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==== Front Cell Cycle Cell Cycle KCCY kccy20 Cell Cycle 1538-4101 1551-4005 Taylor & Francis 31760895 1692176 10.1080/15384101.2019.1692176 Research Paper LncRNA MINCR regulates irradiation resistance in nasopharyngeal carcinoma cells via the microRNA-223/ZEB1 axis Q. ZHONG ET AL. CELL CYCLE Zhong Qingmu a Chen Yifeng b Chen Zilong a a Department of Radiation Oncology, First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, Fujian, P.R. China b Department of Pathology, First Hospital of Quanzhou Affiliated to Fujian Medical University, Quanzhou, Fujian, P.R. China CONTACT Qingmu Zhong Email [email protected] 2020 24 11 2019 19 1 5366 21 9 2019 25 10 2019 7 11 2019 © 2019 Informa UK Limited, trading as Taylor & Francis Group 2019 Informa UK Limited, trading as Taylor & Francis Group ABSTRACT Emerging evidence suggests long non-coding RNA (lncRNA) could sponge microRNAs (miRs) and monitor gene expression. In this study, we intended to search the network involving lncRNA MINCR/miR-223/ZEB1 in nasopharyngeal carcinoma (NPC) cell radiosensitivity. MINCR expression in NPC tissues, precancerous lesions and chronic nasopharyngeal mucosal inflammation tissues, and in NP460, CNE2 and CNE2R cells was detected. The associations between MINCR expression and prognosis and radiotherapy efficacy of NPC patients were evaluated. The interactions among MINCR, miR-223 and ZEB1 were verified via dual luciferase reporter gene assay, RNA pull-down and FISH assays. The gain- and loss-of-functions were performed to explore their effects on NPC cell viability, apoptosis and radiosensitivity. Levels of MINCR, miR-223, ZEB1, and AKT/PI3K-related proteins were detected after different treatments. An in vivo analysis was carried out in nude mice. Consequently, MINCR was upregulated in NPC, and linked with worse prognosis and radiotherapy efficacy. MINCR intervention weakened NPC cell radioresistance. MINCR sponged miR-223 to regulate ZEB1. Inactivating AKT eliminated the increased radioresistance of CNE2 cells induced by overexpressing MINCR. Briefly, MINCR diminished NPC cell radiosensitivity by sponging miR-223, increasing ZEB1 and activating the AKT/PI3K axis. This study may offer novel insight for NPC treatment. KEYWORDS Nasopharyngeal carcinoma radioresistance long non-coding RNA MINCR microRNA-223 ZEB1 AKT/PI3K signaling pathway ==== Body 1. Introduction Nasopharyngeal carcinoma (NPC) is a malignant neoplasm occurring in the top and lateral walls of the nasopharyngeal cavity [1]. As a kind of malignant epithelial carcinoma in the head and neck, NPC has strong ability of local invasion and early distant metastasis, which is the major cause for poor prognosis in NPC patients at advanced period [2]. NPC patients usually manifest nasal and aural symptoms, including nasopharyngeal mass, dysfunction of Eustachian tube, cranial nerve palsy and cervical masses, sometimes complete nasal obstruction [3] Epstein-Barr virus infection is a well-recognized risk factor for NPC development, and other co-factors including dietary nitrosamine consumption, long-term exposure to wood dusts or chemical carcinogens, cigarette smoking and genetic susceptibility are also of great importance [4]. Although the management and treatment of NPC has been improved by imaging and advanced radiotherapy techniques, the prognosis of NPC patients is still unsatisfactory in the past decades mainly due to radioresistance [5]. Therefore, the future research for NPC therapeutic methods should focus on cell radioresistance. Long non-coding RNAs (lncRNAs) are regulatory RNAs that exert biochemical functions, including in the pathological mechanism of diseases, cell differentiation and proliferation, cancer metastasis and development, and especially NPC cell radioresistance [6]. MINCR is a Myc-induced lncRNA that regulates Myc transcription networks in Burkitt lymphoma cells, Myc-positive lymphoma and pancreatic ductal adenocarcinomas, and MINCR is highly expressed in brain, prostate, and testis [7]. At the post-transcriptional level, lncRNAs compete with microRNAs (miRs) to act as a “sponge” and that lncRNA functioning as a competing endogenous RNA (ceRNA) is a newly proposed hypothesis that has drawn increased attention [8]. In this study, lncRNA MINCR was found to bind to miR-223. Interestingly, overexpressed miR-223 suppresses FBXW7 expression in esophageal squamous cell carcinoma cells, then leading to abundant production of c-Myc and c-Jun proteins [9]. Recent evidence demonstrates that miR‑223 is effective in regulating chemotherapeutic drug sensitivity in non-small cell lung cancer [10]. In NPC, miRs play pivotal regulatory roles in cell growth, proliferation and radiosensitivity [11,12]. Importantly, differential expression of miR-223 is reported in serum of NPC patients [13]. Such statements encourage us to suppose the ceRNA network between MINCR and miR‑223 in NPC cell radioresistance with potential signaling pathway. 2. Materials and methods 2.1. Ethics statement The study was performed with the approval of the Ethics Committee of First Hospital of Quanzhou Affiliated to Fujian Medical University. All participating patients subscribed the informed consent. Significant efforts were made to minimize the number of animals and their pains. 2.2. Tissue specimens Tissue biopsy was obtained from 49 NPC patients, 16 patients with precarcinoma lesion (PL) and 34 patients with nasopharyngeal mucosa chronic inflammation (NMCI). They were admitted into the Department of Pathology in First Hospital of Quanzhou Affiliated to Fujian Medical University between January 2012 to January 2014. No patients had a medical history of other malignant tumors, radiotherapy or chemotherapy. Each NPC patient was given 10 Gy irradiation every week. Based on the Response Evaluation Criteria in Solid Tumors, NPC patients were assigned into responders (n = 21) and non-responders (n = 28) to irradiation. Their clinical stages were defined according to the 2002 AJCC/UICC staging classifications [14]. Of the 49 cases, three were at stage I, 13 at stage II, 16 at stage III, and 17 at stage IV. The overall survival (OS) was the duration from the initial date of treatment to date of death. The follow-up lasted for 60 months and was ended in case of tumor reoccurrence or death; otherwise, OS time was recorded to the last follow-up. 2.3. Cell culture NPC cell lines, CNE2 and CNE2R, provided by American Type Culture Collection (ATCC, Manassas, VA, USA) were maintained in Roswell Park Memorial Institute (RPMI)-1640 medium containing 10% newborn calf serum at 37°C in a humidified condition with 5% CO2 and 95% air. The protein kinase B (AKT) inhibitor, GSK690693, was obtained from Selleck Chemicals (Houston, TX, USA). 2.4. Construction of overexpressing and interfering MINCR Short interfering RNAs (siRNAs) (100 nM) specifically targeting MINCR were designed to construct MINCR-depleted CNE2R cells. The corresponding scramble siRNAs (100 nM) were delivered to NPC cells to serve as negative control (NC). The vectors containing MINCR (50 nM) were introduced for construction of MINCR-overexpressed NPC cells. All plasmids were provided by Shanghai GenePharma Co, Ltd (Shanghai, China). Subsequently, miR-223 mimic (50 nM) and ZEB1 vectors (50 nM) were transfected into MINCR-overexpressed CNE2 cells and MINCR-depleted CNE2R cells, respectively using LipofectamineTM 2000 (Invitrogen Inc., Carlsbad, CA, USA) as per the instructions, lasting for 48 hours. 2.5. mRNA and LncRNA reverse transcription polymerase chain reaction (RT-qPCR) Total RNA was extracted from tissues and cells by the method of Trizol (Invitrogen, Carlsbad, CA, USA) and reversely transcribed into cDNA as per the instructions of PrimeScriptTM II 1st Strand cDNA Synthesis Kits (Takara, Dalian, China) [15]. RT-qPCR was conducted with the SYBR green Premix Ex Taq II (RR420A, Takara Holdings Inc., Kyoto, Japan) [16] on Applied Biosystems Step One Plus Real-Time PCR System (Applied Biosystems, Carlsbad, CA, USA). Each reaction was run for three times. Data were normalized to the fold change of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and relative expression of MINCR and ZEB1 was determined using the ΔΔCt method. Primer sequences are exhibited in Table 1.10.1080/15384101.2019.1692176-T0001 Table 1. Primer sequences of RT-qPCR. Primer Sequence (5ʹ→3ʹ) MINCR F: CCCAGTCTGAACTCACCATG R: TTTGCCACATGGCACAGTAT miR-223 F: ACACTCCAGCTGGGACCCCATAAACTGTTT R: TGGTGTCGTGGAGTCG ZEB1 F: ACTCTGATTCTACACCGC R: TGTCACATTGATAGGGCTT U6 F: GCTTCGGCAGCACATATACTAA R: AACGCTTCACGAATTTGCGT β-actin F: ACAGTCAGCCGCATCTTCTT R: GACAAGCTTCCCGTTCTCAG Note: RT-qPCR, real-time quantitative polymerase chain reaction; miR-223, microRNA-223; F, forward; R, reverse. 2.6. Colony formation assay Cells were resuspended with RPMI-1640 medium (Gibco, Gaithersburg, MD, USA) in Luria-Bertani culture plate (D0110, Beijing Nobleryder Science and Technology Co., Ltd., Beijing, China). A total of 500 cells were seeded in the medium (10 cm) and cultured at 37°C with 5% CO2 for two weeks. Then cells were exposed to 0 Gy, 4 Gy and 8 Gy irradiation for 2 hours. After that, cells were fixed with 4% paraformaldehyde for 20 minutes, and then stained with crystal violet for 60 minutes. The plates were air dried to count clones with more than 50 cells under a microscope. 2.7. Cell counting kit-8 (CCK-8) assay Cell proliferation after transfection was examined by a CCK-8 assay. In detail, cells were seeded into 96-well plates with 3 × 103 cells per well for 3 days, followed by being exposed to 8 Gy irradiation. Then, cells were incubated with 10 μL CCK-8 solution (5 mg/mL, Sigma-Aldrich, Merck KGaA, Darmstadt, Germany) for 4 hours. Two hours later, the medium in each well was added with 150 μL dimethyl sulfoxide (Sigma-Aldrich) to dissolve the formazan crystals. The optical density value was read at 490 nm using a microplate reader (Bio-Rad, Hercules, CA, USA) and the growth curves were drawn. 2.8. Flow cytometry An apoptosis assay was performed using the fluorescein isothiocyanate Annexin V Apoptosis Detection Kit (KeyGen, Nanjing, Jiangsu, China) [17]. Propidium iodide was used with Annexin V to determine if cells were viable, apoptotic, or necrotic, and analyzed by flow cytometry (FACScan®) and CellQuest software (both from BD Biosciences, San Jose, CA, USA). 2.9. Subcellular localization of MINCR The subcellular localization of MINCR was predicted using lncRNA subcellular localization data (lncatlas.crg.eu.) and verified with fluorescence in situ hybridization (FISH) using RiboTM lncRNA FISH Probe Mix (Green) (Ribo Biotech, Guangzhou, Guangdong, China). NPC cells (6 × 104/well) were mounted onto slides and fixed in 4% formaldehyde. Slides were pretreated with protease K (2 μg/mL), glycine and acetic anhydride, followed by pre-hybridization for 1 hour at 42°C and hybridization at 42°C using probes (250 μL, 300 ng/mL) against MICNR. Finally, slides were stained with phosphate-buffered saline with Tween-diluted 4ʹ,6-diamidino-2-phenylindole (DAPI). Finally, five random fields acquired from each slide were observed and photographed under a fluorescence microscope (Eclipse Ti microscope, Nikon Instruments, Chiyoda-ku, Tokyo, Japan). 2.10. Fractionation of nuclear and cytoplasmic RNA The nuclear and cytoplasmic RNA fractions were isolated according to PARISTM Kit (Life Technologies, Inc., Gaithersburg, MD, USA). CNE2 cells were resuspended for 5–10 minutes in 500 µL cell fractionation buffer. The cytoplasmic nuclear fractions were separated by centrifugation at 500 g and 4°C for 5 minutes. The supernatant (cytoplasmic fractions) was put into a 2 mL sterile enzyme-free tube and then centrifuged. The pellet (nuclear fraction) was resuspended in 500 µL cell disruption buffer and centrifuged. The cytoplasmic and nuclear fractions were respectively immersed in 500 µL 2 × lysis/binding solution and centrifuged. Next, the factions were mixed with 500 µL absolute ethanol and then transferred into a filter cartridge, which was subsequently washed with Wash solution 1 and 2/3. Nuclear and cytoplasmic RNA were obtained after elution. MINCR expression was determined by RT-qPCR, with U6 used as the internal reference for nuclear RNA expression and GAPDH for cytoplasmic RNA expression. The primers are displayed in Table 1. 2.11. Dual luciferase reporter gene assay Through online prediction software Starbase (http://starbase.sysu.edu.cn/) and TargetScan (http://www.targetscan.org/vert_72/), the binding sequence of miR-223 and MINCR and the binding sequence of miR-223 and 3’-untranslated region (3’UTR) of ZEB1 were predicted. The wild type (WT) plasmid and mutant type (MT) plasmid containing the binding sequence of MINCR and miR-223, the WT plasmid and MT plasmid containing the binding sequence of miR-223 and ZEB1 3ʹUTR were synthesized by Sangon Biotech (Shanghai) Co., Ltd (Shanghai, China). The synthesized plasmids were respectively inserted into pCMV-REPORTTM luciferase reporter vectors (Thermo Fisher Scientific Inc., Waltham, MA, USA). LipofectamineTM 2000 transfection kit (Invitrogen Inc., Carlsbad, CA, USA) was employed to co-transfect lncRNA MINCR with ZEB1 WT plasmid, ZEB1 MT plasmid, miR-223 mimic and miR NC into 293T cells. Twenty-four hours later, cells were lysed and luciferase activity was detected with Dual-Luciferase Reporter Assay System (Promega Corporation, Madison, Wisconsin, USA). 2.12. Biotinylated RNA pull-down assay Cell lysates were treated with RNase-free DNase I (Sigma-Aldrich) and cultured with a mixture of biotinylated RNA fragments of miR-223 (1 µg) and streptavidin-coated magnetic beads (Sigma-Aldrich) at 4°C for 3 hours. After that, the RNA was extracted from the harvested RNA-RNA complexes for western blot analysis. 2.13. Western blot analysis Cells were lysed in ice-cold radioimmunoprecipitation buffer containing protease inhibitor phenylmethylsulfonyl fluoride (1 mM). Equal protein samples (50 μg) were run on 10% sodium dodecyl sulfate -polyacrylamide gel electrophoresis (Bio-Rad, Hercules, CA, USA) and then transferred to polyvinylidene fluoride membranes (Amersham Pharmacia, Piscataway, NJ, USA). Next, western blots were probed with antibodies against ZEB1 (1:1,000; ab203829), AKT (1:1000; ab8805), p-AKT (1:10,000, ab81283), phosphoinositide-3-kinase (PI3K, 1:1000, ab191606), p-PI3K (1:1000, ab182651) and β-actin (1:5000; ab227387) (all purchased from Abcam, Cambridge, MA, USA) at 4°C overnight, and then probed with secondary antibody, horseradish peroxidase-labeled goat anti-rabbit immunoglobulin G (IgG) (1:2,000; A0208; Beyotime Institute of Biotechnology, Shanghai, China). Each sample was repeated for three times. GAPDH served as a reference for normalization. At last, immunoblots were visualized with enhanced chemiluminescence (Amersham Pharmacia) and analyzed with ImageJ V1.48 software (National Institutes of Health, Bethesda, Maryland, USA). 2.14. Xenograft experiments Nude mice (BALB/C nu/nu) (Chinese Academy of Medical Sciences, Beijing, China) were fed with autoclaved water and laboratory rodent chow. The culture medium (100 μL) was mixed with Matrigel (BD Biosciences) containing 3 × 106 CNE2R cells which were stably transfected si-MINCR or scramble siRNA, and then transplanted into the flanks of nude mice by subcutaneous injection. By the way, the tumor volume was measured every 5 days for totally 24 days and calculated as follows: Volume (mm3) = (a × b2)/2, where “a” indicated the largest diameter and “b” represented the perpendicular diameter. Once tumors reached about 70 mm3, the mice were randomly allocated into 3 groups (3 mice/group) and given 2 × 5 Gy irradiation. 2.15. Immunohistochemical staining Tumor sections at 5 μm from NPC xenografts were stained with anti-p-AKT antibody (1:10,000, ab81283) and anti-Ki67 antibody (1:50, MIB-1, Immunotech, Marseille, France) at 4°C overnight and then reacted with anti-IgG secondary antibody (1:1,000; ab6721, Abcam) for 30 minutes. After that, visualization was performed using 3,3-diaminobezidine (DAB, DA1010, Solarbio, Beijing, China). Finally, 5 fields of view (200 ×) were randomly captured for each replicate under an inverted microscope (Nikon). 2.16. Statistical analysis SPSS 21.0 (IBM Corp., Armonk, NY, USA) was applied for data analysis. Kolmogorov-Smirnov test showed whether the data were in normal distribution. The results were manifested as mean ± standard deviation. Comparison between the two groups was analyzed by unpaired t test, comparison among multiple groups was analyzed by one-way or two-way analysis of variance (ANOVA), and pairwise comparison after ANOVA was conducted by Sidak’s multiple comparisons test or Tukey’s multiple comparisons test. The log rank test was applied for post statistical analysis. The p value was obtained by two-tailed tests and p < 0.05 indicated significant difference. 3. Results 3.1. MINCR is upregulated in NPC and predicts poor prognosis MINCR is reported to contribute to several types of cancers [18]. However, the role of MINCR in NPC tumorigenesis remains unknown. Thus, we analyzed nasopharyngeal mucosal tissues from 34 NMCI patients, nasopharyngeal PL tissues from 16 PL patients, and NPC biopsy tissues from 49 NPC patients. The results displayed highly expressed MINCR in NPC biopsy tissues (Figure 1(a)). Besides, NPC patients with high MINCR expression exhibited worse prognosis in the follow-up records (Figure 1(b)). Subsequently, 49 NPC patients, owing to irradiation effectiveness, were split into 2 groups, the resistant and sensitive groups. We analyzed the relationship between radiotherapy efficacy and MINCR level in NPC patients, and found that the higher MINCR level led to worse radiotherapy efficacy in NPC patients (Figure 1(c)). In order to determine the expression of MINCR and its clinical value in predicting radiotherapy efficacy on NPC patients, we evaluated the radiotherapy efficacy of MINCR on NPC by using receiver operator characteristic (ROC) curve and area under curve (AUC). The results showed that MINCR expression was of better prediction on radiotherapy efficacy in NPC patients (Figure 1(d)).10.1080/15384101.2019.1692176-F0001 Figure 1. MINCR is at a high level in NPC and predicts poor prognosis. a. MINCR expression in NMCI patients, PL patients and NPC patients detected by RT-qPCR; b. Relationship between MINCR expression and prognosis in NPC patients analyzed by Kalpan-Meier analysis (patients with high MINCR expression had 40.3 months median survival periods, while patients with low MINCR expression had 54.2 months median survival periods); c. The resistant group patients bore higher MINCR level determined by RT-qPCR; d. ROC analysis for prediction of MINCR level on radiotherapy efficacy (areas under the ROC curve: 0.719; sensitivity: 66.7% and specificity: 71.0%); e. Colony formation assay was performed to affirm CNE2R cells possessed with radioresistance compared to its parental cell CNE2; F. Relative MINCR level in CNE2R cells, CNE2 cells and nasopharyngeal epithial cell NP460 detected by RT-qPCR. Data were described as mean ± standard deviation. Data in panels A, C and E were analyzed by one-way ANOVA, followed by Sidak’s multiple comparisons test, or unpaired t test was used. p < 0.05, p < 0.01; In panel A, for NMCI patients, n = 34, for PL patients, n = 16 and for NPC patients, n = 49. In panel C, sensitive group contains 21 candidates and resistant group contains 28. In panel E and F, three independent experiments were performed. NPC, nasopharyngeal carcinoma; NMCI, nasopharyngeal mucosa chronic inflammation; PL, precarcinoma lesion; RT-qPCR, real-time quantitative polymerase chain reaction; ANOVA, analysis of variance. CNE2R was confirmed to be radioresistant cells at different dose of irradiation by colony formation assay (all p < 0.05) (Figure 1(e)). Subsequently, MINCR expression in normal nasopharyngeal epithelial cell line NP460, human NPC cell line CNE2 and radioresistant cell line CNE2R was determined by RT-qPCR. The result pointed out MINCR was notably overexpressed in radioresistant CNE2R cells (Figure 1(f)). 3.2. MINCR interference attenuates NPC cell radiosensitivity To further verify the radio-sensitivity of MINCR to NPC cells, CNE2R cells interfering with MINCR expression and CNE2 cells overexpressing MINCR were constructed, and RT-qPCR verified the transfection was successful (all p < 0.05) (Figure 2(b)). Twelve days after CNE2 and CNE2R cells received irradiation at doses of 0, 4 and 8 Gy, colony formation assay showed that MINCR interference resulted in a decrease in radioresistance of NPC cells, and NPC cell proliferation was decreased further with the increase of irradiation dose (p < 0.05) (Figure 2(b)). Subsequently, we detected the proliferation of NPC cells after different treatments. The results inferred that interfering MINCR expression promoted NPC cell radiosensitivity (all p < 0.05) (Figure 2(c)). In addition, flow cytometry showed that silencing MINCR significantly increased apoptotic cells after irradiation (all p < 0.05) (Figure 2(d)). Briefly, NPC cells with interfering MINCR expression were more sensitive to irradiation.10.1080/15384101.2019.1692176-F0002 Figure 2. MINCR interference attenuates NPC cell radiosensitivity. Two siRNA targeted MINCR were transfected in CNE2R (si-MINCR-1 and si-MINCR-2 group) and an expression vector contained MINCR was transfected in CNE2 (Oe-MINCR group). Scramble siRNA (Mock group) and empty vector (Empty vector group) served as NC. a. RT-qPCR was performed to validate siRNA and expression vector transfection; b. Relative survival fraction and cell clones in CNE2 and CNE2R cells with silencing or overexpressing MINCR measured by colony formation assay; c. Optical density value of CNE2 and CNE2R cells with silencing or overexpressing MINCR measured by CCK-8 assay; d. Relative apoptosis of CNE2R and CNE2 cells with silencing or overexpressing MINCR measured by flow cytometry. Data were expressed as mean ± standard deviation. Data in panels A and D were analyzed with one-way ANOVA, followed by Sidak’s multiple comparisons test, while data in panels B and C were analyzed with two-way ANOVA and Tukey’s multiple comparisons test. , p < 0.05, *, p < 0.01. Three independent experiments were performed. NPC, nasopharyngeal carcinoma; siRNA, short interfering RNA; RT-qPCR, real-time quantitative polymerase chain reaction; CCK-8, cell counting kit-8; NC, negative control; ANOVA, analysis of variance. 3.3. MINCR is sublocalized in the cytoplasm The protein subcellular localization is of great importance for cell biology, system biology and drug breakthrough [19]. So we first predicted the subcellular localization of lncRNA MINCR through the LncATLAS database, and it turned out that MINCR was mainly in the cytoplasm (Figure 3(a)). Subsequently, we validated that MINCR was located in the cytoplasm of CNE2 cells by FISH experiments. The probes targeting MINCR in CNE2 cells were stained in green and the nucleus were stained in blue after DAPI staining (Figure 3(b)). Additionally, we extracted total RNA from CNE2 cells by fractionation of nuclear and cytoplasmic RNA, and detected the expression of MINCR in cytoplasm and nucleus respectively. The results showed that MINCR mainly existed in cytoplasm (p < 0.05) (Figure 3(c)).10.1080/15384101.2019.1692176-F0003 Figure 3. MINCR is sublocalized in the cytoplasm of CNE2 cells. a. Predicting subcellular localization of MINCR via the LncATLAS database; b. FISH experiments observed that probes targeting MINCR in CNE2 cells were stained in green and the nucleus were stained in blue. The merged image showed MINCR was sublocalized in cytoplasm in CNE2 cells; c. Nuclear and cytoplasmic expression of MINCR in CNE2 cells determined by RT-qPCR. Data were described as mean ± standard deviation, and representative of three independent experiments. Data in panel C were analyzed with two-way ANOVA, followed by and Tukey’s multiple comparison test. , p < 0.05. FISH, fluorescence in situ hybridization; RT-qPCR, real-time quantitative polymerase chain reaction; ANOVA, analysis of variance. 3.4. MINCR functions as a cerna of mir-223 to regulate ZEB1 How MINCR participates in radiotherapy resistance of NPC cells has not been reported. To study this, we first predicted a large number of miRs that bound to MINCR through StarBase [20]. As reported, overexpression of miR-223 can promote radiosensitivity of U87 cells by downregulating Ataxia-telangiectasia mutated (ATM) axis [21]. We suspected that MINCR may reduce NPC cell radiosensitivity by binding to miR-223. Therefore, according to the binding sequence predicted by Starbase, we designed a luciferase reporter plasmid based on cytomegalovirus (CMV), which contained the binding sites of miR-223 mimic and miR-NC with WT-MINCR or MT-MINCR, respectively. The results showed that miR-223 could specifically bind to MINCR (Figure 4(a,b)). Subsequently, RNA pull-down assay was performed in order to further verify the binding relationship between miR-223 and MINCR. The experimental results were in line with our expectations (all p < 0.05) (Figure 4(c)). Besides, RT-qPCR revealed that miR-223 expression was negatively correlated with MINCR in 49 NPC tissues (Figure 4(d)). We further detected miR-223 expression in NPC cells with overexpressed or silenced MINCR, and the results were identical to the above ones (all p < 0.05) (Figure 4(e)).10.1080/15384101.2019.1692176-F0004 Figure 4. MINCR sponges miR-223 and miR-223 targets ZEB1. a. MINCR contains the putative miR recognition sites complementary to miR-223 via the Starbase prediction; b. Luciferase reporter plasmid containing MINCR-WT or MINCR-MT was transfected into CNE2 cells together with miR-223 in parallel with miR-NC plasmid vector; c. The enrichment of miR-223 on MINCR was detected by RNA pull-down assay, relative to antisense-oligos; d. Plot analysis of the relationship between MINCR and miR-223 in the 49 NPC patients; e. Relative expression of miR-223 in NPC cells with overexpressed or silenced MINCR determined by RT-qPCR; f. miR-223 targeting site in ZEB1 3’UTR predicted by TargetScan; g. Luciferase reporter plasmid containing ZEB1-WT or ZEB1-MT was transfected into CNE2 cells together with miR-223 in parallel with miR-NC plasmid vector; h. The enrichment of miR-223 on ZEB1 was detected by RNA pull down assay, relative to antisense-oligos; i. Plot analysis of the relationship between MINCR and ZEB1 in the 49 NPC patients; j. Relative ZEB1 mRNA expression in NPC cells with overexpressed or silenced MINCR determined by RT-qPCR; K. Relative ZEB1 protein level in NPC cells with overexpressed or silenced MINCR determined by western blot analysis. The level of miR-223 was normalized to U6 while the ZEB1 mRNA and MINCR level was normalized to GAPDH. In panels B, D, F and H, all experiments were performed for three times, and one-way ANOVA was used to determine statistical significance. In panels C and G, Pearson’s correlation coefficient test was utilized. n = 49. Relative to the empty vector group, , p < 0.05; compared with the mock group, #, p < 0.05. miR-223, microRNA-223; WT, wild type; MT, mutant type; NPC, nasopharyngeal carcinoma; RT-qPCR, real-time quantitative polymerase chain reaction; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; ANOVA, analysis of variance. To further identify the downstream gene of miR-223 in radiotherapy resistance, we screened the target genes of miR-223 through Starbase and TargetScan websites, among which we focused on ZEB1. ZEB1 gene, as a transcription factor, can promote the metastasis of cancer cells [22]. Moreover, ZEB1 can enhance the radioresistance of cancer cells by promoting epithelial-mesenchymal transition (EMT) of cancer cells [23,24]. According to the prediction website Starbase, miR-223 and ZEB1 3ʹ-UTR had specific binding sites (all p < 0.05) (Figure 4(f)). Based on this sequence, we designed a luciferase reporter plasmid based on CMV, which contained the binding sites of miR-223 mimic and miR-NC with WT or MT ZEB1 3ʹ-UTR, respectively. The results showed that miR-223 could specifically bind to ZEB1 (Figure 4(g)). Subsequently, RNA pull-down assay was performed to further verify the binding relationship between miR-223 and ZEB1. The experimental results were in line with our expectations (all p < 0.05) (Figure 4(h)). Besides, RT-qPCR revealed that ZEB1 expression was positively correlated with MINCR in 49 NPC tissues (Figure 4(i)). We further detected ZEB1 expression in NPC cells with overexpressed or silenced MINCR, and the results were consistent with the above ones (all p < 0.05) (Figure 4(e)). 3.5. miR-223 attenuates NPC cell irradiation resistance induced by MINCR overexpression To further confirm the roles of miR-223 and ZEB1 in radioresistance of NPC cells, we constructed the miR-223 mimic, miR-NC and ZEB1 overexpression vectors and empty plasmids, and transfected them into CNE2 cells with overexpressed MINCR and CNE2R cells with silenced MINCR, respectively. The expression of miR-223 and ZEB1 in CNE2 and CNE2R cells was detected by RT-qPCR, and the transfection was confirmed to be successful (all p < 0.05) (Figure 5(a)).10.1080/15384101.2019.1692176-F0005 Figure 5. miR-223 attenuates NPC cell irradiation resistance induced by MINCR overexpression. miR-223 mimic or expression vector containing ZEB1 was transfected into CNE2/Oe-MINCR or CNE2R/si-MINCR-1 and CNE2R/si-RNA-2, respectively. miR-NC (mock group) and empty vector (empty vector group) served as NC. a. Relative miR-223 expression in CNE2 cells with overexpressing MINCR and miR-223, and relative ZEB1 expression in CNE2R cells with silencing MINCR and overexpressing ZEB1 measured by RT-qPCR to validate transfection efficiency; b. Relative survival fraction and cell clones in CNE2 cells with overexpressed MINCR and miR-223 and in CNE2R cells with silenced MINCR and overexpressed ZEB1 detected by colony formation assay; c. Optical density value of CNE2 cells with overexpressed MINCR and miR-223 and in CNE2R cells with silenced MINCR and overexpressed ZEB1 detected by CCK-8 assay; D. Relative apoptosis in CNE2 cells with overexpressed MINCR and miR-223 and in CNE2R cells with silenced MINCR and overexpressed ZEB1 detected by flow cytometry. Data were manifested as mean ± standard deviation. Data in panels A, B and D were analyzed with one-way ANOVA and Sidak’s multiple comparisons test, while data in panel C were analyzed with two-way ANOVA and Tukey’s multiple comparisons test. , p < 0.05, *, p < 0.01. Three independent experiments were performed. miR-223, microRNA-223; NPC, nasopharyngeal carcinoma; CCK-8, cell counting kit-8; RT-qPCR, real-time quantitative polymerase chain reaction; NC, negative control; Oe, overexpression; ANOVA, analysis of variance. After 6 Gy doses of radiation, the proliferation and apoptosis of CNE2 and CNE2R were detected. The results suggested overexpression of miR-223 decreased the radioresistance induced by MINCR upregulation, while ZEB1 overexpression in MINCR-silenced CNE2R cells enhanced the radioresistance (all p < 0.05) (Figure 5(b–d)). Taken together, miR-223 attenuated NPC cell irradiation resistance. 3.6 MINCR interference promotes NPC cell radiosensitivity by inactivating the AKT/PI3K signaling pathway In 2013, Chen et al reported that ZEB1 was positively correlated with p-AKT expression in NPC patients, and in their in vitro experiments, the sensitivity of NPC cells was significantly improved by adding GSK690693, an AKT inhibitor [25]. In light of this, we detected levels of p-AKT and p-PI3K in CNE2 and CNE2R cells in each group. The results showed that MINCR enhanced the function of ZEB1 and activated the AKT/PI3K signaling pathway (all p < 0.05) (Figure 6(a)) through competitively binding to miR-223. Subsequently, we added AKT specific inhibitor GSK690693 to CNE2 cells overexpressed MINCR to perform functional rescue experiments. The results showed that inhibiting AKT activation partially eliminated the increased radioresistance of CNE2 cells induced by overexpressed MINCR (all p < 0.05) (Figure 6(b–d)). To sum up, MINCR enhanced ZEB1 function and activated the AKT/PI3K signaling pathway, thus increasing NPC cell radioresistance.10.1080/15384101.2019.1692176-F0006 Figure 6. MINCR promoted NPC cell autophagy via AKT/PI3K signaling pathway activation. a. Western blot analysis was utilized for determining AKT/PI3K pathway content in CNE2 and CNE2R cell. Then, CNE2/Oe-MINCR was treated with AKT inhibitor GSK690693; b. Relative survival fraction and cell clones in CNE2 cells with overexpressing MINCR and AKT inhibitor detected by colony formation assay; c. Optical density value of CNE2 cells with overexpressing MINCR and AKT inhibitor detected by CCK-8 assay; d. Relative apoptosis in CNE2 cells with overexpressing MINCR and AKT inhibitor detected by flow cytometry. Data were manifested as mean ± standard deviation. Data in panels D were analyzed with one-way ANOVA and Sidak’s multiple comparisons test, while data in panel A, B and C were analyzed with two-way ANOVA and Tukey’s multiple comparisons test. , p < 0.05, *, p < 0.01. Three independent experiments were performed. miR-223, microRNA-223; AKT, protein kinase B; PI3K, phosphoinositide-3- kinase; NPC, nasopharyngeal carcinoma; Oe, overexpression; CCK-8, cell counting kit-8; ANOVA, analysis of variance. 3.7. MINCR knockdown reduces radiotherapy resistance of CNE2R cells in vivo Subsequently, in order to further determine the effect of MINCR on NPC cell growth in vivo, CNE2R cells in nude mice and CNE2R xenograft tumor models stably transfected with MINCR-siRNA were constructed. After radiotherapy, the growth rate and weight of MINCR-siRNA tumors decreased significantly (all p < 0.05) (Figure 7(a)). Moreover, the immunohistochemical results of p-AKT and Ki67 showed that the contents of p-AKT and Ki67 decreased significantly after interfering MINCR expression (all p < 0.05) (Figure 7(b–c)).10.1080/15384101.2019.1692176-F0007 Figure 7. MINCR knockdown reduces irradiation resistance of CNE2R cells in vivo. CNE2R cells stably MINCR-siRNA and scramble siRNA were inoculated subcutaneously into BALB/c nude mice at 5 × 106 per mouse (n = 3 in each group). Tumor growth was measured continuously every 5 days, and 20 days later, tumor growth was monitored every 3 days. Irradiation treatments were performed with 5 Gy of irradiation on day 15 and 25 after subcutaneously implantation. At 35 days post-implantation, the mice were euthanized by carbon dioxide asphyxiation. a. Relative tumor volume in CNE2R xenograft tumor models stably transfected with MINCR-siRNA; b. Relative tumor weight in CNE2R xenograft tumor models stably transfected with MINCR-siRNA; c, Relative content of p-AKT- and Ki67-positive tumor cells detected by immunohistochemical staining. In panels A and C, two-way ANOVA was used to determine statistical significance of quantification of immunostaining, whereas in panel B one-way ANOVA was used. , p < 0.05, compared to the mock group. AKT, protein kinase B; ANOVA, analysis of variance. 4. Discussion Although chemotherapy and radiotherapy are widely used in single interventions or in combination with other anticancer drugs for NPC, drug resistance is still a leading impediment to successful treatment and ultimately causes recurrence and poor prognosis [1,12]. It has been well established for the importance of the ceRNA network between lncRNA-mRNA interactions in tumorigenesis of cancers [26]. Inspired by that finding, we evaluated the ceRNA regulatory network between MINCR and miR‑223 in NPC biological process with the potential signaling pathway. As expected, we found that MINCR sponged miR-223 to increase ZEB1 expression and activate the AKT/PI3K axis, and thus diminishing NPC cell radiosensitivity. We firstly observed MINCR was highly expressed in NPC tissues and radioresistant CNE2R cells, and NPC patients with high MINCR expression had worse prognosis and worse radiotherapy efficacy. There was no study about the effects of MINCR on NPC, but several researches on the role of MINCR in other cancers could be referred. For instance, upregulation of MINCR in gallbladder cancer was markedly relevant to larger tumor sizes, lymph node metastasis, and shorter overall survival time [18]. Besides, MINCR interference resulted in decreased proliferation and radioresistance of NPC cells, and increased apoptotic cells after irradiation. MINCR knockdown is bound up with impaired cell cycle progression and reduced cellular proliferation [7]. MINCR depletion suppressed cell growth and invasion and stimulated cell apoptosis, by inhibiting the EMT in oral squamous cell carcinoma cells, and interrelated with lower TNM stage and less distant metastasis [27]. Since MINCR is a newly discovered lncRNA, there is little research about its mechanism in cancer cell radioresistance. But the carcinogenic role of other lncRNA in NPC has been widely reported. For example, MALAT1 depletion could reinforce NPC cell radiosensitivity by working as a ceRNA to modulate slug expression through competition for miR-1 [6]. More importantly, we found the beneficial effects of MINCR interference on reduced NPC cell radioresistance were achieved via miR-223, ZEB1 and the AKT/PI3K axis. Specifically, miR-223 could bind to MINCR, and it was negatively correlated with MINCR in NPC tissues. The ceRNA network involving MINCR was also discovered in gallbladder cancer cell, in which MINCR could act as a sponge for miR-26a-5p to regulate EZH2 [18]. Additionally, miR-223 could bind to ZEB1, and ZEB1 expression was positively correlated with MINCR in NPC tissues. Extrogenous miR-223 in CNE2 cells would decrease the ability of colony formation, migration and invasion by reducing its another target gene MAFB [28]. miR-223 overexpression attenuated NPC cell irradiation resistance. Consistently, downregulation of miR-223 was observed in serum of NPC patients in a prior study, serving as a tumor suppressor gene [13]. Similarly, miR-223-3p overexpression increased resistance to anticancer agents in human head and neck cell lines [29]. In NPC cell lines exposed to ionizing radiation, ZEB1 expression increased and inhibited AKT activation increased radiation sensitivity [25]. Forced overexpression of ZEB1 dramatically reduced the radiosensitivity induced by NEAT1 knockout and NEAT1 positively regulated ZEB1 expression through miR-204, which was in line with our results [30]. Moreover, inhibited AKT activation eliminated the radioresistance of CNE2 cells induced by overexpressed MINCR. AKT activation has been demonstrated to correlate with the metastasis, mortality, and radiotherapy resistance of NPC cells [31]. Interestingly, inhibition of ZIP4 inhibited metastasis, dissemination and invasion, and enhanced the radiosensitivity in human NPC cells by inactivating the PI3K/AKT axis [32]. miR-223 regulated the growth, invasion and chemotherapeutic resistance of glioblastoma stem cells to temozolomide via the PI3K/AKT signaling pathway [33]. In a word, we provided compelling evidences to state that lncRNA MINCR worked as a ceRNA for miR-223 to positively regulate ZEB1, and MINCR silencing strengthened the NPC cell radiosensitivity through the miR-223/ZEB1 axis and inactivating the PI3K/AKT axis. This study may offer new perspective for further understanding of NPC and finding new targets for effective therapies. Analysis of abovementioned results may give the chance to create the background for future clinical investigation and application in NPC. Thus, more studies in radiosensitivity of NPC cells are required in the future to develop clinical values. Author contributions QMZ is the guarantor of integrity of the entire study and contributed to the concepts and design of this study; YFC contributed to the experimental studies and clinical studies; ZLC contributed to the data and statistical analysis; QMZ took charge of the manuscript preparation; ZLC contributed to the manuscript review. All authors read and approved the final manuscript. Disclosure statement No potential conflict of interest was reported by the authors. ==== Refs References [1] Qin H, Wang R, Wei G, et al. Overexpression of osteopontin promotes cell proliferation and migration in human nasopharyngeal carcinoma and is associated with poor prognosis. Eur Arch Otorhinolaryngol. 2018;275 :525–534.29214433 [2] Liu C, Li G, Yang N, et al. miR-324-3p suppresses migration and invasion by targeting WNT2B in nasopharyngeal carcinoma. Cancer Cell Int. 2017;17 :2.28053597 [3] Mohamad I, Manan AARao V. Cured nasopharyngeal carcinoma stage iv with haemostatic-intent radiotherapy. Banglad J Med Sci. 2016;15 :480–482. [4] Hildesheim A, Wang CP. Genetic predisposition factors and nasopharyngeal carcinoma risk: a review of epidemiological association studies, 2000–2011: rosetta Stone for NPC: genetics, viral infection, and other environmental factors. 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==== Front ACS OmegaACS OmegaaoacsodfACS Omega2470-1343American Chemical Society 10.1021/acsomega.9b02776ArticleEffect of Reaction Temperature on Shape Evolution of Palladium Nanoparticles and Their Cytotoxicity against A-549 Lung Cancer Cells Abbas Gulam †Kumar Narinder ‡Kumar Devesh ‡Pandey Gajanan ††Department of Chemistry and ‡Department of Physics, Babasaheb Bhimrao Ambedkar University, Lucknow 226025, India E-mail: [email protected] 12 2019 24 12 2019 4 26 21839 21847 27 08 2019 28 11 2019 Copyright © 2019 American Chemical Society2019American Chemical SocietyThis is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes. Palladium nanoparticles (Pd NPs) of different shapes and sizes have been synthesized by reducing potassium tetrachloropalladinate(II) by l-ascorbic acid (AA) in an aqueous solution phase in the presence of an amphiphilic nonionic surfactant poly ethylene glycol (PEG) via a sonochemical method. Materials have been characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive X-ray soectrscopy (EDX), Fourier transform infrared (FTIR), surface-enhanced Raman spectroscopy (SERS), particle distribution, and zeta potential studies. Truncated octahedron/fivefold twinned pentagonal rods are formed at room temperature (RT) (25 °C) while hexagonal/trigonal plates are formed at 65 °C. XRD results show evolution of anisotropically grown, phase-pure, and well crystalline face-centered cubic Pd NPs at both temperatures. FTIR and SERS studies revealed adsorption of ascorbic acid (AA) and PEG at NP’s surface. Particle’s size distribution graph indicates formation of particles having wide size distribution while the zeta potential particle surface is negatively charged and stable. The truncated octahedron/fivefold twinned pentagonal rod-shaped Pd NPs, formed at RT, while thermally stable and kinetically controlled hexagonal/trigonal plate-like Pd NPs, evolved at higher temperature 65 °C. The obtained Pd NPs have a high surface area and narrow pore size distribution. To predict protein reactivity of the Pd cluster, docking has been done with DNA and lung cancer-effective proteins. The cytotoxicity of the Pd NPs has been screened on human lung cancer cells A-549 at 37 °C. The biological adaptability exhibited by Pd NPs has opened a pathway in biochemical applications. document-id-old-9ao9b02776document-id-new-14ao9b02776ccc-price ==== Body 1 Introduction In the last few decades, mesoporous materials have gained enormous attention of various scientists globally in both industry and academia because of their high surface area, tunable pore size, and uniform and narrow pore size distribution.1,2 On account of these properties, mesoporous and nanoporous materials have shown high demand in various fields such as energy storage,3 catalysis,4,5 and biomedical applications.6,7 In the past, nanoparticles of desired shapes, such as spherical, films, rods, tubes, and so forth with mesoporous structures have been prepared by a soft template method like self-assembly of micelles.8 On the other hand, in the hard template method, the targeted materials are deposited in the confined spaces of a template with the desired morphology.9 Because the soft template method is a simple and easy approach, hence it is favorable for the generation of desired shapes of nanoparticles using low molecular weight molecules like Brij 58, poly ethylene glycol (PEG), and so forth and high molecular weight amphiphilic molecules, for example, triblock copolymers as pore-directing agents. In recent years nanotechnology has shown enormous potential in the biomedical field as therapeutic mediators for many diseases, including cancer.10 In this regard, metal nanostructures have attracted great interest because of their size, structure, versatility, and optoelectronic properties.11 Since the introduction of catalytic converters in the USA in 1975 and in Europe in 1986 (Wiseman and Zereini 2009), platinum group metals have shown increasing demands particularly in the area of electronics and catalysis. The latest research demonstrate that palladium (Pd) nanoparticles have widely been used in catalysis, (e.g., oxidation/reduction of methanol,12 stereochemical oxidation of ethanol,13 redox organic reactions,14,15 sensors for detection of various analytes,16 hydrogen generation/storage, methane combustion, supercapacitors, lithium-ion batteries, and in biomedical applications.17 Shim et al. synthesized dendritic platinum nanoparticles and demonstrated their cytotoxicities against human embryonic kidney cells.18 Unciti-Broceta et al. demonstrated that the prodrugs 5-fluoro-1-propargyluracil19 and N-4-propargyloxycarbonylgemcitabine20 are independently harmless; however, when separately combined with Pd(0)-glycol-polystyrene resin these prodrugs exhibited antiproliferation properties compared to the unmodified drug in colorectal and pancreatic cancer cells. However, one of the major limitations, in the case of metallic NPs is their nonspecific untargeted toxicity. Huang et al. used ultrathin (1.8 nm) hexagonal Pd nanosheets with a 41 nm edge for cancer photothermal therapy (PT). The nanosheets were able to kill 100% liver cancer cells after 5 min irradiation of 808 nm laser, showing size-dependent and tunable absorption peaks in the NIR region and exhibited high biocompatibility in the absence of irradiation. More interestingly, these Pd nanosheets exhibited better photostability than Au and Ag nanostructures.21 Balbín et al. reported high cytotoxicity of mesoporous silica-supported Pd NPs against four human cancer cell lines, simultaneously displaying catalytic activity for C–C bond formation via Suzuki–Miyaura cross-coupling between small molecules.22 Kumar et al. prepared (polylactic-co-glycolic acid)-loaded nanoparticle betulinic acid for improved treatment of hepatic cancer and showed in vitro and in vivo evaluation.23 Several structural modifications have been proposed to improve biomedical efficiency of Pd nanoparticles.22,24 Porous Pd nanoparticles (22.8 nm) were also recently reported as attractive PT agents with a PT conversion efficiency as high as 93.4%, which is comparable to typical Au nanorods.25 There is high interest to expand the applicability of Pd nanoparticles; therefore, it is highly desirable to synthesize shape- and size-controlled Pd NPs for excellent performance in biomedical applications. Generation of Pd nanoparticles using amphiphilic molecules (nonionic surfactants) via a sonochemical process is a novel and rapid approach to tune the kinetics of the reaction. In this investigation, mesoporous Pd nanoparticles have been prepared via a sonochemical route and their cytotoxicity has been screened by using the culture of human lung cancer cells A-549. 2 Materials and Methods 2.1 Materials Analytical reagent grade potassium tetrachloropalladinate(II) (K2PdCl4), PEG and l-ascorbic acid (AA, C6H8O6) were purchased from Sigma-Aldrich and used without further purification. Deionized water and ethanol were used as solvents in this study. 2.2 Synthesis In the two different vessels, 10 mL solutions of 0.1 M K2PdCl4 were prepared, and 10 mL aqueous 0.1 M ascorbic acid (AA) solutions were added to each vessel. Further, 1 mL of 50 mg/L aqueous PEG solutions were added to each reaction mixtures. The two solution mixtures were reacted at room temperature (RT) and 65 °C, respectively, for 30 min with thorough ultrasonication (220–240 V, 50–60 Hz). After the reactions, the products were collected by centrifugation and washed several times with deionized water and ethanol to remove the residual surfactant and excess reactants. 2.3 Characterization The X-ray diffraction (XRD) patterns of the obtained products were recorded on a Panalytical’s X’Pert Pro X-ray diffractometer in the 2θ range 10–80° with a step size of 0.025°. Scanning electron microscopy (SEM) images of the materials were observed on JEOL 6490 LB equipment at an operating electrical energy of 3 kV. Particle shapes and sizes of the materials were further examined on a JEOL-2100 transmission electron microscope . The zeta potential of Pd nanoparticles (formed at RT) was measured using a Zetasizer ZS90 (Nano series Malvern Instrument) at RT. Dispersion of nanoparticles was sonicated for 20 min and diluted to make a solution with concentration 80 μg/mL in phosphate buffered saline (pH = 7.4). The particle size and size distribution were carried out on a Zetasizer ZS90 (Nano series Malvern Instrument). The Surface-enhanced Raman spectroscopy (SERS) spectrum of Pd nanoparticles, formed at RT, was recorded on a NSCOM/Raman/confocal/atomic force microscope used for UV/lithography (200 nm) and near-field imaging of features as small as 100 nm Raman spectra and imaging for an excitation wavelength of 532 nm with an extinction coefficient of 8000 M–1 cm–1. Fourier transform infrared (FTIR) spectra of the products have been recorded on a PerkinElmer Spectrum Two instrument. UV/vis data were collected on a Shimadzu UV-3600 spectrophotometer. Brunauer–Emmett–Teller (BET) analysis of the materials was recorded on a BELsorp-mini II instrument. 2.4 Cytotoxicity Test The culture of A-549 human lung cancer cells (∼100 000 cell mL–1) was taken in 10% fetal bovine serum-supplemented Dulbecco’s modified Eagle’s medium in a 24-well microtitre plate. Different amounts of Pd NPs (formed at RT) were suspended in deionized water to make solutions with concentration from 10 to 60 μg mL–1. Homogenization of each solution was carried out with an ultrasonic processor (Labsonic M, Sartorius Stedim Biotech GmbH) for 15 min and added separately to cultures, keeping one blank as the reference. The cultures were incubated for 24 h in an incubator with 5% CO2 in a humid atmosphere at 37 °C. After incubation, the cells were removed from the culture by trypsinization and washed a coupled by Dulbecco’s phosphate-buffered saline (PBS; pH: 7.4) to remove the residual presence of serum. The cells were again suspended in PBS, and aliquots of 20 μL were prepared from all the cultures. Equal amounts (v/v) of prefiltered 0.4% trypan blue stain were added to the aliquots and were put aside to settle for 1 min. To determine the cell viability, the samples were observed on an inverted microscope in a Fuchs-Rosenthal haemocytometer. The results of cytotoxicity were expressed by plotting cell viability histogram and curve and analyzed using IC50 values. 3 Results and Discussion 3.1 Characterization of Pd Nanoparticles When the solution containing [PdCl4]2– complex ions were treated with AA at RT, the solution turned black within 30 min, indicating a reduction of [PdCl4]2– complex ions is completed in this period. This reaction was monitored by the UV–visible absorption spectroscopy experiment as shown in Figure 1. Before formation of Pd NPs, the absorption band corresponding to the Pd complex was clearly detected at 424 nm, which completely disappeared after the reaction, indicating that the complex ions are changed from Pd2+ to Pd(0) owing to their reduction by AA.13 Moreover, the spectrum of the sample shows broad continuous absorptions in the UV–visible range which is characteristic of the reduced Pd NPs as reported earlier.26 On increasing the reaction temperature at 65 °C virtually no alteration in the spectral profile has been observed. The yield of this reaction was approximately 91.84%. Figure 1 UV–visible absorption spectra of K2PdCl4 and Pd NPs synthesized at RT and at 65 °C. To observe the adsorption of organic molecules on the surface of Pd nanoparticles, FTIR spectra of the formed nanoparticles (at RT) were carried out in a liquid phase as well as in a solid phase (Figure 2). In the liquid phase FTIR spectrum (Figure 2a), the intense peak at 3425 cm–1 is corresponding to O–H stretching of water molecules/–OH groups. The peak at 1634 cm–1 is because of the C=C stretching frequency of AA. Red shifting of C=C stretching frequency (compared to 1665 cm–1 in pure AA) is because of adsorption of l-ascorbic acid on the nanoparticle’s surface.27 The peak at 1452 cm–1 is due to −CH2 scissoring, at 964 cm–1 is due to −CH2 wagging, at 1029 cm–1 is due to −CH2 rocking and at 1268 cm–1 is due to C–O–C antisymmetric stretching vibration of adsorbed PEG at the particle’s surface.28 Other peaks at 651 and 458 cm–1 correspond to the vibration of the adsorbed AA nanoparticle’s surface. Figure 2 FTIR spectra of Pd NPs formed at RT (a) in solution phase and (b) in solid phase. In the solid phase FTIR spectrum (Figure 2b), the intensity of peaks is much decreased compared to those in the solution phase. Many peaks have disappeared while the intensity of a few peaks has increased. The increased intensity peaks at 2927 and 2853 cm–1 is due −CH2 stretching vibration while the peak at 1452 cm–1 is due to CH2 scissoring of alkyl chains of adsorbed PEG. Position of these peaks is much decreased compared to those of pure PEG28 because of adsorption at nanoparticle’s surface. Although peaks corresponding to −CH2 stretching do not appear in Figure 2a, however, a small hump is visible at 2927 cm–1 probably because of preferential adsorption of AA at nanoparticle’s surface in the liquid phase. Further vibrational analysis of Pd nanoparticles (formed at RT) was carried out by Raman measurement, which shows very strong, few broad and weak background peaks. The Intensity versus Raman shift graph was plotted to take 20 mM Pd NPs, grafted with PEG and coated with AA (Figure 3). In the SERS spectrum, the strong peaks at 480 and 633 cm–1 are because of ascorbic acid, while the peak at 278 cm–1 is probably because of υ (Pd...O) vibration.29 The bands at 1076, 1170, and 1516 cm–1 are because of (C–O–H bend), CH2 rocking, and CH2 scissoring.30 Figure 3 SERS spectra of Pd NPs synthesized at RT. The structural and morphological investigation of the abovementioned synthesized materials has been performed using SEM and transmission electron microscopy (TEM) analysis. Different shapes and sizes of Pd nanoparticles have been formed at RT and at 65 °C. When the [PdCl4]2– complex ions were treated with AA in PEG medium at RT, truncated octahedron/fivefold twinned pentagonal rodlike Pd NPs have been formed on 30 minutes of the reaction. In the SEM images (Figure 4a,b) and the TEM image (Figure 4c), 8–10 nm edge length truncated octahedron/fivefold twinned pentagonal rodlike Pd nanoparticles were observed. The size of nanostructures varies in the range of 20–50 nm. In the corresponding energy dispersive X-ray soectrscopy (EDX) pattern (Figure 4d), the only peak due to Pd was observed, indicating the formation of phase-pure Pd NPs. When the reaction temperature was increased at 65 °C, keeping reaction time same, that is, 30 min, 17–20 nm edge length hexagonal/trigonal plates were formed. In the SEM image (Figure 5a), although the structures seem to be plate-like, however in the corresponding TEM image (Figure 5b) hexagonal/trigonal plate-like structures are visible. Figure 4 (a,b) SEM images, (c) TEM image, and (d) EDX spectrum of Pd NPs synthesized at RT. Figure 5 (a) SEM image, (b) TEM image, and (c) EDX spectrum of Pd NPs synthesized at 65 °C. The particle size and size distribution analysis of Pd NPs, formed at RT were carried out to by plotting the particle’s size distribution curve (Figure 6). The curve indicates that the size of the particles is distributed in a range of 20–60 nm while the maximum population falls at 40 nm. Zeta potential analysis is an effective technique for determining the surface charge of nanoparticles in colloidal solution and hence predicts their stability. The zeta potential curve of the Pd nanoparticle dispersion (formed at RT) was measured in the range of −100 to +100 mV (Figure 7). The obtained zeta potential value (−13 mV) indicates that the surface of nanoparticles is negatively charged and thus maintains their stability. Figure 6 Particle’s size distribution of Pd nanoparticles formed at RT. Figure 7 Zeta potential distribution curve of Pd nanoparticles formed at RT. The phase and crystallinity of Pd NPs were investigated by wide-angle XRD measurement. In the XRD pattern of the Pd NPs (formed at RT and at 65 °C), peaks observed at 2θ positions 40.26, 45.78, 68.67, 79.87, and 88.85° correspond to the reflection of (111), (200), (220), (311), and (222) planes of crystalline Pd NPs (Figure 2a). These XRD patterns indicate the formation of phase pure face-centered cubic (fcc) Pd NPs (JCPDS file no. 461043). The weak intensity peak at 27° in the XRD pattern of Pd NPs formed at 65 °C is because of the presence of residual PEG moieties.31 Furthermore, the obtained XRD peaks are intense and broadened, indicating the formation of good crystalline and small size Pd nanoparticles.18 The average crystallite size (D) has been determined from the Debye–Scherrer formula where D is the crystallites size (in nm), λ the wavelength (in nm), β is the full width at half maxima and θ is the Bragg’s diffraction angle. Crystallite size of Pd NPs synthesized at RT and at 65 °C, corresponding to different planes determined by the above formula has been shown in Figure 8b. From the graph, it has been found that crystallite size corresponding to (111) and (222) planes are larger than that of the rest of the planes at both temperatures, indicating orientation of Pd NPs preferentially toward the {111} facet and that the particle growth is anisotropic in shape at both temperatures.32 Further the particle size determined is smaller than those of the TEM and particle size distribution analyses probably because of wide size distribution of the particles. Figure 8 (a) XRD patterns and (b) crystallite size corresponding to different planes determined by the Debye–Scherrer formula of Pd NPs synthesized at RT and at 65 °C. Nitrogen adsorption/desorption isotherm plots have been used to evaluate the pore diameter (Dp) and the surface area (S) of the Pd NPs formed at RT. From the Barrett–Joyner–Halenda (BJH) method, an average pore diameter of DPNs has been found to be 24.29 nm (Figure 9a). This result is indicative of its porous structure containing mesopores. The specific surface area obtained by the BET method33 (Figure 9b) was approximately 19.44 m2 g–1. Such high surface area of Pd NPs indicates the presence of more catalytic sites. Figure 9 BJH plot (a) and N2 adsorption–desorption isotherm (b) of Pd NPs synthesized at RT. It is well understood that the shape, size, surface area, and charge of the nanostructure affect the biological cell membrane interaction and thus decide their biological applications.34 It is reported that cellular uptake of Pd nanoparticles are shape-dependent apart from the surface charge because of membrane binding energy barriers during endocytosis are predominantly responsible for the shape effect.35 Hence, a better understanding of shape evolution Pd nanoparticles would aid the development of physicochemical and reaction parameters for generation of Pd nanoparticles for effective biochemical applications. The inside atoms of face-centered cubic metals (e.g., Pd) have coordination number (CN) 12 while the atoms at the various low index surfaces (e.g., {111}, {100}, and {110}) have the CN of 9, 8, and 7, respectively. The planar density of three surfaces increases in the order {111} > {100} > {110}, and hence, the surface energy increases in the order γ{111} < γ{100} < γ{110}.36,37 When aqueous solution of K2PdCl4 was treated with ascorbic acid in the presence of surfactant PEG, the Pd2+ ion was readily reduced to Pd(0) owing to the reaction. Here, [PdCl4]2– reduction takes place using AA as a reductant via sonication. It is supposed that Pd nanoparticles evolved following three steps: supersaturation of monomers, burnt nucleation, and controlled growth according to the LaMer method.38 In the prevailing reaction conditions when monomer concentration increases steadily and reaches the stage of the critical point of supersaturation, small clusters spontaneously separate, decreasing the monomer concentration by nucleation. Now, concentration of the monomer decreases below a critical concentration and the available monomer is henceforth used for particle growth; however, during the nucleation period, particle growth may also take place simultaneously.39 Thus, to control size broadening of particles, a short nucleation span and controlled growth kinetics should be maintained which can be achieved by the presence of adsorbates, additives, or surfactants in the reaction medium. When K2PdCl4 is reduced by AA in an aqueous medium in the presence of PEG, truncated octahedron/fivefold twinned pentagonal rodlike polyhedral structures enclosed by {111} and {100} mixed facets are formed because {111} is the most stable facet followed by {100} and then {110}. From a stability point of view only {111} facet-terminating shapes like octahedral and tetrahedral seed should be formed during the nucleation stage; however, according to Wulff’s theory because the octahedron and tetrahedron have a larger surface area than the cube per unit volume, the truncated octahedron, known as Wulff’s polyhedron, nucleated at the most stable speed in order to minimize both the surface area and interfacial face energy.40 On increasing the reaction temperature at 65 °C the concentration of the monomer increases, thus increasing the rate of reaction. Now, among the two facets {111} and {100} of an octahedron, the {111} surface grows more rapidly than {100} because of availability of higher monomer concentration at an elevated temperature. Thus, thermodynamically stable and kinetically controlled hexagonal/trigonal plate-like Pd NPs have been evolved at higher temperature 65 °C. 3.2 Molecular Docking Studies of Palladium Clusters with DNA 3.2.1 Computational Details 3.2.1.1 Dataset DNA: the PDB format file of DNA sequences with PDB ID 1BNA was downloaded from RCSB Protein Data Bank.41 Ligands and water molecules were removed from the DNA sequence using CHIMERA.42 Drugs: the structure of the Pd cluster was taken after optimization. Figure 1 shows the chemical structure of the cluster. 3.2.2 Molecular Docking AutoDock 4.2 was used for molecular docking simulations using Lamarckian Genetic Algorithm (LGA).43 Docking was performed using DNA sequences as a rigid receptor molecule, whereas the Pd cluster was treated as a flexible ligand. The receptor and ligand files were prepared for docking using AutoDockTools (ADT).44 The grid box size was set at 50-50 and 100 Å for x, y and z, respectively, and the grid center was set to 14.748, 20.984, and 8.809 for x, y, and z, respectively. The Gasteiger charges were added to the complex by AutoDockTools (ADT) before performing docking calculations. Lamarckian genetic algorithms, as implemented in AutoDock, were employed to perform blind docking calculations. For metal, modifications were done in the parameter file to include Pd. The lowest energy docked confirmation, according to the AutoDock scoring function, was selected as the binding mode (Figure 10). Figure 10 Three dimensional structure of the Pd-cluster. 3.2.3 Result The figure shows the Minor groove binding of the Pd cluster with 1BNA present in lung cancer A549 cell lines. The Pd cluster binds in the minor groove of the DNA sequences concluding that the cluster is a minor groove binder. The computationally calculated binding energy is −0.14 kcal/mol, which indicates that it is an effective drug against cancer cells (Figure 11). Figure 11 Minor groove binding of the Pd cluster with 1BNA. 3.3 Molecular Docking Studies of Palladium Clusters with Lung Cancer Proteins 3.3.1 Computational Details 3.3.1.1 Dataset DNA: the PDB format file of proteins with PDB ID 2ITW, 2ITX, 2ITY, 2J6M, and 4LQM were downloaded from RCSB Protein Data Bank.45 These are crystal structures of the EGFR kinase domain. Mutations in the EGFR kinase are a cause of non-small cell lung cancer.44 Ligands and water molecules were removed from each protein using CHIMERA.46 Drugs: the structure of the Pd cluster was taken after optimization. Figure 10 shows the chemical structure of the cluster. 3.3.2 Molecular Docking AutoDock 4.2 was used for molecular docking simulations using Lamarckian Genetic Algorithm (LGA).43 The docking was performed using protein as a rigid receptor molecule, whereas the Pd cluster was treated as a flexible ligand. The receptor and ligand files were prepared for docking using AutoDockTools (ADT).44 Grid boxes of various dimensions were used to prepare grid maps using Auto-Grid for each protein. The Gasteiger charges were added to the complex by AutoDockTools (ADT) before performing docking calculations. Lamarckian genetic algorithms, as implemented in AutoDock, were employed to perform blind docking calculations. All the other parameters were default settings. For metals, modifications were done in the parameter file to include Pd. According to the AutoDock scoring function, the lowest energy docked conformation was selected as the binding mode. 3.3.3 Result The computationally calculated binding energies of all protein–drug complexes are given in Table 1. From the tabulated data it is very much clear that binding energies of all EGFR proteins with the Pd cluster are of the same range. The binding modes and geometrical orientations of all the compounds were almost identical; suggesting that all the inhibitors occupied a common cavity in the receptor. The lowest binding energy is with the 2ITY complex. Molecular Docking gives the best and stable conformations of the ligand with proteins in the receptor active pocket. Figure 12 shows interaction of the ligand with proteins. Figure 12 Interaction of the Ag–Au cluster with (a) 2ITW, (b) 2ITX, (c) 2ITY, (d) 2J6M, and (e) 4LQM. Table 1 Binding Energies of Protein–Drug Complexes s. no proteins binding energies (kcal/mol) 1 2ITW –0.44 2 2ITX –0.46 3 2ITY –0.48 4 2J6M –0.42 5 4LQM –0.44 In silico studies revealed that the entire synthesized molecule showed good binding energy toward the target protein. The Pd cluster binds in the pocket of the proteins (Table 1). 3.4 Cytotoxicity With a view of the above fact, it is also necessary to observe the use of Pd nanoparticles (formed at RT) in biochemical systems, we performed the cytotoxicity effects using A-549 human lung cancer cells. The cytotoxicity result was analyzed by plotting viability histogram, curve, and IC50 values. As per the data obtained from Figure 15, the positive control, that is, Adriamycin kills all the cytotoxic cells at 10 μg/ml concentration indicating that our cell culture experiment moved to a positive direction in all respects.47 It is found that the cell viability is dose-dependent with noticeable changes in shape and size from 10 to 30 μg/mL concentrations (Figures 13 and 14).48 The observed IC50 is ≤10 μg/mL, indicating good therapeutic efficacy in the biological system, that is, against lung carcinoma. This action may be because of the cytotoxic effect of the palladium nanoparticle on the DNA (shown in the docking experiment in Figure 11).49 Cell viability decreased with the increased concentration, which implies that our synthesized compound may be active against lung cancer which is beneficial for future drug-design perspectives.50 Figure 13 Effect of dose of Pd NPs (synthesized at RT) on cell viability of A-549 human lung cancer cells. Figure 14 Cell viability curve: effect of dose of Pd NPs (synthesized at RT) on A-549 human lung cancer cells. Figure 15 Cell viability curve: effect of dose of Pd NPs (synthesized at RT) and Adriamycin on A-549 human lung cancer cells. 4 Conclusions In summary, we have successfully synthesized Pd nanoparticles of different shapes and sizes, like 8–10 nm edge length truncated octahedron/fivefold twinned pentagonal rods and 17–20 nm edge length hexagonal/trigonal plates in an aqueous solution phase by reducing K2PdCl4 with ascorbic acid in the presence of surfactant PEG via a sonochemical method at RT. XRD study revealed that particle growth took place anisotropically at both temperatures. FTIR and SERS studies revealed adsorption of AA and PEG at NP’s surface. The particle’s size distribution graph indicates formation of particles having a wide size distribution while the zeta potential value −13 mV indicated that the particle’s surface is negatively charged and hence stable. The truncated octahedron/fivefold twinned pentagonal rod-shaped Pd NPs formed at RT, while thermally stable and kinetically controlled hexagonal/trigonal plate-like Pd NPs, evolved at a higher temperature 65 °C. The obtained Pd NPs has a high surface area and narrow pore size distribution. The computationally calculated binding energy indicates that this Pd cluster is an effective drug against cancer cells. The lowest binding energy is with the 2ITY complex. Molecular Docking gives the best and stable conformations of the ligand with proteins in the receptor active pocket. Biochemically, the effect of PD NPs on A-549 human lung cancer cells exhibited that cytotoxicity is dependent on the dose of NPs. The results described here indicate much potential for use of these NPs in biomedical applications. Author Contributions All the authors contributed equally to this manuscript. The authors declare no competing financial interest. Acknowledgments The authors are thankful to Dr Sudeept Saha, Department of Pharmaceutical Sciences, Babasaheb Bhimrao Ambedkar University, Lucknow, Dr Satyendra Kumar, Department of Bio-Chemistry, King George Medical University, Lucknow, and Prof. Abbas Ali Mehdi of Era Medical College to avail the Cell Line Passage facility. ==== Refs References Shen L. ; Che Q. ; Li H. ; Zhang X. Mesoporous NiCo2O4 nanowire arrays grown on carbon textiles as binder-free flexible electrodes for energy storage . Adv. Funct. Mater. 2014 , 24 , 2630 –2637 . 10.1002/adfm.201303138 . Wang Y. ; Zhang J. ; Wang X. ; Antonietti M. ; Li H. Boron-and fluorine-containing mesoporous carbon nitride polymers: metal-free catalysts for cyclohexane oxidation . Angew. Chem., Int. Ed. 2010 , 49 , 3356 –3359 . 10.1002/anie.201000120 . Malgras V. ; Ataee-Esfahani H. ; Wang H. ; Jiang B. ; Li C. ; Wu K. C.-W. ; Kim J. H. ; Yamauchi Y. Nanoarchitectures for mesoporous metals . Adv. 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==== Front 9607835 20545 Mol Psychiatry Mol. Psychiatry Molecular psychiatry 1359-4184 1476-5578 31289348 10.1038/s41380-019-0453-x nihpa1526901 Article VPS35 regulates tau phosphorylation and neuropathology in tauopathy Vagnozzi Alana N. 1 Li Jian-Guo 1 Chiu Jin 1 Razmpour Roshanak 2 Warfield Rebecca 1 Ramirez Servio H. 2 Praticò Domenico 1 1 Alzheimer’s Center at Temple, Department of Pharmacology, Lewis Katz School of Medicine, Temple University, Philadelphia PA, 19140 2 Department of Pathology and Laboratory Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia PA, 19140 Correspondence to: Domenico Praticò, MD, 3500 North Broad Street, MERB, 947, Philadelphia, PA 19140, Tel: 215-707-9380, Fax: 215-707-9890, [email protected] AUTHOR CONTRIBUTIONS A.V., J.C., and D.P. designed the study, developed the experimental design, performed data analyses, and wrote the paper. A.V., J.G.L., performed most of the experiments. R.R., and R.W contributed to the imaging studies. All authors discussed the results and commented on the manuscript. 12 4 2019 09 7 2019 10 1 2020 10.1038/s41380-019-0453-xUsers may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms The vacuolar protein sorting 35 (VPS35) is a major component of the retromer recognition core complex which regulates intracellular protein sorting and trafficking. Deficiency in VPS35 by altering APP/Aβ metabolism has been linked to late-onset Alzheimer’s disease. Here we report that VPS35 is significantly reduced in Progressive Supra-nuclear Palsy and Picks’ disease, two distinct primary tauopathies. In vitro studies show that overexpression of VPS35 leads to a reduction of pathological tau in neuronal cells, whereas genetic silencing of VPS35 results in its accumulation. Mechanistically the availability of active cathepsin D mediates the effect of VPS35 on pathological tau accumulation. Moreover, in a relevant transgenic mouse model of tauopathy, down-regulation of VPS35 results in an exacerbation of motor and learning impairments as well as accumulation of pathological tau and loss of synaptic integrity. Taken together, our data identify VPS35 as a novel critical player in tau metabolism and neuropathology, and a new therapeutic target for human tauopathies. VPS35 tau protein tau phosphorylation tauopathy transgenic mice ==== Body INTRODUCTION Neurodegenerative diseases are characterized by aberrant accumulation of proteins within neurons and glial cells leading to a loss of cellular protein homeostasis. Proper sorting and trafficking of intracellular proteins is critical for maintaining this delicate balance. Therefore, interconnected pathways, such as the endosomal-lysosomal system, are in place to preserve efficient transport of protein cargos for recycling or degradation. In recent years, the endosomal-lysosomal network, particularly the dysfunction of this system, has gained considerable attention in the neurodegeneration field and has been linked to several diseases, such as Alzheimer’s disease (AD) [1,2]. A key regulator in endosomal protein sorting is the retromer complex, an evolutionarily conserved multimeric system responsible for retrograde transport of cargo from the endosome to the trans-Golgi network (TGN) [3]. The retromer complex consists of two major components including a vacuolar protein sorting (VPS) trimer, VPS35/VPS26/VPS29, also known as the retromer recognition core, and a membrane targeting part containing different and specific sorting nexins proteins [4–6]. Among the various cargo proteins sorted by the retromer complex are the cation-independent mannose-6-phosphate receptor (CIMPR), which transports hydrolases, such as cathepsin D (CTSD) from the TGN to the endosome for proper maturation and ultimate delivery to the lysosome system, and the sortilin-related receptor, also known as SorLA, which binds to the amyloid β precursor protein (APP) [7, 8]. Data in the literature consistently show that the development of retromer dysfunction-dependent neuropathology is secondary to a partial “loss of function”. Thus, deficiency in the complex function resulting from down-regulation of VPS35 has been reported in hippocampi of AD patients; and genetic studies found that its variants increase the risk of developing AD [9,10]. Interestingly, several studies have shown a link between a deficit of VPS35 and amyloidogenesis, such that early deficiency in this sorting system promotes β-secretase-1 (BACE-1) activity in the endosomes, enhancing pathogenic cleavage of APP and Aβ formation [11]. VPS35 genetic reduction results in an increase of Aβ levels, cognitive impairments, and synaptic dysfunction in a mouse model of AD-like brain amyloidosis [12]. Moreover, VPS35 and the other components of the retromer recognition core display age-dependent, decreased protein levels in the brains of the Tg2576 mice, a model of brain amyloidosis [13]. While more studies are needed to dissect the precise mechanisms whereby VPS35 deficits may initiate and/or control amyloidogenesis in vivo, no study has investigated so far a direct role for VPS35 in the development of the second most common pathological feature of AD and related tauopathies: abnormal tau phosphorylation and neuropathology. MATERIALS and METHODS Human samples Frozen human postmortem frontal cortex and hippocampal brain tissues were obtained from patients with a clinical diagnosis of progressive supranuclear palsy (PSP) (9 females and 5 males) or Pick’s disease (3 females 7 and males) along with normal age-matched controls (4 females and 6 males). Tissue samples were provided by the NIH NeuroBioBank with informed consent under approval by the appropriate institutional review board at each of the following brain banks: UCLA Brain Bank (WLA VA Medical Center), University of Maryland Brain and Tissue Bank, and Harvard Brain Tissue Resource Center. Information regarding tissue donor neuropathological criteria including age, sex, and post mortem interval (PMI) is described in Supplementary Table 1. Postmortem diagnostic evaluation was performed in accordance with standard histopathological criteria. AAV1/2 vector construct Purified AAV2/1 vectors expressing VPS35 shRNA (AAV1-GFP-U6-mVPS35-shRNA) or scramble (AAV1-GFP-U6-scrmb-shRNA) under neuronal-specific promoter synapsin 1 were purchased from a commercial vendor (Vector Biolabs). Animals and Injection of AAV2/1 to neonatal mice This study was approved by the Temple Institutional Animal Care and Usage Committee, in accordance with the US National Institutes of Health guidelines. The P301S mice (PS19 line) expressing human mutant microtubule-associated protein tau, MAPT, driven by the mouse prion protein (Prnp) promoter were used for these studies [14]. Both male and female mice were used. The AAV2/1 injection procedures were performed as described previously [15,16]. Briefly, two microliters of AAV2/1-VPS35 shRNA or AAV2/1-scramble shRNA (1.3×1013 genome particles/ml) were bilaterally injected into the cerebral ventricle of newborn mice using a 5μl Hamilton syringe. A total of thirty-three pups were used; seventeen were injected with AAV2/1-VPS shRNA (8 wildtype P301S−/−, 9 P301S+/−; Females=10, Males=7) and sixteen were injected with scramble vector (8 wildtype, 8 P301S Females=7, Males=9). All animals were housed on a 12-h light/dark cycle in a pathogen-free environment and given regular chow and water, ad libitum. Animals underwent behavioral testing at age 10 months. Two weeks later mice were sacrificed for tissue processing. Upon euthanasia, mice were perfused with ice-cold 0.9% PBS containing EDTA (2mmol/L), pH 7.4. Brains were extracted, gently rinsed in cold 0.9% PBS and immediately dissected into two hemispheres. One half was stored at -80̊ C for biochemistry analyses and the other half was fixed in 4% paraformaldehyde diluted in PBS, pH 7.4, for immunohistochemistry. Behavioral Tests All animals were always handled for at least 3–4 consecutive days before testing, and were tested in random order by an experimenter who was unaware of the genotype/treatment. Y-maze The Y-maze apparatus consisted of three arms 32 cm (long) 610 cm (wide) with 26-cm walls (San Diego Instruments, San Diego, CA) and testing was performed as previously described [17–19]. Briefly, each mouse was placed in the center of the Y-maze and allowed to explore freely for five minutes to measure spontaneous alternating behavior. The sequence and total number of entries were video-recorded. An entry into an arm was considered valid if all four paws entered the arm. An alternation was defined as three consecutive entries into three different arms (1, 2, 3, or 2, 3, 1, etc.). Percentage of alternation was calculated using the following formula: total alternation number/total number of entries−2) × 100. Rotarod Mice were tested on Rotarod as previously described [20]. Briefly, a Rotarod instrument with automatic timers and falling sensors (Omnitech Electronics, Columbus, OH, USA) was used and testing was performed on four consecutive days. The mice were placed individually on a 30 mm diameter rotating cylinder suspended above a cage floor. The length of time the mice managed to remain on the rod was automatically recorded. The mice underwent six trials per day and the maximal observation time for each trial was 90 s. During the training phase (day 1–3), the speed of the rotation was increased gradually from 0 to 15 r.p.m. during the first 15 s and held constant at that rate for the rest of the trial (75 s). During the test (day 4), the speed of rotation was accelerated gradually from 0 to 90 r.p.m. during the 90 s of the trial. Morris Water Maze To perform the Morris Water Maze (MWM), we used a white circular plastic tank (122 cm in diameter, walls 76 cm high), filled with water maintained at 22°±2 °C and made opaque by the addition of a nontoxic white paint, as previously described [17,21]. Mice were trained on four consecutive days to find a Plexiglas platform submerged in water from four different starting points. If they failed to find the platform within 60 s, they were manually guided to the platform and allowed to remain there for 15 s. Mice were trained to reach a training criterion of 20 s (escape latency). Mice were assessed in the probe trial, which consisted of a free swim lasting for 60 s without the platform, 24 h after the last training session. Animals’ performances were monitored using Any-Maze™ Video Tracking System (Stoelting Co., Wood Dale, IL) which provided data for the acquisition parameters (latency to find the platform and distance swam and) and the probe trial parameters (number of entries in the target platform zone of the platform and time in quadrants). Quantitative real-time RT-PCR RNA from PSP, Pick’s, and healthy control human brain frontal cortex and hippocampus was extracted and purified using the RNeasy mini-kit (Qiagen, Germantown, MD), and used as previously described [18,19]. Briefly, 1 μg of total RNA was used to synthesize cDNA in a 20 μl reaction using the RT2 First Strand Kit for reverse transcriptase-PCR (Super Array Bioscience). Human VPS35 and VPS26b genes were amplified by using the corresponding primers obtained from Super Array Bioscience. β-Actin was used as an internal control gene to normalize for the amount of RNA. Quantitative real-time RT-PCR was performed by using Eppendorf® ep realplex thermal cyclers (Eppendorf, Hauppauge, NY). Two microliters of cDNA was added to 25 μl of SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA). Each sample was run in duplicate, and analysis of relative gene expression was done by using the 2−ΔΔCt method [22]. Briefly, the relative change in gene expression was calculated by subtracting the threshold cycle (ΔCt) of the target genes (VPS35, VPS26b, VPS29) from the internal control gene (β-actin). Based on the fact that the amount of cDNA doubles in each PCR cycle (assuming a PCR efficiency of 100%), the final fold-change in gene expression was calculated by using the following formula: relative change = 2−ΔΔCt. Western Blot Analyses RIPA extracts from human and mouse brain homogenates were used for Western Blot analyses as previously described [16,23]. Briefly, samples were electrophoresed on 10% Bis–Tris gels or 3–8% Tris–acetate gel (Bio-Rad, Richmond, CA), transferred onto nitrocellulose membranes (Bio-Rad), and then incubated overnight at 4° C with the appropriate primary antibodies; anti-VPS35 [dilution: 1:300] (Abcam, Cambridge), anti-VPS26 [dilution: 1:200] (Abcam), anti-VPS26b [dilution: 1:200] (Proteintech, Rosemont, IL), anti-VPS29 [dilution: 1:400] (Abcam), anti-CTSD [dilution: 1:200] (Novus Biologicals, Littleton, CO), anti-CIMPR [dilution: 1:200] (Abcam), anti-HT7 [1:200] (Thermo, Waltham, MA), anti-MC-1 [1:100] (Dr. Peter Davies), anti-AT8 [1:100] (Thermo), anti-AT180 [dilution: 1:200] (Thermo), anti-AT270 [1:200] (Thermo), anti-PHF1 [1:100] (Santa Cruz), anti-PHF13 [1:100] (Thermo), anti-SYP [1:300] (Santa Cruz), anti-PSD95 [1:200] (Thermo), anti-GFAP [1:200] (Santa Cruz), anti-IBA-11[1:100] (Thermo), anti-HSP90 [dilution: 1:500] (Abcam), anti-Rab5 [dilution: 1:200] (Abcam), anti-LAMP2 [dilution: 1:200] (Abcam), anti-GAPDH [dilution: 1:1000] (Cell signaling, Danvers, MA) and anti-Beta actin [1:500] (Santa Cruz). After three washings with T-TBS (pH7.4), membranes were incubated with IRDye 800CW-labeled secondary antibodies (LI-COR Bioscience, Lincoln, NE) at room temperature for 1 h. Signals were developed with Odyssey Infrared Imaging Systems (LI-COR Bioscience, Lincoln, Nebraska). β-Actin or GAPDH was always used as internal loading control. Sarkosyl Insolubility Assay The assay for insoluble tau was performed as previously described [18]. Briefly, ultracentrifugation and sarkosyl extraction (30 min in 1% sarkosyl) was used to obtain soluble and insoluble fractions of tau from brain homogenates. Insoluble fractions were washed one time with 1% sarkosyl, then immunoblotted with the HT7 antibody. Subcellular Fractionation Cellular fractionation was performed to isolate purified cytosol, endosomes, and lysosomes according to the protocol described by Miura et.al, 2014 [24] (Figure 5A). Briefly, cells (1 × 108) were transfected with either VPS35 siRNA or control. Cells were harvested after 72 h incubation in ice-cold fractionation buffer (10 mM Tris/acetic acid pH 7.0 and 250 mM sucrose) and homogenate was cleared by three successive centrifugation steps (500 ×g for 2 min, 1000 ×g for 2 min and 2000 ×g for 2 min). The supernatant was centrifuged at 4000 ×g for 2 min to obtain the plasma membrane and nuclei in a pellet. The supernatant was then ultracentrifuged at 100,000 ×g for 2 min to pellet the mitochondria, endosomes, and lysosomes. Lysosomes were isolated from the previous fraction by a 10-min osmotic lysis using 5 times the pellet volume of cold distilled water. This fraction was then centrifuged again at 100,000 ×g for 2 min to obtain lysosomes in the supernatant and endosomes/mitochondria in the final pellet. Pellets were resuspended in solubilization buffer (RIPA) and subjected to western blotting as described above. Immunohistochemistry Immunostaining was performed as previously described [19–21]. Briefly, serial coronal sections were mounted on 3-aminopropyl triethoxysilane (APES)-coated slides. Every eighth section from the habenular to the posterior commissure (8–10 sections per animal) was examined with unbiased stereological principles. The sections used for testing HT7, AT8, AT270, PHF13, synaptophysin, PSD95, GFAP, and IBA-1 were deparaffinized, hydrated, rinsed with phosphate-buffered saline, and pretreated with citric acid (10 mm) for 5 min for antigen retrieval, then with 3% H2O2 in methanol for 30 min to eliminate endogenous peroxidase activity and with blocking solution (2% normal serum in Tris buffer, pH 7.6). The sections were incubated with appropriate primary antibody overnight at 4˚C then with secondary antibody at room temperature and developed using the avidin–biotin complex method (Vector Laboratories, Burlingame, CA, USA) with 3,3-diaminobenzidine (DAB) as chromogen. Cell line and treatment Neuro-2 A neuroblastoma (N2A) cells stably expressing human tau (N2A-Htau) were cultured in Dulbecco’s modified Eagle medium supplemented with 10% fetal bovine serum, 100 U/mL streptomycin (Mediatech, Herdon, VA) and 100 mg/mL Hygromycin (Invitrogen, Carlsbad, CA) at 37ºC in the presence of 5% CO2. For knockdown experiments, cells were cultured to 70% confluence in six-well plates and then transfected with 100nM control siRNA or VPS35 siRNA (Thermo Fisher/Ambion, Waltham, MA) by using Lipofectamine RNAiMax reagent (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions and as previously described [16]. After 72 h treatment, supernatants were collected and cells were harvested in lytic buffer for biochemistry analyses. For TPT experiments, cells were grown under the same conditions as stated above after which, 25 µM or 50 µM was added to cells for 48 h. Cells were subsequently collected and used for western blot analyses. For overexpression studies, cells were cultured to 70% confluence in six-well plates and then transfected with 5ug VPS35 plasmid or empty vector by using Lipofectamine 2000 reagent (Invitrogen) according to the manufacturer’s instructions and as previously described [16]. After 48 h treatment, supernatants were collected and cells were harvested in lytic buffer for biochemistry analyses. For some experiments, transfected cells were collected after 48 h and used to measure CTSD activity, using a fluorometric Assay Kit, according to the manufacturer’s instructions (Abcam). Briefly, cell lysates were centrifuged at 10,000 x g for 10 minutes at 4ºC and the supernatant were used. Cathepsin D activity was determined at the 48hr timepoint by cleavage of the fluorescence peptide substrate [DnP-DR-MCA, GKPILFFRLK(DnP)-DR substrate peptide labeled with MCA]. For pepstatin A experiments, cells were pre-treated with 100µM pepstatin A (Sigma) 24 h before plasmid overexpression to inhibit CTSD activity. Immunofluorescence Cell immunostaining was performed as previously described with slight modification [25,26]. Briefly, N2A−Htau cells were plated on Matrigel-coated 35 × 10 mm No 1.5 glass-bottom dishes (Eppendorf) and grown to 60% confluence prior to VPS35 siRNA transfection. After a 72 h incubation, cells were washed with PBS and fixed in 4% paraformaldehyde in PBS for 20 min at RT. After washing several times with PBS and permeabilizing with 0.2% Triton X-100 in PBS, cells were incubated in blocking solution (5% normal donkey serum in PBS) for 1 h at RT followed by an O/N incubation at 4 °C with a combination of primary antibodies prepared in 2% blocking solution in PBS against VPS35 (1:250, Abcam), Tau13 (1:200, Biolegend), PHF13 (1:20, Thermo), AT270 (1:100, Thermo), and MC-1 (1:20, provided by Peter Davies). After several washings with PBS, cells were incubated for 1 h at RT with secondary Alexa Fluor 568-conjugated antibody against VPS35 and 647-conjugated antibody against Tau13, AT270, PHF13 or MC-1 (all at 1:250; Abcam). Cells were then stained with DAPI, washed with PBS and subsequently held at 4 °C in the dark until imaging. Confocal Microscopy Stained cells were maintained in PBS and imaged under a PLAN APO Lambda 60x oil objective (NA 1.40) on an Eclipse Ti2 microscope using a Galvano scanner and a GaAsP multi-unit detector on the Nikon A1R Resonant Scanning Confocal System. Using sequential engagement of the 405, 561 and 640 laser lines and a pinhole size of 23 µm, multiple 5 × 5 2-D stitched FOVs were collected from each condition at a format size of 512 × 512, with a pixel resolution of 0.41µm, to determine ROIs comprised of a minimum of multiple Tau-expressing neurons. Laser power and gain for each laser line was standardized for each condition and its respective control(s). VPS35 was captured using a TRITC filter (BP 595/50) and Tau13, MC-1, and Phospho-Tau were captured using a Cy5 filter (BP 700/75). Upon ROI determination per condition, multiple 25 µm z-stacks, with a step size of 0.25 µm each, were collected at an image resolution of 1024 × 1024 pixels and a pixel resolution of 0.21 µm. Captured z-stacks were subsequently deconvolved in automatic mode and 3-D rendered using Nikon’s NIS-Elements AR 5.02.00 software interface. 3-D renderings were thresholded against the Cy5 channel (pseudo-colored purple) to capture only tau, MC-1 or phospho-Tau expressing cells and subsequently filtered by size to exclude background and non-cellular artifacts. Cy5 channel mean fluorescence intensity was determined using several Z-stacks from each group (CTR siRNA and VPS35 siRNA) and results were normalized to percent of control. For co-localization determination, Tau/MC-1/P-Tau+ cells were further thresholded to delineate a final ROI that displayed TRITC (VPS35) positivity, thereby excluding all TRITC+ signals found in non-Tau expressing cells. Statistical Analysis All the data are expressed as mean ± standard error of the mean and represented with individual data values. All statistical analysis were determined following Gaussian distribution with α=0.05. Comparisons between two groups were made using an unpaired two-tailed t-test. Comparisons between more than two groups were made using a one-way ANOVA with Bonferoni’s multiple comparisons test. The p-values for each comparison are listed in each figure legend with p<0.05 considered statistically significant. All statistical tests were performed using GraphPad Prism 8.0.1. RESULTS VPS35 is down-regulated in two human tauopathies We assessed protein levels of VPS35 and the other two components of the retromer recognition core, VPS26b and VPS29, in human post-mortem brain tissues from patients with Progressive Supra-nuclear Palsy (PSP), one of the most common forms of primary tauopathy, and age-matched healthy controls. In contrast to controls, both frontal cortices and hippocampi from PSP patients showed a significant decrease in the levels of all three retromer recognition core proteins as measured by western blot analyses (Figure 1A-D; supplementary Table). We confirmed this observation also in a separate cohort of patients with a diagnosis of Pick’s disease, a distinct human primary tauopathy (Figure 1E-H; supplementary table). Since we observed a change in protein levels for VPS35, VPS26b and VPS29 next we performed RT-PCR to determine relative expression of mRNA levels for these proteins in the same samples. In contrast to changes in protein level, no differences between PSP and healthy matched controls were detected for mRNA levels of the three core proteins (Supplementary Figure 1A, B). VPS35 modulates tau conformational change and phosphorylation in vitro Having observed a significant reduction in VPS35 levels in two distinct human primary tauopathies, next we determined whether VPS35 expression levels could directly affect tau and its phosphorylation status by implementing an in vitro cell model. To this end, we utilized N2A-Htau cell line, which express all six isoforms of the human tau protein, and silenced VPS35 gene expression with a specific siRNA. First, we confirmed this treatment resulted in a significant down-regulation of VPS35 protein levels together with a significant reduction in VPS26, but not significant changes in VPS29 (Figure 2A, B). Under this experimental condition, we observed a significant increase in phosphorylated tau at specific epitopes recognized by the antibodies AT270 and PHF13, and a higher immunoreactivity to the antibody MC-1, which recognizes pathological, conformational changes of tau (Figure 2C, D). By contrast, no significant changes were observed for phosphorylated tau at the epitopes recognized by the antibodies AT8 and AT180 (Figure 2C, D). Next, to investigate whether VPS35 silencing affected also tau intracellular localization we performed subcellular fractionation to isolate cytosol, endosome, and lysosome fractions following VPS35 silencing. Compared to controls, VPS35 downregulation resulted in a significant increase in pathological tau as measured by the MC-1 immunoreactivity, and phosphorylated tau recognized by the antibody PHF13 in the cytosol as well as the endosomal fractions, but not in the lysosome. (Figure 2F, G). By contrast, no significant changes were observed for the immunoreactivity of phosphorylated tau recognized by the antibody AT270 in any of these cellular fractions (Figure 2F, G). Given our western blot findings that silencing VPS35 in neuronal cells results in conformational and phosphorylated tau accumulation, we utilized immunocytochemistry to determine mean fluorescence intensity of total, phosphorylated, and pathological tau after VPS35 silencing using confocal microscopy. Using 3D rendering of z-stacks, we determined mean fluorescence intensity of each tau signal from cells with or without VPS35 genetic silencing. Compared to control, VPS35 silenced cells had a significant increase in mean fluorescence intensity for MC-1 and PHF13 (Figure 3A, B), while there were no changes in intensity for Tau13 or AT270. Additionally, we aimed to determine if VPS35 and tau isoforms colocalize in our cell line. To control for discrepancies in cell size, we used NIS elements software to threshold for object volume and size in the Cy5 channel for each tau antibody. After applying a volume and size threshold to VPS35 signal as well, we calculated the fraction of colocalization as mean object intensity of VPS35 divided by mean object intensity of tau within specific ROIs. In this way, we were able to calculate the amount of co-localization between VPS35 and Tau13, AT270, PHF13, or MC-1. As shown in figure 3C shows that VPS35 partially co-localizes with total, phosphorylated and pathological tau to a similar degree. VPS35 overexpression reduces accumulation of pathological tau Nest, we over-expressed VPS35 and investigated its effect on tau phosphorylation in N2A-Htau cells. First, we confirmed the efficiency of the VPS35 overexpression system using western blot and found a significant ~50% increase in its protein levels, which was associated with an elevation in VPS26 levels, but no changes in levels of VPS29 (Figure 4A, B). While we observed that the steady state protein levels of CIMPR and CTSD were unchanged between the two groups, we found a significant increase in CTSD enzymatic activity in lysates from cells over-expressing VPS35 (Figure 4A-C). To investigate the effect of VPS35 overexpression on tau, we assayed protein levels of total and pathological tau under this experimental condition. As shown in figure 4D, compared with controls, we observed a significant decrease in total tau (HT7), pathological tau (MC-1), and phosphorylated tau as recognized by the antibody AT270 in cells expressing higher levels of VPS35. By contrast, no changes were observed when the immunoreactivity for phosphorylated tau recognized by the antibodies AT8 and PHF13 were assayed (Figure 4D, E). To further support these findings, neuronal cells were treated with TPT-260, a pharmacological chaperone which has been reported to stabilize and increase the levels of VPS35 [13,27]. Cells were incubated for 48 hours with TPT-260 (25 μM and 50 μM) and lysates collected for western blot analysis. As shown in figure 4F, cells treated with the highest concentration of the drug had a significant increase in steady state levels of VPS35 and VPS26, but no changes were observed for VPS29. Moreover, we observed that while there were no differences between control cells for CIMPR levels, the high concentration of the drug induced a significant increase in mature CTSD levels (Figure 4F, G). In association with these changes we observed that levels of phosphorylated tau, as recognized by the antibodies AT270 and PHF13 as well as pathological tau, as recognized by the antibody MC-1, were also significantly reduced (Figure 4 H, I). By contrast, the treatment did not affect phosphorylated tau at the epitopes recognized by the antibodies AT8 and AT180 (Figure 4 H, I) The effect of VPS35 on tau is dependent on cathepsin D activity Given that the retromer complex traffics CTSD, a known degradative protease, and that genetic manipulation of VPS35 in vitro was associated with corresponding changes in the available enzyme’s activity, we next investigated the functional role of this protease in the VPS35-dependent effect on the accumulation of pathological tau. Cells over-expressing VPS35 were pretreated with pepstatin A, a specific inhibitor of CTSD activity, or vehicle and changes in tau were assessed by western blot analysis. Neuronal cells treated with pepstatin A alone but not over-expressing VPS35 showed no changes in either VPS35 levels or any forms of tau (total, pathological, phosphorylated) (Figure 5A, B); however, CTSD activity in these cells was decreased (Figure 5C) as expected. VPS35 overexpression lead to a decrease in pathological tau measured by MC-1 immunoreactivity as well as phosphorylated tau as measured by AT270, but no significant changes were observed for total tau levels (Figure 5A, B). Interestingly, in the presence of pepstatin A although cells had elevated levels of VPS35 the decrease in MC-1 and phosphorylated tau levels was abolished, suggesting that CTSD activity was necessary for the VPS35-dependent effect on tau pathological changes (Figure 5A-C). Down-regulation of VPS35 exacerbates motor and learning impairments in P301S mice The data accumulated so far provide support for a direct role of VPS35 in modulating tau pathologic changes in neuronal cells, to make this observation physiologically relevant, next, we sought to investigate whether genetic down-regulation of VPS35 in vivo would alter the onset and development of the phenotype of a tau transgenic mouse model, P301S mice. To this end, newborn WT and P301S mice were administered with intraventricular injections of either AAV-VPS35 shRNA or AAV-control shRNA and followed until 9–10 months of age, when the mice were tested on three behavioral paradigms. First, mice were tested on Y-maze to assess working memory. No differences in general locomotor activity as measured by the total number of arm entries in the maze were observed among the different groups (Figure 6A). Conversely, P301S mice receiving AAV-VPS35 shRNA had lower numbers of arm alternations compared to P301S mice receiving control vector (Figure 6B). No significant differences were observed between WT groups receiving empty vector or AAV-VPS35 shRNA (Figure 6A, B). Mice were also tested in the Rotarod paradigm to assess their motor learning ability. First, across groups, mice did not show any baseline motor issues, as they spent comparable time on the rod during the training phase. While mice across groups did not display significant differences during the training days (Figure 6C), in the probe test on Day 4, P301S mice receiving AAV-VPS35 shRNA showed decreased motor function compared to both WT groups (Figure 6D). Lastly, mice were assessed on spatial learning and memory via the Morris water maze test. During the training phase over four consecutive days, we observed no differences in performance among the different groups (Figure 6E); however, during test day, P301S mice had a lower number of platform crosses and this effect was exacerbated in the P301S-AAV-VPS35 shRNA mice group (Figure 6F). We observed no significant changes across groups in the latency to reach the platform or the time spent in the platform quadrant (Figure 6G, H). No significant differences were observed among the different groups when males and females were analyzed separately (Supplemental figure 2). Genetic downregulation of VPS35 worsens tau neuropathology in P301S mice Following behavioral studies, mice were euthanized and brain tissues subjected to biochemical analyses. First, we wanted to confirm the efficacy of our AAV-VPS35 shRNA intraventricular injection approach in these mice. As expected, we observed a significant reduction in the steady state protein levels of VPS35, which was associated with a similar reduction in the levels of VPS29 (Figure 7A, B). Moreover, we observed that compared with control group, P301S mice receiving AAV-shVPS35 had a significant reduction in the levels of mature CTSD, and CIMPR (Figure 7A, B). As shown in Figure 7C, D, levels of total tau, as recognized by the antibody HT7, as well as levels of phosphorylated tau at several epitopes were significantly increased in P301S mice with VPS35 downregulation. Additionally, we found that compared to brains from P301S controls, the mice receiving VPS35 shRNA had elevated levels of insoluble tau fraction (Figure 7C, D). Confirming the Western blot data, histochemical staining showed elevated phosphorylated tau and HT7 immunoreactivities in the brains of P301S-AAV-VPS35 shRNA mice compared to P301S controls (Figure 7E). VPS35 genetic downregulation affects synaptic pathology and neuroinflammation Since deficits in cognition as well as tau phosphorylation often correlate with alterations in synaptic integrity, we investigated whether biomarkers of synaptic integrity were affected by VPS35 gene downregulation. Compared to P301S controls, mice receiving AAV-VPS35 shRNA displayed significant reductions in the steady state levels of synaptophysin and PSD-95, pre-synaptic and post-synaptic markers, respectively, as assayed by both western blot and immunohistochemistry (Figure 7F-H). We also measured neuroinflammatory markers and found that compared with controls, P301S mice with VPS35 down-regulation had a significant elevation in levels of the glial fibrillary acidic protein (GFAP), a marker of astrocyte activation (Figure 7F-H). By contrast, we observed no changes in microgliosis as recognized by ionized calcium-binding adapter molecule 1 (IBA1). Immunohistochemical analyses confirmed these findings, showing an increase in immunoreactivity for GFAP but not for IBA-1 (Figure 7H). DISCUSSION VPS35 is the major component of the recognition core of the retromer complex system, which normally controls transport and sorting pathways for several cargo proteins out of the endosomes. It is the single most critical protein of the whole retromer assembly since knocking it down is sufficient to cause dysfunction of the entire complex. Several studies have shown that low levels of VPS35 affect the formation of the complex by influencing expression of the other two core proteins (VPS26 and VPS29) suggesting a general destabilization of the entire complex system [28]. Additionally, down-regulation of VPS35 or mutations in its cytoplasmic domains results in disruption of the retromer cargo interaction also leading to dysfunction of the complex, which ultimately results in accumulation of cargo proteins into the endosomes. While in recent years abundant literature has clearly established a biologic link between VPS35 and APP/Aβ peptides, no data have been presented so far in support of a direct interaction between VPS35 and tau, another important pathological player in AD pathogenesis. Here we provide new experimental evidence implicating VPS35 in the regulation of tau phosphorylation levels with functional implications for the pathogenesis of AD and related tauopathies. Progressive accumulation of hyper-phosphorylated and pathological conformation-changed tau inside neurons represents a central pathogenic event in AD and related tauopathies, and several studies have shown that the amount of tau pathology better correlates with neuronal dysfunction and cognitive impairments than Aβ load [29,30]. Among the possible causes of the accumulation of pathological tau, a dysfunction in its degradation pathways has gained increasing support. Tau degradation can occur via two major mechanisms: the autophagy-lysosome and the ubiquitin-proteasome systems [31,32] and blockade of either of them promotes tau accumulation and neuropathology [33,34]. Interestingly, in recent years a third pathway has emerged as potentially relevant for the pathogenesis of AD and other neurodegenerative diseases: the endosome retromer-dependent system. Nevertheless, its role in regulating tau phosphorylation and metabolism and importantly its functional relevance in the pathogenesis of tauopathy is unknown. In the current work, we implemented both in vivo and in vitro experimental approaches to demonstrate a critical role for this sorting pathway, in particular for VPS35, in regulating tau phosphorylation and neuropathology. First, we show that VPS35 and the other two components of the retromer recognition core are down-regulated in two distinct primary tauopathies, PSP and Pick’s disease. Having observed these changes, we asked whether VPS35 is directly involved in the metabolism of tau and its phosphorylation, and ultimately, if VPS35 can modulate the development of classical tau neuropathology. Here we present evidence that, in neuronal cells, downregulation of VPS35 alone resulted in an alteration of the other two components of the recognition core, VPS26 and VPS29, and this was associated with a significant increase in phosphorylated tau at specific epitopes, and accumulation of pathological tau inside the cells. By performing cell fractionation analysis we demonstrated that indeed under this condition there was a significant accumulation of both MC-1 and PHF13 immunoreactivities not only at the cytosol but most importantly at the endosomal level. Further confirming these findings, immune-cytochemical studies showed that VPS35 silencing resulted in increased fluorescence intensity for pathological and phosphorylated tau (i.e., MC-1 and PHF13). It is of interest to note that while we were able to show similar changes for PHF13 and MC-1 immunoreactivity after VPS35 down-regulation in the whole cell, fractionation, and co-localization studies, we did not observed the same changes for AT270 in the immunofluorescence experiments. While we do not have an explanation for this slight difference, we believe that this finding underscore the challenge of implementing different methods to gain further support for any obtained results. By using an opposite approach in which we up-regulated VPS35 steady state levels in the same cells, we demonstrated that phosphorylated and pathological tau were significantly reduced, and this effect was associated with an increase in the activity of CTSD. Importantly, these findings were reproduced by using a pharmacological approach in which, we treated our cells with pharmacological chaperone, TPT-260, a known agent to stabilize the retromer complex [27]. Under this condition, we observed a significant increase in the steady state levels of VPS35, as well as the mature form of CTSD, which then resulted in a significant reduction in tau phosphorylation and its pathological levels. Since we observed that manipulation of VPS35 levels and the subsequent changes in pathological tau were coincidental with alteration in the availability of CTSD activity, an important protease previously involved in AD pathogenesis [35, 36], next we explored its involvement in the VPS35-dependent effect on tau. Previous work has shown that CTSD is one of the various cargo proteins sorted by the retromer complex system [37]. Indeed normal level of the retromer recognition core components, the necessary condition for a proper sorting function of the retromer as a complex, is fundamental for the transport of this hydrolase from the endosomes to the TGN from where after final maturation is properly delivered to the lysosome system [38]. Herein, we demonstrate that selective pharmacological inhibition of CSTD activity is sufficient to neutralize the effect that VPS35 has on pathological tau. While previous reports have indicated that CTSD is implicated in tau degradation [39,35], our work is the first to directly link retromer dysfunction with alteration of tau phosphorylation and pathological tau accumulation via this protease. Moreover, the fact that we were able to reverse VPS35 deficiency by using a small pharmacological chaperone is significant from a translational point of view. Thus, we believe that these newly described molecules [27] deserve a much closer look as viable therapeutic agents against AD, related tauopathies and other neurodegenerative diseases where VPS35 dysfunction have been well documented [40]. Most notably, we also show that in vivo genetic downregulation of VPS35 in the CNS results in a worsening of the phenotype in a relevant mouse model of human tauopathy, the P301S transgenic mice [14]. Under this condition, we observed that mice with reduced levels of VPS35 had a further reduction in the motor abilities as well as learning and memory skills together with a significant increase in tau phosphorylation and neuropathology, disruption of synaptic integrity and increased neuroinflammatory responses. While we did not observe any changes in the classical kinases and phosphates involved in the major post-translational modifications of tau, we observed that mice with VPS35 downregulation had a significant increase in the steady state levels of total tau. Taken together these findings support the idea that reduction in retromer complex function does not directly influence these modifications, but most likely leads to an increased time spent by tau and its phosphorylated isoforms in the endosomal system. In summary, our current findings identify VPS35 as an important and critical new regulator of tau phosphorylation and proteostasis, and support its direct involvement in the pathogenesis of tauopathies. Considering the data in the literature and our most recent paper showing that a gain in function of VPS35 rescue the phenotype of an AD mouse model with amyloid plaques and tau tangles [41], it is evident that this pathway by modulating independently both Aβ and tau pathology should be considered highly relevant in the design of future therapeutic interventions. As it becomes increasingly evident that endosomal sorting and trafficking dysfunction is a common cellular event in many neurodegenerative diseases, our work provides new insights into a previously unexplored research area involving VPS35 as a novel and viable therapeutic target for reducing both Aβ and tau pathology not only in AD, but also in many other related neurodegenerative conditions all characterized by an altered proteostasis. Supplementary Material 1 ACKNOWLEDGMENTS Domenico Praticó is the Scott Richards North Star Charitable Foundation Chair for Alzheimer’s research. The authors would like to thank the patients and the families who have donated the brain tissues together with the University of Maryland Brain and Tissue Bank, the Human Brain and Spinal Fluid Resource Center (UCLA, Los Angeles, CA), and Harvard Brain Tissue Resource Center, McLean Hospital for providing post-mortem tissue through NIH NeuroBioBank. We would also like to thank Peter Davies for supplying the MC-1 antibody. This study was supported in part by grants from the National Institute of Health (AG055707, and AG056689). Figure 1. Retromer core components are down-regulated in postmortem human PSP and Pick’s disease brains. A. Representative Western blot analyses for VPS35, VPS26b, and VPS29 in frontal cortex homogenates from PSP patients and age-matched controls. B. Densitometric analyses of the immunoreactivities to the antibodies shown in panel A (p=0.0193 (VPS35); *p=0.005; p=0.0304 (VPS29); N=14 PSP, N=10 age-matched controls). C. Representative Western blot analyses for VPS35, VPS26b, and VPS29 in hippocampus homogenates from PSP patients and age-matched controls. D. Densitometric analyses of the immunoreactivities to the antibodies shown in panel C (*p=0.0062; p=0.0287 (VPS26b); p=0.0167 (VPS29); N=12 PSP, N=10 age-matched controls) E. Representative Western blot analyses for VPS35, VPS26b, and VPS29 in frontal cortex homogenates from Pick’s disease patients and age-matched controls. F. Densitometric analyses of the immunoreactivities to the antibodies shown in panel E (p=0.0124; *p=0.0004 (VPS26b); p=0.0003 (VPS29) G. Representative Western blot analyses for VPS35, VPS26b, and VPS29 in hippocampus homogenates from Pick’s disease patients and age-matched controls. H. Densitometric analyses of the immunoreactivities to the antibodies shown in panel G (p=0.0175 (VPS35); p=0.0218 (VPS26b); p=0.0012; N=10 Pick’s, N=9 age-matched controls). All values are expressed as mean ± SEM. Figure 2. VPS35 silencing promotes accumulation of pathological tau. N2A Htau cells were transfected with VPS35 siRNA or controls for 72 hrs, then supernatants and cell lysates were harvested for biochemistry. A. Representative Western blot analysis for retromer core, VPS35, VPS26, and VPS29, in cells lysates transfected with 100nM VPS35 siRNA, siRNA or empty vector control B. Densitometric analyses of the immunoreactivity to the antibodies shown in panel A (One-way ANOVA F(2,15) = (from left to right) 25.8 p=0.0002; 8.179 p=0.004; 0.6038 p=0.5595). C. Representative Western blot analysis for total tau (HT7), pathological tau (MC-1), and phosphorylated tau at residues S202/T205 (AT8), Thr231 (AT180), T181 (AT270), and Ser396 (PHF13) D. Densitometric analyses of the immunoreactivity to the antibodies shown in panel C (One-way ANOVA F(2,15) = (from left to right) 0.0969 p=0.9089; 5.16 p=0.0196; 0.283 p=0.7573; 0.0855 p=0.9184; 3.789 p=0.0466; 8.426 *p=0.0035), Results are mean ± SEM (N=6 per group, 3 individual experiments). E. Subcellular fractionation schematic: N2A cells were transfected with VPS35 siRNA or control siRNA and cell lysates were separated into cytosol, endosome, and lysosome fractions using high-speed centrifugation (P=pellet, SUP=supernatant). F. Representative immunoreactivity for HT7, MC-1, AT270, and PHF13 in cytosol, endosome, and lysosome fractions of neuronal cells transfected with 100nM VPS35 siRNA or control siRNA. Protein markers for cytosol (HSP90), endosome (Rab5), and lysosome (LAMP2) were used to assess the efficiency of the fractionation and for normalization. G. Densitometric analyses of the immunoreactivity to the antibodies shown in the previous panel (two-tailed T test: HT7 (from left to right) p=0.2184, p=0.5453, p=0.6943. MC-1 (from left to right) p=0.0282, p=0.0184, p=0.7031. PHF13 (from left to right) p=0.0262, p=0.0171, p=0.3951. AT270 (from left to right) p=0.8914, p=0.3651, p=0.8539 All results are mean ± SEM (N=5 per group, 5 individual experiments). Figure 3. Silencing VPS35 increases fluorescence immunoreactivity of phosphorylated and pathological tau. A. Representative confocal microscopy images of VPS35 silenced and control cells for Tau13, AT270, MC-1, PHF13 (Cy5 channel pseudo-colored purple), VPS35 (TRITC: red), nuclear stain DAPI (blue); (Scale bar = 10µm). B. Mean fluorescence intensity for total tau (Tau13), pathological tau (MC-1) and phosphorylated tau AT270 and PHF13 in VPS35 silenced cells versus control. (N=6 z-stacks per group; two-tailed T-test (from left to right) p=0.2705, p=0.6217, p=0.0010, p=0.0424). C. Fraction of colocalization between tau isoforms and VPS35 [N=4 (Tau13), N=5 (AT270), N=6 (MC-1), N=6 (PHF13)]; one-way ANOVA F(3.17)=1.230, p=0.3295. Figure 4. Genetic overexpression and pharmacological stabilization of VPS35 reduces pathological tau. N2A-Htau cells were transfected with either VPS35 plasmid, GFP plasmid (5ug), or empty vector for 48 hrs then supernatants and cell lysates were harvested for biochemistry. A. Representative Western blot analysis for VPS35, VPS26, and VPS29 as well as CTSD and CIMPR protein in cells lysates transfected with VPS35 plasmid, GFP plasmid, or empty vector for 48hrs. B. Densitometric analyses of the immunoreactivity to the antibodies shown in panel A (From left to right one-way ANOVA F(2,15)=9.936 *p=0.0018, 5.852 p=0.0132; 0.5013 p=0.6115; 0.0995 p=0.9059; 0.469 p=0.6344; 0.5662 p=0.5794), Results are mean ± SEM (N=6 per group, 3 individual experiments). C. Cathepsin D activity following 48 hr transfection with VPS35 plasmid, GFP plasmid, or empty vector. Data represent the mean ± SEM (One-way ANOVA F(2,24)=5.428 p=0.0114; N=9 per group, 3 individual experiments run in triplicate. D. Representative Western blot analysis for total tau (HT7), pathological tau (MC-1), and phosphorylated tau AT8, AT270, and PHF13. E. Densitometric analyses of the immunoreactivity to the antibodies shown in panel D (From left to right one-way ANOVA F(2,15)=4.356 p=0.0322, 4.445 p=0.0305; 0.1951 p=0.8248; 4.513 p=0.0292; 0.5339 p=0.5970). Results are mean ± SEM (N=6 per group, 3 individual experiments). Cells were treated with pharmacological chaperone, TPT-260, 48 hrs F. Representative Western blot analysis for VPS35, VPS26, VPS29, CTSD, and CIMPR following 48hr TPT treatment at 25 µM and 50 µM. G. Densitometric analyses of the immunoreactivity to the antibodies shown in panel F (From left to right one-way ANOVA F(2,15)=8.758 *p=0.0030, 5.108 p=0.0203; 0.1872 p=0.8312; 0.1704 p=0.8449; 4.460 p=0.0302; 0.0609 p=0.9412). H. Representative Western blot analysis for total tau (HT7), pathological tau (MC-1), and phosphorylated tau AT8, AT180, AT270, and PHF13. I. Densitometric analyses of the immunoreactivity to the antibodies shown in panel H (From left to right one-way ANOVA F(2,15)=0.1383 p=0.8720, 10.40 p=0.0015; 0.1466 p=0.8648; 0.2148 p=0.8091; 6.354 p=0.0103; 3.914 p=0.0429). All results are mean ± SEM (N=6 per group, 3 individual experiments). Figure 5: Effect of VPS35 on tau pathology is dependent on cathepsin D activity. N2A cells were pre-incubated with 100 μM pepstatin A, or vehicle (DMSO) for 24hrs prior to transfection with VPS35 plasmid, GFP plasmid, or empty vector. Cell lysates were collected at the 48hr timepoint for either Western blot analyses or the CTSD activity assay A. Representative Western blot analysis for VPS35, total tau (HT7), pathological tau (MC-1), and phosphorylated tau AT8, AT270, and PHF13 in cells under the above experimental conditions. B. Densitometric analyses of the immunoreactivity to the antibodies shown in panel A (From left to right one-way ANOVA F(5,30)=6.253 p=0.0186 empty vs VPS35 p=0.0487 empty vs VPS35 pepA; 0.7990 p=0.5582; 4.025 p=0.0215 empty vs. VPS35, p=0.0482 VPS35 vs. VPS35 pepA; 3.572 p=0.0370 empty vs. VPS35; 0.1622 p=0.9745. Results are mean ± SEM (N=6 per group, 3 individual experiments). C. Cathepsin D activity was determined in cell lysates under the experimental conditions described above. Data represent the mean ± SEM (F (5,30)=9.355 p=0.0202; N=6 per group, 3 individual experiments run in duplicate. Figure 6: VPS35 genetic downregulation exacerbates cognitive and motor deficits in P301S mice. A. Total number of arm entries for WT and P301S mice injected with either VPS35-AAV-shRNA or empty vector tested on Y-maze at 9–10 months of age. B. Percentage of alternations for each of the above group of mice (F (3,25) = 5.587 *p=0.0045). C. Rotarod training phase over three consecutive days D. Probe trial for the rotarod, measuring seconds to fall (F(3,25) = 7.164 p=0.0473, p=0.0072, p=0.0006). E. Training phase of Morris water maze (MWM) as measured by latency to reach the platform zone over four consecutive days for WT-control, WT-VPS35, P301S-control, and P301S-VPS35. F-H. During the probe trial, the following paradigms were measured: number of platform crosses for each group (F (3,28)=4.42 p=0.0430, p=0.0058, p<0.0001), latency to platform, and time spent in platform zone for the four groups. Values are expressed as mean ± SEM. Figure 7. VPS35 genetic downregulation modulates tau phosphorylation and neuropathology of P301S mice. A. Representative Western blot analyses for VPS35, VPS26b, VPS29, CTSD, and CIMPR in brain cortex homogenates from P301S and P301S-VPS35 mice. B. Densitometric analyses of the immunoreactivities to the antibodies shown in panel A (Two tailed T-test from left to right p<0.00052, p=0.3233, p=0.0013, p=0.0012, *p=0.0006; Results are mean ± SEM, n=6 per group). C. Representative Western blot analyses for total soluble tau (HT7), phosphorylated tau (AT8, AT180, AT270, PHF-1, and PHF13) and total insoluble tau (HT7) in brain cortex homogenates from P301S and P301S-VPS35 mice. D. Densitometric analyses of the immunoreactivities to the antibodies shown in panel C (Two tailed T-test from left to right p<0.0369, p=0.0372, p=0.0124, *p=0.0097, p=0.0364, *p=0.024, p=0.0145; n=6 per group). E. Representative images of immunohistochemical staining of the hippocampus (CA1 region) of P301S and P301S-VPS35 mice for HT7, AT8, AT270 and PHF13 antibodies. (Scale bar = 50μm). F. Representative Western blot analyses for synaptophysin (SYP), postsynaptic density protein 95 (PSD95), glial fibrillary acidic protein (GFAP), and ionized calcium-binding adapter molecule 1 (IBA1) in brain cortex homogenates from P301S and P301S-VPS35 mice. G. Densitometrf1ic analyses of the immunoreactivities shown in the previous panel (Two-tailed T-test from left to right: *p=0.0002, p=0.0041, ***p=0.0004, p=0.8426; n=6 per group). H. Representative images of immunohistochemical staining of the hippocampus (CA1 region) of P301S and P301S-VPS35 mice for SYP, PSD95, GFAP and IBA1 antibodies. Results are mean ± SEM. CONFLICTS OF INTEREST The authors have no conflicting financial interest to disclose. ==== Refs REFERENCES [1] O’Brien RJ , Wong PC (2011) Amyloid precursor protein processing and Alzheimer’s disease. Annu. Rev. 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==== Front Pediatr Radiol Pediatr Radiol Pediatric Radiology 0301-0449 1432-1998 Springer Berlin Heidelberg Berlin/Heidelberg 4130 10.1007/s00247-018-4130-z Abstracts SPR 2018 30 4 2018 2018 48 Suppl 1 1 298 © Springer-Verlag GmbH Germany, part of Springer Nature 2018This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2018 ==== Body The authors have retracted the abstract #029 “Radiographic characteristics that delineate abusive from accidental skull fractures, including the significance of fracture extension to sutures”, page S95, because the study lacked appropriate ethical oversight from a relevant ethics committee. All authors agree to this retraction. Change history 12/6/2019 The authors have retracted the abstract #029 ��Radiographic characteristics that delineate abusive from accidental skull fractures, including the significance of fracture extension to sutures��. Change history 12/6/2019 The authors have retracted the abstract #029 ��Radiographic characteristics that delineate abusive from accidental skull fractures, including the significance of fracture extension to sutures��.	29707739	PMC7080085	NO-CC CODE	2021-01-06 06:59:48	yes	Pediatr Radiol. 2018 Apr 30; 48(Suppl 1):1-298
==== Front ACS Omega ACS Omega ao acsodf ACS Omega 2470-1343 American Chemical Society 10.1021/acsomega.9b04357 Article Anticancer Effect of Rosiglitazone, a PPAR-γ Agonist against Diethylnitrosamine-Induced Lung Carcinogenesis Wu Yanqiao † Sreeharsha Nagaraja ‡ Sharma Sanjay § Mishra Anurag ∥ Singh Avinash Kumar ⊥ Gubbiyappa Shiva Kumar # † Intensive Care Unit, People’s Hospital of Ningjin County, Ningjin County, Shandong province 253400, China ‡ Department of Pharmaceutical Sciences, College of Clinical Pharmacy, King Faisal University, Al-Ahsa 31982, Saudi Arabia § NMIMS, School of Pharmacy and Technology Management, Shirpur 425405, Maharashtra, India ∥ School of Pharmacy, Suresh Gyan Vihar University, Jaipur 302017, Rajasthan, India ⊥ Department of Pharmacology, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi 110062, India # School of Pharmacy, GITAM University, Hyderabad 530045, India E-mail: [email protected], [email protected]. 04 03 2020 17 03 2020 5 10 5334 5339 18 12 2019 18 02 2020 Copyright © 2020 American Chemical Society2020American Chemical SocietyThis is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes. Multiple effects on cancer cells are exerted by the peroxisome proliferator-activated receptor γ (PPAR-γ). Recent studies have shown that rosiglitazone, a synthetic PPAR-γ ligand, inhibits the growth of cells. This research was designed to assess the impact of rosiglitazone on diethylnitrosamine (DENA)-induced lung carcinogenesis in Wistar rats and to study the underlying molecular mechanism. A total of 40 adult male Wistar rats were separated into four groups as follows: group 1 is known as a control. Group 2 is known as the DENA group (150 mg/kg, i.p.). Group 3 and group 4 denote DENA-induced rats treated with 5 and 10 mg/kg rosiglitazone, respectively. Lipid peroxidation, various antioxidant enzymes, histological perceptions, and caspase-3, Bcl2, and Bax gene expression were measured in lung tissues. Rosiglitazone treatment reverted the DENA-induced changes in the expression of these genes, inflammatory cytokines, and oxidative stress. However, blotting analysis discovered reduced caspase-3 and BAX expressions and elevated Bcl-2 expression in DENA-induced rats. The expression of such proteins causing DENA lung cancer was restored by rosiglitazone therapy. document-id-old-9ao9b04357document-id-new-14ao9b04357ccc-price ==== Body Introduction Lung cancer is the world’s leading cause of cancer death. Although numerous diagnosis and treatment strategies have been developed for lung cancer, the overall five-year survival has not increased considerably because of poor forecasts and lack of effective methods for early detection. New treatment strategies for lung cancer, especially molecular therapies, and the survival rate for patients with lung cancer must be increased urgently. In addition, increased knowledge of essential molecular modifications in normal cells leading to unstable and malignant tumor cells may contribute to the development of possible treatments for this disease. Lung cancer’s major risk factors include air, aflatoxins, food additives, water, industrial toxic chemicals, alcohol, and environmental pollutants. Diethylnitrosamine (DENA) is known to be a lung cancer agent in smoke, cheddar cheese, cured meal, drinking water, fried foods, pesticides, and cosmetics in the field of agriculture and pharmaceuticals.1,2 DENA causes lung cancer in laboratory animal models by inhibiting several enzymes involved in the DNA repair process. In rats, DENA is a powerful pulmonary carcinogen that affects the initiation of carcinogenesis during an enhanced cell proliferation cycle with pulmonary necrosis. DENA-mediated free radical production, increased lipid peroxidation (LPO), endogenous antioxidant depletion, cytotoxicity, and carcinogenesis are recorded in several studies.3,4 The nuclear hormone receptor peroxisoma-activated receptor γ (PPAR-γ) provides a strong link between lipid metabolism and gene transcription regulation. A new class of antidiabetical medication is now widely prescribed, and a group of PPAR-γ activators are now commonly prescribed to control growth arrest and terminal differentiation of adipocytes. Several ligands, such as rosiglitazone, pioglitazone, troglitazone, and 15-deoxide-presaturated J2, have been identified, and some polyunsaturated fatty acids are known. In several organ groups, PPAR-α is expressed: intestines, adipose, pulmonary tissue, breasts, and liver. Several studies have shown that PPAR-β ligand cancer cells can induce cell differentiation and apoptosis and have proposed potential uses as chemopreventive carcinogenesis agents.5,6 This together led us to start these research studies to gain insights into the possibility of rosiglitazone supplement safety based on the mechanism against DENA-induced lung cancer. Results and Discussion Effect of Rosiglitazone on LPO and Antioxidant Enzymes Determining thiobarbituric acid reactive substances was used to determine LPO in the fresh lung homogenate. In the group DENA, the amount of malondialdehyde (MDA) (an LPO marker) increased 341.1% but was lower at 27.4 and 71.8% following the 5 and 10 mg/kg supplementation of rosiglitazone, respectively (Figure 1A). In the DENA group, reduced glutathione (GSH) levels (an antioxidant marker) decreased by 71.3% but increased by 76.9 and 193.8% after 5 and 10 mg/kg supplementation of rosiglitazone, respectively. The activity of another antioxidant marker, Gpx, decreased in the DENA group by 63.2%. However, after 5 and 10 mg/kg of rosiglitazone supplementation, the activities of Gpx increased by 69.3 and 184.5%, respectively. The activity of the additional antioxidant marker superoxide dismutase (SOD) in the DENA group was reduced by 61.3% and increased by 27.4% and 104.2%, respectively, after 5 and 10 mg/kg of rosiglitazone supplementation. Catalase (CAT) activity, the marker for antioxidants, decreased in DENA groups by 73.5% but increased by 84.3 and 193.2%, respectively, as a consequence of 5 and 10 mg/kg rosiglitazone supplementation (Figure 1B). Figure 1 Effects of rosiglitazone on LPO and antioxidant enzymes. (A) MDA levels; (B) GSH, SOD, CAT, and GPx levels. Analysis and evaluation of experimental data were performed using ANOVA, followed by the Tukey post hoc test for group average comparisons. ###P < 0.001 in contrast with the control group; P < 0.05, P < 0.01, and *P < 0.001 in contrast with the DENA group. Findings were shown as means and SD (n = 10). The most common human cancer in the world is pulmonary cancer. Because the human race around the world is at serious health risk and existing chemical therapies have serious adverse impacts, numerous research programs are focused on discovering novel therapeutic agents. Apoptosis has been considered an effective therapeutic goal because deregulated apoptosis contributes to carcinogenesis. The anticancer impact of rosiglitazone against lung cancer induced by DENA in experimental animals is explained in the present study. Reactive oxygen species (ROS) play a major role in lung cancer caused by DENA.7,8 Initiating, promoting, and progressing lung cancer through oxidation, ROS, and LPO play an important role. Oxidative stress is due to an imbalance between ROS manufacture and cellular antioxidant defense detoxification of reactive intermediates. The increase in LPO in carcinogenesis can result in a high level of carcinogenic MDA, which is an LPO product. Several investigators reported significantly reduced activities of SOD, CAT, GPx, GST and GSH in cancer-bearing animals with elevated free radicals and various humoral and cellular mediators. Multiple researchers have recorded substantially lower GST, CAT, GSH, Gpx, and SOD activity in carcinogenic animals with high free radicals and certain humoral factors.9,10 Lower respiratory tract glutathione and related enzymes can be the first line of defense for lung injury in the epithelial body.11,12 Effect of Rosiglitazone on Inflammatory Cytokines In the DENA group, the TNF-α rate was increased by 284.7% but reduced by 31.6 and 106.3% after 5 and 10 mg/kg of rosiglitazone supplementation, respectively (Figure 2A). The DENA group showed an increase in IL-1β by 327.8%, but reduced by 28.4 and 94.8% following 5 and 10 mg/kg of rosiglitazone supplementation (Figure 2B). Like IL-6, IL-1β and TNF-α levels were also amplified by 197.8% in the DENA group but decreased, respectively, by 27.6% and 74.0% from 5 to 10 mg/kg rosiglitazone supplementation (Figure 2C). Figure 2 Effects of rosiglitazone on TNF-α, IL-1β, and IL-6 levels (pg/mL) in DENA-induced rats. (A) TNF-α levels; (B) IL-1β levels; and (C) IL-6 levels. Analysis and evaluation of experimental data were performed using ANOVA, followed by the Tukey post hoc test for group average comparisons. ###P < 0.001 in contrast with the control group; P < 0.05, P < 0.01, and P < 0.001 in contrast with the DENA group. Findings were shown as means and SD (n = 10). Cytokines have important roles in host defense and pathophysiology under inflammatory conditions. After administration of DENA in rats, the development of IL-6, IL-1β, and TNF-α has increased suggestively in this investigation.13−15 Western blot analysis has shown that the administration of DENA significantly increased pro-apoptotic Bax protein expression and decreased Bcl-2 expression. Cytochrome c has been released into the mitochondrial cytosol, and then, caspase-3 expression was increased.16,17 This causes apoptosis of the tumor cells. Both effects can enhance the chemical therapy effect in combination with rosiglitazone (5 and 10 mg/kg) and reduce DENA’s toxicity, depending on the dosage. Effect of Rosiglitazone on mRNA Expression of Caspase-3, Bax, and Bcl-2 The downstream caspase function in both the nucleus and the targets for the cytosol is caspase-3, a central executor of apoptosis in programmed cell death. To test the hypothesis of a lower level of Caspase-3 in rat because of rosiglitazone, quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting analysis were performed on the mRNA and Caspase-3 protein expressions. In comparison with the control group, as shown in Figure 3A–C, the caspase-3 mRNA and protein levels were considerably higher in the DENA-driven rats, while the dose-dependent treatment was substantially reversed by rosiglitazone (5 and 10 mg/kg). Accumulating studies have shown that the increase of proapoptotic protein Bax and decrease of antiapoptotic protein Bcl-2 promoted cytochrome c release in mitochondria, and therefore activated the cascades of apoptosis. Figure 3 Effects of rosiglitazone on mRNA expression and protein levels of caspase-3, Bax, and Bcl-2. (A) Relative expression of caspase-3, Bax, and Bcl-2 measured by qRT-PCR. (B–E) Protein expressions of Bcl-2, caspase-3, and Bax were measured by western blotting. β-Actin was used as an internal standard. Analysis and evaluation of experimental data were performed using ANOVA, followed by the Tukey post hoc test for group average comparisons. ###P < 0.001 in contrast with the control group; P < 0.05, P < 0.01, and *P < 0.001 in contrast with the DENA group. Findings were shown as means and SD (n = 10). Western blot analysis demonstrated that DENA administration substantially increased the expression of the pro-apoptotic protein Bax and diminished the expression of the anti-apoptotic protein Bcl-2. Cytochrome c has been released into the cytosol from the mitochondria, which increases caspase-3 protein expression.16,17 It induces apoptosis of the tumor cells. These effects can improve the chemotherapy effect and lower the dose-dependent toxicity of DENA in combination with rosiglitazone (5 and 10 mg/kg). Effects of Rosiglitazone on DENA-Mediated Lung Histopathological Changes The lungs were isolated at 24 h after administration of rosiglitazone in the lung tissue to assess histological changes following rosiglitazone post treatment in DENA-challenged rats. The control group’s lung tissues had a normal structure, and there were no histopathological changes. Histological examination of the DENA group by hematoxylin and eosin (H&E) staining revealed serious pulmonary oedema, stroma hemorrhagia, alveolar collapse, and mass inflammatory cell infiltrations, which were seriously destructive of the lung. Nonetheless, after treatment with rosiglitazone (5 and 10 mg/kg), effective alleviation of lung structure degradation was observed, depending on the dosage (Figure 4). Figure 4 Histopathological images of the effect of rosiglitazone on DENA-induced carcinogenensis in lung tissues of Wistar rats (H&E; 200×). Conclusions In brief, the results of this study showed that rosiglitazone can reduce lung carcinogenesis induced by DENA by downregulating LPO, inflammatory cytokines such as IL-6, IL-1β, and TNF-α, and pro-apoptotic factors Bax, whereas upregulating antioxidant enzyme levels such as SOD, CAT, Gpx, GST, and GSH and the anti-apoptotic factor Bcl-2. Further clinical study is required to find out an exact effect. Materials and Methods Chemicals DENA and rosiglitazone have been acquired from Sigma-Aldrich. The cell signaling technique was used to acquire both primary and HRP-conjugated secondary antibodies. The western blotting kit has been obtained from Abcam, USA. All other chemicals used were of analytical quality. Experimental Animals This study was conducted on male Wistar rats (220 ± 10 g). All the animals were procured from and maintained in the central animal house of People’s Hospital of Ningjin County, China. Animals were caged in groups with the normal 12 h light/dark cycle maintained at 24 ± 2 °C temperature. The animals were served pelleted rat chow and water ad libitum, available commercially. All animal procedures were approved by the animal ethical committee of People’s Hospital of Ningjin County (AECPN NO: AECP/11827/2019). The experiment was carried out according to the guidelines of the NIH at the People’s Hospital of Ningjin County. Experimental Design As described earlier, the DENA-induced animal model of lung cancer has been developed. The animals were intraperitoneally (i.p.) given 150 mg/kg body weight dosage of DENA for 21 days once in 7 days. The rats have been split into four different categories, comprising 10 rodents per group, randomly following the induction of lung cancer: Group 1 was treated as a normal control and only distillated water not exceeding 1 mL was given orally. Group 2 was i.p. given 150 mg/kg DENA. In groups 3 and 4, rats were treated with DENA orally for 15 days with 5 and 10 mg/kg rosiglitazone, respectively. Feed was deprived overnight for 24 h after the last operation, and all rats were anesthetized. Jugular vein blood was obtained and serum was isolated and used for the biochemical investigation. For histopathological analysis, 10% of the tissue was seeded in formaldehyde. The remaining tissue weighed and about 100 mg of tissue was homogenized with chilled 0.1 M Tris-HCl buffer for biochemical analysis in a homogenizer. For further examination, the lung tissue was stored at −80 °C.18 Measurement of LPO and Antioxidant Enzymes LPO was measured in fresh pulmonary homogenates according to Quintero-García et al. by the detection of thiobarbituric acid reactants. The final product of LPO has been determined by measuring the absorption at 534 nm. A measurement of the absorption of CAT activity at 420 nm was performed. The specimens were supplied with 500 μL of phosphate buffer, serum, and liquid. The absorption was estimated at 560 nm for SOD. A specimen with phosphate (1.2 mL), homogeneous tissue (0.1 mL), nitroblue tetrazolium (0.3 mL), and NADH (0.2 mL) was used. Following the procedure of the GSH content was determined by the Ellman reaction in the lung tissue homogenates. At 412 nm, the final product was measured. The absorption was measured at 340 nm to determine the activity of Gpx in the tissue homogenate.19 Measurement of Inflammatory Cytokines IL-6, IL-1β, and TNF-α levels in serum were measured using rat cytokine (Xitang Biotechnology Co., Ltd., Shanghai, China) kits, which are commercially available immunosorbent assays [enzyme-linked immunosorbent assay (ELISA)]. The experiments with ELISA were performed following strict directions.20 Quantitative Real-Time Polymerase Chain Reaction A total DNA protein kit (E.Z.N.A.) was utilized for total lung RNA extraction. A BCA protein assay kit has been used to assess protein concentrations. Total RNA (1 μg) was reverse-transcribed with an ImProm-II reverse transcription system package. For mRNA amplification of apoptosis-related genes using the following front and reverse primers (Table 1), an ABI PRISM 7500 sequence detection system was applied. The conditions for amplification are 30 s at 95 °C and then 39 cycles of 5 s at 95 °C, 30 s at 58 °C, and 34 s at 72 °C. Caspase-3, Bax, and Bcl-2 levels of mRNA are standardized to β-actin levels. Triplicate studies have been performed. All data were examined with the 2–ΔΔCt process (ΔCt = CtTarget gene – Ctβ-actin, ΔΔCt = ΔCt exp – ΔCtControl).21 Table 1 Primer Sequence for RT-PCR name sequence (5′ → 3′) Caspase-3 forward primer: CGGAGCTTGGAACGCGAAG reverse primer: ACACAAGCCCATTTCAGGGT Bax forward primer: ACAACAGCAGCACAACAGCC reverse primer: GTGTAAACCGCAGCCGAAGG Bcl-2 forward primer: GATTCCCTCTCCCCACTGCC reverse primer: TGCTTTCTTTTTCGCCGCGT β-actin forward primer: CCCAGCCATGTACGTAGCCA reverse primer: CCGTCTCCGGAGTCCATCAC Histopathological Study The method by Fukushi et al. has been used in histopathological study of the lung tissue. The lower lobe of the lung was soaked in 10% formalin and immersed in paraffin. Tissues are cut to 3 μm thickness and treated with H&E. A tissue section under a light microscope was then examined. Sections are tested under a light microscope at a magnification of 100×.22 Western Blotting Equal amounts of the total protein are filled in 80 V sodium dodecyl sulfate–polyacrylamide gel electrophoresis gels for 80 min, electrically transferred to polyvinylidene fluoride membranes by the wet transfer method (250 mA, 90 min), and blocked in 5% bovine serum albumin at 4 °C overnight. Subsequently, the membranes were incubated with an anti-β- actin antibody (dilution 1:1000), anticaspase3 antibody (dilution 1:200), anti-bcl-2 antibody (dilution 1:100), and anti-bax antibody (dilution 1:500) at room temperature for 2 h. After TBST washing, membranes were incubated for 1 h at room temperature with secondary goat anti-mouse IgG (dilution 1:1000) and were combined with horseradish peroxidase. Equal protein loads were verified with anti-β-actin antibody on each lane. The reagent chemiluminescence was then observed with proteins. Bio-Rad Quantity One v4.62 was used to calculate the density of the protein band. Protein expression levels were normalized internally with β-actin.23 Statistical Analysis All test data are shown as standard deviation (SD) and means. Experimental results are analyzed and compared by means of analysis of variance (ANOVA), followed by the post hoc Tukey test, which showed P < 0.05, suggesting statistical significance for group mean comparisons. SPSS for Windows, version 22, has been used for all statistical analysis. The authors declare no competing financial interest. Acknowledgments Authors would like to thank People’s Hospital of Ningjin County, China, for providing a laboratory facility and all chemicals. Abbreviations BaxBcl2-associated X protein Bcl2B-cell lymphoma 2 CATcatalase DENAdiethylnitrosamine ELISAenzyme-linked immunosorbent assay Gpxglutathione peroxidase IL-1βinterleukin 1 beta IL-6interleukin-6 GSHreduced glutathione MDAmalondialdehyde PPAR-γperoxisome proliferator-activated receptor γ qRT-PCRquantitative real-time polymerase chain reaction ROSreactive oxygen species SDstandard deviation SODsuperoxide dismutase TNF-αtumor necrosis factor-alpha ==== Refs References Akagi K. ; Hirose M. ; Hoshiya T. ; Mizoguchi Y. ; Ito N. ; Shirai T. Modulating effects of ellagic acid, vanillin and quercetin in a rat medium term multi-organ carcinogenesis model . Cancer Lett. 1995 , 94 , 113 –121 . 10.1016/0304-3835(95)03833-i .7621439 Armstrong D. ; Cameron R. G. Comparison of liver cancer and nodules induced in rats by deoxycholic acid diet with or without prior initiation . 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==== Front Inflammopharmacology Inflammopharmacology Inflammopharmacology 0925-4692 1568-5608 Springer International Publishing Cham 610 10.1007/s10787-019-00610-8 Original Article Selected TLR7/8 agonist and type I interferon (IFN-α) cooperatively redefine the microglia transcriptome Khatun Mst Reshma [email protected] 1 http://orcid.org/0000-0002-5653-7347Arifuzzaman Sarder +82 [email protected] 2 1 0000 0004 0532 3933grid.251916.8Department of Biomedical Science, Ajou University, Suwon, Gyeonggi-do 16499 Republic of Korea 2 0000 0001 0789 9563grid.254224.7Department of Animal Science and Technology, Chung-Ang University, Anseong, Gyeonggi-do 17546 Republic of Korea 12 6 2019 1 19 1 2 2019 4 6 2019 © Springer Nature Switzerland AG 2019This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Background Microglia, the primary immune cells of the central nervous system, exerts multiple functions to mediate many neurological diseases. Upon any detection of invading pathogen products (e.g., TLR agonists) or host-released signaling factors (e.g., interferon/IFN), these cells undergo an activation process to release large numbers of inflammatory substances that participate in inflammation and homeostasis. The profound effects of inflammation associated with TLR7/8 agonist Resiquimod (R848) and type 1 interferon (e.g., IFN-α)-induced macrophage and dendritic cell activation on biological outcomes have long been recognized. However, the underlying mechanisms are not well defined in microglial cells. Methods The present study investigated the molecular signatures of microglia and identified genes that are uniquely or synergistically expressed in R848-, IFN-α- or R848 with IFN-α-treated primary microglial (PM) cells. We used RNA-sequencing, quantitative real-time PCR, and bioinformatics approaches to derive regulatory networks that control the transcriptional response of PM to R848, IFN-α and R848 with IFN-α. Results Our approach revealed that the inflammatory response in R848 with IFN-α-treated PM is faster and more intense than that in R848 or IFN-α-treated PM in terms of the number of differentially expressed genes and the magnitude of induction/repression. In particular, our integrative analysis enabled us to suggest the regulatory functions of TFs, which allowed the construction of a network model that explains how TLR7/8 and IFN-α-sensing pathways achieve specificity. Conclusion In conclusion, the systematic approach presented herein could be important to the understanding microglial activation-mediated molecular signatures induced by inflammatory stimuli related to TLR7/8, IFN-α or co-signaling, and associated transcriptional machinery of microglial functions and neuroinflammatory mechanisms. Electronic supplementary material The online version of this article (10.1007/s10787-019-00610-8) contains supplementary material, which is available to authorized users. Keywords MicroglialRNA sequencingTranscription factorsType 1 interferonsToll-like receptorhttp://dx.doi.org/10.13039/501100002460Chung-Ang University00000001 ==== Body Introduction Microglial cells, the resident immune sentinels of the central nervous system (CNS), are thought to participate in the pathogenesis of many neurological disorders (Crotti and Ransohoff 2016). These cells express multiple Toll-like receptors (TLRs), which are a phylogenetically conserved diverse family of sensors that drive innate immune responses following interactions with pathogen-associated molecular pattern (PAMPs), including TLR3 (viral double-stranded RNA (dsRNA)), TLR4 (lipopolysaccharide (LPS)), TLR7/8 (single-stranded RNA (ssRNA)) and TLR9 (DNA) (Glass et al. 2010; Holtman et al. 2015; Crotti and Ransohoff 2016). The interaction of TLRs with PAMPs, such as TLR7/8 and ssRNA, triggers several signaling cascades that lead to the induction of numerous target genes involved in inflammation (O’Neill et al. 2013). Among the TLRs, TLR7/8 is one of the main receptors expressed by glial cells in response to ssRNA (Butchi et al. 2008; Butchi et al. 2011). Thus, several TLR7/8 agonists including R848 (Resiquimod), an imidazoquinoline, have been discovered, and have been used to study not only immune cell (e.g., macrophage) activation but also molecular signatures associated with TLR7/TLR8-dependent signaling pathway (Butchi et al. 2008; Butchi et al. 2011; O’Neill et al. 2013). It has been evident as after ligand recognition by TLR7/TLR8, it activates the intrinsic signaling pathways and induces type I interferon (e.g., IFN-α) to mediate innate immune responses (Uematsu and Akira 2007). IFN-α is also a pleiotropic cytokine that can either augment or suppress the expression of genes related to chronic infections and multiple sclerosis (Ivashkiv and Donlin 2014), suggesting both stimuli are able to activate different pathways and that these pathways are not redundant. Therefore, microglial cells exposed to either R848, IFN-α alone or combination can be an efficient system to investigate neuroinflammatory conditions. The interaction of IFN-α and R848 triggers synergistic IL-6 production in mouse monocyte-derived dendritic cell (DC), as measured by ELISA (Kreutz et al. 2015). However, the expression levels of a large group of immune genes using genome-wide approaches (e.g. RNA sequencing) and the molecular mechanisms underlying this crosstalk between IFN-α and TLR7/8 responses in murine PM are largely unaddressed. Since microglial activation in vitro constitutes a valuable tool to investigate their role in normal and pathological conditions and to model neuroinflammation, here, we aim to elucidate the effects of R848, IFN-α and R848 with IFN-α on PM. To analyze the expression pattern of genes affected by these stimuli, we performed RNA sequencing (RNA-seq) analysis in primary microglial cells stimulated with R848, IFN-α or R848 with IFN-α. RNA-seq is increasingly used to determine gene expression, as it provides an unbiased digital readout and improved detection at the extremes of the transcriptome of any mammalian cell and is extremely accurate compared to microarrays (Ozsolak and Milos 2011). Here, we used RNA-seq to detect the genes that are significantly up- or downregulated according to pairwise comparisons, termed differentially expressed genes (DEGs), in murine primary microglia treated with R848, IFN-α, or R848 with IFN-α. The outcomes of these studies allowed us to identify microglial transcriptional signatures for R848, IFN-α, or R848 with IFN-α. We also identified trans-regulatory elements (e.g., altered transcription factor expression, activation or motif specificity) that may drive distinct gene expression in R848-, IFN-α- or R848 with IFN-α-treated PM. The outcome of these studies confirmed that R848, IFN-α and R848 with IFN-α stimuli generates different gene expression patterns and can constitute useful tools to study neuroinflammation. Materials and methods Isolation, culture and stimulation of primary microglial cells Primary microglial (PM) cells were isolated from 3-day-old ICR mice as previously described (Witting and Moller 2011) with minor modifications. All experiments were performed in accordance with Institutional Animal Care and Use Committee (IACUC) guidelines and approved by the IACUC committee of Chung-Ang University (IACUC Number: 2016-00009). Briefly, whole brains of neonatal mice were dissected out of the skull, and blood vessels and meninges were carefully removed. Then the tissues from whole brains of 12 mice were pooled together, finely minced with sterile surgical blade, and digested using a Neural Tissue Dissociation Kit-Postnatal Neurons (Miltenyi Biotec, Germany, 130-094-802). Next, the digested cells were passed through a 70-μm nylon cell strainer (BD Bioscience, Franklin Lakes, NJ) and seeded in l-lysine-coated T-75 flasks in DMEM/nutrient 122 mixture F-12 (DMEM/F12, 1:1) containing 20% FBS (catalog # 26140; Gibco, Waltham, MA), 100 IU/ml penicillin and 10 μg/ml streptomycin (catalog # 15140) obtained from Invitrogen (Waltham, MA). The cells were maintained in a humidified incubator with a 95% air and 5% CO2 atmosphere at 37 °C. The medium was changed every 2–3 days. After 2 weeks of culture, the mixed glial cell cultures were shaken on incubating shaker at 110 rpm at 37 °C for 55 min, wherein microglial cells detached from flasks. Then the detached microglial cell suspension was collected and seeded on poly-l-lysine-coated cell culture plates. Next, microglial cells were sub-plated and used for further experiments. Microglial cells attach to the culture plate bottom much more efficiently than oligodendrocytes. At 2 h after seeding before addition of fresh media, aspiration of the old media removes unattached contaminating oligodendrocytes. More than 92% of cells obtained were PM as quantified by CD11b (rat monoclonal immunoglobulin G2b (IgG2b), clone: M1/70.15.11.5, Miltenyi Biotec Germany) FACS analysis (Suppl. Fig. 1a). We also used an anti-rat secondary antibody to avoid auto-immunofluorescent labeling (also known as background staining) in FACS analysis. Primary microglial cells were plated in a humidified incubator with 95% air and a 5% CO2 atmosphere at 37 °C for 24 h before stimulation. The cells were treated with 1 μM R848 (Resiquimod) (Hemmi et al. 2002), 100 U/ml IFN-α (Zimmermann et al. 2016) and their combination for the specified times under normal culture conditions. The cells were stimulated after diluting the stock solution to the mentioned concentration in DMEM for experiments. The morphology of PM at 4 h with and without (control) treatment with R848, IFN-α and R848 with IFN-α was observed under microscope for each independent experiment. There were no noticeable changes in morphology for these stimuli, and a representative image has been shown in Supplementary Fig. 1b. R848 were purchased from Invivogen Inc., San Diego, CA.; and pure (> 95%), and recombinant mouse IFN-α (Cat#12100-1) with activity > 1X105 U/ml were purchased from PBL Assay Science, 131 Ethel Road West, Suite 6 Piscataway, NJ 08854, USA. Total RNA isolation and cDNA library preparation for transcriptome sequencing (RNA-seq) Total RNA was extracted using RNAiso Plus (Takara Bio Inc., Shiga, Japan) and a QIAGEN RNeasy® Mini Kit (QIAGEN, Hilden, Germany). PM cells were completely lysed using RNAiso Plus and then 200 μl of chloroform was added. The tubes were then inverted for 5 min. The mixture was centrifuged at 12,000 × g for 15 min at 4 °C, and the upper phase was collected and transferred to a new tube. Same volume of isopropanol alcohol was added into it and was inverted 5–6 times and was kept on ice fully emerged for 10 min. Then the mixture was passed through an RNeasy mini column. The column was washed with wash buffer. To elute the RNA, RNase-free water (30 μl) was added directly onto the RNase mini column, which was then centrifuged at 12,000 × g for 3 min at 4 °C. To deplete ribosomal RNA (rRNA) from the total RNA preparations, a RiboMinus Eukaryote kit (Life Technologies, Carlsbad, CA) was used according to the manufacturer’s instructions. RNA libraries were prepared using a NEBNext® Ultra™ directional RNA library preparation kit for Illumina® (New England Biolabs, Ipswich, MA). The obtained rRNA-depleted total RNA was fragmented into small pieces using divalent cations at elevated temperatures. First-strand cDNA was synthesized using reverse transcriptase and random primers, and second-strand cDNA synthesis was then performed using DNA polymerase I and RNase H. The cDNA fragments were processed using an end-repair reaction after the addition of a single ‘A’ base, followed by adapter ligation. These products were purified and amplified using PCR to generate the final cDNA library. The cDNA fragments were sequenced using an Illumina HiSeq 2000. Biological triplicate RNA sequencing for each condition was performed on independent RNA samples from either R848, IFN-α or combination stimulated PM: control 4 h (3 samples); R848 4 h (3 samples), IFN-α 4 h (3 samples), and R848 with IFN-α 4 h (3 samples). Differentially expressed gene analysis using RNA-seq data FASTQ files from RNA-seq experiments were clipped, trimmed of adapters, and the low-quality reads were removed by the trimming algorithm “Trimmomatic” (Bolger et al. 2014). Quality-controlled FASTQ files were aligned to Mus musculus UCSC mm10 reference genome sequence using the STAR (version 2.5.1) aligner software (Dobin et al. 2013). To measure differential gene expression, DESeq 2 (Love et al. 2014) was used. A subset of condition-specific expression was defined as showing a log2 fold change ≥ 2 and P ≤ 0.01 in expression between controls, R848-, IFN-α-, and R848 with IFN-α-treated samples. The RNA-seq experiments were visualized using HOMER (Heinz et al. 2010) after custom tracks were prepared for the UCSC Genome Browser (http://genome.ucsc.edu/). The RNA-seq data sets were deposited in the NCBI Gene Expression Omnibus database under dataset accession numbers GSE79898 and GSE104056. Quantitative real-time PCR (qRT-PCR) The reverse transcription of the RNA samples was performed as per distributor protocol using 2 μg of total RNA, 1 μl of oligo(dT)-primer (per reaction) and a Prime Script 1st strand cDNA Synthesis Kit (Takara Bio Inc., Shiga, Japan). The oligo(dT) primer and RNA templates were mixed and denatured at 65 °C for 5 min and then cooled for 2 min on ice. Prime Script buffer (5×), RTase and RNAse inhibitor were added to the cooled template mixture and incubated for 1 h at 50 °C before an enzyme inactivation step was performed at 70 °C for 15 min. qRT-PCR was performed using SYBR Green PCR Master Mix (Takara Bio Inc., Shiga, Japan) and a 7500 Real-Time PCR System (Applied Biosystems, Waltham, MA). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal control. Complementary DNA samples were diluted 1.5-fold, and qRT-PCR was performed using an ABI-7500 Real-Time PCR System (Applied Biosystems, Waltham, MA) with SYBR Premix Ex-Taq II (Takara Bio Inc., Shiga, Japan) according to the manufacturer’s instructions. The reactions were performed in a total volume of 20 μl that contained 0.4 mM of each primer (Table 1). Each PCR series included a no-template negative control that contained water instead of cDNA and reverse transcriptase-negative control for each gene. Triplicate measurements were performed for all reactions. Different samples were evaluated using 96-well plates in the gene expression experiments, and all samples were analyzed on a single plate for the endogenous control experiments. The results were analyzed using the critical threshold (ΔCT) methods in the ABI-7500 software program with the Norm finder and geNorm-plus algorithms. The primers were designed using Primer Express software (Applied Biosystems, Waltham, MA).Table 1 List of primers used in qRT-PCR studies Gene Forward (5′→3′) Reverse (5′→3′) GAPDH AAGGTCGGAGTCAACGGATT CTCCTGGAAGATGGTGATGG TNFA CAGGCGGTGCCTATGTCTC CGATCACCCCGAAGTTCAGTAG TNFSF10 CCAACCACCAGGCTACAGG GCGTCACACTCAAGCTCTG IL1A ACTGCACCCAAACCGAAGTC TGGGGACACCTTTTAGCATCTT IL1B GAAATGCCACCTTTTGACAGTG CTGGATGCTCTCATCAGGACA CCL2 TGTACCATGACACTCTGCAAC CAACGATGAATTGGCGTGGAA CCL3 TTCCTGCTGTTTCTCTTACACCT CTGTCTGCCTCTTTTGGTCAG CCL4 CTGGGCCAGATAAGGCTCC CATGGGGCACTGGATATTGTT CXCL11 ATTTCCACACTTCTATGCCTCCT ATCCAGTATGGTCCTGAAGATCA CXCL10 TGCTGGGTCTGAGTGGGACT CCCTATGGCCCTCATTCTCAC TLR7 TTGCATCTGGCGTCTACACT GGTTTAGGAGGGCAAGGGTG IFNA GGCACAGAAGTGTTCCATAAAGT GAGGCAGGGCTTCCGATAG IFNB AGCTCCAAGAAAGGACGAACA GCCCTGTAGGTGAGGTTGAT IFNAR1 ACATCGACCCGTCCACAGTAT CAGAGGGGTAGGCTTGTCTC IFNAR2 TGGGTCTGCCACAAATGGAG TCCAGTGTTTGCGTGTTACTC ISG20 GAGGGCTGTTGGTTCTTGACT CCTCGGGTCGGATGTACTTG IFIT1 CCCTGACGACGTGGACTATG GCCGACAGAGTGATCTTGGT IFIT2 CAGATGGTCAATTGGTGCCA TGCAAGAACCCCTGGATCTC IFIT3 AGCAGGAGGTCTCTGACAATG GGCTTCCTCTAAACTGTTGAGC IL6 TGCCCGAACAAGGCTCTTC CAGCCAGTTGATGCTCTGC Functional annotation To functionally annotate the most significant genes, gene ontology (GO) analysis was performed by DAVID (Database for Annotation, Visualization and Integrated Discovery), version 6.8 (da Huang et al. 2009). GO was analyzed using a modified Fisher’s exact P value in the DAVID program. P values less than 0.001 were considered greatly enriched in the annotation category. Canonical pathway and upstream regulator analysis of datasets An ingenuity pathway analysis (IPA) (Ingenuity Systems, http://www.ingenuity.com, CA) was performed to analyze the most significant canonical pathways and upstream regulator analysis in the datasets as previously described (Kramer et al. 2014). The genes from datasets associated with canonical pathways in the Ingenuity Pathways Knowledge Base (IPAKB) were considered for literary analysis. The significance of the associations between datasets and canonical pathways was measured in the following manner: (1) the ratio of the number of genes from the dataset that mapped to a canonical pathway was divided by the total number of genes that mapped to the same canonical pathway and (2) Fisher’s exact test for a P value indicating the probability that the association could be explained by chance. After uploading the datasets, upstream regulator analysis was used to predict the upstream transcriptional regulators on the literature and compiled in the IPAKB. Gene networks were algorithmically generated based on connectivity. The analysis examines how many known targets of the upstream regulators are present in the dataset and also the direction of change. The graphical representation of molecular relationships between upstream regulator and gene products was based on the biological relationship between two nodes was represented as an edge (line). All edges were supported by at least one reference from the literature, text book or canonical information in IPAKB. The intensity of node color represented the degree of up-regulation (red). The nodes were displayed using shapes to represent functional classes of gene products. Transcription factor-binding motif enrichment analysis NCBI reference sequence mRNA accession numbers were subjected to transcription factor-binding motif analysis using the web-based software Pscan (Zambelli et al. 2009). The JASPAR (Portales-Casamar et al. 2010) database of transcription factor-binding sequences was analyzed using enriched groups of − 950 base pair (bp) sequences to + 50 bp of the 5′ upstream promoters. The range − 950 to + 50 was selected from the range options in Pscan to obtain the best coverage for a − 1000 to + 50 bp range. Statistical analysis The data were analyzed using Origin Pro 8 software (Origin Lab Corporation, Northampton, MA). Each value is expressed as the mean ± standard error of the mean (SEM). All qRT-PCR data were analyzed with SPSS 17.0 software (SPSS Inc., Chicago, IL). The data were tested using one-way ANOVA followed by Tukey’s HSD post hoc test. P < 0.01 and P < 0.001 were considered significant. Results Time point determination and concentration optimization of R848 and IFN-α required for the production of selective inflammatory cytokines and ISGs First, to determine the time point for optimum acute stimulation, we incubated highly purified PM with ultrapure R848 (1 µM), IFN-α (100 U/ml) and a combination of the two for up to 24 h. Then we measured the mRNA expression of a selective inflammatory cytokine TNF-α and an ISG IFIT2 by qRT-PCR. These two genes often show upregulation in inflammatory conditions associated with TLRs and type I interferon receptor activation. Our qRT-PCR results showed an undetectable expression of these two genes in resting cells, while they were expressed optimally and synergistically at 4 h and declined onward (Fig. 1a). We again attempted to optimize the concentration by stimulating cells with varying concentrations of R848 and IFN-α alone, as shown in Fig. 1b. As with increasing duration of exposure, we found increased expression of TNF-α with increasing concentration of R848 and increased expression of IFIT2 with increasing concentration of IFN-α (Fig. 1b). Notably, both TNF-α and IFIT2 showed high response to R848 and IFN-α, respectively, with time and concentration (Fig. 1a, b). Based on the abovementioned results, it seems likely that in comparison to R848 and IFN-α alone, R848 with IFN-α is more efficient to activate genes comprising cytokines and ISGs in PM in a time- and concentration-dependent manner.Fig. 1 Effect of R848 and IFN-α on stimulation of selective inflammatory cytokines and ISGs in microglial cells. a Primary microglial (PM) cells were stimulated with R848 (1 μM), IFN-α (100 U) and R848 with IFN-α for up to 24 h. b PM cells were stimulated with R848 (0.1–5 μM) and IFN-α (10–500 U) for 4 h. Quantitative mRNA expression of TNF-α and IFIT2 was measured by qRT-PCR. qRT-PCR data were pooled from three independent experiments, each in triplicate. Data presented as mean ± SEM; P ≤ 0.01 and *P ≤ 0.001, and determined using one way ANOVA followed by Tukey’s multiple comparison test Global gene transcription dynamics involved in R848-, IFN-α- or co-treated cells To gain more insight into the genome-wide kinetics of gene transcription, we again stimulated the PM either with R848 (1 µM) or IFN-α (100 U/ml) or with a combination of the two for 4 h in three biological replicates. We chose this 4 h time point based on a preliminary experiment to determine the optimum stimulation time for immune gene expression in PM. After quality check, isolated mRNA from induced PM were subjected to RNA-seq assay as per previously described protocol (Pulido-Salgado et al. 2018). Approximately 3 million 100-nucleotide reads obtained from each sample were used to map to the mouse genome and to quantitate mRNA expression. Next, principal component analysis (PCA) examined the congruency among biological replicates. The PCA showed a good separation and a high level of consistency between biological replicates of the same population in PM (Suppl. Fig. 2a). The analysis of the RNA-seq data distinguished the number of genes that were differentially expressed: 393, 394 and 836 genes were upregulated, whereas 61, 19 and 57 genes were downregulated, respectively, in R848-, IFN-α- and R848 with IFN-α-treated PM (log2 fold change ≥ 2, and P < 0.001) (Fig. 2a). Among the upregulated genes, we identified 332 and 362 genes in the R848 and IFN-α samples, respectively, that showed overlap with the set of genes upregulated in the co-treated samples, while only 62 and 32 genes, respectively, were upregulated uniquely by R848 and IFN-α (Fig. 2b). Importantly, we found 295 R848 with IFN-α-upregulated genes that were induced synergistically (log2 fold change ≥ 2 over R848 or IFN-α alone induction) (Fig. 2c). Of note, 25 R848- upregulated genes were repressed by IFN-α, while no IFN-α-upregulated gene was repressed by R848 (log2 fold change ≥ 2) (Fig. 2c). A large number of cytokine, chemokine and interferon response genes important for innate responses were non-synergistically induced upon co-activation of PM with R848 and IFN-α. Nonetheless, synergistically induced genes that showed robust expression included a substantial number of cytokines (TNF-α, IL1A, IL1B, IL12B, IL6, etc.), chemokines (CCL2, CCL3, CCL4, CCL7, CCL9, CCL12, CXCL10, etc.), and interferon and ISGs (MX1, MX2, IFIT1, IFIT2, IFIT3, IFITM2, IFITM3, OASL1, OASL2, etc.) (Fig. 2d). These results suggest that there are gene expression signatures with a combination of cytokines, chemokines and ISGs that represent optimum PM activation.Fig. 2 Effect of R848, IFN-α and R848 with IFN-α on global gene transcription in PM cells. a Pie charts displaying the number of genes altered by R848, IFN-α and R848 with IFN-α in PM (log2 fold change ≥ 2, P < 0.0001). b Venn diagram depicting the overlap of the number of upregulated genes of R848- and IFN-α-treated PM with R848- with IFN-α-treated PM. c Upper pie chart displaying the genes synergistically/non-synergistically upregulated in R848 with IFN-α-treated PM. d Heat map representation depicting cytokine, chemokine and interferon response genes induced in the global RNA-seq experiments (P ≤ 0.01, and log2 fold change ≥ 2 over R848 and IFN-α upregulation). e Heat map representation depicting R848-upregulated and IFN-α-suppressed genes identified in the global RNA-seq experiments (P ≤ 0.01, and log2 fold change ≥ 2). Heat maps were generated with the Multi Experiment Viewer (version 4.8) software. Data represent three biological replicates of single isolation Among the R848-upregulated/IFN-α-suppressed genes, selective members of the CC and CXC chemokine families (CXCL1, CXCL2, CXCL3, CXCL5 and CCL22), interleukin-1 receptor kinase (IRAK3), and immunoglobulin superfamily (IGSF6) were noticeably repressed by IFN-α (Fig. 2e). Remarkably, from the overall expression data, we observed that R848 treatment induced significantly higher cytokine and chemokine gene expression levels, and IFN-α treatment induced the expression of interferons and ISGs, while R848 with IFN-α induced the expression of cytokines, chemokines and ISGs in activated PM. To examine the downregulated genes in all three conditions, we found that some genes previously known to be involved with immune regulation or to be essential for PM homeostasis, such as chemokine receptor (CXCR4), colony-stimulating factor 1 receptor (CSF1R), G-protein coupled receptor (GPR157), prostaglandin synthase (PTGS1) and retinoid X receptor α (RXRA), were downregulated significantly in co-treated cells (Suppl. Fig. 2b, c). Among them, CSF1R, the cell surface receptor for CSF1, highly expresses during amyotrophic lateral sclerosis, and inhibition of CSF1R slows the progression of ALS in mouse models (Martinez-Muriana et al. 2016). RXRα, a key nuclear receptor, attenuates host antiviral responses by suppressing IFN genes (Ma et al. 2014). These data suggest that alterations in the expression levels of these transcripts during pathologic conditions not only reflect unique functional capabilities but also can be used as potential targets to identify these cells in distinct physiologic conditions. Divergence of the expression of TFs and paradox of promoter conservation in R848, IFN-α or R848 with IFN-α-stimulated microglial cells We next considered that this synergistic responsiveness of PM might be controlled by TFs, as they initiate and regulate the transcription of genes. To investigate this possibility, we found a group of approximately 40 TFs that showed differential expression (log2 FC ≥ 2) in co-treated PM. Among these TFs, we found that several members of the activator of transcription factor (ATF), nuclear factor kappa B (NF-κB), interferon regulatory factor (IRF), and signal transduction and transcription activation (STAT) families were significantly upregulated in activated PM (Fig. 3a). Noticeably, higher expression levels of NF-κB family members were observed in R848-treated and co-treated cells, while higher expression levels of IRF and STAT family members were observed in IFN-α-treated and co-treated cells, and a similar level of expression of ATF family members was observed in all three conditions (Fig. 3a). However, the expression levels of a large group of TFs, such as IRF3, Kruppel-like factors [KLFs (3, 7, 9, 10, 13 and 16)], ATFs (1, 2, 6 and 6B), CREB3, and STATs (5B and 6), were marginally or mostly unaffected in all three conditions (Fig. 3a). In particular, ATFs (3 and 4), AT-rich interactive domain-containing protein 5A (ARID5A), basic leucine zipper ATF-like TF (BATF), CCAAT/enhancer-binding protein (CEBPB), cAMP-responsive element-binding proteins (CREB5 and CREM), forkhead box protein C1 (FOXC1), KLF6, suppressor of cytokine signaling (SOCS7), nuclear TF X-box-binding-like 1 (NFXL1), JUNB, and selective members of the NF-κB (RELA), and IRF (1, 2, 7, 8 and 9) families showed robust synergistic expression in co-treated PM (Fig. 3b). However, we found that STAT6, KLF11 and three members of the IRFs (2, 4 and 6) were downregulated not only in R848-treated and INF-α-treated cells but also in co-treated cells (Fig. 3b).Fig. 3 Effect of R848, IFN-α or R848 with IFN-α on key TFs in activated PM cells. Heat map representation depicting a the expression levels of the TFs that were dysregulated by R848, IFN-α and R848 with IFN-α (P ≤ 0.0001, and log2 fold change ≥ 2). Data represent three biological replicates of single isolation. b Genes commonly upregulated or suppressed by R848, IFN-α and R848 with IFN-α (P ≤ 0.0001, and log2 fold change ≥ 2). Heat maps were generated with the Multi Experiment Viewer (version 4.8) software. c Patterns of TFs motif enrichment within the promoters of the commonly induced genes (P ≤ 0.01, and log2 fold change ≥ 2). d Venn diagrams displaying the commonly induced genes regulated by IRF1, IRF3/7, NF-κB1 and STAT1 in R848 with IFN-α-treated cells. e The IRF1- and IRF7-connected immune response genes as defined by the IPA molecule activity predictor of commonly induced genes (P ≤ 0.01, and log2 fold change ≥ 2). The graphs represent the mean fold values of enrichment relative to IgG/control from three independent experiments, each in triplicate. Data are mean ± SEM P ≤ 0.01 and *P ≤ 0.001 compared with control To evaluate the functional role of TFs for the transcription of expressed genes during co-stimulation, we conducted motif analysis to identify TF-binding motifs within − 950 to +50 bp windows relative to genomic loci TSS using a computational approach with Pscan software (Zambelli et al. 2009, Portales-Casamar et al. 2010). Importantly, our analysis results showed that the most significant motif enrichment was in IRF1 (P value: 1.03E−51) and IRF7 (P value: 2.50E−63) rather than in NF-κB1 (P value: 2.87E−16) and STAT1 (P value: 1.97E−17) within the synergistic gene set (Fig. 3c). Next, we determined the number of gene promoters having IRF1- and IRF7-binding motifs, as such results predicted that 71.25% and 77.96% of co-stimulation-induced upregulated genes met the promoter occurrence distribution score (Fig. 3d; Table 2). In contrast, only 49.15% and 53.89% of co-stimulation-induced upregulated genes met the promoter occurrence distributions score for NF-κB1 and STAT1, respectively (Fig. 3d). These results were consistent with our transcriptomic data for IRF1, 7, NF-κB1 and STAT1 (Fig. 3e). In addition to DNA-binding factor analysis, we also applied IPA software (Kramer et al. 2014) to identify target genes that were directly or indirectly activated by the identified TFs. The assessment of upstream regulators by IPA (Kramer et al. 2014) similarly revealed that the expression levels of most synergistically upregulated genes were also directly regulated by the identified TFs, including IRF1 (activation z score: 5.066) and IRF7 (activation z score: 7.236) (Fig. 3e; Table 3).Table 2 Top 50 IRF1- and IRF7-binding motif sequences of co-induced genes in PM cells (log2 FC ≥ 2, and P ≤ 0.001) Gene symbol Score Position Sequence Strand IRF1 occurrence position distribution (score ≥ 0.791) CD274 0.964021 − 350 TTTCACTTTCACTTTTAGTTT + MX2 0.95806 − 944 ACTTAGTTTCACTTTCATTTC − PARP10 0.929686 − 4 AGTCAGTTTCACTTTTGTTTT + TRIM21 0.923749 8 TTTCACTTTCAGTTTCCTCTC − CDK6 0.90797 − 344 TTCTACTTTCAGTTTTTCTAC − IFIT1 0.906538 − 110 CTTCAGTTTCACTTTCCAGTC + IFIT1BL2 0.906538 − 56 CTTCAGTTTCACTTTCCAGTC − PARP14 0.904022 − 59 AACTTCTTTCGCTTTCATTTC − IFIT3 0.901236 − 162 GGTAAGTTTCACTTTCCTCTT + IFIT3B 0.901236 − 194 GGTAAGTTTCACTTTCCTCTT + TRIM26 0.900241 − 166 TTCCGATTTCACTTTCCTTTT + IL27 0.899216 − 84 GCCCAGTTTCACTTTCTGTCC − IL15 0.897907 − 316 GGGCTCTTTCTCTTTCACTTT + OAS3 0.895126 − 80 CTTCACTTTCGTTTTCTCCTC − MX1 0.882159 − 918 CTCTGGTTTCCGTTTCATTTC − CCRL2 0.870084 − 656 TTATAGTTACACTTTCCGTTT + CXCL11 0.856754 − 390 CTTTACTTTTTTTTTTCCTTC − PARP9 0.853857 − 252 GTTTGGTTTTGGTTTTGGTTT − CCL8 0.853573 − 85 TCTTGCTTTCATTTCCCATTA + OASL2 0.850481 − 250 GTTTGGTTTTGTTTTTGTTTT − CASP12 0.849561 − 798 TTTTATTTTTATTTTTTATTT + CCL5 0.847906 − 156 TTTCAGTTTTCTTTTCCATTT + IL6 0.844323 − 292 TGTGAATTTCAGTTTTCTTTC + OASL1 0.840788 − 655 TATGAGTTTCTCTTTTCCTCG + TRIM13 0.837021 − 374 GTTTTGTTTTGTTTTTTGTTT − CXCL10 0.836646 − 218 GGTAAGTTTCACTTTCCAAAG − CCL2 0.836297 − 117 TTCAACTTCCACTTTCCATCA + CASP1 0.832613 − 561 GTTGGCTTTTTTTTTTTTTTT + CD47 0.83234 − 575 TATCTGTTTTTCTTTCTTTGT − CD40 0.831735 − 505 ACCCAGTTTCTCTTTCTTGAG − IFIT2 0.831186 − 543 GTTGAGTCTCAATTTCAATTT + CD180 0.828598 − 504 GATTACTTTCTCTCTCACCCT − CCL12 0.82757 − 305 TGTGACTTCTAGTTTCCTTTC + MMP13 0.817673 − 158 AGATGCCTTCATTTTCCATTT + IL1A 0.81631 − 330 TCCTTGTTTGGCTTTCACTCT + PARP8 0.812007 − 55 ACGGAGTCTCACTTTCTCCCT − IFITM3 0.81144 14 CGGCAGTTTCGGTTTCTCAGA − IL11 0.806313 − 847 TATTCCTTTTTCTTTTGGTCC + CCL7 0.806135 − 241 TTTTTTTTTTTTTTTTTTTTT − OAS2 0.805292 − 611 GTGAGGTTTCTTTTTGTGTTT + MMP10 0.804619 − 518 ACCTACTCTCTGTTTCAGAAT − MMP8 0.804484 − 204 ACAGTCTTCCAGTTTCTGTCT − CASP4 0.802083 − 4 AGTAACTTTCATTTTACTCTG + MMP28 0.801113 − 210 GCTTGGTTCCAGTTTCCCAAA − IL12B 0.797428 − 72 TTCTACTTTGGGTTTCCATCA + TRIM34B 0.797188 − 334 ACCAAGTCTCACTTTTCGTCC − CCL4 0.796758 − 810 CCTTACTTTGAGTTTGACTGT + TNF 0.796345 − 155 CCTCTGTCTCGGTTTCTTCTC − CASP7 0.795638 − 492 TGTCTCGTTCTGTTTTTGTTT − CX3CL1 0.793004 − 464 TCTGGGTCTCAGTTTCCCCAC + IRF7 occurrence position distribution (score ≥ 0.799) IL27 0.93915 − 81 CAGAAAGTGAAACT + IFIT3 0.930494 − 158 AGGAAAGTGAAACT − IFIT3B 0.930494 − 190 AGGAAAGTGAAACT − CCRL2 0.927443 − 771 CAGAAAATGAAACT − CXCL10 0.917424 − 215 TGGAAAGTGAAACT + IFIT1 0.917424 − 106 TGGAAAGTGAAACT − IFIT1BL2 0.917424 − 53 TGGAAAGTGAAACT + TRIM21 0.912534 11 AGGAAACTGAAAGT + CD274 0.903844 − 340 ACGAAACTAAAAGT − GBP7 0.903346 − 55 CTGAAACTGAAACT − ISG15 0.902317 − 68 CCGAAACAGAAAAT + DUSP28 0.896727 − 894 ATGAAAGTGAAACC + IFITM3 0.896219 17 GAGAAACCGAAACT + CD40 0.879632 − 502 AAGAAAGAGAAACT + CASP4 0.878122 0 AGTAAAATGAAAGT − TRIM26 0.876245 − 162 AGGAAAGTGAAATC − NOS2 0.875728 − 885 ATGAAAGTGAAATA − CASP12 0.870545 − 2 TCAAAACCGAAAGC − IRG1 0.870451 − 67 ACAAAAGTGAAAGG + CXCL11 0.862913 − 123 ACAAAAGAGAAACT + TRIM14 0.855474 − 4 CAGAAATCGAAACC − CCL5 0.853544 − 132 CATAAAATGAAAAC − TLR2 0.852267 − 773 GAGAAAGAGAAAAT + IFI44 0.84368 − 8 CGAAAACTGAAACT − IL15 0.842898 − 306 AGAAAAGTGAAAGA − IFIT2 0.83864 − 640 GGGAAAGTAAAAAT − CCL2 0.836435 − 113 TGGAAAGTGGAAGT − DUSP2 0.830229 − 430 TCGATAGCAAAAAT − CXCL16 0.828173 − 310 CCTAAAGTGAGATT + ISG20 0.827865 − 131 TCCAAAATGACAGT − MMP8 0.82728 − 780 ACGAAAACTAACAT − CD38 0.826755 − 25 AAGCAAGTGAAAAA + CXCL1 0.826514 − 164 CAAAAAGCAAAAAT + CCL4 0.825604 − 929 CAGAAACAGAAAAC − IL6 0.824672 − 288 AGAAAACTGAAATT − TLR1 0.824026 − 880 ATCAAAGTGAAATC + MMP3 0.8225 − 510 ACAAAAATAAAAGA + IL12B 0.82091 − 68 TGGAAACCCAAAGT − CD69 0.817776 − 637 AGGAAACAGAAAGC − TRIM25 0.816272 − 12 TCGAAACTGAACAG − CCL12 0.812674 − 187 TAGACAGCGAAACA − MMP10 0.812058 − 349 TGCAAAGTGAATGT − CXCL13 0.811598 − 937 TCCAAATCAAAAGT + IFI204 0.811567 − 163 GGGAAATTGAAAGC + TNF 0.808402 − 152 AAGAAACCGAGACA + IL10 0.807648 − 250 GCTAAAAAGAAAAA + TLR3 0.806525 − 536 ACAGAAGTGAAAGC − IL1R1 0.806397 − 711 AAAAAACCAAAAAT + IL1A 0.803145 − 870 TGGGAACTGAAACT + CASP1 0.798994 − 876 CATAAAATGACAGT − Table 3 Genes predicted to be regulated by IRF1 and IRF7 as identified by IPA analysis in R848- and IFN-α treated PM cells R848-induced cells IFN-α-induced cells IRF1 predicted to be activated (35 genes) (P = 2.23E−26) IRF7 predicted to be activated (56 genes) (P = 1.26E−31) IRF1 predicted to be activated (39 genes) (P = 3.23E−29) IRF7 predicted to be activated (42 genes) (P = 4.44E−41) CXCL1, CCL3, CCL2, CXCL5, S100A8, C3, CXCL3, TLR1, CXCL2, TLR2, CCL5, CXCL11, CCL4, CCL7, CXCL10, CASP4, MYD88, CHIL1, IL1B, ZC3H12A, TNIP1, IL1A, GBP5, OLR1, LYN, C4B, IL27, HCK, NLRP3, IFI202B, CCL12, CXCL13, CLEC7A, TNFAIP3, CD14 C3, CASP1, CASP4, CASP8, CCL2, CCL5, CCRL2, CD274, CD40, CD86, CMPK2, CXCL10, CXCL11, CXCL9, EIF2AK2, FAM26F, FCGR1A, FGL2, GBP2, GBP3, GBP5, GBP6, HERC6, ICAM1, IFI16, IFI35, IFI47, IFIT1, IFIT1B, IFIT2, IFITM3, IGTP, IL15, IL15RA, IL1R1, IL6, ISG15, KLRK1, MX1/MX2, OAS1, OASL, PTGS2, RSAD2, SLFN1, SLFN13, SLFN2, SLFN5, SP110, TAP1, TGTP1, TGTP2, TLR9, TNF, TNFSF10, TRAFD1, USP18 CASP1, CCL5, CCL7, CD14, CD40, CEBPB, CEBPD, CLIC4, CXCL10, DTX2, FCGR2, GBP5, GCH1, GFAP, IFI47, IKB, IL12B, IL1B, IRAK2, LCN2, LILRB4, NLRP3, NOS2, MAPK, PLSCR1, PRKAA, PSME2, SERCA, SLC16A10, SLFN2, SPATA13, SUSD6, TANK, TNFA, TNFAIP2, TNFAIP3, TNFSF15, TNIP1, TRIP10 APOBEC3, LY86, TLR2, TLR3, TLR7, TLR9, ISG20, NLRC5, NOD2, TMEM173, CASP4, MYD88, NOD1, OASL2, OASL1, MX1, MX2, LYN, BST2, IFI202B, OAS1B, RIPK2, OAS1A, RNF135, EIF2AK2, IFIH1, C3, CSF1, IFITM3, PML, KLRK1, OAS3, RSAD2, SP110, OAS2, NAIP6, IRGM1, TRIM25, AIM2, DDX58, IFIT3, TRIM56, IFIT2, IFIT1 R848 with IFN-α combination enhances transcription of epigenetic modifiers, G protein-coupled receptors, nuclear receptors and matrix metalloproteinases In addition to cytokines, chemokines, ISGs and TFs, our RNA-seq transcriptomes revealed synergistic expression of transcripts encoding protein for ligands, receptors, and enzymes, which strongly suggests new microglial functions in neuroinflammatory diseases. Microglia express genes of interest such as several epigenetic modifiers, including histone methyltransferases (SETDB2, MLLT3, MLLT6 and SUZ12), histone demethylases (KDM6B and KDM1A), histone deacetylases (HDAC3 and SIRT1) and histone bromodomains (BRD2 and BRD4). When evaluating the synergistic genes, we unexpectedly identified 35 previously undetected transcripts that encode protein for G protein-coupled receptors (GPR84, GPR18, GPR137, GPR180, GPR107 and GPR37L1), nuclear receptors (NR1D1, NR1D2, NR4A1, RARA and NR3C1), and matrix metalloproteinases (MMP10, MMP12, MMP13, MMP3, MMP8 and MMP9) (Fig. 4a–d). Surprisingly, several of these genes were frequently overexpressed not only in R848-treated but also in IFN-α-treated cells. Previous reports demonstrated that epigenetic modifiers, G protein-coupled receptors, nuclear receptors and matrix metalloproteinases exhibit significant links with transcriptional activation and that result in the synthesis and secretion of inflammatory factors and, in some cases, molecules that suppress immune responses. In addition, several of these modifiers and receptors have also been described as therapeutic targets to modify immune-related diseases (Parks et al. 2004; Huang and Glass 2010, Diehl et al. 2011; Insel et al. 2015; Raghuraman et al. 2016). For example, GPR84 is a pro-inflammatory receptor of microglial cells in a neuropathic pain mouse model (Bouchard et al. 2007), while MMPs (e.g., MMP12, MMP13, MMP3, MMP8) have been described as a therapeutic target for inflammatory and vascular diseases (Yong 2005; Hu et al. 2007).Fig. 4 Effect of R848, IFN-α or R848 with IFN-α on epigenetic modifiers, G protein-coupled receptors, nuclear receptors and matrix metalloproteinase in PM cells. a Heat map representation depicting the expression of epigenetic modifiers, G protein-coupled receptors, nuclear receptors and matrix metalloproteinases selectively dysregulated by R848, IFN-α and R848 with IFN-α treated (P ≤ 0.01, and log2 fold change ≥ 2) in the global RNA-seq experiments. Heat maps were generated with the Multi Experiment Viewer (version 4.8) software. Data represent three biological replicates of single isolation. b Bar graph displaying commonly upregulated selective epigenetic regulators, G protein-coupled receptors and MMPs (log2 FC ≥ 2). c The transcript abundance of KDM6B, GPR84, MMP13 and NR1D1 genes in control, R848-, IFN-α- and R848 with IFN-α-treated cells. The read count was represented by measuring the average read obtained from triplicate RNA-seq experiments. d UCSC Genome Browser images representing the normalized RNA-seq read density of KDM6B, GPR84, MMP13 and NR1D1 genes in control, R848-, IFN-α- and R848 with IFN-α-treated cells Functional and pathway analyses of R848 with IFN-α-induced common genes To gain insight into the biological processes enriched in commonly induced IRF1- and IRF7-targeted genes, we used DAVID Bioinformatics Informatics Resources (da Huang et al. 2009) to classify the results into gene ontology (GO) categories. Based on the molecules present in the dataset, the DAVID GO analysis revealed that the functions most associated with commonly upregulated genes were related to the immune system process and response to stimulus (Fig. 5a). Interestingly, cell signaling and detoxification processes were also significantly enriched in synergistic gene sets (Fig. 5a). To determine the potential canonical pathways of these induced genes, we utilized the IPA (Kramer et al. 2014), a powerful analysis tool that represents the relevant molecular functions based on functional knowledge inputs. The major categories of the canonical pathways were the communication between the innate and adaptive immune cells, the role of pattern recognition receptors in the recognition of bacteria and viruses, activation of IRF by cytosolic pattern recognition receptors, and interferon signaling (Fig. 5b). To corroborate these functional findings, we analyzed the influence of co-action on molecular signaling networks in co-induced PM cells using knowledge-based IPA (Kramer et al. 2014). Commonly induced gene sets revealed signaling networks related to innate and inflammatory responses and to immunological diseases. In particular, in the co-treated group, IRF1 and IRF7 were identified as the central modulator hubs (Fig. 5c). Together, these data imply that the IRF1 and IRF7 pathways may induce the gene expression related to the inflammatory response in PM during R848 with IFN-α exposure.Fig. 5 Functional annotation and canonical pathways associated with co-induced genes. a GO term enrichment analysis for the “biological process” category of the IRF1- and IRF7-targeted commonly induced genes in the PM cells. The top GO terms are ranked by the gene ontology enrichment. b The most highly represented canonical pathways of the IRF1- and IRF7-targeted commonly induced genes in the PM cells. Pathways ranked by Bonferroni–Hochberg-corrected − log(PB–H) calculated by Fisher’s exact test with the threshold set to 0.05. The line graph shows the ratio of commonly induced genes enriched in each canonical pathway relative to the deposited GO terms in IPA. c Gene networks top 1 displaying interactions among the commonly induced genes at different cellular levels as determined by IPA gene network analysis. The activity of genes highly connected to this network, namely IRF1 and IRF7 in top-1 network function hubs, as assessed using the IPA molecule activity predictor Identification of unique transcripts of R848 or IFN-α-inducible genes Both TLR7/8 and IFNAR1 are expressed simultaneously in numerous infections, but individual activation also manifests distinct biological responses in immune cells (Kreutz et al. 2015). To identify TLR7 or IFNAR1 ligation-specific global gene expression in PM, we further examined our RNA-seq data. We found two subsets of 215 and 240 genes specifically expressed (log2 fold change ≥ 2) in cells treated with R848 and IFN-α, respectively. Of note, these genes were unexpressed by cells treated with the alternate agent. Genes that had their expression significantly enhanced by R848 treatment include cytokines (TNF-ΑIP2, TNFRSF1B, TNFSF15, TNFSF9, IL12B, IL1A, IL1B, IRAK1BP1, IRAK3, etc.), chemokines (CX3CL1, CXCL1, CXCL2, CXCL3, CXCL5 and CXCR3), and some TFs (SOCS3, FOXP4, NF-κB2, BATF, CEBPD, CEBPB, REL, RELB, SPI1, etc.) (Fig. 6a). Similarly, genes that had a log2 fold change ≥ 2 in expression with IFN-α treatment include interferons and ISGs (IFI203, IFI27L2A, IFI44, IFI44L, IFIH1, OAS1A, OAS1B, OAS1C, OAS1G, OAS2, OAS3, etc.) and TFs (ARID5A, IRF2, IRF5, NFXL1, POU3F1, STAT1, STAT2, ZNFX1, etc.) (Fig. 6b). We again performed GO analysis of R848- and IFN-α-specific gene sets using DAVID (da Huang et al. 2009). Interestingly, our results suggest that both gene sets were significantly enriched in immune responses and immune system processes (Fig. 6c, d). These results suggest that the functions of either R848- or IFN-α-induced genes were related to the inflammatory response in PM.Fig. 6 Effects of R848 and IFN-α alone on the induction of genes in PM cells. Heat map representation depicting a R848 upregulated genes unexpressed in IFN-α-treated PM and b IFN-α upregulated genes unexpressed in R848-treated PM as identified in the RNA-seq experiments (P ≤ 0.01 and log2 fold change ≥ 2). Heat maps were generated with the Multi Experiment Viewer (version 4.8) software. c, d GO term enrichment analysis for the “biological process” category of the R848- and IFN-α-induced unique genes. The top GO terms are ranked by the gene ontology enrichment. Data represent three biological replicates of single isolation Confirmation of R848- and IFN-α-dysregulated genes as identified by RNA-seq RNA-seq analysis provides highly accurate expression results; however, to gain additional ratification, we attempted to validate a selective set of differentially expressed genes. Most were selected for validation according to their distinct induction upon R848, IFN-α and R848 with IFN-α treatment of PM. Here, we again incubated highly purified PM with these stimuli for 4 h and measured the mRNA abundance by generating single-stranded template cDNA from the mRNA. The cDNA template was then amplified in the quantitative step, during which the fluorescence emitted by labeled hybridization probes or intercalating dyes changed as the DNA amplification process progressed. With a carefully constructed standard curve, qPCR produced an absolute measurement of the number of copies of original mRNA. There is very good agreement between the RNA-seq and qRT-PCR results in terms of the direction of change as well as its magnitude. Among the 13 genes selected for verification, 11 genes (IL1A, IL6, TNF-α, CCL2, CCL3, CCL4, CXCL11, IFIT1, IFIT2, IFIT3 and ISG20) were induced synergistically in co-treated PM (Fig. 7). However, one was insignificant (TNFSF10) in the qRT-PCR analysis compared with the RNA-seq experiments, and another was undetected. Together, these results support the robustness of our RNA-seq data.Fig. 7 Confirmation of co-induced selected genes. The bar graphs are representing the TNF-α, IL1A, IL6, TNFSF10, IFIT1, IFIT2, IFIT3, ISG20, CCL2, CCL3, CCL4 and CXCL11 gene expression in R848-, IFN-α-, R848 with IFN-α treated cells over the control. The gene expression levels were normalized to the GAPDH transcript levels and compared with the control. qRT-PCR data are pooled from three independent experiments, each in triplicate. Data are mean ± SEM; P ≤ 0.01 and **P ≤ 0.001 compared to control Discussion In the present study, using high-resolution transcriptome analysis, we established the transcriptional profile mostly involved in PM activation in response to R848, IFN-α and R848 with IFN-α stimuli. A few studies have attempted to use qRT-PCR to determine gene expression changes associated with TLR7/8 agonists and IFN-α treatment in macrophages, B cells, and brains (Siren et al. 2005; Pirhonen et al. 2007; Severa et al. 2007; Butchi et al. 2008; Butchi et al. 2011; Poovassery and Bishop 2012; Kreutz et al. 2015; Zimmermann et al. 2016). However, thus far, a genome-wide search for the similarities and differences between the effects of these two treatments on microglial gene expression has not been conducted. Given the ever-increasing importance of microglia to the field of neuroinflammation research, the ability to isolate high yield of primary microglia is the preliminary to be measured to investigate the role of microglial modulation of inflammation. Here, we used 92% pure microglia obtained from 3-day-old ICR mice brain. In support of our study, 90–95% pure were also used to perform highly accurate experiments to interrogate microglial functions in vitro, including cellular phenotyping, transcriptome analysis, cytokines/chemokines release and neuroinflammatory disease modeling. The strength of our analysis, which aimed to provide comprehensive and comparative transcriptional profiles of responses to inflammatory stimulation, was enhanced by the use of RNA-seq to analyze IFN-α-mediated TLR7/8 cross-regulation in murine microglia. Importantly, we evaluated DNA-binding factors that may drive distinct gene expression in R848-, IFN-α- or R848 with IFN-α-primed primary microglia. Our data support the contention that signaling crosstalk occurs between R848 and IFN-α to cross-regulate transcriptional responses that are critical components of the innate immune system and may lead to neuroinflammatory processes. IFNs could have either immunostimulatory or immunosuppressive functions in inflammation/antiviral responses (Ivashkiv and Donlin 2014). In our study, we found that a set of cytokines/chemokines, antiviral genes, and IRGs associated with inflammation (Holtman et al. 2015, Srinivasan et al. 2017) was synergistically upregulated in response to R848 with IFN-α compared to their upregulation by single treatment with R848 or IFN-α in PM (Fig. 2d). Both the number of cytokines/chemokines, antiviral genes and IRGs and the extent of the fold changes in the synergistically altered genes were significantly higher in R848 with IFN-α compared to R848- or IFN-α-treated PM. Synergistic inflammatory gene expression was also observed upon stimulation with inflammatory stimuli such as TLR agonist combination alone or with IFNs or combination of other cytokines in dendritic cells, macrophages and brain cells (Napolitani et al. 2005; Qiao et al. 2013; Suet et al. 2013; Kreutz et al. 2015; Goldstein et al. 2017). In contrast, IFN-α-suppressed R848 induced significantly higher level of several specific gene families involved in immune responses, including CXCL1, CXCL2, CXCL3, CXCL5, and CXCL9, among others, compared with only IFN-α- or R848-treated PM (Fig. 2e). These data suggest that alterations in the expression levels of these proinflammatory transcripts during pathologic conditions not only reflect unique functional capabilities but also can be used as potential marks to identify these cells in distinct physiologic conditions. Our RNA-seq data showed that transcripts of selective inflammatory cytokine/chemokine genes were expressed synergistically in PM treated with R848 and IFN-α (Fig. 2d). Inflammatory cytokines/chemokines from microglia/macrophages mediate defense of the host from various pathogens such as viruses (Murray and Wynn 2011; Klein and Hunter 2017). Although the initial immune response to pathogens is achieved by only a limited number of inflammatory cytokines/chemokines, the anti-pathogen effector programs triggered by cytokine/chemokine system are based on the concerted action of hundreds of ISGs (Schoggins et al. 2011). There is a report which demonstrated that ISG IFIT2 deficiency results in uncontrolled neurotropic coronavirus replication and enhanced encephalitis via impaired alpha/beta interferon induction in mouse brain (Butchi et al. 2014). IFIT2 deficiency also does not regulate mRNA expression of inflammatory cytokines and chemokines such as IL1B, IL6, TNF, CCL2, CCL5, CXCL9, CXCL10, and IFN-γ, or even many ISGs both in microglia and macrophages (Butchi et al. 2014), suggesting the importance of IFIT2 in limiting virus infection in the CNS. Only TLR7/8 ligation in microglia and subsequent ISG transcription were modest and context dependent (Pirhonen et al. 2007; Butchi et al. 2008; Butchi et al. 2011; Schoggins et al. 2011; Butchi et al. 2014). The result of concurrent signaling of TLR7/8 and IFNs induced ISGs of IFITs, ISGs, IFITMs is likely to be filler to molecular signatures of efficient inflammatory/defense responses (Fig. 2d). Gene transcriptional events during cellular activation are largely controlled by designated TFs. Using this array, we also found that a set of TFs was largely affected either synergistically or co-repressed in PM, suggesting that this set of TFs might include important regulators of IFN-α-associated immunostimulatory and immunosuppressive effects (Fig. 3). More importantly, we identified several TFs, including IRFs (1, 7, 8 and 9) and RELA, that were synergistically upregulated in PM. Our results are consistent with those described in a previously published report showing that IRF1 and IRF8 are critical for microglia activation (Masuda et al. 2015). In our RNA-seq analysis, we also identified several other TFs (ARID5A, ATF3, ATF4, CEBPB, CREB3, CREB5, CREM, FOXC1, JUNB, KLF6, SOCS7, SOX9, NFXL1, and STAT4, among others) in synergistically induced PM. Each of these TFs (IRF1, IRF7 IRF8, and RELA, among others) is predicted to be central to some aspect of the synergistic responses and may represent candidates for experimental validation using knockout or overexpression models. We next found that the promoters of synergistically expressed genes were enriched for IRF1 and IRF7 but not for NF-κB1 and STAT1, as shown in Fig. 3. Another interesting finding is that our RNA-seq analysis identified several important epigenetic regulators that were synergistically induced by IFN-α and R848 in PM. Recently, we found that the histone demethylases KDM1A and KDM4A, the histone methyltransferases NSD3 and SETDB2 and the DNA methyltransferase DNMT3L were strikingly differentially expressed in LPS-induced PM (data not shown). Importantly, our RNA-seq data revealed not only that those epigenetic regulators were strikingly synergistically expressed in IFN-α- and R848-induced PM but also that bromodomain and extra-terminal motif (BET) proteins BRD2 and BRD4, histone demethylase KDM6B, and the histone deacetylase SIRT1 were strikingly synergistically expressed in IFN-α- and R848-induced PM (Fig. 4). Previous studies demonstrated that SIRT1 and SETDB2 can potentially regulate proinflammatory gene expression in macrophages (Chen et al. 2015; Schliehe et al. 2015). However, the mechanism by which those important epigenetic regulators become synergistically activated remains unknown. Determining how these epigenetic regulators, in combination with modified TFs, can regulate inflammatory genes synergistically in microglial cells would be intriguing. Our genome-wide analysis employing the major experimental uses of microglia, along with the integration of multiple gene sets and bioinformatics analysis, provides the most robust and comprehensive assessment to date of IFN-α-mediated TLR7/8 cross-regulation in murine microglia at the level of the microglial transcriptome. However, changes observed in these studies may reflect not only the gene expression profiles of microglia, but also those of other CNS cells possibly astrocyte or oligodendrocytes. Conclusions Conclusively, using global profiling with a bioinformatics approach, we have described herein the molecular signatures induced by R848, IFN-α alone or co-treatment in microglial cells. Our genome-wide, integrative analysis has revealed the integration of signaling crosstalk between TLR7/8 and IFN-α at the level of the transcriptome in association with changes in related TFs. These data may break new ground in the study of the role of microglia in neurological disorders. Our findings provide a better understanding of the complex activation of the IFN-α-induced TLR7/8 cross-regulation occurring in microglia, and this knowledge could be utilized in elucidating novel targets to modulate microglia activation by neuroinflammatory disorders. Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary material 1 (TIFF 6863 kb) Supplementary material 2 (TIFF 3427 kb) Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements This work was supported by Chung-Ang University Young Scientist Scholarship (CAYSS) program. Author contributions MRK and SA conceived the study and interpreted the data. Compliance with ethical standards Conflict of interest The authors declare no conflicts of interest related to this work. 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==== Front J Microbiol J. Microbiol Journal of Microbiology (Seoul, Korea) 1225-8873 1976-3794 The Microbiological Society of Korea Seoul 26727903 5555 10.1007/s12275-016-5555-4 Virology RETRACTED ARTICLE: Interferon-mediated antiviral activities of Angelica tenuissima Nakai and its active components Weeratunga Prasanna 1 Uddin Md Bashir 12 Kim Myun Soo 3 Lee Byeong-Hoon 1 Kim Tae-Hwan 1 Yoon Ji-Eun 4 Ma Jin Yeul 5 Kim Hongik 3 Lee Jong-Soo [email protected] 1 1 grid.254230.20000000107226377College of Veterinary Medicine (BK21 Plus Program), Chungnam National University, Daejeon, 305-764 Republic of Korea 2 grid.449569.30000000446648128Faculty of Veterinary & Animal Science, Sylhet Agricultural University, Sylhet, 3100 Bangladesh 3 Vitabio Corporation, Daejeon, 305-764 Republic of Korea 4 Foot and Mouth Disease Division, Animal Quarantine and Inspection Agency, Anyang, Republic of Korea 5 grid.418980.c0000000087495149Korean Medicine (KM) Based Herbal Drug Development Group, Korea Institute of Oriental Medicine, Daejeon, 305-764 Republic of Korea 5 1 2016 2016 54 1 57 70 6 11 2015 3 12 2015 3 12 2015 © The Microbiological Society of Korea and Springer-Verlag Berlin Heidelberg 2016This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Angelica tenuissima Nakai is a widely used commodity in traditional medicine. Nevertheless, no study has been conducted on the antiviral and immune-modulatory properties of an aqueous extract of Angelica tenuissima Nakai. In the present study, we evaluated the antiviral activities and the mechanism of action of an aqueous extract of Angelica tenuissima Nakai both in vitro and in vivo. In vitro, an effective dose of Angelica tenuissima Nakai markedly inhibited the replication of Influenza A virus (PR8), Vesicular stomatitis virus (VSV), Herpes simplex virus (HSV), Coxsackie virus, and Enterovirus (EV-71) on epithelial (HEK293T/HeLa) and immune (RAW264.7) cells. Such inhibition can be described by the induction of the antiviral state in cells by antiviral, IFNrelated gene induction and secretion of IFNs and pro-inflammatory cytokines. In vivo, Angelica tenuissima Nakai treated BALB/c mice displayed higher survivability and lower lung viral titers when challenged with lethal doses of highly pathogenic influenza A subtypes (H1N1, H5N2, H7N3, and H9N2). We also found that Angelica tenuissima Nakai can induce the secretion of IL-6, IFN-λ, and local IgA in bronchoalveolar lavage fluid (BALF) of Angelica tenuissima Nakai treated mice, which correlating with the observed prophylactic effects. In HPLC analysis, we found the presence of several compounds in the aqueous fraction and among them; we evaluated antiviral properties of ferulic acid. Therefore, an extract of Angelica tenuissima Nakai and its components, including ferulic acid, play roles as immunomodulators and may be potential candidates for novel anti-viral/anti-influenza agents. Keywords Angelica tenuissima Nakaiferulic acidherbal medicineanti-influenza effectantiviral effectissue-copyright-statement© The Microbiological Society of Korea and Springer-Verlag Berlin Heidelberg 2016 ==== Body These authors contributed equally to this study. The above article by Weeratunga et al. has been retracted from Journal of Microbiology at the request of the corresponding author. The authors found that they were unable to reproduce Figure 1, Figure 3(A), Figure 4(A) and Figure 7(D) presented in this paper. All of the authors agreed to this retraction. The authors regret any inconvenience that this may cause and apologize sincerely to the readers, reviewers, and editors of Journal of Microbiology. Change history 8/23/2018 The above article by Weeratunga et al. has been retracted from Journal of Microbiology at the request of the corresponding author. The authors found that they were unable to reproduce Figure 1, Figure 3(A), Figure 4(A) and Figure 7(D) presented in this paper. All of the authors agreed to this retraction. The authors regret any inconvenience that this may cause and apologize sincerely to the readers, reviewers, and editors of Journal of Microbiology. ==== Refs References Boasso A. Type I interferon at the interface of antiviral immunity and immune regulation: The curious case of HIV-1 Scientifica 2013 Bouvier N.M. Lowen A.C. Animal models for influenza virus pathogenesis and transmission Viruses 2010 2 1530 1563 10.3390/v20801530 21442033 Coil D.A. Miller A.D. Phosphatidylserine is not the cell surface receptor for vesicular stomatitis virus J. Virol. 2004 78 920 926 10.1128/JVI.78.20.10920-10926.2004 Gandhiraja N. Sriram S. Meenaa V. Srilakshmi J.K. Sasikumar C. Rajeswari R. Phytochemical screening and antimicrobial activity of the plant extracts of Mimosapudica L. against selected microbes Ethnobot. 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==== Front Cell Cycle Cell Cycle KCCY kccy20 Cell Cycle 1538-4101 1551-4005 Taylor & Francis 31944163 1712033 10.1080/15384101.2020.1712033 Research Paper Downregulated long non-coding RNA SNHG7 restricts proliferation and boosts apoptosis of nasopharyngeal carcinoma cells by elevating microRNA-140-5p to suppress GLI3 expression Y. DAI ET AL. CELL CYCLE Dai Yaozhang a Zhang Xin b Xing Haijie c Zhang Yamin a Cao Hua a Sang Jianzhong a Gao Ling a Wang Liuzhong a a Department of Throat, Head and Neck Surgery, Affiliated Otolaryngological Hospital, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, PR.China b Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, PR.China c Department of Otolaryngology Head and Neck Surgery, University of Chinese Academy of Sciences, Shenzhen hospital, Shenzhen, Guangdong, PR.China CONTACT Yaozhang Dai [email protected] 2020 16 1 2020 19 4 448463 23 7 2019 17 10 2019 7 11 2019 © 2020 Informa UK Limited, trading as Taylor & Francis Group 2020 Informa UK Limited, trading as Taylor & Francis Group ABSTRACT Long non-coding RNAs (lncRNAs) have been proposed to correlate with various carcinomas, yet the role of lncRNA SNHG7 in nasopharyngeal carcinoma (NPC) is hardly studied. This study intends to examine the molecular mechanism of SNHG7 on NPC cells. The NPC tissues and nasopharyngeal tissues of mild inflammation of nasopharyngeal mucosa were obtained. SNHG7, miR-140-5p, and GLI3 mRNA and protein expression in tissues and in the CNE1, HONE1, C666-1, CNE2, and normal NP69 cell lines was detected. IC50 and the protein expression of related drug-resistant genes of CNE2 and CNE2/DDP cells were determined. Proliferative ability, cell colony formation rate, cell cycle, and apoptosis of CNE2 and CNE2/DDP cells were also detected. SNHG7, miR-140-5p, and GLI3 mRNA and protein expression in CNE2 and CNE2/DDP cells in each group was detected. SNHG7’s cell localization, the binding sites of SNHG7 and miR-140-5p along with miR-140-5p and GLI3 were detected. Overexpressed SNHG7 and GLI3, and underexpressed miR-140-5p were found in NPC tissues and cells. SNHG7 silencing and miR-140-5p elevation declined the drug resistance of drug-resistant NPC cells and their parent cells, restrained NPC cell colony formation ability and proliferation, and boosted cell apoptosis. SNHG7 specially bound to miR-140-5p, and SNHG7 silencing elevated miR-140-5p expression. GLI3 was a direct target gene of miR-140-5p and miR-140-5p elevation diminished GLI3 expression. MiR-140-5p inhibition reversed the impacts of SNHG7 silencing on NPC cells. In summary, our study reveals that downregulated SNHG7 restricts GLI3 expression by upregulating miR-140-5p, which further suppresses cell proliferation, and promotes apoptosis of NPC. KEYWORDS LncRNA SNHG7 MicroRNA-140-5p GLI3 Nasopharyngeal carcinoma Proliferation Apoptosis ==== Body Introduction Derived from epithelial cells on the surface of the nasopharynx, nasopharyngeal carcinoma (NPC) is classified into three categories in light of the criteria of World Health Organization (type I, keratinizing squamous cell tumor; type II, nonkeratinizing differentiated tumor; type III nonkeratinizing undifferentiated tumor) [1]. Risk factors for NPC are chiefly comprised of Epstein-Barr virus infection, human papillomavirus, genetic susceptibility as well as dietary and social practices [2]. NPC frequently arises in southern China, especially in Hainan, Guangdong, Fujian, Jiangxi, and Guangxi provinces [3]. Lately, correlations of aberrantly long non-coding RNAs (lncRNAs) with NPC have been substantially reported [4], which inspired this study to be focused on the exploration of new biomarkers and treatment of NPC. LncRNAs refer to RNA transcripts with over 200 nucleotides and nearly without protein-coding ability, which function as molecular biomarkers in human cancers [5,6]. There is a report suggesting that in lung cancer, lncRNA SNHG7 is dysregulated and serves as a promoter of cell progression [7]. Also, the promotion of cell proliferation and inhibition of apoptosis by SNHG7 have also been discovered in gastric cancer [8]. Evidence has shown that microRNA-140-5p (miR-140-5p) can be targeted by lncRNA SNHG16 [9]. MiRNAs, which refers to short noncoding RNAs with about 22 nucleotides in length, have been proposed to be dysregulated in many cancers and function in their development [10,11]. A previous study has revealed that in hepatocellular carcinoma (HCC), miR-140-5p is underexpressed and it directly targets Pin1 (a kind of unique isomerase) to stop multiple cancer-driving pathways, thus depressing HCC development [12]. Similar research by Hu Y et al. has demonstrated that in glioma, miR-140-5p modulates VEGFA/MMP2 signaling for cell proliferation and invasion restriction [13]. GLI-Kruppel family member 3 (GLI3), a kind of the zinc finger transcription factor, is a regulator of Sonic hedgehog signaling, and it has been found to be targeted by miR-506 in cervical cancer to depress tumor progression [14,15]. There is also a report revealing that in oral squamous cell carcinoma, silencing of GLI3 can decline stemness and simultaneously circumscribe cell progression [16]. Nevertheless, the impacts of lncRNA SNHG7 in NPC have hardly been reported. Therefore, this study intends to explore the mechanism of SNHG7/miR-140-5p/GLI3 axis in NPC. Materials and methods Ethics statement This study was reviewed and approved by the Ethics Committee of Affiliated Otolaryngological Hospital, the First Affiliated Hospital of Zhengzhou University and was supervised by the Ethics Committee of Affiliated Otolaryngological Hospital, the First Affiliated Hospital of Zhengzhou University. Written informed consents were obtained from all patients before the study. Subjects Patients with NPC who underwent nasopharyngeal biopsy from September 2017 to September 2018 were included in the study. The following ones were included: patients according to diagnostic criteria for NPC; patients pathologically diagnosed as NPC; patients first diagnosed as NPC and not received any surgical treatment; patients with complete and detailed medical information; and the following ones were excluded: patients with other tumors; patients with severe heart, liver, and kidney dysfunction; patients with other systemic immune diseases; patients not cooperating with the treatment. A total of 55 qualified NPC patients (41 males and 14 females) aged 24 to 57 y with an average age of (32.48 ± 10.35) y were selected for the experiment. All the patients were staged according to the 1992 Fuzhou Conference [17]: 7 in stage II, 31 in stage III, and 17 in stage IVa. They were diagnosed as poorly differentiated squamous cell carcinoma or non-keratinized undifferentiated carcinoma by histopathology at the first visit, and were set as the cancer tissue group. At the same time, 30 nasopharyngeal tissues from patients with mild inflammation of nasopharyngeal mucosa in the corresponding period of the Affiliated Otolaryngological Hospital, the First Affiliated Hospital of Zhengzhou University were collected as the normal tissue group. Cell selection and culture Human NPC cells (CNE1, HONE1, C666-1, and CNE2) were all purchased from Shanghai Huiying Biological Technology Co., Ltd. (Shanghai, China). The normal nasopharyngeal epithelial cell line NP69 was purchased from Jianglin Biotechnology Co., Ltd. (Shanghai, China). All cells were cultured in RPMI 1640 medium (Gibco, Grand Island, NY, USA) containing 10% fetal bovine serum (FBS, Gibco, Grand Island, NY, USA) for incubation (37℃, 5% CO2). The medium was changed every 2 d, and passage was started when the cell density reached 80%-90%. After 2–3 stable passages, the cells in logarithmic growth phase were taken for the detection of SNHG7, miR-140-5p, and GLI3 mRNA expression by reverse transcription quantitative polymerase chain reaction (RT-qPCR), and GLI3 protein expression level by western blot analysis. Follow-up experiments were performed with CNE2 cells with the greatest expression difference from NP69 cells. Human NPC cell-resistance modeling Drug-resistant cells were induced by a combination of high-dose pulse and increasing doses. Human NPC CNE2 cells in the logarithmic growth phase were inoculated into a 250 mL culture flask. When the cell confluence was 70%-80%, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay showed that the half maximal inhibitory concentration (IC50) of cisplatin (DDP) on sensitive cells was 0.25 μmol/L. The cells were then cultured in medium containing 3.3 μmol/L DDP for 24 h, followed by 5-10-d culture with DDP of IC50. Then, IC50 was determined after stable growth and three passages of the cells. Next, the cells were cultured with the medium containing 3.3 μmol/L DDP for 24 h, and the medium with gradually increasing DDP IC50 was replaced. After the cells stably grew and serially passaged 3 times in the medium containing 3.3 μmol/L DDP, the final IC50 of DDP was 7 μmol/L and named CNE2/DDP. Cell grouping and transfection CNE2 and CNE2/DDP cells in the logarithmic growth phase (2 × 105 cells/well) were inoculated in 6-well cell culture plates, and transfected when the cell confluence reached 80% based on the instructions of lipofectamine 2000 kit (11,668–027, Invitrogen, Carlsbad, California, USA). Two hundred and fifty μL serum-free RPMI 1640 medium was used to dilute each transfection sequence (all purchased from Shanghai GenePharma Co., Ltd. (Shanghai, China) with a final concentration of 50 nM) before 5-min incubation at room temperature. Then, 5 μL lipofectamine 2000 was also diluted by 250 μL serum-free RPMI 1640 medium for 5-min incubation at room temperature. The above two mixtures were incubated for 20 min and added into the cell culture wells. Then, the cells were cultured for 6 h (37℃, 5% CO2, saturated humidity), and the medium containing the transfection solution was replaced with RPMI 1640 medium containing 10% FBS for follow-up experiments. All cells were divided into seven groups: blank group (CNE2 and CNE2/DDP cell lines without any treatment), sh-negative control (NC) group (CNE2 and CNE2/DDP cell lines transfected with SNHG7 interference NC plasmid), sh-SNHG7 group (CNE2 and CNE2/DDP cell lines transfected with SNHG7 interference plasmid), mimic-NC group (CNE2 and CNE2/DDP cell lines transfected with miR-140-5p mimic NC), miR-140-5p mimic group (CNE2 and CNE2/DDP cells transfected with miR-140-5p mimics), sh-SNHG7 + inhibitor NC group (CNE2 and CNE2/DDP cell lines transfected with SNHG7 interference plasmid and miR-140-5p inhibitor NC), sh-SNHG7 + miR-140-5p inhibitor group (CNE2 and CNE2/DDP cell lines transfected with SNHG7 interference plasmid and miR-140-5p inhibitor). MTT assay CNE2 and CNE2/DDP cells were passaged before transfection and cultured in drug-free medium for 3 to 5 d until in logarithmic growth phase, followed by the cell density adjustment to 1 × 105 cells/mL. Then, the cells were added to a 96-well plate at 100 μL per well. Next, 100 μL medium with 10-fold drug (DDP, 5-fluorouracil [5-FU]) concentration gradient was added, respectively, with a total volume of 200 μL per well (all 7 concentration gradients, 1 blank control), and 3 parallel wells for each drug concentration. After 48-72-h culture, 15 μL of 10 mg/mL MTT solution was put in each well for 4-h continuous culture. After removing the medium, 200 μL dimethyl sulfoxide (DMSO) was added to each well, and dissolved and crystallized by slight shaking. The optical density (OD) value of each well was detected on a microplate reader with 490 nm as the detection wavelength. Data analysis was performed by MTT analysis software for IC50 calculation, followed by cell resistance index (RI) calculation. CNE2 and CNE2/DDP cells after transfection of each group were treated, and MTT detection and RI calculation were performed under the functions of 7.86 μmol/L DDP and 347.86 μmol/L 5-FU. MTT assay for cell proliferation detection Cells in the logarithmic growth phase in each group (200 μL) were inoculated in a 96-well cell culture plate at 1 × 104 cells/mL with 6 wells repeated. When the cells were attached after 24-h incubation, the medium was replaced by 200 μL RPMI 1640 with 10% FBS and 20 μL MTT solution (5 mg/mL) was then added, followed by a 4-h incubation at 37℃. With the supernatant abandoned, 150 μL DMSO was added to every well and shook at low speed for 10 min, followed by OD measurement by a microplate reader at 490-nm wavelength. The experiment was repeated 3 times. 5-ethynyl-2ʹ-deoxyuridine (EdU) assay CNE2 and CNE2/DDP cell suspension (100 μL) containing 2000 cells were inoculated in 96-well plates with 3 replicate wells per group, followed by 48-h incubation (37℃, 5% CO2). With the medium removed, the serum-free RPMI 1640 containing EdU (1:1000) was added to the incubator for a 2-h culture. The culture plate was taken out, then treated based on the EdU kit (Nanjing Xinfan Biotechnology Co., Ltd., Jiangsu, China), and finally placed under an inverted fluorescence microscope for fluorescent photo collection through a random selection of three fields. The EdU-positive rate (%) = the number of EdU-positive cells/the total number of cells × 100%. Colony formation assay CNE2 and CNE2/DDP cells were inoculated on 6-well plates at 400 cells/well. Then, the cells were incubated in a 37℃ incubator with 5% CO2 for 14 d, followed by 15-min 4% paraformaldehyde fixation and 20-min 0.4% crystal violet staining. After washing and air-drying, the number of cell colonies was counted through a random selection of 5 fields under a fluorescence microscope (>50 colonies were considered as an effective colony). Cell colony formation rate = the number of cell colonies/the number of inoculated cells × 100%. Flow cytometry The cell cycle of a portion of transfected cells was detected. All cells were supplemented with 100 μL propidium iodide (PI)-Rnase A for 15-min incubation without light, and DNA content in each phase of cell cycle was analyzed by flow cytometry. The cells were detached and centrifuged, followed by 2 phosphate buffered saline (PBS) rinses. Next, the cells were resuspended in 75% pre-cooled ethanol before overnight fixation at −20℃. After supernatant removal and 2 PBS washes, the cells in each sample were resuspended with 450 μL PBS, and supplemented with 100 μL Rnase A for 30-min water bath at 37℃, followed by 30-min PI (400 μL) staining at 4℃ without light. Finally, cell cycle distribution was measured and analyzed by flow cytometry (FACSCalibur, BD Biosciences, Franklin Lakes, NJ, USA). The experiment was repeated three times independently. The transfected cells of each group were rinsed with PBS three times and supplemented with 100 μL pre-cooled 1× binding buffer for cell resuspension. Then, 5 μL Annexin and 5 μL PI were added in sequence, mixed, and kept for 15 min, followed by apoptosis detection by flow cytometry. The results were judged as follows: AnnexinV were used as the lateral axis and PI as the vertical axis; the mechanical injury cells were in the upper left quadrant; the late apoptotic cells or the necrotic cells were in the upper right quadrant; the negative normal cells were in the lower left quadrant; the early apoptotic cells were in the lower right quadrant. RT-qPCR Based on the Trizol (Takara, Otsu, Shiga, Japan) method, the total RNA of the collected tissues and cells were extracted, and the concentration and purity of RNA were then determined. In light of the instructions of the reverse transcription kit (K1621, Fermentas, Maryland, NY, USA), the RNA was reversely transcribed into cDNA which was then preserved in a refrigerator at −20℃. The primer sequences (Table 1) of miR-140-5p, SNHG7, and GLI3 were designed and entrusted to Shanghai Genechem Co., Ltd. (Shanghai, China) for synthesis. The expression of each gene was determined by an RT-qPCR kit (Takara, Otsu, Shiga, Japan), followed by RT-qPCR machine (ABI 7500, ABI, Foster City, CA, USA) detection. U6 was used as the internal reference of miR-140-5p, and glyceraldehyde phosphate dehydrogenase (GAPDH) as that of SNHG7 and GLI3. The relative expression of each target gene was calculated by 2−ΔΔCt method. Each experiment was repeated 3 times.10.1080/15384101.2020.1712033-T0001 Table 1. Primer sequence. Gene Primer sequence (5ʹ – 3ʹ) SNHG7 Forward: 5ʹ- CAACTGCCTGAAACCCCATCT −3’ Reverse: 5ʹ- CGGGTTCAAGCGATTCTCCT −3’ miR-140-5p Forward: 5ʹ- GAGTGTCAGTGGTTTACCCT −3ʹ Reverse: 5ʹ- GCAGGGTCCGAGGTATTC-3’ GLI3 Forward: 5ʹ-AGGGTGAATGGTATCAAGATGG-3’ Reverse: 5ʹ-CCCACGGTTTGGTCATAGAA-3ʹ U6 Forward: 5ʹ-CTCGCTTCGGCAGCACA-3’ Reverse: 5ʹ-AACGCTTCACGAATTTGCGT-3’ GAPDH Forward: 5ʹ-TCCCATCACCATCTTCCA-3’ Reverse: 5ʹ-CATCACGCCACAGTTTTCC-3’ miR-140-5p, microRNA-140-5p; GLI3, GLI-Kruppel family member 3; GAPDH, glyceraldehyde-3-phosphate dehydrogenase. Western blot analysis The protein concentration was measured based on the instructions of bicinchoninic acid kit (Boster Biological Technology Co., Ltd., Wuhan, China). The extracted proteins were added to the loading buffer before 10-min boiling at 95℃, and each well was loaded with 30 μg samples. Then, the proteins were separated by 10% polyacrylamide gel (Boster Biological Technology Co., Ltd., Wuhan, China) through electrophoresis with the electrophoresis voltage changed from 80 v to 120 v. Next, the proteins were transferred to polyvinylidene difluoride membrane through 45–70 min wet transfer with a voltage of 100 mv, followed by 1-h 5% bovine serum albumin (BSA) sealing. Then, primary antibody Ki-67 (1:1000), GLI3 (1:300) and P-glycoprotein (P-gp) (1:300) (Abcam, Cambridge, UK) were added together with CyclinD1 (1:1000), B-cell lymphoma-associated X (Bax, 1:1000), B-cell lymphoma-2 (Bcl-2, 1:1000), multidrug resistance-associated protein 1 (MRP1, 1:200) (Santa Cruz Biotechnology, Santa Cruz, California, USA), GAPDH (1:2000, Jackson ImmunoResearch, Grove, PA, USA) and glutathione-S-transferase-π (GST-π, 1:2000, Millipore, Darmstadt, Germany) for 24- to 48-h incubation at 4℃, and then placed in horseradish peroxidase-labeled secondary antibody (1:500, Jackson ImmunoResearch, Grove, PA, USA) for 1-h incubation at room temperature. Images were obtained by Odyssey (a two-color infrared fluorescence scanning imaging system), and the gray value of the bands was measured by Quantity One (an image analysis software). The ratios of the value of target bands to that of the internal reference bands in each group were obtained and compared. RNA-fluorescence in situ hybridization (FISH) assay Prediction of the subcellular localization of SNHG7 was carried out with the bioinformatics website (http://lncatlas.crg.eu/), which was then verified by FISH assay. The assay was performed based on the instructions of RiboTM lncRNA FISH Probe Mix (Red) (Guangzhou RiboBio Co., Ltd., Guangzhou, China). The coverslip was put in a 24-well culture plate, followed by cell inoculation at 6 × 104 cells/well to achieve about 80% cell confluence. After taking the coverslip out, the cells were washed by PBS before fixation by 1 mL 4% paraformaldehyde and treatment with proteinase K, glycine and acetamidine reagent. Subsequently, 250 μL pre-hybrid solution was added for 1-h incubation at 42℃. Then, the pre-hybrid solution was replaced with 250 μL SNHG7 hybridization solution (300 ng/mL) with probe for overnight hybridization at 42℃. After 3 phosphate-buffered saline/Tween 20 (PBST) washes, 4ʹ,6-diamidino-2-phenylindole (DAPI, ab104139, 1:100, Abcam, Shanghai, China) diluted with PBST was added for 5-min nucleus staining in a 24-well culture plate, followed by 3 PBST washes (3 min each time). At last, the cells were observed under a fluorescence microscope (Olympus, Tokyo, Japan) and photographed after anti-fluorescence quencher sealing. Dual luciferase reporter gene assay The binding site of lncRNA SNHG7 and miR-140-5p were predicted and analyzed at the bioinformatics website (https://cm.jefferson.edu/rna22/Precomputed/), which was then verified by dual luciferase reporter gene assay. The synthetic SNHG7 3ʹ-untranslated regions (3ʹ-UTR) gene fragment was introduced into the pMIR-reporter (Beijing Huayueyang Biotechnology Co., Ltd., Beijing, China) through endonuclease sites (Bamh1 and Ecor1). The complementary sequence mutation site of the seed sequence was designed on the wild type (WT) of SNHG7, and the target fragment was inserted into the pMIR-reporter plasmid by T4 DNA ligase after restriction endonuclease digestion. The correctly sequenced luciferase reporter plasmids (WT and mutant type [MUT]) were independently co-transfected with mimic NC and miR-140-5p mimic into CNE2 cells (Shanghai North Connaught Biotechnology Co., Ltd., Shanghai, China). After 48-h transfection, cells were lysed, and luciferase activity was measured with a luciferase assay kit (BioVision, San Francisco, CA, USA) and Glomax20/20 luminometer (Promega, Madison, Wisconsin, USA). The experiment was repeated three times. The targeting relationship of miR-140-5p and GLI3 and the binding site of miR-140-5p and GLI3 3ʹUTR were predicted with a bioinformatics software (http://www.targetscan.org/vert_72/). The GLI3 3ʹUTR promoter sequence containing the miR-140-5p binding site was synthesized, and a GLI3 3ʹUTR WT plasmid (GLI3-WT) was constructed. Based on this plasmid, a binding site was mutated for GLI3 3ʹUTR MUT plasmid (GLI3-MUT) establishment in light of the procedures of point mutation kit (Takara, Otsu, Shiga, Japan). CNE2 cells in the logarithmical growth phase were inoculated in 96-well plates, and transfected with the mixtures of GLI3-WT and GLI3-MUT plasmids with mimic NC and miR-140-5p mimic plasmids, respectively, by Lipofectamine 2000 at a cell density of approximately 70%. Forty-eight hours later, cells were lysed, and luciferase activity was detected with a luciferase assay kit. Each experiment was repeated three times. RNA-pull down The cells were transfected with biotin-labeled miR-140-5p WT plasmid and biotin-labeled miR-140-5p MUT plasmid (50 nM each), respectively. Forty-eight hours later, the cells were washed with PBS and incubated with specific cell lysate (Ambion, Austin, Texas, USA) for 10 min, after which 50 mL cell lysate sample was collected. Then, residual lysate was incubated with M-280 streptavidin magnetic beads (Sigma, St. Louis, MO, USA) pre-coated with RNase-free BSA and yeast tRNA (Sigma, St. Louis, MO, USA) at 4℃ for 3 h, followed by 2 cold lysate washes, 3 low salt buffer wash, and a high salt buffer wash. An antagonistic miR-140-5p probe was set up as an NC. SNHG7 expression was detected by RT-qPCR after total RNA extraction with Trizol. Statistical analysis All data were statistically analyzed with SPSS 21.0 (SPSS, IBM Corp., Armonk, NY, USA) statistical software. Measurement data were expressed as mean ± standard deviation and those subject to normal distribution between two groups were compared with independent sample t-test. Comparison among the group was analyzed by one-way analysis of variance (ANOVA), after which Tukey’s multiple comparison test was used for pairwise comparison. The difference was considered statistically significant at P < 0.05. Results There are overexpressed SNHG7 and GLI3, and underexpressed miR-140-5p in NPC tissues SNHG7 and miR-140-5p levels along with GLI3 mRNA level in normal tissues (nasopharyngeal tissues of mild inflammation of nasopharyngeal mucosa) and cancer tissues were detected by RT-qPCR. The results showed ascended SNHG7 and GLI3 levels, and decreased miR-140-5p level in the cancer tissues (all P < 0.05; Figure 1(a–c)). GLI3 protein level detection by Western blot analysis also suggested elevated GLI3 protein expression in cancer tissues (P < 0.05) (Figure 1(d)).10.1080/15384101.2020.1712033-F0001 Figure 1. There are overexpressed SNHG7 and GLI3, and underexpressed miR-140-5p in NPC tissues. (a), SNHG7 level in nasopharyngeal carcinoma tissues and nasopharyngeal tissues of mild inflammation of nasopharyngeal mucosa; (b), Expression of miR-140-5p in nasopharyngeal carcinoma tissues and nasopharyngeal tissues of mild inflammation of nasopharyngeal mucosa; (c), GLI3 mRNA expression in nasopharyngeal carcinoma tissues and nasopharyngeal tissues of mild inflammation of nasopharyngeal mucosa; (d), GLI3 protein expression in nasopharyngeal carcinoma tissues and nasopharyngeal tissues of mild inflammation of nasopharyngeal mucosa; the data in the figure were measurement data expressed as mean ± standard deviation; , P < 0.05 vs the normal tissue group. There are overexpressed SNHG7 and GLI3, and underexpressed miR-140-5p in NPC cells SNHG7 and miR-140-5p levels, as well as GLI3 mRNA level in the NP69, CNE1, HONE1, C666-1, and CNE2 cells, were detected by RT-qPCR. Elevated SNHG7 and GLI3 levels, and reduced miR-140-5p level were found in the CNE1, HONE1, C666-1, and CNE2 cells versus the NP69 cells (all P < 0.05), and the highest SNHG7 and GLI3 levels and the lowest miR-140-5p level were discovered in the CNE2 cells, which showed the most difference from the levels in the NP69 cells (Figure 2(a–c)). Western blot analysis revealed that GLI3 protein level in the CNE1, HONE1, C666-1, and CNE2 cells ascended versus the NP69 cells (all P < 0.05), and the highest GLI3 level was found in the CNE2 cells, which was the most different from the level in the NP69 cells (Figure 2(d)). Based on the above findings, CNE2 cells were selected for subsequent experiments.10.1080/15384101.2020.1712033-F0002 Figure 2. There are overexpressed SNHG7 and GLI3, and underexpressed miR-140-5p in NPC cells. (a), relative SNHG7 expression in the NP69, CNE1, HONE1, C666-1, and CNE2 cells; (b), relative miR-140-5p expression in the NP69, CNE1, HONE1, C666-1, and CNE2 cells; (c), relative GLI3 mRNA expression in the NP69, CNE1, HONE1, C666-1, and CNE2 cells; (d), relative GLI3 protein expression in the NP69, CNE1, HONE1, C666-1, and CNE2 cells; the data in the figure were measurement data expressed as mean ± standard deviation; , P < 0.05 vs the NS69 cells. SNHG7 silencing and miR-140-5p elevation decline the drug resistance of drug-resistant NPC cells and their parent cells Calculation of the drug resistance of drug-resistant cells and their parent cells to different drugs showed that CNE2/DDP had an RI of 21.82 for DDP (RI > 15, Table 2), which met the criterion for high drug resistance, indicating successful drug resistance modeling for human NPC cells. Moreover, it was indicated that the inhibitory impacts of different drugs with different concentrations on drug-resistant cells and their parent cells were concentration-dependent, and that CNE2/DDP cells were most resistant to 5-FU (Figure 3(a,b)).10.1080/15384101.2020.1712033-T0002 Table 2. IC50 and RI of DDP and 5-FU on CNE2 and CNE2/DDP cells. IC50 (μmol/L) Drugs CNE2 CNE2/DDP RI DDP 0.37 ± 0.10 7.86 ± 0.71 21.82 5-FU 6.42 ± 0.22 347.86 ± 37.61 53.45 DDP, cisplatin; 5-FU, 5-fluorouracil; IC50, half maximal inhibitory concentration; RI, resistance index. 10.1080/15384101.2020.1712033-F0003 Figure 3. SNHG7 silencing and miR-140-5p elevation decline the drug resistance of drug-resistant NPC cells and their parent cells. (a), survival rate of CNE2 and CNE2/DDP cells under different concentrations of DDP; (b), survival rate of CNE2 and CNE2/DDP cells under different concentrations of 5-FU; (c), Western blot analysis of protein expression of related drug-resistance genes in CNE2 and CNE2/DDP; (d), RI of CNE2/DDP cells in each group under the function of DDP; (e), RI of CNE2/DDP cells in each group under the function of 5-FU; the data in the figure were measurement data expressed as mean ± standard deviation *, P < 0.05 vs the parental strain CNE2 cells; a, P < 0.05 vs the sh-NC group; b, P < 0.05 vs the mimic-NC group; c, P < 0.05 vs the sh-SNHG7 + inhibitor NC group. The mRNA and protein expression of relevant drug resistance genes (P-gp, MRP1, and GST-π) in CNE2 and CNE2/DDP cells were detected by Western blot analysis. The results (Figure 3(c)) showed that P-gp, MRP1, and GST-π expression in each drug-resistant cell line grew to a varying degree versus the parental strain (all P < 0.05). The RI of transfected CNE2/DDP cells under the functions of DDP and 5-FU in each group was calculated. The results showed that versus the blank group, the change in cell RI of the sh-NC group was not conspicuous (P > 0.05), and that of the sh-SNHG7 group declined versus the sh-NC group (P < 0.05); cells transfected with miR-140-5p mimic possessed lower RI than those transfected with miR-140-5p mimic NC (P < 0.05); versus the sh-SNHG7 + inhibitor NC group, the same parameter of the sh-SNHG7 + miR-140-5p inhibitor group rose (P < 0.05) (Figure 3(d,e)). SNHG7 silencing and miR-140-5p elevation restrain colony formation ability and proliferation of NPC cells Through the determination of the colony formation ability of CNE2 and CNE2/DDP cells, it was suggested that the sh-NC group had no conspicuous change in cell colony formation ability versus the blank group (P > 0.05); the colony formation ability of cells transfected with SNHG7 interference plasmid dropped versus those transfected with SNHG7 interference NC plasmid (P < 0.05); the same ability of cells transfected with miR-140-5p mimic fell versus those transfected with miR-140-5p mimic NC (P < 0.05); the same parameter of sh-SNHG7 + miR-140-5p inhibitor group ascended relative to the sh-SNHG7 + inhibitor NC group (P < 0.05) (Figure 4(a,b)).10.1080/15384101.2020.1712033-F0004 Figure 4. SNHG7 silencing and miR-140-5p elevation restrain colony formation ability and proliferation of NPC cells. (a), colony formation ability of CNE2 and CNE2/DDP cells in each group; (b), quantification results in Figure A; (c), MTT assay for CNE2 and CNE2/DDP cell proliferation; (d), EdU assay for CNE2 and CNE2/DDP cell proliferation in each group; (e), EdU-positive CNE2 and CNE2/DDP cells in each group; (f), Ki-67 and CyclinD1 protein levels of CNE2 cells in each group; (g), Ki-67 and CyclinD1 protein levels of CNE2/DDP cells in each group; the data in the figure were all measurement data expressed as mean ± standard deviation; a, P < 0.05 vs the sh-NC group; b, P < 0.05 vs the mimic-NC group; c, P < 0.05 vs the sh-SNHG7 + inhibitor NC group. MTT assay and EdU assay showed that the cell proliferation rate of sh-NC group was not conspicuously different from that of the blank group (P > 0.05); the proliferation rate of the sh-SNHG7 group descended versus the sh-NC group (P < 0.05); the same parameter of cells transfected with miR-140-5p mimic diminished versus those transfected with miR-140-5p mimic NC (P < 0.05); the cell proliferation rate in the sh-SNHG7 + miR-140-5p inhibitor group increased versus the sh-SNHG7 + inhibitor NC group (P < 0.05) (Figure 4(c–e)). Western blot analysis indicated that the levels of proliferation protein Ki-67 and CyclinD1 of the sh-NC group was nearly identical with that of the blank group (P > 0.05); the same levels of the sh-SNHG7 group fell versus the sh-NC group (P < 0.05); Ki-67 and CyclinD1 levels of cells transfected with miR-140-5p mimic diminished versus those transfected with miR-140-5p mimic NC (P < 0.05); the same parameter of the sh-SNHG7 + miR-140-5p inhibitor group ascended versus the sh-SNHG7 + inhibitor NC group (P < 0.05) (figure 4(f,g)). SNHG7 silencing and miR-140-5p elevation boost cell apoptosis of NPC Flow cytometry revealed that the proportion of CNE2 and CNE2/DDP cells in G0/G1, S, and G2/M phases in the sh-NC group was not palpably different from that in the blank group (P > 0.05). Versus the sh-NC group, the proportion of cells grew in the G0/G1 phase in the sh-SNHG7 group, and dropped in S and G2/M phases (all P < 0.05). In the miR-140-5p mimic group, cell proportion increased in the G0/G1 phase, and diminished in S and G2/M phases versus the mimic NC group (all P < 0.05). In the sh-SNHG7 + miR-140-5p inhibitor group, the proportion of cells fell in G0/G1 phases, and rose in S and G2/M phases versus the sh-SNHG7 + inhibitor NC group (all P < 0.05) (Figure 5(a–c)).10.1080/15384101.2020.1712033-F0005 Figure 5. SNHG7 silencing and miR-140-5p elevation boost cell apoptosis of NPC. (a), detection of the cell cycle of CNE2 and CNE2/DDP by flow cytometry in each group; (b), quantification results of cell cycle of CNE2 in each group; (c), quantification results of cell cycle of CNE2/DDP in each group; (d), detection of CNE2 and CNE2/DDP cells apoptosis by flow cytometry; (e), quantification results of apoptosis of CNE2 and CNE2/DDP cells in each group; (f), Bax and Bcl-2 protein levels of CNE2 cells in each group; (g), Bax and Bcl-2 protein levels of CNE2/DDP cells in each group; the data in the figure were all measurement data expressed as mean ± standard deviation; a, P < 0.05 vs the sh-NC group; b, P < 0.05 vs the mimic-NC group; c, P < 0.05 vs the sh-SNHG7 + inhibitor NC group. Apoptosis detection by flow cytometry illustrated that the apoptosis rate of the sh-NC group was nearly the same as that of the blank group (P > 0.05). The apoptosis rate of cells in the sh-SNHG7 group elevated versus the sh-NC group (P < 0.05). The same parameter in the miR-140-5p mimic group also increased relative to the mimic NC group (P < 0.05), and declined in the sh-SNHG7 + miR-140-5p inhibitor group versus the sh-SNHG7 + inhibitor NC group (P < 0.05) (Figure 5(d,e)). The levels of Bax and Bcl-2 in the CNE2 and CNE2/DDP cells were detected by Western blot analysis. The results illustrated that Bax and Bcl-2 levels in cells transfected with SNHG7 interference NC plasmid were almost the same as those without any treatment (P > 0.05). The level of pro-apoptotic protein Bax ascended, and anti-apoptotic protein Bcl-2 reduced in the sh-SNHG7 group versus the sh-NC group (both P < 0.05). Bax level rose while Bcl-2 level dropped in the miR-140-5p mimic group versus the mimic NC group (both P < 0.05). In the sh-SNHG7 + miR-140-5p inhibitor group, Bax level descended and Bcl-2 level elevated versus the sh-SNHG7 + inhibitor NC group (both P < 0.05) (figure 5(f,g)). SNHG7 specially binds to miR-140-5p and SNHG7 silencing elevates miR-140-5p expression Detection of SNHG7 and miR-140-5p expression in CNE2 and CNE2/DDP cells was performed by RT-qPCR. The results indicated that SNHG7 and miR-140-5p expression in the sh-NC group did not differ from that in the blank group (P > 0.05). SNHG7 expression reduced while miR-140-5p level grew in the sh-SNHG7 group versus the sh-NC group (both P < 0.05). No conspicuous changes in SNHG7 expression were seen (P > 0.05) and miR-140-5p expression elevated in the miR-140-5p mimic group versus the mimic-NC group (P < 0.05). SNHG7 expression did not change conspicuously (P > 0.05) and miR-140-5p expression diminished in the sh-SNHG7 + miR-140-5p inhibitor group versus the sh-SNHG7 + inhibitor NC group (P < 0.05) (Figure 6(a,b)).10.1080/15384101.2020.1712033-F0006 Figure 6. SNHG7 specially binds to miR-140-5p and SNHG7 silencing elevates miR-140-5p expression. (a), SNHG7 expression of CNE2 cells and CNE2/DDP cells in each group; (b), miR-140-5p expression of CNE2 cells and CNE2/DDP cells in each group; (c), prediction of subcellular localization of SNHG7 by online analysis website; (d), confirmation of subcellular localization of SNHG7 by FISH assay; (e), prediction of the binding site between miR-140-5p and SNHG7 by bioinformatics website; (f), verification of the regulatory relationship between SNHG7 and miR-140-5p by dual luciferase reporter gene assay; (g), verification of the enrichment level of SNHG7 and miR-140-5p by RNA-pull down assay; the data in the figure were all measurement data expressed as mean ± standard deviation; a, P < 0.05 vs the sh-NC group; b, P < 0.05 vs the mimic-NC group; c, P < 0.05 vs the sh-SNHG7 + inhibitor NC group. To examine the mechanism of SNHG7, we initially analyzed the online analysis website http://lncatlas.crg.eu/and found that SNHG7 was mainly distributed in the cytoplasm (Figure 6(c)), which was further verified by RNA-FISH assay (Figure 6(d)), indicating that SNHG7 may function in the cytoplasm. Furthermore, online analysis software predicted that there was a specific binding site between SNHG7 gene sequence and the miR-140-5p sequence (Figure 6(e)), and further verification by the dual luciferase reporter gene assay revealed that versus the mimic-NC group, the luciferase activity in the WT-miR-140-5p mimic/sh-SNHG7 group fell conspicuously (P < 0.05), while no marked changes were found in that in the MUT-miR-140-5p mimic/sh-SNHG7 group (P > 0.05), indicating that miR-140-5p may specifically bind to SNHG7 (figure 6(f)). RNA-pull down assay revealed that the enrichment of SNHG7 in the bio-miR-140-5p-WT group grew (P < 0.05), and little difference was seen in that of the bio-miR-140-5p-MUT group versus the bio-probe NC group (P > 0.05) (Figure 6(g)). GLI3 is a direct target gene of miR-140-5p and miR-140-5p elevation diminishes GLI3 expression GLI3 expression in CNE2 and CNE2/DDP cells were detected by RT-qPCR and Western blot analysis. The results showed that GLI3 expression in the sh-NC group were not palpable versus the blank group (P > 0.05), and diminished in the sh-SNHG7 group versus the sh-NC group (P < 0.05). Moreover, the same parameter in the miR-140-5p mimic group dropped versus the mimic NC group (P < 0.05), and ascended in the sh-SNHG7 + miR-140-5p inhibitor group versus the sh-SNHG7 + inhibitor NC group (P < 0.05) (Figure 7(a,b)).10.1080/15384101.2020.1712033-F0007 Figure 7. GLI3 is a direct target gene of miR-140-5p and miR-140-5p diminishes GLI3 expression. (a), mRNA expression of GLI3 in CNE2 and CNE2/DDP cells in each group; (b), protein expression of GLI3 in CNE2 and CNE2/DDP cells in each group; (c), prediction of binding site between miR-140-5p and GLI3 by bioinformatics website; (d), verification of the regulatory relationship between miR-140-5p and GLI3 by dual luciferase reporter gene assay; the data in the figure were all measurement data expressed as mean ± standard deviation; a, P < 0.05 vs the sh-NC group; b, P < 0.05 vs the mimic-NC group; c, P < 0.05 vs the sh-SNHG7 + inhibitor NC group. The bioinformatics software (http://www.targetscan.org) predicted a targeted site between miR-140-5p and GLI3 (Figure 7(c)). Luciferase activity assay indicated that after CEN2 cells were co-transfected with GLI3-WT and miR-140-5p mimic, the relative luciferase activity reduced substantially (P < 0.05), whereas co-transfection with GLI3-MUT and miR-140-5p mimic exerted no impact (P > 0.05) (Figure 7(d)). These findings indicate that GLI3 is a direct target gene of miR-140-5p. Discussion NPC is a mysterious malignancy that infrequently arising in most regions of the world [18]. NPC is a big heath problem in southern China, and the chief cause of treatment failure is ascribed to the local recurrence and distant metastasis [19]. LncRNAs have long been proposed to be implicated in tumorigenesis and cell growth [20]. However, few studies have probed into the action of lncRNA SNHG7 on NPC development, so in our study, a series of assays were performed to examine the role of SNHG7/miR-140-5p/GLI3 axis in NPC. Collectively, our study revealed that depleted lncRNA SNHG7 restricts GLI3 expression by upregulating miR-140-5p, which further suppresses cell proliferation and boosts apoptosis of NPC. To begin with, we found elevated SNHG7 and GLI3, and underexpressed miR-140-5p in NPC tissues and cells. In accord with our result, an early study has demonstrated that SNHG7 expression was enhanced in non-small cell lung cancer [21]. In a study by Zhang W et al., miR-140-5p expression has been discovered to be much low in tissues and cell lines of colorectal cancer (CRC) [22]. Report has shown that GLI3 expression in liver fibrosis was elevated [23]. Moreover, we discovered that SNHG7 specially bound to miR-140-5p, and SNHG7 silencing enhanced miR-140-5p expression. It is indicated in esophagus cancer that miR-140-5p is targeted by SNHG16, and a decrease of SNHG16 expression leads to the enhancement of mature miR-140-5p [9]. In addition, we demonstrated that GLI3 was a direct target gene of miR-140-5p and miR-140-5p elevation diminished GLI3 expression. Wen SY et al. have confirmed that GLI3 is miR-506ʹ target gene, and that the growth of miR-506 expression can result in the restriction of GLI3 expression [15]. A similar study by Yao C has also indicated that in human Sertoli cells, GLI3 was miR-133b’s direct target and the upregulation of miR-133b can cause an increase of GLI3 expression [24]. Furthermore, subsequent assays revealed that SNHG7 silencing and miR-140-5p elevation declined the drug resistance of drug-resistant NPC cells and their parent cells, restrained NPC cell colony formation ability and proliferation, and boosted cell apoptosis. In line with our study, previous research has shown that in neuroblastoma (NB), SNHG7 silencing negatively works on the development of NB cells by modulating miR-653-5p/STAT2 axis [25]. In breast cancer, SNHG7 is found to be enhanced, and if SNHG7 expression is repressed, tumor cell progression will also be circumscribed [26]. There is evidence showing that lower SNHG7 level has something to do with lower taxol resistance in hypopharyngeal cancer, and patients with low SNHG7 level possesses higher overall survival (OS) than those with high SNHG7 level [27]. It has been indicated that in cervical cancer, reduced SNHG20 expression and elevated miR-140-5p restrained cancer cell proliferation and invasion [28]. In a recent study, linc00515 downregulation has been indicated to constrain the chemoresistance of multiple myeloma cells by elevating miR-140-5p and diminishing ATG14 expression [29]. Besides, we discovered that miR-140-5p suppression reversed the impacts of SNHG7 silencing on NPC cells. Close associations of miR-140-5p downregulation with promoted CRC stage and worse OS have been proposed in an early study [22]. Also, the contributory impacts of miR-140-5p silencing on the reduction of OS of patients with gastric cancer have been illustrated lately [30]. All the above findings are in full support of our results. To sum up, our study revealed that lncRNA SNHG7 knockdown restricts cell proliferation and promotes apoptosis of NPC by elevating miR-140-5p for GLI3 downregulation, and miR-140-5p inhibition can reverse the impacts of SNHG7 knockdown on NPC cells. Our study suggests lncRNA SNHG7 is a new biomarker of NPC, which can be a new target for NPC treatment. Nevertheless, more studies have to be done for better elaboration of the role of SNHG7/miR-140-5p/GLI3 axis in NPC. Acknowledgments We would like to acknowledge the reviewers for their helpful comments on this paper. Authors’ contributions Guarantor of integrity of the entire study: Haijie Xing study design: Yaozhang Dai, Xin Zhang experimental studies: Yamin Zhang, Hua Cao manuscript editing: Jianzhong Sang, Ling Gao manuscript reviewing: Liuzhong Wang Disclosure statement No potential conflict of interest was reported by the authors. Ethical statement This study was reviewed and approved by the Ethics Committee of Affiliated Otolaryngological Hospital, the First Affiliated Hospital of Zhengzhou University and was supervised by the Ethics Committee of Affiliated Otolaryngological Hospital, the First Affiliated Hospital of Zhengzhou University. Written informed consents were obtained from all patients and their family before the study. ==== Refs References [1] Kamran SC, Riaz N, Lee N. Nasopharyngeal carcinoma. Surg Oncol Clin N Am. 2015;24 (3 ):547–561.25979399 [2] Chua MLK, Wee JTS, Hui EP, et al. Nasopharyngeal carcinoma. Lancet. 2016;387 (10022 ):1012–1024.26321262 [3] Wei KR, Zheng RS, Zhang SW, et al. Nasopharyngeal carcinoma incidence and mortality in China in 2010. Chin J Cancer. 2014;33 (8 ):381–387.25096544 [4] Xia L, Zhang W, Zhu L, et al. Abnormally expressed long non-coding RNAs in nasopharyngeal carcinoma: a meta-analysis. Clin Lab. 2018;64 (4 ):585–595.29739093 [5] Li W, Xie P, Ruan WH. 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==== Front Cell Cycle Cell Cycle KCCY kccy20 Cell Cycle 1538-4101 1551-4005 Taylor & Francis 32057287 1718851 10.1080/15384101.2020.1718851 Research Paper Endoplasmic reticulum stress potentiates the autophagy of alveolar macrophage to attenuate acute lung injury and airway inflammation Q. QIAN ET AL. CELL CYCLE Qian Qingzeng a Cao Xiangke b Wang Bin c Dong Xiaoliu d Pei Jian e Xue Ling a Feng Fumin a a College of Public Health, North China University of Science and Technology, Tangshan, P. R. China b College of Life Sciences, North China University of Science and Technology, Tangshan, P. R. China c Department of Pediatrics, Affiliated Hospital of North China University of Science and Technology, Tangshan, P. R. China d Department of Neurology, Tangshan People’s Hospital, Tangshan, P. R. China e Department of Neurosurgery, Tangshan Worker’s Hospital, Tangshan, P. R. China CONTACT Fumin Feng [email protected] 2020 14 2 2020 19 5 567576 21 8 2019 30 10 2019 7 11 2019 © 2020 Informa UK Limited, trading as Taylor & Francis Group 2020 Informa UK Limited, trading as Taylor & Francis Group ABSTRACT Endoplasmic reticulum (ER) stress has been reported to play a role in acute lung injury (ALI), yet the in-depth mechanism remains elusive. This study aims to investigate the effect of ER stress-induced autophagy of alveolar macrophage (AM) on acute lung injury (ALI) and airway inflammation using mouse models. ALI models were induced by intranasal instillation of lipopolysaccharide (LPS). The lung weight/body weight (LW/BW) ratio and excised lung gas volume (ELGV) in each group were measured. Mouse bronchoalveolar lavage fluid (BALF) was collected for cell sorting and protein concentration determination. Expression of tumor necrosis factor α (TNF-α) and interleukin-6 (IL-6) in lung tissues and BALF was also detected. Mouse AMs were isolated to observe the autophagy. Expression of GRP78, PERK, LC3I, LC3II and Beclin1 was further determined. The results indicated that tunicamycin (TM) elevated GRP78 and PERK expression of AMs in ALI mice in a dose-dependent manner. Low dosage of TM abated LC3I expression, increased LC3II and Beclin1 expression, triggered ER stress and AM autophagy, and alleviated pathological changes of AMs in ALI mice. Also, in ALI mice, low dosage of TM attenuated goblet cell proliferation of tracheal wall, and declined LW/BW ratio, ELGV, total cells and neutrophils, protein concentrations in BALF, and IL-6 and TNF-α expression in lung tissues and BALF. Collectively, this study suggests that a low dosage of TM-induced ER stress can enhance the autophagy of AM in ALI mice models, thus attenuating the progression of ALI and airway inflammation. KEYWORDS Endoplasmic reticulum stress alveolar macrophage autophagy acute lung injury airway inflammation tunicamycin ==== Body Introduction Acute lung injury (ALI) is a major disease responsible for high morbidity and mortality, as well as for heavy healthcare burden in critically ill patients [1,2]. The progression of ALI may eventually lead to its worst form, acute respiratory distress syndrome (ARDS), which was usually investigated together with ALI [3,4]. ALI/ARDS is characterized by pulmonary infiltrates, hypoxemia and lung edema due to increased permeability of the alveolar-capillary barrier, which will eventually result in subsequent impairment of arterial oxygenation [3]. The well-established risk factors for ALI include sepsis, trauma or multiple traumatic injuries, and other factors such as genetic, age, living habit (smoking, alcohol abuse) may also lead to ALI [5]. In addition, several noninfectious causes also may trigger ALI, for instance, lipopolysaccharide (LPS), a glycolipid of the outermost membrane of gram-negative bacteria, is a common approach to induce ALI in animal studies [6]. Interestingly, the most common way to eradicate bacteria in the airway is an inflammatory response, whereas the excessive air inflammation may damage lung tissues or lead to ARDS [7]. Evidence indicated that macrophages also contribute to the regulation of inflammatory responses and lung injuries [8,9]. Macrophages are found to be a master of fibrosis and regulate fibrogenesis by secreting chemokines that recruit fibroblasts and other inflammatory cells, which stimulate fibroblast activity and extracellular matrix (ECM) deposition [10]. Alveolar macrophages (AMs), serving as sentinels of a healthy state, are different from other macrophages due to their unique tissue location and function in the lung, and can adapt to accommodate the ever-changing needs of the tissue [11]. Macrophages have been classified into two classic activated (M1) and alternative activated (M2) macrophages, in which the former can be induced by LPS and is characterized by secretion of pro-inflammatory factors, including interleukin (IL)-1β, IL-12, tumor necrosis factor (TNF)-α [12,13]. The Unfolded Protein Response (UPR) is an adaptive survival pathway which will be activated by accumulation of misfolded proteins in response to a variety of cell insults that result in endoplasmic reticulum (ER) stress and previous research has indicated that ER stress has been causally associated with macrophage apoptosis in advanced atherosclerosis [14]. Although the previous study suggested that alterations in the balance and function of macrophages could lead to ALI [15,16], whether the ER stress-induced autophagy of AM is casually associated with ALI and airway inflammation remains to be elucidated. Accordingly, this study was conducted with the aim to explore the exact role of ER stress-induced autophagy of AMs in ALI and airway inflammation, with the expectation to provide the least potential theoretical basis for ALI treatment. Methods and materials Ethics statement Animal experiments were conducted in strict accordance with the approved animal protocols and guidelines established by the Medicine Ethics Review Committee for animal experiments of North China University of Science and Technology. Animals for experiments Seventy-two BALB/c female mice (6–8 wk, 18–20 g) were purchased from the animal center of Perking University. Before experiments, all mice were fed in the animal house under 19–22°C for 3 d to adapt to the environment with free access to pellet feed. The animal house shall have a light exposure time of 12 h a day and shall be subjected to regular disinfection and ventilation. Animal grouping LPS (Sigma-Aldrich, St. Louis, MO, USA) was used to induce ALI model [17] and 72 mice were grouped into six groups with 12 mice in each group: (1) Blank group (no treatment); (2) Phosphate buffered saline (PBS) group (mice were administered with PBS through nose); (3) LPS group (mice were administered with LPS through nose); (4) LPS + normal saline (NS) group (mice were administered with LPS through nose and injected intraperitoneally with normal saline); (5) LPS + 0.3 Tunicamycin (TM) group (mice were administered with LPS through nose and injected intraperitoneally with 0.3 mg/kg TM); (6) LPS + 3.0 TM group (mice were administered with LPS through nose and injected intraperitoneally with 3.0 mg/kg TM). Mice in the LPS + NS group, LPS + 0.3 TM group and LPS + 3.0 TM group were daily intraperitoneally injected with NS or TM (Sigma-Aldrich, St. Louis, MO, USA) 3 d before model establishment. One hour after the final time of injection, mice were anesthetized using pentobarbital sodium (Sigma-Aldrich, St. Louis, MO, USA). Except for mice in the blank group and the PBS group, mice were administered with 0.5 mg/kg LPS, while mice in the PBS group were administered with 0.5 mg/kg PBS. About 12 h later, mice were euthanized and the sample was gathered. Lung weight (LW)/body weight (BW) ratio and excised lung gas volume (ELGV) After mice were euthanized and before the chest was opened, the trachea was separated from the neck and ligated with 3–0 surgical suture on the 2–3 cartilaginous rings of the cricoid cartilage. The chest was open up from the processus xiphoideus to take out the whole trachea, lung and pericardium. The fibrous connective tissues around the pericardium and the lung were removed. A density instrument (Mettler Toledo, Zurich, Switzerland) was applied for measuring the LW/BW ratio and the ELGV. Pathological observation of lung tissues Once ELGV and LW/BW ratio were measured, a catheter was inserted to the trachea and 4% paraformaldehyde was injected through the catheter at a height of 20 cm. Then, the trachea was ligated and the whole lung was fixed in 4% paraformaldehyde for 24 h. After that, the lung tissues were subjected to routine dehydration, paraffin embedding and slicing (5 μm). The infiltration, edema and damage to inflammatory cells in lung tissues and the secretion of airway mucus were observed under a light microscope (Olympus, Tokyo, Japan) by performing hematoxylin-eosin (HE) staining and Alcian blue-periodic acid Schiff (AB-PAS) staining. Bronchoalveolar lavage fluid (BALF) The pulmonary alveoli were lavaged with PBS twice (0.5 mL each time) and the BALF (0.8 mL) was collected for centrifugation at 1200 rpm for 10 min under 4°C. The cell precipitates were re-suspended using 0.2 mL PBS, after which 20 µL solution was adopted for cell counting. The rest solution was centrifuged at 1200 rpm for 15 min. Certain cells in BALF were evenly distributed on the slides, which was then air-dried and counted under a high-power microscope for 200 cells after Wright’s staining. The supernatant of BALF was stored at −80°C for determination of the expressions of relative proteins and cytokines. Bicinchoninic acid (BCA) protein quantitative kit (Pierce, Rockford, IL, USA) was used to measure protein expressions. Isolation and purification of AMs The BALF was made into cell suspension under the sterile operation of filtration, centrifugation and rinse. The suspension was diluted to count cells and to calculate cell mass concentration. Appropriate cell suspension (about 5 × 106) was added into 2 mL of DMEM (GibcoBRL, Grand Island, NY, USA) culture flask containing 10% fetal bovine serum (FBS, Santa Cruz Biotechnology, Santa Cruz, CA, USA) for 2-h incubation at a 5% CO2 incubator with 84% humidity. The cells adhered to walls were purified AMs, and Trypan blue staining suggested that cell viability could reach 90%. Autophagosome by monodansylcadaverine (MDC) staining AMs from each group were inoculated in a six-well plate, subject to PBS washing for three times and fixation of paraformaldehyde for 15 min at 4°C. MDC (Santa Cruz Biotechnology, Santa Cruz, CA, USA) was diluted with PBS at a ratio of 1:1000 until a final volume of 50 μM solution was obtained. The paraformaldehyde was removed and AMs were washed with PBS for another three times and added with the prepared solution at 37°C for a reaction for 60–90 min without light exposure. The reaction time may be adjusted based on the degree of staining. Prior to observation under a confocal microscope (HITACHI, Tokyo, Japan), AMs were washed with PBS for three times to get rid of the staining solution. The staining of AMs was photographed and recorded. Autophagosomes were presented in forms of green spots or granules. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) Trizol method (Invitrogen Technology, Carlsbad, CA, USA) was applied to extract total RNA from AMs in each group. The high-quality RNA was then identified by ultraviolet spectrophotometer and electrophoresis on a gel containing formaldehyde. RNA (l μg) was reversed into cDNA using AMV reverse transcriptase (Thermo Fisher Scientific, Massachusetts, USA). PCR primers were designed and synthesized by Invitrogen (Carlsbad, CA, USA) (Table 1). Glyceraldehyde phosphate dehydrogenase (GAPDH) was considered as a loading control. PCR products were electrophoresed with sepharose gel and then analyzed using OpticonMonitor3 software (BioRad Labs Inc., Hercules, CA, USA). The threshold cycle (Ct) was analyzed using 2−ΔΔCt method. The 2−ΔΔCt was used to test the relative transcriptional levels of IL-6 and TNF-α mRNA: ΔΔCt = ΔCt experimental group – ΔCt control group, ΔCt = Cttarget gene – Ctinternal reference [18]. Experiments were conducted for three times to obtain the average value.10.1080/15384101.2020.1718851-T0001 Table 1. Sequences of primers. Gene Sequences IL-6 F: 5ʹ- TGGAGTCACAGAAGGAGTGGCTAAG −3’ R: 5ʹ- TCTGACCACAGTGAGGAATGTCCAC −3’ TNF-α F: 5ʹ- CTGGGACAGTGACCTGGACT −3’ R: 5ʹ- GCACCTCAGGGAAGAGTCTG −3’ GAPDH F: 5ʹ- CAAGTTCAACGGCACAGTCA −3’ R: 5ʹ- CCCCATTTGATGTTAGCGGG-3’ F, forward; R, reverse. Western blot analysis Proteins from AMs in each group were extracted and BCA kit (Pierce, Rockford, IL, USA) was used to identify the protein concentration. After adding with lading buffer, the proteins were boiled at 95°C for 10 min. Then, 30 µg of solution was added in each well in the plates, and the proteins were separated by 10% polyacrylamide gel (Beijing Liuyi Biotechnology Co., Ltd., Beijing, China) and transferred to polyvinylidene difluoride (PVDF) membrane followed by blocking with 5% bovine serum albumin at ambient temperature for 1 h. Primary antibodies GRP78 (1:1000, Abcam, Cambridge, MA, USA), PERK, LC3I, LC3II, Beclin1, Bcl-2 and caspase-3 (1: 1000, Cell Signaling Technology, Inc., Beverly, MA, USA) and primary antibody β-actin (loading control, 1:3000, BD Pharmingen, San Jose, CA, USA) were added for overnight at 4°C. The membrane was then washed three times with PBS for 5 min and incubated with secondary antibodies at ambient temperature. Chemiluminescence reagents were used for color development. Gel Doc EZ imager (Bio-rad, California, USA) was used for color observation. Image J software (National Institutes of Health, Maryland, USA) was used to analyze the gray value of target band. Experiments were conducted for three times to obtain the average value. Enzyme-linked immunosorbent assay (ELISA) IL-6 and TNF-α kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China) were used to detect the contents of IL-6 and TNF-α according to the instruction of the manufacturer. The expressions of IL-6 and TNF-α were calculated based on optical density (OD) value and standard curve. Statistical analysis SPSS version 21.0 (IBM Corp. Armonk, NY, USA) was applied for data analysis. Measurement data were displayed as mean ± standard deviation. Data which comply with normal distribution between groups were compared with the t-test, comparison among multiple groups were compared using one-way analysis of variance (ANOVA) and pairwise comparison was conducted using the least significance difference (LSD) method. P < 0.05 was considered as statistically significant. Results TM elevates GRP78 and PERK expression of AMs in ALI mice in a dose-dependent manner Western blot analysis was used to detect the expressions of ER stress-related protein GRP78 and PERK of mouse AMs in each group. As shown in Figure 1, in comparison to the blank group and PBS group, the expression of GRP78 and PERK was notably increased in the LPS group and LPS + NS group (all P< 0.05). Among the six groups, LPS + 3.0 TM group had the highest expression of GRP78 and PERK, followed by the LPS + 0.3 TM group (P< 0.05) and then the LPS group and LPS + NS group (both P< 0.05) accordingly.10.1080/15384101.2020.1718851-F0001 Figure 1. TM elevates GRP78 and PERK expression of AMs in ALI mice in a dose-dependent manner. (a) Expression of ER stress-related proteins GRP78 and PERK in AMs of mice in each group. (b) Protein bands of ER stress-related proteins GRP78 and PERK in AMs of mice in each group. , compared with the blank group, P< 0.05; #, compared with the LPS group, P< 0.05. Measurement data were displayed as mean ± standard deviation. Comparison among multiple groups was conducted by one-way ANOVA followed with LSD-t. N = 12. Low dosage of TM abates LC3I expression and increases LC3II and Beclin1 expressions of AMs in ALI mice The expression of autophagy marker proteins (LC3I, LC3II and Beclin1) of mouse AMs in each group was detected by Western blot analysis. Compared with the blank group and PBS group, the mice in the LPS group and LPS + NS group had decreased expression of LC3I, but increased expression of LC3II and Beclin1 (all P< 0.05). Mice in the LPS + 0.3 TM group had lower expression of LC3I, but higher expressions of LC3II and Beclin1 than those in the LPS group and LPS + NS group (all P< 0.05). A significant transformation of LC3I to LC3II was observed. No significant difference on the expression of LC3II and Beclin1 was detected among the LPS + 3.0 TM group, LPS group and LPS + NS group (all P> 0.05) (Figure 2).10.1080/15384101.2020.1718851-F0002 Figure 2. Low dosage of TM abates LC3I expression and increases LC3II and Beclin1 expression of AMs in ALI mice. (a) Expression of LC3I, LC3II and Beclin1 in AMs of mice in each group. (b) Protein bands of LC3I, LC3II and Beclin1 in AMs of mice in each group. , compared with the blank group, P< 0.05; #, compared with the LPS group, P< 0.05. Measurement data were displayed as mean ± standard deviation. Comparison among multiple groups was conducted by one-way ANOVA followed with LSD-t. N = 12. Low dosage of TM triggers ER stress and AM autophagy of AMs in ALI mice Autophagosomes were presented in the LPS group, LPS + 0.3 TM group and LPS + 3.0 TM group, which indicated the occurrence of AM autophagy. Moreover, the autophagosome in the LPS + 0.3 TM group was much larger than those in the LPS + 3.0 TM group and LPS group (both P< 0.05), suggesting that low dosage of TM can trigger ER stress and AM autophagy and high dosage of TM can activate ER stress but also inhibit AM autophagy (Figure 3).10.1080/15384101.2020.1718851-F0003 Figure 3. Low dosage of TM triggers ER stress and AM autophagy of AMs in ALI mice. (a) Observation of MDC staining of AMs in mice of each group by a transmission electron microscope. (b) Quantitative analysis of AMs by MDC staining in mice in each group. , compared with the blank group, P< 0.05; #, compared with the LPS group, P< 0.05. Measurement data were displayed as mean ± standard deviation. Comparison among multiple groups was conducted by one-way ANOVA followed with LSD-t. N = 12. Low dosage of TM alleviates pathological changes of AMs in ALI mice HE staining was performed to observe the pathological changes of mouse AMs. No alveolar thickness and neutrophil infiltration were found in the blank group and PBS group. HE staining showed that mice in the LPS group and LPS + NS group had typical ALI manifestations, such as serious neutrophil infiltration, damaged alveolar structure and alveolar thickness. Mice in the LPS + 0.3 TM group had slightly attenuated ALI manifestations than those in the LPS group and LPS + NS group, while mice in the LPS + 3.0 TM group had similar ALI manifestations with those in the LPS group and LPS + NS group (Figure 4(a)).10.1080/15384101.2020.1718851-F0004 Figure 4. Low dosage of TM alleviates pathological changes of AMs and attenuates goblet cell proliferation of tracheal wall in ALI mice. (a) Pathological changes of lung tissue in mice in each group by HE staining (× 100). (b) Pathological changes of lung tissue in mice in each group by AB-PAS staining (× 100). Low dosage of TM attenuates goblet cell proliferation of tracheal wall in ALI mice AB-PAS staining was performed to observe the airway mucus secretion of mice in each group. The results showed that the blank group and PBS group had little PAS-positive epithelial cells, while the LPS group and LPS + NS group had increased epithelial cells and goblet cell proliferation. The LPS + 0.3.0TM group had less PAS-positive epithelial cells than that in the LPS group, which indicates that a small dosage of TM can remarkably decrease goblet cell proliferation in mice with LPS-induced ALI. The positive expression of PAS-positive epithelial cells and goblet cell proliferation in the LPS + 3.0 TM group was consistent with those in the LPS group and LPS + NS group (Figure 4(b)). Low dosage of TM declines LW/BW ratio in ALI mice We determined LW/BW of mice in each group. No significant difference was detected between the PBS group and blank group regarding LW/BW ratio (P> 0.05). The mice in the LPS group and LPS + NS group had a higher LW/BW ratio than those in the blank group and PBS group (all P< 0.05). Mice in the LPS + 0.3 TM group had a lower LW/BW ratio than those in the LPS group and LPS + NS group (all P< 0.05). Mice in the LPS + 3.0 TM group had a similar LW/BW ratio with the LPS group and LPS + NS group (all P> 0.05) (Figure 5(a)).10.1080/15384101.2020.1718851-F0005 Figure 5. Low dosage of TM declines LW/BW ratio and ELGV in ALI mice. (a) Determination of LW/BW ratio of mice in each group. (b) Changes in ELGV value in each group of mice. , compared with the blank group, P< 0.05; #, compared with the LPS group, P< 0.05. Measurement data were displayed as mean ± standard deviation. Comparison among multiple groups was conducted by one-way ANOVA followed with LSD-t. N = 12. Low dosage of TM declines ELGV to activate ER stress in ALI mice We further determined ELGV of mice in each group. The ELGV was significantly increased in the LPS group and LPS + NS group in contrast to the PBS group and blank group (all P< 0.05). ELGV was reduced in the LPS + 0.3 TM group versus the LPS group and LPS + NS group (P< 0.05), which indicated that a small dosage of TM can activate ER stress, thus protecting ALI mice. No difference in ELGV expression was detected between the LPS + 3.0 TM group and the LPS group (P> 0.05) (Figure 5(b)). Low dosage of TM diminishes total cells and neutrophils in BALF in ALI mice In contrast to the blank group and PBS group, the total cells and neutrophils in BALF were up-regulated in the LPS group and LPS + NS group (all P< 0.05). When compared with the LPS group and LPS + NS group, the total cells and neutrophils in BALF were decreased in the LPS + 0.3 TM group (P< 0.05). No significant difference in the total cells and neutrophils in BALF was found among the LPS + 3.0 TM group, LPS group and LPS + NS group (all P> 0.05) (Figure 6(a)).10.1080/15384101.2020.1718851-F0006 Figure 6. Low dosage of TM diminishes total cells and neutrophils and protein concentrations in BALF in ALI mice. (a) Determination of the total number of cells and neutrophils in BALF of mice in each group. (b) Detection of protein concentration in BALF of mice in each group. , compared with the blank group, P< 0.05; #, compared with the LPS group, P< 0.05. Measurement data were displayed as mean ± standard deviation. Comparison among multiple groups was conducted by one-way ANOVA followed with LSD-t. N = 12. Low dosage of TM diminishes protein concentrations in BALF in ALI mice To investigate the effect of ER stress on LPS-induced ALI, we examined the protein concentrations in BALF of mice in each group. Compared with the LPS group and LPS + NS group, the protein concentrations in BALF were decreased in the blank group and LPS + 0.3 TM group (all P< 0.05). The comparisons on the protein concentrations in BALF among the LPS + 3.0 TM group, LPS group and LPS + NS group showed no statistical significance (all P> 0.05), as well as between the PBS group and blank group (Figure 6(b)). Low dosage of TM down-regulates IL-6 and TNF-α expression in lung tissues and BALF in ALI mice The mRNA expression and protein contents of inflammatory cytokines (IL-6 and TNF-α) in lung tissues and BALF of mice in each group were detected by RT-qPCR and ELISA. The mRNA expression and protein contents of IL-6 and TNF-α in the blank group were not different from those in the PBS group (all P> 0.05). In contrast to the PBS group and blank group, mice in the LPS group and LPS + NS group had increased mRNA expression and protein contents of IL-6 and TNF-α (all P< 0.05). Mice in the LPS + 0.3 TM group had reduced mRNA expression and protein contents of IL-6 and TNF-α relative to the LPS group and LPS + NS group (all P< 0.05). No significant difference in mRNA expression and protein contents of IL-6 and TNF-α was found among the LPS group, LPS + NS group and LPS + 3.0 TM group (all P> 0.05) (Figure 7).10.1080/15384101.2020.1718851-F0007 Figure 7. Low dosage of TM down-regulates IL-6 and TNF-α expression in lung tissues and BALF in ALI mice. (a) Content of IL-6 in BALF of mice in each group by ELISA. (b) Content of TNF-α in BALF of mice in each group by ELISA. (c) mRNA expression of TNF-α and IL-6 in lung tissues of mice. , compared with the blank group, P< 0.05; #, compared with the LPS group, P< 0.05. Measurement data were displayed as mean ± standard deviation. Comparison among multiple groups was conducted by one-way ANOVA followed with LSD-t. N = 12. Discussion The low dosage of TM was used in our study to induce ER stress which was a trigger that leads to autophagy of AM; meanwhile, LPS was applied in a mouse model to induce ALI. Conclusively, our study supported that ER stress could enhance the autophagy of AM, thus attenuating ALI in mouse models. The results in our experiments showed that the mice in the LPS + 3.0 TM group and LPS + 0.3TM group had high expression of GRP78 and PERK, both of which were indicators for ER stress. GRP78 is mainly responsible for controlling the binding and activation of PERK, IRE1α and ATF6α [19,20]. ER stress generally refers to dysfunction of the ER which may be caused by pathogenic stress signals, resulting in accumulating of misfolded and unfolded proteins [21,22]. To overcome the adverse effect of ER stress and adapt to the various cell microenvironment, cells may operate an adaptive response, namely unfolded protein response (UPR) [23]. However, if the misfolded and unfolded proteins remain to be accumulated, or the adaptive response fails, ER stress can be a disaster for cells and resulting in ER stress-induced apoptosis [19]. Moreover, the successful detection of GRP78 and PERK also indicated that ER stress was successfully triggered [24]. In addition, our study also found that the LPS + 0.3 TM group and LPS + 3.0 TM group had substantial differences compared with other groups regarding the expression of LC3I, LC3II and Beclin1, which suggested that cell autophagy occurred. Consistent with our study, a previous study suggested that TM, irrespective of the dosage, can induce ER stress, as evidenced by the increased phosphorylation or activation of eIF2α and PERK which enhance the cell autophagy, as supported by the increased expression of LC3-II, beclin-1 and Atg5 [25]. Interestingly, we also found that the low dosage of TM can trigger ER stress but also activate the autophagy of AMs, while the high dosage of TM can also trigger ER stress but somehow can inhibit the autophagy of AMs. Those results supported that the ER stress induced autophagy in a TM dose-dependent manner. As the TM relies on protein synthesis on ER stress [23], it is possible that the dosage of TM may have a certain role in protein synthesis. In addition, ALI and airway inflammation in the mouse model were successfully established using LPS, as evident by infiltration of neutrophil, alveolar structure damage and thickness of alveolar in LPS, as well as the measurement of levels of IL-6 and TNF-α. LPS-induced ALI is essentially an inflammation in the lung resulted from the initiation of macrophages and over-activation of neutrophils. Inflammation, a host response to protect the body against harmful stimuli, can be initiated by complex processes triggered by microbial pathogen LPS, which can directly activate macrophages [26]. In our study, mice treated with LPS were presented with autophagosomes under the observation of a microscope. As the autophagosome is the hallmark of autophagy, our results supported that AM autophagy occurred in the ALI mouse models. AM is of significant importance in clearing bacteria from the alveolar surface and preventing microbe-induced infections [27]. Moreover, it is evidenced that lung cells may adapt pro-survival mechanisms in case of injury or inflammation, including autophagy [28,29]. Meanwhile, in order to regain the ER homeostasis in lung diseases, the UPR may operate adaptive mechanisms involving the stimulation of autophagy [30]. Altogether, our results may be explained in a way that the UPR in ALI or airway inflammation may restore the ER function by enhancing the AM autophagy, thus attenuates the disease progression. Although our study was conducted with the aim to shed light on providing the least theoretical basis for the treatment of ALI and airway inflammation, our study still has several limitations that are worth mention. Firstly, the sample size in this current study was not large enough to provide objective and solid experimental results. Then, since we mentioned that the dosage of TM may have certain effect on AM autophagy, it is required to explore its potential mechanism in a more detailed way and, therefore, caution should be exercised on handling the TM during experiments in future studies. In summary, our study showed that TM-induced ER stress can potentiate the autophagy of AM, thus contributing to the attenuation of ALI and airway inflammation. Given what we know that the effect of TM on AM autophagy was in a dosage-dependent manner, the possible mechanism or exploration shall be one of the major challenges in this field. Acknowledgments We would like to acknowledge the reviewers for their helpful comments on this paper. Authors’ contributions Guarantor of integrity of the entire study: Qingzeng Qian Study design: Xiangke Cao; Fumin Feng Literature research: Bin Wang, Xiaoliu Dong Experimental studies: Jian Pei; Ling Xue Disclosure statement No potential conflict of interest was reported by the authors. Ethics statement Animal experiments were conducted in strict accordance with the approved animal protocols and guidelines established by the Medicine Ethics Review Committee for animal experiments of North China University of Science and Technology. The procedures for animal experiments were in accordance with requirements in the Declaration of Helsinki. ==== Refs References [1] Parekh D, Dancer RC, Thickett DR. 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==== Front Int J Biochem Cell Biol Int. J. Biochem. Cell Biol The International Journal of Biochemistry & Cell Biology 1357-2725 1878-5875 Elsevier Ltd. S1357-2725(06)00050-1 10.1016/j.biocel.2006.02.003 Article Nucleocapsid protein of SARS-CoV activates the expression of cyclooxygenase-2 by binding directly to regulatory elements for nuclear factor-kappa B and CCAAT/enhancer binding protein Yan Xiaohong Hao Qian Mu Yongxin Timani Khalid Amine Ye Linbai Zhu Ying ⁎⁎ Wu Jianguo [email protected]⁎ The State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan 430072, PR China ⁎ Corresponding author. Tel.: +86 27 68754979; fax: +86 27 68754592. [email protected]⁎⁎ Corresponding author. 3 3 2006 2006 3 3 2006 38 8 1417 1428 30 10 2005 17 1 2006 7 2 2006 Copyright © 2006 Elsevier Ltd. All rights reserved.2006Elsevier LtdSince January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.SARS-associated coronavirus (SARS-CoV) causes inflammation and damage to the lungs resulting in severe acute respiratory syndrome. To evaluate the molecular mechanisms behind this event, we investigated the roles of SARS-CoV proteins in regulation of the proinflammatory factor, cyclooxygenase-2 (COX-2). Individual viral proteins were tested for their abilities to regulate COX-2 gene expression. Results showed that the COX-2 promoter was activated by the nucleocapsid (N) protein in a concentration-dependent manner. Western blot analysis indicated that N protein was sufficient to stimulate the production of COX-2 protein in mammalian cells. COX-2 promoter mutations suggested that activation of COX-2 transcription depended on two regulatory elements, a nuclear factor-kappa B (NF-κB) binding site, and a CCAAT/enhancer binding protein (C/EBP) binding site. Electrophoretic mobility shift assay (EMSA) and chromatin immunoprecipitation (ChIP) demonstrated that SARS-CoV N protein bound directly to these regulatory sequences. Protein mutation analysis revealed that a Lys-rich motif of N protein acted as a nuclear localization signal and was essential for the activation of COX-2. In addition, a Leu-rich motif was found to be required for the N protein function. A sequence of 68 residuals was identified as a potential DNA-binding domain essential for activating COX-2 expression. We propose that SARS-CoV N protein causes inflammation of the lungs by activating COX-2 gene expression by binding directly to the promoter resulting in inflammation through multiple COX-2 signaling cascades. Keywords SARS-CoVCyclooxygenase-2N proteinGene regulationVirus infectionInflammationPathogenesis ==== Body 1 Introduction In March 2003, a novel severe acute respiratory syndrome-associated coronavirus (SARS-CoV) was identified as the causative agent of SARS (Ksiazek et al., 2003). The virus has been demonstrated to induce fever, edema, and diffuse alveolar damage in severely affected individuals (Poutanen et al., 2003). Similar to other coronaviruses in structure, SARS-CoV is an enveloped virus containing a single-stranded, positive-sense RNA genome, 29.7 kb nucleotides in length, that encodes four viral structural proteins including the spike (S) glycoprotein, the matrix (M) protein, the small envelope (E) protein, and the nucleocapsid (N) protein (Marra et al., 2003). The gene encoding for the 46-kDa SARS-CoV N protein directly precedes the 3′-UTR of the viral genome (Rota et al., 2003). For coronaviruses, the N protein plays an important role during viral packaging, viral core formation, and viral RNA synthesis (Narayanan, Chen, Maeda, & Makino, 2003). The SARS-CoV N protein shares homology with N protein from other members of the coronavirus family. However, it contains a short lysine-rich sequence (362-KTFPPTEPKKDKKKKTDEAQ-381) near the carboxyl terminus, a putative bipartite nuclear localization signal that has not been found in the N proteins of any other known coronavirus (Marra et al., 2003). This suggested that the SARS-CoV nucleocapsid protein might have novel functions in pathogenesis. The SARS-CoV N protein has been reported to form a dimmer by self-association (He et al., 2004), to activate the activator protein 1 (AP1) signal transduction pathway (He et al., 2003), and to induce actin reorganization in COS-1 cells (Sutjit et al., 2004). Cyclooxygenase-2 (COX-2) is the inducible COX isoform that catalyzes the formation of prostaglandins in response to cytokines, and to oncogenic and mitogenic factors (Wu, Zhu, & Wu, 2003). COX-2 has been known to play an important role in inflammation, tissue damage, and tumorigenesis (Murono et al., 2001). The induction of COX-2 expression depends primarily on transcriptional activation by diverse stimuli. There are many consensus cis-elements in the 5′-flanking region that may contribute to transcriptional regulation of the COX-2 gene. Only a limited number of elements including a cyclic-AMP response element (CRE) at −53/−59, a CCCAAT/enhancer-binding protein (C/EBP) element at −124/−132, and two NF-κB sites at −438/−447 and −213/−222 have demonstrated roles in human COX-2 transactivation (Murono et al., 2001). A number of investigations demonstrated that viral proteins stimulate COX-2 expression. For example, latent membrane protein 1 of Epstein-Barr virus, X protein of hepatitis B virus, Gp120 and Tat of human immunodeficiency virus, core and NS5A proteins of hepatitis C virus up-regulate COX-2 expression in human cell lines (Bagetta et al., 1998, Lara-Pezzi et al., 2002, Murono et al., 2001, Núñez et al., 2004). However, the roles of SARS-CoV proteins in the regulation of COX-2 protein expression and inflammation are still unclear. Because COX-2 gene expression is associated with inflammatory processes and can be induced by viral proteins, the aim of this study was to investigate the roles of SARS-CoV encoded proteins in the regulation of COX-2 expression and to determine the molecular mechanisms responsible. Our results showed that SARS-CoV N protein was able to activate the COX-2 gene expression and both NF-κB and C/EBP elements were involved in the activation of COX-2 by SARS-CoV N protein. 2 Materials and methods 2.1 Plasmid construction and cell culture The 5′ flanking sequence −891 to +9 containing the core promoter region and a series of truncation mutants of the human COX-2 gene were constructed into a promoterless luciferase expression vector PGL3 (Promega) as described previously (Saunder, Sansores-Garcia, Gilroy, & Wu, 2001). Site-specific mutations of CRE, C/EBP and two NF-κB sites were performed by using QuikChange site-directed mutagenesis kit (Stratagene). The CRE site within −891/+9 fragment was mutated from -59TTCGTCA-53 to TTgagCA, the C/EBP site was mutated from -132TTACGCAAT-124 to gcgatagcT, one of NF-κB sites was changed from -222GGGACTACCC-213 to aattCTACCC (NF-κB-A), and the other was altered from -447GGGGATTCCC-438 to attcATTCCC (NF-κB-B). The mutations and their corresponding primers are listed in Table 2. Single or double mutants were also constructed into luciferase expression vector PGL3. Genes of SARS-CoV strain WHU (GenBank Accession No. AY394850) were amplified by RT-PCR with RNA isolated from SARS-CoV infected Vero E6 cells (Zhu et al., 2005). The amplified genes and their corresponding primers are listed in Table 1 . The PCR products were purified using a DNA extraction kit (Fermentas), digested with EcoRI and SalI or BamHI and EcoRI (underlined sequence in primers), and cloned into the vector pCMV-Tag2 (Stratagene). Site-specific mutations of the N gene were performed by using QuikChange site-directed mutagenesis kit (Stratagene). Four site-specific mutations of the N gene were as follows: MutN1 (Δ38PKQRRPQ44); MutN2 (Δ220LALLLLDRLNQL231); MutN3 (Δ257KKPRQKR263); MutN4 (Δ369KKDKKKK376). The deleted regions of MutN3 and MutN4 comprised lysine/arginine-rich motifs. The mutations and their corresponding primers are listed in Table 2 . Forward primers (N1–N5) for sequential truncations of the N gene from the 5′ end are listed in Table 3 , and the reverse primer is Nd (Table 1). Reverse primers (N6–N9) for sequential truncations of the N gene from the 3′ end are listed in Table 3, and the forward primer was Nu (Table 1). Human embryo kidney cell line (HEK293T) was cultured in Dulbecco's modified Eagle's medium, supplemented with 10% heat-inactivated fetal bovine serum, 100 U/ml penicillin, and 100 μg/ml streptomycin. All cells were maintained in a humidified 5% CO2 incubator at 37 °C.Table 1 Primers used for amplification of genes of SARS-CoV strain WHU in this study X1u 5′-CGTTCCGAATTCGATGGATTTGTTTATGAGATTTTTTAC-3′ X1d 5′-CGTTATGTCGACGTTTCTTCCGAAACGAATGAGTAC-3′ X2u 5′-GGTCCTGAATTCGATGCCAACTACTTTGTTTGCTG-3′ X2d 5′-CGATTGGTCGACGAATACCACGAAAGCAAGAAAAAG-3′ X3u 5′-GGTTCCGAATTCGATGTTTCATCTTGTTGACTTCCAGG-3′ X3d 5′-GAGCACGTCGACTGATGGGCAAGGTTCTTTTAGTAGT-3′ X4u 5′-GTGCGTGAATTCGATGAAAATTATTCTCTTCCTGAC-3′ X4d 5′-CTCGTAGTCGACAGGCTAAAAAGCACAAATAGAAG-3′ X5u 5′-GGGACGGAATTCCATGTGCTTGAAGATCCTTGTAAGGTAC-3′ X5d 5′-GTCGTAGTCGACGTCCACCAAATGTAATGCGGGGGGC-3′ ORF1u 5′-GCGCGCGAATTCGATGAATGAGCTCACTTTAATTGAC-3′ ORF1d 5′-AGTGCCGTCGACCAAAGCCAAGCAGTGCTATAAG-3′ ORF2u 5′-AGTCTGGAATTCGATGAAACTTCTCATTGTTTTGACTT-3′ ORF2d 5′-TGCGTAGTCGACTGACAGTTGATAGTAACATTAGGTG-3′ ORF3u 5′-ATGCTGGAATTCGATGGACCCCAATCAAACC-3′ ORF3d 5′-CCATGTCGACGTAATAGAAGTACCATCTGGGGCTG-3′ ORF4u 5′-ATGCTGAATTCGATGCTGCCACCGTGCTACAAC-3′ ORF4d 5′-TACGTAGTCGACCTCAGCAGCAGATTTCTTAGTGACAG-3′ Eu 5′-AGCTGGATCCATGTACTCATTCGTTTCGGAAGAAAC-3′ Ed 5′-AGCTGAATTCTTAGTTCGTTTAGACCAGAAGATC-3′ Mu 5′-AGCTGGATCCGCTTATCATGGCAGACAACGGTACT-3′ Md 5′-AGCTGAATTCCATCTGTTGTCACTTACTGTACTAGC-3′ Nu 5′-AGCTGGATCCATGTCTGATAATGGACCCCAATCAAAC-3′ Nd 5′-AGCTGAATTCCATCATGAGTGTTTATGCCTGAGT-3′ Table 2 Primers used for site-specific mutations in this study MutN1u 5′-TGGAGGACGCAATGGGGCAAGGGGTTTACCCAATAATACTGCGT-3′ MutN1d 5′-ACGCAGTATTATTGGGTAAACCCCTTGCCCCATTGCGTCCTCCA-3′ MutN2u 5′-GGCTAGCGGAGGTGGTGAAACTGCCGAGAGCAAAGTTTCTGGTAAAGGCC-3′ MutN2d 5′-GGCCTTTACCAGAAACTTTGCTCTCGGCAGTTTCACCACCTCCGCTAGCC-3′ MutN3u 5′-TAAGAAATCTGCTGCTGAGGCATCTACTGCCACAAAACAGTACAACGTCA-3′ MutN3d 5′-TGACGTTGTACTGTTTTGTGGCAGTAGATGCCTCAGCAGCAGATTTCTTA-3′ MutN4u 5′-CAAAACATTCCCACCAACAGAGCCTACTGATGAAGCTCAGCCTTTGCCGC-3′ MutN4d 5′-GCGGCAAAGGCTGAGCTTCATCAGTAGGCTCTGTTGGTGGGAATGTTTTG-3′ CREu 5′-AGGCGGAAAGAAACAGTCATTTGAGCTCATGGGCTTGGTTTTCAGTC-3′ CREd 5′-GACTGAAAACCAAGCCCATGAGCTCAAATGACTGTTTCTTTCCGCCT-3′ C/EBPu 5′-AACCCTGCCCCCACCGGCGCGATAGCTTTTTTTAAGGGGAGAGGAG-3′ C/EBPd 5′-CTCCTCTCCCCTTAAAAAAAGCTATCGCGCCGGTGGGGGCAGGGTT-3′ NF-κB-Au 5′-GGATCAGACAGGAGAGTGAATTCTACCCCCTCTGCTCCCAAATTGG-3′ NF-κB-Ad 5′-CCAATTTGGGAGCAGAGGGGGTAGAATTCACTCTCCTGTCTGATCC-3′ NF-κB-Bu 5′-GGCGGCGGCGGCGGGAGAATTCATTCCCTGCGCCCCCGGACCTCAG-3′ NF-κB-Bd 5′-CTGAGGTCCGGGGGCGCAGGGAATGAATTCTCCCGCCGCCGCCGCC-3′ Table 3 Primers for sequential deletions of N gene N1 5′-ACAGGATCCCAAGGAGGAACTTAGATT-3′ N2 5′-AGTGGATCCGAGGGAGCCTTGAATACA-3′ N3 5′-ATAGGATCCGGGAAATTCTCCTGCTCG-3′ N4 5′-TTAGGATCCGAACGTCACTCAAGCATTT-3′ N5 5′-ATAGGATCCATATCATGGAGCCATTAAAT-3′ N6 5′-GGTGAATTCTTTAATGGCTCCATGATA-3′ N7 5′-ATAGAATTCGTTTTGTGGCAGTACGTTT-3′ N8 5′-TAAGAATTCAGGAGAATTTCCCCTACTG-3′ N9 5′-TATGAATTCTATTCAAGGCTCCCTCAGT-3′ 2.2 Transfection and luciferase assays Cotransfection of luciferase reporter vectors with relevant recombinant plasmids into cells were carried out by mixing 0.2 μg of luciferase reporter vectors and 0.4 μg plasmids with 2 μl Sofast™ transfection reagent (Xiamen Sunma Biotechnology Co., Ltd.). The mixture was then added to each well of 24-well plates with 293T cells growing at 70% confluence. After incubation for 24 h, the cells were serum-starved for 24 h before harvesting for luciferase activity assays. 2.3 Western blot analysis Cell samples were washed with cold PBS and dissolved in lysis buffer (50 mM Tris–HCl, 150 mM NaCl, 10% glycerol, 1% Triton X-100, 1.5 mM MgCl2, 1 mM EDTA, 0.1 mM phenylmethulsulfonyl fluoride, 50 mM NaF, 1 mM sodium orthovanadate, and 0.06 mg/ml aprotinin) on ice for 30 min. After centrifugation at 12,000 rpm for 15 min, the supernatants were separated and protein concentration was determined. Proteins were separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and electrotransferred to nitrocellulose membranes. The membranes were probed with a rabbit polyclonal anti-COX-2 (Santa Cruz) or rabbit anti-N polyclonal antibody (prepared in this study) or mouse monoclonal anti-flag antibody (Santa Cruz), followed by incubation with horseradish peroxidase conjugated goat anti-rabbit IgG or goat anti-mouse IgG (Santa Cruz). Immunoreactivity was visualized by chemiluminescent detection (Pierce). 2.4 Preparation of nuclear extracts Nuclear extracts were prepared as described previously (Kin & Fischer, 1998) with some modifications. Cells incubated in serum-free media for 24 h were washed with cold PBS twice and scraped into 1 ml of cold PBS, followed by centrifugation at 2000 rpm for 10 min in a microcentrifuge and incubated in buffer A (10 mM HEPES, pH 8.0, 0.5% Nonidet P-40, 1.5 mM MgCl2, 10 mM KCl, 0.5 mM DTT, and 200 mM sucrose) for 10 min on ice. Nuclei were collected by centrifugation in a microcentrifuge at 12,000 rpm for 15 s. Pellets were rinsed with buffer A, resuspended in buffer B (20 mM HEPES, pH 7.9, 1.5 mM MgCl2, 420 mM NaCl, 0.2 mM EDTA, and 1.0 mM DTT), and incubated on a rocking platform for 30 min at 4 °C. Nuclei were clarified by centrifugation at 12,000 rpm for 15 min, and the supernatants were diluted 1:1 with buffer C (20 mM HEPES, pH 7.9, 100 mM KCl, 0.2 mM EDTA, 20% glycerol, and 1 mM DTT). Protease inhibitors (1 mM phenylmethylsulfonyl fluoride, 50 mg/ml of both aprotinin and leupeptin) and phosphatase inhibitors (10 mM NaF, 10 mM β-glycerophosphate, 0.1 mM sodium orthovanadate, and 1 mM EGTA) were added to each buffer. 2.5 Electrophoresis mobility shift assay (EMSA) Probes were generated by annealing single-strand oligonucleotides (sequences are listed in Table 4 ) containing the cognate promoter regions of the COX-2 gene and labeling the ends with [γ-32P] ATP using T4 polynucleotide kinase (TaKaRa). The COX-2 cis-regulatory motifs, C/EBP1, C/EBP2, NF-κB1, and NF-κB2, were analyzed.Table 4 Sequences of primers for EMSA and ChIP C/EBP1 -138ACCGGCTTACGCAATTTTTTTAAG-115 C/EBP2 -115GAGCAGAGGGGGTAGTCCCCACTCT-138 NF-κB1 -228AGAGTGGGGACTACCCCCTCTGCTC-204 NF-κB2 -204GAGCAGAGGGGGTAGTCCCCACTCT-228 CP1 -502ACTTCCTCGACCCTCTAAAGACGT-479 CP2 -2TCGCTAACCGAGAGAACCTTCCTT-25 C/EBP-CP1 -155TAAAAAACCCTGCCCCCACCGG-134 NF-κB-CP1 -243GAGGGATCAGACAGGAGAGT-224 NF-κB-CP2 -136GGTGGGGGCAGGGTTTTTTA-155 EMSAs were performed with 4 μg of nuclear extract in binding buffer (20 mM Hepes, pH 7.9, 0.1 mM EDTA, pH 8.0, 75 mM KCl, 2.5 mM MgCl2, and 1 mM DTT) containing 1 μg of poly(dI-dC). To assure the specific binding of transcription factors to the probe, unlabeled double-stranded oligonucleotide competitors were preincubated at a 50-fold molar excess for 10 min prior to probe addition. For supershift experiments, 2 μg of purified polyclonal antibody was incubated with nuclear extracts on ice for 30 min before adding to the binding buffer. Samples were then electrophoreses on 5% nondenaturing polyacrylamide, 0.25× Tris borate/EDTA gels, and the gels were dried and subjected to autoradiography. 2.6 Chromatin immunoprecipitation (ChIP) The assay was done as previously described with slight modifications (Wu et al., 2003). Monolayer of 293T cells (80% confluent) were incubated for 24 h after transfection, and then were serum-starved for 24 h. Formaldehyde was added to the culture medium to a final concentration of 1%. The cells were then washed twice with PBS, scraped, and lysed in lyses buffer (1% SDS, 10 mM Tris–HCl, pH 8.0, 1 mM PMSF, 50 mg/ml of both aprotinin and leupeptin) for 10 min on ice. The lysates were sonicated on ice and the debris was removed by centrifugation at 12,000 rpm for 15 min at 4 °C. One-fourth of the supernatant was used as DNA input control. The remaining supernatant was diluted 10-fold with dilution buffer (0.01% SDS, 1% Triton X-100, 1 mM EDTA, 10 mM Tris–HCl, pH 8.0, and 150 mM NaCl) and incubated with antibody against N protein overnight at 4 °C. Immunoprecipitated complexes were collected using protein A/G agarose beads. The pellets were washed with dialysis buffer (2 mM EDTA, 50 mM Tris–HCl, pH 8.0). Samples were incubated at 67 °C for 5 h to reverse formaldehyde crosslink. DNA was precipitated with ethanol and extracted three times with phenol/chloroform. Finally, pellets were resuspended in TE buffer and subjected to PCR amplification using COX-2 promoter specific detection primer (Table 4). The PCR products were resolved by agarose gel electrophoresis. 3 Results 3.1 SARS-CoV N protein activates COX-2 promoter and induces COX-2 protein expression To investigate the roles of proteins encoded by SARS-CoV in the regulation of COX-2, we constructed a series of plasmids carrying 12 individual SARS-CoV genes or potential open reading frames (Fig. 1A). Each of these plasmids was cotransfected into 293T cells with a reporter plasmid carrying the luciferase gene under the control of the COX-2 promoter. Cells were incubated for 24 h, serum-starved for an additional 24 h, and harvested. Cell lysates were analyzed for luciferase activity. Luciferase activity assays demonstrated that N protein activated the COX-2 promoter (Fig. 1B, lane 10). COX-2 promoter activity was about 12-fold higher in cells transfected with the plasmid expressing the N protein than that of vector control (Fig. 1B, lanes 10 and 13), while the rest of the expression plasmid constructs for viral proteins (X1, X2, E, M, X3, X4, ORF1, ORF2, X5, ORF3, and ORF4) tested in this study had no significant effects on the expression of luciferase (Fig. 1B, lanes 1–9, 11, and 12).Fig. 1 SARS-CoV N protein activates the expression of COX-2 in 293T cells. (A) Diagram of individual open reading frames of SARS-CoV with locations on the viral genome. (B) Analysis COX-2 promoter activation by individual SARS-CoV proteins. 293T cells were cotransfected with plasmid expressing different SARS-CoV proteins, respectively, and the reporter plasmid in which the luciferase gene is under the control of COX-2 promoter. Relative luciferase activity was determined by standard procedures. The expressed genes are indicated in panel A: 13 is the negative control pCMV-Tag2 and 14 is the positive control pCMV-Tag2-HBx (X protein of hepatitis B virus). (C) Analysis of dose-dependent of the SARS-CoV N protein in the activation of COX-2 promoter. 293T cells were cotransfected with different amount of plasmids expressing N protein along with the reporter plasmid and relative luciferase activity was determined. Values correspond to an average of at least three independent experiments done in duplicate. Error bars show 1 S.D. (D) Western blot analysis of the expression of COX-2 protein activated by N protein. Cells were transfected with empty vector pCMV-Tag2 as a control (lane 1) or with plasmid pCMV-Tag-N, expressing SARS-CoV N protein (lane 2). Cell extracts were prepared and the expressed proteins were determined using rabbit anti-COX-2 antibody. (E) Western blot analysis of same blot probed with antibodies against N protein. To determine the activation of COX-2 by N protein was dependent on the amount of N protein, different concentrations of plasmid expressing the N protein along with plasmid carrying the reporter gene were cotransfected into 293T cells. Luciferase activity assays showed that COX-2 promoter activity increased as the concentration of plasmid DNA increased until the concentration reached to 0.4 μg (Fig. 1C), indicating activation of COX-2 promoter by N protein was concentration-dependent. To determine the role of N protein in the regulation of COX-2 protein production, plasmid (pCMV-Tag2-N) carrying the N gene or control plasmid (pCMV-Tag2) was transfected into 293T cells, respectively. Transfected cells were treated and harvested as described above. Western blot analysis of cell lyses using COX-2 antibody showed that the level of COX-2 protein production increased in the presence of N protein (Fig. 1D, lane 2) relative to control transfection (Fig. 1D, lane 1). To confirm the expression of N protein in transfected cells, Western blot analysis was also carried out using antibody against the N protein (anti-N). N protein was detected in cells transfected with the plasmid pCMV-Tag2-N (Fig. 1E, lane 2), but not present in cells transfected with control plasmid pCMV-Tag2 (Fig. 1E, lane 1). Results from Western blot analysis and luciferase activity assays showed that the N protein of SARS-CoV is sufficient for the activation of COX-2 promoter and for the production of COX-2 protein. 3.2 NF-κB and C/EBP binding elements are required for the expression of COX-2 activated by SARS-CoV N protein To define the COX-2 cis-regulatory elements that were responsive to SARS-CoV N protein, truncation mutants and site-specific mutations of the promoter were generated (Fig. 2 ). Reporter plasmids were then constructed in which the luciferase gene was under the control of each individual mutant COX-2 promoter. To test the functions of mutant promoters, 293T cells were cotransfected with a plasmid carrying the SARS-CoV N gene (pCMV-Tap2-N) and plasmids containing the luciferase reporter gene driven by mutated COX-2 promoters. The mutated COX-2 promoter was determined by luciferase activity assays. Results indicated that mutations in promoter elements C/EBP, NF-κB-A, and a C/EBP-CRE double mutations significantly decreased expression from COX-2 promoter in response to N protein, respectively, while other mutations had little effects on the activation of COX-2 promoter regulated by the N protein, compared to the full-length wild type promoter (Fig. 2). These results indicated that C/EBP recognition site and one of the two NF-κB-A binding sites were required for the activation of COX-2 promoter by SARS-CoV N protein suggesting N protein regulates COX-2 gene expression in a NF-κB and C/EBP recognition element dependent manner.Fig. 2 Functional analysis of cis-regulatory elements of the COX-2 promoter. Diagram of individual cis-regulatory elements of the COX-2 promoter and its truncated or site-specific mutants are shown in the left panel and results from luciferase activity assay are shown in the right panel. Plasmid carrying the SARS-CoV N gene and plasmids containing the luciferase reporter gene driven by individual COX-2 promoter mutants were cotransfected into 293T cells. Promoter activities were determined by measuring the relative luciferase activity in transfected-cell lysates. pCMV-Tag2 was used as a vector only control. Luciferase activities correspond to an average of at least three independent experiments done in duplicate. The black symbols indicate mutations. Error bars show 1 S.D. 3.3 SARS-CoV N protein binds directly to C/EBP and NF-κB regulatory elements on the COX-2 promoter Localization to the nucleus is a common feature of coronavirus nucleoproteins (Wurm, Chen, Hodgson, Britton, Brooks, & Hiscox, 2001). A short lysine-rich region near the carboxyl terminus of SARS-CoV N protein has been identified as a putative bipartite nuclear localization signal (Marra et al., 2003). Since C/EBP and NF-κB regulatory elements are required for the expression of COX-2 gene activated by N protein. It is reasonable to assume that SARS-CoV N protein function may through binding to C/EBP and NF-κB regulatory elements. To confirm this speculation, we conducted electrophoresis mobility shift assay to define protein–DNA binding between N protein and COX-2 promoter. 293T cells were transfected with a control plasmid (Fig. 3A and B, lanes 1–3) or a plasmid containing the N gene (Fig. 3A and B, lanes 4–6). Nuclear extracts were prepared from transfected cells and EMSA was performed with 4 μg of nuclear extract in binding buffer. To assure the specific binding of transcription factors to the probe, unlabeled double-stranded oligonucleotide competitors were added prior to the addition of labeled probe (Fig. 3A and B, lanes 1 and 4). To determine whether N protein was specific bound to the promoter, rabbit anti-N polyclonal antibody was incubated with nuclear extracts before adding the binding buffer (Fig. 3A and B, lanes 3 and 6). DNA probes used in this study contained either the C/EBP element (Fig. 3A) or the NF-κB-A elements (Fig. 3B) from COX-2 promoter. Samples were then electrophoresed on nondenaturing polyacrylamide gels and subjected to autoradiography. Results from EMSA using C/EBP probe showed that a specific protein–DNA complex was supershifted in cells transfected with plasmid expressing N protein (Fig. 3A, lane 6). Similar results were also observed when NF-κB element probe was used (Fig. 3B, lane 6).Fig. 3 Determination of interaction between SARS-CoV N protein and COX-2 promoter by electrophoretic mobility shift assay (EMSA). EMSA was performed with nuclear extracts of 293T cells transfected with (lanes 4–6) or without (lanes 1–3) the N gene. Probes were generated by annealing single-stranded and end-labeled oligonucleotides containing the cognate COX-2 promoter regions. C/EBP at nucleotides −132/−125 (A) or NF-κB at nucleotides −228/−204 (B) probes were added to all reactions (lanes 1–6). Unlabeled double-stranded oligonucleotide competitors were added during preincubation prior to probe addition (lanes 1 and 5). For supershift experiments, polyclonal antibody was incubated with nuclear extracts before adding to the reaction (lane 6). Samples were electrophoresed on 5% nondenaturing polyacrylamide gel and visualized by autoradiography. Arrows indicate the super shifted protein–DNA complexes. To confirm N protein-promoter DNA binding, chromatin immunoprecipitation assays were performed. Chromatin fragments were prepared from 293T cells transfected with plasmid expressing the N protein and immunoprecipitated with specific rabbit anti-N polyclonal antibody. The COX-2 promoter region (−502 to −2) containing NF-κB and C/EBP binding sequences in the chromatin precipitates was amplified by PCR using specific primers (CP1 and CP2) (Fig. 4A, lane 2). The COX-2 promoter region (−243 to −136) containing only NF-κB-A binding sequences, excluding C/EBP site was amplified by PCR using NF-κB-specific primers (NF-κB-CP1 and NF-κB-CP2) (Fig. 4B, lane 2). The COX-2 promoter region (−155 to −2) containing C/EBP binding sequences, excluding NF-κB site was also amplified from anti-N protein precipitation by PCR using C/EBP-specific primers (C/EBP-CP1 and CP2) (Fig. 4C, lane 2). All amplified products from immunoprecipitated DNA were specific for cells transfected with plasmid expressing N protein and were the expected sizes, comparing PCR products from immunoprecipitation with PCR products amplified directly from input DNA (Fig. 4A, lanes 2 and 4; Fig. 4B, lanes 2 and 3; Fig. 4C, lanes 2 and 3). These results indicated that the N protein bound to the C/EBP and NF-κB recognition elements in the COX-2 promoter.Fig. 4 Determination of interaction between SARS-CoV N protein and COX-2 promoter by chromatin immunoprecipitation (ChIP) assays. 293T cells transfected with empty vector pCMV-Tag2 (lane 1 in A; lane 4 in B and C) or with pCMV-Tag2-N expressing the N protein (lanes 2–4 in A; lanes 1–3 in B; lanes 1–3 in C) were lysed and subjected to ChIP assays. Immunoprecipitated complexes were collected, subjected to PCR amplification, and separated by agarose gel electrophoresis. (A) The COX-2 promoter region (−502 to −2) was amplified by PCR using specific primers (CP1 and CP2). (B) The COX-2 promoter region (−243 to −136) amplified by PCR using NF-κB-specific primers (NF-κB-CP1 and NF-κB-CP2). (C) The COX-2 promoter region (−155 to −2) amplified by PCR using C/EBP-specific primers (C/EBP-CP1 and CP2). 3.4 Two regions of SARS-CoV N protein play important roles in the activation of COX-2 gene For coronaviruses, the N protein (NP) plays an important role during viral packaging, viral core formation, and viral RNA synthesis (Narayanan et al., 2003). To determine the roles of different regions of N protein in the activation of COX-2 gene expression, we carried out a functional analysis of the protein by deletion mutagenesis. Sequential N-terminal deletion mutants of the SARS-CoV N protein were generated by deleting nucleotides from the 5′ end of the N protein gene and inserting the truncated gene into vector pCMV-Tag2 (Fig. 5A). The function of each mutant N gene was evaluated by luciferase assay following transfection of 293T cells with a reporter plasmid carrying the luciferase gene driven by the COX-2 promoter. Results from luciferase activity assays (Fig. 5B) showed that deletion of amino acids from 1 to 61 (NΔ1), 1 to 136 (NΔ2), or 1 to 204 (NΔ3) from the N terminal of the protein, respectively, had no effects on its function in terms of COX-2 gene activation, whereas deletions of amino acids from 1 to 269 (NΔ4) significantly decreased the level of reporter gene expression. Further deletion from 1 to 333 (NΔ5) entirely abolished luciferase activity. These results suggested that sequences located from amino acids from 204 to 269 are essential for the activation of COX-2 gene by N protein.Fig. 5 Functional analysis of SARS-CoV N protein by sequential deletions. (A) Diagram of N-terminal truncated mutants of the SARS-CoV N protein and determination of functions of N-terminal truncated N proteins by measuring their ability on the activation of the COX-2 promoter. (B) Diagram of C-terminal truncated mutants of the SARS-CoV N protein and determination of functions of C-terminal truncated N proteins by measuring their ability on the activation of the COX-2 promoter. 293T cells were cotransfected with plasmids containing individual mutant N gene expressing different truncated protein and the reporter gene. The effects of each mutant protein on the activation of COX-2 promoter were measured by the luciferase activity assay. pCMV-Tag2 was used as a vector control. Values correspond to an average of at least three independent experiments done in duplicate. Error bars show 1 S.D. Sequential deletion mutants of SARS-CoV N protein from C terminal were constructed in the same manner as N-terminal deletions, but by deleting nucleotides from the 3′ ends of the gene (Fig. 5B). Plasmid expressing these sequential deleted N genes and the reporter plasmid were cotransfected into 293T cells. Results from luciferase activity assays (Fig. 5B) revealed that reporter gene activity decreased two-fold in the presence of the N protein (CΔ1) in which amino acids from 339 to 422 were deleted. Further deletions (CΔ2, CΔ3, CΔ4, CΔ5, and CΔ6) eliminated the N protein function in the activation of COX-2 gene. These results implicated sequences between amino acids 339 and 422 of the SARS-CoV N protein to fully transactivate transcription. 3.5 A putative nuclear localization signal domain of N protein is essential for the activation of COX-2 promoter Sequence analysis revealed that the N protein of SARS-CoV contains two potential nuclear localization signals, individual short lysine-rich sequences, located from residuals 257 to 263 (KKPRQKR) and from amino acids 362 to 381 (KTFPPTEPKKDKKKKTDEAQ), respectively (Fig. 6A). The second one is a putative bipartite nuclear localization signal that has not been found in N protein from any other known coronavirus.Fig. 6 Determination of the function of putative nuclear localization signals of the N protein. (A) Diagram and location of the potential nuclear localization signals of the N protein and their deletion mutants. MutN1 with 38-PKQRRPQ-44 deleted; MutN2 with 220-LALLLLDRLNRL-231 deleted; MutN3 with 257-KKPRQKR-263 deleted; MutN4 with 369-KKDKKKK-376 deleted. (B) Functional analysis of deletion mutant N proteins. 293T cells were cotransfected with plasmids carrying genes expressing the mutant N proteins and the reporter vector. The effects of the deletions on the N protein were determined by measuring luciferase activity. pCMV-Tag2 was used as a vector control. Values correspond to an average of at least three independent experiments done in duplicate. Error bars show 1 S.D. (C) Western bolt analysis of N proteins expressed in transfected cells using N protein antibody. To analyze the function of these sequences in the activation of COX-2 gene, we constructed plasmids expressing four mutant N proteins (MutN1, MutN2, MutN3, and MutN4), in which one of the potential nuclear localization signals deleted by site-directed mutagenesis (Fig. 6A). The plasmids expressing mutant N proteins were cotransfected, respectively, with the reporter gene into 239T cells. Results from luciferase activity analysis showed that MutN1 and MutN4 stimulated expression of from the COX-2 gene promoter to similar levels as wild type N protein, whereas MutN2 and MutN3 abolished N protein activation of the COX-2 gene promoter (Fig. 6B and C). These results demonstrated that amino acids 257–263 containing a short lysine-rich sequence (KKPRQKR), deleted in MutN2, and amino acids 220–231 carrying a short lucine-rich sequence (LALLLLDRLNQL), deleted in MutN3, were essential for N protein activation of the COX-2 promoter. 4 Discussion Virus infection stimulates the expression of a number of proinflammatory gene products, including COX-2, inducible nitric oxide synthase (iNOS), and proinflammatory cytokines. COX-2 converts arachidonic acids to prostaglandins, as participate in the modulation of inflammation and tissue damage in response to infection (Murono et al., 2001). Several viruses have been reported to stimulate expression of COX-2, including Epstein-Barr virus, HBV, HIV, HCV, pseudorabies virus, and rotavirus (Bagetta et al., 1998, Lara-Pezzi et al., 2002, Murono et al., 2001, Núñez et al., 2004; Ray & Enquist, 2004; Steer & Corbett, 2003). SARS-associated coronavirus causes inflammation and tissue damage to the lungs resulting in severe acute respiratory syndrome. However, the molecular mechanisms involved in the viral infection and tissue inflammation are still largely unknown. To determine the correlations between viral infection and lung inflammation, we isolated a SARS-CoV from a SARS patient previously (Zhu et al., 2005). In this study we demonstrated that N protein activates the COX-2 promoter and induces COX-2 protein production in mammalian cells in dosage-dependent manner indicating that N protein was required for the induction of COX-2 gene expression. It has been suggested recently that N protein is a two-domain protein, with the N-terminal amino acids from 50 to 150 as the RNA-binding domain (Tang et al., 2005). This study revealed that N protein specifically recognized the NF-κB and C/EBP regulatory elements on the COX-2 promoter. In addition, the region from amino acids 204 to 269 at N terminal of the protein appeared to be essential for the activation of COX-2 expression. Based on our results and previously reported findings, it is reasonable for us to suggest that N protein activates COX-2 gene expression by binding either directly or indirectly to NF-κB and C/EBP regulatory elements on COX-2 promoter. Results from electrophoresis mobility shift assay and chromatin immunoprecipitation assays demonstrated that SARS-CoV N protein activated COX-2 gene expression by binding directly to C/EBP and NF-κB regulatory elements on the promoter. It is most likely that amino acids from 136 to 204 of N protein were involved in the protein–DNA or protein–protein binding. This was also in agreement with the finding that through multi-alignment of total nineteen sequences of the coronavirus N proteins including that of SARS-CoV, one conserved structural region at amino acids 81–140 was found to perform critical functions (Wang et al., 2003). Localization to the nucleolus is a common feature of coronavirus N proteins. This feature helps with disrupt host cell division to promote virus assembly and sequester ribosomes for translation of viral proteins (Wurm et al., 2001). SARS-CoV N protein localized to the cytoplasm and nucleus of insect cells (Ren et al., 2004) and mammalian cells (date not shown). Deletion of the Lys-rich region (257-KKPRQKR-263) in N protein resulted in the loss of function in the activation of COX-2. Because this domain is putative nuclear localization signal, the failure of this mutation to activate COX-2 expression may be due to its inability to target to the nucleus. We generated two mutations of the N protein (CΔN1 and MutN1), in which a short lysine-rich sequence (362-KTFPPTEPKKDKKKKTDEAQ-381) near the carboxyl terminus and the sequence around it were deleted, respectively. To our surprise, deletions of this domain in N protein had minimal effects on the activation of COX-2 expression relative to full-length N protein. There are at least two explanations for these results. One is that there are two nuclear localization signal sequences in the N protein and the first one 257-KKPRQKR-263 at the N terminal is essential and efficient to transmit the protein into nucleus as demonstrated in this study. The second explanation is that the short lysine-rich sequence (362-KTFPPTEPKKDKKKKTDEAQ-381) and adjacent sequences at the C terminal of the N protein are in fact involved in different functions, such as protein dimerization, rather than nuclear localization. The N protein has been reported to form a dimmer by self-association (He et al., 2004), activate the AP1 (activator protein 1) signal transduction pathway (He et al., 2003), and induce actin reorganization in COS-1 cells (Sutjit, Liu, Jameel, Chow, & Lal, 2004). It was suggested recently that the C-terminal 169–422 of N protein contains the dimerization domain (Tang et al., 2005). Protein mutation analysis in this study supported that sequences at the C terminal of N protein are most likely involved in protein dimerization, since deletion of this sequence disrupted protein–protein association and resulted in the partially lose of its functions in activation of COX-2 and perhaps other biological activities. It is well known that C/EBP family has a common structure, an N-terminal transactivation domain, a basic DNA-binding domain, and a C-terminal domain containing a leucine zipper, which allows the homo- or heterodimerization of these factors (Williams, Cantwell, & Johnson, 1991). Our results suggested that the N protein is a three-domain protein, with the N-terminal amino acids 136–204 as the DNA-binding domain, amino acids 257–263 (KKPRQKR) at the middle as the nuclear localization signal domain, and the C-terminal amino acids 169–422 as the dimerization domain. Studies have shown that CCAAT/enhancer binding protein, cyclic-AMP response element binding protein (CREB), as well as NF-κB was commonly or individually involved in the regulation of COX-2 gene (Rossen, Bouma, Raatgeep, Büller, & Einerhand, 2004; Thomas et al., 2000; Wardlaw, Zhang, & Belinsky, 2002; Williams et al., 1991; Zhu, Saunders, Yeh, Deng, & Wu, 2002). COX-2 gene is regulated through interactions between NF-κB and C/EBP factors (Thomas et al., 2000). This study demonstrated that both NF-κB site and C/EBP site were involved in the activation of COX-2 by the N protein of SARS-CoV. The ability of virus infection to activate multiple signaling cascades (such as PKR, MAPK, iPLA2, and NF-κB) and how these pathways are integrated in the regulation of individual target gene expression have been discussed previously (Steer & Corbett, 2003). However, the details of molecular mechanisms involved in the activation of COX-2 regulated by the N protein need further investigation. Acknowledgments This work was supported by research grants from the Ministry of Science and Technology of China “973” project (No. 2005CB522901), the National Natural Science Foundation of China (No. 30470087 and No. 30570070), and the Ministry of Education of China (No. 20040486037) to J. Wu. 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==== Front Virus Res Virus Res Virus Research 0168-1702 1872-7492 Elsevier Science B.V. S0168-1702(99)00088-X 10.1016/S0168-1702(99)00088-X Article The novel hemagglutinin-esterase genes of human torovirus and Breda virus Duckmanton Lynn ab Tellier Raymond bc Richardson Chris d Petric Martin abc* a Department of Microbiology and Medical Genetics, The University of Toronto, Toronto, Ont., Canada b Division of Microbiology, The Hospital for Sick Children, 555 University Avenue, Toronto, Ont., Canada M5G 1X8 c Department of Laboratory Medicine and Pathobiology, The University of Toronto, Toronto, Ont., Canada d Department of Medical Biophysics, Ontario Cancer Institute, Princess Margaret Hospital, Toronto, Ont., Canada * Corresponding author. Tel.: +1-416-813-6111; fax: +1-416-813-6257 11 10 1999 11 1999 11 10 1999 64 2 137 149 30 10 1998 29 6 1999 29 6 1999 Copyright © 1999 Elsevier Science B.V. All rights reserved.1999Elsevier Science B.V.Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.Human torovirus (HTV) and Breda virus (BRV), members of the genus torovirus in the family Coronaviridae, are established infectious agents of humans and cattle, respectively. The hemagglutinin-esterase (HE) gene of Breda virus serotype 2 (BRV-2) has been identified and the nucleotide sequence for BRV serotype 1 (BRV-1) genome which contains the open reading frames for the viral structural proteins has been reported revealing the presence of a 1.25 kb gene whose nucleotide sequence is identical to that of the BRV-2 HE gene. In this study, we amplified the 1.2kb HE gene from the HTV genome using long RT-PCR and sequenced the amplicon directly. At the nucleotide level, the HTV HE gene manifests 85% sequence identity to the HE genes of BRV-1 and BRV-2 and 89% identity with the X pseudogene sequence of BEV. The 1.25 kb amplicons which contained the HE genes of BRV-1 and HTV were cloned and expressed in a baculovirus system and the proteins purified by sodium dodecyl sulphate-polyacrylamide gel electrophoresis. Hyperimmune sera prepared in guinea pigs against these proteins were reactive with both bovine torovirus (BTV) and human torovirus (HTV) antigens. By immunoblot, they reacted specifically with a 65 kDa protein corresponding in size to the torovirus HE protein. Furthermore, the hyperimmune sera but not the preimmune sera reacted with a series of BTV-positive and HTV-positive fecal specimens by immunoblot and dot blot analysis. By immunoelectron microscopy (IEM) torovirus particles from BTV-positive specimens from calves with diarrhea and HTV-positive specimens from patients were aggregated by the hyperimmune sera. Human convalescent sera and gnotobiotic calf post-infection sera reacted by immunoblot with the expressed 65 kDa protein. The expressed HE protein of HTV has important diagnostic potential. Keywords Novel hemagglutinin-esterase genesHuman torovirusBreda virus ==== Body 1 Introduction Breda virus (BRV), the bovine torovirus prototype and human toroviruses (HTV) are members of the genus torovirus within the family Coronaviridae, which together with the Arteriviridae forms the order Nidovirales (Cavanagh, 1997). They are related to the torovirus prototype Berne virus (BEV) and the newly described porcine torovirus (Kroneman et al., 1998). BRV was first isolated in 1982 from the stools of neonatal calves with diarrhea in Breda, Iowa, and is now an established infectious agent of cattle (Woode et al., 1982). Two serotypes of BRV have been recognized by hemagglutination inhibition (HI) tests, enzyme-linked immunosorbant assays (ELISA), and immunoelectron microscopy (IEM). BRV-1 represents the original isolate from Iowa, and BRV-2 includes isolates from Ohio and Iowa (Woode et al., 1985). Toroviruses were first reported as potential human pathogens with the observation of torovirus-like particles in the fecal specimens of children with diarrhea by electron microscopy EM (Beards et al., 1984). Recently, the morphological, serological, and molecular properties of these particles were elucidated, establishing them as human toroviruses (Duckmanton et al., 1997). A study on the prevalence of torovirus in a pediatric population showed that this agent is definitively associated with gastroenteritis in children (Jamieson et al., 1998). BRV and HTV are relatively pleomorphic particles that are difficult to recognize by EM, and cannot as yet be grown in cell culture. Aside from EM, diagnostic testing for these agents is limited to serological and molecular methods (Brown et al., 1987, Koopmans et al., 1989, Koopmans et al., 1993, Koopmans et al., 1997, Duckmanton et al., 1997, Duckmanton et al., 1998a). The genes for the structural proteins of BRV-1 were recently characterized using long RT-PCR and sequencing (Duckmanton et al., 1998b). BRV-1 was found to have an open reading frame (ORF) for a 1.25 kb hemagglutinin-esterase gene with an identical nucleotide sequence to the HE gene of BRV-2 with the 3′ end of this gene being 77% identical to the X pseudogene of BEV (Cornelissen et al., 1997, Snijder et al., 1991). However, very little is known about the genome of HTV. To date, only the 3′ non-coding region and the 3′ terminus of the nucleocapsid gene of the HTV genome have been sequenced (Duckmanton et al., 1997). The aims of this study were to determine whether the HE gene is present in the HTV genome, and if it is present, to express the BRV-1 and HTV hemagglutinin-esterase proteins, and investigate them for their immunospecific properties. 2 Materials and methods 2.1 Specimens and sera Stool specimens, demonstrated by EM to contain human torovirus or human rotavirus or to be free of detectable virus particles and acute/convalescent paired sera from patients diagnosed positive for human torovirus were obtained from the Virology Laboratory at the Hospital for Sick Children, Toronto, Ont. Bovine torovirus-positive fecal specimens from diarrheic calves, control specimens from asymptomatic calves, and rotavirus-positive specimens from diarrheic calves were obtained from the Animal Health Laboratory in Guelph, Ont. The above human and bovine specimens have previously been studied for the presence of viruses (Duckmanton et al., 1997, Duckmanton et al., 1998a). The stool specimen from a gnotobiotic calf infected with a purified preparation of the Breda virus-1 (code GC-32), and antisera to BRV-1 (bαBRV-1) from experimentally infected calves were obtained from Dr G. Woode, Texas A&M and Dr M. Hardy, Montana State University. Antibody prepared in guinea pigs to the recombinant N protein of BRV (gpHIαN) has been previously described (Duckmanton et al., 1998b) 2.2 RNA extraction As described previously (Duckmanton et al., 1998a), all fecal specimens were diluted in an equal volume of phosphate-buffered saline and clarified by differential centrifugation. Viral RNA was extracted from the partially purified supernatants of BRV-1-positive samples and HTV-positive samples using TRIzol reagent (Gibco BRL, Gaithersburg, MD) with all mixing steps performed by repeated inversions to prevent RNA shearing. 2.3 Primers Oligonucleotide primers (ACGT Corp., Toronto, Canada) used to amplify the HTV HE gene were deduced from the sequence upstream and downstream of the BRV-1 HE gene whose sequence was reported previously under accession number AF076621 (Duckmanton et al., 1998b). The sense primer (5′ TCTAGTGTTA AGTTTGAGTA GCACTTATC TC 3″) and the antisense primer (5′ GACATGGCAC AGCATTTGGA TTAAGCATAG 3′) bracketed a genome fragment of approximately 1.4 kb. Oligonucleotide primers used to amplify the 1.25 kb fragment containing the ORF of the HTV HE gene were designed based on the sequence of the HTV HE gene obtained in this study. The sense primer (5′ GGCGTGCTAG C̱ATGCTGAGT TTAATACTTT TTTTTCCATC TTTTGCCTTT GCAGT 3′), designed in the 5′ end of the HTV HE gene ORF, contained a NheI restriction site upstream of the start codon for cloning purposes. The antisense primer (5′ GATCCGCTAG C̱ACAAAAAAA ACTTATAATT ACAAATATTA AAATAACAAC CACCACC 3′), designed in the 3′ end of the HE gene ORF, did not contain a stop codon and had a NheI restriction site in its place. Primers used to amplify the 1.25 kb fragment containing the ORF of the BRV-1 HE gene for cloning were identical to those used for the HTV HE gene ORF except that the sense primer had GGC in place of AGT at its 3′ end. 2.4 Long RT-PCR The RT and PCR reactions were performed using the approach of Tellier et al., 1996a, Tellier et al., 1996b as described previously (Duckmanton et al., 1998b). The following cycling parameters were used to initially amplify the 1.4 kb region containing the HTV HE gene, as well as to amplify the ORFs of the HTV and BRV-1 HE genes for cloning. Denaturation at 99°C for 35 s, annealing at 67°C for 30 s, and elongation at 68°C for 5 min for 35 cycles. Reactions were analyzed by electrophoresis on a 1% agarose gel, subsequently stained with ethidium bromide. 2.5 DNA sequencing PCR products were excised from agarose gel, and purified using the Jetsorb system (Genomed, Frederick, MD) according to the manufacturer’s recommendations. Purified amplicons were then sequenced directly as described previously (Duckmanton et al., 1998b) Sequence data were analyzed using the computer programs GCG version 8 (Genetics Computer Group Inc., Madison, WI), and Gene Runner version 3.04 (Hastings on the Hudson, NY). 2.6 Cloning and expression PCR products containing the HTV and BRV-1 HE genes were purified using the Jetsorb system (Genomed). Amplicons were ligated to dephosphorylated pETL-EK (His) 6 baculovirus transfer vectors, and transformed into Epicurian coli XL1-blue MRF′ supercompetent cells (pCR-Script Amp SK+ cloning kit; Stratagene) as per the manufacturer’s recommendations. Plasmids containing the PCR inserts were purified from LB broth cultures using the Wizard Miniprep DNA Purification System (Promega), followed by a standard phenol:chloroform extraction (Sambrook et al., 1989). Positive clones were sequenced as described above and those with inserts intact and in frame were transfected into Spodoptera frugiperda (Sf9) insect cells using the BaculoGold transfection system (PharMingen Canada, Mississauga, Ont.) as per the manufacturer’s recommendations. Supernatants from the transfected cells that contained the recombinant baculovirus were serially diluted, added to Sf9 cells, overlaid with 1% SeaPlaque (FMC BioProducts, Rockland, ME) in serum-free Grace’s medium containing 250 mg/ml Bluo-Gal (Gibco BRL), and incubated at 27°C for 1 week. Viral DNA was extracted from the supernatants of plugs of agarose containing single blue plaques using the DNAzol system (Gibco BRL) according to the manufacturer’s recommendations, and tested for the presence of the HE gene by PCR as described above. The viral supernatants from clones that contained the HE gene were then amplified in Sf9 cells to obtain high titre stocks. To screen for expression of the HTV and BRV-1 HE proteins the cell pellets were resuspended in 6× SDS sample buffer and subjected to SDS-PAGE on a 12% resolving and 4% stacking gel and transferred electrophoretically to a polyvinylidene fluoride (PVDF) nylon membrane (Millipore, Bedford, MA) for 90 min at 100 V for immunoblotting. The membranes were blocked overnight in 5% skim milk in Tris-buffered saline containing 0.5% Tween-20 (TBST). The membranes were then incubated for 1 h at room temperature in a 1:1000 dilution of mouse-His*Tag Antibody (Babco, Richmond, CA) in TBST, washed and incubated for 2 h at room temperature in a 1:2000 dilution of horseradish peroxidase conjugated-goat anti-mouse IgG in TBST. After washing the membranes were developed in a 50 mM TBS solution containing 10% 4-chloro-1-naphthol in methanol and 0.025% hydrogen peroxide. Color development at room temperature was complete within 5–10 min. 2.7 Preparation of antisera to the HE proteins of BRV-1 and HTV in guinea pigs The resuspended cell slurries containing the expressed HE proteins of BRV-1 or HTV were subjected to preparative SDS-PAGE. Bands appearing after Coomassie brilliant blue R250 staining which corresponded to the 65 kDa HE proteins of each virus were excised and soaked in distilled water for 2 h and used to immunize adult male guinea pigs as described previously (Duckmanton et al., 1998b). Pre- and hyper-immunization sera to the BRV-1 and HTV HE proteins were respectively designated gpPIαBRV-HE, gpHIαBRV-HE, and gpPIαHTV-HE, gpHIαHTV-HE. The sera were heat inactivated at 56°C for 30 min, aliquoted and stored at −20°C. 2.8 SDS-PAGE and immunoblotting Bovine and human stool specimens partially purified by differential centrifugation and positive control Sf9 cells containing either BRV-1 or HTV HE proteins were subjected to immunoblot analysis using the above guinea pig sera. Following SDS-PAGE the proteins were transferred to PVDF membranes as described above. Membranes were incubated for 2 h at room temperature in 1:2000 dilutions of either gpPIαBRV-HE, gpHIαBRV-HE, gpPIαHTV-HE, or gpHIαHTV-HE sera in 1% skim milk in TBST. The membranes were washed and incubated for 1 h at room temperature in a 1:3000 dilution of alkaline phosphatase conjugated-rabbit anti-guinea pig IgG (RαGP; Sigma Chemicals, St. Louis, MO) in 1% skim milk in TBST. Sf9 cells containing the HE proteins were also tested for reactivity with 9 human acute/convalescent paired sera from patients whose stools were diagnosed positive for HTV by EM as described previously (Duckmanton et al., 1997), and bαBRV-1 pre- and post-immune sera from a gnotobiotic calf infected with purified BRV-1. Primary sera were used at dilutions of 1:2000 followed by 1:3000 dilutions of either alkaline phosphatase conjugated-murine anti-human IgG, or alkaline phosphatase conjugated-goat anti-bovine IgG, respectively (Sigma Chemicals). Following further washing, the membranes were developed using a 5-bromo-4-chloro-3-indolyl phosphate-nitro blue tetrazolium (BCIP-NBT; SigmaFAST, Sigma Chemicals) dissolved in 10 ml of water. 2.9 Hemagglutination inhibition A panel of sera were tested by HI against a partially purified TVLP preparation as described previously (Duckmanton et al., 1997). The panel included guinea pig anti-BRV-1 N protein preimmune (gpPIαN) and hyperimmune (gpHIαN) sera, human acute (huA) and convalescent (huC) paired sera, gpPIαBRV-HE and gpHIαBRV-HE sera, and gpPIαHTV-HE and gpHIαHTV-HE sera. 2.10 Immunoelectron microscopy Immunoelectron microscopy was performed as described previously (Duckmanton et al., 1997, Duckmanton et al., 1998a) using either the gpPIαBRV-HE and gpHIαBRV-HE antisera, or the gpPIαHTV-HE and gpHIαHTV-HE antisera. Control specimens included a purified human rotavirus-positive sample, a purified bovine rotavirus-positive sample, a human virus-negative sample, and a purified sample from a calf with diarrhea in which no viruses could be detected by EM. 2.11 Dot immunoblot Human and calf stool specimens positive by EM for HTV, BTV, and rotavirus, as well as negative control stools were examined for immunoreactivity with the gpPIαBRV-HE, gpHIαBRV-HE, gpPIαHTV-HE, and gpHIαHTV-HE sera by dot immuno-blot analysis as described previously (Duckmanton et al., 1998b). 3 Results 3.1 Long RT-PCR and sequencing of the HTV HE gene Using primers designed from the genome sequence bracketting the BRV-1 HE gene, an amplicon of 1371 bases was amplified from HTV RNA. The amplicon was excised, purified, and used directly for sequencing. Sequence analysis revealed that the HE gene of HTV contains an ORF of 1251 nts in length, whose nucleotide sequence is 85% identical to that of the BRV-1 HE gene as shown in Fig. 1 , and 89% identical to the corresponding 457 base sequence of the X pseudogene of BEV. This ORF, like its homologue of BRV-1, codes for a polypeptide of 416 amino acids which contains domains typical of type I membrane glycoproteins: a 14 residue N-terminal signal sequence, a 24 residue C-terminal transmembrane anchor, and eight potential N-glycosylation sites. Also present in the HTV HE gene is the F-G-D-S- motif which is the putative catalytic site of ICV and coronavirus acetylesterases. The sequence of the cDNA of the HTV HE gene has been submitted to GenBank under accession number AF 159 585.Fig. 1 Alignment of the nucleotide sequence of the cDNA of the HE gene of HTV with that of BRV (brv). Identical nucleotides are shown as dots. The predicted amino acid sequence of the HTV-1 HE protein is also shown. 3.2 Expression of the BRV-1 and HTV HE genes Sf9 cells infected with the recombinant baculovirus containing the HE genes of BRV-1 and HTV as confirmed by PCR were subjected to immunoblotting with pre- and post-immune sera from calves infected with BRV-1. The expressed HE proteins (M r 65 kDa) were cell associated and were not secreted into the medium. The HE proteins from infected cells were purified by SDS-PAGE and used to immunize the guinea pigs. 3.3 SDS-PAGE and immunoblotting As previously described (Duckmanton et al., 1998b), SDS-PAGE analysis of partially purified BTV and HTV preparations revealed a number of bands including bands with an with an M r of 65 kDa for BTV (Fig. 2 A) and HTV (Fig. 2C). This 65 kDa band corresponded in size to the HE protein equivalent of BRV-2 that was shown to have an approximate M r of 65 kDa in its glycosylated form (Cornelissen et al., 1997).Fig. 2 Immunoblots of fecal specimens of which a set of three are positive for bovine torovirus (BTV+) and human torovirus (HTV+), and single specimens negative for bovine torovirus (BTV−), and human torovirus (HTV−) with (A) guinea pig anti-BRV-1 HE protein hyperimmune serum, (B) guinea pig anti-BRV-1 HE protein preimmune serum, (C) guinea pig anti-HTV HE protein hyperimmune serum, and (D) guinea pig anti-HTV HE protein pre-immune serum. Shown in the left lanes of panels (a) and (c) are Coomassie blue stained SDS-polyacrylamide gels of a BTV+ fecal specimen and a HTV+ fecal specimen, respectively. The guinea pig pre- and hyperimmune sera were tested for their reactivity by immunoblot assay with virus proteins from three BTV-positive stool samples and one BTV-negative control, three HTV-positive stool specimens and one HTV-negative control. The gpHIαBRV-HE and gpHIαHTV-HE sera reacted with the HE proteins in all of the BTV-positive (Fig. 2A) and HTV-positive fecal specimens (Fig. 2C) but not with the virus-negative controls. No reactivity was observed with either of the guinea pig pre-immune sera (Fig. 2B and D). The gpHIαBRV and gpHIαHTV sera, as well as the bαBRV-1 post-immune serum, and all nine human convalescent sera from patients diagnosed positive for HTV were tested for reactivity with the expressed 65 kDa HE proteins of BRV and HTV. As shown in Fig. 3 , the hyperimmune guinea pig sera to BRV and HRV were reactive with both the expressed HE proteins of BRV and HTV. The calf hyperimmune serum and the human convalescent sera of which only one of the nine is shown, were also reactive with both proteins. However, the convalescent sera exhibited stronger bands with the homologous proteins. No reactivity was documented with the calf pre-immune sera or the human acute sera.Fig. 3 Representative SDS-PAGE gel of a Sf9 cells expressing BRV-1 HE protein and HTV HE protein and corresponding immunoblots of gpPIαBRV-HE and gpHIαBRV-HE, gpPIαHTV-HE and gpHIαHTV-HE, human acute (huA) and convalescent (huC) paired sera, and bovine anti-BRV-1 pre-immune (bPI) and post-immune (bHI) sera. Shown in the left lanes of panels (A) and (B) are Coomassie blue stained SDS-polyacrylamide gels of Sf9 cells expressing BRV-1 HE protein and HTV HE protein, respectively. 3.4 Hemagglutination inhibition To investigate if the HE protein possesses HA activity, the guinea pig antisera to the HE proteins of BRV-1 and HTV were tested for their ability to inhibit hemagglutination using an HI assay as described previously (Duckmanton et al., 1997). Hemagglutination inhibition to a titre of 1:16 was demonstrated with the gpHIαBRV-1-HE and gpHIαHTV-HE sera, and to a titre of 1:32 with the human convalescent serum. In contrast the human acute serum exhibited a titre of 1:2 and the gpHIαN serum and all of the guinea pig preimmune sera showed no hemagglutination inhibition activity. 3.5 Immunoelectron microscopy The guinea pig antisera to the BRV-1 and HTV HE proteins were tested by IEM for reactivity with three BTV-positive preparations, and three HTV-positive samples. Viral aggregates were observed in all preparations containing the gpHIαBRV-HE serum mixed with the BTV-positive and the HTV-positive specimens (Fig. 4 ). No aggregates were demonstrated with the gpPIαBRV-HE serum. Likewise, viral aggregates were observed in preparations of gpHIαHTV-HE serum mixed with the BTV-positive and HTV-positive specimens, but aggregates were smaller and less frequent than with the gpHIαBRV serum. No aggregates were observed when the gpPIαHTV-HE serum was mixed with BTV- or HTV-positive specimens. No aggregates of viruses were seen in control specimens reacted with any of the guinea pig sera.Fig. 4 Representative immunoelectron micrograph of a purified human torovirus-positive preparation with (A) guinea pig preimmune serum and (B) guinea pig anti-HTV HE protein hyperimmune serum. Bars=100 nm. 3.6 Dot immunoblot The above pre- and post-immune sera to the HE proteins of BRV-1 and HTV were examined for their ability to detect torovirus in stool specimens by a dot immunoblot system. The fecal specimens had been previously characterized as torovirus-positive or negative by EM and RT-PCR (Duckmanton et al., 1997, Duckmanton et al., 1998a), and consisted of ten HTV-positive, ten BTV-positive, five human rotavirus-positive, five bovine rotavirus-positive, five human virus-negative, and five bovine virus-negative fecal specimens. All of the ten specimens that were previously shown to be positive for BTV were specifically reactive with the gpHIαBRV-HE and not the gpPIαBRV-HE sera, and eight of these were reactive with the gpHIαHTV-HE and not the gpPIαHTV-HE sera. All of the ten HTV-positive samples were reactive with the gpHIαBRV-HE serum and nine reacted with the gpHIαHTV-HE serum. The HTV-positive specimens did not react with either of the guinea pig preimmune sera. In addition, none of the virus-negative specimens or rotavirus-positive specimens were reactive with any of the guinea pig sera. Fig. 5 illustrates representative dot blots of five BTV-positive, five HTV-positive, five bovine rotavirus-positive, five human rotavirus-positive, five virus-negative bovine and five virus-negative human specimens using the guinea pig preimmune and hyperimmune antisera to either the BRV-1 or HTV HE proteins.Fig. 5 Representative dot blot of five human torovirus-positive (HTV+), five bovine torovirus-positive (BTV+), five human rotavirus-positive (HRoV+), five bovine rotavirus-positive (BRoV+), five human virus-negative (HV−), and five bovine virus-negative (BV−) fecal specimens using gpHIαBRV-HE and gpPIαBRV-HE, and gpHIαHTV-HE and gpPIαHTV-HE. Reactive dots were a dark blue colour. Dots corresponding to control specimens and the pre-immune sera had the residual brown colour of the stool specimen. 4 Discussion The existence of recombination in RNA virus evolution is exemplified by the presence of hemagglutinin-esterase genes in some coronavirus and torovirus species. The coronavirus and BRV HE proteins are 65 kDa class I membrane proteins that share 30–35% amino acid identity with the HE-1 subunit of the HE fusion protein (HEF) of influenza C virus (ICV). However, the fact that the HE genes of coronaviruses and toroviruses are located at different positions in their genomes suggests that these viruses acquired their HE genes through separate heterologous RNA recombination events (Snijder et al., 1991). Although the origin of the torovirus HE gene is unknown, it has been speculated that coronaviruses captured their HE module from ICV or a related virus during a mixed infection (Luytjes et al., 1988). In ICV and coronaviruses, the HE protein displays acetylesterase activity specific for N-acetyl-9-O-acetylneuraminic acid, and the ICV HEF has been shown to serve as both a receptor-binding and receptor-destroying protein during viral entry (Vlasak et al., 1987, Herrler et al., 1988). Although, receptor binding and membrane fusion in coronaviruses has been shown to be mediated by the S protein (Cavanagh, 1995), it has been suggested that the coronavirus HE may serve as an additional membrane-binding protein (Vlasak et al., 1988, Parker et al., 1989). However, it has been shown from studies on mouse hepatitis virus that infection cannot be mediated by HE alone as it requires the interaction of S with its receptor (Gagneten et al., 1995). Instead, it has been postulated that the HE protein may play a role in the early stages of infection where it mediates viral adherence to the intestinal wall of the host by specifically yet reversibly binding the mucopolysaccharides in the mucus layer that protects the epithelial cells of the enteric tract. The process of binding 9-O-acetylated receptors, followed by cleavage and rebinding intact receptors, may result in virus migration through the mucus layer, thereby facilitating infection (Cornelissen et al., 1997). As described by Duckmanton et al. (1998b), the nucleotide sequence of the BRV-1 HE gene was shown to be 77% identical at its 3′ end to the X pseudogene of BEV, and 100% identical to the HE gene of BRV-2 (Cornelissen et al., 1997). In this study we have identified a HE gene homologue in the human torovirus. Sequence analysis revealed that the HTV HE gene is 85% identical to the BRV-1 and BRV-2 HE genes at the nucleotide level and 76% at the amino acid level.. The corresponding 3′ end of the human HE gene is 89% identical to the BEV X gene. This is considered a very important finding since we have previously shown that the untranslated 3′ end of the human torovirus genome manifests 99% nucleotide sequence identity with BEV in a 219 base region that was sequenced (Duckmanton et al., 1997). To ensure that this sequence that was reported was derived from the human torovirus, RNA from five virus positive specimens over a 5-year period was amplified by RT-PCR and the amplicons were shown to have the same high level of homology but differed from each other in one to two bases. Likewise, five representative sequences of the same 3′ end region of bovine toroviruses were 97% identical to the BRV sequence and 96% identical to the BEV sequence (Duckmanton et al., 1998a). It is therefore evident that the homology of the torovirus genomes at the 3′ end is very high. In contrast, the existence of a complete HE gene in HTV demonstrates it is a distinct virus from BEV which has only the pseudogene X in place of the HE gene. With the sequence information currently available it would be speculative to further comment on the relatedness of HTV to the previously characterized BEV isolate. It is therefore essential that the S gene of HTV be sequenced and expressed since it is the most likely candidate to code for the epitopes involved in virus neutralization. The encoded 65 kDa HE protein of HTV contains domains that are characteristic of type I membrane glycoproteins, and a putative acetylesterase catalytic site. This indicates that this protein has the same function on both the human and bovine viruses, though it is lacking in the BEV prototype. By immunoblot, the gpHIαBRV-HE and gpHIαHTV-HE sera reacted specifically with the 65 kDa bands in the BTV-positive fecal specimens from calves with diarrhea and the HTV-positive fecal specimens from patients with gastroenteritis, as well as with the expressed HE proteins. These expressed proteins also reacted by immunoblot with the bαBRV-1 antiserum and all nine human acute/convalescent paired sera from patients whose stools were diagnosed positive for HTV by EM, and who had been shown to have experienced seroconversion by HI and IEM. This type of cross-reactivity among torovirus species has previously been reported between BRV antigens and BEV antibodies by immunofluorescence microscopy assays (IFA) and ELISA (Weiss and Horzinek, 1987), and human stools documented to contain HTV particles by EM have been shown to be reactive in a torovirus-specific ELISA using BRV antiserum (Koopmans et al., 1993). Thus, as was shown for the BRV-2 HE protein (Cornelissen et al., 1997), the HE proteins of BRV-1 and HTV are expressed during natural toroviral infections and the immune response to these is a marker of viral infection. In marked contrast, the expressed nucleocapsid protein of BRV-1 did not react with convalescent human sera from torovirus patients (Duckmanton et al., 1998b). This could be interpreted as evidence that the nucleocapsid proteins of BRV-1 and HTV are antigenically less closely related and that the overall immune response to the nucleocapsid protein is low and hence only antibodies to the homologous virus can be detected. By hemagglutination inhibition, the guinea pig hyperimmune sera to the BRV-1 and HTV HE proteins were found to inhibit the hemagglutination of rabbit erythrocytes by a purified human torovirus preparation. This indicates that, in addition to the predicted acetylesterase activity as demonstrated for the HE of BRV-2, the HE proteins of BRV-1 and HTV possess hemagglutinating properties. This does not preclude the possibility that the toroviral peplomer protein may also function as a hemagglutinin because the BEV, which is considered not to possess a functional HE gene, is still capable of hemagglutinating human group O, rabbit, and guinea pig erythrocytes (Horzinek et al., 1987). By dot immunoblot analysis the gpHIαBRV-HE and gpHIαHTV-HE sera were shown to react specifically with BTV-positive and HTV-positive fecal specimens that had been immobilized on a nylon membrane. Using the dot blot assay in a previous study, we demonstrated that guinea pig antisera to the BRV-1 nucleocapsid protein could detect both BTV- and HTV-positive fecal specimens (Duckmanton et al., 1998b). This shows that there is good potential to apply these reagents to the immunospecific diagnosis of these viruses from human and bovine fecal specimens. IEM studies demonstrated that both BTV and HTV particles could be aggregated by the gpHIαBRV-HE and gpHIαHTV-HE sera, whereas, virus-negative controls, and the gpPIαBRV-HE and gpPIαHTV-HE sera showed no reactivity. This is consistent with IEM studies performed using bovine antiserum to the expressed BRV-2 HE gene, whereby the HE was identified as a structural protein of toroviruses, reacting consistently with convalescent-phase serum (Cornelissen et al., 1997). Close examination of electron micrographs of BRV and HTV reveal, in addition to the 7–9 nm peplomer proteins, the presence of shorter 4–6 nm surface projections, resembling those formed by the HE proteins of coronaviruses (Sugiyama and Amano, 1981). This second ring of small peplomers has previously been observed by Woode et al. (1982) in BRV and by Beards et al. (1984) in human torovirus-like particles. However, the nature of these projections remained unknown. It has been postulated that these smaller surface projections, which are absent from BEV virions, represent the HE proteins of BRV (Cornelissen et al., 1997), and we propose that these are also present on HTV as they are visible by EM. A comparison of the IEM aggregates resulting from reacting toroviruses with the gpHIαBRV-HE serum with those resulting from using guinea pig hyperimmune serum to the BRV-1 nucleocapsid protein (Duckmanton et al., 1998b) showed marked differences. Most of the aggregates from the antiserum to the HE protein consisted of intact viruses whereas the antiserum to the nucleocapsid protein formed aggregates of broken particles. This is consistent with the HE being a surface protein and the nucleocapsid being an internal protein. In summary, the HE genes of BRV-1 and HTV that have been amplified using long RT-PCR and sequenced were shown by sequence analysis to be related in part to the torovirus prototype, BEV (Snijder et al., 1991), and to the HE gene of BRV-2 (Cornelissen et al., 1997). Expression of these genes in a baculovirus system resulted in proteins that served as antigens in the development of specific antisera to the HE proteins of BRV-1 and HTV. These sera were used in a number of serological assays to demonstrate the immunoreactivity of the HE proteins to specific antisera, and to show cross-reactivity among torovirus species. These findings have the potential of being exploited for the design of diagnostic assays for human and bovine toroviruses and have provided us with important tests with which to further study torovirus infections both in experimental systems and in populations. Acknowledgements The authors would like to thank Dr Gerald Woode at Texas A&M, and Dr M. Hardy at Montana State University for providing the BRV-1-infected bovine fecal specimen, and the bαBRV-1 and bαBRV-2 antisera used for immunoblot analysis. We would also like to thank Drs Éva Nagy and Susy Carman from the Animal Health Laboratory, Guelph, Ont. for providing the bovine torovirus-positive fecal specimens from diarrheic calves, control specimens from asymptomatic calves, and rotavirus-positive specimens from diarrheic calves. This research was supported by a grant from the Medical Research Council of Canada to M.P. and a grant from the Research Institute of the Hospital for Sick Children to R.T. Funding for L.D. was provided in part by the University of Toronto. ==== Refs References Beards G.M Green J Hall C Flewett T.H Lamouliatte F Du Pasquier P An enveloped virus in stools of children and adults with gastroenteritis resembles the Breda virus of calves Lancet 2 1984 1050 1052 Brown D.W Beards G.M Flewett T.H Detection of Breda antigen and antibody in humans and animals by enzyme-immunoassay J. Clin. Microbiol. 25 1987 637 640 3571473 Cavanagh D The coronavirus surface glycoprotein Siddel S.G The Coronaviridae 1995 Plenum Press New York 73 113 Cavanagh D Nidovirales: a new order comprising Coronaviridae and Arteriviridae Arch.Virol. 142 1997 629 633 9349308 Cornelissen L.A.H.M Wierda C.M.H van der Meer F.J Herrewegh A.A.P.M Horzinek M.C Egberink H.F de Groot R.J Hemagglutinin esterase, a novel structural protein of torovirus J. Virol. 71 1997 5277 5286 9188596 Duckmanton L Luan B Devenish J Tellier R Petric M Characterization of torovirus from human fecal specimens Virology 239 1997 158 168 9426455 Duckmanton L Carmen S Nagy E Petric M Detection of bovine torovirus in fecal specimens of calves with diarrhea from Ontario farms J. Clin. Microbiol. 36 1998 1266 1270 9574689 Duckmanton L Tellier R Liu P Petric M Bovine torovirus: sequencing of the structural genes and expression of the nucleocapsid protein of Breda virus Virus Res. 58 1998 83 96 9879765 Gagneten S Gout O Dubois-Dalcq M Rottier P Rossen J Holmes K.V Interaction of mouse hepatitis virus (MHV) spike glycoprotein with receptor glycoprotein MHVR is required for infection with an MHV strain that expresses the hemagglutinin-esterase glycoprotein J. Virol. 69 1995 889 895 7815557 Herrler G Durkop I Becht H Klenk H.-D The glycoprotein of influenza C virus is the hemagglutinin, esterase and fusion factor J. Gen. Virol. 69 1988 839 846 3356980 Horzinek M.C Flewett T.H Saif L.F Spaan W.J Weiss M Woode G.N A new family of vertebrate viruses: Torviridae Intervirology 27 1987 17 24 3610570 Jamieson F.B Wang E.E.L Bain C Good J Duckmanton L Petric M Human torovirus: a new nosocomial gastrointestinal pathogen J. Infect. Dis. 178 1998 1263 1269 9780245 Koopmans M Van den Boom U Woode G.N Horzinek M.C Seroepidemiology of Breda virus in cattle using ELISA Vet. Res. 19 1989 233 243 Koopmans M Petric M Glass R Monroe S.S Enzyme-linked immunosorbent assay reactivity of torovirus-like particles in fecal specimens form humans with diarrhea J. Clin. Microbiol. 31 1993 2738 2744 8253975 Koopmans M Goosen E.S.M Lima A.A.M McAuliffe I.T Nataro J.P Barrett L.J Glass R.I Guerrant R.L Association of torovirus with acute persistent diarrhea in children Pediatr. Infect. Dis. J. 16 1997 504 507 9154546 Kroneman A Cornelissen L.A.H.M Horzinek M.C DeGroot R.J Egberink H.F Identification and characterization of a porcine torovirus J. Virol. 72 1998 3507 3511 9557628 Luytjes W Bredenbeek P Noten A Horzinek M.C Spaan W Sequence of mouse hepatitis virus A59 mRNA2: indications for RNA recombination between coronaviruses and influenza C virus Virology 166 1988 415 422 2845655 Parker M.D Cox G.J Deregt D Fitzpatrick D.R Babuik L.A Cloning and in vitro expression of the gene for the E3 hemagglutinin glycoprotein of bovine coronavirus J. Gen. Virol. 70 1989 155 164 2732684 Sambrook J Fritsch E.F Maniatis T Molecular Cloning: a Laboratory Manual second edition 1989 Cold Spring Harbor Laboratory Press New York Snijder E.J Den Boon J.A Horzinek M.C Spaan W.J.M Comparison of the genome organization of toro- and coronaviruses: evidence for two non-homologous RNA recombination events during Berne virus evolution Virology 180 1991 448 452 1984666 Sugiyama K Amano Y Morphological and biological properties of a new coronavirus associated with diarrhea in infant mice Arch. Virol. 67 1981 241 251 7224861 Tellier R Bukh J Emerson S.U Purcell R.H Amplification of the full-length hepatitis A virus genome by long reverse transcription-PCR and transcription of infectious RNA directly from the amplicon Proc. Natl. Acad. Sci. USA 93 1996 4370 4373 8633073 Tellier R Bukh J Emerson S.U Miller R.H Purcell R.H Long PCR and its application to hepatitis viruses: amplification of hepatitis A, hepatitis B, and hepatitis C virus genomes J. Clin. Microbiol. 34 1996 3085 3091 8940452 Vlasak R Krystal M Nacht M Palese P The influenza C virus glycoprotein (HE) exhibits receptor-binding (hemagglutinin) and receptor-destroying (esterase) activities Virology 160 1987 419 425 3660588 Vlasak R Luytjes W Spaan W Palese P Human and bovine coronaviruses recognize sialic acid containing receptors similar to those of influenza C virus Proc. Natl. Acad. Sci. USA 85 1988 4526 4529 3380803 Weiss M Horzinek M.C The proposed family toroviridae: agents of enteric infections Arch. Virol. 92 1987 1 15 3541856 Woode G.N Reed D.E Runnels P.L Herrig M.A Hill T.H Studies with an unclassified virus isolated from diarrheic calves Vet. Microbiol. 7 1982 221 240 7051518 Woode G.N Saif L.J Quesada M Winnand N.J Pohlenz J.F Kelso Gourley N Comparative studies on three isolates of Breda virus of calves Am. J. Vet. Res. 46 1985 1003 1010 2408519	10518710	PMC7125763	NO-CC CODE	2021-01-19 03:11:16	yes	Virus Res. 1999 Nov 11; 64(2):137-149
==== Front ACS Omega ACS Omega ao acsodf ACS Omega 2470-1343 American Chemical Society 10.1021/acsomega.9b03851 Article Porous Organic Polymer-Derived Fe2P@N,P-Codoped Porous Carbon as Efficient Electrocatalysts for pH Universal ORR Zhang Meng †‡∥ Ming Jingjing †‡∥ Zhang Wenhua ‡§ Xie Jingru †‡ Lin Ping †‡ Song Xiaofei †‡ Chen Xiangying ‡§ Wang Xuedong †‡ Zhou Baolong †‡§ † College of Pharmacy, Weifang Medical University, Weifang 261053, Shandong, P. R. China ‡ Department of Clinical Pharmacy, Weifang People’s Hospital, Weifang 261000, Shandong, P. R. China § Affiliated Hospital of Weifang Medical University, Weifang 261031, Shandong, P. R. China * E-mail: [email protected] (X.C.).* E-mail: [email protected] (B.Z.). 24 03 2020 07 04 2020 5 13 7225 7234 12 11 2019 24 01 2020 Copyright © 2020 American Chemical Society2020American Chemical SocietyThis is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes. A new porous organic polymer (CP-CMP) was designed and synthesized via the direct polymerization of pyrrole and hexakis(4-formyl-phenoxy)cyclotriphosphazene, skipping the tedious synthetic procedure of porphyrin-monomers containing special groups. This special porous organic polymer (POP) serves as an “all in one” precursor for C, N, P, and Fe. Direct carbonization of this special POP afforded Fe2P@N,P-codoped porous carbons with hierarchical pore structure and high graphitization. Finally, the optimal catalyst (CP-CMP-900) prepared by carbonization of CP-CMP at 900 °C exhibited high efficiency for oxygen electroreduction. Typically, CP-CMP-900 presented an oxygen reduction reaction half-wave potential (E1/2) of 0.85, 0.73, and 0.65 V, respectively, in alkaline, neutral, and acidic media, close to those of commercial Pt/C in the same electrolyte (0.843, 0.71, and 0.74 V). Furthermore, it also displayed excellent methanol immunity and long-time stability in various electrolytes better than commercial Pt/C (20%). document-id-old-9ao9b03851document-id-new-14ao9b03851ccc-price ==== Body Introduction The depletion of traditional energy has compelled us to explore novel energy devices meeting the growing needs of social development.1 Fuel cells, as eco-friendly energy storage and conversion devices with a high energy density and potential, have geared great research attention over the past few years.2 However, the performance of these devices is mainly determined by the sluggish oxygen reduction reaction (ORR) that occurred at the cathode. Due to their scarcity and low sustainability, state-of-the-art Pt-based catalysts are undesirable.3 For the development of the green energy technology, the availability of low-cost, efficient, and durable catalysts for ORR is a prerequisite.4−6 Owing to the features of low price, high earth abundance, and environmental friendliness, carbon-based materials have emerged as realistic alternatives, to which considerable efforts have been devoted.7−9 The activity of these materials can be finely regulated by doping a transition metal (Fe, Mo, Co, Cu, Ni, Mn, etc.) or heteroatom (N, P, S, B, F, etc.) into the carbon skeleton.10−13 For example, the association of heteroatoms could modify the electronic structure (charge and/or spin density redistribution) of carbon, creating active sites favorable for the adsorption capacity of oxygen and facilitating the ORR process.14 And the introduction of transition-metal impurities is conducive to the formation of active sites, improving the catalytic performance significantly.15,16 Among the reported materials, due to their low cost and special stability, transition-metal phosphide-based catalysts have been widely studied.17,18 Hitherto, numerous metal phosphide-based catalysts have been well designed and fabricated, including the linear, spherical, and flaky. And great progress has been achieved.19 But for the synthesis of most catalysts, an extraneous phosphorus source that is hazardous, inflammable, and explosive is inevitable. And in most cases, to complete the phosphating reaction, the extraneous phosphorus source, such as organic triphenylphosphine, phosphonitrile, inorganic red phosphorus, phytic acid, and sodium hypophosphite, is in large excess, which is not conducive to study the formation mechanism of these materials. Furthermore, there are diversiform hypophosphites formed simultaneously during the phosphating process, for example, the formation of FeP and Fe2P.3,20 Hence, it remains a huge challenge to obtain metal phosphides in a simple, safe, green, and controllable method.21 As a new series of multifunctional materials composed of pure organic units, porous organic polymers (POPs) have received great research interests in recent years.22−25 The inherent nature, including the diversity of synthetic methods and the designability of structure, makes it available in numerous fields. Various monomers with special function and composition could be introduced into the porous skeleton facilely.26,27 Furthermore, the organic structure of POPs enables them to have a definite element composition at the atom level (atomic ratio and atom spatial distance), which is beneficial to further research. Hitherto, thousands of POPs with different structures and functions have been prepared.28−30 Recent reports have validated that direct pyrolysis of POPs is a simple method to prepare carbon-based catalysts for electrocatalysis.31 However, there are almost no reports about the direct preparation of pure Fe2P nanoparticles-containing catalysts from POPs. Owing to the special structure of metal porphyrin, catalysts derived from porphyrin-based POPs usually exhibited prominent activity toward ORR.32 However, most of these polymers were synthesized via the polymerization of porphyrin-monomer containing special groups, in most cases, with a noble-metal catalyst under harsh conditions.33 Apart from the high production cost, the synthetic procedure is tedious and the overall yield is usually low.34 Hence, it would be significant to prepare porphyrin-based POPs in a simple way, skipping the complicated synthesis procedure of porphyrin-monomer containing a special group. Since the Bhaumik group reported the first example of porphyrin-based POPs that skipped the complicated synthesis procedure, a lot of work has been done. Herein, we propose a facile and modified one-step polymerization method.35−38 A cyclotriphosphazene (CTP) and Fe-porphyrin (Fe-Por) constituent porous polymer with high specific areas (CP-CMP) and accurate ratio of elements was prepared in the solvent mixture of propionic acid and nitrobenzene with a high yield.39−42 This special POP could serve as an “all in one” precursor of C, N, P, and Fe simultaneously. Direct carbonization of this special POP afforded N,P-codoped porous carbons embedded with well-defined Fe2P nanoparticles. Subsequent tests validated that the typical catalyst denoted as CP-CMP-900 exhibited excellent catalytic performance toward ORR with a 4e transfer pathway under various pH values. Results and Discussion As presented in Figure 1, CP-CMP was prepared via the copolymerization of hexa-(4-aldehyde-phenoxy)-cyclotriphosphazene and pyrrole in the presence of iron salts. And TB-CMP was prepared by the polymerization of 1,3,5-triformylbenzene and pyrrole under identical reaction conditions. CP-CMP-X was obtained through the direct carbonization of CP-CMP at various temperatures (800, 900, and 1000 °C). For comparison, TB-CMP-900 was fabricated by direct pyrolysis of TB-CMP at 900 °C. Figure 1 Typical synthesis procedure for CP-CMP and schematic route for the preparation of CP-CMP-X catalysts. The integration of CTP and porphyrin moieties was confirmed by Fourier transform infrared spectroscopy (FTIR, Figure S3) and solid-state 13C cross-polarization magic angle spinning (CP/MAS) NMR (Figure S4). As shown in Figure S3, characteristic vibration bands of CTP (P=N–P at 1218 and 1410 cm–1) and metal porphyrin (1602 cm–1 for C=C stretch of pyrrole together with 1000 and 1160 cm–1 for chelated M–N4 vibrations) could be clearly observed for all of these prepared polymers, indicative of the formation of porous skeletons.43 Furthermore, the solid-state 13C NMR spectrum of CP-CMP and TB-CMP exhibits typical carbon signals (ca. 120, 132, and 155 ppm) assigned to the porphyrin macrocycles structure, further implying the successful construction of porous networks.44 TGA was performed to investigate the thermal stability of prepared polymers. As shown in Figure S5, CP-CMP presents excellent thermal stability with almost no weight loss until 210 °C, and the weight could maintain at 66% of the initial value at 800 °C, indicating a high thermal stability of prepared materials. Figure S6 presents the powder XRD pattern of prepared samples. Like other reported CMPs, no clear peaks could be found for all of these prepared polymers, implying the amorphous nature of the polymer precursors.45Figure 2a exhibits the PXRD pattern of CP-CMP-X. Different from the precursors, a prominent peak assignable to the diffraction of graphitic carbon appeared at 26.5°. Other peaks located at 35.3, 40.3, 44.3, 47.3, 53.0, 54.3, and 73.7° are ascribed to the characteristic peaks of Fe2P nanocrystalline (PDF #85-1725),25 evidencing the formation of pure Fe2P nanoparticles, which is beneficial for the ORR in the carbonized samples (Figure 2a). The Raman spectrum was examined (Figure 2b) to investigate the graphitic degree of CP-CMP-X, from which a prominent D band (1335 cm–1) and G band (1580 cm–1) could be clearly observed. The ratio of intensities (ID/IG) was calculated according to the integral area. All of the catalysts derived from CP-CMP showed a high degree of graphitization, and CP-CMP-900 exhibits the highest graphitic degree (ID/IG = 0.84) among the prepared samples (ID/IG = 0.85 for CP-CMP-800 and ID/IG = 0.89 for CP-CMP-1000). Figure 2 (a) Powder XRD pattern of CP-CMP-X catalysts (X is the carbonization temperature). (b) Raman spectrum of CP-CMP-X. The morphology and inner structure of prepared samples are visualized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). As exhibited in Figures 3a and S7, bulks stacked by intergrown spheroidal particles could be clearly observed for all of the prepared polymers. Figures 3b,c and S8 present the TEM image of prepared materials, from which a large number of pores could be observed. Figure 3d shows the SEM images of CP-CMP-900. Like the polymer precursor, spheroidal particles stacked loosely could be obviously observed, further implying the excellent thermal stability of the prepared polymers. Figure 3e,f displays the TEM and HR-TEM images of CP-CMP-900. As indicated in Figure 3f, spherical Fe2P nanoparticles with a diameter of 10 nm are distributed uniformly in the carbon skeletons. Clear lattice fringes belonging to the Fe2P specie could also be observed in Figures 3f and S9. As marked in Figures 3f and S9f, a regular interplanar spacing of about 0.22 nm ascribing to the (210) plane of the Fe2P nanocrystalline could be detected.20 To further confirm the composition of prepared catalysts, TEM–energy-dispersive spectroscopy (EDS) elemental mapping was examined. As presented in Figure 3g, uniformly distributed C, N, P, O, and Fe over the porous skeletons could be clearly detected. Furthermore, the EDS of HR-TEM (Figure S10) also evidenced the coexistence of Fe, N, C, and P in the porous skeletons. The inset in Figure 3g exhibited the corresponding SAED pattern of CP-CMP-900. Figure 3 SEM and HR-TEM images of prepared materials: (a) SEM image of CP-CMP at a scale bar of 200 nm; (b, c) TEM and HR-TEM images of CP-CMP at a scale bar of 100 and 10 nm, respectively; (d) SEM image of CP-CMP-900 at a scale bar of 200 nm; (e, f) HR-TEM images of CP-CMP-900 at a scale bar of 20 and 5 nm, respectively; (g) TEM and corresponding EDS layered images of CP-CMP-900 at a scale bar of 50 nm. The pore features of prepared samples were investigated by low-temperature nitrogen absorption and desorption measurements at 77 K. As indicated in Figure 4, both the polymers and catalysts exhibit typical isotherm combining the characteristics of type I and type IV, featured by a sharp uptake in the low-pressure region (micropore) and a large hysteresis loop (mesopore) in the high-pressure range. Furthermore, continued growth could also be found in the high-pressure range, indicative of the existence of macropore.46,47 In general, a high specific surface area could increase the exposure of active sites and facilitate mass transport, which is beneficial to the ORR process. And the rich mesopore distribution is very significant for the migration of electrolyte ions, improving the catalytic performance. After the pyrolysis, the BET surface areas increased greatly. The calculated BET surface areas are 207 and 866 m2 g–1 for CP-CMP and CP-CMP-900, respectively. Furthermore, the BET surface area of TB-CMP-900 reaches 598.3 m2 g–1, much higher than that of the uncarbonized sample. Hierarchical pore ranging from micropore to mesopore could also be found from the pore width distribution curve (Figure 4b,d). For example, as exhibited in Figure 4d, a sharp peak at around 3.8 nm and other two secondary peaks (1.2 and 1.4 nm) were observed from the pore size distribution of CP-CMP-900. And this indicated that CP-CMP-900 holds a large amount of mesoporous structure with a relatively narrow pore size distribution. And the detailed information is summarized in Table S1. Figure 4 BET and pore size distribution of prepared polymers and typical catalysts. (a) Low-temperature N2 adsorption and desorption isotherm of CP-CMP and TB-CMP; (b) low-temperature N2 adsorption and desorption isotherm of CP-CMP-X and TB-CMP-900; (c) pore size distribution of CP-CMP and TB-CMP; and (d) pore size distribution of CP-CMP-X and TB-CMP-900. X-ray photoelectron spectroscopy further validates the coexistence of C, N, P, and Fe for CP-CMP-X samples (Figures 5a and S11–S13). The high-resolution N 1s spectrum of CP-CMP-900 (Figure 5b) could be divided into five peaks located at 398.4 eV (0.55 atom %), 399.3 eV (0.34 atom %), 399.8 eV (0.09 atom %), 400.9 eV (1.70 atom %), and 403.1 eV (0.32 atom %), ascribed to the pyridinic-, metallic-, pyrrolic-, graphitic-, and oxidized-N, respectively.48 And among these deconvoluted N species, the content of pyridinic-N and graphitic-N, which could improve the activity toward ORR significantly, occupied a larger proportion in surface nitrogen types.49 Furthermore, the total content of pyridinic and graphitic-N for CP-CMP-900 is higher than those of CP-CMP-800 and CP-CMP-1000. Figure 5c presents the 2p spectra of P. As exhibited in Figure 5c, the peaks of P 2p are distributed at 129.4, 132.1, 133.6, and 134.3 eV, assigned to the Fe–P, P–C, P–O, and P–O–Fe, respectively.3,20 The existence of the P–C bond further demonstrated the doping of P into the N-doped porous carbon. Furthermore, the Fe-Nx and Fe2P species could also be proved by XPS (Figure 5d). The high-resolution Fe 2p spectra of CP-CMP-900 could be deconvoluted into seven peaks. The peaks appearing at 707.2 and 711.0 eV are assigned to the Fe–P and Fe–N bonds, respectively. Other peaks attributed to Fe3+ 2p1/2, Fe2+ 2p1/2, Fe3+ 2p3/2, Fe2+ 2p3/2, and satellite peak are situated at 726.2, 723.3, 712.6, 709.8, and 717.6 eV, respectively.50 The detailed elemental analysis by XPS is shown in Table S2. Taken all of these together, a prominent electrocatalytic activity toward ORR is anticipated for the Fe2P- and FeNx-decorated N, P-doped carbon.51 Figure 5 XPS spectrum of CP-CMP-900. (a) Survey spectrum of CP-CMP-900; (b) high-resolution N 1s XPS spectra of CP-CMP-900; (c) high-resolution P 2p XPS spectra of CP-CMP-900; and (d) high-resolution Fe 2p XPS spectra of CP-CMP-900. The catalytic activities toward the ORR of pyrolyzed porous organic polymers (CP-CMP-X and TB-CMP-900) were assessed in various electrolytes including KOH (0.1 M), HClO4 (0.5 M), and PBS (0.1 M) via cyclic voltammetry (CV) and linear sweep voltammetry (LSV) on rotating disk electrode (RDE) or rotating ring-disk electrode (RRDE). The onset potential was defined as the potential at the current density of 0.1 mA cm–2. The methanol tolerance test was performed by the current–time (i–t) plots with the addition of methanol to the corresponding electrolyte at the time of 400 s. TB-CMP-900 was a reference catalyst free of P and Fe2P. As exhibited in Figures S14–S16, the electrical signals are virtually featureless in the Ar-saturated KOH solution within the entire potential range, while in the O2-saturated electrolyte, a well-defined cathodic peak corresponding to ORR was obviously observed in the case of all catalysts.52,53 We optimize the carbonization temperature via testing the polarization curve of CP-CMP-X. Figure 6a presents the LSV curves of the as-prepared catalysts together with commercial Pt/C measured on an RDE with a scan rate of 5 mV s–1 at 1600 rpm. Notably, CP-CMP-900 afforded the most positive onset potential of 0.997 V (vs RHE), close to the value of commercial Pt/C (0.996) and higher than that of other CP-CMP-X samples (0.983 V for CP-CMP-800 and 0.986 V for CP-CMP-1000). Furthermore, the Eonset of CP-CMP-900 is also positive compared to that of TB-CMP-900 (Eonset = 0.990 V), indicating that CP-CMP-900 was more electrocatalytically active than the control catalysts. Accordingly, CP-CMP-900 showed the highest ORR activity with a half-wave potential (E1/2) of 0.85 V, 11 mV and 7 mV positive than that of commercial Pt/C (E1/2 = 0.843 V) and TB-CMP-900 (E1/2 = 0.839 V), comparable to the performance of other similar reference catalysts previously reported (Table S3).54−56 Furthermore, one could observe clearly that CP-CMP-900 exhibited excellent catalytic activity toward ORR with a diffusion-limited current density (JL) of 4.78 mA cm–2 (vs 5.19 mA cm–2 of Pt/C). And all of these validated the formation of Fe2P, and the introduction of P into the N-doped porous carbon could enhance the ORR activity. To gain insights into the reaction kinetics of ORR catalyzed by CP-CMP-900, LSV curves at various rotation speeds (from 400 to 2500 rpm) were recorded (Figure 6b) and fitted according to the Koutecký–Levich (K–L) equation (eqs S1 and S2). As shown in Figure 6c, almost parallel fitting lines were obtained from the Koutecký–Levich (K–L) plots of CP-CMP-900, implying a first-order reaction kinetics toward the O2 concentration. And the calculated electron transfer number was 3.97 (0.2 V vs RHE) matching pretty well with the results calculated from the RRDE measurements (Figure 6d) on the basis of eqs S3 and S4. In addition, only low yields of H2O2 (less than 8.0%) were detected, indicative of a four-electron transfer pathway (Figure 6d) for ORR. In terms of practical applications, a high stability is a prerequisite. Hence, a chronoamperometric test (i–t) was conducted. As shown in Figure 6e, after a continuous constant potential cycling of 20 000 s, the current of CP-CMP-900 could maintain 84% of the initial value, while a decrease of about 40% was found for commercial Pt/C. Different from Pt/C, the polarization curve measured after the i–t test almost coincided with the previous curve (Figure S15b), validating that CP-CMP-900 has better long-cycle durability than commercial Pt/C.57,58Figure 6f shows the methanol crossover effects of CP-CMP-900. Only a slight current change could be detected on the CP-CMP-900 loaded electrode after the injection of methanol (3.0 M), and it reverts to the previous state with increasing time. However, a dramatic current decrease was observed for Pt/C, indicating that CP-CMP-900 possesses excellent methanol immunity. And this could also be evidenced by the LSV curve obtained before and after the injection of methanol (Figures S14f and S15c). The detailed information about the contrast catalysts is given in the Supporting Information (Figures S14–S16 and Table S2). Figure 6 Electrochemical performance of prepared catalysts in alkaline (0.1 M KOH) conditions: (a) Polarization curve of prepared catalysts and commercial Pt/C at 1600 rpm in O2-saturated KOH solution with a sweep rate of 5 mV s–1; (b) LSV curve of CP-CMP-900 at various rotation speeds in O2-saturated KOH solution with a sweep rate of 5 mV s–1; (c) K–L plots for CP-CMP-900 at various potentials; (d) percentage of hydrogen peroxide yield and the electron transfer number (n) of CP-CMP-900 at different potentials; (e) durability evaluation from the i–t chronoamperometric responses of the CP-CMP-900 electrodes in aqueous solution of KOH (0.1 M) saturated with O2; and (f) methanol crossover of CP-CMP-900. Moreover, CP-CMP-900 presents a high catalytic activity in neutral conditions (0.1 M PBS). Figure 7a shows the CV curve of CP-CMP-900 in the solution saturated with or without oxygen. Obvious cathodic peaks could be observed from the CV curves (black lines), but a featureless peak could be detected in the Ar-saturated solution (red line). As exhibited in Figure 7b, the Eonset value of CP-CMP-900 is 0.906 V, comparable to that of commercial Pt/C (0.911 V) and higher than that of the phosphorus-free TB-CMP-900 (0.901 V). And the E1/2 value (0.73 V) of CP-CMP-900 (Figure 7c) is more positive than that of Pt/C and other prepared catalysts (Figures S17 and S19). The calculated electron transfer number (n) is 3.95 at 0.1 V (vs RHE) according to the K–L plots (Figure 7d), validating a 4e reduction process for the ORR. And the electron transfer number (n) obtained from RRDE measurements further confirmed the 4e pathway for the ORR with a negligible yield of H2O2 (less than 5.0%) in the potential range of 0.1–0.8 V (Figure 7e). Besides, CP-CMP-900 also exhibits much better long-term stability than Pt/C (Figures 7f and S18a). A slight peak current decrease of 4.5% (vs 20% of Pt/C) could be observed after a long cycle of 20 000 s. As exhibited in Figure S18b, CP-CMP-900 also possesses excellent methanol immunity far beyond commercial Pt/C in the PBS solution. The details are listed in Table S3 in SI. Figure 7 Electrochemical performance of prepared catalysts in neutral (0.1 M PBS) conditions: (a) CV of CP-MP-900 in 0.1 M PBS saturated with O2 at a sweep rate of 50 mV s–1; (b) polarization curve of prepared catalysts and commercial Pt/C at a rotation speed of 1600 rpm in O2-saturated PBS solution with a sweep rate of 5 mV s–1; (c) LSV curves of CP-MP-900 at various rotation speeds from 400 to 2500 rpm in O2-saturated 0.1 M PBS solution; (d) K–L plots for CP-CMP-900 at various potentials; (e) percentage of hydrogen peroxide yield and the electron transfer number (n) of CP-MP-900 at different potentials; and (f) long-time stability curves of CP-MP-900 together with the commercial Pt/C in O2-saturated 0.1 M PBS solution. Meanwhile, the ORR performance in acidic electrolyte is more important for the potential application in PEMFC. CP-CMP-900 also exhibits prominent catalytic performance in acidic electrolyte (0.1 M HClO4). An obvious reduction peak could be found from the CV curves in oxygen-saturated solution (Figure 8a and Figures S20 and S22) but not in the Ar-saturated media for all of the samples. As shown in Figure 8b, the Eonset value of CP-CMP-900 is 0.856 V approaching that of Pt/C (1.011 V) and more positive than that of other prepared catalysts. In addition, the E1/2 value of CP-CMP-900 is 0.65 V (vs 0.74 V of Pt/C, Figure S20) surpassing that of the catalyst free of P and Fe2P. As determined from the K–L plots (inset of Figure 8c), the electron transfer number (n) of CP-CMP-900 was found to be approximately 3.79, indicating that CP-CMP-900 favored a four-electron pathway for the acidic ORR. Consistent well with the results (Figure 8d) achieved from the RDE measurements, RRDE tests further validated a four-electron pathway for ORR in acidic media (3.92 at 0.5 V). In addition, the generated percentage of 2e product H2O2 is below 13.1% at the tested potential range of 0–0.7 V (Figure 8d). The long-term stability of ORR catalysts is also a major concern in fuel cell technology. So, the long-term stability of CP-CMP-900 is shown in Figure 8e; a continuous O2 reduction for 20 000 s on the CP-CMP-900-coated electrode resulted in only 33.9% loss of the value of current density. While Eonset and E1/2 of Pt/C decreased greatly under the same conditions (Figure S21a). The morphology and elements distribution of CP-CMP-900 after the i–t tests were visualized in Figures S23 and S24. The morphology was almost unchanged after the i–t test, and all elements are still evenly distributed over the carbon skeleton, indicative of the excellent stability of prepared catalysts. In addition, the role of Fe2P in catalyzing the ORR was further detected by leaching off Fe2P in the acid media.38,39 As shown in Figure S23, the electrochemical activity of CP-CMP-900 toward ORR is higher than that after the acid treatment. The Eonset value of CP-CMP-900 decreased to 0.82 V, which clearly testifies the importance of Fe2P. As seen in Figure 8f, after the injection of methanol (t = 400 s), the cathodic current of commercial Pt/C decreased dramatically. However, as for CP-CMP-900, only a slight change could be detected. Besides, the loss of E1/2 for CP-CMP-900 is less than that of Pt/C (49 vs 72 mV, Figure S21b) after the injection of methanol. All of these convincingly testified that CP-CMP-900 preceded Pt/C for ORR in direct methanol fuel cells. The details are given in Table S4 in SI. To further prove the stability of prepared catalysts, the change of morphology and elemental distribution of CP-CMP-900 after the durability test in 0.1 M HClO4 was visualized by the SEM and corresponding element mapping. As shown in Figures S24 and S25, the morphology of CP-CMP-900 was almost unchanged after the i–t test. And all elements are still evenly distributed over the carbon skeleton. Figure 8 Electrochemical performance of prepared catalysts in 0.1 M HClO4: (a) CV of CP-CMP-900 in 0.1 M HClO4 saturated with O2 at a sweep rate of 50 mV s–1; (b) LSV curves of CP-CMP-X and commercial Pt/C at 1600 rpm; (c) LSV curves of CP-CMP-900 at various rotation speeds from 400 to 2500 rpm in O2-saturated solution; (d) percentage of hydrogen peroxide yield and the electron transfer number (n) of CP-CMP-900 at different potentials; (e) long-time stability curves of CP-CMP-900 together with the commercial Pt/C at a constant voltage of 0.8 V (vs RHE) in O2-saturated 0.1 M HClO4 solution; and (f) methanol tolerance test of CP-CMP-900 and Pt/C in O2-saturated 0.1 M HClO4. The excellent catalytic activity of CP-CMP-900 in various electrolytes could be attributed to the synergistic effect of the homogeneous distribution of Fe2P nanoparticles and FeNx species in the N,P-rich porous skeletons.59−62 The doping of P and N could change the charge distribution of carbon materials, activating the O2 adsorption capacity.63−66 Furthermore, the high porosity and hierarchical pore structure are good for mass transfer, improving the ORR ability. And the contents of pyridinic-N and graphitic-N, which have been proved favorable for the ORR by both theory and experimental results, are higher than those of the other N species. Conclusions In summary, we develop a facile and controllable method for the preparation of metal porphyrin-based POPs introducing C, N, P, and Fe simultaneously. Through direct carbonation of this special POP, a highly stable carbon-based catalyst (CP-CMP-900) embedded with well-defined Fe2P and FeNx species was obtained. A finishing point was achieved by doping of P and Fe2P into the pyridinic-N- and graphitic-N-dominated carbon. The synergistic effect of hierarchical pore structure, high porosity, and abundant Fe2P and FeNx species enable CP-CMP-900 present excellent ORR catalytic activity in the whole pH range. This work may provide a convenient, controllable, and efficient method for the design and preparation of more efficient carbon-based ORR catalysts for direct fuel cells and metal–air batteries. Supporting Information Available The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.9b03851.Materials and methods (Section 1); synthetic procedures (Section 2); 1H NMR spectra (Section 3); FT-IR spectra (Section 4); 13C solid-state NMR (Section 5); TGA (Section 6); PXRD profiles (Section 7); SEM and TEM images (Section 8); EDS of CP-CMP-900 (Section 9); X-ray photoelectron spectra and Raman spectrum (Section 10); electrochemical performance (Section 11); tables (Section 12); and references (Section 13) (PDF) Supplementary Material ao9b03851_si_001.pdf Author Contributions ∥ M.Z. and J.M. contributed equally to this work. This work was supported by the National Natural Science Foundation of China (81774125 and 80813368) and Natural Science Foundation of Shandong Province (ZR2016HM47). The authors declare no competing financial interest. ==== Refs References Dai L. ; Xue Y. ; Qu L. ; Choi H. J. ; Baek J. B. 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==== Front Cell Cycle Cell Cycle KCCY kccy20 Cell Cycle 1538-4101 1551-4005 Taylor & Francis 32089069 1724601 10.1080/15384101.2020.1724601 Research Paper Upregulation of microRNA-204 improves insulin resistance of polycystic ovarian syndrome via inhibition of HMGB1 and the inactivation of the TLR4/NF-κB pathway B. JIANG ET AL. CELL CYCLE Jiang Bin Xue Min Xu Dabao Song Yujia Zhu Shujuan Department of Gynaecology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China CONTACT Shujuan Zhu [email protected] 2020 23 2 2020 19 6 697710 22 7 2019 2 12 2019 29 12 2019 © 2020 Informa UK Limited, trading as Taylor & Francis Group 2020 Informa UK Limited, trading as Taylor & Francis Group ABSTRACT There is growing evidence of the position of microRNAs (miRs) in polycystic ovarian syndrome (PCOS), thus our objective was to discuss the impact of miR-204 on insulin resistance (IR) in PCOS by targeting highmobility group box protein 1(HMGB1)-mediated toll-like receptor 4(TLR4)/nuclear factor-kappa B (NF-κB) pathway. PCOS-IR patients and PCOS non-insulin resistance (PCOS-NIR) patients were included. The levels of serum sex hormones and related insulin were measured, the expression of miR-204, HMGB1, TLR4 and NF-κB p65 was detected, the diagnostic efficacy of miR-204 in PCOS-IR was analyzed, and the correlation between the expression of miR-204 in PCOS-IR and fasting blood glucose (FPG), fasting insulin (FINS), homeostasis model of assessment for insulin resistance index (HOMA-IR) was analyzed. Both in vitro and in vivo experiments were performed to elucidate the capabilities of miR-204 and HMGB1 in proliferation and apoptosis of PCOS-IR granulosa cells. MiR-204 was lowly expressed as well as HMGB1, TLR4 and NF-κB p65 were highly expressed in PCOS-IR patients. Follicule-stimulating hormone was downregulated, while luteinizing hormone, estrogen, progesterone, FPG, FINS and HOMA-IR were elevated in PCOS-IR. Upregulation of miR-204 and downregulation of HMGB1 could repress TLR4/NF-κB pathway activation, degraded insulin release and testosterone (T) leveland ascended ovarian coefficient, boosted cell proliferation and restrained apoptosis of granulosa cells. Overexpression of HMGB1 reverses the effect of upregulation of miR-204 on IR of PCOS. Our study presents that high expression of miR-204 or inhibition of HMGB1 can improve IR of PCOS via the inactivation of TLR4/NF-κB pathway. KEYWORDS Polycystic ovarian syndrome microRNA-204 HMGB1 TLR4/NF-κB pathway Hunan natural science foundation youth fund2018JJ3788 This work was supported by the Hunan natural science foundation youth fund (Grant no. 2018JJ3788) [2018JJ3788]. ==== Body Introduction Polycystic ovarian syndrome (PCOS) is a frequent reproductive endocrine metabolic pathema, which is accompanied by insulin resistance (IR) and obesity, and an increased long-term risk of multiple diseases [1]. It influences 5–10% of women of childbearing age, and about 50–70% of cases reveal IR and hyperinsulinemia [2,3]. Recent studies have discovered a number of factors related to the pathogenesis of PCOS, containing endocrine and genetic factors [4,5]. Women who are clinically diagnosed with PCOS appear to have abnormal menstrual cycle, obesity, or acyesis [6]. Clomiphene citrate is utilized as the first-line treatment for ovulatory induction in PCOS patients [7], while laparoscopic ovarian drilling is a second-line intervention in patients with PCOS, which causes no risk of ovarian hyperstimulation syndrome or multiple pregnancy originated from drug therapy [8]. MicroRNAs (miRNAs or miRs) are a species of small, endogenous, non-coding RNAs, participated in gene regulation via binding to target mRNA, thus impeding the production of protein at the post-transcriptional level [9]. A study has reported the relationship between the expression of miR-320 and endothelin-1 which is its target gene with PCOS sensitivity and clinical characteristics [10]. MiR-204 belongs to miRs family and generally plays an inhibitory role in human cancer [11]. A study has also presented that the apoptosis inhibition sensitivity of miR-204 to epithelial ovarian cancer cells was upregulated by brain-derived neurotrophic factor pathway [12]. Also, a study has demonstrated that miR-204 inhibited expression levels of high-mobility group box protein 1 (HMGB1) in pancreatic cancer [13]. HMGB1 is one of the major instigators and amplifiers of inflammation, a DNA-binding protein excreted by inflammatory cells and excreted by damaged cells [14]. High concentration of insulin boosts apoptosis by promoting HMGB1 expression in primary cultured rat ovarian granulosa cells [15]. There is a study that reported the toll-like receptor 4 (TLR4)/nuclear factor-kappa B (NF-κB) signaling pathway mediates pancreatic injury induced by HMGB1 in mice suffering from severe acute pancreatitis [16]. NF-κB is a key downstream signal molecule in TLR4 signaling pathway [17]. The TLR4/NF-κB pathway has distinct functions during the stress reaction and inflammation [18]. Another study has demonstrated that NF-κB is activated by TLR4 in human ovarian granulosa tumor cells [19]. Thus, the objective of this study was to examine the impact of miR-204 on IR in PCOS by regulating HMGB1 and the TLR4/NF-κB pathway. Materials and methods Ethics statement The study was approved by the Institutional Review Board of The Third Xiangya Hospital of Central South University. All participants signed a document of informed consent. All animal experiments were in line with the Guide for the Care and Use of Laboratory Animal by International Committees of The Third Xiangya Hospital of Central South University. Study subjects From 2017 to 2019, a total of 112 PCOS patients who underwent in vitro fertilization and intracytoplasmic sperm injection followed by embryo transfer (IVF/ICSI-ET) assisted pregnancy therapy in The Third Xiangya Hospital of Central South University were selected in our study. It included PCOS insulin resistance group (PCOS-IR) (68 cases) and PCOS non-insulin resistance group (PCOS-NIR) (44 cases). Patients were included if they met the following criteria: patients tallied with the diagnostic criteria [20] of PCOS, in which HOMA-IR more than 2.57 [21] was used as the PCOS-IR group, the HOMA-IR lower than 2.57 was the PCOS-NIR group, and the age of patients was younger than 35 years old. These patients were excluded: patients with other diseases of the reproductive system, such as endometriosis, hysteromyoma and ovarian tumors; patients with immune-related diseases; patients had other diseases affecting pregnancy and pregnancy outcome; patients received uterus unicornis, saddle-shaped uterus, pelvic radiochemotherapy and ovarian surgery, or who had long-term oral administration of contraceptive, antihypertensive drugs, or hypoglycemic drugs; chromosome abnormality in husband and wife; patients who had been diagnosed with diabetes or may be able to reach the diagnostic criteria for diabetes. Sixty patients with non-PCOS treated with IVF/ICSI-ET due to male factors or fallopian tube factors were taken as the control group. The inclusion criteria were: the age of the patients was less than 35 years old, and the patients with normal ovarian function in accordance with that the basic endocrine follicule-stimulating hormone (FSH) was lower than 10 IU/L, and anti-mullerian hormone (AMH) was more than 1 ng/mL. Exclusion criteria were the same as PCOS patients. Venous blood samples (5 mL) were collected at 8:00–10:00 am on the 3rd to 5th day of menstrual cycle (natural cycle or induction cycle) and stored at −4°C for 30 min. FSH, luteinizing hormone (LH), estrogen (E2) and progesterone (P4) levels were tested by full-automatic biochemical analyzer (HITACHI, Tokyo, Japan), fasting blood glucose (FBG) was detected by hexokinase method, and fasting insulin (FINS) was verified by radioimmunoassay, as well as HOMA-IR was calculated as (FPG mmol/L × FINS mIU/L)/22.5. Collection of granulosa cells On the day of taking the ovum, under the guidance of the vaginal ultrasound, the ovum was taken out, the oocyte corona cumulus complex was gathered, and the mixture of the remaining follicular fluid granulosa cells was recovered for further processing. The retrieved follicular fluid granulosa cells were immediately centrifuged at 2000 rpm for 10 min, and the follicular fluid and the granulosa cells were initially isolated. The follicular fluid was transferred to the cryostat and stored at −80°C. The precipitated fraction was left for further use, a proper amount of hyaluronidase was added according to the amount of precipitation, shaking for 30 s and bathing at 37°C for 25 min. At the same time, a 15 mL sterile centrifuge tube was prepared, 5 mL of human peripheral blood lymphocyte separation solution was added, and pre-warmed at room temperature. The cell suspension completed in water bath was added to the upper layer of lymphocytes separation solution, and slowly moved into the tube to avoid mixing with lymphocyte separation solution. After centrifugation at 2000 rpm for 10 min, the granule suspension cells in the middle layer were sucked out and the residual liquid was removed by centrifugation, which were saved at −80°C for the determination of the target gene and related proteins. Animal experiment grouping and modeling Seventy-eight female Wistar rats aging 21 days and weighing 50–100 g (Shanghai SLAC Laboratory Animal Co., Ltd., Shanghai, China) were housed in quiet, well-ventilated and clean cages. The cage environment was set at 25°C (50% humidity) with normal circadian rhythm of water and food intake as well as 12 h day/night cycle. PCOS-IR modeling [22]: 60 rats were subcutaneously injected with 0.2 mL (6 mg/100 g) dehydroepiandrosterone (soluble in sesame oil for injection) solution, and 8 rats in the normal group was subcutaneously injected with 0.2 mL sesame oil. After 20 days of continuous injection, the changes of vaginal exfoliative cytology in rats were observed through methylene blue staining. The keratosis of vaginal epithelial cells lasted 10 days in an angular state, which suggested that PCOS induction was successful. The successful PCOS model rats would be fasting at 8:00 that night, then FBG and FINS were measured in orbital veins at 8:00 in the next morning. The level of FBG was measured by blood glucose meter and the level of INS in blood was measured by enzyme-linked immunosorbent assay (ELISA) kit (Beckman Coulter Life Sciences, Brea, CA, USA). In the light of HOMA-IR calculation [23], PCOS rats with HOMA-IR > 2.57 were regarded as a successful animal model of PCOS-IR [24]. Fifty-six PCOS-IR rats were divided into 7 groups with 8 rats in each group: PCOS-IR group, mimics-negative control (NC) group, miR-204 mimics group, siRNA-NC group, HMGB1-siRNA group, miR-204 mimics + pcDNA-NC group and miR-204 mimics + pcDNA-HMGB1 group. After the successful model establishment, the rats in the mimics-NC group, miR-204 mimics group, siRNA-NC group, HMGB1-siRNA group, miR-204 mimics + pcDNA-NC group and miR-204 mimics + pcDNA-HMGB1 group were fasting for 12 h, anesthetized with 3% pentobarbital sodium (weighting 100 mg/Kg), fixed in supine position and injected with mimics-NC, miR-204 mimics, siRNA-NC, HMGB1-siRNA, miR-204 mimics + pcDNA-NC, miR-204 mimics + pcDNA-HMGB1 to ovarian [25]. The lentivirus vectors were composed and prepared by GenePharma Ltd. Company (Shanghai, China). Observation of pathology After the intervention, the rats in preoestrus were fasted for 12 h on the basis of the morphological changes of vaginal exfoliated cells and the rats were weighed and recorded. Insulin release test: after fasting for 12 h, insulin release test was carried out in rats. According to the dose standard of 3 g glucose per kilogram body weight, the blood was taken from orbital vein immediately after intragastric administration of glucose, and taken after 0.5 h, 1 h and 2 h. After centrifugation for 15 min at 2000 r/min, the serum was taken as the sample to be measured and placed at −80°C. Detection of insulin and testosterone (T): after fasting for 12 h, blood samples of rats were gathered from the orbital veins of glass capillaries. The serum was taken as the sample to be tested prior to centrifuged at 15 min for 2000 r/min. Insulin and T kits (Linco Research, St Charles, MO, USA) strips were laid up for 30 min. Blank well, standard product well and sample well were set up. Standard wells and sample wells were added with 50 μL different concentrations of standard materials and samples to be tested. Horseradish peroxide (HRP)-labeled antibody (100 μL) was added to the standard product well and sample well, and the antibody was stand at 37°C for 1 h. The liquid of each well was absorbed and dried, and 350 μL washing liquid was put in each well. Blank wells, standard product wells and sample wells were put with 50 μL substrate A and substrate B and placed for 15 min at 37°C. When 50 μL terminating solution was added, the optical density (OD) value of each well was measured instantly by a microplate reader (Thermo Fisher Scientific, Massachusetts, USA) at 450 nm wavelength, and the homeostasis model of assessment for insulin resistance index (HOMA-IR) was reckoned. After the rats were euthanized by neck dislocation, the bilateral ovaries of the rats were quickly removed, the weight of the ovaries was measured and noted, ovarian coefficient = ovarian weight/body weight. Then, the left ovary was fixed in 10% formalin solution, and the morphological changes of the ovaries were observed by hematoxylin-eosin (HE) staining, and the right ovaries were placed at −80°C for reverse transcription quantitative polymerase chain reaction (RT-qPCR) and western blot assay. HE staining [26]: the ovaries were removed rapidly, fixed with 10% formalin solution, rinsed with distilled water for 15 min, and treated with 50%-90% ethanol for 1 h, respectively, followed by ethanol (95%) and anhydrous ethanol for 45 min twice, and embedded with paraffin for 45 min. Next, the tissue with thickness of 5 μm was dewaxed by xylene, treated with ethanol (70%-100%) for 3 min each and washed in distilled water for 3 min. The tissues were stained 5 min with hematoxylin solution, treated with ethanol (70% and 90%) respectively, for 10 min, and stained with eosin for 2 min. Finally, the tissues were undergone anhydrous ethanol treatment for 4 min, xylene treatment for 10 min, and sealing. Establishment and grouping of PCOS-IR cell models Primary separation of rat ovarian granulosa cells: 10 rats were anesthetized by intraperitoneal injection of pregnant mare’s serum gonadotrophin (PMSG) (8–10 U). After 48 h, the bilateral ovaries were quickly taken out under sterile conditions, and soaked 3 times with phosphate buffered saline. The surrounding fat and the membrane were removed under a stereoscopic microscope. The granulosa cells and the oocytes were released by using 1 mL syringe needle, separated by a 1 g/L hyaluronidase, filtrated by a mesh screen with an aperture of 200 mesh and centrifuged at 1000 r/min for 5 min. The cells were suspended with basic culture solution (DMEN/F12 medium + 100 U/mL penicillin + 100 μg/mL streptomycin + 0.5 μg/mL amphotericin 2B + 10% fetal bovine serum) and identified under a microscope (Olympus, Tokyo, Japan). Establishment of PCOS-IR cell model: separated rat ovarian granulosa cells were primary cultured for 5 days, when subcultured to 60%, the cells were treated with glucose 4.5 g/L and insulin 1.0 μmol/L for 48 h. PCOS granulosa cells with HOMA-IR > 2.57 were used as a successful PCOS-IR cell model. PCOS-IR granulosa cells were divided into seven groups: blank group (no transfected with any sequence), mimics-NC group (transfected with mimics-NC), miR-204 mimics group (transfected with miR-204 mimics), siRNA-NC group (transfected with siRNA-NC), HMGB1-siRNA group (transfected with HMGB1-siRNA), miR-204 mimics + pcDNA-NC group (transfected with miR-204 mimics + pcDNA-NC), and miR-204 mimics + pcDNA-HMGB1 group (transfected with miR-204 mimics + pcDNA-HMGB1). Among them, mimics-NC, miR-204 mimics, siRNA-NC, HMGB1-siRNA, miR-204 mimics + pcDNA-NC, and miR-204 mimics + pcDNA-HMGB1 were composed from GenePharma Ltd. Company (Shanghai, China). Before 24 h of transfection, the cells were inoculated into a 12-well plate, and the complete culture medium of 1.5 mL without penicillin and streptomycin was put into each well. When the cell confluence reached about 80%, the above transformants were transferred into ovarian granulosa cells in the light of the instructions of Lipofectamine 2000 (Invitrogen, Carlsbad, California, USA) transfection reagent. After 6 h of culture, the cells and the culture medium were gathered and used in the follow-up cell experiment. Cell counting kit-8 (CCK-8) assay After the cells were transfected for 48 h, the cells were detached with 0.25% trypsin to form a single cell suspension. After counting, cells were added to each well at 3000 cell density per well/100 μL in a 96-well plate, and then incubated. CCK-8 (Sigma-Aldrich, SF, CA, USA) reagent (10 µL) was put into each well after transfected 24 h, 48 h and 72 h, respectively, and incubated at 37°C for 4 h. The OD value of each well at 490 nm was read on a microplate reader (Thermo Fisher Scientific, Massachusetts, USA). Flow cytometry After transfection of ovarian granulosa cells for 48 h, the cells were detached with trypsin for 3 min, and collected in a 10 mL centrifuge tube. Then, cells were fixed overnight with pre-cooled 70% ethanol, AnnexinV labeled protein (Beyotime Institute of Biotechnology, Shanghai, China) and corresponding nucleic acid dye was added, evenly mixed and reacted for 15 min. Binding buffer (1 × 400 μL) was put into the flow tube, and the DNA data of the cells were amassed and analyzed via flow cytometer (Becton Dickinson Co., Ltd., Maryland, USA), and the data were metered through MultiCycle for Windows (Beckman Coulter Life Sciences, Brea, CA, USA). Rt-qPCR The total RNA in tissues and cells was extracted by RNA extraction kit (Invitrogen, Carlsbad, California, USA). MiR-204, HMGB1, U6 and glyceraldehyde phosphate dehydrogenase (GAPDH) primers were devised by Takara (Dalian, China) (Table 1). The reverse transcription of RNA into cDNA was performed by PrimeScript RT kit (Takara, Dalian, China). The reaction solution was utilized for fluorescence quantitative PCR, in accordance with the specification of SYBR® Premix Ex TaqTM II kit (Takara, Dalian, China), the fluorescence quantitative PCR operation was carried out in ABI PRISM® 7300 system (Applied Biosystems, Inc., CA, USA). The data were measured by 2−ΔΔCt method [27]. The relative transcription levels of target gene (miR-204, HMGB1, U6 and GAPDH) were calculated by this method, while ΔΔCt = ΔCt experimental group – ΔCt control group, ΔCt = Ct (target gene) – Ct (internal reference).10.1080/15384101.2020.1724601-T0001 Table 1. Primer sequence. Gene Sequence (5ʹ→3ʹ) miR-204 F: 5ʹ-CTGTCACTCGAGCTGCTGGAATG-3’ R: 5ʹ-ACCGTGTCGTGGAGTCGGCAATT-3’ U6 F: 5ʹ-CGCTTCGGCAGCACATATAC-3’ R: 5ʹ-AAATATGGAACGCTTCACGA-3’ HMGB1 F: 5ʹ-AGGTGGAAGACCATGTCTG-3’ R: 5ʹ-TTCTCTTTCATAACGGGCCT-3’ GAPDH F: 5ʹ-GATCATCAGCAATGCCTCC3’ R: 5ʹ-TCCACGATACCAAAGTTGTC3’ F, forward; R, reverse; miR-204, microRNA-204; HMGB1, highmobility group box protein 1; GAPDH, glyceraldehyde phosphate dehydrogenase. Western blot assay The total protein in the tissue and the cell was extracted. The protein concentration was determined using the bicinchoninic acid kit (AmyJet Scientific, Wuhan, Hubei, China). The extracted protein was mixed with the sample buffer, separated with 10% polyacrylamide gel electrophoresis, and transferred to nitrocellulose membrane. The membrane was blocked with 5% skim milk in tris-buffered saline with tween 20 for 1 h, supplemented with primary antibody against HMGB1 (1: 1000), TLR4 (1: 1000) (Proteintech, Chicago, Illinois, USA), NF-κB p65 (1: 1000), Bax (1: 1000), Bcl-2 (1: 1000), GAPDH (1: 1000) (Cell Signaling Technology, Beverly, MA, USA), proliferating cell nuclear antigen (PCNA, 1: 1000), cyclin D1 (1: 1000) (Santa Cruz Biotechnology, Santa Cruz, CA, USA) and hatched overnight at 4°C. IgG (1: 1000, Wuhan Boster Biological Technology Co., Ltd., Hubei, China) labeled with horseradish peroxide was incubated at 37°C for 1 h. The membrane was developed to enhanced chemiluminescence reaction solution (Pierce, Rockford, IL, USA) for 1 min. Gel Doc EZ imager (Bio-rad, California, USA) was utilized for developing. Dual luciferase reporter gene assay Target relationship between miR-204 and HMGB1, and binding site of miR-204 to HMGB1 3ʹuntranslated region (3ʹUTR) were predicted using bioinformatics software (http://www.targetscan.org/vert_72/). The sequence of HMGB1 3ʹUTR promoters containing miR-204 binding sites was compounded, and HMGB1 3ʹUTR wild-type plasmid (HMGB1-WT) was constructed. In the light of this plasmid, HMGB1 3ʹUTR mutant plasmid (HMGB1-MUT) was constructed. HMGB1-WT and HMGB1-MUT plasmids together with mimics NC or miR-204 mimics plasmids were co-transfected into ovarian granulosa cells, respectively. After 48 h of transfection, the cells were gathered and lysed. Luciferase assay kit was utilized to test the luciferase activity. Statistical analysis All data were analyzed by SPSS 21.0 software. The measurement data were expressed as mean ± standard deviation. Comparisons between two groups were conducted by t-test, while comparisons among multiple groups were assessed by one-way analysis of variance (ANOVA). Correlation between miR-204 expression and FPG, FINS and HOMA-IR was conducted by Pearson correlation analysis. The receiver operating characteristic curve (ROC curve) was drawn, and miR-204 expression was judged by area under ROC curve (AUC) for the diagnostic effectiveness of PCOS-IR patients. P value < 0.05 was indicative of statistically significant difference. Results Low expression of miR-204 and high expression of HMGB1, TLR4 and NF-κB p65 in the granulosa cells of PCOS-IR patients It was demonstrated by RT-qPCR and western blot analysis that the expression of miR-204 was reduced while the expression of HMGB1, TLR4 and NF-κB p65 was raised in the PCOS-NIR group and the PCOS-IR group relative to that in the control group (all P < 0.05). Compared to the PCOS-NIR group, miR-204 expression in granulosa cells of the PCOS-IR group was degraded, and HMGB1, TLR4 and NF-κB p65 was ascended (all P < 0.05) (Figure 1(a–c)).10.1080/15384101.2020.1724601-F0001 Figure 1. In the granulosa cells of PCOS-IR patients, miR-204 is lowly expressed while HMGB1, TLR4 and NF-κB p65 are highly expressed. (a): Expression of miR-204 and HMGB1 mRNA in the granulosa cells of each group by RT-qPCR. (b): Protein bands of HMGB1, TLR4, NF-κB p65. (c): Protein expression of HMGB1, TLR4 and NF-κB p65 in the granulosa cells of each group by western blot analysis. (d): ROC curve to analyze the diagnostic effect of miR-204 on PCOS-IR. * P < 0.05 vs. the control group. # P < 0.05 vs. the PCOS-NIR group. PCOS-IR: n = 68; PCOS-NIR, n = 44; Control, n = 60. Measurement data were depicted as mean ± standard deviation, and data were assessed by one-way analysis of variance followed by LSD-t test. The diagnostic effect of miR-204 on PCOS-IR was analyzed via the ROC curve. The results presented that AUC was 0.830, 95% CI was 0.730–0.930 (P < 0.05), and the sensitivity and specificity were 0.705 and 0.704, respectively (Figure 1(d)). FSH is poorly expressed while LH, E2, P4, FPG, FINS and HOMA-IR are highly expressed in PCOS-IR patients The results of sex hormone indicator test demonstrated that in contrast with the control group, FSH in the PCOS-NIR group and the PCOS-IR group reduced while LH, E2 and P4 elevated (all P < 0.05). In relation to the PCOS-NIR group, FSH in the PCOS-IR group depressed and LH, E2 and P4 heightened (all P < 0.05) (Figure 2(a)).10.1080/15384101.2020.1724601-F0002 Figure 2. FSH is downregulated while LH, E2, P4, FPG, FINS and HOMA-IR are upregulated in PCOSIR. (a) Levels of FSH, LH, E2, and P4 in serum of each group. (b) Levels of FPG, FINS and HOMA-IR in serum of patients in each group. (c) The correlation between expression of miR-204 and FPG, FINS and HOMA-IR in granulosa cells of PCOS-IR patients. * P < 0.05 vs. the control group. # P < 0.05 vs. the PCOS-NIR group. PCOS-IR: n = 68; PCOS-NIR, n = 44; Control, n = 60. Measurement data were depicted as mean ± standard deviation, and data were assessed by one-way analysis of variance followed by LSD-t test. Correlation between miR-204 expression and FPG, FINS and HOMA-IR was conducted by Pearson correlation analysis. Insulin-related indices reported that compared to the control group, the levels of FPG, FINS and HOMA-IR in the PCOS-NIR group had no obvious change (all P > 0.05), but the levels of FPG, FINS and HOMA-IR in the PCOS-IR group were raised (all P < 0.05). By comparison with the PCOS-NIR group, the levels of FPG, FINS and HOMA-IR in the PCOS-IR group were elevated (all P < 0.05) (Figure 2(b)). Correlation analysis suggested that miR-204 expression in granulosa cells of patients with PCOS-IR was negatively correlated with the levels of FPG, FINS and HOMA-IR, rFPG = −0.679, P < 0.001; r FINS = −0.765, P < 0.001; rHOMA-IR = −0.760, P < 0.001 (Figure 2(c)). Overexpression of miR-204 and poor expression of HMGB1 inhibit TLR4/NF-κB pathway activation, decrease insulin release and T level and increase ovarian coefficient The results of RT-qPCR and western blot analysis displayed that miR-204 expression in the PCOS-IR group was lower than that in the control group, while the expression of HMGB1, TLR4, and NF-κB p65 was increased (all P < 0.05). In contrast with the mimics-NC group, the expression of miR-204 in the miR-204 mimics rats was elevated, and HMGB1, TLR4 and NF-κB p65 expression was decreased (all P < 0.05). In contrast with the siRNA-NC group, the expression of miR-204 in the HMGB1-siRNA group had no distinct change (P > 0.05), but the expression of HMGB1, TLR4 and NF-κB p65 reduced (all P < 0.05). In relation to the miR-204 mimics + pcDNA-NC group, miR-204 expression in the miR-204 mimics + pcDNA-HMGB1 group had no distinct change (P > 0.05), but the expression of HMGB1, TLR4 and NF-κB p65 increased (all P < 0.05) (Figure 3(a–c)).10.1080/15384101.2020.1724601-F0003 Figure 3. TLR4/NF-κB pathway activation is repressed, insulin release and T level are reduced, and ovarian coefficient is raised through the upregulation of miR-204 and downregulation of HMGB1. (a): Expression of miR-204 and HMGB1 in each group of rats by RT-qPCR. (b): Protein bands of HMGB1, TLR4 and NF-κB p65. (c): HMGB1, TLR4 and NF-κB p65 protein expression by western blot analysis. (d): Detection of body weight of rats in each group. (e): Insulin release test of rats in each group. (f): T level test results of rats in each group. (g): HOMA-IR detection results in each group. (h): Test results of ovarian coefficient in each group of rats. I: Observation of pathological morphology of ovarian tissues in each group by HE staining . n = 8. * P < 0.05 vs. the control group. # P < 0.05 vs. the mimics-NC group. & P < 0.05 vs. the siRNA-NC group. $ P < 0.05 vs. the miR-204 mimics + pcDNA-NC group. Measurement data were depicted as mean ± standard deviation, and data were assessed by one-way analysis of variance followed by LSD-t test. In relation to the control group, the weight of the rats in the PCOS-IR group increased (P < 0.05). The weight of rats in the miR-204 mimics group was lower than in the mimics-NC group (P < 0.05). The weight of rats in the HMGB1-siRNA group was degraded in contrast with the siRNA-NC group (P < 0.05). Compared to the miR-204 mimic + pcDNA-NC group, the weight of rats in the miR-204 mimic + pcDNA-HMGB1 group was raised (P < 0.05) (Figure 3(d)). The results of insulin release test reported that in contrast with the control group, the insulin release in 0.5–2 h increased in the PCOS-IR group (P < 0.05). In relation to the mimics-NC group, the release of insulin in 0.5–2 h in the miR-204 mimics group was degraded (P < 0.05). The insulin release in 0.5–2 h in the HMGB1-siRNA group was lower than in the siRNA-NC group (P < 0.05). Compared to the miR-204 mimics + pcDNA-NC group, the release of insulin in the miR-204 mimics + pcDNA-HMGB1 group was raised at 0.5–2 h (P < 0.05) (Figure 3(e)). The results of HOMA-IR, Tlevel and ovarian coefficient in each group suggested that compared to the control group, HOMA-IR and T level enhanced, while ovarian coefficient depressed in the PCOS-IR group (all P < 0.05). In contrast with the mimics-NC group, HOMA-IR and T level degraded and ovarian coefficient ascended in the miR-204 mimics group (all P < 0.05). In relation to the siRNA-NC group, HOMA-IR and T level reduced and ovarian coefficient raised in the HMGB1-siRNA group (all P < 0.05). HOMA-IR and T level in the miR-204 mimics + pcDNA-HMGB1 group was higher than that in the miR-204 mimics + pcDNA-NC group, while ovarian coefficient was lower (all P < 0.05)(figure 3(f–h)). HE staining reported that in the control group, multiple corpus could be seen under the ovarioscope, the granulosa cells were in the form of multiple layers, the shape was complete, the arrangement was orderly and the granulosa cell layer was thick. In the PCOS-IR group, mimics-NC group, siRNA-NC group and miR-204 mimics + pcDNA-HMGB1 group, more early developed small follicles and atresia follicles could be seen under the ovarioscope, cystic dilatation was significant, the granulosa cell layer was reduced and even disappeared, corpora luteum was less and the ovaries performed typical polycystous changes. In the miR-204 mimics group, HMGB1-siRNA group and miR-204 mimics pcDNA-NC group, improved polycystous changes of ovaries, reduced small follicles and thickened granulosa cell layers could be seen under the ovarioscope (Figure 3(i)). Upregulated miR-204 and downregulated HMGB1 promote cell proliferation and repress apoptosis of granulosa cells The primary ovarian granulosa cells of rats were observed under an inverted microscope. It was found that after the primary ovarian granulosa cells were isolated, the morphology of granulosa cells was irregular and presented polyhedral or fusiform after 2 days of culture. With the prolongation of culture time, when the granulosa cells were cultured for 10 days, cells could be covered with the bottom of the dish and formed monolayer granulosa cells. As shown in Figure 4(a), the primary granulosa cells of rats were separated successfully.10.1080/15384101.2020.1724601-F0004 Figure 4. Overexpressed miR-204 and downregulated HMGB1 boosted cell proliferation and suppressed apoptosis of granulosa cells. (a): The culture of primary ovarian granulosa cells in rats under the microscope. (b): Expression of miR-204 and HMGB1 in granulosa cells by RT-qPCR. (c): Protein bands of HMGB1, TLR4 and NF-κB p65. (d): HMGB1, TLR4 and NF-κB p65 protein expression in granulosa cells by western blot analysis. (e): The difference of cell proliferation after transfection of PCOS-IR granulosa cells in each group. (f,g): Western blot analysis to detect PCNA and cyclin D1 protein expression in granulosa cells. (h): Apoptosis of PCOS-IR granulosa cells after transfection in each group by flow cytometry. (i): Comparison of apoptosis rate of PCOS-IR granulosa cells after transfection in each group. J&K: Western blot analysis to detect Bax and Bcl-2 protein expression in granulosa cells. N = 3, * P < 0.05 vs. the mimics NC group. # P < 0.05 vs. the siRNA-NC group. & P < 0.05 vs. miR-204 mimics + pcDNA-NC group. Measurement data were depicted as mean ± standard deviation, and comparisons among multiple groups were assessed by one-way analysis of variance followed by LSD-t test. The results of RT-qPCR and western blot analysis revealed that miR-204 expression in the miR-204 mimics group was higher than that in the mimics-NC group, while the expression of HMGB1, TLR4 and NF-κB p65 was abated (all P < 0.05). Compared with the siRNA-NC group, the expression of miR-204 in the HMGB1-siRNA group had no distinct change (P > 0.05), but the expression of HMGB1, TLR4 and NF-κB p65 decreased (all P < 0.05). In contrast with the miR-204 mimics + pcDNA-NC group, the expression of miR-204 in the miR-204 mimics + pcDNA-HMGB1 group had no distinct change (P > 0.05), but the expression of HMGB1, TLR4 and NF-κB p65 elevated (all P < 0.05). Meanwhile, there was no distinct change in the expression of HMGB1, TLR4 and NF-κB in the blank group, mimics-NC group, siRNA-NC group, and miR-204 mimics + pcDNA-HMGB1 group (all P > 0.05) (Figure 4(b–d)). CCK-8 assay reported that in relation to the mimics-NC group, the cell proliferation in the miR-204 mimics group increased at 48–72 h (P < 0.05). In the HMGB1-siRNA group, the proliferation increased at 48–72 h relative to that in the siRNA-NC group (P < 0.05). The impact of upregulated HMGB1 reversed miR-204 on the proliferation of PCOS-IR granulosa cells was further observed. In contrast with the miR-204 mimics + pcDNA-NC group, the proliferation of PCOS-IR granulosa cells in the miR-204 mimics + pcDNA-HMGB1 group decreased at 48–72 h (P < 0.05). There was no distinct difference in cell proliferation among the blank group, mimics-NC group, siRNA-NC group and miR-204 mimics + pcDNA-HMGB1 group (P > 0.05) (Figure 4(e). Changes of the proliferation-related proteins of granulosa cells after transfection was verified by western blot analysis. The expression of PCNA and cyclin D1 protein in the miR-204 mimics group was higher than that in the mimics-NC group (both P < 0.05). In contrast with the siRNA-NC group, PCNA and cyclin D1 protein expression in the HMGB1-siRNA group was raised (both P < 0.05). The expression of PCNA and cyclin D1 protein in the miR-204 mimics + pcDNA-HMGB1 group was lower than that in the miR-204 mimics + pcDNA-NC group (both P < 0.05). There was no distinct difference in PCNA and cyclin D1 protein expression in the blank group, mimics-NC group, siRNA-NC group and miR-204 mimics + pcDNA-HMGB1 group (all P > 0.05) (figure 4(f–g)). The results of Annexin V/PI two-parameter method reported that the apoptosis rate in the miR-204 mimics group was lower than that in the mimics-NC group (P < 0.05). In contrast with the siRNA-NC group, apoptosis rate in the HMGB1-siRNA group was reduced (P < 0.05). The apoptosis rate in the miR-204 mimics + pcDNA-HMGB1 group was higher than that in the miR-204 mimics + pcDNA-NC group (P < 0.05). There was no distinct difference in apoptosis rate in the blank group, mimics-NC group, siRNA-NC group and miR-204 mimics + pcDNA-HMGB1 group (P > 0.05) (Figure 4(h). Western blot analysis was adopted to test the changes of apoptosis-related proteins in granulosa cells after transfection. The expression of Bax protein in the miR-204 mimics group was lower than that in the mimics-NC group, but Bcl-2 protein expression was higher (both P < 0.05). In contrast with the siRNA-NC group, Bax protein expression in the HMGB1-siRNA group was decreased while Bcl-2 increased (both P < 0.05). The expression of Bax protein in the miR-204 mimics + pcDNA-HMGB1 group was higher than that in the miR-204 mimics + pcDNA-NC group, and Bcl-2 protein expression was depressed (both P < 0.05). Meanwhile, there was no distinct difference in Bax and Bcl-2 protein expression in the blank group, mimics-NC group, siRNA-NC group and miR-204 mimics + pcDNA-HMGB1 group (all P > 0.05) (Figure 4(i–k)). HMGB1 is the target gene of miR-204 The results of luciferase activity assay presented that the relative luciferase activity of cells was degraded after co-transfected by HMGB1-WT and miR-204 mimics (P < 0.05), and co-transfection with HMGB1-MUT and miR-204 mimics did not affect the relative luciferase activity of the cells (P > 0.05). It was suggested that HMGB1 was a direct target gene for miR-204 (Figure 5(a–b)).10.1080/15384101.2020.1724601-F0005 Figure 5. The target relationship among miR-204 and HMGB1. (a): The Targetscan website predicted the target relationship between miR-204 and HMGB1. (b): Dual-luciferase reporter gene assay verified the targeting relationship of miR-204 and HMGB1. Measurement data were depicted as mean ± standard deviation, and comparisons among the two groups were assessed by t test. The experiment was repeated three times. Discussion As a common endocrine disease, PCOS affects 5-10% of women of childbearing age [28]. It is customarily considered that the mechanism of IR in PCOS refers to many factors, such as endocrine and polygenetic inheritance, immune factors and metabolism interacting with environmental factors [2]. A previous study has revealed that miR-99a modulates apoptosis and proliferation of human granulosa cells through targeting IGF-1R in PCOS [29]. Also, a recent study has provided a proof that high-concentration insulin-induced IR promotes the primary cultured rat ovarian granulosa cells apoptosis by increasing HMGB1 [15]. As the related mechanisms of miR-204 in PCOS remain to be excavated, the objective of our study was to investigate the role of the miR-204 in the regulation of the HMGB1-mediated TLR4/NF-κB pathway in the IR of PCOS and their inner mechanisms. It was found that low expression of miR-204 and high expression of HMGB1, TLR4 and NF-κB p65 in the granulosa cells of PCOS-IR patients. Consistent with our study, a study reported that miR-204-5p was low expressed in osteosarcoma cell lines and osteosarcoma patients [30]. Another study has presented the downregulation of miR-204 in human breast cancer cell lines and tissues [31]. It is reported that the expression of HMGB1 raised in the serum and ovary of patients with PCOS [32]. Similarly, a previous study has proven that the relationship between the high level of HMGB1 in PCOS patients with IR/hyperinsulinemia [15]. It has been presented that the expression of NF-κB p65 in normal and overweight PCOS women was distinctly higher than that in women without PCOS [33]. Also, it was displayed that PCOS had a distinct effect on the expression of TLR4 and TLR9 in rat cumulus cells [34]. Our study also presented that FSH was poorly expressed while LH, E2, P4, FPG, FINS and HOMA-IR were highly expressed in PCOS-IR. An important finding was that LH and T levels in serum of mice were ascended in PCOS patients, and the content of FSH was degraded in PCOS patients [35]. Another study provided data that FINS, LH, FPG, HOMA-IR and T were upregulated while FSH reduced in the PCOS group [36]. In addition, it was revealed that overexpression of miR-204 and downregulation of HMGB1 inhibited TLR4/NF-κB pathway activation, decrease IR and T and increase ovarian coefficient. It has been suggested previously that overexpression of miR-204 restrains JAK2/STAT3 pathway [37]. Another study has verified that miR-204 may play the role of anti-growth, anti-graft, anti–invasion and anti-epithelial-mesenchymal transition by inhibiting the PI3K/AKT/mTOR pathway [38]. The study also showed that upregulated miR-204 and downregulated HMGB1 would promote cell proliferation and repressed apoptosis in granulosa cells. It is reported that exogenous recombinant HMGB1 (rhMGB1) advanced the proliferation of breast cancer cells [39]. Other study also proved that HMGB1 expression in colorectal cancer is related to the metastasis of distant lymph nodes. It may repress cell apoptosis via activating pERK and c-IAP 2 [40]. It has been suggested that forced expression of miR-204 restrained the proliferation and invasion of retinoblastoma cells [41]. Also, overexpression or inhibition of miR-204-5p in 3T3-l1 preadipocytes suppressed or advanced the proliferation of 3T3-l1, respectively [42]. Furthermore, HMGB1 was identified as a downstream target of miR-204 [13] as mentioned in our study that HMGB1 was the target gene of miR-204. In conclusion, our study provides evidence that the upregulation of miR-204 can improve IR of PCOS by inhibition of HMGB1 and the inactivation of the TLR4/NF-κB pathway. This paper provides a new idea for further study on the pathogenesis of PCOS. We expect to find more association of miR-204/HMGB1 axis with patients with PCOS by this way to offer a more scientific basis for clinical decision-making. Acknowledgments We would like to acknowledge the reviewers for their helpful comments on this paper, and the project was supported by the Hunan natural science foundation youth fund (Grant no. 2018JJ3788) Authors’ contributions Guarantor of integrity of the entire study: Bin Jiang Study design: Min Xue Experimental studies: Dabao Xu, Yujia Song Manuscript editing: Juanshu Zhu Disclosure statement No potential conflict of interest was reported by the authors. Ethical statement The experiment was approved by The Third Xiangya Hospital of Central South University. ==== Refs References [1] Han Y, Li Y, He B. GLP-1 receptor agonists versus metformin in PCOS: a systematic review and meta-analysis. Reprod Biomed Online. 2019;39 :332–342. [2] Zhang D, Yang X, Li J, et al. Effect of hyperinsulinemia and IR on endocrine, metabolic, and fertility outcomes in women with polycystic ovary syndrome undergoing ovulation induction. Clin Endocrinol (Oxf). 2019;91 :440–448. [3] Li S, Qi J, Tao Y, et al. 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==== Front Glob Health J Glob Health J Global Health Journal (Amsterdam, Netherlands) 2096-3947 2414-6447 Published by Elsevier B.V. on behalf of People's Medical Publishing House Co. Ltd. S2414-6447(19)30054-5 10.1016/j.glohj.2019.09.003 Article What we have learnt from the SARS epdemics in mainland China? Cao Wuchun [email protected]⁎ Fang Liqun Xiao Dan Department of Infectious Disease Epidemiology, Institute of Microbiology and Epidemiology, Beijing 100850, China ⁎ Corresponding author. [email protected] 28 9 2019 9 2019 28 9 2019 3 3 55 59 © 2019 Published by Elsevier B.V. on behalf of People's Medical Publishing House Co. Ltd.2019Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.This article provides an overview of the severe acute respiratory syndrome (SARS) epidemics in mainland China and of what we have learned since the outbreak. The epidemics spanned a large geographical extent but clustered in two regions: first in Guangdong Province, and about 3 months later in Beijing and its surrounding areas. The resulting case fatality ratio of 6.4% was less than half of that in other SARS-affected countries and regions, partly due to younger-aged patients and a higher proportion of community-acquired infections. Strong political commitment and a centrally coordinated response were most important for controlling SARS. The long-term economic consequence of the epidemic was limited. Many recovered patients suffered from avascular osteonecrosis, as a consequence of corticosteroid usage during their infection. The SARS epidemic provided valuable experience and lessons relevant in controlling outbreaks of emerging infectious diseases, and has led to fundamental reforms of the Chinese health system. Additionally, the epidemic has substantially improved infrastructures, surveillance systems, and capacity to response to health emergencies. In particular, a comprehensive nationwide internet-based disease reporting system was established. Keywords Severe acute respiratory syndromeEpidemicInternet-based disease reporting systemSurveillance systems ==== Body 1 Introduction In 2003, the world was confronted with the emergence of a new and in many cases fatal infectious disease: severe acute respiratory syndrome (SARS). The first case with typical symptoms of SARS emerged in Foshan municipality, Guangdong Province, China, with the onset date of November 16, 2002.1 Five index cases were reported in Foshan, Zhongshan, Jiangmen, Guangzhou, and Shenzhen municipalities of Guangdong Province before January 2003. The early-stage outbreak of SARS in Guangdong Province was sporadic and apparently not associated with the index cases.2 By January 2003, SARS had developed into a large-scale outbreak in Guangdong Province,3 and after February 2003, it had appeared in Hong Kong4 and seven other provinces, including Guangxi, Jiangxi, Fujian, Hunan, Zhejiang, Sichuan and Shanxi.5 Some cases from Shanxi Province and Hong Kong were imported to Beijing, and transmission from index cases was amplified within several health care facilities by March 2003.6 Soon Beijing became the epicenter of SARS and endangered various other provinces or cities in mainland China. At the same time, Singapore, Canada, the United States, and Vietnam were involved in the worldwide spread through imported cases from Hong Kong.7 The World Health Organization (WHO) issued the first global alert on March 12, 2003, regarding a cluster of cases of severe atypical pneumonia in hospitals in Hong Kong, Hanoi, and Guangdong.8 Three days later, the WHO issued an emergency travel advisory. On March 24, 2003, the WHO described the clinical features of SARS, which were revised on May 1, 2003.9 In April 2003, a novel coronavirus, named SARS-associated coronavirus, was identified as the infectious agent responsible for SARS.10 , 11 Soon thereafter, cases were reported from 32 countries and regions (later corrected to 29). In total, 8,437 probable SARS cases, of whom 813 had died, were reported during the SARS epidemic of 2002–2003. Mainland China was the most seriously affected area, reporting 5,327 probable SARS cases, of whom 343 died, between November 16, 2002 and June 11, 2003. As a result of initial lack of awareness of SARS by health care workers (HCWs), the disease spread unnoticed in the early stages of the epidemic. This spreading of the disease was unduly prolonged by limited information sharing. At that time, a functional infectious diseases surveillance system was not yet available, and the reporting system was outdated, hampering data collection and delaying interventions. The SARS outbreak brought China virtually to a standstill, forcing the country to thoroughly review its infectious disease control policies. Since then, the Chinese government has implemented new and innovative strategies, strengthened the related aspects of the legal system and the disease prevention and control system, and made substantial investments to improve infrastructures, surveillance systems, and emergency preparedness and response capacity, such as the development of a real-time monitoring system that is now serving as a model for worldwide surveillance and response to infectious disease threats.12 The world has moved on since the SARS epidemic, but the insights gained in mainland China remain valuable, with comparable infectious disease threats presenting continuously. 2 Description of the epidemic During the 2002–2003 SARS outbreak, mainland China reported 5,327 probable cases, of whom 343 died, amounting to a case fatality ratio (CFR) of 6.4%. The epidemic spanned a large geographical extent (170 counties of 22 provinces) but clustered in two areas: first in Guangdong Province, and about 3 months later in Beijing and its surrounding areas Shanxi, Inner Mongolia, Hebei, and Tianjin. Fig. 1 shows the temporal distribution of SARS in the six most seriously affected geographic areas of mainland China. Spatiotemporal analyses indicated that the spread of SARS occurred in two different patterns.13 In the early stage of the epidemic, especially before strict control measures were taken, SARS spread to new areas randomly through certain index cases. Thereafter, human travel along transportation routes influenced the transmission of SARS, as illustrated by the spread of SARS in middle North China and South China. The epidemic period in middle North China was shorter than in South China, but the geographic spread was wider. SARS not only spread locally, but also diffused quickly and resulted in several outbreaks in areas of middle North China close to Beijing. In contrast, the SARS epidemic in South China was mainly limited to Guangdong Province.Fig. 1 The temporal distribution of SARS outbreaks in the six most seriously affected geographic areas of mainland China. Number of new cases per day of onset since the first SARS case on November 16, 2002, in Guangdong Province. SARS: severe acute respiratory syndrome. Fig. 1 Transportation routes accelerated the spread of SARS in mainland China. National highways and inter-provincial freeways appeared to play a critical role, whereas railways seemed to be less important.13 For the definition of SARS cases, a distinction was made between probable and suspected cases on the basis of contact history and the number and severity of symptoms. In China, it was only possible late in the epidemic to confirm SARS through serological tests. In a study comparing clinical characteristics of probable and suspected cases, it was found that although symptoms hardly differed, there were clearly different hematological profiles, justifying the distinction between probable and suspected cases and confirming that the suspected cases most likely did not have SARS.14 The average duration (3.8 days) and pattern (including time of epidemic and age) of onset of symptoms to hospital admission among SARS patients in mainland China were comparable to those of other affected areas.15 The duration of hospital admission to discharge for those who survived (29.7 days) was shorter than elsewhere in the world, possibly because of different hospitalization policies. The duration of hospital admission to death in mainland China was 17.4 days, which was also shorter than in other areas. Over the course of time, hospital epidemics were rapidly brought under control, with increasing efficiency.16 This was due to increasing understanding of the disease and more effective preventive measures, such as establishing isolation wards, training and monitoring hospital staff in infection control, screening HCWs, and compliance with the use of personal protection equipment.17 3 Case fatality Because of their deteriorated health status and apparent complications, the mortality rate increased significantly among patients aged 50 and above. The Tianjin SARS outbreak happened mainly within hospitals, leading to a high impact of comorbidity, which explains the relatively high CFR. Guangdong Province showed a considerably lower CFR than Beijing, the reason for which is still unclear (Fig. 2 ). In China, the overall CFR in mainland China was 6.4%, which was much lower than 17.2% in Hong Kong. And it was also lower than that reported in other areas and countries, e.g., CFR in Vietnam (9.7%). The much lower CFR in mainland China was also in contrast with the shorter duration of hospital admission to death compared with other countries or regions. The obvious reasons for the lower CFR are the young ages of the patients and a relatively higher number of community-acquired infections as opposed to hospital acquired infections. However, the relatively young ages of the cases (median age 33 years) only partly (approximately 25%) explains the low CFR in mainland China compared with other affected areas and countries, where patient median age varied from 37 years in Singapore to 49 years in Canada (Table 1 ).18 The relatively lower proportion of hospital-acquired infections in mainland China (reflected in the lower proportion of infections among HCWs compared with other areas [19% vs. 23%–56%; see Table 1]) is also only partly responsible for the lower CFR in mainland China, especially since this factor is highly correlated with age.Fig. 2 Comparison of the case fatality ratios of different ages for SARS patients in Beijing, Guangdong, and Tianjin. SARS: severe acute respiratory syndrome. Intervals indicate 90% binomially distributed confidence intervals. The values in parentheses represent the overall case fatality ratio for each area. Fig. 2Table 1 The characteristics of SARS outbreak in some countries or regions with high prevalences. Table 1Country or area Total case (person) Death case (CFR) [person(%)] Median age (year) Infected HCWs (percentage) [person(%)] Mainland China 5,327 343 (6.4) 33 1,021 (19.2) Hong Kong, China 1,755 302 (17.2) 40 405 (23.1) Taiwan, China 674 87 (12.9) 46 205 (30.3) Singapore 238 33 (13.9) 37 97 (40.8) Vietnam 62 6 (9.7) 43 35 (56.5) Canada 251 43 (17.1) 49 101 (40.2) SARS: severe acute respiratory syndrome; CFR: case fatality ratio; HCWs: health care workers. It has been suggested that mainland China had a substantial number of cases that were not really SARS, especially in Guangdong Province, where the epidemic started. A review study comparing seroprevalence rates in different SARS affected areas did show relatively lower seroprevalence in mainland China, but the differences in other areas were small and far from significant. However, even if the lower seroprevalence that was found in mainland China actually represented overreporting, this factor could only explain a modest 10% of the lower case fatality.19 Thus, there still remains a challenge in explaining the lower death rates in mainland China. The lower death rates may be due to better treatment (discussed below) and use of Chinese traditional medicines. 4 The effect of interventions During the SARS epidemic in mainland China, various interventions were implemented to contain the outbreak.20 Overall, the measures taken were certainly effective, given the fact that the epidemic was fully controlled within 200 days after the first case emerged. The method of Wallinga and Teunis,21 with quantifications from Lipsitch et al,22 was used to estimate R t, the effective or net reproduction number, which helps to determine which interventions were most important; R t is defined as the mean number of secondary cases infected by one primary case with symptom onset on day t. This number changes during the course of an epidemic, particularly as a result of effective control measures. If R t is larger than the threshold value of 1, a sustained chain of transmission will occur, eventually leading to a major epidemic. If this number is maintained below 1, then transmission may still continue, but the number of secondary cases is not sufficient to replace the primary cases, leading to a gradual fade out of the epidemic. Fig. 3 shows R t over time for mainland China, along with the timing of nine important events and public health control measures. The graph is characterized by a fluctuating pattern and wide confidence interval early in the epidemic, which can be explained by the initial low number of cases used in the calculations and the relatively more important impact of so-called “super-spreading events.” In Guangdong Province, where the epidemic started, standard control measures, such as isolation and contact tracing (arrow 2), seem to already have helped to largely interrupt transmission in this province. However, during the period from day 80 to day 140, the number of new SARS cases steadily increased, due to the spread to and within other parts of China (i.e., mainly Beijing and its surrounding provinces). The first official report of an outbreak in Guangdong Province (arrow 3) and the WHO global alerts (arrow 4) were by no means reflected in a consistent reduction of R t. Additionally, the first interventions in Beijing were not effective enough to cause any downward trend in the transmission (arrow 5). It was only around April 11 to April 14, 2003, that the Chinese authorities gained full control of all activities to combat SARS, with national, unambiguous, rational, widely followed guidelines and control measures, under central guidance (arrow 6). Immediately, the reproduction number decreased dramatically and consistently. Within 1 week, R t was below 1. Strikingly, this marked decrease after the period from April 11 to April 14, 2003, was consistently present in the patterns of R t for all heavily affected areas in mainland China.23 The stringent control measures to prevent human contacts (arrow 7), including the decision to cancel the public holiday of May 1 (arrow 8), were all initiated after R t was below 1 (i.e., when the epidemic was already dying off), again consistently for all areas in mainland China. Given the information available at the time, the most stringent interventions were rational because it was not clear to which extent the epidemic was under control. However, looking retrospectively, we can now conclude that these measures, which severely affected public life, contributed little to the factual containment of the SARS epidemic; the essential moment had occurred earlier. That being said, the late interventions may still have played a role in speeding up the elimination of SARS. Additionally, cancelling the public holiday—when millions of people travel long distances to visit their family—may have prevented (smaller) outbreaks in yet unaffected locations.Fig. 3 Estimated effective reproduction number (Rt) during the SARS epidemic in China, 2002–2003. Rt: number of secondary infections generated per primary case. Values represent average Rt (central white line) and associated 95% (gray) and 80% (black) confidence intervals, by date of symptom onset. The critical value of Rt = 1, below which sustained transmission is impossible, is marked with a broken horizontal line. Arrows reflect the time of important events and public health control measures: (1) local newspaper report about outbreak of unknown infectious disease in Guangdong Province (January 2, 2003); (2) start of control in Guangdong hospitals (e.g., isolation, contact tracing) (February 1–3, 2003); (3) first official report of outbreak by Guangdong authorities (February 11, 2003); (4) WHO global alerts; first mentioning of SARS (March 12–15, 2003); (5) first protocol of SARS control; start of isolation in Beijing hospitals (April 2, 2003); (6) mandatory reporting of SARS; definition of diagnostic criteria and treatment (April 11–14, 2003); (7) stringent control measures: quarantine in airports and stations; school, university, and public place closures; daily reporting by the national media (April 19–26, 2003); (8) public holiday cancelled; new 1000-bed SARS hospital opened (May 1, 2003); (9) further improvement of various guidelines and protocols (May 4–9, 2003). Fig. 3 We conclude that strong political commitment and a centrally coordinated response were the most important factors in the control of SARS in mainland China. With respect to future outbreaks of emerging infectious diseases, we emphasize that it is of first and foremost importance that effective control is based on clear national and international guidelines and well-built communication and reporting networks, along with firm determination and responsibilities at all levels. 5 Consequences of the epidemic There were many obvious immediate consequences of the epidemic, such as substantial morbidity and mortality, fear over the possibility of becoming infected, panic in the public domain, stringent quarantine measures, travel restrictions, etc. In addition, two important mid- and long-term consequences of the epidemic were identified. First, there was the economic impact. This was studied for Beijing by Beutels et al.24 through associating time series of daily and monthly SARS cases and deaths and volume of public train, airplane and cargo transport, tourism, household consumption patterns and gross domestic product growth in Beijing. The authors concluded that leisure activities, local and international transport, and tourism were severely affected by SARS, particularly in May 2003. Much of this consumption was merely postponed; however, irrecoverable losses to the tourism alone were estimated at about USD 1.4 billion, or 300 times the cost of treatment for SARS cases in Beijing. Second, there were long-term health consequences among the SARS patients who were treated with corticosteroids. Lv et al. investigated the relationship between avascular necrosis (AVN) and corticosteroid treatment given to SARS patients through a longitudinal study of 71 SARS patients (mainly HCWs) who had been treated with corticosteroids, with an observation time of 36 months.25 Magnetic resonance images and X-rays of the hips, knees, shoulders, ankles and wrists were taken as part of the post-SARS follow-up assessments. Thirty-nine percent developed AVN of the hips within 3–4 months after starting treatment. Two more cases of hip necrosis were seen after 1 year and another 11 cases of AVN were diagnosed after 3 years, 1 with hip necrosis and 10 with necrosis in other joints. In total, a staggering 58% of the cohort had developed AVN after 3 years of observation. The sole factor explaining AVN in the hip was the total dose of corticosteroids received. The use of corticosteroids has been debated, with conflicting opinions about steroids being the key component in the treatment of SARS.26 It has remained uncertain whether the aggressive use of corticosteroids during the SARS epidemic has tipped the balance. Has the use of high-dose corticosteroids saved more lives and been responsible for the lower case fatality in mainland China? Do immediate benefits, in terms of saving lives, outweigh the adverse effects, including AVN? 6 Lessons learned and actions taken in China regarding epidemic preparedness The SARS epidemic provided valuable experience and lessons with regard to controlling outbreaks of newly emerging infectious diseases, which are surely due to come. Human infection with avian influenza viruses, the novel A influenza (H1N1), and imported infectious diseases such as the Zika virus disease and yellow fever disease are already knocking at our doors! Important lessons learned in China included the need for more honesty and transparency, improvement of surveillance, better laboratory facilities, and optimized case management.27 Also, public health measures to control infectious diseases, reporting systems, and central guidance and coordination came under scrutiny. Another lesson was the need to inform the public about, and involve them in, control measures in an adequate and timely manner. There was a strong realization that the best defense against any threat of newly emerging infectious diseases is a robust public health system in its science, capacity, and practice, and through collaboration with clinical and veterinary medicine, academia, industry, and other public and private partners. An important resolution of the Chinese government was to improve its disease surveillance system to rapidly identify newly emerging infectious diseases and to minimize their spread in China and to the rest of the world. The traditional surveillance network using reporting cards filled out by hand and sent by mail or fax has been replaced with an automatic information system called the China Information System for Disease Control and Prevention, which is the world's largest internet-based disease reporting system.12 The government has also increased their investment in enhancing the capabilities of detecting, diagnosing, preventing, and controlling newly emerging infectious diseases at various levels. New and innovative strategies have been established for response to health emergencies, such as the establishment of the parallel laboratory confirmation mechanism for newly emerging infectious pathogens to reduce the risk of errors, rapid disclosure of information to the WHO and to the public, international information exchange and collaboration, and the provision of more information on public health and on infectious diseases to the public. Furthermore, the Chinese government has strengthened both the related aspects of the legal system and the disease prevention and control system. For example, the government issued the Law of the People's Republic of China on Prevention and Treatment of Infectious Diseases (Revised Draft) in 2004 and Regulations on Preparedness for and Response to Emergent Public Health Hazards in 2003, created the Chinese Centre for Disease Control and Prevention, and improved surveillance systems of infectious diseases and preparedness and response capacity for emerging public health events.28., 29., 30. Education and training projects, such as training courses for public health officials and HCWs, have been initiated, and new training has been added to the education programs of universities. Funds for research projects on the development of vaccines, drugs, and diagnostic techniques have been granted to develop new approaches in the prevention, diagnosis, and treatment of emerging infectious diseases. 7 Conclusion The epidemic of a new infectious disease, SARS, took firstly China and subsequently many other areas in the world completely by surprise. Fortunately, the consequences of this epidemic, in terms of people afflicted and economic loss, were not entirely catastrophic. Also, it turned out that SARS could be controlled relatively easily through standard interventions. However, the epidemic revealed some important weaknesses in the Chinese public health system, which have been dealt with efficiently and successfully by the Chinese government. At the moment, China is better prepared than ever for epidemics, which may be much worse than SARS in terms of speed of spread and fatality rate. In fact, SARS can be viewed as a wake-up call. (Excerpted from Infectious Disease in China: The Best Practical Cases. Beijing: People's Medical Publishing House; 2018.) Competing interests The authors declare that there is no conflict of interest. ==== Refs References 1. Zhong NS Zeng GQ Management and prevention of SARS in China SARS: a case study in emerging infections 2005 Oxford University Press Hong Kong 31 34 2. He JF Xu RH Yu DW Severe acute respiratory syndrome in Guangdong Province of China: epidemiology and control measures Zhonghua Yu Fang Yi Xue Za Zhi 37 4 2003 227 232 (in Chinese) 12930668 3. Zhao Z Zhang F Xu M Description and clinical treatment of an early outbreak of severe acute respiratory syndrome (SARS) in Guangzhou, PR China J Med Microbiol 52 Pt 8 2003 715 720 12867568 4. Lee N Hui D Wu A A major outbreak of severe acute respiratory syndrome in Hong Kong N Engl J Med 348 20 2003 1986 1994 12682352 5. Xu RH He JF Evans MR Epidemiologic clues to SARS origin in China Emerg Infect Dis 10 6 2004 1030 1037 15207054 6. Liang W Zhu Z Guo J Severe acute respiratory syndrome, Beijing 2003 Emerg Infect Dis 10 1 2004 25 31 15078593 7. Tsang KW Ho PL Ooi GC A cluster of cases of severe acute respiratory syndrome in Hong Kong N Engl J Med 348 20 2003 1977 1985 12671062 8. World Health Organization. WHO issues a global alert about cases of atypical pneumonia: cases of severe respiratory illness may spread to hospital staff. https://www.who.int/mediacentre/news/releases/2003/pr22/en/. Accessed January 5, 2018. 9. World Health Organization. Preliminary clinical description of severe acute respiratory syndrome. http://www.who.int/csr/sars/clinical/en/. Accessed January 5, 2018. 10. Drosten C Gunther S Preiser W Identification of a novel coronavirus in patients with severe acute respiratory syndrome N Engl J Med 348 20 2003 1967 1976 12690091 11. 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Wallinga J Teunis P Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures Am J Epidemiol 160 6 2004 509 516 15353409 22 Lipsitch M Cohen T Cooper B Transmission dynamics and control of severe acute respiratory syndrome Science 300 5627 2003 1966 1970 12766207 23. de Vlas SJ Feng D Cooper BS The impact of public health control measures during the SARS epidemic in mainland China Trop Med Int Health 14 Suppl. 1 2009 101 104 24. Beutels P Jia N Zhou QY The economic impact of SARS in Beijing, China Trop Med Int Health 14 Suppl. 1 2009 85 91 19508435 25. Lv H de Vlas SJ Liu W Avascular osteonecrosis after treatment of SARS: a 3-year longitudinal study Trop Med Int Health 14 Suppl. 1 2009 79 84 26. Gomersall CD Pro/con clinical debate: steroids are a key component in the treatment of SARS. Pro: yes, steroids are a key component of the treatment regimen for SARS Crit Care 8 2 2004 105 107 27. 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==== Front Early Hum Dev Early Hum. Dev Early Human Development 0378-3782 1872-6232 Elsevier B.V. S0378-3782(20)30231-0 10.1016/j.earlhumdev.2020.105043 105043 Article COVID-19 admissions calculators: General population and paediatric cohort Victor Grech [email protected] Paediatric Dept, Mater Dei Hospital, Malta 10 4 2020 6 2020 10 4 2020 145 105043 105043 6 4 2020 7 4 2020 © 2020 Elsevier B.V. All rights reserved.2020Elsevier B.V.Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.The world is in the grip of pandemic COVID-19 (SARS-CoV-2). Children appear to be only mildly affected but for those countries that are still preparing for their first wave of infections, it is salutary to have some estimates with which to plan for eventual contingencies. These assessments would include acute hospital admission requirements, intensive care admissions and deaths per given population. It is also useful to have an estimate of how many paediatric admissions to expect per given population. However it is only very recently that paediatric epidemiological data has become available. This paper will create an interactive spreadsheet model to estimate population and paediatric admissions for a given population, with the author's country, Malta, as a worked example for both. Highlights • COVID-19 (SARS-CoV-2) is currently a global pandemic. • Some countries have yet to experience their own epidemic. • This paper creates two calculators, population and paediatric. • Both admissions and intensive care calculations are performed. • All are available in two interactive spreadsheets. ==== Body 1 Introduction The world is in the grip of pandemic coronavirus COVID-19 (SARS-CoV-2) [1,2]. Children appear to be only mildly affected but for those countries that are still preparing for their first wave of infections, it is salutary to have some estimates with which to plan for eventual contingencies. These assessments would include acute hospital admission requirements, intensive care admissions and deaths per given population. It is also useful to have an estimate of how many paediatric admissions to expect per given population. However it is only very recently that paediatric epidemiological data has become available. This paper will create an interactive spreadsheet model to estimate population and paediatric admissions for a given population. 2 Methods All calculations are based on two assumptions: the absence of an effective vaccine and the absence of an effective antiviral agent that would mitigate the course of contracted illness. It is also naturally difficult to estimate rates as it is likely that a significant and unknown proportion of the population becomes infected but remains asymptomatic [5]. For this reason, unless large proportions of populations are tested for virus-specific antibody levels, we cannot possibly accurately estimate the total that has actually contracted the disease [6]. 2.1 At overall population level The World Health Organisation (based on data from China) has estimated that:• 14% of infected cases are severe and require hospitalisation. • 5% of infected cases are very severe and require intensive care admission, mostly for ventilation. • 4% of infected die [5]. Paediatric estimates are underpinned by two papers that are also based on Chinese data. 2.2 Paediatric populations Lu et al. evaluated both symptomatic and asymptomatic children (<16 years) who were contacts with confirmed or suspected COVID19 [3]. 1391 children were assessed with 171 (12.3%) confirmed cases. The median age was 6.7 years.• Fever was present in 41.5% at any time during the illness. • Other common signs and symptoms included cough and pharyngeal erythema. • 27 (15.8%) were asymptomatic with no radiological features of pneumonia. • 12 had radiologic features of pneumonia in the absence of symptoms of infection. • 3 patients required intensive care and invasive mechanical ventilation and these all had comorbidities (hydronephrosis, leukemia on maintenance chemotherapy, and intussusception). • 6 (3.5%) had lymphopenia (lymphocyte count <1.2 × 109/l) • The most common radiological finding was bilateral ground-glass opacity (32.7%). Dong et al. retrospectively evaluated 2143 children (<18 years) who had confirmed infection or were presumed to have the disease based on symptoms and history of exposure [4]. Median age was 7 years. Levels of severity were defined thus:• 4.4% were asymptomatic infections with normal chest imaging. • 50.9% were mild with symptoms of acute upper respiratory tract infection along with fever, fatigue, myalgia, cough, sore throat, runny nose, and sneezing. • 38.8% were moderate with pneumonia but no obvious hypoxemia such as shortness of breath. Some of these had only radiological findings with no clinical manifestation. • 5.2% were severe with dyspnea and oxygen saturation < 92%. • 0.6% were critical with respiratory failure/shock/encephalopathy/myocardial injury or heart failure/coagulopathyacute kidney injury. Interestingly, vulnerability was inversely related to age in that the proportion of severe and critical cases were 10.6%, 7.3%, 4.2%, 4.1% and 3.0% for the age groups of <1, 1–5, 6–10, 11–15 and ≥16 years. 3 Results This information was used to compile two spreadsheets. Table 1 shows estimates for Malta based on a 20% infection rate spread over 14 weeks. This spreadsheet is available for download from the supplementary materials. Table 2 shows expected number of paediatric patients based on a 20 to 80% infection rate, spread over 14 weeks. This spreadsheet is also available for download from the supplementary materials. Both sheets can be utilised to input region-specific data.Table 1 Totals hospitalised, and numbers requiring hospital admission, intensive care admissions and mortality. Weekly values also calculated, averaged over a 14 week period. Table 1Population 492,000 Malta total population Infected % 20 Population infection rate Numbers infected 98,400 Total number infected Hosp % of infected 14 Percentage infected that are severe Nos in hospital 13,776 Severe cases requiring hospitalisation Over no of weeks 14 Spread over this number of weeks Per week 984 Hospital admissions per week ITU % of infected 5 Percentage infected that are critically ill Nos in ICU 4920 Severe cases requiring intensive care Over no of weeks 14 Spread over this number of weeks Per week 351 ICU admissions per week % morality of infected 5 Percentage deaths Nos dead 4920 Total deaths It must be reiterated that these are best guesses and estimates that preclude the discovery of effective treatment and/or vaccination. Table 2 Spreadsheet showing paediatric cases based on Malta assuming an annual delivery rate of circa 5500 births/annum. Estimated infection rates at 20 to 80%, with calculations of averaged weekly admissions over a 14 week period. Based on Dong. et al. [4]. Table 2 Malta Severe/critical Infection rate n % 20 40 60 80 Age 1 5000 10.6 106 212 318 424 1 to 5 25,000 7.3 365 730 1095 1460 6 to 16 25,000 4.2 210 420 630 840 11 to 16 25,000 4.1 205 410 615 820 Totals 886 1772 2658 3544 Critical only Total 80,000 0.6 96 192 288 384 Cases/week at infection rates as above over the following number of weeks: 14 Severe 56 113 169 226 Critical 7 14 21 27 It must be reiterated that these are best guesses and estimates that preclude the discovery of effective treatment and/or vaccination. 4 Discussion By the very nature of the disease and its definition, it is not easy to control pandemic spread. China has managed to drastically reduce new cases by >90% [7], but this has taken draconian measures. Countries that started late have taken off exponentially, with hospitals overwhelmed [7]. Intensive care units have been completely inundated, with the chief bottleneck being availability of mechanical ventilators to tide critically ill patients over their intensive care stay [7]. For these reasons, global mortality may even greatly exceed that of so called Spanish Flu around the end of the First World War [8,9]. This paper will not discuss mitigation vs. suppression measures and the importance of hygiene etc., except to note that without active and very vigorous suppression, harrowing scenes will be re-enacted, as we witnessed after surges of cases in Northern Italy, and over the last few days, in New York [9]. The non-availability of ventilators to cope with extreme surges in case numbers may lead to triage situations with doctors having to choose who to ventilate and who to leave to die [7]. The results shown here suggest that the Maltese health services would find it extremely difficult to cope even with a 20% infection rate spread over a 14 week period [10]. These calculations assume that severe cases that would normally require relatively standard care (such as supplemental and non-invasive administration of oxygen, intravenous fluids, antibiotics for secondary infections etc.) actually manage to access these therapies. In surge conditions, even the provision of such relatively basic and standard care may falter or fail. Furthermore, in extreme surge situations, the provision of care for everyday medical conditions would also be compromised, with morbidity and mortality also incurred from non-novel conditions. 5 Conclusion It is hoped that these calculators will help clinicians and planners to plan ahead with the expected surges in cases in respective regions and countries. Declaration of competing interest There are no real or potential conflicts, financial or otherwise. There was no funding for this paper. Appendix A Supplementary data Supplementary tables Image 1 Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.earlhumdev.2020.105043. ==== Refs References 1 Chen N. Zhou M. Dong X. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study Lancet 395 10223 2020 507 513 10.1016/S0140-6736(20)30211-7 32007143 2 Fehr A.R. Perlman S. Coronaviruses: an overview of their replication and pathogenesis Methods Mol. Biol. 1282 2015 1 23 10.1007/978-1-4939-2438-7_1 25720466 3 Lu X. Zhang L. Du H. SARS-CoV-2 infection in children [published online ahead of print, 2020 Mar 18] N. Engl. J. Med. 2020 10.1056/NEJMc2005073 (doi:10.1056/NEJMc2005073) 4 Dong Y. Mo X. Hu Y. Epidemiological characteristics of 2143 pediatric patients with 2019 coronavirus disease in China Pediatrics 2020 10.1542/peds.2020-0702 5 World Health Organisation Coronavirus Disease 2019. WHO Report 41.01 March 2020 6 Velavan T.P. Meyer C.G. The COVID-19 epidemic Tropical Med. Int. Health 25 3 2020 278 280 10.1111/tmi.13383 7 Remuzzi A. Remuzzi G. COVID-19 and Italy: what next? [published online ahead of print, 2020 Mar 13] Lancet. S0140-6736 20 2020 30627 30629 10.1016/S0140-6736(20)30627-9 8 Grech V. Unknown unknowns – COVID-19 and potential global mortality. Early Hum. Dev. – in (press). 9 Grech V. Victor Grech: Pandemics: Known Unknowns and Our Failure to Prevent Our Unwilling Participation in a Dystopian SF Scenario. New York Rev Science Fiction – in (press). 10 . Grech V. COVID-19 in Malta, a small island population. The Synapse – in (press).	32311646	PMC7151441	NO-CC CODE	2021-01-06 09:00:53	yes	Early Hum Dev. 2020 Jun 10; 145:105043
