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Early versus late nephrology referral and patient outcomes in chronic kidney disease: an updated systematic review and meta-analysis | 9c240f00-aa76-4c9a-a9c5-f62731cda925 | 11737272 | Internal Medicine[mh] | As a public health problem, chronic kidney disease (CKD) has attracted more and more attention due to its increasing prevalence and mortality. The global prevalence of chronic kidney disease is estimated between 11%–13% with the majority stage 3 . A systematic review including 123 countries or region register systems has reported that 2.6 million people received renal replacement therapy (RRT) in 2010 and is estimated to exceed 5.4 million in 2030 . Chronic kidney disease resulted in 1.2 million deaths worldwide in 2017 and is predicted to become the fifth leading cause of mortality globally by 2040 . Numerous studies have shown that consulting a nephrologist can affect the clinical outcome of patients with chronic kidney disease. A meta-analysis in 2005 showed that patients referred to nephrologists early had lower mortality rates and fewer early hospitalizations compared to those referred late . The other meta-analysis in 2014, consistent with the previous analysis, showed a decrease in mortality and better dialysis access preparation in patients with early nephrology referrals . However, the benefits of early referral remain controversial due to heterogeneity and bias from confounding factors (i.e., comorbidity, age, and residual renal function). Pooled analysis using adjusted estimates is necessary for minimizing bias and enhancing the generalizability of the findings. Besides, an increasing number of studies have compared the clinical outcomes among patients with early versus late referral to nephrologists in the past few years. There is a growing need for an updated meta-analysis to identify the patient outcomes associated with referral patterns based on the latest research. Therefore, we performed an updated meta-analysis to examine outcomes related to referral patterns in patients with advanced CKD. The study with subgroup analyses also examined whether the mortality risk of early versus late nephrology referral is influenced by dialysis duration, dialysis modalities, and referral entry points. A systematic review and meta-analysis was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The pre-specified protocol for this study was registered with PROSPERO (CRD42023423608). Search strategy and study selection We searched for randomized clinical trials, cohort studies, and case–control studies that compared outcomes in patients with early referral versus patients with late referral using PubMed, Embase, and the Cochrane Library until June 1, 2022. We designed search strategies by combining all relevant terms of referral, chronic kidney disease (Supplementary Appendix ). Two authors (LC, YC) independently screened all records by title and abstract and retrieved the full text of potential records. The third author (NH) independently made a determination in case of any disagreement. For inclusion, the studies had to meet all criteria as follows: (1) being a randomized clinical trial or a case–control or a cohort study; (2) defining late and early nephrology referral by the time at which patients were referred to nephrologists; (3) including patients with stage 4–5 of CKD or ESRD; (4) being English literature, and (5) reporting either all-cause mortality, emergency start, initial use of catheter, arteriovenous access creation or initial use of arteriovenous access. Studies were excluded for either one of the criteria as follows: (1) participants younger than 18 years old; (2) patients on pre-existing renal replacement therapy; and (3) defining late and early nephrology referral by either referral frequency, preparation of vascular access or estimated glomerular filtration rate (eGFR). Data extraction and quality assessment Two authors (LC, YC) collected data into a chart independently, including data source, the definition of late and early nephrology referral, follow-up duration, dialysis modality, sample size, age, sex, eGFR or creatinine clearance (Ccr) at the first referral and the first dialysis session, and adjusted confounders of all-cause mortality. We evaluated the methodological quality according to criteria from the Newcastle–Ottawa Scale including selection, comparability, and outcome. More than 5 points were regarded as a low risk of bias. Outcome measures The primary outcome of interest was all-cause mortality risk after initiation of dialysis in the early referral (ER) versus late referral (LR) patients. The secondary outcomes included various clinical parameters in the ER versus LR group, including the length of initial hospital stay, the rate of kidney transplantation, the emergency start of dialysis, initial catheter use, arteriovenous access creation, and initial use of arteriovenous access. Initial hospitalization was in connection with the start of renal replacement therapy. Renal transplant recipients included patients receiving transplantation before and after dialysis. The emergency start was defined as the first dialysis within 24 h after medical consultation or unavoidable first dialysis for life-threatening disorders including severe hyperkalemia, pulmonary edema, encephalopathy, pericarditis, and metabolic acidosis. Catheters included non-tunneled and tunneled catheters. Arteriovenous access included arteriovenous fistula and arteriovenous graft. Statistical analysis We summarized data using the risk ratio and 95% confidence interval (CI) for dichotomous variables, mean and deviation means or median and range for quantitative variables, percentages for categorical variables, and hazard ratio (HR) and 95% CI for time-to-event data. When estimates of effect were unavailable directly, we calculated relevant effect estimates by extracting data from figures or transforming them from raw data. If adjusted estimates were available in the studies, we used the best-adjusted estimates of effect for each study, otherwise, we used the unadjusted estimates. We measured heterogeneity among studies by I 2 statistic. If severe heterogeneity cannot be avoided ( I 2 > 50%), we chose the Random-effects inverse-variance model with the DerSimonian-Laird method for the meta-analysis, otherwise, we used the fixed-effect model. We assessed the publication bias using funnel plot and Egger test. We used the trim-and-fill method to obtain the pooled estimates adjusted for publication bias. To examine the robustness of the meta-analysis, we performed a sensitivity analysis by removing each included study. For all analyses, statistical significance was considered when a two-tailed p < 0.05. Engauge Digitizer version 11.1 was used to extract data from graphs. R version 4.1.3 (The R Foundation for Statistical Computing) was used to perform all analyses. We searched for randomized clinical trials, cohort studies, and case–control studies that compared outcomes in patients with early referral versus patients with late referral using PubMed, Embase, and the Cochrane Library until June 1, 2022. We designed search strategies by combining all relevant terms of referral, chronic kidney disease (Supplementary Appendix ). Two authors (LC, YC) independently screened all records by title and abstract and retrieved the full text of potential records. The third author (NH) independently made a determination in case of any disagreement. For inclusion, the studies had to meet all criteria as follows: (1) being a randomized clinical trial or a case–control or a cohort study; (2) defining late and early nephrology referral by the time at which patients were referred to nephrologists; (3) including patients with stage 4–5 of CKD or ESRD; (4) being English literature, and (5) reporting either all-cause mortality, emergency start, initial use of catheter, arteriovenous access creation or initial use of arteriovenous access. Studies were excluded for either one of the criteria as follows: (1) participants younger than 18 years old; (2) patients on pre-existing renal replacement therapy; and (3) defining late and early nephrology referral by either referral frequency, preparation of vascular access or estimated glomerular filtration rate (eGFR). Two authors (LC, YC) collected data into a chart independently, including data source, the definition of late and early nephrology referral, follow-up duration, dialysis modality, sample size, age, sex, eGFR or creatinine clearance (Ccr) at the first referral and the first dialysis session, and adjusted confounders of all-cause mortality. We evaluated the methodological quality according to criteria from the Newcastle–Ottawa Scale including selection, comparability, and outcome. More than 5 points were regarded as a low risk of bias. The primary outcome of interest was all-cause mortality risk after initiation of dialysis in the early referral (ER) versus late referral (LR) patients. The secondary outcomes included various clinical parameters in the ER versus LR group, including the length of initial hospital stay, the rate of kidney transplantation, the emergency start of dialysis, initial catheter use, arteriovenous access creation, and initial use of arteriovenous access. Initial hospitalization was in connection with the start of renal replacement therapy. Renal transplant recipients included patients receiving transplantation before and after dialysis. The emergency start was defined as the first dialysis within 24 h after medical consultation or unavoidable first dialysis for life-threatening disorders including severe hyperkalemia, pulmonary edema, encephalopathy, pericarditis, and metabolic acidosis. Catheters included non-tunneled and tunneled catheters. Arteriovenous access included arteriovenous fistula and arteriovenous graft. We summarized data using the risk ratio and 95% confidence interval (CI) for dichotomous variables, mean and deviation means or median and range for quantitative variables, percentages for categorical variables, and hazard ratio (HR) and 95% CI for time-to-event data. When estimates of effect were unavailable directly, we calculated relevant effect estimates by extracting data from figures or transforming them from raw data. If adjusted estimates were available in the studies, we used the best-adjusted estimates of effect for each study, otherwise, we used the unadjusted estimates. We measured heterogeneity among studies by I 2 statistic. If severe heterogeneity cannot be avoided ( I 2 > 50%), we chose the Random-effects inverse-variance model with the DerSimonian-Laird method for the meta-analysis, otherwise, we used the fixed-effect model. We assessed the publication bias using funnel plot and Egger test. We used the trim-and-fill method to obtain the pooled estimates adjusted for publication bias. To examine the robustness of the meta-analysis, we performed a sensitivity analysis by removing each included study. For all analyses, statistical significance was considered when a two-tailed p < 0.05. Engauge Digitizer version 11.1 was used to extract data from graphs. R version 4.1.3 (The R Foundation for Statistical Computing) was used to perform all analyses. Characteristics of the included studies A total of 19,850 publications were identified based on the search strategy and 142 were retrieved in the full text. Finally, 72 cohort or case–control studies with a total sample size of more than 630,000 patients were included in this review (Fig. ) . There was no randomized clinical trial regarding referral patterns and outcomes. The baseline characteristics of eligible studies are given in Table . The studies were published between 1998 and 2019, with follow-up duration ranging from 2 months to 5 years. A total of 31 studies enrolled patients before 2003, 25 studies enrolled patients after 2003, one study did not specify the enrollment period, and 16 studies spanned across 2003. Among the patients, more than 321,000 were ER patients and more than 309,000 were LR patients. The average age of patients was 35.5 to 87.4 years and the proportion of males was 38.3% to 78%. The cut-off point of late and early nephrology referral varied among studies. The cut-off point of 1, 3, 4, 6, and 12 months before dialysis initiation was used in 9, 35, 15, 14, and 12 studies, respectively. The average eGFR/Ccr was from 3.8 to 23.7 ml/min/1.73 m 2 at the first visit to nephrologists. Thirty-one out of 72 studies reported either serum creatinine, eGFR, or Ccr of the cohorts at the initiation of dialysis. Among these, 25 studies compared residual kidney function between the LR and ER groups. The eGFR at the initiation of dialysis in the LR and ER groups varied across studies, whether in the pre-2003 or post-2003 cohorts. Eight of the 14 studies in the pre-2003 cohort and 4 of the 11 studies in the post-2003 cohort reported significant differences in eGFR between the LR and ER groups. The average eGFR at initiation of dialysis ranged from 3.4 to 10 mL/min/1.73 m 2 in the pre-2003 cohort and from 5.3 to 10.5 mL/min/1.73 m 2 in the post-2003 cohort. ER patients initiated dialysis at an eGFR of 3.9 to 8.5 mL/min/1.73 m 2 and 4.9 to 9.9 mL/min/1.73 m 2 in the pre- and post-2003 cohorts, respectively, while LR patients initiated dialysis at an eGFR of 3.4 to 8.9 mL/min/1.73 m 2 and 5.4 to 11.2 mL/min/1.73 m 2 in the respective cohorts. According to the Newcastle–Ottawa Scale, the majority of studies presented a low risk of bias (Supplementary Table S2). All-cause mortality In the 56 studies reporting all-cause mortality, more than 245,000 ER patients and 275,000 LR patients were assessed. The all-cause mortality rate of ER patients was 33% lower than that of LR patients (HR = 0.67, 95% CI: 0.62–0.72, Fig. ). Adjusted estimates from each study were combined to reduce potential bias from confounding. Of note, mortality outcomes were adjusted for different sets of variable factors. Among 22 studies available, 20, 16, and 9 studies were adjusted for age, comorbidity, and residual renal function, respectively. Pooled analysis showed that the adjusted mortality rate was 27% lower in ER patients than in LR patients (HR = 0.73, 95% CI: 0.69–0.78). The unadjusted HR was 0.63 (95% CI: 0.56–0.71) in the 34 studies. Further analysis of mortality rates stratified by follow-up duration is presented in Fig. . The 6-month, 1-year, 2-year, 3-year, 4-year, and 5-year mortality rates between ER and LR were reported in 22, 41, 21, 18, 15, and 17 studies, respectively. ER patients had a lower risk of mortality at 6 months, 1 year, and 2, 3, 4, and 5 years after the start of dialysis compared to LR patients (6 months: HR = 0.52, 95% CI: 0.40–0.68; 1 year: HR = 0.57, 95% CI: 0.51–0.65; 2 years: HR = 0.54, 95% CI: 0.47–0.63; 3 years: HR = 0.62, 95% CI: 0.53–0.71; 4 years: HR = 0.63, 95% CI: 0.54–0.73; 5 years: HR = 0.67, 95% CI: 0.60–0.74). To evaluate the short- and long-term effect of referral timing, the survival outcomes at 6-month and 5-year dialysis were obtained. Figure shows the relative mortality risk and absolute survival rates of ER versus LR at 6-month and 5-year dialysis when cut-off points were set at 3, 4, and 6 months before the first dialysis. Compared to LR patients, patients who were referred for at least 3 and 6 months had a lower likelihood of 6-month and 5-year mortality. Among ER patients, the survival rate increased with longer durations of pre-RRT care from ≥ 3 months to ≥ 6 months. The mortality risk of ER versus LR patients on hemodialysis (HD) and peritoneal dialysis (PD), HD only, and PD only was reported in 27, 22, and 6 studies, respectively (Fig. ). Compared to LR patients, ER patients showed a lower likelihood of mortality risk, irrespective of dialysis modalities (HD and PD: HR = 0.68, 95% CI: 0.62–0.75; HD: HR = 0.61, 95% CI: 0.53–0.69; PD: HR = 0.83, 95% CI: 0.72–0.95). Six and 10 studies reported adjusted mortality risk for cohorts initiating dialysis before and after 2003, respectively. A lower mortality risk was observed in ER patients in both time periods (pre-2003: HR = 0.69, 95% CI: 0.59–0.81; post-2003: HR = 0.72, 95% CI: 0.60–0.87) (Supplementary Fig. ). Pooled data from 6 post-2003 cohorts with a mean age above 60 showed a 20% lower mortality risk in the ER group (HR = 0.80, 95% CI: 0.71–0.89) (Supplementary Fig. S2). Other clinical outcomes Secondary outcomes of interest were durations of initial hospitalization, kidney transplantation, arteriovenous access creation, emergency first dialysis, initial use of arteriovenous access, and first catheter use before dialysis initiation, which were reported in 9, 10, 8, 14, 21, and 23 studies, respectively. Relative risk for each outcome between ER patients versus LR patients was highly heterogeneous with the I 2 ranging from 81 to 99% (Fig. ). All 9 studies reported that ER patients had shorter hospital stays beginning at dialysis than LR patients. Compared to LR patients, ER patients were more likely to undergo kidney transplantation during a follow-up period ranging from 4 months to 34.4 months in the included studies (RR = 1.41, 95% CI: 1.12–1.78, Fig. a). ER patients presented a higher likelihood of arteriovenous access creation (RR = 3.34, 95% CI: 2.43–4.59, Fig. b) and initial use of arteriovenous access (RR = 2.60, 95% CI: 2.18–3.11, Fig. c). Besides, ER patients were less likely to undergo emergency first dialysis (RR = 0.39, 95% CI: 0.28–0.54, shown in Fig. d) or start dialysis with catheters (RR = 0.43, 95% CI: 0.32–0.58, Fig. e). Sensitivity analysis We conducted a sensitivity analysis by excluding each included study. The pooled HR was not significantly altered, indicating that the result was relatively robust (Supplementary Fig. S3). Publication bias The publication bias was assessed regarding the outcome of all-cause mortality, with the largest number of included studies. There was significant publication bias by Egger ‘s test ( p = 0.037) and funnel plot. Therefore, a sensitivity analysis was conducted using the trim-and-fill method. After adding 17 unpublished studies, the trim-and-fill analysis showed a similar result (HR = 0.72, 95% CI: 0.66–0.78, Supplementary Fig. S4). A total of 19,850 publications were identified based on the search strategy and 142 were retrieved in the full text. Finally, 72 cohort or case–control studies with a total sample size of more than 630,000 patients were included in this review (Fig. ) . There was no randomized clinical trial regarding referral patterns and outcomes. The baseline characteristics of eligible studies are given in Table . The studies were published between 1998 and 2019, with follow-up duration ranging from 2 months to 5 years. A total of 31 studies enrolled patients before 2003, 25 studies enrolled patients after 2003, one study did not specify the enrollment period, and 16 studies spanned across 2003. Among the patients, more than 321,000 were ER patients and more than 309,000 were LR patients. The average age of patients was 35.5 to 87.4 years and the proportion of males was 38.3% to 78%. The cut-off point of late and early nephrology referral varied among studies. The cut-off point of 1, 3, 4, 6, and 12 months before dialysis initiation was used in 9, 35, 15, 14, and 12 studies, respectively. The average eGFR/Ccr was from 3.8 to 23.7 ml/min/1.73 m 2 at the first visit to nephrologists. Thirty-one out of 72 studies reported either serum creatinine, eGFR, or Ccr of the cohorts at the initiation of dialysis. Among these, 25 studies compared residual kidney function between the LR and ER groups. The eGFR at the initiation of dialysis in the LR and ER groups varied across studies, whether in the pre-2003 or post-2003 cohorts. Eight of the 14 studies in the pre-2003 cohort and 4 of the 11 studies in the post-2003 cohort reported significant differences in eGFR between the LR and ER groups. The average eGFR at initiation of dialysis ranged from 3.4 to 10 mL/min/1.73 m 2 in the pre-2003 cohort and from 5.3 to 10.5 mL/min/1.73 m 2 in the post-2003 cohort. ER patients initiated dialysis at an eGFR of 3.9 to 8.5 mL/min/1.73 m 2 and 4.9 to 9.9 mL/min/1.73 m 2 in the pre- and post-2003 cohorts, respectively, while LR patients initiated dialysis at an eGFR of 3.4 to 8.9 mL/min/1.73 m 2 and 5.4 to 11.2 mL/min/1.73 m 2 in the respective cohorts. According to the Newcastle–Ottawa Scale, the majority of studies presented a low risk of bias (Supplementary Table S2). In the 56 studies reporting all-cause mortality, more than 245,000 ER patients and 275,000 LR patients were assessed. The all-cause mortality rate of ER patients was 33% lower than that of LR patients (HR = 0.67, 95% CI: 0.62–0.72, Fig. ). Adjusted estimates from each study were combined to reduce potential bias from confounding. Of note, mortality outcomes were adjusted for different sets of variable factors. Among 22 studies available, 20, 16, and 9 studies were adjusted for age, comorbidity, and residual renal function, respectively. Pooled analysis showed that the adjusted mortality rate was 27% lower in ER patients than in LR patients (HR = 0.73, 95% CI: 0.69–0.78). The unadjusted HR was 0.63 (95% CI: 0.56–0.71) in the 34 studies. Further analysis of mortality rates stratified by follow-up duration is presented in Fig. . The 6-month, 1-year, 2-year, 3-year, 4-year, and 5-year mortality rates between ER and LR were reported in 22, 41, 21, 18, 15, and 17 studies, respectively. ER patients had a lower risk of mortality at 6 months, 1 year, and 2, 3, 4, and 5 years after the start of dialysis compared to LR patients (6 months: HR = 0.52, 95% CI: 0.40–0.68; 1 year: HR = 0.57, 95% CI: 0.51–0.65; 2 years: HR = 0.54, 95% CI: 0.47–0.63; 3 years: HR = 0.62, 95% CI: 0.53–0.71; 4 years: HR = 0.63, 95% CI: 0.54–0.73; 5 years: HR = 0.67, 95% CI: 0.60–0.74). To evaluate the short- and long-term effect of referral timing, the survival outcomes at 6-month and 5-year dialysis were obtained. Figure shows the relative mortality risk and absolute survival rates of ER versus LR at 6-month and 5-year dialysis when cut-off points were set at 3, 4, and 6 months before the first dialysis. Compared to LR patients, patients who were referred for at least 3 and 6 months had a lower likelihood of 6-month and 5-year mortality. Among ER patients, the survival rate increased with longer durations of pre-RRT care from ≥ 3 months to ≥ 6 months. The mortality risk of ER versus LR patients on hemodialysis (HD) and peritoneal dialysis (PD), HD only, and PD only was reported in 27, 22, and 6 studies, respectively (Fig. ). Compared to LR patients, ER patients showed a lower likelihood of mortality risk, irrespective of dialysis modalities (HD and PD: HR = 0.68, 95% CI: 0.62–0.75; HD: HR = 0.61, 95% CI: 0.53–0.69; PD: HR = 0.83, 95% CI: 0.72–0.95). Six and 10 studies reported adjusted mortality risk for cohorts initiating dialysis before and after 2003, respectively. A lower mortality risk was observed in ER patients in both time periods (pre-2003: HR = 0.69, 95% CI: 0.59–0.81; post-2003: HR = 0.72, 95% CI: 0.60–0.87) (Supplementary Fig. ). Pooled data from 6 post-2003 cohorts with a mean age above 60 showed a 20% lower mortality risk in the ER group (HR = 0.80, 95% CI: 0.71–0.89) (Supplementary Fig. S2). Secondary outcomes of interest were durations of initial hospitalization, kidney transplantation, arteriovenous access creation, emergency first dialysis, initial use of arteriovenous access, and first catheter use before dialysis initiation, which were reported in 9, 10, 8, 14, 21, and 23 studies, respectively. Relative risk for each outcome between ER patients versus LR patients was highly heterogeneous with the I 2 ranging from 81 to 99% (Fig. ). All 9 studies reported that ER patients had shorter hospital stays beginning at dialysis than LR patients. Compared to LR patients, ER patients were more likely to undergo kidney transplantation during a follow-up period ranging from 4 months to 34.4 months in the included studies (RR = 1.41, 95% CI: 1.12–1.78, Fig. a). ER patients presented a higher likelihood of arteriovenous access creation (RR = 3.34, 95% CI: 2.43–4.59, Fig. b) and initial use of arteriovenous access (RR = 2.60, 95% CI: 2.18–3.11, Fig. c). Besides, ER patients were less likely to undergo emergency first dialysis (RR = 0.39, 95% CI: 0.28–0.54, shown in Fig. d) or start dialysis with catheters (RR = 0.43, 95% CI: 0.32–0.58, Fig. e). We conducted a sensitivity analysis by excluding each included study. The pooled HR was not significantly altered, indicating that the result was relatively robust (Supplementary Fig. S3). The publication bias was assessed regarding the outcome of all-cause mortality, with the largest number of included studies. There was significant publication bias by Egger ‘s test ( p = 0.037) and funnel plot. Therefore, a sensitivity analysis was conducted using the trim-and-fill method. After adding 17 unpublished studies, the trim-and-fill analysis showed a similar result (HR = 0.72, 95% CI: 0.66–0.78, Supplementary Fig. S4). In the analysis of 72 studies involving more than 630,000 patients, we showed the survival benefits of early nephrology referral among pre-dialysis populations, irrespective of dialysis modalities. Further, we identified that patients referred earlier had shorter lengths of initial hospitalization and better preparation for renal replacement therapy. Nephrology care involves patient education, complication management, consultations of treatment modality, and preparation of dialysis access. Timely pre-RRT nephrology care provides enough time for multidisciplinary cooperation to optimize strategies in advanced CKD and generally leads to improved outcomes. Kidney Disease: Improving Global Outcomes (KIDGO) guidelines have recommended timely nephrology consultations for RRT planning in people with progressive CKD . However, population heterogeneity and selection bias were potentially high in this meta-analysis. Results from observational studies may be confounded by case-mix characteristics and clinical statuses, such as age, laboratory parameters, and comorbidity. Our study suggested a trend toward initiating dialysis at slightly higher eGFR levels over the past two decades. eGFR at the initiation of dialysis has proven to be a significant risk factor influencing patient prognosis. Data from the Initiating Dialysis Early and Late randomized controlled trial showed no significant differences in mortality risk or adverse event frequency between early- and late-start groups (eGFR of 10–14 mL/min/1.73 m 2 vs. 5–7 mL/min/1.73 m 2 ) . Moreover, a meta-analysis of 15 cohort studies found that a higher adjusted mortality risk was associated with initiating dialysis at higher GFRs, even after accounting for confounding factors . Therefore, eGFR at dialysis initiation was included as a key confounder in our analysis. Additionally, age and comorbidity are prognostic factors affecting patients' survival. The differences in confounders for adjustment existed across studies. Riley et.al proposed to define at least a minimum set of factors for adjustment to reduce confounding bias in meta-analysis of observational studies . To minimize the effect of confounding factors, we presented pooled mortality risk using estimates adjusted for potential confounding factors such as age, comorbidity, and eGFR. We observed a 27% reduction in adjusted mortality risk associated with early nephrology referral. The persistent survival benefits of early referral were observed in both the post-2003 cohort and older populations, demonstrating that early nephrology referral continues to be a critical factor in improving patient outcomes. Additionally, in line with previous meta-analyses, the present study found that the survival benefits from early nephrology care persisted for years after dialysis initiation. The hypothesis of survival benefits in ER patients could be partly caused by a lower likelihood of emergency start and initial catheter use and a higher likelihood of permanent access creation and permanent access first use. Data from the French Renal Epidemiology and Information Network have shown that emergency first dialysis is independently associated with worse three-year survival . Non-tunneled CVCs (central venous catheters) are typically applied in short-term, inpatient dialysis including emergency induction . Central venous catheters are associated with a higher likelihood of death, cardiovascular events, and infection . Arhuidese et al. showed that reliable arteriovenous access positively impacted prognosis in patients receiving chronic dialysis . Kidney transplantation is the best therapy for kidney failure, with proven benefits in life quality and survival over dialysis . Our findings showed that the ER patients had a higher rate of transplantation compared to LR patients, again reiterating that adequate nephrology care plays a role in further prospective management of CKD patients. The steps prior to kidney transplantation are multiple, involving patient education, referral to transplant clinics, medical evaluation, and wait-listing . Gill et al. suggested that the death rate increased with a longer waiting time before transplantation . Early nephrology referral has been associated with pre-emptive kidney waiting-list placement and transplantation , suggesting better nephrology care drives referral to transplant clinics. Early RRT planning discussions with patients at high risk of ESRD should be promoted. As chronic kidney disease is common and represents a heavy societal burden, there is a need to explore the proper timing of nephrology consultations for adequate preparation of RRT. Pooled analysis of survival data with different referral points showed an increasing trend of survival rate with longer durations of nephrologist follow-ups. However, caution is needed in interpreting these results, as selection bias cannot be completely avoided. Saggi et.al suggested that preparation for RRT should begin early enough in the course of CKD to consider therapy modality and establish permanent access for dialysis choice . Given the burden and integrated care associated with advanced CKD, KDIGO guidelines suggest at least 1 year is required to ensure appropriate education, understanding, and referrals to other practitioners (e.g., vascular access surgeons, transplant team, etc.) . The current study has several strengths. Firstly, our findings involved a large cohort of CKD patients and enhanced statistical power to quantify the association of referral patterns and outcomes. Secondly, our study focused on significant outcomes related to ESRD patients including mortality and kidney transplantation. The latter was not reported in previous systematic reviews. Further analysis explored associations of survival and length of nephrology care. However, our study is limited by the observational nature of the included studies. The heterogeneity across studies is largely attributed to population selection criteria, sample size, statistical methodology, and referral practices. The referral pattern was defined as months before dialysis initiation without considering eGFR. Besides, the sample population for analysis consists of patients with CKD at different stages and thus, lead-time bias cannot be avoided. Additionally, our meta-analysis included pre-2003 cohorts, which limited its ability to accurately reflect the current dialysis population. Therefore, a subgroup analysis was conducted to assess the mortality risk associated with the two referral patterns, focusing on cohorts from before and after 2003. Furthermore, publishing bias existed in this study. Studies with negative findings that are less likely to be published might affect the results. However, it is not feasible to conduct randomized controlled trials to address this issue due to ethical limitations. A large-scale prospective study is awaited to draw a conclusion. To conclude, our study showed that early referral to nephrologists for patients with advanced CKD was associated with a reduced risk of mortality, shorter initial hospitalization durations, and improved readiness for RRT. Early nephrology care should be promoted to improve the management of advanced chronic kidney disease. Supplementary Material 1. |
Elastic Bands Improve Oral Appliance Treatment Effect on Obstructive Sleep Apnoea: A Randomised Crossover Trial | b9580356-5745-4647-ac0a-09d1ace44553 | 11680498 | Dentistry[mh] | Background Obstructive sleep apnoea (OSA) is characterised by frequent collapse of the upper airway during sleep, causing hypoxia, hypercapnia and sleep fragmentation . Consequences of OSA, if left untreated, may include excessive daytime sleepiness, impaired quality of life (QoL) and comorbid cardiovascular conditions . It has been estimated that approximately 425 million adults aged 30–65 suffer from moderate to severe OSA globally . Positive airway pressure (PAP) is generally considered the first‐line treatment option for OSA . Even though PAP commonly achieves optimal treatment effects, the main challenge for PAP is treatment adherence . Oral appliance (OA) treatment is often the go‐to treatment for patients without adherence to PAP. Although OA treatment generally is regarded effective , as many as 25%–45% may be considered failures, defined as ≤ 50% reduction in baseline respiratory event index (REI). Hence, the need to improve OA treatment effect is evident. OAs come with different designs. Bibloc appliances are generally considered the gold standard due to better treatment effect and adherence , compared to monobloc appliances. However, some studies have shown that patients may benefit from OAs that limit mouth opening during sleep by using monobloc appliances . The application of elastic bands on bibloc appliances may limit mouth opening during sleep, and in many ways transform a bibloc appliance to a monobloc appliance. A small randomised pilot study with crossover design investigated application of elastic bands in OA treatment and showed higher success rate with elastic bands compared to without (90% success vs. 70% success). The greatest differences were shown in two patients with positional‐dependent OSA (POSA) . Cartwright defined POSA as REI supine > 2 × REI nonsupine . The prevalence of POSA in a previous study was found to be 18%–34% . Elastic bands application in OA therapy in patients with POSA has shown to significantly increase success rates (> 75% reduction of REI) compared to OA without elastic bands (67.5% vs. 36.2%) . It is hypothesised that patients treated with bibloc appliances without connectors (e.g., SomnoDent Fusion, SomnoMed Ltd.) will benefit from using elastic bands, mainly due to prevention of opening of the mouth and mandibular retrusion during sleep in supine position. In mouth opening, the mandible retrudes together with the tongue and the soft palate, which may lead to decreased upper airway volume , and thus, reduced effect of the OA. Consequently, patients with POSA are hypothesised to benefit the most from applying elastic bands when treated with OA. Generally, there is a lack of evidence regarding application of elastic bands in OA treatment, and above‐mentioned studies have several limitations, with limited number of patients or weak study design . Thus, there is a need for randomised controlled trials with appropriate sample sizes to investigate the effect of elastic bands in OA treatment. The primary objective of this study was to investigate if elastic bands improve OA treatment success based on > 50% reduction in REI. A further aim was to explore if there were differences regarding adherence, side effects and subjective effects between the two treatment modalities and if investigated variables can predict the need for elastic bands in OA treatment.
Methods The study was designed as a single‐centre, randomised crossover trial of patients with OSA. Eligible patients were men and women aged 18 years and older referred for treatment of moderate or severe OSA. All patients were nonadherent to PAP treatment and were thus referred for OA treatment. Eligible patients participated in the ‘Sleep Registry’ at the Center for Sleep Medicine, Haukeland University Hospital. Patients were excluded if they had mild or no OSA, had insufficient number of teeth to retain an OA, had complete dentures, did not speak and/or read the Norwegian language and were not capable of giving informed consent. All eligible patients were in a consecutive order invited to participate in the trial until the estimated number of patients was included. All parts of the treatment took place at the Center for Sleep Medicine, Haukeland University Hospital. 2.1 Device At the Center for Sleep Medicine, the ‘Narval CC’ appliance (ResMed) is prioritised as the primary choice for OA treatment, decided by a tender. The indication for treatment with the ‘SomnoDent Fusion’ appliance (SomnoMed) at the Center for Sleep Medicine is insufficient retention on remaining teeth to support a ‘Narval CC’ appliance (ResMed). Eligible patients were all considered for treatment with ‘Somnodent Fusion’ but were excluded if they were suitable for treatment with the ‘Narval CC’ appliance. Maximal protrusion was measured using the George Gauge bite fork, and the increase of occlusal vertical dimension was reduced to the minimal height required for the ‘SomnoDent Fusion’ appliance (4–5 mm). The OAs were produced in 63% or 69% of maximal protrusion for patients with moderate and severe OSA, respectively. These positions have been reported as being optimal for the mandible following stepwise, objective titration . Elastic bands were attached to anteriorly placed hooks on both the upper and lower appliance, bilaterally (see Figure ). The elastic bands were carefully selected so as not to interfere with retention of the OA. The strongest elastic bands that did not interfere with retention of the OA were selected, within the range of 85–170 g (3/8″–3/16″). 2.2 Randomisation Sequence Patients were randomised to initiate treatment either with or without elastic bands with simple random allocation, by drawing one of two identical balls labelled ‘with elastic bands’ or ‘without elastic bands’. The patients drew the balls from a container without visual access to the balls in the drawing process. Drawing was performed initially at the first visit of the trial, subsequent to an oral repetition of study details with the patients. Simple randomisation method was selected based on the assumption that there was no sequence effect within the study . Therefore, it is presumed that imbalance in the treatment sequence has minimal effect on the results . 2.3 Intervention Participants were treated with OA, both with and without elastic bands for 3 weeks. The effect of the treatment was investigated with home respiratory polygraphy registrations (PG) with Type IV devices (Nox T3, Nox Medical), including manual scoring, and questionnaires at the end of each 3‐week period. Thereafter, patients changed treatment modality (with or without elastic bands), with identical assessment of treatment effect after 3 weeks of treatment. After both treatment modalities were tested, patients were informed of the results from the PG recordings, and patients chose their preferred modality for further treatment. Treating dentist advised the patients to choose the treatment modality resulting in the largest REI reduction. Success was categorised into four criteria from Gjerde et al. based on the reduction in REI following OA treatment: Success criterion 1 was defined as REI < 5 postop, criterion 2 was defined as REI < 10 + REI reduction > 50% from baseline, criterion 3 as REI reduction > 50% from baseline and criterion 4, also termed treatment failure, as REI reduction ≤ 50% from baseline. If a patient accomplished Success Criterion 1 or 2 with the chosen treatment modality after the intervention, no titration was performed. In the event of Success Criterion 3 or treatment failure with the chosen modality, the OA was titrated stepwise by 1 mm incrementation to locate the optimal position. Every titration was controlled using PG recordings to examine its impact. For patients not able to use their OA due to side effects at any point during the 3‐week period, the issues causing discomfort were handled adequately, and control of the treatment with PG and questionnaires were postponed for 2 weeks if remaining time until the planned control was less than 2 weeks. Patients were encouraged to make contact if side effects/discomfort related to the treatment occurred. If a patient was unable to complete the intervention protocol due to side effects/discomfort, the treatment was changed to the other treatment modality (i.e., from OA with elastic bands to OA without elastic bands), and the OA was titrated optimally. If a patient was unable to use any of the treatment modalities, and thus is nonadherent to OA treatment, PAP treatment was reoffered. Similarly, if a patient was deemed as nonresponder to OA treatment after both interventions, and further titration to optimise the OA proves ineffective, the patient was reoffered PAP treatment. 2.4 Outcomes The primary outcome measure of this study was to investigate differences in success, defined as > 50% reduction of REI, comparing OA treatment with and without elastic bands. The secondary outcome measures included assessing differences in the subjective effect of OA treatment with and without elastic bands through questionnaires. These questionnaires aimed to explore variables regarding treatment adherence, side effects, quality of life, partner perception of the treatment, insomnia, snoring, daytime somnolence and anxiety/depression. 2.5 Measurements 2.5.1 Respiratory Polygraphy Registrations The PG recordings were manually scored by the same clinician blinded to the treatment modality (with or without elastic bands) using criteria in accordance with the American Academy of Sleep Medicine 2012 guidelines . 2.5.2 Questionnaires A questionnaire used in all patients referred to the sleep apnoea clinic at the Centre for Sleep Medicine, Haukeland University Hospital, is included in the local ‘Sleep Registry’ and applied in this study. This contains questions regarding the patient's history of sleep disorders and the patient's sleep habits, subjective evaluated health, QoL, experienced snoring, breathing cessations and daytime somnolence during the past 3 months, Epworth Sleepiness Scale (ESS), questions regarding subjective nasal congestion, Bergen Insomnia Scale (BIS), questions regarding restless legs and circadian rhythm, the Hospital Anxiety and Depression Scale (HADS), questions regarding smoking, alcohol and coffee consumption and the Fatigue Severity Scale (FSS 7‐item). ESS is used for measuring daytime sleepiness through eight questions regarding the propensity to fall asleep in different everyday situations. Each question is scored from 0 to 3, with a total score based on a scale of 0–24. Scores > 10 indicate excessive daytime sleepiness . BIS is a validated scale for measuring insomnia consisting of six items scored on an 8‐point scale (0–7) . Chronic insomnia is defined as scoring ≥ 3 in one or more of Items 1–3 in addition to scoring ≥ 3 in one or more of Items 5–6. HADS is a questionnaire containing 14 questions that can be divided into two subquestionnaires regarding anxiety (HADS‐A) and depression (HADS‐D). The intention of HADS is to identify patients with high possibility and probability of anxiety and/or depression in a nonpsychiatric hospital clinic . HADS‐A and HADS‐D scores between 0 and 7 are considered asymptomatic, and scores > 7 indicate symptoms of anxiety and depression, respectively. FSS 7‐item measures subjective fatigue through seven questions scored on a 7‐point Likert scale (1 = strongly disagree, 7 = strongly agree) . FSS score was reported as the mean score of the seven questions, and scoring of high fatigue is defined as mean FSS score ≥ 5 . In addition, the questionnaire contained questions about whether the patients previously had been diagnosed with myocardial infarction, stroke, diabetes, hypertension, chronic obstructive pulmonary disease, asthma, angina pectoris and/or depression. At the end of both 3‐week treatment periods, the patients answered an additional questionnaire regarding reported adherence to treatment, subjective effects and side effects. Regarding treatment adherence, patients were asked two questions: The first question was ‘On average, how many nights per week do you use your oral appliance’, with five response options: ‘0 nights/week’, ‘1–2 nights/week’, ‘3–4 nights/week’, ‘5–6 nights/week’ and ‘7 nights/week’. The second question was ‘On average, how many hours do you use your oral appliance per night’, with alternatives: ‘0–2 h/night’, ‘2–3 h/night’, ‘3–4 h/night’, ‘4–5 h/night’ or ‘> 5 h/night’. Concerning subjective effect, patients were asked ‘how is your daytime sleepiness affected by the oral appliance treatment’. Response options were ‘increased daytime sleepiness’, ‘unchanged’, ‘reduced daytime sleepiness’ and ‘complete relief of daytime sleepiness’. Furthermore, patients were asked ‘how is your quality of life affected by the oral appliance treatment’, with response options ‘worsening’, ‘unchanged’ and ‘improved’. Regarding side effects, patients were asked to check boxes in the questionnaire if they had experienced any of the following side effects when using their oral appliance: ‘temporomandibular joint pain’, ‘headache’, ‘pain in masticatory muscles’, ‘increased salivation’, ‘dry mouth’ and ‘occlusal changes’. 2.6 Sample Size Calculation Prior to the study, sample size calculation was performed with 5% level of significance and 80% power, using data from above‐mentioned pilot study with similar study design , where treatment success (> 50% reduction of REI) with and without elastic bands was 90% and 70%, respectively. The result of this calculation determined that 124 patients (62 per group) were sufficient for this study. Being a crossover trial, this would correspond to 62 patients in total. To account for 10% attrition rate, 69 patients were recruited in total. Sample size calculations were performed using the statistical software ‘Stata 18’ (StataCorp LLC) . 2.7 Statistics All statistical analyses were performed using Stata 18 (StataCorp LLC). Student's t ‐test was performed to investigate differences between continuous variables following OA treatment with and without elastic bands, and Pearson's chi‐squared test was performed to investigate differences between categorical variables posttreatment. Values pre‐ and posttreatment were used to investigate effect within each treatment modality (with and without elastic bands), and delta values (change pre‐ to posttreatment per treatment modality) were used to investigate differences between the two treatment modalities. Logistic regression analysis was performed to investigate variables predicting treatment success exclusively with OA treatment with elastic bands, with ‘treatment success exclusively with elastic bands’ (yes or no) as a dependent, bivariate variable. Selection of independent variables for the analysis encompassed variables that theoretically could influence treatment outcomes based on previous research on prediction of responders to OA treatment . Patients who were nonadherent to OA treatment with or without elastic bands were excluded from the analysis. To adjust for the repeated design (crossover), we applied logistic regressions with cluster robust variance estimates for two observations per individual when investigating differences in success with elastic bands between men and women. Differences in side effects between the two treatment modalities were investigated using binomial probability tests, investigating the probability of observing identical or higher frequency of side effects with elastic bands compared to OA treatment without elastic bands. In addition, binomial probability tests were used to investigate differences in frequency of ESS, BIS, HADS‐A, HADS‐D and FSS between the two treatment modalities. Student's t ‐test was performed to investigate differences between patients completing both interventions and patients dropping out from the study for the variables age, gender, BMI, neck circumference, REI baseline, REI supine baseline, SaO 2 , SaO 2 nadir, overjet, overbite and maximum mouth opening. The level of significance was set to p < 0.05 for all above‐mentioned statistical tests. 2.8 Ethics The trial was approved by the Regional Ethics Committee of Western Norway (Protocol No: 550079 REK Vest). The study was also approved by the health and social services representative of both the University of Bergen and Haukeland University Hospital. Written informed consent was obtained by all participants before treatment started. The study was registered at clinicaltrials.gov prior to trial start (ID: NCT05987618).
Device At the Center for Sleep Medicine, the ‘Narval CC’ appliance (ResMed) is prioritised as the primary choice for OA treatment, decided by a tender. The indication for treatment with the ‘SomnoDent Fusion’ appliance (SomnoMed) at the Center for Sleep Medicine is insufficient retention on remaining teeth to support a ‘Narval CC’ appliance (ResMed). Eligible patients were all considered for treatment with ‘Somnodent Fusion’ but were excluded if they were suitable for treatment with the ‘Narval CC’ appliance. Maximal protrusion was measured using the George Gauge bite fork, and the increase of occlusal vertical dimension was reduced to the minimal height required for the ‘SomnoDent Fusion’ appliance (4–5 mm). The OAs were produced in 63% or 69% of maximal protrusion for patients with moderate and severe OSA, respectively. These positions have been reported as being optimal for the mandible following stepwise, objective titration . Elastic bands were attached to anteriorly placed hooks on both the upper and lower appliance, bilaterally (see Figure ). The elastic bands were carefully selected so as not to interfere with retention of the OA. The strongest elastic bands that did not interfere with retention of the OA were selected, within the range of 85–170 g (3/8″–3/16″).
Randomisation Sequence Patients were randomised to initiate treatment either with or without elastic bands with simple random allocation, by drawing one of two identical balls labelled ‘with elastic bands’ or ‘without elastic bands’. The patients drew the balls from a container without visual access to the balls in the drawing process. Drawing was performed initially at the first visit of the trial, subsequent to an oral repetition of study details with the patients. Simple randomisation method was selected based on the assumption that there was no sequence effect within the study . Therefore, it is presumed that imbalance in the treatment sequence has minimal effect on the results .
Intervention Participants were treated with OA, both with and without elastic bands for 3 weeks. The effect of the treatment was investigated with home respiratory polygraphy registrations (PG) with Type IV devices (Nox T3, Nox Medical), including manual scoring, and questionnaires at the end of each 3‐week period. Thereafter, patients changed treatment modality (with or without elastic bands), with identical assessment of treatment effect after 3 weeks of treatment. After both treatment modalities were tested, patients were informed of the results from the PG recordings, and patients chose their preferred modality for further treatment. Treating dentist advised the patients to choose the treatment modality resulting in the largest REI reduction. Success was categorised into four criteria from Gjerde et al. based on the reduction in REI following OA treatment: Success criterion 1 was defined as REI < 5 postop, criterion 2 was defined as REI < 10 + REI reduction > 50% from baseline, criterion 3 as REI reduction > 50% from baseline and criterion 4, also termed treatment failure, as REI reduction ≤ 50% from baseline. If a patient accomplished Success Criterion 1 or 2 with the chosen treatment modality after the intervention, no titration was performed. In the event of Success Criterion 3 or treatment failure with the chosen modality, the OA was titrated stepwise by 1 mm incrementation to locate the optimal position. Every titration was controlled using PG recordings to examine its impact. For patients not able to use their OA due to side effects at any point during the 3‐week period, the issues causing discomfort were handled adequately, and control of the treatment with PG and questionnaires were postponed for 2 weeks if remaining time until the planned control was less than 2 weeks. Patients were encouraged to make contact if side effects/discomfort related to the treatment occurred. If a patient was unable to complete the intervention protocol due to side effects/discomfort, the treatment was changed to the other treatment modality (i.e., from OA with elastic bands to OA without elastic bands), and the OA was titrated optimally. If a patient was unable to use any of the treatment modalities, and thus is nonadherent to OA treatment, PAP treatment was reoffered. Similarly, if a patient was deemed as nonresponder to OA treatment after both interventions, and further titration to optimise the OA proves ineffective, the patient was reoffered PAP treatment.
Outcomes The primary outcome measure of this study was to investigate differences in success, defined as > 50% reduction of REI, comparing OA treatment with and without elastic bands. The secondary outcome measures included assessing differences in the subjective effect of OA treatment with and without elastic bands through questionnaires. These questionnaires aimed to explore variables regarding treatment adherence, side effects, quality of life, partner perception of the treatment, insomnia, snoring, daytime somnolence and anxiety/depression.
Measurements 2.5.1 Respiratory Polygraphy Registrations The PG recordings were manually scored by the same clinician blinded to the treatment modality (with or without elastic bands) using criteria in accordance with the American Academy of Sleep Medicine 2012 guidelines . 2.5.2 Questionnaires A questionnaire used in all patients referred to the sleep apnoea clinic at the Centre for Sleep Medicine, Haukeland University Hospital, is included in the local ‘Sleep Registry’ and applied in this study. This contains questions regarding the patient's history of sleep disorders and the patient's sleep habits, subjective evaluated health, QoL, experienced snoring, breathing cessations and daytime somnolence during the past 3 months, Epworth Sleepiness Scale (ESS), questions regarding subjective nasal congestion, Bergen Insomnia Scale (BIS), questions regarding restless legs and circadian rhythm, the Hospital Anxiety and Depression Scale (HADS), questions regarding smoking, alcohol and coffee consumption and the Fatigue Severity Scale (FSS 7‐item). ESS is used for measuring daytime sleepiness through eight questions regarding the propensity to fall asleep in different everyday situations. Each question is scored from 0 to 3, with a total score based on a scale of 0–24. Scores > 10 indicate excessive daytime sleepiness . BIS is a validated scale for measuring insomnia consisting of six items scored on an 8‐point scale (0–7) . Chronic insomnia is defined as scoring ≥ 3 in one or more of Items 1–3 in addition to scoring ≥ 3 in one or more of Items 5–6. HADS is a questionnaire containing 14 questions that can be divided into two subquestionnaires regarding anxiety (HADS‐A) and depression (HADS‐D). The intention of HADS is to identify patients with high possibility and probability of anxiety and/or depression in a nonpsychiatric hospital clinic . HADS‐A and HADS‐D scores between 0 and 7 are considered asymptomatic, and scores > 7 indicate symptoms of anxiety and depression, respectively. FSS 7‐item measures subjective fatigue through seven questions scored on a 7‐point Likert scale (1 = strongly disagree, 7 = strongly agree) . FSS score was reported as the mean score of the seven questions, and scoring of high fatigue is defined as mean FSS score ≥ 5 . In addition, the questionnaire contained questions about whether the patients previously had been diagnosed with myocardial infarction, stroke, diabetes, hypertension, chronic obstructive pulmonary disease, asthma, angina pectoris and/or depression. At the end of both 3‐week treatment periods, the patients answered an additional questionnaire regarding reported adherence to treatment, subjective effects and side effects. Regarding treatment adherence, patients were asked two questions: The first question was ‘On average, how many nights per week do you use your oral appliance’, with five response options: ‘0 nights/week’, ‘1–2 nights/week’, ‘3–4 nights/week’, ‘5–6 nights/week’ and ‘7 nights/week’. The second question was ‘On average, how many hours do you use your oral appliance per night’, with alternatives: ‘0–2 h/night’, ‘2–3 h/night’, ‘3–4 h/night’, ‘4–5 h/night’ or ‘> 5 h/night’. Concerning subjective effect, patients were asked ‘how is your daytime sleepiness affected by the oral appliance treatment’. Response options were ‘increased daytime sleepiness’, ‘unchanged’, ‘reduced daytime sleepiness’ and ‘complete relief of daytime sleepiness’. Furthermore, patients were asked ‘how is your quality of life affected by the oral appliance treatment’, with response options ‘worsening’, ‘unchanged’ and ‘improved’. Regarding side effects, patients were asked to check boxes in the questionnaire if they had experienced any of the following side effects when using their oral appliance: ‘temporomandibular joint pain’, ‘headache’, ‘pain in masticatory muscles’, ‘increased salivation’, ‘dry mouth’ and ‘occlusal changes’.
Respiratory Polygraphy Registrations The PG recordings were manually scored by the same clinician blinded to the treatment modality (with or without elastic bands) using criteria in accordance with the American Academy of Sleep Medicine 2012 guidelines .
Questionnaires A questionnaire used in all patients referred to the sleep apnoea clinic at the Centre for Sleep Medicine, Haukeland University Hospital, is included in the local ‘Sleep Registry’ and applied in this study. This contains questions regarding the patient's history of sleep disorders and the patient's sleep habits, subjective evaluated health, QoL, experienced snoring, breathing cessations and daytime somnolence during the past 3 months, Epworth Sleepiness Scale (ESS), questions regarding subjective nasal congestion, Bergen Insomnia Scale (BIS), questions regarding restless legs and circadian rhythm, the Hospital Anxiety and Depression Scale (HADS), questions regarding smoking, alcohol and coffee consumption and the Fatigue Severity Scale (FSS 7‐item). ESS is used for measuring daytime sleepiness through eight questions regarding the propensity to fall asleep in different everyday situations. Each question is scored from 0 to 3, with a total score based on a scale of 0–24. Scores > 10 indicate excessive daytime sleepiness . BIS is a validated scale for measuring insomnia consisting of six items scored on an 8‐point scale (0–7) . Chronic insomnia is defined as scoring ≥ 3 in one or more of Items 1–3 in addition to scoring ≥ 3 in one or more of Items 5–6. HADS is a questionnaire containing 14 questions that can be divided into two subquestionnaires regarding anxiety (HADS‐A) and depression (HADS‐D). The intention of HADS is to identify patients with high possibility and probability of anxiety and/or depression in a nonpsychiatric hospital clinic . HADS‐A and HADS‐D scores between 0 and 7 are considered asymptomatic, and scores > 7 indicate symptoms of anxiety and depression, respectively. FSS 7‐item measures subjective fatigue through seven questions scored on a 7‐point Likert scale (1 = strongly disagree, 7 = strongly agree) . FSS score was reported as the mean score of the seven questions, and scoring of high fatigue is defined as mean FSS score ≥ 5 . In addition, the questionnaire contained questions about whether the patients previously had been diagnosed with myocardial infarction, stroke, diabetes, hypertension, chronic obstructive pulmonary disease, asthma, angina pectoris and/or depression. At the end of both 3‐week treatment periods, the patients answered an additional questionnaire regarding reported adherence to treatment, subjective effects and side effects. Regarding treatment adherence, patients were asked two questions: The first question was ‘On average, how many nights per week do you use your oral appliance’, with five response options: ‘0 nights/week’, ‘1–2 nights/week’, ‘3–4 nights/week’, ‘5–6 nights/week’ and ‘7 nights/week’. The second question was ‘On average, how many hours do you use your oral appliance per night’, with alternatives: ‘0–2 h/night’, ‘2–3 h/night’, ‘3–4 h/night’, ‘4–5 h/night’ or ‘> 5 h/night’. Concerning subjective effect, patients were asked ‘how is your daytime sleepiness affected by the oral appliance treatment’. Response options were ‘increased daytime sleepiness’, ‘unchanged’, ‘reduced daytime sleepiness’ and ‘complete relief of daytime sleepiness’. Furthermore, patients were asked ‘how is your quality of life affected by the oral appliance treatment’, with response options ‘worsening’, ‘unchanged’ and ‘improved’. Regarding side effects, patients were asked to check boxes in the questionnaire if they had experienced any of the following side effects when using their oral appliance: ‘temporomandibular joint pain’, ‘headache’, ‘pain in masticatory muscles’, ‘increased salivation’, ‘dry mouth’ and ‘occlusal changes’.
Sample Size Calculation Prior to the study, sample size calculation was performed with 5% level of significance and 80% power, using data from above‐mentioned pilot study with similar study design , where treatment success (> 50% reduction of REI) with and without elastic bands was 90% and 70%, respectively. The result of this calculation determined that 124 patients (62 per group) were sufficient for this study. Being a crossover trial, this would correspond to 62 patients in total. To account for 10% attrition rate, 69 patients were recruited in total. Sample size calculations were performed using the statistical software ‘Stata 18’ (StataCorp LLC) .
Statistics All statistical analyses were performed using Stata 18 (StataCorp LLC). Student's t ‐test was performed to investigate differences between continuous variables following OA treatment with and without elastic bands, and Pearson's chi‐squared test was performed to investigate differences between categorical variables posttreatment. Values pre‐ and posttreatment were used to investigate effect within each treatment modality (with and without elastic bands), and delta values (change pre‐ to posttreatment per treatment modality) were used to investigate differences between the two treatment modalities. Logistic regression analysis was performed to investigate variables predicting treatment success exclusively with OA treatment with elastic bands, with ‘treatment success exclusively with elastic bands’ (yes or no) as a dependent, bivariate variable. Selection of independent variables for the analysis encompassed variables that theoretically could influence treatment outcomes based on previous research on prediction of responders to OA treatment . Patients who were nonadherent to OA treatment with or without elastic bands were excluded from the analysis. To adjust for the repeated design (crossover), we applied logistic regressions with cluster robust variance estimates for two observations per individual when investigating differences in success with elastic bands between men and women. Differences in side effects between the two treatment modalities were investigated using binomial probability tests, investigating the probability of observing identical or higher frequency of side effects with elastic bands compared to OA treatment without elastic bands. In addition, binomial probability tests were used to investigate differences in frequency of ESS, BIS, HADS‐A, HADS‐D and FSS between the two treatment modalities. Student's t ‐test was performed to investigate differences between patients completing both interventions and patients dropping out from the study for the variables age, gender, BMI, neck circumference, REI baseline, REI supine baseline, SaO 2 , SaO 2 nadir, overjet, overbite and maximum mouth opening. The level of significance was set to p < 0.05 for all above‐mentioned statistical tests.
Ethics The trial was approved by the Regional Ethics Committee of Western Norway (Protocol No: 550079 REK Vest). The study was also approved by the health and social services representative of both the University of Bergen and Haukeland University Hospital. Written informed consent was obtained by all participants before treatment started. The study was registered at clinicaltrials.gov prior to trial start (ID: NCT05987618).