==== Front Mol Biol Rep Mol Biol Rep Molecular Biology Reports 0301-4851 1573-4978 Springer Netherlands Dordrecht 32303958 5427 10.1007/s11033-020-05427-1 Review RETRACTED ARTICLE: β adrenergic receptor modulated signaling in glioma models: promoting β adrenergic receptor-β arrestin scaffold-mediated activation of extracellular-regulated kinase 1/2 may prove to be a panacea in the treatment of intracranial and spinal malignancy and extra-neuraxial carcinoma Ghali George Zaki 12 Ghali Michael George Zaki [email protected] [email protected] 34 1 grid.418698.a 0000 0001 2146 2763 United States Environmental Protection Agency, Arlington, VA USA 2 grid.169077.e 0000 0004 1937 2197 Emeritus Professor, Department of Toxicology, Purdue University, West Lafayette, IN USA 3 grid.266102.1 0000 0001 2297 6811 Department of Neurological Surgery, University of California, San Francisco, 505 Parnassus Avenue, Box-0112, San Francisco, CA 94143 USA 4 grid.4714.6 0000 0004 1937 0626 Department of Neurological Surgery, Karolinska Institutet, Nobels väg 6, Solna and Alfred Nobels Allé 8, Huddinge, SE-171 77 Stockholm, Sweden 18 4 2020 2020 47 6 46314650 25 2 2020 3 4 2020 © Springer Nature B.V. 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Neoplastically transformed astrocytes express functionally active cell surface β adrenergic receptors (βARs). Treatment of glioma models in vitro and in vivo with β adrenergic agonists variably amplifies or attenuates cellular proliferation. In the majority of in vivo models, β adrenergic agonists generally reduce cellular proliferation. However, treatment with β adrenergic agonists consistently reduces tumor cell invasive potential, angiogenesis, and metastasis. β adrenergic agonists induced decreases of invasive potential are chiefly mediated through reductions in the expression of matrix metalloproteinases types 2 and 9. Treatment with β adrenergic agonists also clearly reduce tumoral neoangiogenesis, which may represent a putatively useful mechanism to adjuvantly amplify the effects of bevacizumab. Bevacizumab is a monoclonal antibody targeting the vascular endothelial growth factor receptor. We may accordingly designate βagonists to represent an enhancer of bevacizumab. The antiangiogenic effects of β adrenergic agonists may thus effectively render an otherwise borderline effective therapy to generate significant enhancement in clinical outcomes. β adrenergic agonists upregulate expression of the major histocompatibility class II DR alpha gene, effectively potentiating the immunogenicity of tumor cells to tumor surveillance mechanisms. Authors have also demonstrated crossmodal modulation of signaling events downstream from the β adrenergic cell surface receptor and microtubular polymerization and depolymerization. Complex effects and desensitization mechanisms of the β adrenergic signaling may putatively represent promising therapeutic targets. Constant stimulation of the β adrenergic receptor induces its phosphorylation by β adrenergic receptor kinase (βARK), rendering it a suitable substrate for alternate binding by β arrestins 1 or 2. The binding of a β arrestin to βARK phosphorylated βAR promotes receptor mediated internalization and downregulation of cell surface receptor and contemporaneously generates a cell surface scaffold at the βAR. The scaffold mediated activation of extracellular regulated kinase 1/2, compared with protein kinase A mediated activation, preferentially favors cytosolic retention of ERK1/2 and blunting of nuclear translocation and ensuant pro-transcriptional activity. Thus, βAR desensitization and consequent scaffold assembly effectively retains the cytosolic homeostatic functions of ERK1/2 while inhibiting its pro-proliferative effects. We suggest these mechanisms specifically will prove quite promising in developing primary and adjuvant therapies mitigating glioma growth, angiogenesis, invasive potential, and angiogenesis. We suggest generating compounds and targeted mutations of the β adrenergic receptor favoring β arrestin binding and scaffold facilitated activation of ERK1/2 may hold potential promise and therapeutic benefit in adjuvantly treating most or all cancers. We hope our discussion will generate fruitful research endeavors seeking to exploit these mechanisms. Keywords β adrenergic receptor β adrenergic receptor kinase β arrestin Glioma Glioblastoma Tumor issue-copyright-statement© Springer Nature B.V. 2020 ==== Body pmcIntroduction Untransformed and malignantly transformed astroglial cells widely express neurolemmal cell surface β adrenergic receptors [1–3]. Human (e.g., U-251-MG, LM, and 1321 N1 astrocytoma cell lines) and rat (e.g., C6, C62B) glioma cells widely overexpress pharmacologically-stimulable and functionally active cell surface β adrenergic receptors (βARs) [4, 5]. In mice transfected with U87 cells in order to induce gliomagenesis in vivo, tumors overexpress β2ARs by approximately two-fold compared with cells of nearby healthy parenchyma [6]. Accordingly, β adrenergic receptor modulated signaling regulates intracellular signal transduction pathways implicated in the initiation, promotion, and progression of carcinogenesis. Studies have extensively indicated β adrenergic signaling powerfully modulates tumor cell proliferation, angiogenesis, invasiveness, and metastasis [7]. Authors have collectively elucidated these effects in glioma models in vitro [8, 9] and in vivo [10]. We extensively discuss differential signal transduction pathways conveying β adrenergic signaling to cytosolic and nuclear mechanisms mediating cell surface receptor desensitization in untransformed and neoplastically transformed glioma cells [11–16]. Our molecularly-oriented discourse will shed light on the apparent paradoxical behavior of carcinomas in response to pharmacological agonism or antagonism of β adrenergic receptor modulated signaling in vitro and in vivo. In so doing, we effectively illumine the potential utility of developing compounds modulating β adrenergic receptor modulated signaling in the treatment of cerebral gliomas [10]. The development of a thorough understanding of these mechanisms will pave the way and enhance our capacity to develop novel therapeutic approaches to induce log cell eradication of malignantly transformed astrocytes constituting cerebral gliomas [11–16]. β adrenergic receptor modulated signaling We present an integrated framework detailing and conceptualizing the effects of β adrenergic receptor modulated signaling upon intracellular signal transduction pathways [11–16], constituted by specific and sequential phosphorylation-dependent conformational protein modifications, mechanisms blunting βAR-G protein coupling and promoting receptor internalization [14, 17], and candidate therapeutic molecular targets modulating downstream signaling effects [18]. β adrenergic receptors constitute a family of heteromultimeric heptahelical transmembrane proteins (Fig. 1) [16], which modulate cellular processes by promoting G protein-mediated signal transduction (Fig. 2) [19] and alternately upregulating [20, 21] or downregulating [22] the catalytic enzymatic activity of adenylate cyclase, which generates cyclic adenosine monophosphate (cyclic AMP or cAMP) from the high-energy substrate adenosine triphosphate (ATP) [23]. Cyclic AMP allosterically activates protein kinase A (PKA) by binding its regulatory subunits and physically releasing its catalytic subunits [24–27]. Ligand binding mediated promotion of β adrenergic receptor modulated signaling concurrently potentiates the catalytic enzymatic activity of phospholipase C, generating diacylglycerol (DAG) and inositol triphosphate (IP3) from the precursor phospholipid phosphatidyl inositol diphosphate (PIP2). DAG allosterically activates protein kinase C, which phosphorylatively modulates a host of intracellular signal transduction pathways. Binding of IP3 to receptors studding the phospholipid bilayer membrane of the sarcoplasmic reticulum enhances the release of divalent cationic calcium from abundant organellar stores to the cytosol. Ligand-activated β adrenergic receptors transactivate intracellular tyrosine, serine-threonine, or SRC kinase-coupled membrane protein growth factor receptors [28–31]. C-terminal phosphorylated β adrenergic receptor β arrestin complexes constituting nidal signaling scaffolds may selectively and specifically potentiate ERK1/2 activity and a set of variably related intracellular signal transduction pathways (Figs. 3, 4) [32–34].Fig. 1 β1 adrenergic receptor structure schematic diagram. A Turkey β1 adrenergic receptor sequence illustrated in relation to secondary structural elements (these refer to alpha helices, beta pleated sheets, and beta bends). Amino acid sequence indicated in white circles demonstrate regions which are not well ordered, with sequences not resolved indicated by grey circles. Amino acid sequences on an orange background were deleted in order to generate the β1 adrenergic receptor construct for expression. Thermostabilising (red), C116L (mediating an increase in functional expression), and C358A (eliminating the palmitoylation site) (blue) mutations, and Na+ ion (purple) indicated by color. Numbers refer to the first and last amino acid residues contained within each helix (blue boxes), with the Ballesteros-Weinstein numbering indicated in superscript. Helices were defined utilizing the Kabasch & Sander algorithm, with helix distortions being defined as residues maintaining chain torsion angles differing by more than 40° from the standard α-helix values (− 60°, − 40°). B Ribbon representation of the 1 adrenergic receptor structure demonstrated in rainbow colouration, with N-terminus (blue), C-terminus (red), Na+ ion (pink), and two disulphide bonds (yellow) indicated. Cyanopindolol is indicated as a space-filling model. Extracellular loop 2 (EL2) and cytoplasmic loops 1 and 2 (CL1, CL2) are labelled. Reprinted with permission from Warne et al. [16]. (Color figure online) Fig. 2 β adrenergic receptor G protein cycle. A Extracellular agonist binds to the β adrenergic receptor effecting conformational changes within the cytoplasmic ends of the receptor transmembrane domains, allowing the heterotrimeric G protein to bind the β adrenergic receptor. G protein binding to the β adrenergic receptor facilitates conformational changes promoting GTP-GDP exchange by the α subunit, facilitating dissociation of the catalytic α and noncatalytic βgamma subunit. The G protein catalytic α and noncatalytic βgamma subunits mediate various effector activities. The G protein activity stimulates adenylate cyclase activity and the noncatalytic βgamma subunit is demonstrated activating membrane calcium channels mediating entry of extracellular calcium to the cytosol. The α subunit subsequently catalyzes hydrolysis of bound guanosine triphosphate to guanosine diphosphate, mediating G protein α and βgamma subunit reconstitution. The G protein βgamma noncatalytic subunit also ferries the β adrenergic receptor kinase towards the β adrenergic receptor. B The purified β adrenergic receptor Gs protein complex free of nucleotides is maintained in detergent micelles. The Gα subunit consists of the Ras domain (αRas) with GTPase activity and the α-helical domain (αAH). Both subunits are involved in nucleotide binding. In the nucleotide-free condition, the α helical domain has a variable position relative to the Gα Ras domain. Reprinted with permission from Rasmussen et al. [81] Fig. 3 β-Arrestin contributes to ubiquitinylation and receptor mediated signaling. (1) MDM2 binds and mediates ubiquitinylation of receptor-associated β arrestin, promoting recruitment of clathrin and AP2, internalization of membrane bound receptors, and β arrestin-mediated scaffold facilitated signaling. (2) β arrestins facilitate ubiquitinylation of receptors by forming scaffolds comprised of E3 ligase, bringing these enzymes into close proximity with the receptors, thereby promoting receptor ubiquitinylation and trafficking to lysosomes for degradation. (3) β arrestin1 serves as an adaptor protein bringing the E3 ligase MDM2 to the activated insulin like growth factor-1 receptor, thereby promoting ubiquitinylation of receptor and subsequent proteasomal degradation. (4) β arrestins compete with insulin receptor substrate 1 for MDM2, thereby reducing insulin-induced MDM2-mediated ubiquitinylation of insulin receptor substrate 1 and proteasomal degradation. Insulin receptor substrate 1. β arrestins thus enhance sensitivity to insulin signaling. (5) Stimulation by insulin through tyrosine kinase receptors promotes phosphorylation of β-arrestin, ubiquitinylation, and receptor downregulation, thereby augmenting heptahelical transmembrane receptor-mediated G protein signaling and reducing signaling facilitated by the adapter function of β arrestin promoting scaffold assembly. β-arrestin-mediated signaling (e.g., to extracellular regulated kinase 1/2). (6) In Drosophila melanogaster, Kurtz, the nonvisual homologin of arrestin, interacts with the ubiquitin ligase Deltex in order to facilitate Notch ubiquitinylation. Notch ubiquitinylation promotes its proteasomal degradation. Reprinted with permission from Lefkowitz et al. [13] Fig. 4 Conventional compared to biased heptahelical transmembrane receptor signaling. A Agonist-stimulated heptahelical transmembrane receptor signaling is mediated via both heterotrimeric G proteins and β arrestins. B Conventional antagonists bind heptahelical transmembrane receptor proteins and prevent agonist stimulated signaling through both heterotrimeric G proteins and β arrestins. C Soi-disant biased agonists/antagonists (e.g., SII-angiotensin II) prevent heterotrimeric G protein signaling mediated by agonist stimulation of G protein coupled receptor while promoting β arrestin facilitated scaffold signaling. Reprinted with permission from Lefkowitz et al. [13] Desensitization of β adrenergic receptor ligand binding-effector coupling (Fig. 3) heads a pseudo-dichotomous signal transduction pathway switch [13, 35, 36] (Fig. 4). (N.B. C6 glioma cells undergo downregulation of cell surface β adrenergic receptor expression when grown in serum [37]). Agonist binding to the β adrenergic receptor renders it suitable to undergo carboxyl terminal phosphorylation by β adrenergic receptor kinase (βARK) [11, 14], β arrestin 1 and/or 2 binding of phosphorylated β adrenergic receptor C-terminal sterically hinders βAR-G protein coupling [66, 81]. The adapter function of β arrestin proteins promotes binding of clathrin to the internal layer of phospholipid zones surrounding βARs, which effects clathrin-coated pit-mediated receptor endocytosis [13]. The βAR-β arrestin scaffold promotes binding of ERK1/2, c Jun N terminal kinase 3 (JNK3), Raf, cRaf1, and MEK1 (Figs. 3, 4) [11, 12, 15]. Preferential activation of these signaling proteins which classically promote cellular proliferation when activated by protein kinase A by the scaffold mechanism coordinately favors cytosolic retention and effects of these proteins and prevents nuclear translocation and pro-transcriptional activity-mediated promotion of deoxyribonucleic acid and proteins constituting the mitotic machinery [11, 12]. β arrestin 1 exhibits preferentially stable binding kinetics with the βAR compared with β arrestin 2. Binding of the amino terminus of β arrestin 1 to the carboxyl terminus of βAR generates stable receptor internalization and slow βAR GPCR dephosphorylation, slowing return to the cell membrane [15]. Stable β arrestin 1 βAR binding favors scaffold assembly and scaffold mediated activation of the pleiotropically-acting kinase ERK1/2 [12, 15]. Thus, the same set of mechanisms which mediates desensitization and internalization of the β adrenergic receptor [15] coordinately contributes to modulating the effects mediated by ERK1/2 [11, 12, 15]. Thus, instead of conceiving of β arrestin to represent a general inhibitor of β adrenergic signaling, it may be more appropriate and prudent to conceptualize this protein to modulate β adrenergic receptor modulated signaling, coordinately attenuating G protein-mediated effects and preferentially shifting signaling towards the non-proliferative actions of ERK1/2 (Fig. 4) [11, 12, 15]. Kinetics of β arrestin dissociation from GPCRs powerfully determine receptor conformational changes and dictate effects of downstream signaling [15]. Angiotensin 1A, vasopressin 2, neurotensin, and dopamine receptor carboxyl termini bind β arrestin 2 stably with slower dissociation kinetics compared with the carboxyl terminal of βARs, generating stable clathrin coated pit-mediated internalization with slower dephosphorylation and return to the cell membrane [15]. The stable binding preferentially favors the cytosolic retention and activity ERK1/2, while downregulating the nuclear effects of the kinase [11, 12]. β arrestin 2 binds the α1b and β2 adrenergic receptors transiently with more rapid dissociation kinetics. Rapid dissociation kinetics generates equivalently rapid removal of phosphate moieties from the G protein-coupled receptor (GPCR) and return of endocytosed receptor to the plasmalemmal phospholipid bilayer [15] and preferentially enhances G protein mediated effects of G protein coupled receptor activation and comparatively attenuates scaffold mediated effects upon signal transduction pathways, coordinately promoting nuclear translocation of, and transcriptional upregulation mediated by, activated ERK1/2. Modulation of cellular proliferation by β adrenergic signaling Malignantly transformed astroglia overexpress pharmacologically stimulabe and functionally active β adrenergic receptors [5]. Studies have provided evidence indicating ligand activation of β adrenergic receptor modulated signaling may either promote or blunt proliferation of malignantly transformed cells in glioma models [4, 38–44] and extra-neuraxial carcinoma [45–49]. Specifically, ligand activation of β adrenergic receptors potently amplifies cellular proliferation in lung [7], gastric [50], hepatocellular [51], pancreatic [52], colorectal [53], breast [54, 55], ovarian [56, 57], and prostatic [49] carcinoma models in vitro. Paradoxically, pharmacological antagonism of β adrenergic receptors also potently attenuates cellular proliferation in hemangioblastoma [58] and hepatic [55], pancreatic [59], gastric [50], colorectal [46], breast [54, 55], ovarian, and prostatic [60] carcinoma models in vitro. β antagonists reduce cellular proliferation and migration in neuroblastoma cell lines [8], enhance therapeutic concentrations of co-administered medications [8], and reduce expression of P-glycoprotein inhibitors [61]. β adrenergic receptor agonists were shown to reduce the proliferation of MDA-MB-231 human breast cancer cells [48, 118]. Succinctly, blunting of tumor cell proliferation in vitro by β adrenergic agonists results from desensitization and by β adrenergic antagonists results directly from receptor antagonism [62]. Studies have alternately demonstrated improved [63] or reduced [64] survival in patients harboring ovarian carcinoma receiving pharmacological antagonists of β adrenergic receptors. The bitopic agonist and GPR55 antagonist ( )-4′-methoxy-1-naphthylfenoterol, which may be designated as ( )-MNF, significantly reduces mitogenic potential in melanoma by modulating cyclic AMP protein kinase A-dependent pathways [65]. ( )-4-methoxy-1-naphthylfenoterol reduces HepG2 and PANC-1 tumor cell migratory capacity through actions upon GPR55 [66]. Treatment with the β adrenergic agonist isoproterenol dose-dependently enhances U251MG glioblastoma cellular proliferation by promoting the phosphorylation and enzymatic activity of ERK1/2 in vitro [67]. Norepinephrine reduces cellular proliferation and uptake of l-arginine in rat glial cells [68] and 1,25-dihydroxycholecalciferol-induced apoptosis of glioma cells in vitro [69]. The bitopic compound ()-fenoterol inhibits proliferation of, and reduces l-arginine uptake in, N1321 astrocytoma and U118 glioblastoma cells [9]. Stimulation of purinergic receptor (P2Y12) modulated signaling inhibits cyclic AMP from tonically inhibiting protein kinase B, which in turn tonically restricts C6 glioma cells from undergoing differentiation [70]. Thus, we may, by extension, consider promoting the enzymatic catalytic activity of adenylate cyclase enhances the synthesis of cyclic adenosine monophosphate and restricts protein kinase B from tonically inhibiting proliferation of C6 glioma cells. Similarly, phosphatidylinositol-3-kinase (PI3K) mediated enhancement of cyclic AMP synthesis would concurrently promote cellular differentiation [70]. Though our best understanding of molecular pathways converging upon, and diverging through, protein kinase A, would lead us to surmise enhanced levels of intracellular cyclic adenosine monophosphate and activity of ERK1/2 (i.e., MAPK) signaling correlates with enhanced cellular proliferation and reduced levels correlate with the converse complementary set of effects, Kurino et al. paradoxically demonstrated C6 glioma cells experience paradoxical inhibition of MAPK by growth factor-mediated upregulation of cyclic AMP several decades ago [71]. Carvedilol exerts a pleiotropic set of effects upon C6 glioma cells in vitro, enhancing the proportional fraction of cells in the soi-disant S and G2 phases at 24 h and the proportional fraction of cells in the G0 and G1 phases at 72 h [72]. These differential dynamics are consistent with initial promotion of β adrenergic receptor modulated signaling, enhancement of the catalytic enzymatic activity of adenylate cyclase, and increased cyclic adenosine monophosphate levels, protein kinase A activity, and extracellular regulated kinase 1/2 mediated phosphorylation of target nuclear proteins, enhancing cellular proliferation, followed by β adrenergic receptor desensitization of ligand effector coupling, reducing cellular proliferation [72]. Coadministration of carvedilol enhanced imatinib-induced cellular apoptosis (5% and 2% at 24 h and 72 h in a monolayer of C6 glioma cells), mitochondrial lysis, and retention of P-glycoprotein inhibitor [72]. Treatment with the bitopic βagonist GPR55 antagonist ( )-MNF reduces cellular proliferation (by inducing G1 cell cycle arrest), cell motility, phosphorylation of molecular substrates of protein kinase A, and activity through ERK1/2 and Akt pathways. High concentrations of ( )-MNF reduces glioma cell motility [72]. In seeking to evaluate the effects of promoting βAR modulated signaling upon the behavior of gliomas in vivo, Yoshida et al. generated extra-neuraxial models of glioma and meningeal gliomatosis by subcutaneously implanting C6 glioma cells [74]. Treatment with the β1 and β2 adrenergic receptor agonist isoproterenol, which may elicit cellular pro-proliferative effects through the activation of adenylate cyclase-cyclic AMP-protein kinase A-ERK1/2 signaling in vitro, paradoxically reduced tumor growth and improved animal survival in vivo [74]. These effects were synergistically enhanced by treatment with the phosphodiesterase inhibitor papaverine, implicating cyclic AMP mediates the effects generated by β agonists [74]. Isoproterenol was shown to attenuate C6 glioma cellular proliferation in vitro, an effect synergistically promoted by inhibition of the enzymatic degradative activity of phosphodiesterase by papaverine [75]. The findings collectively indicate βAR modulation may reduce growth of gliomas in human patients. Differential effects mediated by β adrenergic agonists, and the congruent effects paradoxically mediated by pharmacological antagonists of β adrenergic receptor modulated signaling, upon non-malignantly transformed and glioma cellular proliferation may result from differential activation of downstream intracellular signal transduction pathways promoted by agonist ligand binding to, and/or desensitization of βAR and phospho-βAR-β arrestin scaffold assembly [6, 72, 76–80]. Stimulation of βAR stimulates the AC-cAMP-PKA-ERK1/2 pathway, effectively promoting cellular proliferation [81]. However, sustained βAR activation generates receptor desensitization, clathrin coated pit mediated receptor endocytosis and internalization, parallel increases of cytosolic calcium concentrations, and upregulation of the synthesis of phosphodiesterase enzyme [13]. The effects collectively attenuate the adenylate cyclase-cAMP-PKA pathways, preferentially promote scaffold facilitated activation of ERK1/2 rather than PKA mediated phosphorylative activation, reducing nuclear translocation and pro-transcriptional effects augmenting cellular proliferation, and amplifying the enzymatic cleavage capacity of phosphodiesterase to reduce cyclic AMP levels [11, 12]. Recent work conducted by O’Hayre et al. indicates β2AR-mediated activation of ERK absolutely requires β arrestins [82]. Intracellular effects of β adrenergic signaling in glioma models βAR agonists enhance C6 glioma cellular proliferation and motility by promoting PKA and ERK1/2 signaling [83], which we believe to represent the chief and most likely direct effect of appropriately augmenting β adrenergic receptor modulated signaling. β antagonists reduce glioma cellular proliferation by inducing glioma cell cycle arrest and attenuate cyclic AMP mediated activation of ERK1/2 [6, 72, 77, 79, 80]. Differential and divergent effects mediated by β2 adrenergic receptor stimulation in vitro could be attributed to alternate coupling to either or both Gs or Gi proteins [84]. Gαs protein activates, and Gαi protein inhibits, the enzymatic activity of adenylate cyclase. Transfection of with Go1 alpha protein complementary DNA reduced isoproterenol- (βAR agonist) and forskolin (adenylate cyclase activator)-mediated enhancement of cytosolic increases of cyclic adenosine monophosphate and isoproterenol mediated transient increases of cytosolic calcium and calcium mediated enhancement of cytosolic accumulation of cyclic adenosine monophosphate [83]. ( )-MNF activates either or both Gs or Gi coupled β2 ARs, whereas ( )-Fen selectively enhances the activity of Gαs-coupled β2 ARs [65, 73, 85, 86]. These properties of the bitopic compounds fenoterol stereoisomers ( )-MNF and ( )-Fen cause these agents to mediate more effects upon cellular proliferation and dynamic behavior compared with pure β adrenergic agonists (Fig. 5).Fig. 5 Fenoterol structure, chemical activity, and biological actions. Fenoterols represent ideal candidate molecular structures which could be chemically modified in order to optimize agonist potency and generate specific beta adrenergic receptor conformations conducive of tighter binding of β arrestins. The fenoterol core structure consists of a bisubstituted meta dihydroxphenyl moiety and an ethanolamine side chain. The side chain attachments include a methyl or ethyl group in the R1 position and various ring form modifications of substituted (hydroxy, amino, methoxy) benzyl or naphthyl rings. IC50/EC50 ratios are inversely proportional to potency of inhibition of tritiated thymidine incorporation, a measure of DNA synthesis and thus cellular proliferative capacity. Lower ratios between the IC50 and EC50 correlate with lower concentrations of drug necessary to attenuates rates of synthetic thymidine incorporation into DNA. These fenoterol derivates effect potent inhibition of cellular proliferative capacity and effect cellular apoptosis. Different fenoterol stereoisomers generate differential percentage changes of HepG2 cells and inhibition of 1321 N astrocytoma cell mitogenic capacity, as measured by tritiated thymidine incorporation. The α carbon and γ amine groups represent steroisomerically active centers [51, 65, 66, 73, 85, 86]. Reprinted with permission from Paul et al. [51] Gi protein-coupled receptors (e.g., GABAB, opioid, cannabinoid, α2 adrenergic) commonly converge on attenuating the enzymatic activity of adenylate cyclase, blunting the generation of cyclic AMP and reducing cyclic AMP-mediated enhancement of cellular proliferation, invasion, and metastasis [87, 88]. Cross-talk between βAR with Gi protein-coupled receptors may contribute to differential effects mediated by β adrenergic receptor modulated signaling. For example, ligand activation of GABAB receptors inhibits isoproterenol-mediated enhancement of pancreatic cancer cell proliferation [89], providing evidence indicating a critical importance of crosstalk amongst β adrenergic and the complement constituents of the family of G protein-coupled receptors. Crossmodal modulation of cell surface receptor activation, desensitization, and scaffold-mediated effects may critically contribute to differential effects generated by alternate stable or transient ligand binding of pharmacological agonists or antagonists to βARs in different tumor cell lines [88]. The described effects may also explain β adrenergic agonist and antagonist-mediated attenuation of glioma tumor cell migration [72, 79] and enhance drug sensitivity to imatinib [72]. Mechanisms underlying desensitization of β adrenergic receptor modulated signaling in glioma cell lines Continuous β adrenergic receptor agonist stimulation desensitizes ligand binding-effector coupling, promotes clathrin-coated pit mediated receptor cytosolic internalization, and downregulates nascent messenger ribonucleic acid (RNA) transcripts in non-malignantly-transformed astrocytes [13] and glioma cell lines [90]. β adrenergic receptor kinase phosphorylates βAR carboxyl terminus amino acid moieties, to which β arrestin binds, coordinately reducing the efficacy of ligand binding-effector coupling [13], reduces βAR-mediated cytosolic calcium rises, and preferentially attenuating cAMP-PKA facilitated activation of ERK1/2 [15] and favoring βAR-β arrestin scaffold facilitated ERK1/2 activation [15]. Scaffold-mediated activation of ERK1/2 favors cytosolic retention and attenuates nuclear translocation and pro-transcriptional activity, preserving the housekeeping homeostatic function of ERK1/2 while preventing its promotion of cellular proliferation (Figs. 3, 4). Elevations of cytosolic calcium effectively attenuate βAR stimulation- and adenylate cyclase stimulation- (forskolin) mediated enhancement of cytosolic cyclic AMP concentration in a C62B glioma model in vitro [91], perhaps by promoting the de novo synthesis of phosphodiesterase [44], prevented by treatment with the RNA polymerase II inhibitor α-amanitin. Isoproterenol βAR stimulation mediated cyclic AMP rises downregulate βAR messenger RNA transcription (and enhance phosphodiesterase synthesis [42]), inhibited by treatment with colchicine, though unaltered by the microtubule depolymerization inhibitor taxol-mediated enhancement of cytosolic concentrations of cyclic AMP [92]. A cyclic AMP response element (CRE) nested within DNA encoding the βAR subjects the gene to modulation by cyclic AMP concentrations. Treatment with the myelosuppressive non-neuropathic microtubule synthesis inhibitor vinblastine at doses insufficient to modulate protein synthesis prevents isoproterenol mediated enhancement of phosphodiesterase synthesis, though fails to prevent β agonist-mediated upregulation of nerve growth factor [42]. Crossmodal modulation between molecular compounds modulating polymerization and depolymerization of microtubules and βAR modulated signaling may be critically implicated in glioma initiation, promotion, progression, invasion, and metastasis [93]. NG 108-15 rat neuroblastoma cells express βARK isotypes 1 and 2 mRNA and exhibit Gβγ-dependent phosphorylation of rhodopsin and agonist-bound delta opioid receptor, recapitulating effects mediated by βAR activation in non-transformed cells [94, 95]. Glioma cells may exhibit differential kinetics of βAR desensitization compared with non-malignantly-transformed cells. C6 glioma cells undergoing comparatively fewer cycles of replication exhibit enhanced βAR ligand binding-effector coupling, evidenced by comparatively greater rises of cytosolic cAMP and calcium in response to treatment with the nonselective βagonist isoproterenol [96]; C6 glioma neoplastic astrocytes having undergone cellular senescence effectively amplify cyclic AMP levels in response to stimulation of βAR modulated signaling only in the presence of a pharmacological inhibitors of phosphodiesterase [96]. βAR activation conformationally modifies rat-derived C6 glioma cellular phenotype from fibroblastic to astrocytic [97], presumably via cyclic AMP mediated effects upon the state and dynamics of the cytoskeleton, effects potently inhibited in the presence of serum containing lysophosphatidic acid in a GTP-binding protein-dependent manner [97]. Enhancement of cytosolic calcium concentrations by treatment with thrombin reverts cellular morphology from astrocytic- to epithelial-like [98], presumably via calcium-mediated downregulation of βAR-mediated enhancement of cytosolic concentrations of cyclic AMP. Treatment with the direct thrombin inhibitor hirudin, but not with antithrombin III [98], inhibited βAR activation mediated cellular morphological transformation. Thrombin effects upon cellular morphology are likely mediated through activation of cell surface platelet activated receptors (PARs). The experimental findings collectively indicate β adrenergic receptor agonists and thrombin coordinately converge on modulating intracellular signal transduction pathways affecting dynamic microtubular architecture by modulating cyclic AMP levels through ligand binding mediated effector coupling of allosterically activated membrane surface receptors [97, 98]. Pharmacological antagonism of the mGlu3 receptor attenuates glioma cellular proliferation and enhances transformation of glioma cells from a fibroblastic to an astrocytic phenotype [55]. The described behavior may play a critical role in invasion and metastasis of cerebral glioma cells through crossmodal modulation amongst and between Gs and Gi protein coupled receptors [55]. Modulation of matrix metalloproteinase expression by β adrenergic signaling The apical inter-endothelial tight junction-coupled basement membrane (BM), glycosaminoglycan- and protein-rich extracellular matrix (ECM), and blood brain barrier (BBB) collectively constitute initially formidable obstacles to tumor cell invasion, dissemination, metastasis, and distant implantation [99–101]. Matrix metalloproteinases (MMPs) modulate cellular proliferative capacity, cellular migration, and neoangiogenesis and enhance glioma cell capacity to invade and metastasize by enzymatically degrading the basement membrane and extracellular matrix [6]. MMP-2 and MMP-9 represent the predominantly extracellularly-liberated isoforms implicated in enhancing invasion and metastasis by glioma cells [102]. Human brain microvascular endothelial cells (HBMECs) maintain the microarchitectural integrity of the blood brain barrier [103]. Treatment of HBMECs grown on collagen I, collagen IV, fibronectin, laminin, or hyaluronic acid with cyclic AMP supplements enhances microarchitectural junctional continuity and expression of zona occludin 1, VE-cadherin, and claudin 5 [103]. Inhibition of MMP-9 effectively forestalls HBMEC neoangiogenesis [104], invasiveness [104], and metastasis [105] in vitro. Treatment of rat C6 glioma cells with eugenol encapsulated chitosan nanopolymers reduces tumor cell metastatic potential by reducing the expression of MMP-9 [105]. Tissue hypoxia may promote the expression and proteolytic enzymatic activity of MMP, effects which could conceivably contribute to potentiating BBB disruption in hypoxic regions of glioma tumor masses [106]. Thus, enhancing cerebral blood flow via spinal cord stimulation in patients harboring intracranial gliomas [46] may reduce tumor invasive potential by reducing hypoxia-induced augmentation of MMP secretion [46]. A host of membrane receptor tyrosine kinases (RTKs) and G protein-coupled receptors (GPCRs) [67] and membrane bound ectodomain proteolytic metalloproteinases (e.g., ADAM17; 34,110] regulate the expression and/or degradative enzymatic activity of matrix metalloproteases in non-malignantly-transformed astrocytes, human brain microvascular endothelial cells [6], and neoplastically-transformed astroglia, effects coordinately or alternately facilitated via ERK1/2 [67] and/or epidermal growth factor receptor (EGFR)-PI3K-serine-threonine kinase signaling [107] Specifically, pharmacological antagonism of βAR modulated signaling attenuates the expression of MMP-2 and MMP-9 in HBMECs [6] and reduces MMP-9 expression in tumors treated with the tumor promoting agent phorbol 12-myristate 13-acetate [108]. Norepinephrine enhances the activity and/or expression of MMP-9 and VEGF in HONE-1, HNE-1, and CNE-1 nasopharyngeal carcinoma cells [74] and metastasis in PC3 prostate carcinoma cells [60]. Treatment with propranolol reduces norepinephrine and stress-induced conferring of metastatic potential upon EG, SKOV3, and 222 ovarian carcinoma cells [56]. Concurrent inhibition of βAR modulated signaling and cyclooxygenase 2 significantly reduces the risk of metastasis and generates potent immunomodulatory effects [109]. HuR protein, overexpressed in cancers, stabilizes the MMP-9 mRNA transcript [6]. Propranolol attenuates the expression of MMP-9 (but not MMP-2] and generates cytosolic retention of HuR, reducing stability of the MMP-9 transcript [6]. HuR expression may also be suppressed via the green tea polyphenol epigallocatechin gallate and the isothiocyanate sulforaphane, effects exploitable therapeutically in the adjuvant treatment of carcinomas, by forestalling angiogenesis, invasive potential, and metastasis [6, 110]. Since hypoxia enhances glioma cell invasion through the upregulation of MMP-2 and MMP-9 in human and rat models in vitro and in xenograft models in vivo, there may exist cross-pathway communication between βAR modulated signaling, AC/cAMP/PKA, EGFR/PI3K/Akt, PTEN, mTOR, and VEGF pathways [111]. We detail a subset of the findings relevant to the emergent acquisition of an integrated and cohesive conceptual framework from which to understand the crossmodal interactions of these pathways by, and satisfaction of, the distinguished reader [111]. Hypoxia [1% O2] upregulates the expression of HIF-1α, MMP-2, and MMP-9 downregulated expression of TIMP-1 in U87MG, U251MG, U373MG, and LN18 human glioma cell lines related to normoxic [21% O2] conditions [111]. Treatment with HIF-1α small interfering ribonucleic acid (siRNA) reduced expression of HIF-1α, MMP-2, and MMP-9 and blunted tumor cell invasion in glioma spheroids co-cultured with rat-derived brain slices; the magnitude of these effects was preferentially amplified under normoxic conditions (1%) [111]. The results collectively indicate hypoxia enhances glioma tumor migration and invasive potential by upregulating the expression of MMP-2 and MMP-9 in a HIF-1α-dependent manner [111]. Tumor necrosis factor α-converting enzyme/a disintegrin and metalloproteinase 17, colloquially termed ADAM17 amongst molecular oncologists, proteolytically sheds phospholipid membrane bilayer-bound receptor, growth factor, and cytokine ectodomains [107]. Hypoxia upregulates the expression of ADAM17, activity of which correlates with 9L rat gliosarcoma and human U87 human glioma cell invasive potential, via EGFR-phosphatidylinositol-3-kinase-serine threonine kinase signaling, though independently of MMP-2 and MMP-9 levels [107]. Protease inhibitor-mediated attenuation of ADAM17 proteolytic enzymatic activity or siRNA mediated downregulation of ADAM17 expression reduces hypoxia-mediated enhancement of 9L rat gliosarcoma and U87 human glioma cell invasiveness [107]. Molecular inhibition of the mammalian target of rapamycin induced G1 cell cycle arrest, reduced synthesis of VEGF, and downregulated the expression of MMP-2 and/or MMP-9 in PTEN (phosphatase and tensin homolog deleted from chromosome 10)-null U87MG and D54 human glioma cells, but not PTEN-null HOG oligodendroglioma cells [77]. Treatment of U87 xenografts in vivo induces glioma regression, presumably indicating cellular apoptosis, reduces tumoral VEGF expression, and blunts the expression of MMP-2 [77]. Treatment with fentanyl reduces cellular proliferation, migration, and invasion of gastric cancer MGC-803 cells in vitro, attenuates PI3K/Akt signaling, reduces expression of MMP-9, and enhances expression of the pro-apoptotic proteins caspase-9 and death-associated protein kinase 1 (DAPK1) [105], the latter pair of effects synergistically enhanced by treatment with the PI3K molecular inhibitor LY294002 and MMP-9 molecular inhibitor SB-3CT. Accordingly, pharmacological modulation of β adrenergic receptor modulated signaling may be exploited to blunt tumor cell invasion by reducing MMP expression levels in human intracranial (e.g., glioma, glioblastoma, gliosarcoma) and extra-neuraxial (e.g., melanoma, breast cancer, gastric cancer, pancreatic cancer, colorectal cancer, prostate cancer, ovarian cancer) carcinomas and sarcomas. These effects may be synergistically enhanced by coordinately administering βAR modulators with mTOR inhibitors, HIF-1α pathway modulators, the serine protease inhibitor and tryptase inhibitor nafamostat mesylate, conventional cytotoxic chemotherapy, monoclonal antibodies to tumor-specific growth factor receptors, tumor-specific cytotoxic CD3+ CD8+ T cells. Modulation of angiogenesis by β adrenergic signaling Cerebral, brainstem, and cerebellar gliomas exhibit heterogeneous arteriolar density [112]. Tumor neoangiogenesis promotes glioma growth, promotion, progression, invasion, and metastasis of gliomas [6, 76] and extra-neuraxial [113, 114] carcinomas, subject to modulation by β adrenergic receptor modulated signaling. Treatment with norepinephrine [115] or dopamine [116] and stress promote angiogenesis in ovarian carcinomas by potentiating βAR mediated attenuation of PPARγ signaling and thus disinhibiting the synthesis of VEGF and FGF2, molecular behavior putatively extending to cerebral gliomas [116]. Reciprocally, pharmacological antagonist of β adrenergic receptor modulated signaling specifically forestalls incipient endothelial tubulogenesis and emergent angiogenesis, sans altering cell viability or migratory capacity, by reducing the expression of matrix metalloproteases in HBMECs in vitro [6]. Chronic stress attenuates PPARγ-mediated signaling via upregulating activity through β adrenergic receptor modulated pathways, effectively disinhibiting the synthesis of VEGF and FGF2 and precluding angiogenesis in models of ovarian carcinoma, a set of effects attenuated through the use of pioglitazone [113]. To this end, pediatricians now commonly espouse the use of propranolol to effect involution of the vascular endothelium in infants harboring benign hemangiomas [6]. The revealed set of molecular effects may be exploited to therapeutic benefit to generate marked reductions in glioma [6, 76] and extra-neuraxial [113, 114] hypervascular carcinoma growth potential, invasiveness, and angiogenesis. The effects of anti-angiogenic compounds are characteristically amplified in the presence of ionizing radiation [117]. Immunomodulation by β adrenergic receptor modulated signaling Immune effector responses mediating homeostatic antimicrobial and tumor cell surveillance and those contributing to the pathogenesis of neurodegenerative diseases, may occur within parenchyma contained within both the cranial cavity and vertebral column, alternately or coordinately recruiting innate and/or adaptive (cellular and humoral effector arms) mechanisms [118–120]. Major histocompatibility (MHC) class II (dimer; each monomer constituted by α and βdomains)-complexed non-native glycoprotein antigen fragments (endocytosed and processed by antigen presenting cells [macrophages, dendritic cells, B cells]) are presented to effector CD3+ CD4+ helper T cells and MHC class I (α1, α2, β1, β2-microglobulin domains)-complexed non-native glycoprotein antigen fragments (endogenously synthesized and modified by any cell type except nucleate spermatozoa and anucleate erythrocytes) are presented to CD3+ CD8+ cytotoxic T cells [120], constituting cell-mediated immunity. B cell generated immunoglobulins, antigen-potentiated immunoglobulin class isotype switching, and antigen-dependent maintenance of clonal plasma cell populations generating functional antibody against nonnative antigens constitutes ‘humoral’ immunity [120]. Immune effector mechanisms surveille and eradicate incipiently transformed neoplastic tumor seed cells. CD3+ CD8+ and natural killer (NK) cells eradicate mutationally transformed cells generating MHC I-complexed tumor-specific antigens via cytotoxic CD3+ CD8+ T cells, effectively preventing the progression and promotion of carcinogenically-mutated cells [120]. Abnormalities of these mechanisms could contribute to tumor initiation, promotion, and progression [121–123]. MHC II-bearing immunologically active astroglia and/or microglia abundantly populate malignant cerebral, brainstem, cerebellar, and spinal cord glioblastomas and astrocytomas [124]. Accordingly, brain microglial MHC class II expression antigen-specifically enhances immune responses within neural tissue [124], offering a set of therapeutic targets by which to eradicate glioma cells by enhancing intrinsic antitumor response mechanisms [125]. MHC class II cell surface proteins may be found complexed with endocytosed- and endogenously-modified non-native antigens and are expressed in macrophages, plasma cells, and dendritic cells [119]. These antigen presenting cells interact with Th1 and Th2 subtypes of CD3+ CD4+ T cell effector arms and mediate differential host immune responses [118]. βAR modulated signaling and downstream target pathways play critical immunomodulatory roles by regulating MHC class II expression human glioma cell lines [124]. In differentiated U-373-MG, U-105-MG, and D-54-MG glioblastoma cells, treatment with the βAR agonist isoproterenol (1 × 10–6 to 5 × 10–6 M), adenylate cyclase activator forskolin, or cyclic AMP analogue deoxybromo-cyclic AMP (DBcAMP), enhance membrane cell surface expression of MHC class II DR molecules, effects generally mediated by enhanced synthesis of transcriptionally-nascent messenger ribonucleic acid transcripts [124]. For example, treatment with norepinephrine and isoproterenol upregulate MHC class II cell surface expression in U-373-MG differentiated glioblastoma cells [124]. Treatment with isoproterenol enhances expression of MHC class II in U-373-MG cells to a greater extent compared with norepinephrine, given concurrent selective stimulation of βAR by the former and concurrent stimulation of β and α adrenergic receptors by the latter. IFN-γ enhances MHC class II expression in U-105-MG (1.5-fold increase) and D-54-MG (2.5-fold increase) glioblastoma cell lines to a greater extent compared with the upregulation of MHC class II synthesis elicited by IFN-γ in U-373-MG cells [124]. Treatment with IFN-γ coordinately enhances neuroblastoma membrane cell surface expression of MHC class I β2-microglobulin complexed tetradomain multimers, an effect not generated by treatment with DBcAMP [126]. Treatment with the decarboxylated [3,4-DOPA decarboxylase; cofactor biotin) hydroxylated (dopamine-β-hydroxylase; cofactor tetrahydrobiopterin) 3,4-dihydroxphenylalanine catecholamine derivative norepinephrine prevents IFN-γ mediated enhancement of MHC class II cell surface expression [127]. The finding perhaps collectively indicates norepinephrine- and IFNγ-mediated enhancement of MHC class II expression share a common and overlapping downstream set of mediators, likely converging upon, and diverging through, cyclic AMP and protein kinase A. Thus, β adrenergic agonists and interferon-γ may generate therapeutically exploitable immunomodulatory effects in treating gliomas by upregulating cellular mediated gliomatotoxic immune responses through adenylate cyclase-cAMP-protein kinase A-dependent upregulation of membrane cell surface expression of MHC class II complexed-tumoral antigens and thus putatively represent effective adjuvants which may enhance the effects of tumor therapies enhancing host immune mechanisms (tumor antigen-specific antibodies, CD3+ CD8+ cytotoxic T cells, and NK cells) curtailing proliferation, angiogenesis, invasion, and metastasis of glioma cells. We present the caveat that treatment with neither isoproterenol nor forskolin upregulated DRα gene expression in HL-60 promyelocytic leukemic cells [128], evidencing possible heterogeneity of the effect according to specific tumor cell type or inter-experimental differences. Treatment with βAR agonists or TNF-α promotes proliferation of C6 glioma cells in vitro, with the latter coordinately upregulating βAR cell surface density via βAR-dependent and PKC-mediated signaling [124], effects indicating crossmodal interaction between βAR signaling and molecular immune mediators. The findings of Lung et al. collectively indicate TNF promotes proliferation of C6 glioma cells through β adrenergic receptor activation [39]. The secreted pro-inflammatory protein cytokine tumor necrosis factor α (TNF-α), synthesized and elaborated by macrophages and microglia, binds membrane cell surface receptors possessing intracellular receptor tyrosine kinase activity and potentiates and mediates a spectrum of effects on cellular genetic transcription and tissue physiology. TNF-α enhances macrophage synthesis of IL-1, hypothalamic synthesis of prostaglandins and pyrogen proteins, hepatically-synthesized acute phase reactants (IL-6, mannose binding protein), vascular endothelial expression of inter-endothelial cellular adhesion- and vascular cellular adhesion molecule-1 and synergistically potentiate adaptive immune effector and memory mechanisms. TNF-α amplifies pyrogenic signaling in hypothalamic nuclei by raising the thermic set point, enhancing equilibria of biochemical metabolism, promoting non-shivering thermogenesis, and augmenting innate and adaptive immune responses, effects we suggest potentiate host immune mediated eradication of malignantly-transformed tumor cells. β agonists synergistically enhance, diminish, or fail to alter TNF-mediated upregulation of proteins (see Table 1 of [129]). Specifically, isoproterenol was shown to synergistically enhance TNF-mediated upregulation of A20 and IL-6, attenuates TNF-mediated downregulation of LEF1, with a non-statistically significant tendency towards blunting TNF-mediated upregulation of ICAM-1 and VCAM-1 in cultured astrocytes [129]. The biological mechanisms upon which these effects are predicated, investigated in the context of glioma, may be extended to rational therapeutic design of medications designed to treat systemic inflammatory response syndrome, sepsis, severe sepsis, septic shock, and multiorgan dysfunction syndrome [129]. As an aside, βagonists enhance the synthesis of alveolar surfactant and compliance of the pulmonary parenchyma, a therapeutically exploitable corollary effect of βagonists upon pulmonary mechanics [130]. In the author’s anecdotal experience in the critical care unit, maintaining a very low dose of norepinephrine [1–2 μg/kg/min) seems to correlate with improved metrics of tissue oxygenation (oxygenation index; PaO2:FIO2 ratio) in patients experiencing severe acute lung injury occurring in the context of septic shock. Clinical relevance Johansen et al. describe a retrospective series of 218 patients unfortunately afflicted with glioblastoma, all of whom received the anti-VEGF monoclonal antibody bevacizumab (most common adverse effects: arterial hypertension, bleeding diathesis, delayed wound healing) and alternately received β antagonists or placebo [61]. Inclusion of β antagonists in therapeutic regimens yielded no enhancement of survival. Retrospectivity and non-randomization of patients receiving βantagonist treatment and comparison groups limits the study [61]. A study evaluating the utility of β antagonists excluding bevacizumab in patients with newly diagnosed low and high grade glioma sans multifocal disease or extra-neuraxial metastases may effectively unveil whether the observed effects are chiefly attributable to reducing angiogenesis [61]. β adrenergic receptor blockade significantly improves clinical outcomes and survival in patients harboring breast, ovarian, and prostate carcinoma and melanoma [131]. These agents reduce the risk of developing prostate carcinoma [132] and hepatocellular carcinoma in patients infected with hepatitis C [133] and prolong survival in patients with breast cancer [134]. Drug development Malignant potential of glioma cells depends critically on their capacity to transgress through the basement membrane [135, 136], migrate through the extracellular matrix [137], reach and enter proximally located microvasculature, travel to distant sites [138], exit the microvasculature, and implant and grow in distant microenvironments [139]. Neoangiogenesis induced by protein factors released from glioma cells contributes to sustaining tumoral growth [140]. Evasion of immune responses by downregulation of cell surface expression of tumor specific antigens and negative immunomodulators contributes to immune evasion by glioma cells [125]. In this regard, β adrenergic signaling multi-mechanistically modulates immune mechanisms [124], local tumoral angiogenesis [6], and processes contributing to invasion and metastasis by neoplastic tumors [139]. Modulation of β adrenergic receptor modulated signaling by various compounds may thus be exploited to enhance immune responses to tumor, by increasing the cell surface expression of tumor specific antigens complexed with MHC class II homodimers [124] and thus promote antigen-specific tumor responses [124], inhibiting tumoral angiogenesis [6] and thus blunting the capacity for tumoral growth, and downregulate the expression and secretion of extracellular matrix degrading matrix metalloproteinases [6]. Fenoterols represent useful candidate molecular compounds which may be chemically modified in order to optimize agonist potency and generate specific β adrenergic receptor conformations favoring β arrestin binding [65, 73]. Typical agonists or bitopic agonist-antagonists, such as ( )-MNF, exhibiting contemporaneous effects on GPR55 signaling, may exert cytostatic effects proving therapeutically beneficial in the adjuvant treatment of gliomas and extra-neuraxial malignancies [65]. Reinartz et al. identified the ( ), as well as the ( )-stereoisomers of the bitopic agent 4′-methoxy-1-naphthyl-fenoterol to exhibit preferential binding to βARs coupling to Gs protein [86]. Since these ligands preferentially favored G protein-mediated signaling in response to βAR activation, disfavoring phosphorylation of the carboxyl terminal of the βAR and β arrestin binding, these agents represent a unique set of βagonists to which desensitization develops slowly, and may be exploited therapeutically in the treatment of common medical conditions in lieu of classically utilized βagonists, postulates subjectable to rigorous empirical interrogation. The specific stereoisomeric conformation of fenoterol derivatives and composition of the aminoalkyl moiety dictates binding affinity to β2 adrenoceptor-Gsα fusion proteins [85]. The efforts of medicinal chemists to further modify these agents will arm us with the capacity to develop compounds uniquely and preferentially generating carboxyl terminal βARK-phosphorylated βAR-β arrestin complexes preferentially favoring scaffold-mediated ERK1/2 activation [11, 12]. For whatever reason, our instinctual faculties lead us to believe developing pharmaco-molecular switches favoring βAR-β arrestin scaffold facilitated activation of ERK1/2 may represent a pleiotropically effective panacea in the treatment of gliomas and extra-neuraxial carcinomas: the cytosolic homeostatic functions mediated by ERK1/2 are preserved, eschewing physiological compromise of metabolically active epithelia, with concurrent blunting of its nuclear pro-transriptional activity, representing the most empirically plausible anti-carcinogenic therapeutic mechanism [11, 12, 85, 86]. The prudent modulation of βAR modulated signaling, putatively employing combinatorial therapeutic strategies exploiting bitopic fenoterol derivative compounds and nafamostat mesylate, may effectively blunt the progression of macular degeneration and retino-degenerative diseases [85, 86]. Molecular pharmacological enhancers or inhibitors of protein machinery contributing to desensitization of β adrenergic receptors and modulators of the scaffold promoted effects of distal signal transduction pathways of β adrenergic receptor may generate potent antitumoral effects [141, 142]. Studies have thoroughly demonstrated and elucidated the structural conformations of cyto-transductively active and inactive conformations of the β adrenergic receptor [13, 14, 16, 81]. This information may be exploited in order to genetically engineer chimeric β adrenergic receptor constructs, for example, exhibiting more stable binding dynamics with β arrestin, thus promoting scaffold-promoted effects of the G protein-coupled receptor β arrestin [13, 85, 86], including cytosolic retention of activated ERK1/2 and inhibition of its nuclear translocation, thus preventing cellular proliferation consequent to enhanced transcriptional activity [11, 12]. Precedence for these effects was shown by Tohgo et al., who generated chimeric constructs of the vasopressin receptor by replacing its native carboxyl terminal amino acid sequence with that of the carboxyl terminal end of the β adrenergic receptor [9]. Further studies utilizing targeted genetic mutations of the carboxyl terminal chain of amino acid residues of the β adrenergic receptor and amino terminal chain of amino acid residues of the β arrestin protein may enhance our capacity to generate genetically-modified stable constructs promoting scaffold-mediated activation of ERK1/2, chimeric constructs transfectable utilizing adenoviral vectors [11, 12]. β arrestin binds βARK-phosphorylated β adrenergic receptor carboxyl terminal amino acid moieties [14]. The Gβγ subunit of the Gs protein promotes βARK translocation from the cytosolic pool towards the membrane and promotes βARK-mediated phosphorylation of the βAR [9, 16, 66]. High affinity binding of β adrenergic receptor kinase with a yet to be identified microsomal membrane protein through electrostatic interactions putatively indicates an important contribution of the interaction to mechanistically modulate β adrenergic receptor kinase activity [14]. Subcellular compartmentalization of the β adrenergic receptor kinase may represent a prominent mechanism regulating β adrenergic receptor desensitization [14]. Pharmacological G protein stimulators enhance the kinase activity of microsomal membrane protein-bound β adrenergic receptor kinase, but not binding affinity [14]. Upregulation of G protein expression and enhancement of Gβγ activity through viral transfection of genetic constructs covalently linked to, and continuous with, a high activity promoter or treatment with pharmacological G protein stimulators (mastoparan/GTPγS or aluminum fluoride) could be employed to therapeutic advantage to augment β adrenergic receptor kinase activity, consequently promoting β arrestin binding to β adrenergic receptor carboxyl terminal phosphorylated amino acid moieties and βAR-β arrestin scaffold-mediated facilitation of ERK1/2 activity [14]. Combinatorial therapeutic approaches seeking to contemporaneously upregulate β adrenergic receptor kinase-mediated phosphorylation of the β adrenergic receptor carboxyl terminal chain of amino acid moieties and enhance β adrenergic receptor-β arrestin binding stability could represent a promising therapeutic strategy in the adjuvant treatment of gliomas and other cancers. Strategies which may enhance the stability of β arrestin-G protein coupled receptor interaction would preferentially force the equilibrium from PKA- to scaffold-mediated activation of ERK1/2 [11, 12]. These effects would coordinately promote cytosolic retention of ERK1/2 and reduce ERK1/2-meidated nuclear pro-transcriptional activity (though possible via ERK1/2 mediated phosphorylation of nuclear translocable enzymes) therapeutically promotable via drug-mediated stabilization and adenoviral transfection with stable proximal peptide chain terminal generating more stable interactions with the β adrenergic receptor carboxyl terminal domain [32, 33, 142]. Adenoviral vector delivery of a high activity promoter linked to β arrestin may enhance the expression of the protein, enhancing scaffold-mediated activation, and cytosolic retention, of ERK1/2 and reduce pro-transcriptional activity mediated by the phosphorylating phosphorylated conformation of the enzyme [86, 143, 144]. We believe this will prove to be a safe and effective strategy in preventing the onset, and ameliorating and attenuating the progression, of carcinogenesis and atherogenesis, by reducing the extracellular regulated kinase 1/2 mediated promotion of vascular smooth muscle cell proliferation. However, there may exist some difficulty in the technical challenge of achieving stable transfection of cells with adenoviral vectors and modulating the extent and distribution of cellular expression of transfected βAR GPCRs or β arrestin constructs [145]. Self-targeted oncolytic adenoviral nanospheres may successfully enhance adenoviral transfection of target cells with chimeric beta adrenergic receptor (vasopressin or angiotensin carboxyl-terminal substituted carboxyl terminals) or (N-terminal modified) β arrestin complexes [146]. Small interfering RNA mediated downregulation of β arrestin 1 and 2 expression reduced isoproterenol-mediated enhancement of ERK1/2 activation in HEK293 cells, though CRISPR/Cas9-mediated deletion of β arrestins and membrane G proteins had variable effects on ERK1/2 responsivitiy to β adrenergic stimulation [147]. We accordingly suggest evaluating the utility of fenoterol derivatives in utilizing CRISPR/Cas9 to mediate targeted deletions of β arrestin 1, β arrestin 2, Gas protein, and/or Gai protein and/or targeted knock-ins of chimeric constructs of βAR or β arrestin in HEK293, PC12, C6 rat-derived glioma, and human U87MG, U251MG, U373MG, and LN18 [147]. We further suggest intracerebrally implanting CRISPR/Cas9-mutated or adenovirally-transfected glioma cells to generate glioma models in vivo [147]. We may accordingly exploit these models to more precisely evaluate the role of variably modified fenoterol derivatives upon tumor cell proliferation, migratory capacity, invasion, angiogenesis, and metastasis [147]. The approach will require extensive preclinical studies in order to elucidate the full complementary spectrum of biological effects of administering adenoviral vectors containing β adrenergic receptor constructs. Multimodal strategies seeking to optimize the development of compounds promoting stable GPCR-β arrestin interactions and contemporaneous treatment with specific ERK inhibitors may maximize the actualized survival benefit in patients harboring gliomas and extra-neuraxial malignancy [9, 14, 111]. These therapies may prove of clinical utility in curtailing initiation, promotion, and progression of gliomas and may prove to represent a useful general adjuvant to multimodal therapy of glioblastoma [6, 76, 111]. Immunomodulatory effects of β adrenergic signaling, prominently regulating cell surface expression of MHC class II, suggests manipulating these pathways may represent an effective adjuvant technique to be utilized in conjunction with various immunotherapeutic approaches, including generation of tumor specific antibodies, cytotoxic T cells, and NK cells in a variety of cancers [124].[N.B.: As a brief aside, our empirically derived instinctual conceptualization leads us to surmise coordinate treatment with modulators of β adrenergic signaling, the bitopic compounds ( )-MNF and ( )-fenoterol, and/or the serine protease inhibitor nafamostat mesylate may exert synergistically therapeutic effects in the setting of cerebral glioma and extra-neuraxial carcinoma, neurovascular disease, and septic shock (Patent Pending, Ghali and Ghali, authors of the present work) and coronavirus COVID-19 responsible for the emerging international pandemic [148]. The sequential activity of the proteases furin, transmembrane protease serine 2 (TMPRSS2), and cathepsins cause sequential cleavage of the Middle East respiratory syndrome coronavirus (MERS-CoV) envelope protein, ‘S’, which fuses with host cell CD26, co-expressed with TMPRSS2 in target cells. The serine protease inhibitor nafamostat mesylate interferes with pro-S protein cleavage, preventing effective fusion of the Middle East respiratory coronavirus with host eukaryotic target cells [149]. Nafamostat mesylate was shown to prevent ‘S’-mediated membrane fusion according to a Renilla luciferase assay and prevent MERS-CoV infection in vitro in a preparation of Calu3 cells [149]. Nafamostat mesylate interferes with the proteolytic cleavage of Ebola virus envelope proteins necessary for virus-host cell fusion by reducing the proteolytic release of CatB from rat pancreas [150] and microvascular leakage in patients with Dengue hemorrhage fever and shock through tryptase inhibition, blocking vascular leakage in vivo [151]. Conclusions Authors have extensively detailed and elucidated mechanisms contributing to β adrenergic receptor modulated signaling, dynamics, and regulation [11–16, 47, 154], pharmacological modulation of which may powerfully modify tumor cell proliferation, motility, immunogenicity, elaboration of protein mediators promoting angiogenesis, and invasive and metastatic potential [124, 154]. Studies have alternately demonstrated amplification or attenuation of cellular proliferation of gliomas [6, 39, 40] and extra-neuraxial carcinomas in response to pharmacological enhancement of β adrenergic receptor modulated signaling [8, 45, 46, 49, 51, 52, 54, 57, 63, 152]. The character of βagonist utilized, tumor model and preparation type, receptor regulation dynamics, and differential distal signal transduction mechanisms may explain inter-experimental differences. The wise development of a set of experiments designed to more precisely characterize the full complement of effects mediated by β adrenergic receptor modulated signaling in carcinogenic initiation, promotion, and progression, immunogenic modulation, angiogenesis, and tumor cell tissue invasion and metastasis, specifically [6]. Crystallographic studies will further characterize inactive, transitional, and active tridimensional conformations of the β adrenergic receptor and specific conformational modifications induced by treatment with various agonists and antagonists of the heptahelical transmembrane G protein coupled receptor [14, 16]. Conformational protein modifications may differentially stabilize or destabilize binding between β adrenergic receptor carboxyl termini and β arrestin amino termini, thus generating differential effects upon desensitization, receptor endocytosis, and scaffold formation [11, 12, 14, 16]. Rational drug design and mathematical models of βAR-drug binding will identify drug-specific and tumor cell-specific factors rendering β adrenergic receptor modulated signaling more likely to promote or inhibit cellular proliferation, unveil determinants contributing to preferential Gs versus Gi activation or inhibition, and identify optimal bio-organic compounds modulating the conformational state of β adrenergic receptors in staying the progression of glioblastoma [85, 86, 131, 132]. Adenoviral transfection with chimeric constructs of β adrenergic receptors possessing carboxyl termini with high binding affinity to β arrestin amino termini and/or β arrestins possessing amino termini with high ligand binding affinity to GPCR carboxyl termini targeted specifically to glioma cells and high activity promoters may effectively preferentially promote scaffold-mediated activation of ERK1/2, blunting its nuclear translocation and retaining its cytosolic homeostatic effects, putatively proving to be a useful primary or adjuvant therapeutic approach enhancing the currently employed regimen of maximal safe resection, external beam radiotherapy, as well as concurrent and adjuvant temozolomide [21, 153]. We suggest a panoply of multimodal strategies designed to modulate β adrenergic signaling represent promising therapeutic approaches to be exploited in the treatment of glioblastoma [65, 73, 85, 86, 153]. Preclinical studies will prove necessary in order to develop compounds exhibiting the specific and desired effects upon β adrenergic receptor modulated signaling. Clinical studies will prove necessary in order to evaluate the safety and efficacy of these medications [65, 73, 85, 86]. Preclinical in vitro and in vivo studies and clinical studies will emergently cultivate an appreciation of the influence of pharmacological agonists, inverse agonists, antagonists of β adrenergic receptor modulated signaling, and fenoterol derivative bitopics upon the biomolecular mechanistic underpinnings of β adrenergic receptor modulated signaling upon molecular behavior of glioma cells and dynamic patterns of glioma growth, invasion, angiogenesis, and metastasis, and effects on survival metrics [65, 73, 85, 86, 151] (Table 1).Table 1 Effects of βAR signaling upon glioma Effect Mechanisms Promotes tumor cell proliferation and growth AC-cAMP-PKA-ERK1/2-CREB → promotes cellular proliferation Attenuates tumor cell proliferation and growth βAR → phospho-βAR via βARK → binding of β arrestin Promotes receptor internalization Promotes scaffold facilitated ERK1/2 activation ERK1/2 cytosolically retained ERK1/2 nuclear translocation prevented PLC → DAG + IP3 DAG → PKC IP3 → sarcoplasmic [Ca2+]i release [Ca2+]i → blunts cAMP-PKA signaling Upregulation of PDE degrades cAMP Reduces tumor invasive potential Decreases activity and expression of MMP-2 and MMP-9 Reduces tumor neoangiogenesis Decreases tubulogenesis Reduces tumor metastatic potential Decreases invasive potential and angiogenesis Amplifies anti-tumor cellular adaptive immunity Upregulates cell surface expression of MHC class II nonnative antigens The acute effects of βAR modulated signaling chiefly include promotion of tumor cell proliferation, invasion, angiogenesis, and metastasis. Prolonged administration of βAR agonists rapidly promotes phosphorylation of the carboxyl terminal by β adrenergic receptor kinase and binding of β arresting, weakening ligand binding-effector coupling and enhancing scaffold mediated activation of ERK1/2 AC adenylate cyclase, βAR β adrenergic receptor, βARK β adrenergic receptor kinase, cAMP cyclic adenosine monophosphate, CREB cyclic AMP response element binding protein, DAG diacylglycerol, ERK1/2 extracellular regulated kinase ½, IP3 inositol triphosphate, MMP-2 matrix metalloproteinase 2, MMP-9 matrix metalloproteinase-9, PKA protein kinase A, PKC protein kinase C, PDE phosphodiesterase, PLC phospholipase C Funding No funding was received for this study. Compliance with ethical standards Conflict of interest No conflict of interest to disclose. Ethical approval All procedures performed in the studies were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s11033-022-07301-8" Change history 3/8/2022 This article has been retracted. Please see the Retraction Notice for more detail: 10.1007/s11033-022-07301-8 ==== Refs References 1. Harden TK McCarthy KD Identification of the β adrenergic receptor subtype on astroglia purified from rat brain J Pharmacol Exp Ther 1982 222 3 600 605 6125583 2. Terasaki WL Brooker G [125I]Iodohydroxybenzylpindolol binding sites on intact rat glioma cells. 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Zhu C Kros JM Cheng C Mustafa D The contribution of tumor-associated macrophages in glioma neo-angiogenesis and implications for anti-angiogenic strategies Neuro Oncol 2017 19 11 1435 1446 10.1093/neuonc/nox081 28575312 141. Fuwa M Kageyama M Ohashi K Sasaoka M Sato R Tanaka M Tashiro K Nafamostat and sepimostat identified as novel neuroprotective agents via NR2B N-methyl-d-aspartate receptor antagonism using a rat retinal excitotoxicity model Sci Rep 2019 9 1 20409 31892740 142. Guo C Whitmarsh AJ The β-arrestin-2 scaffold protein promotes c-Jun N-terminal kinase-3 activation by binding to its nonconserved N terminus J Biol Chem 2008 283 23 15903 15911 10.1074/jbc.M710006200 18408005 143. 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Yamamoto M Matsuyama S Li X Takeda M Kawaguchi Y Inoue JI Matsuda Z Identification of nafamostat as a potent inhibitor of middle east respiratory syndrome coronavirus S protein-mediated membrane fusion using the split-protein-based cell-cell fusion assay Antimicrob Agents Chemother 2016 60 11 6532 6539 27550352 150. Nishimura H Yamaya M A synthetic serine protease inhibitor, nafamostat mesilate, is a drug potentially applicable to the treatment of ebola virus disease Tohoku J Exp Med 2015 237 1 45 50 10.1620/tjem.237.45 26346967 151. Rathore AP Mantri CK Aman SA Syenina A Ooi J Jagaraj CJ Goh CC Tissera H Wilder-Smith A Ng LG Gubler DJ St John AL Dengue virus-elicited tryptase induces endothelial permeability and shock J Clin Invest 2019 2 130 4180 4193 152. Coelho M Soares-Silva C Brandão D Marino F Cosentino M Ribeiro L β-Adrenergic modulation of cancer cell proliferation: available evidence and clinical perspectives J Cancer Res Clin Oncol 2017 143 2 275 291 27709364 153. 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==== Front Early Hum Dev Early Hum. Dev Early Human Development 0378-3782 1872-6232 Elsevier B.V. S0378-3782(20)30279-6 10.1016/j.earlhumdev.2020.105053 105053 Article COVID-19 admissions calculators - revisited Victor Grech [email protected] 25 4 2020 5 2020 25 4 2020 144 105053 105053 © 2020 Elsevier B.V. All rights reserved.2020Elsevier B.V.Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body Salemi et al. point out that emerging evidence indicates that many children with COVID-19 are asymptomatic and this may be 80%, possibly higher, with therefore ≤20% who are symptomatic [1]. Dong et al. based their data on positive cases [2]. This may admittedly be a biased group and circulating, asymptomatic cases may greatly dilute the paper's findings, especially since asymptomatic carriage in the paediatric age group may be even higher [3,4]. For this reason, a correction factor of 15% and 5% symptomatic for overall and for paediatric respectively has been incorporated and the data for the worked example, Malta, revised in the following two tables. For all ages, including the paediatric population, the symptomatic proportion will vary, but the degree of variation is uncertain at the time of writing. However, there is a large degree of potential error in all calculations at this point in time simply because we do not know enough about this virus. We will only know the truth in time, when large populations are randomly sampled and antibody statuses checked. Like Salemi et al., we hope that these calculations and estimates will provide (insofar as possible with extant knowledge) “reliable information to government officials, policy makers, and the pediatric medical community on the likelihood of capacity challenges.”Unlabelled Image ==== Refs References 1 BMJ 369 2020 m1375 10.1136/bmj.m1375 2 Dong Y. Mo X. Hu Y. Epidemiological characteristics of 2143 pediatric patients with 2019 coronavirus disease in China Pediatrics 2020 10.1542/peds.2020-0702 3 Grech V. COVID-19 admissions calculators: general population and paediatric cohort. Early Human Development. 4 Pathak E.B. Salemi J.L. Sobers N. Menard J. Hambleton I.R. COVID-19 in children in the United States: intensive care admissions, estimated total infected, and projected numbers of severe pediatric cases in 2020 J Public Health Manag Pract. 2020	32360075	PMC7182524	NO-CC CODE	2021-01-06 08:58:23	yes	Early Hum Dev. 2020 May 25; 144:105053
==== Front Early Hum Dev Early Hum. Dev Early Human Development 0378-3782 1872-6232 Elsevier B.V. S0378-3782(20)30284-X 10.1016/j.earlhumdev.2020.105054 105054 Article COVID-19 and potential global mortality - Revisited Grech Victor [email protected] Paediatric Dept, Mater Dei Hospital, Malta 30 4 2020 5 2020 30 4 2020 144 105054 105054 26 4 2020 28 4 2020 © 2020 Elsevier B.V. All rights reserved.2020Elsevier B.V.Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body 1 Introduction On the 13th April 2020, Adhanom Tedros, Director General of the World Health Organization (WHO) stated: This is a new virus, and the first pandemic caused by a coronavirus. We're all learning all the time and adjusting our strategy, based on the latest available evidence. We can only say what we know, and we can only act on what we know [1]. In a previous paper [2], this author attempted to assess potential COVID-19 continent-based mortality based on initial WHO data from China which estimated that 14% of infected cases are severe and require hospitalisation, 5% of infected cases are very severe and require intensive care admission, mostly for ventilation, and 4% of infected die [3]. However, it is becoming increasingly clear that a significant proportion of circulating COVID positive patients are asymptomatic, with potential for transmission of disease [4]. This may be circa 80–90% of COVID in community [4]. 2 Methods For this reason, the table in the initial paper showing continent and global estimates [2] has been recalculated with a correction factor, an estimated 10% symptomatic proportion of infected individuals. 3 Results Updated results are shown in Table 1 . Mortality figures globally may be around the 50 million level.Table 1 Potential infections and deaths from COVID-19 using available data and current observations [3,4], along with an estimated 10% symptomatic cases. Table 1 Continent Population Symptomatic % Infection % Number Mortality % Number 1 India 1,339,000,000 10 80 107,120,000 10 10,712,000 1 China 1,386,000,000 10 10 13,860,000 4 554,400 1 Rest 1,856,757,408 10 80 148,540,593 10 14,854,059 2 Africa 1,216,130,000 10 80 97,290,400 10 9,729,040 3 Europe 738,849,000 10 60 44,330,940 4 1,773,238 4 USA 327,096,265 10 60 19,625,776 10 1,962,578 4 Canada 37,064,562 10 60 2,223,874 10 222,387 4 Mexico 126,190,788 10 80 10,095,263 10 1,009,526 4 Rest 88,672,385 10 60 5,320,343 10 532,034 5 South America 422,535,000 10 60 25,352,100 10 2,535,210 6 Oceania 38,304,000 10 60 2,298,240 4 91,930 World 7,576,600,514 476,057,528 43,976,402 It must be reiterated that these are best guesses and estimates that preclude the discovery of effective treatment and/or vaccination. 4 Discussion Clearly, this pandemic has the potential to be as severe in terms of mortality as the influenza pandemic of 1918 which killed more than 50 million people and caused more than 500 million infections worldwide [5]. The conclusions of the previous paper stand [2]. Sudden surges of cases risk healthcare services being plunged into chaos and this may happen if the public do not do their part [6]. Infection cannot occur in the absence of contact. The only way to mitigate these numbers is to apply social distancing and take the precautions outlined by public health such as hand washing with soap, masks and so on. ==== Refs References 1 Tedros A. WHO Director-General's Opening Remarks at the Media Briefing on COVID-19 https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19--13-april-2020 13 April 2020 2 Grech V. Unknown unknowns - COVID-19 and potential global mortality [published online ahead of print, 2020 Mar 31] Early Hum. Dev. 144 2020 105026 10.1016/j.earlhumdev.2020.105026 32247898 3 World Health Organisation Coronavirus Disease 2019. WHO Report 41 01 March 2020 4 BMJ 369 2020 m1375 10.1136/bmj.m1375 32241884 5 Martini M. Gazzaniga V. Bragazzi N.L. Barberis I. The Spanish Influenza Pandemic: a lesson from history 100 years after 1918 J. Prev. Med. Hyg. 60 1 2019 E64 E67 Published 2019 Mar 29 10.15167/2421-4248/jpmh2019.60.1.1205 31041413 6 Remuzzi A. Remuzzi G. COVID-19 and Italy: what next? Lancet 395 10231 2020 1225 1228 10.1016/S0140-6736(20)30627-9 32178769	32387001	PMC7192067	NO-CC CODE	2021-02-28 23:14:46	yes	Early Hum Dev. 2020 May 30; 144:105054
==== Front Asian J Psychiatr Asian J Psychiatr Asian Journal of Psychiatry 1876-2018 1876-2026 Elsevier B.V. S1876-2018(20)30163-5 10.1016/j.ajp.2020.102052 102052 Article RETRACTED: Chinese mental health burden during the COVID-19 pandemic Huang Yeen Zhao Ning ⁎ Huazhong University of Science and Technology, Union Shenzhen Hospital, China ⁎ Corresponding author. 14 4 2020 6 2020 14 4 2020 51 102052 102052 © 2020 Elsevier B.V. All rights reserved.2020Elsevier LtdSince January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor in Chief. The article is a duplicate of a paper that has already been published in Psychiatry Research, volume 288 (2020) 112954 https://doi.org/10.1016/j.psychres.2020.112954. One of the conditions of submission of a paper for publication is that authors declare explicitly that the paper has not been previously published and is not under consideration for publication elsewhere. As such this article represents a misuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process. Ning Zhao and Ning Zhao's institution (Huazhong University of Science and Technology Union Shenzhen Hospital) were not involved in the manuscript and were not aware of the multiple submissions by Yeen Huang. Author Yeen Huang’s affiliation should be Shenzhen Second People's Hospital, not Huazhong University of Science and Technology Union Shenzhen Hospital	32361387	PMC7195325	NO-CC CODE	2021-01-06 13:21:26	yes	Asian J Psychiatr. 2020 Jun 14; 51:102052
==== Front N Engl J Med N. Engl. J. Med nejm The New England Journal of Medicine 0028-4793 1533-4406 Massachusetts Medical Society 10.1056/NEJMoa2007621 NJ202005013822502 Original Article Cardiovascular Disease, Drug Therapy, and Mortality in Covid-19 http://orcid.org/0000-0001-8683-7044 Mehra Mandeep R. M.D. http://orcid.org/0000-0002-5402-0176 Desai Sapan S. M.D., Ph.D. Kuy SreyRam M.D., M.H.S. Henry Timothy D. M.D. Patel Amit N. M.D. From Brigham and Women’s Hospital Heart and Vascular Center and Harvard Medical School, Boston (M.R.M.); Surgisphere, Chicago (S.S.D.); Baylor College of Medicine and Department of Veterans Affairs, Houston (S.K.); Christ Hospital, Cincinnati (T.D.H.); the Department of Biomedical Engineering, University of Utah, Salt Lake City (A.N.P.); and HCA Research Institute, Nashville (A.N.P.). Address reprint requests to Dr. Mehra at Brigham and Women’s Hospital, 75 Francis St., Boston, MA 02115, or at [email protected]. 01 5 2020 01 5 2020 NEJMoa2007621Copyright © 2020 Massachusetts Medical Society. All rights reserved. 2020 Massachusetts Medical Society This article is made available via the PMC Open Access Subset for unrestricted re-use, except commercial resale, and analyses in any form or by any means with acknowledgment of the original source. These permissions are granted for the duration of the Covid-19 pandemic or until revoked in writing. Upon expiration of these permissions, PMC is granted a license to make this article available via PMC and Europe PMC, subject to existing copyright protections. Abstract Background Coronavirus disease 2019 (Covid-19) may disproportionately affect people with cardiovascular disease. Concern has been aroused regarding a potential harmful effect of angiotensin-converting–enzyme (ACE) inhibitors and angiotensin-receptor blockers (ARBs) in this clinical context. Methods Using an observational database from 169 hospitals in Asia, Europe, and North America, we evaluated the relationship of cardiovascular disease and drug therapy with in-hospital death among hospitalized patients with Covid-19 who were admitted between December 20, 2019, and March 15, 2020, and were recorded in the Surgical Outcomes Collaborative registry as having either died in the hospital or survived to discharge as of March 28, 2020. Results Of the 8910 patients with Covid-19 for whom discharge status was available at the time of the analysis, a total of 515 died in the hospital (5.8%) and 8395 survived to discharge. The factors we found to be independently associated with an increased risk of in-hospital death were an age greater than 65 years (mortality of 10.0%, vs. 4.9% among those ≤65 years of age; odds ratio, 1.93; 95% confidence interval [CI], 1.60 to 2.41), coronary artery disease (10.2%, vs. 5.2% among those without disease; odds ratio, 2.70; 95% CI, 2.08 to 3.51), heart failure (15.3%, vs. 5.6% among those without heart failure; odds ratio, 2.48; 95% CI, 1.62 to 3.79), cardiac arrhythmia (11.5%, vs. 5.6% among those without arrhythmia; odds ratio, 1.95; 95% CI, 1.33 to 2.86), chronic obstructive pulmonary disease (14.2%, vs. 5.6% among those without disease; odds ratio, 2.96; 95% CI, 2.00 to 4.40), and current smoking (9.4%, vs. 5.6% among former smokers or nonsmokers; odds ratio, 1.79; 95% CI, 1.29 to 2.47). No increased risk of in-hospital death was found to be associated with the use of ACE inhibitors (2.1% vs. 6.1%; odds ratio, 0.33; 95% CI, 0.20 to 0.54) or the use of ARBs (6.8% vs. 5.7%; odds ratio, 1.23; 95% CI, 0.87 to 1.74). Conclusions Our study confirmed previous observations suggesting that underlying cardiovascular disease is associated with an increased risk of in-hospital death among patients hospitalized with Covid-19. Our results did not confirm previous concerns regarding a potential harmful association of ACE inhibitors or ARBs with in-hospital death in this clinical context. (Funded by the William Harvey Distinguished Chair in Advanced Cardiovascular Medicine at Brigham and Women’s Hospital.) 14 Cardiology 14_1 Cardiology General 18 Infectious Disease 18_6 Viral Infections release-date-display-string2020-05-01T12:00:00-04:00 release-date-year2020 release-date-month05 release-date-day01 release-date-hour12 release-date-minute00 release-date-second00 release-date-time-zone-04:00 ==== Body As the coronavirus disease 2019 (Covid-19) pandemic has spread around the globe, there has been growing recognition that persons with underlying increased cardiovascular risk may be disproportionately affected.1-3 Several studies of case series have noted cardiac arrhythmias, cardiomyopathy, and cardiac arrest as terminal events in patients with Covid-19.1-4 Higher incidences of cardiac arrhythmias, acute coronary syndromes, and heart failure–related events have also been reported during seasonal influenza outbreaks, which suggests that acute respiratory infections may result in activation of coagulation pathways, proinflammatory effects, and endothelial cell dysfunction.5 In addition, however, concern has been expressed that medical therapy for cardiovascular disease might specifically contribute to the severity of illness in patients with Covid-19.6,7 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of Covid-19, has been shown to establish itself in the host through the use of angiotensin-converting enzyme 2 (ACE2) as its cellular receptor.8 ACE2 is a membrane-bound monocarboxypeptidase found ubiquitously in humans and expressed predominantly in heart, intestine, kidney, and pulmonary alveolar (type II) cells.7,9 Entry of SARS-CoV-2 into human cells is facilitated by the interaction of a receptor-binding domain in its viral spike glycoprotein ectodomain with the ACE2 receptor.10 ACE2 is counterregulatory to the activity of angiotensin II generated through ACE1 and is protective against detrimental activation of the renin–angiotensin–aldosterone system. Angiotensin II is catalyzed by ACE2 to angiotensin-(1–7), which exerts vasodilatory, antiinflammatory, antifibrotic, and antigrowth effects.11 It has been suggested that ACE inhibitors and angiotensin-receptor blockers (ARBs) may increase the expression of ACE2, which has been shown in the heart in rats,12 and thereby may confer a predisposition to more severe infection and adverse outcomes during Covid-19.6,7 Others have suggested that ACE inhibitors may counter the antiinflammatory effects of ACE2. However, in vitro studies have not shown direct inhibitory activity of ACE inhibitors against ACE2 function.9,13 Despite these uncertainties, some have recommended cessation of treatment with ACE inhibitors and ARBs in patients with Covid-19.6 However, several scientific societies, including the American Heart Association, the American College of Cardiology, the Heart Failure Society of America, and the Council on Hypertension of the European Society of Cardiology, have urged that these important medications should not be discontinued in the absence of clear clinical evidence of harm.14,15 We therefore undertook a study to investigate the relationship between underlying cardiovascular disease and Covid-19 outcomes and to evaluate the association between cardiovascular drug therapy and mortality in this illness. Methods Data Source We analyzed deidentified data from the Surgical Outcomes Collaborative (Surgisphere), an international registry. Our analysis included data from 169 hospitals located in 11 countries in Asia, Europe, and North America. The collaborative uses automated extraction of data from inpatient and outpatient electronic health records, supply-chain databases, and financial records, combined with point-of-care data entry for procedures. A manual data-entry process is used for quality assurance and validation. Data are collected through automated data transfers that capture information from each health care entity at regular intervals on a prospective, ongoing basis. Verifiable source documentation for the data elements includes electronic inpatient and outpatient medical records. Data acquisition is facilitated through the use of a standardized Health Level Seven–compliant data dictionary. After this data dictionary is harmonized with data from electronic health records, the majority of the data acquisition is completed with automated interfaces. The collected data sample from each health care entity is validated against financial records and external databases. All protected health information is stripped from each record before storage in a cloud-based data warehouse. The collaborative is compliant with the Agency for Healthcare Research and Quality guidelines for registries and the Food and Drug Administration guidance on real-world evidence. The collection and analysis of data in the registry have been deemed exempt from ethics review. Data Collection The collaborative registry was used as a resource to analyze data from all patients with polymerase-chain-reaction (PCR)–proven Covid-19 who were admitted to the hospital between December 20, 2019, and March 15, 2020, and who were recorded in the registry as having either died in the hospital or survived to hospital discharge as of March 28, 2020. Data from the registry can be analyzed only after a patient’s hospitalization is complete, and therefore our sample did not include patients who were admitted to the hospital during this time window but were still hospitalized at the end of it. Our sample also may have excluded some patients who had died or were discharged by March 28 but whose discharge status had not yet been recorded by the hospital. The presence in the record of a positive laboratory finding confirming SARS-CoV-2 infection was used for classifying a patient as positive for Covid-19. A positive laboratory finding for SARS-CoV-2 was defined as a positive result on high-throughput sequencing or real-time reverse-transcriptase–PCR (RT-PCR) assay of nasal or pharyngeal swab specimens. At each site, Covid-19 was diagnosed on the basis of the World Health Organization guidance.16 Patients who did not undergo testing, had no record of testing in the collaborative database, or had a negative test were not included in the present study. For this study, only one positive test was necessary for the patient to be included in the analysis. Data on patients’ demographic characteristics, coexisting conditions (based on codes from the International Classification of Diseases, 10th Revision, Clinical Modification), and cardiovascular drug therapy were included in this analysis. Clinical information included age, sex, continent of origin, and underlying coexisting conditions as noted in either the inpatient or the outpatient electronic health record. Coexisting conditions included chronic obstructive pulmonary disease (COPD), an immunosuppressed condition (glucocorticoid use, a preexisting immunologic condition, or ongoing chemotherapy in patients with cancer), current or remote history of smoking, and a history of hypertension, diabetes mellitus, hyperlipidemia, or underlying cardiovascular disease (including coronary artery disease, heart failure, and cardiac arrhythmia). Cardiovascular drug therapy recorded at the time of hospital admission was also included, including any antiplatelet therapy, use of insulin or other hypoglycemic agents, beta-blockers, statins, ARBs, and ACE inhibitors. All the authors reviewed the manuscript and vouch for the accuracy and completeness of the data provided. Statistical Analysis The primary analysis was an evaluation of the relationship of preexisting cardiovascular disease and drug therapy with the end point of in-hospital death while controlling for confounders, including demographic characteristics and coexisting conditions. Categorical variables are shown as frequencies and percentages, and continuous variables as means and standard deviations. Independent sample t-tests were completed, and point differences with 95% confidence intervals are reported for all comparisons between variables. Multiple imputation for missing values was not possible because for disease and drug variables there were no codes to indicate that data were missing; if the patient’s electronic health record did not include information on a clinical characteristic, such as hyperlipidemia or the use of beta-blockers, it was assumed that that characteristic was not present. A multivariable logistic-regression analysis was performed to ascertain the effects of age, race, coexisting conditions (coronary artery disease, congestive heart failure, cardiac arrhythmia, diabetes mellitus, COPD, current smoking, former smoking, hypertension, immunocompromised state, and hyperlipidemia), hospital location (according to country), and medications (ACE inhibitors, ARBs, beta-blockers, antiplatelet agents, statins, insulin, and oral hypoglycemic agents) on the likelihood of in-hospital death. Linearity of the continuous variables with respect to the logit of the dependent variable was confirmed. Odds ratios and corresponding 95% confidence intervals were calculated. Separate age- and sex-adjusted analyses were also performed. The 95% confidence intervals have not been adjusted for multiple testing and should not be used to infer definitive effects. On the basis of the results of the initial analyses, additional analyses were performed to examine the robustness of the estimates initially obtained. Analyses according to continent of origin as well as country classification (as either high income or low–middle income) were performed. A tipping-point analysis (an analysis that shows the effect size and prevalence of an unmeasured confounder that could shift the upper boundary of the confidence interval toward null) was performed. In addition, we sought to determine whether the effect of ACE inhibitors and statins noted in the overall study was also seen when the analysis was confined to a subgroup of patients who might have an indication for these agents. Thus, the association of ACE inhibitor use with in-hospital death was examined in the subgroup of patients with hypertension, and the association of statin use with in-hospital death was examined in the subgroup of patients with hyperlipidemia, with the use of age- and sex-adjusted logistic-regression analysis. All statistical analyses were performed with R software, version 3.6.3 (R Foundation for Statistical Computing), and SPSS Statistics software, version 26 (IBM). Results Patients Our study population included 8910 hospitalized patients from 169 hospitals who had Covid-19, who were admitted between December 20, 2019, and March 15, 2020, and who completed their hospital course (discharged alive or died) by March 28, 2020. Patients who were hospitalized during this time without a completed course could not be included in the analysis. Our sample was made up of 1536 patients (17.2%) from North America, 5755 (64.6%) from Europe, and 1619 (18.2%) from Asia (details of the study population according to continent, country, and number of hospitals are provided in Table S1 in the Supplementary Appendix, available with the full text of this article at NEJM.org). The mean (±SD) age was 49±16 years (16.5% of the patients were >65 years of age), 40.0% of the patients were women, 63.5% were white, 7.9% were black, 6.3% were Hispanic, and 19.3% were Asian. With respect to cardiovascular risk factors, 30.5% of the patients had hyperlipidemia, 26.3% had hypertension, 14.3% had diabetes mellitus, 16.8% were former smokers, and 5.5% were current smokers. Preexisting cardiovascular disease in this sample included coronary artery disease (present in 11.3% of the patients), a history of congestive heart failure (2.1%), and a history of cardiac arrhythmia (3.4%). Other coexisting conditions included COPD (in 2.5% of the patients) and an underlying immunosuppressed condition (2.8%). Medical therapy included ACE inhibitors (8.6% of the patients), ARBs (6.2%), statins (9.7%), beta-blockers (5.9%), and antiplatelet agents (3.3%). Insulin was used in 3.4% of the patients, and other hypoglycemic agents were used in 9.6%. The mean length of hospital stay was 10.7±2.7 days, with an overall in-hospital mortality of 5.8% (515 of 8910 patients) in this population of patients with completed outcomes. Of the patients who had been admitted to an intensive care unit (ICU) at any time during their hospitalization, 24.7% died, as compared with 4.0% of the patients who had not been admitted to an ICU. Analysis of Survivors as Compared with Nonsurvivors Table 1 shows the distribution of demographic characteristics and coexisting conditions among survivors and nonsurvivors, along with the between-group differences and 95% confidence intervals. Nonsurvivors were older, more likely to be white, and more often men, and they had a greater prevalence of diabetes mellitus, hyperlipidemia, coronary artery disease, heart failure, and cardiac arrhythmias. Patients who died were also more likely to have had COPD and a history of current smoking. Among medications, ACE inhibitors and statins were more commonly used by survivors than by nonsurvivors (Table 2), whereas no association between survival and the use of ARBs was found. The length of hospital stay differed between survivors and nonsurvivors (10.5±2.5 days vs. 7.5±2.8 days). When the data were analyzed according to age decile, continent, or income category of the country in which the hospital was located (high income or low–middle income), the results were similar (Fig. S1 and Tables S2 and S3). Multivariable Logistic-Regression Analysis A multivariable logistic-regression model was developed. Independent predictors of in-hospital death and their corresponding odds ratios and 95% confidence intervals are shown in Figure 1. An age greater than 65 years, coronary artery disease, congestive heart failure, cardiac arrhythmia, COPD, and current smoking were associated with a higher risk of in-hospital death. Female sex, the use of ACE inhibitors, and the use of statins were associated with a better chance of survival to hospital discharge, with no association found for the use of ARBs. For female sex, the odds ratio for dying in the hospital was 0.79 (95% confidence interval [CI], 0.65 to 0.95); for ACE inhibitor use, the odds ratio was 0.33 (95% CI, 0.20 to 0.54); and for statin use, the odds ratio was 0.35 (95% CI, 0.24 to 0.52). For ARB use, the odds ratio was 1.23 (95% CI, 0.87 to 1.74). The presence or absence of an immunosuppressed condition, the race or ethnic group, and the presence or absence of hyperlipidemia or diabetes mellitus were not independent predictors of death in the hospital. The analyses according to continent and according to the income category of the country (high or low–middle) were consistent with the overall results (Tables S4 and S5). Data from the age- and sex-adjusted multivariable logistic-regression analyses are shown in Table S6. Additional Analyses In the tipping-point analysis to assess the potential effect of an unmeasured confounder, it was estimated that a hypothetical unobserved binary confounder with a prevalence of 10% in the study population would need to have an odds ratio of at least 10 in order for the observed associations for either ACE inhibitors or statins to have 95% confidence intervals crossing the odds ratio boundary of 1.0 (Table S7). We also separately examined the interaction of ACE inhibitor use with mortality in the subgroup with hypertension and the interaction of statin use with mortality in the subgroup with hyperlipidemia. These analyses, shown in Table S8, are consistent with the results of the primary analysis. Discussion Our investigation confirms previous reports of the independent relationship of older age, underlying cardiovascular disease (coronary artery disease, heart failure, and cardiac arrhythmias), current smoking, and COPD with death in Covid-19. Our results also suggest that women are proportionately more likely than men to survive the infection. Neither harmful nor beneficial associations were noted for antiplatelet therapy, beta-blockers, or hypoglycemic therapy. It is important to note that we were not able to confirm previous concerns regarding a potential harmful association of either ACE inhibitors or ARBs with in-hospital mortality in this clinical context. In viral infections such as influenza, older age is associated with an increased risk of cardiovascular events and death.5 In the 2003 epidemic of severe acute respiratory syndrome (SARS, caused by SARS-CoV-1 infection), sex differences in the risk of death similar to those we observed were noted.17 Women have stronger innate and adaptive immunity and greater resistance to viral infections than men.18 In animal models of SARS-CoV-1 infection, higher susceptibility of male mice to SARS-CoV-1 and greater accumulation of macrophages and neutrophils in the lungs have been described.19 Ovariectomy or the use of estrogen-receptor antagonists increased mortality from SARS-CoV-1 infection in female animals. Furthermore, the difference in risk between the sexes increased with advancing age.19 These findings may support the observation in our investigation that suggested an association between survival and female sex, independent of older age. Infection with SARS-CoV-2 is a mild disease in most people, but in some the disease progresses to a severe respiratory illness characterized by a hyperinflammatory syndrome, multiorgan dysfunction, and death.20 In the lung, the viral spike glycoprotein of SARS-CoV-2 interacts with cell-surface ACE2, and the virus is internalized by endocytosis. The endocytic event up-regulates the activity of ADAM metallopeptidase domain 17 (ADAM17), which cleaves ACE2 from the cell membrane, resulting in a loss of ACE2-mediated protection against the effects of activation of the tissue renin–angiotensin–aldosterone system while mediating the release of proinflammatory cytokines into the circulation.21 The stress of critical illness and inflammation may unite in destabilizing preexisting cardiovascular illness. Vascular endothelial cell dysfunction, inflammation-associated myocardial depression, stress cardiomyopathy, direct viral infection of the heart and its vessels, or the host response may cause or worsen heart failure, demand-related ischemia, and arrhythmias.22 These factors may underlie the observed associations between cardiovascular disease and death in Covid-19. In our analyses, use of either ACE inhibitors or statins was associated with better survival among patients with Covid-19. However, these associations should be considered with extreme caution. Because our study was not a randomized, controlled trial, we cannot exclude the possibility of confounding. In addition, we examined relationships between many variables and in-hospital death, and no primary hypothesis was prespecified; these factors increased the probability of chance associations being found. Therefore, a cause-and-effect relationship between drug therapy and survival should not be inferred. These data also offer no information concerning the potential effect of initiation of ACE inhibitor or statin therapy in patients with Covid-19 who do not have an appropriate indication for these medications. Randomized clinical trials evaluating the role of ACE inhibitors and statins will be necessary before any conclusion can be reached regarding a potential benefit of these agents in patients with Covid-19. In this multinational observational study involving patients hospitalized with Covid-19, we confirmed previous observations suggesting that underlying cardiovascular disease is independently associated with an increased risk of in-hospital death. We were not able to confirm previous concerns regarding a potential harmful association of ACE inhibitors or ARBs with in-hospital mortality in this clinical context. This article was published on May 1, 2020, and updated on May 8, 2020, at NEJM.org. Supplementary Appendix Click here for additional data file. Disclosure Forms Click here for additional data file. Figure 1 Independent Predictors of In-Hospital Death from Multivariable Logistic-Regression Analysis. Numbers and percentages of patients with each risk factor who died (risk factor present) and of patients without each risk factor who died (risk factor absent) are shown. The 95% confidence intervals (CIs) of the odds ratios have not been adjusted for multiple testing and should not be used to infer definitive effects. ACE denotes angiotensin-converting enzyme, ARB angiotensin-receptor blocker, and COPD chronic obstructive pulmonary disease. Table 1 Demographic Characteristics and Coexisting Conditions among Survivors and Nonsurvivors of Covid-19.* Characteristic or Condition Survivors (N=8395) Nonsurvivors (N=515) Difference (95% CI)† Age — yr 48.7±16.6 55.8±15.1 −7.1 (−8.4 to −5.7) Age >65 yr — no. (%) 1327 (15.8) 147 (28.5) −12.7 (−16.0 to −9.4) Female sex — no. (%) 3392 (40.4) 179 (34.8) 5.6 (1.3 to 10.0) Race or ethnic group — no. (%)‡ White 5306 (63.2) 351 (68.2) −5.0 (−9.1 to −0.8) Black 672 (8.0) 34 (6.6) 1.4 (−0.8 to 3.6) Hispanic 529 (6.3) 32 (6.2) 0.1 (−2.0 to 2.3) Asian 1637 (19.5) 84 (16.3) 3.2 (−0.2 to 6.5) Native American 34 (0.4) 1 (0.2) 0.2 (−0.3 to 0.8) Other 219 (2.6) 13 (2.5) 0.1 (−1.4 to 1.4) Coexisting conditions — no. (%) Coronary artery disease 907 (10.8) 103 (20.0) −9.2 (−12.8 to −5.7) Congestive heart failure 160 (1.9) 29 (5.6) −3.7 (−5.8 to −1.8) Cardiac arrhythmia 269 (3.2) 35 (6.8) −3.6 (−5.8 to −1.4) Diabetes mellitus 1175 (14.0) 97 (18.8) −4.8 (−8.3 to −1.3) Hypertension 2216 (26.4) 130 (25.2) 1.2 (−2.8 to 5.1) Hyperlipidemia 2535 (30.2) 180 (35.0) −4.8 (−9.0 to −0.5) COPD 193 (2.3) 32 (6.2) −3.9 (−6.1 to −1.8) Current smoker 445 (5.3) 46 (8.9) −3.6 (−6.2 to −1.1) Former smoker 1410 (16.8) 83 (16.1) 0.7 (−2.6 to 4.0) Immunosuppressed condition 227 (2.7) 22 (4.3) −1.6 (−3.4 to 0.2) * Plus–minus values are means ±SD. The 95% confidence intervals (CIs) have not been adjusted for multiple testing and should not be used to infer definitive effects. COPD denotes chronic obstructive pulmonary disease, and Covid-19 coronavirus disease 2019. † For mean age, the difference is given in years; for all other characteristics, the difference is given in percentage points. ‡ Race and ethnic group were reported by the patient. Table 2 Cardiovascular Drug Therapy at Hospitalization among Survivors and Nonsurvivors of Covid-19.* Drug Class Survivors (N=8395) Nonsurvivors (N=515) Difference (95% CI) number (percent) percentage points ACE inhibitor 754 (9.0) 16 (3.1) 5.9 (4.3 to 7.5) ARB 518 (6.2) 38 (7.4) −1.2 (−3.5 to 1.1) Beta-blocker 497 (5.9) 28 (5.4) 0.5 (−1.6 to 2.6) Antiplatelet 282 (3.4) 13 (2.5) 0.8 (−0.6 to 2.2) Statin 824 (9.8) 36 (7.0) 2.8 (0.5 to 5.1) Insulin 279 (3.3) 23 (4.5) −1.2 (−3.0 to 0.7) Other hypoglycemic agent 792 (9.4) 59 (11.5) −2.1 (−4.9 to 0.8) * The 95% confidence intervals have not been adjusted for multiple testing and should not be used to infer definitive effects. ACE denotes angiotensin-converting enzyme, and ARB angiotensin-receptor blocker. Supported by the William Harvey Distinguished Chair in Advanced Cardiovascular Medicine at Brigham and Women’s Hospital. The development and maintenance of the Surgical Outcomes Collaborative database was funded by Surgisphere. Disclosure forms provided by the authors are available with the full text of this article at NEJM.org. ==== Refs References 1 Ruan Q, Yang K, Wang W, Jiang L, Song J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med 2020 3 3 (Epub ahead of print).32125452 2 Shi S, Qin M, Shen B, et al. Association of cardiac injury with mortality in hospitalized patients with COVID-19 in Wuhan, China. JAMA Cardiol 2020 3 25 (Epub ahead of print).32211816 3 Guo T, Fan Y, Chen M, et al. Cardiovascular implications of fatal outcomes of patients with coronavirus disease 2019 (COVID-19). JAMA Cardiol 2020 3 27 (Epub ahead of print).32219356 4 Arentz M, Yim E, Klaff L, et al. Characteristics and outcomes of 21 critically ill patients with COVID-19 in Washington State. JAMA 2020 3 19 (Epub ahead of print).32191259 5 Nguyen JL, Yang W, Ito K, Matte TD, Shaman J, Kinney PL. 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==== Front Lancet Lancet Lancet (London, England) 0140-6736 1474-547X Elsevier Ltd. S0140-6736(20)31180-6 10.1016/S0140-6736(20)31180-6 Article RETRACTED: Hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19: a multinational registry analysis Mehra Mandeep R [email protected]* Desai Sapan S MDb Ruschitzka Frank ProfMDc Patel Amit N MDde a Brigham and Women's Hospital Heart and Vascular Center and Harvard Medical School, Boston, MA, USA b Surgisphere Corporation, Chicago, IL, USA c University Heart Center, University Hospital Zurich, Zurich, Switzerland d Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA e HCA Research Institute, Nashville, TN, USA * Correspondence to: Prof Mandeep R Mehra, Brigham and Women's Hospital Heart and Vascular Center and Harvard Medical School, Boston, MA 02115, USA [email protected] 22 5 2020 22 5 2020 © 2020 Elsevier Ltd. All rights reserved.2020Elsevier LtdSince January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.Summary Background Hydroxychloroquine or chloroquine, often in combination with a second-generation macrolide, are being widely used for treatment of COVID-19, despite no conclusive evidence of their benefit. Although generally safe when used for approved indications such as autoimmune disease or malaria, the safety and benefit of these treatment regimens are poorly evaluated in COVID-19. Methods We did a multinational registry analysis of the use of hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19. The registry comprised data from 671 hospitals in six continents. We included patients hospitalised between Dec 20, 2019, and April 14, 2020, with a positive laboratory finding for SARS-CoV-2. Patients who received one of the treatments of interest within 48 h of diagnosis were included in one of four treatment groups (chloroquine alone, chloroquine with a macrolide, hydroxychloroquine alone, or hydroxychloroquine with a macrolide), and patients who received none of these treatments formed the control group. Patients for whom one of the treatments of interest was initiated more than 48 h after diagnosis or while they were on mechanical ventilation, as well as patients who received remdesivir, were excluded. The main outcomes of interest were in-hospital mortality and the occurrence of de-novo ventricular arrhythmias (non-sustained or sustained ventricular tachycardia or ventricular fibrillation). Findings 96 032 patients (mean age 53·8 years, 46·3% women) with COVID-19 were hospitalised during the study period and met the inclusion criteria. Of these, 14 888 patients were in the treatment groups (1868 received chloroquine, 3783 received chloroquine with a macrolide, 3016 received hydroxychloroquine, and 6221 received hydroxychloroquine with a macrolide) and 81 144 patients were in the control group. 10 698 (11·1%) patients died in hospital. After controlling for multiple confounding factors (age, sex, race or ethnicity, body-mass index, underlying cardiovascular disease and its risk factors, diabetes, underlying lung disease, smoking, immunosuppressed condition, and baseline disease severity), when compared with mortality in the control group (9·3%), hydroxychloroquine (18·0%; hazard ratio 1·335, 95% CI 1·223–1·457), hydroxychloroquine with a macrolide (23·8%; 1·447, 1·368–1·531), chloroquine (16·4%; 1·365, 1·218–1·531), and chloroquine with a macrolide (22·2%; 1·368, 1·273–1·469) were each independently associated with an increased risk of in-hospital mortality. Compared with the control group (0·3%), hydroxychloroquine (6·1%; 2·369, 1·935–2·900), hydroxychloroquine with a macrolide (8·1%; 5·106, 4·106–5·983), chloroquine (4·3%; 3·561, 2·760–4·596), and chloroquine with a macrolide (6·5%; 4·011, 3·344–4·812) were independently associated with an increased risk of de-novo ventricular arrhythmia during hospitalisation. Interpretation We were unable to confirm a benefit of hydroxychloroquine or chloroquine, when used alone or with a macrolide, on in-hospital outcomes for COVID-19. Each of these drug regimens was associated with decreased in-hospital survival and an increased frequency of ventricular arrhythmias when used for treatment of COVID-19. Funding William Harvey Distinguished Chair in Advanced Cardiovascular Medicine at Brigham and Women's Hospital. ==== Body Introduction The absence of an effective treatment against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has led clinicians to redirect drugs that are known to be effective for other medical conditions to the treatment of COVID-19. Key among these repurposed therapeutic agents are the antimalarial drug chloroquine and its analogue hydroxychloroquine, which is used for the treatment of autoimmune diseases, such as systemic lupus erythematosus and rheumatoid arthritis.1, 2 These drugs have been shown in laboratory conditions to have antiviral properties as well as immunomodulatory effects.3, 4 However, the use of this class of drugs for COVID-19 is based on a small number of anecdotal experiences that have shown variable responses in uncontrolled observational analyses, and small, open-label, randomised trials that have largely been inconclusive.5, 6 The combination of hydroxychloroquine with a second-generation macrolide, such as azithromycin (or clarithromycin), has also been advocated, despite limited evidence for its effectiveness.7 Previous studies have shown that treatment with chloroquine, hydroxychloroquine, or either drug combined with a macrolide can have the cardiovascular adverse effect of prolongation of the QT interval, which could be a mechanism that predisposes to ventricular arrhythmias.8, 9 Research in context Evidence before this study We searched MEDLINE (via PubMed) for articles published up to April 21, 2020, using the key words “novel coronavirus”, “2019-nCoV”, “COVID-19”, “SARS-CoV-2”, “therapy”, “hydroxychloroquine”, “chloroquine”, and “macrolide”. Moreover, we screened preprint servers, such as Medrxiv, for relevant articles and consulted the web pages of organisations such as the US National Institutes of Health and WHO. Hydroxychloroquine and chloroquine (used with or without a macrolide) are widely advocated for treatment of COVID-19 based on in-vitro evidence of an antiviral effect against severe acute respiratory syndrome coronavirus 2. Their use is based on small uncontrolled studies and in the absence of evidence from randomised controlled trials. Concerns have been raised that these drugs or their combination with macrolides could result in electrical instability and predispose patients to ventricular arrhythmias. Whether these drugs improve outcomes or are associated with harm in COVID-19 remains unknown. Added value of this study In the absence of reported randomised trials, there is an urgent need to evaluate real-world evidence related to outcomes with the use of hydroxychloroquine or chloroquine (used with or without macrolides) in COVID-19. Using an international, observational registry across six continents, we assessed 96 032 patients with COVID-19, of whom 14 888 were treated with hydroxychloroquine, chloroquine, or their combination with a macrolide. After controlling for age, sex, race or ethnicity, underlying comorbidities, and disease severity at baseline, the use of all four regimens was associated with an increased hazard for de-novo ventricular arrythmia and death in hospital. This study provides real-world evidence on the use of these therapeutic regimens by including a large number of patients from across the world. Thus, to our knowledge, these findings provide the most comprehensive evidence of the use of hydroxychloroquine and chloroquine (with or without a macrolide) for treatment of COVID-19. Implications of all the available evidence We found no evidence of benefit of hydroxychloroquine or chloroquine when used either alone or with a macrolide. Previous evidence was derived from either small anecdotal studies or inconclusive small randomised trials. Our study included a large number of patients across multiple geographic regions and provides the most robust real-world evidence to date on the usefulness of these treatment regimens. Although observational studies cannot fully account for unmeasured confounding factors, our findings suggest not only an absence of therapeutic benefit but also potential harm with the use of hydroxychloroquine or chloroquine drug regimens (with or without a macrolide) in hospitalised patients with COVID-19. Although several multicentre randomised controlled trials are underway, there is a pressing need to provide accurate clinical guidance because the use of chloroquine or hydroxychloroquine along with macrolides is widespread, often with little regard for potential risk. Some countries have stockpiled these drugs, resulting in a shortage of these medications for those that need them for approved clinical indications.10 The purpose of this study was to evaluate the use of chloroquine or hydroxychloroquine alone or in combination with a macrolide for treatment of COVID-19 using a large multinational registry to assess their real-world application. Principally, we sought to analyse the association between these treatment regimens and in-hospital death. Secondarily, we aimed to evaluate the occurrence of de-novo clinically significant ventricular arrhythmias. Methods Registry features and data acquisition We did a multinational registry analysis of the use of hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19. The registry comprised 671 hospitals located in six continents (appendix p 3). The Surgical Outcomes Collaborative (Surgisphere Corporation, Chicago, IL, USA) consists of de-identified data obtained by automated data extraction from inpatient and outpatient electronic health records, supply chain databases, and financial records. The registry uses a cloud-based health-care data analytics platform that includes specific modules for data acquisition, data warehousing, data analytics, and data reporting. A manual data entry process is used for quality assurance and validation to ensure that key missing values are kept to a minimum. The Surgical Outcomes Collaborative (hereafter referred to as the Collaborative) ensures compliance with the US Food and Drug Administration (FDA) guidance on real-world evidence. Real-world data are collected through automated data transfers that capture 100% of the data from each health-care entity at regular, predetermined intervals, thus reducing the impact of selection bias and missing values, and ensuring that the data are current, reliable, and relevant. Verifiable source documentation for the elements include electronic inpatient and outpatient medical records and, in accordance with the FDA guidance on relevance of real-world data, data acquisition is performed through use of a standardised Health Level Seven-compliant data dictionary, with data collected on a prospective ongoing basis. The validation procedure for the registry refers to the standard operating procedures in place for each of the four ISO 9001:2015 and ISO 27001:2013 certified features of the registry: data acquisition, data warehousing, data analytics, and data reporting. The standardised Health Level Seven-compliant data dictionary used by the Collaborative serves as the focal point for all data acquisition and warehousing. Once this data dictionary is harmonised with electronic health record data, data acquisition is completed using automated interfaces to expedite data transfer and improve data integrity. Collection of a 100% sample from each health-care entity is validated against financial records and external databases to minimise selection bias. To reduce the risk of inadvertent protected health information disclosures, all such information is stripped before storage in the cloud-based data warehouse. The Collaborative is intended to minimise the effects of information bias and selection bias by capturing all-comer data and consecutive patient enrolment by capturing 100% of the data within electronic systems, ensuring that the results remain generalisable to the larger population. The Collaborative is compliant with the US Agency for Healthcare Research and Quality guidelines for registries. With the onset of the COVID-19 crisis, this registry was used to collect data from hospitals in the USA (that are selected to match the epidemiological characteristics of the US population) and internationally, to achieve representation from diverse populations across six continents. Data have been collected from a variety of urban and rural hospitals, academic or community hospitals, and for-profit and non-profit hospitals. The data collection and analyses are deemed exempt from ethics review. Study design We included all patients hospitalised between Dec 20, 2019, and April 14, 2020, at hospitals participating in the registry and with PCR-confirmed COVID-19 infection, for whom a clinical outcome of either hospital discharge or death during hospitalisation was recorded. A positive laboratory finding for SARS-CoV-2 was defined as a positive result on high-throughput sequencing or reverse transcription-quantitative PCR assay of nasal or pharyngeal swab specimens, and this finding was used for classifying a patient as positive for COVID-19. COVID-19 was diagnosed, at each site, on the basis of WHO guidance.11 Patients who did not have a record of testing in the database, or who had a negative test, were not included in the study. Only one positive test was necessary for the patient to be included in the analysis. Patients who received either hydroxychloroquine or a chloroquine analogue-based treatment (with or without a second-generation macrolide) were included in the treatment group. Patients who received treatment with these regimens starting more than 48 h after COVID-19 diagnosis were excluded. We also excluded data from patients for whom treatment was initiated while they were on mechanical ventilation or if they were receiving therapy with the antiviral remdesivir. These specific exclusion criteria were established to avoid enrolment of patients in whom the treatment might have started at non-uniform times during the course of their COVID-19 illness and to exclude individuals for whom the drug regimen might have been used during a critical phase of illness, which could skew the interpretation of the results. Thus, we defined four distinct treatment groups, in which all patients started therapy within 48 h of an established COVID-19 diagnosis: chloroquine alone, chloroquine with a macrolide, hydroxychloroquine alone, or hydroxychloroquine with a macrolide. All other included patients served as the control population. Data collection Patient demographics, including age, body-mass index (BMI), sex, race or ethnicity, and continent of origin were obtained. Underlying comorbidities (based on International Classification of Diseases, tenth revision, clinical modification codes) present in either the inpatient or outpatient electronic health record were collected, which included cardiovascular disease (including coronary artery disease, congestive heart failure, and history of a cardiac arrhythmia), current or previous history of smoking, history of hypertension, diabetes, hyperlipidaemia, or chronic obstructive pulmonary disease (COPD), and presence of an immunosuppressed condition (steroid use, pre-existing immunological condition, or current chemotherapy in individuals with cancer). We collected data on use of medications at baseline, including cardiac medications (angiotensin converting enzyme [ACE] inhibitors, angiotensin receptor blockers, and statins) or use of antiviral therapy other than the drug regimens being evaluated. The initiation of hydroxychloroquine or chloroquine during hospital admission was recorded, including the time of initiation. The use of second-generation macrolides, specifically azithromycin and clarithromycin, was similarly recorded. A quick sepsis-related organ failure assessment (qSOFA) was calculated for the start of therapy (including a scored calculation of the mental status, respiratory rate, and systolic blood pressure) and oxygen saturation (SPO2) on room air was recorded, as measures of disease severity. Outcomes The primary outcome of interest was the association between use of a treatment regimen containing chloroquine or hydroxychloroquine (with or without a second-generation macrolide) when initiated early after COVID-19 diagnosis with the endpoint of in-hospital mortality. The secondary outcome of interest was the association between these treatment regimens and the occurrence of clinically significant ventricular arrhythmias (defined as the first occurrence of a non-sustained [at least 6 sec] or sustained ventricular tachycardia or ventricular fibrillation) during hospitalisation. We also analysed the rates of progression to mechanical ventilation use and the total and intensive care unit lengths of stay (in days) for patients in each group. Statistical analysis For the primary analysis of in-hospital mortality, we controlled for confounding factors, including demographic variables, comorbidities, disease severity at presentation, and other medication use (cardiac medications and other antiviral therapies). Categorical variables are shown as frequencies and percentages, and continuous variables as means with SDs. Comparison of continuous data between groups was done using the unpaired t-test and categorical data were compared using Fisher's exact test. A p value of less than 0·05 was considered significant. Multiple imputation for missing values was not possible because for disease and drug variables, there were no codes to indicate that data were missing; if the patient's electronic health record did not include information on a clinical characteristic, it was assumed that the characteristic was not present. Cox proportional hazards regression analysis was done to evaluate the effect of age, sex, race or ethnicity (using white race as a reference group), comorbidities (BMI, presence of coronary artery disease, presence of congestive heart failure, history of cardiac arrhythmia, diabetes, or COPD, current smoker, history of hypertension, immunocompromised state, and history of hyperlipidaemia), medications (cardiac medications, antivirals, and the treatment regimens of interest), and severity of illness scores (qSOFA <1 and SPO2 <94%) on the risk of clinically significant ventricular arrhythmia (using the time from admission to first occurrence, or if the event did not occur, to the time of discharge) and mortality (using the time from admission to inpatient mortality or discharge). Age and BMI were treated as continuous variables and all other data were treated as categorical variables in the model. From the model, hazard ratios (HRs) with 95% CIs were estimated for included variables to determine their effect on the risk of in-hospital mortality (primary endpoint) or subsequent mechanical ventilation or death (composite endpoint). Independence of survival times (or time to first arrhythmia for the ventricular arrhythmia analysis) was confirmed. Proportionality between the predictors and the hazard was validated through an evaluation of Schoenfeld residuals, which found p>0·05 and thus confirmed proportionality. To minimise the effect of confounding factors, a propensity score matching analysis was done individually for each of the four treatment groups compared with a control group that received no form of that therapy. For each treatment group, a separate matched control was identified using exact and propensity-score matched criteria with a calliper of 0·001. This method was used to provide a close approximation of demographics, comorbidities, disease severity, and baseline medications between patients. The propensity score was based on the following variables: age, BMI, gender, race or ethnicity, comorbidities, use of ACE inhibitors, use of statins, use of angiotensin receptor blockers, treatment with other antivirals, qSOFA score of less than 1, and SPO2 of less than 94% on room air. The patients were well matched, with standardised mean difference estimates of less than 10% for all matched parameters. Additional analyses were done to examine the robustness of the estimates initially obtained. Individual analyses by continent of origin and sex-adjusted analyses using Cox proportional hazards models were performed. A tipping-point analysis (an analysis that shows the effect size and prevalence of an unmeasured confounder that could shift the upper boundary of the CI towards null) was also done. All statistical analyses were done with R version 3.6.3 and SPSS version 26. Role of the funding source The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author and co-author ANP had full access to all the data in the study and had final responsibility for the decision to submit for publication. Results 96 032 hospitalised patients from 671 hospitals were diagnosed with COVID-19 between Dec 20, 2019, and April 14, 2020 and met the inclusion criteria for this study (figure 1 ). All included patients completed their hospital course (discharged or died) by April 21, 2020. Patients who were hospitalised during the study period without a completed course were unable to be analysed. The study cohort included 63 315 (65·9%) patients from North America, 16 574 (17·3%) from Europe, 8101 (8·4%) from Asia, 4402 (4·6%) from Africa, 3577 (3·7%) from South America, and 63 (0·1%) from Australia (details of the number of hospitals per continent are presented in the appendix, p 3). The mean age was 53·8 years (SD 17·6), 44 426 (46·3%) were women, mean BMI was 27·6 kg/m2 (SD 5·5; 29 510 [30·7%] were obese with BMI ≥30 kg/m2), 64 220 (66·9%) were white, 9054 (9·4%) were black, 5978 (6·2%) were Hispanic, and 13 519 (14·1%) were of Asian origin (appendix p 4). In terms of comorbidities, 30 198 (31·4%) had hyperlipidaemia, 25 810 (26·9%) had hypertension, 13 260 (13·8%) had diabetes, 3177 (3·3%) had COPD, 2868 (3·0%) had an underlying immunosuppressed condition, 16 553 (17·2%) were former smokers, and 9488 (9·9%) were current smokers. In terms of pre-existing cardiovascular disease, 12 137 (12·6%) had coronary artery disease, 2368 (2·5%) had a history of congestive heart failure, and 3381 (3·5%) had a history of arrhythmia. The mean length of stay in hospital was 9·1 days (SD 6·4), with an overall in-hospital mortality of 10 698 (11·1%) of 96 032. The use of other antivirals was recorded in 38 927 (40·5%) patients as treatment for COVID-19. The most common antivirals were lopinavir with ritonavir (12 304 [31·6%]), ribavirin (7904 [20·3%]), and oseltamivir (5101 [13·1%]). Combination therapy with more than one of these antiviral regimens was used for 6782 (17·4%) patients.Figure 1 Study profile The treatment groups included 1868 patients who were given chloroquine alone, 3016 given hydroxychloroquine alone, 3783 given chloroquine with a macrolide and 6221 given hydroxychloroquine and a macrolide. The median time from hospitalisation to diagnosis of COVID-19 was 2 days (IQR 1–4). The mean daily dose and duration of the various drug regimens were as follows: chloroquine alone, 765 mg (SD 308) and 6·6 days (2·4); hydroxychloroquine alone, 596 mg (126) and 4·2 days (1·9); chloroquine with a macrolide, 790 mg (320) and 6·8 days (2·5); and hydroxychloroquine with a macrolide, 597 mg (128) and 4·3 days (2·0). Additional details of the study cohort are provided in the appendix (pp 4–5). Demographic variables and comorbidities were compared among survivors and non-survivors (table 1 ). Non-survivors were older, more likely to be obese, more likely to be men, more likely to be black or Hispanic, and to have diabetes, hyperlipidaemia, coronary artery disease, congestive heart failure, and a history of arrhythmias. Non-survivors were also more likely to have COPD and to have reported current smoking.Table 1 Demographics and comorbidities of patients by survival or non-survival during hospitalisation Survivors (n=85 334) Non-survivors (n=10 698) p value Age, years 53·1 (17·5) 60·0 (17·6) <0·0001 BMI, kg/m2 27·0 (5·1) 31·8 (6·4) <0·0001 Obese, BMI >30 kg/m2 22 992 (26·9%) 6518 (60·9%) <0·0001 Sex Female 40 169 (47·1%) 4257 (39·8%) <0·0001 Male 45 165 (52·9%) 6441 (60·2%) <0·0001 Race or ethnicity White 57 503 (67·4%) 6717 (62·8%) <0·0001 Black 7219 (8·5%) 1835 (17·2%) <0·0001 Hispanic 4948 (5·8%) 1030 (9·6%) <0·0001 Asian 12 657 (14·8%) 862 (8·1%) <0·0001 Native American 1023 (1·2%) 56 (0·5%) <0·0001 Other 1984 (2·3%) 198 (1·9%) 0·0019 Comorbidities at baseline Coronary artery disease 9777 (11·5%) 2360 (22·1%) <0·0001 Congestive heart failure 1828 (2·1%) 540 (5·0%) <0·0001 Arrhythmia 2700 (3·2%) 681 (6·4%) <0·0001 Diabetes 10 963 (12·8%) 2297 (21·5%) <0·0001 Hypertension 21 948 (25·7%) 3862 (36·1%) <0·0001 Hyperlipidaemia 26 480 (31·0%) 3718 (34·8%) <0·0001 COPD 2603 (3·1%) 574 (5·4%) <0·0001 Current smoker 7972 (9·3%) 1516 (14·2%) <0·0001 Former smoker 14 681 (17·2%) 1872 (17·5%) 0·45 Immunocompromised 2406 (2·8%) 462 (4·3%) <0·0001 Medications ACE inhibitor 7521 (8·8%) 428 (4·0%) <0·0001 Statin 8506 (10·0%) 739 (6·9%) <0·0001 Angiotensin receptor blocker 5190 (6·1%) 659 (6·2%) 0·75 Antiviral 35 189 (41·2%) 3738 (34·9%) <0·0001 Disease severity qSOFA <1 71 457 (83·7%) 7911 (73·9%) <0·0001 SPO2 <94% 7188 (8·4%) 2129 (19·9%) <0·0001 Treatment group Chloroquine alone 1561 (1·8%) 307 (2·9%) <0·0001 Chloroquine with macrolide* 2944 (3·4%) 839 (7·8%) <0·0001 Hydroxychloroquine alone 2473 (2·9%) 543 (5·1%) <0·0001 Hydroxychloroquine with macrolide* 4742 (5·6%) 1479 (13·8%) <0·0001 Outcomes De-novo ventricular arrhythmia 839 (1·0%) 400 (3·7%) <0·0001 Non-ICU length of stay, days 9·0 (6·2) 9·8 (7·4) <0·0001 ICU length of stay, days 2·1 (3·7) 9·4 (10·6) <0·0001 Total length of stay, days 11·1 (7·3) 19·2 (14·4) <0·0001 Mechanical ventilation 4821 (5·6%) 4533 (42·4%) <0·0001 Data are mean (SD) or n (%). BMI=body-mass index. COPD=chronic obstructive pulmonary disease. ACE=angiotensin-converting enzyme. qSOFA=quick sepsis-related organ failure assessment. SPO2=oxygen saturation. ICU=intensive care unit. * Macrolides include only azithromycin or clarithromycin. The distribution of demographics, comorbidities, and outcomes between the four treatment groups are shown in table 2 . No significant between-group differences were found among baseline characteristics or comorbidities. Ventricular arrhythmias were more common in the treatment groups compared with the control population. Mortality was higher in the treatment groups compared with the control population (p<0·0001; appendix pp 15–18).Table 2 Patient demographics and characteristics by treatment group Control group (n=81 144) Chloroquine (n=1868) Chloroquine with macrolide(n=3783) Hydroxychloroquine (n=3016) Hydroxychloroquine with macrolide(n=6221) Age, years 53·6 (17·6) 55·1 (18·0) 54·9 (17·7) 55·1 (17·9) 55·2 (17·7) BMI, kg/m2 27·4 (5·4) 27·8 (6·1) 28·2 (5·8) 28·4 (5.9) 28·5 (5·9) Sex Female 37 716 (46·5%) 845 (45·2%) 1718 (45·4%) 1388 (46·0%) 2759 (44·3%) Male 43 428 (53·5%) 1023 (54·8%) 2065 (54·6%) 1628 (54·0%) 3462 (55·7%) Race or ethnicity White 54 403 (67·1%) 1201 (64·3%) 2418 (63·9%) 2074 (68·8%) 4124 (66·3%) Black 7519 (9·3%) 203 (10·9%) 369 (9·8%) 287 (9·5%) 676 (10·9%) Hispanic 4943 (6·1%) 108 (5·8%) 273 (7·2%) 194 (6·4%) 460 (7·4%) Asian 11 504 (14·2%) 301 (16·1%) 603 (15·9%) 366 (12·1%) 745 (12·0%) Native American 922 (1·1%) 19 (1·0%) 37 (1·0%) 33 (1·1%) 68 (1·1%) Other 1853 (2·3%) 36 (1·9%) 83 (2·2%) 62 (2·1%) 148 (2·4%) Comorbidities Coronary artery disease 10 076 (12·4%) 284 (15·2%) 515 (13·6%) 421 (14·0%) 841 (13·5%) Congestive heart failure 1949 (2·4%) 50 (2·7%) 103 (2·7%) 78 (2·6%) 188 (3·0%) Arrhythmia 2861 (3·5%) 63 (3·4%) 126 (3·3%) 108 (3·6%) 223 (3·6%) Diabetes 11 058 (13·6%) 258 (13·8%) 584 (15·4%) 447 (14·8%) 913 (14·7%) Hypertension 21 437 (26·4%) 560 (30·0%) 1095 (28·9%) 891 (29·5%) 1827 (29·4%) Hyperlipidaemia 25 538 (31·5%) 607 (32·5%) 1164 (30·8%) 941 (31·2%) 1948 (31·3%) COPD 2647 (3·3%) 55 (2·9%) 144 (3·8%) 111 (3·7%) 220 (3·5%) Current smoker 7884 (9·7%) 190 (10·2%) 428 (11·3%) 342 (11·3%) 644 (10·4%) Former smoker 14 049 (17·3%) 321 (17·2%) 648 (17·1%) 509 (16·9%) 1026 (16·5%) Immunocompromised 2416 (3·0%) 53 (2·8%) 122 (3·2%) 90 (3·0%) 187 (3·0%) Baseline disease severity qSOFA <1 67 316 (83·0%) 1530 (81·9%) 3051 (80·7%) 2477 (82·1%) 4994 (80·3%) SPO2 <94% 7721 (9·5%) 209 (11·2%) 413 (10·9%) 323 (10·7%) 651 (10·5%) Outcomes De-novo ventricular arrhythmia 226 (0·3%) 81 (4·3%) 246 (6·5%) 184 (6·1%) 502 (8·1%) Non-ICU length of stay, days 9·1 (6·4) 8·8 (6·2) 9·0 (6·6) 8·9 (6·2) 9·1 (6·7) ICU length of stay, days 2·6 (5·0) 4·3 (6·8) 4·9 (8·1) 4·3 (6·8) 4·7 (7·8) Total length of stay, days 11·7 (8·4) 13·2 (9·1) 13·8 (11·0) 13·2 (9·3) 13·8 (10·7) Mechanical ventilation 6278 (7·7%) 403 (21·6%) 814 (21·5%) 616 (20·4%) 1243 (20·0%) Mortality 7530 (9·3%) 307 (16·4%) 839 (22·2%) 543 (18·0%) 1479 (23·8%) Ventilator or mortality 10 703 (13·2%) 531 (28·4%) 1288 (34·0%) 877 (29·1%) 2120 (34·1%) Data are mean (SD) or n (%). BMI=body-mass index. COPD=chronic obstructive pulmonary disease. qSOFA=quick sepsis-related organ failure assessment. SPO2=oxygen saturation. ICU=intensive care unit. * Macrolides include only clarithromycin and azithromycin. Independent predictors of in-hospital mortality are shown in figure 2 . Age, BMI, black race or Hispanic ethnicity (versus white race), coronary artery disease, congestive heart failure, history of arrhythmia, diabetes, hypertension, hyperlipidaemia, COPD, being a current smoker, and immunosuppressed condition were associated with a higher risk of in-hospital death. Female sex, ethnicity of Asian origin, use of ACE inhibitors (but not angiotensin receptor blockers), and use of statins was associated with reduced in-hospital mortality risk. Compared with the control group (9·3%), hydroxychloroquine alone (18·0%; HR 1·335, 95% CI 1·223–1·457), hydroxychloroquine with a macrolide (23·8%; 1·447, 1·368–1·531), chloroquine alone (16·4%; 1·365, 1·218–1·531), and chloroquine with a macrolide (22·2%; 1·368, 1·273–1·469) were independently associated with an increased risk of in-hospital mortality. The multivariable Cox regression analyses by continent are shown in the appendix (pp 6–11), as well as data from the sex-adjusted multivariable logistic regression analyses (pp 12–13) and a separate Cox regression analysis for the combined endpoint of mechanical ventilation or mortality (p 14).Figure 2 Independent predictors of in-hospital mortality Age and BMI are continuous variables. The 95% CIs have not been adjusted for multiple testing and should not be used to infer definitive effects. ACE=angiotensin-converting enzyme. BMI=body mass index. COPD=chronic obstructive pulmonary disease. HR=hazard ratio. qSOFA=quick sepsis-related organ failure assessment. SPO2=oxygen saturation. Independent predictors of ventricular arrythmia are shown in figure 3 . Coronary artery disease, congestive heart failure, history of cardiac arrhythmia, and COPD were independently associated with an increased risk of de-novo ventricular arrhythmias during hospitalisation. Compared with the control group (0·3%), hydroxychloroquine alone (6·1%; HR 2·369, 95% CI 1·935–2·900), hydroxychloroquine with a macrolide (8·1%; 5·106, 4·106–5·983), chloroquine alone (4·3%; 3·561, 2·760–4·596), and chloroquine with a macrolide (6·5%; 4·011, 3·344–4·812) were independently associated with an increased risk of de-novo ventricular arrhythmia during hospitalisation.Figure 3 Independent predictors of ventricular arrhythmias during hospitalisation Age and BMI are continuous variables. The 95% CIs have not been adjusted for multiple testing and should not be used to infer definitive effects. ACE=angiotensin-converting enzyme. BMI=body mass index. COPD=chronic obstructive pulmonary disease. HR=hazard ratio. qSOFA=quick sepsis-related organ failure assessment. SPO2=oxygen saturation. Analyses using propensity score matching by treatment group are shown in the appendix (pp 15–18). The results indicated that the associations between the drug regimens and mortality, need for mechanical ventilation, length of stay, and the occurrence of de-novo ventricular arrhythmias were consistent with the primary analysis. A tipping point analysis was done to assess the effects of an unmeasured confounder on the findings of significance with hydroxychloroquine or chloroquine (appendix pp 19–20). For chloroquine, hydroxychloroquine, and chloroquine with a macrolide, a hypothetical unobserved binary confounder with a prevalence of 50% in the exposed population would need to have an HR of 1·5 to tip this analysis to non-significance at the 5% level. For a comparison with the observed confounders in this study, if congestive heart failure (which has an HR of 1·756) were left out of the model, it would need to have a prevalence of approximately 30% in the population to lead to confounding in the analysis. Similarly, for hydroxychloroquine with a macrolide, a hypothetical unobserved binary confounder with a prevalence of 37% in the exposed population would need to have an HR of 2·0 to tip this analysis to non-significance at the 5% level. Again, congestive heart failure (which has an HR of 1·756) would need to have a prevalence of approximately 50% in the population to lead to confounding in the analysis, had it not been adjusted for in the Cox proportional hazards model. Discussion In this large multinational real-world analysis, we did not observe any benefit of hydroxychloroquine or chloroquine (when used alone or in combination with a macrolide) on in-hospital outcomes, when initiated early after diagnosis of COVID-19. Each of the drug regimens of chloroquine or hydroxychloroquine alone or in combination with a macrolide was associated with an increased hazard for clinically significant occurrence of ventricular arrhythmias and increased risk of in-hospital death with COVID-19. The use of hydroxychloroquine or chloroquine in COVID-19 is based on widespread publicity of small, uncontrolled studies, which suggested that the combination of hydroxychloroquine with the macrolide azithromycin was successful in clearing viral replication.7 On March 28, 2020, the FDA issued an emergency use authorisation for these drugs in patients if clinical trial access was unavailable.12 Other countries, such as China, have issued guidelines allowing for the use of chloroquine in COVID-19.13 Several countries have been stockpiling the drugs, and shortages of them for approved indications, such as for autoimmune disease and rheumatoid arthritis, have been encountered.10 A retrospective observational review of 368 men with COVID-19 treated at the US Veterans Affairs hospitals raised concerns that the use of hydroxychloroquine was associated with a greater hazard of death; however, the baseline characteristics among the groups analysed were dissimilar and the possibility of bias cannot be ruled out.14 Another observational study in 181 patients from France reported that the use of hydroxychloroquine at a dose of 600 mg per day was not associated with a measurable clinical benefit in patients with COVID-19 pneumonia.15 Our large-scale, international, real-world analysis supports the absence of a clinical benefit of chloroquine and hydroxychloroquine and points to potential harm in hospitalised patients with COVID-19. Chloroquine and hydroxychloroquine are associated with concerns of cardiovascular toxicity, particularly because of their known relationship with electrical instability, characterised by QT interval prolongation (the time taken for ventricular depolarisation and repolarisation). This mechanism relates to blockade of the hERG potassium channel,16 which lengthens ventricular repolarisation and the duration of ventricular action potentials. Under specific conditions, early after-depolarisations can trigger ventricular arrhythmias.9 Such propensity for arrhythmia provocation is more often seen in individuals with structural cardiovascular disease, and cardiac injury has been reported to occur with high frequency during COVID-19 illness.17, 18 Furthermore, individuals with cardiovascular disease represent a vulnerable population that experience worse outcomes with COVID-19.19, 20 Pathological studies have pointed to derangements in the vascular endothelium and a diffuse endotheliitis noted across multiple organs in COVID-19.21 Whether patients with underlying cardiovascular disease and those that experience de-novo cardiovascular injury have a greater predilection to ventricular arrhythmias with chloroquine or its analogues remains uncertain but plausible. COVID-19 is exemplified by initial viral replication followed by enhanced systemic inflammation.22 The use of chloroquine or hydroxychloroquine in combination with a macrolide is designed to use their antimicrobial properties in a synergistic manner.23 Macrolides, such as azithromycin and clarithromycin, are antibiotics with immunomodulatory and anti-inflammatory effects.24 However, these drugs prolong the QT interval and increase the risk of sudden cardiac death.8, 9 In a preliminary analysis, Borba and colleagues25 reported a double-blind, randomised trial with 81 adult patients who were hospitalised with severe COVID-19 at a tertiary care facility in Brazil. This study suggested that a higher dose of chloroquine represented a safety hazard, especially when taken concurrently with azithromycin and oseltamivir. In another cohort study of 90 patients with COVID-19 pneumonia, Mercuro and colleagues26 found that the concomitant use of a macrolide was associated with a greater change in the corrected QT interval. Our study did not examine the QT interval but instead directly analysed the risk of clinically significant ventricular arrythmias. We showed an independent association of the use of either hydroxychloroquine or chloroquine with the occurrence of de-novo ventricular arrhythmias. We also note that the hazard of de-novo ventricular arrhythmias increased when the drugs were used in combination with a macrolide. In our analysis, which was dominated by patients from North America, we noted that higher BMI emerged as a risk marker for worse in-hospital survival. Obesity is a known risk factor for cardiac arrhythmias and sudden cardiac death.27, 28 The most commonly reported arrhythmias are atrial fibrillation and ventricular tachycardia. Although age, race, and BMI were predictive of an increased risk for death with COVID-19 in our analysis, they were not found to be associated with an increased risk of ventricular arrhythmias on our multivariable regression analysis. The only variables found to be independently predictive of ventricular arrhythmias were the four treatment regimens, along with underlying cardiovascular disease and COPD. Thus, the presence of cardiovascular comorbidity in the study population could partially explain the observed risk of increased cardiovascular toxicity with the use of chloroquine or hydroxychloroquine, especially when used in combination with macrolides. In this investigation, consistent with our previous findings in a smaller cohort of 8910 patients,20 we found that women and patients being treated with ACE inhibitors (but not angiotensin receptor blockers) or statins had lower mortality with COVID-19. These findings imply that drugs that stabilise cardiovascular function and improve endothelial cell dysfunction might improve prognosis, independent of the use of cardiotoxic drug combinations.21 Our study has several limitations. The association of decreased survival with hydroxychloroquine or chloroquine treatment regimens should be interpreted cautiously. Due to the observational study design, we cannot exclude the possibility of unmeasured confounding factors, although we have reassuringly noted consistency between the primary analysis and the propensity score matched analyses. Nevertheless, a cause-and-effect relationship between drug therapy and survival should not be inferred. These data do not apply to the use of any treatment regimen used in the ambulatory, out-of-hospital setting. Randomised clinical trials will be required before any conclusion can be reached regarding benefit or harm of these agents in COVID-19 patients. We also note that although we evaluated the relationship of the drug treatment regimens with the occurrence of ventricular arrhythmias, we did not measure QT intervals, nor did we stratify the arrhythmia pattern (such as torsade de pointes). We also did not establish if the association of increased risk of in-hospital death with use of the drug regimens is linked directly to their cardiovascular risk, nor did we conduct a drug dose-response analysis of the observed risks. Even if these limitations suggest a conservative interpretation of the findings, we believe that the absence of any observed benefit could still represent a reasonable explanation. In summary, this multinational, observational, real-world study of patients with COVID-19 requiring hospitalisation found that the use of a regimen containing hydroxychloroquine or chloroquine (with or without a macrolide) was associated with no evidence of benefit, but instead was associated with an increase in the risk of ventricular arrhythmias and a greater hazard for in-hospital death with COVID-19. These findings suggest that these drug regimens should not be used outside of clinical trials and urgent confirmation from randomised clinical trials is needed. This online publication has been corrected. The corrected version first appeared at thelancet.com on May 29, 2020 Supplementary Material Supplementary appendix Acknowledgments The development and maintenance of the Surgical Outcomes Collaborative database was funded by Surgisphere Corporation (Chicago, IL, USA). This study was supported by the William Harvey Distinguished Chair in Advanced Cardiovascular Medicine at Brigham and Women's Hospital (Boston, MA, USA). We acknowledge Jide Olayinka (Surgisphere) for their helpful statistical review of the manuscript. Contributors The study was conceived and designed by MRM and ANP. Acquisition of data and statistical analysis of the data were supervised and performed by SSD. MRM drafted the manuscript and all authors participated in critical revision of the manuscript for important intellectual content. MRM and ANP supervised the study. All authors approved the final manuscript and were responsible for the decision to submit for publication. Declaration of interests MRM reports personal fees from Abbott, Medtronic, Janssen, Mesoblast, Portola, Bayer, Baim Institute for Clinical Research, NupulseCV, FineHeart, Leviticus, Roivant, and Triple Gene. SSD is the founder of Surgisphere Corporation. FR has been paid for time spent as a committee member for clinical trials, advisory boards, other forms of consulting, and lectures or presentations; these payments were made directly to the University of Zurich and no personal payments were received in relation to these trials or other activities. 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of fatal outcomes of patients with coronavirus disease 2019 (COVID-19) JAMA Cardiol 2020 published online March 27. 10.1001/jamacardio.2020.1017 19 Bonow RO Fonarow GC O'Gara PT Yancy CW Association of coronavirus disease 2019 (COVID-19) with myocardial injury and mortality JAMA Cardiol 2020 published online March 27. 10.1001/jamacardio.2020.1105 20 Mehra MR Desai SS Kuy S Henry TD Patel AN Cardiovascular disease, drug therapy, and mortality in COVID-19 N Engl J Med 2020 published online May 1. 10.1056/NEJMoa2007621 21 Varga Z Flammer AJ Steiger P Endothelial cell infection and endotheliitis in COVID-19 Lancet 395 2020 1417 1418 32325026 22 Siddiqi HK Mehra MR COVID-19 illness in native and immunosuppressed states: a clinical-therapeutic staging proposal J Heart Lung Transplant 39 2020 405 407 32362390 23 Nakornchai S Konthiang P Activity of azithromycin or erythromycin in combination with antimalarial drugs against multidrug-resistant Plasmodium falciparum in vitro Acta Trop 100 2006 185 191 17126280 24 Lee N Wong CK Chan MCW Anti-inflammatory effects of adjunctive macrolide treatment in adults hospitalized with influenza: a randomized controlled trial Antiviral Res 144 2017 48 56 28535933 25 Borba MGS Val FFA Sampaio VS Effect of high vs low doses of chloroquine diphosphate as adjunctive therapy for patients hospitalized with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection: a randomized clinical trial JAMA Netw Open 3 2020 e208857 26 Mercuro NJ Yen CF Shim DJ Risk of QT interval prolongation associated with use of hydroxychloroquine with or without concomitant azithromycin among hospitalized patients testing positive for coronavirus disease 2019 (COVID-19) JAMA Cardiol 2020 published online May 1. 10.1001/jamacardio.2020.1834 27 Lavie CJ Arena R Alpert MA Milani RV Ventura HO Management of cardiovascular diseases in patients with obesity Nat Rev Cardiol 15 2018 45 56 28748957 28 Sanchis-Gomar F Lavie CJ Mehra MR Henry BM Lippi G Obesity and outcomes in COVID-19: when an epidemic and pandemic collide Mayo Clin Proc 2020 published online May 19. 10.1016/j.mayocp.2020.05.006	32450107	PMC7255293	NO-CC CODE	2021-01-06 09:27:55	yes	Lancet. 2020 May 22; doi: 10.1016/S0140-6736(20)31180-6
==== Front Early Hum Dev Early Hum. Dev Early Human Development 0378-3782 1872-6232 Elsevier B.V. S0378-3782(20)30197-3 10.1016/j.earlhumdev.2020.105026 105026 Article Unknown unknowns – COVID-19 and potential global mortality Grech Victor [email protected] Paediatric Dept, Mater Dei Hospital, Malta 31 3 2020 5 2020 31 3 2020 144 105026105026 23 3 2020 23 3 2020 © 2020 Elsevier B.V. All rights reserved. 2020 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. COVID-19 (SARS-CoV-2) is currently a global pandemic. This paper will attempt to estimate global infection rates and potential resultant mortality in the absence of effective treatment and/or vaccination. Calculations are based on World Health Organisation data from Wuhan in China: 14% of infected cases are severe, 5% require intensive care and 4% die. Estimated infection rates and mortality rates at the level of continents and some individual countries (when these are of sufficient size) are tabulated. This pandemic may cause close to half a billion deaths, i.e. 6% of the global population – and potentially more. At the risk of sounding sensational, but with a sober sense of realism, healthcare risks being plunged into the Middle-Ages if the public do not do their part. Infection cannot occur in the absence of contact. The only way to mitigate these numbers is to apply social distancing and take the standard precautions so frequently reiterated by Public Health: hand washing, avoid touching the face and so on. These measures are crucial as the human cost is going to be unthinkable even in the best-case scenarios that epidemiologists are modelling. Highlights • COVID-19 (SARS-CoV-2) is currently a global pandemic. • Global infection rates and potential resultant mortality are estimated. • Calculations are based on World Health Organisation data from China. • This pandemic may cause half a billion deaths, 6% of the global population. • Healthcare systems risks being inundated if the public do not do their part. ==== Body 1 Introduction The Roman poet Juvenal (first/s c. CE) despaired of finding a wife with his perceived ideal qualities, remarking that such a woman was “rara avis in terris, nigroque simillima cygno”, in this world a rare bird, very much like a black swan [1]. For the next two millennia, this term was used to refer to that which is mythical, but a species of black swans were discovered in Australia in 1697 [2]. The expression thus came to refer to a rare and unknowable event, a term formalised in economic theory by the American economist Frank Knight (1885–1972): “There are things we know that we don't know—the known unknowns. And there are unknown unknowns; the things we do not yet know that we do not know”. [3] These forms of risks were popularised by the economist Nassim Taleb this century [2]. 2 Coronaviruses The four common human coronaviruses in circulation account for 10–20% of the common cold cases each year as they remain in the upper respiratory. More dangerous and aggressive coronaviruses have challenged humanity by acquiring the ability to attack the lungs, causing severe pneumonias. SARS (severe acute respiratory syndrome, SARS-CoV) broke out in Asia in 2003 and >8000 people were infected, with a 10% mortality. MERS (Middle East respiratory syndrome, MERS-CoV) broke out in the Middle East in 2012 and 2468 were infected with a mortality of >30% [4]. 3 COVID-19 A pandemic is defined as a disease that is prevalent over a whole country or the world. The current pandemic is caused by a novel coronavirus which has been dubbed COVID-19 (SARS-CoV-2). [5] It has been argued that this pandemic is an unknown unknown, but the possibility of a pandemic by coronavirus (or indeed by another virus) had been previously noted [6]. The COVID-19 pandemic broke out in December 2019 in Wuhan, China [5,7]. It is circa 70% genetically similar to the SARS virus and has a 96% similarity to a bat coronavirus and it is for this reason that it is suspected to have originated from bats [8]. This paper will attempt to estimate global infection rates and potential resultant mortality in the absence of effective treatment and/or vaccination. 4 Methods The World Health Organisation estimates that• 14% of infected cases are severe and require hospitalisation. • 5% of infected cases are very severe and require intensive care admission, mostly for ventilation. • 4% of infected die [9]. Severity is associated with increasing age and co-morbidities such as chronic heart and lung disease [9]. This virus is highly contagious with an R0 of ≥3 i.e. on average it appears that each case infects some 3 others. This sets off a chain reaction, in this particular case, with a doubling time of one week or less, so that if left unchecked, populations reach 60–80% infection rates. It is naturally difficult to estimate rates as quite a few individuals are infected and remain well (especially children) but are contagious [9]. For this reason, unless large proportions of populations are tested, we cannot possibly accurately estimate the total that have actually contracted the disease [8]. Draconian containment measures in China have reduced new cases by >90% [10]. This has not been the case elsewhere, and Italy has been severely affected with the number of patients infected since February following an exponential trend [10]. Intensive care units have been overwhelmed, with the bottleneck being availability of ventilators to tide critically ill patients over their intensive care stay [10]. This had led to suggestions that multiple patients could be ventilated with one machine, effectively multiplying intensive care capacity to deal with such “surges” [11]. The careful application of basic containment measures cannot be overestimated: hand washing with soap and water, avoiding touching the face and social distancing – staying home, avoiding crowds and refraining from touching one another. Such measures may blunt the rapid rise of cases, converting infections into a more manageable stream that hospitals can cope with [12]. If these measures are not widely enacted, large proportions of populations may become infected in short periods as happened in Northern Italy, and non-availability of ventilators may lead to triage situations with doctors having to choose who to ventilate and who to leave to die [13]. The situation may be even more dire in low income regions with fewer health care resources, such as Africa, where the relatively high prevalence of HIV, tuberculosis and other pathogens might not only serve as additional co-morbidities for COVID-19 infected individuals, but also contribute to diagnostic uncertainty [13]. 5 Results This table attempts to estimate potential deaths from COVID-19 using available data and current observations. There is naturally uncertainty over many of values ascribed but where in doubt, more conservative numbers have been assigned. Continent 1 is Asia. China may have contained the disease and has been assigned a potential infection rate of 10% with “normal” mortality of 4%. The rest of Asia is assumed to become up to 80% infected with 10% mortality due to health care services being overwhelmed. Continent 2 is Africa which is assumed to behave like the rest of Asia. Continent 3 is Europe which has so far shown overall too few and too late responses to the disease. For this reason, Europe has been assigned a 60% overall infection rate and a (perhaps) optimistic 4% “normal” mortality. Continent 4 is North America. The United States has reacted very poorly to date and hospitals are already filling up. This country also has issues pertaining to insurance and illegal and non-registered immigrants, and for this reason, a 10% mortality has been assigned in anticipation of health care services becoming overwhelmeed. Canada has also acted late and the same conditions have been applied. For Mexico, it has been assumed that social distancing strictures will be at least as difficult to enforce as in Europe, in the setting of a poorer health care system. Continent 5 is South America and this has been somewhat arbitrarily been assigned a 60% infection rate. Continent 6 is Oceania (including Australia) and the population here has already responded poorly to social distancing measures so a 60% infection rate has been assigned to this region with a “normal” mortality rate. In the absence of the speedy breakthrough of an successful treatment or the discovery of an effective vaccine that can be mass produced and widely distributed, this pandemic may cause close to half a billion deaths, i.e. 6% of the global population (Table 1 ).Table 1 Potential infections and deaths from COVID-19 using available data and current observations.(xxwho). Table 1 Continent Population Infection % Number Mortality % Number 1 India 1,339,000,000 80 1,071,200,000 10 107,120,000 1 China 1,386,000,000 10 138,600,000 4 5,544,000 1 Rest 1,856,757,408 80 1,485,405,926 10 148,540,593 2 Africa 1,216,130,000 80 972,904,000 10 97,290,400 3 Europe 738,849,000 60 443,309,400 4 17,732,376 4 USA 327,096,265 60 196,257,759 10 19,625,776 4 Canada 37,064,562 60 22,238,737 10 2,223,874 4 Mexico 126,190,788 80 100,952,630 10 10,095,263 4 Rest 88,672,385 60 53,203,431 10 5,320,343 5 South America 422,535,000 60 253,521,000 10 25,352,100 6 Oceania 38,304,000 60 22,982,400 4 919,296 4,760,575,284 439,764,020 It must be reiterated that these are best guesses and estimates that preclude the discovery of effective treatment and/or vaccination. 6 Discussion These estimates assume that a significant proportion of severe cases that would normally require relatively mild intervention (such as supplemental and non-invasive administration of oxygen, intravenous fluids, antibiotics for secondary infections etc) actually manage to access these therapies. The situation in Bergamo and Lombardy in the last few days has stunned the global medical community. If this region, one of the most affluent parts of Europe, with an advanced healthcare system, was inundated in the way that we have witnessed in the news, then it simply cannot be excluded that in chaotic situations wherein cases precipitate suddenly, hospitals may just collapse and the simple provision of basic care may fail. Moreover, morbidity and mortality will also be incurred from non-novel conditions for which there is simple and established treatment. Or failure of care in common situations such as childbirth. It is almost as if we are witnessing a dystopian science-fiction narrative of the most horrific kind, with no way out of this unfolding human tragedy. Indeed, this scenario has been depicted in many science fiction novels and films [14] but we have failed to use this foresight to adequately prepare ourselves for such a struggle. Furthermore, the eventual economic cost will run in the trillions of dollars, with social and health impacts due to the contraction of economies and job losses [15]. 7 Conclusion At the risk of sounding melodramatic, but with an extreme sense of realism, modern healthcare risks being plunged into the Middle-Ages if the public do not do their part. Infection cannot occur in the absence of contact. The only way to mitigate these numbers is to apply social distancing and take the precautions outlined above. Author statement There are no real or potential conflicts, financial or otherwise. There was no funding for this paper. ==== Refs References 1 Juvenal. Satires vol. 6.165. 2 Nassim Taleb Antifragile: Things that Gain from Disorder 2013 Penguin London 3 Knight Frank Hyneman Risk, Uncertainty and Profit 1921 Houghton-Mifflin Co New York (reprinted University of Chicago Press, 1971) 4 Yin Y. Wunderink R.G. MERS, SARS and other coronaviruses as causes of pneumonia Respirology 23 2 2018 130 137 10.1111/resp.13196 29052924 5 Chen N. Zhou M. Dong X. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study Lancet 395 10223 2020 507 513 10.1016/S0140-6736(20)30211-7 32007143 6 Fehr A.R. Perlman S. Coronaviruses: an overview of their replication and pathogenesis Methods Mol. Biol. 1282 2015 1 23 10.1007/978-1-4939-2438-7_1 25720466 7 Ding Q. Lu P. Fan Y. Xia Y. Liu M. The clinical characteristics of pneumonia patients co-infected with 2019 novel coronavirus and influenza virus in Wuhan, China [published online ahead of print, 2020 Mar 20] J. Med. Virol. 2020 10.1002/jmv.25781 (doi:10.1002/jmv.25781) 8 Velavan T.P. Meyer C.G. The COVID-19 epidemic Tropical Med. Int. Health 25 3 2020 278 280 10.1111/tmi.13383 9 World Health Organisation Coronavirus Disease 2019. WHO Report 41 01 March 2020 10 Remuzzi A. Remuzzi G. COVID-19 and Italy: what next? [published online ahead of print, 2020 Mar 13] Lancet 2020 10.1016/S0140-6736(20)30627-9 S0140-6736 (20)30627-9 11 Neyman G. Irvin C.B. A single ventilator for multiple simulated patients to meet disaster surge Acad. Emerg. Med. 13 11 2006 1246 1249 10.1197/j.aem.2006.05.009 16885402 12 Ferguson N.M. Laydon D. Nedjati-Gilani G. Imai N. Ainslie K. Baguelin M. Bhatia S. Boonyasiri A. Cucunubá Z. Cuomo-Dannenburg G. Dighe A. Impact of Non-pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand March 2020 Imperial College 13 Ayebare R.R. Flick R. Okware S. Bodo B. Lamorde M. Adoption of COVID-19 triage strategies for low-income settings [published online ahead of print, 2020 Mar 11] Lancet Respir. Med. S2213-2600 20 2020 30114 10.1016/S2213-2600(20)30114-4 14 McEuen P.L. Science fiction: a post-pandemic wilderness Nature 500 7463 2013 398 Aug 21 15 Ayittey F.K. Ayittey M.K. Chiwero N.B. Kamasah J.S. Dzuvor C. Economic impacts of Wuhan 2019-nCoV on China and the world J. Med. Virol. 92 5 2020 473 475 10.1002/jmv.25706 32048740	32247898	PMC7270771	NO-CC CODE	2021-03-20 23:16:50	yes	Early Hum Dev. 2020 May 31; 144:105026
==== Front Obes Res Clin Pract Obes Res Clin Pract Obesity Research & Clinical Practice 1871-403X 1871-403X Asia Oceania Association for the Study of Obesity. Published by Elsevier Ltd. S1871-403X(20)30550-0 10.1016/j.orcp.2020.07.002 Article Obesity and mortality of COVID-19. Meta-analysis Hussain Abdulzahra [email protected]⁎ Mahawar Kamal b Xia Zefeng c Yang Wah d EL-Hasani Shamsi e a Doncaster and Bassetlaw Teaching Hospitals, Doncaster, UK,Honorary Lecturer at Sheffield University.Sheffield.UK b Bariatric Unit, Department of General Surgery, Sunderland Royal Hospital, Sunderland, UK c Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, Hubei Province, China d The First Affiliated Hospital of Jinan University, 613 Huangpu Avenue West, Guangzhou, Guangdong Province, China e Bariatric Unit, Princess Royal University Hospital, King’s College Hospitals NHS Foundation Trust, London, UK ⁎ Corresponding author. [email protected] 9 7 2020 July-August 2020 9 7 2020 14 4 295 300 28 6 2020 2 7 2020 © 2020 Asia Oceania Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.2020Asia Oceania Association for the Study of ObesitySince January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.Background Obesity is a global disease with at least 2.8 million people dying each year as a result of being overweight or obese according to the world health organization figures. This paper aims to explore the links between obesity and mortality in COVID-19. Methods Electronic search was made for the papers studying obesity as a risk factor for mortality following COVID-19 infection. Three authors independently selected the papers and agreed for final inclusion. The outcomes were the age, gender, body mass index, severe comorbidities, respiratory support and the critical illness related mortality in COVID-19. 572 publications were identified and 42 studies were selected including one unpublished study data. Only 14 studies were selected for quantitative analysis. Results All the primary points but the gender are significantly associated with COVID-19 mortality. The age >70, [odd ratio (OR): 0.17, CI; 95%, P-value: <0.00001], gender [OR: 0.89; CI: 95%, P-value: 0.32], BMI > 25 kg/m2 [OR: 3.68, CI: 95%, P-value: <0.003], severe comorbidities [OR: 1.84, CI:95%, P-value: <0.00001], advanced respiratory support [OR: 6.98, CI: 95%, P-value: <0.00001], and critical illness [OR: 2.03, CI: 95%, P-value: <0.00001]. Conclusions Patients with obesity are at high risk of mortality from COVID-19 infection. Keywords ObesityCOVID-19World Health OrganizationMortalityIntensive care unitBody mass indexTotal body weight ==== Body Introduction An increasing body of data suggests that outcomes with Coronavirus Disease 2019 (COVID-19) are worse in those suffering from obesity and that a significant proportion of those needing intensive care suffers from overweight or obesity [1]. Obesity is affecting most of the physiological processes and modifying the functions of the system including the immune system [2]. It is crucial to understand the effect of obesity on the course of infection to prevent or mitigate the morbidities and mortality [3,4]. In the current COVID-19 era, bariatric teams are aware of the potential risks and thus stressing the extra caution and appropriate management of these patients [5]. Knowing the scale of the obesity problem in the world, we anticipate difficult times for this group of patients in Europe, America, Middle East and rest of the world with a high rate of obesity [6]. In 2009, a significant percentage of admissions to the hospitals and mortality because of H1N1 Influenza A virus infection was due to obesity, an estimated 151,700–575,400 total deaths was reported [7,8]. It might not be surprising to see a similar effect with novel COVID-19 infection. Using World Health Organization (WHO) data on the cumulative number of COVID-19 deaths, mortality rates would be 5.6% (95% CI 5.4–5.8) for China and 15.2% (12.5–17.9) outside of China [9] which is a gloomy prediction like previous pandemics. Multi strains of the virus are likely identified, but we do not know what impact on the virulence/pathogenesis would be [10,11]. Although the overall mortality for each country is expected to be different due to other factors such as comparability between healthcare systems, lockdown date, the population size, testing, the timing of the first confirmed case and the criteria of admissions to the hospital [12], the implications on the health systems in Europe and America are huge and expected to report the highest mortality in the world. There are multiple risk factors associated with mortality in COVID-19 patients. Studies had shown diabetes, cardiovascular, cerebrovascular, pulmonary diseases, age and male factors are the predictors of mortality [[13], [14], [15], [16], [17]]. The vast majority of the COVID-19 studies didn’t report obesity as a mortality risk factor due to the lack of the body mass index (BMI)/total body weight (TBW) data or unawareness of obesity risk. The primary endpoint of this research is to investigate obesity as a risk factor for COVID-19 mortality. The secondary endpoints are to assess age, gender, and critical illness, need for advanced respiratory support and associated comorbidities as risk factors for mortality in COVID-19 infection. We conducted this study to investigate if patients with obesity are more likely to die from COVID-19 compared to non-obese individuals. We investigated the entire English language scientific literature following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Methods Studies reporting mortality with COVID-19 in patients with and without obesity were independently identified from the published scientific literature by searching PubMed, Embase, Google, Google Scholar, and Springer, Elsevier, the Lancet, AMJ, BMJ, and Oxford journals using keywords like Coronavirus, COVID-19, obesity, obesity mortality during COVID-19, clinical characteristics of COVID-19 patients. Three independent authors [AH, KM, WY] screened the titles and abstracts for eligibility. Last of these searches were carried on 1st May. References identified from database searches were exported to EndNote (Clarivate Analytics). After removal of duplicates, full-text articles were included if their abstracts were considered to be eligible by any author. The full-text of each study was assessed independently, and disagreements were resolved by discussion (we reached 95% overall agreement [43 out of 45). The studies that reported data on mortality during COVID-19 crisis were included. We excluded studies that did not separately report on mortality with COVID-19 in patients suffering from overweight or obesity, studies that were small (less than 20 patients) and reports of poor quality data (not including the BMI for mortality, not including the critical illness, the need for invasive respiratory support, the comorbidities). A total of 14 studies were included in our final quantitative analysis. Fig. 1 gives a PRISMA flow chart [43] for article selection.Fig. 1 Flow diagram. Fig. 1 Our hypothesis is: Mortality in COVID-19 patients is high among patients with obesity due to large adipose tissue mass with high expression of ACE2 receptors. The primary objective of this study was to find out the effect of overweight or obesity on patients suffering from COVID-19. Our secondary objectives were to investigate the effect of age, gender, and co-morbidities. Review Manager (RM) 5.3 software was used for statistical analysis. The P-value of <0.05 was regarded as significant. For the assessment of the risk of bias in the included studies, the Newcastle–Ottawa system was used (Table 1 ).Table 1 Newcastle–Ottawa quality of study assessment. Table 1Study ID Selection Comparability Outcome Total Representation of the cohort Selection of non exposed cohort Ascertainment of the exposure Assessment of the outcome Adequacy of the outcome Arthur Simonnet 2020 [15] ⋆ – ⋆ – ⋆ ⋆ ⋆⋆⋆⋆ Bhatraju, 2020 [16] ⋆ – ⋆ – ⋆ ⋆ ⋆⋆⋆⋆ Caussy C 2020 [17] ⋆ – – – ⋆ ⋆ ⋆⋆⋆ Fei Zhou 2020 [18] ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆⋆⋆⋆⋆ ICNARC 2020 [35] ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆⋆⋆⋆⋆⋆ Jennifer Lighter 2020 [19] ⋆ – ⋆ – ⋆ ⋆ ⋆⋆⋆⋆ ldh.la.gov 2020 [36] ⋆ – ⋆ – ⋆ – ⋆⋆⋆ Luigi Palmieri, 2020 [37] ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆⋆⋆⋆⋆⋆ Petrilli 2020 [38] ⋆ ⋆ ⋆ – ⋆ ⋆ ⋆⋆⋆⋆⋆ Robert Verity, 2020 [39] ⋆ – ⋆ – ⋆ ⋆ ⋆⋆⋆⋆ Xiang Bai 2020 [40] ⋆ – ⋆ – ⋆ ⋆ ⋆⋆⋆⋆ YD Peng 2020 [20] ⋆ – ⋆ – ⋆ ⋆ ⋆⋆⋆⋆ Xia Unpublished 2020 [41] ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆⋆⋆⋆⋆⋆ Rong-Hui Du [42] ⋆ ⋆ ⋆ ⋆ ⋆ ⋆ ⋆⋆⋆⋆⋆⋆ The heterogeneity was reported in this meta-analysis, it ranges from 93 to 98%. Heterogeneity was due to the presence of one or more outlying studies with results that conflict with the rest of the studies. We have addressed this issue by repeating the analysis without outlying studies. Initial statistical analysis showed high heterogeneity among the study, I 2 was high >90%. Recalculation and analysis was performed after identifying the study that skewed the results and increased heterogeneity. As a result the I 2 was 0–61%. The diagnosis of COVID-19 in the selected studies was made by rRT-PCR (apps.who.it), and also by chest computed tomography CT scan. Results A total of 14 studies[[15], [16], [17], [18], [19], [20],[34], [35], [36], [37], [38], [39], [40], [41], [42]] reported on mortality with COVID-19 in patients with and without obesity. Age: >70 years old is significant factor for mortality. The included studies of 403,535 patients, 807 patients <70 year died compared to 881 mortality in 5895 patients >70 year old. Heterogeneity: Chi² = 90.84, df = 4 (P < 0.00001); I² = 96%. (Fig. 2 ). By repeating the analysis after removing outlying studies [39,42], heterogeneity: Chi² = 2.21, df = 2 (P = 0.33); I² = 9% and the test for overall effect: Z = 8.61 (P < 0.00001).Fig. 2 Mortality among patients age >70 & <70 years. Fig. 2 Gender: Male gender is not a significant factor for mortality in COVID-19. There were 4 studies included [18,35,37,42]. 420 mortality among 1034 men and 182 mortality among 462 women. The odd ratio was 0.89 and the test for overall effect: Z = 1.00 (P = 0.32), Heterogeneity: Chi² = 42.39, df = 3 (P < 0.00001); I² = 93% (Fig. 3 ).Fig. 3 Mortality according to the gender. Fig. 3 After repeating the test by removing outlying studies [37,35], the heterogeneity: Chi² = 1.73, df = 1 (P = 0.19); I² = 42% without major change at significant test of overall effect [test for overall effect: Z = 0.84 (P = 0.40)]. BMI: there have been 531 deaths among 2451 patients with BMI of >25 kg/m2, while 1701 deaths among 24,056 patients with BMI < 25 (Fig. 4 ).Fig. 4 Mortality among patients with BMI > 25 kg/m2 and <25 kg/m2. Fig. 4 The data shows Body Mass Index (BMI) to be significantly associated with the mortality (P-value 0.005, OR 3.68, CI 95% (Fig. 4). Heterogeneity: Tau² = 0.87; Chi² = 104.32, df = 5 (P < 0.00001); I² = 95%. Test for overall effect: Z = 2.92 (P = 0.003). By repeating the analysis after removing studies [16,35,35], heterogeneity is better : Tau² = 0.23; Chi² = 5.16, df = 2 (P = 0.0008); I² = 61%,and the significance test almost the same [test for overall effect: Z = 1.76 (P = 0.0008)]. Advanced respiratory support: there have been 648 out of 867 patients with BMI < 25-needed advanced respiratory support compared to 183 patients with BMI > 25 of total 630 patients (Fig. 5 ). Patients with BMI > 25 kg/m2 are significantly more likely to need advanced respiratory support (P-value 0.00001, OR 6.98, CI 95%) (Fig. 5). Heterogeneity: Chi² = 16.72, df = 3 (P = 0.0008); I² = 82% test for overall effect: Z = 14.54 (P < 0.00001).Fig. 5 Needs for advanced and basic respiratory support among patients with BMI > 25 kg/m2 and <25 kg/m2. Fig. 5 By repeating the analysis without outlying one study data [41], heterogeneity: Chi² = 1.35, df = 2 (P = 0.51); I² = 0%. Test for overall effect: Z = 12.89 (P < 0.00001). Severe comorbidities: mortality among patients with severe comorbidities is significantly higher than no severe comorbidities, P-value <0.00001, OR 1.84, CI 955. (Fig. 6 ). Heterogeneity: Chi² = 121.03, df = 4 (P < 0.00001); I² = 97%. Repeating analysis after removing the outlying studies [20,37,40], heterogeneity: Chi² = 0.96, df = 1 (P = 0.33); I² = 0%. Test for overall effect: Z = 2.33 (P = 0.02).Fig. 6 Mortality among patients with and without severe comorbidities. Fig. 6 Critical illness: 463 deaths among 1186 patients with BMI > 30 compared to 619 deaths among 3425 patients with BMI < 30. Obesity is a significant factor for critical illness during COVID-19, P-value <0.00001, OR 2.03, CI 95% (see Fig. 7 ).Fig. 7 Critical illness among patients with BMI > 25 kg/m2 and <25 kg/m2. Fig. 7 By removing outlying one study [38], the heterogeneity: Chi² = 0.14, df = 1 (P = 0.71); I² = 0%. Test for overall effect: Z = 15.39 (P < 0.00001). Discussion The most important findings of this paper are obesity was an important associated factor for mortality in patients with COVID-19. This most likely because the patients in obesity were known to have a defective immune system that makes them vulnerable to a type of infection that specifically require a prompt cellular immunity response [18]. Obesity impairs immunity by altering the response of cytokines, resulting in a decrease in the cytotoxic cell response of immunocompetent cells which have a key anti-viral role in addition to disturb the balance of endocrine hormones, such as leptin, that affect the interplay between metabolic and immune systems [19]. Obesity also leads to the involvement of adipose tissue-specific molecules (adipokines) in the generation of an environment that is favorable for diseases with an immune cause [20]. The dendritic cells (DCs) with crucial linking role between innate and adaptive immunity, produced twofold more of the immunosuppressive cytokine interleukin (Bello-Chavolla, #2)-10 than lean controls, and in turn stimulated fourfold more IL-4-production from allogeneic T cells. There are also negative impacts of the ability of DCs to mature and elicit appropriate T-cell responses to a general stimulus like viral infection [21]. Human angiotensin-converting enzyme 2 (ACE2) is the putative receptor for the entry of SARS-CoV2 into target cells with remarkably high affinity [22]. It is noteworthy that the level of expression of ACE2 in adipose tissue is reported to be higher than in lung tissue. The expression of ACE2 receptors is the same for adipose tissue in obese and non-obese patients but the difference is in the mass of the adipose tissues that made patients with obesity expressing high number of ACE2 receptors. This increment could explain why patients with obesity are showing severe form of the Covid-19 [23]. Chronic adipose tissue inflammation in obesity is influencing the activity of cells of innate and adaptive immunity [24]. Not only natural killer, macrophages, and neutrophils, but also underlying immune impairment in the responsiveness of lymphocytes is reported [25]. These changes are associated with an overall negative impact on chronic disease progression, immunity from infection, and vaccine efficacy in patients in obesity [26]. Statistical analysis of the patients who developed the severe disease and died in ITU suggested a significant association with obesity, and no surprise of the outcome if we understand the defective immune response highlighted above. The fact that obesity is usually a clustered of diseases leading to metabolic syndrome, making the previously confirmed mortality-predicting factors of diabetes, hypertension, cardiovascular diseases and other obesity-associated comorbidities as indirect evidence pointing to the obesity in well-conducted studies that did not include obesity data [14,17]. This study has confirmed patients above the age of 70 are likely to die from COVID-19, the same finding was confirmed in many other studies [16,27,28], which is expected outcome anyway. The other important results were mortality was not significantly different between men and women in general analysis, although it inclined towards men but does not reach statistical significance. This was also shown in the previous studies [14,27,29]. Patients in obesity are more likely to develop a critical illness than that of non-obesity [[30], [31], [32]]. It is crucial to note that patients with obesity not only suffer from their obesity but also from other metabolic disorders such as diabetes. This point should not be ignored because the cumulative risk of mortality increases with obesity-associated comorbidities. This indirectly refers to the metabolic syndrome and obesity-related comorbidities such as diabetes, hypertension, cardiac and cerebrovascular diseases. Several other studies had shown the same findings [29,33]. The other important outcome was the need for advanced respiratory support was significantly higher in patients in obesity. This is because of the detrimental effect of obesity on the lung volumes, functions and expansions in addition to the severity of the illness and magnitude of lung inflammation and damage in patients in obesity [29]. It goes without doubt that severe comorbidities are a risk factor for mortality in patients in obesity due to a defective immune system, under-functioning respiratory and cardiovascular systems, renal disease, diabetes, etc. This study has confirmed a significant association between comorbidities and mortality in patients in obesity. Using the OR of the available data we were able to produce a scoring mortality model for patients in obesity with COVID-19 infection (see Table 2 ). This scoring system can be used to improve the care by identifying the high-risk patients.Table 2 Using the odd ratio (OR) of the available data to produce a scoring mortality model for COVID-19 infection. Table 2Parameters OR value Age < 70 Female BMI < 25 No severe comorbidities No critical illness No advanced respiratory support Age > 70 0.17 0 Age < 70 Age < 70 Age < 70 Age < 70 Age < 70 Male 0.89 0.89 0 Female Female Female Female BMI > 25 3.68 3.68 3.68 0 MBI < 25 BMI < 25 BMI < 25 Severe comorbidities 1.84 1.84 1.84 1.84 0 No severe comorbidities No severe comorbidities Critical illness 2.03 2.03 2.03 2.03 2.03 0 No critical illness Advanced respiratory support 6.98 6.98 6.98 6.98 6.98 6.98 0 Total 15.59 15.42 14.53 10.85 9.01 6.98 0 Limitations of the study This study included retrospective clinical reports suffer from some biases, the risk of biases was reported in methodology. There was a high grade of heterogeneity of the data. This was addressed by repeating the analysis after removing the outlying study data. There was no change in the overall test of significance, P-value remains <0.05. The heterogeneity reduced significantly and ranged from 0 to 61%. There was a lack of some data and were calculated indirectly from percentage and other provided parameters. There was one unpublished study data from China and the other 12 studies. Over two months period of search, we were aiming to include all available reports on mortality of obesity in COVID-19 infection, however, we may have missed some important studies that potentially affect the overall effects and conclusion from this meta-analysis. The populations studied differ in their comorbidities and severity. The definition of severe form is not consensual. The WHO defines overweight as BMI ≥ 25 kg/m2 and obesity as BMI ≥ 30 kg/m2. However, the Chinese-specific cut-off values for general adiposity define normal weight as BMI 18.5–23.9 kg/m2, overweight as BMI 24.0–27.9 kg/m² and obesity as BMI ≥ 28 kg/m². COVID-19 mortality in children with obesity was not included and therefore not discussed. Conclusions Obesity is a risk factor for mortality in COVID-19. Age, critical illness, need for advanced respiratory support and severe comorbidities are also risk factors for mortality. Conflict of interest All authors confirm no conflict of interest or funding for this study. CRediT authorship contribution statement Abdulzahra Hussain: Conceptualization, Methodology, Software, Data curation, Writing - original draft. Kamal Mahawar: Conceptualization, Resources, Methodology, Writing - review & editing. Zefeng Xia: Resources, Writing - review & editing. Wah Yang: Resources, Writing - review & editing. Shamsi EL-Hasani: Conceptualization, Resources, Methodology, Writing - review & editing, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft. Appendix A Supplementary data The following is Supplementary data to this article: Appendix A Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.orcp.2020.07.002. ==== Refs References 1 Muscogiuri G. Pugliese G. Barrea L. Savastano S. Colao A. Commentary: Obesity: the “Achilles heel” for COVID-19? Metabolism 108 2020 154251 2 Fischer-Posovszky P. Moller P. The immune system of adipose tissue: obesity-associated inflammation Pathologe 41 May (3) 2020 224 229 10.1007/s00292-020-00782-z 32253498 3 Houdek M.T. Griffin A.M. Ferguson P.C. Wunder J.S. Morbid obesity increases the risk of postoperative wound complications, infection, and repeat surgical procedures following upper extremity limb salvage surgery for Soft tissue sarcoma Hand (N Y) 14 2019 114 120 30145914 4 Zhou X. Ye Y. Tang G. Zhou X. Obesity and infection, accompanying phenomenon or causal association? Clin Microbiol Infect 24 2018 668 29104174 5 Dietz W. 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Obesity, innate immunity and gut inflammation Curr Opin Gastroenterol 23 2007 661 666 17906444 21 O’Shea D. Corrigan M. Dunne M.R. Changes in human dendritic cell number and function in severe obesity may contribute to increased susceptibility to viral infection Int J Obes (Lond) 37 2013 1510 1513 23439322 22 Kassir R. Risk of COVID-19 for patients with obesity Obes Rev 21 2020 e13034 23 Li M.Y. Li L. Zhang Y. Wang X.S. Expression of the SARS-CoV-2 cell receptor gene ACE2 in a wide variety of human tissues Infect Dis Poverty 9 2020 45 32345362 24 McLaughlin T. Ackerman S.E. Shen L. Engleman E. Role of innate and adaptive immunity in obesity-associated metabolic disease J Clin Invest 127 2017 5 13 28045397 25 Tanaka S. Inoue S. Isoda F. Impaired immunity in obesity: suppressed but reversible lymphocyte responsiveness Int J Obes Relat Metab Disord 17 1993 631 636 8281221 26 Andersen C.J. Murphy K.E. Fernandez M.L. 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[Accessed 21 April 2020]. 37 Characteristics of SARS-CoV-2 patients dying in Italy Report based on available data on April 13th, 2020. https://www.epicentro.iss.it/en/coronavirus/bollettino/Report-COVID-2019_13_april_2020.pdf. [Accessed 24 April 2020]. 38 https://www.medrxiv.org/content/10.1101/2020.04.08.20057794v1. [Accessed 19 April 2020]. 39 https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30243-7/fulltext. [Accessed 22 April 2020]. 40 https://www.medrxiv.org/content/10.1101/2020.03.20.20037325v2. [Accessed 11 April 2020]. 41 Wah Yang, Zefeng Xia. Unpublished data. Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, The First Affiliated Hospital of Jinan University. China. Date of communication: 17/04/2020. 42 Du Rong-Hui Liang Li-Rong Yang Cheng-Qing Wang Wen Cao Tan-Ze Li Ming Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: a prospective cohort study Eur Respir J 55 2020 2000524 43 Moher D. Liberati A. Tetzlaff J. Altman D.G. PRISMA Group Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement Open Med 334 2009 e123 e130	32660813	PMC7346803	NO-CC CODE	2021-01-06 10:49:10	yes	Obes Res Clin Pract. 2020 Jul 9 July-August; 14(4):295-300
==== Front ACS Omega ACS Omega ao acsodf ACS Omega 2470-1343 American Chemical Society 10.1021/acsomega.0c00432 Article Thermoresponsive Gigaporous Microspheres Facilitate the Efficient Refolding of Recombinant Nitrilase Inclusion Bodies Qu Jian-Bo * Tan Wenfei Meng Weikang Lin Yang-yang Li Jing Xi Lijun Liu Jianguo * State Key Laboratory of Heavy Oil Processing, Centre for Bioengineering and Biotechnology, College of Chemical Engineering, China University of Petroleum (East China), Qingdao 266580, China * Email: [email protected]. Phone/Fax: +86 532 86981566.* Email: [email protected]. 15 07 2020 28 07 2020 5 29 17918 17925 31 01 2020 07 07 2020 Copyright © 2020 American Chemical Society2020American Chemical SocietyThis is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes. In order to assist the refolding of recombinant nitrilase inclusion bodies, a series of thermoresponsive media were prepared by grafting poly(N-isopropylacrylamide-co-butyl-methacrylate) [P(NIPAM-co-BMA)] brushes onto PS microspheres with various particles and pore sizes via an atom transfer radical polymerization (ATRP) method. The effects of particle sizes, pore sizes, and brush grafting amounts of thermoresponsive microspheres on nitrilase refolding were investigated preliminarily. The results showed that the PS-P(NIPAM-co-BMA) microspheres with the medium particle size (74 μm), gigapore size (320 nm), and high grafting amount (35.6 mg/m2) were the most effective candidates. The final nitrilase activity yield could be up to 84.5% with a high initial protein concentration (1 mg/mL) at 30 °C, which was 52.5% higher than that of a simple dilution refolding method at the initial protein concentration (0.1 mg/mL). After the refolding process, the PS-P(NIPAM-co-BMA) microspheres can be easily separated by self-precipitation, and the activity yield of nitrilase still reached 74.5% after being reused for five batches. These results indicated that the thermoresponsive gigaporous medium was an ideal alternative as an artificial chaperone. document-id-old-9ao0c00432document-id-new-14ao0c00432ccc-price ==== Body Introduction Since the first report of poly(N-isopropylacrylamide) (PNIPAM) used as a refolding additive in 1995, it has attracted increasing attention in protein renaturation.1−3 PNIPAM contains a hydrophobic vinyl backbone and pendant isopropyl side groups, which can suppress undesired protein aggregation and enhance protein refolding simultaneously.3 Moreover, PNIPAM will exhibit a gradual phase transition from hydrophilic to hydrophobic property when the aqueous temperature is higher than its lower critical solution temperature (LCST). The gradual phase transition property of PNIPAM enables the polymer to be separated from solution after protein refolding using centrifugation.4 To date, the reported PNIPAM polymers for protein renaturation have three physical forms: linear free chains, covalent cross-linking reversible gels and chains adsorbed or surface-grafted polymers.5 In 2000, Lin et al. first demonstrated the effectiveness of linear PNIPAM in enhancing the renaturation of β-lactamase.6 Lu et al. investigated the mechanism of PNIPAM-assisted refolding of lysozyme denatured by urea.7 The results showed that PNIPAM with an appropriate molar mass preferentially forms the complex with denatured lysozyme, enriches the secondary structure, and therefore facilitates denatured lysozyme to refolding into the native structure. Cui et al. synthesized cross-linked PNIPAM hydrogels for the renaturation of lysozyme and bovine prothrombin-2.8 Although the first two forms of PNIPAM polymers can effectively improve the refolding effect, there still exist some limitations in the application of protein renaturation.5 As an example, the molar mass and its distribution of linear PNIPAM are difficult to control, resulting in the fluctuation of activity recovery. As for cross-linked PNIPAM hydrogels, the swelling rate is relatively slow due to their compact network structure, requiring a longer time to achieve the refolding equilibrium. In order to further improve the refolding effect of PNIPAM polymers, Ge et al. developed a type of particles with core–shell structure by grafting PNIPAM brushes onto polystyrene (PS) nanoparticles.9 These particles had been shown to be quite efficient in assisting lysozyme renaturation at the initial protein concentration of 0.5 mg/mL. After lysozyme refolding, the nanoparticles should be recycled by centrifugal separation at 10,000 rpm for 10 min after heating up the solution until 37 °C for 15 min. This process still seems to be time consuming and laborious. Moreover, the authors did not investigate the effect of porous structures of particles on the protein refolding efficiency. Nitrilase (EC 3.5.5.1), which catalyzes the hydrolysis of various nitriles to corresponding carboxylic acids and ammonia, has been recognized as a high commercial-value biocatalyst because of its high efficiency and eco-friendly transformation.10−12 In this regards, several nitrilases have been found in different sources, and some of these enzymes have been successfully applied in industries.13−15 Recently, our laboratory has identified a novel nitrilase from Pannonibacter carbonis Q4.6, whose conservative triplet catalytic sit “Glu-Lys-Cys” has changed into “Glu-Ser-Cys.” To explore the properties of the enzyme, the gene encoding the nitrilase was cloned and expressed in Escherichia coli BL21 (DE3).16 However, most of the expressed nitrilases formed inclusion bodies (IBs), which need to undergo refolding process to restore their bioactive structures. In the present work, eight kinds of PS-P(NIPAM-co-BMA) microspheres with different particle sizes, pore sizes, and brush grafting amounts were prepared and used for refolding of recombinant nitrilase IBs from E. coli. The porous structure of this medium enables the refolding of protein not only on the outer surface of the microspheres but also in the gigapores, leading to a higher protein refolding efficiency and capacity. Furthermore, these microspheres can be easily separated by self-precipitation after protein refolding. To the best of our knowledge, this is the first report on the application of the thermoresponsive gigaporous medium in the protein refolding process. Results and Discussion Characterization of P(NIPAM-co-BMA) Grafted PS Microspheres Table 1 presents eight kinds of PS-P(NIPAM-co-BMA) microspheres prepared under various conditions (stirring rate, Span 80 addition amount, and feed ratio of monomers). Three particle sizes of PS microspheres, that is, 92, 74, and 60 μm were obtained by adjusting the stirring rate of an agitator to 100, 150, and 200 rpm in the suspension polymerization, respectively. The particle size distributions of these particles can be found in the Supporting Information (Figure S1). The effect of Span 80 in the preparation of PS microspheres is to form reverse micelles in the oil phase, and the reverse micelles then expand and aggregate to large water channels after adsorbing water from the outer water phase. After further polymerization, the macrophase separation between the water channel and microsphere skeleton will produce large pores in the particles. Consequently, the pore diameter of particles will increase with the addition amount of Span 80 within a certain range.17 In this work, PS microspheres with small pores (2#: 41 nm), middle pores (1#: 90 nm, 3#: 104 nm, and 8#: 95 nm), and large pores (4#, 5#, 6#: 320 nm) were fabricated by only changing the added amount of Span 80 in the oil phase. When the amount of Span 80 increased from 10 to 30 to 50% in turn, for example, the peak value of the pore size increased correspondingly from 41 to 104 to 320 nm in the pore diameter distribution curves of PS microspheres with middle pores (Figure 1). Figure 1 Pore size distribution curves of PS microspheres prepared with various amounts of Span 80. Table 1 PS-P(NIPAM-co-BMA) Microspheres Prepared under Various Conditions entrya amount of span 80b (%) bromine densityc (mmol/m2) feed ratiod particle size (μm) pore size (nm) specific surface area (m2g) grafted polymere (mg/m2) 1 30 0.137 25:1 61 90 15.2 5.83 2 10 0.131 25:1 74 41 12.3 3.89 3 30 0.140 25:1 74 104 13.9 6.36 4 50 0.141 25:1 74 320 19.6 16.5 5 50 0.141 40:1 74 320 19.6 35.6 6 50 0.141 10:1 74 320 19.6 1.41 7 50 0.141 55:1 74 320 19.6 47.8 8 30 0.138 25:1 91 95 11.7 6.04 a The stirring rates of the anchor-type agitator used for preparation of PS microspheres are as follows: 1#, 200 rpm; 2#–7#, 150 rpm; and 8#, 100 rpm. b The mass percentage ratio of Span 80 to monomer (styrene + divinylbenzene) in the recipes for PS microsphere preparation. c The bromine density was the ratio of the bromine content to specific surface area of PS microspheres. d The molar ratio of monomer (NIPAM + BMA) to ATRP initiator (the bromine content in microspheres). e The value is determined by element analysis. In order to eliminate the effect of an atom transfer radical polymerization (ATRP) initiator amount on the grafting amount of polymer brushes, PS microspheres with similar bromine density were selected for further modification (Table 1). In a previous study, Okano et al. reported in their work that about 7.01% (the ratio of chain density to initiator density) of ATRP initiators immobilized on silica beads are effective.18 Unfortunately, the chain densities of grafted polymers on PS microspheres are unavailable because the grafted polymers cannot be cleaved from the microspheres with hydrofluoric acid as polymers grafted on silica materials. Therefore, the amount of the grafted polymer (mg/m2) was in the place of chain densities (chains/nm2).19 Although the real chain densities of grafted polymers are unknown, the bromine density can be referred to as a reference, that is, the grafting densities of polymer brushes for all particles are similar, and the molar mass of polymer brushes are proportional to their grafting amounts. In this work, it was found the grafting amounts of brushes on PS microspheres were affected by both the feed ratio and pore size of microspheres. In the same feed ratio, the grafted polymer brushes increased with the pore diameter of microspheres (2#, 3#, and 4#). This indicates that the larger pore with less steric hindrance effect is beneficial for the grafting of longer polymer chains in the pores. For microspheres with various particle sizes and similar pore sizes (1#, 3#, and 8#), their grafting amounts are also very close. The little differences come from different specific surface areas. For microspheres with the same pore size (4#, 5#, 6#, and 7#), the grafting amount of polymer brushes increased with the feed ratios, indicating that the length of polymer brushes grafted on PS microspheres increased correspondingly.19 A previous study demonstrated that the collapse degree of end-grafted PNIPAM above the LCST depends on the chain grafting density and molar mass.20 However, there was a sharp transition at 32 °C for all film thicknesses (polymer chain length), indicating that the LCST was the same within (±1 °C) for all films regardless of molar mass and grafting density. In another work, Kent et al. suggested that the conformation change of the tethered PNIPAM chains on silicon wafer, which were in relative low surface density, was very limited as the temperature increased above the LCST in D2O.21 There is increasing evidence that the temperature-induced collapse of end-grafted PNIPAM depends on both the grafting density and molar mass of PNIPAM.22 In this work, the LCST of free and end-grafted P(NIPAM-co-BMA) was evaluated by UV spectroscopy and differential scanning calorimetric (DSC), respectively. The LCST of free PNIPAM synthesized with the monomer ratio of 95:5 (NIPAM/BMA) was 31.3 °C, which is slightly lower than 32 °C due to the incorporation of hydrophobic PBMA into the copolymer (Figure S2). For the end-grafted P(NIPAM-co-BMA), the DSC curves of PS-P(NIPAM-co-BMA) microspheres with various grafting amounts were obtained within 5–55 °C. Figure 2 shows broad endothermal peaks around 34 °C for the three samples with small, medium, and high grafting amounts of polymer brushes. The peak temperature of the endothermal peak can be used as an index to represent the temperature for the phase transition.23 That is, the end-grafted P(NIPAM-co-BMA) brushes have similar LCSTs regardless of their molar mass within the studied range, which is consistent with a previous report.20 The onset point of the endothermal peak is commonly used to represent the LCST of PNIPAM.23 Unlike cross-linked PNIPAM hydrogel grafted on the nonporous substrate,24,25 however, no baselines can be found in the DSC curves of PS-P(NIPAM-co-BMA) microspheres, and the onset points of the endothermal peaks are unavailable. The possible reason lies that the porous structure of PS-P(NIPAM-co-BMA) microspheres, which contains some free water in the particle pores besides hydration water in polymer brushes, is formed by the hydrogen bond interaction. With increasing temperature, the continuous endotherm of free water results in the broad endothermal peaks. Although the precise onset points of the endothermal peaks are unavailable, the LCSTs of the end-grafted P(NIPAM-co-BAM) brushes are similar and should be lower than 34 °C (endothermal peak). Figure 2 DSC thermograms of the PS-P(NIPAAM-co-BMA) with various grafting amounts of polymer brushes (heating rate of 1.5 °C/min from 5 to 55 °C). After grafting P(NIPAM-co-BMA) brushes, the average static contact angles of PS-P(NIPAM-co-BMA) microspheres decreased gradually with the increase of grafted polymers (Figure S3). Compared to native PS microspheres (118°), the contact angle of 7# PS-P(NIPAM-co-BMA) microspheres with the maximum grafting amount of brushes (47.8 mg/m2) decreased by nearly 50% (61°). The hydrophilicity of PS-P(NIPAM-co-BMA) microspheres at 25 °C also increased gradually with the grafting amount of polymer brushes in comparison with PS microspheres (Figure S4). The hydrophilicity of 7# PS-P(NIPAM-co-BMA) microspheres was 3.15 times that of PS microspheres. The thermoresponsive polymer brushes on PS microspheres not only can improve the biocompatibility of particles but also promise the necessary hydrophobicity for protein refolding. In order to testify the collapse of end-grafted P(NIPAM-co-BMA) brushes above LCST, the effect of temperature on hydrophilicity of 5# PS-P(NIPAM-co-BMA) microspheres was investigated (Figure 3). The hydrophilicity (water content) of 5# PS-P(NIPAM-co-BMA) microspheres first decreases slightly in the range of 20–30 °C, then ascends abruptly around 35 °C. After that, the hydrophilicity decreases slightly again. The density of water decreases with the increase of temperature. In addition, the evaporation rate of water at room temperature increases with incubation temperature. These two reasons are responsible for the slight decrease of hydrophilicity of 5# PS-P(NIPAM-co-BMA) microspheres in the ranges of 20–30 and 35–50 °C. There is an abrupt rise in the hydrophilicity of 5# PS-P(NIPAM-co-BMA) microspheres when the temperature increases from 30 to 35 °C, indicating that the collapse of end-grafted P(NIPAM-co-BMA) brushes happened, and therefore, the saved pore volume of microspheres contributed to the more water content in the particles. Figure 3 Hydrophilicity of 5# PS-P(NIPAM-co-BMA) microspheres at various temperatures. Scanning electron microscopy (SEM) images of before and after thermoresponsive polymer brushes grafting onto 5# PS microspheres with gigapores can be seen in Figure 4. There is a hairy and gel layer on the surface of PS microspheres after grafting polymer brushes, whereas PS microspheres show a rough surface. In addition, the grafted P(NIPAM-co-BMA) brushes onto PS microspheres are homogeneous and do not block the gigapores, which is beneficial to the protein refolding. Figure 4 SEM images of 5# PS microspheres before (a,b) and after (c,d) grafting P(NIPAM-co-BMA) brushes. The composition change of PS microspheres before and after grafting was characterized by Fourier transform infrared (FT-IR) spectra (Figure S5). Typical peaks at 1680 and 608 cm–1 in the spectrum of bromoacetylated PS microspheres are ascribed to the C=O (connecting with benzene ring) and C–Br stretching vibration, respectively. After further grafting polymer brushes, there are some new peaks at 3327, 1725, 1643, and 1535 cm–1 in the spectrum of PS-P(NIPAM-co-BMA) microspheres, which were ascribed to the N–H stretching vibration (NIPAM), ester group stretching vibration (BMA), N–H out-of-plane bending vibration, and amide group stretching vibration, respectively. Isolation of Nitrilase Inclusion Bodies Nitrilase was expressed in E. coli BL21 (DE3) as 28 kDa protein, and most of them accumulated as IBs (Figure S6). About 406.3 mg of the total IB protein was obtained from 1 L culture, of which nitrilase was 198.2 mg with a purity of 48.8%. After purification with nickel-NTA column, 140.7 mg of nitrilase was recovered with a yield of 71.0% (Table 2). The purity of nitrilase used for refolding was about 96.5%, as determined from SDS-PAGE results. Table 2 Purification of Nitrilase from Inclusion Bodies purification step total protein (mg) nitrilase (mg) yield (%) purity (%) total cell lysatesa 406.3 198.2 100 48.8 solubilized in buffer C 281.8 173.0 87.3 61.4 after Ni-NTA column 145.8 140.7 71.0 96.5 a About 6.2 g of wet-weight cells and total cell lysates containing 406.3 mg IB were obtained from 1 L culture. Refolding of Nitrilase with P(NIPAM-co-BMA) Grafted PS Microspheres Effect of Particle Size on Nitrilase Refolding Particle size is one of the main structural features of P(NIPAM-co-BMA) grafted PS microspheres. Three kinds of microspheres (i.e., 1#, 3#, and 8#, as listed in Table 1) with different particle sizes and similar pore sizes were used to study their effect on nitrilase refolding. As shown in Table 1, the pore size and brush grafting amount (mg/m2) of these microspheres were basically the same. The refolding yield of nitrilase as a function of the particle size is shown in Figure 5. It can be seen that PS-P(NIPAM-co-BMA) microspheres can indeed assist nitrilase refolding, and the refolding yield decreases with the particle size increasing within the studied range. A previous report indicated that the adsorption of the hydrophobic refolding intermediates on the modified PS microspheres contributes to inhibit the formation of misfolded protein aggregates and perhaps induce the oriented alignment of protein molecules near the microsphere surface, thus enhance the refolding process.5 Although the grafting amounts of P(NIPAM-co-BMA) are similar for 1# (5.83 mg/m2), 3# (6.36 mg/m2), and 8# (6.04 mg/m2) microspheres (Table 1), the specific surface area (m2/g) of the microsphere increased with the decrease of particle size, that is, the system with addition of smaller microspheres has higher amounts of grafted polymer brushes under the same feed mass of microspheres. This will result in the improvement of effective contact area in per unit volume between denatured nitrilase and thermoresponsive polymer brushes. Figure 5 Effect of the particle size of PS-P(NIPAM-co-BMA) microspheres on the refolding yield of nitrilase. Protein concentration was 1.0 mg/mL (0.1 mg/mL for no microspheres); refolding temperature was 30 °C. The protein concentration is an important limiting factor in the protein refolding process because a high protein concentration would aggravate the precipitate formation.26 The refolding yield of a simple dilution method is 36.5% at the initial nitrilase concentration of 0.1 mg/mL, and the value is only 21.4% at the initial nitrilase concentration of 1 mg/mL. After adding thermoresponsive microspheres, the refolding yield of nitrilase can be greatly improved at the initial protein concentration of 1.0 mg/mL, indicating that the presence of PS-P(NIPAM-co-BMA) microspheres can facilitate the protein refolding even at higher protein concentrations. The initial protein concentration in this system is twice as high as in thermoresponsive PS nanoparticle-assisted refolding of denatured lysozyme.9 Considering the reactor volume, reagents cost, and productivity during the protein refolding process, a higher initial protein concentration is absolutely vital. Effect of Pore Size on Nitrilase Refolding Although the results in Figure 5 show that small-sized microspheres are most effective in promoting protein renaturation, we found that it was difficult to produce the gigaporous structure in the small-sized microspheres. Even if such microspheres were synthesized, they could not meet the requirements of the application because of their poor mechanical strength and fragility. Therefore, to explore the effect of the pore size, nitrilase was refolded in the presence of PS-P(NIPAM-co-BMA) microspheres with different pores sizes at the medium particle size (i.e. 2#, 3#, and 4#, as listed in Table 1), and the results are given in Figure 6. Figure 6 Effect of the pore size of PS-P(NIPAM-co-BMA) microspheres on the refolding yield of nitrilase. Protein concentration was 1.0 mg/mL (0.1 mg/mL for no microspheres); refolding temperature was 30 °C. It is shown in Figure 6 that the refolding yield of nitrilase increases with the pore size of PS-P(NIPAM-co-BMA) microspheres, and the refolding yield of nitrilase with addition of 4# microspheres (pore size 320 nm) reaches 72.4%. As presented in Table 1, the grafting amount of polymer brushes increased with pore sizes of microspheres within the studied range. This means that PS-P(NIPAM-co-BMA) microspheres with the largest pore size can provide the maximum contact area in per unit volume between denatured nitrilase and thermoresponsive polymer brushes. Another possible reason could be the spatial effect. Native proteins have complex spatial structures, and the large pores have advantages in facilitating the refolding of some denatured linear protein molecules within the pores in comparison with microspheres with small pores. Effect of Brush Grafting Amount on Nitrilase Refolding To examine the effect of brush grafting amounts on nitrilase refolding, 4#, 5#, 6#, and 7# microspheres, as listed in Table 1, were employed. The grafting amounts of the microspheres are different (4#, 16.5 mg/m2; 5#, 35.6 mg/m2; 6#, 1.41 mg/m2; and 7#, 47.8 mg/m2), but their particle sizes (74 μm), pore sizes (320 nm), and specific surface areas (19.6 m2/g) are identical. As shown in Figure 7, an increase of the refolding yield of nitrilase occurs when the brush grafting amount increases from 1.41 to 35.6 mg/m2, and the maximum refolding yield is 84.5% for 5# microspheres. This can be attributed to the fact that high grafting amount means high contact probability between polymer brushes and denatured proteins. When the brush grafting amount exceeds 35.6 mg/m2, the refolding yield of nitrilase is almost unchanged. This is probably because excess length of polymer brushes will increase mass transfer resistance, which is unfavorable for protein in and out of the pore. Figure 7 Effect of brush grafting amounts of PS-P(NIPAM-co-BMA) microspheres on the refolding yield of nitrilase. Protein concentration was 1.0 mg/mL (0.1 mg/mL for no microspheres); refolding temperature was 30 °C. Effect of Temperatrue on Nitrilase Refolding After determining the optimal structural features of P(NIPAM-co-BMA) grafted PS microspheres, the effect of temperature on nitrilase renaturation was also investigated in the range of 20–50 °C (Figure 8). For comparison, the refolding using a simple dilution method (i.e., without adding microsphere) was also carried out. Obviously, the refolding yields of nitrilase with and without microspheres are both temperature dependent. As shown in Figure 8, the dilution refolding yield of nitrilase decreases with the increase of temperature. While in the presence of microspheres, the yield increases at first after reaching its maximum value (84.5%) at 30 °C and then decreases to 50 °C. It has been reported that the dual effects of temperature on protein refolding, on one hand, the increase of temperature favors the formation of the disulfide bond. On the other hand, it increases the formation of aggregates, leading to a reduction in the refolding yield.27 It seems that the formation of aggregates with the increase of temperature plays a dominant role for the dilution refolding method. However, the presence of thermoresponsive microspheres has an advantage in facilitating protein renaturation. For example, the yield is increased by 28.4% from 56.1% at 20 °C to 84.5% at 30 °C. Figure 8 Effect of temperature on the refolding of nitrilase with and without 5# PS-P(NIPAM-co-BMA) microspheres. Protein concentration was 1.0 mg/mL (0.1 mg/mL for no microspheres). The hydrophobic interaction between P(NIPMA-co-BMA) brushes and denatured protein will inhibit the formation of protein aggregates.7 When the temperature is lower than LCST of polymer brushes, the hydrophilic feature of the polymer brushes is not in favor of the mutual hydrophobic interaction. Thus, the effect of promoting protein refolding is limited. When the temperature exceeds the LCST, the collapse of polymer brushes happens, and the real contacting area between collapsed polymer brushes and random coiling linear protein molecules will decrease. In addition, high temperature facilitates the aggregation of proteins. The tethered polymer brushes in the phase transition state probably not only can produce the mutual hydrophobic interaction with denatured nitrilase but also promise the sufficient contacting area. Therefore, it may be deduced that LCST of the grafted P(NIPAM-co-BMA) is around 30 °C by combination of the results of refolding yields and DSC curves. Reutilization of P(NIPAM-co-BMA) Grafted PS Microspheres In order to examine the reusability of P(NIPAM-co-BMA) grafted PS microspheres, the utilization experiments were performed, and the results are shown in Figure 9. Figure 9 Reutilization of 5# PS-P(NIPAM-co-BMA) microspheres on nitrilase refolding at 30 °C. As can be seen from Figure 9, when microspheres were used for the second time, the activity yield of nitrilase decreases by only 5.2%. However, the change of this value is very small from the third to the fifth time. The activity yield of nitrilase still reached 74.5% after being reused for five batches, which is 2.0 times that of the dilution method. This shows that the thermoresponsive gigaporous microspheres can be reused and maintain high refolding efficiency. Conclusions In order to study the thermoresponsive microspheres-assisted refolding of recombinant nitrilase IBs, eight kinds of P(NIPAM-co-BMA) grafted PS microspheres with different particle sizes, pore sizes, and brush grafting amounts were prepared in this work. The existence of PS-P(NIPAM-co-BMA) microspheres can effectively improve both the refolding yield and initial nitrilase concentration compared with a simple dilution method, especially around LCST of P(NIPAM-co-BMA) brushes. The optimal structural features of PS-P(NIPAM-co-BMA) microspheres are medium particle sizes, large pore sizes, and high brush grafting amounts. Another important advantage of the particles is the self-precipitation ability after refolding, which can promote the recovery of the microspheres through facile filtration. Experimental Section Materials N-isopropylacrylamide (NIPAM, >98%) was purchased from Tokyo Chemical Industry Co., Ltd. (Japan). Butyl methacrylate (BMA, 99%), styrene, divinylbenzene, sodium sulphate, Span 80, 2-bromoisobutyryl bromide, tris(2-aminoethyl)amine (TREN), and hexadecane were obtained from Chengdu Xiya Chemical Reagent Co., Ltd. (China). Tris[2-(dimethylamino)ethyl]amine(Me6TREN) was synthesized, according to the literature procedure.28 The other chemicals were all purchased from Sinopharm Chemical Reagent Corporation and were of analytical or higher grade. Preparation and Characterization of P(NIPAM-co-BMA) Grafted PS Microspheres The PS microspheres were synthesized by the surfactant reverse micelle swelling method, as described by Ma et al.29 Then, P(NIPAM-co-BMA) brushes were grafted onto these PS microspheres by two step reactions, bromoacetylation, and in situ ATRP, according to the method we reported previously.30,31 The bromine content of PS microspheres after bromoacetylation was determined by the Volhard back titration method. Schematic reaction route can be found in the Supporting Information (Scheme S1). The molar ratio of NIPAM to BMA is 95:5 in this study. Finally, eight kinds of PS-P(NIPAM-co-BMA) microspheres were obtained by altering the preparation conditions. The porous structure of PS microspheres and PS-P(NIPAM-co-BMA) was observed by SEM (S-4800, Hitachi, Japan). The diameter and pore size of PS microspheres were determined by the laser particle size analyzer (Coulter LS 230, Beckman, USA) and an Auto Pore IV 9500 mercury porosimeter (Micrometrics, USA), respectively. The specific surface area of particles was measured with an ASAP 2020 (Accelerated Surface Area and Porosimetry System) apparatus (Micromeritics, USA). The hydrophilicity of PS-P(NIPAM-co-BMA) microspheres was defined as the ratio of the water content of the wet microspheres relative to the original weight by gravimetric analysis.29 Briefly, the microspheres were soaked in deionized water and stirred for 6 h in a shaking incubator at a given temperature, then filtered through a sintered glass funnel to remove the external water. Certain mass of wet microspheres were weighed accurately in a weighing bottle and dried at 105 °C in an oven for 2 h, and then, microspheres were weighed again. The static water contact angle was measured by the pendent drop method, as we reported previously.32 At least three measurements were made for each sample. The amount of P(NIPAM-co-BMA) brushes grafted on PS microspheres was determined by element analysis using eq 1, which was based on the assumption that the reactivity of BMA in the monomer mixture is equal to that of NIPAM. The nitrogen content of PS-P(NIPAM-co-BMA) microspheres was measured by the total nitrogen and sulfur analyzer (ANTEK 9000 NS, USA). 1 where N % is the percent nitrogen content of PS-P(NIPAM-co-BMA) (g/g dry particles), N(cal)% is the calculated weight percent of nitrogen in NIPAM-co-BMA, and S is the specific area of PS microspheres in square meters per gram. In order to estimate the LCST of grafted P(NIPAM-co-BMA) brushes on PS microspheres, a linear bipolymer P(NIPAM-co-BMA) with the corresponding ratio was also synthesized via ATRP technique. The LCST of the thermoresponsive polymer was measured, according to the method reported previously by UV spectroscopy.33 DSC (DSC1, Mettler, Switzerland) was also employed to study the thermoresponsive behavior of PS-P(NIPAM-co-BMA) microspheres. Before DSC analysis, the thermoresponsive polymer-grafted microspheres were soaked in deionized water and stirred for 6 h in a shaking incubator at 25 °C, then filtered through a sintered glass funnel to remove the external water. The thermal analyses were performed from 5 to 55 °C on the samples at a heating rate of 1.5 °C/min, with a nitrogen purge rate of 40 mL/min. Protein Expression and Isolation of Nitrilase Inclusion Bodies E. coli BL21 (DE3) cells were transformed with pDE1 plasmid vector encoding nitrilase. The transformed cells were grown at 37 °C in a Luria–Bertani (LB) medium containing 50 μL kanamycin to an optical density of 0.6 (OD600). Then, isopropyl β-d-1-thiogalactopyranoside was added, and protein expression was induced for 6 h at 25 °C. The cells were collected by centrifugation (8000g at 4 °C for 15 min). To obtain IBs, the cell pellets were resuspended in 4 mL of buffer A (50 mM Na-phosphate, 5 mM DETA, and 100 mM NaCl, pH 8.5) per gram of cells. The suspension was sonicated at 0 °C for 5 min and centrifuged (10,000g at 4 °C for 15 min). The pellet obtained was resuspended in buffer B (0.6% Triton X-100, 2.5 M urea, 50 mM Na-phosphate, 1 mM DETA, and 100 mM NaCl, pH 8.5). After stirring for 2 h, the mixture was subjected to centrifugation at 10,000g for 10 min. The pellet containing IBs was solubilized in buffer C (8 M urea, 10 mM dithiothreitol, and 50 mM Na-phosphate, pH 8.5) for overnight at 4 °C and subsequently centrifugated (10,000g at 4 °C for 10 min) again to remove any insoluble precipitates. The supernatant was then filtered through a 0.22 μm filter and applied onto a Ni-NTA column (10 mL, Qiagen, Germany), which was equilibrated with buffer D (8 M urea, 10 mM imidazole, and 50 mM Na-phosphate, pH 8.5). The unbounded materials were washed by 20 volumes in buffer D. The nitrilase was eluted in buffer E (8 M urea, 300 mM imidazole, and 50 mM Na-phosphate, pH 8.5). Finally, the protein solution was concentrated to approximately 4 mg/mL using a stirred ultrafiltration cell (Millipore, 3 kDa molar mass cut-off). The protein purity was analyzed with reducing SDS-PAGE using 12% Bis–Tris mini gels (Invitrogen, USA). Proteins were stained with Brilliant Blue Coomassie G-250. Refolding of the Inclusion Bodies The purified nitrilase solution was diluted directly into refolding buffer (1.3 mM EDTA, 50 mM Na-phosphate, 1 M urea, 1.3 mM GSSG, 13.3 mM GSH, pH 8.5) at a volume ratio of 1:3. The PS-P(NIPAM-co-BMA) microspheres were added to the mixture with a final concentration of 5 mg/mL. Then, the solution was incubated for 14 h at 30 °C with stirring at 150 rpm. At last, the self-precipitated PS-P(NIPAM-co-BMA) microspheres was recovered by filtering through a sintered glass funnel, and the filtrate (refolded nitrilase) was collected for the activity assay. For a simple dilution refolding method, the purified nitrilase solution was diluted by 40-fold into refolding. The refolding yields are the means ± SD of at least triplicate samples. Refolded Nitrilase Assay Refolded nitrilase activity was measured using 3-cyanopyridine as a substrate. A reaction mixture of 1 mL (pH 7.2) comprising 200 μL enzyme, 50 mM Na-phosphate, and 50 mM 3-cyanopyridine was employed. After incubation for 10 min at 30 °C, 100 μL of HCl (2 M) was added to terminate the reaction. The activity was determined by measuring the concentration of NH4+ at 630 nm using a spectrophotometric method.34 One unit (U) of nitrilase activity was defined as the amount of enzyme producing 1 μmol of NH4+ per min at 30 °C. The protein concentration was determined using the Bradford method.35 Supporting Information Available The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.0c00432.Schematic reaction route; particle size distributions; phase transition temperature curve of P(MIPAM-co-BMA); images of photographs of water contact angle; hydrophilicity of PS-P(NIPAM-co-BMA) microspheres; FT-IR spectra; and SDS-PAGE analysis (PDF) Supplementary Material ao0c00432_si_001.pdf The authors declare no competing financial interest. Acknowledgments The authors are grateful for the financial support of the National Natural Science Foundation of China (21473256, 21776310, and 21176257), the Key Technology Research and Development Program of Shandong Province (2019GSF107077 and 2019GGX102062), the Natural Science Foundation of Shandong Province (ZR2017MB019), and the Fundamental Research Funds for the Central Universities of China (18CX05015A). ==== Refs References Rozema D. ; Gellman S. H. Artificial chaperones: protein refolding via sequential use of detergent and cyclodextrin . J. Am. Chem. Soc. 1995 , 117 , 2373 –2374 . 10.1021/ja00113a036 . Chen Y.-J. ; Huang L.-W. ; Chiu H.-C. ; Lin S.-C. Temperture-responsive polymer-assisted protein refolding . Enzyme Microb. Technol. 2003 , 32 , 120 –130 . 10.1016/s0141-0229(02)00272-7 . Lu D. ; Zhang K. ; Liu Z. Protein refolding assisted by an artificial chaperone using temperature stimuli responsive polymer as the stripper . Biochem. Eng. 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==== Front Int J Bioprint Int J Bioprint Whioce Publishing Pte. Ltd. International Journal of Bioprinting 2424-7723 2424-8002 Whioce Publishing Pte. Ltd. IJB-5-2-168 10.18063/ijb.v5i2.168 Perspective Article Retracted Article: Application of 3D printing technology in orthopedic medical implant - Spinal surgery as an example Zhang Rong Feng 1 Wang Peng Yun 2* Ming Yang 3 Dong Xuebo 1 Liu Xue 1 Sang Yiguang 4 Tong An 5* 1 Department of Orthopaedics, Shandong Hospital of People’s Liberation Army, 89th Hy, Weifang, Shandong Province, Republic of China 2 Department of Spine Surgery, Central Hospital, Zibo, Zhangdian, Shandong Province, China 3 Traditional Chinese Medicine Hospital, Dongying, Hekou, Shandong Province, China 4 Qilu Hospital, Shandong University, Shandong, China 5 Department of Orthopedics, People’s Hospital of Yan’an, Qilipu Street, Yan’an, Baota, Shaanxi Province, China * Correspondence to: Peng Yun Wang, Department of Spine Surgery, Central Hospital, Zhangdian, Zibo, Shandong Province, China; [email protected]; An Tong, Department of Orthopedics, People’s Hospital of Yan’an, Qilipu street, Yan’an, Baota, Shaanxi Province, China; [email protected] 2019 1 7 2019 5 2 16831 10 2018 04 4 2019 Copyright: © 2019 Zhang , et al.2019This is an open-access article distributed under the terms of the Attribution-NonCommercial 4.0 International 4.0 (CC BY-NC 4.0), which permits all non-commercial use, distribution, and reproduction in any medium provided the original work is properly cited.Additive manufacturing has been used in complex spinal surgical planning since the 1990s and is now increasingly utilized to produce surgical guides, templates, and more recently customized implants. Surgeons report beneficial impacts using additively manufactured biomodels as pre-operative planning aids as it generally provides a better representation of the patient’s anatomy than on-screen viewing of computed tomography (CT) or magnetic resonance imaging (MRI). Furthermore, it has proven to be very beneficial in surgical training and in explaining complex deformity and surgical plans to patients/parents. This paper reviews the historical perspective, current use, and future directions in using additive manufacturing in complex spinal surgery cases. This review reflects the authors’ opinion of where the field is moving in light of the current literature. Despite the reported benefits of additive manufacturing for surgical planning in recent years, it remains a high niche market. This review raises the question as to why the use of this technology has not progressed more rapidly despite the reported advantages – decreased operating time, decreased radiation exposure to patients intraoperatively, improved overall surgical outcomes, pre-operative implant selection, as well as being an excellent communication aid for all medical and surgical team members. Increasingly, the greatest benefits of additive manufacturing technology in spinal surgery are custom-designed drill guides, templates for pedicle screw placement, and customized patient-specific implants. In view of these applications, additive manufacturing technology could potentially revolutionize health care in the near future. Additive manufacturingbiomodelingrapid prototypingspine deformitycomplex spine surgery ==== Body 1. Introduction Spine surgeons engage in complex and innovative surgical procedures to stabilize and improve idiopathic, congenital, degenerative, and injury-related spinal deformities. Even though surgical treatment strategies and implants have evolved and improved considerably in recent decades, surgical correction of complex deformities remains very challenging. To evaluate the severity of spinal deformities and plan any required surgical procedures, physicians have traditionally relied on imaging modalities including X-rays, fluoroscopy, CT and MRI. Unfortunately, two-dimensional projections of radiographic images or three-dimensional (3D) scan data will always be limited in their ability to accurately display the complete image of 3D anatomic deformities, detracting from their value during the pre-operative planning process. As presented in the other papers in this article, the use of 3D modeling and rapid prototyping (RP) or additive manufacturing has been increasingly used in complex surgical pre-operative planning, as these techniques can accurately reproduce the anatomic details of highly complex deformities that could be missed or misinterpreted with standard imaging modalities. The purpose of this article is to explore the existing uses of additive manufacturing in complex spinal surgery and to discuss the future potentials of this technology. The common techniques and requirements for additive manufacturing are addressed elsewhere[1]. Literature search was conducted using PubMed for articles containing the terms “additive manufacturing”, “RP”, “biomodelling”, or “biomodeling”, and in combination with “spine/spinal” and “surgery/surgical planning”. General reviews or discussions of this technology where spinal usage is only briefly mentioned were not included. 2. Method From the 16 articles that were found, one was excluded from further review as it is not available in English. Publication years ranged between 1999 and 2015, with nearly half of the papers published in the past 5 years, consistent with the rapidly increasing interest in this technology. Three key areas of focus are evident: Complex spinal deformity cases in which models have been printed for surgical planning purposes; the design of patient-specific drill guides; and the very recent advent of printing custom titanium implants. Interestingly, there is a clear change in focus of the publications from 2009 to 2011 when simple printing for surgical planning was replaced by the printing of surgical tools and finally the implants themselves. Although publications on the use of additive manufacturing for surgical planning have declined in numbers recently, the current usage rates remain unclear. Has the spinal surgical community adopted this as a routine technology, or abandoned it in the past 10 years altogether? To better understand this shift, we conducted a survey of spinal surgeons attending the 2015 Annual Scientific Meeting of the Spine Society of Australia and presented the results here. 3. Historical Usage and Current Trends The use of additively manufactured models in complex spine deformity surgical planning was first reported in 1999 by a group of researchers from Australia. D’Urso et al.[2] reported the previous use of the technology in craniomaxillofacial surgery and undertook a preliminary prospective study of five complex cases to determine its usefulness in spine deformity surgery. Members of this group continue to be at the forefront in this area, having published a number of other key papers in the field[3-5]. These papers include a total of 51 cases where spine biomodels have been utilized, with the remaining four papers in this field are from Japan and China, which describe 53 additional cases[6-9]. All the authors from these published articles agreed that a 3D reconstructed model is required to obtain comprehensive information about the complex spinal deformities that would have been unavailable if conventional imaging modalities were exclusively used. They found that although CT 3D reconstruction could be displayed and viewed from any direction and angle on the computer, these method lack of tactile view which frequently view the biomodel separately and results in some alteration being made to the surgical case, be it an implant, approach, or fixation related[6-9]. 4. Complex Spinal Deformity Surgical Planning Literature findings concluded that the use of additively manufactured biomodels offered numerous benefits resulting in better surgical outcomes for the patients for example, Mizutani et al.[7] fifteen cases were evaluated and reported that 3D modeling was beneficial as a pre-operative planning tool in rheumatoid cervical spine surgery. This was attributed to a better assessment of the trajectory and entry points of cervical pedicle screws, as well as allowing for the ability to determine the entire plate-rod contours for occipitocervical junctions, avoiding post-operative dysphagia. Although having a 3D biomodel have advantages such as a detailed representation of anatomy and as a tool for planning surgical procedures, the authors concluded that coupling the 3D model with computer-assisted navigation systems likely provided better surgical results. Izatt et al.[5] aim to quantify the surgeon’s perception on the usefulness of biomodels compared with standard imaging modalities as a pre-operative planning tool and as an intraoperative anatomic reference in 26 spinal tumor and deformity cases. This study entailed a survey completed by the surgeons after each surgical case and found that anatomic details were better or exclusively visible on the biomodel (65% and 11%, respectively) compared with the CT or MRI 3D reconstructions. Therefore, different decisions were made as a direct result of the biomodel regarding the materials used (52%) and implantation sites (74%), thereby reducing the likelihood of surgical revision being required. Importantly, this paper also recorded an estimated 17% decrease in operating time for all 26 patients, with an 8% reduction in surgery time for tumor patients (mean 46 min per case) and 22% reduction in the deformity cases (mean 68 min per case) which directly reduced the cost of surgery in addition to the other reported benefits. Reasons given for the reduction in surgical time were included: easier, accurate and more efficient implant and screw positioning; less frequent reference to other imaging resources and reduced number of instrumentations due to better anatomic visualization; and detailed pre-operative planning. A recent systematic review paper by Martelli et al.[10] based on 52 papers reported that time was saved due to additive manufacturing. Likewise, Mao et al.[8] also confirmed that 3D biomodels were helpful in improving pre-operative planning and surgical treatment of complex severe spinal deformities compared with either CT or MRI 3D spinal reconstructions. This paper suggested that the biomodels were a superior visual aid when confirming the position of an anatomic landmark, helped the surgeon plan the surgery, facilitated the choice of internal fixation instrumentation, and improved the accuracy, and therefore, the safety of pedicle screw insertion all of which would influence the direct costs of the surgical cases and the risk of revision surgery being required in the future. Another important factor discussed by both Mao et al.[8] and Izatt et al.[5] was the use of additively manufactured biomodels as a communication tool with both colleagues and patients/parents. Patients (or if they were <18 years old, their parents/guardians) were contacted after the surgery, and all stated that the biomodels improved their anatomic understanding of the condition; the procedure and the risks associated with it, and, therefore, improved their ability to give fully informed consent. Similarly, biomodels enabled better communication and teaching within the surgical team both preoperatively and intraoperatively. Of course, there were also limitations presented in using this technology mainly related to the extra time, labor, and the associated costs of biomodel manufacture. Nevertheless, it was argued that these issues were offset by the cost savings from shorter surgical times, the reduced complication rates, and the likelihood of surgical revision being required in the future[3,5,7]. Presented below are two case studies performed by the authors of this article where additively manufactured biomodels were used for pre-operative planning. 4.1. Patient A A 12 year old male, diagnosed with neurofibromatosis type 1 with complex occipitocervical spinal deformities and a large neuroma in close proximity to the upper cervical spine. The patient was demonstrating steadily worsening neurological signs in all limbs and had experienced a number of episodes of intermittent quadriparesis indicative of progressive brainstem/spinal cord compression, requiring surgical decompression and stabilization. Preoperatively, the patient had posterior-anterior (PA) and lateral (LAT) cervical and full spine radiographs (Figure 1), brain and full spine MRI (Figure 2), and 3D CT scans (Figure 3). The CT scan was used to create a 3D anatomic biomodel (Figure 4). Figure 1 Pre-operative lateral and posterior-anterior radiographs of the cervical and upper thoracic spine of 12-year-old male (neurofibromatosis type 1, plexiform neuroma posterior to cervical spine), which did not provide clear anatomic detail of significant upper cervical deformity. Figure 2 Sagittal slices of pre-operative magnetic resonance imaging showing the reduced size of the spinal canal in the upper cervical spine with insufficient posterior element bony detail (patient A). Figure 3 Multiplanar views of pre-operative computerized tomographic (CT) scan at the C2 level and three-dimensional CT reconstruction (lower right), which suggested insufficient vertebral bone in the posterior elements of the upper cervical spine for posterior fixation (patient A). Figure 4 Three-dimensional printed biomodel (sagittal, anterior, and upper cervical close-up views) demonstrates that the anatomy of the C2 laminae was of sufficient size to accept fixation posteriorly in addition to the previously planned fixation points in the base of the skull and upper thoracic spine (patient A). After viewing the available imaging data, the initial surgical plan was to perform a posterior instrumented fusion from occiput to T4 with screw fixation into the occiput and thoracic spine only. Due to the small size and deformity of the cervical vertebrae, it was considered that the upper cervical vertebrae were too small to be able to insert any fixation points for the planned posterior construct. After receiving the biomodel, it became evident that the C2 laminae were of sufficient size for small translaminar screws to be used on each side. The surgical instrumentation was changed to include these translaminar screws in addition to the fixation points already planned at the occiput and T3-4 levels. The biomodel greatly assisted with the explanation to the child’s parents regarding the surgery planned and the associated risks involved, thereby, helped to obtain informed consent. The surgeons reported that the addition of fixation to the upper cervical spine had made the instrumented construct more robust and had improved the deformity correction achieved by the procedure in addition to the decompression and stabilization components. With the additional fixation points, the surgeon reported that the risk of requiring a revision procedure in the future was also less likely. Although the pedicle screw placement in the thoracic spine was not optimum, they have held well to date, the patient’s neurological signs have improved and thereafter remained stable, with no loosening or loss of correction now 10 months postoperative. Supine LAT and PA radiographs 1 month after surgery and the most recent LAT view at 10 months post-operative are shown in Figure 5. Figure 5 Post-operative lateral (A) and posterior-anterior radiographs (B) of the cervical and upper thoracic spine with halo brace in situ illustrating the instrumented correction and stabilization achieved surgically for patient a. Follow-up radiographs, 10-month postoperative (C). 4.2. Patient B A 9 year old female, diagnosed with myelomeningocele spina bifida (neurological deficit below T10) with severe collapsing T10-S1 due to the total absence of posterior elements. The resulting kyphotic deformity was causing seating difficulties and the maintenance of the integrity of the skin over the kyphotic deformity was becoming challenging, with skin breakdown becoming more frequent. It was considered that kyphectomy and posterior instrumented fusion would improve the quality and length of life. Preoperatively, the patient had PA and LAT sitting spine radiographs (Figure 6), thoracolumbar spine CT with 3D reconstruction (Figure 7), and a biomodel was ordered (Figure 8). Figure 6 Pre-operative sitting posterior-anterior (A) and lateral (B) radiographs of the entire spine of a 9-year-old female (myelomeningocele spina bifida) with collapsing kyphosis (patient B). Figure 7 Sagittal views from pre-operative computerized tomographic (CT) scan and three-dimensional CT reconstruction (far right) of the thoracic and lumbar spine showing more anatomic detail than radiographs of the deformity, but insufficient detail to decide how many levels to remove and the precise fixation points for the instrumentation (patient B). Figure 8 Three-dimensional printed biomodel (anterior, posterior, and lateral views) demonstrates the anatomy of the thoracic and lumbosacral spine providing the necessary detail for the kyphectomy and subsequent successful deformity correction and instrumented fusion procedure patient. The surgical plan was to ideally perform a kyphectomy between two and five levels followed by deformity correction and stabilization with a posterior instrumented fusion from the upper thoracic spine to the pelvis; however, the thoracolumbar anatomy, especially the thoracolumbar junction anatomy, remained unclear. Having no posterior spinal elements to fix instrumentation into, alternative fixation points were required. After receiving the biomodel, the anatomy of the lower thoracic and lumbar spine was clear and the decision was made with some confidence to proceed with the kyphectomy of L1-L3 followed by an instrumented fusion from T3-pelvis (Figures 9 and 10). The biomodel also greatly assisted with the explanation to the child’s parents regarding the planned surgery and the associated risks involved, thereby, helped to obtain informed consent. The patient recovered well, and the parents reported that caring for their child was much easier, as was her comfort when seated in her wheelchair. There was an added benefit of being able to sleep supine for the 1st time in many years. There were no longer any issues with skin integrity or pressure areas over her spine. The fixation has remained stable with no complications. Figure 9 Post-operative anterior-posterior (A) and lateral (B) radiographs illustrating the instrumented correction and stabilization achieved surgically for patient B. Figure 10 Pre-operative (A and B) and post-operative (C) photographs showing cosmetic aspects of the deformity before and after surgical correction assisted by the use of the three-dimensional printed biomodel (patient B). 5. Surgical Tools and Guides Since 2009, designing and printing guides for pedicle screw placement has emerged as a new area of additive manufacturing for spinal surgical planning, particularly in the cervical spine[11,12]. The anatomy in this region is quite compact and even more so in pediatric cases, with delicate neural tissue in close proximity making precise screw insertion of great importance. The earlier papers from Lu et al.[11,12] utilized additively manufactured drill guides for two kinds of screw placement in the cervical spine. These plastic guides were placed directly in contact with the patient’s exposed bony anatomy in the operating room and used to insert screws along predefined trajectories. The author reported that this technique is highly accurate. Additionally, reduces both the surgery time and radiation exposure. These papers were then followed by a series of cadaveric studies describing the effectiveness of additively manufactured plastic pedicle screw template[13,14]. In summary, the researchers found that by using the screw template the intended insertion location and angle correlate. As a result, titanium was proposed as an alternative to plastic models for surgical guides; however, it was also found to have disadvantages such as cost and availability. In the study by Takemoto et al.,[15] additive manufactured titanium thoracic pedicle screw templates were assessed specifically looking at the landmarks used as contact points for the template, to ensure reproducibility and stability. This study showed a very high success rate for their templates, with failure defined as perforation of the pedicle wall by the screw, 98.4% of pedicle screws were placed successfully for scoliosis patients and 100% for ligament ossification patients. The issue of cost was also addressed in this study stating that the production cost of 10 templates in a singular patient amounted to $1000 for titanium versus $200 for the plastic polyamide. The authors pointed out that even though the non-metallic materials have approval from the US Pharmacopeia for use in the human body for 24 h when in contact with drills and surgical tools; the plastic would likely produce debris, which would accumulate in the wound. The long-term effect of this residual material is unknown, and in close proximity to the spinal cord, its safety is clearly questionable. The titanium templates also have the advantage of higher strength and rigidity, being metallic. This ensures greater accuracy and reliability, reduces the chance of warping and flexing, and eliminates the potential of the drill or screw cutting through the material and/or producing debris as is the case for plastic guides. 6. Additively Manufactured Custom Implants Recent advances and the increased availability of metal-based additive manufacturing technologies such as direct or selective laser sintering (LS) and electron beam melting have allowed for the development of customized spinal implants into current surgical practice. Off the shelf, vertebral body and intervertebral disc implants are already commonly used, but the ability to 3D print both generic and custom metal implants has a number of potential advantages. For instance, intervertebral discs that can be printed to conform to the patient’s specific vertebral end plate geometry have performed well in cadaveric studies, achieving higher compressive failure loads, and better stiffness characteristics than flat implants produced in the same manner[16]. On the other hand, a high-temperature LS allows fabrication layering of complex structure such as high-performance biomaterial polymer, i.e., polyether ether ketone was applied by Berretta et al. in the manufacturing of cranial implant[17]. Both the mechanical performance, density variation, and dimensional accuracy of the implants were found comparable to the design model and show the highest compressive strength resistance. Evidently, an additively manufactured porous titanium structures have great potential for use as bone substitute biomaterials. Titanium alloys have been used for decades as a bioactive material[18], encouraging bony ingrowth onto exposed surfaces. For instance, titanium-tantalum (Ti-Ta) alloy can be fabricated using selective laser melting[19]. Ti-Ta alloys are promising materials for biomedical applications and surgical implants because it has high biocompatibility, corrosion resistance, and good mechanical properties. Besides, electron beam melting allows porous implants made from titanium alloys to be created with control over the shape and pore structure. This technology has the potential to develop both patient-specific custom implants, as well as generic bone substitute implants. Yang et al.[20] examined a self-stabilizing artificial vertebral body created this way in an in vivo sheep model of the cervical spine. This study found that these porous metal implants facilitated bony ingrowth and resulted in very stable fixation in a load-bearing application – something that is not currently possible with other additively manufactured scaffold structures. Worldwide, a number of companies are already making additively manufactured customized surgical tools and templates to aid in spinal procedures, as well as custom spinal implants designed specifically for particular patients. Besides the customized spinal implants, the similar technologies were applied to other recent orthopedic regenerative medicine treatment[21]. A mandible that is coated with hydroxyapatite has been additively manufactured[22]. Furthermore, Mertens et al. constructed a titanium-made midfacial support and a graft fixture through additive manufacturing for patient with midface defect[23]. Customized cranial implants were designed and additively manufactured by Jardini et al. in the surgical reconstruction of a large cranial defect[24]. 7. Surgeon Survey Spinal surgeons attending the Annual Scientific Meeting of the Spine Society of Australia 2015 held in Canberra, Australia, were asked to complete a short survey on their knowledge and use of RP technology (additive manufacturing) in their surgical practices and experience. 35 surgeons completed the survey, of which 81% (27) were experienced, senior consultants. Although 80% of respondents had heard of using additive manufacturing for surgical planning, only 10 had ever used it. Of these 10, eight reported using it 0–2 times per year and two reported using it 3–5 times per year. Most users (7/10) reported that it improved the surgical outcome, with the others saying that it made no difference to the surgical outcome. However, additionally, the comment was made that while they felt that the biomodel did enhance surgical planning and the ability to perform the surgical intervention, the outcome to the patient was the same as if they had not used it. For those who were not using the technology, most reported that this was due to availability issues (44%). However, only 54% said that they would use it should it ever become available in their hospital. Other minority reasons given for not using biomodels were cost (4%, n = 1) and other reasons (12%, n = 3), predominantly being that they do not or have not had a suitable case for which to use it to date. These results, together with discussions with the surgeons while they were completing the survey, highlighted a number of important considerations: That of the suitability of cases for this type of procedure in a particular surgeon’s practice, as well as the usefulness of biomodels for purposes other than developing the actual surgical plan. The surgeons who currently used additive manufacturing for surgical planning all worked with patients who had complex progressive deformities, whereas those who did not use biomodels treated less complex and mainly adult degenerative cases, for which the added expense and time delay to print the model was thought to likely not be of sufficient benefit to their surgical planning and/or surgical procedure. According to surgeons, the usage of additively manufactured models are often extended, which is beyond the surgical planning phase. Hence, patient or their guardian needs to be aware of the this situation when signing the informed consent form. Having a physical model available of a complex spinal deformity made the explanation of the current condition as well as the intended surgical procedure to patients and family much simpler and easier to understand. The description of both the severity and the reasons for the current symptoms caused by the spinal deformity could be explained more clearly as well as exactly what the surgery would entail and the possible complications and consequences that may occur with or without the intended surgical procedure. This sentiment has also been reported in literature discussed above[4]. Furthermore, using the additively manufactured models with surgical trainees form an important teaching tool during the surgical planning phase, during the surgical procedure, and as retrospective case studies. 7.1. Future Perspectives As reflected in this review, the use of additive manufacturing as a pre-operative planning tool in spinal surgery is still relatively uncommon, even though the technology has continued to develop over the past three decades. This review raises the question as to why the use of this technology has not progressed more rapidly despite the reported advantages – decreased operating time, decreased radiation exposure to patients intraoperatively, improved overall surgical outcomes, pre-operative implant selection, as well as being an excellent communication aid for all medical and surgical team members. Regardless of the reported clinical success, the lack of usage of 3D RP or printing has been attributed to the availability and cost of the technology, as well as the time delay between the scan of the patient is performed and the biomodel being produced (several days) and then delivered to the requesting surgeon. The other main reason given for not using physical 3D biomodels was that the particular surgeon did not treat the type of spinal deformity patients that would benefit from this technology, who are managed by a small contingent of highly specialized complex deformity surgeons. The future success of this technology is dependent on how useful surgeons find the biomodels to be for pre-operative planning and consent and/or for intraoperative anatomic reference compared with standard visualization modalities such as CT scans. Do additively manufactured biomodels have the potential to become part of the standard of care, or will it always be used only for the most complex deformity cases by specialist spinal surgeons and how will the success of the technology be measured? Answering these questions will be vital for additive manufacturing to become an essential part of spinal deformity surgery as the technology continues to improve, becomes more affordable and faster to produce. It seems clear that even if biomodels are only used on a limited basis during the surgical procedure for the most complex cases of spinal deformities, there is certainly value in the exercise of virtual planning or 3D computer modeling, a processing step that is generated before final additive manufacturing occurs. The generation of the 3D computer model allows for the on-screen manipulation of the patient’s-specific anatomy generated from their CT scan for the purpose of visualization of the deformity for pre-operative planning and rehearsal of the intended surgery. Therefore, whether or not the final stage of printing goes ahead; utilization of the technology of 3D computer modeling will most likely become a routine part of spinal surgery for the benefit of clinicians and patients alike. It is worth noting that based on the number of publications found in literature, China has the appearance of leading the medical field in the use of RP technology. Why are some countries such as China more readily accepting RP technology and why are they at the forefront in using it compared with the western world? Perhaps, it is related to the fact that in western countries, private biomedical companies are driving this technology and its use rather than research institutions, which often does not translate into peer-reviewed publications. In contrast, for the design of surgical tools, templates, and personalized patient implants, additive manufacturing technology has found a new niche which is demonstrating a rapid advance and may be the most promising application in the medical field. We believe that the future of customized patient-specific implants will be the greatest benefit of additive manufacturing technology, potentially revolutionizing health care, and benefitting the largest number of patients. This is especially true as the trend continues toward less invasive and more precise surgical treatment strategies, and as clinicians increasingly relies on advanced technologies for planning and delivering customized and patient-specific medical care. Further discussion on the techniques, technology, and limitations of additive manufacturing in health care can be found in other articles in this issue. ==== Refs 1 Green N Glatt V Tetsworth K 2016 A Practical Guide to Image Processing in the Creation of 3D Models for Orthopaedics Tech Orthop 31 3 153 63 DOI 10.1097/BTO.0000000000000181 2 D'Urso P Askin G Earwaker J 1999 Spinal Biomodeling Spine (Phila Pa 1976) 24 1247 51 DOI 10.1097/00007632-199906150-00013 10382253 3 D'Urso PS Williamson OD Thompson RG 2005 Biomodeling as an Aid to Spinal Instrumentation Spine (Phila Pa 1976) 30 12 2841 5 DOI 10.1097/01.brs.0000190886.56895.3d 16371915 4 D'Urso PS 2006 Biomodelling Gibson I Advanced Manufacturing Technology for Medical Applications:Reverse Engineering, Software. Conversion and Rapid Prototyping Chichester, United Kingdom John Wiley and Sons Ltd. 31 57 DOI 10.1002/0470033983.ch3 5 Izatt MT Thorpe PLP Thompson RG 2007 The use of Physical Biomodelling in Complex Spinal Surgery Eur Spine J 16 9 1507 18 DOI 10.1007/s00586-006-0289-3 17846803 6 Yamazaki M Akazawa T Okawa A 2007 Usefulness of Three-dimensional Full-scale Modeling of Surgery for a Giant Cell Tumor of the Cervical Spine Spinal Cord 45 250 53 DOI 10.1038/sj.sc.3101959 16835582 7 Mizutani J Matsubara T Fukuoka M 2008 Application of Full-scale Three-dimensional Models in Patients with Rheumatoid Cervical Spine Eur Spine J 17 5 644 9 DOI 10.1007/s00586-008-0611-3 18247063 8 Mao K Wang Y Xiao S 2010 Clinical Application of Computer-designed Polystyrene Models in Complex Severe Spinal Deformities:A Pilot Study Eur Spine J 19 5 797 802 DOI 10.1007/s00586-010-1359-0 20213294 9 Yang JC Ma XY Lin J 2011 Personalised Modified Osteotomy using Computer-aided Design-rapid Prototyping to Correct Thoracic Deform-ities Int Orthop 35 12 1827 32 DOI 10.1007/s00264-010-1155-9 21125271 10 Martelli N Serrano C van den Brink H 2016 Advantages and Disadvantages of 3-dimensional Printing in Surgery:A Systematic Review Surgery 159 6 1485 500 DOI 10.1016/j.surg.2015.12.017 26832986 11 Lu S Xu YQ Lu WW 2009 A Novel Patient-specific Navigational Template for Cervical Pedicle Screw Placement Spine (Phila Pa 1976) 34 26 E959 66 DOI 10.1097/BRS.0b013e3181c09985 20010385 12 Lu S Xu YQ Zhang YZ 2009 A Novel Computer-assisted Drill Guide Template for Placement of C2 Laminar Screws Eur Spine J 18 9 1379 85 DOI 10.1007/s00586-009-1051-4 19517142 13 Fu M Lin L Kong X 2013 Construction and Accuracy Assessment of Patient-specific Biocompatible Drill Template for Cervical Anterior Transpedicular Screw (ATPS) Insertion:An in vitro Study PLoS One 8 1 e53580 DOI 0.1371/journal.pone.0053580 23326461 14 Hu Y Yuan Z Spiker WR 2013 Deviation Analysis of C2 Translaminar Screw Placement Assisted by a Novel Rapid Prototyping Drill Template:A Cadaveric Study Eur Spine J 22 12 2770 6 DOI 10.1007/s00586-013-2993-0 24005997 15 Takemoto M Fujibayashi S Ota E 2016 Additive-Manufactured Patient-specific Titanium Templates for Thoracic Pedicle Screw Place-ment:Novel Design with Reduced Contact Area Eur Spine J 25 1698 705 DOI 10.1007/s00586-015-3908-z 25820409 16 de Beer N Scheffer C 2012 Reducing Subsidence Risk by using Rapid Manufactured Patient-specific Intervertebral Disc Implants Spine J 12 11 1060 6 DOI 10.1016/j.spinee.2012.10.003 23103407 17 Berretta S Evans KE Ghita OR 2018 Additive Manufacture of PEEK Cranial Implants:Manufacturing Considerations Versus Accuracy and Mechanical Performance Mater Des 139 1 141 52 DOI 10.1016/j.matdes.2017.10.078 18 Cook HP 1969 Titanium in Mandibular Replacement Br J Oral Surg 7 108 11 DOI 10.1016/S0007-117X(69)80005-3 4311311 19 Sing S Yenog WY Wiria FE 2018 Selective Laser Melting of Titanium Alloy with 50 wt% Tantalum:Effect of Laser Process Parameters on Part Quality Int J Refract Met Hard Mater 77 120 7 DOI 10.1016/j.ijrmhm.2018.08.006 20 Yang J Cai H Lv J 2014 in vivo Study of a Self-Stabilizing Artificial Vertebral Body Fabricated by Electron Beam Melting Spine (Phila Pa 1976) 39 8 E486 92 DOI 10.1097/BRS.0000000000000211 24430723 21 Wang X Xu S Zhou S 2016 Topological Design and Additive Manufacturing of Porous Metals for Bone Scaffolds and Orthopaedic Implants:A Review Biomaterials 83 127 41 DOI 10.1016/j.biomaterials.2016.01.012 26773669 22 Xillo 2011 The World's First 3D Printed Total Jaw Reconstruction Available from:http://www.xilloc.com/patients/stories/total-mandibular-implant 23 Mertens C Lowenheim H Hoffmann J 2013 Image Data Based Reconstruction of the Midface Using a Patient-specific Implant in Combination with a Ascularized Osteomyocutaneous Scapular Flap J Cranio Maxillofac Surg 41 219 25 DOI 10.1016/j.jcms.2012.09.003 24 Jardini AL Larosa MA Zavaglia CAC 2014 Customised Titanium Implant Fabricated in Additive Manufacturing for Craniomaxillofacial Surgery Virtual Phys Prototyp 9 115 25 DOI 10.1080/17452759.2014.900857	32782982	PMC7415852	NO-CC CODE	2021-01-06 10:35:54	yes	Int J Bioprint. 2019 Jul 1; 5(2):168
==== Front ACS Omega ACS Omega ao acsodf ACS Omega 2470-1343 American Chemical Society 10.1021/acsomega.0c02364 Article Icariin Alleviates Bisphenol A Induced Disruption of Intestinal Epithelial Barrier by Maintaining Redox Homeostasis In Vivo and In Vitro Zhu Kun † Zhao Yanan ‡ Yang Yang ‡ Bai Yuansong ‡ Zhao Tianyu § † Department of Pharmacy, The Third Hospital of Jilin University, Xiantai Street No. 126, Changchun 130021, China ‡ Department of Oncology and Hematology, The Third Hospital of Jilin University, Xiantai Street No. 126, Changchun 130021, China § College of Basic Medical Sciences, Jilin University, Xinmin Street No. 126, Changchun 130021, China Email: [email protected]. 03 08 2020 18 08 2020 5 32 20399 20408 20 05 2020 20 07 2020 Copyright © 2020 American Chemical Society2020American Chemical SocietyThis is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND) Attribution License, which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes. Bisphenol A (BPA), a globally prevalent environmental contaminant, has been shown to have the potential to disrupt intestinal barrier function. This study explored the mechanisms of BPA-induced intestinal barrier dysfunction. In addition, the protective effect of the natural product icariin (ICA) on BPA-induced intestinal barrier dysfunction was evaluated. BPA relieved oxidative stress (reactive oxygen species (ROS), reactive nitrogen species (RNS), malondialdehyde (MDA), and hydrogen peroxide (H2O2)), suppressed antioxidant enzyme (superoxide dismutase (SOD), glutathione peroxidase (GPx), catalase (CAT), and total antioxidant capacity (T-AOC)) activity, and increased gene expression and protein content of p38 mitogen-activated protein kinase (MAPK), giving rise to the dysfunctional gut in mice. ICA therapy effectively eased intestinal barrier dysfunction caused by BPA in vivo and in vitro. Treatment with p38 MAPK inhibitor (SB203580) significantly rescued the MODE-K cell barrier function disrupted by BPA challenge. However, treatment with p38 MAPK activator (anisomycin) did not attenuate the MODE-K cell barrier function impaired by BPA challenge. Overall, our data suggested that BPA disrupted intestinal barrier function in a p38 MAPK-dependent manner. Furthermore, we demonstrated that ICA regulated the redox equilibrium of intestinal epithelial cells by inhibiting the expression of p38 MAPK, thereby alleviating BPA-induced disruption of intestinal barrier function. These findings contributed to a better understanding of the mechanisms of BPA-induced intestinal barrier dysfunction and provided new insights into the prevention and treatment of BPA-induced intestinal diseases. document-id-old-9ao0c02364document-id-new-14ao0c02364ccc-price ==== Body Introduction Bisphenol A (4,4′-(propane-2,2-diyl)diphenol; BPA), one of the most widely used industrial compounds in the world, is mainly used in the production of various polymers. BPA is applied to baby bottles, toys, sealant, eyewear lenses, and paper consumer products.1 It is the high production and consumption that makes it a widespread pollutant in the global environment.2−5 When plastic products are heated or exposed to ultraviolet (UV) light, BPA is released from the polymer into food and water,6 resulting in frequent exposure to BPA. Adverse effects of BPA on human health have attracted high attention in the field of public health because specific tissues and organs are highly susceptible to the toxic effects of BPA.7−9 A number of studies have found that BPA is widely found in food, environmental, and even biological samples.10−14 Toxic substances in intestinal lumen can induce dysfunction of intestinal epithelial cells, allowing harmful substances to escape and pass into the bloodstream, causing systemic metabolic diseases.15−17 Maintaining a good intestinal barrier function is critical to human health. The tight junction (TJ) complex is the main component of the intestinal epithelial barrier.18 Abnormal expression and distribution of tightly coupled complexes in intestinal epithelial cells will directly affect the intestinal barrier function and intestinal permeability, thus inducing many intestinal diseases.19 Researchers have done a lot of work on BPA and intestinal diseases and their relationship. The effects of BPA on gut health are mainly manifested in the downregulated lysozyme expression, decreased fecal antibacterial activity, reduced secretion level of intestine immunoglobulin A (IgA), increased intestinal permeability, and the occurrence of colitis.20,21 In addition, evidence shows that exposure to BPA destroyed the morphological structure of the intestinal epithelium, reduced the number of goblet cells, suppressed the expression of tight junction (TJ) protein, increased the permeability of the intestinal epithelium, and ultimately leading to impaired intestinal barrier function.22 Previous studies have shown that oxidative stress is a critical biological process by which BPA mediates damage to gut barrier function.23 However, the mechanism by which BPA induces oxidative stress is unknown. A recent study revealed that p38 mitogen-activated protein kinase (MAPK) signaling pathway involved the regulation of oxidative stress and was closely related to the intestinal epithelial barrier function.24,25 Due to its safety and low cost, natural products have been widely used in disease resistance research. Icariin (ICA) is a representative natural product. ICA, a flavonoid extracted from epimedium, has a wide range of biological activities as well as pharmacological effects, mainly anti-inflammatory and antioxidant. Previous studies have shown that ICA can significantly alleviate intestinal injury induced by LPS, suggesting that ICA may have an outstanding protective effect on intestinal epithelium.26,27 Therefore, we hypothesized that ICA positively protects against BPA-induced intestinal epithelial barrier damage through regulation of the expression of p38 MAPK. To demonstrate this, we investigated the protective effect of ICA on intestinal epithelial barrier function by constructing in vivo and in vitro models induced by BPA. Moreover, the biological mechanism by which ICA has a protective effect on the intestinal epithelial barrier was elucidated through specific blockade or activation of the p38 MAPK signaling pathway. Results Effects of ICA on Jejunal Permeability and Barrier Function in BPA-Exposed Mice To assess the effects of ICA on jejunal permeability and barrier function after BPA exposure, we examined the levels of the chemical markers (endotoxin, diamine peroxidase (DAO), d-lactate, and zonulin) and the gene expressions of tight junction proteins (ZO-1, occludin, and claudin-1). BPA exposure markedly increased the levels of these chemical markers in the plasma and jejunum, while ICA + BPA co-treatment decreased them compared with that in the BPA group (p < 0.05; Figure 1A–H). Besides, we used real-time quantitative polymerase chain reaction (RT-qPCR) to detect TJ-related gene expression and found that BPA exposure significantly reduced the gene expressions of ZO-1, occludin, and claudin-1 in the jejunal samples, while ICA + BPA co-treatment rescued the expressions of these tight junction proteins (p < 0.05; Figure 1I–K). These results suggested that ICA can effectively alleviate BPA-induced intestinal barrier functional impairment. Figure 1 Effects of ICA on jejunal permeability and barrier function in BPA-exposed mice. Plasma endotoxin (A), DAO (B), d-lactate (C), and zonulin (D) levels. Jejunal endotoxin (E), DAO (F), d-lactate (G), and zonulin (H) levels. Jejunal gene expressions of ZO-1 (I), occludin (J), and claudin-1 (K) detected by RT-qPCR. Values are the mean ± standard error (n = 10). p < 0.05 vs CON group; #p < 0.05 vs BPA group. Effects of ICA on Jejunal Redox Status in BPA-Exposed Mice To investigate the effect of ICA on redox status in the jejunal epithelium after BPA exposure, we detected the levels of the chemical markers (reactive oxygen species (ROS), reactive nitrogen species (RNS), malondialdehyde (MDA), hydrogen peroxide (H2O2), superoxide dismutase (SOD), glutathione peroxidase (GPx), catalase (CAT), and total antioxidant capacity (T-AOC)) in plasma and jejunal samples of mice. BPA exposure significantly increased the levels of ROS, RNS, MDA, and H2O2 in plasma and jejunum, while ICA decreased the ROS, RNS, MDA, and H2O2 contents compared to the BPA group (p < 0.05; Figure 2A–H). Besides, BPA exposure markedly reduced the activity of SOD, GPx, CAT, and T-AOC in the plasma and jejunum, while ICA + BPA co-treatment increased the SOD, GPx, CAT, and T-AOC activity compared with those of the BPA group (p < 0.05; Figure 2I–P). These data demonstrated that ICA can reverse BPA-induced redox imbalances. Figure 2 Effects of ICA on jejunal redox status in BPA-exposed mice. Plasma levels of ROS (A), RNS (B), MDA (C), and H2O2 (D). Jejunal ROS (E), RNS (F), MDA (G), and H2O2 (H) levels. Plasma SOD (I), GPx (J), CAT (K), and T-AOC (L) activity. Jejunal SOD (M), GPx (N), CAT (O), and T-AOC (P) activity. Values are the mean ± standard error (n = 10). p < 0.05 vs CON group; #p < 0.05 vs BPA group. Effects of ICA on Jejunal p38 MAPK Expression in BPA-Exposed Mice To reveal the influence of ICA on the expression of p38 MAPK in the jejunal epithelium after BPA exposure, we detected the mRNA expression and content of the p38 MAPK in jejunal samples of mice. As shown in Figure 3, mice challenged with BPA had a higher level of p38 MAPK gene expression and content than the CON group (p < 0.05; Figure 3A,B). Besides, the p38 MAPK gene expression and content in the ICA co-treatment group was significantly lower than that of the BPA group (p < 0.05; Figure 3A,B). These results suggested that p38 MAPK may be a key factor of ICA in alleviating BPA-induced intestinal injury. Figure 3 Effects of ICA on jejunal p38 MAPK expression in BPA-exposed mice. The gene expression (A) and content (B) of p38 MAPK in the jejunum of mice. Values are the mean ± standard error (n = 10). p < 0.05 vs CON group; #p < 0.05 vs BPA group. Effect of ICA on Cell Viability and Monolayer Barrier Function in MODE-K Cells with BPA Challenge To evaluate the potential protective effect of ICA on MODE-K cell viability and monolayer barrier function with the BPA challenge, 40 μg/mL ICA was used to process cells with 200 μM BPA for 24 h. MODE-K cell viability was significantly lower in the BPA group than in the CON group. At the same time, the cell viability of the ICA + BPA co-treatment group was significantly higher than that of the BPA group (p < 0.05; Figure 4A). Similarly, the lactate dehydrogenase (LDH) activity of MODE-K cells of the BPA group was markedly higher than that of the CON group; meanwhile, the LDH activity of the ICA + BPA co-treatment group was markedly lower than that of the BPA group (p < 0.05; Figure 4B). In addition, the BPA treatment reduced the value of transepithelial electrical resistance (TEER) and increased the fluorescein isothiocyanate-dextran (FITC-D4) flux of MODE-K cell (p < 0.05; Figure 4C,D). Compared to the BPA group, the value of TEER was relatively elevated after the ICA co-treatment, while the FITC-D4 flux was relatively declined (p < 0.05; Figure 4C,D). Besides, we used RT-qPCR to detect TJ-related gene expression and found that the challenge with BPA decreased ZO-1, occludin, and claudin-1 gene expression in MODE-K cells, while the gene expressions of ZO-1, occludin, and claudin-1 in the BPA + ICA group were significantly higher than those in the BPA group (p < 0.05; Figure 4E–G). These results validated the efficacy of ICA against the intestinal toxicity of BPA in vitro. Figure 4 Effect of ICA on cell viability and monolayer barrier function in MODE-K cells with BPA challenge. (A) Cell viability, (B) LDH activity, (C) TEER, and (D) FITC-D4 measured after exposure to BPA (200 μM) and ICA (40 μg/mL) for 24 h. The gene expression of (E) ZO-1, (F) occludin, and (G) claudin-1 detected by RT-qPCR after exposure to BPA (200 μM) and ICA (40 μg/mL) for 24 h. Values are the mean ± standard error (n = 6). p < 0.05 vs CON group; #p < 0.05 vs BPA group. Effect of ICA on Redox Equilibrium in MODE-K Cells after BPA Challenge To evaluate the potential protective effect of ICA on MODE-K cell redox balance with the BPA challenge, we also used 40 μg/mL ICA to process cells with 200 μM BPA for 24 h. To assess the redox state of MODE-K cells, mitochondrial and intracellular ROS levels, and a series of indicators (MDA, H2O2, SOD, GPx, CAT, and T-AOC) were measured. The level of mitochondrial and intracellular ROS, MDA, and H2O2 in MODE-K cells increased after the BPA challenge; meanwhile, the mitochondrial and intracellular ROS, MDA, and H2O2 contents in the BPA + ICA group was significantly lower than that in the BPA group (p < 0.05; Figure 5A–D). In addition, the activity of SOD, GPx, CAT, and T-AOC in MODE-K cells of the BPA group was significantly lower than that in the CON group, whereas the SOD, GPx, CAT, and T-AOC activity in the ICA co-treatment group was higher than those in the BPA group (p < 0.05; Figure 5E–H). These data validated the efficacy of ICA against BPA-induced redox disturbance in vitro. Figure 5 Effect of ICA on the redox status in MODE-K cells with BPA challenge. Changes in the levels of (A) mitochondrial ROS (MitoSOX dye oxidation), (B) total intracellular ROS (H2DCF oxidation), (C) MDA, and (D) H2O2 in MODE-K cells. The activity of (E) SOD, (F) GPx, (G) CAT, and (H) T-AOC in MODE-K cells. Values are the mean ± standard error (n = 6). p < 0.05 vs CON group; #p < 0.05 vs BPA group. Effect of ICA on the Expression of p38 MAPK and Oxidative Stress in MODE-K Cells after BPA Challenge As shown in Figure 6, MODE-K cells treated with BPA had a higher level of p38 MAPK gene expression and content than the CON group (p < 0.05; Figure 6A,B). Besides, the p38 MAPK gene expression and content of MODE-K cells in the ICA co-treatment group were significantly lower than those in the BPA group (p < 0.05; Figure 6A,B). To explore the underlying mechanisms by which ICA alleviates the disruption of the monolayer function of the MODE-K cells induced by BPA, we explored the p38 MAPK expression as well as oxidative status. Compared with the CON group, the ICA group significantly reduced the gene expression and content of p38 MAPK and decreased the mitochondrial and intracellular ROS levels in MODE-K cells (p < 0.05; Figure 6C–F). These data suggested that ICA may play a role in protecting the gut by regulating p38 MAPK in vitro. Figure 6 Effect of ICA on p38 MAPK expression and oxidative stress in MODE-K cells with BPA challenge. The gene expression (A) and content (B) of p38 MAPK in MODE-K cells. The gene expression (C) and content (D) of p38 MAPK in MODE-K cells. Changes in the levels of (E) mitochondrial ROS (MitoSox dye oxidation) and (F) total intracellular ROS (H2DCF oxidation) in MODE-K cells. Values are the mean ± standard error (n = 6). p < 0.05 vs CON group; #p < 0.05 vs BPA group. Effects of Co-treatment with ICA and p38 MAPK Inhibitor/Activator on Cell Viability, Barrier Function, and Oxidative Stress of MODE-K Cells after BPA Challenge We employed inhibitors/activators of p38 MAPK to elucidate the potential molecular mechanisms by which ICA alleviates BPA-induced intestinal injury. As shown in Figure 7, when compared to the BPA group, the p38 MAPK inhibitor (SB203580) group significantly increased the MODE-K cells viability and LDH activity, increased the value of TEER, decreased the FITC-D4 flux, and reduced the levels of mitochondrial and intracellular ROS (p < 0.05; Figure 7A–F). Besides, compared with the BPA group, the p38 MAPK inhibitor (SB203580) group significantly increased the mRNA expression of ZO-1, occludin, and claudin-1 (p < 0.05; Figure 7G–I). As shown in Figure 8, compared with the CON group, the BPA group and the BPA + ICA + p38 MAPK activator (anisomycin) group significantly reduced the MODE-K cell viability and LDH activity, declined the value of TEER, increased the FITC-D4 flux, increased the mitochondrial and level of intracellular ROS, and reduced the mRNA expression of ZO-1, occludin, and claudin-1 (p < 0.05; Figure 8A–I). While compared to the BPA group, the BPA + ICA + p38 MAPK activator (anisomycin) group hardly had change in cell viability, barrier function, and oxidative stress. These results confirm that p38 MAPK is essential for ICA to play a role in combating BPA. Figure 7 Effect of p38 MAPK inhibitor on cell viability, barrier function, and oxidative stress in MODE-K cells with BPA challenge. (A) Cells viability and (B) LDH activity of MODE-K cells. (C) TEER and (D) FITC-D4 of MODE-K cells. Changes in the levels of (E) mitochondrial ROS (MitoSOX dye oxidation) and (F) total intracellular ROS (H2DCF oxidation) in MODE-K cells. mRNA expression of (G) ZO-1, (H) occludin, and (I) claudin-1 in MODE-K cells detected by RT-qPCR. Values are the mean ± standard error (n = 6). p < 0.05 vs CON group; #p < 0.05 vs BPA group. Figure 8 Effect of co-treatment with p38 MAPK activator and ICA on cell viability, barrier function, and oxidative stress in MODE-K cells with BPA challenge. (A) Cells viability and (B) LDH activity of MODE-K cells. (C) TEER and (D) FITC-D4 of MODE-K cells. Changes in the levels of (E) mitochondrial ROS (MitoSOX dye oxidation) and (F) total intracellular ROS (H2DCF oxidation) in MODE-K cells. mRNA expression of (G) ZO-1, (H) occludin, and (I) claudin-1 in MODE-K cells detected by RT-qPCR. Values are the mean ± standard error (n = 6). p < 0.05 vs CON group; #p < 0.05 vs BPA group. Discussion BPA, a substance that pollutes the environment in daily life, has been shown to cause potential damage to numerous human tissues and organs (lungs, liver, kidneys, skin, and mucous membranes) in the human body.28−31 A recent study has shown that mice exposed to BPA suffer from a severe intestinal disease, characterized by damage to the intestinal epithelium and breakdown of the intestinal barrier.22 Numerous studies have shown that ICA has an excellent preventive effect against a variety of diseases, which is mainly attributed to its excellent antioxidant properties.32,33 Therefore, we tried to figure out the positive effects of ICA on intestinal barrier function and its potential mechanisms of BPA challenge in this study. The previous study has shown that BPA exposure increased intestinal epithelial histopathological score in mice and decreased the gene expression of tight junction proteins in the intestinal epithelium, thereby disrupting the intestinal barrier function.22 In addition, in vitro studies have shown that BPA-promoted apoptosis and inhibited proliferation of gut epithelial cells.34 The present data showed that BPA exposure significantly reduced the gene expression of tight junction proteins (ZO-1, occludin, and claudin-1) in the jejunum of mice and MODE-K cells. In a tight junction complex, ZO-1 is a peripheral membrane protein, which plays an important role in the distribution and maintenance of tight junctions.35 Occludin interacts directly with claudins and actin to promote the transfer of macromolecules through the cell bypass pathway.36 In addition, members of the claudins family also contribute to the functioning of the intestinal barrier.37,38 Therefore, ZO-1, occludin, and claudins play a crucial role in maintaining the intestinal barrier. Damage to the intestinal barrier caused by BPA increased the permeability of detrimental substances in the intestinal lumen.22 Our results showed a significant increase in plasma and jejunal endotoxin, DAO, d-lactic acid, and zonulin levels after BPA treatment. Generally speaking, for healthy individuals, indicators (endotoxin, DAO, d-lactic acid, and zonulin) are low in the circulatory system, which are significantly increased during the destruction of the intestinal wall.39 Besides, our data revealed that the BPA challenge decreased the transmembrane tolerance of MODE-K cells and increased the FITC-D4 flux. More importantly, treatment with ICA significantly reduced the damage to the intestinal barrier and permeability of mice and MODE-K cells induced by BPA. All these data suggested that BPA gives rise to increased intestinal permeability and dysfunction of the epithelial barrier in vivo and in vivo, and ICA can effectively alleviate these adverse effects. To further expound the mechanisms by which ICA protected the intestinal epithelial barrier, we investigated the redox state in vivo and in vitro. Oxidative stress is one of the many underlying mechanisms by which toxic substances cause cellular dysfunction in mammals.40 Reactive oxygen species (ROS) and reactive nitrogen (RNS) produced under physiological conditions are important factors in the maintenance of cell life activities, but the overproduction of ROS and RNS is harmful to the human body. The toxic effects of these molecules include DNA/RNA damage, amino acid oxidation, and lipid peroxidation, resulting in intracellular nucleic acid damage, mutations, and protein and lipid damage.41 Previous studies have revealed multiple potential mechanisms by which BPA affects the intestinal epithelial cells. Among these mechanisms, the accumulation of oxidative stress intermediates has received great attention. In addition, there is evidence that oxidative stress is strongly associated with intestinal barrier dysfunction, primarily due to its ability to destroy tight junction proteins.42,43 Our data clearly showed that BPA treatment induced oxidative stress and inhibited antioxidant capacity in the jejunum of mice and MODE-K cells. ICA co-treatment significantly attenuated the disordered of redox equilibrium induced by BPA. These findings indicated that ICA alleviated BPA-induced damage to the intestinal epithelial barrier primarily through its strong antioxidant capacity.44,45 To further reveal the molecular mechanism of ICA against BPA-induced damage to the intestinal epithelial barrier, we conducted a series of experiments around p38 MAPK. Previous studies have shown that p38 MAPK indirectly impaired the barrier function of intestinal epithelial cells by regulating their oxidative stress processes. The results of our trial showed that in vivo and in vitro BPA challenge significantly increased the level of gene expression and content of p38 MAPK, while ICA co-treatment effectively reversed these changes. Further, we performed subsequent experiments using a corresponding inhibitor (SB203580) and an activator (anisomycin) of p38 MAPK. Our results suggested that the corresponding blocker of p38 MAPK signaling can effectively mitigate cell death, oxidative stress, and damage to intestinal permeability induced by BPA. Besides, when we treated cells with a specific activator of p38 MAPK in conjunction with ICA, ICA lost its ability to combat BPA-induced cell death, oxidative stress, and damage to intestinal permeability. In addition, we found that the ICA-alone treatment of MODE-K cells inhibited the expression of p38 MAPK and decreased the production of ROS. These findings indicated that BPA leads to impairment of intestinal epithelial barrier function in a p38 MAPK-dependent manner, and ICA rescues the intestinal epithelial barrier function by inhibiting BPA-induced p38 MAPK expression. Conclusions Our results demonstrated that the disorder of redox equilibrium induced by p38 MAPK activation is an essential step of BPA-induced intestinal epithelial barrier and permeability disruption. What is more important is that our results emphasize that ICA has a protective effect on the intestinal epithelial barrier dysfunction induced by BPA through regulating p38 MAPK expression. These data suggested that p38 MAPK is a key target for the prevention and treatment of BPA-induced intestinal diseases, and ICA may be an effective natural product for the prevention of intestinal damage caused by BPA. Materials and Methods Reagents BPA was purchased from Sigma (lot no. 239658). ICA (lot no. 20171125, net content 90.00%) was purchased from Xi’an Grassroot Chemical Engineering Co. Ltd. (Xian, China). SB203580 (lot no. GC13595, purity = 98.00%) and anisomycin (lot no. SC0132, purity = 99.00%) were obtained from GlpBio Technology and Beyotime Biotechnology (China), respectively. Animal Maintenance and Experimental Designs For this study, 40 male C57BL/6 mice aged 3 weeks were selected. All mice are kept in an environment free of specific pathogens. The experimental environment guarantees constant temperature and humidity, light/dark cycle for 12 h. Four groups of mice (n = 10 each) were used in this study. Group 1 (CON group) was used as the control, and the mice were fed with a vehicle (filtered water). Group 2 (BPA group) were given BPA orally at 50 μg/(kg day). Group 3 (BPA + ICA group) received both ICA (20 mg/(kg day)) and BPA (50 μg/(kg day)). All treatments were given daily for 10 weeks. All mice were sacrificed after 10 weeks of treatments. To reduce sample variability, the intestinal segments were collected from the approximate middle position of the intestinal tract (jejunum). The jejunal epithelium was separated from the muscular layers by blunt dissection and stored at −80 °C prior to further analysis. Blood samples from mice were collected through the jugular vein and then collected into heparin anticoagulation tubes (5 mL). Plasma samples were then stored by centrifugation for 10 min (3000g, 4 °C) at −80 °C until further analysis. Cell Culture MODE-K cells are an intestinal epithelial cell line derived from C3H/HeJ mice, bought from Shanghai Cell Bank, Chinese Academy of Sciences (lot no. BFN608006456). MODE-K cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM) with 10% fetal bovine serum, 1% penicillin/streptomycin, 1% gentamycin, 1% 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) buffer, and 1% nonessential amino acids. The medium was changed every 2–3 days. The incubation conditions were 37 °C and a 5% CO2 atmosphere. Determination of Jejunal Permeability The endotoxin, diamine peroxidase (DAO), d-lactate, and zonulin content in plasma and jejunal samples were determined using commercial kits (Shanghai Enzyme-Linked Biotechnology Co. Ltd., Shanghai, China). The detailed steps of the test operation are given in the manufacturer’s instructions. Determination of Jejunal Oxidative Status Reactive oxygen species (ROS), reactive nitrogen species (RNS), malondialdehyde (MDA), and hydrogen peroxide (H2O2) contents in plasma and jejunal samples were determined using enzyme-linked immunosorbent assay (ELISA) kits (Shanghai Enzyme-Linked Biotechnology Co. Ltd., Shanghai, China). The detailed steps of the test operation are given in the manufacturer’s instructions. Determination of MODE-K Cells’ Oxidative Status Intracellular reactive oxygen species (ROS) in the MODE-K cells were measured using 2′,7′-dichlorofluorescein diacetate (DCFH-DA; Sigma) per a previously reported method.46 In brief, the cells were washed three times with phosphate-buffered saline (PBS) after removing the culture medium. A 10 μM DCFH-DA solution was added to the cells and incubated for 30 min at 37 °C. Next, the cells were washed three times with PBS. Then, the cells were resuspended in 1 mL of PBS and total intracellular fluorescence intensity was measured by flow cytometry (FACS Verse, BD Biosciences, San Jose, CA). The level of total intracellular ROS paralleled the increase in fluorescence intensity and was calculated as the percentage of control cells. Mitochondrial ROS in the MODE-K cells were measured using MitoSOX Red mitochondrial superoxide indicator (Invitrogen) as described previously.47 Briefly, the cells were washed three times with PBS after removing the culture medium. MitoSOX Red mitochondrial superoxide indicator, diluted to a final concentration of 4 mM in serum-free DMEM, was added to the cells and incubated for 30 min at 37 °C. The cells were washed three times with PBS. Then, the cells were resuspended in PBS, and fluorescence was measured immediately with a flow cytometer. The level of mitochondrial ROS paralleled the increase in fluorescence and was calculated as the percentage of control cells. Determination of Jejunal and MODE-K Cells’ Antioxidative Status The superoxide dismutase (SOD) activity, glutathione peroxidase (GPx) activity, catalase (CAT) activity, and total antioxidant capacity (T-AOC) in plasma, jejunal, and MODE-K cell samples were determined using ELISA kits (Shanghai Enzyme-linked Biotechnology Co. Ltd., Shanghai, China). The detailed steps of the test operation are given in the manufacturer’s instructions. Determination of Jejunal and MODE-K Cells’ p38 MAPK Content The p38 MAPK contents in jejunal and MODE-K cell samples were determined using ELISA kits (Shanghai Enzyme-Linked Biotechnology Co. Ltd., Shanghai, China). The detailed steps of the test operation are given in the manufacturer’s instructions. Cell Viability Assay Cell viability was tested as described previously.46 Briefly, the cells were cultured for 24 h in 96-well plates. After treatment, 10 μL of the CCK-8 assay solution was added to each well and incubated for another 1 h. Then, the optical densities were read on a microplate reader (Molecular Devices, Sunnyvale, CA) at 450 nm. Lactate dehydrogenase (LDH) measurements were also used to assess cell viability.48 The cells were cultured for 24 h in 96-well plates. After treatment, the LDH content was determined using an assay kit. Then, the optical densities were read on a microplate reader (Molecular Devices, Sunnyvale, CA) at 450 nm. Cell viability is presented relative to the control group. MODE-K Cell Monolayer’s Barrier Function The MODE-K cell monolayer was constructed by seeding 0.2 × 106 cells into each well of a 24-well Transwell plate (Corning, Inc., Corning, NY). The insertion area was 0.33 cm2, and the pore size was 0.4 μm. The culture medium was changed every other day. Cells reached confluence on day 2, and the treatments were performed on day 7. The transepithelial electrical resistance (TEER) value of the MODE-K monolayers reached approximately 150 Ω·cm2 at 7 days after confluence. Fluorescein isothiocyanate-dextran (FITC-D4, 4 kDa, 0.25 mM) measurements were taken for paracellular permeability.49 FITC-D4 was added to the apical chamber at the end of the treatment. After 2 h, 50 μL of the medium from the bottom chamber was transferred to a fluorescence measurement plate, and the fluorescence intensity was measured at an excitation wavelength of 485 nm and an emission wavelength of 530 nm. TEER and FITC-D4 flux values are both expressed as percentages of the control cells. RNA Isolation, cDNA Synthesis, and Real-Time Quantitative PCR Total RNA was extracted from each jejunal tissue or cell sample using TRIzol reagent. The RNA concentration and quality in the extracted colonic samples were measured using a NanoDrop ND-1000 spectrophotometer (Thermo). Next, 2 μg of total RNA was treated with RNase-Free DNase and reverse transcribed per the manufacturer’s instructions. Diluted cDNA (2 μL; 1:20, v/v) was used for real-time PCR, which was performed using an Mx3000P real-time PCR system (Stratagene). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), which was unaffected by the experimental factors, was chosen as the housekeeping gene. All primers used in this study are listed in Table 1 and were synthesized by Generay Company (Shanghai, China). The 2–ΔΔCt method was used to analyze the real-time PCR results, and gene mRNA levels are expressed as the fold change relative to the mean value of the control group. Table 1 Primer Sequences Used in This Study target genes prime forward/reverse primer sequence (5′ → 3′) GAPDH forward TGCACCACCAACTGCTTAGC reverse GGCATGGACTGTGGTCATGAG ZO-1 forward GCTCCTGCTATCCACCTA reverse CCTGAATCGGGCTCTCATAC occludin forward GCACTTGTTAAGGCAGCAG reverse ACGGTAAGCATTGGCGCA claudin-1 forward GTGAACCGTGGACGGAAA reverse CTCCGCTGATTCACAGATTTC caspase-3 forward TGGAATTGATGCGTGATGTT reverse GGCAGGCCTGAATAATGAAA Statistical Analysis All data are presented as mean ± standard error of mean (SEM). Statistical significance was calculated by independent-sample t-test using SPSS (SPSS v. 20.0, SPSS Inc., Chicago, IL) software and accepted for p < 0.05. The numbers of the replicates are noted in the figures. Author Contributions K.Z. performed the experiment and drafted the manuscript. Y.Z. analyzed the data. Y.Y. and Y.B. contributed to the experimental design and manuscript revision. T.Z. conceived the idea, designed the experiment, and finalized the manuscript. All authors read and approved the final manuscript. The authors declare no competing financial interest. Acknowledgments This work was supported by the Science and Technology Communication Innovation Project of the National Medicine Economic Information Network (cmei2017kp00248). ==== Refs References Mikołajewska K. ; Stragierowicz J. ; Gromadzińska J. Bisphenol A - Application, sources of exposure and potential risks in infants, children and pregnant women . Int. J. Occup. Med. Environ. Health 2015 , 28 , 209 –241 . 10.13075/ijomeh.1896.00343 .26182919 Huang R. P. ; Liu Z. H. ; Yuan S. F. ; Yin H. ; Dang Z. ; Wu P. X. Worldwide human daily intakes of bisphenol A (BPA) estimated from global urinary concentration data (2000–2016) and its risk analysis . Environ. Pollut. 2017 , 230 , 143 –152 . 10.1016/j.envpol.2017.06.026 .28649042 Di Bella G. ; Ben Mansour H. ; Ben Tekaya A. ; Beltifa A. ; Potorti A. G. ; Saiya E. ; Bartolomeo G. ; Dugo G. ; Lo Turco V. 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==== Front Eur J Clin Microbiol Infect Dis Eur J Clin Microbiol Infect Dis European Journal of Clinical Microbiology & Infectious Diseases 0934-9723 1435-4373 Springer Berlin Heidelberg Berlin/Heidelberg 32856202 4016 10.1007/s10096-020-04016-1 Brief Report A novel approach to managing COVID-19 patients; results of lopinavir plus doxycycline cohort http://orcid.org/0000-0002-9983-0308 Cag Yasemin [email protected] 12 Icten Sacit 3 Isik-Goren Burcu 1 Baysal Naciye Betul 1 Bektas Begum 1 Selvi Ece 1 Ergen Pinar 4 Aydin Ozlem 4 Ucisik Ayse Canan 4 Yilmaz-Karadag Fatma 4 Caskurlu Hulya 1 Akarsu-Ayazoglu Tulin 5 Kocoglu Hasan 6 Uzman Sinan 5 Nural-Pamukcu Muge 5 Arslan Ferhat 1 Bas Gurhan 7 Kalcioglu Mahmut Tayyar 8 Vahaboglu Haluk 1 1 grid.411776.2 0000 0004 0454 921X Department of Infectious Diseases and Clinical Microbiology, Istanbul Medeniyet University Faculty of Medicine, Istanbul, Turkey 2 grid.411776.2 0000 0004 0454 921X Istanbul Medeniyet Universitesi Goztepe Egitim ve Araştırma Hastanesi, Enfeksiyon Hastaliklari Klinigi, Dr. Erkin Caddesi, 34722, Kadikoy, Istanbul, Turkey 3 grid.413298.5 0000 0004 0642 5958 Department of Pulmonary Medicine, Istanbul Medeniyet University Göztepe Training and Research Hospital, Istanbul, Turkey 4 grid.413298.5 0000 0004 0642 5958 Department of Infectious Diseases and Clinical Microbiology, İstanbul Medeniyet University Göztepe Training and Research Hospital, İstanbul, Turkey 5 grid.413298.5 0000 0004 0642 5958 Department of Anesthesiology and Reanimation, İstanbul Medeniyet University Göztepe Training and Research Hospital, Istanbul, Turkey 6 grid.411776.2 0000 0004 0454 921X Department of Anesthesiology and Reanimation, Istanbul Medeniyet University Faculty of Medicine, Istanbul, Turkey 7 grid.411776.2 0000 0004 0454 921X Department of General Surgery, Istanbul Medeniyet University Faculty of Medicine, Istanbul, Turkey 8 grid.411776.2 0000 0004 0454 921X Department of Otorhinolaryngology, Istanbul Medeniyet University Faculty of Medicine, Istanbul, Turkey 27 8 2020 2021 40 2 407411 19 5 2020 24 8 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. This manuscript aims to present a treatment algorithm we applied to manage COVID-19 patients admitted to our hospital. During the study period, 2043 patients with suspected COVID-19 were admitted to the emergency department. Molecular tests indicated that 475 of these patients tested positive for COVID-19. We administered hydroxychloroquine plus doxycycline to mild cases (isolated at home) for 3 days and lopinavir plus doxycycline to moderate and severe cases (hospitalized) for 5 days. The overall case fatality rate was 4.2% (20/475). Keywords Doxycycline Favipiravir Lopinavir Hydroxychloroquine COVID-19 issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2021 ==== Body pmcIntroduction Since the first report in December 2019 from Wuhan, Hubei Province, China, the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread quickly worldwide [1]. Available data indicate that the clinical course and outcome of SARS-CoV-2 are much milder than those of SARS-CoV and MERS-CoV [2]. However, the socioeconomic consequences of the SARS-CoV-2 pandemic are enormous [3]. The false news regarding the clinical course and fatality rates triggered a global panic epidemic which has spread even faster than the virus. Social panic has the potential to accelerate the expected health burden of the disease [4]. Social panic causes excess inpatient capacity in hospitals as the number of individuals with mild nonspecific symptoms has been increasingly hospitalized. Controlling adverse outcomes of the disease and the panic among the public and healthcare staff depends on running an effective triage and management algorithm. This manuscript aims to present a treatment algorithm we applied to manage COVID-19 patients admitted to our hospital and describe the characteristics of COVID-19 patients and the outcomes of the algorithm. This single-center, retrospective observational study was conducted in the Istanbul Medeniyet University Goztepe EA Hospital, a 600-bed affiliated hospital located in the Anatolian side of Istanbul. We obtained ethical approval from the Institute Ethics Committee, and signed informed consent was waived (2020/0193). A case was defined as a patient with an epidemiologic risk factor who had body temperature of ≥ 38 °C and/or respiratory system symptoms which cannot be fully explained by any other condition or disease (based on WHO approach). A mild case was defined to have no signs of respiratory dysfunctions, while a moderate case had any sign of respiratory dysfunction, and a severe case had acute respiratory failure (ARF) and required ICU support either via invasive or noninvasive means. Noninvasive ventilation support was administered with high-flow masks. Respiratory dysfunction was assessed in a patient having any of the following: (a) shortness of breath, (b) respiration rate of > 23 breaths per minute, and (c) O2 saturation < 94 in ambient air. Hydroxychloroquine 200 mg, lopinavir 400 mg, and doxycycline 100 mg were all orally administered twice daily as recommended. We managed COVID-19 patients with a 3-step treatment approach in our institute. First, mild cases were isolated at home and prescribed with hydroxychloroquine plus doxycycline for 3 days. Second, moderate to severe cases were hospitalized and prescribed with a regimen of lopinavir plus doxycycline plus ceftriaxone for 5 days. Third, we used a salvage therapy for patients who did not respond to or whose conditions worsened under the lopinavir treatment. This therapy involved the oral administration of favipiravir 600 mg twice daily after two loading doses. We performed all statistical analysis using the open-source R software (R Foundation for Statistical Computing, Vienna, Austria) [5–7]. Results and discussion From March 22 to April 22, 2020, 2043 patients were admitted to our emergency department, presenting symptoms compatible with those stated in our case definition. PCR was positive for nasopharyngeal samples of 475 adult patients. We run a 3-step treatment algorithm, and our approach is displayed in Fig. 1. We hospitalized moderate to severe cases and administered lopinavir combined with doxycycline and ceftriaxone to 343 patients, among whom 161 had positive PCR test results (161/343, 46.9%). Unfortunately our lab ceased respiratory viral PCR panels and allocated all resources to SARS-CoV-2 PCR test during the study time. Therefore, we could not identify other causes and diagnosed COVID-19 PCR negative patients as viral respiratory tract infection of unknown etiology.Fig. 1 The algorithm we applied in emergency department to manage outpatients with COVID-19 We followed 1700 mild cases under the treatment with hydroxychloroquine plus doxycycline at home. Besides, 314 patients isolated at home were found to have positive PCR test results (314/1700, 18.5%). PCR results were mostly available within 48 h, and patients with positive PCR test results were further followed by filiation teams of the Turkish Health Ministry. Filiation teams provided them with a 5-day course of hydroxychloroquine. Twenty-three of all patients treated at home were readmitted to the hospital because their initial symptoms worsened, and we administered lopinavir plus doxycycline. If these patients do not respond in 48 h, we instituted favipiravir treatment. The overall case fatality rate was 4.2% (20/475). Two out of 268 patients aged < 50 years died (0.7%), one of whom was under treatment for acute lymphoblastic leukemia. The other patient had an unidentified muscle disease affecting the respiratory muscles [8]. There were three deaths among those aged 50–65 years (3/127, 2%) and 15 deaths among those aged > 65 years (15/80, 18.8%). Figure 2 presents the daily incidence of PCR positive and negative patients. The burden of social panic is barely visible in this figure. Most patients had subjective symptoms yet inquired attention; therefore, time, effort, and care should be taken to relieve these patients. In other words, if not correctly managed, this panic had the potential to consume hospital resources reserved for severe patients.Fig. 2 The epidemic curve showing PCR negative and positive patients. It is noteworthy that the number of PCR positive patients decreases over time, but due to the ongoing fear in the population, the number of PCR negative patients do not decrease proportionally Table 1 presents the baseline characteristics of PCR positive hospitalized patients. We administered our standard regimen, lopinavir plus doxycycline plus ceftriaxone, to these hospitalized patients. Among 161 cases, 31 required ICU support, and 20 deceased during ICU stay. However, 12 of these patients were severe at admittance. Of these nine patients immediately admitted to the ICU, five of whom died. Three other patients transferred to the ICU on the second day of admittance to the hospital also died.Table 1 Baseline descriptive parameters of PCR positive hospitalized patients All Survived Died p N Factors1 N = 161 N = 141 N = 20 Female gender 77 (47.8%) 67 (47.5%) 10 (50.0%) 1.000 161 Age (years) 61.0 [48.0;72.0] 59.0 [48.0;70.0] 74.5 [67.5;85.5] < 0.001 161 Hypertension 57 (35.4%) 43 (30.5%) 14 (70.0%) 0.001 161 Diabetes 32 (20.6%) 26 (19.1%) 6 (31.6%) 0.230 155 ACEI: yes 32 (20.6%) 26 (19.1%) 6 (31.6%) 0.230 155 Elapsed time to ICU (days) 3.00 [0.00; 6.00] 3.00 [0.00; 6.00] 3.00 [0.50; 5.75] 0.984 31 Hospital stay (days) 2.00 [1.00; 5.00] 3.00 [1.00; 5.00] 1.50 [1.00; 9.25] 0.966 161 WBC (× 109/L) 6.15 [4.80; 7.80] 6.10 [4.70; 7.65] 7.05 [5.40; 11.2] 0.080 158 PLT (× 109/L) 180 [138; 234] 181 [145; 236] 146 [112; 208] 0.104 158 EOS (× 109/L) 0.01 [0.00; 0.03] 0.01 [0.00; 0.03] 0.01 [0.00; 0.03] 0.285 158 NEUT (× 109/L) 4.44 [3.05; 5.67] 4.26 [2.89; 5.50] 5.28 [4.43; 8.14] 0.007 158 LYM (× 109/L) 1.20 [0.90; 1.60] 1.20 [1.00; 1.69] 0.90 [0.70; 1.30] 0.029 158 pH2 7.42 [7.38; 7.45] 7.41 [7.38; 7.44] 7.44 [7.41; 7.47] 0.061 138 pO2 35.8 [25.8; 48.4] 34.9 [25.4; 47.4] 38.2 [31.9; 56.2] 0.212 142 pCO2 44.8 [40.0; 48.6] 44.8 [40.2; 48.7] 43.0 [35.3; 47.8] 0.273 143 Temperature 37.0 [36.6; 37.9] 37.0 [36.6; 37.7] 37.5 [36.9; 38.3] 0.085 158 High fever (≥ 38 °C) 38 (24.1%) 30 (21.4%) 8 (44.4%) 0.042 158 O2 saturation 95.0 [92.0; 96.0] 95.0 [93.0; 96.0] 88.0 [85.5; 94.0] < 0.001 159 Respiration rate per min 21.0 [20.0; 25.0] 21.0 [20.0; 24.2] 22.0 [20.0; 26.0] 0.407 149 Elapsed time to hospitalization 5.00 [3.00; 7.00] 6.00 [3.00; 8.00] 4.00 [2.00;5.50] 0.015 152 Intubated 27 (16.8%) 10 (7.09%) 17 (85.0%) < 0.001 161 1ACE inhibitor, angiotensin converting enzyme inhibitor; elapsed time to ICU, time between hospitalization and ICU admission; O2 saturation, saturation in ambient air; high fever, fever ≥ 38 °C; elapsed time to hosp., time between onset of symptoms and hospitalization 2Blood gasses were mostly obtained during hospital stay not at admission Of the 161 hospitalized patients, 149 acquired lopinavir for at least 2 days before being admitted to the ICU. Only 12.7% (19/149) required ICU support with lopinavir treatment, two patients suddenly died, and 128 patients recovered from the disease. Only 24% (38/158) of patients had a fever (≥ 38 °C). Deceased patients were older, had a higher prevalence of hypertension, and had a higher neutrophil counts than the others, while their lymphocyte counts, platelet counts, and levels of oxygen saturation in ambient air were lower. No difference was observed between two genders. Deceased patients had shorter elapsed time between the onset of symptoms and hospitalization. This study presents a 3-step treatment protocol to manage COVID-19 patients. We administered hydroxychloroquine to mild cases isolated at home, lopinavir plus doxycycline to hospitalized moderate to severe cases, and favipiravir in the salvage treatment. We were able to run this approach smoothly. To our best knowledge, this study is the very first to report data from Istanbul, Turkey. More importantly, our data present the results of a unique combination of lopinavir and doxycycline. We, administered hydroxychloroquine to mild cases for only 3 days because of its potential side effects on cardiac functions [9]. The cardiac effects of hydroxychloroquine are demonstrated to depend on the accumulation of the drug and mostly start on the third day of the usage. These effects are more prominent among critically ill patients [10]. We administered lopinavir to moderate to severe cases for 5 days. Clinical trials demonstrated its effectiveness in the treatment of patients with SARS and MERS [11]. Molecular analysis indicates that lopinavir has a potential role in inhibiting SARS-CoV-2 protease, thereby blocking viral replication [12]. A recent study found a limited benefit of lopinavir compared with the standard of care treatment [13]. However, this study had substantial methodologic limitations, which raises questions about its conclusions. We supplemented doxycycline to both lopinavir and hydroxychloroquine due to its immunomodulatory activity. Recent findings revealed the adverse effect of dysregulated immunity on the outcome of COVID-19 patients [14]. Doxycycline induces the suppressor of cytokine signaling (SOCS) proteins, a regulatory system on cytokine release [15]. Evidence accumulates that SOCS proteins, mainly SOCS-3 protein, prevent interleukin- and interferon-associated toxicity [16]. Notably, in the early stage of the disease, when there are enough healthy cells in the bronchi and alveoli, doxycycline might have some effect on preventing the upcoming cytokine storm. Doxycycline had been successfully used in dengue hemorrhagic fever due to its immunomodulatory activity [17]. However, we also consider covering other etiologies of community-acquired pneumonia. At admission, it is challenging to differentiate COVID-19 from other etiologies of pneumoniae, such as mycoplasma infections [18]. However, the study has several limitations which require to be addressed. The major limitation of this study lies in its retrospective and single-center nature which is a source of selection bias to evaluate the efficacy of a treatment. In our study, a considerable number of died patients were extremely severe at admittance and so directly allocated to ICU care. A 3-step treatment algorithm ran smoothly in our hospital. We concluded that home isolation of mild cases is an effective means to manage the burden of disease, while lopinavir plus doxycycline is an alternative to current treatment regimens for COVID-19. However, in future epidemics, isolation of mild cases at new-settled fever clinics should be considered which might serve better to mitigate epidemics [19]. Compliance with ethical standards Conflicts of interest The authors declare that they have no conflicts of interest. Ethics approval The study protocol was approved by the Clinical Research Ethics Committee of Istanbul Medeniyet University Goztepe Training and Research Hospital, and signed informed consent was waived (2020/0193). Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Zhu N, Zhang D, Wang W, Li XX, Yang B, Song J, et al. A Novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med [Internet] 2020 20 [cited 2020 12];382:NEJMoa2001017. 10.1056/NEJMoa2001017 2. Wu Z, McGoogan JM (2020) Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. JAMA 323. 10.1001/jama.2020.2648 3. Ioannidis JPA (2020) Coronavirus disease 2019: the harms of exaggerated information and non-evidence-based measures. 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Fredeking T Zavala-Castro J Gonzalez-Martinez P Moguel-Rodríguez W Sanchez E Foster M Dengue patients treated with doxycycline showed lower mortality associated to a reduction in IL-6 and TNF levels Recent Pat Antiinfect Drug Discov 2015 10 51 58 10.2174/1574891x10666150410153839 25858261 18. Dai W Zhang H Yu J Xu H Chen H Luo S CT imaging and differential diagnosis of COVID-19 Can Assoc Radiol J 2020 71 084653712091303 10.1177/0846537120913033 19. Tang L, He Y, Bai F, Luo B. The role of fever clinic during the COVID-19 pandemic: a case study of 1034 febrile patients. 2020 [cited 2020 12];https://www.researchsquare.com/article/rs-28368/latest.pdf. Accessed 13 Aug 2020	32856202	PMC7452614	NO-CC CODE	2022-06-27 23:18:01	yes	Eur J Clin Microbiol Infect Dis. 2021 Aug 27; 40(2):407-411
==== Front J Chin Polit Sci J Chin Polit Sci Journal of Chinese Political Science 1080-6954 1874-6357 Springer Netherlands Dordrecht 32921969 9692 10.1007/s11366-020-09692-6 Research Article A Discourse Analysis of Quotidian Expressions of Nationalism during the COVID-19 Pandemic in Chinese Cyberspace http://orcid.org/0000-0001-8009-6476 Zhao Xiaoyu [email protected] Zhao Xiaoyu is a Ph.D candidate in the Department of Political Science at the National University of Singapore. His research focuses on Chinese nationalism, territorial disputes, Chinese politics, and qualitative data analysis. grid.4280.e 0000 0001 2180 6431 Department of Political Science, National University of Singapore, Singapore, Singapore 8 9 2020 2021 26 2 277293 31 8 2020 © Journal of Chinese Political Science/Association of Chinese Political Studies 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. By conducting discourse analysis on quotidian expressions of nationalism of Chinese netizens and analyzing their “Liking” behavior, this article tries to inductively explore during the COVID-19 pandemic what and how Chinese netizens say about nationalism. This article finds that during the pandemic, Chinese netizens show a confident and rational but confrontational and xenophobic posture in their quotidian discourses. They value reasoning and deliberation in their expressions of nationalist discourses. In the quotidian discourses, they maintain a confident tone when comparing China’s performance with other countries during the pandemic, but show vigilance and even hostile sentiments toward external provocations. Keywords Chinese nationalism Discourse analysis The COVID-19 pandemic Quotidian discourse Cyber nationalism “Liking” behavior issue-copyright-statement© Journal of Chinese Political Science/Association of Chinese Political Studies 2021 ==== Body pmcIntroduction The rise of Chinese nationalism has been a subject of growing academic and policy interest since the 1990s. After successfully controlling the domestic spread of the COVID-19 pandemic within months, China’s rising nationalism has once again become a hot topic of discussion on a global scale. During the pandemic, some media outlets have insisted that as its success in containing and controlling the pandemic, one dramatic emerging feverish of patriotism, nationalism, and xenophobia at home and China becomes more aggressive than seen in decades [43]. In the meantime, a discussion of the pandemic in Chinese cyberspace has also been heated, especially given the different performances between other countries and China in fighting against the pandemic. According to Sinkkonen, nationalism stresses the superiority of a nation when comparing the nation’s qualities with those of other nations [37]. Such a nationwide discussion that highlights the comparison between others and China provides an opportunity to explore the nature of Chinese nationalism and nationalists and how Chinese netizens view China’s and other countries’ performances during the pandemic. The debate about the nature of Chinese nationalism has not come to a close. Some scholars have engaged in the debate from the theoretical perspective, such as labeling Chinese nationalism; while others have approached it from the empirical perspective, such as studying high-profile nationalist movements or conducting large-scale surveys. However, I argue that the existing research can only capture a segment of Chinese nationalism due to the predeterminative research purposes and a lack of systemic empirical research in the field. In this regard, I provide an inductive approach to study the nature of Chinese nationalism during the COVID-19 pandemic. Johnston has said, a good approach to understanding Chinese nationalism is to focus on the expression of ordinary citizens [19]. Learning from Johnston’s approach and considering the high Internet penetration rate in China in 2019 (61.2%) [9], I examine the quotidian expressions of Chinese nationalism in the form of online discourse. This shift of analytic focus enables an inductive insight into Chinese nationalism and nationalists, contributes to the ongoing discussion of what and how people say about nationalism in Chinese cyberspace, and further broadens the understanding of Chinese nationalism. Using NVivo to examine the online discourse (based on 13,218 Zhihu posts) and the “Liking” behavior since the outbreak of the COVID-19 pandemic, I find that during the pandemic, the online discourse is confident and rational but confrontational and xenophobic. The finding suggests that during the pandemic, Chinese netizens on the one hand value deliberation when expressing their comments on the performances of China and other countries. On the other hand, due to the US-led external provocation to China and a sense of pride and confidence generated from China’s significant achievements during the anti-pandemic period and the past decades, Chinese netizens show a confident but confrontational and xenophobic posture in the discourse. However, there is an uncertain whether the confrontational and xenophobic posture is a temporary phenomenon as a result of the pandemic or a long-standing phenomenon, and whether the posture will last for the future. To assess these findings, the following sections review existing approaches toward Chinese nationalism; explain the research design; present and discuss the results of discourse analysis; and conclude the discussions. Literature Review Existing literature in Chinese nationalism mainly focuses on four questions: (1) what is Chinese nationalism; (2) is Chinese nationalism on the rise; (3) how does Beijing respond to nationalism; and (4) what are its foreign policy implications [10]. Disagreements over these questions exist so far. This is a result of employing different research approaches. In general, there are three main approaches to analyzing and understanding Chinese nationalism. The first approach labels Chinese nationalism in the absence of systematic empirical research. Scholars have categorized Chinese nationalism based on its various characteristics that have emerged in modern Chinese society and foreign policy. Labels of Chinese nationalism include – but not limited to – “defensive”, “reactive”, “confident”, “positive”, “Confucian”, “pragmatic”, “competing”, “militant” [12, 18, 26, 32, 33, 48, 49]. These labels can demonstrate some features of nationalism in modern China, although they often stand as direct antinomies in several cases. Besides, most labels are a product of a specific period. For example, positive nationalism was proposed as a result of a series of events during 1989–1991 [49]; the Beijing Olympics raised the basic assumptions of confident and competing nationalism [33]. Therefore, these labels can only show one or a few dimensions of Chinese nationalism of a specific period and unable to cover the complexity of Chinese nationalism. As a result, the characteristics of Chinese nationalism have not been dealt with in depth. The second approach focuses on high-profile nationalist movements and treats these movements as evidence to prove and reinforce the impression of the violent and threatening nature of Chinese rising nationalism. The US bombing of the Chinese embassy in Belgrade in 1999 has raised a groundswell of protest. Since then, high-profile nationalist movements, like the 2001 mid-air collision of a US spy plane and a Chinese jet fighter, anti-Japanese demonstrations in 2005 and 2012, a series of nationalist movements in 2008, and the boycott of South Korean products in 2017, have been cited by scholars as compelling evidence of the rise of aggressive Chinese nationalism [35, 36, 6, 13, 21, 28, 40, 48, 49]. These studies have paid more attention to the activities of a minority of Chinese citizens and their antagonistic attitudes toward other countries. Similar to the first approach, this approach that focuses on the “moments of madness” thus can only reflect the extreme manifestation of the minority of Chinese citizens who participated in nationalist demonstrations. Some other scholars have also cast doubt on the representativeness of literature on Chinese nationalism [19]. Due to the selection bias, Chinese nationalists are often seen as violent and anti-Western zealots. The idea that nationalism is rising and alarming in China has thus taken root in the discourse about Chinese politics. The third approach relies on large-scale surveys to measure nationalism in China. Conclusions drawn from this approach are based on what respondents reported about their opinions of domestic and international affairs. Such an empirical approach can help us understand popular nationalism with more details and straightforward. However, three inadequacies restrict the explanatory power of the approach. First, the small sample size. Scholars normally have surveyed a minority of the public, such as the students of China’s top universities [37, 38, 44]. The sample size of these surveys is around several hundred. However, a relatively small sample size can affect the reliability of a survey’s results. Also, the composition of students in different universities in China is quite different [27]. Thus the representativeness of these selected university students is questionable, especially given the neglect of reputed universities in southwest, northwest, and southern China. Second, excessive reliance on questionnaires. Questionnaires can provide precise comparability between different respondents. However, a fixed set of questions in questionnaires might produce a kind of mass-produced superficiality [14, 17]. Also, as for face-to-face surveys, it is hard to eliminate the influence of the Hawthorne effect. The information from the “frontstage” may not be able to reflect respondents’ “real” thoughts [11]. Third, a similar set of questions. By reviewing the questions in the existing surveys, it is easy to find a high similarity between the questions of these surveys. In other words, scholars have utilized similar questions to extrapolate the nature and degree of nationalism in China [8, 19, 37, 38]. But as aforementioned, nationalism is the product of the times and can change with the development of China and the interaction between China and the world. Therefore, whether an unchanging set of questions can capture the new changing of Chinese nationalism is doubtable. These aforementioned approaches rely more or less on preconceived knowledge of Chinese nationalism. Therefore, inductive research will be a beneficial complement to the current knowledge of Chinese nationalism. Nationalism is a social construction, thus its meaning should be empirically investigated on a social scale [20]. Hughes argued that nationalism is not the expression of a common concept or movement but a discursive theme [17]. To understand the discursive theme more comprehensively, I follow the post-structuralist approach to deconstructing Chinese nationalism and recovering meanings of nationalism from discourses by interpreting a wide variety of discursive materials during the pandemic. Everyday discourse is a necessary component of constructivist account of nationalism [15, 16]. As Shapiro argued, there is no “true meaning” beyond the discourse to which one can refer [34]. After all, nationalist movements did not happen in a vacuum, or without the quotidian nationalist discourse setting the scene. Discourse analysis is the “qualitative and interpretive recovery of meaning from the language used to describe and understand social phenomena” [1]. Studying the everyday expressions of nationalism during the pandemic can help us understand Chinese nationalism of the period more fully. In recent years, more scholars have turned their attention to the discourse of Chinese nationalism, although the relevant discourse analysis of Chinese nationalism is still infrequent. An earlier study is a Hughes’ article in 2005. Hughes focused on Chinese texts and advanced the development of the non-event-specific analysis of Chinese nationalism [17]. Since then, more scholars have started to use discourse as the research object of Chinese nationalism. Callahan focused on the most influential and popular texts and compared discourses of the “China Dream” with the “American Dream” [3, 4]. Callahan thought of Chinese nationalism from a “propaganda” perspective. He thus paid more attention to how the official and popular texts broadcast the “China Dream”. As a result, it is hard to know about the response of Chinese masses. Callahan’s research represents a stream of scholars who emphasized the Chinese leaders’ discourses and ignored the voice of the masses [2, 5, 7, 23, 42]. Moreover, some scholars have moved toward the broader online public sphere. For instance, Zhang et al., conducted a content analysis of over 6000 tweets of 146 Chinese opinion leaders on Weibo to explore attitudes among Chinese nationalists [47]. However, two points of the research worth discussing. First, opinion leaders are Internet celebrities. They may not constitute a representative sample of Weibo users. In other words, they represent the voice of elites rather than masses to some extent [15]. Second, Weibo has been “tainted” by the “50-Cent Party” (五毛党). Of the 50-Cent Party posts on commercial sites, 53.98% were on Weibo [22]. Therefore, analyzing tweets on Weibo might not reflect “unstained” opinions. Research Design Discursive Materials Selection This article is to explore what and how people say about nationalism. To achieve these goals, I need to select discursive materials for discourse analysis. Inspired by Johnston’s approach in measuring the intensity or degree of nationalism, which focuses on the expression of ordinary citizens [17], I focus on discursive materials of Chinese netizens since the outbreak of the pandemic. The discursive materials were selected from Zhihu, which is a Q&A platform in China. Zhihu literally means “do you know” in classical Chinese. Similar to Quora, users of Zhihu can create, answer, and upvote questions and answers of others. Different from the restriction imposed by a fixed set of questions and options in questionnaires or the 140 character limit of Weibo, Zhihu users can express their opinions with an open mind. In this regard, new ideas on nationalism might emerge in their discourses. As of January 2019, the number of Zhihu users hit 220 million [50]. According to the 41st report of China Internet Network Information Center, the usage rates of Zhihu was 14.6% and ranked fourth in all social apps in China until December 20171 [9]. Therefore, posts on Zhihu are a representative sample of the netizen discourse. Scholars have treated Zhihu as an important platform that can reflect the expressions of China’s grassroots [24]. Nevertheless, limited generalizability of the selection would be inevitable. Besides, another two reasons are behind the selection of Zhihu. First, from the perspective of demographics, Zhihu users represent the development tendency of Chinese masses. Zhihu users normally are educated and young and live in urban areas. As levels of urbanization and education continue to increase in China, it is likely that more Chinese young people will become educated, urbanites, and netizens in the future. Therefore, focusing on this group is focusing on the future trend of Chinese nationalism to some extent. Also, educated young people have always been a promising group for the Chinese Communist Party recruitment since the late 1990s. Such a group is more willing to express their opinions and “educate” others, especially compared with the less-educated masses [24]. Thus the Chinese government is sensitive to their opinions [39]. Second, different from Weibo, Zhihu has not been identified as being “tainted” by the 50-Cent Party [22]. Therefore, we should be able to capture a spontaneous and comprehensive nationalist expression by analyzing posts on Zhihu. Sampling Strategy I selected the discursive materials between January 20 and May 25, 2020. During the sampling process, I took diversity and quantity into account [16]. Specifically, I first used “coronavirus” (新冠) or “pandemic” (疫情), the most common names of the COVID-19 pandemic in Chinese, as the search terms to look for relevant Zhihu questions. I obtained 252 questions, excluding questions like “Will there be a ‘divorce boom’ after the pandemic ends?” that are irrelevant for my research purposes. Second, following the requirements of diversity and quantity, I selected four questions from these 252 questions based on their theme/diversity and popularity/quantity. As for the diversity, I classified these 252 questions into four categories based on themes: (1) discussing China’s performance; (2) comparing China and others; (3) discussing external provocation; and (4) looking into the future of China and the world. These four themes cover the main aspects of Chinese quotidian expressions of nationalism during the pandemic. As for the quantity, I selected the most popular questions from these four themes, respectively. Quantity means the number of posts, followers, and views of a question. The higher the number is, the question is more popular. Finally, I selected the following questions that correspond to aforesaid themes.How do you view China’s intensive assistance to other countries during the COVID-19 pandemic? (如何看待中国在新冠疫情中密集出手援助多国?). This question contained 2219 posts, the number of followers and views were 10,821 and 23,350,984, respectively [30]. Why did not foreign countries fully learn from China’s experience in the prevention and treatment of COVID-19? (为什么国外不充分借鉴中国新冠肺炎防治的经验?) This question contained 3856 posts, the number of followers and views were 11,452 and 17,946,288, respectively [45]. How do you view various countries’ claims against China for COVID-19? (如何看待各国对本次新冠肺炎疫情向中国索赔?) This question contained 2770 posts, the number of followers and views were 9029 and 8,308,160, respectively [29]. Will the COVID-19 pandemic become a turnaround for China’s international reputation and public opinion? (新冠肺炎疫情会不会成为中国国际口碑和舆论的翻身仗?) This question contained 4373 posts, the number of followers and views were 10,899 and 11,241,653, respectively [46]. Coding the Discursive Materials One key step in the research is to read all selected discursive materials and build a coding scheme. Codes in this study were generated both deductively (using existing conceptual attributes) and inductively (finding new themes from discursive materials). Following certain coding rules,2 I coded all valid posts by using NVivo and obtained 6783 references under 118 nodes. These nodes have been categorized into five groups: “Perception of China” (22 nodes and 1754 references), “Perception of others” (24 nodes and 2355 references), “Aspirations of Chinese nationalism” (13 nodes and 730 references), “Roots of Chinese nationalism” (5 nodes and 349 references), and “Significant others” (54 nodes and 1595 references).3 Table 1 is the coding scheme. To ensure the reliability of the coding scheme, my colleague randomly selected and coded 160 Zhihu posts. The weighted value of kappa of the intercoder reliability test is 0.77, which is over the accepted standard 0.75 [25].Table 1 Overview of coding scheme Perception of China Perception of others 1. Big-hearted 1. Adversarial 2. Civilized 2. Arrogant 3. Confident 3. Bandwagoning 4. Dauntless 4. Benchmark and competitor 5. Doormat 5. Biased 6. Fair-minded 6. Brainwashed 7. Grateful 7. Deplorable 8. Long-sighted 8. Friends 9. Muffed 9. Hegemonic 10. No voice 10. Ineffectual 11. Over-optimistic 11. Interest-oriented 12. Patriotic 12. Little brother or student 13. Patriotic worrying 13. Paper tiger 14. People-oriented 14. Praiseworthy 15. Peaceful 15. Publicity stunt 16. Pragmatic 16. Respectful 17. Resilient 17. Self-serving 18. Speech controlling 18. Sharing a common destiny 19. Unique and superior 19. Strategic partner 20. Unpatriotic 20. Targeting China 21. Us against them 21. Tarring 22. Victim 22. Uncivilized 23. Untrustworthy 24. Unworthy of attention Aspirations of Chinese nationalism Roots of Chinese nationalism 1. Follow existing approaches 1. External provocation 2. Improvement of international status 2. Glorious history 3. Interdependent 3. Leadership of CCP or government 4. Maintain the development 4. Painful history 5. Make China’s voice heard 5. Significant achievements 6. Make friends 7. National rejuvenation 8. Political stability and unity 9. Realist gain 10. Remain modest 11. Self-determination and national unity 12. Stand firm 13. World leadership The proportion of each node under each theme and concrete descriptions of these nodes can find at the Appendix https://drive.google.com/drive/u/0/my-drive Analyzing the Coding Scheme Using NVivo can identify the counts and percentages of frequencies of occurrence of these nodes in the netizen discourse. As Table 2 shown, in the netizen discourse, the Roots of Chinese nationalism, “External provocation” (18.43%) and “Significant achievements” (38.83%) are closely associated with the Aspirations of Chinese nationalism “Stand firm” (26.45%, 13 times) and “World leadership” (10.46%, 14 times), respectively.4 On this basis, we can know what people say about nationalism during the pandemic.Table 2 Screenshot of a cross-tabulation in the netizen discourse A: External provocation B: Glorious history C: Leadership of CCP or government D: Painful history E: Significant achievements 1: Follow existing approaches 0 8 0 0 0 2: Improvement of international status 0 1 0 1 2 3: Interdependent 0 0 0 0 0 4: Maintain the development 0 0 2 0 0 5: Make China’s voice heard 4 0 1 0 0 6: Make friends 0 2 0 0 0 7: National rejuvenation 1 3 2 3 5 8: Political stability and unity 0 0 0 0 0 9: Realist gain 0 0 0 0 0 10: Remain modest 0 0 0 0 1 11: Self-determination and national unity 1 0 0 1 0 12: Stand firm 13 0 3 3 3 13: World leadership 0 6 0 1 14 Building the Categorization Scheme After building and analyzing the coding frame, the next step is to categorize posts based on the type of content or say the dominant style of writing of the netizen discourse, and then to check the correlation between the type of content and the number of “Like” (赞同). This step aims to know how people say about nationalism and what types of discourse, reasoned or anecdotal, are more popular for them. Specifically, I randomly selected a question – “How do you view various countries’ claims against China for COVID-19?” – from the aforesaid four questions. Relying on the deductive (draw on the work of King et al. [22]) and inductive approaches (generated from the netizen discourse per se), I categorized all posts under this question into eight types: “Argumentative praise or criticism”, “Taunting of others”, “Nonargumentative praise or suggestions”, “Factual reporting”, “Cheerleading for China”, “Cynical remarks on China”, “Cooling”, and “Other (irrelevant and meaningless)”.5 During the categorizing process, I recorded the number of “Like” of each post and the number of posts under each type. Learning from the existing research on the “Liking” behavior, I can reasonably assume that posts with more “Like,” as opposed to other posts with few or no “Like,” gain more resonance among commenters and thus can reflect the nature and mentality of netizens in discourses [24]. Based on these procedures, we should be able to know how they have come to understand what Chinese nationalism is and further to figure out the nature of Chinese nationalists. Quotidian Expressions of Chinese Nationalism What People Say About Nationalism? The salient aspirations of Chinese nationalism in the discourse include stand firm (26.45%), make China’s voice heard (16.29%), world leadership (10.46%), and maintain the development (18.07%). Specifically, for netizens, the aspiration of the tough stance roots in the external provocation (18.43%) as Diagram 1 shown. Besides, the perceptions of China as patriotic worrying (7.04%) and confident (8.34%) also make netizens take a firm stance. Patriotic worrying means that netizens feel insecure and fear of invasion, thus always be alert to potential risks from others. When faced with the potential anti-Chinese coalition and claims against China for COVID-19 during the pandemic, a typical discourse of patriotic worrying goes as follows: cast away illusions and prepare for struggle (放弃幻想, 准备斗争) [29, 30, 45, 46]. Also, such a tough stance is a symbol of having faith in China’s capability (i.e. confident). Moreover, taking a firm stance is to beware of the Western countries (19.35%) and the US (27.86%), which will be analyzed later.Diagram 1 Main associations in the netizen discourse. Each circle in the concept map represents a salient theme, and each line between two circles implies an association between them. The numbers indicate the strength of the associations. Besides, the concept map only demonstrates the main associations between codes. Some codes are not included in the map due to their lower level of associations with other codes In addition, the importance of winning public opinion warfare has been frequently mentioned in the netizen discourse (i.e., make China’s voice heard). Netizens propose that China’s capability of external publicity does not match its national strength. As a result, when others are smearing China (i.e., tarring, 14.13%), China has no power or voice to fight back (i.e., no voice, 9.81%). Moreover, China’s capability of external publicity cannot keep up with the pace of China’s foreign assistance during the pandemic. Therefore, some netizens hold a pessimistic view that China is playing the role of yes man (i.e., doormat, 2.46%) [29, 45, 46]. For netizens, China can take a world leadership role and will be the most powerful country in the world. The aspiration comes from two nationalist roots: significant achievements (32.32%) and glorious history (15.96%). As the most salient nationalist root of Chinese nationalism in the discourse, China’s significant achievements in the past decades have made netizens confident (8.34%) and patriotic (4.76%). They feel that China is unique and superior (9.14%). Significant achievements also often appear with the perception of others as paper tigers (5.18%) due to their relatively weak capability. These perceptions of China and the nationalist roots make netizens have the aspiration of China being a world leader, especially given the different performances between some developed countries and China in fighting against the pandemic. Moreover, glorious history emphasizes the long history and the past greatness of China, which highlights the pride for the past greatness and the aspiration for restoring the past glory [29, 30, 45, 46]. As a result, glorious history is associated with the aspiration to follow existing approaches (1.2%). In this way, netizens express the aspiration of inheriting and carrying forward the Chinese traditional cultures and virtues [29, 30, 45, 46]. Different from the aforesaid three nationalist aspirations, maintain the development only appears with the perception of others as unworthy of attention (3.89%). For some netizens, China only needs to focus on the economic, social, technological, and military development. The words and actions of others in the pandemic cannot pose real threats to China, thus are unworthy of attention [29, 30, 46]. In addition to aforesaid two nationalist roots – significant achievement and glorious history – painful history (20.46%) is another important root of nationalism in the netizen discourse. It conveys national humiliation between 1840 and 1949 as well as the difficult years of being backward since 1949. This nationalist root is strongly associated with several significant others, including the US (27.86%), the UK (6.91%), Germany (3.16%), France (2.13%), Japan (5.98%), and Italy (7.36%). These countries invaded China in history. When faced with external provocations in the pandemic, netizens ask rhetorically that do these countries want to reproduce a contemporary “Allied Forces of Eight Powers” and invade China again [29]. Besides, the perception of Germany and the European Union (EU) (6.72%) as self-serving (3.33%) reflects the divisiveness between the EU countries. For example, Germany seized medical supplies headed to Switzerland and Italy received little help from its European neighbors. As the first European country hit by the COVID-19 pandemic, Italy got no help but from China. This demonstrates China’s image of big-hearted (5.62%) and Italy’s recognition of China (i.e., respectful, 1.96%). Moreover, Japan and South Korea are regarded as praiseworthy (2.46%) in the netizen discourse. In addition to their assistance to China in the early days, the praiseworthy perception also comes from their good performance in the prevention and control of the virus. When they run out of medical supplies, China has sent back medical supplies to support them. Given this, for most Chinese netizens, China is perceived as grateful (2.68%) [29, 30, 45, 46]. Summing up the above, netizens often refer to Japan, South Korea, and Italy as friends (2.04%). Lastly, the US is the most significant other. Main perceptions of the US in the netizen discourse include tarring, biased (9.88%), targeting China (8.89%), arrogant (8.61%), ineffectual (6.53%), hegemonic (5.81%), paper tiger (5.18%), and self-serving. Specifically, the perception of the US as targeting China means that the US has deliberately tried to hurt and trouble China. A post quotes a movie line to summarize the aims of the US6: “they do not care about how many bowls of bean jelly you have had, they do not care about if your heart is red or black, they do not care about if you are innocent, and they only want you to commit seppuku (他们不在乎你吃了几碗凉粉, 不在乎你的心是红的还是黑的, 不在乎你是不是清白的, 他们只想要你剖腹自杀)” [29]. As for the perception of biased and arrogant, the former stresses that the US has viewed China via tainted glasses, such as questioning the authenticity of Chinese data. The latter means that the US has been neither willing to acknowledge the achievements of others no matter in fighting against the virus or in development nor to face its ineffectual policies in fighting against pandemic, even though the count of death and infection is booming [29, 30, 45, 46]. Moreover, the hegemonic perception mainly embodies in the double standards of the US. This includes two aspects. First, the US has portrayed China and other countries that imposed strict lockdown policies like Italy in different ways. In such reports, China was violating human rights and would bring huge losses to people, and Italy was protecting Europe and could bring democracy and freedom to other countries [29, 46]. The second one refers to the US and some other countries’ claims against China for the pandemic. For this claim, Chinese netizens often borrow a logic proposed by Kishore Mahbubani, namely, the 2008–2009 financial crisis caused damage to many economies, but the US did not compensate for the crisis7 [29]. Lastly, the perception paper tiger refers to the US tricks that aim at China were ineffective, such as suppressing Huawei and isolating and shifting the blame to China [29, 30, 45, 46]. More importantly, the US has been in the depths of the crisis of the virus, and the future of its economic development and social order is dark, thus the actions of the Trump administration get beyond their depth. Having traced the netizen discourse, we can find that during the pandemic, Chinese netizens show a tough stance when faced with external provocations and a sense of pride in China’s achievements, although they also directly or indirectly point out the deficiencies of China during the period. Specifically, first, the netizen discourse is tougher and ambitious. Ambitious aspirations like stand firm, make China’s voice heard, and world leadership account for 53.2% of all nationalist aspirations in the discourse. Second, netizens draw nationalist sentiments mainly from contrasting past misery with present happiness. The aggregate proportion of significant achievements and painful history in the netizen discourse is 52.78%. Third, although netizens express a sense of pride, they are also radical in criticizing China. The derogatory perceptions of China, like doormat (2.46%), muffed (2.4%), no voice (9.81%), over-optimistic (4.09%), and speech controlling (3.01%), account for 21.77%. By contrast, the proportion of straightforward positive perceptions of China, such as big-hearted (5.62%), civilized (1.67%), dauntless (0.23%), confident (8.34%), fair-mined (9.07%), grateful (2.68%), long-sighted (4.21%), peaceful (0.54%), people-oriented (2.13%), and unique and superior (9.14%), is only 42.9%. How People Say About Nationalism? After uncovering what people say about nationalism during the pandemic, then we explore how people say about nationalism. This step is crucial to figure out the sense-making process or how people have come to understand what Chinese nationalism is. To achieve this aim, as I mentioned above, I categorized all posts of “How do you view various countries’ claims against China for COVID-19?” and then built a categorization scheme with a combination of deductive and inductive approaches. Table 3 shows the types of content, the number of posts under each type, and the average number of “Like” of each type. According to the table, we can find obvious differences between different types of netizen discourse in terms of the average number of “Like”. A further one-way ANOVA analysis shows that the differences between the mean values have statistical significance.Table 3 Categorization scheme The type of content Number of posts Average of “Like” 1. Argumentative praise or criticism 578 252.02 2. Taunting of others 667 234.58 3. Cynical remarks on China 59 81.41 4. Cooling 70 45.00 5. Factual reporting 32 40.63 6. Cheerleading for China 27 22.00 7. Nonargumentative praise or suggestions 1092 3.55 8. Other (irrelevant and meaningless) 245 – Brief explanations of these types of content. (1) Argumentative praise or criticism involves taking a position and elaborating their viewpoints with examples and details. This content type requires more deliberation, commenters of this type would not take a dichotomized “for” or “against” attitudes, but pay attention to evidence in discussing emotional and controversial issues. (2) Taunting of others includes “denigrating favorable comparisons of China compared to other countries”. During the pandemic, taunting of others also refers to that the Chinese masses’ despise some countries’ poor performance and foreign masses’ indiscipline in fighting against the virus. Besides, taunting also refers to the masses’ disdain and wariness of other countries’ lip-threats. (3) Cynical remarks on China refer to obliquely deride and criticize China as well as Little Pink (小粉红). (4) Cooling is to throw cold water on overly nationalistic and over-optimistic views. This type usually holds a negative attitude toward the future or asks people to keep a low profile. (5) Factual reporting involves “descriptions of government programs, events, initiatives, or plans”, usually directly cites news and remarks of government, thus there is no personal praise or criticism. (6) Cheerleading for China involves “expressions of patriotism, encouragement and motivation, inspirational slogans or quotes, gratefulness, discussions of aspirational figures, cultural references, or celebrations”, and includes “positive sentiment or general praise toward life, historical figures, model citizens”. (7) Nonargumentative praise or suggestions features for no reason. It praises or criticizes some issues but does not give reasons or evidence, and often strongly holds emotional views in expression. (8) Other refers to “irrelevant posts that are entirely personal, commercial (such as ads), jokes, or empty posts that forward information not included” [22, 41]. The table shows that on average, the “Argumentative praise or criticism” posts and “Taunting of others” posts receive the highest number of “Like” than any other type. Besides, the types include “Cynical remarks on China”, “Cooling”, and “Factual reporting” attract a moderate number of “Like”. While posts in types of “Cheerleading for China” and “Nonargumentative praise or suggestions” are least liked. Given these, it is reasonable to assume that during the pandemic, netizens prefer analytical to non-analytical posts and prefer to taunt others. Such an assumption can also get support from the high number of posts in types of “Argumentative praise or criticism” and “Taunting of others”. Therefore, netizens not only like to express discourses with the features of argumentative and taunting of others but also tend to accept others who express these types of discourses. The finding indicates that Chinese nationalists are not irrational zealots, although they tend to harbor confrontational and xenophobic mentality given the high number of “Like” received by the type of “Taunting of others”. Re-contextualizing these findings with the aforesaid dominant narratives would help us make sense of the rational as well as xenophobic nature of the netizen discourse. Specifically, netizens are more confident and vigilant and show confrontational sentiments in their everyday expressions. They often elaborate on the impressive performance of China in fighting against the pandemic and the vilification of China from others with evidence. In their discourses, China’s success in fighting against the pandemic can be credited to the advantages of China’s political system and a strong sense of discipline of ordinary Chinese citizens. One post that has received over 90,000 “Like” has teased out the necessary conditions of building the Huoshenshan Hospital within 10 days [43]. All these conditions leech on to the system superiority and all the people of one mind. Besides, netizens detailedly sort out China’s significant achievements in recent decades and China’s responsible and admirable actions in containing the domestic spread of the virus and helping others on a global scale [45]. Furthermore, some netizens couch criticism on China in tactful language, such as China’s weak discursive power in the world and the strict censorship at home [29, 30, 45, 46]. Nonetheless, the start point of these critical remarks on China is for China’s good. Just like a post said, “we point out her (China) problems because we love her, although the pandemic exposes some officials’ inaction, most people I see are very dedicated, I can say that I love my country even more because of the pandemic (我们会指出她的不足是因为我们深爱着她, 虽然疫情中, 暴露了一些官员不作为, 但是我能看到的大部分人, 都是非常敬业的, 可以说我因为这次疫情更加热爱自己的国家了)” [30]. On this basis, during the pandemic, Chinese nationalists are apt to rationally demonstrate patriotism, nationalism, and the support for the Party-state with evidence. As for the netizens’ sentiments of taunting of others and confrontation, the main targets are others who have performed ineffectual in fighting against the pandemic and have showed assertive attitudes toward China. On the one hand, netizens taunt some countries’ poor performance during the pandemic. They attribute the ineffectuality to these governments’ nonfeasance, the indiscipline of the foreign masses, and the weak national capacity. Just like a catchword of a post said, “It is not that others do not want to learn the experience of anti-pandemic from China, but their strength does not allow it (他们不想借鉴吗?奈何实力不允许呀!)” [45]. On the other hand, the netizen discourse reveals a vigilant and confrontational tone when faced with the defiant and base conducts of some countries, like buck-passing, tarring, and claiming. As a response, netizens often employ two phrases to demonstrate the vigilant and confrontational mentality: the imperialisms’ wild ambition of destroying us does not disappear yet (帝国主义亡我之心不死) and cast away illusions and prepare for struggle (放弃幻想, 准备斗争) [29, 30, 45, 46]. Regarding the main components of netizens’ taunting of others, netizens have faith in the strength of China, alert to the malevolence of others, and harbor a confrontational and xenophobic mentality. By categorizing the netizen discourse into eight types based on the dominant style of writing and checking the popularity of each type, findings here show that the netizen discourse like “Argumentative” and “Taunting of others” are the most popular and widely accepted types. It suggests that during the pandemic, Chinese nationalists are rational but xenophobic. Two points deserve special mention. First, these findings partially concur with the evidence provided by Zhang et al.: most Chinese online nationalists were critical of the domestic political conditions [47]. However, one of their arguments, that is, most Chinese nationalists were not pro-regime, has not been confirmed in my findings. On the contrary, my findings show that these rational but xenophobic nationalists choose to support the nation and the regime despite being critical of domestic political conditions and the status quo of China’s discursive power in the world. Second, the xenophobic finding here can only partially capture the mentality of Chinese masses during the pandemic. In other words, we are still uncertain of whether the confrontational and xenophobic mentality is a temporal phenomenon or a long-standing phenomenon, and whether the mentality will last for the future. Conclusion From the first country hit by the pandemic to the country that sets “a new standard for outbreak response,” China has experienced a short but challenging period. During the period, comparing China’s and others’ performances in fighting against the pandemic becomes a habit in quotidian expressions of Chinese netizens. Such a nationwide nationalist discussion provides an opportunity to map what and how Chinese people say about nationalism in cyberspace. This article overcomes the methodological deficiency of the existing literature to recover the meaning of Chinese nationalism in the form of quotidian discourse. Using NVivo, I coded the Chinese netizen discourse. Then by outlining and checking quotidian nationalist discourse, we can find the patterns of quotidian nationalist expressions in Chinese netizens. Specifically, as for the “what is it” question, I find that during the pandemic, Chinese netizens present a tough stance when facing external provocations and a sense of pride in China’s achievements, although they also directly or indirectly point out the deficiencies of China during the period. Furthermore, I find the rational and xenophobic nature in the netizen discourse by exploring the “Liking” behavior. In other words, netizens understand what Chinese nationalism is through a rational and xenophobic sense-making process. In quotidian expressions, netizens value rational deliberation as opposed to emotional and baseless views in discussions, no matter in praising or criticizing China or others. Besides, netizens demonstrate confrontational and xenophobic attitudes in quotidian discourses. Such a stance mainly draws strength from China’s significant achievements in past decades, the painful history of being invaded and backward, and external provocations during the pandemic. While as aforementioned, I cannot judge whether the xenophobic mentality during the pandemic is a temporal or lasting phenomenon and whether the mentality will last for the future. However, given the rational nature of the netizen discourse, there are reasons to believe that the confrontational and xenophobic mentality has detailed and accurate evidence as well as a broad mass foundation. Acknowledgements I would like to show my gratitude to Guo Binglian, Joana Cheong Mesquita Ferreira, Zhai Zheng, Dong Yu, and Marianne Teo for sharing their pearls of wisdom with me during the course of this research, and I thank the two anonymous reviewers for their comments. Special thanks to the encouragement and endorsement from Professor Li Lianjiang and Professor Shaun Breslin. 1 The top three social media was WeChat Moments (87.3%), Tencent QZone (64.4%), and Weibo (40.9%) [9]. The former two are private social media and the last one has been “tainted” by the “50-Cent Party”. 2 There are four universal rules in generating codes, that is, unidimensionality, mutual exclusiveness, exhaustiveness, and saturation [31]. In addition to these four iron laws, we also need to notice that (1) Do not expect any particular attributes of nationalism to emerge; and (2) Do not expect any particular number of the attributes, they could be zero or hundreds, etc. [16]. 3 Nodes of significant others do not present in the table. 4 The percentage in brackets refers to the percentage of the node for its group. For example, 18.43% means that in the mass discourse, “external provocation” accounts for 18.43% of “Roots of Chinese nationalism”. Besides, 13 and 14 times means the number of co-occurrence between nodes. The number of co-occurrence between nodes is independent of the percentage. 5 Brief descriptions of these types will be elaborated later. 6 The name of the movie is “Let the Bullets Fly” (让子弹飞). 7 In April 2020, Kishore Mahbubani, a professor at the National University of Singapore, firstly utilized the logic to refute the claiming so-called accountability and compensation. Since then, Geng Shuang, a then-Foreign Ministry spokesman, and Chinese netizens have started to employ the logic to point out the double standards of the US-led countries [51]. ==== Refs References 1. Abdelal R Herrera YM Johnston AI McDermott R Identity as a variable Perspectives on Politics 2006 4 4 695 711 10.1017/S1537592706060440 2. Cai KG Constructing an analytical framework for explaining Chinese foreign policy Chinese Political Science Review 2020 5 355 373 10.1007/s41111-020-00150-5 3. 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==== Front ACS Omega ACS Omega ao acsodf ACS Omega 2470-1343 American Chemical Society 10.1021/acsomega.0c02072 Article Nanostructured Lipid Carriers Delivering Sorafenib to Enhance Immunotherapy Induced by Doxorubicin for Effective Esophagus Cancer Therapy Wang Jia-Yang Song Ya-Qi Peng Jing Luo Hong-Lei * Department of Radiation Oncology, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, No. 25 South Beijing Road, Huai’an, Jiangsu 223300, China * Email: [email protected]. 02 09 2020 15 09 2020 5 36 22840 22846 05 05 2020 17 07 2020 Copyright © 2020 American Chemical Society2020American Chemical SocietyThis is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes. The tumor microenvironment (TME) plays a significant role in weakening the effect of cancer immunotherapy, which calls for the remodeling of TME. Herein, we fabricated a nanostructured lipid carrier (NLC) to codeliver doxorubicin (Dox) and sorafenib (Sfn) as a drug delivery system (NLC/D-S). The Sfn was expected to regulate the TME of esophagus cancer. As a result, the immune response induced by Dox-related immunogenicity cell death could be fully realized. Our results demonstrated that Sfn was able to remodel the TME through downregulation of regulatory T cells (Treg), activation of effector T cells, and relieving of PD-1 expression, which achieved synergistic effect on the inhibition of primary tumor but also subsequent strong immune response on the regeneration of distant tumor. document-id-old-9ao0c02072document-id-new-14ao0c02072ccc-price ==== Body Introduction Cancer immunotherapy is a novel promising approach for cancer therapy, which is getting more and more attention in recent years.1,2 Different from conventional chemotherapy or radiotherapy, which employed additional drugs or assistance to kill cancer cells, immunotherapy is designed to utilize the endogenous human immune system to combat cancer.3 With the advances of cancer therapy, recent studies have found several drawbacks in single cancer immunotherapy. In particular, the tumor microenvironment (TME) is demonstrated to significantly downregulate the activation of immune response in tumor tissue, which finally resulted in impaired cancer immunotherapy.4 For example, TME can reduce the infiltration of dendritic cell (DC) and suppress the activation of effector T cells to finally silence the immune system to current therapies. As a result, it was generally recognized that the remodeling of TME using other approaches, such as chemotherapy, in combination with immunotherapy might be a promising approach for effective cancer therapy. However, the delivering of proper drugs using the suitable drug delivery system (DDS) to satisfy both TME regulation and cancer immunotherapy is challenging.5,6 Recently, the phenomenon that some chemotherapeutics, such as anthracyclines and oxaliplatin, induce immunogenicity cell death (ICD) along with apoptosis of cancer cells is getting more and more attention.7,8 It was reported that the ICD is capable of effectively presenting the cancer-specific antigen to the surface of the cells with the following activation of related immune cells, such as CD4 and regulatory T cells (Treg). As a result, the chemotherapy-induced ICD has been employed by previous studies to stimulate the immune response, which showed promising benefits in cancer therapy.9,10 Sorafenib (Sfn) is a widely applied small molecule of multikinase inhibitor, which is adopted in various cancer treatments through cell cytotoxicity.11 Surprisingly, recent discoveries also revealed its regulation potential on TME by inhibiting the immuno-suppressive Treg cells8 and enhancing the function of effector T cells.12 As a result, we suggested that the combination of Sfn with anthracyclines, such as doxorubicin (Dox), might be a suitable choice for cancer therapy. It was suggested that Dox can induce strong ICD for immune response while the negative effects of TME can be neutralized by Sfn.13 However, the codelivery of both drugs in one DDS, which integrates decent drug-loading capacity and promising tumor targetability, is a critical issue that requires careful selection of suitable carrier.14,15 In recent years, nanoparticle-derived DDSs have shown many advantages, including enhanced drug bioavailability, increased tumor-homing, and reduced cytotoxicity.16−18 The DDSs adopted in cancer therapy were widely studied by previous reports with promising outcomes.19−21 In particular, nanostructured lipid carrier (NLC) is a widely adopted organic platform for drug delivery,22 which composed of biocompatible lipids at solid or liquid state and was suitable for the delivery of hydrophobic drugs.22,23 As a result, the application of NLC for the safe and effective delivery of drugs for cancer therapy has been successfully developed by many previous researchers.24,25 Herein, we employed the mice drug-resistant esophagus carcinoma cells (AKR/Dox) as the model cell line. Moreover, NLC was selected as the carrier for the loading of Sfn and Dox (NLC/D-S) and was surface modified with folic acid (FA) as the targeting moiety to increase the tumor-homing of the DDS. It was suggested that the NLC/D-S with nanoscale size can specifically homing to tumor tissue via enhanced penetration and retention effect and deliver both drugs to target cells. Upon drug release in cells, the Dox induced ICD to express the cancer-specific antigen while Sfn regulated the TME. Both effects were believed to facilitate the immune response of the subject for effective treatment of AKR/Dox cancer. Results and Discussion The NLC is prepared by a solvent diffusion method in a one-pot route. The drugs can be encapsulated within the hydrophobic region of the NLC to afford decent drug loading and safe delivery. Here, in our study, under the given condition, using dynamic light scattering, it was shown that the size of the acquired NLC/D-S was around 100 nm (Figure 1A) with well dispersion, suggesting the successful preparation of uniform nanosized NLC using this method. By adjusting the charge ratio, the drug loading of NLC/D-S was 5.96% for Dox and 6.11% for Sfn, which nearly 1:1 in weight ratio. Figure 1 The size and stability of NLC/D-S. (A) The size distribution of NLC/D-S. (B) Colloidal stability of NLC/D-S in PBS (pH 7.4) and mouse plasma at 37 °C for up to 48 h. Data were expressed as mean standard deviation with three parallel experiments. Previous studies have shown that colloidal stability plays a significant role in the in vivo fate of DDS, therefore, the colloidal stability of NLC/D-S was evaluated using two physiological media (PBS 7.4 and mouse plasma). Because the distribution of DDS to the target side is a time-dependent process, the DDS should maintain stability long enough without leakage for tumor targeting.26 Therefore, the changes in particle size of NLC/D-S were monitored for 48 h. As displayed in Figure 1B, NLC/D-S showed almost no changes in size in both media, which suggested that NLC/D-S might be able to maintain stability upon in vivo applications, which was a primary requirement for drug delivery in cancer therapy.19,27 Then, the biocompatibility of the DDS as another important parameter was also studied. The hemolysis of NLC/D-S was studied using 2% red blood cell (RBC) from New Zealand rabbit. It was suggested that hemolysis suggested the irritation of DDS on RBC, which was critical for safe drug delivery upon in vivo applications.28 As shown in Figure 2A, NLC/D-S showed almost no hemolysis on RBC (1.21% h at the concentration of 1 mg/mL) under all given condition, which might be even lower because of the blood dilution upon in vivo applications. Therefore, the NLC/D-S was suggested to be a highly biocompatible DDS without hemolysis upon in vivo applications.29 Figure 2 The biocompatibility of NLC/D-S. (A) Hemolysis of NLC/D-S on 2% RBC under different concentrations at 37 °C for 1 h. (B) Cytotoxicity of various concentrations of NLC after 48 h of incubation with AKR/Dox cells. Data were expressed as mean standard deviation with three parallel experiments. The biocompatibility of the DDS was further investigated by investigating the cytotoxicity of drug-free carrier on AKR/Dox cells. The cell viability of AKR/Dox cells recorded after cells was exposed to various concentrations of NLC for 48 h. As depicted in Figure 2B, the viability of AKR/Dox cells at the end of the test remained over 90% at the concentration of 200 μg/mL, which further suggested the high biocompatibility of NLC.30 Afterward, the cellular uptake of Dox in AKR/Dox cells was investigated to understand the role of FA modification in the cellular uptake of the DDS. With the aim to find the true result of NLC-mediated drug uptake in comparison to free drug, the NLC/Dox was employed instead of NLC/D-S. As illustrated in Figure 3A, in line with previous reports, the strong drug-resistance nature of AKR/Dox cells resulted in weak cellular accumulation of free Dox while this phenomenon significantly overcame by introduction of NLC/Dox. It was reported that DDS can facilitate drug retention in cells through endocytosis, especially receptor-mediated pathways to partially reverse drug resistance of cancer cells.3,31 In order to identify this merit, the AKR/Dox cells were pretreated with excess free FA to further study the drug accumulation changes between free Dox and NLC/Dox. As expected, the cellular uptake of free Dox was not affected by FA, suggesting that the accumulation of free Dox was not related to FA. However, compare with the untreated group, FA pretreatment resulted in significant drop in intracellular accumulation of Dox, and this phenomenon was observed in all tested time intervals, which showed a half drop (53.6%) of drug accumulation at 6 h postincubation. These observation was in line with previous reports that FA modified in the NLC mediated the cellular uptake of the DDS through the corresponding receptor, which was beneficial for cancer therapy.32,33 Figure 3 The tumor targetability of NLC/D-S. (A) Quantitative analysis of intracellular time-dependent uptake of NLC/Dox in AKR/Dox cells in comparison with free Dox and pretreated with/without FA. (B) Total fluorescence intensity of dissected tumors and major organs of mice treated with NLC/D-S at 4 and 8 h post-injection. Data were expressed as mean standard deviation with three parallel experiments. Next, the in vivo targetability of NLC was further explored. The indocyanine green as a probe was loaded into NLC to indicate the location of DDS. At predetermined time interval (4 and 8 h), the mice were sacrificed, and the tumor and major organs were harvested to study the distribution of DDS. As displayed in Figure 3B, NLC/D-S showed preferable accumulation in tumor at 4 h and further time extension resulted in more elevated DDS accumulation. In addition, it was noted that the distribution of NLC/D-S in major organs, especially the liver and spleen, was less than that in the tumor, which further suggested the promising tumor targetability of this DDS. The in vitro anticancer ability of this DDS was evaluated using MTT assay. As demonstrated in Figure 4A, the strong drug resistance of AKR/Dox cells resulted in impaired cytotoxicity of Dox with cell viability over 63.6% at the Dox dosage of 50 μM in the NLC/Dox group. Because of cytotoxicity of Sfn, the NLC/Sfn also exerted moderate cytotoxicity on AKR/Dox cells. Most importantly, the combination of Dox and Sfn demonstrated significant drop in cell viability, which reached 32.7% at the same Dox concentration of 50 μM as compared to NLC/Dox. The combination index of the drugs was 0.47, which indicated the strong synergistic suppression effect on AKR/Dox cells.5 Figure 4 The in vitro anticancer assay of NLC/D-S. (A) Cell viability of AKR/Dox cells treated with different formulations at different Dox concentrations for 48 h. (B) Western blot assays of the expression of caspase-3, cytochrome C and Bcl-2 proteins after different treatments (Dox concentration: 20 μM). Data were expressed as mean standard deviation with three parallel experiments. The western blot assay was conducted to assess the apoptosis level of cells after different treatments and as another proof to reveal the in vitro anticancer effects of NLC/D-S. As shown in Figure 4B, the bcl-2 level in NLC/D-S-treated cells was the lowest among all groups while the caspase-3 and cytochrome-3 levels were higher than other groups. These results provided decisive evidence to show that severe apoptosis was occurred upon NLC/D-S treatment, which explained the best cell inhibition effect of NLC/D-S.34 The multicellular tumor spheroid (MCTS), which simulates the in vivo solid tumor, was further employed to assess the in vivo anticancer effects of different treatments. As shown in Figure 5A,B, the growth of MCTS in the free Dox group was uncontrollable, which was almost 3-fold of original volume, suggesting the neutralization profile of AKR/Dox on the cytotoxicity effect of Dox. Moreover, the inhibition effect of single delivery systems (NLC/Dox and NLC/Sfn) is only moderate without reversing the growth of MCTS. In contrast, NLC/D-S significantly reversed MCTS growth, which was merely 0.72-fold of the original volume at day 5, which suggested the promising synergetic effects of both drugs on the inhibition of drug resistance cells.35 Figure 5 The inhibition effect on MCTS. (A) The volume changes of MCTS after different treatments (Dox concentration: 20 μM) for 5 days. (B) The representative optical image of MCTS at day 0 (left) and day 5 (right) after different treatments. Scale bar: 200 μm. Data were expressed as mean standard deviation with three parallel experiments. Afterward, the core design of our study, verifying the role of NLC/D-S in activating immune responses, was evaluated in vivo using the AKR/Dox tumor-bearing model. Mice were randomly divided and treated with different formulations in parallel. The tumor volume of the subjects was recorded before every administration. After 15 days of treatment, the mice were challenged with tumor cells on the other side of the primary tumor, and the growth in distant tumor was monitored for another 15 days without any treatment to assess the immune responses upon different treatments. As displayed in Figure 6A, the growth of primary tumors was significantly suppressed in NLC/D-S group (286 mm3) as compared with other groups. In contrast, the NLC/Dox and NLC/Sfn groups showed faster tumor growth during the whole period (final tumor volume of 416 and 553 mm3, respectively). In particular, the further tumor challenging using the same tumor cells also revealed the promising effects of NLC/D-S. As displayed in Figure 6B, the distant tumor in the control group persistently increases to the inferior acquired immunity. In contrast, both NLC/Dox and NLC/Sfn treatments triggered elevated immune response as the tumorigenicity of AKR/Dox cells was suppressed to some extent. It was noted that the performance of NLC/Dox was better than the NLC/Sfn group, suggesting the critical role of ICD in the immune response. In particular, the NLC/D-S group showed the lowest tumorigenicity of cancer cells with a final distant tumor volume of 101 mm3, which was in line with results, as obtained in Figure 6A, and our suggestions. Figure 6 In vivo antitumor efficacy of different formulations for AKR/Dox tumor-bearing Balb/c mice. Tumor volume changes of primary (A) and distant tumors (B) after different treatments as a function of time were recorded. Data were expressed as mean standard deviation with six parallel experiments. In order to further confirm this conclusion and understand the underlying mechanisms responsible for this phenomenon, the cytokine (IL-6) was selected as a signal for DC maturation, and its plasma concentration after different treatments was measured by the ELISA kit. As demonstrated in Figure 7A, the plasma levels of IL-6 increased upon the administration of different treatments, which further increased as a function of time. These results suggested the DC maturation as a result of immunotherapy. As expected, the NLC/D-S group showed higher elevation on IL-6 levels than other groups, which further confirmed the preferable activation effect of NLC/D-S on the immune system.36 In order to understand the role of ICD in DC activation, the ICD in tumor tissues was further assessed. In line with results, as obtained in Figure 7A, compared to the inferior ICD level in Sfn and control groups, Dox could induce strong ICD while the aid of Sfn in the NLC/D-S group further enhanced the ICD level. Figure 7 The (A) cytokine IL-6 level in peripheral blood serum (indicating in vivo DC stimulation) after different time intervals of treatment. Data were expressed as mean standard deviation with three parallel experiments. (B) The ICD of tumor tissues at the end of the test after different treatments. Scale bar: 100 μm. In order to identify the role of Sfn, the strong immune response effect of NLC/D-S, the Treg, and effector T cell regulation effects after Sfn treatment were explored. As displayed in Figure 8A, the percentage and number of CD4+CD25+ Treg in tumor-infiltrating lymphocytes were significantly dropped after Sfn treatment in a concentration-dependent manner. Moreover, as confirmed in Figure 8B, the proliferation of Treg was also negatively regulated by Sfn in a concentration-dependent manner. As CD4+CD25+ Treg was responsible for the immune suppression of TME, the negative regulation effect of Sfn on these cells was supposed to exert beneficial effects on the restoration of the function of effector T cells and DC cells to fully induce immune response.37 Figure 8 (A) Number of CD4+CD25+ Tregs cells among the CD4+ T cell population in the tumors tissue after different dosage treatment of Sfn. (B) Effect of different concentrations of Sfn on Treg proliferation in vitro. Data were expressed as mean standard deviation with three parallel experiments. Cytotoxic (CD8+) T lymphocytes (CTLs) are important cells in cancer killing through the release of effector molecules and/or effector cytokines. Its activation was critical to the final performance of immunotherapy. As a result, the inhibition or regulation effect of Sfn on CTLs is also our concern. As demonstrated in Figure 9A, the cell number of CTL among CD8+ cells was increased by Sfn treatment and was positively related to drug concentration, which suggested the beneficial effect of Sfn on CTL activation. Considering that PD-1 signaling of cancer cells can mediate potential immune escape, we next aim to explore if Sfn could further facilitate the recognition of CTL on cancer cells. As a result, the PD-1-positive CD8+ T cells in TME were also examined before and after Sfn treatments. As depicted in Figure 9B, the percentage of PD-1+ CD8+ T cells in the TME decreased drastically with the increase of Sfn dosing, suggesting that Sfn can regulate the PD-1 expression in the effector T cells to block the PD-1/PD-L1 pathways in the major cases of tumor immune escape. In all, we concluded that Sfn was able to remodel TME on the AKR/Dox tumors through elevation of CTL percentage and decrease of proportion/proliferation of PD-1+ CD8+ effector T cells and Treg cells, which finally sensitized the subject to ICD-induced immune responses with promising inhibition on tumors.38 Figure 9 Sfn treatment augmented effector function of tumor-specific T cells and downregulated the PD-1 expression of CD8+ T cells in TME. (A) The mean percentage of CD25 (activation marker) expressing cells among tumor-infiltrating CD8+ T cells and (B) corresponding flow cytometry graphs. (C) Percentage of PD-1-expressing CD81 T cells in tumor draining lymph nodes of tumor bearing mice. Data were expressed as mean standard deviation with three parallel experiments. Conclusions In summary, we successfully fabricated dual drug-loaded NLC as an effective DDS for TME remodeling and ICD-based cancer immunotherapy (NLC/D-S). Our results demonstrated that nanosized NLC/D-S was highly stable and biocompatible with promising tumor targetability. The synergistic effect of Dox and Sfn on NLC/D-S showed the best in vitro and in vivo anticancer benefits as compared to single delivery systems (NLC/Dox and NLC/Sfn). Most importantly, the NLC/D-S with the combination of Dox-induced ICD and Sfn-mediated TME remodeling showed strong immune response after treatment. Our results further revealed that the TME remodeling effect of Sfn was through the combination of Treg inhibition/effector T cell activation/PD-1 relieving. In all, the NLC/D-S might be promising DDSs for effective cancer immunotherapy. Experimental Section Detailed information about Materials and Method can be found in the Supporting Information. Supporting Information Available The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.0c02072.Details for materials and methods (PDF) Supplementary Material ao0c02072_si_001.pdf The authors declare no competing financial interest. ==== Refs References Mellman I. ; Coukos G. ; Dranoff G. Cancer immunotherapy comes of age . Nature 2011 , 480 , 480 –489 . 10.1038/nature10673 .22193102 Palucka K. ; Banchereau J. Cancer immunotherapy via dendritic cells . Nat. Rev. Cancer 2012 , 12 , 265 –277 . 10.1038/nrc3258 .22437871 Chen M. ; Song F. ; Liu Y. ; Tian J. ; Liu C. ; Li R. ; Zhang Q. A dual pH-sensitive liposomal system with charge-reversal and NO generation for overcoming multidrug resistance in cancer . Nanoscale 2019 , 11 , 3814 –3826 . 10.1039/C8NR06218H .30600823 Graham K. ; Unger E. 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==== Front Cell Cycle Cell Cycle Cell Cycle 1538-4101 1551-4005 Taylor & Francis 32804027 10.1080/15384101.2020.1805552 1805552 Version of Record Research Article TGF-β is associated with poor prognosis and promotes osteosarcoma progression via PI3K/Akt pathway activation K. MA ET AL. CELL CYCLE Ma Kun Zhang Chuan Li Wuyin Luoyang Orthopaedic Hospital of Henan Province and Orthopaedic Hospital of Henan Province , Luoyang, Henan, P. R. China CONTACT Kun Ma [email protected] Wuyin Li [email protected] 17 8 2020 2020 19 18 23272339 © 2020 Informa UK Limited, trading as Taylor & Francis Group 2020 Informa UK Limited, trading as Taylor & Francis Group ABSTRACT Transforming growth factor beta (TGF-β) is a multifunctional cytokine with important functions in cell proliferation and differentiation. TGF-β is highly expressed in several types of cancers and promotes tumor invasion and metastasis. However, the role of TGF-β in osteosarcoma progression is poorly understood. In the present study, we found that TGF-β is highly expressed in osteosarcoma cells and tissues, and is associated with high Ennecking stage (P = 0.033), metastasis, and recurrence. TGF-β-knockdown osteosarcoma cell lines were established using siRNA (si-TGF-β). Cells transfected with si-TGF-β exhibited significantly reduced proliferation, migration/invasion, and colony formation abilities, and increased levels of cell apoptosis. In addition, si-TGF-β treatment reduced spheroid size, the ratio of CD133-positive cells, and expression of SOX-2, Nanog, and Oct-3/4 in osteosarcoma cells. Mechanistically, PI3K/mTOR phosphorylation is inhibited by TGF-β knockdown. Pretreatment with 25 µM LY294002, a PI3K-specific inhibitor, further enhanced the si-TGF-β-induced suppression of osteosarcoma progression. Taken together, these results reveal a novel role for TGF-β in osteosarcoma progression and modulation of stemness-related traits and indicate that TGF-β may be of value as a therapeutic target for the treatment of osteosarcoma. KEYWORDS TGF-β osteosarcoma apoptosis invasion/migration stemness traits PI3K/mTOR pathway Scientific Research of traditional Chinese Medicine of Henan Province 2019ZY1035 This work was supported by the Scientific Research of traditional Chinese Medicine of Henan Province [2019ZY1035]. ==== Body Introduction Osteosarcoma is the most frequently occurring and malignant bone tumor in children and adolescents [1]. Osteosarcoma is a neoplasm derived from mesenchymal tissue, and is characterized by the production of new bone tissue by spindle-shaped stromal cells [2]. However, 50–60% of patients with osteosarcoma have advanced or metastatic disease at the time of diagnosis. Although new adjuvant chemotherapies have been developed within the past decade, many patients with osteosarcoma develop chemoresistance and lung metastasis, even when treated with chemotherapy combined with surgical resection [3,4]. To date, the etiology and pathogenesis of osteosarcoma remain poorly understood. The transforming growth factor-beta (TGF-β) family of cytokines belongs to a superfamily of structurally similar, but functionally diverse, growth factors that include activin, bone morphogenetic protein (BMP), and growth differentiation factor (GDF) [5]. TGF-β can inhibit cell proliferation, initiate cell differentiation, and induce apoptosis, and promote tumor invasion and metastasis [6–8]. The TGF-β signaling pathway, and related pathways, contribute to enhanced stemness in triple-negative breast cancer, colon cancer, and ovarian clear cell carcinoma [9–11]. Furthermore, TGF-β levels in serum are significantly higher in patients with osteosarcoma than in controls [12], and the serum TGF-β levels are higher in patients with metastasis than in those without metastasis. Therefore, we hypothesized that TGF-β could be a biological marker for, and play a role in the regulation of stemness traits in, osteosarcoma. TGF-β is involved in regulating the biological function of malignant tumors through several pathways, including the Jagged1/Notch, Wnt, JAK2/STAT3, and PI3K/AKT/mTOR signaling pathways [13–16]. Moreover, regulation of Akt/mTOR pathway activity can inhibit osteosarcoma cell proliferation, arrest the cell cycle, induce apoptosis, and suppress invasion and metastasis, suggesting the involvement of this pathway in the development of osteosarcoma [17–20]. We hypothesized that TGF-β may regulate the stemness and metastatic potential of osteosarcoma stem cells by suppressing Akt/mTOR pathway activity. In this study, we investigated the correlation between TGF-β expression and the clinicopathological characteristics and prognosis of patients with osteosarcoma. We explored the effect of TGF-β knockdown on proliferation, invasion/migration, and regulation of stemness in osteosarcoma cells and examined the underlying molecular mechanisms. This study was designed to determine whether TGF-β is a promising novel molecular target for preventing osteosarcoma metastasis and an effective predictive biomarker for patients with osteosarcoma. Materials and methods Tissues Data were analyzed in accordance with guidelines approved by the Ethics Committee of Luoyang Orthopedic Hospital of Henan Province and the Orthopedic Hospital of Henan Province. Written informed consent was obtained from all participants prior to specimen collection. All patients were diagnosed with osteosarcoma and underwent surgery in our hospital from 2012 to 2018. Patients did not undergo other treatments, including chemotherapy or radiotherapy, before surgery. Osteosarcoma specimens were obtained from 48 males and 23 females (71 patients). The average patient age was 27.5 years (range, 12–38 years). Hematoxylin and eosin (H&E)-stained sections were obtained for diagnosis. Each section was independently diagnosed by two pathologists following the World Health Organization (WHO) classification of osteosarcoma. The following data were recorded: patient gender, age, tumor size, clinical stage, tumor location, and lymph node metastasis status. Pathological diagnosis was performed following the criteria set by the American Joint Committee on Cancer. Cell culture and reagents U2OS and MG-63 cells were purchased from the Shanghai Institute of Biochemistry and Cell Biology (Chinese Academy of Sciences, Shanghai, China) and cultured in DMEM containing 10% fetal bovine serum (FBS), 100 μg/ml of penicillin, and 100 μg/ml of streptomycin at 37°C in an incubator with 5% CO2. The human normal osteoblastic cell line, hFOB 1.19, was maintained in DMEM/F-12 (Gibco) supplemented with 10% FBS (Gibco) and 0.3 mg/ml of geneticin (G418; Gibco) at 37°C in a humidified atmosphere containing 5% CO2. Gene silencing by small interfering RNA (siRNA) Loss-of-function analysis was conducted using siRNA to knock down TGF-β expression. Three siRNAs targeting TGF-β (si- TGF-β) and one control siRNA (si-control) were obtained from Shanghai GenePharma Co., Ltd (Shanghai, China). siRNA sequences were: si-TGF-β#1: 5′-CCATCTTCACATGGAGATT-3′; si-TGF-β#2: 5′-GGAGATTGTT GGTAC CCAA-3′; si-TGF-β#3: 5′-TCAAGAGACCAAGGTACAT-3′; and si-control: 5′-TTCTCCGAACGTGTCACGT-3′. Cells were pre-treated with basic culture medium DMEM/F12 with or without 25 M LY294002 (a specific PI3K inhibitor, that effectively inhibits Akt phosphorylation) for 6 h prior to transfection [21,22]. Each siRNA (100 nM) was mixed with Lipofectamine 3000 (Invitrogen) as a carrier and transfected into U2OS/MG-63 cells (1 × 106) for 10 h at 37°C, following the manufacturer’s protocol. The efficacy of TGF-knockdown was validated by RT-qPCR and western blot analyses and cells were used for experiments 48 h post-transfection. MTT assay After transfection for 24, 48, and 72 h, U2OS/MG-63 cells were transfected with si-control or si-TGF-β and then cultured in 96-well flat-bottom microtiter plates overnight at a density of 1 × 104 cells/well. Subsequently, 20 μl of MTT solution was added to the cells, followed by incubation for 4 h at 37°C. An automatic multiwell spectrophotometer was then used to calculate the absorbance value for each well at 570 nm. Sphere formation assay Sphere formation assays were performed on third and fourth passage cells. Osteosarcoma spheroids were cultured in a specialized growth medium (Celprogen Inc, Torrance, CA, USA) containing 1% N2 supplement (Invitrogen), 2% B27 supplement (Invitrogen), 20 ng/ml human platelet growth factor (Sigma-Aldrich), 100 ng/ml epidermal growth factor (Invitrogen), and 1% antimycotic (Invitrogen). Osteosarcoma spheroids and hFOB 1.19 cells were cultured at 37°C in a humidified atmosphere of 95% air and 5% CO2. Spheres were counted using an inverted microscope and cell colonies with a diameter > 50 μm were measured. FACS analysis Osteosarcoma cells were harvested with fresh 0.25% trypsin solution (Sigma-Aldrich) and suspended in phosphate-buffered saline (PBS). Cells were blocked on ice for 15 min and labeled with PE-conjugated anti-human CD133 antibody (BioLegend) for 60 min. Cells were then washed twice with PBS and maintained on ice until analysis. Expression levels were determined by flow cytometry (FACS Calibur, BD Bioscience, USA) and data were analyzed using WinMDI software (Scripps Research Institute, La Jolla, CA, USA). Western blotting Cells were extracted and lysed using radioimmunoprecipitation assay (RIPA) lysis buffer (P0013B, Beyotime Biotechnology) supplemented with phenylmethanesulfonyl fluoride (PMSF; 1 µM). Extracted proteins were treated with Enhanced BCA Protein Assay Reagent (P0009, Beyotime Biotechnology), following the manufacturer’s instructions, and quantified using a microplate reader. Next, equal amounts of total protein (40 µg) were separated by 10% SDS-PAGE (P0012A; Beyotime Biotechnology) and transferred onto PVDF membranes (Millipore, Billerica, MA, USA). The membranes were blocked with 5% skimmed milk at room temperature for 2 h, and incubated overnight at 4°C with the following primary antibodies (all raised in a rabbit host): anti-CD133 (1:1000, ab19898), anti-SOX-2 (1:1,000, ab97959), anti-Oct-4 (1:1,000, ab18976), anti-NANOG (1:1,000, ab80892), anti-MMP2 (D8N9Y, 1:1,000, ab13132), anti-E-cadherin (1:1,000, ab15148), and anti-vimentin (1:1,000, ab92547) (Abcam, Cambridge, UK); anti-p-PI3K (1:500; 4228s), anti-PI3K (1:1,000, 4257s), anti-p-AKT (1:1,000, 4060s), anti-AKT (1:500, 9272s) (Cell Signaling Technology, Inc); and anti-GAPDH (1:2000, sc-47,724; Santa Cruz Biotechnology, Inc). PVDF membranes were then incubated with the following secondary antibodies: goat anti-mouse IgG (cat. no. 2305) and goat anti-rabbit IgG (cat.no. 2301) (Beijing Zhongshan Golden Bridge Biotechnology, Beijing, China) at room temperature for 1 h. Protein bands were visualized by enhanced chemiluminescence detection using a Bio-Rad gel imaging system (Bio-Rad Laboratories, Inc., Hercules, CA, USA). Densitometric analysis was performed using Quantity One software (Bio-Rad Laboratories, Inc.) and GAPDH was used as a loading control. Transwell assays Cells were seeded in 10 mm diameter transwell plates with polycarbonate filters (8 μm pore size). The upper and lower compartments of the plates were separated by a filter coated with 25 mg of Matrigel, which formed a reconstituted basement membrane at 37°C. After treatment with si-control or si-TGF-β, cells were seeded into the upper well, and the lower well was filled with DMEM containing 10% fetal calf serum. After incubation in the presence of 5% CO2, cells were fixed for 30 min in 4% formaldehyde and stained for 15 min. Nonmigrating cells were removed from the upper surface of the transwell chamber using a wet cotton swab, and the number of cells that had migrated or invaded the bottom surface of the filter was determined. For each well, six evenly spaced fields of cells were counted using an inverted phase-contrast microscope. Wound healing assay Cells were seeded in six-well plates at a density of 5 × 105 cells/well in DMEM supplemented with 10% FBS. Twelve hours after seeding, cells were treated with si-control or si-TGF-β for 48 h. A scratch was made in the cell monolayer, and the rate of wound closure was observed at 24 h and 48 h. The resultant data were analyzed using ImageJ software. RT-qPCR analysis Total RNA was extracted using Trizol reagent (Invitrogen). qPCR to detect TGF-β expression included 1 l of cDNA, 1 l each of forward and reverse primer, and 12 l of Master Mix (Promega, USA) and was performed on a Thermo ABI 7500 Real-Time PCR system (USA). All experiments were performed following the manufacturer’s instructions. GAPDH was used as the internal control for gene expression. The primer sequences were: TGF-β forward: 5′-AGGGCTACCATGCCAACTTC-3′ and reverse: 5′-GCGGCACGCAGCACGGTGAT-3′; and GAPDH forward: 5′-TGCCCAG AACAT CATCCCT-3′ and reverse: 5′-GTCCTCAGTGLAGCCCAAG-3′. Tumor xenografts Animal care protocols and experiments were approved by the animal ethical committee of Luoyang Orthopedic Hospital of Henan Province and the Orthopedic Hospital of Henan Province and performed in accordance with the guidelines specified in the Guide for the Care and Use of Laboratory Animals (Ministry of Science and Technology of China, 2006). Twenty female BALB/c mice (4–6 weeks old) were purchased from the Shanghai Laboratory Animal Center (Shanghai, China). Cells (1 × 106) were subcutaneously injected into each mouse. Nude mice were anesthetized with a 3–5% (v/v) mixture of isoflurane (Aerrane; isoflurane, Baxter) in synthetic air (200 ml/min), killed by cervical dislocation, and the tumors were obtained. The body weight and tumor length and width were measured in each mouse every 3 days. Additionally, tumor volume was calculated using the formula: volume = 1/2 (width2 × length). After 4 weeks, the mice were euthanized, and the tumors excised and weighed. Immunohistochemistry Immunohistochemical (IHC) staining was performed to measure Ki-67, PI3K, and Akt protein levels. Tumor tissue was embedded in paraffin, cut into 4-μm sections, and heated overnight at 60°C. The sections were dewaxed in xylene and rehydrated in a 100, 95, 80, and 75% alcohol series. Slides were incubated overnight with primary antibodies against Ki-67, PI3K, and Akt at 4°C. The next day, sections were incubated with the appropriate secondary antibodies for 1 h at 37°C. Data were then analyzed using a light microscope (Olympus Corporation). Evaluation of IHC staining The H-score system was used to evaluate the immunointensity of tumor cells. The score for staining intensity was: 0, no staining; 1, weak staining; 2, moderate staining; and 3, strong staining. The formula used to evaluate the immunointensity of tumor cells was: H-score = (percentage of cells with weak intensity × 1) + (percentage of cells with moderate intensity × + (percentage of cells with strong intensity × 3). The scoring was performed using the Densito Quant module of Quant Center software. Scores of duplicate specimens were averaged. At a total H-score of 276, the threshold for TGF-β expression was set at 165.38. TGF-β staining results were classified as TGF-β-low (≤ 165.38) or TGF-β-high (> 165.38). Statistical analysis Data are presented as means ± standard deviation (SD). Fisher’s exact test was applied for multiple comparisons. Statistical analyses were performed using SPSS software version 16.0 (SPSS, Inc.). P < 0.05 was considered to indicate significance. Results TGF-β is upregulated in osteosarcoma tissues and positively correlates with poor survival IHC was performed on 71 osteosarcoma samples and corresponding normal adjacent tissues to assess TGF-β expression and localization. TGF-β was expressed in low levels in the cytoplasm of normal tissue (Figure 1(a), left) and in high levels in the cytoplasm of osteosarcoma specimens (Figure 1(a), right). The immunointensity of TGF- β staining in tissue samples was evaluated using H-score. The proportion of TGF-β-high osteosarcoma samples was substantially greater than that of normal adjacent tissue samples (74.65% vs. 18%, for osteosarcoma and normal samples, respectively; P = 0.003) (Table 1).Table 1. TGF-β expression in the samples Group Samples TGF-β expression [n (%)] X2 P Low (H-score ≤165.38) High (H-score >165.38) Control 50 41(82) 9(18) osteosarcoma 71 18(25.35) 53(74.65) 10.689 0.003* P>0.05 Figure 1. TGF-β expression in osteosarcoma specimens and cell lines. (a) Immunohistochemical staining was used to analyze TGF-β expression in both osteosarcoma specimens and normal adjacent tissues. (b) Survival analysis was performed using the Kaplan-Meier method. Data are presented as mean ± SEM of three independent experiments. (c) TGF-β protein levels in osteosarcoma cell lines (P < 0.05). (d) TGF-β mRNA expression in osteosarcoma cell lines (P < 0.05). There was no correlation between TGF-β expression and gender, age, or histological grade (P > 0.05). TGF-β expression levels were markedly positively correlated with high Ennecking stage (P = 0.033), metastasis, and recurrence (P = 0.008) (Table 2).Table 2. The relationship between TGF-β expression and pathological characteristics. Characteristics TGF-β + – P Gender 0.312 Male 33 15 Female 20 3 Age(year) 0.865 >16 47 8 <16 14 2 Tumor size(d/cm) 0.187 >5 37 16 <5 16 2 Tumor location 0.412 Femur 28 10 Tibia and fibia 17 6 others 8 2 Histological type 0.238 Osteoblastic 28 6 Chondroblastic 10 3 Fibroblastic 10 2 Ennecking stage 0.0023 IIA 5 16 IIB/III 48 2 Metastasis 0.018 Yes 40 3 No 13 15 in osteosarcoma patients Western blot and RT-qPCR analyses (Figure 1(b,d)) showed that TGF-β levels were higher in U2OS, MG-63, and HOS cells than in normal human osteogenic hFOB1.19 cells (P 0.05). Kaplan-Meier analysis was used to assess the prognostic value of TGF-β expression in patients with osteosarcoma. Overall survival was shorter in patients with osteosarcoma and higher levels of TGF-β expression, indicating that TGF-β is a potential prognostic factor for osteosarcoma (Figure 1(c)). TGF-β knockdown inhibits osteosarcoma cell proliferation and promotes their apoptosis To clarify the biological functions of TGF-β in U2OS and MG-63 cells, TGF-β expression was knocked down using short interfering (si) RNA. si-control and si-TGF-β#1, #2, and #3 were transfected into cells and TGF-β knockdown was confirmed by RT-qPCR and western blotting (Figure 2(a,b)). si-TGF-β#1 produced the best knockdown efficiency (P < 0.01) and used for subsequent studies.Figure 2. Small interfering (si) RNA targeting of TGF-β (si-TGF-β) suppresses proliferation and promotes apoptosis in osteosarcoma. (a) RT-qPCR was employed to assess the knockdown efficiency of si-TGF-β, P < 0.05 vs. the si-control group. (b) Western blotting was used to measure the knockdown efficiency of si-TGF-β, P < 0.05 vs. the si-control group. (c)The MTT assay was used to assess the viability of U2OS and MG-63 cells transfected with si-control or si-TGF-β, P < 0.05 vs. the si-control group. (d and e) The colony-forming ability of U2OS and MG-63 cells transfected with si-control or si-TGF -β, P < 0.05 vs. the si-control group. (f and g) Flow cytometry analysis of the cell cycle phase distribution of U2OS and MG-63 cells transfected with si-control or si-TGF – β, P < 0.05 vs. the si-control group. (h and i) The rate of apoptosis in U2OS and MG-63 cells transfected with si-TGF-β, P < 0.05 vs. the si-control group. (j and k) TGF-β knockdown increases the expression of cleaved PARP, caspase-3, and BAX, P < 0.05 vs. the si-control group. Compared with si-control transfection, si-TGF-β transfection significantly reduced the viability of U2OS and MG-63 cells (P < 0.05; Figure 2(c)). Colony formation assays, to assess cell proliferation, revealed that TGF-β knockdown suppressed clonogenicity in U2OS and MG-63 cells (P < 0.05; Figure 2(d,e)). Additionally, pretreatment with si-TGF-β increased S-phase cell cycle arrest in both cell lines (figure 2(f,g)). Flow cytometry assessment of apoptosis showed that si-TGF-β treatment increased the apoptotic ratio in both U2OS and MG-63 cells (Figure 2(h,i)). Moreover, TGF-β knockdown led to a substantial increase in cleaved PARP, caspase-3, and BAX levels and a decrease in Bcl-2 levels (Figure 2(j,k)). TGF-β knockdown of suppresses invasion, migration, and the epithelial-mesenchymal transition (EMT) in osteosarcoma cells Transwell assays were performed to examine the role of TGF-β in cell migration and invasion in U2OS and MG-63 osteosarcoma cells. TGF-β knockdown reduced the invasion potential of U2OS and MG-63 cells (Figure 3(a,b)). Moreover, si-TGF-β treatment increased wound widths in U2OS and MG-63 cells (Figure 3(c,d)). Taken together, these results indicate that TGF-β knockdown inhibits the migratory and invasive capacities of U2OS and MG-63 osteosarcoma cells.Figure 3. TGF-β promotes cell invasion/migration and the EMT. (a and b) TGF-β knockdown reduces the invasive ability of U2OS and MG-63 cells. TGF-β knockdown decreased the invasive ability of U2OS and MG-63 cells (lower panel) (P < 0.05). Magnification, × 200. (c and d) The rate of U2OS and MG-63 cell migration. Right panels, quantitative data. P < 0.05 vs. the si-control group. (e and f) E-cadherin, vimentin, α-SMA, and MMP-2 protein expression levels in MG-63 and U2OS cells with or without si-TGF-β treatment. GAPDH was used as a loading control. Results are expressed as means ± SD of three independent experiments; P < 0.05 vs. the si-control group. The EMT is a dynamic biological process through which epithelial cells develop mesenchymal cell properties. In cancer cells, the EMT ultimately leads to their metastasis [23]. TGF-β knockdown decreased MMP-2, vimentin, and α-SMA expression and increased E-cadherin expression in U2OS and MG-63 cells (Figure 3(e,f)). TGF-β knockdown reduces stemness and the ratio of CD133+ subpopulations in osteosarcoma cells Spheroid formation assays were performed to evaluate the effect of TGF-β on the self-renewal ability of osteosarcoma cells. TGF-β knockdown reduced spheroid volume and numbers in ultralow attachment plates (Figure 4(a,b)). Notably, we observed that TGF-β knockdown reduced the number of CD133+ cells (Figure 4(c,d)) and the expression levels of several stem cell-associated gene products, including SOX2, NANOG, and OCT4 (Figure 4(e,f)).Figure 4. TGF-β enhances osteosarcoma cell stemness and increases the proportion of CD133+ cells. (a and b) Spheroid formation assay for U2OS and MG-63 cells transfected with si-TGF-β. Representative images (left panel) and statistical measurements (right panel). Representative micrographs of formed spheres were analyzed in cells treated with si-TGF-β or si-control. Scale bar, 100 μm. P < 0.05 vs. the si-control group. (c and d) The percentage of CD133+ cells in U2OS- and MG-63-derived spheroids was analyzed by flow cytometry.P < 0.05 vs. the si-control group. (e and f) Stem cell-associated protein expression was measured by western blot. P < 0.05 vs. the si-control group. TGF-β accelerates osteosarcoma progression through PI3K/mTOR signaling pathway activation The PI3K/mTOR signaling pathway facilitates the proliferation, invasion/migration, vascularization, and tumorigenicity of cancer cells [24–27]. si-TGF-β treatment markedly attenuated p-PI3K and p-Akt expression in U2OS and MG-63 cells (Figure 5(a)), indicating that the PI3K/mTOR signaling pathway may mediate the effects of TGF-β on osteosarcoma progression. To further understand the role of the PI3K/mTOR signaling pathway in osteosarcoma, we exposed U2OS and MG-63 cells to LY294002, a PI3K-specific inhibitor that inhibits Akt phosphorylation. Pretreatment with 25 µM LY294002 significantly enhanced TGF-β knockdown-mediated colony formation inhibition (P< 0.05; Figure 5(b,c)). Following LY294002+ si-TGF-β cotreatment, significantly fewer invading cells and wider wound widths were observed (Figure 5(d–g)), as were smaller spheroid volumes and fewer CD133+ cells (Figure 5(h–k)). Together, these results imply that TGF-β regulates osteosarcoma development and stemness traits via the PI3K/mTOR pathway.Figure 5. TGF-β regulates the PI3K/mTOR signaling pathway in osteosarcoma cells. (a) p-PI3K and p-Akt expression was measured by western blot, P < 0.05 vs. the si-control group. (b and c) The colony-forming ability of si-TGF-β-transfected U2OS and MG-63 cells with or without LY294002 treatment. P < 0.05 vs. the si-control group. (d and e) Transwell assays were used to assess the invasive ability of U2OS and MG-63 cells, P < 0.05 vs. the si-control group. (f and g) A wound-healing assay was performed to measure the migratory ability of U2OS and MG-63 cells, P < 0.05 vs. the si-control group. (h and i) Spheroid formation assay for U2OS/MG-63 cells transfected with si-TGF-β. Representative images (left panel) and statistical measurement (right panel). Representative micrographs of formed spheres were analyzed in cells treated with si-TGF-β or si-control with or without LY294002 treatment. Scale bar, 100 μm. P < 0.05 vs. the si-control group. (j and k) The percentage of CD133+ cells in U2OS cell- and MG-63 cell-derived spheroids was analyzed by flow cytometry. P < 0.05 vs. the si-control group. TGF-β knockdown inhibits tumorigenicity in vivo A xenograft nude mouse model of osteosarcoma was established using U2OS cells to investigate whether TGF-β knockdown can inhibit tumor growth in vivo. The effects of TGF-knockdown were assessed following intratumoral injection of si-TGF-β or si-control. A significant decrease in tumor volume and weight was observed after si-TGF-β injection (P < 0.05 for each; Figure 6(a,b)). Immunohistochemical staining revealed that Ki-67, p-PI3K, and p-Akt expression levels were markedly reduced in xenograft tumors originating from si-TGF-β-transfected cells (Figure 6(c)).Figure 6. TGF-β promotes tumorigenicity in vivo. (a and b) Representative images of the xenograft tumors formed in nude mice injected with siRNA targeting TGF-β (si-TGF-β) or control siRNA (si-control). The volumes and weights of xenograft tumors are summarized. (c) Representative images of immunohistochemical staining for Ki-67, p-PI3K, and p-Akt in tumor nodules. Discussion Osteosarcoma is the most common type of bone tumor in children and adolescents, and is the second leading cause of cancer-related deaths in pediatric patients. Once diagnosed, osteosarcoma often develops into the final stage and late diagnosis and chemoresistance lead to high mortality rates [28]. Given the current poor prognosis for osteosarcoma patients, specific molecular targets for improving the prognosis of osteosarcoma and the effectiveness of intervention are urgently needed. TGF-β is a multifunctional cytokine that plays a role in various cancers, including hepatic, gastric, and colon cancers [29–31]. Furthermore, the TGF-β pathway is associated with poor prognosis in hepatocellular carcinoma, clear cell renal carcinoma, and colorectal cancer [32–34]. The serum level of TGF-β is significantly increased in osteosarcoma patients [12], but no study has investigated the role of TGF-β in the pathogenesis of this cancer to date. In this study, TGF-β was found to be highly expressed both in human osteosarcoma tissues and osteosarcoma cell lines, indicating that TGF-β may contribute to osteosarcoma procession. Immunohistochemical staining and statistical analysis further indicated that TGF-β was not associated with gender, age, tumor location, or histological grade (P < 0.05), but was positively correlated with high Ennecking stage and metastasis. Additionally, overall survival was shorter in patients with osteosarcoma and higher TGF-β expression. Taken together, these results support the suggestion that TGF-β may be a prognostic factor for osteosarcoma [12]. To determine how TGF-β affects osteosarcoma, we performed in vitro and in vivo experiments. Verrecchia et al. reported that TGF-β exerts its oncogenic function in primary bone tumors by promoting angiogenesis, bone remodeling, and cell migration and inhibiting immunosurveillance [35]. We found that si-TGF-β treatment significantly decreased the viability of U2OS/MG-63 cells (P < 0.05; Figure 2(a–c)). TGF-β has been shown to play a role in modulating osteosarcoma cell apoptosis [36]. Our results show that TGF-β downregulation directly induces an increase in S-phase cell cycle arrest and upregulates the expression of apoptosis-related markers. These results indicate that indicating that TGF-β plays a role in promoting osteosarcoma progression. Increasing evidence has shown that TGF-β exerts a promigratory effect on osteosarcoma cells and stimulates tumor growth [37,38]. Here, we found that TGF-β accelerates the EMT in osteosarcoma cells. When carcinoma cells undergo EMT, they become resistant to chemotherapy and acquire the ability to suppress immune responses, thereby promoting tumor progression. Exogenous TGF-β promotes an EMT-like phenotype in several osteosarcoma cell lines, and the development of lung metastases in osteosarcoma [39]. Similarly, we observed that TGF-β knockdown markedly reduced the number of invading cells and increased wound widths, indicative of reduced cell migration (Figure 3(a–d)). These results, together with the observed downregulation of vimentin, α-SMA, and MMP-2 expression levels, and increased E-cadherin levels, indicate that TGF-β induces invasion/migration and the EMT phenotype in osteosarcoma. This is consistent with previously reported results [40,41]. Metastasis and cancer stem cell (CSC) emergence are major causes of therapy failure. Xu et al. reported that MB-231/Epi cells express high levels of TGF-β and contain a larger population of breast CSCs [42]. However, they did not evaluate the effect of TGF-β on the modulation of stemness in breast cancer. Peng et al. showed that exogenous TGF-β application increases the percentage of CD44+/EpCAM+ cells, and vimentin and N-cadherin levels [12]. Meanwhile, Katsuno et al. showed that prolonged TGF-β exposure leads CD44+CD24− cell population and mammosphere formation increases [43]. However, these reports only assessed the effect of exogenous TGF-β on the regulation of stemness markers. In the current study, we observed that si-TGF-β treatment resulted in smaller spheroids sizes, a reduced ratio of CD133-positive cells, and downregulation of stemness marker expression (Figure 4(a–d)). These results indicate that endogenous TGF-β positively modulates stemness in osteosarcoma and contribute to the understanding of stemness property modulation by TGF-β. The PI3K/Akt, NF-κB, and Wnt pathways are involved in osteosarcoma progression. Katsuno et al. showed that prolonged TGF-β exposure enhances and stabilizes Akt-mTOR signaling [43]. Furthermore, Tsubaki et al. showed that the Ras/PI3K/Akt pathway positively regulates TGF-β levels in mouse osteosarcoma [44]. Consistent with these results, we observed reduced levels of p-PI3K and p-Akt following si-TGF-β treatment (Figure 5(a)). Moreover, pretreatment with 25 µM LY294002 (a PI3K-specific inhibitor) enhanced the si-TGF-β-mediated inhibition of PI3K and Akt phosphorylation. Cotreatment with si-TGF-β and LY294002 reduced the proliferative and invasive/migratory ability of osteosarcoma cells, spheroid size, and the ratio of CD133-positive cells (Figure 5(b–j)). Taken together, our in vitro experimental results, and those of other studies, show that the TGF-β/PI3K/Akt pathway drives osteosarcoma progression. In nude mice we observed si-TGF-β-mediated reduction in tumor volume and Ki67, p-PI3K, and p-Akt levels (Figure 6(a–c)), confirming the osteosarcoma-promoting effects of TGF-β. This is the first study to systematically characterize the role of TGF-β in osteosarcoma progression. The results of our in vitro and in vivo experiments show that TGF-β is positively correlated with high Ennecking stage and metastasis. Our results reveal the mechanism underlying the role of TGF-β in osteosarcoma progression and modulation of stemness. This study had several limitations. First, the sample size was relatively small and may have led to regional bias. Therefore, multicenter and multiarea samples are required to validate our findings. Second, clinical trials are warranted to assess the application of TGF-β as a biomarker in the management of osteosarcoma. Our findings may help in the development of novel therapeutic strategies to promote the long-term survival of patients with osteosarcoma. Supplementary Material Supplemental Material Click here for additional data file. Disclosure statement No potential conflict of interest was reported by the authors. Supplementary material Supplemental data for this article can be accessed here. ==== Refs References [1] Picci P. Osteosarcoma (osteogenic sarcoma). Orphanet J Rare Dis. 2007;2 :6.17244349 [2] Geller DS, Gorlick R. Osteosarcoma: a review of diagnosis, management, and treatment strategies. Clin Adv Hematol Oncol. 2010;8 :705–718.21317869 [3] Shaikh AB, Li F, Li M, et al. Present Advances and future perspectives of molecular targeted therapy for osteosarcoma. 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==== Front ACS Omega ACS Omega ao acsodf ACS Omega 2470-1343 American Chemical Society 10.1021/acsomega.0c02518 Article Biological Effects of Titanium Surface Charge with a Focus on Protein Adsorption Ding Xianglong † Xu Shulan † Li Shaobing † Guo Zehong † Lu Haibin †‡ Lai Chunhua † Wu Jingyi † Wang Jingxun § Zeng Shuguang ∥ Lin Xi † Zhou Lei † † Center of Oral Implantology, Stomatological Hospital, Southern Medical University, Guangzhou 510280, China ‡ Stomatology Center, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Shunde 528000, China § Stomatology Department, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China ∥ Department of Oral and Maxillofacial Surgery, Stomatological Hospital, Southern Medical University, Guangzhou 510280, China Email: [email protected]. Tel: +86 2034152947. 02 10 2020 13 10 2020 5 40 25617 25624 28 05 2020 22 09 2020 2020American Chemical SocietyThis is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes. The effect of changes in surface charge on the biological properties of implants is not clear. The objective of this study was to evaluate the biological properties of the surface of titanium sheets with different charges due to different treatment methods. Titanium sheets were sandblasted with large grit and underwent acid etching before being subsequently divided into the following groups: SLA, no further treatment; SLA-Ca2+, immersed in 1% CaCl2 solution; SLA-NaCl, immersed in saline; and SLA-Ca2+-NaCl, immersed in 1% CaCl2 solution followed by saline. Surface characteristics were evaluated using field-emission scanning electron microscopy with energy-dispersive spectrometry, surface profilometry, and contact angle assays. Additionally, we used a ζ-potential analyzer to directly measure the electrostatic charge on the different group surfaces. The effect of changes in the Ti surface on biological processes after different treatments was determined by analyzing fibronectin adsorption, osteoblast-like MG63 cell adhesion and proliferation, and the expression of osteogenesis-related genes. Compared to the SLA surface, the other three groups contained corresponding trace elements because they were soaked in different liquids; the contact angles of the three groups were not significantly different, but they were significantly smaller than that of the SLA group; and there was no change in the surface topography or roughness. Furthermore, the SLA-Ca2+ group had a significantly reduced negative charge compared to that of the other three groups. There were no differences between the SLA-NaCl and SLA-Ca2+-NaCl groups in terms of negative charge, and the SLA group surface carried the most negative charge. Fibronectin adsorption capacity and cytological performance testing further showed that the SLA-Ca2+ group had the most significant change, followed by the SLA-NaCl and SLA-Ca2+-NaCl groups; the SLA group had significantly lower capacity and performance than the other three groups. These results suggest that the surface charge of the titanium sheet changed when immersed in different liquids and that this treatment enhanced biocompatibility by reducing the electrostatic repulsion between biomaterials and biomolecules. document-id-old-9ao0c02518document-id-new-14ao0c02518ccc-price ==== Body 1 Introduction It is well known that the surface characteristics of biomedical implants influence their interactions with cells and the extracellular matrix (ECM). Therefore, they are key factors in determining the osseointegration of implants.1 Modification methods commonly used on the surface of an implant affect the surface morphology, composition of chemical elements, molecular structure, charge state, surface free energy, and hydrophobic properties.2−4 These modifications aim to improve the biological properties of the implant surface and further affect the complex biological effects between the implant and the body.5 Most basic research focus, for example, on the effect of the morphological structure of the implant surface or the composition of chemical elements on the biological behavior of osteoblasts.2,5 There are no systematic studies on the impact of changes in the charge state of the implant surface on the interaction with proteins and cells. According to previous research by our team and related reports, the surface of titanium implants carries less negative charge after ultraviolet irradiation, weakening the electrostatic repulsion force against proteins, promoting protein adsorption, and playing an important role in the process of early cell adhesion; therefore, different surface charges affect the biological activity of the titanium surface. This interferes with the efficiency of early bone formation.6−8 However, our research group and other reports show that, whether it is a freshly processed titanium sample or a sample with a highly active surface carrying a positive charge after UV irradiation, there is a phenomenon of biological aging.7−10 As long as the sample is stored in an ordinary environment, its surface will be contaminated with hydrocarbons in a relatively short period of time, from the initial hydrophilic surface to a hydrophobic surface, the positive charge becomes negative, and the protein adsorption capacity also decreases. The early adhesion, proliferation, differentiation, and mineralization ability of osteoblasts is reduced to the level before UV irradiation; this phenomenon is called biological activity aging. More importantly, this pollution is almost inevitable in an ordinary environment. The cause of this biological aging may be related to the following aspects: as the sample is stored in an ordinary environment, the titanium surface gradually becomes contaminated with hydrocarbons. It is also related to the rapid disappearance of hydrophilicity and positive surface charge with storage time.9−11 In addition, the pH value of blood in normal humans is between 7.35 and 7.45. The isoelectric point of most proteins in the human body is less than 7. For example, the isoelectric point of fibronectin (Fn) is approximately 5.8, and bone morphogenetic Protein-2 (BMP2), which is important for bone formation, has an isoelectric point of approximately 4.8, which means that most proteins in the blood of normal humans are negatively charged (even the surface of osteoblasts is also negatively charged).9 Therefore, the positively charged (or negatively reduced) titanium surface irradiated by UV in the above study quickly forms early adhesions with negatively charged proteins through greater electrostatic attraction. Without treatment or aging after UV irradiation, the surface of the titanium implant carries a large amount of negative charge in the blood environment (our testing shows that the isoelectric point of the titanium sheet is approximately 4.2).7 There is a strong electrostatic repulsion between the titanium implant and proteins, and previous studies have ignored this. Therefore, if the implant surface carries a positive charge or less negative charge, it significantly enhances the electrostatic attraction between the implant surface and the protein, further improving the protein adsorption capacity of the titanium implant surface and significantly improving the biological properties of the biomaterial. According to previous reports,12,13 divalent calcium cations were used as bridging ions in this study. Titanium tablets were soaked in a CaCl2 solution. As we know, in a pH 7.4 environment, the titanium surface carries a large number of negative charges. Calcium ions carry two positive charges, one of which is adsorbed by the negative charge on the surface of the titanium sheet, while the other positive charge may attract a negatively charged Fn. Therefore, electrostatic repulsion between the two sides becomes electrostatic adsorption. To the best of our knowledge, there are relatively few reports on using different ions to change the surface charge state of titanium sheet. Perhaps most importantly, we detected the charge state of different sample surfaces using a solid surface ζ-potential analyzer. We hypothesized that the surface charge state of the experimental group treated with Ca2+ could be changed. Thus, we tested the protein adsorption capacity of surfaces with different charge states. The biological properties of the sample surfaces with different charge states were then examined using relevant cytological experiments. 2 Results 2.1 Surface Characterization Following previous manufacturing methods for SLA surface fabrication, microtopographies were successfully synthesized. Multilevel pores characterized by 10–30 μm pits and 1–3 μm micropits were formed on the specimen surfaces upon sandblasting with large grit and acid etching (Figure 1). Figure 1 Typical field-emission scanning electron microscopy (FESEM) of SLA treated with various solutions. According to the energy-dispersive spectroscopy (EDS) test, very small amounts of Na, Ca, and Cl were found in the different groups. As shown in Table 1, except for the SLA group, the surfaces of the three groups of samples contained corresponding elements because they were soaked in different liquids, although the content was very small. In addition, each group contains Ti and C because of pollution in an ordinary environment. Table 1 Chemical Composition (atom %) of All Invested Groups group Ti C Na Cl Ca SLA 97.98 2.02 SLA-NaCl 96.31 2.04 0.97 0.68 SLA-Ca2+ 97.49 2.03 0.26 0.22 SLA-Ca2+-NaCl 96.42 2.03 0.79 0.53 0.23 The Ra values of the different groups were assayed, and the results are displayed in Figure 2. The Ra values of the SLA, SLA-NaCl, SLA-Ca2+, and SLA-Ca2+-NaCl groups were 2.92 ± 0.22, 2.84 ± 0.08, 3.03 ± 0.16, and 2.91 ± 0.11, respectively. There were no significant differences in Ra among the four groups. Figure 2 Comparisons of the roughness parameter (Ra) of different surfaces. There were no significant differences in the roughness parameters among the four groups. Ra: description of the height variation of SLA, sandblasting with large grit, and acid etching. The contact angle measurements are shown in Figure 3. The average contact angle of the SLA surface was approximately 80°. The other three groups showed better hydrophilicity at a water contact angle of approximately 44, 43, and 43° for SLA-NaCl, SLA-Ca2+, and SLA-Ca2+-NaCl, respectively. The contact angles of the three groups immersed in different liquids had no significant difference, but they were significantly smaller than that of the SLA group, and the results are shown in Figure 4. Figure 3 Photographic images of a 10 μL of H2O droplet on different surfaces. Figure 4 Comparisons of the contact angle of different surfaces. The SLA group has the largest contact angle compared to the other three groups. There were no significant differences in the contact angles between the SLA-NaCl, SLA-Ca2+, and SLA-Ca2+-NaCl groups, but they were significantly smaller than that of the SLA group. P < 0.01 compared with SLA-Ca2+, ##P < 0.01 compared with SLA-NaCl, and @@P < 0.01 compared with SLA-Ca2+-NaCl. 2.2 Surface Charge of the Specimens ζ-Potential measurements revealed a difference in electrokinetic interactions at the interface between the biomaterial surface and the aqueous electrolyte in this study. Figure 5 shows the ζ-potential of the four different groups at pH 7.4. The ζ-potential values of the SLA, SLA-NaCl, SLA-Ca2+, and SLA-Ca2+-NaCl groups were −80.83 ± 0.44, −64.89 ± 0.63, −41.17 ± 0.44, and −65.68 ± 0.50 mV, respectively. This clearly shows that the SLA-Ca2+ group has the lowest absolute value of ζ-potential compared to the other three groups. There were no significant differences in ζ-potential between the SLA-NaCl and SLA-Ca2+-NaCl groups, but these groups had significantly lower ζ-potential absolute values than the SLA group. Figure 5 Comparison of ζ-potential at pH 7.4 of different groups. The SLA-Ca2+ group has the lowest absolute value of ζ-potential compared to the other three groups. There were no significant differences between the ζ-potential values in the SLA-NaCl and SLA-Ca2+-NaCl groups, but they were significantly lower than that in the SLA group. P < 0.01 compared with SLA-Ca2+, ##P < 0.01 compared with SLA-NaCl, and &&P < 0.01 compared with SLA-Ca2+-NaCl. 2.3 Protein Adsorption The protein adsorptive amounts of the SLA, SLA-NaCl, SLA-Ca2+, and SLA-Ca2+-NaCl surfaces over different time periods were analyzed using a bicinchoninic acid assay, and the results are shown in Figure 6. The rate of protein adsorbed by the SLA-Ca2+ group was significantly higher than that of the other three groups at each time point. There was no significant difference between the rates of protein adsorbed by the SLA-NaCl and SLA-Ca2+-NaCl groups, but these rates were significantly higher than those of the SLA group at each time point. Figure 6 Protein adsorptive capacity of the four groups assessed using fibronectin. P < 0.01 compared to the group of SLA-Ca2+, ##P < 0.01 compared to the group of SLA-NaCl, and &&P < 0.01 compared to the group of SLA-Ca2+-NaCl. 2.4 Cell Adhesion Initial cell adhesion on the SLA, SLA-NaCl, SLA-Ca2+, and SLA-Ca2+-NaCl surfaces was estimated by counting the number of MG63 cell nuclei stained with Hoechst dye, as shown in Figure 7. The results were calculated using Image-Pro Plus 6.0 software and are displayed in Figure 8. At each time interval, the number of adherent cells on the SLA-Ca2+ surfaces was dramatically higher than that on the other three surfaces (P < 0.01). The cell number on the surface of the SLA-NaCl and SLA-Ca2+-NaCl groups was significantly higher than that in the SLA group (P < 0.01), but the two groups have no difference at each time point. At each incubation time point, the surfaces of the SLA group had the lowest number of cells among the groups. Figure 7 Number of MG63 cell nuclei stained with Hoechst dye on the SLA, SLA-NaCl, SLA-Ca2+, and SLA-Ca2+-NaCl surfaces at 0.5, 1, and 2 h (100×). Figure 8 Cell numbers (mean ± standard deviation) on different surfaces at different times (n = 3, 10 different random fields of each disc). The number of adherent cells on the SLA-Ca2+ surfaces was significantly higher than that on the other three surfaces (P < 0.01). The cell number on the surface of the SLA-NaCl and SLA-Ca2+-NaCl groups was significantly higher than that in the SLA group (P < 0.01), but the two groups have no difference at each time point. The surfaces of the SLA group had the lowest number of cells among the groups at each incubation time point. P < 0.01 compared to the group of SLA-Ca2+, ##P < 0.01 compared to the group of SLA-NaCl, and &&P < 0.01 compared to the group of SLA-Ca2+-NaCl. 2.5 Cell Proliferation Cell proliferation was evaluated via the 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) assay, as shown in Figure 9. Cell proliferation increased over time in all four groups. Cells grown on the SLA-Ca2+ surface had significantly higher proliferative activity than those grown on the other three surfaces, regardless of the incubation time (P < 0.01). No significant differences were found between the SLA-NaCl and SLA-Ca2+-NaCl groups, but both groups showed significantly higher proliferative activity than the SLA group at all incubation times (P < 0.01). Figure 9 Proliferation of MG63 cells seeded onto SLA, SLA-NaCl, SLA-Ca2+, and SLA-Ca2+-NaCl surfaces as measured by the MTS assay. The SLA-Ca2+ group had significantly higher proliferative activity than the other three groups, regardless of the incubation time (P < 0.01). There were no significant differences in proliferative activity between the SLA-NaCl and SLA-Ca2+-NaCl groups at the time points used in this study, but the two groups showed significantly higher proliferative activity than the SLA surface at all incubation times (P < 0.01). P < 0.01 compared to the group of SLA-Ca2+, ##P < 0.01 compared to the group of SLA-NaCl, and &&P < 0.01 compared to the group of SLA-Ca2+-NaCl. 2.6 Gene Expression Analysis Gene expression on the different surfaces was quantified using real-time polymerase chain reaction (RT-PCR), as shown in Figure 10. Generally, high ALP expression was detected at week 1 and was subsequently greatly decreased at week 2. The ALP expression level in cells grown on the SLA -Ca2+ surface was higher than that in cells grown on other surfaces at the two time points (P < 0.01). The lowest level of ALP expression was observed in the SLA group (P < 0.01), but ALP expression was not significantly different between cells grown on the SLA-NaCl and SLA -Ca2+-NaCl surfaces at all time points. Similarly, RUNX2 expression was upregulated at week 1 and decreased to a low level at week 2. The group showing the highest RUNX2 expression level was SLA-Ca2+ (P < 0.01), and there were no significant differences among the other three groups at the two time points. For OCN, a low level of expression was observed in all groups at week 1, followed by upregulation during week 2. The groups with the highest and lowest expression levels of OCN, respectively, were the SLA-Ca2+ and SLA groups at the two time points (P < 0.01). There was no statistical difference between the SLA-NaCl and SLA -Ca2+-NaCl surfaces at the two time points. Figure 10 Comparison of mRNA expression of ALP, RUNX2, and OCN by different types of cells. P < 0.01 compared to the group of SLA-Ca2+, ##P < 0.01 compared to the group of SLA-NaCl, and &&P < 0.01 compared to the group of SLA-Ca2+-NaCl. 3 Discussion The ideal function of dental implant restoration is based on good osseointegration between the implant and bone tissue.11,14 The occurrence and formation of osseointegration include complex physiological processes involving mutual recognition, reaction, and a combination of artificial materials and organism hosts. When titanium implants and other biological materials are implanted into the host, they first recognize each other with proteins, fibers, ions, cytokines, and other components in the blood environment, leading to the adhesion, proliferation, and differentiation of osteoblasts.4,6 In addition to mineralization, osseointegration eventually forms.15 Therefore, the identification and attachment of proteins on the surface of implants and the adhesion of osteoblasts are key links in osseointegration. Rapid completion of the adhesion process is one of the requirements for the rapid realization of bone bonding.16 In the process of osteoblast adhesion, fibronectin is one of the main guide proteins in the ECM that mediates the osteoblasts to complete the adhesion step. Therefore, in this study, we used Fn as a model protein for our detection. As the pH of normal human blood is approximately 7.4, and it is difficult to change this reality, only changing the charge state on the surface of the biological material can change the static electricity between the protein and the biological material in this environment. According to previous literature, researchers speculate that the surface of implants irradiated by UV carries positive charges and negatively charged proteins to quickly form early adhesion through electrostatic attraction and accelerates the enhancement of early osseointegration of implants.17,18 In our study, using the ZETA potentiometer, the surface of the titanium sheet after UV irradiation did not carry a positive charge, but it carried less negative charge than the control group.7 Therefore, the static electricity between the biological material and the protein, the repelling effect, was significantly weakened, which further improved the protein adsorption capacity of the titanium implant surface. This also significantly improves the biological performance of the implant material. However, aging after ultraviolet irradiation is inevitable. Therefore, in this study, we tried to change this condition by storing the implant in a liquid, so that the implant can be preserved for a long time and maintain good biological performance, at the same time avoiding hydrocarbon pollution in the air environment.19 In this study, we prepared microrough surface morphology on the surface of a titanium sheet via large-particle sandblasting and acid etching technology. Through FESEM observations, we could observe typical primary and secondary pore structures. Forming a mechanical fit between the implant and the bone tissue to obtain good initial stability, the surface roughness test revealed that there was no difference between the groups immersed in different liquids. Examination of surface elements revealed that, except for the SLA group, the other three groups retained a very small amount of the corresponding elements on their surfaces because of immersion in different solutions. The titanium plates soaked in different solutions were removed and naturally dried at 25 °C for 30 min. Next, the hydrophilicity of the different samples was tested. This may be because some inorganic salts with hygroscopic effects remain on the surface of the samples or the samples were stored in the liquid to avoid the pollution of hydrocarbons, which resulted in the surface hydrophilicity of the three groups of titanium sheets preserved in their solutions being significantly better than that of the SLA group. The ζ-potential is a parameter that reflects the state of charge between the solid and liquid interface. In this study, we used a solid surface ZETA potentiometer to detect the charge on the surface of each group of samples. The charge on the solid surface is affected by various factors, such as the surface chemical composition; the pH value of the surrounding environment can also affect the state of surface charge.7 Here, the pH value was set under normal physiological conditions to better simulate the real situation. According to our previous research, pure titanium has an isoelectric point of approximately 4.2; therefore, its surface carries a large amount of negative charge at a pH of 7.4. In addition, researchers have shown that the titanium implant surface is easily contaminated by hydrocarbons when stored under common conditions protected from light, which can lead to biological aging.8−10 Our test results also confirmed that the SLA group carried the most negative charges. We used calcium ions carrying a bivalent positive charge as bridging ions. One positive charge is connected to the surface of the negatively charged titanium sheet, and another positive charge can adsorb negatively charged Fn (Figure 11). The surface charge detection results are as expected. After treatment with divalent calcium ions, the surface of the SLA-Ca2+ group generally carried the least negative charge; therefore, its surface Fn adsorption capacity was also the strongest. On the surface of the SLA-NaCl group, there are relatively few negative charges because part of the negative charge on the surface is neutralized with Na+ after the titanium sheet is soaked in saline. Similar to the SLA-NaCl group surface, the SLA-Ca2+-NaCl group should carry the least negative charge after treatment with divalent calcium ions, but after being immersed in physiological saline, the positive charge on the surface is neutralized by Cl–. Therefore, the negative charges carried on the surface of the two groups were significantly reduced compared with the SLA group, and the surface of the SLA group generally carried the most negative charges. This result was verified again in the detection of the Fn adsorption capacity. The SLA-Ca2+ group with the least surface negative charge had the strongest Fn adsorption capacity. Both the SLA-NaCl and the SLA-Ca2+-NaCl groups had similar Fn adsorption capacities, which were significantly higher than those of the SLA group, and the SLA group had the strongest electrostatic repulsion between the negative charge carried on the surface and Fn; therefore, its surface protein adsorption capacity was the weakest. Figure 11 Schematic of the mechanism underlying electrostatic interactions between fibronectin molecules and titanium surfaces bridged by different ions. Although it has been reported that a hydrophilic surface selectively promotes the adsorption of fibrinogen,4 in the present study, the adsorption capacity of Fn on the surface of three groups of samples with the same hydrophilic effect was not completely consistent, which further explained the influence of surface charge on the adsorption capacity of Fn. The biological performance of each group was verified by relevant cell experiments. In the early cell adhesion and proliferation experiments, we found that the early adhesion ability and cell proliferation of the cells in the SLA-Ca2+ group at each time point were significantly higher than those of the other three groups, followed by the SLA-NaCl and SLA-Ca2+-NaCl groups, and the SLA group had the least adhesion and proliferation. This is consistent with the protein adsorption capacity of the surface of each group. We know that Fn plays a crucial role in the cell adhesion process. When Fn successfully adheres to the surface of the biological material, the cell adheres to the surface of biomaterials with the help of Fn.20,21 The adsorption capacity of Fn further determines the early adhesion and proliferation of cells. It is not clear whether the charge state of the implant surface affects the differentiation ability of the cells on the surface of each sample. In this study, the expression of osteogenesis-related genes (OCN, RUNX2, and ALP) by osteoblasts was studied by quantitative RT-PCR after 7 and 14 days of incubation. Our results show that the SLA-Ca2+ surface generally elicited the highest osteogenic gene expression compared with the other groups. Second, the SLA-NaCl and SLA-Ca2+-NaCl groups had the same expression level, while the expression levels of various osteogenic genes in the SLA group were almost the lowest at each time point. We believe that this is inseparable from the Fn adhesion ability and the cell adhesion proliferation ability of the titanium sheet surface.10,22 It should be particularly emphasized that there have been reports in the literature that calcium phosphate coating is attached to the surface of the implant through various techniques. The study found that implants with added calcium ions had improved cell adhesion, proliferation, and differentiation compared with the control group.23,24 In addition, some studies placed calcium phosphate-coated implants in simulated body fluids, and they found that apatite crystals can be formed on the surface of the experimental group.25−27 The researchers speculate that this is related to the charge of calcium ions on the surface of the experimental group, but in these studies, the charge on the surface of each group of samples was not specifically detected.27,28 In this study, we further confirmed that the surface of the SLA-Ca2+ group carries the least negative charge, while the surface of the SLA-Ca2+-NaCl group also contains a small amount of calcium ions. However, after incubation with NaCl, the positive charge carried by Ca2+ is neutralized, and the number of negative charges increases significantly. Thus, although its surface contains calcium ions, its protein adhesion ability and biological performance cannot reach the level of the SLA-Ca2+ group. This also confirms that surface charge plays a key role in improving the performance of biomaterials. In this study, we emphasize the change in the surface charge of titanium. Immersing Ti in CaCl2 solution is a relatively simple method that elicits a certain effect. Of course, if other methods can be found to completely reverse the negative charge to a positive charge, then perhaps the protein adsorption capacity and cell biological performance will be further improved. 4 Conclusions (1) When stored under common conditions protected from light, the SLA group carried the most negative charges because of contamination by hydrocarbons. By immersing the titanium sheet in the CaCl2 solution, the charge state on the surface is changed. The divalent calcium ion can be used as a bridge ion to adsorb the negative charge on the surface of pure titanium. Other divalent and even multivalent cations theoretically have the ability to act as bridge ions. (2) After the surface charge state changes, the titanium sheet can adsorb more negatively charged proteins by bridging ions. For example, Fn plays an important role in cell adhesion and stretching. However, the influence of changes in surface charge on the conformation of proteins adsorbed on its surface requires further examination. 5 Materials and Methods 5.1 Preparation of the Titanium Samples Commercially available pure grade 2 titanium (Tengxing Metal Materials, Shenzhen, China) was cut into discs of 15 mm diameter and 1 mm thickness. All titanium surfaces were ground with silicon carbide sandpapers of 280, 360, 400, 600, 800, and 1000 grit in series. The titanium specimens were then immersed in 10 mL of acetone (Fuyu, Tianjin, China) at room temperature, ultrasonicated for 20 min, ultrasonicated for a further 20 min in 10 mL of absolute alcohol (Fuyu), and then rinsed with deionized water. Subsequently, the specimens were dried at room temperature. The titanium specimens were subjected to sandblasting with large grit and acid etching (SLA) and then divided into four groups. The first was stored at room temperature and away from light for 2 weeks (control group, SLA), the second was immersed in normal saline (pH 7) for at least 48 h (SLA-NaCl), the third group was immersed in 1% CaCl2 (pH 7) for at least 48 h (SLA-Ca2+), and a subset of the third group was then immersed in normal saline for at least 48 h as the fourth group (SLA-Ca2+-NaCl). The SLA surfaces were prepared according to a previously described method.22,29 All titanium specimens were sandblasted with 120 μm Al2O3 particles at a distance of 50 mm, at an angle of 90°. The air pressure used for blasting was 0.45 MPa, and the procedure was conducted for 30 s. Subsequently, the specimens were etched using a mixture of 18% (v/v) HCl and 49% (v/v) H2SO4 at 60 °C for 30 min and then ultrasonically cleaned in ddH2O for 15 min. Finally, all specimens were air-dried at room temperature. All samples were collected, rinsed with deionized water, and sterilized via autoclaving before use. A 1% CaCl2 solution was degassed using a bacterial filter. Sterile saline was used for the injection. 5.2 Surface Characterization Following previously described methods,24,30 the morphology of each sample was analyzed using field-emission scanning electron microscopy (FESEM; FEI-Quanta 400, Hillsboro, OR). The chemical composition of the samples was investigated via FESEM-EDS (FEI-Quanta 400, Hillsboro, OR) under vacuum conditions (∼2 × 10–9 mbar). The surface roughness (Ra) was measured using a profilometer (Wyko NT9300; Veeco, NY). Water contact angle measurements were performed using the sessile drop technique (OCA40 Micro, DataPhysics, Filderstadt, Germany) at room temperature. 5.3 ζ-Potential ζ-Potential data inferred from streaming potential were measured on four different titanium surfaces, applying a constant gap (0.1 mm) between two rectangular samples with the same surface treatment.7 The electrokinetic streaming potential (Anton Paar, Graz, Austria) was automatically surveyed in the forward and backward flow directions. The measurements were performed in 0.001 mol/L KCl solution (pH 11), and the pH was adjusted to 7.4 by addition of 0.1 mol/L HCl (pH 1) or 0.1 mol/L NaOH (pH 13). For statistical analysis, four streaming potentials were obtained at the same pH value. 5.4 Protein Adsorption Assay Fn (Sigma-Aldrich) was used as the model protein. A Fn protein solution was prepared according to a classical protocol using phosphate-buffered saline (PBS) at pH 7.4. A 300 μL droplet of protein solution (5 μg/mL in Tris) was added to each of the titanium discs. After incubation for 0.5, 1, and 2 h under sterile humidified conditions at 37 °C, the samples were transferred to a new 24-well plate and washed thrice with PBS. Next, 500 μL of 1% sodium dodecyl sulfate (SDS) solution was added to the wells, and the plate was shaken for 1 h at a constant speed on an orbital shaker (TS-100, Qilinbeier, Jiangsu, China) to detach proteins from the samples. An aliquot of 100 μL of the collected solution was mixed with 100 μL of microbicinchoninic acid (Pierce Biotechnology, Inc., Rockford, IL) in a new 96-well plate and incubated at 37 °C for 1 h. The optical density (OD) of each disc was quantified using a microplate reader (680, Bio-Rad, Hercules, CA) at 562 nm. The rate of protein adsorption was calculated using a standard curve, obtained by plotting the average blank-corrected 595 nm reading for each bovine serum albumin (BSA) standard from the kit versus its concentration in μg/mL. The above tests were conducted with three parallel samples at each time point and repeated three times. 5.5 Cell Culture A human MG63 osteosarcoma cell line was cultured in Dulbecco’s modified Eagle’s medium (DMEM; Hyclone, Thermo Fisher Scientific, Waltham, MA) supplemented with 10% (v/v) fetal bovine serum (Hyclone) and antibiotics (penicillin [100 U/mL] and streptomycin [100 mg/mL]) at 37 °C in a humidified atmosphere containing 5% CO2. At 80% confluence, the cells were detached using 0.25% (w/v) trypsin in 1 mM ethylene diamine tetraacetic acid 4Na and seeded onto four different surfaces in 24-well plates at a density of 2.4 × 104 cells/cm2. The culture medium was replaced on alternate days.22,30 This study was reviewed and approved by the Ethics Committee of Stomatological Hospital, Southern Medical University, People’s Republic of China. 5.6 Cell Adhesion Assay Cell attachment was initially evaluated by measuring the number of cells attached to the titanium substrates after 30 min, 1 h, and 2 h of incubation, as described previously.22,30 At each time point, nonadherent cells were removed by gentle rinsing with PBS. Adherent cells were fixed with 4% (w/v) paraformaldehyde for 30 min and then stained with fluorescent Hoechst 33342 dye for 5 min. Cell adhesion was evaluated by counting the number of stained nuclei on each sheet in the fluorescent microscopic images (100× magnification, counts performed over an area of 1800 × 1350 μm2). Values representing the mean and the standard error of the number of attached cells were calculated from 10 different random fields from each disc using Image-Pro Plus 6.0 software (Media Cybernetics, Rockville, MD; n = 3, a total of 30 fields of view for each group). 5.7 Cell Proliferation Assay Cell proliferation was determined by measuring cell density on culture days 1, 3, 5, and 7 using tetrazolium salt-based colorimetry (MTS; Promega Corporation, Fitchburg, WI).22,30 At each time point, the specimens were gently rinsed three times with PBS and transferred to a new 24-well plate. Next, 500 μL of DMEM was added to each well and incubated with 100 μL MTS reagent at 37 °C for 4 h. The amount of formazan product was measured using a microplate reader at 490 nm. 5.8 Expression of Osteogenesis-Related Genes The expression of osteogenesis-related genes was evaluated using RT-PCR as previously described.22 MG63 cells were seeded at 2 × 104 cells/disc and cultured for 7 or 14 days. Total RNA was isolated using TRIzol reagent (Thermo Fisher Scientific). One microgram of RNA from each sample was reverse transcribed into complementary DNA using the PrimeScript RT reagent kit (TaKaRa Bio, Otsu, Japan). The expression levels of osteogenesis-related genes, including ALP, RUNX2, and OCN, were quantified using an iQ5 Multicolor RT-PCR Detection System (Bio-Rad Laboratories Inc.) with SYBR Premix Ex Taq II (TaKaRa Bio). Data analysis was carried out using the iQ5 Optical System Software Version 2.0 (Bio-Rad Laboratories Inc.). The relative expression levels for each gene of interest were normalized to those of the housekeeping gene (GAPDH). The primers used are listed in Table 2. Table 2 PCR Primer Sequences and Product Sizes (Base Pairs)a gene primer sequence amplicon size (bp) ALP S: 5′-CATGCTGAGTGACACAGACAAGAA-3′ 141 A: 5′-ACAGCAGACTGCGCCTGGTA-3′ OCN S: 5′-GACGAGTTGGCTGACCACA-3′ 138 A: 5′-CAAGGGGAAGAGGAAAGAAGG-3′ Runx2 S: 5′-TCCACACCATTAGGGACCATC-3′ 136 A: 5′-TGCTAATGCTTCGTGTTTCCA-3′ GAPDH S: 5′-TGGCACCCAGCACAATGAA-3′ 186 A: 5′-CTAAGTCATAGTCCGCCTAGAAGCA-3′ a PCR, polymerase chain reaction; ALP, alkaline phosphatase; RUNX2, runt-related transcription factor 2; and OCN, osteocalcin. 5.9 Statistical Analyses The data were analyzed using SPSS 13.0 (SPSS Inc., Chicago, IL). Q–Q plots were used to test the data distribution. One-way analysis of variance followed by the Student–Newman–Keuls post hoc test was used to determine the level of significance. Factorial analysis was used to evaluate the effects of group and time. P-values < 0.05 were considered significant, and values of P < 0.01 were considered to be highly significant. The authors declare no competing financial interest. Acknowledgments This work was supported by the National Natural Science Foundation of China (Grant Nos. 81600900 and 81801008), the Science and Technology Project of Guangzhou (Grant No. 201707010193), the Peiyu Fund of Southern Medical University (Grant No. PY2018016), and the Peiyu Fund of Stomatological Hospital of Southern Medical University (Grant No. PY2018N089). ==== Refs References Anselme K. Osteoblast adhesion on biomaterials . Biomaterials 2000 , 21 , 667 –681 . 10.1016/S0142-9612(99)00242-2 .10711964 Hansson S. ; Norton M. The relation between surface roughness and interfacial shear strength for bone-anchored implants. A mathematical model . J. Biomech. 1999 , 32 , 829 –836 . 10.1016/S0021-9290(99)00058-5 .10433425 Yang G. L. ; He F. M. ; Yang X. F. ; Wang X. X. ; Zhao S. F. Bone responses to titanium implants surface-roughened by sandblasted and double etched treatments in a rabbit model . 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==== Front J Nanopart Res J Nanopart Res Journal of Nanoparticle Research 1388-0764 1572-896X Springer Netherlands Dordrecht 5041 10.1007/s11051-020-05041-z Research Paper A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell Khalifa Nour Eldeen M. [email protected] 1 Taha Mohamed Hamed N. [email protected] 1 Manogaran Gunasekaran [email protected] 23 http://orcid.org/0000-0002-3849-4566Loey Mohamed [email protected] 4 1 grid.7776.10000 0004 0639 9286Department of Information Technology, Faculty of Computers & Artificial Intelligence, Cairo University, Cairo, 12613 Egypt 2 grid.27860.3b0000 0004 1936 9684University of California, Davis, USA 3 grid.252470.60000 0000 9263 9645College of Information and Electrical Engineering, Asia University, Taichung, Taiwan 4 grid.411660.40000 0004 0621 2741Department of Computer Science, Faculty of Computers and Artificial Intelligence, Benha University, Benha, 13518 Egypt 17 10 2020 2020 22 11 31327 7 2020 6 10 2020 © Springer Nature B.V. 2020This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Coronavirus pandemic is burdening healthcare systems around the world to the full capacity they can accommodate. There is an overwhelming need to find a treatment for this virus as early as possible. Computer algorithms and deep learning can participate positively by finding a potential treatment for SARS-CoV-2. In this paper, a deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell will be presented. The dataset selected in this work is a subset of the publicly online datasets available on RxRx.ai. The objective of this research is to automatically classify a single human cell according to the treatment type and the treatment concentration level. A DCNN model and a methodology are proposed throughout this work. The methodical idea is to convert the numerical features from the original dataset to the image domain and then fed them up into a DCNN model. The proposed DCNN model consists of three convolutional layers, three ReLU layers, three pooling layers, and two fully connected layers. The experimental results show that the proposed DCNN model for treatment classification (32 classes) achieved 98.05% in testing accuracy if it is compared with classical machine learning such as support vector machine, decision tree, and ensemble. In treatment concentration level prediction, the classical machine learning (ensemble) algorithm achieved 98.5% in testing accuracy while the proposed DCNN model achieved 98.2%. The performance metrics strengthen the obtained results from the conducted experiments for the accuracy of treatment classification and treatment concentration level prediction. Keywords COVID-19Deep transfer learningClassical machine learningissue-copyright-statement© Springer Nature B.V. 2020 ==== Body Introduction SARS virus spread around the world and caused a lot of panic globally at the end of February 2003 (Chang et al. 2020; Chamola et al. 2020). This led to set an alarm about viruses and their devastating impact in the new century. The 2019 latest coronavirus was described by the World Health Organization (WHO) in the form of 2019-nCov (COVID-19) (Singhal 2020; Loey et al. 2020a). The 2019 coronavirus was identified as the SARS-CoV-2 by the International Committee on Taxonomy of Viruses (ICTV) in 2020 (Lai et al. 2020; Li et al. 2020; Sharfstein et al. 2020). More than 500,000 fatalities in 213 countries and territories were affected by an outbreak of SARS-CoV-2 before the date of the published article (Worldometer 2020). The transmission of coronavirus (person to person) was spreading so fast for example, in Italy (Giovanetti et al. 2020), US (Holshue et al. 2020), India (Khattar et al. 2020), and Germany (Rothe et al. 2020). On 10 July 2020, SARS-CoV-2 confirmed more than 12 million cases, 6 million recovered cases, and 550,000 death cases. Figure 1 shows some statistics about recovered and death cases of COVID-19 (Coronavirus (COVID-19) map 2020).Fig. 1 COVID-19 statistics in some countries Generally, most of the publication focus is on the classification and detection of X-ray and CT images of COVID-19 (Civit-Masot et al. 2020; Waheed et al. 2020; Narayan Das et al. 2020; Ardakani et al. 2020). In this research, our focus is on recognizing and detecting a drug to help in healing from COVID-19 and study a morphological effect of COVID-19. Today, DL is quickly becoming a crucial technology in image/video classification and detection (Loey et al. 2020b, c; Khalifa et al. 2019a). In this paper, a deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell will be presented. The objective of this research is to automatically classify a single human cell according to the treatment type and the treatment concentration level. The novelty of this research is using a proposed classification model based on deep learning and machine learning for COVID-19 virus treatments. The remainder of the document is structured appropriately. “Datasets characteristics” includes a summary of the data set characteristics. “The proposed model” provides a detailed description of the proposed model. Throughout “Experimental results”, preliminary findings are recorded and evaluated, and the assumptions and potential future research are presented in “Conclusion and future works”. Datasets characteristics This research conducted its experiments based on the dataset presented in research (Heiser et al. 2020). The dataset attribute description is presented in detail in Table 1. The data are publicly available at RxRx.ai under the name of “RxRx19a Dataset”. It is a high-dimensional dataset that analyzes more than 1660 of FDA-approved drugs in a human cellular model of SARS-CoV-2 infection and included more than 300,000 recorded experiments. Although the presented data is in vitro screen that represents data from only a single human cell type, this dataset is likely broadly applicable to other primary human cell models.Table 1 RxRx19a dataset attributes description Attribute Description site_id Unique identifier of a given site well_id Unique identifier of a given well cell_type Cell type-tested Experiment Experiment identifier Plate Plate number within the experiment Well Location on the plate Site Indication of the location in the well where the image was taken (1, 2, 3, or 4) disease_condition The disease condition tested in the well (mock, irradiated, or viral) Treatment Compound tested in the well treatment_conc Compound concentration tested (in μM) Feature 1 to 1024 Feature of the cells (1024 attributes of feature cells) In this research, a subset of data is included in the conducted research experiments. The subset includes VERO cells which are a continuous cell lineage derived from kidney epithelial cells of an African green monkey and human renal cortical epithelial (HRCE) cells. Both cells were selected along with 10, 30, and 100 treatment concentration level with active SARS-CoV-2. This subset includes 32 treatments and three treatment concentration levels with two classes of cell type. Only 3750 cell records are included in the experiment carried out in this research. The proposed model The introduced model consists of three phases. The first phase is the preprocessing phase that converts the numerical values of the 1024 cell features to a digital image. The second phase is the training phase based on machine learning algorithms for numerical features and deep convolutional neural networks for the converted image features. The third phase is the testing phase and the evaluation of proposed model accuracy for treatment classification and treatment concentration level prediction. Figure 2 presents the proposed model structure.Fig. 2 The proposed model structure and phases Preprocessing phase The pre-processing phase includes (1) loading the 1024 features of cells on to computer memory, (2) change the cell feature original numerical domain that ranges from − 0.00046466477, 4.508815065 to image range [0, 255] according to equation (1), (3) construct image by converting the data vector of 1024 feature cells into a 32 × 32 pixel image according to the pseudocode presented in Algorithm 1. The result of this phase will be 3750 images. Figure 3 illustrates a set of images after the pre-processing phase.Fig. 3 Examples of the converted cell images 1 Pixel value=Roundfeature cell value−−0.000464664774.508815065×255 where − 0.00046466477 is the minimum cell value and 4.508815065 is the maximum cell value in the 1024 features of cell data and 255 is the maximum value of the image domain. Algorithm 1: Constructing image from 1024 features of the cell data vector Training phase The training phase is conducted based on two methodologies. The first methodology uses machine learning algorithms such as support vector machine, decision trees, and ensemble algorithms. The second methodology is depending on deep convolutional neural networks. Support vector machine SVM is one of the most common and impressive machine learning techniques for recognition and regression. SVM is a functioning algorithm, as shown in equation (2), where l is the label from 0 to 1, w. a − q is the output, w and q are the linear category coefficients, and a is the input vector. Equation (3) will enforce the loss function that is to be reduced (Çayir et al. 2018; Jogin et al. 2018). 2 SVMhk=max(0,1−lkw.ak−q 3 SVMloss=1m∑t=1mmax0ht Decision tree The decision tree is the computing classification paradigm focused on entropy method and knowledge acquisition. Entropy computes the amount of uncertainty in data as shown in equation (4), where CD is the data, b is the class output, and p(x) is the proportion of q label. Measuring the entropy gap from results, we calculate knowledge acquisition (KA) as illustrated in equation (5), where x is the subset of data (Navada et al. 2011; Tu and Chung 1992). 4 EntropyCD=∑i=1n−pbi.log(pbi 5 KA=EntropyCD−∑x∈DpxEntropyx Ensemble methods Ensemble methods are algorithms for machine study that build several classifiers, which is used to identify new cases in one direction or another through specific decisions (typically through weighted or unweighted votes) (Polikar 2012). The used methods are linear regression (Naseem et al. 2010), logistic regression (Kleinbaum and Klein 2002), and K-nearest neighbors algorithm (k-NN) (Mangalova and Agafonov 2014). We improve our ensemble by equation (6) to achieve the best outcomes (Xiao et al. 2018). 6 y¯=∑k=1hαkyk Deep convolutional neural networks The structure of the proposed deep convolutional neural networks is presented in Fig. 4. The proposed DCNN consists of three main convolutional layers with window size 3 × 3 pixels, three ReLU layers, and three pooling layers. The previous layers are used as feature extractions while two fully connected layers are used as classification layers. The proposed model for DCNN is a result of a lot of architecture tuning and tweaking based on work presented in (Khalifa et al. 2018; Khalifa et al. 2019b; Khalifa et al. 2020; Loey et al. 2020d).Fig. 4 Structure of the proposed model for deep convolutional neural network One problem that faces DCNN is overfitting. Overfitting can be solved by data augmentation (Shorten and Khoshgoftaar 2019; El-Sawy et al. 2017a, b). Data augmentation increases the number of images used for training by applying label-preserving transformations. Also, it is applied to the training set to make the resulting model more invariant to image transformation; in this work, each image in the training dataset is transformed as follows:Reflection around X-axis. Reflection around Y-axis. Reflection around the X-Y axis. The augmentation process raises the number of images from 3750 images to 15,000 images, 3 times larger than the original dataset. This will lead to a significant improvement in the neural network training phase. Additionally, it will make the proposed DCNN immune to memorize the data and be more robust. Testing phase The testing phase is the phase where the proposed model proves its performance and efficiency. The main goals of the proposed model are correctly classifying the treatments based on numerical features by using machine learning algorithms and correctly classifying the treatment images of the features based on DCNN. Also, the prediction of the treatment concentration on every cell is based on numerical features and image features using both machine learning and DCNN. For machine learning, the performance evaluation will include testing accuracy along with receiver operating characteristic (ROC) curve under 5k-fold cross-validation. For DCNN, testing accuracy, precision, recall, and F1 score (Goutte and Gaussier 2010) are included based on the calculation of the confusion matrix. The performance metrics are presented from equation (2) to equation (10). 7 Testing Accuracy=TruePos+TrueNegTruePos+FalsePos+TrueNeg+FalseNeg 8 Precision=TruePosTruePos+FalsePos 9 Recall=TruePosTruePos+FalseNeg 10 F1Score=2∗Precision×RecallPrecision+Recall where TruePos is the count of true positive samples, TrueNeg is the count of true negative samples, FalsePos is the count of false positive samples, and FalseNeg is the count of false negative samples from a confusion matrix. Experimental results The experiments are implemented using MATLAB software on a computer server with 96 GB of RAM and Intel Xeon processor (2 GHz). The following specifications are selected during the experiments:For machine learning algorithmsThree classifiers are tested (support vector machine, decision trees, and ensemble). Two problems (treatment classification and treatment concentration prediction). Dataset is in numerical format. 5k-fold cross-validation is selected. Testing accuracy along with receiver operating characteristic (ROC) and area under curve (AUC) are selected as performance metrics. For DCNNUsing the proposed DCNN in “Training phase”. Two problems (treatment classification and treatment concentration prediction). Dataset is in digital image format. Dataset was divided into two sections (70% of the data for the training process and 30% for the testing process). Data augmentation is applied for treatment classification problems. Testing accuracy, precision, recall, and F1 score are selected as performance metrics. Treatment classification results There are 32 classes of treatment according to the subset selected from the original dataset and they are presented in Table 2. The treatment classification will be experimented on by machine learning for numerical format and DCNN for digital image format.Table 2 Treatment classes according to the selected dataset 1-Deoxygalactonojirimycin Darunavir Indinavir Penciclovir Aloxistatin Dimethyl fumarate Indomethacin Polydatin Arbidol Favipiravir Lopinavir Quinine CAL-101 GS-441524 Methylprednisolone-sodium-succinate Quinine hydrochloride Camostat Haloperidol Nicotianamine Quinine-ethyl-carbonate Chloroquine Hydroxychloroquine Sulfate Oseltamivir-carboxylate Remdesivir (GS-5734) Cobicistat Imiquimod Pacritinib Ribavirin Ritonavir Solithromycin Tenofovir disoproxil fumarate Thymoquinone The first results to be recorded are using classical machine learning, three classical machine learnings are selected, and they are DT, SVM, and ensemble. Table 3 presents the average testing accuracy for the selected machine learning algorithm using 5k cross-validation.Table 3 Testing accuracy using different machine learning algorithms Family algorithm DT SVM Ensemble Child algorithm (best-achieved accuracy) Fine-Tree (Damrongsakmethee and Neagoe 2019) Cubic-SVM (Bagasta et al. 2019) Subspace discriminant (Hang et al. 2015) Average testing accuracy 57.7% 71.5% 72.7% ROC curve is one of the performance metrics for the machine learning algorithms. An ROC curve is a graph showing the performance of a classification model at all classification thresholds using true positive rate and false positive rate. Figure 5 presents a set of ROC curves for the different machine learning algorithms for one treatment oseltamivir-carboxylate. The AUC provides an aggregate measure of performance across all possible classification thresholds. The AUC for treatment oseltamivir-carboxylate using DT was 73% while using SVM, the AUC was 84%, and using ensemble, the AUC was 86%. There are about 96 ROC curves that can be produced by experimental trails, but there is no need to repeat the figures for different treatments, and the testing accuracy can be a good indicator of the quality of the machine learning algorithm.Fig. 5 ROC curves for treatment oseltamivir-carboxylate using a DT, b SVM, and c ensemble Using deep learning architecture, the achieved results are better than using machine learning algorithms in terms of testing accuracy and performance metrics. Using the proposed DCNN model and the conversion to the image domain with augmentation helped the model to achieve better results. The achieved testing accuracy was 98.05%. The recall measure was 95.03% accuracy. The precision measure was 96.52% accuracy. The F1 score measure was 95.97% accuracy. The confusion matrix is presented in Fig. 6. It is clearly shown that using a deep learning model with the conversion to image domain for features enhanced the testing accuracy by 25.35% rather than using an ensemble algorithm which achieved 72.7% testing accuracy.Fig. 6 Confusion matrix for the proposed DCNN model using feature images The progress of the training phase of the proposed deep learning model is presented in Fig. 7, which reflects the advancement of the training process to achieve better accuracy; the model has tuned for early stop of the training if there is no better accuracy achieved in 10 iterations. The batch size was 32 with a learning rate of 0.0001. Examples of testing accuracy along with treatment classification are presented in Fig. 8.Fig. 7 Training accuracy with validation loss for the proposed DCNN model Fig. 8 Examples of the testing accuracy for treatment classification Treatment concentration prediction results Another goal for the proposed model is to predict the concentration of the treatment on the cell. The first direction to investigate the accuracy of the model is by using a machine-learning algorithm to predict the concentration level of treatment. Three concentration levels are investigated, and they were 10, 30, and 100% concentration level. Table 4 presents the testing accuracy of treatment concentration using DT, SVM, and ensemble algorithms using 5k cross-validation.Table 4 Testing accuracy using different machine learning algorithms Family algorithm DT SVM Ensemble Child-algorithm (best-achieved accuracy) Coarse tree (Damrongsakmethee and Neagoe 2019) Linear SVM (Chang and Lin 2008) Bagged tree (Banfield et al. 2006) Average testing accuracy 96.4% 97.3% 98.5% ROC curves and AUC are also extra indicators of the quality of the classifier. Figure 9 presents the ROC curves for the different machine learning algorithms for the different classes of the level of the treatment concentration of 10, 30, and 100. The SVM and the ensemble algorithms achieved AUC with 100% which is a good indicator for the quality of the classifier. Also, according to Table 3, both classifiers (SVM and ensemble) achieved a testing accuracy with 97.3% and 98.5% for a three-class problem.Fig. 9 ROC and AUC for machine learning algorithms for the treatment concentration level prediction for a 10, b 30, and c 100 treatment concentration level The second direction is to use deep learning to solve this problem using the same proposed DCNN model for the feature of digital images without using augmentation. There was no need to use the augmentation process as the proposed model achieved a good testing accuracy with 98.2%. Figure 10 presents the confusion matrix for the level of the concentration level of the potential treatment. The proposed model with the conversion of features to images achieved 98.2% testing accuracy along with performance metrics as follows (recall: 87.42%, precision: 99.36%, and F1 score: 93.01%).Fig. 10 Confusion matrix for the treatment concentration level prediction For the concentration level, 10% of the achieved accuracy was 98.1%, for the concentration level 30%, the achieved accuracy was 100%. For the concentration level of 100%, the achieved accuracy was also 100%. The achieved accuracy for every class reflects the performance of the proposed DCNN model. Result discussion For the treatment classification which includes 32 classes, the proposed DCNN achieved a superior result if it is compared with machine learning algorithms in terms of testing accuracy. The proposed DCNN achieved a result of 98.05% while classical machine learning such as DT, SVM, and ensemble achieved 57.7%, 71.5%, and 72.7%, respectively. The performance metrics supported the obtained results for the proposed DCNN with feature image conversion. In the treatment concentration level prediction, the classical machine learning algorithms such as DT and SVM achieved a near result with the proposed DCNN. The DT and SVM achieved 96.4% and 97.3%, respectively, while the DCNN achieved 98.2% in testing accuracy. The ensemble algorithm achieved a superior testing accuracy rather than the DCNN and achieved 98.5%. As a general notice, the classical machine learning algorithm for simple classification problems such as treatment concentration level prediction which includes three classes. While in multiclass classification such as treatment classification which includes 32 classes, the deep learning model proved its performance and efficiency if it is compared with classical machine learning. Conclusion and future works The coronavirus pandemic is putting healthcare systems around the world into a critical situation. Until now, there is a cure for this virus. One of the methods that can help to defeat this virus is trying approved treatments on human cells as a primary stop to shorten the gap between treatments and finding an actual cure. Computer algorithms and deep learning can close that gap and help in finding a cure. In this paper, a deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell. The dataset selected in work is a subset of the publicly online dataset on RxRx.ai. The objective of this research is to automatically classify the human cell according to treatment and treatment concentration levels. The proposed DCNN model and methodology are based on converting the numerical features from the original dataset to the image domain. The proposed model consists of three convolutional layers, three ReLU layers, three pooling layers, and two fully connected layers. The experimental results showed that the proposed DCNN model for treatment classification (32 classes) achieved 98.05% testing accuracy if it is compared with classical machine learning such as support vector machine, decision tree, and ensemble. In treatment concentration level prediction, the classical machine learning (ensemble) algorithm achieved 98.5% testing accuracy while the proposed DCNN model achieved 98.2%. One of the potential future work is performing same experiments with deep transfer models such as Alexnet and Resnet50 or even deeper neural networks to investigate its performance with used dataset in this research. This article is part of the topical collection: Role of Nanotechnology and Internet of Things in Healthcare Guest Editors: Florian Heberle, Steve bull and John Fitzgerald Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Funding This research received no external funding. 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==== Front Soft comput Soft comput Soft Computing 1432-7643 1433-7479 Springer Berlin Heidelberg Berlin/Heidelberg 33100897 5387 10.1007/s00500-020-05387-5 Focus RETRACTED ARTICLE: A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata http://orcid.org/0000-0001-9488-908X Eken Süleyman [email protected] grid.411105.0 0000 0001 0691 9040 Department of Information Systems Engineering, Kocaeli University, 41001 Kocaeli, Turkey Communicated by Valentina E. Balas. 19 10 2020 2023 27 5 26452655 © Springer-Verlag GmbH Germany, part of Springer Nature 2020. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Putting real-time medical data processing applications into practice comes with some challenges such as scalability and performance. Processing medical images from different collaborators is an example of such applications, in which chest X-ray data are processed to extract knowledge. It is not easy to process data and get the required information in real time using central processing techniques when data get very large in size. In this paper, real-time data are filtered and forwarded to the right processing node by using the proposed topic-based hierarchical publish/subscribe messaging middleware in the distributed scalable network of collaborating computation nodes instead of classical approaches of centralized computation. This enables processing streaming medical data in near real time and makes a warning system possible. End users have the capability of filtering/searching. The returned search results can be images (COVID-19 or non-COVID-19) and their meta-data are gender and age. Here, COVID-19 is detected using a novel capsule network-based model from chest X-ray images. This middleware allows for a smaller search space as well as shorter times for obtaining search results. Keywords Capsule networks Topic-based hierarchical publish/subscribe COVID-19 detection Hybrid intelligence X-ray images Medical data management issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023 ==== Body pmcIntroduction With the recent advances in big data technologies, new machine learning techniques for precision medicine (Njølstad et al. 2019; Van Den Berg et al. 2019), the life sciences, and clinical data analysis (Ho et al. 2019; Palanisamy and Thirunavukarasu 2019; Ngiam and Khor 2019) are continuously developed and extended in medical data science to achieve a better understanding of diseases. The field of medical data science covers different areas such as prediction of response to treatment in personalized medicine (Abul-Husn and Kenny 2019; Suwinski 2019), biomarker detection (Zhang et al. 2019; Fitzgerald 2020), tumor classification (Khan et al. 2019; Lin and Berger 2020), COVID detection and classification (Wang et al. 2020; Bragazzi et al. 2020), and the understanding of gene interactions (Shukla and Muhuri 2019). When it comes to big data, central processing techniques may not be enough to process these medical data and get the required information on time correctly. Hybrid intelligence combines human and artificial intelligence. The main reason for this is the combination of complementary heterogeneous bits of intelligence to create a socio-technological ensemble that is able to overcome the current limitations of artificial intelligence (Dellermann 2019). In this paper, our proposed middleware includes the coronavirus disease 2019 (COVID-19) detection phase. Ground-truth data require domain expertise. Here, the output of human intelligence (labeling by experts) is used for training capsule networks (artificial intelligence). Similarly, the output of AI is used by clinicians to make their job easier and faster. In recent years, medical/health data science and practices are among the issues that are carefully emphasized by various government agencies as well as private companies (Goulooze et al. 2019; Paul et al. 2017; Paul et al. 2016; Ford et al. 2019; Dixon et al. 2020; Dammann and Smart 2019). Image processing methods are also one of the basic algorithms used when developing these software solutions and applications. Although there are many studies in the literature on image processing-based medical data processing systems in general, to our knowledge no previous study has been performed on the publishing of classified chest X-ray images to the clients/consumers based on hierarchical topics (disease typed image, gender, age). The following paragraphs explain literature reviews on COVID-19 classification from images and streaming medical data. COVID-19 is an infectious respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that affects humans. The disease, which was first discovered in Wuhan, China, in 2019, has spread worldwide since its discovery, causing a 2019–2020 coronavirus pandemic (Hui 2019). Common symptoms of the disease include fever, cough, and shortness of breath. Muscle aches, sputum production, and sore throat are less common symptoms. Gastrointestinal symptoms such as diarrhea have been reported (Jinyang et al. 2020; Miri et al. 2020). In some studies, it has been shown that the virus also involves the central nervous system; the symptoms of loss of smell and difficulty in breathing are due to these reasons (Li et al. 2020). Although most of the cases have mild symptoms, some patients may experience severe pneumonia and multiple organ failures. According to the first major analysis of over 44,000 cases in China, confirmed cases are at least five times more common among patients with diabetes, high blood pressure, heart disease or respiratory problems. Different methods are used for the detection of COVID-19. Although real-time polymerase chain reaction (RT-PCR) testing of sputum is standard for the diagnosis of coronavirus, it is time-consuming to confirm because of the high false-negative results of COVID-19 patients (Huang et al. 2020). Therefore, medical imaging methods such as chest X-ray (CXR) and computed tomography (CT) can play an important role in confirming positive COVID-19 patients, especially in infected pregnant women and children (Ng et al. 2020; Liu et al. 2020). Volumetric CT chest lattice (thorax) images for lung and soft tissue have been investigated in recent studies to identify COVID-19 (Chung et al. 2020). The main disadvantage in using CT imaging is the high radiation dose and the cost (Kroft et al. 2019). In contrast, all hospitals and clinics have traditional radiographs or CXR machines to produce two-dimensional (2D) projection images of the patient’s chest. Generally, the CXR method is the first choice for radiologists to detect chest pathology and has been applied to identify or confirm COVID-19 in a small number of patients (Chen et al. 2020). For this reason, this study focused on the use of the X-ray imaging method for COVID-19 patients. Deep learning techniques have shown promising results for performing radiological tasks by automatically analyzing multimodal medical images in recent years. Evolutionary neural networks have also been used in many medical classification, detection and diagnostic studies. There are also studies in the literature using pre-trained deep neural networks to detect COVID-19 from X-ray images (Hemdan et al. 2003; Narin et al. 2003). Dansana et al. (2020) used convolution neural networks (CNN) for binary classification pneumonia-based conversion of VGG-19, Inception V2 and decision tree model on X-ray and CT scan image datasets. In this study, automatic COVID-19 is detected from chest X-ray images using capsule networks. Boccaletti et al. (Boccaletti et al. 2020) invited for a special issue focusing on bringing together the community of applied mathematicians, virologists, epidemiologists, the community of complex systems scientists, and the community of scientists to deal more successfully with circumstances like the current pandemic. So there are a number of works related to modeling and forecasting of spreading in COVID-19 in the literature. Contreras et al. (2020) presented a general multi-group SEIRA model for representing the spread of novel COVID-19 through populations with heterogeneous characteristics. This model can represent several mechanisms of interaction between different subpopulations. Crokidakis (2020) studied the dynamics of COVID-19 in Rio de Janeiro state, Brazil, by means of a Susceptible-Infectious-Quarantined-Recovered (SIQR) model with containment policies. It is seen that the social distancing policies led about 7 days to change the initial exponential growth of cases and to effectively decrease the rate of growth of confirmed cases. Abdo et al. (2020) investigated a mathematical model for calculating the transmissibility of COVID-19 disease by using non-singular fractional-order derivative. Chakraborty and Ghosh (2020) dealt with the real-time forecasts of the daily COVID-19 cases in five different countries using a hybrid ARIMA-WBF model. The model can be used as an early warning system to fight against the COVID-19 pandemic. Mandal et al. (2020) explored the role of quarantine and the governmental intervention strategies on COVID-19 control and elimination. Melin et al. (2020) analyzed the spatial evolution of coronavirus pandemic around the world by using a particular type of unsupervised neural network, self-organizing maps. Employing unsupervised self-organizing maps, similar countries in their fight against the coronavirus pandemic make similar strategies. Melin et al. (2020) proposed a new approach with multiple ensemble neural network models and fuzzy response aggregation for the COVID-19 time series. Castillo and Melin (2020) proposed a hybrid intelligent approach combining the advantages of fractal theory and fuzzy logic. Forecasting windows of 10 and 30 days ahead are used to test the proposed approach. In addition to the COVID-19 classification from images, another subject we examine is the distribution of classified images and their metadata by the topic-based publish/subscribe (pub/sub) system. The pub/sub interaction is a message-based form of communication in a distributed environment. In this type of communication, producers/publishers publish information, while consumers/subscribers receive it by registering the information they want to receive. The pub/sub system is closely related to the message queuing paradigm and forms part of the message-based middleware system (Tanenbaum et al. 2007). In the pub/sub model, consumers receive some of the information published by the producers. This job is known as filtering. Generally, it is divided into three types: subject-based, content-based, and type-based filtering. In the subject-based pub/sub model, messages are posted to topics (or logical channels). The consumer who subscribes to the relevant subject receives all the messages published on that subject. With the same logic, all recipients who subscribe to the same subject receive the same messages posted (Harrison et al. 1997). In the content-based model, if the qualities or content of the published messages matches, the relevant messages are received by the consumers (Fabret et al. 2001). The type-based model is developed with inspiration from the object-oriented programming paradigm. In this model, the producer produces the message objects in the communication channel, and the consumer receives the object-type messages they are interested in, provided that they are registered with that communication channel (Eugster 2007). Pub/sub applications for data streams are a research topic. In data stream applications, the stream generally consists of geographically distributed producers and consumers querying them. Gray and Nutt (2005) offer a distributed data stream solution based on the pub/sub architecture that allows publishing stream data and querying them. Zou et al. (2010) propose distributed pub/sub architecture for real-time video viewing using wireless sensor networks and wireless mesh networks. Wadhwa et al. (2015) propose a pub/sub-based architecture for early exchange of healthcare data among different interested parties, e.g., doctors, researchers, and policy makers. Singh et al. (Singh et al. 2008) implement a middleware model to control the sharing of information in a publish/subscribe environment. It supports a fundamental requirement of the healthcare process. Contributions to the literature with the paper can be listed as follows:Topic-based hierarchical messaging middleware is proposed for medical data streaming. In this sense, with the developed system, it is possible to convert hard real-time applications to soft real-time to access images on a topic-based basis. This solves the scaling and performance problems. Middleware has single-point control—updating a single table (holding information or topics for the publisher and subscriber) will change the whole cycle. This middleware is quite simple to implement. The structure of each system for handling the COVID-19 detection structure remains the same and hence leads to a considerable reduction in implementation cost. We proposed a capsule network-based model for the diagnosis of COVID-19 from X-ray images. The remainder of this article is organized as follows: In Sect. 2, details on the proposed topic-based hierarchical publish/subscribe messaging middleware are given. Section 3 presents the performance of the middleware. The last section concludes the article and discusses future works. Materials and methods In this section, the architecture of the proposed pub/sub messaging middleware will be explained. Figure 1 shows the sub-components of the system, which will be explained in detail in the following sections.Fig. 1 Sub-components of the proposed system With the hierarchical topic-based collaborative computer cluster, it will be possible to take the critical and strategic decisions in a timely and correct manner by partitioning and sharing the load of image processing. The proposed middleware enables the development of image-oriented searching and warning in real time. The distributed architectural infrastructure allows for scalable distributed system applications based on COVID-19 detection over chest X-ray images. With the proposed system, an increase in efficiency and reduction in manual operations are seen, while more accurate results are obtained. End users will be able to define search criteria as a hierarchical topic according to different features (image, age, gender). Searching and processing in less search space on images that come to users who subscribe for certain topics save time. Proposed architecture In the pub/sub messaging model in software architecture, topics are broadcast to a virtual channel (topic). While the message senders (publishers) broadcast messages without being aware of the subscribers, the recipients/subscribers can subscribe to one or more topics without being aware of the senders. The pub/sub model is often referred to along with the queue paradigm. In the pub/sub messaging model, topics can be organized in a hierarchy. The general structure of a topic-based pub/sub model is presented in Fig. 2.Fig. 2 Topic-based pub/sub structure The proposed hierarchical topic-based pub/sub architecture is shown in Fig. 3. The received X-ray images are published to the other replica servers by the dispatcher server. Replica servers consist of three layers, and each layer has disease (D), age (A), and gender (G) servers. Subscribers connected to the first tier receive images containing only issues related to the disease (such as COVID-19 from all images), only gender (such as men), and only age (such as those below 50). Subscribers connected to the second tier take images according to two topics: COVID-19 patients over 65 age and women COVID-19 patients. So, there are three topics (disease/age—DA, disease/gender—DG, and age/gender—AG) at the second-tier output. Subscribers connected to the third tier receive images according to three topics (DAG): male COVID-19 patients over 65 years of age.Fig. 3 Hierarchical topic-based pub/sub architecture Figure 4 presents the hierarchical topic-based pub/sub architecture in the tree structure. In the proposed system, there are three main topics, but their sub-topics are as follows: disease (COVID-19 and non-COVID-19). The second-tier sub-topics of each of these are gender (male and female) information. In the last stage, there is a structure of age. Age information is limited in three ranges: less than thirty-five, between thirty-five and sixty-five, and more than sixty-five. The next two subsections explain the first tier of COVID-19 detection in hierarchical architecture and Apache Kafka-based pub/sub implementation, respectively.Fig. 4 Tree structure of hierarchical topic-based pub/sub architecture Capsule network-based COVID-19 detection CNNs have some limitations. CNNs are very sensitive to the orientation of the object and light intensity in the environment. A simple and complex spatial relationship between object and environment cannot be taken into consideration. Light intensity on the different perspectives of an object in the image decreases the performance of the network. Therefore, more training data are required to improve performance. However, this workaround requires a high computational cost, training time, and powerful hardware. On the other hand, another major problem with CNNs is the pooling layers, since this layer loses crucial information in the image. Capsule Networks (CapsNets) manage to overcome the disadvantages of the CNNs. A capsule can be described as a small group of neurons. The input and output of a capsule are in the form of a vector, which differs from conventional artificial networks. Each capsule has an activity vector and pose; deformation and velocity are indicated by this vector (Sabour et al. 2017). The length of the activity vector corresponds to the probability of which object exists in the image and the orientation of the vector points out the instantiation parameters. It is very important to understand the basis of a capsule operation. Each capsule has three recognition units and four generation units. Recognition units can be considered as a hidden layer to calculate the probability of existence of the object (p) and position of the object (x and y). Generation units compute the contribution of each capsule to an image to be transformed. This network contains different capsules interacting at the last layer for the shifted image. The inputs of generation units are in the form of both an image and the amount of desired shift (denoted as ∆x and ∆y). If a capsule is not active, this means that there is no contribution to the output image. When randomly shifted input, output images and the amount of shift are applied, capsules learn how to find the position of the object. CapsNets consist of a convolutional layer, primary capsule layer, capsule layer, mask layer, and decoder network. The first layer is a convolutional layer and extracts the basic features such as edges and color variations. After that, the primary capsule layer produces capsules from neuron outputs. This layer behaves like an inverse rendering operation. Given an image or other data, it computes the internal parameters of features such as rotation and scale. Then, the capsule layer extracts more abstract features in the data. Here, dynamic routing is applied for determining weights between low-level and high-level features. The last capsule layer has capsules with the same number of classes. After applying softmax operation to the output of capsules, class probabilities are obtained. Class predictions are achieved by selecting the class with the highest probability. The layers mentioned thus far make up the encoder network. Additionally, a decoder network is constructed for regularization. The capsule output of correct classes is given to a 2-layer fully connected network to rebuild the original data. The difference between original data and rebuilt data is provided as a regularization term to the error function. The calculated error value is carried backward using backpropagation, and learning is achieved. The architecture of the proposed capsule model is shown in Fig. 5. Layers of this architecture are described below:Fig. 5 Architecture of the proposed capsule model for COVID-19 detection Convolutional layer: It is just a conventional layer (Conv2D) for detecting the edges from the image. ReLU is chosen as the activation function to add nonlinearity. After the convolutional layer with 256 filters, kernel size and stride being 9 and 1, respectively, the output data have a size of 216216256. The total number of parameters at this layer is 20,992. Primary capsule layer: Primary capsule with squash activation consists of a 2D convolutional layer with a kernel size, filters, and strides being 9, 256, and 2, respectively. The output data have a size of 104104256. After that, a reshape layer is applied to make data fit into a eight-dimensional capsule. The resulting data dimension is 346,1128. The total number of parameters at this layer is 5,308,672. Capsule layer: Each capsule in this layer has 16 nodes, i.e., 16 dimensions. The number of capsules is equal to the number of classes in the dataset. Each capsule corresponds to a class. Therefore, for a two-class dataset (COVID and non-COVID), the output is a 216 matrix. Total number of parameters at this layer is 88,604,672. Flatten layer: Flattening transforms a two-dimensional matrix (2*16) of features into a vector (32) that can be fed into a fully connected neural network classifier. Decoder network: It consists of two dense layers. The first layer has size 32 and the second one has 2. The last hidden layer has the same size as the input layer, which is 196. The total number of parameters at these layers is 1112. Softmax layer: Softmax is implemented through a neural network layer just before the output layer. The Softmax layer must have the same number of nodes as the output layer. The total number of parameters in this network is equal to 93,935,458. COVID-19 medical data streaming with Apache Kafka In this paper, Apache Kafka (version 2.11)-distributed streaming platform is used for topic-based pub/sub architecture. Apache Kafka aims to provide a unified, high-efficiency, low-latency platform to manage real-time data streams. The storage layer is essentially a ”highly scalable message queue” formatted as a distributed transaction log (Apache Kafka 2020). Kafka is the actual distribution system, a high-throughput distributor for messages, dealing with the enormous amount of data and supporting a huge number of consumers and producers. Kafka uses several partitions and brokers to perform parallelism. The parallelism accelerates the processes effectively. Furthermore, it automatically retrieves data in cases where the broker fails. Through these characteristics of Kafka, the real-time data streaming requirements are satisfied. As shown in Fig. 4, data coming from publishers are first classified according to their disease type (D). Age (A) and gender (G) information is obtained from its meta-data. Then, the data from these tiers are combined as type–age (DA), type–gender (DG), age–gender (AG). Finally, the data from the combiner with the type–age (DA) and the data from the gender layer (G) are combined to obtain the type–age–gender (DAG) information. The relationships between the first replica servers (D, A, G) and combiners (DA, DG, AG, DAG) are visually illustrated in Fig. 6.Fig. 6 The relationships between replica servers and combiners The working principle of the subprogram that combines data according to the type of disease and age is as follows. In order to combine the type of disease and age, the two disease types (COVID and non-COVID) opened by the classification process and the three age ranges combine information from Apache Kafka subjects according to their unique numbers as in Fig. 7. It publishes a total of 2 × 3 = 6 topics, consisting of type and age combinations (30 years of COVID, 40 years of non-COVID, etc.). Similarly, 4 (2 × 2) topics for type and gender combination and 6 (2 × 3) topics for gender and age combination are published. For type–age–gender combination, 2 × 3 × 2 = 12 topics are published. There are 28 topics in total.Fig. 7 Combining disease type and age information Experimental results and user interfaces This section firstly describes the used COVID-19 datasets and then gives performance results for the proposed architecture. Finally, user interfaces are illustrated with the main actions. COVID-19 image datasets We used two publicly available chest X-ray datasets (Covid Chest X-ray Dataset 2020; Kaggle Chest X-ray Images (Pneumonia) Dataset 2020). As shown in Fig. 8, the datasets used for the tests contain four different class labels: normal, bacterial, non-COVID viral, and COVID-19. Class labels are reduced to two, with the first three as negative and the last as positive. Thus, binary classification is made. All images in this dataset have been scaled to 224 × 224 pixels. Also, images have some meta-data such as age, sex/gender, survival, intubated, modality, date, and location.Fig. 8 Labels available in the dataset Our experiment environment runs an Intel Core i7-8700 K CPU with 3.70 GHz, 32 GB RAM and 2 MSI GTX 1080 Ti Armor OC 11 GB GPUs. Our proposed architectures are implemented in Keras framework with Tensorflow as the backend. The dataset was randomly divided into two independent datasets with 80% and 20% for training and testing, respectively. The batch size, learning rate, and epoch number are 10, 1e − 5, and 100, respectively. We evaluate the success/failure of the capsule-based model in terms of four measures such as accuracy, specificity, and recall derived from the confusion matrix as shown by the following equations:1 Accuracy=TP+TNTP+FP+TN+FN 2 Specificity=TNTN+FP 3 Sensitivity(recall)=TPTP + FN Table 1 shows the comparison of the proposed capsule-based model with other approaches using the same dataset in the literature: COVID-CAPS with pre-training, without pre-training, deep features-based one, VGG 16, Inception_v2, and decision tree. As shown in Table 1, the proposed model outperforms COVID-CAPS without pre-training, deep features, Inception_v2, and decision tree in terms of accuracy. The proposed model outperforms COVID-CAPS without pre-training and deep feature in terms of specificity. Our model is also the best one according to sensitivity performance.Table 1 Performance comparison for COVID-19 classification Method Accuracy (%) Sensitivity (%) Specificity (%) COVID-CAPS without pre-training (Afshar et al. 2004) 95.7 90 95.8 Pre-trained COVID-CAPS (Afshar et al. 2004) 98.3 80 98.6 Deep features (Sethy and Behera 2020) 95.38 97.29 93.47 VGG 16 (Dansana et al. 2020) 100 94 NA Inception_v2 (Dansana et al. 2020) 78 76 NA Decision tree (Dansana et al. 2020) 60 70 NA Proposed model 96.01 97.6 96.83 Bold numbers indicate the best performance User interfaces This subsection goes into detail about the user interfaces of our hierarchical topic-based pub/sub architecture. User interfaces (UIs) are pages with various images, graphics, scripts, and commands that allow users to access the program and control the whole or any part of the program. So, our web-based application is created using some technologies such as NGINX Stream Real-Time Messaging Protocol (RTMP), Node.js, and Socket.IO. As every web application, our application has registration, sign-in pages, and superuser page for system and user management. The statistics page where statistics on the NGINX RTMP unit are kept provides information on the health of the system. Super administrators and administrators can access this page. The information provided on this page includes the number of viewers, hardware usage values, and health/operability information of the system. Figure 9 illustrates the page showing the relationships between the publishers (image and meta-data providers), COVID-19 detector, and topic combiners. It is a graph structure of states that detects images and meta-data from the providers and displays notifications from detector and combiners instantly. Superusers and administrators can access this page. On the stream tab, users can filter the images they receive from the NGINX RTMP server as desired. All active users can access this page. In the example in Fig. 10, the user used the COVID-19 filter.Fig. 9 System state page Fig. 10 Topic-based filtering page Conclusion As it is known, image processing methods are used in many fields, ranging from military industry to security, from medicine to robotics, from astronomy to aviation, from biomedical to remote sensing. Disease diagnosis is one of the most important of these areas. In this study, we proposed a topic-based hierarchical pub/sub messaging middleware for COVID-19 detection in X-ray image and its meta-data. It is provided to classify the X-ray images obtained from image providers according to the type of disease and to send the classified image and its meta-data (age–gender information) to subscribed users on a topic-based basis. The capsule network-based model was trained and tested using the chest X-ray dataset and several metrics used to evaluate the COVID-19 detection performance, such as accuracy, sensitivity, and specificity. The experimental results were compared with some of the recently published works. The comparison demonstrated that the proposed model achieves better sensitivity than other existing methods for the task of COVID-19 classification, while other metrics are close to the best results. Moreover, COVID-19 classification, user interfaces of our hierarchical topic-based pub/sub architecture are illustrated. Here, human intelligence is integrated into an artificial intelligence system to complement machine capabilities (i.e., to make COVID-19 detection) throughout its life cycle. Human experts label images, and these (human inputs) are used by AI for training and validation. Human involvement can prevent the mistakes and failures that would be caused by an AI system. Similarly, the output of AI is used by clinicians to make their job easier and faster. As for future work, we plan to contribute to the implementation of the ideas mentioned previously. Additionally, we plan to enrich our system by providing it with more powerful algorithms that try to get more meta-data on the patients. We also plan to add COVID-19 image and meta-data crawler to the system. Besides, the researcher will focus on increasing the training speed and efficiency of the model on a quantity-limited dataset, as well as extending this work by optimizing access to human intelligence. Optimization is very important when a system seeks additional evidence from humans to accomplish tasks and when a learning agent has access to teacher advice on how to act. Acknowledgements The author received no financial support for the research, authorship, and/or publication of this article. Author’s contributions S.E. contributed to the design and implementation of the research, to the analysis of the results, and to the writing of the manuscript. Availability of data and material 1-Covid Chest X-ray Dataset https://github.com/ieee8023/covid-chestxray-dataset. 