Results A total of 69 patients were included in the study (19 females and 50 males). In total, 52 patients completed both arms of the trial (16 females and 36 males). A chart for a detailed description of the participants’ study flow is shown in Figure . Their baseline characteristics are described in Table . All included patients ( n = 52) were noncompliant with PAP therapy, and six patients (11.5%) had previously undergone surgical treatment for OSA/snoring indication. Among included patients, 23 (45.1%) were diagnosed with hypertension, five (9.6%) with diabetes, seven (13.5%) with asthma, three (5.8%) with chronic obstructive pulmonary disease, one (1.9%) with angina pectoris and one (1.9%) with a history of myocardial infarction. None had previously suffered from a stroke, and 10 (19.2%) patients reported depression at baseline. The average protrusion of the OAs was 5.7 mm, ranging from 2.1 to 9.6 mm. The average thickness of the OAs, meaning the interincisal distance with the OA in situ, was 5.4 mm, ranging from 4 to 8 mm. When including the vertical overbite of the patients, the average increase of the total vertical opening with the OA in situ was 8.4 mm, ranging from 4 to 12 mm. 3.1 Comparison Between the Treatment Modalities 3.1.1 Objective Measured Results of the Intervention The success rates (> 50% reduction of REI) with OA treatment with and without elastic bands were significantly in favour of treatment with elastic bands (53.9% vs. 34.6% success, p = 0.002). Results from PG registrations, displaying outcomes of both interventions are further described in Table . 3.1.2 Subjective Measured Results of the Intervention The frequency of reporting ‘improved quality of life’ after treatment was significantly higher using OA without compared to elastic bands (71.1% vs. 56.3%, p = 0.018). Daytime sleepiness (ESS) was reduced after OA treatment compared to baseline values, both with (7.9 to 6.2, p < 0.001) and without (7.9 to 6.6, p < 0.002) elastic bands, without significant differences between the two treatment modalities. However, the frequency of participants with excessive daytime sleepiness (ESS > 10) was only significantly reduced without elastic bands, but not with elastic bands (Table ). A total of 66.7% and 69.6% with and without elastic bands, respectively, reported reduced daytime sleepiness on the question ‘how is your daytime sleepiness affected by the oral appliance treatment’. Complete relief of daytime sleepiness was reported by 7.8% (with elastic bands) and 8.7% (without elastic bands) on the same question. These results did not differ between treatment modalities. No significant differences were observed in reported chronic insomnia (based on BIS), anxiety (HADS‐A), depression (HADS‐D) or fatigue (FSS 7‐item) following both interventions (Table ). 3.1.3 Reported Adherence to the Treatment A total of 91.8% of the patients reported using their OA with elastic bands 5 nights or more per week compared to 95.9% reported usage without elastic bands ( p = 0.01). Overall, 94.5% reported usage of their OA with elastic bands 4 h or more per night compared to 98.6% without elastic bands (NS). 3.1.4 Side Effects No statistically significant differences were observed in reported side effects between the two treatment modalities: Temporomandibular joint pain occurring within the treatment period was reported by 16.7% and 15.2% with and without elastic bands, respectively. The corresponding figures for headache were 8.3% and 6.5%, pain in masticatory muscles 8.3% and 6.5%, increased salivation 25.0% and 23.9%, dry mouth 22.9% and 19.6% and occlusal changes 18.8% and 13.0%. 3.1.5 Predictive Factors for Superior Effect Using Elastic Bands After patients had completed treatment with both interventions, 13 (25.0%) patients had been successfully treated exclusively with elastic bands. The remaining 39 patients (75%) that completed both interventions were either successfully treated both with and without elastic bands (28.8%), successfully treated exclusively without elastic bands (5.8%) or failed with both treatment modalities (40.4%). Of the 13 patients successfully treated exclusively with elastic bands, 11 (84.6%) were men and 2 (15.4%) were women. Logistic regression showed that a significantly greater chance of reducing REI > 50% from baseline with elastic bands was observed for men (OR:2.9, 95% CI: 1.35–6.21, p = 0.006) but not for women (OR: 1.30, 95% CI: 0.53–3.20, NS). In addition, maximum mouth opening at baseline was significantly larger in the group exclusively successful with elastic bands, with 54.5 mm (SD:6.0 mm) maximum mouth opening, compared to 50.1 mm (SD:6.0 mm) in the remaining patients (OR:1.13, 95% CI: 1.01–1.27, p = 0.035). 3.1.6 Subgroup Analysis Subgroup analysis was performed for the group of patients predicted to be responders to OA treatment with elastic bands, which were men with maximum mouth opening above 50 mm (50th percentile). The subjective variables that showed significant differences in favour of OA treatment without elastic bands were investigated for this subgroup of patients (frequency of reporting ‘improved quality of life’, frequency of participants with excessive daytime sleepiness (ESS > 10) and higher frequency of reported usage ≥ 5 nights/week). No significant differences were shown for any of the investigated variables for men with maximum mouth opening above 50 mm in the study population that completed both interventions. 3.1.7 Missing Data Analysis Of the included 69 patients, a total of 17 (24.6%) dropped out during the intervention for various reasons (Figure ). In missing data analyses, no significant differences were observed between study drop‐outs ( n = 17) and completers ( n = 52) for the investigated variables age (52.9 years vs. 50.8 years), gender (30.8% females vs. 17.7% females), BMI (31.1 vs. 31.7), neck circumference (41.5 cm vs. 42.9 cm), REI baseline (30.3 vs. 34.1), REI supine baseline (41.4 vs. 49.9), SaO 2 mean (92.5% vs. 91.9%), SaO 2 nadir (77.6% vs. 77.2%), overjet (2.5 mm vs. 2.8 mm), overbite (3.0 mm vs. 3.0 mm) or maximum mouth opening (51.2 mm vs. 51.6 mm).
Comparison Between the Treatment Modalities 3.1.1 Objective Measured Results of the Intervention The success rates (> 50% reduction of REI) with OA treatment with and without elastic bands were significantly in favour of treatment with elastic bands (53.9% vs. 34.6% success, p = 0.002). Results from PG registrations, displaying outcomes of both interventions are further described in Table . 3.1.2 Subjective Measured Results of the Intervention The frequency of reporting ‘improved quality of life’ after treatment was significantly higher using OA without compared to elastic bands (71.1% vs. 56.3%, p = 0.018). Daytime sleepiness (ESS) was reduced after OA treatment compared to baseline values, both with (7.9 to 6.2, p < 0.001) and without (7.9 to 6.6, p < 0.002) elastic bands, without significant differences between the two treatment modalities. However, the frequency of participants with excessive daytime sleepiness (ESS > 10) was only significantly reduced without elastic bands, but not with elastic bands (Table ). A total of 66.7% and 69.6% with and without elastic bands, respectively, reported reduced daytime sleepiness on the question ‘how is your daytime sleepiness affected by the oral appliance treatment’. Complete relief of daytime sleepiness was reported by 7.8% (with elastic bands) and 8.7% (without elastic bands) on the same question. These results did not differ between treatment modalities. No significant differences were observed in reported chronic insomnia (based on BIS), anxiety (HADS‐A), depression (HADS‐D) or fatigue (FSS 7‐item) following both interventions (Table ). 3.1.3 Reported Adherence to the Treatment A total of 91.8% of the patients reported using their OA with elastic bands 5 nights or more per week compared to 95.9% reported usage without elastic bands ( p = 0.01). Overall, 94.5% reported usage of their OA with elastic bands 4 h or more per night compared to 98.6% without elastic bands (NS). 3.1.4 Side Effects No statistically significant differences were observed in reported side effects between the two treatment modalities: Temporomandibular joint pain occurring within the treatment period was reported by 16.7% and 15.2% with and without elastic bands, respectively. The corresponding figures for headache were 8.3% and 6.5%, pain in masticatory muscles 8.3% and 6.5%, increased salivation 25.0% and 23.9%, dry mouth 22.9% and 19.6% and occlusal changes 18.8% and 13.0%. 3.1.5 Predictive Factors for Superior Effect Using Elastic Bands After patients had completed treatment with both interventions, 13 (25.0%) patients had been successfully treated exclusively with elastic bands. The remaining 39 patients (75%) that completed both interventions were either successfully treated both with and without elastic bands (28.8%), successfully treated exclusively without elastic bands (5.8%) or failed with both treatment modalities (40.4%). Of the 13 patients successfully treated exclusively with elastic bands, 11 (84.6%) were men and 2 (15.4%) were women. Logistic regression showed that a significantly greater chance of reducing REI > 50% from baseline with elastic bands was observed for men (OR:2.9, 95% CI: 1.35–6.21, p = 0.006) but not for women (OR: 1.30, 95% CI: 0.53–3.20, NS). In addition, maximum mouth opening at baseline was significantly larger in the group exclusively successful with elastic bands, with 54.5 mm (SD:6.0 mm) maximum mouth opening, compared to 50.1 mm (SD:6.0 mm) in the remaining patients (OR:1.13, 95% CI: 1.01–1.27, p = 0.035). 3.1.6 Subgroup Analysis Subgroup analysis was performed for the group of patients predicted to be responders to OA treatment with elastic bands, which were men with maximum mouth opening above 50 mm (50th percentile). The subjective variables that showed significant differences in favour of OA treatment without elastic bands were investigated for this subgroup of patients (frequency of reporting ‘improved quality of life’, frequency of participants with excessive daytime sleepiness (ESS > 10) and higher frequency of reported usage ≥ 5 nights/week). No significant differences were shown for any of the investigated variables for men with maximum mouth opening above 50 mm in the study population that completed both interventions. 3.1.7 Missing Data Analysis Of the included 69 patients, a total of 17 (24.6%) dropped out during the intervention for various reasons (Figure ). In missing data analyses, no significant differences were observed between study drop‐outs ( n = 17) and completers ( n = 52) for the investigated variables age (52.9 years vs. 50.8 years), gender (30.8% females vs. 17.7% females), BMI (31.1 vs. 31.7), neck circumference (41.5 cm vs. 42.9 cm), REI baseline (30.3 vs. 34.1), REI supine baseline (41.4 vs. 49.9), SaO 2 mean (92.5% vs. 91.9%), SaO 2 nadir (77.6% vs. 77.2%), overjet (2.5 mm vs. 2.8 mm), overbite (3.0 mm vs. 3.0 mm) or maximum mouth opening (51.2 mm vs. 51.6 mm).
Objective Measured Results of the Intervention The success rates (> 50% reduction of REI) with OA treatment with and without elastic bands were significantly in favour of treatment with elastic bands (53.9% vs. 34.6% success, p = 0.002). Results from PG registrations, displaying outcomes of both interventions are further described in Table .
Subjective Measured Results of the Intervention The frequency of reporting ‘improved quality of life’ after treatment was significantly higher using OA without compared to elastic bands (71.1% vs. 56.3%, p = 0.018). Daytime sleepiness (ESS) was reduced after OA treatment compared to baseline values, both with (7.9 to 6.2, p < 0.001) and without (7.9 to 6.6, p < 0.002) elastic bands, without significant differences between the two treatment modalities. However, the frequency of participants with excessive daytime sleepiness (ESS > 10) was only significantly reduced without elastic bands, but not with elastic bands (Table ). A total of 66.7% and 69.6% with and without elastic bands, respectively, reported reduced daytime sleepiness on the question ‘how is your daytime sleepiness affected by the oral appliance treatment’. Complete relief of daytime sleepiness was reported by 7.8% (with elastic bands) and 8.7% (without elastic bands) on the same question. These results did not differ between treatment modalities. No significant differences were observed in reported chronic insomnia (based on BIS), anxiety (HADS‐A), depression (HADS‐D) or fatigue (FSS 7‐item) following both interventions (Table ).
Reported Adherence to the Treatment A total of 91.8% of the patients reported using their OA with elastic bands 5 nights or more per week compared to 95.9% reported usage without elastic bands ( p = 0.01). Overall, 94.5% reported usage of their OA with elastic bands 4 h or more per night compared to 98.6% without elastic bands (NS).
Side Effects No statistically significant differences were observed in reported side effects between the two treatment modalities: Temporomandibular joint pain occurring within the treatment period was reported by 16.7% and 15.2% with and without elastic bands, respectively. The corresponding figures for headache were 8.3% and 6.5%, pain in masticatory muscles 8.3% and 6.5%, increased salivation 25.0% and 23.9%, dry mouth 22.9% and 19.6% and occlusal changes 18.8% and 13.0%.
Predictive Factors for Superior Effect Using Elastic Bands After patients had completed treatment with both interventions, 13 (25.0%) patients had been successfully treated exclusively with elastic bands. The remaining 39 patients (75%) that completed both interventions were either successfully treated both with and without elastic bands (28.8%), successfully treated exclusively without elastic bands (5.8%) or failed with both treatment modalities (40.4%). Of the 13 patients successfully treated exclusively with elastic bands, 11 (84.6%) were men and 2 (15.4%) were women. Logistic regression showed that a significantly greater chance of reducing REI > 50% from baseline with elastic bands was observed for men (OR:2.9, 95% CI: 1.35–6.21, p = 0.006) but not for women (OR: 1.30, 95% CI: 0.53–3.20, NS). In addition, maximum mouth opening at baseline was significantly larger in the group exclusively successful with elastic bands, with 54.5 mm (SD:6.0 mm) maximum mouth opening, compared to 50.1 mm (SD:6.0 mm) in the remaining patients (OR:1.13, 95% CI: 1.01–1.27, p = 0.035).
Subgroup Analysis Subgroup analysis was performed for the group of patients predicted to be responders to OA treatment with elastic bands, which were men with maximum mouth opening above 50 mm (50th percentile). The subjective variables that showed significant differences in favour of OA treatment without elastic bands were investigated for this subgroup of patients (frequency of reporting ‘improved quality of life’, frequency of participants with excessive daytime sleepiness (ESS > 10) and higher frequency of reported usage ≥ 5 nights/week). No significant differences were shown for any of the investigated variables for men with maximum mouth opening above 50 mm in the study population that completed both interventions.
Missing Data Analysis Of the included 69 patients, a total of 17 (24.6%) dropped out during the intervention for various reasons (Figure ). In missing data analyses, no significant differences were observed between study drop‐outs ( n = 17) and completers ( n = 52) for the investigated variables age (52.9 years vs. 50.8 years), gender (30.8% females vs. 17.7% females), BMI (31.1 vs. 31.7), neck circumference (41.5 cm vs. 42.9 cm), REI baseline (30.3 vs. 34.1), REI supine baseline (41.4 vs. 49.9), SaO 2 mean (92.5% vs. 91.9%), SaO 2 nadir (77.6% vs. 77.2%), overjet (2.5 mm vs. 2.8 mm), overbite (3.0 mm vs. 3.0 mm) or maximum mouth opening (51.2 mm vs. 51.6 mm).
Discussion To our knowledge, this is the first randomised crossover trial comparing OA treatment with and without elastic bands, based on an appropriate sample size estimation. Overall, the application of elastic bands significantly improved OA treatment success in this study. However, several subjective variables came out in favour of OA treatment without elastic bands. The main benefit of applying elastic bands was a greater reduction in REI when sleeping in supine position. REI nonsupine did not differ between the two treatment modalities (Table ). POSA has previously been recognised as a predictive factor for success with OA treatment in several studies . However, the majority of studies that identified POSA as a predictor of success with OA treatment used OA designs that restricted mouth opening during sleep, such as monobloc appliances . Studies that did not identify POSA as a predictive factor for OA success tend to use bibloc OAs that allow mouth opening to a greater extent, such as those used in the present study . Opening of the mouth with an OA in situ has been shown to increase pharyngeal collapsibility , consequently leading to less effective OA treatment . This mechanism is more pronounced in supine position due to gravity . A recent study comparing a monobloc appliance with a bibloc appliance with elastic bands showed significantly better treatment effect in the bibloc group . This finding indicates that the application of elastic bands on bibloc appliances enhances the positive functional mechanisms of the monobloc appliance, reducing the pharyngeal collapsibility. These results are supported by the findings in the current study. Hence, patients who might benefit from reduced mouth opening when treated with an OA should be considered for treatment with bibloc appliances with elastic bands rather than monoblocs for several reasons. In addition to superior treatment effect, biblocs are generally simpler to manufacture and titrate, and biblocs with elastic bands can easily be converted into a standard bibloc if issues regarding adherence or other problems occur. A larger maximum mouth opening was associated with success exclusively with elastic bands. OAs that allow involuntary mouth opening exceeding the vertical range of the appliance might impair treatment efficacy during these episodes of sleep. Consequently, the appliance wings no longer hold the mandible in the desired protruded position leading to loss of pharyngeal patency. Hence, patients with large maximum mouth opening capacity should be considered for OA with elastic bands to prevent ‘losing’ the mandibular protrusive position. A significantly greater chance of success with OA was observed for men when treated with elastic bands compared to women in the present study. This finding can be compared with a previous study evaluating the treatment effect of a monobloc appliance with nasal CPAP . All patients included were men with moderate OSA, in addition to being categorised with POSA. The study showed no significant differences in treatment effect between the monobloc and nasal CPAP, meaning that men with moderate OSA and POSA may achieve excellent treatment effects with an OA that restricts mouth opening. The previously mentioned study from Marklund , using a monobloc device, identified POSA as a predictive factor for treatment success in men, but not in women. Similarly, reduction of supine AHI with elastic bands was significantly greater for men, but not for women, in the present study. This may indicate that men with POSA generally should be considered for OA designs that restrict mouth opening. The frequency of reporting ‘improved quality of life’ during OA treatment was significantly higher without elastic bands. In addition, frequency of participants with excessive daytime sleepiness (ESS > 10) was significantly lower without elastic bands, as well as significantly higher frequency of reported usage ≥ 5 nights/week, compared to OA with elastic bands. These findings may indicate that the patients who completed this study preferred OA treatment without elastic band as a group. The fact that five patients dropped out due to nonadherence with elastic bands emphasises this. Previous studies on subjective outcomes of OA treatment comparing different OA designs did not identify a superior modality in this regard . Furthermore, studies on patient preferences comparing OAs with monobloc design to bibloc design have conflicting results: Some studies show higher preferences towards bibloc designs , whereas other comparable studies are in favour of monobloc designs . However, in the subgroup analysis, investigating differences between above‐mentioned subjective variables for men with large maximum mouth opening, no significant differences were observed. This indicates that with appropriate patient selection, the use of elastic bands may not affect patient preferences. There is seemingly not one single OA design that is selected by all patients, and it seems necessary to individualise OA design per patient to increase both treatment effect and adherence. Therefore, future research needs to emphasise patient preferences to identify variables that may predict this on an individual level, in addition to investigating variables predicting treatment efficacy. The main limitation of our study is the number of dropouts during the intervention. A total of 52 patients completed both interventions, meaning that the number of patients determined through sample size estimation ( n = 62) was not reached due to considerably higher attrition rate than expected. Still, a significant difference in the primary outcome measure was observed, and the dropouts did not differ significantly from the patients completing both interventions in any of the investigated variables. However, variables predicting treatment success with elastic bands, such as male gender and large maximum mouth opening, cannot be generalised to apply for all OSA patients but need to be put in context with other similar studies before application. It should also be noted that some patients had missing data regarding REI nonsupine at baseline (Table ), preventing the assessment of their POSA status. This resulted in a reduced sample size when investigating POSA as a predictive variable in the study population. Furthermore, it was not possible to monitor objective adherence with the two treatment modalities because an adherence chip on the OAs was not included in the study. The rather short follow‐up time per treatment modality indicates an initial preference towards treatment without elastic bands, however, long‐term follow‐up studies are needed to investigate how elastic bands may affect adherence to OA treatment over time. Due to the clinically implemented recruitment strategy in this study, patients meeting the inclusion criteria (being nonadherent to PAP) were consecutively invited by all clinicians performing follow‐up controls on their PAP therapy. Hence, it was not possible to estimate the exact number of patients who were assessed for eligibility. In addition, patients who are nonadherent to PAP may possess personality traits that also make them prone to be nonadherent to OA. Probably, an ‘unselected’ group of patients would have been preferable, although the study design reflects guidelines and the practice in most sleep centres internationally, selecting PAP as the primary choice of treatment. We did not apply a wash‐out period between the two treatment modalities due to ethical considerations, meaning we found it difficult to argue that patients deemed in need of treatment for moderate or severe sleep apnoea should be advised to discontinue treatment for a given period. Furthermore, we assumed no or limited carry‐over effect between the two treatment modalities, and we therefore did not consider a wash‐out period as obligatory. The strength of this study is primarily its design. A randomised crossover trial is an optimal environment for comparing two treatment modalities, where neither has any significant persisting effects, eliminating most confounding factors which could be present in other randomised controlled trials with unmatched groups. In addition to this, we investigated the effect of OA treatment on several subjective variables using validated questionnaires, such as ESS, BIS, FSS 7‐item and HADS, which have been lacking in previous reports on the same theme. Furthermore, the study population was highly representative of noncompliant patients referred for OA treatment, as the patients were recruited in a clinical environment with relatively wide inclusion criteria. Hence, the results of this study are easily applicable in clinical practice.
Conclusion The application of elastic bands to restrict mouth opening in OA treatment significantly improved treatment success compared to OA treatment without elastic bands (53.9% vs. 34.6%). These results apply to OA treatment using appliances that allow mouth opening. Patient groups that seemed to benefit from the application of elastic bands were men with large maximum mouth opening, reducing the REI in supine position. However, patients in the current study preferred OA treatment without elastic bands, emphasising the necessity of selecting patients who will benefit from them, rather than universally applying elastic bands on all OAs.
All authors contributed to concept and study design. U.L.O. collected the data. U.L.O., M.B. and A.J. analysed the data. S.L. and B.B. interpreted the data. U.L.O. wrote the paper. M.B., S.L., B.B. and A.J. revised the paper. All authors approved the final draft of the manuscript before submission.
The authors declare no conflicts of interest.
The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer‐review/10.1111/joor.13870 .
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Influence of engaging female caregivers in households with adolescent girls on adopting equitable family eating practices: a quasi-experimental study | 4be273c4-4011-48e9-a05f-61a35e9b4951 | 10518164 | Family Medicine[mh] | Improving adolescent girls’ nutritional status is a critical milestone in achieving the Sustainable Development Goals . Eating together as a family has many positive outcomes, including equitable food distribution and improved nutritional and health status in low-income settings. Families who eat together tend to have a more equitable food distribution, with all members receiving a fair share of available food . Though eating together as a family has beneficial health outcomes, family eating practices are often influenced by prevailing social norms, making it challenging to promote the equable distribution of food within the household . Social norms often dictate food consumption, including who, when, and how much to eat. Influential adult community members and caregivers often enforce the norms . Within households, female caregivers have a more significant role in food preparation and allocation, often guided by the prevailing social norms . In low-income countries, discriminatory social norms often make young adolescent girls eat less frequently or less, especially during periods of scarcity . Food allocation social norms that lead to unequal consumption can severely affect adolescent girls’ food security and nutritional status . In addition, such social norms can lead to insufficient consumption to meet daily dietary requirements resulting in poor mental and physical health outcomes . Adolescent girls with depleted nutrients may experience fatigue, weakness, and difficulty concentrating in school, limiting their potential and opportunities in their educational and career development . When families eat together, everyone around the table has a better opportunity to share the available food fairly, even if there is a shortage in the household . This is especially important for vulnerable groups like adolescent girls, who might otherwise be left with little or nothing unless they eat together with other family members . To improve equitable household food distribution practices for adolescent girls, breaking the food inequality cycle resulting from gender-biased social norms is crucial . Social Analysis and Action (SAA) promotes community dialogue among the people who influence the norms ( e.g ., community leaders, religious leaders, mothers-in-law, and husbands) to shape existing expectations, decisions, and behaviors around household food allocation. For the purpose of this study, the community dialogue centered around adolescent girls’ nutrition. SAA is regarded as a community-led social change process. In this approach, community members are active and central in identifying social issues, setting goals, and implementing positive social changes . Previous research has suggested that simple information or education about healthy eating practices may not be sufficient to change social norms and promote behavioral changes. Instead, targeted interventions that address specific social norms are more effective in promoting behavior change . The engagement of influential norm holders and gatekeepers is critical to bring about sustainable and acceptable changes in social norms. Nevertheless, research on social norms related to food allocation inequality is scarce in Ethiopia. Therefore, this study assesses the effect of engaging norm influencers on improving family eating practices.
Study setting, design, and population The study was conducted in the West Hararghe zone of the Oromia regional state in Ethiopia. The study was conducted in three Woredas, where two weredas implemented SAA intervention and one Wereda served as a comparison area. The majority of the population in these areas belongs to the Oromo ethnic group and follows the Islam faith. In addition, agriculture-based living styles are dominant in the area. This article utilized data from a large quasi-experimental study that assessed various interventions to address structural issues affecting young adolescent girls in the Western Hararghe Zone, Oromia Region, Ethiopia . This study’s population was female caregivers with at least one young adolescent girl (age 13 and 14) in the household. The intervention primarily targeted female caregivers who are responsible for food preparation and allocation in the households. Intervention (SAA) The SAA was initiated by assembling female caregivers along with other influential adults in the community, including religious leaders and other norm holders. The group consists of about 30 influential adults to ensure adequate dialogue and greater impact by engaging influential adults in the process, which promotes open and candid discussion, seeking solutions to identified challenges, and promoting actions to address the challenges. The process starts with the training of the partcipants by trained facilitators. The SAA was facilitated by selected and trained community members from each group. Trained support staff were assigned to support the groups in person or via telephone to maintain fidelity to the intervention. The SAA is designed to change community social norms through open dialogue . The groups met monthly, and each discussion session lasted an hour or more. The central discussion topics girls related to this research outcome were adolescent girls’ nutrition. The group met every month for 3 years during the intervention period. Mechanisms/causal pathways The intervention (SAA) influences the behavior of individuals or groups, leading to changes in their decision-making, habits, or actions related to family eating practices, focusing on adolescent girls. For instance, understanding adolescent girls’ nutritional needs and correcting cultural miss conception might lead to changes in intrahousehold family eating practices. In addition to this, Socio-cultural contexts can play a role in how individuals or groups behave in a given situation. These factors might mediate or modify treatment effects . Sampling procedure The study population was selected by a two-stage cluster sampling method, as it was described in the previous article . First, for each study arm (intervention and comparison arms), 38 clusters (locally known as development areas or ‘Gere,’ a division within the smallest administrative unit, the kebele) were randomly selected after preparing the list of all clusters in the study woredas. Then, in each cluster, the list of households with adolescent girls (aged 13–17 years) was prepared by conducting a complete household listing. In the final stage, 30 households with eligible adolescent girls were randomly selected from the list. Then, female caregivers in the sampled households were invited to participate in the study. Data collection A structured questionnaire was used to collect relevant data. The questionnaire contains questions related to socio-demographic variables from the Ethiopian Demographic and health survey (EDHS) and standard questions used for the household food security assessment . The questionnaire was first developed in English and then translated into the local language, Afaan Oromo. Data were captured before the intervention (at baseline) and after a period of implementing the intervention for 3 years (at end line) using an open data kit (ODK), which is an electronic data collection platform. Data were collected by trained enumerators fluent in the written and spoken local language, Afaan Oromo. Measurement The effect of the intervention (social analysis and action) was measured by the number of families eating together, which was assessed at baseline before the intervention commenced and at the end-line after the intervention. The family was considered as eating together if a family mostly ate at the same time from the same dish, as reported by female caregivers. Then, the family eating practice, the outcome variable, was dichotomized into eating together or not. Data analysis The data analysis was done on a combined baseline and end-line dataset. A time variable was created as ‘time,’ the baseline dataset was coded as ‘0,’ and the endline was coded as ‘1’. We compare differences in eating practices between the study groups using chi-square tests. The Difference-in-Difference (DID) approach is a method to assess the program’s impact by comparing changes over time between the comparison group. For this study, the DID model is based on a mixed-effect logistic regression. The model assumes that in the absence of the intervention, the difference between the intervention and comparison group would be constant over time, which is a parallel trend assumption. Difference-in-Difference is implemented by adding an interaction term between time and program group dummy variables in a regression model and can be specified as follows: [12pt]{minimal}
$$\!\!\!\!\!\! {Y_i} = {{b_0} + {b_1*[Time_i]} +{ b_2*[program_i]} + {b3* [Time_i*Program_i]} + {b_4*[Covariates]} + {_i}}$$ Y i = b 0 + b 1 ∗ [ T i m e i ] + b 2 ∗ [ p r o g r a m i ] + b 3 ∗ [ T i m e i ∗ P r o g r a m i ] + b 4 ∗ [ C o v a r i a t e s ] + ε i in which [12pt]{minimal}
$ {y_i}$ y i is the outcome variable, which is family eating practice measured at the baseline and end line in both intervention and comparison group. [12pt]{minimal}
${_0}$ β 0 is the intercept, which is the value of the outcome variable when all of the other variables are equal to zero. [12pt]{minimal}
$ Time_i$ T i m e i is a time variable, which is the baseline or end-line period that takes the value 0 or 1 respectively. [12pt]{minimal}
$ Program_i$ P r o g r a m i is a program variable, which is the control or treatment groups that coded as 0 or 1 respectively. [12pt]{minimal}
$ {(Time _i*program_i)}$ ( T i m e i ∗ p r o g r a m i ) is an interaction term between time and program, which used to test the effect of the intevention. [12pt]{minimal}
$ {_i}$ ϵ i is the error term, which captures the effect of all factors that affect the outcome but the model could not adequately represent. The models were adjusted for clustering and unbalanced covariates between the intervention and control group. A mixed-effect model was used to control the clustering effect as the data is clustered. As the outcome variable was binary, we used mixed-effect logistic regression. We used the Difference-in-Difference (DID) model with the interaction terms to determine the intervention effect. The impact was examined based on a statistically significant difference of p -value < 0.05, the crude odds ratios (COR), and adjusted odds ratios (AOR) with 95% confidence intervals (CI) of the interaction term (program × time), using Stata version 14 statistical software. We checked for multicollinearity and outliers during the analysis. Mixed effect logistic regression controlled for clustering effect and other variables such as female caregiver’s educational status and income, male caregivers’ education status and income, household wealth and food security status, intervention, time, and difference in the different interaction terms. Ethical considerations The study protocol was reviewed and approved by the Ethical Review Board of the Addis Continental Institute of Public Health (Ref No. ACIPH/IRB/005/2016). Due to the low literacy level of the community and the minimal risk involved in the study, informed verbal consent was obtained after explaining the purpose of the study. Participants were informed about their rights to refuse participation and/or withdraw their consent at any time. All interviews took place in private settings. No personal identifiers were linked with the dataset made available for this study.
The study was conducted in the West Hararghe zone of the Oromia regional state in Ethiopia. The study was conducted in three Woredas, where two weredas implemented SAA intervention and one Wereda served as a comparison area. The majority of the population in these areas belongs to the Oromo ethnic group and follows the Islam faith. In addition, agriculture-based living styles are dominant in the area. This article utilized data from a large quasi-experimental study that assessed various interventions to address structural issues affecting young adolescent girls in the Western Hararghe Zone, Oromia Region, Ethiopia . This study’s population was female caregivers with at least one young adolescent girl (age 13 and 14) in the household. The intervention primarily targeted female caregivers who are responsible for food preparation and allocation in the households.
The SAA was initiated by assembling female caregivers along with other influential adults in the community, including religious leaders and other norm holders. The group consists of about 30 influential adults to ensure adequate dialogue and greater impact by engaging influential adults in the process, which promotes open and candid discussion, seeking solutions to identified challenges, and promoting actions to address the challenges. The process starts with the training of the partcipants by trained facilitators. The SAA was facilitated by selected and trained community members from each group. Trained support staff were assigned to support the groups in person or via telephone to maintain fidelity to the intervention. The SAA is designed to change community social norms through open dialogue . The groups met monthly, and each discussion session lasted an hour or more. The central discussion topics girls related to this research outcome were adolescent girls’ nutrition. The group met every month for 3 years during the intervention period.
The intervention (SAA) influences the behavior of individuals or groups, leading to changes in their decision-making, habits, or actions related to family eating practices, focusing on adolescent girls. For instance, understanding adolescent girls’ nutritional needs and correcting cultural miss conception might lead to changes in intrahousehold family eating practices. In addition to this, Socio-cultural contexts can play a role in how individuals or groups behave in a given situation. These factors might mediate or modify treatment effects .
The study population was selected by a two-stage cluster sampling method, as it was described in the previous article . First, for each study arm (intervention and comparison arms), 38 clusters (locally known as development areas or ‘Gere,’ a division within the smallest administrative unit, the kebele) were randomly selected after preparing the list of all clusters in the study woredas. Then, in each cluster, the list of households with adolescent girls (aged 13–17 years) was prepared by conducting a complete household listing. In the final stage, 30 households with eligible adolescent girls were randomly selected from the list. Then, female caregivers in the sampled households were invited to participate in the study.
A structured questionnaire was used to collect relevant data. The questionnaire contains questions related to socio-demographic variables from the Ethiopian Demographic and health survey (EDHS) and standard questions used for the household food security assessment . The questionnaire was first developed in English and then translated into the local language, Afaan Oromo. Data were captured before the intervention (at baseline) and after a period of implementing the intervention for 3 years (at end line) using an open data kit (ODK), which is an electronic data collection platform. Data were collected by trained enumerators fluent in the written and spoken local language, Afaan Oromo.
The effect of the intervention (social analysis and action) was measured by the number of families eating together, which was assessed at baseline before the intervention commenced and at the end-line after the intervention. The family was considered as eating together if a family mostly ate at the same time from the same dish, as reported by female caregivers. Then, the family eating practice, the outcome variable, was dichotomized into eating together or not.
The data analysis was done on a combined baseline and end-line dataset. A time variable was created as ‘time,’ the baseline dataset was coded as ‘0,’ and the endline was coded as ‘1’. We compare differences in eating practices between the study groups using chi-square tests. The Difference-in-Difference (DID) approach is a method to assess the program’s impact by comparing changes over time between the comparison group. For this study, the DID model is based on a mixed-effect logistic regression. The model assumes that in the absence of the intervention, the difference between the intervention and comparison group would be constant over time, which is a parallel trend assumption. Difference-in-Difference is implemented by adding an interaction term between time and program group dummy variables in a regression model and can be specified as follows: [12pt]{minimal}
$$\!\!\!\!\!\! {Y_i} = {{b_0} + {b_1*[Time_i]} +{ b_2*[program_i]} + {b3* [Time_i*Program_i]} + {b_4*[Covariates]} + {_i}}$$ Y i = b 0 + b 1 ∗ [ T i m e i ] + b 2 ∗ [ p r o g r a m i ] + b 3 ∗ [ T i m e i ∗ P r o g r a m i ] + b 4 ∗ [ C o v a r i a t e s ] + ε i in which [12pt]{minimal}
$ {y_i}$ y i is the outcome variable, which is family eating practice measured at the baseline and end line in both intervention and comparison group. [12pt]{minimal}
${_0}$ β 0 is the intercept, which is the value of the outcome variable when all of the other variables are equal to zero. [12pt]{minimal}
$ Time_i$ T i m e i is a time variable, which is the baseline or end-line period that takes the value 0 or 1 respectively. [12pt]{minimal}
$ Program_i$ P r o g r a m i is a program variable, which is the control or treatment groups that coded as 0 or 1 respectively. [12pt]{minimal}
$ {(Time _i*program_i)}$ ( T i m e i ∗ p r o g r a m i ) is an interaction term between time and program, which used to test the effect of the intevention. [12pt]{minimal}
$ {_i}$ ϵ i is the error term, which captures the effect of all factors that affect the outcome but the model could not adequately represent. The models were adjusted for clustering and unbalanced covariates between the intervention and control group. A mixed-effect model was used to control the clustering effect as the data is clustered. As the outcome variable was binary, we used mixed-effect logistic regression. We used the Difference-in-Difference (DID) model with the interaction terms to determine the intervention effect. The impact was examined based on a statistically significant difference of p -value < 0.05, the crude odds ratios (COR), and adjusted odds ratios (AOR) with 95% confidence intervals (CI) of the interaction term (program × time), using Stata version 14 statistical software. We checked for multicollinearity and outliers during the analysis. Mixed effect logistic regression controlled for clustering effect and other variables such as female caregiver’s educational status and income, male caregivers’ education status and income, household wealth and food security status, intervention, time, and difference in the different interaction terms.
The study protocol was reviewed and approved by the Ethical Review Board of the Addis Continental Institute of Public Health (Ref No. ACIPH/IRB/005/2016). Due to the low literacy level of the community and the minimal risk involved in the study, informed verbal consent was obtained after explaining the purpose of the study. Participants were informed about their rights to refuse participation and/or withdraw their consent at any time. All interviews took place in private settings. No personal identifiers were linked with the dataset made available for this study.
A total of 812 caregivers in the intervention arm and 913 in the control arm were included in the study. The mean ± Standard Deviation age of female caregivers was 39.85 ± 12.39 in the intervention arm and 39.40 ± 10.87 in the control arm. Most respondents in both arms had primary school education and were ever married, Muslim, and from food-insecure households . The baseline balance for covariates showed no significant differences between the comparison groups except for male caregiver income . The effect of the intervention The family eating practice, eating together, improved in both study arms but more in the intervention arm. There was a 20.57% increase in the intervention arm compared to 8.58% in the comparison group. Overall, the intervention group showed an 11.99% increase compared to the comparison group (Pr = 0.001) . After controlling for potential confounders and clustering effect, the DID analysis showed a significant difference in eating together between the comparison groups. The adjusted odds ratio showed that eating together was twice as likely in the intervention arm as compared to the control arm (AOR 2.08 (95% CI [1.06–4.09]), p -value of 0.033). In addition, male caregivers’ education, income, and household food security status were significantly associated with the outcome . Heterogeneity analyses We also did a heterogeneity analysis by adolescent girls’ ages (13 and 14 separately) and female caregivers’ education (never attended and primary and above). After controlling for potential confounders and clustering effect, the DID interaction term showed a significant difference in eating together among the younger adolescents (AOR 2.96 (95% CI [1.09–7.99]), p -value of 0.032) and among female caregivers with better educational status (AOR 4.03 (95% CI [1.01–16.13]), p -value of 0.049) .
The family eating practice, eating together, improved in both study arms but more in the intervention arm. There was a 20.57% increase in the intervention arm compared to 8.58% in the comparison group. Overall, the intervention group showed an 11.99% increase compared to the comparison group (Pr = 0.001) . After controlling for potential confounders and clustering effect, the DID analysis showed a significant difference in eating together between the comparison groups. The adjusted odds ratio showed that eating together was twice as likely in the intervention arm as compared to the control arm (AOR 2.08 (95% CI [1.06–4.09]), p -value of 0.033). In addition, male caregivers’ education, income, and household food security status were significantly associated with the outcome .
We also did a heterogeneity analysis by adolescent girls’ ages (13 and 14 separately) and female caregivers’ education (never attended and primary and above). After controlling for potential confounders and clustering effect, the DID interaction term showed a significant difference in eating together among the younger adolescents (AOR 2.96 (95% CI [1.09–7.99]), p -value of 0.032) and among female caregivers with better educational status (AOR 4.03 (95% CI [1.01–16.13]), p -value of 0.049) .
The findings of this study showed that the intervention (SAA) improved family eating practices (eating together as a family) that favor adolescent girls after controlling for potential confounding factors such as female caregiver’s educational status, adolescent girls’ age and education, male caregivers income and household wealth and food security status. The heterogeneity analysis showed that the intervention was more effective among female caregivers with younger adolescent girls (13 years old compared to 14 years) and female caregivers with at least primary level education. In many societies, food has values attached to it, often related to household power dynamics. Thus, allowing young adolescent girls to eat together with family can promote gender equality and rights within a household, giving girls a fair chance to share what is available . In addition, giving young adolescent girls a chance to eat together creates a platform for them to develop healthy eating habits and a sense of belongingness and respect, which are essential for their general well-being . Intrahousehold food allocation social norms directly or indirectly shape family eating practices . Families conform with prevailing social norms because they face sanctions and undue pressure from influential community members who are often the norm holders . Engaging influential community members to deliberate on intrahousehold food allocation norms allows them to understand the negative consequences of the norms, which is a crucial step toward promoting equitable household food allocation that benefits adolescent girls . Furthermore, shifting social norms within a community becomes more feasible when better and acceptable alternatives are offered by the community members . Efforts to shift social norms require understanding the community’s health belief systems . The Social Analysis and Action Action (SAA) approach considers perceived barriers and stimulating cues to action in the community. SAA engages norm-holders and influential community members to encourage healthy behavior and influence the belief system . As a result, the community is empowered to overcome the threats of sanctions and adopt positive behaviors. Our finding aligns with the studies that show the effectiveness of a similar intervention in enhancing equitable attitudes, beliefs, and behaviors . A similar intervention in Kenya to improve social support for mothers’ complementary feeding practices also showed positive results . Another study in Benin helped to successfully address social norms related to family planning . The family eating practices could significantly impact health outcomes for adolescent girls and promote family cohesion. The involvement of influential community members is in line with the traditional value given to these groups of people in decision-making in patriarchal societies. Using mixed-effect logistic regression in this study was appropriate as it allowed for the analysis of the clustering effect. Furthermore, the difference in difference analysis employed in this study was critical to control confounding factors. However, the study was conducted in the rural part of the country; thus, the findings may not be generalizable to urban settings and other socio-cultural contexts. Moreover, the study design may not fully control for all potential confounding factors; thus, the influence of residual confounding cannot be ruled out. Finally, the social desirability bias is possible due to the reliance on self-reported family eating practices. In conclusion, the study indicated that the involvement of female caregivers, along with other influential adult community members, significantly improves the family practice of eating together in households, which is greatly beneficial in promoting equitable allocation of family to adolescent girls. The intervention has great potential to minimize household food allocation inequalities and thus improve the nutritional status of young adolescents if implemented successfully at scale. Further studies are necessary to evaluate the effectiveness of the intervention in different socio-cultural contexts to adopt a policy and guidelines for scale-up.
10.7717/peerj.16099/supp-1 Supplemental Information 1 Quantitative baseline end line merged Stata dataset used for the analysis. Click here for additional data file.
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Infectious diseases epidemiology, quantitative methodology, and clinical research in the midst of the COVID-19 pandemic: Perspective from a European country | 02556c36-8560-43eb-827c-efd5819cf05d | 7581408 | Health Communication[mh] | Introduction We have to return to 1918, the time of the H1N1 influenza pandemic, the Spanish flu, to encounter a health crisis that had to be confronted without adequate medicinal products, prior to even the concept of vaccination, poor scientific knowledge (viruses had not been discovered), and with little or no historic registration or surveillance data available . Arguably, such an invasive health crisis has a profound transformational impact on virtually all aspects of society. Spinney , in a chronicle of the Spanish flu, asserts that this 1918 pandemic, responsible for a death rate in the order of magnitude of 100 million people (rescaled to today's world population; directly or because of induced comorbidities, in particular also bacterial infections), was at least equally impactful as both world wars for shaping the world as we have known it until the end of 2019. It is interesting from a historic perspective, and crucial in understanding today's evolution, to examine how the Spanish flu impacted society (politics and geopolitics, social relationships, economic power, etc.). Unquestionably, the SARS-CoV-2 induced COVID-19 epidemic holds the same disruptive power. Our focus is on how such a global public health crisis transforms clinical research, and in particular epidemiological, (bio)statistical and clinical trials research. It is insightful to remember that, at the onset of Spanish flu, scientists thought that it was bacterial, before it catalyzed discovery and then the study of viruses and their induced ailments. In more traditional communities around the world, religious, cultural, (e.g., Confucian) or even environmental explanations were given (such as miasma or bad air). Spanish flu was often confused with bacteria-induced typhus, also known as typhus fever. In the absence of proper diagnostic testing, the occurrence of typhus was confirmed as soon as the characteristic rash occurred. Other than that, milder cases of the Spanish flu were hard to set apart based on the symptoms they induced. For severe cases, there was less doubt (e.g., due to partial or full-body dis-coloring), but by that time it was usually too late. While the details are different, the broad-brush similarity between 1918 and 2020 is striking . The post-Spanish flu public health world looked very different from what it was before. The importance of hygiene to fight and prevent disease had been understood since the seminal contributions of Florence Nightingale and the key discoveries of Louis Pasteur regarding bacteria. A key factor had been the discovery of penicillium and eventually antibiotics. This brings to the fore two important ways to confront infections: hygiene as an archetypical non-pharmaceutical intervention (NPI) and antibiotics as an essential example of a pharmaceutical intervention (PI). But, for the Spanish flu, antibiotic development was in its infancy (arsphenamine, discovered by German physician Ehrlich in 1909, was used for syphylis at the time), which remained the case until the discovery and mass production of penicillin during WWII. More importantly, antibiotics do not work for viral infections. While a century ago the world was less globalized than it is now, the mass movement of people due to WWI, but also transatlantic vessels, offered transmission opportunities to H1N1; our hyper-interconnected world did the same for SARS-CoV-2. This meant that Spanish flu had to be tackled with a variety of NPIs and some PIs, including social distancing, facial masks, quarantining after improvements in diagnosis, and more adequate treatment for H1N1-induced pneumonia. Eventually, likely already in 1919, the virus mutated to a less lethal strain, a typical competitive advantage for a virus, even though the mutation between Spring and Fall 1918 was uncharacteristic: the virus became more lethal over the summer, causing a horrendous second wave of infections . It was clear in 1918 that little or no records, apart from anecdotal evidence, were kept about past influenza epidemics. Fast forwarding to 2020, arguably influenza is properly understood, from a viral, epidemiological, epidemic modeling, and vaccination standpoint. National and international surveillance is well developed, e.g., to determine the components of the upcoming season's influenza vaccine and to monitor the emergence of strains with pandemic potency . We have to admit, though that, even though SARS-CoV-1 and MERS-CoV provided a wakeup call, data on coronavirus induced pathology, in contrast, are rare. Vijgen et al. suggest that the Russian flu of 1890 was the birth of hCoV-OC43, rather than H2N2, the 1957–1958 influenza pandemic in East Asia. Like H1N1, also SARS-CoV-2 induced a sense of urgency and mobilized societal, political, and research forces that are in non-pandemic periods unheard of, except in wartime and in the face of catastrophes such as a financial meltdown. On March 10, 2020, Tomas Pueyo, a product and marketing leader at Course Hero, addressing politicians, wrote on medium.com : “The coronavirus is coming to you. It's coming at an exponential speed: gradually, and then suddenly. It's a matter of days. Maybe a week or two. When it does, your healthcare system will be overwhelmed. Your fellow citizens will be treated in the hallways. Exhausted health care workers will break down. Some will die. They will have to decide which patient gets the oxygen and which one dies.” While it sounded alarmist to some, it has proven an accurate vision for countries and regions in all continents except Oceania. The combined fields of biostatistics, epidemiology, survey science, and clinical trials research, in close collaboration and at the service of virology, immunology, and infectiology, contribute towards the following broad areas: understanding the virus and its dynamics by extending and reformulating existing mathematical and statistical models and used to estimate key epidemiological parameters (e.g., basic reproduction number, incubation period, serial interval, generation interval, etc.); studying the immunological response to SARS-CoV-2 exposure, including the determination of the (sero-)prevalence in the population, T-cell mediated and humoral immunity responses, and potential cross-immunity; monitoring the global pandemic and its epidemics (country-wide, regional, city specific) by observing a set of characteristics and using a variety of modeling tools; gathering additional information by way of (longitudinal) survey sampling to gauge the epidemiological effect as well as the societal side effects (social and economic) of NPIs; making short-, medium-, and longer-term predictions – in view of monitoring health care capacity in early phases, NPI exit strategies, and the building of lines of defense towards surveillance in the post-peak period; contributing to clinical research for the development of diagnostic tools, antiviral medicinal products, and vaccines. Every one of these areas has seen tremendous and rigorous scientific development in the pre-pandemic era, both theoretical and applied. In that sense, the body of knowledge in 2020 cannot possibly be compared with the fragmented knowledge in 1918, and still, the knowledge about key aspects (seasonality, immunity, prevalence) is partial and speculative at best. The field of mathematical and statistical modeling of infectious diseases is well established as is, of course, epidemiology and clinical trial methodology. When a pandemic suddenly breaks out, all of these areas are strongly forced to collaborate, whereas scientific areas, even within medicine, tend to be compartmentalized. Researchers in the same field across the globe should work together. In addition, time is of the essence, so that certain principles need to be relaxed out of necessity, while others stand like a rock. A natural consequence for the need and willingness to collaborate is making available all potentially relevant data and an uncompromised commitment to an open access policy. We return to this key lesson in Section 11. Note that the need for open access to data should be paralleled by an open access to code in order to harness the power of the Internet and make research efficient on a worldwide scale. A sobering thought is that, in spite of all of this knowledge, at the outset, all one can do is enlist the key questions that emerge and quickly report early but key findings An epidemic, or even pandemic, of a different nature was the HIV-induced AIDS epidemic . Confronted with a lethal viral infection that affected predominantly younger people and hence led to a considerable number of life years lost, a massive response ensued, with large academic and collaborative AIDS research groups formed around the globe, predominantly in the United States (e.g., the AIDS Clinical Trials Group). It led to the acceptance of placebo controlled trials with frequent interim analyses overseen by an Independent Committee . Also, coerced by the ‘fourth player’ (i.e., the patients and their advocacy groups, next to the three other players: regulators, industry, academia), co-enrolment in several trials simultaneously was grudgingly accepted, but arguably led to the development of highly active anti-retroviral therapy (HAART; ). Undoubtedly, it dynamized the clinical research community and arguably paved the way for dynamic treatment regimes and a new emphasis on personalized medicine. It is evident that any deviation from standard practice poses methodological challenges that may be partially addressed during a crisis. A WWII example thereof is Wald's development of the sequential trial framework: there simply was no time for the established rigorous but slow industrial quality control processes . The new paradigm proposed by Wald led to further developments that continue to influence clinical research today . The current pandemic, just like the earlier ones, shows the need to trust the good faith of experts, and the good intentions of health professionals, rather than build onerous and time-consuming systems that are premised on the possibility of fraud and misbehavior. It is instructive to point out that the position of science and scientists was questioned in 1918 as well as today. Some referred to a “totalitarian system of science” . It is natural that the position of biomedical science and its biostatistics and epidemiological counterparts is debated because seldom are they so prominently present in the public debate. To understand this, note that an epidemic is somewhat archaically referred to as a “crowd disease” . It is natural to consult a physician for an ailment and, the more severe the condition, the more a patient is willing to accept side effects, as long as there is a sufficiently strong therapeutic effect. In fact, this is not different in a crowd disease. In the absence of PIs, the NPIs are society's only therapeutic class. Prescription, dosing, and monitoring of side effects then becomes a societal responsibility, where expert advice is blended with policymaking by mandated politicians, and with input from advocacy groups.