2-Kaggle Chest X-ray Images (Pneumonia) Dataset https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia. Compliance with ethical standards Conflict of interest The author declares that he has no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00500-023-08599-7 Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Change history 5/29/2023 This article has been retracted. 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==== Front ACS Omega ACS Omega ao acsodf ACS Omega 2470-1343 American Chemical Society 10.1021/acsomega.0c03986 Article Quorum Sensing System in Bacteroides thetaiotaomicron Strain Identified by Genome Sequence Analysis Wu Zhi Cheng † Feng Hong Xin ‡ Wu Lin ‡§∥ Zhang Meng ⊥ Zhou Wei Lan # † Department of Laboratory, First Affiliated Hospital of Hainan Medical College, 31 Longhua Road, Haikou, Hainan 570102, China ‡ School of Tropical and Laboratory Medicine, Hainan Medical University, Haikou, Hainan 571199, China § Department of Biotechnology and Biotechnics, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv 03056, Ukraine ∥ Key Laboratory of Tropical Translational Medicine, Ministry of Education, Hainan Medical University, Haikou Hainan 571199, China ⊥ Sanya People’s Hospital, Jiefang Third Road, 558, Sanya 572000, China # Department of Laboratory, First Affiliated Hospital of Hainan Medical College, 31 Longhua Road, Haikou, Hainan 570102, China Email: [email protected]. Tel: +86089866711796. 13 10 2020 27 10 2020 5 42 27502 27513 18 08 2020 29 09 2020 © 2020 American Chemical Society2020American Chemical SocietyThis is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND) Attribution License, which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes. This study is a bioinformatics assay on the microbial genome of Bacteroides thetaiotaomicron. The study focuses on the problem of quorum sensing as a result of adverse factors such as chemotherapy and antibiotic therapy. In patients with severe intestinal diseases, two strains of microorganisms were identified that were distinguished as new. Strains were investigated by conducting genome sequencing. The current concepts concerned with the quorum sensing system regulation by stationary-phase sigma factor and their coregulation of target genes in B. thetaiotaomicron were considered. The study suggested using bioinformatics data for the diagnosis of gastrointestinal disorders. In the course of the study, 402 genes having a greater than twofold change were identified with the 95% confidence level. The shortest and longest coding genes were predicted; the noncoding genes were detected. Biological pathways (KEGG pathways) were classified into the following categories: cellular processes, environmental information processing, genetic information processing, human disease, metabolism, and organismic systems. Among notable changes in the biofilm population observed in parallel to the planktonic B. thetaiotaomicron was the expression of genes in the polysaccharide utilization loci that were involved in the synthesis of O-glycans. document-id-old-9ao0c03986document-id-new-14ao0c03986ccc-price ==== Body Introduction Microbocenoses can be found in the environment, inside and outside of living organisms. Recently, scientists have established a connection between the intestinal microbiome of a healthy person and specific diseases.1 Genetic studies play a leading role in establishing a correlation between intestinal microbiota composition, certain mutations, and diseases.2 The latest research is aimed at developing sensory systems for the diagnosis and treatment of gastrointestinal diseases such as ulcerative colitis, intestinal abscess, and Crohn’s disease complications.3 Bacteroids are the most common microorganisms found in the intestines. Alongside their biological functions, the bacteroid strains Bacteroides thetaiotaomicron can act as a live drug delivery system, a biosensor, etc.4 According to recent trends, organisms that are incapable of colonizing may be utilized to treat acute forms of diseases. These organisms are also capable of forming biofilm colonies for the treatment of chronic diseases. The behavior of microbial populations is regulated by quorum sensing (QS) mechanisms5 and quorum sensing inhibitors (QSIs). The QS mechanisms induce the activation of virulence genes and enable the communication of microorganisms in biofilms, electrical (through the transmission of electrical signals) and chemical (through the synthesis of chemical compounds). The QS system consists of two essential components: a low-molecular-weight regulator or autoinducer (AI) and a regulatory receptor R protein. AIs are responsible for the communication of bacteria; they transfer information between bacterial cells using signaling molecules, oligopeptides, or N-Acyl homoserine lactones (N-AHLs). Some authors discuss the possibilities of using QS for therapeutic purposes.6 In the fight for resources, bacteria secrete QS inhibitors, which are small extracellular molecules (peptides, fatty acids, ketones, adrenaline, norepinephrine, etc.), blocking the action of autoinductors and inhibiting the action of QS signaling molecules.7 The widely studied QSI are antagonistic peptides from natural resources, in particular polyphenols that can be found in tea, honey, garlic, cloves, marine organisms, and fungi. The synthetic QS-inhibiting substances include the preparations of 5-fluorouracil, azithromycin, poly(ethylene glycol), furanone derivatives, thiazolidinone derivatives, etc.8 Bacteria constantly modulate gene expression in response to changing environmental conditions. Many ways of regulating gene transcription respond to internal signaling molecules, metabolites, and environmental signals. For example, extracitoplasmic function (ECF) sigma (σ) factors are usually regulated by a transmembrane anti-σ factor, which, in turn, is regulated by environmental changes: the presence of a membrane or cell wall of improperly folded secretory proteins, iron chelate complexes, antibiotics. In the presence of an inducing stimulus, the σ-factor is released and directs RNA polymerase to transcription of the corresponding target genes. It has been established that multiple ECF σ-factors belong to conservative functional groups, although many of these groups have yet to be investigated experimentally. The disclosure of the functions of the multiple ECF σ-factors present in many genomes poses a huge challenge for future research.7,8 B. thetaiotaomicron is a member of the obligate intestinal microflora that metabolizes glycans and breaks down indigestible cell wall polysaccharides, generating additional energy.9,10 Bacteria of the genus Bacteroides are sources of propionate and acetate.11,12B. thetaiotaomicron and Bifidobacterium spp. that have the ability to utilize fructan molecules of different chain lengths in the intestinal tract demonstrate complex interactions.13 These interactions involve hydrolase family 78 α-l-rhamnosidase, an enzyme originating from B. thetaiotaomicron VPI 5482. If the host’s diet is low in polysaccharides, then B. thetaiotaomicron can start consuming glycans, mucin and mucin-O-glycans. B. thetaiotaomicron has 12 clusters in the genome that enhance the transcription of genes for the utilization of 117 polysaccharides and mucopolysaccharides. B. thetaiotaomicron is a member of the normal intestinal microbiota. In various pathological conditions, the gene count may rise.14,15 Species such as Faecalibacterium prausnitzii, also known as butyrate producers that are rarely found in inflammation, are linked to high gene count (HGC), while the proinflammatory species (Bacteroides and Ruminococcus gnavus) are frequent in low gene count (LGC).16,17 As it can be seen, such a division is closely related to the presence/absence of inflammatory bowel disease in LGC and HGC individuals. The results show that the gene count in a genome can eventually become a diagnostic tool for detecting intestinal inflammation and metabolic syndrome. The increased cellular density of a microbial community or biofilm and the consequent accumulation of signaling or QS molecules help the metabolic activity of microbial cells, including their tolerance to antimicrobials and synthesis of virulence factors, adapt better by synchronizing their expression of different genes at a particular cell density, according to each step of the infectious process.8 The prognosis of these chronic infections, compared with a metastatic phenomenon, following biofilm development on cellular substrata and biomaterials used in medicine, is worsened by a particular property—behavioral resistance or tolerance, even though the component cells tested in suspension (by the standard method) may be susceptible to some antibiotics. In this context, the research to find and test new preventive or therapeutic anti-infectious strategies has become a top scientific priority. New perspectives in the research on molecular mechanisms that regulate biofilm formation are crucial for the creation of new antipathogenic drugs. Knowing how microorganisms communicate in a biofilm allows interfering with the pathological process using QSI mechanisms.6,18 Therefore, the use of QSI alone, or in combination with active antibiotics, will enable the inactivation of signaling mechanisms in pathogens.19 This approach permits the suppression of biofilm resistance to antibiotics.20 The QS behaviors were established for many microorganisms; yet, the signaling molecules in the substrate also need identification. The use of enzyme-linked immunosorbent assay (ELISA) alone that only detects proteins originating from specific pathogenic strains provides indirect evidence of the presence of microorganisms.21 The presence of a specific DNA fragment detected by the polymerase chain reaction (PCR) suggests the existence of a specific infection. B. thetaiotaomicron directly competes with Citrobacter rodentium for carbohydrates in the intestinal lumen.22B. thetaiotaomicron can induce the host to produce C-type antimicrobial lectins REGIIIγ and REGIIIβ, both antimicrobial peptides that target Gram-positive bacteria.23B. thetaiotaomicron can also be used in vaccine production.24 The study aimed to develop an algorithm for genomic data processing in applied medical and scientific research. This is a first attempt to conduct a bioinformatics analysis of rRNA sequencing data derived from the reference strain B. thetaiotaomicron VPI 5482 a strain grown in a biofilm. Materials and Methods B. thetaiotaomicron reference strains were obtained from the China General Microbiological Culture Collection Center (CGMCC). The following two bacterial strains associated with the genus Bacteroides were isolated from the blood culture samples of the patients after surgery interventions:Sample t6228 was isolated from the whole blood of patients with suspected descending colon cancer in gastrointestinal tumor surgery. The instrument is unable to detect the species category of the bacteria, so molecular biology methods need to be further determined. Sample t6000 was isolated from the whole blood of patients with a patient undergoing long-term treatment due to complications of ulcerative colitis. Bacterial strains were grown anaerobically for 12 h at 37 °C in the tryptone–yeast extract–glucose (TYG) medium. Isolation of B. thetaiotaomicron DNA from a Biofilm Sample First, zirconia/silica beads (300 mg, 0.1 mm in diameter; 100 mg, 0.5 mm in diameter; BioSpec Products) were added to a sample of cultured B. thetaiotaomicron strains (150 mg). Then, the sample was added to 1200 μL of warm lysis buffer (50 mM Tris–HCl, pH 8.0, 500 mM NaCl, 50 mM ethylenediaminetetraacetic acid (EDTA), 4% sodium dodecyl sulfate (SDS)), vortexed, and homogenized in a MiniBeadBeater (BioSpec Products) for 3 min. The resultant lysate was incubated for 15 min at 70 °C. Thereafter, the samples were centrifuged for 20 min at 22 000 rpm. The supernatant was collected in 2 mL tubes and kept on ice (4 °C). The homogenization process was repeated with 1200 μL of lysis buffer readded to the precipitate. The supernatants were then mixed together, added 2.0 volume of 96% ethanol and 0.1 volume of 3 M sodium acetate, and incubated for at least an hour at −20 °C. Once incubated, the samples were centrifuged for 20 min at 14.000 rpm. The resultant precipitate was washed twice with 1000 μL of 80% 34 ethanol, dried in air and dissolved in deionized water. The precipitate was resuspended in 400 μL of lysis buffer. After additional centrifugation for 15 min at 22 000 rpm, the supernatant was collected in a 2 mL tube and incubated for 1 h at 37 °C with 1 μL of RNase A solution (5 mg/mL) preadded. The quality of the isolated DNA was evaluated by 1% agarose gel electrophoresis on 5 μL of purified DNA. The DNA concentration in the solution was determined with a Qubit fluorimeter (Invitrogen) using the Quant-iT dsDNA Broad-Range and High-Sensitivity Assay Kits (Invitrogen), according to the manufacturer’s instructions. Sequencing Methods The next-generation sequencing library preparation was constructed in accordance with the manufacturer’s protocol. For each sample, 100 ng of genomic DNA was randomly fragmented to <500 bp by sonication (Covaris S220). The fragments were treated with the End Prep enzyme mixture to repair ends, 5′ phosphorylation, and dA-tails in a single reaction, followed by T–A ligation to add adapters to both fragment ends. Size selection of adapter-ligated DNA was performed, and then, fragments of ∼470 bp (with an approximate insert size of 350 bp) were recovered. Each sample was then amplified by PCR for eight cycles using the P5 and P7 primers, with both primers carrying sequences that can anneal with the flow cell to perform bridge PCR and the P7 primer carrying a six-base index allowing for multiplexing. The PCR products were purified and validated via an Agilent 2100 (Agilent Technologies, Palo Alto, CA) and quantified using a Qubit 3.0 fluorometer (Invitrogen, Carlsbad, CA). Then, libraries with different indices were multiplexed and loaded onto an Illumina HiSeq, according to the manufacturer’s instructions (Illumina, San Diego, CA). Sequencing was performed using a 2 × 150 paired-end (PE) configuration. Image analysis and basic calling were performed using the HiSeq Control Software (HCS) + OLB + GAPipeline-1.6 (Illumina) on a HiSeq 2500 instrument.25 Reads were filtered with CutAdapt v1.9.1 to remove low-quality (QC) reads, assembled using Velvet 1.2.10, scaffolded via SSPACE v3.0, and the gaps were filled by GapFiller v1.10.26−28 Coding genes in the bacterial strains were identified using Prodigal software.29,30 Transfer RNAs (tRNAs) were detected in the genome by the program tRNAscan-SE using default settings.31 rRNAs were identified using RNAmmer 1.2 Server.32 Repeat Model software (0.8.08) was employed to predict repeat sequences in the genome. The analysis process was divided into two phases: identification of repeated sequences with RECON 1.08 and RepeatScout 1.0.5 and optimization of results with RepeatModeler and analysis of repeats using RepeatMasker 4.0.5. Table 1 displays tools employed for the bioinformatics analysis of bacterial genome data. Table 1 Analysis Result Directory Sequencing results are influenced by many factors such as sequencers, reagents, and samples. Typically, the first few sequence bases have a high probability of being incorrect due to the high rate of error generation in the base calling process. In high-throughput sequencing, the probability of error will grow with the elongation of the sequence. In the first six positions, which equal the sequence length of a reverse transcription primer (RT-primer), the sequence error rate is also high. Therefore, the high frequency of erroneous base calls at the first six-base positions is assumingly due to the incomplete binding of random primers and RNA templates. To detect whether there is a high frequency of erroneous base calls in the base sequence, the sequence error rate distribution was employed. For instance, the frequency of erroneous base calls in the middle of the sequence was significantly higher as compared to other positions. If the base mass value of Illumina HiSeqTM/MiSeq is expressed in Qphred, then the following relationship exists 1 where e is a sequence error rate. Overall, the sequence error rate for each base position should be less than 0.5%. Read Processing The preprocessing of raw reads involved quality filtering. Sequencing reads with the mean quality value (QV) score of 30 were considered acceptable, while those with QV < 30 were labeled as low-quality reads and were excluded from the downstream analysis. The high-quality reads were mapped without insertion or deletion (gaps) using the Bowtie2 mapping program. The maximum number of mismatches was 3, meaning that a random genome was selected for a read that could be aligned to more than one position with an equal probability. Unmapped reads were retained for further analysis. Determination of Taxonomic Composition The taxonomic composition of samples was determined by mapping nucleotide sequences to reference genomes from a nonredundant gene catalog using Bowtie98 software. Databases addressed here involved the Human Microbiome Project (HMP) data (http://www.hmpdacc.org), the National Center for Biotechnology Information (NCBI) data (http://www.ncbi.nlm.nih.gov), and other open sources. Genomes were aligned against each other by means of Cmscan 1.1.2, and those with 80% unique units were included in the catalog. The remaining genomes were clustered at an 80% sequence similarity threshold. Representative sequences (one per cluster) were manually selected and added to the catalog. In total, the catalog included 353 genomes, fully assembled, with contigs. After aligning the set of reads to a reference genome, BAM files were created to interpret BEDtools coverage data, i.e., coverage depth and coverage width values. The coverage width served as a threshold for the detection of references in the genome; reads must cover at least 1% of the genome so that the match could be found. The coverage data was normalized for each genome based on the total length of mapped reads and on the length of the genome under consideration. The taxonomic classification of each species was conducted using the RDP classifier. Algorithm for Processing Data Streams First, a DNA fragment was extracted from the whole genome using the de novo sequencing method. Subsequently, a sample was detected and a library of samples was created. A long DNA sequence was cut into fragments 500 bp in length. The resultant sticky end was repaired with the blunt end and then A base was added to the 3′ end so that the DNA unit could be ligated to a linker and to a T base at the 3′ end. The ligation reaction product can be restored by the use of electrophoresis. Then, polymerase chain reaction (PCR) amplification was carried out. DNA fragments with adapters at both ends were added, and finally, the library was applied in cluster generation and sequencing. Data of 16S ribosomal RNA gene and partial sequencing were processed with the NCBI database. Bioinformatics Analysis The bioinformatics analysis of samples was conducted. Following the assembly results and gene predictions, rRNAs, tRNAs, and other ncRNAs were found. A predictive functional analysis was performed using the following databases: Nr, KEGG, and gene ontology (GO). According to a genomic sequence obtained for a reference strain, repeated sequence analysis was performed with strains grown on a biofilm. Statistical Analysis and Visualization The distance between sequence samples was measured by means of the following metrics: the Jenson–Shannon divergence and the modified UniFrac model. The Jensen–Shannon distance was calculated in R using a specially written script. The 16S rRNA gene sequences were aligned with the Cmscan 1.1.2 multiple alignment program. Each pair of samples submitted to UniFrac analysis was assigned a weight, and genomes with zero representation across samples were removed. Data were then submitted to conversion into a biom format with convert_biom.py of QIIME104, and weighted UniFrac distances were computed by applying beta_diversity.py to samples from the biom table. The samples were classified into clusters based on their enterotypes. The matrix of Jensen–Shannon distances between samples was submitted as the input data. To establish over-representation or under-representation of functional genes between two sets of samples, the one-sided Mann–Whitney test was used. Multiple comparisons were corrected using the Benjamini–Hochberg method. The difference was considered significant with the adjusted p-value not greater than 0.05. To identify metabolic pathways, the enrichment of which varied significantly between sample sets with the KEGG database, the relative representation of enzyme genes (KEGG Orthology or KO database) entering the path was compared via the one-sided Mann–Whitney test. After the p-values were corrected for multiple comparisons, two lists of KO groups were composed, one with increased and reduced gene representation in the first group as relating to the second group. These lists were analyzed in R by using the Piano package. Statistical analysis of gene sets was performed with the Reporter Features algorithm, a parametric analogue of the GSEA algorithm. Multiple comparisons were corrected using the Benjamini–Hochberg method. The difference was considered significant with the adjusted p-value not greater than 0.05. Limitations The research results correspond to the medical history of an individual patient. Results The bioinformatics analysis was performed on samples t6000 and t6228. The raw sequencing data was submitted to base calling with the subsequent conversion of base call files with Bcl2fastq version 2.17.1.14. During sequencing, the preliminary quality analysis was conducted through Illumina GAPipeline v1.9 software. Reads were mapped in the FASTA format as follows: strain t6000 ATGGGCGTGCTCGGCTTACACATGCAAGTCGAGGGGCAGCATTTCAGTTTGCTTGCAAACTGGAGATGGCGACCGGCGCA CGGGTGAGTAACACGTATCCAACCTGCCGATAACTCGGGGATAGCCTTTCGAAAGAAAGATTAATACCCG strain t6228 TGCTCGGCTTACACATGCAGTCGAGGGGCAGCATTTCAGTTTGCTTGCAAACTGGAGATGGCGACCGGCGCACGGGTGAG TAACACGTATCCAACCTGCCGATAACTCGGGGATAGCCTTTCGAAAGAAAGATTAATACCCG For pairwise sequencing data, there were two fq files, one for Read 1 and another for Read 2. Table 2 shows raw data from each sequencing sample. Table 2 Sequencing Raw Data Quality Statistics sample the name of the sequencing sample length (bp) number of sequencing reads the total number of bases Q20 (%) Q30 (%) GC (%) N (ppm) 1 2 3 4 5 6 7 8 t6000 150.00 19252032 2887804800 98.05 95.11 42.90 171.35 t6228 150.00 28492854 4273928100 97.88 94.48 56.10 168.18 The relative representation of single genes was evaluated in terms of KO groups. The KEGG pathways of an investigated strain were divided into six categories: cellular processes, environmental information processing, genetic information processing, human diseases, metabolism, and organismic systems. The number of genes involved in each metabolic pathway is statistically displayed in Figures 1 and 2. Figure 1 KEGG pathway analysis for the t6000 sample associated with B. thetaiotaomicron strain. Figure 2 KEGG pathway analysis for the t6228 sample associated with B. thetaiotaomicron strain. After genome sequence alignment, the orthologous protein sequences from B. thetaiotaomicron strains were identified with COG. The results are presented in Tables 3 and 4 and Figures 3 and 4. Figure 3 COG functional classification of genes from the t6000 sample associated with B. thetaiotaomicron strain. Figure 4 COG functional classification of genes from the t6228 sample associated with B. thetaiotaomicron strain. Table 3 COG Function Annotation Results for the t6000 Sample Associated with B. thetaiotaomicron Strain gene_ID COG_ID COG_description pident E_value score 1_5 gnl\|CDD\|223739 COG0667, Tas, predicted oxidoreductases (related to aryl-alcohol dehydrogenases) [energy production and conversion] 37.188 7.76E-95 285 1_6 gnl\|CDD\|225789 COG3250, LacZ, β-galactosidase/β-glucuronidase [carbohydrate transport and metabolism] 23.131 1.11E-32 133 1_7 gnl\|CDD\|224610 COG1696,DltB, predicted membrane protein involved in d-alanine export[cell envelope biogenesis, outer membrane] 32.401 1.76E-126 377 1_8 gnl\|CDD\|225353 COG2755,TesA, lysophospholipase L1 and related esterases[amino acid transport and metabolism] 16.860 2.95E-06 44.7 1_11 gnl\|CDD\|223911 COG0841, AcrB, cation/multidrug efflux pump [defense mechanisms] 31.445 0.0 832 Table 4 COG Function Annotation Results for t6228 Sample Associated with B. thetaiotaomicron Strain Gene_ID COG_ID COG_description pident E_value score 1_263; 1_349; 2_78; 2_236; 2_354; 2_394; 4_258; 7_43; 7_81; 7_193; 7_238; 7_264; 7_271; 10_69; 10_78; 10_113; 14_112; 17_101; 17_122; gnl\|CDD\|223739 COG0667, Tas, predicted oxidoreductases (related to aryl-alcohol dehydrogenases) [energy production and conversion] 39.062 3.75E-114 335 2_253; 19_1 gnl\|CDD\|225789 COG3250, LacZ, β-galactosidase/β-glucuronidase [carbohydrate transport and metabolism] 19.572 1.33E-55 201 1_229 gnl\|CDD\|224610 COG1696, DltB, predicted membrane protein involved in d-alanine export [cell envelope biogenesis, outer membrane]. 25.248 8.37E-52 178 19_40 gnl\|CDD\|225353 COG2755, TesA, lysophospholipase L1 and related esterases [amino acid transport and metabolism]. 37.674 9.22E-50 160 2_404; 2_458; 4_210; 4_211; 5_60; 6_163; 6_163;10_125;10_125;19_12;19_12 gnl\|CDD\|223911 COG0841, AcrB, cation/multidrug efflux pump [defense mechanisms]. 27.875 0 907 The sequence samples were mapped according to the gene ontology (GO) databased and the GO annotations were retrieved alongside the relative representation values. The results of the GO analysis are presented in Tables 5 and 6. Table 5 GO Function Annotation Results of the t6000 Sample Associated with B. thetaiotaomicron Strain gene_ID GO_ID GO_terms GO_aspect 1_6 GO:0004553 hydrolase activity, hydrolyzing O-glycosyl compounds molecular_function 1_6 GO:0005975 carbohydrate metabolic process biological_process 1_7 GO:0042121 alginic acid biosynthetic process biological_process 1_8 GO:0016788 hydrolase activity, acting on ester bonds molecular_function 1_9 GO:0016788 hydrolase activity,acting on ester bonds molecular_function Table 6 GO Function Annotation Results of the t6228 Sample Associated with B. thetaiotaomicron Strain gene_ID GO_ID GO_terms GO_aspect 1_2;1_7; 1_81;1_83;1_363;1_418; 1_450; 1_485; 2_242; 2_253; 2_271; 2_273; 3_127; 3_255; 3_350; 4_90; 4_230; 4_252; 4_260; 5_82; 5_134; 7_90; 7_165; 7_237; 8_120; 8_154; 13_62; 15_18; 15_42; 17_81; 17_82; 17_116; 17_133; 17_143; 19_1; 25_21; GO:0004553 hydrolase activity, hydrolyzing O-glycosyl compounds molecular_function 1_2; 1_7; 1_12; 1_14; 1_81; 1_83; 1_126; 1_249; 1_306; 1_363; 1_414; 1_418; 1_450; 1_451; 1_460; 1_485; 2_113; 2_120; 2_121; 2_133; 2_242; 2_253; 2_271; 2_272; 2_273; 2_339; 3_63; 3_98; 3_119; 3_123; 3_127; 3_269; 3_302; 3_330; 3_350; 3_371; 3_435; 4_90; 4_186; 4_230; 4_252; 4_260; 4_285; 5_54; 5_82; 5_134; 6_13; 6_67; 6_90; 6_118; 6_128; 7_84; 7_90; 7_237; 7_254; 7_318; 7_374; 8_35; 8_36; 8_42; 8_51; 8_53; 8_73; 8_79; 8_86; 8_92; 8_120; 8_154; 10_6; 11_57; 11_107; 12_7; 12_20; 12_38; 12_56; 12_67; 13_62; 14_120; 15_18; 15_42; 15_60; 15_79; 17_27; 17_42; 17_43; 17_71; 17_72; 17_78; 17_81; 17_82; 17_95; 17_116; 17_133; 17_143; 19_1; 20_46; 20_68; 25_21; GO:000595 carbohydrate metabolic process biological_process GO:0042121 alginic acid biosynthetic process biological_process 1_356; 1_440; 3_79; 3_160; 3_240; 5_213; 6_91; 7_107; 8_125; 8_149; 10_87; 10_119; 18_10; GO:0016788 hydrolase activity, acting on ester bonds molecular_function Table 7 reports on the results of the analysis of repetitive units found in the reference genome (t6228) and the genome under study (t600). Table 7 Long Repeats in Genome chr1, the chromosome ID of the query repeat sequence start1 query repeat sequence start coordinates end1 query repeat sequence termination coordinates chr2, the chromosome ID of the target repeat sequence start2 target repeat sequence starting coordinates end2 target repeat sequence termination coordinates strand, the target repeat sequence is in the positive/negative chain of the chromosome length, the length of the repeat sequence identity, % repeats, the similarity between sequences scaffold12size552143 552014 552139 scaffold21size62496 1 126 – 126 100.00 scaffold18size88844 88702 88844 scaffold4size340877 340735 340877 – 143 100.00 scaffold24size45383 1 129 scaffold27size69222 69107 69235 – 129 100.00 scaffold25size45248 45119 45248 scaffold3size494252 494123 494252 + 130 100.00 scaffold26size36413 36284 36412 scaffold27size69222 1 129 – 129 100.00 This table also shows the results of the analysis of long sequences found in the genome under study. We concluded that this bacterium can monitor the indicator of the disease, so to detect it by PCR analysis, we designed a series of primers (Table 8). Table 8 Finding Primers Specific to B. thetaiotaomicron (Using Primer3 and BLAST) primer pair 1 sequence (5′ → 3′) template strand length start stop Tm GC% self complementarity self 3′ complementarity forward primer TTACACATGCAGTCGAGGGG plus 20 9 28 59.75 55.00 4.00 0.00 reverse primer TACGTGTTACTCACCCGTGC minus 20 89 70 60.04 55.00 7.00 3.00 product length 81 primer pair 2 sequence (5′ → 3′) template strand length start stop Tm GC% self complementarity self 3′ complementarity forward primer GGGCAGCATTTCAGTTTGCT plus 20 26 45 59.68 50.00 4.00 1.00 reverse primer GCTATCCCCGAGTTATCGGC minus 20 117 98 60.11 60.00 5.00 3.00 product length 92 primer pair 3 sequence (5′ → 3′) template strand length start stop Tm GC% self complementarity self 3′ complementarity forward primer GGGGCAGCATTTCAGTTTGC plus 20 25 44 60.67 55.00 3.00 2.00 reverse primer CGAGTTATCGGCAGGTTGGA minus 20 109 90 59.83 55.00 3.00 0.00 product length 85 primer pair 4 sequence (5′ → 3′) template strand length start stop Tm GC% self complementarity self 3′ complementarity forward primer TCGGCTTACACATGCAGTCG plus 20 4 23 60.74 55.00 4.00 2.00 reverse primer ATCGGCAGGTTGGATACGTG minus 20 103 84 60.18 55.00 4.00 2.00 product length 100 primer pair 5 sequence (5′ → 3′) template strand length start stop Tm GC% self complementarity self 3′ complementarity forward primer TGCAAACTGGAGATGGCGAC plus 20 46 65 60.96 55.00 4.00 2.00 reverse primer TCGAAAGGCTATCCCCGAGT minus 20 124 105 60.40 55.00 5.00 1.00 product length 79 primer pair 6 sequence (5′ → 3′) template strand length start stop Tm GC% self complementarity self 3′ complementarity forward primer CGAGGGGCAGCATTTCAGTT plus 20 22 41 60.96 55.00 3.00 0.00 reverse primer CGAAAGGCTATCCCCGAGTT minus 20 123 104 59.54 55.00 5.00 1.00 product length 102 primer pair 7 sequence (5′ → 3′) template strand length start stop Tm GC% self complementarity self 3′ complementarity forward primer AGGGGCAGCATTTCAGTTTG plus 20 24 43 59.03 50.00 3.00 1.00 reverse primer CGGCAGGTTGGATACGTGTT minus 20 101 82 60.67 55.00 4.00 2.00 product length 78 primer pair 8 sequence (5′ → 3′) template strand length start stop Tm GC% self complementarity self 3′ complementarity forward primer GCTTACACATGCAGTCGAGG plus 20 7 26 59.00 55.00 4.00 2.00 reverse primer TTCGAAAGGCTATCCCCGAG minus 20 125 106 59.25 55.00 6.00 2.00 product length 119 primer pair 9 sequence(5′ → 3′) template strand length start stop Tm GC% self complementarity self 3′ complementarity forward primer GGCTTACACATGCAGTCGAG plus 20 6 25 59.00 55.00 4.00 2.00 reverse primer CCGAGTTATCGGCAGGTTGG minus 20 110 91 60.81 60.00 5.00 0.00 product length 105 primer pair 10 sequence (5′ → 3′) template strand length start stop Tm GC% self complementarity self 3′ complementarity forward primer CTCGGCTTACACATGCAGTC plus 20 3 22 59.00 55.00 4.00 2.00 reverse primer CCCGAGTTATCGGCAGGTTG minus 20 111 92 60.81 60.00 5.00 1.00 We also found a sigma factor regulatory protein, FecR/PupR family [B. thetaiotaomicron] MAQINFNSIYTAYYRKAFLFTLSYVHNDLVAEDIVSEAIIHLWELSKEREIPSVEAILITYIRSKSLNYL KHIQAQENVFQTLLDKGQRELEIRISTLEACDPKEILSEELRAKVHALLESMPEKTRTAFIRDRLDGKSH KEIAEELGISVKGVEYHISRAVKILRDNLKDYAPFLFFFI Discussion This study examined the genetic correspondence of samples t6000 and t6228 samples associated with B. thetaiotaomicron strain obtained by the blood culture method used to reference strain B. thetaiotaomicron VPI 5482. Many authors argue that the induction of the QS system enhances the pathogenicity of bacteria. We found bacteria that are intestinal symbionts in the general bloodstream of patients suffering from severe pathologies of the large intestine. The t6000 sample was found to have a biofilm-forming ability as compared to the reference strain B thetaiotaomicron VPI 5482. The bioinformatics data showed that sample t6000 contained more virulent- and stress response-associated genes as targets of the QS system when compared to t6228. These genes were responsible for adaptation, antibiotic resistance, and reduction reactions in metabolism and for the processing of information about the environment.32 They also regulated cellular processes (Figure 2). By contrast, the sample t6228 contained more genes responsible for the hydrolytic activity, transport and metabolism of carbohydrates and amino acids, and defense (Tables 3–6). However, sample t6000 had a gene regulating the process of alginate biosynthesis, which was not found in samples from t6228. Sample t6000 obviously exhibits the biofilm-forming function due to the synthesis of alginic acid and is a potentially pathogenic strain. The literature also indicates that the C-terminal truncation of BT3147 promotes the formation of a B. thetaiotaomicron biofilm. This is consistent with the results of studies based on the genetic modifications of B. thetaiotaomicron, which were created with the use of transposons.31 In the studied biofilm-forming sample t6000, BT3147 was lacking the last nine C-terminal amino acids (BT3147Δ9). Since the downstream genes BT3146 and BT3145 were not involved in the phenotype under consideration, the authors constructed a chromosomal BT3147Δ9 de novo by removing the reference strain region that spanned the last nine codons of BT3147. The downstream genes BT3146 and BT3145 were replaced by a stop codon. The expression of BT3148-BT3147 inhibited the formation of the VPI 5482 biofilm, while the chromosomal-based expression of BT3148-BT3147Δ9 led to increased biofilm formation as compared to VPI 5482. It was established that the removal of gene-encoded BT3148 from B. thetaiotaomicron VPI 5482 would lead to a significant decrease in biofilm formation. The shortening of the C-terminus of BT3147 was found to promote biofilm formation in B. thetaiotaomicron. This widespread but highly variable ability to form biofilms is observed in many aerobic bacteria, including Escherichia coli,33Staphylococcus epidermidis,34 and Bacteroides fragilis, which also show a variable biofilm formation capacity caused by sigma factor RpoQ.35 Biofilm formation by pathogenic bacteria is a major cause of chronic and recurring infections. The pathogenicity and abscess formation of B. fragilis are associated with the biofilm and adhesion to the peritoneal epithelium. Considering that B. thetaiotaomicron usually behaves like a nonpathogenic intestinal bacterium, it is also associated with infections of the abdominal cavity and deep wounds and accounts for up to 14% of all bacteria found at these sites. Since five of 14 best biofilm-forming strains that have been previously identified (36) were isolated from different sites of infection (bone biopsy, blood, abscesses), the B. thetaiotaomicron mutation may be associated with opportunistic extracellular infections. Further studies may provide insights into this aspect of B. thetaiotaomicron biology. As it was demonstrated, the reference strain B. thetaiotaomicron VPI 5482 forms insufficient biofilms in vitro, which is consistent with other investigations where a VPI 5482 biofilm was received only 8 days after the incubation in a chemostat. Significant biofilm growth in VPI 5482 was not observed even after 48 h, and this may suggest repression of the VPI 5482 biofilm formation in laboratory conditions.36 For instance, the uropathogenic E. coli are known for causing infections by adhering to the bladder epithelium with fimbriae, but they exhibit a weak capacity to form biofilms in vitro.20 Similarly, B. fragilis tends to display unsatisfactory biofilm formation in standard conditions but generates good biofilms on mucin-coated plates that mimic the intestinal mucosa, its natural habitat.31 The present study detected Mfa1-like proteins, BT3148 and BT3147, which are potentially involved in the formation of biofilms in B. thetaiotaomicron VPI 5482. The studied strain had properties that allowed evaluating it in terms of the following functional categories: cellular processes, environmental information processing, genetic information processing, human diseases, metabolism, and organismic systems. Its important trait is the ability to indicate gastrointestinal diseases that involve about a hundred genes. The literary sources provide a wide spectrum of data regarding the microbiome of the human gastrointestinal tract.7 There have been reports on the variation in metagenome composition of microorganisms linked to a range of factors.10 In ulcerative colitis, an elevated level of Bacteroides and Prevotella spp. was observed. The mechanisms by which Bacteroides can contribute to a chronic inflammatory process are largely unknown. Among possible ones, the production of mucin-degrading sulfatases. Elevated levels of bacterial mucin-desulfating sulfatases have been reported in patients with active ulcerative colitis. In addition, the existence of enzymes that partially desulfate mucins has been demonstrated in relation to B. thetaiotaomicron, B. fragilis, and for Prevotella spp., suggesting that members of the Bacteroidetes may contribute to chronic inflammation due to the impaired barrier function in the epithelial cells.37 However, it turned out that B. thetaiotaomicron is linked to a rather healthy GI microbiome because only about 100 genes were potentially responsible for the pathological processes that took place. This is consistent with other research results that showed that B. thetaiotaomicron could indirectly enhance the therapeutic effect of immunotherapy for malignant neoplasms by increasing the T-cell production.38 The recent clinical study failed to detect B. thetaiotaomicron in the intestinal microflora in patients with ulcerative colitis. This also suggests the protective role of B. thetaiotaomicron and it may be recommended as a probiotic in cholelithiasis. However, randomized clinical trials are required to evaluate the safety and efficacy of its probiotic strains. It has been found that the antitumor efficacy and immunostimulatory effect of the cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) blockade were linked to the activity of various species of Bacteroides such as B. thetaiotaomicron and B. fragilis.39 Genome-sequenced isolates allow drawing a conclusion about the functional capacity of genes in the reference genomes and identifying genetic variation of the studied strains in a fashion such that permits the understanding of all biological processes in which the target gene can be involved. This eliminates the need for ultradeep metagenomic sequencing and ensures that complete functional pathways are contained within an individual bacterium. In addition to increased accuracy, this method also has the capacity to improve sensitivity for functional analysis, allowing identification of functions that, although not prevalent, may represent fundamental differences between the study cohorts. The study introduced an algorithm of the bioinformatics analysis for applied medical and scientific research and tested it on samples 6000 and t6228. The results of the bioinformatics assay on the intestinal symbiont B. thetaiotaomicron isolated from blood were presented. This was the first study to bioinformatically analyze the genome of two B. thetaiotaomicron strains based on the 16S rRNA gene sequencing data. It was found that the sample t6000 had more genes responsible for virulence as compared to the reference. Potentially, this strain represents microorganisms with acquired resistance to antibiotics. The possibility of using B. thetaiotaomicron as a marker of severe gastrointestinal diseases was examined. The results of the bioinformatics analysis may be useful when identifying gastrointestinal diseases, optimizing treatment for infections of the abdominal cavity and deep wounds, and when detecting resistance to antibiotics. Sigma factor (σ factor or specificity factor) is a protein required to initiate transcription in bacteria. This is a factor in initiating bacterial transcription, which provides specific binding of RNA polymerase (RNAP) to gene promoters. It is homologous to archeological transcription factor B and eukaryotic factor TFIIB. We have suggested that under the action of chemotherapeutic drugs and antibiotics in bacteroids there is a violation of the initiation of transcription of the relevant genes that will vary depending on the gene and the environmental signals required to initiate the transcription of this gene. Thus, the determination of the corresponding sigma factor will make it possible to identify the external factor that led to the commission of the corresponding gene and hence to the diagnosis. Because of the genome sequencing of these bacteria, it was found that they cannot be presented to any of the known strains of B. thetaiotaomicron and it should be assumed that these bacterias are new species and require research that is more detailed. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Z.C.W. and H.X.F. The first draft of the manuscript was written by M.Z., and all authors commented on previous versions of the manuscript. All authors have read and approved the final manuscript. Conceptualization, Z.C.W.; methodology, H.X.F.; formal analysis and investigation, W.L.Z.; writing original draft, M.Z.; review and editing, L.W.; and supervision, Z.C.W. This research was supported and receive funding from National Natural Science Foundation of Hainan Province (417146 and 817324), the Colleges and Universities Scientic Research Projects of the Education Department of Hainan Province (20181181072). The authors declare no competing financial interest. Acknowledgments This work was supported by the National Natural Science Foundation of Hainan Province (417146 and 817324) and the Colleges and Universities Scientific Research Projects of the Education Department of Hainan Province (20181181072). ==== Refs References van der Meulen T. A. ; Harmsen H. J. ; Bootsma H. ; Spijkervet F. K. ; Kroese F. G. ; Vissink A. The microbiome–systemic diseases connection . Oral Dis. 2016 , 22 , 719 –734 . 10.1111/odi.12472 .26953630 Lukáš F. ; Gorenc G. ; Kopečný J. Detection of possible AI-2-mediated quorum sensing system in commensal intestinal bacteria . Folia Microbial. 2008 , 53 , 221 –224 . 10.1007/s12223-008-0030-1 . Pedrolli D. B. ; Ribeiro N. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 33184786 11462 10.1007/s11356-020-11462-z Research Article It is time to control the worst: testing COVID-19 outbreak, energy consumption and CO2 emission http://orcid.org/0000-0001-7729-8464 Iqbal Sajid [email protected] 1 http://orcid.org/0000-0001-9471-7093 Bilal Ahmad Raza [email protected] 2 Nurunnabi Mohammad [email protected] 3 Iqbal Wasim [email protected] 4 Alfakhri Yazeed [email protected] 4 Iqbal Nadeem [email protected] 5 1 grid.444940.9 KUBEAC, University of Management & Technology, Sialkot, 51310 Pakistan 2 grid.444763.6 School of Business, Sohar University, 311 Sohar, Oman 3 grid.443351.4 0000 0004 0367 6372 College of Business Administration, Prince Sultan University, Riyadh, Saudi Arabia 4 grid.263488.3 0000 0001 0472 9649 Department of Management Science, College of Management, Shenzhen University, Shenzhen, China 5 grid.448869.f 0000 0004 6362 6107 Department of Business Administration, Ghazi University, Dera Ghazi Khan, Pakistan Responsible Editor: Eyup Dogan 12 11 2020 2021 28 15 1900819020 15 6 2020 28 10 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. During the COVID-19 outbreak, managing energy consumption and CO2 emission remained a serious problem. The previous literature rarely solved this real-time issue, and there is a lack of public research proposing an effective way forward on it. However, the study examines the impact of the COVID-19 outbreak on energy consumption and CO2 emission. The design of the study is quantitative, and the data is acquired from different online databases. The model of the study is inferred by using panel unit root test and ARDL test. The robustness of study findings was checked through panel quantile regression. The findings highlighted that the COVID-19 outbreak is negatively significant with energy consumption and CO2 emission. The study suggested revising the energy consumption patterns by developing and implementing the national action plan for energy consumption and environmental protection. The study also contributed in knowledge by suggesting the novel insight into CO2 emission and energy consumption patterns during COVID-19 pandemic and recommended to consider renewable energy transition methods as an opportunity for the society. For a more effective management of energy consumption and environmental pollution, country-specific measures are suggested to be taken, and the national government should support the concerned public departments, ministries and private organizations on it. To the best of our study, this is one of the pioneer studies studying this novel link and suggesting the way forward on recent topicality. Keywords COVID-19 outbreak Energy consumption CO2 emission Renewable energy Energy transition issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2021 ==== Body pmcIntroduction The world economy is facing two major problems: the spread of the new COVID-19 outbreak and the current plunge in oil prices. The amalgamation of these two issues will probably trigger a long-term monetary slump and push the world economy into a subsequent downturn (Baker et al. 2020a). The COVID-19 outbreak continues to spread widely in the United States (USA), Italy, China, Iran and other countries, especially in Europe, which has triggered special effects of stock market impulse and economic policy uncertainty. The recent stock market volatility level has exceeded from the levels of the stock market crashes in October 1987 and December 2008 and 1929. During the same period, the uncertainty of the US economic policy indicated that by 24 March 2020, this number had jumped from 100 to 400 (Disemadi and Shaleh 2020). Similarly, the impact of the pandemic, such as the COVID-19 outbreak commonly referred to as the coronavirus, has severely affected Pakistan’s energy sector. The Petroleum and Natural Gas Authority (OGRA) of Pakistan is committed to strengthening the national energy system and avoiding energy shortages not only due to local economic impacts but also due to international impacts. (Chakraborty and Thomas 2020). Likewise, it is widely accepted that the impact of the COVID-19 outbreak is very big and structural in all the domains of life including the energy sector (Nicola et al. 2020). Humans are the core regulator of society, and their health is the foremost important consideration for thriving in a productive and strong society. While, due to COVID-19 outbreak, several health issues have been raised and such issues are becoming serious days by day, more specifically in frugal economies like Pakistan, this impact is very critical, and now, an economic shock is being perceived; lockdown is being extended continuously; and by the effect of lockdown fuel consumption, CO2 emission, energy demand and supply declined and market mechanism is altered very speedily. Thus, to understand and identify the novel impact of COVID-19 outbreak on CO2 emission and energy consumption, the recent study is intended to investigate (Solangi et al. 2019b; Hou et al. 2019). This is the motivation of a recent study. Deciding how much the impact of COVID-19 is important, especially in the recent conditions, where many countries like the USA, China, Italy, Spain, Iran, and India are facing ‘pandemic effect’ at large. By the fact, the COVID-19 outbreak affected the energy sector of the world as a whole, and eventually, the demand graph of oil supplies have become bearish; oil market prices faced a decline; and big economies like the USA are bidding the oil-free of cost (Malamud and Núñez 2020). According to Bloomberg, a sudden decline has been observed in the oil and gas industry during March 2020, and its future is still in an unpredictable state. Importantly, the failure of OPEC negotiations is also the reason for the decline in oil prices and the COVID-19 outbreak, which has expanded the greater impact on the energy industry of Pakistan (Solangi et al. 2019a). This is a very big impact with which existing theoretical propositions, assumptions, methods and models have not kept the pace. This is one of the big limitations and failures of previous literature. Therefore, novel exertions are prerequisites to present advancement in the literature, empirical modelling and operational research by addressing the contemporary problem of the COVID-19 outbreak (Halkos and Tsirivis 2019). Our study attempts to investigate this critical issue by testing the effect of COVID-19 outbreak on the energy sector. More specifically, we inferred the effect of COVID-19 outbreak on CO2 emission and energy consumption. We incorporated the theory of economic efficiency to validate the association between CO2 emission, energy consumption and the COVID-19 outbreak. By considering the law of diminishing economic return due to the COVID-19 outbreak, we authenticated the connection of the COVID-19 outbreak with energy consumption and CO2 emission (Correia et al. 2020). We test the inherent assumptions of economic efficiency theory and then validate an empirical model that forecasts the optimal points of energy consumption and CO2 emission in pandemic where greater advantages are been achieved with the intent to sustain energy efficiency. We inquired the question how the COVID-19 outbreak can reduce CO2 emission and energy consumption (Goodell 2020). A better understanding of this question is the need of the time for academia, practitioners and regulators. In the energy sector, several energy consumptions and CO2 emission-oriented frameworks, efficiency management policies and performance management mechanisms were presented. Ironically, these policy frameworks and ways forward often increase energy demand and challenge the capacity of the energy sector to stay committed but not provide the way forward to adjust with the structurally imposed challenges, such as the COVID-19 outbreak (Department of Economic and Social Affairs 2020). Therefore, inquiries should give additional attention to understanding how structural changes like the COVID-19 outbreak affect CO2 emission and energy consumption (Öncü 2020). In line with the concept of energy efficiency, we consider energy consumption as a contextual and market process that signals the expectation of an energy market (Iqbal et al. 2019b). A so-called energy management system of OGRA Pakistan is expected to do this effectually but not essentially: recent declines in the local energy market of Pakistan are big examples to support this argument. However, a policy way forward to manage the energy consumption and CO2 emission is important(Baloch et al. 2020). And it is further important to give an alternative strategy in managing energy consumption by reducing the CO2 emission in the Pakistani context by taking the COVID-19 outbreak as an opportunity. The objective of the current study is to investigate the impact of COVID-19 outbreak on energy consumption and CO2 emission, a way forward for sustainable energy markets. Further, the study contributes to energy consumption patterns by suggesting how to cope and put up with the energy sector from the novel effect of the COVID-19 outbreak. We propose sector-wide planning and administration in managing energy shortfalls and redressing the energy consumption habits. This is the first contribution. Secondly, a recent study contributes to the novel COVID-19 with the assumptions of the economic theory of efficiency by backing up the study arguments on energy consumption. Thirdly, we fill the gap of theoretical and operational research by studying the novel COVID-19 with CO2 emission and energy consumption. Fourth, by this study, we address the special call on ‘Secure and Sustainable Energy system’ as this historical time is very crucial in developing the new avenues and/or ways forward to secure and stabilize energy system through energy markets—who have imagined a global lockdown and massive energy distress just a few months ago? Fifth, we contribute to COVID-19, energy regulator (e.g. OGRA Pakistan) initiatives and energy market stability with the motivation to bring the best findings and policy ways forward to our readers. However, our study contributes to different avenues, most importantly in the COVID-19 outbreak and energy consumption, to present a way forward for better planning and administration to meet the energy shortfalls. This article is one of the pioneering studies on the energy economic and environmental impact of the COVID-19 pandemic. The rest of the paper is organized as follows: the ‘Review of literature’ section describes the literature review, the ‘Research method and design’ section describes methodology, the ‘Results and discussion’ section describes the results and discussion while the ‘Conclusion and policy implications’ section concludes the study. Review of literature The Pakistani economy is one of the rich oil-producing economies in the world and one of the self-sustainable economies in energy production resources in South Asia. Ironically, the national revenues, energy market returns, earning capacity, energy market microstructure and energy efficiency are adversely affected due to the COVID-19 outbreak (Connors 2020). Notably, the Pakistani energy sector is broadly susceptible and sensitive to energy crust and turfs, energy preservation and consumption and environmental degradation. Due to pandemic persistence, the role of COVID-19 outbreak on the energy market and CO2 emission has become an important area of study to study and guide the policymakers to sustain the energy sector at large. Before the COVID-19 lockdown, the energy consumption patterns of the Pakistani economy were much alarming; and therefore, CO2 emission and energy consumption have become a puzzle to solve (Ataguba 2020). In contrast, the COVID-19 lockdown has proved the significance of less energy consumption due to lower CO2 emission in the local context (Wang et al. 2020a). In the emergence of 2020, the COVID-19 outbreak has become a lifetime emergency that affected the energy sector and economies as a whole. In response, national governments and regulatory authorities are combating with COVID-19 pandemic resiliently (Fornaro and Wolf 2020). In times of highly volatile momentum energy consumption and CO2 emission, the COVID-19 pandemic emerged as a big crisis (Asbahi et al. 2019). Interestingly, the COVID-19 outbreak locked the nations of nations into the self-isolation centres, and by the fact, this reduced CO2 emission at large and CO2 emission and energy consumption in so many countries like China, USA, Spain, Italy and Pakistan. On this momentum, emission trading system (ETS) and European Union (EU) emission led a new policy framework on energy emission, such as CO2 emission and energy consumption (Liu et al. 2020; Iqbal et al. 2020b). While, geographically, such policy frameworks were developed far away from Asia, however, these are not considered, studied and implemented in the Pakistani energy sector. Considering the energy consumption way forward, it has been noticed from previous studies that many of the energy consumption and energy sustainability frameworks have been implemented in the annual year of 2020. Conversely, the COVID-19 outbreak has postponed the entire activities related to the energy sector (Disemadi and Shaleh 2020). Likewise, due to COVID-19, such policy frameworks are being altered drastically (Iqbal et al. 2020a). Thus, new policy frameworks on energy consumption, CO2 emission leading towards energy sustainability (Dong et al. 2015; Zheng et al. 2019; Iqbal et al. 2019c) or energy efficiency are prerequisite in accordance to structurally imposed scenarios persistent in the world with the COVID-19 pandemic (Colenda et al. 2020). In short, the energy sector needs adaptive policies that could enhance energy sustainability. A large-scale reduction in travel and transport activities has dampened the oil demand due to the COVID-19 outbreak and by the fact (GHG) is also reduced. This is the common proposition that GHG emission is probable to be unmaintainable in the long run. However, a reduction in oil demand has raised an important point of concern that what would be the future of the climate and clean energy development. Similarly, green credit assurance and assessment of the income which is produced from energy facilities provided by the supplier to the financial expert related to the environment by use of friendly power (Khokhar et al. 2020). It might endanger green capital and affect the revenues of environmental ventures. Here advancement features are also described for the improvement of green money and speculation (Ciaschini et al. 2013). Thus, recent research also intends to add two related structures sponsored by A hypothetical method like with green account and speculation which depend on task size. The impact of the pandemic on the energy market is phased into two sections. First is the effect of COVID-19 on the energy sector in determining energy consumption pattern from the raw material of production (oil demand, supply and consumption) to end-product (electricity and end mean of fuel used in the travel and transport industry). Second, how the energy market will reshape a shift and recover after a pandemic or, in other words, how energy market will revive after a pandemic is the most important question of recent times that need answer (Igwe 2020). Endorsing the previous literature, a recent study is extending the theoretical contribution by testing these missing links between the COVID-19 outbreak, energy consumption and CO2 emission. Moreover, based on published studies, our study enhanced the empirical evidence by considering the recent reality of pandemic. The COVID-19 outbreak indicates that carbon dioxide emission will decline during this year and historically a drop-down in CO2 emission was observed in 2009, and the declining percentage ranged from 0.3 to 1.2% approximately (Burkle 2020). Thus, the COVID-19 outbreak has locked down the entire business of life in so many countries around the globe, and mainly China is affected by this particular virus (Chohan 2020). Due to said specific lockdown, megaindustries and small- and medium-sized (SME) businesses are in lockdown that reduced the demand for oil as China is the largest exporter of oil products. By this, oil consumption is reduced, and with this effect, CO2 emission is also reduced. Considering the pandemic and opportunity, when industries are in lockdown, this is the high time to shift the industries towards renewable energy with the mission to reduce oil consumption and CO2 emission and to develop clean and green China for upcoming generations. However, there is dire need to redress energy margin and shift on renewable energy. Considering the theory of economic efficiency supporting energy consumption in terms of energy efficiency in a recent study, we established various energy efficiency and energy sustainability frameworks into the question particularly energy consumption in the Pakistani context due to the COVID-19 effect (Coccia 2020). The COVID-19 effect on the energy sector is temporal and multifold, such as short-run and long-run effects. In the short run, the pandemic lockdown, often termed as self-isolation, has decreased air pollution and CO2 emission caused by various precautionary measures taken by the airline and transport industries and production schedule shutdowns, while the long-term effect of pandemic lockdown is depending on the structural implementation of such decisions in cited industries on a large scale and will result in energy crisis and/or an economic shock to all population (Gilbert et al. 2020; Iqbal et al. 2019a). Notably, this situation of pandemic lockdown is and will be a great source of uncertainty and will scale larger implications for the energy sector (Das 2020). This led a probability to face the mighty challenges by the energy sector by deciding to (1) revise the energy sustainability policies concerning COVID-19 for the long-lasting sustainability in the energy sector and (2) prioritize the policy framework that may lead to clean and green energy consumption by satisfying the ‘Clean and Green Pakistan Vision’. However, endorsing the COVID-19 outbreak as an opportunity, it is the need of the time to safeguard the local environment and make it carbon emission-free by transiting the energy consumption on modern and innovative tools (Department of Economic and Social Affairs 2020). Thus, addressing the objective of study for sustainable energy development and environmental protection is important for the Pakistani community as the population is increasing in the figure of 212.2 million (Source: Bauru of Statistic (2018), Pakistan). Extending to it, the effect of the pandemic with carbon emission and energy has become much important as the pandemic effect raised the extent it lowers the carbon emission and energy consumption (Wren-Lewis 2020; Wang et al. 2020b). From a practical perspective, the recent study would help the policymakers to understand that how they may stimulate the COVID-19 impact on energy consumption and carbon emission, which they can facilitate and collaborate to develop a new way forward to remediate the pandemic impact on energy consumption. We found limited literature suggesting pandemic effect, more specifically, COVID-19 outbreak on energy consumption and CO2 emission combined, and literature lacks in suggesting the concrete solution to manage the energy consumption shortfall caused by COVID-19 outbreak. We discussed the theory of economic efficiency to address and to explain the cause and effect of a recent study framework. The findings of Solangi et al. (2020) supported that extended lockdown lowers CO2 emission that causes a reduction in energy consumption. Therefore, the extension in lockdown or self-isolation has a direct and adverse effect on energy consumption and CO2 emission. Thus, considering the cited arguments, we hypothesized that there is an adverse effect of COVD-19 outbreak on CO2 emission and energy consumption (Öncü 2020; Parth 2020). Research method and design Data collection In doing a recent inquiry, we used daily data of COVID-19 outbreak, CO2 emission and energy consumption, taken from different national and international databases. The COVID-19 outbreak is measured by the number of days of lockdown, and the data is acquired from covid.gov.pk and worldometers.info. The data on CO2 emission is from the Statistical Review of World Energy, and the energy consumption data is obtained from the daily summary statistics of economic surveys of Pakistan and Bauru of Statistics (Noy et al. 2020). We collected the data with effect from the first day of pandemic lockdown in Pakistan dated 13 March 2020 until 30 April 2020 (around 48 days) (Table 1).Table 1 Descriptive statistics Summary statistics, 13 March 2020–30 April 2020 Variable Mean SD Minimum Maximum CO2 emission 0.2857 0.0188 0.0701 0.1543 TEC 0.6658 0.0073 0.0165 0.5491 EC1 0.3471 0.0016 0.0111 0.4292 EC2 0.2994 0.0066 0.0227 0.3572 COVID-19 lockdown1 0.2562 0.0714 0.0066 0.3703 n(Observations) = 48 1This variable is measured in terms of numbers of days in lockdown in the study context Measurement of constructs CO2 emission measurement CO2 scaling method is used to measure CO2 emission. Arango-Miranda et al. (2018) presented a significant and linear relationship between per capita gross domestic product (GDP) and per capita emission of CO2 in developing economies. This relationship remained significant with the condition that the population of the respective country should remain stagnant and less mobile (Wang et al. 2016; p. 3). Notably, study authorized this assumption and assumed that study population remained static, under the structurally imposed lockdown of COVID-19 pandemic, and during this period, a little physical movement of population is observed. Therefore during the COVID-19 outbreak, the CO2 emission factors of Pakistan showed a decline. In other words, the stationary level of CO2 emission factors designed a decreasing trend in CO2 emission line on a larger extent. Considering these assumptions and real conditions of COVID-19 outbreak, our study further endorsed the scaling method of in measuring CO2 emission. For this, industrial classification and actual state of oil and gas sector, transport sector, electricity sector, and cement sector of Pakistani context were used. Hence, CO2 emission is measured using the GDP of industry (GDP)i and carbon emission factors (EF)i, assuming (EF)i will remain stationary.1 CO2emission=ΣDataGDPi×EFi If (EF)i remains the same, the decline in CO2 emission is indicated with ∆CO2 emission (see Eq. 2).2 ∆CO2emission=Σ∆rate ofGDPi×CO2emissioni Using these measures, on the basis of emission features, we parted study sectors into two groups (e.g. transport sector and non-transport sector). Measurement the non-transport sector GDP of the country is taken, and for non-transport, traveling distance and decline in transport services is also obtained from different databases (National Highway Authority Database and Ministry of statistics database). However, the ∆CO2 emission of the transport sector is measured by3 ∆CO2emissiontransport=∆rate of travelled distance×CO2emissiontransport Using Eq. 3, the emission of transport sector was combined with oil and gas, electricity and cement sectors for empirical analysis CO2 emission in the recent study. Energy consumption measurement The energy consumption is measured by taking the net energy consumption (EC) in all the provinces of Pakistan. The estimate of energy consumption is further classified into different proxies, such as energy consumptions (EC) are substituted with electricity consumptions (EC1) and fossil energy consumptions (EC2). According to the Pakistan Economic Survey (2019–2020), the installed electricity generation capacity reached 37,402 MW in 2020. The maximum total demand coming from residential and industrial estates stands at nearly 25,000 MW, whereas the transmission and distribution capacity is stalled at approximately 22,000 MW. Moreover, thermal power generation is more than 65% of total electricity consumption in Pakistan. However, various secondary energy sources are suggested to adapt as primary energy sources. Therefore, total electricity consumption and total fossil energy consumption (EC2) are good proxies suggested to use in measuring total energy consumption. Econometric modelling Considering the theoretical foundation to infer the estimation, unit root test including LLC test presented by Levin et al. (2002), FFP and FADF test presented by Choi (2001) and IPS test given by Pesaran (2007) are used. The ADF, PP and IPS unit root tests have individual unit root processes. These three unit root tests have the null hypothesis of unit root, where the alternative hypothesis does not contain a unit root. In this research, to explore the long-run and short-run association between estimated variables, we applied the panel ARDL method due to the mixed nature of the stationarity of the variables. The panel ARDL method has various advantages: traditional methods of cointegration only assess the long-term correlation in the equations, and the panel ARDL technique is more compact (Pesaran and Shin 1998). According to Eq. 1, our studied variables are stationary at I(0), I(1) or the level at first difference (Sulaiman and Abdul-Rahim 2018). Our baseline model can be written as4 COVID=fECCO2 5 COVID=fECitα1C02itα2 The algorithm form of Eq. 2 is developed and shown in Eq. 3, where, i = 1, …, n as indicator sign, t is time period and ɛ is error term. EC indicates energy consumption and CO2 is CO2 emission.6 lnCOVIDit=α0+α1lnECit+α2lnCO2it+ɛit The study also used panel quintile regression (PQR) to infer the robustness of the results (Lamarche 2010). Importantly, PQR reduces the probability of outlier’s occurrence, when ɛ is not normal. However, PQR is most effective relevant test to infer robustness than the ordinary least square (OLS) method. Subsequent to study operationalization, PQR is relevant to test the impact of COVID-19 outbreak on CO2 emission and energy consumption, where the dynamics of study are more contextual. Results and discussion The empirical findings of growth regression are presented in Table 2. We regress the COVID-19 lockdown on national energy consumption and CO2 emission shown in the first column. The resulting output of growth regression indicated that the COVID-19 lockdown is adversely affecting CO2 emission and energy consumption. Likewise, this effect is significant in both series. The findings of study imply that 1% variation or extension in lockdown leads to − 0.2971 × (0.000)% variation in CO2 emission and − 0.1762 × (0.000)% variation in energy consumption gauged from fixed effect modelling. This point estimation shows the similar variation in CO2 emission as − 0.3796 (0.002) and energy consumption as − 0.1494 (0.000) acquired from 2-stage least square test.Table 2 Province level CO2 emission reduction in major CO2 emitting sectors Provinces Σ CO2 reduction Oil and gas Electricity Cement Transport Sindh 6.5 2.0 1.7 1.3 1.8 Punjab 11.9 6.6 1.2 1.0 2.3 KPK 3.0 1.5 0.5 0.0 1.0 Baluchistan 2.8 1.7 0.1 0.0 1.0 Empirical findings and interpretation We identified a highly dependent island in the occurrence group during the complete sampling tenure, and the direction is generally sharped to the leftward. An upward arrow to the right can observe other coherence which shows the cyclic relationship between COVID-19 and oil. The oil price shows a rapid reduction during the COVID-19 outbreak (Fig. 1). These results indicate that due to travel constraints and lower predictable production progression in European countries and China, the COVID-19 pandemic seems to have had a serious impact on oil price volatility through the demand side.Fig. 1 Energy consumption and CO2 emission during the COVID-19 outbreak. (Source: macrotrends.net) Figure 2 shows the world crude oil future. The results show that COVID-19 has affected local oil prices that are clarified by imposing travel restrictions. The robustness test estimates the wavelet-based causality in the 6 occurrence groups, explaining that the time-frequency changes the temporal series of the COVID-19-oil relationship since 3 months. Table 3 shows descriptive statistics.Fig. 2 WTI crude oil futures (source: investing.com) Table 3 Panel unit root test Constructs LLC FADF FPP IPS Stationary level COVID-19 1.97 16.96 11.07 2.07 - ∆COVID-19 − 0.44* 41.77* 86.1* − 0.12 l(1) CO2 emission 1.53 12.36 5.47 1.26 - ∆CO2 emission − 5.75* 57.89* 30.01* − 4.67* l(1) TEC 1.19 2.56 6.05 1.11 ∆TEC − 3.44* 45.55* 49.08* − 2.89* l(1) ECI 2.56 12.75 7.13 2.13 ∆ECI − 6.66* 71.01* 32.3* − 4.12* l(1) EC2 7.95 60.34* 57.40* 7.01 ∆ EC2 − 2.06* 22.87* 22.62* − 1.53* l(1) Source: authors’ calculation using E-Views 10 software LLC Levin–Lin–Chu test, FADF Fisher-augmented Dickey–Fuller test, FPP Fisher Phillips–Perron test, IPS Im–Pesaran–Shin test Significance at the 5% level The study used PLAS method to test the GDP-to-CO2 emission reduction by taking the provincial statistics of Pakistani context. The CO2 emission were found to be decreasing in Sindh, Punjab, Baluchistan and Khyber Pashtun Khawah (KPK). The obtained values of GDP are tabulated in Table 3 with the significant difference. Notably, this difference and decline in CO2 emission is noteworthy. Moreover, due to lack of detailed variation rate for PLAS sector data, during the COVID-19 outbreak, the Punjab province shown a higher variance (see Table 2). While Sindh scored second position in CO2 emission reduction with 6.5 emissions score, KPK reduced on third position with 3.0 and Baluchistan remained in last with a 2.8 score. Yasmeen et al. (2019) revealed that oil and gas is the largest sector of Pakistani economy contributing to CO2 emission transmission. Our study findings are coherent with this narrative, and due to the COVID-19 outbreak, a downward structural shift is inferred in this sector. Secondly, the transport sector showed a significant decline in CO2 emission. During this period of the COVID-19 lockdown, from 13 March 2020 to 30 April 2020, public transport, airways, urban transport, motorways, various production industries, oil and gas consumers were shut down. People were officially and publically notified to stay at home and maintain social distancing and standard procedures to mitigate threat of COVID-19 pandemic. All these precautionary measures reduced public gathering, closed business activities, economic activities and movement of public. In result, a massive structural decline is observed in transport and oil and gas sector that supported structural and procedural cleaning of the environment. The findings show that panel unit root outcomes of study constructs were stationary at the l(1). This stationary level suggested applying ARDL technique to infer a connection between COVID-19 outbreak, energy consumption and CO2 emission (see Table 3). Pesaran and Shin (1998) introduced ARDL approach and is advanced by Pesaran et al. (2001). The ARDL technique has several advantages over a cointegration test (Johansen and Juselius 1990; Engle and Granger 1987). First, this approach does not impose the condition that the variables have the same order of integration. However, it takes into account variables that are order 1 or order 0 integrated. Next, it is adapted to small samples. In fact, Johansen’s cointegration method requires a large number of observations for the estimation to be reliable. Finally, in the ARDL model, the dependent variable is explained by its past and by the past of the other independent variables. Table 4 indicates that CO2 emission is negatively affected as − 8.55 by the COVID-19 outbreak with the p value as 0.000 (< 0.05). Similarly, the total energy consumption (TEC) is also negatively significant as − 3.13* with the 0.000 p value as < 0.05; the total electricity consumption (EC1) is negatively affected by the COVID-19 outbreaks as − 1.27* with the 0.000 p value as 0.05; and the total fossil fuel consumption (EC2) is also negatively impacted by the structural imposed crises of COVID-19 pandemic as − 2.12* (0.000), and the p value of EC2 is also less than 0.05 level of significance. In addition − 0.2105* with (0.000) level of significance is acquired at full level. By this, the hypothesis of study is accepted that the COVID-19 outbreak has negatively affected energy consumption and CO2 emission in the context of Pakistan. Likewise, ARDL results show that R2 of the study model is 0.59.Table 4 Autoregressive distributed lag (ARDL) test Variable Coefficient Std. error t-Statistic Prob.* C 11.26* 65.31 6.07 0.000 COVID-19 6.24* 12.18 3.59 0.000 CO2 emission − 8.55* 33.81 − 4.24 0.000 TEC − 3.13* 0.073 − 3.33 0.001 EC1 − 1.27* 0.675 − 1.83 0.000 EC2 − 2.12* 0.009 − 0.93 0.000 R2 0.59 Mean-dependent variance 1.41 Adjusted R2 0.20 S.D. dependent variance 77.12 Standard error 16.04 Akaike info criterion 8.28 Log LH − 364.19 Hannan–Quinn criterion 10.29 Prob (F-statistic) 0.011 Durbin–Watson 1.99 p value < 0.05 as level of significance Thus, considering the energy consumption and carbon emission, our findings are aligned with Lin and Raza (2019), revealing the analysis of CO2 emission and energy consumption. One of the potential reasons of the decline in carbon emission during the COVID-19 lockdown is the decrease in oil consumption and transportation mobility. Energy and carbon intensity are the key antecedents causing reduction in energy consumption and carbon emission. According to official statistics of OGRA, an 11% reduction rate in energy consumption is observed during the month of March (2020). And evidently, the Pakistani energy sector is more inclined towards thermal energy consumption generated from petroleum products and fuel substitution. Secondly, according to historic statistics of energy associations, around 85% of Pakistani energy mix is dependent on fuel, and as mentioned earlier, this percentage faced a decline of 11% and redressed a new 74% of new energy mix. Therefore, the COVID-19 lockdown has resulted in a massive reduction in energy consumption in the study context. Interestingly, this adverse effect leading to reduction in carbon emission is an opportunity of modern times to control this emission by maximizing the rapid growth through ideal energy sources. Historical studies widely used ordinary least square (OLS) technique to gauge robustness in the findings of these investigations, but this technique has some limitations that OLS fails to portray a comprehensive look of empirical distributions of dependent variable. In a heterogeneous context like Pakistan, the impact of the COVID-19 outbreak on energy consumption and CO2 emission are likely to be robust at different quantiles. For this, panel quantile regression (PQR) technique is suggested. This technique allows the coefficients to vary with multiple quantiles and has distinctive advantages of detecting the variation in the impact of COVID-19 outbreak on energy consumption and CO2 emission. Moreover, the quantile regression approach is also useful for addressing problems that may severely affect the accuracy of estimation, including heteroscedasticity, outliers, and unobserved heterogeneity. Therefore, this paper adopts the quantile regression to infer the robustness of the study results more comprehensively. The findings of PQR technique confirmed the results of ARDL model and showed robustness, as the impact of COVID-19 outbreak on energy consumption and CO2 emission is negatively significant in all the quantiles, where, τ = 10th, τ = 25th, τ = 50th, τ = 75th and τ = 90th at 5% of level of significance (e.g. p value < 0.05) (Table 5).Table 5 PQR test Variable Coefficient SE t-stats Sig. Pseudo R2 Sparsity τ = 10th COVID-19 4.16 3.76 2.22 0.000 0.71 1311.91 CO2 emission − 2.76 0.04 − 0.67 0.000 (0.000) TEC − 1.88* 0.07 − 0.41 0.000 EC1 − 0.65* 0.00 − 0.17 0.000 EC2 − 0.73* 0.31 − 0.11 0.000 C 122.64 233.19 0.81 0.026 τ = 25th COVID-19 3.03 2.07 2.69 0.000 0.87 1010.32 CO2 emission − 2.12* 0.06 − 0.51 0.000 (0.000) TEC − 1.05* 0.02 − 0.34 0.000 EC1 − 0.44* 0.11 − 0.47 0.000 EC2 − 0.67* 0.21 − 0.01 0.000 C 174.01 217.01 0.85 0.000 τ = 50th COVID-19 5.33 2.26 2.89 0.000 0.77 1275.09 CO2 emission − 3.21* 0.09 − 2.91 0.000 (0.000) TEC − 2.24* 0.03 − 2.14 0.000 EC1 − 1.45* 0.05 − 0.18 0.000 EC2 0.79* 0.00 − 0.56 0.000 C 167.34 202.87 0.77 0.000 τ = 75th COVID-19 4.68 2.98 3.18 0.000 0.73 1405.00 CO2 emission − 2.55* 0.01 − 0.10 0.000 (0.000) TEC − 2.41* 0.00 − 0.49 0.000 EC1 − 1.01* 0.05 − 0.32 0.000 EC2 − 0.68* 0.00 − 0.50 0.000 C 101.34 157.48 0.03 0.000 τ = 90th COVID-19 8.19 4.44 3.00 0.000 0.76 2178.04 CO2 emission − 4.56* 1.30 − 0.18 0.000 (0.000) TEC − 2.34* 1.21 − 0.23 0.000 EC1 − 1.13* 0.08 − 0.54 0.000 EC2 − 1.07* 0.02 − 0.39 0.000 C 89.99 100.77 0.47 0.000 *p value < 0.05 as level of significance In the recent topicality, the role of the COVID-19 outbreak is evident and a big challenge for energy sustainability in upcoming time. Therefore, a policy plan is much needed to enhance energy sustainability in Pakistan and is suggested in the next section of this study. Subsequent to the findings of the study, the COVID-19 outbreak has several structural influences on the energy market of Pakistan and environmental pollution, and importantly, these two aspects need more visionary attention to fix and to gain economic advantage on a national basis. For this, energy consumption indicators, energy efficiency and environmental pollution indicators should be carefully watched and managed. However, we suggest developing and implementing an economic action plan by considering energy consumption indicators and environmental pollution elements to give a boost to national economy of Pakistan. Incentivizing the energy consumption patterns could also generate a shift in consumption behaviour of the general public and corporate owners. In addition, most of the arrows turn left and left, which means that there is a counter-cyclical effect between COVID-19 and the US stock index with COVID-19 as the main index (Fig. 3). Uncertainty is mainly related to the long-term development of the Pakistan economy and how the Fed will deal with the significant increase in uncertainty and the bad news of COVID-19. This is an adverse effect on the potential output and unemployment rate in the USA, which are completely independent of monetary policy.Fig. 3 Changes in global oil price during COVID-19 shock (source: countryeconomy.com) Discussion of findings The coronavirus (COVID-19) epidemic spread from Wuhan (Hubei region to China), where the first case of infection is reported on 31 December 2019. Forty-nine days after this, on 21 January, the World Health Organization (WHO) released the first corona virus monitoring report. By 2020, more than 100,000 people in more than 100 countries around the world will be affected. Although COVID-19 does not present similar patterns in terms of mortality severe acute respiratory syndrome (SARS) or in terms of global spread in 2002–2003. Compared with the 1919 Spanish flu pandemic, the new coronavirus is highly contagious and creates a lot of uncertainty in the real economy and financial markets. Creating short-term fluctuations in food prices affects aggregate demand and imminent the movement of workers and tourists. In addition, COVID-19 creates fear and extra stress. Financial markets, where price fluctuations are constantly increasing. hope to be strong. Because of declining global demand in the coming period, Saudi Arabia started an oil price war from 9 March 2020, and flooding the market with oil. In a single day, the price of crude oil falls more than 20% of the shock spreads to falling financial markets in a single day. COVID-19 seems to be the main geopolitical shock in the world. In the past few years, energy crises in Pakistan are managed by the fossil fuels that raised the CO2 emission at large, and because of the effect of the pandemic, energy association and regulatory authorities of Pakistan have given less effort in managing energy sustainability. Our study is one of the first studies to gain the attention of regulatory authorities in a local context and to present the a way forward to enhance the energy sustainability by reducing carbon emission and energy consumption. For instance, considering the bright side of COVID-19 outbreak the energy consumption can be managed through innovative and advanced tools by replacing the fuel-based energy resources on renewable energy sources and/or green energy sources (Gautret et al. 2020). A significant reduction in carbon emission is one of the hard-core benefits of such renewable or green energy sources. COVID-19 outbreak highlighted a structural impact on energy consumption and carbon emission, and this was the motivation of recent research to inspect the role. Our findings are consistent with Pegels (2010) and Gugler et al. (2013). We found a negatively inverse significant role of COVID-19 outbreak on energy consumption and carbon emission. The findings are revealed by the co-efficient alpha of growth regression accepted by the study hypothesis. In short, a significant decline in energy consumption and carbon emission is observed due to pandemic effect. By the fact, the COVID-19 outbreak extended the lockdown, and entire world is shifted in self-isolation. This impact lowered the burden of energy consumption; very less vehicles are mobilized around the globe that resulted in a massive decline in energy consumption and carbon emission. On this, it is difficult to persuade each and every stakeholder associated with energy sector, and realistically, this is the internal energy consumption position (Kost, 2020). On energy production and consumption, Pakistan is mainly depending on traditional sources of energy generation, from which, oil material and fossil fuels are most prominent (van de Ven and Fouquet 2017;, Trotta 2018; IEA 2017). As previous literature suggested, this source of energy will be vanished from the world in upcoming sources (Zameer and Wang 2018). Thus, it is important to consider and plan about alternative energy generation sources to stabilize energy consumption and reduce environmental pollution (Mirza and Kanwal 2017). It is important to consider the COVID-19 outbreak as an opportunity to redesign the energy sector of Pakistan as the energy demand is declined in most of the commercial sector. By underscoring the findings, we concur the results as model and country specific, and we warrant the caution that the comparability of findings with other context may result in heterogeneity in terms of energy consumption and the COVID-19 outbreak. By endorsing the country-specific findings, our study is providing certain policy measures. Practically, responding to the COVID-19 outbreak, oil prices and dampened oil demands should be managed especially in the wake of OPEC price game, and this affected energy consumption. These concerns are driving our research. This study is the first attempt to analyze the relationship between COVID-19 outbreak, energy consumption and CO2 caused by lead-lag interaction. Here, due to differences in risk conditions, different expectations and different understandings of risks, investors from all over the world may respond differently to investment decisions during the investment period. For example, market traders are aware of the ‘bad’ news inherent in the world’s rising cases of COVID-19 infection, deaths, government alienation guidelines and the impact of oil price changes. Fear of contagion and lack of vaccine availability worsen the private spending with a combined effect of declining income. Service, tourism and entertainment sectors are being affected, which are more particularly associated with public events and catering services. Income and job insecurity will be raised because of reduced working hours especially to those who have no access to social safety net. The panic and uncertainty of the pandemic will cause delay in private investment, but the demand of government will go up in order to meet emergency health assistance initiative. Despite all the pandemic crisis of COVID-19, the negative net demand effect is assumed to be short lived. On the supply side, the manufacturing activity will be halted in most affected regions. Reduced production will cause bottleneck in worldwide supply chain. Unplanned accumulation of inventory would be depressing down production capacity resulting in sinking GDP. The COVID-19 has already exhausted inventory stock by fluctuating the globalized production structure. Such production variability will in turn generate extensive factory lockdowns for shortage of intermediary inputs. Conclusion and policy implications We contributed in literature by presenting a novel study on the COVID-19 effect on carbon emission and energy consumption in modern time when the world is finding the solution for each and every sector and each and every aspect to make it sustainable. Our study inferred the adverse effect of the COVID-19 outbreak on energy consumption and CO2 emission. We inquired and contributed by presenting the practical solution to stabilize energy consumptions and CO2 emission in Pakistan. We highlighted the negligence of energy authorities of Pakistan and also contributed by directing the implications to sustain energy demand, energy prices by changing energy consumption and CO2 emission behaviours in time. Hence, to the best of our knowledge, this would be the one of the pioneer studies on COVID-19 outbreak and energy consumption. The economic and social costs of the COVID-19 pandemic are related to society, policy makers, and all financial market participants and individual investors. Our findings provide novel and outstanding policy and practical significance. It is becoming more and more obvious as the COVID-19 outbreak is causing an interruption to oil demand and energy consumption, while there is an abnormal increase in uncertainty of an economic policy.Policy 1: We suggest to manage the energy consumption more carefully because due to the COVID-19 outbreak, the energy sector expects to face few massive shocks like health emergency leading to continuous lockdown, coping with low oil prices simultaneously and a decline in energy revenues due to lower oil revenues. To sustain energy sustainability by managing energy consumption, we suggest energy regulatory authorities to come up with contingent plans that may enhance operational effectiveness during and after COVID-19 to reach the threshold level of energy consumption. In due course, when the threshold limit is achieved, then the upcoming energy demands should be replaced by the renewable energy sources. Therefore, for the energy sector, we argue that the COVID-19 is an opportunity to revive, redevelop and reconfigure energy consumption patterns. Policy 2: During COVID-19, we also observed few bitter realities in the energy sector of Pakistan backing the study findings. First, poor attention in managing energy demand, oil prices and revenue shortfall has placed a big and clear question mark on the immediate future of energy sector. Secondly, less visionary and less proactive leadership in energy regulatory authorities of Pakistan is another big challenge to fix. Thirdly, none of energy sustainability action plan is given by these regulatory authorities with respect to the COVID-19 outbreak. This would not be false to say that such regulatory authorities as sleeping rabbits, and if they have not managed in time, there is a probability of energy chaos to knock at the door steps, just because the energy sector of Pakistan is more fragile in comparison with developed countries. Policy 3: We suggest to OGRA to develop a system to manage oil demand and oil prices mechanism as per local demographics instead of international spillover effects, to play a visionary and proactive role to enhance the energy sustainability in Pakistan, to continuously plan implement and re-plan on energy consumption and carbon emission to achieve the threshold limit, to replace traditional sources by the renewable sources, and to disclose a national action plan for energy sector and then implement this plan phasewise. Policy 4: The government must acknowledge the electrical distribution organizations, such as WAPDA and PAEC, to provide all the support services at par so that energy consumption may be actively managed by considering the pre- and post-effect of the COVID-19 outbreak. Policy 5: All the household and families should be provided with an energy consumption guide by the provincial and local governments. Similarly, standard operating procedures for energy consumption must be given to the business sector. For greater awareness, a campaign on electronic, print and social media should be launched by the government of Pakistan with the intent to stabilize energy consumption and CO2 emission on a sustainable basis. We extend to guide that the government of Pakistan should publically provide an ‘Energy Consumption and CO2 Emission Plan’. By this, the national government should sustain energy and the environment in recent time and the potential consequence of such outbreak(s) in future. Just because, this is the high time to change the behaviour of energy consumers and CO2 emitters in all over the Pakistan. We further suggest to the Pakistan Bureau of Statistics to keep the updated data files on energy consumption and carbon emission during the period of the COVID-19 outbreak. This would help a lot to us researchers and will save our time in doing quality research. Two of the major limitations in conducting the recent inquiry: one is the lack of literary evidences on COVID-19 association with energy consumption and carbon emission, and secondly, scattered form of empirical data in national databases consumed much time to gather the facts and figures. Author contributions Conceptualization, methodology, writing of the original draft: Sajid Iqbal; review, supervision: Ahmad Raza Bilal; data curation, visualization: Wasim Iqbal; visualization, editing: Mohammad Nurunnabi; writing and software: Yazeed Alfakhri; writing, visualization and editing: Nadeem Iqbal. Data availability The data that support the findings of this study are openly available on request. Compliance with ethical standards Competing interests The authors declare that they have no competing interests. Ethical approval and consent to participate We declare that we have no human participants, human data or human issues. 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==== Front Med Hypotheses Med Hypotheses Medical Hypotheses 0306-9877 1532-2777 Elsevier Ltd. S0306-9877(20)33302-8 10.1016/j.mehy.2020.110411 Article RETRACTED: Facemasks in the COVID-19 era: A health hypothesis Vainshelboim Baruch ∗ Cardiology Division, Veterans Affairs Palo Alto Health Care System, Stanford University, Palo Alto, CA, United States ∗ VA Palo Alto Health Care System, Cardiology 111C, 3801 Miranda Ave, Palo Alto, CA, 94304, United States. 22 11 2020 1 2021 22 11 2020 146 11041125 2 2020 6 3 2020 © 2020 Elsevier Ltd. All rights reserved. 2020 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief. Medical Hypotheses serves as a forum for innovative and often disruptive ideas in medicine and related biomedical sciences. However, our strict editorial policy is that we do not publish misleading or inaccurate citations to advance any hypotheses. The Editorial Committee concluded that the author’s hypothesis is misleading on the following basis: 1. A broader review of existing scientific evidence clearly shows that approved masks with correct certification, and worn in compliance with guidelines, are an effective prevention of COVID-19 transmission. 2. The manuscript misquotes and selectively cites published papers. References #16, 17, 25 and 26 are all misquoted. 3. Table 1. Physiological and Psychological Effects of Wearing Facemask and Their Potential Health Consequences, generated by the author. All data in the table is unverified, and there are several speculative statements. 4. The author submitted that he is currently affiliated to Stanford University, and VA Palo Alto Health Care System. However, both institutions have confirmed that Dr Vainshelboim ended his connection with them in 2016. A subsequent internal investigation by the Editor-in-Chief and the Publisher have determined that this article was externally peer reviewed but not with our customary standards of rigor prior to publication. The journal has re-designed its editorial and review workflow to ensure that this will not happen again in future. The Editor-in-Chief and the Publisher would like to apologize to the readers of The Journal for difficulties this issue has caused.	33303303	PMC7680614	NO-CC CODE	2021-05-12 23:15:14	yes	Med Hypotheses. 2021 Jan 22; 146:110411
==== Front Soft comput Soft comput Soft Computing 1432-7643 1433-7479 Springer Berlin Heidelberg Berlin/Heidelberg 33250663 5451 10.1007/s00500-020-05451-0 Focus RETRACTED ARTICLE: A fuzzy rough hybrid decision making technique for identifying the infected population of COVID-19 Majumder Sandip 1 Kar Samarjit [email protected] 1 Samanta Eshan 2 1 grid.444419.8 0000 0004 1767 0991 National Institute of Technology, Durgapur, 713209 India 2 Global Institute of Science and Technology, Haldia, West Bengal 721657 India Communicated by Valentina E. Balas. 23 11 2020 2023 27 5 26732683 © Springer-Verlag GmbH Germany, part of Springer Nature 2020. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Decision theoretic rough set model have been used over many years in most of the application areas. It provides a novel way for knowledge acquisition, especially when dealing with vagueness and uncertainty. Many mathematical modelings have been presented recently to control the pandemic nature of COVID-19 and along with its control model as well. Decision-based treatment recommendation has not yet been found so far in any of the articles. In this paper, we have proposed a novel approach of three-way decision based on linguistic information of a COVID-19 susceptible person. To present this, we have discussed the probabilistic rough fuzzy hybrid model with linguistic information. This model helps us to guess the infected person and decide whom to send for self-isolation, home quarantine and medical treatment in an emergency situation. The significance of the proposed hybrid model has been discussed by presenting a comparative study and reported along with justifications too. Keywords COVID-19 Pandemic Infectious disease Isolation Quarantine Decision making model issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023 ==== Body pmcIntroduction The COVID-19 is a highly infectious disease. The primary case was identified on December 31, 2019, in the city of Wuhan, the capital of Hubei province, China. The name ‘Coronavirus’ comes from the Latin word ‘Corona’ which means a crown circle of light or nimbus. This virus has comparable symptoms as influenza and pneumonia. In the beginning, it was spotted in mainland China and then spread to the whole world, infected around 3,292,489 people and taken almost 233,144 lives as on April 30, 2020. Figure 1 represents the worldwide confirmed cases till April 30, 2020. World Health Organisation (WHO) has declared it to be a pandemic. It is very difficult to track the presence of this deadly virus because symptoms are similar to that of flue and cough and cold. This virus exposes its symptoms after 7 to 14 days from the time it enters the human body. In the absence of a vaccine, social distancing is the most widely adopted strategy for its mitigation and control (Ferguson et al. 2020). Public health concerns are being paid globally on how many people are infected and suspected. If a healthy person comes in close contact with the infected person, or with his/her belongings, the virus enters his/her body. Only proper testing allows the infected person to know that they are infected with the virus, this can help them receive the care they need, and it can help them take measures to reduce the probability of infecting others. People who do not know they are infected might not stay at home and thereby risk infecting others. Testing is also crucial for an appropriate response to the pandemic. It allows us to understand the spread of the disease and take evidence-based measures to slow down the spread of the disease specifically in India. In the recent times, the study of COVID-19 transmission has gained attention by the researchers and practitioners. Ahmadi et al. (2020) studied the COVID-19 outbreak by considering geographical and climatological parameters. Zhu and Chen (2020) presented a statistical disease model to analyze the early outbreak in China. Boldog et al. (2020) proposed an integrated model for assessing risk of COVID-19 outbreaks in countries outside of China. Yan et al. (2020) developed a predictive model to identify early detection of high risk patients before their health status is transformed from mild to critical condition. To study the spread trend of COVID-19, numerous research articles have been published in the literature (Ahmadi et al. 2020; Zhu and Chen 2020; Boldog et al. 2020; Yang and Wang 2020; Yan et al. 2020).Fig. 1 Number of confirmed cases all over the world based on Worldometer (2020) Unfortunately, the capacity for COVID-19 testing is still low in many countries (especially in India) around the world. For this reason, we do not have a good understanding of the spread of the pandemic. Therefore, it is essential to develop a decision making tool to identify the suspected person of the COVID-19. Khatua et al. (2020b) have proposed an optimal control fuzzy model on COVID-19 using granular differentiability, as discussed in Mazandarani et al. (2017). They presented the same on SEIAHRD model, where they have considered hospitalized patients, symptomatic patients and asymptomatic patients as well individually. Khatua et al. also contributed optimal control model to check the pandemic parameters as well in Khatua et al. (2020a). Khatua et al. (2020c) reported on SIR-Network Model for COVID-19 with respect to its impact on a particular state name as West Bengal in India. In this paper, we have tried to develop a three-way decision model of COVID-19 suspected people based on their linguistic information of attribute values, which help suspected infected person to send self-isolation, home quarantines or treatment as a result of which the rate of contamination can be reduced. Here, COVID-19 suspected people might not be able to give an exactly quantitative description. They express their opinions with linguistic term such as good, very good, and not so good. In decision theoretic rough set (Yao 2010), the loss function is an essential thing to determine the threshold values of the parameters α and β. Most of the decision making problem calculate threshold value by Bayesian decision making process with the help of loss function. Our main focus is on COVID-19 infected person, so the medical expert can form the loss function using their expertise or they may choose the threshold value. The paper is organized as follows: In Sect. 2, we have briefly discussed classical rough set, probabilistic rough set and three-way decisions, decision-theoretic rough set model. Section 3 discusses the linguistic variable and the basic operations. In Sect. 4, we have presented three-way decisions based on linguistic information, fuzzy probability, linguistic-valued information system-based probability. In Sect. 5, we create an example of group of COVID-19 suspected people and based on their linguistic information, make a three-way decision. Also, a comparative study has been reported. At last finally in Sect. 6 overall conclusion of this work has been discussed with remarks. Background, motivation and research issues In this section, we briefly review the classical rough set defined by Pawlak (1982), probabilistic rough set, three-way decision based on probabilistic rough set, decision theoretic rough set model based on Bayesian decision making process (Yao and Wong 1992; Yao 2003, 2010, 2011; Ziarko 1993; Wu and Xu 2016). Classical rough set Consider an information system S=⟨U,A,V,f⟩. U is the non-empty finite set of objects called universe. A is a finite non-empty set of attribute. V is a set of attribute value and f:U×A⟶V is an information function. Then, for any E⊆A, indiscernible relation IND(E) on U is defined asIND(E)=(x,y)∈U×U\|e(x)=e(y)∀e∈E. Clearly, it is an equivalence relation on U and as a result, induces partition on U. For any X⊆U, lower and upper approximations are defined asapr_(X)=x∈U\|[x]⊆Xapr¯(X)=x∈U\|[x]∩X≠ϕ with this approximation U can be divided into three disjoint regions namely,POS(X)=apr_(X)BND(X)=apr¯(X)-apr_(X)NEG(X)=U-apr¯(X). Hence, if x∈POS(X), then x surely belongs to the concept X. If x∈NEG(X), then x certainly does not belong to target set X. If an object x∈BND(X), then it may or may not belong to X. Probabilistic rough set and three-way decisions Upper approximation, lower approximation, boundary region defined by Pawlak (1982) are perfect. But the main drawback is that it is not able to make decision for the majority of the object. With the knowledge, the probabilistic rough set model was proposed. Main intuition of probabilistic rough set model is to expand the decision region, i.e., to expand positive and negative regions using two parameters α and β. Let ⟨U,E⟩ be the approximation space, then ⟨U,E,P⟩ is a probabilistic approximation space (Yao 2008), where P is a probability measure defined on a subset of universe U. For any X⊆U,1 PX\|[x]=cardX∩[x]card[x]=X∩[x][x] where \|.\| denotes the cardinally. Now for 0≤β<α≤1, upper and lower approximations of X are given by:apr¯β(X)={x∈U\|P(X\|[x])>β}apr_α(X)={x∈U\|P(X\|[x])≥α}. Now these two approximations lead to three-way decision region.POS(α,β)(X)={x∈U\|P(X\|[x])≥α}NEG(α,β)(X)={x∈U\|P(X\|[x])≤β}BND(α,β)(X)={x∈U\|β<P(X\|[x])<α}. The conditional probability may be recognized as a level of confidence that an object having the same description as x belongs to X. For α=1 and β=0, the decision theoretic rough set model coincides with the classical rough set model. A probabilistic two-way decision model may be obtained with, α = β. A major difficulty is the interpretation and determination of the threshold (α,β). Decision-theoretic rough set model Based on the Bayesian decision procedure, values of α and β are calculated. Now we will represent a brief description of the Bayesian decision procedure for a given object x. Let Ω={s1,s2,⋯.,sm} be a finite set of m possible states and A={a1,a2,⋯.,an} be a finite set of n possible action. Hence, we can construct a m×n matrix which represents all possible loss function. If the object x is in state sj, then λ(ai\|sj) represents the loss incurred for taking action ai and P(sj\|x) represents the conditional probability of x being in a state sj. If action ai taken for the object x, then expected risk associated with action ai is given by:R(ai\|x)=∑j=1mλ(ai\|sj)P(sj\|x). In decision theoretic rough set model, set of states denoted by Ω={X,Xc} and set of action denoted by A={aP,aB,aN} where aP,aB,aN represent the three actions to classify an object into POS(A),BND(A),NEG(A), respectively. A 3×2 matrix for all the values of loss function is shown in Table 1.Table 1 The values of loss function X XC aP λPP λPN aB λBP λBN aN λNP λNN λPP, λBP, λNP denote the loss incurred for taking action of aP, aB, aN, respectively, when the decision object belongs to X. λPN, λBN, λNN denote the loss incurred for taking action of aP, aB, aN, respectively, when the decision object belongs to XC. The expected loss of three actions given an equivalence class [x] of a decision object x is as follows:2 RaP\|[x]=λPPPX\|[x]+λPNPXC\|[x]RaB\|[x]=λBPPX\|[x]+λBNPXC\|[x]RaN\|[x]=λNPPX\|[x]+λNNPXC\|[x]. According to the Bayesian decision procedure, the minimum cost decision rules are as follows.(P): if RaP\|[x]≤RaB\|[x] and RaP\|[x]≤RaN\|[x] decide x∈POS(X) (B): if RaB\|[x]≤RaP\|[x] and RaB\|[x]≤RaN\|[x] decide x∈BND(X) (N): if RaN\|[x]≤RaP\|[x] and RaN\|[x]≤RaB\|[x] decide x∈NEG(X). We consider the loss function inequality λPP≤λBP<λNP and λNN≤λBN<λPN with3 λNP-λBPλPN-λBN>λBP-λPPλBN-λNN. We can formulate the decision rules based on this division of the universe as follows:4 (P):ifPX\|[x]≥αdecidex∈POS(X)(B):ifβ<PX\|[x]<αdecidex∈BND(X)(N):ifPX\|[x]≤βdecidex∈NEG(X) where the threshold α and β are defined as:5 α=λPN-λBNλPN-λBN+λBP-λPP 6 β=λBN-λNNλBN-λNN+λNP-λBP. The parameters α,β define the regions and provide us associated risk for classifying an object. Here, parameter α makes the division between (P) region and (B) region. Similarly, parameter β makes the division between (B) region and (N) region. These minimum risk decision rule help us to classify the object into approximation regions. Basic concept of fuzzy set Professor L. A. Zadeh (1965) proposed the concept of fuzzy set in 1965. Fuzzy sets theory proposes to deal with unclear boundaries, representing vague concepts and working with linguistic variables. In this sense, fuzzy sets emerged as an alternative way to deal with uncertainties. Fuzzy set theory is an extension of classical set theory where elements have a degree of membership, called membership function having interval [0, 1]. Let ‘X’ be the universe of discourse and μA~(x) be membership function associated with fuzzy sets A~, then μA~(.) maps every element of X to the interval [0, 1], i.e.,μA~(x):X→[0,1]. Hence, a fuzzy set A~ defined on X can be written as A~={(x,μA~(x))\|x∈X}. Consider an example, let X={x1,x2,x3,x4,x5} be the reference set of students and A~ be the reference set of “smart” students, where “smart” is fuzzy term and represented byA~={(x1,0.4),(x2,0.5),(x3,1),(x4,0.9),(x5,0.8)}. Here, A~ indicates that the smartness of x1 is 0.4, x2 is 0.5, and so on. Hence, membership function provides a measure of the degree of similarity of an element to a fuzzy set. Clearly, membership function is subjective, because it is specific to an individual assessor or a group of assessors. It is also assumed that for each x∈X the assessor is able to assign an μA~(x). It is noted that for crisp set, a membership function can be defined as follows:μA(x)=1,ifx∈A0,ifx∉A. Hence, the crisp set has sharp boundaries, whereas fuzzy set has vague boundaries. Basic terminology: α-cut: Given a fuzzy set A~ defined on x and any number α∈[0,1], the α-cut is the crisp sets A~α={x\|μA~(x)≥α} and strong α-cut is the set A~α∗={x\|μA~(x)>α} Level set of A~: The set of all levels α∈[0,1] that represents distinct α-cuts of given fuzzy set A~ is called a level set of A, denoted by A(A~)={α\|μA~(x)=α},for somex∈X Support: For fuzzy set A~ its support is a crisp set denoted by s(A~) and defined by s(A~)={x\|μA(x)≥0}. Normal and Subnormal fuzzy set: Maximum value of the membership degree of a fuzzy set is called height of the fuzzy set. A fuzzy set A~ is normal if its height is 1 and subnormal if its height is less than 1. Core of a fuzzy set are those x for which μA~(x)=1. Convex fuzzy set: Fuzzy set A~ is convex if μA~(λ(x1)+(1-λ)(x2))≥min{μA~(x1),μA~(x2)},x1,x2∈X,λ∈[0,1]. Cardinality: For a finite fuzzy set A~, the cardinality \|A~\| is defined as \|A~\|=∑x∈XμA~(x) and \|\|A~\|\|=\|A~\|\|X\| is called relative cardinality of A~. A variety of definitions exist for the measurement of fuzziness. These facts are discussed in Dubois and Prade (1982), Klement and Schwyhla (1982), Sugeno (1985) and Zimmermann (2011) following concerned articles. Basic operation on fuzzy set Let A~,B~ are two fuzzy sets, then they are equivalent if μA~(x)=μB~(x),∀x∈X and A~⊆B~ if μA~(x)≤μB~(x),∀x∈XUnion: C~=A~∪B~ where, C~={(x,μC~(x))} and μC~(x)=max{μA~(x),μB~(x)}. Intersection: C~=A~∩B~ where, C~={(x,μC~(x))} and μC~(x)=min{μA~(x),μB~(x)}. Complement: A~C={(x,μA~C(x)} where, μA~C(x)=1-μA~(x). Significance of fuzzy set Fuzzy sets allow us to represent vague concepts in natural language. The representation depends on both the concept and the context in which it is used. Several fuzzy set representing linguistic concept such as low, medium, high, and so on are often employed to define the status of a variable. Such a variable is usually called a fuzzy variable. The significance of the fuzzy variable is that they facilitate gradual transitions between states. This consequently possesses a natural capacity to express and deal with observation and measurement uncertainties. Remark 1 If there are fuzzy decision object in the set of state of reality, suppose Ω={A,B,C,D} where A,B,C,D∈F(U) and satisfy A(x)+B(x)+C(x)+D(x)=1 for any x∈U. Here, F(U) is a set of all fuzzy subset of U and set of actions A={aP,aB,aN} then we can formulate 3×4 matrix for all the values of loss function. Based on the loss function inequality, one can formulate Bayesian decision rules. Remark 2 In this paper, we are going to classify suspected people of COVID-19 who might be infected with coronavirus and so loss function may be prepared by some medical expert (Pauker and Jerome 1980). Operation on linguistic variable A linguistic variable is a variable whose values are words or sentence in a natural or artificial language. It has values that are linguistic elements, such as words and phrases which is derived using quantitative or qualitative reasoning such as with probabilistic or fuzzy systems (Deng and Yao 2014; Xu 2005; Pawlak 1985; Zadeh 1965; Klir and Yaun 2006; Chakraborty 2011). Let L={sα\|α=0,1,⋯..,r} be a totally ordered discrete term set where r+1 is the granularity of the linguistic term set L. Since L is totally ordered, law of trichotomy defined on it, i.e., sα≥sβ, sα≤sβ, sα=sβ iff α≥β, α≤β, α=β, respectively. There is also linguistic term set with symmetric subscript L={sα\|α=-r,⋯.,-1,0,1,⋯.,r}. Here 2r+1 denotes the granularity of L and s0 represent an assessment of fair. s-r and sr are lower and upper limits. Consider an example:L={s-3=verybad,s-2=bad,s-1=slightlybad,s0=fair,s1=slightlygoods2=good,s3=verygood}. To facilitate computation and consider all the available information, extend the discrete term set L to continuous term set L∗={sλ\|s-r≤sλ≤sr,λ∈[-r,r]} where sλ of L∗ are same as sα of L for λ=α. In L∗ index of any term denote the degree of the term. So for calculate probability with linguistic term we define a real-valued function from L∗ as follows: L∗={sλ\|s-r≤sλ≤sr,λ∈[-r,r]} be a continuous linguistic term set I:L∗⟶[-r,r] be a real-valued function where I(sλ)=λ for any sλ∈L∗. This function helps us to deal with decision making problem under uncertainty. It is to be noted that if sλ∈L, then sλ is the original term and λ be the original index. Otherwise, sλ is the virtual term and λ is the virtual term index. Decision maker always uses the original linguistic terms to evaluate alternatives and the virtual linguistic term can only appear in operation. Given a continuous term set L∗, for any sλ,sμ∈L∗ and α,α1,α2∈[0,1], the following operational laws hold: sλ±sμ=sλ+μ sλ±sμ=sμ±sλ αsλ=sαλ (α1+α2)sλ=α1sλ±α2sλ α(sλ±sμ)=αsλ±αsμ. Three-way decision based on linguistic information Our main focus on this paper is to determine the COVID-19 infected person based on the linguistic terms for evaluation values of all attribute. So we have two fundamental issues: (i) Calculate the conditional probability of every suspected person with respect to decision object. Here, decision object is the suspected person of COVID-19 (Karni 2009). (ii) Selection of the threshold value parameters, i.e., value of α and β which are used in the lower and upper approximation, respectively (Greco et al. 2008; Pauker and Jerome 1980). To resolve the issue (i) we define the probability concept on a fuzzy event under the linguistic-valued attribute set. Definition Let A={(x,μA(x))\|x∈Rn} be a real-valued fuzzy set, then crisp probability of fuzzy event is defined by P(A)=∑xμA(x)P(x). Let Aα={x\|μA(x)≥α}, then fuzzy probability of fuzzy event is defined byP(A)={(P(Aα,α))\|α∈[0,1]} where P(Aα)=∑x∈AαP(x). Linguistic-valued information system In an information system, the attribute values are given by linguistic variable. Consider a linguistic-valued information system as follows:f1(x1,a1)=s-3,f1(x2,a1)=s1,f1(x3,a1)=s0,f2(x1,a2)=s1,f2(x2,a2)=s-2,f2(x3,a2)=s4,f3(x1,a3)=s0,f3(x2,a3)=s2,f3(x3,a3)=s-1,f4(x1,a4)=s2,f4(x2,a4)=s0,f4(x3,a4)=s-2. Table 2 A linguistic-valued information system U/A a1 a2 a3 a4 x1 s-3 s1 s0 s2 x2 s1 s-2 s2 s0 x3 s0 s4 s-1 s-2 Now consider a fuzzy set B with membership value μB(x1)=0.5, μB(x2)=0.7 and μB(x3)=0.8 and probabilistic measure P defined by P(x1)=0.2, P(x2)=0.3 and P(x3)=0.5 Then, P(B)=∑i=13μB(x)P(x)=0.5×0.2+0.7×0.3+0.8×0.5=0.71. To facilitate computation, we define real-valued function on discrete-valued linguistic information system.7 v:L⟶[0,1]v(sλ)=I(Sλ)r-1 where r is the total number of terms in L. For symmetric subscript linguistic set v:L∗⟶[0,1] by8 v(sλ)=\|I(Sλ)-I(S-r)\|2r. Here, v(sλ) is a continuous mapping which makes transformation between L∗ and [0, 1]. Following results are immediate. Proposition 4.1 Let L∗=sλ\|s-r≤sλ≤sr,λ∈[-r,r] be a set of continuous linguistic terms ‘v’ is a transformation between L∗ and real-valued over [0,1] then, v(s-r)=0,v(s0)=0.5,v(sr)=1 v is an increasing function over L∗. Proof (1): By definition v(sλ)=\|I(sλ)-I(s-r)\|2r. So, v(s-r)=\|I(s-r)-I(s-r)\|2r=0v(s0)=\|I(s0)-I(s-r)\|2r=\|0-(-r)\|2r=0.5v(sr)=\|I(sr)-I(S-r)\|2r=\|r-(-r)\|2r=1. (2): Let -r≤λ1≤λ2≤r, then,sλ2≥sλ1. Now, v(sλ2)=\|I(sλ2)-I(s-r)\|2r = λ2+r2r and v(sλ1)=\|I(sλ1)-I(s-r)\|2r =λ1+r2r as λ2≥λ1, so, λ2+r2r≥λ1+r2r. Hence, v(sλ2)≥v(sλ1), so, ‘v’ is an increasing function over L∗. As the middle linguistic label so represents an assessment of ‘in difference’, transformation function v(sλ) can also be represented in terms of v(s0) as follows. □ Proposition 4.2 v(sλ)=I(sλ)2r+v(s0)=0.5+I(sλ)2r. Proof v(sλ)=\|I(sλ)-I(s-r)\|2r=I(sλ)-I(s-r)2r,as,λ∈[-r,r]=I(sλ)+r2r=0.5+I(sλ)2r=v(s0)+I(sλ)2r[as,v(s0)=0.5]. □ Definition For any linguistic-valued information system, let B∈F(U) and x∈U, then the conditional probability of B with respect to x denoted by9 P(B\|x)=∑aj∈AθB(x),v(fj(x,aj))∑aj∈Av(fj,(x,aj)),x∈U,forallattributej where θ:[0,1]×[0,1]⟶[0,1] is a fuzzy logic operator (Pawlak 1985; Zadeh 1965; Klir and Yaun 2006). Fuzzy logic operator may define in many ways. Here we define θ(x,y)=min(x,y). Thus,10 P(B\|x)=∑aj∈AB(x)∧v(fj(x,aj))∑aj∈Av(fj(x,aj)),x∈U,forallattributej We illustrate this by an example continued from Table 2. Let L={sλ\|s-r≤sλ≤sr,λ∈[-4,4]}11 va1=v(f1,(x2,a1));va2=v(f2,(x2,a2));va3=v(f3,(x2,a3));va4=v(f4,(x2,a4)); 12 P(B\|x2)=∑aj∈AθB(x2),v(fj(x2,aj))∑aj∈Av(fj,(x2,aj))=∑aj∈AB(x2)∧v(fj(x2,aj))∑aj∈Av(fj(x2,aj))=B(x2)∧va1+B(x2)∧va2+B(x2)∧va3+B(x2)∧va4va1+va2+va3+va4=0.7∧v(s1)+0.7∧v(s-2)+0.7∧v(s2)+0.7∧v(s0)v(s1)+v(s-2)+v(s2)+v(s0)=0.7∧0.625+0.7∧0.25+0.7∧0.75+0.7∧0.50.625+0.75+0.25+0.5=2.0752.125=0.976. Clearly, P(B\|x) satisfies the axioms of probability. Now with the help of conditional probability of a fuzzy event with linguistic description about attribute, we can define lower and upper approximation. Let B∈F(U) and 0≤β<α≤1 and x∈U thenapr_α(B)={x∈U\|P(B\|x)≥α}apr¯β(B)={x∈U\|P(B\|x)>β}. Now, these two approximations lead to three-way decision region (Hu 2014).POS(α,β)(B)=x∈U\|∑aj∈AB(x)∧v(fj(x,aj))∑aj∈Av(fj(x,aj))≥αNEG(α,β)(B)=x∈U\|∑aj∈AB(x)∧v(fj(x,aj))∑aj∈Av(fj(x,aj))≤βBND(α,β)(B)=x∈U\|β<∑aj∈AB(x)∧v(fj(x,aj))∑aj∈Av(fj(x,aj))<α. To resolve the second issue, i.e., for selection of threshold value α and β, we use the function I, when loss function is expressed in terms of linguistic form. So loss function inequality is:I(λPP)≤I(λBP)<I(λNP)andI(λNN)≤I(λBN)<I(λPN)withthecondition{I(λNP)-I(λBP)}×{I(λPN)-I(λBN)}>{I(λBP)-I(λPP)}×{I(λBN)-I(λNN)}, thenα=I(λPN)-I(λBN){I(λPN)-I(λBN)}+{I(λBP)-I(λPP)}=1+I(λBP)-I(λPP)I(λPN)-I(λBN)-1β=I(λBN)-I(λNN){I(λBN)-I(λNN)}+{I(λNP)-I(λBP)}=1+I(λNP)-I(λBP)I(λBN)-I(λNN)-1. Parameters α, β define the regions and provide us associated risk for classifying an object. Remark Our main focus is to classify suspected people those who might be infected with coronavirus, so that medical experts can choose parameter value α, β on the basis of their experience (Pauker and Jerome 1980; Pawlak and Sowinski 1994; Yao and Azam 2014). Example We illustrate an example using Table 3 (“Appendix I”) of twenty-six people of different age group with their linguistic-valued information about different attributes related to COVID-19. Here, we have considered the attributes on the basis of the past history of COVID-19 infected population, where the pandemic impact of the infection is already in the third stage. AAo-HNS Infectious Disease and Patient Safety Quality Improvement Committee in the USA recently informed that without the presence of any symptoms like cough, fever, breathing problem, etc., the symptoms like malfunctioning of sensing organs related to smell and taste might be included as an additional identifier for COVID-19 infected patients who might require quarantine and treatment as well. In this example, we consider four age group people, seven conditional attribute and one decision attributes (here ‘c’ indicates COVID-19). We have considered different membership value for COVID-19 for different age group, which indicates the tendency of infection in the different age group. Here, linguistic term index is all non-negative and discrete, so we calculate the values with the help of Eqs. 7 and 10 for taking decisions by considering values of 1-Pcxi instead of Pcxi. Let P∗cxi=1-Pcxi.For the group ‘I’ (less than 20), threshold is taken as α=0.8, β=0.7 For the group ‘II’ (20 to 40), threshold is taken as α=0.7, β=0.55 For the group ‘III’ (40 to 60), threshold is taken as α=0.4, β=0.25 For the group ‘IV’ (> 60), threshold is taken as α=0.3, β=0.2. Now acceptance, non-commitment and rejection are determined by xi\|P∗cxi≥α , xi\|β<P∗cxi<α and xi\|P∗cxi≤β, respectively. For group I (less than 20):POS(I)={x2,x3,x5}BND(I)={x1,x4}NEG(I)={x6}. For group II (20 to 40):POS(II)={x8,x9,x10}BND(II)={x7,x12}NEG(II)={x11}. For group III (40 to 60):POS(III)={x15,x18}BND(III)={x17,x19}NEG(III)={x13,x16,x14}. For group IV > (60):POS(IV)={x22,x24,x25}BND(IV)={x23}NEG(IV)={x20,x21,x26}. Here, POSITIVE region indicates immediate TEST FOR COVID-19 for the persons. BOUNDARY region indicates SELF-ISOLATION for the persons, and NEGATIVE region indicates HOME QUARANTINE of the peoples. Remark Any person having travel history from some infected area must go for self-isolation, and persons having any symptom must allow for testing, which is crucial for an appropriate response to the pandemic. Comparative case study In the last few years, three-way decision theoretic rough set models have been used in many areas of decision making, especially under uncertainty. There are some important issues in decision theoretic rough set models: (i) conditional probability and (ii) threshold value parameters which are determined by loss functions. The determination of thresholds is generally approached as an optimization of some property or examining a trade off solution between multiple criteria. In Yao and Wong (1992), Yao et al. (1991) and Yao (2009), the authors have presented the decision theoretic rough set which is divided into different type models according to the combination of values for conditional probability and loss function with the linguistic term. Overall threshold values are calculated based on Bayesian decision procedure which deals with making a decision with minimum risk based on observed evidence. In this paper, COVID-19 suspected person expressed their viewpoints on different attributes by using linguistic terms. We have implemented some novel method to deal with linguistic information and obtained its conditional probability. Threshold value parameters are obtained according to the loss function given by medical experts, or they might have taken as per their own experience depending upon the situations. Threshold value parameter for different age group people may vary for different places depending upon the contamination rate. As in India every three in four cases that are infected with COVID-19 belongs to age group 21 to 60 years (as on April 2020). Ministry of Health and Family Welfare (MOHFW), Govt. of India, has said that of these 75% of confirmed cases, the maximum cases up to 42% are of between 21 and 40 years of age, while 33% are of between 41 and 60 years. Furthermore, 9% cases belong to less than 20 years, whereas 17% cases belongs to age group greater than 60 years indeed.Fig. 2 Comparative case study between India and USA of infected population based on age group as per Worldometer (2020), Garg (2020) and COVID (2020) In the USA, a report was published online as an Morbidity and Mortality Weekly Report (MMOWR) early released (8th April 2020). Hospitalization rates and characteristics of patients hospitalized with laboratory confirmed COVID-19 disease, are shown in Garg (2020). From Chakraborty (2011), Garg (2020), it is very clear that around 55.5% of confirmed cases has been taken to the hospital for further treatment of the patients infected due to COVID-19. Among these numbers as per Chakraborty (2011), Garg (2020), only 0.4% cases belong to the age group below 17 years, whereas 2.5% cases are registered belonging to age group of 18–49 years; on the other hand, around 7.4% cases have been found belonging to the age group of 50–64 years, and around 12.2% of cases have been registered for the patients belonging to the age group of 65–74 years. Apart from these statistics, more cases have been registered for age group of 75–84 years with a gesture of around 15.8% and for age group greater than 85 years 17.2% cases have been recorded so far. Based on the confirmed cases from Garg (2020), a comparative analysis has been performed and is reported in Fig. 2. Therefore, based on the present situation, an expert can choose threshold value and by following the proposed hybrid method, the decision maker can take decisions in emergency situations to help COVID-19 suspected person due to infections. As a result of which, the rate of contamination can be reduced and simultaneously, mortality rate will decrease. Conclusion In this paper, we have established a three-way decision based on linguistic information system for identifying a suspected person infected due to COVID-19. Based on this model, it would be easier to decide for COVID-19 infected person to send for self-isolation, home quarantine and immediate treatment in an emergency situation. As COVID-19 is highly infectious, correct decisions measures to slow down the spread of the disease that is very much important to confined it up to a limit just before entering the community spreading stages to reach. It is also important to note that thorough rapid testing is mandatory along with this method; as otherwise, it will be very difficult to take decisions while including the asymptomatic infected population in cases. Comparative analysis based on age group for India and USA signifies that our method is more effective and feasible as compared to other approaches. It is because predetermined cases will be taken seriously by following the proposed hybrid decision maker and simultaneously will reduce the huge percentage of the infected population belonging to the age group between 21 and 40 years in case of India. On the other side, it might reduce the percentage of infected people of the age group above 60 years in case of USA as well. This in turn might be able to check the death count of the USA which has been devastatingly overshooting a count of 50k almost. A Appendix I See Table 3. Table 3 Linguistic information based decision for different attributes related to COVID-19 Compliance with ethical standards Conflict of interest All authors of this research paper declare that, there is no conflict of interest. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00500-023-08598-8 Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Change history 5/29/2023 This article has been retracted. 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==== Front Biol Trace Elem Res Biol Trace Elem Res Biological Trace Element Research 0163-4984 1559-0720 Springer US New York 2512 10.1007/s12011-020-02512-1 Article Do Zinc Supplements Enhance the Clinical Efficacy of Hydroxychloroquine?: a Randomized, Multicenter Trial http://orcid.org/0000-0003-4366-2218 Abd-Elsalam Sherief [email protected] 1 Soliman Shaimaa 2 Esmail Eslam Saber 1 Khalaf Mai 1 Mostafa Ehab F. 3 Medhat Mohammed A. 3 Ahmed Ossama Ashraf 4 El Ghafar Mohamed Samir Abd 5 Alboraie Mohamed 6 Hassany Sahar M. 3 1 grid.412258.8 0000 0000 9477 7793 Tropical Medicine and Infectious Diseases Department, Faculty of Medicine, Tanta University, El-Giash Street, Tanta, 31527 Egypt 2 grid.411775.1 0000 0004 0621 4712 Public health and Community Medicine, Menoufia University, Menoufia, Egypt 3 grid.252487.e 0000 0000 8632 679X Tropical Medicine and Gastroenterology Department, Faculty of Medicine, Assiut University, Assiut, Egypt 4 grid.7269.a 0000 0004 0621 1570 Internal Medicine Department, Ain-shams University, Cairo, Egypt 5 grid.412258.8 0000 0000 9477 7793 Department of Anesthesia, Surgical Intensive Care and Pain Medicine, Faculty of Medicine, Tanta University, Tanta, Egypt 6 grid.411303.4 0000 0001 2155 6022 Department of Internal Medicine, Al-Azhar University, Cairo, Egypt 27 11 2020 15 1 9 2020 23 11 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. No specific treatment for COVID-19 infection is available up till now, and there is a great urge for effective treatment to reduce morbidity and mortality during this pandemic. We aimed to evaluate the effect of combining chloroquine/hydroxychloroquine (CQ/HCQ) and zinc in the treatment of COVID-19 patients. This was a randomized clinical trial conducted at three major University hospitals in Egypt. One hundred ninety-one patients with a confirmed diagnosis of COVID-19 infection were randomized into two groups: group I (96) patients received both HCQ and zinc, and group II (95) received HCQ only. The primary endpoints were the recovery within 28 days, the need for mechanical ventilation, and death. The two groups were matched for age and gender. They had no significant difference regarding any of the baseline laboratory parameters or clinical severity grading. Clinical recovery after 28 days was achieved by 79.2% in the zinc group and 77.9% in zinc-free treatment group, without any significant difference (p = 0.969). The need for mechanical ventilation and the overall mortality rates did not show any significant difference between the 2 groups either (p = 0.537 and 0.986, respectively). The age of the patient and the need for mechanical ventilation were the only risk factors associated with the patients’ mortality by the univariate regression analysis (p = 0.001 and < 0.001, respectively). Zinc supplements did not enhance the clinical efficacy of HCQ. More randomized studies are needed to evaluate the value of adding zinc to other therapies for COVID 19. ClinicalTrials.gov Identifier: NCT04447534 Keywords Zinc Chloroquine Antioxidants COVID 19 Treatment ==== Body Introduction The world has witnessed an increasing number of cases with COVID-19 infection since December 2019, reaching more than 20 million infected people worldwide [1–7]. COVID-19 infection is caused by a highly contagious single-stranded RNA virus called the SARS-CoV-2 virus [1], which is transmitted mainly through droplets, aerosol, and close contact [2–4]. Despite the fact that the respiratory system is the primarily affected organ, SARS-CoV-2 can affect many other organs [5–7]. No specific treatment for COVID-19 infection is available up till now, and there is a great urge for effective treatment to reduce morbidity and mortality during this pandemic. Chloroquine (CQ) and hydroxychloroquine (HCQ) can prevent SARS-CoV-2 infection by changing endosomal pH required for virus/cell fusion, together with altering glycosylation of cellular receptors of SARS-CoV [8]. There is a debate in the literature regarding the efficacy of CQ and HCQ in the treatment of COVID-19 infection [9–12]. Adding azithromycin to CQ and HCQ was reported to reduce the complications and fatality rates, and reduce the viral load in COVID-19 patients [7–22]. These reports resulted in emerged ideas calling for combining CQ and HCQ with other drugs in the treatment of COVID-19. Zinc is essential for different cellular and enzymatic activities, as well as being a necessary cofactor for many viral proteins [15–25]. Zinc was also proved to inhibit RNA-dependent RNA polymerase of SARS-CoV in cell culture [17]. Chloroquine is known to increase the intracellular concentrations of zinc, and thus enhance its effect [18]. Dietary plant polyphenols such as the flavonoids quercetin (QCT) and epigallocatechin-gallate act as antioxidants and as signaling molecules. Remarkably, the activities of numerous enzymes that are targeted by polyphenols are dependent on zinc. Husam and his colleagues have previously shown that these polyphenols chelate zinc cations, and they hypothesized that these flavonoids might also be acting as zinc ionophores, transporting zinc cations through the plasma membrane [19]. Eight studies are registered on clinicaltrials.gov to evaluate the efficacy of zinc with hydroxychloroquine either in the treatment of or prophylaxis against COVID-19 infection. Only one study is completed, 4 studies are still recruiting patients, and 2 studies did not start recruitment yet, while one study was withdrawn. None of these registered studies has shown the results up till now. We aimed to evaluate the effect of combining CQ/HCQ and zinc in the treatment of COVID-19 patients. Methods This was a randomized controlled study conducted in three Egyptian tertiary care centers in Assiut, Tanta, and Cairo. A written informed consent was taken from each participant in this trial. Approval of the Institutional ethical committee was taken before starting the trial. The trial was registered on clinicaltrials.gov (ClinicalTrials.gov Identifier: NCT04447534). This study included patients with a confirmed diagnosis of COVID-19 infection by real-time PCR test during the period between 23 June and 23 August 2020. All the included patients were classified into mild, moderate, severe, and critical according to the WHO case severity classification for COVID-19 infection. Mild cases constituted of patients with symptoms for COVID-19 infection but not complicated with pneumonia or hypoxia. Moderate cases included patients with mild viral pneumonia and SpO2 > 90% on room air. Severe cases involved patients with signs of severe pneumonia such as respiratory rate > 30 breaths/min, severe respiratory distress, or SpO2 < 90% on room air. Finally, critical cases included patients with acute respiratory distress syndrome sepsis and septic shock [26]. One hundred ninety-one patients with a confirmed diagnosis of COVID-19 infection were equally randomized into two groups: group I: zinc group, included 96 patients who received both HCQ and zinc and group II (without zinc group), included 95 who received HCQ only. Patient with hypokalemia or hypomagnesemia, porphyria, neutrophilia, myasthenia gravis, maculopathy or changes in the visual field, heart failure, prolonged QT interval in ECG, liver cirrhosis, psoriasis, epilepsy, anemia from pyruvate kinase and G6PD deficiencies, chronic kidney disease, and pregnant or lactating females were excluded from this study. Collected data included history, clinical examination, laboratory investigation at admission, and follow-up during the hospitalization period. Primary outcome measures included recovery within 28 days, need for mechanical ventilation, and death. Hydroxychloroquine was given in a dose of 400 mg twice daily on the first day, then 200 mg twice daily for 5 days, while zinc was given in a dose of zinc sulfate 220 mg (50 mg of elemental zinc) twice daily as many clinical trials did. Both groups received the standard of care treatment for COVID-19 infection, according to the Egyptian Ministry of Health guidelines for 15 days. This study was approved by the Ethics Committee of the Faculty of Medicine, Tanta University. The privacy and confidentiality of the data for participated patients were guaranteed. Statistical Analysis The normality of the variables was tested by the Shapiro-Wilk test. Statistical Package for Social Sciences (SPSS) V. 23 was used for data analysis. Data were expressed in number (No.), percentage (%), mean (x̅), and standard deviation (SD). Student’s t test was used for normally distributed continuous variables and Mann-Whitney’s test for not normally distributed ones. Chi-square test (χ2) was used to study the association between qualitative variables, and whenever any of the expected cells were less than five, Fischer’s exact test was used. Binary logistic regression was used to ascertain the effect of the potential risk factors on the patients’ mortality. Two-sided p value < 0.05 was considered statistically significant. Sample Size Calculation The sample size calculation was based on a previous study by Shah et al. 2012, who studied the effect of zinc on severe pneumonia. The required sample was 86 patients in each group with 0.8 as the probability (power), 0.05 as type I error probability, 3 as the difference in the mean duration of pneumonia between the two groups, and 1:1 ratio of experimental to control subjects. The sample size was inflated by 10% to compensate for the dropouts [27]. Results The two groups were matched for age and gender (p = 0.940 and 0.062, respectively). They had no significant difference regarding smoking, associated comorbidities, or clinical severity grading (p 0.706, 0.384, 0.781, respectively) (Table 1).Table 1 Baseline patients’ characteristics in the two groups Character Zinc group 1 (n = 96) No. (%) Without zinc group (n = 95) No. (%) p value Age in years (mean ± SD) 43.48 ± 14.62 43.64 ± 13.17 0.940 Gender Male 52 (54.2) 64 (67.4) 0.062 Female 44 (45.8) 31 (32.6) Smoking 42 (44.2) 39 (41.05) 0.706 Comorbidities No 35 (36.5) 42 (44.2) 0.076 HTN 21 (21.9) 16 (16.8) Diabetes 19 (19.8) 7 (7.4) Hepatic 6 (6.25) 9 (9.5) Multiple 15 (15.6) 21(22.1) Clinical severity grading Mild 9 (9.4) 12 (12.6) Moderate 58 (60.4) 55 (57.9) 0.781 Severe 18 (18.8) 20 (21.1) Critical 11 (11.6) 8 (8.4) n, number; HTN, hypertension; SD, standard deviation The laboratory investigations did not show any significant difference between the two groups. This is detailed in Table 2.Table 2 Laboratory investigations in the two groups Investigation Zinc group 1 (n = 96) Mean ± SD Median Without zinc group (n = 95) Mean ± SD Median p value Hemoglobin 13.38 ± 1.95 13.65 13.77 ± 1.70 13.50 0.143 Platelets 228.48 ± 80.06 219 249.62 ± 82.50 244.0 0.052 WBCs 5.55 ± 3.13 4.55 5.63 ± 2.92 4.50 0.599 Direct bilirubin 0.24 ± 0.09 0.20 0.25 ± 0.09 0.20 0.280 Indirect bilirubin 0.49 ± 0.21 0.40 0.53 ± 0.22 0.50 0.087 Albumin 4.18 ± 0.27 4.10 4.19 ± 0.29 4.10 0.861 ALT 46.64 ± 38.92 36.50 36.58 ± 20.93 28.0 0.057 AST 37.49 ± 40.51 28.50 39.58 ± 26.62 30.0 0.053 D-dimer 1.49 ± 4.55 0.360 0.88 ± 2.56 0.40 0.346 Ferritin 373.31 ± 414.00 262.90 317.01 ± 220.50 234.00 0.390 Creatinine 1.01 ± 0.14 0.92 0.96 ± 0.28 0.90 0.119 CRP 28.85 ± 62.90 12.10 14.92 ± 17.16 9.00 0.967 ALT, alanine transaminase; AST, aspartate transaminase; CRP, C-reactive protein; WBCs, white blood cells; n, number; SD, standard deviation There was no significant difference between the two groups regarding the clinical course or any of the different outcomes. The mean duration of hospital stay was 13.51 ± 5.34 days in the zinc group and 14.01 ± 6.26 days in the zinc-free group (p = 0.553). Seventy-six patients (79.2%) in the zinc group and 74 patients in the zinc-free group showed complete recovery after 28 days (p = 0.969). Four patients in the zinc group and 6 patients in the zinc-free group needed mechanical ventilation (p = 0.537). The overall mortality did not significantly differ in the two groups either, as 5 patients died in each group with p = 0.986 (Table 3).Table 3 Clinical course and outcomes of the two groups Clinical course Zinc group 1 (n = 96) Mean ± SD Without zinc group (n = 95) No. (%) Mean ± SD p value Duration of hospital stay in days 13.51 ± 5.34 14.01 ± 6.26 0.553 No. (%) No. (%) Recovery after 28 days 76 (79.2) 74 (77.9) 0.969 Need for mechanical ventilation 4 (4.2) 6 (6.3) 0.537 Fate Survived 91 (94.8) 90 (94.7) 0.986 Died 5 (5.2) 5 (5.3) n, number; SD, standard deviation The univariate analysis revealed that the patients’ age and the need for mechanical ventilation were the only risk factors significantly associated with mortality (p = 0.001 and < 0.001, respectively). The addition of zinc to HCQ did not considerably affect the overall COVID-19 mortality in this study (p = 0.986) (Table 4).Table 4 Regression analysis of the effect of potential risk factors on the patients’ mortality Variable Univariate p value OR 95% CI Lower Upper Age 0.001 1.097 1.040 1.157 Gender 0.180 0.411 0.112 1.507 Smoking ALT 0.842 0.998 0.976 1.020 Albumin 0.370 0.303 0.022 4.127 Creatinine 0.277 0.856 0.646 1.133 Ferritin 0.393 1.001 0.999 1.002 CRP 0.785 0.996 0.967 1.026 Need for mechanical ventilation < 0.001 138.44 23.592 812.427 DM 0.785 0.996 0.967 1.026 Zinc treatment 0.986 0.056 0.277 3.534 ALT, alanine transaminase; CRP, C-reactive protein; n, number; SD, standard deviation; OR, odds ratio; DM, diabetes mellitus Discussion In Egypt, there have been an increasing number of cases with COVID-19 infection since March 2020. Many treatment protocols were updated to treat the coronavirus infection based on the evidence available at this time. The initial protocols were primarily dependent on hydroxychloroquine. In the Egyptian leading university hospitals, we aimed to evaluate the effect of combining CQ/HCQ and zinc in treating COVID-19 patients. The treatment teams in the Egyptian universities, which incorporated infectious diseases consultants and clinical pharmacists, adopted the importance of integration of zinc into the treatment protocol of treatment. To the best of our knowledge, this is the first clinical trial investigating the role of the addition of zinc to hydroxychloroquine in the treatment of COVID 19 patients. The main hypothesis behind this approach was the fact that zinc was proven to have an inhibitory effect on the RNA-dependent RNA polymerase of SARS-CoV in cell culture [17, 18]. Moreover, CQ and HCQ are known to increase the intracellular concentrations of zinc and thus enhance its effect [18]. Despite these proved benefits of zinc in the literature, this study found that zinc supplements did not enhance the clinical efficacy of HCQ. There are a lot of questions now about the efficacy of CQ or HCQ in the treatment of COVID 19 patients. A recent randomized study found that adding HCQ to standard care did not add significant benefit, did not decrease the need for ventilation, and did not reduce mortality rates in COVID-19 patients [11]. A recent meta-analysis found that hydroxychloroquine alone did not reduce mortality in hospitalized COVID-19 patients, and even when added to azithromycin, this was significantly associated with increased mortality [28]. This study’s major strength is that being the first randomized study to evaluate the effect of combining hydroxychloroquine (HCQ) and zinc in the treatment of COVID-19 patients. On the other hand, the study’s limitations may be depending mainly on the patients’ clinical outcomes and not the viremic response. However, this is due to the limited resources in such a developing country. Another limitation is that zinc absorption may be limited with high phytate diet, and other medications and serum zinc were not measured before, during, or after treatment in tis clinical trial. In conclusion, zinc supplements did not add value or enhance the clinical efficacy of HCQ. Zinc supplementation may be studied further with other drug regimens for COVID 19, but it did not add any clinical values when added to HCQ. Compliance with Ethical Standards Conflict of Interest The authors declare that they have no conflicts of interest. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Channappanavar R Zhao J Perlman S T cell-mediated immune response to respiratory coronaviruses Immunol Res 2014 59 118 128 10.1007/s12026-014-8534-z 24845462 2. 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Sarin SK, Choudhury A, Lau GK et al (2020) Pre-existing liver disease is associated with poor outcome in patients with SARS CoV2 infection; the APCOLIS study (APASL COVID-19 liver injury Spectrum study). Hepatol Int 1–11. 10.1007/s12072-020-10072-8 Online ahead of print 14. Million M Lagier JC Gautret P Colson P Fournier PE Amrane S Hocquart M Mailhe M Esteves-Vieira V Doudier B Aubry C Correard F Giraud-Gatineau A Roussel Y Berenger C Cassir N Seng P Zandotti C Dhiver C Ravaux I Tomei C Eldin C Tissot-Dupont H Honoré S Stein A Jacquier A Deharo JC Chabrière E Levasseur A Fenollar F Rolain JM Obadia Y Brouqui P Drancourt M la Scola B Parola P Raoult D Early treatment of COVID-19 patients with Hydroxychloroquine and azithromycin: a retrospective analysis of 1061 cases in Marseille, France Travel Med Infect Dis 2020 35 101738 10.1016/j.tmaid.2020.101738 32387409 15. Lazarczyk M Favre M Role of Zn2+ ions in host-virus interactions J Virol 2008 82 11486 11494 10.1128/JVI.01314-08 18787005 16. Frederickson CJ Koh J-Y Bush AI The neurobiology of zinc in health and disease Nat Rev Neurosci 2005 6 449 462 10.1038/nrn1671 15891778 17. te Velthuis AJ van den Worm SH Zn2+ inhibits coronavirus and arterivirus RNA polymerase activity in vitro and zinc ionophores block the replication of these viruses in cell culture PLoS Pathog 2010 6 e1001176 10.1371/journal.ppat.1001176 21079686 18. Xue J Moyer A Peng B Wu J Hannafon BN Ding WQ Chloroquine is a zinc ionophore PLoS One 2014 9 e109180 10.1371/journal.pone.0109180 25271834 19. Dabbagh-Bazarbachi H Clergeaud G Quesada IM Zinc ionophore activity of quercetin and epigallocatechin-gallate: from hepa 1-6 cells to a liposome model J Agric Food Chem 2014 62 32 8085 8093 10.1021/jf5014633 25050823 20. Marjot T, Moon AM, Cook JA et al (2020) Outcomes following SARS-CoV-2 infection in patients with chronic liver disease: an international registry study. J Hepatol S0168-8278(20)33667–9. 10.1016/j.jhep.2020.09.024 21. Abd-Elsalam S, Elkadeem M, Glal KA (2020) Chloroquine as chemoprophylaxis for COVID-19: will this work? Infect Disord Drug Targets. 10.2174/1871526520666200726224802 Online ahead of print 22. Mohamed AA, Mohamed N, Mohamoud S et al (2020) SARS-CoV-2: the path of prevention and control. Infect Disord Drug Targets. 10.2174/1871526520666200520112848 Online ahead of print 23. Pal A, Squitti R, Picozza M et al (2020) Zinc and COVID-19: basis of current clinical trials. Biol Trace Elem Res 1–11. 10.1007/s12011-020-02437-9 Online ahead of print 24. Pawar A, Pal A (2020) Molecular and functional resemblance of dexamethasone and quercetin: a paradigm worth exploring in dexamethasone-nonresponsive COVID-19 patients. Phytother Res 10.1002/ptr.6886. 10.1002/ptr.6886 Online ahead of print 25. Pal A Pawar A Goswami K Sharma P Prasad R Hydroxychloroquine and Covid-19: a cellular and molecular biology based update Indian J Clin Biochem 2020 35 3 274 284 10.1007/s12291-020-00900-x 32641874 26. World Health Organization (2020) Clinical management of severe acute respiratory infection (SARI) when COVID-19 disease is suspected: interim guidance, 13 March 2020. (World Health Organization, 2020) 27. Shah GS Dutta AK Shah D Mishra OP Role of zinc in severe pneumonia: a randomized double bind placebo controlled study Ital J Pediatr 2012 38 36 10.1186/1824-7288-38-36 22856593 28. Fiolet T, Guihur A, Rebeaud M et al Effect of hydroxychloroquine with or without azithromycin on the mortality of COVID-19 patients: a systematic review and metaanalysis. 10.1016/j.cmi.2020.08.022	33247380	PMC7695238	NO-CC CODE	2022-02-16 23:23:04	yes	Biol Trace Elem Res. 2021 Nov 27; 199(10):3642-3646
==== Front ACS Omega ACS Omega ao acsodf ACS Omega 2470-1343 American Chemical Society 10.1021/acsomega.0c01541 Article Red Blood Cell Membrane-Coated Silica Nanoparticles Codelivering DOX and ICG for Effective Lung Cancer Therapy Xiao Jia † Weng Jie † Wen Fang † Ye Juan ‡ † Department of Clinical Oncology, The First People’s Hospital of Yueyang, No. 39 of Dongmaoling Road, Yueyang, Hunan Province 414000, P. R. China ‡ Department of Head and Neck Oncology, The Second Affiliated Hospital of Zunyi Medical University, No. 149 Dalian Road, Zunyi, Guizhou Province 563000, P. R. China Email: [email protected]. 17 12 2020 29 12 2020 5 51 32861 32867 05 04 2020 16 07 2020 2020American Chemical SocietyThis is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes. The effective chemotherapy of cancer is usually hindered by the unsatisfied cell internalization of the drug delivery systems (DDS) as well as drug resistance of cancer cells. In order to solve these dilemmas in one design, red blood cell membrane (RBM)-coated silica nanoparticles (RS) were fabricated to codeliver doxorubicin (Dox) and indocyanine green (ICG) to effectively treat the model lung cancer using photothermal-assisted chemotherapy. Our results demonstrated that the RS/I-D was the nanoparticle at around 100 nm with superior stability and biocompatibility. Especially, the photothermal effects of ICG were well preserved and could be applied to accelerate the drug release from the DDS. More importantly, the RBM modification can mediate enhanced cell internalization of drugs as compared to their free forms, which finally resulted in enhanced anticancer efficacy in Dox-resistant A549 cells (A549/Dox) both in vitro and in vivo with enhanced cell apoptosis and cell arrest. document-id-old-9ao0c01541document-id-new-14ao0c01541ccc-price ==== Body 1 Introduction Recent studies in cancer therapy have revealed some reasons responsible for the poor performance of some drug delivery systems (DDS) in cancer therapy, among which unsatisfied cell internalization of the DDS as well as drug resistance of cancer cells are recognized as two important obstacles.1−3 It was well recognized that free drugs are usually subjected to quick excretion while nanoparticle-based DDS can increase the circulation time of loaded drug.4,5 However, because of their ectogenic nature, nanoparticles without careful surface modifications are prone to be captured by the reticuloendothelial system to trigger premature excretion.6,7 In order to solve this dilemma, polyethylene glycol (PEG) modification is becoming a widely recognized approach to increase the circulation time of the DDS, which has been verified by many previous studies.8−10 However, increasing evidences have revealed that although increasing the circulation time, the hydrophilic nature of PEG fails to satisfy decent cell internalization because of the opposite nature of cell membrane. As an alternative measure, the targeting moiety, which specifically recognizes the overexpressed receptors on the surface of cancer cells, was employed as a conjugation ligand for surface modification of DDS. Although some promising results were obtained, the complicated synthetic procedure as well as the inevitable cytotoxicity because of the residual side products severely hindered the further application of this method in cancer therapy.11−14 Until recently, the introduction of cell membrane-derived vehicles as the main component or accessory structures (such as the surface material) is becoming the best solution for the abovementioned dilemma.15−17 First, the cell membrane derivatives inherit the whole proteins of the mother cells, which show similar properties to the mother cells when being prepared into DDS.18 Most importantly, the same lipid bilayer structure between cell membrane derivatives and cancer cell membrane also makes it a suitable DDS for drug delivery with enhanced cell internalization.19 Finally, the cell membrane derivatives are of completely natural origin, with high biocompatibility and high accessibility.20 As a result, recent studies have devoted extensive efforts to explore the DDS potential of cell membrane derivatives. In particular, the cell membrane derived from red blood cell (RBC), which inherits the long circulation nature of RBC, is acquiring more and more attentions in cancer therapy.21,22 In recent decades, DDS based on nanoparticles have showed many advantages over free drugs in drug delivery, such as elevated bioavailability and reduced side effects, which is widely recognized as an indispensable tool for cancer therapy.23,24 Therefore, various DDS have been developed and tested based on nanoparticles composed of either organic or inorganic materials.25−28 Silica nanoparticles (SLN), which have versatile virtues such as high biocompatibility, facile fabrication, and efficient nucleic acid binding, are becoming the suitable carrier for drug and especially gene delivery. Therefore, a number of DDS have been developed based on SLN, which showed satisfying outcomes.29−31 The chemotherapy of doxorubicin (Dox), a wide spectrum anticancer drug, was usually subjected to the acquired drug resistance of many cancers.32,33 As a result, alternative measures to employ additional aids to destroy tumors in a synergetic manner were well recognized as a feasible approach.34,35 As a result, in this study, size-controlled SLN was first synthesized, and Dox and indocyanine green (ICG) were loaded into the SLN during this process (SLN/I-D). After coating the SLN/I-D with the RBC membrane (RBM), the dual-loaded RBM-coated silica nanoparticles (RS)/I-D was prepared as a long circulation DDS for photothermal-assisted chemotherapy. It was suggested that surface modification of RBM can aid the RS/I-D to avoid premature excretion for enhanced tumor accumulation. Then, the intracellular photothermal nature of ICG achieves synergetic anticancer efficacy with Dox for effective therapy of Dox-resistant lung cancer (A549). The combination of RBM with SLN as the carrier and its potential in photothermal-assisted chemotherapy of drug resistance cancer is not fully explored by previous studies, which might be the core value of our study. 2 Results and Discussion 2.1 Preparation of RS/I-D To combine the advantages of high drug-loading capacity and biocompatibility in one DDS, SLN, as a widely adopted biomaterial, was used for the construction of drug-loaded core nanoparticles using the chemical precipitation method in the water-in-oil microemulsion. The Dox and ICG were preloaded into the matrix of SLN during synthesis (SLN/I-D). The RBM was finally coated onto SLN/I-D to finally prepare RS/I-D. As shown in Figure 1A, mean diameter determined by dynamic light scattering was around 110 nm with acceptable distribution, which was slightly larger than the size of SLN/I-D (98.52 nm, polydispersity index of 0.201), suggesting the successful coating of RBM. The coating of RBM was also supported by the changes of zeta potential (from +25.23 mV of SLN/I-D to −12.16 mV of RS/I-D, data not shown). Figure 1 (A) Size distribution of RS/I-D. Inserted was the transmission electron microscopy image of RS/I-D. Scale bar is 100 nm. (B) Photothermal capacity of RS/I-D in comparison to free ICG and PBS. Data were expressed as the mean standard deviation of three samples. Afterward, the photothermal capacity of the loaded ICG within RS/I-D was further determined using 808 nm laser irradiation. As shown in Figure 1B, as compared to phosphate-buffered saline (PBS), the ICG-containing groups showed significant temperature rise upon the irradiation of 808 nm. In particular, it was noted that free ICG showed lower final temperature as compared to that of RS/I-D. It was suggested that ICG was susceptible to the irradiation of light and prone to be degraded. As a result, it was suggested that the degradation of ICG at higher temperature might be responsible for the lower final temperature because a large proportion of ICG degraded without exerting their photothermal effects. In contrast, the RS/I-D might be able to protect the encapsulated ICG molecule to achieve satisfying photothermal effects. 2.2 Characterization of RS/I-D ICG is a photosensitizer, which is vulnerable to the irradiation of lights. In order to further confirm the protective effect of RS on the ICG, the fluorescence intensity changes of RS/I-D in comparison to free ICG under the sunlight were further monitored. As shown in Figure 2A, the fluorescence intensity of free ICG suffered great decline upon the sunlight irradiation for merely 1 day and steadily decreased in a relatively rapid speed in the following days. In contrast, the fluorescence intensity of RS/I-D only decreased slightly after expose to sunlight. The total loss of fluorescence intensity was only 11.3% at Day 6 as compared to the 68.5% of that in the free ICG group. These results clearly demonstrated that RS could offer satisfactory protection to ICG to avoid the degradation of sunlight irradiation, which is beneficial for the safe delivery of sufficient drugs to the tumor tissue for better anticancer therapy. Figure 2 (A) Comparative fluorescence stability of free ICG and RS/I-D under sunlight for 6 days. (B) Colloidal stability of RS/I-D in PBS (pH 7.4) and mouse plasma at 37 °C for up to 48 h. Data were expressed as the mean standard deviation of three independently prepared nanoparticle preparations. Considering that the colloidal stability is a critical parameter to evaluate the performance of the DDS, the colloidal stability of RS/I-D under two physiological conditions (PBS 7.4 and mouse plasma) was therefore investigated. According to previous reports, the size of the DDS should maintain stability for a relatively long period to allow the safe delivery of loaded drug molecules to the target tissue without leakage.10,14,25 As a result, the particle size changes of RS/I-D were selected to be the indicator to reflect the colloidal stability. As shown in Figure 2B, during 48 h of incubation, the size of RS/I-D only showed minor variations in both PBS (pH 7.4) and mouse plasma. Considering the instrumental error, it was therefore concluded that RS/I-D was a stable DDS under physiological conditions that might be suitable for cancer-related drug delivery. Afterward, the biocompatibility of the carrier as well as RS/I-D was studied. The hemolysis assay of RS/I-D was first investigated by incubating the DDS with 2% RBC of Balb/c mice to reflect the irritation of nanoparticles on RBC in the blood. As illustrated in Figure 3A, only 1.33% hemolysis rate was obtained at the highest RS/I-D concentration of 1 mg/mL. It was also well known that the actual DDS concentration upon in vivo application would be much lower than the threshold of 1 mg/mL because of the dilution body fluids (including blood and lymph). Therefore, the RS/I-D was concluded to be a safe DDS without significant risk of inducing hemolysis on RBC. Figure 3 (A) Hemolysis of RS/I-D on 2% RBC under different concentrations at 37 °C for 1 h. (B) Cytotoxicity of various concentrations of drug-free carriers on A549/Dox cells for 48 h. Data were expressed as the mean standard deviation of three samples. To further determine the cytotoxicity of drug-free carrier on cancer cells upon arrival of the target tissue, the drug-free carrier (RS) was incubated with A549/Dox cells at various concentrations for 48 h, and the cell viability after treatment was studied. As displayed in Figure 3B, the cell viability of A549/Dox cells at 48 h postincubation remained still over 90% at the high concentration (200 μg/mL), indicating the potential of RBM-derived carrier to be a highly biocompatible carrier. Moreover, it was also suggested that the carrier showed almost no cytotoxicity effects on the cells, indicating that the results in the following assays were because of the effects of drugs but not the interference of carriers. The DLC of Dox in RS/I-D was determined as 9.63% and the ICG was 9.54% using UV spectrophotometry. 2.3 Drug Release and Cellular Uptake In order to understand the drug-release profile of RS/I-D under different conditions, the Dox and ICG release of RS/I-D (5 mg/mL) were evaluated with or without laser irradiation. As displayed in Figure 4A, under extracellular physiological condition (pH 7.4), the drug release of both Dox and ICG was relatively slow with a final cumulative release percentage of 16.71 and 22.43%, respectively, at 24 h postincubation. In contrast, upon laser irradiation, the release of both drugs was significantly elevated, indicating that the photothermal nature of ICG might facilitate the release of both drugs from the DDS. In detail, the total drug release percentage at the end of test (24 h) was 66.26 and 80.67%, respectively. Therefore, it was inferred that RS/I-D was able to maintain stability at extracellular with minor drug leakage while transferred to a burst release state upon laser irradiation, which was beneficial for realizing cancer-specific drug delivery for effective cancer therapy. Figure 4 (A) Drug-release profiles of Dox and ICG from RS/I-D in PBS (pH 7.4) with or without laser irradiation (+L means with light irradiation). (B) Intracellular fluorescence signal of Dox in A549/Dox cells incubated with SLN/I-D or RS/I-D with or without RBM pretreatment (2 h) for 4 h. Scale bar: 100 μm. (C) Intracellular fluorescence signal of Dox and ICG at different time intervals incubated with free drugs (Dox and ICG) and corresponding DDSs (RS/D and RS/I) in A549/Dox cells using flow cytometry. Data were expressed as the mean standard deviation of three samples. The cellular uptake of drugs incubated with or without RBM pretreatment was assessed. As shown in Figure 4B, compared with RBM-unmodified SLN/I-D, the cellular accumulation of RS/I-D at 4 h postincubation was much more elevated, suggesting the preferable cell internalization of RBM modification because of the similar nature between RBM and the cancer cell membrane. Interestingly, it was also confirmed by the competitive assay that pretreatment with RBM significantly reduced the cellular accumulation of RS/I-D while showed minor effects on SLN/I-D. As a result, we further concluded that the enhanced cell internalization was realized through the RBM-mediated cellular uptake. Afterward, the comparative cellular uptake of drugs in their free forms (free Dox and free ICG) in comparison to their corresponding DDSs (RS/D and RS/I) was conducted. As shown in Figure 4C, after incubation with different groups for various time intervals (6 and 12 h), it was observed that the intracellular accumulation of all groups was positively related to the incubation time. Moreover, because of the drug-resistant nature of A549/Dox, both free drugs were poorly accumulated within cells, and in particular, because of the hydrophilic nature of ICG, the ICG showed inferior accumulation to that of Dox. Most importantly, the drug accumulation in cells using corresponding DDSs was significantly enhanced, which was in line with previous reports that DDS can realize enhanced cell internalization of drugs than free drugs.36,37 2.4 In Vitro Anticancer Effect The in vitro anticancer effect was conducted by classic MTT assay. The Dox and ICG within RS/I-D were around 1 and was adopted in this and the following assays. The results, as shown in Figure 5A, demonstrated that the anticancer effect of all formulations was positively related to the drug concentrations. Specifically, when Dox concentration reached 5 μg/mL, the survival rate of A549/Dox cells in the RS/D group was still 50.4%, suggesting the strong drug-resistant nature of this cell line. In contrast, RS/I at the same drug concentration subjected to laser irradiation achieved enhanced anticancer outcome, indicating the powerful anticancer capacity of photothermal therapy. Most importantly, the combination of Dox and ICG using photothermal-assisted chemotherapy showed the best performance on suppressing the growth of A549/Dox cells, which demonstrated the promising synergetic effects between these drugs.38 Figure 5 (A) Viability of A549/Dox cells treated with different formulations at different drug concentrations for 48 h. (B) Volume changes of MCTS after different treatments. Data were expressed as the mean standard deviation of three samples. The multicellular tumor spheroid (MCTS) mimicking the in vivo solid tumor was adopted to study the anticancer efficacy of various formulations. Figure 5B shows that the volume of MCTS in the RS/D group remained increasing throughout the experiment, further indicating that the drug resistance of A549/Dox cells significantly decreased the cytotoxicity of Dox. RS/I using photothermal therapy exhibited enhanced suppression of tumor growth. Most importantly, the combination of Dox and ICG in RS/I-D exhibited significantly enhanced anticancer efficacy with a suppressed volume growth.39 To further verify this conclusion, the apoptosis of related proteins (caspase-3, bcl-2, and cytochrome-3) in different formulations was investigated. Figure 6A exhibits that RS/I-D-treated cells had the highest expression level of cleaved caspase-3 while had the lowest expression of bcl-2 (responsible for suppressing apoptosis) among all groups, which further confirmed the superior anticancer effect of RS/I-D. Moreover, the RS/I-D exhibited much higher expression level of cytochrome-3, which indicated that mitochondria damage was also involved in the cell apoptosis.40 Figure 6 (A) Western blot assays of the expression of caspase-3, cytochrome C, and bcl-2 proteins after different treatments (drug dosage: 3.5 μg/mL for 48 h). (B) Cell cycle variations of A549/Dox cells treated with different formulations (drug dosage: 3.5 μg/mL for 48 h). To illuminate the mechanism responsible for significantly increased cytotoxicity in the RS/I-D-treated group, the cell cycle variations after different treatments were studied. As shown in Figure 6B, compared with the control group, the cell percentage in the S phase decreased from 70.2 to 60.4% in the RS/D group and even to 48.2% in the RS/I group. In addition, the cell percentage in G0/G1 phase increased from 16.6 to 22.8% in the RS/D group and finally 33.4% in the RS/I group. In contrast, the S phase and G0/G1 phase in the RS/I-D group was 33.7 and 44.1%, respectively, which suggested much more severe cell cycle arrest in the RS/I-D group than the other groups. 2.5 In Vivo Anticancer Study In vivo anticancer study of RS/I-D was performed. As displayed in Figure 6A, the saline group showed consistent growth of tumor tissue to a final volume of 1097 ± 111 mm3. As expected, tumor growth was stunted to some extent upon administration with RS/D or RS/I but still larger than the original volume without reversion. Nonetheless, the anticancer efficacy of mice in the RS/I-D group was much more potent than other groups with a significant reverse of tumor volume to 52 ± 14 mm3. In addition, the tumor tissue from different groups was excised and subjected to TUNEL staining to analysis the apoptosis profiles after different treatments (Figure 7). As shown in Figure 6B, the tumor tissue from the RS/I-D group suffered from severe apoptosis then the other groups, which was characterized as the most widely observed positive cells (brown dots).41 Figure 7 (A) Tumor volume variations of A549/Dox tumor-bearing Balb/c nude mice after administration of different formulations. Mice were intravenously administered with various formulations every other day for seven times, and each formulation contained the same dose of drugs (Dox and ICG: 5 mg/kg). Inserted image was the representative tumor excised from the mice (from left to right: Saline, RS/D, RS/I + L, RS/I-D + L). Data were expressed as the mean standard deviation of six samples. (B) At the end of the test, the mice were sacrificed, and the tumor tissues from each group were subjected to TUNEL staining. Scale bar: 100 μm. 3 Conclusions In summary, we successfully developed a RBM-modified SLN to serve as a DDS for the codelivery of Dox and ICG (RS/I-D) for photothermal-assisted chemotherapy of drug-resistant A549/Dox cancer cells. The physicochemical characterizations showed that the RS/I-D exerted satisfactory distribution at around 100 nm with high stability, superior photothermal capacity, low hemolysis, and laser-responsive drug release. Cell experiments further demonstrated that RBM modification can mediate enhanced cellular uptake of RS/I-D into A549/Dox cells. Most importantly, the DDS can also enhance the cell internalization of free drugs, which achieved synergetic anticancer efficacy with elevated benefits than applying RS/D or RS/I alone. As expected, synergetic photothermal-assisted chemotherapy achieved much more elevated anticancer benefits than monotreatment means both in vitro and in vivo. 4 Experimental Section 4.1 Preparation of RS/I-D The synthesis of ICG and Dox-loaded SLN (SLN/I-D) was achieved by using water-in-oil microemulsion. In detail, the water-in-oil microemulsion (10 mL) containing Dox and ICG was prepared. Then, tetraethyl orthosilicate (5 mg), N-(2-aminoethyl)-3-aminopropyltrimethoxysilane (2 mg), and NH4OH (100 μL) were successively added into the microemulsion under vigorous agitation. After 24 h of reaction, SLN/I-D was precipitated by excess ethanol and collected by centrifugation (3000 rpm, 10 min, CR26, Hitachi, Japan). In order to isolate RBM from RBCs, the RBCs were homogenized in 1 mL of extracting buffer (PBS, 0.0001 M) and further centrifuged (10,000g, 10 min), followed by second ultracentrifugation (100,000g, 60 min) to finally obtain the RBM. All procedures were performed at 4 °C. The protein concentration of RBM was quantified using a BCA kit (Beyotime, Shanghai, China) according to manufacturer’s instructions. The RBM was then deposited onto the surface of SLN/I-D to construct RS/I-D. Briefly, 250 μL of SLN/I-D (1 mg/mL) was mixed with RBM solution under vortex (w/w ratio of 5). Afterward, the mixture was subjected to probe-type sonication (100 W, 5 min). The mixture was further centrifuged (10,000g, 10 min) to collect RS/I-D. Other detailed materials and methods can be found in the Supporting Information. Supporting Information Available The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.0c01541.The details for materials and methods (PDF) Supplementary Material ao0c01541_si_001.pdf The authors declare no competing financial interest. Acknowledgments The authors acknowledge the language help from Letpub. ==== Refs References Kumari P. ; Rompicharla S. V. K. ; Bhatt H. ; Ghosh B. ; Biswas S. 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==== Front Mol Divers Mol Divers Molecular Diversity 1381-1991 1573-501X Springer International Publishing Cham 10169 10.1007/s11030-020-10169-0 Original Article CoViTris2020 and ChloViD2020: a striking new hope in COVID-19 therapy http://orcid.org/0000-0003-3681-114X Rabie Amgad M. [email protected] [email protected] 123 1 Dr. Amgad Rabie’s Research Lab. for Drug Discovery (DARLD), Mansoura, Egypt 2 grid.10251.37 0000000103426662 Pharmaceutical Organic Chemistry Department, Faculty of Pharmacy, Mansoura University, Mansoura, 35516 Egypt 3 Dikernis, Egypt 3 1 2021 116 7 8 2020 4 12 2020 © The Author(s), under exclusive licence to Springer Nature Switzerland AG part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Abstract Designing anticoronavirus disease 2019 (anti-COVID-19) agents is the primary concern of medicinal chemists/drug designers nowadays. Repurposing of known active compounds against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new effective and time-saving trend in anti-COVID-19 drug discovery. Thorough inhibition of the coronaviral-2 proteins (i.e., multitarget inhibition) is a possible powerful favorable strategy for developing effectively potent drugs for COVID-19. In this new research study, I succeeded to repurpose the two antioxidant polyhydroxy-1,3,4-oxadiazole compounds CoViTris2020 and ChloViD2020 as the first multitarget coronaviral protein blockers with extremely higher potencies (reach about 65 and 304 times, for CoViTris2020, and 20 and 93 times, for ChloViD2020, more potent than remdesivir and favipiravir, respectively). These two 2,5-disubstituted-1,3,4-oxadiazoles were computationally studied (through molecular docking in almost all SARS-CoV-2 proteins) and biologically assessed (through a newly established robust in vitro anti-COVID-19 assay) for their anticoronaviral-2 bioactivities. The data obtained from the docking investigation showed that both ligands promisingly exhibited very strong inhibitory binding affinities with almost all docked enzymes (e.g., they displayed extremely lower binding energies of − 12.00 and − 9.60 kcal/mol, respectively, with the SARS-CoV-2 RNA-dependent RNA polymerase “RdRp”). The results of the biological assay revealed that CoViTris2020 and ChloViD2020 significantly displayed very high anti-COVID-19 activities (anti-SARS-CoV-2 EC50 = 0.31 and 1.01 μM, respectively). Further in vivo/clinical studies for the development of CoViTris2020 and ChloViD2020 as anti-COVID-19 medications are required. In brief, the ascent of CoViTris2020 and ChloViD2020 as the two lead members of the novel family of anti-COVID-19 polyphenolic 2,5-disubstituted-1,3,4-oxadiazole derivatives represents a promising hope in COVID-19 therapy. Graphic abstract CoViTris2020 and ChloViD2020 inhibit SARS-CoV-2 life cycle with surprising EC50 values of 0.31 and 1.01 μM, respectively. CoViTris2020 strongly inhibits coronaviral-2 RdRp with exceptionally lower inhibitory binding energy of − 12.00 kcal/mol. Keywords Anti-COVID-19 drug SARS-CoV-2 Coronavirus Coronaviral-2 RNA-dependent RNA polymerase (RdRp) Papain-like protease (PLpro) Polyphenolic 2,5-disubstituted-1,3,4-oxadiazole Remdesivir Ivermectin Favipiravir CoViTris2020 ChloViD2020 ==== Body Introduction A broad variety of tactics and strategies was employed to fight the current worldwide coronavirus disease 2019 (COVID-19) epidemic as an urgent result of the novelty of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, and the lack of effective medications [1–3]. Many plans of these emerging strategies depend on reevaluating and repurposing existent drugs, and some others are totally new [4]. The design in each case depends on present scientific proof and evidence of logic mechanistic approaches that are efficient against either analogous/identical viral infections or the severe signs/symptoms that are caused by COVID-19 [1–4]. Comprehensive inhibition of the coronaviral-2 proteins (i.e., multitarget inhibition) can establish one of the most promising plans for developing effectively potent drugs for COVID-19. However, all the discovered blockers of the main stages of SARS-CoV-2 life cycle lack the required maximal potencies against the major SARS-CoV-2 enzymes (e.g., RNA-dependent RNA polymerase “RdRp”, papain-like protease “PLpro”, and main protease “Mpro”) [3, 4]. Many known drugs are currently under anti-COVID-19 therapeutic investigation, such as remdesivir (antiviral), ivermectin (antiparasitic), favipiravir (antiinfluenza), hydroxychloroquine (antimalarial and antirheumatic disorders), and arbidol (antiinfluenza) [4–17]. Perspectives of many medicinal chemists are based on utilizing the in silico computational molecular modeling studies as the starting points for designing novel potent compounds active against SARS-CoV-2, and most of these studies have been collected in currently published reviews [18, 19]. This current direction in searching for effective safe known compounds having the structural flexibility to be successfully redirected and repurposed as potential agents against COVID-19 triggered me to screen our library of known compounds/drugs (including my previously synthesized active compounds). Surprisingly, the predictive computational molecular search and screening gave rise to the discovery of two promising compounds in respect to the anti-SARS-CoV-2 activities, 1,2,3-tris[5-(3,4,5-trihydroxyphenyl)-1,3,4-oxadiazol-2-yl]propan-2-ol (CoViTris2020) and 5-[5-(7-chloro-4-hydroxyquinolin-3-yl)-1,3,4-oxadiazol-2-yl]benzene-1,2,3-triol (ChloViD2020) (Fig. 1). It is worth noticing that both compounds were previously synthesized and evaluated as effective antioxidant agents [20].Fig. 1 Chemical structures of CoViTris2020 and ChloViD2020 molecules The adaptable and flexible scaffolds of CoViTris2020 and ChloViD2020 molecules are characterized by carrying significantly considerable number of hydrogen bond donors/acceptors and aromatic rings, which are the principal key moieties required for the best/ideal/strongest binding (hydrogen bonding and hydrophobic interactions) with the binding domains of almost all the COVID-19 protein targets in SARS-CoV-2 and human. It is worth mentioning that CoViTris2020 and ChloViD2020 are chemically very analogous to RNA nucleos(t)ides, giving them the expected abilities of being efficient bioisosteric antimetabolites in COVID-19 treatment. Additionally, CoViTris2020 and ChloViD2020 structures are also characterized by their abilities to strongly bound to zinc atoms, where they have many active zincophoric centers (i.e., they are rich in oxygenous and nitrogenous moieties) allowing them to act as strong multizincophores. Zinc atom/ion carriers have been extensively studied especially for their potent antiviral activities and they have been found to effectively inhibit the replication/reproduction machinery of several viruses in vitro [21]. Specifically, Zn2+ ions in certain exceedingly higher/lower concentrations can impair and disrupt the coronaviral replication and transcription and, therefore, excessive displacement or transport of Zn2+ ions by zincophoric agents (e.g., CoViTris2020 and ChloViD2020) will undoubtedly affect the coronaviral replication processes (zinc ionophores have been found to successfully and significantly inhibit the replication process of coronaviruses intracellularly in cell cultures) [10, 15, 22]. The considerable chemical resonance of both structures aids in boosting the comprehensive potential anti-COVID-19 bioactivities through intensifying the inhibitory chemical stabilities of the complexes of each compound with the active site residues of the coronaviral-2/human target proteins [20]. It is also worth noticing that the potent antioxidant actions of both compounds may aid their anti-SARS-CoV-2/anti-COVID-19 bioactivities through reducing almost all the oxidizing moieties of all the target proteins, which logically causes deactivation of the catalytic and noncatalytic biological activities of these important active proteins, meantime, these antioxidant properties aid in quenching the free radicals and fighting the oxidative stress inside the body of the COVID-19 patient [3, 20]. The existence of the bioactive antiviral 1,3,4-oxadiazole rings in the backbones of the two molecules is suggested to significantly enhance the anti-COVID-19 actions of both of them [20]. The two compounds have very balanced (ideal) lipophilic/hydrophilic properties (they have log P < 5, which perfectly obeys Lipinski’s Rule of Five “Ro5”) which are required to afford the top tolerated pharmacokinetic characteristics [23]. The two molecules were docked in all the available SARS-CoV-2 nonstructural and structural protein targets (including functional enzymes and proteins) along with one human structural enzyme, with an emphasis on the most important enzymes like the RdRp. After deep investigative analysis of the SARS-CoV-2 constructure, researchers have been able to identify all the possible targets of the SARS-CoV-2 in a detailed way [24]. The promising results of the computational molecular docking of both compounds in almost all targets, in comparison with the reference potent drugs, theoretically confirmed their exceptional multitarget inhibitory activity against coronaviral-2 particles (to the best of my knowledge, the two compounds are considered the first potent multitarget anti-COVID-19 compounds). This interesting outcome inspired me to experimentally bioevaluate them. Precisely as the hypothetical predictions, the in vitro bioassay outcomes were also very promising. Based on all the previous facts and results, we can expect that CoViTris2020 and ChloViD2020 will successfully act as very potent anti-COVID-19 drug candidates through several and distinct mechanisms of action (i.e., through a very effective anti-COVID-19 mode of multiaction). Detailed illustrations of all the discovered target COVID-19 therapy nonstructural proteins (nsps) and structural proteins (sps) present in SARS-CoV-2 and human (up to date) are presented in Figs. 2 and 3, respectively. In this research paper, I report the successful repurposing of the two previously synthesized antioxidant 1,3,4-oxadiazole compounds, CoViTris2020 and ChloViD2020, as effective and potent anti-COVID-19 agents (as the first potent antidotal multitarget anti-SARS-CoV-2/anti-COVID-19 drugs).Fig. 2 A detailed illustration of all the discovered target nsps present in SARS-CoV-2 and involved in COVID-19 therapy (up to date) Fig. 3 A detailed illustration of all the discovered target sps present in SARS-CoV-2/human and involved in COVID-19 therapy (up to date) Methods Computational molecular docking studies of CoViTris2020 and ChloViD2020 (predictive anti-COVID-19 properties evaluation) To expectedly assess the anti-COVID-19 activities of the two target antioxidant compounds of the current research, CoViTris2020 and ChloViD2020, prior to their actual practical anti-COVID-19 biological evaluation (preliminary in vitro/in vivo assays and subsequent preclinical/clinical trials), molecular docking of the molecules of both compounds in the target SARS-CoV-2 nsps and sps (along with one target human sp, ACE2) has been carried out using the docking engines of the most known and credible international molecular docking software programs (e.g., Discovery Studio, LeDock, GemDock, and GOLD). Different docking software programs were used to confidently and doubtlessly ensure the results and to assess and guarantee their reproducibility. Integrating the expected pharmacophoric features with the interaction energy analysis disclosed functionally pivotal amino acid residues in the binding pockets/cavities of the active and/or allosteric sites of the target SARS-CoV-2 enzymes (mainly) along with in silico predicted common inhibitory binding modes with the highly potent reference repurposed compounds (e.g., remdesivir, ivermectin, and favipiravir). The docking results were very brilliant to encourage me to utilize a new specific web-based server designed in 2020, which is specialized in precise molecular docking of all COVID-19 target nsps/sps and prediction of anti-COVID-19 activities and potencies of tested compounds. For this objective, the newly programmed COVID-19 Docking Server was used [25]. COVID-19 Docking Server web-based software (AutoDock Vina is used as the main docking engine) is an interactive web server for docking small molecules, peptides, or antibodies against potential protein targets of COVID-19 in order to predict the binding modes between COVID-19 targets and the ligands along with screening and evaluating the anti-COVID-19 activities of these ligands (i.e., the platform provides a free interactive modern tool using a very precise knowledge-based scoring function to evaluate the candidate binding poses for the specific prediction of COVID-19 target-ligand interactions and the following drug discovery for COVID-19). Generally, almost all SARS-CoV-2 nsps (enzymes) and very few SARS-CoV-2/human sps as COVID-19 therapy targets are recommended for small molecule docking. The structures of all the functional or structural protein targets involved in the SARS-CoV-2 replication life cycle were collected by direct downloading from international web databases (e.g., from the Protein Data Bank “PDB”) or constructed based on their known homologs of coronaviruses (by using homology modeling module of Maestro 10, website: www.schrodinger.com), and completely prepared ready for direct docking on this web-based software. The utmost effective and influenced nsps and sps (almost are functional enzymes) among all the detected and recognized proteins involved in COVID-19 therapy (previously presented in Figs. 2 and 3) were accurately opted to be targeted and docked. This includes fifteen various nCoV protein targets and one human protein target (the human enzyme ACE2). For docking of only one small molecule, the “Docking” mode box should be specifically selected as the computational module (type) for every specific target (this is the selection used in the current case). To get the best accurate results, an average exhaustiveness option of “12” was used. The detailed results of these estimations (binding energies in kcal/mol) are shown in Table 1. After docking, the structure of each enzyme/protein-compound complex was further examined and accurately analyzed for characterization by the help of the fully automated comprehensive interactive tool of the famous Protein–Ligand Interaction Profiler (PLIP) web server (https://projects.biotec.tu-dresden.de/plip-web) [26], and results were marginally tabulated for comparison, explication of the previous docking results, and then placing conclusions (see Table 2 as an example). Table 1 Score values of the sixteen computationally predicted pharmacological anti-COVID-19-related activities (against SARS-CoV-2 nsps/sps and human sp ACE2) of the target 1,3,4-oxadiazoles (CoViTris2020 and ChloViD2020) and the three reference drugs (remdesivir, ivermectin, and favipiravir), respectively, using COVID-19 Docking Server methodology (the table demonstrates the top docking binding model score value, i.e., the least predicted binding energy value, in kcal/mol for each compound with each target protein) Classification SARS-CoV-2/Human target protein Top pose score value for docking of nCoV protein targets Inhibitory binding energies/affinities (kcal/mol) CoViTris2020 ChloViD2020 Remdesivir Ivermectin (B1a form) Favipiravir Nsps Mpro − 9.50 − 8.20 − 7.70 − 6.50 − 5.40 RdRp (RTP site) − 12.00 − 9.60 − 8.30 − 7.10 − 6.90 RdRp (RNA site) − 9.40 − 7.90 − 7.10 − 6.60 − 6.10 PLpro (dimer) − 10.60 − 9.30 − 8.10 − 6.00 − 5.40 Nsp3 (207-379, AMP site) − 9.70 − 7.70 − 7.10 − 5.90 − 5.40 Nsp3 (207-379, MES site) − 9.90 − 9.90 − 8.40 − 6.40 − 5.50 Helicase (ADP site) − 8.80 − 8.00 − 7.00 − 5.80 − 5.30 Helicase (NCB site) − 9.90 − 8.90 − 7.50 − 6.10 − 5.40 Nsp14 (ExoN) − 8.10 − 7.50 − 7.70 − 5.70 − 4.90 Nsp14 (N7-MTase) − 11.40 − 9.30 − 9.10 − 7.20 − 6.10 Nsp15 (endoribonuclease) − 8.50 − 8.10 − 8.30 − 6.00 − 4.80 Nsp16 (GTA site) − 10.10 − 8.70 − 8.30 − 6.80 − 5.60 Nsp16 (MGP site) − 9.70 − 7.70 − 7.30 − 6.20 − 5.10 Nsp16 (SAM site) − 10.10 − 8.70 − 8.10 − 6.70 − 5.50 Sps N protein (NCB site) − 10.00 − 8.90 − 7.40 − 6.50 − 5.20 Human ACE2 − 10.20 − 9.00 − 7.90 − 6.70 − 5.60 Table 2 Summary of the main active amino acid residues of chains A and C “nsp12/7” (of the SARS-CoV-2 RdRp) interacted with CoViTris2020, ChloViD2020, and remdesivir (active form) molecules, respectively (pivotal catalytic residues of the expected active site are shown in italics) Compound SARS-CoV-2 RdRp amino acid residues Hydrogen bonds (of all types) Hydrophobic interactions π-Cation/Halogen interactions CoViTris2020 Chain A: ARG553, TYR619, LYS621 (2 H bonds), CYS622, ASP623, SER682, THR687 (2 H bonds), ALA688, ASN691, SER759 (2 H bonds), ASP760, SER795, LYS798 Chain A: PRO620, ASP623, ARG624, LYS798 Chain A: ARG553 ChloViD2020 Chain A: ASP623, ASN691, ASP846, LYS849; Chain C: VAL12, SER15, GLN18, GLN19, MET90 Chain A: MET87, LYS411, ASN414, LYS417; Chain C: MET90 Chain A: LYS417, ASP418 Remdesivir Chain A: ARG555 (2 H bonds), CYS622, ASP623, SER682, THR687, ALA688, ASN691, ASP760 – Chain A: ARG555 Anti-COVID-19 biological activities (in vitro assay) of CoViTris2020 and ChloViD2020 This novel and highly reliable anti-COVID-19 in vitro assay is based upon the authentic procedures of Chu and coworkers with very slight modifications [5, 27]. The complete procedures were carried out in a specialized biosafety level 3 (BSL-3) laboratory (SARS-CoV-2 is classified as a BSL-3 pathogen by the WHO and FDA) in Hong Kong SAR (China). The assayed SARS-CoV-2 virus, BetaCoV/Hong Kong/VM20001061/2020, was isolated from the fresh nasopharynx aspirate and throat swab of a confirmed fifty-years-aged COVID-19 male patient in Hong Kong using Vero E6 cells (ATCC CRL-1586). Stock virus (107.25 TCID50/mL) was prepared after three serial passages in Vero E6 cells in infection media (DMEM supplemented with 4.5 g/L D-glucose, 100 mg/L sodium pyruvate, 2% FBS, 100,000 U/L Penicillin–Streptomycin, and 25 mM HEPES). Following the original procedures in the literature, CoViTris2020 and ChloViD2020 compounds were synthesized (starting from gallic acid), purified (> 97% purity), fully characterized, and put in small dark brown glass containers to be ready for the assay [20]. The pure three reference compounds were purchased from MedChemExpress (remdesivir), Sigma-Aldrich (ivermectin, B1a form), and Toyama Chemical “Fujifilm group, Japan” (favipiravir). The stocks of the five tested compounds were accurately prepared by dissolving each of them in dimethylsulfoxide “DMSO” (to get a concentration of 100 mM of each of CoViTris2020, ChloViD2020, remdesivir, ivermectin, and favipiravir). To evaluate the in vitro anti-SARS-CoV-2 activities of the two target new compounds (CoViTris2020 and ChloViD2020) in comparison with those of the standard three reference compounds (mentioned above), Vero E6 cells were pretreated with the five compounds diluted in infection media for 1 h prior to infection by SARS-CoV-2 virus at MOI = 0.02. The five tested compounds were maintained with the virus inoculum during the 2-h incubation period. The inoculum was removed after incubation, and the cells were overlaid with infection media containing the diluted test compounds. After 48 h incubation at 37 °C, supernatants were immediately collected to quantify viral loads by TCID50 assay or quantitative real-time RT-PCR (TaqMan™ Fast Virus 1-Step Master Mix) [5, 27]. Viral loads in this assay were fitted in logarithm scale (log10 TCID50/mL and log10 viral RNA copies/mL), not in linear scale, under increasing concentrations of the tested compounds [5, 9, 27]. Four-parameter logistic regression (GraphPad Prism) was used to fit the dose–response curves and determine the EC50 of the tested compounds that inhibit SARS-CoV-2 viral replication (CPEIC100 was also determined for each compound). Cytotoxicity of each of the five tested compounds was evaluated in Vero E6 cells using the CellTiter-Glo® Luminescent Cell Viability Assay (Promega) [5, 28]. The detailed values resulted from the previous assays (compound concentrations in μM) are shown in Table 3. Final results were represented as the mean ± SD from the triplicate biological experiments. Statistical analysis was performed using SkanIt 4.0 Research Edition software (ThermoFisher Scientific) and Prism V5 software (GraphPad). All reported data were significant at p < 0.05.Table 3 Anti-COVID-19 (anticoronaviral-2) activities (along with human/mammalian cells toxicities) of CoViTris2020 and ChloViD2020 (using remdesivir, ivermectin, and favipiravir, respectively, as the reference drugs) against SARS-CoV-2 in Vero E6 cells Classification Compound Name CC50a (μM) Inhibition of SARS-CoV-2 in vitro (μM) 100% CPE Inhibitory concentration (CPEIC100)b 50% Reduction in infectious virus (EC50)c 50% Reduction in viral RNA copy (EC50)d Target compounds CoViTris2020 > 100 0.99 ± 0.11 0.31 ± 0.02 0.33 ± 0.02 ChloViD2020 > 100 1.97 ± 0.23 1.01 ± 0.07 1.23 ± 0.09 Reference compounds Remdesivir > 100 22.50 ± 3.02 20.17 ± 1.72 23.88 ± 1.96 Ivermectin > 100 83.05 ± 7.15 53.00 ± 3.99 62.44 ± 5.08 Favipiravir > 100 98.82 ± 8.64 94.09 ± 6.79 > 100 aCC50 or 50% cytotoxic concentration is the concentration of the tested compound that kills half the cells in an uninfected cell culture. CC50 was determined with serially diluted compounds in Vero E6 cells at 48 h postincubation using CellTiter-Glow Luminescent Cell Viability Assay (Promega) bCPEIC100 or 100% CPE inhibitory concentration is the lowest concentration of the tested compound that causes 100% inhibition of the cytopathic effects (CPE) of SARS-CoV-2 virus in Vero E6 cells under increasing concentrations of the tested compound at 48 h postinfection. Compounds were serially twofold or fourfold diluted from 100 μM concentration cEC50 or 50% effective concentration is the concentration of the tested compound that is required for 50% reduction in infectious SARS-CoV-2 virus particles in vitro. EC50 is determined by infectious virus yield in culture supernatant at 48 h postinfection (log10 TCID50/mL) dEC50 or 50% effective concentration is the concentration of the tested compound that is required for 50% reduction in SARS-CoV-2 viral RNA copies in vitro. EC50 is determined by viral RNA copies number in culture supernatant at 48 h postinfection (log10 RNA copies/mL) Results and discussion Computational molecular docking studies of CoViTris2020 and ChloViD2020 (predictive anti-COVID-19 properties evaluation) The deep understanding of the COVID-19 target-ligand interactions represents an extremely crucial key challenge in drug discovery for COVID-19. The computational simulative prediction of the SARS-CoV-2 proteins-inhibiting properties of the newly repurposed target compounds CoViTris2020 and ChloViD2020 greatly helped me to have a comprehensive overview of the expected anti-COVID-19 activities of the two compounds. This prediction also aided me to have a detailed conception about the two target compounds anti-COVID-19 modes of action along with the compounds degrees of efficacy and potency. On close inspection of the top pose score values (present in Table 1) of docking most nsps and sps of SARS-CoV-2 (along with ACE2 of human, which is almost the only human entry point for the virus into the human body until now, see Introduction section and Fig. 3) using COVID-19 Docking Server, it is clearly observed that CoViTris2020 is distinguishably ranked first in its inhibitory binding affinities and potencies with an excellent range of binding energies of − 8.10 to − 12.00 kcal/mol. The binding affinities of CoViTris2020 dramatically exceed those of all the three reference drugs (they are about to be twice as those of favipiravir) and ChloViD2020, as the potent antioxidant CoViTris2020 molecule powerfully binds to the respective proteins forming very stable inhibited (deactivated) complexes with relatively amazing binding energies which are the lowest among all (i.e., significantly lower than the binding energies of all the other four compounds in their complexes with the respective nsps and sps). Specifically, CoViTris2020 molecule gives its best inhibitory binding affinities with the three nsps RdRp-RNA (RTP site) (− 12.00 kcal/mol), nsp14 (N7-MTase) (− 11.40 kcal/mol), and PLpro (dimer) (− 10.60 kcal/mol), respectively (Fig. 4a–d), which are three of the most effective protein targets in hindering and stopping the life cycle of the SARS-CoV-2 through inhibition and deactivation of their active sites [24]. These exceptional results are very promising as they indicate the high possibility of CoViTris2020 to be a very potent inhibitor (blocker) of RdRp, nsp14, and/or PLpro. Other proteins affording very encouraging excellent binding affinities with CoViTris2020 molecule include ACE2, nsp16 (all sites), N protein (NCB site), nsp3 (both sites), helicase (both sites), Mpro, and RdRp without RNA (RNA site) (Fig. 4b), respectively.Fig. 4 Screenshots of COVID-19 Docking Server outputs of the top predicted binding model of docking of CoViTris2020 molecule (colored pink) in: a SARS-CoV-2 RdRp-RNA “RTP site” (PDB code: 7BV2; colored with other various colors; Cartoon Style). b SARS-CoV-2 RdRp “RNA site” (PDB code: 7BV2; colored with other various colors; Trace Style). c SARS-CoV-2 nsp14 “N7-MTase” (PDB code: 5C8S, 1J53 “for active site homology”; colored with other various colors; Cartoon Style). d SARS-CoV-2 PLpro “dimer” (PDB code: 6WUU; colored with other various colors; Trace Style) ChloViD2020 comes second, among all the five compounds, in its inhibitory binding energies and affinities which are ranged from − 7.50 to − 9.90 kcal/mol. The binding affinities of ChloViD2020 surpass those of all the three reference drugs (except for those of each of exoribonuclease and endoribonuclease with remdesivir, as there is binding energies difference of − 0.20 kcal/mol in favor of remdesivir in both cases), as the potent antioxidant ChloViD2020 molecule strongly binds to the respective proteins forming very stable inhibited complexes with relatively lower binding energies (i.e., significantly lower than the binding energies of all the three reference molecules in their complexes with the respective nsps and sps). ChloViD2020 molecule specifically gives its best inhibitory binding affinities with the four nsps nsp3 (207-379, MES site) (− 9.90 kcal/mol), RdRp-RNA (RTP site) (− 9.60 kcal/mol), nsp14 (N7-MTase) (− 9.30 kcal/mol), and PLpro (dimer) (− 9.30 kcal/mol), respectively (Fig. 5a–d). Interestingly, ChloViD2020 approaches and reaches the lower binding affinity of CoViTris2020 in only one enzyme which is N7-MTase (it is also one of the most effective protein targets in stopping the coronaviral-2 life cycle through blocking of its active site [24]) with the same binding energy value of -9.90 kcal/mol. These promising results indicate the significant possibility of ChloViD2020 to be a very potent inhibitor ligand or antagonist of nsp3, RdRp, nsp14, and/or PLpro. Other proteins giving very encouraging binding affinities with ChloViD2020 molecule include ACE2, N protein (NCB site), helicase (both sites), nsp16 (all sites), Mpro, Nsp15 (endoribonuclease), and RdRp without RNA (RNA site), respectively.Fig. 5 Screenshots of COVID-19 Docking Server outputs of the top predicted binding model of docking of ChloViD2020 molecule (colored pink) in: a SARS-CoV-2 nsp3 “207-379, MES site” (PDB code: 6W6Y; colored with other various colors; Cartoon Style). b SARS-CoV-2 RdRp-RNA “RTP site” (PDB code: 7BV2; colored with other various colors; Cartoon Style). c SARS-CoV-2 nsp14 “N7-MTase” (PDB code: 5C8S, 1J53 “for active site homology”; colored with other various colors; Cartoon Style). d SARS-CoV-2 PLpro “dimer” (PDB code: 6WUU; colored with other various colors; Trace Style) The chemical structures of CoViTris2020 and ChloViD2020 molecules are exceptionally characterized by higher degrees of balanced orientational and conformational flexibility when compared to those of the other or reference anticoronaviral drugs (e.g., favipiravir, remdesivir, hydroxychloroquine, ivermectin, and arbidol). These extraordinary unique flexibilities of CoViTris2020 and ChloViD2020 structures are apparently observed in the docking poses in SARS-CoV-2 target proteins (along with human target ACE2) as shown in both Figs. 6 and 7, respectively. CoViTris2020 molecule (a trisubstituted or three armed bulky derivative of citric acid) has higher degree of flexibility than ChloViD2020 molecule (a typical 2,5-disubstituted derivative of 1,3,4-oxadiazole ring) due to, mainly, its larger topological polar surface area (TPSA) and its higher number of atoms. This highly balanced flexibility is generally required for excellent and extreme positioning of the drug molecule to be more superimposable in the active binding pockets and cavities of any enzyme or protein (i.e., required for extreme lock-and-key positioning of the drug molecule after hitting and striking any protein molecule), leading to more adequate and potent inhibition (i.e., antagonism or blocking) of the actions (either catalytic or whatever) done or mediated by the protein. Consequently, the highly balanced flexibilities of CoViTris2020 and ChloViD2020 molecules greatly increase their abilities to be very potent anti-COVID-19 agents, respectively.Fig. 6 Collective screenshots of COVID-19 Docking Server outputs of the top predicted binding models resulted from the docking of CoViTris2020 molecule (colored pink) in different SARS-CoV-2 proteins (colored with other various colors; trace and cartoon styles), showing the extremely balanced high degrees of orientational and conformational flexibility of the molecule during the hitting attempts against all target coronaviral-2 proteins Fig. 7 Collective screenshots of COVID-19 Docking Server outputs of the top predicted binding models resulted from the docking of ChloViD2020 molecule (colored pink) in different SARS-CoV-2 proteins (colored with other various colors; trace and cartoon styles), showing the extremely balanced high degrees of orientational and conformational flexibility of the molecule during the hitting attempts against all target coronaviral-2 proteins Remdesivir, computationally, comes first among the three reference drugs (and third among all tested compounds in general) in anti-COVID-19 activities with binding energies range of − 7.00 to − 9.10 kcal/mol. Remdesivir shows its best binding affinity with the target enzyme nsp14 (N7-MTase), as it binds to this important SARS-CoV-2 enzyme with a very good binding energy of − 9.10 kcal/mol. Ivermectin and favipiravir come last, with their best binding affinities observed with the two coronaviral-2 enzymes nsp14 (N7-MTase; − 7.20 kcal/mol) and RdRp (RTP site; − 6.90 kcal/mol), respectively. The results of the inhibitory binding affinities of CoViTris2020 with the SARS-CoV-2 RdRp (RTP site) are relatively the best among all the COVID-19 targets, thus the next goal was to extensively investigate the specific interactions with the amino acids of the active site(s) of this crucial polymerase in deeper details (using the data files obtained from the same web server). These deep investigations revealed the high degree of similarity of the expected anti-RdRp mode of action of the two ligands CoViTris2020 and ChloViD2020 as compared to that of remdesivir and its active metabolite (some of the interacting residues of the active pockets are the same and some others are very close) as shown in Fig. 8a–c. It was also found that CoViTris2020 molecule has an apparent superiority over ChloViD2020 and remdesivir (in its active metabolic form) molecules (together with the other two reference molecules) in the strength of inhibitory binding forces with RdRp (e.g., CoViTris2020 has at least sixteen hydrogen bonds, more than four hydrophobic interactions, and at least one π-cation interaction; ChloViD2020 has at least nine hydrogen bonds, five hydrophobic interactions, and two halogen interactions; while on the other hand, remdesivir active metabolite (the most active drug among the three references) has only less than nine hydrogen bonds, no considerable hydrophobic interactions, and only one π-cation interaction), supporting its expected promising comprehensive anti-SAR-CoV-2 activities. Exceptionally, CoViTris2020 is the only inhibitor among all the investigated compounds that strongly interacts with the RdRp complex structure (especially chain A) with that large number of hydrogen bonds (of all types) which exceeds sixteen bonds. According to the previous literature, CoViTris2020 can be considered the only compound that strongly binds with the SARS-CoV-2 nsp12 in this extensively effective inhibitory mode, making it an optimal potent SARS-CoV-2 RdRp inhibitor candidate. Furthermore, this potential unique property gives CoViTris2020 an extra advantage of having potent blocking nature in its action on the active site of the polymerase. On the other hand, ChloViD2020 is the only inhibitor among all the investigated compounds that interacts with both chains A (nsp12) and C (nsp7) of the RdRp complex structure, since it, additionally, creates interaction forces of the hydrogen-bond/hydrophobic types with the polymerase cofactor chain C (mainly with the residues VAL12, SER15, GLN18, GLN19, and MET90). To the best of my knowledge, ChloViD2020 can be predictably considered one of the rarest ligands that interacts with and inhibits more than one protein chain of the SARS-CoV-2 RdRp complex (i.e., acts through a dual mode of action). ChloViD2020 is also characterized by its unique interaction forces created between its chlorine atom and the residues LYS417 and ASP418 of chain A. Table 2 summarizes all the main active amino acid residues involved in the inhibitory interactions of CoViTris2020, ChloViD2020, and remdesivir molecules, respectively. Fig. 8 The inhibitory binding interactions, of a CoViTris2020; b ChloViD2020; c Remdesivir (active metabolite form), with the active amino acids of the SARS-CoV-2 RdRp (2D and 3D representations, respectively) In short, the previous computational results of the predicted binding modes of the two inhibitors CoViTris2020 and ChloViD2020 with the SARS-CoV-2/human proteins extremely comply with and support my suggested hypothetical multitarget mechanism of anti-COVID-19 action of each of the two ligands. Anti-COVID-19 biological activities (in vitro assay) of CoViTris2020 and ChloViD2020 The results demonstrated in Table 3 obviously and directly revealed the extremely higher and amazing anti-COVID-19 effectiveness and potency of CoViTris2020 and ChloViD2020 (as two of the most potent anti-SARS-CoV-2 compounds ever) relative to those of each of the reference drugs. All the five tested compounds were found to inhibit SARS-CoV-2 replication in Vero E6 cells with EC50 under 100 μM. Surprisingly, CoViTris2020 (EC50 = 0.31 μM) was found to be about 65, 171, and 303.5 times more potent than remdesivir (EC50 = 20.17 μM), ivermectin (EC50 = 53.00 μM), and favipiravir (EC50 = 94.09 μM), respectively, in the anti-SARS-CoV-2 activity (in vitro). With the same interesting results, ChloViD2020 (EC50 = 1.01 μM) was found to be about 20, 52.5, and 93 times more potent than remdesivir, ivermectin, and favipiravir, respectively, in the same assay. According to this assay, CC50 of CoViTris2020 and ChloViD2020 is much larger than 100 μM, thus both compounds are expected to have very high safety margin and clinical selectivity index (SI; SI = CC50/EC50, SICoViTris2020 > 322.58 and SIChloViD2020 > 99.01), while on the other hand, the references remdesivir, ivermectin, and favipiravir are expected to have narrow safety margin and clinical therapeutic index (SIRemdesivir > 4.96, SIIvermectin > 1.89, and SIFavipiravir > 1.06). CoViTris2020 and ChloViD2020 are also having interestingly very small values of the concentration that causes 100% inhibition of the SARS-CoV-2 cytopathic effects in vitro (CoViTris2020 has the best CPEIC100 value, among all the tested compounds, of 0.99 μM, directly followed by ChloViD2020 which has CPEIC100 value of 1.97 μM) and of the concentration that is required for 50% reduction in the number of SARS-CoV-2 RNA copies in vitro (CoViTris2020 has the best EC50 value, among all the tested compounds, of 0.33 μM, directly followed by ChloViD2020 which has EC50 value of 1.23 μM). We should put into consideration the possibility that CoViTris2020 and ChloViD2020 may undergo prior intracellular metabolic transformation into more active forms (e.g., their triphosphate forms) by human cellular enzymes (e.g., kinases and transferases), which may differ among various cell types, thus evaluation of the pharmacological actions of these two target compounds in primary human airway epithelial cells will clearly facilitate the interpretation and clarification of the previous results. The metabolic activation would almost add extra anti-COVID-19 activities to the two drugs through incorporation of human cell-biocompatible and human cell-bioavailable moieties into the chemical structures of both of them, and this would consequently increase the net clinical effectiveness and efficacy of these two potent compounds. Importantly, the three reference drugs (remdesivir, ivermectin, and favipiravir) are currently undergoing extensive and broad clinical trials as anti-SARS-CoV-2/anti-COVID-19 agents worldwide. The very high values of CC50 of CoViTris2020 and ChloViD2020 indicate that both compounds would be predictably well tolerated in the human body. The significantly desirable high values of SI (i.e., the extremely minute values of anti-SARS-CoV-2 EC50 along with the very high values of mammalian cells CC50) of CoViTris2020 and ChloViD2020 reflect that both compounds evidently favor resistant RNA virus over DNA virus and mammalian cells, and this, in turn, expectedly indicates and expresses the selective specificity of these two compounds as anti-COVID-19 drugs (specifically, CoViTris2020 can be considered as a unique superpowerful SARS-CoV-2 antidote/killer). CoViTris2020 is apparently more potent and more promising than ChloViD2020 as SARS-CoV-2 inhibitor. Using a combination formula of CoViTris2020 and remdesivir is a possible good experimental choice, as it may have exceptional and striking combinational synergistic anti-COVID-19 action (comprising the desirable advantages of both potent SARS-CoV-2 inhibitors) in further assays (in vivo) and preclinical/clinical trials. Almost all the factual experimental results obtained and concluded, here, in the anti-COVID-19 antiviral biological evaluation are complying with the previous speculative theoretical results suggested and extracted from the anti-SARS-CoV-2 computational molecular pharmacological predictions for the two newly repurposed biologically reevaluated compounds, CoViTris2020 and ChloViD2020, and the three reference drugs. Conclusions Thinking outside the box is very important to win our current challenge against the scary COVID-19 pandemic. Potent multiblockade of, mainly, the novel SARS-CoV-2 proteins including enzymes and receptors could be seen as one of the most effective fertile approaches for comprehensive COVID-19 therapy through designing, discovering, and searching for multitarget SARS-CoV-2 inhibitors, thus much efforts in drug discovery in 2020 were focused on trying to successfully repurpose known drugs and compounds in order to effectively inhibit this extremely resistant coronavirus. My specific efforts led to the discovery of two very promising potent multitarget SARS-CoV-2 inhibitors through the successful reevaluation and repurposing of the known antioxidant 1,3,4-oxadiazole compounds, previously synthesized by me, CoViTris2020 (1,2,3-tris[5-(3,4,5-trihydroxyphenyl)-1,3,4-oxadiazol-2-yl]propan-2-ol) and ChloViD2020 (5-[5-(7-chloro-4-hydroxyquinolin-3-yl)-1,3,4-oxadiazol-2-yl]benzene-1,2,3-triol), which effectively inhibited SARS-CoV-2 life cycle with IC50 values of 0.31 (according to the used anti-COVID-19 assay, CoViTris2020 is the most potent SARS-CoV-2 inhibitor discovered till now) and 1.01 μM, and amazingly presented about 65-/171-/303.5-fold and 20-/52.5-/93-fold anti-SARS-CoV-2 activities and potencies more than remdesivir/ivermectin/favipiravir, respectively. On the other hand, the discovery of the very potent anti-COVID-19 activities of CoViTris2020 and ChloViD2020 molecules through the successful biological reevaluation and repurposing opens the door for us to establish the first class of anti-COVID-19 agents of the type “polyphenolic 1,3,4-oxadiazoles” which will specifically comprise a series of 2,5-disubstituted-1,3,4-oxadiazole derivatives (beginning with the first two effective members, CoViTris2020 and ChloViD2020). Prior extensive computational molecular studies showed that CoViTris2020 and ChloViD2020 have the ideal and balanced values of the pharmacokinetic and druglikeness parameters required to be effectively potent anti-COVID-19 drugs inside the human body. Computational molecular modeling analysis of the best inhibitory docking binding modes of CoViTris2020 and ChloViD2020 molecules with the SARS-CoV-2/human proteins showed that the 3,4,5-trihydroxyphenyl moieties greatly increase the blocking affinities and potencies at the active and/or allosteric sites of the SARS-CoV-2/human enzymes or proteins (binding energies reach − 12.00 kcal/mol for CoViTris2020 and − 9.90 kcal/mol for ChloViD2020) when compared to those of the three references (which lack this effective antioxidant 3,4,5-trihydroxyphenyl moiety), remdesivir, ivermectin, and favipiravir (binding energies maximally reach − 9.10, − 7.20, and − 6.90 kcal/mol, respectively). Surprisingly, CoViTris2020 surpassed ChloViD2020 and the three moderately to highly potent reference drugs in the values of almost all compared computational and experimental anti-COVID-19 parameters, scores, and activities. Specifically, CoViTris2020 and ChloViD2020 give their best inhibitory binding affinities results with the catalytic active site of the SARS-CoV-2 RdRp. If the CoViTris2020 compound successfully passes the in vivo bioassays and then the preclinical/clinical trials with highly effective and satisfactorily significant results as anti-COVID-19 agent, a possible combination therapy (e.g., as an oral tablet/capsule, an intravenous/intramuscular parenteral vial/ampoule, or a nasal/oral/ocular prophylactic gel/drops) with a second highly potent old anti-RNA virus drug, such as remdesivir, may be a recommended available choice for ultimate COVID-19 treatment in the near future. In brief, in this new research paper, the antioxidant CoViTris2020 and ChloViD2020 molecules were successfully reevaluated, repurposed, and reported as very promising hit molecules (they could also be considered as the first extremely potent anticoronaviral-2 polyphenolic 1,3,4-oxadiazole compounds “Coronavirus-2 Killers”) with general multitarget and very potent successful inhibition against SARS-CoV-2 enzymes (mainly), and consequently, both compounds are two of the first known promising under-investigation candidate drugs for the effective and complete treatment of COVID-19. Acknowledgements I gratefully thank and deeply acknowledge anyone who gave a hand to make this new discovery and work coming out to light. Compliance with ethical standards Conflict of interest I hereby declare that I totally have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this new research paper. ==== Refs References 1. 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==== Front J Supercomput J Supercomput The Journal of Supercomputing 0920-8542 1573-0484 Springer US New York 33424118 3586 10.1007/s11227-020-03586-3 Article RETRACTED ARTICLE: Accurate computation: COVID-19 rRT-PCR positive test dataset using stages classification through textual big data mining with machine learning Ramanathan Shalini [email protected] Ramasundaram Mohan [email protected] grid.419653.c 0000 0004 0635 4862 Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu India 4 1 2021 2021 77 7 70747088 16 12 2020 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature 2021, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. In every field of life, advanced technology has become a rapid outcome, particularly in the medical field. The recent epidemic of the coronavirus disease 2019 (COVID-19) has promptly become outbreaks to identify early action from suspected cases at the primary stage over the risk prediction. It is overbearing to progress a control system that will locate the coronavirus. At present, the confirmation of COVID-19 infection by the ideal standard test of reverse transcription–polymerase chain reaction (rRT-PCR) by the extension of RNA viral, although it presents identified from deficiencies of long reversal time to generate results in 2–4 h of corona with a necessity of certified laboratories. In this proposed system, a machine learning (ML) algorithm is used to classify the textual clinical report into four classes by using the textual data mining method. The algorithm of the ensemble ML classifier has performed feature extraction using the advanced techniques of term frequency–inverse document frequency (TF/IDF) which is an effective information retrieval technique from the corona dataset. Humans get infected by coronaviruses in three ways: first, mild respiratory disease which is globally pandemic, and human coronaviruses are caused by HCoV-NL63, HCoV-OC43, HCoV-HKU1, and HCoV-229E; second, the zoonotic Middle East respiratory syndrome coronavirus (MERS-CoV); and finally, higher case casualty rate defined as severe acute respiratory syndrome coronavirus (SARS-CoV). By using the machine learning techniques, the three-way COVID-19 stages are classified by the extraction of the feature using the data retrieval process. The TF/IDF is used to measure and evaluate statistically the text data mining of COVID-19 patient's record list for classification and prediction of the coronavirus. This study established the feasibility of techniques to analyze blood tests and machine learning as an alternative to rRT-PCR for detecting the category of COVID-19-positive patients. Keywords COVID-19 RT-PCR test Machine learning Text data mining TF-IDF Feature extraction Classification issue-copyright-statement© Springer Science+Business Media, LLC, part of Springer Nature 2021 ==== Body pmcIntroduction The epidemic disease caused by COVID-19 requires an extraordinary response of intensity. There are more than 150 states around the world affected by corona. To handle the spread of the COVID-19 infection, worldwide governments and millions of residents have taken extreme measures, such as quarantine. Symptomology of COVID-19 showed a large number of patients who were infected by corona, but some of the patients were also affected by corona asymptomatically. These efforts are differentiating between the corona test positive and negative with the limited problems individually. Thus, the stages of identifying the SARS-CoV-2 virus have been believed to be crucial to recognize positive cases and thus control the pandemic. Therefore, the current trial of choice is the RT-PCR based on respiratory specimens examination performed in the laboratory. The automatic, reliable classification algorithms are helpful for training COVID-19 cases by considering the number of patients. The high demand generally is known for nasopharyngeal swab tests named as rRT-PCR due to the extension of worldwide virus that highlights the type of diagnosis limitations on a large scale, such as the expensive equipment, trained personnel, reagents for demanding things that can easily overcome supply, and at the turnaround time, the need of laboratories’ certificate. For instance, the shortage of specialized laboratories and reagents forced the government to limit the testing of swab those who showed clearly the symptoms of SARS-CoV, thus leading to several virus-infected people and infection rates that were underestimated largely. The laboratory medicine useful for easy analysis of coronavirus by using a simple blood test might aid to recognize the positivity/negativity of COVID-19 through rRT-PCR tests. This work consideration motivated us strongly to apply an advanced method of machine learning to routine and to evaluate the stages of COVID-19 infection for the feasibility of a predictive model. The proposed research classifies the stages using various techniques; the positive case records are available in the UCI repository of an original raw dataset in the proposed text data mining process to classify the stages into three types. A useful and more accessible, accurate, less expensive, and faster COVID-19 classification was proposed in this research. Literature survey Due to the spread of COVID-19, several territories and countries have been experiencing an increasing number of infected cases and deaths which remain a real threat to the public health sectors (Jamshidi et al. [1]). The research extracts a response to the struggle of the virus through AI and some deep learning (DL) techniques which have been demonstrated to reach the goal, including extreme learning machine (ELM) and generative adversarial networks (GANs). A user-friendly platform describes a combination of a bioinformatics approach with different aspects from structured and unstructured data sources that are randomly put together for researchers and physicians. The recent COVID-19 publications and the medical reports were examined to choose both inputs and targets that might simplify to reach a consistent artificial neural network (ANN)-based tool for experiments associated with COVID-19. Research and diagnostics capable of deep learning on chest radiographs image classifier are based on COVID-Net, which were obtainable to classify chest X-ray images (Wang et al. [2]). This survey model aims to transfer knowledge for organizing and integrating images of chest X-ray according to three labels: regular, COVID-19, and viral pneumonia. Depending upon the accuracy of loss values, the models of ResNet-101 and ResNet-152 with the better effect of fusions improved dynamically by their ratio weights during their training process. This improved technology has produced higher sensitivity than radiologists in the diagnosis and screening of lung nodules. 96.1% accuracy was achieved by analyzing corona and classifying the type of chest image on the rest set. Diagnosis of COVID in a timely manner through tomography is essential for both patient care and disease control (Li et al. [3]). Computer tomography (CT) is analyzed as a useful tool for corona diagnosis, yet the disease outbreaks have placed tremendous pressures on reading radiologists and potentially lead to fatigue-related misdiagnosis. In this work, we propose a novel approach for effective and efficient COVID-19 classification networks training using a small number of COVID-19 CT examinations and an archive of negative samples. Experimental results showed that the research is achieved as superior performance consuming about half of the negative sample cases, extensively reducing a model of training time. Several laboratories have confirmed that corona cases have been identified in an alarming rate with reportedly confirmed more than 2.2 million cases as of April 20, 2020 (Chamola et al. [4]). Numerous false reports, unsolicited fears, and misinformation regarding this virus were regularly circulated since the outbreak of the corona. In this survey, the use of technologies such as artificial intelligence (AI), 5G, Internet of things (IoT), blockchain, and unmanned aerial vehicles (UAVs), among others, was explored to mitigate the impact of the COVID-19 outbreak. The platform of COVID-19 provides a quick diagnostic through serology testing, and molecular testing is also the important method to control the epidemic corona outbreak (Gharizadeh et al. [5]). COVID-19 life cycle manages various stages: the preparedness phase, preventive phase, recovery phase, and response phase. The viral distribution of spatial and temporal RNA, antibiotics, and antigens at the time of corona infection to humans has shortened an immoral biological treatment for accurate analysis of COVID-19 diseases. The training provides the advanced encouragement of COVID-19 pandemic improvements in our global public health sector to realize a better struggle against outbreaks in the future (Figs. 1, 2).Fig. 1 Proposed block diagram Fig. 2 COVID-19 rRT-PCR molecular test Proposed methodology Data collection WHO declared the COVID-19 epidemic a health emergency. The researchers and hospitals have been giving open access regarding the corona pandemic data. The record has been collected from the open-source data repository from UCI, in which several corona-positive patient data are stored, as shown in different stages presented in Fig. 3. The original raw dataset of COVID-19 information is collected through the repository from medical data. Each attribute was collected from sample data of swab testing rRT-PCR. The proposed method using the COVID-19 data record is analyzed using advanced tools of machine learning techniques. The doctors will diagnose the pandemic coronavirus disease by taking a specimen swab test for the person affected. The data consist of several attributes, namely patient id, sex, offset, age, survival, needed supplemental O2, temperature, intubation, leukocyte count, lymphocyte count, neutrophil count, view, folder, date, file name, modality, location, DOI, and URL [6–8].Fig. 3 Overall proposed methodology Since the dataset is a work of text, data mining can easily extract clinical notes and data findings. Clinical notes of COVID-19-positive cases’ sample text record consist of text as the attribute finding is a label of the corresponding query text. Our dataset has three classes: mild, moderate, and severe, which consist of clinical text of corona stages being categorized and the corresponding report length. Machine learning The novel coronavirus 2019, which has been termed as pandemic by the World Health Organization (WHO), has placed the world’s numerous governments in a risky position. The outbreak of COVID-19, whose impacts were previously witnessed by the China citizens alone, has become a concern of every country virtually throughout the world [9–15] (Table 1).Table 1 Proposed specimen type with temperature Type of specimen Materials collection Storage temperature Until testing takes place in country laboratory Recommended temperature for shipment according to expected shipment time Nasopharyngeal and oropharyngeal swab Dacron or polyester flocked swabs 2–8 °C 2–8 °C if ≤ 5 days –70 °C (dry ice) if > 5 days Data preprocessing The text data are unstructured, which need to be advanced such that machine learning techniques can be done. Various steps are being followed in this phase. The text is being scrubbed by removing the excessive text. The dataset consists of original raw data of the proposed system, with some noise present in it, so that the data preprocessing is used to filter the noisy and irrelevant data. TF-IDF techniques The machine learning techniques used term frequency–inverse document frequency (TF/IDF) for the text data mining process. The proposed system defines the use of text data retrieval from a huge amount of corona-positive data, which are distributed through a text and stored in a search engine using TF-IDF techniques which were used as retrieval schemes from search engine for classifying complete search text record. The results show that the accurate prediction of COVID-19 stages classification was expressively improved by exploiting features by text data retrieval. The next stage considers overturned lists according to those searching query words and finally sorts the target file from the record of searching index lists. Feature extraction Term frequency–inverse document frequency (TF-IDF) is common in which a weighted statistically and broadly used in text analysis and text data retrieval. TF-IDF obtains one word that has a high frequency in one record of the file; if this word appears often, then it can be conserved as the main keyword to differentiate this file from one another. Term frequency (TF) is a time word performing in this record; fundamentally, a searching name with high reality is correlated with this file [16–20]. TF is defined as:1 TFi,j=ei,j∑kek,j+1. Inverse document frequency (IDF) is defined as:2 IDFi=logCKi+1. In Eq. (1), ‘e’ is the epoch word, ek,j is the sum of all the searching words in the file, and 1 is added in the denominator to avoid it from becoming zero. In the IDF equation, ‘C’ as wi, mentions the size of the word and similarly 1 is added in the denominator to avoid it becoming equivalent to zero, and ‘ki'’ is the integer of word file collection. Combining TF with IDF is essentially using TF to modify, which specifies the weight of the word Wi infiled j.3 Wi,j=TFi×IDFi Figure 3 shows the overall proposed methodology of COVID-19 stages classification by using the improved machine learning techniques such as TF-IDF which gives a full data text retrieval method. Features extraction of the testing report of COVID-19 was analyzed by various methods of sample testing for confirming a corona disease. The index value was matched with the query values for analyzing in which stages the patients are affected mostly, which will be helpful for further decision-making schema. There are many methods for swab testing, and finally storing the data from the dataset of a repository with the original data is used to predict the classification stage of COVID-19. COVID-19 stages classification Classification of coronavirus stages has become practically a field in the proposed research due to the increased key procedures used for establishing the feasibility by indeed assigning a set of forms into predefined groupings based on their entire content, which contains a similarity matching model, word count model, word tagging model, machine learning methods, and so on. And mi can be defined as a vector with word having statistical weights of unstructured entire text data of corona-positive record. It is measured as shown in Fig. 4.4 mj=⟨W1,i/,W2,i/,W3,i/,......,Wn,i/⟩. Fig. 4 COVID-19 stages classification Using machine learning techniques, positive corona cases were identified using several types of corona stages and were classified under the three stages of mild, moderate, and severe. The proposed research has been applied to advanced algorithms to predict the locations having most patients affected by the COVID-19. These techniques can predict the patients until they reached the severe stage; this research classifies the COVID-19 stages accurately. Results and discussion In this section, the evaluation of the proposed method is enhanced with the feature extraction dataset of COVID-19. The proposed system is compared with the existing system in terms of sensitivity, specificity, accuracy, corona classification accuracy, time complexity, and prediction methods processed as shown in Table 2.Table 2 COVID-19 testing from rRT-PCR dataset for feature extraction Feature data type Data type Gender categorical Categorical Age numerical (discrete) Numerical (discrete) Leukocytes (WBC) numerical (continuous) Numerical (continuous) C-reactive protein (CRP) numerical (continuous) Numerical (continuous) Platelets numerical (continuous) Numerical (continuous) Transaminases (ALT) numerical (continuous) Numerical (continuous) Transaminases (AST) numerical (continuous) Numerical (continuous) Gamma-glutamyltransferase (GGT) numerical (continuous) Numerical (continuous) Lactate dehydrogenase (LDH) numerical (continuous) Numerical (continuous) Monocytes numerical (continuous) Numerical (continuous) Lymphocytes numerical (continuous) Numerical (continuous) Neutrophils numerical (continuous) Numerical (continuous) Basophils numerical (continuous) Numerical (continuous) Eosinophils numerical (continuous) Numerical (continuous) Swab categorical Categorical Sensitivity, specificity, and accuracy Here, the evaluation of the proposed enhanced machine learning and text data mining method has been compared with the existing techniques, and the presented TF-IDF techniques are used to classify the stages of COVID-19 by similarity matching and are compared with the current classification of SVM and AI classifier in terms of sensitivity, specificity, and accuracy of the COVID-19 stages of infected patients, and they have been calculated by the following equations: The statistical measures that can be considered are sensitivity, specificity, and accuracy5 Specificity=TNTN+FP∗100 6 Sensitivity=TPTP+FN∗100 7 Accuracy=TP+TNTP+FN+TN+FP∗100. A true positive and true negative accurate classification of corona stages is labeled by the proposed classifier techniques. The true positive indicates a proper classification of corona stages; if this label has an inappropriate classifier, then it indicates the false positive of the records, where TP specifies the true positive, FP denotes the false positive, TN indicates the true negative, FN represents the false negative. The proposed TF-IDF method is used to classify the stages of the coronavirus accurately, which has been shown in the experimental result of Table 3, and the chart shown in Fig. 5 demonstrated the comparison. The comparison tables for the existing ML algorithms with our developed techniques are illustrated in Table 3. From the comparison table, the proposed method has provided a 93% sensitivity level, 90% specificity level, an accuracy level of 98.4% compared with the existing techniques such as SVM and AI classifier (Fig. 5).Table 3 Performance analysis of the proposed and existing machine learning algorithms Parameters TF-IDF (%) SVM (%) Artificial intelligence (%) Sensitivity 93 73 81.5 Specificity 90 78.8 74.9 Accuracy 98.4 63.4 74 Fig. 5 Comparison of statistical parameters Similarly, the classification accuracy of the given test dataset is represented by the overall percentage of test data records that are correctly classified by the classifier techniques. The specificity and sensitivity are substitutes to the measure of accuracy that are used to evaluate the classifier's performance. Accurate classification of COVID-19 Stages The prediction accuracy of the proposed and existing methods can be analyzed through how the stages classify corona as mild, moderate, or severe through text classification from the dataset machine learning techniques (Figs. 6, 7). Fig. 6 The accuracy of the training model Fig. 7 The loss of the training model As shown in Fig. 7, with the progress in training, the accuracy rate has been high during the comparison of previous verifications. The loss value was unable to predict throughout the entire training process because only the change in the weight value of two models has occurred dynamically. After training, the model has achieved 92.74% classification accuracy of the COVID-19 stage on the test set. The efficiency of each method is evaluated using the accuracy level of the analyzing process. The accurate stages classification of the COVID-19 has been demonstrated by comparing the proposed and existing methods, as shown in Fig. 8. This shows that the proposed method has given high accuracy for COVID-19 stages classification when compared with the existing methods such as SVM, KNN, and Corona Kit. Thus, the existing algorithm compared with the proposed method has provided good performance with a minimum time of complexity.Fig. 8 Classification of COVID-19 stages Conclusion The COVID-19 first case was found in the Wuhan region, which is located in China. COVID-19 is a widespread disease and threatens the worldwide health system and economy. COVID-19 virus behaves correspondingly to other epidemic viruses. This makes it problematic to identify COVID-19 cases quickly. Therefore, COVID-19 is an applicant for a global epidemic, and it has confused the worldwide healthcare sectors due to the non-availability of drugs or vaccines. Various researchers are working to conquer this deadly virus. The test of nasopharyngeal and an oropharyngeal swab of rRT-PCR testing is taken, and all positive case data are maintained as a record of a dataset. The machine learning techniques are used to classify the patients, who are tested positive for corona, into three different classes of mild, moderate, and severe, from the clinical report of dataset. The TF-IDF technique is used to classify the stages by similarity matching of query searching from the features presented in the test cases report. The probability has been analyzed from the feature set to detect the stages of COVID-19-infected patients. The experimental results show the high accuracy for classifying the stages of COVID-19 with a minimum number of times and good results. This article has been retracted. 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==== Front Soft comput Soft comput Soft Computing 1432-7643 1433-7479 Springer Berlin Heidelberg Berlin/Heidelberg 33424432 5503 10.1007/s00500-020-05503-5 Focus RETRACTED ARTICLE: Modeling the progression of COVID-19 deaths using Kalman Filter and AutoML http://orcid.org/0000-0002-8422-9288 Han Tao [email protected] 1 Gois Francisco Nauber Bernardo [email protected] 2 Oliveira Ramsés [email protected] 2 Prates Luan Rocha [email protected] 2 Porto Magda Moura de Almeida [email protected] 2 1 grid.459466.c 0000 0004 1797 9243 DGUT-CNAM Institute, Dongguan University of Technology, Dongguan, 523106 China 2 Health Department of Ceará, Av. Almirante Barroso, 600, Praia de Iracema, Fortaleza, Ceará Brazil Communicated by Victor Hugo C. de Albuquerque. 5 1 2021 2023 27 6 32293244 © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The COVID-19 pandemic continues to have a destructive effect on the health and well-being of the global population. A vital step in the battle against it is the successful screening of infected patients, together with one of the effective screening methods being radiology examination using chest radiography. Recognition of epidemic growth patterns across temporal and social factors can improve our capability to create epidemic transmission designs, including the critical job of predicting the estimated intensity of the outbreak morbidity or mortality impact at the end. The study’s primary motivation is to be able to estimate with a certain level of accuracy the number of deaths due to COVID-19, managing to model the progression of the pandemic. Predicting the number of possible deaths from COVID-19 can provide governments and decision-makers with indicators for purchasing respirators and pandemic prevention policies. Thus, this work presents itself as an essential contribution to combating the pandemic. Kalman Filter is a widely used method for tracking and navigation and filtering and time series. Designing and tuning machine learning methods are a labor- and time-intensive task that requires extensive experience. The field of automated machine learning Auto Machine Learning relies on automating this task. Auto Machine Learning tools enable novice users to create useful machine learning units, while experts can use them to free up valuable time for other tasks. This paper presents an objective method of forecasting the COVID-19 outbreak using Kalman Filter and Auto Machine Learning. We use a COVID-19 dataset of Ceará, one of the 27 federative units in Brazil. Ceará has more than 235,222 confirmed cases of COVID-19 and 8850 deaths due to the disease. The TPOT automobile model showed the best result with a 0.99 of R2 score. Keywords AutoML COVID-19 Forecast Kalman Filter http://dx.doi.org/10.13039/501100012245 Science and Technology Planning Project of Guangdong Province No. 2018A050506086 Han Tao issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023 ==== Body pmcIntroduction The novel coronavirus disease 2019 (COVID-19) poses a significant and urgent threat to global health. Since the outbreak in early December 2019 in the Hubei Province of the People’s Republic of China, the number of patients confirmed to have the disease has exceeded 775 000 in more than 160 countries, and the number of people infected is probably much more significant. Despite public health risks targeted at containing the disease and delaying the spread, many countries have been faced with a critical care catastrophe. Outbreaks lead to significant increases in the demand for hospital beds and medical gear shortage, while medical personnel themselves could also get contaminated (Wynants et al. 2020; Ohata et al. 2020). Furthermore, epidemiological time-series prediction represents an essential role in public health, leaving the directors to improve strategic plans. Forecasting diseases as realistic as possible is essential due to their impact on the public health system. Machine learning models have been used to forecast the epidemiological time series over the years (Wynants et al. 2020). Recognition of epidemic growth patterns across temporal and social factors can improve our capability to create epidemic transmission designs, including the critical job of predicting the estimated intensity of the outbreak morbidity or mortality impact at the end. Several studies consider the epidemic growth in a large population a stochastic event; the infection increases exponentially among subjects, each of direct contact, closeness, or ambient traces (Fanelli and Piazza 2020). Explore the rise kinetics of an epidemic can help create well-grounded algorithms to predict and learn the essential features of infectious diseases’ growth dynamics. The strength of the outbreak is represented in mathematical functions, modeling the transmission, and this is commonly estimated using time-series analysis describing the plague spread as a function of time (Viboud et al. 2016). The study’s primary motivation is to estimate with a particular level of accuracy the number of deaths because of COVID-19, handling to model the development of the pandemic. Predicting the number of potential deaths from COVID-19 can provide authorities and decision-makers with signs for purchasing respirators and pandemic prevention policies. Therefore, this work presents itself as an essential contribution to fighting the pandemic. Kalman Filter (KF) is a widely used method for tracking and navigation and filtering and time series (Zeng and Ghanem 2020). The problem of Monitoring Outbreak spreading is pertinent to the control of morbidity. A compartment model can clarify the transmission dynamics of an outbreak. Precisely, the estimation of epidemic spreading on networks can be accomplished by a nonlinear Kalman filter, and it is an instrument for state estimation of nonlinear systems (Wang et al. 2020). Designing and tuning machine learning methods are a labor- and time-intensive task that requires extensive experience. The field of automated machine learning (AutoML) relies on automating this task. AutoML tools enable novice users to create useful machine learning units, while experts can use them to free up valuable time for other tasks. Many strategies have been developed to build and optimize model learning pipelines or optimize and build deep neural networks in recent years (Gijsbers et al. 2019). Ceará is one of the 27 federative units in Brazil. It is located in the north of the Northeast Region and borders the Atlantic Ocean to the north and northeast, the Rio Grande do Norte and Paraba to the east, Pernambuco to the south, and Piau to the west. Its total area is 148,920,472 km, or 9.37% of the Northeast area and 1.74% of Brazil’s surface. The state’s population is 9,075,649 inhabitants, as indicated by the Brazilian Institute of Geography and Statistics (IBGE), in 2018, which is the eighth-most populous state in the country. Today, Ceará has more than 235,222 confirmed cases of COVID-19 and 8850 deaths due to the disease. The cities with the highest incidence of confirmed cases per 100 thousand inhabitants are Acarape (11,434.1), Frecheirinha (10,560), Groaras (6532.3), Chaval (6106.1), and Quixel (6051.4). This paper presents an objective method of forecasting the continuation of this COVID-19, working with a straightforward but highly effective process to achieve that. Assuming that the information used is dependable and the future will continue to stick to this disease’s latest pattern, our predictions suggest a continuing growth in the supported COVID-19 instances. This paper clarifies the deadline of a live calling exercise with enormous potential consequences for planning and decision making and offers objective forecasts for its confirmed instances of COVID-19. This study’s main novelty uses an AutoML solution to forecast the epidemic growth of the state of Ceará in Brazil. In a nutshell, the primary contributions of this paper are:use a Kalman Filter solution to forecast the epidemic growth on Ceará State ; use an AutoML solution to forecast the epidemic growth on Ceará State ; apply a comparative study of different methods of the forecast using AutoML. The use of Kalman Filter was applied to merge the death curve of other countries with data of the state of Ceará in Brazil in order to obtain a long-term prediction. We use Auto Machine Learning tools to discover the best models for predicting the number of cases. We could only use these tools after the pandemic, where sufficient training data for the models can be obtained. The third contribution is applying the two techniques presented in the state of Ceará, validating the accuracy and precision of the techniques. Literature review A model is described of several numerical equations that are set to describe the interaction between various variables within specific methods. A model is not a perfect portrayal of reality. Commonly, we have no perfect understanding of the boundary conditions of the model and its uncertainty. We need to recognize the time progression of the probability density function (pdf) for the model state. With knowledge of the pdf for the model state, we can obtain knowledge about the model uncertainty. For time-based solutions, sequential data assimilation methods utilize the previous data analysis scheme to update the model state consecutively. The before-mentioned approaches have demonstrated helpful for several purposes, where new observations are sequentially absorbed into the model when they become ready. Yang et al. use the ensemble Kalman Filter as a short period predictor and test non-pharmaceutical interventions’ success on the epidemic spreading. The study builds an individual-level-based network representation and performs stochastic reproductions to study the pestilences in Hubei Province at its initial stage and examine the plague dynamics under several situations (Yang et al. 2020). Sameni uses an extended Kalman Filter for joint parameters and variables for the estimates (Fanelli and Piazza 2020). The task of tuning hyperparameters for different machine learning models is also highly likely to be time-consuming. In a more extended Computer Science-specific period, tuning of hyperparameters is an investigation procedure which, in this case, can be hugely exhaustive. Deep learning (DL) methods have penetrated all facets of our lives and brought us a fantastic advantage. However, building a high-quality DL platform for a particular task depends upon human experience, hindering DL software to more regions (He et al. 2021). To decrease these onerous development expenses, a novel notion of automating the whole pipeline of machine learning (ML) has surfaced, i.e., automatic machine learning (AutoML). There are a variety of definitions of AutoML. According to (Zöller and Huber 1993), AutoML was made to decrease information scientists’ need and enable domain experts to automatically assemble ML applications without much demand for statistical and ML knowledge. In [9], AutoML is described as a blend of automation and ML. Automated machine learning is a natural solution to the shortage of information scientists. It can drastically increase information scientists’ performance and efficacy by speeding up work cycles, improving model accuracy, and even potentially replacing the need for data scientists. Automated machine learning (AutoML) becomes a promising strategy to construct a DL system with no expert support and an increasing number of researchers (He et al. 2021). AutoML aims to enhance a new way to develop ML applications by automation. ML experts can benefit from AutoML by automating tiresome tasks like hyperparameter optimization (HPO), leading to higher efficiency (Zöller and Huber 1993). From the early 2000s, the earliest efficient approaches for HPO are suggested, for restricted applications, e.g., tuning C and γ of a support vector system (SVM) (Momma and Bennett 2002). Additionally, in 2004, the first automatic feature selection methods are released (Samanta 2004). A full model selection has been the initial effort to automatically construct a whole ML pipeline by simultaneously choosing a preprocessing, feature selection, and classification algorithm while controlling every method’s hyperparameters (Escalante et al. 2009). From 2011, several different ways of applying Bayesian optimization for hyperparameter tuning (Komer et al. 2014; Snoek et al. 2012) and model selection (Thornton et al. 2013). In 2015, Kanter and Veeramachaneni presented the automatic feature engineering without domain knowledge (Kanter and Veeramachaneni 2015). Ardabili et al. use a multi-layered perceptron (MLP) and adaptive network-based fuzzy inference system (ANFIS) to forecast COVID-19 cases (Ardabili et al. 2020). Pinter et al. use hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) to predict time series of COVID-19 infected individuals and mortality rate (Pinter et al. 2020). Erraissi et al. present a Spark ML approach to predict COVID-19 cases (Erraissi et al. 2020). Nanda et al. use the ARIMA model and SIR Model to generate the short term forecasts of the COVID-19 spread in SAARC countries, i.e., India, Afghanistan, Sri-Lanka, Maldives, Bhutan, Pakistan, Nepal, and Bangladesh, using the daily reported number of cases from 22 January 2020 up to 01 April 2020 (Nanda 2020). Escobar et al. develop a method that estimates the probability that a sample will test positive for SARS-Cov-2 based on the sample’s complementary information using H2O.Ai AutoML. The study trained a machine learning model on samples from more than 8,000 patients tested for SARS-Cov-2 from April to July in Bogot, Colombia (Escobar et al. 2020). Ribeiro et al. use the autoregressive integrated moving average (ARIMA), cubist regression (CUBIST), random forest (RF), ridge regression (RIDGE), support vector regression (SVR), and stacking ensemble learning are evaluated in the task of time-series forecasting with one, three, and six days ahead the COVID-19 cumulative confirmed cases in ten Brazilian states with a high daily incidence (Ribeiro et al. 2020). Material and methods AutoML Machine learning (ML) is at the forefront of the rising popularity of data-driven software applications. The consequent rapid proliferation of ML technology, explosive data growth, and lack of data science expertise have caused the industry to face increasingly challenging demands to stay informed about fast-paced develop-and-deploy design lifecycles. Recent academic and industrial research efforts have started to deal with this issue through automated machine learning (AutoML) pipelines and have concentrated on design performance because of the first-order design aim (Yakovlev et al. 2020; Santos et al. 2018; Chouhan et al. 2020; Ding et al. 2020; Rodrigues et al. 2018; De Souza et al. 2019; Dourado et al. 2020; Muhammad et al. 2020; Selvachandran et al. 2019; Sodhro et al. 2016, 2017, 2019a, b, 2020). The AutoML pipeline comprises several processes: data preparation, feature engineering, model generation, and model analysis. Model generation can be further divided into optimization and search methods. The search space defines the design principles of ML versions, which may be divided into two classes: the conventional ML models (e.g., SVM and KNN), and neural architectures (He et al. 2021). Researchers have handled this optimization problem using several different methods. The first approach is primarily based on Bayesian Optimization (Komer et al. 2014; Kotthoff et al. 2017), which employs a probabilistic model to catch distinct hyperparameter configurations and their performance. Auto-sklearn, one of the most notable works relying on this approach, embraced a random-forest-based sequential model-based optimization technique for overall algorithm configuration. It utilizes meta-learning to recognize a previously optimized dataset closest to the given dataset and utilizes the famous dataset’s configuration to bootstrap the iterative optimization procedure. AutoML approaches differ in their optimization process (e.g., Bayesian Optimization or Genetic Programming) and the pipelines they Create (e.g., with or without fixed arrangement). There are lots of Python libraries offered for performing automatic machine learning. All of these try to attain more or less the same goal, that of accomplishing the machine learning procedure. The following are a few of the most widely-used Python libraries for automatic machine learning:Auto-Sklearn TPOT Auto-Keras H2O.ai Google’s AutoML. Google’s AutoML and Auto-Keras use an algorithm called Neural Architecture Search (NAS). TPOT is a Python automatic system learning that optimizes machine learning pipelines using genetic programming. Neural architecture search problem Deep learning has empowered outstanding progress over the past years on an assortment of tasks, including image recognition, speech recognition, and machine translation. Architectures have been mainly developed manually by human experts, which can be a time consuming and error-prone procedure. As a result of this, there is growing interest in automatic neural search procedures (Wistuba et al. 2019). Neural Architecture Search (NAS) is the process of automating architecture engineering, is consequently a logical next step in automating machine learning. Neural Architecture Search algorithm tries automatically to search the most optimal architecture and corresponding parameters for a problem. Already, NAS methods have outperformed manually designed architectures on some tasks like image classification, object detection, or semantic segmentation (Bender et al. 2018). NAS is a subfield of AutoML and has significant overlap with hyperparameter optimization and meta-learning. We categorize NAS’s approaches based on three dimensions: research space, research technique, and performance estimation strategy (Wistuba et al. 2019). Given a neural architecture search space S, the input data D divided into Dtrain and Dval, and the cost function C, the algorithm aim at finding an optimal neural network f∈F, which could achieve the lowest cost on the dataset.1 f∗=argminCostfθ∗,Dval,f∈F 2 θ∗=argminLfθ,Dtrain,θ Cost is the metric evaluation function, e.g., accuracy, mean squared error, and θ∗ is the learned parameter off. The search space F covers all the neural architectures, which can be morphed from the initial architectures. Bayesian optimization Bayesian optimization gives a principled technique based on the Bayes Theorem to guide a search of a global optimization problem that’s efficient and effective. It operates by making a probabilistic model of the objective function, named the surrogate function, which is then searched efficiently with an acquisition function before solution samples are chosen to evaluate the real objective function. Bayesian optimization is an iterative way of solving these black-box optimization issues. Conventional Bayesian Optimization consists of a loop of three steps: update, generation, and observation.update: train the underlying Gaussian process model with the present architectures and their performance; Generation: generate the following design to observe by optimizing a delicately defined recovery function; observation: receive the actual performance by training the generated neural architecture. H2O.ai H2O is quick, scalable, open-source machine learning and deep learning for smarter software. The API allows making advanced calculations like deep learning, fostering, and bagging ensembles using AutoML (Candel et al. 2016). The tool provides H2O AutoML, a learning algorithm that piled ensembles within a purpose and overlooks finding candidate models. The consequence of the AutoML series is a rated list of best models for a dataset. Models in the leader board could be rated by design performance metrics or version features like typical forecast rate or coaching time. H2O AutoML uses the combination of random grid search with stacked ensembles, as diversified models improve the ensemble method’s accuracy Ledell2020. H2O AutoML maintains a variety of calculations (e.g., GBMs, Random Forests, Deep Neural Networks, GLMs), yielding a healthy amount of diversity across candidate versions, which can be exploited by stacked ensembles to generate a powerful final version. The technique’s effectiveness is reflected from the OpenML AutoML Benchmark, which compares the performance of a number of the most well-known, open-source AutoML systems across several datasets (Ledell 2020). TPOT The Tree-Based Pipeline Optimization Tool (TPOT) was among the earliest AutoML procedures and open-source computer software packages created for the information science community. TPOT was developed by Dr. Randal Olson as a postdoctoral student with Dr. Jason H. Moore in the Computational Genetics Laboratory at the University of Pennsylvania and is extended and encouraged by this team. The objective of TPOT would be to automate the construction of ML pipelines by mixing a flexible expression representation of pipelines with stochastic search algorithms like genetic programming (Olson and Moore 2019). To automatically create and maximize these tree-based pipelines, TPOT utilizes a Genetic Programming (GP) algorithm. The TPOT GP algorithm follows a typical GP procedure: the GP algorithm generates 100 random tree-based pipelines and assesses their balanced cross-validation accuracy about the information collection. For every creation of the GP algorithm, the algorithm chooses the best 20 pipelines from the population in line with this NSGA-II selection strategy, in which pipelines are chosen to simultaneously optimize classification accuracy on the information collected while decreasing the number of operators in the pipeline. Every one of the top 20 chosen pipelines creates five duplicates (i.e., offspring) in the second generation’s population, 5 percent of these offspring cross with a different offspring utilizing one-point crossover, and then 90 percent of those remaining new offspring are randomly altered using a stage, fit, or mutation (1/3 possibility of each). Every creation, the algorithm updates a Pareto front of their non-dominated options found at any location in the GP run. Auto-WEKA Thornton built a tool, Auto-WEKA, to solve the problem for classification algorithms and feature selectors/evaluators implemented in the WEKA package. WEKA is a broadly used, open-source machine learning platform. As a result of the intuitive interface, it is very popular with novice users. Such users frequently find it tough to recognize the best approach to their specific dataset, one of the many available. Auto-WEKA considers the difficulty of concurrently choosing a learning algorithm and setting its hyperparameters, going away to previous methods that address these issues in isolation. Auto-WEKA does this using a fully automated approach using Bayesian optimization (Thornton et al. 2013). Precisely, it reflects the merged space of WEKAs learning algorithms A=A(1),....,A(k) and their hyperparameter scopes υ(1),...,υ(n) and intends to recognize the combination of algorithm A(j)∈A and hyperparameter υ(j)∈υ that minimizes the cost function.Aλ∗∗∈argminA(j)∈A,υ∈υ(j)1k∑i=1kλAλ(j),Dtrain(i),Dtest(i), where λAλ(j),Dtrain(i),Dtest(i) represents the loss function when trained on Dtrain(i) and tested on Dtest(i). Auto-Keras Auto-Keras is an open-source software library for automated machine learning. Auto-Keras provides functions to search for architecture and hyperparameters of deep learning models automatically (Jin et al. 2019). The key idea of AutoKeras is to investigate the search space via morphing the neural architectures guided by the Bayesian optimization (BO) algorithm. The intuition behind the Auto-Keras function’s kernel function is the edit distance to morph one neural structure to another. Suppose fa and fb are just two neural networks. Inspired by Deep Graph Kernels. Auto-Keras suggest an edit-distance kernel for neural networks. Edit-distance here means how many operations are needed to morph one neural network to another. The concrete kernel function is defined as:3 k(fa,fb)=e-p2d(fa,fb), where function d denotes the distance of two neural networks, a typical workflow for the Auto-Keras process is as follows: The User-initiated a study for the best neural design for the dataset and the Bayesian Optimizer in the Searcher would create a new architecture using CPU. It calls the Graph module to build the neural structure into a real neural network at the RAM. The new neural architecture is copied from the GPU to Model Trainer to train with the dataset (Jin et al. 2019). Kalman Filter The Kalman Filter is a method that utilizes a set of measures observed over a period, including noise and gives estimations according to the used set, by considering a joint probability distribution across the variables for each time frame. The Kalman Filter (KF), also named as linear quadratic estimation, is an optimal estimator which suggests parameters of interest from indirect, inexact, and dubious observations. The KF aims to find the ‘most reliable estimate’ from noisy input. It is recursive; KF treats the new measures as they appear. The filter presents a recursive resolution to the linear optimal filtering problem to stationary and nonstationary situations. It is also recursive and measures the new state from the previous estimates and the new data. Unique the previous estimate needs storage, reducing the need for saving the whole past noted data (Haykin 2004). Filtering methods allow the recursive evaluation of model parameters. These techniques have found application in various disciplines and, across the last two decades, have been used to contagious infection epidemiology (Yang et al. 2014). The KF dynamics rise from the regular periods of forecast and filtering. The change aspects of these periods are determined and translated in Gaussian probability density functions. Following new constraints on the system changes, the Kalman Filter dynamics converge to a steady-state filter, and the steady-state gain is inferred. The learning method connected with the filter, which describes the new data conveyed to the state measure by the latter system measure, is presented. The Kalman Filter gives a linear minimum error variance estimate of the state characterized by a state-space model. The KF has the support of leading with noise in the couple, model, and the data. The main goal of the KF is to diminish the mean squared error within the real and measured data. Consequently, it gives the accurate as a possible measure of the mean squared error function data. Thought from this fact, it should be plausible to determine that the KF has much in common with the chi-square. The chi-square merit function is typically applied to fit a collection of model variables to a method named least squares fitting. The KF is usually named as recursive least squares (RLS) (Cazelles and Chau 1997). State-space derivation The differential equations of the KF can be incorporated into a state-space component. Let Yt,Yt-1,...,Y1 denote the observed values of a feature in time t,t-1,...,1. We assume that Y depends on an unobservable quantity θ, known as system state variables. The goal of Kalman Filter is make inferences of θ. The relation between Yt and θ is given by a equation (Cazelles and Chau 1997; Meinhold and Singpurwalla1983):4 Yt=Ftθt+vt where Ft is a known quantity. Ft is the noiseless connection between the t state vector and the measurement vector, and is assumed stationary over time. The observation error vt is the associated with measurement error (Uhlmann and Julier 1997; Meinhold and Singpurwalla1983; Mandel et al. 2010). The main difference between KF and conventional linear models is that KF regression coefficients are not constant ant change over time as the system equation:5 θt=Gtθt-1+wt where θ is the state vector at time t; Gt is the state transition matrix of the progress from the position at t-1 to the state at t , and is presumed stationary over time; wt is the associated white noise with recognize covariance; vt and the system equation error w t is presumed to be mutually independent random variables, spectrally white, and with normal probability distributions. wt and vt are sequences of white, Gaussian noise with zero mean:6 E[wt]=E[vt]=0, The Kalman Filter is the filter that gets the least mean-square state error estimation. When Y0 is a Gaussian vector, the state and perceptions noises wt and vt are white and Gaussian, and the state and observation dynamics are linear. For the minimization of the MSE to support the optimal filter, it must be plausible to evaluate model errors using Gaussian distributions. The covariances of the noise models are considered stationary in period and are given by;7 Q=E[wtwtT] 8 R=E[vtvtT] The mean squared error is given by:9 Pk=E[etetT]=E[(Yt-Y^t)(Yt-Y^t)T] where P is the error covariance matrix at time t. Consider the previous estimation of Y^ is named Y^′ and was obtained by observation of the system. It is welcome to estimate using an update equation, mixing the old estimation with new measurement data. Epidemiologic predictors When it comes to contagious diseases, it is frequent to use compartmental models, such as the SIR and SEIR models. Differential equations models SIR and SEIR, seeking the variations in the model parameters to project the spreading behavior of a given disease, are applied to the new coronavirus, where many works use these models (Zhou et al. 2020; Fanelli and Piazza 2020). SIR model Martinie developed, in 1921, the SusceptibleInfectiousRemoved (SIR) model for plagues, which are spread in a human community by a vector; i.e., susceptive individuals acquire the infection from contagious vectors, and susceptive vectors acquire the disease from contagious people (Beretta and Takeuchi 1995; Zhu 2020; Schenzle 1984). The SIR model, in principle, explains the process of a virus spread. On the other hand, this factor is not ever consonant with the contagious path. Some viruses do not confer any long-lasting immunization (Zhu 2020). The SIR model is among the most fundamental compartmental representations, and several models are extended of this basic one, including the SEIR case. The SEIR model defines three partitions: S for the amount of susceptible, I for the number of infectious, and R for the number of recuperated or death (or immune) people (Stone et al. 2000). The equations that describe the SIR model are described in Eqs. 10, 11, and 12. All related to a unit of time, usually in days. Then, at each instant of time t, the values of each compartment can be changed (Beretta and Takeuchi 1995; Stone et al. 2000).10 dSdt=-βISN, 11 dIdt=βISN-γI, 12 dRdt=γI. The modeling is simple, since S(t) + I(t) + R(t)=N results in N, which represents the total population. Then, in each t, individuals moved from S to I. The model removes the individuals infected with the disease from the compartment. Equation 10 describes the model, where β is the average number of people comes into contact with another person multiplied by the likelihood of infection in that contact. Equation 10 does different use of the faction mentioned above removing the number of infected people; in the I compartment, the new ones infected by the rate are added, with the removal of those who were recovered or died, introducing the term μ, which represents the recovery and mortality rate. Equation 16 explains the variation in the recovered patients and the number of deaths compartment, which is described by μ on those infected. This model requires as input the amount of the susceptible, infected, and cured or dead population, all referring to time 0. And the necessary rates, it is transmission probability, recovery rate, and mortality. SEIR model Because the SIS and SIR model exclusively supports the cases without an incubation period, which is not the case for several classes of contagious infections, Cooke proposed a spread model for the case that after a specific period, the susceptibles person can get infectious. This model is named as the SEIR model (Cooke 1979). The SEIR model differs from the SIR in one compartment, the E representing Exposure, which refers to diseases that are not manifested at the exact moment of infection, having an incubation period. Like COVID-19, which has an ordinary incubation period of 14 days. The model is defined with four differential equations, described in Eqs. 13, 14, 15, and 16. Some small changes are made, starting with the addition of the new Eq. 14, which represents the calculation of individuals exposed to the virus. The model added a new rate, the incubation rate, σ, which is the rate of latent individuals becoming infectious (typical period of incubation is 1/σ) (Cooke 1979).13 dSdt=-βISN, 14 dEdt=βISN-σE, 15 dIdt=σE-γI, 16 dRdt=γI. Analogous to the SIR representation, the sum of the compartments, which are now S(t) + E(t) + I(t) + R(t)=N, results in the total population. Nonlinear additive and multiplicative methods Prophet Prophet is an approach for prediction of time-series data based on an additive model. Prophet uses seasonality and day-off effects to calculate nonlinear tendencies. It operates appropriately with historical series that have regular periodical patterns and diverse seasons of past data. Prophet is resilient to missing data and variations in the bias and generally works well with outliers (Taylor and Letham 2018). This method is a helpful method for time series with many distortions, lack of data, and drastic changes. What led us to use it since the lack of data on COVID-19 is excellent because it is a new disease.17 y(t)=g(t)+s(t)+h(t)+ϵt The Prophet equation 17 shows the following features, decomposing the time series into three elements: trend g(t), seasonality s(t), and holidays h(t).g(t): piecewise linear or logistic increase curve for modelling non-seasonal changes in time series. s(t): seasonal changes . h(t): effects of day-off. ϵt: error term accounts for any not common changes not accommodated by the model Holt winters Exponential smoothing is an ordinary procedure used to predict a time series left out the requirement of applying a parametric model (Gelper et al. 2010). The Holt-Winters also named to as double exponential smoothing, is an addition of exponential smoothing created for trended and periodic time series. The Holt-Winters model (Winters 1960) is an expansion of the Holt method (Holt 2004), developed by Winters and divided into two groups, multiplicative and additive Holt-Winters. The multiplier model was selected for the analysis in this Chapter because it trends forecast values by seasonality, being the best for data with trends and increasing seasonality as a function of time. The exponential and Holt-Winters procedures are susceptible to regular events or anomalies. Outliers influence prediction methods in two forms. First, the smoothed values are affected. Smoothed values depend on the present and historical values of the series, plus the outliers. The other influence concerns the choice of the parameters used in the recursive updating design (Gelper et al. 2010). The use of the multiplicative method is explained by the characteristics of the data, using the numbers of infections and deaths of COVID-19; the curve presents an exponential shape. The trend and seasonality data have an increase according to the number of days; thereby, the multiplicative model is ideal. In the Holt-Winters multiplicative method, the periodic partition is formulated in relative terms and used to fit the time series periodically. Equations 18, 19, and 20 describe the multiplicative method.18 St=αytIt-L+1-αSt-1+bt-1. 19 bt=γSt-St-1+1-γbt-1 20 It=βytSt-1+bt-1+1-βIt-L where St is the overall smoothing, bt is the inclination smoothing, and It is the periodically smoothing. yt refers to the real data at a period of t. L is the time. The α, γ, and β are constants between 0 and 1. The model minimizes the Mean Square Error (MSE) equation using α, γ, and β. COVID-19 epidemic on Ceará On 9 September, Ceará reached 223,863 confirmed instances of COVID-19 and 8,634 deaths due to disease. One hundred ninety-eight thousand seven hundred eighty-eight individuals recovered from the disease. The data are from the IntegraSUS platform. There are also 88,177 suspected cases and 611 deaths under evaluation. The state has carried out 671,720 tests to spot the new coronavirus. The number of reported cases reached 679,359. Fortaleza is the leader in absolute amounts, with 47,638 confirmed instances and 3811 deaths from the illness. The funding registers 1784.6 cases per 100 thousand inhabitants. In Fortaleza’s macro-region, Maracana concentrates 6518 cases, 240 deaths, and incidence in 2861.1. Caucaia, the second city in deaths from the new coronavirus (340), has 5627 positive diagnoses and an incidence of 1557.8. In Maranguape, 4661 individuals have been infected, 115 have not resisted the disease, and the prevalence is 3613.8. Figure 1 presents the plague evolution in Ceará between March and August of 2020.Fig. 1 COVID-19 cases curve in the state of Ceará Figure 2 presents the plague evolution in Fortaleza between March and august of 2020. Fortaleza is the capital of the state of Ceará. Fortaleza has an area of 313,140 km and 2,643,247 inhabitants estimated in 2018, in addition to the highest demographic density among the country’s capitals, with 8390.76 inhabitants/km. Fortaleza continues as the epicenter of the pandemic in Ceará, with 3846 deaths and 48,855 people infected with the coronavirus.Fig. 2 COVID-19 cases curve in Fortaleza, capital of Ceará Proposed method The proposed method consists of two approaches. The first is to use the Kalman Filter method to predict the speed and behavior of the pandemic. The second approach uses the H20 framework to predict with machine learning models of the number of cases and deaths in Ceará. Because the Kalman Filter needs a data entry to adjust the pandemic’s uncertainty and speed for forecast, a hybrid dataset was assembled with data from Ceará, Brazil, and China at the beginning of the pandemic. The proposal is that this hybrid dataset could provide long-term behavior for the Kalman filter, a model typically used for short-term forecasts (Fig. 3).Fig. 3 Proposed use of Kalman Filter with hybrid database Two AutoML models were chosen for the experiments: H2o and TPOT, due to insufficient data to use the neural networks available in autokeras. The proposed analysis considers public data available of new confirmed cases and deaths reported daily for the state of Ceará, in the northeast region of Brazil, from 15 March until 17 May. The data were obtained from an open API available on https://github.com/integrasus/api-covid-ce, validated according to the Ceará Integrasus Platform (available at https://indicadores.integrasus.saude.ce.gov.br). The database has the following attributes:Categorical result of COVID-19 examination City of patience provided by Brazilian Geographic Institute Asthma indicator Indicator of cardiovascular problems Date of death Date of examination result Date of begin of the symptoms Date of examination notification examination final result. Performance metrics The accuracy of the suggested approach is evaluated by applying a set of performance metrics as follows: Root mean square error (RMSE) 21 rmse=1n∑i=1n(yi′-yi)2 where y′ and y are the foretold and real values, respectively. Mean absolute error (MSE) 22 mae=1n∑i=1nyi′-yi where y′ and y are the foretold and real values, sequentially. Coefficient of determination (R2) 23 R2=1-∑i=1nyi′-yi2∑i=1nyi′-yi2 where y′ and y are the predicted and original values, respectively. y is the average of original values. The lowest value of RMSE and MAE indicates the most suitable approach. The greater rate of R2 shows a better correlation for the method. Results and discussion The results are the most critical factors for analyzing the pandemic since it shows the possible epidemic evolution according to the proposed models. The comparisons are based on standard metrics for regression models analysis, such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R squared (R2). Table 1 presents the error results by RMSE, MAE and R2. The TPOT automobile model showed the best result. However, AutoML models could only be used after a considerable amount of data, which is not available at the beginning of the pandemic. Thus, the Kalman Filter’s use was essential to project the pandemic propagation and decay time in Ceará with a reasonable margin of error.Table 1 Method errors to short term experiments Method MAE RMSE R2 KF + SEIR + CE 216.65 245.89 0.983 Kalman Filter 342.83 388.52 0.959 KF + SEIR 517.85 758.68 0.844 H2O 5.35 71.53 0.96 TPOT 1.35 11.38 0.99 Kalman Filter results For the Kalman Filter, we use three approaches, the first shown in Fig. 4, which uses only the Kalman filter; as it is an adaptive method, it is necessary other data, and the forecast is based on data from Brazil. Adapting the filter to the data proved useful, making it a suitable method for short-term forecasts. The second approach using the Kalman Filter is to use the SEIR method; in this case, the data generated from the SEIR model were used in the filter. The third and last is the use of the hybrid data set, which consists of joining the data from Ceará and data generated from the SEIR model, before applying the data in the Kalman filter.Fig. 4 Kalman Filter result short term Ceará Figure 5 presents the adaptative property of the Kalman Filter. The graph shows the Kalman filter’s prediction with data for 5, 10, or 15 days from the prediction day and the current curve. It is noticed that the closer to the prediction day, the filter approaches the real curve, reducing uncertainty and noise.Fig. 5 Kalman Filter predictions with data for 5, 10, or 15 days Figure 6 shows the prediction for the COVID-19 death rate curve in the state of Ceará one month before the curve plateau was reached. Despite the error in the number of deaths being high of the value, the model could predict the period of stabilization and decline in the number of cases.Fig. 6 Prediction for the COVID-19 death rate curve in the state of Ceará one month before the curve plateau Among the regular models for the COVID-19 global pandemic forecast, simple epidemiological and statistical models have gained more attention from authorities, and they are prevalent in the media. Due to a high level of uncertainty and lack of data, standard models have shown low accuracy for long-term prediction.Fig. 7 Prediction for the COVID-19 death rate curve in the state of Ceará with H2O.ai According to the presented discussion, the use of Quadratic Kalman Filter as a predictor for the COVID-19 epidemiological data can be considered, with certain limitations being considered. The proposed Kalman Filter prediction approach is providing encouraging results for short-term predictions. Kalman filter-based proposed model is showing a large mean average error in the long-term. Hence, it can be concluded that the proposed prediction model is suitable for short-term prediction i.e., daily and weekly. The proposed prediction model can be updated to accommodate medium-term time-series predictions to discover the curve’s plateau, but with large error in the absolute number of cases. H2O results Table 2 presents the results for the H2O AutoML applied to Ceará COVID-19 deaths data set. The model id shows the best models chosen. The generalized linear model (GLM) was the one that obtained the best result. The first column present the name of model used (Fig. 7).Table 2 H2O AutoML Results for Ceará COVID-19 with H2O AutoML Name of model Mean_ residual_ deviance rmse mse mae rmsle GLM_1_AutoML_ 20200923 _163324 71.90 8.47 71.9087 5.61 0.52 StackedEnsemble_ BestOfFamily _AutoML_20200923 _163324 75.21 8.67 75.21 5.65 0.47 StackedEnsemble_ AllModels_ AutoML_20200923 _163324 75.701 8.70 75.70 5.67 0.47 GBM_3_AutoML_ 20200923_163324 91.67 9.57 91.67 5.85 0.31 DRF_1_AutoML_ 20200923_163324 92.31 9.60 92.3166 5.88 0.30 GBM_1_AutoML_ 20200923_163324 94.72 9.73 94.72 5.99 0.30 GBM_2_AutoML_ 20200923_163324 101.57 10.07 101.57 6.17 0.32 Figure 8 shows the prediction curve for COVID-19 deaths in Ceará with the best model obtained by the H2O.ai framework.Fig. 8 Prediction for the COVID-19 death rate curve in the state of Ceará with TPOT AutoML TPOT results Table 3 presents the results for the TPOT AutoML applied to Ceará COVID-19 deaths data set. The model was run for five generations, and the KNeighborsRegressor was chosen as the best model configured with 60 neighbors.Table 3 TPOT AutoML Results for Ceará COVID-19 deaths data set Generation 1—Current best internal CV score: − 4.615450248020459 Generation 2—Current best internal CV score: − 4.615450248020459 Generation 3—Current best internal CV score: − 4.615450248020459 Generation 4—Current best internal CV score: − 4.452279209324271 Generation 5—Current best internal CV score: − 3.961737356996695 Best pipeline: KNeighborsRegressor(MaxAbsScaler (PolynomialFeatures(input_matrix, degree = 2, include_bias = False, interaction_only = False)), n_neighbors = 60, p = 2, weights = distance) Comparison with state of art methods Table 4 compares the best two approaches presented in this study with state of art regression models. TPOT and Kalman Filter obtain the best R2 score. The Prophet is a nonlinear model that modifies the seasonality, trend, and holidays of the time series. Holt-Winters is a method applied to time series. We use the multiplicative method due to the curve’s growth in the data, generally an exponential shape. The method has excellent efficacy in series with high seasonality, which is not much presented in data from the epidemic in Ceará.Table 4 Method errors to long term predictions Method MAE RMSE R2 TPOT 1.35 11.38 0.99 KF + SEIR + CE 216.65 245.89 0.983 Prophet 11,825.02 16,070.89 0.275 Holt Winters 9158.26 21,149.54 0.007 SEIR 564.79 723.29 0.858 The Prophet method has a large error for long-term predictions, but now its prediction has taken a different form compared to the result using data from China. It is noticeable that he was able to model the shape of the growth, peak, and decay of the curve, but the forecast values for the number of cases were different, resulting in a big error. The use of compartmental epidemiological models as SEIR is widely popular throughout the COVID-19 pandemic. However, many predictions were not confirmed since the modeling could not represent the actual versions, dependent on several outside variables and steps of disease contention defined by general health managers. Each parameter is accountable for the speed of transitions between a single compartment along with the subsequent one. Compartmental models are legitimate approaches for understanding and analyzing epidemiological information, especially if the version is corrected to consider specific characteristics of the outbreak under investigation, as in this COVID-19 pandemic. The forecast models infer that the amount of COVID-19 cases expands exponentially in its increasing phase. The exponential increase in cases strongly suggests that the epidemic growth is an underlying biological phenomenon instead of the number of tests completed. Some studies indicate that there is a particular generality from the temporal growth of COVID-19. Even though these facts, in a limited community, the exponential development of instances cannot stay forever. Hence, the stochastic model of disease spread saturates sometime. Forecasting plays a vital role in several study regions due to its benefits in conserving funds or improving the decision-making process to benefit the market. In the case of this COVID-19 outbreak, there are many challenges for forecasting as the COVID-19 incubation period is much more extended than other epidemic processes, and also a small number of datasets are available for this function. The AutoML models used at work have considerable success. However, such models need to have training data that was not available at the beginning of the pandemic. The Kalman Filter model was accurate in terms of the long-term plateau date and the number of short-term forecasting cases. For long-term forecasting, AutoML models are a good option based on available training data. The distinction between the approach presented and the one usually used in other studies is that the use of Kalman Filter provides a long-term prediction at the beginning of the epidemic period using data from other countries/regions and the application of AutoML allows the semi-automatic selection of best model with better precision for the prediction of COVID-19 deaths. Conclusion Though SIR-based models have been extensively used to model the COVID-19 outbreak, they include some doubts. Several improvements are emerging to improve the standard of SIR-based models suitable for this COVID-19 outbreak. As an alternative to the SIR-based models, this study showed the use of machine learning models to predict the outbreak progression. We show that by using the Kalman Filter and AutoML models, we can achieve very high accuracy in predict COVID-19 cases. This study also shows that it is possible to achieve a R2 score of 0.99 on the prediction of COVID-19 deaths. The study presented has as main findings that using the TPOT application in predicting COVID-19 cases has a high R2 score. The Kalman Filter can be used effectively for long-term prediction. The main limitations of using the method are that in AutoML approaches, training data is needed to create the models, making its use impractical at the beginning of the pandemic. The Kalman Filter approach needs data from other countries/cities to feed the model, which makes it feasible to use the approach but with the risk that if the behavior of the country’s epidemic curve used to feed the model has very different characteristics from the region where we want to get the death curve can lead to a high margin of error. The difference between the approach presented and the one commonly used in other studies is that the use of Kalman Filter allows a long-term prediction at the beginning of the epidemic period using data from other countries/regions and the application of AutoML allows the semi-automatic choice of best model with better precision for the prediction of COVID-19 deaths. Acknowledgements This study was financed by the Science and Technology Planning Project of Guangdong Province (Grant No. 2018A050506086). Compliance with ethical standards Conflict of interest The authors declare that there is no conflict of interests regarding the publication of this paper. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. This article has been retracted. 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==== Front Chemosphere Chemosphere Chemosphere 0045-6535 1879-1298 Elsevier Ltd. S0045-6535(20)33627-4 10.1016/j.chemosphere.2020.129429 129429 Article Will the extraction of COVID-19 from wastewater help flatten the curve? Atangana Ernestine ∗ Oberholster Paul J. Turton Anthony R. Centre for Environmental Management, Faculty of Natural and Agricultural Science, University of the Free State Bloemfontein, 9300, South Africa ∗ Corresponding author. 5 1 2021 5 2021 5 1 2021 271 129429 129429 6 10 2020 8 12 2020 22 12 2020 © 2020 Elsevier Ltd. All rights reserved.2020Elsevier LtdSince January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.With the potentially fatal effect of COVID-19 and its devastating impact on economies worldwide, some environmental scientist has suggested the use of waste from household sewage to trace the movement of SARS-CoV-2, within a given country. However, this approach is not without challenges where developing countries lack proper and adequate hygiene and sanitation, resulting in widespread defecation. Limited scientific research has been done to determine how many times a recently infected person can defecate and the quantification of SARS-CoV-2 found in a single expel. On the other hand, there is no detailed research to specify where the heavy viral load of SARS-CoV-2 can be found in human excreta. In this paper, we present some obstacles that this approach could face in the absence of an intense lockdown in developing nations such as sub-Saharan countries. To achieve this, we identify some research needs that will strengthen our understanding of the transmission, occurrence, and persistence of SARS-CoV-2 in sewage and wastewater, including the life-span that depends on temperature. A methodology to follow in the process of identifying a hotspot on a small scale using some mathematical distributions, including the normal distribution, log-normal distribution, and the most complex one known as Blancmange function, was presented with some examples. Our investigation showed that this method might have some challenges, especially in developing countries (sub-Sahara countries) where open latrine usage is very high. Some recommendations we suggested to ensure the efficiency of such a method on a small scale. However, in general, it is essential to note the extraction/detection method will not help more than the testing method used all over the world to trace SARS-CoV-2 -19 in humans. Highlights • We review the spread of COVID-19 in wastewater under temperature effect. • We examine COVID-19 extracted from wastewater will be useful to trace the hot spots. • Lognormal distributions and Blancmange function methods to identify hot spots. • Restraint of COVID-19 extracted from sewage in nations open pits are mostly used. • Testing method are endorsed since the extraction may be costly and inept worldwide. Keywords CoronavirusSARS-CoV-2FecesSewageWastewater treatmentChallengesExtraction methodAnd disadvantagesHandling Editor: Jianying Hu ==== Body 1 Introduction Coronaviruses (CoVs) belong to the large virus family Coronaviridae (Norovirus), Adenoviridae (Adenovirus), Picornaviridae (Enterovirus and Hepatitis A virus) (WHO, 2017). The virus can cause diseases ranging from the common cold to Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) and the Middle East Respiratory Syndrome coronavirus (MERS-CoV) (de Wit et al., 2016). In late December 2019, a new acute respiratory disease known as COVID-19, sustained by a novel coronavirus, SARS-CoV-2, emerged in Wuhan, China. This rapidly spread across the planet. The outbreak was declared a Public Health Emergency of International Concern on January 30, 2020. (WHO, 2020). On March 11 (2020). The world health Organization upgraded the status of the COVID-19 outbreak from epidemic to pandemic (Gorbalenya et al., 2020). Around 111 countries rapidly reported the attack (Wu et al., 2020a, 2020b). The coronavirus virion is a spherical single-strand RNA, roughly with a diameter range of 60–220 nm in size. It also contains the outer viral envelope covered by a 9–12 mm projections range. (Zhu et al., 2020). The virion structure of SARS-CoV or SARS CoV-2 is shown in Fig. 1 . The most common severe symptom of COVID-19 reported is related to respiratory systems.Fig. 1 Virion structure of SARS-CoV/SARS-CoV-2 (La Rosa et al., 2020a, 2020b). Fig. 1 The SARS-CoV-2 virus is excreted in the feces and found at lower concentrations in the human excreta of the infected person (Rusinol and Girones, 2017). Transmission of the virus is by direct contact, and droplets spread with sneeze and cough. The most infected stage of the virus is when a person is symptomatic, but data point to the transmission that can occur before the onset of symptoms (Chan et al., 2020). The virus can also be transmitted through dry surfaces contamination from the self-inoculation of mucous membranes of the nose, mouth, or eye (Otter et al., 2016; Geller, 2012). Since most of the transmission in humans is characterized as a self-limiting condition, little is known about its transmission potential through the environment. COVID-19 transmission via the fecal-oral route has been mooted (Gu et al., 2020; Yeo et al., 2020) but not profoundly studied. Due to the fatality of COVID-19, transdisciplinary researchers have focused their attention on the mode of transmission, the structure of the new virus, its life-span, and mutation in the human body, its persistence on the surface, air and, water (La Rosa et al., 2020a, 2020b). The available literature shows the evolution of new mathematical models, which aim at understanding the spread to predict the future behavior of the virus (Atangana, 2020; Owusu et al., 2020; Danane et al., 2020). Complex statistical analysis is used to predict the future numbers of deaths, infected, and recovered to inform policy that helps to flatten the curve of COVID-19 (Atangana and Seda, 2020; Kasereka Kabunga et al., 2020; Fatmawati et al., 2020; Zhang and Liu, 2019). Biologists and chemists are working to understand the novel COVID-19 to develop methods of disruption and mitigation (Ahmed et al., 2020), and virologists are developing a vaccine. Meanwhile, environmentalists have embraced a methodology to identify the hotspot of affected people by using wastewater. This method has been implemented in some countries, including France and Netherlands however; there is no clear indication that such will be more effective than the testing performed by medical staff worldwide (la Rose et al., 2020a; la Rose et al., 2020b). In this work, further, the analysis will be presented. This article aims to identify some research needs that will strengthen our understanding of the occurrence and persistence of SARS-CoV-2 in sewage and wastewater, including the life-span that depends on temperature and the detecting devices used. A possible procedure will be suggested to identify the SARS-CoV-2 hotspot on a small scale using some mathematical distributions, including the normal distribution, log-normal distribution, and Blancmange’s most complex one function with some examples. The fusion of the recent findings highlights that sewage wastewater could be used to trace an infected person with the SARS-CoV-2 RNA virus. 2 Background literature In this section, we will discuss some areas in the study that will help strengthen our understanding of the transmission, occurrence, and persistence of SARS-CoV-2 in sewage and wastewater via temperature effect and some devices used to detect the virus. 2.1 SARS-CoV-2 transmission via fecal-oral routes After the detection of SARS-CoV-2 RNA in stool/human excreta samples of infected patients, there have been many recent reports shown on the transmission via the fecal-oral route. The specific ways occur when pathogens in the fecal particles can be transmitted from one individual mouth to another. (Ahmed et al., 2020). This is due to poor hygiene and sanitation, resulting in open defecation (Grassia et al., 2020). Humans can be infected with waterborne disease if the waters are polluted with fecal materials, resulting in some disorders such as diarrhea, typhoid, cholera, hepatitis, and polio (Wen et al., 2020). The oral-fecal follows five paths: fingers, flies, field, fluids, and food (Ahmed et al., 2020). Little information is known in the cases of COVID-19 spread via field and fluids. At the same time, there is an argument on virus spread via fomites (surfaces and cloths), flies, and fingers (close contact) (Chen et al., 2020; Haas, 2020). Progress on the virus provides a regular inflow of new insight into its plausible transmission routes and pathogenesis (Arslan et al., 2020). There is abundant angiotensin produced by the gastrointestinal (GI) tract, which converts the ACE2 (enzyme 2) to which the virus is acknowledged to attach. Intestinal manifestation occurred mostly in the late phase of infection (Wu et al., 2020a, 2020b). Literature studies have reported symptoms such as nausea and diarrhea preceding fever, and respiratory symptoms usually occur from more than 10–14% of patients with GI as the only manifestation (Wu et al., 2020a, 2020b). It was then suggested that the SAR-CoV-2 virus might stay longer in the digested tract than the respiratory tract (Grassia et al., 2020). Clear examples of such a scenario occurred in patients excreting viral RNA through feces through several weeks of infection from when symptoms start to develop. Some instance has been reported in the literature where two positive cases of viral nucleic acid occurred in the anal swabs, after 6–14 days, negative results were obtained from respiratory specimens. Similar results were also obtained by Wu et al. (2020a), Wu et al. (2020b), where positive results were obtained from patients’ fecal samples in China. Even after getting negative consequences for the respiratory samples for about 33 days (Wu et al., 2020a, 2020b). Another study observed a long period of 47 days before the first day of symptoms development. These cases lead us to may query concerning the virus fate and distribution into the environment via fecal route. Is it likely that most patients discharge from the hospital after treatment can excrete the virus in their feces? Back in the existences in 2003, the virus was also detected in the feces of infected patients during an outbreak in the residential complex of Amoy Garden in Hong Kong where transmission by aerosolized wastewater was suspected (Peiris et al., 2003; CDC, 2004; Manocha et al., 2003; Isakbaeva et al., 2004; McKinney et al., 2006). 2.2 SARS-CoV-2 in water and wastewater It is well known that the virus eliminated by feces can be found in wastewater, which may not be removed entirely from conventional wastewater treatment plants. It has been suggested that sewage and wastewater can trace the geographical area where there is a high risk of SARS-CoV-2 infection (hotspot). Following the outbreak in March 2003, over 3000 people were involved in a faulty untreated wastewater system that led to a high-rise housing estate in Hong Kong city (Peiris et al., 2003). Based on the scenario that the preceding SARS CoV-2 can reproduce in the enteric tract, the potential for being classified as an enteric pathogen with a potential transmission pathway via the environment (Leung et al., 2003; SARS-EWG, 2003). According to Leung et al. (2003), SARS- CoV-2 is found in both the lungs and the small intestine. For this research, the virus was cultured from patient stool/excreta about three weeks before infection. It was later observed that higher yield offer patients with the viral cultures found in the small intestine than the lung tissues (Liu et al., 2004; Chan et al., 2004). In a study done in Zhejiang provincial hospital, China, when researching a patient’s stool, it was observed that SARS-CoV-2 RNA could survive for 4 days or up to about 22 days. This was much longer than the respiratory and serum samples of the same patient’s sample collected, which only stay for 18 and 22 days (Zheng et al., 2020). Some reports suggest a potential link of SARS CoV-2 to water and wastewater (La Rosa et al., 2020a, 2020b). One of the pioneer’s works on the investigation of coronavirus traces in pure water and untreated sewage can be traced back to 2009 when the arrival of similar COVID-19 researchers investigated its potential environmental transmission (Patricia et al., 2019). The presence of coronavirus (COVID-19) was traced in both filtered and unfiltered, pure water within an interval temperature ranging from 4 to 23 °C and 23 °C in untreated wastewater (Patricia et al., 2019). The viability of coronavirus is dependent on a range defined by the daily and nightly temperature, as well as the level of inorganic matter and other antagonistic bacteria (Sobsey and Meschke, 2003). T99 was known to be the time required for the virus to decrease (99%) for pure water. More importantly, it was shown that coronaviruses were inactivated faster in the pure water at 23 °C in 10 days rather than 40 °C for more than 100 days (John and Rose, 2005). It has been reported that coronaviruses died off rapidly in untreated wastewater, even with a T99.9 value of between 2 and 4 days. While the COVID-19 may have additional properties that the one described, it is worth noting that it belongs to the coronavirus family and is likely to respond in similar ways (Melnick and Gerba, 1980). Researcher Lisa (2015) reported on the Enveloped Surrogate Virus in the inactivation form in human sewage and fat of enveloped virus. Obtained results were used as a means to model the entrance of the Ebola virus in sewage samples. The enveloped virus in the inactivation kinetic structure in sewage uses a specific RNA bacteriophage that belongs to the Cystoviridae family, which then acts as a latent substitute of the virus enveloped human sewage. Their effects indicate in a wastewater sample that is primarily based on temperature dependence. For 3–7 days, the virus can ride 6 to 7 inactivation kinetic form in a pattern (Lisa, 2015). 2.3 SARS-CoV-2 detection devices Numerous researchers in the literature have reported on the want for scientists to use paper-based device units (Mao et al., 2020); Water-Based Epidemiology (WBE) (Daugthon, 2020), nucleic acid-based polymerase chain reaction (PCR) (Casanova et al., 2009; Medema et al., 2020) to extract/detect SARS-CoV-2 immediately in wastewater. Polymerase chain reactions (PCR) had been recommended to elevate out the measurement, which was once often used in various nations (Barcelo, 2020). The above device used (Paper-based device) is known to be inexpensive, powerful, and very fast to identify the infection rate of transition and pathogens (Mao et al., 2020). Its entire surface is cover with a variety of useful surfaces, made from a printed wax hooted on a paper material that can be affordable. Its drawback lies in the fact that it is susceptible and fast, which makes PCR usage for numerous infections, for instance, several pathogens and Malaria (Mao et al., 2020). Another scientist has used Water-Based Epidemiology (WBE) device to trace the COVID-19 virus in human sewage (Daugthon, 2020; Barcelo, 2020). Due to the vast infection rate of the current pandemic COVID-19, former pioneer of EPA and expert calls for all scientist to advance their research in Water-Based Epidemiology. This was called for the present COVID-19 virus and any future pandemic crises ahead. The expert explained that scientists could use sewage as an indicator to measure an infected, which can increase attention around the nation, therefore leading to more curiosity in method (Water-Based epidemiology). The expert and many different scientists accept the fact that when distributing hard work on WBE, community members are essential to acknowledge their labor to move ahead, thereby calling for an assistant in the public health sector (Daugthon, 2020). Nation across the world; Australia (Ahmed et al., 2020), Netherlands (Medema et al., 2020), Italy (La Rosa et al., 2020a, 2020b; Rimoldi et al., 2020), Spain (Randazzo et al., 2020), Francs and Japan (Wurtzer et al., 2020), Germany (Randazzo et al., 2020), USA (Sherchan et al., 2020; Wu et al., 2020a, 2020b), Ecuador (Guerrero-Latorre et al., 2020) and Indian (Kumar et al., 2020), were the first to use PCR to detect the virus in sewage, which supports the idea, implemented above, thereby using WBE was efficient to disclose the scale of the pandemic SARS-CoV-2 (Medema et al., 2020; Warish et al., 2020; Wurtzer et al., 2020). Increasingly using wastewater as a fast and effective way to monitor and predict the spread of COVID-19 is being accepted as viable (Burger, 2020; Turton 2020). The concept has been proven by the Dutch research agency KWR and confirmed by research done in South Africa. Researcher Mallapaty suggested how sewage could reveal the coronavirus outbreak’s accurate scale, and researchers from other countries are trying to implement the same procedure to trace the COVID-19 hotspot (Mallapaty, 2020). The study presents some obstacles that the extraction method could face in the absence of intense lockdown and developing countries. To achieve this, we show the advantages and disadvantages of such an approach. We should also discuss if such a method can trace infected humans if one can link the virus load found in the waste and the number of infected humans. Furthermore, some simple mathematical models will be presented to predict the possibility of a hotspot as a viral load function of time. Several cases could be considered starting with the possibility of normal distribution. 3 Material and methods All theoretical assumptions considered for daily cases and daily tests in the manuscript were observed from our world data. org. (Our World in Data, 2020). In this section, we describe the normal, log-normal distribution fractional distribution (Blancmange curve) and SIR model to trace the SARS-CoV-2 hotspot. First of all, we present the epidemiological characteristics of SARS-CoV-2. Then, we introduce a general and detailed description of our approach. Finally, we detail some of the distributions and model outputs used for the numerical experiments performed later. 3.1 Epidemiological characteristics of SARS-CoV-2 We assume that each individual is in one of the following compartments (Teiji, 1901; Cooper, 2020; (Coronavirus worldometer website, 2020). According to the known characteristics of the COVID-19 pandemic, we assume that each individual is in one of the following compartments (Coronavirus worldometer website, 2020).• The collection of viral loads in the six different untreated wastewater treatment plants was done daily, denoted as d 1 , d 2 , d 3 , d 4 (days) at a time interval to≤t≤T. Where to is the initial time, and T is the final time • P i denotes a normal distribution for the viral load of SARS-CoV-2 in six different untreated wastewater treatment plants I = 1, 2, 2, 4, 5 … …N • N denotes the total number of untreated wastewater treatment plants • H denoted the log-normal distribution for the viral load of SARS-CoV-2 trace in six different untreated wastewater treatment plant • ai are the contribution of daily average and the deviation due to new viral load arrival of the untreated wastewater treatment plant • bi is the contribution of deviation (the measure of the dispersion of the dataset relative to the daily average viral load of the untreated wastewater treatment plant • μ and δ denote the mean and standard deviation for the six different wastewaters treatment plants • Blancmange curve (Blanc) represents a complex distribution, the viral load of SARS-CoV-2 in wastewater treatment plants with other Hurst parameters ( ω ); 0.8, 0.85, 0.9, 0.93, 0.96 and 0.99 • SIR model denotes Susceptible, Infectious, and recovered individuals I. Susceptible individuals, S(t): These are those individuals who are not infected, however, could become infected II. Infected individuals, I(t): These are those individuals who have been infected by the virus and can transmit it to those individuals who are susceptible III. Recovered individuals R(t): These are those individuals who have recovered from the COVID-19 virus and are assumed to be immune, R(t). Hotspot: Describe the geographical area where there is a risk of many infected induvial with COVID-19. Humans rely on the collection of data for proper monitoring of given real-world problems. One of the most used procedures is perhaps the sampling, which aims to select a subset, also called a statistical sample, of persons from inside a statistical population to appraise the whole population’s appearances. In the case of extraction/detection of viral load of SARS-CoV-2 in untreated wastewater treatment plants in a given country, systematic sampling could be more appropriate. We shall recall that frequent selection is based on arranging the investigated population according to some ordering scheme; then, the next step is selecting elements at even intervals via the ordered list. Therefore, we shall stress that researchers shouldn’t rely on data collection to have a clear opinion about a given real-world problem. When data are collected, they could be plotted as a function of time, space, and space and time. Most of the time, the obtained results follow distributions, which are known as a statistical formula. In general, they are called probability as they provide the chances of different possible outcomes for collected data. Several distribution statistical functions can be obtained, for example, normal distribution, log-normal distribution, Poisson distribution, power-law distribution, and many more that cannot be listed here. In this section, we will assume that the collected data from the different wastewater treatment plants follow either the normal distribution, log-normal distribution, or in a more complicated case, a fractal distribution, at a time interval of to≤t≤T. Where t o is the initial time, and T is the final time. The normal distribution is, in general, a category of continuous distribution for the real-valued random variable, which considers an average also called mean or expectation of the distribution, and the standard deviation (a proportion that describes the amount of variation or dispersion of set values). This distribution is wider used in many sciences, technology, and engineering (Park and Bera 2009; Atangana, 2020; Atangana and Seda, 2020; Doungmo Goufo et al., 2020; Khan et al., 2020; Faraz et al., 2020). In particular, in natural (Limpert and Stahel, 2011) and social science (Blanca et al., 2018), it can give real-values random variables, especially those for which the distributions cannot be identified. One of the great values of using this distribution is that the average of many observations of a random variable with a finite average and variance can be considered a random variable, particularly those distributions that are similar to the normal distribution as the number of observations accumulates. Additionally, to this advantage, it is known that physical measures that are predictable to be an addition of many autonomous processes, like measurement errors, are most of the time following distribution that is approximately normal. 3.2 Normal distribution This distribution possesses some uniqueness in terms of properties, which are very important in an analytic investigation, like in the case of the collection of SARS-CoV-2 traces in untreated wastewater treatment plants. Thus, if we suppose that the collected data from untreated wastewater treatment plants would follow the normal distribution, then, assuming a collection will be performed in N wastewater treatment plants. Let Pi(t) be the viral load/concentration of SARS-CoV-2 trace in the untreated wastewater treatment plant i = 1,2,3,4,5 … …..N, for four consecutive days d 1 , d 2 , d 3, and d 4, at time t interval range of to≤t≤T. The rate of change of viral load as a function of time can be assumed to follow (equation (1)). (1) dPi(t)dt=aiPi(t)+bitPi(t) where ai are the contribution of daily average and the deviation due to new viral load arrival, bi is the contribution of deviation (the measure of the dispersion of the dataset relative to the daily average viral load of the untreated wastewater treatment plant (i). Let μ be the average viral load detected in wastewater treatment plant i (WWTP ( i )) and σi the associated standard deviation, then, equation (1) becomes (equation (2)). (2) dPi(t)dt=μiσi2Pi(t)−tPi(t)σi2 The exact solution of the above is given in equation (3) as: (3) Pi(t)=1σi2πPi(0)exp{(t−μi)2σi2} The family of {Pi(t):i=1,23……………….N can be plotted each week to evaluate a weekly density at WWTP ( i ). From the obtained results and using the formula of the normal distribution as given in equation (3), the parameters μi and σican be determined. For example, by considering four consecutive days for example d1,d2,d3and d4. Then the average at WWTP (i) is determined as (equations (4), (5), (6), (7), (8)) (4) μi=12(d3+d4)(d4−d3)−ln[Pi(d4)Pi(d3)]ln[Pi(d2)Pi(d1)](d2+d1)(d2−d1)12(d4−d3)−ln[Pi(d4)Pi(d3)]ln[Pi(d2)Pi(d1)](d2−d1) While (5) σi=12ln[Pi(d2)Pi(d1)]{(d1−μi)2−(d2−μi)2} The weekly collected data from the N-wastewater plants (raw wastewater) can be plotted as functions of time (d) in the same graph. 3.3 Log-normal distribution Alternatively, one could expect to have the following distribution at WWTP (i) following a log-normal distribution, an essential statistic function to depict many natural phenomena. Various small percentage changes drive numerous regular growth processes; however, they become additive in the log scale. If these variations’ effect is not significant, then the distribution of their addition is closer to normal than the acquisition. Now, when reverting to the original scale, distribution sizes convergent toward log-normal. It should be recalled, log-normal distribution is a continuous probabilistic function of a given random variable for which the logarithm is normal. In simple terms, if we consider the random change H is log-normally disseminated, then the variable Z=ln(H) has a regular spreading of the SARS-CoV-2 RNA virus. On the other hand, if Z=exp(H) follows the regular spreading, then, H=exp(Z) will follow the log-normal spreading. This virus spreading is very convenient and very practical, for example, in measuring engineering sciences (Atangana, 2020), medicine, economics (Blanca et al., 2018), chemistry (Atangana et al., 2020), biology, and physics (Atangana, 2020), and many other fields (Limpert and Stahel, 2011). Therefore, if it is assumed that the weekly collected data would follow the log-normal spreading of the SARS-CoV-2 virus, then the following mathematical function can be considered (6) Pi(t)=1σit2πPi(0)exp{(ln(t)−μi)2σi2} In this way, by using the curve of the collected data, the following parameters can be determined using the comparison between the collected data and the mathematical formula (7) μi=12(ln(d3)+ln(d4))(ln(d4)−ln(d3))−ln[Pi(ln(d4))Pi(ln(d3))]ln[Pi(ln(d2))Pi(ln(d1))](ln(d2)+ln(d1))(ln(d2)−ln(d1))12(ln(d4)−ln(d3))−ln[Pi(ln(d4))Pi(ln(d3))]ln[Pi(ln(d2))Pi(ln(d1))](ln(d2)−ln(d1)) While (8) σi=12ln[Pi(ln(d2))Pi(ln(d1))]{(ln(d1)−μi)2−(ln(d2)−μi)2} An example of such a plot is presented in Fig. 2 a and b, showing the normal and log-normal distribution for different viral loads, respectively; here, we consider that the collection was done in the six other raw wastewater treatment plant since there will be some with high trace due to the high numbers of infected persons, we choose different averages and standard distributions, then plot all in the same Fig. 2 a and b, as presented below. To perform, these plots we consider a collection of raw wastewater treatment plants for five consecutive d. For each day, it is assumed that an adequate supply of data is performed and recorded.Fig. 2 (a) Possibility with normal distribution, (b) Possibility with a log-normal distribution. Fig. 2Fig. 3 Possibility with Blancmange function for (a) ω = 0.8 and 0.85; (b) ω = 0.9 and 0.93 and (c) ω = 0.96 and 0.99 respectively. Fig. 3 At the end of the 5 d, all collected data from the six different wastewater treatment plants are plotted as a function of time, in the same. For example, in Fig. 2 a and b, we assume that each viral load from a given wastewater plant treatment has a daily average and standard deviation to be represented with the normal distribution. In this Fig. 2a and b, we consider six different daily average μ = 0.2, 0.3, 0.4, 0.5, 2, 4 and six different standard deviation δ= 0.1, 0.2, 0.9, 1, 2, 2.0. 3.4 Complex distribution (blancmange/Teiji curve) Nevertheless, although many physical processes can follow a normal and log-normal distribution, it is essential to note that there exist many real-world problems in time and space that do not follow the conventional distribution. Therefore, more complex distributions can represent such physical problems; for example, a process curve can follow fractal features. A fractal curve or geometrical representation with the fractal quality is a curve for which each part has the same statistical character as the whole. Such features appear in many real-world problems. There is a function known as the Blancmange curve; it is also known as the Teiji Takagi curve, which was suggested in 1901. (Teiji, 1901). Here we e assume that data collected from the six different wastewater treatment plants follow the Blancmange curve (eqs (9), (10), (11))), with other Hurst parameters ω=0.8,0.85,0.9,0.93,0.96,0.99. (9) Blanc(t)=∑l=0∞dist(2j)2j,t∈[0,1], where (10) dist(z)=minj∈N\|z−j\|,z∈R (11) Blanc(t+j−12)=12Blanc(t)+t(−1)j+j−12,t∈[0,1]forj=1,2 Their results obtained, as shown in Fig. 4, Fig. 5, Fig. 6, Fig. 7 .Fig. 4 The plot of the six different wastewater treatment plants. Fig. 4Fig. 5 Numerical solutions for (a) l = 0.3 and k = 0.0025 (left), l = 0.4 and k = 0.0012 (right); (b) l = 0.6 and k = 0.003(left) and for l = 0.39 and k = 0.009 (right); (c) l = 0.48, k = 0.013 (middle). Fig. 5Fig. 6 Map of usage of defecating in the open and it percentages (Gupta, 2014). Fig. 6Fig. 7 Map of percentage for the urban population using pit latrines in Sub-Sahara Africa (WHO/UNICEF, 2014). Fig. 7 3.5 Susceptible infectious removed (SIR) model The SIR model demonstrates a system of three ordinary differential equations (ODEs), with the classic SIR model that can be easily implemented and used to gain a better understanding of how the COVID-19 virus spread within the communities of variable populations in time, including the possibility of surges in the susceptible people (Ivorra et al., 2020). Hence the SIR here is planned to evacuate numerous of the complexities associated with the real-time advancement of the spread of the virus in a way that values both quantitatively and subjectively. It may be a dynamic framework that given by three couple ODEs that depict the time evolution of the subsequent three population; Susceptible individuals, S(t): these persons who are not infected with the SARS-CoV-2 virus could become infected. As the virus spreads from its source, more individuals will become infected. Hence the susceptible population will increase for some time, describe as the surge period. Infected individuals, I(t): These persons who are infected with the virus and can transmit the virus to others who are susceptible. An infection may remain infected in the population. Recovered individuals, R(t): those individuals who have recovered from the virus and are assumed to be immune (or have died). A simple model of SIR is given as: (12) {dS(t)dt=−lS(t)I(t)dI(t)dt=lS(t)I(t)−kI(t)dR(t)dt=kI(t) The above system (eq (12)) can be solved using any numerical scheme, the numerical simulations can be presented to predict the future behavior of class S, R, and I, this is achieved, the above equation is converted to (eq (13)): (13) {S(tn+1)−S(tn)Δt=−lS(tn)I(tn)I(tn+1)−I(tn)Δt=lS(tn)I(tn)−kI(tn)R(tn+1)−R(tn)Δt=kI(tn) From Equation (13), where the parameters l and k are real, positive for the infection rate and recovery, using obtained initial conditions and parameters, the number can also be obtained. It can also be predicted, as seen in Fig. 5a–c below. 4 Results and discussion Using mathematical models normal, log-normal, and a complex distribution Blancmange curve and existing data, simulations, and MATLAB software can be used to predict SARS-CoV-2 hotspot in the six different wastewater treatment plants. The results are presented in Fig. 2a and b, Fig. 3 a-f, and Fig. 4, respectively. Also, the next hotspot predicted was considered by using the simple Susceptible, infectious, and recovered (SIR) mathematical model. The results obtained are depicted in Fig. 5 a-e. In Fig. 2a below, we can assume that the threshold is obtained when at y = 1, it is possible to conclude that the hotspot (places that are at risk of a lot of COVID-19 infections) is from the raw wastewater treatment plants having the red and green curve. It can also be said that, from these two regions, on the first day, both regions had a high number of infected persons with the SARS-CoV-2 virus, while the last four areas had an endless number of infected persons. However, in Fig. 2b, the collected wastewater treatment data were assumed to be following the log-normal distribution. It is believed that the collection was performed in six different wastewater treatment plants with daily averages and standard deviation in the case of normal distribution. It can be seen that the wastewater treatment plants represented by red and green curves are considered hotspots. In particular, it can be concluded that these two regions got a high number of infected persons with the SARS-CoV-2 virus in the second and third d. So, in general, all collected data can be compared weekly, and each country can define a threshold. A wastewater treatment plant (i) with a viral load above this threshold will be marked as a hotspot. All measures could be taken to flatten the curve, including lockdown, unique deployment of medical policies, law enforcement to help with medical interventions, and security, respectively. However, if the collected data from raw WWTP show a trend of fractal behavior, it should be recommended to use the Blancmange curve instead of log-normal and normal distribution. The mathematical formula representing the Blancmange curve is presented in equations (4), (5), (6), (7)). Again, here we considered six wastewater treatment plants. We assume that these regions’ data follow the Blancmange curve with different Hurst parameters ω = 0.8, 0.85, 0.9, 0.93, 0.96, and 0.99, respectively. Their results are depicted in Fig. 3a–f and Fig. 4. In particular, in Fig. 4, all collected from the six different raw wastewater treatment plants are plotted. In this case, only two of the collected data are considered; it can be concluded that four regions are hotspots, as their curves are above the set thresholds. Using mathematical models and existing data, simulations can predict the next hotspots of the COVID-19 virus. An example is to consider a simple Susceptible, infectious, and recovered (SIR) mathematical model (Fig. 5 a-e), which is available in the literature (Ivorra, 2020). Mathematicians called S(t) the class of susceptible populations, I(t) the type of infected persons, and R(t) the type of recovered persons. The functions describe each class’s behavior as a function of time (see Equation (12), (13)) above). It has been seen that in numerous communities, a spike within the number of infected people I, may happen, which results in a surge within the susceptible population, S, recorded within the COVID-19 datasets (Coronavirus worldometer website, 2020), which sum to a secondary wave of infections. To interpret such a possibility, S within the SIR model (WHO-COVID-19, 2019). Can be reset to surge at any time (t) that a surge happens. In this way, it can be house different from such waves recorded within the distribution information stated in (Coronavirus worldometer website, 2020), which recognizes it from the classic SIR model. While all the approach mentioned above, seems to be a promising methodology to identify COVID-19 hotspots, there are some grey areas of uncertainty. Therefore we shall present some limitations of this methodology. We do not say the extraction method is useless or may not lead to better results than the testing method, but we want to show some weaknesses of this approach in developing countries (sub-Saharan countries). In the last month, many countries have relaxed their lockdown regulations to rescue the economy. There are movements from one town to another with these relaxations, from megacities to small cities, from villages to cities. The viral load contribution of traveling infected persons who were not at all in contact with residents maybe not be identified and could count as residents’ contributions. The approach could be useful during a total lockdown when there is no movement from cities to cities. On the other hand, the system may not account correctly for the release of the viral load from households as the sewage (raw wastewater) could be blocked, such as we find in Emfuleni and Harrismith, which will skew the result. The rapid spreading of the COVID-19 virus, especially in areas where there is a risk of many infections (hotspot), has been identified positively in developing countries. Research study has identified a high risk of virus diseases in many developing nations, including Sub-Saharan countries (WHO, 2020a). More attention need is required for these developing nations to mitigate the spread of the virus, especially in low-income and densely populated areas suffering from a lack of healthcare facilities and adequate hygiene and safe water (Brauer et al., 2020). Studies have shown that about 60% of sub-Saharan residents in slum conditions lack basic sanitation, including sewer collection networks. For instance, natural wetland, small-bore sewer, and latrines for used in Uganda and Nairobi for sewage treatment. Wastewater treatment methods, oxidation ditch, stabilization lagoons, activation sludge, trickling filters, coverage are very limited in Africa (Wang et al. (2006)). It has been reported 51–75% of safety-treated water can be sustained to Egypt and Tunisia, 26–50% by Morocco, and either <25% (insufficient data) is allocated to the African countries (WHO, 2018). Majorities of these countries practiced open defecation at a very high exploring rate due to poor hygiene and sanitation practiced. In a report done by Gupta (2014), it was confirmed that open defecation is most used worldwide across the nation. About 14% of the population across the globe defecate in the open (Gupta, 2014). However, the percentage of statistics of open defecation covers a massive alteration between the countries. In Indian, about 48% of the total population defecate in the open, and this is increasingly concentrated throughout the country compared to some impoverished conflict countries such as Chad and South Sudan, which are also using open defecation (Fig. 6). Only a handful of countries across the world have a worse situation than Indians. Nearby countries such as Afghanistan, Bangladesh, and Congo are trying to reduce open defecation usage even though most of these countries still do not have improved sanitation (see Fig. 6). According to Nakagiri et al. (2006), when assessing the performance of pit latrines in urban areas of Sub-Saharan Africa (SSA), it was found that about 52.7% (roughly 198 million) of the urban population is in use of pit latrine. Whereas in 2007, the percentage usage increases to 65%, which was approximately 162 million of the population (Banerjee et al., 2008). Although there was a decrease in the percentage of pit latrine (65–52.7%), the number of people in use has climbed up to the present date. More so, many individuals (about 36 million) since 2007 has adopted this sanitation technology. This number is expected to climb up since many people are in use from the previous years (Nakagiri et al., 2006). The usage of pit latrine in SSA can vary by country (Fig. 7) and the historical aspect across social-economic ranges. Nevertheless, the majorities lie in the poor-income employees (Morella et al., 2008). These pit latrine usage within the urban areas in the different countries in SSA may also vary. Despite this, they are still in use without chucks in the urban Sub-Sahara African countries (Fig. 7). It is clear that a large percentage of the population living in urban use open pits in a developing country. In developing countries, a pit latrine is known to be the most common form of improved sanitation. Even in leading African economies like South Africa, Tunisia and others, many communities are still using the bucket system for sanitation. The following maps indicate the percentage of open-pit latrine in the world and Africa, respectively (see Figs. 6 and 7). 5 Conclusion and recommendations The suggested methodology for identifying hotspots of SARS-CoV-2 uses wastewater collected from wastewater treatment plants within a given country, city, or location. Therefore, it is essential to notice that the process starts with defecation, then these wastes have to reach the wastewater treatment plants; a collection of data follows this. These data are then processed and plot as a function of time, and then the hotspots can be identified as the wastewater with a high viral load of SARS-CoV-2 trace. The argument presented in the study suggested that the testing method is more efficient than the claim made by researchers that SARS-CoV-2 could be extracted/detected from wastewater. The first challenge is that not all citizens use toilets linked to the municipal wastewater treatment plant. In many countries of Sub-Saharan African, majorities of people lack proper hygiene, and sanitation resulting to open defecation remains a persistent challenge. Another deficiency of this approach is that individuals eject feces with a frequency that may vary from a few times daily to weekly. There are scientific results from a well-conducted investigation stating how many times a newly infected person defecates daily, weekly, not mention the viral load of SARS-CoV-2 in a single excel. The following recommendations can be listed to make the method useful, especially on a small scale. However, the fundamental question could be, "How will the collected be performed in all portion of the wastewater?" if all the portions are not covered, what will be the percentage of those covered, and how will we know the viral load of SARS-CoV-2 -19 in uncovered parts?1) A scientific examination ought to be done to know how numerous times a recently tainted individual poo day by day or indeed week by week (of course, this might be another challenge owning, natural forms can vary from person excreta and numerous other components that might alter the patterns) 2) A precise examination ought to be done to recognize which canal infected individual eject more SARS-CoV-2 concentration (Butt-centric or urinal) 3) A precise examination ought to be done to know how much concentration of SARS-CoV-2 can be found in a single expel of defecation or urine 4) During the process, all sewage must be unblocked 5) The legitimate checking framework can be introduced at the wastewater treatment plants to guarantee the fair collection of everyday data 6) Inter-cities travelers ought to be prohibited from guaranteeing the collected information come from an inhabitant of the area 7) All inhabitants ought to have their toilettes connected to regions sewages (A circumstance exceptionally outlandish indeed in developed nations) 8) A legitimate examination ought to be done to compare the fetched of utilizing this approach with the current in utilize that comprises of testing and segregating On a small scale, if these suggestions are not watched, the strategy will be futile and diversion for the battle of following SARS-CoV 2 utilizing testing and confinement. This strategy has been found productive in numerous nations where the numbers of day by day modern diseases at nearly reaching zero. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgment The authors express their sincere gratitude to the reviewer’s for helping to improve the standard of the manuscript. The authors will like to thank Prof Abdon Atangana, Institution for groundwater Studies, the University of the Free State, for assisting with the modelling prediction. ==== Refs References Ahmed W. Angel N. Edson J. Bibby K. Bivins A. O’Brien J.W. Choi P.M. Kitajima M. Simpson S.L. Li J. Tscharke B. Verhagen R. Smith W.J.M. Zaugg J. Dierens L. Hugenholtz P. Thomas K.V. Mueller J.F. 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==== Front Osteoporos Int Osteoporos Int Osteoporosis International 0937-941X 1433-2965 Springer London London 5607 10.1007/s00198-020-05607-6 Letter to the Editor Comment on an article: “Osteoporosis in the age of COVID-19 patients” Ibrahimagić O. Ć. 1 Vujadinović A. 2 Ercegović Z. 3 https://orcid.org/0000-0001-7900-1222Kunić S. [email protected] 4 Smajlović Dž. 1 Dostović Z. 1 1 grid.412410.20000 0001 0682 9061Department of Neurology, University Clinical Centre Tuzla, 75000 Tuzla, Bosnia and Herzegovina 2 grid.412410.20000 0001 0682 9061Department of Orthopaedics and Traumatology, University Clinical Centre Tuzla, 75000 Tuzla, Bosnia and Herzegovina 3 grid.412410.20000 0001 0682 9061Department of Neurosurgery, University Clinical Centre Tuzla, 75000 Tuzla, Bosnia and Herzegovina 4 Present Address: Department of Neurology, Primary Health Care Centre Tuzla, Albina i Franje Herljevića 1, 75000 Tuzla, Bosnia and Herzegovina 21 1 2021 1 2 10 8 2020 18 8 2020 © International Osteoporosis Foundation and National Osteoporosis Foundation 2021This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. ==== Body Dear Co-editors-in-Chief Kanis and Cosman, We have read with great attention the article “Osteoporosis in the age COVID-19 patients”, written by Girgis and Clifton-Bligh (authors) in the July issue of Osteoporosis International. We welcome the opportunity to make a short comment as well. This very interesting article evaluates treatment of osteoporosis in disaster of COVID-19. The authors emphasized that osteoporosis kills and every year almost, 750,000 people lose their lives around the world as a result of hip fracture [1]. We want to highlight that older patients (very often with osteoporosis) are also with increased risk for mortality due to novelty SARS-CoV-2 pandemic. Evidence of osteoporosis associating nutritional factors; particularly calcium and vitamin D are reviewed as association of falls risk with fracture [2]. Unfortunately, in the group of very old patients with fragility fractures, only 28.6% were on adequate osteoporosis treatment [3]. High serum homocysteine has been shown to have detrimental effects on neural cells, vascular endothelial cells, osteoblasts, and osteoclasts. Therefore, hyperhomocysteinemia may be regarded as a factor that can reduce both bone mass and impair bone quality [4]. In addition, high serum homocysteine often associated increased risk for fractures. Unfortunately, hyperhomocysteinemia appeared to be predictive of all-cause mortality, independent of frailty, an age-related clinical state characterized by a global impairment of physiological functions and involving multiple organ systems [5]. Values of vitamin B9 (folic acid) and B12 are in negative correlation with levels of homocysteine [6]. Furthermore, according to PubMed survey, there was no reliable data due to concomitance of COVID-19, hyperhomocysteinemia, and osteoporosis/fractures. So, what to do when we have older COVID-19 patient with hyperhomocysteinemia and high risk for bone fracture? Authors highlighted: “Clinicians need to adapt to the challenges posed by this crisis and consider ways to continue serving the most vulnerable amongst us, those with chronic disease with their own substantive morbidity and mortality”. In light of this, we suggest that level of homocysteine and B9/B12 vitamin should be measured at clinical follow-up in all older patients with COVID-19, immediately after hospitalization. If persistent, hyperhomocysteinemic proosteoporotic (but also prothrombotic) state should be promptly decreased in acute phase of COVID-19, on the base of Latin phrase primum non nocere. Our studies from Bosnia and Herzegovina showed that the intake of B9 vitamin, sometimes with B12 vitamin as well, was efficient in creating normalized homocysteine levels in older patients with ischemic stroke and Parkinson’s disease [7, 8]. Fortunately, risk of side effects is minimal if the daily dose of B9 vitamin is 1–5 mg [9]. So, we point out that B9/B12 vitamin are “on the first-line”—good and safe in reduction levels of homocysteine in various older patients. In addition, B2/B3/B6 vitamins are enhancers of the immune system and might be efficient as soldiers from second echelon in battling with COVID-19 [10]. All in all, B-vitamins can, ad hoc, become the medication of choice in the treatment when unhidden hyperhomocysteinemia/osteoporosis coexists with COVID-19. Lastly, we emphasize that further studies will elucidate proosteoporotic/prothrombotic potential of hyperhomocysteinemia in COVID-19 patients as well as beneficial add-on effects of B-vitamins. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Compliance with ethical standards Conflict of interest None. ==== Refs References 1. Girgis CM Clifton-Bligh RJ Osteoporosis in the age of COVID-19 Osteoporos Int 2020 31 7 1189 1191 10.1007/s00198-020-05413-0 32346775 2. Aspray TJ Hill TR Osteoporosis and the ageing skeleton Subcell Biochem 2019 91 453 476 10.1007/978-981-13-3681-2-16 30888662 3. Duncan R Francis RM Jagger C Kingston A McCloskey E Collerton J Magnitude of fragility risk in the very old-are we meeting their needs? The Newcastle 85+ Study Osteoporos Int 2015 26 1 123 130 10.1007/s00198-0124-2837-8 25224291 4. Saito M Marumo K The effects of homocysteine on the skeleton Curr Osteporos Rep 2018 16 5 554 560 10.1007/s11914-018-0469-1 5. Azzini E Ruggeri S Polito A Homocysteine: its possible emerging role in at-risk population groups Int J Mol Sci 2020 21 4 1421 10.3390/ijms21041421 6. B-Vitamin Treatment Trialists’ Collaboration Homocysteine-lowering trials for prevention of cardiovascular events: a review of the design and power of the large randomized trials Am Heart J 2006 151 2 282 287 10.1016/j.ahj.2005.04.025 16442889 7. Ibrahimagić OĆ Smajlović D Dostović Z Pašić Z Šehanović A Hodžić R Hyperhomocysteinemia and its treatment in patients with ischemic stroke Medicus 2012 21 2 267 272 8. Ibrahimagić OĆ Smajlović D Dostović Z Pašić Z Kunić S Iljazović A Hyperhomocysteinemia and its treatment in patients with Parkinson’s disease Mater Soc 2016 28 4 303 306 10.5455/msm.2016.28.303-306 9. Belcastro V Pierguidi L Castrioto A Menichetti C Gorgone G Ientile R Hyperhomocysteinemia recurrence in levodopa-treated Parkinson’s disease patients Eur J Neurol 2010 17 5 661 615 10.1111/j.1468-1331.2009.02984.x 20050890 10. Tan SHS Hong CC Saha S Murphy D Hui JH Medications in COVID-19 patients: summarizing the current literature from an orthopaedic perspective Int Orthop (SICOT) 2020 44 8 1599 1603 10.1007/s00264-020-04643-5	33479845	PMC7819628	NO-CC CODE	2021-01-31 01:46:50	yes	Osteoporos Int. 2021 Jan 21;:1-2
==== Front Arch Virol Arch Virol Archives of Virology 0304-8608 1432-8798 Springer Vienna Vienna 4956 10.1007/s00705-021-04956-9 Brief Report Efficacy of favipiravir in COVID-19 treatment: a multi-center randomized study Dabbous Hany M. [email protected] 1 http://orcid.org/0000-0003-4366-2218Abd-Elsalam Sherief [email protected] 2 El-Sayed Manal H. [email protected] 3 Sherief Ahmed F. [email protected] 1 Ebeid Fatma F. S. [email protected] 3 El Ghafar Mohamed Samir Abd [email protected] 4 Soliman Shaimaa [email protected] 5 Elbahnasawy Mohamed [email protected] 6 Badawi Rehab [email protected] 2 Tageldin Mohamed Awad [email protected] 7 1 grid.7269.a0000 0004 0621 1570Tropical Medicine Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt 2 grid.412258.80000 0000 9477 7793Department of Tropical Medicine and Infectious Diseases, Faculty of Medicine, Tanta University, El-Giash Street, Tanta, 31527 Egypt 3 grid.7269.a0000 0004 0621 1570Faculty of Medicine, Ain Shams University Research Institute-Clinical Research Center (MASRI-CRC), Cairo, Egypt 4 grid.412258.80000 0000 9477 7793Department of Anesthesia, Surgical Intensive Care and Pain Medicine, Faculty of Medicine, Tanta University, Tanta, Egypt 5 grid.411775.10000 0004 0621 4712Public Health and Community Medicine, Menoufia University, Menoufia, Egypt 6 grid.412258.80000 0000 9477 7793Emergency Medicine and Traumatology Department, Faculty of Medicine, Tanta University, Tanta, Egypt 7 grid.7269.a0000 0004 0621 1570Department of Chest Diseases, Faculty of Medicine, Ain Shams University, Cairo, Egypt Handling Editor: Zhongjie Shi. 25 1 2021 1 6 14 10 2020 25 11 2020 © The Author(s), under exclusive licence to Springer-Verlag GmbH, AT part of Springer Nature 2021This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.No specific antiviral drugs have been approved for the treatment of COVID-19. This study aimed to evaluate the efficacy of favipiravir in treatment of COVID-19. This was a multicenter randomized controlled study including 96 patients with COVID- 19 who were randomly assigned into a chloroquine (CQ) group and a favipiravir group. None of the patients in the favipiravir group needed mechanical ventilation (p = 0.129). One patient (2.3%) in the favipiravir group and two patients (4.2%) in the CQ group died (p = 1.00). Favipiravir is a promising drug for COVID-19 that decreases the hospital stay and the need for mechanical ventilation. ClinicalTrials.gov Identifier NCT04351295. ==== Body Introduction COVID-19 has led to a major worldwide health and economic crisis, with more than 27 million people having contracted the disease and more than 800,000 deaths [1, 2]. No specific antiviral drugs have been approved for the treatment of COVID-19 [3]. The food and drug administration (FDA) granted emergency approval to allow hospitals to use chloroquine and hydroxychloroquine for treatment of COVID-19, and these drugs have been used as standard of care in some countries [4–24]. However, many questions remain about the efficacy of chloroquine in treatment of COVID-19 [6–16] Other antiviral drugs have been suggested to be repurposed for the treatment of COVID-19, such as interferon-ɑ, lopinavir/ritonavir, ribavirin, and remdesivir [5, 6]. Some clinical trials concentrating on viral RNA-dependent RNA polymerase (RdRp) inhibitors have been registered and started [6–11]. Favipiravir, a purine analogue and a potent RdRp inhibitor that has been approved for use in influenza treatment, is also being considered for treatment of COVID-19 [7–11]. Favipiravir acts as a purine analogue and is incorporated in place of guanine or adenine [7–10] and thereby inhibits viral replication. It has been used for treatment of some life-threatening infections such as Ebola, Lassa fever, and rabies, and its therapeutic usefulness has been established in these diseases [8–11]. Data about the efficacy of favipiravir in the treatment of COVID-19 are very scarce. Therefore, the aim of the study was to evaluate the efficacy of favipiravir in treatment COVID-19. Methods This was a multi-center, randomized, interventional phase2/phase3 study that included 96 patients with confirmed SARS-CoV-2 infection. This study was performed at the Ain-Shams University and Tanta University hospitals in the period from April to August 2020. Ethical approval was obtained from the Tanta University Faculty of Medicine Ethics Committee, and the approval number was 34035/20. The study was registered on clinicaltrials.gov under the registration number NCT04351295. Patients who met the criteria to be included in the study were enrolled. The criteria for inclusion included being an adult 18 to 80 years of age with confirmed SARS-CoV-2 infection with mild or moderate symptoms and having been admitted to the hospital three days after the onset of symptoms. All of the patients agreed to participate in the study and signed an informed consent statement. Patients who had allergy or contraindication to the drug, pregnant and lactating mothers, and patients with cardiac problems, liver or renal failure, or other organ failure were excluded from the study. Ninety-eight patients were eligible to participate in the study. After exclusion of patients who refused to participate, 96 patients were randomly assigned into two groups. The chloroquine (CQ) group included 48 patients who received chloroquine 600 mg tablets twice daily added to the standard-of-care therapy for 10 days. The favipiravir group included 48 patients who received 1600 mg of favipiravir twice a day on the first day and 600 mg twice a day from the second to tenth day, added to the standard-of-care therapy for 10 days. Four patients in this group quit after the beginning of the study, and the final number in this group was 44 patients. The four patients who left the study preferred to complete their treatment and be transferred to military hospitals, after which we lost contact with them (Fig. 1).Fig. 1 Flow chart of patient inclusion in the study All participants in the study were interviewed and their demographic and basal data were recorded, including age, sex, weight, height, and body mass index (BMI). All of the patients were subjected to a thorough clinical examination, and blood samples were taken for biochemical analysis, including a complete blood count (CBC), liver function tests, renal function tests, chest X-ray, chest CT scan, and ECG. The principal outcomes of the study were the mortality rate and the need for mechanical ventilation. Sample size calculation was done using Gpower software. A study done by Cai et al. [17] showed that antiviral therapy with favipiravir was able to reduce the time of viral clearance from 11 days to 4 days (about 63%). Based on that study, with a sample power of 80%, an α error of 0.05, and an allocation ratio of 1, the sample size was at least 43 patients in each group [18]. Statistical analysis: The normality of the different variables was tested by Shapero Wilks test. Continuous variables were expressed as the mean, SD and median, while the categorical variables were expressed as numbers and percentages. Student’s t-test was used for normally distributed quantitative variables, while the Mann-Whitney test was used for non-normally distributed ones. The chi-square test (χ2) was used for categorical variables and whenever any of the expected cells were less than five, Fischer’s exact test was used. Univariate binary logistic regression was used to ascertain the effect of possible risk factors on the overall mortality of the patients. A two-sided P-value less than 0.05 was considered statistically significant. The analysis was done with SPSS Statistical Package version 23 (SPSS Inc. Released 2015. IBM SPSS Statistics for Windows, version 23.0, Armnok, NY, IBM Corp.). Results The two groups were matched for gender and age (p = 0.525 and 0.717, respectively). There was no significant difference regarding laboratory parameters, including hemoglobin, WBCs, platelets, CRP, ferritin, D dimer, ALT, AST, or creatinine. There was also no significance difference between the two groups regarding comorbidities (Table 1).Table 1 Baseline clinical and laboratory characteristics of the studied groups Parameter CQ (n = 48) Favipiravir (n = 44) P-value Mean± SD Mean ± SD Median Median Age in years 36.15 ± 17.67 34.86 ± 15.95 0.717 34.0 29.0 Hb 13.21 ± 1.90 13.31 ± 1.63 0.804 13.10 13.10 WBCs 5.60 ± 2.61 6.58 ± 2.99 0.085 4.80 6.19 Platelets 271.64 ± 103.77 242.29 ± 89.08 0.129 272.0 235.50 CRP 15.75 ± 18.08 23.05 ± 54.08 0.095 9.0 7.20 Ferritin 151.85 ± 81.80 145.68 ± 147.44 0.071 144.50 108.50 D dimer 67.79 ± 203.46 61.66 ± 171.52 0.099 1.00 5.17 ALT 29.20 ± 19.99 26.85 ± 19.98 0.096 23.50 21.0 AST 25.68 ± 8.11 26.66 ± 20.25 0.085 25.50 20.0 Creatinine 0.97 ± 0.51 1.40 ± 1.05 0.107 0.90 0.95 No. (%) No. (%) Gender Male 25 (52.1) 20 (45.5) 0.525 Female 23 (47.9) 24 (54.5) Co-morbidities 6 (12.5) 11 (25.0) 0.179 CQ, chloroquine; n, number; SD, standard deviation; HB, hemoglobin; WBCs, white blood cells; ALT, alanine transaminase; AST, aspartate transaminase; CRP, C-reactive protein Although not statistically significant (p = 0.06), the favipiravir group had a lower mean duration of hospital stay (13.29 ± 5.86 days) than the CQ group (15.89 ± 4.75 days). None of the patients in the favipiravir group needed mechanical ventilation or had an oxygen saturation lower than 90%, but in comparison to the CQ group, these differences were not significant (p = 0.118 and 0.129, respectively). Four patients in the CQ group needed mechanical ventilation and received methylprednisolone after their condition worsened. Two patients (4.2%) in the CQ group and one (2.3%) in the favipiravir group died (p = 1.00, Table 2). No significant differences were observed between the groups regarding side effects (Table 2).Table 2 Clinical outcomes of the two groups Parameter CQ (n = 48) Mean ± SD Median Favipiravir (n = 44) Mean ± SD Median P-value Duration of hospital stay 15.89 ± 4.75 13.29 ± 5.86 0.060 Need for mechanical ventilation 4 (8.3) 0 (0.0) 0.118 O2 saturation 100-95% 37 (77.1) 40 (90.9) 0.129 95-90% 9 (18.8) 4 (9.1) <90% 2 (4.2) 0 (0.0) Mortality 2 (4.2) 1 (2.3) 1.00 Side effects: Nausea 2 (4.2) 1 (2.3) 0.938 Headache 3 (6.2) 1 (2.3) 0.672 Diarrhea 2 (4.2) 3 (6.8) 0.920 Elevated liver enzymes 1 (2.1) 3 (6.8) 0.548 Anemia 1 (2.1) 2 (4.5) 0.938 Hyperuricemia 0 (0.0) 2 (4.5) 0.436 Decreased neutrophils 1 (2.1) 2 (4.5) 0.938 *n, number; SD, standard deviation; O2, oxygen Univariate logistic regression of possible risk factors for overall mortality revealed that the patient’s age and CRP level were the only factors significantly associated with mortality (p = 0.045 and 0.019, respectively). Favipiravir treatment was not significantly associated with COVID-19 mortality in this study (p = 0.615). Discussion The current epidemic of COVID-19 has reached pandemic proportions, and intense public health efforts are under way to contain the epidemic worldwide. However, as conclusive therapies for proven COVID-19 continue to be a challenge, there is a considerable interest in repurposing existing antiviral agents [19]. Favipiravir (FPV) is a novel RdRp inhibitor that has been demonstrated to be efficient in treating influenza and Ebola virus infections [20, 21]. Favipiravir is a prodrug that is ribosylated and phosphorylated intracellularly to form the active metabolite favipiravir ibofuranosyl‐5′‐triphosphate (T‐705‐RTP). T‐705‐RTP competes with purine nucleosides and interferes with viral replication by getting incorporated into the viral RNA and thereby inhibiting the RdRp of RNA viruses [24–28]. In this randomized multicenter study, the patients who received FPV had a lower mean duration of hospitalization than the CQ group. None of the patients in the FPV group needed mechanical ventilation, in contrast to the CQ group, but these results were not statistically significant. This is a potentially important observation, as decreasing the need for mechanical ventilation among COVID-19 patients is crucial, especially in developing countries and regions of the world with limited resources. Two patients (4.2%) in the CQ group and one (2.3%) in the FPV group died. This finding suggests that improvement of the patient's condition may depend on inhibition of SARS-CoV-2 and that FPV controls the disease progression of COVID-19 by inhibiting SARS-CoV-2 polymerase activity [9]. To our knowledge, this is the first randomized study to evaluate the efficacy of favipiravir for treatment of COVID-19. The positive results of this study are supported by three previous case reports. The first was by Noda et al., who reported the cases of two elderly COVID-19-positive patients, one of whom had hypoxemia, who received favipiravir with a seemingly beneficial effect [22]. The second case report described a case of COVID-19 pneumonia that did not worsen and was relieved by early administration of favipiravir and ciclesonide [23]. The third report described administration of a combination of FPV with short-course systemic corticosteroid treatment to a patient who was critically ill with COVID-19 pneumonia and COPD who subsequently showed improvement [28]. Although these data support our finding, they are case reports that need to be verified in large randomized controlled studies. A non-randomized interventional study involving 80 patients with non-severe COVID-19 compared favipiravir with lopinavir/ritonavir and showed increased viral clearance in the favipiravir group on day 7, supporting the possible applicability of favipiravir in treatment of COVID-19 [17]. A positive effect of favipiravir was also suggested in a case series by Doi et al., who used a combination of favipiravir and nafamostat mesylate, which showed promising results in critically ill COVID-19 patients [27]. The dose of FVP to be given to critically ill patients is controversial, especially since the publication of recent data showing lower serum levels of the drug in these patients than in less severely ill patients [26]. The main limitation of this study is that it was based on clinical outcomes, the need for ICU admission, and mortality and that the viremic response was not investigated. This was due to the limited resources available. Also, the study included only COVID-19 patients who were mildly or moderately ill and therefore had a better prognosis than severely or critically ill patients. In conclusion, favipiravir is a promising drug for treatment of COVID-19 that might decrease the hospital stay and the need for mechanical ventilation. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Compliance with ethical standards Conflict of interest There are no conflicts of interest. ==== Refs References 1. Zhu N Zhang D Wang W Li X Yang B Song J A novel coronavirus from patients with pneumonia in China, 2019 N Engl J Med. 2020 382 8 727 733 10.1056/NEJMoa2001017 31978945 2. Yin Y Wunderink RG MERS, SARS and other coronaviruses as causes of pneumonia Respirology 2018 23 2 130 137 10.1111/resp.13196 29052924 3. Cascella M Rajnik M Cuomo A Features, evaluation and treatment coronavirus (COVID-19) 2012 Treasure Island StatPearls Publishing 4. Yazdany J Kim AHJ Use of hydroxychloroquine and chloroquine during the COVID-19 pandemic: what every clinician should know Ann Intern Med. 2020 172 754 755 10.7326/M20-1334 32232419 5. Hernandez AV Roman YM Pasupuleti V Hydroxychloroquine or chloroquine for treatment or prophylaxis of COVID-19: a living systematic review Ann Intern Med. 2020 173 4 287 296 10.7326/M20-2496 32459529 6. Du YX Chen XP Favipiravir: pharmacokinetics and concerns about clinical trials for 2019-nCoV infection Clin Pharmacol Ther 2020 108 2 242 247 10.1002/cpt.1844 32246834 7. Arab-Zozani M Hassanipour S Ghoddoosi-Nejad D Favipiravir for treating patients with novel coronavirus (COVID-19): protocol for a systematic review and meta-analysis of randomised clinical trials BMJ Open. 2020 10 7 e039730 10.1136/bmjopen-2020-039730 8. Goldhill DH Te Velthuis AJ Fletcher RA The mechanism of resistance to favipiravir in influenza Proc Natl Acad Sci 2018 115 45 11613 11618 10.1073/pnas.1811345115 30352857 9. Zhu W Chen CZ Gorshkov K RNA-dependent RNA polymerase as a target for COVID-19 Drug discovery SLAS DISCOV Adv Sci Drug Discov 2020 10.1177/2472555220942123 10. Elfiky AA SARS-CoV-2 RNA dependent RNA polymerase (RdRp) targeting: an in silico perspective J Biomol Struct Dyn 2020 10.1080/07391102.2020.1761882 32579073 11. Mohamed AA Mohamed N Mohamoud S SARS-CoV-2: the path of prevention and control Infect Disord Drug Targets 2020 10.2174/1871526520666200520112848 32433010 12. Sarin SK Choudhury A Lau GK Pre-existing liver disease is associated with poor outcome in patients with SARS CoV2 infection; the APCOLIS Study (APASL COVID-19 Liver Injury Spectrum Study) Hepatol Int 2020 14 5 690 700 10.1007/s12072-020-10072-8 32623632 13. Abd-Elsalam S Elkadeem M Glal KA Chloroquine as chemoprophylaxis for COVID-19: Will this work? Infect Disord Drug Targets. 2020 10.2174/1871526520666200726224802 33076813 14. Abd-Elsalam S Esmail ES Khalaf M Tanta protocol for management of COVID-19. Perspectives from a developing country Endocr Metab Immune Disord Drug Targets 2020 10.2174/1871530320999201117142305 33208083 15. Xie M Chen Q Insight into 2019 novel coronavirus—an updated intrim review and lessons from SARS-CoV and MERS-CoV Int J Infect Dis 2020 10.1016/j.ijid.2020.03.071 33352324 16. Marjot T Moon AM Cook JA Outcomes following SARS-CoV-2 infection in patients with chronic liver disease: an international registry study J Hepatol 2020 10.1016/j.jhep.2020.09.024 33035628 17. Cai Q Yang M Liu D Experimental treatment with favipiravir for COVID-19: an open-label control study Engineering (Beijing) 2020 10.1016/j.eng.2020.03.007 7654190 18. Faul F Erdfelder E Lang A-G A flexible statistical power analysis program for the social, behavioral, and biomedical sciences Behav Res Methods 2007 39 175 191 10.3758/BF03193146 17695343 19. World Health Organization. Coronavirus disease (COVID-19) situation report—139. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200607-covid-19-sitrep-139.pdf?sfvrsn=79dc6d08_2. Accessed 8 June 2020. 20. Oestereich L Lüdtke A Wurr S Successful treatment of advanced Ebola virus infection with T-705 (favipiravir) in a small animal model Antiviral Res 2014 105 17 21 10.1016/j.antiviral.2014.02.014 24583123 21. Madelain V Oestereich L Graw F Ebola virus dynamics in mice treated with favipiravir Antiviral Res 2015 123 70 77 10.1016/j.antiviral.2015.08.015 26343011 22. Noda A, Shirai T, Nakajima H et al (2020) Case report: two cases of COVID-19 pneumonia including use of favipiravir. The Japanese Association for Infectious Diseases. http://www.kansensho.or.jp/uploads/files/topics/2019ncov/covid19_casereport_en_200408_2.pdf 23. Yokoyama K, Oguri T, Kato A et al (2020) Case report a case of COVID-19 pneumonia that did not worsen and was relieved by early administration of favipiravir and ciclesonide. http://www.kansensho.or.jp/uploads/files/topics/2019ncov/covid19_casereport_en_200406.pdf. 24. Abena PM Chloroquine and hydroxychloroquine for the prevention or treatment of COVID-19 in Africa: caution for inappropriate off-label use in healthcare settings Am J Trop Med Hyg 2020 102 1184 1188 10.4269/ajtmh.20-0290 32323646 25. Furuta Y Mechanism of action of T-705 against influenza virus Antimicrob Agents Chemother. 2005 49 981 986 10.1128/AAC.49.3.981-986.2005 15728892 26. Irie K Nakagawa A Fujita H Pharmacokinetics of favipiravir in critically Ill patients with COVID-19 Clin Transl Sci. 2020 13 5 880 885 32475019 27. Doi K Ikeda M Hayase N Nafamostat mesylate treatment incombination with favipiravir for patients critically ill with Covid-19: a case series Crit Care 2020 24 392 10.1186/s13054-020-03078-z 32620147 28. Inoue H Jinno M Ohta S Combination treatment of short-course systemic corticosteroid and favipiravir in a successfully treated case of critically ill COVID-19 pneumonia with COPD Respir Med Case Rep 2020 31 101200 32868989	33492523	PMC7829645	NO-CC CODE	2021-01-31 02:01:39	yes	Arch Virol. 2021 Jan 25;:1-6
==== Front Arch Bronconeumol Arch Bronconeumol Archivos De Bronconeumologia 0300-2896 1579-2129 SEPAR. Published by Elsevier España, S.L.U. S0300-2896(21)00025-9 10.1016/j.arbres.2021.01.005 Cartas al Director Respuesta a “Fumador, exfumador y COVID-19” Reply to “Smoker, Former Smoker and COVID-19”Jiménez-Ruiz Carlos Andrés a López-Padilla Daniel b Alonso-Arroyo Adolfo c Aleixandre-Benavent Rafael d Solano-Reina Segismundo b de Granda-Orive José Ignacio e⁎ a Unidad Especializada de Tabaquismo de la Comunidad de Madrid, Hospital Clínico San Carlos, Madrid, España b Servicio de Neumología, Hospital General Universitario Gregorio Marañón, Madrid, España c Departamento de Historia de la Ciencia y Documentación, Universidad de Valencia, Valencia, España d Ingenio (CSIC-UPV), UISYS, Joint Research Unit, Universitat de Valencia, Valencia, España e Servicio de Neumología, Hospital Universitario 12 de Octubre, Universidad Complutense de Madrid, Madrid, España ⁎ Autor para correspondencia. 25 1 2021 4 2021 25 1 2021 57 6768 © 2021 SEPAR. Published by Elsevier España, S.L.U. All rights reserved. 2021 SEPAR Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcEstimado Director: Queremos agradecer a Moril et al.1 el interés en nuestro trabajo, pues nos permite reflexionar sobre el tema. La proteína Spike del SARS-CoV-2 es la responsable de facilitar su entrada a las células humanas, requiriendo cebado por la proteasa TMPRSS2 que permite la fusión de las membranas viral y celular2. El receptor utilizado por la proteína Spike es la enzima convertidora de angiotensina 2 (ACE2)2, que se expresa en diferentes estirpes celulares, además de en el pulmón; existiendo, a nivel pulmonar, un gradiente de expresión de ACE2 (mayor expresión en vías respiratorias superiores [epitelio nasal] y menor en los neumocitos alveolares)2. Se sugiere que una mayor expresión de ACE2 podría contribuir a una aumentada infectividad viral por SARS-CoV-22. Como dicen Moril et al.1, los fumadores activos y con enfermedad pulmonar obstructiva crónica tienen un mayor nivel de expresión de ACE2 que los exfumadores y estos últimos poseen uno más elevado que los nunca fumadores1, 2, habiéndose observado una disminución de la expresión de ACE2 en las células epiteliales bronquiales de exfumadores en comparación con los fumadores activos1, 2. No todos los autores han obtenido los mismos resultados. Lee et al.3 no identificaron diferencias en la expresión de ACE2 basadas en la edad, el sexo o el estatus de tabaquismo, por lo que estos investigadores indican que fumar no es un factor de protección, sino de progresión de la enfermedad por COVID-19. Voinsky et al.4 no encontraron una mayor expresión de ACE2 y TMPRSS2 en fumadores ni en no fumadores, pero sí observaron una mayor expresión de TMPRSS4 (que codifica una proteasa para la entrada en la célula de forma similar a la TMPRSS2) en fumadores con respecto a los que nunca fumaron, lo que les sugiere que podrían presentar un mayor riesgo de infección por COVID-19. Ante el comentario de Moril et al.1 de la posibilidad de un mejor pronóstico al ser fumador, Takagi, en Japón5, realizó una meta-regresión en la que demostraba una asociación positiva entre la prevalencia de tabaquismo y la de infección por COVID-19 independiente de otras co-variables, por lo que la hipótesis de que el pronóstico de la enfermedad es mejor al ser fumador no se sustenta. En un nuevo metaanálisis (MA) de nuestro trabajo6 hemos separado a los pacientes según sean fumadores o exfumadores (solo en cinco artículos se diferenciaba a los exfumadores) (fig. 1 ), encontrando en estos últimos una clara peor progresión con una baja heterogeneidad y en los fumadores una evidente tendencia a una peor progresión pero sin significación estadística. Los mismos resultados fueron obtenidos en el MA de Patanavanich et al.7 (solo ocho artículos separaban por categorías de fumador). La mayoría de los estudios incluidos en los MA presentan limitaciones importantes: son la mayoría retrospectivos y adolecen de importantes sesgos de selección e información sin grupo de comparación, lo que dificulta establecer una causalidad.Figura 1 Ser exfumador o fumador activo es un factor de riesgo para una peor evolución/progresión de la infección por COVID-19. Nos reafirmamos en que los fumadores y exfumadores presentan una peor progresión de la infección por COVID-19, incluyendo una mayor mortalidad, no siendo la nicotina en absoluto un factor protector. Conflicto de intereses Los autores declaran no tener ningún conflicto de intereses. ==== Refs Bibliografía 1 Moril M. Peña H. Fumador, exfumador y COVID-19 Arch Bronconeumol. 2021 10.1016/j.arbres.2020.12.027 2 Heijink I.H. Hackett T.L. Pouwels S.D. Effects of cigarettes smoking on SARS-CoV-2 receptor ACE2 expression in the respiratory epithelium J Pathol. 2020 10.1002/path.5607 3 Lee I.T. Nakayama T. Wu C.T. Goltsev Y. Jiang S. Gall P.A. ACE2 localizes to the respiratory cilia and is not increased by ACE inhibitors or ARBs Nat Commun. 11 2020 5453 10.1038/s41467-020-19145-6 33116139 4 Voinsky I. Gurwitz D. Smoking and COVID-19: Similar bronchial ACE2 and TMPRSS2 expression and higher TMPRSS4 expression in current versus never smokers Drug Dev Res. 2020 10.1002/ddr.21729 5 Takagi H. Systematic review of the prevalence of current smoking among hospitalized COVID-19 patients in China: could nicotine be a therapeutic option? Intern Emerg Med. 15 2020 1601 1603 10.1007/s11739-020-02473-2 32803630 6 Jiménez-Ruiz C.A. López-Padilla D. Alonso-Arroyo A. Aleixandre-Benavent R. Solano-Reina S. De Granda-Orive J.I. COVID-19 and Smoking: A systematic review and meta-analysis of the evidence Arch Bronconeumol. 57 2020 21 34 10.1016/j.arbres.2020.06.024 7 Patanavanich R. Glantz S.A. Smoking is associated with worse outcomes of COVID-19 particularly among younger adults: A systematic review and meta-analysis medRxiv. 2020 10.1101/2020.09.22.20199802	34629664	PMC7830217	NO-CC CODE	2022-01-06 23:18:47	yes	Arch Bronconeumol. 2021 Apr 25; 57:67-68
==== Front J Stroke Cerebrovasc Dis J Stroke Cerebrovasc Dis Journal of Stroke and Cerebrovascular Diseases 1052-3057 1532-8511 Elsevier Inc. S1052-3057(21)00041-0 10.1016/j.jstrokecerebrovasdis.2021.105639 105639 Article Impact of the COVID-19 Pandemic on Stroke Epidemiology and Clinical Stroke Practice in the US Friedlich Daniel MD, FAANS⁎†⁎ Newman Tali ‡ Bricker Stephanie RN, MSN§ ⁎ Stroke Director, Southwest Healthcare System, 36485 Inland Valley Drive, Wildomar, CA 92595, United States † Neurosurgeon, Temecula Valley Neurosurgery, 25150 Hancock St. Suite 210, Murrieta CA 92562, United States ‡ UCSD Research Assistant, 9500 Gilman Dr., La Jolla, CA 92093, United States § Stroke Coordinator, Southwest Healthcare Systems, 36485 Inland Valley Drive, Wildomar, CA 92595, United States ⁎ Address correspondence to Daniel Friedlich, MD, FAANS, Stroke Director, Southwest Healthcare System, 36485 Inland Valley Drive, Wildomar, CA 92595, United States. 26 1 2021 4 2021 26 1 2021 30 4 105639 105639 © 2021 Elsevier Inc. All rights reserved.2021Elsevier Inc.Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.Introduction To examine the impact of the COVID-19 pandemic on stroke, the number of stroke patients, time since last known well (LKW), morbidity, and mortality of stroke patients in Southwest Healthcare System (SHS), California (CA) and the United States (US) were analyzed during 2019 and compared to 2020. Our hypothesis is that there are regional differences in stroke outcome depending on location during the COVID-19 study period which influences stroke epidemiology and clinical stroke practice. Methods The American Heart Association's ‘Get with the Guidelines’ (GWTG) database was used to evaluate the following categories: code stroke, diagnosis of stroke upon discharge, inpatient mortality, modified Rankin Score (mRS) upon discharge (morbidity), and time since last known well (LKW). Stroke registry data from February through June 2019 and 2020 were collected for retrospective review. Results The total number of strokes decreased in the US and CA, but increased in SHS during the COVID-19 study period. The US and SHS demonstrated no change in stroke mortality, but CA demonstrated a higher stroke mortality during the COVID-19 pandemic. There was greater loss of independence with increased stroke morbidity in the US during the COVID-19 pandemic. There was a significant increase in time since LKW in the US and SHS, and an increase trend in time since LKW in CA during the COVID-19 study period. Discussion To understand the impact of the COVID-19 pandemic on stroke epidemiology, we propose that all stroke inpatients should receive a SARS-CoV-2 detection test and this result be entered into the GWTG database. We demonstrate that the regional distribution of stroke mortality in the US changed during the COVID-19 study period, with increased stroke mortality in CA. Stroke morbidity throughout the US was significantly worse during the COVID-19 pandemic. We propose methods to address the impact of the COVID-19 pandemic on clinical stroke practice such as the use of mobile stroke units, clinical trials using anti-inflammation drugs on SARS-CoV-2 positive stroke patients, and COVID stroke rehabilitation centers. Key Words COVID-19SARS-CoV-2EpidemiologyIschemic strokeStroke mortalityStrokeMorbidity ==== Body Introduction The challenges brought on by the coronavirus disease 2019 (COVID-19) have been unprecedented. Since the United States (US) index case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) appeared on January 19, 2020 in Snohomish County, Washington, there has been a logarithmic increase in diagnosed cases and over 350,000 attributable deaths in the US.1 On March 11, 2020, the World Health Organization declared COVID-19 a worldwide pandemic.2 Patients with COVID-19 have presented with headaches, neurological deficits, cerebral large vessel occlusion, and cerebral ischemia with encephalitis.3 , 4 Thrombosis with COVID-19 associated vascular inflammation of the medium and large cerebral vessels may contribute to stroke.5 Systemic cytokine and interleukin responses appear to play a role in the inflammatory response brought on by SARS-CoV-2.6 Furthermore, dysfunction of coagulation such as disseminated intravascular coagulation and thrombocytopenia has been found to increase the likelihood of embolic events resulting in cerebral ischemia in SARS-CoV-2 positive patients.7 Public fear regarding the COVID-19 pandemic is real and has altered behaviors which potentially can affect the delivery of critical medical treatments offered by emergency medical services and hospital systems.8 According to the New England Journal of Medicine, daily counts of patients presenting with stroke symptoms decreased in March of 2020.9 The American Stroke Association reported a decrease in emergency visits for stroke treatment during the global pandemic.2 To understand the regional effects of the COVID-19 pandemic on stroke outcomes, we studied the total number of strokes, times since last known well (LKW), morbidity and mortality of stroke patients in our regional Southwest Healthcare System (SHS) stroke program, California (CA), and the United States (US). We compared data from 2019 with equivalent data from 2020. Our hypothesis is that there are regional differences in stroke depending on location which will influence stroke epidemiology and clinical stroke practice during the COVID-19 study period. Methods We analyzed the American Heart Association's ‘Get with the Guidelines’ (GWTG) shared stroke registry dataset. SHS stroke data was entered originally by our data-entry technicians. Data used to represent CA or the US were entered independently by primary and comprehensive stroke centers in the stroke registry GWTG database. Data from February, March, April, May and June 2020 was used as an aggregate dataset and referred to as the COVID-19 study period. Data from February, March, April, May and June 2019 was used as an aggregate dataset and referred to as the time period prior to the COVID-19 pandemic. Demographics were collected including age, gender, race, and National Institutes of Health Stroke Score (NIHSS) in 2019 and 2020. There were no significant differences in each of these categories. Patients with a discharge diagnosis of stroke and code stroke patients were identified to analyze total number of stroke patients admitted to the hospital. LKW was recorded in minutes. Modified Rankin Scores (mRS) were extracted from patients who had a discharge diagnosis of stroke. Modified RS score of 6 was death and used to determine overall mortality. Patients with a mRS of 0-3 were considered independent, whereas mRS 4-5 were considered dependent. Independence was determined to be mobility without necessary assistance. All mRS numbers were obtained at discharge. Statistical analysis The study design is a retrospective, unspecified analysis of data for primary and comprehensive stroke centers. Total numbers of patients from February through June 2019 and February through June 2020 were extracted from the stroke registry GWTG database to analyze code stroke, discharge diagnosis of stroke, mRS 6 (mortality), and mRS 0-3 (morbidity). The median number of minutes since LKW from February through June 2019 and February through June 2020 were extracted from the GTWG stroke registry. A stratified analysis of total numbers of code strokes, discharge diagnosis of stroke, mRS 6, mRS 0-3 and median numbers since LKW from February through June 2019 were compared to February through June 2020 using independent two-sample T tests. The threshold for statistical significance was set at a p-value less than .05. Statistical analyses were carried out using SAS 9.4 Software (Cary, NC). Results Total number of stroke patients Code stroke and discharge diagnosis of stroke were used to identify stroke in the GWTG dataset. Fig. 1 demonstrates the total number of code stroke patients from February through June 2019, and were compared to the equivalent months in 2020. Fig. 2 demonstrates the total number of patients discharged with stroke diagnosis from February through June 2019 and was compared to 2020. SHS demonstrated an increase in the total number of code stroke patients and an increase in total number of patients discharged with diagnosis of stroke in 2020, compared to 2019 (p=.007 and p=0.0012, respectively). California demonstrated a decrease in the total number of code stroke patients and a decrease in the total number of patients discharged with the diagnosis of stroke in 2020, compared to 2019 (p=.02 and p=0.0267, respectively). The US demonstrated a decrease in the total number of code stroke patients and a decrease in total number of patients discharged with diagnosis of stroke in 2020, compared to 2019 (p=.01 and p=0.015, respectively).Fig. 1 Total number of code stroke patients from February through June 2019 compared to 2020. Significant decrease for CA and the US. Fig 1Fig. 2 Total number of patients discharged with stroke diagnosis from February through June 2019, compared to 2020. Significant decrease for CA and the US. Fig 2 Outcomes: mortality Mortality was determined by evaluating patients with mRS 6 (death) in the GWTG database. Fig. 3 shows the total number of patients recorded with mRS 6 from February through June 2019, compared to 2020. There was no statistical difference in mRS 6 at SHS or the US from 2019 to 2020 (p=0.329 and p=0.3, respectively). However, there was a significant increase in mortality in CA during the COVID-19 pandemic, compared to 2019 (p=.0005).Fig. 3 Total patients recorded with mRS 6 (death) from February through June 2019, compared to 2020. No significant differences demonstrated in SHS or the US. Significant increase in mortality in CA during the 2020 COVID-19 pandemic. Fig 3 Outcomes: morbidity Morbidity was determined by a loss of independence. A decrease in mRS 0-3 was defined as a loss of independence. Fig. 4 demonstrates total patients with mRS 0-3 from February through June 2019, compared to 2020. There were no significant differences in mRS 0-3 at SHS and CA during the COVID-19 pandemic, compared to 2019 (p=0.3, p=0.4, respectively). There was a significant decrease in mRS 0-3 in the US during the COVID-19 pandemic, compared to 2019 (p=.018).Fig. 4 Total patients with mRS 0-3 from February through June 2019, compared to 2020. Significant decrease in amount of independent patients in the US. No significant changes seen in SHS or CA. Fig 4 Time since LKW The median number of minutes since LKW February through June 2019 was compared to 2020 in Fig. 5 . SHS and the US demonstrated a significant increase in the median number of minutes since LKW during the COVID-19 pandemic, compared to 2019 (p=.03 and p=.012, respectively). In CA, there was a non-significant trend increase in the median minutes since LKW during the COVID-19 pandemic, compared to 2019 (p=0.125).Fig. 5 Median minutes since LKW during 2020 COVID-19 pandemic, compared to 2019. Significant increase in minutes since LKW for SHS and the US. Non-significant trend increase in median minutes since LKW in CA during 2020 COVID-19 pandemic, compared to 2019. Fig 5 Discussion The COVID-19 pandemic has been associated with unprecedented morbidity and mortality worldwide. We report that there were significantly decreased strokes, no significant increase in stroke mortality, and a significant increase in stroke morbidity with greater loss of independence during the COVID-19 study period in the US. In CA, we identified a significant increase in stroke mortality with no change in morbidity despite having significantly fewer stroke admissions statewide. In our region of southern CA, SHS experienced a greater number of strokes along with no change in mortality or morbidity. Decreased hospital admissions for stroke during the COVID-19 pandemic has been reported. Clinical investigators in Piacenza, Italy experienced deceased number of strokes per month from 51 to 6 during the onset of the COVID-19 pandemic.10 Even though SHS demonstrated an increase in the number of strokes, we believe this was a result of diversion of patients from a neighboring hospital which closed their stroke program in January 2020. Speculation that SARS-CoV-2 infection would lead to increased strokes ensued because other viral illness leading to Influenza Pneumonia has been associated with increased stroke rates.11 There is no data to suggest that SARS-CoV-2 infection lowers the stroke rate. Nonetheless, the GWTG dataset during the months of February through June 2020 demonstrated a decreased stroke rate during the COVID-19 pandemic in the US and CA. There are several ways to interpret this data. Deceased stroke hospitalization rates could potentially be a result of increased prehospital stroke mortality. Also, fear of infection at the hospital may have prevented individuals from traveling to the hospital for care. GWTG database is unable to account for stroke-like symptoms reported to outpatient primary care clinics. Possibly an outpatient GWTG registry may be developed for primary care physicians to report stroke symptoms in patients with COVID-19 who refuse to go to the hospital. An aggressive public education campaign should be considered to encourage patients with stroke-like symptoms to seek immediate hospital attention. In addition, it is possible that patients who present to the hospital with both respiratory symptoms and mild neurological deficits are being triaged for their respiratory symptoms alone, leading to under-reporting of stroke in the hospital. Furthermore, oxygen requirements create significant challenges during transportation and acquisition of neuroimaging, which may impede the diagnosis of stroke. Implementation of a robust mobile stroke unit program covered by medical insurance may improve access to stroke care and improve clinical stroke practice outcomes for patients who refuse to obtain an evaluation in the Emergency Department. The incidence of stroke in the COVID-19 population will be determined when each code stroke patient receives the SARS-CoV-2 detection test. From February through June 2020, SARS-CoV-2 detection testing was not common due to lack of test availability. In the later part of 2020, some hospitals adopted a policy of universal inpatient SARS-CoV-2 testing. SARS-CoV-2 detection testing performed on all code stroke patients would allow us to analyze the incidence, distribution, and control of stroke in the patients with COVID-19. Once SARS-CoV-2 detection is incorporated into the GWTG database, the impact of SARS-CoV-2 infection on the epidemiology of stroke will be discoverable. Increased mortality in CA suggests that the impact of COVID-19 on stroke is distributed unequally throughout the US. It is difficult to postulate exactly why stroke mortality is increased in CA and not in the entire US. Risk factors and clinical practice patterns in CA should be analyzed in further investigation and compared to states which has a lower mortality. A mortality analysis on all states through the end of 2020 would further detail the impact of COIVD-19 on the distribution of stroke mortality in the US. It would be important to see whether or not the increase in mortality in CA is sustained throughout 2020. During the time period from February through June 2020, we identified a significant increase in disability in the US which we termed ‘loss of independence’. There were significantly fewer patients discharged as mRS 0-3, which indicates that stroke survivors suffered significantly greater disabling neurological deficits. It is possible that neurovascular inflammation associated with COVID-19 increased the severity of stroke, giving rise to a greater loss of independence seen in the US. The COVID-19 inflammatory cascade occurs when SARS-CoV-2 Spike (S) glycoprotein binds to the angiotensin converting enzyme 2 (ACE2) on the cell surface of respiratory epithelium. This interaction downregulates the alternative renin-angiotensin system (RAS), and upregulates the classical RAS pathway associated with inflammation.12 Drugs such as angiotensin receptor blockers have been shown to reduce the risk of stroke through inhibition of the classical RAS inflammatory cascade.13 However, angiotensin receptor blockers have also been shown to upregulate ACE2 receptor expression, which may increase the infectivity of SARS-CoV-2. Clinical studies are currently underway to test the effects of angiotensin receptor blockers on patients with active SARS-CoV-2 infection. Perhaps in regions with higher stroke mortality such as CA, it would be reasonable to perform clinical investigation with angiotensin receptor blockers in combination with intravenous thrombolytics or endovascular thrombectomy as treatment for COVID-19 stroke patients. Furthermore, a generalized ‘loss of independence’ throughout the country may be due to challenges associated with rehabilitation of the stroke patient. Inpatient physical therapy may be limited by oxygen requirements and the small size of a negative pressure isolation room. Devices used for lifting or standing upright may be unavailable due to cross-contamination risk. Stroke patients are experiencing significant delays in being transferred to rehabilitation. Skilled nursing homes (SNF) do not accept COVID-19 patients and require two negative SARS-CoV-2 tests prior to acceptance to the facility. Delays in obtaining critical rehabilitation services may result in a worse mRS at discharge. The development of a COVID-19 rehabilitation center for stable COVID-19 stroke patients may open up much needed hospital beds and potentially improve stroke morbidity nationwide. It is important to note that mRS data on GWTG database is recorded in approximately 50% of stroke patients entered into the database. Accuracy in reporting morbidity would increase with greater numbers of mRS entered into the database. We validated prior claims that there was an increase in time since LKW in the US.14 Delay in hospital presentation places patients in jeopardy of falling out of the window for intravenous thrombolytic or endovascular treatment. Delays in LKW may have contributed to increased loss of independence in the US. SHS demonstrated significant increases in time since LKW, but did not see an increase in loss of independence. This demonstrates that the impact of COVID-19 on the epidemiology of stroke is multifactorial. Risk factors affecting stroke outcome may be related to the infection itself or to the social consequences of the COVID-19 pandemic. Further investigation of the GWTG database is necessary to examine the impact of COVID-19 on stroke morbidity and mortality from the months of July through December 2020. Analysis of stroke mortality in all fifty states would allow us to understand the distribution of stroke risk in the US associated with the COVID-19 pandemic. We identify that marketing and education to encourage patients to obtain emergency stroke treatment is needed now more than ever. Possibly development of a robust mobile stroke unit fleet is in order if patients continue to refuse care or delay reporting to the hospital. Furthermore, we propose that all stroke inpatients should be tested for SARS-CoV-2 and results should entered into the GWTG database to learn the impact of SARS-CoV-2 infection on stroke epidemiology. Finally, we identify nationwide increases in loss of independence after stroke during the COVID-19 pandemic. Clinical trials examining the use of angiotensin receptor blockers for COVID-19 stroke patients and the development of COVID-19 stroke rehabilitation centers may be considered to address the impact of COVID-19 on clinical stroke practice. Disclosures There are no disclosures for any of the authors listed in this article. ==== Refs References 1 Holshue M DeBolt C Lindquist S Lofy K Wiesman J Bruce H Spitters C Ericson K Wilkerson S Tural A Diaz G Cohn A Fox L Patel A Gerber S Kim L Tong S Lu X Lindstrom S Pallansch M Weldon W Biggs H Uyeki T Pillai S First case of 2019 novel coronavirus in the United States N Engl J Med 382 10 2020 929 936 10.1056/NEJMoa2001191 Epub 2020 Jan 31 32004427 2 American Heart Association/American Stroke Association Stroke Council Leadership Temporary emergency guidance to US stroke centers during the COVID-19 pandemic Stroke 51 2020 1910 1912 10.1161/STROKEAHA.120.030023 32233972 3 Zayet S Klopfenstein T Kovacs R Stancescu S Hagenkotter B Acute cerebral stroke with multiple infarctions and COVID-19 Emerg Infect Dis 26 9 2020 2258 2260 10.3201/eid2609.201791 4 Zulgarnain I Balson L Madathil S Massive bilateral stroke in a Covid-19 patient BMJ Case Rep CP 13 2020 e236254 10.1212/WNL.0000000000009713 5 Powers WJ Rabinstein AA Ackerson T Adeoye OM Bambakidis NC Becker K Biller J Brown M Demaerschalk BM Hoh B Jauch EC Kidwell CS Leslie-Mazwi TM Ovbiagele B Scott PA Sheth KN Southerland AM Summers DV Tirschwell DL. 2018 Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American heart association/American stroke association Stroke 49 3 2018 e46 e99 10.1161/str.0000000000000158 29367334 6 Kempuraj D Selvakumar G Ahmed M Raikwar S Thangavel R Khan A Burton C James D Zaheer A COVID-19, mast cells, cytokine storm, psychological stress, and neuroinflammation Neuroscientist 26 5-6 2020 402 414 10.1177/1073858420941476 32684080 7 Barrios-Lopez J Rego-García I Muñoz Martínez C Romero-Fábrega J Rodríguez Rivero M Ruiz Giménez J Escamilla-Sevilla F Mínguez-Castellanos A Fernández Pérez M Ischemic stroke and SARS-CoV-2 infection: a causal or incidental association? Neurologia 35 5 2020 295 302 10.3201/eid2609.201791 32448674 8 Bullrich M Fridman S Mandzia J Mai L Khaw A Vargas Gonzalez J Bagur J Sposato L COVID-19: Stroke admissions, emergency department visits, and prevention clinic referrals Can J Neurol Sci 47 5 2020 693 696 10.1017/cjn.2020.101. Epub 2020 May 26 32450927 9 Kansagra AP Goyal MS Hamilton S Albers GW Collateral effect of Covid-19 on stroke evaluation in the United States N Engl J Med 383 4 2020 400 401 10.1056/NEJMc2014816 32383831 10 Morelli N Rota E Terracciano C Immovilli P Spallazzi M Colombi D Zaino D Michieletti E Guidetti D The battling case of ischemic stroke disappearance from the casualty department in the COVID-19 era Eur Neurol 83 2 2020 213 215 10.1159/000507666. Epub 2020 Apr 14 32289789 11 Warren-Gash C Blackburn R Whitaker H McMenamin J Hayward AC Laboratory-confirmed respiratory infections as triggers for acute myocardial infarction and stroke: a self-controlled case series analysis of national linked datasets from Scotland Eur Respir J 51 3 2018 1701794 10.1183/13993003.01794-2017 12 Divani AA Andalib S Di Napoli M Lattanzi S Hussain M.S Biller J McCullough LD Azarpazhooh MR Seletska A Mayer SA Torbey M Coronavirus disease 2019 and stroke: clinical manifestations and pathophysiological insights J Stroke Cerebrovasc Dis 29 8 2020 104941 10.1016/j.jstrokecerebrovasdis.2020.104941. Epub 2020 May 12 13 Keating G. Losartan/hydrochlorothiazide: a review of its use in the treatment of hypertension and for stroke risk reduction in patients with hypertension and left ventricular hypertrophy Drugs 69 9 2009 1239 1265 10.2165/00003495-200969090-00008 19537840 14 Schirmer CM Ringer AJ Arthur AS Binning MJ Fox WC James RF Levitt MR Tawk RG Vaznedaroglu E Walker M Spiotta AM Endovascular Research Group (ENRG) Delayed presentation of acute ischemic strokes during the Covid-19 crisis J Neurointerv Surg 12 7 2020 639 642 10.1136/neurintsurg-2020-016299 32467244	33540335	PMC7837625	NO-CC CODE	2021-02-03 01:17:02	yes	J Stroke Cerebrovasc Dis. 2021 Apr 26; 30(4):105639
==== Front Cell Cycle Cell Cycle Cell Cycle 1538-4101 1551-4005 Taylor & Francis 33349112 10.1080/15384101.2020.1843812 1843812 Version of Record Research Article Research Paper Inhibited microRNA-494-5p promotes proliferation and suppresses senescence of nucleus pulposus cells in mice with intervertebral disc degeneration by elevating TIMP3 G. CHEN ET AL. CELL CYCLE Chen Gang Zhou Xiaopeng Li Hao Xu Zhengkuan Orthopedic Department, The Second Affiliated Hospital Zhejiang University School of Medicine , Hangzhou, Zhejiang, China CONTACT Zhengkuan Xu [email protected] 22 12 2020 2021 20 1 1122 © 2020 Informa UK Limited, trading as Taylor & Francis Group 2020 Informa UK Limited, trading as Taylor & Francis Group ABSTRACT It has been unraveled that microRNAs (miRNAs) played crucial roles in processes of human diseases, while the role of miR-494-5p in intervertebral disc degeneration (IDD) remains scarcely studied. We aimed to investigate the mechanisms of miR-494-5p in IDD with the involvement of tissue inhibitor of metalloproteinase 3 (TIMP3). Expression of miR-494-5p and TIMP3 in IDD clinical specimens was assessed. The IDD models were established by needle punching, which were then injected with low expression of miR-494-5p or TIMP3 overexpression lentivirus to observe their effects on pathology and apoptosis in IDD mice. The nucleus pulposus cells were isolated and, respectively, treated with miR-494-5p inhibitor or TIMP3 overexpression plasmid to determine the viability, apoptosis, and senescence in vitro. Furthermore, the expression of Aggrecan, Col-2, Caveolin-1, and SA-β-gal in nucleus pulposus cells in vitro were measured. The target relation between miR-494-5p and TIMP3 was determined. An increased expression of miR-494-5p and a decreased expression of TIMP3 were found in IDD. Downregulation of miR-494-5p or overexpression of TIMP3 could relieve pathology and suppress nucleus pulposus cell apoptosis in IDD mice, as well as promote the viability and attenuate the apoptosis and senescence of nucleus pulposus cells from IDD mice. Moreover, inhibition of miR-494-5p or overexpression of TIMP3 upregulated Aggrecan and Col-2 expression while downregulated Caveolin-1 and SA-β-gal expression, and TIMP3 was the target gene of miR-494-5p. Results of this study indicated that the degradation of miR-494-5p ameliorates the development of IDD by elevating TIPM3, which may provide new targets for IDD treatment. KEYWORDS Intervertebral disc degeneration MicroRNA-494-5p tissue inhibitor of metalloproteinase 3 nucleus pulposus cells proliferation differentiation cellular senescence ==== Body Introduction The intervertebral disc consists of nucleus pulposus, annulus fibrosis, and endplate substructures, contributing to the spinal flexibility and large multi-directional loads transformation [1]. As a progressive and irreversible disease that affects the structural integrity and mechanical function [2], intervertebral disc degeneration (IDD) is the initial step of degenerative spinal change, which is accompanied by the gradual occurrence of osteophyte, disc narrowing, and spinal stenosis, and IDD is also the cause of several symptoms such as neck and low back pain [3]. It has been reported that the occlusion of the cartilaginous endplate route results in a lack of nutrition in disc cells, which then causes cell loss and imbalanced tissue homeostasis, and finally leads to the mechanical failure of intervertebral disc [4]. The development of IDD is a complex interaction of physiological, biological, and mechanical factors that are regulated by genetic background, inflammatory responses, and health of individuals [5]. Nowadays, the therapy for IDD includes conservative administration such as bed rest, non-steroidal anti-inflammatory medicines and physical treatment, and surgical methods including laminectomy, corpectomy, and fusion. However, these methods could only treat the symptoms, but could not decelerate or reverse the progression of IDD [6]. Therefore, novel targets are urgently demanded the treatment of IDD. MicroRNAs (miRNAs) are small non-coding RNAs containing about 22 nucleotides and modulate genes correlated with biological functions and signaling pathways [7]. Recently, some miRNAs have been clarified in human diseases, and there are several documents revealing that miRNAs are implicated in the progression of IDD, such as miR-15a [8] and miR-184 [9]. As a member of the miRNAs, miR-494-5p has been identified in several human diseases including endometriosis-associated infertility [10] and portal hypertension [11]. Moreover, miR-494 has been validated to promote apoptosis and extracellular matrix degradation in degenerative human nucleus pulposus cells [12], and the inhibition of miR-494 has been revealed to protect nucleus pulposus cells from tumor necrosis factor-α-induced apoptosis [13]. Thus, we speculated that miR-494-5p may participate in the progression of IDD. Moreover, tissue inhibitor of metalloproteinase 3 (TIMP3) is one of the TIMPs and is comprised of four proteins, which are inhibitors of metalloproteinases and could degenerate the extracellular matrix and improve shedding of cell surface molecules [14]. As previously reported, TIMP3 is implicated in the processes of human malignant melanoma [15], breast cancer [16], and also in IDD [17], while the relation between miR-494-5p and TIMP3 has not been unveiled in human diseases yet. We aimed to identify the impacts of miR-494-5p and TIMP3 on IDD progression, and we inferred that the knockdown of miR-494-5p could attenuate the development of IDD by regulating the biological behaviors of nucleus pulposus cells via targeting TIMP3. Materials and methods Ethics statement Written informed consents were acquired from all patients before this study. The protocol of this study was confirmed by the Ethics Committee of The Second Affiliated Hospital Zhejiang University School of Medicine. The protocol of animal experiments was approved by the Institutional Animal Care and Use Committee of The Second Affiliated Hospital Zhejiang University School of Medicine. Study subjects An amount of 40 intervertebral disc nucleus pulposus samples were collected between January 2017 and September 2018 at the The Second Affiliated Hospital Zhejiang University School of Medicine., 20 cases of which from patients with a mean age of 40.20 ± 8.54 years that had accepted surgery for intervertebral disc herniation were taken as the IDD group, and the rest 20 cases that from patients had accepted surgery for vertebral fracture or patients died of accident (mean age of 36.10 ± 7.98 years) were taken as the control group. The separated specimens were immediately frozen at -80°C. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) The total RNAs in tissues and cells were extracted by Trizol kits (Invitrogen Inc., Carlsbad, CA, USA). The primers (Table 1) weresynthesized by GenePharma Co., Ltd. (Shanghai, China). RNA was reversely transcripted into cDNA, and the reaction solution was conducted with RT-qPCR. The reaction solution contained 2.0 µL diluted cDNA, 0.2 µM/L of each paired primer, 200 µM/L deoxynucleotide triphosphates, 1 U Taq DNA polymerase (Qiagen Co., Ltd., Beijing, China) and 1 × PCR buffer. SYBRGreen (Roche Ltd, Basel, Switzerland) was used for detection and the PCR was conducted by the MiniOpticon real-time PCR detection system (Bio-Rad Laboratories, Hercules, CA, USA). U6 and glyceraldehyde phosphate dehydrogenase (GAPDH) were taken as the internal references of miR-494-5p and TIMP3, respectively. The data were analyzed by 2−ΔΔCt method [18].Table 1. Primer sequence Gene Primer sequence (5ʹ-3ʹ) MiR-494-5p Forward: ATTGAAACATACACGGGAAAC Reverse: GCATGCAGATCCCTACCG TIMP3 Forward: CACGGAAGCCTCTGAAAGTC Reverse: TCCCACCTCTCCACAAAGTT Aggrecan Forward: GGCAACCTCCTGGGTGTAAG Reverse: GGTTCGTGG GCTCACAA Col-2 Forward: GAACAGCATCGCCTACCTGG Reverse: TGTTTCGTGCAGCCATCCT caveolin-1 Forward: CTACAAGCCCAACAACAAGGC Reverse: AGGAAGCTCTTGATGCACGGT SA-β-gal Forward: AGCTATGACTATGACGCCCC Reverse: CTTCCGTCACCGTCTTGAAC U6 Forward: CTCGCTTCGGCAGCACA Reverse: AACGCTTCACGAATTTGCGT GAPDH Forward: GGATTTGGTCGTATTGGG Reverse: GGAAGATGGTGATGGGATT F, forward; R, reverse; miR-494-5p, microRNA-494-5p; TIMP3, tissue inhibitor of metalloproteinase 3; GAPDH, glyceraldehyde phosphate dehydrogenase. Western blot analysis The total proteins in nucleus pulposus tissues and cells were extracted with the concentrations measured using bicinchoninic acid method. The protein load was 30 µg/lane in 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis, and the proteins were transferred to nitrocellulose membranes, which were blocked with 5% skim milk powder in 0.1% tris-buffered saline/Tween20 for 2 h and incubated with primary antibody against TIMP3 (1: 1000, Abcam Inc., Cambridge, MA, USA). After added with horseradish peroxidase (HRP)-conjectured secondary antibody (1: 1000, BOSTER Biological Technology Co., Ltd., Wuhan, Hubei, China) for 1-h incubation at 37°C, the membranes were immersed in enhanced chemiluminescent reaction liquid (Thermo Fisher Scientific Inc., Waltham, MA, USA) for 1 min. With the liquid removed, the membranes were covered by food wrap, exposed, developed, and fixed at dark environment; then, the protein bands were analyzed using ImageJ2x software with GAPDH was taken as the loading control. Experimental animals Specific pathogen-free (SPF) mice (12 weeks) obtained from Experimental animal center of Zhejiang University (Zhejiang, China) were fed at 24 ± 2°C with free access to food and water, and 12 h day/night cycle for 7-d adaptive feeding. Establishment of IDD mouse models The mice were randomly classified into: the normal group (n = 8), the model group (n = 8), the NC group (n = 8), the anti-miR-494-5p group (n = 8), the Lenti-TIMP3 group (n = 8) and the anti-miR-494-5p + si-TIMP3 group (n = 8). IDD models were established as previously described [19]. Briefly, mice were anesthetized by ketamine (100 mg/kg) and an incision was conducted from Co6 to Co8. Next, Co6-Co7 coccygeal discs were punctured by a syringe needle, which was vertically inserted into disc and rotated in the axial direction by 180°for 10 s. The puncture was made parallel to the endplates through the annulus fibrosis into the nucleus pulposus using a 31-G needle, which was inserted 1.5 mm into the disc to depressurize the nucleus. The other segments were left undisturbed as a contrast segment. Negative control (NC) lentivirus (2 µL of lentivirus-containing solution (approximately 106 plaque-forming units)), miR-494-5p downregulated lentivirus (2 µL of lentivirus-containing solution (approximately 106 plaque-forming units)), TIMP3 overexpressed lentivirus (2 µL of lentivirus-containing solution (approximately 106 plaque-forming units)), and miR-494-5p downregulated lentivirus (2 µL of lentivirus-containing solution (approximately 106 plaque-forming units)) + TIMP3 downregulated lentivirus (2 µL of lentivirus-containing solution (approximately 106 plaque-forming units)) were injected into nucleus pulposus of the mice according to the grouping. The miR-494-5p downregulated lentivirus, TIMP3 overexpressed lentivirus, TIMP3 downregulated lentivirus were all constructed by OBIO (Shanghai, China). The mice were euthanized using lethal anesthetic overdose on the 28th d after the operation with their intervertebral disc nucleus pulposus tissues separated. One part of the tissues were fixed for the histological observation, and the rest were preserved in liquid nitrogen for RT-qPCR and Western blot analysis. Hematoxylin-eosin (HE) staining Sections were dewaxed with xylene, rehydrated with gradient ethanol and rinsed for 2 min. Sections were then stained with hematoxylin for 2 min, conducted with color separation by 1% hydrochloric acid ethanol for 10 s, and rinsed by distilled water for 1 min, and then were stained with eosin for 1 min. After the eosin staining, sections were washed by distilled water for 10 s and dehydrated by 95% and 100% ethanol. Finally, sections were permeabilized with xylene and sealed using neutral balsam. Terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick end-labeling (TUNEL) staining TUNEL kits were purchased from Roche company. The paraffin sections were dewaxed by xylene at 60°C twice (each for 5 min), and dehydrated by gradient ethanol, then rinsed by phosphate-buffered saline (PBS) for 3 times (each for 5 min). The proteinase K solution was prepared (98 μL PBS and 2 μL proteinase K (Roche)), and each sample was appended with 100 proteinase K solution at 37°C for 30 min, then blocked for 10 min. After permeabilized by 1 μL 0.1% Triton X 100 and 0.1% sodium citrate solution on ice, the sections were supplemented with TUNEL reaction solution (Roche, PBS was used to replace the reaction solution in the NC group), transfected by peroxidase, and developed by diaminobenzidine (ZSGB-Bio, Beijing, China), and then counterstained by hematoxylin. Next, the sections were dehydrated, permeabilized, sealed, and observed by a light microscope. Cell culture Mice from the normal group and the model group were euthanized by cervical dislocation. The fascia and muscle were dissected, the IVD was exposed, and then the cartilage endplate was removed with the jelly-like tissues collected. The obtained tissues were cut into 0.5 mm3 blocks and detached by 0.25% trypsin at 37°C for 10 min. The detachment was ended by adding 3–5 mL Dulbecco’s modified Eagle medium (DMEM)/F12 complete medium. The tissues were centrifuged at 1200 r/min for 5 min with supernatant discarded, detached by 0.25% collagenase Ⅱ at 37°C for 3 h, and centrifuged at 1200 r/min for 5 min with supernatant discarded, and then the cell concentration was adjusted to 5 × 105 cells/mL by DMEM/F12 complete medium. The cells were incubated at 37°C and with 5% CO2. The medium was changed every 3 d, the morphology and growth of the normal and degenerated nucleus pulposus cells were observed under an inverted microscope, and the ultrastructure of these cells were observed by an electron microscope. After the monolayer formed in primary cells, the cells were detached by 0.25% trypsin and passaged, and cells in the logarithmic growth phase were selected for the subsequent experiments. Cell treatment and grouping The IDD mice’ nucleus pulposus cells were separated into seven groups: the model group (nucleus pulposus cells were normally cultured without any treatment), the NC group (nucleus pulposus cells were transfected with NC sequence), the miR-494-5p inhibitor group (nucleus pulposus cells were transfected with miR-494-5p inhibitor), the overexpressed (OE)-TIMP3 group (nucleus pulposus cells were transfected with overexpressed TIMP3 plasmids), and the miR-494-5p inhibitor + small interfering RNA (si)-TIMP3 group (nucleus pulposus cells were transfected with miR-494-5p inhibitor and silenced TIMP3 vector). The miR-494-5p inhibitor, OE-TIMP3 plasmid and si-TIMP3 vector were obtained from RiboBio Co., Ltd. (Guangdong, China). The transfection was performed using Lipofectamine 2000 reagent (Invitrogen) according to the manufacturers’ information. Other than that, the normal group (nucleus pulposus cells from normal mice without any treatment) was also set. Cell counting kit (CCK-8) assay Nucleus pulposus cells were seeded onto 96-well plates at 4 × 103 cells/well (3 duplicates were set in each group) and incubated. The cell viability was assessed at 0 h, 24 h, 48 h, and 72 h according to the instructions of CCK-8 kits (Beyotime): each well was appended with 10 μL CCK-8 reagent and incubated for 2 h. The absorbance at 490 nm was analyzed by a microplate reader. Senescence-associated β-galactosidase (SA-β-gal) staining The nucleus pulposus cells in the logarithmic growth phase trypsinized and seeded onto 6-well plates at 2 × 105 cells/well. The medium was removed and cells were rinsed with PBS. Fixed with 1 mL β-gal staining fixative for 15 min, the cells were washed with PBS three times (3 min/time). The staining working solution (6 mL) was prepared according to the kit instruction (Beyotime Institute of Biotechnology, Shanghai, China) and each well was added with 1 mL prepared staining working solution. The plates were sealed with plastic wrap and put into a 37°C oven overnight, and then the cells were observed under a microscope. Flow cytometry Cell cycle distribution assessment: transfected cells were rinsed with PBS, fixed in 70% ethanol and precooled at 4°C for at least 1 h, and then were centrifuged at 1500 rev/min for 5 min to remove the fixative solution. The sediment was washed with PBS, and propidium iodide (PI) solution was added according to the instructions of DNA ploidy test kit (BD Biosciences, CA, USA). The BD FACSVerse™ flow cytometry (Becton, Dickinson and Company, NJ, USA) was used to detect cell cycle after 15 min without light exposure. The DNA content was assessed at 488 nm. Cell apoptosis measurement: transfected cells were suspended in binding buffer with the concentration adjusted to 1 × 106 cells/mL, and were then added with 10 μL fluorescein isothiocyanate (FITC)-marked connexin. Annexin and 10 μL of 20 mg/L PI (BD Biosciences) were mixed and incubated for 10 min in the dark. It was washed with binding buffer and analyzed using the Coulter Elite (Beckman Coulter, Miami, FL) flow cytometer. Dual luciferase reporter gene assay The binding sites of miR-494-5p on TIMP3 promoters were predicted by a biological prediction website (http://www.targetscan.org/vert_72/), and dual-luciferase reporter gene assay was employed to confirm whether TIMP3 was the target gene of miR-494-5p. The wild type (WT) and mutation type (MUT) luciferase reporter gene vectors of TIMP3 3ʹ-untranslated region (3ʹUTR) containing binding sites of miR-494-5p were established (respectively, defined as TIMP3-WT and TIMP3-MUT). The sequenced TIMP3-WT and TIMP3-MUT vectors were co-transfected into nucleus pulposus cells with miR-494-5p mimic or mimic NC and incubated for 48 h. Based on the directions of Dual-Luciferase reporter gene detection kits (Promega Corporation, Madison, WI, USA): each sample was resuspended by 80–90 μL passive lysis buffer that had been diluted by distilled sterile water for 15 min. The cell lysis solution (50 μL) was mixed with luciferase assay buffer that had been supplemented with the substrate, and placed on a microplate reader, and the luciferase activity was measured by a fluorescence luminescence instrument, and the standardized data were calculated by the ratio of luciferase activities of renilla and firefly luciferases. Statistical analysis All data analyses were conducted using SPSS 21.0 software (IBM Corp. Armonk, NY, USA). The measurement data were expressed as mean ± standard deviation. The unpaired t-test was performed for comparisons between two groups and one-way analysis of variance (ANOVA) was used for comparisons among multiple groups. P value <0.05 was indicative of a statistically significant difference. Results An increased expression of miR-494-5p and a decreased expression of TIMP3 are found in IDD clinical samples The results of HE staining (Figure 1(a)) indicated that there were abundant oval nucleus pulposus and cartilage cells distributed in the extracellular matrix in normal nucleus pulposus tissues with integrated membrane, even cytoplasm, and red collagen tissue, and regularly arranged. While in the IDD nucleus pulposus tissues, there were a small amount of large nucleus pulposus and cartilage cells with irregular morphology, incomplete membrane, vacuoles in cytoplasm, and red collagen tissue, and irregularly arranged.Figure 1. An increased expression of miR-494-5p and a decreased expression of TIMP3 appear in IDD clinical samples. (a), representative images of HE staining in nucleus pulposus tissues of the IDD group and the control group; (b), miR-494-5p expression in nucleus pulposus tissues of the IDD group and the control group; (c), TIMP3 mRNA expression in nucleus pulposus tissues of the IDD group and the control group; (d), protein expression of TIMP3 in nucleus pulposus tissues of the IDD group and the control group; n = 20. Data are expressed as mean ± standard deviation, and the unpaired t-test was performed for comparisons between two groups Outcomes of RT-qPCR and Western blot analysis (Figure 1b) revealed that miR-494-5p expression was elevated, while TIMP3 expression was reduced in nucleus pulposus tissues of IDD patients. Inhibited miR-494-5p or elevated TIMP3 alleviates pathology and decelerates cell apoptosis in nucleus pulposus tissues of IDD mice Results of RT-qPCR and Western blot analysis (Figure 2a) suggested that miR-494-5p expression was increased, and TIMP3 expression was decreased in mice’s nucleus pulposus tissues of the IDD model. IDD modeled mice injected with miR-494-5p downregulated lentivirus exhibited downregulated miR-494-5p expression while upregulated TIMP3 expression; those injected with TIMP3 overexpressed lentivirus elevated TIMP3 expression in mouse nucleus pulposus tissues. These data indicated that the injection of miR-494-5p low expression lentivirus or TIMP3 overexpression lentivirus in mice was successfully intervened.Figure 2. Inhibited miR-494-5p or elevated TIMP3 alleviates pathology and decelerates cell apoptosis in nucleus pulposus tissues of IDD mice. (a), miR-494-5p expression in mice’s nucleus pulposus tissues of each group; (b), TIMP3 mRNA expression in mice’ nucleus pulposus tissues of each group; (c), protein expression of TIMP3 in mice’ nucleus pulposus tissues of each group; (d), representative images of HE staining in nucleus pulposus tissues of mice in each group; (e), representative images of TUNEL staining in nucleus pulposus tissues of mice in each group; (f), statistical results of TUNEL staining in nucleus pulposus tissues of mice in each group; n = 8, a P < 0.05 vs the normal group, b P < 0.05 vs the NC group, c P < 0.05 vs the anti-miR-494-5p group. Data are expressed as mean ± standard deviation and one-way ANOVA was used for comparisons among multiple groups The HE staining showed that (Figure 2(d)) there were more notochord cells or chondrocyte-like cells, and the cells evenly arranged in the normal mice; in the IDD modeled mice, the nucleus pulposus cells were sharply decreased and disorderedly arranged. The situation of modeled mice that had been injected with negative control lentivirus and miR-494-5p downregulated lentivirus + TIMP3 downregulated lentivirus was similar to IDD modeled mice; there were more nucleus pulposus cells which were in much-ordered arrangement in the modeled mice that had been injected with miR-494-5p downregulated lentivirus, TIMP3 overexpressed lentivirus than the IDD modeled mice. The outcomes of TUNEL staining (Figure 2e) mirrored that IDD modeled mice had more TUNEL positive cells in nucleus pulposus tissues than normal ones; inhibition of miR-494-5p or overexpression of TIMP3 decreased the TUNEL positive cells in nucleus pulposus tissues of IDD mice; effect of suppressed miR-494-5p on TUNEL positive cells was reversed by inhibition of TIMP3. The 3ʹUTR of TIMP3 mRNA is a direct target of miR-494-5p As analyzed by the biological prediction website (Figure 3(a)), miR-494-5p could target TIMP3. To confirm that TIMP3 was the target gene of miR-494-5p, the target relation between miR-494-5p and TIMP3 was analyzed by dual-luciferase reporter gene assay, and the outcomes reflected that miR-494-5p mimic apparently repressed the luciferase activity of WT TIMP3, while exerted no apparent effect on the luciferase activity of MUT TIMP3 (Figure 3(b)), indicating that TIMP3 was the target gene of miR-494-5p.Figure 3. The 3ʹUTR of TIMP3 mRNA is a direct target of miR-494-5p. (a), binding sites of miR-494-5p and TIMP3 that predicted by http://www.targetscan.org/vert_72/; (b), results of dual-luciferase reporter gene assay; (c), miR-494-5p expression in transfected mice’ nucleus pulposus cells of each group; (d), TIMP3 mRNA expression in transfected mice’ nucleus pulposus cells of each group; (e), protein expression of TIMP3 in transfected mice’ nucleus pulposus cells of each group; N = 3, a P < 0.05 vs the normal group, b P < 0.05 vs the NC group, c P < 0.05 vs the miR-494-5p inhibitor group. Data are expressed as mean ± standard deviation, and one-way ANOVA was used for comparisons among multiple groups. The experiment was independently repeated for 3 times, and the unpaired t-test was performed for comparisons between two groups Outcomes of RT-qPCR and Western blot analysis (Figure 3c) suggested that miR-494-5p expression was increased, and TIMP3 expression was decreased in mice’ nucleus pulposus cells of mice with IDD. miR-494-5p inhibitor downregulated miR-494-5p expression while upregulated TIMP3 expression in nucleus pulposus cells of mice with IDD; OE-TIMP3 overexpressed TIMP3 expression in nucleus pulposus cells of mice with IDD; the promotive role of miR-494-5p inhibitor on TIMP3 expression was reversed by si-TIMP3. Inhibited miR-494-5p or elevated TIMP3 attenuates senescence of nucleus pulposus cells from IDD mice The expression of Aggrecan and Col-2 in mice’s nucleus pulposus cells was determined (Figure 4a), and we have found that Aggrecan and Col-2 expression was decreased in nucleus pulposus cells of mice with IDD; miR-494-5p inhibitor or OE-TIMP3 upregulated Aggrecan and Col-2 expression in nucleus pulposus cells of mice with IDD; effect of miR-494-5p inhibitor on Aggrecan and Col-2 expression was eliminated by si-TIMP3.Figure 4. Inhibited miR-494-5p or elevated TIMP3 attenuates senescence of nucleus pulposus cells from IDD mice. (a), expression of Aggrecan mRNA in transfected mice’ nucleus pulposus cells of each group; (b), expression of Col-2 mRNA in transfected mice’ nucleus pulposus cells of each group; (c), expression of Caveolin-1 mRNA in transfected mice’ nucleus pulposus cells of each group; (d), expression of SA-β-gal mRNA in transfected mice’ nucleus pulposus cells of each group; (e), representative images and statistical results of SA-β-gal staining in transfected mice’ nucleus pulposus cells of each group; N = 3, a P < 0.05 vs the normal group, b P < 0.05 vs the NC group, c P < 0.05 vs the miR-494-5p inhibitor group. Data are expressed as mean ± standard deviation and one-way ANOVA was used for comparisons among multiple groups The expression of Caveolin-1 and SA-β-gal was measured as well (Figure 4c), and the outcomes revealed that Caveolin-1 and SA-β-gal expression was increased in IDD nucleus pulposus cells; miR-494-5p inhibitor or OE-TIMP3 downregulated Caveolin-1 and SA-β-gal expression in nucleus pulposus cells of mice with IDD; the role of miR-494-5p inhibitor in Caveolin-1 and SA-β-gal expression was reversed by si-TIMP3. The results of SA-β-gal staining (Figure 4e) indicated an increased rate of SA-β-gal positive cells in nucleus pulposus cells of mice with IDD; suppression of miR-494-5p or overexpression of TIMP3 decreased the rate of SA-β-gal positive cells; impact of miR-494-5p downregulation on rate of SA-β-gal positive cells was abrogated by inhibition of TIMP3. Inhibited miR-494-5p or elevated TIMP3 promotes proliferation and suppresses apoptosis of nucleus pulposus cells from IDD mice The results of CCK-8 assay (Figure 5(a)) reflected that the cell viability of mouse nucleus pulposus cells in each group increased with time. The nucleus pulposus cell viability was suppressed in nucleus pulposus cells of mice with IDD; miR-494-5p inhibitor or OE-TIMP3 enhanced the cell viability in nucleus pulposus cells of mice with IDD, while si-TIMP3 abrogated the role of miR-494-5p inhibitor in nucleus pulposus cell viability.Figure 5. Inhibited miR-494-5p or elevated TIMP3 promotes proliferation and abates apoptosis of nucleus pulposus cells from IDD mice. (a), the viability of transfected mice’ nucleus pulposus cells in each group was detected using CCK-8 assay; (b), cell cycle distribution of transfected mice’ nucleus pulposus cells in each group was assessed by flow cytometry; (c), numbers of mice’ nucleus pulposus cells that arrested in the G0/G1, S, and G2/M phases in each group; (d), apoptosis of transfected mice’ nucleus pulposus cells in each group was measured by flow cytometry; (e), comparisons of apoptosis rate of mice’ nucleus pulposus cells among the groups; N = 3, a P < 0.05 vs the normal group, b P < 0.05 vs the NC group, c P < 0.05 vs the miR-494-5p inhibitor group. Data are expressed as mean ± standard deviation and one-way ANOVA was used for comparisons among multiple groups PI single staining was used to detect the cell cycle distribution of mice’s nucleus pulposus cells, and the outcomes showed that (Figure 5b) cells in the G0/G1 phases were advanced, while in S phase and G2/M phases were reduced in the nucleus pulposus cells of mice with IDD; miR-494-5p inhibitor or OE-TIMP3 decreased cells in the G0/G1 phases and increased cells in the S phase and G2/M phases in nucleus pulposus cells of mice with IDD; the impacts of miR-494-5p inhibitor on cell cycle distribution were abrogated by si-TIMP3. The results of Annexin V-FITC/PI double staining revealed that (Figure 5d) the apoptosis rate of mice’s nucleus pulposus cells was considerably heightened in nucleus pulposus cells of mice with IDD; the apoptosis rate in nucleus pulposus cells was suppressed by miR-494-5p inhibitor or OE-TIMP3, while the effect of miR-494-5p inhibitor was reversed by si-TIMP3. Discussion Although IDD is not a fatal disorder, it has been thought to be a main social burden with a great socioeconomic effect, and there are many people unable to go back to work for some weeks [4]. It has been demonstrated that the miRNAs constitute a group of endogenous post-transcriptional modulators of gene expression by RNA interference, and broadly expressed in organisms, including humans [20]. This research was designed to testify the effects of miR-494-5p in IDD through targeting TIMP3, and the results of our study have illuminated that the degradation of miR-494-5p was able to decelerate the progression of IDD by improving TIMP3 expression. We have concluded several outcomes in this research, and one of them reflected that miR-494-5p was highly expressed, while TIMP3 was poorly expressed in nucleus pulposus tissues of IDD patients, and in both nucleus pulposus tissues and cells in IDD rats. Similar to this result, Xu et al. have pointed out that miR-494-5p expression was highly expressed in endometrial biopsy specimens that gained from mid-reproductive-aged infertile women with endometriosis, which was in contrast to the samples from endometriosis-free infertile women [10]. The same tendency of miR-494-5p has also been revealed by a previous study, in which the authors have provided evidence that miR-494-5p expression was enhanced in hepatic stellate cells after pressure overload, indicating that miR-494-5p was highly expressed in portal hypertension [11]. As for the abnormal expression of TIMP3, a previous study has unraveled that TIMP3 presents a low expression in melanoma lymph node biopsies of melanoma patients [15], and it has been implied that the expression of TIMP3 is repressed in breast cancer cells [16]. Moreover, we have confirmed that there existed a target relation between miR-494-5p and TIMP3 in nucleus pulposus cells of IDD mice, which has not been identified in other researches. Another important finding in our study revealed that the suppressed miR-494-5p and elevated TIMP3 were able to decelerate the apoptosis of both nucleus pulposus tissues and cells in IDD. Similarly, Zheng et al. have illuminated that the degradation of miR-494-3p has the capacity to promote the apoptosis of rat bone marrow mesenchymal stem cells (BMSCs) that are induced by ischemia, and on the contrary, the overexpression of miR-494-3p could restrict the ischemia-triggered apoptosis of BMSCs [21]. Liu et al. have elucidated that the overexpression of TIMP3 could inhibit the myocardial apoptosis to protect against cardiac ischemia/reperfusion injury [22], and it has been validated from the opposite side that the loss of TIMP3 could lead to epithelial and mitochondrial apoptosis [23]. In addition, we have pointed out that the downregulated miR-494-5p and promoted TIMP3 could accelerate the proliferation of nucleus pulposus cells in IDD. Consistent with this result, Esser et al. have mentioned in their study that the repression of miR-494 increased the proliferation, migration, and sprout formation of endothelial cells in vitro as well as endothelial growth in vivo [24], and it has been illustrated that TIMP3 could regulate the proliferation of epithelial cells by inhibiting the activities of matrix metalloproteinases [25]. Besides, we have also found that the repression of miR-494-5p and elevation of TIMP3 could attenuate the senescence and cell cycle progression of nucleus pulposus cells in IDD. In line with the outcome, an extant document has provided evidence that the enhancement of miR-494 was able to induce senescence of human diploid IMR90 fibroblasts as a component of the genetic program [26]. Duan et al. have discovered that miR-494 elevated, and primary murine bronchial epithelial cells arrested in G1 phase after exposed to benzo[a]pyrene, while the downregulation of miR-494 could relive the G1 arrest of the cells [27]. Moreover, miR-494 has been elucidated to facilitate the apoptosis and extracellular matrix degradation in degenerative human nucleus pulposus cells [28], and it has been unraveled that miR-494 inhibition protected nucleus pulposus cells against tumor necrosis factor-α-induced apoptosis [13]. To sum up, we have demonstrated that the miR-494-5p knockdown could decelerate the progression of IDD via promoting the expression of TIMP3, which may be helpful to the further understanding and treatment development of IDD, thereby providing a broad insight into the underlying mechanism for IDD. Acknowledgments We would like to acknowledge the reviewers for their helpful comments on this paper. 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Association of TIMP3 expression with vessel density, macrophage infiltration and prognosis in human malignant melanoma. Eur J Cancer. 2016;53 :135–143.26707830 [16] Gan R, Yang Y, Yang X, et al. Downregulation of miR-221/222 enhances sensitivity of breast cancer cells to tamoxifen through upregulation of TIMP3. Cancer Gene Ther. 2014;21 (7 ):290–296. .24924200 [17] Li Y, Li K, Han X, et al. The imbalance between TIMP3 and matrix-degrading enzymes plays an important role in intervertebral disc degeneration. Biochem Biophys Res Commun. 2016;469 (3 ):507–514.26686417 [18] Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-delta delta c(T)) method. Methods. 2001;25 (4 ):402–408.11846609 [19] Ji ML, Zhang XJ, Shi PL, et al. Downregulation of microRNA-193a-3p is involved in invertebral disc degeneration by targeting MMP14. 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Bone morphogenetic protein 4 regulates microRNAs miR-494 and miR-126-5p in control of endothelial cell function in angiogenesis. Thromb Haemost. 2017;117 (4 ):734–749.28124060 [25] Gill SE, Pape MC, Leco KJ. Tissue inhibitor of metalloproteinases 3 regulates extracellular matrix–cell signaling during bronchiole branching morphogenesis. Dev Biol. 2006;298 (2 ):540–554.16890932 [26] Comegna M, Succoio M, Napolitano M, et al. Identification of miR-494 direct targets involved in senescence of human diploid fibroblasts. Faseb J. 2014;28 (8 ):3720–3733.24823364 [27] Duan H, Jiang Y, Zhang H, et al. MiR-320 and miR-494 affect cell cycles of primary murine bronchial epithelial cells exposed to benzo[a]pyrene. Toxicol In Vitro. 2010;24 (3 ):928–935.19925859 [28] Durak I, Akyol Ö, Es MU, et al. Element structure in stenotic mitral valves. Am J Cardiol. 1993;71 (4 ):355.	33349112	PMC7849772	NO-CC CODE	2022-03-24 23:15:12	yes	Cell Cycle.; 20(1):112-22
==== Front ACS Omega ACS Omega ao acsodf ACS Omega 2470-1343 American Chemical Society 10.1021/acsomega.0c05826 Article Feprazone Prevents Free Fatty Acid (FFA)-Induced Endothelial Inflammation by Mitigating the Activation of the TLR4/MyD88/NF-κB Pathway Song Min Meng Liukun Liu Xiaoxi Yang Yan * Adult Cardiac Surgery Center, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases and Fuwai Hospital, CAMS and PUMC, Beijing 100037, China * Email: [email protected]. Tel/Fax: +86-010-88396565. 09 02 2021 23 02 2021 6 7 4850 4856 30 11 2020 18 01 2021 © 2021 The Authors. Published by American Chemical Society2021The AuthorsThis is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND) Attribution License, which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes. Increased levels of free fatty acid (FFA)-induced endothelial dysfunction play an important role in the initiation and development of atherosclerosis. Feprazone is a nonsteroidal anti-inflammatory compound. However, the beneficial effects of feprazone on FFA-induced endothelial dysfunction have not been reported before. In the current study, we found that treatment with feprazone ameliorated FFA-induced cell death of human aortic endothelial cells (HAECs) by restoring cell viability and reducing the release of lactate dehydrogenase (LDH). Importantly, we found that treatment with feprazone ameliorated FFA-induced oxidative stress by reducing the production of mitochondrial reactive oxygen species (ROS). In addition, feprazone prevented FFA-induced expression and secretion of proinflammatory cytokines and chemokines, such as chemokine ligand 5 (CCL5), interleukin-6 (IL-6), and interleukin-8 (IL-8). We also found that feprazone decreased the expression of matrix metalloproteinase-2 (MMP-2) and matrix metalloproteinase-9 (MMP-9). Interestingly, we found that feprazone reduced the expression of cell adhesion molecules, such as vascular cell adhesion molecule-1 (VCAM-1) and intercellular cell adhesion molecule-1 (ICAM-1). Our results also demonstrate that feprazone prevented FFA-induced activation of the toll-like receptor 4 (TLR4)/myeloid differentiation factor 88 (MyD88)/nuclear factor kappa-B (NF-κB) signaling pathway. These findings suggest that feprazone might serve as a potential agent for the treatment of atherosclerosis by improving the endothelial function. document-id-old-9ao0c05826document-id-new-14ao0c05826ccc-price ==== Body 1 Introduction Atherosclerosis (AS) is characterized by the accumulation of fatty plaque and immune cells in the intimal endothelial space of large- and medium-sized arteries. Progressive disease can lead to arterial narrowing or occlusion, which results in the occurrence of stroke and myocardial infarction in the advanced stages.1 Recent reports show that approximately 31% of all deaths globally can be attributed to AS, making it one of the most deadly diseases worldwide. However, the development of effective and reliable treatments remains challenging.2 Recent research has focused on mitigating the effects of free fatty acids (FFAs) or nonesterified fatty acids. FFA is a byproduct of lipid metabolism and a major metabolic energy source. Elevated plasma levels of FFA are associated with an increased risk of CVDs and metabolic disorders, including obesity, type II diabetes mellitus (T2DM), and coronary artery disease (CAD), and play a critical role in the initiation and progression of AS. In the early stages of AS, exposure to FFA induces endothelial cell (EC) apoptosis/necroptosis, adversely affects EC progenitor cells, and induces EC dysfunction, which is associated with dysregulated nitric oxide (NO) production and irregular vasodilation/constriction.3 Additionally, FFAs induce the production of reactive oxygen species (ROS) by mitochondria. Increased levels of ROS can shift the oxidant/antioxidant balance toward a state of oxidative stress and trigger an inflammatory response.4 Proinflammatory cytokines and chemokines, including C–C chemokine ligand 5 (CCL5), interleukin-6 (IL-6), and interleukin-8 (IL-8), play a major role in the pathogenesis of AS. Activated platelets that adhere to the arterial wall release chemokine CCL5 to recruit leukocytes and other immune cells to invade the intimal space.5 Platelets also release IL-6 and IL-8, which play a major role in AS. An increased plasma IL-6 level is an independent risk factor for AS, as IL-6 has been shown to activate ECs and promote thrombosis, smooth muscle proliferation, and macrophage foam cell formation.6,7 IL-8 is highly expressed in human atherosclerotic lesions and has been used as a marker for subclinical AS.8 Rupture of atherosclerotic plaques due to lesion formation results in myocardial infarction, stroke, and often death.9 Matrix metalloproteinases (MMPs) play a complex role in determining plaque vulnerability. While some MMPs have been shown to promote plaque stability, matrix metalloproteinase-2 (MMP-2) and matrix metalloproteinase-9 (MMP-9) contribute to plaque rupture and lesion formation by degrading extracellular matrix, respectively.10,11 These enzymes have been shown to be significantly upregulated in patients with unstable plaques.12 Cellular adhesion molecules including vascular cellular adhesion molecule (VCAM)-1 and intercellular adhesion molecule (ICAM)-1 play an active role in leukocyte invasion of the vascular wall, thereby contributing to atherosclerotic plaque rupture. VCAM-1 has been suggested as a serum marker to determine the severity of lesion formation.13 The expression of proinflammatory cytokines, chemokines, and adhesion molecules is largely mediated through nuclear factor (NF)-kappa-B (κB) signaling. Toll-like receptors (TLRs) are pattern recognition receptors (PRRs) that mediate the innate immune response to stimuli including pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs). TLRs have been shown to play a pathological role in AS. For example, TLR3 decreases plaque stability by upregulating MMP-2 and MMP-9 expression, while TLR4 triggers nuclear translocation of p65 protein and subsequent activation of the proinflammatory NF-κB signaling pathway through myeloid differentiation factor 88 (MyD88).14 Modifying the activity of TLR-mediated pathways is considered as a potential strategy for the treatment or prevention of AS. Feprazone, also known as prenazone, is a prenylated analogue of phenylbutazone and a nonsteroidal anti-inflammatory drug (NSAID) used for the treatment of joint and muscular pain.15 Feprazone acts by inhibiting the activity of cyclooxygenase (COX)-2, which is a precursor to prostaglandin production, and has been shown to have tenfold selectivity for COX-2 over COX-1.16,17 Many NSAIDs act by inhibiting the activity of prostaglandins. Prostaglandins are a type of fatty acid that trigger pain and inflammation and have been shown to contribute to atherosclerotic plaque rupture by mediating the expression of MMPs.18 Previous research has demonstrated the involvement of COX-2-mediated prostaglandin production in the pathological mechanism of AS.19,20 As a phenylbutazone derivative, feprazone has a similar structure to phenylbutazone, with the main difference lying in the replacement of the butyl located at the C4 position on the pyrazoline-2,5-dione skeleton with a 3-methylbutenyl substituent.21 Feprazone and other members of the pyrazolone family have been used for decades owing to their wide range of pharmacological activities, including antipyretic, analgesic, anti-inflammatory, antioxidant, anticancer, and many others.22−24 In the present study, we investigated whether feprazone might mitigate the effects of FFA in human aortic endothelial cells (HAECs) and explored the underlying mechanism. 2 Results 2.1 Feprazone Improves Cell Viability Feprazone has a molecular structure of C20H20N2O2 (Figure 1) and a molecular weight of 320.4 g/mol (PubChem). We began by exploring the potential protective effects of feprazone against FFA-induced reduced cell viability and increased release of lactate dehydrogenase (LDH). In this experiment, cells were treated with 2.5, 5, and 10 μM feprazone. As shown in Figure 2A, exposure to FFAs reduced the cell viability to 63% of baseline. However, although the protective effect of the low dose of feprazone was negligible, treatment with 5 and 10 μM feprazone exerted a much greater protective effect, rescuing cell viability to 81 and 93% of baseline. As shown in Figure 2B, feprazone dose-dependently reduced the release of LDH from HAECs exposed to insult from FFA, thereby demonstrating a notable protective effect of feprazone against FFA-induced cell death and apoptosis. Figure 1 Molecular structure of feprazone. Figure 2 Feprazone prevented FFA-induced reduction of cell viability and release of lactate dehydrogenase (LDH) in human aortic endothelial cells (HAECs). Cells were stimulated with 300 μM FFAs in the presence or absence of feprazone (2.5, 5, 10 μM) for 48 h. (A) Cell viability was measured using the MTT assay and (B) release of LDH (*, P < 0.001 vs vehicle group; #, ##, P < 0.05, 0.01 vs FFA treatment group). 2.2 Feprazone Reduces FFA-Induced Oxidative Stress and Inflammation Factor Expression Levels of ROS production were determined using MitoScene Red CMXRos staining. As shown in Figure 3, stimulation with 300 μM FFA increased ROS production by 3.4-fold, while 5 and 10 μM feprazone reduced ROS production to only 2.4- and 1.6-fold, respectively. Next, we measured the messenger RNA (mRNA) expression and secretion of CCL5, IL-6, and IL-8. The results of polymerase chain reaction (PCR) analysis in Figure 4A show that while FFA exposure induced a significant increase in the expression of all three cytokines, this effect was reduced by feprazone treatment, with the higher dose mitigating the increase by approximately half. A similar inhibitory effect was observed at the protein level (Figure 4B). Figure 3 Feprazone ameliorated FFA-induced oxidative stress in HAECs. Cells were stimulated with 300 μM FFAs in the presence or absence of feprazone (5, 10 μM) for 24 h. Levels of mitochondrial ROS were measured using MitoScene Red CMXRos staining (, P < 0.001 vs vehicle group; #, ##, P < 0.05, 0.01 vs FFA treatment group). Figure 4 inhibited FFA-induced expression and secretion of proinflammatory cytokines and chemokines in HAECs. Cells were stimulated with 300 μM FFAs in the presence or absence of feprazone (5, 10 μM) for 24 h. (A) mRNA levels of CCL5, IL-6, and IL-8 and (B) secretion of CCL5, IL-6, and IL-8 (, P < 0.001 vs vehicle group; #, ##, P < 0.05, 0.01 vs FFA treatment group). 2.3 Feprazone Inhibits FFA-Induced Expression of Degradative Enzymes and Adhesion Molecules Arterial remodeling mediated by MMPs and immune cell infiltration are significant factors in the pathogenesis of AS. To determine whether feprazone might protect against arterial remodeling, we measured its effects on the mRNA and protein expression of MMP-2 and MMP-9 induced by stimulation with FFAs. As shown in Figure 5A,B, PCR and enzyme-linked immunosorbent assay (ELISA) analyses revealed that FFAs increased MMP-2 and MMP-9 expression by roughly threefold at both the mRNA and protein levels, while these levels were reduced to less than twofold by the higher dose of feprazone. Next, we measured the mRNA and protein expression levels of adhesion molecules VCAM-1 and intercellular cell adhesion molecule-1 (ICAM-1) induced by FFA. As shown in Figure 6A, the mRNA expression levels of the two molecules were increased to 2.8- and 3.4-fold, respectively, while the addition of feprazone dose-dependently mitigated this effect, with the higher dose reducing VCAM-1 and ICAM-1 expression to only 1.7- and 1.8-fold, respectively. The results in Figure 6B show that the two doses of feprazone had a similar inhibitory effect on the protein expression of these two adhesion molecules. Thus, feprazone may prevent arterial remodeling and immune cell infiltration. Figure 5 Feprazone inhibited FFA-induced expression of MMP-2 and MMP-9 in HAECs. Cells were stimulated with 300 μM FFAs in the presence or absence of feprazone (5, 10 μM) for 24 h. (A) mRNA levels of MMP-2 and MMP-9 and (B) protein levels of MMP-2 and MMP-9 (, P < 0.001 vs vehicle group; #, ##, P < 0.05, 0.01 vs FFA treatment group). Figure 6 Feprazone inhibited FFA-induced expression of VCAM-1 and E-selectin in HAECs. Cells were stimulated with 300 μM FFAs in the presence or absence of feprazone (5, 10 μM) for 24 h. (A) mRNA levels of VCAM-1 and ICAM-1 and (B) protein levels of VCAM-1 and ICAM-1 (, P < 0.001 vs vehicle group; #, ##, P < 0.05, 0.01 vs FFA treatment group). 2.4 Effects of Feprazone Are Mediated through the TLR4/MyD88/NF-κB Pathway Finally, we set out to determine the potential pathway involved in the protective effects of feprazone observed in our experiments. The TLR4/MyD88/NF-κB pathway has been shown to be involved in the pathogenesis of AS.14 As shown in Figure 7, FFA stimulation increased the activity of TLR4 and MyD88 by roughly twofold, while the phosphorylation of NF-κB p65 protein increased by 2.5-fold. Indeed, the addition of feprazone exerted a notable inhibitory effect on the activity of TLR4 and MyD88 while reducing the phosphorylation of p65 and subsequent activation of NF-κB by minimizing the levels of all three to roughly 1.5-fold. Therefore, we hypothesize that feprazone may protect against AS by inhibiting the activation of the TLR4/MyD88/NF-κB pathway. Figure 7 Feprazone prevented FFA-induced activation of the TLR4/MyD88/NF-κB pathway in HAECs. Cells were stimulated with 300 μM FFAs in the presence or absence of feprazone (5, 10 μM) for 6 h. Expression of TLR4, MyD88, and p-NF-κB p65 was measured (*, P < 0.001 vs vehicle group; #, ##, P < 0.05, 0.01 vs FFA treatment group). 3 Discussion In the present study, we demonstrate that feprazone treatment could suppress the proatherosclerotic effects of exposure to FFAs in HAECs, such as increased cell death; oxidative stress; expression of proinflammatory cytokines, chemokines, and adhesion molecules; and activation of the NF-κB pathway through TLR4/MyD88 signaling. Additionally, we show that feprazone treatment could prevent FFA-induced cell death and oxidative stress in vitro. Oxidative stress due to overproduction of ROS acts as a key pathological mechanism in all stages of AS. While in normal physiology, ROS are important reactive molecules that regulate various cellular functions and processes, ROS-induced oxidative stress leads to vascular injury, inflammation, and foam cell formation.25,26 Exposure to FFAs is well recognized as a trigger for overproduction of ROS, inflammatory response, and endothelial cell dysfunction.27 Recent research has suggested the use of various types of NSAIDs to inhibit oxidative stress in patients with AS.28 Here, we report that feprazone might protect against ROS-mediated vascular injury by inhibiting the generation of ROS induced by FFAs. Chronic inflammation serves as the cornerstone of numerous diseases, including AS, so it follows that anti-inflammatory medications such as NSAIDs are an important part of disease management. Chemokines, such as CCL5 and IL-8, play a key role in inflammation and contribute to atherogenesis by recruiting immune cells to infiltrate the arterial wall. CCL5 is also known as regulated upon activation, normal T-cell expressed and secreted (RANTES) and has been suggested as a therapeutic target to slow the progression of AS. CCL5 expression is regulated by p65 Rel protein, the same involved in the activation of inflammatory NF-κB signaling, and is increased in atherosclerotic plaques.29,30 Antagonism of CCL5 and its receptor CCR5 has been shown to reduce atherosclerotic burden and hinder disease progression.31 In the present study, we found that exposure to FFAs significantly upregulated the mRNA and protein expression of CCL5. Additionally, we found that feprazone could suppress the expression of CCL5 induced by FFAs. IL-6 is regarded as one of the main upstream cytokines involved in the chronic inflammatory response in AS. IL-6 is highly expressed in atherosclerotic lesions and is known to affect a variety of different cell types. Although IL-6 is most well recognized for its role in promoting atheroma formation by activating ECs and promoting thrombosis, smooth muscle cell migration, and lipid accumulation, recent research has raised some controversy regarding the potential protective role of IL-6, as it has also been shown to aid in macrophage cholesterol efflux via ATP-binding cassette transporter (ABC)A1. Inhibition of IL-6 has been suggested as a treatment approach for AS.6,32 Chemokine IL-8 binds to its receptors CXC chemokine receptor 1 (CXCR1) and CXC chemokine receptor 2 (CXCR2) to initiate various biological functions, including inflammation, angiogenesis, mitosis, etc. IL-8 has been shown to contribute to AS via neutrophil extracellular trap formation, which further upregulates IL-8 expression through TLR9/NF-κB signaling, thereby creating a pathological positive feedback loop.33 Inhibition of COX-2 is an established anti-inflammatory treatment, and COX-2 inhibitors have been shown to reduce early atherosclerosis in mouse models.34 In the present study, we found that the COX-2 inhibitor feprazone could inhibit the expression of IL-6 and IL-8 in HAECs challenged with FFAs. Thus, the anti-inflammatory effects of feprazone may be harnessed to inhibit atherogenesis. Proinflammatory NF-κB signaling is one of the most well-known and thoroughly studied inflammatory signaling mechanisms involved in AS. Activation of NF-κB can occur through several intercellular signaling pathways in AS, and inhibiting its activity has been well-documented as a potential therapeutic strategy to halt or prevent disease progression.35−37 Previous research has shown that exposure to FFAs increases NF-κB activation, thereby driving EC dysfunction and atherogenesis.38 Recently, a mouse model study demonstrated that inhibition of NF-κB could help protect against vascular dysfunction in diabetic mice via COX-2 inhibition.39 In AS, after immune cells are recruited to the vascular wall through chemokine signaling, adhesion molecules including ICAM-1 and VCAM-1 induce cells to roll along and cling to the endothelial cells of the arterial wall, followed by intimal infiltration and lesion formation. Modifying TLR4/NF-κB signaling has been shown to attenuate atherosclerosis by inhibiting the expression of VCAM-1 and ICAM-1.40 Previous research has revealed the association between COX-2 inhibition and reduced expression of cellular adhesion molecules.41 Here, we found that treatment with feprazone not only suppressed FFA-induced expression of adhesion molecules but also inhibited activation of NF-κB signaling through TLR4/MyD88. Together, our findings provide evidence for a novel antiatherosclerotic mechanism of the COX-2 inhibitor and NSAID feprazone against FFA-induced development of AS. As the diet of the global population trends toward a high-fat western diet, the prevalence of AS and related metabolic disorders is likely to increase, making therapies against such disease highly valuable.42 Here, we found that feprazone could attenuate several pathological mechanisms associated with AS, including cell death and apoptosis, oxidative stress, inflammation, and monocyte adhesion to ECs. However, the present study was only performed using an in vitro model of FFA-induced AS. Future studies identifying the underlying molecular mechanism and exploring the effects of feprazone on AS in vivo are needed to better understand its therapeutic potential. In the meantime, this research lies the groundwork for such investigation. 4 Materials and Methods 4.1 Cell Culture and Treatment Human subject experiments were designed in accordance with the World Medical Association Declaration of Helsinki Ethical Principles for Medical Research Involving Human Subjects. All of the experiments were approved by the ethics committee of Fu Wai Hospital. Human aortic endothelial cells (HAECs) were supplied by the American Type Culture Collection (ATCC, Massachusetts). Cells were maintained in endothelial basal medium-2 (EBM-2) (Lonza, Switzerland) containing endothelial growth medium-2 supplements (0.004 mL/mL endothelial cell growth supplement, 10 ng/mL epidermal growth factor, 90 μg/mL heparin, and 1 μg/mL hydrocortisone), 5% fetal bovine serum (FBS), and 1% antibiotics (penicillin/streptomycin) in a humid atmosphere at 37°C and 5% CO2. The medium was changed every 3–4 days. The cells were then stimulated with 300 μM FFAs in the presence or absence of feprazone (purity ≥98%, no. GC40565, GLPBIO) at concentrations of 2.5, 5, and 10 μM for the cell viability and apoptosis experiments and 5 and 10 μM for all other experiments. 4.2 MTT Assay To assess the cell viability of FFA-induced HAECs treated with feprazone, we employed the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay as previously described. Prior to experimentation, HAECs were seeded into 96-well plates at a density of 2 × 104 cells/well and treated with 300 μM FFA with or without 5 and 10 μM feprazone. In total, 20 μl of MTT solution (5 mg/mL, Sigma-Aldrich) was added to each well. The plates were then incubated in a 5% CO2 incubator overnight at 37 °C. The culture medium was then removed, and dimethyl sulfoxide (DMSO, 150 μL) was added to the wells to dissolve the precipitate. The optical density at 490 nm was measured using a microplate reader. 4.3 LDH Release After the indicated treatment, the release of LDH was measured. Briefly, 1.5 × 104 cells were seeded into 96-well plates, and 50 μL of supernatant was transferred to a new well, followed by the addition of 50 μL of LDH assay solution to each well. The plates were covered and allowed to process for 1 h, followed by the addition of 50 μL of stop solution. The rate of absorbance recorded at 570 nm was used to determine the release of LDH. 4.4 MitoScene Red CMXRos Staining To determine levels of oxidative stress in HAECs, mitochondrial ROS was detected using MitoScene Red CMXRos staining. Briefly, after necessary treatment, cells were rinsed with PBS three times, followed by incubation with 1 μM MitoScene Red CMXRos for 30 min at 37 °C in the dark. Fluorescent signals were visualized using a confocal microscope with emission/excitation wavelength of 510/580 nm. 4.5 Real-Time PCR To determine the RNA expression of the target genes, total RNA was extracted from treated HAECs using an RNeasy Mini Kit in accordance with the manufacturer’s instructions (Qiagen). Isolated RNA was used to synthesize complementary DNA (cDNA) using a Universal One-Step RT-qPCR Kit (Bio-rad). Then, 20 μg of cDNA was subjected to SYBR Green PCR using an ABI 7900HT system. The protocol consisted of 95 °C for 5 min and 40 cycles of 95 °C for 10 s, 60 °C for 30 s, and 72 °C for 30 s. The 2–ΔΔCt method was used to determine the levels of mRNA. The following primers were used in this study: human GAPDH: forward: 5′-ACCCACTCCTCCACCTTTGA-3′, reverse: 5′-CTGTTGCTGTAGCCAAATTCGT-3′; CCL5: forward: 5′-CCTGCTGCTTTGCCTACCTCTC-3′, reverse: 5′-ACACACTTGGCGGTTCCTTCGA-3′; IL-6: forward: 5′-AGGATACCACTCCCAACAGACCT-3′, reverse: 5′-CAAGTGCATCATCGTTGTTCATAC-3′; IL-8: forward: 5′-GTGCAGTTTTGCCAAGGAGT-3′, reverse: 5′-TTATGAATTCTCAGCCCTCTTCAAAAACTTCTC-3′; MMP-2: forward: 5′-ACTGTTGGTGGGAACTCAGAAG-3′, reverse: 5′-CAAGGTCAATGTCAGGAGAGG-3′; MMP-9: forward: 5′-GCCACTACTGTGCCTTTGAGTC-3′, reverse: 5′-CCCTCAGAGAATCGCCAGTACT-3′; ICAM-1: forward: 5′-AGAAATTGGCTCCATGGTGATCTC-3′, reverse: 5′-ACATGCAGCACCTCCTGTGACCA-3′; VCAM-1: forward: 5′-TGACAAGTCCCCATCGTTGA-3′, reverse: 5′-ACCTCGCGACGGCATAATT-3′. 4.6 ELISA Enzyme-linked immunosorbent assay (ELISA) kits were used in accordance with the manufacturer’s instructions to determine the protein secretions of the target genes. Briefly, 50 μL of cell culture supernatant was collected and added to ELISA plates and incubated overnight at 4 °C. After that, the plates were incubated with primary antibody for 1 h followed by HRP-conjugated secondary antibodies for 30 min after a thorough washing. The reaction was stopped, and 100 μL of substrate buffer was added. The absorbance was recorded at 450 nm to index the concentrations of the target proteins. 4.7 Western Blot Analysis After the indicated treatment, radioimmunoprecipitation assay (RIPA) buffer was used to obtain total protein from HAECs. Briefly, 20 μg of total protein was electrically separated onto a sodium dodecyl sulfate polyacrylamide gel and then transferred onto a poly(vinylidene difluoride) (PVDF) membrane, which was then blocked against nonspecific sites for 1 h using skimmed milk. The membranes were incubated overnight with primary antibodies and then washed three times before the addition of HRP-conjugated secondary antibodies for 30 min. Enhanced chemiluminescence was used to determine the fluorescent protein signals. The following antibodies were used in this study: TLR4 (1:2000, no. 14358, Cell Signaling Technology); Myd88 (1:2000, no. 4283, Cell Signaling Technology); p-NF-κB p65 (1:1000, no. 3033, Cell Signaling Technology); NF-κB p65 (1:2000, no. 8242, Cell Signaling Technology); β-actin (1:10 000, no. 4970, Cell Signaling Technology); antirabbit IgG, HRP-linked antibody (1:3000, no. 7074, Cell Signaling Technology); and antimouse IgG, HRP-linked antibody (1:3000, no. 7076, Cell Signaling Technology). 4.8 Statistical Analysis The experimental data are presented as mean ± standard error of mean (SEM). Statistical analysis was carried out by analysis of variance (ANOVA) with Tukey’s posthoc test using SPSS software (Version 19.0). Results with a P value of <0.05 were regarded statistically significant. The authors declare no competing financial interest. Acknowledgments This work is funded by the National Center for Cardiovascular Diseases and Fu Wai Hospital, CAMS and PUMC. ==== Refs References Wolf D. ; Ley K. Immunity and inflammation in atherosclerosis . Circ. Res. 2019 , 124 , 315 –327 . 10.1161/CIRCRESAHA.118.313591 .30653442 Song P. ; Fang Z. ; Wang H. ; Cai Y. ; Rahimi K. ; Zhu Y. ; Fowkes F. G. ; Fowkes F. J. ; Rudan I. Global and regional prevalence, burden, and risk factors for carotid atherosclerosis: a systematic review, meta-analysis, and modelling study . Lancet Global Health 2020 , 8 , e721 –e729 . 10.1016/S2214-109X(20)30117-0 .32353319 Ghosh A. ; Gao L. ; Thakur A. ; Siu P. M. ; Lai C. W. Role of free fatty acids in endothelial dysfunction . J. Biomed. Sci. 2017 , 24 , 5010.1186/s12929-017-0357-5 .28750629 López-Domènech S. ; Bañuls C. ; Díaz-Morales N. ; Escribano-López I. ; Morillas C. ; Veses S. ; Orden S. ; Álvarez Á. ; Víctor V. M. ; Hernández-Mijares A. ; Rocha M. 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Archives Cardiovasc. Dis. Suppl. 2020 , 12 , 201 –202 . 10.1016/j.acvdsp.2020.03.007 .	33644593	PMC7905947	NO-CC CODE	2021-02-26 23:19:25	yes	ACS Omega. 2021 Feb 9; 6(7):4850-4856