Epidemiological background The biomedical and public health community, as well as the world population, have quickly been learning crucial lessons about SARS-CoV-2 and COVID-19. To date, several aspects, though, remain uncertain or simply unknown. It is useful to briefly review some basic concepts of infectious disease modeling. While more complex models for COVID-19 are undoubtedly more appropriate to account for heterogeneity related to gender and age (in relation to social contact behavior, acquisition of infection, infectivity per average person, symptomatology of infected individuals and corresponding risks of hospitalization as well as subsequent mortality risks), spatial heterogeneity, and/or variation in risks due to societal position, the so-called basic Susceptible-Infected-Recovered (SIR) compartmental model provides a reasonable starting point (see,e.g., ). Although simplistic in the face of the current epidemic, the SIR model does contain the essential ingredients. Abrams et al. (2020) have developed a much more elaborate model, i.e., an age-structured, stochastic model, tailored to the dynamics of SARS-CoV-2 transmission and the subsequent human response upon contracting the disease, both at the level of the symptomatology as well as in terms of humoral immunity responses within hosts . In the basic SIR model, at any time t ≥ 0, the population is divided into three fractions or compartments: S ( t ) represents the susceptible fraction, I ( t ) is the infected (and infectious) fraction, and R ( t ) is the recovered fraction (immune survivors and potentially deaths). The initial states are S (0), I (0), and R (0). Flows of individuals between these states can be described using (ordinary) differential equations. The model is further influenced by two critical numbers: the recovery rate k , and the basic reproduction number R 0 . While R 0 is an implicit model parameter, the force of infection, i.e., the instantaneous rate at which susceptible individuals become infected, determines the basic and time-varying effective reproduction numbers, together with the recovery rate, through the so-called next generation matrix, providing information about the next generation of infected individuals resulting from a single typical infected individual. This basic model is rigid in that it assumes homogeneous (random) mixing within the population, and requires the population to be a closed system. In reality, as in Abrams' model, a population consists of various subgroups, or silos, with different behaviors (such as different levels of social contacts), and borders in a country like Belgium are merely administrative lines between neighboring countries . Also, the three-fraction system is often too simple. For SARS-CoV-2, we need to add an exposed state in which exposed individuals are not yet infectious while viral load is gradually building up, a pre-symptomatic compartment in which individuals are able to infect others even though they do no have symptoms yet, and compartments including asymptomatic individuals and individuals with mild symptoms, severely ill, hospitalised, and intensive care unit (ICU) admitted persons. Recovery and death are ideally kept separate as well. Such an elaborate model is essential if it is to be used against the background of the hospital capacity available, and to gauge the death toll. Consider now the reproduction number R 0 , defined as the average number of susceptible individuals infected by a single typical infected individual during his/her entire infectious period, at least in a fully susceptible population. There is a whole world “not” captured by a single R 0 value. First, it may depend on the initial population characteristics (age distribution, geographical spread], etc.). Second, as time evolves and S ( t ) depletes, the effective reproduction number R e is more relevant. The basic reproduction number as a measure of transmissibility of a pathogen is very different for seasonal influenza, where it is usually around 1.5, as compared to COVID-19, where it is estimated around 2.5 without medication or vaccines, and without NPIs. For an overview of COVID-19 related R 0 estimates, see Abrams et al. (2020) [1]. An early estimate for COVID-19, based on the Wuhan outbreak, can be found in Zhou et al. . As is now everyday knowledge, R 0 < 1 (and R e < 1) implies dampening of the epidemic, whereas with R 0 > 1 (and R e > 1) it picks up until immunity is sufficiently widespread or the susceptible reservoir is depleted. Depending on R 0 and the generation interval (time between infection events in an infector-infectee pair, see ) building up immunity can be a lengthy process, even if no interventions are implemented, which we seem to see with the current pandemic. Moreover, uncertainty surrounding the nature and extent of immunity is considerable, because humoral immunity seems to wane over time and the role of T-cell immunity is yet to be studied in more detail. At the onset of the Wuhan outbreak, there was considerable uncertainty regarding key epidemiological parameters, in particular R 0 , but also the associated (case and infection) fatality rate. We now know that both R 0 and the infection fatality rate (IFR) are relatively high, the latter being highly variable with age. Although highly dependent on the population under study, some additional examples of R 0 values from other infections: measles ( R 0 ≃ 15), mumps ( R 0 ≃ 5), SARS ( R 0 ≃ 2.5). See Riccardo et al. , Chowell et al. , and He et al. . Several other quantities are of epidemiological interest: infectious period (roughly about a week, versus a few days for influenza); age-specific contact rate (the typical number of social contacts of a certain type a member of the population has, see ); mode of transmission (for COVID-19, the mode of transmission was established early as airborne droplets, but this mode was later supplemented with others); probability of transmission upon a contact between a susceptible and infectious individual; shedding of viral load depending on the severity of symptoms; contribution of children to the infection process; high-risk contacts and their influence on disease dynamics (e.g., superspreading events). The infectiousness is strongly person-dependent (cf. the so-called superspreaders) and here secondary transmission via the airborne route is key, i.e., via aerosols . A key population characteristic is the contact rate, i.e., the frequency and intensity of physical social contacts between population members. The number and intensity of social contacts is not a constant. There are group- and individual-specific aspects to the contact rate and, importantly, it can be modified. During the time frame without PIs, modifying the contact rate and intensity (briefly or for a very extended period of time) is essentially the only option available. For a variety of reasons, describing and predicting a real-life epidemic curve may be very difficult. As the pandemic has been unfolding, the country and state specific epidemic curves take about any possible non-linear shape ( https://coronavirus.jhu.edu/data/new-cases-50-states ), underlining the importance and extent of heterogeneity in infectious disease dynamics. 2.1 The non-pharmaceutical intervention period Let us turn to the three possible strategies to modify the aforementioned contact rate, because when the house is on fire, and neither medicinal products nor vaccines are yet available, NPIs are all that one has got. The first strategy is suppression. It essentially means that the reproduction number is forced below one by imposing severe contact restrictions at the population level, as was done in China outside of Hubei (in Hubei, where the Chinese authorities were taken by surprise, this was at first not possible). Of course, a large fraction of the population is then kept in the susceptible state and hence they do not contribute to the build-up of herd immunity. As a consequence, measures should be put in place to avoid the epidemic from flaring up after measures are relaxed, while monitoring very effectively so that, if it does, suppression measures can be enacted again. Clearly, China is in this situation, and will be until vaccines and medication are available. Cheap, widespread, sensitive and specific diagnostic tools help maintain control, potentially supported by electronic means such as smartphone apps, as well as contact tracing and isolation . Needless to say, international travel in and out of susceptible regions is and remains problematic. Suppression is only possible when the viral spread is radically suppressed at an early stage, however, SARS-CoV-2 has stealth characteristics. Its incubation period is relatively long, with a very infectious period near the end of the incubation period . To aggravate matters, there is a large fraction of pre-symptomatic and asymptomatic but infectious cases (possibly 40–50%, although estimates vary widely and could be even higher). These characteristics, combined with a high reproduction number, make the epidemic resemble a bush fire: one match is sufficient to ignite it, after which the fire starts to spread at ground level, invisible to the naked eye until it suddenly evolves in an all-out fire. For this reason, in Europe, suppression was not a viable option after the initial outbreak in Northern Italy. The second strategy is mitigation . This was practiced by about all European countries during their first wave, to more (Italy, Spain) or lesser (Sweden) degrees. Here, measures are taken to bring the reproduction number down, such as reducing the number and nature of social contacts to a pre-specified level, so that the epidemic is slowed sufficiently and the number of critically ill cases at any time can be handled by the health care system. The difference between suppression and mitigation is that the latter aims to build up herd immunity, in such a way that the health care system is able to cope. It can be supplemented by a temporary capacity increase of the system (e.g., field hospitals, annexes to existing hospitals). The third strategy, or perhaps absence thereof, is counting solely on herd immunity . Generally, it will typically produce a shorter epidemic than with mitigation, and afterwards the population will be immune at population level. That is, the fraction of recovered people (immune for a certain time, e.g., the rest of the season) will be large enough, i.e. above the critical threshold, so that the re-emerging virus will not find enough susceptible population members to push the reproduction number above one, and the epidemic will soon decrease and become seasonal (where transmission is typically increased during winter months, as it is for influenza virus and other, more benign, betacoronaviruses, such as hCoV-OC43). It was anticipated, early March 2020, that mitigation in a country or region would lead to a population with roughly 30% of recovered, hence immune, members, whereas herd immunity could lead to 60–70% immunity, at least in the absence of clusters . The latter is sufficient to prevent further outbreaks, or to ensure that they would be short-lived. That is, provided that immunity is sufficiently strong and sufficiently long-lasting. Unfortunately, none of this has played out as anticipated. Sero-prevalence has been building up depressingly slowly . In Belgium, sero-prevalence was estimated at roughly 3% by the end of March, 6% mid-April, 7% mid-May, and back down to 6% and 5% in early June and July, respectively. This points to waning of IgG antibodies, after their discovery has been ridden between a long delay in onset of detectability and relatively poor sensitivity. At the time of writing, this suggests that other aspects of immunity, such as T-cell immunity, need to be scrutinized . A key limitation to herd immunity strategies is the high fraction of critically ill patients, leading to overburdening of the health care system, and the high IFR . In the Belgian non-nursing home population, the IFR is about 0.4%, but this figure masks the strong age gradient, with an IFR close to 0% in the population under 25, but rising to 2.5% in the 85+ population outside of nursing homes, and to 35% for the 85+ in nursing homes. Not surprisingly, the death toll in nursing homes is very large (two thirds of the nearly 10,000 COVID-19 related deaths in Belgium are among nursing home residents). This has been observed in a large number of countries around the globe . The death toll has been quoted as an argument for why lockdowns and other NPIs are unavoidable. In Europe, an estimated 3 million deaths have been avoided by lockdown measures . For Belgium, this boils down to a figure between 50,000 (with a coping health care system) and 250,000 (for a strongly overwhelmed health care system). How to proceed with the mitigation strategy when the peaks in the relevant curves lie in the past? Given the large reproduction number (super-spreading virus combined with a long infectious period), relaxation of NPIs needs to be done with utmost care. Re -emergence of the epidemic is likely as the virus will have built up reservoirs already. Reservoirs take the form of animal species that harbor the virus during time periods when there is no human epidemic(e.g., geese and pigs in the case of influenza). Changing tactic and opting for herd immunity is extremely difficult because it would undo the effects of NPIs, including the hardships they will have induced. It is only a viable strategy if supplemented with sufficiently promising PIs (antiviral medication and vaccines). While pharmaceutical breakthroughs are happening at an unprecedented speed, it is unrealistic to expect major relief from this end in less than a year. It is more realistic to move towards suppression, or a combination of mitigation and suppression when the epidemic is sufficiently under control, i.e., when the number of new infections falls below a certain level. At that point, contact tracing and quarantine measures, needed for suppression, become a viable strategy, supported by increased reliability and capacity of diagnostic testing, the use of electronic tracing (e.g., based on apps) in addition to human tracing (by infectiologists and health inspectors). A final but extremely important aspect is whether or not contact between populations will be possible in periods when there are no peaks or outbreaks. The answer is that this could well be detrimental. Not only is travel itself a risk factor, as is clear from the early introductions around the globe, but contact between populations in different epidemic stages is complex. China's cautious protection of its borders after its initial peak, as well as Europe's initially prudent but now complex international travel situation, even within the Schengen zone of the European Union, are cases in point. Inevitably, new outbreaks will keep emerging until immunity is sufficiently widespread or adequate vaccines are available. Antivirals will not stop this but may prove important in turning mitigation strategies into a success . Note that this provides an interesting link between NPIs and PIs, between mathematical modeling and the outcome of successful clinical trials. The seasonality of COVID-19 (and its successors in subsequent years, i.e., COVID-20, etc.) is poorly understood at this point, although Kissler et al. provide useful predictions, based on knowledge from coronaviruses OC43 and HKU1. Corona virus-induced diseases (typically but not exclusively, common cold) are seasonal, but less so than, for example, influenza. Kissler et al. report that outbreaks are possible at any time of year, with more acute outbreaks in autumn and winter. Depending on the extent of (non-permanent) immunity, either annual or biennial outbreaks are more likely. Other scenarios would be possible if immunity is lifelong (i.e., outbreaks in cycles of 5 years or more). Also, cross-immunity with other betacoronaviruses HCoV-OC43 and HCoV-HKU1 will play an important role in temporal SARS-CoV-2 dynamics.
The non-pharmaceutical intervention period Let us turn to the three possible strategies to modify the aforementioned contact rate, because when the house is on fire, and neither medicinal products nor vaccines are yet available, NPIs are all that one has got. The first strategy is suppression. It essentially means that the reproduction number is forced below one by imposing severe contact restrictions at the population level, as was done in China outside of Hubei (in Hubei, where the Chinese authorities were taken by surprise, this was at first not possible). Of course, a large fraction of the population is then kept in the susceptible state and hence they do not contribute to the build-up of herd immunity. As a consequence, measures should be put in place to avoid the epidemic from flaring up after measures are relaxed, while monitoring very effectively so that, if it does, suppression measures can be enacted again. Clearly, China is in this situation, and will be until vaccines and medication are available. Cheap, widespread, sensitive and specific diagnostic tools help maintain control, potentially supported by electronic means such as smartphone apps, as well as contact tracing and isolation . Needless to say, international travel in and out of susceptible regions is and remains problematic. Suppression is only possible when the viral spread is radically suppressed at an early stage, however, SARS-CoV-2 has stealth characteristics. Its incubation period is relatively long, with a very infectious period near the end of the incubation period . To aggravate matters, there is a large fraction of pre-symptomatic and asymptomatic but infectious cases (possibly 40–50%, although estimates vary widely and could be even higher). These characteristics, combined with a high reproduction number, make the epidemic resemble a bush fire: one match is sufficient to ignite it, after which the fire starts to spread at ground level, invisible to the naked eye until it suddenly evolves in an all-out fire. For this reason, in Europe, suppression was not a viable option after the initial outbreak in Northern Italy. The second strategy is mitigation . This was practiced by about all European countries during their first wave, to more (Italy, Spain) or lesser (Sweden) degrees. Here, measures are taken to bring the reproduction number down, such as reducing the number and nature of social contacts to a pre-specified level, so that the epidemic is slowed sufficiently and the number of critically ill cases at any time can be handled by the health care system. The difference between suppression and mitigation is that the latter aims to build up herd immunity, in such a way that the health care system is able to cope. It can be supplemented by a temporary capacity increase of the system (e.g., field hospitals, annexes to existing hospitals). The third strategy, or perhaps absence thereof, is counting solely on herd immunity . Generally, it will typically produce a shorter epidemic than with mitigation, and afterwards the population will be immune at population level. That is, the fraction of recovered people (immune for a certain time, e.g., the rest of the season) will be large enough, i.e. above the critical threshold, so that the re-emerging virus will not find enough susceptible population members to push the reproduction number above one, and the epidemic will soon decrease and become seasonal (where transmission is typically increased during winter months, as it is for influenza virus and other, more benign, betacoronaviruses, such as hCoV-OC43). It was anticipated, early March 2020, that mitigation in a country or region would lead to a population with roughly 30% of recovered, hence immune, members, whereas herd immunity could lead to 60–70% immunity, at least in the absence of clusters . The latter is sufficient to prevent further outbreaks, or to ensure that they would be short-lived. That is, provided that immunity is sufficiently strong and sufficiently long-lasting. Unfortunately, none of this has played out as anticipated. Sero-prevalence has been building up depressingly slowly . In Belgium, sero-prevalence was estimated at roughly 3% by the end of March, 6% mid-April, 7% mid-May, and back down to 6% and 5% in early June and July, respectively. This points to waning of IgG antibodies, after their discovery has been ridden between a long delay in onset of detectability and relatively poor sensitivity. At the time of writing, this suggests that other aspects of immunity, such as T-cell immunity, need to be scrutinized . A key limitation to herd immunity strategies is the high fraction of critically ill patients, leading to overburdening of the health care system, and the high IFR . In the Belgian non-nursing home population, the IFR is about 0.4%, but this figure masks the strong age gradient, with an IFR close to 0% in the population under 25, but rising to 2.5% in the 85+ population outside of nursing homes, and to 35% for the 85+ in nursing homes. Not surprisingly, the death toll in nursing homes is very large (two thirds of the nearly 10,000 COVID-19 related deaths in Belgium are among nursing home residents). This has been observed in a large number of countries around the globe . The death toll has been quoted as an argument for why lockdowns and other NPIs are unavoidable. In Europe, an estimated 3 million deaths have been avoided by lockdown measures . For Belgium, this boils down to a figure between 50,000 (with a coping health care system) and 250,000 (for a strongly overwhelmed health care system). How to proceed with the mitigation strategy when the peaks in the relevant curves lie in the past? Given the large reproduction number (super-spreading virus combined with a long infectious period), relaxation of NPIs needs to be done with utmost care. Re -emergence of the epidemic is likely as the virus will have built up reservoirs already. Reservoirs take the form of animal species that harbor the virus during time periods when there is no human epidemic(e.g., geese and pigs in the case of influenza). Changing tactic and opting for herd immunity is extremely difficult because it would undo the effects of NPIs, including the hardships they will have induced. It is only a viable strategy if supplemented with sufficiently promising PIs (antiviral medication and vaccines). While pharmaceutical breakthroughs are happening at an unprecedented speed, it is unrealistic to expect major relief from this end in less than a year. It is more realistic to move towards suppression, or a combination of mitigation and suppression when the epidemic is sufficiently under control, i.e., when the number of new infections falls below a certain level. At that point, contact tracing and quarantine measures, needed for suppression, become a viable strategy, supported by increased reliability and capacity of diagnostic testing, the use of electronic tracing (e.g., based on apps) in addition to human tracing (by infectiologists and health inspectors). A final but extremely important aspect is whether or not contact between populations will be possible in periods when there are no peaks or outbreaks. The answer is that this could well be detrimental. Not only is travel itself a risk factor, as is clear from the early introductions around the globe, but contact between populations in different epidemic stages is complex. China's cautious protection of its borders after its initial peak, as well as Europe's initially prudent but now complex international travel situation, even within the Schengen zone of the European Union, are cases in point. Inevitably, new outbreaks will keep emerging until immunity is sufficiently widespread or adequate vaccines are available. Antivirals will not stop this but may prove important in turning mitigation strategies into a success . Note that this provides an interesting link between NPIs and PIs, between mathematical modeling and the outcome of successful clinical trials. The seasonality of COVID-19 (and its successors in subsequent years, i.e., COVID-20, etc.) is poorly understood at this point, although Kissler et al. provide useful predictions, based on knowledge from coronaviruses OC43 and HKU1. Corona virus-induced diseases (typically but not exclusively, common cold) are seasonal, but less so than, for example, influenza. Kissler et al. report that outbreaks are possible at any time of year, with more acute outbreaks in autumn and winter. Depending on the extent of (non-permanent) immunity, either annual or biennial outbreaks are more likely. Other scenarios would be possible if immunity is lifelong (i.e., outbreaks in cycles of 5 years or more). Also, cross-immunity with other betacoronaviruses HCoV-OC43 and HCoV-HKU1 will play an important role in temporal SARS-CoV-2 dynamics.
Modeling and monitoring the epidemic 3.1 Modeling Jewell underscores the importance of high-quality mathematical and statistical models for epidemics. Using such models, key epidemiological quantities are estimated: numbers of infected cases, hospitalizations, people in ICU, and deaths. Some models also permit short-term, medium-range, and long-term predictions, and allow to examine how such quantities change with changing human behavior and measures taken, such as social distancing, face masks, hygiene and, eventually, vaccination programs. It is useful to cast predictions according to a variety of scenarios, to inform policy makers, other scientists, and the public opinion. Each model has its strengths and pitfalls, and simultaneously considering various models strengthens prediction. Some models operate at macro level (e.g., to study the number of cases in the population of an entire country), while others operate regionally or locally. Models are informed by data, mathematical infectious disease theory, and assumptions. Each model provides a piece of the jigsaw puzzle, and it requires a good amount of expertise and skills in infectious disease modeling to lay the entire puzzle. Model uncertainty and sensitivity analysis must accompany every modeling effort. Models, no matter how refined, will never be able to capture every detail of human behavior. In fact, there are striking examples of models that were poorly predictive because they ignored behavioral aspects, such as the need for college students to gather and party . Also, important epidemiological quantities, such as the ones referred to earlier, are (typically) fully unknown at the onset of a pandemic. Over the first half year of the crisis, several quantities have been estimated with increasing precision, though sometimes with hiccoughs (e.g., the length of the pre-symptomatic period). Others, such as seasonality, remain hazy. The determination of immunity has been a roller coaster of progressing insight (see Section 5). In a growth model, such as a logistic or Richards model , hospital admissions, number of tests, etc. are used to compute how the spread of the virus evolves over time. This approach lends itself naturally to estimating how the growth factor of the epidemic changes according to measures taken, or under the influence of a changing testing strategy. Transmission trees aim at mapping the chain of infections among people . One examines the genetic similarity of the virus among people or one makes use of contact tracing. For COVID-19, contact tracing was applied at the onset of the epidemic to find out in which region a person could have been infected. As the epidemic in March 2020 gained strength and the number of infected people increased, contact tracing was no longer feasible in Belgium. However, it is considered a vital component of a suppression strategy for second and later waves. Transmission trees are helpful to estimate key characteristics of SARS-CoV-2, such as the basic and effective reproduction number, and the generation interval, i.e., the time lapse in a so-called infector-infectee pair, the serial interval, i.e., the time between symptom onset in the infector and in the infectee, and the incubation period. Based on COVID-19 data from China and Singapore, Ganyani et al. were able to show that R 0 is larger when estimated from the generation interval as compared to the serial interval, pointing for the first time to pre-symptomatic infections, its associated risks, and implications for an exit strategy A meta-population model is a robust, large-scale model, that allows to incorporate people's mobility. It divides the population into groups based on age category, residence, etc. Each of these groups follow an underlying mathematical model for the spread of the epidemic. Such a model assigns people to the various compartments (susceptible, exposed, infected, recovered). By mimicking interaction between such groups according to various scenarios (e.g., little or a lot of contacts with people outside the household, little or more mobility between towns, etc.), it is possible to predict how the number of infected people changes over time, in the short run as well as over longer time intervals. Important sources of information are the number and the nature of social contacts of people in various age categories, the mobility patterns of people in different regions, etc. An individual-based model, based on the number of hospitalizations, performs well in terms of (1) describing the spread of the disease, and (2) examining the consequences of relaxing the measures taken, i.e., candidate exit strategies. In such a model, each individual is assigned to a family, a school category, type of workplace environment, and the population at large. This assignment is guided by data available from school registries as well as employment data. The model mimics behavior of individuals on a day-by-day basis. It accounts for changes in behavior on weekend days relative to weekdays, during holiday periods and, importantly, also as a result of measures taken, such as school closure and reduced social contacts. When investigating the consequences of exit strategies (e.g., reopening of schools and certain workplaces), the team also examines the added value of household bubbles (i.e., a combination of members from multiple households, matched to have a similar structure in terms of age, composition, etc.), allowing repeated contacts within bubbles and lensuring a reduction in community-level mixing, and contact tracing to monitor and avoid new infections. Although simple deterministic compartmental models, such as the basic SIR model introduced previously, have been used in the initial phase of the pandemic, making an abstraction of some of the important properties of both the pathogen as well as the infected host, an additional layer of complexity is of utmost importance to incorporate in order to adequately describe the dynamics of COVID-19 and to make reliable predictions of the future course of the epidemic. As more and more evidence has been accumulating throughout the progression of the pandemic, it became clear that age-specific differences exist in susceptibility to infection, infectiousness upon infection, probability of being symptomatic and disease severity, thereby leading to large differences in hospitalization and mortality risks upon contracting SARS-CoV-2. Research groups with ample of experience in infectious disease modeling were well equipped to expand and refine existing models for disease spread to account for such complexities. In a Belgian context, an individual-based model (STRIDE) previously developed for influenza was adapted to describe COVID-19 dynamics in the Belgian population, a meta-population model accounting for mobility patterns was adapted to study the impact of exit strategies in the aforementioned setting and a stochastic age-structured compartmental model was designed and specifically tailored to the spread of SARS-CoV-2 following earlier though related work on asymptomatic infections and their role in disease spread . On top of that, preparedness after previous epidemics, such as but not limited to the Ebola virus epidemic in West Africa (2013–2016), and experience in modeling infectious disease dynamics under pressure allows one to go beyond the application of simple models. Complexities imposed by intervention measures taken, such as stringent lockdown measures, and their impact on social contact behavior, pose additional challenges for modeling. Consequently, there is a need to directly relate the spread of the disease to social contact behavior and to inform transmission rates using social contact data. All of the approaches mentioned before (Individual-based, meta-population and stochastic models) rely on such social contact data, besides other sources of information, to calibrate and relate these models to the given epidemiological situation. Needless to say, model outputs and predictions require continuous fine-tuning and validation. Long-term predictions, while very useful should be seen as plausible scenarios at best, that demonstrate the impact of assumptions and variations in behavior. A collection of the aforementioned statistical and mathematical models developed by the team at the Universities of Hasselt, Antwerp and Leuven in Belgium can be found at www.simid.be and https://www.uhasselt.be/dsi-covid19-en . While not always obvious, there are clear links between statistical and mathematical modeling of the epidemic, and COVID-19 clinical trials research. A convincing illustration is found in Torneri et al. , who establish the vital role of antiviral medication in local outbreak control, in other words, the impact of non-pharmaceutical and pharmaceutical interventions can form a virtuous couple. In retrospect, when a number of predictions have been cast, under a variety of scenarios, at most one of these will come close to what actually happened, at least for the country or region for which it was intended. But, in a pandemic, countries and regions around the globe, with varying characteristics, all exhibit their own curves. For example, the Southern and Western states in the US exhibit a very different curve than the Northeastern states (cf. https://coronavirus.jhu.edu/map.html ). While care needs to be taken when comparing an observed curve with a prediction intended for a different geographical entity (or subpopulation), it is useful information for epidemic monitoring as well as for future model refinement and calibration, not only for future pandemics, but also for subsequent peaks of the ongoing one. 3.2 Nowcasting and early warning Modeling the event history of COVID-19 is important for public health policy, especially towards critically ill patients . Event history analysis includes studying the timing (or delay) between different events: infection, symptom onset, confirmed case, hospitalization, recovery and death. First, due to the incubation period and the delay of reporting and/or hospitalization, the impact of intervention measures is only observed after several days. For example, if the sum of the incubation period and delay of reporting is 10 days, then we expect to see an impact of the interventions on the number of confirmed cases after 10 days. However, as the delay time varies from individual to individual, the effect of the intervention is spread over several days. The delay distribution of the incubation period and the time between symptom onset and hospital admission ] is therefore crucial, as is understanding the heterogeneity in the delay times among individuals. Good knowledge of such delay distributions allows one to back-calculate the number of newly (symptomatic) infected cases, known as nowcasting, from either the number of confirmed cases or hospitalised cases, and assess the impact of intervention measures. Second, the length of stay in hospital is important, which varies among individuals and among countries due to different health systems. Information about the length of stay in hospital is important to predict the number of required hospital beds, both for beds in general hospital and beds in the ICU, and to track the burden on hospitals . Individual-specific characteristics, such as, for example, sex, age, comorbidity, and frailty of the individual, can explain differences in length of stay in the hospital and are therefore important to correct for. The estimation of the length of stay is complicated by the truncated and interval-censored nature of the data collected during the unfolding epidemic . Third, the time delay from infection and illness onset to death is important for the estimation of the case fatality ratio . A naïve case fatality ratio based on the proportion of reported deaths to reported cases during an outbreak is generally biased upwards, due to both the delay between case and death incidence and underreporting of cases. An early warning system to monitor COVID-19 trends and forecast increases of the hospital burden are essential in times of a pandemic . Multiple data streams are used as predictors of increases at the national and provincial level in Belgium. The mobility of individuals (tracked via mobile phone data), absenteeism at work, the number of patients with respiratory diseases visiting their general practitioner and the proportion of positive tested cases are important predictors for the immediately following two-week period . This is especially relevant at crucial times during an epidemic with multiple waves. Nowcasting is essential at the onset of the epidemic and when the curve begins to flatten and a peak is reached. It is also relevant when the rate of decrease slows, an often missed signal. While a decreasing curve is qualitatively a favorable evolution, it is important to constantly monitor the rate of decrease: if the decrease slows down while the curve is still at a relatively high level, it might be an early sign that it might eventually stop and then, unfortunately, start to increase again.
Modeling Jewell underscores the importance of high-quality mathematical and statistical models for epidemics. Using such models, key epidemiological quantities are estimated: numbers of infected cases, hospitalizations, people in ICU, and deaths. Some models also permit short-term, medium-range, and long-term predictions, and allow to examine how such quantities change with changing human behavior and measures taken, such as social distancing, face masks, hygiene and, eventually, vaccination programs. It is useful to cast predictions according to a variety of scenarios, to inform policy makers, other scientists, and the public opinion. Each model has its strengths and pitfalls, and simultaneously considering various models strengthens prediction. Some models operate at macro level (e.g., to study the number of cases in the population of an entire country), while others operate regionally or locally. Models are informed by data, mathematical infectious disease theory, and assumptions. Each model provides a piece of the jigsaw puzzle, and it requires a good amount of expertise and skills in infectious disease modeling to lay the entire puzzle. Model uncertainty and sensitivity analysis must accompany every modeling effort. Models, no matter how refined, will never be able to capture every detail of human behavior. In fact, there are striking examples of models that were poorly predictive because they ignored behavioral aspects, such as the need for college students to gather and party . Also, important epidemiological quantities, such as the ones referred to earlier, are (typically) fully unknown at the onset of a pandemic. Over the first half year of the crisis, several quantities have been estimated with increasing precision, though sometimes with hiccoughs (e.g., the length of the pre-symptomatic period). Others, such as seasonality, remain hazy. The determination of immunity has been a roller coaster of progressing insight (see Section 5). In a growth model, such as a logistic or Richards model , hospital admissions, number of tests, etc. are used to compute how the spread of the virus evolves over time. This approach lends itself naturally to estimating how the growth factor of the epidemic changes according to measures taken, or under the influence of a changing testing strategy. Transmission trees aim at mapping the chain of infections among people . One examines the genetic similarity of the virus among people or one makes use of contact tracing. For COVID-19, contact tracing was applied at the onset of the epidemic to find out in which region a person could have been infected. As the epidemic in March 2020 gained strength and the number of infected people increased, contact tracing was no longer feasible in Belgium. However, it is considered a vital component of a suppression strategy for second and later waves. Transmission trees are helpful to estimate key characteristics of SARS-CoV-2, such as the basic and effective reproduction number, and the generation interval, i.e., the time lapse in a so-called infector-infectee pair, the serial interval, i.e., the time between symptom onset in the infector and in the infectee, and the incubation period. Based on COVID-19 data from China and Singapore, Ganyani et al. were able to show that R 0 is larger when estimated from the generation interval as compared to the serial interval, pointing for the first time to pre-symptomatic infections, its associated risks, and implications for an exit strategy A meta-population model is a robust, large-scale model, that allows to incorporate people's mobility. It divides the population into groups based on age category, residence, etc. Each of these groups follow an underlying mathematical model for the spread of the epidemic. Such a model assigns people to the various compartments (susceptible, exposed, infected, recovered). By mimicking interaction between such groups according to various scenarios (e.g., little or a lot of contacts with people outside the household, little or more mobility between towns, etc.), it is possible to predict how the number of infected people changes over time, in the short run as well as over longer time intervals. Important sources of information are the number and the nature of social contacts of people in various age categories, the mobility patterns of people in different regions, etc. An individual-based model, based on the number of hospitalizations, performs well in terms of (1) describing the spread of the disease, and (2) examining the consequences of relaxing the measures taken, i.e., candidate exit strategies. In such a model, each individual is assigned to a family, a school category, type of workplace environment, and the population at large. This assignment is guided by data available from school registries as well as employment data. The model mimics behavior of individuals on a day-by-day basis. It accounts for changes in behavior on weekend days relative to weekdays, during holiday periods and, importantly, also as a result of measures taken, such as school closure and reduced social contacts. When investigating the consequences of exit strategies (e.g., reopening of schools and certain workplaces), the team also examines the added value of household bubbles (i.e., a combination of members from multiple households, matched to have a similar structure in terms of age, composition, etc.), allowing repeated contacts within bubbles and lensuring a reduction in community-level mixing, and contact tracing to monitor and avoid new infections. Although simple deterministic compartmental models, such as the basic SIR model introduced previously, have been used in the initial phase of the pandemic, making an abstraction of some of the important properties of both the pathogen as well as the infected host, an additional layer of complexity is of utmost importance to incorporate in order to adequately describe the dynamics of COVID-19 and to make reliable predictions of the future course of the epidemic. As more and more evidence has been accumulating throughout the progression of the pandemic, it became clear that age-specific differences exist in susceptibility to infection, infectiousness upon infection, probability of being symptomatic and disease severity, thereby leading to large differences in hospitalization and mortality risks upon contracting SARS-CoV-2. Research groups with ample of experience in infectious disease modeling were well equipped to expand and refine existing models for disease spread to account for such complexities. In a Belgian context, an individual-based model (STRIDE) previously developed for influenza was adapted to describe COVID-19 dynamics in the Belgian population, a meta-population model accounting for mobility patterns was adapted to study the impact of exit strategies in the aforementioned setting and a stochastic age-structured compartmental model was designed and specifically tailored to the spread of SARS-CoV-2 following earlier though related work on asymptomatic infections and their role in disease spread . On top of that, preparedness after previous epidemics, such as but not limited to the Ebola virus epidemic in West Africa (2013–2016), and experience in modeling infectious disease dynamics under pressure allows one to go beyond the application of simple models. Complexities imposed by intervention measures taken, such as stringent lockdown measures, and their impact on social contact behavior, pose additional challenges for modeling. Consequently, there is a need to directly relate the spread of the disease to social contact behavior and to inform transmission rates using social contact data. All of the approaches mentioned before (Individual-based, meta-population and stochastic models) rely on such social contact data, besides other sources of information, to calibrate and relate these models to the given epidemiological situation. Needless to say, model outputs and predictions require continuous fine-tuning and validation. Long-term predictions, while very useful should be seen as plausible scenarios at best, that demonstrate the impact of assumptions and variations in behavior. A collection of the aforementioned statistical and mathematical models developed by the team at the Universities of Hasselt, Antwerp and Leuven in Belgium can be found at www.simid.be and https://www.uhasselt.be/dsi-covid19-en . While not always obvious, there are clear links between statistical and mathematical modeling of the epidemic, and COVID-19 clinical trials research. A convincing illustration is found in Torneri et al. , who establish the vital role of antiviral medication in local outbreak control, in other words, the impact of non-pharmaceutical and pharmaceutical interventions can form a virtuous couple. In retrospect, when a number of predictions have been cast, under a variety of scenarios, at most one of these will come close to what actually happened, at least for the country or region for which it was intended. But, in a pandemic, countries and regions around the globe, with varying characteristics, all exhibit their own curves. For example, the Southern and Western states in the US exhibit a very different curve than the Northeastern states (cf. https://coronavirus.jhu.edu/map.html ). While care needs to be taken when comparing an observed curve with a prediction intended for a different geographical entity (or subpopulation), it is useful information for epidemic monitoring as well as for future model refinement and calibration, not only for future pandemics, but also for subsequent peaks of the ongoing one.
Nowcasting and early warning Modeling the event history of COVID-19 is important for public health policy, especially towards critically ill patients . Event history analysis includes studying the timing (or delay) between different events: infection, symptom onset, confirmed case, hospitalization, recovery and death. First, due to the incubation period and the delay of reporting and/or hospitalization, the impact of intervention measures is only observed after several days. For example, if the sum of the incubation period and delay of reporting is 10 days, then we expect to see an impact of the interventions on the number of confirmed cases after 10 days. However, as the delay time varies from individual to individual, the effect of the intervention is spread over several days. The delay distribution of the incubation period and the time between symptom onset and hospital admission ] is therefore crucial, as is understanding the heterogeneity in the delay times among individuals. Good knowledge of such delay distributions allows one to back-calculate the number of newly (symptomatic) infected cases, known as nowcasting, from either the number of confirmed cases or hospitalised cases, and assess the impact of intervention measures. Second, the length of stay in hospital is important, which varies among individuals and among countries due to different health systems. Information about the length of stay in hospital is important to predict the number of required hospital beds, both for beds in general hospital and beds in the ICU, and to track the burden on hospitals . Individual-specific characteristics, such as, for example, sex, age, comorbidity, and frailty of the individual, can explain differences in length of stay in the hospital and are therefore important to correct for. The estimation of the length of stay is complicated by the truncated and interval-censored nature of the data collected during the unfolding epidemic . Third, the time delay from infection and illness onset to death is important for the estimation of the case fatality ratio . A naïve case fatality ratio based on the proportion of reported deaths to reported cases during an outbreak is generally biased upwards, due to both the delay between case and death incidence and underreporting of cases. An early warning system to monitor COVID-19 trends and forecast increases of the hospital burden are essential in times of a pandemic . Multiple data streams are used as predictors of increases at the national and provincial level in Belgium. The mobility of individuals (tracked via mobile phone data), absenteeism at work, the number of patients with respiratory diseases visiting their general practitioner and the proportion of positive tested cases are important predictors for the immediately following two-week period . This is especially relevant at crucial times during an epidemic with multiple waves. Nowcasting is essential at the onset of the epidemic and when the curve begins to flatten and a peak is reached. It is also relevant when the rate of decrease slows, an often missed signal. While a decreasing curve is qualitatively a favorable evolution, it is important to constantly monitor the rate of decrease: if the decrease slows down while the curve is still at a relatively high level, it might be an early sign that it might eventually stop and then, unfortunately, start to increase again.
Mortality reporting Mortality among COVID-19 patients is relatively high when measured by IFRs . The overall IFR is estimated around 0.6% in many countires, but is very strongly age dependent, and the risk is higher for males than for females. This was clear even from early reports . In a pandemic like the current one, it is not uncommon to have (at least) double mortality reporting. For example, in Belgium, Statistics Belgium reports overall mortality, while the Belgian health institute Sciensano reports COVID-19 mortality. Excess mortality can be deduced from overall mortality, providing an alternative estimate for, and perhaps a better one, than COVID-19 mortality . Hence, this is a place where official statistics, epidemiology, and demography meet. Bustos Sierra et al. and Molenberghs et al. from a Belgian perspective, and Aron et al. from an international standpoint, reported that Belgium's excess mortality agrees very closely with COVID-19 mortality. This is because Belgium reports not only confirmed COVID-19 deaths in hospitals, but also suspected deaths regardless of the place of occurrence. In contrast, these authors found that in the Netherlands the reported COVID-19 mortality accounts for only 62% of excess mortality. Arguably, excess mortality, when carefully teased out from overall mortality, is a better estimate of COVID-19 mortality, than reported COVID-19 mortality itself. For example, the number of deaths per million on July 4, 2020, was 843 in Belgium, 650 in the UK, 607 in Spain, 576 in Italy, 458 in France, and 357 in the Netherlands. But, after correction for underreporting, these figures become 1012 for Spain, 860 for Italy, 813 for the UK, 766 for Belgium, 575 for the Netherlands, and 472 for France ( https://github.com/owid/covid-19-data/tree/master/public/data ). Something that has become saliently clear is the very steep IFR curve as a function of age . This, combined with the superspreading context that nursing homes provide has led, in many countries, to a huge death toll in such settings , which has in turn triggered dedicated epidemiological research.
Prevalence determination and other surveys Unlike testing and tracing, which is aimed at finding as many new cases as possible, prevalence determination is aimed at reliably estimating what fraction of the population is recovered and hopefully immune. Apart from the viral and immunological issues related to prevalence determination, it should be done based on representative samples. Hence, sample survey methods can be used, although often alternative methods are used. Prevalence determination is important to gauge IFRs and to assess whether or not herd immunity is building up. 5.1 Prevalence determination An obvious way of prevalence determination is by means of the sero-prevalence, based on the detection of antibodies in blood serum samples. Herzog et al. proceeded via a nationwide cross-sectional survey of residual blood samples tested for the presence of Immunoglobulin G (IgG) antibodies against SARS-CoV-2. This method, as we know now, is ridden with a number of issues, such as time to IgG seroconversion, detectability, and waning . In Belgium, sero-prevalence around April 1, 2020, was around 3%, three weeks later it was 6%, rose to nearly 7% mid-May, and then started to drop to 5.5% (around June 10) and even 4.5% around July 1. In other words, as mentioned in the literature , waning of IgG antibody concentrations is also observed in this sero-epidemiological study, and the primary route for immunity may not be these antibodies but rather T-cell mediated immunity or other antibodies not measured so far. As a consequence, IFR determination and the status of a population's immunity are referred back to the drawing board and the interpretation of (serial) sero-prevalence studies have to be reconsidered. Evidently, the decrease in seroprevalence implies that the status but also extent of immunity may be very different when based on T-cell mediated and humoral immunity responses. Also, cross-immunity with endemic coronaviruses, especially beta-coronaviruses such as hCoV-OC43, is a relevant study subject, but one about which there is little or no knowledge available . Note that different survey sampling methods and different sub-populations considered (e.g., blood donors, or people spontaneously reporting at hospitals) may well yield different estimates. Apart from immunological issues with prevalence determination, the quality of the representative sampling method used influences the reliability of the findings. 5.2 The role of public opinion surveys It is important to keep the finger on the pulse of the public opinion, for various reasons. Well-conducted surveys are vital to get a feel for how the population perceives risk, the impact of measures taken, acceptance and compliance to NPIs, etc. At the same time, it can be a component of an early warning system (Section 3.2) if the occurrence of symptoms is queried. One such example is the “Big Corona Study” (see also ), an online survey that can be filled in by all members of the public on every Tuesday since March 17, 2020; from June 2, 2020 onwards, the survey shifted to a bi-weekly frequency. It collects data about public adherence to measures taken by the government, contact behavior, mental and socio-economic distress, and spatio-temporal dynamics of COVID-19 symptoms' incidences. While public participation is useful as a low-cost method to collect timely information within the context of a pandemic, caution should be exercised at the analysis stage; online surveys, based on self-reporting, often do not reach every societal group equally . It typically causes response rates to vary among citizens of different ages, genders, cultures and economic statuses. This is particularly the case in 2020, where the perception of the seriousness of the COVID-19 pandemic varies considerably between individuals and has become politically coloured. This then translates to increased difficulties to correct for unrepresentative samples, even after standardization methods such as inverse probability weighting are performed. In essence, these problems all relate to non-random missingness patterns , where the absence of information is driven by complex processes. These processes do not lie far from opportunistic sampling phenomena that often occur in biodiversity studies that make use of citizens to collect data . For example, using such surveys to pinpoint areas of increased disease incidence necessitates careful investigation, since response rates' spatial dynamics may be stochastically dependent on the underlying spatial process that generates heterogeneity in the symptoms' incidences. If present, this opportunistic sampling phenomenon, termed preferential sampling , invalidates statistical inference on the spatial dynamics of COVID-19 symptoms. This can be accommodated by using a shared latent process approach where a geostatistical binomial model for the proportion of participants of each Belgian municipality that experiences COVID-19 symptoms shares a spatial random effect with a model for the response rates. The result of this approach is shown in , which depicts predicted symptoms' incidence, corrected for preferential sampling, using data of 397,529 individuals collected during the third round of the “Big Corona Study” (March 31, 2020). The above is an example of how survey sampling methods, citizen science, and spatial statistics come together to gauge the public opinion regarding COVID-19 and, in turn, to inform policy makers. Unsurprisingly, several suveys are undertaken simultaneously. For example, the Belgian health institute Sciensano has conducted several waves of a COVID-19 Health Interview Survey . This study has a longitudinal component; participants can indicate whether or not they are willing to have their responses linked across waves. Smaller scale (longitudinal) surveys towards the public's perceived vulnerability and acceptance of measures are undertaken too . A general perspective on the role of social and behavioral science in the response to COVID-19 research can be found in Van Bavel et al. . In many countries, all such surveys take place in an ad hoc fashion. It can be beneficial, though, to make use of a permanent (online) representative panel for public opinion research. Such a panel exists in the Netherlands . Catalyzed by the current pandemic, a panel of this type is likely to be initiated in Belgium as well.
Prevalence determination An obvious way of prevalence determination is by means of the sero-prevalence, based on the detection of antibodies in blood serum samples. Herzog et al. proceeded via a nationwide cross-sectional survey of residual blood samples tested for the presence of Immunoglobulin G (IgG) antibodies against SARS-CoV-2. This method, as we know now, is ridden with a number of issues, such as time to IgG seroconversion, detectability, and waning . In Belgium, sero-prevalence around April 1, 2020, was around 3%, three weeks later it was 6%, rose to nearly 7% mid-May, and then started to drop to 5.5% (around June 10) and even 4.5% around July 1. In other words, as mentioned in the literature , waning of IgG antibody concentrations is also observed in this sero-epidemiological study, and the primary route for immunity may not be these antibodies but rather T-cell mediated immunity or other antibodies not measured so far. As a consequence, IFR determination and the status of a population's immunity are referred back to the drawing board and the interpretation of (serial) sero-prevalence studies have to be reconsidered. Evidently, the decrease in seroprevalence implies that the status but also extent of immunity may be very different when based on T-cell mediated and humoral immunity responses. Also, cross-immunity with endemic coronaviruses, especially beta-coronaviruses such as hCoV-OC43, is a relevant study subject, but one about which there is little or no knowledge available . Note that different survey sampling methods and different sub-populations considered (e.g., blood donors, or people spontaneously reporting at hospitals) may well yield different estimates. Apart from immunological issues with prevalence determination, the quality of the representative sampling method used influences the reliability of the findings.
The role of public opinion surveys It is important to keep the finger on the pulse of the public opinion, for various reasons. Well-conducted surveys are vital to get a feel for how the population perceives risk, the impact of measures taken, acceptance and compliance to NPIs, etc. At the same time, it can be a component of an early warning system (Section 3.2) if the occurrence of symptoms is queried. One such example is the “Big Corona Study” (see also ), an online survey that can be filled in by all members of the public on every Tuesday since March 17, 2020; from June 2, 2020 onwards, the survey shifted to a bi-weekly frequency. It collects data about public adherence to measures taken by the government, contact behavior, mental and socio-economic distress, and spatio-temporal dynamics of COVID-19 symptoms' incidences. While public participation is useful as a low-cost method to collect timely information within the context of a pandemic, caution should be exercised at the analysis stage; online surveys, based on self-reporting, often do not reach every societal group equally . It typically causes response rates to vary among citizens of different ages, genders, cultures and economic statuses. This is particularly the case in 2020, where the perception of the seriousness of the COVID-19 pandemic varies considerably between individuals and has become politically coloured. This then translates to increased difficulties to correct for unrepresentative samples, even after standardization methods such as inverse probability weighting are performed. In essence, these problems all relate to non-random missingness patterns , where the absence of information is driven by complex processes. These processes do not lie far from opportunistic sampling phenomena that often occur in biodiversity studies that make use of citizens to collect data . For example, using such surveys to pinpoint areas of increased disease incidence necessitates careful investigation, since response rates' spatial dynamics may be stochastically dependent on the underlying spatial process that generates heterogeneity in the symptoms' incidences. If present, this opportunistic sampling phenomenon, termed preferential sampling , invalidates statistical inference on the spatial dynamics of COVID-19 symptoms. This can be accommodated by using a shared latent process approach where a geostatistical binomial model for the proportion of participants of each Belgian municipality that experiences COVID-19 symptoms shares a spatial random effect with a model for the response rates. The result of this approach is shown in , which depicts predicted symptoms' incidence, corrected for preferential sampling, using data of 397,529 individuals collected during the third round of the “Big Corona Study” (March 31, 2020). The above is an example of how survey sampling methods, citizen science, and spatial statistics come together to gauge the public opinion regarding COVID-19 and, in turn, to inform policy makers. Unsurprisingly, several suveys are undertaken simultaneously. For example, the Belgian health institute Sciensano has conducted several waves of a COVID-19 Health Interview Survey . This study has a longitudinal component; participants can indicate whether or not they are willing to have their responses linked across waves. Smaller scale (longitudinal) surveys towards the public's perceived vulnerability and acceptance of measures are undertaken too . A general perspective on the role of social and behavioral science in the response to COVID-19 research can be found in Van Bavel et al. . In many countries, all such surveys take place in an ad hoc fashion. It can be beneficial, though, to make use of a permanent (online) representative panel for public opinion research. Such a panel exists in the Netherlands . Catalyzed by the current pandemic, a panel of this type is likely to be initiated in Belgium as well.
Diagnostic and serological testing The battle against a novel emerging pathogen such as COVID-19 requires the development of a rigorous screening strategy to detect the virus, with the objective to mitigate its public health impact and to bring the pandemic under control. Aiming to achieve a rapid scale-up of diagnostic testing capacity has rarely, if ever, been attempted at the current pace . Testing is not merely an instrument to diagnose a given individual and to determine individual-level risk factors, it is also a prerequisite to a proper disease surveillance system, serving in monitoring and managing the epidemic. Testing allows unraveling a number of key uncertainties concerning the epidemic, such as the number of infected people, or the proportion of the population that is effectively immune against the virus. Early literature , i.e., from the first quarter of 2020, is a testimony that at first, diagnostic instruments for SARS-CoV-2 were lacking and needed to be developed in a speedy fashion. The SARS-CoV-2 tests that were developed since the start of the COVID-19 outbreak can broadly be categorized in so-called real-time (diagnostic) reverse-transcriptase PCR (RT-PCR) and serological tests. Patients with symptoms are often diagnosed based on RT-PCR tests allowing the detection of viral nucleic acid in oropharyngeal or nasopharyngeal swabs. Such tests identify whether someone has the virus. Serological tests on the other hand, determine the presence of antibodies. With the advent of COVID-19, new serological tests have been emerging, creating new opportunities for an assessment of the SARS-CoV-2 epidemic. Serologic tests are most of the time ineffective at detecting early stages of the infection, since antibody titers only gradually increase days or weeks after infection, but are able to detect past infections providing, in theory, an indication of the proportion of the population that has been infected with the virus, at least when lifelong humoral immunity is conferred. Serological analysis may be useful to actively identify close contacts, define clusters of cases and linking clusters of cases retrospectively to delineate transmission chains and ascertain how long transmission has been ongoing or to estimate the proportion of asymptomatic individuals in the population . Serological tests help to understand the epidemiology and to evaluate vaccine responses, but the reliability for diagnosis in the acute phase of illness and the assumption of protective immunity have been questioned . Detection capabilities of tests may further depend on the delay since the onset of the infection or symptoms . Furthermore, higher antibody levels not necessarily correlate well with an increase in protection against reinfection. Despite their value, serological tests do not allow, given the many current unknowns and uncertainties, to confirm whether or not a person is contagious or if he/she is protected against the virus, unless a correlate of protection is well-established, and do not allow, in other words, the delivery of an “immunity passport”. In the initial phase of an epidemic, knowledge on diagnostic test performance is scarce and not fully reliable. Samples are usually collected from a limited number of patients, and negative controls are not always present. A correct assessment of the limitations and performance of each of these tests is nevertheless crucial to demonstrate their accuracy and clinical utility and to design a correct testing strategy. The performance of a diagnostic test is typically characterized by its sensitivity and specificity. RT-PCR tests are considered reliable for detecting the presence of the virus, and are considered the standard by some, despite a non-negligible rate of false negative results, i.e., a low sensitivity - in some circumstances (see, for example, ). False negatives can complicate governmental decisions to lift confinement restrictions. False-negative results have an impact on the manner in which serological testing might be used to support non-pharmaceutical interventions, as well as implications for the development of large-scale testing pathways . The current evidence about the diagnostic accuracy of COVID-19 serology tests is characterized by high risks of bias and heterogeneity, with limited generalizability to outpatient populations . A full comparison of the performance of serological tests has not yet been conducted on a large set of identical samples. The duration of antibody rises is currently unknown, and the utility of these tests for public health management purposes has been reported as uncertain . Variation in performance characteristics between assays indicates the urgent need for evaluation of the large number of SARS-CoV-2 serology tests that have become rapidly available . Evaluating the performance of diagnostic tests is usually based on comparing test results with a gold standard, but such a “perfect test” is often unavailable. Moreover, even if the diagnostic sensitivity and specificity are considered fixed values, intrinsic to the diagnostic test (i.e., constant and universally applicable), many examples illustrate that these values can fluctuate depending on the context . Estimations of test characteristics are often obtained from studies under well-controlled conditions. The sensitivity of RT-PCR tests used for the diagnosis of COVID-19 may, for example, depend on factors such as the type of specimen, the timing of sampling and the sampling technique . Yet, quantifying the performance of a given test in real-world conditions is essential when interpreting test results, measuring its predictive value, or when choosing a test for a specific use case: screen asymptomatic patients, monitor contacts, identify clusters, support contact tracing, and as a preventive measure. Hitchings et al. explain how the so-called test positive fraction correlates with the incidence in a given population, turning this into a useful surveillance tool. In hospital settings, sensitive and specific diagnostic tests for active infection with SARS-CoV-2, allow guiding the care for individual patients, but a fast and repeated testing strategy at the expense of e.g. a lower test sensitivity may be more effective as a public health strategy . A public health strategy – with the goal to reduce transmission - may indeed ask for the use of rapid tests, removing the focus from the usual dogma of high sensitivity and specificity towards a test to be practically useful, also accounting for factors such as costs, speed, and logistical constraints. A proper evaluation of diagnostic performance in the absence of a gold standard can be done by using latent class models, which do not require a priori knowledge of the infection status. Umemneku Chikere et al. provide an overview of these and other models that allow using the combined information of multiple different tests applied on the same samples and Kostoulas et al. present standards for the reporting of such diagnostic accuracy studies. Models used to analyze the results of multiple diagnostic tests assume that there is an unknown prevalence, sometimes referred to as a latent class, and that the sensitivity and specificity of the diagnostic tests are unknown. This “latent prevalence” can then be linked to the apparent prevalence (i.e., the observed proportion of positive results of the diagnostic tests) through a set of equations allowing estimating all parameters at stake (i.e., prevalence, sensitivities and specificities of each of the tests used) . Further context on issues surrounding diagnostic tests is given in Tang et al. . Once diagnostic tools are available and properly evaluated, their use may be hampered by constraints such as a lack of reagents, limited laboratory capacity, and personnel. Pooling samples may be used to addresss this concern, increasing the number of individuals tested with an available number of tests and providing a cost-effective alternative to individual testing. Over the years, an entire body of research has indeed been developed around group testing in a diagnostic context, for example when resources are scarce and/or under time pressure . This is precisely the situation we are confronted with the current pandemic, creating an opportunity to roll out and test reliable and new methodologies (see, e.g., ). It is another example where existing and seasoned methodology can be tailored to differing circumstances, such as the need for repeated testing, as described by Augenblick et al. . Test results can be compared with the results from non-pharmaceutical components of early warning systems (Section 3.2). Knowledge on the test characteristics can be used and integrated when interpreting survey results (Section 5.2).
Vaccine development While a number of effective vaccines have been developed over the last half century, such as for measles, rubella, smallpox, hepatitis B, Ebola, etc., vaccine development remains a challenging area. For example, no succesful vaccine has been found so far for HIV . Even the determination of the seasonal influenza vaccine, a yearly exercise, is one of hits and misses, due to the volatile nature of the influenza virus. Of particular importance to us is that traditionally coronaviruses (hCoV-229E, hCoV-NL63, and hCoV-OC43) have received little or no attention from a vaccine development standpoint. This changed for SARS-CoV-1 and MERS-CoV but in these cases there was no opportunity to put potential vaccines to the test. While existing vaccine-constructs (e.g., adeno-based, adjuvants, etc.), in particular for influenza and the aforementioned coronaviruses, can provide a step-up for SARS-CoV-2, success is not automatically guaranteed. Because the general consensus is that the global population will be able to return to normalcy only after the development of effective vaccines and the implementation of large vaccination programmes, the challenge is to develop a vaccine at unprecedented speed. Evidently, global collaboration is essential. A candidate vaccine developed in one part of the world may have to be put to the test in another, depending on the succession of epidemic waves. The state of urgency poses ethical questions, such as whether one can, besides the traditional phase 3 efficacy studies, set up controlled human infection model (CHIM) studies where healthy subjects are infected to test a vaccine, while effective treatment may not yet be available. A further challenge is that vaccines need to be developed while the immunology associated with SARS-CoV-2 is still unclear, and knowledge is accumulating, with trial and error. Several pharmaceutical companies have taken the unprecedented step to plan and build production capacity in parallel with candidate vaccine development and testing. A fascinating new chapter is currently being written to bring future vaccines to market; many lessons will be learned that fall beyond the scope of the present paper.