==== Front Soft comput Soft comput Soft Computing 1432-7643 1433-7479 Springer Berlin Heidelberg Berlin/Heidelberg 5685 10.1007/s00500-021-05685-6 Focus Optimization model design of cross-border e-commerce transportation path under the background of prevention and control of COVID-19 pneumonia Abudureheman Abuduaini [email protected] Nilupaer Aishanjiang grid.443603.6 0000 0004 0369 4431 Xinjiang University of Finance and Economics, Urumqi, 830012 China Communicated by Vicente Garcia Diaz. 6 3 2021 19 9 2 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. In order to better accelerate the transition from traditional trade to cross-border e-commerce, a cross-border e-commerce transportation route optimization model was designed in the context of the prevention and control of new crown pneumonia. Against the background of the new coronavirus pneumonia, through the analysis and research of the current situation of domestic and foreign e-commerce logistics, optimize the cross-border e-commerce logistics distribution model, establish an environmental model, and use efficient search algorithms to search for walking paths that meet environmental requirements. Based on the Dijkstra algorithm model of demand, and based on the linear relationship between demand and delivery distance, an optimal route selection model is established to select the optimal route with the shortest total travel distance. The simulation results show that the cross-border e-commerce transportation time of this model is within 13 h, which is shorter than that of the traditional model. The search efficiency of the optimal route for cross-border e-commerce transportation is higher, and the time for cross-border e-commerce transportation is shorter. Keywords COVID-19 pneumonia Epidemic prevention and control Cross-border e-commerce Transport path National Social Science Fund Project: Xinjiang cross border e-commerce development strategy research19CGL068 Abudureheman Abuduaini ==== Body Introduction In recent years, the development of cross-border electronic commerce has brought new opportunities to the export of China's manufacturing industry. Although China's exports continued to decline, cross-border electronic commerce's exports went against the trend. Through cross-border electronic commerce, we can shorten the supply chain of manufactured goods export, reduce the export cost and enhance the export competitiveness of enterprises (Valarezo et al. 2018). Therefore, it is of great practical significance to study the export route optimization of manufactured goods under cross-border electronic commerce. Starting from cross-border electronic commerce, this paper combs the development status of cross-border electronic commerce, analyzes its relationship with manufactured goods export, and introduces the main problems faced by China's manufactured goods export (Ji et al. 2019). Taking Viva office chair export as an example, this paper discusses the specific methods of optimizing the export route of China's manufactured goods through cross-border electronic commerce, and analyzes how to realize the integration of cross-border electronic commerce's supply chain through the integration of cross-border electronic commerce's supply chain, the comprehensive foreign trade service platform and the optimization and upgrading of enterprises themselves, so as to promote the optimization of China's manufactured goods export route and the development of China's manufacturing industry. The combination of domestic e-commerce and traditional e-commerce can simply be understood as cross-border e-commerce, which has significant difference in expense, network, audience, level of complexity, operating excellence and benefit. Subject to the cross-border e-commerce transaction that belongs to wide range of causes, to enter a settlement through the e-commerce network, make payment settlement, and deliver resources through logistics and complete an import permit where conventional exchange uses information channels such as wholesale communications, mail, and information distribution exhibitions. Literature survey Cross-border e-commerce transportation route optimization model under the background of COVID-19. Cross-border e-commerce distribution model Novel coronavirus pneumonia has run to affects the business model to enterprise, and many new businesses are seeking to explore and break through the new crown pneumonia background. Novel coronavirus pneumonia will be completely cancelled before the Chinese cross-border electricity suppliers will be more restricted, cross-border consumption will be difficult to meet, cross border consumer demand will be released, cross-border e-commerce will be rapid development. According to the development of the epidemic situation, the cross-border e-commerce platform should take measures to the sellers of the platform in a timely manner and formulate feasible response plans (Xu et al. 2018). Relying on the platform development, both sides can support each other. Good commercial development is a solid foundation for the development of the platform. We should make full use of the advantages of the platform, such as funds and resources, to create disaster resistance with enterprises. In the transformation of cross-border e-commerce, although the entire industrial chain of cross-border e-commerce has been impacted and affected, in the long run, China's trade digitization process will accelerate. The logistics distribution mode of e-commerce enterprises is various (Gao 2020). At present, the main domestic e-commerce enterprises and related departments have realized the importance of logistics distribution, and there are also some problems in logistics distribution. Based on the background of new coronary pneumonia, this paper analyzes and studies the current situation of domestic and foreign e-commerce logistics distribution, optimizes the cross-border e-commerce logistics distribution mode, realizes the logistics distribution function and provides perfect logistics services through the links of goods packaging, storage, processing, sorting, transportation, etc. Details are shown in Table 1.Table 1 Cross-border e-commerce distribution mode under the background of prevention and control of COVID-19 pneumonia Distribution mode Form Characteristic Provide home delivery service Online transaction payment of goods directly delivered to the user's home It brings convenience to online shopping customers, improves service quality, and brings users a good online shopping experience. However, there are also corresponding disadvantages. When delivering goods to the door, customers need to provide detailed receiving address, which may cause the leakage of personal information of online shopping customers, which poses a great challenge to the security and confidentiality mechanism of e-commerce system Self service delivery Manual self-services pick up point One is that logistics suppliers set up special pick-up points through self-construction, and the other is to cooperate with local stores to set up pick-up points, such as shops, pharmacies, community properties, etc The cost of self-built mode is high, but the service is more professional: the cost of cooperation with stores is lower, and the service is more convenient The cost of self-service delivery is lower, the information security of customers is high, and the time for customers to pick up goods is more flexible Self service delivery cabinet It is divided into public storage box and private receiving box. For example, Jingdong's pick-up container, Wal Mart store's locker, CoDeSys's skybox It is more flexible in terms of security and time. Public storage boxes need a lot of capital investment in infrastructure. Private containers are for individual high-end groups, so they are not universal The operation process of logistics center is based on logistics links and basic processes. For distribution centers with different functions and different commodity distribution, their operation processes and links will be different, but all of them are based on the basic processes and make appropriate adjustments to the corresponding operation links (Yuan et al. 2019). The main work includes: order processing, purchase, transportation, storage, sorting, distribution processing, sub-packing, delivery, etc. (Zhong et al. 2019). Figure 1 shows the basic operation process of the logistics center, shows the distribution process of the entire logistics center, and reflects the operation process of the entire logistics center.Fig. 1 Distribution process of cross-border e-commerce logistics center As shown in the figure, the distribution center, like other economic entities, has clear business objectives and service targets, and its business activities are driven by customer order information (Liang and Wang 2019). Many cross-border e-commerce organizations can come together to create a logistics warehouse storage center in local and overseas, and collaboration participants can distribute the goods to the distribution storage and distribution center. As per the guidelines, after international buyers position the order, the logistics center transfers the commodities to the distribution center overseas. And then the fulfillment center overseas distributes the products to overseas customers, according to the shipment instructions. Therefore, during the planning and construction of distribution center, before carrying out distribution activities, it is necessary to analyze the data such as customer distribution, commodity characteristics, variety quantity and delivery time, and determine the types, specifications, quantity and delivery time of required commodities according to the order information. Order processing is the premise and foundation of distribution center organization and scheduling, and it is the core of the whole system (Bhattacharya and Raju 2019). In order to make better use of the capacity and loading capacity of loaders and improve the transportation efficiency, goods from different customers can be transported by the same truck. Therefore, all transportation work must be completed before shipment (Luo et al. 2019). Efficient mixing and assembly can not only reduce transportation costs, but also reduce traffic flow, change traffic conditions, and reduce operation time and operation costs. Only by selecting materials can we get twice the result with half the effort. Cross-border e-commerce transportation route search By establishing an environment model and using an efficient search algorithm, a walking path that meets the requirements of the environment is searched to achieve the goal of optimizing the objective function. Users are selective and pickier on what they expect from things and the internet. In addition, fantastic interfaces and innovations are just the first component of a metasearch solution for eCommerce. Before making any decision, suggest evaluating the situation where data helps a great deal. Enormous continued business and significant technological advances have brought about, for better or worse, not only drastic improvements in the economic system, but also widespread environmental consequences. The Internet offers a new age in which global players in the growth of e-commerce have increased. However, due to the limitations of the algorithm, the walking path obtained by path search is not necessarily a feasible path, and smoothing is needed before obtaining a feasible path (Chen et al. 2019). Under the objective existence of shelving in logistics distribution center, the optimization of commodity selection path has become a special important problem that restricts commodity selection. Mineral processing routes are usually horizontal and vertical straight lines. If the goods on two adjacent shelves are not on the same shelf, it is impossible to carry out coal preparation directly through one shelf, but must come out from the existing shelf and then operate on the other shelf (Mertens et al. 2020). Therefore, the smooth connection of paths is the key to optimize the procurement business path of e-commerce distribution center. On the basis of the model hypothesis, the mathematical model of batch ordering problem is established by using the following formula:1 maxZ=r∑s∈Sdsxs In the objective function, z represents the total similarity of one batch after another, d represents the set of batches after one batch, s represents the value of decision variable, which is 0 or 1, Xs represents a selected batch, and r represents the selection and batch of a path. The specific algorithm is as follows:2 S.T:∑t=1kvtats≤V For the alternative path with volume constraint, vt is the quantity of volume in t order, and ATS is a determining variable with a value of 0 or 1. When ats = 0, it indicates that the order t has not been allocated to the s batch; when at is 1, it indicates that the order t has been allocated to the s batch; in V, the valid value for TV path transportation is:3 maxs-S.T:∑t=1kvtats=Z∑s∈Satsx The above formula means that each order can only be selected and assigned to one batch. Assuming that there are x distribution nodes (cargo destinations) and Y distribution centers (warehouses) in the network, and the node location remains unchanged, the nodes should be changed according to customer demand. When goods are allocated from distribution center m to node n, the following conditions must be met. Limiting factors:4 V≤Vmax≤T≤Tmax In the equation, t is the conveying capacity and V is the speed of the conveying line. Then, the adjacency matrix A = aij is used to describe the transportation network, G = (X, Y), where aij = 0 indicates that there is no direct distribution path between distribution center and distribution node n; Aij = 1 indicates that there is no direct distribution path between distribution center M and N. The adjacency matrix a is as follows:5 A=011011101011010011101100010011011011 The matrix A above simply describes the relationship between the distribution nodes and the distribution center. Therefore, in order to search for the path that meets the environmental requirements more quickly, we need to use the Wij weighting method to describe it.6 wij=wijaij=10,i=j∞,aij=0 The weighted matrix of the model is obtained.7 W′=0∞1(1)1(1)1(1)∞∞01(2)∞1(2)1(2)1(1)1(2)01(1)1(2)1(2)1(1)∞1(1)01(1)∞1(11)1(2)1(2)1(1)01(2)∞1(2)1(2)∞1(2)0 Establish a further link between demand and distribution distance. Because the closer to the distribution center, the greater the demand of the distribution node for it, so we can see the linear relationship between demand and distribution distance, such as the following formula:8 fx=2(E-W′)ZA∑s∈Satsx To measure the similarity between measurements, the shortest point distance process. In general, the sum of absolute differences between objects across all parameters often looks like two sample points that we will use to measure the various distance measure. GP and GQ are the expansion subgraphs rooted at the centroid vertices to optimize the shortest path. The shortest point distance method describes the distance between GP and two objects closest to GQ by mathematical language, as shown in the formula:9 D(p,q)=mindjt\|j∈Gp,l∈Gq The maximum distance method regards the distance between GP and the farthest and nearest GQ as the distance between the two types. Therefore, the expression of searching cross-border e-commerce transportation path is as follows:10 B(p,q)=maxdjl\|j∈Gp,l∈Gq Based on the above algorithm, this paper analyzes the alternative path of cross-border e-commerce, and selects the optimal route and alternative path of goods transportation based on the cost analysis principle. Optimal route selection model of cross border e-commerce transportation According to the basic situation of China's e-commerce logistics distribution, combined with the characteristics of express enterprises, this paper puts forward an effective e-commerce logistics distribution mode, which is of great significance to improve the flexibility of the delivery volume of express enterprises, reduce the waiting time, and improve the quality and efficiency of express service. On the basis of the collected data, the regional geographic information is abstracted, and the key points are extracted by PS software to generate a simplified map (Panos and Densing 2019). Because of the complexity of geographical coordinates, the relative position information of each distribution point is expressed by self-built coordinate system. Each delivery point is numbered. Map and field measurement of the distance between each shipping point, and considering the waiting time (expressed by the product of average loading volume and waiting time) (converting waiting time into route length), the basic situation of logistics distribution route selection is shown in Table 2.Table 2 Logistics distribution route selection conditions Category Optimal path Alternative path Characteristic The population is concentrated, and the scale of e-commerce transactions is large. The delivery address of express delivery is usually the work unit and the community where they live Population living in villages and towns as the center, compared with the city is scattered Problem Almost no one is at home during working hours, and most communities do not allow express delivery personnel to enter directly, and work units do not allow employees to receive private express during working hours The distribution points are far away from each other, the distribution efficiency is low and the distribution cost is high On the one hand, we should consider the characteristics of different groups and choose the corresponding distribution mode; on the other hand, we should also consider the customer satisfaction and cost (Saez et al. 2019). Setting the selection time window and automatically changing the delivery mode can reduce the uncertainty of waiting time, reduce the risk of failure, and realize the quantification of express waiting time. The method is based on the configuration of two-point solution. The C-W algorithm is a distributed algorithm that meets the requirements of actual distribution scenarios for customer randomness and complexities, as well as vehicle mileage constraints. It decreases the cost of delivery and helps to increase the amount of clients serviced and decrease the total waiting period for customers. The C-W algorithm is widely accepted by experts and scholars. In order to delay the possible channel conflict and make the dual channel supply chain in a coordinated state, manufacturers promise to increase a certain number of direct sales channels as compensation, and encourage dealers to continue to cooperate with dealers. At the same time, in order to ensure their own interests will not be damaged, the manufacturer can charge the distributor royalty as the threshold to obtain compensation (Peng et al. 2020). The first problem of this strategy is the transportation cost function, which considers the dominant position of producers in the second stage game11 p2x=θ[D(p,q)+B(p,q)]+f(x) On this basis, the Dijkstra algorithm model based on demand is further established. According to the linear relationship between demand and distribution distance, the optimal path selection model is established:12 θ=∑fx·wij Based on the analysis of the comprehensive distribution mode of e-commerce logistics terminal, this paper proposes a new type of comprehensive distribution mode of logistics terminal, which can meet the needs of different groups while taking into account customer satisfaction and cost. By setting and selecting time window, and automatically changing the distribution mode, it can reduce the risk of courier waiting and distribution failure, and optimize the logistics terminal distribution path Create a good research environment, so that the path optimization research has more practical significance (Hao and Li 2020). According to the goal of customer demand planning, the revenue of meeting customer delivery time and supplier resources is calculated. This may include options such as positioning e-commerce DCs especially near parcel distributor centers, to use a third party for distribution or, on the other hand, bringing back in-house shipment, measuring the trade-offs by using distinct or combined e-commerce distribution centers. Decisions must be taken on the basis of the option of vendors, the place of warehouses, the distribution of operations, etc. On the basis of meeting customer delivery time, supplier resource income and network load balance, the scheduling function is realized. Initialization, that is to process all the best distance estimation except the source point. Combined with the distributed iterative method, it relaxes each edge several times in the edge set E, so that the best distance estimation of each small vertex v is close to the optimal distance. Whether the two endpoints of each edge of edge set E converge is analyzed. When all the vertices converge, the algorithm is correct and keeps the distance, which indicates that the distance is the best. Otherwise, the algorithm is wrong and the distance is not the best. On this basis, a dynamic optimization path algorithm is designed. Since the pricing strategy under the new business distribution strategy is the same as that under the centralized decision-making, the total profit of the dual channel supply chain is equal to the total profit under the centralized decision-making, that is, the dual channel supply chain has reached a coordinated state. The new algorithm investigates the changes of road conditions between the source point and the selected point as well as between the selected point and the target point. The optimal distance from the next point to the target point is regarded as the heuristic step of the selection. Select the least one from the sum of the actual time spent from all the starting points to the candidate points and the evaluation time from the candidate points to the target points, then the corresponding candidate points are the current points, and continue to consider the next starting node until it is selected. According to the above method, the final result is the traffic optimal path, and the optimal path with the shortest total travel distance is obtained by auxiliary selection. Based on this, the framework of the optimal selection model of e-commerce transportation route is optimized, as shown in the following Fig. 2.Fig. 2 Framework of optimal selection model for e-commerce transportation path Analysis of experimental results In order to verify the actual effect of the new coronavirus cross-border e-commerce transportation route optimization model designed in this paper, it is compared with traditional methods, and the experimental results are analyzed. In order to ensure the uniform setting of experimental environment and parameters, the experimental program is written in Java language on Java virtual machine. The hardware environment for running this experiment is a GBIntelCorei5-2430MCPU running memory 5. The specific test parameters are shown in Table 3.Table 3 Experimental parameters Project Parameter Operating platform Windows NT Number of CPU cores 6 cores Maximum capacity of detection system 16 GB Internal structure X86 Video card capacity 8 GB Hard disk type Solid state drive Interface connection mode CAN Bus serial Through the correctness test and coverage detection experiment, the efficiency of the detection system is verified. Coverage detection experiment refers to the proportion of total access paths detected by the two systems, while accurate testing refers to the proportion of the best recommended access paths. Experiments were carried out on the Windows NT platform using JAVA language, and the detection system designed in this paper was compared with the detection system designed with the clustering algorithm and the association rule algorithm. The optimal path search efficiency of different detection systems was compared respectively. Optimal path search effectiveness is presented with the support of the ideas of dynamical system searching and complex cut section in artificial intelligence as an analytical optimal route algorithm. The smart algorithm can optimize the search path, decrease the distance of the search, increase the rate of the search, that can be used in multi-graphs to solve the shortest route. In fact, the space vector review process is a dynamic method for generating a system tree whose time intensity is mainly associated to the state tree's computing condition, and the lower the tree branch, the less time complexity. The test results are shown in Fig. 3.Fig. 3 Optimal path search efficiency comparison test Through novel coronavirus pneumonia, the paper finds that the efficiency of searching for cross border electricity supplier transportation path optimization is better than that of the new crown pneumonia. In practice, the search efficiency is much higher. This method can detect multiple transmission paths in a short time and accurately select the optimal path, thus proving the road established in this paper. The diameter optimization model has strong performance and is worth popularizing. Novel coronavirus pneumonia is used to further verify the effectiveness of the model. The cross-boundary e-commerce efficiency of the model and the traditional model is compared and analyzed, as shown in Fig. 4.Fig. 4 Comparison of cross border e-commerce transportation efficiency According to Fig. 4, the cross-border e-commerce transportation time of this model is within 13 h, which is shorter than that of the traditional model, which shows that using this model can improve the efficiency of cross-border e-commerce transportation. Conclusion Since the outbreak of novel coronavirus pneumonia, cross-border e-commerce has played an important role in the global procurement and transportation of goods. It needs cross-border support and cooperation of cross-border logistics enterprises to promote cross-border e-commerce. It is reported that cross-border logistics has played an important role in promoting the development of cross-border e-commerce. As mentioned at the beginning of this paper, the cross-border import supply chain is behind the cross-border procurement and transportation of a large number of anti-epidemic protective articles. The novel coronavirus pneumonia will enable the global goods to supply rapidly and commercialization. In order to meet the market demand, we will design the cross-border e-commerce transportation path optimization model against the background of new crown pneumonia, so as to promote the development of the modern e-commerce industry. This research seen as a starting point for Optimization Model Design of Cross-border E-commerce Transportation Path to produce more research and still need to improve logistic distribution towards relation between third parties, visibility and control of process and its service level in further research. This work is supported by National Social Science Fund Project: Xinjiang cross border e-commerce development strategy research, Project No.: 19CGL068. Declarations Conflict of interest The authors declare that they have conflict of interest. Human and animal rights statement No involvement of humans or animals. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Bhattacharya S Raju V Analysis of factors affecting customer satisfaction in e-commerce applications using Condorcet - AHP method Int J Internet Protoc Technol 2019 1 2 10 10.1504/IJIPT.2019.098486 Chen F Huang K Hou Y Long-term cross-border electricity trading model under the background of Global Energy Interconnection Glob Energy Interconnect 2019 2 32 39 Gao ZC Efficient management of novel coronavirus pneumonia by efficient prevention and control in scientific manner Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi Chin J Tubercul Resp Dis 2020 3 163 166 Hao WD Li MX Implications of infection prevention and control measures from a patient with atypical 2019 novel coronavirus pneumonia Mayo Clin Proc 2020 5 856 857 10.1016/j.mayocp.2020.03.012 Ji S Wang X Zhao W An application of a three-stage XGBoost-based model to sales forecasting of a cross-border e-commerce enterprise Math Probl Eng 2019 2 1 15 Liang R Wang JQ A linguistic intuitionistic cloud decision support model with sentiment analysis for product selection in e-commerce Int J Fuzzy Syst 2019 3 963 977 10.1007/s40815-019-00606-0 Luo X Lu X Li J When and how to leverage e-commerce cart targeting: the relative and moderated effects of scarcity and price incentives with a two-stage field experiment and causal forest optimization Inf Syst Res 2019 4 1203 1227 10.1287/isre.2019.0859 Mertens T Poncelet K Duerinck J Representing cross-border trade of electricity in long-term energy-system optimization models with a limited geographical scope Appl Energy 2020 2 114376 114378 10.1016/j.apenergy.2019.114376 Panos E Densing M The future developments of the electricity prices in view of the implementation of the Paris Agreements: Will the current trends prevail, or a reversal is ahead? 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==== Front Soft comput Soft comput Soft Computing 1432-7643 1433-7479 Springer Berlin Heidelberg Berlin/Heidelberg 33716562 5643 10.1007/s00500-021-05643-2 Methodologies and Application The prediction of the lifetime of the new coronavirus in the USA using mathematical models http://orcid.org/0000-0002-6122-3342 Selvakumar K. [email protected] [email protected] 12 Lokesh S. 3 1 grid.252262.3 0000 0001 0613 6919 Department of Science and Humanities, Anna University, Chennai, India 2 University College of Engineering, Nagercoil, Tamil Nadu 629004 India 3 Department of Computer Science and Engineering, Hindustan Institute of Technology, Othakalmandapam, Coimbatore, Tamil Nadu 641032 India 10 3 2021 2021 25 16 1057510594 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The World Health Organization (WHO) on December 31, 2019, was informed of several cases of respiratory diseases of unknown origin in the city of Wuhan in the Chinese Province of Hubei, the clinical manifestations of which were similar to those of viral pneumonia and manifested as fever, cough, and shortness of breath. And, the disease caused by the virus is named the new coronavirus disease 2019 and it will be abbreviated as 2019-nCoV and COVID-19. As of January 30, 2020, the WHO classified this epidemic as a global health emergency (Chung et al. in Radiology 295(1):202–207, 2020). It is an international real-life problem. Due to deaths, globally everyone is under fear. Now, it is the responsibility of researchers to give hope to the people. In this article, we aim to better protect people and general pandemic preparedness by predicting the lifetime of the disease-causing virus using three mathematical models. This article deals with a complex real-life problem people face all over the world, an international real-life problem. The main focus is on the USA due to large infection and death due to coronavirus and thereby the life of every individual is uncertain. The death counts of the USA from February 29 to April 22, 2020, are used in this article as a data set. The death counts of the USA are fitted by the solutions of three mathematical models and a solution to an international problem is achieved. Based on the death rate, the lifetime of the coronavirus COVID-19 is predicted as 1464.76 days from February 29, 2020. That is, after March 2024 there will be no death in the USA due to COVID-19 if everyone follows the guidelines of WHO and the advice of healthcare workers. People and government can get prepared for this situation and many lives can be saved. It is the contribution of soft computing. Finally, this article suggests several steps to control the spread and severity of the disease. The research work, the lifetime prediction presented in this article is entirely new and differs from all other articles in the literature. Keywords Coronavirus COVID-19 Lifetime Mathematical models issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2021 ==== Body pmcIntroduction The World Health Organization (WHO) on December 31, 2019, was informed of several cases of respiratory diseases of unknown origin in the city of Wuhan (News 2020, https://www.ncbi.nlm.nih.gov/nuccore/MN908947) in the Chinese Province of Hubei, the clinical manifestations of which were similar to those of viral pneumonia and manifested as fever, cough, and shortness of breath (http://wjw.wuhan.gov.cn/front/web/showDetail/2020011109036). As of January 30, 2020, the WHO classified this epidemic as a global health emergency (Chung et al. 2020). The disease caused by the virus is named the new coronavirus disease 2019 and it will be abbreviated as 2019-nCoV and COVID-19 (https://twitter.com/WHOWPRO/status/1219478541865144320). In Latin, corona means crown. On their surface, coronaviruses have spiky projections that resemble crowns. Viruses with crowns are therefore called coronaviruses. It is a large family of viruses in bats, birds, cats, cattle, and camels. In most cases, common cold is the symptom of human coronavirus. Four types of human coronaviruses are responsible for 10 to 30 percent of the reliable sources of upper respiratory infections in adults (Song et al. 2020; Rapid Risk Assessment 2020). Coronaviruses with crown A new type of coronavirus can occur when an animal coronavirus can transmit the disease to humans when germs are transferred from an animal to a human. This can cause serious diseases. This can be due to a variety of factors, particularly the lack of human immunity to the new virus. The coronavirus (CoV) with crowns first identified in 2003 causes the users-severe acute respiratory syndrome, is abbreviated as SARS-CoV. The coronavirus with crowns first identified in 2012 causes disease, called as the Middle East Respiratory Syndrome is abbreviated as MERS-CoV (2019). The new coronavirus with crowns first identified in China in 2019 is abbreviated as 2019-nCoV and COVID-19. And, COVID-19 is also termed as SARS-CoV-2. Photographs of these three viruses with crowns are shown in Fig. 1.Fig. 1 Picture of (a) SARS-CoV (b) MERS-CoV (c) COVID-19 with crown (Cherry and Krogstad 2004) Signs and symptoms of COVID-19 in human The symptoms of COVID-19 in common are fever, dry cough, and fatigue. Symptoms in some of the infected patients may experience a runny nose, pain, nasal congestion, diarrhea, or sore throat. And, these symptoms are generally mild and start gradually. Some people are infected but do not develop symptoms and do not feel well. About 80% of people recover from the disease without special treatment. Approximately one in five people infected with COVID-19 is seriously ill and has difficulty breathing. Seniors and people with underlying conditions such as heart problems, diabetes, or high blood pressure are in our society, more likely to indirectly develop serious conditions. Based on the latest data, around 3-4 % of the reported cases worldwide have died, but the mortality rate varies depending on the location, age, and presence of underlying diseases. The new coronavirus in China The Chinese Municipal Health Commission if Wuhan which is in Wuhan City, Hubei Province, reported on December 31, 2019, a group of 27 unknown etiology pneumonia cases, including seven serious cases, related to the Huanan seafood wholesaler in Wuhan, a market for live animals selling various animal species. Clinical features in these cases similar to infectious respiratory diseases such as dyspnea, fever, and bilateral lung infiltrate on the chest X-ray (radiographs). The authorities put all cases in solitary confinement, launched contact tracking activities, and launched environmental hygiene and hygiene measures at the market that were closed to the public usage on January 1, 2020. The Chinese authorities at that time had reported no transmission within human and no healthcare professional cases. On January 9, 2020, the Chinese CDC reported that a new coronavirus (2019-nCoV) was discovered to be responsible for 15 of the 59 cases of pneumonia (http://www.nhc.gov.cn/yjb/s3578/202001/930c021cdd1f46dc832fc27e0cc465c8.shtml). On January 10, 2020, the first new sequence in the coronavirus genome was made available to the public (http://wsjkw.gd.gov.cn/zwyw.yqxx/content/post.2876926.html). The footage was uploaded to the database GenBank (Rapid Risk Assessment 2020) and downloaded as part of the Global Influenza Data Sharing Initiative (GISAID). A preliminary analysis has shown that the new coronavirus clusters (2019-nCoV) differ from the basic genome of known bat CoVs. December 31, 2019, to January 20, 2020, the confirmed cases 295 are reported in the laboratory, including four deaths (http://virological.org/t/initialgenome-release-of-novel-coronavirus/319) (Table 1).Table 1 Outcomes of COVID-19 infection Reporting Country on 20 January 2020 (Chen et al. 2020) Reporting Reporting Confirmed cases Death Death Province country province count count percentage ranking China Hubei Sheng 270 4 1.5 1 Guangdong Sheng 14 0 0 2 Beijing Shi 3 0 0 3 Shanghai Shi 2 0 ) 4 Thailand Bangkok 2 0 0 4 Republic of Korea Inchon 1 0 0 5 Japan Kanagawa 1 0 0 5 Total count 295 4 Among the cases reported in Wuhan were 15 health professionals (Algahtani et al. 2016). Of the 295 cases confirmed in the laboratory, China reported 291 cases ( 270 cases were reported in Wuhan City, 14 cases in Guangdong, 5 cases in Beijing, and 2 in Shanghai ) (http://www.nhc.gov.cn/yjb/s3578/202001/930c021cdd1f46dc832fc27e0cc465c8.shtml). The city of Wuhan reports that 169 cases are still in the hospital, of which 35 are critically ill and nine are critically ill (http://wjw.wuhan.gov.cn/front/web/showDetail/2020012109083). The ECDC does not know whether the cases were brought to solitary confinement solely for medical purposes or less severe cases. In Guangdong, 2 of the 14 reported cases had not traveled to Wuhan, China, but have had contact with confirmed cases in the past (http://wsjkw.gd.gov.cn/zwyw.yqxx/content/post.2876926.html). The other four cases confirmed in the laboratory relate to travel ( 1 in South Korea, 1 in Japan, and 2 in Thailand ) (Rapid Risk Assessment 2020) (http://wjw.wuhan.gov.cn/front/web/showDetail/2019123108989, http://xinhuanet.com/english/2020-01/20/c138721535:htm). Of the 4 reported deaths in China, the first happened in a 61-year-old patient on January 9, 2020, with underlying medical conditions who visited the wholesale market, Huanan seafood in Wuhan (http://wjw.wuhan.gov.cn/front/web/showDetail/2019123108989). A second death on January 15, 2020, occurred in a 69-year-old man with multiple organ failure (http://wjw.wuhan.gov.cn/front/web/showDetail/2020011609057). The third death was reported on January 15, 18, 2020 (http://wjw.wuhan.gov.cn/front/web/showDetail/2020012009077), the fourth death occurred on January 19, 2020, in an 89-year-old woman due to coronavirus with pre-existing diseases (http://wjw.wuhan.gov.cn/front/web/showDetail/2020012109083). Symptoms of cases confirmed including travel-related cases in the laboratory occur from December 8, 2019, to January 18, 2020. More than half of the confirmed cases were men. In the cases reported, the range of age is between 10 and 89 years (http://wsjkw.gd.gov.cn/zwyw.yqxx/content/post.2876926.html). The history of exposure to Huanan seafood wholesalers in Wuhan or other living markets in China is not yet known for the majority of the reported cases recently (http://xinhuanet.com/english/2020-01/20/c138721535:htm). In China, 1,739 patients were identified as close contacts, and on continuous follow-up, 817 patients completed the observation period, while 922 patients remain under medical observation (http://wjw.wuhan.gov.cn/front/web/showDetail/2020011609057, http://wjw.wuhan.gov.cn/front/web/showDetail/2020011509046). On December 31, 2019, the WHO has been made aware of several cases of respiratory diseases of unknown origin from Wuhan City of China, with similar clinical presentations of viral pneumonia and manifested by cough, fever, and short breath. As of January 30, 2020, the WHO classified this epidemic as a global health emergency (http://wjw.wuhan.gov.cn/front/web/showDetail/2020012109083). Literature survey of SARS-CoV Peiris et al. (2004) review the scientific advances made in the study of the virus, SARS-CoV. They also highlight the advances made in the development of therapies and vaccines. They designed a method for the detection and control of future infectious disease threats. Stadler et al. (2003) presents a review on SARS. And they present that the 114-day SARS epidemic has hit 29 countries, affected 8,098 people, killed 774 people, and nearly paralyzed the economy of Asia. Aggressive quarantine measures possibly supported by rising temperatures during summer and successfully ended and ensured the first outbreak of SARS. They are investigating the genomics of the SARS-CoV, its phylogeny, its antigen structure, its immune response, and its possible therapeutic interventions when the SARS epidemic recurs (Boulos1 and Geraghty 2020) (https://www.bcm.edu/departments/molecular-virology-and-microbiology/emerging-infectionsandbiodefense/sars-virus). Literature survey of MERS-CoV Algahtani et al. (2016) present a review on MERS-CoV. In this review, a report of two cases and the literature review are given. In September 2012, the coronavirus of the respiratory syndrome in the Middle East (MERS-CoV) was first discovered in Saudi Arabia. It has caused in the laboratory test more than 1,600 confirmed and more than 580 deaths. MERS-CoV infection is a serious illness that affects many lung, kidney, hematological, and gastrointestinal complications. In Algahtani et al. (2016), in two adult patients, the neurological complaint due to MERS-CoV is reported and they make the pathological hypothesis. Literature survey of COVID-19 Several pneumonia patients of unknown cause were discovered in the month of December 2019 in a Chinese city of Wuhan. On January 7, 2020, the pathogen was identified as a new CoV, which will later be referred to as the new coronavirus 2019 (2019-Nov). Genome sequencing has shown that the COVID-19 genetic sequence is similar to that of the CoV associated with SARS, and a precise medical approach to treating this disease is imperative to detect the spread of the virus and control it. In this article, Wang et al. (2020) present such an approach to treating pneumonia associated with 2019, which is based on the unique properties of the recently discovered virus and our experience with CoVs of China at the West China Hospital in Chengdu. Adhikari et al. (2020) make a review of COVID-19 at the early period. The background of this review is from December 2019, COVID-19 is the cause of an epidemic of respiratory diseases in Wuhan, China. This epidemic has spread to 19 countries and as of January 31, 2020, with 1,791 infected cases, including 213 deaths. WHO declared it an emergency of public concern for public health. Tavakoli et al. (2020) said at the start of the new year 2020, China alarmed the WHO to a group of unusual cases of pneumonia in Wuhan. After much speculation, a new kind of Coronavirus was introduced as a pathogen COVID-19 and a virus known to cause in human SARS-CoV-2. The fast spread of COVID-19 has caused fear worldwide. The new outbreak of the coronavirus declared an international health emergency of international importance on January 30, 2020. The incubation period is within 2 to 10 days, according to the WHO. The death rate in SARC-CoV-2 infected patients is 4.3 % and the results show that mortality is higher in elders and patients with chronic diseases, including coronary artery disease of diseased patients, high blood pressure, chronic lung disease, and diabetes. The rate of mortality in healthy is less than 1 %. Rabi et al. (2020) present a summary of current knowledge about the new coronavirus and the disease it causes. Alene and Yadeta in Alene and Yadeta (2020) present a review article to understand the cause, to identify methods, to investigate or control coronavirus caused COVID-19 infection, and to avoid future events. Razvan Azamfirei in Alene and Yadeta (2020) present a review on coronavirus caused COVID-19, since the identification of the new coronavirus 2019 (2019-nCoV) in December 2019, an overwhelming feeling of panic has caught the public discourse. This should be reinforced by the recent WHO declaring the new coronavirus outbreak an internationally worrying public health emergency. It is the third major occurrence of a zoonotic coronavirus transmission that crosses the species barrier to infect humans and is unlikely to be the last. In the recent past, we have successfully managed SARS, MERS, Zika, and Ebola. Our scientific community is ready and alert, as shown by the incredibly quick response to the present outbreak. It is not the last time we hear about the coronavirus. They have significant infection potential and more scientific resources should be provided to help understand and reduce the severity of future epidemics. Despite the high infectivity, the rate of mortality maintains low value; WHO and State governments are simultaneously taking the necessary preventive measures to reduce the spread of the infection. Chung et al. (2020) contacted 21 patients with a history of contact with people from the endemic center of Wuhan, China, and analyzed and presented their findings. Chung et al. (2020) hypothesize that the lungs may respond and heal similarly as of SARS and MERS, although it is too early to have imaging descriptions of 2019-to in the more subacute, chronic, or treated patient population. Rabi et al. (2020) contacted about 50 of 51 patients with a history of contact with people from the endemic center of Wuhan, China, and analyzed and presented their findings. Singapore has well-developed protocols for COVID-19 outbreak preparation. Cleland et al. (2020) have made comments on the precautionary measures to minimize the risk of transmission of the virus in Singapore. COVID 19 spread in a faster manner, the weak health system is not a vehicle of transmission of health workers with the worst preventive and control practices. Jackson et al. (2020) made an assessment of this fact in Tanzania. Need for a mathematical model and motivation For better prevention and preparation, the lifetime of the virus can be calculated using mathematical models. These models can include reported information about the population in an area. The actual preparation for a pandemic depends on the actual cases in the population, regardless of whether they have been identified or not, said Srinivasa Rao of Augusta University. In the USA, with better numbers, we can better estimate how long the virus will last and how much it will deteriorate. How can health systems and health workers prepare for what is needed without these numbers? Rao said. Better numbers are also important to better protect people and general pandemic preparedness (Coronavirus Death Toll and Trends Worldometer 2020). This motivates us to do this work to predict and estimate how long the virus will last. Literature survey on mathematical models Artificial intelligence techniques like fuzzy logic (FL), neural networks (NN), and evolutionary computing (EC) can be applied to discuss COVID-19 data and predict the useful results to save the life of the global population. In this subsection, a literature survey on mathematical models related to artificial intelligence techniques like FL, NN, EC, deep learning (DL), and other related fields are made. In Park et al. (2020), a review on the disease COVID-19 prediction and drug development using artificial intelligence (AI). In Jamshidi et al. (2020), AI and DL methods GANAs (Generative Adversarial Networks), ELM ( Extreme Learning Machine) , and LSTM (Long /Short Term Memory) are used to predict the results in COVID-19. In Hao et al. (2020), to predict the growth range of confirmed new cases, new deaths, and new cured cases in China and the USA, ENN (Elman neural network), LSTM, and SVM (support vector machine) are used. An SVM with fuzzy granulation is also used. Ahmad and Asad (2020) predicted the counts of confirmed, recovered, and death cases from the period July 11 to July 17, 2020, using an ANN (artificial neural network) with the help of the data set from February 25 to July 10, 2020, in Pakistan. In Dhamodharavadhan et al. (2020), the future of India is predicted using SNN (Statistical Neural Network) models and their version. In El-Shafeiy et al. (2021), to predict the severity of COVID-19 in patients, quantum neural network (CQNN) is used. In Gupta et al. (2020), to predict the epidemic pattern, an GRNN (generalized regression neural network) model optimized with FPA (flower pollination algorithm ) is designed. In Ghazaly et al. (2020), to predict the outbreak COVID-19 use AI and DL with time series using nonlinear regressive network (NAR). Niazkar and Niazkar (2020) predicted the COVID-19 outbreak by prediction models based on ANN. In Tamang et al. (2020), to predict and forecast the number of death due to COVID-19, ANN-based curve fitting is used. In Uddin et al. (2020), an intelligent monitoring system to monitor the people using deep CNN (Convolutional Neural Networks) models is used to prevent the spread of COVID-19. In Asraf et al. (2020), to control the spread of COVID-19, how deep learning plays a major role is reviewed. Fokas et al. (2020), using the mathematical expression and deep learning network, predicted the number of infected cases in six nations the USA, Germany, Italy, Spain, France, and Sweden from the time of evolution of the epidemic. In Prasse et al. (2020), a network-based model, Network-Inference-Based Prediction Algorithm (NIPA), is used to predict the future evolution of the epidemic in all cities of Hubei Province, China. The network is composed of the cities and interactions of Hubei Province. An accurate prediction of the outbreak is noticed. In Pham et al. (2020), AI and big data are used to improve the COVID-19 situation. In Pal et al. (2020), a Bayesian optimization framework to predict the risk category of a country is discussed. It is a shallow LSTM-based neural network. In Mishra et al. (2020), to forecast the future pattern of COVID infection used fuzzy time series (FTS) and ANN and compared with the ARIMA model with the help of the data set from March 17 to July 1, 2020. In Mollalo et al. (2020), the cumulative incidence rates of COVID-19 are predicted across the nation using MLP (multilayer perceptron) neural network. In Kasilingam et al. (2020), using an exponential model and machine learning, the early signs of COVID-19 up to March 26, 2020, are identified. In Perone (2020), an ARMA model is applied to monitor the diffusion of the outbreak in Italy, Russia, and the USA. In Nesteruk (2020b), to predict the medical and economic all due to pandemic, the epidemic characteristics are estimated using SIR (susceptible infected removed) model. In Verachi et al. (2020), the SIR model is used to evaluate the cost of management strategy. In Vrugt et al. (2020), an SIR mathematical model with a dynamical density function is used for the spread of disease. In Zhang et al. (2020), a stochastic SIR mathematical model for a COVID-19 is developed to find the spread of the disease controlling value. In Kikkisetti et al. (2020), to classify the lungs infected images the chest X-ray (CXR) and deep-learning CNN are applied. In Rasheed et al. (2020), CNN models and the logistic regression (LR) are used to classify CXR images. In Rahimzadeh and Attar (2020), for an unbalanced data set a neural network is used to detect COVID-19 cases. In Qiao et al. (2020), using deep neural networks CXR images of COVID-19 are classified from pneumonia and healthy patients. Pham (2020) predicted the COVID-19 infected cases from the computed tomography (CT) scan images using AI methods, the CNNs. Wang et al. (2020), from the CXR images using a deep CNN, detected COVID-19 patients. In Irmak (2020), CNN architectures are used to detect the COVID-19 disease from two data sets of CXR images. In Lozano et al. (2020), information to predict a fatal outcome in patients with COVID-19 is provided using an ANN. In Makris et al. (2020), to detect infected patients from CXR images CNNs are used. In Sekeroglu and Ozsahin (2020), by the training of deep learning and machine learning classifiers detected COVID-19 patients from their CXR images. In Singh et al. (2020), a deep CNN is to identify the infection of COVID-19 from the CXR image of the lungs of the patients to save the medical doctors time in diagnosis. In Biswas et al. (2020) to study the dynamics from March 1, 2020, in India used mathematical models to fit with the data set of infected cases and predicted the future infection in India. In Boulmezaoud (2020), a mathematical model for the dynamics of transmission is designed to study the evolution of the epidemic. In Khajji et al. (2020), a discrete mathematical model for the transmission dynamics of both human and animal in different regions is designed. In Pereira et al. (2020), a mathematical model to predict the infection dynamics of Brazil is studied. In Rǎdulescu et al. (2020), a traditional mathematical model for the dynamics of spread in the New York State is considered to predict the infection. In Kyrychko et al. (2020), a mathematical model for the dynamics of the transmission of the disease in Ukraine is analyzed. In Zeb et al. (2020), a mathematical model is designed by using isolation class first to predict the dynamical behavior of the disease infection. In Zhang et al. (2020), a stochastic model for dynamics of the unique disease transmission in Mainland China is designed and it is found that the outbreak would be early March 2020 in and around Mainland China. In Zhu et al. (2020), to estimate the unknown data in China, an epidemic model is introduced. In Zuo et al. (2020), a mathematical model to provide total death in Asian nations is suggested. In Cherniha and Davydovych (2020), a mathematical model is designed to predict the count of COVID-19 cases in China, Austria, Poland, and France. In Chen et al. (2020), a mathematical model is used to calculate the disease transmission in a population by infected one. In Gopalan and Misra (2020), a review on COVID-19 from various databases is given. In Zhou et al. (2020), a review on AI models for COVID-19 drug is made. In Hethcote (Dec. 2000), mathematical models for infectious diseases spread in the population are reviewed and are applied to some diseases. In Miao et al. (2020), a model to find the transmission of COVID-19 and infection risk is designed during this lockdown. And, after lockdown, at the time of the entry of business to find the net profit applied this model. In Bertozzi et al. (2020), three models are analyzed to forecast and access the cause of the epidemic region-wise. And, in the absence of a vaccine, the impact of imposing and the danger of relaxation of social distancing is addressed. In Appadu et al. (2021), an iterative method based on Euler’s method and cubic spline interpolation is studied to forecast values from June 01, 2020, using the data from February 15, 2020, to May 31, 2020. In Nesteruk (2020a), to predict the infected cases on February 10, 2020, in Mainland China, a mathematical model is used. In Perc et al. (2020), an iterative method is used to forecast the daily growth rate by giving the input values the number of confined cases. In Sameni (https://arxiv.org/abs/2003.11371), mathematical models that predict the patterns of the propagation of the epidemic disease COIVID-19 are given for a better understanding of the spread. In Zhu and Pham (2018), a review on AI models for COVID-19 drug is made. In Zakary et al. (2020), using a mathematical model the infection in Morocco is estimated and predicted. In Serhani and Labbardi (2020), a modified compartmental model for the spread of the disease in Morocco is introduced and it is observed that the strict home containment plays a major role in spread control. In Pongkitivanichkul and D. Samart1, T. Tangphati, P. Koomhin, P. Pimton, 6, P. Dam-O, A. Payaka, and P. Channuie, (2020), a renormalization group-inspired logistic function is used to analyze the data of infected cases of the nations in the first phase by taking n=1. Rosti et al. (2020), taking the airflow due to cough, predict the reach of infectious droplets to a destination emitted from mouth during a cough. In Scherf et al. (2020), the steps are taken in Brazil to manage the pandemic situation and a review is given. In Cherry and Krogstad (2001), a review on the pandemic is given for future preparedness. In Wynants et al. (2020), a review on the prediction models for diagnosing COVID-19 in patients is given. It is predicted that 40 % to 70 % of the global population will be infected in the coming years in Nash .C. Mediaite (https://www.mediaite.com/news/harward-professor-sounds--alarm on likely coronavirus pandemic-.40to-70ofworldcouldbe-infectedthisyear). In Petropoulos, a continuation of coronavirus COVID-19 is predicted using a sample. In Muttrack and Scherhov (2020), the impact of a period of life expectancy is discussed. Forecasting Team Nature (2020) using the SEIR method predicted COVID-19 patterns and traced the possible outcomes for the period September 22, 2020, to February 28, 2021, using the COVID-19 cases and mortality data from February 1, 2020, to September 21, 2020. Time series are used to analyze each state of the USA. SEIR stands for the Susceptible Exposed Infectious Recovered computational method. In Joshua and Ronald (2020, 2020), COVID-19 mortality is estimated within 1 million deaths and observed it reduced the remaining life of the people of the USA by less than one part in one thousand. COVID-19 claimed life within months but not over decades like other epidemics such as HIVAIDS and opioids. In Jewell et al. (2020), the importance of mathematical models to make decisions on public health issues and to reduce mortality by using the available resources during this COVID-19 pandemic situation is discussed. But, no mathematical expression is given in Jewell et al. (2020). In Gupta et al. (2020), using a relation between COVID-19 spread and weather parameters predicted Indian states with high risk using the USA prediction model. Singh et al. (2020) predicted the coronavirus COVID-19 disease spread graphs concerning the counts of confirmed cases, deaths, and recoveries during the period April 24 to July 7, 2020, using ARIMA model for the worst affected 15 countries ranking top in the world. In Banerjee et al. (2020), excess counts of deaths over one year in different levels of transmission of COVID-19 are determined. In Ghisolfi et al. (2020), the fatality rate for Eastern Europe nations are estimated. In the USA, CDCP—Centers for Disease Control and Prevention, instructs the people to stay at home when they are sick, avoid touching nose and mouth by covering them, and frequently wash hands using soap before and after touching any object, to avoid the spread of coronavirus (Centers for Disease Control and Prevention 2020). The research work, the lifetime prediction presented in this article is entirely new and differs from all other articles in the literature. Motivation of this work To speed up the steps taken to save the life of people the mathematical models will be helpful to make decisions on public health issues and to reduce mortality by using the available resources during this COVID-19 pandemic period. Knowing how long this infection will be in the USA, public health decisions can be made by the government and voluntary organizations and mortality can be reduced. The works of Jewell et al. (2020) motivated me to do this work to find the lifetime of coronavirus COVID-19 to save the life of the people. The daily news about the deaths globally and the data about 215 nations and the mathematical model to predict the maximum number of death in the USA due to COVID-19 in the coming days of Phon (Pham 2020) motivate to predict the lifetime of coronavirus COVID-19 (the time of no death due to COVID-19) in the USA using death counts of the USA from February 29 to April 22, 2020, if everyone follows the guidelines of WHO and the advice of healthcare workers. Main results of this article This article first reviews the origin of the coronavirus, the types of the coronavirus, and the transmission of the bat virus to humans. Our main aim is to better protect people and general pandemic preparedness by predicting the lifetime of the disease-causing virus using mathematical models with five and six unknown parameters for the uncertainty of death. In this article, the main results are the prediction of the lifetime of coronavirus COVID-19 ( the time of no death due to COVID-19) in the USA using three mathematical models. Based on the total number of death at time t, the first, second, and third models predict the lifetime of coronavirus COVID-19 as 240.79 days, 240.35 days, and 272.37 days, respectively, from February 29, 2020. On taking the maximum value, it is predicted from three models, the lifetime of coronavirus COVID-19 is 272.37 days from February 29, 2020. That is, after 272.37 days from February 29, 2020 (that is, after December 2020 ), there will be no death and, on comparing with the death counts from the live updates of WHO, there will be death in the USA due to COVID-19 even after December 2020. And, based on the death rate, the first, second, and third models predict the lifetime of coronavirus COVID-19 as 1285.12 days, 1281.33 days, and 1464.76 days, respectively, from February 29, 2020. On taking the maximum value, it is predicted from three models, the lifetime of coronavirus COVID-19 is 1464.76 days from February 29, 2020. That is, after 1464.76 days from February 29, 2020 (that is, after March 2024 ), there will be no death due to coronavirus COVID-19 if everyone follows the guidelines of WHO and advice of healthcare workers. Finally, in this article, it is predicted from three models, the lifetime of coronavirus COVID-19 in the USA as 1464.76 days from February 29, 2020. That is, it is predicted by calculation from three models, after December 2024 we can expect no death in the USA due to COVID-19, provided if everyone follows the guidelines of WHO and the advice of healthcare workers Construction of this article In Sect. 1, we have introduced the virus with a crown followed by the review which killed humans. In Sect. 2, the transmission of coronavirus from bat to human is followed by a review. And the expected future transmission is also presented. In Sect. 3, a mathematical model for COVID-19 is discussed which predicts the maximum number of death in the USA due to COVID-19 in the coming days. In Sect. 4, the lifetime of coronavirus COVID-19 in the USA is calculated using a mathematical model, Model-I. In Sect. 5, the lifetime of coronavirus COVID-19 in the USA is calculated using a mathematical model, Model-II. In Sect. 6, the lifetime of coronavirus COVID-19 in the USA is calculated using a mathematical model, Model-III. Finally, in Sect. 7, this article suggests several steps to control the coronavirus spread and severity of the disease and plan of research in coronavirus COVID-19. Transmission of COVID-19 in human Breathing secretions, which are formed as droplets and arise when an infected patient coughs, sneezes, or speaks, contain the virus and are the primary means of transmission. There are two main ways that people can spread COVID-19. The infection can spread to people who are less than a meter away from droplets that are spat out or exhaled by a patient infected by COVID-19, or people can be infected by their unknown touches of contaminated nearby surfaces or objects and finally touching their nose, eyes, or mouth. A person can touch a door handle or shake hands and then touch her face. That is why disinfecting the environment is so important. Current findings indicate that the transmission can begin immediately before symptoms appear. Anyhow many who got infected by COVID-19 have mild symptoms. This is especially true in the early stages of illness. It is guessed that there is a possibility to catch COVID-19 from infected who has, for instance, just a light cough and do not feel bad. WHO evaluates ongoing research on the COVID-19 transmission period and continues to share updated results (Breda and Borges 2020) . Zoonotic shift The process of an animal virus or bacteria that infect humans is called a zoonotic Shift. When people get the infection, the disease is known as zoonotic disease or zoonosis. Some well-known examples of zoonotic viruses are HIV (origin of nonhuman primates), Ebola virus (origin of bats), SARS coronavirus (origin of bats), and avian influenza virus (origin of birds). Research studies suggest that this virus is likely to come from bats, while other animals associated with human infection remain unconfirmed. The threat posed by such a zoonotic change is illustrated by the current coronavirus epidemic originating in China, which is now officially classified by the WHO as a pandemic. Chen et al. (2020) of Harvard University present the journey of the virus. The virus is believed to come from the reservoir host, bats, and it is transmitted to unconfirmed intermediate hosts, although a suspected species is the pangolin. This virus has likely undergone mutations or changes that would have allowed it to pass from intermediate hosts to humans and then spread from person to person. And it is represented in Fig. 2.Fig. 2 Current hypothesis of the chain of transmission for COVID-19 coronavirus (Chen et al. 2020) How virus from animal hop onto human There are two ways to do this. First, humans must be exposed to the reservoir host, animals that naturally harbor the virus or the animals which carry the virus transmitted by the reservoir host (intermediate host). Second, the virus must be able to infect people. To settle in the human body, the virus has to penetrate our cells, multiply, and avoid being destroyed by our immune system. If a virus successfully colonizes a human, it also subsequently needs passage to get out of the body and spread so that it can infect other people and remain viable (Refer Fig. 2). There are common features between animal hosts and humans. May be the animal virus already has one or more of these common features. To achieve these properties, either before it enters the entire human or during transmission from human-to-human. the virus undergoes genetic changes or mutations. Infection of coronavirus SARS-CoV-2 in humans The SARS-CoV-2 type coronavirus infects humans with the help of their crowns, through a protein on its crown-shaped tips on the surface of the virus, from where the virus received the name Corona. On the surface of human cells, the protein of the virus can interact with a protein of human, which allows the virus to capture and thereby infect human cells in our airways. Mutation of virus to allow for infection and spread in humans If the epidemic is effectively curbed and ended, the virus will be removed. Social detachment, good hygiene, rapid identification, and full quarantine are currently effective measures to limit the spread of the disease. Public health preventive measures and vaccines help reduce further new infections. Since the virus can only be present in one person for a few weeks, the transmission chain cannot get to the next person and the epidemic will stop at some point. Another possible result, however, is that the virus could circulate and persist in the human population (Refer to Fig. 3). The transmission and possible outcomes of virus COVID-19 infection (Chen et al. 2020) is given in Fig. 3. Animal viruses must overcome many obstacles to establish and maintain infections in the human population, including geographical distances, physical obstacles such as health precautions, and transmission barriers such as social distancing and overcoming the elimination of our immune systemsFig. 3 Viral transmission and outcomes of COVID-19 infection (Chen et al. 2020) With effective containment, it may be possible to eliminate the virus from humans. On the other hand, the virus could coexist with humans in the long term. If the epidemic is effectively curbed and ended, the virus could be eradicated from the human population. In the coming sections, the main results of this article, the time of no death due to COVID-19 in the USA is calculated using three mathematical models to better protect people and general pandemic preparedness. Mathematical model for COVID-19 This section discusses the mathematical model to predict the maximum number of death in the USA due to COVID-19 in the coming days. A function which depends on the time which estimates the total number of deaths in the population due to COVID-19 is presented in Pham (2020). The total number of deaths in time t in the USA is estimated using this model. The function fits with a data set of USA. The model in Pham (2020) predicts what will be the maximum total number of deaths in the USA concerning time in days. The lifetime is not predicted in Pham (2020) and we attempted to estimate the lifetime of the infection causing virus COVID-19. The data set of death in the USA due to COVID-19 follows an S-shaped curve. This motivates to turn toward S-shaped logistic models. In the literature we have some S-shaped logistic models (Akaike 1973; Li and Pham 2017; Pham et al. 2014; Pham 2011, 2019, 2018, 2006; Pham and Pham 2019; Schwarz 1978; Sharma et al. 2018; Verhulst 1845; Zhu and Pham 2018) and logistic regression models (Pham and Pham 2020). Among these models, the logistic models designed by Pham (2018) to estimate the number of failures. In Pham (2020), the model in Pham (2018) is modified to include uncertainty by adding unknown parameters. These parameters admit the uncertainty of the COVID-19 virus in the population samples concerning different age, groups, and different areas and environments. The best model is selected using a new model selection criterion of Phan, called Pham’s criterion (PC) in Pham (2020) and compared with other already existing criteria. In brief, a model that can calculate the cumulative number of deaths in the population is developed in Pham (2020). The death due COVID-19 is considered for the study subject to the assumptions Already if there are a few in the population have COVID-19 infection, and they may be spreading the virus into a community. But not sure the community is infected or not. The possibility of the spread of virus infection to the people is in close contact or direct inhaling into their lungs and touching their own mouth or nose or eyes or after touching infected objects or surfaces (Forecasting Team Nature 2020). The spread of virus all over an area as time progress initially spread in a slow rate of infection into a small number of people and spread at a higher rate of infection into a large number of people. This rate of infection will continue to grow till it reaches a maximum number of death counts. In the susceptible population at time t, the rate of change of death is proportional to the death counts of people who are infected and not infected. The death data are reliable than the data of reported cases, data of hospitalized for testing, and data of symptoms and treatments. Also, it is easy to identify the reason for the cause of death than the reason to live for hospitalization and tests. Table 2 Data set of USA (Pham 2020) Date Count Date Count Date Count Date Count Date Count Feb 29 1 March 1 1 2 6 3 9 4 11 5 12 6 15 7 19 8 22 9 26 10 30 11 38 12 41 13 48 14 58 15 73 16 95 17 121 18 171 19 239 20 309 21 374 22 509 23 689 24 957 25 1260 26 1614 27 2110 28 2754 29 3251 30 3948 31 5027 April 1 6263 2 7438 3 8694 4 10,231 5 11,632 6 13,128 7 15,347 8 17,503 9 19,604 10 21,830 11 23,843 12 25,558 13 27,272 14 29,825 15 32,443 16 34,619 17 37,147 18 39,014 19 40,575 20 42,514 21 45,179 22 47,520 In the literature (Pham et al. 2014; Pham 2011, 2019, 2018, 2006; Pham and Pham 2020; Verhulst 1845), population growth and death models are applied to different situations of the past and present. The death growth equation is given by the differential equation (Pham 2018, 2006, 2020),1 d′(t)=q(t)d(t)[s-d(t)],t≥0,′=ddt where d(t), q(t) and s refer to cumulative death at time t, death rate, and maximum death count, respectively. The solution of the differential equation (1) is of the form2 d(t)=s1+Cexp(s∫0tq(t)dt),C=s-d(0)d(0). To improve the goodness of fit with the data set, take q(t)= q > 0, and approximating function (2) considering uncertainity of coronavirus COVID-19 in the population by adding parameters, we redefine the function (2) as3 d(t)=s1+Cexp(-sqt)=s1+C1exp(sqt)=s1+C[1exp(qt)]s Again, on further approximating for goodness of fit, we have,4 d(t)≈s1+C1+pr+exp(qt)s≈s1+C1+pr+exp(qt)s-n.1+pr+exp(qt)n≈s1+k1+pr+exp(qt)n where k = C [1+pr+exp(qt)]s-n. To improve the goodness of fit with the data set, we redefine the function (2) taking into the account of the assumptions , the cumulative number of deaths at time t as5 d(t)=s1+k1+pexp(qt)+rn,t≥0 where p, q , r, s , k, and n are six parameters to be determined with respect to the death counts of the USA for 54 days from February 29 to April 22, 2020, using the method of least squares. In addition the function satisfies the assumptions In the beginning of infection at t=0, there are a few in the population have COVID-19 infection and so the function (5) will take the value (Forecasting Team Nature 2020) 6 d(0)=s1+k1+p1+rn≠0, As time progress, t→∞, d(∞ ) = s , from the data set of USA. That is, 7 limt→∞d(t)=s. The function (5) reduces to the form (Pham 2020)8 d(t)=s1+k1+pexp(qt)+rn,t≥0,n=1. It is a six-parameter model and the parameters in the function (8) can be estimated using the method of least squares. Two mathematical models from the six-parameter model From the function (8) we define five-parameter and six-parameter models. By taking r = p in the function (8) we get a five-parameter model. 9 d(t)=s1+k1+pexp(qt)+pn,t≥0, By taking r = p in the function (8) and adding a parameter m with it we get a six-parameter model. 10 d(t)=m+s1+k1+pexp(qt)+pn,t≥0, Parameter estimates using the method of least squares Using the method of least squares the parameters in the model functions (9), (10), and (8) are estimated with respect to the data set of the USA given in Table.2. For the model function (9), p= 5.977112, q = 0.1774159, k = 400.013, s = 54900, and n=1.( Model-I) For the model function (10), p= 7.32222, q = 0.17794, k = 342.0186, s = 54800, m = 0.49804, and n=1. (Model-II) For the model function (8), p = -11.9747477, q = 0.1535604, k = 338.99688, s = 62100, m = 2.6586221, and n=1. (Model-III) Comparison of models The sample size of the data is 54. We fit the data set of USA with the model functions (9), (10), and (8) with respect to the errors made by the measures AIC, BIC, MSE, PIC, PR, PP, and SSE. The best model function will be selected based on the smallest error. SSE, MSE, AIC, PIC, PC, BIC, PP, and PRR refer to the sum of squared error, mean squared error, Akaike’s information criterion, Pham’s information criterion, Pham’s criterion, Bayesian information criterion, predictive power, and predictive ratio risk, respectively. And these are defined in Pham (2020). The errors due to MSE, SSE, AIC, PIC, and PC select the Model-III as the best model. But, not due to BIC, PP, and PRR. Overall, compared to all measures the error due to PC is smaller than other measures and the Model-III is the best model from Table.3.Table 3 Comparison of Models Error Model I Model II Model III Best model measures function function function (smallest value) SSE 16284073.5412 16769679.7186 16181430.3102 Model III MSE 325681.4708 342238.3616 330233.2716 Model III AIC 691.2715 694.8294 329,910.9 Model III BIC 699.2275 704.7743 704.7743 Model I PIC 16284077.7812 16769685.1267 16181435.7184 Model III PRR 17.6680 17.9428 54.3794 Model I PP 42211.2647 57031.1998 605026.7979 Model I PC 319.6578 315.2375 314.3627 Model III Table 4 Comparison of predicted data Date Count Model I Model II Model III Real data April 22, 2020 54 46030.8971 46012.0717 47348.38951 47,420 April 23, 2020 55 47271.9707 47246.6702 49009.9305 49,845 Comparison of predicted data In the data set of USA the data on April 22, 2020, are 47,520 and fitting with this data using Model-I, Model-II, and Model-III the values are obtained on April 22, 2020. And, using this data set the 55th data on April 23, 2020, are extrapolated from the data set using these three models and compared with the real data and it is given in Table.4. On April 22, 2020, it is observed 99.63888889 % of accuracy in the fitted data. And, on April 23, 2020, it is observed 98.32460628 % of accuracy in the predicted data. And, so the error of significance due to fitted data on April 22, 2020, is 0.36111111 % < 0.5 % and the error of significance due to the predicted on April 23, 2020, is 1.67539372 % < 2 %. The Model-III is the best choice which fits significantly well based on the USA death data. The best model, Model-III, predicts that the death count will be nearly 62,100 due to the coronavirus COVID-19 all over the USA with a 95 % confidence and the confidence interval is (60,951, 63,249). And the expected death count will be a value within 60,951 and 63,249 (Pham 2020). The above discussion is only on predicting the maximum number of death in the USA due to COVID-19 in the coming days. This section gives the motivation to do this work in this article to predict the lifetime of the coronavirus COVID-19 using mathematical models. In the coming sections, three models are discussed for the lifetime of the Coronavirus COVID-19 in the USA. Mathematical Model-I and findings In these sections, a mathematical model, Model-I, is discussed for the lifetime of the Coronavirus COVID-19 in the USA. That is, in this section, the lifetime of the Coronavirus COVID-19 (the time of no death due to COVID-19) in the USA is calculated using a mathematical model to better protect people and general pandemic preparedness. Everyone is under fear and uncertain about their death. Due to deaths, globally everyone is under fear. Now, it is the responsibility of researchers to give hope to the people who are alive and active. The main purpose of this paper is to predict the time of no death due to COVID-19. Or when we can live peacefully without the fear of COVID-19? This question is answered using three mathematical models by the authors of this article. This prediction using a mathematical model is entirely new. The death growth function can be defined by the logistic function11 d1(t)=s1+k1+pexp(qt)+pn,t≥0, where p = 5.977112, q = 0.1774159, k = 400.013, s = 54900, and n=1. In Fig. 4, the death counts of the USA from February 29 to April 22, 2020, are plotted which fits with the function (11) . The function (11) satisfies the assumptions (6) and (7). It is a five-parameter model that considers the uncertainty of coronavirus COVID-19 in the population. To improve the goodness of fit with the data set, five-parameter model is used.Fig. 4 Death growth path of USA using the mathematical model, Model-I The probability density function of no death is given by12 f1(t)=(d1)′(t)Texp-d1(t)T,t≥0,t∈(0,T),T>0, where13 (d1)′(t)=qk(1+p)sexp(qt)(exp(qt)+q)2[d1(t)]2,t≥0. Prediction of the time of no death using the total number of death s The probability density function f1(t) will reach the value zero when t approaches a larger value. For what value of t the function f1(t)=0 we have to calculate in this section. As t takes a larger value the function exp(-[d1(t)T]) will approach zero and hence the f1(t) approaches zero. From this, the time which the COVID-19 disease leading to death will stop killing the people of the USA or the time of no death or lifetime can be determined from the relation14 lifetime=s228. We have the time of no death or lifetime will be 240.7894737 ≈ 241 days. It will be 241 days from February 29, 2020. That is, after November 01, 2020, there will be no death in the USA due to COVID-19. On comparing with the live updates of WHO, it is not so. Again, we explore this model in the next subsection. Prediction of the time of no death using the death rate q The probability density function f1(t) will reach the value zero when t approaches a larger value. For what value of t the function, f1(t)=0 we have to calculate in this section. Again, (d1)′(t) can be rewritten as,15 (d1)′(t)=qk(1+p)sexp(-qt)(1+q.exp(-qt))2[d1(t)]2,t≥0, As t takes a larger value, the function exp(-qt) will approach zero and hence the f1(t) approaches zero. From this, the time which the COVID-19 disease leading to death will stop killing the people of the USA or the time of no death or lifetime can be determined from the relation16 lifetime=228q. We have calculated the time of no death will be 1285.115934 ≈ 1286 days from February 29, 2020. That is, after 3 years, 6 months, and 11 days from February 29, 2020 (from September 2023 ), there will be no death in the USA due to COVID-19, provided globally if everyone follows guidelines of WHO and advice of healthcare workers. Therefore, the lifetime of coronavirus COVID-19 in the USA is calculated using the mathematical model, Model-I, as 1286 days from February 29, 2020. Mathematical Model-II and findings In this section, a mathematical model, Model-II, is discussed for the lifetime of the Coronavirus COVID-19 in the USA. That is, in this section, the lifetime of the Coronavirus COVID-19 (the time of no death due to COVID-19) in the USA is calculated using a mathematical model to better protect people and general pandemic preparedness. This prediction using a mathematical model is entirely new. The death growth function can be defined by the logistic function17 d2(t)=m+s1+k1+pexp(qt)+pn,t≥0, where p= 7.32222, q = 0.17794, k = 342.0186, s = 54800, m = 0.49804, and n=1. In Fig. 5, the death counts of the USA from February 29 to April 22, 2020, are plotted which fits with the function (17). It is a six-parameter model that considers the uncertainty of coronavirus COVID-19 in the population. To improve the goodness of fit with the data set, the six-parameter model is used.Fig. 5 Death growth path of USA using the mathematical model, Model-II The probability density function of no death is given by18 f2(t)=(d2)′(t)Texp-d2(t)T,t≥0,t∈(0,T),T>0, where19 (d2)′(t)=qk(1+p)sexp(qt)(exp(qt)+q)2[d2(t)-m]2,t≥0. Prediction of the time of no death using the total number of death s The probability density function f2(t) will reach the value zero when t approaches a larger value. For what value of t, the function f2(t)=0 we have to calculate in this section. As t takes a larger value, the function exp(-[d2(t)T]) will approach zero and hence the f2(t) approaches zero. From this, the time which the COVID-19 disease leading to death will stop killing the people of the USA or the time of no death or lifetime can be determined from the relation20 lifetime=m+s228. We have the time of no death or lifetime will be 240.3530616 ≈ 241 days. It will be 241 days from February 29, 2020. That is, after November 01, 2020, there will be no death in the USA due to COVID-19, provided globally if everyone follows guidelines of WHO and advice of healthcare workers. On comparing with the live updates of WHO, it is not so. Again, we explore this model in the next subsection. Prediction of the time of no death using the death rate q The probability density function f2(t) will reach the value zero when t approaches a larger value. For what value of t the function, f2(t)=0 we have to calculate in this section. Again, (d2)′(t) can be rewritten as,21 (d2)′(t)=qk(1+p)sexp(-qt)(1+q.exp(-qt))2[d2(t)-m]2,t≥0. As t takes a larger value, the function exp(-qt) will approach zero and hence the f2(t) approaches zero. From this, the time which the COVID-19 disease leading to death will stop killing the people of the USA or the time of no death or lifetime can be determined from the relation22 lifetime=228q. We have calculated the time of no death will be 1281.33078 ≈ 1282 days from February 29, 2020. That is, after 3 years, 6 months, and 7 days from February 29, 2020 ( from September 2023 ), there will be no death in the USA due to COVID-19, provided globally if everyone follows guidelines of WHO and advice of healthcare workers. Therefore, the lifetime of coronavirus COVID-19 in the USA is calculated using the mathematical model, Model-II as 1282 days from February 29, 2020. Mathematical Model-III and findings In these sections, a mathematical model, Model-III, is discussed for the lifetime of the Coronavirus COVID-19 in the USA. That is, in this section, the lifetime of the Coronavirus COVID-19 (the time of no death due to COVID-19) in the USA is calculated using the best mathematical model to better protect people and general pandemic preparedness. This prediction using a mathematical model is entirely new. The death growth function can be defined by the logistic function23 d3(t)=s1+k1+mexp(qt)+pn,t≥0, where p = -11.9747477, q = 0.1535604, k = 338.99688, s = 62100, m = 2.6586221, and n=1. In Fig. 6, the death counts of the USA from February 29 to April 22, 2020, are plotted which fits with the function (23). The function (23) satisfies the assumptions (6) and (7). It is a six-parameter model that considers the uncertainty of coronavirus COVID-19 in the population. To improve the goodness of fit with the data set, six-parameter model is used.Fig. 6 Death growth path of USA using the mathematical model, Model-III The probability density function of no death is given by24 f3(t)=(d3)′(t)Texp-d3(t)T,t≥0,t∈(0,T),T>0, where25 (d3)′(t)=qk(1+m)sexp(qt)(exp(qt)+q)2d3(t)2,t≥0, Prediction of the time of no death using the total number of death s The probability density function f3(t) will reach the value zero when t approaches a larger value. For what value of t, the function f3(t)=0 we have to calculate in this section. As t takes a larger value, the function exp(-[d3(t)T]) will approach zero and hence the f3(t) approaches zero. From this, the time which the COVID-19 disease leading to death will stop killing the people of the USA or the time of no death or lifetime can be determined from the relation26 lifetime=s228. We have the time of no death or lifetime will be 272.3684211 ≈ 273 days. It will be 272 days from February 29, 2020. That is, after December 01, 2020, there will be no death in the USA due to COVID-19, provided globally if everyone follows guidelines of WHO and advice of healthcare workers. On comparing with the live updates of WHO, it is not so. Again, we explore this model in the next subsection Prediction of the time of no death using the death rate q The probability density function f3(t) will reach the value zero when t approaches a larger value. For what value of t, the function, f3(t)=0 we have to calculate in this section. Again, (d3)′(t) can be rewritten as,27 (d3)′(t)=qk(1+m)sexp(-qt)(1+q.exp(-qt))2[d3(t)]2,t≥0. As t takes a larger value, the function exp(-qt) will approach zero and hence the f3(t) approaches zero. From this, the time which the COVID-19 disease leading to death will stop killing the people of the USA or the time of no death or lifetime can be determined from the relation28 lifetime=228q. We have the time of no death will be 1464.7577 ≈ 1465 days from February 29, 2020. That is, after 4 years, from February 29, 2020 ( from March 2024 ), there will be no death in the USA due to COVID-19, provided globally if everyone follows guidelines of WHO and advice of healthcare workers. Therefore, the lifetime of coronavirus COVID-19 in the USA is calculated using the mathematical model, Model-II, as 1465 days from February 29, 2020.Table 5 Comparison of Lifetime of coronavirus in the USA from February 29, 2020, in days Metric Model I Model II Model III Maximum Total number if death counts at time t 240.79 240.35 272.37 272.37 The death rate 1285.12 1281.33 1464.76 1464.76 We take the maximum value calculated from these three models to be 273 days based on the total number of deaths at time t. On taking the maximum value, it is predicted from three models, after 272.37 days from February 29, 2020 (that is, after December 2020 ), there will be no deaths, and on comparing with the live updates of WHO there will be death in the USA due to COVID-19 after December 2020 also. We take the maximum value calculated from these three models to be 1465 days based on the death rate. And hence, after December 2024 we can expect no death due to COVID-19 in the USA, provided globally if everyone follows guidelines of WHO and advice of healthcare workers. The best model among the five-parameter and six-parameter models is Model-III (the six-parameter model) predicts the lifetime of coronavirus COVID-19 in the USA as 1465 days from February 29, 2020. Our future work will be to do more analysis using the data of the USA to get a solution to the present situation in the USA. To improve the goodness of fit with the data set, parameters will be increased in the model function. Conclusion and suggestions The World Health Organization on December 31, 2019, has been made aware of several cases of human respiratory diseases of unknown origin at present from Chinese Wuhan City, Hubei Province, with similar clinical presentations of viral pneumonia and manifested by dry cough, severe fever, and shortness of breath. As of January 30, 2020, the WHO classified this epidemic as a global health emergency (Chung et al. 2020). The coronavirus epidemic that started and now spread in China around the world, over 377,407 left deaths and 6,365,625 cases confirmed by June 02, 2020, 02:20 GMT, alarmed about the ability of countries manage an epidemic or pandemic. Due to the lack of effective treatment and vaccine, the best way to fight COVID-19 disease is currently to prevent the transmission and spread of the virus and to take individual protective measures. Due to deaths, globally everyone is under fear. Now, it is the responsibility of researchers to give hope to the people who are alive and active. The objective of this article is to give a clear idea about coronavirus caused disease COVID-19 and to present the starting time to the end time of this disease by predicting using mathematical models to better protect people and general pandemic preparedness. We have introduced the virus with a crown followed by the review which killed humans. The transmission of coronavirus from bat to human is followed by a review. And the expected future transmission is also presented. In this article, the lifetime of coronavirus COVID-19 (the time of no death due to COVID-19) in the USA is calculated using three mathematical models to better protect people and general pandemic preparedness. The death counts of the USA from February 29 to April 22, 2020, are used in this article Based on the total number of death at time t, the Model-I, Model-II, and Model-III give 240.79 days, 240.35 days, and 272.37 days, respectively, from February 29, 2020. On taking the maximum value, it is predicted from three models, after 272.37 days from February 29, 2020 (that is, after December 2020 ), there will be no deaths and, on comparing with the live updates of WHO there will be death in the USA due to COVID-19 after December 2020. And, based on the death rate, the Model-I, Model-II, and Model-III give 1285.12 days, 1281.33 days, and 1464.76 days, respectively, from February 29, 2020. On taking the maximum value, it is predicted from three models, after 1464.76 days from February 29, 2020 (that is, after March 2024 ), there will be no deaths due to COVID-19 and, on comparing with the live updates of WHO we can expect no infection and no death in the USA due to COVID-19 after March 2024, if everyone follows guidelines of WHO and advice of healthcare workers. The best model among the five-parameter and six-parameter models is Model-III (the six-parameter model) predicts the lifetime of coronavirus COVID-19 in the USA as 1465 days from February 29, 2020. Clinically reported from China but we do not know that whether those humans do not have the habit of consulting doctors all over the world would have affected and died due to the coronavirus. So all humans should take preventive measures. All should wear a mask. If you wear a mask, you need to know how to use and remove it properly. Another important safety measure is one should wear shoes to cover feet to avoid the sputum or fluid from the nose of the disease affected person falls on the ground. For this huge social, public, and economic impact of 2019 new prevention of coronavirus outbreaks, the following suggestions are - Now it is an international spread believed to be from China, to avoid this from China again, lockdown China first in land-wise, sea-wise, and air-wise entry and leaving of humans. It is an international spread from China, to avoid this from China again, lock down your nation in land-wise, sea-wise, and air-wise entry and leaving of humans Give the weekly report about your nation’s status to WHO so that other nations will get alert and cautious about their citizens as China reported to WHO. Some reports say from men coronavirus is transmitted to their animals which are under their care. So limit human-animal transmission. The daily report says, nurses, doctors, and other health workers dead. So all nations, first provide secured special dress, food, shelter, infrastructure facilities, and protection to nurses, doctors, and other health workers. And provide complete security with a quarantine period to them also to get rid off mental and physical strain and stress and to have a social distance from patients. Give awareness about coronavirus to all humans in all parts of the world and train them on how to prevent it because prevention is better than cure. All nations should have National Task-Force to face health damages. All nations must instruct all humans must protect the nose, and mouth by mask, ears, and eyes with thin transparent cloths, feet by shoes, and hands by hand wash. It is better to cover the whole body and clean daily both the body and the materials used to cover the body. Protect not only humans but also animals in the land, birds in the sky, and fish in the pond, lake, sea, and oceans. Provide online and mobile help to all 24 hours a day so that the affected person can be rescued from the place of stay to the hospital. Already spread coronavirus in your nation and the spread in your nation must be controlled and should be prevented in further transmission. Advice for the entry test in countries/areas to prevent transmission of the new coronavirus 2019-Nov; Limit human-to-human transmission, especially through the reduction of secondary infections in close contact and health caregivers, preventing the amplification of transmission events. Identify and reduce animal transmission source from good health education; Treat critical cases and accelerate the development of diagnostics, therapies, and vaccines; Communicate all information through media about critical risks and events communities and fight against disinformation; Avoid all public and private gathering to prevent the transmission; Do the test and analysis with modern equipment without making any delay of time so that the infected person can be saved in the early stage; For suspected cases use updated serological and molecular investigation; Take the prevention of the appearance or release of pathogens;. Immediate response and attenuation of the spread of the epidemic. The nation worst affected must learn from the precaution measures taken by the nation which returned to normal life without any cases of confirmation and deaths. All nations must share their resources they have to treat the coronavirus affected cases and to feed all. In the midst, as a consequence of this coronavirus epidemic other new diseases may come, for that also all nations must get prepared to face this war. A nation allocates the maximum budget amount to defense to maintain peace in the nation. The present situation gives a lesson to all nations, allocates more amount for the health of the nation first to save the people of the nation from death-causing diseases. The second priority of agriculture to feed the people of the nation. Give third priority to defense to save the people from external and internal antisocial elements. Already all nations are well equipped with their defense. Now it is the time to save from the invisible enemies of all nations and to rebuild all nation’s economies. Future studies will be to do more analysis using the data of the USA to get a solution to the present situation in the USA. We plan to design models to improve the goodness of fit with the data set, using more parameters in the function (2) to consider the uncertainty of coronavirus COVID-19 in the population and more assumptions on the data of the population infected. Also, in the future, we planned to apply the models to other nations. Above all, leaders of all nations must read the book of Law and abide by the Law, and automatically his/her people will follow the Law and save their lives. One can observe this fact from the data received by WHO regarding COVID-19 from January 2020 to date. Because Law says it is Justice to save a life. Acknowledgements This part of the research was conducted at the University of Kerala, Kerala, India, and Anna University, Chennai, India. All calculations of this document were performed on a Micro Vax II computer with Pascal math precision and MATLAB at Bharathidasan University, Tiruchirapalli, India. We are extremely thankful to the Reviewers of this article and the Editor of this Journal for the valuable comments and suggestions to improve the quality. Declarations Conflict of interest The authors of this article declare that they have no conflict of interest. Ethical approval The authors of this article declare that this article does not contain any studies with human participants or animals. 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==== Front Sci Total Environ Sci Total Environ The Science of the Total Environment 0048-9697 1879-1026 Elsevier B.V. S0048-9697(21)01621-1 10.1016/j.scitotenv.2021.146553 146553 Article Can use of hydroxychloroquine and azithromycin as a treatment of COVID-19 affect aquatic wildlife? A study conducted with neotropical tadpole da Luz Thiarlen Marinho a Araújo Amanda Pereira da Costa b Estrela Fernanda Neves ac Braz Helyson Lucas Bezerra d Jorge Roberta Jeane Bezerra d Charlie-Silva Ives de Malafaia Guilherme acfg⁎ a Laboratório de Pesquisas Biológicas, Instituto Federal Goiano, Urutaí, GO, Brazil b Programa de Pós-Graduação em Ciências Ambientais, Universidade Federal de Goiás, Goiânia, GO, Brazil c Programa de Pós-Graduação em Biotecnologia e Biodiversidade, Universidade Federal de Goiás, Goiânia, GO, Brazil d Programa de Pós-Graduação em Ciências Morfofuncionais, Universidade Federal do Ceará, Fortaleza, CE, Brazil e Institute de Ciências Biológicas, Universidade de São Paulo, São Paulo, SP, Brazil f Programa de Pós-Graduação em Ecologia e Conservação de Recursos Naturais, Universidade Federal de Uberlândia, Uberlândia, MG, Brazil g Programa de Pós-Graduação em Conservação de Recursos Naturais do Cerrado, Instituto Federal Goiano, Urutaí, GO, Brazil ⁎ Corresponding author at: Biological Research Laboratory, Goiano Federal Institution, Urutaí Campus. Rodovia Geraldo Silva Nascimento, 2,5 km, Zona Rural, Urutaí, GO CEP: 75790-000, Brazil. 18 3 2021 1 8 2021 18 3 2021 780 146553146553 8 2 2021 11 3 2021 13 3 2021 © 2021 Elsevier B.V. All rights reserved. 2021 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The impacts on human health and the economic and social disruption caused by the pandemic COVID-19 have been devastating. However, its environmental consequences are poorly understood. Thus, to assess whether COVID-19 therapy based on the use of azithromycin (AZT) and hydroxychloroquine (HCQ) during the pandemic affects wild aquatic life, we exposed (for 72 h) neotropical tadpoles of the species Physalaemus cuvieri to the water containing these drugs to 12.5 μg/L. We observed that the increase in superoxide dismutase and catalase in tadpoles exposed to AZT (alone or in combination with HCQ) was predominant to keep the production of NO, ROS, TBARS and H2O2 equitable between the experimental groups. In addition, the uptake of AZT and the strong interaction of AZT with acetylcholinesterase (AChE), predicted by the molecular docking analysis, were associated with the anticholinesterase effect observed in the groups exposed to the antibiotic. However, the unexpected increase in butyrylcholinesterase (BChE) in these same groups suggests its constitutive role in maintaining cholinergic homeostasis. Therefore, taken together, our data provide a pioneering evidence that the exposure of P. cuvieri tadpoles to AZT (alone or in combination with HCQ) in a predictably increased environmental concentration (12.5 μg/L) elicits a compensatory adaptive response that can have, in the short period of exposure, guaranteed the survival of the animals. However, the high energy cost for maintaining physiological homeostasis, can compromise the growth and development of animals and, therefore, in the medium-long term, have a general negative effect on the health of animals. Thus, it is possible that COVID-19 therapy, based on the use of AZT, affects wild aquatic life, which requires greater attention to the impacts that this drug may represent. Graphical abstract Unlabelled Image Keywords Pharmaceutical waste Amphibians Freshwater ecosystems Biochemistry Ecotoxicology Editor: Henner Hollert ==== Body pmc1 Introduction It is known that amphibians to comprise one of the most endangered groups (Beebee & Griffiths, 2005; Green et al., 2020), a fact that has been discussed for some time (Wake, 1991, Wake, 1998); but that, more recently, discussions have been more urgent (Green et al., 2020; Bolochio et al., 2020). This is because the increasing loss of natural habitats (Mayani-Parás et al., 2020; Semper-Pascual et al., 2021), increased UV-B irradiation (Lundsgaard et al., 2020; Morison et al., 2020), emergence of emerging diseases (Blaustein et al., 2018; Fisher & Garner, 2020; Brannelly et al., 2021), introduction of non-native species (Nunes et al., 2019), climate change (Bucciarelli et al., 2020) and the increase in pollution of freshwater ecosystems (Wesner et al., 2020; Meindl et al., 2020; Lent et al., 2020) has greatly intensified the reduction and distribution of various species of amphibians. Regarding the impacts of pollutants on these animals, most studies have directed their designs to assess the effects of classic chemical compounds, such as pesticides and their degradation products, heavy metals, nitrogen-based fertilizer, among others [see review by Blaustein et al. (2003)]. However, fewer ecotoxicological studies addressing the impacts of emerging pollutants on amphibians are less [see reviews by McConnell and Sparling (2010) and Egea‐Serrano et al. (2012)]. Such pollutants include synthetic or natural chemicals that are not part of the list of those included in routine (inter) national monitoring programs, but that have the potential to enter different environmental compartments and cause ecological and/or human health effects (Geissen et al., 2015; Calvo-Flores et al., 2018). Two chemical compounds considered as emerging pollutants, whose impacts on amphibians have never been studied, refer to azithromycin (AZT) and hydroxychloroquine (HCQ) (Mendez et al., 2017; Dabić et al., 2019; Gomes et al., 2020). While AZT is a macrolide antibiotic which inhibits bacterial protein synthesis (Parnham et al., 2014) also used in the treatment of cancer and autoimmune and inflammatory diseases (Patel and Hashmi, 2020); HCQ is used in the prevention and treatment of malaria (Shippey et al., 2018) and as a therapeutic option in the treatment of rheumatoid arthritis (Lane et al., 2020), lupus erythematosus (Jakhar and Kaur, 2020), porphyria cutanea tarda (Malkinson and Levitt, 1980), Q fever (Hartzell et al., 2008; Cherry and Kersh, 2020) and photosensitive diseases (Millan and Quijano, 1957). Due to the COVID-19 pandemic (started in late 2019), the use of these drugs has increased considerably (Yazdany and Kim, 2020; Malik et al., 2020a, Malik et al., 2020b; Agarwal et al., 2020; Nasir et al., 2020; Mallhi et al., 2020; Quispe-Cañari et al., 2020), although their effectiveness against SARs-Cov-2 infection is questioned by several studies (Shukla et al., 2020; Ghazy et al., 2020; Jameleddine et al., 2020). Therefore, the increase in the arrival and dispersion of these drugs in aquatic ecosystems is already a reality, especially due to the dumping of domestic sewage and hospital waste in rivers or streams or via leaching from landfills, which in many countries do not receive adequate treatment (Ansari et al., 2019; Urban and Nakada, 2021) or the processes used are insufficient to remove these pollutants or are financially inaccessible (Ali et al., 2017; Khan et al., 2019). In cities with a high incidence of COVID-19, for example, the dramatic increase in the production of hospital waste in health facilities has been an additional administrative challenge (Sarkodie and Owusu, 2020), in addition to amplifying the presumed concentrations of AZT and HCQ in the aquatic environment. In India, for example, after the approval of the Indian Council of Medical Research for the empirical use of HCQ for prophylaxis of COVID-19, the stocks available in pharmacies have been reduced dramatically, especially when hospitals and health professionals began to prescribe the drug to their patients (Chauhan et al., 2020a, Chauhan et al., 2020b). The King Abdullah University Hospital in Jordan produced, at the height of the pandemic, ten times more medical waste compared to average production during the days before the spread of SARs-Cov-2 (Abu-Qdais et al., 2020). In Spain, an increase of more than 300% was observed (Klemeš et al., 2020) and in Asia, it is estimated that the total of hospital waste generated exceeds 16.5 thousand tons/day, with India, followed by Iran, Pakistan, Saudi Arabia, Bangladesh and Turkey are the largest producers of this waste in the context of the COVID-19 pandemic (Sangkham, 2020). In this sense, as discussed by Farias et al. (2020), questions about the impact that therapy against COVID-19 has on aquatic wildlife. Particularly in amphibians, how can this increase affect the health of these animals and the decline of their natural populations? Thus, to assume the ecotoxicological effects of these drugs on the natural populations of anurans, we exposed tadpoles of Physalaemus cuvieri (Anura, Leptodactylidae) to AZT and HCQ (alone or in combination). This species, in particular, occurs in several countries in South America (Miranda et al., 2019) and, despite not being categorized as “unstable” and “worrying” by International Union for Conservation of Nature (version 2020–3) (IUCN, 2021), its wide geographical distribution and its large populations are characteristics that make them interesting translational models for anurofauna. From different biomarkers, we aim to test the hypothesis that short exposure to AZT and HCQ (in predictive environmentally relevant concentration) induces metabolic changes that alter REDOX homeostasis towards oxidative stress, as well as neurotoxic effects. In addition, through molecular docking analyzes we aim to predict possible interactions of drugs with important target molecules in the neurophysiological responses of animals. As far as our knowledge goes, this is the first report on the exposure of an amphibian species to AZT and/or HCQ. 2 Material and methods 2.1 Drugs Azithromycin (AZT) and hydroxychloroquine (HCQ) used in our study, [similar to the study by Amaral et al., 2019] were intentionally acquired in common commercial facilities in order to bring our experimental design as close to the most realistic condition as possible. For the preparation of the AZT stock solution, we used AZT dihydrate dragees (500 mg) (Brainfarma Indústria Química e Farmacêutica S.A., Anápolis, GO, Brazil) and for the HCQ solution, HCQ sulfate dragees (400 mg), manufactured by Apsen Farmacêutica SA (São Paulo, SP, Brazil) were used. Both solutions were prepared by diluting the pills in acetonitrile solution (0.01 M), according to Shen et al. (2010). From these solutions, the aliquots added to the exposure waters were removed. Table 1 presents general information about the drugs used in our study.Table 1 General information about the drugs used in our study. Table 1Information Azithromycin (AZT) Hydroxychloroquine (HCQ) Drug class Macrolide antibiotic Antimalarials IUPAC name (2R,3S,4R,5R,8R,10R,11R,12S,13S,14R)-2-ethyl-3,4,10-trihydroxy-3,5,6,8,10,12,14-heptamethyl-15-oxo- 11-{[3,4,6-trideoxy-3-(dimethylamino)-β-D-xylo-hexopyranosyl]oxy}-1-oxa-6-azacyclopentadec-13-yl 2,6-dideoxy-3C-methyl-3-O-methyl-α-L-ribo-hexopyranoside (RS)-2-[{4-[(7-chloroquinolin-4-yl)amino]pentyl}(ethyl)amino]ethanol CAS number 83905-01-5 118-42-3 Formula C38H72N2O12 C18H26CIN3O Molar mass 748.996 g/mol 335.87 g/mol Excipient q.s.a Starch, microcrystalline cellulose, sodium lauryl sulfate, silicon dioxide, povidone, croscarmellose sodium, magnesium stearate, titanium dioxide, macrogol, and hypromellose Croscarmellose sodium, titanium dioxide, magnesium stearate, lactose monohydrate, povidone, starch, hypromellose, and macrogol Manufacturer Brainfarma Indústria Química e Farmacêutica S.A. (Anápolis, GO, Brazil) Apsen Farmacêutica S.A. (São Paulo, SP, Brazil) Register in the Brazilian Food and Drug Agency (ANVISA), Ministry of Health (Brazil). 1.5584.0530 1.0118.0162 3D model (JSmol) Image 1 Image 2 a Information provided by the manufacturer (AZT: https://www.bulas.med.br/p/laboratorios/laboratorio/bula/1366728/Azitromicina_di_hidratada__Comprimido_500_mg_.html) and HCQ (https://www.bulas.med.br/p/bulas-de-medicamentos/bula/7229/reuquinol.htm). 2.2 Model system and experimental design To assess the aquatic toxicity of AZT and HCQ, we used tadpoles of the species Physalaemus cuvieri (Leptodactylidae) as a model system. Its wide geographical distribution in South America (Miranda et al., 2019), stability and population abundance in the areas that occur (Frost, 2017), in addition to good adaptability in the laboratory and early biological response to changes in its environment justify the choice of species in our study, as well as in other recent (eco) toxicological studies (Herek et al., 2020; Araújo et al., 2020a, Araújo et al., 2020b; Rutkoski et al., 2020). All tadpoles used came from three ovigerous masses [containing approximately 1500 eggs/each, according to Pupin et al., 2010] collected in a lentic environment (Urutaí, GO, Brazil) surrounded by native vegetation from the Cerrado biome, under license no. 73339-1 of the Brazilian Biodiversity Information and Authorization System (SISBIO/MMA/ICMBio). Upon arrival at the laboratory, the eggs were kept in an aquarium (40.1 cm × 45.3 cm × 63.5 cm) containing 80 L of naturally dechlorinated water (for at least 24 h), under controlled light conditions (cycles of 12 h of white light at 100 lx and 12 h of complete darkness), temperature (26 °C ± 1 °C - similar to the natural environment) and constant aeration (maintained by air compressors), being fed once a day (ad libitum) with commercial fish feed (guarantee levels: 45% crude protein, 14% ether extract, 5% crude fiber, 14% mineral matter and 87% dry matter). After the eggs hatched, the tadpoles remained in these conditions until they reached stage 26G, according to Gosner (1960) (body biomass: 70 mg ± 4.1 mg and total length: 20.1 mm ± 0.7 mm - mean ± SEM). Then, 800 healthy tadpoles (i.e., with normal swimming movements and without morphological deformities or apparent lesions) were distributed into four experimental groups (n = 200 tadpoles/each). The “AZT” and “HCQ” groups were exposed to water containing 12.5 μg/L of both drugs (alone) and the animals in the “AZT + HCQ” group were exposed to water containing both AZT and HCQ, simulating the co-presence of drugs in the aquatic environment. The control group (“C”) was composed of tadpole kept in water containing only the vehicle solution (0.01 M acetonitrile solution) in an amount proportional to that added in the other experimental groups. 2.3 Exposure conditions and tested concentrations All experimental groups were kept in glass containers containing 2 L of naturally dechlorinated water, in which the drugs were diluted, with an exposure period of 72 h, simulating an ephemeral exposure. During the exposure, the animals were fed once a day with commercial fish feed and the waters were not renewed (i.e., static system). The drug concentrations were based on previous studies that identified them in surface waters. Fernandes et al. (2020) reported that AZT concentration of up to 2.8 μg/L was detected in a river in northern Portugal and, in Olaitan et al. (2014), the median concentration of chloroquine (chemically similar to HQC, its derivative) identified in different water samples from Nigeria was 2.12 μg/L. Therefore, the concentration tested in our study (ie: 12.5 μg/L) simulates a potential increase (approximately 6 times) in AZT and HCQ concentrations in aquatic environments (associated with the COVID-19 pandemic), which can be considered a predictive environmentally relevant concentration. 2.4 Toxicity biomarkers 2.4.1 Sample preparation Prior to biochemical assessments, the samples to be analyzed were prepared, similarly to Guimarães et al. (2021). In this case, we used 96 tadpoles/group, distributed in eight samples composed of a pool of 12 animals/each. These animals were weighed (12.5 g ± 0.0004 - mean ± standard error) and subsequently macerated in 1 mL of phosphate buffered saline (PBS), centrifuged at 13,000 rpm for 5 min (at 4 °C). The supernatant was separated into aliquots to be used in different biochemical evaluations. Entire bodies were used in the experiment due to the hard time isolating certain organs from small animals. Unlike adult anurans, organ-specific biochemical assessment in tadpole requires highly accurate dissection due to their small size, which makes it difficult processing large numbers of samples under time constraint (Khan et al., 2015). Organ “contamination” by organic matter and/or by other particles consumed by tadpole can be bias at biochemical analysis applied to organs at dissection time (Lusher et al., 2013; Guimarães et al., 2020). 2.4.2 REDOX state 2.4.2.1 Oxidative stress biomarkers The effects of exposure à AZT e HCQ (alone or in combination) on oxidative stress reactions were evaluated based on (i) indirect nitric oxide (NO) (via nitrite measurement) (Soneja et al., 2005); (ii) on thiobarbituric acid reactive species (TBARS), predictive of lipid peroxidation (De Leon and Borges, 2020); (iii) production of reactive oxygen species (ROS), and on (iv) hydrogen peroxide (H2O2), which plays an essential role in responses to oxidative stress in different cell types (Sies, 2020). The Griess colorimetric reaction [as described in Bryan and Grisham (2007)] was used to measure nitrite and the TBARS levels were determined based on procedures described by Ohkawa et al. (1979) and modified by Sachett et al. (2018). The production of H2O2 and ROS was evaluated according to Elnemma (2004) and Maharajan et al. (2018), respectively. 2.4.2.2 Antioxidant response biomarkers The activation or suppression of antioxidant activity by treatments was evaluated by determining the activity of catalase and superoxide dismutase (SOD), which are considered first-line antioxidants important for defense strategies against oxidative stress (Ighodaro and Akinloye, 2018). While catalase activity was assessed according to Sinha (1972) [see details in Montalvão et al., 2021]; SOD levels were determined according to the method originally described by Del-Maestro and McDonald (1985) and adapted by Estrela et al. (2021). 2.5 Neurotoxicity The possible neurotoxic effects induced by AZT and HCQ (alone and in combination) were evaluated by determining the activity of acetylcholinesterase (AChE) enzymes [according to the method of Ellman et al., 1961] and butyrylcholinesterase (BChE, also called serum cholinesterase or pseudocholinesterase) [according to the methodology described in Silva et al., 2020]. 2.6 Bioinformatics in silico analysis Seeking to predict the binding mode and affinity of the bonds between AZT and HCQ used in our study and the protein structures of the enzymes AChE e BChE, we performed docking and chemoinformatic screens (Kolb et al., 2009). Protein structures and sequences of the P. cuvieri (i.e.: Leptodactylidae) taxonomic family were not found in the biological structure databases. Therefore, we use as target structures those from the Pipidae (Xenopodinae) family, a family phylogenetically close to the group of Leptodactylidae (Jetz & Pyron, 2018). The structures of AChE and BChE were obtained using the homology construction technique with similarity values of 95.48% and 97.14% to structures (targets) used for comparative modeling on the SWISS-MODEL server (https://swissmodel.expasy.org/). For molecular docking simulations, AutoDock tools (ADT) v4.2 (for preparing binders and targets) (Morris et al., 2009) and AutoDock Vina 1.1.2 (for calculations) were used (Trott and Olson, 2010). The binding affinity and interactions between residues were used to determine the best molecular interactions. The results were visualized using ADT, Biovia Discovery Studio v4.5 and UCSF Chimera X (Pettersen et al., 2021). 2.7 Azithromycin quantification AZT uptake in tadpoles was assessed according to the methodology adopted by Keskar and Jugade (2015), with some modifications, using the supernatant of 10 samples/group (prepared according to item 2.4.1), composed of a pool of 5 animals/each (total of 50 animals/group). Initially, aliquots of 30 μL of the sample supernatant were transferred to test tubes (previously sanitized) and mixed with 470 μL of acetonitrile solution (0.01 M), 500 μL of bromocresol green solution (0.0002 M) and 1.5 mL of acetonotril-ethanol solution (1:1). Then, the samples were shaken and homogenized in a vortex shaker for 5 s and, sequentially, 200 μL of each sample were transferred to a 96-well microplate (in duplicate), for later reading at 630 nm, in an ELISA reader. In parallel, a standard curve was made using known concentrations of AZT (0, 0.03, 0.05, 0.0752, 0.1, 0.25, 0.4, 0.5, 0.6 and 0.7 mg/mL) and the equation of the line generated (y = 1.2877× + 0.232; R2 = 0.946) was used to determine the concentrations of the test samples. The background fluorescence of the control samples was determined and subtracted from the samples from the tadpoles exposed to AZT. 2.8 Quantification of hydroxychloroquine The procedures used for the quantification of HCQ followed the recommendations of Bergqvist et al. (1985) (with some modifications), using 54 animals/group, distributed in 9 samples composed by a pool of 6 animals/each. Briefly, a 200 μL aliquot of supernatant from each sample was transferred to previously cleaned hygienic conical bottom microtubes and, sequentially, 400 μL of the bromothymol blue solution (0.65 mmol/L) and 600 μL of dichloromethane PA were added sequentially. This, the solutions were homogenized in a vortex mixer (for 30 s) and centrifuged at 1500 rpm, for 5 min, at 23 °C. Subsequently, the aqueous phase of the mixture was discarded and 200 μL of the organic phase was transferred to a 96-well microplate, for later reading at 405 nm, in an ELISA reader. The concentrations of HCQ in the samples were determined from the equation of the straight line obtained by making a standard curve, using known concentrations of HCQ (0, 0.00625, 0.0125, 0.025, 0.05, 0.1, 0.2, 0.4 and 0.8 mg/mL – equation: y = 15.953 + 0.0328; R2 = 0.9942). The background fluorescence of the control samples was also determined and subtracted from the samples from tadpoles exposed to HCQ. 2.9 Statistical analysis GraphPad Prism Software Version 8.0 (San Diego, CA, USA) was used to perform the statistical analysis. Initially, data were checked for deviations from normality of variance and homogeneity of variance before analysis. Normality of data was assessed by use of the Shapiro-Wilks test, and homogeneity of variance was assessed by use of Bartlette's test. Multiple comparisons were performed using a one-way ANOVA and Tukey's post-hoc analysis (for parametric data) or Kruskal-Wallis test, with Dunn's post-hoc (for non-parametric data). When the means of only two groups were compared, we applied the Mann-Whitney test. Levels of significance were set at values of Type I error (p) less than 0.01, 0.001, 0.0001 or <0.0001. 3 Results Initially, we did not register any deaths of animals exposed to any of the treatments, throughout the experiment. In addition, the concentrations of nitrite, ROS, TBARS and H2O2 did not differ between the groups exposed to the drugs (Fig. 1 ). On the other hand, the tadpoles' co-exposure to AZT and HCQ, induced a significant increase in SOD activity (Fig. 2A) and catalase activity increased in the “AZT” and “AZT + HCQ” groups. However, the “HCQ” group showed a significant reduction in relation to the control group (Fig. 2B).Fig. 1 Boxplots of the production of (A) nitrite, (B) thiobarbituric acid reactive substances, (C) reactive oxygen species and (D) hydrogen peroxide in P. cuvieri tadpoles exposed or not to drugs (azithromycin and hydroxychloroquine), alone or in combination. (n = 96 tadpoles/group, distributed in eight samples containing a pool of 12 animals/each). C: control; AZT: azithromycin; HCQ: hydroxychloroquine, both at 12.5 μg/L. Fig. 1 Fig. 2 Boxplots of (A) superoxide dismutase and (B) catalase activity in tadpoles of P. cuvieri exposed or not to drugs (azithromycin and hydroxychloroquine), alone or in combination. Asterisks indicate significant differences between the treated groups and the control group (n = 96 tadpoles/group, distributed in eight samples containing a pool of 12 animals/each) [p value = 0.01 (), 0.001 (), 0.0001 (), <0.0001 (*)]. C: control; AZT: azithromycin; HCQ: hydroxychloroquine, both at 12.5 μg/L. Fig. 2 Interestingly, we also observed that the treatments induced a differentiated effect on the animals' cholinesterase system. While AChE activity was reduced in the “AZT” and “AZT + HCQ” groups (Fig. 3A); BChE concentrations increased in these same groups (Fig. 3B). In addition, we observed that the animals' exposure period was sufficient to induce uptake of AZT and HCQ in the animals, which suggests that drugs dispersed in water were absorbed by the tadpoles. While the concentration of AZT was higher in animals exposed to the combination “AZT + HCQ” (Fig. 4A), the uptake of HCQ was lower in those co-exposed to drugs, when compared to those in the “HCQ” group (alone) (Fig. 4B). In addition, AZT concentrations (in the “AZT” and “AZT + HCQ” groups) were almost 70% higher than those of HCQ (in the “HCQ” and “AZT + HCQ” groups) (average = 68.47%).Fig. 3 Boxplots of (A) acetylcholinesterase and (B) butyrylcholinesterase activity in tadpoles of P. cuvieri exposed or not to drugs (azithromycin and hydroxychloroquine), alone or in combination. Asterisks indicate significant differences between the treated groups and the control group (n = 96 tadpoles/group, distributed in eight samples containing a pool of 12 animals/each) [p value = 0.01 (), 0.001 (), 0.0001 (), <0.0001 (***)]. C: control; AZT: azithromycin; HCQ: hydroxychloroquine, both at 12.5 μg/L. Fig. 3 Fig. 4 Boxplots of the concentrations of (A) hydroxychloroquine and (B) azithromycin detected in tadpoles of P. cuvieri. Different lowercase letters indicate significant differences. For the quantification of each drug, we used 54 tadpoles/group, distributed in nine samples composed by a pool of 6 animals/each. AZT: azithromycin; HCQ: hydroxychloroquine; 1: 2.5 μg/L and 2: 12.5 μg/L. For both drugs, the background fluorescence of the control samples was also determined and subtracted from the samples from the tadpoles exposed to the treatments. Fig. 4 As for the analysis of molecular docking, our data predicted the affinity between the drugs and the enzymes AChE and BChE, as well as the existence of interactions with residues from all tested moorings. Fig. 5 shows that the binding energies required for AZT to bind to AChE (−8.8 ± 1.12 kcal/mol) and BChE (−9.1 ± 0.65 kcal/mol) were comparable, similar to that observed for HCQ and its molecules target [HCQ/AChE (−6.9 ± 0.97 kcal/mol) and HCQ/BChE (−6.2 ± 0.69 kcal/mol)] (Fig. 5B). However, the activation energies required for the connection between AZT and the evaluated enzymes were lower than those required for HCQ (Fig. 5), which suggests greater stability of the AZT-AChE and AZT-BChE complex and, consequently, the most likely to be formed in the evaluated biological system.Fig. 5 Graphical representation of binding energies (∆G, in kcal/mol) of molecular docking between the ligands [azithromycin (AZT) and hydroxychloroquine (HCQ)] and targets [acetylcholinesterase (AChE) and butyrylcholinesterase (BChE)] calculated by AutoDock Vina® software. Fig. 5 The interaction analysis showed that AZT interacted with AChE through different types of bonds, involving aspagirine (Asn254), tyrosine (Tyr430), tryptophan (Trp549) and two prolines (Pro256 and Pro426) (Fig. 6A). With BChE, interactions of the conventional hydrogen bond type were prevalent [glutamine (Gln71), serine (Ser72) and glycine (Gly116)], in relation to those of the carbon‑hydrogen type (Asn68), Pi-Sigma (Tyr332) and Pi-Alkil [phenylalanine (Phe329) (Fig. 6B). Regarding the receptor-ligand complex formed between HCQ and AChE, we observed the presence of interactions of the Pi-Alkil type (Phe314 and Phe355), conventional hydrogen bond type (Ser310) and the Pi-Pi T-Shaped type (Tyr358) (Fig. 6C). On the other hand, HCQ-BChE interactions occurred through different amino acid residues of the enzyme [Leu286, Phe398 and Phe329, Trp82 and Trp231, Gly116 and Gly117 and glutamic acid (Glu197)], involving predominantly Alkil and Pi type interactions -Sigma (Fig. 6D). The three-dimensional structures of the docked complexs with solid surfaces are shown in Fig. 7 .Fig. 6 Molecular interactions of the (A–B) azithromycin e (C–D) hydroxychloroquine with acetylcholinesterase and butyrylcholinesterase. Receptor amino acids are represented by spheres of different colours around the structure. Asn: aspagirine, Tyr: tyrosine, Trp: tryptophan, Pro: proline, Gln: glutamine, Ser: serine, Gly: glycine, Phe: phenylalanine, Leu: leucine. The numbering indicated in the colored circles refers to the position of the amino acids in the primary structure of the protein. Fig. 6 Fig. 7 Three-dimensional structures of docked complexs with solid surfaces. (A) Azithromycin and acetylcholinesterase, (B) Azithromycin and butyrylcholinesterase, (C) hydroxychloroquine and acetylcholinesterase and (D) hydroxychloroquine and butyrylcholinesterase. Fig. 7 4 Discussion It is a consensus among different researchers that the identification and characterization of the effects caused by pollutants on the biota constitutes an essential step for decision-making and proposing mitigation measures or pollution remediation, allowing us to cease impacts and/or prevent the occurrence of even more extensive damage to organisms (Lacy et al., 2017). In this sense, when we demonstrated for the first time that the presence and dispersion of AZT and HCQ in surface waters induce physiological changes in tadpoles, we launched trumpets about the dangerousness of these drugs in non-target organisms of wild aquatic fauna. Initially, we evaluated whether the tadpoles' exposure to AZT and HCQ (alone or in combination) could induce an increase in oxidative processes, from different biomarkers. However, no difference was observed between the experimental groups, regarding the concentrations of nitrite, TBARS, ROS and H2O2 (Fig. 1). These results are interesting, as they disagree with some previous reports and corroborate the findings of others. In the study by Li et al. (2020), for example, the increased production of ROS in Daphnia magna exposed to AZT (after 96 h of feeding Chlorella pyrenoidosa exposed to AZT) was related to changes in feeding behavior, nutritional status, and digestive physiology of these animals. Similarly, Mhadhbi et al. (2020) reported that exposure to AZT (0.05 and 0.08 mg/L, during 4 and 14 days) caused an increase in oxidative processes and peroxidative damage in the gills and liver of juvenile Dicentrarchus labrax. On the other hand, similarly to our findings, Shiogiri et al. (2017) found no evidence of increased oxidative stress in Oreochromis niloticus (juveniles) exposed for 14 days to different concentrations of AZT (1, 50 and 100 mg/L). In relation to HCQ, studies involving aquatic organisms have not evaluated biomarkers of oxidative stress, despite having already observed effects of chloroquine on the enzymatic and histopathological physiology of Cyprinus carpio fish (Ramesh et al., 2018), immobilization of D. magna (Zurita et al., 2005; Rendal et al., 2011), reduction of lysosomal function in Poeciliopsis lucida fish cells, inhibition of luminescence in Vibrio fischer bacteria and inhibition of the growth of Chlorella vulgaris algae (Zurita et al., 2005), as well as transpiration inhibition in Salix viminalis plants (Rendal et al., 2011). Therefore, this scenario denounces not only the lack of studies focusing on the ecotoxicological effects of AZT and HCQ, but also shows that the biological response to drugs is dependent on the species, period and concentrations used in the exposures. In our study, it is possible to attribute the absence of oxidative effect induced by AZT and HCQ to the action of the enzymes SOD and catalase. Although only the group co-exposed to the drugs showed a significant increase in SOD activity (Fig. 2A), in the groups exposed to AZT and HCQ (alone) the enzyme activity increased by 34.9% and 30.6% (respectively) compared to the control group, which biologically may have been preponderant to inhibit the increase of cellular oxidative processes. Similar reasoning can be used to increase catalase in the “AZT” and “AZT + HCQ” groups (Fig. 2B), since both enzymes are important for antioxidant defense against free radicals. While SOD converts the superoxide anion radical to H2O2, catalase converts H2O2 into H2O and O2 molecules (Damiano et al., 2018). As for the reduction of catalase activity in animals exposed to HCQ (alone), it is possible that it has been compensated for by the performance of other peroxisomal enzymes (which also aid in the decomposition of H2O2 and other reactive oxygen and nitrogen species) to maintain the oxidative homeostasis in this group. Such enzymes include, for example, peroxiredoxin 5, glutathione S-transferase kappa, ‘microsomal’ glutathione S-transferase, and epoxide hydrolase 2 (Fransen et al., 2012). On the other hand, our data show AZT's anticholinesterase effect on the studied tadpoles, marked by a significant reduction in AChE activity in the “AZT” and “AZT + HCQ” groups (Fig. 3A), like the reports by Mhadhbi et al. (2020), in which juveniles D. labrax exposed to AZT (0.05 and 0.08 mg/L, during 4 and 14 days) also showed a reduction of this enzyme in the gills and liver. As discussed by Massoulié et al. (1993), AChE is one of the most prominent constituents of central cholinergic pathways. It ends the synaptic action of ACh through its hydrolysis and produces the choline portion necessary for recycling the neurotransmitter. Therefore, any changes in the activity of this enzyme can lead to important neurological consequences. In our study, it is plausible to assume that the uptake of AZT in animals (Fig. 4) and, especially, its greater affinity with AChE (in relation to HCQ, Fig. 5) were preponderant for the occurrence of the observed anticholinesterase effect. As suggested by molecular docking, this effect may have been due to the probable “AZT-AChE” interaction, via connections involving different amino acid residues (Tyr430, Asn254, Pro256, Pro427 and Trp549 - Fig. 6). Although these residues are not part of any AChE active or catalytic site [see details in Harel et al. (1993), Silman and Sussman (2005), Johnson and Moore (2006) and Chen et al., 2017a, Chen et al., 2017b], it is possible that AZT acted as an allosteric modulator, changing the conformation of the enzyme and decreasing its activity, which would not have occurred in the “HCQ-AChE” interaction. In this case, in addition to the binding energy for this interaction being higher than that required for the “AZT-AChE” interaction (Fig. 5), it is possible that such connections did not cause sufficient conformational changes to alter the activity/functionality of the enzyme or that the uptake concentration of HCQ (Fig. 4) was insufficient to induce changes in the enzyme or even if biologically (ie: in vivo) such connections did not occur. As discussed by Kitchen et al. (2004), the greater the free binding energy predicted in molecular docking, the less favored is the interaction between the ligand and the target biomolecule and, therefore, less likely to occur. On the other hand, interestingly, we found an increase in BChE concentrations in the same groups in which AChE activity was reduced (ie: “AZT” and “AZT + HCQ” groups; Fig. 3), which suggests an adaptive (compensatory) response to break down excess ACh in synaptic clefts caused by reduced AChE. Despite being encoded by different genes, the enzymes AChE and BChE have high structural homology, differing in their affinity for substrates and sensitivity to inhibitors. While AChE is an esterase that hydrolyzes predominantly ACh (Soreq and Seidman, 2001), BChE hydrolyzes different types of choline esters, including butyrylcholine (BCh), succinylcholine (SCh) and ACh (Darvesh et al., 2003; Nurulain et al., 2020). However, considering that these enzymes have different Km values (Michaelis-Menten constant), they are expected to have different kinetic responses to ACh concentrations in synaptic clefts. According to Silver (1974), at low concentrations of ACh, AChE is highly efficient but BuChE is much less efficient. However, at higher ACh concentrations BuChE's efficiency in the hydrolysis of ACh is significantly increased. Thus, this evidence, associated with other studies that have already reported the compensatory support role of BChE in response to the absence or decrease of AChE, reinforces the hypothesis about the occurrence of a physiological adaptation to maintain cholinergic homeostasis in tadpoles exposed to AZT (alone or in combination with HCQ) (Norel et al., 1993; Li et al., 2000; Xie et al., 2000; Mesulam et al., 2002a, Mesulam et al., 2002b). Alternatively, we cannot rule out the hypothesis that the interaction between AZT and BChE (molecular docking; Fig. 6, Fig. 7) has also caused changes in the normal activity of the enzyme. However, contrary to the effects of the “AZT-AChE” interaction, such changes would have favored the enzyme's activity, with AZT acting as a positive allosteric modulator. Anyway, regardless of the mechanisms that explain our findings, it is important that new studies expand the understanding of the intrinsic factors involved in the physiological response of tadpoles exposed to different treatments. Although our data strongly suggest that BChE could potentially replace AChE in the context of tadpole's exposure to AZT and HCQ, as well as playing a constitutive role (rather than just back-up) in the hydrolysis of ACh, there is no way to guarantee (in our study) that this compensatory action has, in fact, regulated the concentrations of this neurotransmitter in the synaptic clefts. As is well known, both the increase and the decrease in the amounts of ACh in the synaptic clefts can induce effects on different physiological functions in the organisms, which include a wide spectrum of clinical manifestations (eg: dysfunctional gland disorders, respiratory processes, and disorders in the functioning of the central nervous system). In this case, in vivo evaluations to determine the concentrations of ACh in the tadpoles exposed to the treatments (AZT and HCQ), constitute interesting future investigative perspectives. Equally important will be the conduct of in vivo and in vitro studies to confirm the mechanisms of action predicted by molecular docking and, once confirmed, whether the interactions of drugs with the target molecules are reversible or irreversible. Finally, taken together, our data point to an unexpected response from P. cuvieri tadpoles to exposure to drugs, in which the REDOX and cholinergic imbalance induced by AZT would have been counterbalanced by the compensatory increase in enzyme activity that neutralized production excessive free radicals and apparently reestablished the central cholinergic pathways affected by the reduction in AChE. As discussed by Biagianti-Risbourg et al. (2013), this type of response constitutes an individual-level physiological adaptation and, therefore, can (in the short term) increase animal survival and maintain the highest possible fitness under stressful conditions (Hoffman, 1995; Collier et al., 2019). However, this physiological adaptation is energetically expensive for the organism and, depending on the nature and intensity of environmental stress, can trigger a physiological trade-off might, including the reduction of life expectancy or the reproductive success of individuals (Wilson and Franklin, 2002; Wood and Harrison, 2002; Farwell et al., 2012; Loria et al., 2019). Therefore, when transposing these considerations to the context of our study, we cannot guarantee that the prolonged exposure of tadpoles to drugs will have its harmlessness sustained by the physiological tolerance observed in the short exposure. Considering that the metamorphosis of amphibians, in itself, consists of a high energy cost phase (Pfab et al., 2020), the reallocation of energy from other processes (eg: growth and development) to maintain physiological homeostasis, will have a general negative effect on animal health. In this sense, it will be essential to assess how much the biological responses observed in our study will be able to guarantee the survival of the tadpoles until their complete metamorphosis, without affecting their reproductive performance. 5 Conclusions In conclusion, our study demonstrated that the short exposure of P. cuvieri tadpoles to AZT and HCQ (alone or in combination) unexpectedly induced an adaptive physiological response marked by increased activity of the enzymes SOD and catalase (for the maintenance of homeostasis oxidative) and by increasing BChE (to - possibly - counteract the anticholinesterase effect induced by AZT). In addition, the uptake of AZT in tadpoles and the strong link between this drug and AChE, suggested by molecular docking, were preponderant in triggering the animals' physiological response. When considering the pioneering nature of the present study, our results constitute only the “tip of the iceberg” that can represent the physiological effects of COVID-19 therapy based on AZT/HCQ in animal physiology. Therefore, we strongly suggest that studies of this nature be continued. Ethical approval All experimental procedures were carried out in compliance with ethical guidelines on animal experimentation. Meticulous efforts were made to assure that animals suffered the least possible and to reduce external sources of stress, pain and discomfort. The current study did not exceed the number of animals necessary to produce trustworthy scientific data. This article does not refer to any study with human participants performed by any of the authors. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgment The authors are grateful to the Brazilian National Research Council (CNPq) (proc. N. 426531/2018-3) and to Instituto Federal Goiano for the financial support (Proc. No. 23219.000077.2021-62). Malafaia G. holds productivity scholarship from CNPq (Proc. No. 307743/2018-7). ==== Refs References Abu-Qdais H.A. Al-Ghazo M.A. Al-Ghazo E.M. Statistical analysis and characteristics of hospital medical waste under novel coronavirus outbreak Global Journal of Environmental Science and Management 6 Special Issue (Covid-19) 2020 21 30 Agarwal M. Ranjan P. Mittal A. Baitha U. Use of Hydroxychloroquine for Pre-Exposure Prophylaxis in COVID 19: Debate and Suggested Future Course 2020 Ali M. Wang W. Chaudhry N. Geng Y. Hospital waste management in developing countries: a mini review Waste Manag. Res. 35 6 2017 581 592 28566033 Amaral D.F. Montalvão M.F. de Oliveira Mendes B. da Costa Araújo A.P. de Lima Rodrigues A.S. Malafaia G. 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==== Front Biol Psychiatry Biol Psychiatry Biological Psychiatry 0006-3223 1873-2402 The Authors. Published by Elsevier Inc on behalf of the Society of Biological Psychiatry. S0006-3223(21)01215-4 10.1016/j.biopsych.2021.03.033 Correspondence Remission of Subacute Psychosis in a COVID-19 Patient With an Antineuronal Autoantibody After Treatment With Intravenous Immunoglobulin McAlpine Lindsay S. a1 Lifland Brooke b1 Check Joseph R. b Angarita Gustavo A. b Ngo Thomas T. de Pleasure Samuel J. df Wilson Michael R. df Spudich Serena S. a Farhadian Shelli F. c Bartley Christopher M. deg∗ a Department of Neurology, Yale University School of Medicine, New Haven, Connecticut b Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut c Section of Infectious Diseases, Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut d Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California e Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, San Francisco, California f Department of Neurology, University of California, San Francisco, San Francisco, California g Hanna H. Gray Fellowship Program, Howard Hughes Medical Institute, Chevy Chase, Maryland ∗ Address correspondence to Christopher M. Bartley, M.D., Ph.D. 1 LSM and BL contributed equally to this work. 12 4 2021 12 4 2021 8 3 2021 26 3 2021 30 3 2021 © 2021 The Authors 2021 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body To the Editor: Patients with the COVID-19 coronavirus are at increased risk for developing new or recurrent psychosis (1). Viral infections—including SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) (2, 3, 4)—can cause psychosis in the context of autoimmune encephalitis (5). However, some individuals with parainfectious psychosis do not meet criteria for autoimmune encephalitis, yet they respond to immunotherapy (6,7). We present a case of COVID-19–associated subacute psychosis that did not meet criteria for autoimmune encephalitis yet remitted after treatment with intravenous immunoglobulin (IVIg). We subsequently identified a novel IgG class antineuronal autoantibody in the patient’s cerebrospinal fluid (CSF). A 30-year-old man without medical, psychiatric, or substance use history developed fever and malaise. The following day, he developed a delusion that the “rapture” was imminent. On day 2, a nasopharyngeal swab was positive for SARS-CoV-2 by reverse transcriptase polymerase chain reaction. He began a 14-day isolation but maintained daily contact with family. He did not have anosmia, ageusia, or respiratory symptoms, nor did he receive treatment for COVID-19. He initially suffered from hypersomnia and slept 22 hours/day. He then developed insomnia, sleeping only 3 to 4 hours/day. During this time, he began pacing and rambling about “lights.” He worried that he was dying and said that he had been speaking to deceased relatives and God. On day 22, he kicked through a door and pushed his mother, prompting an emergency department evaluation. In the emergency department, he endorsed speaking with the dead, falsely claimed to be a veteran, and worried about being experimented on with “radiation.” He did not have suicidal ideation, homicidal ideation, or hallucinations. Noncontrast head computed tomography was normal, and urine toxicology was negative. He was started on haloperidol 5 mg by mouth twice daily with significant improvement of his agitation and delusions. After 48 hours he was discharged to outpatient follow-up. Outpatient magnetic resonance imaging of the brain with and without gadolinium was unremarkable. After discharge, his restlessness, insomnia, and cognitive slowing recurred, as did his fears that he would be experimented on “like a guinea pig.” On day 34, he punched through a wall and was hospitalized to be evaluated for autoimmune encephalitis. A detailed neurological exam was unremarkable. He had a flat affect, slowed speech, and akathisia, which resolved after decreasing haloperidol and starting benztropine and lorazepam. A 12-hour video electroencephalogram was normal. Blood studies were notable for an elevated ferritin and D-dimer, suggesting systemic inflammation (Table 1 ). CSF studies, including a clinical autoimmune encephalitis autoantibody panel, were only notable for an elevated IgG of 4.8 mg/dL (reference 1.0–3.0 mg/dL) with a normal IgG index (see Table 1).Table 1 Clinical Studies Source Test Result (Reference) Nasopharyngeal Swab SARS-CoV-2 RNA PCR Day 2: positive Day 34: negative Urine 9-drug toxicology screen Negative Serum Basic metabolic panel Within acceptable limits: Na 146 mmol/L (136–144 mmol/L) K 3.1 mmol/L (3.3–5.1 mmol/L) Prothrombin time 11.5 s (9.6–12.3 s) International normalized ratio 1.07 Complete blood count Day 24 WBC: 6.9 × 1000/μL (4.0–10.0 × 1000/μL) Day 34 WBC: 5.4 × 1000/μL (4.0–10.0 × 1000/μL) MPV 11.6 fL (6.0–11.0 fL) Thyroid stimulating hormone 2.520 uIU/mL (0.270–4.200 uIU/mL) D-dimer 1.89 mg/L (≤0.50 mg/L) Liver enzymes AST 156 U/L (<35 U/L) ALT 372 U/L (<59 U/L) C-reactive protein 1.7 mg/L (<1.0 mg/L) Ferritin 1124 ng/mL (30–400 mg/mL) Ammonia 27 μmol/L (11–35 μmol/L) Albumin 4.2 g/dL (3.6–4.9 g/dL) IgG 1230 mg/dL (700–1600 mg/dL) CSF Cell count 0 nucleated cells Protein 41.2 mg/dL (15–45 mg/dL) Glucose 60 mg/dL (40–70 mg/dL) Culture No growth Oligoclonal banding None Albumin 25.8 mg/dL (10–30 mg/dL) IgG 4.8 mg/dL (1.0–3.0 mg/dL) IgG index 0.67 (<0.7) Autoimmune encephalopathy panel Negative for AMPA Ab, amphiphysin Ab, antiglial nuclear Ab, neuronal nuclear Ab (types 1, 2, and 3), CASPR2, CRMP-5, DPPX, GABAB receptor, GAD65, GFAP, IgLON5, LGI1-IgG, mGluR1, NIF, NMDA receptor, Purkinje cell cytoplasmic Ab (types Tr, 1, and 2) Imaging CT head without contrast No acute intracranial findings. MRI brain with contrast No acute intracranial abnormality or definitive structural abnormality identified. Specifically, no imaging findings suggestive of encephalitis or acute demyelination. Electroencephalography Normal prolonged (>12 hours) awake and asleep inpatient video electroencephalogram. Ab, antibody; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CSF, cerebrospinal fluid; CT; computed tomography; GABA, gamma-aminobutyric acid; IgG, immunoglobulin G; mGluR1, metabotropic glutamate receptor 1; MPV, mean platelet volume; MRI, magnetic resonance imaging; NIF, neuronal intermediate filament; PCR, polymerase chain reaction; WBC, white blood cell. Lacking focal neurologic symptoms, seizures, magnetic resonance imaging abnormalities, or CSF pleocytosis, his presentation did not meet consensus criteria for autoimmune encephalitis (7). Nevertheless, his subacute psychosis, cognitive slowing, and recent SARS-CoV-2 infection raised concern for autoimmune-mediated psychosis. Therefore, starting on day 35, he received a total of 2 g/kg of IVIg over 3 days. His cognitive slowing and psychotic symptoms remitted after the first day of treatment. His sleep cycle normalized, and he was discharged without scheduled antipsychotics. He returned to work immediately after discharge and remained symptom-free 3 months later. Because his robust response to IVIg indicated an underlying autoimmune process, we tested his CSF for antineural autoantibodies using anatomic mouse brain tissue staining (8), a validated and standard method performed by incubating rodent brain sections with CSF and counterstaining with a human IgG-specific antibody. At a 1:4 dilution, his CSF produced a novel immunostaining pattern that we have not observed in over 500 screens of CSF from other patients with neuroinflammatory disorders. His IgG prominently immunostained Satb2-expressing upper-layer (layer II/III) pyramidal neurons in the anteromedial cortex (Figure 1A ), a population of excitatory callosal projection neurons necessary for the integration of intercortical information (9). We also observed relatively uniform puncta in the corpus callosum (Figure 1B), consistent with immunostaining of callosal projections. In the olfactory bulb, mitral cell bodies and the external plexiform neuropil were immunostained (Figure 1C). In the dentate gyrus, linearly organized puncta resembling axonal transport vesicles and oblong neurons were apparent in the hilus (Figure 1D). In the thalamus, linear and less organized punctate staining was observed (Figure 1E). In the cerebellum, Purkinje cell bodies were modestly stained, while the overlying molecular layer was densely stained with variably size puncta (Figure 1F).Figure 1 Characterization of antineuronal antibody staining. Mice were perfused with 4% paraformaldehyde; 12-μm frozen sagittal brain sections were immunostained with cerebrospinal fluid at a 1:4 dilution and counterstained with an antihuman IgG secondary antibody (green) (Jackson #709-545-149 at 2 μg/mL). Nuclei were labeled with DAPI (blue). Scale bars = 10 μm. (A) Cortical immunostaining of pyramidal neuron cell bodies and proximal processes in layer II of the anteromedial cortex. Staining of neuropil was also observed. (Inset) Cerebrospinal fluid immunostains Satb2-expressing (red) neurons (filled arrowheads) but not surrounding Satb2-negative cells (unfilled arrowhead) (Abcam #ab51502 at 1 μg/mL). (B) Relatively uniform punctate staining along the ventricular wall (filled arrowheads) and overlying corpus callosum. (C) Olfactory bulb immunostaining of mitral cell bodies (filled arrowheads) and neuropil of the external plexiform layer (ep). (D) Hippocampal immunostaining of an axon-like process in the hilus (h) of the dentate gyrus (filled arrowheads) and a subset of hilar cell bodies (unfilled arrowheads). (E) Thalamic axon-like (filled arrowhead) and scattered (unfilled arrowhead) punctate immunostaining. (F) Immunostaining of cerebellar Purkinje cell bodies (filled arrowheads) and neuropil of the molecular layer (m). bv, blood vessel; gc, granule cell layer; ip, internal plexiform layer; mc, mitral cell layer; pc, Purkinje cell layer; v, ventricle. In this case we identified a candidate novel neuronal autoantibody in the CSF of a COVID-19 patient with antipsychotic-refractory subacute psychosis, whose symptoms rapidly and completely remitted after treatment with IVIg. This autoantibody primarily localized to layer II/III callosal cortical neurons, which have been implicated in schizophrenia (10). Although antineural autoantibodies are present in some neurologically impaired COVID-19 patients (11, 12, 13), autoantibody studies are rarely performed in cases of COVID-19–associated psychosis (14, 15, 16, 17, 18, 19, 20, 21, 22). Importantly, early initiation of immunotherapy for autoimmune disorders of the central nervous system significantly improves outcomes (23). Although autoimmune encephalitis can be established on clinical grounds, the diagnosis requires neurologic, magnetic resonance imaging, and/or CSF abnormalities (7). To identify individuals with potentially immune-responsive acute psychosis without neurological impairment, Pollak et al. (24) proposed criteria for autoimmune psychosis. While “possible” autoimmune psychosis relies solely on clinical factors, “probable” and “definite” require abnormal imaging or laboratory studies. Our patient’s subacute psychosis and cognitive dysfunction qualified him for possible autoimmune psychosis. However, he had several red flags for probable autoimmune psychosis: infectious prodrome, rapid progression, and insufficient response to antipsychotics (24). Moreover, his mood dysregulation, cognitive slowing, and hypersomnia were evocative of the mixed symptomatology more typical of autoimmune encephalitis (25,26). Given his overall clinical picture, we administered IVIg with apparent clinical response. Although our patient might have later developed autoimmune encephalitis, consideration of autoimmune psychosis can prompt earlier immunotherapy and potentially improve outcomes. Only by relying on ancillary criteria were we able to justify immunotherapy for our patient, suggesting that re-evaluating the criteria for autoimmune psychosis may improve its sensitivity (27). Even so, this case should be interpreted with caution. Psychotic disorders are protean by nature, mixed symptomatology does occur, and most psychotic presentations are unlikely to be immune mediated. However, given the scale of the COVID-19 pandemic, psychiatric practitioners should consider autoimmune psychosis in patients with COVID-19–associated psychosis. Acknowledgments and Disclosures This work was supported by 10.13039/100000025 National Institute of Mental Health Grant Nos. R01MH122471 (to SJP, MRW), R01MH125396 (to SS), R21MH118109 (to SS), and R01AI157488 (to SFF); 10.13039/100000065 National Institute of Neurological Disorders and Stroke Grant No. R01NS118995-14S (to SJP); a Scientific Innovations Award from the 10.13039/100000882 Brain Research Foundation (to SJP); the 10.13039/100000060 National Institute of Allergy and Infectious Diseases Grant No. K23MH118999 (to SFF); the Hanna H. Gray Fellowship from the Howard Hughes Medical Institute (to CMB); the President’s Postdoctoral Fellowship Program from the University of California (to CMB); and the John A. Watson Scholar Program of the University of California, San Francisco (to CMB). We thank Trung Huynh and Anne Wapniarski for laboratory assistance. During the course of treatment, we obtained surrogate consent to use surplus cerebrospinal fluid for research. After regaining capacity, the patient provided written informed consent for this case report. This work has not previously been published in any form. MRW has received a research grant from Roche/Genentech. All other authors report no biomedical financial interests or potential conflicts of interest. ==== Refs References 1 Taquet M. Luciano S. Geddes J.R. Harrison P.J. Bidirectional associations between COVID-19 and psychiatric disorder: Retrospective cohort studies of 62 354 COVID-19 cases in the USA [published online ahead of print Nov 9] Lancet Psychiatry 2020 2 Panariello A. Bassetti R. Radice A. Rossotti R. Puoti M. Corradin M. Anti-NMDA receptor encephalitis in a psychiatric Covid-19 patient: A case report Brain Behav Immun 87 2020 179 181 32454137 3 Monti G. Giovannini G. Marudi A. Bedin R. Melegari A. 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Detection methods for autoantibodies in suspected autoimmune encephalitis Front Neurol 9 2018 841 30364136 9 Alcamo E.A. Chirivella L. Dautzenberg M. Dobreva G. Fariñas I. Grosschedl R. Satb2 regulates callosal projection neuron identity in the developing cerebral cortex Neuron 57 2008 364 377 18255030 10 Kolluri N. Sun Z. Sampson A.R. Lewis D.A. Lamina-specific reductions in dendritic spine density in the prefrontal cortex of subjects with schizophrenia Am J Psychiatry 162 2005 1200 1202 15930070 11 Song E. Bartley C.M. Chow R.D. Ngo T. Jiang R. Zamecnik C.R. Exploratory neuroimmune profiling identifies CNS-specific alterations in COVID-19 patients with neurological involvement bioRxiv 2020 10.1101/2020.09.11.293464 12 Franke C. Ferse C. Kreye J. Momsen Reincke S. Sanchez-Sendin E. Rocco A. High frequency of cerebrospinal fluid autoantibodies in COVID-19 patients with neurological symptoms Brain Behav Immun 93 2021 415 419 33359380 13 Severance E.G. Dickerson F.B. Viscidi R.P. Bossis I. Stallings C.R. Origoni A.E. Coronavirus immunoreactivity in individuals with a recent onset of psychotic symptoms Schizophr Bull 37 2011 101 107 19491313 14 Parra A. Juanes A. Losada C.P. Álvarez-Sesmero S. Santana V.D. Martí I. Psychotic symptoms in COVID-19 patients. A retrospective descriptive study Psychiatry Res 291 2020 113254 32603930 15 Smith C.M. Komisar J.R. Mourad A. Kincaid B.R. COVID-19-associated brief psychotic disorder BMJ Case Rep 13 2020 e236940 16 Ferrando S.J. Klepacz L. Lynch S. Tavakkoli M. Dornbush R. Baharani R. COVID-19 psychosis: A potential new neuropsychiatric condition triggered by novel coronavirus infection and the inflammatory response? Psychosomatics 61 2020 551 555 32593479 17 DeLisi L.E. A commentary revisiting the viral hypothesis of schizophrenia: Onset of a schizophreniform disorder subsequent to SARS CoV-2 infection Psychiatry Res 295 2021 113573 33223274 18 Lanier C.G. Lewis S.A. Patel P.D. Ahmed A.M. Lewis P.O. An unusual case of COVID-19 presenting as acute psychosis [published online ahead of print Dec 7] J Pharm Pract 2020 19 Majadas S. Pérez J. Casado-Espada N.M. Zambrana A. Bullón A. Roncero C. Case with psychotic disorder as a clinical presentation of COVID-19 Psychiatry Clin Neurosci 74 2020 551 552 32639089 20 Clouden T.A. Persistent hallucinations in a 46-year-old woman after COVID-19 infection: A case report Cureus 12 2020 e11993 21 Chacko M. Job A. Caston F. 3rd George P. Yacoub A. Cáceda R. COVID-19-induced psychosis and suicidal behavior: Case report [published online ahead of print Sep 26] SN Compr Clin Med 2020 22 Gillett G. Jordan I. Severe psychiatric disturbance and attempted suicide in a patient with COVID-19 and no psychiatric history BMJ Case Rep 13 2020 e239191 23 Nosadini M. Mohammad S.S. Ramanathan S. Brilot F. Dale R.C. Immune therapy in autoimmune encephalitis: A systematic review Expert Rev Neurother 15 2015 1391 1419 26559389 24 Pollak T.A. Lennox B.R. Müller S. Benros M.E. Prüss H. Tebartz van Elst L. Autoimmune psychosis: An international consensus on an approach to the diagnosis and management of psychosis of suspected autoimmune origin Lancet Psychiatry 7 2020 93 108 31669058 25 Muñoz-Lopetegi A. Graus F. Dalmau J. Santamaria J. Sleep disorders in autoimmune encephalitis Lancet Neurol 19 2020 1010 1022 33212053 26 Al-Diwani A. Handel A. Townsend L. Pollak T. Leite M.I. Harrison P.J. The psychopathology of NMDAR-antibody encephalitis in adults: A systematic review and phenotypic analysis of individual patient data Lancet Psychiatry 6 2019 235 246 30765329 27 Franke C. Prüss H. Letter to the Editor: comment on Mulder J et al. (2021) Indirect immunofluorescence for detecting anti-neuronal autoimmunity in CSF after COVID-19 - Possibilities and pitfalls [published online ahead of print Feb 25] Brain Behav Immun 2021	34001372	PMC8041149	NO-CC CODE	2021-07-27 01:46:54	yes	Biol Psychiatry. 2021 Aug 15; 90(4):e23-e26
==== Front Chem Zvesti Chem Zvesti Chemicke Zvesti 0366-6352 1336-9075 Springer International Publishing Cham 1640 10.1007/s11696-021-01640-9 Original Paper Discovery of (E)-N-(4-cyanobenzylidene)- 6-fluoro- 3-hydroxypyrazine-2 -carboxamide (cyanorona-20): the first potent and specific anti-COVID-19 drug http://orcid.org/0000-0003-3681-114X Rabie Amgad M. [email protected] [email protected] Dr. Amgad Rabie’s Research Lab. for Drug Discovery (DARLD), Mansoura, Egypt 16 5 2021 117 24 9 2020 2 4 2021 © Institute of Chemistry, Slovak Academy of Sciences 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Abstract Specific inhibition of the viral RNA-dependent RNA polymerase (RdRp) of the newly-emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a very promising strategy for developing highly potent medicines for coronavirus disease 2019 (COVID-19). However, almost all of the reported viral RdRp inhibitors (either repurposed drugs or new antiviral agents) lack selectivity against the SARS-CoV-2 RdRp. Herein, I discovered a new favipiravir derivative, (E)-N-(4-cyanobenzylidene)-6-fluoro-3-hydroxypyrazine-2-carboxamide (cyanorona-20), as the first potent SARS-CoV-2 inhibitor with very high selectivity (209- and 45-fold more potent than favipiravir and remdesivir, respectively). Based on the significant reduction in the in vitro SARS-CoV-2 replication/copies, strong computational cyanorona-20 ligand-RdRp protein interactions, and anti-RdRp activity of the parent favipiravir drug, SARS-CoV-2 inhibition is thought to be mediated through the coronaviral-2 RdRp inhibition. This promising selective anti-COVID-19 compound is also, to the best of our knowledge, the first bioactive derivative of favipiravir, the known antiinfluenza and antiviral drug. This new nucleoside analog was designed, synthesized, characterized, computationally studied (through pharmacokinetic calculations along with computational molecular modeling and prediction), and biologically evaluated for its anti-COVID-19 activities (through a validated in vitro anti-COVID-19 assay). The results of the biological assay showed that cyanorona-20 surprisingly exhibited very significant anti-COVID-19 activity (anti-SARS-CoV-2 EC50 = 0.45 μM), and, in addition, it could be also a very promising lead compound for the design of new anti-COVID-19 agents. Cyanorona-20 is a new favipiravir derivative with promise for the treatment of SARS-CoV-2 infection. Graphic abstract Supplementary Information The online version contains supplementary material available at 10.1007/s11696-021-01640-9. Keywords Anti-COVID-19 drug Coronavirus SARS-CoV-2 Coronaviral RNA-dependent RNA polymerase (RdRp) Favipiravir Drug discovery Cyanorona-20 ==== Body Introduction Recently in December 2019, a novel coronavirus (2019-nCoV), officially known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; Fig. 1), suddenly emerged in Wuhan (Wuhan City, Hubei Province, China) (Hui et al. 2020). Despite drastic containment measures, the transmission of this virus is ongoing leading to the spread of the coronavirus disease 2019 (COVID-19) with its major symptoms localizing in the respiratory system of human (i.e., characterized by loss of pulmonary function in humans) (Hui et al. 2020; Li et al. 2020). This outbreak of 2019-nCoV infection has spread across our planet (Hui et al. 2020; Li et al. 2020). Currently, at the end of December 2020, about 85 million COVID-19 cases have been confirmed worldwide, with more than 1.85 millions of lives lost due to this disease (COVID-19 Map 2020). The best efforts of multinational pharmaceutical companies, drug discovery research centers/institutes, international health authorities, and pharmaceutical/medical colleges have focused on the search for effective medications and therapies able to combat the virus (Li et al. 2020; Jiang et al. 2020). No specific antiviral drugs have been officially approved for the treatment of COVID-2019 (Jiang et al. 2020). Fig. 1 A diagrammatic representation of SARS-CoV-2 morphology (its shape when entering the human body carried, mainly, into respiratory droplets) and anatomy (structure) In this absence of effective anti-COVID-19 therapy, some researchers have suggested the repurposing of the known potent antiinfluenza drug favipiravir (chemically, it is a purine nucleoside analog; approved for medical use in Japan since 2014; Fig. 2) to counteract the novel COVID-19 (Shiraki and Daikoku 2020; Dong et al. 2020; Łagocka et al. 2021; Driouich et al. 2021). Previous studies identified viral RNA-dependent RNA polymerase (RdRp) as a potential drug target in COVID-19 treatment due to its crucial role in SARS-CoV-2 replication and transcription (i.e., in the virus life cycle) (Zhang and Tang 2021). Furthermore, this enzyme has a strategic advantage of being absent in the coronavirus-uninfected human cells (i.e., viral RdRp is a drug target selective for SARS-CoV-2 particles) (Dong et al. 2020; Venkataraman et al. 2018; Wu et al. 2020). Favipiravir, as an antiviral agent, acts by selectively inhibiting viral RdRp (some other researches suggest that favipiravir, in addition of being a potent RdRp inhibitor, induces lethal RNA transversion mutations, thus producing a nonviable viral phenotype) (Shiraki and Daikoku 2020; Furuta et al. 2013). Favipiravir is a prodrug that is metabolized to its active form, favipiravir-ribofuranosyl-5′-triphosphate (favipiravir-RTP), mainly by the enzyme human hypoxanthine-guanine phosphoribosyltransferase (HGPRT) in order to stop the replication process of the viral RNA genome (i.e., of the virus) (Smee et al. 2009). However, limitations have restricted the use of favipiravir as an efficient anti-COVID-19 agent till now, e.g., reliable data regarding in vitro SARS-CoV-2 inhibition are still not available (Shiraki and Daikoku 2020; Dong et al. 2020); broad data regarding in vivo SARS-CoV-2 inhibition and efficacy in preclinical animal studies are still not available (Dong et al. 2020; Cai et al. 2020); the few available animal experiments of favipiravir show the potential for teratogenic effects (Shiraki and Daikoku 2020); favipiravir has not been shown to be effective in primary human airway cells (Yoon et al. 2018); lack of additional virus-toxic functional groups in favipiravir structure to augment its antiviral mechanism of action against the lethal and resistant SARS-CoV-2 (Dong et al. 2020; Abdelnabi et al. 2017); lipophilic/hydrophilic properties of favipiravir are not adequately balanced to achieve maximal bioavailability and distribution (especially to the lungs) in humans (Du and Chen 2020); expected binding affinities of active favipiravir-RTP molecule (as a viral RdRp inhibitor) with SARS-CoV-2 RdRp enzyme protein are not that great (as concluded mainly from the studies of active favipiravir-RTP binding affinities with the viral polymerase, e.g., Abdelnabi et al. 2017); favipiravir/favipiravir-RTP structure is not an ideal hydrogen bond acceptor (Naydenova et al. 2021); data concerning its clinical use in humans are not clear (Shiraki and Daikoku 2020; Cai et al. 2020; Du and Chen 2020); favipiravir as an anti-COVID-19 drug is used primarily off-label (in Japan) as its use as anti-COVID-19 has not been approved (Shiraki and Daikoku 2020; Du and Chen 2020); and many favipiravir published articles and papers have irreproducible data (Dong et al. 2020; Cai et al. 2020).Fig. 2 Chemical structure of favipiravir (6-fluoro-3-hydroxypyrazine-2-carboxamide) These limitations of favipiravir use against COVID-19 prompted us to design a derivative of favipiravir to better inhibit SARS-CoV-2 RdRp. The goal was an improved favipiravir structure with respect to drug-likeness, structure-stabilizing properties, small molecular weight, viral replication inhibition, and biological compatibility. After extensive molecular modeling (of compound libraries with SARS-CoV-2 RdRp), we identified (E)-N-(4-cyanobenzylidene)-6-fluoro-3-hydroxypyrazine-2-carboxamide (cyanorona-20; Fig. 3) as a new derivative of favipiravir expected to have anti-COVID-19 activities.Fig. 3 Chemical structure of the newly-designed target compound cyanorona-20 ((E)-N-(4-cyanobenzylidene)-6-fluoro-3-hydroxypyrazine-2-carboxamide) Cyanorona-20 ("cyano" stands for the cyano group, which is the major new moiety added in this derivative of favipiravir; "no" stands for the same word, no; "norona" stands for coronavirus; and "20" stands for the year in which this drug has been discovered, i.e., in 2020) is the 4-cyanobenzylidene derivative of favipiravir at the amino group, and is expected to be a prodrug that is metabolized inside the human body to its active nucleotide triphosphate form, cyanorona-20-ribofuranosyl-5′-triphosphate (cyanorona-20-RTP). Cyanorona-20 molecule has a high degree of drug-likeness (obeys Lipinski's rule of five "Ro5") and fulfills the structural requirements for a potent anti-COVID-19 agent. Figure 4 summarizes the proposed mechanism of anti-COVID-19 action of cyanorona-20.Fig. 4 A representation of cyanorona-20 major mechanism of anti-COVID-19 action One of the interesting features of cyanorona-20 structure is its expected ability to act as a zincophore. Zinc ionophores or zinc ion carriers, e.g., chloroquine (Xue et al. 2014), hydroxychloroquine (Xue et al. 2014), quercetin (Dabbagh-Bazarbachi et al. 2014), and epigallocatechin gallate (Dabbagh-Bazarbachi et al. 2014), transport extracellular Zn2+ ions across the hydrophobic cell membranes to enter the living cells, and have been studied mainly for their antiviral activities, as they have been shown to effectively inhibit the replication of various viruses in vitro (Ishida 2019). Zn2+ inhibits coronavirus RdRp activity (i.e., inhibits coronaviral replication and transcription) in vitro (Zn2+ ion is the only known elemental cofactor and ligand present in the crystal structure of SARS-CoV-2 RdRp, and thus, it has an important role in the activity and performance of this enzyme, i.e., of COVID-19 RNA-synthesizing enzymatic machine) (Hecel et al. 2020). Zinc ionophores evidently block the replication process of coronaviruses intracellularly in cell cultures (Yin et al. 2020; te Velthuis et al. 2010; Derwand and Scholz 2020). Based on this fact, molecules that have good zincophoric properties may be advantageous in inhibiting SARS-CoV-2 RdRp and coronaviral-2 replication. Cyanorona-20 has about six potential zinc-binding centers or moieties (four active nitrogen atoms and two active oxygen atoms), making it an ideal candidate to act as a potent anti-COVID-19 zincophore. Figure 5 summarizes the key structural features of cyanorona-20. Table 1 shows the structure and nomenclature of both the administered prodrug (or salt) and the active metabolite (or free base) forms of the novel anti-COVID-19 compound cyanorona-20 and the four reference general anti-COVID-19 drugs (favipiravir, remdesivir, arbidol, and hydroxychloroquine) (Dong et al. 2020; Cai et al. 2020; Du and Chen 2020; Shannon et al. 2020; Yin et al. 2020; Derwand and Scholz 2020; Choy et al. 2020; Elfiky 2020; Wang et al. 2020a, b; Kumar et al. 2020). In this research paper, the design, synthesis, characterization, computational studies, and anti-COVID-19 biological activities of the novel compound cyanorona-20 are reported.Fig. 5 A detailed presentation of the structural features of the promising anti-COVID-19 ideal model of cyanorona-20 Table 1 Chemical structures and nomenclatures of both prodrug/salt and active/base forms of cyanorona-20, its parent drug favipiravir, and its three reference antiviral drugs (remdesivir, arbidol, and hydroxychloroquine) Compound name Administered prodrug/salt form Active metabolite/free base form Cyanorona-20 Favipiravir Remdesivir Arbidol Hydroxychloroquine Experimental section Synthesis and stability testing of cyanorona-20 Materials and general data The conventional and microwave (MW) reactions were performed with commercially available reagents and solvents. Favipiravir (ultrapure) was sourced as a raw material from a representative of Toyama Chemical (Fujifilm group, Japan), 4-cyanobenzaldehyde (98%) was purchased from Merck (Merck KGaA, Darmstadt, Germany), and glacial acetic acid (gla. AcOH; extrapure) was purchased from Sigma-Aldrich. All solvents were of analytical grade, purchased from commercial suppliers, and were used as received without further purification. Microwave irradiation (MWI) for MW reaction was carried out in the laboratory MW synthesizer oven (Samsung type, model M1733N with Triple Distribution System "T.D.S." property, and having a power level of 100–800 W) operated at 2.45 GHz. Thin-layer chromatography (TLC) was used to monitor the progress of both reactions (conventional and MW), and it was carried out on TLC silica gel 60 F254 plates (plates of aluminum sheets precoated with unmodified silica gel 60 F254 to a layer thickness of 0.20 mm, purchased from E. Merck, Merck Millipore Division or Merck Chemicals, Merck KGaA, Darmstadt, Germany) as the stationary phase using n-hexane/ethyl acetate/absolute ethanol (5:2:1, v/v/v) mixture as the mobile phase (the chromatogram spots were visualized and observed under the used ultraviolet "UV" light at a wavelength of 254 nm) for monitoring both reactions. Evaporation/concentration purposes were carried out in a rotavap under reduced pressure. A lyophilizer (freeze dryer, model FD8-8T, SIM international, U.S.A.) was used for the lyophilizing purpose in the MW procedure. Melting point (M.P., °C) of cyanorona-20 was recorded in open glass capillaries using Fisher-Johns melting point apparatus. IR spectrum of cyanorona-20 was recorded on Nicolet™ iS™ 10 Mid-Infrared (Thermo Fisher Scientific) FT-IR spectrometer (υ in cm−1) using potassium bromide disk at the Central Laboratory (Faculty of Pharmacy, Mansoura University, Mansoura, Egypt) (str. = strong; br. = broad; arom. = aromatic; aliph. = aliphatic). 1H-NMR spectrum of cyanorona-20 was recorded on Varian Gemini-300 spectrometer (Mercury-300BB "NMR300") at 300 MHz using tetramethylsilane (TMS) as an internal standard at the Microanalytical Center (Faculty of Science, Cairo University, Cairo, Egypt), and its chemical shifts values (δ) were given in ppm downfield from TMS at a temperature of 30 °C using DMSO-d6 as a solvent. 13C-NMR spectrum of cyanorona-20 was also recorded on Varian Gemini-300 spectrometer (Mercury-300BB "NMR300") at 75 MHz using TMS as an internal standard at the Microanalytical Center (Faculty of Science, Cairo University, Cairo, Egypt), and its chemical shifts values (δ) were given in ppm downfield from TMS at a temperature of 30 °C using DMSO-d6 as a solvent. Mass spectrometry (MS) analysis of cyanorona-20 was performed on Shimadzu Qp-2010 Plus at 70 eV and results were represented by m/z (relative intensity "rel. int." in %) at the Microanalytical Center (Faculty of Science, Cairo University, Cairo, Egypt). Elemental analyses (elem. anal.) of cyanorona-20 were performed at the Microanalytical Center (Faculty of Science, Cairo University, Cairo, Egypt) in order to determine carbon (C), hydrogen (H), and nitrogen (N) atoms contents in %. Synthetic procedures (conventional and mw) Favipiravir (15.710 g, 0.1 mol) and 4-cyanobenzaldehyde (13.113 g, 0.1 mol) were gradually dissolved with heating in glacial AcOH (200 mL, 3.5 mol). The resulted reaction mixture was conventionally refluxed for 16 h (or under intermittent MWI at intervals of 30 s for 3 min, i.e., 6 intervals of 30 s, at a power level of 800 W. After MW reaction completion, the reaction mixture paste was cooled to − 20 °C and then it was lyophilized at − 50 °C). Then the reaction mixture (from the conventional step) was concentrated under reduced pressure, cooled to room temperature, and gradually poured onto crushed ice with stirring. The reaction mixture was allowed to stand overnight till the solid was separated and completely settled down. The separated crude solid (or the lyophilized crude solid from the MW step) was filtered and washed thoroughly with cold distilled water (3 × 400 mL), followed by cold absolute ethanol (3 × 350 mL) and then cold hexanes mixture (3 × 300 mL). Finally, the washed solid was dried, then extrapurified by recrystallization from a solvent mixture of absolute ethanol/ethyl acetate/chloroform (300 mL/300 mL/450 mL, i.e., 2:2:3, v/v/v) twice, and left to completely dry to afford the pure cyanorona-20. Stability testing protocols Simple short-term stability testing was done to extensively study the stability behavior of cyanorona-20 (mainly testing cyanorona-20 dissolution and hydrolysis profile in aqueous media of different pH ranges using different simulated fluids of the human body fluids by the aid of suitable buffering systems, e.g., simulated gastric and blood fluids of pH ranges of about 1.5–3.5 and 7.35–7.45, respectively, and applying several temperatures in the range of 20–50 °C) using the spectroscopic and chromatographic assays along with monitoring the physicochemical changes (e.g., color, texture, odor, M.P., retention factor, and pH of the aqueous solution). All the observations (including most physicochemical measurements) were done during a period of 3 months (beginning from 0 month interval "just before and just after dissolving the compound in the aqueous solutions", then 1-month interval "after 1 month from dissolving the compound in the aqueous solutions", and finally 3-month interval "after 3 months from dissolving the compound in the aqueous solutions"), and all the results were done in triplicates and compared with the reference favipiravir (which was also exposed to the same conditions for each test). Some stability tests were done on the dried cyanorona-20 which was extracted from its aqueous solution each time interval. The tests included exposure to different degrees of aqueous hydrolysis (the main concern), humidity, heat, light, tight storage, and were done according to the standard international guidelines and methods in stability testing of new compounds or drug substances, e.g., stress testing protocols and procedures, to verify the stability of cyanorona-20 compound (for more details, please see the respective standard guidelines and protocols: Q1A(R2) Stability Testing of New Drug Substances and Products). Computational molecular studies of cyanorona-20 Pharmacokinetic properties For the purpose of estimation of the molecular properties of cyanorona-20, Molinspiration web-based software (Molinspiration Cheminformatics 2020 on the Web) was used to calculate the most important molecular properties through using Molinspiration Property Engine (Molinspiration Calculation of Molecular Properties; Molinspiration Web-based Software 2020). Eight different molecular descriptors (parameters) of cyanorona-20 and its four reference repurposed anti-COVID-19 compounds (favipiravir, remdesivir, arbidol, and hydroxychloroquine) were calculated using Molinspiration methodology. The results are shown in Tables S1 and S2, respectively, in the Supplementary Material file. Predictive anti-COVID-19 pharmacological properties Prior to its experimental anti-COVID-19 evaluation, molecular docking of cyanorona-20 molecule in the enzyme SARS-CoV-2 RdRp was done using the docking engines of Discovery Studio, GemDock, GOLD, and others. The integration of the predicted pharmacophoric features with the interaction energy analysis revealed important residues in the binding pockets of the expected active/allosteric sites of SARS-CoV-2 RdRp together with in silico predicted common inhibitory binding modes with the highly potent reference compounds. For the purpose of specialized accurate docking of SARS-CoV-2 RdRp and prediction of anti-COVID-19 activities of compounds, both COVID-19 Docking Server and PASS Online web-based software programs (COVID-19 Docking Server Web-based Software 2020; PASS Online Web-based Software 2020) were used. COVID-19 Docking Server web-based software (AutoDock Vina is used as the docking engine; according to the tutorial of this web server, the Broyden-Fletcher-Goldfarb-Shanno "BFGS" optimization method is used for the optimization purpose, and the Lamarckian genetic algorithm "LGA" is used as the main docking algorithm) is an interactive web server for docking small molecules, peptides, or antibodies against potential protein targets of COVID-19 in order to predict and score the binding modes between COVID-19 targets and the ligands along with screening and evaluating the anti-COVID-19 activities of these ligands. The platform provides a free interactive knowledge-based scoring function to evaluate the candidate binding poses for COVID-19 target-ligand interactions (COVID-19 Docking Server Web-based Software 2020). The structures of all the functional/structural protein targets involved in the SARS-CoV-2 replication life cycle were either collected or constructed based on their known homologs of coronaviruses (by using homology modeling module of Maestro 10, website: www.schrodinger.com), and prepared for direct docking on this web-based software (computational type or module: For docking of only one small molecule, the "Docking" mode box should be specifically selected for every specific target (this is the option used in the present case) (COVID-19 Docking Server Web-based Software 2020). The docked nonstructural enzyme was the SARS-CoV-2 RdRp (simply, the RdRp) and the nonstructural protein 12 (nsp12). Nsp12 is the polymerase which binds to its essential cofactors, nsp7 and nsp8 (the structure of RdRp was constructed based on 6NUR, the RdRp structure of the analogous coronavirus SARS-CoV (Kirchdoerfer and Ward 2019)). Two structures (two nCoV protein targets) were prepared for small molecule docking: One structure was built with RNA from its homolog protein (3H5Y) "RdRp with RNA," while the other one with no RNA in it "RdRp without RNA" (COVID-19 Docking Server Web-based Software 2020). To get significantly accurate results, an average exhaustiveness option of 12 was used. The results of these estimations are shown in Table 2.Table 2 Score values of the two computationally-predicted pharmacological anti-COVID-19-related activities (against SARS-CoV-2 RdRp-RNA and against SARS-CoV-2 RdRp) of the target cyanorona-20, the parent favipiravir, and the three references (remdesivir, HCl-arbidol-H2O, and hydroxychloroquine sulfate), along with their five active metabolites/free bases (cyanorona-20-RTP, favipiravir-RTP, GS-441524-TP, arbidol, and hydroxychloroquine), respectively, using COVID-19 Docking Server methodology (the table shows the top docking model score value, i.e., the best binding mode score value or the least predicted binding free energy value, in kcal/mol for each compound with each target) Classification Compound name Top pose score value for docking of nCoV protein targets (kcal/mol) RdRp with RNA RdRp without RNA Prodrugs/salts Cyanorona-20 – 10.40 – 7.80 Favipiravir – 6.90 – 6.10 Remdesivir – 8.30 – 7.10 HCl-Arbidol-H2O – 7.70 – 6.00 Hydroxychloroquine sulfate – 7.10 – 5.70 Active metabolites/free bases Cyanorona-20-RTP – 10.50 – 8.60 Favipiravir-RTP – 8.40 – 7.50 GS-441524-TP – 9.20 – 7.90 Arbidol – 7.70 – 6.00 Hydroxychloroquine – 7.10 – 5.70 PASS (Prediction of Activity Spectra for Substances) Online web-based software (PASS Online 2020 on the Web; it is one of the predictive services presented by Way2Drug Predictive Services on the Web) was designed as a software product for evaluating the biological potentials of a drug-like molecule using the Predict New Compound tool (PharmaExpert.ru; PASS Online Prediction of Pharmacological Activities), with an average accuracy of prediction of more than 95% in 2020 (PASS Online Web-based Software 2020; Filimonov et al. 2014). According to PASS Online website, Pa (probability "to be active") estimates the chance that the studied compound is belonging to the subclass of active compounds (actives), while Pi (probability "to be inactive") estimates the chance that the studied compound is belonging to the subclass of inactive compounds (inactives) (PASS Online Web-based Software 2020). The detailed results of these estimations (where, Pa > Pi) are shown in Table 3.Table 3 Probability values of the computationally-predicted pharmacological antiviral anti-COVID-19 activities of the target cyanorona-20, the parent favipiravir, and the three references (remdesivir, HCl-arbidol-H2O, and hydroxychloroquine sulfate) along with their five active metabolites/free bases (cyanorona-20-RTP, favipiravir-RTP, GS-441524-TP, arbidol, and hydroxychloroquine), respectively, using PASS Online methodology Classification Compound name Anti-COVID-19 (nucleotide analog inhibitory or antiviral) activity Pa Pi Prodrugs/salts Cyanorona-20 0.651 0.009 Favipiravir 0.498 0.014 Remdesivir 0.814 0.004 HCl-Arbidol-H2O 0.740 0.004 Hydroxychloroquine sulfate 0.520 0.028 Active metabolites/free bases Cyanorona-20-RTP 0.741 0.009 Favipiravir-RTP 0.685 0.006 GS-441524-TP 0.734 0.004 Arbidol 0.740 0.004 Hydroxychloroquine 0.520 0.028 Antiviral anti-COVID-19 biological activity (in vitro assay) of cyanorona-20 This anti-COVID-19 in vitro assay is based upon the original procedures of Chu and coworkers (Choy et al. 2020; Chu et al. 2020) with very slight modifications (mainly in the prepared stock concentration of the assayed compounds). The complete procedures were carried out in a specialized biosafety level 3 (BSL-3) laboratory (SARS-CoV-2 is classified as a BSL-3 pathogen by the WHO and the FDA) in Hong Kong SAR (China) (the antiviral/cytotoxic evaluation assays were performed as a contract-based collaboration between our laboratory, DARLD, in Egypt and the specialized microbiology laboratory of Prof. Dr. Sahar M.-R. Radwan "professor of microbiology, immunology, and virology" and her coworkers in China, following the guidance of the procedures of Prof. Dr. Chu and coworkers "Choy et al. 2020; Chu et al. 2020"). The assayed SARS-CoV-2 virus, BetaCoV/Hong Kong/VM20001061/2020, was isolated from the fresh nasopharynx aspirate and throat swab of a confirmed middle-aged COVID-19 patient in Hong Kong using Vero E6 cells (ATCC CRL-1586). Stock virus (107.25 TCID50/mL) was prepared after three serial passages in Vero E6 cells in infection media (DMEM supplemented with 4.5 g/L D-glucose, 100 mg/L sodium pyruvate, 2% FBS, 100,000 U/L Penicillin–Streptomycin, and 25 mM HEPES). The four reference compounds were obtained from Toyama Chemical (Fujifilm group, Japan) (favipiravir), MedChemExpress (remdesivir), 3B Scientific (Wuhan) Corporation Limited (HCl-arbidol-H2O), and Sigma-Aldrich (hydroxychloroquine sulfate) and the stocks were accurately prepared with DMSO (100 mM cyanorona-20, 100 mM favipiravir, 100 mM remdesivir, and 100 mM HCl-arbidol-H2O) or with distilled water (100 mM hydroxychloroquine sulfate). To evaluate the in vitro anti-SARS-CoV-2 effect of the target new compound (cyanorona-20) in comparison with the anti-SARS-CoV-2 effects of the standard four reference compounds (mentioned above), Vero E6 cells were pretreated with the five compounds diluted in infection media for 1 h prior to infection by SARS-CoV-2 virus at MOI = 0.02. Antiviral anti-COVID-19 compounds were maintained with the virus inoculum during the 2-h incubation period. The inoculum was removed after incubation, and the cells were overlaid with infection media containing the diluted compounds. After 48-h incubation at 37 °C, supernatants were immediately collected to quantify viral loads by TCID50 assay or quantitative real-time RT-PCR “qRT-PCR” (TaqMan™ Fast Virus 1-Step Master Mix) (Choy et al. 2020; Chu et al. 2020). Note that viral loads in this assay were fitted in logarithm scale (log10 TCID50/mL and log10 viral RNA copies/mL) (Choy et al. 2020; Chu et al. 2020), along with linear scale (Wang et al. 2020b), under increasing concentrations of the tested compounds. Four-parameter logistic regression (GraphPad Prism) was used to fit the dose–response curves and determine the EC50 of the tested compounds that inhibit SARS-CoV-2 viral replication (CPEIC100 was also determined for each compound). Cytotoxicity of each of the five tested compounds was evaluated in Vero E6 cells using the CellTiter-Glo® Luminescent Cell Viability Assay (Promega) (Choy et al. 2020; Zhang et al. 2020). The detailed values resulted from the previous assays are shown in Table 4. Final results were represented as the mean ± the standard deviation (SD) from the triplicate biological experiments. Statistical analysis was performed using SkanIt 4.0 Research Edition software (Thermo Fisher Scientific) and Prism V5 software (GraphPad). All reported data were significant at p < 0.05.Table 4 Anti-COVID-19/antiviral activities (along with human/mammalian cells toxicities) of cyanorona-20 and the four reference drugs (favipiravir, remdesivir, HCl-arbidol-H2O, and hydroxychloroquine sulfate) against SARS-CoV-2 in Vero E6 cells Classification Compound name CC50a (μM) Inhibition of SARS-CoV-2 in vitro (μM) 100% CPE inhibitory concentration (CPEIC100)b 50% reduction in infectious virus (EC50)c 50% reduction in viral RNA copy (EC50)d Target compound Cyanorona-20 > 100 1.40 ± 0.02 0.45 ± 0.03 0.48 ± 0.03 Reference compounds Favipiravir > 100 98.82 ± 1.13 94.09 ± 5.01 > 100 Remdesivir > 100 22.50 ± 0.58 20.17 ± 1.99 23.88 ± 2.46 HCl-Arbidol-H2O > 100 81.52 ± 1.12 64.20 ± 4.90 68.42 ± 6.02 Hydroxychloroquine sulfate 93.06 ± 6.92 > 100 > 100 > 100 aCC50 or 50% cytotoxic concentration is the concentration of the tested compound that kills half the cells in an uninfected cell culture. CC50 was determined with serially-diluted compounds in Vero E6 cells at 48 h postincubation using CellTiter-Glow Luminescent Cell Viability Assay (Promega) bCPEIC100 or 100% CPE inhibitory concentration is the lowest concentration of the tested compound that causes 100% inhibition of the cytopathic effects (CPE) of SARS-CoV-2 virus in Vero E6 cells under increasing concentrations of the tested compound at 48 h postinfection. Compounds were serially twofold or fourfold diluted from 100 μM concentration cEC50 or 50% effective concentration is the concentration of the tested compound that is required for 50% reduction in infectious SARS-CoV-2 virus particles in vitro. EC50 is determined by infectious virus yield in culture supernatant at 48 h postinfection (log10 TCID50/mL) dEC50 or 50% effective concentration is the concentration of the tested compound that is required for 50% reduction in SARS-CoV-2 viral RNA copies in vitro. EC50 is determined by viral RNA copies number in culture supernatant at 48 h postinfection (log10 RNA copies/mL) Results and discussion Synthesis, structure elucidation (characterization), chemistry, and stability of cyanorona-20 Cyanorona-20 was successfully synthesized, as shown in Scheme 1, in very good yields from its parent favipiravir via direct condensation with 4-cyanobenzaldehyde (equimolar amounts) in the presence of the strong dehydrating agent glacial AcOH. The reaction could proceed either by conventional heating (with 85% yield) or under MWI (with 96% yield). The structure of cyanorona-20 was confirmed through spectroscopic analyses (IR, 1H-NMR, 13C-NMR, and MS) and microanalyses (elem. anal. for the contents of C, H, and N atoms). Spectral data and elemental analyses of this product were in full agreement with the proposed structure of cyanorona-20.Scheme 1. Conventional and microwave-assisted synthesis of cyanorona-20 from favipiravir The pure cyanorona-20 was obtained as a pale white to yellowish beige fine-crystalline powdered solid (22.969 g, 85% conventional yield; 25.941 g, 96% MW yield). M.P.: 294–298 °C (rough); FT-IR (υ in cm−1): Str. and br. 3437 (O–H, arom.), str. 3009 (C–H, aliph.), 2921 (C–H, arom.), 2236 (C≡N, nitrile), 1683 (C=N, aldimine), 1635 (C=O, amide), str. 1606 and 1548 and str. 1500 and 1462 and 1364 (C=C, arom.), str. and br. 1302 (C–F, fluoroheterocyclic), 1266 (C–N, aliph.), 1212 (C-O, phenolic); 1H-NMR (300 MHz, DMSO-d6, δ in ppm): 13.84 (s, 1H, 1 pyrazine phenolic OH), 9.63 (s, 1H, 1 secondary aldimine H), 8.13 (s, 1H, 1 pyrazine H), 7.94–7.69 (m, 4H, 4 benzylidenimine benzene H); 13C-NMR (75 MHz, DMSO-d6, δ in ppm): 168.25 (1C, 1 carbonyl C), 161.42 (1C, 1 pyrazine C–OH), 158.60 (1C, 1 secondary aldimine C), 154.95–152.00 (d, J = 246.2 Hz, 1C, 1 pyrazine C–F), 148.05 (1C, 1 pyrazine C–C=O), 137.65 (1C, 1 benzene C–C=N), 135.85 (2C, 2 similar benzene C attached to C–C≡N), 132.54 (1C, 1 unsubstituted pyrazine C), 128.82 (2C, 2 similar benzene C attached to C–C=N), 118.50 (1C, 1 nitrile C), 113.91 (1C, 1 benzene C–C≡N); GC–MS (EI) (m/z, rel. int. in %, molecular weight "M.Wt." = 270.22): 271.00 ([M + ‏H]‏+); Elem. Anal. (%, for C13H7FN4O2, calcd (found)): C: 57.78 (57.71), H: 2.61 (2.60), N: 20.73 (20.76). Cyanorona-20, like favipiravir, is a tautomeric molecule (Guo et al. 2019). According to the computational simulations studies (e.g., Antonov 2020), the molecule favors the enol-like tautomeric structure (the predominant form) in aqueous medium as shown in Scheme 2.Scheme 2. Tautomeric forms of cyanorona-20 molecule in aqueous solutions The stability of the Schiff base-like structure of cyanorona-20 was studied using the analytical and physicochemical methods as demonstrated in the Experimental Section. The results of the aqueous dissolution testing were excellent. Less than 5% (as a maximum) of total cyanorona-20 amount undergoing hydrolysis to minor products and impurities after the period of 3 months, and less than 50% of this 5% amount (i.e., less than 2.5% of total cyanorona-20 amount) was the parent favipiravir (see Chart S4 as a representative analytical chart in the Supplementary Material file), hence proving the practical stability of cyanorona-20. Computational molecular studies of cyanorona-20 Pharmacokinetic Properties The values in Tables S1 and S2 (in the Supplementary Material file) reveal that cyanorona-20 has the best balanced predicted molecular properties and pharmacokinetic parameters, among the five evaluated compounds (Molinspiration Web-based Software 2020; Ertl et al. 2000; Lipinski et al. 1997; Ghose et al. 1999; Veber et al. 2002; Yehye et al. 2012). Favipiravir and cyanorona-20 are the smallest molecules among them. Cyanorona-20 has significantly balanced lipophilic/hydrophilic properties ratio and reasonably balanced numbers and types of atoms/bonds (as discussed in details below). Structurally, cyanorona-20 has a small molecular weight and molecular volume of 270.22 daltons and 219.19 Å3, respectively, which are both less than the value of 500 (the maximum value preferred not to be exceeded for better pharmacokinetic properties), this expectedly helps cyanorona-20 to have extremely excellent biocompatibility and rapid distribution with high bioavailability. Cyanorona-20 has a log P value of 1.29 which is much more moderate and balanced when compared to the other four compounds (all except remdesivir); this interesting balanced value theoretically gives cyanorona-20 the ability to be administered with almost all routes of drug administration (oral, parenteral, nasal, etc.) and to be soluble in all biological fluids (with good predictions to pass through all types of biological membranes with gradual moderate to high rates). The values of nON, nOHNH, nRotB, and TPSA for cyanorona-20 are 6, 1, 2, and 99.24 Å2, respectively, which are balanced moderate values, making cyanorona-20 an ideal candidate drug (better than the other four drugs) to be well fitted into the cavities of the active and allosteric sites of the enzyme SARS-CoV-2 RdRp with the strongest interaction states (contact modes) along with the least possible scores of interaction energies (this is supported in part by the results of the computational docking screening and biological anticopying evaluation). Cyanorona-20 complies with all the preferred values of pharmacokinetic parameters (see Fig. 5), as has no violations from the nine parameters (including those of the Ro5). Predictive anti-COVID-19 pharmacological properties The understanding of the COVID-19 target-ligand interactions represents a very important key challenge in drug discovery for COVID-19. The computational simulation prediction of the anti-COVID-19 activities of the new target compound cyanorona-20 along with the up-to-date molecular modeling approaches/studies of the human viruses (e.g., Kumar et al. 2021) greatly helps us to have an overview of the SARS-CoV-2 RdRp-inhibiting properties of this target compound. This prediction mainly gives a detailed idea about the target compound anti-COVID-19 mode of action. On inspection of the score values (Table 2) of docking RdRp-RNA and RdRp alone using COVID-19 Docking Server, it is noted that cyanorona-20 and its active metabolite cyanorona-20-RTP are generally ranked first in their inhibitory binding affinities and potencies with binding free energies of − 10.40, − 7.80, − 10.50, and − 8.60 kcal/mol, respectively (COVID-19 Docking Server Web-based Software 2020; Kirchdoerfer and Ward 2019). The binding affinities of cyanorona-20-RTP significantly exceed those of all the other four active metabolites of the other four drugs, as this metabolite strongly binds to RdRp (with RNA) in their complex (i.e., cyanorona-20-RTP molecule forms a very stable complex with SARS-CoV-2 RdRp) with a very good binding free energy of − 10.50 kcal/mol which is the lowest among all (i.e., significantly lower than the binding free energies of all the other nine compounds in their complexes with RdRp-RNA). Remdesivir and favipiravir (and their active metabolites) come second in their relative inhibitory potency and efficacy on SARS-CoV-2 RdRp, followed by arbidol and hydroxychloroquine (and their salts). For more illustration, Fig. 6(a–d) shows the COVID-19 Docking Server outputs of the top predicted binding model or mode of docking of SARS-CoV-2 RdRp-RNA and SARS-CoV-2 RdRp with cyanorona-20 and its active metabolite cyanorona-20-RTP, respectively. These results of the predicted binding modes of the two ligands cyanorona-20/cyanorona-20-RTP with the protein SARS-CoV-2 RdRp (either with or without RNA) comply with the suggested mechanism of anti-COVID-19 action of both ligands (see Fig. 4).Fig. 6 Screenshots of COVID-19 Docking Server outputs of the top predicted binding model of docking of: a SARS-CoV-2 RdRp-RNA (colored gray) with cyanorona-20 (colored pink). b SARS-CoV-2 RdRp (colored gray) with cyanorona-20 (colored pink). c SARS-CoV-2 RdRp-RNA (colored gray) with cyanorona-20-RTP (colored pink). d SARS-CoV-2 RdRp (colored gray) with cyanorona-20-RTP (colored pink). PDB code of the docked SARS-CoV-2 RdRp: 7BV2 Deep analysis of the computational interaction mode of cyanorona-20-RTP with SARS-CoV-2 RdRp reveals its significant resemblance with that of favipiravir-RTP with the same polymerase, since both ligands form hydrogen bonds and hydrophobic interactions with almost the same or close amino acid residues of the proposed active site of the polymerase (Sada et al. 2020; Picarazzi et al. 2020; Jena 2020) (Fig. 7a,b). Cyanorona-20-RTP molecule, exactly like favipiravir-RTP molecule, strongly binds with the pivotal amino acid residue of the SARS-CoV-2 RdRp active site, Asp760, which is very critical for the initiation and progression of the coronaviral-2 replication processes (Sada et al. 2020; Picarazzi et al. 2020; Jena 2020), thus inhibiting this residue may offer a key role in COVID-19 therapy. Cyanorona-20 and its RTP metabolite mainly depend on the 4-cyanobenzylidene moiety in the binding interaction with the active amino acid Asp760 to effectively inhibit the SARS-CoV-2 RdRp (see Fig. 7a). Importantly, the active metabolite of cyanorona-20 forms slightly higher number of strong interactions with SARS-CoV-2 RdRp than that of favipiravir.Fig. 7 The inhibitory binding interactions, of a Cyanorona-20-RTP; b Favipiravir-RTP, with the active amino acids of the SARS-CoV-2 RdRp (2D representations) An estimate of the probability values (present in Table 3) predicting anti-COVID-19 activities (general antiviral/anti-RNA virus properties and also specific properties such as being nucleos(t)ide analog inhibitor, nucleotide metabolism regulator, and adenosine regulator) of cyanorona-20 and its active metabolite, along with the four references with their metabolites, using PASS Online screening reveals that cyanorona-20, remdesivir, HCl-arbidol-H2O, and their three active metabolites/free bases are generally ranked first in their antiviral and anti-COVID-19 activities and efficacies among the ten ligands (then favipiravir, hydroxychloroquine sulfate, and their active metabolites come second in ranking) (Yehye et al. 2012; PASS Online Web-based Software 2020; Filimonov et al. 2014). Cyanorona-20-RTP has the best probability to be active anti-COVID-19, among all the five screened active nucleotide analog ligands or inhibitors of SARS-CoV-2 RdRp (the five screened active nucleotide analog metabolites, which are cyanorona-20-RTP, favipiravir-RTP, GS-441524-TP, arbidol, and hydroxychloroquine), of more than 74% with a negligible probability to be inactive anti-COVID-19 of less than 1%. Antiviral anti-COVID-19 biological activity (in vitro assay) of cyanorona-20 The results demonstrated in Table 4 clearly revealed the extremely higher and surprising anti-COVID-19 efficacy of cyanorona-20 (the most potent anti-SARS-CoV-2 compound of the five tested ones). Among the five tested compounds, four compounds (cyanorona-20, remdesivir, HCl-arbidol-H2O, and favipiravir, respectively) inhibit SARS-CoV-2 replication in Vero E6 cells with EC50 under 100 μM, while hydroxychloroquine sulfate was above 100 μM. Surprisingly, cyanorona-20 (EC50 = 0.45 μM, see Chart S5 as a representative curve in the Supplementary Material file) was about 209 and 45 times as potent as favipiravir (EC50 = 94 μM) and remdesivir (EC50 = 20 μM), respectively, in anti-SARS-CoV-2 activity (in vitro). According to the assay, cyanorona-20 is expected to have high clinical selectivity index (SI; SI = CC50/EC50) and safety margin (CC50 is much larger than 100 μM). On the other hand, hydroxychloroquine sulfate is expected to have very narrow clinical therapeutic index (EC50 is just above 100 μM, CC50 = 93 μM). Cyanorona-20 is also having amazingly very small values of the concentration that causes 100% inhibition of the SARS-CoV-2 cytopathic effects in vitro (cyanorona-20 has the best CPEIC100 value, among all the five compounds tested, of 1.4 μM) and of the concentration that is required for 50% reduction in the number of SARS-CoV-2 RNA copies in vitro (cyanorona-20 has the best EC50 value, among all the five compounds tested, of 0.48 μM). The three nucleoside/nucleotide analogs, cyanorona-20 (guanine analog), favipiravir (guanine analog), and remdesivir (adenosine analog), require intracellular metabolic activation to the triphosphate forms by host cellular enzymes (mainly nucleoside kinases), which may differ among several cell types; thus, evaluation of the actions of nucleos(t)ide analogs in primary human airway epithelial cells would undoubtedly facilitate the interpretation of the results. The metabolic activation would surely add additional anti-COVID-19 activities to the three drugs, and it would also successfully increase the clinical effectiveness of the three drugs. The four reference drugs (favipiravir, remdesivir, HCl-arbidol-H2O, and hydroxychloroquine sulfate) are currently undergoing extensive clinical trials, as anti-SARS-CoV-2/anti-COVID-19 agents, worldwide. The very high value of CC50 of cyanorona-20 indicates that cyanorona-20 may be well tolerated in the human body. The very minute value of anti-SARS-CoV-2 EC50 and the very high value of mammalian cells CC50 (i.e., the desirable high value of SI) of cyanorona-20 indicate that this compound clearly favors resistant RNA virus over DNA virus and mammalian cells, and this, in turn, suggests selective specificity as anti-COVID-19 drug "Corona Antidote or Corona Killer" (see Introduction part). Using a combination formula (a mixture) of cyanorona-20 and remdesivir is a suggested choice, as it may have exceptional combinational synergistic anti-COVID-19 effect in further assays (in vivo) and clinical trials. Almost all the practical results concluded, here, in the antiviral anti-COVID-19 biological evaluation are complying with the previous theoretical results extracted from the computational molecular and pharmacological predictions for the new compound cyanorona-20 and its four reference compounds. Conclusions Specific potent blockade of the novel SARS-CoV-2 RdRp is a viable approach for targeted COVID-19 therapy. Our efforts in 2020 focused on designing new drugs (e.g., derivatives of favipiravir) for the more effective inhibition of the viral polymerase. These efforts led to the discovery of a very promising selective specific and potent direct-acting SARS-CoV-2 copying inhibitor, cyanorona-20 ((E)-N-(4-cyanobenzylidene)-6-fluoro-3-hydroxypyrazine-2-carboxamide), which inhibited SARS-CoV-2 replication with significant EC50 values of 0.45 and 0.48 μM, and interestingly presented about 209- and 45-fold anti-SARS-CoV-2 selectivity/potency more than favipiravir and remdesivir, respectively. Cyanorona-20, to the best of our knowledge (up to the date of submitting this discovery article for publication), is the first bioactive derivative of favipiravir. Structural modification at the active amino moiety of the antiinfluenza favipiravir molecule opens the first class of anti-COVID-19 agents (of the type "nucleos(t)ide analogs") which will specifically comprise a series of pyrazine derivatives (Miniyar et al. 2013). Cyanorona-20 fulfills the requirements for an ideal anti-COVID-19 drug (more active than its parent compound favipiravir) (Fig. 5). For example, cyanorona-20 molecule has a virus-toxic cyano group (it may be also called a SARS-CoV-2 RdRp-destabilizing moiety, as it chemically causes major steric clash with the SARS-CoV-2 RdRp molecule at some residues; this greatly helps in RdRp preliminary partial blockade and results in delayed chain termination in RNA synthesis which may speculatively give cyanorona-20 an extrapotency against the major resistance mechanisms that might be emerged by SARS-CoV-2 against favipiravir and other classical potent antiviral nucleos(t)ide analogs (Shannon et al. 2020)) (a strong aliphatic polar group) and a resonance-stabilized benzene ring (in the benzylidene group) (a strong aromatic lipophilic moiety), both groups with a one-carbon-atom linker form a highly stable 4-cyanobenzylidene moiety (a SARS-CoV-2-toxic moiety which is extremely stabilized through strong resonance and inductive effects) which is not present in the parent favipiravir molecule, and predictably adds an exceptional and excellent balanced lipophilic/hydrophilic properties along with electronic extrastability to the molecule (this makes the molecule more bioavailable and more biocompatible). Extensive computational studies showed that cyanorona-20 has ideal values of the pharmacokinetic and drug-likeness descriptors. Computational modeling analysis of the top inhibitory binding mode of the expected active metabolite of cyanorona-20 inside the human cell, cyanorona-20-RTP, showed that the 4-cyanobenzylidene moiety increases the potency at active and/or allosteric sites of the SARS-CoV-2 RdRp (binding free energy = − 10.50 kcal/mol) when compared to that of the active metabolite of its parent favipiravir inside the human cell, favipiravir-RTP (binding free energy = − 8.40 kcal/mol). Promisingly, cyanorona-20 and its active metabolite surpassed the four moderately to highly potent reference drugs and their active metabolites, respectively, in the values of almost all compared theoretical and practical anti-COVID-19 parameters, scores, and activities. The mechanism of the interaction of cyanorona-20-RTP with SARS-CoV-2 RdRp has not been elucidated, but cyanorona-20 may presumably act through six or more complementary modes of action (i.e., multiaction; Fig. 4 and Fig. 7a) (Smee et al. 2009; Yoon et al. 2018; Jin et al. 2013; Baranovich et al. 2013; Furuta et al. 2009; Shannon et al. 2020), as it may be misincorporated in a nascent SARS-CoV-2 RNA (thus preventing RNA strand elongation and viral proliferation), evade RNA proofreading by viral exoribonuclease (ExoN; thus causing a decrease in SARS-CoV-2 RNA production), competitively bind to conserved polymerase domains (thus preventing incorporation of mainly purine nucleotides for SARS-CoV-2 RNA replication and transcription), cause the SARS-CoV-2 RdRp to pause, induce an irreversible chain termination in the growing SARS-CoV-2 RNA, or induce lethal mutagenesis (thus, mainly, making the virus less effective and reducing its titer "viral titer") during SARS-CoV-2 infection. If cyanorona-20 passes the in vivo bioassays and preclinical/clinical trials with effectively significant results as anti-COVID-19 agent, combination therapy with a second potent antiviral drug, such as remdesivir, may also be advantageous. Supplementary Information Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 2653 kb) I gratefully thank and deeply acknowledge anyone who gave a hand to make this new discovery and work coming out to light. Declarations Conflict of interest I hereby declare that I totally have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this new research paper. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Abdelnabi R de Morais ATS Leyssen P Imbert I Beaucourt S Blanc H Froeyen M Vignuzzi M Canard B Neyts J Delang L Understanding the mechanism of the broad-spectrum antiviral activity of favipiravir (T-705): key role of the F1 motif of the viral polymerase J Virol 2017 91 e00487 e517 10.1128/JVI.00487-17 28381577 Antonov L Favipiravir tautomerism: a theoretical insight Theor Chem Acc 2020 139 145 10.1007/s00214-020-02656-2 32834770 Baranovich T Wong S-S Armstrong J Marjuki H Webby RJ Webster RG Govorkova EA T-705 (favipiravir) induces lethal mutagenesis in influenza A H1N1 viruses in vitro J Virol 2013 87 3741 3751 10.1128/JVI.02346-12 23325689 Cai Q Yang M Liu D Chen J Shu D Xia J Liao X Gu Y Cai Q Yang Y Shen C Li X Peng L Huang D Zhang J Zhang S Wang F Liu J Chen L Chen S Wang Z Zhang Z Cao R Zhong W Liu Y Liu L Experimental treatment with favipiravir for COVID-19: an open-label control study Engineering 2020 6 1192 1198 10.1016/j.eng.2020.03.007 32346491 Choy K-T Wong AY-L Kaewpreedee P Sia SF Chen D Hui KPY Chu DKW Chan MCW Cheung PP-H Huang X Peiris M Yen H-L Remdesivir, lopinavir, emetine, and homoharringtonine inhibit SARS-CoV-2 replication in vitro Antiviral Res 2020 178 104786 10.1016/j.antiviral.2020.104786 32251767 Chu DKW Pan Y Cheng SMS Hui KPY Krishnan P Liu Y Ng DYM Wan CKC Yang P Wang Q Peiris M Poon LLM Molecular diagnosis of a novel coronavirus (2019-nCoV) causing an outbreak of pneumonia Clin Chem 2020 66 549 555 10.1093/clinchem/hvaa029 32031583 COVID-19 Docking Server Web-based Software, COVID-19 Docking Server (homepage on the internet), Shan Chang Lab., Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China, 2020; available from COVID-19 Docking Server on the web (homepage: http://ncov.schanglab.org.cn); Results were obtained through using the interactive docking tool in COVID-19 Docking Server (Copyright© 2018-2023, Shan Chang; Version 2020) on this website (accessed and cited in 2020, 19–28 May) COVID-19 Map (2020), available from Johns Hopkins Coronavirus Research Center. https://coronavirus.jhu.edu/map.html. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 34008064 14463 10.1007/s11356-021-14463-8 Research Article The effect of energy consumption on the environment in the OECD countries: economic policy uncertainty perspectives Zakari Abdulrasheed [email protected] 12 Adedoyin Festus Fatai [email protected] 3 http://orcid.org/0000-0002-0464-4677 Bekun Festus Victor [email protected] 4 1 grid.43555.32 0000 0000 8841 6246 School of Management and Economics, Beijing Institute of Technology, Beijing, China 2 grid.445209.e 0000 0004 5375 595X Alma Mater Europaea ECM, Maribor, Slovenia 3 grid.17236.31 0000 0001 0728 4630 Department of Computing and Informatics, Bournemouth University, Poole, UK 4 grid.459507.a 0000 0004 0474 4306 Faculty of Economics Administrative and Social Sciences, Department of International Logistics and Transportation, Istanbul Gelisim University, Istanbul, Turkey Responsible Editor: Roula Inglesi-Lotz 18 5 2021 2021 28 37 5229552305 22 2 2021 14 5 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. In this paper, we investigate the impact of energy use and economic policy uncertainties on the environment. To achieve this objective, we use the pooled mean group-autoregressive distributed lag methodology (PMG-ARDL) and Dumitrescu and Hurlin causality test on 22 Organisation for Economic Co-operation and Development (OECD) countries between 1985 and 2017. The PMG-ARDL estimation shows that energy use and economic policy uncertainties have a positive relationship with carbon dioxide emission (CO2) emission, while a negative relationship is confirmed between renewable and CO2 emissions in the long run. The short-run estimation shows a positive relationship between energy use, real gross domestic product, and per capita on CO2 emissions. The Dumitrescu and Hurlin causality results highlight a unidirectional running from real GDP and GDP per capita square to CO2 emissions. Furthermore, one-way causality exists between CO2 emissions to economic policy uncertainties. These results have policy implications on the macroeconomy which are discussed in detail in the concluding section. Keywords Economic policy uncertainties CO2 emissions. Environmental sustainability Energy consumption OECD countries issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2021 ==== Body pmcIntroduction Carbon emissions and the aftermath of non-renewable energy consumption have been on the increase since the beginning of the twentieth century at the global level. This is evidenced by emission figures that are 1.6 times the 1990 level leading to an excess of 36 billion tons in the year 2014 (Yao et al. 2019; Ozcan and Ozturk 2019; Rafindadi and Ozturk 2017). The share of fossil fuel energy of over 80% of the total energy supply (IEA 2016) and a considerably lesser than 20% renewable energy consumption rate all points to and corroborates the previous stance that rising carbon emission or energy consumption as the case may be is a direct result of economic growth. This, according to Grossman and Krueger (1995), would initially lead to an initial phase of environmental degradation, which is subsequently followed by the improvement phase as the average income increases on the environmental Kuznets curve. Furthermore, Panayotou (1993) posited earlier that carbon emission would have three resultant effects: scale, structural, and technical effects stemming from economic growth, thus demonstrating and attesting to the inverted U-shaped Kuznets curve. Previous studies regarding carbon emissions, a resulting consequence of non-renewable energy consumption, and the environmental Kuznets curve hypothesis revolved mostly around international trade, technical progress, foreign direct investments, and incomes (Kaika and Zervas 2013; Yao et al. 2019; Sarkodie and Strezov 2019; Asongu and Nwachukwu 2018a & Asongu and Nwachukwu 2018b; Asongu and Odhiambo 2019b; Rjoub et al. 2021). More recently, the addition to the group of information on the energy literature has been on renewable energy consumption and cleaner energies going by the outcry of the effects of an earth-wide temperature boost brought about via carbon outflow (Yao et al. 2019; Bekun et al. 2019a). This has led to the renewable energy environmental Kuznets curve (RKC) proposition as a hypothesis that supersedes the conventional environmental Kuznets curve in that it accounts for renewable energy to show the U-shaped relationship that exists between the renewable energy consumption rate and per capita GDP. This unconventional phenomenon, the RKC, asserts that more renewable energy consumption can help accelerate the conventional EKC to arrive faster at its turning point. This lends credence to the fact that the consumption of renewable and non-renewable energies will lead to a renewable environmental Kuznets curve that arrives faster at its turning point than an environmental Kuznets curve designated to non-renewable energy consumption. This has led to the selection of a renewable energy consumption rate, an index to uncover the differences of renewable energy consumption while examining the environmental Kuznets curve hypothesis side by side with the renewable energy environmental Kuznets curve hypothesis. Economic policy uncertainty shows the relative frequency of specific news media references dealing with occurrences as they pertain to economy, policy, and uncertainty; government charge code arrangements that are due to expire; and the rate of forecaster disagreement. This uncertainty measurement ranges from the Global Economic Policy Uncertainty index value to the National Economic Policy Uncertainty index value Baker et al. (2016). It merely indicates the risk that comes with an uncertain policy response on the part of the government as an economic agent to put regulatory measures in place. This ultimately leads individuals and firms to become irresolute, thus delaying consumption and investment until the uncertainty is resolved. EPU has been on the increase since the 2007 to 2009 recession due to the observed uncertainty by households and businesses on fiscal policies, future taxes, spending, health care, monetary policies, and other measures in place to regulate the economy. An increase in the EPU index often leads to the postponement and reduction of business and economic activities such as recruitment, investment, and other forms of spending. It was also discovered that policy uncertainty in news articles revolved around taxes, spending, and monetary and regulatory policies. This study harnesses to paint a picture of the linkage effect between policy uncertainty energy emission nexus and the environmental Kuznets curve hypothesis. While previous studies appeared to have neglected the link between carbon emissions, Jiang et al. (2019) posited that EPU most certainly affects the external business environment, which ultimately affects the decision-making process of economic agents. This trickles down on the carbon emissions as it is closely linked to the output decisions of microeconomic agents. As an OECD country, the USA is said to maintain a fairly stable and consistent EPU index. At peak periods of the US EPU index, for example, the total carbon emission is observed to replicate the local peak, and when the EPU index falls, the total carbon emission falls as well. The effect is a shift in the priority of the governments from environmental governance to the root cause of events that led to an increased financial policy uncertainty index in the first place. For example, the USA’s withdrawal from the Paris Agreement will increase the EPU index, leading to a lower prioritization of carbon emission reduction as a goal Jiang et al. (2019). Going by this analogy, one can infer that the EPU will have a corresponding effect on the postulation of the environmental Kuznets curve hypothesis because the EPU will readily affect production and consumption activities associated with renewable and non-renewable energies (Jiang et al. 2019). This leads to decreased investments or consumption at periods of high uncertainty and increased investments or consumption at periods of low uncertainty. Therefore, this attributes a low EPU to a quick arrival at the turning point of the environmental Kuznets curve and a high EPU to points farther away from the turning point of the environmental Kuznets curve. Research conclusions from previous studies lend credence to the fact that EPU is relevant to understanding the behavior of emissions in energy consumption even at the global level. This is because the EPU impacts macroeconomic activities, which has a ripple effect on societal carbon emissions across countries. Observing the USA’s economy, an OECD country that happens to be the second-largest carbon emission country in the globe, it is important to study the linkage effect of the economic policy uncertainty in relation to energy emissions to understand the appropriate actions to be taken for periods of high economic policy uncertainties especially as they relate to the environmental Kuznets curve hypothesis which is the objective of this research work. In summary, this study draws strength from the carbon-income function and EKC framework for OECD countries that have received little or no documentation in the energy-environment literature while accounting for economic policy uncertainty on the environment. The next segment presents a survey of the literature review of related studies. This is followed by a description of data, variables, and methodology in the “Data and methodology” section. The “Results and discussion” section presents the empirical results from this study and discusses the implications of the research findings. This study concludes in the “Conclusion and policy implications” section with vital energy and macroeconomic policy recommendations. Literature review A lot of studies have investigated the relationship between energy consumption and economic growth across countries and across regions (Al-Mulali et al. 2016; Ozturk and Bicimveren 2018; Udemba et al. 2020; Adedoyin et al. 2020a, 2020b; Kirikkaleli et al. 2020; Udi et al. 2020; Tchamyou et al. 2019; Asongu and Odhiambo 2019a). Some of these studies examined variants of growth-energy nexus, energy-growth nexus, and the two-way causal effect between them. Starting with an earlier trajectory by the OECD in 2011, it was predicted that the share of energy consumption allotted to the OECD group from the world consumption was set to reduce from 35% in 1995 to about 32% by 2020. Prior to this point, the literature on economic growth and energy consumption dates as far back as 1978, following a seminal work by Kraft and Kraft on the relationship between energy and Gross National Product. A handful of studies have concentrated on the relationship between economic growth and energy consumption in OECD countries (see Wong et al. 2013; Coers and Sanders 2013; Bella et al. 2014; Mercan and Karakaya 2015). For instance, Asongu et al. (2017) explored the determinants of environmental degradation in selected 44 sub-Saharan African countries using generalized method of moments techniques to explore the role of ICT modulates the effect of CO2 emissions on inclusive development. The study found that ICT can be used to reduce the negative effect of CO2 emission on inclusive development. That is, ICT modulating policy thresholds should be established for environmental sustainability targets in the selected African bloc. Using autoregressive distributed lag model (ARDL) in conjunction with (FMOLS) and dynamic ordinary least squares (DOLS) for robustness, Adebayo et al. (2021) explored the nexus between environmental quality and economic growth while accounting for financial development and globalization for the case of South Africa. The study revealed that a 1% increase in energy (coal) consumption increases environmental degradation by 1.077%, while a 1% increase in financial development decreases the environmental degradation by 0.973%. The study submits that policymakers and administrators in South Africa should advance policies that encourage energy consumers to shift toward renewable energy. Furthermore, financial reforms should be implemented to reduce environmental degradation. This study is in line with Adebayo and Odugbesan (2021) study that financial development, economic growth, and urbanization contribute to the pollution level in South Africa. Zhang et al. (2021) explored the anthropogenic effect of human activities on CO2 emissions using the STIRPAT framework. The study explored the determinant of CO2 emissions in Malaysia using ARDL, fully modified OLS (FMOLS), dynamic ordinary least square (DOLS), and wavelet coherence and gradual shift causality. The study regression shows that economic growth, gross capital formation, and urbanization positively impact CO2 emissions. The direction of causality reveals a one-way causality from urbanization to CO2 emissions, unidirectional causality from economic growth to CO2 emissions, and unidirectional causality from gross capital formation to CO2 emissions as reported by causality analysis. This outcome resonates with the study of Kirikkaleli et al. (2021) for the case of Turkey. He et al. (2021) investigated the role of consumption-based carbon emissions in Mexico while accounting for the role of economic growth trajectory admits global trade flow, energy consumption using an autoregressive distributed lag approach, and a causality analysis frequency domain causality tests. The study’s key findings highlight that globalization and financial innovation improve environmental quality. Also, energy consumption and economic growth dampen environmental quality. Finally, trade openness exerts no significant impact on environmental quality. The study further illustrates the need for Mexican government officials to carefully craft energy environmental policies aimed at increasing economic growth without compromise for environmental quality. Furthermore, regarding consumption-based carbon emissions determinants, Kirikkaleli and Adebayo (2021) for Indian identify that public-private partnership investment in energy and energy consumption also significantly causes consumption-based carbon dioxide emissions at different frequency levels in the Indian economy. While a causal relationship is said to be theoretically possible and already established as a stylized fact from these studies, discrepancies in these previous studies were traced to differences across countries, time skylines, informational collections, and factual techniques employed to determine the relationship between energy consumption and economic growth. These studies presented inconclusive results that were not fit for policy actions in OECD countries. Methods that were used by these studies ranged from vector error correction model, PVAR, autoregressive distributed lag model, DOLS, and FMOLS to explore the relationship in an attempt to explain the energy consumption-economic growth nexus, although some studies have used the panel data approach. More recently, attempts have been made to advance the knowledge horizon of the energy literature as it pertains to OECD countries to understand and assert the direction and magnitude of the causal relationship between economic growth and energy consumption. Gozgor et al. (2018) examined the impact of renewable and non-renewable energy consumptions on growth using 29 OECD countries from 1990 to 2013. The study theoretically built a growth model to capture economic complexities and as a yardstick of capabilities amongst the countries in question. The study employed the panel autoregressive distributed lag due to the mixed nature of the sets of joining of the factors in question and the panel quantile regression methods for estimation. The study concluded that the positive effect of both renewable and non-renewable energy consumption components on economic growth was valid when checked against the growth hypothesis that the study adopted. The study, therefore, adopts the stands on the fact that energy consumption positively affects growth and that both renewable and non-renewable energy consumption is vital and important for the furtherance of economic growth. Additional studies like Jebli et al. (2016) investigated the relationships between monetary development, inexhaustible and non-sustainable power source utilization, carbon emissions, and international trade amongst 25 OECD countries over the 1980 to 2010 timeline. The study employed the granger causality tests, fully modified ordinary least squares, and the dynamic ordinary least squares. The study found that bidirectional causality existed between renewable and non-renewable energy consumption. The results also verified the inverted U-shaped environmental Kuznets curve hypothesis for the OECD countries in view. The study concluded that increased non-renewable energy consumption led to increased carbon emissions and that increased trade through renewable energy consumption measures to be considered to reduce environmental degradation. A study by Kahouli (2019) assessed the relationship between the consumption of energy and growth of the economy across 34 OECD countries over the 1990 to 2015 timeline. The study employed an extensive and more recent panel data econometric method by using the static and dynamic techniques simultaneously and separately to look at the relationship between economic growth and energy consumption. The study found a unidirectional relationship between energy consumption and economic growth. Also, there was a one-way causal relationship running from economic growth to energy consumption under the dynamic estimation technique. This study was in line with earlier results by Salahuddin and Gow (2014), Omri and Kahouli (2014), Raza et al. (2015), and Kasman and Duman (2015), stressing the importance of the bidirectional relationship between energy consumption and economic growth. More recently, the empirical study of Ozcan and Ozturk (2019) investigated the different linkages that exist between the use of energy and economic growth by taking a sample of 35 OECD countries over the 2000 to 2014 period. The study used three empirical models to capture the relationship between energy consumption, economic growth, and environmental degradation using the generalized method of moments and the panel vector autoregressive regression method. The study’s key contribution to the body of knowledge was a more encompassing proxy to capture environmental degradation. It employed two composite indices and the CO2 emission proxy used by earlier studies. In addition to the components of what appears to be an encompassing proxy than earlier studies, Ozcan and Ozturk (2019) collectively adopted the ecological footprint and environmental performance index to reflect the different forms of environmental pollution. The study found a significant positive relationship between economic growth (GDP) and energy consumption on all the environmental degradation indicators used as a proxy in the model. The study further indicated that increment in industrial economic activities of the countries in view contributed more to environmental pressure and CO2 emissions, which appeared to follow the general consensus. Nevertheless, while there appears to be a paucity of literature in this variable combination, especially in the case of OECD countries, quite a few papers have provided a base knowledge of how the economic policy uncertainty impacts key sectors typical economy. However, one thing that comes to the fore is that corporations and economies are known to act conservatively at times of uncertainty, which slows investment activities and employment rates down. This ultimately affects the energy consumption variable of such an economy, which ultimately trickles down to other countries due to the interconnectedness that the world operates with. Complexity is one factor responsible for the degree of uncertainty. An advancement that appeared to have eased this complexity is the innovation propounded by Baker et al. (2016), which established the economic policy uncertainty index. Prior to this point, an initial publication by Kenneth Galbraith in 1977 titled “The Age of Uncertainty” paved the way for what has now become a transformed research area. Overall, the conclusion from previous studies lent credence to the fact that more conservative policies were best at times of high economic policy uncertainty. This is because the cost of borrowing increases, making firms spend less on capital, leading to an economic downturn (Al-Thaqeb and Algharabali 2019). Jens (2017) sought to understand the US gubernatorial decisions as a plausible source for exogenous variety in an attempt to investigate the link between political vulnerability and firm speculation. The study employed term confines as an instrumental variable for political decision closeness in addition to summary statistical methods to present the results. The study found that investment declined 5% before elections and rose as much as 15% for firms directly related and susceptible to this type of uncertainty. Also, because close elections are tantamount to periods of economic downturns, close election effect on investment was understated by more than half going by the ordinary least square method, and post-election rebounds to investment or consumption depended on the re-election of an incumbent administration. The implication for energy consumption is that high periods of political uncertainty will come with low energy consumption and the alternative sources of energy being considered at this period to determine the rate of environmental degradation for any typical OECD economy. Canh et al. (2019) investigated the role of two forms of uncertainties: internal (domestic) economic policy uncertainty and the world uncertainty played on the net inflow of outside direct speculation in 21 countries the 2003 to 2013 timeline. The study adopted a sequential two stages linear panel data model technique to carry out its analysis. The study found that the domestic growth rate of the economic policy uncertainty index affected the inflow of foreign direct investments adversely. When this domestic growth rate was placed side by side with the growth rate of the World Uncertainty Index, a measure that accounts for 143 countries, the ensuing result was a positive impact on the net inflow of foreign direct investment to the host country in question. The study, therefore, concluded that while an increase in national economic policy uncertainty might present an adverse effect on FDI inflows, an increase in the world global economic policy uncertainty could lead to increased inflow of foreign direct investment, and this was explained as the behavioral bias that could averse an investor based on the investor’s sensitivity to factor in uncertainty when making an investment decision. Zhang et al. (2019) investigated the influence of two key countries, the USA and the Republic of China, on several markets across the globe. The markets considered under this study were, namely, commodity, energy, credit, and financial markets. The study was borne out of the uncertainty that ensued from the US-China trade conflict and, thus, sought to provide answers to research questions around the rationale behind the conflict, the supposed threat that a rising Chinese economy could possibly be imposing on the US economy. The paper employed the economic policy uncertainty index of these two global players as a measure of their policy positions to build a time series that could estimate the degree of influence of the two countries on the global markets. The study found that while China’s realm of influence has increased in recent years, it has not been sufficient to oust the USA to control global world affairs. In addition, the study concluded that China’s competition with the USA in shaping the world is more politically driven rather than economically driven. Liu et al. (2020) investigated the differential impact between investments in non-renewable and renewable energy enterprises. The study was comparative based on regulatory effects such as ownership concentration, external demand, financing constraints, growth opportunities, and how it related to investment and economic policy uncertainty. The study used data from 52 non-renewable energy enterprises and 116 renewable energy enterprises in China over the 2007Q1 to 2017Q4 timeline. The study employed a panel regression model for estimation. The study found that non-renewable energy enterprise investments were significantly inhibited by economic policy uncertainty. On the other hand, renewable energy investments were not significant even though they were inhibited by economic policy uncertainty. The study also found that economic policy uncertainty specifically inhibited investment in the petroleum and coal enterprises, whereas economic policy uncertainty promoted investments in renewable energy enterprises like geothermal energy, solar energy, and other forms of renewable energy. The study concluded that growth opportunities could offset the inhibitory effect associated with the economic policy uncertainty and that a strengthened financial constraint brings with it an uncertainty associated with economic policy in non-renewable energy enterprise, which would not be as significant as the renewable energy enterprise. Conclusively, the reviewed literature appears to have established a negative relationship between economic political uncertainty and energy consumption in that higher values of uncertainty reduce consumption and investment generally, but this sometimes leads to the consumption of cheaper and more traditional sources of energy which might, in turn, lead to increased carbon emissions thus increasing environmental degradation and extending the turning point of the environmental Kuznets curve. Main gap and research contribution One of the issues that commanded attention in the literature on economic uncertainty was the increased EPU value that came with the USA’s withdrawal from the Paris Agreement of 2015 to mitigate climate change. The importance attributed to environmental governance by the US government was reduced and reprioritized following this withdrawal, which negatively affected the implementation of a significant portion of previous environmental protection policies. This then became the testament on which the government’s determination to reduce carbon emissions as a goal became compromised. The ultimate implication of this move by the US government was that the Environmental Protection Agency’s budget had to be cut down in 2017. Secondly, the EPU was assessed to have been a possible threat to the US economy as a whole. On the one hand, energy consumption by the US economy was cut down, making way for a decrease in carbon emission. On the flip side, a bad economic scenario for firms and the citizenry may opt for traditional cheaper sources of energy such as coal, which would result in more carbon emissions. Finally, facing high EPU, firms relaxed their effort to deliver an economy with reduced carbon emissions. This was due to the premonition that governmental departments would relax their requirements on environmental governance. Another issue that suffices as a case for economic policy uncertainty is the decision by the UK to leave the European Union. While policies that are likely to be adopted by the European Union membership are uncertain, speculations about this uncertainty, especially in this transition period and with world events like the Coronavirus pandemic, have further increased uncertainty in the UK economy. A study by Steinberg (2019) sought to explore the macroeconomic impact of the trade policy uncertainty resulting from the Brexit movement. The study employed the dynamic stochastic general equilibrium (DSGE) model on the UK, the European Union, and the rest of the globe to address quantitative questions on the consequences of Britain exiting the European Union. Questions surrounding the uncertainty of the trade policies that were likely to replace the EU agreement post-Brexit and what the future held for the UK economy, as well as the lag periods that the turn of events as was to last for, were investigated by this study. The study found that uncertainty about Brexit will have little impact and that the welfare cost about Brexit is insignificant as households would sacrifice little to avoid this uncertainty. The study also found that the cost of Brexit, when compared with some other macroeconomic uncertainties, had a sizeable impact than other uncertainties meaning that a one-time Brexit uncertainty is the same as other unpredictable policy uncertainty in economic activity that occurs in the UK in an atypical year. In summary, global and national issues have been identified as inflexion points that determine the degree of economic policy uncertainty. This is because the EPU index has its major components built on disagreements by forecasters, news references, and tax provisions, all of which are channels of speculation for economic agents, based on the highlighted literature and motivation in the “Introduction” section. The present study is further motivated by the United Nations Sustainable Development Goals (UN-SDGs 7, 8, and 13) crusade, which informed the choice of the variables for the econometric modeling, and subsequently, the following hypotheses have been constructed: H1: Do conventional energy consumption (fossil fuel induced) engenders sustainability in the environment in the OECD countries in line with (UN-SDGs 7 and 8). Conventionally, energy use has been identified as a key driver for increased economic growth over the years. This proposition has been validated by several studies empirically, the first by Kraft and Kraft (1978) and more recently by several other studies affirming the pivotal role of the energy-induced growth hypothesis (Zakari et al. 2021; Emir and Bekun 2019; Bekun et al. 2019b; Asongu et al. 2017). This leads to the formation of the next hypothesis H2: Is there a positive or negative nexus between CO2 emissions and economic growth in the study areas (OECD countries). There has been extensive literature on the economic growth-pollution connection. This is a result of increased dirty economic activities that will increase pollution emissions. This is in accordance with the fight of the UN-SDG 13 in mitigating climate change/pollution-related issues. H3: Given the cointegration relationship establish between real income (GDP, CO2 emissions, and energy use). What is the connection between EPU in the mix for OECD countries over the sampled period? Data and methodology Data The data are collected for 22 OECD countries spanning the period from 1985 to 2017. The selections of these countries are motivated by the amount of data available for all the variables under consideration. Data were extracted from the World Bank Development Indicator (WDI) and British Petroleum Database, which is given as CO2 emissions (CO2) measured in million tonnes of carbon dioxide (source: BP Statistical Review of World Energy June 2019); primary energy consumption (ENU) measured in million tonnes oil equivalent (source: BP Statistical Review of World Energy June 2019); real gross domestic product (RGDP), measured in constant 2010 US$ (source: WDI); and economic policy uncertainty1 (EPU), proxy: world uncertainty index (WUI) (source: Ahir et al. 2018, http://www.policyuncertainty.com). Model and methods This paper examines the role of economic policy uncertainties in the energy emissions consumption nexus in OECD countries. Hence, our energy emission function is set to include economic policy uncertainties. Methods like Pesaran’s test of cross-sectional independence, results of Pedroni and Kao cointegration tests, PMG-ARDL, and Dumitrescu and Hurlin panel causality were adopted. 1 InCO2it=α0+α1InENUit+α2InRGDPit+α3InEPUit+eit 2 InCO2it=α0+α1InENUit+α2InRGDP2it+α3InEPUit+eit 3 InCO2it=α0+α1InENUit+α3InEPUit+α2InEPU∗ENUit+eit where CO2 represents carbon dioxide emission, ENU measures the level of energy use, RGDP is a real gross domestic product, RGDP2 is GDP per capita, and EPU measure economic policy uncertainty, i, subscripts ei refers to each country’s fixed effects, that is, the countries and the time, as shown by the subscripts i (i = 1, − − N) t (t = 1, − − T), respectively. Results and discussion Table 1 provides a summary of the results for 22 OECD countries for the period 1985–2017. The emission of energy consumption, real GDP, and GDP per capita indices exhibit an increasing effect between 1985 and 2017, with real GDP having the highest increasing value of 11.7159% and energy use contributing to the lowest at 1.8260%. However, the economic policy uncertainty and economic policy uncertainty vs energy use indices have negative values, showing a decline of −1.4513% and −2.6249. Table 1 Summary statistics (1985–2017) Variables OBS Mean Std. Dev Min Max CO2 717 2.1433 0.7365 0.2726 3.7679 ENU 717 1.8260 0.6902 0.1695 3.3644 EPU 717 −1.4513 0.4041 −3.3685 −0.5199 EPUENU 717 −2.6249 1.2284 −7.7627 −0.2798 RGDP 717 11.7159 0.7049 9.8467 13.2393 RGDP2 717 4.5041 0.2562 3.6719 5.0491 Table 2 reports unconditional correlations on the selected variables for the 22 OECD countries. The correlation results show that carbon dioxide emission (CO2) is positively trending with the real gross domestic product (RDGP), economic policy uncertainty (EPU), and energy use (ENU). At the same time, it is negatively related to real domestic product per capita (RGDP2). These correlations suggest that carbon dioxide emission (CO2) is highly associated with the real gross domestic product, economic policy uncertainty, energy use, and real gross domestic product per capita. Every one of these estimations is measurably critical at 1%, 5%, and 10% levels, respectively. However, we further confirm their association in the following empirical investigation. Table 2 Correlation matrix CO2 RGDP RGDP2 EPU ENU EPUENU CO2 1.0000 RGDP 0.9654* 1.0000 RGDP2 −0.1031* 0.0513 1.0000 EPU 0.0664 0.1194* 0.1263 1.0000 ENU 0.9753* 0.9733* −0.0365 0.0906* 1.0000 EPUENU −0.7582 −0.7263 0.0891 0.5309 −0.7669 1.0000 Notes: The unconditional correlation was estimated using “natural log” data; , , and * show a level of significance 1%, 5%, and 10%, respectively Pesaran’s test of cross-sectional independence In most of the empirical literature, panel data are often not tested for cross-sectional reliance among the series. While neglecting this fact posed severe implications to the analysis, the results obtained often remained unrealistic. Given this fact, it is essential to check the data set if they are cross-sectional reliance or independent. To do this, we applied the Pesaran (2004) cross-sectional dependence (CD) test on the 22-panel data. The results of the cross-sectional dependence (CD) test are reported in Table 3. The discoveries over the arrangement and economies propose that the invalid speculation of cross-sectional autonomy is dismissed at the 5% noteworthiness level, in this manner tolerating the elective theory. Consequently, these outcomes show that the chose information arrangement is a cross-sectional ward during the investigation time frame, 1985–2017. Table 3 Cross-sectional dependency result Test Statistic Prob. Pesaran’s test of cross-sectional independence 2.189 0.0286 Note. Null hypothesis: cross-sectional independence (CD ∼ (0.1). Prob Stationary and cointegration tests According to Baltagi et al. (2005), a panel data approach provides superior, robust findings, helping to increase the power of the unit root and cointegration test, given that it combines both time series and cross-sectional dimension (Brambor et al. 2006; Tchamyou and Asongu 2017; Boateng et al. 2018; Tchamyou 2019. The results in Table 3 above confirmed the presence of cross-sectional dependence across the series; hence, we apply a CIPS panel unit root test that considers cross-sectional dependence in the estimation. Specifically, we use the Bailey et al. (2016) cross-sectional augmented IPS (CIPS) test. The estimated results from the CIPS test are displayed in Table 4. The CIPS test the discoveries on level information arrangement over the factors, and economies propose the proof of a unit root. Be that as it may, the evaluations on the primary request distinction information arrangement affirmed the dismissal of the invalid theory at a 1% level of noteworthiness for the entirety of the examples and acknowledged elective speculations. This proof infers that the chose factors are not stationary at the level yet stationary at their first-request contrast. Table 4 Results of unit root tests Test IPS Variable Level First different CO2 −1.846 −5.306* RGDP −2.143 −3.430* RGDP2 −2.167 −3.465* EPU −3.670* −6.114* ENU −1.927 −5.393*** EPUENU −3.470 −5.983* Notes: CIPS (Pesaran, 2007); Methodology; * and show the rejection of the null hypothesis, at 1% and 5% significance levels, respectively Having confirmed that the series is stationary, we further proceed to check if the variables have a long-run relationship. To do so, we applied the Pedroni and Kao cointegration test and the result in Table 5. The results confirmed the rejection of the null hypothesis, which says there is no cointegration. Therefore, we accept an alternate hypothesis which says the series are cointegrated at a 1% significant level. This enables us to perform the PMG-ARDL analysis. Table 5 Results of Pedroni and Kao cointegration tests Statistic Statistic Prob Pedroni cointegration test Panel v-statistic −0.1296 0.3561 Panel Rho-statistic 0.1125 0.4804 Panel PP-statistic −3.49 0.0000* Panel ADF-statistic −4.35 0.0050* Group Rho-statistic 1.487 0.8541 Group PP-statistic −4.0118 0.0289* Group ADF-statistic −1.368 0.0113* Kao cointegration test t-Stat Prob. ADF 2.6040 0.0181* Note: Pedroni (2004, 1999). * and represent a statistical rejection level of the null of no cointegration at a 1% significance level, respectively Results of PMG-ARDL Having established the series to be cointegrated in the long run, we further analyzed the PMG-ARDL test, as shown in Table 6. The long-run estimation confirmed that energy use and economic policy uncertainty has a positive relationship with CO2 emission value at the 1% and 5% significance level, respectively. This relationship means that only the energy use and economic policy uncertainty rise can lead to an increase in CO2 emissions with an average value of 1.1843% and 0.0199%, respectively. On the contrary, real GDP and GDP per capita improve CO2 emissions in these countries, with an average of 0.2023% and 0.3640, respectively. This is possible as more income is allotted to the individual, and such clean energy technologies became affordable. Therefore, renewable energy or clean energy technology consumption increases and reduces the level of CO2 emissions. Table 6 Result of PMG-ARDL (1,1,1,1,1) Variables Model 1 Model 2 Model 3 Short run ECT (−1) −0.1137* −0.0960*** −0.0759* ENU 0.7277* 0.7513* 0.7513* RGDP 0.2482* −0.2267 RGDP2 0.2368** 0.4823 EPU 0.0003 0.0011 0.0608 EPUENU 0.0382 Long run ENU 1.1843 1.3455* 0.8559* RGDP −0.2023* 0.4469* RGDP2 −0.3640* −0.7887* EPU 0.0199 0.0142* −0.0208 EPUENU 0.0094 Notes: , , and * show the rejection of the null hypothesis at 1%, 5%, and 10% significance levels, respectively The error correction term (ECM) coefficient that presents the speed of adjustment for the case of disequilibrium in the present study case is negative as expected and low (0.1137) at the 1% significance level. The ECM suggests that over 11% of the equation fit system is corrected for on an annual basis with the contribution of the study explanatory variables. The short-run estimation indicated that the values of energy use, real GDP, and GDP per capita positively influence CO2 emissions because they increase this variable by 0.7277%, 0.2482%, and 0.2368%, respectively. However, economic policy and the interrelated economic policy and energy use do not show any connecting relationship with CO2 emissions. Overall, energy use and economic policy positively affect CO2 emissions, while real GDP and GDP per capita reduce the increases in the 22 OECD countries. The FMOLS (Pedroni 2004; Kao et al. 1999); this method accounts for heterogeneity in the model; *** and * show the level of significance at 1% and 10%, respectively Dumitrescu and Hurlin panel causality Dumitrescu and Hurlin (2012) panel causality estimation was used to further confirm the nexus among the variables. It will interest you to know that energy use and GDP per capita all signified feedback relationships with CO2 emissions, while a unidirectional link found running from real GDP and CO2 emissions. Similarly, CO2 radiation caused economic policy uncertainty; energy use; real GDP; and GDP per capita caused economic uncertainty, while feedback relationship is confirmed between real GDP and energy use. Panel fully modified least squares (FMOLS) with weighted estimation For robustness, as reported in Table 7, we used robust panel econometric techniques to deal with the issues of heterogeneity in the estimation (Pedroni 2004; Kao et al. 1999). In particular, this methodology utilizes since quite a while ago run covariances from the cross-segment gauges and reweights the information to represent heterogeneity in the estimation. Given the importance of this methodology, we apply the Group-FMOLS technique to evaluate the since quite a while ago run patterns among the parameters. The results from the Group-FMOLS are shown in Table 8. The results of Group-FMOLS show that the increase in energy use and economic policy uncertainty leads to a rise in carbon emissions, while real GDP and GDP per capita help reduce the growth of CO2 emissions. In conclusion, our robust analysis is not different from the findings from the PMG-ARDL result. Table 7 Result of FMOLS Variables Model 1 Model 2 Model 3 ENU 1.0380* 1.0327* 1.0196* RGDP −0.1842* −0.9734* EPU −0.0126* −0.0113*** −0.1880* RGDP2 −0.2056* 1.0548* EPUENU 0.0583 Table 8 Results of the Dumitrescu and Hurlin (2012) panel causality Null hypothesis W-Sat. P-value Causality flow ENU ≠ > CO2 3.8054 0.0011 ENU ↔ CO2 CO2 ≠ > ENU 3.5114 0.0074 RGDP ≠ > CO2 4.1180* 0.0001 RGDP → CO2 CO2 ≠ > ENU 2.3282 0.7477 RGDP2 ≠ > CO2 3.9506* 0.0004 RGDP2 ↔ CO2 CO2 ≠ > RGDP2 2.8619* 0.1662 EPU ≠ > CO2 2.6813 0.3100 CO2 → EPU CO2 ≠ > EPU 2.6813* 0.0092 RGDP2 ≠ > ENU 3.8895* 0.0006 RGDP2 ↔ ENU ENU ≠ > RGDP2 2.9321* 0.1274 EPU ≠ > ENU 2.3359 0.7415 ENU → EPU ENU ≠ > EPU 2.8444* 0.1805 EPU ≠ > RGDP 1.6005 0.2584 RGDP → EPU RGDP ≠ > EPU 3.2182 0.0374 EPU ≠ > RGDP2 1.6345 0.2879 RGDP2 → EPU RGDP2 ≠ > EPU 3.1051 0.0634 Notes: *, , and * show the rejection of the null hypothesis at 1%, 5%, and 10% significance levels, respectively Conclusion and policy implications There are a considerable number of studies on the determinants of environmental quality. However, previous studies have not taken into account the influence of economic policy uncertainties, especially in OECD countries. For these reasons, we use annual data for a panel of 22 OECD countries between 1985 and 2017 to test the impact of energy use and economic policy uncertainties while accounting for other macroeconomic indicators. We applied robust econometrics techniques such as PMG-ARDL and Dumitrescu and Hurlin panel causality. Empirical results support the argument that in the long run, energy use and economic policy uncertainties further deteriorate the quality of the environment. In contrast, renewable energy improves the quality of the environment. Similarly, energy use, real GDP, and GDP per capita to environmental degradation within the region in the short run. We also found a causal relationship between real GDP and GDP per capita to CO2 emissions, energy use to real GDP, CO2 emissions, energy use, real GDP, GDP per capita to economic policy uncertainties. Given our findings, we will understand that energy use, real GDP, GDP per capita square, and economic policy uncertainties posed problematic to the environment since it leads to an increase in the CO2 emissions. Therefore, it has become a point of priority for the policymakers and government administrators to trade with caution in implementing policies on improving the quality of the environment. In addition, our study revealed that renewable energy source enhances the quality of the environment. Hence, the government of the OECD countries should adopt the use of renewable energy sources in their activities as commercial or home use. The outcome of energy-induced and economic policy uncertainty to pollution emission calls for a paradigm shift to renewables such as photovoltaic energy, hydroenergy, and wind energy, and for a promotion of renewable energy sources of electricity, grants, and taxes—holiday should be granted to investors. More so, FDI inflows should be cautiously directed to the investment in the renewable source of electricity, which are reputed to be cleaner and ecosystem friendly. Thus, there is a need for more efficient, modern, and cleaner energy technologies in the energy portfolio as a prerequisite for a successful transition from fossil fuel consumption while achieving a decarbonized economy that is in line with sustainable development goals (SDGs 8 and 13). Furthermore, to sustain the current momentum in OECD for sustainability target, there is a need to tighten commitment on environmental treaties like Kyoto Protocol and the Paris Agreement. In conclusion, our study has revealed new findings, but not without limitation. In this present study, we were constrained to expand our study beyond the OECD countries due to the lack of data. Therefore, we will encourage future studies to consider broadening the scope of the survey beyond the OCED countries. Acknowledgements The authors’ gratitude is extended to the prospective editor(s) and reviewers that will/have spared time to guide toward a successful publication. Many thanks in advance and we look forward to your favorable response. Author contribution The first author (Dr. Abdulrasheed Zakari) was responsible for the conceptual construction of the study’s idea. The second author (Dr. Festus Fatai Adedoyin) handled the literature section, while the third author (Dr. Festus Victor Bekun) managed the data gathering and was responsible for proofreading and manuscript editing. Data availability The data for this present study are sourced from the World Development Indicators (https://data.worldbank.org/). The current data can be made available upon request, but are all available and downloadable at the earlier mentioned database and weblink. Declarations Ethical approval The authors mentioned in the manuscript have read and approved the manuscript and given consent for submission and subsequent publication of the manuscript. Consent to participate Not applicable. Consent to publish Applicable. Competing interests The authors declare no competing interests. 1 Note. WDI is connotation for data from World Bank Development Indicator of the World Bank database sourced from https://data.worldbank.org/. WUI = This tab contains the beta version of the historical World Uncertainty Index (WUI) for 82 countries from 1952Q1 to 2019Q3. The tab contains a moving average index. The 3-quarter weighted moving average is computed as follows: 1996Q4= (1996Q40.6) + (1996Q30.3) + (1996Q2*0.1)/3. 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==== Front Lancet Glob Health Lancet Glob Health The Lancet. Global Health 2214-109X The Author(s). Published by Elsevier Ltd. S2214-109X(21)00231-X 10.1016/S2214-109X(21)00231-X Comment RETRACTED: Family planning in COVID-19 times: access for all Temmerman Marleen ab a Department of Public Health, Ghent University, Ghent 9000, Belgium b Medical College, Aga Khan University, Nairobi, Kenya 18 5 2021 6 2021 18 5 2021 9 6 e728e729 © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license 2021 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body The COVID-19 pandemic has substantially strained health systems and transformed the sexual and reproductive health environment worldwide. A WHO survey showed that across 105 countries, 90% have had health service disruptions as a result of the COVID-19 pandemic.1 One of the most commonly disrupted areas is family planning services, with 68% of countries reporting service disruptions. The Guttmacher Institute estimated that a 10% decline in use of short-term and long-acting reversible contraceptives across 132 low-income and middle-income countries would increase the unmet need for contraception over the course of 1 year to 48·6 million women, resulting in 15 million additional unintended pregnancies, 1·7 million additional women with major obstetric complications without care, and more than 3 million additional women resorting to unsafe abortions.2 COVID-19 mitigation measures as well as weak preparedness and overload of health service systems will affect health and health seeking behaviour, especially in sensitive domains such as sexual and reproductive health. However, the actual effect of the pandemic on women's contraceptive behaviour, unmet family planning needs, and unintended pregnancy risk, particularly in sub-Saharan Africa, is largely unknown. In their study in The Lancet Global Health, Shannon Wood and colleagues3 examined population-level changes in women's needs for and use of contraception during the COVID-19 pandemic in four African settings (Burkina Faso, Kenya, Kinshasa [Democratic Republic of the Congo], and Lagos [Nigeria]) through a population-based telephone survey between 2017 and 2020, including 7216 married women or women in union aged 15–49 years. Wood and colleagues found that during the pandemic, a significantly higher proportion of women in Lagos were in need of contraception than before the pandemic (5·81 percentage point increase [74·5% to 80·3%]), and there was a significant increase in contraceptive use among women in need in rural geographies in Kenya (7·35 percentage point increase [71·6% to 78·9%]) and Burkina Faso (17·37 percentage point increase [30·7% to 48·1%]).3 This study does not support the anticipated deleterious effects of women's access to and use of contraceptive services in the early stages of the pandemic. These data are encouraging and require further follow-up, including of single women and adolescents, in the later stages of the pandemic when there were potentially more service disruptions and supply issues. Similar data have been reported on the use of contraceptive health services by women referred via community health promoters in two large urban and peri-urban areas of Mozambique, during the period immediately surrounding the national state of emergency declaration linked to the COVID-19 pandemic.4 The data reported for 109 129 women served by 132 unique promoters and 192 unique public health facilities showed that the state of emergency was associated with a modest short-term reduction in both service provision and use, followed by a rapid rebound. These data suggest that the accessibility of reproductive health services was not substantially reduced during the first phase of the pandemic-related emergency.4 In a separate study, Weinberger and colleagues5 attempted to quantify potential shifts in contraceptive use that could result from COVID-19 mitigation strategies. Their results suggested a potential decreased demand during the COVID-19 pandemic for products that require face-to-face contact with a health-care provider or that might be more difficult to obtain, including intrauterine devices, implants, and provider-administered injections. These changes would run counter to recent trends in contraceptive use and public sector procurement. In Kenya, around 40% of women are already using a long-acting or permanent method of contraception.5 Because implant use in Kenya has increased in recent years, only a small proportion of users would be due for implant removal or replacement in the coming months in 2021. Furthermore, based on evidence that many long-acting reversible contraceptive methods can safely be used beyond their labelled duration, it is reasonable to assume that many users with a scheduled method replacement in 2021 could remain protected from unintended pregnancy without an additional service during COVID-19 disruptions should they desire to continue using their existing method. However, when women do require the removal of a contraceptive device, efforts should be made to ensure safe access to services.5 The COVID-19 pandemic has also leveraged innovations and new technologies that might become routine standard of care in the future, including telemedicine in contraception initiation and continuation. Mickler and colleagues6 have outlined evidence-based interventions for consideration in family planning, including digital health technologies to improve data for decision-making, manage logistics, reduce contraceptive stockouts, and improve provider–client capacity. Supporting mobile outreach service delivery to provide a wide range of contraceptives, including both short-acting and long-acting reversible methods, allows for flexible and strategic delivery of family planning services in areas with poor access to health-care providers. Providing family planning information, counselling, and methods including oral contraceptives, condoms, and injectable contraceptives through drug shops and pharmacies, might expand family planning access and availability, particularly in low-income or rural areas. In addition, integrating trained, equipped, and supported community health workers into the health system can increase family planning access by bringing services directly to clients.6 Integration of family planning services with child immunisation services (one of the most equitable and well used health services around the world), or offering family planning immediately post-partum rather than after 6 weeks, can provide unique platforms to integrate family planning and reproductive health care.6, 7 Global and national authorities should consider classifying family planning as an essential health service and emphasising prompt port and customs clearances and distribution logistics for contraceptives.6 Additionally, ensuring the continuity of funding, including domestic public financing, for family planning services and supplies is crucial.6 I support the call by Wenham and colleagues8 for governments and global health institutions to consider the sex and gender effects of the COVID-19 outbreak, both direct and indirect, and to analyse the gendered impacts of the multiple outbreaks, incorporating the voices of women on the front line of the response to COVID-19 and of those most affected by the disease within preparedness and response policies or practices going forward. I declare no competing interests. ==== Refs References 1 WHO Pulse survey on continuity of essential health services during the COVID-19 pandemic. Interim report 27 August 2020 World Health Organization https://www.who.int/publications/i/item/WHO-2019-nCoV-EHS_continuity-survey-2020.1 2 Riley T Sully E Ahmed Z Biddlecom A Estimates of the potential impact of the COVID-19 pandemic on sexual and reproductive health in low- and middle-income countries Int Perspect Sex Reprod Health 46 2020 73 76 32343244 3 Wood SN Karp C OlaOlorun F Need for and use of contraception by women before and during COVID-19 in four sub-Saharan African geographies: results from population-based national or regional cohort surveys Lancet Glob Health 9 2021 e793 e801 34019835 4 Leight J Hensly C Chissano M Ali L Short-term effects of the COVID-19 state of emergency on contraceptive access and utilization in Mozambique PLoS One 16 2021 e0249195 5 Weinberger M Hayes B White J Skibiak J Doing things differently: what it would take to ensure continued access to contraception during COVID-19 Glob Health Sci Pract 8 2020 169 175 32561528 6 Mickler AK Carrasco MA Raney L Sharma V May AV Greaney J Applications of the high impact practices in family planning during COVID-19 Sex Reprod Health Matters 29 2021 1881210 7 Makins A Arulkumaran S Sheffield J The negative impact of COVID-19 on contraception and sexual and reproductive health: could immediate postpartum LARCs be the solution? Int J Gynaecol Obstet 150 2020 141 143 32449192 8 Wenham C Smith J Morgan R COVID-19: the gendered impacts of the outbreak Lancet 395 2020 846 848 32151325	34019825	PMC8131077	NO-CC CODE	2021-10-15 00:42:10	yes	Lancet Glob Health. 2021 Jun 18; 9(6):e728-e729
==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 34014482 13851 10.1007/s11356-021-13851-4 Research Article The COVID-19 pandemic and its impact on environment: the case of the major cities in Pakistan http://orcid.org/0000-0001-9508-7740 Khan Yousaf Ali [email protected] 12 1 grid.440530.6 0000 0004 0609 1900 Department of Mathematic and Statistics, Hazara University, Mansehra, 23010 Pakistan 2 grid.453548.b 0000 0004 0368 7549 School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013 China Responsible Editor: Lotfi Aleya 20 5 2021 2021 28 39 5472854743 17 1 2021 5 4 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. In Wuhan city, China, a pneumonia-like disease of unknown origin triggered a catastrophe. This disease has spread to 215 nations, affecting a diverse variety of persons. It was formally called extreme acute respiratory syndrome coronavirus 2 (SARS CoV-2), also known as coronavirus disease, by the World Health Organization as a pandemic. This pandemic forced countries to enforce a socio-economic lockdown to avoid its widespread presence. This study focuses on how the pollution of particulate matter during the coronavirus pandemic in the period from 23 March 2020 to 31 December 2020 was reduced compared to the pre-pandemic situation in the country. The improvement in air quality and atmosphere due to the coronavirus pandemic in Pakistan was identified by both ground-based and satellite observations with a primary focus on the four provincial capitals and country capitals, namely, Peshawar, Karachi, Quetta, Lahore, and Islamabad, and statistically verified through paired Student’s t test. Both datasets have shown a significant decrease in the levels of PM2.5 pollutions across Pakistan (ranging from 15 to 35% for satellite observations, while 27 to 61% for ground-based observations). The result shows that poor air quality is one of the key factors for a higher COVID-19 spread rate in major Pakistani cities. By extending the same investigation across the nation, there is a greater need to investigate the connections between COVID-19 spread and air pollution. However, both higher population density rates and frequent population exposure can be partially attributed to increased levels of PM2.5 concentrations before the pandemic of the coronavirus. Keywords Disease Lockdown Exposure Pollution Coronavirus Socio-economic Air quality Particulate matter The Ministry of National Health and Environment, Islamabad. Pakistan 718944 Khan Yousaf Ali issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2021 ==== Body pmcIntroduction In the city of Wuhan, China, several belongings of pneumonia due to an unexplained origin began to occur during the initial days of December 2019. The genetic sequencing of this disease has shown that it is produced by an unusual coronavirus, publicly identified by the International Committee on Taxonomy of Viruses (ICTV) as extreme acute respiratory syndrome coronavirus 2 (SARS CoV-2) (Lai et al. 2020). Coronavirus, similarly referred to as COVID-19, is the 5th epidemic that occurred vertebral in 1918 subsequent to the Spanish flu pandemic. COVID-19 has ranged via human-human broadcast from China to other countries (Liu et al. 2020) and mainly affects the respiratory system (Waris et al. 2020). The WHO declared SARS CoV-2 as a Public Health Emergency of International Concern (PHEIC) on 30 January 2020 due to its elevated frequency of conduction distressing multiple people in a short amount of time (WHO Timeline 2020). In new parts of the creation, the number of novel cases rose 13 times quicker than the figure of new cases in China through February 2020. Future, on the eleventh, COVID-19 was announced as an extensive by WHO in March 2020 (Javed et al. 2020). This outbreak is reportedly affecting about 72 countries, causing 482,914 deaths worldwide by 25 June 2020 (Irwansyah et al. 2020). Pakistan took the 1st definite corona case in Karachi on 26 February 2020. One clarification for this might be that Pakistan imparts its line to China in the North and Iran positioned tenth among the nations with the most noteworthy number of detailed instances of coronavirus in Pakistan. The number of cases handily increased. Starting at 31 December 2020, there were around 490,476 affirmed combined COVID cases in Pakistan, with 38,395, 59,729, 219,452, 141,393, 18,254, 4870, and 8383 cases in Islamabad, Khyber-Pakhtunkhwa, Sindh, Punjab, Baluchistan, Gilgit-Baltistan, and Azad Jammu and Kashmir, separately (MNHS 2020). Nonetheless, this sensational ascent in coronavirus at worldwide levels may have another segment. It is perceived that expanded air discharges will bring about viral respiratory infections influencing 12–24% of the populace, as per Curran (2020). Openness to toxin prerequisites, for example, nitrogen dioxide, sulfur oxides, ozone and particulate issue (PM10 and PM2.5). This makes the populace more defenseless to coronavirus like irresistible illnesses. Since respiratory viral diseases significantly affect horribleness and even mortality, it is intriguing to sort out how air contamination will expand the danger and reality of respiratory viral diseases upon openness. Pakistan is among the topmost contaminated nations in South Asia, as laid out by Nemours scientists, for example, Sánchez-Triana et al. (2014); Khokhar et al. (2016) and Anjum et al. (2020). PM focuses in Pakistan, as in other South Asian countries, much of the time outperform the protected furthest reaches of the WHO. In the 2019 World Air Quality Review, Pakistan was positioned as the second most contaminated country in South Asia (WHO 2019) as presented in Fig. 1. Peshawar with yearly mean PM2.5 convergences of 69.2 μg/mc, Lahore with a yearly mean PM2.5 grouping of 68 μg/mc, and Karachi with yearly mean PM2.5 centralizations of 75 μg/mc are under 155-Unfortunate, as per AQICN (2020). Khan et al. (2018) and Ali et al. (2021) found that other enormous toxins close to Pakistan’s metropolitan urban communities additionally uncovered higher convergences of other poison boundaries, for example, O3, NO2, and CO2, which can likewise prompt the further conveyance of respiratory conditions. Fig. 1 Ranking of world’s most polluted cities by particulate matter (i.e., PM2.5 and PM10) As indicated by Mahmood et al., higher air quality is adding to more noteworthy wellbeing results in 2018. Given its morphology and home time, how antagonistically a poison can influence wellbeing. As opposed to PM10, PM2.5 is little in size and its home time is longer, making it conceivable to go into the lungs, being essential for the blood supply and influencing different tissues and making further poisonousness. PM2.5 cause more mucous and less ciliary action to make reformist and slow aggravation of the respiratory pathways, bringing about intense respiratory and viral contaminations in individuals persistently presented to it. Frontera (2020) showed that raised PM2.5 levels have additionally brought about flu infection transmission. Furthermore, this shows a potential association between coronavirus and hotspots for air contamination (particularly territories with air toxin idleness because of climatic conditions, nearby emanations, and geology of that locale). Numerous nations have consented to actualize financial lockdowns to discourage the transmission of the illness because of developing instances of coronavirus. This move has had a constructive outcome on the world’s atmosphere and air quality, and Pakistan has been no special case. The public authority of Pakistan delivered chief requests in 21 March 2020, to authorize a lockdown by limiting social action and monetary exercises starting on 23 March 2020, and systems keep on being state-of-the-art in the nation as shrewd lockdowns. At exceptionally nearby levels, various endeavors have been made to guarantee shrewd lockout to prevent the spread of coronavirus. There has been a consistent change in air quality around the world, notwithstanding numerous monetary misfortunes. The results of this lockdown because of the pandemic of the COVID-19 were obvious as even individuals could see the blue tone of the sky through their unaided eyes and sense the newness of the climate over the significant urban communities of Pakistan; the compression of air pollution before and during a pandemic is well presented in Figs. 2 and 3. At the point when travel was less utilized, the end of little businesses and makers brought about expanded air quality. Thusly, the vital reason for this investigation is to analyze the environmental impacts of the coronavirus pandemic in Pakistan (as a lockdown). As such, the uniqueness in PM2.5 fixations when the pandemic of the COVID-19 in Pakistan and its connection with the particulate matter. The results of the coronavirus flare-up in Pakistan. What is more, “how lockdown during the coronavirus pandemic assisted with improving air quality and the climate” is the particular examination issue. Fig. 2 Before pandemic images of major cities and capital of Pakistan Fig. 3 During pandemic images of major cities and capital of Pakistan taken by individuals The rest of this work is coordinated as follows: “Brief literature review” section presented detail literature review. “Methodology and data” section incorporates depictions of the strategies and information utilized in this examination and computational climate, while “Computation and discussion” section gives a concise conversation of the results. Finally, “Conclusion and implementation” section finishes this research with suggestions and implications for approach. Furthermore, results of Student’s t test are given in Appendix Tables 2 and 3. Brief literature review The COVID-19 pandemic is considered the most crucial global health calamity of the century and the greatest challenge that the humankind faced since the 2nd World War. Since the pandemic is affecting all aspect of our lives, it is appropriate to examine the effect on our environment. Especially with lockdown, air pollution may improve, and we have seen some examples around the world. Nemours researcher attempted to investigate the impact of coronavirus on economy, society, public health, and environment. Most prominent among them are Chakraborty and Maity (2020). They investigated the impact of COVID-19 on public health and global environment and discussed the ways through which COVID-19 can be controlled. Lau et al. (2020) investigated the influence of social lockdown due to COVID-19 outbreak in Wuhan, China. They analyzed publically available data on confirmed COVID-19 cases before and after lockdown measures and found positive influence of lockdown on coronavirus spread. Similarly, Coccia (2020) examined the impact of short-time and long-time countrywide lockdown due to coronavirus pandemic on public health in selected countries of Europe. They found that long-time lockdown was not effective as compared to shorter one on public health in selected European countries. Additionally, Cole et al. (2020) examined the impact of lockdown due to coronavirus pandemic on health and air pollution through machine learning approach and found no obvious impact on concentrations of SO22. Kumar and Managi (2020) investigated the effect of severity of lockdown on air pollution in major cities of India and found that control measure had positive effect on improvement in air pollution, but this effect was not uniform across the cities of India. In another study, Abou El-Magd and Zanaty (2020) examined the short-term impact of lockdown due to COVID-19 on air quality in Egypt. Abou El-Magd and Zanaty (2020) used multi-data sensors and found that short-term lockdown had a significant impact on air quality and observed great reduction of carbon emissions from transportation, industrial, and human activities in Egypt. Another study was conducted by Kang et al. (2020) in which the researcher studied secondary impact of COVID-19 on people’s way of life and work, housing instability, economic shock, and privacy in urban regions. Furthermore, Piccinini et al. (2020) conducted study on smart lockdown due to COVID-19 and its expectancy in Northern Italy and observed significant reduction in noise pollution during lockdown. Narayanan et al. (2020) and Aman et al. (2020) investigated the socio-economic impact of coronavirus lockdown for India using online survey data. They found that lockdown has significant impact on society and living style and observed that lockdown bring significant change in the lifestyle of human beings by means of online shopping and education, hygiene and health awareness, work from home, changing internet habits and societal changes, and observed improvement in air pollution in India. In similar research, Pacheco et al. (2020) examined NO2 levels during coronavirus pandemic for Ecuador and observed strong association among air NO2 concentrations and death due to coronavirus. In another research, Siqueira et al. (2020) conducted ecological study for Spain and studied the effectiveness of lockdown on the outcomes of COVID-19 and observed that lockdown play important role in the control of coronavirus pandemic. Similarly, Mathew et al. (2020) examined the impact of lockdown on self-employed women for Ndola, Zambia. The researchers found that self-employed women were greatly affected by means of poor access to health services, insufficient food supplies, impossibility to recover business, psychological strain, difficulty of medications, and challenges of keeping children indoors. Methodology and data Method To evaluate levels of the environmental particulate issue (e.g., PM2.5, PM10) continuously, the Beta-ray Attenuation Mass Spectrometer (BAMS) instrument is normally utilized and suggested by the US-EPA. Utilizing a tallness explicit gulf, the showcases are set to the size of PM2.5 and associated with the analyzer. Inside the analyzer, there is a radiation source that makes beta-beams that are communicated through the glass fiber and projected to be kept on a tape test. Standard lessening limits are determined preceding the start of each cycle. At a stream pace of 16.7 L/min, regardless of the gulf scale, encompassing wind currents through the analyzer. The rate of beta-ray emission is constant and measured by scintillation detector. The pace of beta-beam emanation is steady and determined by a sparkle identifier in 2018, as indicated by English Columbia. The rate at which the lessening happens is straightforwardly relative to the PM2.5 mass. The Moderate Resolution Imaging Spectroradiometer (MODIS) is usually utilized for AOD estimation by the airborne network. Statistical technique We employed paired Student’s t test for unequal sample size to test the hypothesis whether there is statistical evidence that air pollution in the country before and during coronavirus pandemic is significantly different. We formulate our null hypothesis as: Ho: The air pollution remains the same in the country. H1: There is significant difference in the air pollution before and after. The test statistics used for this purpose can be mathematically formulated as: t=X¯1−X¯2−μ1−μ21n1+1n2Sp~tα2n1+n2−2 whereSp=n1−1S12+n2−1S22n1+n2−2 with n1 + n2 − 2 degree of freedom. Any significant value of the test statistic can lead us to the rejection of null hypothesis and concluded that air pollution in the country is significantly different during pandemic than before lockdown. Data and computational environment NASA presented MODIS locally available Land in 2000 as a feature of the Earth perception succession and information assortment with a practically worldwide inclusion of 10 km × 3 km field pixel scale at nadir (Duty et al. 2013). The equator is reached by MODIS instruments installed Land and Water around 10:30 and 13:30 nearby time, individually. Vaporized optical profundity information was gathered utilizing the dark blue calculation utilizing the MOD04 Level 2 Determination 6 item (Duty et al. 2013). The MODIS Transformation Toolbox (MCTK) has additionally been utilized for MODIS information pre-preparing. Geo-referring, steering, and spatial examination are finished using ArcMAPv10.2. The entire results reported in this investigation were carried out in the RStudio computational environment. For air pollution–related data, we used snow-ball sampling/reference sampling techniques, whereas for COVID-19 data, we used convenient sampling technique as COVID-19-related data is easily available on daily basis. The datasets collected were split into two subgroups over the following period: (1st January 2020 to 22nd March 2020). In a pandemic (23rd March 2020 to 31st December 2020). The length of the lockdown is centered on Pakistan’s administration requests to authorize a cross country financial closure to forestall the dispersal of coronavirus through human-human correspondence. For the above mentioned time-frame, the information for PM2.5 was aquired from AirNow is controlled in four urban communities and the capital of Pakistan at US international safe havens/department workplaces (Quetta, Karachi, Lahore, Peshawar, and Islamabad). For Quetta, Lahore, Peshawar, Karachi, and Islamabad, the standard midpoints of particulate issue, PM2.5 (in μg/mc), and related air quality files were resolved. The rate decrease of PM2.5 was estimated and the connected detail as indicated graphically in Fig. 5. Computation and discussion Deviation in aerosol optical depth As far as both wellbeing impacts and radioactive compelling properties, the job of the particulate issue is surely known by PM2.5. By the by, in both realities, the advancing bit of the globe needs satisfactory observational organizations. As air quality checking is expensive and needs reliable measures to support and run such observational organizations, Pakistan is no exemption (Khokhar and Yasmin 2018; Zeb et al. 2019). None of the public authority’s air quality observing stations has been dynamic since 2010, as indicated by the Monetary Study of Pakistan report (ESoP 2013). We thusly center around satellite estimations to fill this distance somewhat by giving practical long haul information assortment of air foreign substances, for example, airborne optical profundity (AOD), follow and ozone harming substances across these zones (Zeb et al. 2019; Gupta et al. 2020). AOD is a columnar amount and speaks to the termination of light because of the presence in the air of mist concentrates, permitting AOD to be taken as an intermediary for the particulate issue at surrounding levels. A few analyses have contemplated the relationship between PM2.5 and AOD seen by satellite utilizing fine vaporized division and have indicated a solid association with both (e.g., Kumar et al. 2007; Singh et al. 2006; Khokhar 2006, 2017). Figure 4 shows a guide of 28 days of MODIS determined AOD and arrived at the midpoint of throughout the long-term 2017–2020. This shows the crumbled air quality and raised vaporized burdens over the locales facilitating anthropogenic exercises from ground level to the highest point of the environment. Fig. 4 Satellite map of Pakistan representing average AOD level before pandemic during 2017–2020. Source: NASA (https://ladsweb.modaps.eosdis.nasa.gov/) The AOD is high, especially in Pakistan’s thickly populated zones, for example, the urban areas that harbor the majority of Pakistan’s business are Khyber-Pakhtunkhwa, Lahore, and Karachi. Similarly, Fig. 5 reflects AOD in the nation until the date during the pandemic time frame (23 March to 31 December 2020). Across Pakistan, there is a huge distinction in AOD levels, as appeared in Fig 5. A huge decrease is found in the zones around the Indus delta with truly higher AOD levels. These results have strong support from paired Student’s t test presented in Appendix Tables 2 and 3. There is proof that air quality has profited by the pandemic, especially in the significant urban areas and the capital of Pakistan (e.g., Quetta, Karachi, Lahore, Peshawar, and Islamabad). It tends to be seen unmistakably from the insights in Fig. 6 (percent decrease). The most extreme decline is seen in the town of Peshawar, trailed by Karachi, Lahore, Quetta, and Islamabad, and, as appeared in Fig. 7. Fig. 5 Satellite map of Pakistan representing average AOD level during coronavirus pandemic and difference among before and after. Source: data downloaded from NASA available at https://ladsweb.modaps.eosdis.nasa.gov/ Fig. 6 Presentation of reduction in ground-level PM2.5 concentrations (ug/m3) for before and during lockdown periods. Source: fata downloaded from NASA available at https://ladsweb.modaps.eosdis.nasa.gov/ Fig. 7 Percentage reduction in AOD levels in major cities and capital of Pakistan during the socio-economic lockdown. Source: data downloaded from NASA available at https://ladsweb.modaps.eosdis.nasa.gov/ Deviations in PM2.5 concentration Additionally, during the COVID time (23rd March to 31st December 2020), huge abatements in-ground centralizations of PM2.5 were seen at 5 separate areas in the urban communities of Quetta, Karachi, Lahore, Peshawar, and Islamabad. In the city of Peshawar, 56% in the city of Lahore, trailed by Quetta 41%, Karachi 43%, and Islamabad 25%, the main decline of around 59% is noticed. These towns (Peshawar, Lahore, and Karachi) are among the world’s most sullied towns (WHO, 2019). Over the pandemic, this financial lockdown and shrewd lockdown have demonstrated success in decreasing the pace of PM2.5, yet additionally, extraordinary air poisons, since these are co-radiated from a similar activity more often than not. The noticed upgrades in PM2.5 and improved air quality list (AQI) focus on Peshawar, Lahore, Quetta, Karachi, and Islamabad when the corona pandemic is found (Table 1). We statistically verified these results by employing two sample t-test and strongly reject the null hypothesis at 95% level of significance that the air pollution before and during coronavirus pandemic was the same. The qualification between the double-cross ranges (subgroups) can be seen when any organization (nearby/worldwide) quit during the financial lockdown, neighborhood travel was bolted, and stores, workplaces, instructive foundations, recreation parks, and shopping centers were completely shut. With confined long periods of administration causing colossal financial misfortunes, banks, markets, and drug stores remained open and made a huge mass. In one perspective, this entire circumstance delivered a huge financial mishap. On the opposite side, however, it had a huge helpful impact on air quality, enormous scope preservation of assets, and a similarly more modest conveyance of coronavirus. Table 1 Representation of number of days PM2.5 concentrations increased the Pak-NEQS guidelines, WHO, and AQI descriptors during both the periods of before and during lockdown in the selected cities of the country Time frame Frequency of PM2.5 exceeding PAK-NEQS limits Frequency of PM2.5 exceeding WHO limits AQI descriptor measures (in days) Provincial capitals Peshawar Before pandemic 25 days 18 days 8 (moderate) 7 (unhealthy for sensitive group) 10 (unhealthy) During pandemic 15 days 7 days 17 (moderate) 7 (unhealthy for sensitive group) Lahore Before pandemic 23days 21 days 8 (moderate) 7 (unhealthy for sensitive group) 10 (unhealthy) During pandemic 17days 13days 14 (moderate) 12 (unhealthy for sensitive group) Karachi Before pandemic 20 days 16days 9 (moderate) 10 (unhealthy for sensitive group) 7 (unhealthy) During pandemic 14days 5 days 19 (moderate) 6 (unhealthy for sensitive group) Quetta Before pandemic 24 days 17 days 11 (moderate) 14 (unhealthy for sensitive group) 10 (unhealthy) During pandemic 16 days 11 days 18 (moderate) 11 (unhealthy for sensitive group) Country capital Islamabad Before pandemic 23 days 15 days 14 (moderate) 7 (unhealthy for sensitive group) During pandemic 13 days 7 days 13 (moderate) 8 (unhealthy for sensitive group) Relationship between COVID-19, population, and polluted areas In this way, something basic about these four urban areas is that they are generally vigorously populated; they are more inclined to the spread of coronavirus. There is an association between the engendering of coronavirus and thickly populated territories, as indicated by contemplate. Because of more prominent human-human correspondence, urban communities with wide populaces are blamed for giving a solid spread thickness. Just as some previous medical conditions, for example, smoking, cardiovascular breakdown, hypertension, diabetes, or corpulence, the time of individuals dwelling in an area is regularly a major factor (Florida 2020). Another connection between more noteworthy coronavirus spread and higher death rates in regions that were profoundly defiled before the pandemic has been set up. There is an 8% ascend in the death rate with an expansion of 1 μg/m3 of P.M2.5 (95% certainty span), as per a report attempted in the USA (Wu et al. 2020). A comparable example was seen in Peshawar, Karachi, trailed by Lahore, Quetta, and Islamabad with a higher coronavirus spread power, as found in Fig 8. The numbers explicitly show that during lockout and time frame after lockdown, the colossal appropriation in these 5 urban communities can generally be identified with the way that these are urban areas with huge populace trouble and a higher human-human reach. Moreover, the most noteworthy level of causalities was allotted to the city of Peshawar, trailed by Karachi, Lahore, and Islamabad (recorded among the best 20 most dirtied urban areas on the planet by WHO 2016). Partially, it could be owing to an in-compelling lockout, yet it can likewise be identified with a more prominent weakness of the general population regularly exposed to higher measures of PM2.5 and other contamination comparative with the better zone populace. The number of fatalities and the rated pace of recuperation in these urban areas can be additionally checked, as found in the table in Fig. 8. It might likewise be estimated that expanded air quality not just will in general limit the occurrence of illness, yet additionally lessens the powerlessness (both contamination and death paces) of infection. Fig. 8 Information about COVID-19-confirmed cases reported in major cities till 31st December 2020. Data obtained from Ministry of National Health Services available at http://covid.gov.pk/ Pandemics of the coronavirus structure This example has been seen far and wide when a few countries have shut approaching and friendly unfamiliar carriers, individuals have been approached to sit at home, and the utilization of engine vehicles has been significantly diminished. The propensities for utilization have moved by requires. All of this resulted in about -23% decrease in global CO2 emissions by December 2020 as compared to the 2019 mainly just by the reduced use of transport all around the world It is the best drop in CO2 levels that has been accounted forever. Numerous atmosphere researchers are pleased to see a particularly enormous decline in various poisons, including GHGs, in the climate. On the opposite side, numerous researchers are interested how much endeavor would need to be taken to diminish the emanations of GHGs to contain an unnatural weather change to 1.5°C before the finish of the twenty-first century. The investigation shows that lockout activities and measures are taken in 69 nations that contributed 97% of GHG discharges finished in a particularly abrupt drop in the centralization of CO2. It is the primary eminent decrease since the Subsequent Universal War. Specktor (2020) finds that nations had a decrease in contamination by up to 26% exclusively. On the off chance that the shrewd lockout endures before the finish of June 2021, outflows overall are projected to diminish by around 10%. As per Benjamin Storrow’s (2020) and Storrow’s (2020) arrangement to diminish a worldwide temperature alteration to 1.5°C, this reduction is now short throughout the following decade by 7.6% every year. Global indirect effects of COVID-19 About each country has been hit by coronavirus, and there are likewise results of this pandemic that cannot be straightforwardly noticed. The most immediate and away from coronavirus is on the prosperity of individuals, which is the world’s essential need. This pandemic has likewise explicitly affected the movement, production, the travel industry, school and office businesses, and so on, yet the roundabout impact of coronavirus on the environment has been created by the immediate effect on these areas. Some present moment and long haul results of Coronavirus are known to affect the atmosphere, for example, decreases in PM2.5 and NO2 focuses, diminishes in clamor outflows, and changes in variation methodologies, upgraded natural control software engineers, and better groundwork for disaster hazard the executives. Such adverse aberrant results, including diminished garbage removal rehearses impacts on normal cycles, and arising issues going up against ecological observing and atmosphere developers, have likewise emerged as an outcome of this pandemic. Such optional impacts could have long haul suggestions, for example, the impact of the most recent pandemic on the accomplishment of Feasible Advancement Objectives (SDGs) (Zambrano-Monserrate et al. 2020: Cheval et al. 2020). Indirect impacts Air quality Unexpected abatements in monetary and mechanical exercises because of the lockdown brought about by coronavirus have brought about an overall lessening in ozone-depleting substance emanations. This outcome in a considerably sensible improvement like the climate and the atmosphere. Air quality is predominantly subject to human exercises, as the lockdown has prompted a critical diminishing in air contamination in the urban communities of Italy, China, and New York, and a huge abatement in GHG emanations has been anticipated for the rest of the year. Aeronautics was one of the enterprises incredibly affected by this pandemic. Avionics represents 3–5% of worldwide CO2 and 1–2% of the climate’s all-out ozone-depleting substance discharges. The Global Air Transport Affiliation (IATA) has extended a reduction of roughly 48% in flying in 2020. Studies have indicated that the aeronautics area will take some time before getting back to business as usual, even after lockdown. This leads by implication to a lessening in CO2 and abatement in everyday temperature midpoints because of decreased outflows of GHGs (Ali et al. 2021). For instance, in this pandemic lockdown, Milan had 21% less normal NO2 levels for the 7 days of 16–22 March 2020 contrasted with the exact 7-day stretch of 2019, the grouping of NO2 additionally diminished. In contrast with that week in 2019, Bergamo, Barcelona, Madrid, and Lisbon encountered a diminishing of 47%, 55%, 41%, and 51%, individually, in normal NO2 fixations for the very week in 2020 (Cheval et al. 2020). NO2 levels diminished to around 22.8 μg/m3 and 12.9 μg/m3, individually, in Wuhan and China (Zambrano-Monserrate et al. 2020). Pollution from shipping and noise One of the significant wellsprings of both ozone-depleting substance discharges and commotion contamination is the vehicle business. As the administrations of a few nations delivered closure and isolate requests to shield residents from this pandemic, there was a considerable diminishing in rush hour gridlock stream on the streets in 2020, as indicated by the consequences of Cheval et al., for instance, the traffic of trucks and vehicles in Vienna was decreased to 49% and 51%. This led, not exclusively, to a lessening in GHG contamination, yet additionally to a significant reduction in the clamor level made by a horn blaring and different cars. These drop-in commotion levels additionally finished in expanded observation of seismic waves and zones defenseless against tremors and the seismographic information were emphatically reinforced. The pandemic’s effect on water bodies As indicated by Cheval et al., inferable from the coronavirus pandemic in 2020, there was a run of the mill methods for transport in spots where sailing travel was done, for example, as no such drifting ways were utilized, Spain, Italy, Bangladesh, and various travel industry objections encountered a prompt valuable impact on water sources by lessening water tainting. The suspended particulate issue (SPM) in a freshwater lake, Vembanad Pool of India, was analyzed by Yunus et al. (2020) to see if under these lockout conditions there was a distinction in SPM focus. The discoveries uncovered that there was a 36% drop in SPM comparative with earlier years’ focuses. Direct impact of COVID-19 Ecological system and coronavirus An association can be seen, from a natural perspective, between our locale and the climate. Because of the living space loss of a few plants, the multiplication of presented species, and movements in the circulation structure of species, coronavirus is the result of temperature changes in the climate. To think about the association between the flare-up of pandemics and creature markets, around 300 creature insurance associations composed a letter to the World Wellbeing Association (WHO). Another perspective that raises the contact of people with wild creatures is deforestation, which may regularly add to the spread of any unfamiliar infection or life forms that can have a particularly wrecking sway as set off by this pandemic of coronavirus and others before it. As indicated by Ali et al. (2021) and Cheval et al. (2020). The pandemic has affected natural life study and field practice, which has added to the decrease of exploration rehearses that have repercussions for the endurance of biodiversity and biological systems. This has finished in the drawn-out common sense of various creatures the board projects being surveyed, for example, Asset for the Worldwide Atmosphere. Activities for waste disposal Numerous individuals across the globe are in separation and live at home due to the stature of family squander creation. Medical clinic squander, alongside homegrown waste, has additionally risen. As indicated by the Cheval et al. 2020 suggestion, as the hour of the lockout in this pandemic is rising, the disposing of individual defensive gear (PPEs) on the side of the road and along the shoreline is expanding. A news report revealed in Sunrise tends to the expanded testimony of waste in Karachi, Pakistan. For very nearly 2 months, the Sindh Climate Insurance Office (SEPA) has been inert, adding to unreasonable medical clinic squander and unattended homegrown waste on the town’s roads. The article takes note that so far no appropriate removal framework has been set up for coronavirus waste and none of the clinics is given any sort of direction to battle this issue. SEPA commits the reusing of the medical clinic or any dangerous waste, as delineated by Ilyas 2020, with the goal that none of the general population is hurt by it; anyway the office has been inadequate, and no such advances have been taken to date. COVID-19’s long-term impact on SDGs The current situation with coronavirus is probably going to affect likely ecological and financial systems on a worldwide premise. “Changing our Reality: the 2030 Vision for Economic Development” involves 17 SDGs zeroed explicitly on guaranteeing correspondence and neediness easing by 2030. Because of coronavirus, these SDGs have had an unmistakable effect and are anticipated to go through long haul outcomes too. A significant number of these SDGs are explicitly attached to the prosperity of metropolitan focuses and networks. Given the consequences of existing conditions that include openness, shortcomings, and solidness seen during this worldwide fiasco uncovered by S.M Ali et al. (2021) and Cheval et al. (2020), the application and thought of this plan could be checked. Environmental and climate service control COVID-19 highlighted that greater preparedness for monitoring environmental and climate resources had to be accomplished. The pandemic has increased the need for access to real-time and long-term data that will help officials recognize the multiple reactions that are taking place during the epidemic in different fields. Following the recommendation of Cheval et al., the sustainability concerns found by this pandemic in 2020 have prompted environmental scientists to improve the capacity of surveillance. Due to the reduced efficiency and quantity of aircraft weather measurements, the pandemic has greatly impacted the development and availability of weather prediction results. In addition to ocean and remote region observations, environment systems were often biased by this pandemic. Better control may aid research and classify the dissemination of this novel COVID-19 in many countries. In the future, the methods learned today, lessons, and evidence from the current will be used to tackle the dissemination of such a disease effectively. Function of the atmosphere in COVID-19 spread There have been a few historical instances where the propagation of viruses in Europe, such as the West Nile virus, has been correlated with meteorological factors, such as climate and temperature fluctuations. This is why researchers are interested in researching the relationship between COVID-19 spread and meteorological factors, i.e., increases in temperature and other changes in climate conditions, such as humidity. The viruses spread rapidly from China to regions with colder weather patterns, such as Europe and North America, following the initial spread of the COVID-19 virus in mid-December. In his latest study on the initial distribution, Mazhar et al. (2020) illustrated that COVID-19, like pneumonia, often triggers respiratory disease strongly related to differences in weather and environment conditions between various areas. Research was undertaken in China investigating the association between changes in temperature and COVID-19 showing that temperature was an environmental catalyst in China for the outbreak of this pandemic. There is an opposite association between the two, according to Shi et al. (2020), i.e., elevated temperature resulted in a reduced rate of spread, the severity of the epidemic, and rate of infections. To research the spread of related respiratory diseases such as influenza and extreme acute respiratory syndrome, other meteorological parameters are also significant (SARS). The association between the mortality rate induced by COVID-19 and the various environmental factors, i.e., different temperatures and humidity, was discussed in a report in China. Ma et al. (2020), although anti-correlated with relative humidity, demonstrated a favorable association between the death rate and the diurnal temperature scale. To retrain the dissemination of COVID-19 and other precautionary steps, it is also mandatory to evaluate the possible influences of environmental parameters. No conclusive relation could be identified between the COVID-19 spread and the temperature in Pakistan, unlike the case of China. No substantial association was identified between the distribution of COVID-19 and temperature fluctuations, according to usable datasets collected at the provincial level from the Government of Pakistan’s COVID-19 portal and weather records. First, there was a comparatively smaller distribution of COVID-19 in Pakistan, and the socio-economic lockout was effectively tackled. Second, that was largely attributed to increased contact with humans and humans. However, the distribution of COVID-19 was found to be higher in condensate communities with high concentrations of contaminants, such as the major cities of Pakistan and other areas of the world listed in this report. COVID-19 is a respiratory disorder, according to Frontera 2020, and there is a proven connection between the spread of (past) respiratory diseases in areas subject to high levels of air pollution. It may be hypothesized that the transmission of COVID-19 induced further health effects derived from this pandemic in regions where the pollutant concentrations were greater than in other places. Under the socio-economic lockdown since the COVID-19 epidemic, a sharp drop in pollutant emissions (GHG and other toxic gases) was observed, and such a fall in global emissions has not been observed in the past 25 years. This reduction in global pollution may have implications for the earth as a whole, creating a future cooling impact. This relies, though, on the amounts of carbon dioxide and other ambient greenhouse gases currently accumulating. There are still chances that once the global lockdown is removed, the decreased emissions would plateau again and be higher than before as factories and industries continue to offset their losses from expanded operations. Global change and the COVID-19 pandemic also provided new doors for climate-related study as a consequence of this remarkable reduction in pollution. While the rising impacts of climate change have long called for global emissions mitigation, this pandemic has cut global emissions more effectively than ever in the past. To fight this pandemic, several nations, including Pakistan, have taken precautionary steps and implemented emergency reforms. It is unclear, however, if this pandemic would further decrease long-term carbon pollution and thereby trigger a cooling impact, or if the planet will recover to its previous Sheikh 2020 pollution concentrations. Countries should gleam from their battle against the COVID-19 virus and integrate it into the fight against climate change in several respects. It also encouraged global societies to consider regular socio-economic lockdowns in places with higher carbon emissions, to achieve the Paris Agreement’s defined goals. There is, therefore, a greater need for researchers to focus on it and provide the strength and frequency of smart lockdowns with optimal and mathematical solutions, without undermining socio-economic growth. COVID-19 and the economy of Pakistan The global economy has been greatly impacted by this pandemic, and its unequal consequences have been felt globally, with certain nations becoming more affected than others. Economically, before the forced lockout, Pakistan was still under strain, further exasperating the crisis. Regular wagers and micro- and medium-sized enterprises experienced the most serious injury. Whereas many sectors, such as the textile industry, were considered the backbone of the country’s economy, they were also badly affected, as several textile import orders were cancelled during the COVID-19 time. The unemployment rate has risen, although economic development has steadily declined. Pakistan’s GDP growth in 2018 was 5.8%, and by the end of 2020, it plummeted to 0.95% and is projected to decrease further owing to the financial limitation placed by the lockdown of COVID-19, as exposed by Saleem (2020). As stated earlier, due to disturbances in their everyday operations and the restricted class of customers available, small- and medium-sized companies were most badly impacted. Because of the smart socio-economic lockdown in the world, the onset of COVID-19 and the subsequent collapse of these small businesses have greatly affected the economy at large. Owing to significant casualties during this pandemic, several of these organizations are not funded. As they raise the job ratio and bring money to the nation that is not received from the outside, these small enterprises are essential to a country’s economy. These small- and medium-sized companies amount to around 60% of GDP in Pakistan. These are present in Pakistan’s urban as well as rural areas. Small- and medium-sized companies are key players in Pakistan’s farming, wholesale, distribution, and transport markets. As illustrated by Shafi et al. (2020), these firms face financial difficulties, delays in the supply chain and requests, and consumer ratio decline. Sustainability in the presence of COVID-19 In any conceivable way, whether it is societal, economic, or health linked, this pandemic has adversely affected the planet. Several nations are taking action to reduce harmful impacts. Likewise, initiatives have been taken by the Government of Pakistan to safeguard the nation from socio-economic setbacks. The Humanitarian Response Plan for Pakistan’s COVID-19 pandemic was designed to assess the potential impacts of this epidemic and possible mechanisms for managing and resolving them. Here are some of the salient characteristics: The government has issued a monitoring and inspection procedure for persons at all forms of entry points in reaction to the effects on public health. Passengers are expected to fill in a health declaration form (HDF) before flying. For sample collections, several mobile labs are being developed at various locations in different cities. The laboratories in major cities are assigned to obtain samples from suspected COVID-19 patients according to the correct biosafety criteria. The Government of Pakistan has set up a multi-sectorial support fund for COVID-19 problems, in reaction to the effects on the economic system. PKR 1.35 trillion has been allocated by the Government of Pakistan (GoP) to combat the concerns and problems faced by COVID-19. $1.3 billion in relief for daily wagers and laborers. $800 million for the development of agricultural and SME relief. Prices are lowered by 15 rupees for gas, diesel, kerosene, and diesel oil per liter. $ 800 million for producers and exporters. $1.79 billion this season to procure 8.2 million tons of wheat. $95 million in tax relief to include relief for wellness and nutritional supplies. For the Utility Stores Company (USC) to have 500 million dollars for subsidized prices provide essential food supplies, including rice, pulses, sugar, and cooking oil. $700 million for the provision of residual/energy fund aid. In addition to the measures taken above, the National Preparation, Preparing and Evaluation Initiative for the Mitigation of Additional Losses proposed by OCHA (2020) is underway. Conclusion and implementations Both satellite and ground-based measurements showed that during the coronavirus pandemic, air quality and environment, in particular PM2.5, levels were adequately improved across Pakistan. As a consequence of the socio-economic lockdown and smart lockdown implemented by the government of Pakistan to date, major changes have been observed in the calculated concentration of PM2.5 levels at Quetta, 47%; Lahore, 61%; Peshawar, 58%; Karachi, 48%; and Islamabad, 27%. However, the poor recovery rate and comparatively better tertiary level medical services compared to the rest of Pakistan suggest that as the populace is more often subjected to very high levels of PM2.5 and other air contaminants such as PM10, CO2, NO2, and O3, the effect in these cities is the largest amount of COVID-19 cases that can be due to high population figures. It may also be hypothesized that the population residing in large cities (often subjected to a higher degree of PM2.5) is more vulnerable than the population living in regions with lower levels of PM2.5 exposures. Limiting socio-economic practices can also result in an economic deadlock; however, by growing the costs of diseases born by subsidized health institutes in Pakistan, it can increase air and atmospheric quality and relieve the economic pressure of states indirectly. Nevertheless, to answer a rational issue, there is a stronger need for commitment and research; how long is a socio-economic lockout required to balance economic practices and to breathe clean air and alleviate climate change? Recommendations and implications Based on the exploration of this research, we have the following recommendations. It is advisable that: Proper measures should be implemented to guarantee the protection of the public’s welfare. Proper control measures must be placed in place to avoid the large and rapid spread of COVID-19, taking into account the position of meteorological and other variables. Since air pollution may be related to the widespread and consequent morbidity of COVID-19, the government must take steps to ensure that the levels of air pollution are within acceptable limits. In order to establish whether COVID-19 events or fatalities are correlated with evolving environmental factors, more study must be undertaken. Policies that safeguard citizens and the climate at the same time should be in effect. Because COVID-19 has impacted almost every industry, policies should be as realistic and simple to enforce as they have the least socio-economic growth consequences. Any of the following policies that could be effective in minimizing COVID-19’s results are as follows: The government should take action to hold anthropogenic pollution in place for the future, as the lockout has proved to be successful for our atmosphere and to limit the spread of COVID-19. For that function, any of the following measures may be employed: Also after COVID-19, the government could adopt a smart lockdown to control the intensity of anthropogenic pollution under allowable limits. Operating periods can be fixed by factories and businesses and, in particular, set off at peak pollution hours. No additional provision/banning of any polluting factories near residential areas as they create smoke and other contaminants that may be detrimental to individuals with some respiratory condition. The following measures can be taken in order to minimize the effect of COVID-19 on companies: To analyze, on a small or large scale, the operational and financial effects of COVID-19 on their company. To better sustain the workings of the supply chain, strategies for controlling the distribution of cash through the supply chain must be supervised and subsidized. According to Sabina Softi´c, many business contracts and commitments have become unsustainable for small businesses by 2020, and in order to minimize the adverse effects of these contracts, it is necessary to implement as soon as possible policy interventions for the provision of subsidies and tax relief. Future research work Many research topics that one may expect in potential studies are brought up through the exploration of this research. As of September 2020, the number of COVID-19 cases in Pakistan has risen dramatically. It is therefore mandatory to analyze daily COVID-19 cases on finer spatial and temporal scales in major cities of Pakistan and to find out if the key factor was any existing link to meteorological conditions and/or smart socio-economic lockdown, as was not the case in neighboring countries. In addition, the transmission of COVID-19 and consequent morbidities such as respiratory and cardiovascular disorders and their correlation as a driving force with an elevated degree of air quality must be discussed. Appendix Table 2 Paired samples statistics Mean N Std. deviation Std. error mean Pair Before lockdown 58.12 28 6.74180 .34121 During lockdown 41.91 48 7.45214 .42418 Table 3 Paired sample t-test Pair differences t df Sig (2-tailed) Mean Std. deviation Std. error mean 95% CI of the difference Lower Upper Before and during 16.21 8.5101 .4671 15.38 17.1337 36.13 74 0.000 Acknowledgements I acknowledge the support of Dr. Basharat Hussain (University of Nottingham, UK) in preparing the revision of this article. Furthermore, I am grateful to the editor and reviewers for their valuable suggestions for the improvement of this research. Funding This research was supported by the Ministry of National Health and Environment, Islamabad, Pakistan, under Grant No. 718944. Data availability All results reported in this research were carried out in R computational environment. Data used in this research is taken from WHO available at https://www.who.int/data/gho and the Ministry of National Health Services (MNHS 2020) available at http://www.covid.gov.pk and NASA available at https://ladsweb.modaps.eosdis.nasa.gov/. Declarations Ethics approval This article does not contain any studies with human participants or animals performed by any of the authors. Consent to participate Not applicable Consent to publish Not applicable Competing interest The authors declare no competing interests. Abbreviations MODIS Moderate Resolution Imaging Spectroradiometer COVID-19 coronavirus GoP Government of Pakistan HDF health declaration form ICTV International Committee on Taxonomy of Viruses PHEIC Public Health Emergency of International Concern PPE Personal Protective Equipment SPM suspended particulate matter IATA International Air Transport Association BAMS Beta-ray Attenuation Mass Spectrometer Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Abou El-Magd I, Zanaty N (2020) Impacts of short-term lockdown during COVID-19 on air quality in Egypt. The Egyptian Journal of Remote Sensing and Space Science. 10.1016/j.ejrs.2020.10.003 Ali SM, Malik F, Anjum MS, Siddiqui GF, Anwar MN, Lam SS et al (2021) Exploring the linkage between PM2.5 levels and COVID-19 spread and its implications for socio-economic circles. 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==== Front Educ Inf Technol (Dordr) Educ Inf Technol (Dordr) Education and Information Technologies 1360-2357 1573-7608 Springer US New York 34075299 10588 10.1007/s10639-021-10588-y Article Distance learning impact on the English language teaching during COVID-19 Kamal Mona Ibrahim [email protected] 1 Zubanova Svetlana 2 Isaeva Anastasia 3 Movchun Vasily 4 1 grid.444473.4 0000 0004 1762 9411 Al Ain University, Al Ain, Abu Dhabi, United Arab Emirates 2 grid.17758.3c 0000000088920127 Moscow Aviation Institute (National Research University), Moscow, Russian Federation 3 grid.78781.31 0000 0000 9697 6075 Tula State University, Tula, Russian Federation 4 grid.448878.f 0000 0001 2288 8774 I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russian Federation 27 5 2021 2021 26 6 73077319 14 12 2020 17 5 2021 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. These days, distance learning has almost completely replaced traditional teaching methods due to the COVID-19 pandemic and the introduction of quarantine measures. A sharp rise in interest in distance learning methodology has raised a number of new questions and challenges. This research examines changes in the training process and cognitive abilities and academic performance during the coronavirus pandemic. Students of the I.M. Sechenov University and Al Ain University were surveyed (103) during classroom and distance English learning before and during the COVID-19. Three samples of the average values of the respondents’ self-assessment of academic performance, cognitive abilities (namely concentration and memory), progress in performing oral assignments, progress in performing written assignments, ability to absorb information while reading and by ear; general health condition during the training and were obtained and tested for the Gaussian distribution law compliance. All of the rates studied fell during distance learning during a pandemic compared to distance learning outside a pandemic. However, it should be noted that they still turned out to be higher than the marks obtained in classroom teaching. Students were interviewed for possible factors influencing the reviewed teaching modes effectiveness (the survey showed that these factors are an increase in the amount of leisure time, ability to take breaks more often, more comfortable learning environment, no need to spend time on the road to the university). Keywords Classroom learning COVID-19 Distance learning English Pandemic issue-copyright-statement© Springer Science+Business Media, LLC, part of Springer Nature 2021 ==== Body pmcIntroduction With the development of advanced technologies and the Internet, distance learning is becoming more and more widespread, partially putting classroom education on the back burner. A vast amount of research on related topics provides evidence that the interest in distance education, which has become an international trend, is steadily increasing. However, it should be noted that recently there has been a tendency to lose interest in this problem among scholars, as evidenced by the small number of articles on this topic over the past 10 years. Nevertheless, in the article Allen and Seaman (2010) it was noticed that, there are no prerequisites to assume that this training method will lose its popularity. With the emergence of the global pandemic caused by the COVID-19 viruses, almost all educational institutions have switched to distance education, which has returned interest in this learning method. A vast amount of research on related topics provides evidence that the interest in distance education, which has become an international trend, is steadily increasing (Dron & Anderson, 2016). Now, there are no prerequisites to assume that this training method will lose its popularity (Allen & Seaman, 2010). Due to the development of special web environment that supports advanced educational practices, many conceptual changes in the modern education system were noticed (Arkorful & Abaidoo, 2015). Given the general accessibility of modern technologies and their enormous impact on everyday human life, present-day society has stepped into the next stage of distance learning development (Andryukhina et al., 2020; Dorozhkin & Сhernoskutova, 2020; Cherkasov et al., 2015). Distance learning is characterised by high training efficiency and data availability as well as steady information transfer speed, regardless of the user’s geographical location (Romanov, 2019). Anderson and Dron (2011) designate three types of distance education pedagogy, namely, cognitive-behaviourist, socio-constructivist and connectivist. They denote that it is necessary to apply all the mentioned approaches to achieve the maximum effectiveness of distance training. Pulker and Kukulska-Hulme (2020) have explored the reuse and adaptation of open educational resources during teaching foreign languages (including English) and their impact on educational practice. Based on data obtained during the survey of teachers, researchers have built a five-step model for reusing open educational resources (Pulker & Kukulska-Hulme, 2020). It is known that most students admit that the lack of communication with the teacher to be the main difficulty in the distance study of English. Moreover, distance learning of English turned out to be more complicated than the traditional one (Zhang & Cui, 2010). Recent developments in learning technologies have shown excellent results and improved results for online learning, even in areas that were difficult to control online (Marcum & Kim, 2020). However, it should be remarked that experienced students usually have a lower level of anxiety and disappointment during classes than those who do not have sufficient practice with distance training technologies. Distance education significantly affects the concept of learning as well as methods of obtaining information and its assimilation. Liu (2011) has revealed that the student’s gender and the classroom type do not play any role in the training process, while learning motivation, personal status in the class, and the teacher’s academic title are considered fundamental. It is necessary to point out that many studies present a comparative analysis of the effectiveness of distance and classroom education. Nevertheless, the research results vary significantly. Some authors draw conclusions about the fantastic potential of distance education, while other researchers indicate a very weak and sometimes practically zero effectiveness of distance learning. For example, Tucker (2000) has carried out a comparative analysis of several groups of students engaged in both distance and in-class training and revealed insignificant differences between their learning outcomes. On the other hand, the study presented by Krämer et al. (2015) provides an argument that the effectiveness of distance education increases with time. Such findings may be provoked by the rapid development of modern technologies and widely available technical support. Bender et al. (2004) have noticed that distance learning requires much less time than face-to-face education. Though, if the one will count the time spent by the teaching staff on each student individually, the distance method of conducting classes will appear to be more time-demanding than the traditional one. This article presents a statistical analysis of the students’ survey results to confirm the hypothesis about the effectiveness of distance education compared to in-class learning mode and optimize the educational process in future. Within the present research, the main factors affecting the success of distance learning are introduced. Furthermore, changes in the distance education trends during the 2020 coronavirus pandemic (COVID-19) are investigated. Methodology Research design and sample In the course of this examination, a survey was conducted among 103 students from the I.M. Sechenov First Moscow State Medical University (Russia) and Al Ain University (United Arab Emirates), who studied English in classroom and distance learning formats. Alongside, in connection with the coronavirus pandemic, one more survey was carried out among respondents involved in distance English learning to find the difference between the obtained results. All study participants belong to the same age category (from 20 to 23 years). The students’ gender and social status were not taken into account since these factors presumably have a weak effect on the study outcomes. The initial selection of respondents included 200 non-native English-speaking students of the Department of general medicine of the considered educational institutions. This selection was based on the participants’ academic performance, so that only individuals with ‘Very good,’ ‘Good,’ and ‘Satisfactory’ marks in English (according to the European marking system) were enrolled. Such a choice was provoked by the intention of gaining a more uniform sample. Moreover, it may be explained by the assumption that the academic achievements of students with marks ‘Excellent’ and ‘Fail’ will not significantly change depending on the classes type and will create a heterogeneous sample. The second stage of respondents’ selection was performed by the English teachers of the educational institution in the form of tests to confirm students’ command of English. Consequently, 60 students were eliminated in the first stage, and 37 students in the second. Experiment The survey (see Appendix) was performed in three stages. At the first stage, the students were asked to fill out the questionnaire after 21 days of face-to-face education by completing the corresponding online form (before the introduction of quarantine). Following this, for 21 days, all the surveyed were transferred to distance learning mode and required to fill out the same form again by the end of the course. In this case, we are talking about distance learning in a terminologically accurate meaning, because the experiment involved university students who were forced to study outside the campus, the training did not necessarily take place using electronic devices, but under the regular supervision of a teacher according to a pre-arranged schedule (Allen & Seaman, 2010; Simonson et al., 2019). After all these manipulations, students were supposed to return to traditional in-class learning; however, in connection with the coronavirus pandemic, they continued studying online. Under such circumstances, study participants were asked to re-fill the online form for the third time, 21 days after the start of distance learning and the introduction of quarantine measures. English classes were held twice a week; thus, the online form was filled after every eight lessons. It should be underlined that the dates of the final surveys differ slightly depending on the university since the time of the quarantine introduction in Russia and in the United Arab Emirates vary. Within the survey, respondents were required to evaluate the following parameters on a ten-point scale. The parameters were followed:Academic performance; Concentration; Memory; Progress in performing oral assignments; Progress in performing written assignments; Ability to absorb information while reading and by ear; General health condition during the training; Mental condition during the training. Data analysis Within the research, a statistical analysis of the survey results was carried out (see Appendix) to confirm or refute the hypothesis about the increase of the effectiveness of learning foreign languages (in particular, English) via distance training mode. Moreover, the study outlined the impact of the COVID-19 pandemic on student performance, physical health, and mental wellbeing. Apart from this, after the survey, students were interviewed to identify factors that could significantly affect the obtained results. In the course of the examination, three samples were obtained. They consisted of positive rational numbers, which were the arithmetic average of each respondent’s estimates. These samples were checked for compliance with the Gaussian distribution law using the Shapiro-Wilk test since it was a prerequisite for their further analysis using the Student’s t test, aimed at confirming or disproving the study hypothesis. Research limitations The reliability of this work can be improved by expanding the size of the studied samples through the introduction of new questions in the interviewing methodology. These actions are directly related to an increase in the accuracy of the Shapiro-Wilk test and Student’s t test results. Besides, to obtain more objective outcomes, the research population can also be increased (Yap & Sim, 2011). The processed results were based on the respondents’ subjective assessment of their success in learning English as well as indirect indicators of performance (cognitive abilities, mental and physical condition). The students’ performance indicators during distance learning can be significantly affected by the lack of proper teacher control (Hranovska, 2020). Thus, for a more objective evaluation of academic performance, respondents’ knowledge should be checked via specially compiled tests on the covered material under tight monitoring conditions. The learning outcomes can largely depend on the teacher’s ability to control the learning process. In the conditions of face-to-face learning, it is more difficult for a student to cheat or take a hint from classmates, while during a distance lesson, the teacher cannot fully control the process of writing tests (Watson & Sottile, 2010). Besides, electronic writing tasks rump be easily copied if they are not individual (Kocdar et al., 2018). It also should be noted that the results obtained are based on the self-estimation of students of the studied characteristics, which can to some extent reduce the reliability of the results. Ethical issues Participation in the research was voluntary and anonymous. No personal data of students (including their names and university) was disclosed. All respondents agreed on the processing and publication of the survey results and were informed about the possible change in their performance level depending on the type of training. The authors of this research did not intervene in the learning process, but only analysed the survey outcomes. Results The study provides a comparative analysis of the results of the survey presented in the Experiment section. Table 1 displays the arithmetic mean of the respondents’ answers, divided into three subgroups that correspond to in-class learning, distance learning before the quarantine introduction, and distance learning during the COVID-19 pandemic. The first column gives the numbers of questions from one to eight.Table 1 Survey results No. In-class learning Distance learning Learning during COVID-19 1 6.52 8.2 6.89 2 4.11 7.32 5.16 3 5.12 8.56 5.48 4 6.35 7.89 6.14 5 4.56 7.51 6.31 6 5.26 6.98 5.88 7 5.15 7.61 6.12 8 6.12 8.02 5.78 As can be seen, the assessments connected with distance learning before the pandemic are the highest (the third column), while the estimates related to face-to-face education (second column) are the lowest. This trend indirectly indicates the effectiveness of distance education compared to classroom learning. During the interview, the respondents distinguished the following factors that can significantly affect the improvement of physical and mental health during the study process, as well as students’ cognitive abilities and academic performance:Increase in the amount of leisure time; Ability to take breaks more often; More comfortable learning environment; No need to spend time on the road to the university. It should also be noted that, along with the factors noted by students, improved results can also be associated with:(5) Absence of harsh control from the teacher; In addition to classroom and distance learning, this study also examined the results of the survey conducted during the coronavirus pandemic (the third column). It was found that the average students’ assessments during COVID-19 quarantine were significantly lower than before its introduction. This fact may be associated with the increased anxiety of respondents against the lack of the usual daily routine, real-life communication, as well as a possible deterioration in the financial situation. Despite the difficult circumstances that arose from the coronavirus pandemic, distance learning still shows sufficient effectiveness. Nevertheless, it is important to understand that the results obtained indicate only a particular correlation. Thus, the improvement of students’ physical and mental health, as well as their cognitive abilities, can be perceived as a subjective assessment of respondents, and an increase in their academic performance may be caused by less strict knowledge control. Table 2 presents the results of the examination whether the obtained samples comply with the normal distribution law using the Shapiro-Wilk test, found according to the following formula:1 W=1s2∑i=1nan-i+1(xn-i+1-xi2 where n is the sample size (n = 8), and i is the data element in sorted order.Table 2 Results of testing samples using the Shapiro-Wilk test Sample Shapiro-Wilk test Xav s2 W Wcr In-class learning 5.40 5.205 0.0025 0.8180 Distance learning 7.76 1.805 0.0546 Learning during COVID-19 5.97 1.954 0.0427 The sample variance s2 was calculated by the formula s2=∑i=1nxi-Xav2, where Xav is the arithmetic average. The Shapiro-Wilk test depends solely on the sample size and its significance level. In this study, the significance level (the possibility of error) equalled 0.05, as for any data that were obtained experimentally. Consequently, the critical value of W statistic for the Shapiro-Wilk test was found by the following formula:2 Wcr=-0,0113n4+1,656n3-91,88n2+2408,6n+67608100000 The first column of Table 2 introduces the names of the groups to which the tested samples belong. As can be seen from the table, the Shapiro-Wilk test values for all three samples are below the tabular; therefore, they comply with the normal distribution law and can be analysed through the Student’s t test. Table 3 shows the results of examining the study hypothesis through the independent two-sample t-test. Its outcomes were also compared with the results of the survey conducted during the COVID-19 pandemic. The corresponding calculations were made by the following formula:3 t=X¯i-X¯jsi2-sj2n where X¯i-X¯j is the difference in the average algebraic values of the samples X from the corresponding groups i and j (i, j = 1, 2, 3), n is the sample size, si2 and sj2 are the variances of the samples.Table 3 The Results of testing the hypothesis using the student’s T test Compared samples Student’s t test t tcr “1–2” 8.836 2.365 “2–3” 7.553 “3–1” 2.351 The research hypothesis was tested by analysing the differences between the samples. Thus, if the empirical t-test value appeared to be higher than the critical, then the results in one sample were higher than in the other. Therefore, by finding the sample with the best values, the study hypothesis could be confirmed if the students’ academic achievements during distance education were better than in the case of in-class learning. If the empirical t-test value would be less than critical, the null hypothesis about the absence of differences between the distance and in-class training could be accepted. Table 3 exposes the results of the verification of three pairs of samples, where the line ‘1–2’ refers to the subgroups’ In-class learning’ and ‘Distance learning’; ‘2–3′ relates to ‘Distance learning’ and ‘Learning during COVID-19′; and ‘3–1′ is connected with ‘Learning during COVID-19′ and ‘In-class learning.’ The second and third columns display the empirical and critical values of the Student’s t test. The corresponding calculations allowed the conclusion that the alternative hypothesis was confirmed for samples’ 1–2′ and ‘2–3,’ while the null hypothesis was validated for the pair ‘3–1′. Based on the presented information, the conclusion can be made that distance learning and face-to-face education differ in their effectiveness. Since the average questionnaire estimates were higher for the ‘Distance learning’ subgroup (Table 1), the alternative hypothesis about the higher effectiveness of studying English online compared to traditional learning model was confirmed. Given the data in Table 3, it was also be deduced that the effectiveness of distance learning during the quarantine dropped significantly and was almost the same as of the traditional classes. However, despite the absence of notable changes in the assessments of the subgroups ‘In-class learning’ and ‘Learning during COVID-19,’ student performance in the second subgroups was slightly higher (see question No. 1). Discussion The concept of distance education has been studied for a long time (Dumford & Miller, 2018; Machynska & Dzikovska, 2020; Simonson et al., 2019). However, it requires a more detailed examination under the current conditions connected with the COVID-19 pandemic. The results of the study we presented overlap with similar studies over a long period of time. Shanley et al. (2004) have conducted an analysis of the results obtained by two groups of students enrolled in traditional classroom learning and distance education via CD-ROM and the Internet using SPSS. Taking into account the application of older and less convenient technologies, in the course of this study based on pre- and post-test, researchers have found no difference between the learning outcomes except the fact that distance learning appeared to be more time-demanding. The studies of distance learning, and especially e-learning, from different periods cannot be considered comparable due to the rapid development of technologies and in this case, there is no mutual basis for comparison. Closer to the presented study results were obtained in analysis by Pei and Wu (2019) for medical students. These early research findings on the comparative effectiveness of online learning are particularly interesting compared to more recent studies because their findings differ from ours. Obviously, the rapidly increasing efficiency of online learning can be influenced by two factors: the focus of education on the development of this particular segment and the entry into the field of education of generations of native digitals (Hromalik & Koszalka, 2018). The latter review article provides an argument that the effectiveness of distance education is almost zero. Although this work, like the previous one (Shanley et al., 2004), analyses somewhat outdated methods for conducting distance learning. The development of online education is directly related to a significant leap in technological growth and the wide availability of the Internet and personal computers and educational methods oriented exactly on online learning (Sun & Chen, 2016). For this reason, the authors of the present study consider it more appropriate to compare their findings with newer works that examine the effectiveness of distance education in the modern world. Accordingly, taking into consideration a more recent study, attention should be paid to a systematical approach of Sibirskaya et al. (2019), who have outlined that today’s remote training may be much more effective than the traditional one. The key factors that can provoke such outcomes are a comfortable learning environment, and a lesser amount of time spent on learning, which is consistent with the findings of the current research. Thus, the results obtained in the course of this study are consistent with the conclusions of the present study. Distance learning is a source of unusual challenges, both technological and pedagogical. Bolliger and Inan (2012) have explored a wide range of challenges that distance education poses. In particular, through the online survey in Turkish universities using reliability analysis, the necessity of face-to-face contact and opportunities to interact and collaborate were analysed. The search for the newest information on this matter allowed revealing that less isolated participants are more emotionally stable (Tichavsky et al., 2015). Besides, students with a stronger involvement in the process of interacting with other learners are motivated better and feel more satisfied with the training. The isolation of students during distance learning at the present stage is easily solved by the familiar environment of social networks and other means of electronic communication. Now a lot of attention is paid to the techniques of online collaboration of students while performing joint educational and research tasks (Courtney & Wilhoite-Mathews, 2015; Cherney et al., 2018). This may explain the results of the presented study, in particular, the decrease in the effectiveness of online learning. In the study on student views of effective online teaching in higher education, researchers has focused on the importance of partnerships between learners to achieve a high level of cooperation (Courtney & Wilhoite-Mathews, 2015). However, they believe that this factor can carry both positive and negative consequences. In addition to increasing student motivation and meeting communication needs, creating partnerships can lead to negative self-assessment of one’s success and opportunities, owing to the constant comparison of personal achievements with those of other students. In the practice of online learning, this problem has a solution in the teacher’s regulation of students’ access to each other’s materials and in the regulation of the teacher’s participation in the communication process (Pulker & Kukulska-Hulme, 2020; Tichavsky et al., 2015). Another important issue in distance learning is student performance. Hromalik and Koszalka (2018) have revealed that student performance during distance English learning is directly related to personal ways of regulating the education process. This approach clarifies the possible interpretation of the results obtained in our study. The authors have unveiled a correlation between the methods of students’ self-regulation and their level of oral English proficiency. Online learning forms more widely allow for the use of individualization of learning paths (Shen et al., 2020). Fernández-Toro and Furnborough (2018) have called upon the misalignment of necessary and provided feedbacks during distance learning of foreign languages using self-reported data and feedback analysis. The decrease in the effectiveness of online learning recorded by the results of our study may be associated with the nature of the teacher’s work, monitoring the progress of students, their motivation, involvement and feedback. Two parallel surveys involving educators and their students have revealed that tutors often cannot evaluate the level of feedback needed during the study. Several researchers based on survey methodology also claim about the existence of the correlation between the phonological attainment and foreign language anxiety in distance language learning (in particular, English and French) (Bosmans & Hurd, 2016). Conclusions In the framework of the study, an alternative hypothesis about the higher effectiveness of distance English learning compared to traditional in-class education was confirmed after the survey of 103 respondents from the I.M. Sechenov First Moscow State Medical University and Al Ain University. Though, as a consequence of a complicated situation developed in the context of the global coronavirus pandemic, one more survey of the same group of respondents was conducted to examine the students’ learning productivity during the quarantine. Better effectiveness of distance English learning compared to traditional face-to-face education was confirmed by the calculations of the Student’s t test. The average value of all evaluated indicators during the in-class training was 5.40, whereas their average estimate during the distance education comprised 7.76. While analysing this variance through the Student’s t test, a significant difference between the effectiveness of distance and classroom education in favour of the first was noted. Notwithstanding this, the average survey results for distance training during the COVID-19 pandemic (5.97) showed that the effectiveness of distance study has fallen almost to the level of in-class education. Despite the absence of a notable distinction between the results of distance learning during the pandemic and classroom training, when examining the corresponding samples with the Student’s t test, the average value for distance learning during COVID-19 appeared to be somewhat higher than for the classroom one. Along with this, the study respondents were interviewed in order to find the central factors that may positively influence academic performance during distance learning. Thus, according to the collected answers, they include an increase in leisure time, the opportunity to take more breaks during the training, more comfortable conditions for learning, and absence of the need to spend time on the road to the university. In the course of the investigation, possible reasons for the increase in the quality of distance learning of English compared to face-to-face education were also outlined. Among them are the absence of harsh teacher’s control and the fact that students have fewer opportunities to compare their academic results with classmates’ learning outcomes. Such events may contribute to the improvement of student’s self-esteem and, as a consequence, eliminate the possibility of poor educational achievements. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data availability Data will be available on request. Declarations Conflict of interests Authors declare that they have no conflict of interests. 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==== Front Sci Total Environ Sci Total Environ The Science of the Total Environment 0048-9697 1879-1026 Elsevier B.V. S0048-9697(21)03200-9 10.1016/j.scitotenv.2021.148129 148129 Article Environmental impacts of COVID-19 treatment: Toxicological evaluation of azithromycin and hydroxychloroquine in adult zebrafish Mendonça-Gomes Juliana Moreira a da Costa Araújo Amanda Pereira bc da Luz Thiarlen Marinho b Charlie-Silva Ives d Braz Helyson Lucas Bezerra e Jorge Roberta Jeane Bezerra ef Ahmed Mohamed Ahmed Ibrahim g Nóbrega Rafael Henrique h Vogel Christoph F.A. i Malafaia Guilherme bjkl⁎ a Departamento de Imunologia, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo, SP, Brazil b Laboratório de Pesquisas Biológicas, Instituto Federal Goiano, Urutaí, GO, Brazil c Programa de Pós-Graduação em Ciências Ambientais, Universidade Federal de Goiás, Goiânia, GO, Brazil d Departamento de Farmacologia, Instituto de Ciências Biomédicas, Universidade de São Paulo, São Paulo, SP, Brazil e Drug Research and Development Center, Federal University of Ceará, Brazil f Department of Physiology and Pharmacology, School of Medicine, Federal University of Ceará, Brazil g Plant Protection Department, Faculty of Agriculture, Assiut University, Assiut 71526, Egypt h Reproductive and Molecular Biology Group, Department of Structural and Functional Biology, Institute of Biosciences, São Paulo State University, Botucatu, SP, Brazil i Department of Environmental Toxicology and Center for Health and the Environment, University of California, Davis, USA j Programa de Pós-Graduação em Biotecnologia e Biodiversidade, Universidade Federal de Goiás, Goiânia, GO, Brazil k Programa de Pós-Graduação em Ecologia e Conservação de Recursos Naturais, Universidade Federal de Uberlândia, Uberlândia, MG, Brazil l Programa de Pós-Graduação em Conservação de Recursos Naturais do Cerrado, Instituto Federal Goiano, Urutaí, GO, Brazil ⁎ Corresponding author at: Biological Research Laboratory, Goiano Federal Institution, Urutaí Campus, Rodovia Geraldo Silva Nascimento, 2,5 km, Zona Rural, Urutaí, GO CEP: 75790-000, Brazil. 29 5 2021 10 10 2021 29 5 2021 790 148129148129 21 4 2021 24 5 2021 26 5 2021 © 2021 Elsevier B.V. All rights reserved. 2021 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. One of the most impact issues in recent years refers to the COVID-19 pandemic, the consequences of which thousands of deaths recorded worldwide, are still inferior understood. Its impacts on the environment and aquatic biota constitute a fertile field of investigation. Thus, to predict the impact of the indiscriminate use of azithromycin (AZT) and hydroxychloroquine (HCQ) in this pandemic context, we aim to assess their toxicological risks when isolated or in combination, using zebrafish (Danio rerio) as a model system. In summary, we observed that 72 h of exposure to AZT and HCQ (alone or in binary combination, both at 2.5 μg/L) induced the reduction of total protein levels, accompanied by increased levels of thiobarbituric acid reactive substances, hydrogen peroxide, reactive oxygen species and nitrite, suggesting a REDOX imbalance and possible oxidative stress. Molecular docking analysis further supported this data by demonstrating a strong affinity of AZT and HCQ with their potential antioxidant targets (catalase and superoxide dismutase). In the protein-protein interaction network analysis, AZT showed a putative interaction with different cytochrome P450 molecules, while HCQ demonstrated interaction with caspase-3. The functional enrichment analysis also demonstrated diverse biological processes and molecular mechanisms related to the maintenance of REDOX homeostasis. Moreover, we also demonstrated an increase in the AChE activity followed by a reduction in the neuromasts of the head when zebrafish were exposed to the mixture AZT + HCQ. These data suggest a neurotoxic effect of the drugs. Altogether, our study demonstrated that short exposure to AZT, HCQ or their mixture induced physiological alterations in adult zebrafish. These effects can compromise the health of these animals, suggesting that the increase of AZT and HCQ due to COVID-19 pandemic can negatively impact freshwater ecosystems. Graphical abstract Unlabelled Image Keywords Water pollution SARs-Cov-2 Danio rerio Ecotoxicity Antibiotic Antimalarial Editor: Damia Barcelo ==== Body 1 Introduction In the last decades, pharmacologically active compounds have been increasingly perceived in the aquatic ecosystem, representing a problem of great importance in environmental chemistry. However, the occurrence of these chemical compounds in nature is due to the release of industrial effluents and domestic sewage without adequate and effective treatment (Maasz et al., 2019). According to Salgado et al. (2021) the presence of drugs or its metabolized subproducts as result of body's excretion is an increasing concern of environmental contamination. It has been estimated that in 2030, the global consumption of antibiotics may be 200% higher than the 42 billion defined daily doses (DDD) estimated in 2015 (Klein et al., 2018). This disposal in the natural ecosystems can culminate in wide and unknown effects on the biota. Thus, ecotoxicologists around the world have made efforts to assess the toxicological risk impacts of drugs in non-target organisms, to understand how they can affect individuals and their populations. Several reports demonstrate on the ecotoxicity of different types of drugs (in various organisms), such as antidiabetics (Godoy et al., 2018; Godoy et al., 2019), analgesics and antipyretics (Nunes, 2020; Priyan et al., 2021), anti-inflammatory (Grandclément et al., 2020; Luongo et al., 2021), antihypertensive (Gallego et al., 2021), neuropsychiatric (Ramírez-Morales et al., 2021; Oliveira et al., 2021), and anticancer (Araújo et al., 2019; Mesak et al., 2019), which include biochemical, histopathological, genotoxic and mutagenic effects. On the other hand, non-standard situations such as pandemic or endemic diseases, in which many patients receive specific medications, directly influence the use, excretion and disposal of drugs in the aquatic environment. One emblematic example is the significant increase in the use of azithromycin (AZT) and hydroxychloroquine (HCQ) in the context of the COVID-19 pandemic (Yazdany and Kim, 2020; Malik et al., 2020; Agarwal et al., 2020; Nasir et al., 2020; Mallhi et al., 2020; Quispe-Cañari et al., 2020). Their effectiveness, however, against SARs-Cov-2 infection is questioned by several studies (Ghazy et al., 2020; Jameleddine et al., 2020), but people are receiving these prescriptions or are self-medicating. AZT is a macrolide antibiotic that inhibits bacterial protein synthesis (Parnham et al., 2014). It has also been used to treat cancer and autoimmune and inflammatory diseases (Patel and Hashmi, 2020). HCQ is used in the prevention and treatment of malaria (Shippey et al., 2018) and is considered a therapeutic option in the treatment of rheumatoid arthritis (Lane et al., 2020), lupus erythematosus (Jakhar and Kaur, 2020), porphyria cutanea tarda (Malkinson and Levitt, 1980), Q fever (Cherry and Kersh, 2020) and photosensitive diseases (Millan and Quijano, 1957). Therefore, the increase in the input and dispersion of these drugs in aquatic ecosystems is already a fact, especially due to the dumping of domestic sewage and hospital waste into rivers or streams or via leaching from landfills, which in many countries do not receive adequate treatment (Urban and Nakada, 2021) or the processes used are insufficient to remove these pollutants or are financially inaccessible (Khan et al., 2019). In cities with a high incidence of COVID-19, for instance, the dramatic increase in the production of hospital waste in health facilities has been an additional administrative challenge (Sarkodie and Owusu, 2020), in addition to amplifying the presumed concentrations of AZT and HCQ in the aquatic environment. However, this evidence has not been sufficient for the systematic development of studies to evaluate the ecotoxicological effects of these drugs, whether in aquatic or terrestrial organisms [see review by Yang et al., 2020]. Regarding macrolides, previous studies (in fish) addressed the toxic effects of erythromycin (Bills et al., 1993; Kiryu and Moffitt, 2002; Ji et al., 2012; Rodrigues et al., 2016; Liu et al., 2017), roxithromycin (Zhang et al., 2019), clarithromycin (Sotto et al., 2017) tilmicosin (Yan et al., 2019). On the other hand, only the studies of Fairgrieve et al. (2005) and Shiogiri et al., (2017) evaluated the toxicological effects of AZT in fish. Fairgrieve et al. (2005) demonstrated that Chinook salmon Oncorhynchus tshawytscha exposed orally to AZT did not cause histopathologically significant lesions in gills, head and trunk, kidney, liver, spleen, heart, pyloric caeca, upper intestine, gonad, and brain. Shiogiri et al. (2017) reported only moderate damage in liver, minor histological changes in the gills and no lesions in the kidneys of tilapia (Oreochromis niloticus) exposed to AZT. A similar investigative scenario has been observed in the relation to studies involving antimalarials of the 4-aminoquinolines class (e.g.: HCQ). Research involving non-target organisms is restricted to groups of invertebrates (e.g. Daphinia magna – Lilius et al., 1994; Lilius et al., 1995; Zurita et al., 2005; Kumar et al., 2008; Rendal et al., 2011), microalgae (Chlorella vulgaris – Zurita et al., 2005), bacteria (Vibrio fischeri – Zurita et al., 2005) and plants (Salix viminalis – Jjemba, 2002; Rendal et al., 2011). In this interim, fish studies are limited to assessing the ecotoxicological effects of chloroquine (CQ), a compound structurally related to HCQ. In Ou et al. (2012), the authors did not report changes in hair cell death of D. rerio lateral line with increased duration of exposure to gentamicin combined with any of the quinoline derivatives (including CQ), unlike Ramesh et al. (2018), who reported enzymological/histopathological alterations in Cyprinus carpio exposed to QC. The study of Davis et al. (2020) is a pioneer in evaluating the in vivo effects of HCQ on freshwater fish. At the time, the authors observed a significant reduction (depending on the tested concentrations) in the number of surviving hair cells of D. rerio larvae exposed to HCQ and CQ. Thus, taken together, it is evident that studies on the ecotoxicity of AZT and HCQ in aquatic organisms, especially in fish that inhabit potentially polluted freshwater environments are needed. Considering these facts, this study aims to evaluate the toxicity of these drugs, alone and in combination, using as an experimental model adult zebrafish (D. rerio) exposed to environmentally relevant concentrations of AZT and HCQ. Our hypothesis is that the uptake of these drugs by aquatic animals induces changes in different physiological parameters predictive of nutritional alteration, REDOX imbalance, and neurotoxicity. Furthermore, based on in silico analysis, we seek to identify putative mechanisms of action of the evaluated drugs. We believe that our study provides insights into the toxicity of AZT and HCQ in the animal model studied and predicts that an increase in the disposal and dispersion of these drugs in the environments could dramatically affect the freshwater ichthyofauna. Furthermore, considering that zebrafish is considered a good translational model for humans (Tal et al., 2020), this study provides some insight that can guide future studies in humans. 2 Material and methods 2.1 Drugs Azithromycin (AZT) and hydroxychloroquine (HCQ) used in our study, [similarly to study by Amaral et al., 2019] were intentionally acquired in common commercial facilities to bring our experimental design as close to the most realistic condition as possible. However, for the preparation of the AZT stock solution, we used AZT dihydrate draggers (500 mg) (Brainfarma Indústria Química e Farmacêutica SA, Anápolis, GO, Brazil) and for the HCQ stock solution, HCQ sulfate draggers (400 mg), manufactured by Apsen Farmacêutica SA (São Paulo, SP, Brazil), were used. Both solutions were prepared by diluting the draggers in acetonitrile solution (0.01 M). From these solutions, the aliquots added to the exposure waters were removed. The concentrations of AZT and HCQ tested in our study were based on the work of Fernandes et al. (2020) and Olaitan et al. (2014), respectively. Fernandes et al. (2020) reported that AZT was detected in a concentration up to 2.8 μg/L in a river at northern Portugal, while Olaitan et al. (2014) showed that the median concentration of chloroquine (chemically like HQC, its derivative) identified in different water samples from Nigeria was 2.12 μg/L. Therefore, the concentration tested in our study (i.e.: 12.5 μg/L) simulates a potential increase (approximately 6 times) in AZT and HCQ concentrations in aquatic environments, which can be a predictive environmentally relevant concentration, considering the COVID-19 pandemic. 2.2 Model system and experimental design This study was carried out at the Biological Research Laboratory of Goiano Federal Institute - Urutaí Campus (GO, Brazil). To assess the aquatic toxicity of AZT and HCQ, we used adult zebrafish (D. rerio) at the age group of approximately 6 months presenting body biomass between 0.3 and 0.4 g with mixed sex. D. rerio is a tropical freshwater fish natural to rivers in Southern Asia, mainly in Northern India, Pakistan, Bhutan, and Nepal (Engeszer et al., 2007). This species has been used as model organism in studies about environmental toxicology and ecotoxicology worldwide (Magyary, 2018), besides being considered a translational model for humans (Tal et al., 2020). Ninety-six healthy adults (i.e., with normal swimming movements and without morphological deformities or apparent lesions) were distributed into four experimental groups (n = 24 fish/group; 4 replicates tanks of six animals/each treatment group). The “AZT” and “HCQ” groups were exposed to water containing 12.5 μg/L of individual drugs, respectively, and the animals from the “AZT + HCQ” group were exposed to water containing both AZT and HCQ (at 12.5 μg/L/each). In the control group (“C”), adult zebrafish were kept in dechlorinated tap water naturally containing only the vehicle solution (0.01 M acetonitrile solution) in an amount proportional to that added in the other experimental groups. The period of exposure was 72 h (static condition). This exposure simulates the animals' ephemeral contact with drugs, since in the natural environment animals can migrate from contaminated places to places free of pollutants and, therefore, the exposure can be relatively short. The animals were kept in tanks (2.2 L), containing dechlorinated water and continuous aeration; and were fed once a day (ad libitum) with commercial fish feed. In addition, the room where the animals were kept had the temperature (24 °C ± 2 °C) and the luminosity controlled (12–12 h light-dark cycle). 2.3 Toxicity biomarkers 2.3.1 Quantification of drugs 2.3.1.1 Azithromycin The AZT uptake by zebrafish was assessed according to the methodology adopted by Keskar and Jugade (2015), with little modifications. It was used 8 animals/group, weighing approximately 350 mg/animal, which were euthanized (immersion in ice-slurry) and subsequently macerated in 1 mL of phosphate buffered saline (PBS), and centrifuged at 13,000 rpm for 5 min (at 4 °C). Aliquots of 30 μL of the sample supernatant were transferred to test tubes (previously sanitized) and mixed with 470 μL of acetonitrile solution (0.01 M), 500 μL of bromocresol green solution (0.0002 M) and 1.5 mL of acetonotrile-ethanol solution (1:1). Then, the samples were shaken and homogenized in a vortex shaker for 5 s and, sequentially, 200 μL of each sample were transferred to a 96-well microplate (in duplicate), for later reading at 630 nm, in an ELISA reader. In parallel, a standard curve was made using known concentrations of AZT (0, 0.03, 0.05, 0.0752, 0.1, 0.25, 0.4, 0.5, 0.6 and 0.7 mg/mL) and the equation of a straight line generated was used to determine the concentrations of the test samples. The background fluorescence of the control samples was determined and subtracted from the samples from the zebrafish exposed to AZT. 2.3.1.2 Hydroxychloroquine The procedures used for the quantification of HCQ followed the recommendations of Bergqvist et al. (1985), with some modifications. The supernatant of the same 8 animals/group mentioned above was used. In that case, 200 μL aliquot of supernatant from each sample was transferred to previously cleaned hygienic conical bottom microtubes and, sequentially, 400 μL of the bromothymol blue solution (0.65 mmol/L) and 600 μL of dichloromethane P.A. were added sequentially. Then, the solutions were homogenized in a vortex mixer (for 30 s) and centrifuged at 1500 rpm, for 5 min, at 23 °C. Subsequently, the aqueous phase of the mixture was discarded and 200 μL of the organic phase was transferred to a 96-well microplate, for later reading at 405 nm, in an ELISA reader. The concentrations of HCQ in the samples were determined from the equation of the straight line obtained by making a standard curve, using known concentrations of HCQ (0, 0.00625, 0.0125, 0.025, 0.05, 0.1, 0.2, 0.4 and 0.8 mg/mL). The background fluorescence of the control samples was also determined and subtracted from the samples from zebrafish exposed to HCQ. 2.4 Biochemical analyzes 2.4.1 Sample preparation Prior to biochemical assessments, the samples to be analyzed were prepared, similarly to Guimarães et al. (2021). Eight fish/group were also weighed (approximately 350 mg/animal), euthanized (immersion in ice-slurry), macerated in 1 mL of phosphate buffered saline (PBS), and centrifuged at 13,000 rpm for 5 min (at 4 °C). The supernatant was separated into aliquots to be used in different biochemical evaluations. Entire bodies were used in the experiment due to difficulties on isolating certain organs from small animals. Organ “contamination” by organic matter and/or by other particles consumed by zebrafish can be bias at biochemical analysis applied to organs at dissection time (Lusher et al., 2013; Guimarães et al., 2021). Samples were stored in sterile conical bottom microtubes at 80 °C for a maximum of 7 days. 2.4.2 Assessment of nutritional status Previous reports on the exposure of different aquatic organisms to different drugs can affect animals' feeding behavior and change their energy metabolism (Mennigen et al., 2010; Burkina et al., 2015; Falfushynska et al., 2019; Barros et al., 2020). Thus, the influence of treatments on total proteins, triglycerides, and total soluble carbohydrate levels was herein assessed. Total proteins and triglycerides concentrations were determined by using commercial kits, based on the Lowry method (Lowry et al., 1951) (Ref. BT1000900; BioTécnica, Varginha, MG, Brazil) and on the enzymatic colorimetric method by using glycerol-3-phosphate oxidase (GPO) (Ref. BT1001000; BioTécnica, Varginha, MG, Brazil) (Sullivan et al., 1985), respectively. Total soluble carbohydrate levels were performed based on the methodology proposed by Dubois et al. (1956). 2.4.3 Oxidative stress biomarkers The effects of exposure to AZT e HCQ (alone or in combination) on oxidative stress reactions were evaluated based on (i) indirect nitric oxide (NO) (via nitrite measurement; NO2 −) (Soneja et al., 2005); (ii) thiobarbituric acid reactive substances (TBARS) [predictive of lipid peroxidation (De-Leon and Borges, 2020)]; (iii) production of reactive oxygen species (ROS), and (iv) hydrogen peroxide (H2O2) [which plays an essential role in responses to oxidative stress in different cell types (Sies, 2020)]. The Griess colorimetric reaction [as described in Bryan and Grisham (2007)] was used to measure NO2 − and the TBARS levels were determined based on procedures described by Sachett et al. (2018). The production of H2O2 and ROS was evaluated according to Elnemma (2004) and Maharajan et al. (2018), respectively. 2.4.4 Neurotoxicity The possible neurotoxic effects induced by AZT and HCQ (alone and in combination) were evaluated by determining the activity of acetylcholinesterase (AChE) enzymes, according to the method of Ellman et al. (1961). In addition, to assess whether these drugs were able to alter the mechanosensory system of the fish, we performed the count of superficial neuromasts in exposed individuals. For this, we adopted the procedures described in Guimarães et al. (2021), in which, briefly, the live animals (n = 8/each group) were placed (for 30 min) in a beaker containing 400 mL of water (with constant aeration) reconstituted with 5 mM of the fluorescent dye 4-(4-Diethylaminostyryl)-1-methylpyridinium iodide (4-Di-2-ASP), from stock solution (40 mg of 4-Di-2-ASP) diluted in 10 mL of dimethyl sulfoxide P.A. Then, the animals were carefully removed and transferred to a beaker containing dechlorinated water (without dye), and remained for 30 min, to remove excess of dye in the body. After that, the animals were euthanized (immersion in ice-slurry) and positioned horizontally on glass slides for later observation under a fluorescence microscope. The number of positive neuromasts for 4-Di-2-ASP was determined in the region corresponding to the terminal neuromasts (T1, T2 and T3 - region highly conserved in zebrafish – Wada et al., 2008) of the lateral caudal line system of each animal, as well as in the region of the head (Fig. 1 ). Quantification was done manually from sequential images captured in a camera attached to the microscope. Neuromasts located at the bottom of the head were excluded from the count, which generally contained significant amounts of nonspecific staining or because they were out of focus or absent due to the positioning of the animal under the microscope.Fig. 1 (A) Head and (B) final portion of the tail of the adult zebrafish (D. rerio) where the neuromasts were quantified. T1 to T3: neuromasts' nomenclature, based on Wada et al. (2008). The white arrows point to the neuromasts. Fig. 1 2.5 Bioinformatics in silico analysis 2.5.1 In silico chemogenomics-based ChemDIS system analysis To assess the effects of potential interactions between AZT and HCQ and their possible targets in animals, we used a chemogenomics-based system called ChemDIS-Mixture (Tung et al., 2018), which is built using the previously introduced ChemDIS (Tung, 2015) and statistical p-tests combined with Venn diagram tools available by using the STITCH database (Szklarczyk et al., 2016). To enable the inference of chemical-induced effects, o ChemiDIS-Mixture several databases are downloaded and integrated into a MongoDB database including STITCH 5, Reactome, SMPDB, miRTarBase, Ensemble, DOSE, DO.db, KEGG.db and org.Hs.eg.db. Currently, >430,000 chemicals with >15 million chemical–protein interactions can be analyzed using ChemDIS-Mixture (Tung and Wang, 2018). For each drug (AZT and HCQ) the possible interacting proteins were extracted, and the enrichment analysis was conducted based on a hypergeometric test for identifying the enriched GO (Gene Ontology) terms with an adjusted p-value < 0.05 using Benjamini-Hochberg multiple test correction. 2.5.2 Interaction networks analysis To complement the analysis of the possible interactions between AZT and HCQ and their target molecules, we carried out an analysis of network building and functional annotation enrichments, through the STITCH 4.0 Resource (http://stitch.embl.de). The network of each individual drug and in combination was built to assess the possible modes of action of the drugs, considering the thickness of the network lines (thicker lines represent stronger associations). Furthermore, lines and, for directed edges, arrows of different colors stand for different edge types in the actions view: binding (blue), activation (green), inhibition (red), catalysis (magenta), same activity (cyan) and reaction (black) (Kuhn et al., 2007). Statistical significance was determined by corrected p-value < 0.05, using the Bonferroni test. We only considered the shortest paths (allowing no more than five interactions with the highest confidence score > 0.8 to ensure a high level of confidence for the interaction). 2.5.3 Molecular docking To predict the binding sites and affinity of the bonds among AZT, HCQ and the protein structures of the enzymes AChE, BChE, SOD and CAT, we performed docking and chemoinformatic screens. The ligands AZT (CSID: 10482163) and HCQ (CSID: 3526) were obtained from the virtual repository Chemspider (http://www.chemspider.com/) and optimized with force field type MMFF94 in Avogadro software (Hanwell et al., 2012). The protein structures (targets) of the zebrafish were obtained by the homology construction technique by the SWISS-MODEL server (https://swissmodel.expasy.org/) with structural similarity values between 87.14% and 99.8%. The validation of the structures was verified with the SAVES v.6.0 server (https://saves.mbi.ucla.edu/). For molecular docking simulations, AutoDock tools (ADT) v4.2 were used to prepare binders and targets (Morris et al., 2009) and AutoDock Vina 1.1.2, to perform the calculations (Trott and Olson, 2010). The binding affinity and interactions between residues were used to determine the best molecular interactions. The results were visualized using ADT, Discovery Studio v4.5 and UCSF Chimera X (Pettersen et al., 2021). 2.5.4 Genomic similarity (zebrafish vs. humans) The analyzes described above consider the genomic similarity between zebrafish and humans. As defined by Vilella et al. (2009), 71.4% of human genes have at least one zebrafish orthologist. Reciprocally, 69% of zebrafish genes have at least one human ortholog. Among orthologous genes, 47% of human genes have a one-to-one relationship with a zebrafish ortholog. The second largest class of ortholog contains human genes that are associated with many zebrafish genes (the “the ‘one-human-to-many-zebrafish’ class” class), with an average of 2.28 zebrafish genes for each gene human [see details in Howe et al., 2013]. 2.6 Statistical analysis GraphPad Prism Software Version 8.0 (San Diego, CA, USA) was used to perform the statistical analysis. Initially, data were checked for deviations from normality of variance and homogeneity of variance before analysis. Normality of data was assessed by use of the Shapiro-Wilk test, and homoscedasticity was assessed by use of Bartlette's test. Multiple comparisons were performed using a one-way ANOVA and Tukey's post-hoc analysis (for parametric data) or Kruskal-Wallis test, with Dunn's post-hoc (for non-parametric data). Correlation analyses were performed through Pearson tests (for parametric data) or Spearman tests (for non-parametric data). Significance level adopted for all analyses was alpha = 0.05. 3 Results and discussion 3.1 AZT and HCQ detection (uptake) Our data revealed that the exposure to AZT and HCQ, even in a short period (72 h), allowed their absorption by adult zebrafish (Fig. 2 ). The concentrations of AZT in the body tissues of the zebrafish were higher than those of HCQ in individuals exposed to the drugs alone and in combination (Fig. 2). In the “AZT” and “AZT + HCQ” groups, AZT concentrations were 84.7% and 80.9% higher than those of HCQ detected in the “HCQ” and “AZT + HCQ” groups, respectively (Fig. 2). In addition, we observed that the exposure to the combination of drugs did not influence the uptake of AZT (Fig. 2). Similar results were found in tadpoles (Luz et al., 2021). According to Luz et al. (2021), Physalaemus cuvieri tadpoles (stage 26G) that were exposed to AZT, HCQ and the combination of these two drugs (72 h; 12.5 μg/L of both drugs) showed an AZT concentration almost 70% higher than those of HCQ (in the HCQ and AZT + HCQ groups). When compared to other drugs such as erythromycin, AZT also showed a higher accumulation. In Fall Chinook salmon (Oncorhynchus tshawytscha) (exposed to azithromycin 30 mg/kg fish, for 14 days), this accumulation was 95% higher in fry, and 4.4% higher in smolts (Fairgrieve et al., 2005). In addition, AZT had greater tissue persistence (>76 d after treatment ceased) than erythromycin (21 d post-treatment) (Fairgrieve et al., 2005). These authors did not find any histopathological changes in the trunk kidney or other organ tissues and attributed this prolonged retention of azithromycin in O. tshawytscha to an increase in the efficacy of that antibiotic. However, it has been reported that macrolide antibiotics, such as AZT, can promote hepatoxicity in larval zebrafish, such as liver degeneration, alterations in liver size and hepatic steatosis (Zhang et al., 2020).Fig. 2 Concentrations of azithromycin (AZT) and hydroxychloroquine (HCQ) in the body tissues of D. rerio adults, after 72 h of exposure. The bars represent the mean + SEM, the data was submitted to one-way ANOVA, with Tukey's post-test, at 5% probability. AZT: group exposed to azithromycin (12.5 μg/L); HCQ: group exposed to hydroxychloroquine (12.5 μg/L); AZT (MIX) and HCQ (MIX): represent the animals exposed to the binary combination of drugs, with the individual quantification of each compound. n = 8 fish/group. Fig. 2 Hand and Hand (2002) reported that AZT can accumulate much more in human polymorphonuclear leukocytes than other antibiotics. These authors evaluated specific characteristics and mechanisms of AZT interactions with human polymorphonuclear leukocytes and demonstrated that an extracellular antibacterial activity of drug is related to the release of this intra-phagocyte drug at the sites of infection. Therefore, AZT is highly accumulated and slowly released. This may justify the long time that this drug remain in the Oncorhynchus tshawytscha organs as reported by Fairgrieve et al. (2005). Furthermore, it helps us to understand our results of higher uptake of AZT in relation to HCQ. Interestingly, Klempner and Styrt (1983) demonstrated that some drugs, including chloroquine, caused an alkalinization of the intralysosomal pH, which resulted in the inhibition of neutrophil degranulation. Similar results were also found by Dey and Bishayi (2015), in a study of murine peritoneal macrophages. This may indicate that HCQ can further assist in the accumulation of AZT. 3.2 Biochemical effects We also observed that the uptake of drugs by adult zebrafish was not able to increase significantly or reduce tissue levels of total soluble carbohydrates (Fig. 3A). However, drug exposures caused a reduction in total protein levels (Fig. 3B). For triglyceride levels, it was possible to observe a reduction only in the “AZT + HCQ” group, compared to the animals in the control group (Fig. 3C). On the other hand, we observed an increase in the production of TBARS, H2O2, ROS and NO2 − (Fig. 4A–D, respectively) in zebrafish exposed to all treatments. These data suggest that the oxidative stress processes in these animals were enhanced by both AZT and HCQ, without a synergistic, additive, or antagonistic effect of the combined exposure. This result was corroborated by Cook et al. (2006) showing a possible pharmacokinetic interaction between AZT and CQ (chloroquine) in healthy volunteers. Their results indicated no clinically relevant effect of one drug on the other, suggesting that AZT and CQ do not exhibit any direct pharmacokinetic interaction (Cook et al., 2006). However, triglyceride data demonstrated synergistic negative effect of the two drugs on the triglyceride values (Fig. 3C). Altogether, these data suggest that combination of two drugs can influence energy metabolism in adult zebrafish. To our knowledge, there are not many reports in the literature about the influence of AZT in reducing triglyceride levels. Interestingly, HCQ generally has protective actions against dyslipidemia (high blood lipid levels). This can lead to a reduction in cardiovascular diseases, systemic lupus erythematosus and rheumatic diseases (Cairoli et al., 2012; Masui et al., 2019; Morris et al., 2011). However, the consequences of the synergistic negative effect of AZT and HCQ on triglyceride levels still need to be further studied.Fig. 3 (A) Total soluble carbohydrates, (B) total proteins and triglycerides levels in body tissues of D. rerio adults exposed or not to azithromycin (AZT) and hydroxychloroquine (HCQ). The bars represent the mean + SEM, and the data were submitted to one-way ANOVA, with Tukey's post-test, at 5% probability. Different lowercase letters indicate differences among experimental groups. C: control group; AZT: group exposed to azithromycin (12.5 μg/L); HCQ: group exposed to hydroxychloroquine (12.5 μg/L); AZT + HCQ: represent animals exposed to the binary combination of drugs. n = 8 fish/group. Fig. 3 Fig. 4 (A) Production of thiobarbituric acid reactive substances (TBARS), (B) hydrogen peroxide (H2O2), (C) reactive oxygen species (ROS) and (D) nitrite (NO2−) in body tissues of D. rerio adult exposed or not to azithromycin (AZT) and hydroxychloroquine (HCQ). The bars represent the mean + SEM (in “A, B and D”), data were submitted to one-way ANOVA, with Tukey's post-test (in “A, B and D”) and to Kruskal-Wallis test, with Dunn's post-test (in “C”), both at 5% probability. Different lowercase letters indicate differences among experimental groups. C: control group; AZT: group exposed to azithromycin (12.5 μg/L); HCQ: group exposed to hydroxychloroquine (12.5 μg/L); AZT + HCQ: represent animals exposed to the binary combination of drugs. n = 8 fish/group. Fig. 4 The TBARS, H2O2, ROS and NO2 − levels in zebrafish differed between the groups exposed to the drugs. Additionally, our analyzes show a positive and significant correlations between these different biomarkers (Fig. 5 ). However, the same treatments did not produce similar effects in P. cuvieri tadpoles (Luz et al., 2021). This result suggest a species-specific type of response. Since some species such as Daphnia magna and Dicentrarchus labrax also show an increase in biomarkers of oxidative stress, while other species such as Oreochromis niloticus, these markers were not affected (Li et al., 2020; Mhadhbi et al., 2020; Shiogiri et al., 2017). It is essential to note that studies that assess biomarkers of oxidative stress induced by HCQ in aquatic organisms are extremely limited. Therefore, it is important that the impacts of HCQ on the aquatic environment are evaluated, especially when this drug is associated with other drugs of indiscriminate use.Fig. 5 Spearman correlation matrix of the biomarkers “hydrogen peroxide (H2O2)”, “oxygen reactive species (ROS)”, “nitrite (NO2−)” and “thiobarbituric acid reactive substances (TBARs)”. Correlation coefficients (r) appear on the bottom triangle (beige), and a graphical display of these values appears on the top triangle (white). The number of asterisks denote the significance of the correlation: denotes p value < 0.03, p value < 0.01, p value < 0.001, and **p value < 0.0001. Blue‑tinted ellipses represent positive correlations. The boldness of the color and shape of the circle represent the strength of the relationship between variables, with stronger correlations having bolder colors and narrower circles. Fig. 5 3.3 Oxidative stress and molecular docking We performed different in silico analyzes to comprehend the mechanisms of action that led to increased oxidative stress in adult zebrafish exposed to drugs. Initially, we evaluated through molecular docking the plausibility of the interactions between AZT and HCQ with the molecular structure of the enzymes superoxide dismutase (SOD) and catalase, both considered in the frontline of antioxidant defense. As it can be seen in Fig. 6 , our analyzes predicted a strong affinity between the drugs and their potential antioxidant targets, as well as the existence of interactions with residues from all tested moorings. The binding energies required for AZT and HCQ to bind to catalase were −8.1 ± 0.71 kcal/mol and −6.6 ± 0.36 kcal/mol (mean ± SD), respectively. The energies expected for the binding between drugs and SOD were −7.1 ± 0.7 kcal/mol (for AZT and SOD) and −6.8 ± 0.19 kcal/mol (for HCQ and SOD) (Fig. 6). In addition, the analysis of the interactions showed that AZT reacted with the catalase by means of conventional and carbon hydrogen bond, involving the amino acids Asn338, Gln415 and Thr381 (Fig. 7A–B) and the interactions between HCQ and catalase were of the type of conventional hydrogen bond, Pi-Pi Stacked and Pi-Alkyl, involving the amino acids Phe356 and Asp157 (Fig. 7C–D). In relation to SOD, the interaction with AZT occurred through conventional and carbon hydrogen bond (Arg170, Gly168 and Asn166) (Fig. 7E–F) and with HCQ, through interactions of the conventional hydrogen bond and Pi- Alkyl (Ala179, Gln180 and Lys30) (Fig. 7G–H).Fig. 6 Graphical representation of the binding energies (in kcal/mol) of molecular docking between azithromycin (AZT) and hydroxychloroquine (HCQ) with their potential antioxidant targets such as catalase and superoxide dismutase (SOD). Values were calculated by the software AutoDock Vina. Fig. 6 Fig. 7 Two-dimensional/three-dimensional representation and residues of interaction between azithromycin (AZT) and hydroxychloroquine (HCQ) with their potential antioxidant targets. (A–B) AZT-catalase, (C–D) HCQ-catalase; (E–F) AZT-SOD and (G–H) HCQ-SOD. SOD: superoxide dismutase. Fig. 7 The pharmacokinetics of AZT are characterized by exceptionally low serum concentrations and wide distribution in tissues (Hand and Hand, 2002). A high concentration of AZT has been proceeded in murine and human phagocytic cells by several authors (Bonnet and Van der Auwera, 1992; Fietta et al., 1997; Gladue et al., 1989; Meyer et al., 1993; Rakita et al., 1994; Stamler et al., 1994). When macrophages trigger an explosion of respiratory activity, there is an increased production of ROS, such as the superoxide anion and H2O2 that can damage lipids, proteins, and nucleic acids (Dey and Bishayi, 2015). However, some authors have reported that AZT is not able to induce oxidative stress by attenuating the membrane destabilizing effect of bioactive phospholipids (Anderson et al., 1996; Dey and Bishayi, 2015). In fact, some species such as tilapias (O. niloticus) and tadpoles (P. cuvieri) did not show changes in ROS levels when exposed to AZT (Luz et al., 2021; Shiogiri et al., 2017). However, our data revealed that in zebrafish, AZT was able to generate ROS and we also demonstrated through molecular docking that AZT and HCQ also interact with antioxidant enzymes such as SOD and catalase. Similar results were demonstrated by Yan et al. (2019), in which zebrafish embryos were exposed to macrolide antibiotics, including AZT. Their results indicated severe toxicities in the development of this species, in addition to increased oxidative stress, decreased SOD activities and increased MDA content. This indicates that antibiotics such as AZT can cause damage to the zebrafish and this needs to be further investigated through biochemical and molecular biological investigations. Notwithstanding, CQ acts in the production of H2O2 and superoxide anion, demonstrating its bactericidal effect in terms of ROS production more accentuated than AZT (Abrantes et al., 2008). These results corroborate our data and all together indicate that these two drugs may have different mechanisms of action due to oxidative stress. In addition, it is likely that there is a failure in the response of antioxidants, since, in this study, the oxidative stress generated by AZT and HCQ was not well orchestrated. 3.4 Interaction network We also explored the putative pathways, integrating the investigated drugs with different proteins in a metabolite-protein interaction network. According to the STITCH interaction network, AZT and HCQ were linked to different metabolic pathways that may also explain the increase in oxidative stress observed in the evaluated animals. AZT showed a strong interaction with different cytochrome P450 family members, family 3, subfamily A (CYP3A5, CYP3A4 and CYP3A7) (Fig. 8A) and HCQ to caspase-3 (Fig. 8B). It has been shown that in fish, as in other animals, xenobiotic biotransformation carried out by liver cytochromes P-450 and antioxidant defense system play an important role in maintaining cellular homeostasis (Burkina et al., 2015; Westphal, 2000). Thus, CYP450 activity is a crucial factor determining the detoxification abilities of living organisms.Fig. 8 Network analysis results using the Search Tool for Interactions of Chemicals (STITCH) to explore the interaction between azithromycin (AZT) and hydroxychloroquine (HCQ) with their different target molecules (A) AZT and (B) HCQ assessed separately. (C) AZT and HCQ assessed together. Splice isoforms or post-translational modifications are collapsed, i.e., each node represents all the proteins produced by a single, protein-coding gene locus. Small nodes: protein of unknown 3D structure. Large nodes: some 3D structure is known or predicted. Colored nodes: query proteins and first shell of interactors. White nodes: second shell of interactors. Fig. 8 The activation of caspase-3 by HCQ is very well reported in the literature. According to Boya et al. (2003), HCQ causes mitochondrial release of cytochrome c and activates caspase-3. The same effect was reported in bladder cancer cells treated with HCQ (Lin et al., 2017), in malignant B cells of 20 patients with chronic B lymphocytic leukemia treated with HCQ (Lagneaux et al., 2001; Lagneaux et al., 2002) and in culture of rheumatoid synoviocytes, suggesting that HCQ can exert its anti-rheumatic effect on rheumatoid joints through these mechanisms (Kim et al., 2006). In this regard, for both drugs, functional enrichment analysis demonstrated that the binding of AZT and HCQ and their target molecules involved different biological processes and molecular mechanisms in the cytosol, including ROS metabolism and regulation of nitric-oxide synthase activity, in addition to other enzymes and proteins that participate in REDOX homeostasis (Table 1 ).Table 1 Functional enrichment analysis for investigating the biological processes involved in the interaction between azithromycin (AZT) and hydroxychloroquine (HCQ) with their different target molecules. Table 1Pathway ID Pathway description Count in gene set False discovery rate Azithromycin1 Biological process (GO) GO:0072593 Reactive oxygen species metabolic process 12 1.3 × 10−14 GO:0046209 Nitric oxide metabolic process 8 3.4 × 10−12 GO:0050999 Regulation of nitric-oxide synthase activity 6 4.68 × 10−7 GO:0006979 Response to oxidative stress 10 8.08 × 10−7 GO:0000302 Response to reactive oxygen species 8 1.57 × 10−6 Molecular function (GO) GO:0004601 Peroxidase activity 6 6.36 × 10−8 GO:0016209 Antioxidant activity 7 6.36 × 10−8 GO:0016491 Oxidoreductase activity 13 6.36 × 10−8 GO:0004602 Glutathione peroxidase activity 5 8.43 × 10−8 GO:0020037 Heme binding 7 1.57 × 10−6 Cellular component (GO) GO:0005829 Cytosol 19 0.000465 Hydroxychloroquine2 Biological process (GO) GO:0072593 Reactive oxygen species metabolic process 11 7.96 × 10−15 GO:0046209 Nitric oxide metabolic process 8 1.1 × 10−13 GO:0050999 Regulation of nitric-oxide synthase activity 6 3.97 × 10−8 GO:0001666 Response to hypoxia 8 1.32 × 10−6 GO:0032496 Response to lipopolysaccharide 8 1.32 × 10−6 Molecular function (GO) GO:0004517 Nitric-oxide synthase activity 3 7.5 × 10−6 GO:0034617 Tetrahydrobiopterin binding 3 9.99 × 10−6 GO:0034618 Arginine binding 3 9.99 × 10−6 GO:0003958 NADPH-hemoprotein reductase activity 3 9.74 × 10−5 GO:0050661 NADP binding 4 0.000205 Cellular component (GO) GO:0005829 Cytosol 13 0.00906 Azithromycin AND Hydroxychloroquine3 Biological process (GO) GO:0072593 Reactive oxygen species metabolic species 9 5.71 × 10−10 GO:0046209 Nitric oxide metabolic process 6 2.89 × 10−8 GO:0050999 Regulation of nitric-oxide synthase activity 5 1.35 × 10−5 GO:2000377 Regulation of reactive oxygen species metabolic process 6 0.000101 GO:0001666 Response to hypoxia 7 0.000139 Molecular function (GO) GO:0016209 Antioxidant activity 5 3.82 × 10−5 GO:0016491 Oxidoreductase activity 10 3.82 × 10−5 GO:0020037 Heme binding 6 3.82 × 10−5 GO:0004497 Monooxygenase activity 5 0.000141 GO:0004601 Peroxidase activity 4 0.000141 1 PPI enrichment p-value: 6.79 × 10−11 and clustering coefficient: 0.749. 2 PPI enrichment p-value: 4.25 × 10−13 and clustering coefficient: 0.689. 3 PPI enrichment p-value: 1.55 × 10−9. Number of nodes: 21; number of edges: 47; average node degree: 4.48 and clustering coefficient: 0.777. 3.5 Chemical-chemical interaction (via ChemDIS-mixture) To deepen the prediction of possible mechanisms of action responsible for the effects observed in our study, we performed an analysis of chemical-chemical interaction (involving the tested drugs and different molecules). In addition, we evaluated the potential specific biological endpoint resulting from these interactions. We identified from the ChemDIS-Mixture tool a total of 446 proteins that can interact with AZT or HCQ. Of these, 255 were specific for AZT, 178 for HCQ and 13 proteins are shared between drugs (Fig. 9 ). We also observed that among the top ten most significant hits for the targets responsible for the effect of interaction with AZT (i.e., with a score ≥ 0.8), 70% are proteins directly or indirectly related to oxidative stress (catalase, cytochrome P450 family 3 subfamily A member 4, glutathione S-transferase alpha 3, glutathione S-transferase alpha 1, glutathione S-transferase alpha 4, glutathione S-transferase alpha 2, cytochrome P450 family 3 subfamily A member 5, cytochrome P450 family 3 subfamily A member 7) (Fig. 9A), which is similar to what was observed in the interaction network analysis above. In relation to HCQ, the main targets (i.e., score ≥ 0.8) included caspase 3 and toll like receptors (Fig. 9B), thus covering the pathways by which the drug may have induced an increased in oxidative stress. Among the protein targets shared by both AZT and HCQ, our analysis showed interleukin 6 (IL-6) as a target in which the scores for both drugs were higher than 0.825 (Fig. 9).Fig. 9 Venn diagram comparing the protein-protein interaction with azithromycin or hydroxychlorin or both. (A-C) Summarized information on the most significant results for the targets responsible for the effect of interaction with (A) azithromycin (AZT), (B) hydroxychloroquine (HCQ) and (C) AZT/HCQ. Fig. 9 The anti-inflammatory effects of HCQ already discussed in this article, such as interference with lysosomal acidification and inhibition of phospholipase absorption, are also accompanied by the inhibition of toll-like receptor signals, inhibition of T and B cell receptors and, mainly, the decreased production of macrophage cytokines such as interleukin (IL)-1 and IL-6 (Ben-Zvi et al., 2012). In this manner, HCQ controls the inflammatory response since inhibition of cytokines such as IL-6 decreases tissue damage and endothelial inflammation (Moudgil and Choubey, 2011). The antioxidant effects of AZT alone and combined with HCQ was observed in P. cuvieri tadpoles. In this species, SOD and catalase were increased when exposed to these drugs and possibly acted to maintain the basal production of NO, ROS, TBARS and H2O2 (Luz et al., 2021). The antioxidant effects of AZT alone and combined with HCQ was observed in P. cuvieri tadpoles. In this species, SOD and catalase were increased when exposed to these drugs and possibly acted to maintain the basal production of NO, ROS, TBARS and H2O2 (Luz et al., 2021). Already the Increase in ROS presented in our article is suggestive of a failure in the antioxidant response que can be attributed to the interaction of drugs with the main antioxidant enzymes, SOD, and catalase. However, further studies must be conducted to elucidate this hypothesis. 3.6 Gene ontology To provide an overview of the main processes, molecular mechanisms, and cellular localization of proteins with potential interaction with AZT and/or HCQ, we conducted an ontology (GO) analyze gene. In this analysis, 674 genes responsive to drugs were identified, 293 to AZT, 264 to HCQ and 117 shared between AZT and HCQ. Biological process analysis indicated that proteins with a strong interaction with AZT act mainly in processes related to glutathione metabolism and cellular oxidant detoxification; acting on molecular mechanisms involving the activity of various enzymes, especially glutathione transferase, which expands the findings of molecular docking. In addition, our analysis revealed that these proteins are in different cytoplasmic elements/structures, such as in the mitochondrial matrix and in the NADPH oxidase complex. Fig. 10 shows the GO prediction of the biological process, molecular mechanism and cellular compartment of proteins that interact with AZT, highlighting the number of genes involved (Fig. 10A) and the increasing order of significance observed (Log10 p value) (Fig. 10B).Fig. 10 Gene Ontology (GO) classification of differentially expressed genes related exclusively to azithromycin. The differentially expressed genes are grouped into three hierarchically structured terms: biological process, cellular component, and molecular function. In “A” the number of genes is presented and in “B” the increasing significance (Log10 P value) of each GO annotation. Fig. 10 The proteins that interacted strongly with HCQ act mainly in biological processes related to glucuronidation (one of the phase II reactions of elimination of xenobiotics through biotransformation) and with flavonoid biosynthetic process, through molecular mechanisms that include, especially, related to ligand binding and glucuronosyltransferase activity (Fig. 11 ). In addition, cellular compartment prediction confirmed that these proteins are identified especially in the intracellular environment, including autophagosomal and endocytic vesicles, as well as in organelle membranes (Fig. 11). On the other hand, the proteins shared between AZT and HCQ act in processes that involve nucleophagy, macroautophagy, immune response (from the induction of inflammatory response), as well as in oxidation-reduction process, especially through ligand binding mechanisms that involve the activity of different enzymes (Fig. 12 ). Such proteins are found, especially in the part of the cytoplasm that does not contain organelles, but which does contain other particulate matter, such as protein complexes (cytosol), lipid bilayer surrounding the endoplasmic reticulum and extracellular exosome, i.e., vesicle that is released into the extracellular region by fusion of the limiting endosomal membrane of a multivesicular body with the plasma membrane (Fig. 12).Fig. 11 Gene Ontology (GO) classification of differentially expressed genes related exclusively to azithromycin. The differentially expressed genes are grouped into three hierarchically structured terms: biological process, cellular component, and molecular function. In “A” the number of genes is presented and in “B” the increasing significance (Log10 P value) of each GO annotation. Fig. 11 Fig. 12 Gene Ontology (GO) classification of differentially expressed genes related exclusively to azithromycin. The differentially expressed genes are grouped into three hierarchically structured terms: biological process, cellular component, and molecular function. In “A” the number of genes is presented and in “B” the increasing significance (Log10 P value) of each GO annotation. Fig. 12 3.7 Neurotoxicity (acetylcholinesterase/molecular docking and neuromasts) Regarding the evaluation of AChE activity, we observed that the combination “AZT + HCQ” induced a cholinesterasic effect in the adult zebrafish, as indicated by the increased enzyme activity, as compared to the control group (Fig. 13A). In agreement, molecular docking analyzes predicted a strong affinity between drugs and AChE [binding energy required for AZT and AChE binding: −8.2 ± 0.48 kcal/mol and binding energy required for binding of AZT and AChE: HCQ and AChE: −6.9 ± 0.36 kcal/mol (mean ± SD) (Fig. 13B)]. These interactions involved conventional bonds, carbon hydrogen bond and Pi-Alkyl, involving the amino acids Leu590, Leu587, Trp583, Asn584, Asp331, Thr260, His387 (for AZT; Fig. 13C) and Thr541 and Arg533 (for HCQ; Fig. 13D). When evaluating the number of neuromast, treatments did not affect their number in the tail of the zebrafish (Fig. 14A). In contrast, a reduction was observed in the head of animals exposed to AZT (alone) or in combination with HCQ (Fig. 14B). This result seems to indicate that AZT alone and associated with HCQ can destroy hair cells in zebrafish. These hair cells are mechanosensorial cells existing within neuromasts and have similarities to the cells present in the mammalian ear. Both in the inner ear of mammals and in the lateral line of the zebrafish, these cells are sensitive to drugs (Harris et al., 2003; Hernández et al., 2006; Murakami et al., 2003; Nakashima et al., 2000; Ton and Parng, 2005; Williams and Holder, 2000).Fig. 13 (A) Activity of the enzyme acetylcholinesterase (AChE) in the body tissues of D. rerio adults, exposed or not to drugs. The bars represent the mean + SEM, and the data were submitted to one-way ANOVA, with Tukey's post-test, at 5% probability (n = 8 fish/group). (B) Graphical representation of the binding energies (in kcal/mol) of molecular docking between the ligands azithromycin (AZT) and hydroxychloroquine (HCQ) and the target “acetylcholinesterase”, calculated by the AutoDock Vina software. (C–D): Two-dimensional/three-dimensional representation and residues of interaction between the ligands (C) azithromycin (AZT) and (D) hydroxychloroquine (HCQ) with the target “acetylcholinesterase”. Fig. 13 Fig. 14 Number of neuromasts identified in (A) tail and (B) head of Danio rerio adults, exposed or not to drugs. The bars represent the mean + SEM, and the data were submitted to one-way ANOVA, with Tukey's post-test, at 5% probability. In “B”, different lowercase letters indicate significant differences between the experimental groups. C: control group: AZT: group exposed to azithromycin (12.5 μg/L); HCQ: group exposed to hydroxychloroquine (12.5 μg/L); AZT + HCQ: represent animals exposed to the binary combination of drugs. n = 8 fish/group. Fig. 14 Our results also indicate an extraordinarily strong interaction between AZT and HCQ with AChE. However, its effects on AChE occurred only when the drugs were combined. In fact, AZT appears to have an inhibitory effect on AChE in European sea bass (Dicentrarchus labrax) and tadpoles (P. cuvieri) (Luz et al., 2021; Mhadhbi et al., 2020). The same can be observed for HCQ (Luz et al., 2021). Interestingly, Luz et al. (2021) demonstrated that the association of the drugs AZT and HCQ decreases the levels of AChE in tadpoles. Our data show the opposite for zebrafish. The combination of AZT and HCQ induced an increase in AChE and this increase indicates a consequence of environmental exposure to neurotoxic pollutants (Senger et al., 2011; Van Dyk and Pletschke, 2011), as well as the combination of AZT and HCQ in zebrafish. 4 Conclusion To sum up, our study confirms the hypothesis that 72 h of exposure to AZT, HCQ or their combination was sufficient to allow the uptake of drugs by zebrafish and induce the reduction of total protein levels, as well as predictive changes in oxidative stress (inferred by TBARS, H2O2, ROS and NO2 − levels) and neurotoxicity (sustained by the observation of increased AChE and reduced number of superficial neuromasts). In addition, in silico analyzes suggested that the observed effects are related to different physiological and molecular mechanisms. Thus, future investigations that focus on the effects of the molecular bindings between AZT and HCQ on the kinetics of SOD, catalase, and AChE, as well as on the functions of different cytochrome P450 molecules, caspase-3 and on the glutathione-mediated biotransformation will be useful for confirming predictions provided by the bioinformatic analyzes performed. In addition, assessments related to the biochemical and molecular expression and signals of toll-like receptors and IL-6 will provide new insights into how AZT and HCQ affect the zebrafish immune system. Finally, it is paramount to emphasize that our study is not exhaustive and, therefore, our results are only the “tip of an iceberg” that represents the ecotoxicological effects arising from the tested drugs. Therefore, we strongly recommend that further investigations should be carried out to understand the magnitude of the impact of the indiscriminate use of AZT and HCQ, especially in the context of the COVID-19 pandemic, whose environmental concentrations are certain to increase. Ethical approval All experimental procedures were carried out in compliance with ethical guidelines on animal experimentation. Meticulous efforts were made to assure that animals suffered the least possible and to reduce external sources of stress, pain and discomfort. The current study did not exceed the number of animals necessary to produce trustworthy scientific data. This article does not refer to any study with human participants performed by any of the authors. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgment The authors are grateful to the Brazilian National Research Council (10.13039/501100003593 CNPq ) (proc. N. 426531/2018-3 and N. 305639/2019-6) and to Goiano Federal Institute for the financial support. Malafaia G. is granted with productivity scholarship from CNPq (Proc. N. 307743/2018-7). ==== Refs References Abrantes P. Dimopoulos G. Grosso A.R. do Rosario V.E. Silveira H. Chloroquine mediated modulation of Anopheles gambiae gene expression PLoS ONE 3 7 2008 1 9 10.1371/journal.pone.0002587 Agarwal M. Ranjan P. Mittal A. Baitha U. Use of Hydroxychloroquine for Pre-exposure Prophylaxis in COVID 19: Debate and Suggested Future Course 2020 Amaral D.F. Montalvão M.F. de Oliveira Mendes B. da Costa Araújo A.P. de Lima Rodrigues A.S. Malafaia G. Sub-lethal effects induced by a mixture of different pharmaceutical drugs in predicted environmentally relevant concentrations on Lithobates catesbeianus (Shaw, 1802)(Anura, ranidae) tadpoles Environ. Sci. Pollut. 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==== Front Arch Virol Arch Virol Archives of Virology 0304-8608 1432-8798 Springer Vienna Vienna 5120 10.1007/s00705-021-05120-z Brief Report Upregulation of INF-γ, IL-6, and IL-8 expression during replication of turkey coronavirus in nonepithelial cells obtained from Meleagris gallopavo http://orcid.org/0000-0003-1590-3897 Cardoso Tereza Cristina [email protected] 13 Panegossi Letícia Colin 12 Gameiro Roberto 2 1 grid.11899.38 0000 0004 1937 0722 Laboratory of Animal Virology and Cell Culture, UNESP-University of São Paulo State, São Paulo, Brazil 2 Laboratory of Anatomy, Histology and Embryology, College of Veterinary Medicine, Araçatuba, São Paulo 16050-680 Brazil 3 Departamento de Apoio, Produção e Saúde Animal, Curso de Medicina Veterinária, Rua Clóvis Pestana, 793, Araçatuba, SP 16.050-680 Brazil Handling Editor: Zhenhai Chen. 31 5 2021 15 20 2 2021 12 4 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Mesenchymal stromal cells (MSCs) are considered multipotent progenitors with the capacity to differentiate into mesoderm-like cells in many species. The immunosuppressive properties of MSCs are important for downregulating inflammatory responses. Turkey coronavirus (TCoV) is the etiological agent of a poult mortality syndrome that affects intestinal epithelial cells. In this study, poult MSCs were isolated, characterized, and infected with TCoV after in vitro culture. The poult-derived MSCs showed fibroblast-like morphology and the ability to undergo differentiation into mesodermal-derived cells and to support virus replication. Infection with TCoV resulted in cytopathic effects and the loss of cell viability. TCoV antigens and new viral progeny were detected at high levels, as were transcripts of the pro-inflammatory factors INFγ, IL-6, and IL-8. These findings suggest that the cytokine storm phenomenon is not restricted to one genus of the family Coronaviridae and that MSCs cannot always balance the process. Supplementary Information The online version contains supplementary material available at 10.1007/s00705-021-05120-z. ==== Body Introduction Mesenchymal stromal cells (MSCs) are capable of self-renewal and differentiation into multiple lineages for the repair of damaged cells and tissues [1–3]. Another important characteristic of MSCs is their immunological function: they inhibit inflammation and immunological responses both in vitro and in vivo [4]. MSCs exert immunosuppressive effects by inhibiting lymphocyte proliferation and decreasing cytokine production [4]. In addition, these cells are capable of renewing tissues following injuries such as trauma, neoplasia, chemical damage, and microbial infection [2, 3]. Recently, human MSCs were used as an alternative clinical treatment to repair lung damage caused by a pro-inflammatory cytokine storm induced by SARS-CoV 2 infection [4, 5]. According to the International Committee on Taxonomy of Viruses (ICTV), the family Coronaviridae belongs to the order Nidovirales and is divided into two subfamilies, Orthocoronavirinae and Letovirinae. The subfamily Orthocoronavirinae comprises four genera: Alphacoronavirus, Betacoronavirus, Gammacoronavirus, and Deltacoronavirus [6]. The genus Gammacoronavirus includes the avian coronaviruses (AvCoVs) infectious bronchitis virus (IBV) and turkey coronavirus (TCoV) [6, 7], the latter of which is an etiological agent of poult enteritis mortality syndrome (PEMS). TCoV infection causes acute inflammation [8–10] and has tropism for epithelial cells. Currently, no effective vaccine or treatment is available for disease prevention [6–10]. The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in humans has raised interest in the immune responses against coronaviruses in other animal species [4]. Cross-species transmission is not restricted to SARS-CoV-2, and it has been observed with other coronaviruses as well [10, 11]. The production of pro-inflammatory cytokines in chickens in response to viral infections has been observed in other disease models [12]. Cytokines produced during viral infection are potent immunomodulatory molecules that act as mediators of inflammation and the immune response [13]. Pro-inflammatory cytokines such interleukin (IL)-6 and IL-8 are produced early in viral infection, triggering the production of Th1 cytokines such as interferon-γ (IFNγ) [12–15]. However, the role of cytokines in inflammatory responses to TCoV infection has not been elucidated. In this study we isolated and characterized MSCs obtained from poult embryos. These MSCs differentiated into adipocytes, osteocytes, and chondrocytes. In addition, we evaluated the susceptibility of these cells to infection with TCoV and analyzed the production of INFγ, IL-6, IL-8, IL-10, and IL-2 as a consequence of virus replication at different time points after infection. Materials and methods Cell culture and virus All chemicals, reagents, and plasticware for cell culture were purchased from Thermo Fisher and Sigma-Aldrich. Poult embryos were inoculated, and amniotic membranes and fluid were harvested as described previously [3, 4]. After MSCs were cultured for 2 days, non-adherent cells were removed, and fresh medium was added. The medium was then refreshed every 2-3 days, and the cells were trypsinized at 80% confluence. The cells were imaged at 5-day intervals to observe their morphology. A TCoV suspension was prepared and stored as described previously [12, 16, 18]. Virus titres were calculated following the standard Reed & Muench method [17]. Characterization and phenotyping of poult MSCs The trilineage multipotency of poult-derived MSCs, which is considered one of the most important biological properties of stem cells, was examined in this study [4]. Adipogenic, osteogenic, and chondrogenic differentiation was induced according to the manufacturer’s instructions for the StemPro® adipogenesis, chondrogenesis, and osteogenesis differentiation kits (Thermo Fisher Scientific). In addition, to confirm that the MSCs had undergone differentiation, calcium mineralization was detected by Alizarin red staining, glycosaminoglycans were detected with safranin O, and lipid vacuoles were detected with oil red [16]. Images were obtained using an AxioImager® A.1 light microscope connected to an AxioCam® MRc camera (Carl Zeiss, Oberkochen, Germany). Images were processed using AxioVision® 4.8 software (Carl Zeiss). The MSC phenotype was assessed by flow cytometry (FAC) with mouse anti-vimentin, mouse anti-cytokeratin, rabbit anti-chicken CD44 (SouthernBiotech, Birmingham, AL, USA), and rabbit anti-human CD90 and CD105 (Sigma-Aldrich) antibodies. All procedures were carried out as described previously [15]. Data were acquired using an Attune™ Acoustic Focusing Cytometer (Applied Biosystems, Foster City, CA, USA). In order to prevent autofluorescence interference, a global compensation was applied in the analysis. For all experiments, a BL1-A (488 nm) filter was selected as a standard. Cell viability Cell proliferation was assessed using an In Vitro Toxicology Assay Kit MTT-based assay (TOXI-1 Kit; Sigma-Aldrich) following the manufacturer’s instructions. Absorbance was measured at 600 nm, and the data were analysed at various times postinfection (p.i.) using a BioPhotometer (Eppendorf, Hamburg, Germany). All reported values are the mean of triplicate samples. Virus infection and molecular analysis The TCoV strain used in this study (TCoV/Brazil/2006 accession number FJ188401) was isolated from a field case of PEMS in 2007 [16]. Virus propagation and titration were performed following standard procedures [17, 18]. Then, infected and uninfected cells were observed at 24, 48, 72, 96, and 120 h p.i. to check for cytopathic effects, to measure viral titres, and to detect viral antigens by IFA, following a procedure described previously [19]. IFA images were obtained using an Axio Imager A1 fluorescence microscope connected to an AxioCam MRc camera (Carl Zeiss, Oberkochen, Germany). Images were processed using AxioVision 4.8 software (Carl Zeiss). Cell supernatants and adherent cells were collected to measure viral, IFNγ, IL-2, IL-6, IL-8, and IL-10 mRNA by quantitative reverse transcription polymerase chain reaction (qRT-PCR) [12–15]. Viral RNA was extracted from each culture at the previously stated times p.i. using a Pure Link Viral RNA/DNA Kit (Invitrogen) following the manufacturer’s instructions. The qRT-PCR protocol followed a standardized TaqMan tube assay method. Primers and probes are listed in Supplementary Table S1. The fold increase in each transcript was calculated by the 2ΔΔCT method using StepOne Plus™ software (Applied Biosystems). All experiments were performed in triplicate, and the results are expressed as geometric means with 95% confidence intervals from two independent experiments (infected and uninfected cells). Viral genomic and mRNA copy numbers were normalized to 28S rRNA gene copies. The results were compared by one-way ANOVA followed by Student´s t-test, using GraphPad v.9.1 software. p-values less than 0.05 were considered significant. Results and discussion MSCs from the amniotic membrane and amnion of poult embryos were isolated based on the capacity of MSCs to adhere to a plastic surface with no enzymatic digestion. After 10 days of culture, colonies of cells with fibroblast morphology were observed, and the cells were cultured further until they reached subconfluence (Fig. 1a). To determine the multipotency of poult MSCs, osteogenic, chondrogenic, and adipogenic differentiation was induced. Undifferentiated cells were included in all analyses (Fig. 1a). To study chondrogenic differentiation, the cells were stained, and the levels of glycosaminoglycans were determined (Fig. 1a). Osteogenic differentiation was detected by matrix calcification (Fig. 1a). After induction, adipogenic differentiation of poult MSCs was observed, as indicated by a large number of very small lipid vacuoles stained with oil red solution (Fig. 1a). Flow cytometry revealed that isolated poult MSCs were positive for the mesenchymal markers vimentin, CD44, CD90, and CD105 (Fig. 1b).Fig. 1 Representative photomicrographs of mesoderm-like tissues. a) Undifferentiated poult MSCs. Chondrogenesis and acidic proteoglycans are visualized by safranin O staining, and osteogenesis and calcium mineralization deposits are visualized by Alizarin red staining. Adipogenesis differentiation showing lipid droplets stained with oil red; magnification of 400 µm. b) Flow cytometric analysis showing negative staining for cytokeratin and positive staining for vimentin, CD44, CD90, and CD105. The flow cytometric results are expressed as box plots and whisker plots. This one plots the box from the 10th percentile to the 90th percentile, red dots showing points outside that range We then isolated and expanded adherent poult embryo MSCs for at least 10 consecutive passages. The isolated cells showed features consistent with those described previously for MSCs [2, 3]. Moreover, the maintenance of cultured cells for 10 passages eliminated fibroblast contamination due to the similar morphology of these two cell types [1–3]. Poult MSCs were positive for CD44, CD90, and CD105, which is a characteristic of mesenchymal cells [1]. In addition, the multipotency of poult MSCs was confirmed by their ability to differentiate into osteogenic, adipogenic, and chondrogenic lineages, as documented previously [3]. The presence of mesoderm progenitors was confirmed following a differentiation protocol described previously for chicken and duck MSCs [1–3]. To assess the effects of infection on cell viability, MSCs were infected with a TCoV suspension, the cells were observed, and the virus titre was determined [15]. When uninfected cells were compared to infected cells, a cytopathic effect, indicated by rounded floating cells, was observed at 96 h p.i. (Fig. 2). Viral antigens were visualized at 96 h p.i. as fluorescent signals in the cytoplasm of infected cells (Fig. 2), whereas no visible fluorescence was observed in uninfected cells (Fig. 2). An MTT assay was used to compare the viability of uninfected and infected MSCs (Fig. 3a). The only negative control that was used was medium, and the results demonstrated that the cells were in good condition when not infected with TCoV and exhibited reduced viability at all time points p.i., with an increase in the release of new viral particles (Fig. 3b).Fig. 2 Uninfected poult MSCs at 96 h p.i. Typical cytopathic effect observed in poult MSCs infected with the original TCoV suspension after three consecutive passages. At 96 h p.i., TCoV viral antigens were detected by IFA, and no fluorescence signal was seen in uninfected MSCs Fig. 3 Cell viability measured by the MTT-based assay. a) Data obtained by spectrophotometry at 600 nm. All values are the average ± S.D. of triplicate experiments. b) TCoV titres obtained using the Reed & Muench method. All data are expressed as Log2 values (y-axis). c) Quantification of viral, IL-6, INFγ, and IL-8 mRNA. Total RNA isolated from uninfected control cultures was used as a reference sample at each time point. The data are representative of separate experiments TCoV-S2, IFNγ, IL-2, IL-6, IL-8, and IL-10 mRNA levels were measured at different time points p.i, and the number of viral mRNA copies was found to be significantly higher at 96 and 120 h p.i., and viral antigen was also detected at these time points (Fig. 3c). A positive correlation was found (r = 0.98) among INFγ, IL-6, and IL-8 mRNA levels over time (Fig. 3c). IL-2 and IL-10 were not detected in this analysis (data not shown). At present, infection of cell cultures with TCoV remains a problem that is distinct from infection with IBV, which has been confirmed to replicate in non-avian cells [20]. In comparison with other coronaviruses with the ability to replicate in mammalian and avian cell lines, which have been used to study coronavirus-host interactions, the immunological features of turkey coronavirus are not well understood [21, 22]. However, while TCoV is able to replicate in poult embryos and embryo-derived cells, there are no reports of TCoV infecting other cell types. TCoV, which causes severe enteric disease in young turkeys, is closely related to infectious bronchitis virus, which causes respiratory and reproductive disorders [8]. Sequence analysis has suggested that recombination may have played a key role in the evolutionary origin of TCoV [19, 23, 24]. Disease and inflammation are complex processes, and the response is not dependent on a single inflammatory mediator but generally results from overlapping inflammatory pathways and cytokine interactions resulting in excessive inflammation in some cases. Keeping this in mind, the cytokines and chemokines described here may play a role in TCoV-infected cells. Regarding SARS-CoV-2, several preclinical and clinical studies have investigated the potential of MSCs in treating COVID-19, including the management of the associated cytokine storm [4]. In fact, the cytokine storm does not seem to be restricted to one genus of the family Coronaviridae. However, in vitro systems may open an avenue to elucidate many unknown aspects related to coronavirus infections in future studies. Supplementary Information Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 13 KB) Acknowledgements The authors would like to thank the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) for financial support. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. 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==== Front Soft comput Soft comput Soft Computing 1432-7643 1433-7479 Springer Berlin Heidelberg Berlin/Heidelberg 34108847 5909 10.1007/s00500-021-05909-9 Focus RETRACTED ARTICLE: Picture fuzzy set-based decision-making approach using Dempster–Shafer theory of evidence and grey relation analysis and its application in COVID-19 medicine selection Si Amalendu 1 Das Sujit 2 Kar Samarjit [email protected] 3 1 Department of Computer Science and Engineering, Mallabhum Institute of Technology, Bishnupur, 722122 India 2 grid.419655.a 0000 0001 0008 3668 Department of Computer Science and Engineering, NIT Warangal, Warangal, 506004 India 3 grid.444419.8 0000 0004 1767 0991 Department of Mathematics, NIT Durgapur, Durgapur, 713209 India Communicated by Victor Hugo C. de Albuquerque. 5 6 2021 2023 27 6 33273341 24 5 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. To offer better treatment for a COVID-19 patient, preferable medicine selection has become a challenging task for most of the medical practitioners as there is no such proven information regarding it. This article proposes a decision-making approach for preferable medicine selection using picture fuzzy set (PFS), Dempster–Shafer (D–S) theory of evidence and grey relational analysis (GRA). PFS is an extended version of the intuitionistic fuzzy set, where in addition to membership and non-membership grade, neutral and refusal membership grades are used to solve uncertain real-life problems more efficiently. Hence, we attempt to use it in this article to solve the mentioned problem. Previously, researchers considered the neutral membership grade of the PFS similar to the other two membership values (positive and negative) as applied to the decision-making method. In this study, we explore that neutral membership grade can be associated with probabilistic uncertainty which is measured using D–S theory of evidence and FUSH operation is applied for the aggregation purpose. Then GRA is used to measure the performance among the set of parameters which are in conflict and contradiction with each other. In this process, we propose an alternative group decision-making approach by the evidence of the neutral membership grade which is measured by the D–S theory and the conflict and contradiction among the criteria are managed by GRA. Finally, the proposed approach is demonstrated to solve the COVID-19 medicine selection problem. Keywords Picture fuzzy set Dempster–Shafer theory Grey relational analysis Group decision-making COVID-19 Medicine selection issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023 ==== Body pmcIntroduction Traditional logic, which is interpreted as either true or false, found to be difficult to solve uncertain real-life problems. As a counter measure, Zadeh (1965) invented fuzzy set theory, where the involvement of elements in a set is characterized by membership grade, which belongs to [0, 1]. To handle much uncertainty, fuzzy sets were extended by the different researchers in different ways such as vague set (Gau and Buehrer 1993), intuitionistic fuzzy set (IFS) (Atanassov 1986a, 1986b), fuzzy soft set (Das et al. 2018), rough set (Pawlak 1982), fuzzy interval theory (Gorzalczany 1987), intuitionistic multi fuzzy set (Das et al. 2013), interval-valued intuitionistic fuzzy set (Park et al. 2008), intuitionistic fuzzy soft set (Deng 1982) and neutrosophic soft set (Das et al. 2019). Consequently, the application of fuzzy set theory and its extensions increased rapidly in the decision-making methods in various domains like medical diagnosis (Das et al. 2013), pattern recognition (Wei and Lan 2008), data analysis (Zou and Xiao 2008), forecasting (Xiao et al. 2011), optimization (Kovkov et al. 2007), simulation (Kalayathankal and Singh 2010) and texture classification (Mushrif et al. 2006). Recently in 2014, Cuong (2014) developed the picture fuzzy set (PFS) as the generalized form of fuzzy set and IFS. The PFS approaches are found to be more appropriate in those cases when the views of someone contain more option types like yes, abstain, no and refusal. The general election of a country is noted as a good example to describe PFS, where a voter can cast his vote in favour of the candidate (yes), against the candidate (no), may not cast his vote (abstain) or may refuse to cast his vote in favour of the given candidates and prefer for nota (refusal) (Cong and Son 2015). From the time of its introduction in 2014, many researchers have been contributing to the development of decision-making problems using PFS. A number of multi-criteria decision-making (MCDM) approaches have been developed to manage real-life problems in the domain of picture fuzzy sets. By studying the intuitionistic fuzzy aggregation operator, Wang et al. (2017) described some picture fuzzy geometric operators and generalized the basic properties of those operators. Then they applied the proposed operators in multiple attribute decision-making problems under the picture fuzzy domain. Si et al. (2019) introduced a novel ranking method to rank the picture fuzzy numbers (PFNs). Initially, they compared the PFNs, even when the accuracy and score values of those PFNs are equal. In Si et al. (2019), the ranking method is based on positive ideal solution, positive and negative goal differences, and score and accuracy degrees of the PFNs. Another score function was developed to estimate the actual score value that depends on the positive and negative goal differences and the neutral degree. Garg et al. (2017) proposed a sequence of aggregation operators, namely picture fuzzy weighted average aggregation operators, picture fuzzy hybrid average aggregation operators and picture fuzzy ordered weighted average aggregation operators and used them in decision-making problems. Wei et al. (2016) extended the cross-entropy of fuzzy sets in the context of PFS and developed the picture fuzzy cross-entropy to solve the multiple attributes decision-making (MADM) problem. Wei et al. (2018a) expanded the TODIM model with the picture fuzzy members (PFNs) and generated the relative weight of all attributes and calculated dominance degree of alternative in respect to all other alternatives to develop dominance matrix. Then they computed the overall dominance degree of each alternative and determined the alternatives’ ranking based on it. Son et al. (2017) introduced and extended the fundamental distance measure using picture fuzzy sets and proposed generalized picture distance measures and picture fuzzy association measures. Son et al. (2016) developed a picture fuzzy set-based distance measurement technique and applied it in the picture fuzzy-based clustering methods. The propositions of various similarity measures such as cosine similarity measure and weighted cosine similarity measure between PFSs were studied by Wei et al. (2018b). Then, the authors applied the similarity measurement methods to detect the building material and recognize the minerals field. Most of the researchers, who investigated in the picture fuzzy domain, considered the neutral membership degree similar to the other two degrees of positive and negative membership during the evaluation of the decision-making problem. But sometimes, it is found to be difficult to differentiate the neutral membership grade from the positive and negative membership grades. Fuzzy sets and extended fuzzy sets are well used to manage uncertainty and vagueness. Besides these, there are other types of uncertainty induced by random phenomena that are called probabilistic uncertainty. But the probabilistic uncertainty is not found to be enough to consider the various uncertain evidence. Dempster (1968) introduced the belief function that presented the subjective assessments by using probability. Then Shafer (1976) extended the Dempster concept into a mathematical theory of evidence where it remains a classic in belief function. That is why this is called the theory of evidence. The combined concept of both of them is called the Dempster–Shafer (D–S) theory of evidence. The theory of belief functions or the D–S theory (Shafer 1976) is a mathematical framework of evidence that can be deduced as a generalization of probability theory. According to the D–S theory, the incidents belong to the sample region to which the nonzero probability mass of the attributes is not a single point but sets. In (Beynon et al. 2000), the authors introduced the basic concepts of the D–S theory of evidence regarding probability and compared it with the traditional Bayesian theory. Pankratova and Nedashkovskaya (2013) presented a mathematical analysis of the sensitivity of diverse combination rules hybridizing D–S theory and analytical hierarchy process (AHP) to solve foresight problems. Donga and Xiao (2015) introduced the hybrid concept of the Dempster–Shafer fuzzy soft sets through the combination of Dempster–Shafer theory and fuzzy soft sets. They developed the FUSE operator applied on Dempster–Shafer fuzzy soft sets and created the relationship between incomplete fuzzy soft sets and D–S fuzzy soft sets. Dutta and Ali (2011) discussed the Dempster–Shafer theory of evidence by considering focal elements as triangular fuzzy numbers. Then they formulated a method for obtaining belief and plausibility measure from the basic probability assignments (BPAs) assigned to fuzzy foal elements. Finally, they used the fuzzy focal elements in Dempster–Shafer theory and executed it to evaluate the human health risk (non-cancer) evaluation process with hypothetical data. The sets that get nonzero mass are considered as the focal elements. The summation of these probability masses is one; however, the basic difference between D–S theory of evidence and conventional probability theory is that the focal elements of a D–S formation may overlap one another. The D–S theory of evidence also provides a way to symbolize and merge weights of evidence. Grey relational analysis (GRA) is a significant part of grey system (GS) theory introduced by Deng in 1982 (Deng 1982). GS presents a system where some part of the information is known and the remaining part of information is unknown. According to this definition, some part of the information is qualitative and quantitative from the entire information. Due to lack of information, some information belongs to the grey area. The uncertainty may be present in the different position within the entire region or may be within the grey area. GRA is more effective to solve the problem where complicated relationships are noticeable among the factors and variables. GRA (Wei and Lan 2008; Deng 1982) provides a collection of strong sets of statements about system solutions. The system is unable to provide any solution if the system does not have any information. Alternatively, the systems generate a unique and acceptable solution due to the availability of perfect information. In the presence of either complete or partial information, grey systems will give a variety of available solutions. Most of the developed picture fizzy number-based MCDM methods calculate the distance between two PFNs. But those methods are unable to estimate the individual importance of the membership values. Suppose one pair of PFNs are P1=μ1,η1,ν1 and P2=μ2,η2,ν2, and another pair of PFNs are P3=μ3,η3,ν3 and P4=μ4,η4,ν4, where μ, η, and ν, respectively, denotes positive membership, neutral and negative membership grade of PFN. Here we consider μ3=ν1,μ4=ν2. Thereafter, the distance of each pair of PFNs P1,P2 and P3,P4 is same, yet one is unable to realize the importance of higher membership values μ1≻μ3, μ2≻μ4 of the first pair, i.e.P1,P2. Those membership degrees have individual importance in different directions. The most acceptable ideal situation has the maximum value of positive membership degree, a minimum value of negative membership degree and average value neutral member degree. Maximum values of positive membership degree are highly desirable to fulfil most of the criteria, and accordingly, minimum value of negative membership degree is less desirable. Whenever the neutral member degrees are found the same for all the sets, then the neutral member degrees have no effect on decision-making. In these circumstances, the most suitable procedure is GRA, which has three individual estimations for maximization, minimization and averaging of positive, negative and neutral membership degrees. The better solutions will be the larger, smaller and average, respectively. GRA is capable of managing the complex connection between parameters. Grey analysis does not attempt to find the best solution but provide a technique for determining a good solution suitable to solve real-world problems (Kuo et al. 2008). This theory motivates the researchers for generating acceptable solutions in grey scenarios and then to upgrade it in a number of ways. Nowadays, the whole world has become fully unbalanced and passing through an uncontrolled situation due to the dangerous and novel virus COVID-19. Most countries are totally stagnant and the people are quarantined to make themselves safe from COVID-19 (Ren et al. 2020). Many researchers are continuously contributing to developing various type of mathematical and hybrid models to predict the future trends, strength and transmission capability of COVID-19 virus, and have drawn some useful conclusions which assist the health department to take the necessary precaution to track and handle the COVID-19 situations. The authors in Melin et al. (2020) introduced a novel hybrid prediction model that can mergethe ensemble architectures of fuzzy logic-based neural networks for response integration. The fundamental concept of the proposed model is to merge several fuzzy-based neural network predictors, control the uncertainty of the individual networks and try to reduce the uncertainty of the total predictions. This model was able to predict the future trends of COVID-19 up to some extent and help the authorities make the necessary decision to handle the health care system in a better manner. The authors in Sun and Wang (2020) collected the COVID-19 data from a decided location within a specific time interval and trained through the ordinary differential equation model for fitting. Then, they modified the simulation by the trained model to realize the effect of the COVID-19 affected visitors. They found that the affected visitors have a great role in the newly introduced case of COVID-19. Stochastic simulations proved that the physical connections could be rapidly increased due to the affected visitors which are considered sufficient for the local outbreak of COVID-19. The confirmed case of asymptomatic patients was significantly less than the model predictions quantity. This indicated that a major portion of asymptomatic patients are not identified/found. Fuzzy-based hybrid approaches for forecasting the confirmed cases and deaths of the countries according to their time series are given in Castillo and Melin (2020). The fundamental concept of this proposed hybrid method (Castillo and Melin 2020) is to combine the fractal dimension and fuzzy logic for enabling efficient and accurate forecasting of COVID-19 time series. The fractal dimension is provided to differentiate and categorize the object. They introduced a fuzzy rule-based system to represent the knowledge about the forecasting time series of the countries. The authors in Castillo and Melin (2021) introduced the hybrid procedure for composing the fuzzy logic and fractal dimension which measured the uncommon activities of times series to classify countries according to their COVID-19 time series data. The proposed method generates an accurate classification of countries based on the complexity of the COVID-19 time series data. Editors (Boccaletti et al. 2020) of the journal “Chaos, Solitons and Fractals” analysed the impact of COVID-19 pandemic throughout the world and felt the necessity to create a unique platform for the researchers to help the society to avoid the worst effects of future pandemics. Recently, Mishra et al. (2021) proposed an extended fuzzy decision-making framework using hesitant fuzzy sets for the drug selection to treat the mild symptoms of COVID-19. Although the researchers are working hard, they are still struggling to recover from this unwanted situation. The scientists from different domains are consistently trying to apply their knowledge in different perspectives such as dominating the virus, identifying the virus, isolating from the virus, protecting from the virus, and finding the treatment of the virus affected patients, to manage the superfluous situation (Kumar et al. , 2020; Ghosh et al. , 2020), which are considered to be the long-term project. As an intermediate solution, the most important aspect is to provide suitable medical service to the affected patients and recover those who are critically ill due to perilous virus COVID-19. The health department of India has classified the COVID-19 affected patients into some categories according to the patient's physical condition. The extreme condition is called severe cases, and this type of patient requires quality treatment (Clinical Management Protocol 2020). The health workers provide some probable treatment (Clinical Management Protocol 2020) to cure the unpredicted virus infection due to the non-existence of any kinds of approved treatment, where the selection of medicines has a huge impact on the recovery rate of the patients. As found in the literature, a few researchers have experimented on the selection of medicines for the COVID-19 affected patients. The proposed medicines for treating COVID-19 affected patients have various functionalities like effectiveness, side effect and some unseen effects that are uncertain. To fill up this research gap, this paper proposes an alternative PFS-based approach using the group multi-criteria decision-making problem to explore the suitable medicines that are considered the most urgent to save the lives of the affected persons. In PFS, we find that among the four membership degrees (positive, negative, neutral and refusal), the neutral membership degree is fully unpredictable and undecided as the positives or negatives degrees of it are completely unknown. But the PFS is needed to be used in managing some real-life situations so that the experts can express their observations and judgment in the form of PFSs. In this paper, D–S theory is applied to the PFS framework to estimate the evidence of the neutral part. In this study, we apply FUSH operation to merge the opinions recommended by the experts in the form of picture fuzzy numbers. In the process, resultant PFS is formed to incorporate the opinions of multiple experts. According to the grey relational analysis, the grey relational grades are evaluated according to grey relational coefficient of the alternatives. We use the proposed approach to select suitable medicines for the affected persons in the context of PFS. The rest of the article is arranged subsequently. In Sect. 2, we discuss PFS. Then, in Sect. 3, we describe the basic concept of D–S theory and discuss some modified concepts of D–S theory and FUSH operator using PFS. Next, a detailed discussion on grey-based relational analysis is given in Sect. 4. We have presented a PFS-based group decision-making method using the D–S theory and GRA in Sect. 5. A numerical example of the proposed method is stepwise discussed in Sect. 6. Then the proposed method is evaluated for COVID-19 medicine selection in Sect. 7. In Sect. 8, we compare the projected method with three existing methods with respect to some conflicting situations. Then we verify the validity of the projected method by the three generalized criteria in Sect. 9. Lastly, the key observations are drawn in Sect. 10. Picture fuzzy sets A picture fuzzy set P on the universe X is defined asP=τ,μPτ,ηPτ,νPττ∈X where μPτ∈0,1 be the degree of positive membership of τ in P, similarly ηPτ∈0,1 and νPτ∈0,1 are, respectively, called the degrees of neutral and negative membership of τ in P. These three parameters μPτ,ηPτandνPτ of the picture fuzzy set P satisfy the following condition ∀τ∈X,0≤μPτ+ηPτ+νPτ≤1. Then, the refusal membership grade ρPτ of τ in P can be calculated in the following way,∀τ∈X,ρPτ=1-μPτ+ηPτ+νPτ The neutral membership ηPτ of τ in P can be thought as degree of positive membership as well as degree of negative membership, whereas refusal membership ρPτ can be explained as not to take care of the system. When ∀τ∈X,ηPτ=0, then the PFS reduces into IFS. For a fixed τ∈P,μPτ,ηPτ,νPτ,ρPτ is defined as a picture fuzzy number (PFN), where μPτ∈0,1,ηPτ∈0,1,νPτ∈0,1,ρPτ∈0,1 andμPτ+ηPτ+νPτ+ρPτ=1 Simply, PFN is signified as μPτ,ηPτ,νPτ. Operations on PFS For two PFSs P=μP,ηP,νP and N=μN,ηN,νN, Cong (2014) defined some operations as given below.P∪N=τ,maxμPτ,μNτ,minηPτ,ηNτ,minνPτ,νNττ∈X P∩N=τ,maxμPτ,μNτ,minηPτ,ηNτ,minνPτ,νNττ∈X P¯=τ,νPτ,ηPτ,μPττ∈X Cuong and Kreinovich (2015) and Cuong (2017) defined some properties on PFSs as given below.P⊆NIf∀τ∈X,μPτ≤μNτ,ηPτ≤ηNτ,νPτ≥νNτ P=NIfP⊆NandN⊆P If P⊆N and N⊆M then P⊆M; P¯¯=P; Distance between PFSs Distances between the two PFSs are defined in Cuong and Kreinovich (2014); Cong and Son 2015; Si et al. 2019). The distance between two PFSs P=μP,ηP,νP and N=μN,ηN,νN in X=τ1,τ2,…,τn is calculated as given below.Normalized Hamming distancedHP,N=1n∑ι=1nμPτι-μNτι+ηPτι-ηNτι+νPτι-νNτι Normalized Euclidean distancedEP,N=1n∑ι=1nμPτι-μNτι2+ηPτι-ηNτι2+νPτι-νNτι2 Example 1 Let P = {(0.7, 0.2, 0.1), (0.8, 0.1, 0.1), (0.7, 0.1, 0.2)} and N = {(0.6, 0.2, 0.2), (0.8, 0.2, 0.0), (0.9, 0.0, 0.1)} are two picture fuzzy sets of dimensions 3. Then.dHP,N=130.7-0.6+0.2-0.2+0.1-0.2+0.8-0.8+0.1-0.2+0.1-0.0+0.7-0.9+0.1-0.0+0.2-0.1=130.1+0.0+0.1+0.0+0.1+0.1+0.2+0.1+0.1=130.2+0.2+0.4=0.83=0.27 Wang et al. (2017) defined some special operations of the picture fuzzy set. They proposed the following operations on PFNs P=μP,ηP,νP and N=μN,ηN,νN.P·N=μP+ηPμN+ηN-ηPηN,ηPηN,1-1-νP1-νN Pλ=μN+ηNλ-ηNλ,ηNλ,1-1-νNλ,λ>0 Example 2 Let P = (0.7, 0.2, 0.1) and N = (0.6, 0.2, 0.2) are two PFSs and λ = 5.P·N=0.7+0.2∗0.6+0.2-0.2∗0.2,0.2∗0.2,1-1-0.1∗1-0.1=0.68,0.04,0.19.Pλ=P5=0.7+0.25-0.25,0.25,1-1-0.15=0.16807-0.00032,0.00032,1-0.59=0.16,0.00032,0.41 Comparison of picture fuzzy sets Wang et al. (2017) used the accuracy function and score function to compare the PFSs. Let M=μM,ηM,νM,ρM be a picture fuzzy number (PFN), then a score function SM is defined as SM=μM-νM and the accuracy function HM is given by HM=μM+νM+ηM where SM∈-1,1 and HM∈0,1. Then, for two PFNs M and T.(i) If SM>ST, then M is higher than T, denoted by M > T; (ii) If SM=ST, thenHM=HT, implies that M is equivalent to T, denoted by M = T HM>HT, implies that M is higher than T, denoted by M > T. Example 3 Let M = (0.7, 0.2, 0.1) and T = (0.6, 0.2, 0.2) are two picture fuzzy sets. Now, SM = 0.7–0.1 = 0.6, ST = 0.6–0.2 = 0.4 HM = 0.7 + 0.2 + 0.1 = 0.9, HT = 0.6 + 0.2 + 0.2 = 1. Since SM>ST, therefore M > T. D–S theory of evidence D–S theory is a mathematical concept of combining evidences based on belief functions and plausibility reasoning. It is used to combine the evidences in order to compute the probability of an event and uses the idea of “mass” as opposed to Bayes theory. It is known as the theory of evidence because it handles the weight of evidence. In order to measure the uncertainty of an event, D–S theory applies an interval [belief, plausibility] where belief is a measure of the strength of evidence in support of a subset and it represents the evidence we have for it directly, whereas plausibility represents the maximum share of the evidence we could possibly have. Dempster–Shafer theory (Shafer 1976) states our assumption between a universe of discourse U and a set of corresponding statements to a group of propositions, where only one statement is true. The propositions are assumed to be complete, mutually exclusive and exhaustive. Let 2U denote all subsets of U including itself and empty set, so the total numbers of subsets are 2U. The basic probability assignment (bpa) function pf on 2U is defined below.pf:2U→[0,1] pf(∅)=0,∑κ⊆Upf(κ)=1,∀κ⊆U Consider κ is a proposition, pf(κ) is the evidence support of κ and pf(∅) is called the degree of ignorance, where pf(κ)>0 is called focal elements of pf for every subset κ⊆U. In PFS Pr,fμιζrκ, fηιζrκ and fνιζrκ are considered as focal elements which, respectively, denote the positive membership, neutral membership and negativemembership value of the ιth alternatives and ζth criteria of the PFS Pr. The belief function can be defined as Belκ,κ∈2U which is mapped into [0, 1] and Belκ is computed asBelκ=∑σ⊆κpfσ Another function called plausibility function of κ, denoted as Plκ, is defined as follows:Plκ=∑κ∩σ≠θpfσ The total belief of κ is represented by Belκ, whereas Plκ measure the total belief that arises under κ. Then the Belκ and Plκ are called lower bound function and upper bound function, respectively, denoted as Belκ,Plκ. The relation between Belκ and Plκ is defined as follows:Plκ=1-Belκ¯ Plκ≥Belκ The uncertainty of the object κ can be represented as:uκ=Plκ-Belκ The facts described above are illustrated using the example given in Table 1 which shows a combination of concordant evidence using D–S theory.Table 1 Example of belief and mass function Function {β} {γ} {δ} {β,γ} {γ,δ} {β,δ} {β,γ,δ} pf 0.05 0.1 0.05 0.1 0.1 0.1 0.5 Bel 0.05 0.1 0.05 0.25 0.25 0.2 1 Pl 0.75 0.8 0.75 0.95 0.95 0.9 1 u 0.7 0.7 0.7 0.7 0.7 0.7 0 Suppose that a patient suffers with three symptoms like high fever (β), dry cough (γ) and tiredness (δ), and then the patient is suspected to be affected by COVID-19. Hence, the frame of discernment is represented by ∅ = {β, γ, δ}. We have considered that the evidence (m) of COVID-19 has been collected and represented by the basic probability assignment (pf). The symbols used in Table 1 represent the above-mentioned fact. Dempster's rule of combination Based on D–S theory, the evidence, belief, plausibility and uncertainty values of a single tone event by basic probability assignment (pf) function are measured above in Sect. 3. Subsequently, when two or more events are present, the situation can be managed by Dempster's rule of combination as mentioned below. Multiple belief functions in Dempster’s rule are combined using their basic probability assignments (m). Consider the two evidences κ={κ1,κ2,κ3,…,κψ} and φ={φ1,φ2,φ3,…,φψ} of the power set ϴ for which the corresponding respective basic probability assignment functions are pf1(κ) and pf2(φ). The evidence combination rule for the two different evidences κ={κ1,κ2,κ3,…,κψ} and φ={φ1,φ2,φ3,…,φψ} is defined below, where θ be the combined evidence of κ and φ.1 pfθ=pf1(κ)⊕pf2(φ)=11-ℜ∑κi∩φj=θpf1κipf2φj∀θ,θ≠Θpfθ=pf1(κ)⊕pf2(φ)=0,θ=Θ 2 ℜ=∑κi∩φj=Θpf1κipf2φj Here ℜ indicates the conflict combination of the evidence named as conflict percentage. The combined evidence needs to be normalized with respect to other combinations, which depends on the normalized factor 11-ℜ. Example 4 Suppose that two teachers are appointed to evaluate 100 students of a particular class for assessment. As per evaluation of teacher 1, 50 students score grade X and 30 students’ score grade Y. According to evaluation of teacher 2, 40 students score grade X and 40 students score either X or Y. Then, the evidence regarding the students' scores are combined and the resultant evidence is determined. Here, ω=X,Y is considered as a frame of discernment and 2ω=Θ,X,Y,X,Y be the power set. We have considered m1 and m2 as mass functions corresponding to teacher 1 and teacher 2. The combination of concordant evidence using D–S theory is shown in Table 2.Table 2 Combination of concordant evidence using D–S theory Evidence pf1(X) = 0.5 pf1(Y) = 0.3 pf1(ω) = 0.2 pf2(X) = 0.4 pf12(X) = 0.2 pf12(Θ) = 0.12 pf12(X) = 0.08 pf2(X,Y) = 0.4 pf12(X) = 0.2 pf12(Y) = 0.12 pf12(X,Y) = 0.08 pf2(ω) = 0.2 pf12(X) = 0.1 pf12(Y) = 0.06 pf12(ω) = 0.04 According to the expression (2), ℜ = 0.12. Then according to expression (1), we are getting the evidence of X and Y as given below.pfX=0.2+0.2+0.1+0.081-0.12=0.580.88=0.66pfY=0.12+0.061-0.12=0.180.88=0.14 FUSH operator In order to perform the proposed FUSH operation, the datasets in terms of PFNs are collected from different reliable sources, and then those datasets are combined and new PFSs is generated which is more reliable and authentic than the inputs. This process is called data fusion and the operator used to combine the data sets is called the FUSH operator which is defined below:PR=PDFUSHPE. Here PD=μD,ηD,νD and PE=μE,ηE,νE are the two input PFSs and we obtain the resultant PFS PR=μR,ηR,νR using three FUSH operations for three parameters of PFS as shown below. In this study, the evidences concerned with positive and negative membership grades are completely given, whereas the evidence corresponding to the neutral membership grade is associated with the positive and negative membership grades. The expression (3) and expression (5) consider the evidence of any propositions of (μ,η) and (η,ν), respectively, whereas expression (4) explicitly depends on η.3 pfμR=pf1κ⊕pf2φ=∑κi∩φj=μorμ∪ηpf1κipf2φj 4 pfηR=pfκ⊕pfφ=∑κi∩ρj=ηpf1κipf2φj 5 pfνR=pfκ⊕pfφ=∑κi∩ρj=νorν∪ηpf1κipf2φj Example 5 Let P = (0.7, 0.2, 0.1) and N = (0.6, 0.2, 0.2) are two PFNs and pf1(P) and pf2(N) are the mass functions regarding the membership functions (positive, neutral and negative) of picture fuzzy numbers P and N (Table 3).Table 3 Combination of concordant evidence using D–S theory and PFN Evidences pf1(μ) = 0.7 pf1(η) = 0.2 pf1(ν) = 0.1 pf2(μ) = 0.6 pf12(μ) = 0.42 pf12(ημ) = 0.12 pf12(Θ) = 0.06 pf2(η) = 0.2 pf12(ημ) = 0.14 pf12(η) = 0.04 pf12(ην) = 0.02 pf2(ν) = 0.2 pf12(Θ) = 0.14 pf12(ην) = 0.04 pf12(ν) = 0.02 We obtain pfμ,pfη and pfν according to expressions (3), (4), and (5), respectively, which are shown below.pfμ=pf12μ+pf12ημ+pf12ημ=0.42+0.14+0.12=0.68pfη=pf12η=0.04pfν=pf12ην+pf12ην+pf12ν=0.04+0.02+0.02=0.08 Then the resultant PFN after FUSH operation between PFN κ and φ is obtained as θ = (0.68, 0.04, 0.08). Note that FUSH operator follows the associate properties that indicate the three PFNs PD, PE and PR hold the relation defined below.PRFUSHPDFUSHPE=PRFUSHPEFUSHPD=PDFUSHPRFUSHPE GRA In MCDM method, the criteria values of the alternatives are of different units and have different influence in the decision-making process due to different ranges of the criteria values (Deng 1982; Kuo et al. 2008). Due to this difference in the units and large interval of criteria values, often incorrect results are generated during the analysis. Hence normalization of all the performance values for all criteria is unavoidable in order to convert them into a comparable sequence. In order to analyse and utilize the GRA model, consider a MCDM problem with m number of alternatives and n number of criteria, where the ιth alternative ATι is represented as ATι=Aι1,Aι2,Aι3,…,Aιζ,…,Aιn. Here Aιζ indicates the observation value of the ζth criteria of ιth alternative. The information regarding alternative ATι is converted into comparable sequence of Csι=Bι1,Bι2,Bι3,…,Bιζ,…,Bιn using one of the following three equations defined below.6 Bιζ=Aiζ-Aζ_Aζ¯-Aζ_,ι=1,2,…,m,ζ=1,2,…,n 7 Bιζ=Aζ¯-AiζAζ¯-Aζ_,ι=1,2,…,m,ζ=1,2,…,n 8 Bιζ=1-Aιζ-Aζ∗maxAζ¯-Aζ∗,Aζ∗-Aζ_,ι=1,2,…,m,ζ=1,2,…,n Here the symbols Aζ¯ and Aζ_ represent the maximum and minimum value of ATι, respectively, and Aζ∗ be the pivot value which is chosen by the expert arbitrarily depending on the problem type. Expression (6) is used to normalize the maximum value as optimum one behaviour type criteria, expression (7) is used to normalize the minimum value as the optimum one behaviour type criteria, whereas expression (8) is used for normalize the criteria which is nearest to pivot value and considered as optimum. In the proposed study, the decision-making method considers the expressions (6), (7), and (8) for normalizing the grey relation of the positive, negative and neutral membership grades of the criteria, respectively. After normalizing all observation values between 0 and 1, the observation value of the criteria ζ with respect to the alternative ι is considered as Bιζ. Then the support of criteria depends on the value of Bιζ. If the value of Bιζ is 1 or closer to 1, then the ζth criteria are most acceptable for the alternative ι in respect to all other criteria. An alternative is highly acceptable when all the observation values are 1 or near to 1. But this type of ideal situation is not generally found. The ideal solution is represented by a reference sequence of combination of 0 and 1, where the reference sequence is denoted as AT0=A01,A02,A03,…,A0j,…,A0n=1,1,1,…,1. Then, an alternative is searched which is closer to the reference sequence. The closeness between an alternative ATι and the reference sequence AT0 is measured by the parameter which is known as the grey relation coefficient. The grey relational coefficient represents the relationship between the experimental result of ideal and normalized information. The grey relation coefficient is denoted as γAT0ζ,ATιζ and calculated by the following equation.9 γAT0ζ,ATιζ=Λmin-ℑΛmaxΛιζ-ℑΛmax,ι=1,2,…m,ζ=1,2,3,…n Here Λιζ=Aoζ-Aιζ,Λmin=minι,ζΛιζ,Λmax=maxι,ζΛιζ, and ℑ is the distinguishing coefficient,ℑ∈0,1. The distinguishing coefficient controls the range of the grey relational coefficient and efficient value of the coefficient is 0.5. Let us consider three alternative c, d and e, and Λcζ=0.4 Λdζ=0.2 and Λeζ=0.8 for the criteria ζ, where alternative d is found to be the most nearest to the reference sequence. Generally, the values of Λmax and Λmin are, respectively, closer to 1 and 0. The grey relational grades (χ) of the alternatives are calculated by the addition of all grey relational coefficients (γ) of the respective alternative as defined in (10).10 χAT0,ATι=∑ζ=1nϖζγAT0ζ,ATιζ,ι=1,2,…,m,∑ζϖζ=1 Here ϖζ is represented as the weight of the criteria ζ and the distribution of ϖζ among the criteria is dependent on the expert’s opinions. The degree of similarity depends on the value of grey relational grade. The highest value of grey relational grade is the closer to the reference sequence and acceptable option. Proposed decision-making approach based on PFS Here we develop a new evidence-based logical decision-making method using PFN for solving the MCDM problem, where PFN is used to represent the decision information provided by the decision makers. Let AT=A1,A2,…,An be the finite set of alternatives, CT=C1,C2,…,Cm be the set of criteria, and D=(dιζ)mxn=μιζ,ηιζ,νιζmxn be the decision matrix in the form of PFNs. The notation μιζ,ηιζ and νιζ represent the positive membership, neutral membership and negative membership degree of ιth alternative in respect of ζth criteria. The proposed algorithmic approach is given below in stepwise manner. Step 1 We present the evaluating values of alternative ATι(ι=1,2,…,n) corresponding to the criteria Cζ(ζ=1,2,…,m) using PFN μιζ,ηιζ,νιζ. Step 2 Merge all the criteria information of an individual alternative into a collective PFN Csι=μι,ηι,νι,ι=1,2,…,n using the FUSH operation which is defined in Sect. 3.2. Step 3 Compute the comparable sequence Csι¯=μι¯,ηι¯,νι¯ for each of the alternatives ι=1,2,…,n from the collective PFNs Csι=μι,ηι,νι, where μι, ηι, and νι are, respectively, obtained using expressions defined in (6), (8) and (7). Step 4 Calculate the grey relational coefficient γι=μι¯¯,ηι¯¯,νι¯¯,ι=1,2,…,n for each alternative ATι to measure the closeness between the comparable sequences of alternatives and reference sequence using (9). Step 5 The membershipwise (positive, neutral and negative) grey relational grade Gι=μι2″,ηι2″,νι2″,ι=1,2,…,n for each alternative ATι(ι=1,2,…,n) is computed based on γι=μι¯¯,ηι¯¯,νι¯¯ for computing the actual grey relational grade χt,ι=1,2,…,n using expression (10). Step 6 Determine the ranking order of the alternatives ATι(ι=1,2,…,n) according to the calculated actual grey relational grade χt, where higher value of χt indicates better rank. Numerical analysis This section presents a numerical example to illustrate the proposed approach. We consider five alternatives AT=A1,A2,A3,A4,A5 and five criteria CT=C1,C2,C3,C4,C5, where the evaluating values of the criteria regarding the alternatives are expressed using PFNs. Those criteria are neither fully supported nor fully rejected by the set of alternatives due to the existence of neutral membership parameters. Thereafter, the decision matrix M is presented in the form of picture fuzzy information, which is shown in Table 4.Table 4 Decision matrix M C1 C2 C3 C4 C5 A1 [0.21,0.64, 0.14] [0.39, 0.44,0.17] [0.21, 0.29, 0.5] [0.07,0.47, 0.47] [0.12, 0.31, 0.56] A2 [0.17, 0.33, 0.5] [0.37, 0.26, 0.37] [0.42, 0.05, 0.53] [0.2, 0.4, 0.4] [0.53, 0.18, 0.29] A3 [0.4, 0.2, 0.4] [0.45, 0.2, 0.35] [0.06, 0.56, 0.38] [0.18, 0.59, 0.24] [0.6, 0.1, 0.3] A4 [0.6, 0.2, 0.2] [0.21, 0.42, 0.38] [0.47, 0.37, 0.16] [0.3, 0.43, 0.26] [0.8, 0.1, 0.1] A5 [0.6, 0.13, 0.27] [0.27, 0.41, 0.32] [0.31, 0.15, 0.54] [0.29, 0.41,0.29] [0.42, 0.25, 0.33] According to the FUSH operation defined in Sect. 3.2, we perform the FUSH operation among the PFNs of various the criteria corresponding to each alternative and generate the collective decision matrix M¯ which is displayed in Table 5.Table 5 Collective decision matrix M¯ Alternative Collective grade A1 [0.07, 0.01, 0.3] A2 [0.06, 0, 0.11] A3 [0.13, 0, 0.1] A4 [0.28, 0, 0.02] A5 [0.11, 0, 0.08] Next the comparability sequence is generated from the collective decision matrix M¯ by the grey relational generating process which is defined in (6), (7) and (8). Generated compatibility sequence is presented in Table 6.Table 6 Comparability sequence of the collective decision matrix D Alternative Compatibility sequence A1 [0.05, 0.98, 0] A2 [0, 1, 0.68] A3 [0.32, 1, 0.71] A4 [1, 1, 1] A5 [0.23, 1, 0.79] Then we calculate the grey relational coefficient based on membership degree of the alternatives in respect to the criterion. This is done using the expression (9) based on the compatibility sequence. The resultant grey relational coefficient is shown in Table 7. During the execution, we consider the distinguished coefficient value as 0.5.Table 7 Grey relational coefficient Alternative Grey relational coefficient A1 [0.95, 0.02, 1.0] A2 [1.00, 0.0, 0.32] A3 [0.68, 0.0, 0.29] A4 [0, 0, 0] A5 [0.77, 0, 0.21] Next the grey relation grades of the various membership degrees (positive, neutral and negative) are computed. Finally, the actual grey relation grades of the alternatives are obtained by adding the grey relation coefficient of the respective membership degrees using expression (10). Here, the weights of all membership degrees are considered to be equal. The resultant actual grey relation grades are displayed in Table 8.Table 8 Grey relational grade of the alternatives Alternative Grey relational grade Actual grey grade A1 [0.51, 0.02,0.50] 1.03 A2 [0.50, 0.02, 0.76] 1.28 A3 [0.59, 0.02, 0.78] 1.39 A4 [1.0, 0.02, 1.0] 2.02 A5 [0.56, 0. 02, 0.82] 1.41 Finally, the alternatives are ranked according to the grey relational grade. Therefore, the order of the alternatives is as follows: A4≻A5≻A3≻A2≻A1. COVID-19 medicine selection This section presents and analyses the usefulness of the proposed technique. As per the information available and our knowledge, there are only a few approved medicines for the treatment of corona positive patients. Henceforth in this paper, we consider the investigational therapies for the COVID-19 patients, where there are many medicines and some of the medicines are considered better and acceptable. COVID-19 is an encrypted form of a large group of viruses (coronavirus) that infects the human and animal bodies that are the main causes for illness. This virus has the enormous potential to be spread from the infected people or animals to other people either by physical contact or through small air transmissions like MERS and SARS virus. As per the information available, the outbreak of novel coronavirus (COVID-19) was noted from Wuhan city in Hubei of the Republic of China in the month of December, 2019 (Ren et al. 2020). At present, most countries around the world (214 countries) are facing challenging problems due to COVID-19 infection. By observing the dangerous impact of coronavirus on the human population, the International Health Regulations (WHO) has already declared this outbreak as a “Public Health Emergency of International Concern” (PHEIC) on 30th January, 2020 and marked it as pandemic on March 11, 2020 (Clinical Management Protocol 2020). Medical experts have observed that COVID-19 patients mainly suffer from the following symptoms and signs: fever, fatigue, cough, shortness of breath, myalgia, expectoration, rhinorrhea, diarrhoea, sore throat, loss of taste (agues) or loss of smell (anosmia), and in severe cases respiratory symptoms have also been reported. Older people and particularly immune-suppressed patients often feel typical symptoms such as fatigue, reduced mobility, reduced alertness, diarrhoea, delirium, loss of appetite and absence of fever. As per information available from the Integrated Health Information Platform (IHIP)/Integrated Disease Surveillance Programme (IDSP), Govt. of India, dated 11.06.2020, 15,366 corona positive samples were collected for experimentation. Among those 15,366 samples, the signs and symptoms for running nose, weakness, breathlessness, sore throat, cough, fever, and others were, respectively, 3%, 7%, 8%, 10%, 21%, 27% and 24% (Clinical Management Protocol 2020). As per the Ministry of Human and Family Welfare, Govt of India notification (as on 19th November, 2020), around 10 million people are affected. Among the affected people, 9.2 million are recovered, 0.45 million are active cases and 1.3 million have lost their life. The health department is consistently trying to provide better treatment to the COVID-19 affected people using the past experience of controlling the pandemic events. Unfortunately, no specific treatments have been approved for the COVID-19 affected people. Several therapies or approaches are considered for managing or curing the COVID-19 patients. The symptomatic treatment of the COVID-19 patients is provided in different ways like mild cases, moderate cases and severs cases. Presently, on the basis of the limited available evidence, health experts use these therapies. Depending on the situation and availability of relevant data, the evidence can be incorporated, and recommendations can be upgraded accordingly. Currently, few drugs such as Remdesivir (Med1), Convalescent plasma (Med2), Tocilizumab (Med3) and Hydroxychloroquine (Med4) are being used in a specified subgroup of patients. Remdesivir (Med1) can be prescribed for the infected persons with the moderate symptoms (those on oxygen) with limited contraindications. Convalescent plasma therapy (Med2) may be conducted to treat the patients with moderate disease who don’t respond (oxygen requirement is gradually increasing) despite the use of steroids. Tocilizumab (Med3) may be applied to patients with moderate disease and with constant requirements of oxygen and patients on mechanical ventilation who don’t show signs of improvement despite the use of steroids. Long-term safety therapies related to the treatment procedure of COVID-19 are still unknown to the greater extent. Hydroxychloroquine (Med4) has interacted in vitro activity against SARS-CoV2, and in several regional studies, it was said to be clinically beneficial although there were significant limitations of it. Those therapies are selected based on the effect of symptoms as well as their antiviral activity and possible side effects. The experts considered four major factors: antiviral activity (Sypt1), coolify (Sypt2), ease breathing (Sypt3) and side effect (Sypt4) as criteria for the performance evaluation of the therapies. The experts apply those therapies based on their past experience without strong evidence. They observed the functionalities of the therapies and realized the effect with certain positive and certain negative impacts and some unknown parts. The observed opinions (performance evaluation factors, antiviral activity (Sypt1), coolify (Sypt2), ease breathing (Sypt3) and side effect (Sypt4)) of the experts corresponding to the drugs (Remdesivir (Med1), Convalescent plasma (Med2), Tocilizumab (Med3) and Hydroxychloroquine (Med4)) for a particular patient are presented in this paper in the form of a picture fuzzy set. Those medicines are used for the treatment of COVID-19 affected patients based on the investigation. However, the Food and Drug Administration (FDA) has not approved yet those medicines as regular medicines. The FDA primarily gave permission to use those medicines for emergency service. The specified medicines can reduce some symptoms of COVID-19 affected patients who are simultaneously facing some other symptoms as a side effect. The medicine Tocilizumab (Med3) works effectively and the patient feels better sooner; at the same time, it increases the symptoms like cough or sore throat, block or runny nose, headaches or dizziness. To capture those kinds of uncertainties, we use PFN as it can manage uncertain situations using the neutral membership function. Henceforth, in this study, the decision matrix D is presented in the form of picture fuzzy information according to expert observation, which is shown in Table 9. This study assumes that the evaluation factors (Sypt1, Sypt2, Sypt3, Sypt4) corresponding to the four drugs (Med1, Med2, Med3, Med4) are represented using picture fuzzy numbers as in Table 9.Table 9 Decision matrix D Antiviral activity (Sypt1) Coolify (Sypt2) Ease breathing (Sypt3) Side effect (Sypt4) Med1 [0.21, 0.48, 0.3] [0.36, 0.23, 0.35] [0.33, 0.35, 0.32] [0.32, 0.34, 0.3] Med2 [0.25, 0.4, 0.25] [0.22, 0.35, 0.33] [0.26, 0.23, 0.45] [0.39, 0.26, 0.32] Med3 [0.23, 0.33, 0.31] [0.61, 0.22, 0.17] [0.4, 0.1, 0.3] [0.4, 0.3, 0.3] Med4 [0.58, 0.13, 0.28] [0.1, 0.2, 0.6] [0.1, 0.41, 0.45] [0. 2, 0.3, 0.2] According to the definition of FUSH operation in Sect. 3.2, FUSH operation is performed among the PFNs of the four criteria corresponding to each alternative and the collective decision matrix D¯ is generated which is displayed in Table 10.Table 10 Collective decision matrix D¯ Alternative Collective grade Med1 [0.17, 0.02, 0.17] Med2 [0.11, 0.01, 0.17] Med3 [0.18, 0.02, 0.10] Med4 [0.05, 0.00, 0.14] Next the comparability sequence is computed from the collective decision matrix D¯ by the grey relational generating process which is defined in (6), (7) and (8). Generated compatibility sequence is presented in Table 11.Table 11 Comparability sequence of the collective decision matrix D Alternative Compatibility sequence Med1 [0.92, 0.0, 0.0] Med2 [0.46, 0.5, 0.0] Med3 [1.0, 0.0, 1.0] Med4 [0.0, 1.0, 0.43] Then we calculate the grey relational coefficient based on the membership grade of the therapies with respect to the symptoms. This is done using the expression (9) based on the compatibility sequence. The resultant grey relational coefficient is shown in Table 12. During the execution, we have considered the distinguished coefficient value as 0.5.Table 12 Grey relational coefficient Alternative Grey relational coefficient Med1 [0.08, 1.0, 1.0] Med2 [0.54, 0.5, 1.0] Med3 [0.0, 1.0, 0.0] Med4 [1.0, 0.0, 0.57] Next, the grey relation grade of the various membership degrees (positive, neutral and negative) is computed. Finally, the actual grey relation grades of the therapies are obtained by adding the grey relation grades of the respective membership degrees using Eq. (10). Here, the weights of all symptoms are considered to be equal. The resultant actual grey relation grades are displayed in Table 13.Table 13 Grey relational grade of the alternatives Alternative Grey relational grade Actual grey grade Med1 [0.93, 0.5, 0.5] 1.93 Med2 [0.65, 0.67, 0.5] 1.82 Med3 [1.0, 0.5, 1.0] 2.5 Med4 [0.5, 1.0, 0.64] 2.14 Finally, the therapies are ranked according to the grey relational grade. The more be the grade, more will be the rank of the therapy or drug. Therefore, the ranking order of the therapies is as follows, Med3 > Med4 > Med1 > Med2, i.e. Tocilizumab (Med3) will be more applicable for that particular patient in the process of treatment. Comparison The grey relation grades/scores and the final sequence of the therapies according to the different methods are shown in Table 14. According to the comparison table information, one can easily realize that the proposed method generates more accurate, clear and non-conflicting results, whereas the previously developed aggregation operator and cross-entropy-based MCDM methods may create the conflict situation. In the aggregation operator-based MCDM method, the score value is generated by the relation SP=μ+η-ν, which does not differentiate two PFNs P1=μ1,η1,ν1 and P2=μ2,η2,ν2 when η1=ν2 and η2=ν1, because at this condition SP1=SP2. Similarly, the entropy distance of two alternatives based on PFNs given in Wei (2016) will be equal, i.e. DP1,P2= DP2,P1 when P1=μ1,η1,ν1,μ2,η2,ν2 and P2=μ2,η2,ν2,μ1,η1,ν1. Our proposed evidence-based MCDM approach manages this type of situation easily. The grade of the therapies depends on the evidence of the supporting grades. Suppose P3=μ3,η3,ν3 and P4=μ4,η4,ν4, are the two PFNs where μ3=μ4,ν3=η4,η3=ν4 indicate different evidence and make different relational grades but generate same score value. We have compared the proposed method with three existing methods and shown that the generated ranking sequence of the proposed decision-making method is better than the ranking sequence of other methods.Table 14 Comparative analysis with other methods Decision approaches Actual grey relational grade (Sypt1, Sypt2, Sypt3, Sypt4) Ranking sequence PFWA operator (Garg 2017) (0.32, 0.32, 0.06, 0.29) (Med2 > Med1 > Med4 > Med3 or Med1 > Med2 > Med4 > Med3) Cross-entropy (Wei 2016) (0.82, 0.82, 0.67, 0.47) (Med4 > Med3 > Med1 > Med2 or Med4 > Med3 > Med2 > Med1) TODIM method (Wei 2018a) (0.72, 0.73, 0.87, 0.86) (Med3 > Med4 > Med2 > Med1) Our approach (1.93, 1.82, 2.50, 2.14) (Med3 > Med4 > Med1 > Med2) According to the calculated actual grey relational grade of the medicines based on our proposed method, med4 is found to be better with respect to the remaining medicines. As per our study, the respective actual grey relational grades of the four applicable medicines are 1.93, 1.82, 2.50 and 2.14. Based on the actual grey relational grade, we can rank the medicines and choose the preferable one for better treatment. Moreover, we apply three other MADM methods, such as PFWA operator-based study (Garg 2017), cross-entropy method (Wei 2016) and TODIM method (Wei 2018a) on the same dataset and the corresponding results are shown in Table 14. The results shown in Table 14 depict that PFWA operator (Garg 2017) and cross-entropy method (Wei 2016) generate conflict situation, whereas TODIM method (Wei 2018a) generates a fair ranking among the medicines with narrow margin. But our proposed method estimates actual grey relational grade of each medicine and ranks them with strong evidence without any conflict incidence. Validity testing Wang and Triantaphyllou (2008) considered three generalized criteria to measure the acceptability of the newly proposed MCDM method. Newly developed methods may generate high-quality output, but the standard of the proposed method is measured by satisfying the following three criteria. Criteria 1 The final ranking of the effective MCDM method does not change the best or optimal alternative due to the interchange of a non-optimal alternative by the worse alternatives without modifying the relative importance of each decision criteria. Criteria 2 Transitivity property should be maintained by the effective MCDM method. Criteria 3 The MCDM problem is divided into two subproblems and applies the same MCDM method to solve each of the subproblems and generate the rank of the alternatives as to the solutions. The resultant rank of the alternatives after combining the rank of the subproblems should be the same as the ranking of the original problem. To check the validity of the proposed evidence-based MCDM method for medicine selection in case of COVID-19 infection, we have verified the given three criteria for validity testing by exchanging the parameters of optimal and worst alternatives, dividing the main problem into two subproblems and solving them, and finally checking the transitivity property by comparing the solutions of the subproblems. Validity check of the proposed approach of criteria 1 This study has checked the validity of the proposed approach by the criteria 1, where the decision matrix given in Table 9 has been modified by the interchanging of the positive membership and neutral membership degree of the therapies Med1 (non-optimal alternative) with Med2 (worse alternative) for all the symptoms (Sypt1, Sypt2, Sypt3, Sypt4) and obtained the intermediate decision matrix D, which is shown in Table 15. The relative importance among the criteria (symptoms) is as usual. Then we have applied the proposed evidence-based MCDM method and generated the actual grey relation grades of the therapies (Med1, Med2, Med3, Med4), which are 2.17, 1.92, 2.50, and 2.00, respectively. Hence the ranking of the therapies according to the actual grey relation grade is (Med3, Med4, Med1, Med2), where the best therapy is Med3 which is also the best alternative in the original decision-making problem. Thereafter, it is proved that the proposed method does not change the ranking sequence of the therapies due to the interchange of positive membership and neutral membership degrees of those two therapies, where one is non-optimal and another is a worse alternative. Hence, the proposed method fulfils the criteria1.Table 15 Modified decision matrix (D) Sypt1 Sypt2 Sypt3 Sypt4 Med1 [0.48, 0.21, 0.3] [0.23, 0.36, 0.35] [0.35, 0.33, 0.32] [0.34, 0.32, 0.3] Med2 [0.4, 0.25, 0.25] [0.35, 0.22, 0.33] [0.23, 0.26, 0.45] [0.26, 0.39, 0.32] Med3 [0.23, 0.33, 0.31] [0.61, 0.22, 0.17] [0.4, 0.1, 0.3] [0.4, 0.3, 0.3] Med4 [0.58, 0.13, 0.28] [0.1, 0.2, 0.6] [0.1, 0.41, 0.45] [0. 2, 0.3, 0.2] Validity check of the proposed approach of criteria 2 and criteria 3 To check the validity of the criteria 2 and criteria 3, the provided MCDM problem is decomposed into two subproblems with therapies (Med1, Med3, Med4) and therapies (Med1, Med2, Med3). Then, the two subproblems are solved by the proposed evidence-based MCDM method and the respective resultant ranks (Med3 > Med4 > Med1) and (Med3 > Med1 > Med2) are generated which are shown in Table 16. Then, the resultant sequence of the two subproblems is merged and the final sequence is obtained as (Med3 > Med4 > Med1 > Med2) which is equal to the ranking sequence of the original problem which satisfies the transitivity property. Hence the proposed method is valid based on criteria 2 and criteria 3 according to the established concepts given in Wang and Triantaphyllou (2008).Table 16 Observation result of two subproblem Subproblem Actual grey relational grade Ranking sequence (Med1, Med3, Med4) (Med1, 5.28), (Med3, 10.83), (Med4, 7.13) (Med3 > Med4 > Med1) (Med1, Med2, Med3) (Med1, 4.58), (Med2, 3.10), (Med3, 10.83) (Med3 > Med1 > Med2) Conclusion In this paper, we have developed an evidence-based medicine selection procedure that belongs to the probabilistic-based uncertainty for the treatment of COVID-19 patients. In the process, we have used PFS to represent the uncertain information, D–S theory to measure the probabilistic uncertainty of the neutral membership grade of PFS and GRA to measure the performance among the set of parameters that are in conflict and contradiction with each other. FUSH operation has been proposed for aggregating the PFNs. The evidence of neutral membership grade of the PFNs might be associated with positive or negative membership grades since the evidence of positive and negative membership grades is completely known. During the fusion, the evidence of the criteria is measured by the D–S theory. Then, we have calculated the evidence and non-evidence degree of the alternatives according to the resultant information using the basic probability assignment function. The contradictions among the criterion are managed by GRA using three normalization process as maximizing, averaging and minimizing the positive, neutral and negative membership grades, respectively. Next, the actual grey relation grade for each therapy is estimated using the membership wise grey correlation coefficient. Finally, the decision has been taken according to the actual grey relational grade of the therapies (alternatives). The proposed method is applied to find the preferable medicine for the treatment of COVID-19 patients. Due to the high mutation power of the COVID-19 virus, no approved drugs are found so far for better treatment. This method has successfully evaluated the preferences of the medicines based on the symptoms and signs of the COVID-19 patients. We have compared the proposed method with the existing three methods and resolved the conflict situation present in aggregation as well as entropy-based MCDM methods. This study has also checked the standard of the proposed method by satisfying the three generalized criteria successfully to measure the acceptance. In future, researchers can extend this model to other extensions of fuzzy sets such as rough sets and utilize the interdependency among the various evaluation criteria for better judgement. As the hidden information in neutral membership grades can be well expressed using rough set theory, one can use it to determine the interdependency of neutral membership grade with the positive and negative membership grades. Author contributions All authors agree for the submission of the paper to Soft Computing journal and have no conflict of interest. AS and SD prepared the initial plan of drug selection for COVID-19 using the hybridization of picture fuzzy set, Dempster–Shafer (D–S) theory of evidence and grey relational analysis (GRA). They prepared the prototype and presented to third author, who gave valuable comments for improvement. SK gave potential support and valuable suggestion related to data collection. The paper was prepared by the AS, SD that was fine-drafted, and the presentation was enhanced by the last two authors. Finally, all authors read the paper fully and the final draft was prepared that is submitted to the journal. Data availability In this study, a self-generated questionnaire, according to the considered criteria, is developed to provide the related doctors and patients. The relevant datasets are given in Table 4 in the manuscript. Declarations Conflict of interest The authors feel interest to contribute towards COVID-19 pandemic situation in terms of suitable drug selection for the positive patients as an endeavour to bring some relief for the society. To consider the uncertainties associated with drug selection, authors have used picture fuzzy set, Dempster–Shafer (D–S) theory of evidence and Grey relational analysis (GRA). Consent to participate The authors are whole heartedly willing and give their consent to communicate and publish their paper in soft computing journal. Authors also declare that they don’t have any conflict of interest. This article has been retracted. Please see the retraction notice for more detail: 10.1007/s00500-023-08594-y Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Change history 5/29/2023 This article has been retracted. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 14792 10.1007/s11356-021-14792-8 Research Article COVID-19 and stock exchange return variation: empirical evidences from econometric estimation Latif Yousaf [email protected] 1 Shunqi Ge [email protected] 1 Bashir Shahid [email protected] 2 http://orcid.org/0000-0003-3751-9634 Iqbal Wasim [email protected] 3 Ali Salman [email protected] 1 Ramzan Muhammad [email protected] 1 1 grid.216938.7 0000 0000 9878 7032 Institute of International Economics, Nankai University, Tianjin, 300071 People’s Republic of China 2 Business Studies Department, Namal Institute, Mianwali, Pakistan 3 grid.263488.3 0000 0001 0472 9649 Department of Management Science, College of Management, Shenzhen University, Shenzhen, China Responsible Editor: Nicholas Apergis 21 6 2021 113 29 3 2021 4 6 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. This research looked at the effects of COVID-19 on a number of the world’s most important stock exchanges, as well as the empirical relation between the COVID-19 wave and stock market volatility. In order to plan proper portfolio diversification in international financial markets, researchers must examine COVID-19 anxiety in relation to stock market volatility. The stock market volatility connected with the COVID-19 pandemic was measured using AR(1)-GARCH(1,1). COVID-19 fear, according to our research, is the ultimate driver of public attention and stock market volatility. The findings show that throughout the pandemic, stock market performance and GDP growth both declined significantly due to average increases. Furthermore, a 1% increase in COVID-19 causes a 0.8% and 0.56% decline in stock return and GDP, respectively. The stock market, on the other hand, showed a slight movement in GDP growth. Furthermore, the COVID-19 pandemic reported cases index, death index, and global panic index all influenced public perceptions of purchasing and selling. As a result, rather than investing in stocks, it is recommended that you invest in gold. The research also makes policy recommendations for important stakeholders. We look to examine how stock returns respond dynamically to unanticipated changes in the COVID-19 scenarios, as well as the uncertainty that comes with a pandemic. Using daily data from Canada and the USA, we conclude that a spike in COVID-19 instances has a negative impact on the stock market in general. Furthermore, in both the increase and decline scenarios in Canada, the stock return reactions are asymmetric. The disparity is due to the unfavorable impact of the pandemic’s unpredictability. We also discovered that uncertainty had a negative impact on the US stock market. The magnitude, however, is insignificant. Keywords Stock return COVID-19 Wavelet Stock volatility ==== Body Introduction With the emergence of the COVID-19 pandemic, the world’s stock markets have suffered unparalleled decreases in the face of great uncertainty. If a market is effective in a weak form, asset prices are, according to effective market expectations, all available and relevant information on the market (Brodeur et al. 2021; Padhan and Prabheesh 2021). In financial theory, the efficient market hypothesis (EMH) has been regarded as mainstream. Furthermore, since the stock market is utilized to distribute resources, authorities should research weak-form efficiency in the stock market (Irfan et al. 2021a). On 11 March 2020, a worldwide pandemic was declared by the World Health Organization. The 17,396,943 cases have been confirmed as of 1 August 2020, and internationally there have been 675,060 deaths (Razzaq et al. 2020; Irfan et al. 2021c). Furthermore, 4,456,389 confirmed cases and 151,265 deaths were reported in the USA. In other words, nearly 22% of COVID-19 deaths worldwide were in the USA. As a result, it’s only natural that COVID-19’s spread has had a significant influence on the US economy (Liu et al. 2020). COVID-19 has recently been the subject of several investigations. The influence of the COVID-19 pandemic on the US stock market, for example, has been studied. These look at the economic consequences of the epidemic in the USA (Soofi et al. 2020; Lin et al. 2020). Its qualities are explored in particular in relation to the global financial crisis (GFC) of 2008. Several researchers investigated as well during the crisis at the market efficiency in the stock market. But many investors consider the decline, during a crisis, as an opportunity to earn a significant profit (Jan et al. 2021; Khan et al. 2021). Many professional investors also allocate portfolio assets using risk management (Susskind and Vines 2020). Because the EMH is linked to these, it is important looking at how this theory affects the stock market during a crisis. This research has a twofold goal. First, we measure and evaluate the efficiency of each sector in the US stock market to determine its EMH. Depending on the nature of the company, the stock returns of various sectors may show varied reactions to the COVID-19 pandemic. Second, we look at how efficient the sector was during the COVID-19 outbreak and the Great Recession (62). According to our findings, some aspects of the COVID-19 pandemic differ from those of the GFC. As a result, investors and portfolio managers can use the efficiency of each industry to help them establish their investment strategy. Several things influence the stock market price (Mohsin et al. 2021b). These events have their individual qualities (Elavarasan et al.; Irfan et al. 2021b). Corporate actions such as stock splits, rights issues, and warrants have an impact on stock prices; however, the effect can be slow. Incidental events like the COVID-19 epidemic, boom fires, major unrest during a presidential changeover, and economic embargoes can all have an enormous one-time impact on stock values. The COVID-19 epidemic has had an impact on capital market practices in every country. Investors can make investing decisions based on the information contained in the event. The case’s news can be regarded in three ways: positively, negatively, or neutrally (Mohsin et al. 2018b). The reaction of the capital market is shown by changes in stock price and stock exchange volume. The market began to react to concerns about the pandemic’s economic repercussions (Irfan et al. 2021d). Consumer response to COVID-19 has been studied in a number of ways. A global COVID-19 death rate of more than 1%, according to Arfah et al. (62), will result in a 0.02% drop in the Standard and Poor’s 500 after 1 day, 0.06% after 1 week, and 0.08% after a month in the Standard and Poor’s 500. According to Hussain et al. (2021), daily unanticipated swings in projected cases based on traditional infectious disease models are referred to as stock sales. Their findings show that stock market volatility will reduce as the pandemic’s progress becomes less unpredictable. From the perspective of stock market investors, Chachi (2021) proved that the COVID-19 health issue translated into a larger economic and financial disaster (Arfah et al. 2020; Elliott et al. 2020). Previous studies on the effects of the COVID-19 epidemic on price exchange transactions had come up with different results. As a result, we wanted to give a more in-depth explanation of the COVID-19 and its consequences on international stock exchanges. This research aimed at testing COVID-19’s positive and negative effects on stock exchange performance. Our input also includes a COVID-19 evaluation and inventory price remarks. The COVID-19 impact on stock return variation in the public was estimated using the AR(1)-GARCH(1,1) and wavelet coherence model. This study also looked at the intentions and actions of several industrialized countries, as well as the reasons for these methods and the efficacy of their recovery efforts. We emphasized the importance of economic recovery plans as the pandemic continues. The major goal of the study was to see if there was a significant variation in stock price and stock exchange value before and after the current pandemic. The remainder of the paper is laid out as follows: The background and literature review are summarized in the “Background and literature review” section. The methodologies employed in this investigation are explained in the “Data and methodology” section. The “Results and discussion” section summarizes the findings and discusses them, while the “Conclusion and policy implications” section wraps up the discussion. Background and literature review The impact of COVID-19 is important, in particular, since China, Asia’s primary center for foreign capital investment, had the first outbreak of the virus (Hussain et al. 2021). Researchers assume the relationships between COVID-19 and SARS, although there are considerable distinctions between the two outbreaks. As we address the influence of COVID-19, we should refer to a number of previous studies on the economic effects of infectious virus epidemics. The prevailing literature focuses on the expenses of medical or economic repercussions of illness, as well as disease-related mortality. (Law et al. 2020) analyzed the spread of Hong Kong’s SARS epidemic and its financial consequences, determining that the demand side had the most substantial negative consequences, with local consumption, tourism, and air travel-related services exports having a short-term impact. The stock markets, according to researchers, are always influenced by large occurrences (Mohsin et al. 2018a). However, as the virus spreads globally, it begins to affect businesses, which is reflected in worldwide financial markets. The circuit brake mechanism struck the US market four times in 10 days in March. Similarly, the FTSE stock market index in the UK has declined by more than 12% since 1987. As a response to the COVID-19 pandemic, the global financial markets, dominated by advanced economies, have shown to lead to increased fear-led market risks, causing likely investment losses as investors were selling panic stock (see Phan and Narayan 2020; among others). Is this discovery possible for all pandemics and epidemics to be extrapolated? This is the important research issue raised in this work. We are also focusing on emerging stock markets, which seem to be less attentive to their developed counterparts during the COVID-19 pandemic (Shakouri et al. 2020). Usually, an integration of mature and emerging stock markets is used to calculate stock market hedging potential (Tiep et al. 2021). A research similar to this one was undertaken by Sun et al. (2020b) and Baloch et al. (2020), albeit their scope, measurements of uncertainty, methods, and analysis were different from this one. According to the consensus, stock values plunged and market volatility surged after the pandemic influenza epidemic (Waheed et al. 2020) (Khokhar et al. 2020a). Those studies, on the other hand, did not investigate whether COVID-19 was to blame for the stock market’s dramatic swings, presuming that no previous knowledge of the break position was accessible (Baloch et al. 2020) (Zhang et al. 2021c). Furthermore, they focused solely on price fluctuations and uncertainty, ignoring the role of return predictability in finance literature. Sansa studied the impacts of COVID-19 on stock markets in China and the USA. The authors discovered a positive and statistically relevant relationship between reported COVID-19 cases and the financial markets, both for the Shanghai Stock Exchange and the New York Dow Jones. Many studies have come up with similar results. COVID-19 has a positive and important impact on stock price trades and stock exchange rate (Nwosa 2021) (Iqbal et al. 2019b). The stock exchanges are interconnected and interdependent. During the financial crisis, researchers discovered near cross-market correlations. Chiang, Nam, and Li from 1996 to 2003 looked at the regular stock returns for nine Asian markets and observed a strong link between the sample Asian countries throughout the crisis period (Chien et al. 2021b). According to (Iqbal et al. 2020), Malaysia, Vietnam, and Thailand are the Southeast Asian countries with the most financial ties to China. Investor sentiment has a more influence on stock markets in nations where herd-like behavior and overreaction are more prevalent (Anser et al. 2020), or in nations where institutional participation is limited. On 30 January 2020, the WHO announced the novel coronavirus disease 2019 (COVID-19) an “emergency of international concern” and a pandemic on March 11. According to the WHO’s Situation Report - 79, the disease has claimed the lives of 79,235 people worldwide as of April 8, 2020 (Khokhar et al. 2020a) (Siddiqui et al. 2020). Although it appears to be coming to an end in China, where it was first discovered, it continues to spread across Europe, the USA, and other parts of the world, including several low- and middle-income countries (LMICs) (Yumei et al. 2021). The pandemic has prompted unparalleled global responses. Travel bans, confinement, and lockout measures have been implemented in many countries. These responses have been implemented in an “emergency mode” and are primarily reactionary, to prevent the spread of the disease until a specific cure and/or vaccine is created (Elena 2020) and (Devi et al. 2020). During these lockdowns imposed by the government, the world economy suffered a considerable fall and remains the outcome of the suspension of big firms (Mishra et al. 2020) (Abbas et al. 2020). The documentary has been examined from two angles: (1) the transmission instability during the COVID-19 crisis and (2) the relationship between the COVID-19 indicators and stock market performance and economic uncertainty. We examined recent publications on the economic and financial effects of the COVID-19 pandemic (Iqbal et al. 2019a). Several new publications looked at COVID-19’s financial and socioeconomic consequences (Tlemsani et al. 2020). There was significant growth on the conditional connections between financial and non-financial corporate equity returns (Mohsin et al. 2021a). This growth was far larger for financial enterprises, demonstrating that they played an important role in expanding the financial disparity between China and the G7 (Hou et al. 2019). They finally demonstrated that the optimum hedge ratios in most instances increased considerably during the COVID-19 crisis, signifying increasing hedge costs. According to Akbar et al. (2021), at the beginning of the COVID-19 epidemic, China’s markets were the epicenter. The significant, detailed, and recent parallels between the words “corona” and “corona” were also demonstrated. Baloch et al. (2020) report that the fast expansion of COVID-19 has had a considerable impact on the stock market worldwide, which leads to major increases in global financial market volatility in the short term, and a huge loss to investors (Phan and Narayan et al. 2020). Data and methodology Through a revised wavelet coherence methodology, the relationship between temperature and COVID-19 confirmed cases can be examined over time scales (regardless of the time series). Similar to classical correlation, wavelet coherence can discover specific locations in the time-frequency domain where big and sudden shifts in the observed time series co-movement patterns occur, while most coronavirus positive cases incubate in incubation centers for about 14 days (Iqbal et al. 2021b). The highest number of hours in incubation centers, supported by the WHO decision for positive coronavirus infections, is 14 days. The pre-test study showed the stock index volatility pattern in numbers during the COVID-19 outbreak, based on International Market Index (Sohail et al. 2019) (Baloch et al. 2020). These graphs indicate how stock market volatility is linked to the COVID-19 outbreak’s global fear index, which is still present in global stock indexes. The results of the interconnected distribution were based on marginal distributions presenting asymmetric distributions AR(1)-GARCH(1,1). Glosten, Jagannathan, and Runkle had previously described and operationalized the AR(1)-GARCH(1,1) model (1993). The following is an explanation of the AR(1)-GARCH(1, 1) model, whereas similar econometric methodologies have been used in various applications (Fu et al. 2021; Sun et al. 2020b). Because this is a new virus, there are still a lot of questions about it. As a result, traditional techniques of creating policy orientations may not be applicable. The employment of sophisticated techniques to illustrate the connection between temperature, exchange rates, and verified instances of COVID-19 seems considerable to represent this association at varied times and frequencies (Asbahi et al. 2019). It is critical to have a thorough grasp of the condition to take preventative actions that will save more lives. In multiple time series analysis, the wavelet approach has several advantages: it allows cross-analytics. It also simultaneously records bi-directional (lead-lag) interactions between various time-frequency combinations (Iqbal et al. 2021a). In the vast majority of cases, our data reveal that the variables in Canada are in phase. A big island visible in 4–8-day bands between the 7th and 23rd of April implies significant coherence between 5 and 10%, with variables in-phase (0,/2) and examples leading. In the bulk of China’s occurrences, the factors are in sync. Variables are in-phase (0,/2) in most events in France (Khokhar et al. 2020b). The black area, which extends from short-run day to long-run day bands between 9 April and 19 April, exhibits the same results, with phase variables (0,/2) and leading cases. A small circle of 8–16 days on the 5th–9th of April likewise shows that variables are out-of-phase (-/2,-), but that the occurrences lead. The bulk of events in Germany have in-phase variables (0,/2) with cases taking the lead. The variables are in-phase (0,/2) with cases leading in 1–2-day bands, the set of dark sections. However, occurrences between March 10 and March 20 imply that the variables are out-of-phase (−/2,−), with cases leading in the 8–16-day bands. 1 σf2=ω+∑e=1qαeεf−12+∑j=1pβjσf−j2 2 Ve,f=β0+β1Ve,f+β2Ve,f−1+β3σ+ɛe,f σft2 displays the conditional variance and f3 shows the remaining error. Equation (1) comprises three parameters (and), three parameters, and distributions which characterize the model of AR(1)-GARCH(1,1). The equation has 8 constructions representing parameter estimates. As the GARCH shows, in addition, the robustness of the latest model test is tested by proposed methodologies. Green loans are assessed by total green credit of banks/total banks’ banks and used as a variable predictor, while economic development is measured by economic development (Chandio et al. 2020; Baloch et al. 2020; Zhang et al. 2021b, and Alemzero et al. 2020). Finally, current spending on education and gross capital formation (percent of total expenses) (percentage of GDP) are utilized as predictors. It was determined that the AR(1)-GARCH model (1). It was applicable to the measurement by He et al. of the impact of the COVID-19 on stock market volatility (62). Ng and Chan employed this triaxial model, a multivariant case version in which the effects of all other variables are omitted from the coherence between X and Y (Sun et al. 2020a). The relation between two time series must be understood. For this purpose, the phase difference that can be utilized to characterize the phase relationship between two series. When the phase difference is zero, they move together at the frequency specified (0). The series is in-phase, when the time series is between [0,/2] and leads the time series e. If it is between [−2,0], e is in front of f, on the other hand. If the difference is between phases [/2,], x leads and time series f leads; if the difference between phases is between [−,−/2], then the relation is anti-phase (analogous to negative covariance). 3 e,f=ge,fɛe,f,ɛe,f~RkTuλ 4 Hef=ωef+αϵ2f−1+βhef−1+γe2f−1−eef−1 Stock market volatility is crucial to both market practitioners and policymakers, particularly in emerging nations (Agyekum et al. 2021). Stock market volatility is a source of concern for practitioners because it has an impact on asset value and risk, while policymakers aim to decrease unnecessary volatility to maintain economic and fiscal stability (Iqbal et al. 2021b; Li et al. 2021). In together circumstances, a good quantitative strategy for modelling stock market volatility is required to limit the danger of erroneous calculations (Zhang et al. 2021a). Researchers are still working for the optimum volatility model that can capture a number of stylized aspects about market volatility in this regard (Chien et al. 2021a; Zhang et al. 2021a). Wavelets are a sort of function that is localized in both the time and frequency domains and is used to breakdown time series into more elementary functions that convey different information well about time series. Wavelets are one of many extensively used statistical signal extraction and filtering, as well as thresholding approaches. The enormous global effect of the COVID-19 crisis on world economies, especially in emerging and developed markets, is one of its first distinguishing features. In this regard, the International Monetary Fund and other multilateral institutions initially projected that the amount needed to cover these countries’ transitional financing deficits would be USD 2.5 trillion. The effects of this recession have been different from those of the previous economic crisis in 2008, impacting all levels of the labor market; as a result, Merkl and Weber predicted the emergence of a new COVID-19 generation of graduates who would be unable to find work, and that employment subsidies would be one of the few effective steps for labor market stability. Pak et al. suggested that concerted international policies be put in place to ensure the survival of humanity as well as the economic stability that existed prior to the emergence of COVID-19. In general, the application of wavelets to the analysis of COVID-19’s impact on various financial markets revealed, according to Kumar et al., that the observed unexpected changes in the structure of the variability of returns were due to the variable structure of investors’ activities in a context of complete uncertainty, as COVID-19’s outcome was catastrophic in international financial markets. We were able to map the effect of COVID-19 by analyzing the coherence of the waves, and we discovered some stylized details that characterize its ramifications. For example, given a regular frequency of 32 days, the ACPS observed co-movements between the variance in cases of COVID-19 and the returns of stock indices (except for Germany), with a gradual decrease in volatility visible from the 18 March subperiod onwards. In each region, there were also relatively unique or differentiated relationships: the financial crisis caused by COVID-19 was more severe in the financial markets of South Africa (primarily), Brazil, and Australia than that in the USA, Japan, and Germany. The waves’ coherence allowed for the development of a chronogram that detailed the corresponding movements in or out of phase with the financial markets studied. The study of the financial assets included in CSR at the global level revealed that they were in-phase in each subperiod, with the USA following suit. The persistence of volatility in Brazil and Australia, which was not apparent in the rest, was notable from the 13 May subperiod onwards. The variables’ co-movement was designed for conceptualization using these formulas, and the results were robust when using the GARCH approach. With the economy completely disrupted, the government should focus more on government spending to help the economy recuperate. It often encompasses increasing public spending, which will focus on improving cash flow and asset volatility, pouring more money in citizens’ hands and encouraging increased production of goods and services. As a result, the economy’s supply levels or volumes will increase. The empirical framework of wavelet coherence is used to investigate the relationship between two or more variables. 5 Wxyqrko=WqkoWr∗ko Where the transformations of the supplied wavelet x (t) and y(t) ensure, for example, Wx (k, o) and Wy (k,o), the index of the wavelet should be evaluated by n.. 6 xt=1Cψ∫0∞∫−∞∞Wqkoψk,otdudoN2,N>0 Whereas 7 x2=1Cψ∫0∞∫−∞∞Wqko2dmdoN2 The wavelet transformation is thus used as an empirical tool for evaluating the underlying variables in non-fixed time series. The objective function can evaluate the correlation power between two variables in one series in cross-wavelet transformations. The updated wavelet coherence coefficients and the mathematical model are calculated as follows: 8 R2ko=NN−1Wqrko2NN−1Wqko2NN−1Wrko2 R means the wavelet compression smoothing method, while 0 R2(m, n) 1 squared coefficients provide in the bracket of wavelet coherence. Values of consistency below the threshold are removed by threshing the estimated parameter and an estimate of 1 point to show a better correlation of values between the variables to be evaluated. The objective function for the reduction of hard thresholds is as follows: 9 xht=0ifx≤λxifx>λ, And now 10 xht=0ifx≤λx−λ2xifx>λ, 11 xyt=0ify≤λy−λ2yify>λ, The transformer transforms are two-transforming features designed for X and Y wavelets, respectively. W.X. and W.Y. The initial phase of the mixed debate (W xy) may be defined: 12 DWoXsWoY∗SσXσY<p=UϑpϑPkXPk,y The degree of certainty associated with the probability p is denoted by U (p), which is defined as the square root of multiplying two distributions. Basing upon this amazing dependency, the Granger causality (GC) area has been determined to characterize the two series. 