Clinical trials for COVID-19 patients The amount of clinical research generated by the COVID-19 pandemic is mind-boggling: on June 15, 2020, a search of the ClinicalTrials.gov website with the keywords “COVID”, returned more than 600 interventional studies currently recruiting patients . For a more complete coverage of trials worldwide, the ReDO database listed 1144 interventional trials for the treatment of COVID-19 infected patients on June 26, 2020 . Reassuringly, 825 (80%) of these trials were controlled and taking place in a hospital setting (because testing capacity was lacking outside of the hospitals at the start of the pandemic). It is beyond the scope of this paper to cover the various treatment approaches that are being tested against COVID-19, whether using repurposed drugs already in use for other indications, new drugs specifically developed against the virus, or non-drug treatments. The World Health Organization (WHO) published a useful classification of treatment types . Here we focus on key features of some of the clinical trials that were designed and conducted in record time in the early days of the epidemic in Belgium. 8.1 Outcome measures The natural history of most diseases is well established, and a consensus has in most cases been reached on outcomes that appropriately capture how a patient feels, functions or survives. COVID-19 infections were, at least initially, largely unknown, hence it was challenging to choose outcome measures that would be clinically relevant as well as statistically sensitive to treatment benefits. The best outcomes to use will undoubtedly emerge as the results of clinical trials begin to appear and clinicians have built experience on how to measure these outcomes. In large randomized trials for hospitalised patients such as RECOVERY (Randomized Evaluation of COVid-19 thERapY), all-cause mortality within 28 days was the primary outcome of interest . While all-cause mortality is unquestionably the ultimate clinical outcome most therapies are trying to impact, cause-specific mortality could be more sensitive and also more relevant if (and only if) the treatments had no impact on deaths due to other causes. In practice both all-cause and cause-specific mortality are typically required to assess all treatment effects, and the designation of either one as the primary outcome may depend on the importance of competing risks of death. Other outcome measures of interest are time to invasive mechanical ventilation, and time to discharge. Besides time to clinically important events, the need to quantify the severity of the COVID-19 infection led to the definition of clinical progression scales. shows one such ordinal scale with scores ranging from 0 to 10 . Less granular ordinal scales have been used (e.g., with scores ranging from 1 to 5) with a similar intent. Various outcome measures can be defined using these scales, e.g. time to a score change (improvement or deterioration) of at least 2 points on the chosen scale, cumulative score or area under the score curve up to day 15, etc. Time will tell which scale and outcome measure are simple enough to be used effectively and sensitive enough to detect treatment benefits. Last but not least, inclusion of patient-reported outcomes (PRO) should be considered in trials of COVID-19 patients with prolonged follow-up . 8.2 Multi-arm designs The main challenges when conducting clinical trials in the COVID-19 context are (a) the multitude of potential treatments, (b) the lack of patients in some regions to conduct several trials in parallel, (c) the pace at which new scientific insights become available, and (d) the push to use treatments based on incomplete preclinical development and unreliable clinical data. Hydroxychloroquine, for instance, made it into preliminary COVID-19 treatment guidelines without proper supporting evidence, thus undermining the use of untreated controls in clinical trials. This has forced statisticians and clinicians to search for flexible designs which allow including additional promising therapies or removing therapies which have shown not to be effective, while simultaneously allowing for optimal use of the limited available patients and drugs. When two treatments A and B are to be compared to standard of care (SOC), a natural choice would be a randomized multi-arm study comparing A, B and SOC (leaving aside the potential difficulties of access to A and B at once). The advantage is that a single SOC group can be used rather than two SOC groups which would be needed in two separate trials comparing A with SOC and B with SOC. However, classical multi-arm studies require all patients enrolled to be eligible for all treatments. In the COVID-19 context, a research treatment often has contraindications which do not allow patients to be randomized to that particular treatment, but allowing patients to be randomized to some of the other treatments under consideration. A possible solution is selective exclusion. While such designs with selective exclusion have been described in the statistical and medical literature , the statistical analysis of such studies has not received much attention. As an example, consider a scenario in which patients are randomized to treatment A, B, or SOC in a (1:2:1) ratio. Interest is in comparing A with SOC, and B with SOC. Further assume that 10% of the population eligible for A and/or B is eligible for A only (subpopulation 1), while 30% is eligible for B only (subpopulation 2). The remaining 60% is eligible for both A and B (subpopulation 3). This situation is graphically shown in . Out of 100 patients eligible for A or B, we expect 10, 30, and 60 subjects in subpopulations 1, 2, and 3, respectively. In each subpopulation, randomization is performed according to the appropriate ratios, i.e., (1:1), (2:1), and (1:2:1), in subpopulations 1, 2, and 3, respectively. When analyzing the effect of treatment A versus SOC, only concurrent controls can be included. Hence the SOC patients from subpopulations 1 and 3, will be compared to all A patients from the same two subpopulations. However, in subpopulation 3, 50% of the patients received B, implying that subpopulation 3 is underrepresented in the comparison of A versus SOC. If the objective is to estimate the marginal effect of A versus SOC, i.e., the effect one would estimate in a placebo controlled trial of A versus SOC, the patients from subpopulation 3 need to be reweighted by a factor 2, in order to restore the balance between subpopulations 1 and 3. The final analysis of A versus SOC is then a weighted analysis of the 2 × 20 patients from the subpopulations 1 and 3 who received either A or SOC, however, the patients from subpopulation 3 get each a weight of 2. Likewise, the marginal effect of B versus SOC can be estimated using a weighted analysis of the 25 SOC patients and the 50 B patients from subpopulations 2 and 3, but the patients from subpopulation 3 need to be reweighted by a factor 4/3 in order to correct for the imbalance due to the removal of the 25% patients on treatment A in subpopulation 3. Note that the gain of the design in is that an expected 15 SOC patients, i.e., 25% of 60% of the study population, can be used twice, once in the comparison with A and once in the comparison with B. The gain obviously highly depends on the eligibility criteria and on the randomization ratios used. Note also that the methodology can easily be extended to trials with more than two research treatments and to trials with adaptive designs allowing for adding new treatments or removing non-promising treatments. 8.3 Factorial designs Factorial designs, a rare exception in trials sponsored by pharmaceutical companies who prefer to focus on a single therapeutic question, were suggested for situations in which more than one treatment could be tested simultaneously in the same patients. As an example of such a design, the COV-AID trial (Treatment of COVID-19 patients with Anti-Interleukin Drugs) simultaneously tested blockade of the Interleukin-1 pathway with Anakinra, and blockade of the Interleukin-6 pathway with either Siltuximab or Tocizilumab, in hospitalised adult patients with COVID-19 infection, acute hypoxia and signs of cytokine release syndrome. The factorial design is premised on the effectiveness of interleukin blockade to prevent hyperinflammation or auto-inflammatory syndromes in COVID-19 infected patients. Interestingly, in such a design, only 2 out of every 9 patients receive usual care while 7 receive usual care plus at least one experimental drug (see ). 8.4 Interim analyses and multi-stage designs In view of the huge uncertainties associated with anticipated clinical outcomes as well as treatment effects, it was generally considered appropriate to include one or more interim analyses for safety and/or futility and/or efficacy in the trial designs. Group sequential trial methodology provides a well-known framework for incorporating as many interim analyses as deemed necessary while adequately controlling the probability of a type I error. Any substantial trial benefits from being monitored by an experienced IDMC (Independent Data Monitoring Committee), and in particular trials with interim analyses of efficacy; however IDMCs are in high demand and short supply, and the flurry of COVID-19 trials will not ease the current shortage. Adaptive design methodology was also considered, though its most common applications (choice of an optimal dose, increase in sample size, or enrichment in specific patient subsets) did not address the most acute need in COVID-19 trials, which was to allow seamless addition or dropping of treatment arms to reflect a changing therapeutic landscape. This is the objective of platform trials, such as the multi-arm multi-stage (MAMS) trials . The PRINCIPLE trial (University of Oxford ), served as a model for the design of a similar trial in Belgium, the DAWN (Direct Antivirals Working against nCov) Ambulatory Care Platform trial. Logistical challenges in the setting of COVID-19 include the timely identification of eligible subjects, obtaining informed consent when isolation at home is needed, as well as the delivery of study medication. Initially, this trial will compare Camostat with standard of care in community dwelling adult patients who are at least 50 years old presenting with signs and symptoms compatible with COVID-19. The aim of this large pragmatic trial is to avoid hospitalization by using a well tolerated antiviral to rapidly treat patients at risk who have first symptoms of COVID-19. Like in PRINCIPLE, the DAWN trial will use Bayesian posterior probabilities to add or drop treatment arms while the study is ongoing, but unlike in PRINCIPLE, randomization will not use adaptive randomization, for there is neither a statistical advantage nor an ethical imperative to do so (see , with discussion). Instead, minimization can be used to allocate treatments in a constant ratio while allowing for several prognostic factors to be balanced across the treatment arms. 8.5 Pragmatism in trial conduct Perhaps the most impressive aspect of clinical trial activities during the pandemic was the collaborative pragmatism that naturally evolved in response to the crisis. Statisticians from academia, the public and the private sectors voluntarily contributed ideas and resources to come up with optimal trial designs to address the most critical clinical questions. Some of these collaborations pre-dated the pandemic, but many were improvised to respond efficiently to the most pressing needs. When it came to launching the trials, the usual delays and bureaucratic hurdles evaporated, and the trials could all be launched within a couple of weeks - instead of the several months usually required to fulfil all administrative requirements. While excessive speed may create challenges, as discussed in Section 10, on balance it may be preferable to unnecessary delays whenever the health of patients is at stake – and this is the case for many non-COVID-19 related health issues. While an overarching priority was given to rigorous trial designs, implementation details were kept as simple as possible. As was already argued prior to the pandemic, simplicity is a virtue in clinical research , but one that does not align with the commercial interests of the clinical research organizations that implement clinical trials for pharmaceutical companies . Many have argued that the absurdly high costs of pivotal clinical trials are due to inefficiencies in the current clinical research process . Examples of inefficiencies include the collection of data of marginal interest, including details of medical history and concomitant medications, complex procedures to measure outcomes, including central reviews and outcome adjudications, strict visit schedules and examinations that do not reflect clinical routine, and so on. Although some of these inefficiencies may be justified for pivotal trials of new drugs, they should generally be avoided in trials of approved drugs or other non-drug treatments. A clear distinction between pragmatic and explanatory approaches to clinical trials was proposed nearly fifty years ago, yet most trials conducted today adopt the explanatory approach, which is unnecessarily onerous . provides a comparison of trial characteristics under the explanatory and pragmatic approaches . The COVID-19 pandemic provided empirical evidence that inefficiencies in clinical research can easily be overcome in pragmatic trials in times of emergency. Will this lesson survive the end of the pandemic?
Outcome measures The natural history of most diseases is well established, and a consensus has in most cases been reached on outcomes that appropriately capture how a patient feels, functions or survives. COVID-19 infections were, at least initially, largely unknown, hence it was challenging to choose outcome measures that would be clinically relevant as well as statistically sensitive to treatment benefits. The best outcomes to use will undoubtedly emerge as the results of clinical trials begin to appear and clinicians have built experience on how to measure these outcomes. In large randomized trials for hospitalised patients such as RECOVERY (Randomized Evaluation of COVid-19 thERapY), all-cause mortality within 28 days was the primary outcome of interest . While all-cause mortality is unquestionably the ultimate clinical outcome most therapies are trying to impact, cause-specific mortality could be more sensitive and also more relevant if (and only if) the treatments had no impact on deaths due to other causes. In practice both all-cause and cause-specific mortality are typically required to assess all treatment effects, and the designation of either one as the primary outcome may depend on the importance of competing risks of death. Other outcome measures of interest are time to invasive mechanical ventilation, and time to discharge. Besides time to clinically important events, the need to quantify the severity of the COVID-19 infection led to the definition of clinical progression scales. shows one such ordinal scale with scores ranging from 0 to 10 . Less granular ordinal scales have been used (e.g., with scores ranging from 1 to 5) with a similar intent. Various outcome measures can be defined using these scales, e.g. time to a score change (improvement or deterioration) of at least 2 points on the chosen scale, cumulative score or area under the score curve up to day 15, etc. Time will tell which scale and outcome measure are simple enough to be used effectively and sensitive enough to detect treatment benefits. Last but not least, inclusion of patient-reported outcomes (PRO) should be considered in trials of COVID-19 patients with prolonged follow-up .
Multi-arm designs The main challenges when conducting clinical trials in the COVID-19 context are (a) the multitude of potential treatments, (b) the lack of patients in some regions to conduct several trials in parallel, (c) the pace at which new scientific insights become available, and (d) the push to use treatments based on incomplete preclinical development and unreliable clinical data. Hydroxychloroquine, for instance, made it into preliminary COVID-19 treatment guidelines without proper supporting evidence, thus undermining the use of untreated controls in clinical trials. This has forced statisticians and clinicians to search for flexible designs which allow including additional promising therapies or removing therapies which have shown not to be effective, while simultaneously allowing for optimal use of the limited available patients and drugs. When two treatments A and B are to be compared to standard of care (SOC), a natural choice would be a randomized multi-arm study comparing A, B and SOC (leaving aside the potential difficulties of access to A and B at once). The advantage is that a single SOC group can be used rather than two SOC groups which would be needed in two separate trials comparing A with SOC and B with SOC. However, classical multi-arm studies require all patients enrolled to be eligible for all treatments. In the COVID-19 context, a research treatment often has contraindications which do not allow patients to be randomized to that particular treatment, but allowing patients to be randomized to some of the other treatments under consideration. A possible solution is selective exclusion. While such designs with selective exclusion have been described in the statistical and medical literature , the statistical analysis of such studies has not received much attention. As an example, consider a scenario in which patients are randomized to treatment A, B, or SOC in a (1:2:1) ratio. Interest is in comparing A with SOC, and B with SOC. Further assume that 10% of the population eligible for A and/or B is eligible for A only (subpopulation 1), while 30% is eligible for B only (subpopulation 2). The remaining 60% is eligible for both A and B (subpopulation 3). This situation is graphically shown in . Out of 100 patients eligible for A or B, we expect 10, 30, and 60 subjects in subpopulations 1, 2, and 3, respectively. In each subpopulation, randomization is performed according to the appropriate ratios, i.e., (1:1), (2:1), and (1:2:1), in subpopulations 1, 2, and 3, respectively. When analyzing the effect of treatment A versus SOC, only concurrent controls can be included. Hence the SOC patients from subpopulations 1 and 3, will be compared to all A patients from the same two subpopulations. However, in subpopulation 3, 50% of the patients received B, implying that subpopulation 3 is underrepresented in the comparison of A versus SOC. If the objective is to estimate the marginal effect of A versus SOC, i.e., the effect one would estimate in a placebo controlled trial of A versus SOC, the patients from subpopulation 3 need to be reweighted by a factor 2, in order to restore the balance between subpopulations 1 and 3. The final analysis of A versus SOC is then a weighted analysis of the 2 × 20 patients from the subpopulations 1 and 3 who received either A or SOC, however, the patients from subpopulation 3 get each a weight of 2. Likewise, the marginal effect of B versus SOC can be estimated using a weighted analysis of the 25 SOC patients and the 50 B patients from subpopulations 2 and 3, but the patients from subpopulation 3 need to be reweighted by a factor 4/3 in order to correct for the imbalance due to the removal of the 25% patients on treatment A in subpopulation 3. Note that the gain of the design in is that an expected 15 SOC patients, i.e., 25% of 60% of the study population, can be used twice, once in the comparison with A and once in the comparison with B. The gain obviously highly depends on the eligibility criteria and on the randomization ratios used. Note also that the methodology can easily be extended to trials with more than two research treatments and to trials with adaptive designs allowing for adding new treatments or removing non-promising treatments.
Factorial designs Factorial designs, a rare exception in trials sponsored by pharmaceutical companies who prefer to focus on a single therapeutic question, were suggested for situations in which more than one treatment could be tested simultaneously in the same patients. As an example of such a design, the COV-AID trial (Treatment of COVID-19 patients with Anti-Interleukin Drugs) simultaneously tested blockade of the Interleukin-1 pathway with Anakinra, and blockade of the Interleukin-6 pathway with either Siltuximab or Tocizilumab, in hospitalised adult patients with COVID-19 infection, acute hypoxia and signs of cytokine release syndrome. The factorial design is premised on the effectiveness of interleukin blockade to prevent hyperinflammation or auto-inflammatory syndromes in COVID-19 infected patients. Interestingly, in such a design, only 2 out of every 9 patients receive usual care while 7 receive usual care plus at least one experimental drug (see ).
Interim analyses and multi-stage designs In view of the huge uncertainties associated with anticipated clinical outcomes as well as treatment effects, it was generally considered appropriate to include one or more interim analyses for safety and/or futility and/or efficacy in the trial designs. Group sequential trial methodology provides a well-known framework for incorporating as many interim analyses as deemed necessary while adequately controlling the probability of a type I error. Any substantial trial benefits from being monitored by an experienced IDMC (Independent Data Monitoring Committee), and in particular trials with interim analyses of efficacy; however IDMCs are in high demand and short supply, and the flurry of COVID-19 trials will not ease the current shortage. Adaptive design methodology was also considered, though its most common applications (choice of an optimal dose, increase in sample size, or enrichment in specific patient subsets) did not address the most acute need in COVID-19 trials, which was to allow seamless addition or dropping of treatment arms to reflect a changing therapeutic landscape. This is the objective of platform trials, such as the multi-arm multi-stage (MAMS) trials . The PRINCIPLE trial (University of Oxford ), served as a model for the design of a similar trial in Belgium, the DAWN (Direct Antivirals Working against nCov) Ambulatory Care Platform trial. Logistical challenges in the setting of COVID-19 include the timely identification of eligible subjects, obtaining informed consent when isolation at home is needed, as well as the delivery of study medication. Initially, this trial will compare Camostat with standard of care in community dwelling adult patients who are at least 50 years old presenting with signs and symptoms compatible with COVID-19. The aim of this large pragmatic trial is to avoid hospitalization by using a well tolerated antiviral to rapidly treat patients at risk who have first symptoms of COVID-19. Like in PRINCIPLE, the DAWN trial will use Bayesian posterior probabilities to add or drop treatment arms while the study is ongoing, but unlike in PRINCIPLE, randomization will not use adaptive randomization, for there is neither a statistical advantage nor an ethical imperative to do so (see , with discussion). Instead, minimization can be used to allocate treatments in a constant ratio while allowing for several prognostic factors to be balanced across the treatment arms.
Pragmatism in trial conduct Perhaps the most impressive aspect of clinical trial activities during the pandemic was the collaborative pragmatism that naturally evolved in response to the crisis. Statisticians from academia, the public and the private sectors voluntarily contributed ideas and resources to come up with optimal trial designs to address the most critical clinical questions. Some of these collaborations pre-dated the pandemic, but many were improvised to respond efficiently to the most pressing needs. When it came to launching the trials, the usual delays and bureaucratic hurdles evaporated, and the trials could all be launched within a couple of weeks - instead of the several months usually required to fulfil all administrative requirements. While excessive speed may create challenges, as discussed in Section 10, on balance it may be preferable to unnecessary delays whenever the health of patients is at stake – and this is the case for many non-COVID-19 related health issues. While an overarching priority was given to rigorous trial designs, implementation details were kept as simple as possible. As was already argued prior to the pandemic, simplicity is a virtue in clinical research , but one that does not align with the commercial interests of the clinical research organizations that implement clinical trials for pharmaceutical companies . Many have argued that the absurdly high costs of pivotal clinical trials are due to inefficiencies in the current clinical research process . Examples of inefficiencies include the collection of data of marginal interest, including details of medical history and concomitant medications, complex procedures to measure outcomes, including central reviews and outcome adjudications, strict visit schedules and examinations that do not reflect clinical routine, and so on. Although some of these inefficiencies may be justified for pivotal trials of new drugs, they should generally be avoided in trials of approved drugs or other non-drug treatments. A clear distinction between pragmatic and explanatory approaches to clinical trials was proposed nearly fifty years ago, yet most trials conducted today adopt the explanatory approach, which is unnecessarily onerous . provides a comparison of trial characteristics under the explanatory and pragmatic approaches . The COVID-19 pandemic provided empirical evidence that inefficiencies in clinical research can easily be overcome in pragmatic trials in times of emergency. Will this lesson survive the end of the pandemic?
Impact of COVID-19 on ongoing clinical trials The COVID-19 pandemic has had, and will continue to have, a major impact on the conduct of almost all ongoing clinical trials, in particular on the treatment of patients and the schedule of their planned protocol visits. Regulatory agencies worldwide have promptly issued guidance on measures to be taken to minimize the impact of COVID-19 on ongoing trials . Given the huge uncertainty associated with the current situation, and the lack of historical precedents, the guidance documents recommend to capture as much information as possible on protocol deviations and other unexpected events, so as to be able to conduct various analyses when the trial is completed. Meyer et al. give an excellent overview of statistical issues and recommendations for clinical trials during the COVID-19 pandemic. From a statistical inference perspective, despite the dramatic health care disruptions caused by the COVID-19 pandemic, intention-to-treat (ITT) analyses of randomized clinical trials remain valid, if (as will generally be the case) protocol deviations impact all randomized treatment groups equally. However, such deviations may induce a dilution of the treatment effect, and as such are likely to result in more conservative estimates of treatment effects (with the exception of non-inferiority trials). In other words, the ITT estimates of treatment effects will in general not be biased by systematic differences between the randomized treatment groups, but they may well underestimate treatment effects that would have been estimated in ‘normal’ circumstances. 9.1 Missing data Missing visits, missing clinical assessments, missing scans or laboratory values, and all such like that result from the COVID-19 pandemic will in general be missing at random (MAR), since the pandemic is an external cause of missingness that bears no relationship to the disease or treatment under investigation. Hence COVID-19 related missing data can be appropriately dealt with by using likelihood based methods or multiple imputation under the MAR assumption. To give a few typical examples: (1) hazard ratios estimated using proportional hazards regression models, e.g., survival times, remain valid under independent censoring (and proportional hazards); (2) treatment effects estimated using mixed models for repeated data, e.g., for longitudinal measurements of visual acuity, remain valid if the outcome data are MAR; (3) generalized estimating equations for longitudinal measurements of responses remain valid if missing data are imputed under the assumption of MAR. Multiple imputation may be feasible when the amount of missing data is limited; however, the potential for multiple imputation is limited when large volumes of data are missing, especially when few patients have observed data that can be used to impute the data for patients with missing values. In multinational or multiregional trials, the COVID-19 pandemic may take a different course in different regions; in addition, regional differences such as distance traveled to health care centers may create very different patterns of missingness across regions. This variability may not create a systematic bias if it affects all treatment arms equally. It does offer an opportunity to perform sensitivity analyses using region as a potential modulator of treatment effect. Other sensitivity analyses (such as shift imputation and tipping-point analyses) will likely play a more prominent role due to the larger than usual volume of missing data. Finally, it will be important to rule out situations of differential drop-out rate between the randomized treatment groups. This could happen, for instance, in open-label trials if patients in the control arm are more likely to miss their planned visits than patients who receive an experimental therapy. Conversely, some trials had to stop the experimental treatment (e.g., immunotherapy in cancer) for fear of an interaction with COVID-19. 9.2 Outcome assessments Missing visits have a direct impact on outcome assessments. For instance, in oncology trials, tumor response and time to progressive disease are assessed through CT-scans performed according to a fixed schedule. Some conventions that are sometimes applied, e.g., to censor patients if they have missed too many visits, become wholly inappropriate when deviations from the intended schedules are systematic and unavoidable. In such cases, these conventions should be used, if at all, only in sensitivity analyses. The proper primary analysis of time to progression should remain an ITT analysis, in which all patients are followed up as thoroughly as possible, regardless of how long it takes to obtain CT-scans, until they have objective confirmation of disease progression. Because in some patients such confirmation may come with considerable delay, interval-censoring analyses may be helpful to complement or even replace the traditional analyses with right censoring only. Some patients may prefer to avoid hospital or office visits during the COVID-19 pandemic. If outcome assessments were due to take place at the hospital or doctor's office (e.g., a 6-minute walk test), it may be preferable to replace these assessments by their home-based equivalent assessments, when available. In most situations, some data are better than no data at all, under the assumption that data taken in less than ideal situations are not grossly erroneous or misleading. In fact, even if assessments taken at home in poorly controlled conditions are less reliable than those taken at the hospital in the most rigorous conditions, the loss in efficiency in detecting a treatment effect may be surprisingly small, assuming no systematic bias between the randomized treatment groups . It is sometimes believed, wrongly, that patients who have symptomatic COVID-19 infections should be removed from trials of other indications. This is unjustified and should not be done unless it is mandated by the patient's safety or personal choice. 9.3 COVID-19 related events It is conceivable that in some cases a randomized trial has its treatment arms differentially affected by the pandemic if the intervention under study is a risk factor for COVID-19. As an example, in oncology, chemotherapy is felt to increase the risk of infection among cancer patients, and some authors have cautioned the medical community about this risk. In a trial comparing chemotherapy with a non-cytotoxic intervention, the incidence of COVID-19 may therefore be higher in the chemotherapy arm. If the infection is a risk factor for one or more of the outcomes of interest (e.g., survival), an association may be created between the exposure (treatment) and the outcome (in this case, survival) through the infection, thus confounding the analysis of such outcome(s), unless cause-specific mortality is used. The reporting of causes of death is generally unreliable and variable from center to center, but COVID-19 related deaths are likely to be reported reliably (respiratory diseases being an exception). It may also be useful to perform competing risks analyses for the outcome of primary interest in the trial (such as disease progression) and COVID-19 infection. It is conceivable that patients with COVID-19 infection will receive treatments that interact with their treatments for other indications. Such interactions would only create a potential bias in randomized trials if they were different for the treatments being compared, an unlikely situation but one that may on occasion occur. A related issue is that most clinical trials forbid the inclusion of patients in other trials of investigational drugs. AIDS advocacy groups argued long ago that co-enrollment in multiple trials was both ethically and scientifically desirable, a view that still prevails today and should be pro-actively implemented in trials . 9.4 Protocol amendments Because the results of randomized clinical trials are, by nature, protected against changes in the environment that affect all randomized groups equally, there will generally be no good reason to amend the statistical sections of the protocols of ongoing studies, except for sample size calculations and the provision of descriptive statistics on the impact of COVID-19 (number of patients with COVID-19 infections and COVID-19 deaths). Some trials will have to stop as a result of the pandemic with a lower sample size than initially planned. For trials that can continue throughout the pandemic, major protocol deviations may result in a lower treatment effect than anticipated, which might justify a sample size increase to compensate the loss in statistical power. Such sample size increases do not affect the type I error.
Missing data Missing visits, missing clinical assessments, missing scans or laboratory values, and all such like that result from the COVID-19 pandemic will in general be missing at random (MAR), since the pandemic is an external cause of missingness that bears no relationship to the disease or treatment under investigation. Hence COVID-19 related missing data can be appropriately dealt with by using likelihood based methods or multiple imputation under the MAR assumption. To give a few typical examples: (1) hazard ratios estimated using proportional hazards regression models, e.g., survival times, remain valid under independent censoring (and proportional hazards); (2) treatment effects estimated using mixed models for repeated data, e.g., for longitudinal measurements of visual acuity, remain valid if the outcome data are MAR; (3) generalized estimating equations for longitudinal measurements of responses remain valid if missing data are imputed under the assumption of MAR. Multiple imputation may be feasible when the amount of missing data is limited; however, the potential for multiple imputation is limited when large volumes of data are missing, especially when few patients have observed data that can be used to impute the data for patients with missing values. In multinational or multiregional trials, the COVID-19 pandemic may take a different course in different regions; in addition, regional differences such as distance traveled to health care centers may create very different patterns of missingness across regions. This variability may not create a systematic bias if it affects all treatment arms equally. It does offer an opportunity to perform sensitivity analyses using region as a potential modulator of treatment effect. Other sensitivity analyses (such as shift imputation and tipping-point analyses) will likely play a more prominent role due to the larger than usual volume of missing data. Finally, it will be important to rule out situations of differential drop-out rate between the randomized treatment groups. This could happen, for instance, in open-label trials if patients in the control arm are more likely to miss their planned visits than patients who receive an experimental therapy. Conversely, some trials had to stop the experimental treatment (e.g., immunotherapy in cancer) for fear of an interaction with COVID-19.
Outcome assessments Missing visits have a direct impact on outcome assessments. For instance, in oncology trials, tumor response and time to progressive disease are assessed through CT-scans performed according to a fixed schedule. Some conventions that are sometimes applied, e.g., to censor patients if they have missed too many visits, become wholly inappropriate when deviations from the intended schedules are systematic and unavoidable. In such cases, these conventions should be used, if at all, only in sensitivity analyses. The proper primary analysis of time to progression should remain an ITT analysis, in which all patients are followed up as thoroughly as possible, regardless of how long it takes to obtain CT-scans, until they have objective confirmation of disease progression. Because in some patients such confirmation may come with considerable delay, interval-censoring analyses may be helpful to complement or even replace the traditional analyses with right censoring only. Some patients may prefer to avoid hospital or office visits during the COVID-19 pandemic. If outcome assessments were due to take place at the hospital or doctor's office (e.g., a 6-minute walk test), it may be preferable to replace these assessments by their home-based equivalent assessments, when available. In most situations, some data are better than no data at all, under the assumption that data taken in less than ideal situations are not grossly erroneous or misleading. In fact, even if assessments taken at home in poorly controlled conditions are less reliable than those taken at the hospital in the most rigorous conditions, the loss in efficiency in detecting a treatment effect may be surprisingly small, assuming no systematic bias between the randomized treatment groups . It is sometimes believed, wrongly, that patients who have symptomatic COVID-19 infections should be removed from trials of other indications. This is unjustified and should not be done unless it is mandated by the patient's safety or personal choice.
COVID-19 related events It is conceivable that in some cases a randomized trial has its treatment arms differentially affected by the pandemic if the intervention under study is a risk factor for COVID-19. As an example, in oncology, chemotherapy is felt to increase the risk of infection among cancer patients, and some authors have cautioned the medical community about this risk. In a trial comparing chemotherapy with a non-cytotoxic intervention, the incidence of COVID-19 may therefore be higher in the chemotherapy arm. If the infection is a risk factor for one or more of the outcomes of interest (e.g., survival), an association may be created between the exposure (treatment) and the outcome (in this case, survival) through the infection, thus confounding the analysis of such outcome(s), unless cause-specific mortality is used. The reporting of causes of death is generally unreliable and variable from center to center, but COVID-19 related deaths are likely to be reported reliably (respiratory diseases being an exception). It may also be useful to perform competing risks analyses for the outcome of primary interest in the trial (such as disease progression) and COVID-19 infection. It is conceivable that patients with COVID-19 infection will receive treatments that interact with their treatments for other indications. Such interactions would only create a potential bias in randomized trials if they were different for the treatments being compared, an unlikely situation but one that may on occasion occur. A related issue is that most clinical trials forbid the inclusion of patients in other trials of investigational drugs. AIDS advocacy groups argued long ago that co-enrollment in multiple trials was both ethically and scientifically desirable, a view that still prevails today and should be pro-actively implemented in trials .
Protocol amendments Because the results of randomized clinical trials are, by nature, protected against changes in the environment that affect all randomized groups equally, there will generally be no good reason to amend the statistical sections of the protocols of ongoing studies, except for sample size calculations and the provision of descriptive statistics on the impact of COVID-19 (number of patients with COVID-19 infections and COVID-19 deaths). Some trials will have to stop as a result of the pandemic with a lower sample size than initially planned. For trials that can continue throughout the pandemic, major protocol deviations may result in a lower treatment effect than anticipated, which might justify a sample size increase to compensate the loss in statistical power. Such sample size increases do not affect the type I error.
The Price of speed: methodological sloppiness 10.1 Uncontrolled trials In times of great pressure, such as when the COVID-19 pandemic erupted, it is very tempting to take shortcuts and experiment with potentially effective treatments in an uncontrolled way, with the hope that some of the treatments tested will be so effective as to constitute real breakthroughs in the management of the disease. Two additional factors may mitigate against conducting properly controlled experiments: the number of patients available, and the severity of their condition (patients admitted to ICU often having a fatal outcome). Yet, despite ethical dilemmas with control arms, randomization was widely considered during the COVID-19 outbreak as the only way to generate reliable, practice-changing evidence . Claims made on the basis of supposedly impressive clinical outcomes of COVID-19 infected patients treated with Chloroquine and Hydroxychloroquine were viewed with skepticism, and the contradictory data that were later published about these drugs, including some that had to be retracted , confirmed that skepticism was indeed in order, and that scientific standards could not be lowered as a result of the pandemic . Observational studies, even when conducted with care, can be so misleading that some authors have argued a moratorium should be placed on reporting them . And indeed, to counteract exaggerated claims based on uncontrolled data, some wide-ranging national or international collaborations were quickly put in place for the conduct of large-scale trials . The SOLIDARITY trial, conducted in 35 countries under the auspices of the WHO, is an example of a large simple trial for hospitalised patients with COVID-19 treated with local standard of care (SOC) against Remdesivir, Lopinavir and Ritonavir, or SOC plus Lopinavir and Ritonavir and Interferon β -1a . Despite the best of intentions, the SOLIDARITY trial ran into contractual and legal difficulties that made its adoption in many countries slow and inefficient. Furthermore, the trial prioritized antiviral agents and other PIs over NPIs, which may have diverted resources away from trials of simple supportive care interventions. Finally, finding international consensus to select or change trial interventions is far more challenging than at the national level. The right balance between national and international efforts will have to be addressed going forward, with the overarching goal of maximizing the efficiency of clinical research. Trial implementation is definitely more efficient at the national level; however the number of patients in a small country like Belgium is insufficient to size the trials properly. Most of the trials started during the early phase of the COVID-19 epidemic will be too small to provide reliable estimates of treatment effects, and it would therefore be advisable to plan prospective meta-analyses of all such trials as soon as possible. Such prospective meta-analyses should be based on patient-level data (see Section 11). In the UK, the large RECOVERY trial tested standard of care against low-dose Dexamethasone, Azithromycin, Tocilizumab or convalescent plasma. This trial accrued 11,303 patients between March and June 2020 and, in this short period of time, was already able to show a highly significant benefit of dexamethasone on mortality , which immediately led to the use of glucocorticoids as standard of care for hospitalised COVID-19 patients. 10.2 Methodological errors Methodological issues have arisen in a number of studies dedicated to prediction models for diagnosis and prognosis (mortality risk, progression to severe disease, length of hospital stay) of patients with COVID-19. Wynants et al. conducted a systematic review and detected 51 studies with methodological issues and errors among a collection of 4909 titles screened. In clinical trials conducted in COVID-19 patients, the statistical methods commonly used are based on the standard Cox proportional hazards model and the Kaplan-Meier estimator (see, for example, , and ). When time to death due to COVID-19 is the outcome of interest, these methods implicitly treat discharged or recovered patients as right censored. Doing so is incorrect, however, as right censoring means that the unobserved time to death can be any time point larger than the observed one, whereas patients who recover may in fact never die from COVID-19. A correct way of analyzing this type of data is through the use of competing risk models, such as the model proposed by Fine and Gray which is based on the subdistribution hazard, or on cure models. To study the impact of incorrectly classifying recovered patients as right censored, Oulhaj et al. simulated data from a fictive clinical trial on COVID-19. Six scenarios representing different situations of the effect of treatment on death and its competing event recovery were considered. The hazard ratio of death and the 28-day absolute risk reduction were estimated using the Cox model and the Fine and Gray model. The Cox model estimated the hazard ratio of death due to COVID-19 and the 28-day absolute risk reduction incorrectly in almost all cases. The magnitude of the estimation bias increased when the process of recovery was faster and/or the chance of recovery was higher. In some cases, the estimates obtained from the Cox model also incorrectly showed a harmful effect of treatment when it was in fact beneficial. The simulation study therefore shows that there is a substantial risk of misleading results in COVID-19 research if recovery and death due to COVID-19 are not considered as competing events, and the assumption of non-informative censoring is violated. This issue, and others related to intercurrent events, is best addressed using the estimand framework, which has now become a regulatory requirement for trials aimed at new drug registration . Another well-known issue with the Cox model is the presence of strongly non proportional hazards. Much literature has recently focused on alternatives to the Cox model, especially for situations where deviations from proportionality are expected or have been observed, e.g., in trials of immunotherapy for cancer patients. Accelerated failure time models and the restricted mean survival time have been advocated in such cases, as have approaches based on generalized pairwise comparisons such as the win ratio and the net benefit . Further experience is needed with these alternative approaches, which might advantageously be considered in COVID-19 trials.
Uncontrolled trials In times of great pressure, such as when the COVID-19 pandemic erupted, it is very tempting to take shortcuts and experiment with potentially effective treatments in an uncontrolled way, with the hope that some of the treatments tested will be so effective as to constitute real breakthroughs in the management of the disease. Two additional factors may mitigate against conducting properly controlled experiments: the number of patients available, and the severity of their condition (patients admitted to ICU often having a fatal outcome). Yet, despite ethical dilemmas with control arms, randomization was widely considered during the COVID-19 outbreak as the only way to generate reliable, practice-changing evidence . Claims made on the basis of supposedly impressive clinical outcomes of COVID-19 infected patients treated with Chloroquine and Hydroxychloroquine were viewed with skepticism, and the contradictory data that were later published about these drugs, including some that had to be retracted , confirmed that skepticism was indeed in order, and that scientific standards could not be lowered as a result of the pandemic . Observational studies, even when conducted with care, can be so misleading that some authors have argued a moratorium should be placed on reporting them . And indeed, to counteract exaggerated claims based on uncontrolled data, some wide-ranging national or international collaborations were quickly put in place for the conduct of large-scale trials . The SOLIDARITY trial, conducted in 35 countries under the auspices of the WHO, is an example of a large simple trial for hospitalised patients with COVID-19 treated with local standard of care (SOC) against Remdesivir, Lopinavir and Ritonavir, or SOC plus Lopinavir and Ritonavir and Interferon β -1a . Despite the best of intentions, the SOLIDARITY trial ran into contractual and legal difficulties that made its adoption in many countries slow and inefficient. Furthermore, the trial prioritized antiviral agents and other PIs over NPIs, which may have diverted resources away from trials of simple supportive care interventions. Finally, finding international consensus to select or change trial interventions is far more challenging than at the national level. The right balance between national and international efforts will have to be addressed going forward, with the overarching goal of maximizing the efficiency of clinical research. Trial implementation is definitely more efficient at the national level; however the number of patients in a small country like Belgium is insufficient to size the trials properly. Most of the trials started during the early phase of the COVID-19 epidemic will be too small to provide reliable estimates of treatment effects, and it would therefore be advisable to plan prospective meta-analyses of all such trials as soon as possible. Such prospective meta-analyses should be based on patient-level data (see Section 11). In the UK, the large RECOVERY trial tested standard of care against low-dose Dexamethasone, Azithromycin, Tocilizumab or convalescent plasma. This trial accrued 11,303 patients between March and June 2020 and, in this short period of time, was already able to show a highly significant benefit of dexamethasone on mortality , which immediately led to the use of glucocorticoids as standard of care for hospitalised COVID-19 patients.
Methodological errors Methodological issues have arisen in a number of studies dedicated to prediction models for diagnosis and prognosis (mortality risk, progression to severe disease, length of hospital stay) of patients with COVID-19. Wynants et al. conducted a systematic review and detected 51 studies with methodological issues and errors among a collection of 4909 titles screened. In clinical trials conducted in COVID-19 patients, the statistical methods commonly used are based on the standard Cox proportional hazards model and the Kaplan-Meier estimator (see, for example, , and ). When time to death due to COVID-19 is the outcome of interest, these methods implicitly treat discharged or recovered patients as right censored. Doing so is incorrect, however, as right censoring means that the unobserved time to death can be any time point larger than the observed one, whereas patients who recover may in fact never die from COVID-19. A correct way of analyzing this type of data is through the use of competing risk models, such as the model proposed by Fine and Gray which is based on the subdistribution hazard, or on cure models. To study the impact of incorrectly classifying recovered patients as right censored, Oulhaj et al. simulated data from a fictive clinical trial on COVID-19. Six scenarios representing different situations of the effect of treatment on death and its competing event recovery were considered. The hazard ratio of death and the 28-day absolute risk reduction were estimated using the Cox model and the Fine and Gray model. The Cox model estimated the hazard ratio of death due to COVID-19 and the 28-day absolute risk reduction incorrectly in almost all cases. The magnitude of the estimation bias increased when the process of recovery was faster and/or the chance of recovery was higher. In some cases, the estimates obtained from the Cox model also incorrectly showed a harmful effect of treatment when it was in fact beneficial. The simulation study therefore shows that there is a substantial risk of misleading results in COVID-19 research if recovery and death due to COVID-19 are not considered as competing events, and the assumption of non-informative censoring is violated. This issue, and others related to intercurrent events, is best addressed using the estimand framework, which has now become a regulatory requirement for trials aimed at new drug registration . Another well-known issue with the Cox model is the presence of strongly non proportional hazards. Much literature has recently focused on alternatives to the Cox model, especially for situations where deviations from proportionality are expected or have been observed, e.g., in trials of immunotherapy for cancer patients. Accelerated failure time models and the restricted mean survival time have been advocated in such cases, as have approaches based on generalized pairwise comparisons such as the win ratio and the net benefit . Further experience is needed with these alternative approaches, which might advantageously be considered in COVID-19 trials.
The need for data sharing During the pandemic, one of the key needs was and remains the collection of personal and medical data at an individual and group level. This need provided impetus for contact tracing and opened possible avenues of research for understanding the spread of the virus throughout the population and specific subgroups. In the discussion regarding the use of existing medical data, the collection of new data and in particular the collection of contact tracing data, some policy makers argued that there was a conflict between the rights guaranteed by the European Union's General Data Protection Regulation (GDPR), and this need for data sharing. This paradoxical dichotomy potentially inhibits the use of valuable data for research purposes within a country, and jeopardises cross-border scientific cooperation in the case of different interpretations of the same regulation within EU-member states. Several authors have argued that there is ample room within the GDPR for a framework allowing for the scientific use of existing and newly collected data to support the international effort to curb the pandemic . These views are echoed by the European Data Protection Board , and confirmed by the Belgian Data Protection Authority. Specifically, one can invoke article 9(i) of GDPR if ‘… processing is necessary for reasons of public interest in the area of public health …’ and 9(j) if ‘… processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1)’. These provisions, together with the European Clinical Trials Regulation and the corresponding Belgian law, provide a solid base for the scientific use and data sharing of medical and personal data . As argued by other authors the COVID-19 pandemic is not a free pass to use these data without any safeguards. The pandemic actually has been an opportunity to show that the principle underlying GDPR can actually be an advantage for data-driven research. The confrontation with a new situation, may also require reflection time, i.e., for a debate, but this time was not available, and hence public interest should prevail, within limits defined by the ethical committees. The availability of granular data from differing sources like individual medical files, or data held by mutual health organizations would provide unique opportunities to support health policy making and develop successful strategies for the current and future pandemics. The adoption of standards for data citation and referencing would also promote data sharing in an international, interdisciplinary, and interdependent research community. Guidelines have been developed by DataCite (https: https://datacite.org/cite-your-data.html ) and DataVerse ( http://best-practices.dataverse.org/data-citation/ ). As far as clinical research is concerned, there has been a remarkable push towards sharing of individual patient data for a number of years, both from publicly-funded trials but also from the pharmaceutical industry . The goal is to share individual patient data from all completed trials within reasonable time after their completion so as to allow for further analyses of these patient data, as well as to help the design of other trials. Such maximization of the use of patient data is certainly in line with greater patient involvement in clinical research, and would pave the way to truly patient-centric research. For the sharing to be maximally useful, the data should be made available as early as possible (without infringing intellectual property rights or publication in full by the trial principal investigators). The COVID-19 pandemic has also made it clear that data should be shared even earlier, albeit confidentially, among the Independent Data Monitoring Committees of trials investigating similar treatments in order to inform decisions about amending or stopping ongoing trials after careful review of all relevant data .
Reflections, concluding remarks, and outlook In this section, we suggest some specific lessons learned for both the modeling and prediction as well as for clinical research. First and foremost, there is a huge need for international collaboration through formal and informal scientific networks during pandemics. While there are local, country specific aspects to the epidemic (culture, population density, demography, health care system), there is commonality from an infectious diseases perspective. The statistical and methodological teams in academia, industry, and government need to connect with each other, nationally and internationally. Steady research capacity is needed, that can quickly scale up in pandemic times in order to respond to pandemics efficiently. For example, statisticians working in other areas (exact sciences, economy, humanities) can be converted quickly to COVID-19 response work provided they are sufficiently broadly trained, and there are pre-existing communication lines (e.g., university wide statistics research centers, learned societies, etc.). More than ever statisticians, modelers, and epidemiologists must be able to communicate and collaborate with research teams from other key fields, such as virologists, health economists, but also economists, social and behavioral scientists, etc. Effective communication lines need to be established between statisticians and other scientific experts, policy makers and international, national, and local policy makers, the public opinion, and the press. A number of statisticians must have received media training and ideally have built up experience in clearly communicating potentially complex statistical matters. An exceptional pandemic situation makes it clear that out-of-the-box thinking is needed. Inevitably, inaccurate or incorrect judgements will be made at some level during the pandemic. It must be acknowledged, and accepted, that knowledge is being built while the response to the crisis is being rolled out. Mutual trust between the parties involved and honest communication towards the public opinion is essential. In this sense, it is fine and even healthy that researchers not automatically agree with one another. Critical reflection and peer review, formally and informally, externally and internally within research groups, is of crucial importance to avoid serious mistakes. A gradually, orderly, and naturally built consensus, can help avoid misguided policies. When the process works, public opinion is ready to accept NPIs, for example, before they are formally announced. A key problem with COVID-19 is the pressure that it can induce on the health care system. It is therefore important to have sufficient reserve capacity (in terms of hospital, staff, supplies). This is difficult because of the cost involved. Statisticians can contribute to planning, health economic evaluation, and, during pandemic times, by monitoring and forecasting hospital load and other capacity. 12.1 Modeling, prediction, prevention To avoid methodological errors, even when research is done at very high speed, and to ensure that models built and data analyses undertaken are as stable, broadly valid, and unbiased as possible, it is imperative to share data at the finest granular level possible, including individual patient data in clinical and epidemiological studies, and spatial data used to monitor the epidemic, to deter or alleviate post-wave outbreaks, etc. As is well-known throughout statistics, a well-fitting model (curve) does not automatically imply good prediction qualities. In meteorology, various weather models are juxtaposed to come to a calibrated weather forecast. Good models imply a subtle interplay between epidemiological theory, sophisticated modeling, and the use of real-world data: data about infections, hospitalization, and mortality on the one hand, and non-pharmaceutical interventions taken as well as their gradual relaxation on the other. In a pandemic epoch, a large number of national, regional, and city-wide epidemics can be compared. To date, excellent international resources are available, such as from the European Centre for Disease Prevention and Control (; https://www.ecdc.europa.eu/en/covid-19-pandemic ), Johns Hopkins University ( https://coronavirus.jhu.edu/map.html ), and Our World in Data ( https://ourworldindata.org/coronavirus ). These offer valuable resources on how the epidemic is playing out elsewhere. Especially in contiguous and highly connected areas, such as in the United States and the European Union (especially the Schengen Zone), the epidemic's evolution cannot be seen in isolation, except at the rare times where state or international borders are virtually closed. 12.2 Clinical research The COVID-19 crisis has provided an exceptional opportunity to question the way in which clinical research is conducted, not just for the treatment of COVID-19 patients but also for all other diseases. One of the priorities today should be to streamline clinical research in diseases with high morbidity and mortality (cancer, cardiovascular disease, etc.) This could entail drastic simplifications of trial set-up (protocol review committees, ethical approval, regulatory submissions, access to drugs from competing drug companies for comparative effectiveness trials, etc.) as well as trial conduct (pragmatic trials comparing standards of care using ultra-simple protocols, real-time electronic data capture, central statistical monitoring, common resources for Independent Data Monitoring Committees, etc.) These ideas are by no means new (see, e.g., https://moretrials.net/ ) but with the lessons learned during the COVID-19 pandemic, they may get more traction than ever before. The need for a strengthened international collaboration in epidemiology should be accompanied by a corresponding international preparedness for clinical research, in order to quickly deploy large simple trials simultaneously in as many countries as possible. If the urgency to carry out clinical trials of treatments against COVID-19 could now be expanded to all other diseases, it would be a revolution in using statistical methodology to improve global health.
Modeling, prediction, prevention To avoid methodological errors, even when research is done at very high speed, and to ensure that models built and data analyses undertaken are as stable, broadly valid, and unbiased as possible, it is imperative to share data at the finest granular level possible, including individual patient data in clinical and epidemiological studies, and spatial data used to monitor the epidemic, to deter or alleviate post-wave outbreaks, etc. As is well-known throughout statistics, a well-fitting model (curve) does not automatically imply good prediction qualities. In meteorology, various weather models are juxtaposed to come to a calibrated weather forecast. Good models imply a subtle interplay between epidemiological theory, sophisticated modeling, and the use of real-world data: data about infections, hospitalization, and mortality on the one hand, and non-pharmaceutical interventions taken as well as their gradual relaxation on the other. In a pandemic epoch, a large number of national, regional, and city-wide epidemics can be compared. To date, excellent international resources are available, such as from the European Centre for Disease Prevention and Control (; https://www.ecdc.europa.eu/en/covid-19-pandemic ), Johns Hopkins University ( https://coronavirus.jhu.edu/map.html ), and Our World in Data ( https://ourworldindata.org/coronavirus ). These offer valuable resources on how the epidemic is playing out elsewhere. Especially in contiguous and highly connected areas, such as in the United States and the European Union (especially the Schengen Zone), the epidemic's evolution cannot be seen in isolation, except at the rare times where state or international borders are virtually closed.
Clinical research The COVID-19 crisis has provided an exceptional opportunity to question the way in which clinical research is conducted, not just for the treatment of COVID-19 patients but also for all other diseases. One of the priorities today should be to streamline clinical research in diseases with high morbidity and mortality (cancer, cardiovascular disease, etc.) This could entail drastic simplifications of trial set-up (protocol review committees, ethical approval, regulatory submissions, access to drugs from competing drug companies for comparative effectiveness trials, etc.) as well as trial conduct (pragmatic trials comparing standards of care using ultra-simple protocols, real-time electronic data capture, central statistical monitoring, common resources for Independent Data Monitoring Committees, etc.) These ideas are by no means new (see, e.g., https://moretrials.net/ ) but with the lessons learned during the COVID-19 pandemic, they may get more traction than ever before. The need for a strengthened international collaboration in epidemiology should be accompanied by a corresponding international preparedness for clinical research, in order to quickly deploy large simple trials simultaneously in as many countries as possible. If the urgency to carry out clinical trials of treatments against COVID-19 could now be expanded to all other diseases, it would be a revolution in using statistical methodology to improve global health.
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Effects of different concentrations of chlormequat chloride on bacterial community composition and diversity in peanut soil | e0503260-369a-49e2-804a-560f9710687a | 11895187 | Microbiology[mh] | Peanuts, a significant source of plant-based oils and proteins, are among the important oil crops in China . Peanut overgrowth is a frequent phenomenon during cultivation, which is primarily characterized by enlarged leaves, shading in the field, and a corresponding increase in the distance between the fruit needle and the ground, which delays needle placement, thereby significantly affecting peanut yield and quality . Therefore, appropriate and effective measures are required to control excessive growth, which can help peanut plants maintain good morphology, optimize nutrient allocation, and promote reproductive growth, thereby improving peanut fruiting rate and plumpness . It has been observed that plant growth regulators can modulate flowering, improve peanut plant morphology and structure, as well as enhance peanut seed quantity and quality . Chlormequat chloride (CC) is a common inhibitory regulator of plant growth, which not only regulates plant growth but also resists lodging. Therefore, it is widely used in the production process of grain crops, vegetables, fruits, medicinal plants, etc . . Recently, it has been reported to be used in peanut production . With increasing studies on sustainable agricultural development and ecological health, the potential impact of CC use is becoming a research hotspot . CC exhibits toxicological properties and has developmental and reproductive toxicity . In the United States CC was detected in urine collected from 2017 to 2022, with a significant increase in urine levels in 2023 . The foundation of agricultural ecosystems is soil biodiversity, which is increasingly recognized as beneficial to human health as it can inhibit pathogenic soil organisms and provide clean air, water, and food . Soil biodiversity is significantly and positively correlated with various ecosystem functions. These functions include nutrient cycling, decomposition, plant production, and reducing the potential for pathogens and underground biological warfare . During peanut cultivation, the application of CC may directly or indirectly affect the structure of soil microbial communities and lead to complex changes in soil biodiversity. Several studies have investigated the effects of the exogenous application of plant growth regulators on soil microorganisms ; however, there are only a few studies on the impact of CC on soil microbial communities. Therefore, this study aimed to elucidate the association between CC and peanut soil diversity, providing an important reference for achieving high peanut yield and quality as well as harmonious coexistence with the ecological environment.
Field experiments In 2023, field experiments were conducted in Xiaochengzi Town, Kangping County, Shenyang City, Liaoning Province, China (E123.35446, N 42.75081). Kangping Xiaochengzi Town belongs to a temperate continental monsoon climate, with distinct four seasons and year-round peanut cultivation. The soil is sandy loam, with an average annual precipitation of about 456.3 mm, an average annual sunshine hours of about 2584.4 h, and an average annual temperature of about 8.1 ℃. Set up a total of 4 processes (3 repetitions/processes). Using the random plot selection method, select 5 × 6 m plot areas with a spacing of 50 cm for each treatment. Peanuts were sown on May 10th, with the tested peanut variety being Baisha. The commercially available 50% CC was prepared in the following concentrations: low concentration (D, 50%CC diluted 5000 times, 45 g ai/ha), medium concentration (M, 50%CC diluted 3000 times, 75 g ai/ha), and high concentration (G, 50%CC diluted 1000 times, 225 g ai/ha). The CK group was not sprayed with CC. On July 5th, foliar sprayed CC. Soil Samples were taken on August 5th. Used the five point sampling method within the field, the peanut rhizosphere soil was collected using the shaking method, and fine roots, plant residues, and stones were removed using a 20 mesh sieve. Soil samples were treated with liquid nitrogen and stored at −80 ℃ for DNA extraction. Soil DNA extraction and sequencing Soil DNA was extracted using the BayBiopure Magnetic Bead Soil DNA Extraction Kit (Guangzhou Bay Area Biotechnology Co., Ltd.), and bacterial PCR amplification was performed using primers F (ACTCCTACGGGAGGCAGCA) and R (CCGTCAATTCMTTRAGTT) in the V3-V4 variable region. The PCR amplification system and amplification conditions were described in reference to the literature . The PCR reaction consisted of 13 μL MOBIO PCR water, 10 μL 5 Prime HotMasterMix, 0.5 μL forward and reverse primers (final concentration of 10 μM), and 1.0 μL genomic DNA. Maintained DNA denaturation at 94℃ for 3 min, amplified at 94℃, 45 s for 35 cycles, 50℃ for 60 s, and 72℃ for 90 s; extended at 72℃ for 10 min to ensure complete amplification. After PCR quality verification, the soil DNA samples were sent to Nanjing Paiseno Biotechnology Co., Ltd. for paired end sequencing of community DNA fragments using the Illumina platform. Soil diversity analysis DADA2 sequence denoising method , used QIIME2 analysis software, first called qiime cutadapt trim paired to remove primer fragments from the sequence and discard sequences with unmatched primers; Then, called DADA2 for quality control, denoising, splicing, and de chimerism through qiime dada2 denoise paired. Merged OTU feature sequences and OTU tables, and removed singletons OTUs. Used R language scripts to statistically analyze the length distribution of high-quality sequences contained in all samples.The final obtainedvalid data was submitted to the Paisenno Cloud platform for data processing and analysis. Sequence processing and analysis The Alpha diversity index was calculated using Mothur software, including Chao1 index and Shannon index. The Chao1 index reflects the richness of the community, while the Shannon index reflects the diversity of the community. Beta diversity analysis was conducted using R3.5.1 language, namely principal component analysis (PCoA). The microbial community structure bar chart was drawn using QIIME software to present the composition and abundance distribution of soil microbial communities at different taxonomic levels .
In 2023, field experiments were conducted in Xiaochengzi Town, Kangping County, Shenyang City, Liaoning Province, China (E123.35446, N 42.75081). Kangping Xiaochengzi Town belongs to a temperate continental monsoon climate, with distinct four seasons and year-round peanut cultivation. The soil is sandy loam, with an average annual precipitation of about 456.3 mm, an average annual sunshine hours of about 2584.4 h, and an average annual temperature of about 8.1 ℃. Set up a total of 4 processes (3 repetitions/processes). Using the random plot selection method, select 5 × 6 m plot areas with a spacing of 50 cm for each treatment. Peanuts were sown on May 10th, with the tested peanut variety being Baisha. The commercially available 50% CC was prepared in the following concentrations: low concentration (D, 50%CC diluted 5000 times, 45 g ai/ha), medium concentration (M, 50%CC diluted 3000 times, 75 g ai/ha), and high concentration (G, 50%CC diluted 1000 times, 225 g ai/ha). The CK group was not sprayed with CC. On July 5th, foliar sprayed CC. Soil Samples were taken on August 5th. Used the five point sampling method within the field, the peanut rhizosphere soil was collected using the shaking method, and fine roots, plant residues, and stones were removed using a 20 mesh sieve. Soil samples were treated with liquid nitrogen and stored at −80 ℃ for DNA extraction.
Soil DNA was extracted using the BayBiopure Magnetic Bead Soil DNA Extraction Kit (Guangzhou Bay Area Biotechnology Co., Ltd.), and bacterial PCR amplification was performed using primers F (ACTCCTACGGGAGGCAGCA) and R (CCGTCAATTCMTTRAGTT) in the V3-V4 variable region. The PCR amplification system and amplification conditions were described in reference to the literature . The PCR reaction consisted of 13 μL MOBIO PCR water, 10 μL 5 Prime HotMasterMix, 0.5 μL forward and reverse primers (final concentration of 10 μM), and 1.0 μL genomic DNA. Maintained DNA denaturation at 94℃ for 3 min, amplified at 94℃, 45 s for 35 cycles, 50℃ for 60 s, and 72℃ for 90 s; extended at 72℃ for 10 min to ensure complete amplification. After PCR quality verification, the soil DNA samples were sent to Nanjing Paiseno Biotechnology Co., Ltd. for paired end sequencing of community DNA fragments using the Illumina platform.
DADA2 sequence denoising method , used QIIME2 analysis software, first called qiime cutadapt trim paired to remove primer fragments from the sequence and discard sequences with unmatched primers; Then, called DADA2 for quality control, denoising, splicing, and de chimerism through qiime dada2 denoise paired. Merged OTU feature sequences and OTU tables, and removed singletons OTUs. Used R language scripts to statistically analyze the length distribution of high-quality sequences contained in all samples.The final obtainedvalid data was submitted to the Paisenno Cloud platform for data processing and analysis.