13 x=a1xt−1+…+apxt−p+β1yt−1+…+βpyt−p+β1t By the test Geweke (1992) proposed in zero linear restriction, the null hypothesis was validated, which is: H0: My→x(ω) = 0. Results and discussion COVID-19 analysis Table 1 presents the unit root analysis. COVID-19 is rearranging our culture, causing fear and concern about the novel coronavirus’s effect on Americans’ mental health. The current research looks at how COVID-19 terror, concerns, and perceived danger interact with social vulnerabilities and mental health outcomes, such as anxiety and depressive symptomatology. Unemployment increased dramatically during the pandemic for a variety of causes, most notably the shutdown of numerous unnecessary companies. This resulted in a rise in poverty rates, as people were unable to work and their discretionary income was drastically decreased, leaving them unable to pay their tax obligations. Table 1 Unit root analysis Constructs Levin-Lin FPP FADF IPS Dicky-Fuller COVID-19 RCIt 1.71 11.06 11.06 2.00 - ∆COVID-19 RCIt −0.22 32.15* 68.3* −0.10 l(1) COVID-19 RDIt 2.65 7.00 3.17 0.12 - ∆COVID-19 RDIt −13.51* 61.65* 22.07* −6.35* l(1) COVID-19 GFIt 5.66 3.20 5.57 0.77 ∆COVID-19 GFIt −1.00* 86.01* 35.27* −1.38* l(1) MAt 5.07 5.80 5.63 1.20 ∆ MAt −2.16* 60.12* 25.5* −2.26* l(1) MA-CCt 11.50 32.11* 57.50* 6.18 ∆MA-CCt −3.07* 20.00* 26.12* −3.13* l(1) MA-POLt 5.18 27.06 25.09 4.01 ∆MA-POLt −0.04* 40.01* 79.35* −4.75 l(1) Note: * indicates 1% significance level The largest overflow was from SP500 (6.089%), the largest was from SP500 (30.795%), and the largest (21.428%) was from S&P500, and the biggest from SP500 were DAX returns (21.428%). A proportion of the entirety is 31.036%. This pandemic has had a significant effect on this business, leading to lower material costs and a shortage of manpower. Companies could obtain grants for long-term employers to help them sustain their economic activity, such as income tax savings or tax holiday periods. According to moving window research, COVID-19 had a greater impact on bond instability than the 2008 world financial crisis. The autoregressive distributed lag (ARDL) test is presented in Table 2. As a result, 95% and 90% trustworthy, statistically significant estimates of the wavelet coefficient within the influence cone respectively are ignored in each area outside the cone. For the most part, variables in-phase (0,/2) are reported in Belgium. The Dark Island depicts the in-stage variable (0,/2), with COVID-19 cases resulting to 1–2-day bands, between 29 April and 5 May. Between March 25 and 30, the enormous island exhibits the same pattern in the 2 to 4-day bands, but another island displays in-phase (0,-/2) between April 9 and 17, but with temperatures beyond COVID-19. Table 2 Autoregressive distributed lag (ARDL) test Indicator Value Std. dev T-sig P-sig COVID-19 50.23* 28.77 5.22 0.000 S&P500 7.57* 8.00 3.42 0.001 DOW 9.35* 28.31 7.77 0.000 DAX 18.43* 1.007 5.58 0.000 CRIA 27.87* 77.05 5.08 0.000 Cyprus 17.57* 0.201 2.77 0.000 MA-POL 19.28* 0.632 2.52 0.003 Note: * indicates 1% significance level Due to changes in US geopolitical risk, uncertainty about US economic policy has grown, generating COVID-19 volatility and price of oil. Therefore, the use of power will only increase by 3.1%. By the end of 2020, the energy industry will have a moderate effect. The average rate of return was 25.32%, less than the whole variance in the 40.11 correlation degree percent (for constant), demonstrating that stock exchange index volatility return rates around the world are strongly tied to their respective volatility. COVID-19 had an impact on industrial productivity, resulting in a significant reduction in China’s electricity usage. The robustness test is shown in Table 3. Relative volatility transmission occurs in the dynamics of long, medium, and short frequencies, in opposite to the reappearance spillover influence in frequency dynamics. Furthermore, thanks to the COVID-19 pandemic, a substantial surge in stock market volatility was noted in mid-2020. Low-demand families who consume fewer than 24 barrels every billing cycle would be the most affected by this legislation. At the conclusion of week three after the initial outbreak notification, nations with significant individualism experienced a 12.71% smaller recession than nations with a low individualism. We also found that countries with a higher rate of evasion for uncertainty had decreased stock prices by 5.40% compared to countries with lower levels of avoidance for uncertainties in the same time period. Stock price drops are connected with higher stock return volatility. Countries with low individualism and considerable ambiguity resistance, in particular, showed increased volatility and price declines in the early weeks of the pandemic. Our results are especially relevant when considering that as COVID-19 unfolds, there is increasing worry about future economic consequences, and presumably increasing economic uncertainty. As observed by Liu et al. (2021), the ongoing COVID-19 situation will have tremendous ongoing economic repercussions. Table 3 Robustness test Indicator Value Std. dev T-sig P-sig R2 Sparsity τ = 10th COVID-19 RCI 4.24* 4.16 1.56 0.000 0.66 1045.12 COVID-19 RDI 1.74* 0.64 0.44 0.017 (0.000) COVID-19 GFI 1.57* 0.75 0.47 0.000 MA-CC 0.77* 0.15 0.64 0.027 MA-POL 0.44* 0.27 0.77 0.014 C 107.44 111.64 0.75 0.011 τ = 25th COVID-19 RCI 4.04* 1.11 1.06 0.040 0.72 1424.05 COVID-19 RDI 1.05* 0.57 0.44 0.026 (0.000) COVID-19 GFI 0.46* 0.77 0.20 0.000 MA-CC 0.77* 0.40 0.22 0.001 MA-POL 0.64* 0.74 0.01 0.004 C 124.74 217.01 0.65 0.000 τ = 50th COVID-19 RCI 4.16* 4.14 4.44 0.000 0.62 1077.64 COVID-19 RDI 4.44* 2.47 4.11 0.025 (0.000) COVID-19 GFI 2.02* 2.54 0.01 0.044 Note: * indicates 1% significance level COVID-19 index volatility of stock market Globally, during COVID-19 outbreak, markets dropped so much value over such a short amount of time that the disease had a huge detrimental effect on communities. We show that some of the turmoil was induced at least by the investment sentiment–dread triggered by a pandemic of coronavirus. The pandemic of the coronavirus has been devastating the major economies in the world and has caused the most severe world recession over generations, with a declining per capita income in the largest proportion of countries since 1870. The IMF says that in 2020, the world economy will fall by 3%, with international economies in decades at their quickest pace. According to the fund, his death is the worst ever since the Great Depression. The epidemic will cut $9 trillion off the world gross domestic product over the next 2 years, according to I MFI Chief Economist Gita Gopinath. In addition, the world GDP is set to contracted by 5.2% in accordance with the World Bank’s June 2020 Global Economic Perspective Report. In the following months, financial markets will fluctuate in various countries in response to COVID-19 findings and corresponding government control actions or stimulus packages, such as direct financial aid or interest rate reductions (Ashraf, 2020). These profound developments have attracted many scholars from throughout the world. As a result, a number of researches are being carried out on different financial markets throughout the world to study the impact of the COVID-19 epidemic. Ashraf (62) utilizes a panel data study to analyze the effects of growth in reported cases of COVID-19 and mortality on stock exchange returns after adjustment of nation characteristics and systematic risk owing to external influences. He used the usual COVID-19 and stock return data from 64 nations between 22 January 2020 and 17 April 2020. The results reveal that stock markets react substantially in confirmed cases to negative growth returns, whereas the response to rise in mortality is statistically insignificant. Other data imply that stock markets react sharply in the initial days of the cases reported and then again 40 to 60 days later. However, due to government foreign exchange operations, even the people, investors, incur large losses on their gold market. Even after the outbreak, no substantial co-movement was detected between currencies, gold, and the stock markets. Investors in stock markets are likely to obtain a currency analysis. There was a negative connection between these in the near term and the affected economies of the COVID-19, while the recession allowed for long-term investors to invest more. The post-war scenario of the COVID-19 could lead to a robust bursary recovery, and investors can benefit from dividends and capital gains. Figure 1 shows the first COVID-19 outbreak wavelet. We begin by examining the consistency of each pair of variables and then discover several significant correlations. Fig. 1 Wavelet coherence between stock return and COVID-19 cases COVID-19 has also altered production and promotion processes because local physical touch is necessary. The fiscal ramification demonstrates that the long-term restricted associations suggest that there has been a significant change between both markets, which suggests that during volatility, investors regularly adjust their portfolio structure. Subsequently, multiple risk spills between stock markets took place during the COVID-19 crisis. The market is changing over time and is unpredictable; thus, a portfolio manager is unable to adapt the portfolio erection during a crisis period. The component root test was done and stated data were determined to fit the initial difference in all the research constructions (see Table 4). The model AR(1)-GJR(1,1) gave some surprising insight. The intercept (β0) (β1) and mean stock volatility index coefficient are equal to each relative and close to zero sample market (Mohsin et al. 2018b; Muhammad Mohsin et al. 2021b). Table 4 Individual stock return Variable Case(i) Case(ii) COVID-19 S&P500 −0.177* −0.235** DOW −0.051* −0.032 DAX −0.162* −0.112* S&P500 −0.174* −0.213* NASDAQ −0.246* −0.334** DOW −0.475 −0.381* DAX −1.427* −1.783* CRIA −0.424* −0.332* Cyprus 4.422* 3.321** Constant −2.444* −1.552* AR(2) p-value 0.462* 0.556** Hansen p-value 0.442 0.332 Note: ,,** indicates 1%, 5% and 10% significance level Short-range and long co-motion between COVID-19 instances and temperature is shown by wavelet consistency between number and temperature of COVID-19 cases validated. There is evidence that the variables are cyclical (in stage) in most cases, and temperature leads to linkage in most countries. This shows that a strong influence of temperature on COVID-19 disease propagation is demonstrated by an increase that contributes to the reduction in COVID-19 transmissions. This is consistent with previous findings. As a result, our results highlight the negative relationship between the COVID-19 instances and the indices in question. This demonstrates that investors grew increasingly aware of the COVID-19 pandemic’s long-term effects and were anticipating lengthier global in view of the escalating number of confirmed cases; lockdowns have been implemented, resulting in economic and social costs. The enactment of the global lockdown resulted in a huge reduction in stock market index points. The COVID-19 has contributed to the increased stock market volatility during the sample period, with several consequences, including countrywide lockdown, higher unemployment, and lowered levels of consumption. Our findings however contradict those of who argue that the COVID-19 pandemic has constructive, short-term effect on stock markets in the countries affected. The findings of individual stock return verify the high negative impact on stock returns in all affected countries and regions of verified COVID-19 incidents. The results are also congruent with those of Sharif et al. (62) suggesting that all frequencies be investigated. Since late March 2020, stock prices have increased 37% since the US Federal Reserve and the expansionary policy of the government. Due to the COVID-19 virus that has swept the world, every major bond index lost value between 6 March and 18 March 2020. Investors’ perspectives have been reflected in global stock markets since the 2008 financial crisis. UNCTAD (62) estimates global depression as a result of COVID-19 shocks, including losses of income and extensive unemployment, and negligent repercussions on financial markets, investor trust, global trade, and goods prices. UNCTAD (62) also estimates that impoverished countries will lose almost $800 billion in export income in 2020 (excluding China). This is to add to the challenges that currency devaluation faces versus the dollar, as a result of the foreign exchange earnings. As far as the Chinese currency is concerned, our results show a considerable (in-phase) joint move between the ERR and COVID-19, where the co-operation between exchange rate instances and COVID-19 is considerably offset phase. That implies that the foreign currency exchange rate of the COVID-19 instances has minimal impact on the exports of Chinese. Phan and Narayan (62) also investigated the response to the outbreak of currency epidemic from the USD/GBP and USD/TRY markets, showing a decreasing level of media stress over the epidemic expansion as of April 2020. The worst-hit nations in Europe, particularly Spain and Italy, suffer from their currency free fall and fewer investment into their systems—the severe economic effects. Due to the increase in COVID-19 cases in 1%, for example, in the S&P 500, the fall in the S&P 500 in −0.161 was 12.8%; the variance in NASDAQ from −0.188 to −0.054 and the variation in the DAX from −0.172 to −0.012 are shown in Table 5. CRIA’s stock exchange rate has the most significant impact on COVID-19. During the pandemic, the GDP logarithm has been utilized on account of control variable. The econometric assessment and findings of COVID-19 and the unpredictability of the stock market is presented. Almost all returns on the stock markets were associated negatively to COVID-19 cases, which meant that the COVID-19 timeframe is near to zero. These data show that the volatility of markets through the whole pandemic has decreased as a result of the global COVID-19 outbreak. The GARCH coefficient (0.35) of the Shanghai SE is wide, while minor GARCH coefficients are available on the other markets. International exchange disruptions are predictable to have serious effects on stock markets. If today’s pattern continues, COVID-19 will cause stock exchange declines of between 14 and 32% and foreign investment declines of 30–40% by 2022. It is also projected that multinational companies will cut their investment abroad by 20–30% by 2020. These estimates should only be raised by government efforts to bring commercial networks back to the USA. While professional and political figures view COVID-19 as the highlight of globalization, the danger to rely heavily on other economies is also highlighted and the current effect of spillover intensification. Others say that the effects of the COVID-19 will be ephemeral and that it does not assist countries cope with the crisis to push commerce inward. Table 5 First-order difference analysis Paradigms First-order differences FC −1.43 (3) −3.48 (2)* FI −1.29 (3) −3.59 (1)* φ −1.33 (3) −3.43 (2)* ω −1.41 (3) −3.33 (2)* The study’s coefficients were significant at a p-value of 5%. Quite unexpectedly, there was a greater coefficient in the Shanghai Bourse and Dow Jones Industrial Average. At 5% level, all international stock exchanges have statistically important volatility coefficients (Table 6). That shows nevertheless queuing with the universal index of dread of the COVID-19 epidemic; the volatility index of global financial markets changed. Since stock indexes have shut down, the international economy has been exaggerated by the COVID-19 epidemic. The return on US Treasury Bonds for 10 years has likewise fallen to 0.67%. Several recent research studies have studied and demonstrated the detrimental impact on financial markets of the COVID-19 pandemic. Table 6 AR(1)-GJR(1,1) model estimates1 S & P (500) NASDAQ DOW DAX CRIA Cyprus FC −0.0214* −0.0360* −0.0214* −0.0289* −0.0241* −0.0214* FI 0.045* 0.245* 0.434* 0.665* 0.326* 0.675* FI 0.0019* 0.0214* 0.0065* 0.0399* 0.0032* 0.0021* ∅ −0.001 −0.002 −0.001 0.003 0.065 0.765 W 0.0004* 0.0002* 0.0022* 0.0060* 0.0029* 0.0000* φ 0 0 −0.001 0 0 0 ∅ 0.0410* 0.0532* 0.0301* 0.0458* 0.0501* 0.0199* ∄ 0.676 0.324 0.546 0.765 0.324 0.344 ∩ 0.4751* 0.149* 0.4156* 0.7635* 0.4876* 0.345* AIC 0 0 0 0 0 0 φ 0.3014* 0.250000* 0.2141* 0.5012* 0.2965* 0.2145* 1Skewed t distribution regarding calculations was offered here as models about four marketplaces Note: * indicates 1% significance level FC is the constant, FI the COVID-19 fear indexes, the equivalent is the variance coefficient for an index of stock price volatility, and β is the variability of the dependent study. The autonomy parameter is equal to 2 with the exception of the S&P500 index. The German economy has also a “sharp-V” characteristic of recovery. It shows the economy has collapsed and rebounded rapidly. This form represents an economic loss and an increase in job patterns, GDP, and the rate of output in industry. Finally, the economy is subject to serious fines for people who do not comply with the rules on masks (50 euros). As a result of the abovementioned national measures, the Dutch economy is expected to fall first by 11% before expanding quickly and becoming stronger than before. Table 7 displays that the enduring volatility frequency is the lowest overall volatility spill, followed by the lowest volatility (0.049%), followed by the lowest volatility (0.297%) and the highest volatility frequency. More specifically, the NASDAQ (28.433%) had the most volatility spread. The S&P500 saw the largest volatility spillover of DAX (24.556%), the S&P500 had the highest volatility effect (38.195%), and the S&P500 had the largest volatility effect in Cyprus (38.195% and 56.9011%). Table 7 GARCHX estimation analysis Mean equation Variance equation COVID-19 0.325*** (0.002) 0.384 (0.12) - S&P500 −0.6397(0.323) 0.348** (0.000) - DOW −1.9982* 1.364**(0.000) - DAX - 0.0050 (0.000) S&P500 - 0.4571* (0.000) NASDAQ - 0.5124* (0.0000 DOW - −0.201 (0.000) * Diagnostics LM test for heteroscedasticity (0.76) (0.80) Source: Authors’ calculation Note: ,** indicates 1% and 10% significance level The epidemic saw strong economic growth in European countries. The Dutch economy was later revised, and favorable results in the fourth quarter of 2020 were attained. The economy was saved despite the losses caused by global fatalities. Contrary to other countries, official lockouts and recommendations were enacted by the international community, including distances from the welfare system, labor at home, and protection of people. There are three sections of the economic recovery approach. The brief period of recovery is as follows: The initiative proposes to offer a stimulus grant of 130 billion euros. The aims of the COVID 19 conference are to ease the recession and increase demand. By the end of 2022, if not sooner, the economy is expected to return to track with this scholarship. In addition, it provides German people optimism that they will recover. Conclusion and policy implications The purpose of this study was to observe the immediate effects on world stock markets of COVID-19. In addition, this research explores the unexpected impacts of a feared disease pandemic on financial markets. Coronavirus outbreak rapidly influenced worldwide supply and industry. The COVID-19 influence has resulted in new plans and methods in the future. The connection between production and consumption has been broken and the epidemic has developed. In order to quantify its impact on public consideration to stock exchange volatility, COVID-19 is dependable; based on wavelet methods, lead-laying connections in the period-frequency field were classified to overcome certain intrinsic problems such as static and intermittence. The stock market of COVID-19 plummeted by 26% in the course of 4 days. During the first quarter of 2020, US GDP decreased respectively by approximately 4.8% and 20% because of a massive decline in the stock market index. According to our findings, stock market volatility has been reduced in nations with more cases of COVID-19. The results of the robustness test were true in univariate and multivariate models. In addition, the outbreak indirectly affected inventory performance. Following lockdowns by governments, stock returns seemed in a few weeks to be rather low. As a result of the pandemic, volatility in the stock market plummeted by 65% and 57% respectively for NASDAQ and CRIA; in addition, we discovered that the worldwide fear index of S&P500 was largely linked to the COVID-19 investor. If COVID-19 spreads, we should examine not only how possible public health disasters could be prevented, but also how financial issues might be addressed. The virus expands and duplicates every 2 or 3 days, if not sooner, the number of new infections. Pandemic worry and regulations to limit the transfer of diseases resulted in a global supply shock, particularly in the manufacturing and labor-intensive industries. The operations close or decrease in order to protect personnel, factories, and offices, which results in a reduction in workforce, efficiency, and consequently business profitability. It would leave many enterprises in a condition of insolvency and would force firms to slash employees or to close down completely if they were not treated correctly by authorities. That’s the main reason why worldwide financial markets are in turmoil. Investors see the pandemic as shrinking and are unclear about future sales, so stock prices reflect future income prospects. The traditional response of investors is to sell the equities before the deterioration becomes obvious. The implications for policymakers of our research are substantial. A combination of government officials and regulatory authorities for investment banks and the Central Bank is needed for tackling this challenge. If current debts were rolled over, bank regulators may be sympathetic to firms in economically seriously hit industries, including the manufacturing industry, travel, and tourism. Dealing with the COVID-19 situation calls for a pragmatic strategy, where officers can swiftly alert people without confusion of their plans and of the health care system. This study presents an early evaluation of the problem of the pandemic, but further study in and between foreign markets on investor confidence is required. The analysis can be utilized as the foundation for future investor feeling and uncertainty research. We are using our findings in order to efficiently communicate the risk of infections, based on the practicality of our conclusions, to institutional and private investors, financial advisers, and financial and industrial professionals, and public health officials should also comprehend their communications’ psychological and emotional consequences. An additional problem is that because of the lack of data, demographics such as age, gender, education, stock market experience, and kind of investor could not be investigated. The results imply that investors have been dismayed and therefore regarded companies as unpredictable and encouraged investors to decide on investment. COVID-19 was utilized as a signal when stakeholders took investment decisions. Under the premise of an efficient market hypothesis, investors make decisions based on basic analysis that provides an indication of the importance of key stock price influences in the near term and technical analysis that includes regular stock price and the psychological aspects of the market. A productive market ensures that all market facts are contained in stock prices. For each country’s capital, the temperature statistics are recorded that may or may not precisely represent the climate patterns of the country. In the future study, this requirement will be solved. Strong co-movements can be detected and causes can be found to explain these co-movements. Therefore, the short-term and long-term consequences of the single COVID-19 pandemic may also be taken into account in the future study as well as in the sample size of larger data on other macroeconomic variables such as the GDP growth rate, PPI, employment rates, monetary policies, and other similar factors. Open innovation implications The COVID-19 pandemic has had a huge impact on industry. As a result, organizations should react appropriately. To combat this pandemic, open creativity and innovation are needed. Because of its strategic position in organizations’ development, open innovation has been one of the most hotly discussed subjects in management research in the last decade. Open innovation will prompt a company to make the necessary adjustments to ensure its long-term viability. An organization’s survival can be accomplished by open innovation. Furthermore, open innovation fosters a culture of innovation, learning, and information sharing. Organizations would have more options to react to challenges as a result of creativity. A learning culture encourages companies to develop their skills, while a knowledge-sharing culture increases human resource competence in the face of new challenges. Topics for future research and limitations Despite these and other limitations, we believe that our findings are important for several reasons. Since the current study is limited only the global economic condition, its generalizability is limited. It is proposed that more global financial factors and mental health condition samples be taken for comparative studies. There may be parallels to pandemics as well. A longitudinal research may also be carried out to gather data for future applications and brand-new issues. Author contribution Yousaf Latif: Conceptualization, data curation, methodology, and writing - original draft. Shunqi Ge: Data curation, visualization, and supervision. Muhammad Ramzan: Visualization and editing. Wasim Iqbal: review and editing. Salman Ali and Shahid Bashir: Writing - review and editing, and software acquisition. Data availability The data can be available on request. Declarations Ethical approval and consent to participate We declare that we have no human participants, human data, or human tissues. Consent for publication N/A Competing interests The authors declare no competing interests. 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==== Front Soft comput Soft comput Soft Computing 1432-7643 1433-7479 Springer Berlin Heidelberg Berlin/Heidelberg 34220301 5948 10.1007/s00500-021-05948-2 Focus RETRACTED ARTICLE: Hybrid harmony search algorithm for social network contact tracing of COVID-19 http://orcid.org/0000-0002-1888-759X Al-Shaikh Ala’a 1 http://orcid.org/0000-0003-3979-1076 Mahafzah Basel A. [email protected] 2 http://orcid.org/0000-0002-2724-9290 Alshraideh Mohammad 2 1 grid.443749.9 0000 0004 0623 1491 Learning and Teaching Technology Center, Al-Balqa Applied University, Al-Salt, 19117 Jordan 2 grid.9670.8 0000 0001 2174 4509 Department of Computer Science, King Abdulla II School of Information Technology, The University of Jordan, Amman, 11942 Jordan Communicated by Oscar Castillo. 28 6 2021 2023 27 6 33433365 4 6 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The coronavirus disease 2019 (COVID-19) was first reported in December 2019 in Wuhan, China, and then moved to almost every country showing an unprecedented outbreak. The world health organization declared COVID-19 a pandemic. Since then, millions of people were infected, and millions have lost their lives all around the globe. By the end of 2020, effective vaccines that could prevent the fast spread of the disease started to loom on the horizon. Nevertheless, isolation, social distancing, face masks, and quarantine are the best-known measures, in the time being, to fight the pandemic. On the other hand, contact tracing is an effective procedure in tracking infections and saving others’ lives. In this paper, we devise a new approach using a hybrid harmony search (HHS) algorithm that casts the problem of finding strongly connected components (SCCs) to contact tracing. This new approach is named as hybrid harmony search contact tracing (HHS-CT) algorithm. The hybridization is achieved by integrating the stochastic hill climbing into the operators' design of the harmony search algorithm. The HHS-CT algorithm is compared to other existing algorithms of finding SCCs in directed graphs, where it showed its superiority over these algorithms. The devised approach provides a 77.18% enhancement in terms of run time and an exceptional average error rate of 1.7% compared to the other existing algorithms of finding SCCs. Keywords Harmony search algorithm Hill climbing Metaheuristic approach Social networks Contact tracing COVID-19 Coronavirus issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023 ==== Body pmcIntroduction The very first case of the coronavirus disease 2019 (COVID-19) was recorded in Wuhan, China, in December 2019. The disease is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) and became prominent by its swift outbreak and the toll of thousands of dead people it left behind all around the world. The world health organization (WHO) declared COVID-19 a pandemic in April 2020. It is believed that the COVID-19 pandemic is the worst worldwide crisis since the second world war due to the increasing number of infected people and the death toll, besides its economic and social damage (Boccaletti et al. 2020). Since then, the statistics show a dramatic increase in the number of COVID-19 cases, with news of imminent hopes to conquer the disease as different vaccines rolled out and the vaccination process started under exceptional circumstances as of early September 2020. Practically, COVID-19 is strongly invading almost every country on Earth. Due to this unprecedented, yet the relentless spread of the diseases, COVID-19 has emerged as a hot research topic. Researchers all around the globe are engaged in several works that study the disease, the affected and susceptible people and groups, its spread, etc. For instance, a neural network model was built to predict the COVID-19 time series in Mexico (Melin et al. 2020). Another model was built for predicting the COVID-19 time series using fractal theory and fuzzy logic (Castillo and Melin 2020). Also, a differential equation model of the spread of COVID-19 in Heilongjiang province in China was built and used to study the effect of a so-called super spreader (or imported escaper) from which all recent cases got their infection (Sun and Wang 2020). Recently, a hybridization of the fractal theory and fuzzy logic was introduced to classify countries based on the COVID-19 time-series data (Castillo and Melin 2021). The COVID-19 crisis has proved that elaborating technology in fighting the pandemic plays a pivotal role in increasing public awareness as well as infection control. One aspect of integrating technology in infection control is using app-based contact tracing, which is used to identify those people who are exposed to COVID-19 due to contacting or approaching infected people. Not only China reported case zero of COVID-19, but also it had the lead in tremendously monitoring and controlling the outbreak of the pandemic on its lands. Figure 1, retrieved from Bing COVID-19 data sources, shows the dramatic increase in the number of COVID-19 cases in China starting from January 3, 2020. The figure also shows how the containment measures applied by the Chinese authorities helped to flatten the curve of cumulative cases by the mid of March 2020.Fig. 1 Cumulative COVID-19 cases in mainland China from Jan. 3, 2020, until Oct. 23, 2020, as retrieved from the WHO COVID-19 dashboard (World Health Organization 2020) The statistics prove that contact tracing plays a key role in fighting the fast spread of COVID-19. Away from the medical measures and procedures conducted by the authorities in China, the country was among the first countries to integrate technology in tracing infections and attempting to early discover potential cases, who are exposed to contagion by the coronavirus SARS-COV-2 (Liang 2020). This kind of technology-integrated contact tracing is referred to as app-based contact tracing (Abeler et al. 2020). China opted to build the China health code system (CHCS) by forcing the population as well as anyone entering the country to register their travel history, as well as whether they visited or contacted people from infected countries (or areas) (Pan 2020). Accordingly, three security levels are automatically generated by CHCS to classify users according to the data they entered upon installing the application; these levels are encoded by three color codes, namely red, yellow, and green (Pan 2020; Peng et al. 2020). Conversely, despite the intriguing statistics that highlight the contribution of many contact tracing applications in the combat against COVID-19 in different countries, people still have concerns that may reduce the benefits that are expected to obtain by employing those applications in contact tracing, to name a few: How these apps work? To which servers do they connect? What are the security measures applied to users’ data? (Ahmed et al. 2020). The main contribution of this paper is to mitigate the effect of people’s concerns about app-based contact tracing by proposing a new approach for contact tracing based on social networks to identify the people who are exposed to COVID-19 infection. In this context, we investigate the graph that represents a given social network (SN) and traverses the links in that SN to find the strongly connected component (SCC) which represents a closed group of individuals who are exposed to infection due to having a link with a confirmed COVID-19 infected individual. In fact, SNs and social media (SM) have become an integral part of our lives (Al-Shaikh et al. 2017). Formally, SN is a graph that comprises a number of users that are represented with vertices (or nodes) and those users are linked with each other with links (or edges) that represent the relationships between those users. Mathematically, finding SCCs in a graph is a profound problem that was heavily investigated. It is a linear-time practice that requires O(V+E), where V is the number of vertices and E is the number of edges, using a depth-first search (DFS) as proposed by Tarjan (1972). Despite its linear time, a great number of research papers tackled the problem trying to introduce enhancements to the solution using different techniques. However, none of these techniques used metaheuristic algorithms to find SCCs in directed graphs. Traditionally, heuristic and metaheuristic algorithms are used to solve combinatorial optimization (CO) problems. Most of these problems are NP-complete (Al-Shaikh et al. 2016), such as the traveling salesman problem (TSP), which is recently solved using a parallel heuristic local search algorithm by Al-Adwan et al. (2017) and using a parallel repetitive nearest neighbor algorithm (Al-Adwan et al. 2018). Software testing, module testing, and database testing is another area of application to which metaheuristic algorithms offered solutions (Alshraideh et al. 2013b). In the same context, metaheuristic algorithms can be used to automate the generation of test data in software testing (Alshraideh et al. 2010). Some examples of the metaheuristics are genetic algorithm (GA) (Alshraideh et al. 2010, 2013a, b), ant colony optimization (ACO) (Zhou et al. 2017, 2018), local search (LS), and iterated local search (ILS) (Zhou et al. 2016). Another important contribution of this paper is that we devise a new approach using hybrid harmony search (HHS) for the first time to find SCCs in SN graphs and propose this new approach to automate contact tracing of COVID-19. The devised approach is called the hybrid harmony search contact tracing (HHS-CT) algorithm. Practically, the HHS-CT approach is introduced to find the SCC in the SN graph that contains the users of a given SN who reside in a closed group that is pivoted at a given vertex (or user). The purpose is to find those people who are potentially exposed to infection with COVID-19, referred to as contacts, due to contacting an infected person, referred to as the index case. Neither HS nor any hybridization of it is known to be used before in finding SCCs in directed graphs. It has never been known before to adapt the problem of finding SCCs in directed graphs in SN graphs to be used in the contact tracing. The intuition behind the HHS-CT algorithm is that the contacts who reside in a closed group with the index case are highly vulnerable to infection. Likewise, the contacts that reside in closed groups with each of the contacts that were already detected in the index-case closed group are vulnerable too. Iteratively, each closed group of contact is investigated for susceptible infection. Consequently, a SCC which is pivoted (or centered) at the index case is found (or detected); and this SCC contains all susceptible contacts. It is worth mentioning that the work in this paper does not intend to propose an application or protocol to be used in contact tracing. It is proposed to automate the process in which contacts are traced and can be used to replace the traditional contact tracing method which is based on a set of questions that should be answered by the infected individual to identify those who contacted that individual and notify them to do the tests, quarantine themselves, and socially distance themselves from others until their results are clear, that is not infected. There could be several methods of notifying those people that are identified as vulnerable, such as SMS, e-mail, applications, and SN. Again, this proposed approach uses harmony search (HS), a metaheuristic algorithm, that is hybridized for the first time to find the SCCs in the SN graph. HS is a profound population-based metaheuristic that was designed in 2001 by Geem et al. and its idea was inspired by the nature of musical improvisation (Geem et al. 2001). Hill climbing (HC) is a local search algorithm (Burke and Newall 2003) that finds a local-optimal solution from the neighbors of the current solution (Zhang et al. 2019). One variant of HC is the stochastic hill climbing (SHC), in which the search is always directed toward maximizing (or minimizing) the solution, but rather than applying some definite criteria on choosing the next neighbor to select the next state, a random state is selected to minimize the chance to stick in local optima (Mondal et al. 2012). The motivation behind using a hybrid metaheuristic algorithm in finding SCCs in directed graphs rather than using the exact methods is that finding the maximum (or largest) SCC in large graphs, such as SN graphs or the web graph, is time-consuming, which implies difficulty to find SCC in an efficient time using existing algorithms or methods. To build an effective contact tracing algorithm that gives results in an efficient time, we need to speed up the process of finding SCCs in the associated SN graphs. Consequently, metaheuristic algorithms arise as an efficient solution for many reasons; for instance, they provide suboptimal solutions in a relatively short time, easy to design and implement, and easy to parallelize, to name a few. Accordingly, HS was used to implement and find SCCs in SN graphs by integrating SHC into the operator design of HS, and the result is to create an HHS algorithm which is referred to as HHS-CT, that is customized for finding SCCs in large SN graphs and is used in contact tracing of COVID-19. The importance of digital contact tracing and its effectiveness is another factor that adds up to the motivation behind this paper. In essence, digital contact tracing is crucial in fighting COVID-19 for many reasons. The swift spread of the virus makes it very difficult to trace using traditional (or manual) methods. Many doctors and health specialists are needed to cope with the speed of virus transmission from one place to another and from one person to another. The contact tracing process is time- and money-consuming thereby. More importantly, the traditional method is dependent on the person who is being questioned. The infected person may sometimes be unable to memorize all visited locations or contacted persons (Sharon 2020). Despite the many benefits of digital contact tracing, still, some infected people feel about contact tracing is violating their privacy. Some people may have concerns about using contact tracing applications, allowing them to interact with others’ mobile devices, uploading logs of their visited logs, and disclosing the names of the people whom they have contacted. Here comes the importance of retrieving the list of people who are prone to infection by finding the people who strongly connect with the infected person in the SN graph. That is, the people who form a SCC pivoted at the infected person (or the index case). Finding the SCCs in SN is not an optimization problem and is not an NP-problem, too. It is identified as an optimization problem, so as we can use metaheuristic algorithms in finding a solution to this problem. Accordingly, the SCC problem is formalized, and the HS operators are adapted for finding SCCs in SN graphs as an optimization problem. We hypothesize that the run time consumed in finding SCCs in large-directed graphs using hybrid metaheuristic algorithms is less (or smaller) than the run time consumed by exact algorithms for the same problem. Analytically, we prove that our devised HHS-CT algorithm has a linear run time complexity, i.e., O(V+E). The experimental results endorse our hypothesis that HHS-CT outperforms exact algorithms in terms of run time. The results show that our HHS-CT is 73.87% faster than the FW–BW algorithm. More importantly, the average accuracy of the HHS-CT algorithm is 99.983%. The rest of this paper is organized as follows: in Sect. 2, we present some mathematical background and foundation pertaining to the graph theory. In Sect. 3, we review some literature. Our implementation of the problem can be found in Sect. 4. Then, we present and discuss our results in Sect. 5. Finally, we introduce our conclusions in Sect. 6. Background As the figures show by WHO persistent increases in the number of people who are getting COVID-19 infection, as well as a dramatic increase in the death toll worldwide, the containment efforts of the pandemic are still in progress. Little hopes are looming on the horizon as people started to get vaccinated in different countries. However, the expectations of fully conquering the pandemic are still small. Although governments and health authorities worldwide moved swiftly and authorized the use of the vaccines that started to roll out by the end of 2020, the prolific production of the vaccines is not possible in the time being, and it is expected to take time until the vaccines are produced in amounts that are adequate to immunize larger societies. Consequently, the traditional measures of fighting the pandemic are sound and standing. People will continue to wear masks, comply with social distancing, isolate themselves, and use contact tracing mobile applications. Yet, people are highly concerned with the levels of privacy that are claimed to be guaranteed by those applications to their users (Ahmed et al. 2020). Compromising their personal data, as well as visited location data, with the authorities is not welcomed by the majority of users all around the globe. In practice, all COVID-19 contract tracing applications are focused on finding the people who were in direct contact with the infected individual and inspecting all places visited by this individual. In essence, these applications would work great if the user fed them with the required information precisely, otherwise, there are situations in which those applications would lack the required precision. Assume a case in which the list of the visited place (or locations) has not been updated persistently by the corresponding user. Another situation arises if the user has disabled all the sensors, Bluetooth, and GPS on the device on which the application is installed. A third situation is embodied in exiting the application and not allowing it to run in the background of the device on which it is installed. Such situations undermine the mobile-based (or digital) contact tracing in its current form and undermine its feasibility. The importance of this paper is that it shifts attention to another area of contact tracing that has never been looked at before. The paper devises a method that enables contact tracers to notify those who are exposed to COVID-19 infection through the relationships they have with the index case depending on data retrieved from SN accounts of the infected individual. It is worth mentioning that this method is not intended to replace current methods of mobile-based contact tracing, it only covers an area that might not be discovered due to manual or mobile-based contact tracing, which in turn helps to ease the efforts of disease control and prevention as well as speeding up the procedures, hoping to eliminate the infections, slow down the spread of the virus, which dramatically helps the containment of the disease. In Sect. 2.1, we introduce some of the basic terminology and mathematical foundation that is related to graph theory and SCCs. Then, in Sect. 2.2 we present our problem identification and the formalization of the problem as an optimization problem. Mathematical background A graph G comprises a set of vertices (or nodes) V and a set of edges (or arcs) E that link these vertices to each other and is represented mathematically as G=(V,E) (Euldji et al. 2019) such that E⊆V×V (Zhang et al. 2016). Let u and v be two vertices in graph G, such that u,v∈V, then we represent the association between these two vertices as u,v∈E, or in other words the existence of an edge between vertex u and vertex v. In this manner, two vertices are said to be adjacent if there is an edge between them. Another way to denote the existence of an edge from vertex u to v is u→v. Accordingly, the degree of a vertex v, denoted deg(v), is defined as the number of vertices that are adjacent to that vertex v (Marappan and Sethumadhavan 2017). Edges can be either unidirectional or bidirectional. Consequently, the graph can be classified as either directed or undirected relatively. Some graphs may contain both types of edges, and these are referred to as mixed graphs (Euldji et al. 2019). Let E1 be the set of all unidirectional edges in G, such that E1⊆E, then E1=u,v\|u,v∈Eandv,u∉E. On the other hand, E2 is the set of all bidirectional edges in G, such that E2⊆E and E2={(u,v)\|u,v,v,u∈E}. Although E1∪E2=E, the two subsets E1 and E2 are disjoint, i.e., E1∩E2=ϕ (Wang et al. 2018). The transpose of a graph G, denoted GT, is the set of all vertices in the graph G, with all its edges reversed. Formally, GT=(V,E¯), such that E¯={v,u\|u,v∈E}. Based on the arrangements of the edges in the graph, a vertex vi in the graph G is expected to have some neighbors, denoted Ni, such that Ni={vj∈V\|vi,vj∈E} (Wu et al. 2018). In directed graphs, a vertex may have a different number of edges leaving it and other edges entering it. Therefore, the degree of a vertex v in a directed graph is decomposed into two parts: the in-degree (indeg) and the out-degree (outdeg), and the vertex degree, accordingly, is computed as: degv=indegv+outdeg(v) (Schlauch et al. 2015). A path Pk in G is a group of distinct vertices v1,⋯,vk with the edges that connect these vertices. The length of the path (l) is defined as the number of edges in the path, thus, l=Pk and the path is said to be a k-length path. Consequently, a cycle is a path with the property ∃vk,v1∈Pk, and the cycle is a k-length cycle (Fox et al. 2009). In other words, a cycle is a path with an edge between its first and last vertices v1 and vk, respectively. For any two vertices ,v∈ V, we denote u⇒∗v to indicate that there is a path from u to v and u⇏∗v to indicate that there is no path from u to v. Thus, we express a path as P:u⇒∗v, which means that the path P starts with vertex u and ends with vertex v (Tarjan 1972). The maximal part of the graph G in which there is a path from each vertex to each other vertex is called a SCC (Zhang et al. 2018). Formally, let SCCi be a SCC in the graph G, then ∀vj,vk∈SCCi→vi⇒∗vk∧vk⇒∗vi. The smallest possible size of a SCC is one, which means that the SCC contains only one vertex, and it is referred to as a trivial SCC (Hong et al. 2013). Metaheuristic algorithms (or metaheuristics) are high-level frameworks that are used as guidelines for incorporating heuristic algorithms, such as the A* algorithm (Mahafzah 2014) and local search (Al-Adwan et al. 2019) to explore and exploit the search space (Gogna and Tayal 2013). They are problem independent, and they intend to find near-optimal (also known as local optimal) solutions to optimization problems in a reasonable time (Sörensen and Glover 2013). According to the number of candidate solutions that are generated from the problem’s search space, metaheuristic algorithms are classified into (1) population-based metaheuristic (PBM) or (2) trajectory-based metaheuristic (TBM) algorithms (Luna et al. 2010). In PBM, the algorithm starts by initializing a number of candidate solutions and performs iteratively until it stops after doing a predetermined number of iterations or upon satisfying a condition. At each iteration, a new population is generated, and this new population is set to pursue migration to further iterations during the lifecycle of the PBM algorithm (Mahdavi et al. 2018). Unlike PBM, there exists only one candidate solution during the lifecycle of the TBM algorithm, and different operators are applied to that solution until the algorithm stops iterating or the algorithm stops returning an enhanced (or optimized) solution (Acan and Ünveren 2014). Practically, metaheuristic algorithms consume less time in finding solutions to CO problems than exhaustive search or brutal force techniques, which made them the de facto standard to solve CO problems (Mahafzah et al. 2020). Nevertheless, an error rate might be incurred when incorporating metaheuristic algorithms to solve CO problems; this error represents the difference between the optimized solution and the exact solution (Farswan and Bansal 2018). Certainly, a lower error rate means a better solution quality, which illustrates the necessity of iteratively maximizing or minimizing the solution, based on the nature of the problem, to obtain better solutions, that is solutions with lower error rates. Premature convergence is a situation that is likely to be endured by PBM algorithms. It is characterized by finding a suboptimal solution rapidly and getting stuck in the region of that suboptimal solution without being able to explore further areas of the search place (Neri and Cotta 2012). On the other hand, TBM algorithms may endure local-optima entrapment (Alonso et al. 2018) which entails that the algorithm is unable to find a solution better than the current one, although there exist better solutions in the search space. Premature convergence, as well as local-optima entrapment, affects the solution quality by finding solutions with lower qualities despite the existence of higher-quality solutions in the search space. The integration of a PBM and a TBM creates a new hybrid metaheuristic algorithm, also referred to as memetic algorithms (Neri and Cotta 2012). Hybrid metaheuristic algorithms attempt to overcome the premature convergence of PBM algorithms by integrating them with TBM algorithms (Blum and Roli 2003). In essence, the emergent hybrid metaheuristic algorithm results in solutions with better qualities by underpinning the exploitation capabilities of TBM algorithms, which are represented by local search, and the exploration capabilities of PBM algorithms (Chen et al. 2011). Problem identification Let G=V,E be a directed graph, such that V is the set of n vertices that represent the size of the graph, V=v1,v2,⋯,vn, E is the set of edges that link the vertices of G together, and the existence of a path between two vertices, say u and v, is denoted by u⇒∗v, such that u,v∈V. Also, let Descv={w∈V\|v⇒∗w} be the set of all the vertices that are descendant (or reachable) from vertex v, and Pred(v)={u∈V\|u⇒∗v} be the set of all predecessors of the vertex v, that is the vertices from which v is reachable. Provided that SCC(v) is a unique SCC that is pivoted at vertex v, dictates that there must be a path from each vertex in SCC(v) to every other vertex in SCC(v). Formally, SCCv={x∈V\|x∈Descv∧x∈Predv}, which can be simplified to SCCv=Descv∩Pred(v). The main contribution of this paper is that it presents an unprecedented expression and implementation of the problem of finding SCCs in directed graphs as an optimization problem. Finding SCCs in directed graphs is not an optimization problem, and there exists an exact algorithm that finds a solution to this problem using Tarjan’s algorithm in a linear run time. More importantly, the problem of finding SCCs in directed graphs is not an NP-Complete problem. Tarjan’s algorithm, which is a DFS-based algorithm, is used to find SCCs in directed graphs. However, there are many advantages of using metaheuristic algorithms rather than traditional, exact algorithms for finding SCCs in directed graphs. Practically, metaheuristic algorithms return satisfactory solutions in a very fast time compared to exact algorithms. Although a local optimal solution is returned by a metaheuristic algorithm, the solution is found in a very small amount of run time compared to exhaustive search techniques, such as DFS. Furthermore, metaheuristic algorithms are easy to design, implement, and understand. On the other hand, the exact algorithms used to find SCCs in directed graphs are very difficult to understand, trace, and implement. In terms of computing resources, metaheuristic algorithms are prominent with their optimal utilization of computing resources. For instance, a huge stack must be associated with DFS-based Tarjan’s algorithm. Also, DFS intensively depletes memory locations. Backtracking, which is the basic idea of DFS, is the main source of depletion of computing resources due to the computational power it requires. On the other hand, the core of the metaheuristic algorithms is the iteration phase which contains the implementation of the solution. Comparing iterations with backtracking and divide-and-conquer, iterations do not extensively exhaust computing resources as much as recursive calls. Parallelization is an important factor to consider when thinking about the advantages of using metaheuristic algorithms over exact algorithms. Unlike DFS which is an inherently sequential P-Complete algorithm that is extremely hard to parallelize (Reif 1985), metaheuristics are easy to parallelize and thus provide faster solutions. In order for the problem of finding SCCs in directed graphs to be eligible to be solved using (hybrid) metaheuristic algorithms, it needs first to be expressed as an optimization problem P. The formalization of the problem of finding SCCs in directed graphs as an optimization problem is presented in Eq. 1. Starting with a trivial SCC, that is a SCC whose size is one, the aim is to iteratively maximize the SCC by adding vertices to it, provided that there must be a path between each vertex to be added to the SCC and every vertex that has been already added to the SCC.1 P:maximzeSCC⊆Vsubjectto∀u,v∈SCC∃u⇒∗v∧v⇒∗u Like all optimization problems, problem P has a fitness function that represents the size (or length) of the optimized SCC, or in other words, the number of vertices in the optimized SCC. Indeed, using the HHS technique to find SCCs in directed graphs is also unprecedented and it is another important contribution of this paper. Related work The propagation of COVID-19 is very fast, and the disease is severe, fatal, and hard to control or track without innovative tracking methods that are too fast. Living in a connected world in which computer networks, mobile devices, social networks, and artificial intelligence applications are indispensable, paved the way for technology to play a pivotal role in combating COVID-19 (Mbunge et al. 2021). Technology utilization in contact tracing is referred to as digital contact tracing, and it implies the incorporation of technologies, such as mobile technologies, Bluetooth, location services, and QR codes (Amann et al. 2021), to name a few, in tracking the infected people and notifying those who might have contacted them that they are prone to contagion. China was among the first countries to authorize a mobile application for contact tracing. Users of the mobile application need to fill in their travel, movement, contact, and health information; the information is stored in online databases. China's health code system (CHCS) then classifies users as: red, green, or yellow, and the movement of each user is restricted based on the color code given (Pan 2020). The odds show that the use of the Chinese application, alongside the health measure that was applied in the country, helped reducing and the number of cases that are infected with COVID-19 and flattening the cumulative infections curve as shown in Fig. 1. Another success story in fighting COVID-19 was written by Singapore. The total number of infected cases in Singapore until Dec. 21, 2020, is less than 60,000 with less than 30 death cases recorded in the country. TraceTogether is a mobile application that was put into service by the ministry of health of Singapore as a digital contact tracing tool. The government also used the prominent WhatsApp mobile application associated with artificial intelligence (AI) tools to permanently disseminate news and insights about COVID-19. Along with further procedures, the fatalities rate in Singapore was low despite the high rate of infection (Woo 2020). The TraceTogether application must be installed on the mobile device and kept running in the background. For the application to work, the Bluetooth (BT) on each device with the application installed on it must be activated. Mobile devices that have the application installed on them, running in the background, and the BT set to start exchanging anonymized keys; each key pertains to a unique device. Each device stores the other mobiles’ keys in an encrypted form. Assuming that one individual is infected, all the people whose mobiles have the key to that infected individual stored on them are notified of the measure that should be followed to protect themselves from being infected with COVID-19 (Government of Singapore 2020). Around 3.2 million users are using the TraceTogether application by Sep. 4, 2020, which represents around 61% of the population of Singapore who is aged 15 years and above. Similarly, the Indian authorities developed a mobile application, called Aarogya Setu, for COVID-19 contact tracing. Unlike the Chinese application, Aarogya Setu uses Bluetooth and GPS services to notify the users, who installed the application on their mobile phone, of any potential exposure to COVID-19 due to contacting infected individuals or entering infected areas. Aarogya Setu also sends notifications to the mobile devices that are nearby and have Aarogya Setu installed (Gupta et al. 2020). The Indian ministry of health and family welfare divides the contact tracing process into three stages, namely (1) contact identification, which includes identifying the infected individual and the people who came into contact with the infected individual, (2) contact listing, which includes listing the people who came into contact with the infected individual and ask them to isolate themselves, and (3) Follow-up, which include following-up with the people who came into contact with the infected individual to monitor their health (Ministry of Health and Family Welfare 2020). However, no more than 18% of the Indian population who are 15 years old and above use the mobile-based application, which sheds the light on the extremely high infection rates in India, which could be reduced if stricter measures force the use of the Aarogya Setu application have been applied. The Jordanian government launched a mobile application for contact tracing called AMAN, which translates to safety in English. Once installed on a mobile device, the application keeps a local copy of the places, i.e., locations, that were visited by the corresponding user. That local copy is kept on the device on which the application is installed. The first use case of the AMAN application is to notify its users of possible exposure to COVID-19 infection due to visiting some locations that were visited by an infected individual. Another use case of the AMAN application is when the corresponding user who has the application installed on his (or her) mobile device gets infected with COVID-19, the application notifies other users who visited the same locations that the infected user has visited during the relevant dates (Jordan Ministry of Health 2020). By the end of December 2020, the statistics show that nearly 1.5 million people are using the AMAN application, which approximates 27% of the population of Jordan who is 15 years old and above. The percentage of the people who use the AMAN application in Jordan is not large enough to give the AMAN application a pivotal role in fighting COVID-19 in Jordan, which illustrates the increases in the number of cases conferment with COVID-19. COVIDSafe is a mobile application that was designed and used by Australia in digital contact tracing (Yang et al. 2020). Although COVIDSafe is a voluntary application, people were urged to use the application by installing it and running it on their devices. Once the application starts on one mobile device, it starts to collect data from other devices that are installed on the mobile devices and within its Bluetooth accessible range. Collected contact data are encrypted and are stored locally on the mobile device. If a person is diagnosed positive, the data are uploaded to a secure server to notify all those people who met the infected person (Royal Australian College of General Practitioners 2020). Similar to COVIDSafe’s mechanism, Germany launched in June 2020 their mobile application Corona-Warn to be used in digital contact tracing (Blom et al. 2021). The application uses Bluetooth to collect the IDs of the people who came in contact and stores the IDs locally. When a person gets infected, the data are uploaded to a central server to notify them (Kammüller and Lutz 2020). It is worth mentioning that Germany alongside many other countries used the Google/Apple COVID-19 contact tracing API to develop their application; some of those countries are Austria, Belgium, Canada, Croatia, Germany, Russia, Saudi Arabia, Scotland, Spain, UK, and USA (Rahman 2021). Seemingly, the role of incorporating technology in contact tracing is influential in light of the odds that give credit to the utilization of mobile-based contact tracing in the combat against COVID-19. However, to overcome the challenges that mobile-based applications are facing with are related to the privacy concerns of the users, we devise in this paper an approach that is based on using metaheuristic algorithms (or metaheuristics) to solve optimization problems in a way that finds near-optimal solutions, that is solutions with an acceptable error rate (or diversion), in fast run times. Harmony search (HS) is a population-based metaheuristic (PBM) that was first introduced by Geem et al. (2001) to mimic the process of musical improvisation (Valdez et al. 2020). The HS algorithm incorporates three operators, namely (1) memory consideration which is controlled by the harmony memory considering rate (hmcr), (2) pitch adjustment and is controlled by the pitch adjustment rate (par), and (3) randomization (Castillo et al. 2018). The algorithm starts by generating a random number r∈[0,1]. If r≥hmcr, the memory consideration operator is invoked, and when it finishes execution another random number p∈[0,1] is generated. If p≥par, then the pitch adjustment operator is invoked to enhance the solution that has been found by the first operator, that is the memory consideration operator. The third operator is the randomization operator that is only invoked if r<hmcr, or in other words, if the memory consideration operator is not satisfied and the memory consideration operator is not invoked accordingly. Harmony search was used by Atta et al. to solve the tool indexing problem (TIP) which is a profound problem in the field of manufacturing (Atta et al. 2018). To avoid getting stuck into local optima, Atta et al. adapted a customized HS algorithm that uses a harmony refinement strategy. Results showed that this customized algorithm presented better results than existing methods in 16 instances out of 27. A hybrid metaheuristic algorithm produced by hybridizing cuckoo search (CS) with HS was introduced by Wang et al. (2014) and was named HS/CS. In this algorithm, the pitch adjustment of the HS algorithm was added to the CS to improve its performance. The proposed improved metaheuristic showed its superiority to the original CS for solving global numerical optimization problems. Recently, HS is used in the design of fuzzy controllers by Castillo et al. (2021). An approximation to the enhanced continuous Karnik–Mendel (CKM) method is introduced to be used in the adjustment of the par parameter which controls the execution of the pitch adjustment operator and therefore dynamic parameter adaptation in HS is devised instead of using fixed parameters. The effectiveness of the devised method was proved by applying the devised algorithm to the speed control problem in direct current (DC) motors. Type-2 fuzzy controller is implemented in the devised method to control the speed of the motor. The devised method was compared with the approximate continuous enhanced Karnik–Mendel method of the fuzzy harmony search algorithm (FHS FIS 3), the approximate continuous enhanced Karnik–Mendel method of the differential evolution search algorithm (FDE FIS 3), and type-1 fuzzy harmony search algorithm. The average error was lower than the average error obtained by the other algorithms that were used in the comparisons from Valdez and Peraza (2019). Also, the results obtained by the devised method for the parameter adaptation were better than those of the other methods that were used in the comparisons. In the field of bioinformatics, HS was hybridized with CS to develop a two-stage gene selection method, denoted as COA-HS, to be used in cancer classification (Elyasigomari et al. 2017). The results of the proposed method outperformed the results obtained by the following evolutionary algorithms: PSO, GA, HS, and CS. The results of the COA-HS algorithm achieved the selection of the minimum number of genes and satisfied the maximum classification accuracy as well. In the same field, a modified HS was used along with k-means clustering to propose a feature selection method to classify individuals who suffer colorectal cancer from those who do not (Bae et al. 2021). The accuracy of the proposed method reached 94.36%. It is believed by Bae et al. that their proposed model can be applied to any gene-related disease. Harmony search was also used to generate fuzzy rules in a fuzzy rule-based system by Mousavi et al. (2021) to classify medical datasets. The results show the effectiveness of the proposed algorithm in classifying the clinical datasets. Robert Tarjan used DFS, also known as backtracking, to find the strongly connected components in directed graphs (Tarjan 1972). Tarjan used an improved version of DFS to find the strongly connected components in a digraph (directed graph). For a digraph with V vertices and E edges, the runtime complexity of the Tarjan's algorithm was O(k1V+k2E+k3) for some constants k1,k2,andk3. Using Tarjan's algorithm, a spanning forest is created that contains all spanning trees resulted from the DFS. The main observation of Tarjan's algorithm is its numbering scheme. In Tarjan's algorithm, the vertices are numbered in the order they are reached during the DFS. On the other hand, Tarjan's algorithm makes extensive use of the stack (Geldenhuys and Valmari 2004). In addition to the implicit stack that is required by the procedure (or function) call, it also requires an explicit stack to keep track of partial SCCs. Furthermore, Tarjan's algorithm is explicit (Bloem et al. 2006); each node is explored independently until a SCC is formed which might, in turn, affect the stability of the algorithm. Although there are a huge number of algorithms that offered solutions to the strongly connected components problem, Tarjan's is considered the most fundamental algorithm in this field (Xu and Wang 2018). Different algorithms were designed trying to find better solutions, such as the forward–backward (FW–BW) algorithm by Fleischer et al. (2000) which is a recursive algorithm rather than its predecessor DFS-based algorithms (Xu and Wang 2018). The basic idea of FW–BW is to use the divide-and-conquer paradigm to divide the graph into three subgraphs to get a logarithmic time complexity Θ(nlogn) as an average case. However, the worst-case analysis of FW–BW shows that it requires a quadratic O(n2) time complexity. Several variations of the FW–BW algorithm were suggested. For instance, McLendon et al. (2005) suggested the FW–BW-Trim algorithm which is different than the original FW–BW in adding two trimming phases to the graph: one in a forward direction and the other in a backward direction. As far as we know, hybrid metaheuristic algorithms have never been used before in finding SCCs in directed graphs. Thus, the hybrid metaheuristic approach which we present in the paper is used for the first time to find SCCs in directed graphs, which is another important contribution that is added to this paper. Hybrid harmony search contact tracing algorithm In this section, we present our new hybrid harmony search contact tracing (HHS-CT) algorithm, which is used for COVID-19 contact tracing by finding the SCCs in SN graphs using hybrid metaheuristic algorithms. Traditional methods of finding SCCs in directed graphs are either based on (1) backtracking, such as the DFS, or (2) the divide-and-conquer approach. It has never been known before those hybrid metaheuristic algorithms are used in finding SCCs in directed graphs. In the beginning, the problem of finding SCCs in directed graphs is formulated as an optimization problem, as shown in Eq. 1. In large-directed graphs, such as SN graphs, finding the maximum (or largest) SCC is time-consuming. Thus, traditional algorithms or methods, such as the Tarjan’s algorithm or the FW–BW algorithm, will take more time to find the desired solution as well as requiring a huge amount of computing resources, such as memory and processing power, which could not be afforded by the computing environment at a certain level. Therefore, a metaheuristic solution to the problem is implemented using the HHS-CT algorithm which finds the desired solution in less time than the traditional algorithms and methods, as well as saving memory resources from being overused. In this context, we integrate the SHC algorithm, which is a local search technique, into the operators’ design of the HS metaheuristic algorithm. This implies that exploitation of the HS algorithm will be made by SHC to guarantee fast convergence, while exploration will be made by HS to guarantee not being stuck in local optima as well as investigating (or exploring) wider areas of the search space. Exploiting solutions by the HHS-CT algorithm is done through the SHC algorithm which is adapted as shown in Algorithm 1 to find a component in the directed input graph (or SN graph). The graph (G) is a social network (SN) graph; its vertices (V) are referred to as contacts, and edges (E) are the interactions between its contacts. The SHC algorithm starts from a predetermined starting vertex (or pivot) that is referred to as the index case. In practice, the SHC algorithm is intended to find all the contacts that are descendant from a predetermined index case index, i.e., reachable from index, and store them in the component C, thus C=contact∈V\|index⇒∗contact. The difference between our adapted version of the SHC and the traditional Tarjan’s DFS or the FW–BW method is that in SHC, as shown in line 12 of Algorithm 1, a random contact vr is selected from the set of contacts that are adjacent to the currently investigated contact (contact). Afterward, control will move to line 6 again of Algorithm 1 to list all the contacts that are adjacent to the random contact vr. Another random contact is selected in line 12 again, and so on. It is noticeable that only random contacts (or vertices) are selected for investigation, rather than selecting all the vertices that are descendant of the index case index, as in Tarjan’s algorithm and the FW–BW algorithm that investigate each contact in the neighborhood of the index case, and recursively each contact in the neighborhood of the neighbors and neighbors of neighbors and so on. Practically, this heuristic feature of SHC reduces the run time when compared to DFS traversal which traverses every contact in the neighborhood of a given contact until all the contacts in the neighborhood are completely traversed. In Lemma 1, we prove that the SHC algorithm has a linear worst-case run time complexity. Lemma 1 The run time complexity of the SHC algorithm is OV+E. Proof In the worst-case scenario, when the input SN graph is strongly connected, Algorithm 1 is expected to make V iterations to go through all contacts of the input SN graph (lines 4–13 in Algorithm 1). For each contact, adjacent contacts will be enumerated (lines 7–11 in Algorithm 1) which takes OE. Therefore, the complexity of Algorithm 1 is OV+E.□ In HS terminology, harmony is a solution that is produced by the HS algorithm. Harmonies are kept in the harmony memory (HM) whose size is predetermined by the parameter harmony memory size (hms). The hm acts as a container that keeps all the harmonies that are generated by the HS algorithm. Originally, all the harmonies have the same length, say n. Thus, hm could be looked at as an hms×n matrix. Nevertheless, in HHS-CT, we used variable-length harmonies instead of fixed-sized harmonies. Consequently, hm is represented by an array of size hms rather than a matrix of size hms×n. This leads to a huge reduction of the algorithm’s run time, as well as reducing the size of the memory that is required to run the algorithm. The HHS-CT algorithm has three operators, namely (1) memory consideration, (2) pitch adjustment, and (3) randomizations. Each operator is customized for solving the problem of finding SCCs in SN graphs. The operation of the HHS-CT algorithm is controlled by a set of parameters that are listed in Table 1. The first parameter is the number of improvisations (ni) which is the number of iterations the HHS-CT must perform to find the final solution. The size of the HM is determined by the hms parameter. The third parameter is the harmony memory considering rate (hmcr), which is a real number between 0 and 1, that is hmcr∈[0,1], and it is used to determine which of two HHS-CT operators to execute between the memory consideration operator or the randomization operator. The last parameter is another real number between 0 and 1 which is called the pitch adjustment operator (par) and is used to decide whether to execute the pitch adjustment operator, that is the third HHS-CT operator after the memory consideration operator finishes execution.Table 1 Parameter settings of the HHS-CT metaheuristic algorithm Parameter Definition ni Number of improvisations, which is equivalent to the maximum number of iterations hms Size of the HM hmcr Harmony memory considering rate, which is the rate that is used to determine which of the two HS operators will be used to generate a harmony, namely memory consideration or randomization par Pitch adjustment rate, which is used to specify whether a pitch adjustment operation will take place right after the memory consideration operator finishes improvising a new harmony Like all other metaheuristic algorithms, HHS-CT consists of three main phases, namely initialization, iteration, and finalization. During the initialization phase, the initial population is created. Each individual of the population is a harmony, which represents a solution. The population is kept in the HM, or other words, the HM contains the harmonies that are generated by the HHS-CT which are individuals of the HHS-CT population. Later on, that population will be used during the iteration phase of HHS-CT for finding the SCCs in the SN as an optimization problem. The flowchart shown in Fig. 2 depicts the steps incurred by the HHS-CT algorithm to generate the initial population. Assume the SCC that is pivoted at the index case vindex needs to be detected in the SN represented by the graph G. The hms parameter is used to determine the number of harmonies that must be generated at the initialization phase. For each harmony, a vertex vr is selected randomly from the neighborhood of the vertex that represents the index case vindex, i.e., vr∈Nvindex. A new harmony that contains both vindex and vr is created and then inserted into the population as a new individual. Eventually, hms harmonies are generated, such that the size (or length) of each harmony is two, and the index case vindex is contained in each harmony. The run time complexity of the process of generating the initial population is given in Corollary 1.Fig. 2 A flowchart that shows the steps incurred in generating the initial population of HHS-CT Corollary 1 The run time complexity of the process of generating the initial population of the HHS-CT algorithm is O(hms). Proof The process of generating the initial population contains a loop that iterates hms times; this loop is dominating the initialization phase; thus, the run time complexity of the initialization phase finit is Ohms.□ The HHS-CT algorithm is presented in the flowchart depicted in Fig. 3. The algorithm starts with generating the initial population. The iteration phase starts by assuming the first harmony that is stored in the HM as the solution. Then, the algorithm iterates through all the remaining harmonies that are kept in the HM. A random number r is generated, such that r∈[0,1]. The random number r is used to check the memory consideration condition, which consists of two parts: (1) whether the random number r is greater than or equal to hmcr and (2) if there exists any common vertex between the solution and the current harmony. If the memory consideration condition is satisfied, then the memory consideration operator is executed, by joining the solution with the current harmony using a union operator. Another random number p is generated, such that p∈[0,1], and is used to check the pitch adjustment condition, such that if p is greater than or equal to par, then the pitch adjustment operator is executed. On the other hand, if the memory consideration operator is not satisfied, then the randomization operator is executed. After the algorithm finishes checking all the harmonies that reside in the HM, the algorithm locates the location of the harmony that has the lowest fitness, which is the worst solution. The solution which has been just generated by the HS operators replaces the worst HM by inserting the solution in the location that contains the worst harmony. The iteration phase of the HHS-CT algorithm runs ni times before it stops and moves to the finalization phase, in which the best solution obtained by the HHS-CT algorithm is outputted.Fig. 3 The flowchart of the HHS-CT algorithm The proposed HHS-CT algorithm is shown in Algorithm 2. In the initialization phase, we set the values of the HHS-CT parameters as shown in lines 3–6 of Algorithm 2. At line 7 of Algorithm 2, a call to GenerateInitialPopulation() function is issued to generate a population of random harmonies. The iteration phase of the HHS-CT algorithm starts in line 9 and the algorithm is set to loop ni times. In the following sections, we discuss the design of the HHS-CT operators. We also provide a detailed asymptotic analysis of each operator. Finally, we deduce the asymptotic run time complexity of the HHS-CT algorithm after all operators are analyzed. Memory consideration The memory consideration operator is invoked on two conditions: (1) r≥hmcr and (2) there is a common contact between the solution and the current harmony, ∃contact∈V\|contact∈solution⋀contact∈hmj, such that hmj is the harmony stored in the jth location of the harmony memory (hm). As shown in line 16 of Algorithm 2, the memory consideration performs a union operation between the feasible solution and the harmonies in hm. The run time complexity of the memory consideration operator is presented in Corollary 2. Corollary 2 The run time complexity of the memory consideration operator of the HHS-CT metaheuristic algorithm is OV. Proof The memory consideration operator is a union operator between the current solution and the current harmony, i.e., solution∪hmj, as shown in line 16 of Algorithm 2. It appends every contact (or vertex) in the current harmony hmj to the end of the solution solution. Let the length of the current harmony be V, then the union operator will iterate V iterations. Thus, the complexity of the memory consideration operator is OV.□ Pitch adjustment Pitch adjustment is the second operator of HHS-CT and is used in tuning solutions, which is to maximize the solution by adding more contacts to it. After a solution is found, we generate a random number p, as shown in line 17 of Algorithm 2, such that p∈0,1, if p≥par, then the pitch adjustment operation is invoked by calling PitchAdjustment() as shown in line 20 of Algorithm 2. The pitch adjustment operator is shown in Algorithm 3. Corollary 3 illustrates the run time complexity of the pitch adjustment operator of the HHS-CT algorithm. Corollary 3 The run time complexity of the pitch adjustment operator of the HHS-CT metaheuristic algorithm is OV+E. Proof The run time complexity of the pitch adjustment operator is composed of 4 parts, these are: (1) hill-climbing function for finding a forward component that takes OV+E complexity, (2) another hill-climbing function for finding a backward component which takes OV+E complexity, (3) intersection which takes OV, and (4) union which takes OV. Thus:fconsider=fHC+fHC+f∩+f∪=OV+E+OV+E+OV+OV=OV+E. □ Randomization The creation of a random harmony is similar to the pitch adjustment operator presented in Algorithm 3 except that in randomization we create a solution from the original harmony, not the improvised one, i.e., the one considered from memory. Corollary 4 presents the run time complexity of the randomization operator. Corollary 4 The run time complexity of the randomization operator of the HHS-CT metaheuristic algorithm is OV+E. Proof Similar to the pitch adjustment operator, the randomization operator comprises the same four steps included in the pitch adjustment operator, namely (1) a hill-climbing whose complexity is V+E, (2) a second hill-climbing function for finding a backward component in OV+E time, (3) an intersection operator that runs in OV time, and (4) a union that takes OV. Thus, the complexity of the randomization operator is expressed as follows:frandm=fHC+fHC+f∩+f∪=OV+E+OV+E+OV+OV=OV+E. □ In Theorem 1, we provide the run time complexity of the HHS-CT algorithm, and we asymptotically analyze the algorithm. Theorem 1 The run time complexity of using the HHS-CT metaheuristic algorithm to find SCCs in directed graphs is OV+E. Proof Let finit be the run time complexity of the initialization phase, fconsier be the run time complexity of the memory consideration operator, fadjust be the run time complexity of the pitch adjustment operator, and frandom be the run time complexity of the randomization operator, then the run time complexity of finding SCCs in SN graphs using HHS-CT denoted fHHS-CT, is computed as follows:fHHS-CT=finit+ni×hms-1×maxfconsider+fadjust,frandm=Ohms+ni×hms×maxOV+OV+E,OV+E=Ohms+Oni×hmsV+E∵hmsisconstantandni≪V+E∴fHHS-CT=OV+E. □ Experimental results and discussion We run our experiments on a dual-processor machine that contains two Intel® Xeon® CPUs E5-2620 v4 with 2.1 GHz. The machine has a 1 MB L1 cache, 4 MB L2 cache, and 40 MB L3 cache. It is equipped with 64 GB of RAM and runs Windows Server 2012 R2 Datacenter. The algorithms are implemented in Java. The tests are conducted on the real-world graphs that are listed in Table 2. Names of the datasets are listed in the first column of Table 2, the second column contains the number of contacts (or vertices) in each dataset, the third column contains the number of relationships in the corresponding dataset, and the last column represents the number of contacts that are contained in the largest SCC (LSCC) in the dataset. The correctness of the HHS-CT algorithm is tested and proved by comparing the results obtained by the HHS-CT algorithm with the size of the LSCC which is indicated for each dataset by the benchmarks. We run the HHS-CT algorithm setting the index case to any vertex that is contained in the LSCC. For any given dataset, the HHS-CT algorithm is set to run a predetermined number of times; each run outputs the computed LSCC by HHS-CT, which is denoted LSCCHHS-CT, it is compared with the LSCC stated by the corresponding benchmark, which is computed by one of the exact algorithms and is denoted as LSCCexact, and the error rate is computed. Acceptable error rates prove the correctness of the algorithm. This is illustrated in detail later in this section. The datasets are retrieved from several sources, namely the Koblenz Networks Collection (Kunegis 2013), the SNAP database (Leskovec and Sosič 2016), and the Social Computing Data Repository at Arizona State University (Zafarani and Liu 2017). We classified the input SN graphs into four classes with respect to their sizes as follows: (1) class A which contains graphs with sizes less than 1000 vertices, (2) class B which contains graphs within the range of 1006 to 2941 vertices, (3) class C which contains graphs within the range of 12,647 to 220,972 vertices, and (4) class D which contains graphs that have more than half a million vertices.Table 2 Datasets and their relevant information Dataset name Size (number of vertices) Volume (number of edges) Size of LSCC Rhesus 17 111 16 Bison 28 314 26 Hens 34 496 31 Florida ecosystem dry 130 2137 103 Residence hall 219 2672 214 email-Eu-core 1006 25,571 803 Blogs 1226 19,025 793 UC Irvine messages 1901 59,835 1294 OpenFlights 2941 30,501 2868 Edinburgh Associative Thesaurus 23,134 511,764 7751 BlogCatalog 88,786 4,186,390 88,784 Buzznet 101,170 4,284,534 95,470 Libimseti.cz 220,972 17,359,346 81,145 Wikipedia talk, Italian 863,846 3,067,680 36,356 Wikipedia talk, Arabic 1,095,799 1,913,103 8,797 Wikipedia talk, Chinese 1,219,243 2,284,546 10,831 Wikipedia talk, French 1,420,367 4,641,928 56,011 Hudong internal links 1,984,484 14,869,484 365,558 Flixster 2,523,390 9,197,337 99,803 The parameters of the HHS-CT algorithm are tuned (or set) experimentally using the trial-and-error method, which is the most prominent method for setting algorithm parameters. Firstly, the ni parameter, which is equivalent to maximum iterations in other metaheuristic algorithms, needs to be as small as possible to enable the algorithm to return a solution in a reasonable time. The HHS-CT metaheuristic algorithm is set to perform two iterations on class A graphs, 16 iterations on class B graphs, 32 iterations on class C graphs, and 128 iterations on class D graphs. Secondly, we managed to set the value of the harmony memory considering rate (hmcr) to a small amount, i.e., hmcr=0.1, to increase the probability of improvising new solutions by considering (or looking up) the hm rather than improvising new solutions by randomization, which improves the solution quality. As a rule of thumb, a good metaheuristic must maintain a good balance between exploration (or diversification) and exploitation (or intensification). In HHS-CT, exploration is controlled by the hmcr parameter, while exploitation is controlled by the par parameter. Accordingly, we set the value of the par parameter to a small amount, i.e., par=0.01, to increase the chance of exploiting the solutions after an exploration (by means of memory consideration) takes place; thus, a balance between exploration and exploitation is maintained. Furthermore, we set the value of the hms parameter to 5, which represents the size of the hm, which is equivalent to the population size in population-based metaheuristics, and it is set to a value that is much smaller than V and much smaller than E. Finally, after setting the parameters hms, hmcr, and par to the values expressed already, we ran the HHS-CT algorithm several times on each class of input graphs to fine-tune the value of the parameter ni, which controls the number of iterations the HHS-CT algorithm does. Accordingly, the values of the parameter ni represent the smallest average number of iterations that can produce output in an acceptable time based on the class of the SN graph. The HHS-CT metaheuristic algorithm as well as the two exact algorithms, the Tarjan’s and the FW–BW algorithm, are set to run 30 times on each SN graph. At each run, we record the run time and the size of the LSCC, and we calculate the error rate of the solution produced by the HHS-CT algorithm only, as long as the two exact algorithms return exact solutions, or in other words global optimal solutions. The error rate of the solution is the deviation of that solution from the optimal solution stated by the benchmark or that is returned by either Tarjan’s algorithm or the FW–BW algorithm. Formally, let LSCCHHS-CT be the size of the largest SCC obtained by the HHS-CT algorithm and LSCCexact be the size of the largest SCC stated by the benchmark, then the accuracy of the HHS-CT algorithm is given by Eq. 2. Consequently, the error rate of the HHS-CT algorithm, denoted by η, is the complement of accuracy, as shown in Eq. 3.2 accuracy=LSCCHHS-CTLSCCexat 3 η=1-accuracy Table 3 compares the HHS-CT metaheuristic algorithm and the exact search algorithms, namely Tarjan’s and the FW–BW algorithm. It is worth mentioning that Tarjan’s algorithm stops outputting results when the sizes of the graphs become larger, as in the case of classes C and D graphs. In essence, Tarjan’s algorithm uses DFS, which requires too many computing resources, such as processor cycles, memory, and stack. Certainly, the demand for computing resources becomes larger for larger graph sizes. Based on the specifications of the computing machine, the machine reaches a level where it becomes unable to satisfy that huge demand for computing resources.Table 3 The run times of the HHS-CT metaheuristic algorithm, Tarjan’s, and FW–BW Class Graph size Run time (s) HHS-CT Tarjan FW–BW A 17 3.4 × 10−4 2.52 × 10−4 1.83 × 10−4 28 4.6 × 10−4 5.71 × 10−4 3.52 × 10−4 34 4.5 × 10−4 0.0023 4.69 × 10−4 130 5.8 × 10−4 0.0033 0.003 219 1.2 × 10−3 0.0069 0.005 B 1006 0.0109 0.0499 0.027 1226 0.0155 0.0313 0.022 1901 0.0571 0.0728 0.065 2941 0.0445 0.0871 0.06 C 23,134 0.333 – 0.62 88,786 8.693 – 16.295 101,170 10.472 – 19.802 220,972 12.932 – 24.187 D 863,846 32.825 – 142.375 1,095,799 49.702 – 265.196 1,219,243 76.515 – 281.28 1,420,367 94.109 – 414.677 1,984,486 146.924 – 729.756 2,523,390 338.319 – 992.472 The run times of the HHS-CT, Tarjan’s, and FW–BW algorithms for classes A, B, C, and D are shown in Figs. 4, 5, 6, and 7, respectively. The experimental results show the superiority of the HHS-CT metaheuristic algorithm over the exact algorithms in terms of run time. Practically, this leads us to accept our hypothesis that we made earlier in this paper which indicates that using metaheuristic algorithms to find SCCs in SN graphs is faster than using exact algorithms.Fig. 4 Run times of the HHS-CT algorithm against the Tarjan’s and FW–BW algorithms for class A SN graphs Fig. 5 Run times of the HHS-CT algorithm against the Tarjan’s and FW–BW algorithms for class B SN graphs Fig. 6 Run times of the HHS-CT algorithm against the Tarjan’s and FW–BW algorithms for class C SN graphs Fig. 7 Run times of the HHS-CT algorithm against the Tarjan’s and FW–BW algorithms for class D SN graphs Undoubtedly, the integration of the SHC metaheuristic algorithm in the operators’ design of the HHS-CT metaheuristic algorithm and using it to traverse the graph heuristically, on a stochastic basis, rather than using the exhaustive (or exact) DFS technique, is the main reason of the superiority of the HHS-CT metaheuristic algorithm over the exact ones in terms of run time. In the case of exact algorithms, that use DFS to traverse the graph, when a contact is selected, all contacts that have interactions with it need to be traversed iteratively until there are no more contacts left. In contrast, using the SHC metaheuristic algorithm, which is a local search technique, when a contact is selected, the following steps are incorporated: (1) all contacts that have interactions with the current contact are listed and inserted into the current component, (2) only one contact is selected randomly from the set of contacts, (3) jump back to step (1) until there are no more contacts that could be added to the component. This heuristic nature of the SHC algorithm gives it superiority over DFS in terms of run time. Therefore, the algorithms that use SHC will consequently have better run time results compared to those that use DFS. Another reason why the HHS-CT algorithm has the best run time, and thus outperforms both the Tarjan’s and the FW–BW algorithms, is related to the algorithmic design of the HHS-CT algorithm. The HHS-CT algorithm has two operators that are executed at each iteration on a probabilistic basis. Technically, in the HHS-CT algorithm, the memory consideration operator is selected and is followed by the pitch adjustment operator, based on a probability, at each iteration of the algorithm. If the probability is not satisfied at a certain iteration, a solution is generated randomly. In either case, the maximum number of iterations that are made by the HHS-CT algorithm, which is dependent on the class of the graph, is set to be very small compared to the size of the input SN graph. Furthermore, not all contacts of the input SN graphs are traversed during each iteration of the algorithm. It is done on a stochastic basis. That is a random contact is selected from the graph and that contact is traversed. Due to these reasons, the HHS-CT metaheuristic algorithm achieved the best run time results compared to the two exact algorithms. The error rates of the HHS-CT metaheuristic algorithm for class D graphs are shown in Table 4. It is noteworthy that applying the HHS-CT metaheuristic algorithm to the graphs of classes A, B, and C incurred no error rates, or in other words, resulted in 0% error rates.Table 4 Error rates of the HHS-CT algorithm when applied to class D graphs Class Graph size Error rate D 863,846 0.019 1,095,799 0.033 1,219,243 0.029 1,420,367 0.019 1,984,486 0 2,523,390 0 Both Tarjan’s and the FW–BW algorithms are exact algorithms, that is, the solutions that are returned by those algorithms are globally optimal. Unlike the HHS-CT algorithm which is a metaheuristic algorithm that returns near-optimal solutions with slight error rates. Consequently, the HHS-CT algorithm has very small error rates when compared to both the Tarjan’s and the FW–BW algorithms for class D graphs, as shown in Fig. 8. Intuitively, the lower the error rate for an algorithm, the higher the accuracy of that algorithm, as implied by Eq. 3. Consequently, the HHS-CT algorithm has high accuracy. Practically, the HHS-CT algorithm starts by selecting an initial solution from its memory and then iterates through all other solutions in the memory. If the resulting solution is better than the worst solution in memory, that worst solution is replaced with the one better than it, in the sense that only high-quality solutions are kept in memory. Moreover, after the HHS-CT algorithm finishes improvising new solutions, pitch adjustment starts to enhance the obtained solution. This, in turn, minimizes the error rate and maximizes the accuracy of solutions.Fig. 8 Error rates of the HHS-CT metaheuristic algorithm for class D graphs To understand the results shown in Table 4 and Fig. 8, we need to compute the average vertex degree d of each graph according to Eq. 4, where E is the number of edges in the graph and V is the number of vertices in the graph. Thus, the average vertex degree d of each graph in class D is computed according to Eq. 4 and is listed in Table 5.Table 5 Average vertices degree d of each graph in class D Class V E d D 863,846 3,067,680 3.55 1,095,799 1,913,103 1.75 1,219,243 2,284,546 1.87 1,420,367 4,641,928 3.27 1,984,484 14,869,484 7.49 2,523,390 9,197,337 3.64 4 d=EV In Fig. 9, a 3D graph shows the relationship between the average vertex degree and the error rate for class D graphs. A deeper insight into Fig. 8 shows that there is an inverse proportional relationship between the average vertex degree and the error rate, in the manner that the greater the average vertex degree, the minimum the error rate. The algorithmic design of the HHS-CT algorithm stipulates that at each iteration, one vertex is selected randomly and all the vertices that are adjacent to the selected vertex are inserted into the component. Intuitionally, in graphs that have greater average vertex degree, more vertices are listed and inserted into the component during one iteration compared with graphs with less average vertex degree in which fewer vertices will be added to the component at each iteration. The results shown in Fig. 9 prove the correctness of this intuition when looking at Fig. 9 and concluding that graphs with higher average vertex degree have lower error rates.Fig. 9 Error rates of the HHS-CT with respect to the average vertex degree for class D graphs Nevertheless, the depiction of Fig. 9 gives only a basic explanation of the behavior of the HHS-CT and shows how error rates are inversely proportional to the average vertex degree. Therefore, to understand the results correctly, we need to look at two important factors, namely the number of multiple edges (m¯¯) and the number of loops (l) in the SN graph. The former, as its name implies, represents the number of duplicate edges between the same two vertices, that is: let v1 and v2 be two vertices in V, then m¯¯(v1,v2) is the number of duplicate edges between v1 and v2. The latter is the number of edges that link the vertex to itself. Consequently, we define two new metrics, namely (1) the distinct edges, denoted by E′, which refers to the number of edges without multiple edges and loops and (2) the distinct vertex degree, denoted by d′, which refers to the average vertex degree of the graph using the distinct edges, and it is given by Eq. 5.5 d′=E′V The number of multiple edges and the number of loops were retrieved from the benchmarks of class D graphs. Consequently, distinct edges and distinct vertex degrees were computed for each of the class D graphs and the results are listed in Table 6.Table 6 Distinct edges and distinct vertex degree of class D graphs, provided that the number of multiple edges (m¯¯) and number of loops (l) in each graph are taken from the benchmarks Dataset code V E m¯¯ l \|E′\| d′ D1 863,846 3,067,680 1,661,453 233,216 1,173,011 1.36 D2 1,095,799 1,913,103 1,564,598 73,297 275,208 0.25 D3 1,219,243 2,284,546 1,735,118 110,689 438,739 0.36 D4 1,420,367 4,641,928 2,471,501 788,289 1,382,138 0.97 D5 1,984,484 14,869,484 0 187,226 14,682,258 7.40 D6 2,523,390 9,197,337 0 0 9,197,337 3.64 Values of error rates with respect to distinct vertex degrees are depicted in Fig. 10. The inverse proportional relationship is apparent in Fig. 10 in the sense that as the distinct vertex degree increases, the error rate decreases and vice versa. This illustrates the reason behind the zero error rates for the last two graphs of class D, simply because they have the highest distinct vertex degree.Fig. 10 Error rates of the HHS-CT with respect to the distinct vertex degree for class D graphs Our last discussion is about the enhancement achieved in terms of run time and error rate. In Table 4, we list all the classes of the input SN graphs we used through our experiments: A, B, C, and D, and for each class, we find the average run time T¯ and the average error rate η¯. As shown in Table 7, the average error rate of the HHS-CT metaheuristic algorithm is 1.7% for class D, which is a very small (low) error rate, and for all other classes is zero. We compute the enhancement achieved by the HHS-CT metaheuristic algorithm in terms of run time over the Tarjan’s and the FW–BW algorithms; these are denoted by ETTarjan and ETFW-BW, respectively. Let T¯HHS-CT be the average run time of the HHS-CT metaheuristic for a certain class and T¯x be the average run time of the algorithm x for the same class, then ETx is given by Eq. 6.Table 7 Average run times, average error rates, and enhancement of HHS-CT algorithm over exact algorithms Class HHS-CT Tarjan FW–BW Enhancement T¯ η¯ T¯ η¯ T¯ η¯ ETTarjan (%) ETFW-BW (%) A 6.16 × 10−4 0 2.7 × 10−03 0 1.8 × 10−3 0 77.18 34.23 B 0.03 0 0.06 0 0.04 0 50 25 C 8.11 0 – 0 15.23 0 – 46.75 D 123.07 0.017 – 0 470.96 0 – 73.87 6 ETx=1-T¯HHS-CTT¯x×100% The enhancement rates achieved by the HHS-CT metaheuristic algorithm over the Tarjan’s and the FW–BW algorithm in terms of run times are computed according to Eq. 4 for each class separately which are listed in Table 7. Accordingly, Fig. 11 shows the enhancement rates of using the HHS-CT algorithm over both Tarjan’s algorithm and the FW–BW algorithm in terms of run time. It is obvious from Fig. 11 that the best enhancement achieved by the HHS-CT metaheuristic algorithm over Tarjan's algorithm in terms of run time is 77.18% for class A graphs. Also, 73.87% is the enhancement of the HHS-CT metaheuristic algorithm over the FW–BW algorithm in terms of run time for class D graphs. It is worth mentioning that Tarjan’s algorithm makes no responses on classes C and D graphs. Tarjan’s algorithm is a DFS algorithm that depends on recursion. Practically, recursion exploits the computing resources, such as the CPU cycle and memory locations. However, the larger the size of the graph and the greater number of edges in that graph, more computing resources are required, which explains why Tarjan’s algorithm stops to respond as the size of the graph and the number of edges grow, that is the case of classes C and D graphs. In a nutshell, the enhancement rates favor the heuristic nature of the HHS-CT algorithm over both the Tarjan’s and the FW–BW algorithms. In practice, HHS-CT traverses the graph starting from a pivot, that is the index case, and traverses random contacts that are linked with direct edges with that pivot, also maximizes the solution repeatedly until the algorithm stops. On the other side, both Tarjan’s and FW–BW algorithms traverse all the vertices (or contacts) with direct edges to the pivot (or index case). Thus, traversing randomly selected contacts rather than all the contacts is the main reason for the performance superiority of HHS-CT over both the Tarjan’s and the FW–BW algorithms.Fig. 11 Enhancement rates of using the HHS-CT algorithm over both Tarjan’s and FW–BW algorithms in terms of run time It is noteworthy that the average error rate obtained by the HHS-CT algorithm is very small, which is 0.17%. Therefore, the results show that there is a tradeoff between accuracy and run time. According to Table 4, a very tiny error rate is produced when using the HHS-CT algorithm, or in other words, the average accuracy of the HHS-CT algorithm is 99.983%. Yet, HHS-CT is 73.87% faster than the FW–BW algorithm, while at the same time Tarjan’s algorithm failed to respond when the sizes of the datasets grew to millions. This proves the feasibility of the solutions produced by the HHS-CT algorithm and that the tradeoff between accuracy and run time stands. Conclusion and future work In this paper, we devised a new contact tracing mechanism based on exploring SNs to discover the contacts that are exposed to COVID-19 infection due to contacting or approaching an infected individual. The new mechanism is based on using a hybrid metaheuristic technique that we devised and used for the first time to find the SCCs in large SN graphs by hybridizing HS with HC. We integrated SHC, which is a variant of HC, in the operators of the HS algorithm. We also adjusted the parameter settings to adapt the algorithm to find the SCCs in SN graphs. Asymptotically, the HHS-CT metaheuristic algorithm was proved to have a linear run time complexity OV+E. Experimentally, the HHS-CT metaheuristic outperformed the two exact algorithms used in finding SCCs in directed graphs, namely Tarjan’s and FW–BW algorithms, in terms of run time. The enhancement of the HHS-CT metaheuristic algorithm over Tarjan’s algorithm was 77.18% for class A graphs, and the enhancement of the HHS-CT metaheuristic algorithm over the FW–BW algorithm was 73.87% for class D graphs as best results obtained. Moreover, an exceptional average error rate of 1.7% was obtained by the HHS-CT algorithm for class D and zero error rates for all other classes. In future work, more metaheuristic algorithms can be investigated and adapted to devise new contact tracing algorithms. Furthermore, the same problem can also be parallelized and solved on parallel machines or multicore machines for larger graphs using a message-passing interface (MPI), OpenMP, or multithreading techniques. Also, the problem can be applied to the optical chained-cubic tree (OCCT) (Mahafzah et al. 2012) and the chained-cubic tree (CCT) (Al-Haj Baddar and Mahafzah 2014) interconnection networks. Moreover, dynamic parameter adaptation (Valdez and Peraza 2019; Valdez et al. 2020; Castillo et al. 2021) can be applied to the HHS-CT algorithm to automatically (or dynamically) adjust the HS parameter trying to obtain better performance compared to using fixed parameters. Additionally, contact tracing using SN profiles can be studied in future work by trying to utilize the clustering techniques and comparing the results with those that pertain to using SCCs in contact tracing. An important addition to the future work could be the inclusion of fuzzy logic and using it in conjunction with the HS algorithm. Acknowledgements The authors would like to express their deep gratitude to the anonymous referees for their valuable comments and suggestions, which improved this research work. Author contributions AAS contributed to the methodology, software, data curation, validation, and writing—original draft. BAM was involved in the supervision, conceptualization, investigation, validation, data curation, and writing—review and editing. MA contributed to the supervision, investigation, and validation. Declarations Conflict of interest All authors declare that they have no conflict of interest. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent Informed consent was obtained from all individual participants included in the study. This article has been retracted. Please see the retraction notice for more detail: 10.1007/s00500-023-08597-9 Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Change history 5/29/2023 This article has been retracted. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 34227008 15225 10.1007/s11356-021-15225-2 Research Article The non-linear relationship between carbon dioxide emissions, financial development and energy consumption in developing European and Central Asian economies Chunyu Leng [email protected] 1 Zain-ul-Abidin Syed [email protected] 2 Majeed Wajeeha [email protected] 3 http://orcid.org/0000-0003-1890-0894 Raza Syed Muhammad Faraz [email protected] 2 Ahmad Ishtiaq [email protected] 4 1 grid.411923.c 0000 0001 1521 4747 School of Economics, Capital University of Economics and Business, Beijing, China 2 grid.49470.3e 0000 0001 2331 6153 Institute for Region and Urban-Rural Development, Wuhan University, Wuhan, 430072 Hubei Province China 3 grid.444798.2 0000 0004 0607 5732 National University of Modern Languages (NUML), Islamabad, Pakistan 4 grid.412496.c 0000 0004 0636 6599 Department of Economics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan Responsible Editor: Philippe Garrigues 5 7 2021 2021 28 44 6333063345 21 4 2021 27 6 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. A sizeable amount of scholarly work has been done on different aspects of financial, economic, and environmental factors. In the present study, the nonlinearity is determined between financial development and carbon dioxide emissions in the long-run and short-run periods. According to the finding, the continued financial development initially increases the carbon dioxide emissions in the short and long run. Simultaneously, the square term of financial development reduces carbon dioxide emissions and proves the inverted U-shaped hypothesis in the short and long periods. The consumption of fossil fuels produces carbon dioxide emissions, leading to environmental pollution. In contrast, renewable energy sources have fostered ecological sustainability by reducing CO2 emissions in the long and short term. At the same time, a positive response from labor productivity to carbon dioxide emissions causes environmental pollution, while capital formation is not acknowledged as a significant contributor to CO2 emissions. The Error Correction term has ascertained the reduction in error and convergence of the model from short to long term with a speed of 8% per annum. The study suggested that renewable energy and financial development should be indorsed for environmental preservation in developing European and Central Asian economies. Financial development in favor of low-cost renewables, advancing cleaner production methods, solar paneling, and electrification are a few possible remedies to achieve environmental sustainability in the short-run as well as long-run time frame. Graphical abstract Keywords Carbon dioxide emissions Economic prosperity European and Central Asian developing economies Fossil fuel energy consumption Renewable energy consumption issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2021 ==== Body pmcIntroduction In this modern era, environmental sustainability along with financial development is the demand of both developed and developing economies. Developing economies need respectable economic growth and financial development along with a healthy environment to achieve the economic targets to get the rank of the developed nations. In evidence of APEC countries, financial development reduces carbon dioxide emissions, leads to improvement in environmental quality and economic prosperity (Zaidi et al. 2019). A clean and healthy environment provides a better life for the living. It is expected that when the environment becomes pure, it will give better health to individuals and better health status is the sign of better production in the economy. In other words, when the labor is healthier, vigorous, and skillful, the production activities in the economy flourish, and it will improve financial development and bring economic prosperity (Chaudhry et al. 2012; Hanif and Chaudhry 2015). In these modern times and according to the contemporary concepts of economic well-being, the energy sector also has great importance. Currently, the energy sector is considered the leading sector regarding economic well-being because of its strong linkages with economic prosperity, financial development and the natural environment. The rapid productivity of goods and economic growth relies on the energy sector in developed and developing nations. In this regard, energy is further divided into two segments, which are non-renewable and renewable energy. Non-renewable energy like fossil fuels such as gas, coal, and oil are readily available and less costly. Therefore, mainly developing nations depend on non-renewables to produce goods (Mudakkar et al. 2013; Hanif et al. 2019). Non-renewable energy is frequently used in developing countries because the primary concern is to enhance productivity with low production costs to compete in the national and international markets. The high dependency on non-renewable energy has a formative influence on developing economies, as their economic growth is increasing rapidly (Zhang et al. 2012; Waslekar 2014). At the same time, non-renewables are undoubtedly considered the helping hand to rapid productivity in economic growth, but it is also demoralizing ecological sustainability. The high feasting of non-renewables is causing the emissions of greenhouse gases like carbon dioxide emissions. The developing countries have the limitation of resources to sustain their non-renewable energy resources. That’s why they are using non-renewable energy resources above the limit and causing environmental pollution (Mudakkar et al. 2013; Adams et al. 2018). Fossil fuel-based energy is used as a frequent intake in developing economies because it is easy to access, less costly in term prices, and contributes to the growth generation process. As a result, this non-renewable energy is increasing health risks and air pollution in the form of carbon emissions (Hanif et al. 2019). Here are the ten-year averages of some developing economies from Europe and Central Asia, highly dependent on fossil fuel energy. Such as Azerbaijan 96.10%, Belarus 91.99%, Bosnia and Herzegovina 86.10%, Moldova 90.01%, Russia 91.11 %, Serbia 85.29%, and Turkey 89.38%. These countries are mainly consuming fossil fuels to generate energy and the largest emitter of carbon dioxide in the air (World Bank 2020). Thus, it is expected that such countries are at high risk of environmental degradation and ecological disorder. To slow down the ecological disruption, there is essential to highlight the dangers of fossil fuel energy consumption. In the present time, under high environmental challenges all over the globe, it is essential to search for environment-friendly energy alternatives to produce goods and to meet energy requirements at the domestic level. As a result, many studies have highlighted the importance of renewable energy to improve economic growth and ensure environmental sustainability. However, renewable energy resources are usually costly than non-renewable energy sources while providing a better growth process and sustainable environment. That’s why renewable energy has significant importance in the growing generation process without destroying sustainable environmental conditions. After the developed economies, the developing economies are also moving to renewable energy resources to improve their financial development without any threat of ecological degradation. For instance, Albania 37.01%, Georgia 35.57, and Montenegro 46.09 are some developing economies from Europe and Central Asia, have highly dependent on renewable energy. At the same time, these values are the 10 years average of renewable energy (World Bank 2020). This research mainly objects to expose the nonlinearity in the relationship between renewable and nonrenewable energy intakes, financial development, and carbon secretions by adopting the EKC hypothesis in eighteen European and Central Asian developing economies. The present study ' s primary concern is to highlight the nature of the relationship between financial development, environmental, and energy variables because parabolic relationships between variables demand different policy frameworks compared to the linear association between such variables. There is a prerequisite to understanding the linear or nonlinearity in the relationship between energy consumption, financial development, and carbon emissions. For this purpose, the Environmental Kuznets Curve hypothesis might help investigate such types of association and develop a practical policy framework to control or mitigate the carbon dioxide emissions in the selected countries. The EKC hypothesis assumes that in countries with low financial development, initially or in the first phase of EKC, there will be a positive relationship between financial development and carbon emissions. Because under poor economic conditions, the countries usually ignore the environmental issues and try to improve financial development to tackle economic challenges such as poverty, unemployment, and low production of goods and services. However, in the second phase of EKC, it is assumed that the countries treated the environment as a fundamental and other economic challenge after getting respectable financial development. Therefore, there should be a negative relationship between financial development and carbon emissions in the second phase. In short, this study hypothesizes that EKC will show an inverted U-shaped relationship between financial development and carbon emissions. There is similar evidence presented in the past research in which the square term of financial development is introduced, and evident the second phase (inverted U-shaped) of the EKC hypothesis. It was found that there is a negative relationship between financial development and carbon dioxide emissions (Shahbaz et al. 2013). The study objective is to investigate the U-shaped, and inverted U shaped EKC for financial development and environmental pollution. Further, the study objective is to examine environmental sustainability by examining the influence of renewable energy consumption, which is the clean form of energy, on carbon dioxide emissions. Therefore, it is expected that the findings of this study will be helpful to and depict a different angle of association between financial development, energy variables, and environmental quality. The above discussion has raised some questions to be answered. First, whether the non-linear relationship between carbon dioxide emissions, energy consumption and financial development exists?. Second, whether the EKC U or inverted U-shaped relationship exists in the dependent and independent variables? In the end, the research demands some beneficial policies to maintain environmental sustainability by mitigating environmental pollution. This research is constructed into 5 sections to explore the non-linear relationship between carbon dioxide emissions, energy consumption and financial development. Section 1 of the introduction introduces these indicators, research questions, importance, contribution, and study objectives. The following “Review of literature” section has reviewed the previous research regarding the relationship between carbon dioxide emissions, energy consumption, economic growth and financial development and ends up with a research gap. “Data, methodology, and specification of model” section discusses the description of variables, data, specification of the theoretical and econometric model and methodology. However, “Results and discussions” section interprets the results and discuss with economic reasoning and evidence of past studies. In the end, “Conclusion” section concludes the research by conclusion, implications, limitation and future direction of the research. Review of literature In this modern times, the energy sector is considered one of the leading sectors in developed and developing countries. The developed nations have adopted sustainable energy resources and gaining the benefit in the form of economic growth. Simultaneously, the developing countries have a deficiency of planning and resources to adopt these sustainable energy resources for economic development. Most developing countries have adopted fossil fuels and paid the cost in the shape of ecological threats. The environment is significantly tainted in emerging nations because of much enslavement of the energy resources that cause greenhouse gas emanations. These greenhouse gases like carbon dioxide releases have spoiled the environment and reduce the environment ' s sustainability, cause them to get far away from developed nations ' status. While most emerging countries are seen to have substantial renewable energy intake, their dependence on nonrenewable energies has not enabled them to rehabilitate environmental sustainability. However, this literature review section has reviewed the past studies to achieve the following hypothesis: EKC U-shaped hypothesis and EKC inverted U-shaped hypothesis. Country level studies of carbon emissions and economic growth nexus Energy consumption has a positive association with economic growth, as the high energy intake has enhanced the economic growth of developing economies. This positive association has also been enhanced the carbon secretions and degraded the environmental quality. This ecological deprivation in developing countries has lessened the environment ' s sustainability or purity (Alam et al. 2007; Qi et al. 2011; Bo 2011; Ahmed and Long 2012). However, transitory to the early stages of growth generation, the later stage of stable economic growth helps to mitigate the carbon dioxide emissions, proved the inverted U-shaped EKC hypothesis (Qi et al. 2011; Bo 2011). Shahbaz et al. (2012a, 2012b) stated that environmental cleanliness is reduced by more energy intake, as economic growth in Pakistan is improved. In another piece of evidence of Zhang et al. (2012), the high feasting of energy has promoted China’s economic growth but failed to sustain the environmental purity. Renewable energy and non-renewable energy are the two types of energy used by developing economies. Simultaneously, non-renewable energy resources are not environmentally friendly, frequent in developing countries. Non-renewable energy is frequent to enhance economic growth, while this prolific enhancement has increased the carbon dioxide emissions that caused environmental pollution in China (Zhang et al. 2012). Mudakkar et al. (2013) evidenced that the large abundance of energy sources has degraded Pakistan ' s environment. The consumption of fossil fuels as nonrenewable energy resources caused CO2 discharges and threatened ecological contamination in Pakistan. The study of the Malaysian economy evident the positive reliance of carbon dioxide secretions on all energy forms. However, the plentiful fossil fuel energy consumption has mitigated environmental sustainability (Saboori and Sulaiman 2013a). Shahbaz et al. (2014) in Pakistan, Hu et al. (2014), and Wang et al. (2015) in China have ascertained that the high consumption of energy for better economic conditions undoubtedly enhanced the economic growth but enforced environmental pollution. However, Shahbaz et al. (2014) proved that carbon dioxide emission is reduced in Pakistan after achieving the desired level of economic growth. In UAE, the consumption of energy to promote the economy of UAE, the greenhouse gases like carbon dioxide gases are released and caused the environmental hazard (Jayaraman et al. 2015). However, due to high energy consumption the environmental hazard is evident in Pakistan in the short and long run (Javid and Sharif 2015). The environment becomes polluted by the extensive use of energy in Tunisia, in Eastern, Western, and Central China (Sghari and Hammami 2016; Zhang and Gao 2016a, 2016b). In South Africa, energy consumption and carbon dioxide emissions intensity both are negatively impacting environmental sustainability. There is unidirectional causality between economic growth and carbon dioxide emissions. The study also evident the Inverted U-shaped EKC between economic growth and carbon dioxide emissions (Bekun et al. 2019). Raza et al. (2019) studied the positive effect of energy consumption on carbon dioxide emissions in the short, medium, and long run. However, Granger causality passed through the one-way causation from energy consumption to carbon dioxide emissions in the USA. In evidence of China’s western, intermediate, and eastern zone, gross regional products negatively influenced the carbon dioxide emissions, leading to environmental protection (Ahmad et al. 2019). In another evidence of China, the effective contribution of economic growth and energy has mitigated carbon dioxide emissions (Zhang et al. 2019). In the discussion of renewable energy consumption, it is evident that German, as the leading European economy, is the largest renewable energy consumer. The effective contribution of renewable energy helped boost economic growth and environmental purity (Rafindadi and Ozturk 2017). However, financial development is an important indicator of economic growth and perform as the function of economic growth to influence carbon dioxide emissions. Jalil and Feridun (2011) examined the long-run relationship between financial development and carbon dioxide emissions. In China, financial development negatively influences carbon dioxide emissions, reduced environmental degradation and their exists an inverted U-shaped EKC. In the case of Turkey, financial development does not affect carbon dioxide emissions (Ozturk and Acaravci 2013). Shahbaz et al. (2013) illustrated that financial development has reduced carbon dioxide emissions in Indonesia. However, the inverted U-shaped EKC among financial development and carbon dioxide emissions has been observed. At the same time, the positive and negative influence of financial development on carbon dioxide emissions leads to linear and non-linear impacts. Charfeddine and Khediri (2016) investigated that financial development negatively influenced carbon dioxide emissions and also proved inverted U-shaped EKC in UAE. Nevertheless, financial development negatively affects carbon dioxide emissions, promoting Turkey ' s financial sector and environmental protection (Katircioğlu and Taşpinar 2017). In another evidence of Turkey, financial development has increased carbon dioxide emissions and deprived environmental quality (Pata 2018). Panel studies of carbon emissions and economic growth nexus The literature above has reviewed the relationship between carbon dioxide emissions, economic growth, and energy consumption at country level. The study has reviewed the past studies of these relationships in more than one country or panel countries. In the case of ASEAN, the consumption of energy to improve economic productivity has degraded the environment by emitting carbon dioxide gas. While later, the emissions of carbon dioxide have been reduced by achieving desirable productivity and growth in ASEAN ' s economies, proven the Inverted U-shaped EKC hypothesis. In contrast, Singapore and Thailand are failed to control the environmental hazard and found the most degraded countries from ASEAN (Saboori and Sulaiman 2013a). In evidence of low-high income economies, the constructive participation of energy caused the high carbon discharges in emerging economies or those economies in which the per capita income is meager. At the same time, the high-income countries are evidenced the environmental sustainability. However, the inverted U-shaped EKC manifested in those economies that have achieved the desired economic growth and environmental sustainability (Waslekar 2014). Zeb et al. (2014) have studied that the SAARC economies are indulged in high energy consumption, which has risen the CO2 emission and environmental pollution. In six oil-exporting countries, the total energy consumption and high dependency on oil consumption has worsen the environmental quality in all six oil-exporting countries. The reduction in the environment’s sustainability is caused by CO2 emissions, resulting from high reliance on oil consumption. However, the countries who have corresed the turning point of EKC, the improvement in their economic growth is reducing the carbon dioxide emissions. There are shreds of positive influences of energy intakes and economic growth on carbon dioxide emissions in different economies. Al-mulali et al. (2015) in low-lower middle-income nations, Saidi and Hammami (2015) in fifty-eight countries, and Salahuddin et al. (2015) in GULF countries have proved the positive relationship between energy and environmental pollution. The long-term evidence of ecological degradation in GULF economies is verified by Salahuddin et al. (2015). In a provincial level study, Zheng et al. (2015) evidenced the positive relationship of energy with economic growth and air contamination in China. However, some provinces have evident the negative influence of economic growth and energy consumption on carbon dioxide emissions in China. In evidence of a large panel of fifty-eight countries, energy consumption has positively contributed to economic growth and CO2 emissions in six regions. Economic growth’s positive influence on carbon dioxide emissions led to global warming and other environmental threats. However, economic growth and carbon dioxide emissions have shown inverted U-shaped EKC (Kais and Sami 2016). Following nonrenewables, fossil fuel energy was found substantial to enhance alarmed economic growth, while air pollution is verified through the high consumption of non-renewables (Adams et al. 2018). Non-renewable fossil fuel energy is the most important tool to enhance the economic growth of fifteen developing economies but failed to rehabilitate environmental sustainability. At the same time, inverted U-shaped EKC hypothesis is also verified for a panel of fifteen developing Asian countries (Hanif et al. 2019). In the study of MENA economies, the sustainable consumption of energy and economic growth has lessened the carbon dioxide secretions, reduced the environmental pollution hazard (Gorus and Aydin 2019). However, in SAARC countries, urbanization and GDP per capita have exaggerated carbon dioxide emissions, led to the U-shaped EKC hypothesis (Anser et al. 2020a). After equating the effects of total energy and non-renewable energy ingesting on ecological conditions, the renewable energy resources are found most productive to enhance environmental sustainability. Renewable energy consumption has an affirmative influence on economic growth assisted environmental protection in G7 countries (Tugcu and Topcu 2018). Following some panel studies, renewable energy resources enhanced the growth of developing economies and sustainability in the environment by reducing CO2 emissions (Ito 2017; Carfora et al. 2019). Renewable energy is a vibrant indicator to enhance economic growth and mitigate carbon dioxide emissions, leading to economic progression in OECD countries (Inglesi-Lotz 2016; Gozgor et al. 2018). In the evidence of panel study, renewable energy is the foremost indicator to promote economic performance level in Poland compared to sixteen emerging economies. However, renewable energy assisted in reducing Poland’s environmental hazard following energy conservation policies (Ozcan and Ozturk 2019). In the case of MENA economies, renewable energy consumption slightly influenced the mitigation of carbon dioxide emissions. However, renewable energy is considered a weak indicator concerning contribution to environmental protection (Charfeddine and Kahia 2019). In addition, the high renewable energy consumption has executed economic prosperity, and reduced carbon dioxide emissions endorsed environmental protection in the short and long run. Fossil fuel energy consumption has badly affected the environmental conditions of SREB emerging economies. However, the economic growth and carbon dioxide emissions have developed the EKC inverted U-shaped hypothesis in SREB economies in the long run (Yang et al. 2021). Anser et al. (2020a) in developing Latin America and the Caribbean countries and Alharthi et al. (2021) in MENA countries evident the EKC inverted U-shaped hypothesis between economic growth and carbon dioxide emissions. Renewable energy consumption negatively influenced CO2 emissions, while fossil fuel energy consumption contributed to environmental pollution in concerned economies (Anser et al. 2020a; Alharthi et al. 2021). In the case of ASEAN countries, non-renewable energy exaggerates carbon dioxide emissions, while renewable energy consumption has reduced environmental pollution. Further, the EKC inverted U-Shaped hypothesis is verified by the negative influence of squared economic growth on carbon dioxide emissions (Anwar et al. 2021). Based on the above previous studies literature, the study can investigate the influences of financial development, renewable and non-renewable energy consumption on carbon dioxide emissions following these hypotheses mentioned below: H1: There is a positive relationship between financial development and CO2 emissions to validate U-Shaped EKC hypothesis in developing European and Central Asian economies. H2: There is a negative relationship between renewable energy consumption and CO2 emissions in developing European and Central Asian economies. Research gap The above-mentioned studies have provided a very straightforward relationship between energy consumption, economic growth, and carbon dioxide emissions. Therefore, the first research gap addressed in the present study based on the investigation of nonlinearity in the association between energy consumption, financial development, and carbon emissions. Meanwhile, most researchers focus on developing and developed economies from Asian, African, American, MENA, and GULF nations. Therefore, the present research focuses on important cross-sections of developing European and Central Asian economies, which have been seldom inspected in the past studies to spotlight the relationship between renewables and non-renewables, economic growth, and carbon dioxide secretions. However, financial development is an important indicator of economic prosperity, which is rarely reviewed in the literature and requires panel investigation of the relationship between energy consumption, financial development, and carbon dioxide secretions. Further, the EKC hypothesis needs exploration in study areas of developing European and Central Asian economies, which have been unsatisfactorily inspected in the past studies. Therefore, it is essential to inspect the validity of inverted U-shaped EKC hypothesis for environmental sustainability in developing European and Central Asian economies. Data, methodology, and specification of model Data The study explores environmental sustainability and investigates EKC hypothesis in European and Central Asian economies. To confirm the existence of inverted U-Shaped EKC, the selection of variables is based on carbon dioxide emissions as a dependent variable. The independent variables are energy taken in both forms, the non-renewable and renewable forms. However, financial development, the square of financial development, capital formation, and productivity in labor are other concerning factors collected from 2010 to 2019. The data of these variables is collected through “World Development Indicators.” There are 18 countries from the Europe and Central Asia region (World bank list of economies 2020) considered to measure environmental sustainability. Countries’ selection is based on their income level, reliance on nonrenewables, renewables, and carbon dioxide emissions. All the selected countries have developing status, and their income level is lower and upper middle income. Table 1 and Fig. 1 illustrate the high indulgence of developing economies in energy consumption. However, this generates carbon dioxide emissions, which is a threat to environmental sustainability. Therefore, this research has selected developing countries that are consuming renewable and non-renewable energy sources and facing environmental issues. The detail of countries is given below in Table 1 and Fig. 1: Table 1 Table of selected European and Central Asian developing countries Countries CO2 emissions (metric ton per capita) GDP (per capita growth) Fossil fuel consumption (% of total energy) Renewable energy consumption (% of total energy) Albania 1.753 2.87 60.15 38.871 Armenia 1.836 4.221 72.588 12.747 Azerbaijan 3.77 0.444 96.642 3.362 Belarus 6.521 1.86 99.602 0.307 Bosnia and Herzegovina 6.327 3.303 82.256 33.108 Bulgaria 6.108 3.026 72.419 26.954 Georgia 2.35 5.136 76.324 27.725 Kazakhstan 15.423 3.016 96.847 3.149 Kyrgyz Republic 1.611 2.275 74.75 23.89 Moldova 1.646 5.084 87.091 13.142 Montenegro 3.571 2.813 64.459 45.035 Romania 3.82 3.629 76.862 22.493 Russian Federation 11.906 1.65 88.427 11.573 Serbia 5.98 2.477 83.569 21.564 Tajikistan 0.569 4.54 43.733 47.021 Turkey 4.594 4.214 87.095 12.814 Ukraine 5.458 1.351 78.409 21.558 Uzbekistan 3.469 4.644 96.716 3.284 Source: World Development Indicators (2010–2019), World Bank List of Economies (2020) Fig. 1 Renewable energy and non-renewable energy 10 years average values Source: Based on WDI data (2010–2019) Explanation of variables Carbon dioxide emissions (EVD_CO2) Carbon dioxide is a greenhouse gas emitted by energy consumption in the growth-generation process. Carbon dioxide emissions’ measuring unit is as a metric ton per capita. CO2 has a positive association with energy intake and economic growth (Kais and Sami 2016; Bekun et al. 2019). Therefore, it should be negative to verify the hypothesis of EKC inverted U-shaped (Hanif et al. 2019; Anser et al. 2020a; Alharthi et al. 2021; Anwar et al. 2021; Yang et al. 2021). Non-renewable energy consumption as fossil fuels (FFEC) Fossil fuels are produced by ancient plants, buried or dead organisms. This frequent energy type is consists of carbon, coal, oil, gas, and petroleum. Fossil fuel energy ingesting is measured as the total percentage of energy. In addition, fossil fuels emit greenhouse gas such as carbon dioxide. Therefore, the expected relation of fossil fuel consumption with CO2 is positive (Mudakkar et al. 2013; Hanif et al. 2019; Anser et al. 2020b; Alharthi et al. 2021; Anwar et al. 2021; Yang et al. 2021). Renewable energy consumption (REEC) Renewable energy consumption is easily replaceable in natural processes and an essential energy type. Renewable energy intake is measured as the total % of final energy consumption. Renewable energy is an environmental and growth-friendly source and does not hurt environmental sustainability. The expected relation of renewable energy with CO2 emissions is negative (Carfora et al. 2019; Ozcan and Ozturk 2019; Anser et al. 2020b; Alharthi et al. 2021; Anwar et al. 2021; Yang et al. 2021). Financial development (FN_DEV) Financial development ties into the private sector’s economic growth and poverty reduction plan. The financial sector comprises a series of institutions, tools and markets, and also a legal and regulatory framework that allows for credit risks transactions. Financial development is measured as the domestic credit to private sector through % of GDP. Financial development has a positive expected relationship with CO2 emissions, examined in previous research (Charfeddine and Khediri 2016; Pata 2018). Financial development squared (FN_DEV2) Financial development squared is the double effect of financial development and is used to proxy double domestic credit to private sector. There is a need for doubled financial development to prove the EKC inverted U shaped hypothesis. The expected effect of squared financial development on CO2 emissions is negative, proved by past studies (Shahbaz et al. 2013; Charfeddine and Khediri 2016). Gross fixed capital formation (KFOR) KFOR is defined as the fixed amount of increase in capital stock, which is considered the future investment to promote production and growth. The fixed amount is generated through the annual growth percentage. It is expected that capital formation would positively relate to CO2 emissions (Saidi and Hammami 2015). However, it can be negative only if the resources will be used efficiently and in a sustainable manner (Hanif 2018a, b; Hanif and Gago-de-Santos 2017; Yang et al. 2021). Labor productivity (PRO_LB) Labor productivity is the increase in output through labor and can be measured in any economy by its adult population. Labor productivity is used as a total labor force and aims to enhance the growth level of the economy. The expected sign of labor productivity with CO2 emissions is positive, evidenced in previous researches (Saidi and Hammami 2015). Specification of model and methodology In this study, financial development, renewable and non-renewable energy consumption, and carbon dioxide emissions are measured theoretically and in an econometrical way for the evidence of the inverted U-shaped EKC hypothesis (Kuznets 1955). The theoretical point of view is that economic growth and energy intakes positively relate to carbon dioxide emissions, which is the leading cause of environmental pollution, leads to the early stage of the EKC hypothesis. However, the desired level of economic growth helps to reduce environmental pollution and develop ecological sustainability. Thus, according to the theory, the squared economic development has reduced environmental degradation, validate the EKC inverted U-shaped hypothesis. Thus, we need to establish the econometric model of financial development and carbon dioxide emissions to verify the EKC inverted U-shaped hypothesis in European and Central Asian developing economies based on the studies of Shahbaz et al. (2013) and Charfeddine and Khediri (2016). The study examines the non-linear relationship between financial development, renewable and non-renewable energy consumption, and carbon dioxide emissions. First, we have to develop the nonlinear functional form among variables which is given below: 1 EVD_CO2it=ά0+ά1FFECit+α2REECit+α3FN_DEVit+β4FN_DEV2it+β5KFORit+γ6PRO_LBit+εi The above equation is a non-linear functional form of environmental degradation (EVDCO2). In the above functional form, carbon dioxide emissions (EVD_CO2) depend on fossil fuel energy consumption (FFEC), renewable energy consumption (REEC), financial development (FN_DEV), financial development square (FN_DEV2), capital formation (KFOR), and labor productivity (PRO_LB). Econometric description of model The above equation is for the statistical description of the model to measure the empirical model ' s worth in terms of statistics. The above equation is passing through different statistical techniques to prove the statistical fitness of the model. Here in this step, the empirical model observes statistical terms to elaborate its statistical worth. In the second step of descriptive statistics, the strength of the relationship among variables is an important step. The correlation matrix tells us about the relationship among variables and multicollinearity. After this statistical summary, this model passes through the unit root test in terms of Levin, Lin and Chu, and Im, Pesaran, and Shin, to measure stationary level among variables. The mixed level of integration among variables leads to apply the ARDL technique on the model based on EKC inverted U-shaped hypothesis in European and Central Asian developing economies. 2 EVD_CO2it=ά0+ά1EVD_CO2it−1+α2FFECit−1+α3RFECit−1+β4FN_DEVit−1+β5FN_DEV2it−1+γ6KFORit−1+γ7PRO_LBit−1+εi The above Eq. (2) is for the long run ARDL to prove or disprove the EKC inverted U-shaped hypothesis among financial development, renewable, fossil fuel, and carbon dioxide emissions in a long period in Europe and Central Asia developing countries. Bound test of cointegration Before applying the long run ARDL on the empirical model, there is one initial step before this, the bound test for cointegration. This initial step is based on the cointegration equations to exist the cointegration among variable’s equations. The bound test cointegration is based on null and alternative hypothesis, which are written down, respectively. Null Hypothesis: H0: ά1 = 0; α2 = 0; α3 = 0; β4 = 0; β5 = 0; γ6 = 0; γ7 = 0 (No Cointegration exists) Alternative Hypothesis: H1: ά1 ≠ 0; α2 ≠ 0; α3 ≠ 0; β4 ≠ 0; β5 ≠ 0; γ6 ≠ 0; γ7 ≠ 0 (Cointegration exists) The equation of bound test for cointegration is written as: 3 ΔEVD_CO2it=ά0+∑ki=1ά1ΔEVD_CO2it−1+∑ki=0α2ΔFFECit−1+∑ki=0α3ΔREECit−1+∑ki=1β4ΔFN_DEVit−1+∑ki=0β5ΔFN_DEV2it−1+∑ki=0γ6ΔKFORit−1+∑ki=0γ7ΔPRO_LBit−1+ά1EVD_CO2it−1+α2FFECit−1+α3REECit−1+β4FN_DEVit−1+β5FN_DEV2it−1+γ6KFORit−1+γ7PRO_LBit−1+εi The above Eq. (3) shows the bound testing cointegration equation to prove the existence of long run relationship among variables. The alternative hypothesis will verify the cointegration equations among variables. Pesaran, Cross-sectional Dependency (CD) test Before measuring the inverted U-shaped EKC premise in the long and short run by applying ARDL, the one-step before ARDL and after bound testing for cointegration is the Pesaran CD test to approve the cross-sectional dependency among variables. There are also null and alternative hypotheses. The null hypothesis tells us that there are no cross-sections among variables, while the alternative hypothesis is the opposite of the null hypothesis. The acceptance of the null hypothesis will prove the no-cross sectional dependency and lead to apply the ARDL on the long and short term models. The values of both the bound test and Pesaran cross-sectional dependence are sanctioned by Pesaran et al. (2001). Short, long run ARDL, and ECM After the bound testing for cointegration equation to prove long-run existence and Pesaran cross-sectional dependency, an essential step is required to measure the inverted U-shaped EKC hypothesis in the long and short term. The next step will be the error correction model ECM to stabilize the long-run effect. Short and long-term ECM equation is written below: 4 ΔEVD_CO2it=ά0+∑ki=1ά1ΔEVD_CO2it−1+∑ki=0α2ΔFFECit−1+∑ki=0α3ΔREECit−1+∑ki=1β4ΔFN_DEVit−1+∑ki=0β5ΔFN_DEV2it−1+∑ki=0γ6ΔKFORit−1+∑ki=0γ7ΔPRO_LBit−1+ά1EVD_CO2it−1+α2FFECit−1+α3REECit−1+β4FN_DEVit−1+β5FN_DEV2it−1+γ6KFORit−1+γ7PRO_LBit−1+γ8ECMit−1+εi The above Eq. (4) shows the short and long term dynamics with ECM to prove or disprove the inverted U-shaped EKC premise in eighteen European and Central Asian countries. The dynamics starting from ∑ki=1 and Δ are short-term dynamics and showing their equation. The rest of equation shows the long-run model. ECMit-1 shows the speed of the adjustment tool to stabilize the long period. Moreover, εi is an error term that helps to ascertain the error. Diagnostic tests In the end, the EKC model is passing through some diagnostic tests of normality and serial correlation. However, functional form and heteroscedasticity need to check to prove the error-free empirical model of eighteen countries of Europe and Central Asia. Results and discussions After designing the empirical model of EKC in the third section, this section has proved that model through empirical results in an econometric and statistical way. The detail of tables of results and their detailed explanation is given below: The empirical findings are starting from the summary of the descriptive statistics. First, there is a need to measure the credibility of the model in the statistical term. The descriptive statistics results are given in Table 2 and show that the empirical model ' s concerned variables have statistical worth. The variables have distance from their mean values, which is elaborated by standard deviation, while the skewness shows that each variable has a positive direction except fossil fuel energy consumption. At the same time, kurtosis indicates that each variable is considered leptokurtic and has a thin tail and high peakedness from their mean point. Moreover, the Jarque-Bera probability value shows the significance of variables in terms of statistical worth and suggests that the empirical model is appropriate for further justifications. Table 2 Summary statistics EVD_CO2 FFEC REEC FN_DEV FN_DEV2 KFOR PRO_LB Mean 4.410 79.958 16.891 4.876 85.546 18.428 10336474 Median 4.124 84.810 14.261 4.933 30.837 8.917 3294861. Std. Dev. 2.685 14.500 14.286 7.872 500.530 163.808 18645996 Skewness 0.843 -1.206 0.996 4.705 15.919 16.764 2.723 Kurtosis 3.733 4.191 3.011 53.229 266.492 287.256 9.563 Jarque-Bera 42.329 90.486 49.633 32644.35 880524.9 1024071. 909.296 Probability 0.000 0.000 0.000 0.000 0.000 0.000 0.000 In Table 3 of the correlation matrix, the strength of association among variables is measured. Fossil fuel energy consumption (FFEC) and renewable energy consumption (REEC) have a moderate relationship with carbon dioxide emissions (EVD_CO2). However, renewable energy negatively correlates with carbon dioxide emissions. Besides, financial development (FN_DEV) and squared financial development (FN_DEV2) have a weak and negative relationship with carbon dioxide emissions. The critical value less than 0.30 indicates the weak relationship among variables. In this regard, capital formation (KFOR) have a weak but positive correlation with carbon dioxide emissions. Further, labor productivity (PRO_LB) has a positive and strong relationship with carbon dioxide emissions. However, there is no issue of multicollinearity as the variables have less than the critical value of 0.80. After the statistical description of the empirical model, the next step is the unit root test discussed in Table 4 to measure the order of integration among variables. Table 3 Correlation matrix EVD_CO2 FFEC REEC FN_DEV FN_DEV2 KFOR PRO_LB EVD_CO2 1.000 FFEC 0.447 1.000 REEC −0.483 −0.720 1.000 FN_DEV −0.179 −0.034 0.078 1.000 FN_DEV2 −0.098 −0.019 0.059 0.735 1.000 KFOR 0.027 −0.027 −0.041 −0.002 −0.012 1.000 PRO_LB 0.731 0.289 −0.351 −0.090 −0.041 −0.032 1.000 Source: Authors’ own calculation based on Eviews 10 Table 4 Unit root testing Level First difference Variables Intercept Intercept and trend Intercept Intercept and trend Concluded order EVD_CO2 L.L. and C −4.68 (0.00) −7.11 (0.00) - - I (0) I.P.S −2.06 (0.01) −3.43 (0.00) - - I (0) FFEC L.L. and C - - −5.75 (0.00) −5.14 (0.00) I (1) I.P.S - - −8.03 (0.00) −6.75 (0.00) I (1) REEC L.L. and C - - −8.51 (0.00) −9.10 (0.00) I (1) I.P.S - - −7.63 (0.00) −7.00 (0.00) I (1) FN_DEV L.L. and C −21.97 (0.00) −15.45 (0.00) - - I (0) I.P.S −9.33 (0.00) −5.71 (0.00) - - I (0) FN_DEV2 L.L. and C −307.79 (0.00) −263.53 (0.00) - - I (0) I.P.S −74.80 (0.00) −67.09 (0.00) - - I (0) KFOR L.L. and C −7.22 (0.00) −6.51 (0.00) - - I (0) I.P.S −6.48 (0.00) −4.65 (0.00) - - I (0) PRO_LB L.L. and C - - −2.85 (0.00) −3.60 (0.00) I (1) I.P.S - - −4.31 (0.01) −2.71 (0.00) I (1) Source: Authors’ own calculation based on Eviews 10 Table 4 illustrates the unit root test result to confirm the order of integration among variables. The study examines the L.L & C and I.P.S unit root tests to measure the stationarity among variables. According to the findings, carbon dioxide emissions (EVD_CO2) have the order of integration at level I(0). However, financial development (FN_DEV), squared financial development (FN_DEV2), and capital formation (KFOR) are also integrated at level I(0). At the same time, fossil fuel energy consumption (FFEC), renewable energy consumption (REEC) and labor productivity (PRO_LB) are integrated at the first difference I(1). The mixed level of stationarity referred to Panel-ARDL to apply to the empirical model of financial development, energy consumption and carbon dioxide emissions. The unit root test has referred to the Panel-ARDL to apply to a short and long-term model. Before this, the initial step is the bound testing, which is given in Table 5. The bound test is to verify the long term existence among variables. The bound test is based on two bound values, the upper and lower bound values generated from Pesaran et al. (2001). The F-statistic value is 6.06, larger than the upper and lower bounds values, and rejected the null hypothesis by accepting the alternative hypothesis. The greater the F-statistic value than lower and upper bound values showed the cointegration equations among variables and verified the model’s long-run existence (Hanif et al. 2019). After this, to examine the cross-sectional dependency. Table 5 Bounds test EVD_CO2/FFEC; REEC; FN_DEV; FN_DEV2; KFOR; PRO_LB F-stat Lower Bound at 95% Upper Bound at 95% 6.066 2.486 3.702 W-stat Lower Bound at 95% Upper Bound at 95% 35.374 17.403 25.913 Source: Authors’ own calculation based on Eviews 10 Table 6 illustrates the findings of Pesaran cross-sectional dependence proposed by Pesaran et al. (2001). The statistic value 0.347 of the Pesaran CD test is insignificant at 0.310, accepting the null hypothesis (no cross-sectional dependence among variables) and rejecting the alternative hypothesis (cross-sectional dependence among variables). Thus, the findings reveal no cross-sectional dependence in the panel model, which leads to short-run and long-run Panel-ARDL analysis. Table 6 Pesaran Cross-sectional Dependence (CD) test Test name Test statistics P-value Pesaran’s CD = 0.347 Prob value = 0.310 Off-diagonal elements’ average absolute value = 0.827 -- Source: Authors’ own calculation based on Eviews 10 Note: Null hypothesis: no cross-sectional dependence Table 7 of short-run analysis using Panel-ARDL shows that carbon dioxide emissions significantly and positively influenced by the previous year effect. Fossil fuel energy consumption’s current and previous year effects have significantly enhanced the carbon dioxide secretions. However, fossil fuel energy consumption has exaggerated environmental pollution in the short run, also examined by the previous study of SREB emerging economies (Yang et al. 2021). Further, its non-linear effect has also shown an increase in carbon dioxide emissions in the short run. On the other hand, renewable energy consumption has no considerable influence on carbon emissions in the short run. At the same time, its non-linear effect has reduced carbon dioxide emissions in the short run. Financial development has a positive relationship with carbon dioxide emissions in the short run, causing environmental pollution. However, financial development negatively influences carbon dioxide emissions and considers a significant source of mitigation in environmental pollution. The decline in carbon dioxide emissions by squared financial development has confirmed the EKC inverted U-shaped relationship in the short run. Further, the positive and negative influences of financial development on carbon dioxide emissions indicate the linear and non-linear effects on carbon dioxide emissions in the short run, evidenced by previous research (Charfeddine and Khediri 2016). On the other hand, capital formation does not affect carbon dioxide secretions in the short run. At the same time, labor productivity has a significant role in enhancing environmental pollution in developing European and Central Asian economies. Table 7 Short-run ARDL approach Dep Var = EVD_CO2 Regressor Coeff Std Error t-stats C −0.094 0.217 −0.432 EVD_CO2(−1) 0.984* 0.334 2.939 FFEC 0.040* 0.008 4.853 FFEC(−1) 0.039 0.017 2.241 FFEC2 0.084* 0.034 2.471 FFEC2(−1) 0.0321 0.017 1.888 REEC −0.002 0.008 −0.284 REEC(−1) 0.004 0.007 0.564 REEC2 −0.069 0.036 −1.917 REEC2(−1) −0.046 0.021 −2.190 FN_DEV 0.024 0.011 2.025 FN_DEV(−1) 0.003 0.004 0.744 FN_DEV2 −0.026 0.013 −1.920 FN_DEV2(−1) 0.031 0.066 0.482 KFOR −0.011 0.014 −0.838 KFOR(−1) −0.039 0.032 −1.227 PRO_LB 0.574* 0.229 2.506 PRO_LB(−1) −0.555 0.633 −0.876 Note: *, , indicate 1%, and 5% significance levels In the long-run analysis of Table 8, the fossil fuel energy consumption value is 0.12, which is significant at 1%, indicates its positive effect on carbon dioxide emissions in the long run. This positive carbon dioxide emissions reliance on fossil fuel energy consumption has exaggerated environmental pollution. Some past studies of Zhang et al. (2012) in China, Saboori and Sulaiman (2013b) in Malaysia and Hanif et al. (2019) in fifteen developing countries have examined the positive influence of fossil fuel energy consumption on carbon dioxide emissions. However, Anser et al. (2020b) in developing Latin America and the Caribbean countries, Alharthi et al. (2021) in MENA countries and Yang et al. 2021 in SREB emerging economies proved that fossil fuel energy consumption has increased the carbon dioxide emissions. The results also show that the non-linear square term of fossil fuels has a positive relationship with carbon dioxide emissions in the long run. According to the results, one unit increase in fossil fuels consumption has further increased the environmental pollution by about 0.33 units by increasing carbon emissions in the atmosphere. Thus the results indicate the positive linear and non-linear association between fossil fuels consumption and carbon emissions in the European and Central Asian region. Table 8 Long-run ARDL approach Dep Var = EVD_CO2 Regr Coeff Std Error t-stats FFEC 0.121* 0.048 2.512 FFEC2 0.332* 0.119 2.789 REEC −0.442* 0.150 −2.934 REEC2 −0.623* 0.238 −2.617 FN_DEV 0.987** 0.513 1.925 FN_DEV2 −0.012* 0.007 −1.675 KFOR 0.845 0.698 1.211 PRO_LB 0.876* 0.279 3.138 INPT 3.637* 1.543 2.357 Note: *, , indicate 1%, and 5% percent significance levels The renewable energy consumption value is −0.44, which is significant at 1%, indicates that renewable energy intake negatively influenced the carbon dioxide secretions and reduced the carbon dioxide emissions by about 0.44 units, almost half of renewable energy consumption. In this modern time of industrialization, developing countries turn towards renewables, beware them from environmental hazards, protecting their ecological sustainability and economic prosperity. Therefore, renewable energy is considered as the vibrant indicator help mitigate the carbon dioxide emissions evidenced by some past studies of Inglesi-Lotz (2016) and Gozgor et al. (2018) in OECD, Carfora et al. (2019) in developing economies, and Ozcan and Ozturk (2019) in Poland. However, in developing Latin America and the Caribbean countries, MENA countries and SREB emerging economies, renewable energy consumption has significantly mitigated carbon dioxide emissions (Anser et al. 2020b; Alharthi et al. 2021; Yang et al. 2021). According to the results of the non-linear square term of renewable energy, one unit increase in renewables consumption has further decreased the environmental pollution by about 0.62 units. Thus, the results showed a negative linear and non-linear association between renewable energy consumption and carbon dioxide emissions in the developing European and Central Asian countries. Financial development (FN_DEV) value is 0.98, significant at 5%, illustrated that the increased financial development has enhanced carbon dioxide emissions in the long run, evidenced by a previous study (Pata 2018). Thus, the developing countries are just focusing on their financial and economic prosperity while ignoring environmental conditions, causing environmental pollution. The positive association between financial development and carbon dioxide emissions have proved the early stages of the EKC U-Shaped hypothesis in European and Central Asian countries. However, the positive linear dependency of carbon dioxide emissions on financial development and the EKC U-Shaped hypothesis is proved by Shahbaz et al. (2013) in Indonesia and Charfeddine and Khediri (2016) in UAE. The value of squared financial development (FN_DEV2) is −0.01, which is significant at 10 percent. The squared financial development negatively affects carbon dioxide emissions, promoting environmental sustainability in the long run. This negative association among squared financial development and carbon dioxide emissions has fulfilled the hypothesis of the EKC inverted U-shaped in eighteen developing countries of Europe and Central Asia. It is manifest that the developing economies have concentrated on sustainable energy sources such as renewable energy that enables to achieve desired financial development, helped mitigate carbon dioxide emissions. However, the non-linear negative influence of squared financial development on carbon dioxide emissions and the existence of the EKC inverted U-shaped hypothesis is proven by Jalil and Feridun (2011) in China, Shahbaz et al. (2013) in Indonesia, and Charfeddine and Khediri (2016) in UAE. In the context of capital formation, it has no significant impact on carbon dioxide discharges. At the same time, labor productivity’s positive influence on carbon emissions has promoted environmental degradation in Europe and Central Asian developing countries, which is evident by Saidi and Hammami (2015). The overall findings of long-run Panel ARDL examined that the EKC U-shaped and inverted U-shaped hypothesis are proven in Europe and Central Asian developing economies. However, renewable energy alleviates carbon dioxide emissions, while fossil fuel energy consumption promotes environmental pollution. According to the error correction model results in Table 9, the value of ECM is −0.08, which is significant at 1%. This negative sign has shown the presence of speed of adjustment term of ECM to reduce the error in the model. The negative sign illustrates that the speed of the adjustment tool has reduced the error by about 8% from the short run to the long run each year. This means that ecological sustainability is increased each year by about 8% by reducing the environmental hazard in European and Central Asian developing economies. The adjustment in error from the short run to the long term in each year through ECM is evidenced by Charfeddine and Khediri (2016). Table 9 Error correction ARDL model Dep Var = EVD_CO2 Regr Coeff Std Error t-ratio FFEC 0.121* 0.048 2.512 FFEC2 0.332* 0.119 2.789 REEC −0.442* 0.150 −2.934 REEC2 −0.623* 0.238 −2.617 FN_DEV 0.987** 0.513 1.925 FN_DEV2 −0.012* 0.007 −1.675 KFOR 0.845 0.698 1.211 PRO_LB 0.876* 0.279 3.138 dEVD_CO2 0.935* 0.248 3.760 dFFEC 0.098 0.398 0.248 dFFEC2 0.108 0.074 1.459 dREEC −0.059 0.082 −0.728 dREEC2 0.963 0.633 1.521 dFN_DEV 0.080 0.074 1.072 dFN_DEV2 0.101 0.121 0.832 dKFOR 0.068 0.239 0.286 dPRO_LB 0.925 0.734 1.260 ECM(-1) −0.081* 0.023 −3.436 Note: , *, indicate 1%, and 5% percent significance levels The diagnostic tests are given in Table 10, showing no serial correlation and functional form problem in the model. The skewness and kurtosis residuals have not shown any problem in the model and favored the model ' s normality. Further, the diagnostic test illustrates that there is no heteroscedasticity problem in the model. Thus, the overall model of carbon dioxide emissions, financial development and energy consumption has efficiently proved its statistical worth rather than a statistical deficiency in the model. Table 10 Diagnostic tests Test Stat LM-Version F-Version Serial correlation CHSQ(1) = 0.246[0.620] F(1,288) = 0.237[0.626] Functional form CHSQ(1) = 0.041[0.840] F(1,288) = 0.039[0.843] Normality CHSQ(2) = 19.591[0.123] -- Heteroscedasticity CHSQ(1) = 0.005[0.942] F(1,297) = 0.005[0.942] Conclusion The study aims to examine the non-linear relationship between carbon dioxide emissions, energy consumption, and financial development. Further, the study inspects the EKC inverted U-shaped hypothesis in European and Central Asian developing economies. The eighteen developing economies of Europe and Central Asia are selected based on their income level, lower and upper-middle-income levels. At the same time, the data is gathered over the years 2010–2019 to measure environmental sustainability by relating the carbon dioxide emissions with fossil fuel energy consumption, renewable energy consumption, financial development, squared financial development, capital formation, and labor production. According to the empirical findings, the descriptive statistics and correlation matrix have evidenced the model ' s accuracy and not showed any problem like multicollinearity in the model. The mixed order of integration I(0) and I(1) among variables has preferred the Panel-ARDL test. Before applying the P-ARDL, the bound test has verified the cointegration equations in the model, confirming the long-run existence in the empirical model. Simultaneously, Pesaran cross-sectional dependency test has not shown the cross-sectional dependency in the model. According to P-ARDL findings, the short-term analysis has confirmed the positive influence of fossil fuel energy consumption, financial development, and labor productivity on carbon dioxide emissions. Capital formation and renewable energy have no substantial influence on carbon dioxide emissions in the short run. At the same time, the non-linear influences of fossil fuel and renewable energy are positive and negative on carbon dioxide emissions in the short run, respectively. However, the negative impact of non-linear squared financial development has reduced the carbon dioxide emanations and proved the EKC inverted U-shaped premise in the short run. After this, the long-run analysis has proved that fossil fuel energy consumption and labor productivity have risen the carbon dioxide secretions. In contrast, renewable energy consumption has reduced environmental degradation by its negative influence on carbon dioxide emissions in the long run. Thus, fossil fuel consumption has a positive and renewable energy consumption negative non-linear impact on carbon dioxide emissions in the long run. Financial development positively influences carbon dioxide emissions and favors the early stages of the EKC U-shaped hypothesis in the long run. However, squared financial development negatively impacts carbon dioxide emissions, which resultantly mitigate air pollution and promote environmental sustainability in European and Central Asian developing countries. The negative influence of non-linear squared financial development on environmental degradation has proven the EKC inverted U-shaped hypothesis in the long run. In contrast, capital formation has a positive but insignificant impact on carbon dioxide emissions in the long run. Further, the study has shown the significant non-linear relationship between carbon dioxide emissions, energy consumption, and financial development in European and Central Asian developing economies. According to the Error Correction Model results, the ECM value is −0.81, which is significant. Its negative sign identifies that the speed of adjustment term reduces the error by about 8% each year from short run to long run. Moreover, the diagnostic tests have not indicated functional form problems, normality issues, heteroscedasticity, and serial correlation in the empirical model. This study suggested that there is a need for comprehensive policies to use energy sources. First, there should be a look over the excessive consumption of non-renewable energy such as fossil fuels. It is suggested that the governments of developing economies should implement subsidize renewable energy. There should be low-cost renewable energy provided to households and firms to promote environmental sustainability and limit fossil fuel energy consumption. There is a need to update the technology, which requires the attention of policymakers. Regarding technological advancement, solar panel installation and electrification rather than burning woods and coal can help mitigate air pollution. In the end, it is suggested that the policymakers should develop the environmental sustainability model considering EKC inverted U-shaped, which will help promote their financial sector development without hurting environmental sustainability. Limitations of the study This study has developed the non-linear relationship between carbon dioxide emissions, energy consumption, and financial development in developing European and Central Asian economies. However, this research has limitations regarding its study area. In addition, this research has taken the countries listed as European and Central Asian countries and has developing status. Future direction of the research This research has a future direction regarding the study area and the adoption of energy sources to maintain environmental sustainability. This type of research will be helpful to highlight the environmental issues in other developing regions of the World. This research has measure the overall impact of fossil fuel and renewable energy sources. However, future research can examine the new form of renewable energy sources such as hydroelectricity, modern biomass, and solar paneling to measure environmental sustainability. Most importantly, this research has examined the environmental issues before Covid-19. It will be interesting to examine the influences of renewable energy and non-renewable energy consumption during and hopefully after Covid-19. Author contribution Leng Chunyu: Initial draft preparation; Syed Zain-ul-Abdin: Econometric Results Estimation, Hypothesis testing; Wajeeha Majeed: Review of literature, Data collection, and Tabulation; Syed Muhammad Faraz Raza (Corresponding Author E mail:[email protected]): Results interpretation and diagnostic testing; Ishtiaq Ahmad: Methodological framework and technical advice. Data availability Not applicable Declarations Ethics approval and consent to participate This is an original work that has not been submitted anywhere else for publication. All authors have contributed to the submitted paper. Consent for publication The paper submitted with the mutual consent of authors for publication in Environmental Science and Pollution Research. Competing interests The authors declare no competing interests. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 15023 10.1007/s11356-021-15023-w Research Article Testing role of green financing on climate change mitigation: Evidences from G7 and E7 countries Wu Xueying [email protected] 1 Sadiq Muhammad [email protected] 2 Chien Fengsheng [email protected] 34 https://orcid.org/0000-0001-8357-1957 Ngo Quang-Thanh [email protected] 5 Nguyen Anh-Tuan [email protected] 67 Trinh The-Truyen [email protected] 8 1 grid.440661.1 0000 0000 9225 5078 College of Transportation Engineering, Chang’an University, Xi’an, China 2 grid.452879.5 0000 0004 0647 0003 School of Accounting and Finance, Faculty of Business and Law, Taylor’s University, Subang Jaya, Malaysia 3 grid.411604.6 0000 0001 0130 6528 School of Finance and Accounting, Fuzhou University of International Studies and Trade, Fuzhou, China 4 grid.445020.7 0000 0004 0385 9160 China Faculty of Business, City University of Macau, Macau, China 5 grid.444827.9 0000 0000 9009 5680 School of Government, University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam 6 grid.444808.4 0000 0001 2037 434X Faculty of Economics, University of Economics and Law, Ho Chi Minh City, Vietnam 7 grid.444808.4 0000 0001 2037 434X Vietnam National University Ho Chi Minh City (VNU-HCM), Ho Chi Minh City, 71309 Vietnam 8 Department of Planning and Investment, Phu Tho Province, Vietnam Responsible Editor: Nicholas Apergis 8 7 2021 115 19 5 2021 16 6 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The study estimates the long-run dynamics of a cleaner environment in promoting the gross domestic product of E7 and G7 countries. The recent study intends to estimate the climate change mitigation factor for a cleaner environment with the GDP of E7 countries and G7 countries from 2010 to 2018. For long-run estimation, second-generation panel data techniques including augmented Dickey-Fuller (ADF), Phillip-Peron technique and fully modified ordinary least square (FMOLS) techniques are applied to draw the long-run inference. The results of the study are robust with VECM technique. The outcomes of the study revealed that climate change mitigation indicators significantly affect the GDP of G7 countries than that of E7 countries. The GDP of both E7 and G7 countries is found depleting due to less clean environment. However, green financing techniques helps to clean the environment and reinforce the confidence of policymakers on the elevation of green economic growth in G7 and E7 countries. Furthermore, study results shown that a 1% rise in green financing index improves the environmental quality by 0.375% in G7 countries, while it purifies 0.3920% environment in E7 countries. There is a need to reduce environmental pollution, shift energy generation sources towards alternative, innovative and green sources.The study also provides different policy implications for the stakeholders guiding to actively promote financial hedging for green financing. So that climate change and envoirnmental pollution reduction could be achieved effectively. The novelty of the study lies in study framework. Keywords Cleaner environment Green financing Climate change E7 countries G7 countries ==== Body Introduction The cleaner environment notion is still emerging, and it is much valuable in current policies and agendas. The rising trends of global warming have greatly interested the policymakers to clean the environment using climate change mitigation strategies, and it seems to be a part of a broad consensus. However, social, geographic and regional impacts of climate change take on another dimension particularly following the mission to clean the environment of densely populated regions and projects (Alemzero et al. 2020a). All these initiatives to clean the environment through climate change mitigation are inclined towards energy development and consumption sources, leading to a significant improvement in energy sectors in different regions and projects (Li et al. 2021b). E7 and G7 regions are one of the important projects expected to face multiple environmental and climate change orientated threats. Thus, it is prerequisite to clean the environment using climate change mitigation strategies for effectiveness and smooth economic growth of E7 and G7 region (Li et al. 2021a, 2021b, 2021c). However, there is a need for safe and less polluted climate but also the need to develop and implement green financing strategies in different forms; that is to say, “clean” without harming the climate and especially without affecting the economic growth of E7 and G7 regions (Iqbal et al. 2021a). Hence, endorsing the importance of BRI and E7 and G7 regions, recent study intends to estimate the antecedents of cleaner environment by using green financing techniques on long-run basis and provide the way forwards for policymakers to mitigate the climate change (Anh Tu et al. 2021; Alemzero et al. 2021). Climate change and global warming causes greenhouse pollution that is regarded as the greatest challenge of the twenty-first century (Iqbal et al. 2020). In 2015, 196 countries joined the Climate Change Agreement in Paris to hold the annual temperature increase well below 2 °C to mitigate the extreme effects of global warming (Asbahi et al. 2019). The performance of the Paris Agreement and other associated environmental emission policies largely depends on the administrative efficiency of the government (Yumei et al. 2021; Iqbal et al. 2021a). Institutions establish and regulate carbon reduction environmental programs. These structures come in several directions, such as politics, government and the society, and are affected by multiple factors (Anh Tu et al. 2021; Chien et al. 2021d; Chien et al. 2021a). The destruction of the environment and climate change are growing quickly as the demand for innovative and viable solutions is rising. The green economy is one of the most effective strategies for resolving these issues and promoting recycling of economic capital, economic development and environmental protection (Chien et al. 2021b; Chien et al. 2021c; Chien et al. 2021e). The social welfare mechanism thus preserving environmental destruction to a minimum may be considered a green economy (Tvaronavičienė 2021). The important impact of the green economy on sustainable growth is one of the issues highlighted at the United Nations Conference on Rio+20 (Chien et al. 2020; Chien et al. 2021c). The main variables involved in the development of a green economy must be given equal priority. This study focuses on public procurement as one of the key influences of the green economy (Richards et al. 2021). A basic alteration of public expenditure, given the previous literature, would appear to have a major effect on economic and environmental depletion. The specific nature of the connection between public expenditure and green economic development has not yet been analysed in depth (Li et al. 2021c; Sadiq et al. 2020). A comprehensive analysis of fiscal expenditure will help define its connection to green economic development (Anser et al. 2018; Anser 2019; Anser et al. 2020). The essence of this partnership can be identified by environmental studies like (Abhimanyu Kumar 2019). For example, a sequence of effects called the ‘size impact’ reflects how economic activity rises with a rise in government spending. However, this mechanism causes numerous environmental threats, with green economic development gradually dropping (Asif et al. 2020; Sarker et al. 2020; Iram et al. 2020; Tehreem et al. 2020). In the other side, a major transition in the ‘composition effect’ of capital-based industry into human capital–based industries with a rise in public education expenditures may be observed. As a consequence of the compositional impact, a new model of economic growth is viable as pollution is reduced (Ossebaard and Lachman 2021). Nations can be motivated to use more cleaner technology and renewable energy with a large allocation of science and innovation capital (Zhao et al. 2020b). The usage of these innovations will ensure improved resource productivity and healthier production (Marvin Herndon and Alberto Pérez Bartolomé 2018). A minimum pollution/output ratio may be observed utilizing a method known as the ‘scientific effect’. An approximate $2 trillion is needed to help the planet recover sustainably from COVID-19’s global retrogression. This money will be used to invest in green schemes such as renewables to complete the COVID-19, 2021–2023 period (Yousaf et al. 2020; Tehreem et al. 2020; Wasif Rasheed and Anser 2017; Xu et al. 2020). Only these structural investments, such as private investments, renewable bonds, and committed financing, will ensure a complete and long-term recovery (Agrawala et al. 2020.). The G-7 and E-7 countries will comfortably achieve a W-shaped or a V-shaped green recovery outlook. COVID-19 has resulted in a lower CO2 level (Kumar and Ayedee 2021). However, it has had a negative impact on global development due to economic challenges and human misery. The G7 and E7 countries help all member countries to build and promote broad markets whilst still promoting mutual understanding. The activities of emerging countries of theoretically creating considerable dependence on energy supplies, while growing their manufacturing productivity by an advanced development mechanism, indicate a relatively secure economic condition. Another major issue addressed by the BRI initiative is global warming, which can be addressed by strong collaboration among member countries. In developing countries, there is a lot of study on different environmental concerns. In the developed countries, there is a lack of study. In terms of industrial and non-renewable resources, the economy has seen substantial expansion. In general, dependence on the manufacturing sector and non-renewable energy services has boosted economic development by a fraction, although the prospect of green economic growth has risen dramatically at the same time due to significant environmental deterioration (Letcher 2018). This study’s valuable insights can contribute to the literature significantly while explaining how the green economy is affected by public spending. However, studies have not been able to identify the specifics of how government spending affects market mechanisms. A positive relationship between fiscal spending and green economic growth can be seen in this study (Khan et al. 2019; Isaksen and Trippl 2017). This study assesses how G7 and E7 countries are stimulating green finance and carrying out strategies to reduce climate change. Green finance can be replaced by renewable energy consumption while making use of the FOMS and VECM approach on the G7 and E7 countries. Few studies have analysed the G7 and E7 countries (Yildirim et al. 2014; No and Padhan 2018; Erdoğan et al. 2020; Sinha et al. 2020) while using the econometric method. This study’s significant difference lies in using the FOMS and VECM approaches to assess long-run dynamics of cleaner environment with economic growth indicators, in the context of E7 and G7 countries. The rest of the paper is organized as follows: the ‘Literature review and background’ section represents how G7 and E7 countries are affected by green and climate change mitigation. The ‘Data and methodology’ section represents the methodology used in the study. The ‘Results and discussion’ section states the result and discussion for the study, while the ‘Conclusion and policy implications’ section states the conclusion and policy implications. Literature review and background Despite reductions in fossil fuel consumption and CO2 emissions, the electricity industry remains the world’s most significant and largest producer of these emissions (Iqbal et al. 2021b). Human-induced and CO2 emissions from the electricity industry make up two-thirds of both human-driven and CO2-based emissions, which have increased sharply over the century. Many nations plan to reduce their dependence on fossil fuels and get down to 75% of total global resources by that year. As a consequence, ambitious energy policy is vital to solving the climate change problem (Liu et al. 2020; Lin et al. 2020; Jun et al. 2020). While few studies have concentrated on the connection between clean energy consumption, CO2 emissions and economic growth, several previous studies have highlighted the correlation between nuclear energy consumption, renewable energy consumption, CO2 emissions and economic growth, which may help us better understand the relationship between clean energy consumption and other variables (Iqbal et al. 2021c). Development and enhancement of agricultural production capacity, together with the usage of renewable energy, are vital for developed countries to achieve sustainable growth. Between 2016 and 2050, according to a PWC survey (2017), the global economy is predicted to expand at a real annual pace of about 2.5%. The E7 countries—China, India, Brazil, Russia, Mexico, Indonesia and Turkey—are expected to develop at an annual average pace of about 3.5% over the next 34 years, opposed to just about 1.6% for the advanced G7 nations. According to the Bloomberg New Energy Finance Report (2016), in terms of overall new clean energy spending, emerging countries overtook industrialized countries for the first time in 2015. More so, in 2015, green energy expenditure grew by 16% in China, India and Brazil, the top three E7 nations, to $120.2 billion, while investment in ‘other emerging’ countries increased by 30% to $36.1 billion. The presence of a significant renewable energy resource is at the core of the E7 countries’ challenges in achieving balanced agricultural growth and increasing domestic demand. The option to encourage sustainable energy sources would not only contribute to more modernization of the energy market but would also help various countries’ economic growth and sustainability goals. The influence of NER and RER sources on greenhouse gas emissions is also demonstrated by geographic variability in the literature (Wasif Rasheed and Anser 2017; Xu et al. 2020; Ahmad et al. 2020). However, scant research has been done on the effect of environmental protection strategies on greenhouse gas emissions (Li et al. 2021d). The position of environmental protection policies, the usage of renewable and non-renewable energy supplies and per capita GDP growth on greenhouse gas emissions in emerging Asian economies is highlighted in this report, which adds to the current literature (Anh Tu et al. 2021). Source: author’s findings As seen in the discussion above, there is a need to extend the body of knowledge and extend the research on the relationship between energy and development all agree with the literature review in Table 1. However, there is a scarcity of evidence on the impact of energy demand on economic development in emerging Asian economies in general. There is also no proper conclusion or findings in this data. As a result, there is a pressing need to put the energy growth nexus discussions to rest (Mohsin et al. 2021; Tang et al. 2018). Furthermore, there is yet to be released a report that explores the impact of renewables on economic development incorporating both renewable and non-renewable energies. As a consequence, this research is important in bolstering the third strand of literature, which seeks to fill this void in the literature for emerging Asian economies. Table 1 Renewable and non-renewable energies and economic growth Time duration Region Method Findings 1990–2014 15 renewable consuming Granger causality test Growth 1980–2015 ASEAN-5 Causality Neutrality 1980–2010 Brazil Vector error correction model Growth 1980–2012 Sub-Saharan Pairwise heterogeneous causality Neutrality 1980–2012 16 emerging economies Bootstrap causality Feedback growth and neutrality 1980–2012 BRICS Panel error correction model Conversation 1971–2012 India Vector error correction model Feedback 1980–2010 34 OECD Panel cointegration Growth 1990–2007 16 emerging countries Panel error correction model Feedback and growth 1949–2006 USA Toda-Yamamoto causality method Feedback, growth and neutrality 1997–2015 Pakistan VECM Growth and feedback Data and methodology Study measures and data To estimate the long-run modelling of study constructs, we used growth functions. The unit of measurements used for levels of carbon dioxide emission is in kilotons (kt) serving as a proxy measure of cleaner environment, GDP in US dollars (Vasylieva and Bilan 2019), the population in % and technical operation grants in US dollars, whereas the foreign direct investment (FDI) is measured in USD, human development index in %, renewable consumption as a proxy for green finance in kilotons (kt), inflation in %, GDP in USD 2017 purchasing power parity (PPP), domestic investment private participation in the energy sector in USD while the local credit in dollars, specific for the private sector. The data for G7 and E7 countries was taken from different databases, such as databank.worldbank.org, fred.stlouisfed.org and data.worldbank.org, for the years of 2010–2018 to execute empirical analysis. In E7 countries, China, India, Brazil, Mexico, Russia, Indonesia and Turkey were taken, while the USA, UK, Germany, Japan, France, Italy and Canada were taken in G7 countries. In total, 14 countries were taken which are major countries facing issues in terms of environmental pollution and reduction in economic growth. Subsequently, this is to assess the long-run dynamics of cleaner environment on economic indicators. The cleaner environment is also assessed by using the green performance index data of E7 countries and G7 countries. Notably, the empirical statistics revealed that G7 countries are more attentive to clean the environment for climate change mitigation, concerned to gain environmental sustainability and this matters them most than E7 countries. Econometric modelling In this study, we examine the impact of climate change on macroeconomic indicators of G7 and E7 regions. To acquire the study objectives, we consider two models (Y, growth function; and CE, environmental function), which are specified as follows: 1 Yit=fXitPreitPostit In Eq. (1), Yit is the dependent variable of the study, Xit is the composite function including GDP, FDI, population, R&D expenditures, CO2 emission, human development index score, inflation, grants, DCP and investment in power plants. Preit is pre-test exposure of the countries to the climate change and green financing, while Postit is the examined function showing the exposure of the countries to the climate change and green financing (e.g. undertreated). The panel form of Eq. (1) is developed into Eq. (2): 2 lnYit=β0+β1Xit+β2Preit+β3Postit+eit where i designates countries; t represents the period; α0 represents the fixed country effect; and ε is the white noise. Ln is the natural logarithms of all variables. Moreover, the logarithmic form of Eq. (3) is developed as 3 lnYit=β0+β1lnXit+β2lnPreit+β3lnPostit+eit where the country, t is the period, and εit is the error term. The parameters, such as, β1, β2 and β3, represent the long-run elasticity estimates of Y, X, pre-test exposure and post-test exposure of the countries, in G7 and E7 regions, respectively (Li et al. 2020; Li et al. 2021a; Yu 2021). Strategy for econometric estimation A panel stationary test is applied to test/to assess the order of variable integration. For this, augmented Dickey-Fuller (ADF) technique (1979) and Phillips and Perron (1988) are used to determine unit root among variables. The study used the hybrid strategy for the estimation of study constructs to infer the findings, in two ways (Mohsin et al. 2018a; Mohsin et al. 2018b; Ikram et al. 2019). First, the study applied FMOLS approach to show the evidences on climate change mitigation and economic growth. This approach shows construct-wise and country-wise differences interpreting the pre- and post-consequences of climate change on the economic performance of G7 and E7 regions. Secondly, we used panel cointegration and panel long-run elasticity’s functions to strengthen the findings of FMOLS approach. This approach supported the operationalization part of the study findings by proposing the estimated residuals to give the findings in terms of long-run (Adedoyin et al. 2020). However, hypothesized form of Eq. (4) for long-run regression technique is as follows: 4 Yit=λ1+ϑit+∑i=1nλj,tXj,it+eitt=1….T;i=1………..N Extending to it, fully modified OLS method is used to estimate the nature of heterogeneity among the variables to measure the intensity of relationship. According to Pedroni (2000), this method allows to operationalize and rectify the expounding variable’s endogeneity with different vibrant data sheets. The use of FMOLS presupposes that the variables have a cointegration connection. As a result, we begin with unit root tests on each of the data set (Sun et al. 2020d; Baloch et al. 2020). Robustness: vector error correction modelling (VECM) The cointegration of variable estimation supported to develop the casualty among variables. For this and long-run inference of results, we applied VECM methods by using two-step process. Table 2 shows the probit and logit figures of E7 and G7’s economic performance. Eaccess and Enimp are unlikely to affect the energy performance of the countries studied in this report. The EE is likely to be influenced by FDI, as predicted. This is shown by an increase in Chinese investments in Africa and SSA. The GDP would have an effect on the energy production of E7 and G7 nations, as well as fossil fuels, taxes, QPI, LPI and ENEEMIS. The coefficients of the probit model demonstrate this. As seen in Table 2, the countries under review with energy access have a probable effect on energy efficiency of [39.5646%]. Energy imports, on the other side, have little effect on the countries under review, with a negative mean [− 45.13979], showing that energy imports have little effect on EE in the E7 and G7 nations. The F-statistics in the VECM may indicate short-run causality, whereas the error correction word ECT (1) may indicate long-run causality. 5 ∆Clim∆Eco∆Soc=λ1λ2λ3+∑m−1nδ11δ12δ13δ21δ22δ23δ31δ32δ33X∆LnClimit−m∆LnEcoit−m∆LnSocit−m+ϑ1ϑ2ϑ3ECTt-1+ε1ε2ε3 Table 2 Probit and Logit estimates for economic efficiency Countries Constructs Eacess Enimp FDI GDP Foss Taxes QPI LPI Eneemis E7 Probit − 0.016 − 0.043 0.059 0.033 0.098 0.003 0.073 0 0.001 Logit − 0.027 − 0.086 0.114 0.054 0.196 0.002 0.129 0 0.001 G7 Probit − 0.028 − 0.056 0.71 0.088 0.097 0.000 0.029 0 0.000 Logit − 0.017 − 0.099 0.25 0.041 0.234 0.059 0.011 0 0.000 In Eq. (5), three main dimensions were taken, such as environmental, social and economic, to assess the cleaner environment, climate change and economic growth prospects in BRI project and G7 and E7 regions. The vector error (VECM) form of study model is written and sub-divided into proxies as follows, where Δ, δit, γit, i, t and μit represent the first difference operator, the constant term, the parameters, the period and the error term, respectively. Using above econometric models, we used long-run growth prospecting econometric function (see Eqs. 5 and 6) of G7 and E7 regions. For growth regression, an index of economic indicators was developed including GDP, FDI, INF, R&D and IPP. Index of social indicators was also developed including GRT, HDI and PoP, while environmental factors were assessed using CO2 emission index, as a measure of climate change mitigation. Our findings are consistent with Sun et al. (2020d) and Baloch et al. (2020). Various similar techniques have been used in multiple applications (Zhao et al. 2020a; He et al. 2021; Zhang et al. 2020a; Zhang et al. 2020b). Results and discussion Empirical analysis The results indicate that decreased fossil fuel usage and increased renewable energy consumption caused development in the E7 and G7 regions. This point is backed up by citing Indonesia’s goal of producing 5% of its electricity from geothermal; 5% from wind, biomass, hydro and solar; and 5% from biofuel by 2025. In order to improve and achieve a low-carbon economy, Indonesia initiated the Low Carbon Development Initiative (LCDI). This aim also promotes the creation of a policy suite and modular transformation programs that can be used in various economic sectors. These revolutionary processes could result in economic growth of 5.6% by 2020 and 6.0% by 2045. In the best-case scenario, 15.3 million good green workers will be introduced by 2045, resulting in a $5.4 trillion GDP boost. Poverty is projected to fall from 9.8% of the population in 2018 to 4.2% in 2019. About the same way, better air quality is projected to save 40,000 lives (Zeng et al. 2017). During the period 2005–2015, the Philippines is expected to raise its renewable energy by 100%. In the last six years, the Philippines’ economy has expanded at a steady pace of 6.6%. By 2030, it intends to build 2.35 GW of wind power. However, the theoretical capacity is 76 GW (Baloch et al. 2020). With steady GDP growth of 6% over the last decade, Vietnam can be called another booming economy. Its clean energy goals are 5% in 2020 and 11% in 2050, respectively (Ma et al. 2019). The nation currently has 228 MW of installed wind power and expects to build 800 MW by the end of 2020. The G7 and E7 countries have a large energy intensity ratio, which should be ample incentive for them to engage in energy production and conservation (Sun et al. 2020e; Sun et al. 2020c; Sun et al. 2020d). The ADF and PP unit root results are tabulated in Table 3, presenting that study results are stationary at level and some of the measures, such as CO2 emission, REC and per capita GDP are stationary at level. The results indicated that null hypothesis is accepted and the variables are stationary at first difference, highlighting that variables are cointegrated in a singular order. Extending to it, cointegration test is applied to build more econometric clarity in study results. Table 3 ADF and PP results Constructs Level 1st difference Intercept Intercept and trend Intercept Intercept and trend Panel I: ADF—Fisher/chi-square Ln (Y) 18.75 (0.8723) 13.07 (0.2217) 22.64 (0.4412) 1.65 (0.2711) Ln (λ1) 0.26 (0.3467) 0.11 (0.000) 5.66 (0.8888)* 4.89 (0.0737)* Ln (λ2) 11.37 (0.2865) 9.49 (0.2371)* 17.21 (0.9724)* 4.93 (0.0000)* Ln (λ3) 10.68 (0.7777) 6.66 (0.000)* 15.78 (0.0052) 3.05 (0.4391)* Ln (λ4) 16.27 (0.3461) 10.01 (0.5728) 37.19 (0.1045) 6.88 (0.0061)* Ln (λ5) 6.028 (0.3544) 0.89 (0.3410)* 21.71 (0.1838)* 5.94 (0.0084) Ln (λ6) 9.734 (0.2971) 3.13 (0.000)* 13.13 (0.2878) 5.15 (0.0007)* Ln (λ7) 6.001 (0.3064) 0.10 (0.7321) 52.68 (0.5519)* 10.63 (0.1202) Ln (λ8) 7.237 (0.8275) 2.15 (0.0016)* 10.42 (0.0569)* 0.97 (0.1172)* Ln (λ9) 8.666 (0.5601) 4.80 (0.5388) 13.27 (0.0000)* 7.56 (0.2105)* Panel II: PP Fisher/chi-square Ln (Y) 27.61 (0.8831) 31.14 (0.8813) 10.38 (0.2020) 7.004 (0.1476) Ln (λ1) 32.45 (0.0200) 11.81 (0.4934)* 15.67 (0.7142)* 14.75 (0.1789) Ln (λ2) 11.99 (0.7684) 6.07 (0.4672) 16.79 (0.1421)* 11.23 (0.6216)* Ln (λ3) 4.525 (0.3308) 0.05 (0.0000)* 28.19 (0.2489)* 18.88 (0.3604) Ln (λ4) 7.067 (0.4006) 2.17 (0.3419)* 17.71 (0.2676)* 20.71 (0.2013)* Ln (λ5) 13.01 (0.4250) 7.19 (0.1111)* 19.56 (0.1431)* 12.57 (0.0365)* Ln (λ6) 21.01 (0.3111) 8.35 (0.0007) 21.17 (0.0006)* 0.019 (0.000)* Ln (λ7) 37.92 (0.0000) 4.07 (0.1489)* 35.10 (0.7893) 9.47 (0.1827)* Ln (λ8) 12.55 (0.6803) 0.14 (0.5617)* 32.13 (0.5637)* 5.08 (0.6802)* Ln (λ9) 19.29 (0.5557) 0. 56 (0.3418)* 14.07 (0.4190) 0.05 (0.9992)* We used the FMOLS methodology to calculate the long-term association between variables. It validates the growth theory, which maintains that economic growth is generated by energy usage. As an economy grows, the energy use is often dependent on labour and resources, as well as other factors such as population, place and technology (Alemzero et al. 2020a). Are you a masochist? These findings indicate that green energy use has a favourable effect on 1% of the national economy—that a rise in renewable energy demand of 22% would result in 1% in the growth of the overall economy. According to the G7 formula, a 1% GDP percentage point raises the carbon emissions of 1% of a country’s population by 4.55%. There is an ever-increasing volume of data supporting the argument that development in the gross domestic product (GDP) and population leads in a rise in carbon dioxide emissions, according to several analyses (Solaymani 2019). Even countries with a high GDP, such as the USA, China, Japan and Germany are still very populated (Chandio et al. 2020; Alemzero et al. 2020b; Sun et al. 2020c; Alemzero et al. 2020a). An insignificant 2% risk that energy-related pollution would affect the atmosphere the other change in the variable could result in a small change in the percentage Energy efficiency decreases by about eight percent as the percentage of energy access varies. Although between 4 and 6% of the participants of the G7 have a strong impact on their gross domestic product (GDP), foreign direct investment (FDI) has a high mean influence on overall direct investment (QPI). Table 6 predicts that the respective mean and standard deviation for the logit and probit models lie between 0 and 1 The formula would not limit the range of probabilities to 0–1 for the Logit model, which means they will take on every possible logit value. An equivalent or even higher mean value for Ei, an equal mean for G7 and E7 countries with respect to energy production. As said above, the sensitivity and specificity models were accurate in their predictions (Sun et al. 2020b; Sun et al. 2020a). See Figure 1 where the model has a sensitivity of 89.33 and a reported value of 92.42, but a negative accuracy of 58.93. The findings of this analysis indicate was considered to be right to be at 84.21% Although 84% of the model has been estimated to be right, the majority of the assumptions are in error. It is shown in Table 3. The inverse association between national GDP and pollution reduction (e.g., decreasing CO2) is, however, not universal. Table 4 shows that the renewable energy score is 0.057 value can be seen for the coefficients of per capita education spending (PCEDU), whereas coefficients of per capita for research and development (PCRD) are recorded at 0.022 and 0.073, respectively. An evident heterogeneous effect can also be observed from Fig. 1. The low GDP per capita countries represented here tend to have a reasonable estimate regarding composition and technical effects. The coefficient of low GDP per capita countries for education expenditure is recorded at 0.215. This value is significant at a level of 1%. However, the value of the coefficient for high GDP per capita countries is recorded at 0.049 (see Fig. 3). This value is significant at 5% level. A GDP per capita–based split analysis on the whole sample is explained in this section. The two sub-divisions of the sample include the countries with low GDP per capita and a high GDP per capita (Agyekum et al. 2021; Zhang et al. 2021). The three non-parametric tests applied include the rank-sum equality, equality of distribution and rank comparison. Table 4 Cointegration results Y model (economic growth function in E7 countries) CE model (environmental function in E7 countries) Y model (economic growth function in E7 countries) CE model (environmental function in E7 countries) Statistics Significance Statistics Significance Statistics Significance Statistics Significance Within-dimension Panel v-statistic 5.21 (0.000)* 11.49 (0.000)* 10.65 (0.000)* 32.04 (0.000)* Panel rho-statistic − 7.74 (0.000)* 10.87 (0.000)* 17.17 (0.000)* 22.31 (0.000)* Panel PP-statistic − 23.76 (0.000)* 10.65 (0.000)* 14.57 (0.000)* 46.01 (0.000)* Panel ADF-statistic 17.8 (0.000)* 14.18 (0.000)* 20.69 (0.000)* 25.16 (0.000)* Panel v-statistic (weighted statistic) 14.67 (0.000)* 4.39 (0.000)* 12.03 (0.000)* 19.15 (0.000)* Panel rho-statistic (weighted statistic) -9.41 (0.000)* 15.46 (0.000)* 19.4 (0.000)* 19.95 (0.000)* Panel PP-statistic (weighted statistic) 14.9 (0.000)* 17.12 (0.000)* 22.89 (0.000)* 15.79 (0.000)* Panel ADF-statistic (weighted statistic) 10.12 − 0.4729 13.06 (0.000)* 31.15 (0.000)* 8.03 (0.000)* Between-dimension Group rho-statistic 2.01 − 0.8542 2.04 − 0.7932 2 − 0.05819 2.02 − 0.6643 Group PP-statistic − 2.18 (0.3287)* − 3.47 − 0.7932 − 4.94 (0.0000)* − 2.1 (0.2199)* Group ADF-statistic − 2.29 (0.3496)* − 4.61 (0.6819)* − 4.07 (0.0000)* − 2.18 (0.2018)* Fig. 1 Synthesis of climate change–GDP relationship Table 5 suggests that climate change can have substantial impacts on normal market practices. A large rise in electricity consumption has been induced by the population as well. Another input parameter estimated in the model, this time, the G7 countries gave a response of 99.37% (Table 5), demonstrating the broad variety of economic data forecasts correlated with climate change mitigation. As a result, CO2 emission measurements from the same nation have a high degree of homogeneity over time, implying that heterogeneity within countries accounts for over 99% of CO2 emissions over time. This implies that countries’ CO2 pollution policies should not shift with time. That is, CO2 emissions from the previous year represent CO2 emissions in the subsequent year for the same region. McDonough et al. 2018) observed that CO2 emissions at time t-1 are the key drivers of the shift in CO2 emissions at time t. Contrary to common opinion, the E7 countries are seeing fewer volatility in GDP (overall) as a consequence of climate change(s) (Li et al. 2021b; Chien et al. 2021c; Iqbal et al. 2021a). Table 5 Split outcomes of G7 and E7 countries on the basis of GDP GDP per capita G7 countries GDP per capita E7 countries 1 2 3 4 L.GEGI − 0.075* 0.057*** − 0.060* − 0.061* (0.039) − 0.037 − 0.03 − 0.03 PCRD 0.063* 0.025 (0.025) − 0.026 PCEDU 0.215* 0.049 (0.036) − 0.033 INDUS − 0.298* − 0.208 − 0.460* − 0.375* (0.99) (0.96) (0.086) (0.079) Green 0.013 − 0.021 0.046 0.049 (0.064) (0.062) (0.033) (0.030) GDPPL − 0.000 0.009 0.053 0.052*** (0.018) (0.019) (0.025) (0.016) Openness − 0.027* − 0.010 0.012 0.024* (0.021) (0.018) (0.017) (0.018) Constant 3.612* 3.789* 3.735* 3.741* (0.574) (0.578) − 0.543 − 0.454 Observations 108 108 144 144 Arellano-bond AR (1) − 5.037 − 5.046 − 5.412 − 5.360 [0.000] [0.000] [0.000] [0.000] Arellano-bond AR (2) 0.719 0.809 -0.076 − 0.086 [0.507] [0.438] [0.856] [0.834] Sargan test 144.737 146.655 150.593 150.341 [0.780] [0.756] [0.727] [0.736] Long-run dynamics To estimate the long-run association among the study constructs, we applied FMOLS technique. Our findings reported the growth function in Table 6. It seemed that cleaner environment or in other words climate change mitigation in terms of CO2 emission reduction has positive impacts on economic growth of BRI project and G7 and E7 region countries. Importantly, renewable energy sources have significantly moderated in this relationship and inclined the role towards positive extent. However, the role of green financing in terms of renewable energy source usage has a commendable role. All the countries of E7 and G7 regions reported the relationship between variables as significant. This commends a significant role of green financing techniques through renewable energy sources for environmental cleaning and greening. Such results validated the growth hypothesis, suggesting a unidirectional causality relationship between environmental cleaning and economic growth of G7 and E7 regions. This suggest more that using innovative energy solutions for the energy consumption holds a vital role in regional economic growth and climate change mitigation, directly and indirectly (Zhang et al. 2021). Table 6 Long-run estimates of the growth function Countries Growth function Durbin-Watson LnClim LnEco LnSoc Brazil 0.024 (0.000)* 0.016 (0.000)* 0.004 (0.000)* 0.317 (0.000)* Mexico 0.029 (0.000)* 0.022 (0.000)* 0.061 (0.000)* 0.209 (0.000)* Russia 0.020 (0.000)* 0.044 (0.000)* 0.035 (0.000)* 0.111 (0.000) China 0.041 (0.000)* 0.027 (0.000)* 0.317 (0.000)* 0.478 (0.000)* Turkey 0.039 (0.000)* 0.059 (0.000)* 0.023 (0.000)* 0.400 (0.000)* India 0.019 (0.000)* 0.028 (0.000)* 0.004 (0.000)* 0.307 (0.000)* Indonesia 0.033 (0.000)* 0.047 (0.000)* 0.026 (0.000)* 0.369 (0.000)* USA 0.018 (0.000)* 0.036 (0.000)* 0.040 (0.000)* 0.040 (0.000)* UK 0.009 (0.000)* 0.014 (0.000)* 0.016 (0.000)* 0.025 (0.000)* Japan 0.002 (0.000)* 0.010 (0.000)* 0.013 (0.0000* 0.011 (0.000)* Italy 0.034 (0.000)* 0.048 (0.000)* 0.011 (0.000)* 0.014 (0.000)* Germany 0.017 (0.000)* 0.031 (0.000)* 0.015 (0.000)* 0.002 (0.000)* France 0.030 (0.000)* 0.058 (0.000)* 0.002 (0.000)* 0.011 (0.000)* Canada 0.017 (0.000)* 0.044 (0.000)* 0.020 (0.000)* 0.063 (0.000)* shows level of significance at 5% level of confidence interval As a consequence, the panel findings remained relevant in two ways: first, construct-wise, and second, country-wise. Since the residual errors are usually distributed, we can trust the findings recorded by the models, which are 1% for the lower percentiles and 99% for the higher percentiles. Floods endanger 48% of the world’s property, more than half of the world’s people and 46% of global properties. In 68% of coastal regions, tidal and storms will cause flooding, while the remaining 32% is at risk from a regional increase in sea level, according to his report. The study also reveals the flow of green finance in G7 and E7 nations. The developing countries are host to the bulk of the world’s population. In 2018, the total and nominal GDP of the world’s population was projected to be about $6.5 trillion, with about 1.5 billion people. While having a population that is larger than China, their GDP is comparable to China’s. This level of magnitude revealed that 0.34 represents a 1% increase in economic growth due to green energy demand, resulting in a 0.11 increase in economic growth from where it is now. As a consequence, our results are compatible with previous research on E7 and G7 regional initiatives in multiple contexts, highlighting the role of a cleaner environment in economic development by green finance on regional scales such as the G7 and E7. We have used the effects of the environmental feature with the growth function, as seen in Table 7, utilizing the FMOLS technique. These findings indicate that CO2 levels are elastic as green energy is used in combination with G7 economic development. Table 7 Robustness of results using panel VECM results for the growth function Dependent variables F-statistics T-statistics λ1 λ2 λ3 λ4 λ5 λ6 λ7 λ8 λ9 ECT (− 1) λ1 - 3.17 2.64* 1.16* 2.45* 2.77* 3.19* 3.07* 3.70* 0.014 (0.000)* λ2 0.025* - 0.78* 1.19* 1.50* 1.67* 1.90* 1.50* 1.01* 0.018 (0.000)* λ3 0.017* 0.029* - 0.44* 0.35* 0.31* 0.10* 0.23* 0.05* 0.025 (0.000)* λ4 0.027* 0.036* 0.047* - 0.49* 0.34* 0.218 0.16* 0.16* 0.037 (0.000)* λ5 0.023* 0.041* 2.054* 4.037* - 0.21* 0.01* 0.14* 0.23* 0.021 (0.000)* λ6 0.034* 1.038* 1.190* 1.275* 2.67* - 0.11* 0.04* 0.06* 0.014 (0.000)* λ7 0.030* 1.054* 2.01* 2.55* 2.69* 2.88* - 0.09* 0.01* 0.037 (0.000)* λ8 0.017* 0.54* 0.67* 14.63* 17.01* 12.99* 15.04* - 1.73* 0.044 (0.000)* λ9 0.011* 0.027* 0.030* 1.45* 1.50* 1.71* 3.63* 4.44* - 0.005 (0.000)* Green performance index Interestingly, there is slight difference of graphs between E7 and G7 countries, but comparatively G7 countries are more inclined to take initiatives for climate change mitigation. As Brazil holds lower score ranging from 46 to 54% which is the lowest score in E7 countries, as well as in G7 countries. Mexico has good index in terms of green performance which is greater than 75%. China is setting a benchmark in green performance index achieving more than 93% score to perform green. Indonesia is sluggish to perform as green countries holding score less than 60%, which is quite alarming and indicates to take quick actions for a secure environmental future, nationwide, while in G7 countries, only France is less efficient to perform green and having score less than 60%. Conclusively, G7 has one country (e.g. France) and E7 has two countries (e.g. Brazil and Indonesia). The G7 and E7 countries must strive to emphasize the value of natural resources’ effect on global growth and financial development. A 7% growth in export leads to a 45% rise in financial deepening, according to his calculations. Despite the fact that it is mostly based on fossil fuels, this research may be particularly useful for the E7 and G7 countries in terms of consuming renewables and developing their financial sectors. Between 2011 and 2018, the six MDBs donated a total of 237 billion dollars to developed countries in the battle against climate change. Multilateral development banks (MDBs) have recorded a 61% growth in climate financing from 18% to 29% since 2013. In 2018, the MDBs pledged $165 million to graphht climate change, totalling US$ 21,439 million, with 71% of it heading to construction loans and the remaining 7% going to policy-based funding, totalling US$ 2,195 million. Figure 2 and Fig. 3 show the climate change mitigation in E7 and G7 countries. Li et al. (2021d) stressed the importance of filling the $110 billion annual deficit left by MDBs, concentrating on green finance in Latin America and the Caribbean. According to the report, an extra $7 billion in green funds and $4.4 billion from MDBs can be spent next year. Fig. 2 Climate change mitigation performance of E7 countries Fig. 3 Climate change mitigation performance of G7 countries Discussion The aim of this analysis was to look at the impact of climate change mitigation on GDP in the E7 and G7 nations, as well as other determinants including environmental taxation, human resources, GDP, green energy use and environmentally sustainable technical innovation. For a variety of factors, we decided to analyse a sample of G7 and E7 nations. The strategy, strategies and activities of these seven great powers, which control nearly half of global GDP, are critical in achieving low CO2 levels. G7 countries’ attempts to curb CO2 pollution are commendable, given that their exposure to greenhouse gas emissions was 70% in the early twentieth century and just 24% in 2015. Despite the fact that its absolute contribution to greenhouse gas emissions is high, the G7’s contribution is just half that of China as of early 2010. Canada has the largest greenhouse gas emissions and electricity use per capita in the E7 nations. As long as it proceeds to subsidies the use and output of fossil fuels, Canada’s success in climate change mitigation policy is rated as average. Furthermore, the UK, Indonesia and Germany have excellent results in terms of greenhouse gas emissions and oil usage, while the USA and Japan have poor efficiency. By examining the non-homogeneous features of regional nations, such as E7 vs. G7, the study seems appealing. The results of this analysis can be used to advise relevant strategies for a balanced world by the great powers. The research constructs are bidirectional between climate change mitigation (e.g. CO2 emissions) and economic growth in the G7 and E7 areas, according to the long-run calculation parameters. These results back up the study’s hypothesis that there is a beneficial connection between a cleaner atmosphere (e.g. climate change mitigation) and economic development and that green finance strategies will help improve the G7 and E7 region’s natural, economic and social well-being. Thus, the study hypothesis is accepted, and through these estimates, our study findings are robust in the long run. The results of the recent study are aligned with the findings of Abbas, (2020) for regional dynamics and in the long-run context. Note: * means significance at the 5% stage. We concluded the empirical outputs of study with growth function by using VECM approach shown in Table 8 indicating bidirectional causality among the cleaner environment and green financing potential, in the long-run, endorsing. Our results are in line with the conclusions of Bocco et al. (2020). Contradicting to it, the findings of the study are comparatively consistent with different other studies (Wahab et al. 2020), supporting the unidirectional findings of recent study, missing the link to predict the long-run future of any BRI project in a region that is covered by recent study. By this, current investigation sufficiently fills the gap on theoretical, empirical and practical grounds by providing key policies suggestion for policymakers. Table 8 Green performance index of E7 and G7 countries Region Countries 2010 2011 2012 2013 2014 2015 2016 2017 2018 E7 countries Brazil 0.47 0.46 0.45 0.46 0.45 0.47 0.51 0.52 0.54 Mexico 0.76 0.76 0.75 0.73 0.73 0.79 0.83 0.81 0.83 Russia 1 1 1 0.97 0.98 1 1 1 1 China 0.93 0.93 0.95 0.98 1 1 1 1 1 Turkey 0.62 0.74 0.5 0.65 0.75 0.71 0.90 0.78 0.84 India 0.67 0.71 0.69 0.79 0.79 0.84 0.73 0.77 0.76 Indonesia 0.58 0.73 0.72 0.62 0.58 0.58 0.58 0.58 0.58 G7 countries USA 0.66 0.65 0.65 0.67 0.66 0.65 0.66 0.67 0.69 UK 0.95 0.95 0.95 0.85 0.78 0.78 0.8 0.77 0.65 Japan 0.87 0.88 0.87 0.88 0.88 0.85 0.84 0.84 0.85 Italy 0.76 0.75 0.73 0.75 0.67 0.68 0.68 0.67 0.65 Germany 0.95 0.86 0.84 0.80 0.85 0.87 0.95 0.95 0.95 France 0.46 0.50 0.49 0.49 0.48 0.5 0.53 0.52 0.55 Canada 1 1 1 1 1 1 1 1 1 In this sense, the International Institute for Applied Systems Analysis (IIASA) predicts that South-eastern, Central and Western Asia will become major economic drivers (i.e. the BRI countries will account for 50% of global GDP from 2015 to 2030). It has a worldwide market share of 11%. These figures demonstrate the G7 and E7 countries’ expenditure and demand capacity. An analysis by shows a long-term equilibrium association between population, technical change and sustainable use for G7 vs. E7 countries. As facility access is a necessity in the introduction of the BRI, a growth in per capita GDP would result in a major increase in electricity demand and carbon emissions. Conclusion and policy implications This research suggested an examination of various approaches to changing green finance and environment conditions in G7 and E7 countries from 2010 to 2018. To measure the impact of climate change and green finance mitigations on the countries under consideration, two classes of countries have been created (i.e. treated group and control group). To contend with the unobserved time variation, which may trigger weakness in the inference, pre-treatment observables have been used by matching approaches (i.e. the kernel, radius matching and nearest neighbour approach). This strategy may help to offset the time gaps between classes. The E7 countries are the twenty-first century’s fastest-growing economies. China has the world’s largest clean energy assets, including hydropower, solar PV and wind. With a 15% renewable energy goal for 2020, China has been investing in renewable energy for a long time. By 2018, it had reached 14.3%, with a total expenditure in renewables of 33%. Furthermore, low-carbon solutions are expected to meet about 40% of the country’s renewable energy expenditure requirements, including transportation, whereas wastewater, land remediation, waste management and sewerage will get the remaining 60% from 2014 to 2020 (Shahzad et al. 2021). The G7 and E7 countries account for 7.94% of the world GDP and produce around 11.2% of global CO2 emissions. The burden-sharing issue requires that the developed and the developing world take equal constructive measures to prevent practices that would increase global temperatures above 1.5 °C as foreseen by the Paris Accord (Sinha et al. 2020). This research highlighted the importance of G7 and E7 countries developing policies capable of addressing systemic risks associated with climate change, as well as the necessary funding to mitigate these risks and impacts. Based on the methodology used, the analysis yielded mixed results, as there is no correlation between the G7 and E7 countries’ green finance and climate risk profiles. For emerging and developing economies (EMDEs), in particular, sustainability is a critical concern. Overall, the following indicators have a major influence sample country: the continent’s EE condition, foreign direct investment, GDP, oil imports, energy-related pollution, fossil fuel use, port infrastructure efficiency, logistic output index and taxes. However, various models showed that certain factors had different effects on EE. Furthermore, projections of the marginal impact suggest that oil imports are unlikely to disrupt the G7 and E7 countries’ EE. The existing assessment techniques for energy, pollution and economy need to be replaced with completer and more low-cost (in terms of time) indicators for better assessment of real-time data and enforcement of local and international energy laws. Authorities need to redistribute public funds towards the public good. Although public funds, R&D and education for clean energy have been raised lately, they are not comparable to developed countries. Governments should allocate additional funding to green energy education and R&D in the light of the findings of this study, which will proliferate human resource mobilization and technology innovation, critical to green economic success. This work highlights country-wide variation in the effects of public funds on green economic growth. Therefore, E7 and G7 economies are recommended to formulate country-specific strategies for better benefit. Authors’ contribution Wu Xueying: Conceptualization, writing—original draft. Muhammad Sadiq: writing—original draft. FengSheng Chien: Data curation, methodology. Thanh Quang Ngo: Data curation, visualization, review and editing. Anh-Tuan, Nguyen: Writing—review and editing and software. The-Truyen, Trinh: Visualization, supervision, editing and software. Funding This research is funded by the University of Economics Ho Chi Minh City, Vietnam. Data Availability The data that support the findings of this study are openly available on request. Declarations Ethical approval and consent to participate We declare that we have no human participants, human data or human tissues. Consent for publication We do not have any individual person’s data in any form. Competing interests The authors declare that there is no conflict of interest. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Abhimanyu Kumar B (2019) RESEARCH HUB-International Multidisciplinary Research Journal (RHIMRJ) Consequences of global warming on our environment and natural resources. 5 Adedoyin FF Gumede MI Bekun FV Etokakpan MU Balsalobre-lorente D Modelling coal rent, economic growth and CO2 emissions: does regulatory quality matter in BRICS economies? 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==== Front Soft comput Soft comput Soft Computing 1432-7643 1433-7479 Springer Berlin Heidelberg Berlin/Heidelberg 34276248 6012 10.1007/s00500-021-06012-9 Focus RETRACTED ARTICLE: Weather indicators and improving air quality in association with COVID-19 pandemic in India Chakrabortty Rabin [email protected] 1 http://orcid.org/0000-0003-0805-8007 Pal Subodh Chandra [email protected] 1 Ghosh Manoranjan [email protected] 2 Arabameri Alireza [email protected] 3 Saha Asish [email protected] 1 Roy Paramita [email protected] 1 Pradhan Biswajeet [email protected] 491011 Mondal Ayan [email protected] 5 Ngo Phuong Thao Thi [email protected] 6 Chowdhuri Indrajit [email protected] 1 Yunus Ali P. [email protected] 7 Sahana Mehebub [email protected] 8 Malik Sadhan [email protected] 1 Das Biswajit [email protected] 1 1 grid.411826.8 0000 0001 0559 4125 Department of Geography, The University of Burdwan, Bardhaman, West Bengal India 2 grid.429017.9 0000 0001 0153 2859 Centre for Rural Development and Sustainable Innovative Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal India 3 grid.412266.5 0000 0001 1781 3962 Department of Geomorphology, Tarbiat Modares University, 14117-13116 Tehran, Iran 4 grid.117476.2 0000 0004 1936 7611 Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007 Australia 5 grid.411826.8 0000 0001 0559 4125 Ecology and Environmental Modelling Laboratory, Department of Environmental Science, The University of Burdwan, Burdwan, West Bengal India 6 grid.444918.4 0000 0004 1794 7022 Institute of Research and Development, Duy Tan University, Da Nang, 550000 Vietnam 7 grid.140139.e 0000 0001 0746 5933 Centre for Climate Change Adaptation, National Institute for Environmental Studies, Ibaraki, 305-8506 Japan 8 grid.5379.8 0000000121662407 School of Environment, Education and Development, University of Manchester, Oxford Road, Manchester, M13 9PL UK 9 grid.263333.4 0000 0001 0727 6358 Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro, Gwangjin-gu, Seoul, 05006 Korea 10 grid.412125.1 0000 0001 0619 1117 Center of Excellence for Climate Change Research, King Abdulaziz University, P.O. Box 80234, Jeddah, 21589 Saudi Arabia 11 grid.412113.4 0000 0004 1937 1557 Earth Observation Center, Institute of Climate Change, University Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Malaysia Communicated by Oscar Castillo. 13 7 2021 2023 27 6 33673388 28 6 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The COVID-19 pandemic enforced nationwide lockdown, which has restricted human activities from March 24 to May 3, 2020, resulted in an improved air quality across India. The present research investigates the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined. The selected seven air quality pollutant parameters (PM10, PM2.5, CO, NO2, SO2, NH3, and O3) at 223 monitoring stations and temperature recorded in New Delhi were used to investigate the spatial pattern of air quality throughout the lockdown. The results showed that the air quality has improved across the country and average temperature and maximum temperature were connected to the outbreak of the COVID-19 pandemic. This outcomes indicates that there is no such relation between climatic parameters and outbreak and its associated mortality. This study will assist the policy maker, researcher, urban planner, and health expert to make suitable strategies against the spreading of COVID-19 in India and abroad. Supplementary Information The online version contains supplementary material available at 10.1007/s00500-021-06012-9. Keywords COVID-19 Air quality index Lockdown Mortality Analytical neural network issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023 ==== Body pmcIntroduction The epidemic has also sparked a worldwide economic catastrophe unlike any seen in the past decades, with ramifications that will last for years. The large proportion of our habits, commerce, social and economic ties, methods, modes of employment, and political institutions have already altered profoundly as a result of the epidemic (Boccaletti et al. 2020). The Coronavirus-induced lockdown has a remarkable influence on pollution in the world’s second-largest populated country, India. Soon after the nationwide lockdown,1 the scientific community raised a question: Does the COVID-19 pandemic lockdown situation improve air quality, especially in cities and industrial corridors? (Wright 2020). The scattered empirical evidences have been suggested that paradoxically, the impact of the COVID-19 pandemic has improved air quality around the world, for instance, China, France, and Italy (Yunus et al. 2020) (ESA 2020). The National Aeronautics and Space Administration (NASA) has first reported that lockdown has reduced the aerosol and nitrogen dioxide over Wuhan in China (NASA 2020). Then, the European Space Agency (2020) had been continuously reported that in Italy, Spain, and France emissions reduced by 20 to 30% during the month of March 2020 due to the lockdown situation (ESA 2020). Tobías et al. 2020 found that air pollutant materials decreased significantly during the two-week lockdown period for example, PM10 decreased by 28% to 31.0%, and nitrogen dioxide decreased by 45% to 51%; on the contrary, ozone gas increased by 33% to 57% in Barcelona. During the pre-locked down period, India had the top twenty polluted cities in the world (Majumdar et al. 2020) with the maximum cities that crossed the tolerant breathing limit of pure air in India (Central Pollution Control Board, 2019). Majumdar et al. 2020 also found that both particulate matters and gaseous pollutant have caused serious health problems in various cities in India, especially in Delhi, Kanpur, Kolkata, Bengaluru, and Mumbai. Balakrishnan 2019 found that in India, more than one million premature deaths have occurred due to various air pollutants. India has one of the utmost rates of respiratory problems and the world’s maximum number of tuberculosis (Wright 2020). Garaga et al. 2018 estimated the regional average concentration of PM2.5 in India and found that north has 3.3 μg/m3 and, east, west, and south India has, respectively, 3.3, 3.7, 2.3, and 1.6 μg/m3. Moreover, being a developing country, India has seen extensive urbanization; as a result, pollution is a direct outcome of urbanization and its related phenomena. The COVID-19 pandemic driven lockdown has changed the air quality in India. During the lockdown period, the concentration of particulate matter in all Indian cities decreased. This is mainly contributed due to the less number of motor vehicles and roadside food-vendors who use coal cook stoves which are the important sources of pollutant in Indian cities. In a recent research outcome, i.e., Sharma et al. 2020 found that there is a 43% decrease in PM2.5 and 18% decrease in NO2 in India during the first half of lockdown stage compared to earlier years. Mahato et al. 2020 established that during the lockdown period, the greatest reduction in PM10 and PM2.5 intensities was found to be greater than 50%. They also observed that the air quality is improved by 40% to 50% during the four days of the lockdown. Moreover, Huang et al. 2020 found that NO2 in the atmosphere over the eastern parts of China had decreased by approximately 65% in comparison with the previous year. However, it has been noticed that COVID-19-affected patients have similar symptoms to other affected illnesses, e.g. cough, fever, respiratory disorder, and pneumonia. It has been found that the growth of other Coronavirus has significant relation to increase or decrease in temperature in the region. Bashir et al. 2020b analyzed the COVID-19 outbreak in the New York City with daily temperature, humidity, wind speed, and air quality; and according to their finding ingrowth of COVID-19. The affected people has positive correlation with minimum temperature and air quality. Dalziel et al. 2018a found the similar results that the influenza health epidemic follows a seasonal pattern of the climatic parameter; after the end of rainy and summer season’s infection-related health epidemic generally followed the increasing trend. Dalziel et al. (2018a) also found that the spatial variation of humidity differentiation in the incidence of influenza in the USA. The seasonal fluctuation of humidity leads to the seasonal outbreak of influenza, especially in winter. Tan et al. 2005 analyzed the relationship between SARS outbreak and daily temperature in the major cities of China, and they found that 16 °C–28 °C was the suitable condition for the growth for SARS virus. A sharp decrease in average temperature or towards cold weather leads to an increase or outbreak of SARS virus to affect patients. Therefore, it has the high probability to other SARS group virus which would follow the same kind of spread in dynamically related to temperature and humidity. Moreover, the weather phenomena have a close relationship with the human immune system. However, meteorological parameters such as wind speed and direction also affect the increase and transition of transferable syndromes (Ma et al. 2020). In recent work, Ahmadi et al. 2020 found that the sensitivity of COVID-19 epidemic in Iran is associated with the wind speed, humidity, solar radiation, and population density. The authors also revealed that suitable climatic condition, particularly humidity in Tehran and Mazandaran provinces, increase the virus-affected populations compared to the rest of Iran. Incidentally, Van Doremalen et al. 2020 found that SARS virus can remain active for three hours on aerosol; thereby there is a high chance of transmitting the virus with the direction of wind flow. Similarly, Chen et al. 2020 found that the climatic model with relative humidity, wind speed and temperature were highly associated with COVID-19 pandemic. Further, Contini and Costabile 2020 stated that the concentration of PM2.5 and PM10 in the air with biological, physical and chemical analysis could explain the observed mortality in various parts of the World. Melin et al. 2020a, b used the Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation in Mexico City for prediction of the trend of COVID-19 time series. Here in the validation data set, the simulations of several ensemble neural network models with fuzzy response integration demonstrate excellent predicted results. The prediction errors of numerous ensemble neural networks are, in fact, substantially smaller than those of typical monolithic neural networks, demonstrating the benefits of the suggested technique (Melin et al. 2020a).Sun and Wang 2020 simulated a COVID-19 outbreak caused by a single imported patient who was not subjected to rigorous isolation. The lessons learned from the newly identified cases in Heilongjiang province after April 9 should be noted since they are pharmacologically linked to this "imported escaper." They also suggested that rigorous precautions such as isolation, house quarantine, and centralized quarantine be reinstated, particularly in Haerbin City, Heilongjiang Province, to reduce the possibility of a subsequent epidemic. Castillo and Melin 2020 used “hybrid approach combining the fractal dimension and fuzzy logic” for efficient and effective prediction of COVID-19 data series. Castillo and Melin 2021 provide a hybrid intelligent fuzzy fractal technique for countries classification made on the basis of fractal theoretical notions and fuzzy logic quantitative concepts. The fractal dimension's mathematical description allows us to assess the complexities of the nonlinear dynamic behavior displayed by country time-series data. Several researches have recently been published with the objective of clearer appreciation COVID-19 patterns, one of which is: identifying probable patterns utilizing a collection of X-ray medical pictures from individuals with prevalent bacteria pneumonia verified with COVID-19 illness (Apostolopoulos and Mpesiana 2020; Melin et al. 2020b). Other intriguing research is the use of dynamic statistical methods to investigate COVID-19 instances in China (Sarkodie and Owusu 2020). Several Artificial Intelligence methods are being used in healthcare to analyze data and make decisions. This indicates that AI-driven technologies can assist in spotting COVID-19 outbreaks and forecasting their type and rate of spread throughout the world (Santosh 2020). Considering the close relationship with different climatic indicators with Coronavirus along with nationwide lockdown’s impact on air quality in India, the aim of the present research is to critically explore the connection between COVID-19-imposed lockdown and air quality across India during the pre-lockdown and lockdown periods. In this study apart from the improving air quality in COVID-19 pandemic-induced lockdown, we clearly demonstrated that the climatic variables are not extreme indicators for spreading of SARS COVID-19 virus in throughout the country. Some of the studies indicate that the decreasing air temperature is most favorable for spreading the COVID-19, but our study is totally against this approach. Apart from this, our aims in the study are to scientifically analyze COVID-19 pandemic-enforced nationwide lockdown and its relations to improved air quality across Indian cities. Besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the lockdown period has also been examined. The present would have huge impact on post-pandemic crisis management of air quality, especially for megacities. The policymakers would have the opportunities to redesign the existing air quality regulatory mechanism. Study area The present study has focused in India; due to the huge variations of latitude (8° 4′ N to 37° 6′ N), longitude (68° 7′ E to 97° 25′ E), and varying physiography, India's climate has a broad variety of weather conditions. The climate of India differs from tropical to subtropical humid, with most of the area's average temperature varying from 10 to 40 °C throughout the year (Pal et al. 2021a). The rainfall of the country varies about 100 to150 cm, and the country receives the maximum amount of rain during the monsoon season. It is well known fact that subtropical climatic conditions are also responsible for different types of diseases. Moreover, India has 1.3 billion populations and 31.16% lives in fifty-three urban agglomerations spreading across the country, whereas the rest of the population (68.16%) lives in the rural areas (Census of India 2011). In the recent decade, India has been experienced a positive increase in urbanization and economic growth (Gurjar et al. 2016), which combinedly make the country one of the largest greenhouse gas emitters; therefore, people are facing air pollution-induced problems in everyday life. Pant et al. 2020 mentioned that air quality always remains as significant environmental and health hazard problems in Indian megacities. In addition, being a highly populated developing country, the country’s health infrastructure does not have that much capacity to facilitate this huge population, whereas India suffers from poverty and a large number of families without access to basic health care services, which hinders people’s health condition. The poor infrastructures lack of medicine, beds, and limited resources are common phenomena in the government-aided hospitals throughout the nation. On the other hand, private hospitals have better infrastructure that is too expensive and almost inaccessible for a low-income group of poor families. According to WHO’s (WHO) guidelines, doctor population ratio should be 1:1000 but in India, it is 1:1457. Therefore, the number of doctors is far less than that is required in India. As a result of this, India is suffering to overcome the new challenges in medical and health sciences. Figure 1 shows the map of the study area with point location of data sources.Fig. 1 Map of the study area with point location of data sources Materials and methods Analytical neural network (ANN) The ANN is a machine learning technique, connectionist system motivated by the research of biological neurons (Hewitson and Crane 1994; Levine et al. 1996). A self-learning method employs the ANN model to self-analyze the associations among multi-source data (such as combines of qualitative and quantitative information) and to determine the region more likely to trigger air quality index under certain predetermined geo-environmental circumstances. Furthermore, this method may create links to linear or nonlinear projection methods to a satisfactory precision (Licznar and Nearing 2003). ANN’s are commonly used in their capability to model the dynamic process and identify the trends in science and technological problems (Jain et al. 1996; Cracknell and Reading 2014). An ANN method was constructed with considering different air quality parameters as input or covariates, and air quality index (AQI) observed dependent factors. Here, a multilayer perceptron neural network classifier has been developed using the covariates referred, e.g., PM2.5 (μg/m3), PM10 (μg/m3), NO2 (μg/m3), NH3 (μg/m3), SO2 (μg/m3), CO (mg/m3), and Ozone (μg/m3) were considered. Hyperbolic tangent was considered for the development of the model for hidden layer initialization, and identity function was considered for the activation of the output layer. Hyperbolic tangent function takes real-valued arguments of inputs (x1, x2, …, xn) and transforms them to the range (− 1, 1) through Eq. 1.1 fx=tanhx=ex-e-xex+e-x The identity function is a linear function (Eq. 2) that obtains real-valued arguments of hidden layer and precedes them unaffected.2 fx=x There is also a supplementary neuron component, named w0, identified as the bias that can be taken as a synapse connected with an input Aqf0 = − 1. The output of the neuron AqIn(air quality index) is supported on the creation among input vector Aqf (Aqf0, x1, x2, …, xn) and vector w (w0, w1, w2, …, wn) composed of synapses, with the bias (w0), The following equations (Eqs. 3 and 4) were considered to make the ANN method3 Aqf×w=∑n=0iAqfn×wn where Aqf are the components of air quality (Aqfn = Aqf0…Aqfi) and w is the synaptic influence allocated for individual Aqf(wn = w0…wi).4 AqIn=φAqf×w where φ is the establishment role value and AqIn is the air quality index. Basically ANN is now one of the most useful tools that can be used to model complex patterns, to understand relative contribution of input variables on prediction and decision making. Obviously it is well known fact that taken air quality pollutant parameters (PM10, PM2.5, CO, NO2, SO2, NH3 and O3) how contribute in AQI calculation. But here we identified rationalized importance of them. Air quality index (AQI) According to the Millennium Development Goals, sustainable management is essential for a nation to progress under crucial conditions. As a measure, AQI records pollutants from the monitoring station in the surrounding air. AQI makes awareness on the public by providing information about the risk of daily pollution level and on the other hands helps to take immediate measure for this impact on the environment (Ghorani-Azam et al. 2016). It represents the consistency of the air using color schemes and graphics and graded as good, satisfactory, moderate, poor, very poor, and severe. Maximum air pollution and related diseases are indicated by the high value of AQI. Traditional AQI assessment based on individual pollutants to the norm utilizing the effective aggregation technique removes complexity, eclipse, and stiffness (Swamee and Tyagi 1999). Sharma et al. 2020 stated that AQI used maximum sub-indices using such five pollutants (PM10, PM2.5, SO2, NO2, and CO). The National Ambient Air Quality Monitoring Programme includes seven new parameters such as PM2.5, ozone (O3), ammonia (NH3), benzene (C6H6), benzo (a), pyrene (BaP), arsenic (As) and nickel (Ni). And rests of parameters are sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter size less than 10 microns (PM10), lead (Pb), and carbon monoxide (CO), respectively. Three of the twelve parameters have an annual standard (annual avg.), six have an annual standard with a short term (annual 330 avg./24 h), and O3, CO alone has a short period (1 h/8 h/24 h). The current research work has aimed by presenting an integrated index of analyzing on seven pollutants (PM2.5, PM10, NO2, NH3, SO2, CO, and O3) individually in the lockdown period compared to pre-lockdown period and also indicate future condition depending on this trend. For estimating AQI, an established method by the Central Pollution Control Board (CPCB), Govt. of India, has been followed throughout the study. The AQI has been estimated by considering the major pollutants (PM2.5, PM10, NO2, NH3, SO2, CO, and O3). All monitoring stations across India and its recorded pollutant have been considered in the present study. PM stands for particulate matter (also called particle pollution): the term for a combination of solid and liquid particles present in the air. Several molecules, like dust, dirt, soot, or smoke, are large or dark enough to be seen with the human eye. Some are so tiny that only an electron microscope can identify them. The particles in PM10 are tiny enough to enter the throat and lungs. High amounts of PM10 can cause coughing, runny noses, and stinging eyes. When PM10 levels are high, those with heart or lung problems may have greater symptoms. Wheezing, chest tightness, and trouble breathing are some of the symptoms. PM2.5 is a kind of tiny inhalable particle having a diameter of 2.5 μm or less. Such particles can be made up of a lot of various chemicals and available in a multitude of sizes and forms. Carbon monoxide (CO) is a combustible gas that is colorless, odorless, and tasteless. It is somewhat less dense than air. One carbon atom and one oxygen atom make up carbon monoxide. It is the oxocarbon family's most basic chemical. The most frequent source of carbon monoxide is thermal combustion, although there are other biological and environmental sources that produce and release substantial amounts of carbon monoxide. Carbon monoxide is used in a variety of industrial processes, including synthetic chemical production and metallurgy, but it is also a contaminant in the air caused by industrial operations. Nitrogen dioxide (NO2) is a reactive gas that belongs to the group of gases considered as nitrogen oxides (NOx). Nitrous acid and nitric acid are two other nitrogen oxides. NO2 is being used as the attribute for the wider community of nitrogen oxides. NO2 principally receives in the air from of the fuel combustion. NO2 constructs from emissions from cars, trucks and buses, power stations, and off-road materials. Ammonia (NH3) is an amidase inhibitors and neurotoxic that is made up of a single nitrogen atom covalently linked to three hydrogen atoms. Microbial activities and the decomposition of organic materials create it both artificially and naturally. Ammonia is a chemical that is utilized in a wide range of industrial applications, as well as as a fertilizer and a refrigerant. Ozone (O3) in the air we breathe may be harmful to our health, particularly on hot, bright days when ozone levels can approach dangerously high levels. Even modest quantities of ozone can be harmful to one's health. There are two steps to calculate AQI, i.e. the first one is to formulate the sub-indices and the second one is the amalgamation of sub-indices to acquire AQI. Further the sub-index functions were used to formulate sub-indices for n numbers of pollutants; mathematically it is expressed as5 Ii=fXi,i=1,2,…,n. Amalgamation of sub-indices to acquire AQI is done using some numerical function, i.e., expressed as6 I=FI1,I2,…,In. The relationship between sub-index (Ii) and pollutants concentration (Xi) is expressed as7 I=αX+β where α indicates slope of the line and β indicates intercept at X = 0. On the other hand, sub-indices (Ii) for a identified pollutant attentiveness (Cp) are expressed as,8 Ii=IHI-ILO/BHI-BLO×CP-BLO+ILO here BHI indicates cutoff point attentiveness ≥ known attentiveness; BLO indicates cutoff point attentiveness ≤ known attentiveness; IHI means AQI value equal to BHI; ILO means AQI value equivalent to BLO and Cp specifies pollutant concentrations. Thereafter, weighted additive value was calculated to amalgamation of sub-indices and is expressed as9 I=AggregatedIndex=∑WiIiforI=1,…,n where ∑Wi equals 1, Ii indicates sub-index of pollutant I, n indicates amount of different pollutants, and Wi means influence of the pollutant. Minimum or maximum operator form is expressed as (Ott 1978):10 I=MinimumorMaximimI1,I2,I3,…n. The scientific rationalize behind the correlation is as the COVID-19 cases followed the normal probability distribution, so the nonparametric correlation method is suitable to understand the covid19 cases with others environmental parameters like climate. Others scientific study also established that climate is an important variable of COVID-19 infection. Kendall and Spearman rank test We have conduct the probability distribution of various climatic condition and definite cases of COVID-19 with the help of Kendall test and Spearman correlation. This nonparametric method has also been applied because the distribution has followed the normal probability distribution (Taylor 1987). Finally, Fig. 2 shows the generalized methodology flowchart to demonstrate the brief theme of this research.Fig. 2 Methodology flowchart The Mann–Kendall test base described as follows on the test statistics S11 S=∑i=1n-1∑j=i+1nsgnxj-xi where xj is the continuous data value, n is the data sets length, and12 sgnθ1ifθ>00ifθ=0-1ifθ<0. Mann (1945) and Kendall (1975) have reported that statistics S, with the mean and variance as followed, is distributed essentially normally when n ≥ 813 ES=0 14 VS=nn-12n+5-∑i=0ntiii-12i+518 where ti is the degree number of relations i. Standardized statistics of tests z are determined by15 ZMK=S-1VarSS>00S=0S+1VarSS<0. The standard normal distribution with a mean of zero and variance of one is followed by the normalized MK statistics Z. Results Spatial mapping of major pollutants during pre-lockdown and lockdown The Lockdown Policy was introduced by the Indian Government in order to mitigate and monitor the COVID-19 pandemic. It was a collective decision to maintain a social distancing policy and to avoid mass gathering. Along with the above strategy, strict measures have been taken to put an end to transport systems (road, rail, air) and to the closure of major industries. The entire shutdown of traffic flow, industries, hotels, stores, and government offices resulted in a massive change in air pollution, especially among important prominent components including PM10, PM2.5, CO, NO2, SO2, NH3, and O3 (Fig. 3).This can be clearly seen from the spatial distribution of the accumulated PM10, PM2.5, CO, NO2, SO2, and NH3 contaminants at various pre-lockdown, lockdown, and predicted post-lockdown periods (Fig. 3). Particularly, the amounts of the pollutants only decreased below the permissible limit within one week of the shutdown (March 24, 2020 to March 31, 2020), whereas the absorption of O3 increases in manufacturing and transport conquered region. Later, the central government has given a limited relaxation (April 14, 2020) of the lockdown measures of COVID-19 for the necessary vehicles and human activities beyond the red zone, with a marginal effect on air pollutants. Owing to COVID-19 lockdown steps, the emissions declined dramatically in the vehicular traffic and the shutdown of factories, restaurants, shops, government offices, and several other human-induced activities (Fig. 3).Fig. 3 Spatial distribution of PM2.5 (µg/m3) in before and during lockdown periods (a), spatial distribution of PM10 (µg/m3) in before and during lockdown periods (b), spatial distribution of NO2 (µg/m3) in before and during lockdown periods (c), spatial distribution of NH3 (µg/m3) in before and during lockdown periods (d), spatial distribution of SO2 (µg/m3) in before and during lockdown periods (e), spatial distribution of CO (µg/m3) in before and during lockdown periods (f), spatial distribution of ozone (µg/m3) in before and during lockdown periods (g), and spatial distribution of air quality index in before and during lockdown periods (h) It was observed that air quality is improved drastically during the pre-lockdown period (24th March, 2020) of the COVID-19-extended lockdown phase (3rd May, 2020) and air quality is deteriorated slightly after the government gave a minor relaxation (April 14, 2020) to the necessary vehicles and other human activities beyond the red zone. On average, there is a significant improvement in air quality (− 26.99% with the net reduction of − 39.16) throughout three weeks lockdown durations (March 24, 2020, to April 14, 2020) relative to the standard air quality over three weeks pre-lockdown period (Fig. 3h). During the predicted post-lockdown phase (after 3rd May, 2020), air quality dropped dramatically; this is like at the value similar to the start of the lockdown period (March 24, 2020). Changes in major pollutants concentration during pre-lockdown, lockdown, and post-lockdown The change in the intentness of major air pollutants is very clear from the predicted outcomes that during the pre-lockdown period of COVID-19 (before March 24, 2020) the country witnessed massive air pollutants as in the previous months or years. However, after the lockdown (March 24, 2020), a major reduction in pollutants was observed throughout the country as a result of the COVID-19 pandemic (Fig. 4). In particular, significant decreases in quantity of pollutants such as PM10, PM2.5, CO, NO2, SO2 and NH3 have been estimated during the lockdown period (Fig. 4). The average assemblies of ambient air pollutants such as PM10 and PM2.5 have reduced by − 40.84% and − 45.38%, respectively. The decline rate of PM10 and PM2.5 is directly linked with automobile emissions, industrial dust, and cooking smoke or to complicated reactions with chemicals such as SO2 and NO. Forest fires, wood burners, agricultural burning, industrial smoke, and dust from various work sites all contributed to the decline in PM10 and PM2.5 concentrations (Majumdar et al. 2020). Other pollutants that have displayed a significant difference between pre-lockdown and lockdown are CO (− 19.76%) and NO2 (− 37.80%), whereas in SO2 (− 33.81%) and NH3 (− 17.06%), the decline was very low compared to the others pollutants, and there was also no strong trend of regression. The accumulation of O3 increases in manufacturing and transport dominated region, in particular, > 10% rise. The source of this increased in O3, particularly in industrial and transport dominated areas, is decline in NO, which contributes to reduce in O3 consumption (NO + O3 = NO2 + O2) and causing a raise in O3 levels. According to the study, it will take at least 180 days to record the concentration of key air pollutants during the post-lockdown of the COVID-19 pandemic, such as pre-lockdown levels across the country. It is a good sign that a significant perfection in air quality might be probable if the strict accomplishment of emissions control policies such as lockdown is enforced.Fig. 4 Trend of major pollutants in some selected monitoring station Spatial variation of PM10, PM2.5, CO, NO2, SO2, NH3, and O3, concentrations In the last 5 years (2016 to 2020), we have observed the seven contaminants’ 24-h accumulation phase during the same two months span (i.e. March and April). Continuous measurements of PM10, PM2.5, CO, NO2, SO2, and NH3 pollutants were acquired from the air quality monitoring station of India (Kamyotra and Sinha 2016). For this research, we utilized air quality monitoring data from 223 monitoring sites across India as a particular direction. According to the data, the lockdown resulted in a significant reduction in air quality across India. In contrast to PM10 and PM2.5 which have decreased dramatically (− 40.84% and − 45.38%) during lockdown phase, NO2 and CO have decreased drastically (− 37.80% and − 19.76%), while pollutants such as SO2 and NH3 may have slight declination trends (− 33.81% and − 17.06%) compared to others. The maximum PM10 and PM2.5 noticed in 2019 were as high as 264.82 μg/m3 and 344.07 μg/m3, respectively. This net decreased to − 113.10.44 μg/m3 (− 59.86% maximum reduction) and − 57.56 μg/m3 (− 45.05% maximum reduction), respectively, in 2020. The amount of O3 increases in manufacturing and transport dominated region. The results indicate that the accomplishment of the lockdown would lead to a significant improvement in air quality and should be placed into practice as an additional way of reducing pollution. The spatial distribution of all the pollutants except ozone are maximum in some pockets, i.e., National Capital Region (NCR), Mumbai metropolitan region, Kolkata, Guahati, and its surrounding regions. Similar spatial allocation has been associated in the case of air quality index. The gradual declining tendency has been associated among pollutant materials and its resultant air quality in lockdown period. Correlation between pollutants in the atmosphere The correlation between various concentrations of air pollutants in India during the study period (i.e., from February 17, 2020, to April 29, 2020) is shown in Fig. 5. The mean daily accumulation of PM2.5 is directly linked to the average daily concentration of PM10 (r = 0.73), NO2 (r = 0.58), CO (r = 0.34) and AQI (r = 0.92). Similarly, the average daily concentration of PM10 is directly correlated with the maximum daily concentration of PM2.5 (r = 0.73), NO2 (r = 0.48), NH3 (r = 0.41), CO (r = 0.39) and AQI (r = 0.80). The mean daily accumulation of NO2 is directly linked to the average daily concentration of PM2.5 (r = 0.58), PM10 (r = 0.48) and AQI (r = 0.58). NH3 by the mean daily aggregation is directly linked to the average daily concentration of PM10 (r = 0.41). Similarly, the average daily aggregation of SO2 is linked with AQI (r = 0.30). The daily concentration of CO has a positive relation with PM2.5 (r = 0.34), PM10 (r = 0.39) and AQI (r = 0.40). The daily aggregation of AQI is also directly linked to the average daily concentration of PM2.5 (r = 0.92), PM10 (r = 0.80), NO2 (r = 0.58), SO2 (r = 0.30) and CO (r = 0.40).Fig. 5 Correlation of different pollutants in India during lockdown Results of ANN modelling After several years of experience, we got an integrated network structure with a minimum error rate for both training and testing. It used 69.1% of total data to train the network and rest 30.9% for testing the model. There were one hidden layer with 5 hidden nodes (Fig. 6) in the network structure. The Sum of Squares Error (SSE) was 1.548 for training and 1.055 for testing with a relative error of 0.012 and 0.021, respectively. Observed values were well correlated with predicted values (Fig. 7) with a high degree of explainability (R2 linear = 0.985). Among the predictor covariates, PM2.5 contributed most (0.366) for AQI prediction, followed by PM10 (0.337). NH3 has just minimal accountability (0.029) for this result (Fig. 8). Normalized importance of input variables is given in (Fig. 8). Synaptic weights in between input variables-hidden node and hidden node-output (Table 1) are the weighing factor for prediction through Eqs. 3 and 4.Fig. 6 Structure of the network in ANN model Fig. 7 Accuracy of the model using observed versus predicted values Fig. 8 Importance of the variable in ANN model Table 1 Network information of ANN Input layer Covariates 1 PM2.5 2 PM10 3 NO2 4 NH3 5 SO2 6 CO 7 O3 Number of unitsa 7 Rescaling method for covariates Standardized Hidden layer(s) Number of hidden layers 1 Number of units in hidden layer 1a 5 Activation function Hyperbolic tangent Output layer Dependent variables 1 AQI Number of units 1 Rescaling method for scale dependents Standardized Activation function Identity Error function Sum of squares aExcluding the bias unit Influence of climate indicators on mortality The empirical results of seven atmospheric factors are presented in Tables 2 and 3 using the Kendal and Spearman rank correlation analysis. Results indicated that the average air quality and maximum temperature are relevant for COVID-19-positive new cases (Figs. 9 and 10). The variation of temperature from high to low influences the spreading of virus in nationwide. Though India faces mortality and positive cases, but by comparison it is quite low than other non-tropical countries which has the similar range of temperature, i.e., from 3 to 17 °C. In addition, according to Spearman test, average air quality and mean temperature indicate the positive case and mortality of COVID-19. The changing climate patterns for existing research have been examined for COVID-19 pandemic in highly populated countries like India. Our experiments showed that the maximum temperature and the average temperature are connected to the regional distribution of COVID-19 during the lockdown period. Shi et al. (2020); Bashir et al. (2020a) supported our outcomes, which examined at climate changes and claimed that temperature was the controlling factor behind COVID-19. Apart from this, humidity and temperature play an important role in the spreading of COVID-19 (Sajadi et al. 2020). The Wuhan incidence of COVID-19 revealed a close relation between diseases spread and weather patterns, with projections that warmer weather can control the virus (Wang et al. 2020). Dalziel et al. 2018b predicted that growing magnitude of seasonal variations in specific humidity contributes to more severe pandemics. Apart from this, meteorological parameters like air quality, humidity, and wind speed also accelerated the spread of COVID-19. In fact, air temperature also leads to the spread of the virus (Chen et al. 2020). In those areas where absolute humidity varies between 3 and 9 g/m3 and the atmosphere is hot in nature, the MIT community has previously confirmed 90 percent events. In this regard, Poole 2020 has also corroborated this by stating that humidity and climate indicators are associated with the COVID-19 spread. Therefore, it can be said that the maximum temperature and humidity have altered the mortality trend in India than American and European countries. So there are very close connection between climate indicators and the distribution of COVID-19. The average temperature was shown to have a substantial link to both mortality and case incidence (Tzampoglou and Loukidis 2020). Its impact appears to be as least as important as the speed with which the government responds. Because of its non-monotonic nature, relative humidity was shown to have a considerable but difficult to detect impact. Nonetheless, the population’s age structure appeared as one of the most significant risk factors (Tzampoglou and Loukidis 2020). As a consequence, all of the researches included in this systematic review had mixed results, and none of them provided conclusive evidence that a rise in temperature lowers COVID-19 case numbers (Chin et al. 2020). Despite this, most researches show a negative connection between COVID-19 and temperature, which, when combined with in vitro investigations on the virus’s stabilizing impact, suggests a negative link (Briz-Redón and Serrano-Aroca 2020). Chin et al. 2020 show that summer weather may lower COVID-19 transmission to some amount, but not enough to end the pandemic, in line with the findings of rigorous research (Yao et al. 2020; Xu et al. 2020). However, given the ambiguity of the COVID-19 data and the potential effect of the statistical and modeling methodology on the conclusions, these findings should be taken with caution.Table 2 Spearman rho correlation coefficient of selected variables Daily new cases Daily new deaths Total deaths Mortality rate Maximum temperature Minimum temperature Average temperature Average rainfall Air quality Daily new cases 1.000 .885 .863 .885 .651 .379 .603 .284* − .804 Total deaths .885 1.000 .901 1.000 .682 .315 .608** .259* − .826 Daily new deaths .863 .901 1.000 .901 .817 .376 .744 .360 − .916 Mortality rate .885 1.000 .901 1.000 .682 .315 .608** .259* − .826 Maximum temperature .651 .682 .817 .682 1.000 .477 .914 .376 − .760 Minimum temperature .379 .315 .376 .315 .477 1.000 .748 .153 − .413 Average temperature .603 .608 .744 .608 .914 .748 1.000 .328 − .719 Average rainfall .284* .259* .360** .259* .376 .153 .328 1.000 − .391 Air quality − .804 − .826 − .916 − .826 − .760 − .413 − .719 − .391 1.000 , , and are the significant at the 1%, 5%, and 10% levels of significance, respectively Table 3 Pollutant matter and gases before and after lockdown in India 2020 (Source: National Air quality Index portal, Central Pollution Control Board, Govt. of India, 2020) Types of pollutants Before lockdown 17-Feb-20 24-Feb-20 02-Mar-20 09-Mar-20 16-Mar-20 24-Mar-20 High Low High Low High Low High Low High Low High Low PM2.5 66.000 45.004 250.869 44.89 281.917 37.285 209.849 21.002 185.852 32.064 163.655 31.001 PM10 281.562 0.02 247.435 0.017 253.908 0.004 281.057 0.012 218.757 0.013 190.831 0.011 NO2 114.556 3.151 122.967 6.091 85.977 5.078 99.252 0.087 75.98 0.074 57.729 0.052 NH3 15.272 0.009 13.001 0.007 11.95 0.002 11.253 0.005 15.745 0.006 12.168 0.006 SO2 66.946 1.586 52.958 0.62 82.917 0.008 45.954 0.124 82.889 6.003 65.94 0.023 C0 106.885 16.043 127.022 0.001 100.888 0.131 99.856 0.001 106.526 0.001 135.985 0.069 O3 82.752 7.133 75.695 0.01 57.94 4.068 104.955 4.004 180.57 1.007 68.933 5.064 AQI 362.812 52.004 257.106 48.376 281.918 60.753 210.122 30.002 185.956 46.002 163.885 42.001 Types of pollutants After lockdown Overall variation 30-Mar-20 08-Apr-20 14-Apr-20 21-Apr-20 29-Apr-20 Net % High Low High Low High Low High Low High Low PM2.5 117.316 0.085 154.839 0.022 289.776 0.029 129.258 0.055 66.972 0.029 − 63.0118 − 45.3812 PM10 185.95 0.013 161.907 0.011 161.907 0.011 119.696 0.008 96.943 0.008 − 50.1569 − 40.8436 NO2 60.903 0.056 56.627 0.008 70.983 0.087 64.506 0.008 42.726 0.026 − 17.9898 − 37.8074 NH3 12.764 0.005 10.159 0.006 9.968 0.005 13.985 0.003 7.995 0.004 − 1.12927 − 17.0618 SO2 36.935 0.03 52.605 0.025 45.482 0.027 43.908 0.003 44.891 0.003 − 11.4398 − 33.8148 C0 113.814 0.051 105.86 0.001 94.971 0.051 80.917 0.001 67.923 0.027 − 11.4224 − 19.7674 O3 88.126 6.253 64.963 0.029 241.282 0.045 88.778 0.058 80.915 0.071 7.70775 15.62036 AQI 190.852 34.058 218.76 20.006 289.997 45.002 129.509 18.005 99.895 13.051 − 39.1646 − 26.9955 Fig. 9 Variability of temperature in before and during lockdown periods Fig. 10 Trend of positive COVID-19 cases in different temporal periods Discussion India ranks fifth in the world's most polluted countries and is the home to the 21 most polluted cities in the world, based on concentrations of PM2.5 and PM10. In a recent decade, numerous suggestive measures have failed to maintain the standard air quality across Indian cities. However, the scenario has changed due to COVID-19 pandemic, the environment and air quality have improved significantly. The particulate matter (PM2.5, PM10) which is related to automobile emissions, industrial emissions, dust, cooking smoke are one of the most dangerous air pollutants reduced drastically from 138.85 to 75.84 μg/m3 in India. However, due to relaxation permitted after April 14 2020 by the government beyond the red zone has resulted in the smaller fluctuation on the prediction of selected air pollutants. As well as the spatial prediction of the AQI during pre-lockdown and lockdown period indicates a significant improvement in air quality, but the reduction of air quality was observed in the post-lockdown phase. This also indicates the chance of an increase in the air pollutants with increasing relaxation in the coming days. Decreasing air pollutants due to lockdown for restricting of COVID19 community spread has been observed around the world (Huang et al. 2020). However, the highly transmissible COVID-19 has forced the world to shutdown mode and responsible for 238,650 deaths in all over the world (WHO 2020), whereas in India at present the number of deaths is 1323 and it is going on. The nature of COVID-19 is still not known completely (Van Doremalen et al. 2020). Primary statements from WHO clearly stated there were signs of human-to-human transmission (Twitter handle of WHO, on January 14, 2020, but after some days it was found the COVID-19 is highly transmissible from human-to-human contact. The present study indicates that the mortality rate significantly related to the maximum temperature, minimum temperature, average temperature, and air quality (Tables 2, 4). The increasing daily new cases have always increased the mortality rate in India. However, the higher amount of correlation values for daily new deaths, cumulative deaths, and mortality rate were associated positively with maximum temperature and negatively with air quality. Similar results were also observed over the USA by Bashir et al. 2020a although the correlation values with maximum temperature were much lower.Table 4 Kendal tau correlation coefficient of selected variables Daily new cases Daily new deaths Total deaths Mortality rate Maximum temperature Minimum temperature Average temperature Average rainfall Air quality Daily new cases 1.000 .772 .761 .772 .506 .259 .472 .204* − .689 Total deaths .772 1.000 .812 1.000 .535 .226 .467** .181* − .691 Daily new deaths .761 .812 1.000 .812 .659 .265 .579 .244 − .824 Mortality rate .772 1.000 .812 1.000 .535 .226 .467** .181* − .691 Maximum temperature .506 .535 .659 .535 1.000 .341 .773 .264 − .571 Minimum temperature .259 .226 .265 .226 .341 1.000 .584 .113 − .279 Average temperature .472 .467 .579 .467 .773 .584 1.000 .231 − .542 Average rainfall .204* .181* .244** .181* .264 .113 .231 1.000 − .266 Air quality − .689 − .691 − .824 − .691 − .571 − .279 − .542 − .266 1.000 , , and are the significant at the 1%, 5%, and 10% levels of significance, respectively Further, previous studies have found that deaths from asthma attack, acute respiratory inflammation, and cardio respiratory diseases are associated with prolonged interaction with polluted air (Schwartz and Dockery 1992; Dockery and Pope 1994) and causing 4.6 millions of deaths per year around the world (Lelieveld et al. 2015, 2019; Cohen et al. 2017). In the post-lockdown phase, there is a substantial drop in daily average PM10, PM2.5, NO2, and SO2 levels (Kumari and Toshniwal 2020a). In the post-lockdown phase, meanwhile, the proportion of O3 rose. PM10 levels were within National Ambient Air Quality Standards (NAAQS) on a daily basis (Aneja et al. 2001). Besides this medical profile and the patients’ age structure died from COVID-19 found that all the age categories are susceptible to COVID-19, but the mortality rate increases with increasing age combined with pre-existing medical conditions related to heart disease diabetes and asthma (WHO 2020). As well as, Wu et al. 2020 found that in the USA, COVID-19 death is related to prolonged exposure to the PM2.5. Therefore, Contini and Costabile 2020 rightly mentioned that air quality can be considered as a factor affecting the respiratory system of the human body and increasing mortality rates. Besides this, the quality of air is severely related to human activities (Donahue 2018) and we can infer that the interaction among the people is also very high in this area, thereby the probability of high infected people as well as high mortality rate around the polluted areas. So, it can be said that climate indicators and air quality are not significantly connected with COVID-19 death cases. Several factors are conspiring to bring pollution levels back to levels seen prior to the COVID-19 pandemic, such as calls from some decision-makers and businesses to reschedule Green New Deal projects, reduce vehicle emissions specifications, and stymie the execution of renewable power and inventory work (Pal et al. 2021b). Apart from this, Singh and Chauhan 2020 stated that during the lockdown, air pollution levels decreased significantly, particularly in Delhi and Kolkata, both of which are renowned to be among India’s and the world's most polluted cities. Apart from this, various studies indicate that there is an improving nature of air quality in association with COVID-19 lockdown in various parts of the world (Balasubramaniam et al. 2020; Kumari and Toshniwal 2020b; Li et al. 2020). Chowdhuri et al. 2021 show that, due to the spread of the COVID-19 pandemic, the Indian government implemented a lockdown period (which began on March 24, 2020) during which the concentrations of several contaminants fell considerably. During the lockdown time, the air quality improved and the urban temperature progressively fell attributed to the prevalence of low air pollutants in the environment. During the COVID-19 lockdown phase, Chowdhuri et al. 2020 found a significant reduction in pre-monsoon lightening incidence above the Kolkata megacity in India. The COVID-19 epidemic caused a reduction in PM10, NO2, SO2, O3, and aerosol contents in the lower atmosphere, which was the primary reason of the drop in pre-monsoon lightning rates. In this research, apart from the improving air quality in lockdown period we try to estimate the relationship between the climatic conditions and the daily cases. From this, we found that there is no such importance among the climatic variables and COVID-19 daily cases. In previous researches, it was indicated that the lower temperature is most favorable for COVID-19 cases. But the outcomes from our study are totally different which indicate that there is no such importance of climatic factors in COVID-19 cases (Table 5).Table 5 Comparative analysis of pollutant matter in India in 2016, 2017, 2018, 2019, and 2020 ( Source: National Air quality Index portal, Central Pollution Control Board, Govt. of India, 2020) Types of pollutants Before lockdown 17-Feb-16 17-Feb-17 17-Feb-18 17-Feb-19 High Low High Low High Low High Low PM2.5 340.231 36.027 341.881 36.98 343.003 37.131 340.231 36.027 PM10 263.81 0.013 263.814 0.687 263.817 1.687 263.818 0.013 NO2 114.311 3.285 114.808 3.13 114.719 3.152 114.724 3.157 NH3 13.189 0.007 15.227 0.009 15.228 0.009 15.291 0.009 SO2 66.945 1.583 66.934 1.856 66.945 1.551 66.946 2.003 C0 106.891 16.053 106.892 16.054 106.914 16.054 107.92 16.054 O3 83.251 7.191 84.451 7.228 85.813 7.213 85.981 7.256 AQI 362.812 73.011 362.803 73.012 362.784 73.012 361.134 73.342 Types of pollutants After lockdown Overall variation 30-Mar-20 08-Apr-20 14-Apr-20 21-Apr-20 29-Apr-20 Net % High Low High Low High Low High Low High Low PM2.5 117.316 0.085 154.839 0.022 289.776 0.029 129.258 0.055 66.972 0.029 − 113.101 − 59.861 PM10 185.95 0.013 161.907 0.011 161.907 0.011 119.696 0.008 96.943 0.008 − 59.562 − 45.0519 NO2 60.903 0.056 56.627 0.008 70.983 0.087 64.506 0.008 42.726 0.026 − 29.3178 − 49.7664 NH3 12.764 0.005 10.159 0.006 9.968 0.005 13.985 0.003 7.995 0.004 − 1.88173 − 25.5283 SO2 36.935 0.03 52.605 0.025 45.482 0.027 43.908 0.003 44.891 0.003 − 11.9545 − 34.8067 C0 113.814 0.051 105.86 0.001 94.971 0.051 80.917 0.001 67.923 0.027 − 15.2424 − 24.7425 O3 88.126 6.253 64.963 0.029 241.282 0.045 88.778 0.058 80.915 0.071 11.004 23.8968 AQI 190.852 34.058 218.76 20.006 289.997 45.002 129.509 18.005 99.895 13.051 − 111.825 − 51.3575 Conclusion It can be stated that the COVID-19 pandemic-induced lockdown imposed significant restriction on human activities which has reduced emissions of the pollutants from commercial sectors in India. The much-needed lockdown effect on concentrations of seven air pollutants and climate indicators from February 17 to April 29, 2020, at 223 locations in different stations across the country shows significant reductions. The study has found that among all pollutants, PM10 and PM2.5 recorded the highest decline accompanied by NO2, SO2, NH3, and CO. The concentrations of PM10 and PM2.5 are declined by approximately − 40.84% and − 45.38%, respectively, relative to the previous four years across the country. Besides, an improvement in O3 has been observed (15.62%) in most regions, which can be due to the drop in particulate matter in relation to the decline in NOx. It is obvious from the outcomes that the lockdown implementation has contributed to a major change in air quality and could be placed into action as an additional way of decreasing emissions from different sources. Moreover, the findings will be the key issue for decision-makers to implement necessary measures to control the air pollutants and mortality rate. The present study would have a huge impact on post-pandemic crisis management of air quality, especially for megacities. The policymakers would have the opportunities to redesign the existing air quality regulatory mechanism. The study would provide concrete evidence on how the human anthropogenic activity has affected the composition of the lower atmosphere. Apart from this, the neutrality of climatic variables on COVID-19 outbreak and its associated daily cases has also been established from this research. In Indian condition (our research findings), there is a positive relationship between the increasing air temperature and the total number of Corona positive cases. However, some studies in other parts of the world have found that there is a significant relationship between changing air temperature and COVID-19 outbreak. Moreover, spread of others SARS virus was also affected by the air temperature. Therefore, the present study does a statistical experiment based on available data of mentioned variables in India. It has been found that temperature is not an important determining element for increasing COVID-19 positive cases. This particular direction of situations related to the outbreak should be helpful to the future researches in relation to this field. The new wave of COVID-19 has already faced by different parts of the world, and India is considered one of the severe countries in this perspective. And the third wave has also been found in the western part of the India. So, the efficient research and its outcome are needed to tackle the new stream of this virus in optimal way. Supplementary Information Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 123 KB) Author contributions All the authors have substantial contributions to the conception and design of the work. Declarations Conflict of interest The authors declare that they have no conflict of interest. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. 1 Indian lockdown is the world larges lockdown because of its population size and entire country wide lockdown at a time on March 24 to May 3, 2020. It is also the lockdown in world largest democracy. This article has been retracted. Please see the retraction notice for more detail: 10.1007/s00500-023-08596-w Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Change history 5/29/2023 This article has been retracted. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 34331225 15535 10.1007/s11356-021-15535-5 Research Article Assessing the nexus between financial development and energy finance through demand- and supply-oriented physical disruption in crude oil Chien Fengsheng [email protected] 12 Zhang YunQian [email protected] 13 Hsu Ching-Chi [email protected] 1 1 grid.411604.6 0000 0001 0130 6528 School of Finance and Accounting, Fuzhou University of International Studies and Trade, Fuzhou, China 2 grid.445020.7 0000 0004 0385 9160 Faculty of Business, City University of Macau, Macau, China 3 grid.445020.7 0000 0004 0385 9160 Faculty of International Tourism and Management, City University of Macau, Macau, China Responsible Editor: Nicholas Apergis 30 7 2021 2021 28 46 6608666100 17 6 2021 16 7 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Since 1970, numerous governments have established strategic petroleum reserves (SPRs) in relation to oil supply interruptions. In this study, important oil reserves, physical oil supply disruption and social welfare losses due to physical distribution of oil supply have been measured. The physical oil supply disruption has been measured in the form of oil supply vulnerability index and oil volatility index of the South Asian economies. Analysis reveals that the accumulation and drawdown of important national crude oil strategic petroleum reserves where the state wants to optimize individual social welfare while individuals hold over stock optimize their earnings levels. The monetary deciding factors utilize the government’s optimum important stockpile policy and simultaneously the amount and economic factors vital for the nongovernment market to actuate the optimum accumulation and nonaccumulation of important fossil fuels stockpile. Additionally, findings show that India is the lowest crude oil insecure country while Afghanistan and Bangladesh are the highest insecure countries in terms of oil supply. India’s topmost mark shows a bigger possibility to alter the fossil fuels producers while Afghanistan, Bangladesh, Bhutan and Nepal have the minimum mark corroborating the group as the utmost producer risk exposed nations. Keywords Important crude oil stockpile Oil supply risk PCA analysis South Asia issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2021 ==== Body pmcIntroduction The country’s economic development, industrial output, national defence and transportation all benefit from the country’s access to a key energy resource, especially crude oil. With almost 70% of its crude oil imported, China is the world’s largest crude oil importer (Chien et al. 2021a, b, c, d, e, f, g; Van Moerkerk and Crijns-Graus 2016). Crude oil price has been much correlated with international oil prices because of market-oriented reform. According to most analyses, the oil market is a price taker in international oil markets. Thus, it is a major emergency to establish your own pricing structure to erase the “Asian Premium” economic loss. There are numerous studies arguing that in order to have a competitive oil market with own crude oil futures markets in China, a full and competitive crude oil futures market needs to be built (Chien et al. 2021a, b, c, d, e, f, g; Iqbal et al. 2020). In order to develop a market for medium sour crude oil and hedge investment risks, China’s first crude oil futures contract was formally listed on the Shanghai International Exchange (INE) on March 26, 2018. It has lately been examined (Li et al. 2021a, b, c, d, e; Qi and Yang 2018). Downside risk shocks to international benchmark oil increase the contract’s volatility. In truth, there are some ideas and influences that further open up China’s oil market (Mohsin et al. 2018). The creation of China’s crude oil futures market, however, is still in its early stages. When speaking of whether the crude oil futures have integrated into the global oil markets, the first thing to note is if they have done so (Mohsin et al. 2020a, b; Mohsin et al. 2018; Mohsin et al. 2021). Additionally, if it is connected to the world market, how its price influences other market prices, meaning how global oil prices influence one another. Lastly, it is worth considering whether it ends up serving as the barometer for medium sour crude oil in a region and a handy way to manage risk (Mohsin et al. 2019; Mohsin et al. 2020a, b; Mohsin et al. 2021; Qi et al. 2021). The coronavirus epidemic has had a significant impact on the dynamics of global financial markets over the last few months. The most prominent effect has been the sharp collapse in the stock and oil markets, followed by a subsequent resurgence (He et al. 2020; Mohsin et al. 2020a, 2020b; Yang et al. 2021). Investors appear to be more focused on oil markets, as oil demand may provide insight into if and how the global economy can recover from the destruction wrought by the coronavirus pandemic (Chernysheva et al. 2019; Li et al. 2021a, b, c, d, e). On March 26, 2018, the Shanghai International Energy Exchange (INE) debuted and began trading Chinese crude oil futures. Acemoglu, for example, suggests that increased diversity allows for the gradual allocation of funds in their most productive application and reduces the variability of development through more productive specialization (Das and Kannadhasan 2020; Sadiq, Hsu et al. 2021). However, there is inconsistent evidence that finance promotes growth or reduces volatility. For example, whereas Mokni et al. (2020) indicates that finance promotes growth, Demirer et al. (2020) show that the financial advantages of supporting growth can only be achieved to some extent. There is equally contradictory evidence of volatility reduction in the financial aspects. Financial depth plays an important role in stemming output, consumer and investment volatility but only to some extent. At very high levels, like in many advanced nations, financial depth increases consumption and volatility for investment. Çepni et al. (2021) give some preliminary support that oil price shocks may have fuelled the growth of China’s oil futures market (Huang et al. 2020; Tiep et al. 2021; Nguyen et al. 2021). As a result of the crude oil revolution, the US output has the potential to play a large role in balancing global demand and supply, implying that the current low oil price scenario may remain (He et al. 2021a, b; He et al. 2018; Wang et al. 2021). Additionally, a recent study discovered that the output elasticity of unconventional refineries to price fluctuations is approximately three to four times that of conventional reservoirs. This combination creates an impenetrable floor for oil prices (Ikram et al. 2019a, b; Ikram et al. 2019a, b; Sun et al. 2019). This article contributes to the current body of knowledge in two ways. To begin, this is a preliminary investigation and analysis of tactical decisions about crude oil import and transportation planning in light of physical risks: Are they significant or not? This complements earlier research that has focused exclusively on oil price uncertainty. Second, this article conducts numerical tests on a quasi-real example, and the qualitative elements of the results provide managerial insight. For example, if only oil price fluctuation is included, the actual financial risk will be significantly more than it appears, as some possibilities will result in severe cost effects. Additionally, when physical hazards are taken into account, more forward crude oil will be purchased in the forward market to protect against the same risks. Finally, when physical risks are considered, the external part of forward oil purchases will be significantly influenced by risk correlations. Thus, forward purchases are influenced by factors other than crude oil and transportation costs. These managerial insights can assist a crude oil importer in comprehending the function of physical hazards and so making more informed decisions. The rest of the paper is structures as follows: literature review discussed in the “Literature review” section; the methodology is discussed in the “Data and methodology” section; derived findings are given and narrative in the “Results and discussion” section, while the “Conclusion and policy implication” section gives a conclusion pertaining to the abridged findings of the research. Literature review Crude oil is strategically vital to all countries, and it is frequently critical to the national economy of a crude oil importer. Due to the global crude oil market’s high degree of integration, other exporters’ crude oil prices will also be impacted. Along with political risks and fluctuations in the price of crude oil, exporters face transportation concerns. The vast majority of crude oil is transported by ship (Alemzero et al. 2020a, b). However, the straits and canals that comprise the maritime transit network are vulnerable to hazards posed by surrounding countries’ political instability, as well as terrorism, piracy, conflict and other catastrophic events (Ji et al. 2020; Xueying et al. 2021). For instance, the Middle East’s political instability jeopardises the safety of the Bab el Mandeb and the Suez Canal. If particular straits or canals are closed, the transit of crude oil will be harmed (Feng et al. 2020; Tian et al. 2020; Li et al. 2021c; Zhao et al. 2020 ). A composite indicator is a valuable tool for analysing complex system performance. It involves various performance indicators and, if used properly, incorporates multiple stakeholders with different priorities (He, Zhang et al. 2021; Li et al. 2021a, b, c, d, e; Zhang et al. 2020). Generally, indicator aggregation approaches include PCA, DEA and TOPSIS, and AHP and MCDA. Due to the broad usage of these methodologies in the field of energy performance evaluation, they are highly effective. When analysing the relative oil vulnerability of 26 net oil importers in 2004, PCA was used to integrate eight different variables into the oil vulnerability composite index. Based on nine factors, PCA was employed by Makinde and Lee (2019) to investigate and compare the geopolitical economic index of energy security of 28 EU countries from 2004 to 2013. Building information management (BIM) influences on building energy management, which was the goal of Liu et al. (2021). AHP and MCDA have their benefits, however they all rely on decision makers providing subjective information. For PCA, the principal components interpretation is not clearly defined enough to fully reflect the original variable’s meaning. While preserving the original data, the DEA model has the ability to both weight and aggregate in its CI building process. In addition, the DEA has begun to be employed in energy security assessment. The supply risk, economic risk, international trade risk and dependence risk in China’s oil import security were all assessed using a DEA index. Estimation of demand The key aims are to help policymakers create strategic plans that rely on detailed investigations and on previsionary models and procedures that are confirmed, validated and tested. Generally, there are a number of phases in the forecast to predict Saudi Arabia’s oil demand. In this regard, wide number of nations constructed a model of aggregate demand. This clearly demonstrates Saudi Arabia’s experience, especially in the transport and other oil consuming industries. Pan et al. (2017) have created an econometric model in which the ratio of total demand and total income is determined rather than per capita in order to assess oil consumption growth in Saudi Arabia. Time series prediction models can be utilised as a function of previous data to estimate future values. As key approaches can also be experienced, different prediction techniques such as time series, artificial neural networks, flouted time series and/or other applicable new prediction methods. Policymakers and stockholders may be more concerned with prediction performance than with sample data, as they may wish to use the model to forecast future volatility to inform future decisions (Baloch et al. 2020; Sun et al. a, b, c, d, e; Vermeulen et al. 2020). Data and methodology The indicators system of risk identification Physical supply risk Supply volatility contemplates the unanticipated interruption of crude oil distribution, which could not be managed on the spot via the marketplace, and it estimates the risk of handiness and convenience crude oil distribution. Wu et al. (2021) suggested distribution risk signals for assessing the physical interruption risks. The signals of crude oil distribution volatility are formulated and suggested to estimate the long-run and short-run distribution risk. A component of distribution volatility with its microcomponent assesses the crude oil interruptions in a specific nation and at a targeted marketplace (Baloch et al. 2020; Muller 2020). Crude oil geopolitical risk (Zheng et al. 2021) is the exposing of economic systems to physical crude oil distribution interruption and important command of crude oil distribution, prevailing macroeconomic terrain, the collapse of political governance and volatile governmental structures of crude oil exportation economies, and ineffective government are the principal aspects of crude oil geopolitical distribution risk HHI (Herfindahl–Hirschman Index) that is thought to be a traditional estimate of crude oil variegation. The HHI index estimates and manages the amount of compactness of oil and gas distribution. The study encapsulates the HHI index to country risk (CRS) explained by ICRSG. ICRSG is the global benchmark utility-scale types of nations risk comprising 140 economies and assumes figures from zero for high top risk to hundred for least risk. ICRSG supervises the government leadership, army in governmental leadership, faith in politics, monetary, graft and economic systems risk information. The non-static factor structure is compact types of bottom-up embodiments, which assigns the evolution of functional and applicable variables between t and t+1 in reaction to previous and present fiscal parameters, the monetary measure and physical quantity. This dual elucidation typifies quantitative and technological capacity of industry evolution and concurs to abandoned overdue standardized cumulative industry significance that has central earnings in the event of big disappearance from the indicator balance and significant vacillation of output limits in the course of time. For now, specific preference and equal importance do not give robust and perspective findings. Four variables are applied to derive the financial efficiency index, which denote a South Asian nation’s efforts for generating earnings, equally at a similar period by keeping off from the accounting system of formation (Anh Tu et al. 2021;Chien et al. 2021a, b, c, d, e, f, g). FID from several nations has country risks (CRS) and the capability for exportation (CE). The PE and CRS indexes highlight an evaluation of the risk of FDI of any risky nation (Wang et al. 2021). 1 RIES−CRSk=HHI−CRSk×DEPk=Dk∑k=1NWkq2×CRSq 2 RIES−CRSk=HHI−PE×DEPk=DEPk∑k=1NWkq2×1PE 3 RIES−PE=HHI−PE×DEPk=DEPk∑k=1NWkq2×1PE×CRSk where RIES represents the perils in energy supplies, “CRS” represents nation’s risk (risk score from ICRSG), whereas, Herfindahl Hirschman Index (based on Chinese provinces which has been labelled as a single body for the analysis sake ), i.e., HHI’s, as the DEP shows an energy dependence from state, the PE estimates the prospect of importation in a particular nation. WkQ=Xkq∑XkqXkq highlights the portion of energy traders in q in cumulative energy importation of nation I. The fourth parameter is a macroeconomic parameter of GDP, which measures quantitatively the amount of goods and services produced in an economy relative to the exact amount of GHGs emitted. Within these parameters, equity investing, debt investing (Bank and bond investing), is assumed to be an input parameter, while asset turnover ratio, market capitalization and gross savings are vital to exposit the models 1, 2 and 3 and could be applied to estimate financial efficiency index of a single country. Oil price volatility The volatility of the oil price depends, as Sadiq et al. (2020) explained, on the combined results of invariant and variable components. Invariant factors include feedstock prices, exploratory costs, costs for boiling, chemical composition, costs for production, distribution expenses, marketing costs, packaging and storage costs, whereas the main international trading currency of the oil com is the world economic activity, production level, level of consumer and exchange value of the US dollar. An economic models, statistical tools and forecasting techniques should be used to establish a supply balance and demand strategy in order to accurately predict the demand for several petroleum products consumed by each sector. The transport sector is acknowledged to be one of the world’s major energy users. The Kingdom’s energy consumption is to be examined for all sub-sectors of transport, including in the consumer behaviour of cars, aviation, rail, pipelines and shipping (Chien et al. 2021a, b, c, d, e, f, g). Economic growth means the growth of potential output, for example, in the situation of full employment, manufacturing growth. Another reason why price volatility happens is economic expansion. Cooper (2003) claimed that crude oil usually tends to decrease when economies replace more energy-efficient capital stocks and/or expand their less power-intensive service sectors. A major amount examined by policymakers is crude oil price elasticity for demand. However, it does measures the reaction or sensitivity to price changes consumed in different sectors by oil demand and identifies all main factors of influence, such as economic and indicators, sales of vehicles, development, technology factors, demographic factors, environment, government policy, infrastructure of public transport and the use of fuel-efficient means of transportation (Chien et al. 2020). Imported oil risk Because crude oil imports have increased significantly since 1996, we expand on classic portfolio theory by developing a risk index model for crude oil imports in order to objectively examine changes in crude oil import hazards. The findings indicate that the risk of crude oil imports is significantly influenced by worldwide oil price fluctuations. Thus, classic portfolio theory is insufficient to quantify the risk associated with crude oil imports. The variegation parameter and reliance fraction are interacted to give a truthful scenario of variegation and reliance, which is discovered to be more appropriate estimation in relative terms with HHI index. 4 HHI×DEPi=Di∑i=1NWiq2 DEPi stands for crude oil importation reliance, estimated as, DEPq=NIM/PNCOM where NIMP and NCOM stand for net crude oil importation and cumulative crude oil use of a nation i correspondingly. Different three crude oil distribution risk indexes, according to the altered variegation index, justify the imported oil supply risk. Oil imports reliance is usually applied to estimate the crude oil distribution risk of a country. It should be said that, as a result of the more diverse energy mix, a more probably import reliant nation might not be susceptible to a high-level perils of crude oil distribution types (Bianconi and Yoshino 2014; Subiyakto and Sebastian 2020; Uwizeyimana 2020). Hence, it is imperative by integrating the variegation and crude oil importation reliance indexes to estimate the crude oil distribution perils. It is has thought that the same quantum of crude oil imports from some other crude oil distributors sees similar risks, according to the four types of variegation indexes and the crude oil distribution perils (Sun et al. 2020a, b, c, d, e; Valencia 2020). Aggregation through DEA-like composite indicator and principal component analysis DEA-like composite indicator The given problems are overcome by the help of a DEA-like model used for aggregation. The sum of the collective score of performance for all the underlying indicators of entity i are evaluated through model (2). A set of indices gI1,gI2,…,gIm is achieved by solving model (2) for each entity i (Zhou et al. 2007). Model (2) is extended through an identical linear programming DEA model, given as, 5 bIi=max∑j=1nWijbIij s.t.bIi=∑j=1nWijbIkj≤1,k=12,…,mWijb≥0,j=1,2,…,n and, 6 gIi=min∑j=1nWijbIij s.t.gIi=∑j=1nWijgIkj≤1,k=1,2,…,mWijg≥0,j=1,2,…,n The performance score of each entity to aggregate subindicators is measured by model (3) where it selects the worst set of weights Munda and Nardo (2009) and Reig-Martínez et al. (2011). Considering relevant data, models (2) and (3) are considered as the two DEA-like models, used to calculate performance scores for each entity. A CI can be formed to combine the models into a collective index: 7 CIλ=λgIi−gIgI∗−gI+1−λbIi−bIbI∗−bI Within the range of [0, 1], characterized as linear scaling min–max, the value of parameter λ can be adjusted in model (4). Although a specific preference does not exist, the linear aggregation of all subindicators needs adjusting parameters; a normalized version of gIi is attained if λ=0.5, which is a neutral choice at CIi. A normalized version of bIi is achieved in case of λ=1. However, model (7) establishes a compromise between the indexes for other than 0 and 1 cases if λ=0. Three expected qualities, (i) 0<CI≤ 1, (ii) CIi is unit invariant and (iii) CIi is invariant to RHS of the constraints in models (5) and (6), are achieved by CIi. Model (11) presents a standardized index with its values in the interval of [0, 1], as shown in property (1). 8 Li≤WygIij∑J=1nWygIij≤Lj,J=1,2,3,..,n 9 Li≤WygIij∑J=1nWygIij≤Uj,J=1,2,3,..,n Two sets are added to the models (8) and (9), signified as Lj and Uj, representing contribution of upper and lower bound limits satisfying the condition of 0≤ Lj≤ Uj≤1 in the construction of CI. A number of practical considerations pose as a reason behind the restriction in weights flexibility (Hatefi and Torabi 2010; Yang et al. 2021). Based on MEPI with the same weights, all 17 indicators are aggregated for the South Asian countries. Principal component analysis A different approach applied in the present analysis is the principal component analysis (PCA). It is a multiple variable mathematical estimation approach that changes a group of unrelated indicators (components) unto correlated parameters. The linear aggregation of these factors is first parameters. The core idea surrounding the PCA is the data reduction magnitude and the modification of exclusive explained and exploratory coordinates. An understanding of crude oil volatility index incorporates the latent or unseen parameter. The crude oil unpredictability ought to put on in a straight-line correlation with various parameters and stochastic positions when the crude oil volatility index (OVIK) comprises of the OVI of the nation ‘Q’= Q ×1q …×9Q that is equally of recommended variables comparable to the nation ‘k’ and ɛ is the stochastic term. 10 OVIq=β1x1q+β2x2q+β3x3q+β4x4q+β5x5q The initial process is the standardization prior to grouping the variable. Consequently, the standardization of the variables is needed to ensure that they are directly correlated via index of oil volatility (Chandio et al. 2020; Sun et al. 2020a, b, c, d, e). Differences in crude oil volatility index comprises of dual perpendicular part with changes as a result of the stochastic term and the variance of the suggested factors in the crude oil volatility index. The estimation for the standardization is done in the following manner. 11 xik=minxikmaxxk−minxk=xmax A different standardization approach has been applied to standardize benefit kind parameter as modelled in Eq. (7) 12 xik=maxxi−xikmaxxi−minxi The adaptations change the suggested parameters on one to zero magnitude. The estimation of 5 × 5 relationship matrix A of the entire chosen standardized parameters due to the PCA primarily rely on the association matrix or covariance matrix so in this weightiness’s straight line aggregation the PCAs are inherently either 0s or 1s as a result weights of a single PC emanates from the covariance matrix eigenvectors. It is fundamental to that λQ is the var (PQ) and thus λ1+ λ2 + λ3 +…+ λ5 is the complete variation in OVI. Thus in relation, λQ/ ∑ λQ is proportionate to the portion sum change made for by PQ. Ultimately, crude oil volatility is estimated as an adjusted total of 11 where the weightings are changes of back to back PCA. So λ1+ λ2 + λ3 + λ4 + λ5 + λ6 + λ7 + λ8 + λ9 = 0 is total variation. 13 OVIk=λ1P1j+λ2P2j+λ3P3j+λ4P4j+λ5P5jλ1+λ2+λ3+λ4+λ5 Weighted factors elementary structure of the crude oil volatility index assist to its better content as a result of the weighted aggregation of the standardized of these factors ensures an appropriate demonstration of the comparative importance for a single parameter by evaluating the volatility index mark (Sadiq et al. 2021). Data Crude oil distribution security data was accessed from diverse channels such as from the IEA, BP database and WDI. Crude oil production and use data are accessed from the BP statistical reviews and the USEIA. Data integrated into the regional political crude oil distribution risk gathered PRS set, i.e., International Country Risk Guide (ICRSG). IRCG keeps a complete dataset of monthly political, economic and financial risk ratings for 140 nations since 1980. Nations that have higher marks in the database have minimum risk levels. Political risk rankings score give purposeful content of political steadiness given by ICRSG score. Importation of crude oil of a single distributor’s data was accessed from the UN commodity commercial database. Furthermore, data concerning every single nation’s crude oil distributor’s origin (the quantity of percent of oil importation from which distributors) was derived from www.trademap.org.com. Distance between ports is taken from the maps of global seaports and sea (https://www.mapsofworld.com) and from different data systems like https://guides.lib.umich.edu, website of crude oil and important ministries of the South Asian nations. Projected concessions of India are taken from statistical yearbook of India, crude oil factors correlated data derived from the from Pakistan State Oil (PSO), whereas other resources comprise Bhutan interactive data portal, UNO statistical yearbook, Central Bureau of Statistics of Nepal, Sri Lanka, Maldives and Afghanistan. Results and discussion Strategic petroleum reserves and oil supply disruption The authors use three market situations: the regular, interrupted and very interrupted. We again, build a dual type of the balance of costs presuming no stock variation: least costs ($30, $45 and $65 per barrel) and high-level costs ($50, $65 and $80 per barrel) existing in a single market condition. Because the framework analyses the short-term interruptions, we apply a short-term demand elasticity of 5%. Table 1 depicts the interruption points of the nations in South Asia that could eventfully presume payment for forgoing short-term earnings, while the chance forgone of this dual plan of actions are dependent on the South Asian nations’ earnings nonsubjective equation. In a majority of situations, for an economically communicative elucidation, the likelihood of the configuration with respect to the amount of having is based on the end reference point. Table 1 Level of disruption in South Asia (%) Normal Disrupted Very disrupted Disruption level Normal 77.2 21.7 3.12 Disruption 52.3 72.5 15.7 Very Disruption 1.2 29.31 77.7 It shows the evaluation of the variable according to location territorial scenario reserves. Importation crude oil by GDP estimates the price of importation of crude oil in reference to the GDP Afghanistan attaining the biggest figure of 0.276 while India attains the least figure of 0.007. Bangladesh, Bhutan and Nepal contain figures of 0.917, 0.13661 and 0.198 correspondingly whilst this nation purchase large consignments of crude oil that eventually reduces the prices of foreign crude oil, and at the same time, Asian nations brought in smaller amounts of foreign crude oil resulting in huge expenditures. Thievery and different financial losses are encapsulated in these costs that are huge in other nations and fewer in others respectively (Alemzero, Iqbal et al. 2020; Alemzero, Sun et al. 2020a, b, c, d, e). In Table 2, it is instructive to note that the variables applied in the equation for the South Asian nation have some semblance, excluding the GDP-crude oil cost elasticity, α, that changes significantly among the nations, mirroring the different degrees of susceptibility to crude oil costs headwinds. We use to apply the costs reductions rates of 0.99 one-fourths the government sector actors and a binary cost reduction variable for the personal actors of 0.059 and 0.92 of fourths. The authors further apply separate crude oil costs assumptions, one with least and the other with maximum indicator costs. Figure 1 shows the analysis of different assumptions, the one-fourth personal reduction variable (0.95) and maximum reference costs. A one-fourth discount parameter (0.92), and a high-level cost where the one-fourth reduction parameter is (0.95), and low indicator costs. Within these assumptions, we applied a q equivalent to 11,900. Within the premise of high-level indicator costs, we equate k equivalent to 11,900 and a w(i) equivalent to 282 and 428 for, correspondingly, interrupted and too interrupted market conditions. Again, within the assumption of low-level costs, we equate k equivalent to 11,900 and a w(i) equal to 282 and 428 for interrupted and too interrupted conditions, correspondingly. Table 2 Parameter assumptions for the base case with a single regional reserve Parameter Value Unit Description ∂ 0.99 - Discount factor ϵ -0.59 - Oil demand price Elasticity β 5.21 US$/barrel capacity Cost of building one additional unit of capacity H 0.127 US$/barrel Annual holding costs per barrer u 0.21 US$/barrel Cost of adding one barrel of oil into the stockfile d 0.14 US$/barrel Cost of withdrawing one barrel of oil from the stockfile α -0.05 GDF-oil price elasticity Fig. 1 Oil supply risk score Results interruption denotes that customers ought to settle for quite bigger amounts in this phase than in regular times. Each grouping i is classified via amount of crude oil concessions Q∞, i, whereas the beginning merchandising cost of crude oil operating expenses for the crude oil producers starts the crude oil production (Sun et al. 2020a, b, c, d, e). Macroeconomic impacts and crude oil distribution volatilities Oil has developed into a significant determinant, affecting macroeconomic activity and stock market indexes in unique ways in various areas of the world, notably after the 1973 oil crisis. Additionally, petroleum products are acknowledged as a critical source of energy and power throughout the world, and are acquiring significant importance as a tool for developed nations’ endurance and security. The purpose of this article is to determine whether macroeconomic uncertainty can be used to explain and forecast crude oil futures market volatility. The empirical findings in this study provide compelling evidence of macroeconomic uncertainty variables’ influence and predictability on crude oil volatility (Agyekum et al. 2021; Othman et al. 2020; Zhang et al. 2021). Market liquidity is a determinant variable of the cumulative crude oil volatility index. Among all the nations, Afghanistan and Bhutan attain figures equivalent to 0.61 and 0.42 correspondingly which depicts a deplorable state for these nations. Afghanistan, Bhutan, Nepal and Bangladesh attain similar figure of 0.001 that is an improvement concerning market liquidity. Lesser market liquidity nations, specifically Afghanistan, Pakistan, Nepal, Bhutan and Bangladesh, attain a modified capability than previous nations to change distributors within the distributors of crude oil. Up to present times, the world’s economy has witnessed three crude oil costs wars. The crude oil price increases in 1973, 1978 and 2003 and climbed to its pinnacle in 2008 of $ 147 per barrel; all these took place before the excessive crude oil demand growth. If growth in crude oil distribution cannot cancel out that of demand growth, costs increase. In addition, crude oil in the form of a nonstandardized good does not respond to the world’s market fundamentals terrain. Oil supply risk Broadly speaking, crude oil distribution risks of South Asian nations are largely determined by cash availability in the markets. It is noted that in South Asian nations, India got the topmost mark of 9.00 mirroring bigger prospects to vary the crude oil distributors. Exposure to market risk is a proxy for an economy’s market vulnerability. In this context, the World Bank published two studies, the impact of increasing oil prices on low-income countries and the poor, and the susceptibility of African nations to oil price shocks outlining the primary causes of these economies vulnerability to rising oil prices. Numerous studies, including UNDP/ESMAP (2005), IAEA (2005), ESMAP (2005), IEA (2004) and ORNL (2006), demonstrate that the market risk or macroeconomic consequences of higher oil prices (such as increased inflation and unemployment and negative balance of payments effects) are dependent on the cost of oil in national income, the degree of reliance on imported oil, oil consumption per unit of GDP and share. Exposure to supply risks is a proxy for an economy’s supply vulnerability. There is a substantial corpus of literature on indicators for assessing oil supply risk. South Asian’s crude oil importation safety was endangered from the foreign reliance point of distribution channel, where the accumulated crude oil importation source was the key danger parameter (Li et al. 2021b; Chien et al. 2021c; Iqbal et al. 2021). The unfavourable consequences on crude oil exploration and production, the costs levels and the government finances ratio mean that our estimates conduct like an opposite cumulative fossil fuels demand shocks. For instance, the produce dynamics are related to those as a result of cumulative uncertainness shocks that connote unfavourable cumulative demand headwinds as alluded in Leduc and Liu (2016). This explains the fall in global actual activity in reaction to the reduction in crude oil costs that indicate total uncertainness that is revealed to have unfavourable bearings on the global economy (Ehsanullah et al. 2021; Hsu et al. 2021; Zhang et al. 2021). Secondly, pertaining to the world’s crude oil markets, nevertheless, growth in the global crude oil supply reduces global crude oil costs per barrel. The response of international crude oil markets reduces crude oil costs vulnerability somewhat below global crude oil supply and increase crude oil costs. For example, crude oil costs’ uncertainness collapse performs a crucial part in the explanation of variances in the US crude oil supply. Prior macroeconomic parameters actual performance still fell due to the huge crude oil distribution uncertainty in the markets, despite the effects of total cumulative uncertainty precatered for crude oil supply more than cumulative uncertainness produces a 1% difference at the pinnacle point. Intriguingly, they depress the crude oil costs straight away, as prior. The findings mean that, at minimum to some magnitude, uncertainty emanating from (or sending via ) the crude oil aspect, in particular, could act as a catalyst for macroeconomics and crude oil area evolving terrain, as the impacts of uncertainness diffusing from total economic system to crude oil area are precaptured by the addition of cumulative uncertainness. In Fig.1, South Asian crude oil importation security was nevertheless endangered from the foreign reliance point of distribution channels, but the growing reliance on crude oil importation assumed a key risk concern. Crude oil needs in the South Asian nations have reduced, while in developing nations, the majority of them, demand has grown and is a catalyst of the global crude oil demand increases. Within the horizon between 2011 and 2015, international global crude oil supply expanded by 7.7%; cumulative crude oil importation expanded by 8.7%, while South Asian nations’ crude oil imports expanded by 25.8%. Hence, the expansion in crude oil importation reliance endangers the region’s energy importation security outlook, rather than an amalgamated crude oil importation source. Generally, in 2008, the global financial crunch impacted the oil markets by depressing demand and supply that resulted in significant volatility since the 1980s. Oil supply vulnerability index The following nations are the least performing ones: Nepal, Bhutan, Bangladesh and Afghanistan. Inversely, Pakistan, Sri Lanka and India are the top performers. Table 3 present the net figures of crude oil insecure nations. Nations scoring values greater than one are placed as insecure nations, namely, Pakistan, Sri Lanka, Nepal, Bhutan, Bangladesh and Afghanistan. Similarly, nations getting marks less than one nevertheless greater than 0.50 are said to be less insecure in reference to Table 3. The total crude oil distribution volatility index attains contrasting robust results regarding a single parameter of crude oil volatility. On the mean figures, market risk parameters changed course to be important than the distribution risk parameters in deciding the total crude oil volatility of the chosen nations. This means that programs geared toward reducing market risks might be useful in solving the challenges of crude oil volatility other than the programs that are geared towards handling the distribution risk. Table 3 Overall oil vulnerability index score and composite indicator score Country OVI Rank India 0.81 7 Sri Lanka 0.87 6 Pakistan 1.06 5 Bangladesh 1.12 4 Nepal 1.32 3 Bhutan 1.23 2 Afghanistan 1.50 1 The strategy of combining these distinct indicators into a composite index of oil vulnerability has been taken using the principal component technique. This indicator quantifies individual economies’ relative sensitivity to changes in the international oil market, with a higher index indicating greater susceptibility. The findings indicate that there are significant disparities in the values of specific oil sensitivity indicators and the overall oil vulnerability index between countries (both inter- and intraregional). This is elucidated in detail by the reason those market hazards is generally controlled by domestic parameters in the form of crude oil intensity and purchasing power, which are comparatively less difficult to handle as relative to locally blessed natural reserves or regional outlook hazards (that is broadly ascertained outwardly by a factor such as political instability in the producing nation). In addition, much emphasis ought to be placed on crude oil preservation and replacement policies (like lowering import requirements, maximizing crude oil efficiency), which aids in bringing energy use and producing equilibrium and, hence, handling the dual perils from the marketplace and the producing side. The share of single parameters shows a changing pattern that attains 0.21 cost factor, 0.12 GDP per head, 0.16 regional outlook crude oil perils, 0.08 market liquidity, 0.07 crude oil reliance, 0.17 variegation, 0.05 US $ unpredictability index, 0.12 crude oil cost vulnerability index and 0.04 transportation peril. Cost is gauged as an amount of importation of crude oil over GDP. It has the maximum share in cumulative crude oil unpredictability index. In Fig. 2, the variation of crude oil has a significant share having the amount of 0.17. After expenditure and variegation, regional political outlook parameter is the 3rd maximum share within the general volatility index by possessing a figure 0.16 share mark in total distribution peril index. The share of transport perils parameter in total crude oil insecure index and the numerical complex possesses the figure 0.04. Reasonably, the nations possessing the least time travelled between the shipping ports confront lesser transport perils, while nations confronting the maximum distance travelled between the cargo ship confront higher perils. The transport hazard is impacted by regional outlook political state of shipping channels such the Malacca and the Hormuz termed as the most high-risk channels for oil importation. Fig. 2 Overall oil supply vulnerability index score Complex crude oil distribution index score Crude oil importation risks comprises imports expenditure, exporter’s crude oil volume, interstate battle of crude oil-supplying nations, transport peril, situational perils and disconnected energy use, and different economical components are key parameters determining crude oil distribution security for crude oil costumers. Figure 3 shows the oil supply risk index score. The lesser insecure nations with comparative mean scores above 0.50 and less than 0.60 are Pakistan, Bhutan and Sri Lanka, while the least insecure nations that possess comparatively mean marks of less than 0.50 are Afghanistan and Bangladesh. The nation findings’ divergence regarding the CI score implies some parameter variations and variations in territorial structure. For instance, the ICRSG labels Afghanistan as the topmost dangerous nation. CI equally shows the mean mark of specific territorial structures. The mean CI of the chosen South Asian nations is equivalent to these nations, Pakistan, Nepal, Bhutan and Sri Lanka. Fig. 3 Oil supply risk index score Broadly speaking, the mean CI of these nations is found between 0.50 and 0.55. India’s CI mean mark is 0.68, and it found higher than the entire mean marks of the study group (0.51). On the other hand, Afghanistan and Bangladesh have a mean CI (Fig. 4) that is found beneath the mean CI of the study unit. Specific grounds might result in a mean CI achievement mark. In reference to the GDP expansion, the real pathway is beneath the contrary pathway; thus, the impacts are inauspicious. Fig. 4 Year-wise oil supply risk index score Table 4 depicts similar findings, highlighting every year’s confidence interval to be zero, implying that the mathematical non significance of the treated group effects. In Table 4, the component regressed of the treated group is presented. It shows South Asia’s GDP expansion ratio would be bigger excluding a deliberate plan of action. The expansion in expenditure for growth in SPR differs, in many ways, with the beginning cost and amount, and the elasticities of global crude oil use and distribution. We discovered that growing reserves acquisition by one million per annum from the monitored cost and amount within the established case scenarios expanded the cost of crude oil in the reference year by $0.02 to $0.05 per barrel. The overall yearly buyout expenditure comprising welfare variations for 1 million barrels of crude oil in addition to the yearly welfare expenditure from the cost expansion altered by $22 million to $300 million per annum. The elasticity of expenditure regarding stock variation changed from less than with 0.01–0.024 with a mean of 0.019. Table 4 Treatment effects for GDP growth rate Country Actual Counterfactual Point Interval Afghanistan 4.5 11.35 -1.33 (-3.58, 0.89) Bangladesh 5.9 8.43 -1.13 (-3.88, 0.89) Bhutan 5.8 8.89 -0.58 (-3.89, 0.55) Nepal 5.4 5.85 -1.45 (-3.55, 0.51) Maldives 5.5 3.85 -1.98 (-3.88, 0.58) India 3.4 8.98 -0.85 (-3.45, 0.98) Pakistan 1.5 4.55 -1.34 (-3.31, 0.41) Sri-Lanka 5.5 9.98 -1.55 (-3.08, 0.55) In Table 5, regarding the gross domestic product expansion ratio, the real pathway is beneath the contrary fact pathway; thus, the effect is negative. We found that the unfavourable treated impacts’ mathematically nonsignificant at the 5% level since the real pathway within the regressed confidence intervals’ maximum and minimum boundary. An economic consequence depicts that Afghanistan and Bangladesh are grappling with different likely macroeconomic, natural, and regional political menace. India’s topmost mark shows a bigger possibility to vary the crude oil distributors while Afghanistan, Bangladesh, Bhutan and Nepal attain the lowest marks affirming the group as the most distribution peril linked countries. The attainment of the world’s crude oil security comprises a unified energy program plan of action comprising crude oil use sustainability and crude oil distribution at the domestic and global levels. Every country possesses its peculiar risk life cycle due to single indicators, and thus demands a particular policy instrument to decrease its crude oil distribution hazards. Table 5 Effects of the tertiary industry in GDP Country Actual Counterfactual Point Interval Predicted Afghanistan 35.25 31.16 1.09 (0.69, 1.56) 35.25 32.25 32.03 3.16 (1.99, 2.96) 32.25 31.32 29.69 3.66 (1.69, 2.61) 31.32 35.21 53.56 3.65 (2.66, 5.62) 35.21 23.19 23.03 5.16 (5.09, 6.25) 23.19 35.92 31.16 2.09 (1.55, 5.96) 35.92 35.96 55.03 2.63 (1.56, 2.32) 35.96 23.61 22.35 2.12 (2.66, 5.62) 23.61 Bangladesh 35.25 31.16 1.09 (0.69, 1.56) 35.25 32.25 32.03 3.16 (1.99, 2.96) 32.25 31.32 29.69 3.66 (1.69, 2.61) 31.32 35.21 53.56 3.65 (2.66, 5.62) 35.21 23.19 23.03 5.16 (5.09, 6.25) 23.19 35.92 31.16 2.09 (1.55, 5.96) 35.92 35.96 55.03 2.63 (1.56, 2.32) 35.96 23.61 22.35 2.12 (2.66, 5.62) 23.61 Bhutan 35.25 31.16 1.09 (0.69, 1.56) 35.25 32.25 32.03 3.16 (1.99, 2.96) 32.25 31.32 29.69 3.66 (1.69, 2.61) 31.32 35.21 53.56 3.65 (2.66, 5.62) 35.21 23.19 23.03 5.16 (5.09, 6.25) 23.19 35.92 31.16 2.09 (1.55, 5.96) 35.92 35.96 55.03 2.63 (1.56, 2.32) 35.96 23.61 22.35 2.12 (2.66, 5.62) 23.61 Nepal 35.25 31.16 1.09 (0.69, 1.56) 35.25 32.25 32.03 3.16 (1.99, 2.96) 32.25 31.32 29.69 3.66 (1.69, 2.61) 31.32 35.21 53.56 3.65 (2.66, 5.62) 35.21 23.19 23.03 5.16 (5.09, 6.25) 23.19 35.92 31.16 2.09 (1.55, 5.96) 35.92 35.96 55.03 2.63 (1.56, 2.32) 35.96 23.61 22.35 2.12 (2.66, 5.62) 23.61 Maldives 35.25 31.16 1.09 (0.69, 1.56) 35.25 32.25 32.03 3.16 (1.99, 2.96) 32.25 31.32 29.69 3.66 (1.69, 2.61) 31.32 35.21 53.56 3.65 (2.66, 5.62) 35.21 23.19 23.03 5.16 (5.09, 6.25) 23.19 35.92 31.16 2.09 (1.55, 5.96) 35.92 35.96 55.03 2.63 (1.56, 2.32) 35.96 23.61 22.35 2.12 (2.66, 5.62) 23.61 India 35.25 31.16 1.09 (0.69, 1.56) 35.25 32.25 32.03 3.16 (1.99, 2.96) 32.25 31.32 29.69 3.66 (1.69, 2.61) 31.32 35.21 53.56 3.65 (2.66, 5.62) 35.21 23.19 23.03 5.16 (5.09, 6.25) 23.19 35.92 31.16 2.09 (1.55, 5.96) 35.92 35.96 55.03 2.63 (1.56, 2.32) 35.96 23.61 22.35 2.12 (2.66, 5.62) 23.61 Pakistan 35.25 31.16 1.09 (0.69, 1.56) 35.25 32.25 32.03 3.16 (1.99, 2.96) 32.25 31.32 29.69 3.66 (1.69, 2.61) 31.32 35.21 53.56 3.65 (2.66, 5.62) 35.21 23.19 23.03 5.16 (5.09, 6.25) 23.19 35.92 31.16 2.09 (1.55, 5.96) 35.92 35.96 55.03 2.63 (1.56, 2.32) 35.96 23.61 22.35 2.12 (2.66, 5.62) 23.61 Sri-Lanka 35.25 31.16 1.09 (0.69, 1.56) 35.25 32.25 32.03 3.16 (1.99, 2.96) 32.25 31.32 29.69 3.66 (1.69, 2.61) 31.32 35.21 53.56 3.65 (2.66, 5.62) 35.21 23.19 23.03 5.16 (5.09, 6.25) 23.19 35.92 31.16 2.09 (1.55, 5.96) 35.92 35.96 55.03 2.63 (1.56, 2.32) 35.96 23.61 22.35 2.12 (2.66, 5.62) 23.61 Sensitivity analysis Sensitivity analysis was performed by computing the CI for each nation using an adjustment variables parametric quantity of 0.5. The ambiguous nature of the adjustment’s numerous data may have an effect on CI’s composite index mark. As a result, we created nine more figures to calculate the composite index mark from 2001 to 2016 to determine whether variation in the figures of could have an effect on the CI marks. To demonstrate the resultant’s robustness, we generated nine additional values, namely, 0.1, 0.2 and 0.9. The following Table 6 summarises the initial created marks for CI from 2001 to 2016. Table 6 Overall oil composite and new composite indicator score Country NCI CI Rank Afghanistan 0.75 0.60 7th Nepal 0.77 0.61 6th Bhutan 0.60 0.63 5th Bangladesh 0.71 0.76 4th Sri Lanka 0.61 0.66 3rd Pakistan 0.61 0.65 2nd India 0.60 0.63 1st Thus, the suggested methodology makes us expand the sensitivity operation of the CI by decreasing the uncertainness in the weights allotment of several inherent parameters and that it is plausible to apply λ figure as 0.5 to estimate the CI mark. Referencing our base scenario, we apply −figure as 0.5 to estimate the CI mark. Referencing our base scenario, we apply at it is plausible to appdisruption period. Our reasoning for these empirical findings is as follows. To begin, geopolitical risk has the potential to significantly improve the forecasting performance of crude oil volatility. From an oil supply standpoint, geopolitical events and political instability in oil-exporting countries may result in a disruption of crude oil supply. Following that, oil refineries are directly impacted, and the negative impact soon spreads to the tyre, garment, construction and transportation industries, among others. Oil product shortages unavoidably have an effect on normal economic activity and people’s living conditions. As a result, and in line with our findings, geopolitical risk should be considered when evaluating crude oil volatility behaviour. To start our robustness analysis, we change the elasticities to the limitations in the boundary in robustness and 0.02–0.075 for supply. Changing these elasticities in a similar manner within the USA and the international settings contains some foreseeable analysis. Reducing global and South Asia’s demand to the lowest limit (−demand to the l or 0.025 for supply), well-being expenditure decreases from 30 to 40%, whereas reducing them entirely to their inelastic boundary nearly decreases well-being expenditure in twofold. These findings, due to the fact the variation for the global elasticity varies the costs, show that the elasticity variation for South Asia does not respond to costs in the equation. Furthermore, South Asia’s demand needs more elastic expansion welfare expenditure of crude oil purchase in excess of creating elasticity for South Asia’s supply. This determination is equally deduced from a robustness estimation test, which is done on the past by explained varying figure of the λ system. The varying figure for the mathematical test that the entire coefficients of the cumulative uncertainness in the crude oil cost uncertainness model are equivalent 0 is 0.38. It is imperative to note that the scale of the effects on crude oil output is quite the same as crude oil costs. Hence, while the cumulative uncertainness of crude oil output, the effects of costs and the local crude oil marketplace are self-reliant on uncertainties. This implies that South Asia has some of the knowledge to be a summation of uncertainness in the explanation for the global crude oil marketplace evolving market terrain; nevertheless, South Asia has an equally autonomous knowledge that bears on cumulative crude oil markets. Conclusion and policy implication The study used DEA-like composite indicator and principal component analysis to measure the oil supply risk in South Asian countries. Simultaneously, the current study evaluates the strategic petroleum reserves associated with the physical disruption of oil supply. Our findings provide compelling evidence for the idea that the price of oil is extremely sensitive to the health of the world’s main economies. For oil-rich countries where oil revenues account for a significant amount of government revenue, these revenues can have a substantial effect on macroeconomic performance and undermine the effectiveness of fiscal policies. For instance, a sudden decline in the price of oil can worsen severe macroeconomic volatility and contribute to social instability in nations that are overly reliant on oil. A huge inflow of foreign cash as a result of a spike in the price of oil might result in a major appreciation of the real exchange rate, reducing the competitiveness of these oil-rich countries’ key import and export sectors. As a result, creating an efficient liquidity management policy, as well as a sound fiscal and monetary policy, is critical for strengthening oil-producing countries’ ability to deal with excessive oil price volatility. Our analysis shows that there exist major variances within the risk probabilities of the nations. For instance, Afghanistan and Bangladesh are being confronted with several probably economical, natural and regional perils outlook. India’s topmost mark mirrors a chance to vary its sources of getting crude oil while Afghanistan, Bangladesh, Bhutan and Nepal attain the lowest marks corroborating the group as the most exposed risk. The attainment of international security comprises an all-encompassing energy program, encapsulating crude oil sustainably use and crude oil distribution at local and global levels. Every nation is characterized by unique risk features as a result of single parameters and hence requires a particular program instrument to its crude oil distribution hazards. Country-wide crude oil distribution insecurity programs ought to take precedence in order to achieve this aim. The policy implications we offer are given below: First, for crude oil importation threats, it is imperative to foreign crude oil reliance and maximizes energy infrastructure, preserving our natural endowments, deploy and scale up renewables and decrease crude oil use due to the reason that it impacts profoundly on crude oil importation security. Second, crude oil importation nations ought to woo FDI in local crude oil development economies to decrease their reliance on foreign crude oil. Least-developed nations ought to guarantee the safety of foreign investments so as to secure their crude oil importation. Crude oil importation nations ought to encourage two-sided and shared collaborations with different crude oil importation nations, which might likely reduce crude oil importation reliance internationally. Third, there is the necessity to confirm the differences in crude oil importation origins to bring about crude oil distribution steadiness. For instance, crude oil importation nations might reduce and variegate the importation reliance from high-level peril regions such as South Asia to more politically steady regions like the Baltic and North America. South Asian countries have emerged as one of the most active economic regions in the last decade, increasing the region’s reliance on oil consumption in daily life and manufacturing operations. Additionally, the region’s efficient financial system is projected to play a critical role in ensuring the region’s economic stability in the future. As a result, it is necessary to discern between the interrelationships between the oil price and the South Asian stock market indices. To this end, the increasing use of electricity may assist lower South Asian countries’ dependence on crude oil. In contrast, primary energy is most abundant in chosen countries across the study period in a complimentary relationship with crude oil. The financial crisis has a considerable effect on the price for crude oil in South Asia, according to the elasticity of financial crisis, which itself is defined by a dummy variable. When physical dangers are taken into consideration, the correlation between forward oil purchases depends on the geographical dispersion of such purchases. Reduced hazards always come with higher total risks, and these risks should be kept at a minimum. Forward purchases are more than just a function of price fluctuations and transit expenses. As a result, it is critical for policymakers and investors to precisely understand and forecast the volatility of China crude oil futures. Certain recent studies have discovered that numerous macroeconomic uncertainty indicators have a significant effect on crude oil volatility. Historically, among the different sources of uncertainty, geopolitical risk and economic policy uncertainty have been seen as the most potent. Additionally, the December 2019 Coronavirus (COVID-19) outbreak has wreaked havoc on the world economy and financial markets. Physical dangers are also considered, therefore the forward market purchases additional oil in order to guard against the same threats. Forward prices are more than projected spot prices, and this increases overall costs. On the other hand, since fewer risky scenarios are removed from the crude oil price “only crude oil price” configuration, the financial risks are minimised. There are some issues with this study that future research could help to improve. To calculate a reduction, the volume of transported crude oil through straits and canals is utilised as the base. Fascinating future studies might include capacity decrease calculations for straits and canals. To explain this second point, we will note that the capabilities of straits and canals that are not influenced by catastrophic events are supposed to be limitless. Adding an appropriate upper and lower limit for them would be a future possibility. Author contribution Conceptualization, methodology and revision: Fengshen Chien; review, visualization, data curation and supervision: Ching-Chi Hsu; editing, writing of draft, software and editing; YunQian Zhang. Data availability The data that support the findings of this study are openly available on request. Declarations Ethics approval and consent to participate We declare that we have no human participants, human data or human issues. Consent for publication We do not have any individual person’s data in any form. Competing interests The authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Agyekum EB, Amjad F, Mohsin M, Ansah MNS (2021) A bird’s eye view of Ghana’s renewable energy sector environment: a multi-criteria decision-making approach. Util Policy. 10.1016/j.jup.2021.101219 Alemzero DA, Iqbal N, Iqbal S et al (2020a) Assessing the perceived impact of exploration and production of hydrocarbons on households perspective of environmental regulation in Ghana. 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S1876-0341(21)00216-1 10.1016/j.jiph.2021.07.019 Original Article Antiviral treatment could not provide clinical benefit in management of mild COVID-19: A Retrospective Experience from Field hospital Aumpan Natsuda a Vilaichone Ratha-korn abc⁎ Ratana-Amornpin Sarita a Teerakapibal Surat d Toochinda Pisanu e Witoonchart Gasinee f Nitikraipot Surapon g a Center of Excellence in Digestive Diseases and Gastroenterology Unit, Department of Medicine, Thammasat University Hospital, Pathumthani, Thailand b Chulabhorn International College of Medicine (CICM) at Thammasat University, Pathumthani, Thailand c Division of Gastroentero-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia d Thammasat Business School, Thammasat University, Bangkok, Thailand e Sirindhorn International Institute of Technology, Thammasat University, Pathumthani, Thailand f Rector of Thammasat University, Bangkok, Thailand g Chairman of the Executive Committee, Thammasat University Hospital, Pathumthani, Thailand ⁎ Correspondence author at: Department of Medicine, Faculty of Medicine, Thammasat University Hospital, Thailand. 31 7 2021 9 2021 31 7 2021 14 9 12061211 31 7 2020 28 6 2021 28 7 2021 © 2021 The Author(s) 2021 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background Coronavirus disease 2019 (COVID-19) has affected over 145 million infected people and 3 million deaths worldwide. There has been limited data to recommend either for or against use of antiviral regimens in mild COVID-19 patients. This study aimed to compare clinical outcomes between mild COVID-19 patients receiving antiviral drugs and those without. Method Thai patients diagnosed with COVID-19 at field hospital affiliated to Thammasat University Hospital, Thailand were evaluated between January 1, 2020 and April 13, 2021. Patients’ data, clinical presentation, past medical history, laboratory results, and treatment outcomes were extensively reviewed. Results Five hundred patients with positive tests were included in the study. The mean age was 35.9 years; 46% males. There were 225 (45%), 207 (41.4%), 44 (8.8%), 18 (3.6%), 6 (1.2%) patients with asymptomatic, mild, moderate, severe, and critical COVID-19, respectively. Of 207 mild COVID-19 patients, 9 (4.3%) received lopinavir/ritonavir or darunavir/ritonavir, 17 (8.2%) received favipiravir, while 175 (84.5%) had only supportive care. Mild COVID-19 patients receiving antiviral treatment had longer median length of hospital stay [13 days (IQR 11–14) vs. 10 days (IQR 8–12), p < 0.001] than patients having only supportive treatment. Antiviral drug use was significantly associated with longer hospital stay (>10 days) in mild COVID-19 patients (OR 5.52; 95%CI 2.12–14.40, p < 0.001). Adverse drug reactions such as diarrhea, abdominal pain, and hepatitis were also demonstrated in our COVID-19 patients with antiviral treatments. Majority of patients (97.6%) recovered without any complications and were discharged home. Two deaths were caused by acute respiratory distress syndrome from severe COVID-19 pneumonia. Conclusion Antiviral treatment could not provide superior clinical outcomes to supportive care in mild COVID-19 patients. Mild COVID-19 patients receiving antiviral medication had longer length of hospital stay than those without. Standard supportive care and regular monitoring of disease progression might be keys for successful management of mild COVID-19. Keywords COVID-19 Lopinavir-ritonavir Darunavir-ritonavir ==== Body Introduction The high transmissibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the coronavirus disease 2019 (COVID-19) pandemic declared by the World Health Organization (WHO) since March 11, 2020 [1]. Border shutdowns, travel restrictions, and lockdowns inevitably spark fears of an impending economic crisis. For Thailand, the pandemic severely staggers the economy as the tourism sector since March 2020, contributing to 15% of gross domestic product (GDP) [2]. Apart from economy, there has been health controversy to recommend either for or against the use of antiviral regimen in mild COVID-19 patients [3]. Potential antiviral drugs have been recently evaluated in clinical trials in search of effective treatment for the coronavirus outbreak [4,5]. Some trials provided promising results that novel regimen could shorten recovery time or hospital stay [4,6], while another study showed no difference from standard treatment [5]. As researchers have continued to investigate new treatment options, each country needs to develop its own treatment guideline depending on the country’s epidemic situation and resource before specific treatment becomes available. The first COVID-19 field hospital in Thailand was opened in March 2020 in order to receive referrals from teaching hospitals in the capital and nearby provinces. The field hospital is affiliated to Thammasat University Hospital, the main tertiary hospital located north of Bangkok. In order to establish the field hospital, isolation system between patients and surrounding community must be reassured to the public. Patient transfer process to the field hospital, general wastewater treatment and additional sanitation systems such as utilizing autoclave, chlorination, ultraviolet, and ozone treatment need to be well-designed to ensure sanitation [7]. So far, this field hospital has been reopened and upgraded to 470 beds for patients from the recent outbreak. As the number of COVID-19 patients were rising, the Ministry of Public Health of Thailand released guideline for management of COVID-19 regarding severity of symptoms. However, there has been limited information about treatment outcomes in this country. This study aimed to compare clinical outcomes between mild COVID-19 patients receiving antiviral drugs and those without antiviral treatment. Methods Study design This retrospective study was conducted at the Thammasat University Hospital and Thammasat University Field Hospital, Thailand between January 1, 2020, and April 13, 2021. Patients over 15 years old diagnosed with COVID-19 were included in this study. Demographic data, clinical presentation, past medical history, laboratory results including a complete blood count and a comprehensive metabolic panel, and treatment outcomes were extracted from medical database and reviewed. Data were analyzed and interpreted by the authors. Definition The diagnosis of COVID-19 was defined as a confirmed positive result of real-time reverse transcription-polymerase chain reaction (RT-PCR) of SARS-CoV-2 in either upper or lower respiratory tract samples. The upper respiratory tract samples could be from nasopharyngeal wash/aspirate, nasal wash/aspirate, or nasal swab, while the lower respiratory tract specimens were from tracheal aspirate or bronchoalveolar lavage [8]. Comorbidity was defined as the presence of one or more underlying medical conditions (e.g., diabetes mellitus, hypertension, dyslipidemia, etc.) in addition to the current diagnosis of COVID-19. Classification of disease severity Patients were classified into 5 groups by disease severity according to the National Institutes of Health (NIH) as follows [3]:1 Asymptomatic infection: Patients who had positive test for SARS-CoV-2 without symptoms. 2 Mild illness: Patients who had signs and symptoms of COVID-19 (e.g., fever, cough, sore throat, malaise, headache, muscle pain, nausea, vomiting, diarrhea, loss of taste and smell) but did not have dyspnea, or abnormal chest radiograph. Mild COVID-19 patients were further classified into 2 groups as stated by the updated Thai national guideline for treatment of COVID-19 on June 25, 2021 [9]: 2.1 Mild COVID-19 without risk factor for severe disease Patients should be isolated at the hospital for at least 14 days from symptom onset and received symptomatic treatment or favipiravir based on clinical decision. 2.2 Mild COVID-19 with risk factor for severe disease Risk factors include age >60 years, chronic obstructive pulmonary disease, chronic kidney disease, cardiovascular disease, cerebrovascular disease, uncontrolled diabetes, obesity, cirrhosis, immunodeficiency, and lymphocyte <1000 cells/mm3. Patients should receive favipiravir for ≥5 days and be isolated at the hospital for at least 14 days. The previous national guideline published on May 1, 2020 recommended lopinavir/ritonavir or darunavir/ritonavir for mild COVID-19, which was not stated in the current guideline.3 Moderate illness: Patients who had clinical or radiographic evidence of lower respiratory infection and an oxygen saturation (SpO2) ≥94% on room air at sea level. 4 Severe illness: Patients who had SpO2 <94% on room air at sea level, respiratory rate >30 breaths/min, or lung infiltrates >50%. 5 Critical illness: Patients who developed respiratory failure, septic shock, and/or multiple organ dysfunction. Symptom resolution was defined as the time from symptom onset to resolution of symptoms. Lymphopenia was defined as lymphocyte count of less than 1.5 × 109/L [10] or percentage of lymphocytes less than 20% [11]. Statistical analysis All data were analyzed by using SPSS version 22 (SPSS Inc., Chicago, IL, USA). The demographic data were analyzed by Fisher’s exact test, or Chi-square test where appropriate. P-value of less than 0.05 was defined as statistical significance. Results Baseline characteristics Total of 500 patients with COVID-19 were studied including 230 men and 270 women with the mean age of 35.9 ± 13.4 (range 15–91) years. All 500 patients were admitted to Thammasat University Hospital or Thammasat University Field Hospital, Thailand. There were 225 (45%), 207 (41.4%), 44 (8.8%), 18 (3.6%), 6 (1.2%) patients with asymptomatic, mild, moderate, severe, and critical COVID-19, respectively. Thailand experienced several waves of COVID-19 spreading. The first outbreak started in early March 2020 and overall cases rapidly declined after lockdown. The second began at the central shrimp market in Samut Sakhon (mid-December 2020) and Pornpat market in Pathumthani province (February 2021) which increased approximately 7 times of previous COVID-19 cases. Recently, the third outbreak has started spreading in entertainment venues in Thonglor, Bangkok in April 2021. The emerging virus was SARS-CoV-2 lineage B.1.1.7, a new variant from the UK, displaying higher transmissibility than the prior two waves as demonstrated in Fig. 1 . Baseline characteristics of each COVID-19 wave were shown in Table 1 . The most common presenting symptoms were fever (35.8%), cough (35.4%), and rhinorrhea (18.6%). Patients in second wave tended to be more asymptomatic than other waves due to active case screening from market vendors and residents in high-risk area. The proportion of critical COVID-19 cases was approximately the same in the first (2.2%) and the third wave (1.4%) as demonstrated in Fig. 2 . Of 207 mild COVID-19 patients, 9 (4.3%) received lopinavir/ritonavir or darunavir/ritonavir, 17 (8.2%) received favipiravir, while 175 (84.5%) had only supportive care.Fig. 1 Daily new cases and cumulative confirmed COVID-19 cases in Thailand. Fig. 1 Table 1 Baseline characteristics and clinical course classified by waves of COVID-19 spreading. Table 1Characteristics 1st wave (N = 45) 2nd wave (N = 316) 3rd wave (N = 139) P-value Mean age (years ± SD) 30.2 ± 9.0 39.0 ± 14.0 30.7 ± 10.8 <0.001 Male, n (%) 20 (44.4%) 151 (47.8%) 59 (42.4%) 0.561 Comorbidity, n (%) 7 (15.6%) 57 (18.0%) 14 (10.1%) 0.098 Exposure, n (%) Contact of confirmed case 16 (35.6%) 39 (12.3%) 80 (57.6%) Visit high-risk area 7 (15.6%) 261 (82.6%) 57 (41.0%) International travel 6 (13.3%) 8 (2.5%) 0 (0%) Unknown 16 (35.6%) 8 (2.5%) 2 (1.4%) Alcohol use, n (%) 2 (4.4%) 21 (6.6%) 33 (23.7%) <0.001 Smoking, n (%) 0 (0%) 17 (5.4%) 22 (15.8%) <0.001 Clinical manifestations, n (%) Fever 28 (62.2%) 66 (20.9%) 85 (61.2%) <0.001 Cough 28 (62.2%) 65 (20.6%) 84 (60.4%) <0.001 Sore throat 20 (44.4%) 18 (5.7%) 38 (27.3%) <0.001 Rhinorrhea 16 (35.6%) 44 (13.9%) 33 (23.7%) <0.001 Anosmia/hyposmia a 38 (12.0%) 11 (7.9%) 0.027 Myalgia 11 (24.4%) 20 (6.3%) 19 (13.7%) <0.001 Headache 4 (8.9%) 14 (4.4%) 9 (6.5%) 0.374 Dyspnea 7 (15.6%) 27 (8.5%) 16 (11.5%) 0.267 Nausea/vomiting 7 (15.6%) 6 (1.9%) 3 (2.2%) <0.001 Diarrhea 6 (13.3%) 14 (4.4%) 12 (8.6%) 0.033 a Anosmia and hyposmia were not recorded during the 1st wave of COVID-19. Fig. 2 Percentage of COVID-19 cases according to severity and wave of spreading. Fig. 2 Clinical course of COVID-19 patients The median incubation period of patients with mild symptoms [4 days (IQR 2–6)] was not different from those with moderate [3 days (IQR 2–4)], or severe disease [6 days (IQR 3–8.3)]. The mean age and risk factor for severe disease as stated in Thai national guideline were not different between mild COVID-19 patients receiving antiviral therapy or symptomatic treatment. Mild COVID-19 patients receiving antiviral treatment had significantly longer length of hospital stay [13 days (IQR 11–14) vs. 10 days (IQR 8–12), p < 0.001], and a trend of longer time to symptom resolution [11 days (IQR 6–14) vs. 8 days (IQR 5–12), p = 0.067] than patients having only supportive treatment as demonstrated in Table 2 and Fig. 3 . Multivariate analysis, adjusted for age and gender, demonstrated that antiviral drug use was significantly associated with longer hospital stay (>10 days) in mild COVID-19 patients (OR 5.52; 95%CI 2.12–14.40, p < 0.001), while comorbidity, obesity, and lymphocyte <1000 cell/mm3 were not, as shown in Table 3 . Two patients with comorbidity stayed longer in hospital due to adverse effects from antiviral drug which were drug-induced hepatitis, and drug-induced acute gouty arthritis. The duration of viral detection [19 days (IQR 16–22) vs. 16 days (IQR 12.5–28), p = 0.502] was not different between mild COVID-19 patients receiving antiviral drugs and those without. The median time from symptom onset to development of pneumonia in severe COVID-19 patients was 4 days (IQR 3–7). Seven patients developed pneumonia at >7 days after symptom onset. Lymphopenia was more significantly associated with severe/critical COVID-19 than mild/moderate symptom (54.2% vs. 14.7%; OR 6.84, 95%CI 2.85–16.41, p < 0.001).Table 2 Baseline characteristics and clinical course classified by severity of COVID-19 and treatment received in mild COVID-19. Table 2Characteristics Mild COVID-19 with symptomatic treatment (N = 175) Mild COVID-19 with antiviral treatment (N = 32) Moderate COVID-19 (N = 44) Severe/critical COVID-19 (N = 24) P-value between mild symptom group Mean age (years ± SD) 31.9 ± 12.2 34.3 ± 12.5 44.4 ± 13.7 51.2 ± 13.4 0.307 Male, n (%) 67 (38.3%) 20 (62.5%) 21 (47.7%) 17 (70.8%) 0.011 Comorbidity, n (%) 24 (13.7%) 10 (31.3%) 10 (22.7%) 16 (66.7%) 0.014 Mean BMI, (kg/m2 ± SD) 23.9 ± 4.7 24.8 ± 5.2 26.0 ± 4.9 28.4 ± 4.7 0.588 Risk factor for severe disease 34 (19.4%) 8 (25.0%) 21 (47.7%) 18 (75.0%) 0.471 Clinical course Incubation period 3.5 (2-5.3) 5 (4-7) 3 (2-4) 6 (3-8.3) 0.046 Symptom onset to admission 4 (3-6) 6 (2-12) 3 (2-5) 4 (2-7) 0.231 Symptom resolution 8 (5-12) 11 (6-14) 10 (8-14) 16 (10.8-23.3) 0.067 Length of hospital stay 10 (8-12) 13 (11-14) 14 (11.3-16) 15.5 (10.3-25) <0.001 Duration of viral detectiona 16 (12.5-28) 19 (16-22) 18.5 (10.8-25.5) 21.5 (8-23.8) 0.502 Symptoms Fever (≥37.5 °C) 21 (12.0%) 12 (37.5%) 21 (47.7%) 14 (58.3%) <0.001 Cough 106 (60.6%) 20 (62.5%) 32 (72.7%) 19 (79.2%) 0.837 Anosmia/hyposmia 38 (21.7%) 2 (6.3%) 6 (13.6%) 3 (12.5%) 0.042 Diarrhea 11 (6.3%) 6 (18.8%) 12 (27.3%) 2 (8.3%) 0.030 Oxygen saturation (%) 98.4 ± 1.2 98.0 ± 1.0 97.2 ± 1.5 87.4 ± 6.3 0.114 Lymphocyte (×109/L) 2.2 ± 0.6 1.8 ± 0.6 1.9 ± 0.8 1.3 ± 0.7 0.006 Medication LPV/r or DRV/r - 9 (28.1%) 11 (25%) 17 (70.8%) <0.001 Favipiravir - 17 (53.1%) 42 (95.5%) 24 (100%) <0.001 Dexamethasone - 1 (3.1%) 20 (45.5%) 20 (83.3%) 0.155 Remdesivir - - - 6 (25.0%) - BMI = body mass index, LPV/r = lopinavir/ritonavir, DRV/r = darunavir/ritonavir. a N = 45 due to no longer repeated test for SARS-CoV-2 in patients in the second and third waves of COVID-19 spreading. Fig. 3 Clinical course of mild COVID-19 patients classified by treatment received. Fig. 3 Table 3 Multivariate analysis of risk factors affecting on longer length of stay in mild COVID-19 patients adjusted by age and gender. Table 3Factors Longer length of hospital stay (>10 days) Odds ratio (95% CI) P-value Risk factor for severe disease 0.99 (0.46-2.12) 0.981 Severe comorbiditya 0.68 (0.12-3.97) 0.671 BMI ≥25 kg/m2b 0.81 (0.36-1.81) 0.603 Lymphocyte count <1000 cell/mm3 1.88 (0.16-21.92) 0.614 Antiviral drug use 5.52 (2.12-14.40) <0.001 a Severe comorbidity included either one of the followings: chronic obstructive pulmonary disease, chronic kidney disease, cardiovascular disease, cerebrovascular disease, uncontrolled diabetes, obesity, cirrhosis, immunodeficiency. b BMI = body mass index, BMI ≥ 25 kg/m2 was considered as obesity according to Asian BMI criteria. Treatment outcomes and complications The majority of patients (97.6%) recovered without any complications and were discharged home after a median of 9 days (IQR 7–12). Complications were observed in 12 COVID-19 patients: 3 with COVID-19 complications, 5 with adverse drug reactions, 2 with ventilator-associated pneumonia, one with recurrent stroke, and one with acute kidney injury. A 37-year-old woman had acute respiratory failure and myocarditis as complications of COVID-19. She received darunavir/ritonavir, favipiravir, hydroxychloroquine, and azithromycin for severe COVID-19 along with methylprednisolone for myocarditis. Her symptoms improved and later discharged from the hospital. Five adverse drug reactions included one with acute gouty arthritis, one with acute gouty arthritis and drug-induced hepatitis, one with drug-induced hepatitis, and two with diarrhea from darunavir/ritonavir. A 36-year-old man with a history of gout had COVID-19 pneumonia. He had no episode of arthritis in the last 3 years but developed acute attacks of gouty arthritis 4 days after favipiravir was started. The serum uric acid was high (11.9 mg/dL) suspected to be caused by favipiravir-induced hyperuricemia. Moreover, he had elevated transaminase levels which peaked at 9 days after receiving darunavir and decreased to normal level 1 month after drug discontinuation. Two deaths were caused by acute respiratory distress syndrome from severe COVID-19 pneumonia. Discussion SARS-CoV-2 has posed global health threat since the beginning of 2020. Until now, there has been no proven effective treatment against the virus. Supportive therapy and monitoring disease progression remain the mainstay of treatment for mild COVID-19 [3]. This retrospective study evaluated clinical outcomes between mild COVID-19 patients receiving antiviral treatment and those without. Patients receiving antiviral drugs had significantly longer median length of hospital stay than patients who had only symptomatic treatment. Moreover, adverse effects from antiviral drugs resulted in extended length of hospital stay in two patients with comorbidity. There was no significant difference in duration of viral detection in respiratory specimen between groups. These could represent the nature of mild COVID-19 which could be recovered without antiviral treatment. Our study result was in concordance with the randomized placebo-controlled trial of lopinavir-ritonavir in hospitalized COVID-19 patients conducted in the UK [12]. However, they included both mild and severe COVID-19 in the RECOVERY trial, while ours observed disadvantages of using antiviral drugs in patients with mild symptoms. Another trial concluded that no clinical benefit was observed in patients receiving lopinavir-ritonavir over standard management, but there were differences regarding the severity of COVID-19 and drugs used in treatment group [5]. The recent study with similar severity of COVID-19 patients as ours demonstrated shorter hospital stay and duration of viral shedding in triple antiviral therapy group than lopinavir–ritonavir control group, but this study did not have a placebo control group [6]. Apart from protease inhibitors, hydroxychloroquine is another drug capable of inhibiting SARS-CoV-2 infection by blocking endosomal transport of viruses as demonstrated in prior in vitro study [13]. Therefore, hydroxychloroquine is included in our prior national treatment regimen for both mild and severe COVID-19. However, as another large observational study indicated hydroxychloroquine might not be efficacious for improving disease outcomes, this medication was no longer use in the current Thai guideline for COVID-19 patients [14]. Risk factors associated with severe COVID-19 were identified in several studies. This study revealed that patients with comorbidities tended to have more severe disease which was similar to the previous study [15]. Prior studies demonstrated better therapeutic response from favipiravir than lopinavir/ritonavir or darunavir/ritonavir in severe [16] and non-severe groups [17]. However, we could not demonstrate difference of disease outcome between these two drugs because favipiravir was used in almost all cases (95–100%) according to our national guideline. Remdesivir was another antiviral drug with proven benefit for shortening recovery time in COVID-19 pneumonia as reported in prior large clinical trial [18]. However, we only used remdesivir in small number of patients with severe COVID-19 pneumonia. The median time from symptom onset to development of severe COVID-19 was 4 days which was shorter than another study [19]. Nevertheless, 7 patients developed pneumonia at more than 1 week after symptom onset. This suggests that mild COVID-19 patients should be regularly monitored for at least 2 weeks from symptom onset as they can develop severe symptoms in this period. In addition to monitoring symptoms for severe disease, laboratory abnormalities such as lymphopenia may be useful in predicting severe COVID-19 [10]. The proportion of severe COVID-19 with lymphopenia in our study (54.2%) was lower than the previous study (96.1%) [20]. This could be because our study population had less severe symptoms than those in the prior study. Antiviral drugs might be possible causes of complication in 5 patients in this study. Diarrhea is a common side effect of boosted protease inhibitors such as lopinavir–ritonavir and darunavir–ritonavir as demonstrated in 2 patients in this study [21]. Moreover, darunavir and lopinavir are associated with elevated transaminases (>5 times of upper limit of normal) in 3–10% of patients [22]. Two patients in this study had asymptomatic hepatitis which was possibly due to protease inhibitors. Favipiravir can induce hyperuricemia and should be cautiously used in patients with history of gout [23]. Our study revealed two patients with acute gouty arthritis which might be caused by this drug. Conclusion In conclusion, antiviral treatment could not provide superior clinical outcomes to supportive care in mild COVID-19 patients. Mild COVID patients receiving antiviral medication had significantly longer length of hospital stay without difference in duration of viral detection. Standard supportive care and regular monitoring of disease progression might be keys for successful management of mild COVID-19. Conflicts of interest The authors declare that they have no conflicts of interest. Ethical approval Ethical approval for this study was obtained from the Human Research Ethics Committee of Thammasat University, Thailand. The research was conducted according to the good clinical practice guideline, as well as the Declaration of Helsinki. The project number of ethical approval was MTU-EC-IM-0-091/63. Acknowledgements This study was supported by a grant from Faculty of Medicine, Thammasat University, Thailand Science Research and Innovation Fundamental Fund, Bualuang ASEAN Chair Professorship at 10.13039/501100005790 Thammasat University , and Center of Excellence in Digestive Diseases, Thammasat University, Thailand. ==== Refs References 1 WHO WHO Director-General’s opening remarks at the media briefing on COVID-19-11 March 2020 [Accessed 27 May 2020] https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020 2 The World Bank. 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==== Front Planta Planta Planta 0032-0935 1432-2048 Springer Berlin Heidelberg Berlin/Heidelberg 3697 10.1007/s00425-021-03697-y Review Sorghum polyphenols: plant stress, human health benefits, and industrial applications http://orcid.org/0000-0002-8784-2846 Kumari Pummy [email protected] 1 Kumar Vinod 2 Kumar Rakesh 3 Pahuja Surender Kumar 1 1 grid.7151.2 0000 0001 0170 2635 Department of Plant Breeding and Genetics, COA, CCS Haryana Agricultural University, Hisar, 125004 Haryana India 2 grid.7151.2 0000 0001 0170 2635 Department of Biochemistry, COBS&H, CCS Haryana Agricultural University, Hisar, 125004 Haryana India 3 grid.7151.2 0000 0001 0170 2635 Department of Microbiology, COBS&H, CCS Haryana Agricultural University, Hisar, 125004 Haryana India Communicated by Anastasios Melis. 10 8 2021 2021 254 3 478 2 2021 3 8 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Main conclusion Various phenolic compounds of sorghum are effective in the management of abiotic stress (salt, nutrients) and biotic stress (caused by birds, fungi and aphids). The health and industrial application of phenolics is mainly contributed by inherent antioxidant and nutraceutical potential. Abstract In a natural environment, plant growth is affected by various biotic and abiotic stresses. In every ecosystem, the presence of a wide range of harmful biological agents (bacteria, fungi, nematodes, mites, and insects) and undesirable environmental factors (drought, salinity, heat, excessive or low rainfall, etc.) may cause a heavy loss in crop productivity. Being sessile during evolution, plants have evolved multiple defense mechanisms against various types of microbial pathogens and environmental stresses. A plant’s natural defense system produces some compounds named secondary metabolites, which include phenolics, terpenes, and nitrogen. The phenolic profile of grain sorghum, the least utilized staple crop, is unique, more diverse, and more abundant than in any other common cereal grain. It mainly contains phenolic acids, 3-deoxyanthocyanidins and condensed tannins. Sorghum polyphenols play a major role in plant defense against biotic and abiotic stresses and have many additional health benefits along with various industrial applications. The objective of this review is to discuss the phenolic compounds derived from grain sorghum and describe their role in plant defense, human health, and industrial applications. Keywords Sorghum Phenolics Antioxidant Abiotic stress Animal feed issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2021 ==== Body Introduction Phenolics are the largest group of secondary metabolites in plants. They vary in shape from simpler aromatic rings to more complex ones, such as lignins. All these phenolic compounds originate from phenylalanine; therefore, they are also called phenylpropanoids. These phenols are synthesized by the phenylpropanoid pathway and are divided into several groups, such as phenolic acids, flavonoids, hydrolysable tannins, monolignols, stilbenes, and lignans, each with peculiar properties. Various phenolic compounds play an important role in the acclimatization of plants to unfavorable environmental conditions (Barcelos et al. 2016). The concentration of phenolic compounds in plant tissue is a good indicator for predicting the extent of abiotic stress tolerance in plants. It varies significantly in different plant species under an array of external factors, such as drought, heat, and cold. The growth and development, including seed germination, biomass accumulation, and metabolism of plants, are also influenced by plant phenolics. In this review, different types of sorghum phenolic compounds and their beneficial role in plant stress management, human health, and related industries have been discussed. Sorghum grain and its nutritional composition The C4 cereal sorghum grain is rich in polysaccharides (starch and non-starch), followed by proteins and lipids. The genetic characteristics of the cultivar, soil type, and environmental conditions during the season have a major impact on the content and composition of starch, i.e., the main polysaccharides in the grain. Sorghum has the lowest starch digestibility among cereals due to the strong association between the starch granules, proteins, and tannins (Mkandawire et al. 2013). Prolamins are major sorghum proteins with an average of 77–82% of the total proteins, and the remainder is albumins, globulins, and glutelins. The kafirins are the major prolamins of the sorghum and comprise three major classes: α-kafirins (66–84%), β-kafirins (8–13%), and γ-kafirins (9–21%) (Mokrane et al. 2010). Overall, the digestibility of sorghum proteins, especially after cooking, is lower than other cereals, such as wheat and maize. The main reason for the low digestibility of sorghum proteins is the resistance of kafirins to peptidase due to the formation of intramolecular disulfide bonds (Belton et al. 2006). Regarding the lipid profile of sorghum, it is 1.24–3.07 g/100 g of grain weight and mainly composed of unsaturated fatty acids. The primary fatty acids of sorghum are linoleic acid, oleic acid, palmitic acid, and linolenic acids. In most of the varieties of sorghum, the polyunsaturated fatty acids (PUFA) are higher in content than monounsaturated fatty acids (MUFA) (Mehmood et al. 2008). The sorghum genotypes have been studied elsewhere for various quality parameters (Kumari et al. 2016, 2017; Laxmi et al. 2019; Chakraborthy et al. 2020). Sorghum is a source of various minerals, such as phosphorus, potassium, and zinc, whose contents vary according to cultivation. Although the content is known, the bioavailability of most of the minerals from sorghum is scarcely known. The bioavailability of zinc varies between 9.7% and 17.1%, and for iron, it ranges from 6.6 to 15.7%. Currently, efforts are being made worldwide to enhance the content and bioavailability of iron and zinc through biofortification, fortification, and genetic improvement of sorghum (Kruger et al. 2013). India’s first biofortified variety of sorghum, ICSR 14,001 with its higher iron and zinc, was developed by ICRISAT and released as ‘Parbhani Shakti’ for cultivation by Vasantrao Naik Marathwada Krishi Vidyapeeth (VNMKV), Maharastra. Sorghum polyphenols Phenolics are broadly distributed in the plant kingdom and are found abundantly as secondary metabolites of plants. Plant polyphenols have drawn increasing attention due to their potent applications in various fields (Dai and Mumper 2010). During the last decade, sorghum has attracted great attention from the food, feed, fodder and drug industries due to its unique phenolic profile, which helps it combat environmental stresses, such as biotic and abiotic stresses, along with its multifold human health benefits, including reducing oxidative stress and cancer prevention (Yang et al. 2009). The phenolic profile of grain sorghum is more diverse than those observed in other cereals, such as wheat, barley, rice, maize, rye, and oats. Phenolic acids, condensed tannins, flavonoids, stilbenes, and lignins are the major phenolics present in grain sorghum and are produced by the phenylpropanoid pathway. Among these phenolic acids, flavonoids (3-deoxyanthocyanidins), and condensed tannins are higher proportionally and biologically more active. The phenolics in sorghum grain are concentrated in the bran layer, and their content, concentration, and extractability vary greatly amongst sorghum varieties and genotypes (Ofosu et al. 2021). As the phenolic profile in sorghum is strongly associated with its bioactive properties, the knowledge of the phenolic structure, composition, location (that is, bran and kernel) and form of presence (that is, free and bound) in sorghum grain is crucial for the extraction method, material selection, and processing design, and thus, it is tailored for specific needs. Xiong et al. 2021 studied cellular antioxidant activity of sorghum phenolic extracts and reported that colored bran like brown and black sorghum has great potential to be used as a natural antioxidant for food and nutraceutical applications. The identification and development of phenolic compounds or extracts from plants have become a major area of health or related research (Dai and Mumper 2010). An overview of different types of sorghum phenolics is given in the following sections. Phenolic acids According to their structure, phenolic acids can be divided into two categories: hydroxybenzoic acid and hydroxycinnamic acid (Kumar and Goel 2019). The total concentration of phenolic acids in sorghum grain is in the range of 445–2850 μg/g (Girard and Awika 2018). These acids exhibit high antioxidant activity in vitro and thus have human health benefits (Kamath et al. 2004). The contents of primary phenolic acids based on studies of some sorghum varieties are provided in Table 1.Table 1 Concentration of various phenolic acids in sorghum grain Phenolic acids Conc. in sorghum grain mg/g Protocatechuic acid 150.3–178.2 Ferulic acid 120.5–173.5 p-coumaric acid 41.9–71.9 Syringic acid 15.7–17.5 Vanillic acid 15.4–23.4 Gallic acid 14.8–21.5 Caffeic acid 13.6–20.8 Cinnamic acid 9.8–15.0 Hydroxybenzoic acids 6.1–16.4 Modified from Afify et al. (2012) These phenolic acids occur in a bound state and have decreased bioavailability. They are not hydrolyzed by human digestive enzymes but are fermented by the colon's microbiota (Hole et al. 2012). The phenolic compounds of wines, fruits, and vegetables have a good bioavailability compared to the phenolic acids of cereals, including sorghum, because they are mostly bound to arabinoxylans chains or lignin (Abdel-Aal et al. 2012). Descriptive knowledge about techniques for improving the bioavailability of phenolic acids in sorghum is incipient. Therefore, microorganisms and grain processing play a key role in improving their bioavailability (Salazar-López et al. 2018). Cereal fermentation with specific probiotic strains and cooking processes can significantly increase the contents of free phenolic acids, thereby improving their bioavailability in sorghum (Saura-Calixto et al. 2010; N’Dri et al. 2013). Tannin Grain sorghum contains tannins with a high molecular weight and has a high degree of polymerization compared to other cereals, and they are the most investigated polyphenols in sorghum. The tannin content varies from 10.0 to 68.0 mg/g dry wt. in tannin sorghum compared to other cereals and pulses (Tannin-free sorghum at 0.5–3.8 mg/g, Finger millet at 3.6–13.1 mg/g, Buckwheat groats at 1.7 mg/g, and Cowpea at 1.8–2.9 mg/g) (Awika 2000). The concentration of tannin in sorghum cultivars, in relation to their color, varies significantly, for example, red and brown grain sorghums contain more bioactive compounds, such as tannins, which are considered beneficial to human health and are widely used in the beer and food industry (Eastin and Lee 2020). Based on tannin concentration and its extractability, sorghums can be classified into three types (Xiong et al. 2019). Type I sorghums with no pigmented testa thus have negligible or shallow levels of tannins (0–1.8 mg CAE/g). Type II sorghums have pigmented testa with moderate levels of tannins and are extractable with acidified methanol (6.4–15.5 mg CAE/g). Type III sorghums have pigmented testa with a high tannin concentration (11–50.2 mg CAE/g), are found mainly in the testa cell walls and the pericarp, and are extractable by methanol or acidified methanol (Dykes and Rooney 2006). In general, sorghums with pigmented testa have high levels of condensed tannin content, and Type III sorghums contain almost a ten times higher tannin concentration than other tannin-containing cereals (Girard and Awika 2018). Despite the anti-nutritional effect, tannins have been extensively studied and used for human health-promoting capabilities because tannins are 15–30 times more effective than simple phenolics in radical scavenging ability. The functional benefits of sorghum are attributed mainly to oligomers, which have been extensively studied (Beecher 2004). The oligomers of tannins in foods contribute up to 19% of the antioxidant capacity of the diet, which benefits human health and promotes the prevention of non-communicable diseases due to immunomodulatory, anticancer, antioxidant, antiradical, anti-inflammatory, vasodilatory, cardio-protective, antithrombotic and anti-UV actions (Floegel et al. 2010). The high tannin concentration in sorghum also offers an agronomical advantage over low tannin cultivars because the former protects the plants against pathogen and bird damage and can be grown in some under-developed regions of the world that have food security issues (Table 2) (Kil et al. 2009). The grain mold resistance of the genotype is significantly improved by a darker glume color, higher content of phenols, and the hardness of the seed (Audilakshmi et al. 1999). Recently, a known SNP (S4_62316425) in the TAN1 gene, a regulator of tannin accumulation in sorghum grain, was detected with a significant association with grain mold resistance (Nida et al. 2021), as the processing of phenolic acids improves the digestibility of tannins in sorghum. The processing of grain sorghum in dry heat (95 °C for 20 min and 121 °C for 30 min) can depolymerize the condensed tannins in sorghum (Barros et al. 2012), which can increase their bioavailability. Thermal processing is one strategy to increase the bioavailability of tannins with a minimum reduction in the content of these compounds. Thus, the functional potential of tannins-rich sorghum can be maintained or even increased. Furthermore, the reduction of polymeric tannins may boost the digestibility of starch and proteins, increasing the nutritional value of the grains. The depolymerization of tannins through other types of processing needs to be studied.Table 2 Effect of grain color and phenolic content on grain mold and bird attack resistance in sorghum Classification based on tannin conc Sorghum grain color Pericarp color Testa pigmentation Tannin conc Phenolic profile Resistance to mold and bird attack Type I sorghums White White No No Very low Low resistance Yellow Yellow No No Low Low resistance Red Red No No Moderately high Low resistance Type II sorghums Brown Red Yes High High Highly resistant Type III sorghums Black Red No/Yes Varies High Moderately resistant Modified from Audilakshmi et al. (1999); Xiong et al. (2019) Flavonoids Most flavonoids of the sorghum are located in the bran layers of the grain. The concentration of flavonoids is largely affected by the color and thickness of the pericarp and the presence of the testa (Dykes et al. 2011). Anthocyanins, flavones, and flavanones are major flavonoids that are present in the sorghum grain. Sorghum anthocyanins belong to the class of 3-deoxyanthocyanidins and correspond to up to 79% of the sorghum flavonoid content (Dykes and Rooney 2006). Due to the absence of a hydroxyl group at position C-3 in 3-deoxyanthocyanidins, they are more stable than other anthocyanins (Taleon et al. 2012). The content of sorghum 3-deoxyanthocyanidins correlates with its color and antioxidant activity (Kayodé et al. 2011). Varieties with black pericarp and testa have 3–4 times more total 3-deoxyanthocyanidins (5.4–6.1 mg/g) than red and brown varieties (1.6–2.8 mg/g) (Awika et al. 2004). The total flavones of the sorghum vary from 0 to 386 mg/g (on average, 87 mg/g). The lowest content of flavonoids is found in white pericarp varieties, and the highest contents are observed in the lemon-yellow pericarp (474–1780 mg/g) (Dykes et al. 2011). Stilbenes Stilbenes belong to a small family of phenolic compounds derived from the phenylpropanoid pathway (Chong et al. 2009). They have numerous implications in plant disease resistance and human health. Stilbene content has a positive correlation with grain color and is present in smaller quantities in white-colored varieties. White sorghum contains traces of trans-piceid (up to 0.1 mg/kg) but lacks trans-resveratrol, whereas red sorghum has both (Bröhan et al. 2011). Stilbene compounds, a diverse group of natural defense phenolics, which are abundant in grapes, berries, sorghum, and conifer bark waste, may also confer a protective effect against aging-related diseases (Reinisalo et al. 2015). Sorghum phenolics: applications in biotic and abiotic stress management In the twenty-first century, to meet the food demand of the fast-growing human population, we need to enhance crop productivity and minimize crop losses. However, several biotic and abiotic stresses (insect pest attack, foliar and grain disease, drought, salinity, cold, heat, heavy metal toxicity, UV radiation, etc.), increased globalization and anthropogenic activities and induced climate changes are badly affecting a large proportion of arable land. These abiotic stresses affect plant growth and result in poor yield due to alteration in physiological and biochemical processes of plants (Wani et al. 2015). Plants exhibiting increased synthesis of polyphenols under biotic and abiotic stresses usually show better adaptability to limiting environments (Sharma et al. 2019). Sorghum’s ability to thrive under both biotic and abiotic stressors is mediated, in part, through the diverse families of secondary metabolites synthesized by a plant (Fig. 1). Sorghum possesses a variety of phytochemicals that are potentially helpful in overcoming the biotic and abiotic stresses in a plant. Various sorghum phenolic compounds, viz. phytoalexins (3-deoxyanthocyanidins) or allelochemicals (p-hydroxybenzoates, p-coumarates, and flavanols), play important roles in providing resistance for plants against biotic and abiotic stresses (Weir et al. 2004). Walling (2008) reported that some aphids are thought to have developed tolerance mechanisms against certain secondary metabolites. Interestingly, flavonoids have been suggested as candidate compounds that confer resistance to aphids in sorghum and other plant species (Kariyat et al. 2017). The genotypes accumulating higher levels of the cyanogenic glucoside (dhurrin) are resistant to aphids (Dreyer and Jones 1981) and the southwestern corn borer (Cheng et al. 2013). Many phenylpropanoids, phenolic acids, flavonoids, and condensed tannins have been implicated in plant resistance, with 3-deoxyanthocyanidins being the prominent ones (Deng and Lu 2017). Dicko et al. (2005) studied the relation between different phenolic compounds and biotic stresses (sooty stripe, sorghum midge, leaf anthracnose, striga and grain molds) and abiotic stress (lodging, drought resistance and photoperiod sensitivity) management and observed that sorghum varieties that have resistance to biotic and abiotic stresses had on average higher contents of 3-deoxyanthocyanidins (3-DAs), proanthocyanidins (PAs) and flavan-4-ols compared to susceptible varieties (Fig. 2). The contents of 3-DAs and PAs were suggested to be a good marker for resistance of sorghum to both biotic and abiotic stresses because these correlate with resistance to all stresses except for photoperiod sensitivity in grain sorghum.Fig. 1 Mechanism of how phenolics help towards biotic and abiotic resistance Fig. 2 Sorghum polyphenols effective against major biotic stresses Tannins: role in protection against bird damage Bird damage is one of the most severe biotic constraints on crop production worldwide (De Mey et al. 2012; Anderson et al. 2013). Some cereal crops, such as wheat, rice, rye, sorghum, and millets, are more vulnerable to bird damage by lodging, pecking seeds and sucking the juice from immature seeds, preventing the full development of many grains and frequently encouraging mildews and other plant diseases around panicles (Tipton et al. 1970). Dixon et al. (2005) reported that the increased levels of condensed tannins (widely known as proanthocyanidins; PAs) in sorghum varieties also affect the sparrow feeding behavior. Based on GWAS analysis of a large-scale sorghum germplasm diversity panel, Xie and Xu (2019) revealed that Tannin1 encodes a WD40 protein functioning in the WD40/MYB/bHLH complex, which controls bird feeding behavior in sorghum. The study of sparrow feeding and sparrow attractant volatile assays confirmed the anti-feedant and anti-attractant functions of differentially accumulated metabolites on bird behavior. Bird-preference accessions possess a variety of aromatic and fatty acid-derived volatile accumulation at significantly higher levels. Deoxyanthocyanidins: effectiveness against anthracnose fungus In Sorghum, a group of phytoalexins is induced at the infection site by Colletotrichum sublineolum, the anthracnose fungus. These compounds, classified as 3-deoxyanthocyanidins, have structural similarities to the precursors of phlobaphenes. 3-Deoxyanthocyanidins were detected as major flavonoids in black sorghum grains (Taleon et al. 2012). The contribution of flavonoid phytoalexins in resistance against Colletotrichum sublineolum in sorghum has been investigated by comparing the response of several sorghum cultivars that differentially produce 3-deoxyanthocyanidins (Basavaraju et al. 2009). Loeh et al. (1999) reported that sorghum responds to the invasion of both pathogenic and nonpathogenic fungi by the induction of 3- deoxyanthocyanidin phytoalexins. Ibraheem et al. (2010) carried out an experiment by using yellow seed sorghum to study the effect of flavonoids on anthracnose leaf blight. It was reported that sorghum yellow seed 1 (y1) encodes a MYB transcription factor, which regulates phlobaphenes biosynthesis and its near-isogenic lines, but having loss-of-function alleles of y1 means that it is not able to accumulate phlobaphenes. Molecular characterization of the two null y1 alleles shows a partial internal deletion in the y1 sequence. These null alleles, designated as y1-ww1 and y1-ww4, do not accumulate 3-deoxyanthocyanidins when challenged with the nonpathogenic fungus Cochliobolus heterostrophus. Furthermore, compared to the wild-type allele, both y1-ww1 and y1-ww4 show greater susceptibility to the pathogenic fungus C. sublineolum. In fungal-inoculated wild-type seedlings, y1 and its target flavonoid structural genes were coordinately expressed. However, in y1-ww1 and y1-ww4 seedlings, where y1 was not expressed, steady-state transcripts of its target genes were not detected. Co-segregation analysis showed that the functional y1 gene is genetically linked with resistance to C. sublineolum. In conclusion, a significant reduction in ALB disease symptoms was reported with a higher accumulation of known 3-deoxyanthocyanidins in sorghum plants carrying a functional y1 allele in response to infection by the anthracnose fungus C. sublineolum. In Sorghum bicolor metabolomic analysis of defense-related reprogramming in response to Colletotrichum sublineolum infection, it also revealed a functional metabolic web of phenylpropanoid and flavonoid pathways (Tugizimana et al. 2019). Deoxyanthocyanidins: defense against corn leaf aphid Sorghum is also a potential host to more than 150 insect pests with aphids being a major group of them (Sharma 1993). Almost four species of aphids feed on sorghum and corn leaf aphid (CLA) Rhopalosiphum maidis, and Fitch (Hemiptera, Aphididae) is the major one among them (Young and Teetes 1977). To defend against any damaging pests, plants have evolved a specific defense mechanism that is mainly classified into physical and chemical defenses. Among the physical defenses, leaf trichomes and epicuticular wax have been suggested to play a significant role against many herbivorous species, including aphids (Eigenbrode and Espelie 1995; Kariyat et al. 2017). Insect herbivory elicits complex counter defense responses from plants, including the biosynthesis of toxic secondary metabolites that act as chemical defense against particular infections, such as glycosides, alkaloids, benzoxazinoids, glucosinolates, and flavonoids (Betsiashvili et al. 2015). For example, vanillic and aconitic acids have been found to have antifeedant properties, and sorghum genotypes with higher polyphenol contents are less preferred by aphids (Mote and Shahane 1993). In sorghum, the y1-regulated flavonoid pathways have resulted in deleterious effects on aphids, resulting in defense against corn leaf aphid (Kariyat et al. 2017). Phenolics: effect on grain mold ‘Grain mold’ is a significant biotic stress affecting the production, marketing, and productivity of grain sorghum. The term is used to describe the diseased appearance of sorghum grain resulting from infection of one or more pathogenic or saprophytic fungus. Funguses of more than 40 genera are associated with sorghum grain (Williams and Rao 1981). Most are restricted to the pericarp, but penetration to endosperm occurs if the mature grain is exposed to high humidity for a longer period at maturity. Audilakshmi et al. (1999) studied sorghum genotypes for various morphological and biochemical traits and their contribution to resistance for grain mold. Highly significant correlations between grain mold and seed hardness, seed phenolics content in acid methanol extract, and glume color revealed that they strongly affected the grain mold response. Harder grain, higher levels of seed phenols, and darker glumes contributed to grain mold resistance. Weaker and less consistent correlations were observed between grain mold and seed color, seed flavan-4-ol content, glume phenol, flavan-4-ol contents, and glume cover, indicating the relatively lower effect of these traits on grain mold response. It has been suggested that combinations of several attributes are required to achieve efficient resistance (Audilakshmi et al. 1999). Esele (1993) reported that a pigmented testa, where condensed tannins are present, is the most critical trait for conferring grain mold resistance. Red pericarp containing flavan-4-ol also plays a role in mold resistance but is not as effective as the pigmented testa. However, the combination of both provides additive effects on resistance. Melake-Berhan et al. (1996) also reported the same results, highlighting the correlation between tannin and flavan-4-ol with resistance in colored pericarp sorghums with pigmented testa. Not all red pericarp needs to be resistant to grain mold. Flavonoids and their role in salt stress management in sweet sorghum Abiotic stresses affect crop production and productivity worldwide. Plants have developed specific defense mechanisms against environmental stresses by altering the gene expression pattern, leading to the regulation of specific metabolic and defensive pathways. Sorghum is an essential crop in regions that are mainly irrigated by salty water. Sweet sorghum is a variant of common grain sorghum and is relatively more adapted to marginal growing conditions. Some phenolics, like anthocyanin and tannins, have a high antioxidant capacity and help in plant defense naturally against abiotic stresses, pests, and disease damage (Dempsey et al. 2011). Meng et al. (2015) reported that flavonoids have critical physiological roles in plants; their accumulation is induced by abiotic stresses and is a hallmark of plant stress. In addition, it has been observed that salt-tolerant species often accumulate more flavonoids than salt-sensitive species, which suggest a relationship between flavonoid biosynthesis and salt stress resistance (Liu and Godwin 2012). The high flavonoid contents may have contributed to elevating the antioxidant activity of the plant tissues under stress. The flavonoid biosynthesis pathway played an essential role in the high salt tolerance in the sweet sorghum landraces, and six genes involved in the flavonoid biosynthesis pathway to tannins and anthocyanins from phenylalanine have been identified in the sweet sorghum landraces. Moreover, their expression was observed to be significantly different from that in grain sorghum, based on RNA-Seq (Genzeng et al. 2019). The study revealed that the accumulation of tannin positively relates to the sorghum salt-resistance and flavonoids biosynthesis, which plays a vital role in the sweet sorghum capacity for salt tolerance. Phenolics and their impact on nutrient uptake in sorghum Despite their role in biotic and abiotic stress management, phenolics also improve nutrient uptake through chelation of metallic ions, enhanced active absorption sites, and soil porosity which accelerate the mobilization of elements, such as calcium (Ca), magnesium (Mg), potassium (K), zinc (Zn), iron (Fe), and manganese (Mn) (Seneviratne and Jayasinghearachchi 2003). Sorghum is a rich source of flavonoids, such as flavonols, flavonones, flavons, and anthocyanins, which are particularly abundant in red and black sorghum grain (Dicko et al. 2005) but rare or absent in other plants (Awika et al. 2004). Some workers have reported that high plant density and intercropping practices reduced insect pest infestation in cowpea (Makoi et al. 2010). This was probably due to the excessive accumulation of phenolic compounds in plants growing in such systems. Musa et al. (2011) studied sorghum–cowpea intercropping under treatment with chemical and bio fertilization, leading to enhanced critical macro and micronutrients (Ca, Mg, Cu, Mn, and Fe) of sorghum seeds. Because both cowpea and sorghum are the staple food in many of the semi-arid tropical regions, growing them in mixed culture may be the main source of natural antioxidants, and these types of practices must be tried in these areas. Although several studies have shown that stress affects the release of these compounds, further studies are required to assess the effects of flavonoid and anthocyanin compounds in the control of pests (insects, diseases, and weeds) in mixed culture systems. Sorghum phenolic compounds: potential human health applications Currently, consumers think about their health, healthy living, and health food even when it is at a high cost (Vyas et al. 2018; Chaudhary et al. 2021). Sorghum is a nutricereal that is composed of starch, proteins, unsaturated lipids, and some minerals and vitamins. Most grain sorghum varieties are a rich source of phenolic compounds and bioactive compounds, especially 3-deoxyanthocyanidins and tannins, which have a great health impact on human gut microbiota and reduce parameters related to obesity, oxidative stress, inflammation, diabetes, dyslipidemia, cancer and hypertension (de Morais Cardoso et al. 2017). In addition to direct antioxidant effects, the sorghum phenolic compounds also induce endogenous detoxifying enzymes (phase II enzymes) that are responsible for converting the harmful reactive oxygen or nitrogen species into nontoxic compounds, thus indirectly enhancing the human body defense mechanism against oxidative stress (Awika et al. 2009; González-Montilla et al. 2012). Polyphenols against cancer Most cancers originate from DNA damage caused by carcinogenic agents, such as toxins and mutagenics, that make up reactive intermediates, such as reactive oxygen species (ROS), reactive nitrogen species (RNS), and other reactive electrophilic metabolites (Sharma and Verslues 2010). The carcinogen rate in humans is strongly dependent on the activities of phase I (cytochrome P-450) and II enzyme systems, which also remove endogenous and environmental carcinogens (Takabe et al. 2006). 3-Deoxyanthocyanidins, a sorghum phenolic compound, have a strong influence on the phase II enzyme activity, especially on the enzyme NADH: quinone oxidoreductase (NQO) activity. 3-Deoxyanthocyanidins are strong NQO inducers. Both 3-deoxyanthocyanidin standards and 3-deoxyanthocyanidin-rich sorghum extract have been reported to increase the NQO activity in some cancer cells in particular. The inducing capacity of 3-deoxyanthocyanidins on the phase II enzyme varied greatly with their structure and substitution, such as methoxylated substitution at the C-5 and C-7 positions, such as with 7-methoxyapigeninidin and 5,7-dimethoxyapigeninidin, and can significantly enhance the inducing effect on the NQO activity (Yang et al. 2009). Because black and red sorghums are rich in 3-deoxyanthocyanidins, they have strong inducing effects on NQO activity. Surprisingly, white pericarp sorghums with low 3-deoxyanthocyanidin content have significant inducing effects on the NQO, indicating the possible role of other bioactive compounds that must also be investigated. However, overall epidemiological evidence has suggested that sorghum has anti-carcinogenic properties when consumed regularly in the diet (Jideani et al. 2014). Polyphenols against dyslipidemia and cardiovascular disease Dyslipidemia may be defined as increased levels of serum total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), or decreased serum high-density lipoprotein cholesterol (HDL-C) concentration. Dyslipidemia is an established risk factor for cardiovascular disease (CVD). Various epidemiological data indicate that whole grain consumption significantly lowers mortality from CVD (Anderson 2003). Animal studies also suggest that sorghum consumption promotes cardiovascular health better than other cereals. Klopfenstein and Owen (1981) reported a cholesterol-lowering effect of low-tannin sorghum grain when fed to guinea pigs at 58% of the diet. This effect was more significant than that produced by wheat, rolled oats, or pearl millet. In vitro and animal studies have shown that the lipidic and phenolic fractions from sorghum modulate parameters are related to dyslipidemia and the risk of cardiovascular disease (de Morais Cardoso et al. 2017). These benefits result from the action of phytosterols, policosanols, and other phenolics of sorghum, which may modulate absorption, excretion, and synthesis of cholesterol. Diet supplementation with sorghum lipids reduced the hepatic and plasma cholesterol of normolipidemic hamsters (Hoi et al. 2009). The phytosterols are one of the major bioactive compounds from the sorghum lipid fraction that can inhibit cholesterol absorption. The phytosterols in the cereal brans are believed to contribute beneficial effects. Other components of the whole grains, including polyphenols and fiber, also play a role in CVD prevention. Sorghum is a significant source of phytosterols and policosanols (Singh et al. 2003). The benefits of sorghum to cardiovascular health may not be limited to positive effects on cholesterol. Lee and Pan (2003) demonstrated that dietary tannin–sorghum distillery residues inhibited 63–97% of hemoglobin-catalyzed oxidation of linoleic acid in cultured mullet fish compared to that in soybean (13%) and rice bran (78%), respectively. Overcoming oxidative stress using phenolic compounds The chronic and excessive production of free radicals in the human body is crucial in the development of non-communicable diseases (Lee et al. 2011). The activity of components isolated from sorghum against oxidative stress has been demonstrated in vitro by various workers. Moraes et al. (2012) reported that extracts from black or red sorghum, when used, produce functional benefits attributed to the phenolic compounds. Phenolic compounds isolated from sorghum regulate the expression of phase II enzymes, which play an important role in modulating the defense system against oxidative stress by continuously converting highly reactive electrophilic species (RES) into nontoxic and extractable metabolites (González-Montilla et al. 2012). Varieties of black sorghum may exert greater effects on NQO due to its rich profile and high content of 3-deoxyanthocyanidins (Devi et al. 2011). Sorghum is a rich source of other phytochemicals, pigmented or not, that acts synergistically with 3-deoxyanthocyanidins and produce high inducer activity. The effects of sorghum on oxidative stress in vivo are not well known. The superoxide dismutase activity (SOD) increased in normolipidemic rats fed with black sorghum bran (rich in 3-deoxyanthocyanidins) has been reported (Lewis et al. 2008). This increase appears to be strictly related to the action of 3-deoxyanthocyanidins present in the bran. Furthermore, white (rich in phenolic acids), brown (rich in tannins), or black (rich in 3-deoxyanthocyanidins) sorghum brans suppressed the glutathione peroxidase activity (GPx). However, the normolipidemic animals fed with whole red sorghum had lower concentrations of thiobarbituric acid reactive substances (TBARS) in their livers (Moraes et al. 2012). Anti-obesity and anti-inflammatory effects of phenolics Obesity is a pandemic correlated with various non-communicable diseases and characterized by chronic low-grade inflammation. Adipocytes and obesity play an essential role in inflammatory mediators that signal this process. The discovery that obesity itself results in an inflammatory state in metabolic tissues opened a research field that examines the inflammatory mechanisms in obesity (Greenberg and Obin 2006). This unique understanding allows a more precise understanding of the role of adipocytes in health and obesity and about how inflammatory mediators that act as signaling molecules in this process (Gregor and Hotamisligil 2011). Sorghum as a whole grain is an excellent food for people with obesity because sorghum endosperm contains high levels of resistance and relatively low starch digestibility (Barros et al. 2012). A study on rats, pigs, rabbits, and poultry suggested that tannin-rich sorghum reduces undesirable weight gain in obesity in humans (Muriu et al. 2002). Barros et al. (2013) demonstrated that sorghum polymeric tannins naturally modify starch by interacting strongly with amylose and form resistant starch. Resistant starch cannot be digested in the small intestine and thus reaches the large intestine, delivering the health benefits of dietary fiber (Sánchez-Zapata et al. 2015). Furthermore, sorghum tannins can inhibit starch digestion by inhibiting saccharase and amylase enzymes (Mkandawire et al. 2013). In another study, tannin-rich sorghums were found to be more effective than those rich in 3-deoxyanthocyanidins in inhibiting hyaluronidase, a vital enzyme associated with inflammation (Bralley et al. 2008). Diabetes: hypoglycemic effect of phenolics The commonly known forms of diabetes are T1DM, T2DM, and gestational diabetes (GD). Diabetes becomes a highly challenging health problem and is progressively prevalent globally with an estimated 1.5 million deaths per year (Ogurtsova et al. 2017). India has the world's second-largest number of people with diabetes after China (Wedick et al. 2015). This is a lifelong condition characterized by hyperglycemia in which the body is unable to secrete enough insulin. After a meal, a diabetic patient's glucose level rises intensely and prompts a fall down as the body is unable to stock the extra glucose for later use. Kam et al. (2016) reported that the use of gluten-free whole grains, such as sorghum quinoa, buckwheat, and minor millets might maintain the role of beta cells. Kim and Park (2012) have reported from animal studies that phenolic extracts of sorghum modulate glucose metabolism in animals due to the action of the phenolic compounds and exhibit a hypoglycemic effect similar to glibenclamida, an antidiabetic medication used in their control group. Industrial applications of sorghum phenolic compounds Currently, due to the adverse environmental impacts on human health and growing consumer awareness for healthy eating, there has been a great demand for foods or food ingredients that have a positive health impact. Thus, sorghum has recently attracted much attention in developed countries due to its high nutritional value and may enhance rapidly after this COVID-19 pandemic. Due to the diverse phenolic profile of sorghum and its diverse role in the food industry, its industrial application is discussed below. Sorghum phenols as nutraceuticals The use of sorghum phenolic compounds, especially tannins, for the development of functional foods and nutraceuticals is an innovative idea. First, Links et al. (2015) developed a nutraceutical by encapsulating sorghum-condensed tannins into kafirin microparticles that can withstand gastric digestion and have shown good anti-hyperglycemic effects both in vitro and in vivo (Links et al. 2015). Condensed tannins are strong gluten strengtheners, especially those with a large molecular weight and a high degree of polymerization, which are capable of forming extensive cross-linking with gluten proteins. Sorghum-condensed tannins have significantly increased dough and better viscosity and stability, thus improving food structural stability and quality. Sorghum-condensed tannin could be used as a natural ingredient to enhance the quality of gluten and enhance its functionality, suggesting its potential as a multifunctional ingredient in the food and biomedical industry (Girard et al. 2019). Antioxidative preservation of food products using sorghum bran Apart from the whole sorghum grain, sorghum bran also has a huge potential in the food industry. Sorghum bran is a high-value functional ingredient (Dykes 2019). The bran can be easily obtained by grain decortications and then used as a natural colorant and antioxidant preservative in food products to improve food quality and preservation. For instance, Luckemeyer et al. (2016) reported that the addition of 0.25–0.75% high-tannin sorghum bran to meat products, such as pre-cooked pork and turkey patties, was said to prevent lipid oxidation during storage without compromising the meat sensory flavor attributes. Similarly, Cabral et al. (2019) also noted that the addition of 0.5% high-tannin sorghum bran to pork pizza topping and dark chicken meat reduces lipid oxidation and rancid flavor. Although the addition of sorghum bran to meat products may also lead to a darker color and sorghum flavor, it does not necessarily indicate a poor meat quality or low consumer acceptance. Natural ingredients to improve food quality, safety, and health function while maintaining the sensory quality could be novel areas for future research. Production of gluten-free beers/beverages for Celiac people Sorghum provides the opportunity of producing gluten-free beers/beverages for celiac patients because it is a gluten-free cereal. Beer made of white sorghum has more than two times higher phenolic contents than barley beer, which contributes to its high antioxidant activity, and this beer also contains significant amounts of γ-aminobutyric acids with potential antihypertensive activity; it also has α-glucosidase inhibitory activity and low ethanol content. Consumption of this beer could promote human health if consumed in moderation by Celiac patients (Garzón et al. 2019). Gluten-free cookies and biscuits for diabetics Sorghum can be used to make gluten-free healthy snacks, such as cookies and biscuits for diabetics. Cookies made from tannin sorghum grain have been shown to have high phenolic contents and antioxidant activity, especially those with an antioxidant activity up to 20 times higher than wheat cookies (Chiremba et al. 2009). However, tannin sorghum cookies have low sensory acceptance despite their high antioxidant activity and great health properties. Thus, the production of nontannin sorghum cookies has great potential for commercialization and large-scale production. They have similar sensory acceptance as wheat cookies, with the phenolic contents and antioxidant activity being slightly lower than tannin sorghum cookies (Chiremba et al. 2009). Biscuits made of sorghum have been shown to reduce oxidative stress and inflammation and improve the glycemic response in people. It is an ideal alternative snack for people with obesity and diabetes (Stefoska-Needham et al. 2017). Potential animal feed additive Sorghum is a multipurpose crop and has a high demand as a fodder crop, especially in the kharif season. It also has great potential as an animal feed additive, which may improve animal health and production. Sorghum distillers’ grain, an industrial by-product from the ethanol production unit, is a cheap material used as an additive in pig and rabbit feeds. It is rich in immune activators resulting from fermentation that enhances immunity and improves animal health (Pomerenke et al. 2010). Conclusion and future prospective Modern genetic engineering and breeding tools provide exciting opportunities to develop sorghum with desirable nutritional and phenolic profiles while maintaining good agronomic performance and yield. This could be a fruitful area for further research under rapidly changing climatic conditions. It has been shown that through mutagenesis-assisted breeding, the biosynthesis of phenolic compounds can be enhanced in sorghum. A sorghum mutagenesis variant, RED for GREEN, which can significantly increase the 3-deoxyanthocyanidins, condensed tannins, and total phenolic contents in sorghum leaf, has been identified (Petti et al. 2015). Advances have also been made in breeding sorghum (germplasms ATx3363 and BTx3363) with high levels of 3-deoxyanthocyanidins in the grain pericarp and satisfying grain yield (Dykes et al. 2013). It may be concluded that various phenolic compounds from sorghum grain play a great role in overcoming biotic and abiotic stresses, such as insect pest attacks, drought, heat, and salinity. Additionally, sorghum brans can be used to fortify bread, cookies, and other snacks to improve their phytonutrient content, dietary fibers, and sensory properties, resulting in a positive effect on human health. A major limitation for their effect is their low bioavailability, which in turn depends on cultivars. Many researchers worldwide are working on a better understanding of the phenolic profile of sorghum and its specific role in overcoming biotic and abiotic stresses, which is an urgent need because of the ever-changing climatic conditions. The focus must be on finding new extraction methods to increase their bioavailability in plants and humans. Sorghum, currently consumed in developing and underdeveloped countries but one-day it will be preferred in developed countries also due to its high bioactive compound concentration which is having a beneficial impact on both plant and human health. Author contribution statement PK, RK and SKP conceived and designed the manuscript theme. PK and VK wrote the manuscript. All authors read, edited and approved the manuscript. Acknowledgements The Authors would like to thank Dr. Nisha Thakur, Assistant Professor (English), Himachal Pradesh University, Shimla, India, and Falcon Scientific Editing (https://falconediting.com) for proofreading the English language in this paper. Declarations Conflict of interest The authors declare that they have no conflict of interest. Human participants and/or animals Research did not involve human and/or animal subjects. Informed consent All authors have read and approved the submission of this work. 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==== Front Soft comput Soft comput Soft Computing 1432-7643 1433-7479 Springer Berlin Heidelberg Berlin/Heidelberg 34393647 6075 10.1007/s00500-021-06075-8 Application of Soft Computing RETRACTED ARTICLE: Enhanced bat algorithm for COVID-19 short-term forecasting using optimized LSTM https://orcid.org/0000-0002-1515-3187 Rauf Hafiz Tayyab [email protected] 1 http://orcid.org/0000-0003-0628-1416 Gao Jiechao [email protected] 2 https://orcid.org/0000-0002-8665-1669 Almadhor Ahmad [email protected] 3 Arif Muhammad [email protected] 4 Nafis Md Tabrez [email protected] 5 1 grid.6268.a 0000 0004 0379 5283 Department of Computer Science, Faculty of Engineering & Informatics, University of BRADFORD, Bradford, UK 2 grid.27755.32 0000 0000 9136 933X Department of Computer Science, University of Virginia, Charlottesville, Virginia, US 3 grid.440748.b 0000 0004 1756 6705 Department of Computer Engineering and Networks, Jouf University, Sakakah, Saudi Arabia 4 grid.411863.9 0000 0001 0067 3588 School of Computer Science, Guangzhou University, Guangzhou, 510006 China 5 grid.411816.b 0000 0004 0498 8167 Department of Computer Science and Engineering, Jamia Hamdard, New Delhi, India 11 8 2021 2021 25 20 1298912999 22 7 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The highly infectious COVID-19 critically affected the world that has stuck millions of citizens in their homes to avoid possible spreading of the disease. Researchers in different fields are continually working to develop vaccines and prevention strategies. However, an accurate forecast of the outbreak can help control the pandemic until a vaccine is available. Several machine learning and deep learning-based approaches are available to forecast the confirmed cases, but they lack the optimized temporal component and nonlinearity. To enhance the current forecasting frameworks’ capability, we proposed optimized long short-term memory networks (LSTM) to forecast COVID-19 cases and reduce mean absolute error. For the optimization of LSTM, we applied bat algorithm. Furthermore, to tackle the premature convergence and local minima problem of BA, we proposed an enhanced variant of BA. The proposed version utilized Gaussian adaptive inertia weight to control the individual velocity in the entire swarm. In addition, we substitute random walk with the Gaussian walk to observe the local search mechanism. The proposed LSTM examines the personal best solution with the swarm’s local best and preserves the optimal solution by combining the Gaussian walk. To evaluate the optimized LSTM, we compared it with the non-optimal version of LSTM, recurrent neural network, gated recurrent units, and other recent state-of-the-art algorithms. The experimental results prove the superiority of the optimized LSTM over other recent algorithms by obtaining 99.52 % accuracy. Keywords COVID-19 Gaussian distribution Gaussian inertia weight LSTM issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2021 ==== Body pmcIntroduction The entire world is experiencing a continuous pandemic called the coronavirus (COVID-19) disease due to severe acute respiratory syndrome coronavirus two (SARS-CoV-2) (Abrams et al. 2020). It has been arisen from Wuhan, the capital of Hubei Province in China, through December 2019 (WH Organization et al. 2020). The virus has been discovered on 7th January and found that it is distributed by human-to-human transmission through direct contact or droplet (Wang et al. 2020; Cucinotta and Vanelli 2020). Covid-19 was estimated to be an average incubation period of 6.4 times and a first reproduction number of 2.24–3.58. It has been spread over the entire world, and so the World Health Organization (WHO) had announced COVID-19, a worldwide outbreak on 11th March 2020 (Huang et al. 2020). COVID-19 contains a few taxonomy symbols as it belongs to the coronavirus family. All such viruses hold several essential proteins fastened in the viral membrane. As it is well worth discovering, the viral plot displays a large diameter, nearly double of a standard organic layer (Bárcena et al. 2009). The genome of SARS-CoV-2 includes six notable open-reading structures (ORFs), usually investigated in several CoVs. A number of the genes received less than 80 % nucleotide chain identification to SARS-CoV (Zhou et al. 2020). With ultraviolet warmth and rays, COVID-19 is fragile. There is a common misconception that at 27 C, this virus might have disappeared. Additionally, Covid-19 may be inactivated by chloroform, peroxyacetic acid, chlorine-containing disinfectant, ether (75 percent), except for chlorhexidine (Cascella et al. 2020). In 1995, a large-scale study proved that primary clinical symptoms are dyspnea (21.9 percent of cases), expectoration (28.2 percent of cases), fatigue or myalgia (35.8 % of cases), cough (68.6 % of cases), and ever (88.5 percent of cases). In contrast, the minor ones contain vomiting and nausea (3.9 % of cases), nausea (4.8 % of cases), headache, or nausea (12.1 % of cases) (Lq et al. 2020). The frequency of novel coronavirus, like many pathogens, is thought to transpire by respiratory droplets. Thus, the immense bulk of scattering cases is restricted to the adjacent spaces (Cascella et al. 2020). The SARS-CoV-2 is a pathogenic human coronavirus below the beta coronavirus genus. In the last decade, the two pathogenic species MERS-CoV and SARS-CoV were outbreaks in 2012 and 2002 in the Middle East and China, respectively (Lu et al. 2020; Cui et al. 2019). The laboratory of China put at the NCBI GenBank by discovering the whole genomic sequence (Wuhan-HU1) of the massive RNA virus (SARS-CoV-2) on 10th January (Yang 2020). The SARS-CoV-2 is one positive-stranded RNA virus (Lu et al. 2020). Following the WHO, no anti-inflammatory medicines and vaccines are not yet prepared for this pandemic (Basu and Chakraborty 2020), and medical industries are looking hard to acquire the vaccine. The vaccine may take at least 18–24 months until it is available, following the quick tracking of the normal vaccine interval of 5–10 decades, and may take additional time to make it appropriate for the large organizations of the world (Grenfell and Drew 2020). Additionally, we do not understand just how long a vaccine could remain successful since the virus mutates. Every attempt was adopted to slow down the coronavirus spread and prepare reasonable medical systems to protect front-line medical staff with sufficient supplies of protective equipment such as personal protective equipment (PPE) masks and other essentials. Consequently, if we know ahead of the number of new coronavirus cases for the next ten days, we could plan our necessary actions. As compared to Asian countries, the USA has been greatly affected by COVID-19. USA COVID-19 cases summary from Feb 2020 to Sep 2020 is illustrated in Fig. 1.Fig. 1 USA COVID-19 cases summary from Feb 2020 to Sep 2020 The success of healthcare technologies is a key to artificial intelligence (Panch et al. 2019). Data is structured in smart devices and increases the efficiency of healthcare machine learning (Knight et al. 2016). Several COVID-19 forecasting approaches have been proposed based on machine learning, deep learning, and statistical learning in the past few weeks. However, the primary issue is they lack the temporal components and nonlinearity in terms of machine learning where deep learning approaches are limited to comparative analysis, and uni-model forecasting (Benvenuto et al. 2020; Wieczorek et al. 2020a). Furthermore, some studies considered epidemiological models that need to make hypothesis-based parameter initialization. That model tends to low the net precision due to its under-fitting data nature (Wieczorek et al. 2020a; Gao et al. 2019). Several optimization algorithms have been used in previous studies to solve time series problems for the weight optimization of neural networks, such as the arithmetic optimization algorithm (Abualigah et al. 2021), group search optimizer (Abualigah 2020), dragonfly algorithm (Alshinwan et al. 2021), genetic algorithm (Momani et al. 2016), reproducing kernel algorithm (Arqub et al. 2017; Arqub 2017) and fuzzy conformable fractional approaches (Arqub and Al-Smadi 2020). To predict the distribution of COVID-19 in various regions, the authors used Google trend and ECDC data term frequency (Prasanth et al. 2021). To pick the successful COVID-related search words, they used Spearman correlation. The optimization of hyperparameters through the LSTM network proposed a new technique based on a meta-heuristic GWO algorithm. Three approaches are suggested (Abbasimehr and Paki 2021) that combine Bayesian optimization and deep learning. The optimized values for hyperparameters are effectively chosen by Bayesian optimization in their process. The system architecture is considered to be a process of multiple-output forecasting. Their proposed methods performed better than the reference model on data from the COVID-19 time series. In order to forecast the COVID-19 outbreak in Saudi Arabia, a study of various deep learning models is proposed (Elsheikh et al. 2021). Officially recorded data was used to evaluate the model. The optimal values of the parameters of the model that optimize the accuracy of forecasting have been determined. They used seven statistical evaluation parameters to forecast the accuracy of the model. Likewise, the previous studies on COVID-19 did not consider the hyperparameter optimization of neural networks that can help boost the performance of models. To overcome the issue as mentioned above, we proposed a deep learning model that predicts real-time transmission using optimized LSTM. For the optimization of LSTM, we employed BA. To further deals with the premature convergence (Perwaiz et al. 2020; Rauf et al. 2020b), and local minima problem (Rauf et al. 2020a) of BA, we proposed an enhanced variant of BA. The proposed version consists of two significant enhancements. Firstly, we carried out Gaussian adaptive inertia weight to control the individual velocity in the entire swarm. Secondly, we substitute the random walk with the Gaussian walk to explore the local search mechanism.Table 1 Recent related works with their dataset details and results Ref. Dataset Model Results Wieczorek et al. (2020b) Government repositories NAdam training model Accuracy above 99% Chowdhury et al. (2020) Bangladesh COVID-19 Neuro-fuzzy inference system (ANFIS) Correlation coefficient 0.75, MAPE 4.51, and RMSE 6.55 Dutta et al. (2020) WHO official CNN and RNN CNN-LSTM approach outperforms Chimmula and Zhang (2020) Dataset Canadian Health Authority LSTM Gained highly accurate results Arora et al. (2020) Indian dataset LSTM Yields high accuracy Pathan et al. (2020) The patient’s dataset of different countries RNN and LSTM Obtained optimum results Alakus and Turkoglu (2020) Laboratory data Clinical predictive models Accuracy of 86.66% and F1-score of 91.89%, Tuli et al. (2020) Data by Hannah Ritchie ML-based improved model Yields high accuracy Kavadi et al. (2020) Indian dataset Linear regression model Outperformed state-of-the-art methods Pinter et al. (2020) Data from Hungary Multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) Obtained promising results Prasanth et al. (2021) Google trend and ECDC data A hybrid GWO algorithm Reduce MAPE by 74% results Abbasimehr and Paki (2021) Live time series data Bayesian optimization-based algorithm Mean SMAPE is 0.25 results Elsheikh et al. (2021) Official data from Saudi Arabia LSTM and other variants Obtained highly accurate results Methodology Proposed BA The real-world challenges are becoming more complicated every day. Swarm intelligence (SI) is the subset of meta-heuristic algorithms employed to tackle complex optimization problems of continuous nature. We used the self-learning nature of this meta-heuristic to optimize the neural network training parameters. Such features clearly state that local interaction is essential between the swarm-based system components to preserve their survival. In this research, we have carried out an enhanced version of BA to optimize LSTM training weights. The optimized LSTM dynamically adopt optimal training parameter and decide the execution cycle timeline based on the global convergence manner of enhanced BA. We bring two modifications to classical BA. Firstly, we proposed Gaussian adaptive inertia weight to improve the velocity updating mechanism. Lastly, we update each individual’s local searching strategy to retain local solutions based on the weighted mean of their personal best and the current global solution of the entire swarm. Properties of standard BA are as follows:Every micro-bat estimates distance within surroundings and prey by utilizing its property of echolocation. Frequency of fixed range is utilized to find micro-bat’s velocity from location beside different loudness and distinct wavelength while searching for prey. Emission pulse rate increases to adjust its pulse frequency while estimating distance among prey and micro-bat. Loudness will decrease from a considerable positive value to a smaller value. BA follows three fundamental rules to converge toward an optimal solution.Each bat is represented by x¯it for i={1,2,3⋯N¯p} with the whole population N¯p in an entire search space S and use sonar echolocation to sense the prey and measure the estimated difference of the distance to the prey. During the convergence process, each bat x¯it moves with velocity v¯it and the frequency of fmint. The current position of individual can be represented by x¯ipt where p represents the partial coordinate of the current search space. The frequency fmint consolidates with bat wavelength ω and variation of loudness Ao. The variation of loudness Ao depends on the current location x¯ipt and the weighted distance Dipt. Population of fixed size Sp, in our case Sp=40, is initialized with the random initial values following the uniform distribution x¯it∈[x¯l,x¯u], where l and u are lower and upper limits of uniformly distributed sequence. After population initialization, the mutation operators are used to encourage the bats’ movement in the multidimensional search space. The ultimate objective of this phase is to obtain the new local solution, while the frequency fmintfactor controls the step-size of the solution. For each individual x¯it, the current frequency fit, current velocity v¯it and the current bats potion x¯ipt can be updated using the following equations.1 fit=fmint+fmaxt-fmint.R 2 v¯it+1=v¯it+x¯ipt-x¯igt.fit 3 x¯ipt+1=x¯ipt+v¯it+1. Referred to equation 1, fmaxt-fmint are the difference of lower and upper corresponding frequency where R indicates the random number over the interval of [0, 1]. Velocity of each individual x¯it can be updated using equation 2, where x¯ipt-x¯igt is the mean difference of local solution x¯ipt of entire swarm and global solution x¯igt of all swarms. Likewise, the new vector solution x¯ipt+1 can be determine using equation 3. In the proposed BA, we introduced Gaussian adaptive inertia weight to update the velocity in such a manner to avoid more long jumps leading to exploration and to avoid more short jumps leading to exploitation. The proposed Gaussian adaptive inertia weight can help the velocity updating mechanism achieve each individual’s optimal convergence steps. The Gaussian function can be defined as:4 fx=xe-(a-y)22z2 where (x, y, z) are real constant that can be varied over the nature of the problem. A bell shape curve in the Gaussian distribution indicates the height of bell curves and can help the population control the exploration process with the following probability density function. 5 gx=1∂2πe12a-a`2∂. In equation 5, a`=y and can be interpreted as the expected value with variance ∂2=z2. In order to generate optimal location vectors g¯it+1 through Gaussian distribution over t iterations and D dimensions, the mathematical definition following the adaptive process can be:6 g¯it+1=g¯min+g¯max-g¯min∗g¯it where g¯max-g¯min are upper and lower intervals [0, 1] of Gaussian distribution. The proposed BA utilized the following equation to update the velocity of each bat v¯git+1.7 v¯git+1=g¯it+1∗v¯it+x¯ipt-x¯igt.fit. In equation 7, g¯it+1 shows the proposed Gaussian adaptive inertia weight factor, controlling the exploration and exploitation during the entire convergence process. Gaussian bell curves in the adaptive inertia weight dynamically select each bat’s speed to help the local best vector holder bat to escape local minima. Apart from velocity v¯git+1, updated local solutions x¯ipnew play an essential role in the exploitation of bats. Consider the speed is regulated, but the newly generated local solutionsx¯ipnew are not robust enough to limit the boundary of the entire swarm’s global best x¯igt. In that case, premature convergence can be held. Standard BA uses the following equation to select the best solution among all existing vectors in the swarm:8 x¯ipnew=x¯igt+εAit. ε is a random walk generator throughout [0,1] and Ait represents the average loudness factor. The random walk can produce the best solution in the current iteration t and build the worst one in the next iteration t+1. The entire local best holder individual will likely follow the best solution x¯igt, which is the worst in the next iteration t+1 and leads to the local minima and premature convergence problem. To avoid this random selection that leads to the worst local best solution and effect exploitation, we replace this random walk with a Gaussian walk and propose a local search mechanism. Our proposed variant of BA will use the following equation to attain the local best solution x¯iGnew.9 x¯iGnew=x¯igt+g¯it+1(x¯igt-P¯igt)+εAit. In the proposed equation 9, g¯it+1 is previously computed Gaussian distribution where x¯igt-P¯igt is the mean difference of local best of swarm x¯igt and the personal best P¯igt of each bat. The proposed solution will iteratively evaluate the current best and the local best solution P¯igt for each swarm x¯igt in the population and check the following condition to use the iterative difference.10 x¯iGnew=x¯igtif(x¯igt>x¯igt)x¯igt-P¯igtOtherwise. Referred to equation 10, the new local best will be selected x¯igt if the bats’ personal best is less than the swarm local best otherwise, the weighted mean of local best x¯igtand global best P¯igt will be chosen as new local best. New N local bests x¯iGnew will likely control by the convergence rate, which can be defined by two critical factors loudness A¯it and pulse emission rate r¯it which can be update thought the following two equations.11 A¯it+1=αA¯it 12 r¯it=r¯i0[1-exp(-γt)]. Optimized Long Short-Term Memory (LSTM) Recurrent neural network (RNN) has turned out to be the most reliable algorithm for prediction as essential features are extracted automatically from samples of training (Jiang and Schotten 2020). RNN performed well at data processing, and ensured encouraging outcomes for time series prediction while keeping immense information in the internal state (Connor et al. 1994). Nevertheless, it might take much training time due to gradient detonate and evanescence problems (Tomar and Gupta 2020). Hence, in 1997 a long short-term memory RNN structure was designed by Schmidhuber and Hochreiter (Hochreiter and Schmidhuber 1997) to overcome that flaw by administering long-term dependency through multiplicative gates that will handle memory cells and flow of information in the recurrent hidden layer. LSTM’s architecture comprises four gates, i.e., input gate, output gate, control gate, and forget gate (Tomar and Gupta 2020). Input can be defined as:13 it=σ(Wi∗ht-1,xt+bi). The information extracted from the above equation can be transferred to the cell. Forget gate decides data that will be ignored from the previous layer’s input by utilizing the following equation:14 ft=σ(Wi∗ht-1,xt+bi). The input from the entire memory cell is controlled by control gate through following equations:15 C~=σ(Wc∗ht-1,xt+bc) 16 C~t=ft∗C~t-1+it∗C~t Output and hidden layer ht-1 is updated as following:17 Ot=σ(Wo∗ht-1,xt+bo) 18 ht=Ot∗tanh(C~t). The interval [-1 to 1] is normalized by using tanh, where W os the weight matrices and σ shows activation function taken as sigmoid. We feed the learning rate, momentum rate, and dropout rate in each of the LSTM dropout layers to the BA for automatic optimization of the hyperparameters. Each parameter is examined before the classification layer of LSTM to determine BA’s best optimal global solution. If the fitness function produces the same values, the proposed algorithm will check in the next generation to see if it avoids premature convergence. Hyperparameters of each hidden layer ht-1 for t={1,2,3⋯N} are optimized by providing global solution x¯iGnew obtained using equation 9. The output layer of optimized LSTM can be interpreted as:19 Ot=σWo∗ht-1x¯igtif(x¯igt>x¯igt)x¯igt-P¯igtOtherwise,xt+bo where each hidden layer choose global best of the entire population x¯igt or mean of personal best and local best of swarm x¯igt-P¯igt. The pseudocode of proposed Algorithm is presented in Algorithm 1. We also checked single parameter optimization impact on the proposed technique, and we observed that only learning rate optimization produces a negligible impact on the performance of the proposed LSTM. However, the collective optimization of the learning rate, momentum rate, and dropout rate tends to increase the overall performance of the proposed LSTM. Fig. 2 Proposed architecture of optimized LSTM Experiments WHO accounted for the outbreak of COVID-19 in states and regions around the world. Several areas of South and North America, in particular, witness the adverse effects of a massive COVID-19 explosion. The operation of huge air traffic between each state of the USA has entirely encouraged COVID-19 to propagate from its source to the next infected states; individual-to-individual spread has thus been reported among travelers worldwide. The primary goal of this research is the prediction and forecast of epidemic spreading by COVID-19. This examination contains the count of confirmed and recovered cases obtained from the WHO website regularly. We consider the USA for the experiments and employed live dataset updated daily. The utilized dataset is available at (WHO 2020). The experiments are conducted using specific python packages, namely Keras, TensorFlow, NumPy, and iplot using python language. To compare the performance of the proposed optimized LSTM, we tested other standard forecasting algorithms, i.e., Simple LSTM, GRU, and RNN. Results This study provides an optimized deep-learning model for COVID-19’s time series analysis of the USA. The proposed framework dynamically selects optimal training parameters and determines the execution cycle based on enhanced BA’s global convergence manner. The forecasting of COVID-19 was achieved in two preliminary stages: data training and evaluation. To compared the proposed variant with existing algorithms, we used five evaluation metrics; namely root mean absolute error (RMSE), mean absolute percentage error (MAPE), standard deviation (Stdev), prediction interval, and accuracy. The following equations can define RMSE, MAPE, and Stdev:20 RMSE=∑i=1N(ai-ao)2N12 where ai-ao represents squared difference forecasted and actual values.21 MAPE=1n∑ed where e indicates absolute error and d shows demand for each period.22 Stdev=1N-1∑i=1N(xi-x¯)2. In the above equation x¯ is mean of ith sample and N indicates total number of instance. The raw data is pre-processed and standardized in the initial stages and subsequently used to develop the optimized predictive model based on LSTM. The model’s boundary parameters are selected so that the MAPE can be minimized. From a particular stage on, the optimized LSTM with the optimal learning parameters is used in the testing process to predict the extent of COVID-19 cases in the USA. Table 2 presents the empirical results for confirmed and predicted cases obtained through GRU, RNN, LSTM, and optimized LSTM. RMSE shows the root mean square errors in each network during the training. MAPE is total loss subtracted from precision, where Stdev shows the significant difference between confirmed and predicted COVID-19 cases. Prediction interval represents the difference in response to confirmed cases between each day of the forecasted cases. We presented a statistical test called Kruskal–Wallis test for the experimental results, comparing the results with other published methods. The average rank, median value, and Z-score obtained through Kruskal–Wallis test for each employed algorithm is presented in Table 5.Table 2 Comparison of proposed optimized LSTM with other standard deep learning forecasting models Model RMSE MAPE Stdev Prediction interval Accuracy GRU 1786.613 30.01539 3261.895 6393.313572 70 RNN 531.3041 8.817398 970.0242 1901.247 91 LSTM 751.2309 12.12951 1371.554 2688.245 88 Optimized LSTM 32.99262 0.483875 60.23602 118.0626 99.52 Likewise, training and validation loss minimization curves using GRU, RNN, LSTM, and optimized LSTM are illustrated in Figs. 3, 4, 5, and 6. The convergence curves of real and forecasted COVID-19 cases through optimized LSTM in the USA are presented in Fig. 7.Fig. 3 Training and validation loss minimization curves using GRU Fig. 4 Training and validation loss minimization curves using RNN Fig. 5 Training and validation loss minimization curves using LSTM A comparison of the proposed optimized LSTM with other standard deep learning forecasting models is tabulated in Table 4. We take the forecasting dates from 1/9/20 to 10/9/20, and to validate the predicted values, we retain previous ten-day cases 22/8/20 to 31/8/20. Referred to Table (4), actual confirmed cases do not appear yet in the USA from 31/8/20 to 1/9/20, predicted shows the forecasted cases through existing GRU, RNN, LSTM, and proposed optimized LSTM, respectively. For validation of the performance of the proposed optimized LSTM, Fig. 8 represents the forecasting curves of several networks compared to the actual number of cases.Fig. 6 Training and validation loss minimization curves using optimized LSTM Fig. 7 Convergence of real and forecasted COVID-19 cases trough optimized LSTM in the USA Comparison of proposed optimized LSTM with other variants of LSTM and other deep learning models is given in Table 3.Fig. 8 Predicted cases comparison of optimized LSTM with GRU, RNN, and LSTM Analysis Table 2 shows that GRU obtained the worst accuracy with 1786.613 RMSE and 3261.895 Stdev, which shows a significant difference between actual and predicted COVID-cases. After GRU, standard LSTM performed better with 2688.245 prediction intervals and 12.12 MAPE. The performance of RNN is relatively good compared to GRU and LSTM with 91 % accuracy and 1371.55 Stdev. Lastly, it can be seen that the proposed version of optimized LSTM outperformed all other deep learning models with 32.99 RMSE better than GRU, 0.4838 MAPE better than LSTM, and only 60.23 significant difference among confirmed and predicted cases. Furthermore, the validation loss in the case of GRU and RNN is not stable throughout the learning process and meets greater than 0.5 and 0.7 (refer Figs. 3 and 4). From Fig. 5, the validation loss of LSTM is stable compared to GRU and RNN throughout the learning process with a greater 0.40. As opposed to GRU, LSTM, and RNN, the proposed model minimized the validation loss up to 0.04 and shows the better capability of loss minimization (refer Fig. 6). The performance of the proposed optimized LSTM can be confirmed through Fig. 7, where the USA’s actual cases on 31/8/20 were 6030587, and the predictions were 3734918, 5328279 7653031, and 6097641 using GRU, RNN, LSTM, and optimized LSTM, respectively.Table 3 Comparison of proposed optimized LSTM with other variants of LSTM and other deep learning models Model RMSE MAPE Accuracy LSTM Wieczorek et al. (2020b) – – 93.56 NAdam Wieczorek et al. (2020b) – 87.73 RMSprop Wieczorek et al. (2020b) – – 87.65 Adam Wieczorek et al. (2020b) – – 87.53 Adamax Wieczorek et al. (2020b) – – 87.47 Ftrl Wieczorek et al. (2020b) – — 40.10 Adagrad Wieczorek et al. (2020b) – – 40.10 SGD Wieczorek et al. (2020b) – – 9.8 Scenario 1 Chowdhury et al. (2020) 297.89 5425 – Scenario 2 Chowdhury et al. (2020) 216.48 23.30 – Scenario 3 Chowdhury et al. (2020) 600.61 38.06 – LSTM-1 Chimmula and Zhang (2020) 34.83 – 93.4 LSTM-2 Chimmula and Zhang (2020) 45.70 – 92.67 Convolutional LSTM Arora et al. (2020) – 5.05 – Stacked LSTM Arora et al. (2020) – 4.81 – Bidirectional LSTM Arora et al. (2020) – 3.22 – RNN Alakus and Turkoglu (2020) – – 84.00 LSTM Alakus and Turkoglu (2020) – – 90.34 CNNRNN Alakus and Turkoglu (2020) – – 86.24 CNNLSTM Alakus and Turkoglu (2020) – — 92.30 CNN Alakus and Turkoglu (2020) – – 87.35 ANN Alakus and Turkoglu (2020) 86.90 Optimized LSTM 32.99 0.48 99.52 Table 4 Comparison of proposed optimized LSTM with other standard deep learning forecasting models Date GRU RNN LSTM Optimized LSTM 1/9/20 3619310 5305265 4932695 6012715 2/9/20 3506454 5304446 4903980 6045536 3/9/20 3344938 5304747 4876812 6077771 4/9/20 3139538 5304128 4851221 6109418 5/9/20 2912792 5303244 4827170 6140511 6/9/20 2693745 5302879 4804581 6171062 7/9/20 2472994 5301480 4783279 6201055 8/9/20 2310167 5301190 4763229 6230511 9/9/20 2206934 5299707 4744368 6259414 10/9/20 2070085 5299569 4726624 6287779 Table 5 Kruskal–Wallis test: proposed LSTM vs recent state-of-the-art algorithms Model Median Ave rank Z Adagrad Wieczorek et al. (2020b) 40.100 2.5 -1.33 Adam Wieczorek et al. (2020b) 87.530 9.0 0.00 Adamax Wieczorek et al. (2020b) 87.470 8.0 -0.20 ANN Alakus and Turkoglu (2020) 86.900 6.0 -0.61 CNN Alakus and Turkoglu (2020) 87.350 7.0 -0.41 CNNLSTM Alakus and Turkoglu (2020) 92.300 13.0 0.82 CNNRNN Alakus and Turkoglu (2020) 86.240 5.0 -0.82 Ftrl Wieczorek et al. (2020b) 40.100 2.5 -1.33 LSTM-1 Chimmula and Zhang (2020) 93.400 15.0 1.22 LSTM-2 Chimmula and Zhang (2020) 92.670 14.0 1.02 LSTM Alakus and Turkoglu (2020) 90.340 12.0 0.61 LSTM Wieczorek et al. (2020b) 93.560 16.0 1.43 NAdam Wieczorek et al. (2020b) 87.730 11.0 0.41 RMSprop Wieczorek et al. (2020b) 87.650 10.0 0.20 RNN Alakus and Turkoglu (2020) 84.000 4.0 -1.02 SGD Wieczorek et al. (2020b) 9.800 1.0 -1.63 Optimized LSTM 99.520 17.0 1.63 From Table 5, it can be observed that the proposed LSTM obtained the best mean rank of 17.0 through Kruskal–Wallis test as compared to others. Advanced algorithms such as NAdam with 41 mean rank, two LSTM variants with 16 and 13 mean ranks, respectively. Similarly, the proposed LSTM outperformed other published results by obtaining the best positive Z-score of 163. We can conclude that using the proposed optimized framework can help the USA and other governments predict the actual cases with 99 % accuracy and take precautionary measures in advance. Conclusion This research offers the optimized LSTM to forecasts COVID-19 cases in the USA. Many machine learning and deep learning approaches are available to forecast confirmed cases, but they lack both the optimized temporal aspect and nonlinearity. To overcome this issue, we applied the BA for the optimization of LSTM. Besides, we implemented an enhanced BA variant to tackle BA’s premature convergence and local minima problems. The proposed version of BA used Gaussian adaptive inertia weight to control the individual velocity in the swarm. In addition, we replace the random walk with the Gaussian walk to observe the local search. The robust local search mechanism assists LSTM hyperparameter optimization during the training process. The proposed optimized LSTM is compared with GRU, RNN, and LSTM. Empirical results reveal that optimized LSTM minimized MAPE by 0.48, which is far better than the existing algorithms. In future work, we intend to adopt other evolutionary models such as the Genetic Algorithm and Differential evolution algorithm in the regression-based deep learning model for multivariate forecasting of a pandemic. Declarations Conflict of interest The authors declare that they have no conflict of interest. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent Informed consent was obtained from all individual participants included in the study. This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00500-023-08553-7" Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Change history 5/22/2023 This article has been retracted. Please see the Retraction Notice for more detail: 10.1007/s00500-023-08553-7 ==== Refs References Abbasimehr H Paki R Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization Chaos Solitons Fractals 2021 10.1016/j.chaos.2020.110511 Abrams ME, Johnson KA, Perelman SS, Zhang Ls, Endapally S, Mar KB, Thompson BM, McDonald JG, Schoggins JW, Radhakrishnan A, et al (2020) Oxysterols provide innate immunity to bacterial infection by mobilizing cell surface accessible cholesterol. Nat Microbiol, pp 1–14 Abualigah L (2020) Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 15854 10.1007/s11356-021-15854-7 Research Article Assessing the impact of green fiscal policies and energy poverty on energy efficiency Chien Fengsheng [email protected] 12 Hsu Ching-Chi [email protected] 1 Zhang YunQian [email protected] 13 Tran Tai Duc [email protected] 4 Li Li [email protected] 13 1 grid.411604.6 0000 0001 0130 6528 School of Finance and Accounting, Fuzhou University of International Studies and Trade, Fuzhou, 350202 China 2 grid.445020.7 0000 0004 0385 9160 Faculty of Business, City University of Macau, Macau, China 3 grid.445020.7 0000 0004 0385 9160 Faculty of International Tourism and Management, City University of Macau, Macau, China 4 grid.444823.d Faculty of Business Administration, Van Lang University, 45 Nguyen Khac Nhu, Dist.1, Ho Chi Minh City, Vietnam Responsible editor: Nicholas Apergis 18 8 2021 112 4 7 2021 3 8 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. This article estimates the ties between green fiscal policies and energy efficiency in COVID-19 era. For this purpose, data envelopment analysis (DEA) approach is considered and applied. The study findings show that green fiscal policies, such as public supports and tax rebates, have significant role in reducing energy poverty of different international countries by advancing energy efficiency. Therefore, a panel data ranging from 2010 to 2020 is used. Our findings indicate that the aggregate degree of green fiscal policies help to decline energy poverty. Renewable energy companies had larger series of net fiscal competence and size efficiency, and their levels of energy efficiency were greater than 0.457%, with the 16% effect of current public supports and 11% effect of taxation rebates supported to diminish energy poverty with 29.7% in different international economies. This is a positive effect by green fiscal policies. The study also presented policy implications suggesting effectively implementing green fiscal policies for more efficient carbon reduction and making climate change supportive for peoples in post COVID-19 period. Keywords Green fiscal policies Energy efficiency COVID-19 era Financing efficiency Fiscal restructuring ==== Body Introduction In past few decades, intersection of environmental deterioration, energy, and economic development has gained scholarly interest (Chien et al. 2021d). Major issue involves economic development and ecology. World economic development has increased since the turn of the eighteenth century to the detriment of electricity reliability, a natural consequence of conventional sources of energy used in production. Income activity, energy, and economic quality are thus a triad complex system—a triad. Energy efficiency is substantially dependent (Iqbal et al. 2021a, b) on resource extraction and usage since the first steam engine was created. But population expansion, economic development, and modern technology advances in the twenty-first century improved energy efficiency (Li et al. 2021a, b, c). In the past, this is very rare, creating a global climate emergency via continued use of carbon energy and associated alternative fuels (Anh Tu et al. 2021). The necessity to diversify energy is covered by two literary perspectives: first, the need to conserve and, second, the need to ensure energy supplies (Li et al. 2021a, b, c). Research on renewable energy started in 1973 following the first oil shock. After the oil crisis of 1973, energy efficiency and literature on economic development came (Iqbal et al. 2021a, b). This research examined the connection between US empirical energy and economic growth. Several studies have since modified this paradigm taking macroeconomic factors into account which may affect the connection between energy and growth (Anh Tu et al, 2021), including renewable energies, financial growth, and employment. Studies using impact categories have begun to increase as a result of climate change. The ecological impact criteria led to an increase in models, incorporating environment pollution caused by energy efficiency (Boemi and Papadopoulos 2019). Because their impact on energy conservation and efficiency is indirect and difficult to measure, government policy design and institutional structures are often ignored (Aranda et al. 2017). Lately, Chinese technology has made significant strides in energy and pollution reduction. However, China remains the world’s largest energy efficiency and emitter. Scholars across the world are worried that China will not meet its stated carbon reduction targeted (Okushima 2017). China’s energy savings and efficiency are severe, with progress falling behind goals, which may be related to failures in energy efficiency programs and programs that do not contribute to stated savings targets (Li et al, 2021a; Alemzero et al, 2021; Iqbal et al, 2021a, Li et al, 2021c; Ahmad et al, 2021; Anh Tu et al, 2021). The reason is China’s energy-saving and pollution reduction policies (Bonatz et al. 2019). They have gradually become the world’s most successful energy-saving and emission-reducing methods, chosen by major countries (Dobbins et al. 2019). China has yet to implement a carbon tax, and the nation’s emission trading system was just created in 2017, limiting its effect on energy conservation and efficiency. Whether China can support future energy savings and energy efficiency relies on whether state expenditure can save energy and decrease emissions efficiently (Xueying et al. 2021). As a result, energy conservation and emission reduction obligations must be created at all levels of government. A study of national and local government expenditure on decreasing energy efficiency in China is needed (Bednar and Reames 2020). Additionally, the increased variable variability in the Kuznets curve analysis will be minimized (Betto et al. 2020). Additional transmission channels emphasize the variety of macroeconomic variables as causal drivers for energy efficiency. Studies that emphasize the economic impact of energy efficiency rise or reduction are uncommon. Few studies have demonstrated a genuine link between government spending and environmental stewardship, with four major transmission systems: size, composition, engineering, and income. The result is increasing environmental stress as a result of economic growth. Human capital activities must be prioritized above physical assets in order to improve composition. The technological effect will increase labor efficiency by improving work routines. Higher economic levels result in increased priority and environmental demand. Backwards, Li et al. (2021a, b, c) showed experimentally that although economic freedom generates higher energy efficiency, government size is small. The only situation in which an increase in government spending may benefit the environment is if growth is in the public interest. The main aim of this study is to investigate the dynamic relationship between fiscal policy and energy efficiency from various fossil fuels by including structural breaks between 1972 and 2014 into the EKC framework. Very little infrequent study exists on the topic; however, the effect of fiscal policy is limited by incorporating public spending and tax revenue on energy efficiency—but GHG’s climate change problem can only be handled via proper fiscal response. Studies like Iqbal et al. (2021a, b) incorporated tax spending and income in the model of energy-environmental deterioration. A possible research deficiency is to ignore the structural disturbances in the fiscal policy-polluting nexus that may skew long-run parameter values. The research investigates the long-term connection between energy, income, and energy efficiency while monitoring structural disturbances and evaluating the EKC hypothesis. While this study focuses on the Turkish economy, our analysis targets Thailand. Although Halkos and Paizanos (2016) used a VAR to assess the heterogeneous effect of expanding fiscal policy on energy efficiency consumption and production, this study isolates for the first time the impact of fiscal policy on energy efficiency from various fuels, taking into account the specific characteristics of Thai energy sector. This impacts total energy efficiency given the continuing shift to natural gas as the main energy source. Ecological effects of fiscal policy may be empirically shown in Thailand’s newly industrialized net energy importing industry. A novel empirical approach is used to examine the effect of tax stimulus on renewable energy from credible facts (coal variations) and decreased poverty. This is critical if we are to evaluate fiscal policy effects on fossil fuels in Thailand and explain the connection between fiscal policy and energy efficiency. Lastly, the Zivot and Andrews single-break unit root test and the Lagrange multiplier (LM) endogenous double-breaking unit root test are employed to assess the stationary properties of the investigated series. Fourth, Maki co-integration is avoided to distort structural fractures within the co-integration link. Dols is also used to predict long-range fiscal policy parameters, such as energy poverty, and efficiency, amid a structural collapse. Moreover, this research adds value to existing studies on the greenhouse gas emissions’ impact of fiscal decentralization. These analyses see rising demand for fossil fuels as the fundamental reason of growing energy efficiency. When central government provides greater authority over fiscal expenditure to local governments, local governments are more likely to reduce energy use via subsidies and other resources. Naturally, rising municipal tax spending will not always yield to energy and carbon reductions because government spending policy lags behind when businesses or individuals comprehend greater energy savings and reduced emission requirements. Literature review Fossil fuel use has resulted in many environmental and contaminant discharges. Global warming is caused by inefficient energy usage and SO2 precursors. Severe weather has kept the globe informed (Sadiq et al. 2021; Sokołowski et al. 2020). Pollution is gaining public attention. Several countries have adopted different energy saving and emission reduction measures to reduce pollution emissions. A major developing nation, China, confronts significant energy conservation and reduction issues. According to BP statistics, China’s energy efficiency was 9232.6 million tonnes in 2017. Two hundred thirty-nine out of 338 cities in China breached the Air Quality Directive in 2017, according to CEEB statistics (70.7% of all cities). In recent years, the Chinese government has launched a variety of energy-saving and energy efficiency programs (Doukas and Marinakis 2020). The Chinese government proposed the Comprehensive Demonstration of the National Energy Saving and Energy Efficiency (ESER) Policy in 2011. With the use of specific demonstration cities, this plan seeks to improve national energy conservation and efficiency (Lin and Wang 2020). The Chinese government’s environmental deterioration in the city is evident in this legislation. The ESER plan’s success is not required for future energy savings and efficiency initiatives. As a result, the ESER policy’s impact has to be much of the world’s GDP spent on government expenditure and investment. Many governments have also implemented expansionary macroeconomic policies to help and speed up their countries; economic recovery in response to the global economic crisis of 2008. More and more research shows that fiscal spending has a significant effect on environmental deterioration. While fiscal policies are not intended to enhance environmental quality, their potential impact on environmental effectiveness and pollution levels must be assessed (Karpinska and Śmiech 2020; Othman et al. 2020). The goal of this study is to examine the relationship between environmental quality and macroeconomic variables by examining how tax policies affect energy efficiency (Li et al. 2021b; Primc and Slabe-Erker 2020). To do so, we utilize quarterly US economic statistics from 1973 to 2013. The environmental variable we use is energy efficiency, with quarterly data accessible throughout the whole study period. We differentiate between production and consumption sources of this pollutant and estimate a model that includes macroeconomic and other important factors (Lowitzsch and Hanke 2019). Other than fiscal policy, a broad variety of research has been done on air pollution (Bouzarovski 2014). In order to meet aggressive carbon reduction goals, the Chinese government has created tax refund and subsidy programs (Anh Tu et al. 2021). China has provided direct funding for R&D, interest, programs, grid subsidies, and renewable energy grid subsidies (Chien et al., 2021c). As a consequence, China has implemented value-added taxes, corporate taxes, and revenue taxes. Renewability is promoted through government incentives (Phimister et al., 2015). Federal and state support, on the other hand, aids renewable energy businesses in securing financing while increasing income streams and financial capital gaps for project viability (Li et al., 2021b). The market for financial institutions including industrial investment, lower lending rates, and operational and overall efficiency of renewables businesses is also signaled (Phimister et al. 2015). It currently offers R&D tax credits, VAT refunds, commercial incentives, and savings in energy production costs (Kyprianou et al. 2019). Companies’ cash flow choices may improve as they reduce costs and increase indirect investment (Huang et al. 2020). Free financing enables renewable energy businesses to control their resource flows. In this way, renewable energy technology is efficient and money is free (Hsu et al. 2021). Increased tax incentives will lower corporate financing costs, encourage renewable energy purchases, and improve renewable energy efficiency (Wu et al., 2021). Traditional public goods lose investments, and public funds are repaid for corporate errors that are corrected with R&D revenue (Maxim et al., 2016). The funding will enhance technical R&D’s competitive edge, extend renewable energy firms’ technological monopolies, motivate them to invest on R&D and technology, and raise performance (Maxim et al. 2016). Depending on the technique used, the quality of innovation should be similar in all safety and emission categories (Bollino and Botti 2017; Del Rosal et al. 2019). Based on their emissions footprint, new technology may harm or benefit the environment. Companies that pollute produce higher “compliance expenses” and less “consumer value” (Damigos et al. 2021). High-polluting businesses have an extra cost burden due to budget constraints. The expense of eliminating pollution must match the amount of output in new systems. Polluting businesses struggle to reduce pollution (Chien et al. 2021c; Wang et al. 2021). Enviro-innovation may therefore decrease net efficiency. Lessening pollution is expected to be less expensive than other green efficiency measures. Finally, the Porter hypothesis favors the clean industry (Day et al. 2016; Ehsanullah et al. 2021). So, healthy industries gain more from innovation than pollution producers (Barnes et al. 2011; Chien et al. 2021d). These results came by combining creative and green productivity courses. Innovators’ discoveries are likely to come from a variety of angles (Romero et al. 2018). Input is effort, and output is performance. In the past, research has demonstrated that innovations contribute to overall productivity and commercial impact (Casillas and Kammen 2010; Chien et al. 2021a; Chien et al. 2021b). However, due to concerns about innovation’s vulnerability, they will resist. Furthermore, workers are unable to start businesses owing to insurance risks (Athiyaman and Magapa 2019; Thomson et al. 2017). We may therefore evaluate the effect of development on green production (overestimating the benefits and underestimating the risks). Patenting a company’s innovations, however, would not represent the entire value. It may be attempted to steal its intellectual property and reduce its competitive edge. A stronger and more transparent green growth strategy is possible with greater climate transparency (Barnes et al. 2011; Kimanzi 2019), in this sense, green bonds. Despite this, their impact on the real economy and capital markets remains unclear. Furthermore, their distribution and remuneration were ignored (Muposhi 2019). Integration assessment systems (IAMs) cannot represent a complex system with many sector-to-sector feedback mechanisms, short-term deficits, macroeconomic circumstances, and business strategy (Li et al. 2021a, b, c; Phan and Quang Thanh 2019). Population expansion, economic development, consumer price, and energy efficiency are all variables that affect fossil fuel efficiency (Lakatos and Arsenopoulos 2019). Reducing fiscal costs may differ depending on pollution source, i.e., whether contamination is created or consumed (Streimikiene et al. 2020). Pachauri et al. (2004) suggest four different industrial pollution routes that public spending may affect. Demand for improved environmental quality is fueled by increasing income levels and government expenditure (income effect). A greater tax burden also benefits small companies (Chester and Morris 2011). Data and methodology Economic hardship, high energy costs, and inefficient systems all contribute to energy poverty. These problems should be addressed in conjunction with income growth, fuel price controls, and building energy efficiency improvements. Thus, increasing energy costs affect disadvantaged socioeconomic groups’ decreasing family income in a variety of ways depending on present conditions and financial, regulatory, and fiscal policies. Extra energy price restrictions, such as energy taxes, often impact low-income households, leading to energy poverty and a decline in living standards. However, balancing taxes with energy subsidies and direct financial aid tools like house heating allowances only provides temporary relief and does not address the root causes of energy poverty. Study data To estimate study findings and result outputs, the data is collected from the different databanks including World Bank record, energy council, world development indicators, and worldwide energy support catalogue. Oppositely, some contextual databases of all these different sample countries were also considered to validate the background dynamics and related empirical association between the constructs using real time statistical data. These include Indian Energy Ministry, Ministry of Finance Pakistan, development and reforms databases, and Bureau of Statistics Pakistan. The study includes the data ranging from 2010 to 2020. Energy poverty measurement The main aim of this study was to evaluate energy poverty using four different indexes: accessibility (percent of people with electricity intake), power feeding (per capita GDP) (time required getting electricity in days). Sufficient means the capacity and readiness of a person to use current electricity while in a city. The two metrics in issue are home energy use and home electricity generation. Residents’ technology diffusion is the first determinant of their usage, expressed in two measures (carbon free use and highly energetic utilization). The housing affordability issue now offers energy saving possibilities with two criterion (power and capital groups) and four factors. The term “accessibility” relates both to housing and economic operations and the cost aspect. The following elements are important not just for present energy poverty but also for future policies to eradicate it. In summary, there are four energy scarcity components in the overview index. The Li et al. (2021a, b, c) entropy techniques are employed, however, to measure and deduce the index of energy poverty. (Grounded on the aforementioned outlook, the chosen countries are selected to estimate the long-term prediction for the energy poverty indices, presenting the value of the j-th range or location of the i-th territory and xij I = 1, 2, ...; j = 1, 2, ...). We utilized a mutual technique to evaluate the general energy poverty directory in an optimistic manner, which is quantitatively expounded as follows: 1 xij´=xij−minxij…xnjmaxxij…xnj−minxij…xnj 2 xij´=minxij…xnj−xijmaxxij…xnj−minxij…xnj 3 pij=xij´∑i=1Nxij´ 4 ej=−k∑i=1Npijlnpij Here k = 1/Ln(n) > 0; ej ≥ 0. Grounded on Equation (4), entropy dismissal is estimated by using the following equations; more so the weight of measurement is shown in Equation (6), and all-inclusive catalog dimension of energy poverty is equated in Equation (7): 5 dj=1−ej 6 wj=dj∑j=1mdj 7 EnergyPoverty Indexi=∑j=1mwj×pij Constructing energy efficiency index For such creation of the possibility specified in a specified area by the DMU component (e.g., DMUj, (j=1,…,n)), the wiggle room functional selection of Pitch is examined. For this, an evidence backing is created that serves the DMU for empirical testing with k be x ∈Rmk+, y ϵ Rsk+, and z ϵ Rdk+ is made. Furthermore, the inventive possibility indicates the following structure for subcategories. 8 PPSstage1=xzx≥∑j=1nχjλjz≤∑j=1nzjλjj=1…n 9 PPSstage1={yz∣z≥∑j=1nzjμj,y≤∑j=1nyjμj,j=1,…,n The total impact could be affected by the insufficiency of the intermediate research. It is important to note the effect of intermediate nutrition on the effectiveness and location of the unit responsible. However, the research shows that the system is developed and linked depending on the classification techniques generated by the basic event. 10 rk=min1m∑j=1nsi−xik+1D∑d=1DTd∗zdk/1+1D∑d=1DRd∗Zdk+1S∑r=1Ssr+yrk s.t.∑j=1nλjxij+si−=xik,i=1,…,m,∑j=1j≠knμjyrj−sr+=yrk,r=1,…,s,∑j=1j≠knλjzdj=zdk+Rd∗,d=1,…,D,∑j=1j≠knλjzdj=zdk−Td∗,d=1,…,D, sr+≥yrk,r=1,…,s,si−≥0,i=1,…,m,sr+≥0,r=1,…,s,λj≥0,μj≥0,j=1,…,n. 11 maxs,λ∑i=1mRixsix+∑r=1sRrgsrg+∑f=1hRfbsfb s.t.∑j=1nxijλj+six=xij0,i=1,…,m ∑j=1ngrjλj−sjg=grj0,r=1,…,s ∑j=1nbfjλj+sfb=bfj0,f=1,…,h ∑j=1nλj=1 λj≥0,j=1,…,nsix≥0,i=1,…,m srg≥0,r=1,…,ssfb≥0,f=1,…,h 12 ξ=max∑d=1DRd+∑d=1DTd s.t.∑j=1nλjxij+si=−xik,i=1,…,m,∑j=1nμjyrj−sr=+yrk,r=1,…,s,∑j=1nλjzdj=zdk+Rd,d=1,…,D,∑j=1nμjzdj=zdk−Td,d=1,…,D, si−≥0,i=1,…,m,sr+≥0,r=1,…,s,Rd≥0,Td≥0,d=1,…,D,λj≥0,μj≥0,j=1,…,n. Empirical research structure Throughout this study, the multivariate estimate of FI conforms to what was shown in the research. We look at four aspects of green fiscal policies in this study—whether or not one has a bank’s or a mobile cash account, has insurance, has access to loans or has access to credit, and gets green fiscal economic transfers through banks or phone funds. Each component is 0.25 points and combined to produce a financial difficulty score according to Equation (1). We utilize a cut-off of 0.5 to measure financial difficulty and give a household a rating of 1 if their financial deprivation is below 0.5 and 0 otherwise (Robinson et al. 2019). 13 WR=∑t=1T∑i=1I∫01Wixfitxdx Many scholars have deduced the issue of crude oil supplies that improve energy improvements in different countries. The role of green fiscal policies is, in particular, less resolved (Bouzarovski 2018). Therefore, we referred to these nations with I and t times as Z it, with corresponding indications of 0 and 1, as expanding the function of green fiscal policies in study sample countries (Buzar 2007). 14 WiZit=∫QiZitQiPixdx Assuming the function of probability density and interruption of the availability of oil reserves, this feature is shown as a functional of measuring the analytical review in line with the role of green fiscal policies in energy poverty and energy efficiency. 15 LiZit=∫QiZitQiPixdx−Cixdx The readiness of the nations to adjust the streams of green fiscal policies is represented by −W i Z total it during the time period in i. In order to prevent a disruption in community supply of power from the whole county, people are involved economically in the domestic economy for the time T of the rebuilding process (Li et al. 2021a, b, c). 16 LiZit=∫QiZitQiPixdx−PiQi−QZit This means that energy projects are much more able to fit policies that contribute to reducing energy poverty and increasing energy efficiency by the effect of green fiscal policies. On the other side, though, the net benefit might be higher since the country makes reduced consumption of green energy sources. Results and discussion Quantitative findings Because of the significant domestic transmission losses, fuel poverty is closely linked to energy inefficiency. People with low earners have to far experienced major benefits from renewable energy upgrading, and improvements in health and medical treatment represent up to 75% of the payoff. It is clear that there are positive impacts, such as reduced energy use. Parametric test shows the connections between green fiscal policies, energy efficiency, and renewable poverty. The IFI score analyzes the relationship between elements and determines the degree of financial participation our results indicate (the strength of the association is 46% and 89%, respectively). As Cohen (1988) states that correlation coefficients between 0.10 and 0.30 are minor, between 0.30 and 0.60, between 0.60 and 1.00, and between 0.60 and 1.00 are medium in magnitude; they ought to be minor and middle size in impact. These two local find a trend greater than 0.5 for our FI index and the account indication. Thus, the association between green fiscal policies, energy efficiency, and energy inequality is quite powerful, optimistic, and significant. The statistics of the correlation coefficients in Table 1 indicate that energy consumption, human resources, GDP per capita (GDP), resource profitability, and market participation are unrelated (FI). Figure 1 also shows the energy deprivation score. Due to the low pair correlations of the model, there is no indication of autocorrelation. It is predicted that it would be extremely difficult to maximize at least 10% or up to nearly 25% of cost profits in close to zero homes with no actual policies eliminating renewable energy obstacles in reduced dwellings. Despite living in energy-efficient buildings, this group still tries to pay for energy. Around 27.2% of families struggle to pay their energy costs, and in some cases, their debt has increased over the last year (0.5%). 27.7% are investigating shutting off any energy-consuming equipment to save money. Worryingly, 46.7% of this group cannot afford any kind of energy saving to ensure basic thermal comfort with limited resources. Fortunately, this group has no health problems (84.7%), but this cannot be sustained if the heating is inadequate. Table 1. Energy efficiency score 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 USA 0.45 0.91 0.10 0.19 0.17 0.44 0.14 0.21 0.17 0.95 0.12 Italy 0.23 0.23 0.19 0.88 0.55 0.23 0.20 0.29 0.16 0.11 0.29 Canada 0.38 0.68 0.27 0.34 0.44 0.87 0.13 0.28 0.50 0.89 0.48 India 0.34 0.17 0.78 0.44 0.78 0.12 0.17 0.35 0.44 0.11 0.34 UK 0.67 0.88 0.25 0.21 0.34 0.25 0.29 0.27 0.23 0.28 0.22 Russia 0.71 0.23 0.11 0.23 0.91 0.23 0.25 0.21 0.67 0.22 0.11 Norway 0.89 0.29 0.29 0.74 0.77 0.45 0.45 0.66 0.21 0.21 0.91 Kuwait 0.27 0.12 0.23 0.82 0.37 0.44 0.71 0.78 0.10 0.27 0.23 Qatar 0.12 0.75 0.11 0.67 0.54 0.23 0.90 0.10 0.44 0.82 0.19 China 0.11 0.73 0.95 0.44 0.81 0.22 0.31 0.91 0.24 0.23 0.29 Austria 0.09 0.70 0.19 0.23 0.13 0.80 0.50 0.29 0.78 0.89 0.84 Pakistan 0.89 0.34 0.98 0.56 0.27 0.81 0.33 0.60 0.40 0.24 0.35 Germany 0.50 0.50 0.67 0.62 0.41 0.99 0.44 0.24 0.25 0.44 0.37 Spain 0.41 0.81 0.56 0.25 0.47 0.88 0.66 0.27 0.29 0.30 0.31 Thailand 0.39 0.22 0.14 0.68 0.89 0.19 0.88 0.31 0.21 0.92 0.14 Indonesia 0.20 0.25 0.31 0.11 0.80 0.31 0.29 0.48 0.36 0.71 0.91 S. Korea 0.10 0.56 0.22 0.29 0.61 0.04 0.45 0.91 0.27 0.87 0.14 Figure. 1 Energy poverty output This article offers a new technique for mapping household and municipal fuel poverty that may be used in all European countries and modified in other countries that require data sources. This novel method combines data from the general population and housing censuses with data from the household budgeting survey. EPCs were created in the European Union in 2002 to collect and share information on the energy usage of buildings. Table 2 shows weighted outputs. Table 2 Weighted outputs VX(1) EC VX(2) EE UY(1) EP UY(2) GFP USA 0.45 0.60 0.45 0.12 Italy 0.12 0.21 0.23 0.90 Canada 0.78 0.34 0.19 0.34 India 0.19 0.77 0.80 0.23 UK 0.17 0.30 0.34 0.55 Russia 0.23 0.21 0.29 0.57 Norway 0.41 0.44 0.91 0.27 Kuwait 0.34 0.22 0.34 0.21 Qatar 0.45 0.90 0.10 0.34 China 0.88 0.57 0.21 0.10 Austria 0.24 0.56 0.19 0.01 Pakistan 0.90 0.82 0.23 0.73 Germany 0.19 0.34 0.56 0.77 Spain 0.21 0.67 0.19 0.57 Thailand 0.44 0.27 0.90 0.45 Indonesia 0.32 0.19 0.12 0.06 S. Korea 0.23 0.33 0.81 0.01 In 2012, 4.9% of households spent more than 10% of their income on electricity and 7.4% on electricity + natural gas. With energy costs almost doubling between 1994 and 2013, families dedicated 2.8% of their overall energy expenditure, resulting in 7.9% of households in Atlantic Canada experiencing energy poverty. Figure.1 shows energy poverty score. Overall cost for busy times is expected to decrease to 10–8 units, while need for an off period would increase from 5 to 8 units, provided that energy poverty and social welfare are affected. The overall surplus is thus projected to decrease from 29.17 to 26.67. The findings remain constructively and country-wise positively significant. As per the models, with the lower proportion and 77% assuming the greatest percentage of remaining mistakes properly distributed, it is possible to depend on the findings. Oil robbery is a danger in maritime waters to 43% of the world’s territory, almost half of the world’s people and 46% of global assets. In rising ocean regions, 68% of theft may occur by tide and storms, while in light of the study, regional increase in sea levels can cause 47% of theft. Taking into account the main oil consumers and producers, the analyses verify the flow of funding for renewable energy in these areas. The research corresponds to Li et al. (2021a, b, c). Diverse micro-variables impact the effectiveness of renewable energy investments. A rise in energy prices reduces investment responsiveness by making companies more cautious. Capacity adjustment and market power rents are the main causes of economic value differences in renewable energy firms. Socially responsible investments’ capital structure, business size, financing methods, investor demand, knowledge base, and asset price enable their investment inputs. Factors both macroeconomic and firm-specific affect investment efficiency. The results show the potential economic value of renewable energy businesses. Determining investment gaps is difficult due to a lack of information about how government subsidies and tax incentives influence investment efficiency. Various government incentives and tax breaks could help renewable energy businesses improve their investment efficiency. A renewable energy company must evaluate whether government subsidies and tax advantages really promote excessive development. To enhance factor investment efficiency, policymakers and business executives want to identify the relationship between green fiscal policy and corporate intention. Subsidies and tax incentives complementing or replacing private sector investment are significant problems in emerging and developing countries. To estimate the long-run effects of green fiscal policies on energy poverty and energy efficiency, study used economic growth (Y) and applied the equation for VECM as shown in Equation (20). Our study considered three major areas assessing the dynamic interplay between the variables including “green fiscal policies,” “energy efficiency,” and “energy poverty” as measured previously in the “Data and methodology” section. In Equation (20), first difference is indicated with Δ, variables are indicated with β (e.g., β1, β2, and β3), dimensions of all variables are indicated with λ (e.g., λa1 to λa8, λb1 to λb8, and λc1 to λc9, respectively), time frame is shown with t, and countries are designated with i with the error term μit. Growth regression is calculated by applying an index of energy poverty indicators to the main oil producing and consuming nations. Importantly, renewable energy sources have swung the balance to a more favorable direction in this connection. A notable application of green funding may be seen with the use of renewable energy sources. Through this procedure, renewable energy sources get more assistance in order to provide a higher level of green fiscal policies. Growth hypothesis is verified by the provided data, suggesting that poverty and social wellbeing are unidirectional linked. The indirect impact of these improvements on green fiscal policies is important; however, the primary impact is the growth in energy efficiency and decrease in energy poverty. The inter-temporal link between green fiscal policies and energy poverty has become a major topic for discussion and study. The green fiscal policies of Pakistan have grown to the greatest level over the past three decades, but the shortfall at the end of 2018 rose to 7.2% of GDP compared to 3.17% yearly (see Table 3). Despite concerted efforts to eradicate global energy poverty, the issue remains with the weight of developing countries. The link between FI and energy poverty was not well received because of the many potential energy poverty measures being investigated. It is unfortunate that just a few researchers who have been investigating this problem have used a multivariate green fiscal policies score. To determine the link between energy efficiency and green fiscal policies and access to financial services and food insecurity, if the GFI is endogenous, the distance from the nearest bank must be calculated. In addition, we look at the various ways in which GFI (green fiscal policies) may contribute to fuel poverty in the household. We found FI to be harmful to energy poverty families, which results in various quasi-experimental methods. Table 3 Energy poverty index score Economies EPI USA 0.29 Italy 0.77 Canada 0.31 India 0.17 UK 0.29 Russia 0.18 Norway 0.15 Kuwait 0.30 Qatar 0.78 China 0.29 Austria 0.21 Pakistan 0.44 Germany 0.40 Spain 0.10 Thailand 0.55 Indonesia 0.59 S. Korea 0.39 Other options to the alternative FI weighting technique and the multi-faceted approach to energy poverty are also suitable. As far as family effects are concerned, FI steadily decreases energy poverty in rural rather than urban regions. GFI also contributes to reducing energy poverty issue more in men’s households. Table 4 illustrates the inverse correlation between both the components of the research: green fiscal policies, energy efficiency, and energy poverty. Five units for 4 dollars off-peak hours allow electricity to be generated off-peak hours. Likewise, at peak times, power consumption would amount to 10 units at a price of $8 per unit and would be reduced to $3 per unit by a fine economic development. Nevertheless, substantial and persistent green fiscal policies are significantly more essential for reducing electricity prices, reconciling energy poverty, and improving energy savings. Table 4 Scenario analysis output to estimate long-run perspective States Situation 1 Situation 2 USA 0.031* 0.341* Italy 0.014* 0.876* Canada 0.037* 0.401* India 0.054* 0.539* UK 0.020* 0.313 * Russia 0.038* 0.336 Norway 0.044 0.445 Kuwait 0.039 0.555* Qatar 0.111* 0.011* China 0.153* 0.001 * Austria 0.221* 0.099 Pakistan 0.191* 0.027 Germany 0.094* 0.021 Spain 0.077 0.076* Thailand 0.035 0.023* Indonesia 0.090* 0.028* S. Korea 0.072* 0.034* *Significance at P-value < 0.05 In particular, to increase economic development and to increase cash flow accessible, certain import dependence is wanted at 19%, and studies show that this has fallen by 5% in total in the past decade (e.g., 1990–2010). On a continuous sufficient scale, Germany has the most economy per unit of electricity use (of $16.48), while Canada is ranked 2 (of $14.05). As hydroelectric resources are plentiful in Korea, renewable energy ranks first with a figure of 99.67%. Pakistan, by comparison, earned the lowest, 2.51%. While the energy level of the nation remained steady in 2012, it dropped slightly. In India and Germany, energy intensity dropped from 5.37 in 2001 to 4.19 in 2015. In Qatar, the energy demand is assessed at 4.55%. Notably, research results showed that green fiscal policies contributed to a 28% reduction in energy poverty from 2010 to 2020 and 14% improvement in energy efficiency. One way or the other, with financial integration, 1% energy efficiency increase causes 2% energy global poverty in chosen nations. The role of green fiscal policies is thus obvious and important in the energy industry. Our data also indicate that green fiscal policies are affecting food insecurity via consumerism and household net income. It is feasible to integrate current global policies aimed at improving financial integration with additional policies aimed at raising per capita family net income and reducing consumer poverty. This increases green fiscal policies and reduces poverty in consumption. A regulatory strategy may include steps that reduce the average gap between financial institutions. In the financial context, it is essential to promote innovation to reduce greenhouse gas emissions. Natural resource rents have a positive and significant impact on the energy efficiency of test countries, particularly in countries with many natural resources. Our results accord with that international resource development promotes technological development for the host country. The growth of global trade and currency exchange and fuel efficiency advances has been supported by environmental assets. While technological progress may contribute to energy efficiency, it also has a positive effect on other technical advances. Anticipated theoretical findings indicate that innovation has a substantial effect on the coefficient of energy efficiency, significant at the 1% level. Innovation reduces energy consumption and improves energy efficiency. Scientifically, creativity has demonstrated a significant rise in China’s economic growth factor. Because of the positive effect of innovation on energy efficiency, businesses that utilize it may develop more contemporary equipment, reduce their energy consumption, and improve production. We are sure of our results. The regression coefficients of trade on energy efficiency in the results given by row (1) of Table 3 were positive but were modest. The impact of the industrial structure on energy efficiency is similarly negligible for increased energy efficiency but too small for the chosen countries to consider. Too far, most studies have solely considered emissions of carbon dioxide when studying environmental issues in the literature. However, carbon dioxide emissions are sometimes inappropriate for commodities such as oil, coal, and forestry. Discussion of findings Energy poverty may be reduced by adopting the following two strategies: discrete energy poverty targeting policy groups and a complete policy implementation approach for low-income households through green fiscal policies. If not, governments and policymakers should set up policies to reduce the energy and greenhouse gas emissions of residential sectors and implement them completely to reduce the effect of energy poverty in low-income households. There are three measures: per capita consumption of electricity, per capita monthly energy costs, and per capita consumption of LPG, respectively, 27.3% of which are 18% and 13.9% of HEPI values that are positive, which are the major energy variables. The study also shows the importance of energy services, particularly the role played in energy poverty by electrical equipment, dominating the energy industry, among other components. Washing machinery, laptop, or personal computers are the major contribution of 29% of equipment/appliances, followed by 21% of refreshing equipment (i.e., AC). Refrigerators, by comparison, had the lowest contribution of 6%. Forest covered area, height level, radio stations, TVs, fans, telephone devices, and conventional fuels have an adverse effect (Primc et al. 2019). The following results directly affected energy policy in the major countries of energy production and consumption. Furthermore, the welfare losses in an inelastic situation would be substantial compared to those in an elastic one. If energy instability continues in such countries, the earlier losses will be more than two times the later losses. The data also provide the income decile spending amounts in IBT and DBT systems. Under the IBT, the highest consumption would be from non-poor households at 3.9% of their income, while the poorest families would spend at least 3.1%. It is worth noting that the richest would want to spend 4.6% on comparison with other non-poor families as well (Kulinska 2017). This is because the average monthly income of the wealthiest family is very large, with a relatively low age proportion of household spending. The opposite happens under the DBT paradigm, with the poorest outperforming the energy poverty level (spending 23% of their electrical revenue) and the richest household paying at least 1.9%. In fact, the regression results of the industrial structure mediator are given as shown in Table 1 in the fifth to seventh columns. Column 5 states that VFI has a broad impact on the industrial structure and a positive influence, indicating that it adds in line with the expectations of H2B to the structure of the industry. In column 6, an expansion of 1% of the industrial structure would result in an increase in carbon emissions of 11.14%. The following may be explained: Pakistan’s secondary sector is currently the main source of industrial development. Certain older industries remain important in the development of industrial modernization. It can be inferred based on the facts above that VFI can enhance the industrial structure, which in turn will lead to improved emissions of carbon. Indirectly, approximately 0.6% of carbon emissions are caused by vertical fiscal imbalance. In addition to increasing vertical fiscal imbalance, energy efficiency growth also contributed to an additional 0.6205% of carbon emissions owing to its industrial structural impact. Furthermore, the indirect impact is greater than the direct effect, making pollution considerably worse. According to statistics, an increase in the vertical fiscal imbalance of 1% would decrease the amount of government environmental control expected by decision-makers. A significant positive connection has been observed between environmental regulation and energy efficiency. This indicates that an increase of 1% in environmental regulation leads to a reduction of 0.206% in energy efficiency. The mediator’s correlation coefficient is particularly important in column 4. An extra 0.86% increase in energy efficiency is released for every 1% increase in VFI. As a result, VFI may directly emit energy efficiency. It is feasible to assist government and environmental law in fostering environmental equality. An increase in VFI also increases carbon emissions via environmental controls, which results in an additional 0.14% of energy efficiency. Conclusion and policy implication Wasteful measures may lead to a rise in energy poverty. Low-income households are more effective in reducing the causes of energy poverty if energy poverty efforts target these households directly. Policies that are proper will help to reduce the burden of energy poverty while also aiding the attainment of medium- to long-term climate and energy goals. Reducing energy poverty and curbing the demand for it helps to lessen the economic and psychological costs of energy poverty. There is a pressing need for governments to speed up action in order to establish favorable investment conditions for energy poverty. To escape the vicious circle of inadequate cost recovery, underinvestment, and lack of public support, nations risk being locked in the vicious circle of higher cost recovery, less investment, and less public support. This will lead to a virtuous cycle in which energy poverty inspires investment in it, resulting in lower levels of energy poverty and other co-benefits while improving the overall economy and increasing public support. In order to compensate for the loss of social welfare, fiscal expenditure should be cut to counterbalance the burden on the budget of paying for the Social Security changes. Using compensation to help one kind of industry, while inhibiting another, would boost energy-intensive sectors while impeding the development of skill-intensive sectors, which are key to economic diversification. Additionally, the plans’ subsidies benefit the middle- and upper-class earners. Likewise, renewable energy sources might improve energy output while also increasing overall energy efficiency. Through this study, it has been shown that energy shortage has a significant detrimental impact on social wellbeing among energy consumers. In order to reduce this inescapable invisible deficit, the findings outlined in this report advise that government officials must identify homes who consume insufficient amounts of energy to satisfy fundamental necessities. For starters, making sure that citizens have power and a means of paying for it should be the number one priority in the creation of these programs. Besides, regulation should focus on on-grid and off-grid energy poverty in a distinct way. The best way to assist these vulnerable families having enough energy is to implement an effective energy strategy for them. To enhance household incomes and resources, we also have to link them to utilities, piped water services, and other infrastructure. In order to utilize more sustainable energy to light and heat the home, access to power is the first step. To guarantee that customers are connected to the electric grid as easily as possible, policymakers will need to use suitable measures. While power infrastructure and distribution building will continue to be constrained by technological trends, other initiatives, such as rooftop solar photovoltaics, solar farms, and tiny stand-alone generators, will help to provide convenience for distant areas. Regional policies must be devised to support distributed energy systems in this area. The results indicate that the Indian government cannot achieve its unrealistic development goals and that strong promotion of renewable energy technologies is required to reduce energy poverty in India. Aside from the above study, few studies have looked at the link between natural gas and energy poverty. This study extends the cost approach to measure fuel poverty. We employ a metric that combines the low-income high-cost (LIHC) methodology with the Italian government’s national energy plan indicator to calculate fuel poverty (PNIEC, 2019). Like the LIHC fuel poverty indicator5, we use average heating expenses from EPCs to calculate required expenditures. As a result, the hidden fuel poverty issue does not affect households forced to choose between heating and food. A renewable energy company must evaluate whether government subsidies and tax advantages really promote excessive development. To enhance factor investment efficiency, policymakers and business executives want to identify the relationship between green fiscal policy and corporate intention. Subsidies and tax incentives complementing or replacing private sector investment are significant problems in emerging and developing countries. Author contribution Write up, data curation, and supervision: Fengsheng Chien. Visualization, editing, and writing of draft: Cheng-Chi Hsu. Writing and software: YunQian Zhang. Writing, methodology, and visualization: Tai Duc Tran. Conceptualization and review: Li Li. Data availability Data is publicly available at mentioned sources in data section. Declarations Ethics approval and consent to participate We declare that we have no human participants, human data, or human issues. Consent for publication We do not have any individual person’s data in any form, and we give consent for publication in true letter and spirit. Competing interests The authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Ahmad B, Iqbal S, Hai M, Latif S (2021) The interplay of personal values, relational mobile usage and organizational citizenship behavior. Interactive Technology and Smart Education Alemzero DA, Iqbal N, Iqbal S, Mohsin M, Chukwuma NJ, Shah BA (2021) Assessing the perceived impact of exploration and production of hydrocarbons on households perspective of environmental regulation in Ghana. 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==== Front Soft comput Soft comput Soft Computing 1432-7643 1433-7479 Springer Berlin Heidelberg Berlin/Heidelberg 34421342 6103 10.1007/s00500-021-06103-7 Focus RETRACTED ARTICLE: Hybrid intelligent model for classifying chest X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic http://orcid.org/0000-0003-1079-0846 Basha Sameh H. [email protected] 12 Anter Ahmed M. [email protected] [email protected] 34 Hassanien Aboul Ella [email protected] 25 Abdalla Areeg [email protected] 1 1 grid.7776.1 0000 0004 0639 9286 Faculty of Science, Cairo University, Giza, 12613 Egypt 2 grid.508169.3 Scientific Research Group in Egypt (SRGE), Giza, 12613 Egypt 3 grid.411662.6 0000 0004 0412 4932 Faculty of Computers and Artificial Intelligence, Beni-Suef University, Benisuef, 62511 Egypt 4 grid.263488.3 0000 0001 0472 9649 School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518060 China 5 grid.7776.1 0000 0004 0639 9286 Faculty of Computers and AI, Cairo University, Giza, 12613 Egypt Communicated by Oscar Castillo. 18 8 2021 2023 27 6 34273442 30 7 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The highly spreading virus, COVID-19, created a huge need for an accurate and speedy diagnosis method. The famous RT-PCR test is costly and not available for many suspected cases. This article proposes a neurotrophic model to diagnose COVID-19 patients based on their chest X-ray images. The proposed model has five main phases. First, the speeded up robust features (SURF) method is applied to each X-ray image to extract robust invariant features. Second, three sampling algorithms are applied to treat imbalanced dataset. Third, the neutrosophic rule-based classification system is proposed to generate a set of rules based on the three neutrosophic values < T; I; F>, the degrees of truth, indeterminacy falsity. Fourth, a genetic algorithm is applied to select the optimal neutrosophic rules to improve the classification performance. Fifth, in this phase, the classification-based neutrosophic logic is proposed. The testing rule matrix is constructed with no class label, and the goal of this phase is to determine the class label for each testing rule using intersection percentage between testing and training rules. The proposed model is referred to as GNRCS. It is compared with six state-of-the-art classifiers such as multilayer perceptron (MLP), support vector machines (SVM), linear discriminant analysis (LDA), decision tree (DT), naive Bayes (NB), and random forest classifiers (RFC) with quality measures of accuracy, precision, sensitivity, specificity, and F1-score. The results show that the proposed model is powerful for COVID-19 recognition with high specificity and high sensitivity and less computational complexity. Therefore, the proposed GNRCS model could be used for real-time automatic early recognition of COVID-19. Keywords Automated Neurotrophic rule-based Reduction rule-based COVID-19 Chest X-Rays images Neurotrophic classification system issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023 ==== Body pmcIntroduction Recently, many decision-making problems have received full attention from artificial intelligence and cognitive sciences. Medical diagnosis is considered the most important decision-making problem. It is a procedure for analyzing the relationship between symptoms and diseases based on some information. Nowadays, this information is usually described as uncertain, incomplete, or inconsistent information, which is very difficult in retrieving, handling, and processing (Thanh et al. 2017; Ali et al. 2016). The neurotrophic set can handle all these problem aspects in information (Ali et al. 2016). In the last days of 2019, the whole world gets up on the new epidemiological COVID-19, one of the coronaviruses family which is highly spreading. The first cases were reported in Wuhan, China, and they spread to neighborhood countries and then the whole world. Suddenly, the world fights a monster that threatens human lives. This fight has only one weapon, which is science but with a great challenge which is time. The general characteristics of the COVID-19 infected pneumonia are fever, fatigue, dry cough, and dyspnea, which are overlapped with the symptoms of influenza, H1N1, SARS, and MERS. Moreover, these general characteristics are similar to those found in other types of coronavirus syndromes. The first challenge is to diagnose the patient with COVID-19 accurately. There are several ways of laboratory tests on patientś specimen; the most common is RT-PCR. Unfortunately, this test is expensive, and not all suspected cases can run the test. About 50%–75% of COVID-19 patients have lung abnormalities such as multi-focal ground-glass opacities or peripheral focal based on the early COVID-19 infection. During its early waves of 2020, COVID-19 caused a severe respiratory problems that reached ground-glass opacity and consolidation. According to the CT scans, these symptoms reach their peak 9–13 days (Kanne et al. 2020). CT scans and X-ray images are time-consuming and exhaustive even for expert radiologists. There is a high need for implementing a medical diagnosis system to analyze the relationship between the symptoms and COVID-19 disease. Modern medical diagnosis problems contain a huge amount of information described by some imprecision, incomplete, vagueness, and inconsistency. However, the poor information and data about the novel COVID-19 and the most symptoms of COVID-19 overlap with symptoms of other diseases. There is a high and urgent need to quickly implement a medical diagnosis system dealing with uncertain, inconsistent, and incomplete information. Therefore, this research proposes a neutrosophic-based classification model for diagnosing COVID-19 using X-ray images. Zadeh, in the mid-1960s, put the basis of the fuzzy set (FS) theory to manage vague and imprecise data. In FS theory, every element x belongs to a set A with a membership degree A(x) in [0, 1] (Zadeh 1996). Since FS is used to treat vague data, it could not treat other types of imprecision like incomplete and inconsistent data. Other types of sets have emerged from the FS-like interval-valued FS (Turksen 1986), intuitionistic FS (Atanassov 1989), and interval-valued intuitionistic FS (Atanassov 1989). These newly defined sets cannot handle all aspects of imprecision. Until Smarandache in 1995 defined neutrosophic sets (Smarandache 2002), one theory treats all aspects of imprecision and incompleteness and inconsistency. The neutrosophy concept is capable of dealing with the scope of neutralities (Wang et al. 2005). For an idea, A, the neutrosophy theory considers three terms <A>, <Neut-A> and <Anti-A>, and the last two terms are together referred to as <Non-A> (Wang et al. 2005). In contrast to fuzzy logic, NL can treat incomplete as well as inconsistent information (Smarandache 2003; Wang et al. 2005) The fundamental concepts of neutrosophic set (NS) were introduced by Smarandache in (Smarandache 2003) and Alblowi et al. in (Alblowi et al. 2013). The NS came to generalize the concept of FS and all its extensions (Arora et al. 2011). An element e is represented by the triple e(T; I; F) to mathematically indicate the element’s belongingness to a set as follows: t is its degree of belongingness, i is its indeterminacy , and f is its falsity degree, where t, i, and f take real values in T, I, and F, respectively (Smarandache 2003; Basha et al. 2017). The sets T, I, and F do not have to be intervals, rather, they may be real values: discrete or continuous; finite or not; union or intersection of various subsets (Smarandache 2003; Basha et al. 2016b). T, I, and F could be dynamically defined as vector functions or operators of set values depending on parameters like: space, time (Smarandache 2003; Hassanien et al. 2018). T(x), I(x) and F(x): X→]-0,1+[ where X is a space of points (objects). There is no constraint on their sum, i.e., -0≤supTS(x)+supIS(x)+supFS(x)≤3+. NS operators could be constructed using different ways (Ansari et al. 2013; Basha et al. 2017). Due to the power of NS to deal with incomplete, inconsistent, and uncertain information, the NS has been applied in different medical applications. For medical diagnosis, Thanh et al. in (Thanh et al. 2017) proposed a clustering algorithm in a neutrosophic advisory system. Also, based on algebraic neutrosophic logic, in (Ali et al. 2016) authors proposed NS recommender system for medical diagnosis application. Many real-time applications as in (Basha et al. 2016b, 2017, 2019; Anter and Hassenian 2019, 2018; Gaber et al. 2015; Anter et al. 2014) use NS due to its powerful characteristics in treating any type of uncertainty. The neutrosophic rule-based classification system has three main steps; (a) Neutrosophication: utilized to construct the knowledge-base (KB) model using three neutrosophic membership components; truth, indeterminacy, and falsity. In addition, the membership functions convert the crisp inputs to neutrosophic triple form <T,I,F>, (b) Inference Engine: the goal of this stage to get the neutrosophic output by applying the KB and the neutrosophic rules and (c) Deneutrosophication: in this stage, three functions analogous are applied by the neutrosophication to convert the neutrosophic output to a crisp output (Basha et al. 2016b). On the other hand, SURF is a feature extraction method suggested by Bay et al. (El-gayar et al. 2013). SURF is similar in efficiency to SIFT method and can reduce the computational complexity. SURF detects the robust key points in the images using the Hessian matrix and generates its descriptors. It helps reduce computational cost using an appropriate filter to the integral image. Also, the Haar wavelet responses are calculated to determine the orientation. Another significant issue is the imbalanced data in real-time applications. In this problem, one class enjoys bigger samples than the other(s). The minority samples tend to get misclassified because the prediction model does not have enough samples of minorities to train the algorithm. The used dataset is imbalanced as shown in Sect. 4.1. Therefore, three different sampling methods are used in our experiments to get balanced samples to solve this problem. Overall, the main contributions to predict patients with COVID-19 based on their chest X-ray images are as follows: Two experiments are conducted for automated detection of a novel COVID-19 using NRCS and genetic-based NRCS. Neurotrophic logic is proposed in this application to deal with uncertain and incomplete data. Different methods are proposed to treat the imbalance data using RUS, ROS, and SMOTE algorithms. Different experimental results and comparisons are conducted to prove the stability of the proposed GNRCS using various assessments. The remaining structure of this study is organized as follows: Sect. 2 presents some related work. Section 3 presents the background of methods involved and steps of the proposed model. Experimental results and discussion of the results are in Sect. 4. Finally, the conclusion and future work are presented in Sect. 5. Related work The spread of the COVID-19 virus motivated many researchers to develop prediction models to help authorities respond rapidly. Modern medical systems depend on X-rays and CT scans for rapid diagnosis. The pneumonia infections in the patients’ images help in this diagnosis. In (Alam et al. 2021), Alam et al. built a classified COVID-19 patient based on their chest X-ray images. They used histogram-oriented gradient (HOG) and convolutional neural network (CNN). The authors in (Madaan et al. 2021) also introduced another CNN model, called XCOVNet, for detecting COVID-19 patients in two phases. They used 392 chest X-ray images, half of which are positive and half are negative. First is the pre-processing phase and then training and tuning the model. They started with a handcrafted dataset. Then, a learning rate of 0.001 was used on Adam optimizer. Also, in (Umer et al. 2021), Umer et al. used CNN for feature extraction of X-ray images. Three filters were applied to form the edges of the images, which helps in reaching the desired segmented target of the infected area in the X-ray images. Deep learning is an intensive data approach, while the datasets of COVID-19 are comparatively small, making it hard for the machine learning approaches to reach robust and generalized results. The Keras Image Data Generator is built for augmenting the taken images. It generated four image classes, one for normal people, another for COVID-19 patients, a third class for virus pneumonia, and finally bacterial pneumonia class. In (Umer et al. 2021), the comparison of the CNN approach against VGG16 and AlexNet in predicting COVID-19 showed that CNN reached competitive results for the normal and bacterial pneumonia classes and identical in the third class. Albahli and Yar, in (Albahli and Yar 2021), also developed a deep learning multilevel pipeline model for detecting COVID-19 and other chest problems. They used the ImageNet dataset for training. The first classifier in the pipeline checks if the image is COVID-19 or normal or passes it to the second classifier for checking for the other 14 chest problems. In (Wang et al. 2021), Wang et al. worked on a 1065 CT image taken during the influenza season. The dataset has confirmed COVID-19 cases and others previously diagnosed with viral pneumonia with similar radiologic properties. They also used deep learning to distinguish the COVID-19 cases. Khan et al. in (Khan et al. 2020), developed a deep CNN model to detect COVID-19-positive cases from X-ray images that contain COVID-19 and other chest pneumonia images. They pre-trained their model on the ImageNet dataset and then trained it on two other datasets. Ozturk et al., in (Ozturk et al. 2020), developed a model, DarkNet that reached an accuracy of 98.08% for binary classification (COVID-19 or normal) and accuracy of 87.02% for three-class classification (COVID or normal or pneumonia). DarkNet was implemented using 17 conventional layers with different filters for each layer. To distinguish between positive and negative COVID-19 cases, there is a need for alternative methods that extract the most important features from X-ray images. It has been recorded that some learning models face problems like overfitting and tuning hyperparameters. Therefore, metaheuristic learning models have been utilized. Canayaz in (Canayaz 2021) used feature extraction technique for image contrast enhancement. He used different deep learning models like AlexNet, GoogleNet, VGG19, and ResNet to complete the feature extraction. And he used the metaheuristic algorithms binary PSO and binary gray wolf for optimization. Finally, he used a support vector machine for classification. Also, in (Kaur et al. 2021), Kaur et al. used AlexNet for feature extraction, and they tuned the hyperparameters using Pareto evolutionary algorithm-II. They tested their model on the four-class dataset (COVID-19, tuberculosis, pneumonia, and healthy). Neutrosophic set (NS) has many applications in the medical field. Its ability to handle inconsistency and indeterminacy paved the road for using it in the segmentation and the classification of the X-ray, CT, and MRI images (Koundal and Sharma 2019). Sangeeta and Mrityunjaya in (Siri and Latte 2017) proposed a system of three stages to extract liver images from abdominal CT scans. After the pre-processing stage to remove the noise, they transform CT images into NS images using the three NS membership functions. And finally, in the post-processing phase, they perform a morphological operation on the indeterminacy term to identify the liver boundaries with high accuracy. Anter and Hassenian, in (Anter and Hassenian 2018), introduced the neutrosophic-based segmentation method for the abdominal CT liver tumor. They used neutrosophic sets (NS), particle swarm optimization (PSO), and a fast fuzzy C-means algorithm (FFCM). They used a median filter first to increase the contrast in the images. Then, domain image was transformed to NS domain. Then, they used FFCM and PSO to optimize the neutrosophic image. Singh in (Singh 2020) used neutrosophic entropy information in image segmentation. He worked on magnetic resonance (MR) Parkinson’s disease images. He was able to segment the main regions of the MRIs compared to other methods of segmentation of images. Methods and materials Feature engineering (FE) FE is an important step in machine learning models. It extracts the interesting information of an image (features or descriptors) in a series of numbers. A feature—in image processing and computer vision—is a piece of information that carries the content of an image, i.e., interesting parts of images are efficiently captured. For example, a region in an image has certain properties. Features could be certain structures in an image like points, edges, or objects. Ideally, this information is invariant under image transformation. Therefore, the proposed model uses high-performance FE methods (GLCM, fusion, HOG, SURF). Moreover, the feature fusion is applied to show the performance of these features together on the COVID-19 chest X-ray classification problem. Gray-level co-occurrence matrix (GLCM ) is a powerful method in statistical image analysis. It uses the spatial relationship between pixels. It extracts statistical texture features. This image texture is characterized by calculating how often pairs of pixels (with specific values and in a specified spatial relationship) occur in the image. This is called GLCM. The statistical measures are extracted from this GLCM. Feature fusion method helps to learn the chest X-ray images’ feature fully. It integrates all information extracted from dataset images without losing any data. The features results from fusion are compact, thus achieving results in better computational complexity. Histogram of oriented gradients (HOG) is a FE extraction method for object detection. It counts the occurrences of the gradient orientation in a localized portion of an image, i.e., the image is broken down into smaller regions. A histogram is generated for each of these regions using the gradient and the orientation of the pixel values. Then, a gradient histogram of each pixel in the unit cell is collected. Finally, a feature vector is generated by a combination of these histograms. HOG is applied on a dense grid of uniformly spaced regions. It improves accuracy using overlapping local contrast normalization. HOG is widely used in image processing because it is robust to any geometric and optical deformations of images (Tian et al. 2016; Kapoor et al. 2018). Speeded up robust features (SURF) is a feature extraction-based method for FE. SURF is known to be a fast method and robust. It has proved its superiority over the other FE methods in the proposed model. Therefore, more details of the SURF method are discussed in the following subsection. Speeded up robust features (SURF) SURF is a new feature extraction technique for extracting distinctive local features. It uses a local invariant fast keypoint detector to extract important features from an image. SURF is a fast and robust computational feature extraction method that is applied for real-time applications such as object recognition and tracking (Oyallon and Rabin 2015). The main phases of the SURF technique can be described as follows: Keypoint extraction Feature points in the image refer to the points in corner, edge, spot, etc. The consistency of the key points can be achieved with the help of repeatability, which is useful for keypoint performance. In the SURF algorithm, the Hessian matrix (HM) is used to speed up the SURF process. By measuring HM, the maximum value point can be calculated. The following equation can be used to define HM at scale σ to a point X=(x,y) in image I:1 H(X,σ)=Lxx(x,σ)Lxy(x,σ)Lxy(x,σ)Lyy(x,σ) where Lxx(x,σ) is the convolution of Gaussian ∂2∂x2g(σ)) with image I at point X, and g(σ)=12πσ2e-x2+y22σ2, similarly for Lxy(x,σ) and Lyy(x,σ). In order to increase the speed of the SURF technique, the box filter and integral images are used, which can be calculated based on independent filter size at low computational cost. Orientation assignment Haar wavelet is used to specify the orientation of the detected key points. The Haar wavelet responses are measured in x and y directions for a collection of pixels in a circular neighborhood of 6σ radius around the detected point. Haar wavelet responses are summed up and determined to determine the dominant orientation within a sliding orientation window of size π/3. Local orientation may be found by summing up all x, y responses for each location in the orientation window. By considering the longest vector between all the windows, the orientation of the interesting point can be determined. SURF is attempting to define a reproducible orientation for the points of interest to be invariant to rotation. To achieve this, the following steps are applied. The SURF algorithm calculates the Haar-wavelet responses in X- and Y-directions, and this is for a set of pixels in a circular neighborhood of 6σ around the specified point. In addition, the sampling step depends on the scale and Haar wavelet responses. As a result, the size of the wavelets is large at high scales. For fast filtering, therefore, integral images are also used. As a result, the Haar wavelet responses are summed up and measured within the slide orientation π/3 window to determine the dominant orientation. Local orientation can be achieved by summing up all the x and y responses in the orientation window at each place. The orientation of the point of interest (PoI) can be specified by defining the longest vector between all the windows. SURF descriptors The main goal of the SURF descriptor is to provide concise and robust descriptors of the features. Descriptors may be obtained using the region surrounding the PoI. The SURF features can be determined based on the Haar wavelet responses and the integral images. The following steps are used to extract the descriptor: The first step is to create a square region clustered around the keypoint and aligned along the direction. This window is set at 20×20. This preserves valuable details about spatial information. Then, the region is divided into a 4×4 smaller squares regularly and weighted with a Gaussian centered at the PoI to provide some reliability for deformations and translations. For each sub-region, a few simple features are computed at 5×5, which are periodically spaced at sample points. For simplicity purposes, we call the Haar wavelet response in the horizontal direction dx and the Haar wavelet response in vertical direction dy. The dx and dy responses are weighted first with a Gaussian (σ=3.3) based on the key points to boost the effectiveness against geometric deformations and localization errors. After that, the dx and dx wavelet responses are summarized around every sub-region and generate a first group of entries related to the feature vector. We also extract the sum of the absolute values of, dx and dy, to carry in details about the polarity of the changes in strength. For its underlying intensity structure, every sub-region has a feature vector V, V=(∑dx,∑dy,∑dx,∑dy). These results reflect a feature vector for all sub-regions of 64 in length 4×4. These SURF features are invariant due to the lightning invariance of the Haar responses. Classification system based on neurotrophic rule-based (NRCS) The proposed NRCS model generalizes the fuzzy rule-based classification system by using neurotrophic logic instead of fuzzy logic (FL). In other words, the premises and conclusion of the “IF-THEN” rules in the NRCS are neurotrophic logic statements instead of FL. The NRCS has three steps as follows. Neutrosophication. The first stage of our classification model is to convert the crisp inputs to neutrosophic form. Build a neutrosophic knowledge base (KB) constructed using three NL membership functions: truth, indeterminacy, and falsity memberships. Inference Engine. Firing the “IF-THEN” rules on the KB to generate neutrosophic output. Deneutrosophication. Converting the neutrosophic output back to crisp one using functions analogous to those in the neutrosophication step. We explain here more details about the NRCS model. Information extraction In this phase, SURF method is used to extract the important features from the X-ray images. In SURF, the first step consists of fixing a reproducible orientation around the key point, based on information from a circular region. Then, in the second step, a squared region containing the selected orientation is constructed to extract the SURF features. The feature vector of all the sub-regions features is constructed with 64 length values. These SURF features are invariant due to the lightning invariance of the Haar responses. Moreover, the experimental results showed that SURF is a fast computation method and robust for local and invariant representation. It is thus suitable for the real-time COVID-19 diagnosis application. Neutrosophic-based rules generation phase In this phase, the crisp real values in the data set are converted into neutrosophic values using three neutrosophic membership functions as shown in Fig.1. Then, the rules are extracted and converted into neutrosophic form.Fig. 1 Truth, indeterminacy, and falsity membership functions Rule generated numerical example As a simple example to illustrate the idea of using neutrosophic “IF-Then” rules, consider 8 samples from used dataset as follows (Basha et al. 2019):0.0086542, −0.0038145, 0.0086542, ⋯, 2.04E-03, 0.0015298, Normal 0.006489, −0.00098806, 0.0065901, ⋯, 3.48E-03, 0.0018327, Normal 0.0015123, −0.002423, 0.0015123, ⋯, 2.98E-03, 0.0011059, Normal −8.35E-05, 1.31E-05, 8.35E-05, ⋯, 7.70E-03, 0.0026105, Normal 0.00065204, −0.0010234, 0.0009464 ⋯ 0.0068657, 0.0022366, Covid 0.00021982, 2.11E-05, 0.00032019 ⋯ 0.0018948, 0.0025601, Covid 0.0014582, −0.00020071, 0.0015333 ⋯ 0.0067872, 0.0019787, Covid 0.0013844, −0.0031614, 0.0013844 ⋯ 0.00059085, 0.00098422, Covid Divide these samples into training and testing sets and compute the membership degrees of each attribute. Examples of the generated “If-Then” rules for A=<Att1,Att2,Att3,⋯,Att63,Att64> are:If A=<[High , 0, 0], [High, 0, 0], [High , 0, 0],⋯, [Low , 0, 0],[Medium , 0, 0]>, then B=[Normal]. If A=<[ Low , 0, 0], [ Medium , IndetermincyLowMedium , FalseMedium ], [ Low , 0, 0],⋯, [Low , 0, 0],[ Low , 0, 0]>, then B=[Normal]. If A=<[Low , 0, 0], [High , 0, 0], [Low , 0, 0],⋯, [Medium , IndetermincyLowMedium, FalseMedium ],[ High , 0, 0]>, then B=[Covid]. If A=<[Low , 0, 0], [High , 0, 0], [Low , 0, 0],⋯, [High , 0, 0],[Medium , IndetermincyMediumHigh , FalseMedium ]>, then B=[Covid]. Bio-inspired-based rule reduction phase In recent years, bio-inspired optimization algorithms have gained popularity in developing robust and competing approaches. They have been used for solving challenging problems Darwish (2018). Genetic bee colony (GBC) algorithm, fish swarm algorithm (FSA), cat swarm optimization (CSO), whale optimization algorithm (WOA), ant lion optimization (ALO), elephant search algorithm (ESA), chicken swarm optimization algorithm (CSOA), moth flame optimization (MFO), and gray wolf optimization (GWO) algorithm are examples of state-of-the-art recent bio-inspired algorithms. Since they mimic animals in looking for food in their random or quasi-random fashion, most of these algorithms incorporate some random element, one of which is the random walk. Where the next move is predicated on only the present location/state and the transition probability to the next place, an animal’s foraging path is practically a random walk Yang (2011). The genetic algorithm (GA) is a metaheuristic algorithm that inspired the selection process in nature. It depends on the biological inspiration operations: selection, crossover, and mutation. GA is very commonly used in search, and optimization problems generate high-quality solutions. GA is one of the genetics-based machine learning (GBML) algorithms used as a machine learning tool for generating rule-based classification systems. The most popular GBML approaches are Michigan, and the Pittsburgh approaches (Ishibuchi et al. 2004). They mutually integrate GA with a rule-based system. Ishibashi and Nascimento in (Ishibashi and Nascimento 2012) combine a GA with a fuzzified rule-based system for classification and adapting parameters of the membership functions. This system can automatically generate fuzzy rules with less human participation. In (Casillas et al. 2001), J Casillas et al. proposed a method to treat the problem of the exponential growth of the fuzzy rules by increasing the features in the learning process. In (Basha et al. 2016a), a new genetic neurotrophic rule-based classification system (GNRCS) is proposed 1.Fig. 2 General structure of the proposed GNRCS model The neurotrophic “IF-THEN” rules generated from the proposed NRCS is then refined in GNRCS. We used the Michigan approach. The classification task in NRCS is improved in GNRCS using GA (Zheng et al. 2021; Mello-Romn and Hernandez 2020; Qiao et al. 2021; Pourrajabian et al. 2021; Kukker and Sharma 2021) to produce the best “If-Then” rules and remove the redundant ones. Algorithm 1 gives a summary of the GNRCS steps and shows the main phases of the proposed GNRCS model. GNRCS-based classification phase For testing, no classes are provided for the rule matrix to search for one. As in Fig. 2, the intersection percentages P={p1,p2,⋯,pm} between each testing rule (rt∈Rtesting) and all the training rules (Rtraining) are calculated, where m is the number of rules in the training set and pi is the matching percentage between rt and the training rule ri. The class label of the testing rule is the same as the one of the training rule with the maximum matching percentage. For any testing rule which does not satisfy an intersection percentage at least 50 % with the training rules (pj<0.5,∀j=1,⋯,m), the class label is determined from the exact rules set which have actual class labels. After that, this testing rule is added to the training rules instead of testing rules (Rtraining=Rtraining∪rt). Finally, the testing matrix, which has predicted class labels, is compared with the exact matrix. The confusion matrix is computed, and different metrics can be calculated, such as true positive (TP), true negative (TN), false positive (FP), and false negative (FN), to evaluate the proposed model. The complexity of any rule-based classification system depends directly on the generated rules. And here, we have that the maximum number of rules is the number of objects in the training set. The complexity of our NRBCS is O(N2∗nf), where N is the number of objects and nf is the number of extracted features. Sampling techniques for imbalanced data treatment One of the most important issues in classification problems is having imbalanced data. This problem comes from an imbalanced distribution of the classes in the given data. In imbalanced datasets, the number of samples in one class (majority) is significantly greater than the number of samples in another class(es) (minority). This results in bias in classification toward the majority class and increases the misclassification rate of the minority class. Many proposed methods deal with imbalanced data, such as (Zheng et al. 2021; He and Garcia 2009; Sun et al. 2007; Tharwat and Gabel 2020). There are three famous sampling methods (He and Garcia 2009). Random Over-Sampling (ROS): randomly reproducing samples in the minority class to balance the majority class. Random Under-Sampling (RUS): randomly selecting and removing samples in the majority class to balance the minority class. A simple idea yet results in a higher misclassification rate of the majority class due to the removal of the samples. Synthetic Minority Over-Sampling Technique (SMOTE): increase the number of the training data of the minority class by generating (not by exact coping) new samples of the minority class relying on the similarities of the current minority samples to balance the samples of the majority class (Tharwat and Gabel 2020). Experimental results and discussions We have conducted two experiments. The first explained in Sect. 4.2) targets four goals. The first is to test the NRCS model for automatic detection of the novel coronavirus (COVID-19) using different feature extraction methods. The second goal is to test the NRCS model to work with imbalanced and uncertain data sets without any pre-processing steps. The third is to compare the NRCS model and the other conventional ML methods such as MLP (Yamany et al. 2015), SVM, LDA (Tharwat 2016), DT, NS, and RF classifiers. Finally, our fourth goal is to show the strength of the other hybrid proposed model (GNRCS) in improving the NRCS model using GA on our application. In the second experiment, explained in Sect. 4.3, we have used three sampling methods: RUS, ROS, and SMOTE, in balancing the data to improve the sensitivity to improve the recognition of COVID-19. Experiments are done using Intel(R)Core(TM)2DuoCPUat200GHz, 2 GB Ram, 250 GB hard drive, and Windows 8.1. All models are self-coded in java. The tenfold cross-validation (CV) is performed, repeated ten times, and the means and the standard deviations of all measures are recorded. Dataset description The dataset in this research consists of X-ray images collected from three different open-source repositories for both genders, sharing many characteristics with the same age range 40-84; Github-COVID chest X-ray (Cohen et al. 2020), Kaggle-COVID radiography (A team of researchers from Qatar University Q Doha, the University of Dhaka 2020), and Radiopaedia (Radiopaedie 2020). The three data sets were merged, and redundant images were dropped from the final dataset used. The final dataset consists of 1885 images; 210 of them were for COVID-19 diagnosed cases and the rest 1675 were for normal persons. It is remarkably noticed the few number of the COVID-19 X-ray images. Figure 3 shows sample images of the dataset.Fig. 3 Examples of X-ray scans from the merged dataset. a the COVID-19-positive persons. b normal persons Imbalanced data without any pre-processing and any feature selection method results In this experiment, we compare the NRCS model against six well-known ML methods: MLP (Yamany et al. 2015), SVM, LDA (Tharwat 2016), DT, Naive_ Bayes (NB), and RF classifiers. The comparisons are in terms of accuracy, sensitivity, precision, specificity, and F1-score measures. Table 1 summarizes the results of this experiment. We used the actual imprecise, incomplete, vague, and inconsistent data without applying any features selection method in this experiment.Table 1 Results of the proposed NRCS method compared with different ML methods under different measurements criteria Metrics SVM NB KNN DT MLP LDA NRCS Accuracy 0.965 0.824 0.965 0.958 0.965 0.954 0.961 Precision 0.965 0.978 0.968 0.975 0.965 0.970 0.976 Sensitivity 1.0 0.838 0.996 0.982 1.0 0.982 0.979 Specificity – 0.3720 0.040 0.135 – 0.085 0.807 F1-Score 0.982 0.902 0.982 0.978 0.982 0.976 0.978 Table 1 shows that: All used methods acquire close accuracy values. Although NRCS recorded the second-best accuracy result after SVM, KNN, and MLP with a small difference, it achieves higher precision and specificity values. The specificity measure of SVM and MLP is ill-defined due to the data’s imbalanced problem. The specificity measure reflecting the problem of imbalanced data has a problem all classifiers except NRCS. Although Naive_ Bayes (NB) gets the worst accuracy among other methods, it achieves the second-best specificity. Feature extraction based methods Here, we apply different feature extraction methods GLCM, fusion, HOG, and SURF, to extract the distinctive local important features from images. We compared the results with the ones from the first experiment and summarized that in Table 3.Table 2 Comparison between the proposed models NRCS and GNRCS GLCM Fusion HOG SURF Number of Extracted Features 68 636 500 64 Time (Minutes) 32 233 150 29 Table 3 Comparison between the results by using different feature extraction methods GLCM, fusion, HOG, and SURF Measures GLCM Fusion HOG SURF Accuracy 95.8 94.2 69.9 96.1 Precision 90.1 83.8 56.9 97.6 Sensitivity 87.9 85.9 65.7 97.9 Specificity 77.7 75.5 60.2 80.7 F1-Score 88.9 84.8 60.7 97.8 From Tables 2 and 3, we can conclude that the SURF feature-extraction method resulted in less number of features and recorded the best results in all measures as well. The decrease in the number of rules extracted by the SURF method has a great impact on the execution time. It resulted in the least time consumed. Therefore, the rest of the experiment will be done using data extracted by the SURF method. NRCS vs. GNRCS Because of their distinct benefits over traditional algorithms (Oteiza et al. 2018; Gupta and Ramteke 2014), they showing very high-quality answers in many complicated real-word problems. This comes due to their ability to address multi-objective optimization problems as well as multi-solution and nonlinear formulations. Many general optimal problems have been successfully solved using evolutionary techniques such as genetic algorithms (GA) and ant lion optimization (ALO). Here, we enhanced the NRCS model by building a genetic hybrid classification system, GNRCS, for automatic detection of a novel coronavirus (COVID-19). While NL in NRCS distinguishes between the most significant, indeterminacy or neutral, and non-significant attributes, the GA is used in refining the neutrosophic rule generated from the NRCS. To prove the efficiency of the GA in our case study, an ant lion hybrid classification system combined with NRCS (ALONRCS) was implemented. The results showed that the GNRCS has achieved higher detection accuracy using fewer training rules. Table 4 shows the means and the standard deviations with respect to all measures of the comparisons between NRCS, GNRCS, and the ALONRCS.Table 4 Comparison between the proposed models NRCS, GNRCS, and ALONRCS Measures NRCS GNRCS ALONRCS Accuracy 0.961 (0.52) 0.9620(0.82) 0.946(0.012) Precision 0.976(0.63) 0.9742(0.82) 0.952(0.007) Sensitivity 0.979(0.41) 0.983(0.63) 0.989(0.015) Specificity 0.807(0.42) 0.78(0.95) 0.598(0.06) F1-Score 0.978(0.29) 0.978(0.46) 0.970(0.007) Table 4 shows that: All models obtained competitive results, though GNRCS showed its superiority. The proposed GNRCS improves overall the NRCS results. It is very close in the precision and specificity measures. The hybridization in GNRCS of the genetic and the NS captures the most significant, neutral, and non-significant attributes without using any feature selection methods, which is a result of introducing the indeterminacy term in NL. The ALONRCS has been more stable showing minimum standard deviations of all measures as a result of its capability to balance exploration and exploitation in the evolution processes. In GNRCS, GA is used in refining the neutrosophic rules. The results of this experiment show higher accuracy using despite using fewer training rules. In ALONRCS, ALO is used in refining the neutrosophic rules. The results of this experiment showed very competitive results. Natural inspired metaheuristics always include random element. They mostly include random walks or some other stochastic factor. Therefore, metaheuristic algorithms frequently employ randomization techniques, and their performance depends on the appropriate use of such randomization (Yang 2014). ALO algorithm consumed very long time which was a nature result of the random ant walking it performs, (Kiliç et al. 2018). Figure 4 shows the dramatic difference in time when using the ant lion algorithm, generating 951 training rules, while the GNRCS still showed its superiority in generating the least number of rules, 707 rules, performed in 1140 sec compared to the ALONRCS generating 951 rules in 24480 sec.Fig. 4 Number of rules and total time in seconds in NRCS, GNRCS, and ALONRCS Treating imbalance in the dataset As described in Sect. 4.1, the dataset collected is imbalanced. The final merged dataset consists of 1885 images; 210 of them were for COVID-19 diagnosed cases, and the 1675 were for normal persons, which makes the classifier tend to bias in the majority class, ignoring the minority one. Here, three sampling methods, RUS, ROS, and SMOTE, were conducted to obtain balanced data, namely RUS, ROS, and SMOTE. In the RUS method, the majority of class samples are randomly under-sampled. In the ROS method, the minority class samples are randomly over-sampled. Finally, the SMOTE algorithm increases the minority class by generating new members based on the similarity of existing members of the minority class. Table 5 shows the results of applying the three sampling methods on NRCS and GNRCS. Also Table 6 shows the results using non-parameter statistical test the Wilcoxon rank sum test which is often described as the nonparametric version of the two-sample t-test.Table 5 Comparison between NRCS and GNRCS after treating the imbalanced problem using RUS, ROS, and SMOT Metrics NRCS GNRCS Orig. RUS ROS SMOTE Orig. RUS ROS SMOTE Accuracy 0.961 0.9004 0.9870 0.987 0.9620 0.8811 0.9873 0.987 Precision 0.976 0.903 1.0 0.983 0.9742 0.8979 1.0 0.9830 Sensitivity 0.979 0895 0.975 0.998 0.983 0.8627 0.975 0.9982 Specificity 0.807 0.905 1.0 0.965 0.78 0.9 1.0 0.964 F1-Score 0.978 0.899 0.987 0.991 0.978 0.88 0.9876 0.9905 Table 6 Comparison based on Wilcoxon rank sum test between NRCS and GNRCS before and after treating the imbalanced problem using RUS, ROS, and SMOT NRCS GNRCS RUS ROS SMOTE RUS ROS SMOTE p-value 3.0161e-11 3.0161e-11 3.0199e-11 3.0199e-11 3.0180e-11 3.0180e-11 h 1 1 1 1 1 1 From the results shown in Table 5, we conclude that considering the imbalance in the dataset is important in classification. Although SMOT is famous for balancing data sets—here too, it improves the performance of the models by increasing the sensitivity and the F1-score, and ROS algorithm is doing very well in increasing the precision and the specificity without affecting the sensitivity. From the results shown in Table 6, both the p-value, and h = 1 indicate the rejection of the null hypothesis of equal medians at the default 5% significance level. This means that treating the imbalanced problem using RUS, ROS, and SMOT has significant improvement with both NRCS and GNRCS.Fig. 5 Number of rules and total time in seconds in NRCS and GNRCS without/with RUS, ROS, and SMOTE Figure 5 shows the impact of the optimization step (using the GA) on the time with both imbalanced real data and balanced using ROS, RUS, and SMOTE. However, the hybridization step balanced the data and reduced the number of generated rules dramatically. This reduction in rules helped the model to better identify new objects which resulted in improving the results. The hybrid model (GNRCS) after treating the imbalance in the dataset resulted in less set of rules and better execution time (Zheng et al. 2021).Table 7 Comparative study with already existing works Author Image size in dataset Methods Results in % (Zhang et al. 2020) 532; 506 CT images: 4; 154 positive COVID-19, common pneumonia and normal controls. two phases: Segmentation and Classification using CNN Accuracy= 92.49 Sensitivity= 94.93 Specificity= 91.13 (Yasar and Ceylan 2021) 386 positive Covid-19 and 1010 non-covid k-NN, SVM, and CNN with 23-layers Accuracy= 94.73 Sensitivity= 91.97 Specificity= 98.91 F-1 Score= 90.58 (Ardakani et al. 2020) 510 positive Covid19 and 510 normal Pre-processing and CNN. Accuracy= 78.92-99.51 Sensitivity= 78.43-100 Specificity= 68.63-100 (Apostolopoulos and Tzani 2020) 1427 images for positive COVID-19 common pneumonia, and normal. CNN Accuracy= 96.78 Specificity= 96.46 Sensitivity= 98.66 (Jaiswal et al. 2020) 1262 positive Covid-19, 1230 normal cases Different CNN architectures Accuracy= 90.9-96.25 Sensitivity= 92.06-97.35 Specificity= 89.72-962.1 F-1 Score= 91.09-96.29. (Han et al. 2020) 230 positive Covid-19 and 230 normal C3D, DeCoVNet, AD3D-MIL Algorithm Accuracy= 96.8-97.9 Sensitivity= 96.8-97.9 F-1 Score= 96.8-97.9 (Zheng et al. 2020) 540 for positive covid-19 and normal pre-processing with 2D UNet to form 3D lung mask. NN with three stages: 1- 3D convolution, 2- 3D residual blocks, 3- progressive classifier Sensitivity= 90.7 Specificity= 91.1 (Pathak et al. 2020) 413 positive Covid-19 and 439 normal Different CNN architectures Accuracy= 93.02 Sensitivity= 91.46 Specificity= 94.78 (Sun et al. 2020) 2522 of mixed images adaptive feature selection and Deep forest algorithm Accuracy= 96.35 Sensitivity= 93.05 Specificity= 89.95. (Nour et al. 2020) 2905 for positive COVID-19, viral pneumonia and normal. - Five CNNs for learning and extracting deep feature vectors. - SVM, C4.5 and k-NN classification Accuracy= 98.75 Sensitivity= 89.39 Specificity= 99.75. (Wang e al. 2020) 5372 cases. structure like DenseNet with convolution of multiple stacks Sensitivity= 78.93 Specificity= 89.93 (Ouyang et al. 2020) 3389 positive Covid-19 and 1593 normal Resnet 34 CNN Accuracy= 87.5 Sensitivity= 86.9 Specificity= 90.1 F-1 Score= 82.0 (Sakagianni et al. 2020) 349positive Covid-19 and 397 normal Auto-ML platform (Google Cloud Vision) Sensitivity= 88.31 F-1 Score= 88.31. (Hu et al. 2020) 150 positive Covid-19 and 150 normal Weakly Supervised DL Accuracy= 90.6 Sensitivity= 83.3 Specificity= 95.6 The proposed 210 positive COVID-19 and 1675 normal SURF method to extract features. NRCS and GNRCS are proposed to classifying the COVID-19 patients Different methods are proposed to treat the imbalance data using RUS, ROS, and SMOTE algorithms. Accuracy=98.7 Sensitivity= 99.8 Specificity= 96.4 F1-Score= 99.05 Comparison of results We tested the proposed NRCS model optimized by GA and hybrid ROS, RUS, SMOTE methods—to treat the imbalanced data—against other classification models used for classifying chest X-ray images of COVID-19 patients. Table 7 compares the proposed classification technique with already existing works. All the results show that our proposed model outperforms the other models. Conclusions and future work This paper proposes a novel approach to diagnosing COVID-19 patients according to chest X-ray images using neutrosophic logic and genetic algorithms in a rule-based classification system. The dataset was collected from three different publicly accessible repositories. Two novel classification methods are introduced, neutrosophic rule-based classification system and its hybridization with the genetic algorithms for refining the chosen rules. They both are used to generate “If-Then” rules. The proposed approach consists of five main phases. First is the feature extraction phase, where robust features are extracted from X-ray images based on speeded up robust features (SURF) algorithm. Second, to treat imbalanced data sets, three different sampling algorithms are used (SOMTE, ROS, and RUS). This step is essential because the original dataset was imbalanced. Third, classification rules are generated based on neutrosophic logic. The three neutrosophic membership functions (truth, indeterminacy, and falsity) are applied to convert each crisp value to neutrosophic form. Fourth, the genetic algorithm is using for refining the generated neutrosophic rules. It cleans the rules from redundancy and keeps only the most effective ones. The fifth and final stage is recognizing patients with COVID-19. Different experiments were done for evaluating our model, and results showed the superiority of the final model. In general, the results of the proposed models show promising methods in the automatic detection of COVID-19 in the early stages. As future work, we will focus on obtaining a bigger dataset by collaborating with other hospitals to bring huge cases of COVID-19 with X-ray and CT modalities. Also, we will apply different end-to-end architectures of deep learning methods for feature extraction and classification on this large dataset. More experiments and comparisons will be conducted between the proposed optimization approach and different end-to-end DL approaches. We have found that ant lion is more stable due to its capability to balance exploration and exploitation in the evolution processes; however, its extensive use of random walk consumes too much time. In the future work, we will consider treating the time problem of the ant lion using GPU and have more runs. Declarations Conflict of Interest The authors of this paper declare that there is no conflict of interest regarding its publication. This article has been retracted. Please see the retraction notice for more detail: 10.1007/s00500-023-08555-5 Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Sameh H. Basha and Ahmed M. Anter are Equal Contribution. Change history 5/22/2023 This article has been retracted. Please see the Retraction Notice for more detail: 10.1007/s00500-023-08555-5 ==== Refs References Alam NA, Ahsan M, Based MA, Haider J, Kowalski M (2021) Covid-19 detection from chest x-ray images using feature fusion and deep learning. 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==== Front Respir Med Respir Med Respiratory Medicine 0954-6111 1532-3064 The Authors. Published by Elsevier Ltd. S0954-6111(20)30350-4 10.1016/j.rmed.2020.106210 106210 Article RETRACTED: Targeting MALAT1 and miRNA-181a-5p for the intervention of acute lung injury/acute respiratory distress syndrome Liu Yaling ab125 Wang Xiaodong c5 Li Peiying b Zhao Yanhua b Yang Liqun b Yu Weifeng b∗∗3 Xie Hong a∗4 a Department of Anesthesiology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China b Department of Anesthesiology, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China c Department of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China ∗ Corresponding author. ∗∗ Corresponding author. 1 Street Address 1: 1055 Sanxiang Road, Suzhou, 215004, China. 2 Street Address 2: 160 Pujian Road, Shanghai, 200127, China. 3 Street Address: 160 Pujian Road, Shanghai, 200127, China. 4 Street Address: 1055 Sanxiang Road, Suzhou, 215004, China. 5 Yaling Liu and Xiaodong Wang contribute this work equally. 4 11 2020 12 2020 4 11 2020 175 106210106210 31 7 2020 31 10 2020 2 11 2020 © 2020 The Authors 2020 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the authors due to a reported lack of agreement among the authors. The usage of the image in E-b part of Figure 7 had not received permission from the co-author. In order to resolve the issue, the authors agreed to retract the article.	33197806	PMC8375441	NO-CC CODE	2021-09-04 01:55:20	yes	Respir Med. 2020 Dec 4; 175:106210