The Alpha diversity index was calculated using Mothur software, including Chao1 index and Shannon index. The Chao1 index reflects the richness of the community, while the Shannon index reflects the diversity of the community. Beta diversity analysis was conducted using R3.5.1 language, namely principal component analysis (PCoA). The microbial community structure bar chart was drawn using QIIME software to present the composition and abundance distribution of soil microbial communities at different taxonomic levels .
Analysis of richness and diversity of soil bacterial and microbial communities Alpha diversity analysis Statistical analysis of sequencing data revealed that after 30 days of drug application, a total of 847,671 raw sequences were obtained from 12 samples. After filtering, a total of 794,468 valid sequences were generated, with at least 66,206 valid sequences produced per sample, resulting in an average of 66,206 valid sequences (Supplementary Table 1). According to the dilution curves of the samples (Supplementary Fig. 1), it can be seen that the curves of all four groups of samples tend to flatten, indicating that the sequencing quantity of the samples is reasonable and the sequencing depth can meet the experimental requirements. Soil bacterial and microbial community OTUs clustering analysis The OTUs of D, M, G, and CK were 5583, 5430, 3910, and 4740, respectively (Fig. ) and the order of OTUs from high to low was D > M > CK > G. The number of OTUs decreased with the increase of CC concentration, indicating that low and medium CC concentrations can increase OTUs values in peanut microbial community. Moreover, the D treatment had the highest OTUs values, whereas the G treatment reduced OTUs of peanut microbial communities. Alpha diversity refers to the indicators of richness, diversity, and evenness of species in a locally uniform habitat, also known as intra habitat diversity. In order to comprehensively evaluate the alpha diversity of microbial communities, richness was characterized by Chao1 and Observed species indices, diversity was characterized by Shannon and Simpson indices , evolutionary diversity was characterized by Faith index , evenness was characterized by Pielou index , and coverage was characterized by coverage index . From Table , it can be seen that there was no significant difference in coverage between the different concentration treatments, indicating that the coverage of the soil sample bank for each treatment was high. The Chao1, Shannon, Simpson, and Pielou indices of low concentration CC treatment were significantly higher than those of other concentration treatments. As the concentration of CC increased, the Chao1, Shannon, Simpson, and Pielou indices showed a decreasing trend. After applyed low concentrations of CC to peanuts, the bacterial diversity and richness of the soil significantly increased. As the CC concentration increased, the bacterial diversity and richness of the soil significantly decreased (Table ). Beta diversity analysis The Beta Diversity Index focuses on the comparison of diversity between different habitats, that is, the differences between samples. By using the Bray Curtis distance method, analyzing the OTU composition of different samples can reflect the differences and distances between samples, such as the closer the sample community structure, the closer the distance reflected in the PCA graph (Fig. ). Inter group difference analysis was conducted using the Python scikit bio package for "permenova" inter group difference analysis, with a permutation test count set to 999. As shown in Fig. a, the distance between soils treated with different concentrations of CC was relatively far, the contribution rates of the two principal components extracted by bacteria were 31.5% and 27.4%, respectively; According to 2b, all treatment groups were greater than 0, indicating significant changes in soil bacterial community structure after applyed different concentrations of CC. Analysis of soil bacterial and microbial community composition The top 5 dominant bacterial phyla in the bacterial community were Proteobacteria (36.67% ~ 56.22%), Firmicutes (16.38% ~ 38.16%), Acidobacteriota (4.05% ~ 8.77%), Bacteroidetes (4.19% ~ 6.99%), and Gemmatimonadota (3.23% ~ 7.39%) (Fig. A). As the concentration of CC increased, the abundance of Proteobacteria decreased compared to CK, and the G group decreased by 24.26%. The abundance of Firmicutes and Bacteroidota increased compared to CK. The abundance of Firmicutes in Group G reached 132.97%, while the abundance of Bacteroidota in Group D was 66.83%. The abundance of Acidobacteriota and Bacteroidota in groups D and M increased compared to CK, with a significant increase under D group, at 36.36% and 46.92%, respectively. In G group, the abundance of Acidobacteriota and Bacteroidetesota decreased compared to CK, reaching 35.40% and 35.79%, respectively (Fig. B). At the genus level, the proportion of bacterial communities without identified genera was relatively high (Fig. ). The dominant bacterial genera in the rhizosphere soil of each group were Clostridium_sensu_ stricto _1, Rahnella1, Pseudomonas, Clostridium sensu -stricto_13, Azovibrio, Polaromonas, RB41, Flavobacterium, and Sphingomonas, Bacillus (Fig. A). Compared with CK, the D, M, and G groups had reduced abundance of Pseudomonas (50.58, 7.27, and 38.95%), Polarimonas (97.59, 98.27, and 87.9%), and Azovibrio (72.87, 91.36, and 96.28%), whereas the abundance of Clostridium-sensu_stricito-13 (36.12, 71.74, and 8.72%) and Clostridium-sensu_stricito-1 (172.4, 55.2, and 792.8%) was increased. Moreover, compared to the CK, Rahnella1 abundance in the D and M groups were decreased (97.83% and 61.5%, respectively), while increased in the G group (147.30%) (Fig. B). Analysis of species differences in soil bacterial microbial communities The LEfSe analysis (Fig. ) identified 100 bacterial biomarkers at the phylum, class, order, family, genus, and species levels. Of these, 9 were identified at the genus level (based on LDA log scores > 4), included Azovibrio , Bacillus , Clostridium- sensu -stricto-1 , Polaromonas , 3.4 Sphingobium , Clostridium- sensu -stricto-13 , Sphingomonas , Massilia , and Rahnella1. The heatmap of species composition (Fig. ) indicated that the most abundant genus in the CK group were Polarimonas and Azovibrio; in the D group, were Bacillus and Sphingomonas; in the M group were Clostridium_sensu_stricto_13; in the G group were Clostridium_sensu_stricto_1 and Rahnella1. D, M and G groups showed a decrease in the abundance of Pseudomonas , polaromonas , and Azovibrio compared to CK, while the abundance of Flavobacterium increased. Functional prediction of metabolites The metabolic pathway (Fig. ) included 4 cellular processing pathways, 2 environmental information processing pathways, 4 genetic information processing pathways, 11 metabolism pathways. The main concentrated metabolic pathways included the metabolisms of nucleotides, terpenoids, polyketides, other amino acids, cofactors, vitamins, lipids, glycan biosynthesis, energy, carbohydrates, xenobiotics, amino acids, and other secondary metabolites. Compared to the CK group, the positive regulatory pathways were significantly different ( p < 0.001 , LogFC > 1) in the D group and included naphthalene degradation and spliceosome, whereas the negative regulatory pathways involved the degradation of polycyclic aromatic hydrocarbons (Fig. ). Furthermore, the markedly different positive regulatory pathways observed in the M group included other polysaccharide degradation pathways, whereas the negative regulatory pathways involved cell apoptosis and vasopressin-modulating water reabsorption pathways. G treatment was related to the following positive regulatory pathways: phosphotransferase system, bacterial invasion of epithelial cells, bacterial invasion of epidermal cells, and bacterial invasion of epithelial cells. The negative regulatory pathways of G treatment included D-arginine and D-ornithine metabolism, cell apoptosis, and styrene degradation pathways.
Alpha diversity analysis Statistical analysis of sequencing data revealed that after 30 days of drug application, a total of 847,671 raw sequences were obtained from 12 samples. After filtering, a total of 794,468 valid sequences were generated, with at least 66,206 valid sequences produced per sample, resulting in an average of 66,206 valid sequences (Supplementary Table 1). According to the dilution curves of the samples (Supplementary Fig. 1), it can be seen that the curves of all four groups of samples tend to flatten, indicating that the sequencing quantity of the samples is reasonable and the sequencing depth can meet the experimental requirements. Soil bacterial and microbial community OTUs clustering analysis The OTUs of D, M, G, and CK were 5583, 5430, 3910, and 4740, respectively (Fig. ) and the order of OTUs from high to low was D > M > CK > G. The number of OTUs decreased with the increase of CC concentration, indicating that low and medium CC concentrations can increase OTUs values in peanut microbial community. Moreover, the D treatment had the highest OTUs values, whereas the G treatment reduced OTUs of peanut microbial communities. Alpha diversity refers to the indicators of richness, diversity, and evenness of species in a locally uniform habitat, also known as intra habitat diversity. In order to comprehensively evaluate the alpha diversity of microbial communities, richness was characterized by Chao1 and Observed species indices, diversity was characterized by Shannon and Simpson indices , evolutionary diversity was characterized by Faith index , evenness was characterized by Pielou index , and coverage was characterized by coverage index . From Table , it can be seen that there was no significant difference in coverage between the different concentration treatments, indicating that the coverage of the soil sample bank for each treatment was high. The Chao1, Shannon, Simpson, and Pielou indices of low concentration CC treatment were significantly higher than those of other concentration treatments. As the concentration of CC increased, the Chao1, Shannon, Simpson, and Pielou indices showed a decreasing trend. After applyed low concentrations of CC to peanuts, the bacterial diversity and richness of the soil significantly increased. As the CC concentration increased, the bacterial diversity and richness of the soil significantly decreased (Table ). Beta diversity analysis The Beta Diversity Index focuses on the comparison of diversity between different habitats, that is, the differences between samples. By using the Bray Curtis distance method, analyzing the OTU composition of different samples can reflect the differences and distances between samples, such as the closer the sample community structure, the closer the distance reflected in the PCA graph (Fig. ). Inter group difference analysis was conducted using the Python scikit bio package for "permenova" inter group difference analysis, with a permutation test count set to 999. As shown in Fig. a, the distance between soils treated with different concentrations of CC was relatively far, the contribution rates of the two principal components extracted by bacteria were 31.5% and 27.4%, respectively; According to 2b, all treatment groups were greater than 0, indicating significant changes in soil bacterial community structure after applyed different concentrations of CC.
Statistical analysis of sequencing data revealed that after 30 days of drug application, a total of 847,671 raw sequences were obtained from 12 samples. After filtering, a total of 794,468 valid sequences were generated, with at least 66,206 valid sequences produced per sample, resulting in an average of 66,206 valid sequences (Supplementary Table 1). According to the dilution curves of the samples (Supplementary Fig. 1), it can be seen that the curves of all four groups of samples tend to flatten, indicating that the sequencing quantity of the samples is reasonable and the sequencing depth can meet the experimental requirements.
The OTUs of D, M, G, and CK were 5583, 5430, 3910, and 4740, respectively (Fig. ) and the order of OTUs from high to low was D > M > CK > G. The number of OTUs decreased with the increase of CC concentration, indicating that low and medium CC concentrations can increase OTUs values in peanut microbial community. Moreover, the D treatment had the highest OTUs values, whereas the G treatment reduced OTUs of peanut microbial communities. Alpha diversity refers to the indicators of richness, diversity, and evenness of species in a locally uniform habitat, also known as intra habitat diversity. In order to comprehensively evaluate the alpha diversity of microbial communities, richness was characterized by Chao1 and Observed species indices, diversity was characterized by Shannon and Simpson indices , evolutionary diversity was characterized by Faith index , evenness was characterized by Pielou index , and coverage was characterized by coverage index . From Table , it can be seen that there was no significant difference in coverage between the different concentration treatments, indicating that the coverage of the soil sample bank for each treatment was high. The Chao1, Shannon, Simpson, and Pielou indices of low concentration CC treatment were significantly higher than those of other concentration treatments. As the concentration of CC increased, the Chao1, Shannon, Simpson, and Pielou indices showed a decreasing trend. After applyed low concentrations of CC to peanuts, the bacterial diversity and richness of the soil significantly increased. As the CC concentration increased, the bacterial diversity and richness of the soil significantly decreased (Table ).
The Beta Diversity Index focuses on the comparison of diversity between different habitats, that is, the differences between samples. By using the Bray Curtis distance method, analyzing the OTU composition of different samples can reflect the differences and distances between samples, such as the closer the sample community structure, the closer the distance reflected in the PCA graph (Fig. ). Inter group difference analysis was conducted using the Python scikit bio package for "permenova" inter group difference analysis, with a permutation test count set to 999. As shown in Fig. a, the distance between soils treated with different concentrations of CC was relatively far, the contribution rates of the two principal components extracted by bacteria were 31.5% and 27.4%, respectively; According to 2b, all treatment groups were greater than 0, indicating significant changes in soil bacterial community structure after applyed different concentrations of CC.
The top 5 dominant bacterial phyla in the bacterial community were Proteobacteria (36.67% ~ 56.22%), Firmicutes (16.38% ~ 38.16%), Acidobacteriota (4.05% ~ 8.77%), Bacteroidetes (4.19% ~ 6.99%), and Gemmatimonadota (3.23% ~ 7.39%) (Fig. A). As the concentration of CC increased, the abundance of Proteobacteria decreased compared to CK, and the G group decreased by 24.26%. The abundance of Firmicutes and Bacteroidota increased compared to CK. The abundance of Firmicutes in Group G reached 132.97%, while the abundance of Bacteroidota in Group D was 66.83%. The abundance of Acidobacteriota and Bacteroidota in groups D and M increased compared to CK, with a significant increase under D group, at 36.36% and 46.92%, respectively. In G group, the abundance of Acidobacteriota and Bacteroidetesota decreased compared to CK, reaching 35.40% and 35.79%, respectively (Fig. B). At the genus level, the proportion of bacterial communities without identified genera was relatively high (Fig. ). The dominant bacterial genera in the rhizosphere soil of each group were Clostridium_sensu_ stricto _1, Rahnella1, Pseudomonas, Clostridium sensu -stricto_13, Azovibrio, Polaromonas, RB41, Flavobacterium, and Sphingomonas, Bacillus (Fig. A). Compared with CK, the D, M, and G groups had reduced abundance of Pseudomonas (50.58, 7.27, and 38.95%), Polarimonas (97.59, 98.27, and 87.9%), and Azovibrio (72.87, 91.36, and 96.28%), whereas the abundance of Clostridium-sensu_stricito-13 (36.12, 71.74, and 8.72%) and Clostridium-sensu_stricito-1 (172.4, 55.2, and 792.8%) was increased. Moreover, compared to the CK, Rahnella1 abundance in the D and M groups were decreased (97.83% and 61.5%, respectively), while increased in the G group (147.30%) (Fig. B).
The LEfSe analysis (Fig. ) identified 100 bacterial biomarkers at the phylum, class, order, family, genus, and species levels. Of these, 9 were identified at the genus level (based on LDA log scores > 4), included Azovibrio , Bacillus , Clostridium- sensu -stricto-1 , Polaromonas , 3.4 Sphingobium , Clostridium- sensu -stricto-13 , Sphingomonas , Massilia , and Rahnella1. The heatmap of species composition (Fig. ) indicated that the most abundant genus in the CK group were Polarimonas and Azovibrio; in the D group, were Bacillus and Sphingomonas; in the M group were Clostridium_sensu_stricto_13; in the G group were Clostridium_sensu_stricto_1 and Rahnella1. D, M and G groups showed a decrease in the abundance of Pseudomonas , polaromonas , and Azovibrio compared to CK, while the abundance of Flavobacterium increased.
The metabolic pathway (Fig. ) included 4 cellular processing pathways, 2 environmental information processing pathways, 4 genetic information processing pathways, 11 metabolism pathways. The main concentrated metabolic pathways included the metabolisms of nucleotides, terpenoids, polyketides, other amino acids, cofactors, vitamins, lipids, glycan biosynthesis, energy, carbohydrates, xenobiotics, amino acids, and other secondary metabolites. Compared to the CK group, the positive regulatory pathways were significantly different ( p < 0.001 , LogFC > 1) in the D group and included naphthalene degradation and spliceosome, whereas the negative regulatory pathways involved the degradation of polycyclic aromatic hydrocarbons (Fig. ). Furthermore, the markedly different positive regulatory pathways observed in the M group included other polysaccharide degradation pathways, whereas the negative regulatory pathways involved cell apoptosis and vasopressin-modulating water reabsorption pathways. G treatment was related to the following positive regulatory pathways: phosphotransferase system, bacterial invasion of epithelial cells, bacterial invasion of epidermal cells, and bacterial invasion of epithelial cells. The negative regulatory pathways of G treatment included D-arginine and D-ornithine metabolism, cell apoptosis, and styrene degradation pathways.
From an environmental perspective, before agricultural chemicals were widely used in agriculture, their toxicological or ecological impacts should be evaluated . It has been observed that the application of plant growth regulators can increase plant biomass . The use of EDTA and CA reduced the diversity and richness of soil bacterial communities, while the combination of DA-6 and EDTA or CA foliar spraying increased the diversity and richness of soil bacterial communities . Most articles have reported that pesticides can reduce the diversity and richness of microbial communities ; But there was no significant difference in the alpha diversity of bacterial communities between the pesticide/fertilizer mixed and single fertilization treatments in sugarcane fields without the addition of pesticides or fertilizers (Huang et al., 2021). However, our research indicated that as the concentration of CC increases, the Chao1, Shannon, Simpson, and Pielou indices showed a decreasing trend, while the low concentration CC increased compared to CK, indicated that the application of low concentration CC on peanuts significantly increased soil bacterial diversity and richness. The results of this study indicated that the dominant bacterial phyla in soil are Proteobacteria, Firmicutes, Acinetobacteria, Bacteroidetes, and Gemmatimonadota, which was consistent with previous research findings . In some studies, it has been found that low application can increased the number of certain beneficial bacteria in soil , such as Azovibrio , Bacillus , Sphingobium and Sphingomonas , which was consistent with the results of our study. Azovibrio can help fix nitrogen in crops and reduce nitrogen fertilizer application . Bacillus can inhibit pathogens, induce systemic resistance, promote growth, and thus promote its widespread use as a biological control bacterium . In addition, sphingolipids and sphingomonas were beneficial rhizosphere microorganisms in plants that can degrade organic pollutants such as chlorpyrifos , thereby inducing plant growth and inhibiting pathogens, thereby enhancing plant disease resistance . In addition, a significant positive correlation has been observed between members of the North Star genus and the concentration of vanadium components in tailings, indicating that the North Star genus has high vanadium resistance or can utilize vanadium for energy metabolism processes . As the concentration increased, it may caused changes in the community structure of soil microorganisms. High concentration application may have serious toxic effects on soil microorganisms. It may damage the cell membrane structure of microorganisms or interfere with their metabolic processes. In this study, high concentrations of CC can reduce the number of bacteria in the soil, leading to interference in processes such as cell apoptosis, and styrene degradation pathways in the soil. The degradation process of pesticides in soil was largely attributed to microorganisms, but there were also other influencing factors . Although our data was only for one year and few monitoring indicators, we have also obtained some preliminary results. In the future, we will further improved it, not limited to planting peanuts in one season, but can conduct research on continuous multi season peanut planting or rotation with other crops. Observed the effects of different concentrations of CC on soil diversity in different seasons and crop rotation, and understanded its variation patterns under different planting systems. At the same time, increased research indicators to enrich the results, such as soil enzyme activity determination, soil physicochemical property analysis, and soil nematode and actinomycete community research.
As the concentration of CC increases, the Chao1, Shannon, Simpson, and Pielou indices showed a decreasing trend. This indicates that the application of low concentration CC to peanuts significantly increased soil bacterial diversity and richness. As the concentration of CC increased, the abundance of Proteobacteria decreased, while Firmicutes and Bacteroidota increased. The abundance of Acidobacteriota and Bacteroidota in groups D and M increased, while in G group decreased. The D, M and G groups showed a decrease in the abundance of Pseudomonas , polaromonas , and Azovibrio compared to CK, while the abundance of Flavobacterium increased. Moreover, Rahnella1 abundance in the D and M groups was decreased, while increased in the G group.
Supplementary Material 1
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Percutaneous Biliary Neo-Anastomosis of Inadvertently Operatively Excluded Right Posterior Bile Ducts: A Durable and Highly Successful Procedure | d337cc3f-418b-44e2-8903-83c7abc6d6f7 | 11889012 | Surgical Procedures, Operative[mh] | The anatomical variant consisting of a low right posterior sectoral duct insertion into the hepatic or even the cystic duct places it at risk to be injured at laparoscopic cholecystectomy . Hepaticojejunostomy has been considered as the mainstay of treatment as these injuries became more frequent with the advent of laparoscopic cholecystectomy. Currently, injuries of the right posterior sector duct at laparoscopic cholecystectomy may well be managed non-operatively by endoscopic or interventional radiological techniques . However, early re-laparotomy may be hazardous after major open hepato-biliary or pancreatic surgery and endoscopic management consisting of sphincterotomy and insertion of nasobiliary catheters or stent (-grafts) may fail if continuity of a transected duct cannot be established. We present four symptomatic consecutive patients with inadvertently excluded right posterior sectoral bile ducts at hepato-biliary or pancreatic (HPB) surgery (Type C injuries according to the Strasberg classification) . As these patients were unfit for early redo bilio-enteric anastomosis ( n = 4) and endoscopic treatment had failed ( n = 1), percutaneous fluoroscopy-guided biliary neo-anastomosis by means of transhepatic sharp recanalization was performed.
Between May 2007 and November 2021, four patients (men, 75%; median 57 years, range 43–66 years) were referred for percutaneous biliary neo-anastomoses at a tertiary care center including liver transplant. History, etiology, indication as well as procedural details for percutaneous biliary neo-anastomoses are reported in Table . The indication for percutaneous biliary neo-anastomosis was made by a multidisciplinary board involving hepato-biliary-pancreatic surgeons, gastroenterologists and interventional radiologists. Two of the four patients with leaking posterior right ducts were recipients of living-donor right hemi-liver transplants, which had been performed for hepatocellular carcinoma in cirrhotic livers. In two patients, major resections for biliary and pancreatic cancers had been performed. Three patients presented with peritonitis related to free intraabdominal bile leakage, and one patient had developed an extrahepatic biloma with recurrent septicemia. All procedures were performed under local anesthesia and moderate sedation. Pre-procedural cross-sectional imaging (both CT and MRI) was reviewed. Attention was paid to perihilar vascular structures (hepatic artery, portal vein) in proximity to the planned neo-anastomosis. The excluded leaking bile duct had been identified on pre-procedural cross-sectional imaging, at fluoroscopy of indwelling biloma drains or percutaneous biliary drains (PTBD) placed via any other duct. Two techniques were utilized to perform the percutaneous biliary neo-anastomosis (PBNA). Both techniques aimed at creating a new connection between an excluded posterior right duct and an intestinal loop ( n = 3, Fig. ) or the common hepatic duct ( n = 1, Fig. ). Technical success was defined as an established connection without any leakage. By means of a 22G Chiba needle the excluded posterior right hepatic duct was percutaneously transhepatically accessed. Vascular sheaths (8F, n = 3 or 10F, n = 1) were then introduced. A 5F catheter with an angled tip was then advanced as far as possible in the duct, followed by 2.7F coaxial microcatheter placement. Opacification of the bowel loop to be anastomosed was obtained in two cases by contrast injection via the PTBD. In the patient with the bilio-bilio-neo-anastomosis, the common hepatic duct (CHD) was easily visible under fluoroscopy due a previously placed stent-graft in the CHD. However, that stent-graft placed by endoscopy had failed to resolve the bile leak. In one patient the puncture of the bowel loop to be anastomosed was guided uniquely by fluoroscopy after review of the pre-procedural cross-sectional imaging depicting the anatomy of the adjacent small bowel. In 3 out of 4 of cases, the stiff back end of a commonly available 0.018″ guidewire (V-18™, Boston Scientific Corp.) was used to access the enteric loop or the CHD. In order to direct this “sharp” recanalization, the tip of the reversed guidewire was bent accordingly. In one case, an Outback® Elite Re-entry catheter (Cordis, Miami Lakes FL, USA) was utilized. A microcatheter was advanced over the 018″ or, respectively, the 0.014″ guidewire through the presumed anastomosis. The correct enteric ( n = 3) or CHD ( n = 1) access was confirmed by means of contrast instillation. Dilatation of the track (2–4-mm-diameter angioplasty balloon) was then performed. Finally, the neo-anastomoses were secured by PTBDs (10.2F, n = 3; 12F, n = 1; Fig. ). The follow-up consisted of repeated cholangiographies via the PTBDs placed across the new anastomosis.
Technical success rate was 100%, as all four patients demonstrated patency of the neo-anastomosis and no bile leak. The PTBDs were removed after a median of 65 days (range 43–103 days). The median follow-up was 2.8 years (range 0.13–17.0 years). Two patients died 7 weeks and 3 years after PBNA of overwhelming sepsis or tumor recurrence. The first presented with recurrent bloody discharge from the PTBD 6 weeks after the neo-anastomosis. Computed tomography depicted a pseudoaneurysm of the right hepatic artery, which was treated with stent-grafting. Due to the time course and the anatomical localization of the pseudoaneurysm, its etiology most likely was the prior bile leakage or a perioperative lesion and not the percutaneous biliary neo-anastomosis per se. Hence, it was not deemed to be procedure-related. The second patient presented with cholangitis 20 months after the creation of the neo-anastomosis. Imaging showed a stenosis of the posterior right hepatic duct due to local tumor recurrence. The subsequently placed 8.5F PTBD remained in place until the death of the patient 16 months later.
The experience regarding iatrogenic bile duct injuries and their treatment has been described previously . However, in these two single- or multicenter series including a large number of patients the bile duct was injured at laparoscopic cholecystectomy. The 30-day morbidity and mortality rates of the repair by hepaticojejunostomy were 26% and 2%, respectively . In the single-center study just 36 out of 800 patients (4.5%) had Strasberg Type C injuries such as the four cases reported herein. Among all injuries, the interventional radiological contribution consisted of PTBDs (7.2%) or percutaneous abdominal drainages (28.2%). In addition, a recent review of the interventional radiological role in the treatment of Strasberg Type C injuries mentioned just hepaticojejunostomy (“preferred”) and PTBD (“sometimes”) as treatment options . This small case series supports the concept that PBNA is technically feasible to treat Strasberg Type C injuries in patients who had prior complex HPB surgery including split-liver transplantation. These patients likely benefit from PBNA as an alternative to a redo bilio-jejunostomy. Due to the operative alteration endoscopic stenting of transected right posterior sectoral ducts may not be feasible. Once the leaking posterior right sectoral duct has been percutaneously accessed, but the target (jejunum or CHD) puncture for a neo-anastomosis fails, any combination of bile duct embolization and ablative sclerosis may still be performed as a bail-out procedure . Our experience in one case suggests that after pre-interventional review of hepatic CT and/or MR imaging, additional enteric target opacification may not be necessary. A question that remains unsolved is the ideal point in time for PTBD removal. In our experience, PTBDs should probably be left in place for at least 6 weeks to create a new anastomosis. This report has limitations related to the small number of patients affected by this operative complication, including the variety of indications and type of HPB surgery. However, all patients had symptomatic Strasberg Type C transections during complex HPB surgery and their repair in common.
In conclusion, percutaneous biliary neo-anastomosis is technically feasible and safe to treat Strasberg C bile duct lesions after major HPB surgery using standard, readily available guidewires and catheters. The healing of these neo-anastomoses seems not to be dependent upon whether performed between a donor liver and its recipient, or after HPB surgeries in non-transplanted patients. Long-term post-interventional observation proves that these neo-anastomoses are durable.
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Birth “outside of guidance”—An exploration of a Birth Choices Clinic in the United Kingdom | 66f999bd-bca4-4149-994a-b2b6e0b77404 | 11829259 | Surgical Procedures, Operative[mh] | INTRODUCTION In the United Kingdom, women and birthing people are encouraged to make choices about their maternity care, including place and mode of birth. , Choice and personalized care have been key themes highlighted by recent national reviews into maternity services. , Midwives have an important role in providing evidence‐based information to facilitate decision‐making and in being respectful of an individual's rights to accept or decline. Although women can employ independent midwives in the UK, the majority of maternity care is provided by National Health Service (NHS) organizations and employees. Recommendations about birthplace and fetal monitoring may be made by clinicians based on risk factors, which are often listed as inclusion or exclusion criteria for birth settings within clinical guidelines. In the UK, national guidelines are formulated by the National Institute for Care Excellence (NICE), which informs guidance at a local level. Clinical guidelines are important in providing guidance to clinicians that is based on the best available evidence to inform their practice. However, if used as a set of rules rather than guidance, guidelines can encourage a prescriptive approach to care at the expense of an individual's autonomy. Care needs to be taken to ensure “guideline‐centered care” does not supersede “person‐centered care,” and that clinicians work in partnership with women to ensure that opting for birth “outside of guidance” is not a seen as “disobeying” guidelines and viewed as deviant. , Choices that fall outside of recommended care could be described as “alternative,” “non‐normative,” “unconventional,” or “outside of guidance”. , This may be at either end of a spectrum, from seeking increased medical interventions, such as a planned cesarean, to planning to birth without medical assistance (known as freebirth). Here we focus on “alternative physiological birth choices” defined as: “Birth choices that go outside of local/national maternity guidelines or when women decline recommended treatment of care, in the pursuit of physiological birth.” Examples of physiological birth choices include choosing to birth in midwifery‐led settings with medical or obstetric risk factors, for example, planning birth at home after a previous cesarean, or choosing to decline routine practices such as fetal monitoring during labor. This area of practice could present personal challenges for practitioners, both in facilitating antenatal discussions and in attending births. Supporting women choosing birth “outside of guidance” requires recognition of a person's right to respect for private and family life and the right to physical autonomy, which is enshrined in UK law. , A caregivers response to alternative choices can influence women's experiences of care, sometimes leading to women feeling coerced into complying with guidelines and affecting the relationship between the woman and clinician. Evidence suggests that midwives are, in principle, supportive of maternal autonomy , but may experience emotional distress and conflicting tensions when person declines care, , especially if these choices may increase the chance of complications. , Midwives have reported fear of poor birth outcomes or of professional accountability for poor outcome, with thorough documentation and personalized care plans developed by senior clinicians cited as the main way of managing tensions and reducing stress. , , Some NHS organizations offer Birth Choices Clinics to facilitate this senior review, supporting both midwives and clinicians. Little is currently known about the prevalence or operation of these clinics across the UK. Decision‐making around birth is complex and influenced by service provision (e.g., what is physically available, staff knowledge or beliefs, and what information is available), and the beliefs, previous experiences, and preferences of women. In addition, the social and political climate and wider attitudes toward “natural birth”, risk, and “safety” may have an influence. Our exploratory descriptive study aims to contribute to knowledge about Birth Choices Clinics (BCC) and alternative physiological birth options, about which currently little is known. METHODS 2.1 Study design and aim This was a descriptive study evaluating the BCC, which aimed to describe the maternal characteristics of women attending BCC who wished to birth in midwifery‐led settings, identify common reasons for referral to BCC, and identify the rationale for opting to birth “outside of guidance”. We aimed to describe the frequency of change in planned birthplace after BCC consultation, and factors that may affect a change in preferred or planned birthplace and actual birthplace. We aimed to describe adverse maternal and perinatal outcomes, mode of birth, and transfers to an obstetric unit (OU) during labor or shortly after birth for those women attending the clinic. 2.2 Ethics This study was a service evaluation. Approved by the NHS Trust Institutional Board Ethics approval ID 8271. 2.3 Birth Choices Clinic The BCC operates within an NHS organization that is responsible for an obstetric unit (OU), an alongside midwifery unit (AMU), four freestanding midwifery units (FMU), and a home birth service. The BCC is run by consultant midwives and a consultant obstetrician and accepts referrals from clinicians working within the organization where women wish to explore their birth choices. During consultations, women are provided with information, clinical guidance, and rationale for recommendations. A detailed history taking of previous experiences and discussion of material risk (i.e., what is important to them) is used. A detailed birth plan is made with women making alternative physiological birth choices, designed to maximize safety in a setting that is not recommended. These plans are shared with women and those providing intrapartum care in advance so assurance can be provided that they have the support of the organization, and there is clear communication of discussions and plans. Participants attended BCC between April 1, 2022 and February 28, 2023, and were referred as they wished to birth in a midwifery‐led setting where they had a characteristic that meant that they were not “included” in the eligibility criteria for home births or midwifery‐led units (MLU). 2.4 Data collection, management, and sources Data were either collected from the maternal clinical record, from the referral form to clinic, or during the clinical consultation. A bespoke data collection tool using Microsoft Xcel was designed. The data were anonymized and held in a password protected file accessible to the authors. 2.4.1 Data from maternal record Data collected in the tool included maternal demographics (age, ethnicity, and parity), which were available from the maternal clinical record. This included the planned birthplace at the start of labor, whether this had changed since the BCC appointment, and if so, the reason for change was collected. Data about transfer during labor to an OU and any reason for this was collected, as well as data about the actual birthplace. The maternal record was also the source for data on outcomes, including mode of birth and any adverse maternal or neonatal events. 2.4.2 Data from the referral form Data about the reason women had been advised to birth in an OU were collected iteratively upon receipt of the referral and described in a free text box. These could include maternal factors, current pregnancy‐related factors, or previous pregnancy factors. 2.4.3 Data from clinical consultation Data were collected about planned birthplace before and after the BCC appointment, and included options of home, AMU, FMU, and OU. Data about the rationale for the decision to birth in a midwifery‐led setting was collected by the clinic team. This rationale was explored and discussed during the clinic appointment and recorded in a free text box. 2.5 Data analysis Maternal demographics were described using frequencies and percentages. Data about planned birthplace, reason for change in birthplace, reason for any transfer, and actual birthplace were described using frequencies and percentages. Textual data describing reasons women were “outside of guidance” were grouped into categories and described using frequencies and percentages. Textual data describing the rationale for choosing birthplace “outside of guidance” was grouped into descriptive themes, and a narrative descriptive analysis of these themes was performed. Where uncommonly serious adverse outcomes occurred, all available data, including free text, were reviewed to understand the circumstances surrounding these events. Study design and aim This was a descriptive study evaluating the BCC, which aimed to describe the maternal characteristics of women attending BCC who wished to birth in midwifery‐led settings, identify common reasons for referral to BCC, and identify the rationale for opting to birth “outside of guidance”. We aimed to describe the frequency of change in planned birthplace after BCC consultation, and factors that may affect a change in preferred or planned birthplace and actual birthplace. We aimed to describe adverse maternal and perinatal outcomes, mode of birth, and transfers to an obstetric unit (OU) during labor or shortly after birth for those women attending the clinic. Ethics This study was a service evaluation. Approved by the NHS Trust Institutional Board Ethics approval ID 8271. Birth Choices Clinic The BCC operates within an NHS organization that is responsible for an obstetric unit (OU), an alongside midwifery unit (AMU), four freestanding midwifery units (FMU), and a home birth service. The BCC is run by consultant midwives and a consultant obstetrician and accepts referrals from clinicians working within the organization where women wish to explore their birth choices. During consultations, women are provided with information, clinical guidance, and rationale for recommendations. A detailed history taking of previous experiences and discussion of material risk (i.e., what is important to them) is used. A detailed birth plan is made with women making alternative physiological birth choices, designed to maximize safety in a setting that is not recommended. These plans are shared with women and those providing intrapartum care in advance so assurance can be provided that they have the support of the organization, and there is clear communication of discussions and plans. Participants attended BCC between April 1, 2022 and February 28, 2023, and were referred as they wished to birth in a midwifery‐led setting where they had a characteristic that meant that they were not “included” in the eligibility criteria for home births or midwifery‐led units (MLU). Data collection, management, and sources Data were either collected from the maternal clinical record, from the referral form to clinic, or during the clinical consultation. A bespoke data collection tool using Microsoft Xcel was designed. The data were anonymized and held in a password protected file accessible to the authors. 2.4.1 Data from maternal record Data collected in the tool included maternal demographics (age, ethnicity, and parity), which were available from the maternal clinical record. This included the planned birthplace at the start of labor, whether this had changed since the BCC appointment, and if so, the reason for change was collected. Data about transfer during labor to an OU and any reason for this was collected, as well as data about the actual birthplace. The maternal record was also the source for data on outcomes, including mode of birth and any adverse maternal or neonatal events. 2.4.2 Data from the referral form Data about the reason women had been advised to birth in an OU were collected iteratively upon receipt of the referral and described in a free text box. These could include maternal factors, current pregnancy‐related factors, or previous pregnancy factors. 2.4.3 Data from clinical consultation Data were collected about planned birthplace before and after the BCC appointment, and included options of home, AMU, FMU, and OU. Data about the rationale for the decision to birth in a midwifery‐led setting was collected by the clinic team. This rationale was explored and discussed during the clinic appointment and recorded in a free text box. Data from maternal record Data collected in the tool included maternal demographics (age, ethnicity, and parity), which were available from the maternal clinical record. This included the planned birthplace at the start of labor, whether this had changed since the BCC appointment, and if so, the reason for change was collected. Data about transfer during labor to an OU and any reason for this was collected, as well as data about the actual birthplace. The maternal record was also the source for data on outcomes, including mode of birth and any adverse maternal or neonatal events. Data from the referral form Data about the reason women had been advised to birth in an OU were collected iteratively upon receipt of the referral and described in a free text box. These could include maternal factors, current pregnancy‐related factors, or previous pregnancy factors. Data from clinical consultation Data were collected about planned birthplace before and after the BCC appointment, and included options of home, AMU, FMU, and OU. Data about the rationale for the decision to birth in a midwifery‐led setting was collected by the clinic team. This rationale was explored and discussed during the clinic appointment and recorded in a free text box. Data analysis Maternal demographics were described using frequencies and percentages. Data about planned birthplace, reason for change in birthplace, reason for any transfer, and actual birthplace were described using frequencies and percentages. Textual data describing reasons women were “outside of guidance” were grouped into categories and described using frequencies and percentages. Textual data describing the rationale for choosing birthplace “outside of guidance” was grouped into descriptive themes, and a narrative descriptive analysis of these themes was performed. Where uncommonly serious adverse outcomes occurred, all available data, including free text, were reviewed to understand the circumstances surrounding these events. RESULTS 3.1 Ethnicity, age and parity Eighty‐two women attended the BCC and were included in the study. The characteristics are described in Table . Most women were from a white ethnic background (84.2%/ n = 69), 11.0% ( n = 9) were from a Black, Asian, or mixed ethnicity background, and 4.9% ( n = 4) of participants ethnicity was unknown. This was compared with the local population using maternity services; people from a global ethnic majority (GEM) background were underrepresented (17.2% of women are from GEM communities in the wider population, compared with 11% in the study population). Most women were aged under 35 (69%), and the majority were under 40 years old (92.7%) ( n = 76/82). Seventy‐one participants were multiparous (one or more previous birth), and 11 were primiparous (no previous births) (86.6% vs. 13.4%). Fifteen were “grand multiparous” (4 or more previous births). 3.2 Factors for which OU birth is recommended Table outlines the risk factors, which meant participants were defined as “higher risk” and therefore birth in an OU was recommended. The most common factors were previous postpartum hemorrhage (PPH) (20), previous cesarean (19), and grandmultiparity (14). Ninety‐six risk factors were identified among the 82 participants. Sixty‐eight women had 1 risk factor (83.9%), and 14 women had 2 or more risk factors in their pregnancy identified (17.1%). For multiparous women, the most common risk factors were previous cesarean, grandmultiparity and previous PPH. For primiparous women, a raised BMI >35 was the most common risk factor, which excluded them from planning birth at home or in a FMU, according to local guidance. 3.3 Motivation for considering place of birth “outside of guidance” Eighty women with obstetric or medical risk factors were considering birth in a midwifery‐led setting, and two women without any risk factors were declining elements of recommended monitoring in labor. Four main themes were identified: The desire to experience water immersion using a birth pool during labor. Pragmatic factors, which meant birth at home or in a MLU, were more practical. This included the MLU being closer to home and logistically easier to organize childcare for older children. Some women reported previous rapid births and they were concerned they would not reach the OU in time to give birth and may even birth in transit. Experience of birth : Women felt the chosen birth setting was more likely to lead to a positive, calm, or more comfortable birth experience. Sometimes this was due to a previous positive experience of birth at home or in an MLU, or due to a poor or traumatic previous experience of birth in an OU. Reduction in obstetric interventions : Women wanted to reduce their risk of experiencing obstetric interventions such as unplanned cesareans or birth with forceps or ventouse and increase their chance of having a physiological birth. 3.4 Place of birth Figure describes the journey of participants to their final birthplace. Thirty‐three participants were planning birth “outside of guidance” at the onset of care in labor. Data on planned birthplace at the start of labor and transfer was unknown for 3 participants (3.7%) as they changed maternity practitioners and were lost to follow‐up. Forty‐six participants (56.1%) either changed their planned birthplace to the OU in pregnancy before the start of care in labor (91.3%, n = 42), or were unable to birth at home or in a MLU due to operational issues such as lack of available staff or no capacity at the chosen location (8.7%, n = 4). The most common reasons for change in planned birthplace were a change in the clinical situation (67.4%, n = 31), including induction or stimulation of labor (45.7%, n = 21), maternal decision in pregnancy (23.9%, n = 11), prolonged rupture of membranes (8.7%, n = 4), requesting an epidural (4.4%, n = 2), preterm birth (4.4%, n = 2), hypertension (2.2%, n = 1), and fetal malpresentation (2.2%, n = 1). Of the 82 participants, 50 gave birth on an OU (61%), 16 gave birth at home (19.5%), 6 gave birth in an AMU (7.3%), 7 gave birth in a FMU (8.5%), and 2 gave birth with a different maternity practitioner, whose birthplace was unknown. 3.5 Transfers to an obstetric unit during labor or immediately postpartum Thirty‐three women (see Figure ) received care at home or on an MLU in labor; 75.7% ( n = 25/33) birthed in this setting with no complications. Eight women (24.2%) transferred to an OU during labor or immediately after birth (3/8 transferred from an AMU, 2/8 transferred from an FMU, and 3/8 transferred from home). Two were primiparous, and six were multiparous women. However, two of the multiparous women had previous cesarean and no previous vaginal births. Reasons for transfer are outlined in Table . 3.6 Mode of birth Fifty‐three participants had a spontaneous vaginal birth (64.7%), 19 women had a cesarean (23.2%), and 8 women had a birth with forceps or ventouse (9.7%). Table reports the mode of birth in the study population overall and by location at the onset of care in labor. A higher proportion of women had a spontaneous vaginal birth who were in a midwifery‐led setting at the onset of care in labor than those in an OU (88% compared with 52%). A lower proportion of women had an unplanned cesarean who were in a midwifery‐led setting at the onset of care in labor than those in an OU (3% compared with 26%). 3.7 Maternal and perinatal outcomes 3.7.1 Adverse outcomes Overall, there were 13/82 adverse events (15.9%), 80.4% ( n = 66) of women experienced no adverse event, and 3.66% ( n = 3) were unknown as lost to follow up. These events were admission to the neonatal unit ( n = 2), term intrauterine death (IUD) diagnosed at the onset of labor care ( n = 1), PPH (blood loss 500–1500 mL) ( n = 10), and uterine rupture diagnosed at cesarean ( n = 1). Ethnicity, age and parity Eighty‐two women attended the BCC and were included in the study. The characteristics are described in Table . Most women were from a white ethnic background (84.2%/ n = 69), 11.0% ( n = 9) were from a Black, Asian, or mixed ethnicity background, and 4.9% ( n = 4) of participants ethnicity was unknown. This was compared with the local population using maternity services; people from a global ethnic majority (GEM) background were underrepresented (17.2% of women are from GEM communities in the wider population, compared with 11% in the study population). Most women were aged under 35 (69%), and the majority were under 40 years old (92.7%) ( n = 76/82). Seventy‐one participants were multiparous (one or more previous birth), and 11 were primiparous (no previous births) (86.6% vs. 13.4%). Fifteen were “grand multiparous” (4 or more previous births). Factors for which OU birth is recommended Table outlines the risk factors, which meant participants were defined as “higher risk” and therefore birth in an OU was recommended. The most common factors were previous postpartum hemorrhage (PPH) (20), previous cesarean (19), and grandmultiparity (14). Ninety‐six risk factors were identified among the 82 participants. Sixty‐eight women had 1 risk factor (83.9%), and 14 women had 2 or more risk factors in their pregnancy identified (17.1%). For multiparous women, the most common risk factors were previous cesarean, grandmultiparity and previous PPH. For primiparous women, a raised BMI >35 was the most common risk factor, which excluded them from planning birth at home or in a FMU, according to local guidance. Motivation for considering place of birth “outside of guidance” Eighty women with obstetric or medical risk factors were considering birth in a midwifery‐led setting, and two women without any risk factors were declining elements of recommended monitoring in labor. Four main themes were identified: The desire to experience water immersion using a birth pool during labor. Pragmatic factors, which meant birth at home or in a MLU, were more practical. This included the MLU being closer to home and logistically easier to organize childcare for older children. Some women reported previous rapid births and they were concerned they would not reach the OU in time to give birth and may even birth in transit. Experience of birth : Women felt the chosen birth setting was more likely to lead to a positive, calm, or more comfortable birth experience. Sometimes this was due to a previous positive experience of birth at home or in an MLU, or due to a poor or traumatic previous experience of birth in an OU. Reduction in obstetric interventions : Women wanted to reduce their risk of experiencing obstetric interventions such as unplanned cesareans or birth with forceps or ventouse and increase their chance of having a physiological birth. Place of birth Figure describes the journey of participants to their final birthplace. Thirty‐three participants were planning birth “outside of guidance” at the onset of care in labor. Data on planned birthplace at the start of labor and transfer was unknown for 3 participants (3.7%) as they changed maternity practitioners and were lost to follow‐up. Forty‐six participants (56.1%) either changed their planned birthplace to the OU in pregnancy before the start of care in labor (91.3%, n = 42), or were unable to birth at home or in a MLU due to operational issues such as lack of available staff or no capacity at the chosen location (8.7%, n = 4). The most common reasons for change in planned birthplace were a change in the clinical situation (67.4%, n = 31), including induction or stimulation of labor (45.7%, n = 21), maternal decision in pregnancy (23.9%, n = 11), prolonged rupture of membranes (8.7%, n = 4), requesting an epidural (4.4%, n = 2), preterm birth (4.4%, n = 2), hypertension (2.2%, n = 1), and fetal malpresentation (2.2%, n = 1). Of the 82 participants, 50 gave birth on an OU (61%), 16 gave birth at home (19.5%), 6 gave birth in an AMU (7.3%), 7 gave birth in a FMU (8.5%), and 2 gave birth with a different maternity practitioner, whose birthplace was unknown. Transfers to an obstetric unit during labor or immediately postpartum Thirty‐three women (see Figure ) received care at home or on an MLU in labor; 75.7% ( n = 25/33) birthed in this setting with no complications. Eight women (24.2%) transferred to an OU during labor or immediately after birth (3/8 transferred from an AMU, 2/8 transferred from an FMU, and 3/8 transferred from home). Two were primiparous, and six were multiparous women. However, two of the multiparous women had previous cesarean and no previous vaginal births. Reasons for transfer are outlined in Table . Mode of birth Fifty‐three participants had a spontaneous vaginal birth (64.7%), 19 women had a cesarean (23.2%), and 8 women had a birth with forceps or ventouse (9.7%). Table reports the mode of birth in the study population overall and by location at the onset of care in labor. A higher proportion of women had a spontaneous vaginal birth who were in a midwifery‐led setting at the onset of care in labor than those in an OU (88% compared with 52%). A lower proportion of women had an unplanned cesarean who were in a midwifery‐led setting at the onset of care in labor than those in an OU (3% compared with 26%). Maternal and perinatal outcomes 3.7.1 Adverse outcomes Overall, there were 13/82 adverse events (15.9%), 80.4% ( n = 66) of women experienced no adverse event, and 3.66% ( n = 3) were unknown as lost to follow up. These events were admission to the neonatal unit ( n = 2), term intrauterine death (IUD) diagnosed at the onset of labor care ( n = 1), PPH (blood loss 500–1500 mL) ( n = 10), and uterine rupture diagnosed at cesarean ( n = 1). Adverse outcomes Overall, there were 13/82 adverse events (15.9%), 80.4% ( n = 66) of women experienced no adverse event, and 3.66% ( n = 3) were unknown as lost to follow up. These events were admission to the neonatal unit ( n = 2), term intrauterine death (IUD) diagnosed at the onset of labor care ( n = 1), PPH (blood loss 500–1500 mL) ( n = 10), and uterine rupture diagnosed at cesarean ( n = 1). DISCUSSION The study suggests there is a demand for birth “outside of guidance” and provides insight into the reasons why women choose midwifery‐led settings when not recommended. Women who are “higher risk” wish to reduce their chances of obstetric intervention, experience water immersion, and believe that midwifery‐led settings will provide them with a more positive birth experience. Women choosing birth at home or in a MLU close to where they lived had legitimate practical concerns about travel time to the OU and their ability to arrange childcare for older children. Midwives and doctors must practice evidence‐based care, ensuring recommendations are based on the best available evidence. In 2015, a landmark legal case changed care in the UK, with the addition that clinicians must ensure the birthing person is aware of any material risks involved in the recommended treatment or plan of care. A “material” risk is one that a reasonable person in the woman's position would likely attach significance to, or if the clinician is or should reasonably be aware that the woman would likely attach significance to it. Therefore, clinicians must listen to and understand their patients, considering what is important to an individual, which will vary from person to person. Our study has revealed a group of women whose choices are sometimes motivated by need rather than personal preference. Clinical consultations need to explore the women's personal situation, as we have found this can affect their “choices.” For example, women who have previously birthed quickly and therefore birth in an OU, although recommended, may not be realistic or achievable. The desire to use water immersion in labor was the most common reason for choosing birth at home or in an MLU. This demonstrates that women value this resource highly and supports the importance of birth pools being available on OUs so that women with pregnancy complications may also have access to water immersion in labor. During the study period, the birth pool on the OU was not in use. This is likely to have increased the number of higher‐risk women choosing to birth in a midwifery‐led setting, as this would have been the only way to access a birth pool in labor. This finding is supported by literature that has explored women's experience of using water immersion as positive, while facing multiple barriers to access. Women described choosing a birth setting that provided a positive experience of birth and reduced the chance of obstetric interventions. Evidence suggests that women are more likely to have a spontaneous vaginal birth if birth is planned in a midwifery‐led setting. , , This group of women were motivated by birth experience and physiological birth and were making choices that would optimize their chance of this. For some women, this was due to a previous traumatic birth experience in an OU and highlights the profound affect that this may have on future birth planning. After the BCC appointment, just over half of women (51.2%) changed their planned birthplace, either due to a change in their preference or in the clinical situation. This is an interesting finding and warrants further investigation, in particular the effect of BCC on decision‐making and, where plans have changed due to operational reasons, what effect this may have on future birth planning. It may be, for example, that this reduces confidence in the care provider. Of those who commenced labor in a midwifery‐led setting as planned, the majority remained in this setting and birthed without complications. Transfer rates in labor or immediately after birth (24.2%) were similar or lower to those found in other studies, although we cannot directly compare as we have not accounted for possible confounding factors. Previous studies suggest women with uncomplicated pregnancies having their first baby have a 36%–45% chance of transfer, and those having second or subsequent births have a 9%–13% chance of transfer to OU during labor or immediately postpartum. , A study of women with a previous cesarean who planned birth at home found transfer rates were 37% and varied widely by parity. There are differences in transfer rates for women who have had a previous vaginal birth compared with those who had not. The numbers in this study were too small to allow for a subgroup analysis of transfer rates by previous vaginal birth or by a specific risk factor. The number of spontaneous vaginal births was higher in this study compared with the general population at the maternity unit in 2022 (65% vs. 45%). Our study has similar findings to the BirthPlace Study, with a higher proportion of women who attended BCC achieving vaginal birth if their labor care at the onset was in a midwifery‐led setting. While there were some adverse outcomes, the risk profile of the women was high, and the majority of women experienced vaginal birth with no complications. Most adverse events were experienced by women who had changed their planned birthplace during pregnancy and attended the OU at the onset of labor ( n = 10/13). We propose that there is a natural process of “filtering” of women either, away from, or toward birthplace “outside of guidance”, depending on the accumulation of additional risk factors. For example, women with a raised BMI may have an otherwise uncomplicated pregnancy, and others may develop preeclampsia or gestational diabetes later in pregnancy, which means they move away from planning birth in a midwifery‐led setting. Perhaps this can be attributed to the power of a having a pregnancy complication diagnosed rather than being counseled about an increased chance of developing a complication that has not actually occurred. Where a pregnancy complication is diagnosed, clinicians may make stronger recommendations or counsel more persuasively. The “filtering” described may suggest that women who birth “outside of guidance” in a midwifery‐led setting are at the lower end of a risk spectrum, than those who recognize the accumulation of multiple risk factors and change planned birthplace because of this. Low risk or high risk is often discussed as a binary concept to categorize pregnancies to decide on pathways of care. We suggest this is an unhelpful concept which groups women into 1 of 2 groups. Considering risk as a spectrum on which a birthing person can move along may more accurately describe the reality of the complexity of individuals. While we focus on the UK experience, there may be benefit in offering a BCC in other maternity settings, particularly where maternity care practitioners may be financially incentivized to provide particular mode/place of birth. 4.1 Strengths and limitations The study design was a service evaluation; designed and conducted to define the current service, with the aim of improving the service for women and clinicians. However, this design and the small heterogenous sample mean the findings are not generalizable. Our evaluation is exploratory and descriptive in nature intended to inform future research on this topic where currently little is known. We recognize the limitations of our approach, particularly with the qualitative data, and caution must be exercised in interpreting the results. 4.2 Conclusion and recommendations Birth is a biopsychosocial‐cultural event, and women make birth choices based on a wide range of factors, depending on what is important to them. Clinicians have a duty to understand what the material risk is for the individual. We suggest that women may recognize a move along the risk spectrum and adjust their birthplace plans accordingly. BCCs can offer the opportunity to maximize safety by developing and communicating clear plans rather than reiterating recommendations that may not be achievable or realistic. We recommend sharing the philosophy of the BCC with women to avoid assumptions that it represents an additional barrier to their choice. Future studies could explore the rationale for and effect of changes in the planned birthplace for those wishing to choose alternative physiological birth options. Evaluation of women's experience of attending Birth Choices Clinics, and midwives' experiences of following Birth Choices Clinic plans for women who choose a birthplace “outside of guidance,” should be explored in future studies. Strengths and limitations The study design was a service evaluation; designed and conducted to define the current service, with the aim of improving the service for women and clinicians. However, this design and the small heterogenous sample mean the findings are not generalizable. Our evaluation is exploratory and descriptive in nature intended to inform future research on this topic where currently little is known. We recognize the limitations of our approach, particularly with the qualitative data, and caution must be exercised in interpreting the results. Conclusion and recommendations Birth is a biopsychosocial‐cultural event, and women make birth choices based on a wide range of factors, depending on what is important to them. Clinicians have a duty to understand what the material risk is for the individual. We suggest that women may recognize a move along the risk spectrum and adjust their birthplace plans accordingly. BCCs can offer the opportunity to maximize safety by developing and communicating clear plans rather than reiterating recommendations that may not be achievable or realistic. We recommend sharing the philosophy of the BCC with women to avoid assumptions that it represents an additional barrier to their choice. Future studies could explore the rationale for and effect of changes in the planned birthplace for those wishing to choose alternative physiological birth options. Evaluation of women's experience of attending Birth Choices Clinics, and midwives' experiences of following Birth Choices Clinic plans for women who choose a birthplace “outside of guidance,” should be explored in future studies. CL and SM have no conflicts of interest to declare. |
Dental Coverage and Care When Transitioning From Medicaid to Medicare | 1e520021-af21-4138-9c40-999a760a4d06 | 11584926 | Dentistry[mh] | The burden of dental disease is projected to rise substantially with the aging of the population. In turn, poor oral health has been associated with cognitive impairment and systemic conditions. , Despite advances in other areas of health care, oral care remains a leading unmet health need in the US, particularly among marginalized populations, , , , contributing to substantial disparities in oral health. , , The implementation of the Affordable Care Act (ACA) was associated with improved insurance coverage rates among adults with low incomes. However, adult dental care coverage in Medicaid varies widely among states, as it is not an essential health benefit for adults within the ACA. Prior studies found that the combination of Medicaid expansion and inclusion of Medicaid dental benefits was associated with improved coverage and access to dental care among adults with low incomes, with improved clinical indicators associated with oral health. , , , Nevertheless, millions of adults with low incomes lose Medicaid eligibility when transitioning to Medicare at age 65 years. This is because Medicare eligibility is based on age, while Medicaid eligibility after age 65 years is determined by income (with more restrictive eligibility limits compared with Medicaid coverage for individuals younger than 65 years), assets, and disability status. Unlike Medicaid in many states, traditional Medicare does not provide dental coverage. Moreover, millions of working adults lose employer-based dental coverage at retirement. Medicare Advantage plans, which frequently include additional benefits, such as dental and vision care, are a major source of dental coverage for older adults. However, these plans can be associated with high out-of-pocket dental costs, creating challenges for those no longer in the labor force. Most recent estimates indicate that nearly 24 million Medicare beneficiaries have no dental coverage, and 47% did not visit the dentist during the previous year. It is important to understand the challenges that adults with low incomes face when transitioning from Medicaid to Medicare. The association of this transition with dental coverage, especially among racial and ethnic minority groups, as well as that with disparities in the use of dental services remain unclear. In this study, we leveraged the natural experiment created by transitioning from Medicaid to Medicare at age 65 years to examine the consequences on coverage and use of dental services. We stratified the analyses according to whether each state’s Medicaid program provides adult dental benefits.