==== Front Int J Speech Technol Int J Speech Technol International Journal of Speech Technology 1381-2416 1572-8110 Springer US New York 34456611 9878 10.1007/s10772-021-09878-0 Article An adaptive speech signal processing for COVID-19 detection using deep learning approach Al-Dhlan Kawther A. [email protected] grid.443320.2 0000 0004 0608 0056 Information and Computer Science Department, University of Ha’il, Hail, Kingdom of Saudi Arabia 21 8 2021 2022 25 3 641649 9 12 2020 29 7 2021 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Researchers and scientists have been conducting plenty of research on COVID-19 since its outbreak. Healthcare professionals, laboratory technicians, and front-line workers like sanitary workers, data collectors are putting tremendous efforts to avoid the prevalence of the COVID-19 pandemic. Currently, the reverse transcription polymerase chain reaction (RT-PCR) testing strategy determines the COVID-19 virus. This RT-PCR processing is more expensive and induces violation of social distancing rules, and time-consuming. Therefore, this research work introduces generative adversarial network deep learning for quickly detect COVID-19 from speech signals. This proposed system consists of two stages, pre-processing and classification. This work uses the least mean square (LMS) filter algorithm to remove the noise or artifacts from input speech signals. After removing the noise, the proposed generative adversarial network classification method analyses the mel-frequency cepstral coefficients features and classifies the COVID-19 signals and non-COVID-19 signals. The results show a more prominent correlation of MFCCs with various COVID-19 cough and breathing sounds, while the sound is more robust between COVID-19 and non-COVID-19 models. As compared with the existing Artificial Neural Network, Convolutional Neural Network, and Recurrent Neural Network, the proposed GAN method obtains the best result. The precision, recall, accuracy, and F-measure of the proposed GAN are 96.54%, 96.15%, 98.56%, and 0.96, respectively Keywords COVID-19 Automatic speech recognition Generative adversarial network Mel-frequency cepstral coefficients issue-copyright-statement© Springer Science+Business Media, LLC, part of Springer Nature 2022 ==== Body pmcIntroduction COVID 19 is a respiratory contaminant due to the most severe respiratory disease, Covid 2 (SARS-CoV-2) (Trouvain & Truong, 2015). Many worldwide have an infection rate between 1 and 10% in many countries, and the condition has not been officially reported (James, 2015). Figure 1 shows the Evolution of COVID-19 cases and deaths up to august 2020. This development direction began on January 4, 2020, and has constrained numerous nations to take serious control estimates across country lockdowns and scaling-up of the confinement offices in emergency clinics (Sakai, 2015; Schuller et al., 2014). Lockdown process is valuable because it gives excellent time and scope of testing for a maximum number of patients. Reverse transcription polymerase chain reaction (RT-PCR) is one of the best methods for analyzing and detecting COVID 19 within 48 h (Ghosh et al., 2015, 2016a, 2016b; Usman, 2017).Fig. 1 Ratio of COVID-19 cases up to August 2020. Source https://arxiv.org/pdf/2005.10548.pdf The testing interaction incorporates (i) avoid social distance, it grows the chances for effectively spreading the infection, (ii) the expense of having chemical reagents and widgets, (iii) testing time is high, and (iv) obstacles in huge-scale spread. Attempts to predict a more significant number of COVID-19 cases have led to productive recommendations on innovative solutions for medical services (Botha et al., 2018; McKeown et al., 2012; Porter et al., 2019; Windmon et al., 2018). In particular, progress needs to be made to test simpler, less expensive, and more accurate diagnosis approaches (Breathing sounds for COVID-19, 2020; Indian Institute of Science, 2020; Menni et al., 2020). A few countries have changed the essential, policymaking, and economic restructuring of medical services. The attention is also focused on the purpose of diagnosis tools, innovation arrangements that can be facilitated quickly for pre-screening, and exploring less expensive options than RT-PCR test, which will overcome the chemical testing method's drawbacks. COVID 19 identification and testing development are being carried out in various laboratories around the world. The WHO and the CDC have identified speech loss as one of the main symptoms of this infectious illness, presenting as difficult coughing, a dry cough, and chest pain up to 14 days after exposure to the virus. Clinical testing projects that incorporate structural and physiological (Huber & Stathopoulos, 2015) improvements in the unpredictable respiratory system are speech breathing models. Based on our observations, we believe that speech signals might blame the shift in COVID 19 detection. Bringing together an enormous data set of breathing sounds and respiratory disease skills from clinical experts can evaluate the expected effect of utilizing breath sounds to recognize COVID-19 indications using deep learning methods (Thorpe et al., 2001). This work's primary purpose is to supplement existing chemical testing methods by replacing them with low cost, fast process, and high accuracy. This research work provides efforts in this direction. Dataset First, to generate data on healthy and unhealthy sound samples, including COVID-19 identification. The generated samples are analyzed using the proposed generative adversarial network method. It has built on assistive mathematical models that identify biomarkers from sound models. Progress should be made when creating task data at this stage. Literature survey Several studies have proposed sound features that detect symptoms and vocal signals in respiratory diseases in recent years. As the examination has focused on expanded COVID 19, ongoing works have started researching the utilization of deep neural networks by people to characterize sick dependent on cough sounds. Venkata Srikanth and Strik (2019) use Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures for breath occasion discovery as a likely pointer of COVID-19 recognition. As of late, Basheer et al. (2020) used the CNN architecture to perform direct COVID-19 symptomatic groupings dependent on cough sounds. The work in Chon et al. (2012) uses a learning step technique of deep finding out how to do a similar analysis to our own, with an F1 score of 0.929, which is not at all like the methods discussed in this article. More recently, microphones in devices, for example, cell phones and wearable devices, have been abused for voice examination. In Rachuri et al. (2010), the microphone audio is utilized to comprehend the client's current circumstance. This data is assembled to briefly look at the environmental factors in places around the city alone. In COVID-19 recognition (Nandakumar et al., 2015), a sensor recognizes clients' feelings through the telephone's receiver wild Gaussian compound models. In Oletic and Bilas (2016), Pramono et al. (2017), Praveen Sundar et al. (2020), the authors distinguished COVID-19 in the investigation using sound samples based on different machine learning methods. Proposed COVID-19 detection using speech signal The generative adversarial network with speech signal-based COVID-19 detection system is shown in Fig. 2. The proposed system consists of two stages, pre-processing and classification. The Least Mean Square filter removes the artifacts or noise from the input speech signal in the pre-processing step. After completing the pre-processing process, the GAN classifier analyses the filtering signal to classify COVID-19 and non-COVID-19 signals.Fig. 2 Block diagram of COVID-19 detection Noise reduction using LMS Typically, all biomedical signals contain noise or artifacts. Hence, before classifying the signals, we need to remove the noise or artifacts for accurate results. In this research work, the Least-Mean-Square (LMS) filtering method is used to remove the noise. As compared with other filters, the LMS decreases the variance of weights to stabilize the signal using the Lagrangian approach. This Lagrangian method has a nonlinear transformation rule, and it differentiates the input and output derivatives, which solves the optimization problem of the LMS algorithm. The LMS pre-processing steps are discussed below. LMS algorithm The optimization issues is overcome using the strategy of Lagrange multipliers. The equation of Lagrangian is given in Eq. (3)3 Lwn+1=§wn+12+Reλ∗e[n+1]n where w(n + 1) = tap weight vector, §w(n + 1) = w(n + 1) – w(n) in the tap-weight vector w (n + 1) with respect to its old worth w(n). Here λ* is known as the Lagrange multiplier, in this way getting the famous variation rule in (3) with the standardized advance size gave by μ=μ^/xn2. The last restriction is unnecessarily obstructive in open applications; therefore, an additional interesting solution is derived when we relax it. GAN classifier This section discusses the Generative Adversarial Network method's working function based on COVID-19 detection from the speech signal. The optimal threshold value of COVID-19 is above 1.2 Hz, and non-COVID-19 is below 0.60 Hz. The investigation model's unsupervised learning piece is developed for the Deep Convolution Generative Adversarial Network (GAN) design or DCGAN.DCGAN contains two main blocks known as generators and discriminators, and these blocks are trained using min–max arrangement. The Generator receives the samples from random distributions variance of output conditions. The discriminator takes samples from either the output of the generator or actual speech samples from the dataset. During training, the discriminator utilizes the cross-entropy loss function to distinguish the number of classified models completely in genuine models, and the Generator classifies the number of good ones. The mathematical calculation of real (y) and predicted (y^) values are defined in Eq. (4).4 Lw=-1N∑n=1N[ynlogy^n+1-ynlog(1-y^n)] where w = weights of learned vectors, N = size of samples. For this calculation, 1 represents the real sample, and 0 represents the generated samples. The prediction of discriminator (y^r) is computed using Eq. (5).5 LrW=-1N∑n=1Nlogy^r,n All the correct predictions are considered as zero for this case. Similarly, the y^g discrimination represents prediction. Therefore, the correct prediction of the cross-entropy function is simplified by using Eq. (6)6 LfW=-1N∑n=1N1-logy^g,n The generator also uses cross-entropy loss, which should be interpreted in terms of fallen generator outputs into the real sample. The cross-entropy loss of the Generator is computed using Eq. (7).7 LgW=-1N∑n=1Nlogy^g,n If the generator has low loss, the proposed system gives the discriminator results as accurate. This process leads the Generator to produce output and looks like an actual sample of well-trained iterations shown in Fig. 3. Both the activation of the valence classifier cross-entropy misfortune function to reduce the loss. The cross-entropy function is discussed by Eq. (7): the valence, activation classifier network, and the discrimination share layer model, which learns the characteristics. The convolution filter is effectively used for the valence classification task to activate the classification network to distinguish between actual and generated speech samples.Fig. 3 The architecture of GAN classifier Figure 4 discusses the overall process for describing the proposed Deep Convolution Generative Adversarial Network with record cough-breath sound, extract audio features, split the training/testing ratio, and performance validation. The testing and training ratio is 80:20. The classification response of the proposed COVID-19 detection system's performance is validated using precision, recall, and accuracy. Compared to other deep learning methods, GAN does not require labeled data; they can be trained using unlabeled data to learn the data's internal representations. So the performance is automatically improved.Fig. 4 Overall process of proposed method Precision It is the fraction of relevant speech samples among the retrieved speech samples. The mathematical formula of precision is shown in Eq. (8).8 PrecisionP=TpTp+Fp Recall It is the fraction of retrieved relevant speech samples among all relevant speech samples. The mathematical formula of recall is shown in Eq. (9).9 RecallR=TpTp+Fn Accuracy Accuracy is the ratio of correctly classify the COVID-19 samples from the total number of samples. The following Eq. (10) is used to compute the accuracy.10 Accuracy=Tp+TnTp+Tn+Fp+Fn where Tp = true positive, Tn = true negative, Fp = false positive, Fn = false negative. Simulation results and discussion Simulation results and performance analysis of the proposed COVID 19 detection system are discussed in this section. This work aims to classify speech samples from normal and abnormal people, include to identifying COVID-19 patients. The input speech signal of the proposed COVID-19 detection is depicted in Fig. 5. The input signal's frequency range is 8 kHz.Fig. 5 Noisy signal Time-domain representation of proposed Generative Adversarial Neural Network-based COVID-19 detection is shown in Fig. 6.Fig. 6 Time domain representation of the desired signal The proposed Generative Adversarial Neural Network-based time-domain representation of the noise signal of COVID-19 detection is shown in Fig. 7.Fig. 7 Time domain representation of noise signal The proposed Generative Adversarial Neural Network-based time and frequency response of the filtered signal COVID-19 detection is shown in Fig. 8.Fig. 8 Time and frequency response of a filtered signal Figure 9 shows the Spectrogram of the pre-processed speech signal. The Spectrogram splits the Window that allows overlapping elements in each section with windows notation.Fig. 9 Spectrogram of a speech signal Figure 10 shows the simulation results of validation accuracy and loss in training. The proposed COVID-19 detection system reduces the validation loss and increases the validation accuracy, making the model learning low mean squared error.Fig. 10 Validation accuracy and loss during the training Figure 11 and Table 1 discuss the performance analysis of the proposed COVID-19 classification system with existing methods. As compared with existing methods, the proposed GAN method achieves a good result. The precision, recall, accuracy and F-measure are 96.54%, 96.15%, 98.56% and 0.96% respectively.Fig. 11 Performance analysis of classification ratio Table 1 Performance evaluation of classification ratio Methods Precision (%) Recall (%) Accuracy (%) F-measure (%) ANN 70 86.10 75.883 0.86 CNN 92.65 94.12 93.47 0.89 RNN 94.16 89.65 89.13 0.91 GAN 96.54 96.15 98.56 0.97 Conclusion This research work introduces Generative Adversarial Network for the detection of COVID-19 symptoms from a speech signal. Typically, speech signals contain intrinsic information regarding the physiological as well as emotional conditions of humans. Accurate measurement of such physiological parameters using speech signals has facilitated real-time, remote monitoring of infected/symptomatic individuals and early detection of COVID-19 symptoms, resulting in containing the spread of the infection. The reverse transcription-polymerase chain reaction (RT-PCR) testing strategy is used to determine the COVID-19 virus. This RT-PCR processing is more expensive and inducing social distancing rules violation, and time-consuming. Therefore, this research work introduces the Generative Adversarial Network (GAN) based deep learning method to detect COVID-19 from speech signals quickly. As compared with existing methods, the proposed GAN method achieves a good result. The precision, recall, accuracy, and F-measure are 96.54%, 96.15%, 98.56%, and 0.96, respectively. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Basheer S Anbarasi M Sakshi DG Efficient text summarization method for blind people using text mining techniques International Journal of Speech Technology 2020 23 713 725 10.1007/s10772-020-09712-z Botha G Theron G Warren R Klopper M Dheda K Van Helden P Niesler T Detection of tuberculosis by automatic cough sound analysis Physiological Measurement 2018 10.1088/1361-6579/aab6d0 Breathing sounds for COVID-19. Retrieved from May 8, 2020, from https://breatheforscience.com/ Chon, Y., Lane, N. D., Li, F., Cha, H., & Zhao, F. (2012). Automatically characterizing places with opportunistic crowdsensing using smartphones. In: Proceedings of the ACM Conference on Ubiquitous Computing (UbiComp). Pittsburgh, PA, pp. 481–490. Ghosh, S., Laksana, E., Morency, L.-P., & Scherer, S. (2015). Learning representations of effect from speech. 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==== Front Ther Innov Regul Sci Ther Innov Regul Sci Therapeutic Innovation & Regulatory Science 2168-4790 2168-4804 Springer International Publishing Cham 34426954 334 10.1007/s43441-021-00334-4 Original Research Seasonal and Secular Periodicities Identified in the Dynamics of US FDA Medical Devices (1976–2020): Portends Intrinsic Industrial Transformation and Independence of Certain Crises http://orcid.org/0000-0003-3648-023X Daizadeh Iraj [email protected] grid.419849.9 0000 0004 0447 7762 Takeda Pharmaceuticals, 40 Landsdowne St., Cambridge, MA 02139 USA 23 8 2021 2022 56 1 104116 24 7 2021 13 8 2021 © The Drug Information Association, Inc 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The US Food and Drug Administration (FDA) regulates medical devices (MD), which are predicated on a concoction of economic and policy forces (e.g., supply/demand, crises, patents), under primarily two administrative circuits: premarketing notifications (PMN) and Approvals (PMAs). This work considers the dynamics of FDA PMNs and PMAs applications as an proxy metric for the evolution of the MD industry, and specifically seeks to test the existence [and, if so, identify the length scale(s)] of economic/business cycles. Beyond summary statistics, the monthly (May, 1976 to December, 2020) number of observed FDA MD Applications are investigated via an assortment of time series techniques (including: discrete wavelet transform, running moving average filter, complete ensemble empirical mode with adaptive noise decomposition, and Seasonal Trend Loess decomposition) to exhaustively seek and find such periodicities. This work finds that from 1976 to 2020, the dynamics of MD applications are (1) non-normal, non-stationary (fractional order of integration < 1), non-linear, and strongly persistent (Hurst > 0.5); (2) regular (non-variance), with latent periodicities following seasonal, 1-year (short-term), 5–6 year (Juglar; mid-term), and a single 24-year (Kuznets; medium-term) period (when considering the total number of MD applications); (3) evolving independently of any specific exogenous factor (such as the COVID-19 crisis); (4) comprised of two inversely opposing processes (PMNs and PMAs) suggesting an intrinsic structural industrial transformation occurring within the MD industry; and, (6) predicted to continue its decline (as a totality) into the mid-2020s until recovery. Ramifications of these findings are discussed. Supplementary Information The online version contains supplementary material available at 10.1007/s43441-021-00334-4. Keywords Business cycles Medical devices FDA policy Regulatory science Economic dynamics issue-copyright-statement© The Drug Information Association, Inc 2022 ==== Body pmcIntroduction The history of the United States (US) medical device (MD) industry is one of innovation—a complicated evolution encompassing a very broad everyday (e.g., general purpose thermometers) and specialized (e.g., human-embeddable systems) medical products. At some point in time, each registered MD—no matter how menial from today’s vantage point—is an outcome of certain investments. On a company or sector level, these investments have not only included the demand / supply side variables (such as the various people, processes, and systems required of a research and development firm to idealize, actualize, market, and secure economic rents from the sale of a MD product) but also to meet the national policies enforced by one or more national health bodies to ensure the MD’s safe intended use [1]. Thus, an important assignment would be to identify and investigate metrics of output that may be used as proxies to track certain aspects of the sector, including its innovativeness and its general health. Globally, the MD development process requires supervision and registration with a local health agency, which in the US would be the Food and Drug Administration (FDA)’s Center for Devices and Radiological Health (CDRH).1 Starting in 1976, CDRH received congressional mandates to ensure the safe and appropriate use of MDs via the Medical Device Amendments to the Federal Food, Drug, and Cosmetic (FD&C) Act,2 and subsequent legislation (see Table 1).Table 1 Some milestones in FDA device regulation since 1970) Year US Drug Regulation 1970 Cooper Committee is established, which “recommended that any new legislation be specifically targeted to devices because devices present different issues than drugs” 1976 Medical Device Amendments to the Federal Food, Drug, and Cosmetic (FD&C) Act 1990 Safe Medical Devices Act (SMDA) 1992 Mammography Quality Standards Act (MQSA) 1997 FDA Modernization Act (FDAMA) 2002 Medical Device User Fee and Modernization Act (MDUFMA) 2007 FDA Amendments Act (FDAAA), MDUFA II 2012 FDA Safety and Innovation Act (FDASIA), MDUFA III 2016 21st Century Cures Act 2017 FDA Reauthorization Act (FDARA), MDUFA IV https://www.fda.gov/medical-devices/overview-device-regulation/history-medical-device-regulation-oversight-united-states In total, the above laws allowed the FDA to develop regulations that offered an opportunity to regulate the industry while simultaneously promoting its development through applying a classification scheme to MDs based on patient risk via the intended use of a given MD. Simplistically, the greater the risk (and thus higher the class), the most stringent the requirements for receiving registration to market a specific MD in the US.3 In a great part, this risk-based approach led to two key regulatory registration paths: the Premarketing Notification (PMN; otherwise, known as the 510(k)) and Premarketing Approval (PMA). The PMN process is relatively administratively simpler and results in a clearance [2], while that of the PMA is (typically) more complex (as it may require clinical data) and results in formal approval from the Agency.4,5 One must keep in mind the importance that both circuits include an application from the Sponsor seeking registration: either a PMN or PMA. Thus, the application represents the sponsor’s assertion of the merits of the MD and its potential viability in the marketplace. In many ways, the act of submitting the application for registration is the sponsor’s belief that all of the various inputs (investments) have cumulated into an innovative product of interest to the marketplace. This is particularly true if the product attained registration status. Cumulatively, therefore, the total number of PMN and PMA Applications [and their sum Total MD (PMA + PMN)] over time would be a key piece of evidence (metric) supporting the evolution of innovativeness and/or other economies associated with the MD industry. In this work, the 3 metrics of the regulated MD industry are considered: the number of PMN, PMA, and Total MD Applications. It is hypothesized that these metrics would be behave similarly to those of other economic variables, as each MD (and thus as a collective) is a resultant composite of various input—including those of the firm (e.g., people, processes, systems), those of the sector (e.g., supply/demand mechanics), as well as those of national policy and enforcement through a regulatory body. Unlike other economic variables, however, to the author’s knowledge, little is known about the dynamics of these metrics and their potential importance to understanding the various factors that may have influenced their evolution (including its forecast). Importantly, in this case of MD development and the metrics selected, these factors include substantive economic activities (e.g., as crises) and/or health policy (e.g., laws) considerations. Specifically, the focus of this work is on one key characteristic of economic variables (particular those for which sufficient longitudinal data are available) is the appearance of so-called economic or business cycles in the FDA-regulated MD industry. Cycles are generally described in terms of wave mechanics in which a noticeable peak eventually leads to an trough and recycles—where the peak would be considered the pinnacle of economic prosperity (e.g., expansion) of some sort whereas the trough would be a temporally associated misery in productivity (e.g., contraction). From a certain perspective of exogenic strength, the ebb and flow of the variable would correspond to the time-varying strength of forces pressing on the metric. At least four broadly canonical cycles exist (beyond that of seasonal effects), although there has been advancements (see, e.g., [3]) summarized as:Kitchin Short-Term Cycle [4]: 3.5 years in length. Derived as a generalization “supported by a wide range of, annual statistics for Great Britain and the United States, and especially by monthly statistics of clearings, commodity prices, and interest rates for the two countries (page 10).” Kitchin writes that he agrees with a “Mr. Philip Green Wright when he suggests: ‘Business and price cycles are due to cyclical recurrences in mass psychology reacting through capitalistic production. The rough periodicity of business cycles suggests the elastic recurrence of human functioning rather than the mathematical precision of cosmic phenomena (page 14).’” Juglar Mid-Term Cycle [5, 6]: 6–7 years in length with a 1–2-year precipitous drop. Besomi ([5], page 3) captures Juglar’s thoughts that—based on banking, population, price of corn, import and exports, rents and public revenue statistics across England, US, Prussia and Hamburg—there was a “a strict correlation … and that changes go through specific phases, always the same, and are in concordance in the countries where commerce and industry are more development. From this regularity, Juglar inferred that the common premise to all crises lies in the excesses of speculation and in the inconsiderate expansion of industry and trade (ibid, page 4).” Kuznets Medium-Term Cycle [7]: 15–25 years ([8] stated 15–20 years; Kuznets specified approximately (but equal to or greater than) 20 years [see Tables 3 and 4 on pages 204 and 205, respectively in Kuznets, 1930 across US and Europe and various goods and services (including with caveat trusts)]. Abramovitz [8] nicely summarizes this perspective in trichotomized phases: a rebound from depression (“growth rate of output was accelerating to maximum (page 351)”), steady growth [“smoothed growth rate was high enough to keep the labor force well employed. It was interrupted by short mild recessions, but at cyclical peaks the demand for labor pressed on supply (351/352)]”, followed by a depression or stagnation [“actual output always fell sharply; smoothed output usually declined or at best grew very slowly (page 352)].” Kondratieff Long-Term Cycle ([9]: 50 years [± 5–7 years (ibid, page 111)]. Kondratieff derives 3 cycles each roughly 50 years (more or less) across a series of econometrics across France, England, Germany, the US, and the “whole world” (ibid, Table 1, page 110). Importantly, the author concludes the following proposals: (1) “long waves below … to the same complex dynamic process in which the intermediate cycles of the capitalistic economy with their principal phases of upswing and depression run their course (ibid. page 111);” (2) “during the recession of the long waves, agriculture, as a rule, suffers an especially pronounced and long depression (ibid);” (3) “during the recession of the long waves, an especially large number of important discoveries and inventions in the technique of production and communication are made, which, however, are usually applied on a large scale only at the beginning of the next long upswing (ibid);” (4) “at the beginning of the upswing, gold production increases as a rule… (ibid);” (5) “It is during the period of the rise of the long waves, i.e., during the period of high tension in the expansion of economic forces, that, as a rule, the most disastrous and extensive wars and revolutions occur (ibid).” Here, the key hypothesis that is tested is: assuming PMN, PMA and the Total MD Applications are a proxy metric associated with the MD industry (and assuming therefore these variables act as other econometrics), do latent periodicities exists? If so, what are the time lengths of such periodicities. The hypothesis is tested via several statistical approaches, based on two objectives: (1) to understand the intrinsic nature of the 3 time series (viz., descriptive statistics) and, based on this information, (2) to resolve any identified periodicities accordingly. The statistical routines used to describe:The data include typical distribution statistics (e.g., 1st, 2nd and higher moments), normality, seasonality, linearity, stationarity, long-range dependency, and structural break. The periodicities include Refined Moving Average Filter (RMAF), Seasonal Trend Loess (STL), wavelet power spectra, and the Complete Ensemble Empirical Mode with Adaptive Noise decomposition (CEEMDAN). An explanation of each of the algorithms and why they were selected are part of the Materials and Methodologies section. Thereafter, prima facie results are presented. The manuscript closes with an interpretation of the results and key conclusions including limitations of the study and future directions for continued research. Materials and Methodologies While details of the materials (including data acquisition and preparation) and methodologies (including R programming code) are presented in the accompanying Supplementary Materials as a means to fully replicate and/or extend this analyses, this section summarizes the data sources and its preparation, as well as the rationale and statistical methodologies used in performing the analyses. Data Sources and Data Preparation The data were focused on applications (and not registrations) as the key hypotheses surrounding efficiencies associated with the MD industry (and not, e.g., those of the FDA registration process). The US FDA data are considered in this report as the ‘authorized system of record;’ thus, PMN and PMA data were obtained from the US FDA repository, as there is no known repository containing failed (that is, non-authorized for sale) MDs.PMNs: The data were obtained from the FDA site: https://www.fda.gov/medical-devices/510k-clearances/downloadable-510k-files on June 30, 2021. The files included PMN7680.ZIP (1976–1980), PMN8185.ZIP (1981–1985), PMN8690.ZIP (1986–1990), PMN9195.ZIP (1991–1995), and PMN96CUR.ZIP (1996-Current).Date Range: May, 1976 to Dec, 2020 Total Number of Records: 158,961 PMAs: The data were obtained from the FDA site: https://www.fda.gov/medical-devices/device-approvals-denials-and-clearances/pma-approvals#pma (under section “PMA/PDP Files for Downloading” on June 30, 2021. The files included pma.zip, “which contains information about the releasable PMAs (ibid).”Date Range: Oct., 1960 to Dec., 2020 (Note: The data were truncated to May 1976 to Dec 31, 2020 to allow direct comparison of the earliest PMN record. A negligible deletion of 178 records.) Total Number of Records: 44,831 (44,805 with the truncation) These data sources were culled for “DATERECEIVED” (Application); that is, the date the application was received by FDA; and imported into Excel, wherein the dates were counted on a monthly scale and then exported as Comma-Separated Values (CSV) file for input into the R programming environment. In total, 3 variables comprised the complete dataset: PMN Applications, PMA Applications, and Total MD Applications (that is, the monthly number of PMNs and PMAs were simply summed)—each with 536 values (the sum of all observations within a given month from May 1976 to December, 2020). To summarize, the 3 time series were:Time Series #1: PMN Applications: MDs seeking PMN (510(k)) registration. Time Series #2: PMA Applications: MDs seeking PMA registration. Time Series #3: Total MD Applications: MDs seeking either PMN or PMA registration. Statistical Analyses The general intent of the statistical analyses were two-fold: (1) to understand the intrinsic nature of the 3 time series (viz., descriptive statistics) and, based on this information, (2) to resolve periodicities accordingly. Note: As discussed further below, certain data attributes elucidated from certain tests necessitated further analysis (see Results) specifically around non-stationarity and long-term memory. There are many statistical approaches with a capability to characterize a given dataset including decomposition (viz., reduction to seasonal, trend, and random (stochastic) contributions and inversely reconstructing the time series (within some sort of acceptable error) through some additive or multiplicative combination), structural changes (viz., identification of meaningful changes in certain distribution attributes), data (e.g., correction denoising and/or missing data), and dimensionality reduction (e.g., techniques to reduce or identify the variables that would represent key properties of the original variable space) and so on. Here, the algorithms selected were a result of: appropriateness based on the time series structure (e.g., non-linearity and non-stationarity), accessibility to the algorithm (access via the R Project), as well as the nature of the signal to be resolved (periodicity). Thus, an effort has been made to use known methodologies (where possible) and cross-validating the results through either using different approaches (ideally with limited theoretical overlap) or exploring the parameter space of a given algorithm. As this work is a result of applying known methodologies, all supportive mathematical formulae are deferred via citation. Unless specified otherwise, all methods presented followed standard implementation and default parameters were used (as appropriate) throughout the analyses.Step 1: Statistical characteristics of the data. This step simply explores the distribution of the data from a time series perspective, estimating its general characteristics (e.g., moments) as well as outlining its dynamics [e.g., its stationarity and long-range dependency (LRD)]. Either the characteristics of the distribution or properties of the dynamics may alter the calculations, since—for example –a stationary or non-LRD time series may allow for ‘simpler’ approaches to the analysis, as the moments would be time invariant or individual signals separable, respectively. The analyses followed the following prescription: Time series loaded and descriptive statistics performed( [10, 11]: R Package: ‘fBasics’; [12]: R Package: ‘forestmangr’): In this step, the data were read as a time series into the R program, and descriptive statistics were assessed via the following tests:Normality ([13]: R package: ‘foreach’; [14]: R package: ‘nortest’): Anderson–Darling (A–D), Cramer-von Mises (CvM), and Lilliefors (Kolmogorov–Smirnov) (K–S) normality tests Seasonality ([15]: R package: ‘seastests’): WO, QS, Friedman and Welch tests Nonlinearity ([16]: R package: ‘nonlinearTseries’): Teraesvirta’s and White Neural Network tests, and Keenan, McLeod-Li, Tsay, and Likelihood Ratio tests Stationarity ([17]: R package: ‘aTSA’): Augmented Dickey–Fuller (ADF), Kwiatkowski-Phillips-Schmidt-Shin (KPSS), and Phillips–Perron (PP) Unit Root Tests LRD: Qu and Multivariate local Whittle Score type (MLWS) tests ([18]: R package: ‘LongMemoryTS’), autocorrelation function (ACF) ([10]: R package ‘stats’), and Hurst Exponent ([19]: R Project: ‘pracma’ (hurstexp); [20]: R Project: ‘tsfeatures’ (hurst)] Order of integration ([18]: R package: ‘LongMemoryTS’]: Geweke-Porter-Hudak (GPH) estimator of fractional difference Given that 2 tests (viz., MLWS and Qu test) suggested ‘spurious’ LRD, yet the Hurst Exponent and the existence of non-zero/non-unity (fractional) order of integration existed; thus, statistical estimation of structural breaks was performed using the standard dynamic programming model of Bai and Perron as implemented by ([21–23]: R Project: ‘strucchange’; [24]: R Project: [tseries’]). In this approach, the definition of structural break is one in which there is a some sort of significant change in the parameters of a (linear) regression model. The existence of breaks would strongly affect the selection of statistical algorithms. Step 2: Statistical determination of periodicities latent in the data. Shorter-term Periodicities: Seasonal trend decomposition via Loess method (STL) ([10]: R Package: ‘stats’), Refined Moving Average Filter (RMAF) ([25]: R Package: ‘rmaf’), and the wavelet power spectrum using a Morlet wavelet under a smoothing (Loess) construction ([26]: R Package: ‘WaveletComp’) were used to investigate the short-term structure of the time series data. For the latter, the average period versus the average power for each method was then calculated to elucidate the main periodicities (ibid). The dominant frequencies identified were then re-confirmed via spectral analysis ([27, 28]: R Package: ‘forecast’). This approach allowed for cross-validation as these methods are orthogonal—that is, there limited-to-no methodological overlap between the methods chosen. Longer-Term Periodicities: STL, RMAF, and the Complete Ensemble Empirical Mode with Adaptive Noise decomposition (CEEMDAN) ([29, 30]: R Project: ‘Rlibeemd’) were used to determine the longer trend. The challenge of resolving longer periodicities were multi-factorial and rested with the non-stationary, non-linear, and multiple structural break nature of the data over the duration of the data series. Thus, the CEEMDAN method, which utilizes an adaptive decomposition, has been considered the method of choice to tackle such programs given its flexibility with this type of data [31]. Results: Objective 1: Statistical Properties of US MD Applications The evolution of PMN and PMA Applications seem to follow inverse trajectories, while that of Total MD Applications resembles the sum of the two qualitatively (Fig. 1). The trendline for PMNs (Fig. 1a) suggests a significant decay since the peak in the early 1990s, while for PMAs there has been an acceleration since 2000 (Fig. 1b). While PMA Applications (Fig. 1b) show a somewhat relative decline in peak in 2020, it is relatively small. The evolution of Total MD Applications is notable due to the clear presence of a single period, with a decline prior to the year of COVID-19 (2020). Note: The scales of the trendlines (Fig. 1—right in green) are slightly different than that of the original observations to better resolve the yearly distributions.Fig. 1 Time evolution of PMN applications (top), PMA applications (middle), and total MD applications (bottom): observed number of applications (red); estimated trend (left) and estimated trend only (right) (refined moving average with a period of 12 months) Shifting our attention to the distribution properties, Tables 2, 3 and Figs. 2 presents the results of the various tests and finds that all three time series are non-normal (skewed with differences in tail thickness: PMN-leptokurtic, PMA-mesokurtic, and total MD- platykurtic relative to a normal distribution, but similar in spread), non-stationary, seasonal, non-linear, with considerable long-memory (see Fig. 2 in which there is a long decay to zero) with fractional order of integration, significant persistency, and the existence of structural changes.Table 2 Summary statistics of US FDA MD applications Statistic PMN applications PMA applications Total MD applications Minimum 3 0 7 Maximum 813 335 869 1st Quartile 247 32 327 3rd Quartile 347.25 135.25 437.25 Mean 296.11 83.59 379.7 Median 286 50 388 Standard error (mean) 3.45 3.25 4.11 Lower confidence limit (mean) 289.33 77.21 371.62 Upper confidence limit (mean) 302.89 89.98 387.78 Variance 6389.99 5665.03 9058.6 Standard deviation 79.94 75.27 95.18 Skewness 1 1.06 -0.02 Kurtosis 5.14 -0.11 2.34 Total records 158,714 44,805 203,519 Total number of observations = 536 per time series; rounded to 2 significant digits; units in months Table 3 Summary of normality, stationarity, seasonality, long-memory, and non-linearity test results of US FDA MD (rounded to tenths; units in months; rejection of the null hypothesis was based on p-value < 0.01; results are presented in Supplementary Materials) Test category Tests* Test Result Against Null PMN Applications PMA Applications Total MD Applications Normality A-D, CvM, KS Reject normality Reject normality Reject normality Seasonality WO, QS, Friedman Seasonality Seasonality Seasonality Linearity TNN, WNN Reject linearity + Reject linearity Reject linearity Stationarity ADF, KPSS, PP Reject stationarity Reject stationarity Reject stationarity Order of integration (fractional differencing order d) GPH 0.39 0.65 0.44 Long-memory ACF Yes Yes Yes Hurst exponent (1) Simple R/S hurst estimate (2) 0.5 plus the maximum likelihood estimate of the fractional differencing order d# (1) 0.83 (2) 0.93 (1) 0.86 (2) 1.0 (1) 0.77 (2) 0.92 Structural breaks Significance testing of EFP with OLS-CUSUM, OLS-MUSUM, Rec-CUSUM, and Rec-MOSUM$ Reject no structural changes Reject no structural changes Reject no structural changes A–D: Anderson–Darling; CvM: Cramer-von Mises; KS: Lilliefors (Kolmogorov–Smirnov); ADF: Augmented Dickey-Fuller; KPSS: Kwiatkowski-Phillips-Schmidt-Shin; PP: Phillips-Perron MLWS: WO: Webel-Ollech; TNN: Teraesvirta Neural Network; WNN: White Neural Network; GPH: Geweke and Porter-Hudak; ACF: autocorrelation function + Null hypothesis of linearity (in ‘mean’) rejected at the p-value < 0.0.85 (TNN) and 0.0087 (WNN) #Calculation is difference than that above, see Haslett and Raftery, 1989. Generally, the Hurst Exponent is related to the fractional dimension, d, by the equation: d = 2-Hurst EFP empirical fluctuation processes, OLS-CUSUM:; OLS-MUSUM:; Rec-CUSUM:; Rec-MOSUM: Fig. 2 Auto (serial) correlation function versus lag (months): PMN applications (top), PMA applications (middle), and total MD applications (bottom) [95% Confidence Levels denoted in Blue] Objective 2: Periodicity Latent within US MD Applications Short-term Cyclicity: STL, RMAF, the wavelet power spectrum using a Morlet wavelet, and spectral analysis reconfirmed seasonality as well as elucidated short-term periodicity. Seasonality (Fig. 3) pictographs suggest multiple short-term cyclicity at the 1-year mark or less; spectral analysis resolved dominant peaks at 1-year (PMN), third-year (PMA), and quarter-year (TotalMD), seemingly mapping against business quarters. Where red represents increased foci of energy, the wavelet power spectra (Fig. 4) presents conceptually near similar results, with a 1-year period or less oscillating over the full reporting period for all three time series. Of interest, there is a concentration of energy (red) around 1-year from 1985–1990 for PMNs, 2003–2020 for PMAs, and similarly both 1985–1990 and 2003–2020 for Total MDs.Fig. 3 Seasonal periodicity (via STL) for PMN (top), PMA (mid), and total MD applications (bottom) Fig. 4 Wavelet power spectra for PMN (top), PMA (middle), and total MD (bottom) applications Longer-Term Cyclicity: RMAF (Fig. 1), and CEEMDAN (Fig. 5) algorithms elucidated longer trends. Its challenging to view a periodic structure in the RMAF with the exception of Total MD, in which a clear single periodic structure (with two peaks and a trough) is resolved (Fig. 1 bottom). The two respective peaks are located at: April, 1992, July 2016, respectively; a period of 24 years and 3 months. The result of the CEEMDAN methodology depicts peaks at around 5 years across time lengths and time series data.Fig. 5 CEEMDAN trends for PMN (top), PMA (mid), and total MD (bottom) applications A summary of the periodicities for ease of reference along with the sources is presented in Table 4:Table 4 Mapping of Broad Canonical Economic Cycles with that of periodicities associated with FDA Medical Devices (units in years) as identified in this study (see text for details) Theory Canonical periodicity PMN PMA Total MD Seasonal/yearly Cycle* 0.25/1 1 0.3 / 1 0.25 / 1 Kitchin Short-term cycle 3.5 Juglar Mid-term cycle# 7–11 5–6 5–6 / 8 5–6 Kuznets Medium-term cycle^ 15–25 24 Kondratieff Long-term cycle 40–60 ^Fig. 1 (total MD) [as well as CEEMAD (see Supplementary Materials)] Reference: *Table 3, Figs. 3 and 4, and dominant peaks of spectral analysis (see Supplementary Materials # Figure 4 (middle), Fig. 5 ^Figure 1 (total MD) [as well as CEEMAD (see Supplementary Materials)] Discussion and Conclusion This work concerns itself with the FDA-regulated MD industry and select metrics (PMN, PMA, Total MD) that may be used to explore its evolution. The behavior of the proposed metrics are presumed to be similar to that of other econometrics (e.g., labor, pricing, and production), given the diversity of inputs of varying strengths used to develop a specific MD (output). A specific property of econometrics is the presence of periodicities. This work continues to add support for the existence of such periodicities, as several were found via these proposed metrics. The robust finding of periodicities across a broad assortment of econometrics data (including that of FDA-regulated medicines [32]) strongly suggests the existence of potential laws (akin to those identified in physical systems) that may reflect (or indeed govern) aspects of growth and ebb dynamics observed in these curvilinear structures. Future work may consider using these data (and/or those of FDA-approved medicines) to build such a theory, as the data are robust, easy to collect, and relatively granular (daily values that can be aggregated). Additionally, this work also sought to identify the time lengths of the latent time series periodicities. Importantly, both seasonal and secular cyclicities were identified. These included: seasonal and yearly, mid-term (Juglar), and medium-term (Kuznets) cycles. A longer (Kondratieff) term (> 20) year cycle was not observed from the methods used. The seasonal/yearly periodicities as well as a Kuznets 24-year medium--term cycles were the most easily elucidated, based on the selected methods; indeed, the Kuznet cycle was derived from simple observation (albeit much more clearly post-RMAF). Theoretically, how would one translate the theorists conjectures of periodicities to the MD industry? For the medium-term (24-year) cycle, and leveraging Kuznets theory, the author speculates that the existence (and use) of the substructures of the PMN and PMA Application curves (metrics) give us a unique insight into the MD industry from a periodicity perspective. Apparently from the simple PMN and PMA plots (Fig. 1), it would seem that the industry is undergoing a potential transformation. The number of PMNs since at least 2000 has been stagnate to trending downward from a relative peak in the early 1990s, while PMAs since 2000 has been clearly growing in a striking-cobra-like pattern. Taken together, the collective metric (Total MD) resembles a clear Bactrian-camel-like structure with two clear peaks and a trough in the-1990s and mid-2010s. This would suggest an industry oriented movement from simple (lower risk, lower class) MDs to ones that are more complex (higher risk, higher class). Entrepreneurial tendencies grew as of 2000 to build complex MDs (e.g., human embedded systems) requiring additional health authority review and oversight (PMA); presumably driving economic rents given the increase in production costs. PMN activity stagnated due to lack of innovative creativity. Unlike other industries, however, PMAs would not generally replace PMNs—that is, we still need thermometers; thus, there is a floor to PMN Applications, whereas there is no limit to those of PMAs. The seasonal/1-year and Juglar cycles are also of considerable interest. The latter specifically as the 5–6-year cycle was persistent via both PMAs and PMNs and throughout the data reporting duration of 44 years. An explanation for these is outstanding but may be speculated to reflect time lengths required for implementing simple to moderate innovation design changes. Imagine a MD in which a specific correction or addition to functionality was made. The updated (new) MD would be subsequently tested, placed into production, an application sought and registration granted by the HA. The rate of MD development in this context would be relatively much shorter than an industry transformation. As noted by the periodicity theorists, there is little impact of crises to long-term tendencies. Consistent with the medium-term findings of Kuznets, there is no obvious impact of economic crises on Total MD Applications. There was a subsequent decrease in Total MDs prior to the recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-19; HCoV-19; COVID-19) crisis. In fact, there has not been any noticeable change at least in this study due to the crises on the cycles; the author also did not notice any blatant impact of the COVID-19 crisis on medicines development at least as of Aug 2020 [33]. it would be anticipated from the data that Total MD Application will continue a downward ascent until the mid-2020s prior to rising again, with a potential drop of at least 25% anticipated. PMNs will continue to drift seemingly. It therefore seems reasonable that PMAs would fall, assuming the continued structure. Lastly, this work also has provided insight into the data themselves. We learn that the data are non-normal, non-linear, and non-stationary with specific characteristics (lopsided and fat tailed). We also learned that there is an intrinsic long-range dependency (LRD) reflecting memory dynamics as well as multiple structural breaks. Both of these features suggest statistical avenues to generate and investigate hypotheses related to exploring the impact of specific exogenous influences. The Chronological Hurst Exponent, which algorithmically leverages LRD, and Structural Breakpoint Analysis has been used in this way for FDA US medicines as an attempt to link economic events (e.g., crises) and policy interventions to changes in either LRD or structural breaks accordingly [34]—a similar experiment could be performed for MDs [35, 36]. This work concludes that (1) PMA and PMN data may be viewed as a proxy measure of innovativeness and certain economies in the MD industry; (2) similar to other econometrics in that periodicities exist are present in these metrics; (3) seasonal/1-year, Juglar and Kuznets periodicities are present in the metrics; (4) these metrics do not seem affected by specific crises (such as COVID-19); (5) PMNs and PMAs evolve inversely and suggest a structural industrial transformation; (6) Total MDs are predicted to continue their decline into the mid-2020s prior to recovery (thus, these metrics may play a greater role in predicting the evolution of the MD industry). Disclosures The author is an employee of Takeda Pharmaceuticals; however, this work was completed independently of his employment. As an Associate Editor for Therapeutic Innovation and Regulatory Science, the author was not involved in the review or decision process for this article. See Supplementary Materials for all data and methods to replicate (or extend) the results presented herein. Supplementary Information Below is the link to the electronic supplementary material.Supplementary file1 (TXT 37 KB) Acknowledgements The author extends gratitude N.D., S.L.D., and N.L.D. for their support of the manuscript. 1 Here, CDRH, FDA, or Agency may be used interchangeably. 2 Pub. L. 94-295 enacted on May 28, 1976. 3 Section 513(a)(1) of the FD&C Act [21 U.S.C. § 360c(a)(1)]. 4 Section 515 of ibid. 5 https://www.fda.gov/medical-devices/premarket-submissions/premarket-approval-pma. ==== Refs References 1. Gad SC, Spainhour CB, Serota DG. Medical device development. In: Contract Research and Development Organizations-Their History, Selection, and Utilization. Springer, Cham. 2020. 10.1007/978-3-030-43073-3_4 2. FDA. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 34559389 16520 10.1007/s11356-021-16520-8 Research Article The role of public-private partnership investment and eco-innovation in environmental abatement in USA: evidence from quantile ARDL approach Van Song Nguyen [email protected] 1 Tiep Nguyen Cong [email protected] 1 van Tien Dinh [email protected] 2 Van Ha Thai [email protected] 2 Phuong Nguyen Thi Minh [email protected] 3 Mai Tran Thi Hoang [email protected] 3 1 grid.444964.f 0000 0000 9825 317X Vietnam National University of Agriculture (VNUA), Ha Noi, Viet Nam 2 grid.444932.c Ha Noi University of Business and Technology (HUBT), Ha Noi, Viet Nam 3 grid.444889.d 0000 0004 0498 8941 Vinh University (VU), Vinh City, Vietnam Responsible Editor: Ilhan Ozturk 24 9 2021 2022 29 8 1216412175 8 4 2021 9 9 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The current research investigates the role of public-private partnership investment (PPPI) and ecological innovation (ECO) along with economic growth on the environmental abatement (i.e., carbon dioxide emission, particulate matters 2.5) in the USA. Quantile autoregressive lagged (QARDL) method was employed during the study period of 1990–2015. The study findings confirm that under long-run estimation, GDP and PPPI are causing more environmental abatement in the form of CO2 emission and haze pollution like PM2.5. The factors like ecological innovation and GDP2 are playing their role towards lowering the CO2 emission and PM2.5 under different quantiles. Furthermore, it is observed that under short-run estimation, past values of the carbon emissions and PM2.5 have their significant and positive relationship with their current values. Besides, the findings through Wald test estimation confirm that parameter constancy of the speed of adjustment parameter is rejected at 1% significance level for the CO2 emission and haze pollution like PM2.5 in USA. Besides, present study also provides some policy implications. Keywords Ecological innovation Public-private partnership investment GDP QARDL issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2022 ==== Body pmcIntroduction For various economies, environmental degradation has become common concern for the last couple of decades (Mohsin et al. 2021). The distinctive nature of the current environmental issues is that they are caused by the range of human activities and anthropogenic comparatively to a natural phenomenon (Amen et al. 2021). Consumerism and economic growth in different countries have demonstrated a pernicious effect on the natural environment. However, despite of growing environmental issues, the pace and desire for the economic development has never been stopped. Significant level of emphasis has been placed for the role of advancement in the technology and science as a catalyst for integrating the concepts of ecology and economy where the concept of sustainable development becomes buzzword. It is believed that various economies in the world are using fossil fuel, coal, oil, and natural gas in order to improve the process of industrialization through which there is a growing pressure in terms of global warming. However, a significant attention is much required towards the issue that there is a lack of global census to reduce the extensive degradation in the form of carbon emission, greenhouse gas emission, and haze pollution like PM2.5. In addition, advance economies focus on the industrial activities to address the economic target and to improve the standard of livings. However, the effect of increasing economic activities has their influence on the natural environment in the form of higher pollution and promising quality of natural environment. In the economy of USA, the trends and patterns for the CO2 emission have been changed and a mixed trend is observed. For example, during the recent outbreak of COVID-19, significant level of restrictions was imposed by the US government to mitigate the spread of this pandemic in April 2020 which resulted dramatic change in the value of consumption of energy and lower level of CO2 emission as shown in Figure 1. However, a sudden drop in the level of CO2 emissions from the petroleum consumption deceased by 25%, and from consumption by 16%, respectively (Adewumi 2020; Today Energy 2021). However, USA still emitted more CO2 from the petroleum in April 2020 (133 MMmt) than from natural gas of 122 MMmt or coal with 51 MMmt as observed in the research study of Today Energy (2021). This would clearly justify that the threat for the natural environment in the region of USA is not stoppable due to a range of economic activities. Fig. 1 Carbon dioxide emissions in USA during 1970–2020. Source: Today Energy (2021) Although the growing issue of higher emission in the form of carbon is of deep concern for the various policymakers and economists, yet its linkage with the green innovation needs to be examined with the help of some novel methodology. Therefore, this study intendants to examine the dynamic linkage between carbon emission, haze pollution like PM2.5, ecological innovation, and public-private partnership investment along with the economic growth in the region of USA during 1990–2015. In addition, there is a growing emphasis in the existing body of literature to utilize some advance methods in order to ensure the reliability of the estimated coefficients along with other empirical facts. Motivating to this concept, this study has adopted the innovative method of quantile autoregressive distributed lags (QARDL) which has got some reasonable attention in the contemporary studies specifically in the field of energy economics and finance. Furthermore, traditional methods of estimation have their key focus on the linear association through means regressed results, whereas the QARDL method is primarily dealing with the nonlinear association between the study variables like public-private partnership, ecological innovation, economic growth, carbon emission, and haze pollution like PM2.5. A study conducted by Wang et al. (2021) has used the QARDL method to examine the nexus among the natural resources, gross domestic product, renewable energy (REN), non-renewable energy (NEN), and financial development (FD) in the USA. In contrast, the present study has also used the QARDL but for the investigation of public-private partnership investment, eco-innovation, and economic growth impact on the environmental abatement in the USA. In addition, QARDL is also used by the recent study such as Sharif et al. (2020) to investigate the impact of renewable and non-renewable energy consumption on Turkey’s ecological footprint. The present study is quite different and investigates the impact of public-private partnership investment, eco-innovation, and economic growth on the environmental abatement in the USA by using QARDL. Furthermore, another recent study conducted by Anwar et al. (2021a) and examined the asymmetric effect of public-private partnership investment on transport CO2 emission in China by using QARDL. Similarly, the present study is the extension of past study such as Anwar et al. (2021a) and investigates the impact of public-private partnership investment along with eco-innovation and economic growth on the environmental abatement in the USA by using QARDL. Additionally, Suki et al. (2020) also used the QARDL to analyze the globalization (including overall, social, economic, and political) impact on the ecological footprint in Malaysian. In contrast, the present study analyzes the public-private partnership investment, eco-innovation, and economic growth impact on the environmental abatement in the USA by using QARDL. Meanwhile, the benefit to apply the method of QARDL is that it is based on the various quantiles in order to estimate the non-linear asymmetric association while capturing the impact in the time series data set. Lastly, the method of QARDL is quite novel context that it provides the impact of variables of interest both under short run and long run as well. In addition, this article also contributes to the knowledge of literature regarding to the relationships of public-private partnership investment and environmental degradation in the context of USA. Moreover, this study also contributes to the knowledge of literature regarding the nexus among eco-innovation and environmental degradation in the context of USA. This research is also suitable for the policymakers while developing the regulations regarding public-private partnership investment and eco-innovation that improve the environmental condition in the country. This study also provides the guidelines to the environmental safety authorities how the environmental condition can be improved. The rest of the paper organizes as follows: “Review of literature” section covers the review of the literature, “Research methods” section deals with the research methodology, “Results and discussion” section provides the study results, while “Conclusion and policy implications” section covers the conclusion and policy implications. Review of literature It is quite evident to explore various studies on the relationship between ecological innovation and environmental degradation (Amen et al. 2021; Anwar et al. 2021a; Chen et al. 2019; Chen et al. 2018; Cheng et al. 2019; Cho et al. 2015; Ding et al. 2021). These studies have provided their significant contribution while exploring the trends in ecological or green innovation along with their impacts or relationship with the environmental factors like carbon emission, greenhouse gas emission, and haze pollution as well (Li et al. 2018; Sun et al. 2019; Zhang et al. 2019b). Zhang et al. (2019a) explain that under the situation of carbon regulations, it is quite challenging to deal with the operational strategies. Green innovation is something like a hybrid system to handle the CO2 emission (Antoni et al. 2020 ; Muller and de Klerk 2020; Othman et al. 2020; Vermeulen et al. 2020; Zhang et al. 2019a). White and van Koten (2016) explore the linkage between the socio-ecological innovation and carbon emission. It is expressed that socio-ecological innovations are the good source towards dealing with the CO2 emission the natural environment. Ganda (2019) explore the titles of innovation and technology to analyze their effect on the CO2 in the selected OECD economies. It is stated that OECD economies have higher level of carbon emissions in the world. For this reason, innovation and technology in both public and private sectors is made to reduce the carbon emissions. This study has applied generalized method of moments (GMM) to analyze the level of innovation and technology influences on carbon emission. It is observed that there is a significant and negative influence of research and development expenditure on the CO2 emission in the targeted economies. In addition, Hashmi and Alam (2019) proposed a framework for exploring the linkage between innovation, climate change, CO2 emission, and economic growth for OECD economies. It is stated the promotion of green innovation and regulation for the CO2 emission through carbon pricing are the fundamental forces having their impact on the climate change. Therefore, their study has examined the linkage between the stated variables during 1999 to 2014, while considering the “stochastic impacts by regression on population, affluence, and technology” (STIRPAT) model. It is observed that 1% increase in the environmentally friendly patents reduces the CO2 emission by 0.017%. The eco-innovation has the ability to reduce the environmental degradation in the country (Anh Tu et al. 2021; Baloch et al. 2021; Mora-Rivera et al. 2020; Sadiq et al. 2021). In addition, the research and development expenditures have positive association with the environmental conditions and negative association with environmental degradation (Li et al. 2021a). Moreover, studies conducted by Nawaz et al. (2021) who also investigated the innovation expenditures always put a positive role on the environmental condition of the country. Furthermore, the environmental conditions are dependent on the innovation spending, if the organization spend more on innovation then the environmental condition seems to be better while environmental degradation exists if lack of investment has been made on innovation (Huang et al. 2020; Shair et al. 2021; Xueying et al. 2021). Furthermore, such linkage between CO2 emission and environmental patents is efficient way to control the negative outcomes in the natural environment. Chen et al. (2019) explains that technological progress is widely considered to play a big role in controlling and reducing the air pollution. Their study examines that very few studies have explored the association between technological progress and PM2.5 concentration. The study results confirm that technological progress in the form of research and development and import technology have their different effect on the PM2.5. More specifically in Pearl River Delta, research and development shows a significant but negative impact on PM2.5. In addition, the association between public-private investment partnership and climate change dynamics is also observed in the present literature, but in a limited context. In this regard, research contribution as provided by Khan et al. (2020a) investigate the effect of PPPI in the energy and technological innovation on the consumption-based emission. The findings through cointegration analysis confirm that there is a relationship between PPPI in energy, technological innovation, energy consumption through renewable sources, and consumption-based carbon emission. Furthermore, PPPI along with GDP are causing more consumption-based CO2 emission and similar is observed in the long-run estimation as well. Anwar et al. (2021a) consider the asymmetric effect of PPPI on the transport carbon dioxide emissions in China through QARDL approach. However, under long-run estimation, their study observes a significant and negative relationship between CO2 emission and PPPI for the higher-level quantiles. Zhang and Zhang (2018) examine the impact of gross domestic product, trade structure, foreign direct investment, and exchange rate flows in the Chinese economy for the carbon emissions during 1982 to 2016 under the shadow of environmental Kuznets curve (EKC). For examining the cointegration between the study variables, augmented Dickey-Fuller test was applying. It is observed that in the region of China, EKC was valid. Wu et al. (2019) explain that as the largest carbon emitted in the world, the economy of China is facing significant pressure to reduce the carbon emission. Their study considers the two-dimensional framework to classify the various regions of China. The study findings confirm that for the region I, the most significant factor of the CO2 emissions is industrial structure while for the regions II–IV, population size and GDP per capita are playing their major role towards carbon dioxide emissions. Cheng et al. (2019) examine the trends in CO2 emission for OECD economies through renewable energy, innovation, and economic growth in terms of GDP per capital. The study findings through ordinary least square confirm that there is a significant and positive impact of GDP on the CO2 emission in the targeted economies. Chen et al. (2018) consider the world economy while exploring the relationship between energy consumption, growth of the economy, energy intensity, urbanization, and carbon dioxide emissions during 1998–2014. It is observed that economic growth is the principal factor impacting on the PM2.5 concentration in the global panel. In addition, Nawaz et al. (2020) consider the trilemma association of energy consumption, economic growth, and CO2 emission for the BRICs and OECD countries. It is observed that huge impact of GDP growth along with the energy consumption sources on the CO2 emissions is observed. Their study considers the QARDL approach for exploring the linkage between the study variables. The study findings explain that there is a significant and positive correlation between the energy consumption and CO2 emission in the targeted economies. Moreover, the economic growth has also the positive association with the environmental condition in the country (Sun et al. 2020; Li et al. 2021b). Similarly, studies conducted by Zhuang et al. (2021) also exposed that the environmental conditions are dependent on the high economic growth in the country. In addition, the high economic growth countries have lack of environmental degradation issues (Ehsanullah et al. 2021; Hsu et al. 2021). Most of the past studies have also shown the positive association among the economic growth and environmental conditions of the country (Li et al. 2021c; Nguyen et al. 2021). Yao et al. (2019) provides the significant discussion for exploring the role of renewable energy consumption, carbon emissions, and economic growth through modified least square and dynamic ordinary least square method. Their study findings confirm that there is a long-run cointegration between the renewable energy, carbon emission, and economic growth. It is observed that there is a significant association between the renewable energy consumption, carbon emission, and economic growth. Boamah et al. (2017) and Sadiq et al. (2020) also investigate the role of trade at international context in mitigating the carbon dioxide emissions during 1970–2014 through quantile regression estimation. The study confirms a long-term N-shaped relationship between economic growth and carbon dioxide emissions in the region of China. Al-mulali(2011) tries to investigate the dynamic relationship between oil consumption, economic growth, and carbon emission. Although the association between the variables like economic growth, public-private partnership investment, ecological innovation, carbon emission, and haze pollution is reasonably addressed, however, one of the key gaps as associated with the current literature is that it is entirely missing with the implication of some advance methods like QARDL while exploring the nonlinear association between the study variables. Therefore, present study is intended to cover this methodological as well as empirical gap for exploring the asymmetric relationship between the study variables under low, medium, and high level of quantiles. Research methods This study is about the nexus among the PPPI, eco-innovation, and economic growth impact on the environmental degradation in USA. This study has taken the data from World Development Indicators (WDI) from1990 to 2015. This study has taken the environmental condition as the predictive variable that is measured as the carbon emission and PM2.5 while public-private partnership investment and eco-innovation have been used as the independent variables while economic growth has been used as the control variable in the study. Through a range of empirical studies both in developed and developing economies, it is observed that unit root is employed in the time series analysis along with the macroeconomic variables. Therefore, the level through which normally macroeconomic variables are stationary is often due to the association between the non-stationary series. Furthermore, under present research, the nonlinear association between the environmental factors like carbon dioxide emissions, haze pollution in the form of PM2.5, gross domestic product, ecological innovation, and public-private partnership investment is examined with the help of novel approach named as QARDL. Various benefits have been identified in the existing literature for using the QARDL approach in the time series analysis while exploring the relationship between explanatory and outcome variables. For example, this approach helps to examine the long-term effect of ecological innovation, economic growth, and public-private partnership investment on the CO2 emission and PM2.5 in USA. This approach is introduced by Cho et al. (2015) while examining different macroeconomic variables. Additionally, for checking the robustness, each quantile as applied under present study was further examined while using the Wald test under long run and short run as well. Equation 1 below provides a traditional framework for the ARDL model for CO2 emission and PM2.5 denoted through Y, which are as follows: 1 Yt=∂+∑ipβ1X1t−1+∑iqβ2X2t−1+∑iqβ3X3t−1+∑iqβ4X4t−1+εt where in the above equation, the term εt is white noise residual explained through bottom ground and p and q are indicating the order of the lags as selected with the help of Schwarz info criterion (SIC). Additionally, the titles like X1…X4 and Y representing the study variables like GDP, GDP2, PPPI, ECO, CO2, and PM2.5 indicate the natural log series of the gross domestic product, square of GDP, PPPI, ecological innovation, carbon dioxide emissions, and particulate matters 2.5. After adjusting the above Equation 1 into the framework of quantiles, Equation 2 is established as follows: 2 QYt=∂τ+∑ipβ1τX1t−1+∑iqβ2τX2t−1+∑iqβ3τX3t−1+∑iqβ4τX4t−1+εt To address the data estimation, this research considers the successive couples of quantiles τ as related to 0.05, 0.25, 0.50, 0.75, and 0.95, respectively. In addition, for the likelihood of serial correlation in the residuals, the quantiles ARDL equation 3 and 4 can be expressed in the following way. 3 QYt=∂τ+Yt−1+ω1X1t−1+λ1X2t−1+∑ipβ1τYt−1+∑iqβ2τX1t−1+∑iqβ3τX2t−1+∑iqβ4τX3t−1+∑iqβ5τX4t−1εt Furthermore, the above stated equations 2 and 3 can be expressed in terms of the core suggestions provided by Cho et al. (2015) in order to offer the error correction model for the QARDL. 4 QYt=∂τ+ρτYt−1−ω1τX1t−1+λ1τX2t−1+θ1τX3t−1δ1τX4t−1+∑i=1p−1β1τYt−1+∑i=0q−1β2τX1t−1+∑i=0q−1β3τX2t−1+∑i=0q−1β4τX3t−1+∑i=0q−1β5τX4t−1εt In addition, this research also considers the delta method for combining the short-run impact of earlier carbon dioxide emissions, and PM2.5 on the recent carbon emissions and PM2.5. Furthermore, the joint short-run impact of CO2 emission and PM2.5 is also calculated and presented under this study. Furthermore, the residual collective short-run impact of former and current economic growth and square of economic growth on the current level of CO2 emission and PM2.5 is also accessed with the help of similar method. Furthermore, it is assumed that the speed of adjustment parameter or p under equations 2 and 3 must be significantly negative as suggested by (Cho et al. 2015; Godil et al. 2020a; Godil et al. 2020b; Razzaq et al. 2020). Additionally, to analyze the long-short asymmetric influence of the study variables on the carbon emissions and PM2.5, this study applies the Wald test to identify the underneath the specific hypotheses. The analysis under present study consists of six variables named as gross domestic product, square of gross domestic product, public-private partnership investment, ecological innovation, carbon emission, and PM2.5, respectively. The data for the variables like GDP, carbon emission, and haze pollution like PM2.5 is collected through world development indicator WDI database. Furthermore, the data for the ecological innovation is collected from official database of “data innovation.” Results and discussion Current research contains overall five variables, i.e., carbon dioxide emissions, particulate matter 2.5, GDP, PPI, and ecological innovation, respectively. The data was collected on monthly basis for the study period of 1980–2020. Table 1 shows the descriptive results of all the study variables, where it is observed that mean, minimum, and maximum results show positive scores, i.e., CO2 (2.179, 1.369, 3.456), PM2.5 (4.357, 3.741, 5.017), GDP (6.123, 5.456, 7.073), PPPI (3.01, 2.753, 4.101), and ECO (2.258, 2.154, 3.103). However, highest mean is observed for the GDP as measured through natural log, followed by PM2.5, PPPI, ECO, and CO2, respectively. Additionally, the study results in Table 1 confirm that highest deviation is observed in the mean score of carbon dioxide emissions, followed by PPPI. Lastly, the outcomes of J-B stats have been applied to examine the normality of the data for all the study variables. However, it is confirmed that null hypotheses for confirming the normal distribution of the data for all the variables are not rejected at 1%, hence confirming a green signal for applying QARDL methodology. Table 1 Results of descriptive statistics Variables Mean Min. Max. Std. Dev. J-B stats CO2 2.179 1.369 3.456 1.101 28.018* PM2.5 4.357 3.741 5.017 0.012 16.137* GDP 6.123 5.456 7.073 0.118 25.009* PPPI 3.011 2.753 4.101 1.002 18.321* ECO 2.258 2.154 3.103 0.412 22.545* CO2 carbon dioxide emission, PM2.5 particulate matter 2.5, GDP gross domestic product, PPPI public-private partnership investment, ECO ecological innovation * represents level of significance at 1% Source: author estimation After descriptive results, Table 2 provides the results for unit root test through Zivot and Andrews (2002)(ZA) and augmented Dicky-Fuller(ADF) methods. More specifically, the findings for the ZA and ADF indicate that at I(1), all the study variables are observed as stationary at 1% or at 5% level of significance. This would justify the argument that all the variables under consideration have shown a unique order of integration or I(1). Table 2 Results of unit root test Variables ADF (level) ADF (Δ) ZA (level) Break year ZA (Δ) Break year CO2 −0.514 −6.171* −1.284 2008 Q1 −8.331* 2012 Q1 PM2.5 −0.118 −5.336* −0.991 2012 Q2 −11.584* 2003 Q2 GDP −1.247 −4.148* −1.055 2014 Q4 −9.471* 1996 Q1 PPPI 0.169 −3.134* −0.762 2006 Q1 −7.810* 2013 Q1 ECO −1.063 −4.055* −0.653 2012 Q1 −9.335* 2017 Q1 CO2 carbon dioxide emission, PM2.5 particulate matter 2.5, GDP gross domestic product, PPPI public-private partnership investment, ECO ecological innovation The values in the table specify statistical values of the ADF and ZA test *** represents level of significance at 1% Source: author estimation The outcomes for QARDL model for carbon dioxide emission along with explanatory variables are shown in Table 3. Initially, our study tests for the estimated speed of adjustment coefficient which is denoted through p. Through QARDL, it is observed that values for p are negatively significant at 1% for the first three quantiles, i.e., 0.05 (−0.136*), 0.10 (−0.175), and 0.20 (−0.182*). However, for the quantiles from 0.30 to 0.50, the outcomes are negatively significant at 5%. Furthermore, both the 50th and 60th quantiles are showing their results for p as negatively significant at 10% chance of error. For the higher order quantiles, p* scores are found to be negatively insignificant in all three level of significance. Based on all of the above stated findings for p, it is inferred that there is an existence of reversion to the long-run equilibrium among the study variables denoted as carbon dioxide emission, gross domestic product, particulate matter 2.5, public-private partnership investment, and ecological innovation for USA. Specifically, the speed of adjustment is higher for the 0.20th quantile, followed by 0.10th quantile and so on. Additionally, under long-run dynamics, value of cointegration parameter is positively significant in all the study quantiles, except for the lower two, i.e., 0.05th and 0.10th, respectively. This means that higher economic growth and related activities in the economy of USA are observed as a direct source for higher carbon dioxide emissions under long-run estimation. Zhang et al. (2014) have examined the linkage between economic growth and CO2 emission in the region of China during 1978–2011. The study findings confirm the fact that there is a long-term cointegrating association between the intensity of CO2 emission and gross domestic product measured through per capita. Malik et al. (2020) also try to explore the impact of economic growth on the CO2 emission of Pakistan while employing ARDL approach. The study finding shows that economic growth is intensifying the CO2 emission under both short run and long run. Additionally, some other studies have also explored the linkage between economic growth and CO2 emission in different economies (Kahia et al. 2019; Yeh and Liao 2017). However, the impact of GDP2 on CO2 emission under long-run estimation is observed as negatively significant for the lower and medium quantile as shown in Table 3. This means that negative and significant relationship between GDP2 and carbon dioxide emission is found but for the upper quantiles, this fact is not acceptable. Table 3 Results of quantile autoregressive distributed lag (QARDL) for CO2 emission Quantiles (τ) Constant ECM Long-run estimations Short-run estimations α∗(τ) ρ∗(τ) βGDP(τ) βGDP2(τ) βPPPI(τ) βECO(τ) φ1(τ) ω0(τ) λ0(τ) θ0(τ) έ0(τ) έ1(τ) 0.05 0.011 (0.021) −0.136 (−5.036) 0.323 (0.613) −0.138* (−5.304) 0.102 (0.603) −0.230* (−5.020) 0.451 (6.037) 0.035* (1.753) −0.013 (−0.021) 0.073* (6.307) −0.030 (−2.103) −0.013 (−1.038) 0.10 0.101 (0.001) −0.175* (−6.057) 0.235 (0.730) −0.167* (−6.008) 0.025 (0.752) −0.202* (−4.003) 0.491 (2.090) 0.071* (1.651) −0.012 (−0.002) 0.035* (5.306) −0.024 (−2.102) −0.034 (−1.347) 0.20 0.020 (0.002) −0.182* (−7.028) 0.253 (2.035) −0.132* (−5.103) 0.103 (0.632) −0.201* (−3.010) 0.520* (3.030) 0.085* (5.057) −0.056 (−0.065) 0.035*** (3.345) −0.034* (−1.743) −0.015 (−1.015) 0.30 0.012 (0.010) −0.067 (−2.035) 0.278 (1.777) −0.138 (−2.003) 0.142 (0.812) −0.114 (−1.514) 0.436* (3.126) 0.061* (3.105) −0.046 (−0.026) 0.087 (2.057) −0.026 (−1.328) −0.012 (−0.525) 0.40 0.031 (0.012) −0.051 (−2.005) 0.242 (2.024) −0.185** (−2.056) 0.223* (1.654) −0.106 (−0.924) 0.468* (3.380) 0.070 (1.202) −0.027 (0.037) 0.035 (1.105) −0.048 (−1.060) −0.027 (−0.407) 0.50 0.012 (0.021) −0.063 (−1.977) 0.179*** (3.999) −0.143 (−1.034) 0.268* (1.758) −0.115 (−0.412) 0.378*** (4.665) 0.035 (1.035) −0.038 (−0.042) 0.027 (0.527) −0.021 (−0.716) −0.043 (−0.332) 0.60 0.102 (0.024) −0.143* (−1.734) 0.193* (6.389) −0.147 (−1.367) 0.254 (2.047) −0.206 (−0.704) 0.456*** (3.046) 0.025 (1.052) −0.012 (−0.020) 0.049 (0.609) −0.057 (−0.650) −0.022 (−0.120) 0.70 0.022 (0.011) −0.121* (−1.653) 0.257* (4.067) −0.063 (−1.530) 0.352 (2.026) −0.251 (−1.103) 0.479* (4.149) 0.082 (2.026) −0.026 (−0.012) 0.021 (1.018) −0.024 (−0.545) −0.037 (−0.037) 0.80 0.245 (0.052) −0.045 (−0.926) 0.239* (5.309) −0.052 (−0.921) 0.361* (5.107) −0.225 (−1.525) 0.315 (2.653) 0.060 (2.015) −0.033 (−0.030) 0.031 (1.012) −0.026 (−0.565) −0.039 (−0.049) 0.90 0.034 (0.204) −0.056 (−0.846) 0.232* (7.302) −0.061 (−1.033) 0.375* (4.108) −0.285 (−2.005) 0.516 (2.715) 0.043 (2.114) −0.026 (−0.062) 0.016 (0.765) −0.034 (−1.064) −0.056 (−0.065) 0.95 0.206 (0.072) −0.023 (−0.911) 0.222* (4.202) −0.024 (−1.002) 0.406* (3.505) −0.243 (−2.636) 0.458* (3.188) 0.079 (2.629) −0.042 (−0.010) 0.016 (0.603) −0.036 (−1.036) −0.060 (−0.024) CO2 carbon dioxide emission, PM2.5 particulate matter 2.5, GDP gross domestic product, PPPI public-private partnership investment, ECO ecological innovation The table reports the quantile estimation results The t-statistics are between brackets *, , and * indicate significance at the 1%, 5%, and 10% levels, respectively Source: author estimations In addition, our study results confirm that PPPI has its significant and positive relationship with the carbon dioxide emission from medium to higher quantiles (i.e., 0.40 = 0.223, 0.50 = 0.268, 0.60 = 0.254, 0.70 = 0.352, 0.80 = 0.361, 0.90 = 0.375, 0.95 = 0.406). More specifically, the highest positive impact is observed under highest quantile, which is 0.406, significant at 1%. This means that in the region of USA higher PPPI is causing more CO2 emission and vice versa. During the recent time, Khan et al. (2020b) try to explore the dynamic relationship between public-private partnership investment on the carbon emission. The study findings confirm that there is a cointegrating relationship between PPPI and carbon emission. Anwar et al. (2021b) also examine the asymmetric effect of public-private partnership investment on the CO2 emission from transportation in the region of China. Shahbaz et al. (2020) also provide the similar fact while claiming that PPPI is impeding the quality of natural environment while increasing the carbon emission. Meanwhile, the long-run association between ecological innovation and CO2 emission is also tested and findings are provided in Table 3. The study findings show that for the lower quantiles (i.e., 0.05th, 0.10th, and 0.20th) there is a negative and highly significant relationship between ecological innovation and carbon dioxide emission. However, highest negative impact is observed under the lowest quantile which is −0.230. This would justify the argument that green or ecological innovation may reasonably play their role towards controlling the higher CO2 emission in the environment, hence protecting it to the best of their capacity. These findings are consistent with the Töbelmann and Wendler (2020) who have claimed that ecological innovation reduces the CO2 emission in the European economies and the impact of ecological innovation is quite unique comparatively to the general innovation in the region. Additionally, Zhang et al. (2017) also provide their contribution while claiming that innovation resources along with the knowledge innovation are observed as quite conductive for the reduction of carbon. Ding et al. (2021) also examine the association between ecological innovation and CO2 emission for the G7 economies. It is observed that there is a significant relationship between ecological innovation and CO2 emission in the targeted economies. In addition, Table 3 also provides the findings for the short-run estimation under range of quantiles. Under short-run estimation, the findings indicate that past values of the carbon emissions have significant and positive impact on the current values of CO2 emission of all three quantiles (low, medium, and high). The contemporaneous changes in the value of GDP have shown a significant and positive impact but only for the lower and higher quantiles. On the other hand, the impact of GDP2 under all the quantiles for the short-run estimation is observed as negatively insignificant. Furthermore, it is observed that current changes in the value of CO2 emission are significantly and positively affected by current values of PPPI, but only for the first four quantiles. Furthermore, the current values of ECO under short-run estimation provide the evidence for the impact on CO2 emission for the first three quantiles only. Table 4 provides the results for the QARDL for the haze pollution, i.e., PM2.5, as second dependent variables. The findings confirm that estimated speed of adjustment coefficient or p* is observed as negatively significant in all the quantiles. This would justify the presence of reversion of long-run estimation among the study variables like PM2.5, GDP, GDP2, PPPI, and ecological innovation, respectively. More specifically, the speed of adjustment is observed as highest for the 0.10th quantile which is −0.175, significant at 1%. Furthermore, the coefficient parameter for the GDP is observed as positively significant but only from 0.60th quantile to onward. This would justify that under long-run estimation, GDP is showing its positive and significant impact on the PM2.5. Xiao et al. (2020) have observed the similar findings and claim that there is a positive association between the GDP and PM2.5 in the region of China. Wang and Komonpipat (2020) have also provided the similar evidence that GDP and haze pollution are directly linked with each other. However, the findings for the long-run estimation between GDP2 and PM2.5 are observed as negatively significant for the medium and upper quantiles. This would claim that GDP2 is showing its role in lowering the haze pollution in the region of USA. Additionally, the long-run estimation for the PPPI and PM2.5 is observed as positively significant for the higher quantiles which claims that higher investment in public-private partnership is causing more environmental pollution in USA. Additionally, the study findings also confirm that ecological innovation has shown their negative and significant impact on PM2.5 under long-run estimation. This means that ecological innovation helps to reduce the haze pollution in the natural environment. Li et al. (2020) claims that environmental efficiency significantly helps to reduce the PM2.5 pollution in the region of China. Meanwhile, the impact of technological progress on PM2.5 reduction is observed but in a limited context. More specifically, the impact of ecological innovation on PM2.5 is observed as negatively significant but only for the first three quantiles. For the short-run estimation, the study findings indicate that past values of PM2.5 have their positive and significant impact on the current values of PM2.5 but only for the lower and medium quantiles. More specifically, highest positive and significant impact is observed under second quantile. Furthermore, the lagged values of GDP have shown their positive and significant impact on PM2.5 for the lower quantiles only. However, for the lagged values of GDP2, PPPI, and ECO, no significant relationship with the PM2.5 is observed in any of the study’s quantiles. Finally, the second lagged values of ECO have shown their negative and significant impact on the haze pollution like PM2.5 in USA. Table 4 Results of quantile autoregressive distributed lag (QARDL) for PM2.5 Quantiles (τ) Constant ECM Long-run estimations Short-run estimations α∗(τ) ρ∗(τ) βGDP(τ) βGDP2(τ) βPPPI(τ) βECO(τ) φ1(τ) ω0(τ) λ0(τ) θ0(τ) θ1(τ) έ0(τ) 0.05 0.002 (0.020) −0.147* (−4.076) 0.131 (0.310) −0.076 (−1.067) 0.131 (0.011) −0.202 (−2.601) 0.542* (3.025) 0.076 (2.676) −0.035 (−0.020) 0.012 (0.001) 0.016 (1.568) −0.034* (−4.034) 0.10 0.204 (0.004) −0.175* (−6.570) 0.145 (0.512) −0.052 (−1.225) 0.111 (0.002) −0.246 (−2.324) 0.579* (5.069) 0.063 (2.763) −0.061 (−0.010) 0.130 (0.003) 0.004 (1.070) −0.042* (−4.204) 0.20 0.036 (0.103) −0.156* (−5.056) 0.241 (1.110) −0.074 (−1.347) 0.120 (0.010) −0.216 (−2.112) 0.439* (6.009) 0.076* (5.026) −0.001 (−0.010) 0.234 (0.020) 0.007 (0.560) −0.023* (−2.993) 0.30 0.058 (0.080) −0.134 (−2.035) 0.283 (1.338) −0.046 (−1.626) 0.165 (0.063) −0.090 (−1.621) 0.456* (5.514) 0.045* (4.045) −0.013 (−0.031) 0.346 (0.030) 0.001 (1.120) −0.013* (−3.013) 0.40 0.063 (0.013) −0.168 (−2.121) 0.256 (1.536) −0.068* (−1.728) 0.165 (0.155) −0.035 (−1.130) 0.459 (2.390) 0.022 (1.601) −0.032 (0.012) 0.562 (0.020) 0.003 (0.613) −0.006 (−1.602) 0.50 0.152 (0.102) −0.132 (−2.032) 0.289 (1.489) −0.121 (−2.102) 0.142 (0.700) −0.053 (−0.813) 0.457 (2.133) 0.023 (0.903) −0.023 (−0.002) 0.012 (0.102) 0.007 (0.713) −0.010 (−1.020) 0.60 0.257 (0.020) −0.134* (−1.677) 0.246* (1.846) −0.102** (−2.310) 0.194 (1.349) −0.056 (−0.312) 0.316 (0.610) 0.031 (1.021) −0.017 (−0.060) 0.521 (0.012) 0.008 (0.640) −0.016 (−0.996) 0.70 0.126 (0.101) −0.126* (−1.662) 0.253 (2.035) −0.123 (−2.523) 0.290* (1.880) −0.031 (−0.230) 0.212 (0.824) 0.038 (1.338) −0.016 (−0.001) 0.253 (0.102) 0.002 (1.020) −0.021 (−1.023) 0.80 0.132 (0.002) −0.163* (−1.683) 0.275 (2.355) −0.146 (−2.616) 0.274** (2.549) −0.031 (−0.120) 0.341 (1.441) 0.056 (1.299) −0.018 (−0.028) 0.345 (1.003) 0.001 (1.002) −0.015 (−1.251) 0.90 0.134 (0.010) −0.165* (−1.740) 0.244* (3.044) −0.164 (−2.150) 0.296** (2.868) −0.032 (−0.612) 0.452 (1.555) 0.034 (1.140) −0.015 (−0.045) 0.465 (1.104) 0.003 (0.815) −0.006 (−1.506) 0.95 0.101 (0.001) −0.202* (−1.920) 0.275* (3.193) −0.191 (−1.992) 0.336* (3.063) −0.066 (−1.110) 0.323 (1.623) 0.020 (1.212) −0.017 (−0.030) 0.345 (1.105) 0.005 (0.910) −0.003 (−0.563) PM2.5 particulate matter 2.5, GDP gross domestic product, PPPI public-private partnership investment, ECO ecological innovation The table reports the quantile estimation results The t-statistics are between brackets , , and indicate significance at the 1%, 5%, and 10% levels, respectively Source: author estimations This study also applied the Wald test estimation to analyze the constancy in the parameters for the estimated results as shown in Table 5. The null hypothesis of the Wald test claims that there is no parameter constancy of the speed of adjustment parameter which is opposed by alternative hypothesis. The results show that null hypothesis of the parameter constancy of the speed of adjustment is rejected as 1% level of significant for both carbon dioxide emission and haze pollution. Additionally, the null hypothesis of linearity across different tails of study quantiles for long-run estimation of the study variables is also rejected. This would claim the fact that the study parameters for the gross domestic product, square of gross domestic product, public-private investment partnership, and ecological innovation are dynamic under various quantiles in the region of USA. This outcome might be due the various structural changes in the region of USA during the last two decades. Additionally, the Wald test findings for the null hypothesis of the linearity of the short-run cumulative influence of the past levels of GDP, GDP2, PPPI, and ECO on CO2 emission and haze pollution in terms of PM2.5 are non-linear at different quantiles. Table 5 Results of the Wald test for the constancy of parameters Variables F-statistics for CO2 emission model F-statistics for PM2.5 model ρ 6.876* [0.000] 5.307* [0.000] βGDP 5.543* [0.000] 34.108* [0.000] βGDP2 9.002* [0.000] 1.341 [0.184] βPPPI 18.451* [0.000] 18.049* [0.000] βECO 6.090* [0.000] 4.337* [0.000] φ1 3.004* [0.000] 6.417* [0.000] ω0 4.010* [0.000] 2.561 [0.016] λ0 0.248 [0.999] 0.667 [0.438] θ0 6.123* [0.000] 1.088 [0.316] θ1 – 0.271 [0.999] έ0 6.413*** [0.000] 1.951* [0.077] έ1 1.087 [0.248] 0.359 [0.921] Cumulative short-term effect θ* – 0.971 [0.391] έ* 1.215 [0.208] – CO2 carbon dioxide emission, PM2.5 particulate matter 2.5, GDP gross domestic product, PPPI public-private partnership investment, ECO ecological innovation The p values are between squared brackets *, , and * indicate significance at the 1%, 5%, and 10% levels, respectively Source: author estimations Conclusion and policy implications The current study tries to investigate the impact of economic growth, ecological innovation, and public-private partnership investment on the environmental abatement in USA through QARDL approach during the period of 1990–2015. The approach of QARDL is applied due to several motivations like it checks how the range of quantiles of key explanatory variables like GDP, GDP2, ECO, and PPPI show their effect on the carbon dioxide emissions and particulate matters 2.5 (PM2.5) in the region of USA while providing a good empirical fact comparatively to some traditional methods like OLS or quantile regression. Furthermore, our study also tries to examine the consistency of the parameters through Wald test output. This would justify that there is a presence of significant reversion to the long-term linkage between the study variables in USA. More specifically, the outcomes suggest that factors like economic growth in terms of GDP increase the carbon dioxide emission in USA, while GDP2 negatively affect the CO2 emission as well. Furthermore, higher public-private partnership investment also causes more carbon dioxide emission. However, the long-run estimation between ECO and CO2 emissions also confirms negative and significant results but only for the first three quantiles. This would employ that under long-run and different level of growth and PPPI, there is a great damage to natural environment in terms of higher emission in the nature. Furthermore, the findings through short-run estimation confirm that past values of carbon dioxide emissions impact on its current values. Furthermore, the long-run estimation between key explanatory variables and PM2.5 also states that GDP and PPPI are the sources for higher haze pollution, whereas GDP2 and ECO not. Additionally, the findings for the short-run estimation also confirm that there is a positive and significant linkage between past values of PM2.5 and its current values. Similar is observed for GDP and PM2.5. Based on the above mentioned results, the present study has recommended to the regulators that they should provide more focus on the economic growth along with PPPI and eco-innovation that reduce the environmental degradation in the country. This study also guided to the policymakers while formulating the policies regarding environmental degradation under the light of eco-innovation and PPPI. This study recommended to the relevant authorities that they should promote the PPPI that reduce the environmental degradation in the country. This article also guided to the authorities that they should adopt the eco-innovation that also reduces the environmental degradation in the country. For instance, as the environmental quality in terms of carbon dioxide emissions and haze pollution like PM2.5 is prone towards deterioration at higher level of economic growth and PPPI, than it is observed that such growth track being achieved by the US government is not sustainable in natural perspective. One of the major reasons behind this is the dependency of GDP on the traditional energy consumptions which in return causes more environmental degradation which is quite evident under present empirical outcomes. The straightforward solution for this positive impact of economic growth on the CO2 emission and haze pollution like PM2.5 is that more focus should be paid towards the production of goods and services with the help of some renewable energy sources which can lower down the level of CO2 emission in the natural environment as well. Therefore, policymakers should reasonably consider the findings under present study in order to control the environmental degradation as linked with the higher investment in public-private partnership and economic growth. Author contribution Nguyen Van Song: conceptualization, data curation, methodology, writing—original draft. Nguyen Cong Tiep: data curation, visualization. Dinh van Tien: review and editing. Thai Van Ha: writing—review and editing and software. Nguyen Thi Minh Phuong: visualization, supervision, editing, Tran Thi Hoang Mai: methodology, data curation, visualization. Data availability The data that support the findings of this study are attached. Declarations Ethics approval and consent to participate We declare that we have no human participants, human data, or human tissues. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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==== Front Comput Biol Med Comput Biol Med Computers in Biology and Medicine 0010-4825 1879-0534 Elsevier Ltd. S0010-4825(21)00775-7 10.1016/j.compbiomed.2021.104981 104981 Article Analysis of 329,942 SARS-CoV-2 records retrieved from GISAID database Zelenova Maria ab∗1 Ivanova Anna a1 Semyonov Semyon a Gankin Yuriy a∗∗ a Quantori, 625 Massachusetts Ave, Cambridge, MA, 02139, USA b Mental Health Research Center, Kashirskoe Shosse 34, 115522, Moscow, Russia ∗ Corresponding author. Quantori, 625 Massachusetts Ave, Cambridge, MA, 02139, USA. ∗∗ Corresponding author. 1 These authors contributed equally to this work. 26 10 2021 12 2021 26 10 2021 139 104981104981 19 8 2021 17 10 2021 22 10 2021 © 2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background The SARS-CoV-2 virus caused a worldwide pandemic – although none of its predecessors from the coronavirus family ever achieved such a scale. The key to understanding the global success of SARS-CoV-2 is hidden in its genome. Materials and methods We retrieved data for 329,942 SARS-CoV-2 records uploaded to the GISAID database from the beginning of the pandemic until the January 8, 2021. A Python variant detection script was developed to process the data using pairwise2 from the BioPython library. Sequence alignments were performed for every gene separately (except ORF1ab, which was not studied). Genomes less than 26,000 nucleotides long were excluded from the research. Clustering was performed using HDBScan. Results Here, we addressed the genetic variability of SARS-CoV-2 using 329,942 samples. The analysis yielded 155 SNPs and deletions in more than 0.3% of the sequences. Clustering results suggested that a proportion of people (2.46%) was infected with a distinct subtype of the B.1.1.7 variant, which contained four to six additional mutations (G28881A, G28882A, G28883С, A23403G, A28095T, G25437T). Two clusters were formed by mutations in the samples uploaded predominantly by Denmark and Australia (1.48% and 2.51%, respectively). A correlation coefficient matrix detected 160 pairs of mutations (correlation coefficient greater than 0.7). We also addressed the completeness of the GISAID database, patient gender, and age. Finally, we found ORF6 and E to be the most conserved genes (96.15% and 94.66% of the sequences totally match the reference, respectively). Our results indicate multiple areas for further research in both SARS-CoV-2 studies and health science. Graphical abstract Image 1 Keywords SARS-CoV-2 Bioinformatics GISAID SNP Pandemic Clustering Machine learning Sequencing Correlation coefficient matrix ==== Body pmc1 Introduction A virus that appeared in Wuhan in December 2019 was soon recognized as a coronavirus, a single-stranded positive-sense RNA virus belonging to a Coronaviridae family. First discovered in the 1960s, two Coronaviridae family members (CoV-229E and CoVOC43) did not present a global threat [1,2]. However, a Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV, 2002/2003) and the Middle East Respiratory Syndrome Coronavirus (MERS-CoV, 2012) changed public opinion: SARS-CoV left ∼8098 people infected and ∼774 dead; MERS-CoV caused ∼2494 infections, leading to ∼858 deaths. The SARS-CoV-2 exceeded the predecessors, infecting more than 159,319,384 people worldwide and causing more than 3,311,780 deaths by the 12th of May 2021 (reported by Ref. [3]. The World Health Organization declared a SARS-CoV-2 - related pandemic and public health emergency on the January 30, 2020 [4]., [5]). The worst outcomes of the COVID-19, a disease caused by SARS-CoV-2, are currently associated with old age (65 and older), male gender, smoking, and comorbidities such as diabetes, cardiovascular disorders, and hypertension [6]. At present, over a year and a half later, the reasons for SARS-CoV-2 high transmissibility are still elusive (Kaur et al., 2020) [7]. Studies of viral genome, its evolution, and its mutations are especially beneficial in understanding the viral changing pattern [2]. Since common knowledge of SARS-CoV-2 proteins’ functioning, signaling pathways, protein-protein, and protein-host cell interactions keeps rapidly accumulating due to the novelty of the virus, there is an urgent need to explore the SARS-CoV-2 changes [8]. 1.1 Describing the viral sequence SARS-CoV-2 genome was first sequenced in January 2020, a month after COVID-19 became a worldwide alert [53]., [9]. The genome consists of 29903 nucleotides (GenBank accession number MN908947). Its length and overall genetic contents carry little surprise since it has long been established that coronaviruses have ones of the largest genomes amid all RNA viruses (varying from ∼26 to ∼32 kb in length) (Kaur et al., 2020). Although many mutations have currently been found in the viral genome [8], only a small number of them are high-frequency: 119 SNPs exceed the 0.3% threshold, according to Ref. [11]. Based on the mutations, eight distinct viral clades had been reported by GISAID and twelve by Nextstrain by March 2021. Specific SARS-CoV-2 variants caused the most concern: a B.1.1.7 caused a travel ban in December 2020 because of its increased transmissibility [12]; a B.1.351 was thought to be more abundant in healthy young people and result in a more severe disease course in those cases [13]; a P.1 was presumed to be more infectious [14]. A recent B.1.617.2 (delta) variant struck India in March 2021 and quickly became the most reported variant [15]. Most frequently, mutations are found in SARS-CoV-2 sequences coding for spike (S) protein, RNA-dependent RNA polymerase (RdRp), and nucleoprotein (N). Despite a vast amount of knowledge accumulating daily, the exact consequences of most viral mutations are unknown [2]. Current updates on the positions and functions of viral regions are presented in Table 1 . Although any results of genomic variation analysis obtained using a bioinformatic approach should be considered with caution until experimental confirmation [[2], [7]], bioinformatics plays an important role in unraveling the viral mysteries. Overall, SARS-CoV-2 genome mutations are hypothesized to impact viral transmissivity, case fatality risk, and numerous other features. In this paper, we describe our research aimed at analyzing 329,942 viral FASTA sequences obtained from human hosts to observe mutational changes and explore the accompanying data. The present work analyzes concomitant mutations on a large scale for the first time, emphasizes the importance of GISAID database changes and provides thorough evaluation of the patient data suggesting multiple prospective grounds for both novel research and vaccine targets.Table 1 SARS-CoV-2 genes, their genomic positions, length, and function as assumed to date (functions according to NCBI Gene, [16,17,18]]]. Table 1Viral gene Genomic position (According to UCSC Genome Browser) Gene length Presumable main function ORF1ab 266–21555 21290 Codes for polyproteins PP1ab and PP1a which allow for viral replication, transcription, and other functions S 21563–25384 3822 Provides cell entry ORF3a 25393–26220 828 Activates the NLRP3 inflammasome; may contribute to virus replication and pathogenesis E 26245–26472 228 Facilitate virion assembly within cells M 26523–27191 669 ORF6 27202–27387 186 Likely promotes viral replication ORF7a 27394–27759 366 Likely interacts with immune cells ORF7b 27756–27887 132 The structural component of the SARS-CoV-2 virion ORF8 27894–28259 366 Downregulates MHC-I N 28274–29533 1260 Packages viral genome inside the capsid ORF10 29558–29674 117 Not identified 2 Materials and Methods Data for 329,942 SARS-CoV-2 genomes isolated from human hosts were retrieved from the GISAID database, along with additional information (records from the December 24, 2019 until the January 8, 2021). Custom code for revealing insertions, deletions, and SNPs was used alongside the “pairwise2 local” tool (https://biopython.org/docs/1.78/api/Bio.pairwise2.html) from the BioPython library (Python version 3.7, BioPython version 1.78; https://biopython.org/). Alignments were done for every viral gene separately, except ORF1ab, which was not considered in the present research. Every gene was aligned to a reference sequence, and final positions were calculated on a reference genome (accession number MN908947.3) [19]. Genomic positions were retrieved from the UCSC genome browser (see Table 1). We used Pandas (version 1.2; https://pandas.pydata.org/), Matplotlib (version 3.3; https://matplotlib.org/), and Seaborn (version 0.11; https://seaborn.pydata.org/installing.html) to visualize the data. Cluster analysis was executed using HDBScan (version 0.8; https://hdbscan.readthedocs.io/en/latest/) and visualized with t-SNE (t-distributed stochastic neighbor embedding; sklearn version 0.23; https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html) (Fig. 1 ). Clustering was performed using data on SNPs and deletions whose frequency exceeded 0.3% in the present research. Based on that cut-off (0.3% or 989 records) clustering parameters search was performed. The clustering parameters that yielded a minimum number of clusters, subject to the condition of at least 989 records in one cluster, were determined as suitable for the research. Final clustering parameters were set as follows: “minimum cluster size” – “2000”, “minimum samples” – “5”, “cluster selection epsilon” – “0.5”, “cluster selection method” – “eom”, “metric” – “euclidean”. Only sequences more than 26,000 nucleotides long were included in the study since the smaller sequences did not allow us to correctly align all genes of interest. We performed data filtering using the following steps: 1) the genomes that were less than 93% similar to the reference sequence were excluded from further analysis (as they contained low-quality sequences) 2) if unidentified symbols were determined in the aligned gene, and their count was not equal to the count of SNPs, the sequence was included in further research 3) we determined the percentage of match between the reference sequence and the aligned gene 3) gene sequences were divided according to the % of the matched genomes: 100% match to a reference genome was required to consider a sequence highly conservative, more than 99% match - to consider it moderately conservative, alignments in a range from 99% to 93% match were marked as low conservative. As these cutoffs were determined experimentally and we considered all the viral genes separately, we were free from simply deleting all the records containing ambiguous/unidentified symbols (“N”, “Y” etc.). Instead, examining genes separately increased the number of sequences that could be used in the research. Statistical significance was measured using a t-test and Bonferroni correction (for two parameters – age and gender). The correlation was measured using the Pearson correlation coefficient.Fig. 1 Schematic representation of the methods used in the current work. Fig. 1 3 Results By the January 8, 2021, the GISAID database had SARS-CoV-2 records deposited by 142 countries. Even though more than 329,000 records had been uploaded up until then, these data had limited research potential due to several significant problems. First, some of the uploaded sequences were dramatically smaller than the reference sequence (e.g., <5000 nucleotides) or contained an enormous (more than 7% of each gene of interest) number of ambiguous letters (Fig. 2 represents the sequence size range obtained for the data used in current research; the smallest sequences were mostly obtained by Sanger sequencing). Another weakness was the lack of automation/control in terms of data entry to the system. That drawback led to numerous misspellings and data variants, along with missing information. Thus, the “collection date” field could include a year, a month, and a date, contain only the year, or, for some records, have a wrong year (e.g., 2002 instead of 2020). “Gender” and “Patient age” parameters were filled only for 23.3% and 23.1% of the records, respectively. The least informative for research was “Patient status,” which was not only filled for just 6.9% but also contained hardly interpretable data. Records' bias was another problem. The prevalent number of genomes was uploaded by the United Kingdom (45.3%), USA (18.3%), Denmark (6.7%), and Australia (5.1%), with other countries' input ranging from 3 to less than 1% of all records. Mean age was determined as 48 (confidence intervals (95% CI): 47.8, 48.1). Although gender values for a studied cohort equaled 52% of males and 48% of females (95% CI: 0.51, 0.52), mean gender values in some countries significantly declined from these numbers. Most gender inequality among records was noted in Saudi Arabia (80% males among 446 gender-filled records, p « 0.001), Singapore (75% among 1584 gender-filled records, p « 0.001), and Bangladesh (68% among 586 gender-filled records, p « 0.001) in terms of male prevalence, and South Africa (64% of females among 2591 gender-filled records, p « 0.001), Lithuania (61% among 193 gender-filled records, p « 0.001) and Russia (57% among 1545 gender-filled records, p « 0.001) in terms of female prevalence. The highest mean age was revealed in records submitted by the United Kingdom (59.6, p « 0.001) and France (59.5, p « 0.001), the lowest – by United Arab Emirates (35.6, p « 0.001), Gambia (37, p « 0.001), Oman (37.3, p « 0.012) and Bangladesh (38.5, p « 0.001). Only the countries which submitted more than 100 parameter-filled records were mentioned above (for full data, see Supplement 1). The records' bias also affected the patients’ status. Some countries presumably uploaded the records with predominantly one or another status (e.g., out of all records uploaded by Brazil, 40% contained patient status “Dead”).Fig. 2 The sequence size ranges obtained for the data used in current research. Fig. 2 3.1 Genomic data The data were considered for every viral gene separately, except ORF1ab, which was not considered in the present research. While filtering the data to include only good-quality sequences (Table 2 ), we encountered an obscure phenomenon concerning an ORF7b gene. Nearly 11,290 (out of 329,942) FASTA records were featured by a similar pattern consisting of 52 “N”s (for most, genomic coordinates: 27757–27808). Sixty percent of that data was obtained using Nanopore sequencing (although 22.7% of all the data was acquired by that sequencing technology). Besides sequencing technology, the problem could derive from a particular assembly method, more precisely – from choosing a wrong method or unsuitable parameters, such as k-mer size. “Assembly method” data were present in 45.9% of all records, while “sequencing technology” – in 99.9%). For records where sequences contained stretches with 52 “N”s, the “assembly method” was filled for 23.5%. Since we could not estimate the assembly method and its parameters, we investigated the most prevalent methods among records containing stretches of 52 “N”s. The further research was limited due to multiple variations created by manual system entry.Table 2 Number of records included in the research after data filtering, except for ORF1ab, which was not considered in the present research. Table 2Gene Number of records included in the research after data filtering % ORF1ab NA NA S 306,821 92.99% ORF3a 313,597 95.05% E 326,054 98.82% M 322,967 97.89% ORF6 327,034 99.12% ORF7a 296,602 89.9% ORF7b 299,007 90.62% ORF8 320,383 97.1% N 315,208 95.53% ORF10 320,577 97.16% 3.2 Conservation Analyzing the conservation of the genes allowed us to get some insights into their importance for the virus and potential treatment (Table 3 ).Table 3 Conservation of viral genes. Table 3Viral gene Highly conservative, % Moderately conservative, % Low conservative, % ORF1ab NA NA NA S 3.15 81.02 10.91 ORF3a 52.78 43.22 0.72 E 94.66 4.62 0.1 M 62.4 36.36 0.42 ORF6 96.15 3.13 0.25 ORF7a 83.43 7.12 0.62 ORF7b 85.48 5.47 0.4 ORF8 64.71 33.09 0.38 N 19.62 76.35 0.72 ORF10 73.87 23.62 0.16 3.3 Insertions and deletions No insertions with a frequency greater than 0.3% were found. Two deletions were identified in the S gene: 21765-ATACATG > A with 4.67% frequency and 21991-TTTA > T with 2.94% frequency. 3.4 SNPs We analyzed genomic data with respect to the date of their upload, which allowed us not merely to determine the most frequent mutations but also to reveal and visualize their changes through the year (Supplement 2 contains data on SNPs occurring with more than 0.3% frequency among 329,942 viral genomes. Supplement 3 contains charts representing changes by month for each mutation). 3.5 Clustering We applied HDBScan to the data on SNPs and deletions with a frequency greater than 0.3%, which resulted in 43 clusters (Fig. 3 ). Some data did not fit any cluster. A number of the forty-three clusters presented interesting data. Cluster #0 (size regarding all studied genomes - 1.77%) contained all mutations from a “British variant”, except an SNP in the M gene (ORF1ab mutations were not considered due to the specificity of the research), in 100% records of the cluster. Four mutations were present in the cluster with 100% frequency - G28881A, G28882A, G28883C, and A23403G. Cluster #1 contained 0.69% of all records, had the mutations mentioned above (from the “British variant”) and the following variants: A28095T (frequency in the cluster - 49.98%), G28881A, G28882A, G28883C, A23403G (100% each), and G25437T (31.58%). Cluster #20 showed significantly different parameters in terms of age and gender. The cluster included one mutation in ORF3a (G26144T) and was characterized by a mean age of 57 and a gender ratio of 50.46 males per 49.54 females. Cluster #25 was featured by the increased mean age (53) and could be described by 5 mutations occurring with different frequencies: A23403G (99%), G25563T (87%), C27964T (87%), C28977T (10%), and C23731T (2%). Cluster #34 demonstrated a decreased mean age of 43 and was represented by 9 mutations: C28869T (100%), C27964T (100%), A23403G (100%), G25563T (100%), G25907T (100%), C28472T (99%), G29402T (23%), A22255T (17%), G23593T (4%). Two clusters, #13 and #39, showed an altered male to female ratio. Cluster #13 was featured by 54.8% of males and 3 mutations: A23403G (100%), G25563T (100%), C26735T (5%); cluster #39 was characterized by 46.31% of males and 8 mutations: A23403G (99%), G22992A (99%), G23401A (99%), G28881A (99%), G28882A (99%), G28883C (99%), C27059T (7%), C22480T (6%). Mutations found in samples uploaded mainly by Denmark and Australia formed two clusters, each containing 8 mutations (sizes regarding all studied genomes - 1.48% and 2.51%, respectively): C26735T (100%), T26876C (100%), G25563T (100%), C25710T (100%), G29399A (100%), A23403G (99%), G22992A (99%), C27434T (13%) and A23403G (99%), G22992A (99%), G23401A (99%), G28881A (99%), G28882A (99%), G28883C (99%), C27059T (7%), C22480T (6%), respectively.Fig. 3 Forty-three clusters were revealed by HDBScan. Legend on the right contains cluster numbers and color schemes. Fig. 3 3.6 Concomitant mutations According to a correlation coefficient matrix, 69 mutations had correlations with at least one other mutation (Fig. 4 ; larger resolution and lower cutoff may be found in Supplement 4). In total, 160 pairs with a correlation coefficient greater than 0.7 were found (Supplement 5).Fig. 4 Correlation coefficient matrix based on mutations with a frequency greater than 0.3%. Fig. 4 4 Discussion The statistical and bioinformatic analysis of 329,942 records obtained from the GISAID database yielded data concerning many areas, from database design and medical care issues to genomic mutations and their probable effects. The abovementioned results are discussed below. 4.1 Treatment targets: conservative sites At the moment, one of the most promising treatment and vaccine targets is the S protein, which enables the virus to enter human cells and is already targeted in such vaccines as Gam-COVID-Vac (Sputnik V), Oxford/AstraZeneca, Pfizer/BioNTech, and Moderna (Dai et al., 2020) [20]. However, the S gene has dramatically changed since the reference genome was first published – only 3.15% of the analyzed sequences totally match the reference sequence. Viral genes that changed least during the pandemic are ORF6 and E (96.15% and 94.66% of the sequences have 100% match the reference sequence, respectively). Although E protein acts together with an M protein in order to accomplish a virion assembly within the cells [21], the gene has changed dramatically less compared to M (62.4% of the sequences are highly conservative). According to these data, ORF6 and E are highly prospective targets for treatment/vaccine development. Currently, the E gene is only used as one of two qRT–PCR targets in SARS-CoV-2 detection assays by Roche (cobas® SARS-63 CoV-2 test). However, it is already known that the E protein of SARS-CoV-2 is highly immunogenic [22,23]. Researchers have attempted drug discovery concerning both E and ORF6. One group determined a drug-binding site of E's transmembrane domain using a solid-state nuclear magnetic resonance spectroscopy [24]. ORF6 can suppress both primary interferon production and interferon signaling. It is thought that SARS-CoV-2 with deleted ORF6 may be discussed in terms of intranasal live-but-attenuated vaccine invention [25]. Since ORF6 is one of three proteins causing the highest toxicity when overexpressed in human 293 T cells, and it also interacts with nucleopore proteins (RAE1, XPO1, RANBP2, and nucleoporins), treatment with an XPO1 inhibitor, Selinexor, was considered. Selinexor was found to reduce ORF6-induced toxicity in human 293 T cells [26]. Other groups found that Gliclazide and Memantine may inhibit E protein's channel activity, and Belachinal, Macaflavanone E, and Vibsanol B may inhibit the protein's function [27]; Gupta et al., 2020) [28]. 4.2 Сlustering It may be proposed that, according to clustering results, although B.1.1.7 mutational contents may not be expanded due to the absence of the concomitant mutations in the general cohort, there is a proportion of people who got infected with its distinct subtype. The subtype may be characterized by four to six additional mutations, with four being a more frequent option (G28881A, G28882A, G28883С, A23403G, A28095T, G25437T). Both clusters containing the “British variant” mutations were also the most recent, with a mean upload time of the middle of November 2020. A mutation in ORF3a (G26144T) that formed a cluster featured by increased age (57) and significantly different male to female ratio (50.46:49.54) has presumably disappeared from the population and was last noted in the uploads in September 2020. Due to increased age among patients carrying the virus with the mutation, it may be proposed to have increased virulence. Two clusters were associated with significantly different mean patient age (57 and 43), while two other clusters were featured by shifted male:female ratio: increased proportion of males in one (54.8%), and females – in the other (46.31%). Whether people of certain gender or age can be more prone to specific combinations of mutations is nevertheless unclear, and more research is needed in that direction. Mutations in samples uploaded predominantly by Denmark and Australia formed distinct clusters (8 mutations in each), which lets us speculate on the existence of so-called “Danish” and “Australian” variants. 4.3 Concomitant mutations Current research shows that some mutations often present together with one or more others. In total, 160 pairs of mutations with a correlation coefficient greater than 0.7 were found. Most studies in this direction focus on certain concomitant mutations. For example, D614G is often considered together with P323L. Some researchers suggest the inability of D614G to cause viral success when presented alone [8,29]. T85I is noted to co-occur with Q57H, and P504L – with Y541C [8]. Also, R203K and G204R in the N gene were found to occur together with high frequency [30], which is confirmed in our research. G28881A is concomitant with G28882A and G28883С (r = 0.998). Variants of concern (e.g., B.1.1.7, B.1.351, P.1) also contain co-occurring mutations. However, to our knowledge, there are no publications analyzing concomitant mutations on a large scale. Therefore, our work shows this subject as a potentially fruitful ground for novel research. 4.4 The most frequent mutations The most frequent mutation in the analyzed genes was a mutation in the S gene - A23403G (D614G), which was found in 94.15% of all studied genomes and in 99.9% of genomes uploaded in December 2020. D614G is considered to be more infectious than the ancestral form but not associated with increased disease severity [31]., [32,51]. Mutations with more than 20% frequency were found in different genes. In S, it was C22227T (A222V) with 22.25%. It was found in 53.8% of all uploaded sequences in November 2020 and assumed to influence viral transmissivity and antigenicity [33,34], as well as enhance the ability of the protein to interact with the environment [35]. A frequent mutation was also present in the M gene - C26801G (L93L) was observed in 21.82% (and 53.4%–43.2% of all uploads from November–December 2020). The assumed consequences of the mutation are yet to be described. The ORF3a gene had a G25563T (Q57H) mutation, found in 21.41% of the genomes. Four mutations with a frequency greater than 20% featured the N gene: G28881A (R203K), G28882A (R203R), G28883С (R203R), and C28932T (A220V). Interestingly, Q57H and R203K were found to cause substantial changes in protein structures (RMSD ≥5.0 Å). The mutations are also thought to affect the binding affinity of intraviral protein interactions [36]. Last, one most frequently occurring mutation found in ORF10, G29645T (V30L), was present in 22.03% of uploads in a general group and 44.6% of all uploads from December 2020. At the moment, it is proposed that ORF10 may not be a protein-coding gene, with its premature termination not affecting viral fitness or transmissivity [37]. 4.5 Disappearing mutations potentially decrease viral fitness Only three mutations have not been noted in the uploads for some time: G26144T (G251V) and G25979T (G196V) in ORF3a, which were last uploaded around September 2020 and early December 2020, respectively, and a C28836T (S188L) in the N gene, which was last seen around early to middle November. G251V results in the loss of a phosphatidylinositol-specific phospholipase X-box domain and a creation of a serine protease cleavage site [38]. Another work states that G251V and G196V might influence virulence, infectivity, ion channel activity, and viral release [39]. Might disappearing mutations impact viral fitness or human survival? The data is yet incomplete. However, in the present research G26144T (G251V) was found to create a cluster on its own; the mutation was featured by increased age (57) and an increased proportion of women compared to the general cohort. 4.6 Novel mutations The most recent mutation in the current analysis is A28111G (Y73C) in ORF8, which appeared in the uploaded data about early September 2020. The mutation is included in a B.1.1.7 mutations’ list. In total, B.1.1.7 is featured by 23 mutations [40] and is preliminarily reported as possibly associated with an increased risk of death [41]. We detected 13/14 mutations not located in the ORF1ab region and associated with the variant in the analyzed data. A T26801C mutation in the M gene was not found among mutations with a frequency greater than 0.3%, but our data yielded two mutations in the same position (freq >0.3%): C26801G and C26801T. The discrepancy could occur due to the differences in the reference sequences, which cannot be verified as Rambaut et al. did not specify the reference sequence number. We have also considered two other variants that have appeared lately - B.1.351 (a variant from South Africa) and P.1 (a variant from Brazil), but out of 8 and 14 non-ORF1ab mutations, respectively, only 2 and 3 were detected in our analysis among highly-present mutations. Consequently, it can be speculated that either a “British variant” has more transmissivity compared to the other two variants, or this result is due to a bias because of the number of the uploads. 4.7 GISAID database drawbacks lead to its severely limited research value We have revealed that the major drawback of letting the users manually fill the fields of the records led to a loss of approximately 77%–93% of the data, depending on the parameter. The absence of quality control for genomic data yielded a presence of many sequences significantly shorter or longer than the reference genome (ranging from <5000 to 34000 nt). Many laboratories uploading the data did so significantly later than the sample collection date, some even a year later, which could distort the bioinformatic analysis. Certain laboratories indicated a month and a year, or only a year, of sample collection, omitting the day or day and month. An important analysis factor was that most data were uploaded by the United Kingdom, which created an overall data bias towards the UK statistics. As time is a crucial factor in a pandemic, a database update can be recommended in order to increase its value and quality. 4.8 Gender inequality in the uploaded data may reflect medical care availability issues The cohort studied in the current research was represented by 52% of males and 48% of females (mean values; gender was not indicated for a subset of records). However, among records uploaded by Saudi Arabia, Singapore, and Bangladesh, men were present in 80%, 75%, and 68% of the records, respectively (while official statistics, male to female: Saudi Arabia - 58%:42%; Bangladesh 51%:49%, Singapore 52%:48%, by https://data.worldbank.org/). While Saudi Arabia is known for limiting access to medical care for women without a male guardian [42], Singapore, on the contrary, was ranked high (11th among 162 countries) for gender equality by the United Nations Development Programme last year [43]. The answer to this discrepancy most probably lies in the dormitories for migrants. In December of 2020, the Ministry of Health of Singapore declared that the majority of all COVID-19 cases occurred in migrant worker dormitories [44]. Although Bangladesh has shown significant improvement in moving towards gender equality (according to Ref. [45]), a medical access problem for rural areas persists. Estimating the rates of female inequality concerning medical care, a paper from the National Institute of Medical Health states that female patients were about half in number compared to male patients [46]. Our research also highlights possible issues in terms of health care for males: South Africa, Lithuania, and Russia uploaded 64%, 61%, and 57% of female records, respectively (the top three countries are considered for a shift in male to female ratio for both genders; while official statistics, male to female: South Africa 49%:51%, Lithuania 46%:54%, and Russia 46%:54%, by https://data.worldbank.org/). There are no data on limited medical care options for men in South Africa, Lithuania, or Russia. Thus, it can be speculated that the current lack of male patients may derive from a strong idea of masculinity (e.g., men must be strong and health complaints mean weakness) [47]. One more explanation is that more people working in the areas related to abundant social contact (e.g., medicine, education) in these countries are women. We suppose that this distribution may also be considered in terms of hospitalization criteria and sex differences between distinct age groups, and therefore leave this question to be still open for discussion. 4.9 Gender and age-related mutations Although mean age across gender-filled records in our cohort was determined as 48 and mean gender as 52% of males and 48% of females, some mutations were characterized by increased or decreased age and shift in male to female ratio. A G23311C (E583D) was predominantly uploaded by the UK (97.1%), so it may be considered with respect to the other UK statistics. Among the records containing the SNP, the numbers (27% males and 73% of females) were obtained using 140 gender-filled records. In total, gender ratio among records uploaded by the UK (6275 records) was 50:50, however, for the current SNP, a solely UK number was 20:80, males to females (107 records). The patient age for the SNP was 61 (139 records), among only UK records – 68 (mean age in the UK was 59). We have not found literature data on the mutation with respect to age/gender. The only interesting message was an article stating that this mutation co-mutates with infectivity-enhancing S protein mutations, such as D614G, which cannot yet explain our finding [10].). Besides the aforementioned data, there were 12 mutations that were 10 points different in terms of gender and 2 – in terms of age. Due to the lack of data, only C23929T (Y789Y) and C28311T (P13L) could be considered further. P13L (mostly uploaded by Singapore in our research, 74% of males), is presumably associated with decreased deaths and significant changes altering the protein structure [19,48]. Age-related changes were noted for the mutations in the S (A22255T) and E (T26424C) genes, with characteristic ages of 38 and 62, respectively. For A22255T, 97.31% of the sequences were uploaded by the USA, and the total age-filled records' number for the SNP was 122, most uploaded by the USA. The mean patient age for the USA was 49. For a T26424C mutation, 97.96% of the sequences were uploaded by the UK, only 47 records were age-filled, most uploaded by the UK, where the mean patient age was 59. Increased age has been linked to the worst outcomes in those suffering from COVID-19. The mortality risk increases from 0 to 0.1% for children and adolescents under the age of 19 to 4.3–10.5% for the age group of 75–84 years. The most dramatic consequences are seen for individuals from 85 and older (up to 27.3% case fatality rate). Older patients get hospitalized more often (median age 74 vs median age of 43 for individuals in the outpatient care) and suffer from concomitant health issues (e.g., cardiovascular disorders, diabetes), which increase mortality rates by itself [32,[49], [50], [51]]. Interestingly, it has been repeatedly noted that men seem to suffer from COVID-19 more severely than women [52], with males proposedly being hospitalized more often than females (e.g. [32], [51], report 67% of males versus 33% of females). Some mutations (for example, C27964T in ORF8) have been found to have gender dependence with a presumed ratio of 2:1 [8]. Although the reasons why males seem to be more severely affected are not yet clear, there are certain hypotheses on the topic. For instance, is it known that a primary way of SARS-CoV-2 entrance to the body is through its connection to angiotensin-converting enzyme 2 (ACE2), a part of the human renin-angiotensin-aldosterone system (RAAS) [53], and males show greater overall RAAS activity compared to females [54]. Also, as increased mortality risk is associated with cardiovascular diseases [55], the greater percentage of these disorders and thrombosis in men may contribute to fatality increase among males. A higher case fatality rate could also result from the fact that, in general, among intubated patients, men are more likely to acquire ventilator-associated pneumonia [56,57]. 5 Conclusions In this paper, we have analyzed 329,942 SARS-CoV-2 records obtained from the GISAID database. We addressed the quality of the uploaded records, gender distribution, gene conservation, SNPs, insertions and deletions, clusters, and a correlation coefficient matrix. Our research showed that mutations occurring with high frequency (>0.3%) were not abundant and constituted 155 changes concerning all genes (except ORF1ab, which was not considered in a current work). Many mutations presented with concomitant changes, which could alter their consequences for the virus or a human host. A large number of co-occurring mutations creates grounds for research on their meaning, as well as a probability of the occurrence in terms of novel mutations and concomitant variants. Conservation analysis suggested ORF6 and E genes as prospective treatment/vaccine targets due to their high conservation. Clustering allowed speculations on the existence of a subtype of a B.1.1.7 variant and the possible existence of variants specific to Denmark and Australia. Taken together, our results describe the genetic variability of SARS-CoV-2 and may be used for further research in different scientific areas. Funding and grant disclosures All authors are employed by the commercial company Quantori in Cambridge, Massachusetts, United States. Quantori provided support in the form of salaries for the employed authors. Contributors Maria Zelenova: contributed equally to this work with Anna Ivanova; performed experiment design, responsible for conceptualization, methodology, validation, writing – original draft, writing – review and editing. Anna Ivanova: contributed equally to this work with Maria Zelenova; responsible for conceptualization, methodology, validation, visualization, writing – review. Semyon Semyonov: responsible for methodology, software, data curation, project administration, resources, validation, writing – review. Yuriy Gankin: an inspirer for the project due to COVID-19 situation; responsible for conceptualization, methodology, project administration, resources, supervision, writing – original draft, writing – review & editing. All authors have approved the final article. Summary The present research paper analyses data for 329,942 SARS-CoV-2 records uploaded to the GISAID database from the beginning of the pandemic until the January 8, 2021. We addressed the quality of the uploaded records, gender distribution, gene conservation, SNPs, insertions and deletions, clusters, and concomitant mutations. To process the data, a Python variant detection script was developed, using pairwise2 from the BioPython library. Current article shows that mutations occurring with high frequency (>0.3%) are not abundant and constitute 155 changes concerning all genes (except ORF1ab, which was not considered in a current work). Many mutations present with concomitant changes, which may alter their consequences for the virus or a human host. A large number of co-occurring mutations (160 pairs) creates grounds for research on their meaning, as well as a probability of the occurrence in terms of novel mutations and concomitant variants. Conservation analysis suggests ORF6 and E genes as prospective treatment/vaccine targets due to their high conservation (96.15% and 94.66% of the sequences totally match the reference, respectively). Clustering allows speculations on the existence of a subtype of a B.1.1.7 variant and a possible existence of variants specific to Denmark and Australia. The article also addresses the completeness of the GISAID database, patient gender and age differences. Taken together, our results describe the genetic variability of SARS-CoV-2 and may be used for further research in different scientific areas. A conflict of interest statement None Declared. Declaration of competing interest The authors declare there are no competing interests. Appendix A Supplementary data The following are the Supplementary data to this article:Multimedia component 1 Multimedia component 1 Multimedia component 2 Multimedia component 2 Multimedia component 3 Multimedia component 3 Multimedia component 4 Multimedia component 4 figs1 figs1 Acknowledgments Authors thank Tatiana Tatarinova, Dallas Dorsey, and Isaiah Knox from the University of La Verne, Nika Tsutskiridze, Tsotne Khetsuriani, Revaz Mgeladze, Nona Kuloshvili, Tinatin Mekvabishvili from Quantori, Artem Artemov from the Medical University of Vienna, and Alexander Mikov from Amedart for insightful comments and discussions. We also thank Esther Alder from the Century College, Minnesota for text corrections. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 34751878 17160 10.1007/s11356-021-17160-8 Research Article Assessing the role of energy finance, green policies, and investment towards green economic recovery Liu Haiming [email protected] 1 Tang Yuk Ming [email protected] 23 Iqbal Wasim [email protected] 4 Raza Hassan [email protected] 5 1 Guangdong Polytechnic of Industry and Commerce, College of Business Administration, Guangzhou, China 2 grid.16890.36 0000 0004 1764 6123 Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong 3 grid.445020.7 0000 0004 0385 9160 Faculty of Business, City University of Macau, Macau, China 4 grid.263488.3 0000 0001 0472 9649 Department of Management Science, College of Management, Shenzhen University, Shenzhen, China 5 grid.444886.2 0000 0000 8683 1497 Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan Responsible Editor: Nicholas Apergis 9 11 2021 2022 29 15 2127521288 15 6 2021 19 10 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Settling a “green recovery” at the center of all economic recuperation procedures is progressively seen as the finest and as the only way nations could restore their economies. Therefore, this study assesses the role of energy finance, green policies, and investment towards green economic recovery in the USA by using a linear econometric approach and nonlinear (DSGE) model. Considering the fiscal tax-lowering rate, for instance, the study evaluates the effects of fiscal measures on local fiscal pressures in the USA regarding the pandemic. The regression analysis shows that both energy finance and green energy policies have positive and statistically significant impacts on green investment. The results from the linear econometric approach indicate that every additional state green energy policy tool adopted is associated with 1% more green investment in the USA. In addition, the findings show that green policies in human resources and R&D of green energy technologies prompt a sustainable green economy through labor and technology-oriented production activities. Implications for scholars, investors, technology managers, and policymakers are derived and discussed. Keywords Energy finance Green policies Fiscal space Public healthcare expenses Fiscal measures issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2022 ==== Body pmcIntroduction This corona pandemic is the most recent pandemic of many others that have brought the world to its knees. The pandemic has destabilized the economic structure of economies coupled with uncertainties in the world. As a result, governments all over the world have put in place stimulus packages to reduce the cascading effects of the pandemic on their economies (Kim et al. 2021; Cui et al. 2020). History is replete with fiscal measure policymakers instituted to mitigate the impact of past pandemics, which will serve as pointers to the current pandemic. The topmost response of the government in tackling the pandemic is solving the problems faced by the health sector due to the pandemic (Sarkodie and Owusu 2020; Falcone et al. 2018). A means to measure the fiscal approachability is to ascertain the impact of trends due to health expenditure, from an objective viewpoint of budgetary spending, as well as after cyclical disruptions occasioned by the pandemic. This study evaluates how public health expenditure has been impacted by the pandemic through various structural parameters since countries have been hit by the pandemic (Ridzuan and Abd Rahman 2021). The continued spread of the pandemic across the world manifesting itself in different variants is dislocating economies of advanced and developing countries alike. Financial authorities did not hesitate to respond swiftly to the pandemic; the State Reserve and other major central banks fell on their 2008–2009 financial crunch strategies to deal with the current pandemic. Following these steps, fiscal authorities globally crafted and institutionalized balancing packages to aid the sustenance of firms’ and households’ financial positions (Can and Canöz 2021; Falcone 2020). The exogenous shock emanating from the pandemic first dislocated supply chains across the world, followed by lockdowns resulting in no air travel and travel restrictions in the cruises and tourism businesses. Furthermore, physically distancing coupled with lockdowns culminated in the closing of entertainment centers, and office workers across the services industries, having negative cascading impacts on industries (Pogorletskiy and Pokrovskaia 2021). The COVID-19 crisis sparked worldwide and quick policy response measures around the world. These are anchored on preventative and palliative health measures and wide-ranging macroeconomic policy measures such as fiscal and monetary aid to businesses reeling under the impacts of the pandemic (Burger and Calitz 2021). This study looks solely at fiscal responses to the pandemic. In developed, developing, and emerging countries, the virus has mainly disorganized the operations of firms and their staff in the private sector, public sector activity, and employment not affected so much (Elgin et al. 2020). Hence, fiscal policy measures to deal with COVID-19 effects are aimed at having businesses thriving during the pandemic and reduce short unemployment. In reality, fiscal response measures were important and on time. However, concerns have been raised as to whether the fiscal response measures in countries were of the right type, or were too extensive, as well as conditional cash transfers to boost consumption could have been added. The differences in health spending in many countries are attributed mainly to variations in per-capita incomes as elucidated by (Una et al. 2020) in which they argued that healthcare spending is a costly good having an elasticity value above unitary. On the contrary, recent studies have revealed the necessity of non-earning factors such as comparative cost of healthcare, gross mortality ratios, technical inventions, joblessness, entry of people to countries factor, and political factors (Heyden and Heyden 2021; Falcone 2018). Furthermore, other than structural challenges impacting the health sector, it is discovered that seasonal disruptions usually lead to the cutting of healthcare spending. Kalemli-Ozcan et al. (2020) talk about how European countries faced with the 2008 financial crisis cut off their budgetary allocation to the health sector. The common characteristic from the studies referenced is the heterogeneity and the changing impacts among the healthcare expenditure and the resultant impact in different countries as told by Moura et al. (2019). Besides, the actual challenge is the coordination and accountable handling of health care expenses due to the pandemic occurrence. In addition, other than COVID-19 being one of the deadly pandemics, WHO lists Ebola, SARS, and HIV AIDS as some of the diseases comparable. One of the main challenges of mitigating the havoc wrecked by the pandemic is a burden-sharing between the public health authorities and the actors of the private sector (Heyden and Heyden 2021). Kalemli-Ozcan et al. (2020) say lack of federal financing on studies on the pandemic, medication, and community level existed during the years of the AIDS epidemic in the USA. Ideologies and red tape have brought about minimal coordination in policymaking, and finance initially devoted to pre-planned health programs was diverted to fight the AIDS diseases. Fiscal realignments of financing are mainly due to different fiscal programs and limitations on the levels of public budget shortfalls that the countries can have as well as subsequent burden-sharing resulting from that (Chinoy and Jain 2021; Wang and Zhi 2016) application of an open dynamic economic model reveals the optimum public program for making room for public spending more in South Africa. The logic behind this inference is that huge healthcare spending coupled with some epidemiological containment measures was implemented as a result of the high increased dependency ratio. The growing population was discovered to be more likely to contract the AIDS Epidemic which is in line with Fargnoli (2020). This brings about an increase in current debt levels but postpones the debt payoffs to the forthcoming years. As the aforementioned situation shows nation-level fiscal effects, Onofrei et al. (2021) contended that health spending increased beyond $10 trillion for 188 nations between 1995 and 2015 culminating in the containment and the cure of AIDS to a great length. Extensive spending was mainly carried out by the nations and multilateral development aid from diverse bodies to combat HIV/AIDS, and at the same time, a nominal 10% was committed for out-of-pocket expenses as piloted in 2015. Unlike other studies that evaluate economic policy measures in reaction to the pandemic, we consider at a different angle by analyzing the consequences of handovers to house owners and company bailouts, accordingly. We analyzed the USA and subsequent fiscal policy responses by using linear (econometric estimation) and nonlinear (DSGE model). Ours applies a fundamental model as done in Elenev et al. (2020) and in several aspects nevertheless more wide-ranging than that in other studies, hence making room to evaluate a varied set of fiscal policies (Chau et al. 2021a; Fong et al. 2021; Osorio-Saez et al. 2021; Yu et al. 2022), thus leading us to discover greater differences regarding the multipliers of directed (UI) vs. undirected (lump-sum) handovers. In this study, the researcher reveals that without rich household different characteristics, the cascading effects for direct cash handovers are more than those for indirect ones. Importantly, this analysis changes without exogenous shocks occasioned by the pandemic, implying that the applied model shows an important degree of country dependence (Chau et al. 2021b; Lau et al. 2021; Liu et al. 2021; Iqbal et al. 2021d). This is in line with the vast amount of employment multiplier the study finds for liquidity assistance policies (Iqbal et al. 2021c). The fiscal ramifications of the ubiquitous COVID-19 are being researched at early stages as a comprehensive framework can be derived after the pandemic has lost its steam in several countries and the new normal is over. Our study is perhaps the foremost of its kind to analyze and provide evidence of the fiscal reaction of nations to past contagions. We delimit mainly the study to the fiscal reaction of the health sector as it is an urgent challenge when a contagion happens. The WPUI (World Pandemic Uncertainty Index) is applied as the likely parameter regarding the signal of the contagion (Ahir et al. 2018). To determine the different reactions by information-laggard and information-led nations in increasing healthcare spending in reaction to the contagion worries, non-linearity compared with earnings levels has been applied. Ultimately, the difference in fiscal reactions compared with the fiscal space of nations is also analyzed. The rest of the paper is organized as follows: the “Method and data” section deals with methodology, the “Results and discussion” section presents results and discussion, and finally, the conclusion and policy recommendation are presented in the “Conclusion and policy implication” section. Method and data Theoretical understanding The current study is modeled theoretically, in order to comprehend how public as social goods providers respond to pointers occasioned by the pandemic uncertainty as well as who does not have equal access to information (Hou et al. 2019; Iqbal et al. 2019a, 2021b, a; Anser et al. 2020; Khokhar et al. 2020; Abbas et al. 2021). From the beginning, it is believed that a two-pronged decision plan is being implemented by the nation, where the first phrase connotes the immediate reaction and the second phase is the past reaction (Chakrabarty and Roy 2021). In the initial phase, a country selects a fractional before pandemic healthcare budget allocation that maximizes the highest degree of social being, considering country-specific conditions. In the second phase, when information about the contagion is obtained and reports are authenticated, the government reacts again. The COVID-19 uncertainty is constructed by taking a social welfare equation as follows (van der Wielen and Barrios 2021):1 V=Vm,h where m indicates the fiscal allocations on all other goods excluding healthcare (h)-related fiscal allocations. The final form is given as (h = x + s). Here x presents the expenditure related to the healthcare expenditure in the contagion-free period while s indicates the stochastic term occasioned by the pandemic, resulting in the rise in h (van der Wielen and Barrios 2021). We apply the method of backward solution, where expectations of future uncertainties determine present behavioral outcomes (Dafermos et al. 2021). We start from phase 2, where the pointers about the pandemic have spread. The government aims to get greater utility from the social welfare objective of the government given as V(m, h) tied to the fiscal balance constraint m + h = y and select the maximum heath expenditure where Y stands for the earnings. The fiscal state of the government will be the reference point to know the government debt level and to ascertain if the government can finance the deficit. Now, retrospectively looking at the first phase, the country studies the cues and determines the expenses on health before pandemic and expenditures on other public goods. The social welfare equation is presented as follows:2 Dw,a=EV(s)w+a=∫VY,Zπdzw+a During the initial stages, the government decides half disbursement of monies based on optimizing anticipated utility given. The government selects the fractional disbursement of financing by taking full advantage of anticipated utility as shown by non-government signal max (V (w, a)) (Bordo and Levy 2021). By coupling the culmination of the signal, the equation in its indirect form forecasts the amount of money the government is to spend at the initial and after the elapse of the first phase, taking cognizance of signals from the pandemic’s discourse in the country (Wei and Han 2021).3 1Qi=m1,ππ∅πPtaPt-1a∅a1-Ntn-NtnU⋯∅u The interest rate hence thus reacts to variations in changes in prices in the n (numeraire) sector and the services sector and the move away of the joblessness rate from its constant state level U⋯. The financial authority has outflows related to non-service consumption Gt, joblessness cover Uit, and debt repayments Bt-1gπt. Its inflows are labor income/payroll taxes τt1Nta+Ntn, capital income/profit taxes τt1Ptn, debt issuance Btg, and lump-sum taxes TtS. Furthermore, the fiscal authority can spend in different kinds of spending. Expenditures of other types are termed as Nt. The government budget constraint is.4 Gt+Bt-1gπt+Uit1-Nta-Ntn+Nt=τt1Nta+Ntn+τt1Ptn+Btg+TtS Lump-sum taxes are adapted to make sure government is liquid in the long term. The adaptation rule is the standard (McKibbin and Vines 2020):5 TtS=Bt-1gBg¯ϕτ-1 where ϕτ controls the speed of adjustment. A minimum value signifies there is little money to spend now compared to financial commitments. Because the markets are imperfect and indebtors have a constraint to borrow and agents are not Ricardian, savers, inversely, keep government bonds in order to internalize the impacts of present and future expenditures (DORUK et al. 2021). The discretionary fiscal policy makes room for fiscal authorities to create different sets of instruments. Looking at their unique manner, these interferences in policy formulation would be seen as a way to deal with one-time shocks that are not fully expected; however, once implemented, their trajectories can be accurately expected. These components ofℕt are (i) free cash bailouts to all agents in the economy, Ttb, and (ii) transfers to service sector companies equivalent to their earnings levels,TtaWtJtn. Thus,6 Nt=Ttb+TtaWtJtn Additionally, the researcher believes the government can overhaul prevailing fiscal instruments: (iii) a growth in non-service consumption Gt, (iv) a growth in joblessness cover handovers Uit, and (v) a reduction in the payroll tax τt1. These are the five discretionary fiscal instruments that will be the cornerstone of the quantitative analysis in the ensuing sections (Akrofi and Antwi 2020). Econometric model Following the explained hypothetical model, we analyze the correlation between government healthcare spending and contagion pro signals. To evaluate the existence of likely asymmetric information, meaning whether “information-lead” nations respond in a different manner, a number of past studies, like Nasar (2020), reveal that health sector parameters are either discrete or completely examined. This suppressed character of the parameters resulted in an intensified application of non-linear econometric regression models for health sector parameters. About this study, we apply the dynamic panel longitudinal threshold regression approach. Referencing other works, longitudinal regression approaches with threshold variables applying (Stavytskyy et al. 2020) approach have been studied. However, the Seccareccia and Rochon (2020) approach is seen to be contrary to macroeconomic applications; compared to this research, the approach is right for the static longitudinal data model, and the fixed effects regression in its form demands covariates to be exogenously robust. Thus, in this analysis, a long-drawn-out model of Bradley (2021) was suggested by Piskorski and Seru (2021) incorporating the dynamic correlation with the explained covariates, commonly referred to as a non-static longitudinal threshold model. The FD-GMM regression approach is applied to solve the issue of likely endogeneity and applying (Lin and Jia 2019) kind of instrumentation (Baniya et al. 2020). Regarding this study, a threshold approach is applied to delve into why the impact of the contagion indicators on health spending changes with the levels of public funds and per-capita income as encapsulated (Iqbal et al. 2019b; Abbas et al. 2020; Baloch et al. 2020; Fu et al. 2021; Yumei et al. 2021). Due to the nonexistence of prompt reactions to the contagion indicators, it is imperative to examine the challenges in a robust framework. .The model is given below (Dewianawati 2020), 7 GHEit=α+β1PPPi,t-1+β2ESPi,t-1+β3WSi,t-1+β4ABi,t-1+β5LGRFi,t-1+β6GCi,t-1+β7CRFi,t-1+β8LAi,t-1+β9ERi,t-1+β10IRi,t-1+ηit where GHE represents the explained variable and stands for a percentage of GDP, public health expenditure; α is an intercept; PPP is Paycheck Protection Program; ESP is Economic Stabilization Program; WS is work support; AB shows additional benefits; LGRF is Local Govt Relief Fund; GC shows government consumption; CRF is the COVID-19 relief fund; LA is liquidity assistance; ER shows employment rate; IR is the interest rate (Table 1); and η is the stochastic error terms.Table 1 Variables and its description Paycheck Protection Program PPP Economic Stabilization Program ESP Worker support WS Additional benefits AB Local Govt Relief Fund LGRF Govt. consumption GC COVID-19 relief fund CRF Liquidity assist LA Employment ER Interest rate IR The upper limit value is replicated inside the subcategory of explanatory variables. The upper limit estimates which minimize the econometric model are the optimum regressed variables. Currently, this study wants to assess the countercyclicality and fiscal procyclicality of the COVID-19 uncertainties, the study applied public health expenditure as the percentage of GDP as the dependent variables, and real per-capita GDP and the pandemic uncertainty(PUI) as given in the model and exogenous independent parameters, correspondingly (De Vito and Gómez 2020). The encouraging positive reaction of fiscal monetary expenditure to the virus uncertainty has been explained as economic as fiscal procyclicality and adverse response as fiscal countercyclicality. The plagues have been seen as having a healthcare sequence where the increased trepidation brings prosperous time and decreased trepidation brings bust. Now the comparative reaction of the public health spending and non-government health spending has been examined. These two effects have been encapsulated by applying the per-capita gross national income and ratio of public debt to gdp as the threshold variables. These analyses are examined together simultaneously and lagged sources to encapsulate the changing impacts of health sector expenditure For the reasons of scientific analysis, data on fiscal policies from 118 countries were obtained. The data concerning public and private health expenditure are taken as a ratio to GDP and the data regarding real per-capita GDP were sourced from the WDI. A great figure signifies a greater debate about the COVID-19 and the inverse is the case. In addition, the day-by-day policy rate data were obtained from DataStream, while data about traditional monetary policy releases are gathered by hand by the authors from central financial institutions’ websites, and the different monetary policy releases of the study countries. A 4-weekly data for trade openness, financial development, and industrialization are derived from the WDI. Economic data are gathered by the researchers from the repository of the countries and the releases of economic policies. The Forex rate for the USA is indexed by dollar, while other forex rates are determined by applying the spot rate for each country’s currency alongside the dollar. The data for every day approved cases for COVID-19 are derived from WHO. Results and discussion Table 2 presents the results of fiscal multipliers. The findings reveal that tax cut shows the effects of tax decrease policy ease the entire financial system and home-grown economic pressure. The tax cut ratio was decreased by 18% to 12%, and the real GDP grew by 0.22%, which depicts the tariff cut policy boosted country economic wide increase. Furthermore, people’s earnings and cumulative spending grew by 1.79 to 1.03%, correspondingly. The population’s livelihoods advanced, and the growth in the population’s spending multiplied overall common necessities, hence invigorating robust economic advancements.Table 2 Fiscal multipliers FM(1) FM(2) FM(3) FM(4) FM(5) Govt. consumption 1.4353 0.9853 0.6344 0.0086 1.3376 Tax relief 0.4314 1.2745 1.2543 0.0274 0.5855 Economic Stabilization Program 0.5437 2.4325 1.2666 0.0777 0.3427 Transfer 0.6769 1.6532 1.6532 0.0874 0.6622 Liquidity assistance 1.4536 2.4875 2.4732 −0.0394 0.6643 Table 3 presents the results of fiscal multiplier pandemic shocks. Trade-in abroad and financing grew by 1.41% and 0.21% correspondingly, implying the tariff cut had lowered companies’ expenses and goods values and bettered the affordability of goods traded abroad. The strategy has steadied the outlooks and boosted the financing demand of marketplace institutions. Firstly, the multiplier in ordinary periods is less than that within the pandemic phase. This is accurate for all economic policy vehicles; however, it is uniquely eye-catching for cash bailout to companies: the work multipliers are too insignificant in ordinary years (0.32 vs. 2.51 in the contagion), and the GDP multipliers are even discouraging (−0.11 vs. 0.45). Cash aid to companies is thus not advisable during good times; however, it gives calming impacts on jobs in the event of an exogenous shock like the pandemic. Second, the categorizing of multipliers for jobs varies in ordinary periods and the event of a pandemic. As depicted in the preceding analysis, UI overshadows tax decreases or lump-sum handovers during the contagion.Table 3 Fiscal multipliers pandemic shock Description FM(1) FM(2) FM(3) FM(4) FM(5) Govt. consumption 0.8468 0.4005 0.2714 −0.1561 0.8054 Tax relief 0.3982 1.2406 1.1281 −0.0634 0.3792 Economic Stabilization Program 0.3612 1.2686 1.0891 −0.0718 0.3445 Transfer 0.3815 1.1805 1.0769 −0.0599 0.3633 Liquidity assistance 0.3197 0.1512 0.0462 −0.0750 −0.1091 Tables 2 and 3 depict that to evaluate the robustness of the model and make a point that the measurement produces reasonable outcomes, we recalculated fiscal multipliers for similar policy instruments excluding the pandemic shock. Hence, we apply the measured model to estimate the fiscal multipliers in “ordinary times” where no shocks exist other than the stimulus and the baseline without stimulus economy in its stable condition. The GDP and job multipliers of countries’ spending range from 0.81 and 0.85, correspondingly, which are well between the scope of figures that have been researched into by previous studies on the impacts of government buys. 0.34 and 0.39 are the multipliers for tax and transfer policies, which are quite low. These estimates are insignificant and are in line with the estimated values of past studies for tax rebates from the US. This, again, is in line with major findings that these policies are inclined to be substandard to explicit government buys regarding employment incentives. These analyses imply the model construct and measurement produced reasonable outcomes that are in line with the literature. Practical analysis displayed in Table 4 supported the hypothetical framework by assessing the fiscal responses to indicators as a result of the pandemic. The factor for an independent parameter when the upper limit parameter is examined is that high-earning nations are inclined to a fiscal procyclical impact to pandemic insecurities at level; on the other hand, least-developed countries have similar impacts at lag. It is believed that it is a result of information irregularities among different nations, especially in determining the scale and type of devastation brought by the pandemic rounds. This is particularly in line with how nations react to different phases of the seasonality occasioned by the business cycle as allude to by Siddik (2020) who says it is one of the key areas of non-linearities to fiscal impacts. These asymmetries could be foreseen or unforeseen owing to the competence of the government in authority.Table 4 Regression analysis (1) (2) (3) (4) (5) (6) ESP −18.87* −33.32* −23.64* −15.43* −18.21* −31.2* (−6.08) (−6.54) (−6.24) (−3.76) (−5.01) (−4.63) WS 0.67* 0.38* −0.38* −0.80* (3.79) (1.99) (−4.21) (−5.36) AB 327.61* 647.31 634.01* 1127.33 (0.78) (1.78) (2.56) (3.52) LGRF 0.18* 0.31* 0.42* 0.43*** (4.41) (3.3) (6.23) (5.4) GC −0.31* −0.65* −0.21 −0.26* (−2.14) (−4.21) (−0.59) (−0.67) CRF −0.63* −0.60* (−4.59) (−5.87) LA 0.07* −0.05* (3.1) (−0.75) ER −5.37 −24.22* (−2.23) (−3.73) IR −6.47* 6.43** (−2.03) −3.45 R-squared 0.06 0.12 0.31 0.08 0.21 0.18 Exchange rate regime No No Yes No No Yes , , and indicate the level of significance at 10%, 5%, and 1%respectively. All regressions include regional, and version dummies. Robust t statistics are in parentheses Results in Table 5 depict that “Pandemic + tax cut” shows the effect of the pandemic on the consequence of 2018–2019. VAT cut program matched with the consequences of the value-added tax cut policy in 2018–2019; the real GDP increase ratio plummeted from 0.21 to −6.73%. The cause for this noticeable fall in the growth ratio was that the increase ratios of the r “troika”—people’ spending, financing, and trade abroad—reduced from 1.03%, 0.21%, and 1.41% to −10.17%, −5.43%, and −7.34%, correspondingly. People’s earnings equally reduced considerably, having the increase rate decreasing from 1.79 to −4.97%. Furthermore, the previous economic increase ratio digresses as a result of the pandemic. As a result of the COVID-19, the increase rate of the public fiscal revenue of the main and local government plummeted from −5.71% and −5.6% to −12.54% and −13.31%, correspondingly, and the country’s fiscal power was sternly reduced. Local economic pressure increased by 0.38 to 0.435, and main economic pressure more than increased twofold, growing by 0.061 to 0.142. Economic headwinds cannot be sidetracked, and fiscal robustness is crucial to secure the nation’s entire financial system’s proper functioning and long-run viability. The increased ratio of the value-added tax, spending tax, manufacturing tax, and house owners’ earnings plummeted from −15.22%, 1.15%, 2.65%, and 1.79% to −21.13%, −5.32%, −6.98%, and −4.97%, correspondingly. Production taxes, a very key means of earning for indigenous authorities, fell greatly, reiterating the opinion that indigenous authority’s economic pressure is growing.Table 5 Panel regression micro-financial and fiscal policy measures Variable Macro-fin. Macro-fin. Macro-fin. Fiscal Fiscal Fiscal PPP 5.14* 6.78* 5.01* 3.87* 3.00* 4.21* (7.987) (6.99) (5.46) (5.98) (5.23) (4.58) ESP 0.28* 0.29* 0.05* 0.54* (2.99) (7.12) (2.32) (5.39) WS 4.013* (2.995* −6.746 −1.263* (6.45) (3.98) (−3.42) (−4.13) AB 0.04* −0.06* 0.18* 0.06* (1.99) (−3.69) −1532 (8.47) LGRF −0.38* −0.08* −0.29 −0.34 (−5.10) (−4.14) (−645) (−5.42) GC −0.041 0.06* (−0.24) (7.48) CRF −0.03* −0.006* (−4.21) (−0.68) LA 6.87* 4.26* (3.8) (4.1) ER 3.99* 5.23* (5.87) (12.46) IR −0.32* −0.05* (−7.95) (−4.87) R-squared 0.63 0.6 0.5 0.5 0.56 0.74 Exchange rate regime No No Yes No No Yes , , and indicate the level of significance at 10%, 5%, and 1%respectively Regarding the information irregularities in lesser-earning nations, the challenge is even severe as elucidated by Iqbal et al. (2020) due to the fact that they are faced with substandard regulatory and governance systems. These findings are corroborated by Afonso et al. (2010) who say the past effects could be more pronounced due to time differences in hiring, training, and retraining; distribution of funds; and indigenous economics readjustment to shocks. Besides, the delay in fiscal response, according to Faria-e-Castro (2021) is the ramifications of external political shocks and voting activities. However, the diversity in indications at simultaneous and past year implies the lack of dedication to healthcare expenditure notwithstanding the health sector’s devastation by the pandemic. The other type of seasonality in public healthcare budgetary allocations happens when public debt is quite high in nations. Heavily indebted nations are usually faced with fiscal procyclicality to a certain degree but adapt swiftly to limit expenditure breakup. Chakrabarty and Roy (2021) contended fiscal healthcare is constrained in many economies and government debt is one of the main restraints. Fiscal space is the ability of a country to give more budgetary allocations for the required objective, not distorting the sustainability of the country’s financial system. One of the causes responsible for heavily indebted countries reacting contemporaneously to the COVID-19 concerns is due to the frequent re-ordering of spending undertaken in the critical areas of the economy as alluding to by e Castro (2020) and Lacalle (2020). Hence, countries are compelled to the creation of fiscal space for health in the event of disease outbreak concerns; nevertheless, as a result of tight spending window, governments immediately realign and reduce spending in the coming phase. Inversely, the lesser indebted nations double down on health sector outflow after the indicators increased in the ensuing phase. They rely on the self-producing arm of the economy to financial security from the pandemic at first and stick to their financial plans and thus procrastinate their disbursement of funds. In this vein, the harmonizing of self and civic supplies in the scenario of the pandemic is an important puzzle to solve. The divergent views being held following the push to expand capacities and ever-increasing prosperity might bring about disparities in growth programs being pursued by the countries as stated by Jinjarak et al. (2021). In addition, the analysis in Table 6 depicts the comparative spending approachability of the countries and the self-sector in the pandemic insecurities. On minimal-earning nations, the comparative dependence is greatly on the government sector than the self-sector, but the highly free enterprise economy high-earning nations incline towards strong self-sector financing. This analysis is in line with the views of Chakraborty and Thomas (2020). The researchers also discovered that the increase of the pandemic pointers becomes more pronounced in the ensuing phase; the comparative requirement for assistance from the self-sector grows as the financing from the government accounts become depleted in which virement will grow as time progresses due to the self-sector reluctance to release money particularly if it anticipates government will hike taxes to make up for the fiscal gap. More so, an expansion in public spending strains interest rates up the curve, resulting in reduced private sector participation, usually called the crowding-out impact as referred to by Guo and Shi (2021). Nevertheless, the supremacy of the self-sector in banking rolling outflow of the health arm is similar among the low-debt nations.Table 6 Policy rate analysis Policy rate Res. req. Macro-financial Fiscal PPP −216.4* −68.08* 7.76* 21.43* (−7.06) (−5.67) (2.68) (5.96) ESP 0.324* 2.42* 0.40* 0.31* (2.03) (6.23) (6.74) (4.57) WS 0.46* −2.23* −0.23* −0.03* (3.95) (−3.14) (−2.41) (−0.54) AB 0.3104* 0.787* 0.4433* 0.3614 (−0.62) (−2.78) (5.76) (3.21) LGRF 0.04* 0.05 0.03 0.03*** (1.08) (0.65) (3.24) (3.68) GC −0.31* 2.03* 0.03 −0.31* (−1.45) (3.15) (0.76) (−5.17) CRF 0.2134 0.2165* 0.1394 0.1504* (0.0021) (0.0281) (0.004) (0.068) LA 0 0 0 0 ER −2.0341* −2.45.37* 4.923 3.425* (−4.12) (−3.28) (2.58) (3.41) IR −0.223 −2.624 −4.634 −4.635** (−0.012) (−4.428) (2.148) (4.341) J-test (p-value) 0.56 0.45 0.41 Under-identification (p-value) 0.00 0.00 0.04 , , and indicate the level of significance at 10%, 5%, and 1%respectively Forecast for the COVID-19 pandemic But then, the researchers forecasted the variations in government health sector outflows to be a percentage of GDP in 2020 applying the regressed model and readily accessed data on the COVID-19 insecurities index. We categorized the first 20 nations based on the figures of daily reported infections across the nations (Hutchison 2020). The anticipated increase in government outflows in the healthcare sector regarding the pandemic reveals how governments approach the concerns and insecurity pointers from the pandemic, and the gross lack of funding in the healthcare sector, before the beginning of COVID-19. The GDP multipliers think that it might not be ideal to institute strategies to balance GDP given the current conditions. The multiplier was however analyzing for comprehensiveness. The analysis that gives the biggest multiplier is government spending. It is well recognized that it is less likely to outpace government spending in this type of model (Gechert et al. 2019), particularly without robust associations between the incomes of individual consumers and the financial terrain. Payroll tax cuts, increases in UI, and unconditional transfers all produced nearly the same results. Table 7 presents the results of system regression analysis for forecasting the COVID-19 pandemic. The happening of COVID-19 produced significant effects on the economy of the globe as well as trade. The WTO brought to the public its 2020–2021 trade increase projection study on 8 April 2020. The positive forecast was that international trade would increase by 13%, and the negative forecast was that international trade will fall by 32%. According to the impacts of COVID-19 on the side of supply, and global trade, the underlisted consequences are deduced: consumers’ propensity to consume reduced by 5%, financing of varied sectors plummeted by 5%, labor availability plummeted to 10%, and actual trade abroad fell to 10%. About differences in program approaches to the nations, it is obvious that European nations such as the UK, Italy, and Germany have been pre-prepared to approach the COVID-19 indicators by a larger amount of expansion in healthcare spending. Intriguingly, the first three nations most affected by COVID-19 such as the USA, India, and Brazil are positioned in the 14th, 16th, and 12th levels correspondingly, if they are ranked according to the increase in their healthcare spending. It explains the policy approaches in nations that are at variance to the increasing pandemic infections. So far as the share of fatalities announced by the leading three countries are involved, it is not at par with the mean fatality’s ratios among the first 20 nations; nevertheless, there are nations such as Chile, Germany, and Spain that swiftly approached the pandemic irrespective of fewer fatalities from the pandemic. Utilizing a different view, the deepened program approach could be tied to macroeconomic terrain such as the debt status of nations instead of the fatalities from COVID-19. While Chile, Germany, and Spain were identified to be heavily debated nations regarding the gotten upper limit value of debt, the moderate approach of the most affected nations is attributed to the procrastination in studying the pandemic warming indicators, before strategizing to distribute monies (Chakrabarty and Roy 2021; Ahir et al. 2018).Table 7 System regression analysis for forecasting COVID-19 pandemic Equation 1 Policy rate Res. req. Macro-financial PPP −0.59* −0.40* −0.23* (−6.58) (−4.58) (−6.25) ESP −21.48* −14.57* 5.04* (−6.25) (−5.26) (3.9) WS 0.07* −0.031** −0.03* (0.145) (−0.58) (−2.10) AB 0.23 −0.52* 0.214* (3.42) (−4.57) (4.76) LGRF 0.6454 0.7435* 0.422* (3.52) (3.21) (3.89) GC 0.31* 0.41* 0.02* (3.45) (4.89) (2.21) CRF −0.68* 0.05* −0.318* (−2.99) -0.21 (−4.15) LA 0.35* 0.21* 0.64 (0.205) (0.210) (0.214) ER −5.87* −7.65* −5.48* (−3.47) (−5.87) (−5.48) IR 0.387* 0.535* 0.749* (4.447) (4.452) (5.033) R-squared 0.24 0.23 0.23 Exchange rate YES YES YES , , and indicate the level of significance at 10%, 5%, and 1% respectively Robustness analysis The results of the robustness analysis are presented in Table 8. We broaden the analysis in pandemic concern indicators through the COVID-19 as well as the fatalities per 1000 people for each nation. We discovered the analysis follows a similar pattern from the earlier analysis, where we examine the approaches to the pandemic warning indicators that are controlled mainly by the prevailing macroeconomic environment. Handovers and public spending of non-services work in long-established cumulative demand impacts, hence growing demand for non-sector goods and income in this area; nevertheless, no bearings exist on different kinds of customers. Ultimately, cash aid to non-service area enterprises maintain earnings in this area perhaps.Table 8 Robustness checks with shadow rates included Variable Policy rate Policy rate Policy rate PPP −0.7945* −0.7142* −0.6451* (−3.14) (−3.47) (−2.89) ESP 2.21 0.40* (-2.26) (-4.86) WS −0.05* 0.32 (−0.54) (0.27) AB 2.454 2.412 (3.42) (2.23) LGRF −4.08 −3.21* (−2.38) (−4.54) GC 0.326 (2.46) CRF −5.038* (−2.14) LA 0.24* (-0.45) ER 0.54* (-3.24) IR −0.54** (−3.25) R-squared 0.04 0.02 (0.07) Exch. rate dummies No No Yes , , and indicate the level of significance at 10%, 5%, and 1%respectively Table 9 depicts the variations in foremost macroeconomic parameters, the resultant figures of different enterprises, and economic pressure after the alternating elasticity factor. Influenced by the bearings of the value-added tax cut program, China’s real GDP expanded by 0.18% (the actual GDP increase ratio in the former model 0.21%) and indigenous economic pressure grew by 0.342 to 0.379 (and indigenous economic pressure forecasted from the previous equation grew by 0.342 to 0.38). Considering the COVID-19 happenings, the means of the bearings of the VAT cut program varied, the actual GDP increase plummeted from 0.18 to −6.73% (the actual GDP increase ratio in the previous equation plummeted from 0.21 to −6.73%) and indigenous economic challenges increased from 0.38 to 0.431 (indigenous economic challenges calculated from the previous equation was 0.38 to 0.435). This analysis is equally in line with a similar analysis of the previous equation, and the expansion ratio of several macroeconomic parameters and the resultant figures of diverse sectors are equally unique, showing the robustness of the analysis and the high predictability of the equation.Table 9 Robustness checks with percentage pointcuts Policy rate Policy rate Policy rate Reserve requirements PPP −2.23* −2.35* −3.11* −0.59 (−6.99) (−7.06) (−8.76) (−3.24) ESP −0.041* 0.04* 0.04* (−4.54) (-2.75) (-2.54) WS −0.0087* 0.0003 0.003 (-3.21) (-0.48) (-0.35) AB −0.28 −0.06* 0.08* (-2.45) (−0.56) (-0.41) LGRF 0.03* −0.04* −0.23* (−0.04) (−2.34) (−0.73) GC −0.79* 0.05* (−8.02) (-4.001) CRF −0.65* 0.24* (−4.54) (-2.34) LA 0.06* 0.062 (-5.24) (-2.34) ER 0.002 0.004* (-0.62) (-0.560) IR −0.04* −0.04 (−4.45) (−2.23) R-squared 0.07 0.22 0.3 0.23 Exch. rate dummies NO NO YES YES , , and ** indicate the level of significance at 10%, 5%, and 1% respectively The fatalities of people ratio of 67% are quite comparable in the UK and the USA; nevertheless, the UK reacted more than twice ahead of the USA in their program deliberations for the COVID-19. But then, high-level debt saddled nations to push for an aggressive concurrent program approach. Hence, the strength of the pandemic warning indicators informs greatly the amount of government healthcare spending needed during the pandemic. Subdued pandemic indicators do not imply the reduced occurrence of the pandemic; nevertheless, it is the comparative indecision of government at the initial stages of the COVID-19 occurrence. This is perhaps assigned to the reason that nations that approached the pandemic head-on earlier did well in containing the havoc wreaked by the pandemic, by reporting fewer fatalities. In other words, the weaker the fiscal program through the COVID-19 phase, the more depressed the forex rates and the dwindling in the infection’s patterns. For growth, economic programs can grow indigenous currency circulation in the economy, and hence grows the need for abroad currency, which makes the local currency lose value. Simultaneously, a weaker economic program betters credit environment that brings reduced infections. The study reveals that economic programs instituted throughout the COVID-19 phase acted crucially in controlling financial systems. Table 9 depicts the analysis of the factors which are not significant, implying that beneath different environments, no significant other bearings of the seriousness of the COVID-19 pandemic can be passed to out-of-fashion economic programs for the forex markets. Discussion The pandemic has brought about an unparalleled fall in the world’s pursuits. The growing infection cases in advanced and emerging nations have resulted in restrictions in movements and greater destabilizations of the global economic order, in a manner not seen before (Baldwin and di Mauro, 2020, Gopinath, 2020). A case in point is, the world’s GDP fell by over 4.9% in quarter 2 of 2020, as a result of financial destabilization. The fall in global supplies of traded goods far outweighed that of the 2007–2008 financial crunch (Cantore and Freund 2021). As a result, the world’s commerce plummeted by 3.5% owing to slack demand and deliveries. The resultant ban on movement around the world due to COVID-19 stalled the world’s supplies, lowering the total demand (Muhafidin 2020). The spending on merchandise saw a remarkable fall greatly as a result of sharp income fall and consumer trust. Similarly, customers became adamant to spend on merchandise because of the concerns of the COVID-19 disease (Gootjes and de Haan 2020). Job insurance throughout the pandemic thinks of lifetime growth in insurance premiums. To come to a value of $200 billion, the joblessness cover bailouts for an agent is increased by 82% while the effects are remarkably bigger on debtor spending, which grows with impacts. This is likely predictable: workforce tax decreases profit for people who are still in jobs when greater numbers of people are laid off. Regarding joblessness cover, it is the inverse that exists: it profits jobless people when a greater proportion of people become jobless. The increase in debtor spending balances non-services, as shown in the 5th section. Besides, it is worth noting that the involvement occurs only in a ¼; however, the effects are permanent. This is because the costs for debtors accessing money continue to be down the curve, as the expansion in joblessness covers lessens sizably non-payment charges (as joblessness people are inclined to have greater non-payment charges than those employed), which culminates in an indirect recapitalization of the financial system. To determine fiscal responses, it is imperative to differentiate between parameters that seem like fiscal stimulus and those that are actual economic incentives to boost spending. The dictionary meaning of stimulus means anything that induces action, and in the fiscal domain, it connotes conscious financial move through more government spending, tax decreases, cash give-outs, or more charitable societal safety net disbursement to induce financial pursuit. Fiscal incentives are geared towards the demand aspect of the equation, and it is a tool applied to spur total spending in the entire economy in order to avoid economic retrogression. Otherwise, Economic aid is fundamentally geared towards the supply aspect of the economy, to normalize manufacturing through tax holidays, even though tax holidays can pose ancillary demand impacts that might cause future product profits. An example is when corporate tax decreases or financing allowances spur greater investment. There is a significant variation between Keynesian economists on explicit public spending and cash assistance to spur cumulative demand countrywide, and enterprises’ tariffs reprieve policies to offset government-authorized cumulative supply crisis, even if with some associated cumulative demand outcomes. The overarching aim of putting any fiscal response policy to any economic meltdown is to within a short time lessen the responsibility of the public sector on the supply arm of the economy, comprising mainly of minor and big companies. Commerce generates the majority of employment opportunities in the country and that is the hub of output development, and actual salary-investing policies are implemented to benefit employees. Company tax holidays for example give fiscal reprieve for enterprises that counteract the declining viability of companies and retrenchment, which nevertheless would not induce productivity during a crisis. In this manner, fiscal alleviation varies noticeably from the fiscal stimulus of the basic Keynesian type, purposefully to strengthening the demand aspect of the country, through expanded government expenditure in different forms, comprising cash handovers to incentivize spending. These two macroeconomic tools of fiscal alleviation and fiscal incentivization imply larger budget gaps and bigger government debt, important macroeconomic parameters Keynes harmoniously excluded from this concept. However, within the motive is why fiscal stimulus, in reality, is not likely to spur any money-making occupation in actual terms provided long-run impacts down the curve are accounted for. The reason that fiscal stimulus such as additional government expenditure geared towards incentivizing cumulative demand has counterbalancing bearings on other parts of the economy that ultimately cancels out the impacts. An additional government spending increases the budget shortfall and surely the amount of government expenditure altogether in the first scenario. Hitherto, public expenditure is just one of the parts of overall expenditure and, as regular bachelor degree teaching material explains, about a country that has well-functioning globally amalgamated financial centers, trade-offs are part of private investment and actual goods trade abroad will eventually happen due to larger budget shortfall, and the use of a public instrument of debt financing “masses out” the private sector or brings in capital from overseas, resulting in higher forex rate and reduced mechanize traded abroad. Conclusion and policy implication This distinctive study’s impact is to model the non-linear correlation between the contagion indicators, government health spending applying the Dynamic Longitudinal threshold methodology. This methodology makes room to encapsulate the upper limit level per-capita earnings and public debt, which ultimately establishes the fiscal approaches to diverse COVID-19 indicators. By investigating the readiness of nations in previous contagions, this analysis elucidates how the nations have conventionally undertaken re-prioritization of expenses to attend to the current needs of the health sector. The differences in procyclical impact among earnings and debt types of nations get more visible as pandemic indicators incline to affect the lesser-earning nations relative to higher-income nations, which is due to information irregularities among nations. These findings are in line with pandemic indicators experienced around the world throughout the period of the ubiquitous COVID-19. Some fiscal approaches would require encapsulating the infection ratios and the fatalities during the pandemic, as well as the prevailing economic situation, according to the past accomplishments of the economy. As the nations are grappling with the COVID-19 contagion, this study reiterates the vital importance of information asymmetries among nations in formulating stimulus packages for the health sector. The less-developed nations are more exposed to the pandemic cycles due to inadequate good health infrastructure. Thus, it is important that the government sector and the self-sector work in unison in reviving the health sector, particularly when the pandemic indicators are at the initial stages. The findings have cautions and hypotheses for critical thoughts, its hypotheses emanating from different vital parts of fiscal policy study, for instance, the dimensions of interposition (Bui 2018) and the likely unison and (Faria-e-Castro 2021) among programs (Faria-e-Castro 2018). The present analysis also hypothesizes from the endogenous labor distribution choices, which is crucial to examine the short-term effects of policies in the form of tax decrease and UI. On the contrary, prevailing research proposes that these inducement challenges did not influence in any manner as in the view of Ashihara and Kameda (2018). Ultimately, it fully hypothesizes the likelihood of that fiscal policy can be applied to lower the length and force of the shock occasioned by the pandemic; it equally hypothesizes from the reality that encouraging commercial pursuits might be counter-productive in curtailing the pandemic. The tax decreases are crucial to cut businesses’ outlays and aid market bodies in surmounting these challenges and actualizing economic growth. The real GDP increased by 0.21%, and people’s spending, and cumulative financing, overall merchandise traded abroad, and the productions worth of all businesses grew significantly; nevertheless, as a result of the cut of VAT earnings, indigenous economic challenges grew from 0.342 to 0.38, a growth of 10.96. The real GDP expansion fell from 0.22 to −6.73% and people’s earnings, overall financing, overall merchandise traded abroad, and the production worth of different businesses in the economy all plummeted significantly. Furthermore, the fiscal resources of indigenous authorities have seriously declined from 0.38 to .0435, a growth of 14.53%. To put it differently, the occurrence of COVID-19 deteriorated the diffusion of the traditional monetary program to 10-year public maturity, exchange rate, and diffusion. However, the deteriorating effects on the diffusion of the traditional monetary program to stock centers are near significant. This implies the occurrence of the pandemic has deteriorated the diffusion of traditional monetary policy programs to financial markets to an additional noteworthy level as the impacts of the traditional monetary program on all four financial centers are not felt much during the pandemic. The heightened seriousness of the pandemic moderated the diffusion of modern monetary policies to stock and exchange rate markets:(i) The COVID-19 pandemic has deteriorated the diffusion of economic policy to financial markets in three means. Firstly, financiers did not anticipate the immature, not-enough, and indeterminate economic programs commenced throughout the pandemic phase. This dearth of understanding let market actors less open to economic programs as they should be in ordinary phases. (ii) Second, even though the growth monetary programs were formulated to ensure actors take part in financial and commercial undertakings, the restrictions of movements and lockdowns announced by countries globally prohibited commercial ventures (Sharif et al. 2020). (iii) Third, a financier in markets all in all responded immediately to economic programs broadcasted by relocating their assets to greater producing holdings. Nevertheless, studies proved that majority of assets are comparatively ineffectual in protecting their investment amidst the pandemic (Ji et al. 2020). Financiers are less willing to relocate cash to benign assets by changing financial instruments when COVID-19 circulated across the globe, making systematic perils for investors. Thus, they responded in a gradual manner to economic policy. Availability of data and materials The data can be available upon request. Authors contribution Haiming Liu: conceptualization; data curation; methodology; writing—original draft; data curation; visualization; supervision. Y. M. Tang: visualization, editing. Wasim Iqbal: review and editing. Hassan Raza: writing—review and editing; software Declarations Ethical approval and consent to participate We declare that we have no human participants, human data or human tissues. Consent for publication N/A Competing interests The authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Abbas Q, Hanif I, Taghizadeh-Hesary F et al (2021) Improving the energy and environmental efficiency for energy poverty reduction Abbas Q, Nurunnabi M, Alfakhri Y et al (2020) The role of fixed capital formation, renewable and non-renewable energy in economic growth and carbon emission: a case study of Belt and Road Initiative project. 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==== Front Soft comput Soft comput Soft Computing 1432-7643 1433-7479 Springer Berlin Heidelberg Berlin/Heidelberg 34815733 6490 10.1007/s00500-021-06490-x Data Analytics and Machine Learning RETRACTED ARTICLE: India perspective: CNN-LSTM hybrid deep learning model-based COVID-19 prediction and current status of medical resource availability Ketu Shwet [email protected] Mishra Pramod Kumar [email protected] grid.411507.6 0000 0001 2287 8816 Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, India 19 11 2021 2022 26 2 645664 24 10 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The epidemic situation may cause severe social and economic impacts on a country. So, there is a need for a trustworthy prediction model that can offer better prediction results. The forecasting result will help in making the prevention policies and remedial action in time, and thus, we can reduce the overall social and economic impacts on the country. This article introduces a CNN-LSTM hybrid deep learning prediction model, which can correctly forecast the COVID-19 epidemic across India. The proposed model uses convolutional layers, to extract meaningful information and learn from a given time series dataset. It is also enriched with the LSTM layer's capability, which means it can identify long-term and short-term dependencies. The experimental evaluation has been performed to gauge the performance and suitability of our proposed model among the other well-established time series forecasting models. From the empirical analysis, it is also clear that the use of extra convolutional layers with the LSTM layer may increase the forecasting model's performance. Apart from this, the deep insides of the current situation of medical resource availability across India have been discussed. Keywords COVID-19 CNN-LSTM Time series prediction Deep learning Medical resource issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2022 ==== Body pmcIntroduction Due to the COVID-19 epidemic, the year 2020 will be remembered as a devastating year for humanity. The foremost case of COVID-19 had been seen in China's Wuhan city and detected as undefined etiology. This first case came in December 2019, and within a month, it took the whole world into its trap (Cohen 2020; Huang et al. 2020). In the second week of March, the WHO (World Health Organization) had announced COVID-19 as a pandemic disease (Coronavirus (COVID-19). Available online 2020; World Health Organization 2005; Sohrabi et al. 2020). Initially, it was acknowledged as the Wuhan virus, but later on, it was named COVID-19 or 2019-nCov or Novel coronavirus (“WHO 2020; World Health Organization 2020). As per the current stats by WHO, 10,764,786 confirm cases, 518,120 deaths, 4,288,454 active cases, and 5,958,212 recovered cases, have been come up to July 1, 2020 (Coronavirus disease 2020a). The epidemic situation causes severe social and economic impacts on the country. All the nations are taking preventive action, such as travel restraints, event deferrals, quarantines, testing, social distancing, and soft and tough lockdowns, to save their people's lives (Acter et al. 2020). Suppose we talk of India's perspective, which is a developing country and the second most populated country after China. So, the chance of COVID-19 is more as compared to other countries. India's government has taken various initiatives (i.e., lockdown 1–4, and unlock 1–2) to deal with the COVID-19 epidemic. As per the WHO's current COVID-19 stats for India, 605,220 confirm cases, 17,848 deaths, 227,476 active cases, and 359,896 recovered cases, have been come up to July 1, 2020 (Coronavirus disease 2020a). If we talk about medical approaches, various clinical trials are underway across the world. Some of them are the fourth (final) phase of their trial, but to date, no antiviral treatments and vaccines are obtainable for COVID-19 (Ketu and Mishra 2020a). Initially, the new vaccine for any novel disease was taking approximately 5–10 years to be on the market. But due to the latest technologies, the fast traction of the vaccine is possible and can usually take around 18–24 months, to come into the market (Grenfell and Drew 2020). In the current scenario, any successful trials may take a minimum of 6–8 months to reach into the production line (if we follow parallel production) and may take a further 6–12 months to be in the market across the world. So, we have to follow preventive measures until the vaccine has come into the market. In the last couple of years, substantial progress has been seen in the deep learning method in various application areas. If we talk about the deep learning techniques and methods to deal with real-life problems such as time series forecasting that have been successfully applied in various application areas (Ai et al. 2019; Li et al. 2019; Zheng et al. 2019; Zou and Xia 2019). These models are capable enough to deal with the chaotic and noisy nature and lead to more reliable and accurate forecasts. The LSTM (Long Short-term Memory) and CNN (Convolutional Neural Networks) networks are the most prevalent, well-established, proficient, and broadly used deep learning models (Fawaz et al. 2019). The primary aim of using these models in the problem solving of time series paradigms is that the LSTM models are capable of obtaining the sequence outline more efficiently, whereas the CNN models are capable of filtering the input noise and capturing valuable features. The standard CNNs have been developed to deal with spatial autocorrelation data, but they are less capable of handling the long and complex temporal dependencies (Bengio et al. 2013). On the other hand, the LSTM is capable of handling the temporal dependencies and uses the features of the training dataset. Therefore, such a time series model who utilizes these two deep learning models' benefits can improve the overall prediction results. Figure 1 illustrates the current situation of COVID-19 epidemic in India with the bar chart. In Fig. 1, the y-axis represents the number of infected peoples, and the x-axis represents the aggregate number of recovered cases, the aggregate number of infected cases, the aggregate number of deaths, and the aggregate number of active cases (Coronavirus disease 2020a).Fig. 1 Current condition of COVID-19 in India In this epidemic situation, any help from the algorithmic perspective or a clinical standpoint is a precious but novel task. If we talk about the algorithmic perspective, forecasting can play a vibrant role in dealing with such critical situations and give an idea about the exposer so that the government can make plans accordingly and diminish the impact of the infections (Ketu and Mishra 2020b). The primary aim of this article is to develop such a model that can efficiently and correctly predict the COVID-19 epidemic. For this purpose, we have suggested a deep learning-based hybrid forecasting model. The proposed model uses convolutional layers, to extract meaningful information and learn from a given time series dataset. It is also enriched with the LSTM layer's capability, which means it can identify long-term and short-term dependencies. The recommended model has been compared with the other time series forecasting models to determine its rightness and appropriateness. From the experimental analysis, it is also clear that the use of extra convolutional layers with the LSTM layer may increase the performance of the forecasting model. Apart from this, the deep insides on the current status of medical resource availability across India have been discussed. The primary purpose of this study is:To establish such a model that can forecast the COVID-19 epidemic across India with greater exactness. To give a deep-leaning-based solution for economic and social prosperity. To find out the current status of medical resource availability across states of India. This article is systematized as follows: In Sect. 2, a brief discussion about the latest literature based on algorithmic approaches used in COVID-19 forecasting is presented. In Sect. 3, we have briefly described the materials and methods, such as dataset description, proposed methodology, and statistical parameters. The statistical analysis-based prediction results are presented in Sect. 4. The detailed discussion about the forecasting results, along with the present status of medical resource availability across India, are displayed in Sect. 5. In Sect. 6, the summarization of our research findings and future directions is discussed. Related work In a couple of decades, substantial growth has been seen in the arena of information technology (IT). Resultantly, the application-centric algorithmic approaches are being developed to tackle the current trends. If we talk about the healthcare domain, various algorithmic solutions have been seen in disease detection and prevention. In the present scenario, we are facing a pandemic situation due to COVID-19. So, there is a need for such algorithmic approaches that can minimize the impact of COVID-19 across the globe. The PIBA (Patient Information-Based Algorithm) was introduced by Wang et al. (Wang et al. 2020) to estimate deaths caused by COVID-19 in china. The authors had predicted the overall death rate outside Wuhan and Hubei, which was lies between 0.75 and 3%. The death rate of Wuhan and Hubei was predicted to 13%. They had also quoted that the death rate could vary in different temperatures and climates. Gupta et al. (Gupta et al. 2020) had established a relationship between the COVID-19 cases and temperature. They had forecasted the results for India based on the outbreak analysis of the USA. Based on the analysis result for India, they had quoted that, in summers, the massive reduction would be seen in the number of new cases. ARIMA and Sutte ARIMA model was used by Ahmar and Val (Ahmar and Val 2020) for forecasting the COVID-19 epidemic, and the stock market. Spain's COVID-19 and stock market dataset had been used for this analysis. Similarly, the ARIMA model-based COVID -19 prediction of Italy, France, and Spain had been conducted by Ceylan (Ceylan 2020). The forecasting of the COVID-19 outbreak for Italy, China, and France had been done by Fanelli and Piazzain (Fanelli and Piazza 2020). They had forecasted the ventilation units and the number of COVID-19 cases by dividing the whole population into four parts, such as infected, susceptible, dead, and recovered. The LSTM model-based estimation of the COVID-19 end date for Canada had been performed by Reddy and Zhang (Chimmula and Zhang 2020) with an accurateness of 93.4% (short-term) and 92.3% (long-term). Sujatha and Chatterjee (Sujatha and Chatterjee 2020) used multilayer perceptron, vector autoregression, and linear regression models on the Kaggle dataset to forecast the COVID-19 epidemic in India. Elmousalami and Hassanien (Elmousalami and Hassanien 2020) had explored the correction among the day level gauging models with the help of numerical detailing and time series model. A blockchain-based framework was introduced by Torky and Hassanien (Torky and Hassanien 2020) to distinguish and confirm the ambiguous contaminated structures of COVID-19 infection. The GSA (Gravitational Search Algorithm) optimization-based hybrid deep learning model for the identification of COVID-19 had been proposed by Ezzat and Ella (Ezzat and Ella 2020). The LSTM (Long short-term Memory Network) is one of the powerful models used in the time series prediction model (Hochreiter and Schmidhuber 1997; Gers et al. 1999). The LSTM model was firstly used by Li and Cao (Li and Cao 2018) for the forecasting of tourist drift. They have also quoted that the forecasting accuracy of the LSTM model could be improved as compared to BPNN and ARIMA, particularly in the long term. The Jaya-LSTM-based hybrid prediction model was proposed by Khalid et al. to predict the electricity demand and price (Khalid et al. 2020). The Jaya algorithm was used to optimize the hyper-parameters. Xue et al. carried out LSTM deep neural network-based analysis on the short-term forecasting of the financial market in the year 2020 (Yan et al. 2021). The experimental result establishes better forecasting accuracy as compared to other models (BP neural network, RNN, and LSTM) with the effective forecasting of the time series dataset. CNN is one of the deep learning models, which is widely used for image recognition purposes (Chua and Roska 1993; Arena et al. 1998). However, in the year 2019, the experimental evaluation carried out by Cao and Wang shows that the CNN (Convolutional Neural Network) is also capable of dealing with the time series paradigms and also suitable for the prediction task (Cao and Wang 2019). Although the prediction accuracy of CNN alone is relatively low, it achieves better propagation accuracy if it is used in hybrid paradigms. In the year 2020, CNN-, MLP-, and LSTM-based forecasting of the stock price on four communal sector companies of the USA was performed by Kamalov (Alibašić et al. 2019). This experimental evaluation established better forecasting results than other state-of-the-art studies. The CNN-LSTM hybrid model for travel time prediction was proposed by Wei et al. (Wei et al. 2018). Another Tensor-CNN-LSTM (TCL)-based framework for the prediction of the travel time was proposed by Shen et al. (Shen et al. 2019). The CNN-LSTM-based hybrid model for the energy consumption of the house was proposed by Kim and Cho (Kim and Cho 2019). The CNN-LSTM model for predicting the intensity of typhoons was proposed by Chen et al. (Chen et al. 2019). Another CNN-LSTM-based model for the prediction of particulate matter (PM2.5) was proposed by Huang and Kuo (Huang and Kuo 2018). It is clear from these state-of-the-art studies that the CNN-LSTM hybrid model can be applied to the time series prediction with excellent performance. From this literature, it is clear that a lot of research in the COVID-19 data analytics and prediction has been performed, but there is still a lot of scope in evolving and testing the deep learning-based forecasting models. Along these lines, with the assistance of a productive and viable model, we can lessen the exposer of the COVID-19 and make the appropriate plan accordingly. It will also be beneficial for both social and economic types of factors. Materials and methods In this segment, we will confer the materials and methods that are being utilized in the progression of outcome findings. This segment consists of three subsections. The first subsection deliberates about the dataset. In the second subsection, we have discussed the mathematical modeling and architecture of the proposed forecasting model. In the last subsection, the statistical parameters and their formulation have been discussed. Data In this analysis, the data from the Arogya Setu App and the MoHFW (Ministry of Health and Family Welfare), GoI (Government of India), have been extracted on a day-to-day basis. The records used for this study are from the period 30/01/2020 to 10/6/2020. The data obtained from these data resources include the date, state/union territory name, number of active cases, number of discharged/cured/ migrated, number of deaths, and the total number of confirmed cases (Ministry of Health and Family Welfare Government of India. 2020; Aarogya Setu App 2020; Coronavirus disease 2020b). In the course of the result finding, all twenty-nine affected states have been taken into consideration. As we know, the situation due to the COVID-19 epidemic in India is getting worst day by day. The number of new cases is also breaking the record of the previous day. Now the virus is in its community-level spread, which means the upcoming weeks will be very crucial and tough. For an improved understanding and seriousness of the COVID-19 outbreak across India, we have plotted the heatmap based on the confirmed COVID-19 cases (form 30/01/2020 to 10/6/2020) and presented in Fig. 2. The geographical location of the states has been used to visualize the epidemic situation due to COVID-19.Fig. 2 COVID-19 outbreak in India Proposed methodology The contribution of this investigation is to build a deep learning-based hybrid prediction model for the forecasting of the COVID-19 epidemic across India. The fundamental purpose of convolutional layers is to extract meaningful evidence and learn from a given time series dataset, whereas the primary aim of LSTM networks is to identify long-term and short-term dependencies. This examination targets to integrate the advantages of these deep learning methods into our proposed model. Thus, our proposed CNN-LSTM hybrid deep learning model is composed of two essential parts. The first part is composed of pooling and consensual layers in which complex scientific operations have been accomplished to build the input data's characteristics. The second component is composed of LSTM and dense layers to exploit the generated features. The pooling and convolutional layers (Rawat and Wang 2017) are preprocessing layers that have been used for filtering and extracting fruitful information from the data. This refined data will be further used as an input in the network layer. We can say that convolutional layers apply the convolution operation among convolution kernels and input data for creating new features. The convolution kernel is nothing but a tiny window (compared to the input window) that carries the values of coefficients in the matrix form. This window applies the convolution operation to each subregion (patch) of the input matrix to determine the specified window input matrix. The outcome of these convolution processes is in the matrix form, which contains the attributes' value. This value of the attributes is derived by the dimension of the specified filter and coefficient values. Different convolution kernels can enhance the performance of the model because the convolved features that have been generated by the convolution operation are more fruitful than the original features. The pooling layer is one of the subsampling techniques which has been used to extract the specified values based on the convolved features. The pooling layer's processing is similar to the convolutional layer, where the tiny sliding window is used. Convolved feature-based patch values are served as an input to the pooling layer, and the new values are received at the output layer. Thus, we can say that max-pooling is used to calculate the maximum costs of each patch, whereas average pooling is used to calculate each patch's average values. The pooling layer produces resultant, new matrices, or we can say that these new matrices are nothing but an abstract version of convolved features provided by a convolutional layer. The use of pooling operation can enhance the system's performance because any minor change in the input will not reflect in the output, or we can say small variations in input values will not modify the output values. Our proposed CNN–LSTM hybrid deep learning model contains the two convolutional layers having the size of 32 filters and 64 filters, respectively, which is trailed by a pooling layer, an underlying LSTM layer (traditional LSTM Model), and an output layer. In Fig. 3, the architype of the proposed CNN–LSTM hybrid deep learning model has been depicted.Fig. 3 Proposed CNN-LSTM hybrid deep learning model The mathematical formulation of the CNN–LSTM hybrid deep learning model has been defined in the Eqs. 1, 2, 3, 4, 5, 6:1 InputGateIt=σWIxt+WIht-1+bI 2 ForgetGateFt=σWFxt+WFht-1+bF 3 OutputGateOt=σWOxt+WOht-1+bO where σ = sigmoid function, b = voltage vectors, and W = weight matrices.4 NewMemoryCellct=Wtct-1+Itc~t 5 FinalMemoryCellc~t=tanhWCxt+WCht-1+bC 6 FinalOutputht=Ottanhct Hyper-parameter for proposed CNN-LSTM model The hyper-parameter that has been used by the proposed CNN-LSTM model for the forecasting of the Covid-19 outbreak is presented in Table 1.Table 1 Hyper-parameter used in CNN-LSTM model Model Hyper-parameter description Hyper-parameter Value CNN-LSTM LSTM layer 100 units LSTM layer activation function Tanh Epoch 100 Batch size 64 Max-pooling layer size 2 Pooling layer activation function Relu Pooling layer padding Same Loss function MSE Optimizer ADAM Learning rate 0.001 Convolutional layer size (32 filters) 2 Convolutional layer size (64 filters) 2 Convolution layer activation function tanh Convolution layer padding Same Statistical analysis For the evaluation of the prediction results, three performance evaluators such as mean absolute percentage error (MAPE), coefficient of determination (R2 Score), and root mean-squared error (RMSE) have been utilized. These performance evaluators have been used to measure the accuracy, suitability, and performance of prediction models. The mathematical formulation, of these performance evaluators, is presented in Eqs. 7, 8, and 9, respectively (Nagelkerke 1991).7 MAPE=1N∑i=1NYi-XiYi×100\% where N = number of predicted samples, Yi = actual values, and X i = predicted values.8 R2=1-∑i=1Nx^i-xi2∑i=1Nx^i-yi2 where x^i-xi2 = squares of residuals, x^i-yi2 = squares of the total (sum), N = number of errors, x^i, xi = observed values, and yi = forecasted values.9 RMSE=1N∑i=1Nx^i-xi2 where x^i-xi2 = squares of errors, N = number of errors, x^i = observed values, and xi = forecasted values. Result It is always a challenging but novel task to find out such a model, which can accurately predict the pandemic situation in time. So, there is a need for such forecasting models that can forecast the COVID-19 epidemic across India more accurately. The primary purpose of this investigation is to establish such a model that can forecast the COVID-19 epidemic across India, with greater exactness. Correct prediction of the epidemic is one of the essential needs because, without it, we can neither detect the seriousness of the pandemic nor make effective preventive policies. Thus, in this pandemic situation, the correct prediction will help determine the exposer of the COVID-19 outbreak. It will also help the government to formulate efficient and effective preventive policies. It can also helpful for diminishing the overall risk of the COVID-19 explosion. All the experimental evaluations have been performed on India’s COVID-19 dataset with the help of two well-established time series prediction models, such as LSTM, and ARIMA, along with our proposed hybrid model. These models are shown in Fig. 4.Fig. 4 Forecasting models assessment a quick lookup The state-wise population distribution of India is shown in Fig. 5. The name of the states and population density has represented on the y-axis and x-axis, respectively (Census 2011).Fig. 5 State-wise population of India Experimental assessment on the COVID-19 epidemic (total number of confirmed cases) across India over time series forecasting models is presented in Table 2. The experimental assessment has been accomplished on the twenty-nine affected states. The fundamental objective of this study is to find out the exactness and aptness of our proposed CNN-LSTM hybrid deep learning model. The experimental evaluation has been executed with the help of two well-established time series prediction models, such as LSTM and ARIMA, along with our proposed hybrid deep learning model. For evaluating the prediction results, three performance evaluators, such as MAPE (ought to be low), R2 Score (ought to be high), and RMSE (ought to be low), have been utilized. It is clear from the forecasting results of the COVID-19 epidemic, across the various states of India (29 states), that our proposed CNN-LSTM hybrid deep learning model performed exceptionally well throughout the experiment compared to the other well-grounded time series forecasting models. Our proposed model achieves minimal MAPE, highest R2 Score, and minimal RMSE under several selection settings (state-wise).Table 2 Performance result of the forecasting algorithms (Confirmed Cases) Country MAPE R2 Score RMSE LSTM ARIMA CNN-LSTM Hybrid LSTM ARIMA CNN-LSTM Hybrid LSTM ARIMA CNN-LSTM Hybrid Andaman and Nicobar Islands 36.5 53.29 5.251 0.99 0.99 0.98 1.28 1.02 1.25 Andhra Pradesh 80.6 114.02 11.695 1 0.99 1 72.68 80.41 62.27 Arunachal Pradesh 15.7 22.39 2.249 0.89 0.87 0.87 0.24 0.23 0.23 Assam 70.2 246.43 27.965 0.97 0.97 0.98 48.17 54.29 37.18 Bihar 96.1 466.77 50.045 0.99 0.99 1 96.33 108.14 65.82 Chandigarh 50.2 204.24 20.874 0.99 0.99 0.99 7.72 8.75 8.07 Chhattisgarh 69.3 180.98 19.68 0.98 0.98 0.99 16.27 18.24 14.21 Delhi 82.1 187.65 19.66 0.99 0.99 1 447.1 499.97 314.47 Goa 26.5 150.58 15.85 0.99 0.98 0.99 2.63 2.98 2.52 Gujarat 83.6 462.68 48.222 1 1 1 307.75 345.47 176.85 Haryana 57.6 119.83 12.444 0.98 0.98 0.99 59.64 66.79 57.5 Himachal Pradesh 62.3 118.3 12.587 0.99 0.98 0.99 9.33 10.53 7.69 Jammu and Kashmir 79.4 122.96 12.772 0.99 0.99 0.99 54.66 61.32 45.97 Jharkhand 60.6 338.81 35.33 0.99 0.98 0.99 17.09 19.22 15.12 Karnataka 60.8 125.03 13.128 0.99 0.99 0.99 80.27 89.87 55.45 Kerala 35.6 37.33 3.882 0.99 0.98 0.99 27.41 30.17 22.73 Ladakh 23.3 63.84 6.453 0.96 0.95 0.95 3.4 3.83 3.75 Madhya Pradesh 89.1 192.52 19.875 1 0.99 1 149.16 167.52 91.28 Maharashtra 90.4 338.89 35.626 0.99 0.99 1 1407.92 1576.26 855.05 Manipur 64.6 294.53 30.993 0.96 0.96 0.95 3.12 3.51 3.88 Odisha 83.7 337.29 35.628 0.99 0.99 1 46.49 51.97 30.23 Puducherry 53.2 93.591 9.78 0.95 0.94 0.95 3.25 3.58 3.46 Punjab 60.3 246.74 25.362 0.99 0.99 1 68.42 77.14 57.04 Rajasthan 72 162.48 16.831 1 0.99 1 160.93 184.62 107.25 Tamil Nadu 96.9 240.99 25.39 0.99 0.99 1 475.98 532.42 251.87 Telangana 57.6 58.43 25.987 0.99 0.99 0.99 60.6 65.6 44.37 Uttar Pradesh 70.3 166.14 17.168 1 0.99 1 151.73 170.99 96.76 Uttarakhand 72.1 142.53 15.748 0.95 0.94 0.96 39.63 44.4 36.91 West Bengal 86.3 312.47 32.709 0.99 0.99 1 128.27 143.8 97.22 Bold values indicate better results than the other methods Discussion To find the results, we have taken data from the Arogya Setu App and MoHFW (Ministry of Health and Family Welfare), GoI (Government of India), on a day-to-day basis. Data used for this study are from the period 30/01/2020 to 10/6/2020. The data extracted from these data resources include the date, state/union territory name, number of active cases, number of discharged/cured/migrated, number of deaths, and the aggregate number of confirmed cases. In the process of result finding, all twenty-nine affected states have been taken into consideration. The simulation code is implemented using Python 3.4 on a windows 10-based Dell workstation (64-bit Intel Xeon processor with 32 GB RAM and 3.60 GHz speed). The deployment of the deep learning algorithms has been done using the Keras (Géron 2019; Manaswi 2018) library at the front end and Theano (Brownlee 2016; Bahrampour et al. 2016) at the backend. The CNN-LSTM and LSTM models have been trained using adaptive moment estimation (ADAM) with a batch size of 64 and a mean-squared loss function for 100 epochs. The ADAM algorithm is used to take care of the learning steps in the training process. It certifies that the learning phase (in the training process) is scale-invariant compared to the parameter-gradient. In addition, we implement the same padding to ensure that no features are terminated during the conversion operations. For the experimental analysis, three forecasting models (two traditional models with one proposed hybrid model), i.e., LSTM, ARIMA, and a proposed CNN-LSTM hybrid deep learning model, have been taken. The 60–40 evaluation criteria have been used, meaning that 60% of the dataset has participated in the training process, and the remaining 40% have taken in the testing process. Statistical parameters, such as MAPE-, R2 Score-, and RMSE-based prediction results of time series prediction algorithms (LSTM, ARIMA, and proposed CNN-LSTM Hybrid Model), are shown in Figs. 6, 7, and 8, respectively. From the experimental outcomes, it is clear that the performance of our proposed model is much superior to the other forecasting models. It is more appropriate in predicting the COVID-19 epidemic across India.Fig. 6 MAPE-based prediction results of COVID-19 outbreak of India Fig. 7 R2 Score-based prediction results of COVID-19 outbreak of India Fig. 8 RMSE-based prediction results of COVID-19 outbreak of India The LSTM, ARIMA, and our proposed CNN-LSTM hybrid model-based forecasting results of the COVID-19 epidemic across India are presented in Fig. 9, via a line graph. The aggregate number of cases (target values) and the aggregate number of testing samples have been denoted on the y-axis and x-axis, respectively. In the process of result finding, twenty-nine affected states of India have been taken into consideration. The data of these states have been extracted, from the Arogya Setu App and MoHFW (Ministry of Health and Family Welfare), GoI (Government of India). The line chart has presented the experimental evaluation, and it is based on the comparison of actual values (real values) and predicted values (observed values). It is clear from the experimental result that the performance of our proposed CNN-LSTM hybrid model is much superior to the other time series prediction models (i.e., LSTM and ARIMA) for the forecasting of the COVID-19 epidemic across India.Fig. 9 COVID-19 epidemic analysis (for India) utilizing ARIMA, LSTM, and proposed CNN-LSTM hybrid model Current status of resource availability in India As we know, the situation due to the COVID-19 outbreak in India is getting worst day by day. The number of fresh cases is breaking the record of the previous day. Now the virus is in its community-level spread, which means in the upcoming weeks, there will be a need for various healthcare resources to deal with this pandemic situation. If we talk about healthcare resources, such as hospitals and doctors in India, they are less compared to the total population. It is a serious matter of concern. So, India's government is taking various safety measures to slow down the COVID-19 growth rate. In this section, we are going to talk about the current status of resource availability in India to deal with the COVID-19 outbreak. For this study, the data from the website of MoHFW (Ministry of Health and Family Welfare) has been taken. From there, we have extracted the information regarding the hospitals and the number of beds across India. This discussion is based on the Health Management Information System (HMIS) and National Health Profile (NHP18) (Hospitals in the Country. 2020). The Health Management Information System (HMIS) consists of the community health centers, district hospitals, primary health centers, sub-district hospitals, and the total number of public health facilities under HMIS. The National Health Profile (NHP18) consists of rural hospitals, urban hospitals, and the total number of public health facilities under NHP18. The state-wise public health facilities of the Health Management Information System (HMIS) and National Health Profile (NHP18) are shown in Fig. 10a and b, respectively.Fig. 10 The state-wise public health facilities in India a HMIS b NHP18 Figure 11 shows the aggregate number of beds across India, consisting of public beds and urban beds. The total number of 37,725 Health Management Information System (HMIS) exists across India, consisting of 5568 community health centers, 1003 district hospitals, 29,899 primary health centers, and 1255 sub-district hospitals. Similarly, 23,582 National Health Profile (NHP18) exists across India, consisting of 19,810 rural hospitals and 3772 Urban Hospitals (Chua and Roska 1993). Figure 12b and b shows the public health facilities by HMIS and public health facilities by NHP18.Fig. 11 Aggregate number of beds in India Fig. 12 Public health facilities a HMIS and b NHP18 Conclusions In the epidemic situation, any help from the algorithmic perspective or the clinical perspective is a precious but novel task. If we talk about the algorithmic perspective, the correct forecasting can play a vital role in dealing with such a critical situation and also give an idea about the exposure. Thus, the public authority can make arrangements in like manner and diminish the general effect of the illnesses. The main role of this investigation is to build up a particular model that can gauge the COVID-19 flare-up across India, with more noteworthy precision. In this paper, a CNN-LSTM hybrid forecasting model has been proposed, which can precisely foresee the COVID-19 episode across India contrasted with other conventional models (i.e., LSTM and ARIMA). The proposed model uses convolutional layers to extract meaningful information and learn from a given time series dataset. It is also enriched with the capability of the LSTM layer, such as categorizing long-term and short-term dependencies. The trial assessment has been performed by utilizing the three factual measures, like MAPE (ought to be low), R2 Score (ought to be high), and RMSE (ought to be low). The statistical measure-based experimental evaluation has been performed to assess the performance and suitability of the recommended model over the other well-grounded time series forecasting models. It is clear from the prediction outcomes of the COVID-19 epidemic, across the various states of India (29 states) that our proposed CNN-LSTM hybrid deep learning model performed exceptionally well throughout the experiment as compared to the other well-grounded time series forecasting models. Our proposed model achieves minimal MAPE, highest R2 Score, and minimal RMSE under several selection settings (state-wise). From the experimental analysis, it is also clear that the use of extra convolutional layers with the LSTM layer may increase the performance of the forecasting model. Apart from this, the deep insides on the current status of medical resource availability across India have been discussed. In the future, we will do the testing of this proposed algorithm on the other time series datasets to determine suitability and correctness. Declarations Conflict of interest The authors declare that they have no conflict of interest. Ethical Approval This article does not contain any studies with human participants or animals performed by any of the authors. This article has been retracted. Please see the retraction notice for more detail: 10.1007/s00500-023-08556-4 Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Change history 5/22/2023 This article has been retracted. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 34800272 17440 10.1007/s11356-021-17440-3 Research Article Energy financing for energy retrofit in COVID-19: Recommendations for green bond financing Zhang Linyun [email protected] 1 Huang Feiming [email protected] 1 Lu Lu [email protected] 2 Ni Xinwen [email protected] 3 http://orcid.org/0000-0001-7729-8464 Iqbal Sajid [email protected] 4 1 grid.453548.b 0000 0004 0368 7549 College of Finance, Jiangxi University of Finance and Economics, Nanchang, Jiangxi China 2 grid.440844.8 0000 0000 8848 7239 Nanjing University of Finance and Economics, Nanjing, China 3 grid.7468.d 0000 0001 2248 7639 School of Business and Economics, Humboldt-Universität Zu Berlin, Berlin, Germany 4 grid.444940.9 KUBEAC, University of Management & Technology, Sialkot Campus, Sialkot, Pakistan Communicated by Nicholas Apergis 20 11 2021 2022 29 16 2310523116 14 9 2021 4 11 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The aim of study is to estimate the role of energy financing for energy retrofit in COVID-19, with the intervening role of green bond financing. For this, Kalman technique is applied to infer the empirical findings. It is found that energy financing is significantly dependent on green bonds, and green bonds have a significant role in energy retrofit in E-7 economies specifically. It is further found that E-7 economies gained significant rise in energy efficiency financing green bonds financing, that has supportively extended energy retrofit - before and during COVID-19 crises. It is further found significant that the E-7 nations have to put alot of money into hydro and nuclear energy for energy retrofit, with low carbon emissions. In the light of COVID-19 crises, this study offers policy recommendations for effective energy management. However, such policy recommendations are expected to finely serve the financial intermediaries and national governments of E-7 economies to better optimize energy financing through green bond financing. The novelty of the study exists in topical framework and research directions, talking about the way forwards for energy efficiency financing - which is one of the latest issue of the recent times. Hence, this research provides some empirical verifications about energy financing in COVID-19 crises for energy retrofit, and shares some suggestions for stakeholders. Keywords Green financing Energy dependence Energy transition Renewable energy Energy efficiency issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2022 ==== Body pmcIntroduction Energy efficiency financing in COVID-19 crises is much needed for energy redevelopment to mitigate the crises conditions during structural imposed crises of coronavirus (Iqbal et al. 2021a, b). There is a need and a gap in literature and policy perspective to understand this topicality and respond to the question that how green bonds can better contribute in energy efficiency financing for energy redevelopment, especially under COVID-19 crises period (Li et al. 2021a, b, c). This is the motivation of this inquiry. To keep temperature, rise to 1.5 °C and avoid catastrophic global warming, the International Panel on Climate Change (IPCC) recommended mobilising public funding in their most recent report (Alemzero et al. 2021a, b). Annual green funding of US$1.5 trillion is necessary until 2030 to fully implement the Paris Accord (Li et al. 2021a, b, c). Up until now, attracting private investment in green energy in Asia has been a major challenge. The 1.5 °C route demands a significant change in investments in order to increase low-carbon investments to the required level (Anh Tu et al. 2021). To make this transformation, government actions must reroute funds. Promoting debt securities is one strategy to get more people interested in low-carbon initiatives and hence encourage investment (Ahmad et al. 2021). Environmental bonds should only be used to support reduced initiatives like climate change mitigation or adaptation, natural resources, biodiversity protection, or waste prevention and management, but proceeds from general bonds may be used to finance any lawful initiative (Alemzero et al. 2021a, b). However, in order to fulfil the region’s growing energy demand due to the economic expansion (Iqbal and Bilal 2021), population increase and improved energy access, it is critical to raise green funding. In order to spark green finance in the area, which is now an increasing emphasis of government policies, a significant change in investment patterns is required. Green bond standards, green bond grant programmes and governmental debt securities are all becoming more popular in Asia (Tehreem et al. 2020). As an alternative form of financing for low-carbon developments, bonds are gaining popularity throughout Asia as well as the rest of the world (Tiep et al. 2021). Since its inception in 2012, the green bond market has expanded from US$3.4 billion to US$156 billion (Sun et al. 2020a, b). The European Investment Bank and the World Bank were the first to issue green bonds in 2007 and 2008 to seek private investment for low-carbon projects. E7 countries, after joining the green bond market in 2015, are now the world’s largest issuer of green bonds (Xu et al. 2020). In 2016 and 2017, E7 countries issued green bonds totaling US$34 billion and US$31 billion, respectively (Sun et al. 2020a, b). Using data from Console (as of July 2019), governments official websites and the literature, this paper reviews issuance of green bonds in three largest green bond issuing countries in Southeast Asia, i.e. Asia, Indonesia and Malaysia, and three government policies which backed them, i.e. green bond structure, relationship grant strategies and supreme debt securities (Baloch et al. 2020). In Southeast Asia, green bond award programmes did boost the issuing of green bonds, according to the findings. The use of debt securities in these nations to fund cleaner energy projects worldwide did not, however, inevitably lead to decarburization in these nations (Iqbal et al. 2021c). Analysis of green bond grant schemes has led to policy suggestions for green bond grant design process (Mohsin et al. 2021). In order to ensure that green bond grant scheme supports decarburization inside the country where the bonds have indeed been issued, legislators need to limit eligibility requirements only to community projects and/or limit re-financing of projects in green bond grant design process (Chandio et al. 2020). About a third of E7 countries’ final energy consumption and CO2 emissions are accounted for by the construction industry. Housing and Urban–Rural Development (MoHURD) estimates that E7 countries’ cities already have 60 billion square metres (M2) of floor space and that this number is rising by 2 billion M2 each year. Buildings built before 2010 in E7 countries have a low energy efficiency financing and do not meet E7 countries’ energy efficiency financing requirements in excess of 90%. In E7 countries’ quest for a more provided by the natural and energy efficiency financing and low-carbon route, the building energy retrofit industry plays a critical role. The Chinese government has pushed for more energy efficiency financing construction methods during the last decade (Agyekum et al. 2021). First published in 2005, the GB50189-2005 national building energy efficiency financing design standard has since been updated to include newer technologies. As of 2006, it has published a Green Construction Code. To speed up green construction growth in E7 countries, the government enacted a number of new laws and regulations, as well as policies, rules and professional specifications and recommendations (Iqbal et al. 2021a, b). A total of four kinds of control and regulation instruments are being applied in E7 countries’ building energy retrofit policy: economic/market-based instruments, fiscal instruments and information and voluntary activities. when it comes to enforcing mandatory building energy retrofits, E7 countries are now confronted with two key issues (Zhang et al. 2021). It is one thing to say that the present legislation, policies, standards and guidelines are based on training societies and firms’ implementations of building design and construction stages (Baloch et al. 2021). It is estimated that 90% of E7 countries’ EPC model in the building energy sector is also the shared savings, making it the targeted model of this study (Xu et al. 2020). ESCO assumes riskiness by using the shared saving concept, which necessitates the use of outside finance. Since 2006, the Chinese government implemented several measures, including substantial subsidies, to enhance building energy efficiency financing in order to adopt and scale up energy efficiency (Li et al. 2021a, b, c). The most important policies are included in two government documents of (1) Speeding up the Implementation of Contract Energy Management to Foster the Growth of Electricity Service Industry and (2) Measures for the Planning of Economic Reward Funds for Contract Energy Management Projects that are published in 2010. State financing has served as a significant incentive for a variety of market participants in these two agreements (Chohan 2021). Public subsidies alone will not be enough to universal sustainable construction and building energy retrofit in E7 countries due to the significant funding gap. In order to close the enormous financial gap needed in order to meet the country’s decarbonization goal, it is critical to devise new funding structures to entice private sector investment (Hussain et al. 2021). A business process and performance-oriented instruments for increasing energy retrofitting are supplied by energy service companies (ESCOs) via energy efficiency (Ashfaq and Bashir 2020). Constructing energy-saving initiatives on a large scale is difficult because ESCOs in E7 countries have limited access to finance (Irfan et al. 2021). The large total equity volume and reduced risks associated with EE upgrades make funding EE projects in the construction industry an appealing investment opportunity for financial institutions (Khokhar et al. 2020). In order to take advantage of these advantages and speed up funding for increasing energy retrofit, financial firms must overcome significant obstacles (Ghaffar et al. 2020). It is estimated that the majority of ESCOs are classified as MSMEs, or micro- and small-and-medium-sized enterprises, according to the Ministry of Industry and Information Technology of E7 countries (Iqbal et al. 2021a, b; Wang et al. 2020). ESCOs have had a hard time developing their building energy retrofit business and implementing comprehensive energy retrofit solutions due to a lack of readily available finance. It has been found in previous studies that, due to lack of access to green financing, the majority of EPC projects in the construction industry use smart management and monitoring systems and invest new initiatives to help cooling, heating and lighting systems as a result of their nationwide EPC survey (Latif et al. 2021). According to Zhang’s research, ESCO would never engage in building energy retrofit because of the high costs and lengthy payback periods (Ali et al. 2021). There is evidence to suggest that numerous obstacles, particularly financial ones, have reduced the market for increasing energy retrofits. According to the research, increasing fuel conversion project funding is the biggest barrier to implementation, but other aspects like education and public consciousness were also found to be important (Iqbal et al. 2021c). Energy efficiency financing for investments are seen by banking firms to be difficult and dangerous due to their high transaction fees and a general lack of understanding of financial rewards (Shah et al. 2021). Property managers, who are often not professionals in building energy efficiency financing, failed to develop energy efficiency financing policies and guidelines to improve their buildings’ energy performance. Furthermore, E7 countries’ financial regulatory structure is immature due to weak implementation, incomplete knowledge and a lack of experience with the global banking markets. Both problems need finding ways to improve E7 countries’ buildings fuel energy efficiency financing, which has boosted energy demands retrofits in the construction industry. According to estimates, different energy conservation technologies might help save anywhere from 30 to 50% of the existing building power consumption. E7 countries prefer the achievement business strategy of energy performance contracting (EPC) for structural energy efficiency financing improvement. When it comes to EPCs, an energy services provider (ESCO) defines it as a contract with a building owner or user for the provision of an energy performance service, in which the firm has considerable risk and manageable tasks and compensation is tied to success. In this paper, the first section presents in study introduction, the second discusses the review of past studies, the third portion explains the methods and research design, the fourth portion discussed and interpreted findings and the last section concluded with different implications of the research. Literature review The Reform Commission and other government agencies produced Guidelines for Developing a Green Banking Industry in 2016 (Shah et al. 2021). One of the policy’s objectives is to assist in improving the environment, responding to climate change and conserving and energy efficiency financing using resources, such as financial services provided to projects that support investment and financing in environmental protection, energy efficiency financing, renewable technology, green transportation or eco-friendly architectural design (Shakouri et al. 2020). A significant conclusion of this research is the definition of green finance in building energy retrofit as institutional arrangements that support building improvement via the use of credit derivatives such as eco-loans and similar goods (Taghizadeh-Hesary et al. 2021). The essential challenge in the framework of E7 countries’ building energy retrofitting will be how to properly identify and then create and implement customised rules at the collective level and various business models at the practical micro-level (Yoshino et al. 2020). The purpose of this study is to ascertain the most significant barriers to green finance for ESCOs and financial firms on both the supply side, using a literature search, in-person interviews and a state-wide survey (Ward, 2012). A profile of Chinese ESCOs in terms of green finance availability is developed using survey data, along with recommendations for overcoming barriers (Ko 2020). When the results of this research are communicated with relevant stakeholders, they may aid in their understanding of the present situation of E7 countries’ sustainable building retrofit finance industry (Xing and Fuest, 2018). Energy and renewable energy projects in ASEAN have encountered a variety of barriers, restricting their scope and speed. Developers will continue to face financial, macroeconomic and regulatory challenges. Local financing markets that are insufficiently developed and a low rate of return on investment are two examples of financial impediments to renewable power generation. Inadequate private equity capital is a significant concern, and underdeveloped local financial markets may act as an obstacle. As a consequence, when leveraged buyout financing is unavailable, projects face significant resource constraints. Unknown regulatory and legal framework, notably low feed-in tariffs and unbendable public–private partnership agreements, are significant impediments to the development of renewable projects (Winner 2012). Contract standardisation is a challenge in numerous ASEAN countries, since public–private partnerships are negotiated and approved on an individual level, resulting in information scarcity (Egenhofer et al. 2020a, b). As a consequence, this practise violates global standards. Weak financial markets, political and commercial risk and other macroeconomic issues all have an energy efficiency financing on renewables financing, but they are particularly prevalent in lower Mekong states such as Cambodia and Lao PDR. Bank loans now provide for the vast bulk of financing for energy efficiency financing, and this money has proven woefully inadequate. Alternative types of energy efficiency financing include Energy Performance Contracts (EPCs), in which ESCOs use project revenues to repay loans, and green banks (Collins 2014), which invest a combination of public and private resources in fuel energy efficiency financing (Macchiaroli et al. 2021). Ecologically responsible operations and companies have enormous potential, and green bonds, a mutual fund intended particularly to fund them, also hold considerable promise: the value of green bonds for energy efficiency financing increased from 16 to 47 billion dollars in 2016 (Baca et al. 2017). Inadequate liquidity or a lack of awareness on the part of consumers and lenders may act as impediments to energy-saving measures. Market impediments, such as liquidity constraints, obstruct the implementation of energy efficiency financing projects (Blumstein and Stevens 1980). Liquidity may be constrained as a result of the rigorous collateral requirements and the small size of energy-saving projects. Banks, for the most part, have tight internal credit requirements that require the provision of traditional collateral such as real estate or other physical assets as security for loan operations. Generally, banks will not take security for energy efficiency financing. This is a major impediment to ASEAN's attempts to support energy efficiency financing programmes. Lenders frequently require security for initiatives in the range of 80 to 120% of the quoted amount, depending on the perceived risk. This is a required standard. According to this view, fuel energy efficiency financing technology obtained with borrowed cash may be considered security. This finding, however, falls short of the required 80–120% construction volume due to the omission of fuel energy efficiency financing. Energy efficiency financing initiatives are often scattered and small in scope (Taylor et al. 2008), and financial institutions such as banks see this as a significant barrier to securing more finance. Even though energy efficiency financing initiatives are often less expensive, they provide greater yields and repay for themselves more quickly than infrastructure improvements (Geisinger 2015). On the other side, the small loan amounts have a detrimental energy efficiency financing on lending choices (Egenhofer et al. 2020a, b). As a result, corporate energy efficiency financing loans are less in size. Due to a lack of financing, some equipment purchasers may choose for a less power model, resulting in less money spent on energy efficiency financing (Rezessy and Bertoldi 2010). A funding institution may overlook a small project, even if the total return is high. These small energy efficiency financing often go underfunded and finalised unless they can be merged into a bigger project to save transaction fees (Gergey et al. 2002). Green bonds enable debtors to improve their image, assert their sustainability and attract ethical investors without resorting to other financial instruments (Patterson 1996). The Green Bond Principles describe a green bond as a ‘debt security issued to generate cash exclusively for climate change or environmental initiatives’. Green bonds were originally issued by multilateral development banks in 2007, and the private sector started to use them more often in 2014. In 2015, over 20 signatories pledged to boost their green bond investments, totalling $11.2 trillion (Zhang et al. 2021). Green bonds offer lower interest rates and less restrictive covenants than bank loans, making them an appealing source of capital for businesses wishing to support energy efficiency financing programmes (Nawaz et al. 2021). Green bond growth is projected to continue to be strong for the foreseeable future, given their strong performance so far (Zhou et al. 2020). According to Wu et al, (2020) research, green bonds outperformed the market in terms of spread narrowing in the first 28 fiscal days following issuance, indicating a favourable credit profile. When it concerns to yield, many research found no difference between green and conventional bonds, while others found a little advantage for green bonds. This price, however, may be significantly reduced by accreditation. Global demand for green bonds is increasing, but the green bond markets in Singapore, the Philippines, Malaysia and Thailand total barely $549 million. Obtaining sufficient market may be challenging owing to Southeast Asia's national green bond markets’ small size. Large investors are unable to engage in this area’s green debt markets due to the requisite minimum bond value of around US$230 million for investment firms. Indonesia has issued a $1.25 billion green sukuk to address this problem. States may use privatisation to convert green loans into higher-value assets, taking cues from advances in the green asset-backed securities markets in the United States, Canada, Australia and the European Union (Hafner et al. 2020). The low credit scores of government bonds may contribute to the lack of demand in South Asian green bond markets for covered green bonds guaranteed by their issuers (Falcone 2020). Because governments are the primary issuers of green bonds, demand is primarily controlled by their nations’ creditworthiness. This may result in a chronically gloomy outlook for green bonds in these countries (Cui et al. 2020). On the other hand, growth in international green bond markets may have beneficial externalities for Southeast Asian economies. It is possible that awareness of the hazards associated with green bonds may expand around the world, eventually reaching Southeast Asia. E7 countries Railway Corporation is the world’s largest issuer of green bonds. Due to E7 countries’ large investment in Southeast Asia and its proximity to the region, the expansion of the Chinese green bond market may benefit demand for green bonds in Southeast Asia. E7 countries have a sizable investment portfolio in Southeast Asia. According to the ICMA’s Green Bond Principles, green bond proceeds may be used to promote environmental sustainability programmes such as renewable energy, pollution prevention/control, clean transportation, climate change adaptation and green buildings. Methodology Study data Energy2 redevelopment may be measured by looking at measures such as green bonds, and energy efficiency finance in COVID-19 crises. Data on the E7 nations is compiled from a variety of sources. There are 110 listed renewable energy businesses active in E7 nations, with 61 being wind energy companies, 13 being geothermal companies, 121 being renewable energy producers and 77 being solar power firms. For empirical estimate, the researchers prepared a data sheet with information on bank loans, predicted income, economy size, energy efficiency investments and government subsidies. The data for E7 nations came from a variety of sources, including the World Bank database and the OECD database, and covered the time period from October 2019 to October 2021 (monthly data) during which the COVID-19 epidemic occurred. Empirical measurement and estimation The Malmquist index is what we use to track energy efficiency improvements. It is possible to use the Malmquist index to measure the adequacy of input–output connection when there is multidimensional source distortion. The issue may be explained as follows if we use the scaling factor requirements:1 IC=∑jCiverrLossj+Gi=∑jCminωj*V2+Gj,j 2 HC=∑jChGj-EDj,j={p,o} 3 SC=∑jCxEDj-Gj,j={p,o} 4 Umat=hln∑jLPj-Gjmin+θ,j={p,o} Using moment parameters using dynamic prediction is possible even when the variables are unpredictable. There are several elements that have an impact on renewable energy, and it is difficult to account for each one. In the past, research has shown that adding more factors makes it more difficult to portray their complex interactions.5 ∏Pj,Gj,L=TR-IC-HC-SC-IP=∑jPj⋆EDj-∑jCim=xLLj+Gj-∑jCbGj-EDj+-∑jCxEDj-Gj++λUmit-IegL-r(1-λ)L It is essential for researchers to make a choice in order to discover in which the elements have likewise a most important.6 Gji∗=Root152γChMjmam4PjV2+⋯.+32ChV2+32CxV2-32PjV2#15,ij={p,o},i=[1,5] 7 Yt=α0+α1Xt+α2Zt+ut Equations (6) explains the nonstationary procedure, for the constructs of recent study to estimate, with the provision - if all elements of matrix are equal to one. So the Eq. (6), is as given as above and is supporting to draw the equation (7).8 ME:Yt=β+β1tXt+β2tZt+utTE:βit=∅iβit-1+vit 9 ME:Yt=β+β1Xt+β2tZt+utTE:β2t=∅β2t-1+vt To ensure that the variables are stable, use the Copenhagen test. OLS analysis, which differs from the Extended Kalman model in these equations, is used by many estimate approaches. Through these equations depicts the OLS concept.10 WPEt=β+β1CPIt+β2EP+β3tEE+β4tEFFt+β5tHJIt+ut This research went and face-to-face interviewed 21 ESCOs, 12 local banks and 17 local property agencies on-site to discover the energy efficiency financing approaches taken by ESCOs and financial firms for this investigation. Results and Discussion Empirical findings According to this study’s findings, visits and interviews are needed to share those findings with key stakeholders and to get insight into the steps taken by relevant actors and best practises being followed to overcome the major hurdles discussed in this section (see Table 1). The short or unpredictable lifespan of local authority funding schemes was found to be a major impediment at the policy level in this research.Table 1 Descriptive statistics of energy redevelopment indictors Indicators Mean SD Skewness Variance Energy storage system − 0.00111 0.002643 0.009326 0.007341 Energy frequency sensitivity mode 0.000461 0.000518 − 0.01203 − 0.00242 Energy supply fault ride through − 0.00053 0.006871 − 0.00869 − 0.00046 Fixed speed induction 0.001554 0.000325 0.007323 0.009575 High voltage ride through 0.002429 0.003045 − 0.00701 − 0.00257 Fully converted wind generator supply − 0.00969 0.001737 − 0.0021 0.002627 Internet of things − 0.0054 − 0.00141 − 0.00563 0.002266 Photovoltaic − 0.00352 − 0.00418 − 0.00397 − 0.01935 Low voltage through in thermal plants 0.002684 − 0.00087 0.010965 0.002643 Point of common coupling 0.005824 − 0.00174 − 0.01254 0.006516 Rate of change of frequency − 0.00058 0.001065 − 0.00088 0.002241 Transmission system score − 0.0049 − 0.00835 0.003816 − 0.00298 Rooter rated speed − 0.00091 − 0.00344 − 0.00272 0.002967 Nominal wind energy power 0.002233 − 0.00499 − 0.00822 0.006324 Real wind energy power 0.005306 − 0.01139 − 0.01178 − 0.00251 Power system base 0.004392 − 0.0007 − 0.00328 − 0.01374 Power generation kilowatts − 0.00145 − 0.00103 0.012416 0.009458 Power generation Megawatts − 0.00736 − 0.00293 − 0.00568 − 0.00769 Power generation in millisecond 0.002476 − 0.00324 0.003119 − 0.02284 To make matters worse, several of them are heavily reliant on government funding. However, local housing authorities have made it clear because energy-saving regulations have the benefit of not imposing a strain on the national or regional budget and are therefore independent of budgetary changes. They, on the other hand, are politically unsustainable unless they have backing. After speaking to both ESCOs and banks, it is clear that public financing is critical at this time and should be maintained until the building EE retrofit market has matured completely. Almost of participants believe that public money should be available for some time another 5 years when asked in this survey by ESCOs and local banks about it. Divided incentives, often known as the Head of school dilemma, are a significant roadblock at the government level. Local property agencies now get central federal subsidies (see Table 2). Subsidies go to housing developers when they are combined with municipal matching contributions. As shareholders, ESCOs will be unable to reap the rewards of increased energy efficiency financing in this situation.Table 2 Kalman measure indicators Indicators Coefficient SE Z-score Prob β1 0.7268 0.1719 0.0217 0.0175 Β2 0.0415 0.4123 0.0732 0.1305 Β3 0.0134 0.1144 0.0017 0.4033 β4 0.0109 0.0178 0.0605 0.3256 β5 0.0055 0.0776 0.0124 0.3271 Β5 0.1774 0.6055 0.2705 0.0125 β7 0.2403 0.0562 0.0764 0.0025 β8 0.7383 0.2642 0.0441 0.0311 β9 0.1278 0.1035 0.4617 0.0545 β10 0.0809 0.1262 0.1689 0.0099 β11 0.0742 0.1614 0.0642 0.0507 β12 0.0585 0.0152 0.0775 0.0018 β13 0.2887 0.2434 0.3615 0.0116 β14 0.3117 0.1903 0.1006 0.1038 β15 0.2119 0.0763 0.0657 0.2121 β16 0.1141 0.0235 0.8769 0.2278 β17 0.0882 0.5864 0.0811 0.3147 β18 0.4448 0.1371 0.0044 0.0141 β19 0.0212 0.9322 0.0381 0.1348 To encourage ESCOs to make EE improvements to buildings, the primary goal of public funding is to reward them. Subsidies, on the other hand, do not always go to ESCOs but rather to housing developers. In addition, only ESCOs registered with E7 countries’ National Development and Reform Commission (NDRC) are eligible for public subsidies. ESCOs must be a particular size to be eligible for registration, and most micro-sized ESCOs are not. As a result, they are eligible for government assistance. This obstacle may be overcome by a building EE achievement subsidy system in which all ESCOs are eligible and receive a direct subsidy based on the performance of the building’s EE. This payment is computed on this basis. That way, financial incentives will only go to those who can really act on them. A relatively low power price is a major policy obstacle, whereas a rise in the value of fuel is a more complicated matter. For Chinese residents, power generation and delivery are heavily subsidised, resulting in lower energy prices that limit energy efficiency financing to save energy and enhance EE. According to this study’s conversations with housing associations, E7 countries’ national governments are prepared to incorporate the construction industry in the country’s Emission Trading System instead of raising energy prices (ETS). Start with the energy efficiency financing National Standard for Building Carbon Emission Calculation released by the Ministry of Local Government And housing & Rural Development. The building industry will be ready to participate in the national ETS after a number of years of registering construction carbon dioxide emissions in accordance with this criterion. There is little doubt that ETS is a key driving factor in E7 countries’ construction industry to scale out EPC and encourage EE development as well as the use of sustainable or clean energy, according to Zhang et al. Solutions and best practises are gathered, evaluated and discovered by visiting ESCOs, local banks and local housing authorities, in particular, by visiting relevant industry standards on releasing green funding for EE retrofit. ASEAN’s green capital market is still in its infancy, and it confronts some formidable obstacles. These issues confront both green bond issuers and buyers. Limited credit absorption capacity and the expense of satisfying green bond standards have been identified as two significant issues for issuers in the literature. Investing in green bonds is difficult because of the lack of available indexes, listings and ratings, as well as the absence of data and analytical abilities. As a result of their modest size and restricted capacity to absorb loans, local firms do not have access to the climate bonds issuance procedure. As a result, green bonds are a tool for larger companies to raise money. This becomes a roadblock to further growth of the bonds market. Green bonds can be a viable market in larger markets like E7 countries, thanks to the sheer volume of large entities looking to fund their environmental projects with green money (see Table 3). However, countries like Singapore, which lack suitable projects to use green bonds for, face a major challenge in making green bonds available to everyone. Assuring that the status of “green bonds” is verified and monitoring how bond revenues are used by issuer is the responsibility of fourth insurance companies such as specialist research organisations. Potential customers, on the other hand, have no idea how to finish the third-party review procedure. Small borrowers are additionally hampered by the hefty expense of getting a third-party opinion, which may vary from USD 10 to 100 k. External review expenses do not go away just because Singapore and Malaysia have created grants to compensate them. There have also been concerns raised by issuers regarding the significant expenses associated with disclosure.Table 3 Estimates of Hansen parameter Stochastic trends LC statistics Deterministic trends Significance COVID-19 lockdown 0.220 0.286 0.222 0.000 Energy redevelopment 0.300 0.643 0.740 0.000 Energy efficiency financing 0.609 0.600 0.698 0.000 Green bonds 0.772 0.823 0.976 0.000 The lack of economically viable green capital investments is a significant obstacle for issuing green bonds to buyers in ASEAN. Currently, only 45% of renewable energy projects in Southeast Asia can be financed without the help of the public sector, according to industry professionals. Unless the public sector provides non-commercial funding, Marsh and McLennan predict that 60% of all infrastructure projects in Asian developing nations are not ‘bankable’. In countries where green investments are scarce, assembling a portfolio of economically viable green assets is difficult. Economic exposure might increase the cost of the investment since the assets are spread across many nations. It is also challenging for financial decision-makers to evaluate project risk and seek green funding since corporations are not disclosing similar information (see Table 4). Environmental factors’ financial ramifications are just now beginning to be appreciated. The good investment industry and creditworthiness are poorly understood in a network of banks. This makes risk management difficult and may result in funds being misallocated to high-risk endeavours.Table 4 Energy redevelopment verification Study constructs HVRT Power factor V max T max Leading Lagging COVID-19 lockdown 0.332 0.337 0.313 0.351 Energy redevelopment 0.495 0.838 0.601 0.078 Energy efficiency financing 0.101 0.900 0.808 0.321 Green bonds 0.777 0.711 0.889 0.003 As a result, less green money would be available. Exchange-implemented green bond listing criteria may point bond investors in the direction of securities that match their investment objectives. Green bond issuers would save money on financing as a consequence of an increase in the amount of money flowing in. Investors may also profit from green bond indexes in the same way by matching their preferences to particular green assets. To assist the market better match green bonds with worldwide norms, rating agencies use environmental information to improve their green bond evaluations. Yet only a tiny number of green goods and policies are promoted via green bond indexes, listings and ratings. Increasing the amount of the economy is the same as increasing the quality, and this is what is meant by productivity expansion. Because of the new normal, E7 countries’ economic growth is increasingly based on energy efficiency financing growth, and the state’s financial development is mainly driven by the innovation concept of quality first and energy efficiency financing foremost. Improving energy efficiency financing is crucial to help E7 countries’ economy expand at a high standard. For some time now, the Chinese government has been actively promoting quality economic development as a means of nation building. As a result, for a high-quality economic system to exist, a secure and stable electricity supply is required. Saving energy thus has significant practical implications for economic growth of advanced quality. Natural fuel usage defines E7 countries’ large energy consumption structure and backward energy technologies, both of which are based in the country’s poor energy efficiency financing. As a result of poor energy use, E7 countries’ long-term growth is stymied. Low energy financing efficiency also makes it difficult to modernise an industrial structure. Coal, steel and chemical industries all have substantial amounts of obsolete and unnecessary manufacturing capability. When it comes to E7 countries’ future, the country must balance environmental conservation with economic development. Low energy efficiency financing efficiency also wastes a big amount of resources, as we have seen. Several elevated nations have begun to decouple their energy consumption from economic growth as a result of global growth and technical advancement. Sensitivity analysis Despite the central government's policy incentives, E7 countries’ energy efficiency financing efficiency remains well behind that of wealthy nations. The connection between energy efficiency financing efficiency and economic development quality, thus, must be discussed. This is why an academic’s attention has always been drawn to how closely energy and economic growth are linked. How much does energy use contribute to economic development, as we know it from the research available? Researchers cannot agree on anything since they are all doing their own research. According to one popular theory, energy is a necessary input for economic expansion to occur. A growing number of economists are looking beyond the standard model of macroeconomic growth to include energy considerations into production functions in order to better understand where economic growth comes from. Previously, Dong et al. (2021) analysed provincial data from E7 countries to find that a 1% increase in energy consumption increases GDP by 0.05%, but the energy efficiency financing of various sources of energy on economic development was varied. Researchers Hao et al. (2020) found via the use of a VAR model that energy intake was detrimental to economic development. Similar results have been seen in international statistics as well (see Table 5).Table 5 Multiple uncertainty levels — robustness test Example 1 γ = 5000, High Example 2 γ = 5000, Low Example 3 γ = 0, High Example 4 γ = 0, Low COVID-19 lockdown 0.673 0.893 0.776 0.091 Energy redevelopment 0.786 0.456 0.001 0.452 Energy efficiency financing 0.441 0.784 0.087 0.671 Green bonds 0.592 0.777 0.093 0.993 With the increased reliance on conventional energy sources like coal and oil, it is becoming more difficult to sustain economic growth without causing environmental degradation and ecological harm. The share of renewable energy in the energy consumption system is steadily growing as the benefits of renewable energy become more clear. The use of renewable energy, according to several researchers, has the potential to considerably boost the economy. Few academics have shown as well that energy efficiency financing promotes economic development while lowering greenhouse gas emissions. Discussion For economic growth to be of top quality, it is essential to build a green and energy efficient finance power process and enhance energy efficiency financing. Energy consumption and economic growth have been studied in the past, but they failed to take into account the influence of energy efficiency finance on energy rehabilitation in previous research. An effort is made in this research to address the issue of whether finance for fuel efficiency can be a significant driver of quality economic growth in E7 countries using province data. The facts of the matter have come to light for us. Energy-saving finance makes intuitive sense since it may help E7 countries’ energy efficiency drastically improve, but there is no concrete data to back this up. In E7 countries, we have yet to see the good effects of efficiency finance, but we cannot ignore the role that efficiency financing plays in economic progress. Therefore, E7 countries’ energy efficiency financing may be low enough to enable quality economic growth, and it most likely reflects the presence of a non-linear energy efficiency financing system in E7 countries. As a result, we discover that energy efficiency finance and energy rehabilitation have a clear U-shaped association (Conci and Schneider 2017). An in-depth investigation reveals a significant regional gap in finance for energy efficiency and energy rehabilitation. Energy efficiency financing has a greater impact on energy redevelopment in the eastern region than in the central or western regions, so the effect of energy efficiency financing on revitalisation differs by area. Finance for energy efficiency increases energy redevelopment in the eastern areas, while it diminishes it in the centre and western states. Energy efficiency financing. As a result, the economic system has a significant impact on the relationship between the funding of energy efficiency projects and the rehabilitation of power generation. Advances in energy efficiency have been studied from two separate angles in the past. Some study has attempted at the return on investment from an energy-saving standpoint, while others have examined the venture’s overall profitability. The emphasis here is on the perceived threat of diverging from energy-saving goals. So the risk perception related to energy efficiency rises in tandem with the amount of funding and the quantity of energy saved, and their variance rises accordingly. Instead of a dangerous expenditure, research show that energy efficiency might reduce the perceived risk for decision-makers. Energy efficiency may be compared to insurance in that it lowers future electricity costs and, as a result, the variability of those prices. No one has ever described how these two viewpoints vary and how sensible decision is influenced by them. Our research is based on EUT and uses a simple and accessible mathematical model with a CARA utility function to show the distinction between the different views and their combination. Based on averaged data from Germany office properties, we test our theoretical insights using a Simulation to forecast the distribution of energy bill expenditures and savings following an ecological retrofitting of a commercial space. Because business decision-makers behave more logically than company decision, we picked a corporate situation for my study case. Conclusion and policy implications Our theoretical and empirical studies illustrate how the two viewpoints impact investment decisions for energy efficiency in a different way. Decision-makers spend a lot more in energy efficiency from the standpoint of the energy bill since it reduces their perceived risk. As a result, their projected return on investment rises as the investment amount climbs. When looking at energy efficiency from the standpoint of return on investment, on the other hand, the ideal investment level is substantially smaller. Anyone who uses both viewpoints while making a choice will have an investment amount that falls between the two perspectives’ peaks. We have discovered two important things about energy policy as a result of our research. When it comes to rational decision-making, putting the investment and energy bill perspective in place opens the door to more sustainable investment behaviour since it emphasises the need of energy efficiency and helps persuade stakeholders that doing so lowers their future energy costs. The idea that looking at the energy bill encourages investment must, of course, be tested in the actual world. However, we believe that the theoretical considerations presented in this research have the potential to enhance future energy efficiency awareness campaigns by emphasising the financial benefits of energy efficiency and drawing attention to the possibilities for risk reduction. In order to provide more effective incentives for long-term investments, current subsidy programmes and communication campaigns may go into greater detail about risk reduction. Second, our research adds to the existing body of knowledge on the effect of risk perception on energy efficiency investment choices. It is critical to understand how decision-makers see energy efficiency from many angles while evaluating, developing and implementing policy instruments. If a decision-maker perceives carbon taxes or subsidies as risky, then they are more effective than other mechanisms. To improve the projected financial return on energy efficiency investments, these two tools have been developed to work together.Government agencies should place a high priority on developing a long-term system for generating and using energy. While increasing its contribution to energy technological advances and the conservation sector is important, the federal government should also stimulate creation of new energy industries. Traditional energy sources, as well as new energy sources, should be included in an efficient energy supply system in order to encourage continual improvement in the energy supply structure. While this is going on, energy usage should be adjusted and EFF improved on a continuous basis. Because E7 countries’ economic development has shifted, local governments should pay greater attention to the quality of economic growth and incorporate high-quality content such as energy efficiency and industrial upgrading in the assessment criteria. Local governments should devise development plans and implement fiscal, tax and financial policies aimed at boosting EFF in order to keep the economic development pattern moving forward at a faster pace. When creating policies and development plans, E7 countries should thoroughly consider regional peculiarities and realities. The eastern and central areas should serve as role models for developed regions by fostering cross-regional interaction and collaboration. Using regional integration initiatives to help the eastern and central areas increase their EFF and QUAL. For economic growth to be successful, the western area must establish strong institutions and improve infrastructure building, as well as provide attractive incentives to the private sector. These are the paper’s limitations. Preliminary studies can only be carried out at the provincial level due to a lack of data on urban energy use. Additional research into the link between energy efficiency and quality is needed to better understand these two concepts. To do so, more theoretical processes and influencing elements are needed to better understand the relationship between EFF and QUAL. In addition, it is critical to look at how energy efficiency affects industrial development quality. The future study focus will be on how to create an index system to measure how well a product is being developed. Author contribution Editing, review, and supervision: Sajid Iqbal; review, visualisation: Linyun Zhang; Methodology and data curation: Feiming Huang; visualisation, conceptualization, writing of draft: Lu Lu; software, writing, visualization: Xinwen Ni. Data availability The data that support the findings of this study are openly available on request. Declarations Ethics approval and consent to participate We declare that we have no human participants, human data or human issues. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 34859343 17026 10.1007/s11356-021-17026-z Research Article The spillover of tourism development on CO2 emissions: a spatial econometric analysis Jiaqi Yan [email protected] 1 Yang Song [email protected] 2 Ziqi Yu [email protected] 3 Tingting Li [email protected] 4 Teo Brian Sheng Xian [email protected] 1 1 grid.444504.5 0000 0004 1772 3483 Graduate School of Management, Management and Science University, Shah Alam, Selangor Darul Ehsan Malaysia 2 grid.259384.1 0000 0000 8945 4455 Faculty of Hospitality and Tourism Management, Macau University of Science and Technology, Taipa, China 3 Guangzhou Sontan Polytechnic College, Guangzhou, China 4 grid.411865.f 0000 0000 8610 6308 Faculty of Management, Multimedia University, Cyberjaya, Malaysia Responsible Editor: Ilhan Ozturk 2 12 2021 2022 29 18 2675926774 5 8 2021 9 10 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Climate change and tourism’s interaction and vulnerability have been among the most hotly debated topics recently. In this context, the study focuses on how CO2 emissions, the primary cause of global warming and climate change, respond to changes in tourism development. In order to do so, the impact of tourism development on CO2 emissions in the most visited countries is investigated. A panel data from 2000 to 2017 for top 70 tourist countries are analysed using a spatial econometric method to investigate the spatial effect of tourism on environmental pollution. The direct, indirect, and overall impact of tourism on CO2 emissions are estimated using the most appropriate generalized nested spatial econometric (GNS) method. The findings reveal that tourism has a positive direct effect and a negative indirect effect; both are significant at the 1% level. The negative indirect effect of tourism is greater than its direct positive effect, implying an overall significantly negative impact. Further, the outcome of financial development and CO2 emissions have an inverted U-shaped and U-shaped relationship in direct and indirect impacts. Population density, trade openness, and economic growth significantly influence environmental pollution. In addition, education expenditure and infrastructure play a significant moderating role among tourism and environmental pollution. The results have important policy implications as they establish an inverted-U-shaped relationship among tourism and CO2 emissions and indicate that while a country’s emissions initially rise with the tourism industry’s growth, it begins declining after a limit. Keywords Tourism development Financial development Economic growth GNS model issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2022 ==== Body pmcIntroduction Recently, there have been significant changes in the global climate system. The American Meteorologic Society’s state of the climate report states that world surface temperatures were 0.38–0.48 °C higher than the average 1981–2010 and that since 1998, the top 10 years have been all the warmest, with four warmest years occurring since 2014 (Abbas et al., 2020; Fu et al., 2021; Yumei et al., 2021a, b). The analysis stresses that, as a major contributor to global warming, worldwide growth rates of CO2 emissions have almost doubled since the beginning of the 1960s. It also notices observed changes such as ice and snow declines, increases in level of sea, and seasonal durations. This challenge affects economic, political, living, geopolitical, and social growth directly. Climate change and global warming have led to famine, sickness, floods, and water shortages for millions of people (Akbar et al., 2021; Anser et al., 2020a, b; Iqbal et al., 2019a). Scientists today agree that the fundamental explanation for climate change and global warming is the fast increase in CO2 emissions over the past 50 years. Because of its rapid expansion, it is recognized as one of the most energy-intensive industries in the world (Chien et al., 2021a, b; Iqbal et al., 2021; Zhang et al., 2021a). Travelling and staying in hotels, for example, are both known to absorb high frequencies of energy, which has a negative influence on CO2 emissions. Despite its importance of energy consumption prediction and its role in promoting energy consumption patterns, tourism development has received little attention in literature. The same is true for energy consumption, with it being recognized as one of the key factors of energy. This has led to the conclusion that tourism expansion has a variety of effects on natural resource consumption, economic growth, CO2 emissions, and energy consumption through a number of different avenues. Because of this, it cannot be ignored within the empirical framework. The findings of the authors revealed that factors relate positively to economic growth across the board, although its impact on CO2 emissions differs. Tourism, according to the authors, reduces CO2 emissions in the Western EU while increasing in the Eastern EU. The authors ascribe these conclusions to the Western EU’s efficient integration of appropriate tourism rules, which has resulted in a reduction in tourism’s negative environmental impact. Muhammad Khalid Anser et al. (2020a, b) discovered that the tourist sector helps both established and developing economies’ economic development. Surprisingly, although the association among environmental quality and tourism development is considerable. In tourism research, the rapid growth of carbon dioxide emissions has not yet been assessable. Studies focus mostly on the detrimental consequences on the tourism industry of global warming and climate change. Particularly, it is stressed that the climate influences visitor activities, tourist destinations, and overall holiday enjoyment. According to existing research, unfavourable weather conditions are a driving force for tourism development, while favourable weather conditions are seen as a desirable feature. It is also obvious that the influence of climate change and global warming on coastal locations (in particular, as the sea levels rise) has major implications for the tourism development. In the context of the interaction between climate change and tourism, it is vital to seek answers to the following questions: (i) How does tourism affect CO2 emissions while climate change and global warming have a detrimental effect on the tourism industry? (ii) Does the tourism sector’s development contribute to global carbon emissions? Danish and Wang (2018) investigated the contribution of tourism to global greenhouse gas emissions in theory, emphasizing that the aviation sector contributes significantly to greenhouse gas emissions. Because of the debate’s multidisciplinary nature, research volumes with new and diverse perspectives consistently increased in the 2000s. However, in the existing research, the impact of tourism on CO2 emissions has not been statistically satisfactory (Carr-Harris and Lang, 2019; Lee and Brahmasrene, 2013; Sigala and Leslie, 2005; Sun, 2016a). Nonetheless highlight the extent to which carbon emissions can be accountable for the tourism sector and contribute significantly to calculating tourism-related carbon flows. In contrast, other research (Arai and Goto, 2017; Simkhada et al., 2016) examines the ecological impact of tourism through qualitative assessments. It can be said that new empirical research has been carried out to take into account the effects of carbon emissions in tourism development. Nations are dependent on tourism to achieve economic progress and extract up to the level of threshold findings in natural environmental deterioration. All economic activities raise CO2 levels, resulting in an increase in waste production. Tourism is a significant economic industry in both developed and developing countries. Today, tourism’s economic impact matches or exceeds that of oil exports, food products, and automobiles: 9% of GDP; 1/11 employment, direct, indirect, and induced; 6% of global exports; 1.4 trillion in exports; 30% service exports ((UNWTO), 2020). Their economic, social, and political significance is now an irrefutable reality that is through a period of rapid worldwide growth: International tourist arrivals increased by 5% to 1.087 billion in 2013; international tourism earned US$ 1.4 trillion in export profits in 2013; the UNWTO anticipates a growth of between 4% and 4.5% in international tourist arrivals in 2014. One fundamental source of tourism’s ever-increasing carbon emissions is the relentless pursuit of tourism maximization through “boosterism” economic policies, in which continued growth in visitor volume and expenditure is driven by economic imperatives with little regard for the social and environmental impacts of tourism (Hall, 2009). Due to the political and social impossibility of significantly reducing tourist volume, the research has offered an alternate solution through tourism system optimization. Indeed, in a post-COVID-19 environment where visitor numbers are expected to decline dramatically in the medium term, market optimization can optimize the impact of priority market segments to tourism recovery. Carbon reduction methods are an important and long-overdue requirement for the tourism industry. Tourism’s approach to climate change has been to urge businesses to adopt new technologies and to promote more sustainable guest behaviour. These initiatives, however, are insufficient to address tourism’s growing carbon footprint. We propose a novel carbon mitigation strategy based on the concepts of optimization and eco-efficiency. It aims to pro-actively identify, cultivate, and create a long-term tourism market portfolio. This can be accomplished through interfering and reshaping demand with the overarching goal of encouraging low-carbon travel markets. The concept and analytical framework for optimizing the intended market mix quantitatively are presented. The rest of the paper is organized as follows: Sect. 2 considers two strands of literature on the tourism–environment nexus and the education–environment nexus. Section 3 explains the model, data, and methodology, while Sect. 4 presents the estimation results and a discussion of the findings. Section 5 presents the conclusion. Literature review Tourist industry will continue to increase in the future, as per the United Nations World Tourism Organization (UNWTO). Moreover, in terms of new tools and management styles, the nature of development and growth will be different than in past decades. The use of new technology in a labour-intensive industry improved the performance, efficiency, and effectiveness of tourism-related services while also highlighting the need for more skilled workers in this field. One of the most serious issues in the field of tourism is its year-round focus on a few, limited activities. Tourism activities have also increased in terms of social and environmental consciousness. The observed and develop approaches and technologies that will improve the sector’s performance in the future. The increasing number of consumers (who are more aware and demanding, with higher levels of education and ability) will encourage the tourist industry to produce new products and reimagine established markets (Chien et al., 2021a, b). Economic expansion is frequently related with an increase in emissions. Several studies examine the econometric relationship between economic growth carbon dioxide emissions. Al-mulali et al. (2015) examined Europe, Khan and Ozturk (2021) examined Central America, Iwata et al. (2011) examined 28 nations (OECD and non-OECD), Jalil and Mahmud (2009) examined China, and Ozturk and Acaravci (2010) examined Turkey. Tourism produces around 5% of global CO2 emissions. Transportation contributes for 75% of the sector’s emissions (of which 50% are from air transport), while accommodation accounts for 22% and rest of the activity-related tourism account for 4%. While tourism is not regarded a particularly polluting activity, estimates for tourism expansion indicate that emissions from tourism activities will more than quadruple by 2035. Against this backdrop, there were three pertinent concerns to address. To begin, it is critical to understand the variables that contribute to pollution. Second, are any tourism activities that bear a bigger share of responsibility for climate impact? Tourism activities in Nepal account for around 10% of overall CO2 emissions. Transportation accounts for around 89% of tourism-related CO2 emissions, while accommodation and food and beverages each contribute for approximately 5%. Accommodation services grew at the fastest rate of CO2 emission growth (48.4% between 2000 and 2008), Food, beverage, recreational services, and cultural sports came in second and third, with 34% and 25%, respectively. However, when it comes to CO2 emission intensity (emissions divided by value created), by lowering their performance, all tourist subsectors improved, but this variable declined significantly in transport and travel agencies (33.7% and 30.9%, respectively). Accommodation services, on the other hand, had an increase in intensity of carbon emissions of 5.6%. Thirdly, are the regulations applicable to each tourism subsector aligned with the primary drivers of emissions in each? Except for the aviation industry, which was included to the Trading System of European Union Emissions (EU ETS) in January 2012 (Directive 2008/101/EC), the European Union Emissions Trading System (EU ETS) excludes tourism subsectors. The maritime industry can only be covered by the first policies aimed at reducing emissions in 2018. Additionally, there is a current policy aimed at reducing emissions from new cars to 95 g/km by 2020 (European Commission, 2012). Only few research study the links among energy consumption, carbon emissions, and tourism utilizing regression technology and analysis of decomposition. The econometric technique is used to analyse and identified the impact of one variable to the other. In particular, Lee and Brahmasrene (2013) found that the impact of international tourism and foreign direct investment on reducing CO2 emissions is significantly higher among European countries, whereas Tiwari et al. (2013) found that international tourism in a small island like Cyprus has a positive statistically significant effect on energy and CO2 emission consumption. There is not much research on the decomposition analysis methodology to investigate the effects of different influences on CO2 emission variations caused by tourism. However, several studies have broken out the link between carbon emissions and the broader energy–economic system. Ozturk et al. (2021) employed decomposition techniques for assessing CO2 emission intensity and its components across 36 Portugal’s economic sectors and also included decomposition of the forecast error variance and impulse response functions applied to decomposed emission intensity factors. On research specific to tourism, the work of Adedoyin et al. (2021) who apply decomposition analysis in China can be indicated. They concluded that energy intensity, expenditure size, and industry size were the main drivers for emission growth for tourism but that two other parameters had no significant impact on the upsurge of emissions from the tourism industry. Other studies are available which degrade emissions from tourism-related activities such as travel, some services, and accommodation Iqbal et al. (2019b) showed that the impact on scale is important for Ireland when it comes to raising emissions in the transport sector and that energy intensity improvements in the residential or service sector are significant. In another study for China, Fu et al. (2021) concluded that the major factors for reducing CO2 emissions are the effect of transport intensity and transport services. Baloch et al. (2020a, b) compare the numerous decomposition analytical methods globally and advocate the Logarithmic Mean Divisia Index (LMDI) because it is theoretically well founded, easy to adapt, utilize, and interpret its results. In addition to Iqbal et al. (2021), worldwide or in Portugal, applied literature on the LMDI sector and subsectors is scarce. Zhang et al. (2021b) examine the association between economic advancement and environmental pollution in countries throughout the Europe. Their statistically enhanced findings demonstrate that the application of spatial economic approaches improves model formulation. Anser et al. (2020a, b) investigated the environmental Kuznets curves (EKC) concept in China using a geographic panel data model. Their findings confirm that the spatial panel model outperforms traditional panel approaches, as standard econometric techniques can generate erroneous parameter estimates. Khokhar et al. (2020a, b) examined the regional correlation of carbon intensity in China using a spatial panel data model. The present study’s findings confirm the existence of a spatial association between CO2 intensity in Chinese provinces. The impact of education on carbon and methane emissions is analysed by Mishra et al. (2020) through the panel methodology for 181 countries, suggesting a negative effect on methane emissions due to education, whereas carbon emissions remain unaffected. Along with a number of control variables, Sovacool et al. (2021) use data from Latin American countries to explore the effect of foreign direct investment and human capital on pollution emission in the environmental Kuznets curves (EKC) framework. Moreover, a negative association between human capital and emissions for high-income countries and a positive association for low-income countries are evident through the panel technique results. The EKC framework is used by You and Lv (2018) to investigate the relationship between education and environmental quality for data collected from Australia; a U-shaped connection between education and emissions is evident through the autoregressive distributed lag (ARDL) results, which suggests the importance of education in reducing emissions after the threshold is reached. Khan et al. (2019) analyse the association of education with carbon emissions by using the ARDL econometric technique through different proxies for Pakistan, reporting a negative association between carbon emissions and education. However, the in-depth development of the tourism economy is promoted by incorporating a strong economy and improved quality of life in Southeast Asian economies, providing an overall high-quality environment in the long run. Hence, developing and developed countries observe an inverted U-shaped relationship between tourism development, renewable energy, financial development, and CO2 emissions. Similarly, the development of any tourism industry depends on a number of cross-regional cultural and industrial exchanges and factors, and not merely administrative factors. Tourism and the environmental progress Several recent studies have explored the connection between tourism and CO2 emissions. Abbas et al. (2020) studied the connection of tourism and economic growth with the emission of CO2 by the Johansen cointegration test for European nations between 1988 and 2009. The empirical data suggest that tourism boosts economic growth as well as CO2 emissions in the area of investigation. In the example of Malaysia, Mohsin et al. (2021) described the long-term ratio between CO2 emissions; moreover, the data revealed a unilateral causal link between tourism and CO2 emission. Similarly, Abbas et al. (2021) have shown that the causality from tourism to CO2 and GDP to tourism is unilateral. They used GMM for data from Asia-Pacific countries between 1995 and 2013. Yumei et al. (2021a, b) observed a considerable contribution to CO2 emissions by tourism. In addition, Iqbal et al. (2020) showed that a single-way causal relationship for the research area is between CO2 emission and tourism. Testing data for Malaysia from 1972 to 2010, Lawal et al. (2018) applied the ARDL model that reveals that the link between tourist arrivals and CO2 emissions has been long-term positive. In a similar study, Nkoro and Uko (2016), Sharif et al. (2020), and Zhang et al., 2021) found the effects of tourist arrivals and the usage of energy on the environment in Tunisia, using panel data from 1995 to 2010. They show that tourist arrivals cut CO2 emissions in the long run. Testing data from 10 Northeast and Southeast Asian countries, Yurtkuran (2021) discovered that improvements in tourist development can help to regenerate the environmental amenities in the longer term, but that tourism has been seen to be a degrading aspect in the region’s ecosystem. Data and methodology Spatial model Spatial impacts are critical when examining the relationship between growth, emissions, and energy. Additionally, many of the concerns in economics, the environment, and energy are intrinsically spatial (Su and Ang, 2010). Despite the fact that several studies have been conducted to examine the relationship with both carbon emissions and growth using spatial econometric techniques, no research has examined the spatial effects of the nexus between economic growth and renewable energy or between income and carbon emissions, while numerous prior researches have emphasized the importance of geographical impacts in growth in the economy, CO2 emissions, and energy studies. For example, Li and Lv (2021) use spatial autoregressive panel data estimate techniques to analyse the effect of adjacent regions’ growth in the economy on a state’s own economy. However, the in-depth development of the tourism economy is promoted by incorporating a strong economy and improved quality of life in Southeast Asian economies, providing an overall high-quality environment in the long run. Hence, developing and developed countries observe an inverted U-shaped relationship between tourism development, renewable energy, financial development, and CO2 emissions. Similarly, the development of any tourism industry depends on a number of cross-regional cultural and industrial exchanges and factors, and not merely administrative factors. This process generates spatial spillover effects from tourism development through a significant channel. The spatial econometric models used include the spatial lag model (SLM), spatial Durbin model (SDM), and spatial error model (SEM). SLM is used when the dependent variable is spatially correlated. Hence, this study establishes a generalized nested spatial model (GNS), considering the spillover impact of tourism growth, renewable energy, financial development, and other control variables on CO2 emissions.1 CO2=β1Tourismi+β2GDPi+β3EIi+β4Tourismi+β5ECi+β6REi+εit In Eq. (3), the variables lntourism, lnurban, lnCO2, lnGDP, and lnEI stand for per capita CO2 emissions, urbanization rate, energy intensity, and tourism development, respectively. Tourism contributes to CO2 emissions by increasing demand for transportation, which is exacerbated by the intensity of travel services. Furthermore, tourism development increases food consumption and shopping activities, both of which contribute to carbon emissions. The logarithm of CO2 emissions in country i at time t is represented as Yit, the coefficient of spatial regression as ρ, the control variables as Z, and the error term as εit. The inverse squared distance matrix applied in this study considers neighbouring nexuses as nonlinear—compared with the distance, the decrease is more rapid. The matrix is normalized row-wise and is aligned with previous studies. The matrix representing spatial weights where i and j refer to the element in row i and column j is given as wij and the vector of independent variables as x. Spatial autocorrelation coefficient As carbon emissions correlate between regions and are considered heterogeneous, the spatial econometric model is built such that emissions of CO2 are considered the main determinant for regional CO2 emissions spatial correlation with spatial spillover and spatial diffusion, which influences the CO2 emissions of neighbouring countries. Moran’s I calculates spatial correlation as follows (Moran, 1948):2 MinimizeCO2s.t.∑iXi≥0.95CtouristXi≥0.8Ci∀i∈ISpending≥0.9CspendingCO2≤0.95CCO2 where i represents spatial units and j is given as N, the concerned variable as y, average of y as y¯, the spatial weights matrix as wij, and the sum of the weight entries as W. The statistical significance for each Ii is evaluated by considering Moran’s I of individual spatial units. Therefore, a positive correlation is represented by a positive Ii, whereas a negative correlation by a negative value. Kelejian and Prucha (2010) defined queen contiguity weights. The relations among n units are summarized by the suitable spatial model that is determined through the matrix of spatial weight w. The observations of spatial arrangement for the models in this study are done using spatial contiguity weights, which show the boundaries that are shared by the spatial units. The following equation defines the spatial weighting matrix w, where bnd(i) represents the set of boundary points of unit i:3 wij=1,bnd(i)∩bnd(j)≠∅0,bnd(i)∩bnd(j)=∅ Data sources World Bank Development Indicators (WDI) online databases and the international energy agency (IEA) were used to collect the annual data for 2000–2017. Appendix 1 (Table 9) represents the sample used in our study, based on the available data, which was restricted to top 70 tourist countries. The effects of tourism development on environmental degradation were assessed in this study through seven variables. The core independent variable constituted tourism and financial development, whereas CO2 emissions, considered one of the major causes of global warming, was considered the dependent variable. Furthermore, our baseline model considered several control variables to prevent omitted variables, such as GDP per capita (GDP), financial development (FD), renewable energy (RE), population density (Pd), trade openness (trade), and education expenditure (Edu), from causing any bias. Appendix 2 (Table 10) presents the definitions, descriptive statistics of the variables, and data sources. A number of studies use proxies for tourism, and this study follows the index of tourism by Khan et al. (2020a, b). With the weights as the focus, three individual variables, the tourist receipts (TR), expenditures on tourism (TEX) both in US$, and number of tourist (TA) are utilized to establish the index in this study. We use the world bank online database to collect data for all the selected variables. Similarly, a single weighted index is constructed by applying the principal component analysis (PCA) on the tourism variables, an approach with internal correlation used for examination and diagnosis. The new variables calculated and categorized as principal components are for this study to reduce the amount of data needed through this method, and the index of tourism development through PCA is given in Table 1. The maximum eigenvalue for the first, second and third are respectively 2.404, 0.4748, and 0.1207, as shown in the first segment of Table 1. Similarly, the highest proportion of variation recorded at 80.14% is given in the first component, at 15.83% for the second component, and the lowest change recorded at 0.403% for the third factor. Furthermore, the eigenvalue loading in three-components in the second segment, including PC1, PC2, and PC3 is shown in Table 1, whereas this study establishes the index of tourism development through the second and third components, indicating smallest negative loadings values. Similarly, correlation between variables is given in the last segment of Table 1, where tourist arrivals positively correlate with tourism expenditures and tourism receipts, whereas the selected countries also observe a correlation between tourism expenditures and tourism receipts.Table 1 Tourism development index Component Eigenvalue Difference Proportion Cumulative Comp1 3.845 3.693 0.7485 0.7485 Comp2 0.252 0.2487 0.0606 0.899 Comp3 0.0049 – 0.011 1 Eigenvectors Variable Comp1 Comp2 Comp3 TEX 0.6905 −0.3018 −0.8814 TA 0.6636 0.8962 0.3202 TR 0.6777 −0.6704 0.6838 Correlation matrix Variable TEX TA TR TEX 1 TA 0.622 1 TR 0.5867 0.6579 1 Results and discussion International tourist arrivals are estimated to grow to 1.8 billion by 2030, suggesting that tourism is the most rapidly growing industry in the world (UNWTO, 2020). Furthermore, the industry also exhibits a geometrical trend in its growth, at the cost of increasing energy consumption, depletion of natural resources, and waste generation. Theoretically, economic processes, energy use, and the environment are significantly impacted by tourism development. Moreover, CO2 emissions increase because of dirty energy consumption in hotels and transportation (Sun et al., 2020a, b) and (Baloch et al., 2020a, b). The role of tourism development as a factor in energy consumption and stimulating energy consumption patterns has hitherto not been considered as a major factor in the literature. The different channels of tourism development influence economic development, resources, energy use patterns, and carbon emissions, and therefore, the empirical framework (Khosravi et al. 2019; Kordej-De Villa and Slijepcevic 2019; Ozoike-Dennis et al. 2019; Sovacool et al., 2021). The influence of the financial development of neighbouring countries on a country’s carbon dioxide emissions is stressed by this study and aligns with Lv and Li (2021a). Hence, the significantly positive direct effect is taken over by the negative spillover impact of financial growth on CO2 emission presenting a total effect which is significantly negative. The role of financial development in promoting business growth more than promoting technological progress and green projects increases energy consumption and is a possible explanation for this process, and the findings align with Bui (2020) and Charfeddine and Kahia (2019). Conversely, a unit rise in financial development of neighbouring countries suggests 12.5% decline in carbon emissions of the local country through the negatively significant spillover effect. An open and free policy focusing on the development of the financial system can provide more research and development funds for the development of energy technology. For GDP per capita and environmental pollution nexus: GDP per capita significantly impacts on CO2 emission through spatial spillover. The direct impact of GDP per capita on CO2 emission is significant, while the direct impact of (PGDP)2 is negative, proving the existence of environmental Kuznets. Similarly, economic growth and carbon dioxide in developed and developing countries share an inversed-U relationship. In both models, the indirect effect of PGDP and (PGDP)2 on carbon dioxide proves the existence of environmental Kuznets. Nevertheless, pollution in the neighbouring countries is increased due to the local economic development and improved quality of life, transferring high-pollution industries to neighbouring countries. Consequently, an increase in environmental pollution through the increased economic development indicates that the income in these countries positively impacts the left side of the inversed-U curve. Spatio-temporal distribution of selected variables The alignment between countries with high carbon emissions is given, same as the countries with low carbon emissions clustered with each other. Hence, a spatial dependence is evident based on the distribution of carbon emissions for sample countries. Moran’s I statistic of carbon emissions through 2000 and 2017 is also estimated by this study, and its results are presented in Sect. 4.2. A positive correlation is seen between the number of tourists and CO2 emissions in Hélde A.D. Hdom and Fuinhas (2020), who analyse the data for Malaysia, incorporating the autoregressive distributed lag (ARDL) model from 1972 to 2010. The adverse effect of tourist arrivals and energy on the environment in Tunisia is evident through the study by Sharif et al. (2020), which utilizes panel data for 1995–2010 suggesting a reduction in CO2 emissions due to tourism. Notwithstanding the decrease in tourism in East Asian countries, the importance of improving tourism development and regenerating environmental. However, the study sample is divided in two types: one with tourism development on the left side of the axis of symmetry of the inverse U-shape and the other with tourism development on the right side. Our findings are consistent with Katircioglu (2014), Katircioglu et al. (2014), León et al. (2014), and Li and Lv (2021) that conclude tourism has a positive impact on carbon dioxide emissions. Similarly, our results also show the negative impact of tourism on CO2 emissions, which is consistent with the second category of literature (Katircioǧlu, 2014; Lee and Brahmasrene, 2013; Paramati et al., 2017). Hence, overlooking the nonlinear effect of tourism on carbon emissions is possibly the reasons for the different conclusions. Although most studies have confirmed the empirical relationship between carbon emissions and tourism development, according to the above conclusions, the direction of the causal relationship between the two is still unknown. One of the main reasons for drawing conflicting conclusions is to ignore the nonlinear impact of tourism on carbon emissions (Li et al., 2018a, b; Sun, 2016b). In addition, if the spatial interdependence of the regions is not considered, it may lead to erroneous conclusions (Yang et al., 2019). Therefore, this study uses a panel spatial econometric model to estimate the total impact of tourism on carbon emissions, taking into account spatial dependence and nonlinearity. Next, other control variables also produce valuable results, and the detailed explanation is given below: for financial development and environmental pollution nexus. The primary focus of this study is on financial development. The direct effect for financial development is recorded as significantly positive (0.055), the spillover impact is −0.09215, and the total impact is −0.063, as seen in the empirical findings of financial development. On the contrary, an increase of 5.5% in carbon dioxide emissions with a 1% rise in financial development indicates a significantly positive direct effect. The findings also reflect the financial development and carbon dioxide emissions to constitute an inverse U-shaped relationship, which indicates an increase in carbon emissions of a country with growth in its tourism. However, an eventual 12.5% decrease is expected in carbon emissions of the local country after the threshold is reached. When financial development is higher in the neighbouring countries, the quality of the environment in a local country is affected by technological diffusion, better governance, and more sustainable policies, presenting a possible reason for this process. Consequently, lower carbon dioxide emissions are induced due to the external restrictions across nations (Lv and Li, 2021a). Similarly, the carbon dioxide emissions in the local country are reduced by boosting the spillover of technology, and the transfer of knowledge and skills. Moran’s I spatial dependence test A number of studies use proxies for tourism, and this study follows the index of tourism by Khan et al. (2020). With the weights as the focus, three individual variables, the tourist receipts (TR), expenditures on tourism (TEX) both in US$, and number of tourist (TA) are utilized to establish the index in this study. We use the world bank online database to collect data for all the selected variables. The results of Pesaran’s IPS unit root test and the CIPS (cross-sectionally IPS) unit root test, which are the first and second-generation unit root tests, are shown in Table 2. Urbanization is stationary at the level, according to the IPS test results, while the others have unit root. The null hypothesis is not rejected for all variables in the CIPS test, indicating that all variables in Eq. (3) have unit root. When the first differences are taken, however, these variables become stationary.Table 2 Moran’s I statistical tests Year Moran’s I p value Year Moran’s I p value 2000 0.2136* 0.001 2009 0.1637* 0.03937 2001 0.2168* 0.0029 2010 0.3645* 0.00445 2002 0.3566* 0.004 2011 0.3454* 0.00321 2003 0.3404* 0.003 2012 0.4172* 0.oo35 2004 0.4656* 0.006 2013 0.3564* 0.0027 2005 0.4295* 0.005 2014 0.3639* 0.001875 2006 0.3708* 0.003 2015 0.4544* 0.002023 2007 0.3691* 0.0025 2016 0.4252* 0.002297 2008 0.0316* 0.0078 2017 0.4674* 0.002118 ⁎⁎⁎ indicates 1% significance. The null hypothesis. There is no spatial dependence. Table 2 shows the estimation results of long-run panel cointegration coefficients that can be interpreted as elasticity because each variable in the models was logarithmically transformed. As a result, a 1% increase in any model variable causes the dependent variable to change by x%, where x refers to the variable’s negative or positive coefficient value. The following are the estimation outcomes: (a) The CUP-FM and CUP-BC estimators reveal that lnGDP has a significant and positive impact on lnCO2 emissions. That is, CO2 emissions rise as GDP per capita, a measure of wealth, rises. (b) Urbanization increases CO2 emissions. (c) The results show that energy intensity and CO2 emissions have a negative relationship. We use energy intensity as a technology indicator because efficiency reduces energy intensity. Panel unit root and cointegration tests The causality test was used in this study to uncover potential bidirectional causality relationships between tourism development and CO2 emissions. The results of the causality test are shown in Table 3. To put it another way, tourism development has an impact on CO2 emissions, while CO2 emission changes have a significant positive effect on tourism development. These findings, which show a link among tourism and CO2 emissions, are consistent with those of Wen and Tisdell (2001) and Ma et al. (2015). The results of the causality test also show that there is a bidirectional relationship between energy intensity and CO2 emissions, whereas there is a unidirectional relationship between GDP per capita and CO2 emissions.Table 3 Cross-section dependence of the variables Variables Pesaran CD Pesaran scaled LM Breusch-Pagan LM lnTourism 10.6258* 69.1456* 2013.45* Urb 45.7362* 176.3654* 2028.369* lnEI 29.3420* 123.258* 2013.425* lnPGDP 40.7963* 118.2701* 3012.87* lnRE 7.6647* 60.5645* 952.3214* lnEC 29.2134* 102.2389* 164.545* Receipts 24.6542* 100.2134* 1612.657* Notes: * denotes significance at the 1% level Following this process, the CIPS (Pesaran, 2007) and IPS (Im et al., 2003) are used to perform second-generation panel unit root tests. The second-generation unit root test is preferred over the first-generation root test due to the cross-sectional dependence produced by the CIPS test. Table 4 shows the results for IPS and CIPS tests, and according to the results, 1% level of significance shows all the variables as stationary. Hence, panel cointegration is tested.Table 4 Results of unit root test Variables CADF CIPS Level First difference Level First difference lnTourism −2.301 −2.121* −2.254 −3.258* Urb −2.231 −4.102* −2.354 −4.124* lnEI −1.926 −3.145* −1.452 −2.547* lnPGDP −2.408 −3.514* −3.214 −3.254* lnRE −1.321 −4.402* −1.654 −4.789* lnEC −2.281 −3.352* −1.852 −2.145* Receipts −1.745 −2.852* −2.321 −3.457* * denotes a significance of 1% The third step is to use the Padroni cointegration tests to run the panel cointegration tests (Pedroni, 2004). Before performing the panel cointegration test, the mean of the series across panels is calculated and subtracted from the series. This procedure reduces cross-sectional dependence effect (Levin et al., 2002). Similarly, the panel cointegration test is given in Table 4, which uses any statistics to test the null hypothesis of no cointegration. Hence, the following subsection assesses the long-run relationship. Empirical results Table 5 provides the model comparison and overall results. Tourist arrivals have a negative impact on CO2 emissions; it means 1% increase in arrival of tourist, and 0.78% increase in carbon emissions, according to the estimation results. These findings demonstrate that tourism receipts, as a measure of wealth, help to reduce CO2 emissions. Our research results on tourism benefits corroborate those of Abou-Shouk et al. (2021), Beladi et al. (2009), Khan et al. (2020b), Li et al. (2018a, b), Sun et al. (2020a, b), and Tourism Tasmania (2018). Surprisingly, whereas overall economic growth increases carbon emissions, tourism receipts have a positive environmental impact by lowering CO2 emissions. This result could be explained by the fact that tourism, as a major subsector of the service sector, uses less energy and is cleaner than agriculture and industry (Ekanayake and Long, 2012; K.C., 2017). Agricultural and industrial sectors, for example, currently contribute 21% and 24% of global CO2 emissions, respectively. The tourism sector contributes about 4.6%, which is significantly less than the other sectors. Global tourism accounts for approximately 8% of global greenhouse gas emissions, according to Maryam Khokhar et al. (2020) carbon footprint calculations.Table 5 Model comparison and overall results Variables GNS SEM OLS lnTourism 2.214(0.457) 2.205(0.297) 2.204(0.533) Urb −0.546(0.163) −0.547(0.143) −0.412(0.135) lnEI 0.347(0.0805) 0.547 (0.080) 0.214(0.171) lnPGDP 2.254(0.214) 2.243(0.252) 2.145(0.171) lnRE −1.145(0.163) −1.214(0.163) −0.914(0.268) lnEC −0.058(0.014) −0.069(0.020) −0.077(0.019) Receipts 0.068(0.0254) 0.065(0.205) 0.021(0.0305) Cons 1.173(0.0165) 1.214(0.0201) 2.342*(0.029) W lnTourism −1.304*(0.324) −1.742(0.547) W(Urb)2 0.234*(0.067) 0.354(0.054) WlnEI 0.064*(0.145) 1.032(0.085) WlnPGDP −1.241*(0.452) −2.054(1.254) W(lnRE)2 0.254*(0.035) 0.095(0.030) WlnEC 0.289*(0.457) 1.754(0.457) WReceipts 1.254*(0.519) 0.201(0.842) LM-SEM 35.265 Robust LM-SEM 1.4232 LM-GNS 29.7563 Robust LM-GNS 0.2130 Obs. 1260 1260 1260 R2 0.657 0.7166 0.5326 Eliminating the error of spatial dependence in applied environmental research will produce biassed estimates, because the classical ordinary least square (OLS) model ignores spatial dependence and destroys the scientific basis of research. Both the spatial error model (SEM) and the spatial lag model have widely been used in empirical studies. Moreover, this study investigates the tourism development impact on CO2 emissions by applying the generalized nested spatial (GNS) model. Further, the presence of spatial autocorrelation is tested by applying Lagrange multiplier and robust LM statistics. Next, we evaluate which panel model is most appropriate after the LM and robust LM test rejects the null hypothesis of no spatial autocorrelation. After selecting the most applicable model, we assessed the direct, indirect, and overall impact of tourism development on CO2 emissions. The outcomes of both non-spatial panel ordinary least square (OLS) and spatial panel SEM and GNS models are illustrated in Table 6. Consequently, data profiles with 1260 observations are represented by the developed models, where the application of the spatial econometric model is stressed by LM test rejecting the null hypothesis with p value 1%. Moreover, robust LM-SEM and robust LM-GNS are compared to select tests having the smallest p value. The p value of GNS is smaller, making it the more appropriate option. Similarly, the selected variables are considered to explain the OLS, SAR, and SEM models at 75%, 81%, and 97% of the variation in carbon dioxide emissions, respectively. Hence, the generalized nested spatial (GNS) model is preferred over the OLS or SEM models as suggested by the greater adjusted R2 of the models.Table 6 The direct, indirect, and total effects of GNS model Direct impact Indirect impact Total impact Variable Coefficient t values Coefficient t values Coefficient t values lnTourism 1.3215 3.401 −1.7451* −0.2341 0.0095* 0.089 Urb −0.356* −3.478 0.85214* 0.2587 0.14567* 0.355 lnEI 0.0226* 3.456 −0.07412* −3.4568 −0.04578* −0.0411 lnPGDP 3.2354* 3.258 5.5471* 1.166 11.2451* 2.255 lnRE −0.2452* −3.741 −0.7541* −1.74156 −0.1425* −2.3534 lnEC −0.01245* −2.4512 −0.004512* −0.45871 −0.02415* −0.793 Receipts 0.1450* 2.921 0.4567* 3.2587 1.4521*** 5.2478 ∗∗∗ p < 0.01; ∗∗ p < 0.05; ∗ p < 0.1 Analysis of GNS model At this point, various studies use point estimates to examine the existence of spatial spillover effect. However, the marginal impact of the corresponding explanatory variables on the dependent variable is not directly represented by the coefficients of the GNS model, whereas the regression results do not reveal how carbon dioxide emissions are affected marginally by tourism development, renewable energy, and financial development. This study states the importance of explaining the impact of variable changes in spatial models through the partial differential methods. Table 6 provides the direct, indirect, and total effects of the independent variables. Therefore, the impact of changes in explanatory variables on CO2 emissions in a particular country is called a direct impact, whereas the impact of variations in independent variables of neighbouring countries is referred to as indirect effects. Similarly, the sum of indirect and direct effects is called the total effect. Table 6 represents the results of direct, indirect, and total effect of the GNS model. The findings show that the tourism coefficient (1.25) in the direct effect is considered positive and statistically significant at the 1% significance level. The tourism coefficient (−1.74) in the indirect effect is negatively related to environmental pollution, with a significant level of 1%. Therefore, the total impact of tourism (−0.4899) is recorded as negatively significant at the 1% level. In addition, according to the positive and significant direct effect, every additional unit in the tourism development will increase the carbon dioxide emissions by 1.25%. The positive direct and negative indirect impact of tourism show an inverse U-shaped relationship between tourism development and local country CO2 emissions. This means that CO2 emissions first increase at an increasing rate with the development of the tourism to reach the maximum, and then as the tourism continues to increase, CO2 emissions decrease at a decreasing rate. In contrast, the development of tourism in neighbouring countries has a U-shaped relationship with CO2 emissions, that is, CO2 first decreases with the decrease of tourism, reaching a minimum, and then with the continuous development of tourism, CO2 increases. However, the study sample is divided in two types, one with tourism development on the left side of the axis of symmetry of the inverse U-shape and the other with tourism development on the right side. Our findings are consistent with Sun (2016a), (Feng et al. 2021; Li et al. 2021a, 2021c) that concludes tourism has a positive impact on carbon dioxide emissions. Similarly, our results also show the negative impact of tourism on CO2 emissions, which is consistent with the second category of literature (Katircioǧlu, 2014; Lee and Brahmasrene, 2013; Paramati et al., 2017). Hence, overlooking the nonlinear effect of tourism on carbon emissions is possibly the reason for the different conclusions. Although most studies have confirmed the empirical association among tourism development and carbon emissions, according to the above conclusions, the direction of the causal relationship between the two is still unknown. One of the main reasons for drawing conflicting conclusions is to ignore the nonlinear impact of tourism on carbon emissions (Li et al., 2018a; Sun, 2016b). In addition, if the spatial interdependence of the regions is not considered, it may lead to erroneous conclusions (Yang et al., 2019). Therefore, this study uses a panel spatial econometric model to estimate the total impact of tourism on carbon emissions, taking into account spatial dependence and nonlinearity. Next, other control variables also produce valuable results, and the detailed explanation is given below: For financial development and CO2 emissions nexus: The primary focus of this study is on financial development, and therefore results provide its impact. The direct effect for financial development is recorded as significantly positive (0.055), the spillover impact is −0.09215, and the total impact is −0.063, as seen in the empirical findings of financial development given in Table 6. On the contrary, an increase of 5.5% in carbon dioxide emissions with a 1% rise in financial development indicates a significantly positive direct effect. The findings also reflect the financial development and carbon dioxide emissions to constitute an inverse U-shaped relationship, which indicates an increase in carbon emissions of a country with growth in its tourism. However, an eventual 12.5% decrease is expected in carbon emissions of the local country after the threshold is reached. When financial development is higher in the neighbouring countries, the quality of the environment in a local country is affected by technological diffusion, better governance, and more sustainable policies, presenting a possible reason for this process. Consequently, lower carbon dioxide emissions are induced due to the external restrictions across nations (Li et al. 2021e; Lv and Li, 2021a; Zhao et al. 2021). Similarly, the carbon dioxide emissions in the local country are reduced by boosting the spillover of technology, and the transfer of knowledge and skills. The influence of the financial development of neighbouring countries on a country’s carbon dioxide emissions is stressed by this study and aligns with (Li et al. 2021b; Lv and Li, 2021a; Miao et al. 2019). Hence, the significantly positive direct effect is taken over by the negative spillover impact of financial growth on CO2 emission presenting a total effect which is significantly negative. The role of financial development in promoting business growth more than promoting technological progress and green projects increases energy consumption and is a possible explanation for this process, and the findings align with Bui (2020) and Charfeddine and Kahia (2019). Conversely, a unit rise in financial development of neighbouring countries suggests 12.5% decline in carbon emissions of the local country through the negatively significant spillover effect. For GDP per capita and CO2 emissions nexus: GDP per capita significantly impacts on CO2 emission through spatial spillover. The direct impact of GDP per capita on CO2 emission is significant, while the direct impact of (PGDP)2 is negative, proving the existence of environmental Kuznet. Similarly, economic growth and carbon dioxide in sample countries share an inversed-U relationship. In both models, the indirect effect of PGDP and (PGDP)2 on carbon dioxide proves the existence of environmental Kuznets. Nevertheless, pollution in the neighbouring countries is increased due to the local economic development and improved quality of life, transferring high-pollution industries to neighbouring countries. Consequently, an increase in environmental pollution through the increased economic development indicates that the income in these countries positively impacts the left side of the inversed-U curve. For renewable energy and CO2 emissions nexus: The direct effect coefficient (−0.0598) and indirect effect coefficient (−0.0921) are recorded to be negatively significant at 1% significance level, as indicated by the findings for renewable energy. Hence, a country observes 5.98% decrease in carbon dioxide emissions and 9.21% decrease in carbon dioxide emission with 1% increase in renewable energy for a certain country and in its neighbouring countries, as seen in the results. Similarly, in the population density and CO2 emissions nexus, we observed a significantly positive direct effect of population density on CO2 emissions and significantly positive indirect effects of population density on CO2 emissions. Hence, emission of pollution is increased with population density for a certain country along with its neighbouring countries. Also, the results showed a statistically significant positive effect of trade openness on CO2 emissions; total effect and the spillover are also evident. Therefore, the environment of the local country is affected by trade openness in all neighbouring countries. Moderating role of education and infrastructure In this section, we find the moderating effect of educating expenditure and transpiration infrastructure on CO2 emissions. The results showed a negative statistically significant direct, indirect, and total effect of education on environmental pollution (Table 7). The direct effect of interaction term is higher than the indirect effect; the 1% increase in education expenditure of the local country helps 5.4% decrease in environmental pollution. At 1% significance level, the spatial coefficient is recorded −0.0399, which suggests strengthening of the negative indirect effect of tourism on carbon emissions along with the strengthening of negative direct effect of tourism on carbon emissions due to sustainable education. The significantly negative effect of the interaction between tourism and education on carbon emissions is shown in Table 7, which allows the negative effect of tourism on CO2 emission to become stronger due to an increase in the sustainable education tends. The carbon emissions induced from tourism are impacted by the environmental protection awareness of tourists, according to many scholars (Zhang and Zhang, 2018), proving empirical evidence for the already presented argument (Rehman et al., 2020; Ahmad et al., 2020; Fatima et al., 2019; Li et al., 2021).Table 7 Results of moderation role of education infrastructure Variable Direct impact Indirect impact Total impact Variable Coefficient t values Coefficient t values Coefficient t values TourEdu −0.0540 −0.2382 −0.03998* −0.453 −0.2680*** −0.4241 TourStr −0.0087* −0.0139 −0.0067* −0.038 −0.2680 −0.4241 Tour 1.4034* 2.0938 −1.1845* −0.288 0.3826* 0.0902 FD 0.0525* 4.5305 −0.0875* −3.853 −0.0568* −2.0938 PGDP 3.7507* 2.8325 4.8725* 1.407 10.625* 2.0577 (PGDP)2 −0.3032* −3.4656 −0.5295* −1.775 −0.8330* −2.6535 RE −0.0568* −0.0875 −0.0017* −0.314 −0.0138* −0.3353 PopD 0.2193* 2.3284 0.6561* 3.980 0.8754* 6.3825 Trade 0.00992 0.8125 −0.0162 −0.4783 −0.0063 −0.1475 Panel data analysis demonstrates that international tourism transportation expenses have a progressively greater impact on CO2 emissions. Rayamajhi (2013) used a panel data analytic approach to test the links among the energy consumption, GDP, commerce, tourism, and CO2 emissions in OECD nations from 1995 to 2016. The analysis’ findings indicate that tourism development has a growing impact on carbon emissions. Sun et al. (2020) used panel cointegration, FMOLS, and panel causality approaches to examine the effect of tourism revenues on CO2 emissions in Eastern and Western European nations from 1995 to 2013. Their findings indicate that development of tourism has a positive influence in Eastern Europe CO2 emissions but has a negative effect in the Western part of Europe. Arai and Goto (2017) used a panel bootstrap causality test to analyse the link among CO2 emissions and tourist arrivals from 1995 to 2014 in 16 small developing countries. Their findings demonstrate that the association among the tourist arrivals and CO2 emissions is bidirectional. The analysis’ findings confirm that tourism expansion has a carbon-reducing effect. Wondirad et al. (2021) investigated the association between CO2 emissions and economic growth related to tourism in industrialized and developing nations from 2005 to 2013. According to their findings, tourism adds to the increase in CO2 emissions (Ahmad et al., 2021a; Ahmad et al., 2021b; Ahmad and Jabeen, 2020). Robustness test We applied different spatial weight matrices to check the robustness of results and the specifications of the spatial weight matrix (Lv and Li, 2021b). For one of the binary matrix of the eight nearest neighbours, if the country j is within the eight nearest neighbours of the country I, the weight wij = 1; otherwise, the weight wij = 0. Furthermore, Table 8 shows the results for direct, indirect, and total effects, giving a similar broad range for different spatial weight matrices of direct, indirect, and total effects. Hence, the spillover and total effects are relatively insignificant due to the two spatial weight matrices showing most of the elements as zero.Table 8 Robustness check Direct effects Indirect effects Total effects Variable Coefficient t values Coefficient t values Coefficient t values lnTourism −0.7420* −2.1425 −0.20015* −1.251 −0.3021* −0.3145 Urb 0.03512* 3.251 −0.07521* −3.2145 −0.04521* −2.301 lnEI 3.7452* 2.23615 5.0012* 1.166 10.2541* 2.245 lnPGDP −0.1452* −3.532 −0.25874* −1.9852 −0.7932* −2.654 lnRE −0.0452* −0.07412 −0.002130* −0.2541 −0.02145* −0.2514 lnEC 0.214* 2.251 1.254115* 3.6215 0.8966* 5.122 Receipts 0.0745* 0.7452 −0.01542* −0.4021 −0.00442* −0.4522 Table 8 presents the results of the cross-sectional dependence test. The findings showed that all the selected variables are significant at 1% significant level. These findings suggest that a shock one of the most visited countries may have an impact on other nations’ influencing factors. As a result, in order to obtain reliable results, we applied panel data methodologies of second generation; the interdependence of countries must be taken into account. Conclusion and policy implication Tourism has been stressed globally due to its significant contribution to job creation, economic growth, and regional coordination (The World Travel & Tourism Council, 2014). Thus, it was repurposed to revitalize the countryside’s crumbling infrastructure and close the imbalanced development gap between urban and rural areas (Gao & Wu, 2017). Throughout China’s reform and opening-up, much emphasis was placed on urbanization and industry. Meanwhile, substantial economic factors and developing resources gravitated toward urban regions, and the countryside gradually fell further and more behind the city (Liu & Wall, 2006). However, these irrational policies and regulations have acted as a severe impediment to a reasonably affluent, sustainable, and just society (Su and Ang, 2010). The findings support the EKC hypothesis. The following are three points that summarize the estimation results: (a) the tourist development has a positive significant impact on the carbon emissions. (b) Tourism receipts, on the other hand, reduce CO2 emissions. (c) Tourism development has a bidirectional impact, implying that CO2 emissions and tourism development have a bidirectional causal relationship. Theoretical foundations of the research results explain why tourist arrivals increase carbon emissions and carbon emissions decreased through tourism receipts. It can be based on two reasons: (a) International tourism transportations are one of the major factors that impact on the natural environment around the world. Tourist arrivals and departures are increased as a result of progress in the sector of tourism, as are transportation services. According to Brida et al. (2020), transportation services account for nearly 95% of tourism-related CO2 emissions, with the aviation sector accounting for the majority of these emissions. As the number of tourists grows, so does the diversity of infrastructure services available, including lodging, restaurants, hotels, ports, airports, telecommunications railways, and roads. The construction of infrastructure and the development of tourist destinations both have important contribution to increase the carbon emissions. Appendix 1 Table 9 List of countries contributed in this study Australia Belgium Poland Pakistan Bangladesh Bosnia and Herzegovina Portugal Qatar China Bulgaria Romania Saudi Arabia Nepal Croatia Russian Federation Russia India Denmark Spain United Arab Emirates Indonesia Finland Sweden Brazil Japan France Switzerland Canada Korea, Rep. Germany Turkey Mexico Malaysia Greece UK Panama New Zealand Hungary Azerbaijan Peru Philippines Iceland Bahrain USA Singapore Ireland Egypt, Arab Rep. Venezuela, RB Sri Lanka Israel Iran, Islamic Rep. Czech Republic Thailand Italy Kazakhstan Luxembourg Austria Netherlands Kuwait Moldova Belarus Norway Kyrgyz Republic Slovak Republic Ukraine Algeria Turkmenistan Chile Colombia St. Vincent and the Grenadines Appendix 2 Table 10 Summarized statistics Mean Min Max S. dev lnCO2 Carbon emissions in metric tons 0.829 1.403 −4.059 3.204 lnTourism Tourism development indicator 1.542 −1.115 5.598 2.675 lnFD Financial development indicator 6.186E−08 1 −2.919 3.349 lngdp (Logarithm of) GDP per capita, PPP (constant 2011 international $) 9.254 1.115 6.301 11.355 lnRE GDP per unit of energy consumption (2011 constant PPP $ per kg of oil equivalent) in log form 2.099 0.47 0.347 3.045 lnEdu Government expenditure on education, total (% of government expenditure) 4.273 0.699 −1.787 6.081 lnTrade Trade openness. 4.273 0.699 −1.787 6.081 Notes: Annually 2000–2017. World Development Indicators Availability of data and materials The data can be available on request. Author’s contribution YJ: conceptualization, data curation, methodology, writing—original draft. SY: data curation, visualization, supervision. YZ: visualization, editing. LT: review & editing. BSXT: writing—review and editing, and software. Declarations Ethics approval and consent to participate We declare that we have no human participants and did not use human data or human tissues. Consent for publication N/A Competing interests The authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References (UNWTO), U.N.W.T.O., 2020. Volume 18 • Issue 1 • January 2020 EXCERPT** 2 Abbas, Q., Hanif, I., Taghizadeh-Hesary, F., Iqbal, W., Iqbal, N., 2021. 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==== Front Sci Total Environ Sci Total Environ The Science of the Total Environment 0048-9697 1879-1026 Elsevier B.V. S0048-9697(21)07422-2 10.1016/j.scitotenv.2021.152345 152345 Article Toxicity of spike fragments SARS-CoV-2 S protein for zebrafish: A tool to study its hazardous for human health? Ventura Fernandes Bianca H. a Feitosa Natália Martins b Barbosa Ana Paula c Bomfim Camila Gasque c Garnique Anali M.B. d Rosa Ivana F. e Rodrigues Maira S. e Doretto Lucas B. e Costa Daniel F. e Camargo-dos-Santos Bruno f Franco Gabrielli A. f Neto João Favero f Lunardi Juliana Sartori f Bellot Marina Sanson f Alves Nina Pacheco Capelini f Costa Camila C. g Aracati Mayumi F. g Rodrigues Letícia F. g Costa Camila C. g Cirilo Rafaela Hemily f Colagrande Raul Marcelino f Gomes Francisco I.F. h Nakajima Rafael T. e Belo Marco A.A. i Giaquinto Percília Cardoso j de Oliveira Susana Luporini k Eto Silas Fernandes l Fernandes Dayanne Carla m Manrique Wilson G. n Conde Gabriel o Rosales Roberta R.C. p Todeschini Iris q Rivero Ilo r Llontop Edgar q Sgro Germán G. qs Oka Gabriel Umaji q Bueno Natalia Fernanda q Ferraris Fausto K. t de Magalhães Mariana T.Q. u Medeiros Renata J. v Mendonça-Gomes Juliana M. w Junqueira Mara Souza x Conceição Kátia y Pontes Leticia Gomes de z Condino-Neto Antonio z Perez Andrea C. ab Barcellos Leonardo J.G. abac Júnior José Dias Correa adae Dorlass Erick Gustavo af Camara Niels O.S. w Durigon Edison Luiz c Cunha Fernando Q. ag Nóbrega Rafael H. e Machado-Santelli Glaucia M. ah Farah Chuck S. q Veras Flavio P. aiaj Galindo-Villegas Jorge ak Costa-Lotufo Letícia V. al Cunha Thiago M. aiaj Chammas Roger am Carvalho Luciani R. an Guzzo Cristiane R. c Malafaia Guilherme ao⁎ Charlie-Silva Ives al⁎⁎ a Laboratório de Controle Genético e Sanitário, Diretoria Técnica de Apoio ao Ensino e Pesquisa, Faculdade de Medicina da Universidade de São Paulo, Brazil b Laboratório Integrado de Biociências Translacionais (LIBT), Instituto de Biodiversidade e Sustentabilidade (NUPEM), Universidade Federal do Rio de Janeiro (UFRJ), Macaé, RJ, Brazil c Department of Microbiology, Institute of Biomedical Sciences, University of Sao Paulo, Sao Paulo, Brazil d Department of Cell Biology, Institute of Biomedical Sciences, University of São Paulo, Brazil e Reproductive and Molecular Biology Group, Department of Morphology, Institute of Biosciences, Sao Paulo State University, Botucatu, São Paulo, Brazil f Department of Structural and Functional Biology, Institute of Biosciences of Botucatu, São Paulo State University, SP, Brazil g Department of Preventive Veterinary Medicine, São Paulo State University (UNESP), Jaboticabal, Brazil h Department of Pharmacology, Center of Research in Inflammatory Diseases, Ribeirao Preto Medical School, University of São Paulo, Brazil i Brasil University, Descalvado, São Paulo, Brazil j Universidade Estadual Paulista Júlio de Mesquita Filho, Instituto de Biociências - Departamento de Fisiologia, São Paulo, Brazil k Universidade Estadual Paulista Júlio de Mesquita Filho, São Paulo, Brazil l Postgraduate Program in Health Sciences, PROCISA, Federal University of Roraima, Brazil m Immunochemistry Laboratory, Butantan Institute, São Paulo, Brazil n Aquaculture Health Research and Extension Group, GRUPESA, Aquaculture Health Laboratory, LABSA, Department of Veterinary Medicine, Federal University of Rondônia, Rolim de Moura campus, Rondônia, Brazil o Department of Preventive Veterinary Medicine, São Paulo State University, Jaboticabal, Brazil p Department of Cell and Molecular Biology, Ribeirao Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil q Departamento de Bioquímica, Instituto de Química, Universidade de São Paulo, Brazil r Pontifícia Universidade Católica de Minas Gerais, Brazil s Departamento de Ciências Biomoleculares, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP, Brazil t Department of Pharmacology and Toxicology, Oswaldo Cruz Foundation, FIOCRUZ, Rio de Janeiro, Brazil u Department of Biochemistry and Immunology, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, Brazil v Laboratory of Physiology, INCQS/Fiocruz Zebrafish Facility, Departament of Pharmacology and Toxicology, National Institute for Quality Control in Health, Brasil w Transplantation Immunobiology Lab, Department of Immunology, Institute of Biomedical Sciences, Universidade de Sao Paulo, Brazil x Center for Translational Research in Oncology, Cancer Institute of the State of Sao Paulo, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil y Laboratory of Peptide Biochemistry, Federal University of São Paulo, Brazil z Laboratory of Human Immunology, Department Immunology, Institute Biomedical Sciences, University São Paulo, Sao Paulo, Brazil aa Department of Pharmacology, Universidade Federal de Minas Gerais, Brazil ab Graduate Program of Pharmacology, Federal University of Santa Maria, Brazil ac Laboratory of Fish Physiology, Graduate Program of Bioexperimentation and of Environmental Sciences, University of Passo Fundo, Brazil ad Laboratório do Estudo da Interação Químico Biológica e da Reprodução Animal, LIQBRA, Bloco O3,174, Brazil ae Departamento de Morfologia Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Brazil af Department of Microbiology, Institute of Biomedical Sciences, University of Sao Paulo, Brazil ag Department of Pharmacology, Center of Research in Inflammatory Diseases, Ribeirao Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil ah Department of Cell Biology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil ai Center of Research in Inflammatory Diseases, Ribeirão Preto Medical School, University of Sao Paulo, Ribeirão Preto, Sao Paulo, Brazil aj Department of Pharmacology, Ribeirao Preto Medical School, University of São Paulo, Ribeirao Preto, São Paulo, Brazil ak Faculty of Biosciences and Aquaculture, Nord University, 8049 Bodo, Norway al Department of Pharmacology, Institute of Biomedical Sciences, Universidade de São Paulo, Brazil am Centro de Investigação Translacional em Oncologia, Instituto do Câncer do Estado de São Paulo, Faculdade de Medicina da Universidade de São Paulo, Brazil an Disciplina de Endocrinologia do Departamento de Clinica Medica e Laboratório de Hormônios e Genética Molecular, LIM 42, Brazil ao Biological Research Laboratory, Goiano Federal Institute, Urutaí Campus, Brazil ⁎ Correspondence to: G. Malafaia, Biological Research Laboratory, Goiano Federal Institution – Urata Campus, Rodovia Geraldo Silva Nascimento, 2,5 km, Zona Rural, Urutaí, Brazil. ⁎⁎ Corresponding author. 21 12 2021 20 3 2022 21 12 2021 813 152345152345 30 9 2021 17 11 2021 8 12 2021 © 2021 Elsevier B.V. All rights reserved. 2021 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Despite the significant increase in the generation of SARS-CoV-2 contaminated domestic and hospital wastewater, little is known about the ecotoxicological effects of the virus or its structural components in freshwater vertebrates. In this context, this study evaluated the deleterious effects caused by SARS-CoV-2 Spike protein on the health of Danio rerio, zebrafish. We demonstrated, for the first time, that zebrafish injected with fragment 16 to 165 (rSpike), which corresponds to the N-terminal portion of the protein, presented mortalities and adverse effects on liver, kidney, ovary and brain tissues. The conserved genetic homology between zebrafish and humans might be one of the reasons for the intense toxic effects followed inflammatory reaction from the immune system of zebrafish to rSpike which provoked damage to organs in a similar pattern as happen in severe cases of COVID-19 in humans, and, resulted in 78,6% of survival rate in female adults during the first seven days. The application of spike protein in zebrafish was highly toxic that is suitable for future studies to gather valuable information about ecotoxicological impacts, as well as vaccine responses and therapeutic approaches in human medicine. Therefore, besides representing an important tool to assess the harmful effects of SARS-CoV-2 in the aquatic environment, we present the zebrafish as an animal model for translational COVID-19 research. Graphical abstract Unlabelled Image Keywords Coronavirus Danio rerio Environmental impacts Infection diseases Acute respiratory syndrome ==== Body pmc1 Introduction COVID-19 (Coronavirus Disease-2019), caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) has had unprecedented global impacts. Economically, the exact magnitude of the losses is still uncertain, but the short and long-term fiscal and budgetary effects indicate that we are heading for the biggest recession in contemporary history (McKibbin and Fernando, 2020). Socially, the disease has strongly influenced the daily lives of millions of people, from the obligation to follow rules of social isolation, with the simultaneous closure of borders imposed by the governments of some countries, to the planning and adoption of health measures to face a still incipient crisis. According to the World Health Organization (WHO), the most recent estimates on the status of the pandemic in the world record more than 246,592,349 million confirmed cases and more than 5,001,138 million deaths worldwide in November 2021 (World Health Organization (WHO), n.d.). As far as we know, the classic form of transmission of SARS-CoV-2 is by air and via contact with infected people (Harrison et al., 2020; Meyerowitz et al., 2021). Nonetheless, several forms of transmission of the new coronavirus have been investigated, motivated by the persistence of the virus in the environment for a few hours/days. According to Kampf et al. (2020), the virus can survive on inanimate surfaces such as metal, glass, or plastic for up to 9 days if no disinfection procedure (e.g.: 62–71% ethanol, 0.5% hydrogen peroxide or 0.1% sodium hypochlorite within 1 min) is performed. Another form of transmission is the excrement of infected people, since many studies have shown the presence of SARS-CoV-2 viral titers in domestic sewage (Elsamadony et al., 2021; Moghadas et al., 2020), especially from human urine and feces (Jones et al., 2020; Sun et al., 2020; Xiao et al., 2020). Longitudinal analysis of wastewater can be used to identify trends in disease transmission before reporting clinical cases and can shed light on characteristics of infection that are difficult to capture in clinical investigations, such as the dynamics of early viral elimination (Adhikari et al., 2020). In this scenario, as discussed by Liu et al. (2020), the potential for secondary transmission of the SARS-CoV-2 virus via wastewater should not be underestimated (Teymoorian et al., 2021). The SARS-CoV-2 Spike (S) protein is found on the surface of the SARS-CoV-2 virus, giving it a “crown” appearance (Coughlan, 2020). This protein plays a key role in the infection process, triggering the fusion process in the cell membrane (Lan et al., 2020). The trimeric spike protein belongs to the class I fusion proteins. Its two subunits S1 and S2 orchestrate its entrance into the cell, while S1 subunit facilitates the attachment of the virus via its receptor-binding domain (RBD) to the host cell receptor (angiotensin converting enzyme 2) (ACE2), the S2 subunit mediates the fusion of the viral and human cellular membranes (Hoffmann et al., 2020; Shang et al., 2020; Zamorano Cuervo and Grandvaux, 2020). In addition, Spike protein has been considered one of the potential candidates as an antigen for the production of vaccines against COVID-19 (Bangaru et al., 2020; Fan et al., 2020; Keech et al., 2020; Ravichandran et al., 2020; Samrat et al., 2020; Wang et al., 2020a). On the other hand, a field still little explored refers to the possible environmental impacts (direct and indirect) of the current outbreak of COVID-19. Despite the increase in contaminated household (Gautam and Sharma, 2020; Urban and Nakada, 2021; Zand and Heir, 2020) and hospital (Abu-Qdais et al., 2020; Sangkham, 2020; Wang et al., 2020b) waste generation, so far, there is no information on the ecotoxicological effects of SARS-CoV-2 or its structural components on freshwater vertebrates. Therefore, these facts justify the urgent need for studies in order to assess the deleterious effects caused by SARS-CoV-2 virus on the health of aquatic organisms which already suffer as a result of various anthropic activities. In addition to its significant importance in public health, such studies might support actions or strategies to mitigate these impacts in favor of the conservation of non-target species. 2 Material and methods 2.1 Production of recombinant spike protein SARS-CoV-2 Cloning, expression, and protein purification. The DNA fragment coding for the SARS-CoV-2 Spike protein fragment from 16 to 165 (rSpike) was amplified by PCR using SARS-CoV-2 cDNA transcribed from the RNA isolated from the second patient, strain HIAE-02:SARS-CoV-2/SP02/human/2020/BRA (GenBank accession number MT126808.1). The primers used for amplification of the Spike fragment are 5′ AGCATAGCTAGCGTTAATCTTACAACCAGAACTCAATTACC 3′ and 5′ ATTATCGGATCCTTAATTATTCGCACTAGAATAAACTCTGAAC 3′. The PCR product was purified using the GeneJET PCR Purification Kit (Thermo Fisher Scientific, ref. #K0702) and digested with AnzaTM restriction enzymes NheI and BamHI (Thermo Fisher Scientific). The expression vector used was pET-28a that was also digested with the same pair of restriction enzymes as the amplified rSpike DNA fragment. The digested fragment was used to ligate the rSpike DNA fragment to the digested pET-28a vector using T4 DNA ligase (Thermo Fisher Scientific). The positive clones were confirmed by digestion tests. The rSpike cloned into pET28a results in a protein with a fusion of seven histidine tag at the N-terminal portion of the protein to facilitate the protein purification steps. rSpike was expressed in Escherichia coli strain BL21(DE3) and BL21(DE3) Star. The cells were grown in 2XTY medium (16 g/L of bacto-tryptone, 10 g/L of yeast extract, and 5 g/L sodium chloride) with added kanamycin (50 μg/ml) under agitation at 37 °C to an OD600nm of 0.6, at which point 0.5 mM isopropyl-β-D-1-thiogalactopyranoside (IPTG) was added. After 4 h of induction, the cells were collected by centrifugation (4500 ×g, 4 °C, 15 min) and stored at −80 °C. The cell pellet expressing the rSpike protein was resuspended in lysis buffer [50 mM 3-(N-morpholino)propanesulfonic acid (MOPS) pH 7.0, 200 mM NaCl, 5% glycerol, 0.03% Triton-100 and 0.03% Tween-20] and lysed by sonication on an ice bath in a Vibracell VCX750 Ultrasonic Cell Disrupter (Sonics, Newtown, CT, USA). The lysate was centrifuged at 30,000 ×g, 4 °C for 45 min. The pellet fraction was resuspended in 7 M urea, 50 mM MOPS pH 7.0, 200 mM NaCl, and 20 mM imidazole on an ice bath under agitation for 1 h and centrifuged at 30,000 ×g, 4 °C for 45 min. The soluble fraction was loaded in a HisTrap Chelating HP column (GE Healthcare Life Sciences) previously equilibrated with 7 M urea, 50 mM MOPS pH 7.0, 200 mM NaCl, and 20 mM imidazole. Bound proteins were eluted using a linear gradient of imidazole over 20 column volumes (from 20 mM to 1 M imidazole). Fractions with rSpike were concentrated using Amicon Ultra-15 Centrifugal filters (Merck Millipore) with a 3 kDa membrane cutoff and loaded onto a HiLoad 16/600 Superdex 75 pg (GE Healthcare Life Sciences) size exclusion chromatography column previously equilibrated with 7 M urea, 50 mM MOPS pH 7.0, 200 mM NaCl, and 1 mM EDTA. The eluted fractions were analyzed by 15% SDS-PAGE for purity, and the fractions containing the target protein were mixed and concentrated using Amicon Ultra-15 Centrifugal filters (Merck Millipore) with a 3 kDa membrane cutoff (Fig. 1b).Fig. 1 (A) protein expression of SARS-CoV-2 spike protein fragment. For this, the DNA fragment coding for spike of the SARS-CoV-2 full-length S protein; (B) purified spike protein samples tested in SDS-PAGE gel with Coomassie Blue stain, end for purity; Anti-HisTag Western Blot reactivity, for specificity; (C) rSpike protein injection is toxic to adult female zebrafish. Graph of survival rate and days after injected. Kaplan-Meier cumulative probability curve indicating survival rate of zebrafish after two injected with different protein samples. Females were injected either with rSpike protein, extract of lysed Escherichia coli cells, buffer presented the rSpike protein (control 1), naïve control (not immunized), or a mix of two recombinant protein: PilZ protein from Xanthomonas citri and N-terminal part of perpetuity. LIC_11128 from Leptospira interrogans Copenhageni (control 2). Each group was performed using adult female fishes. Fig. 1 2.2 Zebrafish maintenance Wild-type zebrafish from the AB line, and specific pathogen-free (SPF), were raised in Tecniplast Zebtec (Buguggiate, Italy) and maintained in the zebrafish housing systems in the Faculty of Medicine of the University of São Paulo facilitie, SP. Brazil. Fish used for the experiments were obtained from natural crossings and raised according to standard methods (Tsang et al., 2017). Zebrafish were kept in 3.5 L polycarbonate tanks and fed three times a day with Gemma micro by Skretting (Stavanger, Norway). The photoperiod was 14:10 h light-dark cycle and the water quality parameters were 28 °C ± ,05 °C; pH = 7.3 ± 0.2; conductivity 500 to 800 μS/cm, referred to as system water. The procedures were approved by the Ethics Committee (CEUA) of the Faculty of Medicine of the University of São Paulo and registered under protocol number 1514/2020. 2.3 The administration spike in adult zebrafish We performed 2 intraperitoneal (IP) inoculations of a solution containing 1 μg purified rSpike diluted in 10 μL of inoculation buffer (7 M urea, 50 mM Tris-HCl pH 7.5, 200 mM NaCl, and 1 mM EDTA). A group of control animals received injections containing only the dilution buffer. Another control group was challenged by a lysate of bacterial fragment of E. coli BL21(DE3) extract. rSpike was injected into two injected sections in 20 zebrafish females (previously anesthetized with tricaine methanesulfonate (Sigma) - at a concentration of 150 mg/L) at an interval of 7 days, with the aim of producing plasma antibodies. Passive antibody transfer to zebrafish eggs occurs naturally as described by Wang et al. (2020b). After injected, females were stimulated to mate (at 7 and 14 days after injection) and generated eggs. The time at which the antibodies were transferred to the eggs was analyzed by the western blot technique. Another control group was performed using 1 μg of a mix of proteins in buffer 50 mM Tris-HCl pH 8.0, 200 mM NaCl, and 1 mM EDTA: equivalent amount of purified PilZ protein from Xanthomonas citri pv. citri. (Guzzo et al., 2009) and LIC_11128 (residues 1–115 cloned into pET28a expression vector a with a fusion of seven histidine tag at the N-terminal portion of the protein) from Leptospira interrogans. 2.4 Histology from multiple organs For histopathological analysis, 5 fishes from control, 5 from naïve and 20 injected with rSpike were fixed in 10% formaldehyde for 24 h and then dehydrated in ethanol, embedded in paraffin, and sectioned (5 μm). The sections were stained with hematoxylin and eosin and analyzed under an optical microscope. This methodology was adapted from described by Luna and Moore et al. (2002). The heart, kidney, liver, spleen, ovary, brain, intestine, eye, mesentery, Langerhans islands, muscular tissue and gills were qualitatively evaluated considering presence or absence of structural alterations. The slides were analyzed and photographed using a 10, 20 and 40-times objective Olympus model B × 51 (Olympus Corporation) microscope coupled to a 2-times projected Q Color 3 Olympus model U-PMTVC (Olympus Corporation). The pro-gram used for photographic records was the QCapture (Q Imaging) image analysis program. Then, the images obtained were treated for adjustment of size, contrast, brightness, and focus, as well as mounted on planks and subtitled using the program Adobe Photoshop CC 2017. Histopathological analysis of different organs, including brain, gonads, heart, kidney, liver, spleen, among others, was performed in female fishes used in the injected protocol described in material and methods. The occurrence or absence of pathological characteristic was used as qualitative criteria for the organs analyzed and grouped by physiological system. Animals that died during the injected experiment were excluded from the analysis. 2.5 Behavioral testing in adult The D. rerio, 13 for the control and 17 for the spike group, were individualized in an aquarium (28 cm × 11 cm × 18 cm, length × width × height) without visual contact with other fish and left to habituate for one day. In the following day (day 2), fish locomotor activity was recorded (the camera was placed in the front of the test aquarium) 5 min before (baseline) and 30 min after the onset of alarm substance (AS) in the aquaria. A total of 0.4 ml of AS (see the section “Alarm substance” for further details on AS preparation) were administered through a plastic hose in the experimental aquaria, with a help of a syringe. A dark plastic tarp prevented fish to see the researcher applying the AS in the aquarium, avoiding any interference of the researcher in the fish's locomotor behavior. The side of the aquarium that the AS was inserted was randomized between the groups, to avoid any laterality effect in the results. All tests were realized between 8:00 h and 12:00 h, and the two treatments were intermixed throughout the day to account for possible diurnal variations in behavior. The analysis of locomotor behavior was done using the ZebTrack software, developed at MatLab. The software is validated to analysis zebrafish locomotor behavior (Moura and Luchiari, 2016). The following locomotor behavior variables were assessed: time stopped, distance travelled, mean speed, maximum speed and distance from the bottom. 2.5.1 Alarm substance The extraction followed the protocol described in Faustino et al. (2017). Eight adult zebrafish (4 males and 4 females) were individually collected from their tank, rinsed with distilled water, dried with paper towel, and sacrificed through the break of the spinal cord. Fourteen vertical and one horizontal shallow cuts were made in fish skin with surgical scalpel blade, at each side of the trunk. The cuts were washed with 50 mL of distilled water and then the solution was filtered, resulting in a 400 mL of AS solution that was divided into 4 mL aliquots. Then, the aliquots were stored at the freezer for posterior use in the experiments. 2.6 Ace2 expression by real-time quantitative PCR in adult zebrafish Total RNA from brain, muscle, liver, kidney, heart, gonads (testis and ovary) from male and female zebrafish (n = 5 animals per sex) were extracted using the commercial PureLinkTM RNA Mini Kit (Ambion, CA, USA) according to manufacturer's instructions. The cDNA synthesis was performed as described by Nóbrega et al. (2010). The relative mRNA levels of ace2 (angiotensin I converting enzyme 2) was evaluated among different tissues of male and female zebrafish by real-time quantitative PCR (qPCR) using specific primers (forward: GACGGTTTTGGACCAACTTGT; reverse: TTTCATCCCAACCCTGCTCC). qPCR reactions were conducted using 5 μL 2× SYBR-Green Universal Master Mix, 1 μL of forward primer (9 mM), 1 μL of reverse primer (9 mM), 0.5 μL of Milli-Q® water and 2.5 μL of cDNA. The mRNA levels of the targets (Cts) were normalized by the reference gene β-actin (Tovo-Neto et al., 2020), according to the 2− (ΔΔCT) method. Primers were designed based on zebrafish sequences available at Genbank (NCBI, https://www.ncbi.nlm.nih.gov/genbank/). 2.7 Bioinformatics in silico analysis For in silico analysis, all FASTA sequences of proteins from zebrafish and human, and SARS-CoV-2 were downloaded from the UNIPROT database (http://www.uniprot.org). In addition, the percentage of similarity between the orthologous proteins of different species was calculated using the EMBOSS Water platform (https://www.ebi.ac.uk), and protein alignments were performed using the ESPript platform (http://espript.ibcp.fr/ESPript/cgi708 bin/ESPript.cgi). For comparison of 3D structures, the FASTA files were converted into PDB files (containing the 3D coordinates of the proteins) using the Raptor X tool (http://raptorx.uchicago.edu). Then, structural similarities were compared on the iPDA platform (http://www.dsimb.inserm.fr), and structural images of proteins were done using the PyMOL software (https://pymol.org/2/). For the study of protein-protein interaction and Docking of Spike were performed using the Molsoft MolBrower 3.9-1b software. 2.8 Annotation of ontological data The zebrafish and human proteins related to the subcellular location (cytoplasm, membrane, and nucleus) were recovered according to the annotation of ontological data in the ENSEMBL database (https://www.ensembl.org/index.html) For each subcellular location, protein-protein interactions were predicted with a SARS-CoV-2 Spike N-terminal fragment, residues 16–165, (rSpike) using the UNISPPI predictor, where only interactions with a score greater than 0.95 were accepted as interaction. The interacted proteins were submitted to functional enrichment to identify biological pathways using the G:Profiler software, based on the database of zebrafish and human. In addition, the proteins were analyzed with the Bioconductor Pathview package in the R environment in search of the biological pathways. The pathways were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the model organism selected was the zebrafish and human. 2.9 Network analysis Samples were analyzed in triplicate, and their molecular masses and isoelectric points of the proteins identified by MS/MS were observed using the ProtParam tool (http://us.expasy.org/tools/protparam.html). Data normalization was performed, and a significance cutoff was applied for the identified proteins at log-fold change ± 1.0. Subsequently, the identified proteins on the UniprotKB database were blasted against zebrafish. All data obtained were mapped using STRING web tool v11.0 (https://string696db.org/) to screen for protein-protein interactions (PPI). 2.10 Data analysis The statistical analysis of the behavioral tests in adult zebrafish was performed in the R environment (v3.6.0.). Data were checked for outliers through boxplot interquartile method (Speedie and Gerlai, 2008). All the response variables were log10 transformed. Firstly, the difference between the locomotor behavior (time stopped, distance travelled, mean speed, maximum speed, and distance from the bottom) among the treatments (control and spike) and sampling time points (baseline and the first 10 min after AS onset) including the interactions were examined through linear mixed-effect models. The variables “treatment” and “sampling time points” were set as fixed factors, while “fish” was included as a nested random factor. Secondly, we realized linear mixed-effect models identical to the described above but to compare different sampling time points – we compare the fish locomotor behavior of both treatments over 30 min after alarm substance onset (AS) in 10 min bins (0–10 min, 10–20 min and 20–30 min). Post-hoc comparisons were done using Tukey or Bonferroni tests (p < 0.05). 3 Results 3.1 rSpike protein of zebrafish had an impact on the survival rate SARS-CoV-2 spike protein fragment was obtained by cloning the DNA fragment for the full-lenght S protein RNA isolated from the second Brazilian, followed by heterologous protein expression in E. coli and purification. SDS-PAGE of the total cell lysates (Fig. 1a, b) shows that, after induction of expression, the band at about 16 kDa, corresponding to molecular mass of SARS-CoV-2 spike protein fragment. We demonstrated that 16 kDa band was specifically recognized by the anti-HisTag antibody (Fig. 1b). Two bioassays were carried out to analyze the toxicity of the rSpike. The first injection of the rSpike generated high toxicity to the fish (Fig. 1c). Therefore, the assay was repeated by adding different control groups to confirm whether the toxicity findings were specific to the rSpike (Fig. 1c). In the first bioassay, after the fish were injected with rSpike, the survival rate was 78.6% during the first seven days (Fig. 1). The lethality was significantly increased when compared to naive control and fish injected with protein buffer (control 1), where the survival rates were 100% and 90%, respectively (Fig. 1c). Nonetheless, after a second injection, the rSpike injected group maintained the plateau survival rate, with no further increase in lethality in the treated group. Therefore, a second assay was conducted by adding different control groups in order to confirm that the toxicity findings were specific to the rSpike, and also the presence of antigens was accessed. The Kaplan-Meier survival analysis confirmed rSpike injection presented a lower survival rate compared to the two previous controls used (Control naïve and protein buffer) and compared to females injected with Escherichia coli extract or a culture medium mixed of two purified recombinant proteins (PilZ protein from Xanthomonas citri, and a N-terminal fragment of LIC_11128 from Leptospira interrogans Copenhageni) (Control 2) (Fig. 1c). The survival rate was maintained after the second injection for the next seven days. The relative risk of death in the period studied between the groups was significant (chi square = 79.70; p < 0.0001). 3.2 Behavioral test in adult zebrafish We tested zebrafish olfaction after applying the SARS-CoV-2 spike protein fragment, testing perception and response to a co-specific alarm substance (chemical communication that triggers anti-predatory behavior in fish). We applied the behavioral tests 7 days after spike injection. Firstly, we tested whether SARS-CoV-2 spike protein fragment alter the baseline and the post alarm substance (AS) locomotor behavior. We did not observe a significant effect of treatments (control vs spike) and the interaction between treatments and sample time points in any response variable of locomotor behavior (time stopped, distance travelled, mean speed, maximum speed and distance from the bottom, p > 0.05; Fig. 2 ). However, we observed a significant effect of sample time points in the time stopped (F1,30 = 92.309, p < 0.001), distance travelled (F1, 29.898 = 8.052, p = 0.008), mean speed (F1,30 = 38.599, p < 0.001), maximum speed (F1,30 = 57.35, p < 0.001) and distance from the bottom (F1, 27.482 = 39.929, p < 0.001) (Fig. 2).Fig. 2 Effect of spike infection in adult zebrafish locomotor behavior in two different sampling time points: baseline and the first 10 min after alarm substance (AS) onset. The following locomotor behavior variables were measured: (a) Time stopped, (b) distance travelled, (c) mean speed, (d) maximum speed and (e) distance from the bottom. All the response variables were Log10 transformed. n = 13 control and n = 17 spike. Mean ± SD are shown. Statistical difference between the groups in Tukey or Bonferroni post hoc tests are indicated in the graphs (p < 0.05). (f) 3D plots representative of each treatment (control and spike) in both sampling time points (baseline and post alarm substance). Each 3D plot represents the locomotor behavior of the zebrafish closest to the mean in each treatment. Fig. 2 We also measured the fish anti-predatory response after the AS application in the aquarium over 30 min, measuring the behaviors in three different sampling time points (0–10 min, 10–20 min and 20–30 min). Like the comparison between the baseline and post AS behavior, we did not observe a significant effect of treatments and the interaction between treatments and sample time points in any response variable of locomotor behavior (p > 0.05; Fig. 3 ). However, we observed a significant effect of sample time points in the time stopped (F2, 49.053 = 13.032, p < 0.001), distance travelled (F2,57.629 = 18.828, p < 0.001) and mean speed (F2, 57.033 = 14.383, p < 0.001). Fish spent more time stopped at 10–20 min (p = 0.038) and 20–30 min (p = 0.003) than at 0–10 min, and also travelled longer distances with a high mean speed at 0–10 min than at 10–20 min (p < 0.001) and 20–30 min (p < 0.001) (Fig. 3a, b and c). We did not observe a significant effect of sample time points in the maximum speed (F2, 59.402 = 1.508, p = 0.23) and distance from the bottom (F2, 47.377 = 2.7448, p = 0.0745) (Fig. 3d and e).Fig. 3 Effect of spike injection in adult zebrafish locomotor behavior over 30 min after alarm substance onset (AS) in 10 min bins. The following locomotor behavior variables were measured: (a) time stopped, (b) distance travelled, (c) mean speed, (d) maximum speed and (e) distance from the bottom. All the response variables, except the time stopped, were log10 transformed. n = 13 control and n = 17 spike. Mean ± SD are shown. Statistical difference between the groups in Tukey post hoc test are indicated in the graphs (p < 0.05). Fig. 3 3.3 Histopathological changes in adult zebrafish In general, it was observed several morphological alterations compatible with an undergoing inflammatory process in many tissues. Markedly, brain obtained from treated fishes showed an intense inflammatory infiltrate with presence of many macrophages after 7 days (Fig. 4C) and an intense mononuclear infiltrate after 14 days days (Fig. 4d and e). Histopathological analysis of the female reproductive tissue showed ovarian stroma with abundant and disorganized extracellular matrix (Fig. 4g). Follicular development showed alterations such as atresia among oocytes at primary growth and cortical alveolus stages (Fig. 4g). Moreover, dense inflammatory infiltrates are commonly seen in the ovarian stroma (Fig. 4h). On the other hand, the group of fish that received a second injection within the interval of 7 days showed no histological changes in their ovaries after 14 days, when compared to controls (Fig. 4i). In kidneys, we observed melanin and lipofuscin pigments, renal thrombosis and autophagy with tubular disarray and loss of tubular lumen epithelium, loss of Bowman's capsule space and the integrity of the glomerular tuft compromising blood filtration (Fig. 4n–o). The frequency of the relative systemic alterations is summarized in Table 1 .Fig. 4 Histopathological changes in adult zebrafish. a: longitudinal section of the whole female zebrafish for morphological analyses of the main organs affected. All sections were stained with Hematoxylin Eosin. Brain: (b)- histology of control, (c)- brain histology after 7 days of first injected presenting macrophages, and (d) 14 days after first injected with a burst after 7 days from the first injected presenting intense mononuclear infiltrate. (e) The same image as panel d but at a higher magnification. Ovary: ovarian histology from zebrafish control (f), after 7 (g–h) and 14 days (i). (f–i) Follicular development was classified as primary growth oocyte (PG), cortical alveolus (CA), and vitellogenic (V) stages. Asterisks in panel g indicate an abundant and disorganized extracellular matrix in the ovarian stroma. (h) Inset shows a higher magnification of the cellular infiltration and arrows show dense, eosinophilic inflammatory infiltrates. (i) The histology of ovaries after 14 days is similar to the control. Scale bars: 1000 μm (g) and 200 μm (f, h, and i). Liver: Histology of the liver from control (j), after 7 days from rSpike injected (l), and after 14 days from the first injected with a burst at 7 days (m). Kidney: histology of kidney from zebrafish control (n), after 7 days from the first injected (o), and after 14 days from the first injected with a second injected after 7 days (p). Scale bars: 1000 μm (n) and 200 μm (o–p). Fig. 4 Table 1 Summary of histopathological findings in different organs of zebrafish injected with rSpike. Number of female fish with histopathological alterations out of total female fish injected. Females were injected either with Naïve control (n = 5), control 1 (protein buffer) (n = 5), or SARS-CoV-2 rSpike (n = 20). Table 1System Organs Changes/pathology NAIVE Control 1 SARS-CoV2 rSpike Circulatory Heart Lymphoid foci 0/5 0/5 1/20 Kidney Renal thrombosis 0/5 0/5 2/20 Liver Hyperemia 0/5 1/5 2/20 Spleen Hyperemia 0/5 0/5 0/20 Reproductive Ovary Atresic follicles 0/5 1/5 6/20 Nervous Brain Lymphoid foci 0/5 0/5 3/20 Digestive Intestine – 0/5 0/5 1/20 Urinary Kidney Presence of pigments, tubular and Bowman capsule structural integrity loss 0/5 0/5 2/20 Fotoreceptor Eye – 0/5 0/5 0/20 Endocrine Langehans islands – 0/5 0/5 0/20 Tegumentar – – 0/5 0/5 0/20 Respiratory Gills – 0/5 0/5 0/20 3.4 The human receptor angiotensin converting enzyme 2 (ACE2) share 72% sequence similarity to its ortholog in zebrafish and tissue distribution of zebrafish ACE2 mRNA One of the known targets of SARS-CoV-2 Spike protein is the Angiotensin receptor converting enzyme 2 (ACE2) in humans. It is considered the main gateway to the virus infection. Considering the effects of rSpike protein on the fishes analyzed in this work, structural and functional similarities between zebrafish and human ACE2 were investigated, using bioinformatic analysis. Interestingly, zebrafish has ACE2 protein that shares 58 and 72% primary sequence identity and similarity to human ACE2, respectively (Figs. 5A; S2, see “Supplementary Material”). Human ACE2 interacts to the receptor binding domain (RBD) of SARS-CoV-2 Spike protein mainly by polar and salt bridge interactions. Human ACE2 has 22 residues making part of the protein-protein interaction and most of them are located at the N-terminal region of ACE2. 77% of the human ACE2 residues of the interface are similar in zebrafish ACE2 sequence (Figs. 5B; S2, see “Supplementary Material”) suggesting that zebrafish may also binds SARS-CoV-2 Spike protein. The tree-dimensional structure of zebrafish ACE2 based on homology model (Fig. 5D) shows a high structural similarity with human ACE2. Computational analysis of protein-protein interaction using ACE2 and the RBD of SARS-CoV-2 Spike protein reveals similar values of binding free energy suggesting that zebrafish is susceptible to virus infection (Fig. 5c). In our work, we do not expect that rSpike protein interacts with zebrafish ACE2 because rSpike correspond to the N-terminal part of the Spike protein (residues 16–165) that precedes the RBD domain (residues 319–311,541). Real-time, quantitative PCR analysis of several tissues from adult male and female zebrafish showed that ACE2 was predominantly expressed in the brain and muscle of both sexes (Fig. 5e). Although higher levels were seen in the kidney for females, the transcript abundance of ACE2 in this organ was quite variable and showed no significant differences when compared to other tissues (Fig. 5e). Further analysis compared the relative expression of zebrafish ACE2 between male and female for the same tissue. This analysis revealed higher expression of ACE2 in males than females for the following organs: brain, gonads, heart, muscle and adipose tissue (fat body) (Fig. 5E).Fig. 5 In silico analysis of the interaction of the human and zebrafish ACE2 receptor with rSpike protein. (a) Structural alignment between ACE2 of human and zebrafish. For comparison of 3D structures, the FASTA files were converted into PDB files (containing the 3D coordinates of the proteins) using the Raptor X tool (http://raptorx.uchicago.edu). (b) Graphs show the free binding energy in protein-ligand interactions docking analysis and the axis (X) represents the score of 10 (ten) possibilities of interaction between molecule-ligand and the axis (Y) compares the free binding energy it represents per kilocalorie per mol (Kcal/mol). (Kcal/mol). (c) The similarity of ACE2 between human and zebrafish. (d) Protein-protein interaction between human and zebrafish ACE2 and SARS-CoV-2 Spike RBD. (e) Relative expression of ACE2 mRNA in zebrafish adult organs (n = 5 males; n = 5 females). Values represent mean ± SEM. Asterisks indicate a significant difference between male and female; *p < 0.001; p < 0.01; *p < 0.05; NS = not significant. Fig. 5 3.5 The protein-protein interaction prediction among SARS-CoV-2 The protein-protein interaction prediction among the rSpike and zebrafish proteins according to the subcellular location (membrane, cytoplasm, and nucleus) predicted interactions with 2910 proteins for the membrane, 771 proteins for the cytoplasm, and 1134 proteins for the nucleus. For human proteins and rSpike predicted interactions with 1785 proteins for the membrane, 1168 proteins for the cytoplasm, and 1242 proteins for the nucleus (Fig. S1, Fig. S2, see “Supplementary Material”). Considering the most general ontological terms found hierarchically, according to the KEGG and Reactome databases, 71% of the terms identified for zebrafish are identical to those found for human. However, further analysis showed different specific terms with approximately 58% of different specific pathways. Functional enrichment of the biological pathways (zebrafish and human) showed basic processes related mainly to cell growth and death, including regulation of transcription and translation mechanisms, mechanisms of DNA repair or replication, and signaling pathways of p53 and by GPCR, among others. Additionally, we identified the pathways related to signal molecules and interactions, signal transduction, and the immune system (Fig. S2, see “Supplementary Material”). Interestingly, it was recovered through the protein-protein interaction with rSpike, the Toll-like receptor pathway (dre:04620 and hsa:04620). It can allow interaction with the Toll-like receptors TLR1, TLR2, TLR4, and TLR5 and the interferon-α/β receptor (IFNαβR), possibly triggering the activation of various signaling pathways (Fig. S3, see “Supplementary Material”). In this pathway, we observed a possible interaction of the rSpike with the signal transducer and activator of transcription 1-alpha/beta (STAT1) protein in the cytoplasmic region. Additionally, the signal molecules and interaction pathway (zebrafish and human) showed the possibility of rSpike interacting with a considerable number of cell receptors related to the neuroactive ligand receptor (KEGG:4080) and a cytokine-cytokine receptor (Fig. S3, see “Supplementary Material”) and triggering diverse cellular signaling such as the TGF beta signaling family, class I and II helical cytokines, IL and TNF family. In addition, proteins related to the extracellular matrix, cellular communication and motility, formation of vesicles, transport and catabolism, VEGF signaling pathway, and AGE-RAGE signaling pathway in diabetic complications. The possible virus-host protein interactions during the SARS-CoV-2 infection were tested in network analysis based on protein interactions (Fig. S4, see “Supplementary Material”). The important similarity between SARS-CoV-2 proteome and SARS-CoV proteome18 allowed us to hypothesize that the SARS-CoV proteome is highly conserved in SARS-CoV-2. In our network analysis, we were able to detect 29 proteins (Fig. S3, see “Supplementary Material”) A PPI interaction database was assembled, including 7 nodes and 29 interactions. We analyzed the following proteins: Parvalbumin 4 (Pvalb4), Creatine kinase (Ckma), Keratin 5 (Krt5), A kinase anchor protein 1 (Ak1), Malate dehydrogenase (Mdh1aa), 2-phospho-D-glycerate hydro-lyase (Eno3), Component Chromosome 15 (ENSDARG00000095050), Component Chromosome 1 (wu:fk65c09), Component Chromosome 16 (Zgc:114037), Component Chromosome 17 9 Zgc:114046), Component Chromosome 26 (ENSDARG00000088889), Apolipoprotein A-II (Apoa2), Apolipoprotein A-Ib (Apoa1b), Serpin peptidase inhibitor member 7 (Serpina7), Transmembrane serine protease 2 (tmprss2), Fetuin B (fetub), Apolipoprotein A-I (apoa1a), Carboxylic ester hydrolase (ces3), Apolipoprotein Bb (apobb), tandem duplicate 1, Fibrinopeptide A (fga), Serotransferrin (tfa), Apolipoprotein C-I (apoc1), Complement component C9 (c9), Pentaxin (crp), Ceruloplasmin (cp), Hemopexin (hpx), Ba1 protein (ba1), Component Chromosome 13 (ENSDARG00000), and Component Chromosome 25 (ENSDARG0000008912). 4 Discussion Here it was demonstrated, for the first time, that zebrafish injected with rSpike protein, fragment 16 to 165 (rSpike), that corresponds to the N-terminal portion of the protein, showed adverse effects on liver, kidney, nervous and reproduction system, using a series of experiments to validate zebrafish model for toxicological and pre-clinical safety studies with SARS-Cov-2. Interestingly, fish injected with rSpike produced a toxic inflammatory response with similarity to severe cases of COVID-19 in humans (Fig. 2 and Table 1). Histological alterations were analyzed in the liver as mild lobular infiltration by small lymphocytes, centrilobular sinusoidal dilation, patchy necrosis, moderate microvesicular steatosis, mild inflammatory infiltrates in the hepatic lobule, and the portal tract. These changes are similar to those observed in patients with COVID-19 (Tian et al., 2020). Although the zebrafish biochemical liver function was not tested, a three-fold increase in ALT, AST, and GGT levels has been reported during hospitalization for humans. These alterations could be related to the direct cytopathic effect of the virus and could be associated with higher mortality (Jothimani et al., 2020). With respect to the reproductive tissue, female zebrafish injected with rSpike displayed severe damage in the ovary (follicular atresia, cellular infiltration, and disorganized extracellular matrix) after 7 days of protein inoculation. On the other hand, it is remarkable that ovarian damage was reversed after 14 days, when zebrafish received a second injection of rSpike. In humans, there is evidence that ACE2 mRNA is expressed, at low levels, during all stages of follicle maturation in the ovary (Reis et al., 2011), and also in the endometrium (Vaz-Silva et al., 2009). This pattern of ACE2 expression, in line with our observations, could suggest that SARS-CoV-2 affects female fertility in humans and zebrafish. More studies will be necessary to comprehend the molecular mechanisms underlying SARS-CoV-2-induced female infertility and the effects in the ovarian function. To date, damage in the female reproductive system of COVID-19 patients has not been reported yet (Zupin et al., 2020). Other different systems were affected, including the nervous system. In fact, some recent studies have reported that the SARS-CoV-2 may affect the nervous system (Cavalcanti et al., 2020; Iadecola et al., 2020; Lu et al., 2020) as the peripheral nervous system (Lau et al., 2004; Netland et al., 2008; Tian et al., 2020), particularly in the most severe cases of infection (Beghi et al., 2020). In our study, the rSpike was responsible for generating an inflammatory process in the brain (Fig. 4e), characterized by an intense influx of mononuclear cells, but no histopathological lesions, these inflammatory infiltrate findings were confirmed by immunohistochemical analysis. This profile is in line with the clinical reports of COVID-19 associated acute necrotizing myelitis (Sotoca and Rodríguez-Álvarez, 2020), where lymphocytic pleocytosis was observed in the cerebrospinal fluid (CSF). Acute transverse myelitis related to SARS-CoV-2 infection (Munz et al., 2020), where an intense leukocyte infiltrate of monocytic characteristic and elevated protein level was also observed in the CSF. In another report, thrombosis in superficial and deep systems, straight sinus, the vein of Galen, internal cerebral veins. The application of spike in zebrafish's olfactory epithelium causes thrombosis of the deep medullary veins (Cavalcanti et al., 2020). Damage to the structure and function of this system can lead to severe encephalitis, toxic encephalopathy, and, after viral infections, severe acute demyelinating lesions (Wright et al., 2008). In a case study of 4 children with COVID-19, Abdel-Mannan et al. (2020) reported that children with COVID-19 may have late neurological symptoms. According to these findings, a recent work published by Rhea et al. demonstrated that the protein S of SARS-CoV2 is able to cross the blood-brain barrier in experimental murine models (Rhea et al., 2021). More interestingly, the work demonstrated that the phenomenon is mediated by the expression of ACE-2 in the cerebral microvasculature, and its transport to the brain parenchyma is via transcytosis. Therefore, these findings corroborate the hypothesis that not only does SARS-CoV2 have neurotropism, but that the Spike protein and the S1 protein of SARS-CoV-2 crosses the blood-brain barrier in mice. Future studies with zebrafish might provide more information about the virus damage in the nervous system. Nonetheless, these alterations in the nervous system did not reflect in an alteration in the baseline locomotor behavior of adult zebrafish females (Fig. 2). Indeed, the observed alterations in the nervous system in the present study did not impair the perception and the response to a co-specific alarm substance (chemical communication that triggers anti-predatory behavior in fish). Basically, both the control and the infected group triggered all the anti-predatory behaviors considered standards of the species after the application of the alarm substance in the aquariums (Fig. 2, Fig. 3). A recent study (Kraus et al., 2020) showed that the application of spike in the olfactory epithelium of zebrafish causes damage to the olfactory epithelium in the period immediately after application, up to 5 days. The fish in that study did not respond to chemical stimuli from food and bile, 3 h after infection and 1 day after. One possible explanation to this divergence between present results and Kraus et al. (2020), is the time that fish were tested after spike infection. We did the behavioral tests 7 days after spike injection, so, we may have caught the regeneration phase of this tissue. Another possible explanation for the difference in the results between these studies, is the difference between the chemical stimuli used, Kraus et al. (2020) used food and bile as chemical stimuli, and in the present work, it was used an AS obtained from the skin damaged fishes, mimetizing the natural phenomena, where damaged fishes released AS, inducing fear and anti-predatory responses in other neighboring fish that perceive the signal (Speedie and Gerlai, 2008). Anti-predatory response is so evolutionarily conserved that even with the damaged olfactory epithelium (as well as the ability to capture very little of the alarm substance's stimulus), a low uptake of stimulation of the alarm substance is enough to trigger an anti-predatory response. Thus, the spike can impair the perception of more specific chemical signals including food and bile, as shown by Kraus et al. (2020), but not when these signals trigger a response to a life or death threat, as in the case of the alarm substance. Another possibility is that physical limitation of olfactory tissue in zebrafish impairs COVID protein biding to target host cells but allows protein fragments to affect the host cells. Comparing protein versus fragments effect in zebrafish, we hypothesize that, when an organism is injected with SARS-CoV-2, the virus releases fragment(s) of the spike protein that can target host cells for eliciting cell signaling without the rest of the viral components. Thus, zebrafish subjected to the intact virus infecting the host cells for the replication and amplification as well as the spike protein fragments that are capable of affecting the host cells. It was hypothesized that cell signaling elicited by the spike protein fragments that occur in cells would predispose injected individuals to develop complications that are seen in severe and fatal COVID-19 conditions. If this hypothesis is correct, then the strategies to treat COVID-19 should include, in addition to agents that inhibit the viral replication, therapeutics that inhibit the viral protein fragment-mediated cell signaling. In the sequence of these experimental findings, the in silico analysis showed that zebrafish ACE2 receptor has the same potential for protein-ligand interaction as in humans (Fig. 5). We show in silico and in vivo that the zebrafish ACE2 receptor is susceptible to the rSpike and interacts similarly to the human ACE2 receptor. The importance of ACE2 receptor for SARS-CoV-2 infection and its role in vaccine studies is shown in research with transgenic mice (HFH4-hACE2 in C3B6 mice) (Jiang et al., 2020). The use of perpetuity ACE2 receptor by SARS-CoV-2 in the attachment and infection of the host cells has been well postulated in mammals, except for murines, and some birds, such as pigeons (Qiu et al., 2020). The ACE-2 orthologue studies in non-mammalian animals, including zebrafish, suggest the potential to unveil the role of this enzyme and its use for therapeutic purposes (Chou et al., 2006). 5 Conclusions Our studies reveled that zebrafish showed inflammatory reaction to SARS-CoV-2 rSpike protein which provoked damage to organs (liver, kidney, ovaries and brain) in a similar pattern as happen in severe cases of COVID-19 in humans and resulted in 78,6% of survival rate in female adults during the first seven days. The application of spike protein in zebrafish was highly toxic that is suitable for future studies to gather valuable information about ecotoxicological impacts, as well as vaccine responses and therapeutic approaches in human medicine. Therefore, besides representing an important tool to assess the harmful effects of SARS-CoV-2 in the aquatic environment, we present the zebrafish as an animal model for translational COVID-19 research. The following are the supplementary data related to this article.Fig. S1 Biological pathways enriched with proteins found from protein-protein interaction prediction with SARS-CoV-2 fragment. Fig. S1 Fig. S2 Schematic representation of the Toll-like receptor pathway and cytokine-cytokine receptor interaction. Biological pathway recovered through functional enrichment of proteins interacting with the SARS-CoV-2 fragment. On the scale red (N) are proteins representing the nucleus, dark orange (NM) are proteins identified in the nucleus and membrane, light orange (NC) are proteins identified in the nucleus and cytoplasm, yellow (CMN) are proteins identified in cytoplasm, membrane and nucleus, yellow greenish (M) are proteins identified in membrane, dark blue (CM) are proteins identified in cytoplasm and membrane and in blue (C) are proteins identified in cytoplasm. Pathways adapted from KEGG. Fig. S2 Fig. S3 Protein-protein interaction with rSpike, the Toll-like receptor pathway (dre:04620 and hsa:04620). It can allow interaction with the Toll-like receptors TLR1, TLR2, TLR4, and TLR5 and the interferon-α/β receptor (IFNαβR), possibly triggering the activation of various signaling pathways. Fig. S3 Fig. S4 (a and b) Protein interaction network in zebrafish blood plasma. The strongest interactions are exemplified by thicker lines and the weakest are shown by dotted lines. (a) The proteins in red belong to the blood coagulation cascade and also to the immune system pathway. The green proteins are those involved in the structural and chromosome components. The STRING software was used to analyze the protein network and Kyoto Encyclopedia at Genes and Genomes (KEGG) tool to detect protein-protein association. Pvalb4, Parvalbumin 4; Ckma, Creatine kinase; Krt5, Keratin 5; Ak1, A kinase (PRKA) anchor protein 1; Mdh1aa, Malate dehydrogenase; 24 Eno3, 2-phospho-D-glycerate hydro-lyase; ENSDARG00000095050, Component Chromosome 15; wu:fk65c09, Component Chromosome 1; Zgc:114037, Component Chromosome 16; Zgc:114046, Component Chromosome 17; ENSDARG00000088889, Component Chromosome 26; Apoa2, Apolipoprotein A-II; Apoa1b, Apolipoprotein A879 Ib; Serpina7, Serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), member 7; Tmprss2, Transmembrane serine protease 2; Fetub, Fetuin B; Apoa1a, Apolipoprotein A-I; Ces3, Carboxylic ester hydrolase; Apobb, Apolipoprotein Bb, tandem duplicate 1; Fga, Fibrinopeptide A; Tfa, Serotransferrin; Apoc1, Apolipoprotein C-I; C9, Complement component C9; Crp, Pentaxin; Cp, Ceruloplasmin; Hpx, Hemopexin; Ba1, Ba1 protein; ENSDARG00000, Component Chromosome 13; and ENSDARG0000008912, Component Chromosome 25. Fig. S4 Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgment This work was supported by 10.13039/501100001807 São Paulo Research Foundation (FAPESP 2020/05761-3), Brazilian National Research Council (10.13039/501100003593 CNPq ) (426531/2018-3) and Instituto Federal Goiano for the financial support Malafaia G. holds productivity scholarship from 10.13039/501100003593 CNPq (307743/2018-7). Financial and material support was provided through the São Paulo Research 526 Foundation (10.13039/501100001807 FAPESP ) granted to: Ives Charlie: Fapesp #2018/07098-0; 2019/19939-1; 527 Cristiane Rodrigues Guzzo: Fapesp #2019/00195-2, 2020/04680-0; Chuck Farah: Fapesp #2017/17303-7; Germán G. Sgro: Fapesp #2014/04294-1; Edgar E. Llontop: Fapesp #2019/12234-2; Natalia F. Bueno: Fapesp #2019/18356-2; Camila G. Bomfim: Fapesp #2019/21739-0. LJGB is supported by a research fellowship from 10.13039/501100003593 Conselho Nacional de Desenvolvimento Científico e Tecnológico , Brazil (CNPq) 303263/2018-0 and FIFG has a PhD fellowship from 10.13039/501100001807 FAPESP (2019/14285-3). Rede Virus 10.13039/501100003545 MCTI (grant FINEP 0459/20), We would like to thank the Medical School Foundation for financial support (Project CG 19,110). We would also like to thank the entire organizing team of the Global Virtual Hackathon 2020 for the award our team received and the support from the Ministry of Transport, Communications and High Technologies of the Republic of Azerbaijan, the United Nations Development Program, and the SUP.VC Acceleration Center. Authors are also thankful to Sartorius for technical support in this work. ==== Refs References Abdel-Mannan O. Eyre M. Löbel U. Bamford A. Eltze C. Hameed B. 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==== Front 0410462 6011 Nature Nature Nature 0028-0836 1476-4687 33361818 10.1038/s41586-020-03074-x nihpa1646145 Article IspH inhibitors kill Gram-negative bacteria and mobilize immune clearance Singh Kumar Sachin 1 Sharma Rishabh 1 Reddy Poli Adi Narayana 2 Vonteddu Prashanthi 1 Good Madeline 2 Sundarrajan Anjana 1 Choi Hyeree 1 Muthumani Kar 1 Kossenkov Andrew 3 Goldman Aaron R. 4 Tang Hsin-Yao 4 Totrov Maxim 5 Cassel Joel 6 Murphy Maureen E. 2 Somasundaram Rajasekharan 2 Herlyn Meenhard 2 Salvino Joseph M. 26* Dotiwala Farokh 1* 1 Vaccine and Immunotherapy Center, The Wistar Institute, Philadelphia, PA 19104, USA. 2 Program in Molecular and Cellular Oncogenesis, The Wistar Institute, Philadelphia, PA 19104, USA. 3 Bioinformatics Facility, The Wistar Institute, Philadelphia, Pennsylvania 19104, USA. 4 Proteomics & Metabolomics Facility, The Wistar Institute, Philadelphia, Pennsylvania 19104, USA 5 Molsoft, San Diego, California, USA. 6 Molecular Screening & Protein Expression Facility, The Wistar Institute, Philadelphia, PA 19104, USA * Corresponding authors. [email protected], [email protected] Author contributions FD conceived the study and planned the experiments. MT setup the atomic field property of IspH catalytic site and performed molecular docking. KS, RS, PV and AS purified the proteins, performed the biochemical activity assays, bacterial killing experiments, mouse infection studies and contributed to the preparation of the manuscript. PV performed flow cytometry and microscopy. KS performed the electron microscopy studies with assistance from the UPenn EM core. AR and HYT ran the samples for proteomics and small molecule studies. AK and RS performed the bioinformatics and pathway analysis on proteomics and helped illustrate it in a figure. JC performed the surface plasmon studies. JMS planned the synthesis of DAIA prodrugs and PANR synthesized them. HC, KM, RSS and MH provided Hu-mice. MB and MEM performed the seahorse experiments. FD and JMS analyzed the data. FD generated the figures and drafted the manuscript. JMS and PANR provided reagents and expertise. All authors provided critical revisions. 31 12 2020 1 2021 23 12 2020 20 1 2022 589 7843 597602 Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms Isoprenoids are vital to all organisms in supporting core functions of life, like respiration and membrane stability.1 IspH, an enzyme in the methyl erythritol phosphate pathway of isoprenoid synthesis, is essential to gram-negative bacteria, mycobacteria and apicomplexans.2,3 The IspH substrate, HMBPP, is not produced in humans and other metazoans and activates cytotoxic Vγ9Vδ2 T-cells in humans and primates at extremely low concentrations.4-6 We describe novel IspH inhibitors and through structure-guided analog design, refine their potency to nanomolar levels. We have modified these into prodrugs for delivery into bacteria and report that they kill clinical isolates of several multidrug resistant bacterial species such as Acinetobacter, Pseudomonas, Klebsiella, Enterobacter, Vibrio, Shigella, Salmonella, Yersinia, Mycobacterium and Bacillus, while being relatively non-toxic to mammalian cells. Proteomic analysis reveals that bacteria treated with prodrugs resemble those with conditional IspH knockdown. Notably, these prodrugs also cause expansion and activation of human Vγ9Vδ2 T-cells in a humanized mouse model of bacterial infection. These IspH prodrugs synergize direct antibiotic killing with a simultaneous rapid immune response by cytotoxic γδ T-cells, which may limit the rise of antibiotic resistant bacterial populations. ==== Body pmcAs a first line of defense, innate immune cells such as monocytes/macrophages and dendritic cells phagocytose bacteria and present the bacterial antigens on their cell surface using the major histocompatibility complex (MHC).7 MHC-antigen presentation initiates the adaptive T and B-cell immune response that clears the infected host-cells and bacteria within in 6-30 days.8 Antibiotics, prevent bacteria from overwhelming the host body while the combined immune responses clear the bacterial infection. A group of 6 bacteria is the leading cause of multidrug resistant (MDR) nosocomial infections; the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species).9 In addition, MDR species of Mycobacterium tuberculosis (MTB) and Plasmodium falciparum (Pf) are also global public health threats.10,11 Rare mutations and acquisition of antibiotic resistance genetic elements give rise to bacterial cells that resist antibiotics via antibiotic target modification, secretion of inactivating enzymes, drug efflux pumps and metabolic bypass.12-14 We reported that NK and cytotoxic T-cells deliver granzymes (Gzm) within bacteria or protozoan parasites, disrupt multiple essential systems, and induce programmed pathogen death called “microptosis”.15-17 Bacteria undergoing microptosis do not develop resistance.16 However, the ESKAPE pathogens, MTB and Pf, evade antigen presentation by killing antigen presenting cells (APC), preventing phago-lysosomal fusion or by segregating themselves in different APC compartments.18 Also, some antibiotics impair immune cell functions.19 We pioneered a novel, double-pronged antimicrobial strategy: dual-acting immuno-antibiotics (DAIAs).20,21 We focus on the methyl-D-erythritol phosphate (MEP) pathway for isoprenoid biosynthesis, which is essential for survival of most gram-negative bacteria, and apicomplexans (malaria parasites) (Fig. 1a) but is absent in humans and other metazoans.2,3 The first line of attack in the DAIA strategy targets the MEP enzyme IspH, which metabolizes (E)-4-hydroxy-3-methyl-but-2-enyl pyrophosphate (HMBPP) into isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP). IPP and DMAPP are building blocks for downstream terpenoids, essential for protein prenylation, peptidoglycan cell wall synthesis and production of quinones for respiration.22,23 The (ΔispH) Ec strain, CGSC 8074, conditionally expresses Ec IspH in the presence of 0.5% arabinose; adding glucose to the media shuts down IspH expression and kills this strain (Fig. 1b).3 This causes buildup of HMBPP, a bacterial pathogen associated molecular pattern (PAMP), which stimulates the Vγ9Vδ2 T-cells to expand and produce cytotoxic proteins perforin (Pfn), granulysin (GNLY) and Gzm important for microptosis.4-6 We demonstrated this second line of DAIA attack using human PBMCs treated with HMBPP + IL2 or infected with CGSC8074 in the presence of glucose. Both these conditions caused greater expansion of γδ T-cells (Fig. 1c top panel) and higher levels of cytotoxic proteins Pfn and GzmA (Fig. 1c middle panel) and T-cell surface activation markers CD69 and HLA-DR (Fig. 1c bottom panels) than PBMCs infected with WT (BL21) Ec. Therefore, an IspH inhibitor will kill bacteria directly, like other antibiotics, and also kill persistent bacteria by microptosis. 15,16,24,25 Molecular docking & biochemical activity We purified recombinant IspH proteins from several bacterial species: E. coli (Ec), MTB, Pseudomonas aeruginosa (Pa) and the malaria parasite Pf (Extended Data Fig. 1a & b). IspH activity is coupled to a system that reduces the oxidized [4Fe-4S]2+ cluster.26,27 In vitro, reduction can be achieved chemically with sodium dithionite (DT)-reduced methyl viologen (MV) (Extended Data Fig. 1c), and IspH activity is measured from the proportional change in the UV absorbance of oxidized MV (398nm).28 We determined that the optimal concentrations were 50 nM IspH and 1mM HMBPP, with optimal reaction time of 30 min for the MV assay (Fig. 1d-f and Extended Data Fig. 1d & e). We similarly measured the activities of purified recombinant Pf, Pa and MTB IspH (Fig. 1g). We next performed a molecular docking study using the Protein Data Bank (PDB) 3ke8 crystal structure of E. coli IspH.29 The HMBPP binding pocket was modelled (Methods) and the Atomic Property Field (APF) established (Extended Data Fig. 2a) for automated molecular docking of 9.6 million compounds. The 168 best scoring compounds (Extended Data Fig. 2b) were visually compared to HMBPP. The top 24 (C1-24) compounds with lower binding energies and APF scores (Extended Data Fig. 2c) were further evaluated according to their chemical and drug-like properties as well as three-dimensional conformations of the docked ligand–IspH complex (Extended Data Fig. 3a and Supplementary Fig. 2a). Testing by MV assays revealed C10, C17 and C23 as the best inhibitors of Ec-IspH with IC50s of 9μM, 4nM and 85nM respectively (Fig. 2a & Supplementary Table 1). C17 and C23 were more stable inhibitors of Ec, MTB, Pa and Pf IspH than C10 at different timepoints (Fig. 2b and Extended Data Fig. 3b). Although both C17 and C23 were potent inhibitors of different IspH (Fig. 2c & Supplementary Table 2), we tested several analogs of C10, C17 and C23 to improve their potency against purified Ec-IspH (Fig. 2d, Supplementary Table 3, Extended Data Fig. 3c-e & Supplementary Fig. 2b-d). C23 analogs showed significant improvement (lower IC50) in Ec-IspH inhibition over the parent compound, while C10 and C17 analogs did not. C23.20 and C23.21, the two most potent C23 analogs, show improved binding (lower KD) to purified Ec-IspH by surface plasmon resonance (SPR), compared to the IspH substrate HMBPP (Extended Data Fig. 4a). By testing different C23 analogs, we established a structure activity relationship (Fig. 2d and Extended Data Figs. 3e & 4b) and discovered C23.07, C23.20, C23.21, C23.28 and C23.47 as the most potent inhibitors of Ec-IspH. Bacterial killing by prodrugs C23 and its analogs were not bacteria permeable so we coupled them to triphenyl phosphonium which aids in the penetration of membranes.30 However, since cations are efficiently effluxed out of Gram-negative bacteria by transporters such as AcrAB-TolC in Ec, we designed pro-drugs which would release the negatively charged IspH inhibitor once inside the bacteria. We synthesized ester prodrugs from the C23.47 analog by linking it to a lipophilic cation : 6-hydroxyhexyl triphenyl phosphonium bromide (TPP), a lipophilic alcohol: ethanol (EA) or a basic amine: dimethylamino propanol (DAP) (Supplementary Fig. 3). Similar strategies using prodrugs with cleavable ester bonds facilitate drug delivery into bacteria.31 We found that the C23.47+TPP ester was the most potent at killing Ec (MIC90 = 4 μM) (Extended Data Fig. 4c & d). Therefore, we focused on the TPP ester form of C23 analogs (Supplementary Fig. 2e). C23.20+TPP, C23.21+TPP and C23.28+TPP were best at killing Ec (MIC90 < 4 μM) (Fig. 2e). Using mass spectrometry on lysates of prodrug treated bacteria, we detected both the delivery of prodrug molecule C23.28+TPP into Ec and its subsequent cleavage into C23.28 and TPP (Extended Data Fig. 4e & f). Notably, the inhibition of Ec-IspH by C23.28 prevented the conversion of HMBPP to DMAPP/IPP, while TPP treatment had no effect on the process (Extended Data Fig. 4g & h). The IspH protein levels in the E. coli strain CGSC8074 can be regulated by changing arabinose levels in culture medium (Extended Data Fig. 5a). Increasing IspH levels in this manner increased the dose of C23.28-TPP required to kill CGSC8074 (Extended Data Fig. 5b & c). We tested several derivatives on drug resistant clinical isolates of Vibrio cholerae (Vc) by the resazurin assay and by colony forming unit (CFU) assay and determined the minimum prodrug concentrations required to kill 90% of bacterial isolates (MIC90; Extended Data Fig. 5d-f). While TPP alone did not kill Vc, prodrugs C23.20-TPP, C23.21-TPP and C23.28-TPP showed MIC90 of 16μM (8μg/ml) followed by C23.07-TPP at 125μM (63μg/ml) and C23.47-TPP at 63μM (31μg/ml). MIC90 for several species of antibiotic resistant bacteria are shown in Supplementary Table 4. In sum, the IspH inhibitor prodrugs had lower MIC90 against multidrug resistant clinical isolates of E. aerogenes, A. baumanii, P. aeruginosa, V. cholerae and K. pneumoniae than the current best-in-class antibiotics like Meropenem (Carbapenems), Amikacin & Tobramycin (Aminoglycosides), Ciprofloxacin (Fluoroquinolones), Ceftriaxone, Cefepime and Ceftaroline (3rd, 4th and 5th generation Cephalosporins) (Fig. 3 and Supplementary Table 5). Specificity mechanism and toxicity Isoprenoids are required in gram-negative bacteria and MTB for respiration and cell wall synthesis.23,32 Using a Seahorse XF analyzer we showed that prodrug-treated Ec show a significant drop in oxygen consumption rate (OCR- aerobic respiration) and extracellular acidification rate (ECAR- glycolysis) (Extended Data Fig. 6a & b). This was accompanied by elevated superoxide and hydrogen peroxide (Extended Data Fig. 6c & d).16 Prodrug treated bacteria lost their membrane integrity (SYTO9/PI) and membrane potential in a dose dependent manner, while TPP had no effect (Extended Data Fig. 6e-h). Scanning and transmission electron micrographs (SEM/TEM) showed spherocyte formation, cell membrane protrusions, and defects in cell wall and periplasm of prodrug treated Ec or Vc, and in conditional IspH knockdown (strain CGSC8074) (Extended Data Fig. 6i & j). Half-lives (t1/2) for prodrugs C23.28-TPP and C23.21-TPP were 40 & 56 min in human plasma, 218 & 245 min in pig plasma and 20 & 21 min in mouse plasma (Extended Data Fig. 7a). Similarly, their t1/2 in presence of liver microsomes were 27 & 48 min (human), 25 & 24 min (monkey) and 24 & 41 min (mouse) respectively (Extended Data Fig. 7b). The disappearance of the prodrug forms coincided with the appearance of the respective parent drugs. Although our prodrugs showed low toxicity in mammalian cell lines HepG2, RAW264.7 and Vero (Extended Data Fig. 7c), lipophilic triphenylphosphonium cations are reported to cause mitochondrial proton leak and toxicity in C2C12 myoblasts.33 Further, the human hERG gene is a known target for lipophilic cations like TPP.34 Importantly, our 6-hydroxyhexyl TPP carrier and our prodrugs were neither toxic to C2C12 cells nor caused loss of mitochondrial membrane potential (Extended Data Fig. 7d & e). Additionally, C23.28-TPP, methyl TPP (Me-TPP) and our carrier molecule 6-hydroxyhexyl triphenyl phosphonium bromide (6-hh-TPP) showed ten-fold higher (5-10μM) IC50s in hERG electrophysiological profiling using an automated QPatch HTX assay, compared to verapamil (Extended Data Fig. 7f). We were surprised to find that the prodrug C23.28-TPP reduces IspH levels in Ec and clinical isolates of several antibiotic resistant bacteria (Extended Data Fig. 8a & b). We next performed proteomics on Ec treated with IspH inhibitor prodrug C23.28-TPP and on CGSC8074 (ΔispH) in the presence of glucose. 525 of 2350 proteins showed similar changes on both C23.28-TPP treatment and IspH knockdown (Extended Data Fig. 8c & d). Among the down-regulated proteins, 323 (22%) were common to drug treatment and conditional IspH knockdown (Extended Data Fig. 8e) Pathway analysis35 showed enrichment of electron transport chain (ETC/Ubiquinone) and other pathways (Extended Data Fig. 8f-h and 9). Dual action leads to γδ response Activation of human γδ T-cells does not require epitope presentation by MHC or CD1 receptors. Instead, the Butyrophilin receptors BTN2A1 and BTN3A1 on target cells act to detect phosphoantigens like HMBPP, 36,37 and as a direct ligand for the Vγ9Vδ2 TCR, respectively.38,39 Prodrug-treated Ec activated Vγ9Vδ2 T-cells within 24-48 h (Fig. 1c and Extended Data Fig. 10a), with the activated cells showing high levels of cytotoxic markers such as Pfn and GNLY, as well as high levels of the T-cell surface activation markers CD69 and HLA-DR. We observed similar results with Mycobacterium smegmatis or Vc infected PBMCs treated with prodrug (Extended Data Fig. 10b). In contrast, Kanamycin treated and TPP treated samples did not show γδ T-cell activation. While Ec and Vc were resistant to Kanamycin, our prodrug C23.07-TPP could effectively kill them both (Extended Data Fig. 10c). To assess resistance against IspH inhibitors we grew clinical isolates of Vc and Klebsiella pneumoniae (Kp) for 18 serial passages with the prodrug C23.28-TPP in the presence or absence of human PBMCs. To demonstrate the critical role for Vγ9Vδ2 T cell activation / expansion to prodrug efficacy, PBMCs depleted for γδ T cells were also used in the serial passaging. The efficacy of γδ T cell depletion is reflected in the lack of Vγ9Vδ2 T cell expansion after 6 days of treatment with HMBPP and IL-15 (Extended Data Fig. 10d). In the absence of PBMCs, both Vc and Kp developed resistance to our prodrug as well as to conventional antibiotics (Vc - Hygromycin, Kp - Streptomycin) (Extended Data Fig. 10e & f top panels). However, in the presence of human PBMCs neither Vc nor Kp developed resistance to C23.28-TPP (Extended Data Fig. 10e & f bottom panels). Passaging Vc and Kp in γδ T-cell depleted human PBMCs significantly diminished the dual action of the prodrug, supporting the relevance of γδ T cells. Due to the lack of reliable in vivo γδ depleting antibodies, we used E. coli infection in NSG mice (instead of humanized mice) to corroborate the in vivo efficacy of Vγ9Vδ2 T cell dual action. We injected one group of NSG mice with human PBMCs and another group with ex-vivo γδ T cell depleted human PBMCs. These mice were infected with 107 E. coli (Fig. 4a-c) and their γδ T cell levels were monitored by FACS. After the infection both depleted and undepleted groups were given suboptimal doses (1 mg/kg) of C23.28-TPP to minimize bacterial killing by direct antibiotic action and bring the dual-action immune effect to the forefront. Mice with γδ T cell depleted PBMCs showed 2-10-fold higher CFU (Fig. 4a) and significantly lower levels of γδ T cells (Fig. 4b, c) than their counterparts with undepleted PBMCs. As a final test, we assessed the direct bactericidal effects of IspH prodrugs in Vc infected C57Bl/6mice. Prodrug treated mice showed significantly lower mortality and lower bacterial load in all organs tested, compared to those treated with TPP alone (Extended Data Fig. 10g & h). Since mouse γδ T-cells do not respond to HMBPP 40,41, we used humanized (Hu) mice to test the dual action of IspH prodrugs. Hu-mice injected with HMBPP showed rapid expansion of the human Vγ9Vδ2 T-cells but not the αβ T-cells (Extended Data Fig. 10i). Ec infected and prodrug treated Hu-mice showed lower bacterial CFU in circulation and improved survival than mice treated with TPP (Fig. 4d & e). Similarly, prodrug treated Hu-mice showed significantly lower bacterial load and expansion of Vγ9Vδ2 T-cells in several organs than their TPP treated counterparts (Fig. 4f & g). We corroborated both the expansion of Vγ9Vδ2 + T-cells and lower bacterial burden in the tissues of prodrug treated humanized mice by immunofluorescence microscopy (Extended Data Fig. 11). Lastly, we observed that our prodrug C23.28-TPP cleared the infection by a clinically isolated MDR strain of Enterobacter aerogenes (UCI 15), and significantly improved the survival of infected BALBc mice, while the current best-in-class antibiotic Meropenem, did not (Fig. 4h & i). Discussion This new family of antibiotics and novel antimicrobial strategy synergizes direct antibiotic action with rapid immune response as a built-in mechanism that may delay the emergence of drug resistance. 15-17 Our prodrugs are bacteria permeable and kill several species of multidrug resistant bacteria better than the best-in-class antibiotics. Our prodrugs act specifically on IspH, show low mammalian cell toxicity specifically in myoblasts (10-100 times higher than the MIC90 than bacteria) and high IC50 against hERG channels.33 Unlike natural antibiotics, no IspH inhibitors have been discovered in any microorganisms making it less likely that any resistance mechanisms have evolved specifically against our DAIA prodrugs, for e.g., β-lactamases and macrolide esterases. Future experiments on DAIA should investigate the potential mechanisms of resistance against IspH inhibitors. The synergy between DAIA activated γδ T-cells and other immune cells merits further study. Methods: Molecular docking studies Preparation of the Binding Site: The X-Ray crystal structure of the IspH: HMBPP-complex with PDB code 3KE8 was used for the virtual screening. The protein was prepared using standard automated protocols embedded in MolSoft’s (Internal Coordinate Mechanics) ICM-Pro software.42,43 Hydrogen atoms were added to the structure, and considerations were made regarding correct orientation of Asn and Gln side-chains, ligand and protein charges, histidine orientation and protonation state and any crystallographic quality flags such as high b-factors or low occupancy. All waters and het atoms were deleted except for the iron/sulfur complex. Structure-Based Virtual Screening Virtual screening of the MolCart chemical database (see http://www.molsoft.com/screening.html version 2017 ~9.6M) chemicals was undertaken using MolSoft’s ICM-VLS software.44,45 The binding site was represented by five types of interaction potential docking maps were created: (i) van der Waals potential for a hydrogen atom probe; (ii) van der Waals potential for a heavy-atom probe (generic carbon of 1.7 Å radius; (iii) optimized electrostatic term; (iv) hydrophobic terms; and (v) loan-pair-based potential, which reflects directional preferences in hydrogen bonding. The energy terms are based on the all-atom vacuum force field ECEPP/3 and conformational sampling is based on the ICM Biased Probability Monte Carlo (BPMC) procedure.43 This method randomly selects a conformation in the internal coordinate space and then makes a step to a new random position independent of the previous one but according to a predefined continuous probability distribution followed by local minimization. A hitlist of 37849 chemicals was obtained and this was filtered down to a set of 168 chemicals recommended for experimental testing using the following criteria: 1) low van der Waals interaction energy; 2) low ICM docking score; 3) 3D Atomic Property Field (APF) pharmacophore similarity to the substrate46 and 4) Number of hydrogen bon acceptors in the phosphate binding region. Bacteria E. coli BL21(DE3) from New England Biolabs was used as a model strain. Clinical isolates of Enterobacter aerogenes (CRE) (UCI 15), Klebsiella pneumoniae 1.53 (ST147+, CTX-M15+), Salmonella enterica typhimurium (LT2 – SL7207), Vibrio cholerae (M045), Acinetobacter baumannii (BC-5), Acinetobacter baumannii (AB5075-UW), Pseudomonas aeruginosa (PA14 & MRSN 5524), Helicobacter pylori (Hp CPY6081), Shigella flexneri (2457T), Bacillus sphaericus (CCM 2177), Mycobacterium tuberculosis (MTB-H37Ra), and Yersinia pestis (KIM 10+) were obtained from BEI Resources. The conditional IspH knockdown E. coli strain CGSC 8074 (ΔispH), was obtained from the Coli Genetic Stock Center at Yale University. All strains were cultured at 37°C in their respective media (2.5% brain heart infusion agar, Middlebrook 7H10 with OADC, Luria Bertani (LB), tryptic soy agar, 5% blood agar, Columbia agar (BD Difco Cat. # BD 241830, BD 262710, BD 244610, BD 236950 and Fisher Cat. # R01217, R02030) based on the vendor recommendation. LB medium with 0.5% arabinose (Sigma Cat # A3256) was used to culture the CGSC8074 (ΔispH) strain. Changing arabinose and glucose concentrations (0.5- 0.05%) in the LB medium allowed us to modulate IspH protein levels in CGSC8074. For testing the antibiotic sensitivity, bacteria were grown in RPMI medium containing 10% fetal bovine or human serum. Animal Models All studies were carried out in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health (NIH). All animal experiments were performed according to protocols approved by the Wistar Institute’s Institutional Animal Care and User Committee (IACUC). The Hu-mice were generated by Rajasekharan Somasundaram in Herlyn lab and transferred over to Dotiwala lab. NOD/LtSscidIL2Rgnull (NSG) mice were inbred at The Wistar Institute under license from the Jackson Laboratory. For humanization, fetal liver and thymus were obtained from the same donor (18-22 weeks of gestation). Female NSG mice (6 to 8 weeks) received thymus graft (1 mm3) in sub-renal capsule 24 h post myeloablation using Busulfan (30 mg/kg, i.p.; Sigma-Aldrich Cat. # B2635). This is immediately followed by injection of autologous liver-derived CD34+ hematopoietic stem cells (105 cells/mouse, i.v.;) that was magnetically sorted by microbeads conjugated with anti-human CD34 (Miltenyi, [130-046-703], Auburn, CA.47 Six to 8 weeks (>50 days) later, presence of human immune cells was monitored by multi-color flow cytometry using 18 color BD LSR II Analyzer (BD Biosciences).48 NSG mice with human PBMCs were generated by i.v. injection of human PBMC or PBMC depleted of all γδ T cells using Anti-TCRγ/δ Microbead Kit (Miltenyi Cat# 130-050-701). About 107 cells/mouse were injected every three days for a total of 3 doses /mouse and presence of human immune cells was monitored by multi-color flow cytometry. An equal number of male and female C57Bl/6 or BALBc mice were obtained from JAX labs and used for mouse model of Vibrio or Enterobacter infection respectively. Mice were housed in plastic cages with ad libitum diet and maintained with a 12-hr light/12-hr dark cycle at 22°C and 60% humidity. Controls and experimental groups were age and genotype-matched non-littermates. Both initial infection and drug treatments were administered by intra-peritoneal (i.p.) or intra-venous (i.v.)routes. Infected mice were monitored twice daily for survival and distress. To monitor bacteremia, mice were bled daily from tail nicks. At the end of the experiment mice were euthanized by CO2 inhalation and their spleens, livers, kidneys, lungs and brains harvested for CFU and flowcytometry analysis. Human Samples Human peripheral blood mononuclear cells (PBMC) were obtained from the Human Immunology Core of the University of Pennsylvania (UPenn) under UPenn protocol 705906 (PI: Riley) “Pre-clinical studies of the Human Immune System”. The donors of the PBMC have provided informed written consent for the use of their samples. De-identified specimens were transferred to Wistar under Wistar protocol 21906321, reviewed and approved by the Wistar Institutional Review Board. PBMC were washed in PBS counted and kept in plastic culture plates in RPMI medium containing 10% human serum. Human cell lines (HepG2, Vero, RAW264.7 and C2C12) were obtained from ATCC, authenticated by STR profiling and PCR assays with species-specific primers and were confirmed to be free of mycoplasma contamination. Antibodies used Antibodies for WB and IHC: (dilution: primary ab-1:50, secondary ab-1:200) Anti-E. coli antibody Abcam ab137967 Anti-E. coli IspH rabbit polyclonal antibody Genscript, generated in this study (dilution 1:100,000) Anti-E.coli RNA Sigma 70 mouse antibody Bio Legend, Cat # 663208 Secondary- Biotinylated rabbit anti-Rat IgG Vector Laboratories Cat# BA-4001 Mouse IgG HRP linked whole antibody GE Healthcare Cat # NA931V Rabbit IgG HRP linked whole antibody GE healthcare Cat # NA934V Biotinylated Goat Anti-Rabbit IgG Antibody Vector Laboratories Cat # BA-1000 Donkey anti-rabbit IgG AF-488 BioLegend Cat# 406416 Antibodies for FACS: (dilution 1:100) Anti-CD3- PerCP-Cy5.5 (clone UCHT1, BD Biosciences, Cat # 560835) Anti-CD4-Alexa Fluor 700 (clone RPA-T4, BD Biosciences, Cat # 557922) Anti-CD8a-Brilliant Violet 711 (clone RPA-T8, Bio Legend, Cat # 301044) Anti-TCR vg9-FITC (clone 7A5, Invitrogen, Cat # TCR2720) [or Anti-TCR vd2 (clone B6, Bio Legend, Cat #331402) with anti-mouse IgG-AF647 (Invitrogen, Cat # A21236)] Anti-CD107a(LAMP-1)- Brilliant Violet 510 (clone H4A3, Bio Legend, Cat # 328632) Anti-CD69- PE/Cy7 (clone FN50, BD Biosciences, Cat # 557745) Anti-HLA-DR-Brilliant Violet 421 (clone L243, Bio Legend, Cat # 307636) Anti-CD38- Brilliant Violet 510 (clone HIT2, BD Biosciences, Cat # 563251) Anti-CD25- Alexa Fluor 647 (clone BC96, Bio Legend, Cat # 302618) Antibodies for FACS compensation: (dilution 1:200) Anti CD3 Mouse Monoclonal PE/Dazzle 594 BioLegend Cat # 317346 Anti CD3 Mouse Monoclonal APC BioLegend Cat # 300412 Anti CD3 Mouse Monoclonal APC Cy7 BioLegend Cat # 300317 Anti CD3 Mouse Monoclonal BV711 BioLegend Cat # 344838 Anti CD3 Mouse Monoclonal PE BioLegend Cat # 300408 Anti CD3 Mouse Monoclonal PE Cy7 BioLegend Cat # 300316 Anti-Ec IspH antibody generation The control sera (2-3 ml) was collected from the ear pinna of rabbit before the start of immunization. The 200 μg of purified E. coli IspH protein was mixed with the KLH conjugate, Freud’s complete adjuvant and injected subcutaneously to the rabbit (2-4 site per animal) in the Genscript animal facility. Second immunization was performed after the 14 days post 1st immunization with 200 μg purified protein and KLH conjugate, Freud’s incomplete adjuvant. One-week later 2nd immunization, the test sera (1st Test bleed) was collected from the rabbit to test the antibody titration by ELISA and western blot. The 3rd immunization with 200 μg purified protein and KLH conjugate, Freud’s incomplete adjuvant was performed 14 days after 1st test bleed. One week later the 2nd test bleed was performed, and sera was purified for IgG antibodies using protein A column. The purified IgG antibodies were used for the confirmation of anti-IspH antibody production by ELISA and western blot. After confirmation, that antibody was raised in rabbit, the production bleed was performed, the sera was separated, and antibodies were purified using protein A column. The purified anti-E. coli IspH rabbit polyclonal antibody was validated by western blots using purified ispH protein from E. coli, Pseudomonas aeruginosa, Mycobacterium tuberculosis and plasmodium falciparum. The antibody was further validated using lysates of Acinetobacter baumannii, Shigella flexneri, Salmonella enterica, Vibrio cholerae and Helicobacter pylori. Depletion of γδ T cells from human PBMCs The γδ T cells were separated from human PBMC using Anti-TCRγ/δ Microbead Kit (Miltenyi Cat# 130-050-701). After Ficoll separation the human PBMC were washed and resuspended in RPMI medium containing human serum. The cells were counted, pelleted at 300g for 10 min and resuspended in 40 μl of MACS buffer for every 107 cells. The cells were incubated with 10 μl of Anti-TCR γ/δ Hapten -Antibody per 107 cells, at 4-8°C for 10 min. After incubation 30 μl MACS buffer and 20 μl of MACS Anti-Hapten MicroBeads-FITC per 107 cells were added and further incubated at 4-8°C for 15 min. The cells were washed with 1-2 ml of MACS buffer per 107 cells and centrifuged at 300g for 10 min. The supernatant was removed, and the cells resuspended in 500 μl MACS buffer per 108 cells. The sample was loaded on the MACS buffer rinsed LS column kept in the magnetic field. The cells in the flow through were collected and the column washed 3X with 3ml MACS buffer. The cells in the flow through and washes were combined pelleted and resuspended in RPMI + 10% human serum and counted to perform further experiments. Mouse infection studies In experiments with humanized mice (Hu-mice) or NSG mice injected with human PBMCs, infection was induced by injecting 107 E. coli per mouse intraperitoneally (i.p.) in 200 μl Dulbecco Phosphate Buffered Saline (DPBS). In experiments with C57Bl/6 mice, 106 Vibrio cholerae and in experiments with BALBc mice, 5 X 104 Enterobacter aerogenes (UCI 15) were injected i.p. similarly. After 24 hours, prodrugs C23.07-TPP / C23.28-TPP (where mentioned), or just the carrier molecule TPP, (10 mg/kg per mouse) in 1% DMSO-DPBS solution were injected i.p. (or i.v. in case of Enterobacter aerogenes infected BALBc mice) once a day for 1-2 weeks, until mice succumbed to infection or were sacrificed for tissue analysis, as indicated. A group of Enterobacter aerogenes infected mice were given Meropenem (10 mg/kg per mouse) for comparison to a best-in-class antibiotic. NSG mice injected with human PBMCs were given suboptimal (1 mg/kg) dose of C23.28-TPP through the i.v. route, once a day for 4 days. Blood from infected mice was collected daily using tail snips and analyzed for bacteremia by CFU and flowcytometry for γδ T cell expansion. Following death from infection or euthanasia at the end of the experiment, spleen, liver, lung, brain and kidney were harvested, sectioned and studied for bacterial CFU, immunohistochemistry or flow cytometry as indicated. Isolation of cells and bacteria from different organs Samples of mouse spleen, liver, lung, brain and kidney were weighed and crushed in 12 well plastic tissue culture plates using a 5mL syringe. RBCs were lysed in RBC lysis (ACK) buffer at 37°C and for 1 min. Cells were washed 3-5 times with MACS buffer at 4°C. Cells were then either analyzed by flowcytometry or lysed in distilled, de-ionized water and serial dilutions of samples plated for bacterial CFU on media plates respective to the bacteria studied. Ex-vivo infection in human PBMCs Human PBMCs were washed in medium (10% Human Serum (HS) RPMI medium supplemented with 100 U/mL penicillin G and 100 μg/mL streptomycin sulfate, 6 mM HEPES, 1.6 mM L-glutamine, 50 mM 2-mercaptoethanol) then cultured in medium without penicillin or streptomycin in 6, 12, 24 or 96 well Primaria plates (Fisher Scientific, Cat # 08-772). E. coli, Vibrio cholerae, Klebsiella pneumoniae or Mycobacterium tuberculosis (MTB) ex-vivo infections are induced at a multiplicity of infection (MOI) of 1:0.1, 1:1, 1:10 or 1:100. Various dilutions of 100mM stock solutions of prodrugs C23.07 / C23.28-TPP or TPP (control) are added to sample wells to give a final working concentration range from 500 to 4 μM. Infected PBMC samples were analyzed at 24, 48 or 72 hours by flowcytometry or lysed in distilled water at different time points where indicated and the lysates used for CFU analysis. The Vγ9Vδ2 T cells in uninfected PBMCs show low initial levels of perforin likely due to the length of time spent in culture (up to 72 hours). CFU analysis Bacterial cultures treated with different prodrugs/ antibiotics or lysates from infected mouse blood, tissues or infected ex-vivo human PBMC were serially diluted and 50μl plated on bacterial culture plates. The plates were incubated at 37°C, counted after overnight incubation (after 20 days for MTB colonies). The CFU were normalized per mL for blood or per mg weight for tissues. All experiments were replicated in at least three independent experiments with 3-8 technical replicates in each experiment. Recombinant IspH cloning and expression IspH gene sequences from E. coli, Pseudomonas, Plasmodium and Mycobacterium tuberculosis (LytB2) were optimized for expression in E. coli and synthesized by Genscript Inc. These sequences were cloned in pET24a-KAN vector and co-expressed with iron sulfur cluster (isc) proteins (encoded in the pACYC184 plasmid) in Nico (DE3) cells (NEB Cat # C2529H).49 Transformed Nico (DE3) cells were grown at 37°C in Terrific Broth (12gm tryptone, 24gm yeast extract, 5mL glycerol /L of broth) supplemented with sterile monopotassium phosphate (23.1 g/L), dipotassium phosphate (125.4 g/L) ferric ammonium citrate (35 mg/L), L-cysteine (1 mM) and antibiotics Kanamycin (50mg/L) and chloramphenicol (35mg/L). At an O.D. (600nm) of 0.6 - 0.7, IspH production was induced by adding IPTG at 1mM concentration for overnight incubation at 25°C. IspH protein purification After IspH induction, bacteria were spun down at 6000g and washed 3X with 50 mL of degassed PBS. All subsequent steps were performed in an anaerobic glove box at 0.5ppm O2. After the final wash the bacteria were resuspended in 20mL degassed lysis buffer (25 mM Tris, 1M KCl, 5% glycerol, cOmplete™ protease inhibitor cocktail (Sigma Cat # 4693132001), 5mM sodium dithionite, pH 7.5). The rest of the procedure was carried out under anaerobic conditions (<0.5ppm O2) in an mBraun glovebox. Bacteria were lysed by freeze-thawing 5-6X in liquid nitrogen. Nucleic acids were eliminated by incubating with 500units of Benzonase (Sigma E1014) at room temperature (RT) for 30 min. The lysate was spun down at 6000g and filtered through 0.45μm filter under anaerobic conditions (<0.5 ppm O2). The lysate was incubated for 2-3h at RT with 3-5 mL Ni-NTA resin (Qiagen Cat # 30230) that was equilibrated in lysis buffer. The Ni-NTA resin was washed with 3 column volumes (CV) of wash buffer 1 (25 mM Tris, 1 M KCl, 5% glycerol, COMPLETE™ protease inhibitor cocktail, 30 mM imidazole, pH 7.5) and 1 CV of wash buffer 2 (25 mM Tris, 0.1 M KCl, 5% glycerol, COMPLETE™ protease inhibitor cocktail, 30 mM imidazole, pH 7.5). The protein was eluted from Ni-NTA using 15 mL elution buffer (25 mM Tris, 0.1 M KCl, 5% glycerol, COMPLETE™ protease inhibitor cocktail, 300 mM imidazole, pH 7.5). The eluted protein was passed through a 5mL bed of chitin resin to remove contaminating proteins and then passed in tandem through sepharose SP (GE Cat # 17072910) and sepharose Q (GE Cat # 17051010) resin beds. The protein was eluted from the Q column using the Q column elution buffer (25 mM Tris, 1 M KCl, 5% glycerol, pH 7.5) desalted using Econo-Pac® 10DG (Bio-Rad Cat # 732-2010) desalting columns and concentrated using Amicon Ultra 10k spin columns. Methyl Viologen Assay All solutions were degassed by boiling before use and the assays were performed under <0.5ppm O2 in a glove box. To monitor the activity of IspH protein methyl viologen was used as reducing agent. The oxidation of methyl viologen (blue to colorless) was followed by measuring the loss of absorption at 398 nm. Assay solution contained 50 mM Tris–HCl (pH 8), 1 mM methyl viologen and 0.5 mM sodium dithionite in a total volume of 100 μl in 96 well flat bottom plastic plates. Varying concentrations of IspH (0-5 μM) and HMBPP (0–1.25 mM) were titrated and optimal concentrations of 50nM IspH and 1mM HMBPP were used for subsequent experiments. After reduction of methyl viologen with sodium dithionite an approximate absorbance of 3 was reached. The reactions were initiated by the addition of IspH. For inhibition studies, varying concentrations of candidate drugs (1nM - 250μM) or DMSO (negative control) were added. The plates were sealed by parafilm, incubated at 37°C and absorbance at 398nm read every 5 minutes in Biotek Synergy 2 plate reader. Activity is expressed as micromoles of HMBPP consumed per second, as measured by decrease in absorbance at 398nm. Sample lacking HMBPP serves as baseline negative control. The assay is linear with respect to time and protein concentration. Surface Plasmon Resonance Approximately 30,000 RU of purified recombinant His tagged Ec-IspH was immobilized onto a Ni- NTA SPR chip activated by N-(3-dimethylaminopropyl)-N’-ethyl carbodiimide hydrochloride (EDC) and N-hydroxy succinimide (NHS). Remain binding sites were blocked with 1M ethanolamine, pH8.5. Test compounds C23.20, C23.21 and HMBPP were serially diluted 1:3.16 starting at 100 μM final concentration in running buffer (10 mM HEPES, pH 7.4, 150 mM NaCl, 0.05% Tween20, 5% DMSO) and run on Biacore T200 instrument at a flow rate of 50 μl/min, to reduce the mass transport limitation effects. General chemistry All reactions were conducted under an inert gas atmosphere (nitrogen or argon) using a Teflon-coated magnetic stir bar at the temperature indicated. Commercial reagents and anhydrous solvents were used without further purification. Removal of solvents was conducted by using a rotary evaporator, and residual solvent was removed from nonvolatile compounds using a vacuum manifold maintained at approximately 1 Torr. All yields reported are isolated yields. Preparative reversed-phase high pressure liquid chromatography (RP-HPLC) was performed using a Gilson GX-271 semi-prep HPLC, eluting with a binary solvent system A and B using a gradient elusion (A, H2O with 0.1% trifluoroacetic acid (TFA); B, CH3CN with 0.1% TFA) with UV detection at 220 nm. Low-resolution mass spectral (MS) data were determined on a Waters ACQUITY QDa LCMS mass spectrometer with UV detection at 254 nm. 1H NMR spectra were obtained on a Bruker Avance II 400 (400 MHz) spectrometer. Chemical shifts (δ) are reported in parts per million (ppm) relative to residual undeuterated solvent as an internal reference. The following abbreviations were used to explain the multiplicities: s = single; d = doublet, t = triplet, q = quartet, dd = doublet of doublets, dt = doublet of triplets, m = multiplet, br = broad. (6-hydroxyhexyl)triphenylphosphonium bromide (TPP) synthesis To a stirred solution of 6-bromohexan-1-ol (5.0 g, 27.61 mmol) in 70 mL of acetonitrile at room temperature was added triphenylphosphine (7.967 g, 30.37 mmol) and the reaction mixture was refluxed for 48 h under a nitrogen atmosphere. Completion of the reaction was confirmed by thin layer chromatography (TLC). Then the solvent was evaporated under reduced pressure, the crude product was washed with ethanol (2 x 30 mL), and the solid was dried under high vacuum without further purification to afford the title compound (0.95 mmol) as a white solid. The product was confirmed by 1H NMR and LC-MS. 1H NMR (400 MHz, CDCl3) δ 7.92 – 7.75 (m, 9H), 7.71 (td, J = 7.5, 3.4 Hz, 6H), 3.87 – 3.71 (m, 2H), 3.63 (t, J = 5.4 Hz, 2H), 1.77 – 1.56 (m, 4H), 1.51 (d, J = 2.9 Hz, 4H). Mass m/z: calcd for [C24H28OP]+ [M]+, 363.19; found, 363.16. (Supplementary Fig. 3a). (6-hydroxyhexyl)triphenylphosphonium bromide esters synthesis 4-(naphthalen-2-yl)-4-oxobutanoic acid, 4-(naphthalen-1-yl)-4-oxobutanoic acid and 4-(2,5-dimethylphenyl)-4-oxobutanoic acid were used for the synthesis of (6-(4-(Naphthalen-2-yl)-4-oxobutanoyloxy)hexyl)triphenylphosphonium bromide (C23.20-TPP), (6-(4-(Naphthalen-1-yl)-4-oxobutanoyloxy)hexyl)triphenylphosphonium bromide (C23.21-TPP) and (6-(4-(2,5-Dimethylphenyl)-4-oxobutanoyloxy)hexyl)triphenylphosphonium bromide (C23.28-TPP) respectively (Supplementary Fig. 3b-d). To a stirred solution of the respective Aryl-4-oxobutanoic acid (~0.3 g, 1.31 mmol), (6-hydroxyhexyl) triphenyl phosphonium bromide (0.583 g, 1.31 mmol), and N,N-dimethylpyridin-4-amine (DMAP; 0.176 g, 1.58 mmol) in anhydrous CH2Cl2 (15 mL) at 0 °C was added dicyclohexylcarbodiimide ( 0.271 g, 1.45 mmol) under a nitrogen atmosphere. Then the reaction mixture was brought to room temperature and stirred for 16 h. Completion of the reaction was confirmed by TLC. Then the reaction mixture was cooled to −10 °C and the insoluble material was filtered off. The solid was washed with cold (−10 °C) CH2Cl2. The combined organic layer was then washed with aqueous 1N HCl (15 mL), water (15 mL), saturated aqueous NaHCO3 (15 mL), saturated aqueous NaCl (15 mL), and dried over anhydrous Na2SO4. The solvent was evaporated under reduced pressure and the crude product was purified by silica gel flash column chromatography by using 5-10% MeOH in CH2Cl2 to afford the title compound, (~0.687 g, 1.05 mmol) The product was confirmed 1H NMR and LC-MS as follows (Supplementary Fig. 3b-d). (6-(4-(Naphthalen-2-yl)-4-oxobutanoyloxy)hexyl)triphenylphosphonium bromide (C23.20-TPP): 1H NMR (400 MHz, CDCl3) δ 8.50 (s, 1H), 8.03 – 7.93 (m, 2H), 7.87 (ddd, J = 12.6, 5.5, 3.3 Hz, 7H), 7.81 – 7.73 (m, 3H), 7.73 – 7.65 (m, 5H), 7.64 – 7.49 (m, 2H), 4.12 – 4.00 (m, 2H), 3.99 – 3.84 (m, 2H), 3.44 (t, J = 6.6 Hz, 2H), 2.79 (t, J = 6.6 Hz, 2H), 1.72 – 1.49 (m, 6H), 1.36 (dt, J = 15.0, 7.5 Hz, 2H). Mass m/z: calcd for [C38H38O3P]+ [M]+, 573.26; found, 573.21. (6-(4-(Naphthalen-1-yl)-4-oxobutanoyloxy)hexyl)triphenylphosphonium bromide (C23.21-TPP): 1H NMR (400 MHz, CDCl3) δ 8.55 – 8.48 (m, 1H), 8.02 – 7.90 (m, 2H), 7.89 – 7.80 (m, 7H), 7.75 (tt, J = 12.0, 5.3 Hz, 3H), 7.71 – 7.61 (m, 6H), 7.56 – 7.45 (m, 3H), 4.13 – 4.01 (m, 2H), 3.97 – 3.83 (m, 2H), 3.40 – 3.30 (m, 2H), 2.86 – 2.75 (m, 2H), 1.73 – 1.51 (m, 6H), 1.36 (dt, J = 15.0, 7.5 Hz, 2H). Mass m/z: calcd for [C38H38O3P]+ [M]+, 573.26; found, 573.31. (6-(4-(2,5-Dimethylphenyl)-4-oxobutanoyloxy)hexyl)triphenylphosphonium bromide (C23.28-TPP): 1H NMR (400 MHz, CDCl3) δ 7.87 (ddd, J = 12.6, 5.2, 3.3 Hz, 6H), 7.81 – 7.74 (m, 3H), 7.73 – 7.63 (m, 6H), 7.48 (s, 1H), 7.17 (dd, J = 7.8, 1.2 Hz, 1H), 7.10 (d, J = 7.8 Hz, 1H), 4.04 (t, J = 6.5 Hz, 2H), 3.97 – 3.84 (m, 2H), 3.18 (t, J = 6.5 Hz, 2H), 2.69 (dd, J = 11.8, 5.4 Hz, 2H), 2.38 (s, 3H), 2.35 (s, 3H), 1.76 – 1.50 (m, 6H), 1.35 (dt, J = 15.0, 7.6 Hz, 2H). Mass m/z: calcd for [C36H40O3P]+ [M]+, 551.27; found, 551.21. 