This cross-sectional study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guidelines. The study was determined not to be human participants research by the institutional review board of Harvard University, which waived informed consent. Study Design and Population We used a regression discontinuity (RD) design and leveraged the sharp discontinuity in Medicare eligibility at age 65 years to examine the association of transitions from Medicaid to Medicare with the study outcomes. We restricted the study sample to adults aged 50 to 90 years with 12 years of education or less (ie, no college education) to capture the population with a low socioeconomic status that would likely be eligible for Medicaid. While education is an imperfect proxy for income, the study design required us to identify those likely to be eligible for Medicaid but use a measure that is not substantially disrupted by turning age 65 years. Income fails this measure, since many adults retire at age 65 years, with substantial changes in their income biasing any comparison of adults older vs younger than 65 years. We also limited the study sample to adults residing in Medicaid expansion states after the 2014 Health and Retirement Study (HRS) survey wave (including those expanded in 2014 and 2015) to focus on the association of dental coverage benefits, rather than overall eligibility for Medicaid. We excluded nonexpansion states and those that expanded Medicaid after 2015. We stratified the sample based on each state’s Medicaid adult dental benefits program. We defined a state as providing adult dental benefits in Medicaid if it offered more than emergency dental coverage to Medicaid beneficiaries. , , We excluded states that changed their dental benefits during the study period. The sample included 27 states and Washington, DC (eTable 1 in ). We also conducted separate analyses by race and ethnicity to capture the association of racism with health care access, as well as socioeconomic status, which is a determinant of Medicaid eligibility. Finally, we examined differences in dental coverage and service use between traditional Medicare and Medicare Advantage respondents aged 65 to 70 years. These results were descriptive, as we did not use the RD design to compare these 2 groups, because there is no comparable age group younger than 65 years stratified by traditional Medicare vs Medicare Advantage. Data Source We analyzed data from the HRS, a nationally representative longitudinal survey of adults 50 years or older. The HRS collects data on key socioeconomic factors, labor force status, health conditions, health coverage, and health care use. HRS data are collected every 2 years. We obtained access to restricted state identifiers through the HRS and used the survey waves from 2014 to 2020. Study Variables The study outcomes included 3 domains: medical coverage (Medicaid, Medicare, dual-eligible coverage, private, and uninsurance), dental coverage (Medicaid, Medicare, private, or none), and use of dental services. Respondents who reported having Medicaid and Medicare coverage were considered dual-eligible. Coverage status was based on the respondent’s coverage at the time of the interview. Use of dental services outcomes included dental visits and out-of-pocket dental spending during the previous 2 years. Spending was measured in US dollars and expressed in real 2020 dollars for comparability across survey waves. All outcomes were self-reported and binary variables, except out-of-pocket dental spending, which was continuous. We stratified the sample by self-reported race and ethnicity into non-Hispanic Black individuals, Hispanic individuals, non-Hispanic White individuals, and individuals of other races (including American Indian, Alaskan Native, Asian, Native Hawaiian, and Pacific Islander individuals). Because we lacked the statistical power, we combined the Black, Hispanic, and other race subpopulations. Statistical Analysis To estimate the association between turning age 65 years and the study outcomes, we estimated the discontinuity using a local linear regression model, with a uniform kernel for each outcome. We used a data-driven approach to select the optimal bandwidth. Because the HRS is a longitudinal cohort with repeated observations over survey years, we estimated the models at the person-year level. We adjusted the models for sex, marital status, years of education, race and ethnicity, and state and year fixed effects. We used robust standard errors clustered by individuals to account for serial autocorrelation. For out-of-pocket dental spending, we first estimated the total annual out-of-pocket dental spending. We then used a 2-part model to account for the skewness of the data by applying the RD design separately in each part of the 2-part model. , The first part of the model determined the probability of having any out-of-pocket dental spending, and the second part estimated the log-transformed out-of-pocket dental spending among those with spending. To examine differences in dental visits between traditional Medicare and Medicare Advantage, we used a multivariable logistic regression and generated predicted probabilities using marginal standardization. We also used multivariable linear regression and calculated marginal means for out-of-pocket dental spending. We adjusted models for sex, education, marital status, and state and year fixed effects. We used the HRS’s survey weights and Stata (StataCorp) for all analyses. Additional Analyses To test the assumptions of the study design and robustness of our findings, we first tested whether there was any evidence of manipulation of the assignment variable by examining the distribution of respondents around age 65 years, even though it is not possible to manipulate age for Medicare eligibility. Second, we conducted a falsification test by examining whether discontinuities in covariates trended smoothly at age 65 years. Third, we tested the robustness of the models to different bandwidths and triangular kernels. Fourth, we repeated the analyses by excluding covariates. Fifth, to ensure the robustness of the 2-part model, we estimated a single generalized linear model. Finally, we excluded individuals aged 65 and 66 years from the analyses of the outcomes of having seen a dentist and out-of-pocket dental spending. We used this donut approach because these questions had a 2-year look-back period, including periods before and after age 65 years. ,
We used a regression discontinuity (RD) design and leveraged the sharp discontinuity in Medicare eligibility at age 65 years to examine the association of transitions from Medicaid to Medicare with the study outcomes. We restricted the study sample to adults aged 50 to 90 years with 12 years of education or less (ie, no college education) to capture the population with a low socioeconomic status that would likely be eligible for Medicaid. While education is an imperfect proxy for income, the study design required us to identify those likely to be eligible for Medicaid but use a measure that is not substantially disrupted by turning age 65 years. Income fails this measure, since many adults retire at age 65 years, with substantial changes in their income biasing any comparison of adults older vs younger than 65 years. We also limited the study sample to adults residing in Medicaid expansion states after the 2014 Health and Retirement Study (HRS) survey wave (including those expanded in 2014 and 2015) to focus on the association of dental coverage benefits, rather than overall eligibility for Medicaid. We excluded nonexpansion states and those that expanded Medicaid after 2015. We stratified the sample based on each state’s Medicaid adult dental benefits program. We defined a state as providing adult dental benefits in Medicaid if it offered more than emergency dental coverage to Medicaid beneficiaries. , , We excluded states that changed their dental benefits during the study period. The sample included 27 states and Washington, DC (eTable 1 in ). We also conducted separate analyses by race and ethnicity to capture the association of racism with health care access, as well as socioeconomic status, which is a determinant of Medicaid eligibility. Finally, we examined differences in dental coverage and service use between traditional Medicare and Medicare Advantage respondents aged 65 to 70 years. These results were descriptive, as we did not use the RD design to compare these 2 groups, because there is no comparable age group younger than 65 years stratified by traditional Medicare vs Medicare Advantage.
We analyzed data from the HRS, a nationally representative longitudinal survey of adults 50 years or older. The HRS collects data on key socioeconomic factors, labor force status, health conditions, health coverage, and health care use. HRS data are collected every 2 years. We obtained access to restricted state identifiers through the HRS and used the survey waves from 2014 to 2020.
The study outcomes included 3 domains: medical coverage (Medicaid, Medicare, dual-eligible coverage, private, and uninsurance), dental coverage (Medicaid, Medicare, private, or none), and use of dental services. Respondents who reported having Medicaid and Medicare coverage were considered dual-eligible. Coverage status was based on the respondent’s coverage at the time of the interview. Use of dental services outcomes included dental visits and out-of-pocket dental spending during the previous 2 years. Spending was measured in US dollars and expressed in real 2020 dollars for comparability across survey waves. All outcomes were self-reported and binary variables, except out-of-pocket dental spending, which was continuous. We stratified the sample by self-reported race and ethnicity into non-Hispanic Black individuals, Hispanic individuals, non-Hispanic White individuals, and individuals of other races (including American Indian, Alaskan Native, Asian, Native Hawaiian, and Pacific Islander individuals). Because we lacked the statistical power, we combined the Black, Hispanic, and other race subpopulations.
To estimate the association between turning age 65 years and the study outcomes, we estimated the discontinuity using a local linear regression model, with a uniform kernel for each outcome. We used a data-driven approach to select the optimal bandwidth. Because the HRS is a longitudinal cohort with repeated observations over survey years, we estimated the models at the person-year level. We adjusted the models for sex, marital status, years of education, race and ethnicity, and state and year fixed effects. We used robust standard errors clustered by individuals to account for serial autocorrelation. For out-of-pocket dental spending, we first estimated the total annual out-of-pocket dental spending. We then used a 2-part model to account for the skewness of the data by applying the RD design separately in each part of the 2-part model. , The first part of the model determined the probability of having any out-of-pocket dental spending, and the second part estimated the log-transformed out-of-pocket dental spending among those with spending. To examine differences in dental visits between traditional Medicare and Medicare Advantage, we used a multivariable logistic regression and generated predicted probabilities using marginal standardization. We also used multivariable linear regression and calculated marginal means for out-of-pocket dental spending. We adjusted models for sex, education, marital status, and state and year fixed effects. We used the HRS’s survey weights and Stata (StataCorp) for all analyses.
To test the assumptions of the study design and robustness of our findings, we first tested whether there was any evidence of manipulation of the assignment variable by examining the distribution of respondents around age 65 years, even though it is not possible to manipulate age for Medicare eligibility. Second, we conducted a falsification test by examining whether discontinuities in covariates trended smoothly at age 65 years. Third, we tested the robustness of the models to different bandwidths and triangular kernels. Fourth, we repeated the analyses by excluding covariates. Fifth, to ensure the robustness of the 2-part model, we estimated a single generalized linear model. Finally, we excluded individuals aged 65 and 66 years from the analyses of the outcomes of having seen a dentist and out-of-pocket dental spending. We used this donut approach because these questions had a 2-year look-back period, including periods before and after age 65 years. ,
Sample Characteristics The study included 15 837 adults (weighted sample, 79 129 322 person-years) . Of these, 1200 adults (weighted sample, 6 436 466 person-years) were in states without dental benefits, whereas 14 637 adults (weighted sample, 72 692 856 person-years) were in states that offer dental benefits. In both groups of states before age 65 years, most participants were White (63.3% and 67.1%, respectively) and female (52.1% and 61.8%, respectively). Changes in Outcomes Associated With Transitions From Medicaid to Medicare at Age 65 Years Turning age 65 years was associated with a marked increase in Medicare coverage in states with (66.5 percentage points [pp]; 95% CI, 58.3-74.7) and without Medicaid dental benefits (67.8 pp; 95% CI, 52.6-83.0) with concurrent reductions in private coverage, Medicaid, and uninsured rates . Before age 65 years, individuals in states without Medicaid dental benefits were more likely to lack dental coverage than those in states providing dental benefits (52.1% vs 35.9%). However, turning age 65 years was associated with a 13.1-pp increase in the likelihood of no dental coverage (95% CI, 10.7-15.5) in states providing dental benefits, largely due to the loss of Medicaid dental coverage (−11.6 pp; 95% CI, −16.1 to −7.1) and a 4.5-pp increase in Medicare dental coverage (95% CI, 3.3-5.7). However, in states without Medicaid dental benefits, there were no changes in the likelihood of dental coverage . For dental use outcomes , in states without Medicaid dental benefits, turning age 65 years was associated with a 15.6-pp increase in the likelihood of any dental visits (95% CI, 6.3-25.0) and 19.2% reduction in out-of-pocket dental spending among those who had any dental expenses during the previous 2 years (95% CI, −33.6 to −1.6). In states with dental benefits, there was a 2.2-pp increase in the likelihood of any out-of-pocket dental spending (95% CI, 1.7-2.8) and 13.0% reduction in out-of-pocket dental spending (95% CI, −24.2 to −0.1) among those with any dental expenses during the previous 2 years. Association of Changes in Outcomes by Race and Ethnicity With Transitions From Medicaid to Medicare at Age 65 Years Turning age 65 years was associated with increased Medicare coverage, reduced private and Medicaid coverage, and reduced uninsured rates for all racial and ethnic groups. This transition was more pronounced for Black individuals, Hispanic individuals, and individuals of other race than White individuals . All racial and ethnic groups experienced an increase in the likelihood of no dental coverage when turning age 65 years in states with dental benefits. However, Black individuals, Hispanic individuals, and individuals of other race had a larger decrease in Medicaid dental coverage (−20.9 pp; 95% CI, −32.3 to −9.5) than White respondents (−8.5 pp; 95% CI, −10.4 to −6.7) and the likelihood of any dental coverage (19.0 pp [95% CI, 11.2-26.9] vs 10.9 pp [95% CI, 5.5-16.4], respectively). Among White respondents, there was a 6.1-pp increase in Medicare dental coverage (95% CI, 4.4-7.9). In states without dental benefits, turning age 65 years was not associated with any significant changes in dental coverage for all racial and ethnic groups. Among Black individuals, Hispanic individuals, and individuals of other race in states without dental benefits, turning age 65 years was associated with a 20.5-pp (95% CI, 11.0-30.0) increase in dental visits during the previous 2 years. However, there was a reduction of 3.9 pp (95% CI, −6.1 to −1.7) for those residing in states providing dental benefits. These discontinuities were not significant among White respondents. Black individuals, Hispanic individuals, and individuals of other race experienced an increase in out-of-pocket dental spending among those with any spending in states without dental benefits. However, White respondents living in states without dental benefits experienced a larger decrease in spending than those in states with dental benefits. Differences in Dental Outcomes Between Traditional Medicare and Medicare Advantage After Age 64 Years We found that a higher percentage of traditional Medicare respondents lacked dental coverage (81.6%) than those with Medicare Advantage (69.2%) . In the full sample and among White respondents, Medicare Advantage respondents were less likely to have had a dental visit during the previous 2 years compared with traditional Medicare respondents (−7.0 pp [95% CI, −9.4 to −4.6] and −10.4 pp [95% CI, −14.4 to −6.4], respectively). However, there were no significant differences in dental visits for Black individuals, Hispanic individuals, and respondents of other races. We also found no significant difference in out-of-pocket dental spending in Medicare Advantage compared with traditional Medicare for the full sample or among racial and ethnic groups (eTable 2 in ). Supplementary Analyses The study findings were generally robust to different sensitivity checks. We did not find any significant discontinuities at age 65 years for demographic characteristics, including sex, race and ethnicity, and marital status, in states without dental benefits; married status was less common for those older than 65 years in states with dental benefits ( ; eFigures 1 and 2 in ). Estimates without individual-level covariates, using different kernels and bandwidths, and from the generalized linear model were similar to our main analyses (eTables 3-5 and eFigures 3 and 4 in ). When we excluded individuals aged 65 and 66 years in analyses of outcomes with a 2-year look-back period, changes remained largely similar to the main analysis (eTable 6 in ).
The study included 15 837 adults (weighted sample, 79 129 322 person-years) . Of these, 1200 adults (weighted sample, 6 436 466 person-years) were in states without dental benefits, whereas 14 637 adults (weighted sample, 72 692 856 person-years) were in states that offer dental benefits. In both groups of states before age 65 years, most participants were White (63.3% and 67.1%, respectively) and female (52.1% and 61.8%, respectively).
Turning age 65 years was associated with a marked increase in Medicare coverage in states with (66.5 percentage points [pp]; 95% CI, 58.3-74.7) and without Medicaid dental benefits (67.8 pp; 95% CI, 52.6-83.0) with concurrent reductions in private coverage, Medicaid, and uninsured rates . Before age 65 years, individuals in states without Medicaid dental benefits were more likely to lack dental coverage than those in states providing dental benefits (52.1% vs 35.9%). However, turning age 65 years was associated with a 13.1-pp increase in the likelihood of no dental coverage (95% CI, 10.7-15.5) in states providing dental benefits, largely due to the loss of Medicaid dental coverage (−11.6 pp; 95% CI, −16.1 to −7.1) and a 4.5-pp increase in Medicare dental coverage (95% CI, 3.3-5.7). However, in states without Medicaid dental benefits, there were no changes in the likelihood of dental coverage . For dental use outcomes , in states without Medicaid dental benefits, turning age 65 years was associated with a 15.6-pp increase in the likelihood of any dental visits (95% CI, 6.3-25.0) and 19.2% reduction in out-of-pocket dental spending among those who had any dental expenses during the previous 2 years (95% CI, −33.6 to −1.6). In states with dental benefits, there was a 2.2-pp increase in the likelihood of any out-of-pocket dental spending (95% CI, 1.7-2.8) and 13.0% reduction in out-of-pocket dental spending (95% CI, −24.2 to −0.1) among those with any dental expenses during the previous 2 years.
Turning age 65 years was associated with increased Medicare coverage, reduced private and Medicaid coverage, and reduced uninsured rates for all racial and ethnic groups. This transition was more pronounced for Black individuals, Hispanic individuals, and individuals of other race than White individuals . All racial and ethnic groups experienced an increase in the likelihood of no dental coverage when turning age 65 years in states with dental benefits. However, Black individuals, Hispanic individuals, and individuals of other race had a larger decrease in Medicaid dental coverage (−20.9 pp; 95% CI, −32.3 to −9.5) than White respondents (−8.5 pp; 95% CI, −10.4 to −6.7) and the likelihood of any dental coverage (19.0 pp [95% CI, 11.2-26.9] vs 10.9 pp [95% CI, 5.5-16.4], respectively). Among White respondents, there was a 6.1-pp increase in Medicare dental coverage (95% CI, 4.4-7.9). In states without dental benefits, turning age 65 years was not associated with any significant changes in dental coverage for all racial and ethnic groups. Among Black individuals, Hispanic individuals, and individuals of other race in states without dental benefits, turning age 65 years was associated with a 20.5-pp (95% CI, 11.0-30.0) increase in dental visits during the previous 2 years. However, there was a reduction of 3.9 pp (95% CI, −6.1 to −1.7) for those residing in states providing dental benefits. These discontinuities were not significant among White respondents. Black individuals, Hispanic individuals, and individuals of other race experienced an increase in out-of-pocket dental spending among those with any spending in states without dental benefits. However, White respondents living in states without dental benefits experienced a larger decrease in spending than those in states with dental benefits.
We found that a higher percentage of traditional Medicare respondents lacked dental coverage (81.6%) than those with Medicare Advantage (69.2%) . In the full sample and among White respondents, Medicare Advantage respondents were less likely to have had a dental visit during the previous 2 years compared with traditional Medicare respondents (−7.0 pp [95% CI, −9.4 to −4.6] and −10.4 pp [95% CI, −14.4 to −6.4], respectively). However, there were no significant differences in dental visits for Black individuals, Hispanic individuals, and respondents of other races. We also found no significant difference in out-of-pocket dental spending in Medicare Advantage compared with traditional Medicare for the full sample or among racial and ethnic groups (eTable 2 in ).
The study findings were generally robust to different sensitivity checks. We did not find any significant discontinuities at age 65 years for demographic characteristics, including sex, race and ethnicity, and marital status, in states without dental benefits; married status was less common for those older than 65 years in states with dental benefits ( ; eFigures 1 and 2 in ). Estimates without individual-level covariates, using different kernels and bandwidths, and from the generalized linear model were similar to our main analyses (eTables 3-5 and eFigures 3 and 4 in ). When we excluded individuals aged 65 and 66 years in analyses of outcomes with a 2-year look-back period, changes remained largely similar to the main analysis (eTable 6 in ).
The findings of this cross-sectional study suggest that turning age 65 years was associated with increased Medicare coverage and reduced private and Medicaid coverage in this sample of US adults with less than a college education. However, the association with dental coverage rates depended critically on the state’s Medicaid dental coverage policies. We found that in states that provided adult dental benefits in Medicaid, turning age 65 years was associated with significant dental coverage loss for all racial and ethnic groups and reduced use of dental services for Black individuals, Hispanic individuals, and individuals of other race. In contrast, for adults living in states without Medicaid dental benefits, the transition was associated with increased use of dental services and no significant changes in dental coverage rates. Adults in both groups of states experienced reductions in out-of-pocket dental spending at age 65 years. These results underscore the importance of dental benefits in Medicaid and their association with disparities in dental coverage during the transition to Medicare. As expected, the loss of Medicaid dental coverage at age 65 years had no association with dental coverage in states that did not offer dental benefits. However, in states providing adult dental benefits under Medicaid, the transition was associated with a significant decrease in the likelihood of having any dental coverage, particularly for Black individuals, Hispanic individuals, and individuals of other races compared with White respondents. Additionally, the gains in Medicare dental coverage were observed only among White individuals. These findings suggest that people of racial and ethnic groups have greater difficulty maintaining coverage after turning age 65 years, raising concerns about exacerbating health disparities. Although people of racial and ethnic minority groups are more likely to enroll in Medicare Advantage plans, , which often offer dental benefits, low-income populations may be less likely to take up Medicare dental coverage or use dental services due to higher cost-sharing in Medicare compared with Medicaid. Our analysis suggested that traditional Medicare respondents 65 years or older were more likely to be without dental coverage than those enrolled in Medicare Advantage. Efforts to support individuals approaching age 65 years in planning their transition to Medicare and understanding sources of dental coverage, particularly those enrolled in traditional Medicare, may reduce these losses and improve access to dental care. Prior studies have shown that transitioning to Medicare after turning age 65 years is associated with increased access to health care and reduced out-of-pocket spending for previously uninsured adults , ; our analysis suggests that this pattern of improved coverage and financial protection applies to dental coverage as well, but only for individuals in states without Medicaid dental coverage. This indicates that Medicare eligibility at age 65 years is associated with reduced financial barriers to accessing dental care, likely through alternative coverage, such as Medicare Advantage. In contrast, in states that provide Medicaid dental coverage, transitioning to Medicare at age 65 years appeared to be associated with reduced affordability of dental care and dental visits for Black individuals, Hispanic individuals, and individuals of other race. Long-term exposure to dental benefits under Medicaid might be associated with individuals’ health care decisions regarding dental coverage as they age into Medicare. There was an increase in Medicare dental coverage only in states with Medicaid dental benefits, suggesting that individuals accustomed to having dental coverage under Medicaid may be more likely to seek ways to maintain it while transitioning to Medicare. Such preferences could be due to an increased awareness of the importance of dental care and an established routine of using dental services and may lead to individuals with Medicaid who are younger than 65 years to later enroll in Medicare Advantage plans that offer supplemental dental benefits instead of traditional Medicare, which lacks dental coverage. Alternatively, this pattern may also reflect differences in Medicare Advantage markets that are associated with the Medicaid state policy environment. We found marked differences in dental expenditure between racial and ethnic groups. Underserved populations bear a disproportionate burden of dental disease , , , ; therefore, they may need complex dental procedures, such as crowns and dentures, that are not covered by insurance. People of racial and ethnic minority groups are disproportionally enrolled in Medicare Advantage plans , ; however, there are wide variations in cost-sharing across these plans, which can confuse beneficiaries, particularly those with low health literacy. , As a result, people of racial and ethnic minority groups may enroll in lower-quality plans, with a low annual dollar cap and higher cost sharing, reducing access to dental care. In our analysis, we found no significant difference in out-of-pocket dental spending for respondents in Medicare Advantage compared with those in traditional Medicare. Limitations This study aimed to examine adults with lower incomes who are likely eligible for Medicaid. Ideally, we would follow up individuals with Medicaid-range incomes from several years before to after turning age 65 years. However, since we could not follow up respondents for several years, we compared individuals younger than 65 years with individuals older than 65 years. Income changes significantly during this age range, so we were limited to using education as a proxy for potential Medicaid eligibility, which may have underestimated the association of the transition. Second, we lacked measures of clinical need to assess needed dental care, as well as data on rurality, which is associated with limited clinician access and transportation challenges. Third, our aim was to examine how state Medicaid dental policy affects individuals transitioning to Medicare at age 65 years. Examining the additional association of traditional Medicare and Medicare Advantage requires a different sampling and analytic approach, because the population younger than 65 years cannot be readily divided into 2 appropriate comparison groups for an RD design. Finally, we only examined states with Medicaid expansion as of 2015 that did not change their dental coverage policies. Additionally, the sample size for states without dental coverage was small. Therefore, our findings may not be generalizable to all states.
This study aimed to examine adults with lower incomes who are likely eligible for Medicaid. Ideally, we would follow up individuals with Medicaid-range incomes from several years before to after turning age 65 years. However, since we could not follow up respondents for several years, we compared individuals younger than 65 years with individuals older than 65 years. Income changes significantly during this age range, so we were limited to using education as a proxy for potential Medicaid eligibility, which may have underestimated the association of the transition. Second, we lacked measures of clinical need to assess needed dental care, as well as data on rurality, which is associated with limited clinician access and transportation challenges. Third, our aim was to examine how state Medicaid dental policy affects individuals transitioning to Medicare at age 65 years. Examining the additional association of traditional Medicare and Medicare Advantage requires a different sampling and analytic approach, because the population younger than 65 years cannot be readily divided into 2 appropriate comparison groups for an RD design. Finally, we only examined states with Medicaid expansion as of 2015 that did not change their dental coverage policies. Additionally, the sample size for states without dental coverage was small. Therefore, our findings may not be generalizable to all states.
The findings of this cross-sectional study suggest that transitioning from Medicaid to Medicare at age 65 years is associated with increased barriers to accessing dental care for beneficiaries in states providing Medicaid dental coverage before age 65 years. This highlights the inadequacy of current Medicare dental provisions, primarily in Medicare Advantage, in closing these gaps. The results support federal initiatives to cover supplemental benefits, including dental benefits, in Medicare, as well as efforts to improve data collection to evaluate Medicare Advantage dental coverage quality. , Additionally, the inclusion of dental care for adults as an essential health benefit may expand dental coverage in the Marketplace, improving dental health for privately insured individuals before age 65 years. At the state level, maintaining and expanding Medicaid adult dental benefits may be associated with long-term cost savings in Medicare by ensuring better oral health during the transition. These findings underscore the need for coordinated state and federal efforts to provide continuous dental coverage and support strategies to mitigate the adverse association of this transition.
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Cervical spine motion during videolaryngoscopic intubation using a Macintosh-style blade with and without the anterior piece of a cervical collar: a randomized controlled trial | 63aa432d-15ba-4274-8fe9-e1a1c7c37dea | 11821687 | Surgical Procedures, Operative[mh] | Ethics The Seoul National University Hospital/Seoul National University College of Medicine Institutional Review Board (Seoul, Republic of Korea) approved this single-centre, parallel-group, randomized controlled trial (reference number: 2217-107-1387; approved 16 January 2023). The trial was registered with CRIS.nih.go.kr (KCT0008151; first submitted 2 February 2023) prior to patient enrolment. All patients provided written informed consent before participating in this trial. The trial was conducted in accordance with the ethical principles of the Helsinki Declaration 2013 and the Good Clinical Practice guidelines, and the manuscript was written in accordance with the applicable Consolidated Standards of Reporting Trials (CONSORT) statements. Participants We recruited adult (≥ 18 yr old) patients scheduled for elective neurointervention under general anesthesia at Seoul National University Hospital. We excluded patients with upper airway disease (tumour, polyp, trauma, or abscess), cervical spine disease, history of surgical treatment of the upper airway or cervical spine, and high risk of aspiration (gastrointestinal reflux disease), bleeding (coagulopathy), or dental injury (weak or loose teeth). Randomization and blinding Before enrolling patients, an anesthesiologist not involved in this study created a random allocation table with computer-generated blocks consisting of six allocations and kept it in an opaque envelope. Just prior to the induction of anesthesia, a nurse not involved in the trial allocated patients to either the posterior-only (wearing only the posterior piece, but not the anterior piece of the cervical collar) or the anterior-posterior (wearing both the anterior and posterior pieces of the cervical collar) group in a 1:1 ratio based on the random allocation table. Patients and anesthesiologists who measured cervical spine angles and investigated intubation-associated complications were blinded to the group allocation. The radiolucent cervical collar used in this study (Philadelphia® Tracheotomy Collar, Össur, Reykjavik, Iceland) also ensured that the anesthesiologist measuring cervical spine angles was blinded to the group allocation. Protocol Prior to the induction of anesthesia, airway-associated variables (modified Mallampati class, interincisal gap, thyromental distance, sternomental distance, neck circumference, and retrognathia) were measured in the sitting position, while thyromental height was measured in the supine position. After preoxygenation with 100% oxygen (6–8 L·min −1 ) until the fraction of expired oxygen reached 0.8, anesthesia was induced with target-controlled infusion of remifentanil (effect site concentration, 4.0–6.0 mg·L −1 ) and intravenous bolus injection of propofol (1.0–2.0 mg·kg −1 ). After loss of consciousness and intravenous bolus injection of rocuronium (0.6–0.8 mg·kg −1 ), manual mask ventilation was performed with 100% oxygen and sevoflurane (1.5–2.0 vol%). The patient’s head and neck were placed in the neutral position without a pillow. In the anterior-posterior group, the anterior and posterior pieces of the cervical collar were worn at the front and back of the neck, respectively, and fastened as tightly as possible. In the posterior-only group, only the posterior piece of the cervical collar was worn at the back of the neck without covering the front of the neck with the anterior piece of the cervical collar. To determine cervical spine angles before intubation, a lateral cervical spine radiograph was obtained using the capture method of a biplane angiographic system (Integris Allura™, Philips, Amsterdam, Netherlands). After confirming the absence of twitches evoked by the train-of-four stimulation, one of two attending anesthesiologists with more than 100 videolaryngoscopic intubations in patients wearing a cervical collar performed videolaryngoscopic intubation. A videolaryngoscope (AceScope™, Ace Medical, Seoul, Republic of Korea) with a disposable Macintosh-style blade (AceBlade™, Ace Medical; size MAC 3 for females and 4 for males) was gently inserted into the oral cavity, avoiding neck extension. After placing the blade tip at the vallecula, the videolaryngoscope was lifted to expose the glottis to a target percentage of glottic opening score of 50% on the monitor. If the glottis was not visible, an assistant performed external laryngeal manipulation, which pressed the thyroid cartilage backward. If the glottis was still not visible, the direct epiglottis elevation method, in which the videolaryngoscope is lifted after placing the blade tip under the epiglottis, was used to expose the glottis. In cases in which the percentage of glottic opening score could not reach 50%, the maximum achievable percentage of glottic opening score was recorded. After then, a tracheal tube (Shiley™ Oral RAE Tracheal Tube with TaperGuard™ Cuff, Covidien, Dublin, Ireland; internal diameter, 7.0 mm for females and 7.5 mm for males) with a malleable stylet bent to 60° at the proximal edge of the endotracheal cuff was advanced until its tip reached the glottic opening. After placing the tube tip at the glottic opening, another lateral cervical spine radiograph was obtained to determine cervical spine angles at intubation. To avoid inconsistent delays in intubation times caused by waiting for the radiologic technologist to take this second lateral cervical spine radiograph, intubation time was defined as the time interval from inserting the videolaryngoscope into the oral cavity to placing the tube tip at the glottic opening just before requesting the radiograph, not to placing the tracheal tube in the trachea. After removing the stylet, the tracheal tube was inserted into the trachea. The presence of a regular capnogram on the patient monitor confirmed intubation success. If intubation time exceeded 3 min or peripheral oxygen saturation decreased to < 90%, the case was recorded as failed intubation, and rescue manual mask ventilation with 100% oxygen and sevoflurane was performed for ≥ 1 min or until the peripheral oxygen saturation reached 100%. In cases of failed intubation, intubation was attempted again by removing the anterior piece of the cervical collar or using a different intubation device. At the end of neurointervention, extubation was performed and the patient was transferred to the postanesthesia care unit. During extubation, the anesthesiologist checked for blood in the oral cavity and tracheal tube as well as injuries to the tongue and teeth. Hoarseness and sore throat were assessed in the postanesthesia care unit and on the ward ward at 1 and 24 hr after intubation, respectively. The severity of sore throat was evaluated using a numeric rating scale, with 0 representing no pain and 10 the most severe pain imaginable. Measurement of cervical spine angles All lateral cervical spine radiographs were saved in the Picture Archiving and Communication System (M6 version 6.0.12.1, IFINITT Healthcare Co. Ltd., Seoul, Republic of Korea). The reference lines of the occiput and C1 were defined as a line connecting the sellar base and the opisthion and that connecting the inferior cortical margin of the C1 anterior arch and the inferior cortical margin of the C1 spinous process, respectively (Fig. ). The reference lines of C2 and C5 were defined as a line connecting the anteroinferior cortical margin of the C2 body and the inferior cortical margin of the C2 spinous process and that parallel to the endplate of the C5 body, respectively. An anesthesiologist, who had previously used the identical method to measure the same cervical spine angles in other studies, measured cervical spine angles before and at intubation at the occiput–C1, C1–C2, and C2–C5 segments at the intersections of the aforementioned reference lines. , Each cervical spine angle was measured twice on the same lateral cervical spine radiograph, and the average of the two measurements was used for analysis to reduce measurement error. Outcomes The primary outcome measure was cervical spine motion during intubation, defined as the change in cervical spine angle (calculated as cervical spine angle at intubation minus that before intubation) at the occiput–C1, C1–C2, and C2–C5 segments. The secondary outcome measures were the cervical spine angles before and at intubation at the three aforementioned segments, intubation performance (intubation success rate, intubation time, percentage of glottic opening score, frequency of external laryngeal manipulation, and direct epiglottis elevation method), and intubation-associated complications (incidence of airway bleeding [oral cavity and tracheal tube], airway injury [tongue and teeth], hoarseness, and sore throat as well as severity of sore throat). Sample size calculation In a previous study, the mean (standard deviation) of cervical spine motion during videolaryngoscopic intubation at the occiput–C1 segment was 6.8 (5.0)° in patients wearing both the anterior and posterior piece of the cervical collar. Also, another study reported a 4° difference (14° − 10°) in median cervical spine motions during videolaryngoscopic intubation at the same segment between manual in-line stabilization and cervical collar application. Based on these findings, we assumed a 3.4° (6.8°/2) difference in cervical spine motions during intubation at the same segment between the posterior-only and anterior-posterior groups. Accordingly, the minimum required sample size was calculated to be 91 when setting the α, β, and effect size to 0.017 (0.05/3), 0.2, and 0.68 (3.4/5.0), respectively. Considering a dropout rate of 10%, we recruited 51 patients per group. Statistical analysis All statistical analyses were performed with the intention-to-treat method using statistical software (IBM SPSS Statistics for Windows version 26, IBM Corp., Armonk, NY, USA). The Shapiro–Wilk test was used to assess the normality of continuous variables. The Student’s t test and Mann–Whitney U test were used to compare normally and nonnormally distributed continuous variables between the posterior-only and anterior-posterior groups, respectively. To calculate confidence intervals (CIs) for difference in means of normally distributed continuous variables between the two groups, the Student’s t test was used. To calculate CIs for difference in medians of nonnormally distributed continuous variables between the two groups, the Hodges–Lehmann estimation was used. The paired t test or Wilcoxon signed-rank test was used to compare cervical spine angles before and at intubation when their distribution was normal and nonnormal, respectively. Fisher's exact test was used to compare categorical variables between the two groups. To calculate CIs for differences in percentages of categorical variables between the two groups, direct computation of the exact CI was used. To compensate for multiple comparisons of cervical spine motion and angles at the occiput–C1, C1–C2, and C2–C5 segments between the two groups, a Bonferroni correction was applied and a P value < 0.017 (0.05/3) was considered statistically significant. Otherwise, a P value < 0.05 was considered to be statistically significant. The Seoul National University Hospital/Seoul National University College of Medicine Institutional Review Board (Seoul, Republic of Korea) approved this single-centre, parallel-group, randomized controlled trial (reference number: 2217-107-1387; approved 16 January 2023). The trial was registered with CRIS.nih.go.kr (KCT0008151; first submitted 2 February 2023) prior to patient enrolment. All patients provided written informed consent before participating in this trial. The trial was conducted in accordance with the ethical principles of the Helsinki Declaration 2013 and the Good Clinical Practice guidelines, and the manuscript was written in accordance with the applicable Consolidated Standards of Reporting Trials (CONSORT) statements. We recruited adult (≥ 18 yr old) patients scheduled for elective neurointervention under general anesthesia at Seoul National University Hospital. We excluded patients with upper airway disease (tumour, polyp, trauma, or abscess), cervical spine disease, history of surgical treatment of the upper airway or cervical spine, and high risk of aspiration (gastrointestinal reflux disease), bleeding (coagulopathy), or dental injury (weak or loose teeth). Before enrolling patients, an anesthesiologist not involved in this study created a random allocation table with computer-generated blocks consisting of six allocations and kept it in an opaque envelope. Just prior to the induction of anesthesia, a nurse not involved in the trial allocated patients to either the posterior-only (wearing only the posterior piece, but not the anterior piece of the cervical collar) or the anterior-posterior (wearing both the anterior and posterior pieces of the cervical collar) group in a 1:1 ratio based on the random allocation table. Patients and anesthesiologists who measured cervical spine angles and investigated intubation-associated complications were blinded to the group allocation. The radiolucent cervical collar used in this study (Philadelphia® Tracheotomy Collar, Össur, Reykjavik, Iceland) also ensured that the anesthesiologist measuring cervical spine angles was blinded to the group allocation. Prior to the induction of anesthesia, airway-associated variables (modified Mallampati class, interincisal gap, thyromental distance, sternomental distance, neck circumference, and retrognathia) were measured in the sitting position, while thyromental height was measured in the supine position. After preoxygenation with 100% oxygen (6–8 L·min −1 ) until the fraction of expired oxygen reached 0.8, anesthesia was induced with target-controlled infusion of remifentanil (effect site concentration, 4.0–6.0 mg·L −1 ) and intravenous bolus injection of propofol (1.0–2.0 mg·kg −1 ). After loss of consciousness and intravenous bolus injection of rocuronium (0.6–0.8 mg·kg −1 ), manual mask ventilation was performed with 100% oxygen and sevoflurane (1.5–2.0 vol%). The patient’s head and neck were placed in the neutral position without a pillow. In the anterior-posterior group, the anterior and posterior pieces of the cervical collar were worn at the front and back of the neck, respectively, and fastened as tightly as possible. In the posterior-only group, only the posterior piece of the cervical collar was worn at the back of the neck without covering the front of the neck with the anterior piece of the cervical collar. To determine cervical spine angles before intubation, a lateral cervical spine radiograph was obtained using the capture method of a biplane angiographic system (Integris Allura™, Philips, Amsterdam, Netherlands). After confirming the absence of twitches evoked by the train-of-four stimulation, one of two attending anesthesiologists with more than 100 videolaryngoscopic intubations in patients wearing a cervical collar performed videolaryngoscopic intubation. A videolaryngoscope (AceScope™, Ace Medical, Seoul, Republic of Korea) with a disposable Macintosh-style blade (AceBlade™, Ace Medical; size MAC 3 for females and 4 for males) was gently inserted into the oral cavity, avoiding neck extension. After placing the blade tip at the vallecula, the videolaryngoscope was lifted to expose the glottis to a target percentage of glottic opening score of 50% on the monitor. If the glottis was not visible, an assistant performed external laryngeal manipulation, which pressed the thyroid cartilage backward. If the glottis was still not visible, the direct epiglottis elevation method, in which the videolaryngoscope is lifted after placing the blade tip under the epiglottis, was used to expose the glottis. In cases in which the percentage of glottic opening score could not reach 50%, the maximum achievable percentage of glottic opening score was recorded. After then, a tracheal tube (Shiley™ Oral RAE Tracheal Tube with TaperGuard™ Cuff, Covidien, Dublin, Ireland; internal diameter, 7.0 mm for females and 7.5 mm for males) with a malleable stylet bent to 60° at the proximal edge of the endotracheal cuff was advanced until its tip reached the glottic opening. After placing the tube tip at the glottic opening, another lateral cervical spine radiograph was obtained to determine cervical spine angles at intubation. To avoid inconsistent delays in intubation times caused by waiting for the radiologic technologist to take this second lateral cervical spine radiograph, intubation time was defined as the time interval from inserting the videolaryngoscope into the oral cavity to placing the tube tip at the glottic opening just before requesting the radiograph, not to placing the tracheal tube in the trachea. After removing the stylet, the tracheal tube was inserted into the trachea. The presence of a regular capnogram on the patient monitor confirmed intubation success. If intubation time exceeded 3 min or peripheral oxygen saturation decreased to < 90%, the case was recorded as failed intubation, and rescue manual mask ventilation with 100% oxygen and sevoflurane was performed for ≥ 1 min or until the peripheral oxygen saturation reached 100%. In cases of failed intubation, intubation was attempted again by removing the anterior piece of the cervical collar or using a different intubation device. At the end of neurointervention, extubation was performed and the patient was transferred to the postanesthesia care unit. During extubation, the anesthesiologist checked for blood in the oral cavity and tracheal tube as well as injuries to the tongue and teeth. Hoarseness and sore throat were assessed in the postanesthesia care unit and on the ward ward at 1 and 24 hr after intubation, respectively. The severity of sore throat was evaluated using a numeric rating scale, with 0 representing no pain and 10 the most severe pain imaginable. All lateral cervical spine radiographs were saved in the Picture Archiving and Communication System (M6 version 6.0.12.1, IFINITT Healthcare Co. Ltd., Seoul, Republic of Korea). The reference lines of the occiput and C1 were defined as a line connecting the sellar base and the opisthion and that connecting the inferior cortical margin of the C1 anterior arch and the inferior cortical margin of the C1 spinous process, respectively (Fig. ). The reference lines of C2 and C5 were defined as a line connecting the anteroinferior cortical margin of the C2 body and the inferior cortical margin of the C2 spinous process and that parallel to the endplate of the C5 body, respectively. An anesthesiologist, who had previously used the identical method to measure the same cervical spine angles in other studies, measured cervical spine angles before and at intubation at the occiput–C1, C1–C2, and C2–C5 segments at the intersections of the aforementioned reference lines. , Each cervical spine angle was measured twice on the same lateral cervical spine radiograph, and the average of the two measurements was used for analysis to reduce measurement error. The primary outcome measure was cervical spine motion during intubation, defined as the change in cervical spine angle (calculated as cervical spine angle at intubation minus that before intubation) at the occiput–C1, C1–C2, and C2–C5 segments. The secondary outcome measures were the cervical spine angles before and at intubation at the three aforementioned segments, intubation performance (intubation success rate, intubation time, percentage of glottic opening score, frequency of external laryngeal manipulation, and direct epiglottis elevation method), and intubation-associated complications (incidence of airway bleeding [oral cavity and tracheal tube], airway injury [tongue and teeth], hoarseness, and sore throat as well as severity of sore throat). In a previous study, the mean (standard deviation) of cervical spine motion during videolaryngoscopic intubation at the occiput–C1 segment was 6.8 (5.0)° in patients wearing both the anterior and posterior piece of the cervical collar. Also, another study reported a 4° difference (14° − 10°) in median cervical spine motions during videolaryngoscopic intubation at the same segment between manual in-line stabilization and cervical collar application. Based on these findings, we assumed a 3.4° (6.8°/2) difference in cervical spine motions during intubation at the same segment between the posterior-only and anterior-posterior groups. Accordingly, the minimum required sample size was calculated to be 91 when setting the α, β, and effect size to 0.017 (0.05/3), 0.2, and 0.68 (3.4/5.0), respectively. Considering a dropout rate of 10%, we recruited 51 patients per group. All statistical analyses were performed with the intention-to-treat method using statistical software (IBM SPSS Statistics for Windows version 26, IBM Corp., Armonk, NY, USA). The Shapiro–Wilk test was used to assess the normality of continuous variables. The Student’s t test and Mann–Whitney U test were used to compare normally and nonnormally distributed continuous variables between the posterior-only and anterior-posterior groups, respectively. To calculate confidence intervals (CIs) for difference in means of normally distributed continuous variables between the two groups, the Student’s t test was used. To calculate CIs for difference in medians of nonnormally distributed continuous variables between the two groups, the Hodges–Lehmann estimation was used. The paired t test or Wilcoxon signed-rank test was used to compare cervical spine angles before and at intubation when their distribution was normal and nonnormal, respectively. Fisher's exact test was used to compare categorical variables between the two groups. To calculate CIs for differences in percentages of categorical variables between the two groups, direct computation of the exact CI was used. To compensate for multiple comparisons of cervical spine motion and angles at the occiput–C1, C1–C2, and C2–C5 segments between the two groups, a Bonferroni correction was applied and a P value < 0.017 (0.05/3) was considered statistically significant. Otherwise, a P value < 0.05 was considered to be statistically significant. Between February 2023 and May 2023, 102 patients were randomized (Fig. ). Lateral cervical radiographs of one patient in the anterior-posterior group were not saved. Table summarizes the baseline demographic characteristics and airway-related variables of both groups. Between the posterior-only and anterior-posterior groups, differences in mean cervical spine motions during intubation were 1.2° (98.3% CI, −0.7 to 3.0), 1.0° (98.3% CI, −0.6 to 2.6), and −0.3° (98.3% CI, −2.2 to 1.7) at the occiput–C1, C1–C2, and C2–C5 segments, respectively (Table ). The cervical spine angles at intubation were significantly larger than those before intubation at the occiput–C1 and C1–C2 segments within each group whereas the cervical spine angles before and at intubation at the C2–C5 segment did not significantly differ within each group (Fig. ). Intubation as per protocol of the allocated group was successful on the first attempt in all patients except one patient in the anterior-posterior group whose videolaryngoscopic intubation was successful on the second attempt only after removing the anterior piece of the cervical collar (Table ). Intubation times were significantly shorter in the posterior-only group. The two groups showed no significant differences in other intubation performance and intubation-associated complications. In this trial, we compared cervical spine motion during videolaryngoscopic intubation between applying only the posterior piece and applying both the anterior and posterior pieces in patients wearing a cervical collar. We found that the differences in cervical spine motion at the occiput–C1, C1–C2, and C2–C5 segments between the posterior-only and anterior-posterior groups during videolaryngoscopic intubation were reliably ≤ 3° and that intubation-associated complications were comparable between both groups. Nevertheless, intubation times were shorter without an anterior piece of cervical collar. Comparison of such clinical outcomes related to videolaryngoscopic intubation with and without an anterior piece of cervical collar has received limited attention in the literature so far. The cervical collar has long been used to immobilize the cervical spine, especially during intubation, in patients with suspected cervical spine instability. , Nevertheless, it is known to make laryngoscopic intubation more difficult by restricting the range of neck motion and mouth opening. In addition, the cervical collar has been reported to increase the lifting force needed to expose the glottis during videolaryngoscopic intubation in some patients. Such an increase in the lifting force can result in increased cervical spine motion during laryngoscopic intubation. Moreover, during intubation, videolaryngoscopes require less lifting force and result in smaller cervical spine motion than direct laryngoscopes even under manual in-line stabilization. Therefore, we anticipated that the force of lifting the videolaryngoscope, which is increased by the anterior piece of the cervical collar, might exceed the force of immobilizing the cervical spine by the cervical collar during videolaryngoscopic intubation. Nevertheless, contrary to our expectation, we found no statistically significant differences in cervical spine motion at the occiput–C1, C1–C2, and C2–C5 segments between applying only the posterior piece and applying both the anterior and posterior pieces in patients wearing a cervical collar. This finding suggests that the disadvantageous effect of the nonimmobilized cervical spine resulting from the absence of the anterior cervical collar piece offset the advantageous effect of the reduced lifting force for the same reason in terms of reducing cervical spine motion during videolaryngoscopic intubation in patients wearing a cervical collar. In this trial, intubation speed was better and all intubations were successful without external laryngeal manipulation and direct epiglottis elevation in patients wearing only the posterior piece of the cervical collar, whereas one failed intubation occurred in patients wearing both the anterior and posterior pieces of the cervical collar. These findings likely are primarily a result of improved mouth opening without the anterior piece of the cervical collar. Approximately 8 sec longer intubation times or a failed intubation on the first attempt may not be critical in general patients, but this can be concerning for patients with suspected cervical spine instability because the risk of aspiration and hypoxia is relatively high and rapid sequence intubation is frequently required in such patients. , We assumed that intubation-associated complications, particularly airway bleeding and sore throat, would be worse in patients wearing both the anterior and posterior piece of the cervical collar because of a more crowded upper airway and increased lifting force. , Nevertheless, although these complications seemed to be more frequent in such patients, we observed no statistically significant differences in these outcomes in this trial. The routine use of intravenous heparin during neurointervention and the predominantly female population in our trial may have contributed to the development of airway bleeding and sore throat even after minor trauma that would not generally cause detectable bleeding and pain, respectively. The likelihood of these phenomena may have been comparatively high in patients wearing only the posterior piece of the cervical collar; nevertheless, we observed no statistically significant differences in these complications as noted, and this trial was not powered to detect such differences. This trial had several limitations. First, because the anesthesiologist who performed videolaryngoscopic intubation was not blinded to the group allocation, performance bias could not be excluded. Second, since this study was conducted in patients without cervical spine instability, removing the anterior piece of the cervical collar may affect cervical spine motion during intubation differently in patients with cervical spine instability who actually require a cervical collar. Also, several patient characteristics in this study, such as Asian race, predominance of female sex, normal body mass index, short neck circumference, and reassuring airway parameters, may limit the generalizability of the results to other populations. In addition, because cervical spine motion during intubation can vary based on the equipment used, the results of this trial may not be reproducible when using other styles or manufacturers of cervical collars and laryngoscope blades, especially a hyperangulated blade, for videolaryngoscopic intubation. Third, because lateral cervical spine radiographs were captured only twice, it is uncertain whether the cervical spine motion measured represented the greatest magnitude that might occur during intubation. Continuous fluoroscopy could potentially capture the latter but would be associated with notable radiation exposure as videolaryngoscopic intubation was sometimes difficult in the anterior-posterior group and was allowed to be attempted for 3 min in this trial. In two previous studies using continuous fluoroscopy, cervical spine angle during videolaryngoscopic intubation at the occiput–C1 measured when placing the tube tip at the glottic opening was not much different from the largest ones measured when obtaining the best glottic view and inserting the tracheal tube into the trachea, resepctively. , Fourth, the lifting force, which is thought to be the main cause of cervical spine motion during videolaryngoscopic intubation, was not measured in this study. Moreover, since the lifting force and resulting cervical spine motion during videolaryngoscopic intubation depend on the target glottic opening, the results may differ in other settings of the target glottic opening. Fifth, ideally, a radiologist specializing in assessing lateral cervical spine radiographs would have measured the cervical spine angles. Nevertheless, the anesthesiologist was able to measure cervical spine angles without difficulty in this trial as their reference lines were clearly defined and their reference points were easily identifiable bony structures. Last, although it is reasonable to consider large cervical spine motions during intubation to be more risky in patients with cervical spine instability, the exact threshold of cervical spine motion during intubation that translates to neurologic injury in such patients remains unknown. Accordingly, a minimum clinically important difference in cervical spine motion during intubation based on adequate evidence could not be defined in this trial. Therefore, caution is warranted when relating the results of this trial—particularly the magnitude of the largest observed mean cervical spine motion during intubation of 10.8° (at the occiput–C1 segment level [Table ])—to neurologic adverse events secondary to intubation. In conclusion, differences in mean cervical spine motions during videolaryngoscopic intubation using a Macintosh-style blade were approximately 1° and reliably ≤ 3° with and without an anterior piece of cervical collar, and the null hypothesis of this study could not be rejected. Additionally, intubation times were shorter without an anterior piece. These findings can be referred to when removing the anterior piece is considered to address difficult videolaryngoscopic intubation because of the cervical collar. |
Effect of Growth Hormone on Branched‐Chain Amino Acids Catabolism in Males With Hypopituitarism | 7689f541-aa1c-4816-9772-c3fa525b3c71 | 11875759 | Biochemistry[mh] | Introduction Hypopituitarism is characterised by the partial or complete loss of anterior pituitary hormones, including GH, luteinising hormone (LH), follicle‐stimulating hormone (FSH), adrenocorticotropic hormone (ACTH) and thyrotropin (TSH) . Due to GH deficiency (GHD), hypopituitarism patients exhibit increased visceral adiposity, IR, dyslipidaemia and hyperglycaemia, increasing the incidence and mortality rate of cardiovascular diseases . Skeletal muscle, a target organ of GH, experiences atrophy and metabolic disorders in the absence of adequate GH levels . In adults with GHD, lean body mass (LBM) and muscle mass are reduced due to disrupted protein metabolism . GH replacement therapy in GHD stabilises protein metabolism by favouring protein synthesis pathways over amino acid oxidation . Muscle atrophy is associated with increased expression of muscle atrophy F‐box protein (MAFbx) and muscle‐specific RING finger 1 (MuRF1), which induce ubiquitination and proteasome‐mediated degradation of target proteins, resulting in rapid muscle mass loss . Russell‐Jones et al. observed that GH supplementation in growth hormone deficiency (GHD) subjects led to an increase in protein synthesis and a reduction in protein oxidation. GH replacement therapy was found to restore protein stabilisation by favouring amino acid utilisation in protein synthesis pathways, thereby ameliorating muscle mass loss. Despite these positive effects on protein metabolism, the specific role of GH in modulating the ubiquitin‐proteasome system during this process remains unknown. Branched‐chain amino acids (BCAAs), namely leucine, isoleucine and valine, are indispensable amino acids that mammals cannot synthesise de novo. Consequently, they must be acquired through dietary intake, as a subset of enzymes essential for their biosynthesis is lacking in human and other mammalian tissues. Amino acids derived from dietary proteins are transported through the circulation to skeletal muscle, where they play a pivotal role in synthesising essential proteins . Elevated concentrations of BCAAs have been implicated in the pathogenesis of IR, type 2 diabetes (T2D) and various cardiometabolic diseases . In parallel with the clinical features observed in obesity and T2D, hypopituitarism presents with characteristics such as central obesity, IR and an increased susceptibility to cardiovascular diseases. The exploration of the intricate interplay between BCAAs and metabolic disturbances in hypopituitarism will provide valuable insights into potential mechanisms underlying these clinical features. Several studies have proposed that the elevated circulating BCAAs observed in patients with IR may result from dysregulated BCAA oxidation pathways in adipose and hepatic tissues, primarily influenced by impaired functions of BCAT and BCKDH . In murine models of obesity and IR, levels of valine and leucine/isoleucine have been reported to increase by 20% and 14%, respectively. This rise in BCAAs has been linked to the downregulation of multiple enzymes in the oxidation pathway . This transcriptional downregulation of BCAA oxidation enzymes has also been observed in human participants with obesity, which can be reversed by weight loss surgery and accompanied by a decrease in circulating BCAA levels . In cases of obesity and IR, minimal changes are observed in BCKDH abundance in the liver. Instead, BCKDH activity is primarily impaired by the induction of BDK and repression of PPM1K, leading to hyperphosphorylation of BCKDH and subsequent inhibition of its enzymatic activities . Moreover, the transplantation of normal adipose tissue into mice lacking BCAT2 has been demonstrated to reduce circulating BCAA levels by 30%–50% . Collectively, these findings suggest an increase in circulating BCAAs in obesity and diabetes, potentially attributed to impaired BCAA oxidation pathways resulting from decreased expression or altered phosphorylation of BCAA oxidation enzymes. To investigate the impact of GH on BCAAs catabolism in males with hypopituitarism, the current study conducted a case–control investigation involving 133 individuals with hypopituitarism and 90 paired controls. Furthermore, we also established animal models of hypopituitarism (hypophysectomized rats). This study provides an in‐depth understanding of the mechanisms underlying GH's effect on BCAAs catabolism in hypopituitarism.
Materials and Methods 2.1 Participant Recruitment Patients and healthy controls were recruited at Ruijin Hospital in Shanghai, China, between January 2016 and December 2018. The recruitment process for the current study adhered to the same protocol as a previously published study , including congenital hypopituitarism and acquired hypopituitarism, with further exclusion criteria of patients with normal GH levels. The study protocol received approval from the Board of Medical Ethics at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China. Ethical considerations and patient confidentiality were maintained throughout the recruitment process and the entire duration of the study. 2.2 Hormone Replacement Plan of Patients With Hypopituitarism Physiologic dosages of glucocorticoids and/or thyroid hormone were administered after diagnosis (median diagnosis age 16.50 year). A subset of these patients had undergone GH replacement during their childhood, subsequently discontinuing the treatment for a minimum of 24 months. Gonadotropin treatment was administered to all patients with LH/FSH deficiency for at least 24 months. 2.3 Measurement of Biochemical Markers in Patients With Hypopituitarism Serum total cholesterol, high‐density lipoprotein cholesterol, low‐density lipoprotein cholesterol and triglycerides were measured by the automated enzymatic method on an autoanalyser (Beckman Coulter, California, USA). Plasma glucose was measured by the glucose oxidase method with the same autoanalyser. Serum insulin was measured by a commercially available RIA kit (Diagnostic Systems Laboratories, Minnesota, USA). IR was quantified using the homeostasis model assessment of insulin resistance (HOMA‐IR) index (HOMA‐IR = insulin [μU/mL] × glucose [mmol/L]/22.5). 2.4 Amino Acids Quantification in Patients With Hypopituitarism Through Untargeted Metabolomics Metabolic profiling of serum samples was conducted on an Agilent 1290 Infinity LC system (Agilent Technologies, California, USA) coupled with an AB SCIEX Triple TOF6600 system (AB SCIEX, California, USA) to measure the amino acid levels. Chromatographic separation was performed on an ACQUITY HSS T3 1.8 μm column. Variable importance in projection (VIP) values for each variable in the orthogonal partial least squares‐discriminant analysis (OPLS‐DA) model were calculated to identify metabolites contributing to classification. Variables with p ‐values < 0.05 and VIP values > 1 were considered statistically significant. Our method allows for a relative quantitative analysis of the AA profile through untargeted metabolomics based on peak areas. The levels of AAs were recorded based on the peak area. 2.5 Hypophysectomized Rat Models and GH Intervention Male Sprague–Dawley (SD) rats aged 3–4 weeks (weighing 70–80 g) were equally randomised into three groups. The hypophysectomy was performed according to the following procedure. A 1.5 to 2.5 cm incision was made along the midline of the neck of the rat, starting from the lower jaw nipple downward. The incision was made bluntly, following the direction of muscle fibres, to separate the subcutaneous tissue and salivary glands, exposing the trachea. The anterior neck fascia was opened on the left or right side of the midline, and the salivary glands were pulled aside. A syringe needle was inserted between the 3rd and 4th tracheal cartilage rings, and after the needle was withdrawn, a PE50 or PE90 tube was inserted along the needle track. The muscle tissue and trachea were pulled apart on both sides to fully expose the base of the skull. The meninges and adhering tissues on the base of the skull were scraped off to expose the sphenoparietal suture. A drill was used to create a hole in the base of the skull, allowing access to the cranial cavity. After removing the membrane covering the surface of the pituitary gland, a suction tube connected to a negative pressure pump (at 320–500 mmHg) was used to aspirate the pituitary gland. After haemostasis was achieved with cotton swabs and gauze in the operative area, the surgical retractor was removed. After the animal regained breathing, the endotracheal tube was removed. The sternocleidomastoid muscle was retracted to fully cover the tracheotomy incision, and the skin incision was sutured. Before the rat regained consciousness, attention should be paid to secretions in the oral cavity or trachea to prevent asphyxiation. After regaining consciousness, clean water should be provided and subcutaneous injection of 10–15 mL of Ringer's solution or glucose saline solution may be considered for rats with significant blood loss during surgery. Because the energy metabolism level of the hypophysectomized rat is lower, attention should be paid to insulation. The success criterion for the model was defined as a postoperative body weight gain of less than 10% after 2 weeks. The rats in the control group were subjected to a sham operation, which involved the administration of general anaesthesia, the cutting and subsequent suturing of the skin on the neck. After 2 weeks' recovery, one of the hypophysectomized groups was provided rhGH (0.1 mg/kg/day) (Genlei, Changchun, China) daily at a relatively fixed time of 11:00–12:00 am with subcutaneous injection, including weekends for 2 weeks . The other groups were injected with 100 μL physiological saline. The dose and duration of rhGH replacement were determined based on the physiological replacement doses of GH in adult GHD. Body weight and fasting blood samples were collected before and 2 weeks after the operation, and 2 weeks after rhGH intervention. At the end of the study, the overnight‐fasted rats were euthanized. The livers and extensor digitorum longus muscles were excised and frozen immediately in liquid nitrogen. Blood samples were collected, and plasma was obtained by centrifugation (2200× g , 4°C) and stored at −80°C. 2.6 Amino Acids Quantification in the Serum of Rats by Targeted Metabolomics The concentrations of serum amino acids were determined by high‐performance liquid chromatography/mass spectrometry. In brief, 80 μL of an ethylene diamine tetraacetic acid sample was deproteinized with 1 mL of methanol and subsequently purified through ion exchange columns. Statistical significance was determined for metabolites based on p ‐values < 0.05 and VIP values > 1. A heatmap visualising the differential expression of these metabolites was constructed across the time points. The LC–MS analysis was conducted by Applied Protein Technology Co. Ltd. (APTBIO, Shanghai, China). 2.7 Four‐Dimensional ( 4D )‐Label Free Phosphorylation Proteomics The protein concentration was quantified using the BCA Protein Assay Kit (Beyotime Biotechnology, Shanghai, China). For filter‐aided sample preparation, 200 μg of protein was mixed with 30 μL of SDT buffer. The phosphopeptides were enriched using the High‐Select Fe‐NTA Phosphopeptides Enrichment Kit (Thermo Scientific, Massachusetts, USA). LC–MS/MS analysis was performed on a timsTOF Pro mass spectrometer coupled to Nanoelute (Bruker, Massachusetts, USA) for 60 min. 4D label‐free phosphorylation proteomics was conducted at Applied Protein Technology Co. Ltd. The differentially expressed phosphoproteins (DEPPs) were identified with standards of p ‐values < 0.05 and VIP values > 1. The gene set enrichment analysis for differentially expressed phosphoproteins was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, which was automatically generated by Metascape ( https://metascape.org ). 2.8 Western Blotting Tissue homogenates were lysed in RIPA lysis buffer supplemented with protease inhibitor cocktail (APExBio, Houston, USA). Approximately 50–70 μg protein was separated by 6%–12% SDS‐PAGE, transferred to PVDF membranes (Bio‐Rad, Hercules, USA), and probed with primary antibodies. The antibodies of BCAT1 D6D4K (88785), BCAT2 D8K3O (79764), BCKDH‐E1α E4T3D (90198) and Phospho‐BCKDH‐E1α (Ser293) E2V6B (40368) all sourced from Cell Signalling Technology (CST, Massachusetts, USA), while MURF1 ab183094 was acquired through Abcam (Abcam, Massachusetts, USA). 2.9 Calculation of Muscle Cross‐Sectional Area ( CSA ) The CSA was calculated assuming the cross‐section to be approximately elliptical. The formula for the area of an ellipse was used, A = π × L /2 × W /2, where A represents area, L is the major axis, W is the minor axis and π is approximately 3.14. The CSA was calculated for 30 cells that were approximately elliptical in shape, and the mean value was determined. One‐way ANOVA was employed to analyse the muscle CSA among three groups of animals. 2.10 Assessment of Cell Density in Skeletal Muscle To evaluate cell density in skeletal muscle, regions of 0.050 mm 2 were selected from HE‐stained images. The number of skeletal muscle cells within these regions was counted. For cells located at the edges of the selected areas, only those with more than half of their volume within the region were included in the count. Four random regions with the same areas were selected, and the mean number of cells was calculated from these counts. 2.11 Statistical Analysis The Kolmogorov–Smirnov statistical test was performed to assess data normality. Continuous variables were presented as the mean ± SD for normally distributed variables or medians (interquartile ranges) for the skewed variables. For multiple metabolites comparisons, including 13 amino acids, in metabolome analysis, ‘ q ‐value’ was used instead of the p ‐value, which has been adjusted using the False Discovery Rate (FDR) method. For comparison of multiple groups, one‐way analysis of variance (ANOVA) was used, followed by Tukey's honest significant difference post hoc test. The correlations between amino acids and metabolism parameters were assessed using Pearson's coefficient. The diagnostic abilities of valine and leucine in hypopituitarism patients were assessed with a receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). Analyses were performed with Graphpad Prism 8.0 (Graphpad, California, USA) or R software. The significance tests were two‐tailed, and statistical significance was set at p < 0.05.
Participant Recruitment Patients and healthy controls were recruited at Ruijin Hospital in Shanghai, China, between January 2016 and December 2018. The recruitment process for the current study adhered to the same protocol as a previously published study , including congenital hypopituitarism and acquired hypopituitarism, with further exclusion criteria of patients with normal GH levels. The study protocol received approval from the Board of Medical Ethics at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, China. Ethical considerations and patient confidentiality were maintained throughout the recruitment process and the entire duration of the study.
Hormone Replacement Plan of Patients With Hypopituitarism Physiologic dosages of glucocorticoids and/or thyroid hormone were administered after diagnosis (median diagnosis age 16.50 year). A subset of these patients had undergone GH replacement during their childhood, subsequently discontinuing the treatment for a minimum of 24 months. Gonadotropin treatment was administered to all patients with LH/FSH deficiency for at least 24 months.
Measurement of Biochemical Markers in Patients With Hypopituitarism Serum total cholesterol, high‐density lipoprotein cholesterol, low‐density lipoprotein cholesterol and triglycerides were measured by the automated enzymatic method on an autoanalyser (Beckman Coulter, California, USA). Plasma glucose was measured by the glucose oxidase method with the same autoanalyser. Serum insulin was measured by a commercially available RIA kit (Diagnostic Systems Laboratories, Minnesota, USA). IR was quantified using the homeostasis model assessment of insulin resistance (HOMA‐IR) index (HOMA‐IR = insulin [μU/mL] × glucose [mmol/L]/22.5).
Amino Acids Quantification in Patients With Hypopituitarism Through Untargeted Metabolomics Metabolic profiling of serum samples was conducted on an Agilent 1290 Infinity LC system (Agilent Technologies, California, USA) coupled with an AB SCIEX Triple TOF6600 system (AB SCIEX, California, USA) to measure the amino acid levels. Chromatographic separation was performed on an ACQUITY HSS T3 1.8 μm column. Variable importance in projection (VIP) values for each variable in the orthogonal partial least squares‐discriminant analysis (OPLS‐DA) model were calculated to identify metabolites contributing to classification. Variables with p ‐values < 0.05 and VIP values > 1 were considered statistically significant. Our method allows for a relative quantitative analysis of the AA profile through untargeted metabolomics based on peak areas. The levels of AAs were recorded based on the peak area.
Hypophysectomized Rat Models and GH Intervention Male Sprague–Dawley (SD) rats aged 3–4 weeks (weighing 70–80 g) were equally randomised into three groups. The hypophysectomy was performed according to the following procedure. A 1.5 to 2.5 cm incision was made along the midline of the neck of the rat, starting from the lower jaw nipple downward. The incision was made bluntly, following the direction of muscle fibres, to separate the subcutaneous tissue and salivary glands, exposing the trachea. The anterior neck fascia was opened on the left or right side of the midline, and the salivary glands were pulled aside. A syringe needle was inserted between the 3rd and 4th tracheal cartilage rings, and after the needle was withdrawn, a PE50 or PE90 tube was inserted along the needle track. The muscle tissue and trachea were pulled apart on both sides to fully expose the base of the skull. The meninges and adhering tissues on the base of the skull were scraped off to expose the sphenoparietal suture. A drill was used to create a hole in the base of the skull, allowing access to the cranial cavity. After removing the membrane covering the surface of the pituitary gland, a suction tube connected to a negative pressure pump (at 320–500 mmHg) was used to aspirate the pituitary gland. After haemostasis was achieved with cotton swabs and gauze in the operative area, the surgical retractor was removed. After the animal regained breathing, the endotracheal tube was removed. The sternocleidomastoid muscle was retracted to fully cover the tracheotomy incision, and the skin incision was sutured. Before the rat regained consciousness, attention should be paid to secretions in the oral cavity or trachea to prevent asphyxiation. After regaining consciousness, clean water should be provided and subcutaneous injection of 10–15 mL of Ringer's solution or glucose saline solution may be considered for rats with significant blood loss during surgery. Because the energy metabolism level of the hypophysectomized rat is lower, attention should be paid to insulation. The success criterion for the model was defined as a postoperative body weight gain of less than 10% after 2 weeks. The rats in the control group were subjected to a sham operation, which involved the administration of general anaesthesia, the cutting and subsequent suturing of the skin on the neck. After 2 weeks' recovery, one of the hypophysectomized groups was provided rhGH (0.1 mg/kg/day) (Genlei, Changchun, China) daily at a relatively fixed time of 11:00–12:00 am with subcutaneous injection, including weekends for 2 weeks . The other groups were injected with 100 μL physiological saline. The dose and duration of rhGH replacement were determined based on the physiological replacement doses of GH in adult GHD. Body weight and fasting blood samples were collected before and 2 weeks after the operation, and 2 weeks after rhGH intervention. At the end of the study, the overnight‐fasted rats were euthanized. The livers and extensor digitorum longus muscles were excised and frozen immediately in liquid nitrogen. Blood samples were collected, and plasma was obtained by centrifugation (2200× g , 4°C) and stored at −80°C.
Amino Acids Quantification in the Serum of Rats by Targeted Metabolomics The concentrations of serum amino acids were determined by high‐performance liquid chromatography/mass spectrometry. In brief, 80 μL of an ethylene diamine tetraacetic acid sample was deproteinized with 1 mL of methanol and subsequently purified through ion exchange columns. Statistical significance was determined for metabolites based on p ‐values < 0.05 and VIP values > 1. A heatmap visualising the differential expression of these metabolites was constructed across the time points. The LC–MS analysis was conducted by Applied Protein Technology Co. Ltd. (APTBIO, Shanghai, China).
Four‐Dimensional ( 4D )‐Label Free Phosphorylation Proteomics The protein concentration was quantified using the BCA Protein Assay Kit (Beyotime Biotechnology, Shanghai, China). For filter‐aided sample preparation, 200 μg of protein was mixed with 30 μL of SDT buffer. The phosphopeptides were enriched using the High‐Select Fe‐NTA Phosphopeptides Enrichment Kit (Thermo Scientific, Massachusetts, USA). LC–MS/MS analysis was performed on a timsTOF Pro mass spectrometer coupled to Nanoelute (Bruker, Massachusetts, USA) for 60 min. 4D label‐free phosphorylation proteomics was conducted at Applied Protein Technology Co. Ltd. The differentially expressed phosphoproteins (DEPPs) were identified with standards of p ‐values < 0.05 and VIP values > 1. The gene set enrichment analysis for differentially expressed phosphoproteins was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, which was automatically generated by Metascape ( https://metascape.org ).
Western Blotting Tissue homogenates were lysed in RIPA lysis buffer supplemented with protease inhibitor cocktail (APExBio, Houston, USA). Approximately 50–70 μg protein was separated by 6%–12% SDS‐PAGE, transferred to PVDF membranes (Bio‐Rad, Hercules, USA), and probed with primary antibodies. The antibodies of BCAT1 D6D4K (88785), BCAT2 D8K3O (79764), BCKDH‐E1α E4T3D (90198) and Phospho‐BCKDH‐E1α (Ser293) E2V6B (40368) all sourced from Cell Signalling Technology (CST, Massachusetts, USA), while MURF1 ab183094 was acquired through Abcam (Abcam, Massachusetts, USA).
Calculation of Muscle Cross‐Sectional Area ( CSA ) The CSA was calculated assuming the cross‐section to be approximately elliptical. The formula for the area of an ellipse was used, A = π × L /2 × W /2, where A represents area, L is the major axis, W is the minor axis and π is approximately 3.14. The CSA was calculated for 30 cells that were approximately elliptical in shape, and the mean value was determined. One‐way ANOVA was employed to analyse the muscle CSA among three groups of animals.
Assessment of Cell Density in Skeletal Muscle To evaluate cell density in skeletal muscle, regions of 0.050 mm 2 were selected from HE‐stained images. The number of skeletal muscle cells within these regions was counted. For cells located at the edges of the selected areas, only those with more than half of their volume within the region were included in the count. Four random regions with the same areas were selected, and the mean number of cells was calculated from these counts.
Statistical Analysis The Kolmogorov–Smirnov statistical test was performed to assess data normality. Continuous variables were presented as the mean ± SD for normally distributed variables or medians (interquartile ranges) for the skewed variables. For multiple metabolites comparisons, including 13 amino acids, in metabolome analysis, ‘ q ‐value’ was used instead of the p ‐value, which has been adjusted using the False Discovery Rate (FDR) method. For comparison of multiple groups, one‐way analysis of variance (ANOVA) was used, followed by Tukey's honest significant difference post hoc test. The correlations between amino acids and metabolism parameters were assessed using Pearson's coefficient. The diagnostic abilities of valine and leucine in hypopituitarism patients were assessed with a receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). Analyses were performed with Graphpad Prism 8.0 (Graphpad, California, USA) or R software. The significance tests were two‐tailed, and statistical significance was set at p < 0.05.
Results 3.1 Physical and Biological Characteristics of Hypopituitarism There were no significant differences observed in age, height, weight and BMI between individuals with hypopituitarism and their age, male‐matched controls. However, elevated levels of fasting triglycerides, total cholesterol, glucose, insulin and HOMA‐IR were noted in individuals with hypopituitarism compared to the control group (Table ). 3.2 Increased Circulating BCAAs in Hypopituitarism In total, thirteen amino acids and metabolites displayed differential abundances in patients with hypopituitarism. Notably, higher levels of alanine, arginine, glutamate, histidine, leucine, phenylalanine, proline, pyroglutamic acid, tryptophan and valine were observed, while glutamine, lysine and norleucine levels were lower in hypopituitarism compared to those in healthy controls (Table ). Significantly higher levels of valine and leucine were observed in hypopituitarism, with a 1.15‐fold increase ( p < 0.001) for leucine and a 1.57‐fold increase ( p < 0.001) for valine. Valine displayed promising potential as a diagnostic biomarker, with an area under the curve (AUC) of 0.8943 (95% CI = 0.8495–0.9392) (Figure ). 3.3 Increased Circulating BCAAs Significantly Correlated With IR in Hypopituitarism Among the amino acids, valine, leucine and glutamate exhibited positive correlations with biomarkers related to lipid and carbohydrate metabolism (Figure ). Specifically, the concentration of valine was positively correlated with triglycerides, insulin and HOMA‐IR ( r = 0.201, p < 0.05; r = 0.278, p < 0.01; r = 0.265, p < 0.01) and the concentration of leucine was also positively correlated with these biomarkers. Moreover, a positive correlation was observed between glutamate and triglycerides, LDL, glucose, insulin and HOMA‐IR. In the healthy control group, no significant correlation was observed between valine, leucine or glutamate and HOMA‐IR. 3.4 Hepatic Steatosis, CSA and IR in Hypophysectomized Rats Hypophysectomized rats were utilised as the animal model for investigating the effects of rhGH intervention (Figure ). Following hypophysectomy, the rats exhibited a noticeable decrease in appetite and experienced a body weight gain of less than 10% within 2 weeks (Table ). However, the administration of rhGH during the subsequent two‐week period resulted in significant weight recovery (Figure ). The successful extraction of the pituitary gland was also confirmed by serum IGF‐1 levels, which decreased by over 80% in hypophysectomized rats (PR group) and significantly increased in the rhGH group (Figure ). Liver tissue analysis revealed hepatic steatosis in the PR group, which was partly restored after rhGH intervention, as evidenced by Oil Red O staining in liver tissue (Figure ). Morphologically, the muscle cells in the WT group were relatively loosely distributed, while those in the PR and rhGH groups were closely packed, as depicted in Figure . However, the weight and volume of thigh/body significantly decreased in the PR group, as evidence of enhanced proteolysis (Figure ). The mean CSA of muscle cells for the WT group was 1630 ± 319.6 μm 2 , for the PR group was 1548 ± 431.4 μm 2 , and for the rhGH group was 1266 ± 266.1 μm 2 . The WT group had the comparatively largest CSA, while the rhGH group had the comparatively smaller CSA area (Table ). The curve of CSA frequency distribution was shown in Figure . Morphologically, the muscle cells in the WT group were relatively loosely distributed, while those in the PR and rhGH groups were closely packed. In an area of 0.050 mm 2 , the cell count for the WT group was 19.75 ± 0.96, for the PR group was 26.25 ± 2.63, and for the rhGH group was 31.60 ± 2.65 (Figure ). There were significant differences among the three groups. The cell density in the PR and rhGH groups was notably increased. HOMA‐IR was relatively higher in the PR group compared to the rhGH group (Figure ). The fasting insulin concentration was paradoxically highest in the PR group, a condition that was mitigated by rhGH replacement (Figure ). 3.5 Increased BCAAs in Hypophysectomized Rats Circulating amino acids were evaluated at various time points in hypophysectomized rats: prior to surgery (D0), before (D14) and after (D28) rhGH replacement. Results revealed substantial perturbation in the levels of 66.7% (20/30) amino acids or derivatives subsequent to hypophysectomy. During rhGH intervention, only four amino acids, encompassing BCAAs, such as leucine, valine, isoleucine, along with hydroxyproline, exhibited a partial normalisation of their concentrations ( p < 0.05, q value < 0.2) between D14 and D28 (Figure , Table ). Specifically, the concentrations of BCAAs undergo a pronounced elevation subsequent to hypophysectomy, followed by a discernible downward trend post intervention with rhGH, as depicted in Figure . Simultaneously, the PR group exhibited no notable variation across the Day 14 and Day 28 time points, as detailed in Figure . 3.6 Regulation of BCAA Degradation and Ubiquitin‐Dependent Proteolysis in Hypophysectomized Rats To elucidate the mechanism underlying elevation of circulating BCAAs in hypopituitarism, a comprehensive 4D label‐free quantitative phosphoproteomics analysis was performed on liver tissues from WT, PR and rhGH groups. A total of 3771 phosphoproteins were evaluated across the three cohorts. A total of 194 upregulated and 361 downregulated differentially expressed phosphoproteins (DEPPs) were identified in comparison between rhGH and the PR group (Figure ). The KEGG pathways analysis revealed DEPPs between rhGH and the PR group involved in ‘mRNA metabolic process,’ ‘diseases of signal transduction by growth factor receptors and second messengers,’ and ‘valine, leucine, and isoleucine degradation’ (Figure ). In the context of hypophysectomized rats, a total of 12 proteins associated with ‘valine, leucine, and isoleucine degradation pathway’ exhibited significantly perturbed phosphorylation levels. These included BCKDHA‐S362, ALDH9A1‐T28, EHHADH‐S720, HADHA‐T392, HADH‐S73, HMGCL‐S22, HMGCS1‐S476, HMGCS2‐S324, ALDH6A1‐S525, PCCA‐Y152, ACAA2‐S332 and BDH1‐S25 (Figure ). No significant differences were observed for the total levels of these proteins, suggesting that the effects of GH on BCAA degradation in the liver primarily occurred at the phosphorylation level rather than the protein expression level (Table ). Among these proteins, the phosphorylation states of EHHADH‐S720, HADH‐S73, ACAA2‐S332, HMGCS1‐S476, BCKDHA‐S362 and ALDH6A1‐S525 were reversed following rhGH intervention. Specifically, the phosphorylation of BCKDHA at the S362 sites was reduced to 0.07‐fold (Figure ), yet it saw a substantial restoration subsequent to GH intervention. The findings from the western blotting corroborated these observations. (Figure ). Given that BCAAs degradation pathway initiated in the skeletal muscle in a manner dependent on BCAT expression, we noted a significant upregulation of BCAT1 and BCAT2 in the tibialis anterior muscle of hypophysectomized rats (Figure ). MuRF1, an ubiquitin ligase in the ubiquitin‐proteasome‐mediated protein degradation, underwent a significant increase post‐hypophysectomy and was significantly restored subsequent to the administration of rhGH.
Physical and Biological Characteristics of Hypopituitarism There were no significant differences observed in age, height, weight and BMI between individuals with hypopituitarism and their age, male‐matched controls. However, elevated levels of fasting triglycerides, total cholesterol, glucose, insulin and HOMA‐IR were noted in individuals with hypopituitarism compared to the control group (Table ).
Increased Circulating BCAAs in Hypopituitarism In total, thirteen amino acids and metabolites displayed differential abundances in patients with hypopituitarism. Notably, higher levels of alanine, arginine, glutamate, histidine, leucine, phenylalanine, proline, pyroglutamic acid, tryptophan and valine were observed, while glutamine, lysine and norleucine levels were lower in hypopituitarism compared to those in healthy controls (Table ). Significantly higher levels of valine and leucine were observed in hypopituitarism, with a 1.15‐fold increase ( p < 0.001) for leucine and a 1.57‐fold increase ( p < 0.001) for valine. Valine displayed promising potential as a diagnostic biomarker, with an area under the curve (AUC) of 0.8943 (95% CI = 0.8495–0.9392) (Figure ).
Increased Circulating BCAAs Significantly Correlated With IR in Hypopituitarism Among the amino acids, valine, leucine and glutamate exhibited positive correlations with biomarkers related to lipid and carbohydrate metabolism (Figure ). Specifically, the concentration of valine was positively correlated with triglycerides, insulin and HOMA‐IR ( r = 0.201, p < 0.05; r = 0.278, p < 0.01; r = 0.265, p < 0.01) and the concentration of leucine was also positively correlated with these biomarkers. Moreover, a positive correlation was observed between glutamate and triglycerides, LDL, glucose, insulin and HOMA‐IR. In the healthy control group, no significant correlation was observed between valine, leucine or glutamate and HOMA‐IR.
Hepatic Steatosis, CSA and IR in Hypophysectomized Rats Hypophysectomized rats were utilised as the animal model for investigating the effects of rhGH intervention (Figure ). Following hypophysectomy, the rats exhibited a noticeable decrease in appetite and experienced a body weight gain of less than 10% within 2 weeks (Table ). However, the administration of rhGH during the subsequent two‐week period resulted in significant weight recovery (Figure ). The successful extraction of the pituitary gland was also confirmed by serum IGF‐1 levels, which decreased by over 80% in hypophysectomized rats (PR group) and significantly increased in the rhGH group (Figure ). Liver tissue analysis revealed hepatic steatosis in the PR group, which was partly restored after rhGH intervention, as evidenced by Oil Red O staining in liver tissue (Figure ). Morphologically, the muscle cells in the WT group were relatively loosely distributed, while those in the PR and rhGH groups were closely packed, as depicted in Figure . However, the weight and volume of thigh/body significantly decreased in the PR group, as evidence of enhanced proteolysis (Figure ). The mean CSA of muscle cells for the WT group was 1630 ± 319.6 μm 2 , for the PR group was 1548 ± 431.4 μm 2 , and for the rhGH group was 1266 ± 266.1 μm 2 . The WT group had the comparatively largest CSA, while the rhGH group had the comparatively smaller CSA area (Table ). The curve of CSA frequency distribution was shown in Figure . Morphologically, the muscle cells in the WT group were relatively loosely distributed, while those in the PR and rhGH groups were closely packed. In an area of 0.050 mm 2 , the cell count for the WT group was 19.75 ± 0.96, for the PR group was 26.25 ± 2.63, and for the rhGH group was 31.60 ± 2.65 (Figure ). There were significant differences among the three groups. The cell density in the PR and rhGH groups was notably increased. HOMA‐IR was relatively higher in the PR group compared to the rhGH group (Figure ). The fasting insulin concentration was paradoxically highest in the PR group, a condition that was mitigated by rhGH replacement (Figure ).
Increased BCAAs in Hypophysectomized Rats Circulating amino acids were evaluated at various time points in hypophysectomized rats: prior to surgery (D0), before (D14) and after (D28) rhGH replacement. Results revealed substantial perturbation in the levels of 66.7% (20/30) amino acids or derivatives subsequent to hypophysectomy. During rhGH intervention, only four amino acids, encompassing BCAAs, such as leucine, valine, isoleucine, along with hydroxyproline, exhibited a partial normalisation of their concentrations ( p < 0.05, q value < 0.2) between D14 and D28 (Figure , Table ). Specifically, the concentrations of BCAAs undergo a pronounced elevation subsequent to hypophysectomy, followed by a discernible downward trend post intervention with rhGH, as depicted in Figure . Simultaneously, the PR group exhibited no notable variation across the Day 14 and Day 28 time points, as detailed in Figure .
Regulation of BCAA Degradation and Ubiquitin‐Dependent Proteolysis in Hypophysectomized Rats To elucidate the mechanism underlying elevation of circulating BCAAs in hypopituitarism, a comprehensive 4D label‐free quantitative phosphoproteomics analysis was performed on liver tissues from WT, PR and rhGH groups. A total of 3771 phosphoproteins were evaluated across the three cohorts. A total of 194 upregulated and 361 downregulated differentially expressed phosphoproteins (DEPPs) were identified in comparison between rhGH and the PR group (Figure ). The KEGG pathways analysis revealed DEPPs between rhGH and the PR group involved in ‘mRNA metabolic process,’ ‘diseases of signal transduction by growth factor receptors and second messengers,’ and ‘valine, leucine, and isoleucine degradation’ (Figure ). In the context of hypophysectomized rats, a total of 12 proteins associated with ‘valine, leucine, and isoleucine degradation pathway’ exhibited significantly perturbed phosphorylation levels. These included BCKDHA‐S362, ALDH9A1‐T28, EHHADH‐S720, HADHA‐T392, HADH‐S73, HMGCL‐S22, HMGCS1‐S476, HMGCS2‐S324, ALDH6A1‐S525, PCCA‐Y152, ACAA2‐S332 and BDH1‐S25 (Figure ). No significant differences were observed for the total levels of these proteins, suggesting that the effects of GH on BCAA degradation in the liver primarily occurred at the phosphorylation level rather than the protein expression level (Table ). Among these proteins, the phosphorylation states of EHHADH‐S720, HADH‐S73, ACAA2‐S332, HMGCS1‐S476, BCKDHA‐S362 and ALDH6A1‐S525 were reversed following rhGH intervention. Specifically, the phosphorylation of BCKDHA at the S362 sites was reduced to 0.07‐fold (Figure ), yet it saw a substantial restoration subsequent to GH intervention. The findings from the western blotting corroborated these observations. (Figure ). Given that BCAAs degradation pathway initiated in the skeletal muscle in a manner dependent on BCAT expression, we noted a significant upregulation of BCAT1 and BCAT2 in the tibialis anterior muscle of hypophysectomized rats (Figure ). MuRF1, an ubiquitin ligase in the ubiquitin‐proteasome‐mediated protein degradation, underwent a significant increase post‐hypophysectomy and was significantly restored subsequent to the administration of rhGH.
Discussion In human cohorts, BCAA and related metabolites are now widely recognised as among the strongest biomarkers of obesity, IR, T2D and cardiovascular diseases . Within the hypopituitarism cohort, a significant elevation in BCAA levels was observed in patients with hypopituitarism compared to healthy controls. Furthermore, there was a positive correlation identified between BCAA concentrations and both triglycerides and IR levels. However, no definitive conclusions can be drawn regarding the potential causal role of BCAAs in disease pathogenesis, based on current correlative metabolic data alone. IR is characterised by reduced sensitivity or responsiveness to the metabolic actions of insulin, encompassing defects in glucose uptake and oxidation, diminished glycogen synthesis and impaired suppression of lipid oxidation . Felig et al. proposed that the elevated levels of BCAAs and aromatic amino acids observed in individuals with obesity may be a consequence, rather than a cause, of IR . However some evidence suggests that BCAAs may independently contribute to IR. Metabolomic studies have indicated that elevated BCAA levels in individuals with normal fasting glycemia are associated with an increased risk of IR and diabetes . Recent human genetic studies investigating variants affecting insulin sensitivity and lipid traits in relation to BCAA levels have proposed a unifying model. According to this model, increases in BCAA observed in pre‐diabetic individuals with obesity may primarily result from IR. However, once elevated, BCAAs could potentially play a causal role in the progression from prediabetes to full‐blown diabetes . It is plausible that elevated BCAAs may be a consequence of IR, yet they might subsequently exert an influence on lipid and glucose metabolism within the context of hypopituitarism. The precise role of BCAAs in hypopituitarism, whether they are mere consequences, causative factors or mere biomarkers of impaired insulin response, remains to be elucidated. The regulation of circulating BCAAs involves various factors, such as dietary consumption, protein synthesis or oxidation, and the rate of proteolysis and release of free amino acids, while de novo biosynthesis is not observed in human tissues . To mitigate the influence of diet‐derived amino acids, known to cause a notable increase in BCAA levels post‐ingestion of animal protein‐rich meals, we assessed amino acid concentrations under fasting conditions. In the hypophysectomized rat model, which is characterised by a severe loss of appetite and a lack of obvious weight gain during the experiment, the elevation of fasting BCAAs is unlikely to be attributed to an excess of diet‐derived amino acids. On the contrary, the notable reduction in the weight or volume of skeletal muscles, as well as the increased density of muscle fibres, indicates the occurrence of muscle atrophy. The mice with GH deficiency showed smaller muscle fibres and normal muscle function according to Mavalli's research . Due to the remarkable degree of intragroup variability within the PR and rhGH groups, there is no significant difference observed in CSA. The compaction of muscle fibres may be related to muscle atrophy, as the reduced interstitial space can impair the nutritional supply to the muscle fibres, potentially triggering their atrophy. Several studies have noted that a more compact arrangement may be associated with a shift in muscle fibre type towards the more densely packed slow‐twitch fibres . Additionally, the substantial increase in the levels of most amino acids and derivatives in both patients with hypopituitarism and the hypophysectomized rat model corroborates the state of enhanced proteolysis. This is further supported by the heightened expression of MuRF1, a key enzyme in the ubiquitin‐proteasome‐mediated protein degradation pathway. Collectively, the increased concentrations of BCAAs may be indicative of elevated proteolysis during the post‐absorptive state. Holeček et al. have proposed that skeletal muscle plays a dominant role in BCAA catabolism and that activated proteolysis and IR can contribute to the elevation of BCAA levels . The ubiquitin‐proteasome system (UPS) is implicated in the degradation of major skeletal muscle proteins and plays a significant role in muscle wasting. MAFbx and MuRF1, as ubiquitin‐protein ligases, are crucial components of the UPS system . Although there is no direct evidence to suggest that GH regulates the expression of MAFbx and MuRF1, one study has shown that Ghrelin, which restores plasma GH levels in burned rats, can decrease proteolysis by modulating MuRF1 and MAFbx . Furthermore, a significant reduction in circulating BCAA levels was observed during fasting with GH replacement therapy, attributed to diminished proteolysis . In the current study, the expression of MuRF1 was significantly augmented following hypophysectomy, a yet it could be mitigated by rhGH intervention, implying its role as a marker of muscle proteolysis regulated by GH. The application of 4D label‐free quantitative phosphoproteomics has revealed the KEGG pathways that are significantly enriched and distinguish the rhGH group from the PR group. Notably, these include pathways involved in ‘mRNA metabolism,’ ‘diseases of signal transduction by growth factor receptors and second messengers,’ and ‘valine, leucine, and isoleucine degradation.’ The inclusion of ‘diseases of signal transduction by growth factor receptors and second messengers’ is rational, given the two‐week rhGH intervention on hypophysectomized rats. Among the 12 proteins exhibiting dysregulated phosphorylation levels in the ‘valine, leucine, and isoleucine degradation pathway,’ the rate‐limiting enzyme BCKDHA in the liver's BCAA degradation pathway showed significantly dephosphorylation. BCKDHA is the α‐subunit of the E1 component of the BCKDH complex, which is responsible for catalysing the second step critical and irreversible reaction in BCAA degradation. The activity of BCKDHA is regulated by BCKDK kinase and PPM1K phosphatase. BCKDK kinase inhibits the activity of BCKDHA through phosphorylation, while PPM1K activates the BCKDH complex by dephosphorylating BCKDHA . Although the phosphorylation site identified in our proteomic analysis was BCKDHA‐S362, we employed the canonical BCKDHA‐Ser293 antibody during the WB validation and obtained a consistent trend. Therefore, the dephosphorylation state of BCKDHA in PR group is an indication of an enhanced BCAA degradation. BCATs are responsible for the first step in the catabolism of BCAAs, catalysing the reversible transamination reaction between BCAAs and their corresponding α‐keto acids (BCKAs). A decline in BCATs expression may lead to a reduction in BCAA catabolism, thereby affecting the concentration of BCAAs in the serum. Evidence including the deficiency of BCAT2 has been demonstrated to reduce circulating BCAA levels by 30%–50% . BCAT2 enhances BCAA uptake to sustain BCAA catabolism and mitochondrial respiration in the development of pancreatic ductal adenocarcinoma . We found the expression of BCATs increased in the PR group, which may indicate an enhanced BCAA degradation, like BCKDHA dephosphorylation. Collectively, these findings suggest that the BCAA degradation pathway is activated in the context of GH deficiency, a state that appears amenable to correction through rhGH intervention. These results were unexpected, given that as IR‐related diseases, such as obesity, typically entail a broad transcriptional repression of the BCAA degradation pathway. The underlying mechanism behind this phenomenon remains unexplored, especially considering the scarcity of research investigating the interaction between GH and BCAA oxidation. Further investigations are warranted to unravel the intricate details of the relationship between GH and BCAA metabolism in GHD. Despite the promising findings, the present study has notable limitations. Firstly, BCAA metabolism has been shown to be sex‐dependent in previous studies , which may limit the generalizability of our findings since only male patients and animal models were included in this study. Secondly, we did not collect data on patients' daily protein intake and activity levels, which can also have an impact on their BCAA metabolism. Thirdly, hypophysectomized rats were chosen as the animal model for hypopituitarism, rather than employing GH receptor knockout transgenic rats. The choice of hypophysectomized rats enabled the demonstration of GH's compensatory effect; however, it also hindered the exploration of potential effects from other pituitary hormones, as no hormone replacement therapies were provided to the rats. Fourthly, the analysis of muscle fibre type and functional study has not been conducted. However, such an analysis is only applicable to fresh samples. This limitation is primarily due to methodological issues, as such analyses are only applicable to fresh samples. Our samples have already been fixed and embedded in paraffin, thus missing the optimal time for detection. To compensate for this missing content, we assessed the cellular arrangement density, as there are certain differences in the arrangement of different fibre types. Generally, slow‐twitch fibres (Type I) have a higher density and more compact arrangement compared to fast‐twitch fibres (Type II). Future work will focus on addressing these methodological limitations by conducting experiments on fresh samples to analyse muscle fibre types and their functional characteristics. Additionally, without data on other comorbidities that patients may have, which can also alter BCAA metabolism. Furthermore, the patients enrolled in our study did not undergo rhGH therapy in adulthood due to economic constraints or concerns related to tumour recurrence, cancer and diabetes risks. Consequently, we were unable to observe the impact of GH intervention on BCAA levels in patients with hypopituitarism. In conclusion, our study establishes a clear association between elevated circulating BCAAs and IR in hypopituitarism. The activation of the BCAA degradation pathway in the state of GHD suggests a complex interplay between GH and BCAA metabolism. The observed increase in fasting BCAAs likely stems from augmented proteolysis and IR, contributing to elevated BCAA levels in the bloodstream that exceed their degradation and utilisation (Figure ). While these findings provide valuable insights into the impact of GH on BCAA catabolism, the study's limitations underscore the necessity for additional investigations to refine our comprehension and potentially contribute to the creation of innovative therapeutic strategies.
Yuwen Zhang: conceptualization (equal), formal analysis (equal), funding acquisition (equal), investigation (equal), resources (equal), validation (equal), writing – original draft (equal), writing – review and editing (equal). Zhiqiu Ye: data curation (equal), methodology (equal), resources (equal), validation (equal), writing – review and editing (equal). Enfei Xiang: methodology (equal), writing – review and editing (equal). Peizhan Chen: conceptualization (equal), funding acquisition (equal), investigation (equal), methodology (equal), project administration (equal), supervision (equal), writing – review and editing (equal). Xuqian Fang: conceptualization (equal), formal analysis (equal), funding acquisition (equal), investigation (equal), methodology (equal), project administration (equal), supervision (equal), writing – original draft (equal), writing – review and editing (equal).
The authors declare no conflicts of interest.
Data S1. Data S2.
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Revolutionizing pediatric orthopedics: GPT-4, a groundbreaking innovation or just a fleeting trend? | 0b964040-8abc-4572-a871-4b1091dff990 | 10651230 | Pediatrics[mh] | GPT-4 presents a valuable tool for aiding orthopedic surgeons in the initial stages of evaluation and diagnosis. This includes areas such as symptom analysis, medical history review, physical examination guidance, disease prediction, research assistance, and improved communication. These capabilities were put to the test using a simulated case of developmental dysplasia of the hip (DDH), a condition commonly encountered in pediatric orthopedics. DDH, which affects approximately 1 in 1000 births, is associated with multiple risk factors. In our study, we created two scenarios incorporating various risk factors for DDH. GPT-4 demonstrated its effectiveness by accurately determining the likelihood of DDH and conveying the diagnosis professionally to the simulated patient’s parents (Supplementary Figure 1, Supplemental Digital Content 1, http://links.lww.com/JS9/B28 ). Despite these positive results, GPT-4 did exhibit limitations in diagnosing conditions that were asymptomatic or had elusive symptoms, such as subacute osteomyelitis. While it shows proficiency in identifying diseases presenting typical symptoms, GPT-4’s accuracy diminishes when dealing with ambiguous symptomatology (Supplementary Figure 2, Supplemental Digital Content 1, http://links.lww.com/JS9/B28 ). As such, GPT-4 should not be seen as a substitute for the expert knowledge and clinical judgment of professional pediatric orthopedic physicians. While GPT-4 holds promise in enhancing efficiency and accuracy in assessment and diagnosis, it is important to underscore that final medical decisions should remain the domain of healthcare professionals. GPT-4 is best utilized as an auxiliary tool to assist in disease assessment and to provide potential diagnostic suggestions. Given the complexity and diversity of clinical diagnoses, the optimization and improvement of GPT-4’s role in clinical diagnostics is an ongoing endeavor. GPT-4 possesses extensive potential for pediatric orthopedic examinations and treatments. It is capable of interpreting imaging and lab results, detecting abnormalities, and suggesting further tests as necessary. Additionally, GPT-4 can guide physical examinations for conditions such as DDH, considering age-related differences and providing a thorough step-by-step examination process (Supplementary Figure 3, Supplemental Digital Content 1, http://links.lww.com/JS9/B28 ). In terms of treatment plans, GPT-4 can provide suggestions that align with the most recent clinical guidelines. As an example, when treating DDH, GPT-4 accurately recommends appropriate treatments for children at different developmental stages (Supplementary Figure 4, Supplemental Digital Content 1, http://links.lww.com/JS9/B28 ). Furthermore, the model aids in preoperative planning for complex procedures by generating a comprehensive list of required equipment, potential complications, and the sequence of surgical procedures (Supplementary Figure 5, Supplemental Digital Content 1, http://links.lww.com/JS9/B28 ). GPT-4’s capability extends to patient education as well. It can effectively translate intricate medical conditions and treatments into language that is easily understandable for both patients and caregivers. This feature enhances the understanding of health conditions, augments medical knowledge, and facilitates more effective communication with healthcare teams. In summary, GPT-4 offers significant support in pediatric orthopedic examinations and treatments. These capabilities improve the quality and efficiency of medical services, enhancing the satisfaction levels of pediatric patients and their caregivers. We eagerly anticipate further advancements of GPT-4 in the realm of pediatric orthopedic examination and treatment. GPT-4 exhibits considerable promise in optimizing pediatric orthopedic postoperative care. It possesses the potential to streamline personalized pain management, elucidate the recovery process to both patients and caregivers, and supply detailed postoperative care instructions. Additionally, GPT-4 can serve as a helpful tool for caregivers in interpreting functional monitoring results and tracking the child’s recovery progress (Supplementary Figure 6, Supplemental Digital Content 1, http://links.lww.com/JS9/B28 ). Considering the challenges faced in China, such as lengthy treatment periods and scarce healthcare resources, GPT-4 becomes particularly beneficial. It can develop tailored follow-up plans, issue reminders for necessary examinations, propose suitable rehabilitation exercises, and promote consistent interaction with medical professionals. Furthermore, it is capable of executing remote recovery monitoring, providing an evaluation of the child’s condition grounded on feedback from parents (Supplementary Figure 7, Supplemental Digital Content 1, http://links.lww.com/JS9/B28 ). Leveraging GPT-4 for online counseling is an appreciable benefit, especially when geographical or financial hurdles present themselves. The technology can promptly address any concerns from parents, distribute educational content, and potentially detect any emerging problems early on (Supplementary Figure 8, Supplemental Digital Content 1, http://links.lww.com/JS9/B28 ). To sum up, integrating GPT-4 into pediatric orthopedic postoperative care could offer substantial clinical support, alleviate stress, ease financial pressures, and enhance the overall productivity of healthcare teams. In doing so, it would pave the way for a more all-encompassing and customized care approach. The advent of GPT-4 may lead to significant advancements in both prognostic and rehabilitative care within the realm of pediatric orthopedics. Its capacity to process and analyze vast amounts of medical and clinical data fosters an in-depth understanding of diseases and related prognosis factors, resulting in more accurate prognostic evaluations (Supplementary Figure 9, Supplemental Digital Content 1, http://links.lww.com/JS9/B28 ). By methodically analyzing an array of factors – including the type and location of the fracture, a patient’s age, sex, nutritional status, and any underlying conditions – GPT-4 can contribute to the development of highly accurate prognostic prediction models (Supplementary Figure 10, Supplemental Digital Content 1, http://links.lww.com/JS9/B28 ). This is of paramount importance in cases such as growth plate fractures, which could potentially influence future skeletal development, and instances where nutritional status may impact the pace of healing and overall recovery time. The ability of GPT-4 to pinpoint negative prognostic indicators enables swift medical interventions, thereby mitigating complications in pediatric orthopedic conditions. When it comes to rehabilitation, GPT-4’s capacity to understand individual patient conditions equips it to craft personalized rehabilitation plans. As an illustration, in patients with DDH undergoing postoperative rehabilitation (Supplementary Figure 11, Supplemental Digital Content 1, http://links.lww.com/JS9/B28 ), GPT-4 can assess patients at various stages of recovery and provide timely guidance on transitioning to subsequent phases. This feature is critical given that a substantial portion of pediatric orthopedic rehabilitation takes place at home. The integration of advanced AI models, such as GPT-4, calls for a meticulously considered strategy, given the multifaceted implications it presents. Preserving the vital role of human judgment in healthcare is imperative. Designed to enhance and not supplant decision-making, AI models like GPT-4 should complement healthcare practices. Paramount to their application is the protection of patient autonomy and privacy, warranting informed consent before their deployment. A significant aspect of this endeavor involves offering patients the option of refusing AI-assisted care. Given the intricacy of AI models, a high level of transparency is required in their design, utilization, and governance. This transparency could be achieved via legislation obliging AI creators to disclose the algorithmic processes and data sources behind their models. Doing so not only enhances understanding but also fosters trust between healthcare practitioners and patients. The establishment of well-defined legal frameworks is essential to ascribe liability in instances of AI-induced errors. The responsibilities of all stakeholders involved – from AI developers and healthcare providers to end-users – must be clearly and precisely specified for the effective application of AI in healthcare. Instituting exhaustive accountability structures could mitigate potential inaccuracies and safeguard patients’ rights and interests. Equity in accessing AI is a vital ethical issue. AI-driven healthcare solutions should not be a privilege reserved solely for affluent regions or institutions. Collaboration between policymakers and healthcare organizations is essential to ensure that underprivileged communities also enjoy the benefits of AI advancements. This may necessitate subsidizing costs or investing in the required infrastructure. In summary, while the incorporation of AI models like GPT-4 in pediatric orthopedics entails challenges, it equally offers substantial potential advantages. If these ethical issues are effectively addressed, AI can significantly boost and advance the level of patient care. The secret resides in adhering to scientifically rigorous, unbiased, and patient-centered methodologies. Transforming these challenges into opportunities holds the potential to redefine healthcare outcomes drastically. GPT-4, a cutting-edge AI technology, has substantial transformative potential in the field of pediatric orthopedics, encompassing aspects such as diagnosis, treatment, ongoing care, and prognosis. Nevertheless, several ethical and legal challenges, including data privacy, the distribution of decision-making responsibilities, and the fair allocation of technology, require attention. Therefore, developing strategies and guidelines for the ethical application of AI is critical. If these challenges can be successfully navigated, the implementation of AI could propel not only pediatric orthopedics but also the broader medical field into a promising digital era. This study did not include any individual-level data and thus did not require any ethical approval. This study did not include any individual-level data and thus did not require any ethical approval. This work was supported by the ‘Planting Plan’ Project for Clinical Research of Shengjing Hospital and the National Nature Science Foundation of China (Grant number: 81772296). S.L., F.C., and L.L.: conceived and designed the study; Y.C. and W.Z.: conceptualized the study and methodology; S.L.: wrote the manuscript; L.D.: conceptualized the study and methodology. All the authors have read and approved the final manuscript. The authors declare that there are no conflicts of interest. Not applicable. Not applicable. Not applicable. Not commissioned, externally peer-reviewed. |
Assessing the efficacy of 3D-printed ear protectors on mask adherence at an academic ophthalmology center | 33b7ddc9-235e-4889-9862-d0d6b0df875c | 9023952 | Ophthalmology[mh] | Materials 3D-printed face mask ear protectors were printed in-house, modeled after the available design 3DPX-013574 from the NIH 3D Print Exchange [Fig. and ]. The ear protectors were 15.5-cm long, 2.5-cm wide, and 3-mm thick. Data extraction 3D-printed face mask ear protectors were distributed to members of the Ophthalmology department in September 2020–January 2021 (during enforced universal masking policy and before the availability of vaccines). Before usage, individuals were asked to complete a brief validated questionnaire asking a series of questions on daily outings, underlying conditions, occupational exposure, and frequency and characteristics of mask usage. Masks were distributed to physicians, administrative staff, technicians, and patients via the University City clinic in Philadelphia. Upon receiving the EP, users were shown a live tutorial by a resident physician on how to loop the ear loops over the hooks on the EP and how to take them off. Users then demonstrated understanding by placing their EP themselves and ensure proper fit. Participants were instructed to use mask extenders all the time (even outside of the workplace) and could be utilized with both surgical masks and cloth masks. Two weeks after the distribution of ear protectors, participants were asked to complete a follow-up survey modeled after previously validated mask comfort and compliance surveys, including questions specific to the frequency of mask usage and the subjective perception of comfort, fit, and overall effect of mask extenders. The study was IRB exempt as a quality improvement project to improve mask compliance in the department. Data analysis Descriptive analyses were performed to characterize survey responses. Fishers Exact paired t test was used to perform statistical comparisons across groups to determine whether the ear protector had a significant effect on the frequency or characteristics of mask usage.
3D-printed face mask ear protectors were printed in-house, modeled after the available design 3DPX-013574 from the NIH 3D Print Exchange [Fig. and ]. The ear protectors were 15.5-cm long, 2.5-cm wide, and 3-mm thick.
3D-printed face mask ear protectors were distributed to members of the Ophthalmology department in September 2020–January 2021 (during enforced universal masking policy and before the availability of vaccines). Before usage, individuals were asked to complete a brief validated questionnaire asking a series of questions on daily outings, underlying conditions, occupational exposure, and frequency and characteristics of mask usage. Masks were distributed to physicians, administrative staff, technicians, and patients via the University City clinic in Philadelphia. Upon receiving the EP, users were shown a live tutorial by a resident physician on how to loop the ear loops over the hooks on the EP and how to take them off. Users then demonstrated understanding by placing their EP themselves and ensure proper fit. Participants were instructed to use mask extenders all the time (even outside of the workplace) and could be utilized with both surgical masks and cloth masks. Two weeks after the distribution of ear protectors, participants were asked to complete a follow-up survey modeled after previously validated mask comfort and compliance surveys, including questions specific to the frequency of mask usage and the subjective perception of comfort, fit, and overall effect of mask extenders. The study was IRB exempt as a quality improvement project to improve mask compliance in the department.
Descriptive analyses were performed to characterize survey responses. Fishers Exact paired t test was used to perform statistical comparisons across groups to determine whether the ear protector had a significant effect on the frequency or characteristics of mask usage.