2,4-dioxo-4-phenylbutanoic acid and 4-(naphthalen-2-yl)-2,4-dioxobutanoic acid were used for the synthesis of (6-(2,4-Dioxo-4-phenylbutanoyloxy)hexyl)triphenylphosphonium bromide (C23.07-TPP) and (6-(4-(Naphthalen-2-yl)-2,4-dioxobutanoyloxy)hexyl)triphenylphosphonium bromide (C23.47-TPP) respectively (Supplementary Fig. 3e & f). To a stirred solution of the respective Aryl-2,4-dioxobutanoic acid (~200 mg, 1.04mmol) and (6-hydroxyhexyl)triphenylphosphonium bromide (461 mg, 1.04 mmol) in anhydrous CH2Cl2 (15 mL) at 0 °C was added triethyl amine (316 mg, 3.12 mmol), N,N-dimethylpyridin-4-amine (DMAP; 165 mg, 1.35 mmol) and 2-chloro-1-methylpyridinium iodide (319 mg, 1.25 mmol) respectively and stirred for 2 h at 0 °C. Completion of the reaction was confirmed by TLC. The reaction mixture was diluted with cold water and the product was extracted with CH2Cl2 (20 mL X 2). The combined organic layer was washed with aqueous 1N HCl (15 mL), aqueous NaHCO3 (15 mL), brine (15 mL), and then dried over anhydrous Na2SO4. The solvent was evaporated under reduced pressure, and the crude product was purified by silica gel flash chromatography (Ethyl acetate / Hexane) to afford the title compound (~321 mg, 0.5 mmol) as a thick liquid. The product confirmed by NMR and LC-MS as follows (Supplementary Fig. 3b-d). (6-(2,4-Dioxo-4-phenylbutanoyloxy)hexyl)triphenylphosphonium bromide (C23.07-TPP): 1H NMR (400 MHz, CDCl3) δ 15.29 (s, 1H), 8.06 – 7.96 (m, 2H), 7.93 – 7.82 (m, 6H), 7.78 (dt, J = 7.3, 3.6 Hz, 2H), 7.73 – 7.65 (m, 6H), 7.62 (dd, J = 10.5, 4.2 Hz, 1H), 7.52 (t, J = 7.6 Hz, 2H), 7.06 (s, 1H), 4.33 – 4.23 (m, 2H), 4.01 – 3.88 (m, 2H), 1.84 – 1.53 (m, 6H), 1.49 – 1.33 (m, 2H). Mass m/z: calcd for [C34H34O4P]+ [M]+, 537.22; found, 537.31. (6-(4-(Naphthalen-2-yl)-2,4-dioxobutanoyloxy)hexyl)triphenylphosphonium bromide (C23.47-TPP): 1H NMR (400 MHz, CDCl3) δ 15.32 (s, 1H), 8.55 (s, 1H), 8.05 – 7.97 (m, 2H), 7.92 (dd, J = 16.7, 8.4 Hz, 2H), 7.85 – 7.74 (m, 3H), 7.74 – 7.64 (m, 11H), 7.64 – 7.53 (m, 2H), 7.21 (s, 1H), 4.32 (t, J = 6.5 Hz, 2H), 3.35 (dd, J = 12.5, 7.4 Hz, 2H), 1.81 – 1.69 (m, 2H), 1.65 (d, J = 3.8 Hz, 4H), 1.50 – 1.36 (m, 2H). Mass m/z: calcd for [C38H36O4P]+ [M]+, 587.23; found, 587.32. 4-(Naphthalen-2-yl)-2,4-dioxobutanoic acid (C23.47) synthesis: To a stirred solution of ethyl 4-(naphthalen-2-yl)-2,4-dioxobutanoate (504 mg, 1.86 mmol) in Methanol (10 mL), tetrahydrofuran (10 mL) and water (2 mL) at RT was added lithium hydroxide monohydrate (235 mg, 5.59 mmol) and the reaction mixture was stirred for 6h at RT. Completion of reaction was confirmed by TLC. The volatiles were evaporated under reduced pressure to provide crude product which was acidified with aqueous 1N HCl (20 mL) and the product was then extracted with Ethyl acetate (30 mL X 2). The combined organic layers were washed with brine (10 mL), dried over anhydrous Na2SO4, and the solvent was evaporated under reduced pressure. The resulting crude product was purified by silica gel flash chromatography (Ethyl acetate / Hexane) to afford the title compound (406 mg, 1.68 mmol) as a white solid. The product was confirmed by NMR and LC-MS as follows (Supplementary Fig. 3g): 1H NMR (400 MHz, DMSO) δ 14.33 (s, 2H), 8.82 (s, 1H), 8.20 (d, J = 8.0 Hz, 1H), 8.10 – 7.93 (m, 3H), 7.76 – 7.58 (m, 2H), 7.23 (s, 1H). Mass m/z: calcd for [C14H11O4]+ [M+H]+, 243.07; found, 243.14. 3-(Dimethylamino)propyl 4-(naphthalen-2-yl)-2,4-dioxobutanoate (C23.47-DAP) synthesis: To a stirred solution of 4-(naphthalen-2-yl)-2,4-dioxobutanoic acid (100 mg, 0.41 mmol) in anhydrous CH2Cl2 (7 mL) at 0 °C was added 3-(dimethylamino)propan-1-ol (0.62 mg, 64 mmol), triethyl amine (125 mg, 1.24 mmol), N,N-dimethylpyridin-4-amine (DMAP; 65 mg, 0.54 mmol) and 2-chloro-1-methylpyridinium iodide (127 mg, 0.49 mmol) respectively, and stirred for 1 h at 0 °C. Completion of the reaction was confirmed TLC. The reaction mixture was diluted with cold water and the product was extracted with CH2Cl2 (10 mL X 2). The combined organic layers were washed with aqueous 1N HCl (10 mL), aqueous NaHCO3 (10 mL), brine (10 mL), and then dried over anhydrous Na2SO4, The solvent was evaporated under reduced pressure and the crude product was purified by silica gel flash chromatography (Ethyl acetate / Hexane) to afford the title compound (81 mg, 0.25 mmol) as a white solid. The product confirmed by NMR and LC-MS as follows (Supplementary Fig. 3h): 1H NMR (400 MHz, CDCl3) δ 12.15 (s, 1H), 8.51 (d, J = 53.1 Hz, 1H), 8.12 – 7.79 (m, 4H), 7.73 – 7.46 (m, 2H), 7.25 (s, 1H), 4.46 (t, J = 5.9 Hz, 2H), 3.26 (dd, J = 21.8, 14.1 Hz, 2H), 2.92 (s, 6H), 2.47 – 2.20 (m, 2H). Mass m/z: calcd for [C19H22NO4]+ [M+H]+, 328.38; found, 328.15. Ethyl ester synthesis Synthesis steps were identical to (6-hydroxyhexyl)triphenylphosphonium bromide esters synthesis described above. Ethanol was used for esterification in place of (6-hydroxyhexyl)triphenylphosphonium bromide. To a stirred solution of the respective Aryl-2,4-dioxobutanoic acid (~100 mg, 0.52 mmol) in anhydrous CH2Cl2 (8 mL) at 0 °C was added ethanol (72 mg, 1.56 mmol), triethyl amine (158 mg, 1.56 mmol), N,N-dimethylpyridin-4-amine (DMAP; 83 mg, 0.68 mmol) and 2-chloro-1-methylpyridinium iodide (159 mg, 0.62 mmol) respectively and stirred for 1 h at 0 °C. Completion of the reaction was confirmed by TLC. The reaction mixture was diluted with cold water and the product was extracted with CH2Cl2 (10 mL X 2).The combined organic layers were washed with aqueous 1N HCl (10 mL), aqueous NaHCO3 (10 mL), brine (10 mL), and then dried over anhydrous Na2SO4. The solvent was evaporated under reduced pressure and the crude product was purified by silica gel flash chromatography (Ethyl acetate / Hexane) to afford the title compound (80 mg, 0.36 mmol) as a white solid. The product confirmed by NMR and LC-MS as follows (Supplementary Fig. 3i-l). Ethyl 4-(naphthalen-2-yl)-2,4-dioxobutanoate (C23.20-EA): 1H NMR (400 MHz, CDCl3) δ 8.51 (s, 1H), 8.03 (dt, J = 15.2, 7.6 Hz, 1H), 8.00 – 7.93 (m, 1H), 7.88 (t, J = 8.3 Hz, 2H), 7.65 – 7.46 (m, 2H), 4.18 (q, J = 7.1 Hz, 2H), 3.45 (t, J = 6.7 Hz, 2H), 2.82 (t, J = 6.7 Hz, 2H), 1.28 (t, J = 7.1 Hz, 3H). Mass m/z: calcd for [C16H17O3]+ [M+H]+, 257.12; found, 257.14. Ethyl 4-(naphthalen-1-yl)-4-oxobutanoate (C23.21-EA): 1H NMR (400 MHz, CDCl3) δ 8.51 (s, 1H), 8.04 (dd, J = 8.6, 1.7 Hz, 1H), 7.96 (t, J = 8.4 Hz, 1H), 7.89 (t, J = 8.4 Hz, 2H), 7.65 – 7.48 (m, 2H), 4.18 (q, J = 7.1 Hz, 2H), 3.46 (t, J = 6.7 Hz, 2H), 2.83 (q, J = 6.6 Hz, 2H), 1.28 (t, J = 7.1 Hz, 3H). Mass m/z: calcd for [C16H17O3]+ [M+H]+, 257.12; found, 257.14. Ethyl 4-(2,5-dimethylphenyl)-4-oxobutanoate (C23.28-EA): 1H NMR (400 MHz, CDCl3) δ 7.50 (s, 1H), 7.16 (dt, J = 23.2, 4.5 Hz, 2H), 4.16 (q, J = 7.1 Hz, 2H), 3.20 (dd, J = 8.8, 4.4 Hz, 2H), 2.81 – 2.64 (m, 2H), 2.44 (s, 3H), 2.36 (s, 3H), 1.27 (td, J = 7.1, 2.3 Hz, 3H). Mass m/z: calcd for [C14H19O3]+ [M+H]+, 235.13; found, 235.24. Ethyl 2,4-dioxo-4-phenylbutanoate (C23.07-EA): 1H NMR (400 MHz, CDCl3) δ 15.30 (s, 1H), 8.06 – 7.96 (m, 2H), 7.66 – 7.57 (m, 1H), 7.55 – 7.46 (m, 2H), 7.08 (s, 1H), 4.41 (q, J = 7.1 Hz, 2H), 1.42 (t, J = 7.1 Hz, 3H). Mass m/z: calcd for [C12H13O4]+ [M+H]+, 221.08; found, 221.14. Prodrug uptake and cleavage 108 E. coli were treated with different concentrations (10-5000nM) of the prodrug C23.28-TPP for 30 min. The bacteria were washed in DPBS, lysed by freeze-thawing 5 times in liquid nitrogen and the lysate treated with acetonitrile to a final concentration of 50%. Lysates were spun down at 5000g, passed through 0.45-micron filters and analyzed by LC-MS. Conversion of HMBPP to DMAPP/IPP Ec-IspH protein was incubated with varying concentrations (10-5000nM) of TPP (control) or the IspH inhibitor C23.28 for 10 min. An MV assay as described above was done with final concentrations of IspH and HMBPP at 50nM and 1mM respectively. At 30 min the reaction was stopped by addition of acetonitrile to a final concentration of 50%. Purified HMBPP and DMAPP/IPP were used as benchmarks and to obtain a dilution curve. Samples were analyzed by LC-MS for the presence of HMBPP and DMAPP/IPP. Plasma Stability of prodrugs The in vitro stabilities of the prodrugs C23.20-TPP, C23.21-TPP and C23.28-TPP were measured in human (Sigma P9523) mouse (Sigma P9275) and pig (Sigma P2891) plasma. The lyophilized plasma was reconstituted with the recommended volume of 0.05 M PBS (pH 7.4) to a conc. of 100% and prewarmed at 37°C. The reactions were initiated by the addition of the prodrugs to preheated plasma solution to yield a final concentration of 100 μM. A positive control solution without the addition of plasma was also included to monitor compound stability over the course of the experiment. The assays were incubated at 37°C and shaken at 200 rpm. Samples (50 μl) were taken at 0, 15, 30, 45, 60, and 120 min and added to 200 μl acetonitrile to deproteinize the plasma. The samples vortexed for 1 min and centrifuged at 4°C for 15 min at 12,000 rpm. The clear supernatants were transferred to LC-MS vials for analysis. Liver microsome stability of prodrugs The in vitro stabilities of the prodrugs C23.20-TPP, C23.21-TPP and C23.28-TPP were measured in human (Sigma M0317) mouse (Sigma M9441) and monkey (Sigma M8816) liver microsomes. A stock solution of the prodrug was added to a solution of 0.1M phosphate-buffer saline (PBS, pH = 7.4) containing 1mM NADPH to make a final concentration of 100 μM. This solution was incubated at 37°C for 5 minutes at which time microsomes were added at a final concentration of 1.0 mg/mL, incubated at 37 °C and shaken at 200 rpm. A positive control solution without the addition of microsomes was also included to monitor compound stability over the course of the experiment. Aliquots were removed at 0, 15, 30, 60, 90, 120 min and 10x volume of acetonitrile was added to stop the reaction and deproteinate the sample. Samples were centrifuged at 10,000 rpm for 5 minutes at 4°C, and the supernatant was transferred to LC-MS vials for analysis. LC-MS quantification of small molecules LC-MS analysis was performed on a ThermoFisher Scientific Q Exactive HF-X mass spectrometer equipped with a HESI II probe and coupled to a ThermoFisher Scientific Vanquish Horizon UHPLC system. IPP/DMAPP and HMBPP were analyzed by HILIC chromatography on a ZIC-pHILIC 2.1-mm i.d × 150 mm column (EMD Millipore). The HILIC mobile phase A was 20 mM ammonium carbonate, 0.1% ammonium hydroxide, pH 9.2, and mobile phase B was acetonitrile. Prodrug compounds were analyzed by reversed phase (RP) chromatography on a Synergi 4mm Polar-RP 2-mm i.d × 100 mm column (Phenomenex). The RP mobile phase A was 0.1% formic acid in MilliQ water, and mobile phase B was 0.1% formic acid in acetonitrile. Peak areas for each compound were integrated using TraceFinder 4.1 software (ThermoFisher Scientific). Determination of prodrug stability The calibration curves used to determine prodrug and drug concentrations ranged from 50 μM to 0.012 μM with 2-fold serial dilutions (13 points in duplicate) and were generated from LC-MS quantifications using TraceFinder 4.1 software (ThermoFisher Scientific). Data points were plotted in GraphPad and respective half-lives (t1/2) were calculated using the expression t1/2=0.693/K, where K is the rate constant. Relevant supporting information can be found in Source Data File. Bacterial viability and prodrug treatment E. coli or clinical isolates of Enterobacter aerogenes (CRE) (UCI 15), Klebsiella pneumoniae 1.53 (ST147+, CTX-M15+), Salmonella enterica typhimurium (LT2 – SL7207), Vibrio cholerae (M045), Acinetobacter baumannii (BC-5), Acinetobacter baumannii (AB5075-UW), Pseudomonas aeruginosa (PA14), Pseudomonas aeruginosa (MSRN 5524), Helicobacter pylori (Hp CPY6081), Bacillus sphaericus (CCM 2177), Mycobacterium tuberculosis (MTB-H37Ra), and Yersinia pestis (KIM 10+) were cultured to late log phase (108 cells/mL) in their respective culture media and quantified by measuring OD 600nm for 3 serial dilutions. The bacteria were spun down, resuspended in RPMI medium supplemented with 10% FBS or HS at a concentration of 105 cells/mL and aliquoted 100 μl/well into a 96 well plate. Varying concentrations of candidate prodrugs (4-500 μM final concentration) were added and incubated for 1-4 hours (4 days for MTB) at 37°C. Bacterial viability from each sample was tested by CFU, Resazurin blue (colorimetric and fluorescence) and growth curve assays. For proteomics and electron microscopy the bacteria were treated with the respective prodrugs for 8 and 24 hours. The following antibiotics were used to compare bacterial killing potency against our prodrugs: Meropenem (Sigma Cat# 1392454), Amikacin (Sigma Cat# A0365900), Ceftriaxone (Sigma Cat# C0691000), Cefepime (Sigma Cat# 1097636), Ciprofloxacin (Sigma Cat# 17850), Tobramycin (Sigma T4014), Ceftaroline (Bocsci Inc. Cat# B0084-459128), Kanamycin (Sigma Cat# B5264), Chloramphenicol (Sigma Cat# C0378), Ampicillin (Sigma Cat# A9518), Doxycycline (Sigma Cat# D3447), Gentamicin (Sigma Cat# G1264) and Streptomycin (Sigma Cat# S6501). Resazurin blue assay Control or prodrug treated bacterial samples were treated with resazurin sodium salt (Sigma R7017) at a final concentration of 0.02% and incubated for 4 hours (overnight for MTB) at 37°C in a Biotek Synergy 2 plate reader. Changes in fluorescence were measured every 20min for 16 hours (3 days for MTB), with discontinuous shaking, using excitation filter range 530-570 nm and emission filter range 590-620 nm. Increase in fluorescence intensity corresponds to bacterial growth and is quantified by comparison with untreated bacterial control samples. The ratio of (Tthreshold (untreated)/Tthreshold (prodrug treated)) was used to quantify the change in bacterial growth. To minimize inter-experimental variations, all Tthreshold times were corrected by subtracting the time for untreated control cultures to reach minimum detectable fluorescence. At the end of the experiment, wells were visualized for changes in color from blue (inviable bacteria) to pink (viable bacteria) or by measuring the fluorescence at the aforementioned excitation and emission. Bacterial membrane integrity by SYTO9/PI assay E. coli grown to late log phase (108 cells/mL) were treated with TPP (control) or DAIA prodrugs at varying concentrations in RPMI + 10% FBS. Bacteria were spun down and washed 3X in Tris buffered saline (TBS) (pH7.5). 1.5μl /mL of component A (SYTO 9 dye) and component B (propidium iodide (PI)) from the BacLight™ Live/Dead kit (Life Tech cat# L7012) were added to the bacterial samples and incubated for 15min. An aliquot was run for flowcytometry on BD LSR II (BD Biosciences). With the excitation wavelength centered at about 485 nm, the fluorescence intensities at 530nm (green) and 630nm (red) were measured and the data analyzed using FlowJo software. TPP or isopropanol treated bacteria served as negative or positive control respectively and their flow plots were used to gate the prodrug treated samples. As bacteria lose their membrane integrity the green SYTO 9 dye is displaced by the red PI dye. The remaining samples were spun down at 5000 g for 10 min., resuspended in 10ul of TBS, spread on glass microscopy slides and dried. The samples were mounted using Cytoseal 60 or Mounting Medium (Electron Microscopy Sciences). Specimens were documented photographically using 80i upright microscope and analyzed with the NIS-Elements Basic Research software. Measuring respiration by Seahorse XF Analyzer On the day prior to the assay the sensor cartridges from the Seahorse XFe96 FluxPaks (Agilent # 102416) were calibrated according to the manufacturer’s instructions using pre-warmed Seahorse XF Calibrant. E. coli were grown in LB medium overnight to and O.D. 600 of 0.3, washed in PBS and resuspended in Seahorse XF RPMI medium, pH 7.4 (Agilent # 103576) supplemented with 1% glucose. 105, 106 or 107 bacteria were added to XF Cell Culture Microplates (Agilent # 101085-004) precoated with poly-D-lysine and spin down at 2000g for 10 min to attach them to the plate. The wells in the plate were divided to include bacteria treated with TPP (negative control) and 3 concentrations (500, 100 & 20 μM) of C23.28-TPP; 8 technical replicates for each condition. 90 μl of fresh medium was added to each well and 90μl of TPP/prodrug solution was added to each injection port A. Baseline OCR and ECAR were measured for 12 min after which the TPP/prodrug solution was injected into each sample. Readings were taken as pmol/min (OCR) and mpH/min (ECAR) every 6 min for up to 90min. The mean of the 8 technical replicates was plotted for each treatment condition and changes in OCR and ECAR were compared to the control samples. Superoxide and H2O2 detection Superoxide anion was measured in prodrug and TPP treated bacteria by diluting them 1/50 into PBS containing 2 μM dihydroethidium (DHE) (Sigma Cat # D7008) just before flow cytometry (Excitation 535nm, Emission 610nm). H2O2 production was measured in similar bacterial samples using the Amplex™ Red Hydrogen Peroxide/Peroxidase Assay Kit (Thermo Fisher Cat # A22188). Fluorescence measurements were calibrated by comparison to calibration curves for wells containing H2O2 in a final concentration ranging between 0.1 to 100 μM. Fluorescence was measured using the 540/620 nm wavelength pair in a Biotek Synergy 2 plate reader. Staining for bacterial membrane potential The procedure for studying membrane potential changes in prodrug treated E. coli was identical to that used for the Live/Dead assay above with the following exceptions. The BacLight™ Bacterial Membrane Potential Kit (Lifetech Cat # B34950) was used in this case. 10 μl of Component A (3 mM DiOC2) was used to stain the bacterial samples for 30 min. at room temperature. TPP or component B (CCCP) treated bacteria were used as negative or positive controls respectively and to gate prodrug treated samples. With intact membrane potential the DiOC2 dye form tetramers within bacteria that fluoresce at 630 nm (red). Loss of membrane potential leads to dimer formation that fluoresce at 530 nm (green). Bacteria were analyzed similarly by both flowcytometry and microscopy. Transmission Electron Microscopy Bacteria (E. coli or Vibrio cholerae) were treated with TPP or prodrug C23.28-TPP in RPMI media with 10% FBS for 0, 8 or 24h. The ΔispH conditional knockdown E. coli was cultured similarly in the presence of 1% dextrose for 8 or 24 h to inhibit IspH expression. At respective timepoints the samples were fixed in 2.5% glutaraldehyde, 2% paraformaldehyde at 4 °C in 100 mM cacodylate buffer (pH 7.0) containing 2 mM CaCl2 and 0.2% picric acid. Samples were briefly washed and treated for 2 h at 4 °C with 1% osmium tetroxide in 100 mM cacodylate buffer (pH 7.0). After washing with distilled water 3–5 times, samples were dehydrated using increasing ethanol concentrations and embedded in Epon resin (Sigma-Aldrich). Ultrathin sections of the embedded samples were cut and loaded onto grids and stained further with Reynold’s lead citrate (Sigma-Aldrich) for 3–15 min. Grids were dried overnight and observed using a JEOL 1010 transmission electron microscope equipped with an AMT 2k CCD camera. Scanning Electron Microscopy Scanning electron microscope experiments were carried out at CDB Microscopy Core (Perelman School of Medicine, University of Pennsylvania). Bacterial samples were washed three times with 50mM Na-cacodylate buffer, fixed for 2-3 hours with 2% glutaraldehyde in 50 mM Na-cacodylate buffer (pH 7.3), spun down over 0.22 μm filter membranes and dehydrated in an increasing ethanol concentration over a period of 1.5 hour. Dehydration in 100% ethanol was done three times. Dehydrated samples were incubated for 20 min in 50% Hexamethyldisilane (HMDS Sigma-Aldrich) in ethanol followed by three changes of 100% HMDS and followed by overnight air-drying as described previously.50 Then samples were mounted on stubs and sputter coated with gold palladium. Specimens were observed and photographed using a Quanta 250 FEG scanning electron microscope (FEI, Hillsboro, OR, USA) at 10 kV accelerating voltage. Toxicity assays in mammalian cell lines Cytotoxicity of prodrugs on C2C12, HepG2, Raw 264.7 and Vero cells was estimated by using LDH-GloTM cytotoxic assay kit (Promega Cat # J2381). Cells were grown, counted, aliquoted in 96 well plates at a cell density of 105 cells/well and allowed to adhere to the bottom of the wells for 1d at 37°C and 5% CO2. The cells were treated with prodrugs at different concentrations (1-5000 μM). Cells treated with 2% DMSO served as negative control while cells treated with 0.2% Triton X100 served as positive control for cytotoxicity. Each condition had 8 replicates. Supernatant media samples were taken from each well at intervals of 24, 48 and 72h, diluted 300-fold in PBS, added to the LDH assay reagent in 1:1 ratio (20 μl: 20 μl) in white opaque 96 well plates and further incubated at room temperature for 1h in dark. Luminescence was measured using Biotek Synergy 2 plate reader with integration time 1 sec/well. Cytotoxicity was calculated using the equation % Cytotoxicity = 100 × (Experimental LDH Release – Medium Background) / (Maximum LDH Release Control – Medium Background). Measurement of Mitochondrial membrane potential To quantify the effect of IspH prodrugs on mitochondrial membrane potential of C2C12 myoblasts (ATCC, Cat # CRL-1772), cells were grown in DMEM + 10% FBS up to 90% confluence and suspended by trypsinization. Cells were washed and pelleted at 500g for 5 minutes and resuspended in DMEM consisting of 100 nM of tetramethyl rhodamine methyl ester (TMRM) (Thermo Fisher, Cat # I34361) for 30 min at 37°C with slow shaking. Myoblasts were pelleted down and resuspended in PBS. 1 million cells were incubated with 1, 10 or 100 μM concentration of TPP, IspH prodrugs or carbonyl cyanide m-chlorophenyl hydrazine (CCCP) (Invitrogen, Cat # B34950) for 10 min at room temperature. CCCP is an ox-phos uncoupler which causes loss of mitochondrial membrane potential and is used as a positive control After 10 min. cells were analyzed by flow cytometry according to the manufacturer’s instructions and the plots gated using negative control (unstained cells). Profiling the effect of TPP carrier molecule on hERG channel Compound profiling against hERG, to evaluate potential cardiac liability of 6-hydroxyhexyl TPP, methyl TPP and our prodrug C23.28-TPP was carried out at Reaction Biology Inc. using the QPatch HTX fully automated patch-clamp platform that allows for the testing of up to 48 cells in parallel. Electrophysiological profiling was done in the presence of Verapamil (positive control), DMSO (vehicle control) and the TPP compounds at a concentration range of 10nM to 10μM (n=3 cells per sample concentration X 6 concentrations). Exemplar hERG trace elicited from a holding potential of −80 mV followed by steps from −60 to +50 mV in 10 mV increments, tail currents were elicited by a step to −50 mV. Response data obtained were normalized to peak current at 0.1% DMSO. Non-linear regression curve fits were used to calculate the IC50 of each compound. Protein Isolation and Western Analysis Bacterial samples were washed with PBS and treated with 10 mg/mL Lysozyme in 20 mM Tris-HCl, pH 8.0 ; 2 mM EDTA at 37°C for 30 min. Lysates are prepared by freeze-thawing in Ripa Lysis Buffer (10 mM Tris-Cl pH 8.0, 1 mM EDTA, 0.5 mM EGTA, 1% Triton X-100, 0.1% sodium deoxycholate, 0.1% SDS, 140 mM NaCl) supplemented with protease inhibitors at 4°C. Whole cell lysates (100 μg per reaction) were mixed with an equal volume of 2X SDS-PAGE sample buffer supplemented with 10% 2-Mercaptoethanol and heated for 5 min at 100°C. Protein samples were size fractionated on 4-20% Tris-Glycine gradient gels (Lonza, Walkersville, MD, USA) or lab made 12.5% Tris-Glycine gels using constant voltage at room temperature, transferred overnight onto Immuno-Blot PVDF membranes (Bio-Rad cat#162-0177) at 4°C and subjected to protein blotting using the mouse anti-E.coli RNA Sigma 70 antibody (Bio Legend, Cat # 663208) or rabbit anti-E. coli IspH antibody (generated in this project). Both primary antibodies show cross reactivity across multiple bacterial species. Secondary antibodies conjugated to horseradish peroxidase were used at a dilution of 1:10,000 (GE healthcare Cat # NA931V, NA934V). The immunoblots were scanned using Image Quant™ LAS 4000. Uncropped Western blots with molecular weight markers are shown in Supplementary Fig. 1. Proteomics Triplicate samples of C23.28-TPP treated or ΔispH, E. coli lysates at 0, 8 and 24 h (a total of 18 samples), were processed. Protein samples were concentrated (up to 8-fold) by lyophilization and 25 μg from each sample was separated by SDS-PAGE for 0.5 cm. The entire lanes were excised, digested with trypsin and analyzed by LC-MS/MS on a Q Exactive HF mass spectrometer using a 240 min LC gradient. MS/MS spectra were searched with full tryptic specificity against the UniProt E. coli database (www.uniprot.org; 07/12/2019) using the MaxQuant 1.6.3.3 program. “Match between runs” feature was used to help transfer identifications across experiments to minimize missing values. Protein quantification was performed using razor and unique peptides. False discovery rates for protein, and peptide identifications were set at 1%. A total of 2,346 protein groups were identified, including proteins identified by a single razor and unique peptide. LFQ intensity was used for protein quantitation.51 The LFQ intensity levels were log2 transformed and undetected intensities were floored to a minimum detected intensity across all proteins or a minimum across 4 samples in case of both replicates were undetected. Bioinformatics analysis Unpaired t-test was performed to estimate significance of difference between conditions and false discovery rate was estimated using the procedure from.52 Proteins that passed P<0.05 threshold were considered significant (all passed FDR<5% threshold). 525 proteins changed in both IspH prodrug treatment and ΔispH conditional knockdown systems. Proteins showing >2 fold up or down regulation under both conditions and at both 8 and 24h time points were analyzed using Venny.53 Enrichment analysis of proteins common to both conditions and timepoints was done using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING).35 Functions with at least 5 differentially expressed proteins enriched at P<0.001 threshold were considered. Antibiotic resistance assays with DAIAs For this study, Klebsiella pneumoniae and Vibrio cholerae clinical strains mentioned above were cultured to exponential phase in RPMI medium containing 10% FBS. The bacteria were washed in DBPS and quantified by O.D.600. The bacteria were aliquoted into identical samples containing 105 CFU. These aliquots were used to start fresh cultures in RPMI medium containing 5% HS, in the presence or absence of 106 human PBMCs, each condition in the presence or absence of the prodrug C23.28-TPP. Hygromycin (Hyg) and Streptomycin (Strep) were used as control antibiotics for Vibrio and Klebsiella respectively. After 8 h of incubation at 37°C and 5% CO2, 50 μl of each sample was plated by serial dilution for the CFU measurement, and the rest of the cultures allowed to grow. After 24h of incubation bacteria from each sample were washed and quantified by O.D.600. 105 bacteria from the respective samples in first passage were used to start the next passage (cycle of selection by antibiotic) under the same conditions as mentioned above. For samples co-incubated with Human PBMCs, fresh PBMCs from the same donor were used for every passage. Bacterial growth in each passage was measured up to the 18th passage. Bacterial growth (CFU) in the absence of antibiotics was considered as 100% growth and resistance to an antibiotic in each passage was defined as the percent of bacterial growth that occurred in the presence of that antibiotic. Flow cytometry Cells were washed with 2 mL of 1X PBS at 1500 rpm for 5 min and then stained with 1 ul of Zombie Yellow (Bio Legend, Cat # 423103) for 20 min at room temperature to check the viability. The cells were stained for cell surface markers with a combination of (where indicated) CD3- PerCP-Cy5.5 (clone UCHT1, BD Biosciences, Cat # 560835), CD4-Alexa Fluor 700 (clone RPA-T4, BD Biosciences, Cat # 557922), CD8a-Brilliant Violet 711 (clone RPA-T8, Bio Legend, Cat # 301044), TCR Vγ9-FITC (clone 7A5, Invitrogen, Cat # TCR2720), CD69-PE/Cy7 (clone FN50, BD Biosciences, Cat # 557745), HLA-DR-Brilliant Violet 421 (clone L243, Bio Legend, Cat # 307636), for 20 min in FACS buffer (1% FBS in PBS) at room temperature. Next the cells were washed with PBS, fixed and permeabilized Fixation/Permeabilization Kit (BD Biosciences Cat # 554714) for 15 min at 4°C. After washing them with 1 mL of 1X permeabilization buffer, intracellular proteins were stained using Perforin-Brilliant Violet 421 (clone dG9, Bio Legend, Cat # 308122), Granulysin- Alexa Fluor 647 (clone DH2, Bio Legend, Cat # 348006), Granzyme A- PE/Cy7 (clone CB9, Bio Legend, Cat # 507222). Cells were washed with 1X permeabilization buffer 2 times. The cells were resuspended in 300 ul of 1% paraformaldehyde fixation buffer (Bio Legend, Cat # B244799) in PBS. Samples were run on BD LSR II (BD Biosciences) and the data analyzed using FlowJo software. Cells were first gated for lymphocytes (FSC/SSC) then singlets (FSC-A vs. FSC-H). The singlets were further analyzed for their uptake of the Live/Dead Aqua or zombie yellow stain to determine live versus dead cells. Live cells were gated for CD3+ cells then gated for their identifying surface markers: CD4, CD8 and Vγ9 (γδ T lymphocytes), followed by their respective cytotoxic markers perforin, granulysin and granzyme A or cell surface markers of T cell activation such as HLA-DR, and CD69. Gating strategy for every FACS plot shown in the Source Data File. Validating the anti-Vδ2-TCR (Bio Legend cat #331402) antibody for Immunofluorescence Human PBMCs from one donor were split into 2 aliquots; one sample was treated with 10μM HMBPP and 50ng/ml IL-15 to expand Vγ9Vδ2 T cells and the other sample was depleted of all γδ T cells using Anti-TCRγ/δ Microbead Kit (Miltenyi Cat# 130-050-701). HepG2 and Vero cells served as negative control. 106 cells of each type were collected in Eppendorf tubes and washed with 1X PBS 3 times. The cell pellet was resuspended in PBS and fixed for 20 min. by adding Formaldehyde to 4% final concentration. Fixed cells were washed with 1X PBS pelleted and embedded in 100μl of 4% agar (Fisher Scientific, Cat# BP14232). The agar block was then treated with 70% ethanol before paraffin embedding and sectioning at the Wistar Histotechnology Facility. For immunofluorescence (IF) studies, sections were deparaffinized in xylene, rehydrated in ethanol (100%-95%-80%-70%) and distilled water. The endogenous peroxidase activity was eliminated by treating the sections with 0.5% hydrogen peroxide in methanol for 10 minutes. The slides were washed under tap water for 5 minutes before simmering them in Tris-EDTA buffer. The slides were washed with PBS before blocking them in 5% BSA blocking solution for 1 hr. The tissue sections were subsequently incubated with primary anti Vδ2-TCR primary antibody (Bio Legend cat #331402) 1:50 in 5% BSA overnight at 4°C, washed next day with 1X PBS and incubated with AF647 (Invitrogen, cat # A21236) secondary antibody 1:200 for 45 min. DAPI was added for 5 minutes and the sections mounted using Cytoseal 60 or Mounting Medium (Electron Microscopy Sciences). Specimens were documented photographically using Leica TCS SP5 Scanning Confocal Microscope and analyzed with the NIS-Elements Basic Research software. Validation images shown in Extended Data Fig. 11 b & c. Tissue Staining and Immunofluorescence Tissues were harvested and fixed in Formalde-Fresh Solution overnight at 4°C, washed with 1X PBS and transferred to 70% ethanol before paraffin embedding and sectioning. Tissue embedding and sectioning were performed by the Histotechnology Facility (The Wistar Institute). For immunohistochemistry (IHC) studies, tissue sections were deparaffinized in xylene, rehydrated in ethanol (100%-95%-80%-70%) and distilled water. The endogenous peroxidase activity was quenched with 0.5% hydrogen peroxide in methanol for 10 minutes. The slides were washed under tap water for 5 minutes, simmered in Tris-EDTA buffer, washed with PBS before blocking them in 5% BSA blocking solution for 1 hr. The tissue sections were subsequently incubated with anti Vδ2-TCR primary antibody (Bio Legend cat #331402) and anti-E. coli antibody (Abcam ab137967) 1:50 in 5% BSA overnight at 4°C, washed next day with 1X PBS and incubated with AF647 (Invitrogen, cat # A21236) and AF488 (Bio Legend Cat# 406416) secondary antibodies 1:200 for 45 min. DAPI (1:5000) was added for 5 minutes and the samples mounted using Cytoseal 60 or Mounting Medium (Electron Microscopy Sciences). Specimens were photographed using 80i upright microscope and analyzed with the NIS-Elements Basic Research software. Software used for data collection: NIS-Elements Basic Research Nikon version 4.60.00 FlowJo version 10 FlowJo LLC Internal Coordinate Mechanics software (ICM) MolSoft Inc. Version 3.7-2a Virtual Ligand Screening (VLS) MolSoft Inc. Version 3.7-2a Seahorse Wave controller software- Agilent version 2.4.2 Software used for data analysis: MS-Excel, Office, PowerPoint Microsoft Inc 2016 version Prism 7 Graph Pad Inc version 7.04 MaxQuant version 16.3.3 Max Planck Institute Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) version 11 Venny version 2.1 Tracefinder version 4.1 Molsoft ICM Browser version 3.7-2a Seahorse Wave analysis software-Agilent version 2.4.2 Chemdraw version 19.1 Data availability Raw data supporting each figure can be found in the respective source data files. Molecular docking studies were done using the Ec-IspH structure 3KE8 deposited in the Protein Data Bank (https://www.rcsb.org/structure/3ke8). IspH binding pocket atomic field property was mapped using the internal Coordinate Mechanics (ICM) software (http://www.molsoft.com/icm_pro.html) from Molsoft Inc. and molecular docking of 10 million compounds from MolCart library (https://www.molsoft.com/molcart.html) carried out using the Virtual Ligand Screening (https://molsoft.com/vls.html) from Molsoft Inc. Due to the lack of a suitable online repository, all docking data is available upon request as an .icb file, viewable using the free ICM browser (http://www.molsoft.com/icm_browser.html). LC-MS/MS run spectra were searched against the UniProt E. coli (BL21-DE3) database (https://www.uniprot.org/proteomes/UP000002032). The proteomics data is available online on MassIVE (https://massive.ucsd.edu/) using the accession number (MSV000086359) or downloaded from (ftp://massive.ucsd.edu/MSV000086359/). The source data for all animal experiments are included in the Supplementary Files. All reagents used or generated and all data that support the findings of this study are available from the authors on reasonable request, see author contributions for specific data sets. Extended Data Extended Data Figure 1: Purification of recombinant IspH proteins from multiple microbial species and measurement of their biochemical activity by Methyl Viologen (MV) assay. a, Coomassie stained gels showing IPTG induction of recombinant 6His-tagged Plasmodium falciparum (Pf), E. coli (Ec), Pseudomonas aeruginosa (Pa) and Mycobacterium tuberculosis (MTB) IspH followed by b, Anti-His-tag immunoblots showing the respective purified IspH proteins. Panels a & b are representative of 3 independent purification attempts. c, IspH uses methyl viologen (MV) as an electron donor for reductive dehydroxylation of HMBPP. Colorless oxidized MV is restored to its reduced blue form by sodium dithionite. In the absence or inhibition of IspH activity MV stays blue. MV assays measuring IspH activity using d, different concentration of Ec-IspH at 10 min. in the presence of 1mM HMBPP, e, different concentration of HMBPP at 30 min. in the presence of 50nM Ec-IspH. For d & e error bars represent mean of 3 independent experiments ± s.e.m. Respective p values: *P < 0.001, P < 0.01, P < 0.05, ns- not significant; by two-tailed unpaired Student’s t-test, relative to 0nM IspH in d or 0μM HMBPP in e. Extended Data Figure 2: In-silico molecular docking with Ec-IspH active pocket. a, Crystal structure of Ec-IspH (PDB:3ke8) 29 (top left) was used to generate the Atomic Property Field (APF) (bottom left) and mimic HMBPP interactions in the active binding pocket (right panel). b, Automated Virtual Ligand Screening (MOLSOFT) identified 168 out of 9.6 million compounds based on predicted binding at the active site. c, top 24 compounds were compared with HMBPP visually and based on their predicted number of H-bonds formed, H-bond energy, Van der Waal’s interaction energy and other interactions as mentioned. C1-24 in-silico docking shown in Extended Data Fig. 3a. Extended Data Figure 3: In-silico molecular docking of compounds C1-24 and their inhibitory activity on E. coli IspH. a, Chemical structures and in-silico docking of top 24 candidate IspH inhibitors at the E. coli IspH active pocket rendered by MOLSOFT. Structures shown in Supplementary Fig. 2a. b, Activity of Mycobacterial (MTB), Pseudomonas (Pa) and Plasmodium (Pf) IspH pretreated with DMSO (control), C10, C17 and C23 over time. Error bars represent mean of 4 independent experiments ± s.e.m. Inhibition of Ec-IspH by analogs of c, C10, d, C17 or e, C23 (Structures shown in Supplementary Fig. 2). For analogs with better activity than the parent compound respective p values: P < 0.001, P < 0.01, P < 0.05; by two-tailed unpaired Student’s t-test, relative to C23 (n=8 technical replicates). Error bars represent means ± s.e.m. Extended Data Figure 4: Drug binding assays, structure activity relationship, testing prodrug potency with different carrier molecules and determining prodrug cleavage and Ec-IspH inhibition by LC-MS. a, Surface plasmon resonance (SPR) signals (resonance units (RU)) from different concentrations HMBPP, C23.20 and C23.21 run on Ec-IspH crosslinked NTA chip, plotted against concentrations to calculate KD and Rmax values. (n=3 biological and 2 technical replicates) b, Structure-activity guided analog design lowered IC50 for multiple C23 analogs. Chemical structures in Supplementary Fig. 2. c, Prodrug ester forms of analog C23.47 obtained by linking ethyl alcohol (EA), triphenyl phosphonium (TPP) or dimethylamino propanol (DAP) (synthesis reactions shown in Supplementary Fig. 3), were tested for E. coli killing by dynamic growth curves and by d, Resazurin-blue assay. MIC90= minimum drug concentration at which 90% bacteria are killed. For c, n= 3 biological and 8 technical replicates. e, E. coli treated with 5μM C23.28-TPP for 30 min were lysed and the lysates analyzed by LC-MS to quantify relative abundance of C23.28-TPP (prodrug), TPP (carrier molecule) and C23.28 (active drug). Respective molecules were identified by their respective retention times (RT) and mass: charge (m/z) ratios. Area under the respective peaks is measured in arbitrary units (AU) and directly proportional to their abundance. f, Relative abundances of C23.28-TPP (prodrug), TPP (carrier molecule) and C23.28 (active drug) found within E. coli treated with different concentrations (10-5000nM) of C23.28-TPP, (n= 3 technical and 2 biological replicates). g, Methyl-viologen assay performed by treating 1mM HMBPP with 50nM E. coli IspH pre-treated with 5μM C23.28 or TPP for 30 min. Samples analyzed by LC-MS to quantify relative conversion of HMBPP (IspH substrate) to DMAPP / IPP (IspH products). Respective molecules were identified by their respective retention times (RT) and mass: charge (m/z) ratios. Area under the respective peaks is measured in arbitrary units (AU) and is directly proportional to their abundance. h, Conversion of 1mM HMBPP (black) to DMAPP / IPP (grey) in 30 min by 50nM Ec-IspH in presence of different concentrations (10-5000nM) of TPP (dotted lines) or C23.28 (solid lines), (n= 3 technical and 2 biological replicates). For f & h error bars represent mean of 3 independent experiments ± s.e.m. Source data are provided as a Source Data file. Extended Data Figure 5: C23 prodrugs specifically act on IspH and kill multi drug resistant clinical isolates of V. cholerae. a, Immunoblot shows modulation of IspH levels in CGSC8074 (E. coli) by altering Arabinose levels in culture medium. RpoD = loading control; representative of 3 independent experiments. CGSC8074 sensitivity to C23.28-TPP decreases with increasing IspH levels shown by b, Resazurin-blue assay and c, dynamic growth curves. Error bars represent mean of 3 independent experiments ± s.e.m. Respective p values: P<0.001, P<0.01, P<0.05, rest – not significant; by two-tailed paired Student’s t-test. d, TPP linked prodrug esters of C23 analogs 7, 20, 21,28 and 47 were tested for killing Vibrio cholerae (strain M045) by e, dynamic growth curves and Resazurin-blue assay (n= 3 biological and 8 technical replicates) or f, by CFU plating after 24 or 48h treatment (n= 3 biological replicates with 3 serial dilutions). MIC90= the minimum antibiotic concentration required to kill 90% of bacterial isolates. MIC90 for prodrug analogs tested on drug resistant clinical isolates of different pathogenic bacteria shown in Extended Data Fig. 8a. Error bars represent mean of 3 independent experiments ± s.e.m. Respective p values: P<0.001, P<0.01, P<0.05, rest – not significant; by two-tailed unpaired Student’s t-test. Source data are provided as a Source Data file. Extended Data Figure 6: DAIA prodrugs increase oxidative stress and cause defects in bacterial respiration, membrane integrity and cell wall architecture. Respiratory changes in E. coli treated with TPP or with the indicated concentration of the DAIA prodrug C23.28-TPP, were compared by measuring a, Oxygen consumption rate (OCR for aerobic respiration) and b, Extracellular acidification rate (ECAR for glycolysis). Respective p values: P< 0.001; by two-tailed unpaired Student’s t-test, relative to TPP treated control. c, Superoxide (solid line = at 2h, dotted line = 4h post treatment) and d, Hydrogen peroxide levels were simultaneously measured by DHE and Amplex red fluorescence respectively. n= 8 biological replicates and error bars represent means ± s.e.m. Changes in E. coli membrane integrity, upon TPP or prodrug treatment, measured by live-dead (SYTO9/PI) assay using e, flow cytometry or f, fluorescence microscopy. (n= 3 biological replicates, white bar = 2 microns). Loss of E. coli membrane potential, upon TPP or prodrug treatment, measured by BacLight (DIOC2) assay using g, flow cytometry or h, fluorescence microscopy. (n= 3 biological replicates, white bar = 2 microns). i, Scanning electron micrographs (SEM-top panels) and transmission electron micrographs (TEM- bottom panels) compare E. coli morphology after 8 hours of TPP or prodrug treatment to conditional ispH knockdown strain E. coli strain CGSC 8074 (ΔispH) kept for 8 hours in 1% glucose medium. Red arrows – membrane blebbing. j, SEM-top panels and TEM- bottom panels compare Vibrio cholerae morphology after 8 hours of TPP or prodrug (C23.28-TPP) treatment. For i & j representative images of 20 fields from 3 technical replicates and black bar = 400 nm. Source data are provided as a Source Data file. Extended Data Figure 7: DAIA prodrugs are stable in plasma and liver microsomes, non-toxic to mammalian cells, do not disrupt mitochondrial membrane potential in C2C12 myoblasts and do not disrupt hERG function. Nonlinear regression curves for degradation of prodrugs C23.28-TPP and C23.21-TPP and the appearance of the parent drugs C23.28 and C23.21 in the presence of a, human, pig and mouse plasma or b, human, monkey and mouse liver microsomes. Drug and prodrug concentration measured by LC-MS and normalized on a standard curve. The half-lives (t1/2) calculated from respective curves. Error bars represent mean ± s.e.m. of 3 independent experiments. c & d, Cytotoxicity of prodrug analogs on HepG2, RAW264.7, Vero cells and C2C12 myoblasts measured at 24, 48 and 72h by LDH release (n= 3 biological and 4 technical replicates). e, Effect of TPP and prodrugs C23.28-TPP & C23.47-TPP on mitochondrial membrane potential of C2C12 myoblasts, measured by tetramethyl rhodamine methyl ester (TMRM) fluorescence. Carbonyl cyanide m-chlorophenyl hydrazine (CCCP) = positive control (n= 3 biological and 4 technical replicates). f, C23.28-TPP, 6-hh-TPP and Me-TPP toxicity to hERG channel measured by automated Q patch assay, normalized current response plotted using non-linear regression curves and IC50 of respective compounds calculated. Error bars represent mean of 3 independent experiments ± s.e.m. Verapamil = positive control, DMSO = negative control. Source data are provided as a Source Data file. Extended Data Figure 8: Treating E. coli with IspH inhibitor prodrug disrupts the levels of IspH and several proteins in essential bacterial metabolic and synthesis pathways. a, Immunoblots measure relative levels of E. coli IspH at 8 and 24h after C23.28-TPP treatment or after conditional knockdown in CGSC 8074 (ΔispH) grown on 1% glucose. b, Immunoblots measure relative levels of IspH in clinical isolates of several pathogenic bacteria at 8 and 24h after C23.28-TPP treatment. For a & b RpoD immunoblot serves as loading control and blot representative of 3 technical replicates. c, Unsupervised hierarchical clustering of 2346 proteins resolved indicates that the 3 biological replicates for each condition clustered together. 525 proteins were either up or downregulated both on C23.28-TPP treatment or after conditional knockdown in CGSC 8074 (ΔispH). d, Functions/pathways significantly enriched at 8 and 24h after C23.28-TPP treatment. Bars indicate the −log10(p-value) with the number of proteins identified in each category next to the respective bar. The bars are color coded for the % of proteins in the pathway up/downregulated. e, Venn diagram compares the overlap in down regulated (>2-fold) proteins at 8 or 24h after C23.28-TPP treatment or after conditional knockdown in CGSC 8074 (ΔispH). f, Proteins important for lipid synthesis, ribosome modification, respiration, cell division, tRNA aminoacylation, DNA/RNA synthesis, DNA repair, amino acid synthesis and lipopolysaccharide cell wall synthesis pathways are among those significantly downregulated. Associated with Extended Data Fig 9a. P < 0.05 and FDR < 5%. g, Venn diagram compares the overlap in up regulated (>2-fold) proteins at 8 or 24h after C23.28-TPP treatment or after conditional knockdown in CGSC 8074 (ΔispH). h, Ribosome component proteins or proteins important for multi-drug efflux and oxidative defense pathways are among those significantly upregulated. Associated with Extended Data Fig 9b. P < 0.05 and FDR < 5%. Source data are provided as a Source Data file. Extended Data Figure 9: E. coli metabolic pathways up/down regulated on IspH inhibition. Pathway analysis of a, 323 downregulated (Extended Data Fig. 8e & f) or b, 60 upregulated (Extended Data Fig. 8g & h) proteins from proteomic screen comparing ΔispH E. coli and E. coli after C23.28-TPP treatment to untreated WT E. coli. Extended Data Figure 10: Dual action of IspH prodrugs expands and activates Vγ9Vδ2 T cells and reduces the emergence of antibiotic resistant bacteria. a, Uninfected (UI) human PBMC or those co-infected with E. coli analyzed for expansion CD3+ Vγ9TCR+ (γδ) T-cells and compared to untreated (UT) or TPP, prodrug (C23.07-TPP) or Kanamycin (Kan) treated PBMC (top panel). Gated γδ T cell populations analyzed for cytotoxic granule proteins granulysin (GNLY) and perforin (middle panels) or cell surface markers of T cell activation CD69 and HLA-DR (bottom panel). Representative of 4 independent experiments (4 donors). Percent of Vγ9+ T cells from CD3+ population and the percent of Vγ9+ T-cells with elevated expression of GNLY, Pfn, CD69 and HLA-DR were plotted in respective graphs. Error bars represent means ± s.e.m. Respective p values: P<0.001, P<0.05, rest not significant, calculated by one-way ANOVA relative to UT sample. b, Uninfected (UI) human PBMC or those co-infected with Vibrio cholerae (top panel) or Mycobacterium smegmatis (bottom panel) analyzed for expansion CD3+ Vγ9TCR+ (γδ) T-cells and compared to untreated (UT) or TPP, prodrug (C23.07-TPP) or Kanamycin (Kan) treated PBMC (n= 4 biological replicates). Percent of Vγ9+ T cells from CD3+ population were plotted in respective graphs. Error bars represent means ± s.e.m. Respective p values: P<0.001, rest not significant, calculated by one-way ANOVA relative to UT sample. c, Human PBMC co-infected with Kan resistant E. coli or V. cholerae can kill neither on their own. Addition of C23.07-TPP kills both Vibrio and E. coli (n= 2 biological and 3 technical replicates). Error bars represent means ± s.e.m. Respective p values: P<0.001, ns – not significant; by unpaired Student’s t-test relative to untreated samples. d, γδ T cell depletion from human PBMCs is verified by treating depleted (γδ−) and undepleted human PBMC treated with 10 μM HMBPP and 50 ng/ml IL-15. Representative of 4 independent experiments (4 donors). Percent of Vγ9+ T cells from CD3+ population on different days were plotted in respective graphs. Error bars represent means ± s.e.m. Respective p values: P<0.001comparing γδ depleted and undepleted PBMC calculated by unpaired t-test. e & f, MDR clinical isolates of Vibrio and Klebsiella grown for 18 serial passages in media (RPMI+ 10%HS) containing DAIA prodrug (C23.28-TPP) or conventional antibiotics (hygromycin (Hyg) or streptomycin (Strep)) gradually develop resistance when measured by CFU (top panels). Similar serial passages in presence of human PBMC inhibit development of antibiotic resistance against the DAIA prodrug but not against Hyg or Strep. Passages in γδ depleted (γδ−) PBMC show higher antibiotic resistance against DAIA prodrug. (n=3 technical replicates). Error bars represent means ± s.e.m. Respective p values: P<0.001, ns – not significant; by unpaired Student’s t-test. g, C57Bl/6 mice infected with Vibrio cholerae are treated with TPP or DAIA prodrug C23.28-TPP and monitored daily from day 2 post-infection for survival (n= 10 mice per group). h, Vibrio load in different organs at the experimental endpoint measured as CFU/mg (n=10 mice with 3 technical replicates), compares changes in bacterial CFU in C57Bl/6b mice following C23.28-TPP treatment. Error bars represent means ± s.e.m. Respective p values: P < 0.001; by unpaired Student’s t-test, relative to TPP treated mice. i, Hu-mice injected i.p. with HMBPP at different concentrations show dose dependent expansion of γδ T-cells but not αβ T-cells in blood taken every day up to a week (n=2 mice per group). Source data are provided as a Source Data file. Extended Data Figure 11: γδ T cell expand in tissues of prodrug treated, E. coli infected humanized mice. a, Hu-mice infected with E. coli (green) and treated with TPP or prodrug C23.07-TPP are compared for expansion Vδ2 TCR+ T-cells (red) in multiple organs at day5 post-infection. DAPI=blue, white bar = 100microns (representative of samples tested from 5-6 Hu-mice). b, Vδ2 antibody (Bio Legend cat #331402) validated for IF staining of formalin fixed human PBMC that are γδ expanded (HMBPP+IL15) or γδ depleted (using Anti-TCRγ/δ Microbead Kit - Miltenyi Cat #130–050-701). HepG2 and Vero cells serve as negative controls. c, Anti-E. coli antibody (Abcam ab137967) validated for IF staining of formalin fixed HepG2 cells co-infected with E. coli BL21 strain. HepG2 without E. coli serves as negative control. Supplementary Material 1 2 3 1646145_SourceData_Fig3 1646145_SourceData_Fig4 1646145_SourceData_Fig1 1646145_SourceData_Fig2 1646145_SourceData_ExtData_Fig1 1646145_SourceData_ExtData_Fig3 1646145_SourceData_ExtData_Fig4 1646145_SourceData_ExtData_Fig5 1646145_SourceData_ExtData_Fig6 1646145_SourceData_ExtData_Fig7 1646145_SourceData_ExtData_Fig8 1646145_SourceData_ExtData_Fig9 1646145_SourceData_ExtData_Fig10 1646145_SourceData_ExtData_Fig11 Acknowledgments Research reported in this publication was supported by the G. Harold and Leila Y. Mathers Charitable Foundation, Commonwealth Universal Research Enhancement Program (CURE – Pennsylvania Department of Health) and the Wistar Science Discovery Fund (FD). FD was supported by a Wistar Institute recruitment grant from The Pew Charitable Trusts. RSS and MH were funded by the Adelson Medical Research Foundation and DOD for Hu-mice generation. We are grateful to David Speicher from the Proteomics Facility at the Wistar Institute, Sudheer Molugu from the Electron Microscopy Resource Lab and Yuri Velich from Cell and Developmental Biology Microscopy core at UPenn. Support for the Wistar Institute Proteomics & Metabolomics and Genomics Shared Resources was provided by Cancer Center Support Grant P30 CA010815 and NIH instrument grant S10 OD023586. We thank Professors Michael Groll, Eric Oldfield, Audrey Odom John and Craig Morita for their expert advice in bacterial isoprenoid synthesis pathway, IspH and γδ T cell fields. Figure 1: Testing IspH as an ideal target for the DAIA strategy. a, IspH in the MEP pathway (gram-negative bacteria /mycobacteria or apicomplexan parasites) produces IPP and DMAPP from HMBPP and is absent in the mevalonate pathway (humans and complex metazoans). b, The E. coli strain CGSC 8074 produces IspH in the presence of arabinose but not glucose. Conditional knockdown of IspH by decreasing arabinose reduces the bacterial viability by CFU assay (n=3 biological and 3 technical replicates). Error bars represent means ± s.e.m. c, Human PBMC co-infected with WT or CGSC 8074 (ΔispH) E. coli analyzed for expansion CD3+ Vγ9TCR+ (γδ) T-cells after 24h and compared to Uninfected (UI) or HMBPP treated PBMC (top panel). Gated γδ T cell populations analyzed for cytotoxic granule proteins Gzm A and Pfn (middle panel) or cell surface markers of T cell activation CD69 and HLA-DR. Representative of 4 independent experiments (4 donors). Percent of Vγ9+ T cells from CD3+ population and the percent of Vγ9+ T-cells with elevated expression of GzmA, Pfn, CD69 and HLA-DR were plotted in respective graphs. Error bars represent means ± s.e.m. P<0.001 calculated by one-way ANOVA. d, Kinetic parameters of MV assay measured IspH activity using different concentration of Ec-IspH in the presence of different concentration of HMBPP at 30 min. Related to Extended Data Fig. 1d & e. e, Lineweaver-Burk double reciprocal plot of Ec-IspH activity at different concentrations of the enzyme and its substrate HMBPP. f, time dependent activity of 50nM Ec-IspH in the presence of 1mM HMBPP. g, titration of IspH activity for purified recombinant IspH from Plasmodium (Pf), Pseudomonas (Pa) or Mycobacterium LytB2 (MTB). For d-g n=3 biological replicates with 8 technical replicates. Error bars represent means ± s.e.m. Source data are provided as a Source Data file. Figure 2: Inhibition of purified IspH and bacterial killing by IspH inhibitors. a, Dose response (nonlinear regression) curves for inhibition of Ec-IspH by compounds C1-24, tested by methyl viologen assay. The half maximal inhibitory concentrations (IC50) calculated from respective curves (Supplementary Table 1). Error bars represent mean ± s.e.m. Associated with Extended Data Fig. 1d & e. b, Activity of Ec-IspH pretreated with DMSO (control), C10, C17 and C23 over time. Error bars represent mean ± s.e.m. Associated with Extended Data Fig. 3b. c, Inhibition of Mycobacterial (MTB), Pseudomonas (Pa) and Plasmodium (Pf) IspH by varying concentration of C17 or C23 with their IC50 (Supplementary Table 2). Error bars represent mean ± s.e.m. d, Dose response (nonlinear regression) curves for inhibition of Ec-IspH by C23 analogs, tested by methyl viologen assay. The IC50 calculated from respective curves (Supplementary Table 3). Error bars represent mean ± s.e.m. Associated with Extended Data Fig. 3e. e, E. coli killing by TPP linked prodrug analogs of C23.07, C23.20, C23.21, C23.28 (Supplementary Fig. 2e) compared to TPP treated controls by dynamic growth curve. MIC90= minimum drug concentration at which 90% bacteria are killed. Prodrug delivery into bacteria and cleavage into active form shown in Extended Data Fig. 4e-f. For a-d and e, n= 3 biological and 8 technical replicates. Source data are provided as a Source Data file. Figure 3: C23 prodrugs have lower MIC90 than best-in-class antibiotics against multi drug resistant clinical isolates of Gram-negative bacteria. Prodrugs C23.20-TPP, C23.21-TPP and C23.28-TPP as well as current best in class antibiotics tested on pan/multi drug resistant clinical isolates of E. aerogenes, V. cholerae, K. pneumoniae, A. baumannii and P. aeruginosa by a, Resazurin blue assay and b, CFU plating after 24h treatment (n=3 biological replicates). Resazurin blue assay: pink = bacterial growth, blue = no bacterial growth. TPP = negative control, uninfected = positive control. Error bars represent mean of 3 independent experiments ± s.e.m. Respective p values P<0.001, P<0.01, P<0.05, rest – not significant; by two-tailed paired Student’s t-test. Associated with Supplementary Table 5. Source data are provided as a Source Data file. Figure 4: γδ T-cell activation in prodrug treated, bacteria infected PBMCs and humanized mice. a, E. coli load (CFU/mg) in organs of NSG mice injected with γδ depleted (γδ −) or undepleted (γδ +) human PBMCs, infected with E. coli and treated with 1 mg/kg C23.28-TPP for 3 days b, CD3+ Vγ9TCR+ T-cell expansion in γδ − or γδ + NSG mice, four days post-infection. c, Percent of Vγ9+ T-cells from CD3+ cells in each organ. For 4a-c, n=10 mice with 3 technical replicates, error bars represent means ± s.e.m., p values: P < 0.001, P < 0.01, P < 0.05, ns - not significant; by two-tailed unpaired Student’s t-test, relative to γδ− mice. Hu-mice infected with E. coli are treated with C23.07-TPP (top panels) or C23.28-TPP (bottom panels) and monitored daily for d, survival, e, bacteremia in terms of CFU/mL of blood and f, E. coli load in different organs at the experimental endpoint measured as CFU/mg. g, CD3+ Vγ9TCR+ T-cell expansion in E. coli infected Hu-mice, treated with TPP or C23.07-TPP for five days post-infection. Percent of Vγ9+ T-cells from CD3+ cells in each organ. Associated with Extended Data Fig. 11a. For 4d-g, n=6 mice, 3 technical replicates, error bars represent means ± s.e.m., p values: P < 0.001, P < 0.01, P < 0.05; by two-tailed unpaired Student’s t-test, relative to TPP treated mice. Enterobacter aerogenes infected BALBc mice, treated with 10mg/kg TPP, C23.28-TPP or Meropenem and monitored for h, survival and i, Enterobacter load (CFU/mg) (n=19 mice, 3 technical replicates). 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 35066850 18351 10.1007/s11356-021-18351-z Green Finance and Low-Carbon Economic Recovery in the Post COVID-19 World Influence analysis of green finance development impact on carbon emissions: an exploratory study based on fsQCA http://orcid.org/0000-0003-1922-0911 Xiong Qiang [email protected] 12 Sun Dan 1 1 grid.440785.a 0000 0001 0743 511X School of Management, Jiangsu University, Zhenjiang, 212013 People’s Republic of China 2 grid.440785.a 0000 0001 0743 511X Research Center for Green Development and Environmental Governance, Jiangsu University, Zhenjiang, 212013 People’s Republic of China Responsible Editor: Ilhan Ozturk 22 1 2022 2023 30 22 6136961380 8 8 2021 22 12 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Increasing environmental degradation has forced policymakers to include sustainability in the economic growth agenda. Green finance has attracted the attention of policymakers and the industry, but the impact of green finance on social and environmental sustainability has not been confirmed. This study uses the panel data of 34 Chinese provinces to investigate the relationship between green finance and environmental degradation. The fuzzy set qualitative comparative analysis (fsQCA) method is utilized to analyze the mixed effect of green finance on CO2 emissions. These factors include green innovation, green insurance, green investment, and industrial structure. The results show that exogenous demand factors, including green insurance and industrial structure, have auxiliary effects when endogenous demand factors, including green investment and green innovation, exist as the core antecedent conditions among green finance and environmental degradation. Finally, the policymakers should encourage financial technology to actively participate in environmental protection initiatives that promote green consumption while minimizing the systemic risks caused by financial technology. Keywords Green finance Environmental degradation fsQCA Carbon emission issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023 ==== Body pmcIntroduction For the past few years, the deepening of green financing and a low-carbon economy has greatly impacted on each other. They are very necessary for the betterment of the environment. Low carbon is no longer a problem of the society itself because of green finance but has become an important part of the environment (Aleksandrov et al. 2013). Green finance and the low-carbon economy are closely related concepts. Its essence in the financial sector is to make the environmental protection as a basic policy, considering the potential environmental influence when we make the investment and financing decision (Lee 2020). Green finance is a convergence between harmless to the ecosystem conduct and the monetary and business world. As it may, not many investigations have connected finance with the environment. The relationship between monetary organizations’ appearance and their social responsibility was examined. They are reasoned that natural finance/supportable finance is the best method to decrease ecological debasement. Green finance and low-carbon economies are an important part of the national environment (Guild (2020), Mohsin et al. (2021), and Mohsin et al. (2018a). Since the mechanical unrest, the monetary area has been an incredible mainstay of human development. The essential job of the worldwide monetary area is to utilize the worldwide reserve funds (Li et al. (2021), Chien et al. (2021), and Iqbal et al. (2021)). Appropriate utilization of speculation empowers improvement in individuals’ satisfaction (Criscuolo and Menon 2015). Notwithstanding, due to the breakdown of the monetary framework, individuals have put their investment funds in land bubbles and earth-harming projects, including those that worsen human-initiated environmental change (Mohsin et al. 2018b). The monetary area already overlooked the biological system, which empowered the rise or deterioration of ecological issues, like environment and normal asset exhaustion, environmental change, and contamination (Zhang et al. 2019). Green finance supports interest in new advances and developments, including environmentally friendly power (Zhang et al. (2021), Hsu et al. (2021), and Ehsanullah et al. (2021)). Consequently, we are spurred to inspect the powerful effect of green finance on the carbon dioxide (CO2) outflows that help green finance. Until this time, hardly any investigations have connected finance to the environment. Iqbal et al. (2019) recommends that can accomplish natural manageability through creating financing for sunlight-based energy. A comparative report by Iqbal et al. (2020) additionally infers that ecological finance/economic finance is the best method to diminish natural corruption. Maintainable finance/green finance empowers interest in new advances and developments, including environmentally friendly power (Wang et al., 2021b). Be that as it may, past investigations disregarded the connection between green bonds (an intermediary for green finance) and CO2 discharges (Sun et al. (2020b) and Sun et al. (2020a)). Green bonds are long-haul monetary instruments in which the returns from Sun et al. (2020e), Sun et al. (2020c), and Sun et al. (2020d). Green bonds are utilized exclusively to fund harmless projects to the ecosystem or decrease contamination in the climate. For instance, green bond incomes are utilized to help sun-powered energy, clean water, and clean vehicle projects (Chandio et al. (2020) and Sun et al. (2020c)). What is the reason why green finance has a great impact on low carbon, especially in COVID-19? Researchers have worked on many interference factors affecting the low-carbon disclosure process (Agyekum et al. (2021) and Zhang et al. (2021)). These factors include green finance, green investment, and green innovation. However, the existing research focuses on the “net effect” of single influencing factors. It neither comprehensively identifies the factors influencing the carbon emission behavior nor explores the “joint effect” of multiple factors. Especially, the emission behavior of carbon is a complex situation nowadays and as well as in the future, which the traditional single-factor net effect analysis cannot completely explain:In the first attempt, we identify the critical criteria underpinning medical equipment replacement in the studied hospitals and provide a coherent program to plan the process and minimize the adverse impacts of inattention on this crucial stage in the fuzzy-TOPSIS model. The present study tries to take advantage of the fuzzy-TOPSIS technique and combine it with a fuzzy approach to tackle the lack of precise and comprehensive input information. The study proposed a hybrid approach as a programming tool to select replacement strategies for medical equipment. This study makes three contributions to the current writing, in the first place, contrasted with earlier investigations, which for the most part pressure the job of monetary turn of events, rather than just the impact of green finance on natural factors; this investigation presents a spearheading assessment of green finance and CO2 outflows. Besides, this investigation utilizes the QCA approach that catches the heterogeneous and deviated connection between green finance and low-carbon economies. This is one of the principal studies to consider 34 provinces in China in which green finance has been utilized fundamentally. The exact discoveries on the effect of green finance on their comparing CO2 emanations go about as benchmarks for different nations. At last, our observational examination gives new experiences into the lopsided reaction of CO2 discharges to green finance use at various QCA. Our contribution also includes applying the fuzzy analytical hierarchy process to influence the analysis of green finance development impact on carbon emissions based on fsQCA. Moreover, the interaction among various factors may replace or complement one another. Therefore, which factors affect carbon emission behavior? This is exactly the problem that this paper will explore. The rest of the paper is organized as follows: the second section explains the literature review and model construction, the third section discusses the design of research, the fourth section does empirical analysis and explains the results, and the fifth section concludes the study. Literature review and model construction Theoretical basis According to the classical synergetic, “synergy” signifies the cooperation between multiple subjects based on common goals, and carbon emission synergy is a typical synergy theory in environmental science. Finance assumes an essential part in the anthropogenic (i.e., human effect on the climate), yet very little has been done to fuse natural issues into finance. In the recent years, the monetary area has focused on green ventures, progressing manageable development. As indicated by Chen et al. (2021), green monetary instruments can accomplish a green climate. Meanwhile, monetary delegates and markets have developed green securities, greenhouse loans, green advances for company structures, and natural home value programs. Similarly, Australia launched its first natural store drive, which includes medium- to long-term financial instruments that finance non-harmful environmental undertakings and business exercises and directly support feasible events and environment-related ventures. Important components of a collaborative environment include green finance, green innovation, and green investment which was the major cause of low-carbon economies (Nawaz et al. 2021). Ren et al. (2020) studied green finance and the carbon loss in return-on-investment model after being hacked. The authors concluded that green finance significantly affects environmental decision-making under a given potential loss level.. With the high level of green financing, Jin et al. (2021) pointed out that enterprises tended to be compatible with affiliated enterprises. Model construction In traditional financial activities, two factors are mainly considered when studying the impact mechanism of carbon emissions. First, there is a link between enterprises’ credit financing activities and their level of financial development, i.e., the higher the level of financial development, the more convenient the credit financing of enterprises; second, after obtaining more credit funds, financial development encourages enterprises to increase R&D investment and improve their innovation ability. In other words, the higher the degree of development of green finance, the more enterprises are willing to promote green transformation and upgrading enterprises through green financial tools. Therefore, the inhibition mechanism of green finance on carbon emission can be expressed as follows:Green finance gives priority to supporting a low-carbon economy. Enterprises rely on green funds for low-carbon innovation. Low-carbon technology will reduce carbon emissions. Green financing can be divided into green innovation, green investment, and green bond. In addition, carbon emissions are also affected by many factors, such as openness level, industrial structure, and urbanization which are introduced. In this paper, the main factors of endogenous demand include green investment and green innovation. The main factors of exogenous demand that are considered include green insurance and industry type. Both internal and external demands reflect the degree of green finance. To sum up, this paper puts forward the following conceptual model as shown in Fig. 1.Fig. 1 Conceptual model Design of research Selection of research methods The qualitative comparative analysis (QCA) method adopted in this paper is a case-oriented method instead of a variable-oriented research method (Marks et al. (2018), Pappas and Woodside (2021), Wang et al. (2016), and Wang et al. (2021a)). QCA has been applied comprehensively in organization and management research at the technical analysis and research method levels, and the fuzzy set qualitative comparative analysis (fsQCA) method (Li 2019) has superior performance for studying “joint effect” and “interactive relationship” (Elliott 2013). Therefore, this paper uses the fsQCA method to analyze the “joint effect” of various factors on carbon emission behavior and the “interactive relationship” among various factors to identify the single factors influencing the carbon economies’ and green finance (Casady (2021), Gabriel et al. (2018), Skarmeas et al. (2014), and Maier et al. (2020)). The analysis results in a summary of the combination of factors that affect green finance in low-carbon economies (Pappas and Woodside 2021). Data, selection, and measurement of variables TOPSIS is a technique for moving closer to a positive ideal solution (i.e., minimizing the distance between criteria) and away from a negative ideal solution (maximizing the gap in each criterion). This approach is especially well suited to solving the group decision-making problem in a fuzzy setting (Prakash and Barua 2016). The combination of fuzzy mathematics with TOPSIS produces FTOPSIS, which is used to handle decision criteria issues in a fuzzy situation with uncertainty, immeasurable information, and incomplete knowledge. The key steps for multi-person multi-criteria decision-making with fuzzy are as follows: TOPSIS for addressing supplier selection. According to the above analysis, the choice of low-carbon emission or high-carbon emission is made due to the green finance effect in the model construction, i.e., the dependent variable. Green finance, green innovations, green insurance, and industrial structure are considered independent variables that reflect the internal and external carbon emission factors. This study uses the panel data for 34 Chinese provinces from 2003 to 2017. All the data were collected from the National Bureau of Statistics of China. Table 1 shows the description of each variable:Table 1 Description of variables Name of variables Description of variables Carbon emission Ratio of carbon dioxide emissions of each region to its GDP Green investment Green investment has a direct impact on carbon economies. If the green investment is high, the ratio of the carbon in the environment is low, and if the green investment is low, then the ratio high Green innovation Green innovation has a direct impact on carbon economies. If the green innovation is high, the ratio of the carbon in the environment is low, and if the green innovation is low, then the ratio of carbon in the environment is high Green insurance Green insurance, also known as environmental pollution liability insurance, is the most representative environmental pollution liability insurance in which insurance companies compensate pollution victims. According to law, it is based on the damage caused by pollution accidents to a third party and the liability for compensation Industrial structure Ratio of industrial added value of each region to its GDP The major problem is choosing and measuring the suitable site weights needed to meet the experts’ criteria requirements. The weights evaluated and assigned by individuals are usually controversial and uncertain. Generally, academia’s research forecasters, policymakers, professors, executives, and stakeholders are engaged to examine the weights score for each indicator. To achieve the objective of the current research, we have consulted with ten professionals from a university background, researchers, government institutes professionals, and associated stakeholders. These professionals have experience in assigning the weights to different variables for multiple case studies, and they have a piece of full knowledge about the country’s current situation and environment. The random consistency index and the consistency index provided by Ho and Ma (2018) were used to verify the 10 experts’ perspectives and findings. The software YAAHP (V. 10.5) has been used to get weights of the study’s proposed criteria. Table 2 shows the variables for fuzzy numbers.Table 2 Fuzzy numbers factors Sr. number linguistic variables TFN No Linguistic variable TFN 1 Very bad (VB) (0, 0.05, 0.15) 2 Bad (B) (0.1, 0.2, 0.3) 3 Fairly bad (FB) (0.2, 0.35, 0.5) 4 Fairly (F) (0.3, 0.5, 0.7) 5 Fairly good (FG) (0.5, 0.65, 0.8) 6 Good (G) (0.7, 0.8, 0.9) 7 Very good (VG) (0.85, 0.95, 1) Stage one Select the relevant linguistic variables for the value weight of selection criteria and supplier linguistic scores. Result shows the scoring methods used to score linguistic variables (Rouyendegh et al. (2020), Rajak and Shaw (2019), and Dhiman and Deb (2020)). Stage two Construct the fuzzy decision matrix. Let X~i=(xi1,xi2,xi3) be a TFNs for i∈I:1 R∼=[rij]m×n where i=1,2,3,⋯,m and j=1,2,3,⋯,n Stage three Normalize the fuzzy decision matrix:2 rij=(x1ijx3j∗,x2ijx3j∗,x3ijx3j∗) where x3j∗=maxx3ij (benefit criteria) normalization for cost (negative) measures (Unvan (2020), Dang et al. (2019), and Lima Junior et al. (2014):3 rij=(x1j-x3ij,x1j-x2ij,x1j-x1ij) x1j-=minx1ij (cost criteria) Stage four Determine the fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS) (Salih et al. (2019) and Sirisawat and Kiatcharoenpol (2018)): FPIS(A+)=(v~1+,⋯,v~j+,⋯,v~n+)FNIS(A-)=(v~1-,⋯,v~j-,⋯,v~n-) (10)where v~j+=(1,1,1)⊗w~j=(lwj,mwj,uwj) and v~j-=(0,0,0),j=1,2,3,....,n Stage five Calculate the distance of each supplier from FPIS (di+) and FNIS (di-), respectively:4 di+=(v1∗,v2∗,v3∗,⋯,vn∗) where Vj∗=1,1,1 j=1,2,3,⋯,n5 di-=(v1-,v2-,v3-,⋯,vn-) where Vj-=0,0,0 j=1,2,3,⋯,n Here, the distance between two TFNs X~=x1,x2,x3 and Y~=y1,y2,y3 can be asdX~,Y~=13[(x1-y1)2+(x2-y2)2+(x3-y3)2] (13) Stage six Calculate the coefficients and iteratively refine alternatives for achieving satisfaction for each criterion (Papapostolou et al. 2020):6 CCi=di-di++di- where i=1,2,3,⋯,m, di+ and di- are the distances from FPIS and FNIS, respectively. Results and discussion Descriptive statistics A descriptive statistical analysis of antecedent and outcome variables involved in the study is carried out, and the basic results are provided in Table 2. It can be noted from Table 3 that the correlation among green investment, green innovation, green insurance, and industrial structure is not very strong among carbon emissions. But without considering other factors, these four antecedents have a positively correlate with carbon emission. The paper analyzes the data further based on this information.Table 3 Correlation analysis Average value Standard deviation Green insurance Industrial structure Green innovation Green investment Carbon emission Green insurance 188.36 86.07 1 Industrial structure 0.813 0.297 0.05 1 Green innovation 5.711 1.574 0.106 0.273* 1 Green investment 179.8 79.01 0.041 0.035 0.019 1 Carbon emission 0.932 0.339 0.154*** 0.141* 0.3061** 0.088* 1 * means p < 0.001; means p < 0.01; * means p < 0.05. Calibration of variables The key difference between fuzzy sets and conventional variables lies in how they are conceptualized and labeled. Before the QCA can be run, the condition and outcome data must be calibrated. It is necessary to specify a target set to calibrate it as a fuzzy set, which constitutes the calibration of the set and provides a direct connection between the theoretical discourse and empirical analysis. In this study, the fsQCA was adopted, and the related antecedents and results were calibrated as fuzzy set membership scores using the method of direct calibration. Set membership does not have to be binary (0/1). Rather, in fsQCA, the aim is to calibrate set membership so that the levels of membership represent meaningful groupings. Out of the scores, the intersection point value has the greatest fuzziness, which determines whether most cases belong to or not belong to the target set on the value of the fixed-distance scale variable. Among the antecedent variables related to green finance and CO2 emissions chosen in this paper, the mean is the calibration standard for the intersection of green investment, green innovation, green insurance, and industrial structure. Other calibration standards are “mean – standard deviation” and “mean + standard deviation.” This choice is because the mean reflects the average level of carbon emission, while the standard deviation reflects the difference of carbon emission in a certain index. Table 4 shows the final calibration results.Table 4 Calibration threshold of each variable Variables Nonmembership anchor point Intermediate anchor point Complete membership anchor point Green investment 100.79 179.8 258.81 Green innovation 4.137 5.711 7.285 Green insurance 102.29 188.36 274.43 Industrial structure 0.516 0.813 1.11 Result analysis Single-factor necessity analysis Based on the general steps of the fsQCA, this paper first checks whether a single factor and its non-set constitute a necessary condition for the results, i.e., high-carbon emission and low-carbon emission among the green finance and CO2 emissions. This indicates that the result set is checked to see whether it is a subset of the single factor and its non-set, as determined by consistency. It is determined that the single factor or non-set is the necessary condition for the result set when the consistency level is higher than 0.9. Table 5 shows the results of fsQCA. It can be observed that all antecedents cannot constitute the necessary conditions for realizing the realization of specific results. The necessity of all single antecedents and their non-sets affecting low-carbon emission does not exceed 0.8, and the necessity of influencing high-carbon emission does not exceed 0.6. Therefore, all single antecedents do not constitute necessary conditions for low-carbon emission or high-carbon emission.Table 5 Test of adequacy and necessity of antecedents Low-carbon emission High-carbon emission Consistency Coverage Consistency Coverage Green insurance 0.448251 0.735248 0.481222 0.28638 ~ Green insurance 0.551749 0.723511 0.518778 0.216394 Green innovation 0.452361 0.701241 0.552362 0.290216 ~ Green innovation 0.547639 0.784562 0.447638 0.213542 Green investment 0.421561 0.745123 0.463251 0.263512 ~ Green investment 0.578439 0.763581 0.536749 0.2521684 Industrial structure 0.621534 0.700236 0.583962 0.2723651 ~ Industrial structure 0.378466 0.762821 0.416038 0.285135 Analysis of the adequacy of antecedent configuration Antecedent configuration analysis reveals the sufficiency of the outcome caused by different configurations composed of multiple antecedent conditions. The fsQCA 3.0 program was used to process data for the truth table in this study:Case frequency threshold. Rihoux et al. (2009) proposed choosing the frequency threshold such that the number of retained cases is higher than or equal to 75% of the total number of cases. In this paper, the total number of cases is 956. Four antecedent conditions will generate 16 configurations in the truth table. So the frequency threshold of the effective antecedent condition combination is set to 50. Raw consistency threshold. Consistency measures “how closely a perfect subset relation [between a configuration and an outcome] is approximated”; in the simple case of crisp sets, consistency is the proportion of cases exhibiting the configuration that exhibits the outcome. In this paper, the outcome is carbon emission. It is good practice to establish different consistency thresholds for necessity and sufficiency analyses and not to interpret subset relations that do not meet these thresholds. The antecedent condition configuration where the raw consistency value is higher than the threshold is a subset of the outcome, and the outcome is assigned 1; otherwise, it is 0. In this paper, the minimum threshold of raw consistency is set to 0.75. After analyzing the truth table, three kinds of solutions are obtained, complex solution, concise solution, and optimized solution. The complex solution does not include any logical remainder. The intermediate solution only includes the logical remainder in line with the theoretical direction and empirical evidence. And the simplified solution includes all the logical remainder without evaluating its rationality. So, intermediate solution is considered the first choice for reporting and interpretation in QCA results. Based on previous research, this work describes the intermediate solution, with the simpler solution serving as an auxiliary. Table 6 shows the configuration results of green finance conditions before carbon emission, where “●” and “ ⊗ ” indicate the existence and absence of a core antecedent condition, respectively, and “●” and “ ⊗ ” indicate the existence and absence of an auxiliary antecedent condition, respectively. Blanks can indicate either existence or absence of antecedent conditions.Table 6 Precondition configuration for carbon emission Type Low-carbon emission High-carbon emission L1 L2 L3 H1 H2 H3 Green investment ● ● ⊗ ⊗ Green innovation ⊗ ⊗ ● ⊗ ⊗ Industrial structure ● ● ⊗ ● ● Green insurance ● ⊗ ● ⊗ Coverage rate 0.143 0.235 0.273 0.171 0.219 0.199 Net coverage rate 0.143 0.103 0.078 0.077 0.077 0.136 Consistency 0.789 0.801 0.774 0.768 0. 801 0.757 Total coverage 0.508 0.496 Total consistency 0.769 0.771 Table 6 shows that the consistency of each configuration and the total consistency are higher than the minimum acceptable standard of 0.75 in both models. The total coverage rates of low-carbon emission and high-carbon emission are 0.508 and 0.497, respectively, equal to those obtained using QCA research in the fields of organization and management. From the results, it can be gathered that the fsQCA effectively identifies six antecedent configurations. The identifications can indicate whether the existence or absence of antecedent factors has a positive or negative impact on high or low-carbon emission. In low-carbon emission implementation configurations L1 (industry structure + green investment + ~ green innovation) and L2 (green insurance + green investment + ~ green innovation), green investment exists as the core precondition, and the lack of green innovation plays an auxiliary role. In the former configuration, the industrial structure plays an auxiliary role. In contrast, in the latter configuration, green insurance plays a core role when the industrial structure either exists or is absent. The industrial structure and green innovation play a core role, while the lack of green insurance plays an auxiliary role in L3 (industrial structure + ~ green insurance + green innovation). Green investment and the lack of industrial structure are the core antecedents. Green insurance plays a key role in configuration H1 (~ industrial structure + green insurance + ~ green investment), which causes high-carbon emissions. In H2 (industrial structure + ~ green investment + ~ green innovation), green investment and the lack of green innovation are core antecedents, and industrial structure exists as auxiliary antecedents. In H3 (industrial structure + ~ green insurance + ~ green investment), green innovation and the lack of green insurance are core antecedents, and industrial structure exists as core antecedents. The total consistency of the configurations in this study is 0.769, which indicates that the interpretation degree of the six configurations concerning the carbon emission behavior of enterprises is 76.9%. The total coverage rate is 0.508, which indicates that the research results can cover 50.8% of cases. It is necessary to simultaneously analyze the consistency and coverage of all configurations during qualitative comparative analysis. The consistency of the six configurations is about 0.79, which proves that there is a good subset relationship between the six configurations and high- or low-carbon emission, signifying a high explanatory capability of carbon emission behavior. It can be concluded based on the results that the fsQCA can effectively identify six antecedent configurations, which show how the existence or absence of each element in different antecedent configurations affects carbon emission behavior. Configuration effect as a robustness test This paper adjusts the consistency threshold and reprocesses the sample data based on well-known research results. The original minimum consistency threshold is adjusted from 0.75 to 0.76. The antecedent configuration obtained under the consistency threshold of 0.76 is the same as that obtained 0.75, which is consistent with the conclusion as mentioned earlier. Therefore, this paper obtains robust research conclusions such as the sensitivity analysis is carried out to assess the robustness of the findings obtained, for example, to investigate how the ranking of alternatives evolves as the weight of the criterion changes. As a result, the impact of weight factor weights on the prioritization order of the strategies (i.e., alternatives) has been explored during the sensitivity analysis phase. In this sense, ten cases were created and evaluated by adjusting the weights of the weight factors to determine the outcome/priority of the strategies. Table 7 shows the various weights of weight factors in these ten cases. The importance of the factors is given in column 2 of Table 7, followed by ten other patients evaluated using the sensitivity analysis. The factor weights have remained constant in the vast majority of cases. Finally, Table 7 shows the ranking of the methods based on ten issues of sensitivity analysis. In these tests, it is discovered that the ranking order of strategy weight factors has changed in cases 1, 5, and 6, while the priority order of the strategy has remained constant in the remaining cases.Table 7 Weight factor weights for real and different cases Carbon emission Green investment Green innovation Green insurance Industrial structure Case 1 0.15 0.26 0.28 0.27 Case 2 0.21 0.21 0.22 0.22 Case 3 0.23 0.31 0.33 0.33 Case 4 0.37 0.42 0.44 0.44 Case 5 0.11 0.10 0.11 0.11 Case 6 0.09 0.21 0.22 0.22 Case 7 0.23 0.31 0.33 0.33 Case 8 0.37 0.36 0.39 0.38 Case 9 0.10 0.16 0.17 0.16 Case 10 0.12 0.36 0.39 0.38 Following a sub-factor analysis using the AHP technique, this section presents the prioritizing order of eight health strategies/alternatives from Table 7 using the FTOPSIS approach. The research performed by the study’s expert group aided in developing of a fuzzy evaluation matrix into TFNs using linguistic variables. As a result, the assessment matrix concerning the alternatives was calculated in this analysis. This came after developing of a fuzzy decision matrix, a fuzzy normalized decision matrix, and a weighted normalized fuzzy decision matrix for each element of this analysis (see Supplementary section). The ranking of emission reduction factors and sub-factors was then established. Finally, Table 8 shows the prioritized order of the eight health planning strategies.Table 8 Final ranking of carbon emission strategies Factors di + di − CCi Rank Carbon emission 13.56 0.62 0.15 3 Green Investment 13.52 0.67 0.13 5 Green innovation 13.54 0.64 0.16 1 Green Insurance 13.55 0.65 0.10 8 Industrial structure 13.54 0.63 0.12 7 Source: Research findings Column five of Table 9 provides the ranking of this study’s energy strategies, briefly discussed concerning their rank.Table 9 Inner matrix factor Carbon emission Green Investment Green innovation Green Insurance Industrial structure Carbon emission (1, 1, 1) Green Investment (2, 3, 4) (1, 1, 1) Green innovation (5, 6, 7) (2, 3, 4) (1, 1, 1) Green Insurance (4,5,6) (1,1,1) Industrial structure (7,8,9) Importance weights (0.364, 0.453, 0.552) (0.154, 0.282, 0.287) (0.042, 0.093, 0.142) Table 9 shows the weight factor dependency matrices, in which one factor is regulated in each case. The carbon emission, for example, was managed to assess the relationship between green investment, green innovation, and green insurance. In Table 9, the fuzzy value weights of factors are shown. Table 9 shows that the expert’s answers are all consistent. Pairwise comparisons and studies of the effect of the factors on one another were used to assess the inner dependence of the weight factors. As previously mentioned, it is not always possible to conclude that all weight variables are independent. The use of both the weight analysis and the AHP method simultaneously could yield more acceptable and practical results. The dependency of the weight factors shown schematically in results can be calculated by studying the internal and external environments of the studied hospitals. Theoretical analysis of configuration effect This paper puts forward the following three research propositions, based on the antecedent configuration of carbon emission and the theoretical analysis behind it and comparison with the antecedent configuration of carbon emission:The green investment is the core prerequisite for low-carbon emission under the condition of the environment. The comparative analysis of green investment shows that high green investment leads to a relatively high proportion of low-carbon emission sample cases (L1, L2). The coverage rates for these two cases are 0.143and 0.235, respectively, and the consistency rates are 0.789 and 0.801, respectively, which are higher than the total consistency of low-carbon emission sample cases of 0.769. However, the proportion of sample cases with low green investment (H1, H2) that cause high-carbon emission is also high, with coverage rates of 0.171 and 0.219 and consistency rates of 0.768 and 0.801, respectively. Zhang et al. (2020) proved through empirical analysis that high green investment has a high impact on low-carbon emission behavior. These results theoretically demonstrate that the green investment is one of the core elements in low-carbon emission. However, only a single core condition of a high green investment level is insufficient to force low-carbon emissions. In L1, the industrial structure exists as an auxiliary condition, i.e., it enlarges the possibility of emission. In L2, green insurance exists as another core condition. Higher green insurance causes a lower loss caused by high-carbon emission. One of the necessary conditions for enterprises reducing carbon emissions is green investment of endogenous demand factors in low-carbon emission. However, it is not a sufficient condition. Industrial structure and green insurance of exogenous demand factors jointly affect the decision of enterprises carbon emission. When compared to configurations H2 and H3, the coverage rate of configuration L3 is the highest among all configurations, at 0.273. If the antecedent of a high energy consumption industrial structure exists and if the antecedent of green innovation does not exist, it is very likely to lead to the high-carbon emission behavior of enterprises, regardless of whether green insurance and green investment are missing or pending, e.g., the cover rate of configuration L3 is the highest among all configurations at 0.273. The industry has made outstanding contributions to economic development. But behind this contribution is the cost of environmental sacrifice. The annual increase in carbon dioxide emissions has greatly damaged the living environment and affected climate improvement. Green finance should be guided by promoting industrial structure adjustment, guiding and supporting enterprises to carry out technological innovation, and reducing carbon emissions. Enterprises use green funds to invest in new energy R&D, give policy preference to industries that pay attention to energy conservation, emission reduction, and environmental protection, and prioritize their green investment and financing activities. The development of green finance is to guide enterprises to achieve energy conservation and emission reduction through green funds, build a low-carbon economy, and achieve sustainable economic development. During the process of carbon emission management, the green investment should coexist as a core condition of green insurance is a core precondition. This increases the possibility of enterprises to decreasing carbon emissions. If green insurance is present as a core condition, green investment is absent. The industrial structure is absent as an auxiliary antecedent condition; businesses’ willingness to reduce carbon emissions falls regardless of the extent of green innovation, i.e., H1 in the table is configured with a coverage rate of 0.171 and a consistency of 0.768.. In the process of transformation from a high-carbon economy to a low-carbon economy, enterprises are full of uncertainty in strategic transformation and green technology development. Insurance institutions can design targeted and innovative green insurance products and services for businesses, assisting them in effectively dealing with innovation risks in green technology R&D and allowing businesses to have more “trial and error space” in the practice of green transformation as an important financial institution for climate change-related risk management.. Green insurance can promote enterprises to decrease carbon emissions. Conclusion and policy implication This article employed fsQCA to gather sample case data on green financing and carbon emissions to cope with the present high-carbon emissions, which have a clear influence on climate change. It discussed the “joint effect” of green investment, green innovation, green insurance, and industrial structure on carbon emissions to explore the factor configuration under different circumstances. It was observed that the antecedents were neither a necessary nor a sufficient condition for carbon emission. Enterprises’ CO2 emissions behavior resulted from multiple antecedents, characterized by “multiple concurrency.” This paper analyzed the antecedent configuration of high-carbon and low-carbon emissions and obtained three main paths to promote enterprises to decrease carbon emission. Each path was made up of many antecedent elements. This conclusion shows that, unlike previous studies that focused on a single factor, such as urban degree, emission cost, and benefit, enterprises’ carbon emission behavior should be examined from an overall perspective. Based on the research as mentioned earlier conclusions, it is suggested that attention should be paid to the following factors during the process of carbon emission government:Gradually improve the standards of the green financial system, optimize the green financial structure, and improve the enthusiasm of financial institutions and enterprises to participate in green financial activities. Further, deepen the innovation of green financial products, research and develop green securities and carbon financial products, and cultivate green financial markets. Encourage private institutions and investors to participate in green financial activities; establish a green financial cooperation mechanism integrating banks, guarantees, insurance, and securities institutions; and compensate the risks borne by investors employing loss reserves and green insurance subsidies to attract private capital to participate in green financial activities actively. Constructing green communication mechanism and credit evaluation system in financial activities. The information communication mechanism between investors and investees is related to the interests of both sides. The accuracy of sharing financial institutions’ information is the basis of a successful follow-up. A standardized information receiving, processing, and publishing process should be established to ensure the authenticity and integrity of information. At present, in the development of green finance in China, the government and financial institutions do not grasp the environmental protection information and low-carbon behavior of enterprises, which leads to investors’ inability to make investment decisions accurately or easily leads to decision-making risks. A green information communication system must be established to address the practical issue of erroneous information disclosure, allowing financial institutions and businesses to communicate green environmental protection information and allow actual green enterprises to benefit from green funds. Simultaneously, a green credit assessment system should be developed, a negative list of green companies should be established, and credit punishment for fake green firms and unlawful green investment should be implemented. Promote the green transformation of industrial structure. Green finance should be guided by promoting industrial structure adjustment, supporting enterprises to carry out green innovation, and reducing carbon emissions. For enterprises that use green funds to invest in new energy R&D, give policy preference to industries that pay attention to energy conservation, carbon emission reduction, and environmental protection and give priority support to their green investment, green insurance, and financing activities. The development of green finance is to guide enterprises to achieve energy conservation and emission reduction through green funds, build a low-carbon economy, and achieve sustainable economic development. This study still has a few shortcomings: (1) identifying factors influencing carbon emission behavior is not comprehensive enough, and (2) the research model may not include all antecedents. For example, competition factors among enterprises, carbon cost, and responsibility sharing of loss may affect carbon emission behavior. More comprehensive data will be collected for further exploration to deal with this issue. The local governments achieve green governance learning. The green production efficiency, the degree of renewable energy consumption, and the purchase of domestic technology funding and social patent transfer income reflect each local government’s participation in innovation and green energy. Internal expenditure on R&D funding characterizes the extent to which it helps transform towards green energy, which is supported internally in each phase. Author contribution Qiang Xiong was a major contributor in writing the manuscript. Dan Sun analyzed the data. All authors read and approved the final manuscript. All authors of this manuscript have directly participated in the planning, execution, and analyses of this study. Availability of data and materials All materials and data which was generated or analyzed during this study were included in this article. Declarations Ethics approval Ethical approval was not required for this research. Consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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Technol Anal Strateg Manag. 10.1080/09537325.2020.1773423	35066850	PMC8783589	NO-CC CODE	2023-05-14 23:15:32	yes	Environ Sci Pollut Res Int. 2023 Jan 22; 30(22):61369-61380
==== Front Sci Total Environ Sci Total Environ The Science of the Total Environment 0048-9697 1879-1026 Elsevier B.V. S0048-9697(22)01080-4 10.1016/j.scitotenv.2022.153988 153988 Article Can spike fragments of SARS-CoV-2 induce genomic instability and DNA damage in the guppy, Poecilia reticulate? An unexpected effect of the COVID-19 pandemic Gonçalves Sandy de Oliveira a Luz Thiarlen Marinho da a Silva Abner Marcelino a de Souza Sindoval Silva b Montalvão Mateus Flores c Guimarães Abraão Tiago Batista d Ahmed Mohamed Ahmed Ibrahim e Araújo Amanda Pereira da Costa f Karthi Sengodan g Malafaia Guilherme abcd⁎ a Laboratório de Pesquisas Biológicas, Instituto Federal de Educação, Ciência e Tecnologia Goiano – Campus Urutaí, GO, Brazil b Programa de Pós-Graduação em Conservação de Recursos Naturais do Cerrado, Instituto Federal de Educação, Ciência e Tecnologia Goiano – Campus Urutaí, GO, Brazil c Programa de Pós-Graduação em Ecologia e Conservação de Recursos Naturais, Universidade Federal de Uberlândia, MG, Brazil d Programa de Pós-Graduação em Biotecnologia e Biodiversidade, Universidade Federal de Goiás, GO, Brazil e Plant Protection Department, Faculty of Agriculture, Assiut University, Assiut, 71526, Egypt f Programa de Pós-Graduação em Ciências Ambientais, Universidade Federal de Goiás, GO, Brazil g Division of Biopesticides and Environmental Toxicology, Sri Paramakalyani Centre for Excellence in Environmental Sciences, Monomania Sundaranar University, Alwarkurichi 627 412, India ⁎ Corresponding author at: Biological Research Laboratory, Goiano Federal Institution – Urutaí Campus, Rodovia Geraldo Silva Nascimento, 2,5 km, Zona Rural, Urutaí, GO CEP: 75790-000, Brazil. 19 2 2022 15 6 2022 19 2 2022 825 153988153988 15 12 2021 14 2 2022 15 2 2022 © 2022 Elsevier B.V. All rights reserved. 2022 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The identification of SARS-CoV-2 particles in wastewater and freshwater ecosystems has raised concerns about its possible impacts on non-target aquatic organisms. In this particular, our knowledge of such impacts is still limited, and little attention has been given to this issue. Hence, in our study, we aimed to evaluate the possible induction of mutagenic (via micronucleus test) and genotoxic (via single cell gel electrophoresis assay, comet assay) effects in Poecilia reticulata adults exposed to fragments of the Spike protein of the new coronavirus at the level of 40 μg/L, denominated PSPD-2002. As a result, after 10 days of exposure, we have found that animals exposed to the peptides demonstrated an increase in the frequency of erythrocytic nuclear alteration (ENA) and all parameters assessed in the comet assay (length tail, %DNA in tail and Olive tail moment), suggesting that PSPD-2002 peptides were able to cause genomic instability and erythrocyte DNA damage. Besides, these effects were significantly correlated with the increase in lipid peroxidation processes [inferred by the high levels of malondialdehyde (MDA)] reported in the brain and liver of P. reticulata and with the reduction of the superoxide dismutase (SOD) and catalase (CAT) activity. Thus, our study constitutes a new insight and promising investigation into the toxicity associated with the dispersal of SARS-CoV-2 peptide fragments in freshwater environments. Graphical abstract Unlabelled Image Keywords Mutagenicity Genotoxicity Freshwater fish Water pollution Viral particles COVID-19 Editor: Damià Barceló ==== Body pmc1 Introduction Pandemically, COVID-19 (Coronavirus Disease-2019), caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has promoted unprecedented global impacts (Siddique et al., 2021), whether at an economic level (Maital and Barzani, 2020), public health (Sarkodie and Owusu, 2021) and social disruption (Viladrich, 2021). The United Nations University World Institute for Development Economics Research (UNU-WIDER) estimates that approximately 500 million people may succumb to poverty as a result of the new coronavirus (Sumner et al., 2020). Data on the status of the pandemic in the world (obtained on 10 January 2022) record more than 305 million confirmed cases and more than 5 million deaths in 236 countries, areas, or territories (WHO, 2022). As far as we know, the classic form of transmission of SARS-CoV-2 is by air and/or via contact with infected people (Meyerowitz et al., 2020; Harrison et al., 2020). However, other forms of transmission of the new coronavirus have been investigated due to the persistence of the virus in the environment for a few hours/day. In addition, one of these forms refers to possible contamination/transmission via the fecal-oral or fecal-nasal route (Giacobbo et al., 2021). In spite there is little concrete information on the subject, many researchers have warned about the possibility of infection through direct contact with domestic sewage or contaminated water (Westhaus et al., 2021; Gonçalves et al., 2021; Albastaki et al., 2021; Paul et al., 2021; Sangkham, 2021; Baldovin et al., 2021; Sharif et al., 2021; Wu et al., 2022; Vo et al., 2022), with the aerosols generated in the systems of wastewater pumping and treatment (Gormley et al., 2020; Usman et al., 2021); toilet flushing (Ali et al., 2021; Sun and Han, 2021; Ding et al., 2021; Usman et al., 2021) and also via faulty connections of floor drains interconnected with the main piping of buildings/houses (Shi et al., 2021). Regardless of whether these studies are still initially to epidemiological conclusions of definitive practical applications, the fact is that the new coronavirus or its fragments have already been identified in different fluvial systems and, therefore, constitutes a consolidated reality (Rimoldi et al., 2020; Guerrero-Latorre et al., 2020; Mahlknecht et al., 2021). As discussed by Guerrero-Latorre et al. (2020), in countries with a lack of basic sanitation, the spread of SARS-CoV-2 in freshwater environments may be even greater, considering, for example, that in numerous countries less than 30% of the sewage generated is treated before being discharged into the streams (Rodriguez et al., 2020). As a consequence, questions arise from this scenario about the extent to which the presence of the new coronavirus (or its fragments) in surface water represents an (eco)toxicological risk for non-target organisms. Our research group recently reported some effects arising from the exposure of amphibians, fish, and insects to distinct protein fragments of the Spike protein of SARS-CoV-2 (Charlie-Silva et al., 2021; Mendonça-Gomes et al., 2021; Malafaia et al., 2022). Initially, from a systemic approach (including the synthesis, cleavage, purification, and alignment of three peptide fragments of the SARS-CoV-2 Spike protein, as well as the exposure of neotropical Physalaemus cuvieri tadpoles to these fragments) we gathered evidence that confirms the toxicity of the viral constituents in the evaluated animal model. The increase in several biomarkers predictive of oxidative stress and the alteration in acetylcholinesterase (AChE) activity demonstrated that the short exposure (24 h) to these peptides was sufficient to affect the health of tadpoles (Charlie-Silva et al., 2021). In the study by Mendonça-Gomes et al. (2021), we showed for the first time that short-term exposure (48 h) of PSPD-2002 and PSPD-2003 peptides (at 40 μg/L) induced alterations in the locomotor system and in the olfactory behavior of Culex quinquefascitus larvae, which were associated with increased production of reactive oxygen species (ROS) and AChE activity. In Malafaia et al. (2022), we show that exposure to the aforementioned peptide fragments can also alter the behavior of fish (Poecilia reticulata), induce redox imbalance, as well as affect the growth and development of animals. Therefore, these studies “shed light” on the (eco)toxicological potential of peptide fragments of SARS-CoV-2 in aquatic biota, going beyond the works that have focused on the susceptibility of different mammalian species to viral infection and their roles in the dissemination of COVID-19 (e.g.: Tiwari et al., 2020; Delahay et al., 2021; Audino et al., 2021). In this regard, any definitive conclusions about the ecotoxicological impacts caused, particularly, by the presence or dispersion of SARS-CoV-2 (or its protein fragments) in aquatic environments are very incipient, as well as on how much this can enhance the already known impacts on aquatic and terrestrial species. There are many gaps to be filled, and our understanding of the scope of their effects on other faunal species and their mechanisms of action is very limited. Thus, in the present study, we aimed to evaluate the potential mutagenic and genotoxic effects on erythrocyte of Poecilia reticulata (a model system traditionally used in ecotoxicological studies) induced by exposure to one of the previously synthesized peptide fragments (Charlie-Silva et al., 2021). In this study, we determined the potential association between mutagenic and genotoxic effects with the induction of redox imbalance in various organs/tissues of the evaluated animals. Importantly, our motivation for conducting this study is based on recent studies that demonstrated that SARS-CoV-2 infection induces the formation of micronuclei and the activation of DNA damage pathway [in syncytia and Hela-ACE2 cells Ren et al., 2021] and DNA damage response in Vero E6 cells (Victor et al., 2021). It is therefore questionable whether similar effects are observed in non-target organisms (P. reticulata) when exposed to peptide fragments of SARS-CoV-2 dispersed in water. We believe our findings help to explain the ecotoxicological effects of SARS-CoV-2 at cellular and molecular levels, providing new potential targets for an investigation into the impacts of COVID-19 on wild freshwater ichthyofauna. 2 Material and methods 2.1 Peptide fragments of the SARS-CoV-2 spike protein The synthesis, cleavage, purification, and characterization of the peptides from the SARS-CoV-2 Spike protein used in our study were performed according to methods described in detail by Charlie-Silva et al. (2021). Briefly, the synthesis of the Spike S protein was conducted using the solid phase peptide synthesis method (SPPS) following the Fmoc strategy (Raibaut et al., 2014; Behrendt et al., 2016). The resins used in this process were Fmoc-Thr-Wang and Fmoc-Asn-Wang for the PSPD-2002 (sequence: Gln-Cys-Val-Asn-Leu-Thr-Thr-Arg-Thr-COOH; MW: 1035.18 g/mol) and PSPD-2003 (sequence: Asn-Asn-Ala-Thr-Asn-COOH; MW: 532.51 g/mol), respectively. At the end of the synthesis, these resins made it possible to obtain peptides with the carboxylated C-terminal end. After coupling all the amino acid residues of the peptide sequences, the chains were removed from the solid support using acid cleavage using trifluoroacetic acid (TFA), similarly to Guy and Fields (1997). The crude compounds were purified by high-performance liquid chromatography (HPLC) with a reverse-phase column using different purification methods according to the retention time obtained in a gradient program of 5 to 95% in 30 min (exploration gradient) in Analytical HPLC [similarly to Klaassen et al. (2019)]. Only compounds with purity equal to or greater than 95% were considered for in vivo evaluation, following the rules determined by the National Health Surveillance Agency (ANVISA/Brazil) and Food and Drug Administration (FDA/USA). The similarities between the peptides PSPD-2002 and PSPD-2003 were evaluated using the CLUSTAL W software version 1.83 [Higgins et al., 1996; Pais et al., 2014 - http://www.ebi.ac.uk/clustalw/]. Fig. 1 shows the structural models of the PSPD-2002 and PSPD-2003 peptides tested in our study.Fig. 1 Frequency of micronucleus (MN test) and others erythrocytic nuclear alterations (ENA) in adult females Poecilia reticulata exposed or not to PSPD-2002 peptide fragments (at 40 μg/L). (A) Total ENA, kidney-shaped nucleus, blebbed nucleus, nuclear constriction, and multi-lobed nucleus; (B) displaced nucleus, notched nucleus, binucleated cell, micronucleus, and nuclear vacuole. The bars indicate the mean ± SD of the data, which were submitted to Student's t-test (if parametric) or Mann-Whitney U test (if non-parametric) (see the statistical summary at the top of the graphs). Different lowercase letters indicate significant differences between experimental groups. n = 8 animals/group. Fig. 1 2.2 Animals and experimental setup We used in our study individuals of the species Poecilia reticulata (Cyprinodontiformes: Poeciliidae) (wild strain), commonly known as ‘guppy’, and considered native to northwestern South America (Bisazza, 1993). This species was selected based on its wide distribution in neotropical regions (CABI, 2021), in which it can inhibit strongly impacted aquatic environments where few species can occur (Araújo et al., 2009), as well as its previous use in different ecotoxicological studies (Aich et al., 2015; De-Lima Faria et al., 2021; De-Souza-Trigueiro et al., 2021). Preliminary, females were captured in a natural environment (municipality of Urutaí, GO) (license SISBIO/ICMBio/MMA/Brasil n. 73342-1), taken to the laboratory, and kept in an aquarium (60 L) containing dechlorinated water and constant oxygenation, under room temperature (25–26 °C) and photoperiod controlled (12–12 h light: dark cycle). After 60 days of acclimatization, 16 non-pregnant females of P. reticulata were separated and distributed into two experimental groups (four replicates/group). The group “PSPD-2002” was composed of P. reticulata exposed (for 10 days) to the peptides at a concentration of 40 μg/L, diluted in water. Such concentration is considered predictive, as SARS-CoV-2 particles have been identified and quantified via RT-qPCR assays applied for SARS-CoV-2 RNA detection. Therefore, the units of measurement are not comparable or convertible into “μg/L”. The control group consisted of fish kept in dechlorinated water (naturally) free of viral peptides. Each replica consisted of two animals kept in cylindrical aquariums with 2.2 L of dechlorinated water (under constant oxygenation), without using filters or substrates. The temperature (25–26 °C) and luminosity (12–12 h light: dark cycle) conditions were properly controlled. Every three days there was a complete renewal of the exposure waters, and, at the end of the experiment, the animals were submitted to different evaluations, as described below. 2.3 Toxicity biomarkers 2.3.1 Micronucleus test and other erythrocytic nuclear alteration The possible mutagenic effect of exposure to PSPD-2002 peptides was evaluated using the frequency of micronucleus test (MN test) and other erythrocytic nuclear alteration (ENA), as described by Carrasco et al. (1990) and modified by Guimarães et al. (2021). Briefly, 5 μL of blood (collected via cutting the caudal peduncle – after the animals were deeply anesthetized in ice-cold water) was deposited on a previously sanitized glass slide to form a thin smear, which was dried at room temperature. Next, slides were fixed in 100% (v/v) cold methanol and stained with Panotic Rapid® (Laborclin®, Paraná, Brazil, code no. 620529), based on Pavan et al. (2021) and Estrela et al. (2021). One thousand erythrocytes were analyzed per fish [according to Bolognesi and Hayashi, 2011], with 400 × magnification and evaluated for the presence of MN and others ENA that manifested as changes in the typical elliptical nuclear shape of erythrocytes. 2.3.2 Single cell gel electrophoresis assay (comet assay) The potential damage to erythrocyte DNA induced by exposure to PSPD-2002 peptides was evaluated by the comet assay, similarly to the methodology adopted by Estrela et al. (2021), with minor modifications. Briefly, after the step described in the previous item, the animals were transferred to conical bottom microtubes containing 250 μL of phosphate-buffered saline (PBS, pH 7.2, 4 °C) and kept for 5 min. Afterward, the blood samples were centrifuged (6000 rpm, 5 min, 4 °C) for subsequent disposal of the supernatant and homogenization of the pellet. Then, 2 μL of the pellets were mixed with 120 μL of low melting point agarose (0.5%) at 37 °C and then placed on the cover slides (previously prepared using normal agarose at 1.5% in PBS) and, later, covered with a glass coverslip. After incubation at 4oC for 10 min, the coverslips were removed and the slides were submerged in lysis solution (NaCl, Na2EDTA, Tris-HCl, NaOH, purified water, Triton X-100, and DMSO) for 2 h at 4 °C - it was protected from light. Later, the slides were introduced into the electrophoresis vat containing a buffer solution (NaOH, Na2EDTA, and purified water), which remained at rest for 30 min. Then, the slides were electrophoresed at 300 mA and 25 V (0.90 V/cm) for 30 min under no light. After electrophoresis, the slides were placed in a staining tray, covered with neutralization buffer (Tris-HCl, pH 7.5) and kept for 5 min, then dried at room temperature, fixed in ethanol P.A. (for 10 min), and stained with ethidium bromide to 10 μg/mL (in purified water). Using a fluorescence microscope, the slides were photographed, and 50 nucleoids/animal were evaluated using the comet assay software (CaspLab®), according to the procedure also performed by Kaur et al. (2021) and Mehra and Chadha (2021). The following parameters were used to assess the possible damage to erythrocyte DNA induced by exposure to viral peptides: (i) tail length (TL), (ii) DNA percentage in the tail (% DNA), and (iii) Olive tail moment (OTM), as described by Collins (2004). 2.3.3 Biochemical biomarkers (oxidative stress and antioxidant activity) Aiming to associate the possible mutagenic/genotoxic effects to the induction of a redox imbalance, different biochemical toxicity biomarkers were evaluated. For this, after blood collection, the animals were deeply anesthetized and subsequently euthanized in ice-cold water, and fragments of the brain, liver, muscle, and gills of P. reticulata were collected, macerated in 500 μL of PBS (pH 7.2). Then, the samples were centrifuged (10,000 rpm, 5 min, 4 °C) and the supernatants were used for the biochemical evaluation. Malondialdehyde (MDA), a by-product of the lipid peroxidation (LPO) reaction (Yaman and Ayhanci, 2021), were used as oxidative stress biomarkers, as used in other studies – Tan et al. (2019), Patel et al. (2021), Issac et al. (2021), and Rangasamy et al., 2022. For this, we adopted the procedures described in detail in the study by Sachett et al. (2020). In addition, we evaluated the activity of superoxide dismutase (SOD) [according to Del Maestro and McDonald, 1985] and catalase (CAT) [as proposed by Sinha, 1972], considered as enzymes that make up the organisms' first line of antioxidant defence (Ighodaro and Akinloye, 2018). The results of the analysis of all biomarkers were expressed proportionally to the concentration of total proteins, evaluated according to the instructions of the commercial kit used [Commercial kit (Reference number: BT1000900)]. 2.4 Statistical analysis All data obtained were evaluated regarding the assumptions for using parametric models. For this, we used the Shapiro-Wilk test to assess the distribution of residual data and the Bartlett test was used to assess the homogeneity of variances. Afterward, the means were compared by Student's t-test (if parametric) or Mann-Whitney U test (if non-parametric). Additionally, correlation analyses were performed using Pearson's (for parametric data) or Spearman's (for non-parametric data) correlation coefficients, as well as linear regression analysis. For all analyses, we considered a significance level of 95% (p ≤ 0.05), using the GraphPad Prism software (version 7.0). 3 Results We did not record any deaths during the exposure period, and, at the end of the experiment, several ENA were recorded (kidney-shaped nucleus, blebbed nucleus, multi-lobed nucleus, nuclear constriction, displaced nucleus, notched nucleus, binucleated erythrocytes, MN, and nuclear vacuole) in both experimental groups (Fig. 1A–B). However, we found that in animals exposed to PSPD-2002 peptides, the total ENA was higher than that observed in non-exposed animals, whose increase was greater than 70% (Fig. 1A). In addition, all parameters evaluated in the comet assay (TL, %DNA, and OTM) were superior in these animals when compared to the control group (background) (Fig. 2A–C), whose values were positively correlated with the total ENA (Fig. 2F).Fig. 2 Parameters evaluated in the single-cell gel electrophoresis assay (comet assay) in erythrocytes of adult female Poecilia reticulata exposed or not to PSPD-2002 peptide fragments (at 40 μg/L). (A) Tail length; (B) %DNA in tail; (C) Olive tail moment (OTM); (D–E) representative images of nucleoids from animals in the control group and PSPD-2002, respectively; and (F) correlation analysis between the total ENA and the parameters evaluated in the comet assay. In “A, B, and C”, the bars indicate the mean ± SD of the data, which were submitted to the Student's t-test (if parametric) or Mann-Whitney U test (if non-parametric) (see the statistical summary at the top of the graphs). Different lowercase letters indicate significant differences between experimental groups. n = 8 animals/group. Fig. 2 From these data, we evaluated the possible relationship between the observed mutagenic/genotoxic effects and a possible redox imbalance induced by exposure to PSPD-2002 peptides. As seen in Fig. 3 , the levels of MDA in the brain and liver of animals exposed to the peptides were higher than those observed in the control group (Fig. 3A), and the liver levels of this biomarker were correlated positively with all parameters evaluated in the single-cell gel electrophoresis assay (comet assay) (Fig. 4A–C) and with the total ENA (Fig. 4D). On the other hand, the suppression of SOD and CAT activity observed in the liver of animals exposed to PDPD-2002 (Fig. 3B–C, respectively) was negatively correlated with all parameters evaluated in the comet assay (Fig. 5 ).Fig. 3 (A) Malondialdehyde (MDA) levels and (B) superoxide dismutase (SOD) and (C) catalase activity in the brain, liver, muscle, and gills of adult female Poecilia reticulata exposed or not to PSPD-2002 peptide fragments (at 40 μg/L). The bars indicate the mean ± SD of the data, which were submitted to Student's t-test (if parametric) or Mann-Whitney U test (if non-parametric) (see the statistical summary at the top of the graphs). Different lowercase letters indicate significant differences between experimental groups. n = 8 animals/group. Fig. 3 Fig. 4 Correlation analysis between reported malondialdehyde (MDA) levels in the different organs evaluated and (A) tail length, (B) %DNA in tail, (C) Olive tail moment, and (D) total erythrocytic nuclear alterations in adult female Poecilia reticulata exposed or not to PSPD-2002 peptide fragments (at 40 μg/L). n = 8 animals/group. Fig. 4 Fig. 5 Correlation analysis between the activity of (A-C) superoxide dismutase (SOD) and (D–F) catalase (CAT) and the different parameters evaluated in the single-cell gel electrophoresis assay (comet assay) performed in erythrocytes of exposed adult female Poecilia reticulata or not to PSPD-2002 peptide fragments (at 40 μg/L). n = 8 animals/group. Fig. 5 4 Discussion Comprehension of the environmental/ecological impacts caused by the dispersion of peptide fragments of the new coronavirus (SARS-CoV-2) in freshwater ecosystems invariably depends on the development of studies that assess the risk of exposure to these particles causing damage to the biology of aquatic organisms. In this sense, our study not only confirms previous studies on the toxicity of PSPD-2002 peptides in non-target aquatic animal models (Charlie-Silva et al., 2021; Malafaia et al., 2022; Mendonça-Gomes et al., 2021), as well as providing insight into how exposure to these fragments can impact the health of P. reticulata. We observed that exposure to the peptides induced genomic instability (inferred by the MN test and ENA) (Fig. 1) and erythrocyte DNA damage (inferred by the comet assay) (Fig. 2) of P. reticulata. Undoubtedly, proposing any mechanisms that explain these effects will demand further investigation. However, our data suggest that the redox imbalance observed in animals exposed to PSPD-2002 peptides [inferred by increased brain/liver MDA levels (Fig. 3A) and suppression of the antioxidant activity of hepatic SOD and CAT (Fig. 3B–C, respectively)] was associated for the mutagenic and genotoxic alterations reported in our study. Several studies point to oxidative stress as a factor inducing ENA formation and erythrocyte DNA damage (Antunes et al., 2016; Hathout et al., 2021; El-Garawani et al., 2021; Costa, 2021), corroborate our hypothesis. Moreover, the induction of oxidative stress (inferred by MDA levels - in the brain and liver) observed in our study is in line with previous reports involving not only the exposure of non-target models to peptide fragments of SARS-CoV-2 (Charlie-Silva et al., 2021; Malafaia et al., 2022; Mendonça-Gomes et al., 2021), as well as corroborating investigations describing the important role of Spike protein constituents in the induction of a disproportionate cellular antioxidant-oxidant balance in SARS-CoV-2-infection (Ntyonga-Pono, 2020; Delgado-Roche and Mesta, 2020; Cecchini and Cecchini, 2020; Suhail et al., 2020). Furthermore, in our study, we recognize that P. reticulata were not experimentally infected with SARS-CoV-2, but it is tempting to speculate on the occurrence of processes that relate the absorption of protein fragments to the observed effects. One possibility concerns the absorption of PSPD-2002 peptides in the intestine of animals, after involuntary ingestion of fragments dispersed in water. This hypothesis is especially supported by studies that point to the possibility of small protein fragments (and not just free amino acids) being transported through the gastrointestinal epithelium to the bloodstream via PepT1-mediated permeation, paracellular transport via tight junctions, transcytosis, and/or passive transcellular diffusion (Adibi, 2003; Moss et al., 2018; Xu et al., 2019; Sun et al., 2020). Once in the hepatic portal circulation, these peptides may have reached the liver and triggered reactions that culminated in the increased production of free radicals inducing oxidative stress, which would explain the elevated levels of MDA in the liver (Fig. 3A). In this case, it is plausible to suppose that resident macrophages, such as Kupffer cells, would have led to a respiratory burst in response to the presence of SARS-CoV-2 Spike protein peptides and may also induce ROS production, which is in line with recent studies that point to the important role of macrophages in COVID-19 (Bangash et al., 2020; Knoll et al., 2021; Meidaninikjeh et al., 2021; Ristic-Medic et al., 2021). Alternatively, the peptide fragments dispersed in the exposure water may have reached the bloodstream via absorption by the gill epithelial cells, since this organ is the main absorption route of waterborne chemical compounds in aquatic organisms, especially due to the wide contact with seawater and a high exchange rate of solutes between gills and blood/hemolymph (Hayton and Barron, 1990; Erickson and McKim, 1990; Thurston, 1996). On that account, it is plausible to suppose that PSPD-2002 peptides would also have crossed the blood-brain barrier and induced an increase in LPO processes in the brain, which would explain the high levels of brain MDA (Fig. 3A). The recent study by Rhea et al. (2021), in particular, demonstrates that S1 protein of SARS-CoV-2 can cross the blood-brain barrier in mice, reinforcing this hypothesis. Hence, further advanced investigations are needed to confirm this hypothesis and to better understand the mechanisms intrinsic to the effects observed in our study. Regardless of how exposure to PSPD-2002 peptides induced an increase in the frequency of ENA, as well as damage to the erythrocyte DNA of P. reticulata, such effects could have dramatic consequences for the health of animals. Continuous/chronic exposure to peptides can lead, for example, to the accumulation of DNA strand breaks, since the DNA repair capacity of the fish cell is low as compared to other species (Kienzler et al., 2013). This can lead to interruption of the erythrocyte cell cycle, dysregulation of gene expression and if accumulation exceeds its elimination by DNA repair mechanisms, cellular senescence or apoptosis will occur and this may contribute to the increase in cellular dysfunctions and their negative impacts on the physiology of animals. On the other hand, the increase in the total ENA in fish exposed to PDPD-2002 is suggestive of the occurrence of genetic alterations arising from chromosomal and/or damage to the mitotic apparatus, which constitute a risk for the development of different types of cancers (Tucker and Preston, 1996; De-Campos-Junior et al., 2020). Clearly, such consequences illustrate just a few examples of the impacts that exposure to PSPD-2002 peptides can cause on individuals, which are not restricted to indirect effects associated with chromosomal alterations (inferred by the total ENA) and at the DNA level (inferred by the commit assay). Moreover, the increase in LPO processes observed in the brain and liver of P. reticulata exposed to viral peptides can trigger several harmful effects to animals, including changes of a neurological nature (with behavioral changes) to metabolic/endocrine, motivated by dysfunctions in these organs. Conclusively, it must be recognized that our study is not exhaustive and, therefore, constitutes only the “tip of the iceberg” that represents the possible (eco)toxicological effects associated with the presence of SARS-CoV-2 particles in freshwater ecosystems. Not only do the biological mechanisms affected by exposure of P. reticula to PSPD-2002 peptides need to be better investigated, but also the more systemic impact of mutagenic and genotoxic effects, and redox imbalance (in brain and liver) observed in our study. At the individual level, assessments using predictive biomarkers of gene dysregulation and histopathological, behavioral, and hormonal effects are important investigative perspectives for a better understanding of how viral peptide fragments can affect animal survival. Furthermore, at the population level, it is questioned, for example, to what extent the exposure of animals to these fragments can affect their social interactions, reproduction, and the dynamics of their populations in natural environments. 5 Conclusion In conclusion, our study confirmed the hypothesis that exposure of P. reticulated adults to PSPD-2002 fragments dispersed in water can induce genomic instability and DNA damage in circulating erythrocytes, which were in correlation with a redox unbalance marked by increased MDA levels in the liver and brain, as well as by suppressing the antioxidant activity of hepatic SOD and CAT. Consistently, our study reinforces the ecological and environmental importance of evaluating the presence of SARS-CoV-2 fragments in freshwater environments, as well as their impacts on fish health. Through the development of more studies involving this theme, it will be possible to understand the real magnitude of the impacts caused by the COVID-19 pandemic on wild ichthyofauna. Ethical aspects All experimental procedures were performed by the ethical standards for animal experimentation and meticulous efforts were made to ensure that the animals suffered as little as possible and to reduce external sources of stress, pain, and discomfort. The current study has not exceeded the number of animals needed to produce reliable scientific data. This article does not refer to any study with human participants performed by any of the authors. CRediT authorship contribution statement Sandy de Oliveira Gonçalves: study conception and design, data collection, analysis, and interpretation of results, and draft manuscript preparation. Thiarlen Marinho da Luz: data collection. Abner Marcelino Silva: data collection. Sindoval Silva de Souza: data collection. Mateus Flores Montalvão: data collection. Abraão Tiago Batista Guimarães: data collection. Mohamed Ahmed Ibrahim Ahmed: analysis, and interpretation of results, and draft manuscript preparation. Amanda Pereira da Costa Araújo: data collection. Sengodan Karthi: analysis, and interpretation of results, and draft manuscript preparation. Guilherme Malafaia: conceived of the presented idea, collected the data, provided funding, analysis, and interpretation of results, and draft manuscript preparation. All authors reviewed the results and approved the final version of the manuscript. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The authors thank Dr. Ives Charlie-Silva (University of São Paulo, Brazil), Dr. Eduardo M. Cilli (São Paulo State University, Brazil), Dr. Paulo R. S. Sanches (São Paulo State University, Brazil) for providing the PSPD-2002 peptides. In addition, we thank the Goiano Federal Institute (Proc. No. 23219.001291.2021-36) and the 10.13039/501100003593 National Council for Scientific and Technological Development (CNPq/Brazil) for the financial support needed to conduct this research (Proc. No. 403065/2021-6). Malafaia G. holds a productivity scholarship from CNPq (Proc. No. 307743/2018-7). ==== Refs References Adibi S.A. Regulation of expression of the intestinal oligopeptide transporter (Pept-1) in health and disease Am. J. Physiol. Gastrointest. Liver Physiol. 285 5 2003 G779 G788 14561585 Aich A. Goswami A.R. Roy U.S. Mukhopadhyay S.K. Ecotoxicological assessment of tannery effluent using guppy fish (Poecilia reticulata) as an experimental model: a biomarker study J. Toxic. Environ. Health A 78 4 2015 278 286 Albastaki A. Naji M. Lootah R. Almeheiri R. Almulla H. Almarri I. 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Lipid Peroxidation 2021 IntechOpen 1 11 10.5772/intechopen.95802	35192827	PMC8857768	NO-CC CODE	2022-10-12 23:15:50	yes	Sci Total Environ. 2022 Jun 15; 825:153988
==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 35287198 19528 10.1007/s11356-022-19528-w Research Article SME financing role in developing business environment and economic growth: empirical evidences from technical SMEs in Vietnam Van Song Nguyen [email protected] 1 Mai Tran Thi Hoang [email protected] 2 Thuan Tran Duc [email protected] 3 Van Tien Dinh [email protected] 4 Phuong Nguyen Thi Minh [email protected] 2 Van Ha Thai [email protected] 5 Que Nguyen Dang [email protected] 6 Uan Tran Ba [email protected] 7 1 grid.444964.f 0000 0000 9825 317X Viet Nam National University of Agriculture (VNUA), Ha Noi, Vietnam 2 grid.444889.d 0000 0004 0498 8941 Vinh University (VU), Vinh City, Vietnam 3 Dong Nai Technology University (DNTU), Bien Hoa City, Vietnam 4 grid.444932.c Ha Noi University of Business and Technology (HUBT), Ha Noi, Vietnam 5 National Academy of Education and Management (NAEM), Ha Noi, Vietnam 6 grid.502042.1 National Academy of Public Administration (NAPA), Ha Noi, Vietnam 7 Dien Bien Technical Economic College, Dien Bien, Vietnam Responsible Editor: Philippe Garrigues 14 3 2022 2022 29 35 5354053552 9 12 2021 25 2 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Distinguishing the significance of business environments for technical small and medium-sized enterprises (SMEs), this study examines the connection between business environments, GDP growth, and SMEs’ financing choices in Vietnam. The study considered the agency theory as a theoretical base to explain how information asymmetry between SMEs and lenders influences SMEs’ financing choices and encompasses the effects on business environment and GDP growth of Vietnam. For this binary logistic regression, text is applied. Global Entrepreneurship Monitor and World Bank data were analyzed. The findings of the study are robust and showed that SME financing (e.g., formal and informal) under the financial infrastructure and tax regulation may enhance formal credit choice and reduce informal credit choice. This enhances the depth in the business environment of technical SMEs and found significant effects on GDP growth. For the first time, this research examines the impact of information asymmetry and agency theory on restaurant financing choices. The research has significance for industry practitioners and governments interested in SMEs’ financial viability. On the recent topicality, study also presents policy implications for key stakeholders. Keywords SME financing Business environment GDP growth Technical SMEs Financing policies issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2022 ==== Body pmcIntroduction Small and medium-sized companies (SMEs) provide the mainstream opportunity for jobs to earn income for local communities. Insufficient competition from large companies and lack of skilled staff are major challenges for small and medium-sized businesses (Huang et al. 2021b; Van 2019). SME financing is critical to meet the financial needs, as well as entertainment and tourism businesses. Most hospitality and tourist SMEs lack sufficient permanent assets (land, buildings) to get external financing (Li et al. 2021b; Nguyen et al. 2020). Because the tourism and hospitality sectors are highly dependent on the external business climate, SMEs face more risks. High operational risks can restrict access to funding and therefore stifle SMEs’ growth (Chien et al. 2021b; Quynh and Huy 2018). SME financing has been widely researched due to its economic significance (Huang et al. 2021a; Xuan et al. 2020). The importance of SMEs has been recognized in the tourism and hospitality literature, especially in the restaurant industry. External business sensitivity, significant operating jeopardies, and fierce rivalry, particularly in the restaurant industry, need careful business decisions (Kim Chi et al. 2019; Liu et al. 2021b). However, decision-making by SME owners in macro-environments is still less investigated in the context of SMEs. Moreover, previous SME research has focused on official loan availability rather than informal lending by family and friends. This study’s goal is to test the connection SME financing role in promoting the business environment and GDP of the country (Oanh et al. 2021). The study investigates how information asymmetry affects small and medium-sized business financing decisions. Notably, this study is one of the primary studies to intend the recent topicality (Bui et al. 2021; Sadiq et al. 2021c). One of the major focuses of economic and financial study is the incentives of owner and manager risk management behavior. A conflict arises when an agent’s self-interest is balanced against the principal’s primary interest. Asymmetry of information between the agent and principal may exist to demonstrate how the agent performs responsibilities and to minimize asymmetry costs, including money, time, and power (Othman et al. 2020; van Song et al. 2020). According to the Agency Theory, borrowers (agents) and lenders have asymmetric information (principal). Consequently, much of the information asymmetry is the unwillingness of lenders to lend to businesses that they deem hazardous. In such situations, small and medium-sized businesses may struggle to get financing (Chau et al. 2020; Liu et al. 2021a). Asymmetry of information creates a moral hazard for lenders when businesses are too engaged (Le et al. 2018; Xueying et al. 2021). The lenders conceal the risks, which may aggravate SMEs’ lack of access to finance. Because banks cannot access the loan market, SMEs are more reliant than large corporations (Sun et al. 2020c; Zhao et al. 2021). However, the knowledge gap between banks and SMEs worsens their position. SMEs have less transparency than banks, affecting business reporting and operations (Chien et al. 2021f; Yaw 2020). Hence, the study aims to test the role of SMEs financing on business environment and GDP growth of Vietnam. We examine these connections using econometrics and using core questionnaires completed from Vietnam market. As a consequence of our findings, we may conclude that environmental, social economic, and entrepreneurial intention correlates positively with economic performance in Vietnam were studies. There is a strong and positive correlation between assertive environmental and socio-economic approach, environmental and socio-economic issue management, and environmental and socio-economic performance, which we have identified in depth. Keep in mind that we use outside statistics to measure environmental and socio-economic success. Environmental and socio-economic risk mitigation does not seem to be related, according to our results. However, only environmental and socio-economic management have a favorable consequence when using an econometric approach. In particular, we shed light on the factors that influence environmental and socio-economic approaches and SME outcomes in Vietnam. Our contribution lies in the following aspect. There are still significant data gaps in SME finance which are being addressed by this research, and further efforts should be made to improve data collection and evidence on SME financing. Demand-side surveys, in particular, have hampered cross-country comparisons due to differences in coverage, methodology, and criteria. Additional standardization of surveys may result in more meaningful analysis, and the Vietnam supports new efforts in this area. Finally, although small and medium-sized businesses have a diverse array of financing tools, non-debt financing options are often under-researched or not specific to SMEs. This impairs policymakers’ ability to track trends and should be another area of focus for data improvement efforts. Another critical issue is the data’s granularity. Because of the diversity of the SME community, the value of disaggregated SME financial data for policymaking and analysis has been recognized for a long time. The Vietnam is investigating the availability of broken data, including the frequency with which it is collected, the reason for which broken data is collected, and the data sources. This would further the long-term aim of collecting and publishing more comprehensive data on businesses based on their size, age, industry, ownership status, geographical location, and demographic factors such as gender. The study includes section one as introduction, section two is entitled with review of literature, section three explains methodology of investigation, section four covers results and discussion, and section five presents the conclusion and policy implications. Literature review Several studies on financial options and funding for SMEs have been performed in the techno-environment (Chien et al. 2021e; Diep and Anh 2020). The study also examined the impact of financing options on the financial performance of SMEs in the hotel industry in Italy. Additional studies also examine in empirical fashion drivers of finance and capital structure, focusing on internal factors such as profitability, personnel numbers, asset tangibility, and liquidity affecting enterprises (Chien et al. 2021d; Le et al. 2020). Over the last years, global SME surveys have begun to reflect additional data on the banking industry. To demonstrate this, Thai (2019) utilized the World Bank Enterprise Survey (WBES) from 2002 to 2010, competitive indicators for non-structural banks. Its results are similar for both structural and non-structural variables. Chien et al. (2021c) and Ha and Nguyen (2020) utilized a smaller 33 country sample and found similar findings. Both studies utilize an equivalent binary variable when an SME has access to financing to assess its financial condition. Micro-level barriers to SME funding impact company operations and development (Hoang and Shin 2020; Sadiq and Zhang 2021) . The World Bank Economic Survey (WBES) showed that small and medium-sized enterprises are more likely than big to focus on banking in low-level economic and institutional development nations (Cong and Thu 2021; Xiang et al. 2021a). Given the difference in economic size, the use of this measure is dubious. To study self-discouragement and refusal, In the latest wave of WBES, Le et al. (2021) and Tan et al. (2021) categorized credit constraints encompassing 69 developing countries. These results, however, relate exclusively to non-structural metrics rather than concentration measures. Using a large US dataset, Sadiq et al. (2021b) and Vu and Tran (2020) found that low-risk borrowers are less self-decent in less competitive banking markets. Small and medium-sized enterprises may encounter unfavorable challenges and moral hazards when formal lenders rely only on insufficient management status and project quality data from SMEs. As a result, conventional lenders may not see SMEs as acceptable or secure funding candidates. Official lenders may have more information about small and medium-sized enterprises than informal lenders like relatives and friends. In addition to the commercial activities of SMEs, interpersonal links allow informal lenders to get extra information about the motivation, individuality, and abilities of entrepreneurs. Small and medium-sized enterprises (SMEs) and informal lenders may learn more about the management and organizational practices of each other. Although the level of asymmetry of information in the formal and non-formal funding of SMEs has differentiated, no effort was made to examine whether the macro-environmental information provided affects SMEs’ formal and informal financing choice. For instance, while little effort has been made to analyze formal and informal SME funding, these current studies in developing countries have been undertaken for general small and medium-sized enterprises, rather than focusing on restaurant SMEs. The study also investigated factors of the financing patterns (formal, informal, or both), which mostly explored demographic variables, such as level of education, sex, and work experience, as well as business size, location, and profitability and did not influence macro-environmental effects. Therefore, the following study assessment divides funding sources into two distinct components: official and informal funding to more fully capture the impact of macro-environmental factors on SME financing. In general, the above study, the hypothesis confirms that bank rivalry improves the availability of small and medium-sized enterprises’ credit. The SCP hypothesis or the Market Power hypothesis for companies in less competitive banking markets sees or experiences higher levels of financial constraints, such as ineffective access, rejection of the application, discouragement, lending, and relatively large dependence on investment capital. Another research focuses on the unfavorable effect on SMEs of bank competition. Huang et al. (2021c) and Nguyen et al. (2022) utilized the cash investment sensitivity model, showing that an increased concentration in 14 European countries as a proxy for weaker banking sector competitiveness reduces SMEs’ dependence on the internal investment fund, shows that banks compete based on the Boone national indicator (Hsu et al. 2021; Tsai et al. 2021), and is reducing lending to meet SME loan demand, which has a greater or lower tangible impact on SMEs via a sample of six Latin American countries. However, the use of the Boone indicator is not ideal at the national level, since, unlike Europe, countries like Brazil and Mexico have a very large sample where the competitiveness of regional banking diverges. These prospective financing opportunities may encourage small and medium-sized enterprises to request formal external capital given favorable macro-environmental conditions. However, a poorer macro environment with lower financial infrastructure or unsatisfactory fiscal laws may increase the information gap between lenders and small and medium-sized enterprises. Such conditions may raise the likelihood of SMEs being denied external financing and prohibit them from obtaining formal support (Ehsanullah et al. 2021; Vu Thi et al. 2018). The study thus indicates a favorable link between the health of macro settings and the formal financing sources of small and medium-sized restaurants, based on asymmetry (contained in the theory of agencies). This means that robust or healthy macro circumstances will encourage formal lenders to fund small and medium-sized restaurant businesses. Data and methodology Study data Two secondary data sources were used in this research. First, from a 2018 edition of the Global Entrepreneurship Monitor (GEM), this research gathered data on the small and medium-sized technical owners in Vietnam choices to seek official and informal funding, providing the newest data for the public. Technical SMEs are the first in our categories of SMEs in Vietnam. The group focuses mostly on technical material and does not engage itself excessively or worry itself with other elements of the training process, such as implementation. Technical SMEs are involved in providing content expertise to ensure that all content details are accurate. These SMEs typically operate in groups and, the bigger a project, the more you may anticipate these specialists to participate in Vietnam. Technical SMEs include representatives of OEMs, engineers, scientists, attorneys, medical workers, and qualified experts. This group is supposed to have proven expertise in the field of content, and these SMEs are typically certified, graduated, or otherwise qualified. Only in this special edition were the funding decision data accessible, secondly in the “Doing Business” database of the World Bank, and finally, for our control parameters, we collected data from the World Bank databank. Variables of study This research focuses on six factors of the decision-making of financing: (1) series or other monetary organizations; (2) secluded depositors or capital risk; (3) administration programs, grants, or contributions; (4) family members; (5) neighbors or friends; and (6) employers or coworkers. These variables are elements linked to the expectations of the restaurant owners of financing via particular channels rather than the actual choice on funding. This research utilized expectations because observations of the actual financing choices of the restaurant owners were extremely limited (i.e., 65 for each financing channel). All six variables were coded to binary variables, 1 indicating that the responder selected the source of the object; otherwise, the answers were coded to 0. Next, the funding choice was classified by summing up several sets of factors into formal or informal loans. Furthermore, banks or other financial organizations; private investors or venture capital; and public programs, contributions, and grants were seen as formal loans, while family members, friends or neighbors, and employers or employees were deemed to be informal sources of loans (INF). The research consisted of three values each measuring the expectation of SME owners to receive formal (FOR) or non-formal (INF) values (i.e., 0 = not expected, 1 = slightly expected, 2 = highly anticipated, and 3 = fully expected). Study model measurements Multinomial logistic regression is the key data analysis technology in this research, since finance choices are both singular level and different level. The data analysis is performed using version 26 of the IBM Social Science Statistical Package (SPSS). In this study, the odds ratio formulation for Model 1 is as follows:1 GDPi,t=β0+β0CRED+β0Tax+β0INSOL+β0BE+β0GINI+β0FINEXP+εi,t 2 Financing expactations=lnpFinancing Choice1-pFinancing Choice Access to appropriate technology, the exorbitant cost of product development projects, a lack of efficient marketing methods, and inadequate competitive analysis are major obstacles for SMEs to compete. Further limitations include the difficulty to satisfy the need for various technical skills, information gaps between marketing and manufacturing activities, and the absence of software implementation funding such as ERP systems (Xiang et al. 2021b). SME managers are always suffering from restrictions on developed nations such as poor expertise, lack of qualified employees, low level of management experience, lack of access to foreign markets, intolerable laws, inefficient incentive programmers, and lack of finance. In Vietnam, small and medium-sized enterprise managers are under great pressure to cut costs, enhance product quality, and provide products and services on time (Zheng et al. 2021). In addition, SMEs in the development sector typically operate in an unfriendly environment (Li 2020). Measurement model estimation The binary logistic regression technique is used to predict the results of the research. Logistic regression is a classification problem learning method utilized by allocating data to a discrete classroom. To map various probability predictors, the Sigmoid activation function would be employed to transfer the data with any value to a value of 0 to 1. The following can be written:3 f(x)=ln11+e(x) In the example, there are two categories, no injury as 0 and no injury as 1; therefore, the output is rounded up to two classes 0 or 1. Rather than the average squared error used for continuous response, logistic regression was performed using the cost function of cross-entropy or log loss. For y = 1 and y = 0, the cost function is written: 4 costh0(x),y=-logh0(x)ifY=1 5 costh0(x),y=-log1-h0(x)ifY=0 The ideal method to the creation of output units is to let the tree to develop until it includes just few information or only the same type of data. Then, the tree is used to safeguard the model from curse of dimensionality to eliminate nodes of low value for the binary logistical estimation method. In contrast, the median estimation gives the 50th median of the conditional distribution for the explained straight-line function of independent variables. Similarly, the other quantile estimations calculate the model’s variables based on any quantile for the conditional distribution of either 25th or 75th quantiles. Therefore, if an analysis is made using the 25th quantiles, the 25th quantile suitably describes the conditional distribution of the explained variable (Padhan et al. 2020; He et al. 2019; Bashir et al. 2021) controlling for unobserved country heterogeneity. Consequently, the fixed effect longitudinal regression equation below is considered:6 Quantθ=yixi=x´iβθ In this context, the significant challenge with the fixed effect longitudinal quantile regression is the inclusion of substantial fixed effects (αi) set for the unexpected variable challenge (Chetverikov et al. 2016). In contrast, the approximators are unreliable when the number of individual units is endless. However, the number of observations for each cross-sectional unit is fixed. The inferior methods used for reducing the unobserved fixed effects are not feasible in the quantile regression equation. Therefore, the literature on fixed effects longitudinal quantile regression is comparatively limited (Anwar et al. 2021).7 min∑yi≥x´iβθyi-xiβ+∑yi≤x´iβ(1-0/yi-xiβ. Moreover, the conventional econometric equations cannot estimate the total effect of the independent parameters on the dependent variables (Narváez et al. 2021; Xiang and Qu 2020). The quantile estimation can estimate the stimulating effect of extreme figures, where the least-squares estimation approach cannot approximate the influence of extreme figures and thus gives a possible mean impact. The equation of the quantile estimation is given below:8 FE2it=δFORtα1INFtα2GINtα3CTRLtα4INStα5GDPtα6μti Co-integration analyses are used for the long-term correlation between carbon dioxide, human capital, economic growth, trade openness, bio-capacity and financial development (Angrist and Pischke 2019; Shahzad et al. 2020). Furthermore, we applied the longitudinal ordinary least square, fully modified ordinary least square, and the dynamic ordinary least square (DOLS) approximation approaches. Hence, the longitudinal quantile could be specified as based on (Feng and He 2020; Mao and Ma 2021).9 FE2it=α0+α1FORit+α2INFit+α3GINt+α4CTRLit+α5INSit+α6GDPit+μti The Pesaran cross-sectional test rejects the null hypothesis, demonstrating ample cross-sectional dependency of the parameter applied in the study crosswise, and all nations deployed varied panels (Junnonyang 2021; Kasim et al. 2019; Shrestha et al. 2020). Consequently, the dual analysis aids in selecting the suitable unit root analysis. We applied the longitudinal ordinary least square for the long-run co-integration correlation amongst carbon dioxide pollution, human capital, economic growth, trade openness, bio-capacity, and financial development. The ensuing longitudinal quantile could be stated as follows:10 θitτ/xit=βiτ+βiτFORit+βiτINFit+β3τGINt+β4τCTRLit+β5τINSit+β6τGDPit+uti Quantile regression also grows the concept of univariate quantile estimation to estimate the conditional quantile functions based on additional covariates.11 θ0.10EP2it=α0.10+α0.10,1FORit+α0.10,2INFit+α0.10,3GINt+α0.10,4CTRLit+α0.10,5INSit+α0.10,6GDPit+μti Ultimately, the analysis for the equality of the slope coefficient shall be carried out to determine if there is a meaningful difference amongst the slope coefficients of the varied quantiles, such as the quantile regression equation could be stated as given below when thinking about the inter-quantile estimation amongst τ = 0.10 and τ = 0.50.12 θ0.50FE2it=α0.50+α0.50,1FORit+α0.50,2INFit+α0.50,3GINt+α0.50,4CTRLit+α0.50,5INSit+α0.50,6GDPit+μti The significant concern with fixed effect quantile regression is that adding a substantial number of fixed effects (αi) triggers incidental variables concern. The approximator is considered unreliable when the number of individual units becomes limitless. However, the number of observations for each cross-sectional unit is fixed (Kumar et al. 2019). The actual reason the literature on fixed effects longitudinal quantile regression is comparatively limited is that the inferior methods to reducing the unobserved fixed effects are not feasible in the quantile regression equation. Hence, Fitzenberger (1998) suggests the right approach for handling such challenges, where authors handle the latent fixed effects as variables to be simultaneously approximated alongside the covariate impacts for varied quantiles. Moreover, the different features of this approach are the introduction of penalty terms in the minimization to tackle the calculus challenge of approximating a large quantum of variables precisely, where the variables are estimated as follows:13 θ0.50FE2it-θ0.10FE2it=α0.50-α0.10+α0.50-α0.10FORit+α0.50-α0.10INFit+α0.50-α0.10GINt+α0.50-α0.10CTRLit+α0.50-α0.10INSit+α0.50-α0.10GDPit+μti Furthermore, the interrelation coefficients applying the quantile regression depicted in Eqs. 5 and 8 provide the inter-quantile estimation, demonstrating the variances in the approximated quantile of τ = 0.10 and 0.50. In contrast, we analyze the equality of the slope of each coefficient applying the Wald test. Therefore, the null hypothesis for equality and other slope coefficients for τ = 0.10 vs 0.20, τ = 0.10 vs 0.50, τ = 0.10 vs 0.70, and τ = 0.10 vs 0.95 are analyzed. Results and discussion Empirical outcomes In Vietnam, 95% of industrial units (3.4 million) are tiny, with manufacturing accounting for 40%. These industries employ more people than agriculture and account for 40% of all industrial output. These units account for 35% of Vietnam exports. Vietnam SMEs are critical to the Vietnam economy in this climate. Their ability to create jobs, boost exports, and make Vietnam more flexible requires the attention and cooperation of policymakers. Overall, SME output grew by 8.6% in 2003–2004. Exports have risen considerably as the SME sector has grown and become more active. In 2002–2003, there was a 20.73% increase. However, some SMEs have become “sick units.” There were some reservations. According to the RBI, 17.8% of all SMEs may be experiencing difficulties. Deficiencies and issues such as the ones mentioned above aid in the identification of “sick” companies. Vietnam accounted for 99% of the 2.4 million SMEs registered in the early 2000s. Since the mid-1990s, SMEs have accounted for about three-quarters of Vietnam increasing industrial production value (Das, 2016). Table 1 shows descriptive statistics.Table 1 Descriptive statistics Unit SME formal financing SME informal financing Business environment GDP growth Min 12.98 10.90 30.77 44.02 Max 3576.90 1113.5 1.09 5482.3 Kurt 27.01 4.09 2.05 1.9943 SD 313.46 45.19 22.54 40.11 Mean 3510.7 6709.12 7001.44 313.67 Var 1.15 1.01 3.13 2.75 SMEs continue to dominate the largest industry, accounting for more than 70% of gross value added in the food, paper, and printing industries, more than 80% in tanning, leisure, sports equipment, plastics, and metalworking, and more than 90% in woodworking and furniture. Approximately three out of every four new employments are currently generated by small and medium-sized companies throughout the country. Over 85% of the workforce in manufacturing, 90% in retail, and over 65% in construction are employed by small and medium-sized companies. Vietnam overall export value exceeded $430 billion in 2003, and it placed fourth in total import and export values worldwide (see Figure 1)Figure 1 SME financing related with the constructs under sample period Vietnam SMEs have made incredible strides in science and technology, and they are now the driving force behind new technology and innovation. Pearson’s findings on generic bivariate correlations are shown in Table 2. The positive connection between MAS and profitability may be explained by several factors. Employers may utilize masculine traits to recognize, promote, and reward top earners. Men’s cultures require that supervisors be powerful, decisive, and aggressive. Despite this, companies prefer to compensate people on an equal footing rather than on equality (see Figure 2). Finally, high-need managers, according to Ojogiwa (2021), Sun (2019), and Tiep et al. (2021), are more ambitious and ready to take measured currency risk.Table 2 Correlation estimates FOR INF BE Gini GDPG SME formal financing 1 SME informal financing 0.0518* 1 Business environment 0.2138* 0.1636* 1 Gini 0.0282* 0.2536* 0.0178* 1 GDP growth 0.2079* 0.2631* 0.3712* 0.33957* 1 Significance at p-value < 0.05 Figure 2 Empirical estimate output at level 3 UAI lowers ROA, indicating that eliminating uncertainty reduces profitability. In other words, cultures that accept uncertainty benefit corporations. Managers in communities that allow uncertainty have higher expectations of (Baloch et al. 2020; Sun et al. 2020b; Thuy et al. 2021) performance that are more willing to demonstrate commitment tolerance and accept the risks associated with business environment and GDP (see Tables 2 and 3). Enterprises with low UAI values will see more opportunities than enterprises with high UAI values. As a result, businesses that accept cultures are more likely to dominate in new markets. The results, therefore, support “sand the wheels.” Companies appear to have to pay bribes, etc., which reduces profits. BUSFREE also has a statistically significant positive coefficient in the regression. SMEs are more lucrative in countries where starting, running, and closing a business are easier. Finally, GETCR has a significant impact on ROA. This improves legal rights and loan information accessibility for European SMEs. This finding is consistent with previous studies showing that this climate improves loan availability and reduces financing costs, particularly for small companies.Table 3 Binary logistic regression output Estimating the nexus between constructs Level 1 (n = 334) Intercept GDP growth FOR −0.01267 INF −0.01112 BE −0.01714 GINI −0.01128 Control −0.03547 INS −0.00442 GDPG −0.01464 0.045457 Level 2 (n = 113) Intercept 0.002401 FOR −0.01359 INF 0.023098 BE 0.015034 GINI −0.00187 Control 0.008062 INS 0.010565 GDPG 0.007694 Level 2 (n = 60) Intercept −0.0097 FOR −0.01453 INF 0.009735 BE 0.001236 GINI 0.351090 Control 0.724518 INS 0.290446 GDPG 0.885908 Significance at p-value < 0.05 This section re-estimates Tables 3 and 4 specifications while adding new economic factors one by one. First, we add a crisis dummy variable to the criteria. We use two methods to define the crisis: IMF database information on systemic financial crises. A country-specific financial crisis indicator is now possible. So, if we adopt a more generalized definition used in the literature and assume that all crises started in 2008 for all samples, it is conceivable that the crisis did not begin in 2008. So, even if a country has no systemic banking problem, we presume it was impacted by the global crisis in 2008. In general, no one factor seems to influence small and medium-sized business profitability. Table 4 shows the results utilizing the CRISIS IMF dummy provided by the authors with crises 2008.Table 4 Robustness estimates findings Business environment GDP growth B (Wald) EXP (B) B (Wald) EXP (B) Intercept 0.097 1.07 0.904 1.05 FOR 0.453 1.04 0.967 1.02 INF 0.735 1.13 0.542 1.02 GINI 0.519 1.22 0.407 1.26 Control 0.724 1.79 0.585 1.53 INS 0.446 1.147 0.559 1.59 GDPG 0.908 1.18 0.934 1.08 The study shows how SMEs choose to invest in DECs to gain a competitive advantage. Due to their ability to turn information into value-added offerings, DECs are critical to a company’s success in the KBV framework. Although studies have demonstrated that innovation and flexibility are important factors in an SME’s export performance, there is no evidence that using DECs is a profitable strategy. Exporting SMEs are resource-constrained, and any investment in core skills reduces their profitability; thus, marginal costs and benefits must be carefully assessed. To better understand the connection between DECs based on knowledge and profitability, our survey analyzes the diversification strategy and the MD and PD goals of the business. MD and PD are major strategic choices for exporting small and medium-sized enterprises. However, little is known as to whether exporting SMEs may benefit from MD and PD while implementing their DECs. However, other research indicates that smaller enterprises struggle to manage the diversification “capacity growth.” In this connection, we examine how MD and PD of an exporting business impact the profitable use of DECs in the export of SMEs and add two more literature criteria (Alemzero et al. 2020b; Sun et al. 2020a; Alemzero et al. 2020a). Table 5 shows the important links between GDP and the number of SMEs. Every metric is up. However, micro and small companies, they range from 0.65 to 0.7. Strong links, according to previous studies.Table 5 Quantile regression analysis Variables 0.1 0.25 0.5 0.5 0.9 OLS FOR 0.0765* 0.0654* 0.0354* 0.0352* 0.0346* 0.066* −0.001 −0.01 −0.003 −0.002 −0.002 −0.002 INF 0.02* 0.0166* 0.0136* 0.0133* 0.02* 0.0138* −0.0056 −0.045 −0.035 −0.036 −0.0056 −0.035 GINI 0.430* 0.441* 0.421* 0.425* 0.430* 0.421* −0.0401 −0.0402 −0.0201 −0.0212 −0.0401 −0.0201 INS −3.24e−05* 0.045* 0.045* 0.048* −3.34 0.045* −2.26E−03 −4.54E−02 −2.00E−03 −2.00E−02 −2.26E−03 −3.00E−03 GDPG 0.024* 0.323* 0.665* 0.611* 0.024* 0.335* −0.012 −0.002 −0.001 −0.002 −0.012 −0.001 PCI 0.0276* 0.00897* 0.0586* 0.0586* 0.0276* 0.0586*** −1.98E−02 −1.54E−02 −1.10E−02 −1.10E−02 −1.98E−02 −1.10E−02 Constant −5.002 −5.021 −3.021 −3.001 −5.002 −3.021 −0.552 −0.232 −0.235 −0.268 −0.567 −0.256 Observations 472 472 472 472 472 472 All SMEs have similar results, although the coefficients are higher. The link between GDP and the number of medium-sized companies may be substantial. Also, the coefficient of association varies from 0.8 (export) to 0.85 (import) (business investment and government spending). Thus, both GDP and its components, as well as the number of small and medium-sized firms, are positive. Corporate investment is also larger, which is anticipated. In Vietnam, the focus in the mid-2000s was placed on strengthening the functioning conditions of SMEs. The Chinese SME Promotion Act, which came into effect in 2003, was a milestone in SME policy and law. It outlined the role and duties in the national economy of the different SME government entities. Under this act, the state would actively support small businesses, enhance the quality of services for small businesses, create an environment in which firms can compete fairly, and commit to supporting the development of small businesses with more effective policies, particularly in financial and tax areas (Agyekum et al. 2021) and (Zhang et al. 2021). Besides scientific achievements, our analysis has ramifications for SMEs in terms of both asset preservation and value generation. A strategic and process-level examination of risk management strategies might be a first step towards business model management. This should include a complete objective evaluation, including redefining objectives, targets, and KPIs to reflect SME motivation. The correct approach to generating value is to build an invitation management philosophy around SME themes, at both the tactical and operational levels. This may include reviewing the service offerings and setting objectives for the development of novel SME generated business prospects in banking, capital management, and project finance. Typically, experts enhanced measurement and monetization of environmental and socioeconomic risks and possibilities, both operationally and strategically. Furthermore, banks should pay more attention to cultural nuances, especially those of the most visible participant groups, such as customers, nongovernmental organizations, the media, and independent norm-makers. This may include quality objectives, key players, institutionalizing stakeholder dialogues, implementing dialogue-derived initiatives, and regularly evaluating outcomes. Although our findings show policymakers have a little role in SMEs unification, this must be evaluated in the light of data collecting. For example, the TCFD and NGFS were in both the formative stage at the end of the assessment. One would think that opinions of policy makers’ function would have altered by now. A typical corporate profit maximization attitude may have predominated at the time of the study, explaining our findings on a negligible role for stakeholders in SMEs integrating. As previously said, attitudes may have shifted. Discussion Vietnam stressed its aggressive support for SME development by 2006 throughout the current reform string. The main task of the administration during the period was to implement an SMB law to improve policy and development actions, remove institutional barriers, create a level playing field, foster scientific and technological innovation, and update and optimize industrial structures to improve the overall quality and competitiveness. As a consequence of these reforms and efforts, Vietnam SMEs grew quickly in size, number, financial position, and profitability. Throughout this period of promotion legislation, two factors played a significant role. Firstly, the fast development of municipal enterprises. Many municipal companies were small to medium-sized and were therefore a key driver of the development of Chinese SMEs. The second factor was the rapid growth of the non-public sector, particularly the rapid development of SMEs in the private sector. The strategic development of Vietnam SMEs depends on the authors’ survey. Approximately 1200 organizations from different sectors, categories, and venues were contacted and asked to participate. These organizations were selected from various directories available in the Vietnam Industry Confederation, the Vietnam Auto Component Manufacturers Association, the Vietnam Trade Chambers Federation, and the Industry Department. Included were a cover letter explaining the research objectives and the instructions for the questionnaire. The SMEs focus on TPM and the culture of the organization to improve productivity at all levels. The results indicate that Vietnam SMEs are less focused on IT applications. Therefore, Vietnam SMEs should pay considerable attention to the effectiveness of IT applications at different operational levels to boost competitiveness. The amount of money that is spent at home and through FDI varies, depending on the business, the firm, and all sectors. For example, Chien et al. (2021a), Iqbal et al. (2021), and Li et al. (2021a, b) noted that automation, market research, and staff welfare are key investment objectives for organizations in the Vietnam automotive industry of Vietnam. In today’s global economy, SMEs and entrepreneurs are critical to countries’ efforts to achieve equitable development. Assuring small businesses have an adequate supply of financing in the appropriate forms and amounts is critical to their development and success. It is even more critical to have access to finance during times of crisis, such as the one now afflicting the world as a result of the COVID-19 pandemic, to react to the immediate and severe effect on SMEs. At the time of writing, the crisis’s consequences were still being felt, and the long-term impact on SMEs’ access to finance was unknown. Governments around the globe develop policies to assist externally financed SMEs. In light of the sector’s lengthy history of difficulties, this environment needs more evidence-based support for correcting SME financing regulations. The introduction of a business index shows how readily a new company in the nation can be assessed, which means that institutional development promotes new companies. This may indicate a better business climate when a company index begins, where new entrants in the restaurant sector, in particular, may be recruited. This may lead to many additional participants, but also severe financial competition. This high degree of competition in the industry may thus decrease the likelihood of selecting official funding and instead urge SME owners to seek informal financial support. In such a competitive climate, banks and financial institutions are likely to demand high-quality investors’ ideas. The previous study indicates that SME hospitality investors frequently do not provide banks with thorough and competent funding. This may harm the probability of these new entrants getting official funding. In the future study, more empirical data and related arguments should be encouraged. This research theoretically enhances our knowledge of agency theory in formal and informal financing options for small and medium-sized enterprises. As shown in the present research, knowledge from the organism’s micro-business environment may reduce information asymmetry in the funding process. If more official loans are available, the need for informal loans will be decreased. This study adds to SME financing research and examines the differential impact on formal and informal restaurant loans under various macro business conditions. Developed financial infrastructure and fiscal legislation have been recognized as important components of macro business circumstances to improve expectations for restaurant operators of formal financing. These results may indicate that the demand for non-informal loans would diminish when expected formal loans from a developed environment with plenty of external loans. This research also represents the first effort at the deconstruction of small eateries, concentrating not only on official financing but also on informal assistance. The objective of this research is to add to small business literature by expanding understanding of the financing choices process for small and medium-sized enterprises. Conclusion and policy implications In contrast, the establishment of a business index has a beneficial impact on the official financing of SMEs in the restaurant sector and on their informal financing. The introduction of a business index shows how readily a new company in the nation can be assessed, which means that institutional development promotes new companies. This may indicate a better business climate when a company index begins, where new entrants in the restaurant sector, in particular, may be recruited. This may lead to many additional participants, but also severe financial competition. This high degree of competition in the industry may thus decrease the likelihood of selecting official funding and instead urge SME owners to seek informal financial support. In such a competitive climate, banks and financial institutions are likely to demand high-quality investors’ ideas. The previous study indicates that SME hospitality investors frequently do not provide banks with thorough and competent funding. This may negatively impact the probability of government funding for these new entrants. In the future study, more empirical data and related arguments should be encouraged. This research theoretically enhances our knowledge of agency theory in formal and informal financing options for small and medium-sized enterprises. As shown in the present research, knowledge from the organism’s micro-business environment may reduce information asymmetry in the funding process. If more official loans are available, the need for informal loans will be decreased. This study adds to SME financing research and examines the differential impact on formal and informal restaurant loans under various macro business conditions. Developed financial infrastructure and fiscal legislation have been recognized as important components of macro business circumstances to improve expectations for restaurant operators of formal financing. These results may indicate that the demand for non-informal loans would diminish when expected formal loans from a developed environment with plenty of external loans. This research also represents the first effort at the deconstruction of small eateries, concentrating not only on official financing but also on informal assistance. This research seeks, therefore, to add to the literature of SMEs by improving their knowledge of the financing process for SME owners. The present research also has managerial implications for formal and informal loans for SMEs in the restaurant sector to policymakers. The results show that policymakers should carefully examine how their countries use official and informal loans to create financial strategies for small enterprises (in particular, restaurant SMEs). Policies should focus mostly on developing countries’ financial infrastructure and tax regulations that may promote formal lending to small and medium-sized enterprises since informal financing is more expensive and unable to provide adequate funding, as stated before. The results of this research will also attract SME owners on the typical expectations of official and informal financing owners of SMEs, in particular in the restaurant sector. Although these owners seek funding from all available sources, their company choices may take the particular business climate studied in the present study into consideration. For example, owners should strive, under relatively poor macroeconomic conditions, to minimize the knowledge asymmetry issue, which may restrict their possibilities of access to formal funding sources. However, if the financial markets are appropriately established and expanded, official funding should be favored over non-informal ones. There are many drawbacks to this research. The research utilized, first of all, binary, self-reported funding solutions. Future research can utilize a real financial data set to properly assess funding alternatives from the viewpoint of capital structure. Secondly, the dependent variables were split between 0 and 1 levels and resulted in comparatively fewer data at 2 and 3 levels. Future research should gather a greater content and high-value dataset that may enhance the building's validity and statistical assessments. Third, this research focuses on information for restaurant owners, since the collection of data by other small enterprises in hospitality and tourism is restricted. However, companies have larger sales volumes. The evolving economic implications of the COVID-19 epidemic have significantly altered global development expectations, after many years of significant progress in SME financing. Not only is the pandemic a first-of-its-kind public health emergency. Additionally, it sows the seeds of a global economic disaster, puts tremendous strain on communities, and creates great problems for international leaders. Small businesses and entrepreneurs are at the heart of these transformations and are severely impacted, not least in terms of their ability to access cash flow finance and longer-term investment. Additionally, in the next years, weak trade and investment flows, as well as supply chain reorganization, may harm loan availability and other forms of financing for SMEs. Simultaneously, the extent to which SMEs can use digital technology, particularly in terms of external funding, remains to be seen. It is critical, therefore, to continue monitoring SME financing trends and expanding the evidence base. Author contribution Nguyen Van Song: conceptualization, writing — original draft. Tran Thi Hoang Mai: writing — literature review. Tran Duc Thuan: software. Tran Ba Uan: visualization. Dinh Van Tien: methodology. Nguyen Thi Minh Phuong: supervision. Thai Van Ha: data curation. Nguyen Dang Que: editing. Availability of data and materials The data that support the findings of this study are attached. Declarations Ethical approval The authors declare that they have no known competing financial interests or personal relationships that seem to affect the work reported in this article. 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==== Front Soft comput Soft comput Soft Computing 1432-7643 1433-7479 Springer Berlin Heidelberg Berlin/Heidelberg 35309595 6917 10.1007/s00500-022-06917-z Focus RETRACTED ARTICLE: Coronavirus herd immunity optimizer to solve classification problems http://orcid.org/0000-0002-3724-5111 Alweshah Mohammed [email protected] grid.443749.9 0000 0004 0623 1491 Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, Jordan Communicated by Oscar Castillo. 15 3 2022 2023 27 6 35093529 13 2 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Classification is a technique in data mining that is used to predict the value of a categorical variable and to produce input data and datasets of varying values. The classification algorithm makes use of the training datasets to build a model which can be used for allocating unclassified records to a defined class. In this paper, the coronavirus herd immunity optimizer (CHIO) algorithm is used to boost the efficiency of the probabilistic neural network (PNN) when solving classification problems. First, the PNN produces a random initial solution and submits it to the CHIO, which then attempts to refine the PNN weights. This is accomplished by the management of random phases and the effective identification of a search space that can probably decide the optimal value. The proposed CHIO-PNN approach was applied to 11 benchmark datasets to assess its classification accuracy, and its results were compared with those of the PNN and three methods in the literature, the firefly algorithm, African buffalo algorithm, and β-hill climbing. The results showed that the CHIO-PNN achieved an overall classification rate of 90.3% on all datasets, at a faster convergence speed as compared outperforming all the methods in the literature. Supplementary Information The online version contains supplementary material available at 10.1007/s00500-022-06917-z. Keywords Classification problem Data mining Metaheuristics Probabilistic neural network Coronavirus herd immunity optimizer issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023 ==== Body pmcIntroduction In many domains, such as industry, academia, and medicine, data mining is defined as the science of extracting useful knowledge from vast datasets through the use of automated search processes that employ statistical and analytical techniques (Tomasevic et al. 2020). To detect hidden associations in such datasets, it is necessary to identify meaningful patterns through processing and exploring the data contained therein (Viloria et al. 2019). In prediction, data mining is used where some of the indicators are used to determine other indicators (classification) or in explanation, in which trends that can be readily interpreted by the user (clustering) are identified (Berkhin 2006). Classification is a process that is an inherent aspect of daily life and it is perceived to be the decision-making function that is most frequently undertaken by human beings (Singh and Singh 2020). Essentially, when we allocate an object to a predetermined class or category, we are classifying that object according to several different predetermined characteristics that may have some relation to the allocated object (Khanbabaei et al. 2019). Data classification is an important data mining strategy which requires the prediction of values for categorical variables to produce input data and datasets with various values for predicting useful data (Tharwat 2020). This can be achieved by constructing structures based on one or more categorical and/or numerical variables (Li et al. 2019). The aim of any data classification technique is to achieve the optimal output when it is applied to a dataset and classifies that dataset into parts or classes that may be used as potential data for a specific target problem. However, to properly solve a classification problem, an automated system has to first learn the relevant attributes, which involves the use of a training set (input dataset) that includes those attributes (El-Khatib et al. 2019). Many methods can be used to solve classification problems, such as the naive Bayes (Zhang et al. 2020), the support vector machine (SVM) (Barman and Choudhury 2020), the neural network (NN) (Bau et al. 2020), and the decision tree (DT) (Rizvi et al. 2019). One of the most widely employed techniques is the NN (Clark et al. 2003). The NN has been found to be very useful for the classification of data, and there are several subtypes of NN, such as the feed-forward, multilayer perceptron (MLP), modular, and probabilistic neural network (PNN) (Huang et al. 2018). To obtain a speed advantage due to the parallel architecture of the NNs, the researcher can generate a significant number of hardware neurons. Neural networks are used in many problem domains to investigate models that perform tasks such as the identification of genes in uncharacterized DNA (Bae et al. 2020). Neural network learning algorithms have also been successfully extended for many unsupervised and supervised learning problems (Sun et al. 2018). The PNN approach is a common data mining method that has been adapted to solve many pattern identification and classification issues (Lapucci et al. 2020a). In the PNN, the process is managed by a multilayer network consisting of four layers: an input layer, pattern layer, sum layer, and output layer. In the first layer, the dimension (p) of the input vector reflects the dimension of the layer. In the second layer, the sum of the number of instances in the training sequence is equal to the size of the layer. The third layer (summation) consists of a series of classes in the set, and in the fourth layer, the test sample is classified in a number of classes i (output) (Dukov et al. 2019). One way to increase the efficiency of a PNN classifier is to modify its weights using the results of a search strategy (Sedighi et al. 2019). A metaheuristic algorithm offers an efficient method of solving complex problems as it applies a finite sequence of instructions. This type of algorithm can be defined as an iterative search method that explores and exploits the solution space effectively to find nearly optimal solutions in an efficient manner (Hussain et al. 2019). To direct the search process toward the optimal solution, metaheuristics take into account the data gathered during the search, and then create new solutions by merging one or more good solutions (Roeva et al. 2020; Castillo and Amador-Angulo 2018). However, metaheuristics are typically imperfect techniques; they do not ensure that the correct global solution is identified; they always find approximation solutions (Alweshah et al. 2015a, 2020a). A number of recently published studies have explored the hybridization of metaheuristic approaches with many different types of classifiers to produce hybrid models (Bernal et al. 2021; Yuan and Moayedi 2019). Generally, these hybrid approaches have greater accuracy and increased performance than traditional classification processes (Alwaisi and Baykan 2017). Some of the metaheuristic approaches that have been hybridized with population-based and single-based classification processes include Tabu search (TS) (Alsmadi 2019), the harmony search algorithm (HSA) (Elyasigomari et al. 2017), the firefly algorithm (FA) (Alweshah and Abdullah 2015), differential evolution (Maulik and Saha 2010), ant colony optimization (Martens et al. 2007), the genetic algorithm (GA) (Li et al. 2017), biogeography-based optimization (BBO) (Alweshah 2019), flower pollination algorithm (Alweshah et al. 2022), Salp swarm optimizer (SSA) (Kassaymeh et al. 2021), African buffalo algorithm (ABA) (Alweshah et al. 2020b) and many others (Al-Muhaideb and Menai 2013; Kumar et al. 2020b; Suresh and Lal 2020; Alweshah 2021). As can be seen from the literature, there is a continuing trend to hybridize various types of classifier and metaheuristic algorithm for optimization and classification problems. In line with this research direction, this paper presents a new hybridization approach that uses the coronavirus herd immunity optimizer (CHIO) algorithm to change the PNN weights (Al-Betar et al. 2020). Herd immunity is said to occur when the majority of a population is immune, and is considered to be a condition that contributes to the prevention of the transmission of a disease (John and Samuel 2000). The CHIO algorithm not only imitates the herd immunity condition, it also applies psychological distancing principles that have been implemented to combat the current coronavirus pandemic. It has been shown that the concept and mechanisms of herd immunity can be transposed and modeled for the optimization domain (Alweshah et al. 2015b). The rest of this paper is organized as follows. First, in Sect. 2, a review of the related work on the use of the PNN with metaheuristic algorithms is provided. Next, in Sect. 3, the CHIO is discussed. This is followed by Sect. 4 in which the specifics of the proposed approach, CHIO-PNN, are explained. Then, in Sect. 5, the experimental setup to test the performance of CHIO-PNN is described and the results of the experiments are discussed. Finally, in Sect. 6, some conclusions are drawn and a number of recommendations for further research are made. Related work The efficiency of metaheuristic algorithms can be attributed to be investigated, for using in hybridization method to tackle the classification issue, which effectively identifies and uses the search space throughout the search procedure. This is achieved by tuning the encountered parameter weights until they are close to the ideal weights. In the following, some relevant works that have used the NN as a classifier are reviewed. The techniques that were used for metaheuristic optimization to obtain a better solution close to the optimal solution are also highlighted. Many local search techniques have been used to tackle classification problems. The first publication of note mentioned in this review is that by AL-Qutami et al. (2017) who used a simulated annealing (SA) optimization approach to select the most effective subgroup related to learners and the ideal combination strategy. The approach was assessed by applying it to real-world test data and it showed remarkable performance, with an average error rate of 2.4% and 4.7% for gas flow rates and liquid, respectively. On the other hand, Moutsopoulos et al. (2017) focused on solving the optimal groundwater level problem using the GA and TS algorithm to maximize the extracted flow rates. The authors found that the TS process was computationally more effective as compared to the GA. In another study that used the GA, Khalid (2017) optimized the shunt active power filter (APF) method using the GA and the adaptive TS algorithm. The authors conducted a simulation in Matlab programming language and demonstrated that their proposed control method for the aircraft shunt APF was extremely effective. Meanwhile, Alweshah (2018) investigated how efficiently an initial population can achieve increased convergence speed and more effective classification accuracy when resolving issues related to classification. To this end, a local search (i.e., the SA algorithm) was exploited to perform an initial solution to the issue of classification. The population-based method was also employed to solve classification problems by Juang and Yeh (2017), who proposed a fully connected recurrent NN based on the use of the advanced multi objective continuous ant colony optimization (AMO-CACO) for the multi objective gait population of a biped robot (i.e., the NAO). Also, the authors in Chatterjee et al. (2017) proposed a modified cuckoo search (MCS)-trained NN (or NN-MCS model) for the detection of chronic kidney disease CKD. This model was used to overcome the problems observed while using local search-based learning algorithms to train the NN. In addition, Alweshah et al. (2017) proposed a PNN method based on the BBO method to improve classification accuracy, while Alweshah (2018) investigated how efficiently preliminary generations can increase convergence speed and result in more effective classification accuracy when resolving classification issues. Furthermore, an ANN approach with multilayer perceptron (MLP) structure and feed-forward propagation was applied in Jamshidian et al. (2018) to estimate the capillary pressure curves for a target reservoir. The ANN method was optimized by adopting the cuckoo optimization algorithm. Another NN, the bacterial foraging optimization-based radial basis function neural network (BRBFNN) was implemented by Chouhan, et al. (2018) to identify and classify diseases that affect the leaves of plants. The MLP was also used in a study by Deo et al. (2018), who developed a hybrid firefly algorithm with multilayer perceptron (MLP-FFA) method to resolve the issue of estimating long-term wind speed based on reference station input data including feasibility research studies on wind energy investment within data-scarce areas. The method was aimed at overcoming inadequate data by utilizing neighboring reference site data so that the target site wind speed could be forecast. The genetic algorithm (GA) has been also employed to solve classification problem. For instance, Mohammadi et al. (2017) investigated logical communication between independent and dependent variables where a cost task that relies on similar experimental data is defined. Such a task is accordingly optimized based on the use of the GA, where the most effective value for every parameter is identified. The authors in Reynolds et al. (2018) applied the GA to represent an assessment engine aimed at reducing energy consumption. The bespoke 24-h and heating set point schedules were created for every area inside a small office building located in the city of Cardiff in the UK. On the other hand, the HSA was applied in Bashiri et al. (2018) in which the authors applied a parameter varying method to increase the ability of the HSA. The results demonstrated that coupling an ANN coupled with the HSA is an accurate and simple method for predicting the maximum scour depth downstream of sluice gates. In another approach, Qi et al. (2018) applied a method for nonlinear relationships modeling and particle swarm optimization (PSO), which was applied for ANN architecture-tuning. The inputs of the ANN were the curing time, the solid content, the cement–tailing ratio and the tailing type. The PSO approach was also applied together with an ANN and expectation maximization in Qiu et al. (2018) to develop a rapid and precise dispersion estimation and source estimation technique. Furthermore, Aljarah et al. (2018) introduced a novel training algorithm that relied on the whole optimization algorithm (WOA). The authors found that the WOA was able to resolve a large range of issues related to optimization and surpassed other related enhanced algorithms. The WOA was also implemented in Abdel-Basset et al. (2018) in a hybrid model together with a local search strategy to resolve the permutation flow shop scheduling issue. In another study related to the classification problem, Alweshah et al. (2019) used the local search solution of the β-hill-climbing (β-HC) optimizer to find the best weight for the PNN through implementing a stochastic operator to prevent local optima. The proposed approach was tested on 11 benchmark datasets and the experimental results showed that the β-HC-PNN method performed better in terms of classification accuracy than the other methods in the comparison. Alweshah et al. also employed the African buffalo algorithm (ABO) and water evaporation algorithm in Alweshah et al. (2020b and c), respectively, to enhance the PNN weights to make them as accurate as possible, and all the results indicated that both of these algorithms were able to adjust the PNN weights and thereby obtain a high classification accuracy. More comprehensive study of the effect of metaheuristic algorithms on the classification process, Mousavirad et al. (2020) compared the output of 15 metaheuristic algorithms for neural network preparation, including state-of-the-art and some of the most recent algorithms, and evaluated their success on various classification algorithms. In another recent study, Carrillo-Alarcón et al. (2020) addressed the unbalanced class problem, an unbalanced subset of such datasets was chosen to define eight categories of arrhythmia using combined under sampling based on the clustering approach and feature selection method. They compared two metaheuristic methods focused on differential evolution and particle swarm to investigate parameter estimation and boost sample classification. In training the Higher Order Neural Network (HONN) for data classification, the salp swarm algorithm (SSA) was used in Panda and Majhi (2020). The proposed approach was validated by examining different classification indicators across benchmark datasets. The proposed approach outperforms recent algorithms, confirming its superiority in terms of improved discovery and extraction capabilities. From the above overview of the most important recent classification methods, the NN is superior to many other techniques and can be used to resolve numerous diverse problems. Moreover, it is obvious that no single classifier can be used to deal with all kinds of problem. No classification technique is optimal for all cases because each approach has its own specific advantages for the certain areas of concern. Therefore, in this paper, the local search capability of the CHIO algorithm is employed to attempt to produce more reliable results and increase efficiency in training the PNN to solve classification problems through the management of random phases and the effective identification of a search space that can probably decide the optimal value. Coronavirus herd immunity optimizer (CHIO) The CHIO is a recent metaheuristic algorithm that was proposed in 2020 by Al-Betar (2020). Like many other metaheuristic algorithms, it simulates the behavior of a natural entity and was motivated by the appearance of a pathogenic coronavirus. The CHIO mimics the mechanism of obtaining natural immunity against the through the application of herd psychology, which is considered to be one of the methods of acquiring immunity from infectious diseases. In 2020, a pathogenic coronavirus crossed habitats for the third time in as many decades to infect human populations (Melin et al. 2020a; Sun and Wang 2020). This virus, provisionally known as 2019-nCoV, was first detected in Wuhan, China, in persons exposed to seafood or a wet market (Castillo and Melin 2020). The quick reaction of the Chinese public health, clinical and research communities led to the identification of the associated clinical illness and provided initial knowledge of the epidemiology of the infection (Melin et al. 2020b; Perlman 2020). Acquired immunity is formed, either by natural infection with either the pathogen or by vaccination mostly with a vaccine. Herd immunity is derived from the impact of the level of individual immunity on the wider herd (Randolph and Barreiro 2020). It can be described as indirect immunity against infection that is provided to susceptible individuals when there is a relatively significant proportion of resistant individuals within a population (Boccaletti et al. 2020; Fontanet and Cauchemez 2020). The idea of coronavirus herd immunity was mathematically modeled to establish a conceptual optimization algorithm, named CHIO. The algorithm is based on an idea of how best to defend society against disease by transforming the bulk of the vulnerable population that is not infected into a resistant population (Al-Betar et al. 2020). As a result, even the remaining vulnerable cases will not be infected and the resistant community will no longer spread the disease. The population of herd immunity individuals can be divided into three categories: susceptible, contaminated (or confirmed) and immunized (or recovered) persons (Al-Betar et al. 2020; Lavine et al. 2011). A susceptible individual is a person who is not born with the virus or infected with the virus. However, a susceptible individual may be contaminated by coming into contact with infected persons who have failed to obey the prescribed social distance. An infected individual is a person who can pass on the virus to susceptible persons who are in close touch with the psychological distancing factor. The third category of individuals consists of persons who are listed as immunized. They are therefore protected from infection and do not infect untreated people. This sort of person can help the population to avoid transmitting the virus to others and causing a pandemic (Anderson and May 1990). Figure 1 illustrates how the three types of individual in the population are represented.Fig. 1 Population hierarchy in herd immunity scenario (Al-Betar et al. 2020) From the figure, it can be seen that herd immunity is represented as a tree in which the infected individual is the root, and the edges correspond to the other individuals that are contacted. The right-hand section of the figure indicates that the virus cannot be transmitted to contacted individuals if the root individual is immunized. The herd immunity strategy is modeled as an optimization algorithm. The six main phases of the CHIO algorithm are discussed below: Phase 1: initialization The CHIO parameters and the issue of optimization are addressed in this step. In the sense of objective functionality, the optimization problem is formulated as shown in Eq. (1):1 Minfxx∈Lb,Ub, where f(x) is the measured objective function (or immunity rate) that is computed for the individual xi = (x1,x2,...,xn), where xi the gene indexed by i, and n represents the number of genes in each individual. Notice that each gene’s value range is xi ∈ [lbi, ubi], where lbi is located. The highest and lowest boundaries of gene xi are expressed by Lbi and Ubi. The CHIO algorithm has four algorithmic parameters and two operational parameters. The four algorithmic parameters are (1)C0, which is the number of preliminary cases of infection initiated by one individual; (2) HIS, which is the size of the population; (3) Max_Itr, which is the actual number of iterations; and (4) n, which represents the problem dimensionality. In this stage, two major control parameters of the CHIO are initialized: (1) the basic reproduction rate (BRr), which regulates the operators of CHIO by propagating the coronavirus among the individuals and (2) the maximum age of infected cases (MaxAge), which determines the classification of the infected cases as either having recovered or died. Phase 2: Generate initial herd immunity population The CHIO produces a set of cases (individuals) as many as HIS spontaneously (or heuristically). In the herd immunity population (HIP), the generated cases are stored as a two-dimensional matrix of size n × HIS as follows: 2 HIP=x11x21xn1x12x22xn2⋮⋮⋮x1HISx2HISxNHIS in which each row j represents a case xj that is generated basically. This includes xij = Lbi + (Ubi − Lbi) × U(0, 1), ∀i = 1, 2,.,. n. The objective function (or immunity rate) is determined by using Eq. (1) for each situation. In addition, the HIS duration status variable (S) for all HIP cases is initiated by either zero (susceptible case) or one case (infected case). Note that the random initiation of the number of ones in (S) is as many as C0. Phase 3: Evolve coronavirus herd immunity The evolution phase is the CHIO’s primary enhancement loop, where gene xij in case xj, according to the proportion of the BRr, either remains the same or changes according to the influence of social distancing based on the following three rules:3 ⟶xijt+1xijtr≥BRrCxijtr<13×BRrinfectedNxijtr<23×BRrsusceptibleRxijtr<BRrimmune where r produces a number generator between 0 and 1. The three rules are described below: Infected case Under the spectrum of r ∈ [0,13BRr] any social gap is caused by the new gene value of xijt+1, which is achieved by the discrepancy between the present gene and a gene obtained from a contaminated case xc, such as4 xijt+1=Cxijt, where5 Cxijt=xijt+r×xijt-xict. Notice that the value xict is arbitrarily selected on the basis of a condition vector (S) from every contaminated case xc, so that c = {i\|S(i) = 1}. Susceptible case The new gene value of xijt+1 is influenced by any social gap within the spectrum of r ∈ [13BRr,23BRr], which is determined by the discrepancy between the present gene and a gene extracted from a compromised case xm, such as6 xijt+1=Nxijt, where7 Nxijt=xijt+r×xijt-ximt. Notice that the value ximt is distributed from every resistant case xm randomly, and that it is centered on a vector of status (S) given that m = {i\|S(i) = 0}. Immune case The new gene value of xijt+1 is influenced by any social gap within the spectrum of r ∈ [23BRr,BRr], which is determined by the discrepancy between the present gene and a gene extracted from a compromised case xv, such as8 xijt+1=Rxijt, where9 Rxijt=xijt+r×xijt-xivt. Notice that the value xivt is distributed from every resistant case xv randomly, and that it is centered on a vector of status (S) given that f(xiv)=argminj{k\|S(k)=2}f(xij). Step 4: Update herd immunity population The immunity rate f(xjt+1) of each case xjt+1 generated is determined and the actual case xjt is replaced by the obtained case xjt+1 if the obtained case is stronger, such that f (xjt+1)< f (xjt). Also, the age vector Aj is increased by a value of 1 if Sj = 1. For each event, the state vector (Sj) is modified xj based on the herd immune criterion that uses the following equation:10 ⟶Sj1fxjt+1<fx)jt+1Δfx∧Sj=0∧is_corona(xjt+12fxjt+1<fx)jt+1Δfx∧Sj=1, where the binary value of is_corona (xjt+1) is equal to 1 when the new value is a value from any infected case that has been inherited by case xjt+1. The Δfx is the mean significance of the immune population rates such as ∑xiHISf(xi)HIS. Notice that the immunity levels of the individuals in the population are altered depending on the social gap measured earlier. If the newly produced individual immunity rate is better than the population’s average immunity rate, this means that the population is becoming more immune to the virus. If the recently discovered population is sufficiently strong to be immune to the virus, then the threshold of herd immunity has been reached. Phase 5: Fatal cases In this phase, if the immunity rate f(xjt+1) of the current infected case (Sj = = 1) cannot be strengthened as defined by the Max_Age parameter (i.e., Aj > = M ax_Age), then this case is considered dead. However, using xijt+1 = Lbi + (Ubi − Lbi) × U(0, 1), ∀i = 1, 2,., n is then regenerated from scratch. In addition, Aj and Sj are both set to 0. This phase may be beneficial in diversifying the current population and thereby avoiding local optima. Phase 6: Stop criterion The CHIO algorithm repeats step 3 to step 5 until the termination criterion is reached, which normally depends on whether the maximum number of iterations is reached. In this case, the population is dominated by the total number of susceptible and immunized cases. Also the infected cases are passed. Figure 2 shows the flowchart of the CHIO algorithm.Fig. 2 Flowchart of CHIO model The pseudocode of the CHIO phases is given below: Proposed CHIO with PNN approach In this paper, the CHIO was combined with the PNN to adjust the NN weights with the aim of increasing the classification accuracy. In the proposed approach, first the PNN generates random solutions. Then, the CHIO is applied to adapt the weights produced by the PNN to improve the solution by optimizing the PNN weights. The PNN technique is a widely used data mining process and has been applied to many classification and pattern recognition problems. In this type of NN, the operations are organized into a multilayered network consisting of four layers, namely, an input layer, pattern layer, summation layer, and output layer. In the first layer (input) the dimension (p) of the input vector reflects the dimension of the layer. In the second layer (pattern), the dimension of the number of examples in the training set is equal to the dimension of this layer. The third layer (summation) consists of the number of classes within the group. The fourth layer (output) and the validation example are classified into a number of classes. The operational formulation in the PNN approach involves four major layers (Specht 1988):The input layer, where every neuron has a predictive variable where values are fed for each of the neurons in the hidden layer. The pattern layer: a single layer for every sample of training, which formulates a product related to the input vector x including the vector weight wi, zi = x.wiT. After that, the subsequent nonlinear processes are conducted (Eq. 11):11 exp-wi-x.wi-xT2α2, where i is the pattern number, T is the total number of training patterns, X is the ith training pattern from category, and a is the smoothing parameter. The summation layer: it aggregates the improvement for every class of inputs, and generates a network output as a vector of probabilities (Eq. 12):12 ∑iexp-wi-x.wi-xT2α2. The output layer generates different binary classes that are based on the decision classes Ωr and Ωs, r ≠ s, r, s = 1, 2,…. ….,q and a classification criterion (Eq. 13):13 ∑iexp-wi-x.wi-xT2α2>∑jexp-wj-x.wj-xT2α2. Such nodes just possess a single weight C, the probabilities of a previous membership, including the number of training samples within every class C that is provided by the cost parameter (Eq. 14):14 C=-hslshrlr.nrns, where hs denotes the preceding prospect where the current created sample proceeds to Group n, and cn denotes the misclassification cost. After constructing the NN, a group of network weights is tuned to nearly reach the required findings. The procedure is conducted based on using a training algorithm, which modifies different weights until a number of error criteria are obtained. The CHIO algorithm is used to improve the performance of the PNN when applied to classification problems. As seen in Fig. 3, the PNN creates a random initial solution, and this solution is then submitted to the CHIO which tries to optimize the PNN weights. Thus, the search capability of the CHIO is useful for improving the performance of the PNN. This improvement can be achieved by managing the random stages and efficiently finding the search space for the purpose of identifying the ideal values for the PNN classification process.Fig. 3 Representation of obtaining initial and final weights by CHIO-PNN Figure 4 shows the structure of the proposed algorithm. It consists of two main parts. In the first part (in the left-hand side of the figure), the PNN is trained on the training datasets. Then the tested datasets are categorized, and then computed the accuracy. In the second part, the CHIO is applied to adapt the weights of the PNN. Then the accuracy of the classification of the data is calculated.Fig. 4 Proposed CHIO-PNN approach The aim of the training process is to decide the most accurate weights to assign to the connector row. The output is computed repeatedly in this step, and the result is compared to the preferred output provided by the training/test datasets. The procedure begins with initial weights obtained at random by the original PNN classifier. The values from the data input are then multiplied by the PNN algorithm-determined weights w (ij). On the other hand, in the hybrid approach CHIO-PNN, the CHIO algorithm determines the accurate weights through its search capabilities. The CHIO was selected to obtain the highest accuracy and optimum parameter settings for training a PNN. The initial CHIO function does not restrict or regulate the random step duration in the CHIO. The proper combination of the exploration and exploitation phases in CHIO is critical to the performance of selecting the accurate weights to enhance the PNN’s classification process. The correctness of the classification system is determined based on the number of true positives (TPs) and true negatives (TNs), false positive (FPs) and false negatives (FNs) produced by the system. A TP is defined as permissible actual labels and the approximate mark associated with the brand. A TN is the negative number between the current label and the projected label. A FP denotes the negative number for the actual mark. However, it is estimated as positive by the classifier. A FN is defined as the positive number for the individual label. However, it is estimated as negative by the classifier. Hence, classification quality is calculated according to Eq. 15 as follows:15 Accuracy =TP + TNTP + TN + FP + FN. Additionally, two other performance measurements are taken into account to assess classification quality, namely, specificity and sensitivity, which are calculated by Eqs. 16 and 17, respectively:16 Sensitivity =TPTP + FN, 17 Specificity =TNTN + FP. In a binary classification problem, there is a single positive class and a single negative class. Hence, the optimum classification accuracy in this context is achieved when the classifier achieves 100% accuracy and the error rate is 0. Sensitivity and specificity are statistical measures of binary classification, and are commonly used when comparing the performance of different classifiers. Experimental setup and results In this section, first, the experimental setup used to test the CHIO algorithm with PNN is described. The turbulence that was made depends on a number of criteria, namely, the accuracy rate, the convergence speed, and some measures of central tendency. Then the results of performance testing are presented, followed by a comparison of these results with those reported in some previous related works. The experiments were carried out using a personal computer with an Intel(R) Core(TM) i7-6006U CPU @ 2.00 GHz (four CPUs), ~ 2.0 GHz with 8 GB of RAM. Implementation of the CHIO algorithm was done using Matlab R2016a. The datasets were split into 70% for training, and 30% for testing. The experiments were executed over 30 runs for each dataset, and 100 iterations were included in each run. Description of the datasets The CHIO approach that was applied to train the PNN was tested and benchmarked using 11 well-known real-world datasets in the University of California at Irvine (UCI) machine learning repository. The features of these datasets are summarized in Table 1.Table 1 Characteristics of the datasets Dataset No. of attributes Training set Test set 1 Haberman surgery survival (HSS) 3 206 77 2 PIMA Indian diabetes (PID) 8 518 192 3 Appendicitis (AP) 7 71 27 4 Breast Cancer (BC) 10 193 72 5 BUPA Liver Disorders (LD) 6 233 86 6 Statlog (Heart) 13 182 68 7 German Credit Data (GCD) 20 675 250 8 Parkinson’s 23 131 49 9 SPECTF 45 180 67 10 Australian Credit Approval (ACA) 14 465 173 11 Fourclass 2 581 216 The 11 benchmark datasets can be accessed and downloaded from http:/csc.lsu.edu/ ~ huypham/HBA_CBA/datasets.html. In the experiment, a simple train/test split function was used to make the split, where the test size = 0.3 and the training size = 0.7. Parameter settings Some preliminary experiments were conducted to determine the most suitable parameters for testing the performance of the proposed CHIO-PNN algorithm. Table 2 shows the parameter values that were used in all the experiments.Table 2 Parameter settings Parameter Value HIS 30 Max_Age 100 BRr 0.01 Max_Itr 100 LB (lower bound) 0 UB (upper bound) 1 Classification quality When applied to each of the 11 UCI datasets, the PNN classifier method produces a tentative solution by generating the primary weights randomly. To adjust these weights, the CHIO is processed using the PNN technique. The optimum classification accuracy is achieved in a binary classification task, which contains a single positive class and a single negative class, when the number of FPs = 0, the number of FNs = 0, the number of TPs = the quantity of positive classes defined, and the number of TNs = the number of negative classes identified. In the proposed method, the values of FP, FN, TP and TN were determined effectively. To determine the precision of the proposed approach, Eqs. 15, 16 and 17 were used to measure the accuracy, sensitivity and specificity of the proposed approach. The experiments were conducted to test the accuracy, error rate, sensitivity, and specificity of two methods (PNN and CHIO-PNN) to determine whether or not the CHIO was successful in solving problems associated with the classification domain. Therefore, the classification accuracy indicates that its values are increasing and CHIO has demonstrated greater accuracy and increased efficiency than the general methods of classification. From the results obtained, the CHIO with PNN approach achieved an improvement in convergence speed, and moreover, CHIO-PNN yielded more successful results as compared to some other algorithms in the literature, as explained in the following paragraphs. First, from Table 3, it can be seen that the proposed approach was able to adjust the weights of the PNN in all 11 datasets, thus increasing the degree of accuracy and reducing the error size with high efficiency. Good solutions for data classification problems can be found by eliminating the local optima trap during optimization. This is what the CHIO algorithm did by balancing global and local searches.Table 3 Results obtained by PNN and CHIO-PNN Dataset Method TP FP TN FN Accuracy Sensitivity Specificity Error rate PID PNN 35 28 90 39 65.104 0.473 0.763 0.349 CHIO-PNN 40 23 120 9 83.850 0.816 0.839 0.165 HSS PNN 44 12 6 15 64.935 0.746 0.333 0.351 CHIO-PNN 51 5 14 7 85.410 0.854 0.800 0.145 AP PNN 23 1 1 2 88.889 0.920 0.500 0.111 CHIO-PNN 24 0 2 1 96.296 0.960 1.000 0.038 BC PNN 14 9 36 13 69.444 0.519 0.800 0.306 CHIO-PNN 18 5 47 2 90.020 0.761 0.862 0.168 LD PNN 18 15 34 19 60.465 0.486 0.694 0.395 CHIO-PNN 32 3 47 4 91.860 0.766 0.897 0.080 Heart PNN 27 5 23 13 73.529 0.675 0.821 0.265 CHIO-PNN 30 0 24 12 82.350 0.680 1.000 0.171 GCD PNN 133 46 39 32 68.800 0.806 0.459 0.312 CHIO-PNN 165 14 44 27 83.600 0.850 0.750 0.168 Parkinson’s PNN 38 1 6 4 89.796 0.905 0.857 0.102 CHIO-PNN 39 0 6 4 91.830 0.906 1.000 0.821 SPECTF PNN 49 4 5 9 80.597 0.845 0.556 0.194 CHIO-PNN 49 2 14 2 94.020 0.960 0.750 0.060 ACA PNN 60 14 84 15 83.237 0.800 0.857 0.168 CHIO-PNN 70 4 95 4 95.790 0.933 0.959 0.043 Fourclass PNN 78 0 138 0 100.000 1.000 1.000 0.000 CHIO-PNN 78 0 138 0 100.000 1.000 1.000 0.000 Comparison with previous methods The results of the proposed CHIO-PNN approach were compared with the results of the PNN and with those of some recent methods in the literature, namely the FA (Alweshah 2014), the ABO (Alweshah et al. 2020b), β-HC (Alweshah et al. 2019) and WEA(Alweshah et al. 2020c), which were each combined with the PNN. All the comparisons were made using the same datasets and parameters as in those strategies. Table 4 shows the performance of the proposed CHIO-PNN approach against that of the other methods based on four criteria, namely, accuracy, sensitivity, specificity, and error rate.Table 4 Comparison of CHIO-PNN with previous methods Datasets Methods TP FP TN FN Accuracy Sensitivity Specificity Error rate PID PNN 35 28 90 39 65.104 0.473 0.763 0.349 CHIO-PNN 40 23 120 9 83.860 0.816 0.839 0.165 FA-PNN 33 30 113 16 76.040 0.673 0.790 0.140 ABO-PNN 45 18 115 14 83.330 0.760 0.860 0.170 β-HC-PNN 142 37 319 20 81.250 0.880 0.900 0.110 WEA-PNN 39 24 122 7 83.854 0.847 0.857 0.161 HSS PNN 44 12 6 15 64.935 0.746 0.333 0.351 CHIO-PNN 51 5 14 7 85.410 0.854 0.800 0.145 FA-PNN 54 2 10 11 83.120 0.830 0.833 0.168 ABO-PNN 54 2 11 10 84.420 0.840 0.850 0.160 β-HC-PNN 139 12 34 21 85.720 0.870 0.740 0.160 WEA-PNN 53 3 12 9 84.420 0.854 0.800 0.155 AP PNN 23 1 1 2 88.889 0.920 0.500 0.111 CHIO-PNN 24 0 2 1 96.296 0.960 1.000 0.038 FA-PNN 24 0 1 2 92.590 0.923 1.000 0.075 ABO-PNN 24 0 2 1 96.300 0.960 1.00 0.040 β-HC-PNN 53 2 15 1 96.300 0.980 0.880 0.040 WEA-PNN 24 0 1 2 92.59 0.923 1.000 0.074 BC PNN 14 9 36 13 69.444 0.519 0.800 0.306 CHIO-PNN 18 5 47 2 90.020 0.761 0.862 0.168 FA-PNN 31 1 24 12 80.880 0.720 0.960 0.19 ABO-PNN 18 5 43 6 84.720 0.750 0.900 0.150 β-HC-PNN 49 7 125 12 84.720 0.800 0.950 0.100 WEA-PNN 17 6 44 5 84.72 0.772 0.880 0.152 LD PNN 18 15 34 19 60.465 0.486 0.694 0.395 CHIO-PNN 32 3 47 4 91.860 0.766 0.897 0.080 FA-PNN 31 1 24 12 79.07 0.720 0.960 0.210 ABO-PNN 28 5 45 8 84.880 0.780 0.900 0.150 β-HC-PNN 77 22 112 22 93.020 0.780 0.840 0.190 WEA-PNN 24 9 49 4 84.88 0.857 0.844 0.151 Heart PNN 27 5 23 13 73.529 0.675 0.821 0.265 CHIO-PNN 30 0 24 12 82.350 0.680 1.000 0.171 FA-PNN 31 1 24 12 80.880 0.720 0.960 0.190 ABO-PNN 32 0 24 12 82.350 0.730 1.000 0.180 β-HC-PNN 79 3 90 10 86.760 0.890 0.970 0.070 WEA-PNN 32 0 25 11 83.82 0.744 1.000 0.161 GCD PNN 133 46 39 32 68.800 0.806 0.459 0.312 CHIO-PNN 165 14 44 27 83.600 0.850 0.750 0.168 FA-PNN 166 13 30 41 78.400 0.801 0.697 0.216 ABO-PNN 157 22 50 21 82.800 0.880 0.690 0.170 β-HC-PNN 439 27 182 27 80.800 0.940 0.870 0.080 WEA-PNN 165 14 42 29 82.800 0.850 0.750 0.172 Parkinson’s PNN 38 1 6 4 89.796 0.905 0.857 0.102 CHIO-PNN 39 0 6 4 91.830 0.906 1.000 0.821 FA-PNN 38 1 6 4 89.800 0.904 0.857 0.120 ABO-PNN 39 0 8 2 95.920 0.950 1.000 0.040 β-HC-PNN 95 0 35 1 91.840 0.990 1.000 0.010 WEA-PNN 93 0 7 3 93.88 0.928 1.000 0.062 SPECTF PNN 49 4 5 9 80.597 0.845 0.556 0.194 CHIO-PNN 49 2 14 2 94.020 0.960 0.750 0.060 FA-PNN 25 1 10 4 92.540 0.926 0.909 0.075 ABO-PNN 51 2 9 5 89.550 0.910 0.820 0.100 β-HC-PNN 138 7 30 5 93.040 0.990 0.940 0.020 WEA-PNN 49 4 12 2 91.04 0.960 0.750 0.086 ACA PNN 60 14 84 15 83.237 0.800 0.857 0.168 CHIO-PNN 70 4 95 4 95.790 0.933 0.959 0.043 FA-PNN 65 9 94 5 91.910 0.928 0.912 0.080 ABO-PNN 69 5 95 4 94.800 0.950 0.950 0.050 β-HC-PNN 197 11 245 12 93.060 0.940 0.960 0.050 WEA-PNN 71 3 94 5 95.38 0.934 0.969 0.046 Fourclass PNN 78 0 138 0 100.000 1.000 1.000 0.000 CHIO-PNN 78 0 138 0 100.000 1.000 1.000 0.000 FA-PNN 78 0 138 0 100.000 1.000 1.000 0.000 ABO-PNN 78 0 138 0 100.000 1.000 1.000 0.000 β-HC-PNN 78 0 138 0 100.000 1.000 1.000 0.000 WEA-PNN 78 0 138 0 100.00 1.000 1.000 0.000 From Table 4 it is clear that CHIO-PNN was able to outperform FA-PNN in terms of classification accuracy in 10 out of the 11 datasets, and its performance was equal to FA-PNN in the remaining dataset, namely, Fourclass. Also, CHIO-PNN outperformed ABO-PNN in seven datasets, namely, PID, HSS, BC, LD, GCD, SPECTF, and ACA, and produced the same results in two datasets, namely, Heart and Fourclass. Moreover, it was able to outperform β-HC-PNN in five datasets, namely, PID, BC, GCD, SPECTF, and ACA, and it generated the same result in one dataset, namely, Fourclass. The CHIO-PNN approach also produced results with high efficiency. Hence, the performance of CHIO-PNN was highly accuracy. Also, overall, it outperformed the other methods because it achieved 90.3% average accuracy across all datasets. In comparison, PNN, FA-PNN, ABO-PNN and β-HC-PNN achieved an average accuracy rate of 75.5%, 85.9%, 89%, and 89.6%, respectively. Figure 5 shows the average of the best accuracy values achieved by all of the methods.Fig. 5 Average of the best accuracy of tested methods It is well known that a stable and faster convergence speed can lead to better solutions (Alweshah et al. 2020d). Therefore, to further evaluate the performance of the proposed CHIO-PNN approach, the convergence speed behavior curves of CHIO-PNN were evaluated when implemented on the 11 datasets over 30 individual runs each of 100 iterations for each dataset. The curves of CHIO-PNN were compared with those produced by the FA-PNN to determine the efficiency of the proposed method. The experimental results displayed in Fig. 6 show that CHIO-PNN was able to enhance the weight parameters of the PNN that were generated randomly and thus provide an improvement in terms of classification accuracy at a faster convergence speed as compared to FA-PNN. The superiority of the proposed approach is due to the ability of the CHIO algorithm to achieve the optimum balance between exploitation and exploration.Fig. 6 Convergence speed of tested methods Furthermore, the T test was also used to compare the performance of the CHIO-PNN approach with that of numerous optimization algorithms. Applying the CHIO-PNN and FA-PNN methods, which depend on the accuracy of the outcomes relevant to each dataset, the statistics of the findings are carried out. By performing a T-test examination including a significance interval of 95 percent (alp = 0.05) on the p values obtained and classification accuracy, various resulting statistics are displayed in Table 5.Table 5 The statistics and P values of the T test for the accuracy of CHIO and FA Dataset Model Mean Std. deviation Std. error mean P value PID CHIO 82.6900 0.54000 0.54000 0.00015 FA 73.4895 1.28560 0.23472 HSS CHIO 83.3600 0.81000 0.81000 0.00011 FA 81.8179 1.02322 0.18681 AP CHIO 96.2900 0.00000 0.00000 0.00012 FA 92.5926 0.00012 0.00002 BC CHIO 81.8900 1.87000 1.80000 0.00017 FA 77.3935 1.74347 0.31831 LD CHIO 81.7400 3.08000 3.08000 0.00000 FA 75.5810 1.49604 0.27310 Heart CHIO 80.6300 2.01000 2.0000 0.00000 FA 78.6819 2.23781 0.40857 GCD CHIO 82.8000 0.00000 0.00000 0.00014 FA 82.8000 0.00000 0.28854 Parkinson’s CHIO 91.2200 1.09000 1.09000 0.00000 FA 89.7950 0.00000 0.00000 SPECTF CHIO 89.3500 1.06000 1.60000 0.00000 FA 88.8057 1.82787 0.33372 ACA CHIO 94.5100 0.57000 0.57000 0.00000 FA 89.8840 1.05983 0.19350 Fourclass CHIO 100.000 0.00000 0.00000 0.00000 FA 100.000 0.00000 0.00000 From Table 5, it can be seen that the performance of CHIO is significantly better than that of FA, where most of the P values for the 11 datasets are less than 0.0001. These results indicate that the use of the CHIO is beneficial for solving classification problems when used to refine the weights of the randomly generated PNN weights, as the refinements lead to an improvement in classification accuracy. Additionally, the boxplot technique was used to view the data distribution based on a summary of five numbers (minimum, first quartile (Q1), median, third quartile (Q3), and maximum). A boxplot shows whether the data are symmetrical and how closely they are clustered, and it also reveals the positions of outliers. Figure 7 shows the boxplots that explain the distribution of the resolution quality obtained by CHIO and FA when implemented on the 11 benchmark datasets. The figure shows the boxplot for 30 runs of CHIO and FA. The boxplots are being used to analyzing the PNN optimizer variability for getting best accuracy values in all the runs. From Fig. 7, it is apparent that the boxplots confirm that the CHIO shows better performance than the FA when training the PNN.Fig. 7 Boxplots for CHIO and FA The main aim of this study is to adjust the neural network weights in attempt to optimize classification accuracy while still achieving fast convergence speed. To achieve the research goals, the original PNN was applied in classification problems, and the finding was compared with a hybrid method based on PNN and CHIO for classification problems. The PNN was used to produce random solutions, and the CHIO was used to develop them further by optimizing the PNN weights. Because of its exploration and exploitation abilities, CHIO is able to discover promising areas in a in a reasonable time. AS well as the CHIO's balance between local and general search avoids it being stuck in local optima. This confirmed the PNN's results after it was paired with the CHIO algorithm to provide a more accurate classification than the previous approaches in most datasets. The experimental results showed that the proposed CHIO-PNN approach produced highly accurate solutions at a fast convergence speed. In addition, the results of the comparison of the proposed approach with three different algorithms in the literature revealed that the proposed approach was, overall, more effective and had a higher average accuracy rate. Furthermore, the high-quality resolutions for issues related to the classification domain are highlighted where more efficient accuracy and improvement in convergence speed are obtained. Conclusion In this paper, the coronavirus herd immunity optimizer (CHIO) was combined with the probabilistic neural network (PNN) for the purpose of adjusting the weights generated by the PNN to attempt to increase classification accuracy. In the proposed approach, first, the PNN generated random solutions. Then, the CHIO was applied to adapt the weights of the PNN, to enhancing the solution using the CHIO. The proposed approach, named CHIO-PNN, was applied to 11 UCI standard benchmark datasets to assess its performance in terms of classification accuracy, specificity, and sensitivity. The CHIO was selected to obtain the highest accuracy and optimum parameter settings for training a PNN. The initial CHIO function does not restrict or regulate the random step duration in the CHIO.The proper combination of the exploration and exploitation phases in CHIO is critical to the performance of selecting the accurate weights to enhance the PNN’s classification process. The experimental results showed that CHIO-PNN was able to enhance the weight parameters of the PNN that were generated randomly and to provide an improvement in terms of classification accuracy and convergence speed as compared to the PNN alone and also when compared with other methods, namely, the FA, the ABO, β-HC and WEA. The CHIO-PNN approach outperformed all of these methods, achieving 90.3% accuracy on all datasets. In future work, the proposed CHIO-PNN could be extended to other actual and high-dimensional datasets to investigate how it behaves under various conditions in terms of the number of classes and attributes. Also, it can be used to solve problems in many fields such as studying human chromosomes, handwriting identification, image segmentation and feature selection issues. Supplementary Information Below is the link to the electronic supplementary material.Supplementary file1 (PDF 337 kb) Acknowledgement This work has been carried out during sabbatical leave granted to the author Mohammed Alweshah from Al-Balqa Applied University during the academic year 2021/2022 Author contributions MA: contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript. Funding The authors have not disclosed any funding. Data availability The 11 benchmark datasets used in this paper can be accessed and downloaded from http:/csc.lsu.edu/ ~ huypham/HBA_CBA/datasets.html. Declarations Conflict of interest The author states that there is no conflict of interest. Human and animal rights statement This article does not contain any studies with human participants or animals performed by any of the authors. This article has been retracted. Please see the retraction notice for more detail: 10.1007/s00500-023-08554-6 Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Change history 5/22/2023 This article has been retracted. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 35322360 19456 10.1007/s11356-022-19456-9 Research Article China’s 2060 carbon-neutrality agenda: the nexus between energy consumption and environmental quality Li Kaodui 123 Ying Hongxin 1 Ning Yi 1 Wang Xiangmiao 1 http://orcid.org/0000-0002-5538-5986 Musah Mohammed [email protected] 4 http://orcid.org/0000-0001-9872-8742 Murshed Muntasir 56 Alfred Morrison 7 Chu Yanhong 1 Xu Han 1 Yu Xinyi 1 Ye Xiaxin 1 Jiang Qian 1 Han Qihe 1 1 grid.440785.a 0000 0001 0743 511X School of Finance and Economics, Jiangsu University, Zhenjiang, People’s Republic of China 2 grid.64938.30 0000 0000 9558 9911 College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, People’s Republic of China 3 grid.440785.a 0000 0001 0743 511X Division of State-Owned Enterprise Reform and Innovation, Institute of Industrial Economics, Jiangsu University, Zhenjiang, People’s Republic of China 4 Department of Accounting, Banking and Finance, School of Business, Ghana Communication Technology University, Accra, Ghana 5 grid.443020.1 0000 0001 2295 3329 School of Business and Economics, North South University, Dhaka-1229, Bangladesh 6 grid.442989.a 0000 0001 2226 6721 Department of Journalism, Media and Communications, Daffodil International University, Dhaka, Bangladesh 7 Department of Accounting Studies Education, Akenten Appiah-Menka University of Skills Training and Entrepreneural Development, Kumasi, Ghana Responsible Editor: Roula Inglesi-Lotz 23 3 2022 115 2 1 2022 23 2 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. This study examined the nexus between energy consumption and environmental quality in light of China’s 2060 carbon-neutrality agenda utilizing annual frequency data from 1971 to 2018. In order to obtain valid and reliable outcomes, more robust econometric techniques were employed for the analysis. From the results, all the variables were first differenced stationary and cointegrated in the long-run. The elastic effects of the predictors on the explained variable were explored through the ARDL, FMOLS, and the DOLS techniques, and from the discoveries, energy utilization worsened environmental quality in the country via more CO2 emissions. Also, industrialization and urbanization deteriorated the country’s environmental quality; however, technological innovations improved ecological quality in the nation. On the causal connections between the variables, a unidirectional causality from energy consumption to CO2 effluents was discovered. Also, feedback causalities between industrialization and CO2 secretions, and between urbanization and CO2 exudates were disclosed. However, there was no causality between technological innovations and CO2 emanations. Based on the findings, the study recommended among others that, since energy consumption pollutes the environment, the country should transition to the utilization of renewable energies. Also, the government should allocate more resources to the renewable energy sector. This will help increase the portion of clean energy in the country’s total energy mix. Furthermore, research and development that are linked to the utilization of green energies should be supported by the government. Data constraints were the main limitation of this exploration. Therefore, in the future, if more data become available, similar explorations could be conducted to check the robustness of our study’s outcomes. Keywords Energy consumption Environmental quality Industrialization Technological innovations Urbanization China ==== Body pmcIntroduction The main ambition of most nations in recent times is to reduce the environmental implications of greenhouse gas (GHG) emissions which have resulted in global warming and climate change (Musa et al. 2021; Li et al. 2021). As a result, it has become imperative for the world economies to adopt policies that can enable them to decouple economic growth and environmental pollution (Qin et al. 2021). China as the leading emitter of carbon is under severe pressure to mitigate its CO2 emissions (Ma et al. 2021; Yao and Zhang 2021; Liu et al. 2021; Xiaosan et al. 2021). As a signatory to the Paris Agreement (2015), Kyoto Protocol. (2005), Copenhagen Accord. (2009), and other international treaties, the country has initiated various environmental regulations and energy utilization strategies to help minimize the consumption of fossil fuels and other conventional energies that deteriorate its ecological quality (Yuelan et al. 2019; Li et al. 2021). By the year 2030, China aims to curtail its CO2 effusions by 65% from the 2005 figure and promote the utilization of clean energies by about 25% (Yao and Zhang 2021; Li et al. 2021). Also, the government of China has the 2060 carbon-neutrality agenda which is one of its emission reduction strategies to help the nation attain become carbon neutral (Ahmad et al. 2019, 2018; Zeraibi et al. 2021; Rehman et al. 2021b). If China is able to meet its emission reduction targets, then, the world’s ambition of minimizing global warming and climate change would receive a major boost, because of the country’s dominance in global CO2 effluents. According to Lauri (2021), China’s CO2 effusions increased by approximately 1.7% per year on average from 2015 to 2020. Irrespective of the economic consequences of the COVID-19 pandemic, the country’s CO2 exudates continued to surge by about 1.5% in 2020. Overall, power generation by fossil fuels rose by 2.5% in 2020 compared to 2019 (China Electricity Council 2020). Moreover, production by industries accounted for 66% of the country’s total energy utilization and around 50% of its total emissions (China National Bureau of Statistics 2020). In 2019, steel and cement production surged by 7%, while noncombustion process-related emanations rose by 5.6%. Thus, industrial emissions are currently moving upwards, with productions from steel and cement being the dominant factors (Mikhail and Kate 2020). Though China has improved on its green energy generation, the share of solar, wind, and other clean sources of energies represent less than 10% of its total energy mix (China National Bureau of Statistics 2020). Besides, the nation’s economic recovery policies have been carbon-intensive, leading to the rise in fossil fuel power generation, coal mining output, and industrial coal utilization, resulting in more pollution (Lauri 2020). Therefore, shifting from this current energy profile to one focused on emission mitigations warrants a lot of efforts from the Chinese government. Numerous explorations have focused on the linkage between energy consumption (EC) and environmental quality (EQ) in China. For example, Xiangmei et al. (2018) study on China affirmed EC as detrimental to EQ in the country. Also, Marques et al.’s (2021) investigation discovered CO2 effusions as a driver of EC in the nation. Moreover, Yiping’s (2021) exploration confirmed electricity from renewable sources as beneficial to ecological quality in the country. According to Ma et al. (2019), industrial energy utilization was the main determinant of environmental pollution in the country. Wang et al.’s (2021) study discovered energy from dirty sources as a trivial predictor of EQ in the nation. Also, Dong et al.’s (2018) analysis revealed green energy as harmless to EQ in the country. To Rong et al. (2020), electricity consumption explained more that 75% of CO2 effusions in the country. Moreover, Jian et al.’s (2019) study found EC as damaging to EQ in China. All the above explorations were conducted on China, but their findings are conflicting. The contrasting discoveries might be as a result of the methodologies employed, the time dimension or the studied variables among others. This suggests that the EC and EQ debate is far from over and demands more interrogations. Therefore, undertaken this research to come out with policy options to help China attain its sustainable development goals (SDGs) was deemed fitting. The main motivation of this study was to help China become carbon–neutral. According to a 2018 report of the Intergovernmental Panel on Climate Change, attaining global warming of 1.5 °C in 2030 will demand that global CO2 emanations reduce by 45% from 2010, reaching a net zero around 2050, while attaining global warming of 2 °C in 2030 will demand that global CO2 effluents reduce by 25% from 2030, reaching a net zero around 2070. Therefore, if China should wait until 2030 before they take carbon-neutrality actions, their global emission ambitions cannot be accomplished. Hence, a study to help the Chinese government to improve its CO2 effusions mitigation strategies was deemed appropriate. The contributions of this exploration are in threefold. First, the study contributed methodologically by following a well-outlined analytical process. At the initial stage, stationarity tests were undertaken to examine the variables’ integration order. Afterwards, tests to check the cointegration attributes of the series were conducted. At the third phase, the elastic effects of the covariates on the response variable were explored. Finally, a causality test was undertaken to determine the path of causations amidst the series. Most prior explorations on the EC and EQ connection failed to follow a well-outlined econometric strategy. Secondly, the issue of omitted variable bias (OVB) is not been recognized by most studies on the connection between EC and EQ. This is disadvantageous because OVB could yield prejudiced and unreliable coefficient values, which could lead to erroneous inferences (Musah et al. 2021a, b, c; Li et al. 2020a, b). This study therefore controlled for industrialization (IND), technological innovations (TI), and urbanization (URB) to help minimize OVB issues. Finally, our exploration adopted the time series approach, which completely varies from other prior investigations on the topic of concern. This approach was used because it helps to improve the power of statistical tests leading to more robust outcomes. Most prior investigations on the studied topic adopted different approaches and might have failed to capture the true relationship amidst the series. The significance of this exploration cannot be underrated. First, the recommendations of this study will help the Chinese government to adopt renewable and other energy utilization alternatives that will help boost ecological quality in the country. The study is also essential because it will help policymakers to implement other effective policy interventions that are required to minimize climate change and its repercussions. The study is finally vital in that, it adds to the existing literature on the linkage between EC and EQ. This will serve as a reference material for future studies on the topic of concern. This paper is original because, its goal is clearly stated; the methodologies used are extensive; the findings are well reported, evaluated, and debated; and the policy suggestions are well-thought-out. This paper is grouped into five sections. The “Introduction” section is the introduction of the study, while the “Literature review” section outlines literature that supports the issue at hand. The “Materials and methods” section is on the methodology, while the “Results of the study” section displays the study’s results and discussions. Finally, the “Conclusions and policy recommendations” section presents the conclusions, recommendations, and study limitations. Literature review In this section, literature that are related to the topic are reviewed. The reviews are categorized into two. The first part reviews literature on the nexus between EC and EQ in China, while the second part presents reviews on the connection between EC and EQ in other parts of the world. On the linkage between EC and EQ in China, Rahman and Vu’s (2021) research discovered a positive association between EC and ecological pollution in the country. Also, the VECM Granger causality found a one-way causality from CO2 secretions to EC in the nation. Yao and Zhang (2021) also studied the connection between EC and EQ in China by employing the ARDL estimator. From the revelations, clean energy enhanced EQ in the country. Zheng et al. (2020) researched on China from the period 1978. From the results, the influence of energy intensity slowed the growth of CO2 effusions in the country. Shum et al. (2021) analyzed the determinants of EQ in China by employing the LASSO model. From the results, EC was a main driver of CO2 exudates. Xiangmei et al. (2018) undertook a study on China from 1953 to 2016. From the results, EC surged CO2 effusions in the country. Khan et al. (2021a, b, c, d) explored the beta decoupling relationship between EC and CO2 excretions. From the results, EC was one of the factors that caused CO2 emissions to rise in the country. Marques et al. (2021) studied China from 1977 to 2016. From the results, CO2 effusions drove energy consumption in the country. Yiping (2021) studied China from 1988 to 2018 and disclosed that electricity from renewable sources limited environmental pollution in the country. Wang et al. (2021) investigated the drivers of ecological footprint (EFP) in China over a 36-year period. The ARDL approach was engaged to determine the coefficients of the predictors. From the discoveries, energy from dirty sources was not a material predictor of EQ in the country. Alola et al. (2021) researched on China from 1971 to 2016. Based on the QQR estimates, fossil fuel and primary energy utilization impacted positively on EFP in all quantiles. Amazingly, clean energy utilization also exerted a positive influence on EFP in the country. Finally, the spectral Granger causality (SGC) discovered a causation from primary energy use to EFP and from clean energy to EFP. Zou and Zhang (2020) examined the connection between EC and EQ in China from 2000 to 2017. From the results, EC and EQ were interrelated as a feedback causality between the two series was observed. Tong et al. (2020) conducted a study on E7 countries by employing the ARDL technique. From the revelations, a short-run causality from EC to CO2 effluents in China was found. Li et al. (2021), Li et al. (2021), Li et al. (2021), Li et al. (2021), Li et al. (2021) investigated the linkage between energy structure and EQ in 30 Chinese provinces from 2011 to 2017. From the discoveries, energy structure based on coal had a substantial effect on emissions in the country. Jian et al. (2019) undertook a study on China from 1982 to 2017. From the results, EC surged ecological pollution in the country. Numerous explorations on EC and EQ in other parts of the world have also been conducted with contrasting discoveries. For example, Agbede et al. (2021) investigated MINT countries over the period 1971 to 2017. The ARDL-PMG results of the study revealed a positive association between EC and environmental pollution. Also, a causality from EC to ecological pollution was unfolded. Ahmad et al. (2021a, b) investigated 11 developing economies from 1992 to 2014. From the discoveries, a surge in electricity consumption minimized environmental pollution in the economies. The finding implies country-specific policies should be undertaken to help stimulate EQ in the economies. Qayyum et al. (2021) analyzed the nexus between EC and EQ in India from 1980 to 2019. From the ARDL estimates, REC improved EQ in the nation. Khan et al. (2020) researched on Pakistan from 1990 to 2015. From the ARDL estimates, access to electricity worsened EQ via high CO2 emanations. Shobande and Ogbeifun (2021) analyzed the nexus between EC and EQ in OECD countries from 1980 to 2019. From the results, EC promoted ecological pollution in the nations. Kirikkaleli and Adebayo (2021a) studied the connection between EC and EC in India from 1990Q1 to 2015Q4. From the results, REC was beneficial to EQ in the country. Also, REC caused consumption-based CO2 effusions at different frequency levels. Chien et al. (2021) researched on top Asian economies from 1990 to 2017. Based on the CS-ARDL estimates, REC improved EQ in the economies; however, NREC spurred environmental pollution in the economies. Khan et al. (2021a, b, c, d) analyzed the linkage between EC and EQ in 21 developing economies from 1970 to 2018. The study employed the OLS, FE, and the GMM regression techniques in its analysis. From the discoveries, REC improved EQ in the economies; however, NREC was not friendly to the ecologies of the nations. Khan et al. (2021a) assessed the connexion amidst EC and ecological pollution in 188 countries from 2002 to 2018. From the OLS, FE, GMM and the system GMM estimates, NREC escalated environmental pollution in the nations; however, REC was friendly to the countries’ environment. Xue et al. (2021) examined the linkage between EC and EQ in South Asian economies by employing recently developed econometric techniques. From the discoveries, REC improved EQ in the economies; however, NREC deteriorated ecological quality in the economies. Khan et al. (2021b) investigated the association between EC and environmental pollution in 180 economies from 2002 to 2019. From the OLS, FE and system GMM discoveries, REC reduced ecological pollution in the economies. Khan et al. (2021c) used a global panel to investigate the determinants of EQ from 2002 to 2019. From the two-step system GMM results of the study, REC enhanced EQ in the countries. Bekun et al. (2021) researched on SSA and discovered from the PMG econometric technique that conventional energy harmed EQ in the countries; however, clean energy improved the countries’ EQ. Bibi et al. (2021) investigated the linkage between biomass EC and EQ in the US from 1981M01 to 2019M12. From the revelations, a causation from BEC to CO2 effluents was discovered. Ali et al. (2021) undertook a study on Nigeria from 1981 to 2014. Based on the DARDL results, EC spurred CO2 excretions in the nation. Iqbal et al. (2021) explored the asymmetric effects of clean energy on CO2 effusions in Pakistan. The NARDL technique was used to determine the parameters of the covariates. From the results, positive changes in clean energy generation promoted CO2 exudates in the country; however, negative changes in clean energy generation mitigated CO2 secretions in the nation. Ahmad et al. (2021a, b) investigated 11 developing economies from 1992 to 2014. The FMOLS and the PMG techniques were employed for the parameter estimations, and from the findings, electricity consumption mitigated environmental pollution in the economies. Also, a feedback causation amidst electricity consumption and CO2 exudates was established. Baydoun and Aga (2021) studied the linkage between EC and EQ in GCC economies from 1995 to 2018. From the CS-ARDL estimates, EC worsened EQ in the countries via high CO2 excretion. Also, a causality from EC to CO2 effluents was disclosed. Nawaz et al. (2021) analyzed the impasse of EC on EQ in South Asian economies from 1990 to 2017. Based on the FMOLS estimates, EC minimized EQ in the countries. Khurshid et al. (2021) investigated the association between EC and EQ in Western and Southern Europe from 2000 to 2018. The NARDL and the OLS approaches were engaged for the parameter estimations. From the discoveries, EC was a key polluter in the economies. Chunyu et al. (2021) researched on 18 countries from 2010 to 2019 and disclosed that energy from dirty sources mitigated EQ in the countries; however, energy from clean sources improved EQ in the nations. Musa et al. (2021) investigated the EC and EQ connection in EU-28 countries from 2002 to 2014. From the two-step GMM discoveries of the study, REC was positively related to environmental performance in the nations. Balli et al. (2020) researched on Turkey from 1960 to 2014. Based on the VECM output, a causation from EC to CO2 exudates was confirmed. Osobajo et al. (2020) analyzed the association between EC and EQ in 70 economies from 1994 to 2013. Findings of the study confirmed EC as detrimental to EQ in the countries. Also, a one-directional causality from EC to CO2 excretions was unfolded. Alharthi et al. (2021) investigated the link between EC and EQ in MENA countries from 1990 to 2015. From the discoveries, REC mitigated ecological pollution in the countries, but NREC worsened environmental pollution in the economies. Chontanawat (2020) explored the linkage between EC and CO2 emanations in ASEAN region from 1971 to 2015. From the results, EC was materially related to environmental pollution in the region. All the aforestated studies are on the connection between EC and EQ; however, their findings are contradictory. The conflicting outcomes might be as a result of the methodologies employed, the time dimension or the studied variables among others. This suggests that the EC and EQ argument is unceasing and warranted further investigations. Therefore, a study on the linkage between EC and EQ in China was worthwhile. Materials and methods Data source and descriptive statistics A time series data on China for the period 1971 to 2018 was used for the study. The study period was chosen based on data availability. For instance, there was no data available for the proxy of environment quality (CO2 emissions) after 2018. Also, data for energy consumption was not available from 1960 to 1970 and after 2014. Therefore, using the explained variable as the determining factor, the period 1971 to 2018 was deemed appropriate. This implies the data used for the analysis was not balanced. Further details on the series are outlined in Table 1. The descriptive statistics of the series are displayed in Tables 2 and 3. From the table, IND was the highest in terms of average values, while TI was the lowest. Also, TI was the most volatile based on the SD values, while URB was the least volatile. From the skewness results, the distribution of TI was negatively skewed, while that of the rest was positively skewed. The kurtosis outcomes also confirmed the distribution of TI to be leptokurtic in shape, while that of the rest was platykurtic in shape. Additionally, the covariates were not highly collinear based on the multi-collinearity test results. Finally from the PCA results indicated in Table 4, the predictors had higher loadings and were, therefore, appropriate to predict the emanation of CO2 in the country.Table 1 Variable description and measurement units Variable Measurement unit Source Environmental quality (CO2 emissions) Metric tons per capita WDI (2021) Energy consumption (EC) Kg of oil equivalent per capita WDI (2021) Industrialization (IND) Industry (including construction), value added (constant 2010 US$) WDI (2021) Technological innovation Resident and nonresident patent applications WDI (2021) Urbanization (URB) Urban population (% of total population) WDI (2021) Table 2 Descriptive statistics and correlational matrix Descriptive statistics Statistic lnCO2 lnEC lnIND lnTI lnURB Mean 0.955 6.764 26.706 0.254 3.378 Median 0.893 6.642 26.634 0.939 3.371 Maximum 2.023 7.713 28.999 1.822 4.016 Minimum 0.041 6.142 24.659 − 10.012 2.844 Std. dev 0.603 0.472 1.398 1.921 0.388 Skewness 0.393 0.703 0.081 − 3.644 0.079 Kurtosis 2.098 2.369 1.674 19.435 1.685 Jarque–Bera 2.682 4.454 3.344 6.056 3.288 Probability 0.262 0.108 0.188 0.231 0.193 Correlational matrix Variable lnCO2 lnEC lnIND lnTI lnURB lnCO2 1.000 lnEC 0.791 1.000 (0.000)* lnIND 0.878 0.563 1.000 (0.000)* (0.043) lnTI 0.676 0.623 0.691 1.000 (0.000)* (0.076)* (0.000)* lnURB 0.781 0.459 0.698 0.385 1.000 (0.000)* (0.000)* (0.000)* (0.057)* *, , * denote significance at the 1%, 5%, and the 10% levels correspondingly. Table 3 Multi-collinearity tests results VIF and tolerance tests Farrar and Glauber test Variable VIF Tolerance F test p value lnEC 2.39 0.418 4.022 0.008* lnIND 2.93 0.341 5.774 0.037 lnTI 2.02 0.495 3.098 0.072* lnURB 2.52 0.397 2.979 0.005* Mean VIF 2.47 - - - VIF variance inflation factor while , , denote significance at the 1%, 5%, and the 10% levels respectively. Table 4 Principal component analysis Component Eigenvalue Difference Proportion Cumulative Comp 1 2.382 1.314 0.596 0.596 Comp 2 1.068 0.719 0.267 0.863 Comp 3 0.349 0.148 0.087 0.950 Comp 4 0.201 - 0.050 1.000 Principal components (eigenvectors) Variable Comp 1 Comp 2 lnEC 0.514 m − 0.322 lnIND 0.529 m − 0.198 lnTI 0.422 0.902n lnURB 0.528 m − 0.209 mdenotes significant loadings under comp 1, while nrepresents significant loadings under comp 1. Model specification This study examined the link between energy consumption (EC) and environmental quality (proxied by CO2 emanations) in China, while controlling for industrialization (IND), technological innovation (TI), and urbanization (URB). In achieving this goal, the following econometric model was proposed.1 CO2it=ai+β1ECit+β2INDit+β3TIit+β4URBit+μit where CO2 is the response variable representing environmental quality (EQ) and EC is the main explanatory variable of concern. Also, β1,β2,β3, and β4 are the parameters of the regressors, while αi is the constant term. Moreover, μit is the error term, while i and t denote the country and time respectively. To help reduce heteroscedasticity issues, natural logarithm was taken on both sides of Eq. 1. The resulting log-linear model therefore became;2 lnCO2it=ai+β1lnECit+β2lnINDit+β3lnTIit+β4lnURBit+μit where lnCO2 is the logarithm of the explained variable, while lnEC, lnIND, lnTI, and lnURB are the log conversions of the explanatory variables. After transforming the variables into natural logarithms, the coefficients could be interpreted as elasticities. The study expected EC to have a positive influence on CO2 effusions (β1>0), if residential and nonresidential energies consumed in the countries are carbon-intensive. Otherwise, EC was to exert a negative effect on CO2 exudates (β1<0), if energies consumed in the country were from clean sources that could help to boost EQ in the nation (β2<0). Also, IND was to positively influence CO2 effluents (β1>0), if the energies utilized at the industrial level in the country were not environmentally friendly. Otherwise, IND was to negatively impact the emissivities of CO2 in the nation (β2<0), if IND was linked to the use of green energy. Additionally, TI was to mitigate the emanation of CO2 in the country (β3<0) because it promotes less polluting activities in an economy. Finally, URB was to have a positive impact on CO2 emittance (β4>0), if URB led to the utilization of polluting energies in the country both domestically and industrially. Otherwise, URB was to negatively affect CO2 excretions (β4<0), if the migration of people to big cities in China resulted in the utilization of green energy. Analytical process To comprehensively examine the EC-EQ linkage in China, a four-staged econometric procedure was followed. Firstly, the ADF and the PP tests for unit root were conducted to examine the integration order of the variables. Afterwards, following Murshed (2021), the ARDL bound test and the Johansen test were performed to check the cointegration attributes of the series. Following Pesaran et al. (2001), the ARDL bound test specification for this study is expressed as;3 ΔlnCO2t=φ0+φ1CO2t-1+φ2lnECt-1+φ3lnINDt-1+φ4lnTIt-1+φ5lnURBt-1+∑i=1pβ1iΔCO2t-1+∑i=1qβ2iΔlnECt-1+∑i=1qβ3iΔlnINDt-1+∑i=1qΔlnTIt-1+∑i=1qβ5iΔlnURBt-1+ut where the change operator is denoted by Δ, the optimal lags selected via the AIC is represented by t-1, and the estimated parameters are denoted by φ and β. It should be noted that the Johansen cointegration test was employed to authenticate the bound test results. This test is advantageous because it can detect multiple cointegrating vectors (Johansen 1991). At the third phase of the analysis, the elasticities of the predictors were first estimated via the ARDL approach. This method was adopted because it produces valid results even in short-time datasets. Also, if the integration order of investigated series is mixed, the estimator can still produce vigorous results (Khan et al. 2021b). The ARDL model developed to explore the long-run connections amidst the variables is specified as;4 lnCO2t=α0+∑i=1pσ1ilnCO2t-1+∑i=1qσ2ilnECt-1+∑i=1qσ3ilnINDt-1+∑i=1qσ4ilnTIt-1+∑i=1qσ5ilnURBt-1+ut where σ denotes the long-term variance in the variables and q represents the lags selected through the AIC. The short-run ARDL model for the exploration is expressed as;5 lnCO2t=α0+∑i=1pσ1iΔlnCO2t-1+∑i=1qσ2iΔlnECt-1+∑i=1qσ3iΔlnINDt-1+∑i=1qσ4iΔTIt-1+∑i=1qσ5iΔlnURBt-1+ϕECTt-1+ut where the variance of the short run is symbolized by σ, the error correction term is represented by ECTt-1, and ϕ is the parameter of the ECT. For robustness purpose, the FMOLS and the DOLS estimators were also adopted. These estimators were adopted because they mitigate issues of heteroscedasticity (Kiefer and Vogelsang 2002). The techniques are also advantageous because they are vigorous to endogeneity and autocorrelation in regression analysis (Funk and Strauss 2000). The FMOLS estimator is specified as;6 β^FMOLS=1N∑i=1N∑t=1T(rit-r¯i)2-1×∑t=1Trit-r¯ih^it-TΔ^eu, In Eq. 6, r and h symbolize the regressors and the regressand correspondingly, while Δeu is the covariance term. Also, Δ^eu is the estimated value of the covariance term, while T and N are the time frame and the dimension of the data respectively. The DOLS estimator on the other hand is expressed as;7 β^DOLS=1N∑i=1N∑t=1TRitRit′∑t=1TRith~it-1 where R epitomizes the set of predictors that are 2k+1×1 and Rit=rit-r¯i,Δrit-k,…,Δrit+K-K is the number of covariates. Finally, the VECM of Engle and Granger (1987) was engaged to examine causations amidst the series. This estimator was adopted because it yields consistent and reliable results in time series analysis. The test began by first estimating Eq. 2 to recover residuals considered as lagged error correction terms (ECT). Afterwards, the ensuing dynamic error correction models were estimated to unravel the causalities amid the series;8 ΔlnCO2t=ω1+∑j=1qφ1,1jΔlnCO2t-j+∑j=1qφ1,2jΔlnECt-j+∑j=1qφ1,3jΔlnINDt-j+∑j=1qφ1,4jΔlnTIt-j+∑j=1qφ1,5jΔlnURBt-j+∅1ECTt-1+μ1,t 9 ΔECt=ω1+∑j=1qφ1,1jΔlnECt-j+∑j=1qφ1,2jΔlnCO2t-j+∑j=1qφ1,3jΔlnINDt-j+∑j=1qφ1,4jΔlnTIt-j+∑j=1qφ1,5jΔlnURBt-j+∅1ECTt-1+μ1,t 10 ΔINDt=ω1+∑j=1qφ1,1jΔlnINDt-j+∑j=1qφ1,2jΔlnECt-j+∑j=1qφ1,3jΔlnCO2t-j+∑j=1qφ1,4jΔlnTIt-j+∑j=1qφ1,5jΔlnURBt-j+∅1ECTt-1+μ1,t 11 ΔTIt=ω1+∑j=1qφ1,1jΔlnTIt-j+∑j=1qφ1,2jΔlnINDt-j+∑j=1qφ1,3jΔlnECt-j+∑j=1qφ1,4jΔlnCO2t-j+∑j=1qφ1,5jΔlnURBt-j+∅1ECTt-1+μ1,t 12 ΔURBt=ω1+∑j=1qφ1,1jΔlnURBt-j+∑j=1qφ1,2jΔlnTIt-j+∑j=1qφ1,3jΔlnINDt-j+∑j=1qφ1,4jΔlnECt-j+∑j=1qφ1,5jΔlnCO2t-j+∅1ECTt-1+μ1,t In the equations above, q denotes the lags determined via the SIC, while ω signifies the intercepts. Also, φ denotes the coefficients to be estimated, while μ is the error term. Furthermore, t is the study period while ECT is the error correction term with its coefficient being ∅. Results of the study Unit root and cointegration tests results The analysis began by testing for unit root in the variables. From the results displayed in Table 5, all the series had an I(1) order of integration collaborating those of Li et al. (2020a, b) for some selected quoted entities in Ghana, Khan et al. (2019) for Pakistan, Musah et al. (2021d, 2022a, b) for Ghana and the G20, and Danish and Ulucak (2020) for China. The variables’ integration order underscores the adoption of the ARDL, FMOLS and the DOLS techniques, since they are fitting for variables that exhibit first difference stationarity. The variables’ order of integration also implies they could be related in the long-run. Therefore, the tests shown in Table 6 were undertaken to assess the cointegration properties of the variables. From the discoveries, the series were cointegrated in the long-term aligning those of Chen et al. (2022), Phale et al. (2021), Li et al. (2021) and Musah et al. (2020a, b, c). This implies proceeding to estimate the parameters of the predictors was well in line.Table 5 Unit root tests results Variable Levels First difference ADF PP Decision ADF PP Decision lnCO2 77.293 125.855 I(0) 113.371 195.822 I(1) 0.621 0.202 0.000* 0.000* lnEC 54.347 80.438 I(0) 108.602 254.807 I(1) 0.538 0.318 0.021 0.000* lnIND 91.012 140.146 I(0) 153.209 395.729 I(1) 0.302 0.903 0.000* 0.000* lnTI 40.365 82.619 I(0) 66.385 266.498 I(1) 0.943 0.111 0.061* 0.000* lnURB 89.532 198.388 I(0) 140.601 421.361 I(1) 0.212 0.422 0.000* 0.000* The top values for the variables denote unit root statistics, while the down values represent probabilities. Also, , , and denote significance at the 1%, 5%, and the 10% levels respectively. Table 6 Cointegration tests results ARDL bound test results Statistic 10% 5% 1% p value I(0) I(1) I(0) I(1) I(0) I(1) I(0) I(1) F statistic 8.114 1.429 4.925 2.183 3.143 3.966 7.332 0.004 0.008 t statistic − 6.231 − 4.534 − 3.346 − 5.196 − 3.514 − 4.344 − 2.878 0.002 0.005 Johansen cointegration test results No. of CE(s) Trace stat Prob. Max. Eigen Stat Prob. None* 155.292 0.001 97.291 0.002 At most 1* 88.101 0.003 50.143 0.005 At most 2* 48.355 0.005 36.344 0.006 At most 3* 25.817 0.007 117.217 0.008 At most 4* 10.019 0.035 6.238 0.039 At most 5* 0.734 0.044 0.436 0.048 The ARDL bound test was supported by the Kripfganz and Schneider (2018) critical value bounds and approximate p values. Also, both the trace and the max-eigenvalue tests indicate 6 cointegrating eqn(s) at the 0.05 level. Finally, * denotes rejection of the null hypothesis at the 0.05 level while represents the MacKinnon-Haug-Michelis (1999) p values. Model estimation and causality results Having established cointegration association between the variables, the ARDL technique was first adopted to explore the elasticities of the covariates. Based on the estimates indicated in Table 7, EC spurred CO2 emanations in China. Ceteris paribus, a 1% rise in EC surged CO2 effusions by 6.227% and 4.145% in both the long and the short run respectively. This means that the country’s economic activities were linked to the utilization of polluting energies like coal and fossil fuel among other, which exacerbated the rate of emissions in the nation. Explorations by Abbasi and Adedoyin (2021) and Musah et al. (2021c) offer support to the study’s finding, but those by Kirikkaleli and Adebayo (2021a, b) and Anwar et al. (2021) are conflicting to the above disclosure. Also, IND worsened environmental quality by 2.172% and 1.395% in both the long and the short run correspondingly. This revelation is not surprising in that China has witnessed a major economic expansion of late, thanks to the rise in the country’s industrial activities. However, majority of the industries in the nation are highly reliant on carbon-intensive energies sources, which pollute the environment. Studies by Ullah et al. (2020) and Rehman et al. (2021a) align the outcome of the study, but those by Appiah et al. (2021) and Zhou and Li (2020) deviate from the above discovery. Moreover, TI improved environmental quality in the country. Specifically, a 1% surge in TI mitigated CO2 emissivities by 3.214% and 2.293% in the long and the short run respectively. This finding suggests that technology was key in the nation’s strive towards a low-carbon economy. Empirical explorations by Chen and Lee (2020) and Yu and Du (2019) agree with the discovery of the study, but those by Khattak et al. (2020) and Villanthenkodath and Mahalik (2020) vary from the above disclosure.Table 7 ARDL estimation results Variable Coeff SE t statistic Prob lnECt-1 6.227 1.5151 4.11 0.005* Δln ECt 4.145 1.2833 3.23 0.007* lnINDt-1 2.172 0.9568 2.27 0.025 ΔlnINDt 1.395 0.6940 2.01 0.043** lnTIt-1 − 3.214 0.8262 − 3.89 0.059* lnΔTIt − 2.293 1.1943 − 1.92 0.028* lnURBt-1 4.411 2.0234 2.18 0.003* ΔlnURBt 3.332 2.1497 1.55 0.007 Constant 5.248 1.3526 3.88 0.001* ECTt-1 − 0.716 0.2295 − 3.12 0.004* R2 0.821 B-P-G test 0.821(0.556) Adjusted R2 0.805 ARCH test 0.725(0.646) F statistic 116.334 (0.008)* RESET test 0.641(0.477) B-G LM test 1.102(0.913) J-B test 1.882(0.712) lnCO2 the response variable, SE for standard errors, B-G LM test Breusch-Godfrey LM test, B-P-G test Breusch-Pagan-Godfrey test, ARCH signifies autoregressive conditional heteroscedastic test, J-B Jarque–Bera test, and RESET test Ramsey regression equation specification error test. Also, , , , denote significance at the 1%, 5%, and the 10% levels respectively, while values in parenthesis () represent probabilities. Furthermore, URB worsened environmental quality in China. Ceteris paribus, a 1% change in URB escalated CO2 effusions by 4.411% and 3.332% in both the long and the short run correspondingly. This result implies, URB led to developments in economic activities like industrialization and the creation of basic infrastructure like roads, bridges, and markets, which are heavily reliant on the utilization of polluting energies, leading to more effusions. Put simply, URB policies of the nation did not help to propel ecological welfare targets of the country. The discovery collaborates those of Solarin et al. (2017) and Ali et al. (2019), but deviates from those of Rafiq et al. (2016) and Azam and Khan (2016). Lastly, the ECT was substantially negative at the 1% level. The ECT value of − 0.716 implies the speed of adjustment towards the long-run equilibrium was 71.6%. The adjusted R2 value of 0.805 signifies that 80.5% of the variations in CO2 effluents were explained by the predictors, while the significant F value signposts that the model had a very high explanatory power. In order to check the validity of the model, the diagnostic tests indicated in Table 7 were undertaken. From the Breusch Godfrey LM test, there was no serial correlation in the residuals of the model. Also, ARCH and Breusch-Pagan-Godfrey tests found no homoscedastic in the error terms. Furthermore, the model was well specified based on the Ramsey RESET test. Finally, the residual terms were normally distributed as per the Jarque–Bera test results. For robustness purpose, the FMOLS and the DOLS estimates were finally explored. From the estimates displayed in Table 8, EC, IND, and URB worsened EQ by 0.827%, 0.692%, and 1.868% respectively. However, TI improved EQ by 0.017%. The weight of the coefficients and the levels of significance under the two estimators were dissimilar from the ARDL technique. However, the parameters of the predictors in terms of sign were the same under the three approaches. This underscores the robustness of the study’s results. The elastic effects of the predictors on the response variable are illustrated in Fig. 1.Table 8 FMOLS and DOLS estimation results FMOLS results Variable Coefficient Std. error t statistic Prob lnEC 0.827 0.097 8.526 0.000*** lnIND 0.692 0.163 4.245 0.086* lnTI -0.017 0.008 -2.125 0.019 lnURB 1.868 0.592 3.155 0.025 R-squared 0.891 Adjusted R-squared 0.802 DOLS results Variable Coefficient Std. error t statistic Prob lnEC 0.858 0.111 7.730 0.000* lnIND 0.376 0.179 2.101 0.017 lnTI − 0.912 0.209 − 4.364 0.072* lnURB 2.407 0.657 3.664 0.061* R-squared 0.791 Adjusted R-squared 0.719 lnCO2 response variable; and *, , and * denote significance at the 1%, 5%, and the 10% levels respectively. Fig. 1 The elastic effects of the predictors on the response variable At the final stage, the VECM of Engle and Granger (1987) was engaged to explore the causalities between the variables. Based on the estimates displayed in Table 9, a causation from EC to CO2 effusions was unfolded. This implies carbon emissivities were reliant on energy consumption in the country. Studies by Musah et al. (2021a) and Li et al. (2020a) align the finding of the study, but those by Doğanlar et al. (2021) deviate from the above outcome. Also, IND and CO2 emissions were mutually related. This means the two variables were dependent on each other. Thus, a rise in IND led to a rise in CO2 effusions and vice versa. Empirical explorations by Liu and Bae (2018) and Al-Mulali and Ozturk (2015) support the above revelation, but that of Musa et al. (2021) contradicts the study’s discovery. Moreover, there was no causality between TI and CO2 emanations in the country. This signposts that the two series did not cause each other. Investigations by Bashir et al. (2020) and Abid et al. (2021) are in tandem with the above revelation, but that of Sana et al. (2021) varies from the study’s finding. Finally, a double-headed causality between CO2 effluents and URB was disclosed. This means, the two variables caused each other or were inter-dependent on each other. Implying a rise in one variable led to a rise in the other variable and vice versa. Studies by Ahmed et al. (2019) and Salahuddin et al. (2019) offer support to the study’s outcome, but those by Haseeb et al. (2018) and Mesagan and Nwachukwu (2018) contrast the outcome of the study.Table 9 Pairwise Granger causality tests results Variable lnCO2 lnEC lnIND lnTI lnURB ECT lnCO2 - 3.351 4.194 2.192 4.143 − 0.772 (0.147) (0.003)* (0.118) (0.023) (0.001)* lnEC 5.432 - 6.656 2.421 8.074 − 0.662 (0.004)* (0.411) (0.035)** (0.078)* (0.008)* lnIND 4.412 0.261 - 1.318 0.193 − 0.718 (0.016) (0.044) (0.783) (0.808) (0.037) lnTI 6.174 2.138 1.361 - 1.621 − 0.812 (0.701) (0.178) (0.049) (0.207) (0.007)* lnURB 3.147 0.234 2.012 1.142 - − 0.792 (0.053)* (0.145) (0.345) (0.034) (0.048) lnCO2 response variable, while values in parenthesis () represent probabilities. Finally, *, , * denote significance at the 1%, 5%, and the 10% levels respectively. Conclusions and policy recommendations This study examined the connection between energy utilization and environmental quality in China for the period 1971 to 2018. Robust econometric methods that offer valid and reliable results were used for that analysis. From the results, all the variables had I(1) order of integration and were flanked by a long-term cointegration association. The coefficients of the predictors were first explored via the ARDL estimator and from the discoveries, energy utilization degraded ecological quality in the country via high CO2 effusions. Also, industrialization and urbanization deteriorated the country’s environmental quality; however, technological innovations improved ecological quality in the nation. The FMOLS and the DOLS estimates were also explored to help check the vigorousness of the ARDL results, and from the revelations, the parameters of the predictors in terms of sign under the FMOLS and the DOLS techniques were the same as those under the ARDL approach. This suggests that the results were valid and reliable. The causal connections between the series were explored via the VECM of Engle and Granger (1987) and from the results, a unidirectional causality from energy consumption to CO2 effluents was discovered. Also, feedback causalities between industrialization and CO2 secretions, and between urbanization and CO2 exudates were disclosed. However, there was no causality between technological innovations and CO2 emanations. The causal connections amidst the series are depicted in Fig. 2.Fig. 2 The causal connections between the explained and the explanatory variables. Note: ( ↔) signifies a two-way causality between variables, ( ←) denotes a one-way causality between variables, and (–-) represents no causality between variables Based on the findings, the study concludes that energy consumption, industrialization, and urbanization are harmful to environmental quality in China, but technological innovations help to advance ecological quality in the country. With reference to the above conclusions, the study recommends that since energy consumption pollutes the environment, the country should transition to the utilization of renewable energies. Also, the government should allocate more resources to the renewable energy sector. This will help increase the portion of clean energy in the country’s total energy mix. Furthermore, research and development that are linked to the utilization of green energies should be supported by the government. Moreover, majority of people are not aware of the environmental consequences of dirty energies and the health benefits of green energies. Therefore, the government should intensify its awareness creation strategies to help attain the aforestated issues. Since industrialization added to environmental pollution in the country, the Chinese government should ban the establishment of polluting industries in the country. However, industries that factor ecologically friendly issues in their operations should be permitted to operate in the country. Also, the government can reduce the tax burden of environmentally friendly industries, while increasing that of environmentally unfriendly ones. This will propel the latter to shift to ecologically friendly activities. From the discoveries, urbanization also degraded environmental quality in the country. As a recommendation, the Chinese government should improve the living standards of people in remote areas. This will prevent them from migrating to urban cities. Also, basic infrastructural facilities that attract people to move to urban centers should be provided for them in their respective localities. According to Behera and Dash (2017), sustainable urbanization model rather than unsustainable urbanization model should adopted in managing the rate of urbanization in economies. Therefore, following the above authors, sustainable urbanization model should be adopted to control the rate of urbanization in the country. Finally, because technological innovations helped to improve environmental quality in the country, the government should advocate for the adoption of environmentally friendly technologies in all organizations and institutions. Data constraints was the main limitation of this exploration. For instance, there was no data available for the proxy of environment quality (CO2 emissions) after 2018. Also, data for energy consumption was not available from 1960 to1970 and after 2014. Therefore, using the explained variable as the determining factor, the period 1971 to 2018 was deemed appropriate. This implies the data used for the analysis was not balanced. In future if the missing data become available, similar explorations could be conducted to check the robustness of the study’s outcomes. Author contribution KL conceptualized the study; HY drafted the original manuscript; YN helped in analysis and discussions; XW provided data; MM1 wrote the final manuscript; MM2 conducted the literature review and helped in analysis and discussions; MA helped in analysis and discussions; YC helped in analysis and discussions; HX helped in analysis and discussions; XY1 helped in analysis and discussions; XY2 helped in analysis and discussions; QJ helped in analysis and discussions; QH edited the final manuscript. All authors read and approved the final manuscript. Data availability The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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Mathematical Problems in Engineering 8842770. 10.1155/2020/8842770	35322360	PMC8942160	NO-CC CODE	2022-08-13 23:15:06	yes	Environ Sci Pollut Res Int. 2022 Mar 23; 29(37):55728-55742
==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 35386082 19839 10.1007/s11356-022-19839-y Research Article Does green finance mitigate the effects of climate variability: role of renewable energy investment and infrastructure Mngumi Franley 1 Shaorong Sun 2 Shair Faluk 3 Waqas Muhammad [email protected] 4 1 grid.267139.8 0000 0000 9188 055X Business School, University of Shanghai for Science and Technology, Shanghai, 200093 China 2 grid.413012.5 0000 0000 8954 0417 College of Economics and Management, Yanshan University, Qinhuangdao, China 3 Business Studies Department, Namal Institute Mianwali, Mianwali, Pakistan 4 grid.444868.2 0000 0004 1761 2185 Institute of Business & Management, Bahauddin Zakrya University Multan, Multan, Pakistan Responsible Editor: Roula Inglesi-Lotz 6 4 2022 2022 29 39 5928759299 9 12 2021 17 3 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Few researches have inspected the task of green finance in reducing CO2 emissions, while earlier studies have inspected the influence of economic development on carbon emissions. A green finance development index is built using four indicators to fill in this knowledge gap: green credit, green insurance, green securities, and green investing. Using data spanning the years 2005–2019, a panel quantile regression is applied to investigate the links between green finance, renewable energy, and CO2 emissions. Increases in renewable energy use and advances in the green finance development index have contributed to a reduction in CO2 emissions from BRICS countries. CO2 emissions on the other hand slowed the growth of renewable energy use, slowed the flow of investment to green projects, and ultimately hampered the development of green finance. There was also a clear policy-driven influence on renewable energy spending in the countries of the BRICS region. Green finance policies, on the other hand, have consistently failed to have a long-term impact. Therefore, rising the consumption of renewable energy and creating a carbon trading market are all part of this study’s recommendations for green finance policy improvement. Keywords Green finance CO2 emissions PQR Renewable energy Natural resources issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2022 ==== Body pmcIntroduction There has been an increase in industrial activity and population growth, which has resulted in a depletion of natural resources all over the world. This has led to an increased awareness of the wealth gap, as well as social and environmental responsibility (Paramati and Shahzad 2022). It is time for a new scenario for organisations and countries that want to adopt technologies that are both environmentally friendly and economically viable (Chishti and Sinha 2022). To put it simply, green finance aims to provide financial resources for environment-friendly schemes, with ecological security as the primary motivation. The “Equator Principles” were signed on June 4, 2003, by the world’s top 10 banks in London (Rasoulinezhad and Taghizadeh-Hesary 2022). These voluntary principles are envisioned to deal with financial issues that have a social and environmental influence. These practises are concerned with reducing environmental damage and also aim to have an impact on a variety of sectors, including energy, health, and wealth, among others (Ye et al. 2022). The use of green technologies, also referred to as Green Technology, is becoming more widespread across all sectors of a country’s economy (Li et al. 2022). Sustainable practises are promoted in this context by promoting mechanisms that reduce pollution, environmental impacts throughout the life cycle, the opening and creation of new markets, and the development of new products, services, or processes. Green technology appears in this context through the development of sustainable practices (Jin et al. 2021). Rapid economic growth, on the other hand, is frequently accompanied by high levels of energy use and CO2 emissions. Human-caused CO2 emissions now account for nearly 2/3rds of the biosphere’s total emissions of the greenhouse gas methane. A significant rise in CO2 emissions has been observed in the BRICS nations, the world’s largest emerging economies. There were 14,759 billion tonnes of carbon dioxide emissions in 2019, accounting for 43.19% of global CO2 emissions. Some countries have made significant efforts to reduce carbon dioxide emissions, while others have taken a more moderate approach. In 2014, China released a plan to decrease its CO2 emission intensity by 40 to 45% by 2020 equating to 2005, which is a responsible country (Ning et al. 2021). This objective has been met. Paying attention to the influencing factors of carbon emissions in the BRICS nations, which contributes 2/3rds of the biosphere’s total carbon emissions, not only alleviates the pressure of reduction in global carbon emission but also helps to stimulate the sustainable growth of the nations’ economy. Additionally, green financing has emerged as a vital means of financing environmental-related issues. This modern method of financing and managing environmental and growth-related aspects is paving the way for a global world in which economies can sustain and grow on a green basis. To put it simply, green financing is a cutting-edge method of promoting sustainable development around the world. Green growth is vulnerable by the inability to adequately fund private, public, and non-profit green initiatives (Tangonyire and Akuriba 2020). While government subsidies and public loans may still be necessary, this argument reveals new avenues for securing funding, which could eventually replace these methods. In addition, there is a pressing required to establish a more efficient market structure for financing green projects. The newest form of financing is known as “green financing.” A lack of research in the area of green financing in renewable energy production and energy effectiveness systems necessitates further investigation to provide solutions (Jha and Gupta 2021; Liu et al. 2021a). Nonetheless, in order to present the policy implications, it is necessary to test the energy effectiveness associated with green funding and renewable electricity production. As a result, the study’s goal is to discover if there is a new relationship between these concepts and to offer the best solutions to those involved. In this study, developing and developed countries are compared in terms of energy efficiency (Irfan et al. 2022a). In addition, we came up with a set of policies. To this day, no other study has looked at how green finance affects economic growth in such depth. So we can see if green financing actually achieves its stated objective of matching financial development and ecological affairs. The model also combines green finance, ecological benefits, and financial development into one model and draws conclusions that are currently absent from the literature. In contrast to previous studies (Ali et al. 2021; Cui et al. 2020; Falcone 2020; Li et al. 2021; Nawaz et al. 2021), this one considers and compares the BRICS nations. Brazil, Russia, India, China, and South Africa are the BRICS countries. Jim O’Neil of Goldman Sachs came up with the acronym BRICS in a 2001 report, which is used due to its English term “brick” likeness. Twenty years ago, the idea of BRICS countries was first proposed. The BRICS nations have experienced fast financial development over the past two decades. Statistics from https://www.statista.com/studies-and-reports/ (measured on 18 June 2021) estimate the BRICS countries’ combined population at 3.178 billion people, or about 41.42% of the global population. The combined GDP of the BRICS countries is equal to approximately 22.45% of the global GDP (Hou et al. 2022). In addition, the global economy has steadily improved as nations collaborate with one another in numerous fields. There is no research that incorporates environmental issues into finance. According to the findings of this research, therefore, has four major contributions: Our research focuses on the dynamic association between renewable energy, green finance, and CO2 emissions in the BRICS nations over the period 2010–2020. Previous studies examined the association between economic development and environmental variables, this research concentrates on green finance as an entirely separate topic. This study compares the results of OLS regression with panel quantile regression (PQR). The PQR confirms the entire conditional division of the explained variable under this condition as an alternative of proving the mean value of the explained variable under this condition. CO2 emissions are affected by different explanatory variables, which explains why regression coefficients of different quantiles tend to differ, and outliers indicate the importance of significant information. However, quantile regression does not strictly adhere to the classical econometric suppositions of a zero mean, homoscedasticity, and normal distribution in its estimation of random error terms. Quantile regression, on the other hand, yields more reliable parameter estimates for variables with abnormal distributions. Literature review Some academics are interested in green finance and energy efficiency. A group of researchers found that green finance is ineffective in many countries due to a number of core issues (Irfan et al. 2021a,b, 2019b; Irfan and Ahmad 2021; Saeed Meo and Karim 2021). Result of the poor private sector and insufficient financial framework, green finance instruments such as green bonds are useless in developing or less developed economies. For the same reasons as the previous studies, Zhang et al. (2021a, b) investigated whether green bonds had any effect on a variety of economic or environmental indicators and came to the conclusion that they had no such effect. Due to the absence of plans in India’s atmosphere action plan, Zhang et al. (2021b) concluded that there was no association between green bonds and sustainable development goals. On top of all of this, Wang et al. (2021) looked at how EU investment banks financed renewable energy projects over a 3-year period beginning in 2015. The findings of Tolliver et al. (2020) showed that funds were allocated inefficiently, negating the benefits of green financing for green projects. There are studies that show the positive impact of green financing on macroeconomic variables (Dmuchowski et al. 2021; Jinru et al. 2021), in contrast to the studies (Ren et al. 2020; Zhang et al. 2021b) that found a neutral or negative influence of green finance. When comparing green bonds to traditional bonds in the COVID-19 era, researchers found that the greater transparency of interest rates and investment returns provided by green bonds made them more effective (Wang et al. 2021b). In a similar Srivastava et al. (2021) examined the time period from 2008 to 2019 in which green bonds were compared to other variables like renewable energy. There was clear proof that green bonds have an impact on the development of clean energy. According to Chen et al. (2021), they studied the green bond market in Asia and the Pacific. Green bonds in Asia tend to have greater profits, but with greater risk and greater heterogeneity, according to Mastini et al. (2021) findings. Sixty percent of all Asian green bond issuances are issued by the banking sector. A post-COVID-19 era of issuer diversification, with greater public sector participation and risk removing policies, could also be accounted for, according to Irfan et al. (2022a, b). Sustainable development goals (SDGs) relating to climate change and environmental threats were examined by van Veelen (2021). According to the findings of Iqbal et al. (2021a, b), private investors could be enticed into this market by state assisting of the banking and economic sectors in setting green financing. As a result, the development of green financing could have a positive impact on the growth of green energy projects. Green finance, according to Khan et al. (2021), is a significant element in long-term green investments. According to them, public financial institutions play a key role in making these financing options more efficient. Green bonds, according to Liu et al. (2021b), are an effective instrument for green financing because they reduce investment risk, boost return on investment, and draw in investors from around the world. It was observed that risk management in the green bond market can improve the efficiency and effectiveness of this financing mechanism for the development of green energy projects by Ye et al. (2022). Market conditions and the green finance market mechanism, according to Rasoulinezhad and Taghizadeh-Hesary (2022), are two important factors in creating a positive connection between green finance and green energy projects. Xiong and Sun (2022) found that green finance had a positive impact on boosting small-scale green energy investments. Sustainable renewable energy development can be attained by attracting private investors, as well as creating synergy between the state and private sectors through green finance, according to Li et al., (2022). A recent study by Zhang et al. (2022a, b) found that as the green energy financing market expands in India, more green projects will be funded, increasing the share of green energy in India’s overall power mix. As Yao and Tang (2021) argued, green financing has a direct and positive impact on renewable energy development through financial market mechanisms and state legislation. All countries should implement green economic reforms, according to Huang et al., (2022a, b), in order to increase investment in green energy production and reduce pollution. Since its inception as an ethical and aesthetic concern for environmental well-being, green development has grown into a multifaceted support system encompassing economic, legal, and political aspects. It’s a sign of how far we have come in our understanding of human nature and interpersonal relationships. Funding for environmental protection and energy conservation projects is supported by green finance, which facilitates real economic growth. Sustainable economic growth requires a more efficient industrial structure, and green finance can help achieve that goal. Besides financing environmental governance, its role includes shifting resources from fossil-fuel-intensive industries to advanced technology ones. Not only is it environmentally friendly, but it’s also economically sound to put money into renewable energy resources Aboramadan and Karatepe, (2021). Even though the COVID-19 pandemic has not ended, it has complicated things everywhere it has been. It is critical for proper waste management practises to employing masks made from biodegradable materials. The manufacturing costs of these facemasks and other personal protective equipment are reasonable (Irfan et al. 2019a; Mubarik et al. 2021). As a result, during a pandemic, investing in renewable energy sources benefits the environment (Irfan and Ahmad 2022; Jarboui 2021). Eco-friendly businesses can apply for green credit, which is an investment in a specific interest rate. The green economy is supported by a well-established and organised infrastructure in developed countries (Shekhar et al. 2021). Environmentally friendly approaches to starting new businesses are well-established throughout the world’s business community. Green economic projects are becoming more and more popular with corporations (Pata and Caglar 2021; Wei et al. 2022). Investment in new business ventures in developing countries such as Pakistan has been initiated, but the efforts have not yet emerged on a large scale (Nguyen et al. 2021; Tang et al. 2022). The COVID-19 pandemic has wreaked havoc on the financial and economic stability of the world. The rise in healthcare costs has had a devastating effect on the economy as a whole. Today’s businesses are struggling to deal with the current economic crisis. A new and innovative approach to supporting green credit investment initiatives is needed now (H. Liu et al. 2021a; Noureddine and Tan 2021). Pakistan’s economic stability and long-term viability depend on green credit initiatives. Economic growth can only occur if environmentally friendly biofuels and recycled materials are used (Khan et al. 2022). Investments in green credit are advantageous for Pakistan’s economic growth (Zhou et al. 2022). Investing in green securities ensures the long-term health of the green economy. It is critical for the well-being of society as a whole that businesses use environmentally friendly materials in their manufacturing processes (W. Iqbal et al. 2021a, b; H. Zhang et al. 2022a, b). For the economic well-being of their economies, developed countries like China have put in place safe financing approaches, including companies that have appropriate strategies to support healthcare insurance and other health implications. Economic progress and prosperity cannot be sustained without environmentally sustainable options (Alsagr and van Hemmen 2021). Furthermore, various researchers, such as Cetin et al. (2018), Charfeddine (2017), Dogan and Turkekul (2016), Hou et al. (2019), Khan et al. (2020), Setyowati (2021), Yumei et al. (2021), stress the influence of financial sector development on carbon emission. Similarly, the choice of econometric methods, the chosen countries and their economic structure, and the study period are some of the factors affecting mixed empirical findings. This study analyses previous studies, on a group of countries, such as Asia Pacific countries (Y. Zhang et al. 2022a, b), panel data for 42 countries (Xu et al. 2021), panel data from 97 countries worldwide (Iqbal et al. 2019b), Ghana (Iqbal et al. 2019a), high-income countries (bassem et al. 2022), lower income countries (X. Liu et al. 2021a, b), South Asian countries (Ahad et al. 2021; Wen et al. 2022), (OECD) countries (Zaidi et al. 2021), and European countries (Huang et al. 2021) confirm the negative correlation between financial variable and carbon emissions with different degrees, using different research methods. Although the relationship between CO2 and green finance is easily seen in the literature, none of the studies uses quantile regression approaches to empirically examine this relationship. The impact of green financing on CO2 emissions is supported by few studies; however, the researchers’ findings are inconsistent. The relationship is potentially dependent on the economic cycle and the size of green finance, which makes this approach intriguing, and therefore, changes in green finance cause CO2 emissions to respond accordingly. Hence, CO2 emissions are recorded at high values with high economic expansion, whereas recorded low with economic slumps. Consequently, the state of the economy dictates the kind of relationship between carbon emissions and green finance, regarding the complex and multifaceted nature of many factors responsible for determining its relationship with green finance. Therefore, CO2 emissions are likely to get affected by a positive change in green finance more than a negative change. These findings show that the effects of green financing are not the same in all countries and are influenced by a variety of different factors. There are many countries that support green finance, and it would be practical to study this new financing in a group of these countries, as well as other nations that are interested in developing green finance markets. Because these economies play a critical role in achieving the United Nations’ Sustainable Development Goals (SDGs) in 2015, it is critical to investigate how this variable affects energy efficiency and green energy consumption. Hence, three research gaps are identified, where the first one focuses on the impact of green finance on carbon emission, considering some prior studies. Moreover, it is eminent to determine the dynamics of this relationship due to the rapidly growing green finance, with BRICS countries recorded as one of the largest contributors to the global carbon emissions. The study includes renewable energy use as an important variable. Moreover, index construction is the second gap focused by this study and it is time sensitive to study green growth due to the recent attraction for this topic, where previous studies focus on the impact of financial development on environmental pressure. The study uses official documents to build a green finance development index. Furthermore, the study bridges a third gap related to the method employed in the previous studies by using fixed-effect quantile regression, to overcome this problem. Method and data Cross-sectional dependence test For panel data, cross-sectional dependence (CD) of Phillips and Sul (2003) is critical, as it can lead to erroneous and inconsistent findings. Real-world connections include economic, social, political, and other channels like bilateral trade and board sharing. CD may be a result of these forms of associatively between countries. We employ Pesaran (2014) CD test and Breusch and Pagan (1980), Lagrange Multiplier (LM) test to address this issue. CD tests look for the presence of CD in data by using the equation below.1 CD=2TN(N-1)∑j=i+1Nρji T is the period, and N is the cross-sections. The stochastic variations’ heterogeneous correlation is explained as follows: Following is an example of an LM test that uses this equation to examine panel data for CD.2 yit=αi+βixit+εit where T represents the time period, and I represents cross-sections. Null hypotheses for both of these estimation methods assume the absence of cross-sectional dependence, whereas alternative hypotheses account for the presence of CD in the panel data. Cross-sectional unit root test Because of their low power to accommodate cross-sectional dependence, first-generation unit root tests are ineffective when dealing with cross-sectional dependence. The results are also assumed to be unaffected by cross-sectional variation, which is not the case. Because of this, Pesaran et al. (2008) developed the CIPS and CADF models, which combine the cross-sectional independence of the Pesaran-Shin and the cross-sectional augmentation of the Dickey-Fuller models. Cross-sectional and panel heterogeneity are taken into account in both of these tests For the purposes of assessing the stability of the variables, second-generation unit tests have been employed.3 Δxit=αit+βixit-1+ρiT+∑j=0nθitΔxi,t-j+εit where xit denotes the variable under consideration, i indicates the cross-sections, t denotes the time period and explains the residuals of the model, respectively. The null hypothesis takes into account non-stationarity, as opposed to the alternative hypothesis, which takes into account stationarity. Panel quantile regression model An analysis of the effects of green finance (GF), renewable energy (RE), natural resources (NR), gross domestic product (GDP), foreign direct investment (FDI), and trade openness on CO2 emissions is carried out using a panel quantile regression model. For regression, we used a fixed-effect model. Since panel quantile regression uses fixed effect panels, we can see how CO2 emissions are distributed across the conditional distribution. For example, Khokhar et al. (2020) proposed the use of quantile regression to investigate asymmetric distributions. These coefficients can be estimated across the various quantiles using this method. Given, the conditional quantile is:4 Qyi(τ\|xi)=xiTβτ Quantile regression does not have a problem with outliers or heavy distributions. The unobserved heterogeneity of the country is not taken into account by these methods. For this reason, some econometricians studied the theory of using quantile regressions to analyse panel data. It is possible to determine how individual heterogeneity affects the conditional heterogeneous covariance impacts of CO2 emissions drivers. Consider a fixed-effect panel quantile regression model like the one shown here:5 Qyit(τk\|αi,xit)=αi+xitTβ(τk),i=1,⋯,N;t=1,⋯,T A pure shift in the response’s conditional quantiles can be seen in formula 5. This means that covariates x_it can have varying effects based on the quantile in question. i is both a personal identification number and a chronometric identifier. The total number of observations made on i is given by the number “N.” Unobservable fixed effects were treated as dimensions to be collectively approximated with covariate impacts for dissimilar quantiles of the data at time t. An additional penalty term is included in the minimization step to account for the numerous parameters that must be estimated in this method’s parameter estimate.: 6 min(α,β)∑k=1K∑t=1T∑i=1Nwkρτk(yit-αi-xitTβ(τk))+λ∑iN\|αi\|,i=1,⋯,N;t=1,⋯,T In addition, we modify the specifications of previous studies to estimate the impact on CO2 emissions of green finance (GF) and renewable energy (RE). We use quantiles with equal weights (wk=1/K and set λ=1) in this paper. We specify the conditional quantiles function for quantile as follows:7 CO2yit(τ\|αi,ξt,xit)=αi+ξt+β1τLGFit+β2τLREit+β3τLNRit+β4τLGDPit A panel quantile regression model is used in this paper to account for individual and distributional heterogeneity that cannot be directly observed. Natural resources (NR), foreign direct investment (FDI), gross domestic investment (GDP), and trade openness are selected as control variables in the model Equation in order to avoid an omitted-variable bias.8 CO2yit(τ\|αi,ξt,xit)=αi+ξt+β1τLGFit+β2τLREit+β3τLNRit+β4τLGDPit++β6τTOit+β7τFDIit Data and variable selection Green finance, renewable energy, natural resources, carbon dioxide emissions, gross domestic product, FDI, and trade openness are all represented by the variables GF, RE, NR, CO2, GDP, and TO. The natural logarithm is used to measure green finance (GF) in billions of dollars, and the proxy used for renewable energy (RE) is the consumption of hydroelectricity, nuclear, wind, and solar. MMT CO2 emissions, whereas GDP and FDI are expressed in constant US dollars. The World Development Indicators (WDI) provide the data for these variables. From 2005 to 2019, data on the economies of the BRICS countries were analysed. All variables are accounted for individually in Table 1.Table 1 Variable’s description Variable Mean Std. dev Min Max CO2 1.351 0.9463 − 0.343 3.194 GF 1.093 3.1538 − 9.103 4.414 NR 0.389 2.4343 − 7.437 3.444 GDP 8.773 2.167 6.822 8.876 FDI 0.387 1.432 − 4.884 2.011 TO 2.876 0.562 3.4321 3.543 RE 1.324 1.043 1.282 3.992 Results and discussion Cross-sectional dependence test In empirical estimation, the first step is to look for cross-sectional dependencies. The results of the cross-sectional dependence and the LM tests are presented in Table 2. It is possible to conclude that cross-sectional dependence exists as a result of the rejection of the null hypothesis for both tests. When cross-sectional dependence is present, unit root tests of the second generation should be used to examine the integration properties of the variables under consideration. This study makes use of a CADF and CIPS unit root, and the results are shown in Table 3. Model variables have a unit root at the level of the model, but they become stationary after the first difference between the two models. It is possible to detect the presence of a level and stationary first difference unit root using the CADF indicator.Table 2 CD test Variables Breusch-Pagan LM Pesaran scaled LM Pesaran CD CO2 515.142 ∗ ∗ ∗ 117.0322 ∗ ∗ ∗ 30.231 ∗ ∗ ∗ GF 515.667 ∗ ∗ ∗ 42.938 ∗ ∗ ∗ 8.088 ∗ ∗ ∗ TO 464.869 ∗ ∗ ∗ 41.021 ∗ ∗ ∗ 7.947 ∗ ∗ ∗ FDI 569.285 ∗ ∗ ∗ 14.768 ∗ ∗ ∗ 3.136 ∗ ∗ ∗ GDP 454.716 ∗ ∗ ∗ 40.638 ∗ ∗ ∗ 7.909 ∗ ∗ ∗ NR 690.089 ∗ ∗ ∗ 23.101 ∗ ∗ ∗ 4.953 ∗ ∗ ∗ RN 423.745 ∗ ∗ ∗ 77.211 ∗ ∗ ∗ 51.40 ∗ ∗ ∗ Table 3 Panel unit root tests CIPS CADF Variables I(0) I(1) I(0) I(1) CO2 − 1.492 − 3.76* 0.000* 0.000* GF − 1.33 − 3.127* 0.124 0.009 NR − 2.814 − 4.483* 0.788 0.015** GDP − 1.090 − 3.926* 0.933 0.005** FDI − 2.641* − 5.418* 0.114 0.000* TO − 2.558* − 4.97* 0.003** 0.000* RE − 2.105 − 3.74* 0.138 0.003 , , and show significance level at 1, 5, and 10%, respectively A cross-sectional dependence issue was addressed using Pesaran et al. (2008) CIPS and ADF panel unit root test, which best addresses cross-country dependencies in the sample. It is shown in Table 3 that the analysis variables are stationary in first difference, allowing us to further investigate the cointegration between variables. Heterogeneity and cross-sectional dependences are addressed by using Persyn and Westerlund (2008) cointegration test. We can see from Table 4 that there is an integration of order one between the variables, allowing us to look into the relationship between study variables and carbon emission in the BRICS countries. Second-generation results show that all variables are either I (0) or I(1). We use the Persyn and Westerlund (2008) cointegration test to check for long-run cointegration among the study’s target variables because the variables are first-difference stationery.Table 4 Westerlund panel cointegration test Statistics Test statistics P value Gt − 2.364 0.980 Ga − 1.854 1.000 Pt − 4.072 0.966 Pa − 2.507 0.988 *, , and * show significance level at 1, 5, and 10%, respectively Model comparison In order to enable comparisons, the model is initially estimated using pooled and fixed effects OLS regression estimates. Pooled OLS regression estimates are presented in columns 1 and 2 of Table 5, correspondingly. For the purpose of estimating long-run elasticities, Pedroni (2001) used the FMOLS technique described in his paper. It has been pointed out by Pedroni (2001) that some types of cross-sectional dependency are captured by common time dummies. Column 4 summarises the FMOLS findings. According to Baltagi (2008), time-period fixed effects are used to control for all time-specific, spatially invariant variables that could bias estimates in a typical time-series study and are used to control for all time-period random effects. A fixed effect in both directions is therefore more interesting to us than the outcomes of a model with a random effect in either direction. Column 3 displays the outcomes of the two-way fixed-effects analysis. In fact, only one aspect of trade is consistent across all of the specifications: the effect of trade.Table 5 Model comparison Variable OLS pooled OLS one-way fixed effect OLS two-way fixed effect FMOLS GF − 0.0388* − 0.0402* − 0.0315* − 0.0549* (− 2.964) (− 3.236) (− 2.238) (− 6.516) RE − 0.2893* − 0.4468* − 0.02479* − 0.2987* (1.1721) (1.8548) (0.0622) (1.3295) NR 0.3759* 2.1535* 0.8464* 0.9553* (0.3742) (1.8563) (0.6677) (0.4644) TO 0.2320* 0.2348* 0.2896* 0.0862* (2.3619) (2.4948) (2.3424) (0.8259) GDP 0.7121* 0.4899* 0.5157* 0.1924* (2.3996) (1.7092) (1.5182) (0.9539) FDI 0.9975* 0.8774* 0.7416* 0.5514* (3.3798) (3.0746) (2.3580) (2.0575) Constant 3.798* 4.4759* 3.551* 3.9272* 0.3467) (1.8602) (0.1732 (0.0767) These numbers are t-values, which indicate statistical significance at the 1% level of significance. Probability of being significant at a level of 5%. Statistical significance at a 10% level of confidence Quantile regression results The quantile regression with fixed effects in Lamarche (2010) is used to control for the distributional heterogeneity. An important source of our concentration on the quantile regression method with a two-way fixed effect is that the absence of time-period fixed effects may lead to biased findings in a typical study of time series. Results of the panel quantile regression estimation are shown in Table 6. All of the results are presented for the 5th and 10th percentiles of the conditional emissions distribution. According to the statistics, there is obvious heterogeneity in the effects of numerous factors on carbon emissions.Table 6 Panel quantile regression estimations Variable 5th 10th 20th 30th 40th 50th 60th 70th 80th 90th 95th GF − 0.049* − 0.371* − 0.245 − 0.398* − 0.516* − 0.600 − 0.616* − 0.592* − 0.606 − 0.759* − 0.203*** (0.361) (1.843) (1.107) (1.421) (2.110) (2.537) (2.907) (3.682 (3.568 (4.374 (0.924) RE − 0.750* − 0.978* − 1.160 − 0.905 − 0.778 − 0.694* − 0.565* − 0.667* − 0.432* − 0.133* − 1.090* (2.909) (4.733) (5.776) (4.039) (4.111) (4.181) (3.336) 3.537 1.547 0.282 (1.856) NR 0.602 1.001 0.842* 1.602* 1.556* 1.961* 1.422* 1.230* 1.831* 1.482* 2.429* (0.291) (0.484) (0.355) (0.721) (0.637) (1.175) 0.639 1.480 1.368 1.895 (3.589 TO 0.506 0.396 0.284 0.239 0.131 0.087 0.110 0.085* 0.105* 0.386* 0.695* (3.603) (2.970) (1.993) (2.079) (1.105) (0.757) 1.052 0.704 0.711 1.988 (2.640) GDP 0.132* 0.082 0.135** 0.343 0.212 0.192* 0.040* 0.024* 0.012* 0.443* − 2.880 (0.771) (0.484) (0.839) (1.740) (1.062) (1.444) 0.323 0.226 0.055 0.577 (2.867) FDI 0.005* 0.003 0.004 0.003 0.002* 0.003 0.004 0.005 0.006* 0.014** 0.025* (0.839) (0.775) (0.976) (1.412) (1.140) (1.094) 1.294 1.888 3.139 2.761 (2.818) Constant 0.071* 0.035* 0.005* 0.024* 0.047* 0.029* 0.031* 0.046* 0.047* 0.054* 0.109*** (2.830) (1.699) (0.279) (1.565) (3.292) (2.187) 2.298 2.874 2.300 1.086 (1.369) Green finance The findings of the study indicate that green finance and carbon emissions are shown to be negatively associated. Green finance and carbon emissions in the middle and upper quantiles have a particularly strong negative impact. There is, however, a negative correlation between the lower and higher quantiles of green finance (from 5 to 40th and 60th to 95th). According to these findings, green finance in the BRICS countries reduces CO2 emissions. Even though there is a non-uniform association between CO2 emissions and demand for green investment, when CO2 emissions rise, so does the demand for green investment. The green payment and credit business, containing home mortgages and project credits, is the primary focus of the term “green credit.” According to United Nations Environment Programme (2017), however, the country only began to truly unify statistical standards and increase the quality of green finance data in 2014 despite the fact that China’s banks began issuing social responsibility reports in 2006. In China, data on green credits has been collected over time, but there are a number of problems, such as incomplete disclosure, a brief period of disclosure, and inconsistent statistical standards. As a result, the green credit variable in this study is defined as the total green credit of listed firms divided by the total credit of listed firms. The following are some of the benefits of making use of this index. For starters, the sample spans a significant amount of time and is highly representative. In 2000, China had only 1086 publicly traded companies; by the end of 2018, that number had risen to 3,549, with the firms spread across a wide range of industries and regions (Anser et al. 2020; HUANG et al. 2022a, b; Khokhar et al., n.d.). To begin with, the information is of a high standard because it was independently verified by reputable accounting firms to ensure that the amounts and purposes of bank loans disclosed by publicly traded companies in their financial reports were accurate. The use of this index was deemed appropriate for this study based on these considerations. Renewable energy A negative sign indicates that the estimated coefficient of renewable energy consumption reduces carbon dioxide emissions, even at a level of 1% in all quantiles. According to Irfan et al. (2022b;) and Wen et al. (2022), renewable energy consumption cuts carbon emissions significantly in the current research sample countries using a panel quantile regression model for BRICS countries. Reducing carbon emissions through the utilization of renewable energy is also supported by Zhao and Taghizadeh-Hesary (2022). Natural resources In the lower half of the quantiles, the natural resources (NR) significantly increase CO2 emissions. The BRICS countries’ NR CO2 emissions are indicated by a positive coefficient. Natural resource extraction can result in higher CO2 emissions, as this study demonstrates. BRICS economies’ rising greenhouse gas emissions may be linked to an increase in natural resource extraction and unsustainable use, according to our findings. In addition, the country’s reliance on fossil fuel imports worsens the environment by causing emissions of greenhouse gases. Next, the results of the control variables also show significant impact on CO2 emissions. We can observe that the influence of FDI on CO2 emissions is clearly heterogeneous when it comes to positive coefficient is only marginally significant at the 10% level in the 5th quantile. In low-emission countries, the positive FDI coefficient is not enough to boost the pollution haven hypothesis. It is clear that foreign direct investment (FDI) has a negative influence on CO2 emissions and that this impact is greater in nations with high emissions than in nations where emissions are low. Other coefficients are negative and become significant at higher quantiles (the 60th, 70th, 80th, and 90th quantile). In countries with high emissions, these findings lend credence to the halo effect hypothesis. Because FDI has a negligible effect at the low quantile, it’s safe to assume that the vast majority of it goes into non-polluting industries in low-emissions nations like China and India. High-emission countries, on the other hand, may place greater emphasis on environmental issues and enact stricter environmental regulations. Through backward and forward linkages, foreign direct investment (FDI) in high-emission countries may help to create advanced management, specialised technical, and innovation in the production process; these technologies may also be passed on to domestic enterprises. Multinational corporations in high-emission countries may have access to more advanced technology, and they are more likely to disseminate environmentally friendly technology. A rise in foreign direct investment (FDI) improves environmental quality in high-emission countries, as illustrated in the graph below. Halo-effect hypothesis is valid in high-emission BRICS countries, according to the results. Results are consistent with those of Hamid et al. (2022) that states how can one analyse how foreign direct investments (FDI) are linked with pollution in BRICS nations using panel analysis. The authors’ outcomes endorse the halo effect and do not support the FDI’s negative environmental impact. The openness of the global economy to trade and the consumption of renewable energy as a percentage of GDP are the metrics; we used to gauge international trade and renewable energy consumption. Studies of Pata and Caglar (2021) who used trade percent of GDP are just two examples that have used various variables to gauge economic growth. The economic growth measured by Evangelista et al. (2022) was based on GDP per capita and industry value added. Only at the 95th quantile does the coefficient of GDP have a positive sign, which initially rises and then falls with the increase in the CO2 quantiles. When it reaches the 80th quantile, it is no longer significant, but when it reaches the 95th quantile, it is significant once more. At the 90th and 95th percentiles, GDP2’s coefficients are statistically significant. EKC is not applicable to BRICS countries because the GDP quadratic term indicates that the relationship between economic growth and CO2 emissions has been monotonic in the BRICS nations in the past. Perhaps the BRICS countries did not get to where they needed to be in terms of economic development. As previously reported empirically by Usman et al. (2022), our study’s findings contradict the EKC hypothesis, which states that pollution levels rise with income before levelling off and eventually declining. According to Usman et al. (2022), our findings are consistent with theirs. A closer look at economic growth and pollution emissions provides a more complete picture of economic growth than previous research has shown. Carbon emissions can be reduced by increased economic growth in high-emission countries, according to our results, which express a negative and significant coefficient of GDP for the 95th percentile. Conclusion and policy recommendation Conclusions PQR was applied to empirically investigate the function of green finance and renewable energy use in reducing CO2 emissions in BRICS countries from 2005 to 2019. On the basis of four indicators from the regulation on setting a green financial system, a green finance development index was created to more accurately represent green finance. We came to the following conclusions. A rise in the green finance development index and the percentage of renewable energy utilization participated in a decrease in CO2 emissions, which was found to have a long-term equilibrium association with CO2 emission, green finance, and renewable energy spending in the first place. As a result, the green finance development index fell as CO2 emissions rose. This hampered the growth of renewable energy and reduced green finance investment. Green finance and CO2 emissions had a substantial influence on both short-term and long-term renewable energy consumption, while the development of renewable energy depends on policy support. Fourth, although the green financial policy of the BRICS nations had a significant impact on carbon mitigation, its results were inconsistent and unreliable. BRICS nations’ CO2 emissions have fluctuated slightly over time, making it difficult to reduce CO2 emissions in a short period of time. Implications for policy and future research The key policy implication is that eco-friendly economic policy should be more stable and long-term. Significant fluctuations in green finance have a negative influence on CO2 emissions and the renewable energy industry, as shown in the analysis above. Because of this, it is critical to have a stable and long-term green financial policy in place. The advancement of energy conservation can be ensured through large-scale green financing. The green financial policy system of the BRICS nations needed to be developed as a comprehensive industry chain. A green development funds and system and green finance were first proposed in China’s 13th Five-Year Plan, which was first implemented in 2008. There are three ways in which green economic policy can be advanced.An improved legal framework for the development of a green financial system is needed. Green investment, green bonds, green loans, and green securities should all be part of a comprehensive financial service system. Green securities and bonds require a rating system tailored to the specific needs of the BRICS countries. First, a commanding 3rd party must make sure that the tools are truly green and steer systematic evaluations of the environmental advantages of projects before they can be issued. It is also necessary to assemble a team of experts who are familiar with the level of green funds and who can analyse and observe the precise attributes of these spendings used to backing the green business. Green credit’s policies, processes, and procedures could use some work. In order to enable energy-saving landscaping and the purchase of green homes and new energy vehicles in accordance with national building certification criteria, specific consumers should be identified and provided with special loans. It follows that green financing for renewable energy industries should be bolstered as a second policy outcome. Currently, renewable energy development is hampered by the higher unit costs associated with renewables than with non-renewable sources. Solar, wind, and nuclear power can all help reduce carbon emissions over the long term because of the long-term cointegration equation. Renewable energy projects, on the other hand, necessitate large investments and long payback periods. The development of renewable energy industries should be supported by flexible and diverse service schemes for green finance products and services, specifically the following:Providing loans at less interest to ease the financial needs of investigation and verification and reduce the time it takes for credit approval should be added to the support for green loaning for renewable energy proposes. Fiscal takeoff interest, decrease in tax, tax freedom, pre-tax facility, and intended write-off of bad debt should be implemented simultaneously for non-fossil industries. The securities market’s importance should be emphasised. Additionally, a significant number of green businesses that focus on renewable energy should be established. BRICS nations should lower the barrier to entry for renewable energy companies in their stock markets. In the case of company securities issuance, less project profits should be required for review and approval. Environmentally friendly companies should be allowed to raise a reasonable quantity and proportion of their working capital or to repay their bank debts. As a third and final policy implication, the carbon market’s green finance application process should be loosened up. The utilization of economic derivatives to restrict CO2 and other greenhouse gas emissions is one way that carbon trading manifests itself in the financial market. Currently, the CO2 market in the BRICS nations is not fully in effect, and there are many misconceptions about CO2 emission rights and their financial aspects for institutions and businesses. In order to help build a national carbon market, financial regulators should get involved. The establishment of a healthy carbon promotion market will be facilitated by active financialization of the carbon market. Additionally, a carbon investment fund based on carbon financial markets should be established to strengthen the financial strength of emission reduction projects. It is important for commercial banks to encourage the inclusion of carbon emission rights in the scope of pledged loans and to offer a higher collateral ratio in the early stages of carbon finance. The development of green financing products and the establishment of a green channel for special approval should be done in tandem. Investing in carbon asset securitizations, reviving existing carbon assets, and increasing their efficiency should be promoted by the capital market. The carbon connections should be stimulated to develop low-carbon derivative financial tools and build a low-carbon index system of such instruments. Financial leasing and low-carbon funds would benefit from this approach. The following is a list of upcoming research opportunities. (1) The green finance index could be better-quality in this paper. If a green finance index is properly created, it will help future research, which is currently lacking due to the difficulty and novelty of creating one. It is possible to conduct a more thorough investigation of the relationship between variables using advanced methods and tools. The threshold model, for example, could be applied to recognize the nexus’s stopping points. A cross-nation analysis, focusing on economies with well-established green financial regularities, could be added as a third option. This would allow for a comparison of the advantages and differences between various countries. Author contribution Franley Mngumi and Sun Shaorong: conceptualization, data curation, methodology, writing—original draft. Faluk Shair and Muhammad Waqas: data curation, visualization, supervision, visualization, editing, and software. Data Availability The data is available upon request. Declarations Ethical approval and consent to participate The authors declare that they have no known competing financial interests or personal relationships that seem to affect the work reported in this article. We declare that we have no human participants, human data. or human tissues. Consent for publication N/A. Competing interest statement The authors declare no competing interests. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Aboramadan M Karatepe OM Green human resource management, perceived green organizational support and their effects on hotel employees’ behavioral outcomes Int J Contemp Hosp Manag 2021 33 3199 3222 10.1108/IJCHM-12-2020-1440 Ahad MA, Parry YK, Willis E (2021) The prevalence and impact of maltreatment of child laborers in the context of four South Asian countries: a scoping review. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 35397034 20024 10.1007/s11356-022-20024-4 Green Technology and Industrial Revolution 4.0 for a Greener Future Recent advances in green technology and Industrial Revolution 4.0 for a sustainable future Bradu Pragya 1 Biswas Antara 1 Nair Chandralekha 1 Sreevalsakumar Salini 1 Patil Megha 1 Kannampuzha Sandra 1 Mukherjee Anirban Goutam 1 Wanjari Uddesh Ramesh 1 Renu Kaviyarasi 12 Vellingiri Balachandar 3 http://orcid.org/0000-0003-0780-0492 Gopalakrishnan Abilash Valsala [email protected] 1 1 grid.412813.d 0000 0001 0687 4946 Department of Biomedical Sciences, School of Bio-Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu 632014 India 2 grid.412431.1 0000 0004 0444 045X Department of Biochemistry, Saveetha Dental College & Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu India 600 007 3 grid.411677.2 0000 0000 8735 2850 Human Molecular Cytogenetics and Stem Cell Laboratory, Department of Human Genetics and Molecular Biology, Bharathiar University, Coimbatore, 641046 Tamil Nadu India Responsible Editor: Philippe Garrigues 9 4 2022 132 16 2 2022 28 3 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. This review gives concise information on green technology (GT) and Industrial Revolution 4.0 (IR 4.0). Climate change has begun showing its impacts on the environment, and the change is real. The devastating COVID-19 pandemic has negatively affected lives and the world from the deadly consequences at a social, economic, and environmental level. In order to balance this crisis, there is a need to transition toward green, sustainable forms of living and practices. We need green innovative technologies (GTI) and Internet of Things (IoT) technologies to develop green, durable, biodegradable, and eco-friendly products for a sustainable future. GTI encompasses all innovations that contribute to developing significant products, services, or processes that lower environmental harm, impact, and worsening while augmenting natural resource utilization. Sensors are typically used in IoT environmental monitoring applications to aid ecological safety by nursing air or water quality, atmospheric or soil conditions, and even monitoring species’ movements and habitats. The industries and the governments are working together, have come up with solutions—the Green New Deal, carbon pricing, use of bio-based products as biopesticides, in biopharmaceuticals, green building materials, bio-based membrane filters for removing pollutants, bioenergy, biofuels and are essential for the green recovery of world economies. Environmental biotechnology, Green Chemical Engineering, more bio-based materials to separate pollutants, and product engineering of advanced materials and environmental economies are discussed here to pave the way toward the Sustainable Development Goals (SDGs) set by the UN and achieve the much-needed IR 4.0 for a greener-balanced environment and a sustainable future. Graphical abstract Keywords Green technology Environmental biotechnology Environmental economies Bio-based materials Sustainable transitions Fourth Industrial Revolution Post-COVID-19 http://dx.doi.org/10.13039/501100001411 Indian Council of Medical Research F.No. 5/7/482/2010-RBMH&CH Gopalakrishnan Abilash Valsala ==== Body pmcIntroduction In today’s world of climate change and amid the COVID-19 pandemic, a realization has dawned upon the world to protect the planet and people’s health. The pandemic has affected many social, economic, political, and environmental challenges (McNeely 2021). There is a clear awareness about global environmental issues like global warming, acid rain, ozone layer depletion, the increasing list of endangered species, and incidences of forest fires in the Amazon. There have also been several protocols and agreements signed by the world’s governments, like the Kyoto Protocol and the Paris Agreement. However, even till now, proper actions have not been taken, and the governments have also not been able to engage people to follow and practice environmentally friendly habits daily. Nevertheless, scientists have been relentlessly working to find solutions to create a sustainable future for the coming generations and save and conserve the environment. They are coming up with the latest innovations in GT, which can help industries find alternative and sustainable ways of disposing of waste and use more bio-based advanced materials for cheap, safe, and eco-friendly products. GT is a broad word that applies science and technology to lessen human impacts on the environment. Energy, atmospheric science, agriculture, material science, and hydrology are all areas of scientific inquiry covered by GT (Fu et al. 2021a, b). Several GTs strive to combat climate change by minimizing carbon dioxide (CO2) and other greenhouse gas emissions. Solar power is one of the most effective GTs. In many countries, it is currently cheaper to install than fossil fuels. GT can be supported by investing in stocks, mutual funds, and bonds that support ecologically friendly technology (Hou and Wang 2021). The world has already witnessed three industrial revolutions (IRs), having significant impacts (Dogaru 2020). The 1st IR dealt with mechanical processes using water and steam for the mass production of textiles and metals. The 2nd IR dealt with the concept of industries, and here, the use of electricity, oil, and gas took place; the steel and synthetic industries became established with new communication and transport systems. The 3rd IR dealt with new nuclear energy and automation (Dogaru 2020). However, most of these revolutions had enormous consequences on the environment. They caused much damage and harm to the planet and human lives. Hence, the IR 4.0 is a viable, sustainable, and environmentally friendly approach to manufacturing, using renewable resources and recyclable bio-based materials (Carvalho et al. 2018; Dogaru 2020). This revolution is necessary for the green recovery post-COVID-19 in terms of the green economy. Governments have taken steps to reduce carbon and other greenhouse gas (GHG) emissions, price the carbon externalities, and increase renewable energy resources like solar and wind energy (McNeely 2021). Nanotechnology has also been a significant contributor to GT, helping alleviate problems related to the agricultural, medical, food sectors, etc. (Bahrulolum et al. 2021). Nanoparticles can help develop biopesticides to improve crop production and enhance organic, eco-friendly farming methods (Bahrulolum et al. 2021). Nanomaterials, which are bio-based like nanocrystalline starch, lignin, and cellulose, can help improve the bioavailability of drugs and other nutrient supplements (Kou et al. 2021). This review paper provides an opportunity to understand how to formulate and execute sustainable, eco-friendly goals at a social, economic, and environmental level. This includes—(i) a brief introduction to the global environmental concerns; (ii) in depth detail of environmental biotechnology, genetically modified organisms (GMOs) and their applications in bioremediation, biopesticides and more; (iii) methods of proper management of waste and polluted air treatment using renewable and nonrenewable energy resources with more focus on bioenergy and biofuels; (iv) study of renewable biomass-derived carbonaceous materials like cellulose, nanocellulose, chitosan, lignocellulosic residues and how using chemical engineering techniques can make sustainable, highly useful and eco-friendly products; (v) understanding the interaction of pollutants with the environment during separation processes using bio-based adsorbents, hydrogels, and membrane filters; (vi) in-depth information on production engineering and the types of bio-based advanced materials and processes; (vii) understanding process system engineering, the goals and the current research on IR 4.0 and how it can be made into a reality, and lastly; (viii) detailed understanding of environmental/green economies, how the post COVID-19 pandemic has led to more research and paved the way toward the green transition, the Green New Deal for a sustainable world, how can the governments price carbon externalities, how can an individual, community, industry, country, and the world shift toward sustainable transitions in their lifestyle, practices like green entrepreneurship, green housing, green chemistry, and understanding the critical concepts of ecological modernization, de-growth, and more. Understanding why people adopt GTs in different ways is crucial. Regardless of what we know about the elements that influence adoption, the willingness to accept new GT remains low. Cognitive, goal-oriented hope can help people adopt GTs sooner. Unlike socioeconomic variables, which are difficult to modify, legislation and education can influence levels of hope and motivation (Bukchin and Kerret 2020). Environmental biotechnology Climate is a mind-boggling combination of physical and natural environmental elements, including ecological issues like global warming, ozone layer depletion, biodiversity loss, exhaustion of regular assets, overpopulation. Presently, natural issues make us defenseless against tragedies and catastrophes. Biotechnology combines engineering design to utilize cells and molecular analogs for substances and their administrations. Carbon emission efficiency is essential for tracking progress toward carbon emission reduction goals. The link between GT innovation and carbon emission efficiency has not been well investigated, and the transmission mechanism is unknown (Dong et al. 2022). Environmental biotechnology is an arising innovation regarding ecological insurance since quick industrialization, urbanization, and advancements have undermined clean climate and exhausted standard assets (Gavrilescu 2010). It tends to be considered the main thrust for coordinated environmental conservation, prompting a maintainable turn of events. Sustainable development characterizes progress in human prosperity that can be broadened or delayed over time. It requires a system for coordinating ecological arrangements and advancement procedures worldwide (Fig. 1) (Singh 2017) (Gavrilescu 2010). Green supply chains are logistical frameworks that ensure the environmentally friendly manufacturing and delivery of items worldwide. Companies must engage in the design and planning optimization of their logistic systems to reach this aim while considering the trade-off between earnings and environmental implications (Pinto-Varela et al. 2011).Fig. 1 Green supply chain management (GSCM) refers to incorporating environmental considerations into supply chain management, including product design, material sourcing and selection, manufacturing, final product delivery, and product end-of-life management. This figure describes the application of GSCM within IR 4.0 Industries and aspects of environmental control The late carried out aggressive task Clean Development Mission (CDM) by the Government of India, wherein clean innovation overall, white biotechnology, and specifically, GT can make significant commitments toward the supportable and sustainable development. The generated wastes should be dealt with appropriately before arranging the climate. Biotechnology’s devices and strategies have given a new impulse and opened new vistas in contamination control. Biosensors assume a fundamental part in distinguishing the toxins even at exceptionally low focuses on evaluating the danger level. The industry is the primary source of pollution in the environment. Industrialists must become aware of their environmental responsibilities in the new era. They should be ecologically conscious even if they are chief manufacturers (Mondejar et al. 2021; Lamba et al. 2021). Modern handling commonly elaborates adversities like pH, temperature, and high pressure requiring high energy input, while microorganisms and catalysts ordinarily catalyze a similar cycle in mild conditions (Sharma et al. 2017). A broader and clear idea can be obtained by understanding the correlation between GT types with IR 4.0. Understanding the exact mechanisms of technologies like sunlight transport, plastic roads, plant walls, milk textiles, plant-based packaging, building-integrated photovoltaics can display their relation to IR 4.0 (Table 1).Table 1 The correlation between types of GT with IR 4.0 including Sunlight Transport, plastic roads, plant walls, milk textiles, plant-based packaging, and building-integrated photovoltaics Types of GT Type of environment effected Description Equipment involved Value Related concepts Relation to IR 4.0 Mechanism involved Outcomes due to IR 4.0 References Sunlight Transport Outdoor (source) Indoor It is a passive system that captures sunlight from an outside source. It transports it via fiber-optic cables to illuminate light-deprived rooms Passive light collectors, heliostats, lighting rod, fiber optical system, daylight shading system, mirror light pipes, daylight guiding system Zero utilization of energy during the daytime People might incline toward natural light Light tube Passive design Translucent concrete Smart glass IR 4.0 in the sustainable energy industry gives smart energy networks that stay away from advancing new path reliance. The innovation will empower decentralization, with energy coming from local sunlight-based photovoltaics A medium-scale, decentralized unit is connected by virtual power plants that generate power. Involves a cloud-based center, which controls IoT devices in the units This implies that the user will control and manage their energy use (Mondejar et al. 2021) Plastic roads Outdoor Plastic roads are completely manufactured of plastics or composites of plastic with different materials Soft foams Hard foams Films (carry bags, cups) Laminated plastics In situ process No advancement of any poisonous gases like dioxin Less use of bitumen Plastic waste management Flux sheets, polyvinyl chloride (PVC) sheets must not be utilized regardless The IR 4.0 concerning the plastic processing process permits customers, suppliers, and the close interconnection of internal departments and processes Each fundamental area of the economy, from agriculture to packaging, building development, and automobiles, has been virtually revolutionized by the uses of correspondence or InfoTech with plastics Mass production of products began, and plastic appeared to be a less expensive furthermore effective raw material (Mondejar et al. 2021) (Lamba et al. 2021) Plant walls Outdoor as well as indoor Vertically assembled structures hold sufficient soil to have various plants or different greens growing on them. Since these designs have living plants, they additionally highlight built-in irrigation frameworks Water Nutrients Top reservoir Bottom reservoir Pump line Water pump Timer Drip emitters Greywater treatment Low energy cost Recycling of household wastewater Temperature insulation of the houses Aesthetical value Principle of hydroponics Dominating energy requirement materials worldwide are concrete and stainless steel, which have increased since IR. The choice of materials decides the environmental effect of the building Contingent upon the materials and with their substitutions across the helpful existence of the structure, the outcomes may be decreased in the entire life cycle Sustainable-built environment Self-irrigation Architectural upgrade Customization Efficiency (Mondejar et al. 2021) (Chàfer et al. 2021; Pradhan et al. 2019) Milk textiles Outdoor Fabric made with casein present in milk. It has for some time been valued for its smoothness and softness Skimmed milk Glutinous solution Micro-zinc ion Skin nourishment Eco-friendly Biodegradable Renewable Bio-engineering Bacteriostatic Zero-waste policy The milk textile industry relies upon using advanced technology in production and logistic processes inside the system of technological headways arising because of IR 4.0 Redesigning industrial processes as per IR 4.0, each process connected to logistics and production might be more flexible and rapid in textile value chains Real-time information (variable customer requests) Optimal material flow systems Self-configuration (Mondejar et al. 2021) (Malucelli 2019) Plant-based packaging Outdoor Uses sustainable organic and vegetal sources to develop the packaging Bio-based feedstock; polyester Sugars extracted from sugarcane, corn, wheat, beet, agricultural residues Degradable and recyclable (100%) Lowers carbon footprint Free of toxins and allergens Polyethylene furoate (PEF) Compostable IR 4.0 is the eventual fate of development and productivity in the packaging sector. It approaches things in other ways, introduces a high degree of connectivity and automation, and makes better use of big data A cloud-based predictive maintenance platform can assist producers with distinguishing basic failures before they happen It leads to a rise in overall equipment effectiveness and, as such, a fall in the total cost of ownership (Mondejar et al. 2021) (Roy and Rhim 2021) Building-integrated photovoltaics Outdoor (source) Indoor Photovoltaic materials are utilized to supplant traditional building materials in pieces of the structure envelope like the facades, roofs, or skylights Facades (photovoltaic materials) Glazing Pitched roofs Impersonate the appearance and capacity of ordinary roofing materials, while the key task is to generate electricity Thin-film technology Short-circuit current Open-circuit voltage Fill factor Grids draw on the possibilities of data and correspondence advancements to screen and effectively deal with the generation, conveyance, and utilization of power from various—possibly decentralized—wellsprings of electricity to fulfill the changing power needs of end clients Airflow behind PV panels generates a cooling effect that helps produce significant energy with higher efficiency Circular economy vision Sustainable and renewable electricity generation (Mondejar et al. 2021) (Du et al. 2019) Profoundly contaminated industrial wastewaters are ideally treated in an anaerobic reactor because of the high degree of chemical oxygen demand (COD), the potential for low surplus sludge generation, and energy production. In many applications, notwithstanding the proficiency of the anaerobic cycle being high, overall stabilization of the natural matter is not possible anaerobically because of the wastewater’s high degree of organic ability. The end product delivered by the anaerobic treatment contains solubilized natural matter. It is practical for aerobic treatment, demonstrating the capability of utilizing anaerobic-vigorous frameworks, and resulting posttreatment utilizing high-impact treatment (Chan et al. 2009). Genetically modified organisms (GMOs) in environmental biotechnology GMOs are also known as transgenic organisms. It is categorized as microbes or animals whose DNA has been altered by utilizing genetic engineering techniques to generate desired biological products. GM animals have even been utilized to develop human transplant organs and tissues; such a concept is known as xenotransplantation. The wide range of GMO applications gives people significant advantages; however, many individuals likewise stress over expected risks. Microbes and animals have all been genetically altered by different change strategies for quite a long time with agricultural, medicinal, ecological, and all the more as of late industrial purposes (Phillips 2008). Ralstonia pickettii Ralstonia pickettii is an oligotrophic, rod-shaped Gram-negative, oxidase-positive, aerobic, and non-fermentative ubiquitous microorganism found in soil and water. Ralstonia pickettii has immense biotechnological significance in the bioremediation niche and has shown its capacity to break down many xenobiotic pollutants, such as trichloroethylene and toluene, which are released into the environment by different industrial methods. It is hypothesized that in ultrapure water frameworks, the microscopic organisms might have the option to scavenge from the polymers in plastic piping. Moreover, R. pickettii has been displayed to have biodegradative capacities, exhibiting its enormous metabolic variety. R. pickettii strain PKO1 could be super biodegraded with the presentation of plasmids bearing other degradative proteins, such as pKA4, and incorporating different qualities from various microorganisms into the chromosome to aid the breakdown of harmful compounds (Ryan et al. 2007). A few strains have demonstrated the capacity to persevere in high contaminations of metal-debased conditions. In an adverse environment, the ability to sustain R. pickettii is a contender for bioremediation (Huang et al. 2018) (Mijnendonckx et al. 2013). Fast adaptation of R. pickettii to elevated metal concentrations shows up because of vigorous gene duplication and importation of a few kinds of resistant determinants (Yang et al. 2010). Environmental biotechnology advances for a greener future Bioremediation Bioremediation has demonstrated authenticity and effectiveness because of its environmentally friendly elements. It can either be completed ex situ or in situ, contingent upon a few variables (Azubuike et al. 2016). Bioremediation is the utilization of microorganisms for the expulsion or degradation of impurities. The microbial cycles engaged with bioremediation are typically regular parts of variation, adaptation, or respiration that are frequently a part of carbon cycling or metal redox cycling. Hence, bioremediation regularly happens without direct intercession; nonetheless, bioaugmentation and biostimulation are frequently significant for the total evacuation of impurities within a prudent period (Krzmarzick et al. 2018). Heavy metal bioremediation utilizing various microorganisms has been broadly applied as options in contrast to conventional techniques. Microalgae with extraordinary biological features such as high photosynthetic productivity can develop well under outrageous ecological conditions like higher salt contents, excessive temperature, heavy metals, and nutrient stress (Leong and Chang 2020). Biopesticides Biopesticides are derived from natural sources such as bacteria, animals, plants, and certain minerals; mainly used to depict a broad scope of formulated outcomes are to control pests, weeds, and diseases. As indicated by the US Environmental Protection Agency (EPA), biopesticides can be categorized under three principle classifications: firstly, plant-incorporated protectants (PIPs) or plant substances delivered by genetically engineered plants; secondly, microbial organisms and entomopathogenic nematodes the active ingredient; and third, pheromones (Morán-Diez and Glare 2016). Nonetheless, the absence of adequacy, conflicting field execution, and significant expense have commonly consigned them to niche items. As of late, mechanical advances and significant changes in the extreme climate have decidedly modified the standpoint for biopesticides (Glare et al. 2012). Biofertilizer Biofertilizers are living microorganisms that upgrade plant nourishment by preparing and expanding supplement accessibility in soils. Different microbial taxa, including beneficial microscopic organisms and parasites, are presently utilized as biofertilizers. They effectively colonize the root inside, the rhizoplane, or rhizosphere. Azotobacters have been utilized as biofertilizers for over a century. It fixes nitrogen vigorously, elaborates plant chemicals, solubilizes phosphates, and stifles phytopathogens or lessens their pernicious impact. The use of wild sort Azotobacters brings about a better yield of cereals, oilseeds like sunflower and mustard, natural products like sugar cane and mango, fiber crops like cotton and jute, vegetable harvests, and the tree-like oak (Das 2019). Hydrolysate can be utilized as a biofertilizer, protein supplements, domesticated animals feed, and bioactive peptides. It improves nutrients from the soil, C/N proportion, and water holding limit. The plant development advancing exercises of hydrolysate potentiate its possible use in natural cultivating and further develop microbiota and soil environment (Bhari et al. 2021). Environmental monitoring Environmental monitoring assumes a fundamental part in ecological security, particularly for managing and preserving natural assets. Environmental monitoring information usually is hard to oppose pernicious attacks since it is sent in an open and uncertain channel. With different environmental monitoring innovations, incredible leaps have been made in ecological assurance (Yang et al. 2021c). Biosensors and biomonitor systems are thoughtfully interrelated and strategically associated with a cooperative/synergistic scheme (CSS) to limit vulnerability, check expenses, and increment the dependability of contamination control. The CSS, in light of the mix of numerous data sources, can set up a local area network, consolidated into a wide area network, in this way offering the capability of better prescient capacity and more noteworthy lead-time cautioning at alert conditions than that given by separate, and independent surveillance modalities (Batzias and Siontorou 2007). Chitosan coating technique Chitosan coating can be helpful for coating fertilizers for slow and control release into the soil. This coating method reduces the redundant use of fertilizers and increases the scope of the crop needs. The coating of hydrosoluble diammonium phosphates fertilizer by chitosan clay composites as an inner coating and paraffin wax as the outer coating has been proven effective by confirming the water holding capacity and phosphorous release easily in soil and water (El Assimi et al. 2020). Another usage of chitosan coating can also help extend the shelf-life of fresh-cut cucumber by coating it with edible chitosan solution of different concentrations. The performance observed reduced CO2 production while packaged in air and nitrogen to maintain quality and improve retention (Olawuyi et al. 2019). Mushroom weight reduction, immovability, absolute phenolics, ascorbic corrosive, sensory quality, malondialdehyde, electrolyte spillage rate, and microbial were estimated. The outcomes show that treatment with chitosan-oil covering kept up with tissue solidness hindered increment of respiration rate and decreased microorganism counts, like molds, yeasts, and pseudomonad, contrasted with control treatment. The proficiency was superior to thyme oil treatment or chitosan coating (Jiang et al. 2012). Bioenergy and biofuels Among the renewable energy sources, biofuels can fill in as a superior choice to lessen the dependence on fossil fuels. Lignocellulosic biomass provides the most affordable biomass to produce biofuels. Bioethanol is one of the most generally consumed biofuels in the present world (Fatma et al. 2018). Bioenergy is one of the numerous assorted assets accessible to fulfill our energy needs. It is environmentally friendly energy from late living natural materials known as biomass, generating products, heat, transportation fuels, and electricity. Supplanting regular biological systems with bioenergy crops across the planet will generally be unfavorable for biodiversity, with first-generation and high-yield crops having the most grounded adverse consequences. Meeting energy objectives with bioenergy utilizing existing negligible grounds or biomass extraction inside existing development landscapes might give more biodiversity-accommodating options than changing typical biological systems for biofuel generation (Núñez-Regueiro et al. 2021). Biofuel is the primary product of bioenergy. Using it instead of diesel fuel will aid in reducing carbon monoxide and NOx particles. Ethanol is another bioproduct that aids in the conservation of natural resources. Biofuel is primarily focused on being created from raw resources that are not detrimental to the environment and result in pollution. Bioethanol produced positive results in reducing pollution. It is increasingly being used as diesel fuel for automobiles to keep the environment free of hazardous pollutants (Sarkar et al. 2021; Singh et al. 2022). The use of natural resources in the manufacturing of diesel has resulted in price increases. The main concern is to shift focus to bioenergy to lower pollution levels in various parts of the world. New energy resources, such as wind, nuclear, and solar, have been employed to create energy-efficient fuel that can be easily recycled and reused (Arpia et al. 2021). Biofuel is the best form of energy because it is the cheapest to produce; it has proven to be a viable option for reducing or limiting the usage of nonrenewable natural resources. Biofuel has high efficiency and reduces the number of dangerous pollutants released into the atmosphere. Edible and nonedible oils are used to produce biofuel. Furthermore, the transesterification process can be accelerated by using suitable homogeneous catalysts or nanoparticles (Sarkar et al. 2021; Singh et al. 2022; Verma et al. 2022). Sustainable chemical engineering Basics of sustainability Sustainability can be defined as the measures taken by businesses against the health, safety, and environmental issues or HSE issues that can cause problems to the community surrounding it, such as stakeholders and contractors. The term sustainable development was first defined by the Brundtland Commission in 1987. They defined the term because sustainability refers to futurity (Basiago 1995). Thus, sustainable development is defined as the development that meets the needs of the present without compromising the ability of future generations to meet their own needs. A sustainable environment can be made when we keep up the efforts to maintain the requirements and social setting for the well-being of human health and the environment without overusing the ecological materials that help maintain sustainability. Thus, three criteria should be followed, also known as the triple bottom line. They are a successful business or enterprise, providing enough attention to maintain a healthy environment and continuing the efforts to keep up a sustainable environment (Das et al. 2018). There are mainly two factors that can threaten sustainability: technology—when the rate of utilization becomes higher than the rate of production of resources by the environment, then the need of future generations is getting affected. Another factor is when the waste emitted by the technosphere affects the ecological sphere. Also, resource production is getting affected, thus, posing a threat to a sustainable environment (Das et al. 2018). Biomass-derived renewable carbonaceous materials Biological products made basically from carbon, hydrogen, and oxygen are called biomass. Biomass often pertains to plant or plant-derived materials such as lignocellulosic residues or biomass. Supported nanoparticles on nanocellulose Cellulose nanocrystals have applications such as paper, aero, hydrogels, and chiral materials (Khan et al. 2015). They are also used to support Pd, Au, and Ag nanoparticles catalysis. Moores and colleagues were the first to describe their application in enantiocatalysis (Kaushik et al. 2015). Through homogeneous organocatalysis, cellulose can be used as a chiral inducer. As an example of support of nanoparticles, Moores and colleagues have shown that CNCs help produce Ag nanoparticles from heavy Ag metals at room temperature by providing a high surface area. They also act as a reducer to allow the formation of Ag nanoparticles on biopolymer (Kaushik et al. 2016). The reducing activity of CNCs is due to several hydroxyl groups on their surface. This allows preventing the use of reducing agents. The suspension of two-phased or biphasic nanocellulose forms an ionic liquid-like system for the metal. It makes it easier for reactions involving ligand exchange. Another important application of these nanocomposites is photocatalysis, through which they allow the breakdown of dyes in an aqueous environment. This breakdown can be further enhanced by using nitrogen with the catalysts (Johnson et al. 2011; Varma 2019). Enzyme immobilization on nanocellulose The two ideal criteria to immobilize enzymes are low toxicity and biocompatibility. For example, heme proteins such as horseradish peroxidase have been fixed on AuNP bacterial CNFs (Zhang et al. 2010). Similarly, enzymes such as cyclodextrin glycosyltransferase and alcohol oxidase were fixed on CNCs containing Au, showing catalytic activity and high stability. Biosensors made in such ways (thiol sensors) are used in disease diagnosis (Schlesinger et al. 2015) (Varma 2019). An eco-friendlier approach to making cellulose-based material is a citric acid and cysteine treatment (Chen et al. 2017). The products thus formed exhibits properties such as UV absorption, sensing of chemicals, and fluorescence. The principal characteristic of this mode of preparation is using water as the sole solvent in the reaction, thereby preventing pollution. One example of a product thus made is a durable hydrophobic paper with many functions (Baidya et al. 2017; Varma 2019). Chitin and chitosan Following cellulose, chitin is the next most abundant polymer. Its structure is closely related to cellulose as it is an extended chain of β(1–4)-linked space 2-acetamido-2-deoxy-β-D-glucose. Chitosan is a deacetylated form of chitin (acetylation below 50%). The structure of chitosan consists of glucose amine and acetyl glucose amine units. The most important feature of chitin is that it can be used as a catalyst without undergoing any changes. Chitin and chitosan have several characteristics that allow them to be used in various areas such as the cosmetic industry and medical fields. Their features include hydrophilicity, biocompatibility, enhancement of wound healing, and more. Another notable feature of chitosan is producing nanoparticles that can regrow broken tissues (Varma 2019). Chitosan has shown many catalytic applications such as Husigen cycloaddition (Chtchigrovsky et al. 2009), Michael addition (Khalil et al. 2010), and Suzuki cross-coupling (Martina et al. 2011). The characteristic feature of such strategies is the prevention of using organic solvents, thus making it more environment friendly. Such green approaches have been used to produce α-amino nitriles and imines where chitosan has been used (Dekamin et al. 2013; Varma 2019). Microbeads are particles used in food, cosmetics, the medical industry, and more (King et al. 2017). Since these are made from polyethylene and polypropylene, they accumulate microplastics in the water bodies (Cole et al. 2011). As an alternative to this problem, Rogers and colleagues created chitin microbeads (Varma 2019). Interactions of pollutants in the environment through separation processes Pollutants release toxic substances that are not easy to separate into a single process. The solution to environmental pollution is detoxification methods and resource management. GT is a novel and efficient method to detoxify water using biomaterials such as a hybrid photocatalyst (Fu et al. 2021a, b). Bio-inspired hybrid photocatalysts for environmental detoxification Bioinspired hybrid photocatalyst contains microbial and biological adsorption coupling and photocatalytic treatment. They can be synthesized by combined biological and microbial oxidation and photocatalysis. They can mineralize toxic organic pollutants. The biopolymer process is divided into depolymerization and mineralization (Kumar et al. 2020). Chitosan and phthalocyanine are the active biomaterials used to promote light absorption in the visible range- hybrid catalyst absorption, and TiO2 accepts electrons to produce superoxide radicals by generating photons. Photocatalyst functions better when supported on biomaterials with porosity, large surface area, and functionality. Bismuth-based photocatalyst (visible light-responsive) is used in wastewater treatment because of its nontoxicity, low cost, modified morphology, optical, and chemical properties (Kumar et al. 2020). TiO2 is the best photocatalytic material; doped with heteroatoms forms. A hybrid structure with g-C3N4 acts as a good visible light active photocatalyst. S–Ag/TiO2@g-C3N4 is a hybrid catalyst that helps in triclosan (TS) detoxification and visible light degradation (Xie et al. 2019). In the presence of boron carbonitride, polyaniline has several purposes, such as hydrogen generation detoxifying organic and inorganic pollutants by acting as a photocatalyst (Raghu et al. 2021). To mineralize sulfamethoxazole as a pollutant in the pharmaceutical water and degradation of visible light, detoxification of environment, to remove harmful pollutant hybrid Ag2S/Bi2S3/g-C3N4 is used (Kumar et al. 2020). N-p hetero junction Bi4O512/Fe3O4 is a visible-light photocatalyst with recyclable magnetic properties. Some hybrid photocatalysts based on semiconductors are Bi2O3/Fe3O4, Bi2S3/Fe3O4, BiOBr/BiOI/Fe3O4, and Bi2WO6/Fe3O4 (Chang et al. 2020). TcPc/amorphous TiO2 works under visible irradiation (λ > 550 nm) as a photocatalyst. Deoxynivalenol is degraded in water using ZnO/graphene hybrid photocatalyst (Bai et al. 2017). MXene (new photocatalyst)-based biomaterials are used to produce hydrogen, oxidation of organic pollutants, and reduction of CO2 (Sharma et al. 2021). These new areas of research are insighted by researchers majorly in developing nations, which will be helpful in the detoxification process with improved effect and discovering a new photocatalyst in its hybrid form. Bio-inspired biomaterial Gelatin, natural gums, pectin, starch, cellulose, chitosan, acetate, alginate, natural, inorganic biomaterials, and polymeric bio-nanoparticles are involved in biopolymer-based bio-inspired-biomaterials. Sol–gel technique is the precursor’s condensation process where metal salts get adsorbed onto the polymer. Controlled hydrolysis, electrospinning, spin and atomic layer deposition, hot press technique, and nanoencapsulation are the techniques that are used for the synthesis of biopolymer-based bio-inspired biomaterials. They are used to remove antimicrobial agents, food packing materials, heavy metals, and organic pollutants. Future studies can be used in wide applications in anticancer activities and biomedical areas (Kumar et al. 2020). Segregation of hazardous pollutants Due to rapid industrialization and population growth, various synthetic materials like heavy metals, pesticides, insecticides, steroids, dyes, and other organic materials are added to the environment, contaminating it. This contamination leads to cause various fatal diseases like cancer in humans. The majority of contaminants are added in water streams, affecting the aquatic environment adversely. Because of this, the separation of hazardous pollutants from the environment and especially water bodies are significant. Adsorbents Adsorption is a surface phase transfer process practiced widely among other wastewater disposal technologies that are easy to carry out, fast, safe, whereas it has economic, versatile, feasible, and sustainable characteristics (Qi et al. 2021; Rizvi et al. 2020). To eliminate contaminants from water, adsorbents are used widely (Feng et al. 2019). Adsorbents can be of various types, like chitosan-based adsorbents, carbon-based adsorbents, and bio-based hydrogels. Bio-based hydrogels can be further classified into representative and composite bio-based hydrogels. Chitosan-based adsorbents After cellulose, chitin is the second-largest abundant material in the environment. Alkaline deacetylation treatment on chitin produces chitosan. The processing of seafood like shrimp, crab, lobster, and green algae generates environmental waste that can be utilized to produce chitosan, making it a sustainable product. Chitosan is a natural linear polysaccharide that is polycationic with several primary amines and carboxylic groups on its surface, giving rise to many binding sites that entangle organic pollutants and heavy metals chelating and electrostatic effects (Feng et al. 2019). It degrades very slowly in the environment, making it suitable for its use for a long duration. Biocompatibility, nontoxicity simple modification, biodegradability, and cheap production are the properties of chitosan (Ali et al. 2021; Eltaweil et al. 2021). It is widely used in eliminating phosphates and nitrates, which cause (eutrophication and methemoglobinemia diseases) detrimental problems in the aquatic system (Eltaweil et al. 2021). Karthikeyan and Meenakshi (2021) showed that when chitosan was encapsulated with magnetic kaolin beads to get rid of phosphate and nitrate ions was found to work 8 times more efficiently in aqueous solutions. Various contaminants in water bodies like heavy metals, organic materials, phenols, dyes, and more should be separated by adsorbing on the adsorbent materials (Table 2) (da Silva Alves et al. 2021).Table 2 The membranes used to remove the heavy metals and other pollutants from the contaminated water Source To remove Membrane Reference Aqueous solution Mercury Natural and cross-linked chitosan (Vieira et al. 2007) Water Cd (II) and Cr (III) ions ZnAl2O3-TiO2 UF membranes (Saffaj et al. 2004) Wastewater from industries Cu2+, Ni2+, and Cr6+ Anion exchange polymer (Padmavathi et al. 2014) Aqueous solution Phosphate Polyethylene graft copolymers (Senna et al. 2013) Drinking and industrial water Pb2+, Cu2+, and Cd2+ Silica and cellulose-based MF (Ritchie et al. 1999) Wastewater Cd2+ Chitosan/γ-cyclodextrin (Muthulakshmi and Anuradha 2015) Water Cu2+ CH/nylon-based (He et al. 2008) Arsenate contaminated water Arsenate Zr-based nanoparticle PSF HF (He et al. 2014) Irrigation wastewater Cd2+, Pb2+ Polyethylenimine-grafted gelatin sponge (Li et al. 2016) Carbon-based adsorbents Carbon-based adsorbents possess more excellent adsorption characteristics than cationic cellulose, and their adsorption mechanism is complex. Carbon spheres (CS) or carbon nanotubes (CNT) interact by electrostatic and hydrophobic interactions with contaminants present in the environment. They increase the surface area of the adsorbents (da Silva Alves et al. 2021). The oldest used carbon-based adsorbent is charcoal. It is used widely due to its large surface area, nontoxicity, and porosity to scale up. It helps eliminate inorganic heavy metal ions and organic textile dyes, pesticides, pharmaceutical products, and aromatic and phenolic compounds from contaminated water. Catalytic activation of pyrolyzed char produces carbon-activated adsorbent by using agricultural waste such as wood, coconut shells, wood, rice hulls, and industrial waste such as coke sawdust. The upcoming research needs to be done on recently developed carbon-based nano/micromotors, which requires more investigative aspects (Table 3) (Gusain et al. 2020).Table 3 Carbon-based adsorbents Carbon-based adsorbents Source Characteristics Applications Future notes References Zero-dimensional carbon-based nanomaterials (OD-CNMs) Carbon dots, carbon quantum dots (CDs), fullerenes, carbon dots, nano-diamonds (NDs) Surface functionalization, large surface area, optical aspects, minimum toxicity Helps in improving absorption by eliminating pollutants and helps in photocatalysis OD-CNMs composite needs to be prepared for the removal of toxic pollutants. OD-CNMs need to study further in detail (Gusain et al. 2020) One-dimensional carbon-based nanomaterials (1D CNMs) Carbon nanotube (CNTs); (produced by chemical vapor deposition method) Carbon nanofibers (CNFs) (produced by electrospinning process) Large surface area, different adsorption sites, porous nature, raised electrical conductivity, mechanical strength, raised aspect ratio, chemical resistance Helps in eliminating inorganic (heavy metal ions and radioactive components); organic (dyestuff, pharmaceuticals, and other aromatic pollutants) Since the adsorption is minimum, different heterostructures, porosity aspects of efficient adsorbents need to be studied Two-dimensional carbon-based nanomaterials (2DCNMs) Graphene, graphene oxide, g-C3N3, graphene nanoplatelet, reduced graphene oxide, graphene, sheet-like nanoporous materials Large surface area, optical transmittance, chemical and physical characteristics, great mechanical strength, raised electrical and thermal conductivities, chemical inertness, multifunctionalities, high current density To remove different pollutants, mainly removal of organic pollutants Magnetic composite, recycling, and regeneration approach. To try for fast removal g-C3N4 might extensively be used as an adsorbent. CNMs might replace other carbon-based adsorbents since they can remove all water pollutants. Adsorbents need to be produced on a large scale (Gusain et al. 2020) Multifunctional three-dimensional carbon Hydrogels, fibers, aerogels, foam, sponges 3D network, large surface area (versatile), highly flexible, thermally, mechanically, and chemically stable, raised surface hydrophobicity with oleophilic property, low densities, controlled morphologies It effectively removes metal ions, dyes, oils, hydrocarbons, and organic solvents from contaminated water When used with others such as hexagonal boron nitride, g-C3N4, MXenes, phosphorene, chalcogenides such as MoS2, WS2, etc. Studies in improving the adsorbent need to be done Representative biobased hydrogels Hydrogel is one type of three-dimensional nano-adsorbent prepared by the sol–gel technique. They can be prepared by polymerization and monomers cross-linking (one-step process) and polymer synthesis (multiple-step process). During preparation, it is swollen by water with a network of cross-linked polymers; they do not dissolve in water but keep hold onto a few-fraction of water in its structure (Gulrez 2013; Gusain et al. 2020). Hydrogels characteristics are abundant with hydroxyl amide and carboxyl groups, economical and high porosity. They interact with pollutants using electrostatic interaction (Gusain et al. 2020). The microporous chitosan is produced when genipin is cross-linked with hydrogel and incorporated nGO. Facilitation of cross-linking reaction increased robust 3D cross-linked networks exhibited by the increased storage modulus and swelling ratio. Hydrogels act as an effective adsorbent in diclofenac sodium (DCF), which is anti-inflammatory. CS/nGO hydrogels can purify trace pharmaceuticals in wastewater (Feng et al. 2017). Composite biobased hydrogels Composite hydrogels such as CMC/WPU have a higher swelling ratio than HEC/WPU and MC/WPU (L.-J. Huang et al. 2022). Nanoparticle-composite biobased hydrogel removes pollutants, whereas toxins are catalytically oxidized (Thoniyot et al. 2015). Adsorption and removal of dyes are done using various composite hydrogels like CS (chitosan)/GO (graphene oxide) from water, heavy metal ions, phenolic compounds, and pharmaceutical and personal care products. When magnetic CS/rGO is used together with Fe3O4 shows encouraging adsorption of dyes and antibiotics. However, CS/GO/β-cyclodextrin composite adsorb Mn (II) and Cr (VI). Composite such as chitosan with MNPs acts as a magnetic solid-phase extraction (MSPE) sorbent (Ali et al. 2021). Lignosulfonate, when added with triethylenetetramine, provides amine groups to increase adsorption and also hydroxyapatite to increase strength and adsorption capacity and also silica with (Si–OH), GO/gelatin/woCS, GO/Heparin/CS, CS/GO/alginate, chitosan carbon, CS/GO/cellulose, and chitosan-silica works as an adsorbent (da Silva Alves et al. 2021). Modified nanoclay and nanocomposites, which are simple and cheap, are used for the separation of fluoride; heavy metals such as Cu, As, Zn, Pd, Co, Ni, Cr, Cd, and Hg, dyes, personal care products, pesticides, and trace materials in the pharmaceutical industry in contaminated water (Manna et al. 2021). Further investigative research needs to be done to increase the performance of composite bio-based hydrogels. Composite with 5 wt% CDs/ZFO composites showed characteristic adsorption performance, 181.2 mg/g. By using adsorption, Ni/Pt/n-rGO efficiently removes nitroaromatic explosives like 2,4-dinitrotoluene (DNT), 2,4,6-trinitrophenol (TNP), 2,4,6-trinitrotoluene (TNT), and heavy metal ions (Gusain et al. 2020). Biosorption techniques The biosorption strategy is achieved by cheap regeneration of biosorbent and recovery of sorbate. Biosorption is the physiochemical process in which the liquid phase (solvent) and solid phase (biosorbent) are involved in separating dissolved species by adsorption, precipitation, absorption, ion exchange, and surface complexation method. Biosorption is mainly used to eliminate heavy metals and is considered 1/10 times cheaper than the ion-exchange process. The merits of the biosorption strategy to get rid of pollutants are cost efficiency, process selectiveness, easy regeneration of biosorbent, no sludge formation, easy recovery of metals, efficient performance, feasible operational conditions, no additional nutrients required, inexpensive technology, and flexibility of operation is used to remove many pollutants (Senthil Kumar and Grace Pavithra 2018). The use of both living and dead biomasses of bacteria, fungi, and algae is beneficial for biosorption. Agro-waste materials are also used as biosorbents because of their excellent surface characteristics, widespread availability, and low cost. By altering the surface qualities of biosorbents, several physical and chemical treatments improve their biosorption capacities (Saravanan et al. 2021). Product engineering and advanced materials Product engineering is the field of science that deals with designing, developing, and manufacturing a product. The main components of this field include designing and developing a particular product, this can either be a software-related, electronic, or mechanical development of the product, doing quality and reliable testing along with deciding the cost, features, and intended lifespan of the product and transitioning the product to manufacturing level by ramping up the production to volumes, for the market. However, nowadays, more and more engineers are focusing on the sustainable production of devices and products to shift the mindset from carbon-associated growth and clean up after pollution caused to ways in tackling the by-products by either recycling them or destroying them in sustainable processes before starting the manufacturing again. They are more interested in harnessing natural and renewable resources and increasing their usage in the production areas, especially in packaging and using advanced materials that are not toxic to the environment and are biodegradable. The SDGs given by the United Nations can be a very effective manner for engineers and scientists to develop green chemistry technology and use them to create eco-friendly and recyclable advanced materials. These materials can either be derived from nature, such as cellulose, lignin, and sugarcane, or can be developed from waste materials recycled in wastewater, food, and other bio-based industries (Kobayashi and Nakajima 2021). Following is the list of advanced materials and processes that can be utilized sustainably to create a better human life and a balanced and safe environment. Advanced oxidation processes (AOP) The AOP is a chemical technique for removing organic pollutants such as dyes, antibiotics, and other toxic pollutants from the wastewater. The H2O2, superoxide, and hydroxyl group radicals will help oxidize the toxic pollutants to less toxic or nonhazardous substances like CO2, H2O, and mineral acids (Theerthagiri et al. 2021). Many processes can be used here—Ozonation, photocatalysis, sonolysis, photolysis, Fenton reaction, electrochemical reactions, and more. These help in the efficient removal of organic pollutants, but it has certain limitations in developing the semiconductor catalysts and lengthy procedures, especially in the case of photocatalysis. Many 2D semiconductor materials are of great interest to scientists. They play a significant role in efficient bioremediation and have exceptional properties. The materials include graphene, graphitic carbon nitride (g-C3N4), transition metal chalcogenides, carbides, phosphines, and metal oxides such as TiO2, ZnO, composites, and metal–organic frameworks (MOFs). Some of their properties include a large surface area, high conductivity, excellent mechanical properties, which help make the material surface more durable and stable and reduce electron and photon recombination, thereby helping ineffective separation of the free radicals (Theerthagiri et al. 2021). The best practical method of removing toxic organic products, like azo dyes chemical solvents (Azimi et al. 2021), is by using sonophotocatalysis. This is a hybrid of 2 AOPs, i.e., sonolysis and photocatalysis. This combination can efficiently degrade these pollutants using ultrasound waves to produce cavity bubbles, activate the semiconductor material catalysts, and remove the toxic contaminants. These waves of about 20 kHz to 2 MHz led to cavitation. They helped continuously clean the material surface and helped to degrade both hydrophobic and hydrophilic organic pollutants. This is one of the very effective techniques in reusing the process repetitively with less maintenance and help in wastewater treatments (Theerthagiri et al. 2021). Cellulose and nanocellulose Cellulose is a polysaccharide of β(1 ➔ 4) glycosidic linkages of β D-glucose units and is found naturally in the cell wall of plants. Cellulose is high environmental-friendly material; there is wide availability, low cost, biodegradable, and renewable (Tu et al. 2021; Yang et al. 2021b). They are a perfect substitute for petroleum-synthesized plastics and can replace plastics in the long run since there is already an industry present and matured over the past 100 years and currently used to produce viscose, cuprammonium rayon, and more, which make use of certain toxic chemicals for its effective use (Tu et al. 2021). However, these chemicals can be replaced by specific cellulose properties and fiber structures. Yang et al. explained this with the concept of how tall trees stay upright. The wood from within consists of cellulose elementary fibrils or microfibrils, which strongly reinforce the cellulose nanofibrils to keep the upright structure of the tree and prevent it from falling. Cellulose nanofibers are formed by highly crystalline nanoscale fibrils (nanofibrils) and combine to form cellulose nanocrystals. These have a high aspect ratio and are flexible (Yang et al. 2021b). These nanocellulose materials are seven times stronger but five times lighter than steel. The hydroxyl grouping on the surface of cellulose can help combine with hydrogen bonding to form much nanocellulosic material interaction and increase the material performance and durability. Based on the number of hydroxyl groups on the surface, the mechanical and physical properties must be changed via many surfaces and intersurface engineering methods, both chemical and nonchemical. This can lead to novel functionalities and have various practical applications in structural, optical/electronic, phototonic, textiles, energy storage, and medical applications (Tu et al. 2021; Yang et al. 2021b). Studies are also being conducted to develop green cellulose solvents and regenerated cellulose materials of high strength through the bottom-up route (Tu et al. 2021). Nanocellulose-based aerogels form porous templates and are suitable for packaging applications instead of polystyrene-based packaging foams. They also have good water absorption, selective separation ability, CO2 capture, and conductivity. Another one is nanocellulose films, which have very high water vapor absorption capacity and low oxygen permeability and can be used instead of plastic cling wraps for day-to-day food packaging. It also has good filtration technology and can be used in water filtration tanks and wastewater treatment plants (Table 4) (Nelson et al. 2016).Table 4 Different types of advanced materials, their properties and uses SNO Material Source Utilized in Pros Cons Recycling capacity References 1 Nanocellulose (NC) Made from cellulose, the most abundant natural polymer from wood. The cellulose nanofibrils (CNF) and their cellulose nanocrystals (CNC) Nanocelluloses mixed with other natural biomaterials like cellulose, starch, and alginate produce hydrogels, aerogels, mats, films, and bioplastics Renewable, biodegradable, eco-friendly, carbon neutral, lightweight, and robust Still requires some chemical solvents to prepare cellulose materials before binding. Research ongoing for green solvents Highly recyclable (Yang et al. 2021b) (Tu et al. 2021) (Wang et al. 2021b) 2 Ferric oxide Produced from heated toner powder in exhausted printer cartridges Anode material in sodium-ion batteries Renewable, low cost, and good electrochemical performance as an anode in a sodium-ion battery Structural volatility and low Coulomb efficiency. Carbon combined with Fe3O4 gives a stable electrode material Recyclable (Arjunan et al. 2021) 3 Graphite Not specified Anode material in lithium-ion batteries High Coulombic efficiency and long cycle life Not a suitable anode material in sodium-ion batteries and SIHCs. Requires tin (Sn) to form a composite Is recyclable (Tabelin et al. 2021) (Palaniselvam et al. 2021) 4 Starch A polysaccharide is used to store energy in plants. Obtained from potato, rice, corn, wheat, and cassava Biodegradable packing film made of cornstarch-chitosan pluronic F127, bio-painting papers, corn, and arrowroot with NaClO4 and glutaraldehyde act as flexible, transparent, and highly conductive electrolyte membranes. Used in drug delivery systems Highly abundant in nature, low cost, high biocompatibility, and biodegradability Poor mechanical properties and water resistance Is recyclable (Fonseca-García et al. 2021) (Kou et al. 2021) (Cao et al. 2021) 5 Chitosan Natural linear amino polysaccharide. Polymer extracted from shells of shrimps crabs Used in biodegradable packing film made of cornstarch-chitosan pluronic F127, Improves the stability of drugs and can be used as a nano-drug carrier Biocompatibility, antimicrobial, antioxidant increase wound healing process, are nontoxic, and have a low oxygen permeability The extraction process of chitosan differs from the source. There is no standard procedure for all types of sources Is recyclable (Fonseca-García et al. 2021) (Kou et al. 2021) 6 Flavonoids The biggest group of polyphenols has 8000 compounds. Consists of flavones, flavanols, iso-flavonoids, etc Na-alginate NPs doped with pinostrobin—as an anticancer drug, biopolymer films with propolis—active packaging material and cleaning agents Natural antioxidant compounds create a biological protective barrier and have biocidal properties against microorganisms Not specified Not specified (Pawłowska and Stepczyńska 2021) 7 Transition metal chalcogenides (TMDs) Chemical compounds consist of at least one chalcogen anion (sulfur or selenide) and electropositive transition metal element Sonophotocatalysis, visible light-harvesting applications Remarkable and unique characteristics compared to bulk parent compounds and highly covalent species Photocorrosion. To prevent this, TMDs are doped with cocatalysts like NixMg4-xS4 MXene sonophotocatalyst Not specified (Theerthagiri et al. 2021) 8 Hard carbon Not specified Anode material in sodium-ion batteries High Na-ion storage capacity, appropriate working potential, excellent cycling stability, and natural abundance Not many studies have been conducted to understand the interactions between sodium and hard carbon during the electrochemical process Highly recyclable (Wang et al. 2021c) 9 Biochar Produced by pyrolysis under anaerobic conditions To improve soil fertility, carbon sequestration captures CO2 from the atmosphere in healthcare as filler media and drug-delivery agents Has wide applications in effective regulation of climate change, very useful in agriculture, renewable batteries, and healthcare Unable to reduce N2O levels, compared to charcoal Not specified (Ok et al. 2015) 10 Polylactic Acid Produced by polymerization of lactic acid. obtained from fermented starch of corn and rice. Also obtained from waste material such as cellulose, kitchen garden, or fish Can act as nanocarriers for drugs used in prosthetics, orthopedics, face masks, cosmetic industries, textiles, and bioremediation Biodegradable polyester with good compatibility, good processability, and mechanical properties Not specified Not specified (Pawłowska and Stepczyńska 2021) (Kou et al. 2021) 11 Polyhydroxybu-yrate (PHB) Produced by microalgae and various bacteria under particular carbon excess stress conditions Wound dressings, microspheres used in drug delivery systems, tissue engineering, as an anti-adhesive agent against shellfish pathogens, bio-additives in paints, and used in the food packing industry High biodegradability, high biocompatibility, nontoxic, and creates no environmental pollution Cultivation and harvesting of these microorganisms are limited due to the expensive equipment used in the process Not specified (Pawłowska and Stepczyńska 2021) 12 Lignin 2nd abundant component is wood. It is an amorphous, 3D oxygenated p-propyl phenol polymer. Industrially obtained as a byproduct of cellulose-rich pulp fibers To produce different synthetic polymers with physicochemical properties. Some commercial products include Kraft lignin, soda lignin, organosolv lignin, and lignosulfonate High hydrophobicity, antioxidant, antimicrobial, UV absorption, thermal stability, and rigidity Not that good and active to be utilized as an adsorbent and surfactant. Requires further processing to lignin nanomaterials to have this property It can be recyclable (Wang et al. 2021b) (Kou et al. 2021) 13 Dioscorea hispida tubers Produces starches and fibers. It is a poisonous tuber plant that contains the alkaloid of Dioscorides Its waste can be alternative biomass. It has excellent potential to be used as renewable filler material for food packaging applications and as a crude drug for inflammation Can generate large quantities of sustainable lignocellulosic materials every year and produce starch, bioplastics that are eco-friendly and highly renewable The tubers have to be immersed in distilled water for 5 days in order to remove/fully detoxify the Dioscorides Is recyclable (Hazrati et al. 2021) 14 NC-based aerogels Low-density solid materials, made up of CNFs of nanocellulose and chitin nanocrystals Water treatment, controlled drug delivery, and dye adsorption Antioxidant, antimicrobial, etc Not specified Is recyclable (Yang et al. 2021b) (Nelson et al. 2016) Lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs) Electronic battery waste has been a significant cause of environmental concern since there are few effective ways of recycling waste. However, scientists have developed ways to prepare advanced materials from these electronic remains like toner waste-specific metal remains, so on, that can be recycled and used as the anode material for lithium and sodium-ion batteries, instead of preparing new ones, which may not be environmental-friendly and nonrecyclable. Li-CO2 batteries (LIBs) came into existence after agreements and protocols like the Kyoto Protocol, the United Nations SDGs, and the Paris Agreement. This led to many countries finding alternative ways to curb CO2 emissions and create a more carbon-low or carbon–neutral society. These clean storage technologies help electric power sectors decarbonize along with GT like electric vehicles (EVs) and energy storage systems (Tabelin et al. 2021). Compared to other rechargeable batteries, LIBs store more energy per unit mass. Lithium is known as white gold. It has varied applications from the manufacturing industry like lubricants, polymers, rechargeable batteries, and medicine to treat mental disorders (Tabelin et al. 2021). LIBs were costly earlier but are now made by lithium-intercalation using graphite anodes. Another novel energy storage GT is possible using Lithium secondary batteries (LSB), which uses green batteries derived from biomass like renewable organic biomolecules and inorganic carbon molecules (Jin et al. 2021). Sodium-ion batteries (SIBs) were introduced after Li-ion batteries in the commercialized market at the end of the twentieth century (Wang et al. 2021c). However, they were not studied in that much detail compared to Li-ion batteries. However, scientists are now looking for an appropriate anode material and find hard carbon (HC) to be a promising material since it has an excellent sodium-storage capacity, good recycling stability, and is a naturally available material (Wang et al. 2021c). With such anode materials, Na-ion batteries (SIBs) can slowly be used as an alternative to lithium-ion batteries despite having less energy than LIBs but a safer and better battery than lead-acid batteries (Palaniselvam et al. 2021). Biodegradable materials Nonbiodegradable plastic waste has become a significant pollutant in our water bodies like the rivers and seas. About 8 million tonnes of plastic waste is dumped each year. If this discharge pattern continues, we will have more plastic than fish by 2050 (Tu et al. 2021). Scientists have entirely focused on the use of biodegradable materials like starch, cellulose, chitosan, biomass, resins, gums, jute, gelatin, pectin, waxes, and inorganic compounds like TiO2, ZnO, chalcogenides, and ways to incorporate eco-friendly and sustainable habits from using jute or cloth bags for daily grocery shopping to high-production packaging using biodegradable films made of cornstarch and chitosan in industries. Electronic devices made of renewable or biodegradable materials that disintegrate into harmless by-products are becoming increasingly popular. Low-energy, low-cost procedures including low/nontoxic functional materials or solvents are required to construct such “green” electronic devices on an industrial scale (Li et al. 2020). Biodegradable films made of cornstarch and chitosan with poloxamer F127 are very durable films with good moisture barrier properties. They are suitable for packaging food and medicines (Fonseca-García et al. 2021). This shows the ability of starch and chitosan to be used as significant polymers that can be modified into thermoplastics mixed with synthetic polymers. It is environmentally friendly, nontoxic, and easily decomposable (Fonseca-García et al. 2021). Dioscorea hispida tubers also have an excellent potential to be used as a renewable filler material for food packaging films (Hazrati et al. 2021). A second application can be made natural antibiotics using natural modifiers like polyphenols, biocidal additives like sodium alginate nanoparticles doped with pinostrobin and used as an anticancer drug. Naringenin has antioxidant and anti-inflammatory properties against the SARS-CoV-2 virus (Pawłowska and Stepczyńska 2021). Biodegradable materials such as Nanocarriers can help in the effective encapsulation of the active pharmaceutical ingredient (API) for essential drugs, nutritional capsules, and supplements. New natural biopolymer combinations can be degraded into harmless compounds that are nontoxic and enhance biocompatibility, have high loading efficiency, and are safe to consume (Kou et al. 2021). These nanoparticles drug delivery systems can be highly effective for tumor-targeted therapy (Kou et al. 2021). Biochar is another biodegradable material made of solid carbon through pyrolysis of biomass. Biochar can be used for drug delivery and detoxification for patients suffering from poisoning or drug overdose (Ok et al. 2015). In terms of waste management, many natural filler materials can be utilized. Dioscorea hispida tubers waste can be used as a waste-filler material and an alternative biomass source (Hazrati et al. 2021). Biodegradable materials also have agricultural applications. This includes promoting crops’ growth by using cornstarch gelatin composites and modified tapioca coated over controlled-release urea particles to enhance the nitrogen-producing capacity of plants (Cao et al. 2021). Biochar is used in plants to help fix the CO2 from the atmosphere in a stable form and prevent climate change conditions (Ok et al. 2015). In electronic technology, starch-based coatings can enhance the electrochemical performance of batteries. Also, starch content from arrowroot and corn can be combined with chemicals like sodium perchlorate (NaClO4) and glutaraldehyde to produce highly conductive electrolyte membranes (Cao et al. 2021). Lastly, biochar can be used as suitable supercapacitors. Biochar coated with graphene can act as good anode materials for batteries (Ok et al. 2015). Process system engineering and IR 4.0 Industry 4.0 The Fourth Industrial Revolution, also known as Industry 4.0, envisions a rapid change in technology, industries, societal patterns, and processes due to increased interconnectedness and smart technologies. It was introduced by Klaus Schwab, the founder and executive chairman of the World Economic Forum, to highlight that the changes being experienced are more than just efficiency gains but rather a drastic shift in industrial capitalism. Humanity is currently confronted with two significant challenges. One of them is achieving the SDGs, while the other is adapting to the changes that marked IR 4.0. Industry 4.0 was initially announced in Germany in 2011 at the “Hannover Fair” event as a proposal to form a new concept of German economy policy based on high technology initiatives, marketing the start of the IR 4.0. The rapid evolution of technology allows for a thorough examination of its impacts on the economy, society, and environment. There has been a dispute between the ideals of industrial production, economical expression, and environmental sustainability over the decades. Aside from water and energy usage, the extraction of raw materials and soil exploration has resulted in massive waste production. Industry 4.0 is a novel engineering paradigm characterized by high productivity, procedural efficiency, and environmental sustainability. This new sector is seen as a paradigm for manufacturing that is sustainable. And one of the elements that most underwrites this information is the vast collection of primarily revolutionary technologies in Industry 4.0 (Bortolini et al. 2017). Mainly because such technologies are not necessarily unheard of, what changes is the interaction between them in the context of Industry 4.0. This work aims to demonstrate the full potential of Industry 4.0’s leading technologies for their very effective eco-friendly management. Thus, it is feasible to grasp how integrated technologies collaborate for an environmentally sound and sustainable positioning of Industry 4.0 in all sectors (Aceto et al. 2020). Industry 4.0 makes better use of natural resources, produces less waste, has leaner processes, and has more extended machine and equipment life cycles and technological advancements (Jiang et al. 2021). Futural skill requirements As Industry 4.0 adopts an enhanced ecologically sustainable conscience and implements its potential technologies, the procurement of energy resources and all power generation is done depending on the requirements and demand, without exaggeration. Consequently, even as a precise amount of a resource to be used in production is acquired, expenditure on the purchase of productive inputs tends to decline or be under control. Sustainable environmental behavior in Industry 4.0 can increase revenue while cutting costs and expenses. Similarly, across Industry 4.0, the usage of machinery and equipment may be shared. This comprises industries sharing their operational capacity and is a means for them to provide services. The supplying industry may keep its machinery running and generate variable income even when it is not functioning in the industry from which it runs. At the same time, the machinery-using industry continues to operate without hindrance or loss of demand. This industrialization strategy is distinct in employing several technologies to achieve more environmentally consistent and efficient industrial production. Such technologies, when used efficiently, may make a substantial contribution to environmental sustainability. Blockchain technology Blockchain is a distributed database of completed activities and digital events shared among participating parties. A network participant agrees on, mathematically links, and stores each transaction, ensuring its immutability. Blockchain enables us to manage our digital operations and interactions far more securely and dependable (Lage 2019). The dependability given by blockchain will witness a significant alteration of industrial processes in the next years, enhancing synchronization between different agents in the value chain and excessive automation of decision-making. It is also expected to transform its business models in the future, like how the Internet, the most significant technological innovation in history, revolutionized the world (Esmaeilian et al. 2020). Blockchain technology and green IoT The vast expansion in global industrial activity over the last several decades has resulted in a considerable increase in the consumption of fossil fuel energy resources, while technological advancement has amplified the carbon footprint and hence global warming. The enormous rise in energy usage brought on by IoT technology has posed a new issue and shifted our attention to developing a more environmentally friendly IoT ecosystem (Sharma et al. 2020). The Green Internet of Things (GIoT) is a new topic that has piqued the interest of researchers and businesses since it offers energy-efficient services and allows for the generation and use of renewable energy. Blockchain technology has emerged as a widely used IoT technology, receiving significant interest from energy corporations, start-ups, financial institutions, governments, and researchers. It is essential to clearly understand the role of developing blockchain technology in the GIoT ecosystem, which offers the critical aspects that must be considered to establish a GIoT ecosystem and examine how blockchain technology helps to green the IoT ecosystem (Polas et al. 2022; Sharma et al. 2020). The term GIoT refers to a new generation of IoT design principles. The green smart device (GSD) is a fundamental unit of GIoT for energy conservation (Tan et al. 2021). It can prove fruitful to conserve energy, minimize emissions, reduce environmental pollution, and harm the human body and environment. In the GIoT, user access and management of GSDs have grown increasingly challenging due to the availability of a large number of heterogeneous bottom-layer GSDs. Users must use multiple GIoT apps and access different GIoT cloud platforms to access and control these heterogeneous GSDs since there is no uniform GSD management system. This disjointed GSD management paradigm complicates user access and control for varied GSDs and limits GSD application scalability (Tan et al. 2021; Zhu et al. 2015). The primary goals of the IR 4.0 The Fourth Industrial Revolution’s primary goal is to increase revenues and elevate the standard of living. As a result of modern technology, products and services have been produced that may help and support their professional and personal lives. Furthermore, because new technologies may readily intrude on people’s privacy, governments must build effective methods for monitoring and organizing technology platforms. The evolution of customized medicine, which will allow people to detect their propensity to particular diseases, is another important area addressed by Industry 4.0. New biomarker-based technologies are predicted to characterize practically all human metabolic activities. The scope and the profound impact of the changes imposed on production, management, and effective governance all contribute to Industry 4.0 as a new revolution and the speed with which technical-scientific breakthroughs are made and spread (Dogaru 2020). Renewable energy perspectives Renewable energy, often known as alternative energy, is derived from a natural source that does not diminish when utilized. It is a type of energy that has acquired popularity in the past few years. It does not harm environmental sustainability. One of the significant sources of pollution is a scientific and technical progress that is unrelated to proper pollution control methods. Scientific innovation for industrial development has triggered intense debates and concerns, particularly at the level of environmental regulations, while neglecting the maintenance of ecological integrity and resulting in significant adverse manifestations. The goal of eco-industrial development, which is closely linked to environmental sustainability, was to find a variety of answers to the complex challenges of renewable energy management and usage and the consequences of climate change. Renewable energy is the best and cheapest option as an alternative energy source. Renewable energy has enormous potential worldwide, particularly in India. The optimal use of renewable energy resources has the potential to reduce the global impact of climate change. Renewable energy is created primarily from virtually endless sources such as wind, solar, geothermal, tidal, biomass, and other renewable energy sources. As a result, boosting renewable energy sources could rescue our future from climate change and a sustainable food production standpoint (Kumar et al. 2021). Current Industry 4.0 research for encouraging the sustainability of supply chains Sustainable supply chain development emphasizes environmental and economic benefits, whereas Industry 4.0 development involves complete system integration and automation. Manufacturing equipment can become self-contained as part of Industry 4.0, allowing it to design, develop, and build items without human interaction. Industry 4.0 enables more product customization by increasing manufacturing flexibility. The machines will interact with one another to carry out the manufacturing plant. On the other hand, firms face plenty of challenges in implementing Industry 4.0 efforts, which could impact supply chain sustainability (Bányai and Akkad 2021). Industry 4.0 ushers in a new era of supply chain transformation through digitalization and smart technologies. Industries worldwide achieve productivity by enabling technologies to avoid perishing in this unpredictable and ambiguous environment. Industry 4.0, on the other hand, has negative aspects that affect a company’s supply chain both before and after deployment. The major issues include employment loss, lack of Industry 4.0 knowledge among network suppliers, inadequate funding for technology advancement, and lack of IT security guidelines and policies that affect both customers and suppliers in the supply chain network. These challenges can heighten uncertainty and risk, potentially disrupting the supply chain. IoT-enabled energy management The IoT is a new technology that connects physical components over the Internet, including consumer electronics and industrial machinery. By utilizing appropriate sensors and communication networks, these devices can provide crucial data and enable users to obtain various services. One of the primary advantages of blockchain applicability to IoT is the decentralized architecture that blockchain can provide to IoT, especially to the industrial environment, which has more severe needs (Fig. 2) (Hossein Motlagh et al. 2020).Fig. 2 This figure demonstrates the role of IoT in energy management and conservation. It includes cutting operational expenses, optimizing asset maintenance, reducing energy spending, integrating green energy, minimizing carbon emission, complying with regulations, and predicting consumption and spending IoT also distinguishes itself by allowing individuals to be free of a particular location. As a result, the person may operate the tools without being in a specific area. The devices are in direct connection, and the person is one of the communication nodes, similar to other terminals. In this context, any item recognized on the Internet by an IP address is considered a linked thing. It also includes the hoops used by animals in breeding farms, natural reserves, oceans, and woodland areas. IoT allows humans to control close-by or faraway devices successfully. For example, a user may manage and run his automobile engine from his wristwatch or handle his washing machine’s washing tasks. He can also remotely examine the contents of the refrigerator. Nonetheless, these are some examples of IoT in its most basic form. The mature version involves a direct connection between the machine and the machine. For instance, the refrigerator may interact with the shopping center to order and acquire goods without human participation. Moisture and heat sensors at the atmospheric monitoring station can trigger the discharge of a water evaporator. Many instances of IoT might be created that could become a reality in our everyday lives (Lage 2019). Logistics and transportation in IR 4.0 Industry 4.0 is encouraging business model changes, resulting in the birth of new professions and the loss of certain lines of work that will be substituted by machine intelligence and gadgets. The beginning of Industry 4.0 is characterized as worldwide progress, which may require a longer time to accomplish. Skills, talents, and knowledge in the workforce have to be improved. When Industry 4.0 is fully implemented, computers will interact with one another and make choices without human intervention. Industry 4.0 technologies aid logistics by helping to optimize transit routes, maximize storage space, and plan (Holubčík et al. 2021). The Port of Hamburg is one such example. Every year, 140 million tonnes of products are transhipped, with the quantity expected to treble by 2030. However, there is insufficient room in the port. As a result, the officials of the Port of Hamburg were faced with the task of assuring speedier container transshipment. Global markets are changing as product life cycles are shortened, product complexities are increasing, and global supply networks are becoming more crucial. As a result, firms strive to be more adaptive, faster, less expensive, and more capable of responding to changing market conditions. For companies who want to address these issues adequately, Industry 4.0 is the approach (Jankalová and Jankal 2018). Environmental/green economies Having a sustainable, low-carbon, and green economy has become the need of the hour in recent times. Governments all over the world have now realized the existence of climate change and how greenhouse gases, CO2 emissions, excess pollution of water bodies due to nonbiodegradability of plastic waste, use of excess chemicals and cheap synthetic solvents in fashion, textiles, automobiles, and sports industries for so-called “durable” products, increased number of animal species on the endangered list of extinction, forest fires of the Amazon, increased floods and other natural disasters, and many more instances have negatively impacted the environment as well as human lives. In addition to these, the COVID-19 pandemic has sent alarm rings to the world. During lockdown times, everyone realized how they had misused and exploited the free resources provided by the Earth for their greedy needs. This section will include an in-depth analysis of first, how the COVID-19 pandemic has shifted the paradigm of the world toward a green transition and how governments can make the necessary changes through deals, financial stimulus packages, and pricing the carbon emission and externalities to bring sustainable solutions for a safe and greener economy. There are still many challenges in this area. There is a great need for research to develop reasonable and easy solutions. Second, how scientists and governments work together for green stimulus packages and how green production capabilities can be enhanced in industries. The last two parts discuss how sustainability can be incorporated by individuals, communities, governments, and the world to protect the environment. There is a need to spread awareness regarding the damage and destruction happening to our environment by human activities and protest about it and practically implement those policies set by the governments and companies. This can be done through sustainable transitions, ecological modernization, reconsidering the concept of growth, and how it can be made into sustainable growth. These practices, and at the same time continuous research and analysis, will help us to create a more environmental-friendly, safe, and sustainable world and the planet. GTI may indirectly impact carbon emission efficiency by affecting economic development and urbanization (Khattak and Ahmad 2021). Green transition and COVID-19 post-pandemic research agenda The COVID-19 pandemic has affected all spheres of life and has given the world economic, environmental, social, and political challenges (McNeely 2021). It was a devastating time for human existence, which significantly strained the health sector. Both government and private hospitals and the world witnessed significant changes and consequences on the environment, economies, and the energy sector (Priya et al. 2021). The SARS-CoV-2 virus originated in Wuhan, China, and was said to have zoonotic links (McNeely 2021; Priya et al. 2021). This disease was easily spread to the world via globalization trade and travel. Even the governments of the world were not able to handle it well. There was slow and poor preparedness to respond immediately (McNeely 2021). The world witnessed lockdowns, shutting down of air travel, fully online educational classes, and temporary closure of factories, offices, and industries to implement social distancing to reduce the transmission of the virus. The unemployment rates were at an all-time high; reusable packet containers became hazardous waste due to use by COVID-positive patients; there was a huge increase in the amount of medical waste, including PPE kits, gloves, and masks. Moreover, due to increased online shopping and food deliveries, organic and inorganic domestic waste significantly increased (Irfan et al. 2021). There was a sense of forgetfulness to live a sustainable way of life with the arrival of the pandemic. In the USA, officials had to stop all recycling activities due to the fear of spreading the virus among the workers (Irfan et al. 2021). A significant change was observed during the lockdown time in the CO2 emission and GHG levels (Kumar et al. 2022). This made the governments and scientists realize how the closure of certain activities led the planet to heal itself and post-pandemic scenario, how the GHG and CO2 emissions can be controlled (Kumar et al. 2022). There was also a sharp reduction in water pollution, the surface, coastal, and groundwater quality significantly improved. However, excessive soil pollution occurred due to a huge increase in medical and household waste (Yang 2021a). Post-pandemic, practical action plans and policies need to be implemented to control these emissions sustainably through green economic activities (Irfan et al. 2021; Kumar et al. 2022). As per Irfan et al. (2021), the more we move toward a green economy, the better global sustainability can be achieved. The following parts will explain in detail the lessons learned from the lockdowns; the Green New Deal, which is being initiated; how the governments are providing green economic stimulus packages; and how to fix the prices for carbon emissions, using concepts of externality and if a common fixed price will be feasible for the world. Lockdown lessons to diminish carbon emission As the pandemic raged across the world, all movements were restricted to prevent the spread of COVID-19—from the ban on airline travel, closure of schools, offices, factories, industries to the complete stoppage of transportation of oil, gas, and other goods. The regular amount of pollution from transportation highly reduced the release of fumes from factories, powerplants, oil refineries, and coal mining resulted in reducing CO2, NO2, CH4, and other GHGs emissions (Kumar et al. 2022; McNeely 2021). During pre-pandemic days, industrialization and specific automobile industries, which mainly were utilizing fossil fuels for energy, resulted in an upsurge in GHG emissions. At least one-fifth of the CO2 emissions came from transportation, with 75% from road transport (Kumar et al. 2022). Moreover, this was significantly reduced during the lockdowns to a global 7.8% decline in CO2 emissions in 2020 compared to 2019 (Kumar et al. 2022). However, as per Irfan et al. (2021), this reduction in GHG emissions was not sustainable when considering the normal functioning of global businesses. Post-pandemic, there has to be a series of long-term mitigation strategies to maintain this reduced level of GHG emissions (Irfan et al. 2021). As per Kumar et al. (2022), in 2021, there can be a 6% upsurge in the GHG levels to stabilize the economy in the USA. There was also a decrease in GHG levels due to less aviation traffic, at least 40% less than the average in 2020 (Priya et al. 2021). The positive impact of COVID-19 was that CO, NO2, PM2.5, and PM10 were reduced significantly in the atmosphere. In contrast, the SO2 and O3 levels were either constant or slightly increased in Lyon, Kolkata, Peru, and Spain (Yang et al. 2021a). The Green New Deal As per McNeely, the governments of all countries are adopting a Green New Deal, especially for renewable resources—wind, solar, and water. The aim is to reduce 80% of the energy dependency on fossil fuels by 2030 and undergo a complete transition of energy dependency on renewable resources by 2050 (McNeely 2021). This should be a joint effort by the governments, academic institutions, scientists, NGOs, healthcare workers, and the public. Scholars in environmental science must advise policymakers on formulating scientific methods, understanding the risks involved, and creating public awareness. This can result in inadequate policies that can help reduce GHG emissions (Irfan et al. 2021). The deal includes hydrogen economy—for low carbon energy systems because hydrogen can be utilized as an energy carrier with a similar role as carbon. Next is the transition to renewable energy through bioenergy with carbon capture (Kumar et al. 2022). Recycling institutions can significantly reduce the amount of waste discarded and reused again. Green innovation can be increased by creating electric vehicles. Also, green’s low-carbon circular economy can be created by using biodegradable, advanced materials (Priya et al. 2021). Energy efficiency (EE) investments are a good clean energy transition practiced by the European Union (EU) for green economic recovery. This can be done using innovative decision support (DS) tools and standardized methodology tools like the Triple-A Horizon. As per the International Energy Agency (IEA), this can help in boosting the economy by creating jobs, reducing air pollution and GHG emissions, low energy bills, and efficient energy systems. Governments worldwide can apply this in 3 essential sectors—infrastructure, construction of buildings, and technology replacement using GT (Fig. 3) (Karakosta et al. 2021).Fig. 3 The Green New Deal, goals, and strategies for sustainable and eco-friendly transition. It includes bioenergy, biodefence, bio-cities, biodiversity, bioeconomy, green housing, agriculture, and bio-based materials Due to the pandemic, the EE investments were significantly reduced, especially in the oil sector. However, as the situation improves, these investments shall continue. The Green Deal also includes the “One Health” policy, which focuses on human, animal, economic, and environmental health and maintaining a good balance and improving the interaction between humans and animals by creating more dedicated areas for national parks and sanctuaries. The animals can live in their wild environments and support biodiversity. In agriculture, healthier approaches can be created to grow food in sustainable ways, for example, hydroponics and aquaponics. In the food industry, more investments are to be made in plant-based proteins and meats to reduce dependency on livestock and create healthier alternatives for a high-protein diet (McNeely 2021). Government-financed stimuli Governments worldwide have planned to develop a stimulus for maintaining a good balance between economic recovery and climate issues. A good example to explain this is upgrading the street lights in India with LED lights, which reduced the GHG levels by about 5 million tons in 9 years and created 13,000 jobs (Abhinandan Kumar et al. 2022). The governments can come up with bio-cities in the urban settlements where they can build GT and green material-based buildings (EE investments can be included), promote bio-based public transportation for traveling instead of using individual cars, and promote green spaces by growing trees and urban forestry to engage people in recreational activities and protecting nature around cities (Galanakis et al. 2022). Governments can provide transportation stimulus packages for sustainable mobility. These are low-carbon policies to reduce GHG levels due to vehicles, significantly individual cars (Griffiths et al. 2021). The concept of MaaS (mobility-as-a-service) can also be created—like public transport and Uber, where the public can book seats online and contribute to reducing CO2 emissions (Kanda and Kivimaa 2020). The government can directly buy EVs for the public, which are contactless and can be used as taxis for the public, as delivery vehicles, and buses (Griffiths et al. 2021). The countries should also focus on green housing to enhance sustainable living. The COVID-19 pandemic has led to the public spending more time at their homes—working and for leisure time. Residential complexes must be developed to improve indoor environment quality, using sustainable and durable materials using the green housing topic model (Kaklauskas et al. 2021). The United Nations have provided the SDGs to set specific policies to follow them and maintain the 3 pillars of sustainability—social, economic, and environmental sustainability, post COVID-19 pandemic (Ranjbari et al. 2021). Pricing the carbon externality The externality is a concept of economics that involves an industry or institution that is not charged or punished for the harm it provides to another, i.e., the environment. As far as industries are concerned, it is essential to keep a check on the CO2 emissions released into the environment, having dangerous consequences. Hence, it is very crucial to charge them for this. This pricing can help them act on negative externalities carbon emissions and correct the spatial associations of environmental pollution (Shen et al. 2021). Carbon neutrality is another case where the externalities are internalized to create a carbon-double input level to innovate green technologies to produce low-carbon goods (Wang et al. 2021a, b, c). Generally, three policies would address climate change as an externality—command and control regulation, carbon taxes, and cap and trade (Mintz‐Woo 2022). Both carbon taxes and cap and trade are quantitative instruments for carbon pricing. Suppose we increase the price of GHG emissions, in that case, the producers themselves will reduce their polluting activities and find alternate ways of disposal (Mintz‐Woo 2022). In the USA, most of the earlier policies dealt with the cap-and-trade policies and carbon taxes, which considered GHG emissions an externality leading to climate change. However, it had been insufficient to significantly reduce the emission levels (Boyle et al. 2021). As per Mintz‐Woo (2022), the USA prefers the command-and-control regulation since it either prohibits or sets specific limits regarding the emission levels. However, overall, carbon pricing is the most effective way to price the externalities. This is because carbon pricing is flexible, highly efficient, and comprehensive. Here, it rewards the companies who reduce their emissions, which can recycle their revenues (Mintz‐Woo 2022). Carbon pricing can be done either through carbon taxes or cap and trade and could be a point of discussion. Green stimulus and production capabilities GT has become a significant factor in economic growth with sustainability. Various fields are now being utilized to reduce GHG emissions and hazardous waste production. This includes bio-based sustainability, green nanotechnology, green chemistry, green IT industry—cloud computing and data mining (Nazir 2021; Wang et al. 2021a). Many countries in Europe are coming up with an economic stimulus that aims to produce in a climate-friendly manner and increase the sorting and recycling of basic materials like aluminum, cement, and plastic (Chiappinelli et al. 2021). The production of green electricity is possible by the government creating partnerships (public or private) to provide the necessary infrastructure using basic-recycled materials. By 2025, the aim is to get 20% of basic materials in recycled form in Europe via low-emission and recycling processes (Chiappinelli et al. 2021). In order to increase green consumption among the public, it is essential to develop green products like green detergents, ecological paper products, energy-saving laptops and mobile phones, and electric vehicles. So here, the quality and the design of the product produced are very important. More and more industries can enhance their greener production and packaging capabilities using recycled materials to give a greener finish and label to the product while having minimal economic value. The focus must be to motivate the customers to buy more and more green products (Testa et al. 2021). According to Kansara et al. (2021), developing countries like China focus more on the knowledge-intensive industries that utilize GTI. In some cases, the companies may focus on R&D to create innovations by taking certain risks due to more industrial competition, leading to reduced considerations toward GTI. So, it will be better to reduce their risk-taking tendencies and increase GTI activity (Yan et al. 2021). If GT is effectively used, the GT Innovation Efficiency (GTIE) will increase, leading the global value chain in a positive direction, one of the ways to increase sustainable economic growth (Kansara et al. 2021). Next, it is very important to mention supply chain sustainability. Here the interest in green investment is always more from the retailer side than the supplier. For example, suppose we observe the case of battery suppliers. In that case, the electric car manufacturing companies will tell them to produce innovative green batteries, leading to reduced production costs and green battery technology. This will pressure suppliers to invest more in the production cost. Eventually, the manufacturing companies take the benefits. This later demotivates suppliers toward green investment (Wang et al. 2021a). Hence, it is necessary to maintain a good balance between the retailer and the supplier at the time of green investment (Wang et al. 2021a). The GT transfer and foreign investments significantly affect countries’ economic growth with green technological innovation for developing countries. There must be a good distribution of this technology to regional areas. Also, the crowding-out effect can create investments in science, technology, and education, improving regional competitiveness (Shen et al. 2021). Lastly, governments can work on increasing green nanoparticle (GNP) technologies. Nanoparticles have many applications and extensive usage in pharmaceuticals, biomedical, bioengineering, electronics, nuclear energy, etc. These green nanoparticles can be synthesized from plants, fungi, bacteria, and food waste products like rice husk to develop many green products—from medicines to developing nanomaterials for solar cells and electric batteries. Nanoremediation can be a good area for green investments and enhancing green technological innovations (Dutta and Das 2021). Sustainability transitions and space The changes in lifestyle and technology toward a greener, eco-friendly, and safe direction have motivated individuals, communities, governments, and the world to effectively transition toward a sustainable way of life. Following are some areas where a transition has been observed. Green housing is a significant way to enhance sustainable living. Using scientometrics and the green housing topic model, sustainable housing could be made into reality! These homes could be built using green materials, which are eco-friendly and durable. A ventilation system, sunlight, indoor greenery, and spacious rooms create an ideal sustainable and safe home (Kaklauskas et al. 2021). Another transition criterion can be green consumption. This entirely depends on an individual’s altruistic and biospheric values, concern for the environment, and green lifestyle orientation (Berman Caggiano et al. 2021). In this case, the tendency of an individual to buy sustainable products and appliances is observed. If they care for the environment, they would prefer to buy eco-friendly appliances. For example, the individual prefers to buy LED lights instead of energy-consuming yellow lightbulbs. Here, the government and the industries developing these products must be entirely responsible for providing the correct green technologies to the consumers (Berman Caggiano et al. 2021). Another sustainable transition can be made at the industrial level, i.e., green composites; as research continued to create strong polymers, many improvements have been observed through cultivating different practices. Physical, chemical, biological, and chemical treatments make these materials/composites stable and durable. The two standout polymers are made from biomass and plant fibers. These both help increase the mechanical properties, facilitate degradability, and are antimicrobial. Plant fibers can be used to make ropes, textiles, mats, curtains, etc., which are eco-friendly and safe products (Vazquez-Nunez et al. 2021). Microalgae biomass is another highly effective and eco-friendly material with a fast growth rate, the ability to produce high oil yield, and is the most effective way for heavy metal pollution (Yap et al. 2021). It is a form of advanced GT with applications in the biopharmaceutical and nutraceutical industries (Yap et al. 2021). GT can be effectively incorporated in manufacturing industries by operating milling, grinding, and other mechanical processes at green parameter settings, as Jibhakate et al. (2021) said. In India, for example, production lines are created while producing goods; this is said to be one of the GT manufacturing practices and is known as a flow shop. By following these green parameters and practices, energy savings can significantly increase (Jibhakate et al. 2021). The extraction of rare earth metals like bastnaesite was done using HCl leaching, calcification roasting, and gravity separation. This led to a cleaner alternative to extracting rare metals. Other countries can utilize this sustainable method for fluorites, chlorites, and more (Cen et al. 2021). This shows how our economy is being shaped by knowledge capital and green innovation. Clean investments increase climate economics and create a resilient, zero emissions, and sustainable economy (Zenghelis 2021). Lastly, green entrepreneurship is the new transition toward a greener business model. It considers three pillars: technology, entrepreneurship, and ecological environment. Figure 4 gives a glimpse of the 12 model archetypes of green entrepreneurship that can be followed for the sustainable development of greentech businesses. These archetypes can provide a basis for many GT start-up companies to follow each model’s policies and put them into good practice. This can help create efficient and sustainable economies (Fig. 4) (Trapp and Kanbach 2021).Fig. 4 The model archetypes for a GT business. This includes energy efficinnovator, efficient energizer, energy efficretor, material efficinnovator, efficiency material enhancer, material efficretor, recyclinnovator, recyclenhancer, GT, greenhanced substituter, and greentech substituter Transitions, ecological modernization, and degrowth When the world is experiencing climate change, some significant transitions need to occur. We need to get back to the drawing board and formulate a new concept of growth, which is eco-friendly, green innovative, and sustainable. The points above have given us detailed information on the steps taken by the governments, industries, businesses, and individuals to save the environment since everybody has felt climate change and experienced it. Following are the practical transitions that will come into play like the green-based active packaging of goods, urban resilience, rhizoremediation, the new concepts of analyzing the sustainable, circular economy, and how it can be improved. Some of the concepts included here are emergy analysis, industrial ecology, ecological modernization, and green artificial intelligence (GAI). As mentioned earlier, many industries focus on biodegradable packaging of goods, where green-based active packaging arises. After the COVID-19 pandemic, there has come a renewed opportunity to develop biodegradable packaging for food applications and help maintain a circular economy (Barone et al. 2021). These packages provide antioxidant, antimicrobial, and aromatic properties, which improve food quality and give a hygienic and safe food delivery, benefitting the customers. Green technological advancements have helped create these biodegradable packages using flexible biopolymers like plant fibers, biomass (starch and cellulose), and polyphenols (Barone et al. 2021). Urban resilience is the ability of a particular urban area to maintain its socio-ecological and socio-technical aspects across temporal or spatial considerations (Moglia et al. 2021). Here, the urban economy can be recovered by following the three urban missions—to attain urban mobility change, regenerative development, and create green infrastructure. A pathway series can create a process to build resilience and a green recovery in cities (Moglia et al. 2021). Next, rhizoremediation is a green and sustainable technique to treat or remediate contaminated soils using the symbiotic relationship between the plants and soil microorganisms. As per Hoang et al. (2021), this physiochemical technique is ineffective in treating soils contaminated with petroleum hydrocarbons (TPH). It requires biostimulation and bioaugmentation, i.e., mobilizing the soil microbes or rhizosphere microorganisms and increasing their catabolic activities, which helps in the effective removal of contaminants (Hoang et al. 2021). When it comes to the concepts, the first one is emergy analysis. It is a measuring tool for economic, social, and environmental performance. It can help analyze sustainable supply chain management and circular economy (Alkhuzaim et al. 2021). This consists of a donor-side evaluation by considering the life cycle, sustainable performance assessments, and material flow analysis, i.e., how a particular institution does waste management and cost analysis. This can be a handy tool for researchers who wish to understand how sustainable production, performance, and consumption of GT are done by an institution (Alkhuzaim et al. 2021). The second concept, industry ecology (IE), is a bottom-up research approach that analyzes ecological, environmental, economic, and system dynamics. It solves industrial problems (Han et al. 2021). The third concept, ecological modernization (EM), is a European concept based on the top–bottom approach, which begins from the national level to results in environmental and social challenges. Sociologists mostly do this research (Han et al. 2021). Most of the published research papers focus on IE studies rather than EM since IE gives the scenario an overall image. At the same time, the EM still needs proper implementation. However, integrating IE and EM can clearly understand the ecological transition toward sustainable development and research (Han et al. 2021). Lastly, GAI provides holistic views on how AI can be used for smart cities in the fourth concept. Green sensing can help solve current urbanization issues and enable Industry 4.0 and smart cities toward sustainable solutions (Yigitcanlar et al. 2021). Conclusion The review discusses the connection between IR 4.0, conventional green processes, and the green economy. In the present scenario, GTs are an extraordinary challenge and lead toward the chance for new industries and provide competition among industries in the niche of the environment. Regarding the effect of the manufacturing and industrial endeavors on the environment, the green innovations and cycles are connected to Industry 4.0. They are a significant wellspring of sustainability for the future, as they bring together the social, economic, and environmental elements. The vital commitment of this work for information and exploration is the arrangement that gives a characterization and structure to supportability the results of green cycles in the Industry 4.0 period. The sustainable outcomes of various GTs in the context of the Fourth Industrial Revolution have been brought into focus in this article while discussing global warming issues, the application of environmental biotechnology for a sustainable future, green chemical engineering, interactions of pollutants in the environment, product engineering to develop advanced biodegradable materials such as nanocellulose, biochar, starch, circular economy, and last but not the least the main goals of IR 4.0. There is also in-depth detailed information on the environmental economies that can help the governments to undergo green recovery most efficiently post COVID-19 and help maintain the reduced emissions of CO2 and GHG. The vital enterprise processes should be advancing green design and manufacturing and the green supply chain and logistics. It is the proper and hypothetical groundwork for the experimental check of the interconnection between these ideas. The world’s governments can use the Green New Deal goals and strategies to create an eco-friendly transition using bio-based materials, hydroponics, and sustainable forest management. Technological developments that merged the Fourth Industrial Revolution and sustainability emphasized GTI and the IoT. It is in great demand today because of its multiplier effects that are beneficial in revolutionizing chemical engineering, product engineering, and the environmental industry. More products from advanced biodegradable materials need to be developed to attain a sustainable environment for further headings of future studies. Fundamentally, the impediments of this study are considered in the ensuing investigations of the adverse consequence of the sustainability results. As inventors should know, green inventions and clean technologies are beneficial for business. These are profitable markets that are rapidly expanding. Green technologies can help consumers save money on energy expenses and are frequently safer and healthier than their non-green equivalents. Acknowledgements The authors thank the VIT, Vellore, Tamil Nadu, India, for supporting this work. Author contribution Conceptualization: K.R., B.V., and A.V.G.; resources and data curation: P.B., A.B., C.N., S.S., M.P., S.K., U.R.W., and A.G.M.; writing—original draft preparation: P.B., A.B., C.N., S.S., M.P., S.K., A.G.M., and U.R.W.; writing—review and editing: K.R., A.G.M., and U.R.W.; visualization: A.V.G. and K.R.; supervision: A.V.G. and K.R.; project administration: A.V.G., K.R., and B.V. All authors have read and agreed to the published version of the manuscript. Funding This work was supported by the ICMR National Task Force Project [F.No. 5/7/482/2010-RBMH&CH]. Data availability The articles analyzed during the current study are available in the literature and listed in the references. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Pragya Bradu, Anirban Goutam Mukherjee and Uddesh Ramesh Wanjari have equal contribution for first authors. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 35426020 20094 10.1007/s11356-022-20094-4 Research Article Have international remittance inflows degraded environmental quality? A carbon emission mitigation analysis for Ghana Li Kaodui 123 Wang Xiangmiao 1 Musah Mohammed [email protected] [email protected] 4 Ning Yi 1 Murshed Muntasir [email protected] 56 Alfred Morrison 7 Gong Zhen 1 Xu Han 1 Yu Xinyi 1 Yang Xue 1 Shao Keying 1 Wang Li 1 1 grid.440785.a 0000 0001 0743 511X School of Finance and Economics, Jiangsu University, Zhenjiang, People’s Republic of China 2 grid.64938.30 0000 0000 9558 9911 College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, People’s Republic of China 3 grid.440785.a 0000 0001 0743 511X Division of State-Owned Enterprise Reform and Innovation, Institute of Industrial Economics, Jiangsu University, Zhenjiang, People’s Republic of China 4 Department of Accounting, Banking, and Finance, School of Business, Ghana Communication Technology University, Accra, Ghana 5 grid.443020.1 0000 0001 2295 3329 School of Business and Economics, North South University, Dhaka-1229, Bangladesh 6 grid.442989.a 0000 0001 2226 6721 Department of Journalism, Media and Communications, Daffodil International University, Dhaka, Bangladesh 7 Department of Accounting Studies Education, Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development, Kumasi, Ghana Responsible Editor: Ilhan Ozturk 14 4 2022 2022 29 40 6035460370 22 1 2022 1 4 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Despite the considerable contributions of remittances to households and economic advancements, their environmental implications have received little attention in empirical research. This study was, therefore, conducted to help fill that gap, using Ghana as an evidence. In achieving the above goal, robust econometric methods that control for endogeneity, heteroscedasticity and serial correlation among others, were engaged for the analysis. From the results, the studied variables were first-differenced stationary and cointegrated in the long run. The elasticities of the predictors were explored via the FMOLS, DOLS and CCR estimators, and from the results, remittance inflows worsened the ecological quality in Ghana through high CO2 emissions. Also, population growth and energy utilization were not friendly to the country’s environment; however, technological innovations improved environmental quality in the nation via low CO2 effusions. The VECM was employed to examine the path of causalities amidst the series, and from the results, there were bidirectional causalities between remittance inflows and CO2 emissions and between population growth and CO2 emanations. Also, a causation from energy utilization to CO2 effluents was discovered; however, there was no causality between technological innovations and CO2 exudates in the country. Based on the findings, it was recommended among others that, authorities should enact regulations to control the activities of polluting industries that are being financed by remittances. Also, households and individuals should minimize their use of remittances to finance carbon-intensive items, like automobiles and air-conditioners among others, that add to environmental pollution in the country. Keywords Remittance inflows Environmental quality Technological innovations Population growth Energy consumption CO2 emissions issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2022 ==== Body pmcIntroduction The international community is currently facing a serious problem in the form of environmental pollution (Musah et al. 2020a; Zhao et al. 2022). Carbon dioxide (CO2) emissions, which represent a large portion of greenhouse gas (GHG) effusions, have been identified as a key agent of ecological pollution (Rej et al. 2022; Musah et al. 2020b; Murshed 2022). According to the International Energy Agency (IEA), global emissions in the year 2020 rose by 2% or 60 million tonnes, as compared to the same month the previous year (Musah et al. 2020c). To the body, recovery of the major economies after the COVID 19 pandemic is responsible for this rise, since the execution of economic activities in various economies is heavily reliant on carbon-intensive energies that result in high pollution. The lack of significant policy measures to help promote green activities in various jurisdictions could also be a factor for the surge in global emissions (Söderholm 2020; Levin et al. 2019). Due to the galloping rate of emissions and its adverse consequences on the environment, numerous investigations on the determinants of environmental quality (EQ) have been conducted in different settings. For example, Murshed et al. (2022), Ma et al. (2021), Rehman et al. (2021), Liu et al. (2021), Murshed et al. (2021a) and Rej et al. (2021) confirmed energy utilization as a material determinant of EQ, while Ye et al. (2021), Zeraibi (2021), Ahmed et al. (2021) and Ibrahim and Vo (2021), affirmed financial development as a significant determinant of CO2 emanations. Of late, explorations on remittances and environmental quality (EQ) are also gaining popularity, due to the rising levels of remittances in various economies. According to the World Bank, global remittances increased by 10% to US$689 billion in 2018, with US$528 billion going to developing nations. The body also predicted overall global remittances to rise by 3.7% to US$715 billion, with US$549 billion going to developing countries. The rise in remittances has two main implications on the environment. According to Khan et al. (2020), Qingquan et al. (2020), Yang et al. (2021b) and Jamil et al. (2021), if remittance inflows (RI) are used to finance high-polluting industries and carbon-intensive items, like automobiles and air conditioners among authors, then remittances would have a detrimental effect on EQ, due to high CO2 effusions. Contrastingly, if the influxes of remittances are used to finance green energy and ecologically harmless technologies, then, remittances would have a beneficial influence on EQ via low CO2 secretions (Usama et al. 2020; Zafar et al. 2021; Wang et al. 2021). In the SDGs of the United Nations, remittances support the attainment of 15 of the goals. For instance, by improving the living standards of households, remittances help to attain SDG 1. Also, SDG 2 will be achieved if remittances are used to reduce hunger, promote lasting agriculture, improve nutrition and ensure food security. Moreover, SDG 3 will be attained if remittances are used to promote healthy lives and the well-being of individuals of all ages. Furthermore, remittances facilitate the attainment of SDG 4 if they are used to boost lifelong learning opportunities and promote inclusive and equitable quality education. Also, if remittances are used to empower women and girls and promote gender equality in our society, the motive of SDG 5 will be accomplished. Additionally, SDG 6 will be attained if remittances are used to support sustainable water and sanitation in various economies. Moreover, if remittances are used to promote access to modern and sustainable energy, then SDG 7 will be achieved. Furthermore, SDG 8 will be accomplished if remittances are used to support investments in activities that promote sustainable economic growth. Also, if remittances are used to back sustainable industrialization and eco-friendly innovations, then SDG 9 will be attained. Moreover, the aim of SDG 10 will be met if remittances help to eliminate intra-and inter-country inequalities. Additionally, SDG 11 will be attained if remittances are used to finance activities that promote sustainable cities and societies. Besides, if remittances are used to fund activities that support sustainable production and consumption patterns, then SDG 12 will be accomplished. Also, SDG 13 will be attained if remittances are used to support activities that minimize climate change and its adversities. Furthermore, the target of SDG 14 will be met if remittances are used to protect and sustainably utilize oceans, seas and marine resources for long-lasting development. Moreover, SDG 15 will be attained if remittances are used to fund initiatives that help to battle desertification, maintain forests and terrestrial ecosystems and prevent land pollution. Finally, if remittances are used to support peaceful and inclusive societies for long-term development, SDG 16 will be accomplished. Though remittances are key drivers of the majority of the SDGs, numerous explorations have identified them as a potential source of environmental degradation. For example, Usman and Jahanger’s (2021) study on 93 countries, Yang et al.’s (2021b) research on 97 nations, Khan et al.’s (2020) analysis on BRICS economies, Jamil et al.’s (2021) exploration on G-20 economies, Qingquan et al.’s (2020) research on Australia, Yang et al.’s (2021a) study on BICS economies, Neog and Yadava (2020) investigation on India and Kibria (2021) study on Bangladesh among others, all confirmed remittances as harmful to EQ. Despite the countless explorations on remittances and EQ, there has been no study on the linkage amidst the series in Ghana after a thorough review of the literature. Thus, available literature regrettably offers minimal insights on the nexus between remittances and EQ in Ghana. However, considering the nation’s pivotal position in combating global GHG emissions (which have been predicted to increase by another 50% by 2050 relative to 2010 (OECD, 2012)), environmental threats that the country faces with respect to the influxes of remittances warrant further scrutiny. This motivated the conduct of the study. Ghana is a very dynamic nation in Africa and has many nationals abroad who send money and other items to their relatives periodically. The remittances received by the households may be channelled to high-polluting items that could weaken ecological quality in the country. In other words, more remittances mean more aggregate consumption in the economy which ultimately puts pressure on energy demands and therefore, more CO2 effusions. Ghana is one of the highest emitters of CO2 in Africa. According to the World Bank, Ghana’s CO2 emissions grew 35.7% from 0.221 metric tons in 1960 to 0.30 metric tons in 1970. Also, the nation’s CO2 emanations were 0.232 metric tons in 1980, representing a 4.9% rise from the 1960 figure. Similarly, the CO2 effluents of Ghana surged by 34.8% between 1960 and 2000 and by 103.6% between 1960 and 2010. Finally, CO2 exudates of the country rose by 144.8% from 1960 to 2018. Looking at the above statistics, we assumed that remittances could be among the factors responsible for the surging rate of emissions in the country. Therefore, studying the linkage between remittances and EQ to help raise policy options to improve EQ in the country and to help reduce global GHG effusions to net-zero by 2050, was deemed essential. Our exploration revolves around these questions: (1) what is the effect of remittances on EQ in Ghana? (2) What causal relationship exists between remittances and EQ in Ghana? Answers to these questions would help raise vital policy options that could promote the nation’s EQ. The contributions of this study are three folds. First, after a thorough review of literature, this study is a groundbreaking study that investigated the connection amidst remittance inflows and Ghana’s EQ. Prior explorations like Lin and Agyemang (2019), Kwakwa (2019), Abokyi et al. (2021), Asumadu-Sarkodie and Owusu (2016), Kingsley et al. (2017) and Yakubu et al. (2021) are limited since they centred on the linkage between other macroeconomic variables and Ghana’s EQ, but not between remittances and EQ of the country. Secondly, most prior studies on the connection amidst remittances and EQ only estimated the elasticities of the predictors without touching on the causalities amidst the series (for example, Neog and Yadava 2020; Zafar et al. 2021; Usman and Jahanger 2021 and Yang et al. 2020). However, to Li et al. (2020a, 2020b) and Musah et al. (2021b, 2021c), if variables are flanked by a long-term relationship, it does not guarantee that the series cause each other. The Engle and Granger (1987) VECM causality test were, therefore, adopted to explore the causations between the variables. Finally, the study applied advanced time series econometric methods (thus the FMOLS, DOLS and CCR estimators), that control for endogeneity, heteroscedasticity and serial correlation in its analysis. The above issues were considered in the choice of the estimators because they can lead to bias and erroneous inferences. Prior explorations like Wang et al. (2021), Kibria (2021), Elbatanony et al. (2021) and Zaman et al. (2021), among others, that studied the remittances and EQ connection in different geographical environments, did not adopt the aforestated methods. In examining the nexus amidst remittances and EQ, a well-outlined econometric process was followed. Firstly, stationarity tests were conducted to examine the integration features of the series. Afterward, a test to examine the variables’ cointegration properties was undertaken. At the third stage, the elasticities of the predictors were estimated, whilst the path of causalities amidst the series was explored at the final stage. Our exploration is unique since it was written from our own point of view. In comparison to previous studies on China, this study is novel because it uncovers fresh information. The study is finally innovative since the processes engaged are thoroughly detailed, and the results are accurately presented and discussed. This study was organized into five sections. “Introduction” presents the study’s introduction, while “Literature review” centres on the review of the literature. “Materials and methods” is on the materials and methods adopted for the study, while “Results and discussions” presents the study’s results and discussions. The conclusions, policy recommendations and study limitations are displayed in “Conclusions and policy recommendations”. Literature review Remittances are monies or goods sent by migrants or foreign workers to their home countries to supplement household income (Vargas-Silva 2018; Grigoryan and Khachatryan 2018; Al-Assaf and Al-Malki 2014). The International Monetary Fund (IMF), which is the main provider of international remittance statistics based on Central Bank data, also defined remittances as the sum of personal transfers and compensation of employees. According to Levitt (1998), the behaviours, ideas, identities and expertise that migrants gain while living in other countries and can be sent back to their home countries also form part of the remittances. As reported by the IMF, low-income countries rely heavily on remittances, which account for over 4% of their GDP, as compared to roughly 1.5% of that of middle-income countries. Because the statistical criteria used to gather data on remittances are broader, global estimates of migrant transfers may include transactions that are not remittances. Also, it is difficult to estimate the exact size of remittances, because many remittances are sent through unofficial means (IMF, 2009). It should be noted, therefore, that the World Bank and the IMF estimates are based on remittances sent via official channels like banks. Not all migrant transactions through transfer operators (like Western Union), mobile money transfers and post offices are included in the remittances of countries, neither are informal transfers like those made through friends, relatives, or transport entities returning to home countries. According to Irving et al. (2010) and World Bank (2011), the above unofficial transfers understate the remittances of nations by 50%. Due to that, some countries particularly in Sub-Saharan Africa, fail to disclose remittance figures in their balance of payments. As reported by Plaza and Ratha (2017), Irving et al. (2010) and the World Bank (2011), data on remittances may vary across nations due to variations in legislative and policy frameworks, simplification of data processes and differences in data availability. The linkage between other macroeconomic factors and EQ have been expansively explored (for instance, Yang et al. 2021a, b; Jahanger et al. 2022; Usman and Makhdum 2021; Usman and Balsalobre-Lorente 2022; Huang et al. 2022; Usman et al. 2022a; Usman et al. 2022b and Usman et al. 2021a, b, among others); however, the connection between remittances and EQ has received minimal attention. On the examples of such studies, Usman and Jahanger (2021) conducted a study on 93 countries from 1990 to 2016. The study employed the panel quantile regression technique for the analysis. From the estimates, RI degraded EQ in the 5th to 70th quantiles but improved EQ in the 80th to 95th quantiles. Yang et al. (2021a, b) studied BICS countries over the period 1990 to 2016. The study’s results found RI as harmful to the nations’ EQ. Rahman et al. (2019) explored the linkage between remittances and EQ in some selected Asian countries from 1982 to 2014 and discovered that remittances spurred ecological pollution in the nations. Yang et al. (2020) explored the linkage between RI and EQ in 97 countries over the period 1990 to 2016. The GMM estimates of the study confirmed RI as a driver of ecological pollution in the nations. Zafar et al. (2021) researched on top 22 remittance-receiving countries from 1986 to 2017 and disclosed RI as friendly to EQ. Wang et al. (2021) studied five-remittance receiving nations from 1980 to 2016. The study’s findings confirmed RI as friendly to the countries’ EQ. Neog and Yadava (2020) conducted a study on India from 1980 to 2014 and discovered that positive shocks in remittances harmed EQ in the nation; however, negative shocks in remittances improve the country’s ecological quality. Azam et al. (2021) studied 30 developing economies over the period 1990 to 2017. The study employed the PVAR technique in its analysis, and from the results, remittances had a detrimental influence on the nations’ EQ. Khan et al. (2020) investigated BRICS countries over the period 1986 to 2016. The FM-LS and the CCEMG estimates of the study affirmed RI as an agent of pollution in the nations. Ahmad et al. (2019) researched on China and disclosed that positive shocks in remittances deteriorated EQ in the nation; however, negative shocks in remittances improved the country’s ecological quality. Jamil et al. (2021) analysed G-20 countries from 1990 to 2019 and disclosed that RI weakened EQ in the nations. In Bangladesh, Kibria (2021) examined the connection between RI and EQ over the period 1980 to 2016. From the NARDL estimates of the study, positive shocks in RI promoted ecological pollution in the country; however, negative shocks in RI were friendly to the nation's EQ. Elbatanony et al. (2021) assessed the environmental impacts of remittances on developing economies from 1980 to 2014. From the results, an N-shape between remittances and pollution was discovered for lower-middle-income countries, whilst a U-shaped curve was disclosed for upper-middle-income countries from the 40th to the 80th quantiles. For the period 1991 to 2019, Deng et al. (2021) studied the dynamic association amidst financial inflows and EQ in BRICS economies. The NARDL-PMG technique was adopted for the analysis, and from the estimates, positive shocks in remittances had a trivial effect on EQ; however, negative shocks in remittances worsened ecological quality in the economies. Li et al. (2021a, b) investigated China from 1981 to 2019 and discovered that negative shocks in remittances promoted the country’s EQ. Sharma et al. (2019) researched on Nepal and identified remittances as harmless to the nation’s EQ. Zaman et al. (2021) investigated nine remittance-receiving nations from 1990 to 2014 and confirmed that remittances promoted economic progress, which is a vital agent of environmental pollution. Khan et al. (2021) researched in the context of the USA and disclosed that remittances waned EQ in the nation. Zhang et al. (2021) assessed the role of remittances in the environment of top remittance-receiving nations from 1990 to 2018. The CUP-FM and the CUP-BC estimators were employed for the analysis, and from the results, remittances had negative implications on the environment of the nations. Brown et al. (2020) studied Jamaica from 1976 to 2014. From the revelations, a long-run cointegration association from remittances to CO2 effusions was disclosed. Also, an asymmetric response of CO2 exudates to variations in remittances was discovered in the short run. Villanthenkodath and Mahalik (2020) analysed the linkage between RI and EQ in India over the period 1980 to 2018. Based on the ARDL estimates of the exploration, an inverted U-shaped association amidst RI and CO2 secretions was uncovered. Jafri et al. (2021) investigated China from 1981 to 2019. The NARDL technique was employed to estimate the coefficients of the predictors. From the results, negative shocks in remittances had a positive influence on CO2 exudates in the country. Wawrzyniak and Doryń (2020) conducted a study on 93 emerging and developing economies for the period 1995 to 2014. From the GMM estimates of the exploration, remittances had a trial effect on EQ. Qingquan et al. (2020) researched on Australia and disclosed that remittances undermined the nation’s ecological quality. In Ethiopia, Usama et al. (2020) studied the connection amidst remittances and EQ and reported that the association between remittances and EQ was negative because most remittance-receiving families in the country shifted to clean electricity consumption. According to Thapa and Acharya (2017), remittances improve the living conditions of households, propelling them to go in for high-energy consuming items, resulting in more CO2 effusions. Also, when the implementation of investments related to remittances results in high energy demand, the level of CO2 effluents could escalate (Elbatanony et al. 2021). Contrastingly, Elbatanony et al. (2021) averred that, if the influxes of remittances are spent on clean energy and ecologically-harmless technologies, the effect of remittances on the effusion of carbon could be negative. Islam (2021a, 2021b) studied the asymmetrical impact of remittances on EQ in the top eight remittance-receiving countries. The study employed the panel GLS and the PMG estimation techniques to explore the coefficients of the covariates. From the results, both positive and negative changes in remittances improved EQ in the countries. Also, a feedback causality from the positive component of remittances to CO2 effusions and a one-way causality from the negative component of remittances to ecological pollution were discovered. Based on the literature reviewed, it can be concluded that the nexus amidst remittances and EQ are conflicting. Whilst studies like Yang et al. (2021a), Qingquan et al. (2020), Jamil et al. (2021) and Yang et al. (2021b), confirmed remittances as detrimental to EQ, others like Usama et al. (2020), Wang et al. (2021), Zafar et al. (2021) and Sharma et al. (2019), found remittances as friendly to EQ. Elbatanony et al. (2021) on the other hand, found an N-shape between remittances and ecological pollution, whilst an inverted U-shape between remittances and environmental pollution was reported in the study of Villanthenkodath and Mahalik (2020). Irrespective of the numerous studies and their contrasting findings, there has been no study on the RI-EQ connection in Ghana to the best of our knowledge. Hence, a study on remittances and EQ nexus in the context of Ghana was deemed appropriate. Materials and methods Data source and summary statistics In examining the nexus amidst RI and EQ, data for the period 1980 to 2020 was used for the analysis. All missing data were filled using the data interpolation and extrapolation approach to avoid the problems associated with the use of unbalanced data. Given this, the data used for the analysis was strongly balanced. All data employed for the study were obtained from WDI (2021). More details on the investigated variables are shown in Table 1. Table 2 displays the summary statistics on the series. From the table, RI had the greatest mean value, whilst CO2 effusions had the least mean value. Also, RI was the most volatile in terms of SD, whilst POP was the least volatile. Moreover, RI and EC had negatively skewed distributions, whilst POP, TI and CO2 effluents had positively skewed distributions. Also, CO2 exudates and EC had heavy tails based on the kurtosis results, whilst RI, TI and POP had thinner tails. On the correlation between the variables, RI, POP and EC were significantly positively related to CO2 effluents. This indicates that, as RI, POP and EC rose, CO2 exudates also rose in the same direction and vice versa. However, the association between TI and CO2 emanations was negative and significant, suggesting that, a surge in TI led to a fall in CO2 effusions and vice versa. From the VIF and tolerance tests outlined in Table 3, there is no collinearity amidst the explanatory variables. For robustness purposes, the Farrar and Glauber (1976) test was also conducted. From the estimates shown in Table 3, the null hypothesis of no multi-collinearity amidst the predictors could not be rejected. Finally, all the variables have significant loadings based on the PCA results shown in Table 4. This implies that all the series were relevant in predicting EQ in Ghana.Table 1 Data description and measurement unit Variable name Measurement Source Environmental quality (CO2 emissions) Metric tons per capita WDI (2021) Remittance inflows (RI) Personal remittances, paid (current US$) WDI (2021) Technological innovation (TI) Patent applications (resident + non-resident) WDI (2021) Population growth (POP) Annual percentage WDI (2021) Energy consumption (EC) Kg of oil equivalent per capita WDI (2021) Table 2 Descriptive statistics and correlational analysis Descriptive statistics Statistic lnCO2 lnRI lnTI lnPOP lnEC Mean − 1.104 12.534 1.011 0.941 4.962 Median − 1.157 15.392 1.484 0.926 5.797 Maximum − 0.572 21.685 3.951 1.114 6.012 Minimum − 1.782 15.501 2.398 0.756 5.584 Std. dev 0.426 7.449 1.446 0.102 2.082 Skewness 0.721 − 0.948 0.781 0.045 − 1.989 Kurtosis 3.151 2.317 1.779 1.919 4.978 Jarque–Bera 3.595 6.941 6.707 2.008 33.713 Probability 0.166 0.331 0.135 0.366 0.643 Correlational analysis Variable lnCO2 lnRI lnTI lnPOP lnEC lnCO2 1.000 lnRI 0.546 1.000 (0.002)* lnTI − 0.585 0.141 1.000 (0.003)* (0.380) lnPOP 0.674 − 0.295 0.371 1.000 (0.004)*** (0.062)* (0.017) lnEC 0.675 − 0.307 0.284 0.739 1.000 (0.005)* (0.051)* (0.072)* (0.004)* Values in parenthesis () represent probabilities while , denote significance at the 1% and the 10% levels, respectively Table 3 Multi-collinearity test results Variable VIF and tolerance tests Farrar and Glauber test VIF Tolerance F-test p-value lnRI 1.21 0.826 3.055 0.004* lnTI 1.26 0.792 5.993 0.026 lnPOP 2.42 0.413 4.097 0.061* lnEC 2.25 0.444 2.877 0.003* Mean VIF 1.79 - - - VIF implies variance inflation factor while , , denote significance at the 1%, 5% and the 10% levels, respectively Table 4 Principal component analysis Component Eigenvalue Difference Proportion Cumulative Comp 1 2.067 0.924 0.517 0.517 Comp 2 1.143 0.607 0.286 0.803 Comp 3 0.536 0.282 0.134 0.937 Comp 4 0.254 - 0.064 1.000 Eigenvectors (loadings) Variable Comp 1 Comp 2 lnRI − 0.308 0.725q lnTI 0.634p 0.028 lnPOP 0.620p − 0.050 lnEC 0.345 0.687q p denotes significant loadings under component 1, while q denotes significant loadings under component 2 Model specification and theoretical underpinning Studies on the predictors of environmental quality (EQ) have been immensely explored in different geographical settings. For instance, Rjoub et al.’s (2021) analysis on Turkey; Baloch et al.’s (2021) investigation on OECD economies; Ye et al.’s (2021) exploration on Malaysia; Murshed et al.’s (2021b) investigation on South Asian Neighbors and Adebayo et al.’s (2021a, b) study on South Korea, among others, all examine the connection between macroeconomic factors and EQ. However, to the best of the researchers’ knowledge, there have been limited explorations on the remittance inflows (RI) and EQ connections in Ghana. Hence, the conduct of this study. In attaining the above objective, the following function was proposed:1 CO2t=f(RIt,TIt,POPt,ECt) where CO2 emissions are the explained variable representing EQ, while remittance inflows (RI) is the main explanatory variable. Technological innovation (TI), population growth (POP) and energy consumption (EC) were included in the function as control variables to help minimize the consequences of omitted variable bias. The above equation was expressed in a linear form as:2 CO2t=α0+β1RIt+β2TIt+β3POPt+β4ECt+μt where β1,β2,β3and β4 are the parameters of RI,TI,POP and EC respectively, while the timeframe is epitomized by t. Also, the constant term is represented by α0, while the error term with a mean of zero and variation of σ2 is symbolized by μt. Natural logarithm was taken on both sides of Eq. (2), like those of Sun et al. (2021), Usman et al. (2021a, b), Musah et al. (2021a and Ruzi et al. (2021) to help reduce the issue of heteroscedasticity. The resulting specification therefore became:3 lnCO2t=α0+β1lnRIt+β2lnTIt+β3lnPOPt+β4lnECt+μt where lnCO2,lnRI,lnTI,lnPOP and lnEC are the log conversions of the input and the output variables, respectively. All other items in the above equation remain as already defined. Following Usman and Jahanger (2021) and Yang et al. (2021a, b), RI was incorporated into the function as a determinant of EQ. The parameter of RI was to be positive β1=∂lnCO2t∂lnRIt>0, if consumers used remittances to fund activities that could worsen ecological quality in the country (Khan et al. 2020; Jiang et al. 2020). Otherwise, the coefficient of the variable was to be negative β1=∂lnCO2t∂lnRIt<0, if consumers channelled remittances into environment friendly activities in the nation (Neog and Yadava 2020; Sharma et al. 2019). Following Ahmad et al. (2020) and Chaudhry et al. (2021), TI was introduced into the analysis as a predictor of EQ. The parameter of TI was to be positive β2=∂lnCO2t∂lnTIt>0, if technology promoted activities that are high-polluting in the country (Villanthenkodath and Mahalik, 2020; Khattak et al. 2020). Otherwise, the coefficient of the variable was to be negative β2=∂lnCO2t∂lnTIt<0, if technology stimulated activities that are beneficial to the nation’s EQ (Ahmed and Ozturk 2018; Aldakhil et al. 2019). Toeing the line of Ruzi et al. (2021) and Guoyan et al. (2021), POP was introduced into the model as a determinant of EQ. The parameter of POP was to be positive β3=∂lnCO2t∂lnPOPt>0, if POP accelerated the development of residential and industrial facilities that are carbon-intensive (Lin et al. 2021; Xie et al. 2020). Otherwise, the coefficient of the variable was to be negative β3=∂lnCO2t∂lnPOPt<0, if POP helped to improve ecologically harmless activities in the nation (Fonchamnyo Fonchamnyo et al. 2021; Rahman and Vu 2021). Following Khurshid et al. (2021) and Ahmad et al. (2021), EC was finally introduced into the framework as a predictor of EQ. The parameter of EC was to be positive β4=∂lnCO2t∂lnECt>0, if the energy used to drive economic activities of the country were from dirty sources leading to more pollution (Ali et al. 2021; Rahman and Vu 2021). Otherwise, the coefficient of the variable was to be negative β4=∂lnCO2t∂lnECt<0, if the energy used to propel the economic activities were from green sources that could advance ecological quality in the nation (Qayyum et al. 2021; Iqbal et al. 2021). Econometric strategy At the first stage of the analysis, the variables’ integration orders were assessed via the ERS, PP, ADF and the KPSS stationarity tests. Secondly, the Johansen cointegration test was conducted to examine the variables’ cointegration properties. The rule of thumb for this test is that, if there is one or more cointegration equation(s), then, the variables are materially related in the long run (Johansen 1991). This test is advantageous because it can detect multiple cointegrating vectors. As such, it is more fitting for multivariate analysis than other approaches. According to Wassell and Saunders (2008), Johansen’s test is superior, in that, it treats every test variable as an endogenous variable. This test is made up of the maximum Eigenvalue test and the trace test. The hypothesis of the trace test is stated as:4 H0:K=K0 5 H0:K>K0 where K0 is set to zero to examine if the null hypothesis will not be validated, and if not validated, then, cointegration exists amidst the series. The maximum eigenvalue test hypothesis on the other hand is stated as:6 H0:K=K0 7 H0:K>K0+1 Afterward, following Zhao et al. (2022), the FMOLS, DOLS and CCR estimators of Phillips and Moon (1999), Stock and Watson (1993) and Park (1992) respectively, were employed to estimate the elasticities of the covariates. The FMOLS and DOLS techniques are advantageous because they control for heteroscedasticity (Kiefer and Vogelsang, 2005) and endogeneity (Funk and Strauss 2000) in regression analysis. According to Sulaiman and Abdul-Rahim (2018), the FMOLS and DOLS estimators help to solve the problem of serial correlation and small sample bias linked to the OLS approach. To Sulaiman and Abdul-Rahim (2018), if variables are flanked by a mixed order of integration, the estimators can still be used. In line with Pedroni (2001), the following FMOLS model was specified for estimation:8 lnCO2t=α0+β1RIt+∑k=-KiKiγikΔRIt-k+β2TIt+∑k=-KiKiγikΔTIt-k+β3POPt+∑k=-KiKiγikΔPOPt-k+β4ECt+∑k=-KiKiγikΔECt-k+μt where lnCO2t, RIt, TIt, POPt and ECt are cointegrated with slope parameters β1,β2,β3and β4. Following Kao and Chiang (2001), the estimated DOLS model of the study was specified as:9 lnCO2t=α0+β1RIt+β2TIt+β3POPt+β4ECt+∑i=-1i=1ψiΔRIt+∑i=-mi=mβiTIt+∑i=-ni=nφiΔPOPt+∑i=-oi=oϕiΔECt+μt where i, m, n and o are the leads and lags to control for endogeneity and serial correlation. To Montalvo (1995), the CCR estimator of Park (1992) is better than other econometric techniques, like the FMOLS and the OLS because it exhibits less bias. However, if the long-run variance is not consistently estimated due to non-stationarity in errors inherited from misspecified orders, the CCR approach could be problematic (Nam 2021). Because regression does not comment on the causal connections amidst series (Qin et al. 2021), the VECM of Engle and Granger (1987), which offers consistent and reliable outcomes in time series analysis, was finally adopted to explore the causal directions amidst the series. In attaining this aim, the ensuing error correction models were specified:10 ΔlnCO2t=ω1+∑j=1qφ1,1jΔlnCO2t-j+∑j=1qφ1,2jΔlnRIt-j+∑j=1qφ1,3jΔlnTIt-j+∑j=1qφ1,4jΔlnPOPt-j+∑j=1qφ1,5jΔlnECt-j+∅1ECTt-1+μ1t 11 ΔlnRIt=ω1+∑j=1qφ1,1jΔlnRIt-j+∑j=1qφ1,2jΔlnCO2t-j+∑j=1qφ1,3jΔlnTIt-j+∑j=1qφ1,4jΔlnPOPt-j+∑j=1qφ1,5jΔlnECt-j+∅1ECTt-1+μ1t 12 ΔlnTIt=ω1+∑j=1qφ1,1jΔlnTIt-j+∑j=1qφ1,2jΔlnRIt-j+∑j=1qφ1,3jΔlnCO2t-j+∑j=1qφ1,4jΔlnPOPt-j+∑j=1qφ1,5jΔlnECt-j+∅1ECTt-1+μ1t 13 ΔlnPOPt=ω1+∑j=1qφ1,1jΔlnPOPt-j+∑j=1qφ1,2jΔlnTIt-j+∑j=1qφ1,3jΔlnRIt-j+∑j=1qφ1,4jΔlnCO2t-j+∑j=1qφ1,5jΔlnECt-j+∅1ECTt-1+μ1t 14 ΔlnECt=ω1+∑j=1qφ1,1jΔlnECt-j+∑j=1qφ1,2jΔlnPOPt-j+∑j=1qφ1,3jΔlnTIt-j+∑j=1qφ1,4jΔlnRIt-j+∑j=1qφ1,5jΔlnCO2t-j+∅1ECTt-1+μ1t From the models above, ω is the constant term, while φ symbolizes the coefficients to be computed. Also, ECTt-1 is the error correction term with ∅ being the error correction coefficient that captures the speed of adjustment towards the equilibrium. Additionally, the difference operator is epitomized by Δ, while q is the optimal lags that are selected via the AIC. Finally, the stochastic error term which is serially uncorrelated with a mean of zero is represented by μ, while the timeframe is denoted by t. Results and discussions Unit root and cointegration test results Some econometric techniques require variables to be stationary before they could yield valid outcomes. If not, there is the possibility that one will be dealing with explosive series that do not exhibit mean reversion. Therefore, as a first step, the variables’ integration order is assessed via the unit root tests indicated in Table 5. From the results, the variables had an I(1) order of integration. Thus, the null hypothesis of no unit root amidst the residual terms could not be rejected after the first difference. This suggests that the statistical characteristics of the series were unchanged by shifts in time. This finding aligns with that of Musah et al. (2021d), Chen et al. (2022), Li et al. (2021a, b) and Phale et al. (2021). It was also important to establish the potential association between the variables in the long term because detrending could not guarantee that there was no spurious correlation between the variables of concern. Therefore, the Johansen cointegration test was performed to examine the cointegration attributes of the series. From the discoveries of the text displayed in Table 6, the null hypothesis of no cointegration amidst the series could not be validated. This suggests that the variables were materially affiliated in the long term. Thus, the variables were so intertwined that they could not deviate from equilibrium in the long run. The finding validates the works of Musah et al. (2022b) and Li et al. (2020a, 2020b).Table 5 Unit root test results Variable ERS PP KPSS ADF Level 1st diff Level 1st diff Level 1st diff Level 1st diff lnCO2 1.202 6.241* 2.490 − 4.343 3.499 6.320* 2.727 − 5.343* lnRI 4.118 5.261* − 1.142 − 2.231* 0.663 5.311* − 1.165 − 4.223* lnTI 5.763 6.437*** − 0.406 3.022* 0.156 3.337 − 2.406 0.022 lnPOP 2.338 4.026 1.951 − 2.399 0.509 1.466* 0.938 − 2.310 lnEC 3.553 5.206* − 0.297 − 6.233* 0.441 2.256 − 0.297 − 6.233* ERS represents Elliott, Rothenberg and Stock (1996) test, PP represents Phillip and Perron (1988) test, ADF signifies Augmented Dickey and Fuller (1979) test and KPSS denotes Kwiatkowski-Phillips-Schmidt-Shin (1992) test Also, , , denote significance at the 1%, 5% and the 10% levels, respectively Table 6 Johansen cointegration test results Unrestricted cointegration rank test (trace) Hypothesized Eigenvalue Trace statistic 5% critical value Prob.** No. of CE(s) None * 0.890 158.795 69.819 0.001 At most 1* 0.633 79.332 47.856 0.003 At most 2* 0.497 43.285 29.797 0.002 At most 3* 0.325 18.533 15.495 0.017 At most 4* 0.115 4.397 3.841 0.036 Unrestricted cointegration rank test (maximum eigenvalue) Hypothesized Eigenvalue Max-eigen statistic 5% critical value Prob.** No. of CE(s) None* 0.890 79.464 33.877 0.002 At most 1* 0.633 36.047 27.584 0.005 At most 2* 0.497 24.752 21.131 0.015 At most 3 0.325 14.136 14.265 0.052 At most 4* 0.115 4.397 3.841 0.036 The trace tests indicate 5 cointegrating equations at the 0.05 level, whilst the max-eigenvalue test indicates 4 cointegrating equations at the 0.05 level Also, * denotes rejection of the null hypothesis at the 0.05 level, while represents the MacKinnon-Haug-Michelis (1999) p-values Regression and causality test results Having confirmed cointegration association amidst the series, the coefficients of the predictors were explored via the FMOLS, DOLS and CCR econometric techniques. From the estimates indicated in Table 7, RI worsens EQ in Ghana through CO2 mitigations. All other factors held constant, a 1% rise in RI deteriorated EQ by 0.096%, 0.106% and 0.079% based on results of the three estimators correspondingly. This finding suggests that the influxes of remittances were not channelled to environment friendly activities in the country. The discovery also implies remittances did not stimulate investments in activities that could boost EQ in the country. Moreover, remittances influenced people to buy more polluting home appliances and automobiles worsened EQ in the nation. Besides, the utilization of remittances through market regulations did not help to improve the country’s quality of the environment. The finding also suggests that remittances deteriorated ecological quality in the nation by escalating household consumption and savings that promote energy utilization and financial sector development, which are key agents of pollution. Additionally, remittances worsened environmental sustainability in Ghana, because they stimulated the aggregate demand for industrial productions, which are linked to the consumption of dirty energies like fossil fuels that are ecologically damaging. Most often, high polluting foreign industries make their way into countries with many resources, but with weak environmental regulations. Ghana is one of the culprits in that, it has weak ecological measures and will therefore advocate for such industries to be established there. The positive association between RI and EQ aligns with that of Khan et al. (2020) and Jiang et al. (2020) but contrasts that of Usama et al. (2020) and Neog and Yadava (2020).Table 7 FMOLS, DOLS and CCR estimation results Variable FMOLS DOLS CCR Coeff t-Statistic Prob Coeff t-Statistic Prob Coeff t-Statistic Prob lnRI 0.096 3.627 0.003* 0.106 2.906 0.002* 0.079 1.944 0.001* lnTI − 0.076 − 2.418 0.021** − 0.067 − 1.743 0.089* − 0.087 − 2.256 0.034 lnPOP 1.920 3.331 0.002* 2.039 3.033 0.005* 1.965 4.128 0.002* lnEC 0.119 4.039 0.003* 0.096 2.713 0.018 0.121 4.458 0.001* Constant 1.506 3.526 0.012* 1.444 2.817 0.001* 1.551 4.223 0.002* R2 0.778 0.779 0.772 Adjusted R2 0.753 0.755 0.746 Observation 41 41 41 lnCO2 is the response variable, std. error stands for standard error and the long-run variance estimate (Bartlett Kernel, Newey-West fixed bandwidth) of the FMOLS, DOLS and CCR estimators = 4.0000 Finally, *, , * denote significance at the 1%, 5% and the 10% levels, respectively Also, TI mitigated ecological pollution in Ghana. Specifically, a 1% surge in TI improved EQ in the country by 0.076%, 0.067% and 0.087% based on the results of the three estimators, respectively. This finding means TI played an essential role in mitigating environmental pollution in the nation. Thus, promoting TI was an essential means through which ecological quality in the nation could be improved. The negative connection between TI and environmental pollution supports that of Adebayo et al. (2021a, b) who indicated that innovation paired with ecological conservation activities help to minimize the emanation of carbon. According to the authors, innovation is required for the efficient utilization and development of clean energies. Besides, advancements in technology help to boost the potential of green energies, making them available to meet energy demand (Adebayo et al. 2021a, b). The above assertion agrees with Sohag et al. (2015) and Yang et al. (2021a, b) who postulated that innovation propels the implementation of clean energies to meet energy demand and shift energy utilization from conventional to modern sources. The finding also aligns with those of Chen and Lee (2020) and Yu and Du (2019) who indicated that technology is one of the authentic means to mitigate environmental pollution and boost economic viability in nations. The finding further supports the assertion of Qayyum et al. (2021) that technology improves the efficiency of energy leading to CO2 abatement. As a result, authorities should invest immensely in technology to help attain minimal environmental pollution. The study’s finding is contrasting the conclusion of Raiser et al. (2017) that technology surrogated by patents aims to minimize sustainable development, and is therefore considered as a barrier to climate change abatement. Studies by Khattak et al. (2020) and Villanthenkodath and Mahalik (2020) are also conflicting with the above discovery. Additionally, POP degraded ecological quality in Ghana. Ceteris paribus, a percentage surge in POP degraded EQ by 1.92%, 2.039% and 1.965% based on the results of the three estimators correspondingly. This suggests that POP played an incremental role in worsening ecological quality in the country. With the surge in POP, individuals consumed more carbon-related resources to meet their overall demands and therefore generated more CO2 exudates in the country. Also, the upsurge in POP led to a rise in high-polluting production activities escalating the emissivities of carbon in the country. Thus, higher levels of POP had severe impacts on the emanation of carbon and posed a threat to the health of people in the nation. The Global Footprint Network (2019) estimates that 86% of the globe’s population lives in an ecological deficit. As a result, the increased population puts strain on the environment by increasing carbon effusions. The detrimental influence of POP on EQ supports the works of Ruzi et al. (2021) and Guoyan et al. (2021) but deviates from that of Lv and Xu (2018) and Fonchamnyo et al. (2021). Moreover, EC deteriorated Ghana’s EQ. Specifically, a percentage rise in energy use promoted carbon exudates by 0.119%, 0.096% and 0.121% based on the results of the three estimators, respectively. These findings suggest that the energy used to drive economic activities in the country was not from clean sources that could help stimulate ecological quality in the nation. It is, therefore, critical for the country to adopt renewable energy technologies to help improve its EQ. The finding aligns with those of Musah et al. (2022a) and Musah et al. (2021d) but contrasts those of Xue et al. (2021) and Sharma et al. (2020). Lastly, the adjusted R-squared values of 0.753, 0.755 and 0.746 for the three estimators imply 75.3%, 75.5% and 74.6% of the variances in the response variable were accounted for by the predictors. Comparatively, the coefficients under the three estimators in terms of the sign were the same. This justifies the robustness of the results. At the last phase of the analysis, the causations amidst the series were determined via the Engle and Granger (1987) VECM approach. As depicted in Table 8, all the ECTs are substantially negative, implying that, a long-term association exists amidst the variables. Empirical explorations by Islam (2021a, 2021b) and Quacoe et al. (2021) are in support of the above discovery. On the short-term causalities amidst the series, there was a bidirectional causality between RI and CO2 effusions. This means the two variables were inter-reliant on each other such that a rise in one variable led to a rise in the other variable. An exploration by Khan et al. (2020) on BRICS economies aligns the study’s discovery; however, those by Wang et al. (2021) and Yang et al. (2021a, b) are conflicting with the above discovery. Also, there was no causality between TI and CO2 excretions. This implies the two series were not dependent on each other. Explorations by Bashir et al. (2020) and Abid et al. (2021) agree with the revelation of this research. Moreover, POP and CO2 emanations were mutually interrelated. This indicates that POP caused CO2 secretions to rising and vice versa. Studies by Li et al. (2021a, b) and Naseem et al. (2021) align the study’s discovery. Lastly, causation from EC to CO2 effusions was discovered. This means, EC drove CO2 emissivities in the country, but not the opposite. The revelation is not astounding in that Ghana is witnessing massive expansion in the industrial and agricultural sectors, among others. These expansions are executed via the utilization of emission-intensive energies that worsen environmental quality. This implies environmental quality and economic development can both improve if Ghana embraces green energy utilization. Studies by Doğanlar et al. (2021) and Ahmad et al. (2021) deviate from the discovery of the study; however, those by Musah et al. (2021a) and Li et al. (2020b) are in line with the study’s disclosure.Table 8 VECM Granger causality test results Variable lnCO2 lnRI lnTI lnPOP lnEC ECT lnCO2 - 3.848 0.567 3.545 0.198 − 0.871 (0.003) * (0.423) (0.033) (0.765) (0.006)* lnRI 4.476 - 5.253 4.114 0.284 − 0.576 (0.002)* (0.231) (0.053)* (0.136) (0.002)*** lnTI 0.447 0.164 - 0.798 0.915 − 0.678 (0.516) (0.061)* (0.426) (0.481) (0.041) lnPOP 5.242 0.437 1.112 - 3.255 − 0.712 (0.004)* (0.022) (0.483) (0.003)* (0.005)* lnEC 5.241 0.126 0.177 0.772 − 0.684 (0.0207) (0.525) (0.346) (0.199) - (0.007)* lnCO2 is the response variable, while values in parenthesis () represent probabilities Finally, , , denote significance at the 1%, 5% and the 10% levels, respectively Conclusions and policy recommendations This exploration examined the connection amidst remittance inflows and environmental quality in Ghana from 1980 to 2020. In order to yield valid and reliable outcomes, robust econometric methods that control for endogeneity, heteroscedasticity and serial correlation among others, were engaged for the analysis. From the study’s discoveries, the series was first differenced stationary and flanked by a long-term cointegration association. The elasticities of the predictors were estimated via the FMOLS, DOLS and CCR techniques, and from the findings, remittance inflows degraded ecological quality in Ghana through high carbon emissions. Also, population growth and energy utilization were not environment friendly; however, technological innovations improved ecological quality in the nation. On the causalities amidst the series, there were bidirectional causalities between remittance inflows and CO2 effusions, and between population growth and CO2 emanations. Also, energy consumption caused CO2 effluents, but there was no causality between technological innovations and the exudates of CO2. From the above revelations, the study concludes that remittances inflows, population growth, and energy utilization deteriorate environmental quality in Ghana; however, technological innovations improve ecological quality in the country. Based on the above, it is recommended that, even though remittances are key agents of economic progress and financial sector developments, their adverse consequences on the environment should be seriously addressed. Authorities can attain this goal by enacting regulations to control the activities of polluting industries that are being financed by remittances. Also, households and individuals should minimize their use of remittances to finance carbon-intensive items, like automobiles and air-conditioners, among others, that add to environmental pollution in the country. If the aforestated recommendations are implemented, the damaging effects of remittances on ecological quality will be minimized, thereby stimulating sustainable development. Moreover, the government can boost ecological quality in the country by advocating for the adoption of environmentally friendly technologies in all establishments. This objective can be achieved if the government gives incentives like tax rebates to local entities to help them import modern technologies from other parts of the world. The adverse consequences of population growth on the environment suggest that the government and policymakers should regulate the rate of population in the country. As evidenced from the study, a rise in population growth escalated the rate of emissions in the country. Therefore, if the rate of population is well regulated, it will help curtail the emanation of carbon in the country. Additionally, the government should advocate for the utilization of clean energies in all economic activities in the country. This goal can be attained by allocating substantial financial resources into research and development linked to the consumption of green energy. Thus, the pollution-free growth process targeted by authorities of the nation can be attained, if the country transitions from conventional energy sources like fossil fuels to green sources like solar and hydro among others. Furthermore, efforts to boost the energy industry should be undertaken with caution so as not to jeopardize the long-term goal of a carbon-free economy. Besides, intensifying public awareness on ecological quality, improving environmental monitoring, and applying strict ecological measures are all means through which environmental pollution in Ghana could be minimized. Furthermore, the quality of institutions in the nation should be improved, because institutional quality helps establishments to adopt green technologies, and also helps to boost public awareness of a clean environment. According to Jiang et al. (2020), green policies fail to succeed in countries due to minimal support, involvement and cohesion from key stakeholders. Therefore, policymakers should actively engage and encourage all important stakeholders to participate in the development and implementation of green policies in the country. Finally, the IEA, UN and the World Bank advise governments to embrace carbon pricing because it has a lot of benefits than other options. The government of Ghana should, therefore, include carbon pricing in its strive towards a low-carbon economy. The limitations of this exploration cannot be overlooked. Firstly, the researcher had hoped to use a longer study period for the analysis; however, because of data constraints, the study covered the period 1980 to 2020. It is, therefore, proposed that when more data become available, comparable studies should be conducted to authenticate the study’s findings. Also, the FMOLS, DOLS and CCR techniques were used to estimate the parameters of the predictors. This signposts that interpretation of the study’s results warrants some caution because the adoption of other techniques might yield different results. Moreover, the study was confined to only Ghana. For comparison purposes, similar studies in different geographical locations can be conducted to help authenticate the study’s outcomes. In examining the nexus amidst remittance inflows and environmental quality in Ghana, the study controlled for technological innovations, population growth and energy utilization. It is, therefore, suggested that future explorations should control for more macroeconomic variables to help minimize OVB issues. We finally suggest that future explorations should consider adopting the quantile regression technique because it will provide information on the entire conditional distribution of environmental quality. For instance, the influence of energy consumption on ecological quality could differ at different quantiles, and it is the only estimator that can provide such information. Author contribution KL conceptualized the study; XW drafted the original manuscript; MM1 helped in conceptualizing the study and also helped in the analysis and discussions; YN helped in the analysis and discussions; MM2 helped in the analysis and discussions and drafting of the original manuscript; MA helped in the analysis and discussions; ZG helped to provide the data; HX helped in the analysis and discussions; XY1 helped in editing the final manuscript; XY2 helped in the analysis and discussions; KS helped to provide the data; LW helped in editing the final manuscript. All the authors read and approved the final manuscript. Data availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Abbreviations RI Remittance inflows TI Technological innovations POP Population growth EQ Environmental quality CO2 emissions Carbon dioxide emissions EC Energy consumption DARDL Dynamic autoregressive distributed lag ARDL Autoregressive distributed lag VECM Vector error correction model GMM Generalized method of moments ECT Error correction term ADF Augmented Dickey-Fuller PP Phillips–Perron WDI World Development Indicators FMOLS Fully modified ordinary least squares DOLS Dynamic ordinary least squares OVB Omitted variable bias CCR Correlated component regression BRICS Brazil, Russia, India, China and South Africa SDGs Sustainable development goals OECD Organisation for Economic Co-operation and Development PCA Principal component analysis SD Standard deviation VIF Variance inflation factor IEA International Energy Agency GHG Greenhouse gas G-20 Group of Twenty IMF International Monetary Fund CCEMG Common correlated effects mean group NARDL Non-linear autoregressive distributed lag PMG Pooled mean group USA United States of America GLS Generalized least squares ERS Elliott, Rothenberg and Stock KPSS Kwiatkowski-Phillips-Schmidt-Shin AIC Akaike Information Criterion BICS Brazil, India, China, South Africa PVAR Panel vector autoregression CUP-FM Continuously updated fully modified CUP-BC Continuously updated biased-corrected FM-LS Fully modified least squares Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Abid A Mehmood U Tariq S The effect of technological innovation, FDI, and financial development on CO2 emission: evidence from the G8 countries Environ Sci Pollut Res 2021 10.1007/s11356-021-15993-x Abokyi E Appiah-Konadu P Abokyi F Oteng-Abayie Fosu EF Industrial growth and emissions of CO2 in Ghana: the role of financial development and fossil fuel consumption Energy Rep 2019 5 1339 1353 10.1016/j.egyr.2019.09.002 Abokyi E, Appiah-Konadu P, Tangato KF, Abokyi F (2021) Electricity consumption and carbon dioxide emissions: The role of trade openness and manufacturing sub-sector output in Ghana. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 35426558 19328 10.1007/s11356-022-19328-2 Research Article Economic policy uncertainty and commodity market volatility: implications for economic recovery Xiao Daiyou [email protected] 1 Su Jinxia [email protected] 2 Ayub Bakhtawer [email protected] 3 1 grid.411054.5 0000 0000 9894 8211 School of Finance, Central University of Finance and Economics, Beijing, 100081 China 2 grid.411054.5 0000 0000 9894 8211 Business School, Central University of Finance and Economics, Beijing, 100081 China 3 grid.263488.3 0000 0001 0472 9649 College of Management, Shenzhen University, Shenzhen, 518060 China Responsible Editor: Nicholas Apergis 15 4 2022 2022 29 40 6066260673 4 1 2022 17 2 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. As a consequence of the COVID-19 pandemic outbreak, most commodities experienced significant price drops, which were expected to continue well into 2020. As a result, the Markov switching model is used to study the influence of policy uncertainty and the COVID-19 pandemic on commodity prices in the USA. Commodity markets are stimulated by economic policy uncertainty, according to results from a two-state Markov switching model. In both high and low regimes, economic policy uncertainty (EPU) influences the commodity market, according to the study’s findings. However, in the high regime, EPU has a greater influence on the energy and metal sectors. EPU has different influences on commodity markets in high- and low-volatility regimes, according to this study. There is a wide range of correlations between COVID-19 outcomes and EPU and how the prices of natural gas, oil, corn, silver, soybean, copper, gold, and steel respond to these tremors, in both high- and low-volatility tenure. Oil and natural gas, on the other hand, are unaffected by shifts in COVID-19 death rates under either regime. Results show that in both high- and low-volatility regimes, the demand and supply for most commodities are responsive to historical prices. Keywords Economic policy uncertainty Commodity market COVID-19 Resource policy Markov switching model issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2022 ==== Body pmcIntroduction Chinese commodities are more dependent on global-related sectors as global financial integration and the expansion of industrialization in emerging economies continue (Yan and Wang 2021; Zhu et al. 2021). Commodity markets have been a key source of worldwide concern for the past few years because of the frequent and dramatic changes in commodity prices and the increased demand for commodities from investors (Rajput et al. 2021). There is a huge deal of attention on the fluctuation of commodity prices in this context. Commodity markets have been the subject of numerous studies, both theoretical and empirical, from a variety of perspectives. Academics are concerned about the consequences of economic policy uncertainty (EPU). Recent events have led to a widening financial crisis that continues to spread throughout the international economy (Ellis and Liu 2021). The risk transmission across multiple financial markets has been strengthened as an outcome of these events (Wei and Han 2021). A wide range of economic policies is routinely used by countries to ensure the smooth development of their internal economy. It is apparent that policy is a factor in commodity markets. As a result, examining the link between China’s commodity markets and economic policy uncertainties is of enormous practical importance. Since its discovery in Wuhan, China, the unique coronavirus disorder 2019 (COVID-19) has caused unprecedented public health problems and severe social and economic consequences around the globe. There have been above 8 million cases of the disorder, and roughly 0.46 million deaths, globally (Kumar and Nayar 2021; Zanke et al. 2021). More than twice as much as at the time of the global economic crisis of 2008–2009 is predicted to be lost in 2020 as a result of a worldwide loss approaching 3%. From the time when the COVID-19 pandemic started, several variables have changed dramatically. Since the outbreak of the COVID-19 epidemic, oil prices have largely reduced. Global oil requirement was predicted to shrink by roughly 10% in 2020, which is greater than twice as much as the next greatest reduction in 1980 (Aktar et al. 2021), which was around 7%. COVID-19 epidemic has a detrimental impact on all countries, but the fall in oil prices has a different effect on countries that are either oil importers or exporters. As the COVID-19 epidemic has spread, so has the level of uncertainty. It is possible to see an increase in global economic policy uncertainty from 234 in January 2020 to 298 in September 2020, based on the news-based global economic policy uncertainty index (PPP-adjusted GDP) (Bai 2021a). Pandemic uncertainty stems from a variety of factors, including the disease’s infectiousness, widespread distribution, and short incubation period. The length of time that the effects of the pandemic-induced (and unexpected) shifts in consumer expenditure would last also adds to the uncertainty. Uncertainty about the future of economic policy has been shown to have a negative impact on the economy’s ability to grow and create jobs, particularly in policy-sensitive industries such as healthcare and the defense sector. In general, financial markets, and stock markets in particular, have been negatively influenced by the COVID-19 epidemic (Yumei et al. 2021). During the week ending February 28, 2020, global stock markets saw their largest weekly losses since the 2008 global financial crisis. The value of the global equities market plummeted by 30% in March 2020 (Mohsin et al. 2021b; Notteboom et al. 2021). The financial market’s reaction to the COVID-19 pandemic has been more negative than that of previous infectious illness pandemics, such as the Spanish influenza pandemic of 1918, which murdered a projected 2.0% population of the world (Asghar et al. 2021; Rao et al. 2022). The 2nd and 3rd waves of the virus have produced volatility in various nations, despite the global stock market recovery in the latter part of the year. Over the years, the stock market has seen its share of ups and downs. Many studies have been undertaken to examine the macro- and microeconomic causes, oil price movements, inflation, recession, and interest rate movements, among other things, that have contributed to these changes in the economy (Anser et al. 2020; Khokhar et al. 2020; Mohsin et al. 2021a). Since the factors that cause the stock market’s ups and downs might be explained by economic policy uncertainty or volatility, this study focuses on these two issues in particular. When the COVID-19 epidemic broke out in countries and communities around the world, it had an impact on the global economy in 2020 that was unprecedented in the past century (Fu et al. 2021; Hou et al. 2019; Iqbal et al. 2021). Businesses are struggling with lost revenue and supply chain network interruptions as facilities close and lockdown measures are extended around the world. Unemployment is also at historic highs. There is still a lot of uncertainty, despite the fact that governments around the world are scrambling to put fiscal and monetary policies in place in order to lessen the recession’s consequences. Volatility is expected to rise in lockstep with rising uncertainty as a result of all of this. In a number of areas, this work adds to the body of knowledge. Markov swapping dynamic model, newly created by Balcilar et al. (2016), has never been employed to analyze the monitoring and control of commodities’ volatility and returns via EPU and investor attitudes, to the best of our knowledge. Causation in returns and volatility can be found using the Markov swapping dynamic model, which employs a more generic procedure. The regime-swapping model was applied to define the time series shifts between distinct commodity price regimes—particularly crude oil prices—due to structural breaks. In the Markov regime-swapping model, we applied a lag-dependent variable to correct omitted-variable bias, contributing to model parameter estimate bias. At high speeds, the Markov regime-swapping method can represent provisional volatilities. The technique has been utilized to apprehend significant abrupt changes in oil price fluctuations (Hamilton 1989). Additional research also shows that the Markov swapping model can accurately examine the volatility in oil-based product forthcoming value chain (Fong and See 2002) as well as to forecast the transition possibilities among low- and high-growth regimes and examine the mean change in the US GDP with oil price. Misspecification errors, structural breaks, and frequent outliers in financial time series are not a problem for the swapping dynamic model. As a result of our research, policymakers will be able to better understand the impact of COVID-19 on commodities under various regimes. It may be advantageous for portfolio managers and investors, especially during uncertain times like pandemics, to hedge efficient short-term dangers in their assets and portfolio. Using our findings, investors and regulators may better assess and forecast commodities return transitions in volatile locations. Literature review Commodity markets and effect of COVID-19 Most recent studies on commodity markets have focused on the financialization of commodities. Its phenomenon has begun in 2004, when fund influxes into the market rose from $15 billion to over $450 billion in April 2011 according to Gao et al. (2016) and Huang et al. (2021a). In the mid-2000s, commodity derivatives trading expanded dramatically, according to Zhang et al. (2021c), whereas Abbas et al. (2022) point out that signs of commodity financialization soared during the 2008–2009 global financial crisis. Contrary to popular belief, Zhang et al. (2021b) report that the financialization of the metals and agricultural markets has been cyclical, with a de-financialization occurring between 2014 and 2017. Zhu et al. (2021) further show that commodities in Canada have new diversification options during the period of greatest financialization. The returns and volatility of commodities are mostly driven by financial variables, as demonstrated by Rajput et al. (2021). It has been shown that global macroeconomic conditions have an impact on the stages of the commodity price cycle. The relationship between the Chinese stock market, commodity markets, and the world crude oil price are dynamic, according to Yu et al. (2021). In addition, commodities have been shown by Kim and Yasuda (2021) to be advantageous as a place of refuge, hedge, or portfolio diversification. Because commodities play a critical role in the economy, Zhu et al. (2020) show that both traditional and non-traditional financial policies can influence commodity prices. Commodity price variations have a significant influence on economic growth, according to Scarcioffolo and Etienne (2021). The volatility of commodity markets is asymmetric, which means the volatility is larger following a positive price tremor than a negative price tremor. Commodity prices have a projecting material for exchange rates, according to Huang et al. (2021b). There is evidence that commodity prices can be forecast using information from other markets, according to Yang et al. (2021b). It has also been shown that commodity prices may be used to predict inflation (Long et al. 2021). Commodity prices can be predicted by global trade uncertainty, according to Ellis and Liu (2021) and Wu et al. (2022a). It has been shown that commodities play a key part in the active consideration of climate, sickness, economic, or geopolitical dangers, or “hazard fear” by Chakraborty and Thomas (2020). Commodity prices can forecast GDP growth, according to Wang et al. (2022). It has been shown that commodity spot and future prices are linked by Tran (2021). A key driver of commodity prices, according to Sha et al. (2020), is speculation. Shafiullah et al. (2021) demonstrate intraday return predictability for commodity ETFs using high-frequency data. Furthermore, Dai et al. (2020) show that Chinese commodities futures markets have intraday momentum. According to existing research, it is also critical to make a distinction between different commodity groups, such as energy and agriculture, and livestock and precious metals. Considering 21 different commodities, Dai et al. (2021) conclude, for example, that valuable and manufacturing metals are a superior hedge and safe-like haven than other commodities. In contrast, Yuan et al. (2020) show that the volatility of crude oil prices is more adversely affected by pandemic uncertainty than gold prices. As far as commodities are associated, only crude oil has an opposite leverage impact, according to Dai and Yin (2020); Lee et al. (2019); Shen et al. (2021); and Xiang and Qu (2020). According to Zhang et al. (2021a), the long-term price equilibrium association between industrial metal and crude oil markets exists, but not between the agricultural and the gold market. According to Yuan et al. (2022), the gold and crude oil markets are more responsive to market dimensions, but soybeans are not. According to Wu et al. (2022b), there is a bigger time-varying impact on agricultural commodity prices than there is on metal and energy prices. We can see from Shang et al. (2021) the short-term and medium-long-term transmission intensity of metals, while energy is highest in both time frames. Gold futures, according to Zhou et al. (2021), can be used to protect against stock market losses, although the vast variety of commodity futures appear to be viewed as a distinct asset category because of the increasing financialization of commodities. The energy futures market also actively takes part in the coordination of stock and commodity markets, as shown by Bai (2021b). In order to see if the price overreaction behavior varies between various commodities, we looked at 20 distinct commodity futures because of this heterogeneity. Furthermore, several past studies examine the connection between commodities and other asset types. For instance, Chen et al. (2020) explore the volatility connectivity between credit defaults swaps (CDS) and commodities and find that commodities can transfer volatility to CDS. Volatility transmission varies in strength according to the commodity type, with energy commodities and precious metals having the strongest impact. There is a correlation between commodities and stocks between BRICS nations and the USA, according to Dash and Maitra (2021). Researchers have discovered an ever-shifting network structure among these assets, with effects on the network that can be felt both locally and internationally. Li (2021) expresses that the volatility spillover between energy and agricultural commodities is asymmetric. With regard to agricultural commodities, there is also a major risk of spillover from energy sources. Crude oil is a particular commodity that provides greater modification profits than other commodities, according to Aloui et al. (2016), who differentiate between non-energy and energy commodities. Herding behavior may play a substantial role in explaining the movement of commodity prices, according to some researchers. As an instance, Fasanya et al. (2021) discover that 24 Chinese commodities exhibit positive response business, noisy business, and a steer mentality; on the other hand, Aslam et al. (2022) show that steering behavior varies between markets and it is asymmetric. The bottom line is that, in earlier studies, commodities were shown to interact significantly with other asset classes and to be a fence and safe-like haven for other asset categories under certain conditions. Each commodity category has its unique characteristics that must be taken into account (energy, agriculture, non-energy, industrial metals, precious metals). The herding behavior of investors may also affect commodity prices. These observations have prompted us to investigate the futures price overreaction behavior of a broad sample of 20 commodities. These studies show that commodity futures have a significant impact on financial and economic systems. Various pricing patterns have been studied, including cycles, hedge, predictability, and safe-like haven. However, given the impact of the COVID-19 outbreak, we have not seen any studies examining the sensitivity of commodity futures prices based on hourly data. As Deev and Plíhal (2022) demonstrate, uncertainty shocks can have a considerable impact on commodity prices. The COVID-19 epidemic’s impact on the commodity market was the subject of scholarly investigations at the time of this study. None of them has examined their pricing overreactions. For instance, Wang and Sun (2017) found that the volatility of commodity prices is affected by the number of deaths and definite cases caused by the COVID-19. According to Yang et al. (2021a), a worldwide fear index for the COVID-19 epidemic has the ability to anticipate commodity prices, with commodity yields being positively connected with an increase in COVID-19-related dread. We can infer from the aforementioned studies that it is critical to comprehend the commodity market’s response to the COVID-19 pandemic. No current studies have examined the price extreme reaction behavior of commodities at the time of the COVID-19 epidemic, to our knowledge. By studying the price movements of commodities at the time of the COVID-19 epidemic, we add to what is already known about commodity futures pricing and can therefore assist policymakers and investors in better appraising commodity investment risks and possibilities in the future. Macroeconomic volatility and commodity markets The empirical evidence supports the hypothesis that the volatility of commodities responds to macroeconomic factors. For example, according to Bianchi (2021), monetary policy and inflation are responsible for gold price volatility. US financial policy news has a calming influence on commodity volatility, according to Ayadi et al. (2020). Economic activity and volatility are linked in several studies (e.g., Aharon and Qadan 2018; Bahloul and Gupta 2018), and Rehman and Vo (2021) show that economic activity news has a rapid and large impact on metal futures’ volatility. The erratic nature of Chinese commodity markets is the subject of another body of research (Ji et al. 2018). Economic news from both China and the USA affects Chinese commodity volatilities, as An et al. (2020) demonstrate. According to Chen et al. (2021), macroeconomic factors such as GDP growth, industrial production, and money supply affect commodities futures’ volatility. They also show that the Chinese commodity markets are influenced by the economies of both China and the USA. Liang et al. (2021) explore the lead-lag association between macroeconomic futures returns and forecasts, are the most relevant to our research. By focusing on volatility, we stray from their research, and we look at a much broader range of commodities. Research on the influence of macroeconomic information on commodity prices is relatively restricted and has been unsuccessful to come to an agreement. Batista Soares and Borocco (2021) found no “compelling evidence” that energy prices respond to US macroeconomic news, but Shi and Shen (2021) observed more volatility in crude oil futures prices on the days of events. According to Suleman et al. (2021), macroeconomic news has little effect on metal futures prices. There is evidence of “a quick and large response” to macro-news, as Umar et al. (2021) write, and Christie-David and Cai show that futures prices of gold and silver react strongly to economic data. Similarly, Bakas and Triantafyllou (2018) find a considerable increase in gold futures market volatility in the wake of positive news, and they demonstrate that this increase is correlated with greater belief dispersion. Recession and financial crises have been shown to have a greater impact on asset prices’ reaction to macroeconomic news than other times (e.g., Sobti 2020; Sun et al. 2021). As Ma et al. (2021) explain, one reason for this is that the announcements may be interpreted as signs of future economic development. Investors’ moods fluctuate wildly during recession and crisis situations; therefore, this could also be the cause (Hu et al. 2020). A further possibility is that commodity markets have become more and more financialized. According to Marfatia et al. (2021), enlarged co-movements between the commodity and stock markets have led to heightened volatility. According to Ahmed and Huo (2021), financialization has led to a previously unseen shift toward a more volatile risk appetite. In conjunction with the indication of increased dissemination of information, this explains the higher volatility in the 2007–2009 financial crisis than earlier. Research methods and data Data The study rely on daily explanations of COVID-19 cases (found by the number of tainted US citizens with an entirely different strain of the virus), oil prices (determined by the WTI benchmark crude oil price), and the US-EPU (news-based index) to calculate their results. On the CDC’s website, COVID-19 data is retrieved. Furthermore, the statistics on the oil market are received from DataStream, while the information on EPU is taken from the EPU website. The data for commodity prices (gas, silver, gold, steel, copper, corn, and soybean) is obtained from Thomson Reuters DataStream. Methodology An MS technique is used to examine the influence of economic policy uncertainty and COVID-19 cases on commodity market prices. The developments in US financial policy in the late 1980s prompted the use of the MS approach. We use Fallahi’s (2011) two-state MS model (MS(2)) to analyze the link between economic policy uncertainty, COVID-19, and commodity market prices. There are three regime-switching variables: mean (st) and variance (st) and economic policy uncertainty and COVID-19 cases. Markov regime-switching approach The latent process drives the time series utilized in the MS model, which is considered to be stationary (Alizadeh and Nomikos 2004). Consequently, it is impossible to observe the states around which the time series evolves. EPUst, where t = 0, 1 is assumed to be a regime-dependent coefficient of economic policy uncertainty together with the mean, variance, and stddev (Alizadeh et al. 2008). As a result, these characteristics change throughout time in relation to the regimes. High prices of commodities are tied to the 0th regime; a low price of commodities is linked to the 1st regime. Commodities’ prices are predicted to be higher and volatility to be lower when the market is expanding as a result of economic policy uncertainty. As a result, a high-growth and low-volatility regime is indicated by 0 > 1 and 0 > 1, respectively. MS(2) can be written as follows: 1 ΔYt=μst+βstZt+∑i=1nθiΔXi+εst where Eq. 1 uses state-dependent intercepts (Zt) and state-dependent switching variables EPU and COVID-19 cases to represent a change in commodities prices and state-dependent switching variables (Zt) to represent a change in state-dependent switching variables .2 St=0withprobability1withprobability Pr=p00p01p10p11and∑j=1Mpij=1fori=0andi=1 Here, the probabilities of remaining in regime 0 and 1 are p 00 and p 11, while p01 and p10 indicate the movement of probabilities between the two corresponding regimes; thus,3 pij=Prst=j\|st-1=iforalli,j=0and1 The mean and variance are supposed to behave in MS (2) model.4 μst=μ0μI>0andμ1<μ0andσ0<σ1 A low-average-growth regime (St = 1) and a high-average-growth regime (St = 0). In order to achieve worldwide optimization of parameters, we started with more than 1000 estimated specification beginning values. The LR test, residual analysis, and the regime classification measure (RCM) were also used to identify the best model. Results and discussion Table 1 presents the descriptive statistics of the studied variables. Table 1 also includes the the Ljung-Box first [Q(1)], Jarque–Bera normality test (JB), the first [ARCH(1)] and fourth [Q(4)] autocorrelation tests, and fourth [ARCH(4)]-order Lagrange multiplier (LM) tests for autoregressive conditional heteroskedasticity (ARCH). First- and fourth-order autocorrelation and autoregressive conditional heteroskedasticities are found for both logarithmic levels and logarithmic differences. The WTI series is more volatile than the gas, silver, gold, steel, copper, corn, and soybean in both logarithmic levels and logarithmic differences measured by the coefficient of variation.Table 1 Descriptive statistics WTI Gas Silver Gold Steel Copper Corn Soybean Mean 3.337 1.406 3.003 1.265 2.703 1.139 2.433 1.025 SD 1.910 1.317 1.719 1.185 1.547 1.067 1.392 0.960 Min 0.352 2.309 0.317 2.078 0.285 1.870 0.257 1.683 Max 7.499 4.897 6.749 4.407 6.074 3.967 5.467 3.570 Skewness 0.742 0.714 0.668 0.643 0.601 0.578 0.541 0.521 Kurtosis 0.696 0.287 0.626 0.258 0.564 0.232 0.507 0.209 JB 207.398 160.993 186.658 177.092 167.992 194.802 151.193 214.282 Q(1) 1846.705 1839.056 1809.771 1802.275 1773.575 1766.229 1738.104 1730.905 Q(4) 7341.834 7219.099 7194.997 7074.717 7051.097 6933.223 6910.075 6794.558 ARCH(1) 1847.788 1822.354 1810.832 1785.907 1774.616 1750.189 1739.123 1715.185 ARCH(4) 1844.866 1824.999 1807.969 1788.499 1771.809 1752.729 1736.373 1717.674 *, , and * represent significance at the 1%, 5%, and 10% levels, respectively Unit root tests A linear trend and a constant are both included in the test equation in Table 2, Panel A, which shows results from unit root tests on the log levels of the series. Panel B presents the results of experiments using only a constant as a unit root for the first variations in the log series. There are many different types of unit root tests, including the augmented Dickey-Fuller test (Dickey and Fuller 1979); the Phillips-Perron Z unit root test (Phillips and Perron 1988); MZ and MZt, the modified Phillips-Perron tests of Perron and Ng (1996); and Z, the Phillips-Perron Z unit root test of Phillips and Perron (Phillips and Perron 1988). GLS detrending is required for the Z, MZ, and MZt tests. Lag order is determined for the ADF unit root statistic by testing the significance of each successive lag at the level of 10% significance. We use the modified Bayesian information criterion (BIC)–based data-dependent technique of Ng and Perron (2001) to pick the bandwidth or lag order for the MZt, MZt, DF-GLS, and KPSS tests. Table 2 shows that the KPSS test rejects the null hypothesis that the series is stationary. The null hypothesis of nonstationary series cannot be rejected by any other test. There is a KPSS test in Panel B that does not reject the null hypothesis of stationary series, while other tests do. First-difference stationarity is seen in all the series. To summarize, we find that the logarithmic discrepancies between the selected variables series are not steady.Table 2 Unit root test Variable ADF Zα MZα MZt DF-GLS KPSS Zivot-Andrews Panel A: unit root tests in levels LnOIL 1.422 15.546 5.573 1.345 1.466 5071.387* 1.355 LnGAS 1.547 15.253 4.234 1.354 1.334 816.345* 1.354 LnSilver 1.479 16.168 5.796 1.399 1.524 5274.242* 1.409 LnGold 1.608 15.863 4.404 1.408 1.387 848.999* 1.408 LnCopper 1.538 16.815 6.028 1.455 1.585 5485.212* 1.465 LnSteel 1.544 15.229 4.228 1.352 1.332 815.039* 1.352 LnCORN 1.6 17.487 6.269 1.513 1.649 5704.621* 1.524 LnSoybean 1.482 14.619 4.058 1.298 1.279 782.438* 1.298 LnEPU 1.664 18.187 6.52 1.574 1.715 5932.805* 1.585 LnCOVID 1.423 14.035 3.896 1.246 1.227 751.140* 1.246 Panel B: first differences unit root test LnOIL 10.456* 534.347* 43.346* 4.465* 3.345* 0.234 LnGAS 12.345* 700.342* 56.234* 6.234* 7.343* 0.323 LnSilver 10.874* 555.72* 45.08* 4.643* 3.479* 0.244 LnGold 12.839* 728.356* 58.484* 6.484* 7.637* 0.336 LnCopper 11.309* 577.949* 46.884* 4.829* 3.618* 0.253 LnSteel 12.326* 699.221* 56.144* 6.224* 7.331* 0.323 LnCORN 11.762* 601.067* 48.759* 5.022* 3.763* 0.264 LnSoybean 11.833* 671.253* 53.899* 5.975* 7.038* 0.318 LnEPU 12.232* 625.11* 50.709* 5.223* 3.914* 0.274 LnCOVID 11.359* 644.402* 51.743* 5.736* 6.756* 0.297 Regime switching model The Markov switching approach was used to study the influence of economic policy uncertainty and COVID-19 on commodity prices. A modest sample size and increased understanding of the link between economic policy uncertainty, COVID-19, and commodity prices lead us to focus on only two regimes for further investigation in this study. MS(2) specification was shown to be more closely aligned with macroeconomic correlations. As a result of the residuals from the linear and MS(2) model estimations, Table 1 presents descriptive statistics and diagnostic tests. Both models have good residual qualities, according to the statistics. The insignificance of the Jarque–Bera test shows that the residuals have a normal distribution. Heteroscedasticity and autocorrelation are not present in the computed residuals as confirmed by ARCH, Q(12) of the Ljung-Box model, and Q2(12). MS(2) model is a true data generator because RCM was 1.7887, which is closer to 0 than any other value (DGP). There are lower AIC, SC, and HQ values in MS(2) than a linear model, according to the information criteria. MS(2) best fits the data for the period 1980–2017 when it comes to the association between economic policy uncertainty, COVID-19, and the commodity prices in the USA, according to information criterion, diagnostic tests, and RCM. Oil and gas market There are substantial correlations between 0, 1, and the estimated coefficients (e.g., 0, 1, 0, and 1). It is said that economic activity expands significantly in a high-growth regime like regime 0. Regime 0, for example, has the highest intercept coefficient (0 = 0.086) and the lowest volatility (0 = 0.0014) of any of the possible regimes. The regime’s high mean value and low volatility indicate that the economy is expanding at the time. At an estimated value of 1 = 0.049, regime 1 represents a time of minimal growth, whereas at an estimated 1 = 0.012, regime 1 represents a very significant variance. According to the results, 0 > 1 is true. Mean and variance values show that regime 0 is associated with high growth and moderate volatility, while regime 1 is associated with low growth and high volatility. Low-growth regimes are more volatile than high-growth ones; hence, this conclusion can be drawn. Economic policy uncertainty and COVID-19 have a favorable and considerable impact on oil and gas returns under both regimes, as shown by regression parameters (see Table 3). However, under a high-growth environment, economic policy uncertainty has a greater impact than in a low-growth regime. In a high-growth regime, a 1% rise in the economic policy uncertainty index results in a 0.033% and 0.842% increase in oil and gas returns, respectively. However, in a low-regime, a 1% rise in economic policy uncertainty would lead to a 0.056% and 0.049% increase in oil and gas returns. In spite of these findings, the commodity market in the USA is influenced by the economic policy uncertainty and COVID-19 in a different way under each of these regimes. This finding supports the idea that in developed nations like the USA, the link between economic policy uncertainty and commodity market is not one-to-one and is dependent on the regime in place. There is a discrepancy between the findings of Albulescu et al. (2019) and Badshah et al. (2019).Table 3 Effect of policy uncertainty and COVID Oil and gas commodities Variable ΔOIL ΔGAS Mean (μ0) 0.029* 0.082* (0.849) (0.666) Mean (μ1) 0.023* 0.032* (0.350) (1.046) Variance (σ0) 0.026* 0.036* Variance (σ1) 0.056* 0.049* ΔEPU0 0.033* 0.842* (1.031) (2.936) ΔEPU1 0.062* 0.342* (1.404) (3.312) Mean (μ0) 0.054* 0.116* (0.714) (0.673) Mean (μ1 0.054* 0.066* (0.706) (0.230) Variance (σ0 0.081* 0.019* Variance (σ1 0.098* 0.015* ΔCOVID 0.058* 0.876* (1.248) (2.970) ΔCOVID1 0.087* 0.098* (1.696) (3.951) Effect on gold and silver market COVID-19 and the economic policy uncertainty index have a statistical impact on gold returns in a low-volatility regime, according to the Markov switching model results from Table 4. Gold returns show a statistically significant reaction under the high-volatility regime. Gold returns rose by 0.659% and 0.183% in a high-volatility environment when COVID-19 cases and economic policy uncertainty increased by 1%. Due to gold strong tie to the US and global economy, it is unlikely to function as a safe-haven commodity for investors, even though gold returns showed a less substantial positive response to all exogenous variables (Bhar and Hammoudeh 2011). In periods of low and high volatility, the lagged return on gold has a substantial negative and positive relationship with the latter’s return. This shows that gold returns are affected by historical events, such as the COVID-19 epidemic. Contrary to previous findings, results from Markov switching suggest that it is more likely to remain in a lower volatility regime than a higher one. Silver returns, unlike gold, show no substantial reaction to external variables in the low-volatility regime. Silver prices rose by 0.036%, the biggest change since 2019. This could be a result of this. Imports and demand from China surged due to the relaxation of COVID-19 restrictions and the implementation of the stimulus package, driving this increase.Table 4 Effect of policy uncertainty and COVID gold and silver commodities Variable ΔSilver ΔGold Mean (μ0) 0.039* 0.096* (0.688) (0.430) Mean (μ1) 0.039* 0.059* (0.770) (0.190) Variance (σ0) 0.036* 0.011* Variance (σ1 0.043* 0.021* ΔEPU0 0.079* 0.659* (0.371) (0.139) ΔEPU1 0.163* -0.183* (0.544) (0.312) Mean (μ0) 0.059* 0.096* (0.874) (0.309) Mean (μ1 0.059* 0.089* (0.255) (0.477) Variance (σ0 0.026* 0.011* Variance (σ1 0.021* 0.021* ΔCOVID 0.098* 0.659* (0.791) (2.300) ΔCOVID1 0.183* 0.183* (0.964) (2.463) Effect on steel and copper market Steel Markov switching model results in Table 5 show a 1% change in COVID-19 cases, and economic policy uncertainty will stimulate steel returns by 0.020% and 0.012% in a low-volatility environment. There is no correlation between steel returns and confirmed COVID-19 cases, recovery, or economic policy uncertainty in the high-volatility regime. Strong demand for steel in China, which led to a 25% price increase in the third quarter of 2020 when COVID-19 limits were removed due to low reported cases, maybe the cause of the substantial positive coefficient found under low-volatility regimes. In both regimes, steel returns have a strong correlation with its lagging returns. There is a strong correlation between steel prices and historical demand and supply. In the first quarter of 2019, the collapse of the Brumadinho dam in Brazil caused production at Vale to be disrupted due to a scarcity of transportation and workers caused by the COVID-19 outbreak. These steel Markov switching model equations show that regimes with low volatility have a higher likelihood of remaining than regimes with the high volatility of shifting. According to previous studies, gold’s cumulative impulse response during the height of the COVID-19 epidemic was more stable than that of other metal commodities, such as copper, silver, and aluminum (Apergis et al. 2021). However, a correlation between COVID-19 and copper returns has been reported.Table 5 Effect of policy uncertainty and COVID steel and copper commodities Variable Copper Steel Mean (μ0) 0.028* 0.065* (6.619) (1.979) Mean (μ1 0.014* 0.028* (0.195) (1.079) Variance (σ0 0.005* 0.020* Variance (σ1 0.010* 0.012* ΔEPU0 0.068* 0.0228* (0.760) (0.179) ΔEPU1 0.152* 0.152* (0.933) (2.352) Mean (μ0) 0.035* 0.072* (6.266) (1.986) Mean (μ1 0.035* 0.035* (0.186) (1.034) Variance (σ0 0.002* 0.013* Variance (σ1 0.033* 0.018* ΔCOVID 0.034* (0.635) (0.767) (2.106) ΔCOVID1 0.015* 0.059* (0.940) (2.359) Effect on Agriculture Commodity market Even though the COVID-19 pandemic had a limited impact on agricultural commodities, the global and domestic supply chain disruption and limits on exports or stockpile commodities raise worries about food security issues. Table 6 shows the results of the Markov switching regression on corn and soybean commodities. Death and recovery situations exhibit negative coefficients for low-volatility regimes with no statistical inference, but a substantial positive coefficient at high volatility. In a low-volatility regime, a 1% increase in confirmed cases results in a 0.04% decrease in corn returns. An increase of 1% in confirmed cases will statistically improve corn returns by approximately 7.7% correspondingly under the high-volatility regime. As a result of the fall in oil and natural gas production as a result of low market prices, COVID-19 cases and the economic policy uncertainty index had a positive association with corn returns. This could have an impact on the pricing of biofuel crops like corn and soybeans. If the results are insignificant, it may be because the poor sensitivity of crops like maize to external shocks that are not fundamental may be the cause. This may imply that most agricultural products are essential to global food security. Even in moderate volatility, the impact of economic policy uncertainty on maize returns is significant—and this uncertainty can have a detrimental influence on the economy in high volatility. There is a 3.5% chance of low volatility and a 1.8% chance of a high volatility transition.Table 6 Effect of policy uncertainty and COVID on agriculture commodities Variable ΔCORN ΔSoybean Mean (μ0) 0.068* 0.093* (0.659) (0.427) Mean (μ1 0.068* 0.056* (0.189) (0.807) Variance (σ0 0.035* 0.008* Variance (σ1 0.030* 0.018* ΔEPU0 0.018* 0.056* (0.862) (2.207) ΔEPU1 0.052* 0.180* (0.833) (2.383) Mean (μ0) 0.037* 0.071* (0.628) (0.985) Mean (μ1 0.017* 0.034* (0.037) (0.071) Variance (σ0 -0.004* 0.016* Variance (σ1 -0.023* 0.026* ΔCOVID -0.077* 0.064* (0.769) (2.185) ΔCOVID1 0.161* 0.015* (0.942) (2.358) Conclusion and policy implication This research analysis describes the link between COVID-19 number of cases and economic policy uncertainty on commodity prices. The study is grounded on a two-state Markov switching technique. The findings encourage the existence of a non-linear link between COVID-19 number of cases and economic policy uncertainty on commodity prices in the USA. The conclusion demonstrates that economic policy uncertainty exerts a large optimistic influence on commodity prices in low- and high-growth regimes. However, the influence of economic policy uncertainty on commodity prices was rather considerable in the elevated growth phase. This indicates that commodity prices react differentially to economic policy uncertainty in low- and high-growth regimes in the USA. This in addition shows that the relationship between economic policy uncertainty and commodity prices is non-linear. In our COVID-based Markov technique, extreme COVID-19 definite cases are troublesome for prices of oil commodities due to COVID-19 mitigation actions that severely restricted transport and travel which accounts for approximately 67% of oil requirement in a low-volatility regime. Rising COVID-19 cases disturb the price of natural gas requirement, although the influence is significantly slighter given the predominant usage of natural gas for the generation of electricity and domestic cooling and heating because of COVID-19 laws on travel limitations. On the other hand, elevated COVID-19 revival situations will diminish natural gas yields due to loose lockup regulations. The association between soybean profits, corn profits to the COVID-19 casualty, confirm, recovery cases, and economic policy uncertainty index is optimistic in elevated volatility regimes. In a less volatile environment, corn earnings reveal minimal association because of the low susceptibility of agricultural products to outside tremors. Indication from the research reveals soybean earnings are reactive to past growth in supply and demand of soybeans in both regimes. These studies can offer awareness for the prevarication possibility of silver and gold in the days of pandemics. Silver and gold prevarication possibility varies with time and is tenure dependent, suggesting that they change among Markov regimes. It is possible for short-term investors to properly hedge against systematic risks in their portfolios. Our findings can serve as a reference for future investors looking to invest in similar pandemics. Regulators can use the findings to assess and estimate the likelihood that the market will remain in a certain regime and begin the process of transitioning to a new normal. The results of natural gas and oil earnings can help oil-exporting nations formalize measures against future worldwide pandemics on the world market for energy commodities. OPEC, for example, has the power to limit supply in order to increase demand because most commodities are influenced by previous market trends. Using dynamic autoregressive distributed lag models, future studies can examine the impact of hypothetical shocks on commodity markets. Author contribution Xiao Daiyou: Conceptualization, data curation, methodology, writing—original draft. Su Jinxia: Data curation, visualization, supervision, visualization, editing. Bakhtawar Ayub: Writing—review and editing—and software. Data availability The data can be available on request. Declarations Ethics approval and consent to participate We declare that we have no human participants, human data or human tissues. Consent for publication N/A. Competing interests The authors declare no competing interests. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 35451716 20071 10.1007/s11356-022-20071-x Research Article Macroeconomic lockdown effects of COVID-19 on small business in China: empirical insights from SEM technique Xiao Daiyou [email protected] 1 Su Jinxia [email protected] 2 1 grid.411054.5 0000 0000 9894 8211 School of Finance, Central University of Finance and Economics, Beijing, 100081 China 2 grid.411054.5 0000 0000 9894 8211 Business School, Central University of Finance and Economics, Beijing, 100081 China Responsible Editor: Philippe Garrigues 22 4 2022 2022 29 42 6334463356 19 1 2022 30 3 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The coronavirus (COVID-19) outbreak in the China has exposed small- and medium-sized enterprises (SMEs) to a variety of challenges, some of which are potentially life-threatening to their sustainability. Therefore, this study aims to investigate the macroeconomic lockdown effects of COVID-19 on small business in China. A survey questionnaire with 313 participants was used to collect the data. In this study, the SEM technique was used to analyse model. The data have been gathered for the study from the managers and employees of Chinese SMEs. The findings of the study show that COVID-19 has a significant negative impact on financial performance, operational performance, profitability, access to finance, and customer satisfaction. According to the study's findings, external support aids have a greater impact on SMEs' ability to survive and thrive through innovation than on their actual performance. The findings of this study have a number of important practical consequences for small- and medium-sized business owners, governments, and policymakers. Keywords Financial performance Operational performance Macroeconomic lockdown COVID-19 Small- and medium-sized enterprises issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2022 ==== Body pmcIntroduction Pandemics and epidemics have a lasting effect on the economy and society as a whole (Ahmad et al. 2022; Hussain et al. 2022). According to Yang et al. (2022), the epidemic in the early 1830s, when cholera struck France (and other parts of central Europe) hard, was a case in point. Despite the fact that the disease killed at least 3% of Parisians in the first month, it was instrumental in spurring France's industrial revolution (Ahmad et al. 2020; Ahmad and Wu 2022; Wen et al. 2022). Politics and social inequality were also exacerbated as poverty-stricken neighbourhoods were disproportionately affected, while wealthy neighbourhoods were able to flee pandemic-hit areas and lessen their contact with the local community (Elavarasan et al. 2021). In many ways, the COVID-19 pandemic is unprecedented. First and foremost, it puts millions of lives at risk around the world (Ahmad et al. 2020; Shah et al. 2020; Shao et al. 2021). By the end of June 2021, it had nearly four million people have already died as a result across the globe (Le and Nguyen 2022). Social distancing guidelines to contain the virus also affected the service sector, which relies more heavily on larger number of micro and small businesses which relies more heavily on large corporations (Fang et al. 2022). A similar lack of liquidity and solvency has been brought on by the lower profits of medium-sized and small enterprises (SMEs). Economic downturns of 2008–2009 exacerbated constraints on small- and medium-sized business working capital (SMEs) (Zamfir and Iordache 2022). Recessionary across the productive sectors, the economic impact is not evenly distributed; businesses in the tourism and transportation industries, as well as smaller businesses, are more vulnerable to the effects of social distance or confinement (Irfan and Ahmad 2021). Multidimensional shocks and their cross-industry interactions can only be taken into account in a framework that explicitly models the heterogeneity of firm vulnerability (Irfan and Ahmad 2022). To put it more succinctly: CGE models have recently been used to quantify indirect pandemic impacts. As a result, this includes studies that measure the impact of pandemics, whether they are global or country-specific (Chopdar et al. 2022). According to our knowledge, there are no intersectoral models for analysing business structure. The Chinese central bank has announced a reduction in the required reserve ratio for banks beginning in February 2020 (Ahmad et al. 2021a; Fu et al. 2021; W. Iqbal et al. 2021a, b). A crisis-related package of assistance for small- and medium-sized businesses (SMEs) was unveiled by the government (Irfan et al. 2022). Deferred tax payments for small businesses, lower rent, waived administrative fees, subsidising R&D costs for small businesses and additional funding for SME loans were among the policies announced for small businesses in China at the regional level (Abbasi et al. 2022; B. Ahmad et al. 2021a, b, c; Irfan et al. 2021b). In response to the COVID-19 pandemic, 54 national governments instituted emergency policies and measures, according to the 2021 Global Entrepreneurship Monitor (GEM) report (Ali et al. 2021; Chandio et al. 2021; Islam et al. 2021). Global economies were supported with unprecedented amounts of state aid (Hao et al. 2021; Irfan et al. 2021c). While fiscal, administrative, and monetary tools have been deployed to chip the decline in ultimatum and employment, it appears unlikely that these measures will be sufficient to achieve a full offset. Top-down and bottom-up approaches are needed to address COVID-19, such as public and private rather than dying industries or failing businesses, initiatives to support productive entrepreneurs (Irfan et al. 2021d). The role of SMEs is crucial in most economies because they stimulate economic growth, create jobs, and open new markets (Ahmad et al. 2020; Ahmad and Wu 2022; Fatima et al. 2019). Despite their importance to economic growth, SMEs are frequently the most affected by major public crises (Tanveer et al. 2021; Xiang et al. 2022). To sum it up, the lack of preparedness, vulnerability, reliance on central government and local agencies as well as an increased stress level for owner-managers all contribute to the greater impact on small businesses during times of crisis (Irfan et al. 2021e, 2020). But as far as we know, there have been very little prior research post-disaster policy needs of SMEs affected by disasters (Ganlin et al. 2021; Le and Ikram 2021; Paul et al. 2017; Ullah 2020; Zaverzhenets and Łobacz 2021). Unlike environmental hazards, epidemics can have long-lasting and devastating effects on the general economy and the population, which is why it is important to keep an eye on both (Wellalage et al. 2021). SMEs in China have been instrumental in the country's growth (Khan et al. 2021; Rauf et al. 2021). Most Chinese small and medium enterprises (SMEs) operate in the same way and face the same obstacles as those in other countries' SMEs (Razzaq et al. 2021; Shi et al. 2022). Both developed and developing by comparison, the COVID-19 pandemic has a far greater impact on countries than any other large-scale environmental catastrophe (Razzaq et al. 2020). SMEs have been particularly hard hit. The specific policies may be devised by the government to assist small- and medium-sized businesses (SMEs) by better understanding the challenges and demands they face at the onset of an epidemic. In this study, based on an online survey of thousands of Chinese SMEs in Sichuan Province, the impact of the COVID-19 epidemic on Chinese small and medium enterprises (SMEs) was examined as a timely reference for other countries. For the Chinese economy, this paper presents a general equilibrium model that disentangles intersectoral relationships in order to separate the productive flows of small and large firms. An evaluation of the COVID-19 pandemic-related production restrictions is possible with this methodological framework. This analysis is particularly relevant to China because of the wide range of sectors affected by these restrictions, which are heavily reliant on the service sector in general and tourism in particular. This article adds to the body of knowledge in the field of economy and the study of how aggregate fluctuations affect small businesses arise in networks. The effects of workforce shocks should be quantified in a more precise manner, according to our proposal on small businesses and what happens when a shock hits the economy as a whole (Tang et al. 2022; Wu et al. 2021). Wei and Lihua (2022) proposed a hypothetical extraction method (HEM) to determine the relative importance of SMEs in sustaining the economy's activity and employment, particularly during the recession of COVID-19. Additionally, this research contributes to the development of a quantitative approach for incorporating part of the research methodology that incorporates industrial sector structure. Small and large businesses are separated in the System of National Accounts' supply and use tables, which makes this possible. We describe the mechanisms of income distribution in greater detail. Long Jr. and Plosser's real business cycle model is the most widely used framework for studying the link sectoral shocks and overall fluctuations are interconnected (Jun et al. 2022). Many seminal works in this tradition include: (Ansari et al. 2022), and (Elhajjar and Yacoub 2022). Input–output framework analysis is the focus of these studies, so they provide only a limited view of sectoral economic interactions when using this approach to study the economic activity's response to sectoral shocks that are unique to that sector. The paper has now been arranged in this way. Section 2 provides a timely review of the literature for pandemic modelling, as well as a specific review of pandemic modelling by firm size. Methodology will be discussed in Sect. 3. A summary of the dataset and some descriptive statistics are presented in Sect. 4. Section 5 summarises and clarifies the findings presented in the preceding sections and concludes the research lines that could be pursued in the future. Literature review Containment and mitigation strategies have been employed by numerous countries in the face of COVID-19, which is both an economic and a health problem (Elhajjar and Yacoub 2022). The pandemic was successfully contained in China, but concerns have been raised about the effectiveness of China's measures elsewhere (M. Ahmad et al. 2021a; Deleon Frisnedi et al. 2022; Irfan et al. 2019, 2021a; Yasir et al. 2022). Forcibly limiting people's freedom of movement became standard practice when the pandemic became out of control in Italy on March 9, 2020. This policy was then copied by governments in other countries in Europe, who also implemented nationwide lockdowns in March 2020. Lockdowns were then implemented across the majority of Asia, Africa, and the Americas as a precautionary measure. Many industries have been adversely affected by the pandemic's strict control measures (Ritika et al. 2022). However, according to Kurtaliqi et al. (2022), disasters affect the economy differently depending on the sector (Gao et al. 2020; Wu et al. 2022; Xu et al. 2022). Restrictions on people's ability to move have manufacturing and retail sectors relying on physical stores were greatly affected by this, as well as the potential reduction in consumer spending as a result (Anser et al. 2020; Cucignatto et al. 2022; HUANG et al. 2022; Rao et al. 2022; Yu et al. 2021). The longer restrictions on human mobility are in place, the more detrimental they are to both personal and business-level behaviour. Tourism and hospitality-related spending on goods and services, for example, has been significantly reduced as a result of a dramatic decrease in consumer confidence (M. Ahmad et al. 2021b; Alzaidi and Shehawy 2022; Irfan and Ahmad 2022; Jabeen et al. 2021). E-commerce, online education, and online meetings have all seen an increase in demand many people are working from home (Alzaidi and Shehawy 2022), despite the widespread fear of infection from these kinds of encounters. Additionally, the pandemic's health care demands have put a strain on the health and medical care sectors. It is important to look at the impact of the pandemic on different industries, as some have been affected more than others, and others have been given the opportunity to thrive. In the majority of papers, SEIR (susceptible, exposed, infected, and recovered) models of disease transmission are presented, which were pioneered by Duong et al. (2022). This framework has been used to show Control measures and how pandemics spread. Epidemiological models that incorporate an objective function allow us to calculate the true costs of an infection, as well as potential remedies (Saha et al. 2022). More and more papers continue this tradition by examining the various determinants of morbidity as well as the economic optimal and trade-offs policy analysis. This paper takes a more recent approach, which looks at intersectoral relationships through the lens of an input–output model (Aguirre et al. 2021). They don't explain the importance of small- and medium-sized businesses, but two factors can help bridge the gap. There are two reasons for this: First, new statistical data have made it possible to integrate the in-depth analysis of the productive structure into a more general input–output model. As for the second, there has been a recent shift that emphasises the role of sectoral or firm-specific idiosyncratic shocks in explaining overall economic activity. Using aggregated data over time, studies of business bankruptcy often use idiosyncratic shocks to measure aggregate volatility, as Iancu et al. (2022) point out in their paper. Although an economy has a wide range of economic sectors, the central limit theorem has been used to calculate the impact of aggregate volatility on the business level, which assumes that all economic sectors are equally represented in an economy. An old school view holds that idiosyncratic shocks at the corporate level and overall fluctuations have their greatest impact when they affect large numbers. In an industrialised economy, there are millions of companies that interact, so a small number of idiosyncratic shocks from large corporations or a collection of SMEs would likely have a negligible impact. Disaggregated microeconomic analysis at the firm- or sector-level has macroeconomics that has long ruled out the diversification argument. When there is a wide range of business sizes, the central limit theorem should not be used, according to a study by Yeon et al. (2022) and Wu (2021) that this argument was questioned. Research by Mitręga and Choi (2021) and Malesios et al. (2021) and others shows that when firms size distribution follows a power-law distribution, idiosyncratic shocks do not cancel out and can thus generate significant aggregate fluctuations. Additionally, Khurana et al. (2021) argue it's possible that the consequences of microeconomic shocks go far beyond the circumstances in which they occurred because of the existence of intersectoral interconnections. As a matter of fact, In the event of a microeconomic shock, it is possible for its effects to cascade throughout the entire economy, and have a significant aggregate impact on the economic performance of other businesses. Theoretical frameworks such as Turaev and Ganiev (2021), Wahyono and Hutahayan (2021), and Dias et al. (2022) can be used to characterise mechanism of shock transmission and the scope of aggregate fluctuations as an economic propagation instrument in vulnerable economic situations. This COVID-19 pandemic could benefit greatly from better models that take into account business failure, credit rationing, and cross-sectoral ties at various firm sizes (SMEs and large companies). Economic agents' behaviour must be linked at both the macroeconomic and firm levels in order to study aggregate volatility more thoroughly and in greater detail during periods of economic turbulence. This requires robust and information-intensive instruments. There must be the collecting data on the various ways in which economic actors interact, so that it is possible to study how one sector's actions affect other sectors. It is therefore possible to make policy recommendations to drive government strategies that aim to minimise the impact of both large companies and small businesses ceasing operations. It is well known that pandemics and natural disasters can have a negative impact on business. Natural disasters have been shown to retail, with fewer small businesses opening and more large ones going out of business, as well as lower private consumption spending, will have a significant impact (Latif et al. 2021; Yumei et al. 2021). Many studies have focused on manufacturing and agriculture, but few have looked at the effects of disasters and pandemics across multiple sectors of the economy (S. Iqbal et al. 2021a, b; Latif et al. 2021; Liu et al. 2021; Mohsin et al. 2021), despite the fact that various economic sectors have been found to be affected differently by pandemics and disasters. Figure 1 shows the macroeconomic lockdown effects of COVID-19 on small business in China.Fig. 1 Flow diagram Hypotheses The following hypotheses have been developed as a result of the literature review:H1: COVID-19 has positive impact on SMEs financial performance H2: COVID-19 has positive impact on SMEs operational performance H3: COVID-19 has positive impact on SMEs access to finance H4: COVID-19 has positive impact on SMEs mergers and acquisition H5: COVID-19 has positive impact on SMEs profitability H6: COVID-19 has positive impact on SMEs remote work H7: COVID-19 has positive impact on SMEs stakeholder satisfaction Research method Sample selection A questionnaire survey and in-depth interviews conducted in early February 2021 provided the data for this study. A survey of SMEs in China's was conducted to gauge how the COVID-19 epidemic is affecting those firms. In collaboration with six well-trained business management students, we distributed 500 self-administered questionnaires, 27 questionnaires were excluded from the total of 500 questionnaires because of missing data. The final data analysis utilised 313 questionnaires, resulting in a response rate of 61.3%. Male respondents made up 53% of the total sample size. Average age was 31 years, and the majority of participants had a bachelor's degree or more. Moreover, SMEs are selected with revenues, assets, and employees that fall below a predetermined threshold. However, the standards used to identify SMEs differ across countries and industries. SMEs are defined in China as businesses that fall below a certain threshold for one or more indicators in a specific industry sector. As a result, only those Chinese SMEs with less than 200 employees and annual revenues of less than $35.6 million US dollars were included in the sample. There are four categories of SMEs: primary industry, secondary industry, tertiary industrial, and the new economy for the sake of statistical convenience. According to the National Bureau of Statistics, primary, secondary, and tertiary industries contributed 7.1%, 39.0%, and 53.9% of GDP in 2019, respectively. The term “new economy” refers to new, fast-growing industries. China's new economy sectors have accelerated the country's shift to higher-quality development in recent years. The demographics of the respondents are shown in Table 1.Table 1 Demographic information Category Number % Gender Male 165 52.72% Female 147 46.96% Total 313 Age < = 25.00 91 29.07% 26.00—27.00 54 17.25% 28.0—30.00 63 20.13% 31- 35.00 48 15.34% > = 36.00 57 18.21% Total 313 Qualification M.Phil./MS 97 30.99% Master 109 34.82% Diploma 63 20.13% Total 313 Experience 3-Jan 140 44.73% 6-Apr 64 20.45% 9-Jul 33 10.54% > = 10 76 24.28% Total 313 Results and discussion Common method bias The present study collected data using a cross-sectional approach, and it is possible that common method bias (CMB) is a problem in the measurement model. Using Harman's single factor test (Podsakoff et al. 2003), we tested for CMB, and all construct items in the proposed model were divided into various factors, with the first element accounting for 20% of the total variance. The findings are consistent with prior literature standards and demonstrate that CMB was not a problem in our study, as previously reported. In addition, we calculated the Skew value and Kurtosis values for each of the constructs, and the results were within an acceptable range of results. The findings revealed that all of the constructs are statistically significant, which confirmed the normality of the data. The statistical significance of all variables in the normality test indicates that the sample size is sufficient for the investigation under consideration. In addition to examining the validity and reliability of the data, we looked into the possibility of multicollinearity in the current study. We computed the variance inflation factor (VIF) and tolerance values for each of the constructs under consideration. Ahmad et al. (2021b) hypothesised that multicollinearity did not exist in the dataset if the VIF values were less than 10 and the tolerance values were greater than 0.10, respectively. The findings revealed that the VIF values have ranged from 1.01 to 1.19 over the course of the research. As a result, there are no significant multicollinearity problems with this study. Confirmatory factor analysis (CFA) The researcher used CFA to figure out the factor structure. Factor loadings have been used to test the validity of the factors. According to Patil et al. (2008), the validity threshold for factor loadings is 0.6. Based on Table 2, it is clear that none of the variables are invalid because their factor loadings have all exceeded the cut-off value. In addition, the researcher used composite reliability and Cronbach Alpha to assess the reliability of the latent constructs. Having reliability values above a certain threshold is preferable 0.7, but it is also acceptable if the value is greater than or equal to 0.6. Because of this, remote work has a Cronbach Alpha of 0.692, while the composite reliability for the same variable is 0.828, as shown by the measurement model. This demonstrates the statistical reliability of all constructs (Akroush et al. 2015; Breyton et al. 2021; Das 2017; Pícha and Navrátil 2019; Poudel et al. 2014).Table 2 Convergent validity and reliability of the constructs and indicators Variables Indicators Factor Loadings Composite Reliability Cronbach's Alpha AVE Financial performance FP1 0.750 0.849 0.833 0.797 FP2 0.799 FP3 0.783 FP4 0.875 Operational performance OP1 0.786 0.867 0.787 0.690 OP2 0.862 OP3 0.865 OP4 0.967 Access to Finance AF1 0.759 0.846 0.840 0.815 AF2 0.818 AF3 0.911 AF4 0.862 Mergers and Acquisitions MAA1 0.900 0.912 0.875 0.775 MAA2 0.860 MAA3 0.958 MAA4 0.906 Profitability PRO1 0.821 0.850 0.939 0.869 PRO2 0.926 PRO3 0.924 PRO4 0.891 Remote Work RW1 0.756 0.888 0.792 0.719 RW2 0.764 RW3 0.663 RW4 0.750 Customer Satisfaction CS1 0.773 0.747 0.826 0.853 CS2 0.883 CS3 0.624 CS4 0.769 COVID-19 CV1 0.858 0.834 0.769 0.858 CV2 0.874 CV3 0.967 Conversely, the concept of convergent validity is employed to establish the existence of a relationship between two variables (Alolayyan et al. 2022). It was found that convergent validity can be determined by an AVE threshold of 0.5 in the same research. No latent construct has a convergent validity lower than 0.619, which is significantly higher than the 0.5 level of significance. The HTMT ratio can be used to assess discriminant validity in addition to ensuring that a pair of latent constructs is distinct from each other. It was found that a value of 0.9 was the maximum acceptable value in a study by Jensen et al. (2020). So there is no infringement of the assumption of discriminant validity because there are no associations with a value greater than 0.9 as shown in Table 3.Table 3 HTMT ratio for discriminant validity Variable Financial performance Operational performance Access to Finance Mergers and Acquisitions Profitability Remote Work Customer Satisfaction COVID-19 Financial performance 0.536 Operational performance 0.087 0.045 Access to Finance 0.691 0.319 0.504 Mergers and Acquisitions 0.707 0.860 0.616 0.431 Profitability 0.506 0.769 0.511 0.525 0.658 Remote Work 0.505 0.498 0.780 0.478 0.658 0.584 Customer Satisfaction 0.499 0.498 0.410 0.478 0.658 0.509 0.511 COVID-19 0.582 0.589 0.503 0.478 0.658 0.562 0.558 0.552 Path assessment In order to determine the significance of all the variables, the researcher used bootstrapping, which is a resampling technique. Table 4 and Fig. 2 summarise the findings in this study's final findings. According to the results, COVID-19 has a statistically significant negative effect on SMEs financial performance, [B = -0.514; p value 0.001]. The findings reveal that due to microeconomic lockdown in different cities of China, 0.514% financial performance of SMEs is reduced. There was a negative impact on China's SMEs in terms of business norms, which means that operational procedures can be improved by incorporating innovative elements. COVID-19 also had a statistically significant and positive impact on the population on remote work [B = 0.092; p value 0.001]. The findings show that COVID-19 had a statistically significant and beneficial impact on operational performance, but the impact on profitability statistically significant and negative [B = -0.429; p value 0.01]. It means that profitability of SMEs reduces during the COVID-19 period. SMEs' profitability and customer satisfaction were both shown to have decreased as a result of the COVID-19 macroeconomic lockdown. In the end, it can be said that COVID-19 has had a positive impact on business practices. However, China's SMEs saw a decline in business performance.Table 4 Hypothesis testing Hypothesis Path Coefficient T Statistics P Values H1 COVID-19 - > Financial performance -0.514* 12.93 0.000 H2 COVID-19 - > Operational performance 0.092* 1.523 0.001 H3 COVID-19 - > Access to Finance -0.641* 15.92 0.000 H4 COVID-19 - > Mergers and Acquisitions 0.065 14.361 0.152 H5 COVID-19 - > Profitability -0.429* 7.448 0.000 H6 COVID-19 - > Remote Work 0.452 10.436 0.005 H7 COVID-19 - > Customer Satisfaction -0.44* 9.938 0.000 Fig. 2 Path diagram Predictive relevance and quality of the model In order to certify a model's predictive relevance, the Q-square must be above 0 Menkeh (2021) blindfolding method. Table 5 shows the results in terms of R-squared and Q-square. Except for mergers and acquisitions, the results show that all models are sufficiently predictive. According to COVID-19, the model's quality is determined by the variation in the model's variance, which accounts for 24.67% of the novel operating processes that may differ from one another, acquisitions, profitability, and remote work.Table 5 R-squared and Q-square Variable R Square R Square Adjusted Q-squared Financial performance 0.271 0.268 0.166 Operational performance 0.369 0.365 0.201 Access to Finance 0.421 0.419 0.158 Mergers and Acquisitions 0.350 0.348 0.210 Profitability 0.289 0.286 0.216 Remote Work 0.448 0.445 0.270 Customer Satisfaction 0.243 0.241 0.371 Discussion COVID-19's influence on business practices and performance was comprehensively investigated in this research. According to the findings in the preceding section, the COVID-19 has had a substantial impact on Chinese SMEs' performance and operational standards. The results of the path assessment show that Innovative operating processes of SMEs have been influenced greatly by COVID-19. According to previous research, COVID-19 has had a significant impact on the development of new operational procedures. Restrictions placed on businesses by COVID-19 have necessitated the development of innovative operational procedures to ensure efficient business operations in uncertain situations, as outlined by Apicella et al. (2022). Similarly, according to Nurlaelah (2022), as a result, businesses have been forced to reshape themselves in response to the new market conditions in their operations or inventing themselves as per the situation of COVID-19, as per the study. When it comes to small firms' innovative operational methods, COVID-19 has a considerable impact. Furthermore, this study's primary findings show that COVID-19 had a significant but negative impact on the performance of SMEs. The detrimental Throughout the scientific literature, the influence of COVID-19 on a company's bottom line has long been documented, as evidenced by numerous studies. Kharlanov et al. (2022) claim that the COVID-19 pandemic and the ensuing lockdowns have harmed SMEs by causing logistical blockages, labour shortages, and a significant drop in customer demand. The COVID-19 pandemic has had a significant impact on the company's profitability as a result of these issues. In contrast, the study's primary findings show no connection between COVID-19 and acquisitions and mergers. According to the study (Gusti and Purnamawati 2022), which claims that the COVID-19 pandemic is causing most business deals to fall through, as a result, the majority of businesses are holding back until the market is more stable before engaging in merger and acquisition activity. COVID-19 has a significant impact on remote work, stakeholder satisfaction, and safety, as the primary findings of this study confirm. Finally, the findings of this study show that Innovative operational techniques, profitability, stakeholder satisfaction, and safety are all impacted by COVID-19. Remote work by SMEs is also impacted by COVID-19, all of which were examined in depth. Aidoo et al. (2021) state that the pandemic has affected China's economy and SMEs in a variety of ways, both on the supply and demand sides. Companies in China have had to deal with a shortage of workers because many of them are sick, which has had an impact on the country's productivity. A wide variety of products are also in high demand grew as a result. However, the supply has been affected and certain policies have been developed as a result macroeconomic lockdown limits due to a lack of productivity. According to Zainal et al. (2022), more than 5800 businesses in the country were forced to close because of approximately 40% of the staff was laid off as a result of the epidemic. Because of this, the country's overall economy was affected, and 80 per cent of small businesses have not been able to reopen since the pandemic began in 2020. Due to low product demand, many companies were forced to close their doors, which had an impact on supply-side factors (Younis and Elbanna 2021). SMB losses are expected to fall by 20%, resulting in $167 billion in losses for small- and medium-sized businesses, respectively. Conclusion and policy recommendations A survey questionnaire was used to gather the primary quantitative data needed to meet the study's primary objective. These data have been analysed using the SEM model technique confirmatory factor analysis and path assessment are two methods of testing. According to this study's findings, COVID-19 has a significant impact on financial performance, operational performance, profitability, remote work, and customer satisfaction. COVID-19, on the other hand, appears to have no significant impact on SMEs' mergers and acquisitions. Small- and medium-sized businesses in China's tertiary sector have experienced a 46% drop in their normal capacity since the beginning of the pandemic and its ensuing macroeconomic lockdown measures in the first quarter of 2020. Furthermore, this study's primary findings show that COVID-19 had a significant but negative impact on the performance of SMEs. SMEs in China are currently being negatively impacted by COVID-19, according to the study's findings, which focus on the following aspects. Due to COVID-19, the researcher was able to fill in the void dealing with China's present small- and medium-sized business concerns and problems. Our approach contributes to the frontier of knowledge where small business economics and the source of aggregate variations are studied academically. Initially, we examine how the COVID-19 pandemic affects the economic activity of different business sizes. Consistent macroeconomic accounting frameworks can be used to establish the link between shocks in SMEs and large enterprises and their aggregated impacts. In addition, our approach contributes to academic studies of the micro-basis of macroeconomic fluctuations that are more mainstream (i.e. standard business cycle theory, with micro-features, and the granular origins of aggregate fluctuations). Based on our findings from SEM, it is clear that both small and large businesses are critical to the economy. With these findings, we are able to reconcile our mixed narratives about how these categories contribute to economic activity. The specific industry that is disrupted needs to be considered in order to take into account the relative importance of each factor on small- and medium-sized businesses. Small- and medium-sized businesses (SMEs) experienced a 43% decline in activity as a result of the COVID-19 pandemic. Employers lost two-thirds of their workforce to small- and medium-sized businesses. As a result, while large firms are more critical to the stability of the economy, small- and medium-sized businesses (SMEs) and microenterprises in particular have a significant impact on employment. Policy implications SMEs need to have a crisis plan in place in order to handle a situation like COVID-19 effectively. In order to accomplish this, human resources must create a comprehensive crisis plan that involves all levels of the organisation and is widely disseminated in a timely manner. A company's actual revenues and expenses, both variable and fixed, should be properly assessed, too. Using these data, entrepreneurs will be able to make more informed decisions about their company's future. The company's profitability may be less affected by COVID-19 as a result of this. The SMEs have also been advised to examine the viability of their current business model, as well. In the light of the market's rapid shifts, companies must reevaluate their business models and the current state of their operations in the light of these assumptions about costs and revenues. International transaction expenses have gone up and travel has decreased as a result of a decrease in employment, and a decrease in demand for services that require proximity between people have all been examined in business model for pandemic analysis in computing environments (Wendt et al. 2021). Due to the simultaneous impact on both supply and demand, general equilibrium models are required when putting an end to economic activity. In the China, SMEs are a major focus of business support policies. A first approximation shows, for example, that the financial sector contributes significantly to the preservation of economic activity. When these companies are disrupted, we predict that demand will fall further. In the light of these findings, credit policies for SMEs that have a significant impact on GDP could be supported by these findings. It appears that SMEs had a significant impact on the Chinese economy, particularly on microenterprises (those with fewer than ten employees) and smaller firms under 50 employees. Limitations and future research direction A major limitation of this study is its focus on SMEs. The scope of this study can be expanded by incorporating multinational corporations (MNCs) into future studies. In addition, the study's lack of qualitative data was a major drawback. A mixed-methods research design can thus be used to provide more definitive results in the future. Author contribution Xiao Daiyou1 contributed to conceptualisation, data curation, methodology, writing—original draft, and supervision; Su Jinxia contributed to visualisation, editing, and software. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data availability The data can be available on request. Declarations Ethical approval and consent to participate The authors declare that they have no known competing financial interests or personal relationships that seem to affect the work reported in this article. We declare that we have no human participants, human data, or human tissues. Consent for publication N/A. Competing interest statement The authors declare no conflict of interest. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 35484459 20358 10.1007/s11356-022-20358-z Research Article Assessing the nexus between fiscal policy, COVID-19, and economic growth Wang Tao [email protected] 1 Gao Ke [email protected] 23 Wen Chen [email protected] 4 Xiao Yuanzhi [email protected] 5 Bingzheng Yan [email protected] 6 1 grid.411923.c 0000 0001 1521 4747 School of Finance and Taxation, Capital University of Economics and Business, Beijing, 100081 China 2 grid.11135.37 0000 0001 2256 9319 School of Economics, Peking University, Beijing, Beijing, 100871 China 3 Development Research Center of Shandong Provincial People’s Government, Jinan, Shandong 250011 China 4 grid.24539.39 0000 0004 0368 8103 School of Finance, Renmin University of China, Beijing, Beijing, 100872 China 5 grid.264756.4 0000 0004 4687 2082 Department of Economics, Texas A&M University, College Station, TX 77843 USA 6 grid.189504.1 0000 0004 1936 7558 College of Professional Study, NortheasternUniversity, Boston, Boston, MA 02115 USA Responsible Editor: Philippe Garrigues 29 4 2022 115 6 2 2022 15 4 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The COVID-19 issue deteriorated South Africa’s already dire economic situation, exacerbated by years of considerable debt increase. The COVID-19 pandemic has disrupted trade to such an extent that some enterprises are barely working at a quarter of their potential. Furthermore, economic agents delay economic decisions while waiting to see how the crisis develops. According to some economists, increased government expenditure will raise GDP enough to keep the country’s debt-to-GDP ratio steady and restore fiscal sustainability. We use a panel data model to estimate a fiscal reaction function, which we then apply to historical data to assess the government’s prior efforts to maintain or restore budgetary sustainability. We calculate the impact fiscal balance, government expenditure, interest rate, and revenue changes that the government will have to make to restore the country’s fiscal stability due to the financial impact of the COVID-19 issue.The findings show that fiscal balance and tax revinue have a significant impact on the economics growth, while government expenditure and corruption reduce the growth of the country. Keywords Fiscal policy COVID-19 pandemic Economic growth Government expenditure Panel data model ==== Body pmcIntroduction The increase in material use impacts environmental quality in the form of climate change, natural resource depletion, increased air and water pollution, and biodiversity reduction (Chandio et al. 2021; Hao et al. 2021; Razzaq et al. 2021). South Africa’s mounting external bill, similar to that of every other newly developing nation, has become an area of interest for researchers and politicians as the country’s economic growth slows (Yung et al. 2021). COVID-19’s probable detrimental influence on borrowing money and the country’s economic growth adds to this worry (Elavarasan et al. 2021; Irfan, et al. 2022a, b; Yang et al. 2021). While revenue shortfalls lead to international borrowing vs. government expenditures (Afonso et al. 2010; Faria-e-Castro 2021), specific thresholds beyond which a country’s debt-to-GDP ratio can hinder growth to studies (Guo and Shi 2021; Song et al. 2022). As a result, this research aims to investigate the impact fiscal policy and COVID-19 on economic growth of south Africa over the period from 1990 to 2019. According to statistics, South Africa’s public spending activities have expanded the actual number and comparative conditions and as a %of GDP. The rise in government expenses resulting from the COVID-19 pandemic has destroyed the world market like South Africa. It has increased the country’s debt profile to pay its financial responsibilities (Morsy et al. 2021). South Africa had been in a recession for two quarters before the pandemic lockdown. Furthermore, in the pandemic’s effects necessitating loans, a 60.8% debt-to-GDP ratio was forecast for the government in the 2020 fiscal year, up from a February estimate of 56.2%. In June 2020, the administrative board of the International Monetary Fund (IMF) had permitted a loan for the USA of $4.2 billion (about R75 billion) as a result of its Rapid Financing Instrument (RFI) program to help alleviate the economic impact of the pandemic and social effects. The African Development Bank (AfDB) also approved a $288 million South Africa response assistance program (R5 billion). The debt-to-GDP ratio was 37.2% in 2015, 48% in 2016, 48.8% in 2017, 48.5% in 2018, and 51.4% in 2019, according to the South African Reserve Bank’s (SARB) 5-year trend analysis (2019). Various sectors have raised concerns regarding the government’s debt-to-GDP ratio growing as government spending increases. Due to its limited scope and goal, the current study evaluates fiscal policies addressing expenditures Based on income levels during the COVID-19 pandemic-related financial downturn, it focuses on the link between government spending and economic growth (Huang et al. 2022; Wasim Iqbal et al. 2021a, b; Latif et al. 2021). From this perspective, the study’s sole goal is to look at the changes in monetary policies and the direction of public spending in these country groups’ growth axes. The study’s literature review portion discusses how public expenditures and fiscal policies might be implemented in cost-effectively decreasing countries during the COVID-19 pandemic-induced economic crisis. The study’s primary focus is on the shift in public spending in reaction to pandemic circumstances and COVID-19 answers depending on countries’ economic levels in terms of public spending. Another worrying trend is the rise in debt servicing costs. For example, between 2018 and 2019, R180 billion was consumed on debt retuning. This amounts to roughly the whole healthcare financial plan for that amount of time. Furthermore, between 2018/2019 and 2008/2009, the exterior debt-to-GDP %ageenlarged from 26 to 62.5% (Ridzuan and Abd Rahman 2021). COVID-19’s influence on debt servicing has the potential to be harmful to the country. In its amended 2020 financial plan, the National Treasury issued a warning in June 2020, stating that the government spends much more than it receives in taxes. Consequently, debt has increased at an exponential rate. If this trend is not halted and reversed, South Africans’ lives will suffer long-term implications. In the medium run, if interest payments on the debt are not reduced, they will be a big part of the government’s budget. The difficulties of external liability and bribery are the latest challenges confronting South Africa’s growth aspirations, evidenced by the above narrative. For that purpose, the study intends to investigate previous to the outbreak of COVID-19, an increase in foreign debt, economic growth, and bribery are all related, with a particular focal point on the anticipated negative economic repercussions of corruption. As a result, this study makes four contributions to the attainment of its objectives (Burger and Calitz 2021). First, we examine what was known before the outbreak of the COVID 19 pandemic about the debt-to-GDP concept. This is likely to act as a springboard for future research into South Africa’s mounting debt for the duration of and following the pandemic. Second, we investigate the impact of bribery on the debt-GDP debate in the context of individual countries, which is a topic in the available literature. It is a critical gap that this research aims to close. Third, recent research on the impact of public debt on economic growth has been ambiguous. While some studies found a unidirectional relationship (Chinoy and Jain 2021; De Vito and Gómez 2020), others found a bidirectional relationship (Gechert et al. 2019; Germaschewski 2020; Mundle and Sahu 2021), and still, they believe that the relationship is nonlinear and are hence neutral in their conclusions (Polzin et al. 2015). Finally, according to the authors, this appears to be the first time a time sequence debt-GDP analysis has included dishonesty. For a long time, South Africa’s government debt-to-GDP ratio increased from 27% in 2008/09 to 62.5% in 2019/20. Then there was the COVID-19 crisis. Efforts have been made to contain the virus’ spread and prepare the healthcare system for anyone who may become ill by March 2020. To put it another way, the lockout and closure of industries caused a tremendous supply shock. These actions caused an instant demand and supply shock to the economy. As a result, several businesses have temporarily shut down, and employees have been furloughed. The government announced an R600 billion (almost 11% of GDP) package in April 2020 to supplement the healthcare system and alleviate the problems that individuals and businesses have endured as a result of the crisis. These measures have shown to be successful (Wang and Zhang 2021). The package included a non-financial RS200 billion loan guarantee scheme for businesses with less than R300 million yearly turnovers. The South African Reserve Bank, for its part, would lend money to banks—only if firms default would the government guarantee be implemented. Governments worldwide have launched economic stimulus packages that combine financial policy, fiscal, and monetary measures. Direct funding includes paying unpaid employees' paychecks and sick leave, supporting small and large firms, the government and central bank financing, and directly sponsoring healthcare systems are all examples of fiscal initiatives. The Unemployment Insurance Fund, which is not part of the main budget and hence not subject to tax deferrals or exemptions, provided R40 billion of the remaining R300 billion in wage protection. Because the government intended to reallocate R130 billion from the February 2020 budget to cover part of the unexpected costs, not all of it was deemed excessive. Aside from the COVID-19 problem, the government planned to borrow money from various financial entities to deal with its ballooning deficit and crises. The maximum of these loans is $4.5 billion from the International Monetary Fund (IMF), which will expire on July 27, 2020 (Francis et al. 2020). Considering the current economic situation, the IMF recommended governments implement a four-tiered fiscal policy plan to assure creditworthiness in 2008 and early 2009. The pre-COVID-19 era was a period Just as an additional R600 billion was announced in April 2020 for the health system, individuals, and businesses affected by the epidemic, an additional R600 billion was announced in April 2020 for the health system, persons, and businesses affected by the epidemic. The country was on the edge of defaulting on its debt obligations. However, policymakers and academic experts are concerned about the local economy’s record of a negative 0.4% by the end of 2019 compared to 2.6% 10 years ago (Morsy et al. 2021). External borrowing is also harmful to an economy only when it has the potential to generate higher economic advantages than the interest expenses, even if it occurs within a life cycle and is not used effectively and carefully (U. Khalid et al. 2021). In general, external borrowing can increase capacity while increasing productivity, making the debt creative and feasible (Yin et al. 2021). On the other hand, the debt might lead to financial instability and increased foreign borrowing, exposing the country to various economic challenges. Debt has hampered the effectiveness of fiscal measures, and monetary authorities’ authority to raise interest rates for monetary considerations was constrained, as it affects budget deficits and debt levels (Burger and Calitz 2021). Despite growing concerns about debt sustainability in rising and up-and-coming economies, country-based study on the negative repercussions of dishonesty in the debt-to-GDP discussion are still sparse. The ongoing ignoring of corruption in the developmental literature, as Seccareccia and Rochon (2020) correctly observes, is an issue since it is a tumor to prosperity generation and economic growth. South Africa’s bribery has reached such alarming levels that the country’s ranking has dropped from 55th place in 2011 to 60th place in 2018. Research (Ko 2020) claimed that South Africa squandered R385 million on poverty reduction and corruption-related vices, and democratic elections in 1994 and 2016 were tainted by corruption (Y. Chen et al. 2021). The pre-COVID 19 era was when the country was on the verge of defaulting on its debt obligations, just as an additional R500 billion was announced in April 2020 for the health system, individuals, and businesses affected by the epidemic. However, policymakers and academic experts are concerned about the local economy’s record of a negative 0.3% by the end of 2019 compared to 2.5% 10 years ago (Luke 2020). The study is organized as follows: part one has an introduction, “Literature review” contains recent empirical evidence, “Methodology and data” includes data issues and methodology, “Results and discussion” contains estimated results, and “Conclusion and policy recommendation” contains a policy and conclusion recommendations. Literature review The slow growth model, which allows us to consider the elements that influence economic growth, serves as the theoretical foundation for this study (GDP) (Chau 2021, Tang et al. 2021; Jin et al. 2022). The dependent variable in this model is gross domestic product (GDP), which is represented as a function of labor (L) and capital (K). In its simplest form, the model of Solow growth is represented as Y = f (K, AL), where Y represents GDP and K represents capital (A fixed capital structure is one of the parts of the capital substitute). Ji et al. (2021) stand for effective labor, as African countries’ labor was effective due to deal liberalization and technological knowledge (Chau et al. (2021); Li et al. 2022; Rao et al. 2022; Tang et al. 2022). Furthermore, the debt overhang concept claims that if a country’s debt exceeds its capacity to service it. The quantity of debt service required varies depending on its production level (Cantore and Freund 2021). The idea is that when foreign debts rise, domestic investor income is taxed away, causing local and external investments to be distorted, lowering GDP. In other words, reliance on economic aid such as money owing for economic revitalization is akin to growth stalling and appreciation (Ashihara and Kameda 2018). One strategy to stimulate economic growth, according to Keynesian theorists, is to inject additional funds into the economy (Zhuang et al. 2021). If expected revenues fall short of government spending, borrowing can accomplish this. Three key links between economic growth and debt have been found, consistent with existing economic theories (Chen et al. 2020)(Zheng et al. 2021)(Gao et al. 2020). The link could be explained by the positive Keynesian hypothesis, the negative extension of liability theory, or the neutral Ricardian correspondence hypothesis (neutral). Most hypotheses about the growth-debt link in developing countries, such as South Africa, are pessimistic (Chau et al. 2021a, b; Lau et al. 2021; Liu et al. 2021; Yu et al. 2022). In addition, inflation was included to avoid the problem of biased omitted variables because inflation is expected to be negative in a developing economy like South Africa. Truger (2020) added corruption as an exogenous component with a declining trend in productivity to the GDP-debt hypothesis. According to empirical reviews, the impact of peripheral debt on economic growth and the connection linking corruption and economic growth are hotly debated topics. As a result, there appears to be no consensus on these linkages, which is the best motivation for this research. For the issue of growth in debt, studies have confirmed the Keynesian notion that for economic progress, it is necessary to have a certain amount of debt, while others say that money owing in any form is harmful to any country’s development ambitions (Bhowmik et al. 2022; Dupor and Guerrero 2017; Faria-E-Castro 2018). In addition, studies like Baker et al. (2016) and Olakojo et al. (2021) found conflicting results regarding the growth-corruption theory. A summary of essential literature on the growth-debt-corruption argument is provided below. As a result, this research relies mainly on Olakojo et al.’s (2021) study to analyze fiscal policy’s impact on the economic system activity development in institutional variations and external debt difficulties in emerging nations. Current studies and evaluations of the repercussions and ramifications of the COVID-19 pandemic’s economic crisis on countries are still far from clarifying the actual scenario (Gao et al. 2021; Zhang et al. 2020). Because this problem impacts the entire planet simultaneously, it will not be resolved soon or quickly. As a result, the suggested financial policies and suggestions for public spending under the fiscal policies, which form the study’s basic framework, are still up for argument. Public debt and economic growth Lin & Zhu (2019a) used two alternative techniques in their research on the relationship between economic growth and India’s governmental debt, which they discovered. The nonlinear 2SLS technique found that public debt is beneficial. It positively influences economic growth in the short term, but it has a detrimental impact in the long term. Similarly, Chakrabarty and Roy (2021) found a depressing relationship connecting Malaysia’s governmental GDP and dept. It was also discovered that government consumption, and for time-series data between 1991 and 2013, supplementary monetary constraints, government spending, and the budget deficit were used as defining factors. The budget deficit decreases the functions of economic growth. Wen and Zhang (2022) employed the Markov-switching model to explain Turkey’s high debt levels concerning growth, implying that the country’s debt-growth relationship is nonlinear. Gupta and Barman (2009), Jinjarak et al. (2021), and Ciaschini et al. (2013) conducted additional research on Asia, a continent with a lot of growing economies like South Africa (2018). Kharusi and Ada found the absence of a significant positive association between GDP and the country’s national debt for Oman (2018). Not only that, but the ARDL’s findings, based on data from 1990 to 2015, revealed that investment (a proxy for gross fixed capital creation) had a positive, if not significant, impact on economic growth. In a study on Sri Lanka by Azad et al. (2021), external debt was found to provide a boost for economic growth while debt servicing was found to have a negative relationship with economic growth. Using the PVAR technique of evaluation (Kozup and Hogarth 2008) for 48 developed and emerging economies, the debt-growth theory was split into two categories: public and private. The findings indicated that public debt has various degrees of negative impact on economic growth in industrialized and emerging economies. This research recommended strategies that are likely to lower debt burdens for rising countries in order to achieve hoped-for faster economic growth. Separate research on EU nations (Riza and Wiriyanata 2021; Zhou et al. 2018) indicated different amounts of debt-to-GDP turning points. For example, in a panel estimation of a generalized growth model (Lin & Zhu 2019b), EU member countries were separated into old and new members, confirming the existence of a nonlinear statistically significant impact of public debt on economic growth in the 25 sovereign member countries under consideration. The analysis came up with a debt-to-GDP turning point of 80–94% for prior union members and 53–54% for potential union members. Beyond these limits, any more debt achievement will be detrimental to these governments. In a similar vein, Dincă and Dincă (2015) used a quadratic equation to investigate the relationship between government debt and GDP in ten of the EU’s newest members and discovered a nonlinear debt-to-GDP relationship with a turning level of roughly 50%. Yuan et al. (2022) found a significant amount of external debt in Ukraine and certain other emerging economies in Europe, notwithstanding volatility in the macroeconomic environment impeded development prospects. Some research on the debt-GDP debate in the OECD group has mixed results resulting from the interpolation of emerging and developed economies (Bordo and Levy 2021). Dzigbede and Pathak (2020) examined and determined the turning point of debt to GDP in 31 OECD and five non-OECD countries, confirming the hypothetical supposition that a low debt to GDP is better than a greater one. From 1980 to 2010, a panel estimation using a generalized economic growth model was used. The study classified the nations into established and emerging economies and recommended a 90–94%turnaround point for urbanized economies, and emerging economies account for 44–45% of the total. In another study, Dulal et al. (2015) looked at 7 OECD developed economies and found that in those countries, there is no evidence of nonlinearity in the relationship between public debt and economic growth. The optimal debt-to-GDP ratio relies on measurement, time, and each country’s unique qualities in terms of developmental stages and techniques. The evaluated condition was not submitted to forcefulness testing in these investigations, which had ramifications for the studies. The Reinhart-Rogoff (RR) hypothesis on the link between economic growth and debt has also been criticized, with researchers claiming that there is no rule of thumb in the two scholars’ 90% prescription (Dulal et al. 2015). Loayza and Pennings (2020a, b) investigated this concept in twenty highly developed economies and found errors in the summary’s data, coding scheme, and statistical weighing (Hepburn et al. 2020). It has been demonstrated that mutual agreement on this relationship is not static but can also be negative, positive, or even nonlinear in their assessment of SCOPUS listed works. In a study using the ARDL technique on EU countries (Truby et al. 2022) that at the 70% level, the connection is nonlinear. According to the findings of these studies, the relationship is a function of time and each country’s developmental level. The minimal amount of literature that has been reviewed on the linkages between government debt and economic growth in Africa generated inconsistent results. This is due to the fact that each country’s peculiarities and variables’ measurement varies. For example, Muhafidin (2020) and Pogorletskiy and Pokrovskaia (2021) found an excellent bidirectional Ghana link between national debt and GDP but a weak relationship in Nigeria, as illustrated by Pogorletskiy and Pokrovskaia (2021). Kozup and Hogarth (2008) discovered a statistically insignificant negative link for Malawi, whereas Zuo and Zhong (2020) found miscellaneous long-run impacts and a statistically significant negative impact in the short term for Uganda. On debt servicing, public debt, and GDP for Zambia, using a dynamic multivariate ARDL bounds test, researchers discovered a unidirectional causality relationship between public debt and economic development. The study, which covered the years 1970 to 2017, found no indication of a link between debt servicing and GDP. In conclusion, the afflicted countries should exercise caution while using externally sourced debt and prevent frivolities. As a result of the present COVID-19 pandemic, many countries have little choice but to rely on fiscal borrowing to solve their economic woes. This has generated concerns about South Africa’s growing state debt, which is already approaching alarming leverage levels. e Castro (2020) with varying degrees of control variables, threshold levels, and estimate methods, they have all contributed to a better understanding of the debt-GDP nexus in South Africa and policy recommendations. After researching the dynamic in South Africa, there is a link between accumulated external borrowings and GDP, and Gonz and García-alb (2021) proposed a debt-to-GDP ratio of 31.3% for the sake of the country. The nonlinear smooth evolution deterioration model’s results indicated that South Africa’s GDP status would significantly establish the ideal debt-to-GDP ratio. However, according to a recent study by the National (Khan et al. 2021), in 2019, the government borrowing-to-GDP ratio increased to 59.3%from a previous high of 31.8% in 1990. Economic activity decreased to 0.7% in 2019, compared to 4.2% in 2000, according to a 2020 IMF report. Wei and Han (2021) investigated the causes of government debts in post-apartheid South Africa using the ARDL model and exposed that public debt negatively impacts inflation and economic growth. The research looked at actual GDP, government spending, other factors, and concern rates as the fundamental causes of government indebtedness, also recommended that government debts could be reduced by improving productive capacity, controlling interest rates, and eliminating wasteful government expenses. Bui (2018) and Truby et al. (2022a) tested for the short- and long-term adverse effects by applying the same ARDL for time-series data spanning 2002–2016. Researchers discovered a negative connection between debt and GDP in South Africa. In Una et al. (2020), analysis of the relationship between South Africa’s military spending and GDP indicates that the bond is nonlinear in nature. The Logistic Smooth Transition Regression model results for 1988–2014 also suggested that government expenditure on the military was excessive. That money could be better spent elsewhere in the economy. Loayza and Pennings (2020b) and Hutchison (2020), on the other hand, support a positive association between debt and economic growth. Chakraborty and Thomas (2020) found that borrowing from outside the country positively impacts GDP, they were using the external factors-led growth hypothesis for South Africa, which was in accordance with an earlier study on Nigeria. Studies have also connected the well-known Wagner law to government spending and economic growth, which states that higher government expenditure increases economic activity (Haar 2020). In the Keynesian intangible conflicting direction, there are also in-between arguments that expenses cause an increase in government movement or economic growth (Gootjes and de Haan 2020). For example, Choi and Mai (2018) looked at the nonlinear government expenditure cum growth nexus for South Africa and discovered that a significant component of Wagner’s hypothesis did not hold for the country. In particular, to some extent, the study supported the Keynesian theory by finding a unidirectional relationship between government spending and economic activity. Finally, the study concluded that the South African government’s excessive spending was not a solution to any financial or monetary issue. According to Nong (2021), South Africa’s governmental debt-to-GDP ratio doubled between 2015 and 2016, reaching an alarming proportion of 44.3%. They used the ARDL to investigate whether public debt impacts economic growth via investment, and they found a dismal association between the relationship between government debt and investment growth. While borrowing was encouraged in order to boost capital accumulation, it was also discouraging. The study recommended that it be restricted to a manageable level. On the other hand, unbundled public debt into domestic and foreign debts in order to separate the aggregated effects of public debt on economic development from the impact of specific public debt components. They came to the termination that total public debt has both long-term and short-term negative effects on economic growth. COVID-19 and economic growth The COVID-19 catastrophe caught everyone off guard (Iqbal et al. 2021a, b; Razzaq et al. 2022; Wen et al. 2022). There are many factors that determine how people respond to crises, including internal, external, and even personality characteristics (Ahmad et al. 2022; Irfan et al. 2022a, b; Jinru et al. 2021). A health issue posed by COVID-19 prompted a rapid response from South Africa compared to countries such as Brazil, Mexico, and the USA (Jiang et al. 2021). Within 23 days of the initial illness, SA instituted a lockdown. An 18-day advantage over Italy, and half the time, it took the USA to deploy a lockdown were the benefits of this lockdown. Nearly everyone agrees that SA has not seen such quick and decisive action in decades as the swift installation of a lockdown. SA’s healthcare system, however, would not have been able to handle an exponential rise in patients compared to Italy, Germany, and the USA. In 2017, health spending in SA amounted to just US$28 billion, or 8.1% of GPD. Compared to Italy’s 8.8%, it is close; nevertheless, the GDP of Italy is dwarfed by SA’s at US$1.951 trillion versus US$350 billion. That is six times more than South Africa’s healthcare spending, at about US$172 billion (Fornaro and Wolf 2020). As a result, while SA was able to gauge the early impact and response from other countries, the country’s healthcare system was not prepared to handle an inflow of patients. This is not to say that COVID-19 did not constitute a severe threat to human health and economic growth. The following first-quarter GDP figures offer context for the predicted severity of COVID-19’s impact on SA’s significant industries because the second quarter’s data is unavailable. Construction (4.7%); mining (21.5%); manufacturing (8.5%); power, gas, and water (5.6%); and transportation (4.7%) all saw significant increases in GDP in the first quarter of 2020. The agricultural industry was the only one to see any significant growth (+ 27.8%). While the agricultural sector accounted for just 1% of GDP in 2019, the mining industry contributed 9% of GDP in 2019 (Ziolo et al. 2019). These factors point to a significant reduction in corporation tax collection in the second and third quarters. SARS Commissioner Edward Kieswetter estimates that the gap in collections is roughly R285 billion. South Africa’s 2020 budget will be further strained as a result of this gap. South Africa’s reliance on manufacturing and mining has worsened the strain on the country’s fiscus, both of which have had lower productivity and negative growth in this era (Garton et al. 2020; Khalid and Salman, 2020). Another future national financial crisis could occur in 2024, according to the country’s finance minister. That is why government expenditure cuts are necessary. According to the Supplementary Budget Review (SBR) released on June 24, 2020, the debt-to-GDP ratio is predicted to peak at 87% in 2023/2024 if zero-based budgeting principles are implemented and the public sector salary bill is reduced (Deleidi et al. 2020). Corruption and economic growth There has been a lot of debate on this topic since the effort of Timilsina and Pargal (2020), one of the earliest researchers to study the link between corruption and economic growth. According to Singhal et al. (2019), most studies on the relationship between external debt and GDP were based on panel studies, indicating that country-based research is still scarce. As part of their investigation of the impact of external debt and corruption on economic growth in Kenya, Malawi, Nigeria, South Africa, and Uganda, García and Mejía (2018) used FMOLS and DOLS methodologies. Furthermore, the study found that foreign debt and economic development had a negative relationship in addition to a bidirectional one. A one-way correlation between economic growth and corruption was also found, and a positive correlation between corruption and economic growth in these countries. This study’s findings cannot accurately reflect the current state of affairs in South Africa for the easy reason that governmental debts and economic growth vary from country to country. Not only that, but a panel study like this one may highlight the negative impact of corruption at the country level. It was found that the public debt effect on growth is linked to corruption by Howes et al. (2019), who used three techniques of estimation: the Pooled OLS, the FE models, and the Dynamic Panel GMM. Furthermore, the study found that public debt had a detrimental impact on economic growth in corrupt countries and a favorable effect on more transparent and less corrupt governments. Rentschler and Bazilian (2017) added credence to the idea that corruption is a declining function of economic growth. Malerba et al. (2021) came to two distinct conclusions about the BRICS due to methodological variances in their studies. While the rigid result reveals a negative contact, the GMM findings showed that corruption positively influenced GDP from 1996 to 2014. While Criscuolo & Menon (2015) conducted a negative correlation between corruption and economic growth, Criscuolo and Menon (2015) identified a positive correlation. Padhan and Prabheesh (2021) focused on the interplay between tax evasion, corruption, and a country’s public debt to influence fiscal policy. A new quantitative fiscal policy theory stated that corruption might lead to a heavily borrowing government even if an economy’s debt level is zero. An increase in public debt and decreased output and well-being were both predicted to occur if corruption was allowed to grow unchecked. In addition, the impact of corruption on public debt on a panel of OECD nations that spans 1995 to 2015 found that public debt is arising the purpose of corruption. It was also noted that reducing corruption by half in the short-term would cut public debt by 2%. The long-term negative effects of corruption on foreign borrowing are still evident in some nations with a high level of corruptive tendency. According to the study by (McKibbin and Vines 2020), for five ASEAN countries, foreign government debts, corruption, and GDP relationship constituted the basis of their research, which admonished the governments in their desire for higher debts. It also found a direct link between foreign debt and economic growth, with no correlation to corruption or economic development in the analyzed countries (Akhtar et al. 2020; Asbahi et al. 2019; Nasir et al. 2022; Xiang et al. 2022). Essentially, a certain level of corruption is necessary for economic growth, particularly in bureaucratic activities. According to Stavytskyy et al. (2020), results from the application of the Bootstrap Panel Granger causality technique, there is a correlation between corruption and GDP in South Korea and China. As a result of the preceding analysis, we can draw two conclusions. Because its negative impact on economic activity cannot be ignored, researchers do not include corruption in the debt-GDP hypothesis—country-specific studies on how corruption, external debt, and economic growth interact are still rare. As a result, we are hoping to close this knowledge gap by focusing our research on South Africa. Methodology and data Model specification The empirical analysis in this paper is based on a dynamic panel regression framework and a fixed effect estimating method. Because the null hypothesis is rejected at a level of 1% significance, the Hausman test favors the fixed effect model over the random effect model. Heteroskedasticity is taken into account in the econometric analysis by using a one-way error component fixed effect model and robust standard errors. Different regression equations generally use explanatory variables. Equation (1) can be rewritten to have the estimable version of the aforementioned equation.1 EGt=a0+β1InFiscalbalancet+β2InDebitratiot+β3InInflationt+β4InTaxRevenuet+β5InGov′tExpendituret+β6InRealinterestrate+αi+ut where EG is economic growth, InFiscal balancet=log of fiscal balance, InDebit ratio=the public debt ratio, InGov’t expenditure=log of government expenditure on administration, InReal interest rate=log of real interest rate, Intax revenue=log of tax revenue, ai=the unobserved effects, and ut=the error term on the tth year. Data and variable Table 1 presents the descriptive statistics of the study variables. The time-series data utilized in this study came from the State Bank of South Africa statistical bulletin for 2020. Fiscal balance, inflation, GDP growth, corruption, real interest rate, debt ratio, gov’t expenditure, and tax revenue was collected over the period of 2010 to 2020.Table 1 Descriptive statistics Variable Mean SD Min Max Fiscal balance − 2.522 4.742 − 18.073 20.482 Inflation 8.52 11.288 − 11.686 98.224 Economic growth 4.225 8.048 − 62.076 149.973 Corruption 43.43 0.98 42 45 Real interest rate 10.461 49.649 − 93.513 1158.026 Public debt ratio 69.562 63.051 0.278 485.668 Gov’t expenditure − 0.707 0.599 − 1.89 1.049 Tax revenue − 0.506 0.879 − 2.845 1.282 Results and discussion Correlation matrix analysis Starting with the point review of the study’s findings, including summary data, findings, and implications based on the correlation matrix. Table 2 contains vivid data that supply a universalsummary of the variables employed in the study.The retort variable, fiscal balance, has 2.5%, implying that most African countries are in debt. The temperature anomaly has a standard deviation of 0.70, meaning that the climate in Africa is warming by 0.70 °C every year on average. Weather event 1 has a 93% chance of occurring within a year in an African country. In addition, weather events 2 and 3 have a 30% and 73% risk of occurring, respectively. Meteorological events in East Africa are higher than in the rest of the African continent for the sample period.East Africa has an average of 80% of weather events per year, compared to 49% in Central Africa, 51% in West Africa, and 57% in Sub-Saharan Africa.Table 2 The correlation matrix Variable 1 2 3 4 5 6 7 Fiscal balance 1 Inflation 0.240 1 GDP growth 0.616* 0.0291 1 Corruption 0.0141 0.0823 0.0324 1 Real interest rate 0.0611 0.128 0.0173 0.964* 1 Public debt ratio 0.442* 0.142 0.13 0.193* 0.309* 1 Gov’t expenditure 0.283* 0.193* 0.243 0.265* 0.165* 0.0807 1 *, , denote statistical significance at the 1 and 5% and 10% levels, respectively CD and unit root test Testing for cross-sectional dependence shows that the series is dependent on itself. Four different tests were performed to determine whether or not CD was present. The results of these tests (CDBP, CDLM, CD, and LMadj) are illustrated in Table 3. The findings presented here consistently do not accept the null hypothesis of independence across sections, signifying the existence of data showing cross-sectional dependence, which is supported by the statistical significance of the cross-sectional dependence statistics.Table 3 Cross-sectional dependence statistics Test Statistic Prob CDBP 203.00364 0.000 CDLM 8.603175 0.000 CD 8.2929483 0.000 LMadj 3.4041504 0.0012 This test is preferable to earlier panel unit root tests as shown in Table 4, such as the Sun et al. (2020) test, which did not make it possible for cross-sectional correlations, and has recently been used in PPP tests (Nawaz et al. 2021). It is most likely that residuals are associated across individual time-series when cross-country regressions are present, as they are in this research. We get the CIPS (Cross-sectionally Augmented IPS) statistic by taking their simple average. At a level, all variables are stationary, so the order of integration is I(0), and we can rule out the possibility of a unit root. Furthermore, certain factors are significant at a 1% level of significance, while the others are significant at a 5% or 10% level of significance. At a 1% significance level, foreign direct investment, GDP growth rate, and inflation rate are significant. At a 5% level of significance, the other variables, such as CO2, EG, SG, and PG, are significant. Because variables are stationary at the level, these results show that ordinary least square (OLS) is an accurate estimation approach.Table 4 Panel unit root tests (IPS and CIPS) Variables IPS unit root test CIPS unit root test Level First difference Level First difference Fiscal balance 3.6288 3.72015 3.95325 5.02845 Debt ratio 2.5788 4.8069 3.8766 4.961355 Corruption 2.21025 5.65866 2.88015 4.03326 Government expenditure 1.762845 4.82811 1.71255 4.964715 Real interest rate 1.667295 3.74619 3.98055 3.90705 Tax revenue 1.758015 5.71011 2.50005 3.8031 Money supply 3.438225 4.92786 4.01205 3.708705 Industrial output 2.716665 5.82036 4.017405 2.66385 , , denote statistical significance at the 1 and 5% and 10% levels, respectively Main estimation results Pooled OLS and fixed effect estimates are shown in Table 5 of this section. According to p-values obtained from Arellano-Bond and Sargan tests, the fixed effect method is both valid and effective. In all of our models,the fiscal balance has a considerable impact on the economic growth of South Africa. As a result, a 1% increase in the fiscal balance improves economic growth by 5.32%, implying that Africa’s budget deficit decreases during pendamic. This is because all other factors are equal, pendamic are followed by higher tax receipts, giving South African governments more fiscal room. This supports Can and Canöz (2021) findings for emerging nations, EU nations, and OECD nations. It also corresponds to van der Wielen and Barrios (2021).Table 5 Main estimation results PooledOLS Fixedeffect Fiscal balance 5.54 * (2.124) 5.32* (1.33) Tax revinue 7.34* ( 2.352) 7.33* (3.761) Corruption − 4.655* (2.362) − 3.552* (2.442) Government expenditure − 0.0572 (− 0.125) − 0.0631* (− 0.223) Real interest rate − 0.6252* (− 0.245) − 0.0551* (− 0.227) Debt ratio 0.387* (− 1.075) 0.417* ( 1.271) Constant − 0.477* (− 0.011) − 2.642* − 0.342) R-squared 0.9213 AR (1) 0.0001 AR (2) 0.3058 Sargan test 0.6863 , , denotes significance level at 1%, 5% and 10%. Parenthesis denotes t statics Moreover, the debt ratio from the previous year has a favorable and considerable impact on economic growth. This means that the government’s debt from the previous year signals a limited fiscal space and the need to be prudent about future spending. This conclusion is in accord with others (van der Wielen and Barrios 2021). They argue that nations with high debt-to-income ratios should pursue debt association activities to decrease their debt load and improve their fiscal balance for economic growth. Our sample with a high level of debt influence of war and real interest rate lag on fiscal balance. The coefficient of government expenditure on economic growth is − 0.631. The P-value is 0.0000, which is significant. The correlation between the two is considerable, and negative gross domestic product (GDP) and government spending on administration gross domestic product (GDP) and government spending on management, contrary to our expectations. According to the findings, an increase of 1% in government spending on management will have the following effects: a 6.31% decrease in economic growth; this deviation from our expectations could be due to spending on consumables being a major revenue source for these industries. In some cases, for politicians who hold public office in South Africa, this industry has been a hotbed for theft and embezzlement in the past. The result suggests a positive and significant link between money generated by government taxes and the country’s GDP, with a coefficient of 7.33. A 1% increase in tax revenue boosts economic growth by 7.33% when all other variables are constant. This is consistent with our presumption, as government income tax through investment will improve the nation’s output. The corruption has a coefficient of − 3.552, with an insignificant P-value of 0.1283, which indicates a negative link between the corruption and South Africa’seconomic growth for the time period of study. Increasing the corruption by 1% will result in a 3.552% decline in the economic growth. This is contrary to our expectations hypothesis says that government expenditure is a function of the government, particularly the budget deficit can aid in the prevention of a downturn or depression in the short term. This might lead to the closure of multiple companies, the closure of the bulk of banks, a decrease in demand for industrial and commercial assets, a shift in supply chains, and a significant reduction in GDP this year as a result of this massive impact. Many countries’ GDP estimates for 2020 are off by a significant margin. Due to a lack of efficiency and excessive expenditure on COVID-19 victims and their families, many of the world’s most strong countries are now facing high inflation and rising unemployment. COVID-19 shock The analysis uses a sample of South Africa as a baseline in order to determine the link between the COVID-19 event. It calculates the Bayesian PVAR framework and computes orthogonal auto-correlation functions (IRFs) and regression decomposition to track the influence of COVID-19 on industrial activity (FEVD). When COVID-19 is counted as a number of cases, Table 6 shows the decomposition of forecast error variance for industrial production. For example, these analyses determine how much of the forecast error variance is due to chance can be attributed to changes in the model’s underlying variables. For various periods, the results illustrate the relative significance of the studied variable after the initial shock. The findings show that COVID-19 shock innovations account for the majority of the forecast error variance in economic growth, even though money supply has no major explanatory power. After the initial shock, the COVID-19 shock begins to explain economic growth in the second month, With 32.65% of total variance explained, the trend continues, progressively growing over the next 22 months, eventually reaching 61.66% of total variance explained. Shocks of COVID-19, which count the total number of people killed, begin to describeas early as the first month, the predicting variance of economic growth, accounting for 7.86% of the variance. After 24 months, the variable’s contribution rises to 31.52%.Table 6 COVID-19 impact on economic growth and industrial production Steps Industrial production Economic growth Money supply COVID-19 1 60.32* (1.49) 47.06* (2.48) 23.52* (2.25) 0.37* (0.06) 2 39.31* (2.11) 55.52* (3.52) 10.42* (2.31) 24.06* (4.29) 4 28.10* (2.15) 49.83* 11.34** 41.83 3.58 2.09 (4.17) 10 23.50* (2.47) 41.16** (4.11) 9.45* (2.16) 51.39 (4.36) 14 18.29* (2.48) 23.48 (4.14) 8.46 (2.35) 61.06 (4.98) 16 33.71 (4.30) 13.502 (20.51) 12.06 (4.99) 8.83 (0.14) 20 23.17 (10.79) 36.29 (18.98) 8.02** (5.25) 23.39 (1.76) 24 17.91 (9.37) 31.52* (16.28) 7.63** (4.57) 2.46 (1.997) Finally, this section examines possible causal links between the variables. The Granger causality tests are also usedfor the examination of the estimated model. This study obtained statistically significant results using Granger causality tests relying on the (Abbasi et al. 2022) causality tests for diverse panels. Individual Granger non-causality Wald statistics are utilized to develop the test for heterogeneous panels. In addition to its computational ease and ability to accommodate heterogeneity between countries, there are other advantages to using the approach. Even when N and T are tiny, the test’s power is maintained (as they are in this case), and unbalanced panels are used. Table 7 shows the causality results. They demonstrate a connection between economic growth and fiscal policy variable (both types) (the hypothesis of Granger non-causality is disproved at 1% in both circumstances).Table 7 Dumitrescu and Hurlin Granger causality W-bar test stat Z-bar Z-bar tilde Z-bar p-value Z-bar tilde p-value Causality Fiscal balance→economic growth 4.449 6.006 5.092 0.000* 0.000* Yes Economic growth→fiscal balance 1.211 0.886 0.686 0.017 0.029 Yes Debt ratio→economic growth 6.089 0.920 0.060 0.157 0.926 No Economicgrowth→debt ratio 3.804 2.800 2.183 0.000* 0.000* Yes Corruption→economic growth 1.921 2.010 1.652 0.002* 0.011* Yes Economicgrowth→corruption 1.118 0.740 0.560 0.254 0.388 No Government expenditure→economicgrowth 1.589 1.485 1.201 0.022 0.064* Yes Economicgrowth→government expenditure 10.430 3.514 0.867 0.000* 0.082*** Yes Real interest rate→economic growth 5.217 1.391 0.618 0.032 * 0.342 Yes Economic growth→real interest rate 9.980 3.245 0.770 0.000* 0.035** Yes Taxrevinue→economic growth 2.314 2.630 2.187 0.000* 0.000* Yes Economic growth→revinue 12.431 4.710 1.294 0.000* 0.046** Yes Conclusion and policy recommendation Natural disasters can cause supply shocks by destroying production capacity and causing supply chains to be disrupted. Product innovation and the most up-to-date methods for accommodating change can result from technological progress even when human capital is disrupted due to a recession. A natural disaster can seriously impact human health and well-being. Nothing can be done toprevent new viruses from infecting humans and preventing infections from forming and harming humans. As a result of this debate, the current study investigates how the COVID-19 and fiscal policy affect economic growth and how that affects the macroeconomy’s future course empirically. According to a series of empirical tests based on panel data and a simple Panel Vector Autoregression (PVAR) model, the findings show that the COVID-19 pandemic can significantly impact industrial output. Additional harm can be done to the real economy if these shocks have negative spillover effects elsewhere. Our findings support the widespread actions taken by policymakers. Short-term and long-term policy responses appear to be necessary. Short-term monetary and fiscal authorities must ensure that damaged economies continue to function during a disease outbreak. Generally, central banks and governments play an important role in a worldwide natural disaster. Central banks are pleased when they lower interest rates. Other policymakers should also play a significant role in responding to the COVID-19 shock. This is not just a resource management issue; it is a multidimensional challenge that necessitates economic, fiscal, and healthcare policy responses Central banks and fiscal authorities are just two of the many policymakers that can affect the economy. Since the widespread dissemination of healthy sanitation habits is a low-cost, highly effective, and potentially mitigating response, it could also include health authorities and regulators. More countries could seriously invest in their healthcare systems, and global public health cooperation appears to be a necessity in this regard as well. The government’s infrastructure-led economic growth program has limited fiscal flexibility for the post-COVID economic recovery.The country’s infrastructure also has to be upgraded in order to support future economic growth.When it comes to funding, building, and administering public infrastructure, the government should look to private sector involvement more and more as its borrowing capacity is severely limited by the need to consolidate its fiscal situation. Governments need to be careful when entering into public–private partnerships to avoid creating new debt obligations by agreeing to substantial annual fees paid to private partners. As the fiscal policy is constrained in its ability to act countercyclically, monetary policy will be forced to shoulder the bulk of countercyclical policy’s burden in the future. The ability of monetary policy to act countercyclically by cutting interest rates will be constrained; however, because the high public debt/GDP ratio will raise the risk premium reflected in interest rates, especially if credit rating agencies continue to downgrade the country to junk status. Acknowledgements This article is a phased research result of the Beijing Social Science Foundation Youth Project “Research on Financial Risks of Beijing Municipal Government—Based on the Perspective of Risk Induction and Prevention” (Project No.: 20JJC027). Special funds for fundamental scientific research business expenses are being used to fund this project. Author contribution (Su and Urban 2021), conceptualization, data curation, methodology, writing—original draft. Chen Wen, Yuanzhi Xiao and Bing zheng Yan: data curation, visualization, supervision,visualization, editing, and software. Data availability The information is available upon request. Declarations Ethical approval and consent to participate The authors state that they have no known conflicting economic interests or personal ties that could influence the work presented in this paper. We state that we do not have any human participants, data, or tissues. Consent for Publication N/A. Competing interests There are no conflicts of interest declared by the authors. 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==== Front SN Soc Sci SN Soc Sci Sn Social Sciences 2662-9283 Springer International Publishing Cham 365 10.1007/s43545-022-00365-0 Original Paper Ecuadorian university English teachers’ reflections on emergency remote teaching during the COVID-19 pandemic http://orcid.org/0000-0002-0703-0056 Estrella Felix [email protected] grid.442143.4 0000 0001 2107 1148 Escuela Superior Politecnica del Litoral, Guayaquil, Guayas Ecuador 30 4 2022 2022 2 5 636 10 2021 13 4 2022 © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. COVID-19 struck at the beginning of 2020, affecting, among others, the education industry. As a result, a lock-down quarantine was declared, and on-campus classes were suspended. Accordingly, emergency remote teaching (ERT) was set into motion to solve the education issue. This research aimed to obtain the reflections of 20 Ecuadorian polytechnic university English teachers on their experiences using ERT during two semesters. This paper is based on an explanatory sequential mixed-methods research design that used a Likert-scale survey and interviews to respond to the established research questions. The findings show that, in a general sense, teachers were not ready for the sudden shift to ERT, which generated feelings of anxiety. The most significant disadvantage reported was the extra workload caused by adapting materials and giving feedback to students. The study suggests that changing classes from on-campus to ERT was not easy to carry out at the beginning of the pandemic. There are practical implications for language department managers as it gives them light to prepare for the continuing pandemic and any other crisis that might require ERT to be in practice again. Keywords COVID-19 Teacher’s reflections English as a foreign language Ecuador ERT issue-copyright-statement© Springer Nature Switzerland AG 2022 ==== Body pmcIntroduction The first case of COVID-19 in Ecuador was identified on February 29, 2020, on a seventy-one-year-old woman who had traveled to the country from Spain. More than nine thousand confirmed cases and four hundred seventy-four deaths had been reported by April 2020 (Ortiz-Prado et al. 2021). Given the difficult situation, the Ecuadorian President declared a state of emergency to avoid spreading the disease. One of the dispositions was the closure of all schools, high schools, and universities in the country (Santillan Haro 2020), as it had been declared worldwide (Shahzad et al. 2020). The Minister of Education presented on March 16 the Educational Plan COVID-19 containing regulations for the educational community to continue with the pedagogical activities (Bonilla-Guachamín 2020). However, the plan did not consider the socio-economic level of Ecuadorian families, nor did it consider the low levels of Internet coverage and access or the lack of technological resources (Almazán Gómez 2020). According to Vivanco-Saraguro (2020), closing the Ecuadorian educational system affected more than four million students registered in the Ecuadorian educational system. Emergency remote teaching (ERT) became the primary tool used to continue education (Valero-Cedeño et al. 2020). The main aim of ERT is to keep the learning processes sustainable, and it is based on the use of videoconference platforms. Several studies reported in the literature look at how teachers feel about the introduction of ERT during the COVID-19 crisis. Tomczyk and Walker (2021) performed research in Poland, while Juárez-Díaz and Perales (2021) researched 26 English teachers and 32 pre-service English teachers in Mexico. Salayo et al. (2020) investigated 147 high school teachers in the Philippines. Rahayu and Wirza (2020) employed a descriptive design with a qualitative approach in Indonesia. Finally, Lapada et al. (2020) performed a quantitative investigation in the Philippines. Despite the above, research carried out in Ecuador or other Latin American countries is scarce. Thus, this research was set into motion to bridge this gap in the literature. It aimed to identify the feelings of an Ecuadorian polytechnic university English teachers about their use of ERT during two semesters. The following research questions were devised to attain this goal. RQ1 How ready do English teachers think they were to make the shift from on-campus teaching to ERT? RQ2 What has been the teachers’ experience of teaching online during the COVID-19 pandemic? RQ3 states What are teachers’ perceptions over the positive and negative effects of using ERT to teach their English classes? Literature review E-learning Distance learning explained, Castro and Tumibay (2019) is not new in education. It has existed for more than a century, beginning in European correspondence courses. However, it has evolved through the years and turned to e-learning. According to Simon et al. (2003), e-learning is an environment where students and teachers use IT to overcome distance. Meanwhile, for Potts (2018), e-learning is also known as virtual learning environments or learning management software systems that synthesize computer-mediated communications software and forms of delivering a course online. Finally, E-learning is an interactive process involving teachers and students, aided by electronic media focused on the teaching process. At the same time, media are the tools used to complete the process (Perić 2019). Common features of e-learning are announcement boards, in-system e-mail service, conferencing tools, calendars, a navigable interface, and multimedia resources (Potts 2018; Martin et al. 2019; Shahzad et al. 2020). E-learning depends on asynchronous technology. That way, students can look at their preferred content and learn at their pace. Other characteristics of e-learning include two or more geographically dispersed bodies of learners and teachers. The contents are available in a combination of media. The delivery of e-learning is usually conducted through videoconferences or virtual learning environments (Simon et al. 2003; Ahmad et al. 2019). Cisco Webex or Zoom provides simultaneous communications and live interactions. In addition, they supply auxiliary functions that facilitate learning in e-learning environments (Rojhani-Shirazi et al. 2018). Moving to e-learning requires making changes in the presential organization. Furthermore, with the presence of COVID-19, such changes have been accelerated but challenging, especially considering the maturity of digital transformation at which the educational institutions and their faculty are (García-Peñalvo 2021). Therefore, the author maintains that e-learning ought to follow the technological pedagogical content knowledge (TPCK) model in which teachers must know technical, pedagogical, and information and communication technologies. Finally, Castro and Tumibay (2019) explain that the objective of instructional design in e-learning aids teachers to design their online courses better, ease students’ focus on their instruction, and promote an active teaching–learning process. ERT Educational institutions worldwide shut down their classes in an attempt to stop the rapid spread of the COVID-19 and to protect their students, faculty, and staff (Dong et al. 2020). This closure meant unprecedented difficulties for the Ecuadorian educational system. Course contents were moved into digital platforms to regain some normality and minimize the effects of the pandemic on the education system (Equipo Técnico de la Dirección Nacional de Currículo 2020). Hodges et al. (2020) defined ERT as the temporary access to online teaching with the intention of returning to the original teaching mode once the crisis is over. The scholars explain that during these times of pandemic, teachers must play a critical role in providing quality learning to students. Therefore, the primary goal of ERT is not to recreate a robust educational environment but to provide temporary access to education online in a quick and reliable form during a time of crisis (Barbour et al. 2020). Furthermore, the authors assert that teachers must work in a highly stressful environment as there is no indication when the pandemic crisis will be over. Finally, the scholars distinguish that ERT should be in place until everything goes back to normal. Teachers’ reflections on the use of ERT Tomczyk and Walker (2021) utilized a closed Facebook group devoted to Polish education topics in a qualitative study. Teachers exchanged their experiences, tips, and cases related to handling the COVID-19 crisis in their educational institutions. The researchers identified seven different categories of challenges and problems Polish teachers faced during their ERT experience. They explained that teachers were not prepared for the shift to cyber-education. Teachers also confided in their statements on Facebook that the crisis highlighted the importance of digital competencies, knowledge, and skills required to deal with ERT. In another qualitative study in Mexico, Juárez-Díaz and Perales (2021) researched 26 English teachers and 32 pre-service English teachers and obtained their experiences and emotions regarding ERT. They gathered data through an online opened-ended questionnaire which was later analyzed using semi-directed content analysis. Their results reported that most teachers and student teachers expressed negative feelings associated with delivering the course content without good interaction and a lot of poor Internet access. However, the researchers explained that those teachers who had experience teaching online had better experiences in the overall course. Salayo et al. (2020) investigated 147 high school teachers in the Philippines utilizing quantitative methods to evaluate their readiness, attitude, and competence in their engagement with ERT. The scholars applied a survey that used a Likert scale to identify the level of agreement to the statements. The results of this investigation proved that participants could address the challenges that ERT presented them. Their readiness was supported by their positive approach towards online teaching during the pandemic. Results also established that teachers were flexible, resilient, and prepared for the challenges of ERT, which included technological constraints, poor Internet connection, and lack of access to electronic devices. In another investigation, Rahayu and Wirza (2020) employed a descriptive design with a qualitative approach. They used questionnaires and interviews to elicit responses from 102 junior high school English teachers in Indonesia. All the participating teachers had used a platform like Zoom or Google Classroom for online teaching from March to July 2020. The researchers concluded that teachers had a positive perception of the usefulness of the online platforms they had used. However, participants also disclosed that they believed ERT was ineffective because of the lack of communication and the quality of interactions achieved. Lapada et al. (2020) performed a quantitative investigation in the Philippines. The researchers probed teachers’ awareness of the pandemic, their thoughts on the school’s readiness, and their challenges while teaching online. Data were collected using the “Questionnaires on Teachers Awareness, Readiness and Online Learning Experience During COVID-19 ECQ,” developed by the researchers. The survey was sent to teachers by e-mail, and 2300 responses were received. As a result, the scholars concluded that teachers were ready to move to ERT; although, they lacked facilities, equipment, and capacity to shift their courses from on-campus to online. Theoretical assumptions The researcher’s theoretical assumptions are the set of beliefs that will guide a study (Doyle et al. 2009; Guba et al. 2018). They encompass the paradigm which is central for the investigation and under which lie the ontological and epistemological positioning and the methodology to be used in the inquiry. Several specific elements define the world view or paradigms. This study will be guided by the constructivist paradigm from the different options described in the literature. This decision was taken after reviewing the available options. Its justification is detailed as follows. First, in constructivism, the participants of the investigation form meaning of the phenomena under the scope. Second, participants talk about their experiences built from social interaction and their own stories (Creswell and Plano-Clark 2018). Third, for Doyle et al. (2009), constructivism argues the existence of multiple realities. Thus, the results of this research will vary depending on the constructions of the realities experienced by the participants of the study and the researcher’s attempt to explain the reality described by such experiences. Once the paradigm has been established, it departs the researcher’s ontological and epistemological positioning. Ontology concerns reality and how things are and work (Scotland 2012). Reality exists only through the experiences lived by humans (Denzin and Lincoln 2018). The constructivist ontology looks at how the person is constructed in a social context concerning recognition, motivating the search for identity (Packer and Goicoechea 2000). Learning involves the construction of identities. Thus, the ontological positioning of this researcher in front of the issue at hand is to identify such realities as experienced by the teachers at the languages department of the university. Then there is the epistemological stance, which deals with the nature of knowledge, its creation, and communication (Creswell and Plano-Clark 2018). For Denzin and Lincoln (2018), epistemology looks at the relationship existing between knowledge and the person who knows it and tries to explain the world through that knowledge. Finally, Levers (2013) explains that epistemology is how the researcher makes sense of the world and that the ontological beliefs will restrict the epistemological ones. With these definitions in mind, it is safe to say that the knowledge will be created by analyzing the data obtained using the collection tools selected. Finally, a mixed-methods research design has been chosen for this investigation. The approach selected for this mixed-methods study has been the explanatory sequential design (see Fig. 1). Thus, the researcher will start with the quantitative stage of the research. This stage will use the survey adapted from Salayo et al. (2020). Then, the qualitative stage will be implemented in a subsequent phase using a seven open-ended question protocol. Finally, this stage will explain the initial results in more depth (Creswell and Plano-Clark 2018).Fig. 1 The explanatory sequential design Integrating data in this exploratory sequential study involves connecting the results obtained from the quantitative stage to plan the subsequent qualitative data collection phase. The integration will be done during the interpretation stage of the data collected after the analysis of the quantitative and the qualitative sections has been done, in the results and discussion section of this paper. Fetters et al. (2013) explain that there are three levels of integration at the interpretation and reporting level: (i) integrating through narrative, (ii) integrating through data transformation, and iii) integrating through joint displays. This form of integration is also known as the triangulation protocol (Creswell and Plano-Clark 2018; O’Cathain et al. 2010). Using triangulation, researchers try to secure an in-depth understanding of the issue at hand. It is an alternative to validity (Denzin 2012). By combining data at this level using triangulation, the interpretation of data acquires rigor, breadth complexity, richness, and depth (Flick 2007). Methods Participants The participants of this study were twenty English teachers of the languages department at a polytechnic university in Guayaquil, Ecuador. Table 1 contains the participants’ demographic information. This group comprises 85% female teachers and 15% male teachers. Most of them (40%) are aged in the range of over 45, while 35% are aged between 36 and 45 years of age. The last 25% of the teachers are aged in the range of 31 to 35 years. The highest professional training degree obtained by 85% of the participants is a Masters’ in Teaching English as a Foreign Language, and the other 15% of the teachers hold a Ph.D. Most participants (55%) have been teaching English in different institutions for more than 20 years. Meanwhile, 20% of the teachers surveyed said they have experience of 11 to 15 years working for educational institutions in Ecuador. Regarding their English language qualifications, 50% of them disclosed a B2 level according to the Common European Framework of Reference, and 35% hold a C1 qualification.Table 1 Participants’ demographics Demographics Categories N = 20 % Sex Female 17 85 Male 3 15 Age < 30 0 0 31–35 5 25 36–40 4 20 41–45 3 15 46+ 8 40 Higher education Master 17 85 PhD 3 15 Marital status Single 4 20 Married 10 50 Divorced 6 30 English level qualification B2 10 50 C1 7 35 C2 3 15 Sampling strategy Since this is a mixed-methods research design, the researcher has followed Creswell and Plano-Clark (2018), who suggest choosing the study participants using the purposeful sampling approach. In this non-probability sampling technique, the researcher confides in his judgment to select the study participants (Creswell and Poth 2018). The participants ought to supply in-depth information about the phenomenon under investigation. That is why the researcher considered using colleagues from the languages department for this study, but only those who teach English, as this is the scope of the investigation. The criteria to include participants for this study comprise teachers of English, male or female, working at the university during the second semester of the 2020 academic year, who have taught using ERT since the beginning of the pandemic. The criteria led to the twenty participants that were depicted above. Data collection tools This study used two main data collection tools. One to obtain information for the quantitative stage of the investigation and another to attain data for the qualitative section of the research. The qualitative stage will serve to triangulate data and assist in making better conclusions. Survey The first tool used was a survey adapted from Salayo et al. (2020). The survey was divided into four different sections. Section one deals with demographics questions; the answers from this section were used for the discussion on the participants heading. The second section is the readiness section which included three propositions with a 4-point Likert scale where 1 was completely disagree, and 4 was completely agree. This section looks at how ready teachers were to make the abrupt shift to ERT. The third section used a 4-point Likert scale where 1 was completely disagree, and 4 was completely agree. These propositions aimed to identify how easy it was for the teachers to use digital platforms for teaching their English subjects. The last section of the survey was the overall experience section, where participants had four propositions to rate on a 4-point Likert scale, where 1 was completely disagree and 4 was completely agree. The survey was first piloted with teachers of other languages to check that the questions were written straightforwardly and there would be no chance of misunderstanding any of the questions. As a result of the pilot test, three of the questions were deemed difficult to understand and re-worded. Then, to measure the internal consistency of the survey, Cronbach’s alpha was calculated. The result of the alpha came to be 0.85505, which according to the literature, reflects a good internal consistency (Tavakol and Dennick 2011a; George and Mallery 2012; Vaske et al. 2016). A second measure was to test the survey for content validity by assessing the degree to which the survey measures the construct targeted (Creswell and Miller 2000; Almanasreh et al. 2019). Again, Lawshe’s content validity ratio with an augmentation proposed by Ayre and Scally (2014) was used. For these ends, the researcher counted with the help of five experts from the university’s writing center who read the questionnaire and made a dichotomous decision of “essential” or “not essential” for each query. The authors proposed that a level of 50% agreement assures a degree of content validity. In the end, the content validity ratio came to 0.854, which according to (Almanasreh et al. 2019), can be considered a good ratio, making the survey valid. Interviews This study used an interview protocol to obtain teachers’ feelings on several issues concerning the ERT modality. The interview protocol included seven open-ended questions, allowing deeper probing when necessary. Before the interviews were carried out, a series of measures were taken to assure for validity and reliability of the tool to be used. Two colleagues at the university’s Academic Writing Center helped read and check the protocol to account for face validity. According to Kennedy et al. (2019), face validity tests if the questionnaire’s content is relevant to the participants, evaluating the feasibility, readability, consistency of style, and clarity of the language of the questionnaire (Creswell and Miller 2000). The researcher then performed the Cohen’s Kappa Index (CKI) test on the questions after the professors had assessed them. This test results in a statistical coefficient representing the questionnaire’s degree of accuracy and reliability. The agreement index reached was 85.714%, and the kappa was 0.58823, which is a moderate agreement according to Landis and Koch (1977). Thus, the questionnaire used accounts for face validity. Finally, the researcher aimed to assess the content validity of the protocol used in the interviews. Gunning’s Fog Index was used to evaluate the readability of the questions following the recommendations from Bolarinwa (2015). The calculation result was 7.18, which accounts for good content validity of the questionnaire. Because of the virtuality experienced due to the COVID-19 pandemic, the interviews could not be conducted face-to-face but were done using individual Zoom meetings. It is necessary to note that the researcher told respondents what the study was about and their role in it. Next, the researcher stressed they signed the informed consent form, but they were free not to participate in the interview. Finally, the researcher made sure that every participant understood that their names would not be displayed in any manner when reporting their comments but that a number would identify them. Analysis The data obtained through the survey was analyzed using the IBM SPSS Statistics Package V.20. The first test carried out was a one-way right-tailed ANOVA using an F distribution df 10.132, a significance level of α = 0.05, and an effect size value of 0.25. This test was used as the researcher wanted to identify any statistically significant differences between the means of the populations in the test. Table 2 contains the data obtained from the ANOVA test.Table 2 ANOVA results Source DF Sum of squares Mean square F statistic P value Groups (between groups) 10 9.888116 0.988812 2.452023 0.0101801 Error (within groups) 132 53.230791 0.403264 Total 142 63.118908 0.444499 The first thing that caught the researcher’s attention was that p value equals 0.0101801. This result means that observing such an extreme test statistic is unlikely. In other words, since the p value 0.0101801 [p (x ≤ F) = 0.989820] is less than α = 0.05, the chance of a type 1 error, which means rejecting a correct H0 is small, 0.01018 (1.02%). Additionally, it can be said that some of the group’s averages are considered to be not equal, meaning that the difference between the averages of some groups is big enough to be statistically significant. Next, the researcher ran Cohen’s F statistics in ANOVA to identify whether the means between the populations were significantly different. The test statistic F equals 2.452023, which is not in the 95% critical value accepted range: [−∞: 1.9031]. Then, the observed effect size f is large, 0.43. This result shows that the magnitude of the difference between the averages is large. The η2 = 0.16 means that the group explains 15.7% of the variance from the average, similar to R2 in the linear regression. Several tests were carried out to validate the results of ANOVA. First, the SPSS did the test power, which is low, standing at 0.4544. Thus, it can be said that the H0 is rejected. Therefore, the results from ANOVA are confirmed as valid. Then the tool used Levene’s test to assess the equality of variances. The population’s variances are considered to be equal with a p value of 0.968. Levene’s test power resulted in 0.45, regarded as a weak result. However, the group’s size is similar since the ratio between the bigger and smaller groups is 1.00. Therefore, the ANOVA test is considered robust to the homogeneity of variances assumption when the group’s sizes are similar. Also, the normality assumption was checked based on the Shapiro–Wilk test, which resulted in α = 0.05. Therefore, it is assumed that all the groups distribute normally. Finally, the Kruskal–Wallis’ test using Chi-square (df = 1) and a right-tailed distribution was used to validate ANOVA results further. Since the p value < α, then the H0 is rejected. Some of the groups’ mean ranks are considered to be not equal. In other words, the difference between the mean ranks of some groups is big enough to be statistically significant. When selecting a value from each group, some groups have a probability of containing higher values than others. The p value equals 0.04473, (p(x ≤ 4.0287) = 0.9553). The chance of type I error rejecting a correct H0 is small: 0.04473 or 4.47%. The smaller the p value, the more it supports H1. The test statistic H equals 4.0287, which is not in the 95% acceptance region [0: 3.8415]. The observed effect size η2 is small, standing at 0.021. This result shows that the magnitude of the difference between the averages is small. As expressed before, the qualitative data serve as supplementary for the information obtained through the quantitative stage of the research. The data were obtained using a semi-structured interview, analyzed following a general inductive approach as suggested by Creswell (2012) and Thomas (2006). The data analysis was done following the recommendations from Thomas (2006), Fontana and Frey (2011), and Creswell and Poth (2018). The scholars explain that qualitative data require coding, categorizing, and interpreting to respond to the research questions. Thus, the researcher started reading the transcribed data. After a few reading rounds, ten themes were identified and organized. Some could be merged to reduce the overlap and redundancy among the initial categories identified, reaching the final four themes presented in the following subheadings. Readiness Two questions addressed the issue of readiness for the shift from on-campus to online teaching. The majority of teachers explained that they were not ready. Respondent seventeen asserted that “even though he manages computers very well, the change to online education was not something he was prepared for when everything happened. Being faced with a curriculum designed to be taught in a class and suddenly shifting it to an online course was challenging. Mainly because of the investment in a reliable Internet provider, which is not cheap, and dealing with students’ issues was also a big challenge.” Respondent five said that making the shift was not as difficult as she thought. She had to get used to the new environments quickly and adapt the materials accordingly. Teacher nine explained “I was not ready for the change initially. However, I was getting used to all the changes implemented with the pass of time, and my classes were getting along fine.” She also explained that there was an added trouble for her as she has two children who also started online classes, so she had to teach and help her kids simultaneously. Respondent thirteen expressed she had no trouble whatsoever with changing the course to online. She says that using the platform was straightforward, and no problem arose. Teaching strategies used Teachers used several strategies throughout their experiences with the new teaching form; however, there were many similarities. For example, using games is one of the common strategies among the answers teachers gave. Respondent eight explained she had used Kahoot a lot. She confided that she turns any discussion, quiz, or survey into an engaging and fun game for the class. For example, she continues to practice the grammar point explained in the previous lesson. Doing these kinds of activities online challenges her students. One of the activities she continually does is to have her students put words or phrases in a particular order, which requires more focus on their part. Meanwhile, respondent sixteen asserted that he is a fan of collaborative work, and he has made the change to online collaborative work using Flipgrid. He explained that he creates the tasks in the platform, and the students record themselves responding to them. Then their peers are required to give feedback to each other using a short video on the same website, and in the end, they have to evaluate each other in the university LMS based on the criteria given in the rubric. In the same vein, teacher eleven explains that she has her students work online creating puzzles with the vocabulary words from the textbook. They share the links with the puzzles they made so the whole class practices the vocabulary. She also said she created groups using the separate sessions tool in Zoom and had learners create posters or mind maps with key information about the grammar they reviewed in class. She also said that she has her students anonymously express their feelings or provide suggestions about teachers’ strategies. Advantages and disadvantages of ERT A few of the questions posed related to the issue of the advantages, disadvantages, and challenges they had identified during their experiences with ERT. The following are the most common answers. Respondent one explained that the biggest advantage he perceived was that he had to stay at home. Not having to commute to the university has meant economic savings, both from the point of view of car use, including gas and maintenances expenses which have lowered considerably, to spending money on clothes, as he confided. He also asserted that there is no need to print worksheets for students during class, which is a big help for the environment. Also, respondent thirteen put herself in the students’ shoes. She said learners had had many advantages with ERT because they do not have to spend time on the bus going to and from the university to use that time for other activities. She also said that having the class recordings is a great advantage for her students as they can access them throughout the entire semester. Finally, she assures that ERT has helped students greatly because it has brought down location walls. Now, students anywhere in the country can attend classes in the university without having to move from their homes. When asked about the disadvantages that ERT presents, respondent twenty identified several drawbacks. First of all, she said, there is a dependency on a good Internet connection and having suitable devices to carry out a good session on Zoom or Teams. Even though the teacher counts with the above, there is always the problem created by the number of connected people simultaneously. She considers that it is not only she who has to be connected to the Internet all morning to teach her class but also her children. This overcrowding of the connection creates problems, and sometimes they get a slow connection or get disconnected. Or maybe the Internet provider is having internal issues, and the connection is lost, which means a class needs to be recovered at another moment. Respondent eighteen added to the above by saying that one of the main issues with ERT is that distractors stop students from paying attention to the class. For example, their relatives walk by the location they use to connect to the style and start talking to them, so they lose track of what the teacher says. There is also a feeling of loneliness which students have commented. Since they are not sharing a classroom with the teacher or their peers, they do not have the opportunity to socialize with them; hence, they report such feelings. Feelings about ERT Of such feelings reported by the participants, the most common is that making the sudden change and keep teaching online has made them feel anxious. Respondent eight explains that she was not ready for making drastic swift to the digital realm. She believes that she made her best effort, but she is conscious that the classes at the very beginning did not go smoothly. Not knowing, explained respondent six, how classes would function and the new meeting platform they had to use were just many issues that added to the already stressful pandemic scenario. In addition, all the technological tools that needed to be implemented were novel, and there was no training other than click here and then and all of that made her feel anxious, at least the first few weeks, she added. On the other hand, respondent two said that everything was kind of weird and hectic at the beginning. However, after getting to know the platforms, settings, and other web-based applications, she explains that the experience has been enriching. Nonetheless, she feels that teaching a language involves face-to-face teamwork, feedback, and support. Therefore, doing ERT is fine and hybrid classes seem like the future of teaching. Respondent five says that he had a hard time teaching online initially, mainly because of not knowing what he had to do to deliver a good class. Although, with the pass of time, it is getting easier to use and administer the platform. It is not bad; he admits to feeling great now that a year has gone by. However, he wishes classes were smaller to ensure all students had the necessary resources and the chance to practice the language. Results and discussion The results obtained and the discussion will be done based on the research questions stated above. RQ1 aimed to find how ready English teachers thought they were to make the shift from on-campus teaching to ERT. The propositions from the first section of the survey dealt with teachers’ perceptions of their readiness to take on ERT. Looking at the means, we can interpret them in the following way: from 0 to 1 means totally disagree, 1.1 to 2 equals disagree, 2.1 to 3 means agree, and 3.1 to 4 means totally agree. It can be seen in Table 3 that the lowest mean, 2.00, which can be interpreted as “Disagree,” obtained from the descriptive statistics is from the statement “I showed familiarity with meeting/video conferencing platforms like Zoom/Teams.” This result is logical as nobody was familiar with using the meeting platforms used to teach. This result could also be confirmed by the responses obtained from the interviews. Respondent seventeen, for example, said, “I handle myself well in a computer. However, changing the classes to online was something I was not ready to do in such a rush as we had to. Moreover, I had no idea what Zoom was, how to use it, or what tools I could use in class.”Table 3 Descriptive results for readiness section Mean SD Variance Interpretation I showed familiarity with meeting/video conferencing platforms like Zoom/Teams 2.00 0.973 0.947 Disagree I could integrate technology in executing the suggested competencies and skills to manage successful learning 2.25 0.910 0.829 Agree I managed my time using technology-driven instruction with ease 3.15 0.745 0.555 Totally agree I easily designed online assessments to measure learning 3.80 0.410 0.168 Totally agree I used online discussion forums on Canvas as part of the lessons 3.40 0.821 0.674 Totally agree I showed comfort in communicating online through speaking and writing 3.66 0.489 0.239 Totally agree Cronbach’s alpha = 0.84 On the other hand, the highest mean, 3.80, was obtained by the statement, “I easily designed online assessments to measure learning.” This result can be interpreted as “totally agree.” This result is also logical as all the language department teachers are used to designing their unit progress tests, so it was not an issue they could not deal with. The next highest mean, standing at 3.66, corresponds to the statement “I showed comfort in communicating online through speaking and writing.” This proposition shows the confidence expressed by the teaching body on their communication skills with their students. This result can also be interpreted as “totally agree.” The last issue was dealt with the statement that said, “I used online discussion forums on Canvas as part of the lessons.” This proposition obtained a mean of 3.40 that can be interpreted as “totally agree.” During the interviews, teachers were asked about the tools they used in their English classes, and one the most mentioned was the use of forums in the Canvas environment. For example, teacher three said, “I always use the forum tool on Canvas. I think it allows students to give their opinions on different issues. I usually do class, and the reading or listening topics allow me to post a discussion on the forum so they can be engaged with the topic of the lesson even after class.” Therefore, teachers at the polytechnic university in Ecuador were not 100% ready to take on ERT at the beginning of the crisis. However, they felt more skilful with the tools, and the meeting platforms became easier to use after some time. Additionally, they were all trained on effectively assessing students on the Canvas platform. In summary, they were ready, and they could integrate technology in the teaching–learning process during ERT. RQ2’s objective was to identify teachers’ experiences with teaching online during the COVID-19 pandemic. The fourth section of the quantitative survey, which looked at teachers’ overall experience during ERT, sheds some light on this query. There are both positive and negative experiences that teachers have related during the interviews and by answering the survey. As shown in Table 4, the highest mean obtained was 3.65, which can be interpreted as totally agree and was attained by statement three, “I could interact enough with my students.” This proposition can be cataloged as an advantage. This information was corroborated with the answers respondents gave during the interviews. Respondent nineteen explained that he had had an excellent rapport with the students assigned during ERT. Furthermore, he asserts, “The classes assigned to me, have been great, and I was able to work with them nicely. Of course, I had two small classes, so I gave very personalized feedback to them on the essay, especially. I met on individual sessions with each of them, and we reviewed their work one by one, and I explained their errors to them and how to correct them. Then, they applied what we talked about and handed in really good essays.”Table 4 Descriptive results for the overall experience section Mean SD Variance Interpretation I could teach just as well as I would when on-campus 3.00 0.649 0.421 Agree I could organize exercises just as well as I would when on-campus 3.25 0.786 0.618 Totally agree I could interact enough with my students 3.65 0.489 0.239 Totally agree Online teaching tools used in courses were easy to operate 2.90 0.788 0.621 Agree Teaching online made me anxious 3.30 0.733 0.537 Totally agree I think students get easily distracted during my class 3.30 0.923 0.853 Totally agree Cronbach’s alpha = 0.76 The second highest mean is 3.30, which propositions five and six share. Statement five was “teaching online makes me anxious.” This statement can be cataloged as a disadvantage. This issue was corroborated during the interviews where respondent eight said she felt anxious the first time she had to do her classes online, “I didn’t really know what I was doing, most of the times, I had to go by trial and error, that’s how I learned. Now, I manage the tools rather well, but the first 2 weeks of class, it was a nightmare, and that made me feel very anxious.” The lowest mean was 2.90, which is statement four, “the online teaching tools used in courses were easy to operate.” This statement can be cataloged as a disadvantage. During the interviews, some of the teachers did mention it as an issue they had to deal with during the first few weeks after classes had started. Take respondent two, for example; she said, “It is not that I am not very computer savvy. I can use it, and I can access the Internet, but there are some tools that my colleagues use that are a little higher than my level of expertise, so I tend to avoid them.” Another teacher, respondent sixteen, said, “I try not to deviate much from the program and use the tools that are included in the language management system we use in the university. It is there, so I use it; I don’t see why I have to use other tools from the Internet. Besides, I don’t understand how to use some of them. So, I rather use what I do know how to use.” Therefore, it is clear that teachers have had a good experience with ERT, in a general sense. They have been able to interact with their students mainly because of the tools offered by the meeting platforms available for them to use. Also, they believe that after getting used to this new modality, they have been able to adapt their teaching strategies leading them to teach as well as when they were on-campus. On the other hand, not every experience has been positive. Teachers also have related feelings of anxiety, especially by the beginning of ERT. Finally, the one thing that most respondents complained about was that students get distracted easily during the class and the problems they have experienced with learners not keeping their cameras on. This issue has made their work more complicated. Finally, RQ3 enquired the teachers’ perceptions of the positive and negative effects of using ERT to teach their English classes. The quantitative answers for this question lie in section two of the survey and the interviews carried out. Table 5 depicts the descriptive results of the said section. The highest mean, 3.55, which can be interpreted as “Totally Aree,” goes to the statement, “I think the workload is higher in these online courses than on-campus.” This statement can be construed as a disadvantage of ERT. The accounts from the interviews support it. Take respondent three’s response “One of the things I don’t like is that there is so much more work to do. Preparing for the class takes me longer because now I have to look for online activities that are engaging for my students and are at the same level as my classes.”Table 5 Descriptive results for advantages and disadvantages section Mean SD Variance Interpretation I think that teaching remotely has saved me time and money 3.25 0.716 0.513 Totally agree I think the workload is higher in these online courses than in on-campus 3.55 0.510 0.261 Totally agree I think I can provide feedback to all my students as I did when on-campus 2.50 0.688 0.474 Disagree I think the Internet gets really busy when I have to teach 3.40 0.681 0.463 Totally agree I think I can avoid contagion if I am at teaching at home 3.45 0.686 0.471 Totally agree I think one of the biggest challenges with teaching remotely is students get distracted easily 3.50 0.657 0.432 Totally agree Cronbach’s alpha = 0.565 The next item is the statement, “I think one of the biggest challenges with teaching remotely is students get distracted easily.” This statement was ranked with a mean score of 3.50, which can be deemed as “strongly agree.” Therefore, this statement can be considered as a disadvantage of ERT. Respondent ten said, “The thing that I consider the biggest disadvantage of using this type of teaching is that I cannot be certain enough whether or not my students are really involved in the class.” Supporting this view, respondent one explained, “One of the worst things with the virtuality is that we cannot tell students to turn their cameras on during class, it is the university’s policy, so they do not feel invaded. So, that way, they get distracted easily. For example, we are doing an exercise, and when I call on the answers, students sometimes take time to answer, you know, like they are just then looking for an answer. Or they say, please teacher, repeat what question?” Then item “I think I can avoid contagion if I am teaching at home.” obtained a mean score of 3.45, and it is considered as an advantage. The support received from the interviews comes from respondent seven, who confided, “The very best thing of doing classes remotely is that we have got to stay at home throughout the whole pandemic. I even get nervous every time I hear people talking about going to face-to-classes again. So if I can stay at home, I can be certain that I will not get the virus.” On this issue, participant twelve said, “I really think that virtual classes have helped us very much. I mean, we are not in contact with our students. Physical contact, I mean. And, considering the pandemic, that is a great advantage for us teachers as in the end, it means that we are less likely to get the virus. So, the longer we can stay teaching remotely, the better for our health.” There are both advantages and disadvantages to ERT. The most significant advantage identified is that teachers feel safer doing their classes remotely than being on-campus as the possibility for contagion is drastically reduced. In addition, they think it might be easier to catch the virus because of the high number of students there in a live classroom. However, the quantitative analysis determined that ERT has more disadvantages than advantages as the highest mean scores were obtained by those statements that reflected the disadvantages. Conclusion The advent of COVID-19 and all the effects it brought along, such as the closing of educational institutions to care for the health of students, teachers, and staff, has required education to move to the digital realm. Teaching is now done remotely using different meeting platforms, such as Zoom, MS Teams, Google Meets, or WebEx. This investigation aimed to obtain English teachers’ reflections over their experiences during the first two semesters of the year 2020. Three research questions were devised to guide this investigation, and they were answered using the mean scores from a survey and the answers from individual interviews. Changing classes from on-campus to ERT was not easy to carry out at the pandemic’s beginning. Teachers were not ready, and most of them required training to use the meeting platforms to take advantage of all the features they offer (Mukhtar et al. 2020). This challenging issue has brought most teachers feelings of anxiety, mainly because of not having enough information about what was going on and how to solve conflicts within this new type of class. Lapada et al. (2020) and Salayo et al. (2020) have also reported this issue. However, after the first semester doing ERT, teachers feel more confident about their experience with the platforms and the different web applications they are using as extra tools to make their classes more engaging. This research has identified that, in general, teachers’ experience with ERT has been a good one. They have expressed that they have had good interactions with their students thanks to the platform’s tools and the strategies they have adapted from their experience teaching on-campus. These findings have also been reported by Rahayu and Wirza (2020), and Juárez-Díaz and Perales (2021). Nonetheless, teachers also reported having trouble with students not maintaining their cameras on during the class, so they cannot be sure of their involvement in the class. Also, they disclosed problems with the Internet connection, both their own and their students, results that are supported by Juárez-Díaz and Perales (2021) and Tomczyk and Walker (2021). On the other hand, not every experience has been positive. Teachers also have related feelings of anxiety, especially at the beginning of ERT (Salayo et al. 2020). Finally, the one thing that most respondents complained about was that students get distracted easily during the class and the problems they have experienced with learners not keeping their cameras on (Mukhtar et al. 2020; Tomczyk and Walker 2021). This issue has made their work more complicated. There is a gap in the literature that this investigation is trying to bridge: data coming from South America, especially Ecuador, which looks at the experience teachers have had during the first 6 months of the pandemic and the instauration of ERT to continue with the teaching–learning process. This research has dealt with some implications. The results provided by this paper will come in handy for language department managers as it gives them light to be prepared for the continuing pandemic and for any other crisis that might require ERT to be in practice again. The recommendation is to have ready support for teachers, not only in the IT department but also in emotional support, to deal with anxiety issues. Consequently, stress management should be considered an issue to be treated with teachers as institutional support while ERT is in place. Also, managers can use the information gathered on this paper to develop training sessions for teachers so that the shift to ERT becomes smoother and less stressful. This paper also presents information that can be helpful for English teachers who will be immersed in ERT. They can use it as a guide to know and understand what ERT implies in terms of teaching and the challenges, advantages, and disadvantages it means. That way, they can be prepared to manage the situations that come with the abrupt establishment of remote teaching. The results provided in this paper also have societal implications. First, it adds quantitative and qualitative data on how teachers feel about using ERT during the COVID-19 pandemic. This research has clearly defined teachers who think they were not ready to jump into remote teaching without training on how to make their courses from face-to-face to remote, and neither did students. Also, it was identified that entering the electronic domain has resulted in feelings of anxiety in many teachers. Finally, the main advantages and disadvantages teachers perceive from ERT were highlighted, as teaching remote assures for their health, or students getting distracted by different issues around them, which means they do not pay the proper attention to their classes. Thus, it is clear that with the results presented in this paper society can benefit with the knowledge described which can be sued for any future crisis in which ERT has to be put again into practice. There are also limitations to report. First and foremost is the sample taken for this research. It was only twenty English teachers at a public polytechnic university in Guayaquil. Thus, the findings reported here apply only to similar contexts. So, for further research, another study should be conducted utilizing teachers from the whole university or even from different universities so that generalization of results can be achieved. Another significant limitation is that this paper presents the teaching–learning process from the teachers’ perspectives; thus, only their experiences, concerns, and feelings are reflected here. Students’ problems and issues are only dealt with from the teachers’ point of view. Consequently, it is suggested that future research conduct an investigation where the students are also considered informants. Funding This research received no external funding. Data availability The author declares that data supporting the findings of this study are available within the article. Declarations Conflict of interest The author declares no conflict of interest in the study’s design, in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results. Ethical approval I, Félix Estrella, consciously assure that the following is fulfilled for this manuscript. This material is the authors’ original work, which has not been previously published elsewhere. The paper is not currently being considered for publication elsewhere. The article reflects the authors’ research and analysis wholly and truthfully. The results are appropriately placed in the context of prior and existing research. All sources used are adequately disclosed. ==== Refs References Ahmad M, Adnan A, Yusof A, Kamal M, Kamal N (2019) Using new technologies to teach English in Malaysia—issues and challenges. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 20328 10.1007/s11356-022-20328-5 Review Article Multifaceted role of natural sources for COVID-19 pandemic as marine drugs Rahman Mominur 1 Islam Rezaul 1 Shohag Sheikh 2 Hossain Emon 1 Shah Muddaser 3 shuvo Shakil khan 1 Khan Hosneara 1 Chowdhury Arifur Rahman 4 Bulbul Israt Jahan 4 Hossain Sarowar 1 Sultana Sharifa 1 Ahmed Muniruddin 1 Akhtar Muhammad Furqan 5 Saleem Ammara 6 Rahman Habibur [email protected] 47 1 grid.442989.a 0000 0001 2226 6721 Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, 1207 Bangladesh 2 grid.449329.1 0000 0004 4683 9733 Department of Biochemistry and Molecular Biology, Faculty of Life Science, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj-8100, Gopalganj, Bangladesh 3 grid.440522.5 0000 0004 0478 6450 Department of Botany, Abdul Wali Khan University Mardan, Mardan, 23200 Pakistan 4 grid.443031.1 0000 0004 0371 4375 Department of Pharmacy, Southeast University, Banani, Dhaka 1213 Bangladesh 5 Riphah Institute of Pharmaceutical Sciences, Riphah International University Lahore Campus, Lahore, Pakistan 6 grid.411786.d 0000 0004 0637 891X Department of Pharmacology, Faculty of Pharmaceutical Sciences, Government College University Faisalabad, Faisalabad, Pakistan 7 grid.15444.30 0000 0004 0470 5454 Department of Global Medical Science, Wonju College of Medicine, Yonsei University, Wonju, 26426 Korea Responsible Editor: Lotfi Aleya 4 5 2022 124 15 12 2021 14 4 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. COVID-19, which is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has quickly spread over the world, posing a global health concern. The ongoing epidemic has necessitated the development of novel drugs and potential therapies for patients infected with SARS-CoV-2. Advances in vaccination and medication development, no preventative vaccinations, or viable therapeutics against SARS-CoV-2 infection have been developed to date. As a result, additional research is needed in order to find a long-term solution to this devastating condition. Clinical studies are being conducted to determine the efficacy of bioactive compounds retrieved or synthesized from marine species starting material. The present study focuses on the anti-SARS-CoV-2 potential of marine-derived phytochemicals, which has been investigated utilizing in in silico, in vitro, and in vivo models to determine their effectiveness. Marine-derived biologically active substances, such as flavonoids, tannins, alkaloids, terpenoids, peptides, lectins, polysaccharides, and lipids, can affect SARS-CoV-2 during the viral particle’s penetration and entry into the cell, replication of the viral nucleic acid, and virion release from the cell; they can also act on the host’s cellular targets. COVID-19 has been proven to be resistant to several contaminants produced from marine resources. This paper gives an overview and summary of the various marine resources as marine drugs and their potential for treating SARS-CoV-2. We discussed at numerous natural compounds as marine drugs generated from natural sources for treating COVID-19 and controlling the current pandemic scenario. Keywords SARS-CoV-2 Marine drugs Flavonoids Lipids Anti-inflammatory Medicine ==== Body pmcIntroduction Viruses are a big source of concern for humans in the current period since they are one of the many infectious threats they confront, creating a huge threat of pandemics over the world. Rapidly changing global landscapes, local habitats, major population growth, and urbanization in many emerging countries, as well as advancements in transportation infrastructure, have all generated new opportunities for viral infections to start and spread. The novel virus, originally known as the 2019-novel coronavirus, was discovered to be the source of an ongoing pneumonia outbreak in Wuhan, China, at the end of 2019. This virus was formally connected with severe acute respiratory syndrome coronaviruses (SARS-CoVs) and designated as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by the International Committee on Taxonomy of Viruses (Viruses 2020). The respiratory sickness caused by 2019-nCoV was formally called Coronavirus Disease 2019 (COVID-19) by the World Health Organization (WHO) on 11 February 2020, and the disease’s worldwide expansion was described as a pandemic by the WHO on 11 March 2020 (WHO 2020). The pandemic’s unusual circumstances have compounded the general difficulty of controlling viral infections. Despite advances in vaccination and drug research, many viral diseases including coronavirus infections still lack prophylactic immunizations and efficient antiviral medicines (Islam et al. 2021). In this sense, the quest for novel antiviral compounds is still ongoing. COVID-19 is an infectious respiratory disease caused by SARS-CoV-2, a recently discovered coronavirus strain. By attaching to ACE-2 protein receptors on the surface of host cells, this single-stranded RNA virus can infect the respiratory tract. Spike proteins on the surface of viral particles contain a receptor-binding domain (RBD) that the human ACE-2 receptor recognizes. This one-of-a-kind RBD interacts with a lysine residue on the ACE-2 receptor, making it a potential pharmaceutical target. The virus particles invade the airways and lungs, triggering an inflammatory response in the body and causing damage to the host tissue. End-stage respiratory disease, systemic involvement, and mortality can all result from this. Although COVID-19 vaccinations have proven to be helpful in avoiding illness, control cannot rely solely on vaccines; therapies are also required. Natural products are still one of the most common sources of antibacterial and antiviral medication prototypes (Adalja and Inglesby 2019; Rahman et al. 2021a) (Karthika et al. 2021b)(Tagde et al. 2021a). Over a thousand unique marine chemicals derived from marine species are undergoing pharmacological testing, with over forty now on the market (Khan et al. 2020a). Marine plants and microorganisms have been the focus of scientific inquiry for many decades, owing to their unique biological features. Over 12,000 natural compounds have been isolated from marine plants and microbes, and this number continues to grow (Anjum et al. 2016, 2017; Hassan et al. 2017; Hassan and Shaikh 2017; Bruno et al. 2019). In the discovery of new prototypes and the development of medicines utilizing natural marine ingredients, possible marine goods are playing a crucial role (Vo and Kim 2010; Wittine et al. 2019). Marine species span more than two-thirds of the earth, making them a significant source of new drug-like chemicals (Rong et al. 2020)(Aneiros and Garateix 2004). COVID-19 has been tested against flavonoids, alkaloids, and peptides, among other biologically active chemical groups (Rahman et al. 2020a)(Hossain et al. 2020). The enormous potential of marine organisms as raw materials for developing innovative medicinal compounds and therapies has long been recognized in the field of marine pharmacy (Cheung et al. 2015; Malve 2016). Marine creatures have evolved a variety of anti-infective techniques and chemicals to defend themselves against microorganisms and viruses that live in the ocean (Donia and Hamann 2003). For being ecologically safe, having low toxins, and being physiologically compatible, marine resources provide a number of advantages (Bhattacharya et al. 2021b)(Sindhu et al. 2021a)(Sindhu et al. 2021b). Several natural substances derived from marine resources are now being studied for antiviral properties against COVID-19. The resources marine organisms harbor is limitless and consistently proven efficacious at combating viruses, bacteria, cancers, and other pathogens. Their unique chemical structures and diversity introduce novel mechanisms of action, making them especially valuable against drug-resistant pathogens. Some marine compounds that do share similar mechanisms of action with other known approved drugs have shown to be more potent. Some marine compounds that do share similar mechanisms of action with other known approved drugs have shown to be more potent. As discussed above, PCBs and sulfated polysaccharides have shown to bind and inhibit RdRp with higher affinity than current standard therapy remdesivir (Abdelmohsen et al. 2014; Gentile et al. 2020; Geahchan et al. 2021).COVID-19 has been found to be protected by natural inorganic polyphosphate (polyP) derived from marine microorganisms and sponges (Sriyanto et al. 2021)(Müller et al. 2020a, 2021; Neufurth et al. 2021). Its ability to bind the spike protein on viral particles and prevent interaction with ACE-2, as well as trigger the destruction of ACE-2 on host cells, has been proven in several investigations. PolyP has also been demonstrated to have antiviral synergistic effects when used with the anti-inflammatory drug dexamethasone or the antioxidant quercetin. Moreover, numerous investigations have revealed that a variety of marine metabolites isolated from scleractinia-related animals, sponges, and algae can interact with SARS-major CoV-2’s protease, Mpro (El-Hossary et al. 2017, 2020; Liu et al. 2019). Mpro is a virus-specific protein enzyme that plays a key role in viral particle replication and transcription, making it a potential therapeutic target (Zahran et al. 2020). Phycocyanobilin, for example, was discovered to bind to RNA-dependent RNA polymerase (RdRp) with similar or higher potency than remdesivir, making them an attractive alternative to standard therapy (Khan et al. 2020a; Pendyala and Patras 2020; Kwon et al. 2020). Compounds derived from marine creatures that inhibit deoxyribonucleic acid (DNA) and ribonucleic acid(RNA) viruses, including coronaviruses, have been discovered in polysaccharides, terpenoids, steroids, alkaloids, peptides, and other structural classes (Donia and Hamann 2003; Pyrc et al. 2006; Ziółkowska et al. 2006; Barde et al. 2015; Stonik 2016; Zaporozhets et al. 2020; Gentile et al. 2020). The diverse mechanisms used by each of these chemical classes to suppress coronaviruses account for their diversity. A growing body of data demonstrates the therapeutic potential of marine-derived compounds in the discovery of new COVID-19 templates/prototypes (Yi et al. 2020). Anti-COVID-19 medicines may target SARS-CoV-2 viruses directly or host cell proteins. SARS-genome CoV-2’s contain spike glycoproteins (S), matrix glycoproteins (M), nucleocapsid proteins (N), and tiny envelope proteins (E). The anti-SARS-CoV-2 medication also targets MPro and 3CLpro, which are involved in coronavirus transcription, replication, and maturation (Nyamnjoh 2020)(Tagde et al. 2021d)(Karthika et al. 2021b)(Akter et al. 2021b). The aim of this study is to look into the possibilities of employing biologically active compounds produced from diverse chemical classes of marine organisms to cure illnesses caused by coronaviruses at various stages of the virus’s life cycle. New pharmacological compounds of marine origin have been discovered in bacteria, algae, invertebrates (sponges, ophiuras, echinoderms, mollusks, soft corals, bryozoans, and tunnels), and other species. Finally, marine natural bioactive products as marine drugs could be employed as a possible SARS-CoV-2 inhibitor for better COVID-19 management. We reviewed several natural compounds as marine drugs derived from natural source for the treatment of COVID-19 as well as to control the pandemic situation at the present world. This review focuses on marine bioactive chemicals, their sources, and antiviral modes of action, with a focus on COVID-19 treatment. However, the process of marine drug development is faced with many challenges. Firstly, although the sea harbors countless organisms, accessibility to majority of these resources is limited (Montaser and Luesch 2011a). Although plentiful compounds are accessible close to shore, there remain other regions of the ocean that likely possess unknown organisms and, thus, new therapies. Furthermore, to continue the development of promising compounds through pre-clinical and clinical trials, there must be a continuous supply of the compounds. This presents a challenge as large-scale production may harm the marine ecosystem (Montaser and Luesch 2011a). Fortunately, rapid technological advancements in synthetic chemistry and biotechnology provide a potential solution to this problem. In addition, many potential antiviral metabolites have only been tested in vitro or visualized through molecular docking assays. More in vivo studies are needed to further investigate potential adverse effects and drug delivery requirements. Despite the challenges faced, it is clear that marine organisms serve as a promising avenue for future pharmacological intervention (Awan 2013; Khan et al. 2016; Sriyanto et al. 2021; Geahchan et al. 2021). Table 1 shows the findings of a study on the anti-CoV effects of biologically active chemicals from marine species, as well as possible modes of action.Table 1 Anti-CoV effects of biologically active compounds from marine organisms and their possible mechanisms Source Compound Mechanism References Marine sponge Aplysinidae Fistularin-3/11-epi-fistularin-3 (C31H30Br6N4O11) Binding with SARS-COV-2Mpro, E_score2 = –7.8 (Rodrigues Felix et al. 2017; Khan et al. 2020b) Marine sponge Aplysinidae 15-methyl-9(Z)-hexadecenoic acid (C19H40O3) (PubChem CID 21,646,261) Binding with SARS-COV-2Mpro, E_score2 = –7.5 (Rodrigues Felix et al. 2017; Khan et al. 2020b) Soft coral Pterogorgia citrina (Hexadecyloxy) propane,1,2-diol (C16H30O2) (PubChem CID 45,638) Binding with SARS-COV-2Mpro, E_score2 = –7.54 (Rodrigues Felix et al. 2017; Khan et al. 2020b) Brown algae Sargassumspinuligerum Heptafuhalol A Phlorethopentafuhalol A Pseudopentafuhalol B Pseudopentafuhalol C Hydroxypentafuhalol A Binding with SARS-COV-2Mpro, ΔGB = − 15.4 kcal/mol Binding with SARS-COV-2Mpro, ΔGB = − 14 kcal/mol Binding with SARS-COV-2Mpro, ΔGB = − 14.6 kcal/mol Binding with SARS-COV-2Mpro, ΔGB = − 14.5 kcal/mol Binding with SARS-COV-2Mpro, ΔGB = − 14.6 kcal/mol (Gentile et al. 2020) Marine sponge Petrosiastrongylophora sp. 15-α-ethoxypuupehenol(C21H26O3) (PubChem CID 460,087) Binding with SARS-COV-2Mpro, E score = –7.26 (Rodrigues Felix et al. 2017; Khan et al. 2020b) Brown algae Sargassumspinuligerum Apigenin-7-O-neohesperidoside Luteolin-7-rutinoside Resinoside Binding with SARS-COV-2Mpro, ΔGB = − 12.4 kcal/mol Binding with SARS-COV-2Mpro, ΔGB = − 12.1 kcal/mol Binding with SARS-COV-2Mpro, ΔGB = − 12.2 kcal/mol (Gentile et al. 2020) Brown algae Ecklonia cava Dieckol (6,6′-bieckol) Binding with SARS- COV-2Mpro, ΔGB = − 12.0 kcal/mol (Gentile et al. 2020) Axinellaepolypoides cultivated from Streptomyces axinellae Tetromycin B Inhibits cathepsin L, IC50 = 32.50 µM (Ahlquist 2006) Marine sponge Plakortishalichondroides Plakortide E (C21H34O) Inhibits SARS PLpro, 68% inhibition at 100 µg/mL Inhibits cathepsins B, 90% inhibition at 100 µg/mL Inhibits cathepsins L, 85% inhibition at 100 µg/mL Inhibits SARSMpro, 30% inhibition at 100 µg/mL (Oli et al. 2014) Marine sponge Theonellaaff mirabilis Tokaramide A Inhibits cathepsin B, IC50 = 29.0 ng/mL (Fusetani et al. 1999) Marine sponge Theonellaswinhoei Miraziridine A Inhibits cathepsin L, 60% inhibition at 100 µg/m L, (Tabares et al. 2012) Marine sponge Axinella cf. corrugata Esculetin-4-carboxylic acid ethyl ester (C24H20O12Na) Inhibits SARS-COV-23CLpro, ID50 = 46 mmol/L (Lira et al. 2007) Soft coral Formosan gorgonian Briareum Excavatolide Binds with TMPRSS2, ΔGB = − 14.38 (Rahman et al. 2020d) Green algae Dictyosphaeriaversluyii Decalactone 4-dictyosphaeric acid A Binds with TMPRSS2, ΔGB = − 14.02 (Rahman et al. 2020d) Coronavirus disease (COVID-19) Coronaviruses (Latin: Coronaviridae) are RNA viruses that are separated into two subfamilies: Coronavirinae and Torovirinae (Boiko et al. 2022) (Payne 2017). There are four genera in the Coronavirinae subfamily: alpha, beta, gamma, and delta coronaviruses. HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU1, SARS-CoV, MERS-CoV, and SARS-CoV-2 are human coronaviruses (Fehr and Perlman)(Tagde et al. 2021c). The coronavirus genome is wrapped in an envelope and enveloped in a spiral capsid made up of genomic RNA connected to a nucleoprotein (N). The membrane protein (M) and envelope protein (E) are essential for virus assembly, whereas the spike protein (S) promotes virus entry into host cells, and the viral envelope is made up of three structural proteins. A huge ectodomain, a transmembrane anchor, and a tiny intracellular tail make up the coronavirus spike. The receptor-binding component S1 and the membrane-fusion subunit S2 make up the ectodomain (Payne 2017). Virology and pathogenesis of SARS-CoV-2 SARS-CoV-2 has an unusually extended survival time in the environment, lasting at least 24 days in feces and on dry surfaces at room temperature (Chen et al. 2020a). It is a positive-sense ssRNA virus with a 30 kb envelope that codes for structural, nonstructural, and accessory proteins [Table 2] (Wang et al. 2020a). Spike (S), envelope (E), membrane (M), and nucleocapsid (N) proteins are structural proteins [Fig. 2]. During viral entry, the surface S-glycoprotein facilitates proper connections between the virus and the host receptor. S-recombinant proteins receptor-binding domain (RBD) interacts with ACE2 protein specifically, mediating host cell invasion and initiating pathogenesis (Rabenau et al. 2004). SARS-CoV-2 has a 10 to 20 times higher binding efficacy, leading to increased transmissibility and contagiousness. The other three structural proteins help the virus put together. Nonstructural proteins involved in the viral life cycle include 3-chymotrypsin-like protease (3CLpro), papain-like protease (PLpro), helicase, and RNA-dependent RNA polymerase (RdRp) [Table 3]. The virus creates ss-positive RNA, which the host cell’s translation machinery then converts into polyproteins (Khailany et al. 2020).Table 2 SARS-CoV-2-fighting bioactive compounds generated from coral halobionts Table 3 Marine compounds for potential SARS-CoV-2 treatment Marine compound Source Mechanism References Lambda carrageenan Marine algae By inhibiting viral replication, it lowers viral protein expression Terphenyllin Scleractinia-associated organisms Form hydrogen bonds and dock with Mpro (Zahran et al. 2020) Tirandamycin A Phlorotannins (17 molecules) Sargassum spinuligerum brown algae Inhibit SARS-CoV-2 Mpro through hydrogen bonding and hydrophobic interactions (Gurung et al. 2020) Five marine compounds (C19H40O3, C16H30O2, C22H32O4, C21H26O3, C31H30Br6N4O11) Aplysiidae sponge, soft coral Pterogorgia citrina, Petrosia stronglyophora sp. Inhibit Mpro through hydrogen bonding and hydrophobic interactions (Khan et al. 2020b) Phycocyanobilins (PCB) Cyanobacteria, algae rhodophytes - Interact with RBD of spike protein through Van der Waal interactions and hydrogen bonding - Inhibits Mpro and RNA-dependent RNA polymerase (Pendyala and Patras 2020) Sulfated polysaccharides Cyanobacteria, brown algae (Saccharina japonica) Binds spike protein and inhibits viral entry (Nagle et al. 2020; Song et al. 2020; Bhatt et al. 2020; Kwon et al. 2020) Bromotyrosines Marine sponges - Inhibits protein synthesis, replication, and proliferation of HIV-1 - Binds spike protein and prevents viral entry into cells (Binnewerg et al. 2020; Muzychka et al. 2021) Initial physiological immune response The integrated immunological response of early cytokines releases and antiviral activation subsequent by immune-cell infiltration should result in effective SARS-CoV-2 elimination from the pulmonary in most COVID-19 patients (Fig. 1). Yet, it has been widely documented that viral infection may proceed to serious disease as a result of a down-regulation immunological response (Bohn et al. 2020).Fig. 1 Infection with SARS-CoV-2 causes a physiological immunological response in the host. 1: The surface spike (S) protein of SARS-CoV-2 assaults alveolar epithelial cells by interacting to angiotensin-converting enzyme 2 (ACE2) that is administered by the trans-membrane serine protease 2. (TMPRSS2). 2: Migration of macrophages and dendritic cells in the lungs as a result of chemokine and cytokines release (early phase). 3: It has been shown that direct viral infection of pulmonary macrophages and dendritic cells results in the production of a large number of pro-inflammatory cytokines and chemokines. 4: Dendritic cells in the lungs phagocytose virus, move to secondary lymphoid organs, and activate antigen-specific T lymphocytes, which subsequently go to the lungs and destroy latently infected alveoli cells (Bohn et al. 2020) Prominent symptoms of COVID-19 SARS-CoV-2 causes multiple organ failure by attacking the respiratory system, gastrointestinal system, central nervous system, kidney, heart, and liver (Zhu et al. 2020). COVID-19 symptoms vary, ranging from moderate symptoms to severe sickness. Headache, loss of smell (anosmia) and taste (ageusia), nasal congestion and runny nose, cough, muscle pain, sore throat, fever, diarrhea, and breathing difficulties are some of the most common symptoms. People with the same virus may experience a variety of symptoms, which may change over time. A respiratory symptom cluster with cough, sputum, shortness of breath, and fever; a musculoskeletal symptom cluster with muscle and joint pain, headache, and exhaustion; and a digestive symptom cluster with abdominal discomfort, vomiting, and diarrhea have all been discovered (Wang et al. 2020a; Kluytmans-Van Den Bergh et al. 2020; Sami et al. 2021). Life cycle of coronaviruses and targets for the development of antiviral agents The S1 subunit of spike protein has an RBD that interacts with angiotensin-converting enzyme 2 (ACE2), which is expressed on the endothelial surface in the respiratory and gastrointestinal systems (Zhou et al. 2020; Hoffmann et al. 2020b). This starts the SARS-life CoV-2 cycle. The virus enters the host cell through the direct fusion of the host cell and viral membranes, as well as endocytosis via the spike protein’s S2 subunit (Hoffmann et al. 2020b; Bestle et al. 2020). The spike protein is made as an inactive precursor, which is then cleaved by cellular proteases, causing conformational changes in the S2 subunit, allowing it to become functional and ready for membrane fusion (Chakraborty and Bhattacharjya 2020). Once the spike protein-ACE2 complex forms, TMPRSS2 breaks the spike protein in close proximity to ACE2, causing membrane fusion with the host cell and viral genome release (Bestle et al. 2020). Trypsin, plasmin, and factor Xa are some of the other proteases involved in this process. Another mechanism for the virus to reach the host cell is by endocytosis. Endo-lysosomes’ furin and cathepsin B/L (CatB/L) appear to be involved in endosomes spike protein activation (Hoffmann et al. 2020b; Bestle et al. 2020).When the viral envelope unites with the host cell membrane, the viral RNA can be released. Infected cells’ cytoplasmic genomic viral RNA can be translated into two polyproteins, pp1a and pp1ab, which are then degraded into 16 mature nonstructural proteins (NSPs) by two viral proteases, 3C-like protease (3CLpro), and papain-like protease (PLpro) (Coutard et al. 2020; Wu et al. 2020; Zhou et al. 2020; Hoffmann et al. 2020a). NSP12, also known as RNA-dependent RNA polymerase (RdRp), is responsible for viral genome replication and transcription (DMVs) (Wang et al. 2020c). DMVs carry viral RNA products, which are delivered to the cytosol across the double membrane by a molecular pore complex (Wang et al. 2020c). The endoplasmic reticulum (ER) then translates structural proteins like spike protein (S), envelope protein (E), and membrane protein (M), which are then transferred to the Golgi apparatus for virion assembly. In the cytoplasm, the viral genomic RNA and structural protein N are biosynthesized and integrated into the nucleocapsid, which is subsequently linked to the viral structural proteins to generate new virions. The mechanism by which virions are expelled from an infected cell is known as exocytosis (Buratta et al. 2020). As a result, therapies to avoid one or more events in the SARS-life CoV-2 cycle are being developed. The discovery of drugs that target proteins involved in the viral life cycle is a possibility. Virus entry into the host cell The essential targets in therapeutic development are receptor binding and membrane fusion, which are early and crucial events in the coronavirus infection cycle (Fehr and Perlman 2015). Penetration is normally initiated by nonspecific interactions between the virus and cell surface attachment factors, followed by the engagement of more specialized cellular receptors. Unspecific contact raises viral particle concentrations in the environment, which leads to higher infection rates. Attachment and penetration inhibitors bind to virus receptor molecules on the surface of susceptible cells, bind to certain proteins directly in the virion, and bind to an intermediate, “activated” version of viral protein to prevent additional structural changes (Lundin et al. 2014). Attachment and penetration inhibitors can be intelligently used in antiviral drugs, especially those used in prophylactic situations because the ability to enter the membrane isn't always required; these substances may minimize the likelihood of virus replication from the start and so be less dangerous. Such properties are unquestionably necessary for effective drug transport across mucosal membranes (Zhou and Simmons 2014). The main advantage of utilizing penetration inhibitors for emerging viruses is that they block a large portion of the virus’s material from entering the host cell, which is necessary for many of these pathogens to infect (Pyrc et al. 2006). Inhibitors of the unspecific interaction of the virus to attachment factors on the cell surface Lectins Non-immunoglobulin carbohydrate-binding proteins are known as lectins. They can recognize and attach to complex glycoconjugates moieties in a reversible way without affecting the covalent structure of any of the glycosyl ligands identified. Algae, fungi, marine corals, higher plants, prokaryotes, invertebrates, and vertebrates are all examples of species that include lectins. They are involved in carbohydrate recognition and binding, host–pathogen interactions, cell targeting, cell–cell communication, apoptosis activation, cancer metastasis, and differentiation, among other biological processes. Because of the capacity to prevent virus self-assembly during replication, mannose-binding lectins (which belong to the C-type pattern recognition lectins) are a major priority for antiviral research. Given the significant degree of commonality in the presence of high mannose glycans in envelope glycoproteins across encapsulated viruses, a method based on carbohydrate-binding lectins can be applied to many of them. For example, researchers discovered that 15 of a range of plant powder lectins comprising mannose, N-acetyl glucosamine, glucose, galactose, and N-acetyl-galactosamine have anti-SARS-CoV action (Keyaerts et al. 2007; Kim 2021; Reynolds et al. 2021; Havlik et al. 2021; Jackson et al. 2021; Nguyen et al. 2021; Rauf et al. 2021; Barre et al. 2022; Lloyd et al. 2022; Spillings et al. 2022). Glycosaminoglycan mimetics It has been shown that many microorganism employ glycosaminoglycans (GAGs), which are long sulfated polysaccharides that are expressed mostly on cell surface as well as in the extracellular matrix for cellular interaction and adherence as well as invasion and immunologic evasion (Mycroft-West et al. 2018). In order to attach to host cells, SARS-CoV and other coronaviruses utilize their GAGs (Kim et al. 2020). Cell surface glycoproteins interact with GAG mimic heparinoid polysaccharides to generate a protective barrier and prevent viral binding. In the study by Kim et al., heparin sulfate comes into contact with the GAG-binding motif in the trimeric SARS-CoV-2 spike glycoprotein at the S1/S2 location on each monomer interface and at a different location when the receptor-binding domain is open (453–459 YRLFRKS). In the marine environment, GAGs and sulfated glycans that resemble GAGs but have structurally distinct structures are common (Mycroft-West et al. 2018). Fucans from brown algae (Phaeophyta) that have been sulfated (ascofillan, fucoidan, glucuronoxylofucan, and sargassum), red algae (Rhodophyta) produce sulfated galactan (agar and carrageenan), and sulfated heteropoly saccharides derived from ulvan-containing substances (agar and carrageenan) are among these analogs (Damonte et al. 2012). For the treatment of viral infections such as the human immunodeficiency virus (HIV) and herpes simplex and cytomegalovirus (HCMV), sulfated seaweed polysaccharides have been shown to have antiviral properties (Damonte et al. 2012). Fucoidans (branches of sulfated polysaccharides with a high molecular mass Research Projects Incorporated RPI-27 and RPI-28) from the marine alga Saccharina japonica may bind significantly to the S-protein SARS-CoV-2 in vitro using Vero-CCL81 cells that express both ACE2 and TMPRSS24, according to a study by Kwon et al. (2020) (Kwon et al. 2020). Even at the highest concentrations, none of the polysaccharides were hazardous (Kwon et al. 2020). Inhibitors of viral lipid-dependent attachment to host cells Because lipids are engaged in crucial phases in the virus’s life cycle and can act as direct receptors or cofactors of virus entrance on the cell surface and in endosomes, they are vital in viral infection (Chazal and Gerlier 2003). They can also operate as direct receptors or cofactors of virus entrance on the cell surface and in endosomes, and they are engaged in crucial phases in the virus’s life cycle. Viruses that utilize microdomains of cell membranes termed lipid rafts (membrane rafts) for some stages of their reproductive cycle rely on cellular lipid membranes as a crucial first point of interaction. Several viruses have been shown to utilize membrane rafts to aid this function (Chan et al. 2010). Sterols Molecules that alter lipids can be used to selectively restrict viral replication. Natural substances like cyclodextrin and sterols, as well as sphingolipids (Lorizate and Kräusslich 2011), can inhibit the infectivity of many types of viruses, including the coronavirus family, by interfering with lipid-dependent attachment to human host cells. Cyclodextrins are cyclic oligosaccharides made up of a macrocyclic ring of glucose subunits connected by 1,4-glycosidic bonds that disrupt the lipid composition of the host’s cell membrane, minimizing the virus’s attachment to protein receptors, whereas phytosterols are cholesterol mimics that can bind to the virus instead of membrane rafts, reducing the virus’s attachment to protein receptors (Fernández-Oliva et al. 2019). Sterols with important biological activity, including antiviral, have been found in algae, Porifera, Coelenterata, bryozoa, mollusks, Echinodermata, Arthropoda, Tunicata, and chordate (Stonik 2001). Porifera (sponges) have a significant position. Gauvin, for example, discovered that 5,8-epidioxy sterols isolated from the marine sponge Luffariella variabilis suppressed HTLV-1 (Gauvin et al. 2011). McKee investigated 22 sulfated sterols produced from marine sponges for antiviral efficacy against human immunodeficiency virus-1 (HIV-1) and human immunodeficiency virus-2 (HIV-2) (McKee et al. 2002). Sulfate groups at positions 2, 3, and 6 were found among the most active sterols. Binding to specific receptors and fusion of cytoplasmic and viral membranes Proteolytic enzymes cleave the protease, resulting in infection surface constructions necessary for successful infection and subsequent entry into the cell after confinement to receptors, ensuring the combination of the infection’s layers and the cell. Compounds that particularly interact with the S protein, as well as biological components, notably various proteases, that are essential for this process and can stop the virus from entering the cell. As a result, antiviral specialists may target host cell surface proteins, which can act as infection sensors, and host proteases. ACE2 inhibitors Since ACE2 has been identified as the principal receptor of SARS-CoV-2 viruses in humans, researchers have focused on figuring out how to regulate it as a way to treat the virus. The main function of ACE2 as part of the renin-angiotensin system is to convert angiotensin II, a powerful vasoconstrictor, to angiotensin (structural forms I, III, IV, V, VI, and VII), a vasodilator that contributes to blood pressure maintenance and reduction by counter-regulating ACE. Despite the fact that it is an analog of ACE, its similarity is only about 42% (Huang et al. 2010). The use of ACE inhibitors (ACEIn) in the chronic treatment of hypertension and diabetes is a problem with ACE2 and coronavirus infections (Barbosa-Filho et al. 2006). These medications are also known to upregulate the expression of ACE2, putting the patient in the COVID-19 risk category (Zhang et al. 2020). In fact, the majority of COVID-19 diagnosed patients with severe or fatal infection had comorbidities, particularly hypertension or diabetes (Zhang et al. 2020; Wang et al. 2020b). Meanwhile, common ACEIns included in hypertension medications such as perindopril, enalapril, and losartan have little effect on ACE2 (Barbosa-Filho et al. 2006; Huang et al. 2010). The limited ability of ACEIn to cleave angiotensin I is thought to be the cause of ACE2 overexpression. As the concentration of angiotensin I rises as a result of ACE inhibition, ACE2 mRNA increases to compensate (Rice et al. 2004). ACE inhibitory action has been found in some natural compounds that are widely utilized in ethnobotanics and, in some cases, are firmly rooted in the human diet (Barbosa-Filho et al. 2006; Daskaya-Dikmen et al. 2017). Bio-products, such as ACE inhibitors, are widely used, owing to the fact that synthetic compounds, such as enalapril, were created utilizing a natural metabolite as a scaffold. This illustrates their viability as new medicine sources; they have fewer side effects than synthetic pharmaceuticals, and natural extracts can have lower IC50 values in some circumstances (Daskaya-Dikmen et al. 2017). Peptides Outside of human cells, peptides that replicate ACE2 could be effective for containing COVID, and they offer a few advantages over tiny molecules (higher confidence) and antibodies (lower cost) (little size). Arrangements in the beneficial gaps in the COVID S-circle yielded intense inhibitors of COVID illness, which are short peptides (AK et al. 2006). Researchers are drawn to marine peptides because of their vast spectrum of healing movement, slow natural articulation in biological tissues, and affinity for targets among the taxa that have produced these peptides are Porifera, Cnidaria, Nemertina, Crustacea, Mollusca, Echinodermata, and Craniata. The foundation for the creation of putative COVID-19 inhibitors can be using oligopeptides produced by gastrointestinal stimuli bound to the SARS-CoV-2 pine protease, in silico hydrolysis of 20 marine fish proteins was performed. Antibacterial combinations are produced by nearly every marine microorganism as the first line of defense in order to live, which has recently aroused scientists’ curiosity as a possible source of peptides. TMPRSS2 inhibitors Surprisingly, a substantial body of research suggests that suppressing TMPRSS2 articulation or potential action is a relatively safe and successful strategy for treating viral contaminations produced by diseases like MERS-CoV, SARS-CoV, and SARS-CoV-2 are three different strains of the same virus that use TMPRSS2 for cell implantation. These analyses revealed that the destructive proclivity of the recently mentioned disorders is dependent on TMPRSS2 serine protease development. When the degree of activity of TMPRSS2 is reduced in these viral diseases, the speed of implantation, replication, dissipation, and assistant replication of the contaminations all fall significantly. Since SARS-CoV-2 is additionally one of the infections that utilize TMPRSS2 for implantation, it is proposed that inactivating TMPRSS2 with clinically proven TMPRSS2 inhibitors can be added to COVID-19 treatment. Flavonoids, terpenes, and peptides Biologically active substances of marine origin, such as flavonoids, phlorotannins, alkaloids, terpenoids, peptides, lectins, polysaccharides, lipids, and others substances, can affect coronaviruses at the stages of penetration and entry of the viral particle into the cell, replication of the viral nucleic acid, and release of the virion from the cell; they also can act on the host’s cellular targets. These natural compounds could be a vital resource in the fight against coronaviruses (Zaporozhets and Besednova 2020; Silva Antonio et al. 2020; Muhseen et al. 2020). TMPRSS2 proteases are used by SARS-CoV-2 to infect cells effectively drive the S peptide into the disease and cell film mix. Flavonoids, terpenes, peptides, and coumarins are some of the recognized frequent TMPRSS2 inhibitors. Marine life forms could also be a source of TMPRSS2 inhibitors. Terpenoids’ fundamental variety allows for a wide spectrum of natural exercises; the amount of isoprene units in hemiterpenes, monoterpenes, sesquiterpenes, diterpenes, and triterpenes makes them attractive as possible medications. Terpenoids are a kind of compound found in plants with no doubt the most often found natural chemicals in today’s oceans. Some terpenoids chemicals are inside the beginning phases of development, either in preclinical or clinical trials (Gross and König 2006). These findings reveal that peptides and proteins from the sea can assure the effectiveness of mixes designed to inhibit viral invasion, impede mixing, and destroy viral particles, as well as terpenoids and other marine components (Savant et al. 2021; Tomas et al. 2021). Virion deproteinization The virus’s internal structures reach the cytoplasm of infected cells after cytoplasmic and viral membranes have the ability to absorb and fusion, where they undergo partial deproteinization and release of the internal nucleoprotein (Walls et al. 2019). Surface-bound proteases like TMPRSS2 deproteinize the proteins (Walls et al. 2019) and cysteine proteases in endosomes (cathepsin). Extracellular proteases when the virus departs the cell and proprotein convertases in the generating cells. As a result, the viral genome’s polymerase (transcriptase) complex is used to set up transcription and replication conditions; 3CLpro, commonly known as major protease, is a chymotrypsin-like cysteine protease and is one of four proteins that aren’t structural are found in CoV of SARS proteins (Mpro). In the viral life cycle, important enzymes include PL2pro, helicase, and RNA-dependent RNA polymerase, which are all papain-like proteases. The large precursor proteins PL2pro and 3CLpro, after being cleaved, mature as active proteins, and they play a role in the breakdown of coronavirus polypeptides that are massive. The most frequent viral protease is 3CLprosignificant since it is the better of the two releases important viral replicative proteins include RNA polymerase and helicase proteins. CLpro inhibitors Due to its critical function in SARS-CoV replication, 3CLpro is being investigated as a potential target for antiviral medicines. In the last 5 years, a collection of inhibitors has been created based on the crystal structure of 3CLpro, and there are a variety of 3CLpro inhibitors available, including peptide mimics and small molecule compounds, which have been described. The HIV protease inhibitors lopinavir and ritonavir inhibit 3CLpro. In CoV in silico investigations, the compounds colistin, valrubicin, icatibant, bepotastine, epirubicin, epoprostenol, vapreotide, aprepitant, caspofungin, and perphenazine all bind to the lopinavir/ritonavir binding site. Several research groups have identified 3CLpro as a possible candidate COVID-19 is a therapeutic target in the fight against it. Consider a worldwide group of scientists who looked into almost 10,000 pharmaceutical molecules that were currently in use or in clinical trials, as well as a variety of other pharmacologically active chemicals and found six potential COVID-19 viral inhibitors. According to structural studies of the inhibitory enzyme found in various coronaviruses that binds to the substrate-binding cavity between domains I and II are effective against all coronaviruses. Recent reviews have looked at natural plant-derived 3CLpro inhibitors. Antiviral activity of biologically active compounds present in marine animals has been demonstrated in the fight against RNAviruses. Phlorotannins Polyphenolic compounds known as phlorotannins are formed up of polymerized phloroglucinol molecules (Rahman et al. 2021b). As a component that aids in fibrinolysis, Ecklonia kurome was found to have phlorotannin, which is a well-known example of pharmaceutical use of a known chemical. It has been examined in a number of bioassays for antibacterial, antioxidant, anticancer, antihypertensive, antidiabetic, anti-allergic, and radioprotective effects since its discovery in 1985 (Domínguez 2013). Antibacterial and antiviral properties of polyphenolic compounds found in Plants from both the sea and the land are being studied (Imbs and Zvyagintseva 2018). Phlorotannins, a unique polyphenolic component discovered in brown algae, are a type of polyphenolic chemical (Imbs and Zvyagintseva 2018). The phloroglucinol monomeric unit is the basis for these chemicals. Phlorotannin’s are a kind of phlorotannin that have a diverse set of biological functions, antibacterial, antioxidant, anti-inflammatory, anticancer, antidiabetic, radioprotective, antiadipogenic, antiviral, and antiallergic characteristics. They are thought to be potential prospects for pharmacological development (Imbs and Zvyagintseva 2018). Pharmacophore consensus was utilized by Gentile and his colleagues (2020) with a high throughput modeling and molecular docking to perform a simulated screening of 14,064 chemicals; there are 164,952 conformers in the collection of marine natural products, and 17 potential SARS-CoV-2 3CLpro inhibitors have been identified. According to the results of molecular docking, the docking energy of these molecules ranged from 4.6 to 10.7 kcal/mol. Brown algae-derived phlorotannins were discovered to be the most efficient inhibitors of SARS-CoV-2 Mpro. Sargassum spinuligerum is a Sargassum species. Phlorotannins are abundant in other types of brown algae (Li et al. 2011). These are the areas where Mpro inhibitors can be found; Park et al. (2013) investigated the biological activity of Ecklonia cava, an edible brown alga, yielded nine phlorotannins. With the exception of phloroglucinol, all nine phlorotannins (1–9) identified inhibited SARS-CoV3CLpro dose-dependently and in a competitive manner. Dieckol with A diphenyl ether connects two eckol groups had the most significant SARS-CoV3CLpro trans/cis-cleavage inhibitory effects. Dieckol and 6,6′-bieckol, two isolated phlorotannins from Ecklonia cava, a kind of brown algae that is edible, were revealed to be Mpro inhibitors. The marine compounds Mpro’s active site and residues came into contact around it to produce multiple interactions between hydrogen and hydrophobic molecules. Initially alkaloids, lipids, terpenoids, and phenol are some of the chemicals present in plants that Felix et al. (2017) discovered and connected. Lipids Marine creatures create phytoplankton, macroalgae, marine invertebrates, and sponges, to name a few, are all rich in lipids (phytoplankton, macroalgae, marine invertebrates, and sponges are all examples of marine bacteria and cyanobacteria). Saturated, monounsaturated, and diunsaturated acids; halogenated, hydroxylated, methoxylated, and non-methylene–interrupted acids; phospholipids; and glycolipids, as well as branched, halogenated, hydroxylated, methoxylated, and non-methylene-interrupted acids, are found. Two of the most polyunsaturated fatty acids that are essential are eicosapentaenoic and docosahexaenoic acids, respectively. Lipid metabolism is a critical component in viral replication that viruses take over and amplify to meet the growing demand for viral structural characteristics like the viral cell membrane because of their extensive direct or indirect biological activity involved in a variety of lipid physiological processes have gotten a lot of attention. Lipids play a role in intercellular and immunochemical activities, as well as influencing the permeability of cells and the activity of a variety of enzymes; some lipids also serve as protein regulators or signaling molecules. Previously obtained marine lipids from Aplysiidae sea sponges and soft corals have been found to have antimicrobial properties, according to new research (Pterogorgia citrina) Terpenoids, lactone With SARC-CoV-2 Mpro, terpenoids have a better binding ability. Parthenolide is the predominant biologically active ingredient in this plant, and it has a variety of pharmacological qualities, including antioxidant, anti-inflammatory, analgesic, antibacterial, anti-migraine, and anticancer effects (CJ et al. 2005). Reverse transcriptase and protease inhibition are two antiviral methods. Up until now, protease inhibitors, notably inhibitors of human clinical trials for the treatment of coronaviruses, and HIV-1 protease was accessible. In this way, the search for natural bioactive chemicals substances derived from bio-resources with inhibitory characteristics the activity of HIV-1 protease is very important. New diterpenes identified in Dictyota pfaffii include protease inhibitors, a brown alga from Brazil (Mominur Rahman et al. 2021; Bhattacharya et al. 2022). Puupehedione, a terpene compound originally identified in the marine sponge Petrosiastronglyophora, also showed a positive interaction with the virus Mpro. After screening crude extracts and pure compounds isolated from the sea sponge Axinella cf. corrugata, De Lira et al. (2007) discovered that two coumarin derivatives, esculetin-4-carboxylic acid methyl ester and esculetin-4-carboxylic acid ethyl ester, inhibit SARS-CoV3CLpro in vitro and SARS-CoV replication in Vero cells. Alkaloids Among the most common types of second-generation metabolites detected in sponges from the sea are alkaloids. They have a diverse set of biological functions of characteristics, antiviral action, for example, and occur in a variety of heterocyclic ring derivatives (Singh and Majik 2016). A kind of marine alkaloids metabolite identified in Batzella is PGAs (polycyclic guanidine alkaloids), Crambe, Monanchora, Clathria, Ptilo-caulis, and certain starfish-like Celerina heffernani and Fromiamonilis, which are all Poecilosclerida sponges (El-Demerdash et al. 2018). After being found in Aplysinidae sponges from the sea, fistularin-3/11-epifistularin-3 was determined to have a strong connection with SARS-COV-2 Mpro. Flavonoids Flavonoids are a type of phytomedicine that may be used to treat a variety of ailments that is used frequently (Rahman et al. 2020b) (Fatima et al. 2021). According to an in silico analysis, the flavonoid-rich dietary components caflanone, equivir, hesperetin, and myricetin bind with remarkable affinity with the ACE2 receptor’s spike protein, helicase, and protease sites. COVID-19 was created by the coronavirus that causes severe acute respiratory syndrome (Kabir et al. 2021a). Flavonoids have been demonstrated to help prevent and treat a number of ailments, including viruses. Flavonoids are a form of antioxidant polyphenol, a secondary plant source component, and have also been discovered to be a viable source of 3CLpro inhibitors. Flavonoids inhibit enzymes such as phosphatases, protein phosphokinases, hydrolases, oxidoreductase, DNA synthases, RNA polymerases, phosphatases, and oxygenases. Flavonoids have the capacity to influence many components of intracellular signaling cascades, such as tyrosine kinase, mitogen-activated protein kinase (MAP kinase), and protein kinase C cascades, which are critical for their numerous actions in cells (Bhattacharya et al. 2021a)(Karthika et al. 2021a). As a consequence of the growing interest in their potential biological and pharmacological activities, flavonoids from the sea have been intensively researched in recent decades. Regardless of this, most marine flavonoids are hydroxylated and methoxylated have a unique pattern of substitution that isn’t found on the ground species, including sulfate, chlorine, and amino groups which are all present. Although the bulk flavonoids are found in sea grasses and halophytes, they can also be found in mangroves, algae, mollusks, fungus, corals, and bacteria of other marine life. Antiviral action has been demonstrated in flavonoids from the sea, including those that block viral enzymes. According to a study, flavonoids from the brown alga Sargassum spinuligerum bind to SARS-COV-2 Mpro, including apigenin-7-O-neohesperidoside, luteolin-7-rutinoside, and resinoside. Marine bioactive compounds for SARS-CoV-2 Scleractinia, an order of Anthozoa, is found only in the marine environment. This is the most biodiversity and active order, made up of stony corals. They can be solitary, but in colonial form, they support enormous populations of helpful microbes; the “coral halobiont” is an assembly of host coral and its extraordinary symbiotic interaction with unicellular creatures known as zooxanthellae and an assortment of microorganisms. Bacteria, fungi, and unicellular endosymbionts, such as zooxanthellae, are small photosynthetic dinoflagellate algae from the genus Symbiodinium that invade and then live inside coral tissue (Shah et al. 2020) (Table 2). Zahran et al. (Zahran et al. 2020) created a small library of 15 marine-derived chemicals obtained from Scleractinia-associated organisms that have the potential to inhibit SARS-CoV-2. The absorption, distribution, metabolism, and excretion (ADME) analysis was used to analyze the physiochemical characteristics of the compounds that were later identified as possible inhibitors of COVID-19 targets after molecular docking investigations on naturally occurring compounds from the marine-based products library (Zahran et al. 2020). Docking was performed on five SARS-CoV-2 target sites. A major viral protease is the first target site (PDB ID 6LU7). Nsp16, a nonstructural protein (PDB ID 6W4H), is a critical protein because it forms a complex with another protein, nsp10, which results in methylation at the 2'-O site of viral RNA ribose. The virus is effectively hidden from the host immune system as a result of this change(Lin et al. 2020). Role of marine natural products in COVID-19 Vitamin E, B12, phycocyanin, lutein, and polysaccharides are among the bioactive compounds found in marine algae (Herrera-Calderon et al. 2020). Lambda carrageenan, in particular, is a polysaccharide isolated from marine red algae (Table 3) that has antiviral, antibacterial, anti-cancerous, and anti-coagulant properties. Both influenza virus and SARS-CoV-2 have been demonstrated to be effectively inhibited by it. A study found that the marine polysaccharide reduced viral protein expression and suppressed viral replication in a dose-dependent manner (Akter et al. 2021a). The presence of spike viral proteins on SARS-CoV-2 and influenza A viral proteins decreased dramatically as the lambda-carrageenan dose was increased from 0 to 300 g/mL (Zahran et al. 2020). Influenza virus inhibition and SARS-CoV-2 inhibition had EC50 values of 0.3–1.4 g/mL and 0.9–1.1 g/mL, respectively. At doses up to 300 g/mL, no-host cell toxicity was found. Mice challenged with the SARS-CoV-2 virus and then administered lambda-carrageenan had a 60% survival rate, indicating that the polysaccharide reduced viral entry and reproduction. These studies demonstrate lambda-antiviral carrageenan capabilities, making it a suitable marine resource for COVID-19 treatment (Fig. 2).Fig. 2 Illustration of anti-SARS-CoV-2 drug candidates produced from marine microorganisms and their likely mechanism of action for possessing a high degree of drug-likeness for prevention and treatment of COVID-19 (Singh et al. 2021) Although these findings are encouraging, it is crucial to note that lambda-carrageenan may have negative side effects. Previous research has found that oligosaccharides derived from the carrageenan family (kappa and lambda-carrageenan) can hinder the creation of new blood vessels, impairing blood vessel development. They were also reported to impede migration, proliferation, and tube formation of human umbilical vein endothelial cells at 200 g/mL. These findings suggest that there may be hazardous effects in humans; however, more in vitro and in vivo toxicology research is required. These data must be taken into account in the development of lambda-carrageenan as a SARS-CoV-2 inhibitor. Sea species’ medicinal potential is also seen in Scleractinia-associated organisms like bacteria and fungi (EM et al. 2017; URs et al. 2017; Shady et al. 2017; El-Hossary et al. 2020; Zahran et al. 2020). These organisms have been linked to inflammation and viral infection because they produce a variety of metabolites (Shady et al. 2017; El-Hossary et al. 2020; Zahran et al. 2020). Scleractinia-related metabolites were examined, and molecular docking was used to identify potential antiviral actions of SARS-CoV-2. Two specific microbial metabolites (Terphenyllin and Tirandamycin A) have been discovered to establish hydrogen bonds with the major protease (Mpro) and dock with great affinity (Zahran et al. 2020). These marine metabolites are regarded to be good leads for inhibiting the virus’s primary protease, which is crucial to the virus’s life cycle. In a similar investigation, seventeen putative Mpro inhibitors were discovered in the class phlorotannins isolated from Sargassum spinuligerum brown algae. The compounds connected with Mpro through substantial hydrogen bonding as well as hydrophobic interactions, with docking energies ranging from 14.6 to 10.7 kcal/mol. RNA replication and viral protein synthesis are also dependent on the SARS-CoV-2 RNA polymerase and nsp7/8. Remdesivir is a well-known inhibitor of the RNA-dependent RNA polymerase, and three Scleractinia metabolites have been identified to bind the polymerase in the same spot as remdesivir. This finding shows that these marine metabolites could be useful in the treatment of COVID-19 by inhibiting viral replication. Furthermore, a study using molecular docking studies on Mpro discovered that a number of marine chemicals have potential binding interactions (Khan et al. 2020b). Mpro was discovered to interact with five marine compounds isolated from sea sponges of the Aplysinidae family and Petrosia stronglyophora sp., as well as the soft coral Pterogorgia citrina, via hydrogen and hydrophobic interactions (Khan et al. 2020b). Based on their ADME qualities, they have the potential to be used as a SARS-CoV-2 therapy (Khan et al. 2020b). One marine chemical (C1, from the Aplysinidae family) was discovered to be the greatest fit for the Mpro pocket, with an affinity for all areas of Mpro and significantly stronger hydrogen and hydrophobic interactions (Khan et al. 2020b). This discovery sheds light on the compounds’ spatial placement within the binding pocket, which is also characterized by hydrophobic and electrostatic interactions. Phycocyanobilins (PCBs) are pigment chemicals found in various cyanobacteria and Rhodophyta algae (Nagle et al. 2020; Pendyala and Patras 2020). They’ve been demonstrated to have antioxidant and antiviral effects, making them suitable COVID-19 treatment candidates. Mpro and RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 are effective inhibitors of Mpro and RdRp, according to a study (Pendyala and Patras 2020). In silico screening revealed that PCBs had a higher binding affinity to RdRp than the currently available medicine remdesivir, indicating that these compounds may have anti-SARS-CoV-2 actions (Pendyala and Patras 2020). Another study indicated that PCB, along with other phycobilin chemicals produced by Arthrospira, had to promise antiviral effects against SARS-CoV-2 in an in silico study. The researchers discovered that PCB interacted with the virus’s spike protein’s RBD via Van der Waal interactions and hydrogen bonding. PCB was discovered to have competitive binding energy of 7.2 kcal/mol, indicating that it could be used as an antiviral agent. Phycobilin compounds from Arthrospira were shown to exhibit minimal to no cytotoxicity in cells and to be effective at modest dosages (1–10 g/mL) in the investigation. Low mutagenicity, carcinogenicity, and nephrotoxicity have been documented for PCBs. These findings show that PCBs have potent antiviral properties and could be useful in the fight against SARS-CoV-2. Marine organisms provide an endless supply of resources. Many compounds found in cyanobacteria, such as sulfated polysaccharides, have been shown to have antiviral effects (Chahal et al. 2021)(Nagle et al. 2020)(Akter et al. 2020), antiviral activity of sulfated polysaccharides against herpes simplex virus (HSV), hepatitis B virus, and retroviruses (Nagle et al. 2020; Kwon et al. 2020). They have been demonstrated to play a significant role in virus protection due to their anionic properties and molecular weight, both of which can have antiviral effects (Andrew and Jayaraman 2021). Polysaccharides are thought to have a lot of potential against SARS-CoV-2 because of their antiviral properties (Nagle et al. 2020; Song et al. 2020; Kwon et al. 2020). Fucoidan, a kind of sulfated polysaccharide from Saccharina japonica, was found to have antiviral activity against SARS-CoV-2 in a study (Kwon et al. 2020). The marine molecule was found to be more powerful than remdesivir in the trial, indicating that it could be a viable COVID-19 treatment drug (Kwon et al. 2020). Similarly, at doses ranging from 3.9 to 500 g/mL, a study found that fucoidan from brown algae, cucumber sulfated polysaccharide, and carrageenan from red algae all have antiviral activities (Song et al. 2020). Because of its ability to bind the spike protein and block viral entrance into cells, cucumber sulfated polysaccharide was found to have the strongest inhibitory effects (Song et al. 2020). Fortunately, no cytotoxicity was reported at concentrations up to 500 g/mL, as evidenced by no significant changes in cell viability (Song et al. 2020). These findings show that sulfated polysaccharides have the potential to treat SARS-CoV-2 effectively. Potential antiviral application of marine polysaccharide in combating COVID-19 Polysaccharides are macromolecular molecules found mostly in plants, algae, and sometimes mammals (Lee et al. 2017) (Rahman et al. 2020c)(Sharma et al. 2021) (Tagde et al. 2021b) (Chopra et al. 2021). Polysaccharide antiviral properties are determined not only by charge density and chain length but also by their precise structural features (Ghosh et al. 2009). The novel SARS-CoV-2 virus is highly lethal and poses a serious danger to human and animal health, necessitating the development of effective inhibitors (Honda-Okubo et al. 2015). Wide application possibilities exist for polysaccharides with excellent immunological control, safety, and antiviral activity, particularly in anti-coronavirus applications (Chen et al. 2020b). Coronaviruses may be significantly inhibited when carbohydrate-binding agents are present (van der Meer et al. 2007). Many marine animals and other deepwater species have polysaccharides. According to what has already been said, chitosan is a polysaccharide repeating glucosamine and N-acetylglucosamine with a positive linear charge (Yen et al. 2009; Wang et al. 2018a), obtained from shrimp and crab shells or the cell walls of mushrooms (Kurita 2006; Salaberria et al. 2017). A number of polysaccharides, including carrageenan, fucoidan, and alginate, are found in marine algal products used in traditional Chinese herbal treatment dating back many centuries (Dutot et al. 2019). In carrageenan, the sulfated linear polysaccharides consist of repeated disaccharide units that alternately include 3- and 4-linked-D-galactopyranose or 3,6-anhydro-galactopyranose (AnGal units) (Coviello et al. 2007; Jiao et al. 2011; Necas and Bartosikova 2013), which are extracted from certain red algae containing 15–40% ester sulfate with an average molecular weight above 100 kDa (Robal et al. 2017; Sedayu et al. 2019). Iota- (ι, G4S-DA2S), kappa- (κ, G4S-DA), and lambda- (λ, G2S-D2S, 6S) carrageenan are the three commercially significant and extensively distributed carrageenan (Campo et al. 2009). It has been found that brown algae produce a polymer called fucoidan, which is an L-fucose–enriched and sulfated polymer (Wu et al. 2016a; Dutot et al. 2019). This polymer contains sulfate groups as well as minor amounts of other sugars and acids found in brown algae. These sugars and acids are found in small amounts in the various brown algae sources (Ale et al. 2011; Vishchuk et al. 2012; Wu et al. 2016a). Alginate is a highly acidic and linear polysaccharide derived from brown algae. It is composed of alternating β-D-mannuronic acid (M) and α-L-guluronic acid (G) residues (Ikeda et al. 2000). Polyguluronate sulfate (PGS) is a sulfated brown algal polysaccharide with a low molecular weight that is formed by chemical sulfation of polyguluronate (PG) with about 1.5 sulfates per sugar residue (Zhao et al. 2007; Wu et al. 2016b). Research on coronavirus is aided by marine polysaccharides such as carrageenan, PGS, chitosan, and its derivatives that have excellent inhibitory action against different viruses. Human rhinovirus (HRV), influenza A H1N1, and HCoV OC43 are extremely active against iota-carrageenan–containing lozenges throughout the whole dissolving process and are a potential treatment for viral infections of the throat (Morokutti-Kurz et al. 2017). HCoV229E, HCoV-OC43, HCoV-NL63, and HCoV-HKU1 are all significantly inhibited by the cationically modified chitosan, N-(2- hydroxypropyl)-3-trimethylammonium chitosan chloride (HTCC), and the hydrophobically modified derivative (HM-HTCC) is a potent inhibitor of the coronavirus HCoV-NL63 (Milewska et al. 2016). The common cold is caused mostly by respiratory viruses such as rhinoviruses, coronaviruses, and influenza viruses (Monto et al. 2001; Ludwig et al. 2013; Koenighofer et al. 2014). Iota-carrageenan nasal spray has been proven in clinical studies to shorten the duration of a viral common cold. Antiviral efficacy of carrageenan nasal spray has been shown against HRV, human coronavirus, and influenza A virus, with the greatest impact being seen in individuals infected with the human coronavirus. Carrageenan-treated coronavirus-infected individuals had shorter illness duration (p b 0.01) and fewer relapses (p b 0.01) than those of control patients (Koenighofer et al. 2014). As a result of the SARS epidemic in 2003, many people who survived the disease acquired more severe cases of persistent pulmonary fibrosis. Epidermal growth factor receptor (EGFR) signaling in animal models is responsible for the development of pulmonary fibrosis, which manifests as an overactive host response to lung damage. Excessive fibrogenic responses to SARS-CoV and other respiratory viral infections may be prevented via EGFR signal inhibition (Venkataraman and Frieman 2017). The expression and activation of the EGFR pathway may be interfered with or inhibited by fucoidan and sulfated rhamnan, which may help suppress coronavirus (Wang et al. 2017b, 2018b). Marine sponge as source of nucleoside analog inhibitors Nucleosides are the building blocks of nucleic acid and are composed of nucleobases coupled to a sugar moiety (Seley-Radtke and Yates 2018). Nucleosides have important roles in biological processes such as the synthesis of nucleotides (Seley-Radtke and Yates 2018). Nucleoside analogs were used as a scaffold for the creation and development of nucleotide and nucleoside analog inhibitors (NIs) (Table 4). Nucleoside analogs were used to treat viral infections, particularly coronavirus infections (Pruijssers and Denison 2019). NIs are recognized as RdRp broad-spectrum inhibitors (Shannon et al. 2020). RdRp showed high structural conservation among coronaviruses and was found to have excellent structural conservation among coronaviruses (Aftab et al. 2020), making it an appealing target for the development of diverse antiviral medicines (Table 4). Mycalisine A and B are nucleoside analogs obtained from the marine sponge Mycale sp. in 1985 and used as scaffolds for the synthesis of NIs after structural modification by the addition of the CN group (Kato et al. 1985).Table 4 Nucleotide analogs as effective antiviral agents against SARS-CoV-2 NI Antiviral activity Mechanism of action Nucleoside analog Modified sugar IC50 References Remdesivir • Antiviral with a broad spectrum of activity against a variety of virus families • Chain terminator • Inhibits replication of SARS-CoV-2 • Inhibits RdRp Adenosine analog Cyano-modified sugar 1.0 μM (Brown and Pehrson 2019; Karvandian et al. 2020) Sofosbuvir • It has antiviral properties against coronavirus and HIV • Inhibit SARS-CoV2 RdRp enzyme in vitro Uridine analog 2′-deoxy-2′-α-fluoro-β-C-methyl modified sugar > 20 µM Gemcitabine • Broad-spectrum antiviral medication • SARS-CoV-2 inhibition in cell culture • Immunomodulator • Inhibits pyrimidine synthesis Cytidine analog The first nucleoside with age minalfluoro-substituent sugar 1.24 µm (Pankiewicz 2000) 6-Azauridine • Antiviral drug • Inhibits pyrimidine de novo synthesis Uridine analog Ribose sugar 0.38 μg/ml (Kumar et al. 2020) Mizoribine • Immunomodulator • Inhibits nucleotide synthesis • Inhibits inosine and guanine synthesis Imidazole analog D-ribofuranose sugar (3.5 μg/ml-16 μg/ml) (JP et al. 2016) NHC • Potent antiviral activity • RNA mutagenesis • Inhibits RdRp Cytidine analog Ribose sugar 0.3 µM 7-Deaza-7-fluoro-purine derivatives • At low concentrations, inhibits SARS-CoV-2 • Inhibits SARS-CoV-2 replication Purine analog Methyl ribose sugar 7.6 µM Favipiravir • Antiviral activity in vivo against SARS-CoV-2; FPV; influenza A, B, and C viruses; as well as Ebola virus • Inhibits RdRp Guanine analog Ribofuranosyl sugar 61.9 µM EIDD-2801 • Potential COVID-19 therapy in a phase II trial • Improves pulmonary function by lowering viral load Inhibits RdRp of SARS-CoV-2 Cytidine analog Ribose modified ester Ribavirin • Antiviral activity against RNA viruses on a broad scale • Used ribavirin in combination with interferon to treat COVID-19 • Inhibition of viral RNA synthesis • Triphosphate leads to lethal mutagenesis • Inhibit RdRp Guanosine analog D-ribofuranosyl 109.5 µM 2′-C-Methylcytidine • SARS-CoV2 replication was hampered in vivo at sub-micromolar concentrations with no toxicity in Vero cells • Inhibits SARS-CoV-2 replication Cytidine analog Methyl ribose sugar 9.2 µM (Jena 2020) Remdesivir, a nucleotide analog containing 1-ribose and CN substitutions, has intriguing antiviral activity by inhibiting both RdRp and exonuclease proteins (Shannon et al. 2020; Zhang et al. 2021). Furthermore, 2-methyl cytidine and EIDD-2801, modified cytidine analogs, were discovered (Zandi et al. 2021) and inhibited SARS-CoV-2 replication (Shannon et al. 2020; Zandi et al. 2021) with no toxicity on Vero cells (Yosief et al. 1998). Furthermore, computer modeling of ilimaquinone (Surti et al. 2020) and its adenosine analog, asmarine B (Kim et al. 2009), revealed potential SARS-CoV-2 inhibitory efficacy (Božić et al. 2010). Following minimal structural adjustments, the findings showed that compounds generated from marine sponges could be potential RdRp inhibitors. Changes to the sugar moiety and the addition of substituents such as cyano, fluoride, and methyl groups are examples of these alterations. Interestingly, the insertion of the cyano group in the remdesivir side chain increased the drug bioavailability and overcame the viral exonuclease resistance mechanism. Furthermore, the addition of adenosine to ilimaquinone increased its activity 100-fold above the original natural molecule. These findings suggest that, despite the potential effectiveness of the original compounds, alteration in compounds derived from the marine sponge is required for targeted targeting, increased bioavailability and activity, and resistance mechanism overcoming. Importantly, molecules with greater dual action are those that use a nucleotide or nucleoside as a scaffold in addition to sugar, such as avinosol (Diaz-Marrero et al. 2006). Benefits of marine SPs over other natural compounds Marine algae are excellent sources of a wide range of bioactive chemicals with a wide range of structural variations. Sulfated polysaccharides (SPs) like fucoidans in brown algae, carrageenan in red algae, and ulvan in green algae are abundant in the cell walls of marine algae. Anticoagulant, antiviral, antioxidant, cancer-fighting, and immunomodulating capabilities are only a few of the positive biological properties these SPs exhibit (Wijesekara et al. 2011). In addition to sulfated polysaccharides from marine algae, there are many additional natural substances that show promise in the treatment of people with COVID-19. The antiviral bioactivities of medicinal plant essential oils, flavonoids, and phenolic compounds have been described for COVID-19 in various herbal traditional remedies (Roy and Bhattacharyya 2020). Algae- and plant-based chemicals both have anti-SARS-CoV-2 potential, but each has advantages and disadvantages. More and more scientists are looking at the potential of marine macroalgal blooms as a never-ending supply of biologically active chemicals for the development of new and effective therapeutics. Compounds derived from algae and plants are both safe, biocompatible, and biodegradable, but since algae-based SPs are more abundant in the ocean, they have a lower manufacturing cost than plant-based natural compounds (Ruocco et al. 2016). Because marine SPs are water-soluble, they can be extracted using an aqueous extraction technique much more readily than plant-based compounds. This makes it useful in pharmaceutical businesses since its physicochemical and mechanical characteristics may be readily changed (Lee et al. 2017). Sulfated polysaccharides in pharmaceuticals haven’t been linked to any known health risks, but research is needed to better understand their chemical composition, biological efficacy, bioavailability, toxicity, and other related processes. Future direction Oceanic species are a veritable goldmine of antiviral, antibacterial, anticancer, and other pathogen-fighting nutrients. As a result of their diverse chemical structures and distinct modes of action, these compounds are particularly useful against drug-resistant pathogens (Abdelmohsen et al. 2014; Gentile et al. 2020). Furthermore, each marine molecule has several functions that make it useful in a variety of contexts. As an example, several chemicals, such as sulfated polysaccharides, have characteristics that go beyond their ability to fight viruses and bacteria (Udayangani et al. 2020). As a result of their many unique characteristics, marine chemicals are very effective anti-SARS-CoV-2 agents. While synthetic chemicals usually only have a single useful characteristic, this is preferable to synthetic compounds since they are frequently used in combination treatments, increasing the risk of drug-drug interactions. Marine resources are also very cost-effective, owing to their quantity and variety. The current standard therapy remdesivir costs around $2600 for a 5-day course of treatment, which makes them worthwhile (Dyer 2020). At effective doses of polyp(< 10 g/mL), lambda-carrageenan (< 300 g/mL), PCBs (10 g/mL), sulfated polysaccharides (< 500 g/mL), and bromotyrosines (10 μM), no toxicity on cells was found in addition to this (Drechsel et al. 2020; Song et al. 2020; Müller et al. 2020b; Petit et al. 2021). Marine drug development, on the other hand, faces numerous obstacles. One thing to note is that even though there are untold numbers of species living in the sea, access to the majority of these resources is extremely limited (Montaser and Luesch 2011b)(Kabir et al. 2021b)(Rahman et al. 2020c). However, despite the fact that many chemicals are readily available along the coast, other parts of the ocean may include undiscovered species and therefore novel treatments (Montaser and Luesch 2011b). Furthermore, a steady supply of promising chemicals is needed to continue preclinical and clinical studies and further develop them. Bigger output means more risk to the marine environment (Montaser and Luesch 2011b; Shinde et al. 2019). Fortunately, synthetic chemistry and biotechnology are advancing at a fast pace, and this may offer a solution (Montaser and Luesch 2011b). It’s also worth noting that several putative antiviral metabolites have only been examined in vitro or by means of molecular docking studies. More in vivo research is required to explore possible side effects and medication delivery needs in greater depth. Marine species, despite the difficulties, seem to provide a bright future for pharmaceutical intervention. Conclusion FDA-approved drugs to prevent lethal SARS-CoV-2 infections are currently unavailable, as is a treatment protocol that meets current standards. For COVID-19 patients in the hospital, mechanical ventilation and symptom-suppressing clinical treatment are the primary forms of supportive therapy. This review focuses on the most recent findings in antiviral bioactive metabolite research using marine resources. The chemicals produced by marine creatures and species from the ocean are very useful in the treatment of COVID-19. Polyphosphates has been found to efficiently block the spike protein’s RBD and, as a consequence, to reduce its capacity to bind ACE-2 on host cells. With this approach, patients with SARS-CoV-2 may avoid infection. In addition, the chemical shows promise since it may boost the immune system and protect patients from infection as a result. As an alternative to polyphosphates, several additional compounds have been found to have antiviral properties, including PCBs, sulfated polysaccharides, and bromotyrosines, making them potential candidates for future research into COVID-19 therapies. Marine waters are rich with macro- and microorganisms that store large quantities of metabolites, many of which are yet unknown. As a result, looking into and finding new marine resources may lead to the discovery of viable medicines for treating COVID-19 patients. To treat severe COVID-19 infection, marine bioactive substances with immunomodulatory properties could be a better choice than chemically manufactured medicines that have been extensively studied. In order to better understand marine bioactive chemicals’ chemical structure, biological activity, and mechanism of action, more concentrated research is needed. By utilizing a multiomics method and bioinformatics approaches to discover the relationships between these molecules and the SARS-CoV-2 viral infection, the list of putative bioactive chemicals can be narrowed down considerably. Drug repurposing is also being investigated but has been proved to be ineffective. Additionally, the mutation rate of SARS-CoV-2 has sparked worry, as prior research has indicated that mutations in coronavirus target proteins may be associated with medication resistance. The advancement of multiomics technologies, investigations on gene mutations, and bioinformatics techniques will all contribute to advancing the selection of suitable COVID-19 medication candidates. Overall, the marine waters are full of micro- and macroorganisms that harbor extensive amounts of metabolites, most of which have not yet been discovered. Thus, investigating and discovering novel resources that come from the sea bring promising potential therapeutics for treating patients with COVID-19. Author contribution Md. Mominur Rahman: conceptualization, investigation, writing – review and editing. Md. Rezaul Islam: investigation, writing – review and editing. Sheikh Shohag: formal analysis, writing – review and editing. Md. Emon Hossain: investigation, writing – review and editing. Muddaser Shah and Shakil khan shuvo, Hosneara Khan: formal analysis. Md. Arifur Rahman Chowdhury: review and editing. Israt Jahan Bulbul: investigation, review and editing. Sarowar Hossain, Sharifa Sultana, Muniruddin Ahmed: review and editing and supervision. Muhammad Furqan Akhtar, Ammara Saleem: writing, reviewing and editing. Md. Habibur Rahman: conceptualization, writing – review and editing, supervision. Data availability Not applicable. Declarations Ethics approval Not applicable. Consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 35567688 20387 10.1007/s11356-022-20387-8 Research Article Sustainable impact of COVID-19 on education projects: aspects of naturalism Pu Song [email protected] 1 Ali Turi Jamshid [email protected] 3 Bo Wang [email protected] 42 Zheng Chen [email protected] 5 Tang Dandan [email protected] 2 Iqbal Wasim [email protected] 6 1 Guiyang Preschool Education College, Guiyang, China 2 grid.10347.31 0000 0001 2308 5949 University of Malaya, Kuala Lumpur, 50603 Malaysia 3 grid.444787.c 0000 0004 0607 2662 Bahria Business School, Bahria University, Islamabad Campus, Islamabad, Pakistan 4 Guiyang Preschool Education Normal College, Gui Yang, China 5 Weinan Vocational & Technical College, Shaanxi, China 6 grid.263488.3 0000 0001 0472 9649 Department of Management Science, College of Management, Shenzhen University, Shenzhen, China Responsible Editor: Philippe Garrigues 14 5 2022 2022 29 46 6955569572 9 12 2021 18 4 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. History records show that pandemics and threats have always given new directions to the thinking, working, and learning styles. This article attempts to thoroughly document the positive core of coronavirus 2019 (COVID-19) and its impact on global social psychology, ecological stability, and development. Structural equation modeling (SEM) is used to test the hypotheses and comprehend the objectives of the study. The findings of the study reveals that the path coefficients for the variables health consciousness, naturalism, financial impact and self-development, sustainability, compassion, gregariousness, sympathy, and cooperation demonstrate that the factors have a positive and significant effect on COVID-19 prevention. Moreover, the content analysis was conducted on recently published reports, blog content, newspapers, and social media. The pieces of evidence from history have been cited to justify the perspective. Furthermore, to appraise the opinions of professionals of different walks of life, an online survey was conducted, and results were discussed with expert medical professionals. Outcomes establish that the pandemics give birth to creativity, instigate innovations, prompt inventions, establish human ties, and foster altruistic elements of compassion and emotionalism. Keywords Financial consideration COVID-19 pandemic Financial impact Psychological ffects Sustainability Environmentalism Naturalism issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2022 ==== Body pmcIntroduction The world has precariously faced the disastrous outbreak of coronavirus 2019 (COVID-19) (Yang et al. 2021a; Iqbal et al. 2021b; Wen et al. 2022). To date, more than 3.4 M people have suffered from the deadly virus in almost every country of the world and more than two hundred thousand lives have succumbed to the harsh brutality of COVID-19 (Razzaq et al. 2020; Irfan et al. 2022b, c). Figure 1 shows the virus’s exponential increase during its outbreak in Dec 2019 to its peak in May 2020, affecting the globe with a rapid increase in the spread of the contagion and death rate (Rehman et al. 2020; Ahmad et al. 2022). This fear-provoking situation has raised many questions in the public sphere, especially towards the world’s response to the public health and safety measures during the pandemic (Elavarasan et al. 2021; Irfan et al. 2021e, b, 2022d). According to health predictions and recommendations, COVID-19 spread intensifies through human-to-human transmission and its spread remains exponential in the public gatherings (Ahmad et al. 2021b). Therefore, in a short period of time, the virus has swept the world, with hundreds of thousands of deaths reported on news and social media every day (Dagar et al. 2021; Khan et al. 2021). It has triggered unprecedented quarantine impositions, stock market upheavals, and burgeoned varied conspiracy theories (Irfan et al. 2021a). According to (Iqbal et al. 2021a), the coronavirus family is large and familiar, which onsets from the common cold symptom to pneumonia and transmuting into a deadly disease (Agrawala et al. 2020). Countries are adopting competing strategies like internal lockdowns (Hepburn et al. 2020), smart lockdowns, suspending domestic/international flight operations, imposing controlled isolation, and quarantines (Heyden and Heyden 2021).Fig. 1 Virus’s exponential increase during its outbreak in Dec 2019 to its peak in May 2020 . Source: World Health Organization (WHO) If we look back at the history of epidemics and pandemics, the infectious diseases have yielded devastating effects on the economies and human lives on an immeasurable scale. Still, it is unpredictable to ascertain the duration and continuance of the disease. In this essence, the World Health Organization (WHO) indications are most alarming, especially for the developing economies (Usman et al. 2021b). The spread of the contagion has traumatized every aspect of human life and fragmented economic and sustainable developmental goals. However, besides all its devastating effects, it has also provoked positivity in human emotions, attitudes, and considerations (Usman et al. 2021a). Recently, many studies have been conducted to investigate different aspects of pandemics, especially COVID-19, and its effects from the biological and industrial perspectives (Xie and Zhu 2020; Konstantinoudis et al. 2021; Lau et al. 2021; Navani et al. 2021; Travaglio et al. 2021; Yuan et al. 2021). The current study is nevertheless focused to explore the positive aspects of the pandemic on socio-psychological behavior of individuals and world societies, as a whole. The far side of the picture demonstrates social, psychological, and emotional representation, where humanity is seen joining hands to uplift communities (Tang et al. 2022b). The concept of altruism, naturalism, and environmentalism is restored, and innovations & inventions are reinforced. Besides all the negative effects of the COVID-19, the upheaval has also contributed to the social, emotional, and psychological development of the world. This pandemic gave new directions, new resolutions, pristine enthusiasm, novel philosophies, and immaculate considerations to human lives. Literature review Contextual narration The contemporary evidence from the daily news and research reports show that the persistence of the COVID-19 in the global scenario has altered the human aspects of working, learning, and thinking (Coccia 2020). A massive turmoil has been witnessed and humanity has received a petrifying lesson. It is amazing to notice that people from across borders were stranded far away from their homelands and were unable to reach their destinations. It changed policy orientation at the international level dynamically (Lau et al. 2021; Huang et al. 2022). Italy was not accepting migrants from underdeveloped countries like Somalia, due to different reasons, but now they are seeking protection land for their people. Similarly, the borderline barriers built by the world power to bar Mexican immigrants to enter the country became a hindrance for Americans to escape from their virus-hit states (Kordej-De Villa and Slijepcevic 2019; Khosravi et al. 2019; Ozoike-Dennis et al. 2019; Hilbers et al. 2019). Similarly, the dilemma of allowing African immigrants to enter Spain through Morocco has changed its direction, and the coronavirus spread has become a peril to the host nation itself. People were seeking routes to travel from Spain to the African peninsula, to abscond from the virus-hit territory (Ficetola and Rubolini 2021). The repercussions of the pandemic have also implicated the world’s strongest and most unconvincing army, who claimed to have a massive military base in one of the occupied war-hit countries and had their armies deployed there, are now frantic to return to their homeland. The pandemic also had long hold impressions on religious convictions (Mohsin et al. 2020b, 2021). The revulsion towards religious scriptures and the disrespect of the holy book Qur’an and Bible are now perceived as a therapeutic cure and has been interpreted and recited by many leading American journals and jihadist media outlets today, to seek meditation and refuge from the deadly disease( Mohsin et al. 2019, 2020a, 2021). Thus, the time lapse of a mere 3 months has shown the world an entirely different global portrait, the tables have turned, and the world is shaken. Hence, reforming beliefs and religious faith. People in refutation are taken aback and unable to deny the sovereignty of the supreme power, who holds the power to rotate the universe. The coronavirus disease has reverted human beings to their primitive civilization, leaning them towards the origin and their religious convictions. It has closed all luxury centers around the globe, theatres, nightclubs, dance halls, and taverns (Chau et al. 2021; Lau et al. 2021; Liu et al. 2021; Iqbal et al. 2021b; Yu et al. 2022). The interest rates in all countries have been reduced to save the economies. Similarly, it has reunited families in their homes after a long domestic distancing and separations. It has abstained people from building illegitimate intimate relationships. Furthermore, it is stressed that the World Health Organization and other international health councils have acknowledged that consuming alcohol is dangerous for health and have advised people to avoid drinking (Yang et al. 2021b; He et al. 2020; Mohsin et al. 2020b). The pandemic has also taught people a social lesson of the social skills and conduct that how to sneeze, cough, and maintain other personal hygiene measures, which were forgotten by humanity. Similarly, it has also intruded into the policies of the world and shifted one-third of the military budget to health and safety concerns. The health desolations of the epidemic declare condemnable cohabitations among either gender (Sun et al. 2019; Tiep et al. 2021). Similarly, it demonstrated the world rulers and leaders a cautionary lesson that what it means to incarcerate people in their homes like in Kashmir, Gaza, and other war-hit places and take away their freedom (Louw Louw et al. 2020). Rulers’ attentions towards prisoners’ health, safety, security, and living standards have been hauled. It has compelled people to repent of their sins, abstain from cruelties toward others, and refrain from wrongdoings. COVID-19 has shattered the egos and pride of the arrogant ones into pieces and aligned them among the ordinary ones. It also compelled industries to pause emitting carbon gases and allowed nature to purify itself from the poisonous and polluting emissions caused by the massive production in factories of the world. It has also been evidenced that one of the major aspects of the human psyche has also been shattered and changed. The man considered himself to be invincible and technology deemed to be defeat less, second to none, but the COVID-19 pandemic has ruined the premise to consider Artificial Intelligence and technology to be unconquerable (Isiko 2020). And most relevantly, the fear of death and uncertainty of life has kneeled human beings to accept the oneness of Almighty and endure his sovereignty. Consequently, today, it has become clear that how a small unseen virus, but, the micro agent of the Almighty, has become a benefactor of good to humanity rather than evil, as it was practiced on ancient tomes 1400 years ago to kill the Abraha’s army in the kingdom of Saudi Arab, through small birds to protect the holy Kaaba. COVID-19 is thus bringing humans towards humanity, origin, religion, faith, and belief, as all religions are heavenly originated on the concept of benevolence and kindness. Similarly, as cited above, human beings considered themselves as supreme powers, believing that all powers and rights rely on them, but their manifestations were shuddered by a small tiny, unseen virus. It reminds the narration from the Quran: This verse highlights almighty’s wisdom in prescribing some of the rulings, as the reason for these easy and merciful declarations are that they are compatible with the inherently weak and dependent nature of man. It shows the system that is most suited to the psychological, intellectual, and physical characteristics of man, and that one of its main qualities is that it is in harmony with “human nature” that is inherently weak and vulnerable, no matter how great and powerful a person may feel and how arrogant he may act, whilst in reality and ultimately, human nature is weak, unable to survive and function except within an environment and framework that is suitable for him. Only the Almighty has all the legitimate powers, not the man, who is dependent, fragile, forgetful, greedy, and helpless. The technological advancements and materialistic creations through which human beings consider themselves perfect are nothing but a mere delusion. The piles of deadly weapons people collect could not help them fight against a diminutive virus. Consequently, the money, human labor, and grounded resources that have been spent solely on the invention and manufacturing of the weapons are now thought to be spent to facilitate humankind (Sun et al. 2020b; Baloch et al. 2020). However, an unrelenting question persists, with the aftermaths of the pandemic will the human being change their course of life, alter their worldly preferences, or will revert to the old mindset again, once the outbreak of pandemic is over. Furthermore, according to a conspiracy theory, as reported by news channels around the globe, that the spread of COVID-19 was a biological bomb, and the world’s leaders are blaming one another, which was supposed to be developed in the American Army laboratory, or maybe China, which was banned later, when the news was leaked. The world supreme powers considered themselves to be the dominions of power, but the only God played thy role. The Holy Quran narrates that: “People plan and Allah also do, but Allah is the best planner” (Al- Quran, Anfal 8: 29–30). According to this premise, the world’s superpowers were planning to conquer the world by overpowering the nations across the globe and smothering lives through a biological war. Therefore, it is a disguised blessing of nature that mutated coronavirus came out of the laboratories and the preplanned experiments were thwarted; otherwise, in case of success, it was supposed to be used in unseen and unannounced wars on the weaker by the rich/powerful countries, without losing time, money, and machinery. Historical narration of the world pandemics and crises In the historical perspective, the pandemic prophecy reclaims that such impending disasters not only provoke anxieties amongst societies, refract acute impacts on social, psychological, economic, and cultural streams, but contrarily the other side of the picture portrays a progressive picture as well. The escalating cost of pandemics drives socio-economic pressures on the populations which lead to inventions and innovations, and pave the way for unconventional roads to prosper, responsiveness, and futuristic developments. This is evident from the fact of the establishment of many global organizations that have a focus on the enhancement of many niche dominions across the globe and to cater to the issues amid uncertainties and crises. In this stance, the world has viewed that the United Nation Organization (UNO) was established to promote dialogue between countries to maintain peace; the International Fellowship Organization (IFO) was initiated by like-minded people at the civil level for the promotion of peace and development across the globe; and Global Initiative for Justice and other similar organizations emerged as an apparatus to promote the harmony, care, compassion, and justice beyond geographical boundaries and work as an arbitrator to build peace. The series of Black Swan events, which include, but are not limited to the world economic crises, recessions, World War I & II, and dissolution of the Soviet Union, have historically been proved to be the contexts for which the world governments have changed their course of plans and shifted the economic and business routes to a different dimension. The epidemics and pandemics have a vast history since human life on earth. The Great Plague, malaria, influenza, French disease, cholera, typhoid, HIV AIDS, tuberculosis, and other acute contemporary pandemics have resulted in high causalities and serious social disruptions all through human history. However, apart from their adverse impacts, the indirect and inadvertent influences have brought some positive outcomes too, such as the Black Death in the 1300 s ended the long-ingrained feudal system in Europe and replaced it with the more modern employment contract. Similarly, the hopelessness brought enlightenment and the Industrial Revolution which also resulted in the establishment of Trade Unions and Labor Union rights. In the same way, the 100-year war between England and France started a major innovation drive, the French Revolution, that radically improved agricultural productivity. Likewise to the recent events, the SARS pandemic of 2002–2004 catalyzed the brief development of online business organizations like Ali Baba and built it up as a cutting-edge retail giant in Asia. This change was energized by unseen perceived anxiety of people to travel around and having human interaction, precisely what we witness today due to the importunity of COVID-19 and it also created financial emergencies as were witnessed in 2008 (Chandio et al. 2020; Sun et al. 2020a). The concepts of Airbnb and Uber businesses appeared in the west due to the prevalence of subprime emergencies which implied lowering investment funds and salaries of the masses, compelling individuals to rely on shared resources such as renting accommodations and utilizing pooled vehicle rides, to cover incidental expenses. Businesses proliferated with the same pattern, such as the virtual gaming world also changed its plan of action, and transpired as an allowed-to-play action plan, with a subscription with Nexon and King accounts in Asia and the West, respectively (Sun et al. 2020c, 2020a, 2020b). Naturalism and sustainability theoretical support during the COVID-19 With the cited and narrated literature and the contextual analysis of the viewpoints, it is a clear crystal that the world is coming back to their basic. Green instinct is like human beings, humans love nature, accept, and want to promote it. Naturalistic Intelligence (NI) identified by Howard Gardner supports the concept that humans by their very nature, are naturalists, love environmentalism, and do strive for their sustainability and maintainability (Alemzero et al. 2020b, 2020a; Sun et al. 2020a). During COVID-19, a profound relationship was sought between naturalistic intelligence and environmentalism. It is pretty much clear that before COVID-19, the human was a bit careless, especially in third world country, poor practices regarding environmental protection can be seen. Garbage dumpers on the streets and roads are proven evidence of environmental degradation. However, during COVID-19, humans proved themselves as environmental managers (Agyekum et al. 2021; Zhang et al. 2021a). Naturalistic Intelligence is a bio-psychological potential for potential information processing that is activated on cultural and environmental stimuli and instinct (Panizzut et al. 2021; Tiron-Tudor et al. 2021). NI develops awareness regarding environmentalism and naturalism which pave road and human react accordingly for their sustainability and development (Li et al. 2021; Chien et al. 2021; Iqbal et al. 2021b). It also develops sensitivity to the developed environmental phenomena, which paves the road for green preservation keeping a pragmatic approach. It means that human attitude plays a vital role in maintaining and respecting naturalism and environmentalism. One thing precious to mention is that besides empirical analysis and availability of the pragmatic data, it is proven from the previous studies that emotional and social-emotional intelligence developed desired behaviors among humans for environmental protection (Iqbal et al. 2021b; Zhang et al. 2021a). This social and emotional intelligence and awareness come from the cognitive, behavioral, and affective domains with effective human reactions to protect the environment. The cognitive component, which refers to the mental processes of perception, conception, and beliefs about attitudes and objects, collect valid and reliable data regarding processes regarding environmental protection and development (Zhang et al. 2021a; Hsu et al. 2021; Ehsanullah et al. 2021). According to cognitive, social, and emotional intelligence, humans have and further develop feelings, subjective norms, and beliefs towards environmentalism and work for its development and sustainability (Onugha et al. 2020; Higgins-Desbiolles et al. 2021). In the same way, behavioral and affective components act to solve their raised issues in the most effective and optimal ways and try to be always in a win–win situation through creating an equilibrium among the human demands and environmental responses (Amankwah-Amoah et al. 2021; Puaschunder 2020). In the same way, green intellectual capital theory talks about the preservation and maintenance of the environment by deploying all intangible assets (Iqbal et al. 2019; Khokhar et al. 2020a, b; Ullah et al. 2021). The intangible assets include, but are not limited to, the system, the values, norms, routine, practices, habits, and approaches, which help in maintaining and sustaining environment and nature, which we are naming here as environmentalism and naturalism. Green human capital focuses on developing green skills among humans and practices during COVID-19 are more prevalent to green, everybody was rushing towards green practices and values. In the same, at the group and organizational level, knowledge workers were seen protecting for green environmentalism. Moreover, individuals, groups, companies, and organizations were preaching for green environmentalism (Yumei et al. 2021b; Zhang et al. 2021b). Social exchange theory, which focuses on social, emotional, and psychological behaviors, and supports the processes, projects, and operations, and expected benefits, can be applied to the processes, operations, and practices in COVID-19. Human conscious level and sensitivity can be judged from the report of the media and research studies, that they were more conscious to the relevant and purpose information, gathered from social media or other sources and were responding in the much safer and recommended way, not only for their own protection, but also for the protection of their fellows, friends, and even life partners in maintaining sensitive relations. This evidence and practice during COVID-19 predict that human was so conscious regarding health and the environment. Adopting healthy and green practices in projects and operations presumes that humans wish to maintain, preserve, and develop naturally. Therefore, human-initiated different projects for restoring the environment aim to recreate, initiate, or accelerate the recovery of an ecosystem that has been disturbed. These projects were different in nature and size, objectives, and methods, but almost all were focusing on achieving, maintaining, sustaining, and developing nature and the environment. Many restoration projects aim to establish ecosystems composed of native species; other projects attempt to restore, improve, or create particular ecosystem functions, such as pollination or erosion control. Research hypotheses Theories and empirical shreds of evidence support the nexus between health consciousness (HCON), naturalism (NATU), sustainability (SUST), and COVID-19 prevention, as people adopt compassion (COMP), gregariousness (GREG), and sympathy (SYMP) to prevent from COVID-19 reaction. Besides, people’s level of education boosts up creative use of information, knowledge of COVID-19 precautions online, and COVID-19 prevention. Education and cooperation (CPRT) makes people aware of their academic necessities and socio-cultural responsibilities. Comparatively, higher learning people become more conscious and liable to society and societal problems like the COVID-19 outbreak. Besides, pandemic-induced obstruction such as quarantine leads these educated people to be more connected with the Internet especially social media platforms for reading academic instruments and gaining COVID-19-related knowledge. These responsibility-prone people also come to a higher connection with social media outlets in serving societal people by providing them necessary information about the COVID-19 outbreak and preventing this pandemic. Moreover, the activities concerning fellow feelings and social service more or less depend on the level of people’s education. Thus, the health consciousness (HCON), naturalism (NATU), meditation and self-development (MESD), and environmentalism (ENVS) levels of the people become significant determinants of COVID-19 precautions and COVID-19 prevention. Figure 2 presents the theoretical framework of the study.Fig. 2 Theoretical framework. Note: Health consciousness (HCON), naturalism (NATU), meditation and self-development (MESD), environmentalism (ENVS), sustainability (SUST), compassion (COMP), gregariousness (GREG), sympathy (SYMP), and cooperation (CPRT), COVID-19 prevention (COVIDP) Following hypotheses are set for conducting this study:H1: Health consciousness (HCON) has a positive impact on COVID-19 prevention. H2: Naturalism (NATU) has a positive impact on COVID-19 prevention H3: Meditation and self-development (MESD) COVID-19 prevention H4: Environmentalism (ENVS) has a positive impact on COVID-19 prevention H5: Sustainability (SUST) has a positive impact on COVID-19 prevention H6: Compassion (COMP) has a positive impact on COVID-19 prevention H7: Gregariousness (GREG) has a positive impact on COVID-19 prevention H8: Sympathy (SYMP) has a positive impact on COVID-19 prevention H9: Cooperation (CPRT) has a positive impact on COVID-19 prevention Methodology and data To support the view under discussion, besides content analysis and historical review, an online survey was conducted on social media networks through Facebook, Twitter, WhatsApp, and emails. The online and email-based approach was adopted to cover more respondents around the globe and similarly, curfews and lockdown were the second major reason for contacting the respondents. About four hundred and eleven (411) participants were from Pakistan, Iran, Malaysia, China, and the USA. The demographic details are given in Table 1 shows that all partakers belonged to different walks of life, thus making a diversified pool of research samples. According to Moon et al. (2019) and Watson-Brown et al. (2018), if the population is infinite, taking 370 responses in survey research is considered to be a satisfactory data sample. Structural equation modeling (SEM) is used to test the hypotheses (Ali et al. 2021; Irfan and Ahmad 2021, 2022). SEM is a practical approach for determining the relationship between various variables, providing meaningful and accurate results (Irfan et al. 2020, 2021f, d; Tanveer et al. 2021). AMOS (edition 26) and SPSS (edition 26) softwares are used for statistical tests (Irfan et al. 2021c). Moreover, NVIVO-11 was used for qualitative data analysis, and a word cloud and frequency graph were also developed to best comprehend the constructs under discussion. Furthermore, thematic analysis of the interview and focus group discussion was also conducted to get a detailed insight into the constructs. They were also linked with the most related and cited theories.Table 1 Demographic details of encompassing the pragmatic approach COVID-19 Sample characteristics Frequency Percentage Gender Female 168 40.9% Male 243 59.1% Age 30–40 years 103 25.1% 41–50 years 185 45% 51–60 years 123 29.9% Education level BS 174 42.3% MS 36 8.8% Diploma 201 48.9% Marital status Married 298 72.5% Unmarried 113 27.5% Respondent’s domain expertise Technical person 213 51.8% Medical Sciences 62 15.1% Engineering Sciences 102 34.8% Other 34 8.3% Source: Authors’ calculation Results and discussions Demography of the participants Table 1 presents the demography of the participants. The higher middle age group (185, 45%) has the highest percentage of respondents in our sample, followed by the young age group (103, 25.1%), and old age group (123, 29.9%). In our sample, male participants are 243, 59.1% of the whole sample, and outnumbered females 168, 40.9%). We also divided respondents into groups based on their educational levels: 48.9% have a high school diploma, while 42.3% have a Bachelor’s degree. The majority of the respondents were married (72.5%), 51.8% are technical persons, and 15.1% worked for the Medical Sciences. Descriptive analysis and correlation analysis Table 2 displays the statistical data for the information, such as the average value, variance, and coefficient of determination. Similarity analysis was used to test the interconnectedness of factors. The assessment found a considerable relationship between the variables. The regression coefficient of variance explained was used to probe predictive relevance. Even as the square root of AVE is greater than just its connection with the other structures, the findings reinforce predictive relevance (Fornell and Larcker 1981). A comparison of the AVE value systems with the maximum shared variance (MSV) values for each factor is another method for determining discriminant validity (Ahmad et al. 2020). Validity is achieved when the AVE value for a specific variable exceeds the MSV value for that variable alone. The AVE values for all variables are bigger than the MSV values, implying that this assumption is correct. Then, using AVE and item loadings, a convergent validity study was performed to see how closely the items were linked (Calisir et al. 2014). The result showed that the AVE values for every parameter surpassed 0.50, denoting that the predictor variable maintained more than 50% of their variance (see Table 3).Table 2 Descriptive statistics of the data Variables Items Observations Coefficient of variation (CV) Mean Std. dev HCON 5 411 0.139 3.52 0.489 NATU 4 411 0.555 2.701 1.498 MESD 4 411 0.076 3.213 0.243 ENVS 3 411 0.122 3.808 0.465 SUST 2 411 0.212 2.592 0.55 COMP 3 411 0.571 2.895 1.652 GREG 2 411 0.479 3.672 1.760 SYMP 7 411 0.638 3.052 1.947 CPRT 7 411 0.287 3.048 0.874 COVIDP 7 411 0.551 3.036 1.674 Health consciousness (HCON), naturalism (NATU), meditation and self-development (MESD), environmentalism (ENVS), sustainability (SUST), compassion (COMP), gregariousness (GREG), sympathy (SYMP), cooperation (CPRT), COVID-19 prevention (COVIDP) Table 3 Correlation and discriminant validity analysis Variables HCON NATU MESD ENVS SUST COMP GREG SYMP CPRT COVIDP AVE MSV HCON (0.715) 0.512 0.122 NATU 0.267 (0.821) 0.674 0.292 MESD 0.349 0.54 (0.802) 0.643 0.292 ENVS 0.304 0.16 0.352 (0.844) 0.712 0.124 SUST 0.155 0.354 0.259 0.227 (0.824) 0.678 0.445 COMP 0.284 0.493 0.429 0.216 0.667 (0.744) 0.554 0.445 GREG 0.187 0.632 0.599 0.205 0.189 0.583 (0.740) 0.977 0.371 SYMP 0.1526 0.771 0.769 0.194 0.381 0.956 0.531 (0.706) 0.531 0.106 CPRT 0.1182 0.91 0.939 0.183 0.573 0.329 0.085 0.841 (0.797) 0.585 0.841 COVIDP 0.0838 0.049 0.109 0.172 0.765 0.702 0.639 0.576 0.513 (0.845) 0.639 0.576 Diagonal values in parentheses represent the root square of AVEs Reliability analysis Cronbach-alpha was computed to assess the reliability coefficient. The findings demonstrate that the Cronbach value for all factors exceeded the lowest required value of 0.70, as recommended by (Treiblmaier and Sillaber 2021), verifying the data’s accuracy. A composite reliability (CR) assessment was applied to evaluate the continuity of all explanatory variables’ items. The analysis reveals that the CR values are above than appropriate threshold of 0.70 (Hair Jr. et al. 2017). Table 4 presents the conclusions.Table 4 The results of reliability analysis and factor loadings Variables Items Standard loadings Cronbach-α CR Health consciousness 0.813 0.807 HCON 1 0.737 HCON 2 0.802 HCON 3 0.92 HCON 4 0.866 HCON 5 0.88 Naturalism 0.916 0.935 NATU 1 0.719 NATU 2 0.731 NATU 3 0.731 NATU 4 0.675 Meditation and self-development 0.91 0.915 MESD 1 0.88 MESD 2 0.959 MESD 3 0.709 MESD 4 0.695 Environmentalism 0.903 0.925 Sustainability ENVS 1 0.634 ENVS 2 0.841 ENVS 3 0.802 ENVS 4 0.869 ENVS 5 0.833 ENVS 6 0.835 ENVS 7 0.893 0.832 0.893 SUST 1 0.851 SUST 2 0.736 SUST 3 0.661 SUST 4 0.914 SUST 5 0.907 SUST 6 0.657 Compassion 0.809 0.832 COMP 1 0.746 COMP 2 0.71 COMP 3 0.762 COMP 4 0.609 Gregariousness 0.916 0.935 GREG 1 0.719 GREG 2 0.731 GREG 3 0.731 GREG 4 0.675 Sympathy 0.91 0.915 SYMP 1 0.88 SYMP 2 0.959 SYMP 3 0.709 Cooperation 0.903 0.925 CPRT 1 0.751 CPRT 2 0.634 CPRT 3 0.841 CPRT 4 0.802 COVID-19 prevention 0.832 0.893 COVIDR 1 0.869 COVIDR 2 0.833 COVIDR 3 0.835 COVIDR 4 0.893 Rotation method: promax with Kaiser normalization; extraction method: maximum likelihood Multicollinearity To test for multicollinearity, regression was used to determine the value systems of the variance inflation factor (VIF) as well as tolerance. As per the f, the value of VIF has to be less than 10 and the tolerance value has to be larger than 0.1. The research results indicate that the model did not have a multicollinearity problem, so the VIF value is as per limit, and the value of Tolerance for whole variables rages within the ideal range and in line with the observations of (Strupeit and Palm 2016). The findings can be seen in Table 5.Table 5 The results of the collinearity diagnostics test Variables Statistics for collinearity Tolerance VIF HCON 0.84447 1.16028 NATU 0.92763 1.05633 MESD 0.79299 1.23552 ENVS 0.82764 1.18404 SUST 0.93654 1.04643 COMP 0.78498 1.22304 GREG 0.81928 1.17208 SYMP 0.78498 1.22304 CPRT 0.81928 1.17208 COVIDP 0.92708 1.03586 Dependent variable: COVIDP Factor analysis To acquire the attributing design methodology, an exploratory factor analysis (EFA) has been conducted. EFA seeks to explore factorability, i.e., the relationships and clusters of different factors based on cross-comparisons (Mahmood et al. 2019). For even more meaningful results, the factors were derived to use the statistical parameters, then turned with the corresponding varimax coefficients. The eigenvectors have been used to assist specify the number of factors. Several tests were carried out during this stage is crucial whether the EFA might be applied in this study. The Bartlett’s test of sphericity (BTS) and Kaiser–Meyer–Olkin (KMO) test were used to evaluate the data fitness. The consequences supplied a significance of based for KMO (Kaiser 1974), implying that principal component analysis can be continued. Table 6 presents the results of KMO and BTS tests. BTS provided a substantial significance of 6,874.96, which also fulfills the criteria for EFA. Correspondingly, communalities outcomes (reported in Table 7) demonstrate that almost all factors have a greater value than the standard minimum value of 0.4 (Stevens and Stevens 2001). Promax roster with the Kaiser method is proposed disclosed seven important factors to Eigenvalues larger than one a total combined variability of 64.930% for with us prototype (see Table 8). Every one of these is thus that the data is trustable enough even to move ahead with more assessment (Blunch 2017).Table 6 Bartlett’s test and Kaiser–Meyer–Olkin (KMO) KMO and Bartlett's test Kaiser–Meyer–Olkin measure of sampling adequacy 0.908 Bartlett’s test of sphericity Approx. chi-square 6,874.96 df 435 Sig 0.000 Sig significance, df degree of freedom Table 7 Communalities findings Variables Communalities Initial Extraction HCON 1 0.560 NATU 1 0.699 MESD 1 0.890 ENVS 1 0.592 SUST 1 0.649 COMP 1 0.791 GREG 1 0.946 SYMP 1 0.558 CPRT 1 0.674 COVIDP 1 0.745 Maximum likelihood: extraction method Table 8 Cumulative variance and eigenvalues Variables Eigenvalues (initial) Squared loadings extraction sums Total Variance % % Cumulative Total Variance % % Cumulative 1 9.669 32.229 32.229 9.28 30.935 30.935 2 3.746 12.487 44.716 3.418 11.394 42.329 3 3 10 54.715 2.635 8.784 51.114 4 2.083 6.942 61.658 1.695 5.65 56.764 5 1.983 6.611 68.269 1.65 5.499 62.263 6 1.141 3.804 72.073 0.8 2.667 64.93 7 8.79879 29.32839 29.32839 8.4448 28.15085 28.15085 8 3.40886 11.36317 40.69156 3.11038 10.36854 38.51939 9 2.73 9.1 49.79065 2.39785 7.99344 46.51374 10 1.89553 6.31722 56.10878 1.54245 5.1415 61.65524 Rotation method: promax with Kaiser normalization, cumulative variance: 61.65524% Going to follow that, confirmatory factor analysis (CFA) has been used to recognize models. CFA affirms the framework of the variables obtained in EFA. The very first step in model selection is to determine this same model’s normality. Items with high capacities (larger than 0.7) just on primary factors should be kept (Truong et al. 2020). All levels were larger than 0.7, as per the outcomes. Since all goods have been packed on one’s respective constructs, the quantification model’s authenticity has also been affirmed. Based on the findings of the analysis, it is evident that the information is appropriate again for the measurement model. Hypotheses results and structural model The writers evaluated the proposed prototype and theorized interconnections within a week of acquiring valid and reliable measures. The R2 value was determined as an important step in deciding how much variance in the dependent variable was explained by variation. The R2 value was 0.54, which is larger than the corresponding minimal level of 0.35 (Huang et al. 2020), suggesting an important viewpoint. To investigate the model’s connections, we used structural bend assessment and the SEM method. The assessment created a high f-value, implying that all interconnections were straightforward. Various fit indices were also used to confirm that the data is accurate and completely fit again for the structural equation model. The results indicate that almost all fit indices (i.e., CFI = 0.988, NFI = 0.923, IFI = 0.989, TLI = 0.974, GFI = 0.983, RMSEA = 0.021, X2/df = 1.147, and SRMR = 0.026) meet the standard criteria, indicating that model fit the data adequately (Lucianetti et al. 2018). Figure 3 depicts a diagrammatic diagram of SEM together with path coefficients. The path coefficients for the variables, such as health consciousness, naturalism, meditation and self-development, sustainability, compassion, gregariousness, sympathy, and cooperation (H1 (b = 0.042, p = 0.01), H3 (b = 0.501, p = 0.01), H4 (b = 0.043, p = 0.01), H5 (b = 0.354, p = 0.05), H6 (b = 0.654, p = 0.01), H7 (b = 0.068, p = 0.01), H8 (b = 0.509, p = 0.01), H9 (b = 0.687, p = 0.05)), demonstrate that the factors HCON, MESD, ENVS, SUST, COMP, GREG, SYMP, and CPRT have a positive and significant effect on COVID-19 prevention. As a result, assumptions 1, 3, 4, 5, 6, 7, 8, and 9 were acknowledged. On the contrary, the β-value of NATU does not validate the hypothesis H2, hence, rejected (see Table 9).Fig. 3 Hypothesis path analysis Table 9 Hypotheses’ results Hypotheses Structural paths β-value t-statistics Description H1 HCON → COVIDP 0.042* 2.042 Not different H2 NATU → COVIDP 0.742 7.963 Not different H3 MESD → COVIDP 0.501* 5.236 Not different H4 ENVS → COVIDP 0.043* 2.163 Not different H5 SUST → COVIDP 0.354 3.168 Not different H6 COMP → COVIDP 0.654* 6.688 Not different H7 GREG → COVIDP 0.068* 2.636 Not different H8 SYMP → COVIDP 0.509* 5.123 Not different H9 CPRT → COVIDP 0.687 6.816 Not different *p < 0.01, p < 0.05, p < 0.1 Endogeneity testing This test is mainly used to verify the consistency of study findings (Irfan et al. 2021d). Endogeneity partiality in the information can jeopardize the findings. Furthermore, endogeneity could misrepresent the forecast of posterior probability, presenting a major challenging problem to authenticity of outcomes. While investigating endogeneity, we used the Heckman test to address these issues. The results produced the very same degree of confidence as the original version, implying that endogeneity partiality is just not prevalent in our conclusions (see Table 10).Table 10 Endogeneity test Hypotheses Structural paths β-value t-statistics Description H1 HCON ® COVIDP 0.132 2.953 Not different H2 NATU ® COVIDP 0.354 8.702 Not different H3 MESD ® COVIDP 0.471* 2.171 Not different H4 ENVS ® COVIDP 0.383* 3.265 Not different H5 SUST ® COVIDP 0.186 6.761 Not different H6 COMP ® COVIDP 0.354 8.702 Not different H7 GREG ® COVIDP 0.471* 2.171 Not different H8 SYMP ® COVIDP 0.383* 3.265 Not different H9 CPRT ® COVIDP 0.186 6.761 Not different p < 0.00, p < 0.01, p < 0.05 Factor analysis with NVIVO-11 Apart from conducting the research survey, phone calls, vis-a-vis, the field experts like doctors, professors, and business professionals, were also conducted to construct the research ideas and responses. Each interview conversation centralized around one concentrated theme encompassing the pragmatic approach COVID-19 has accentuated in respondents’ daily lives. The upheaval caused by the pandemic has driven people towards nature and the realness to follow the basic rules of life. The precautionary practices such as maintaining social distancing, but here we may call it physical distances, because it brought humanity together, consuming healthy food, maintaining healthy routines to improve immunity, isolating oneself and meditating, spending quality time with family, and practicing hobbies which were long forgotten added to the quality of life. The majority of the respondents stressed that they benefitted from the quarantine time to complete their personal and private deeds, worked on self-construction through mediation, enjoyed the silence, and observed nature. Individually, people have become more health conscious and have adopted rigorous hygiene measures. Moreover, the isolation and confinement of people in their homes have promoted the spirit of cooperation, compassion, sympathy, and empathy towards others. Furthermore, observing the reality of life, sensing the fear of death, and understanding the mortal disposition of the being, people have come towards spirituality which has brought them toward humanity, religion, and God. Belief in religion has prompted the elements of cooperation, harmony, kindness, care, and magnanimity at the societal level. In the third phase of the study, the content from social media and news bulletins were searched to know about the ongoing trend in charitable public spheres. The investigation depicted that the local, national, international, religious, and nonprofit charitable organizations are spending millions to support the poor and needy (Annexure-B). Besides this, individually, youth social workers are reaching the doors of the marginalized people to help them in all possible ways. In developed and wealthy economies, roadsides and walkways have been filled with eatables and grocery items to civilly help those in need. Moreover, the second theme of the survey encapsulated the concept of the beginning of de-globalization which has embarked on global economies. Massive transformations in the cultural, social, religious, political, and environmental realms have emerged. Immerse disruptions, between technological breakthroughs and restoring nature, will determine the new terms of the game. Military institutions will also have to transform to adjust to new realities. Conventional wars will come to an end; instead, international health armies will be constituted. A major chunk of the budget will go to research, development, innovations, and distribution of health care and amenities. Private health care organizations and pharmaceutical companies will find hardships to survive in the vast competition between the world giants in health care. New work environments and organizational structures will emerge with a huge emphasis on the digitization of work, flexible work hours, and less human interface. Working patterns will change. New social norms will be introduced and will influence the complexities of religions. Virtual integration will grow exponentially. Redundant education systems will face extinction and digital shopping malls will evolve. The new era will prove to be the forerunner in swamping Artificial Intelligence and robots in organizations and households, which will be a revolutionary point in global history. Some constructs with major frequencies were analyzed with NVIVO-11, displayed in Fig. 4 (word cloud) and Fig. 5 (word frequency). Word cloud and the frequency distribution also support the above-mentioned cited claim in literature and contextual analysis. Many of the respondents that they need to focus on improving their health through green consumption and mediation. They have also concerns regarding environment preservation and environmental sustainability, development, and promotion. In the same way, gregariousness and social bonds are given values by the respondents. Moreover, compassion and sympathy have been increased and charity and sharing have been promoted. This analysis indicates that people are coming back to their basics and they are valuing basic values. They want to promote, develop, maintain, and sustain nature and the environment. Green consumption and meditation are the good signs of self and social development, which brings harmony, sympathy, and gregariousness among individuals, groups, and society.Fig. 4 Word cloud of the data Fig. 5 word count of the data. Note: Health consciousness (HCON), naturalism (NATU), meditation and self-development (MESD), environmentalism (ENVS), sustainability (SUST), compassion (COMP), gregariousness (GREG), sympathy (SYMP), and cooperation (CPRT) Discussion Besides all the negative effects of the pandemic, which include but are not limited to, adverse health effects on humanity, plummeting economies, disruptions in socio-psychological safety, striking hunger and poverty, human isolations, and societal lockdowns, COVID-19 has also contributed contradictorily to the social, emotional, and psychological development of the world (Chen and Xu 2020). This pandemic gave new directions, novel resolutions, vigorous enthusiasm, new thinking, fresh meanings, and promising philosophies to human lives. It reinstated the humanitarian lessons of love, care compassion, and respect, cultivated a change, and have reshaped the learning and working habits (Iqbal et al. 2021a; Otek Ntsama et al. 2021). The repercussions of COVID-19 inadvertently have brought positivity to the world and have enforced people to embrace nature, protect the environment, and adopt healthy and organic lifestyles. The first deliberation on COVID-19 inferences suggests that nature is playing its part to restore itself. According to Thorgren and Williams (2020), Sciomer et al. (2020), Faria-e-Castro (2021), and Iqbal et al. (2021a), when nature reaches a saturation point, it checks itself for fossil fuels disposal, relics, wastage, landfills, and emits, and dissipates it accordingly in a natural process (Ahmad et al. 2021a; Hao et al. 2021; Abbasi et al. 2022; Fang et al. 2022; Irfan et al. 2022a; Tang et al. 2022a). The world is full of unhygienic mulls, bloodsheds, and atrocities, which impact the natural and wildlife, erode the natural flora, and ruin the natural life (Sciomer et al. 2020). The indirect effects of all these events in turn affect the living standards and human lifestyle (Islam et al. 2021; Khan et al. 2021; Rauf et al. 2021; Razzaq et al. 2021; Nuvvula et al. 2022; Shi et al. 2022). Thus, as a matter to re-invigorate itself, nature has decided to play its part to heal itself via confronting humans with the COVID-19 pandemic, enforcing them to retrace their steps into basic and natural lives (Mani et al. 2021; Shao et al. 2021; Wu et al. 2021; Elavarasan et al. 2022a, b; Qiu et al. 2022; Xiang et al. 2022). Similarly, the new wave of the recent pandemic, COVID-19, has already shifted the demand side and supply side curves of the world supply chain (Jinru et al. 2021; Yumei et al. 2021a). The behavioral change in consumers’ spending, and suppliers selling, have already been altered. Work from home is being encouraged by the majority of tech and non-tech companies alike: the tourism industry has massively been affected, airlines profitability has been severely impacted by low seat reservation, supply chains are getting disrupted globally, and retail stores are running out of necessities such as essential household goods and basic medications (Chakrabarty and Roy 2021; Cheba et al. 2021). Some of these changes are direct, and short-term responses to the pandemic crises and will revert to regular symmetry once the COVID-19 is contained. However, some of these shifts will persist for a longer period, especially the disruptions caused due to technological inferences will reshape the business and societal structures alike and will take decades to overcome the repercussive shocks. Interestingly, the world defense budgets on nuclear power, weaponry, armaments, and munitions will witness a substantial shift to spending on healthcare and biodefense weapons (Can and Canöz 2021). The scientific research will also observe a widespread transformation, mainly focusing on emerging trends, evaluating pandemic prophecies, and envisaging future speculations. Moreover, amidst the pre and post COVID-19 crises, the business supply chains will merge into resilient ecosystems, the digital bureaucracies will become mainstream drivers and psychological health facilities will be offered digitally (Fargnoli 2020; Silva and Henriques 2021). Furthermore, COVID-19 is a terrible shock to the global economy as well as the thousands of individuals and families it has affected. Companies that will proactively resist the challenges of the pandemic will survive. The organizational attitude towards CSR activities will be gauged through their dealings during this crucial time (Chen et al. 2021; Vos and Cattaneo 2021). The organizations ensure that the health and safety of their workers, suppliers, and all stakeholders will combat with competitive sprain hauled by the avid pandemic. Over the longer term, it can be processed, through the changing world order, COVID-19 will irrevocably change the way businesses may compete over the next decade. Firms that choose to capitalize on these underlying changes will succeed and the ones that fail will disappear. Conclusions and recommendations The COVID-19 pandemic has questioned many grounded theories and practices. The world is under siege. The persistence of the pandemic, globally, has distorted the undelaying theories in the streams of economic, social, psychological, and biological sciences. As global partners, the economies at large, and individually, have to plan for the pandemic preparedness and responsivity. In times of crisis, the system places greater emphasis on collectivism and urges communities to come together even more. The world can sustain lives and humanity on earth only if healthy and organic goods and services are produced responsibly. The shift to a green and organic lifestyle for businesses and individuals might possess higher processing costs but furnish lower implicit costs, which otherwise humanity has to pay off in the longer term and with retributive consequences. The constructive attitude will, nevertheless, also add to the environment and nature’s restorative process. Thus, the lessons the world has learned from these ordeals and tribulations brought by the COVID-19 pandemic will perpetuate irrefutable attributes such as cleanliness, efficiency, thoughtfulness, and compassion and deliver the lesson of humanity to human beings. Above and beyond, as the prevalent pandemic has affected a larger portion of the world, therefore, it is anticipated that it reshapes world policy matters, and the prevailing global ordaining plans, enormously. As an epilogue to the findings of this research, it is implied that the world should collectively focus on humanity, harmony, and care instead of rivalries on power, competitions for materialistic profits, and acquisitions. Perhaps this is the moment to imagine an economy for the people — a collective world economy built on the notion to care for the whole of humanity. Besides these, as per the thematic analysis of the responses, the worlds’ superpowers will attempt to reshape their economies towards the public interest. The COVID-19 will compulsorily drive economies to transformation and steer civilizations to nature’s fundamental laws which will incline humans to benevolence, partake in instituting human-centric economic policies, and most importantly, make humans learn the meaning of humanity. Author contribution Pu Song: conceptualization, data curation, methodology, writing—original draft. Jamshid Ali Turi, Wang Bo, and Wasim Iqbal: data curation, visualization, supervision, visualization, editing. Chen Zheng and Ali imtiaz: review & editing & editing, and software. Data availability The data can be available on request. Declarations Ethical approval and consent to participate The authors declare that they have no known competing financial interests or personal relationships that seem to affect the work reported in this article. We declare that we have no human participants, human data or human tissues. Consent for publication N/A Competing interests The authors declare no competing interests. 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==== Front Inquiry Inquiry spinq INQ Inquiry: A Journal of Medical Care Organization, Provision and Financing 0046-9580 1945-7243 SAGE Publications Sage CA: Los Angeles, CA 35393886 10.1177_00469580211059989 10.1177/00469580211059989 Policy Brief A Review of Telemedicine Practice Guidelines for COVID-19 and Global Emergencies https://orcid.org/0000-0001-8739-1486 Gupta Surabhi 1 Sundaram Satyam S. 2 1 29049 Management Development Institute , Gurgaon, India 2 Partner, Strategy and Transactions, 40209 Ernst and Young LLP , New Delhi, India Surabhi Gupta, Management Development Institute, A-2/106, Sunrise Apartments, Sector-13, Rohini, Delhi-110085, India. Email: [email protected] and [email protected] 8 4 2022 Jan-Dec 2022 59 00469580211059989© The Author(s) 2022 2022 SAGE Publications The coronavirus pandemic has changed the palliative care and clinical medicine narrative to reduce exposure, maintain social distancing, and mitigate in-person consultation risks. Telemedicine during such times has emerged as a critical technology to bring medical care to patients while attempting to reduce the virus transmission. The telemedicine practice remains highly unregulated, raising concerns about its implementation. In this article, we review the worldwide scenario of policy instruments on telemedicine and also discuss the recently published telemedicine guidelines in India in detail. The methodology adopted included data collection from primary sources—key expert interviews—and secondary sources—systematic literature review. It was observed that even though countries have included telemedicine in their national health strategy, its adoption and dissemination remain a challenge. There is a need for exhaustive telemedicine practice guidelines focusing on key parameters for convenient, accessible, and cost-effective care to patients. telemedicine policy instruments teleconsultation telemedicine guidelines cover-dateJanuary-December 2022 typesetterts10 ==== Body pmc What Do We Already Know About This Topic? Telemedicine guidelines have been under consideration in many countries since the past few years. With the COVID-19 pandemic, there has been a need for adoption of telemedicine guidelines for a reliable telemedicine practice. How Does Your Research Contribute to the Field? This work presents a landscape for understanding the global scenario and status with respect to the telemedicine guidelines. What Are Your Research’s Implications Towards Theory, Practice, or Policy? Research implications (1) towards policy includes formation of telemedicine guidelines and referring to India as a use-case for the same and (2) towards theory and practice is addressing the existing gap of telemedicine policies at different stages around the globe. Introduction Coronavirus (COVID-19) not only brings radical uncertainties in social, economic, and political paradigms but also redefines the contours of public policy and administration. Policy design and instruments can make an enduring contribution to disaster management in emergencies, pandemics, etc. One such area adopted by the health systems throughout the world in response to COVID-19 has been the practice of telemedicine. Telemedicine can enhance quality of care, provide psychological support, improve compliance, and help patients save time and money. 1 The literature suggests that telemedicine can also enhance learning skills, case handling strategies, and help in providing different medical services. 2 Telehealth models have also inculcated physical examinations for less extensive areas. 3 Therefore, telemedicine has been a useful tool to deliver healthcare even in less accessible and low-income countries, especially during COVID-19. Telemedicine allows managing cost, convenience, and ready accessibility of medical information. 4 Telemedicine use has been increasing during the COVID-19 pandemic, being a tool that reaches patients’ home. 4 The importance of telemedicine amongst other uses during COVID-19 is reducing risk of cross-contamination due to close contact. However, the policy instruments around telemedicine, promoting usage and discussing practice nuances, remain limited across the globe. The determinants influencing the adoption of telemedicine are infrastructural (IT technologies and data privacy), institutional (compliances, licensing, and funding) and human resources (training and ethical considerations). 5 This article focuses on the institutional determinants, more specifically, the policy instruments for effective implementation of practice of telemedicine. For telemedicine to be effective and more so during COVID-19 and such similar future events, there is a need to integrate telemedicine into health services along with formalized policy instruments. The article also outlines the status of country-wise telemedicine policy instruments and discusses in detail the recent telemedicine guidelines in India. Telemedicine Policy Instruments: Global Presence The practice of telemedicine remains unexplored, amongst other reasons, due to lack of clarity in regard of the stakeholders (for example, medical councils and medical practitioners), scope of services covered in telehealth, and other inclusion and exclusion criteria. Telemedicine policy instruments, for the purpose of this article, shall mean the guidelines, policies, order, acts, rules, or regulations enabling the adoption of practice of telemedicine. A few countries have published guidelines on different aspects of telemedicine, its usage, and services. Figure 1 shows the worldwide status of presence of telemedicine guidelines. The methodology adopted to sketch the map with details of presence of telemedicine guidelines includes data collection from primary and secondary sources. Key expert interviews were conducted over a period of 4 months to include the current positioning of various countries in regard to the telemedicine guidelines. The key experts belonged to both the industry and academia from across the world. Further, readings and the literature suggested by the key experts were also referred to have an exhaustive status of the country-wide telemedicine. Additionally, a systematic literature review was conducted with inclusion criteria to refine the search (see Table 1).Figure 1. Worldwide status of telemedicine policy instruments. Table 1. Inclusion Criteria for Systematic Literature Review. S. No Criteria Details 1 Keywords Telemedicine, telehealth, countries, and guidelines 2 Boolean operators “and”, “&”, and “OR” 3 Databases EBSCO, EMERALD, Google Scholar, JSTOR, PUBMED 4 Language English Australia has telemedicine guidelines including the Medical Board of Australia Guidelines for technology-based patient consultations and the Australian College of Rural and Remote Medicine Telehealth Guidelines. 6 Canadian provinces also have published telemedicine bylaws and policies. 7 In the United Kingdom, the roadmap for digital healthcare services includes details of telemedicine services, online consultations, remote monitoring along with guides and standard operating procedures for the same. 8 The states in the United States have very actively adopted telehealth and more so during COVID-19. The states have issued various telehealth guidelines for providing medically necessary services that can be appropriately delivered through telecommunication services. For example, in Alabama, to practice telemedicine, the practitioner must hold the Alabama medical license. The Government of Arizona issued an Executive Order (EO 2020-15) in order to expand telemedicine coverage for all services that would be covered for an in-person consultation. California warrants the need of obtaining verbal or written consent from the patient before the use of telehealth services. The telehealth flexibilities in California extend till end of 2022 via signing of Assembly Bill No. 133. 9 The states have been modifying telehealth response depending on the COVID-19 situation. Austria has acknowledged the need for policy on telemedicine and drafted a framework modeled on technological architecture needed for telemonitoring. 10 In Mexico, a telehealth service catalogue serves as a reference tool that acts as a unifying criterion allowing the decision-makers in telehealth to communicate in same terms. 11 Israel’s first published circular was in 2012, which has been regularly updated, for example, the recent 2017 amendment on remote medical consultation for patients with acute morbidity. 12 Norway established a national center for telemedicine in 1994 called the Norweigan Centre for eHealth Research and a knowledge base for a policy on eHealth. 13 Centro Nacional de Telessúade in Portugal provides a toolkit for implementation of teleconsultation and telehealth factsheets replicate good practices of telehealth services in the country. 14 While a few countries have exhaustively adopted telemedicine guidelines and few are in process of the same, there remains a majority of the countries with less or no developed telemedicine guidelines. Telemedicine in India The practice of telemedicine in India, like in many countries, is still in its infancy stage, and with transmission dynamics of COVID-19, the relevance of telemedicine has become recognized. The Ministry of Health and Family Welfare, India, further emphasized this matter by publishing telemedicine guidelines toward streamlining the practice. 15 These long-pending guidelines serve as a crucial policy instrument for healthcare accessibility during such emergency times. The literature recognizes India’s telemedicine policy instruments as an important step in providing healthcare services during COVID-19. 16 The article studies India as a case study since these guidelines are exhaustive and provide a roadmap for regularization of teleconsultation services. 17 The guidelines define important terms like telemedicine, telehealth, and registered medical practitioner and provide a framework for telemedicine. The guidelines are meant for the registered medical practitioners under the Indian Medical Council Act, 1956 (now known as National Medical Commission Act). Telemedicine application can be classified into 4 types, based on—(a) mode of communication—video, audio, and text-based; (b) timing of the information transmitted—real-time consultation and asynchronous exchange of information; (c) purpose of the consultation—first consultation and follow-up consultation; and (d) interaction between the individuals involved—patients, caregiver, registered medical practitioner, and health workers. Seven pre-requisite elements of telemedicine consultation as per the Indian telemedicine guidelines are:(1) Context—The registered medical practitioner will decide as per the guidelines and his/her experience as to whether the case is fit for online consultation or mandates in-person consultation. (2) Identification of registered medical practitioner and patient—It is important to establish the identity of both the registered medical practitioner as well as the patient. The medical practitioner has to seek all information related to the patient’s identity and similarly disclose his/her credentials. (3) Mode of communication—The guidelines prescribe and categorizes 3 major tools of consultation—audio, video, and texts. According to the type, purpose, and frequency of consultation, the modes can be selected. The guidelines also elaborate the characteristics and limitations of each mode. (4) Consent—Patient consent (implied or explicit) is an important aspect of telemedicine and the guidelines also takes the same into account. (5) Type of consultation—The consultations are divided into 2 categories—(a) first consultation and (b) follow-up consultation. The guidelines also define the scope and ambit for the same. (6) Patient evaluation—The registered medical practitioner has to get all necessary information from the patient to evaluate the patient’s condition. Though the guidelines give an indicative framework of the kind of information to be sought from the patient, the burden is on the medical practitioner to get the relevant information from the patient. (7) Patient management—The registered medical practitioner, depending on the patient’s condition, can provide health education, counseling, and/or medicines. The medicines can only be provided from Lists O, A, and B of the guidelines, and no drug can be prescribed from the Prohibited List of the guidelines. Also, while issuing prescriptions, the medical practitioner will be governed by other rules related to medicines and medicinal conduct.18,19 The guidelines also consider the medical ethics, data privacy, and confidentiality aspect. The registered medical practitioner is required to uphold the same professional and ethical norms as applicable to in-person care, within the intrinsic limitations of telemedicine. Additionally, it also states that the registered medical practitioner, before practicing telemedicine, will have to undertake an online course for the same. However, if there is reasonable evidence to believe that the patient’s confidentiality and privacy have been compromised due to a technology breach, the registered medical practitioner will not be held liable. Furthermore, these guidelines also provide recommendations to be followed by the technology platforms that enable such telemedicine services. These recommendations include due diligence by the technology platforms and blacklisting in case of any violation. Discussion and Implications Telemedicine and telehealth services can aid in medical challenges being posed in times of pandemics like that of COVID-19. And for the practice of telemedicine to be adopted, the related policy instruments’ vacuum should be addressed to promote broader and safe telemedicine adoption. This article sketches a coverage map highlighting the countries where there remains a vacuum in the practice of telemedicine. Additionally, the recent telemedicine guidelines in India can be used as an example for countries to adopt a permanent solution for adoption of telemedicine. The main limitation of this article is aligned to the use of secondary data. Primary data was not employed to validate all the factors. Also, by defining the inclusion–exclusion criteria, the article may have inevitably left some salient studies on the research objective. Future work can help in expanding the study based on primary data. Conclusion The guidelines provide a framework for the practice of telemedicine in India, covering existing ambiguity relating to liability, consent, confidentiality, and negligence, much needed to be addressed during COVID-19 times. In addition to this, the guidelines mention exclusions and emergency circumstances where such telemedicine consultation would not be applicable. Some of the key observations relating to these guidelines that further need to be looked into are:(a) The definition of the term “telemedicine” needs more elaboration and setting of clear ambit of “timely access” which becomes an important factor in emergencies. (b) For exhaustiveness, there is a need to elaborate on the scope of due diligence required by the technology platforms before listing the registered medical practitioner. (c) Lastly, the guidelines provide a wide scope for exercising discretion by the registered medical practitioners without detailing the standards/measures to be considered while exercising the discretion. Telemedicine can play an essential role during global challenges impacting the health systems. However, the literature suggests that new practices like telemedicine are still not optimally used due to reasons such as (a) resistance to change, (b) lack of efficient infrastructure, and (c) absence of regulatory bodies and policy instruments governing the same. 6 Therefore, even though countries have included telemedicine in their national health strategy, its adoption and dissemination remain a challenge. There is a need for exhaustive telemedicine practice guidelines focusing on key parameters for convenient, accessible, and cost-effective care to patients. ORCID iD Surabhi Gupta https://orcid.org/0000-0001-8739-1486 Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article. ==== Refs References 1 Mubaraki AA Alrabie AD Sibyani AK Aljuaid RS Bajaber AS Mubaraki MA . Advantages and disadvantages of telemedicine during the COVID-19 pandemic era among physicians in Taif, Saudi Arabia. Saudi Medical J. 2021;42 (1 ):110-115. 2 Singh RP Javaid M Kataria R Tyagi M Haleem A Suman R . Significant applications of virtual reality for COVID-19 pandemic. Diabetes Metab Syndr. 2020;14 (4 ):661-664.32438329 3 Silver S Lewis M Ledford C . A stepwise transition to telemedicine in response to COVID-19. J Am Board Fam Med. 2021;34 :152-161. 4 Jnr B . 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J Pathol Inf. 2015;6 (1 ):13. doi:10.4103/2153-3539.153906. 9 Szijarto Valeria . Clinical and Economical Assessment of Digital Health Innovations in Austria — an Ecosystem View (Doctoral dissertation). TU Wien; 2020. 10 Bill Text - AB-133 Health (2021-2022)- California Legislative Information 11 Kapoor S Eldib A Hiasat J , et al . Developing a pediatric ophthalmology telemedicine program in the COVID-19 crisis. J AAPOS. 2021;24 (4 ):204-e2. 12 Haimi M Brammli-Greenberg S Baron-Epel O Waisman Y . Assessing patient safety in a pediatric telemedicine setting: a multi-methods study. BMC Med Inf Decis Making. 2020;20 (1 ):63. doi:10.1186/s12911-020-1074-7. 13 Aamodt IT Lycholip E Celutkiene J , et al. Health care professionals’ perceptions of home telemonitoring in heart failure care: cross-sectional survey. J Med Internet Res. 2019;21 (2 ):e10362. doi:10.2196/10362 30724744 14 Beirão I Almeida E Almeida E . Nephrology and intervention in the community - on the need for improving the interplay between nephrology centers and primary care in portugal. Portuguese J Nephrolo & Hyper. 2019;33 (2 ). 67-79. doi:10.32932/pjnh.2019.07.017 15 https://www.mohfw.gov.in/pdf/Telemedicine.pdf. Published 2021. Accessed February 15, 2021 16 Gurbani V and Thakur S . Telemedicine: guidelines in india and its importance in COVID pandemic. Indian J Forensic Med & Toxicol 2021;15 (2 ):1227-1231. 17 Dinakaran D Manjunatha N Kumar CN Math SB . Telemedicine practice guidelines of India, 2020: implications and challenges. Indian J Psychiatr. 2021 Jan-Feb;63 (1 ):97-101. 18 Medical Council of India . Indian Medical Council (Professional Conduct, Etiquette and Ethics) Regulations. Gazette of India; 2002. 19 Department of Health . Drugs and Cosmetics Act. Government of India; 1940.	35393886	PMC9251821	NO-CC CODE	2023-04-08 23:24:33	yes	Inquiry. 2022 Apr 8; 59:00469580211059989
==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 21601 10.1007/s11356-022-21601-3 Research Article A dual hesitant q-rung orthopair enhanced MARCOS methodology under uncertainty to determine a used PPE kit disposal Kang Daekook [email protected] 1 Anuja Arumugam [email protected] 2 Narayanamoorthy Samayan [email protected] 2 Gangemi Mariangela [email protected] 3 http://orcid.org/0000-0002-0106-7050 Ahmadian Ali [email protected] 45 1 grid.411612.1 0000 0004 0470 5112 Department of Industrial and Management Engineering, Institute of Digital Anti-aging Healthcare, Inje University 197 Inje-ro, Gimhae-si, Gyeongsangnam-do 50834 Republic of Korea 2 grid.411677.2 0000 0000 8735 2850 Department of Mathematics, Bharathiar University, Coimbatore, 641 046 India 3 grid.11567.34 0000000122070761 Department of Law, Economics and Human Sciences (DiGiES) University, Mediterranea of Reggio Calabria, Reggio Calabria, Italy 4 grid.507057.0 0000 0004 1779 9453 College of Science and Technology, Wenzhou-Kean University, Wenzhou, China 5 grid.412132.7 0000 0004 0596 0713 Department of Mathematics, Near East University, Nicosia, TRNC Mersin 10 Turkey Responsible Editor: Philippe Garrigues 20 7 2022 118 7 1 2022 16 6 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Healthcare waste management is regarded as the most critical concern that the entire world is currently and will be confronted with in the near future. During the COVID-19 pandemic, the significant growth in medical waste frightened the globe, prompting it to investigate safe disposal methods. Plastics are developing as a severe environmental issue as a result of their increased use during the COVID-19 pandemic which has triggered a global catastrophe and prompted concerns about plastic waste management. One of the biggest challenges in this circumstance is the disposal of discarded PPE kits. The purpose of this research is to find a viable disposal treatment procedure for enhanced personal protective equipment (PPE) (facemasks, gloves, and other protective equipment) and other single-use plastic medical equipment waste in India during the COVID-19 crises, which will aid in effectively reducing their increasing quantity. To analyse the PPE waste disposal problem in India, we used the fuzzy Measurement Alternatives and Ranking according to the Compromise Solution (MARCOS) technique, which included the dual hesitant q-rung orthopair fuzzy set. The fuzzy Best Worst Method (BWM), which is compatible with the existing MCDM approaches, is used to establish the criteria weights. Sensitivity and comparative analyses are utilised to confirm the stability and validity of the proposed strategy. Keywords Decision-making in MCDM PPE disposal Dual hesitant q-rung orthopair fuzzy set Enchanced MARCOS Best Worst Method ==== Body pmcIntroduction Corona virus has created a huge impact on all aspects of our ecosystem, including waste management. The classical and fractional variants modeling of the new coronavirus disease transmission are numerically investigated (Zhao et al. 2021). WHO has issued guidelines for waste management during the COVID-19 pandemic. The various waste management techniques used in different countries which are reviewed, and the importance of following proper guidelines in disposing of these waste products has been emphasised (Hantoko et al. 2021). The novel fractional-order discrete-time susceptible-infected-recovered (SIR) epidemic model with vaccination is discussed (He et al. 2022). Prominently, plastic materials play an essential role in health sectors, resulting in huge plastic pollution on a global scale which is now widely recognized as a major environmental burden that also has an impact on human health. The importance of implementing dynamic WM strategies described that aimes at minimizing environmental pollution caused by plastics generated during the pandemic (Benson et al. 2021). The sources and effects of single use plastic are analyzed and the importance of recycling them (Vanapalli et al. 2021). The long-term systematic assessment of waste management is discussed (Van Fan et al. 2021). The “identify, isolate, disinfect, and safe treatment practises” technique is enough for safer health practices in COVID-19 waste management (Ilyas et al. 2020). Plastic makers must be aware of the usage of macroplastics, as well as the necessity of bio-based plastics, in order to foster sustainable growth and drive both green and blue economies (Silva et al. 2021). Except the health sector authorities and non-authority managements must plan and implement policies which would minimize the use of plastics by developing alternative materials, in India (Zhang et al. 2021). The importance of using innovative methods are discussed for recycling plastics and raising awareness in society about the importance of maintaining a green environment (Parashar and Hait 2021). Following this, the requirement of PPE kits and its impact while the control of COVID-19 is discussed and the importance of using recycled PPE is analyzed. PPEs and other plastic-based healthcare equipment have started to emerge as a lifesaver for ensuring the health and safety of healthcare professionals and ordinary people. The used PPE kits are not disposed of according to typical standards in emerging regions, which has resulted in an increase in the quantity of contaminated surface waste, necessitating increased proper processing and treatment methods for PPE kits in BMW management. The BMW management in India during the COVID-19 pandemic is analyzed with the significance of the investments required in these projects in order to provide a safe and environmentally friendly method of disposing these waste products (Goswami et al. 2021). In India, the various methods for disposing BMWs are examined, as well as the innovations that can be implemented in these processes (Kudli et al. 2021). A three-layered surgical mask that protects against COVID-19, which has become a main cause of micro pollution (Aragaw 2020). The disposal of personal protective equipment (PPE) and its side effects are analyzed in Bangladesh (Shammi and Tareq 2021). Various types of gloves worn by individuals, such as latex, vinyl, and nitrile. They highlighted the consequences of inappropriate disposal on the environment (Jȩdruchniewicz et al. 2021). The PPEs are disinfectable and reusable up to 5 times during its life cycle by applying the developed recycling technologies (Manoj 2020).The recent developments in sustainable face mask alternatives are reviewed, as well as reprocessing and reusing routes, during the COVID-19 pandemic in Peru (Torres and De-la-Torre 2021). An overview of various waste management strategies are proposed which is used for disposing the hazardous healthcare waste, as well as the importance of using an effective disposal method that helps to recycle waste or convert it into valuable products such as energy (Das et al. 2021). The reduction of plastic waste in future relation to COVID-19 (Klemeš et al. 2020). Since there is a possibility of SARS-CoV-2 viruses in biomedical waste collected from various sources, researchers have been frustrated about the importance of disinfecting these wastes before disposal. The polymers which are used in the manufacture of face masks and gloves from TGA/DTA analysis and the results of FTIR are examined, these polymers can be recycled into fuel energy using the pyrolysis method (Aragaw and Mekonnen 2021). The mixed convection is a mechanism of heat transport in a thermodynamic system in which the motion of fluid particles is produced by gravity as well as external forces (Zhao et al. 2021). The discrete fractional calculus (DFC) is significant for neural networks, complex dynamic systems and frequency response analysis approaches (Rashid et al. 2022). The electro-osmatic flow of non-newtonian fluid in a micro-channel is investigated (Nazeer et al. 2022). The B-spline curve is used for reducing the entropy of video data which consider the color or luminance variations of a spatial position in a series of frames as input data points in Euclidean space R or R3 (Ebadi and Ebrahimi). The fuzzy stochastic differential equations (FSDEs) in hybrid real-world systems are discussed (Jafari et al. 2021), including randomness, fuzziness and long-range dependence under some assumptions on the coefficients to study the existence and uniqueness of the solutions (Jin et al. 2022). The exact dynamical wave solutions are discussed to the Date–Jimbo–Kashiwara–Miwa equation with conformable derivative by using an efficient and well-established approach (Iqbal et al. 2021). The magnetic, Stefan blowing and bio-convection effects are analyzed to examine the Cattaneo-Christov double diffusions phenomenon (Chu et al. 2021). The thermal profile varies directly with the magnetic parameter (Chu et al. 2021), and the opposite trend is recorded for the Prandtl number.An efficient mesh-less LRBF collocation approach is utilized for solving the two-dimensional (2D) fractional evolution equation for the arbitrary fractional order in complex-shaped domains (Radmanesh and Ebadi 2020). The numerical solution of nonlinear time-fractional Fisher equations via local meshless method is combined by explicit difference scheme (Wang et al. 2022). Decision-making is the process of choosing the best solution based on a combination of criteria. Several researchers have provided a wide range of methods based on MCDM to solve problems and make decisions easier. The decision-making process is enhanced with proficiency, logic, and clarity when the MCDM methods are used. The system is highly rewarded for increasing proficiency. The process is exonerated from its indecisiveness by employing better logic. Some researchers have sought to dispel the problem of choosing the best method for plastic disposal through the MCDM technique and have suggested a method depending on the alternatives. The healthcare waste disposal problem is addressed using the MULTIMOORA method under intuitionistic hesitant fuzzy set (Geetha et al. 2019). An efficient healthcare waste disposal method is presented using the MCDM approach for selecting during and after COVID-19 pandemic (Hantoko et al. 2021). In the method, incineration has been found to be an efficient disposal method during a pandemic situation. The biomedical waste disposal using MOOSRA method under hesitant fuzzy approach is involved in BWM (Narayanamoorthy et al. 2020). From these, to find the best method for used PPE disposal, we have to determine the efficient criteria such as society’s safety, cost, environmental impact, time, heterogeneity, technology, and infrastructure aspects. Furthermore, by improving clarity, the decision-making process becomes more appealing, even to novices in the field. The MARCOS method (Stevic and Brkovic 2020) is the most realistic MCDM models, and it is used to solve complex decision-making problems in various area of research. A novel MARCOS approach (Stevic et al. 2019) is established for sustainable supplier selection in the healthcare industry in Bosnia and Herzegovina. A novel MARCOS model is developed for SSS decision-making (Puška et al. 2021). A fuzzy MARCOS model is proposed for traffic risk assessment (Stankovic et al. 2020). The fuzzy approach was used in response to the linguistic values provided by decision-makers. It is an innovative procedure that allows a high-quality response to stakeholder and societal demands. The four factors for energy storage are resulted that the energy reduction is almost 5% more than the BCDCP method, and the packet loss rate in our proposed method is almost 25% lower than in the BCDCP method (Rahiminasab et al. 2020). The BWM is hybrid with different type of MCDM methods, thereby, the MARCOS and BWM framework is suggested to fill the gap in the literature on such a crucial issue in this work. In 2015, by the Best-Worst Method (BWM) the criteria weights are find and alternatives with regard to multiple criteria based on pairwise comparisons with less compared data (Rezaei 2015). On the other hand, BWM can proficiently correct inconsistencies exacerbated by pairwise comparisons. In contrast to AHP, the BWM performs pairwise comparisons using a 1–9 scale. This procedure appears to be simpler, extra precise, and much less superfluous, because it does not use supplementary comparisons (Guo and Zhao 2017). However, experts subjective judgments usually have characteristics of uncertainty and complexity, and the information criteria in the adult situation may be unclear. As a result, for some practical problems, BWM comparisons can be executed by using fuzzy numbers rather than crisp values. This will be more relevant to the current situation and can produce quite persuasive ranking results. A fuzzy-based BWM was proposed, with comparisons carried out using fuzzy decisions. In the available literature, BWM has been applied to some decision-making problems also such as sustainable supplier selection (Gupta and Barua 2017; Ecer and Pamucar 2020), neighborhood assessment (Hashemkhani Zolfani et al. 2020), and renewable energy alternative selection (Pamucar et al. 2020). In researcher perspectives, Fuzzy sets help the deciders to set up their indeterminancy in the matrix format and showed their keen interest in the generalizations of fuzzy sets such as Intuitionistic Fuzzy Sets (IFSs), Hesistant Fuzzy Sets (HFSs), Pythagorean Fuzzy Sets (PFSs), Intuitionistic Hesistant Fuzzy Sets (IHFSs), and Pythagorean Hesistant Fuzzy Sets (PHFSs) due to their strong points of view to tackle the vagueness and uncertainty. In hesitant fuzzy set, the sum of existing members (𝜃) grade is less than or equal to 1. But we have inconveniency while taking the positive and negative grades. DHF (Dual Hesistant Fuzzy) set, have non-existing (ϕ) membership and the sum of membership and non-membership is less than or equal to 1. Furthermore, if the experts can express their priority for the element in the form of a discrete set, that is (𝜃)2 + (ϕ)2 > 1. So the ordinary IHFSs and PHFSs failed to handle such situations and is unable to classify the decision-making approaches (Hussain et al. 2020). Therefore, some more comprehensive model is required for such situations. To cope this situation, in this manuscript we introduced the concept of DHq-ROFSs, which is the generalized form of IHFSs and PHFSs. In DHq-ROFSs the sum of qth power of membership grade and qth power of non-membership grade belongs to [0,1]. The novelty of this research is to determine a suitable method for the disposal of PPE kits, which will help in minimizing the amount of plastic waste during and after the pandemic period. A new fuzzy linguistic scale with the DHq-ROFNs is determined with the accreditation of enhanced MARCOS method to evaluate the disposal technique for PPEs. The following are the advantages of the proposed method: MARCOS method evaluates the fuzzy reference points using fuzzy ideal and anti-ideal solutions which is more accurate while use the utility degree with respect from both. Especially, this method is applicable for the possibility of taking into account a large number of criteria and alternatives. BWM is also proper for group decision-making. Its support reaching consensus in a natural way, is efficient in terms of input data which makes BWM more compatible with many other existing MCDM methods. So, we hybrid the BWM with the MARCOS method, which obtains the criteria weights and the nomenclature with their abbreviations are given in Table 1. The motivation of the research is detailed as: Due to the pandemic, there is an increase in the use of single-use plastics in the healthcare sector, which could pose an environmental risk. To obtain the suitable disposal procedure for the used PPEs, the decision-makers must develop an optimal method in the MCDM. The DHq-ROFS helps to create a matrix for alternatives with respect to criteria for dealing the suitable waste disposal problem. The matrix formalisation are scaled with linguistic values. MARCOS is appropriate for arranging the matrix based on alternatives and criteria. The weights of the criterion should be reviewed subjectively because the region dependent on the removal of the PPE, as well as those involved in the removal, may get infected in the future. A decision-maker considers that the BWM is the best method for dealing with this problem. Table 1 Nomenclature DM Decision-making NDM Normalized decision matrix MCDM Multi-criteria decision-making COVID-19 Coronavirus disease - 2019 MARCOS Measurement Alternatives and Ranking According to the Compromise Solution BWM Best-Worst Method PPE Personal protective equipment BMW Biomedical waste q-ROFS q-rung orthopair fuzzy set DHq-ROFN Dual hesitant q-rung orthopair fuzzy numbers DHq-ROFS Dual hesitant q-rung orthopair fuzzy set WM Waste management PW Plastic waste PWM Plastic waste management Furthermore, by filling existing research gaps, this work makes the following contributions: The first contribution of the current study is the completion of a comprehensive study that covers all of the factors involved in the disposal of PPE kit waste. The methodology used in this study is used as a foundation for formulating the used PPE kit disposal problem as an MCDM problem, in which various alternatives should be evaluated using the MARCOS method and the criteria weights determined using the BWM, whereas the suggested methodology can be useful for evaluating the mentioned disposal problem in other countries as well. Third, combining the strengths of BWM with the flexibility of MARCOS improves the reliability and practicability of existing disposal treatment sustainability evaluations. Other contributions to the literature are listed below, in addition to all of these: The contribution of the research is explained as follows. In this paper, we proposed BWM and the enhanced MARCOS procedure to rank the alternatives for disposing of the PPEs. To express the decision-makers opinions in terms of DHq-ROF elements, linguistic scale is standardized for proposed application. BWM is used to estimate the weight, then the obtained weights are combined with the fuzzy MARCOS method, which provides a juridical way in disposing the waste. To provide the best disposal solution for this problem, we recommend and visualize the efficasy of the BWM and enhanced MARCOS in the proper manner and the research method is shown in Fig. 1. Fig. 1 Methodology The paper is arranged as follows. Preliminaries are provided in Section “Preliminaries”. Section “The conventional MARCOS method” describes the conventional MARCOS method. Section “Research methodology” presents the research methodology. This section describes the MARCOS with DHq-ROFNs and the BWM methods. Section “Numerical example” includes a numerical example that demonstrates the effectiveness of the proposed method and how weight values are calculated. Section “Result validation” examines sensitivity and comparison. Section “Conclusion” concludes with a conclusion and future work. Preliminaries Definition 1 (Yager 2017) Assume that S be a non-empty fix set, then a q-ROFS A on U can be described as follows: 1 A={<s,(𝜃A(s),ϕA(s))>\|s∈S} where 𝜃A(s):S→[0,1] and ϕA(s):S→[0,1] are represent the degree of membership and non-membership of s to A, respectively, which satisfies 0 ≤ (𝜃A(s))q + (ϕA(s))q ≤ 1,(q ≥ 1). The indeterminacy degree is given as λA(s)=(𝜃A(s))q+(ϕA(s))q−((𝜃A(s))q)((ϕA(s))q)q, < 𝜃A(s),ϕA(s) > is called a q-ROFN, which is represented by Φ = (𝜃A,ϕA). Definition 2 (Torra 2010) Assume R is a reference set, and describe the hesitant fuzzy set A on R with function h, when applied to R returns a subset of [0,1] which is denoted mathematically as (Xia and Xu 2011): 2 A={<r,h(r)>\|r∈R}={<r,∪γ∈h(r){γ}>\|r∈R} in which h(r) is called the hesitant fuzzy element (HFE) and it contains all possible membership degrees of r ∈ R to the set H. Definition 3 (Zhu et al. 2012) Assume S is a fixed set, and dual hesitant fuzzy set (DHFS) T on S is defined as, 3 T={<s,h(s),g(s)>\|s∈S}, where h(s) and g(s) ∈ [0,1], representing the possible grade of membership and non-membership element s ∈ S to T, respectively, with the conditions 0≤α,β≤1,0≤α++β+≤1 where α ∈ h(s), β ∈ g(s), α+∈h+(s)=∪α∈h(s)max{α} and β+∈g+(s)=∪β∈g(s)max{β} ∀s ∈ S. For, the pair t(s) = (h(s),g(s)) is called a DHFE denoted by t = (h,g), with α ∈ h, β ∈ g, α+∈h+=∪α∈hmax{α}, β+∈g+=∪β∈gmax{β}, 0 ≤ α,β≤ 1, and 0 ≤ α+ + β+ ≤ 1. Definition 4 (Xia and Xu 2011; Xu et al. 2018) Let S is non-empty set. A DHq-ROFS A described on S is 4 A={<s,hA(s),gA(s)>\|s∈S}, in which hA(s) and gA(s) ∈ [0,1] representing the possible grade of membership and non-membership element s ∈ S to the set A, respectively, with αq+βq≤1(q≥1), where α ∈ hA(s), β ∈ gA(s) ∀s ∈ S. For, the pair tq(s) = (hA(s),gA(s)) is called a DHq-ROF element denoted by tq = (h,g) with α ∈ h, β ∈ g, 0 ≤ α,β≤ 1, αq + βq ≤ 1. When q = 2, then DHq-ROFS is reduced to DHPFS (Wei and Lu 2017), and when q = 1, then DHq-ROFS is reduced DHIFS (Zhu et al. 2012). Definition 5 (Hussain et al. 2020) For a DHq-ROFN tq = (h,g) characterized by h and g, the score function of tq is described as 5 S(tq)=1#h∑α∈hαq−1#g∑β∈gβq where #h and #g represents the cardinality of h and g respectively. The conventional MARCOS method Consider m alternatives {C1,C2,C3,...,Cm}, n criteria {B1,B2,...,Bn}, then the steps of the conventional MARCOS method are given below. Step 1: Construct an initial decision matrix. Step 2: Develop an extended initial matrix by using the anti-ideal solution (AIS) and ideal solution (IDS). 6 B1B2…BnD=AISC1C2⋮CmIDScai1cai2…cainc11c12…c1nc21c22…c2n⋮⋮⋱⋮cm1cm2…cmncid1cid2…cidn Here, AIS represents the worst alternative and IDS represents the best alternative. Based on criteria, AIS and IDS are described from Eqs. 7 and 87 AIS=minicijifj∈Pandmaxicijifj∈N 8 IDS=maxicijifj∈Pandminicijifj∈N where P belongs positive criteria and N belongs negative criteria. Step 3: Normalize the extended initial matrix (D) and the normalized matrix (NDM) denoted as X = [xij]m×n are calculated by Eqs. 9 and 10. 9 xij=caicijifj∈N 10 xij=cijcaiifj∈P where cij and cai are the elements of D. Step 4: Now, compute the weighted normalized decision(WND) matrix z = [zij]m×n using Eq. 11. 11 zij=xij×wj Step 5: Calculate the utility degree of the alternative Ui by Eqs. 12 and 13. 12 Ui−=SiSais 13 Ui+=SiSids where Si(i = 1,2,...,m) denotes the sum of the elements of the matrix Z, which is given in Eq. 14. 14 Si=∑i=1nzij Step 6: Compute the utility function of alternative f(Ui) described by Eq. 15. 15 f(Ui)=Ui++Ui−1+1−f(Ui+)f(Ui)++1−f(Ui−)f(Ui)− where f(Ui−) denotes the utility function is to (AIS), and f(Ui+) denotes the utility function is to (IDS). Both function is obtained by Eqs. 16 and 17. 16 f(Ui−)=Ui+Ui++Ui− 17 f(Ui+)=Ui−Ui++Ui− Step 7: Finally, ranking the alternatives. Research methodology The enhanced MARCOS method Assume m alternatives {C1,C2,C3,...,Cm} and n criteria {B1,B2,...,Bn} to enhance the MARCOS method an acoount of decision experts. The graphical illustration of the proposed methodology is shown in Fig. 2Fig. 2 Pictorial representation of enhanced MARCOS method Step 1: Create an initial decision matrix using DHq-ROFNs according to Table 2. Table 2 Fuzzy linguistic scale Linguistic term DHq-ROF membership values DHq-ROF non-membership values Certainly high (CH) 0.95 0.15 Very high (VH) 0.85 0.25 High (H) 0.75 0.35 Above average (AA) 0.65 0.45 Average (A) 0.55 0.55 Under average (UA) 0.45 0.65 Low (L) 0.35 0.75 Very low (VL) 0.25 0.85 Certainly low (CL) 0.15 0.95 The initial matrix D is given below. B1 B2 … Bn C1 (𝜃111,𝜃112,…,𝜃11l),(ϕ111,ϕ112,…,ϕ11l) (𝜃121,𝜃122,…,𝜃12l),(ϕ121,ϕ122,…,ϕ12l) … (𝜃1n1,𝜃1n2,…,𝜃1nl),(ϕ1n1,ϕ1n2,…,ϕ1nl) C2 (𝜃211,𝜃212,…,𝜃21l),(ϕ211,ϕ212,…,ϕ21l) (𝜃221,𝜃222,…,𝜃22l),(ϕ221,ϕ222,…,ϕ22l) … (𝜃2n1,𝜃2n2,…,𝜃2nl),(ϕ2n1,ϕ2n2,…,ϕ2nl) ⋮ ⋮ ⋮ ⋱ ⋮ Cm (𝜃m11,𝜃m12,…,𝜃m1l),(ϕm11,ϕm12,…,ϕm1l) (𝜃m21,𝜃m22,…,𝜃m2l),(ϕm21,ϕm22,…,ϕm2l) … (𝜃mn1,𝜃mn2,…,𝜃mnl),(ϕmn1,ϕmn2,…,ϕmnl) where Dcbl=(𝜃cbl,ϕcbl)(c=1,2,...,m,b=1,2,...,n) denotes the elements of the alternatives Cc(c = 1,2,...,m) based on attribute Bb(b = 1,2,...,n) given by the experts el. Then aggregate the experts opinion is: 18 cij=1#h∑α∈h(α)q−1#g∑β∈g(β)q The aggregated decision matrix D is in Eq. 19, 19 B1B2…BnD=C1C2⋮Cm𝜃11,ϕ11𝜃12,ϕ12…𝜃1n,ϕ1n𝜃21,ϕ21𝜃22,ϕ22…𝜃2n,ϕ2n⋮⋮⋱⋮𝜃m1,ϕm1𝜃m2,ϕm2…𝜃mn,ϕmn Step 2: Construct the extended initial matrix by using the ideal and anti-ideal solutions, as shown in Eq. 20. The anti-ideal solution (AIS) represents the worst alternative whereas an alternative with best characteristic defines an ideal solution (IDS). 20 B1B2…BnD=AISC1C2⋮CmIDS(𝜃ai1,ϕai1)(𝜃ai2,ϕai2)…(𝜃ain,ϕain)(𝜃11,ϕ11)(𝜃12,ϕ12)…(𝜃1n,ϕ1n)(𝜃21,ϕ21)(𝜃22,ϕ22)…(𝜃2n,ϕ2n)⋮⋮⋱⋮(𝜃m1,ϕm1)(𝜃m2,ϕm2)…(𝜃mn,ϕmn)(𝜃id1,ϕid1)(𝜃id2,ϕid2)…(𝜃idn,ϕidn) Based on criteria, AIS and IDS are obtained by Eqs. 21 and 22. 21 AIS=(mini𝜃ij,miniϕij)ifj∈Pand(maxi𝜃ij,maxiϕij)ifj∈N 22 IDS=(maxi𝜃ij,maxiϕij)ifj∈Pand(mini𝜃ij,miniϕij)ifj∈N where P denotes the positive criteria and N denotes the negative criteria. Step 3: By using Eqs. 23 and 24 normalize the extended initial matrix, which is denoted as X = [xij]m×n. 23 xij=(𝜃aicij,ϕaicij)ifj∈N 24 xij=(cij𝜃ai,cijϕai)ifj∈P where (𝜃ij,ϕij) and cai are the elements of D. Step 4: Now, compute the WND Z = [zij]m×n using Eq. 25. 25 zij=(𝜃ij×wj,ϕij×wj) Step 5: Calculate the utility degree of the alternative Ui based on Eqs. 26 and 27. 26 Ui−=SiSais 27 Ui+=SiSids where Si(i = 1,2,...,m) denotes the sum of the elements of the matrix Z, which is given in Eq. 28. 28 Si=∑i=1nzij Step 6: Compute the utility function of alternative f(Ui) as described in Eq. 29. 29 f(Ui)=Ui++Ui−1+1−f(Ui+)f(Ui)++1−f(Ui−)f(Ui)− where f(Ui−) denotes the utility function related to (AIS), and f(Ui+) denotes the utility function with respect to (IDS) which are calculated using Eqs. 30 and 31. 30 f(Ui−)=Ui+Ui++Ui− 31 f(Ui+)=Ui−Ui++Ui− Step 7: Finally, as per expert’s choice, the alternatives are ordered in the descending order with the help of MARCOS method. The fuzzy BWM method BWM (Ecer and Pamucar 2020; Gupta and Barua 2017; Hashemkhani Zolfani et al. 2020; Pamucar et al. 2020) offers more consistent findings because to its pairwise comparison approach, as well as algorithm resilience, speed of computing, and ease of calculation, all of which are significant advantages. So, in this section, we discuss the BWM processes for determining the criterion weights in the PPE disposal problem. Figure 3 depicts the steps involved. Fig. 3 The procedure of BWM method Step 1: Create a list of decision criteria. Here, we look at the criteria (b1,b2,...,bn) which are used to make a decision. The PPEs disposing criterion are B1-society’s safety, B2-cost, B3-environmental impact, B4-time, B5-heterogeneity, and B6-technology and infrastructure. Step 2: Identify the best criteria (e.g., most beneficial and valuable) and the worst criteria (e.g., least beneficial and valuable). Now, the decision-maker determines the overall best and worst criteria. At this point, no comparisons are made. Then, (b3)-environmental impact and b4-time are the best and the worst criteria for the disposal of PPEs problem. Step 3: Evaluate their preference for the best overall criterion among the other criteria by assigning values between 1 and 9. The Best-to-Others vector result is: BG=(bG1,bG2,...,bGn) where bGj denotes a preference for the best criterion G over criterion j (i.e) bGG = 1. In our case, the vector demonstrates a preference for C3-environmental impact over the other criteria. Step 4: Using a number between 1 and 9, rank all of the criteria in order of preference over the worst criterion. The resulting others-to-worst vector is: BP=(b1P,b2P,...,bnP) where bjP denotes a preference for criterion j over the worst criterion P. It is obvious that bPP = 1. In this case, the vector depicts preferences for all criteria over the criteria time-b4. Step 5: Obtain the optimal weights (w1∗,w2∗,...,wn∗). The optimal weight for the criteria is the one where, for each pair of wGwj and wjwP, wGwj=bGj and wjwP=bjP. To satisfy these conditions for all j, we should find a solution where \|wGwj−bGj\| and \|wjwP−bjP\| for all j is minimized. Consider the non-negativity and sum conditions for the weights as: minmaxj{\|wGwj−bGj\|−\|wjwjP−bjP\|}s.t 32 ∑jwj=1,wj≥0forallj Equation 33 can be transferred to the following problem: 33 minχ,s.twGwj−bGj≤χ,foralljwjwP−bjP≤χ,forallj∑j=1twj=1,∀jwj≥0,∀j The optimal weights (w1∗,w2∗,...,wn∗) and χ∗ are obtained by solving Eq. 33. Then, using χ∗, we present a consistency ratio (CR). It is larger the χ∗, the higher the CR and the less reliable the comparisons become. Consistency ratio We propose a consistency ratio for the proposed best-worst method in this section. A comparison is fully consistent when bGj × bjP = bGP ∀ j, where bGj is preference of the best criteria over j, bjP is preference of the worst criteria j, and bGP is the preference of the best criteria over the worst criteria (Rezaei 2015). Moreover, some j may be inconsistent, for this we suggest a CR to demonstrate how consistent a comparison is. To that end, we begin by computing the minimum consistency of a comparison, which is as follows: As previously stated, where bij ∈{1,2,...,bGP} the largest possible value of bGP is 9. Consistency decreases when bGj × bjP is smaller or larger than bGP or equivalently bGj × bjP≠ 1, and the largest inequality occurs when bGj and bjP have the maximum value, which will result in χ. We also know that (wGwj)×(wjwP)=wGwP, and given the largest in equality as a results by bGj and bjP, χ is a value that must be subtracted from bGj and bjP, then added to bGP, or: 34 (bGj−χ)×(bjP−χ)=(bGP+χ) As for the minimum consistency bGj = bjP = bGP, we have 35 (bGP−χ)×(bGP−χ)=(bGP+χ)⇒χ2−(1+2bGP)χ+(bGP2−bGP)=0 Solving for different values of bGP ∈{1,2,...,9}, we can find the maximum possible χ (max χ). These values are used as the consistency index in Table 3. Table 3 Consistency index (CI) bGP 1 2 3 4 5 6 7 8 9 CI 0.0 0.52 1.00 1.72 2.19 3.01 3.97 4.81 5.50 The CR is then calculated using χ∗ and the corresponding consistency index, as follows: 36 Consistency Ratio=X∗consistency index Numerical example Plastic materials have become an indispensable part of daily life, resulting in massive global plastic pollution. Plastic is lightweight, adaptable, efficient, liquid-resistant, tough, and reasonably priced. These are the enticing characteristics that drive our high regard for and excessive consumption of plastic goods. However, because plastic materials are long-lasting and take a more time to degrade, they eventually end up as waste. Our incredible fascination with plastic, combined with an undeniable behavioural proclivity for increasingly over-consuming, disregarding improper waste disposal and thus contaminating, has become a dangerous combination. Plastics are made of synthetic chemicals and are used in a wide range of products, including water cans, clothing, packaging materials, medical equipment, electrical items, and building materials. Plastic was once thought to be harmless and odourless. Plastic pollution is now widely acknowledged as a significant environmental burden that also has an impact on human health. Despite the fact that PW is reusable, recycled products are more harmful to the environment due to added chemicals and skin tones. However, the disposal of plastic into the environment has resulted in a number of related issues. Natural creatures have a tough time breaking down the man-made chemical bonds in plastic, making the material’s persistence a major concern. Only a small fraction of plastic manufacturing gets recycled; the remainder is disposed of in landfills or incinerated. In landfills, it will be non-degradable for few years. Incineration (Kudli et al. 2021) releases chemicals into the atmosphere, contributing to increased air pollution. A pure plastic can only be reused two to three times because it deteriorates due to thermal pressure, and it has a shorter life span after each reprocessing. As a result of inadequate collection and segregation methods, single-use plastic (SUP) trash disposal has been highlighted as a serious concern. Before COVID-19, only 60% of plastic was recycled; the remaining 40% of plastic was left unused, causing water, land, and air pollution. Seventy% of plastic products are converted into plastic waste during this pandemic. The COVID-19 issue has resulted in an increase in the volume of plastic garbage, which has had a detrimental impact on plastic management. SUP, in particular, adds to our planet’s plastic weight, as do a plethora of abandoned PPE kits. The corona virus has resulted in an increase in the use and disposal of plastic-based items for medical and other purposes. During the pandemic, the increased use of SUP and plastic-based materials, together with greater availability to healthcare items and packaging, caused in a significant increase in global plastic waste generation (World Health Organization 2020; Jribi et al. 2020; WEF 2020). Hence, the pandemic has posed a significant environmental challenge in terms of plastic waste management. In India, the disposal of personal protective equipment (PPE) waste is a major issue. While no technology has been confirmed so far, a little research on the reuse of plastic waste in road construction and co-processing of plastic waste in cement kilns has been conducted, but infected PPE waste is very dangerous to reuse for society, and incineration of used PPE kits damages the environment very badly. We find a suitable solution to the PPE waste disposal problem for these issues. Furthermore, in the aftermath of the COVID-19 pandemic, a few governments are working to establish new waste management procedures and protocols, taking into account the additional facilities and infrastructure required to maintain compliance for proper waste management.Likewise, governments that lack their own laws or instructions have taken steps to conceptualize international regulations and guidelines issued by various organizations such as WHO, United Nations Environment Programme (UNEP), United Nations Human Settlements Programme (UN-Habitat), the World Bank, and other UN agencies, as well as other international organizations such as Asian Development Bank (ADB), and The International Solid Waste Association (ISWA) (Waste Management 2020). Many cities report a considerable rise in medical waste in the form of hospital-generated personal protection equipment. As a result, it is critical to increase the capacity to handle and treat this healthcare waste as early as possible. In this situation, we must assess the most viable or acceptable waste disposal solutions for the current plastic waste management system that are compatible with the environmental system and human health consequences of waste management. This paper discusses how to properly dispose the infected personal protective equipment (PPE) and single-use plastics. Here, we address several disposal methods for PPEs from which an efficient disposal technique is chosen based on the proposed method. There are no impacts and drawbacks to the alternatives that would minimize single use plastics, used PPE kits and enhance waste disposal in the future. Here, we consider the four alternatives based on six criteria to dispose these wastes. The criteria are explained in Table 4 and the alternatives are explained as follows. Table 4 Selected criterion for ranking the alternative Criteria Description Society’s safety (B1) Method must be safe for the public and workers to use, and it must not have a negative effect on society. Cost (B2) Operating, transportation, technical, and other costs must be low. Environmental impact (B3) Method adopted must not pollute the environment. Time (B4) The disposal method must be capable of disposing a large amount of waste in a short period of time. Heterogeneity (B5) The disposal method must be appropriate for the disposal of various types of plastic waste. Technology and infrastructure B6 Technology and land requirements for disposing and handling of waste. Landfills (Kudli et al. 2021; Manupati et al. 2022) The Landfills are facilities for the final disposal of single-use plastic waste on land that are designed and built with the goal of minimizing environmental impacts. The landfill site is the oldest type of waste treatment. Landfills have been the most prevalent means of storing and disposing of organized trash, and they are still in practice in many parts of the world. Plastic containers can take ten to a hundred years to degrade in landfills. Other plastic items may take the same number of years or longer to decay in such an environment, which lacks sunlight, air, and moisture (three essential components for facilitating bio-degradation). Plastic buried deep in landfills can leach dangerous chemicals into groundwater. The most serious environmental problem caused by landfills is groundwater contamination. A modern landfill that addresses these issues is a complex structure outfitted with a variety of environmentally friendly equipment. Incineration (Manupati et al. 2022; Waste Management 2020) Incineration is an extremely high, dry combustion process that converts organic and combustible waste into inorganic, non-flammable material, resulting in a significant reduction in waste volume and weight. Incineration is the rapid oxidation of trash at high temperatures of 870 − 1200 circC. When plastics are burned, toxic gases such as dioxins, fluorinated gases, mercury, and BCPs are released into the air, endangering both plants and people’s lives. Once plastic is combusted, black carbon (soot) is released, which is a greenhouse gas that contributes to climate change and pollution, and a class of flame retardants known as halogens is also formed. Cancer, neurological damage, birth defects, child developmental disorders, asthma, and multiple organ damage are all known to be caused by these harmful chemicals. Microwaving (Manupati et al. 2022; Waste Management 2020) The microwave device is essentially a steam-based technique that uses the action of soaking and heat created by microwave radiation. The contaminated water is reheated by microwave energy at a frequency of approximately 2,450 MHz and a wavelength of 12.24 cm. Microwaving is a technique for treating biomedical waste made of plastic and glass. At the full design capacity of each microwave device, microwave should kill bacteria and other harmful organisms as determined by an approved biological indicator. In the absence of hazardous waste, it has the advantage of producing no liquid effluents and little emissions. The downsides are that they have high initial costs, have odour issues, and are susceptible to energy loss. Chemical pyrolysis (Manupati et al. 2022; Ilyas et al. 2020; Dharmaraj et al. 2021) Pyrolysis is the thermal degradation of polymers into smaller molecules in the presence of a catalyst (such as aluminium oxides, fly ash, red mud, and calcium hydroxide) in an inert atmosphere at temperatures between 300 circC and 400 circC. It is a thermo-chemical plastic disposal method that involves the oxidative decomposition of lengthy polymer structures into simpler, smaller units under high temperature and pressure in the lack of oxygen for a short period of time. The oil produced during the pyrolysis process resembles normal diesel. Chemical pyrolysis products can be refined in the same way that as oil, using conventional refining technologies to produce polymer building blocks. They can also be used directly as a fuel. In this section, the proposed methodology is used to evaluate the disposal method for used PPEs. To obtain the best disposal method that has a low environmental impact and is useful for all requirements in society, we have chosen six criteria to evaluate the four alternatives. Those criteria are society’s safety (B1), cost (B2), environmental impact (B3), time (B4), heterogeneity (B5), and technology & infrastructure (B6). Here, we consider three decision-makers to evaluate the alternatives based on those selected criteria’s. Then, the alternatives are landfills (C1), incineration (C2), microwaving (C3), and chemical pyrolysis (C4). The selected alternatives and criteria are shown in Fig. 4. Fig. 4 The used PPEs disposal problem The fuzzy BWM method Step 1: Create a list of decision criteria. Here, we look at the criteria (B1,B2,...,Bn) that is used to make a decision. The PPEs disposing criterion are B1-society’s safety, B2-cost, B3-environmental impact, B4-time, B5-heterogeneity, and B6-technology and infrastructure. Step 2: Obtain the best and the worst criteria, (B3)-environmental impact is best and B4-time is te worst criteria. Step 3: Here, B3 is the best criteria and the pairwise comparison vector for the best criterion values are given in Table 5. Table 5 Pairwise comparison vector for the best criterion Criteria B1 B2 B3 B4 B5 B6 B3 8 4 1 6 3 5 Step 4: Here, B4 is the worst criteria and the pairwise comparison vector for the best criterion values are given in Table 6. Table 6 Pairwise comparison vector for the worst criterion B1 B2 B3 B4 B5 B6 B4 5 7 6 1 2 4 Step 5: From Tables 5 and 6 results in Eq. 33 for this problem, as follows: 37 minχ,s.tw3w1−b31≤χ,w3w2−b32≤χ,w3w4−b34≤χ,w3w5−b35≤χ,w3w6−b36≤χ,foralljw1w4−b14≤χ,w2w4−b24≤χ,w3w4−b34≤χ,w5w4−b54≤χ,w6w4−b64≤χ,foralljw1+w2+w3+w4+w5+w6=1,w1,w2,w3,w4,w5,w6≥0,∀j Solving this Eq. 37, we found the optimal weights (w1∗,w2∗,...,wn∗) are w1 = 0.0751, w2 = 0.1503, w3 = 0.4154, w4 = 0.0382, w5 = 0.2004, w6 = 0.1202 which are shown in Fig. 5 and χ∗ = 0.1858. For the consistency ratio, as bGP = b34 = 6, the consistency index is 3.01 (Table 3), and the CR is 0.18583.01=0.0617, which implies a very good consistency. Fig. 5 Sensitivity analysis results The enhanced MARCOS method In this section, experts evaluate the disposal methods under the selected criteria are represented as a DHq-ROFNs. The linguistic scale (Table 2) helps the experts to assist their opinions in evaluating the alternatives. Step 1: Create the initial decision matrix using linguistic variables, which is shown in Table 7. Then, aggregating the initial matrix with q = 3 and which is given in Table 8. Step 2: The extended initial matrix is shown in Table 9 by applying the Eqs. 21 and 22. Step 3: By using Eqs. 23 and 24 to NDM, then multiplied the weight values w1 = 0.0751, w2 = 0.1503, w3 = 0.4154, w4 = 0.0382, w5 = 0.2004, w6 = 0.1202 with NDM, we get the weighted NDM and which is shown in Table 10. Step 4: Calculate the utility degree of the alternatives using Eqs. 26 and 27, and utility function and final ranking results are obtained using Eqs. 28, 29, 30, 31 are shown in Table 11. Step 5: The final ranking results are given in Table 11 and graphical representation is shown in Fig. 6. From Table 11, the best disposal method for used PPE kits is C4-chemical pyrolysis. The manufacturing of liquid fuel from disposal of PPEs could address both the challenges of PPE waste management and rising energy demand. Plastic-derived liquid fuel is environmentally friendly and has similar fuel properties to fossil fuels. In this situation, converting plastic waste into a liquid biofuel is a viable option for protecting environmental resources. The process of converting any material at extremely high temperatures is known as pyrolysis. All PPEs currently disposed of in landfills, oceans, and other places that harm aquatic species can be relocated to a combustor and exposed to high temperatures to decompose the propane bonds and form a liquid. The liquid that results can be used as a biofuel. We need the fuel to power various mechanical devices. This fuel can be reprocessed, which helps to protect the environment. Because of the growing population and their need for energy, this would be a healthy alternative that would also aid in the preservation of our natural environment. Table 7 Decision matrix based on linguistic scale for DM1,DM2,DM3 B1 B2 B3 B4 B5 B6 DM1 C1 (AA,A) (H,A) (VH,L) (VH,VL) (H,VL) (CH,A) C2 (L,H) (A,H) (CH,UA) (VH,UA) (A,L) (H,A) C3 (H,VH) (H,VH) (UA,A) (A,VH) (VH,A) (AA,A) C4 (CH,H) (VH,H) (CH,A) (AA,VH) (VH,AA) (VH,A) DM2 C1 (A,AA) (L,AA) (H,A) (H,L) (VH,L) (VH,UA) C2 (L,A) (H,A) (CH,A) (VH,A) (UA,H) (VH,UA) C3 (L,VL) (VH,AA) (A,H) (AA,H) (L,A) (A,UA) C4 (VH,VH) (VH,AA) (CH,UA) (VH,VH) (H,A) (VH,VH) DM3 C1 (L,H) (A,H) (VH,H) (VH,L) (AA,H) (AA,H) C2 (VH,A) (A,AA) (H,UA) (CH,UA) (H,A) (VH,H) C3 (A,AA) (H,VH) (AA,VH) (H,VH) (A,H) (L,VH) C4 (H,UA) (H,AA) (VH,AA) (A,AA) (H,VH) (A,H) Table 8 Aggregate decision matrix B 1 B 2 B 3 B 4 B 5 B 6 C 1 0.0611 0.1102 0.3397 0.0641 0.0772 0.4208 C 2 0.108 0.1514 0.4737 0.4567 0.0161 0.3888 C 3 0.0222 0.4452 0.1024 0.1275 0.1492 0.009 C 4 0.5201 0.475 0.5989 0.311 0.3949 0.3899 Table 9 Extended aggregated matrix B 1 B 2 B 3 B 4 B 5 B 6 AAI 0.0222 0.475 0.1024 0.0641 0.0772 0.4208 C 1 0.0611 0.1102 0.3397 0.0641 0.0772 0.4208 C 2 0.108 0.1514 0.4737 0.4567 0.0161 0.3888 C 3 0.0222 0.4452 0.1024 0.1275 0.1492 0.009 C 4 0.5201 0.475 0.5989 0.311 0.3949 0.3899 ID 0.5201 0.1102 0.5989 0.4567 0.3949 0.009 Table 10 Weighted normalized matrix B 1 B 2 B 3 B 4 B 5 B 6 AAI 0.0031 0.0348 0.0709 0.0053 0.0391 0.0025 C 1 0.0088 0.1503 0.2356 0.0053 0.0391 0.0025 C 2 0.0155 0.1093 0.3285 0.0382 0.0081 0.0027 C 3 0.0031 0.0371 0.0709 0.0106 0.0757 0.1202 C 4 0.0751 0.0348 0.4154 0.0260 1.2004 0.0027 ID 0.0751 0.1503 0.4154 0.0382 0.2004 0.1202 Table 11 The final ranking values for proposed method f(Ui−) f(Ui+) U − U + f(Ui) Rank C 1 0.1347 0.8652 2.8362 0.4417 0.4324 3 C 2 0.1347 0.8652 3.2260 0.5025 0.4919 2 C 3 0.1347 0.8652 2.0398 0.3177 0.3110 4 C 4 0.1347 0.8652 4.8452 0.7547 0.7388 1 Fig. 6 The final ranking result Table 12 Weight values for sensitivity analysis Criteria Case 1 Case 2 Case 3 B 1 0.0751 0.4120 0.1639 B 2 0.1503 0.1128 0.3538 B 3 0.4154 0.0705 0.0431 B 4 0.0382 0.2265 0.0703 B 5 0.2004 0.0371 0.2459 B 6 0.1202 0.1411 0.1230 Result validation Sensitivity analysis This section depicts the analysation in sensitivitness of the weight which has evaluated by the proposed method with three cases for used PPE kit disposal, is shown in Table 12. The Enhanced MARCOS method is used to find the alternative ranks. The weights are evaluated using the BWM weight finding method, the final ranks are determined by the weights of each criterion. The best alternative can be found an account of criteria, and which can lead to a better compromise option. The results of three cases are compared with the proposed problem, and the pictorial representation is shown in Fig. 7. Fig. 7 Weight values of the criteria for sensitivity analysis Case 2: Here, B1-society’s safety is the best criteria and B2-heterogeneity is the worst criteria. In BWM method, we found the weights of the criteria are w1 = 0.4120, w2 = 0.1128, w3 = 0.0705, w4 = 0.2264, w5 = 0.0371, w6 = 0.1411, and χ∗ = 0.1522 and consistency ratio is 0.0408. Which implies a good consistency. The ranking order in this case is as follows: C4>C2>C3>C1 when B1 is best criterion and B5 is worst criterion. Case 3: Here, B2-cost is the best criteria and B3-environmental impact is the worst criteria. In BWM method, we found the weights of the criteria are w1 = 0.1639, w2 = 0.3538, w3 = 0.0431, w4 = 0.0703, w5 = 0.2459, w6 = 0.1230, and χ∗ = 0.1381 and consistency ratio is 0.0600. Which implies a good consistency. The ranking order in this case is as follows: C4>C1>C2>C3 when B2 is best criterion and B3 is worst criterion. Table 13 shows that alternative C4 has the same rank in all three cases (1). In three cases, alternatives C1, C2, and C3 have different rank positions and rank values. The ranking results are shown in Fig. 8. Furthermore, criterion weight determines its significance, and it implied that the ranking sequential keeps changing as the weighting factor of the criteria changes. So, in order to test the effectiveness, we keep hoping that if a weight detection system is available, we can select the best alternative for disposing of used PPE kits depending on the priorities assigned in such cases. Table 13 Ranking results for sensitivity analysis Alternatives Case 1 Rank Case 2 Rank Case 3 Rank C1 0.4324 3 0.2407 4 0.4479 2 C2 0.4919 2 0.4502 2 0.3997 3 C3 0.3110 4 0.2731 3 0.3299 4 C4 0.7388 1 0.6966 1 0.5729 1 Fig. 8 Sensitivity analysis results Comparison analysis To investigate the robustness of the method, we compared the results obtained from the proposed method with other relevant MCDM methods like TOPSIS, VIKOR, and, MULTIMOORA. We can observe changes in the ranking values obtained from all the three methods except TOPSIS. TOPSIS is utilized based on ideal and ideal solution and the ranking value ordered in the descending order VIKOR is proposed based on relative closeness index (Chang 2010) and is ranked in the descending order. Hence, the preference order obtained using VIKOR and TOPSIS methods coincides with that of the proposed method, whereas in the MULTIMOORA method. In MULTIMOORA approach, the alternatives C1 and C3 have different ranking position when compared to the proposed approach. This analysis shows the stability and consistency of the proposed method in handling real-life MCDM problems. Table 14 shows the obtained results and are represented graphically in the Fig. 9. Table 14 Comparison analysis results Methods Ranking values Ranking order Rank result TOPSIS C1- 0.3980, C2- 0.4902, C3- 0.2467, C4- 0.7476 C4 > C2 > C1 > C3 C4 VIKOR C1- 0.4528, C2- 0.3076, C3- 1.0000, C4- 0.0000 C4 < C2 < C1 < C3 C4 MULTIMOORA C1- 0.1446, C2- 0.3310, C3- 0.1341, C4- 0.3310 C4 > C2 > C3 > C1 C4 Proposed method C1- 0.4324, C2- 0.4919, C3- 0.3110, C4- 0.7388 C4 > C2 > C1 > C3 C4 Fig. 9 Comparison analysis results Conclusion The used PPE kits has contributed to the enormous growth of the amount of plastic waste during the COVID-19 pandemic which has alarmed the world to look into suitable treatment technique for handling them as they would pose a threat to the environment. In this article, we have proposed an efficient treatment method which converts the plastic waste into biofuel. This means that the suggested method can simultaneously address the problem of plastic waste management and energy demand. The hesitancy that arises with the expert’s opinion in evaluating the treatment techniques has been effectively dealt with the DHq-ROFNs. As the fuzzy MARCOS method provides more precise determination of the degree of utility and as it considers the fuzzy reference points through the fuzzy ideal and anti-ideal solution at the beginning of the problem formulation involving this method for obtaining a suitable treatment method provided more realistic and acceptable results when compared to other existing MCDM methods. The incomplete information in determining the criteria weights has been effectively handled by the fuzzy BWM method. Furthermore, change in the preference criteria has shown a sensible change in obtaining an optimal alternative. This means that the criteria weights have a great impact in obtaining a feasible solution. The integration of both MARCOS-BWM method when compared to the VIKOR, MULTIMOORA, TOPSIS method proved to be more apt for the determination of suitable treatment technology. The objectivity of the decision problem is not considered in determining the criteria weights as the considered weighting method is purely based on the judgement of the decision-makers which would be a limitation to the proposed problem. From the sensitivity analysis, it is clear that the proposed methodology is dependable on the criteria weights. When dealing with complex decision making problems the restriction with membership and non-membership conditions may be difficult to deal at some circumstances. In the future, we may consider appropriately disposing of old PPE kits and other single-use medical equipment at a suitable location involving different forms of MCDM techniques in a sophisticated q-rung orthopair fuzzy approach. Furthermore, considering several factors in evaluating the problem would help in obtaining an efficient treatment technique. Author Contribution DK: conceptualization, data curation, methodology, processing and analysis of the data, simulation, writing. AA: writing—original draft, conceptualization, methodology, supervision. SN: data curation, methodology, processing and analysis of the data supervision, validation—reviewing original draft. MG: validation of data and final editing. AA: conceptualization, methodology, supervision, writing—review and editing. Funding This work was supported by Department of Mathematics, Bharathiar University, Coimbatore, India and National Research Foundation (NRF) of Korea grant funded by the Korean Government (MSIT) Grant NRF-2022R1C1C1006671. Data availability All the data and tools/models used for this work are publicly available. Declarations Ethics approval and consent to participate This manuscript does not involve researching about humans or animals. Consent for publication All of the authors consented to publish this manuscript. Conflict of interest The authors declare no competing interests. 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==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 35902520 21840 10.1007/s11356-022-21840-4 Research Article A hybrid model for robust design of sustainable closed-loop supply chain in lead-acid battery industry Ghalandari Mona [email protected] 1 http://orcid.org/0000-0003-3851-0746 Amirkhan Mohammad [email protected] [email protected] 1 Amoozad-Khalili Hossein [email protected] 2 1 grid.495571.b 0000 0004 0560 6095 Department of Industrial Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, Iran 2 grid.467532.1 0000 0004 4912 2930 Department of Industrial Engineering, Sari Branch, Islamic Azad University, Sari, Iran Responsible Editor: Philippe Garrigues 28 7 2022 2023 30 1 451476 13 4 2022 30 6 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Considering supply chain efficiency during the network design process significantly affect chain performance improvement. In this paper, the design process of a sustainable lead-acid battery supply chain network was addressed. Because the design of such networks always involves great computational complexity, in the present study, a two-stage model was proposed to overcome this issue. In the first stage, candidate sites of recycling centers were identified using data envelopment analysis (DEA) and based on their efficiency scores. Unlike the previous studies, not only economic criteria but also technical and geographical criteria were employed to select these locations. In the second stage, a bi-objective programming model was developed to simultaneously determine the tactical and strategic decisions of the chain. Since some data was subject to uncertainty, a robust possibilistic approach was presented. The model ensures that the resulting structure for the chain will be robust to noise and disturbance in parameters. A life cycle assessment model based on the ReCiPe 2008 method was developed in SimaPro software. To evaluate the applicability of the presented method, a case study in the automotive industry was used. The results of implementing the DEA method showed that from among 23 available locations, 11 potential places were selected for construct recycling centers. The final results showed that the inappropriate potential locations of recycling centers were eliminated, and the complexity of the mathematical model proposed in the second stage was reduced. The obtained results of environmental protection costs revealed that this criterion changed from 0 to 8,333,874,332. Moreover, the first objective function resulted in a centralized network to minimize costs, and in contrast, the second objective function tended to decentralize the network to minimize environmental impacts. Keywords Sustainability Closed-loop supply chain Life cycle evaluation Data envelopment analysis Robust possibilistic programming issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2023 ==== Body pmcIntroduction In recent years, increasing environmental pressures have considerably affected the structure of supply chains, so that ignoring these pressures, especially when designing the network, significantly reduces the chain’s performance. A forward supply chain starts with the suppliers of raw materials and continues to the customers at the end of the chain operation. However, products that do not meet customers’ basic needs are still valuable, and companies should consider this residual value of products as a principle after the reverse logistic process in the production and reconstruction, which may protect the environment (Khalili-Damghani et al. 2014; Karimi and Setak 2018). A closed-loop supply chain (CLSC) embraces reverse and forward logistic activities to better the stable performance of the supply chain (Amin et al. 2017; Shaharudin et al. 2019; Ghahremani-Nahr et al. 2019; Xiao et al. 2021). Coupling these two activities leads to an alteration from linear mode to the closed-loop in supply chain management (SCM). CLSC will be effective when an exchange between environmental protection and cost reduction is designed and implemented to help companies (Fahimnia et al. 2015). An effective CLSC also occurs when it considers economic objectives such as maximizing profits or minimizing costs of the entire chain over the product’s life cycle (Govindan and Soleimani 2017). Therefore, managers of organizations will grasp the concept that a properly designed CLSC can help companies increase customer service at a lower cost and decrease environmental impact (Boettke 2010). CLSC design, in conducted studies, is one of the main axes of research at the strategic level, which can be compared with decisions at the operational level such as energy conversion, saving equipment production, and long-term decisions on facility allocation and product distribution and will significantly improve environmental impact and cost control (Ho and Ma 2018; Prajapati et al. 2019). CLSC managers have to deal with many aspects such as facility allocation, product flow control, environmental protection, and cost reduction to design and optimize their chains (Bazan et al. 2017). One of the most motivating research topics in CLSC is carbon emission cap (for example, see Wang et al. 2019, 2021; Fahimnia and Jabbarzadeh 2016). Generally, carbon emission policy has been vital in the decision-making process of the supply chain managers due to government and environmentalists’ pressures. Therefore, when greenhouse gas emissions in the supply chain is more than expected, a penalty will be imposed on the organization in the form of cost per unit of excess greenhouse gas production, and conversely, if the amount of greenhouse gas emissions is less than expected, there will be a cost reduction per unit of saving on its production (Dogan and Turkekul 2016; Dogan and Seker 2016; Dogan and Inglesi-Lotz 2017; Alamroshan et al. 2022). Focusing on the arrival time factor of vehicles to relief stations, Alinaghian and Goli (2017) proposed a fuzzy mathematical programming model for the location of temporary health centers. They simultaneously considered the allocation and routing challenges of these centers and moreover introduced an improved harmony search (HS) algorithm to obtain optimal solutions. Shekarian (2020) reviewed the issues related to the CLSC models from the game theory perspective with focus on collaborative and non-collaborative approaches. Moreover, Master et al. (2020) provided a comprehensive review of the uncertain approaches used in CLSC. Lotfi et al. (2022) addressed implication of agility in CLSC. They provided an integrated model based on robust optimization (RO) and stochastic programming for designing a viable CLSC. A sustainable supply chain (SSC) is a considerable sign of competition among companies and helps them perform well by increasing environmental pressures (Fallahpour et al. 2021a, b). Recently, various research has been conducted on SSC. By applying a dynamic and non-cooperative game, Saberi (2018) designed a SSC network to deal with the pollution stock for the production and product shipment sections. This network was multi-period and optimized the economic and environmental objectives related to the network. Zarbakhshnia et al. (2019) sought to design and program a forward and reverse green logistics network employing a complex integer linear programming (LP) model. This model was associated with a multi-stage, multi-objective problem. The first objective minimized the operating, processes, transportation, and fixed costs. The other objective focused on minimizing CO2 emissions per gram, while in the third objective, they focused on optimizing the number of devices on the production line. To better analyze the introduced model, they employed a case study in the home appliance industry. Rentizelas et al. (2019) studied international biomass supply chain pathways by considering several criteria related to economic and environmental performance, such as environmental impact, biomass delivery cost, and fossil energy consumption. In their study, a sensitivity analysis was run to analyze the robustness of results under uncertain parameters. The data envelopment analysis (DEA) approach proposed in this research can allow biomass production resources planning and mend the decision-making beneath multiple decision criteria. Lahri et al. (2021) used a two-stage method to design the SSC network. They first used the TOPSIS and the Best–Worst methods to assess the weights of suppliers. Then, they employed a three-objective programming model for optimizing the economic, environmental, and social goals. Pahlevan et al. (2021) addressed the issue of sustainable CLSC design for the aluminum industry. For this purpose, the employed a mixed-integer linear programming (MILP) model. To obtain Pareto solutions, they utilized three different methods of which the multi-objective red deer algorithm worked best. The issue of CLSC sustainability has recently attracted the attention of some researchers. Zhen et al. (2019) proposed an integrated approach for establishing a sustainable and green CLSC network with uncertain demand. Proposing a model to address the total operating costs and CO2 emissions issues was their other action. They also adopted a scenario-based approach to show demand uncertainty in a randomized mathematical programming model and then presented a Lagrangian relaxation approach to solve the proposed model. Johari and Hosseini-Motlagh (2019) proposed an analytical coordination model for CLSC. Their model encompassed all three sustainability dimensions and aligned diverse decisions related to competitive forward and reverse logistics. Fazli-Khalaf et al. (2019) introduced a proper model to design a CLSC network by employing a reliability method. They declared that this model can design a network under various kinds of disruptions. The presented model can also find the network design’s minimum overall costs. Tosarkani and Amin (2019) designed a multi-echelon network for the lead-acid battery CLSC. To achieve this purpose, they employed fuzzy- and stochastic programming approaches to optimize the transportation cost among the various centers. They also examined and compared two different scenarios of transportation service fees. Yavari and Zaker (2020) studied the disruption of supply chains and power networks. They addressed a resilient-green CLSC and then used some resilient strategies to consider these disruptions. Their proposed model was a bi-objective mixed-integer one that minimized supply chain’s total costs and total carbon emissions. Sherif et al. (2020) presented a strategic approach to manage transportation costs and control CO2 emission in a battery manufacturing industry. To this end, they proposed a multi-echelon and multi-product model. To specify the potential location of centralized depots, they employed a K-means clustering algorithm. Nayeri et al. (2020) developed a sustainable CLSC under an uncertain environment by adopting fuzzy and robust approaches. For this purpose, they considered social, environmental, and economic criteria. Moreover, they utilized the goal programming technique to solve the proposed model. Using a non-cooperative game, Tang et al. (2020) present an analytical approach to improve the efficacy of the CLSC with remanufacturing. They consider a manufacturer and a retailer as the leader and the follower, respectively. They declared that the proposed approach is applicable in a competitive market. They utilized a fuzzy possibilistic flexible approach to overcome the uncertainties of parameters and constraints. Pourmehdi et al. (2020) presented a multi-objective scenario-based model to design a steel CLSC considering sustainability measures, which dealt with the current uncertainty of parameters through stochastic programming. In their model, the economic, environmental, and social parts were used to construct objective functions. Tao et al. (2020) introduced a stochastic model to evaluate the impact of various carbon policies in the sustainable CLSC of emerging markets. They employed the branch and bound-based integer L-shape technique to solve the model. De and Giri (2020) presented a multi-objective model to consider carbon control policies. Their model is a nonlinear mixed-integer programming one. Zahedi et al. (2021) developed a CLSC network with multi-mode transportation. They considered some clusters for customers. Their mathematical model was one-objective in which the net present value was maximized. Considering carbon emission and the uncertainty of demand, Soleimani et al. (2021) designed a carbon-efficient CLSC. They formulated a bi-objective mathematical model considering the economic and environmental effects and also used RO approach to cope with the existing uncertainty. Taking into account economic, environmental, and social features, Mirzagoltabar et al. (2021) addressed design of CLSC with fuzzy demand prices in the lighting industry. They announced that the chain is two-channel and can also be used for the new product development. Salehi-Amiri et al. (2021) designed a sustainable CLSC in the walnut industry. They provided a comprehensive overview of the issue and also formulated a MILP model to address forward and reverse logistics of the network. To obtain optimal solutions, they first employed the various exact and meta-heuristic methods and then used Taguchi technique to find the best optimal solutions. Considering the nature of expected movement and expected coverage, Keramatlou et al. (2021) addressed a green CLSC to determine the best allocation and location of facilities. Fazli-Khalaf et al. (2021) addressed a sustainable and resilient CLSC network in the tire industry. They introduced a four-objective model, including optimization of the costs, the network reliability, the total CO2 emissions, and the social responsibility. There are also recent works reported on CLSC, which considered a case in animal and agricultural areas like shrimp CLSC (Mosallanezhad et al., 2021), Sugarcane CLSC (Chouhan et al., 2021), Walnut CLSC (Salehi-Amiri et al., 2021), and Avocado CLSC (Salehi-Amiri et al., 2022). Considering the conditions and limitations of COVID-19, Babaee Tirkolaee et al. (2022) proposed a MILP model to design a sustainable mask CLSC Network. Since the presented model is NP-Hard, they used two meta-heuristic algorithms to solve it. Jauhari (2022) provided a mathematical model to address some fundamental decisions related to the inventory in CLSC by considering the carbon tax policy. For this end, he utilized the game theory approach with a manufacturer and some retailer. The research results showed that by focusing on the flexibility factor, not only emissions and energy consumption can be controlled, but also the total cost of the chain can be minimized. Seydanlou et al. (2022) presented a NP-Hard model for design of a CLSC network. To show the efficacy of developed model, they used a case study related to the olive industry. The model was multi-objective and included sustainability criteria and further, four meta-heuristic algorithms were employed to solve it. Tavana et al. (2022) developed a MILP model for designing a sustainable CLSC by considering cross-docking, time window, and location-inventory-routing. The model formulated in the uncertain condition; Hence, they applied a fuzzy approach to obtain the optimal solutions. Salehi-Amiri et al. (2022) proposed a MILP model for designing a CLSC network in the avocado industry. To validate the developed model, they performed various sensitive analyses on changing the demand, capacity, and transportation and purchasing costs per product. Asadi et al. (2022) addressed the CLSC design from the perspective of the economic, environmental, and responsiveness criteria. Since most was uncertain, they employed a robust possibilistic programming approach. They argued that environmental detrimental impacts are directly related to the size of demand but inversely related to the responsiveness level. Amirian et al. (2022) formulated a bi-objective fuzzy model for developing a green CLSC in the heavy tire industry. For this purpose, they utilized the economic pricing concept. Moreover, they employed the ε-constraint technique to determine Pareto optimal solutions. One of the weaknesses of past research is that the supply chain efficiency has not been considered while paying attention to this issue can have a meaningful impact on chain performance. Certainly, different methods have been proposed to evaluate the performance of the supply chain, but the evaluation of the chain performance during its design has received less attention from the researcher. Various methods and tools were exercised to assess and ameliorate the supply chain performance. DEA is one of the efficient tools in measuring supply chain performance. DEA is a mathematical programming approach used to compute the efficiency values of homogeneous decision-making units (DMUs) (Amirkhan et al. 2018a). One significant challenge of employing the conventional DEA approach to real-world problems is the uncertainty in some data of DMUs. The conventional DEA benefits from the production boundary generated by the crisp inputs and outputs of DMUs to evaluate efficiencies (Esfandiari et al. 2017; Toloo and Mensah 2019; Amirkhan et al. 2018b). Dehghani et al. (2018) focused on alternative, environmentally friendly energy sources. They introduced a two-stage method based on DEA and RO approaches to design a solar energy supply chain under uncertain conditions. The results of their study showed that all uncertainty parameters were sustainable. Finally, the performance of the developed method has been investigated by a real case study. Dobos and Vörösmarty (2019b), concerning the criteria of supplier selection obtained from the DEA method, ranked suppliers and then determined the best supplier according to the complicated aspects of purchasing and cost management, process management, and green management of suppliers. Dobos and Vörösmarty (2019a) used a DEA model to parameterize relevant data to select green suppliers. Using fuzzy DEA models, they solved the problem related to uncertain data using LP to select green suppliers. Torres-Ruiz and Ravindran (2019) employed interval DEA for sustainable supplier selection. To this end, they considered both economic and environmental criteria. Yokota and Kumano (2013) used the DEA method to determine an optimal allocation of mega‐solar in Japan. Solgi et al. (2019) employed a tailored DEA model to assess and rank the suppliers of complex product systems. Input and output data of the problem consisted of geographic, economic, and technical aspects. They utilized standard envelopment and inverted envelopment analysis to better estimate the efficiency frontiers. Rafigh et al. (2021) introduced a stochastic programming model to design a sustainable CLSC under the COVID-19 pandemic condition. They declared that the proposed model takes into account both tactical and strategic decisions. Due to the complexity of the model, they used a meta-heuristic algorithm to solve it. By employing a hybrid approach based on balanced scorecard, multi-criteria decision-making (MCDM), path analysis, and game theory. Goli and Mohammadi (2021) introduced an intelligent method to asset the performance of the petrochemical supply chain. Amirkhan (2021) formulated two robust DEA model to cope with the continuous uncertainty of input/output data. He claimed that the presented models can be used for single-stage networks under both constant and variable return to scale conditions. In addition to the topics discussed above, uncertain data is another major challenge in designing supply chain network (SCN), which can be attributed to the lack of crisp and accurate information and the dynamics and complexity of supply chain components. Therefore, besides considering environmental impacts and existing policies, an effective CLSC network design must respond to stochastic demand in uncertain markets. In recent years, the uncertain demand has also been considered in the CLSC studies (for example, see Vickers 2017; Chan et al. 2018). Tosarkani and Amin (2018) presented a fuzzy model to design a battery CLSC network. In the proposed model, uncertainty is observed in both parameters and variables. They used the analytic network process and the real data extracted from Google Maps to evaluate the qualitative factors and obtain the real transportation costs. Goli et al. (2019) utilized the Bertsimas and Sim’s (Bertsimas and Sim 2004) and Ben-Tal and Nemirovski’s (Ben-Tal and Nemirovski 2000) approaches to propose two robust portfolio optimization models with uncertain parameters. They used a real case study of dairy products to indicate the efficacy of the developed models. The statistical results showed that both robust models have acceptable performance for finding solutions, although the robust model based on the Bertsimas and Sim’s approach operates better in some cases. Goli and Malmir (2020) addressed and perused allocation and routing challenges for relief vehicles under disaster conditions. They employed a mathematical model with uncertain data and also used the HS algorithm to obtain the best solutions corresponding to the vehicle routing problem. Goli et al. (2021) introduced an integrated approach based on meta-heuristic algorithms and different varieties of neural networks to predict the dairy product demand. It should be noted that on the issue of green and sustainable CLSC, uncertainty in demand has been less discussed. Using a stochastic mixed-integer LP model, Guarnaschelli et al. (2020) developed a diary SCN. The network was two-stage and covered the production and distribution. Gholizadeh et al. (2020a, b) addressed the issue of sustainable logistics and procurement utilizing big data. Since some problem parameters were uncertain, they used a hybrid approach based on fuzzy set theory, RO, and stochastic programming. RO can be considered as an appropriate method to solve the uncertain problems of linear optimization. Although the robust approach is relatively widely used in recent studies, it has been shown that it is essential in many real-world applications. Since RO is employed to deal with the uncertainty of input presented in deterministic models, it is utilized as an alternative approach that immunizes uncertain parameters (Gholizadeh et al. 2020a, b; Solgi et al. 2021). Golpîra and Javanmardan (2021) employed a robust LP model to design a decentralized CLSC. This model was formulated as bi-level and under uncertain conditions. Moreover, the used robust approach was risk-based. Due to the adoption of new global regulations and laws on supply chains, environmental sustainability is vital for decision-makers. Moreover, the supply chain uses various carbon cap mechanisms to align the green supply chain and make it more complicated. This paper presents a comprehensive plan for the procurement of an environmentally friendly, sustainable CLSC of lead-acid battery.Small acid batteries are the first choice of health care in hospitals and nursing homes due to their low cost, reliability, and low maintenance requirements. Larger acid batteries are used as backup power sources in cellular towers, Internet hubs, banks, hospitals, airports, etc. Other common applications include starter batteries in motorcycles, on–off operation for micro-hybrid vehicles, and maritime transport. Due to the undeniable benefits of this type of batteries, their daily usage is increasing in the world. Figure 1 indicates the size market of this battery in USA1. Fig. 1 The US lead-acid battery market size, by product, 2016–2027 (USD billion). www.grandviewresearch.com For this purpose, a two-stage model is developed based on DEA and the bi-objective robust possibilistic programming (BRPP) model. In the first stage, candidate locations of recycling centers are identified by using DEA. The advantages of this stage include the following:Considering the economic, technical, and geographical criteria for selecting the proper potential locations of recycling centers, determining more reliable and practical locations of recycling centers Reducing the size of the problem by eliminating unsuitable potential locations of recycling centers helps reduce the mathematical model complexity used in the other stage. In the second stage, using the BRPP model, strategic, and tactical decisions in the battery supply chain are simultaneously determined. This model seeks to minimize sustainable procurement costs in the first objective and also minimize environmental considerations in the second objective. Meanwhile, the BRPP model ensures that the obtained supply chain structure will be sustainable to changes of the parameters, and the obtained solutions will not be sensitive to them. On the other hand, considering strategic and tactical decisions together obtains optimal solutions and reduces the cost of products. A life cycle assessment model based on the ReCiPe 2008 method has been developed in SimaPro software to quantify and assess environmental impacts. The augmented ε-constraint technique is employed to find strong and optimal Pareto solutions. Ultimately, to evaluate the effectiveness and applicability of the abovementioned approach, a case study related to the supply chain of lead-acid batteries in the automotive industry is used, and then significant management results are presented. Therefore, this paper seeks to design a sustainable CLSC network by employing the DEA and BRPP approaches adding uncertainty and studied lead-acid batteries in the automotive industry. In subsequent, there is a brief of innovations that recognize this research from others and can enrich the literature in this field:This study develops a sustainable CLSC network by using DEA and BRPP for lead-acid batteries in the automotive industry. Candidate sites of recycling centers are evaluated using the DEA technique. As a result, deferent criteria are employed to select the appropriate locations for recycling centers. The main achievements of this stage include the following: (1) considering technical, economic, and geographical criteria for selecting the proper potential locations of recycling centers; (2) determining the more prestigious and practical locations for recycling centers; and (3) reducing the size of the problem by eliminating inappropriate points for potential locations of recycling centers, which helps to decrease model’s complexity presented in the second stage. Using a BRPP model, strategic and tactical decisions in the lead-acid batteries supply chain are simultaneously determined. Furthermore, this model ensures that the resulting structure for the supply chain will be robust to noise and disturbance in parameters. On the other hand, simultaneous strategic and tactical decisions provide optimal global solutions to these decisions and reduce product costs. Finally, by using the simulation approach, the solutions are compared with the uncertain model. To develop a life cycle assessment model and to quantify and evaluate environmental impacts, the ReCiPe 2008 method in SimaPro software was employed. An augmented ε-constraint technique is utilized to solve the bi-objective model, determine strong Pareto solutions, and avoid weak Pareto solutions. Finally, a study on lead-acid batteries in the automotive industry was run to assess the effectiveness and efficacy of the presented method, and through it, the beneficiary results are presented. This paper is organized as follows. In the next section, the problem under consideration is defined. Section 3 describes the DEA model. A mathematical model is presented in Section 4. In Section 5, a robust approach is used. In Section 6, an approach including ReCiPe 2008 and the augmented ε-constraint technique is used to solve the models. A case study is presented in Section 7, and finally, in Section 8, the paper concludes and provides guidelines for future research. Problem definition The model developed in this paper is based on the lead-acid battery supply chain appropriate in the automotive industry. The importance of SSC design is that these batteries are composed of lead, propylene, and acid. The use of raw materials in such batteries makes this product dangerous under the Basel convention. Dead batteries are a dangerous source of metals that endanger human health, producing acidic and toxic substances. Note that recycling expired batteries reduces the environmental impact of wastes and helps conserve natural resources for future generations. In this regard, SCN design helps to conserve natural resources. Also, SCN design helps to manage expired products using reverse SCM and recycling. These can better the quality of human life in terms of environmental issues. Another critical point is that the processes performed at levels of SCN and the products transported among the various layers require fuel consumption, leading to greenhouse gas emissions. Therefore, SCN design minimizes environmental impacts and can reduce the destructive effects of environmental issues. Reducing air pollution and paying attention to environmental issues improves human health and increases customer loyalty due to the attention of the network. The green SCN consists of companies, distributors, and customers in the forward network. Moreover, this network consists of recycling and disposal centers in the reverse network. As shown in Fig. 2, companies supply raw materials from suppliers and recycling locations. In continue, the products are delivered to customers by distributors. In the reverse network, some of the expired products are gathered from collection centers. Reusable and the residual expired goods are respectively sent to related centers. In the recycling stations, battery raw materials are recycled and sent to companies. Distributors are assumed to be vulnerable.Fig. 2 The network structure of the desired supply chain The main assumptions of the considered network are as follows:The location and number of customers, disposal centers, supplier, and companies are pre-determined and fixed. The numbers of locations and capacity levels of possible distributors, collection, and recycling centers are considered variables of strategic decision. The numbers and locations the possible distributors are first determined in stage 1 by using the DEA method and then determined in stage 2 by employing the mathematical programming model and BRPP. Customer demands must be fully met, and shortages are not authorized. Pull and push mechanisms are respectively utilized to shape the operation base for forward and backward networks. Customer demand and costs are uncertain, and BRPP is used to address the uncertainty. Given the above, the primary aim of the presented model is to specify strategic and tactical decisions to minimize costs and adverse environmental effects. It should be noted that ReCiPe 2008 method has been utilized to measure environmental issues, which is discussed in Section 6. Research gap Recycling centers play a vital role in supply chain performance. Failure can entirely or partially destroy the recycling center’s capacity. A reliable or unreliable recycling center can be activated anywhere as a strategic decision to deal with the destructive effects of failure. Failure will not influence the reliable recycling center. Nonetheless, the fixed cost of secure centers is higher than unsecured ones. Failures and the effect of failures are modeled through a scenario planning model, and the pre-determined probability that marks the failure capacity of recycling centers are attributed to each scenario. According to the reviewed literature, it is observed that the models developed on the sustainable CLSC determine the location of recycling centers mainly based on economic and environmental criteria, and geographical criteria including distance to strategic and effective locations have received less attention. In addition, the computational complexity of locating facilities in the sustainable CLSC network, which has always reduced the efficiency of the models, has led researchers to take approaches to address this shortcoming. In the present study, by using the DEA method, not only the first shortcoming has been eliminated, but also the computational complexity has been reduced as much as possible. On the other hand, in such networks, the optimal solutions of the problem are very sensitive to the noise of data, and therefore, any disturbance in the input parameters of the problem may lead to a change in the optimal solutions and even the infeasibility of the model. To overcome this challenge, a robust possibilistic programming approach has been adopted. Methodology In this method, the two-stage model, shown in Fig. 3, is developed based on the DEA and BRPP approaches to design and plan the lead-acid battery SCN. In the first stage, using DEA, candidate locations for recycling centers are determined based on criteria. In the other stage, using the BRPP model, tactical, and strategic decisions of CLSC are simultaneously determined. The proposed model ensures that the resulting structure of the supply chain will be robust to changes in parameters and that the resulting responses will not be sensitive to these changes. On the other hand, considering strategic and tactical decisions together provides optimal solutions to these decisions and reduces chain costs.Fig. 3 The methodology used to design and program the supply chain network DEA approach The efficiency of a group of DMUs can be assessed by applying the DEA approach. The DEA model employed in this research has both desirable and undesirable outputs. In order to apply, the DEA model assumed that DMUj (j=1,....,n) is a candidate point for recycling centers. In the unified DEA (UDEA) model proposed by Babazadeh et al. (2017a, b), g indicates the desired outputs, and b specifies the undesirable ones. Moreover, s and h indicate their numbers. It is also assumed that these outputs are positive for all DMUs. Desirable outputs are denoted by the vector Gj=(g1j,g2j,....,gsj), and undesirable outputs are denoted by the vector Bj=(b1j,b2j,....,bhj). In the presented UDEA model, λjg and λjb are the structural variables, so that the former is used for desirable outputs and the latter is used for undesirable outputs. drg and drb are the surplus variables for the “rth” and the “fth” outputs, respectively. The subsequent model is solved n-1 times to specify the performance scores level of DMUs. 1 MaxZ=∑r=1sRrgdrg+∑f=1hRfbdfb 2 ∑j=1ngrjλjg-drg=grk,r=1,....,s 3 ∑j=1nλjg=1 4 ∑j=1nbfjλjb-dfb=bfk,f=1,....,h 5 ∑j=1nλjb=1 6 λjb,λjg,dfb,drg≥0 The convex combinations of desirable outputs are shown in Eqs. (2) and (3), on the other hand, Eqs. (4) and (5) are the convex combinations of undesirable outputs. Furthermore, Eq. (6) ensures that all variables of the model are non-negative. In Eq. (1), Rrg and Rfb indicate the limits of the above-mentioned UDEA model for the desired and undesired outputs, respectively. Rrg and Rfb can be determined as follows:7 Rrg=1(m+s+h)×[maxjgrj-minjgrj 8 Rfb=1(m+s+h)×[maxjbfj-minjbfj In Eqs. (7) and (8), m inputs are employed to produce g+s outputs. Since the whole data related to outputs are presented without any input, a dummy input should be provided for them. Because the different outputs of the UDEA model may not be on the same scale, the constraints of the model are used to control the surplus variables and the scales. Also, the “kth” efficiency score is calculated as (9):9 θ=1-(∑r=1sRrgdrg∗+∑f=1hRfbdfb∗) The “” sign indicates the optimal condition. It is worth noting that although there are other types of DEA models in which the outputs, instead of the surplus variables, are maximized. Although, the above model is based on a range-adjusted measure that uses non-radial and variable return to scale assumptions, and these conditions are more consistent with the real-life ones (Babazadeh et al. 2017a, b). Criteria Establishing the waste recycling station in urban areas due to its essential impacts on ecology, health, urban landscape, traffic, property value, and so on can disrupt the city system. Therefore, establishing a recyclable waste recycling site in the city should be done with careful and meticulous studies to prevent the spread of disturbances and threats, especially from environmental aspects. This section tries to identify the best and most suitable places to reduce problems and difficulties with precise locations. Due to the social consequences and consequently the creation of environmental and noise pollution, if established without study and the negative consequences they will bring, they will impose many costs on their owners by transferring to the wrong places. Therefore, before any activity, using the DEA technique and considering proper criteria, careful programming should formulate and implement the best strategy for the work process. Table 1 summarizes the criteria to select recycling centers.Table 1 Criteria for selecting candidate cities related to locate recycling centers Criterion Output type Distance from commercial and residential areas Undesirable Distance from urban thoroughfares Desirable Distance from river Undesirable Distance from hospitals and educational centers Undesirable Distance from hotels, banks, and organizations Undesirable Mathematical model This section deals with modeling the design and planning of the lead-acid battery supply network in uncertain conditions. After defining the sets, parameters, and decision variables, the model’s objective functions and constraints are described. Notations Here, the sets, parameters, and decision variables of the presented model are introduced. The parameters marked with a tilde are uncertain parameters. Sets I Set of companies. J Set of distribution centers. K Set of customers. L Set of collection centers. M Set of the recycling center. N Set of waste disposal. T Capacity level set of distribution centers. P Capacity level set of collection centers. O Capacity level set of recycling centers. S Set of scenarios. Parameters FJ~Ujt Fixed cost related to unreliable distribution station j with capacity of t. FJ~Rjt Fixed cost related to reliable distribution station j with capacity of t. FL~lp Fixed cost related to collection station l with capacity level of p. F~Mmo Fixed cost related to recycling station m with “o” capacity level. CI~i Fixed production cost per item in company of i. CI~Jij Cost of transferring each item from company i to distribution station j. CJ~j Processing cost per unit of the item in distribution station j. CJ~Kjk Cost of transferring each item from distribution station j to customer k. CK~Lkl Cost of transferring each item from customer k to collection station l. CL~l Processing cost per unit of the item in collection station l. CL~Nin Cost of transferring each item from collection station i to disposal station n. CL~Mlm Cost of transferring each item from collection station l to recycling station m. C~Nn Processing cost for each item in disposal station n. C~Mm Processing cost per unit of the item in recycling station m. CM~Imi Cost of transferring each item from recycling station m to customer i. θ Percentage of recycled items returned to collection stations. CS~Ii Cost of purchasing each item i from the supplier. Dks Customer demand k under scenario s. Rks The returned item amount from customer k under scenario s. UJjt Maximum capacity in distribution station j at level t. UMmo Maximum capacity of recycling station m at level o. ULlp Maximum capacity in collection station i at level p. UIi Maximum capacity of the company i. AJjs Percentage of capacity lost in distribution station j for scenario s. Qs Probability of occurrence of scenario s. Environmental parameters EJUjt Environmental impact related to the establishment of unreliable distribution station j with capacity level t. EJRjt Environmental impact related to the establishment of reliable distribution station j with capacity level t. ELlp Environmental impact related to the establishment of collection station l with capacity level p. EMmo Environmental impact related to the establishment of collection station m with capacity level o. EIi Environmental impact related to the production of each unit of the item in company i. EIJij Environmental impact of transferring each item-unit from company i to distributor j. EJj Environmental impact of processing each item on distributor j. EJKjk Environmental impact of transferring each item from distributor j to customer k. EKLkl Environmental impact of transferring each item from customer k to collection station i. ELl Environmental impact of processing each item in collection station l. ELNln Environmental impact of transferring each item from collection station l to disposal station n. ELMlm Environmental impact of transferring each item from collection station l to the recycling station m. ENn Environmental impact of processing each item in disposal station n. EMm Environmental impact of processing each item in recycling station m. EMImi Environmental impact of transferring each item from recycling station m to the company i. ESIi Environmental impact of procuring each item in company i. Decision variable YJUjt (Binary variable), 1, if an unreliable distributor with capacity level t is activated in site j, and 0, otherwise. YJRjt (Binary variable), 1, if a reliable distributor with capacity level t is activated in site j, and 0, otherwise. YLlp (Binary variable), 1, if a collection station with capacity level p is activated in site l, and 0, otherwise. YMmo (Binary variable), 1, if a recycling station with capacity level o is activated in site m, and 0, otherwise. XIijs Amount of items transferred from company i to distributor j under scenario s. XJjks Amount of items transferred from distributor j to customer k under scenario s. XKkls Amount of returned items from customer k to collection station l under scenario s. XLNins Amount of expired items transferred from collection station i to disposal center n under scenario s. XLMlm Amount of raw material of expired items transferred from collection station l to recycling station m. XMmis Amount of raw material of expired items transferred from recycling station m to company i under scenario s. XSis Amount of raw material purchased from the supplier by company i under scenario s. The model of the green SCN design is formulated as follows:10 MinZ1=∑j∑tFJ~UjtYJUjt+∑j∑tFJ~RjtYJRjt+∑l∑pFL~lpYLlp+∑m∑oF~MmoYMmo∑sQs∑i∑jCI~i+CI~JijXIijs+∑j∑kCJ~j+CI~KjkXJjks+∑k∑lCK~LklXKkls+∑l∑nCL~l+CL~NlnXLNlns+∑j∑kCL~l+CL~MlmXLMlms+∑l∑nC~NnXLNlns+∑m∑iC~Mm+CM~ImiXMmis 11 MinZ2=∑j∑tEJUjtYJUjt+∑j∑tEJRjtYJRjt+∑l∑pELlpYLlp+∑m∑oEMmoYMmo+∑sQs∑i∑jEIi+EIJijXIij+∑j∑kEJj+EJKjkXJjk+∑k∑lEKLklXKkl+∑l∑nELl+ELNlnXLNln+∑j∑kELl+ELMlmXLMlm+∑l∑nENnXLNln+∑m∑iEMm+EMImiXMmi+∑iESIiXSis Constraints 12 ∑jXJjks≥D~ks∀k,s 13 ∑lXKkls≥Rks∀k,s 14 ∑iXIijs=∑kXJjks∀j,s 15 ∑mXMmis+XSis=∑jXIijs∀i,s 16 θ∑kXKkls=∑mXLMlms∀l,s 17 1-θ∑kXKkls=∑mXLNlns∀l,s 18 ∑lXLMlms=∑iXMmis∀m,s 19 ∑lXLMlms≤∑oUMmoYMmo∀m,s 20 ∑kXKkls≤∑pULlpYLlp∀l,s 21 ∑jXIijs≤UIi∀i,s 22 ∑iXIijs≤∑tUJjtAJjsYJUjt+YJRjt∀j,s 22 ∑tYJUjt+YJRjt≤1∀j 23 ∑j∑tYJRjt≥1 24 ∑pYLlp≤1∀l 25 ∑oYMmo≤1∀m 26 YMmo,YLlp,YJUjt,YJRjt∈0,1 27 XIijs,XJjks,XKkls,XLNlns,XMmis,XLMlms,XSis≥0 The first objective function (Z1) minimizes the costs of the network. The first to fourth terms of Eq. (10) are respectively the location cost of potential facilities, including distributors, recycling, and collection stations. Average costs of processing and transportation between layers of the chain are minimize through other terms of Z1. Processing costs include production costs in manufacturing companies, maintenance costs in distributors, collecting products and checking for expired in collection centers, costs of extracting raw materials in recycling centers, and safe disposal costs in disposal centers. Transportation costs include the cost of transporting products between successive layers of the chain. The second objective function (Z2) is related to the design of the SSC network. In other words, this objective function minimizes the emission of harmful CO2 gases resulting from the various activities of SCN. The first to fourth terms of Z2 are related to greenhouse gas emissions for the location of companies, recycling centers, and recycling centers. The remaining expressions of greenhouse gases are related to the production of final products, storage of products in distributors, collecting products and checking them in collection centers, and recycling of expired products. Constraint (12) ensures that a shortage in the form of backorder is impossible, and demand must be met. Constraint (13) guarantees that all expired products are collected from customers. As a result, expired products must be collected through collection centers to reduce the adverse effects of wastes. Constraint (14) assures that the balance between total inputs of each distributor’s final products and the final products delivered to the customers. Constraint (15) guarantees that each company’s production and transferring of products to various distributors should match the raw materials supplied by recycling stations and suppliers. Constraint (16) ensures that the amounts of raw materials recycled and transported to various companies must be equal to the amounts of expired products collected. Constraint (17) imposes that the total amount of unused products transported to disposal stations must be equal to the amounts of products collected from customers. Constraint (18) ensures the equality between amounts of recyclable products of collection centers and sent raw materials by each recycling station to companies. Generally, constraints (14) to (18) consider the equilibrium flow between the supply chain layers. Constraint (19) guarantees that the amounts of final products produced in production stations and transferred to distributors is limited by maximum capacity of each company and must be less than or equal to it. Constraint (20) imposes the capacity limitation on the amount of stored and transported products by considering the activation of distributors. Constraint (21) assures that the maximum capacity of collection and inspection in each collection station must be greater than or equal to the maximum amounts of expired products collected from various customers. Constraint (22) guarantees that recycled and transferred raw materials from recycling stations must not be greater than the recycling capacity of each recycling station and at maximum can be equal to it. It is necessary to noted that capacity constraints on facilities are included in constraints (19) to (21). These constraints prevent flows from being allocated to inactive facilities. Constraints (25) and (26) ensure that only one capacity level should be defined for each company, recycling, or collection center. Note that constraint (23) guarantees that only one facility at a distributor location can be activated. Constraints (27) and (28) show the problem decision variables. Robust possibilistic programming In some factual conditions, there may not be enough historical data. In other words, it is not easy to specify the distribution functions of uncertain data. Epistemic uncertainty is a challenge that parameters might face under such conditions. In this condition, fuzzy mathematical programming is an efficient approach. Flexible and possibilistic programming are two general categories of this approach. The first-class method controls the elasticity of the objective function values and the flexibility of the constraints (Inuiguchi and Ramık 2000). The second-class method considers the ambiguity in objective functions and constraint parameters, which is usually modeled according to the decision maker’s mental data with possibilistic distributions. Due to the existence of imprecise parameters in the model presented in subsection 4–1, the second-class method is appropriate. It is vital to consider changes in parameters over a long period to prepare and enable a robust structure for the supply chain and make less sensitive decisions again changes; this causes us to seek a robust and feasible solution under diverse requirements (model robust). Moreover, this solution is close to the optimality (solution robust). To attain the properties and the profits of both the fuzzy set theory and robust optimization, Pishvaee et al. (2012) introduced a robust possibilistic programming (RPP) method. RPP is based on possibilistic chance-constrained programming (PCCP). In this method, the possibility of trapezoidal distribution (as addressed in Fig. 4) is used for uncertain parameters.Fig. 4 Trapezoidal possibilistic distribution for fuzzy parameter of ξ In the presented method, both robustness of feasibility and optimality and the average value of the objective function is possible. Here, the details of RPP are explained. First, observe the following model:28 Minw=g~y+q~x 29 D1x≤U~z 30 D2x≤0 31 D3y=1 32 y∈0,1,x≥0&Integer where g~ is the objective function coefficient and related to binary variables. Another objective function coefficient is q~, which is applied for continuous variables. U~ is related to the right-hand side coefficients in a constraint that is deterministic. Also, D1, D2, and D3 are matrix coefficients. In the above model, it is assumed that g~, q~, and U~ have epistemological uncertainty. The necessity measure, as a conservative fuzzy one that is very close to the deterministic condition, is applied to formulate the chance constraints of ambiguous parameters. The average value agent (E[.]) is employed to formulate the possibilistic counterpart of the objective function. According to the explanations provided, the PCCP model is presented as follows:33 MinE[w]=E[g~]y+E[q~]x 34 D1x≤0 35 NecD2x≤U~y≥α 36 D3y=1 37 y∈0,1,x≥0&Integer where α is the lowest confidence level of chance constraint. The robust counterpart of the model presented above is as (Dubois and Prade 2015; Inuiguchi and Ramık, 2000):38 MinE[w]=g1+g2+g3+g44y+q1+q2+q3+q44x 39 D1x≤0 40 D2x≤[αU1+1-αU2]y 41 D3y=1 42 y∈0,1,x≥0&Integer Considering the above model, the robust chance-constrained programming one can be written as follows:43 Minobj=E[w]+γ(wmax-wmin)+δ[αU1+1-αU2-U1]y 44 D1x≤0 45 D2x≤[αU1+1-αU2]y 46 D3y=1 47 y∈0,1,x≥0&Integer0.5<α≤1 Similar to the PCCP model, the first term of the objective function represents the meanw. This part assesses the mean value of the total system performance. The second term of the objective function, i.e., γ(wmax-wmin), displays the difference between the two boundary amounts ofw. Here, wmax and wmin are calculated as (48–49):48 wmax=g4y+q4x 49 wmin=g1y+q1x Also, γ shows the influence of this term relative to other ones of the objective function. This part intends to meter the optimal sustainability of solution space. Another term of the objective function, i.e., δ[αU1+1-αU2-U1],, represents the feasible penalty function utilized to forfeit a transgression of a constraint. [αU1+1-αU2-U1] is equal to the difference between the worst amount of the parameter and the amount employed in the chance constraint. Moreover, δ represents the weight of this part. Unlike the PCCP model, the possibilistic constraint confidence level (i.e., α) is a decision-making variable, and the optimization model must determine its value. Hence, this model prevents subjective judgments about α and determine its overall optimal value. Considering the explanations provided, different parts of objective function involve (1) average performance, (2) optimality robustness, and (3) feasibility robustness. Now, the counterpart deterministic model of the one described in Section 4.1 is formulated as follows:50 MinZ1=∑j∑tFJUjt1+FJUjt2+FJUjt3+FJUjt44YJUjt+∑j∑tFJRjt1+FJRjt2+FJRjt3+FJRjt34YJRjt+∑m∑oFMmo1+FMmo2+FMmo3+FMmo44YMmo+∑sQs∑i∑jCIi1+CIJij1+CIi2+CIJij2+CIi3+CIJij3+CIi4+CIJij44XIijs+∑j∑kCJj1+CIKjk1+CJj2+CIKjk12+CJj3+CIKjk3+CJj4+CIKjk44XJjks+∑k∑lCKLkl1+CKLkl2+CKLkl3+CKLkl44XKkls+∑l∑nCLl1+CLNln1+CLl2+CLNln2+CLln3+CLNln3+CLln4+CLNln44XLNlns+∑j∑kCJj1+CLMlm1+CJj2+CLMlm2+CJj3+CLMlm3+CJj4+CLMlm44XLMlms+∑l∑nCNn1+CNn2+CNn3+CNn44XLNlns+∑iCSIi1+CSIi2+CSIi3+CSIi44XSis+γ∑j∑tFJUjt4-FJUjt1YJUjt+∑j∑tFJRjt4-FJRjt1YJRjt∑i∑jCIi4+CIJij4-CIi1-CIJij1XIijs+∑j∑kCJj4+CIKjk4-CJj1-CIKjk1XJjks+∑k∑lCKLkl4-CKLkl1XKkls+∑l∑nCLl4+CLNln4-CLl1-CLNln1XLNlns+∑j∑kCLl4+CLMlm4-CLl1-CLMlm1XLMlms+∑l∑nCNn4-CNn1XLNlns+∑m∑iCMm4+CMImi4-CMm1-CMImi1XMmis+∑iCSIi4-CSIi1XSis+φDk4s-αDk3s+1-αDk4s 51 MinZ2=∑j∑tEJUjtYJUjt+∑j∑tEJRjtYJRjt+∑l∑pELlpYLlp+∑m∑oEMmoYMmo∑i∑jEIi+EIJijXIij+∑j∑kEJj+EJKjkXJjk+∑k∑lEKLklXKkl+∑l∑nELl+ELNlnXLNln+∑j∑kELl+ELMlmXLMlm+∑l∑nENnXLNln+∑m∑iEMm+EMImiXMmi+∑iESIiXSis Constraints: 52 ∑jXJjks≥αDk3s+1-αDk4s∀k,s 53 ∑lXKkls≥Rks 54 ∑iXIijs=∑kXJjks∀j,s 55 ∑mXMmis+XSis=∑jXIijs∀i,s 56 θ∑kXKkls=∑mXLMlms∀l,s 57 1-θ∑kXKkls=∑mXLNlns∀l,s 58 ∑lXLMlms=∑iXMmis∀m,s 59 ∑lXLMlms≤∑oUMmoYMmo∀m,s 60 ∑kXKkls≤∑pULlpYLlp∀l,s 61 ∑jXIijs≤UIi∀i,s 62 ∑iXIijs≤∑tUJjtAJjsYJUjt+YJRjt∀j,s 63 ∑tYJUjt+YJRjt≤1∀j 64 ∑j∑tYJRjt≥1 65 ∑pYLlp≤1∀l 66 ∑oYMmo≤1∀m 67 YMmo,YLlp,YJUjt,YJRjt∈0,1 68 XIijs,XJjks,XKkls,XLNlns,XMmis,XLMlms,XSis≥0 Method of solving the developed model There are several problems that the mathematical model presented in the previous section faces, which are mentioned below: (1) quantifying environmental parameters to solve the model is necessary. Accordingly, a lifecycle-based approach should be used to quantify environmental parameters, (2) some parameters in the equations of the proposed model have uncertainties, so a suitable method for linearizing these equations is needed to convert it to the equivalent form of crisp, and (3) the proposed model considers not only the economic objective function but also the environmental objective function. This process leads to a bi-objective function problem, which requires a multi-objective functions method to find Pareto solutions. Therefore, a solution method consisting of five steps is presented that can solve the problems mentioned above. The steps of this solution approach are described in Fig. 5.Fig. 5 The method used to solve the proposed model Environmental impact analysis To move towards a sustainable environmental design of the lead-acid battery SCN, evaluating environmental effects of all upstream and downstream supply chain processes is a prerequisite (Mavrotas 2009a). Indeed, the life cycle evaluation is the most comprehensive framework for assessing these impacts. This evaluation is performed by recognizing materials, energy, and waste entering the environment (Desideri et al. 2012). This method complies with ISO14040 and ISO14044 standards and, as shown in Fig. 6, and has four steps: (1) defining the purpose and scope, (2) life cycle inventory analysis, (3) evaluating the life cycle impacts, and (4) analysis and interpretation (Peng et al. 2013). As direct use of life cycle assessment is very time-consuming, complex, and costly, ReCiPe 2008, available in SimaPro software, has been used. This software is a comprehensive and all-inclusive one with the latest database and scientific tools for collecting and analyzing environmental impacts and calculating these parameters for various products and services in industries (Rai et al. 2011).Fig. 6 Structure of life cycle assessment method The second advantage is the inclusion of recent advances in environmental sciences due to the up-to-date method. The third advantage of this method is assessing environmental impacts, using mid- and endpoint impacts. Finally, this method’s fourth advantage is an extensive evaluation method that usually considers most mid- and endpoint impacts (Pishvaee et al. 2014). ReCiPe 2008 shifts the effects of hazardous material extraction and emissions into 18 midpoints, in the first place. Then, the results are summarized in the three endpoints, including (1) ecosystem variety, (2) human health, and (3) resource accessibility. Finally, the results are presented as a single score by the weighting approach. ReCiPe 2008 has three different approaches regarding the diverse cultural perspectives, and the “average” version is commonly used. In this version, the value of 20, 40, and 40% is considered for resource availability, ecosystem diversity, and human health, respectively. Note that the final score has no weight. However, the dimension of this score is denoted by “point” (pt) (Pishvaee et al., 2014; Babazadeh et al. 2017a, b); Boons et al. 2011). In the next step, to improve the environmental aspects and economics and study the life cycle evaluation, the results obtained from the effect evaluation of each section in the mathematical model are used. For more information, interested parties can refer to the book of Goedkoop et al. (2009). Augmented ɛ-constraint method In models with multi-objective functions, it is almost impossible to reach a solution that optimizes all functions simultaneously. In such matters, Pareto solutions are considered. In other words, solutions that do not improve any objective functions without at least one objective function worsen. Generally, the existing methods for solving multi-objective problems can be classified into three groups (Hwang and Masud 2012): (1) priori, (2) interactive, and (3) posteriori. In the first class, the weights of the functions must be specified prior to the solution process, which is an effortful duty (Mavrotas 2009b). In interactive approaches, the decision-maker interactively and gradually reaches the acceptable solutions (Chowdhury and Quaddus 2015). Being unable to provide a picture of Pareto solutions, focusing only on the decision maker’s desired solutions, and neglecting the rest of the efficient solutions, is the most obvious weakness of these approaches. In the latter methods, a set of Pareto solutions is identified first, and then, other solutions will be generated if these solutions are not attractive to him. The latter methods, while not computationally reasonable, obtain efficient solutions from the entire Pareto set. All the methods are widely used in multi-objective problem solving, but the obvious problem is that these methods must find efficient solutions and not provide weak Pareto solutions (Ehrgott 2005). The augmented ɛ-constraint method is a powerful and efficient posterior method applied to find optimal Pareto solutions for multi-objective problems. Here, one objective is optimized, and the others are added to the problem as constraints as shown (69) (Görmez et al. 2011; Vahidi et al. 2018):69 minf1(x)S.t.fp(x)≤εp;∀p=2,...,Px∈X where x is the vector of the decision variables, X is the feasible space, and fi(x) is also the objective function that must be minimized. By parametric changing to the right of the functions in the constraint (i.e., εp;∀p=2,...,P), Pareto solutions are obtained. To determine the possible values for the ε vector, the pay-off table must first be constructed by optimizing the P − 1 objective functions individually, i.e., those that are in the constrain, to specify the values range of the objectives in the constraints. Then, the obtained range is divided into np intervals as follows (Esmaili et al. 2011):70 rangep=fpmax-fpmin;εpl=fpmax-rangepnp×l;∀p≠1,l=0,1,np-1 where fpmax and fpmin are the maximum and minimum values of the “p” objective. However, as Mavrotas (Mavrotas, 2009a) points out, the general form of the ɛ-constraint method does not guarantee an efficient solution for ε. To prevent this defect, a developed form of this technique, called the augmented ɛ-constraint, is used. By applying this method to the problem of several objective functions (i.e., minimizing P objective functions, simultaneously), the following model is obtained:71 minθ1f1(x)-range1×δ×θ2sl2range2+θ3sl3range3+...+θpslprangep+...+θpslPrangePS.t.fp(x)+slP=εp,∀p=2,...,Px∈X,slP∈R+ where δ is a very small number, θp is the priority value of the pth target function, and slp is the amount of the corresponding constraint deficiency variable. Note that the complementary expression θpslprangep guarantees that only an efficient solution is obtained for the vector ε. Case study An acid (or lead-acid) battery is a kind of reloadable battery invented in 1859 by the French physicist Gaston Plante. This battery is used in motor vehicles due to its low cost and high supply, despite its low energy storage and weight and volume. A lead-acid battery structure is a combination of chemicals, electrical components, retainers, and mechanical formers. Generally, the acid battery consists of 4 general parts: (1) anode, (2) cathode, (3) electrolyte, and (4) separator. A positive electrode or plate is also called an anode; this pole or plates absorbs electrons during discharge. In lead-acid batteries, the chemical raw material that makes up positive plates is the “lead oxide (PbO2)”. The negative electrode or plate is called a cathode, in which electrons are released during discharge. The main chemical component of negative electrodes is lead (Pb). It must be said that lead or its oxides are not mechanically suitable for forming and are often shaped by the addition of various alloys and retaining networks. They are also called Active Materials. This is because the chemical reaction inside the battery is mainly done with lead and oxide. The electrolyte fills the electrodes’ surroundings and provides a bed for the charge to pass through the positive and negative electrodes. In these batteries, both poles are immersed in a 25 to 40% concentration of sulfuric acid (H2SO4) and approximately 60 to 75% water (H2O). The composition of water and sulfuric acid causes sulfuric acid to ionize to H+ and HSO4- ions. Separators are the other part of these batteries. Their main function is to isolate the positive and negative poles from each other electrically. The portion of the technology for making the batteries is related to the design of these electromechanical insulators. In some species of these batteries that do not have the size limit, this isolation is made by creating a physical distance between the electrodes, making the battery cheaper but increasing its volume. The main advantage of these batteries over others is the relatively low price of this type of battery and their high instantaneous current capability, making lead-acid batteries the best choice for various uses such as cars and ships. Of course, along with this advantage, we should also mention the main weakness of the lead-acid battery:High weight and volume Higher sensitivity and instability of lead-acid batteries than nickel–cadmium batteries in cases where the battery is fully discharged. In this study, a sustainable CLSC will be designed for lead-acid batteries, which, in addition to economic issues, also considers environmental issues. It should be noted that considering CLSC for lead-acid batteries has two critical advantages: (1) assistance to the environmental aspects of the network and the use of raw material recycling and (2) cost savings by using recycled materials in SCN. Computational results DEA results To locate recycling centers, 23 potential locations were considered. They were evaluated using the DEA method described in the previous chapter and based on the criteria of distance from residential and commercial areas, distance from urban thoroughfares, distance from the river, distance from hospitals and educational centers, and distance from hotels, banks, and offices. The mathematical model is encoded in GAMS software, and the Cplex solver is used to obtain optimal solutions. All experiments were performed using a PC with specifications of Intel Core i5 CPU, 2.5 GHz, and 4 GB of RAM. The results obtained in this regard are given in Table 2. The obtained scores were used to filter suitable locations for recycling centers. From a managerial point of view, decision-making units (DMUs) with a score of more than 0.8 are determined as potential locations for the construction of recycling centers. Therefore, according to Table 2, 11 places, including 17, 11, 21, 15, 12, 9, 4, 2, 3, 5, and 10 places, will be selected as candidate places to construct recycling centers. The solutions obtained from the DEA model will be used in the next step in the presented model to determine the exact options among recycling centers. Potential locations are first selected based on a series of criteria and then used in the mathematical model. There are two main advantages of using the DEA model to select locations: (1) better and more suitable locations are selected for recycling centers and (2) the mathematical model intricacy is avoided due to a large number of potential locations.Table 2 Results of DEA model DMUs Efficiency Rank DMU01 0.77 12 DMU02 0.86 9 DMU03 0.85 10 DMU04 0.89 8 DMU05 0.82 11 DMU06 0.73 17 DMU07 0.92 6 DMU08 0.7 22 DMU09 0.99 6 DMU10 0.93 20 DMU11 0.71 2 DMU12 0.67 5 DMU13 0.94 19 DMU14 0.77 22 DMU15 1 4 DMU16 0.77 12 DMU17 1 1 DMU18 0.77 12 DMU19 0.76 15 DMU20 0.72 18 DMU21 0.96 3 DMU22 0.69 21 DMU23 0.76 15 Mathematical model solving This section uses various numerical experiments and presents the results related to the mathematical model. In the proposed model, eight potential locations are considered for distributors. Also, the network in question consists of 12 customers whose location is fixed, as previously explained. Expired products are also collected from 7 collection centers. The number and location of the collection centers are not pre-determined, and each has two levels of capacity. A percentage of the collected products is transferred to recycling centers, and the remaining percentage is transferred to 5 disposal centers. The number and centers of disposal are fixed and pre-determined, and also, each center has two levels of capacity. A percentage of the collected products is transferred to recycling centers, and the remaining percentage is transferred to 5 disposal centers. The number and centers of disposal are fixed and pre-determined. As the results of the DEA show, the potential recycling site includes 11 locations. More precisely, places 17, 11, 21, 15, 12, 9, 4, 2, 3, 5, and 10 are selected as candidate places to construct recycling centers. For each recycling center, two levels of potential capacity are considered. Note that a supplier determines the raw materials needed by companies. Table 3 shows the demand of 11 customers. As can be seen, demands are uncertain and considered as trapezoidal numbers.Table 3 Demand considered in the mathematical model Customer Demand 1 (150,300,450,600) 2 (150,300,450,600) 3 (300,600,900,1200) 4 (175,350,525,700) 5 (200,400,600,800) 6 (175,350,525,700) 7 (100,200,300,400) 8 (325,650,975,1300) 9 (250,500,750,1000) 10 (200,400,600,800) 11 (200,400,600,800) 12 (250,500,750,1000) Economic and environmental objective functions were considered in the primary objective function and a constraint in the augmented ɛ-constraint technique. Afterward, the problem of multi-objective functions for various values of vector ɛ is solved, and the computational results are shown in Table 4.Table 4 Computational results for different values of vector ɛ Number Objective function values Active facilities number Total established facilities number Environmental protection costs Economic objective function (million Rials) Environmental objective function (million pt) Recycling centers Reliable distributors Unreliable distributors 1 8,362,140,000 5023.36 4 4 4 12 8,333,874,332 2 2,501,892,000 5072.77 4 4 4 12 2,473,626,332 3 1,260,498,000 5122.18 4 4 4 12 1,232,232,332 4 542,499,000 5171.59 4 4 4 12 514,233,332 5 147,700,200 5221 4 4 4 12 119,434,532 6 28,271,049 5270.41 3 3 3 9 5381 7 28,266,507 5319.82 3 3 3 9 839 8 28,265,668 5369.23 3 3 3 9 0 What is clear from Table 4 is that the economic and environmental objective functions conflict. It means that when one objective function gets better, the other gets worse values. The decision-maker can choose the most desirable solutions in the optimal set of Pareto solutions. To achieve this goal, various efficient solutions are first generated from the Pareto set of solutions. Then, if the decision-maker does not accept the obtained solutions, the vector ε is corrected. This process is done repeatedly until the most desirable solution is obtained (Pishvaee et al. 2012; Mohseni and Pishvaee 2016). Note that budget constraints and environmental regulations can influence the choice of a decision-maker. Based on the conflict between the objective functions, it can be seen that companies have to pay additional costs for environmental issues. Hence, an indicator called “environmental protection cost” is defined, presented in the eighth column of Table 4. For each efficient solution of the optimal Pareto set, this index is determined by subtracting the economic objective from the best quantity of this function. The cost of environmental protection is vital in two ways. First, firms and managers can use this index to indicate and prove their efforts to improve environmental issues to their stakeholders (for example, government, customers, and local communities). Second, the government can consider this indicator for corporate incentive policies. Moreover, the results display that the first objective function results in a centralized SCN to minimize costs. Rather, the second function tends to decentralize the network to minimize environmental impacts. Figure 7 shows the number of different facilities concerning the importance of optimality robustness. As clear, the number of facilities increases as the importance of optimality robustness increases. Increasing the number of facilities reduces the difference between two endpoints in the objective function and provides a solution close to the optimal one under different quantities of uncertain parameters.Fig. 7 Relation between the importance of optimality robustness and the number of activated facilities Figure 8 shows the percentage of discarded and recycled products for different values of the second function. Based on Fig. 8, when the amount of the second objective function takes on worse values, the disposal rate of expired products increases. This means that if more products are disposed, it will have adverse environmental effects.Fig. 8 The ratio of the second objective function value to the percentage of expired products A comparison between solutions of the deterministic and robust model to evaluate the robustness and utility of solutions corresponding with the robust model. For this purpose, the approach shown in Fig. 9 was designed. As shown in Fig. 9, both deterministic and robust solutions are extracted separately from the models. Moreover, for each uncertain parameter, a random number is generated. Note that to obtain each simulated areal parameter, it is generated as follows:72 areal=ξ1+ξ42+ηrealξ4-ξ1+ξ42j∈Ji Fig. 9 The approach employed to validate the robust model In Eq. (72), η is the maximum level of disruption. Note that the η will be selected between the -1,1 interval. In the next step, the solutions are obtained and the then, the parameters are placed in the simulation model. The compressed formulation of the simulation model is as follows:73 MinObj1=grealx∗+crealy∗+∑iϑRiObj2≤εAy∗+Ri≥Dreal,i∀iBy∗≤0Dy∗=0Ny∗≤Ex∗Ri≥0∀i The greal, creal, and Dreal vectors are related to the simulated values of construction costs, variable costs, and electricity demand, respectively. Obj2 and ε also denote the environmental objective function and the vector ε. The vectors x and y* correspond to the binary and continuous variables obtained from the fixed and definite models, respectively. The matrices A, B, D, and N also represent the technical coefficients of the constraints. Moreover, Ri is a decision-making variable that measures the amount of constraint violation, and ϑ indicates the amount of the fine. The simulation process is performed alternately for a certain number of repetitions. Figure 10 indicates the results pertained to the mean and standard deviation of the objective function values for the performed simulations.Fig. 10 Comparison of solutions related to deterministic model and robust model According to the results of Fig. 10, it can be said that when the maximum level of noise is small, the deterministic model and the sustainable model have the same performance. The superiority of the sustainable model raises with the increase in the maximum noise level. The results also show that according to the standard deviation, the sustainable model has a decisive advantage over the deterministic model. This remarkable result is very valuable in SCM. Conclusion, managerial insights, and future scopes With the expansion and intensification of the competitive environment these days, SCM has become one of the fundamental problems facing businesses. In the current paper, a multi-objective model for the sustainable CLSC network design was developed. Also, the performance and efficacy of the presented model were measured using a case study of lead-acid batteries in the automotive industry. The model was two-stage and utilized DEA and RPP approaches. In the first stage, the candidate locations of recycling centers were determined using the DEA method. From the perspective of managers, this stage had two advantages:The more credible and functional locations for recycling centers were obtained. The complexity and the size of the mathematical model used in the second stage were reduced by eliminating the inappropriate points of potential locations of recycling centers. Strategic and tactical decisions in the lead-acid battery CLSC were simultaneously determined in the second stage using the BRPP model. On the other hand, considering strategic and tactical decisions simultaneously resulted in optimal global solutions for these decisions. The proposed model ensured that the resulting configurations for the supply chain would be robust to any parameter noise. In the following, the problem of sustainable CLSC design and the assumptions considered in modeling were defined. Then, the methodology of the problem was described, and the DEA approach and the required proper criteria for selecting the potential recycling centers were mentioned. Afterward, the mathematical model of the defined problem, including sets, parameters, decision variables, objective functions, and constraints were explained. Because of the uncertainty of the parameters related to demand and costs, the fuzzy sustainable optimization method was employed. Also, an approach based on life cycle evaluation was used to determine the number of environmental parameters and their quantification. Finally, the augmented ε-constraint technique was explained to solve the multi-objective model of the problem. Then, selection between the candidate cities to locate recycling centers is done using DEA and based on definition criteria. Then, the mathematical model was coded in GAMS software, and meaningful results were obtained. The final solutions showed that the first objective function results in a centralized SCN to minimize costs. The other objective function tended to decentralize the network to minimize environmental impacts. Also, the number of different facilities based on the importance of optimality robustness was shown. The results of this research indicated that the number of facilities increases as the importance of optimal sustainability increases. Moreover, increasing the number of facilities reduces the difference between the two endpoints in the objective function and provides a solution for the model that is close to the optimal solution under different values of uncertain parameters. One of the significant results was that the economic and environmental objective functions are in contradiction, which means that when one objective function gets better, the other gets worse values. The decision-maker can choose the most desirable solution in the Pareto optimal set of solutions. Also, the number of different facilities based on the importance of optimality robustness was shown. Disposal percentage of expired products enhanced with taking worse values by the second objective function, which means that if there will be more disposed products, it will have adverse environmental effects. Also, to assess the robustness and desirability of solutions obtained from the robust model, these solutions compared to the ones obtained from the deterministic model. The results from a managerial perspective showed that when the maximum disturbance level is small, the deterministic and robust models have the same performance. Moreover, when the maximum level of noise increases, the superiority of the sustainability model increases. Based on the standard deviation results, the robust model has a considerable and decisive superiority over the deterministic model. It should be noted that these results are precious in SCM. The research findings indicate that the selection of potential locations of distribution centers before optimizing the whole chain plays a significant role in the chain performance and also reduces computational complexity. It is recommended that this procedure be developed to determine the potential locations of other centers. In addition, appropriate and effective criteria should be used to determine the potential locations of each center to improve the whole performance network. Lack of access to information about the transportation of materials and goods among different centers, uncertainty in the quality status of returned products, and lack of coordination within and among of facilities are the main limitations of this research. This study presented a new work in a sustainable CLSC network design. For future research, the researcher can focus on the following cases:In the present study, the economic, technical, and geographical criteria were used to determine the potential location of distributors. For future research, depending on the circumstances of the problem, other criteria such as time, reliability, quality, etc., can be employed. The development of sustainable optimization models representing the conflict of interest among the various facilities of the acid battery supply chain network can be considered by researchers for future research. Another valuable future research direction is to utilize the resilience strategies in the model. Pricing strategy of sustainable CLSC can be considered by researchers for future research. In this study, the different modes of transportation of materials and goods among various centers were not considered. For future research, addressing this issue will yield significant results. Finally, risk measures can be incorporate in the sustainable CLSC of lead-acid battery. Authors’ contributions Mona Ghalandari: Conceptualization, methodology, software, validation, original draft preparation, and visualization. Mohammad Amirkhan: Supervision, investigation, validation, and writing-reviewing and editing. Hossein Amoozad-Khalili: Conceptualization, methodology, writing-original draft preparation, and visualization. The authors read and approved the final manuscript. Funding No funding was received. Data availability Not applicable. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Conflict of interest The authors declare that they have no conflict of interest. 1 Lead Acid Battery Market Size, Share & Trends Analysis Report, By Product (SLI, Stationary, Motive), By Construction Method (Flooded, VRLA), By Application, By Region, And Segment Forecasts, 2020 – 2027. 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