In total, 48 participants completed pre- and post-surveys after wearing the face mask extender for 2 weeks. The average age of the population was 40.4 years old (SD = 12.2) with 31.3% female, 25% Caucasian, 22.9% African-American, and 43.8% Asian participants. Nine (16.8%) participants endorsed underlying conditions, including 14.6% with diabetes and 4.2% with asthma . When asked for reasons for not wearing masks, 75% of individuals listed discomfort as the main contributor, with 37.5% listing lack of fit . With respect to pre-mask extender mask usage, 72.9% of participants endorsed being “very likely” to wear masks while exercising or walking outside, with only 16.7% saying they were “not so likely” or “not likely at all” to wear masks. Furthermore, 95.8% of participants endorsed being “very likely” to wear masks while grocery shopping . Mask usage while visiting friends was more varied, with 52.1% of participants “very likely” to wear a mask, 31.5% “somewhat likely,” 4.2% “not so likely” and 12.5% “not likely at all.” Furthermore, 85.4% of participants endorsed masked usage while working at an office or workplace, with only 14.6% being “somewhat likely” or “not so likely” to wear masks . Although changes in mask usage were not statistically significant, post mask extender responses demonstrated increases in mask usage across all activities. After mask extender use, 83.3% of participants were very likely to wear a mask during exercise or walks, 100% of participants were very likely to wear a mask during grocery shopping, and 91.7% were very likely to wear a mask while working at an office or workspace . After mask extender usage, there was a decrease in the percentage of participants who had previously said they were “not likely at all” to wear a mask while visiting friends; however, there was no increase in participants “very likely” to wear masks while visiting friends . Frequency of mask removal during a variety of activities was also explored. Before the intervention, a majority of individuals (79.2%) endorsed removing their masks 0–5 times per hour (times/h), with 12.5% endorsing not wearing a mask at all or removing it >20 times/h while exercising or walking outside. The vast majority of participants (97.9%) endorsed removing masks only 0–5 times/h during grocery shopping . A smaller majority compared to the other activities (81.3%) reported removing masks 0–5 times/h while working at an office or workplace. In contrast, only 62.5% reported removing masks 0–5 times/h while visiting friends, with 16.7% reporting mask removal of greater than 20 times/h or not wearing a mask at all . Although the effects were not statistically significant, the mask extender decreased the frequency of mask removal in all categories with almost no participants reporting mask removal of >15 times/h after using the mask extender. The effects of the mask extender while visiting friends showed a 100% reduction in participants reporting removing 15–20 times/h and 75% reduction for more than 20 times/h or complete mask noncompliance [ and ]. The effect of the mask extender was greatest for low mask utilizers. In this low mask utilization cohort, there were no individuals reporting mask removal frequency of >15 times/h during any activity, and the greatest effects of increased usage occurred in activities including visiting friends or exercising or walking outside . Lastly, the subjective experience of the mask extender was queried among study participants: 91.9% of participants reported improved comfort, 91.9% reported improved fit, and 81.6% reported increased mask usage with the use of the mask extender . Of note, none of the participants reported mask strap breakage while engaging the ear protector with the mask strap. Additionally, every participant noted that the discomfort was related to the pressure on the ears and resultant headaches. Each participant used an elastic face mask that had loops around their ears. Surgical face masks that had ties were reserved only for surgical cases and their discomfort was not evaluated. N95 masks were reserved for the ICU and were not assessed.
Medical face masks have been demonstrated to prevent transmission of COVID-19 in community and hospital settings. However, while 85% of Americans claim to wear a mask, a much lower percentage wear masks “regularly” or all the time when outside or in areas of potential transmission. This study aimed to investigate the perception of mask compliance, reasons for mask noncompliance, and the effect of a simple, cost-effective, 3D-printed mask attachment (EP) to decrease mask discomfort in the goals of increasing mask compliance in healthcare workers and patients in ophthalmology. Although the use of 3D-printed mask extenders have been reported in the literature, to our knowledge, this is the first study that both implements 3D mask extenders and assesses the effect of extenders on mask usage and comfort in follow-up on a population with a relatively high risk of exposure. The participants represented a diverse demographic, though they were also relatively healthy, with only 19.7% endorsing underlying chronic diseases, making them more susceptible to COVID-19 complications and thus more mask compliant. The majority of individuals reported discomfort and lack of fit to be the main reasons for choosing not to wear a mask, further supporting the need for simple measures such as the mask extender to ameliorate discomfort and fit to improve compliance. This discomfort was likely related to the ear loops on the ears given the resolution as the EP offset the pressure and most reported resolution of discomfort. Overall, participants in this study tended to be high mask utilizers, influencing the pre-intervention responses to conform to a skewed relatively high compliance of mask usage during activities. However, while the majority of participants stated they were “very likely” to wear masks while exercising, grocery shopping, visiting friends, and working, the numerical frequency of mask removal was more revealing. This is exemplified by the fact that while 85.4% of participants reported being likely to wear masks while visiting friends, 37.5% reported the frequency of mask removal to be >6 times/h, with 16.7% reporting wearing no mask at all. This suggests there was much room for improvement in mask compliance in situations of high transmission even among the healthcare worker population with presumably higher health literacy and compliance. Although changes in mask usage after the introduction of mask extenders were not statistically significant, post-intervention responses demonstrated increases in the likelihood of mask usage across all activities with the greatest effects during exercise or walking outside. Mask extender usage also resulted in 100% of participants reporting being very likely to wear masks while grocery shopping and 91.7% (7.3% increase) while working at an office or workplace. Similarly, the mask extender decreased the frequency of mask removal in all categories, with almost no participants reporting mask removal of >15 times/h after using the mask extender. The effect of the mask extender was greatest for low mask utilizers, resulting in no individuals reporting mask removal frequency of >15 times/h during any activity and the greatest effects of increased usage while visiting friends (100% decrease in the frequency of mask removal >15 times/h) or exercising or walking outside in this relatively lower mask utilizer cohort. Overall, post-intervention, there were no noncompliant participants. Subjective experience of the mask extender was also promising. The vast majority reported improved comfort, improved fit, and increased mask usage, demonstrating that this simple extender may have real effects on improving mask compliance. It is also important to further discuss the importance of facilitating appropriate mask usage and comfort in ophthalmology specifically. Among specialties, ophthalmology was one of the top three specialties with the highest proportion of confirmed COVID-19 disease burden, likely explained by the often close exams. For example, during the slit-lamp exam, the distance between the ophthalmologist’s and patient’s face is often less than 1 foot. Beyond the proximity of the patient, certain ophthalmic diseases, including conjunctivitis, are known to be complications of the coronavirus and may serve as additional pathogenic routes for transmission. Therefore, proper mask usage and comfort are important to prevent transmission in an ophthalmic exam that requires a very close working distance and contact with infectious secretions as in conjunctivitis. It is also important to note that the implications of 3D printing for ophthalmologists are far broader than just the creation of these ear protectors. Alongside these ear protectors, 3D printing has multiple other uses within ophthalmology. For example, 3D printing has been used to create mounts for lenses that connect to imaging devices such as smartphones to allow for mobile fundus photography. Furthermore, it has been useful in surgical planning of orbital cases and is often used in fracture repairs. While these results have implications for improvement of both compliance and the benefit of simple cost-effective mask attachments, this study is limited by several factors. First, this study represents a small subset of primarily healthcare workers with relatively high mask utilization prior to the introduction of the mask extenders. Therefore, this study may not be able to truly appreciate the effect of mask extenders on compliance in the general population, as suggested by the greater effects on low mask utilizers . In addition, it may be difficult to engage the mask in the ear protector and remove it, which may prove challenging for the average layperson and older age individual to use. Similarly, subjective measures of mask compliance derived from survey studies are inherently limited due to self-reporting bias and the inability to assess whether individuals are wearing masks correctly and/or whether the mask extender significantly improves the probability of “correct” mask usage. Third, this study is limited in its ability to draw prescriptive conclusions by the lack of a control group. Given the status of the COVID-19 crisis at the time, the authors wanted to supply the mask extender to as many individuals as possible and therefore did not have a control group without access to the mask extenders. In addition, subjects enrolled in the study may have been more likely to be compliant (or report compliance) as they knew they would be measured over the 2 week period. Therefore, it is difficult to quantitatively compare differences in compliance with extender usage. Overall, this study demonstrates that there is room for improvement in mask compliance, driven by the need for better fit and comfort, even in a high compliance ophthalmology department at an academic institution. While mask extenders have been employed in certain healthcare worker populations, access to and use of them has been limited. Even among ophthalmologists, where the clinical exam necessitates close proximity to patients, almost no subjects had used an EPs before. Beyond the proximity of the patient, certain ophthalmic diseases, including conjunctivitis, are known to be complications of the coronavirus and may serve as additional pathogenic routes for transmission. Along with proper face mask usage, comprehensive protection with eye shields has been shown to have a relative decrease in infection risk by 10.6%. In addition, although not assessed in this study, the pediatric population may benefit greatly from these as their cranial circumference is much less than an adult and most of the widely available face masks are designed for adults. Further studies may assess its utility in children.
This study demonstrates that these simple cost effective extenders may improve fit, comfort, and overall mask compliance among healthcare workers, with potential implications for the population at large. We hope that this study will drive broad public health efforts to further investigate the effect and availability of these mask attachments to potentially improve mask compliance in the population through better fit and comfort. Financial support and sponsorship CARES grant to support the production of 3D-printed ear protectors. Conflicts of interest There are no conflicts of interest.
CARES grant to support the production of 3D-printed ear protectors.
There are no conflicts of interest.
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Review of Applications of Cyclodextrins as Taste-Masking Excipients for Pharmaceutical Purposes | ea3b549b-2102-44df-b36d-277d3fd5c197 | 10574773 | Pharmacology[mh] | The application of cyclodextrins (CDs) as pharmaceutical excipients is highly appreciated and well established. A lot of review papers focusing on various beneficial properties of CDs have been published, such as their roles as drug delivery systems , solubilizers and absorption promoters , agents that improve drug stability , or even active pharmaceutical ingredients (APIs) . However, so far, no reviews have focused on the taste-masking properties of these cyclic oligosaccharides. Therefore, the aim of this work is to provide insight into the studies in this area. Particular attention has been given to the methods of evaluation of the taste-masking properties, and the reviewed cases have been grouped according to the pharmacological groups of the studied APIs. We hope that this article will provide an easy-to-follow overview of the application of CDs as excipients that can be effectively used to mask the unpleasant tastes of APIs. Prior to addressing flavor-masking strategies, it is important to understand how people transmit taste . Human taste receptors can detect a wide range of substances, but they can only discriminate between five basic tastes: sour, umami, salty, sweet, and bitter . There is a distinct channel for each fundamental sense of taste that detects the transfer of flavor signals . G-protein-coupled receptors are triggered by sweetness, umami, and bitterness . Both sour and salty tastes simultaneously communicate taste signals along ionic channels; salty tastes have the potential to activate epithelial sodium channels, but this taste transmission has not received much attention yet . One flavor might influence another taste’s perception if they share the same sort of channel, which may be used in taste masking. For instance, sweets work better to cover up bitterness than acidic substances do . Many APIs that are commonly used in the pharmaceutical industry have a sour or bitter taste . This is the reason why taking these kinds of drugs can be challenging for some patients, especially children, leading to a decrease in adherence . Therefore, the application of taste-masking agents, defined as substances that improve the drug’s taste and/or smell, is in many cases necessary. Taste-masking strategies may be broadly divided into two groups based on how tastes are transmitted. The first kind includes the use of flavoring chemicals and bitterness inhibitors to impede taste transmission channels. Flavorings can aid in mitigating the unpleasant taste of medication by competing with the medication to excite taste receptor cells (TRCs) . The second technique involves employing several substances, such as ion-exchange resins, solid dispersions, polymer coatings, prodrugs, microcapsules, liposomes, and nanoemulsions, to prevent the release of the unpleasant-tasting medication into the oral cavity. One of the techniques in this second category is the use of cyclodextrins to disguise tastes. Regardless of the mechanism of action, the requirements for taste-masking agents are strictly defined. They can only be used to mask the bitter taste of substances, and they cannot interact with other compounds or exhibit pharmacological properties . While they are usually solid organics, taste-masking agents can also be used in liquid form: for instance, a pharmacopeial simple syrup is an aqueous solution containing 64% sucrose, commonly applied as an excipient. The high concentration of sugar masks the unpleasant taste of APIs. Additionally, the increased density of the solution keeps the bitter substance dispersed, limiting its contact with taste buds. Furthermore, the simple syrup does not interact with the ingredients of the drug, and it is easy to prepare . Other substances that have similar properties to simple syrup and are commonly used as excipients are mannitol, sorbitol, glycerol, aspartame, and saccharin sodium salt. Among the substances that improve the taste, there are also those that have flavoring properties that also eliminate the smell of the drug. These mainly include essential oils (rose, dill, anise, lemon, mint) or flavored waters. What is more, concentrated fruit juices (raspberry, cherry, or tinctures from oil raw materials) are also being used for such purposes . Other methods of masking the taste or smell of an API include using an insoluble form of the drug, emulsification, the addition of small amounts of anesthetics to block the taste nerve endings, such as sodium phenolate or menthol, and the addition of effervescent substances that anesthetize the taste buds during the release of carbon dioxide. As stated above, one of the methods that can be used to mask the taste of an API is based on the formation of its complexes. The molecular explanation behind this application is multiform. Firstly, complexes may not exhibit an affinity toward taste receptors, in contrast to the noncomplexed API, due to their apparent structural differences, including the size and shape of the molecule. In addition, the delayed release of the drug into a solution delays the bitterness perception. Further, the slowed release could result in lower quantities of the API present in the mouth that are below the bitterness threshold of the particular API. Cyclodextrins are the perfect choice for such purposes due to their ability to form host–guest inclusion complexes with a wide variety of molecules. The industrial use of CDs has experienced a sharp rise in interest since the 1970s . This expansion has been accompanied by a clear confirmation of CDs’ nontoxicity and a significant drop in their price. In 1957, when French mistakenly labeled CDs as “toxic” , they were claimed to be hazardous. Thankfully, Szejtli proposed the absence of toxicity, a hypothesis that was carefully researched and, in the end, broadly accepted . Due to their unique features, CDs are largely employed in medicinal compositions . By forming host–guest complexes, they increase the solubility of poorly soluble drugs and protect molecules from environmental influences, including light, humidity, and heat . The beneficial characteristics of CDs in terms of increasing the solubility of guest molecules (i.e., APIs) can be explained at the molecular level. CD molecules have a “donut” ring shape that may trap small, usually non-polar substances. Due to the presence of hydroxyl groups, the exterior parts of CD molecules are polar. The host–guest complex created when an API enters the cyclodextrin molecular hole is polar and is consequently more soluble than the isolated guest molecule. Large-ring cyclodextrins (LR-CDs), which range in size from nine to more than several hundred units, are also being researched and employed, although the three CDs that are most frequently used are those that contain six, seven, and eight glucose subunits . In addition to native (non-substituted) CDs, the food industry, medicines, cosmetics, biomedicine, and textiles have all found significant usage for these derivatives . The outcome of a flavor evaluation experiment serves as a reference point for evaluating taste-masking technology and modifying manufacturing settings. It is therefore crucial to go into depth about the frequently utilized taste evaluation tools and procedures. The most common methods used to evaluate the taste-masking abilities of cyclodextrins are (1) the employment of volunteers who recognize the flavors of the substances and (2) an electronic tongue. The first method includes healthy volunteers, usually both male and female, in the age range of 18–63 (usually n = 3–30, ), who rate the bitterness of the substances on a scale, e.g., of 0–6.25, using the following scale: 0 ¼ tasteless, 1 ¼ very slightly bitter, 2 ¼ slightly bitter, 3 ¼ moderately bitter, 4 ¼ moderately to strongly bitter, 5 ¼ strongly bitter, 6 ¼ very strongly bitter. Other scales, e.g., 1–5, are also encountered . Most frequently, the volunteers are asked to randomly take one tablet and keep it on their tongues for a few seconds. Tablets may include the analyzed substances or their complexes with cyclodextrins. Before the experiment, the volunteers are usually asked to drink a cup of water. They may be asked to score the formulations’ first taste, aftertaste, mouthfeel, flavor, and general acceptability during the taste test. The second method to evaluate the taste-masking abilities of cyclodextrin involves the use of a device called an electronic tongue . An analytical tool called an “Electronic Tongue” (or “e-tongue”) consists of a number of non-specific, imperfectly selective chemical sensors that are only partially specific to various components in a solution, as well as a suitable system for pattern recognition and/or multivariate calibration for data processing. The stability of sensor behavior and improved cross-sensitivity, which is interpreted as a consistent response of a sensor to as many species as feasible, are of the utmost significance. The e-tongue can recognize the quantitative and qualitative compositions of multicomponent solutions of various natures if correctly designed and calibrated. The e-tongue is a prospective analytical instrument to evaluate the masked bitterness of pure medicinal substances by non-medicinal components . The application setup required to perform an analysis using an electronic tongue consists of preparing the solution at a constant temperature (25 °C). The next step is loading the solution (10–100 mL) into the e-tongue test beakers. The seven-sensor assembly and reference electrode are then submerged into each test beaker for an acquisition period (i.e., 120 s). To avoid cross-contamination or the carryover of residues from earlier samples, two rinse beakers, each containing new non-deionized distilled water, are then sequentially filled for 10 s. This series is repeated a couple of times (usually 4–9 times) in rotation. The e-tongue software measures and records the potential difference that is produced between each individual sensor and the reference electrode. In the reviewed works, we have found only one in which these two methods of taste evaluation were used at the same time . The authors asserted that simultaneous analysis by these two methods is beneficial for the study; while the human taste trial validates the acceptance of the chosen potential formulations, the electronic taste-detecting system (e-tongue) data may be utilized to give direction on the selection of flavor-masked formulations. Finally, it should be noted that there are many other methods for evaluating the taste-masking effect, such as facial expression and organoid-based methods, in addition to electronic tongue and human volunteer tasting. However, so far, they have not been used in studies including CDs as excipients. The APIs that were chosen to create complexes with cyclodextrins in order to remove their bitter taste were from several pharmacological groups. Most of them were from R06, antihistamines for systemic use (13). The most popular ones were meloxicam (4) from M01, cetirizine dihydrochloride (4) from R06, and levocetirizine dihydrochloride (4) from R06. Below is a list of APIs used to prepare complexes with CDs, grouped by their ATC (Anatomical Therapeutic Chemical) classification ( https://www.whocc.no/atc_ddd_index/ (accessed on 26 July 2023)). The numbers in the brackets indicate the number of studies devoted to the particular API. A02: ranitidine hydrochloride (1), famotidine (3); A03: propantheline bromide (1), oxyphenonium bromide (1); A07: prednisolone (1), loperamide hydrochloride (1); A16: 4-phenylbutyrate (1); C01: lidocaine hydrochloride (1), Indomethacin (1); C03: furosemide (1); C05: diltiazem hydrochloride (1); C09: captopril (1); D04: promethazine hydrochloride (2); D08: triclosan (1); G04: vardenafil (1); J01: cefixime trihydrate (2), lomefloxacin hydrochloride (1), cefuroxime axetil (3); J05: oseltamivir phosphate (1); M01: meloxicam (4), lornoxicam (1), ibuprofen (1), aclofenac (1); N02: rizatriptan benzoate (1), sumatriptan succinate (1); N03: lamotrigine (1), gabapentin (1); N05: aripiprazole (1), hydroxyzine (1); N06: fluoxetine (1), donepezil (1), paroxetine hydrochloride (1), atomoxetine hydrochloride (1); P01: primaquine phosphate (1), artemether (1); R05: dextromethorphan hydrobromide (1); R06: cetirizine hydrochloride (3), cetirizine dihydrochloride (4), levocetirizine dihydrochloride (4), diphenhydramine epinastine (1), DL-chlorpheniramine (1); Others: bromelain hydrolysate (1), chitosan (1), allicin (1), arundic acid (1), bitterness suppressants of berberine hydrochloride (1). Apart from pure APIs, sometimes mixtures or plant extracts are also used for this purpose, for example, goat’s milk, soybean meal, bitter gourd extract (BGE), and beany off-flavors in plant-based meat analogs. The complete list of reviewed works, together with additional information, can be found in . While the list presented in and reveal the complete index of APIs that have been complexed in order to mask their unpleasant tastes, in this section, the most interesting examples will be described in more detail. To increase the clarity, the complexed APIs have been grouped according to their ATC classifications. 7.1. Alimentary Tract and Metabolism Drugs (A) Famotidine : In , the authors investigated the potential of a ternary system (comprising famotidine, β-CD or its derivatives, and a hydrophilic polymer) as an approach for enhancing the aqueous solubility and masking the bitter taste of famotidine. The presence of modified β-CDs, notably sulfobutyl ether β-CD (SBE-β-CD), increased the solubility of famotidine in water, and the combination of SBE-β-CD and polyvinyl pyrrolidone (Povidone) K30 increased this solubility even more. By using kneading and freeze-drying techniques, solid binary (drug-SBE-β-CD) and ternary (drug-SBE-β-CD-Povidone K30) systems were created. These solid complexes have substantially faster rates of dissolution than the API by itself. To test the ternary complexation’s capacity for taste-masking capabilities, a taste perception investigation was conducted first by using a taste-sensing device and then by using volunteers. The findings showed that SBE-β-CD and Povidone K30 work well together to increase famotidine’s solubility and rate of dissolution as well as to mask its harsh taste. This beneficial effect was achieved as a combination of two factors. First, the solubility of famotidine in the studied formulation was higher than that of pure famotidine due to the presence of CD. Also, the delay in the release caused by the presence of the polymer prevented the contact of the API with the taste buds, which was the reason behind the successful taste masking. Also, in , the authors evaluated the potential of ternary complexation (comprising the drug, cyclodextrin, and a polymer) as an approach for taste masking. To assess the potential of ternary complexation (a method for masking taste that combines a medication, cyclodextrin, and polymer), famotidine, which has a bitter taste, was chosen as a model API. By creating a ternary complex with the hydrophilic polymer hydroxy propyl methyl cellulose (HPMC) as the third component, the improvement in the taste-masking ability of cyclodextrin toward famotidine was examined. Both the binary (drug–cyclodextrin and drug–polymer) and ternary (drug–cyclodextrin–polymer) systems underwent a phase solubility investigation at 25 °C. The ternary complex was created utilizing the solution method, and it was then further examined using microscopic, PXRD, DSC, and FT-IR techniques. In order to ascertain how ternary complexation affects drug release (D.E 15 min = 90%), in vitro dissolution research was carried out. The improved complexation of famotidine in the ternary system as compared to the binary system was thought to be the cause of efficient taste masking, and this was validated by characterization experiments. The study concluded by showing that ternary complexation may be used as a different strategy for efficient flavor masking. Loperamide hydrochloride : In , the authors compared two maltodextrins (MDs, dextrose equivalent: MD1-17 and MD2-13) to well-known water-soluble CDs in terms of their solubilizing and taste-masking abilities. Dextromethorphan hydrobromide and loperamide hydrochloride, two APIs with low solubility, were utilized to compare the effects of CDs and MDs. Dextromethorphan hydrobromide and loperamide hydrochloride became more soluble when 5 mM CD or MD was added to an oversaturated API solution. These APIs are considered to have a bitter flavor. Through the use of electronic taste-sensing devices, or “e-tongues”, the taste-masking effects of CDs and MDs on dextromethorphan hydrobromide, loperamide hydrochloride, and the extremely soluble cetirizine hydrochloride were assessed. Principal component analysis was used to analyze the data from the bitterness sensor and provide a relative API complex rating. The analysis showed greater bitterness masking with MD1 for loperamide hydrochloride and dextromethorphan hydrobromide but better bitterness masking with CD for cetirizine hydrochloride. These findings suggest that MDs have superior solubility effects to CDs for all examined APIs and that MDs may have stronger taste-masking properties than CDs for specific APIs. They came to the conclusion that using MDs offers a promising new strategy for creating medication formulations. Similarly to the other studies, an apparent correlation between the enhanced solubility and taste-masking properties was visible. The possible explanation is that the dissolution of the complex is more favorable than the dissolution of the crystalline API, which increases the solubility of the drug. However, it is also possible that the complex formation delays the initial burst effect and hence masks the bitter taste. 4-Phenylbutyrate : In , the characteristics of the complexation of 4-phenylobutarate (PB) with native CDs, specifically α-, β-, and γ-CDs, in solution, as well as their ability to mask PB’s disagreeable flavor, were assessed. It was demonstrated that mixing PB with the CDs can make the PB less bitter. Notably, the creation of a soluble inclusion complex with PB by α-CD significantly reduced the bitter taste of PB to 30% of the initial response at a 1:1 molar ratio in vitro. Thus, the study offers a solid foundation for the formulation of PB as a flavor-masked CD complex that can be given orally or nasogastrically to urea cycle disorder (UCD) patients of all ages. Therefore, the shortcomings of current PB formulations are addressed by this taste-masking strategy. 7.2. Cardiovascular System Drugs (C) Diltiazem hydrochloride : Diltiazem hydrochloride is a drug with a bitter taste. However, showed that by combining the β-CD and freeze-drying methods, it may be successfully taste-masked. Oro-dispersible tablets (ODTs) were made using the taste-masked complex. Doshion P544 (4%), crospovidone (8%), and sodium starch glycolate (4%), used as a superdisintegrant in tablet formulation, exhibited quicker disintegration and drug release. The developed mixture produced notable improvements in taste and bioavailability. Spironolactone : In , the authors showed that the addition of HP-β-CD results in an increase in the solubility of spironolactone; however, this did not significantly improve the taste of this API. This is most likely because of the weak host–guest interactions, as was confirmed by NMR analysis and molecular docking calculations. These results imply that the inclusion complex formation between the API and CD is a necessary but not sufficient requirement for taste-masking effectiveness when utilizing CDs to cover up the disagreeable taste of a particular API. Although the investigated HP-CD-containing solution did not significantly improve the unpleasant taste of spironolactone, the authors concluded that the physicochemical and computational findings of this study may support the design and preparation of other cyclodextrin-containing oral pediatric formulations, including solid dosage forms (such as spray-dried powders and oro-dispersible mini-tablets). 7.3. Dermatological Drugs (D) Triclosan : The goal of this work was to create triclosan (TC)-containing fast-dissolving films for local distribution to the oral cavity. For the purpose of optimizing the composition of quickly dissolving films, several film-forming agents, film modifiers, and polyhydric alcohols were tested. The authors looked into how Poloxamer 407 and HP-β-CD could increase the solubility of TC. The ingredients hydroxypropyl methylcellulose (HPMC), xanthan gum, and xylitol were combined to create fast-dissolving films. The solubility of TC was significantly improved by the use of Poloxamer 407 and HP-β-CD. Fast-dissolving films made with the TC-HP-β-CD complex and TC-Poloxamer 407 were tested for their in vitro microbiological properties and dissolution profiles. In comparison to films containing the TC-HP-β-CD complex, those containing TC-Poloxamer 407 had improved in vitro dissolution profiles and in vitro antibacterial activity. Human volunteers were used to assess how adding eugenol affected the in vivo performance of films containing TC-Poloxamer 407. Films that contained eugenol enhanced the TC-Poloxamer 407 films’ acceptability in terms of flavor masking and tongue refreshing without affecting the in vivo dissolution time. In this study, the authors used two methods of taste masking: the formation of complexes with CD, namely, HP-β-CD, as well as the addition of eugenol. The authors did not attribute the effect to the particular component of the drug formulation. 7.4. Genito-Urinary System and Sex Hormone Drugs (G) Vardenafil : One study found that vardenafil’s (VDR) inclusion complex with ß-CD at a 1:2 molar ratio reduced its bitter taste and increased its solubility in water. The newly developed tablet has a masked taste, an appropriate degree of hardness, and a rapid period of disintegration for the medication’s rapid release. The newly prepared VDR complex grants a higher bioavailability than commercially available tablets, according to the pharmacokinetic analysis results. According to in vivo tests, the oral absorption of VRD from ODTs was likewise clearly higher than that from the currently available tablets. Additionally, the t max for the commercially available pills was lowered from 2 h to 1 h, demonstrating the rate at which VRD started working and eventually enhancing patient efficacy. 7.5. Anti-Infectives for Systemic Use Drugs (J) Cefixime trihydrate : In , cefixime trihydrate’s (CFX) solubility and dissolution were improved as a result of its complexation with β-CD. The complex chosen for the film’s (ODF) formation was created using the freeze-drying technique since it had the maximum drug concentration and an excellent dissolution profile. The films were homogeneous in weight and thickness, smooth, flexible, elegant, and edible and had a sufficient drug content and a short disintegration time. When compared to films without inclusion complexes, the improved formulation of an inclusion complex (C4) releases the API more quickly, as it also contains tween 80, which accelerates drug release. Because of this, the ODF containing CFX with improved solubility and palatability will be a more sophisticated method of oral drug delivery, helping to increase patient compliance, especially in dysphagic patients. Cefuroxime axetil : In this study , cefuroxime axetil (CFA) binary and ternary complexes were made using the kneading process. The researchers came to the conclusion that PVP K-30 can function as a ternary component to enhance the medicinal properties of CFA with β-CD. Ternary complexes outperformed binary complexes in terms of the dissolution profile, releasing >85% of the drug within 30 min. This is because basic PVP K-30 was added, which dramatically increased the Ks and CE phase solubility parameters while also interacting with CFA and β-CD via electrostatic interactions and salt production. An in vitro taste-masking study revealed that the ternary complex was effective in masking the drug’s disagreeable taste. As a result, it can be said that ternary systems of CFA with β-CD and PVP K-30 are a workable way to enhance the medicinal properties of cefuroxime axetil. 7.6. Musculo-Skeletal System Drugs (M) Aceclofenac : This study’s goal was to improve the taste-masking and solubilizing abilities of β-CD by making aceclofenac acid-soluble taste-masked granules (ASTMGA) using citric acid and mannitol. Citric acid, mannitol, and β-CD combined in ASTMGA enhanced -cyclodextrin’s ability to mask bitter tastes and increased aceclofenac’s solubility both by facilitating the drug’s entrapment within the molecule and by boosting the inclusion complex’s water solubility. The best formulations of FDT including ASTMGA were quickly developed using a general factorial design in order to have quick disintegration, a palatable flavor, and improved drug dispersion in both simulated salivary fluid and simulated stomach fluid. Ibuprofen : In , ibuprofen (IB)-β-CD inclusion complex was prepared by spray drying technique with a 1:1 molar ratio. ODTs were created using the direct compression technique and different superdisintegrant ratios of Ac-Di-Sol ® and Kollidon ® CL. DSC and FT infrared spectroscopy were used to describe the inclusion complex. ODTs’ physicochemical characteristics and rate of dissolution were assessed. The Dissolution Efficiency (DE 60 ) and Dissolved Drug Concentration at 60 Minutes (Q 60 ) were also determined. IB-β-CD was successful in hiding the acrid taste of IB. The IB-β-CD (1:1) inclusion complex produced using the spray-drying process improved the water solubility and concealed the disagreeable taste compared to the IB flavor by itself. 7.7. Nervous System Drugs (N) Rizotriptan benzoate : In , in order to hide the bitter taste of rizatriptan benzoate, HP-β-CD was used in the preparation of tablets. By mixing HP-β-CD with rizatriptan, a taste-masked complex was created, which was then tested for drug content and bitterness. Crospovidone was used as a superdisintegrant in the direct compression method used to create the tablets. The weight variation, mechanical strength, wetting duration, water absorption ratio, and in vitro release characteristics of the tablets were all assessed. When compared to the common form of API, the complex demonstrated good taste masking. Data on hardness and friability showed that the tablets had good mechanical strength. The tablets quickly dispersed in 35 to 71 s, with rizatriptan benzoate releasing at a quicker rate. The three-month stability test revealed no appreciable changes in the tablet quality. As a result, the current investigations demonstrated HP-β-CD’s potential for taste masking and for enhancing rizatriptan benzoate’s dissolution profile following complexation. Paroxetine hydrochloride : With the aid of HP-β-CD and the freeze-drying procedure, the bitter API paroxetine hydrochloride may be successfully taste-masked and used to create oro-dispersible films, as was described in . Faster disintegration and drug release were seen in films made with HPMC E-5 (59%), PG (36%), and PVP K-30 (10%) as superdisintegrants. The developed mixture produced notable improvements in taste and bioavailability. Finally, it can be said that taste-masked PXT oro-dispersible films can be successfully created, with PVP K-30 acting as a superdisintegrant and HP-β-CD serving as a taste-masking agent. This makes the films suitable for anxious and depressed patients, as well as bedridden and dysphasic patients. Gabapentin : Due to its short biological half-life of 5–7 h and limited bioavailability (60%), gabapentin was chosen to produce an oral controlled-release dry solution in . Since gabapentin has a harsh taste, an effort was made to cover it up. The goal was to create and assess a controlled-release dry suspension for reconstitution in order to improve bioavailability and reduce the medication’s acridity. Nanosponges made of β-CD were created using the previously described melt process. The nanosponge–drug complexes were analyzed for taste and saturation solubility as well as by FTIR, DSC, and PXRD physicochemical methods. Using a suspension-layering approach, the complexes were coated on Espheres before being covered in ethyl cellulose and Eudragit RS-100. Drug nanocavities were partially trapped by the complexes. In comparison to the pure API, the complexes had significantly decreased solubility (by 25%) in several media. For 12 h, the nanosponge complex’s microspheres displayed the desired regulated release profile. When the reconstituted suspension was stored for 7 days at 45 °C/75% RH, minimal API leakage was seen. The regulated release of the API and improved taste masking were both supplied by the coated polymers and nanosponges, which successfully masked the taste of gabapentin. According to in vivo research, the controlled-release solution has a 24.09% higher bioavailability than the pure medication. As an appropriate controlled-release drug delivery system for gabapentin, the dry powder suspension filled with microspheres of nanosponges complexes can be suggested. Donepezil hydrochloride : The purpose of this study was to create an ODF for donepezil (DP) that is tasty to aid in swallowing and to look into how cyclodextrin affects taste masking based on dynamic processes and in vivo drug absorption. To cover up the bitter taste, DP was complexed with HP-β-CD, and the complexes were then integrated into the ODF using the solvent-casting process. An e-tongue was used to assess the effectiveness of taste masking, and an in vivo investigation was used to examine the pharmacokinetic behavior of DP/HP-β-CD ODF. The optimized film was bioequivalent to DH and, according to the results, was more palatable than the DH film. Phase solubility analysis, FT-IR, DSC, PXRD, and molecular modeling methods were used to determine the molecular properties of the complex. The creation of DP/HP--CD, which was caused by a modest interaction between DP and HP--CD, was attributed to taste masking. The stomach’s acidic environment, which made it easier for DP to be absorbed, reduced the stability of DP/HP-CD. The findings, according to the authors, improved the knowledge of the use of cyclodextrin complexation and offered recommendations for the design of ODFs, particularly for medications with unpleasant tastes. 7.8. Antiparasitic Products, Insecticides, and Repellents (P) Artemether : In order to disguise the bitterness, improve the drug release, and create a stable, palatable formulation of artemether (ARM) specifically for pediatrics, the authors of looked into the inclusion complexation of ARM with β-CD. To investigate the inclusion complexation, a physical combination and kneaded system were created. The gustatory sensation test was used to measure the bitterness score. Additionally, at pH values of 1.2 and 6.8, the physical mixture and kneaded system showed improved drug release. Based on the bitterness score, the 1:20 ratio physical mixture was chosen in order to provide a tasty reconstitutable dry suspension of ARM. The physical mixture prepared as a reconstitutable dry suspension demonstrated perfect bitter taste masking, high flowability, and simple redispersibility. The reconstitutable dry suspension made with pure ARM was evaluated for taste by human volunteers and scored as tasteless with a score of 0. This proved that ARM could be reconstituted as a stable and tasty dry suspension for flexible pediatric dosing utilizing CD inclusion complexation. 7.9. Respiratory System (R) Cetirizine dihydrochloride : Cetirizine dihydrochloride complexation with α-, β-, and γ-CDs was studied using nuclear magnetic resonance (NMR), ultraviolet (UV), and isothermal titration calorimetry (ITC) methods in . Overall, the taste of bitterness for the cetirizine-CD solutions was consistent across all examiners. Cetirizine-β-CD solutions had the greatest taste-masking effect, followed by cetirizine-α-CD solutions. When compared to pure cetirizine, the taste-masking impact of cetirizine-γ-CD formulations was the worst. This is most likely caused by the association constant’s low value. The ratio of the CD that was utilized also had an impact on the taste masking; solutions with a molar ratio of 1:5 cetirizine-CD displayed better taste than those with a ratio of 1:2. This is most likely caused by the extra CD, which makes cetirizine primarily complexed. The cetirizine-β-CD solutions, which had a molar ratio of 1:5 cetirizine-CD, had the best taste-masking effects. Of the 13 tasters, 5 and 7 said there was no bitterness or only a mild bitterness, respectively. β-CD’s extraordinary taste-masking ability may be attributed to its sweet flavor and strong connection with the cetirizine molecule, as indicated by its higher association constant than the other two native CDs, α- and γ-CD. The cetirizine-γ-CD solution, which had a 1:5 molar ratio of cetirizine-CD and the weakest taste-masking characteristics, was described as being extremely bitter by 11 out of 13 panelists. Despite the fact that all three native CDs exhibit acceptable complexation with cetirizine, only β-CD is suitable for the formulation of oral pharmaceutical dosage forms . 7.10. Others Chitosan : In , several gram-scale macromolecular adducts were made by joining chitosan and β- and γ-CDs together through succinyl or maleyl bridges. Their ability to conceal bitter natural extracts (artichoke leaves, aloe, and gentian) was evaluated in a panel test using serial caffeine doses as a reference scale. The highest efficacy was demonstrated by the β-CD-chitosan adduct, and the bitterness mitigation was statistically significant. Allicin : By creating an inclusion with α-CD utilizing a straightforward and environmentally safe manufacturing method, allicin’s disagreeable taste and odor were successfully hidden in . This method of preparation was much easier than the conventional techniques for the preparation of CD inclusion complexes, and it only took 10 min to obtain the desired product. Allicin-α-CD’s stability, which was 33 times better than that of their physical mixture, also experienced significant improvements. The results of molecular dynamics simulations showed that the allicin thiocarbonyl group was partially contained by α-CD, indicating a potential molecular distribution and interaction of allicin-α-CD. It has made a significant improvement in customer compliance and given allicin’s application a fresh, practical approach. Arundic acid : Arundic acid, an oily medication, has a low solubility in water and a strong bitter/irritating taste. To create arundic acid medicinal formulations, these physicochemical qualities must be improved. In , the authors showed how arundic acid interacts with native cyclodextrins (CDs) and hydroxypropyl beta cyclodextrin (HP-β-CD), as well as their ability to powderize, dissolve, and conceal tastes. The most effective CD for arundic acid solubilization was HP-β-CD. Studies using UV and 1 H NMR spectroscopy proved that arundic acid and CDs in solution produced inclusion complexes at a molar ratio of 1:1. The oily form of arundic acid was converted to a solid form through complexation with CDs. According to research on gustatory perception, HP-β-CyD and γ-CD, among other CDs, had the strongest taste-masking effects in solutions and powders, respectively. The response of the electric potential brought on by the adsorption of arundic acid to the taste sensor was greatly diminished by HP-β-CD. These findings imply that multifunctional excipients for creating liquids and powders containing arundic acid may be made from hydrophilic CDs. Bitter gourd extract (BGE) : Although bitter gourd extract (BGE) has several antioxidants and anti-diabetic ingredients that support human health, its bitter flavor makes it difficult to incorporate into cuisine. The authors of examined how carboxymethyl cellulose and β-CD affected the bitterness and other characteristics of BGE. A trained sensory panel assessed the level of bitterness, and the physicochemical characteristics, such as viscosity, total saponin, polyphenol content, antioxidant capacity, and -amylase inhibitory activity, were also identified. The bitterness of BGE with 0.75% w/v β-CD was discovered to have been lowered by more than 90%. Additionally, the creation of a complex between β-CD and components of BGE was confirmed by FTIR, 1 H NMR, and thermogravimetric analysis. The findings of this study also revealed that taste-masking agents did not inhibit the biological activity of BGE. Famotidine : In , the authors investigated the potential of a ternary system (comprising famotidine, β-CD or its derivatives, and a hydrophilic polymer) as an approach for enhancing the aqueous solubility and masking the bitter taste of famotidine. The presence of modified β-CDs, notably sulfobutyl ether β-CD (SBE-β-CD), increased the solubility of famotidine in water, and the combination of SBE-β-CD and polyvinyl pyrrolidone (Povidone) K30 increased this solubility even more. By using kneading and freeze-drying techniques, solid binary (drug-SBE-β-CD) and ternary (drug-SBE-β-CD-Povidone K30) systems were created. These solid complexes have substantially faster rates of dissolution than the API by itself. To test the ternary complexation’s capacity for taste-masking capabilities, a taste perception investigation was conducted first by using a taste-sensing device and then by using volunteers. The findings showed that SBE-β-CD and Povidone K30 work well together to increase famotidine’s solubility and rate of dissolution as well as to mask its harsh taste. This beneficial effect was achieved as a combination of two factors. First, the solubility of famotidine in the studied formulation was higher than that of pure famotidine due to the presence of CD. Also, the delay in the release caused by the presence of the polymer prevented the contact of the API with the taste buds, which was the reason behind the successful taste masking. Also, in , the authors evaluated the potential of ternary complexation (comprising the drug, cyclodextrin, and a polymer) as an approach for taste masking. To assess the potential of ternary complexation (a method for masking taste that combines a medication, cyclodextrin, and polymer), famotidine, which has a bitter taste, was chosen as a model API. By creating a ternary complex with the hydrophilic polymer hydroxy propyl methyl cellulose (HPMC) as the third component, the improvement in the taste-masking ability of cyclodextrin toward famotidine was examined. Both the binary (drug–cyclodextrin and drug–polymer) and ternary (drug–cyclodextrin–polymer) systems underwent a phase solubility investigation at 25 °C. The ternary complex was created utilizing the solution method, and it was then further examined using microscopic, PXRD, DSC, and FT-IR techniques. In order to ascertain how ternary complexation affects drug release (D.E 15 min = 90%), in vitro dissolution research was carried out. The improved complexation of famotidine in the ternary system as compared to the binary system was thought to be the cause of efficient taste masking, and this was validated by characterization experiments. The study concluded by showing that ternary complexation may be used as a different strategy for efficient flavor masking. Loperamide hydrochloride : In , the authors compared two maltodextrins (MDs, dextrose equivalent: MD1-17 and MD2-13) to well-known water-soluble CDs in terms of their solubilizing and taste-masking abilities. Dextromethorphan hydrobromide and loperamide hydrochloride, two APIs with low solubility, were utilized to compare the effects of CDs and MDs. Dextromethorphan hydrobromide and loperamide hydrochloride became more soluble when 5 mM CD or MD was added to an oversaturated API solution. These APIs are considered to have a bitter flavor. Through the use of electronic taste-sensing devices, or “e-tongues”, the taste-masking effects of CDs and MDs on dextromethorphan hydrobromide, loperamide hydrochloride, and the extremely soluble cetirizine hydrochloride were assessed. Principal component analysis was used to analyze the data from the bitterness sensor and provide a relative API complex rating. The analysis showed greater bitterness masking with MD1 for loperamide hydrochloride and dextromethorphan hydrobromide but better bitterness masking with CD for cetirizine hydrochloride. These findings suggest that MDs have superior solubility effects to CDs for all examined APIs and that MDs may have stronger taste-masking properties than CDs for specific APIs. They came to the conclusion that using MDs offers a promising new strategy for creating medication formulations. Similarly to the other studies, an apparent correlation between the enhanced solubility and taste-masking properties was visible. The possible explanation is that the dissolution of the complex is more favorable than the dissolution of the crystalline API, which increases the solubility of the drug. However, it is also possible that the complex formation delays the initial burst effect and hence masks the bitter taste. 4-Phenylbutyrate : In , the characteristics of the complexation of 4-phenylobutarate (PB) with native CDs, specifically α-, β-, and γ-CDs, in solution, as well as their ability to mask PB’s disagreeable flavor, were assessed. It was demonstrated that mixing PB with the CDs can make the PB less bitter. Notably, the creation of a soluble inclusion complex with PB by α-CD significantly reduced the bitter taste of PB to 30% of the initial response at a 1:1 molar ratio in vitro. Thus, the study offers a solid foundation for the formulation of PB as a flavor-masked CD complex that can be given orally or nasogastrically to urea cycle disorder (UCD) patients of all ages. Therefore, the shortcomings of current PB formulations are addressed by this taste-masking strategy. Diltiazem hydrochloride : Diltiazem hydrochloride is a drug with a bitter taste. However, showed that by combining the β-CD and freeze-drying methods, it may be successfully taste-masked. Oro-dispersible tablets (ODTs) were made using the taste-masked complex. Doshion P544 (4%), crospovidone (8%), and sodium starch glycolate (4%), used as a superdisintegrant in tablet formulation, exhibited quicker disintegration and drug release. The developed mixture produced notable improvements in taste and bioavailability. Spironolactone : In , the authors showed that the addition of HP-β-CD results in an increase in the solubility of spironolactone; however, this did not significantly improve the taste of this API. This is most likely because of the weak host–guest interactions, as was confirmed by NMR analysis and molecular docking calculations. These results imply that the inclusion complex formation between the API and CD is a necessary but not sufficient requirement for taste-masking effectiveness when utilizing CDs to cover up the disagreeable taste of a particular API. Although the investigated HP-CD-containing solution did not significantly improve the unpleasant taste of spironolactone, the authors concluded that the physicochemical and computational findings of this study may support the design and preparation of other cyclodextrin-containing oral pediatric formulations, including solid dosage forms (such as spray-dried powders and oro-dispersible mini-tablets). Triclosan : The goal of this work was to create triclosan (TC)-containing fast-dissolving films for local distribution to the oral cavity. For the purpose of optimizing the composition of quickly dissolving films, several film-forming agents, film modifiers, and polyhydric alcohols were tested. The authors looked into how Poloxamer 407 and HP-β-CD could increase the solubility of TC. The ingredients hydroxypropyl methylcellulose (HPMC), xanthan gum, and xylitol were combined to create fast-dissolving films. The solubility of TC was significantly improved by the use of Poloxamer 407 and HP-β-CD. Fast-dissolving films made with the TC-HP-β-CD complex and TC-Poloxamer 407 were tested for their in vitro microbiological properties and dissolution profiles. In comparison to films containing the TC-HP-β-CD complex, those containing TC-Poloxamer 407 had improved in vitro dissolution profiles and in vitro antibacterial activity. Human volunteers were used to assess how adding eugenol affected the in vivo performance of films containing TC-Poloxamer 407. Films that contained eugenol enhanced the TC-Poloxamer 407 films’ acceptability in terms of flavor masking and tongue refreshing without affecting the in vivo dissolution time. In this study, the authors used two methods of taste masking: the formation of complexes with CD, namely, HP-β-CD, as well as the addition of eugenol. The authors did not attribute the effect to the particular component of the drug formulation. Vardenafil : One study found that vardenafil’s (VDR) inclusion complex with ß-CD at a 1:2 molar ratio reduced its bitter taste and increased its solubility in water. The newly developed tablet has a masked taste, an appropriate degree of hardness, and a rapid period of disintegration for the medication’s rapid release. The newly prepared VDR complex grants a higher bioavailability than commercially available tablets, according to the pharmacokinetic analysis results. According to in vivo tests, the oral absorption of VRD from ODTs was likewise clearly higher than that from the currently available tablets. Additionally, the t max for the commercially available pills was lowered from 2 h to 1 h, demonstrating the rate at which VRD started working and eventually enhancing patient efficacy. Cefixime trihydrate : In , cefixime trihydrate’s (CFX) solubility and dissolution were improved as a result of its complexation with β-CD. The complex chosen for the film’s (ODF) formation was created using the freeze-drying technique since it had the maximum drug concentration and an excellent dissolution profile. The films were homogeneous in weight and thickness, smooth, flexible, elegant, and edible and had a sufficient drug content and a short disintegration time. When compared to films without inclusion complexes, the improved formulation of an inclusion complex (C4) releases the API more quickly, as it also contains tween 80, which accelerates drug release. Because of this, the ODF containing CFX with improved solubility and palatability will be a more sophisticated method of oral drug delivery, helping to increase patient compliance, especially in dysphagic patients. Cefuroxime axetil : In this study , cefuroxime axetil (CFA) binary and ternary complexes were made using the kneading process. The researchers came to the conclusion that PVP K-30 can function as a ternary component to enhance the medicinal properties of CFA with β-CD. Ternary complexes outperformed binary complexes in terms of the dissolution profile, releasing >85% of the drug within 30 min. This is because basic PVP K-30 was added, which dramatically increased the Ks and CE phase solubility parameters while also interacting with CFA and β-CD via electrostatic interactions and salt production. An in vitro taste-masking study revealed that the ternary complex was effective in masking the drug’s disagreeable taste. As a result, it can be said that ternary systems of CFA with β-CD and PVP K-30 are a workable way to enhance the medicinal properties of cefuroxime axetil. Aceclofenac : This study’s goal was to improve the taste-masking and solubilizing abilities of β-CD by making aceclofenac acid-soluble taste-masked granules (ASTMGA) using citric acid and mannitol. Citric acid, mannitol, and β-CD combined in ASTMGA enhanced -cyclodextrin’s ability to mask bitter tastes and increased aceclofenac’s solubility both by facilitating the drug’s entrapment within the molecule and by boosting the inclusion complex’s water solubility. The best formulations of FDT including ASTMGA were quickly developed using a general factorial design in order to have quick disintegration, a palatable flavor, and improved drug dispersion in both simulated salivary fluid and simulated stomach fluid. Ibuprofen : In , ibuprofen (IB)-β-CD inclusion complex was prepared by spray drying technique with a 1:1 molar ratio. ODTs were created using the direct compression technique and different superdisintegrant ratios of Ac-Di-Sol ® and Kollidon ® CL. DSC and FT infrared spectroscopy were used to describe the inclusion complex. ODTs’ physicochemical characteristics and rate of dissolution were assessed. The Dissolution Efficiency (DE 60 ) and Dissolved Drug Concentration at 60 Minutes (Q 60 ) were also determined. IB-β-CD was successful in hiding the acrid taste of IB. The IB-β-CD (1:1) inclusion complex produced using the spray-drying process improved the water solubility and concealed the disagreeable taste compared to the IB flavor by itself. Rizotriptan benzoate : In , in order to hide the bitter taste of rizatriptan benzoate, HP-β-CD was used in the preparation of tablets. By mixing HP-β-CD with rizatriptan, a taste-masked complex was created, which was then tested for drug content and bitterness. Crospovidone was used as a superdisintegrant in the direct compression method used to create the tablets. The weight variation, mechanical strength, wetting duration, water absorption ratio, and in vitro release characteristics of the tablets were all assessed. When compared to the common form of API, the complex demonstrated good taste masking. Data on hardness and friability showed that the tablets had good mechanical strength. The tablets quickly dispersed in 35 to 71 s, with rizatriptan benzoate releasing at a quicker rate. The three-month stability test revealed no appreciable changes in the tablet quality. As a result, the current investigations demonstrated HP-β-CD’s potential for taste masking and for enhancing rizatriptan benzoate’s dissolution profile following complexation. Paroxetine hydrochloride : With the aid of HP-β-CD and the freeze-drying procedure, the bitter API paroxetine hydrochloride may be successfully taste-masked and used to create oro-dispersible films, as was described in . Faster disintegration and drug release were seen in films made with HPMC E-5 (59%), PG (36%), and PVP K-30 (10%) as superdisintegrants. The developed mixture produced notable improvements in taste and bioavailability. Finally, it can be said that taste-masked PXT oro-dispersible films can be successfully created, with PVP K-30 acting as a superdisintegrant and HP-β-CD serving as a taste-masking agent. This makes the films suitable for anxious and depressed patients, as well as bedridden and dysphasic patients. Gabapentin : Due to its short biological half-life of 5–7 h and limited bioavailability (60%), gabapentin was chosen to produce an oral controlled-release dry solution in . Since gabapentin has a harsh taste, an effort was made to cover it up. The goal was to create and assess a controlled-release dry suspension for reconstitution in order to improve bioavailability and reduce the medication’s acridity. Nanosponges made of β-CD were created using the previously described melt process. The nanosponge–drug complexes were analyzed for taste and saturation solubility as well as by FTIR, DSC, and PXRD physicochemical methods. Using a suspension-layering approach, the complexes were coated on Espheres before being covered in ethyl cellulose and Eudragit RS-100. Drug nanocavities were partially trapped by the complexes. In comparison to the pure API, the complexes had significantly decreased solubility (by 25%) in several media. For 12 h, the nanosponge complex’s microspheres displayed the desired regulated release profile. When the reconstituted suspension was stored for 7 days at 45 °C/75% RH, minimal API leakage was seen. The regulated release of the API and improved taste masking were both supplied by the coated polymers and nanosponges, which successfully masked the taste of gabapentin. According to in vivo research, the controlled-release solution has a 24.09% higher bioavailability than the pure medication. As an appropriate controlled-release drug delivery system for gabapentin, the dry powder suspension filled with microspheres of nanosponges complexes can be suggested. Donepezil hydrochloride : The purpose of this study was to create an ODF for donepezil (DP) that is tasty to aid in swallowing and to look into how cyclodextrin affects taste masking based on dynamic processes and in vivo drug absorption. To cover up the bitter taste, DP was complexed with HP-β-CD, and the complexes were then integrated into the ODF using the solvent-casting process. An e-tongue was used to assess the effectiveness of taste masking, and an in vivo investigation was used to examine the pharmacokinetic behavior of DP/HP-β-CD ODF. The optimized film was bioequivalent to DH and, according to the results, was more palatable than the DH film. Phase solubility analysis, FT-IR, DSC, PXRD, and molecular modeling methods were used to determine the molecular properties of the complex. The creation of DP/HP--CD, which was caused by a modest interaction between DP and HP--CD, was attributed to taste masking. The stomach’s acidic environment, which made it easier for DP to be absorbed, reduced the stability of DP/HP-CD. The findings, according to the authors, improved the knowledge of the use of cyclodextrin complexation and offered recommendations for the design of ODFs, particularly for medications with unpleasant tastes. Artemether : In order to disguise the bitterness, improve the drug release, and create a stable, palatable formulation of artemether (ARM) specifically for pediatrics, the authors of looked into the inclusion complexation of ARM with β-CD. To investigate the inclusion complexation, a physical combination and kneaded system were created. The gustatory sensation test was used to measure the bitterness score. Additionally, at pH values of 1.2 and 6.8, the physical mixture and kneaded system showed improved drug release. Based on the bitterness score, the 1:20 ratio physical mixture was chosen in order to provide a tasty reconstitutable dry suspension of ARM. The physical mixture prepared as a reconstitutable dry suspension demonstrated perfect bitter taste masking, high flowability, and simple redispersibility. The reconstitutable dry suspension made with pure ARM was evaluated for taste by human volunteers and scored as tasteless with a score of 0. This proved that ARM could be reconstituted as a stable and tasty dry suspension for flexible pediatric dosing utilizing CD inclusion complexation. Cetirizine dihydrochloride : Cetirizine dihydrochloride complexation with α-, β-, and γ-CDs was studied using nuclear magnetic resonance (NMR), ultraviolet (UV), and isothermal titration calorimetry (ITC) methods in . Overall, the taste of bitterness for the cetirizine-CD solutions was consistent across all examiners. Cetirizine-β-CD solutions had the greatest taste-masking effect, followed by cetirizine-α-CD solutions. When compared to pure cetirizine, the taste-masking impact of cetirizine-γ-CD formulations was the worst. This is most likely caused by the association constant’s low value. The ratio of the CD that was utilized also had an impact on the taste masking; solutions with a molar ratio of 1:5 cetirizine-CD displayed better taste than those with a ratio of 1:2. This is most likely caused by the extra CD, which makes cetirizine primarily complexed. The cetirizine-β-CD solutions, which had a molar ratio of 1:5 cetirizine-CD, had the best taste-masking effects. Of the 13 tasters, 5 and 7 said there was no bitterness or only a mild bitterness, respectively. β-CD’s extraordinary taste-masking ability may be attributed to its sweet flavor and strong connection with the cetirizine molecule, as indicated by its higher association constant than the other two native CDs, α- and γ-CD. The cetirizine-γ-CD solution, which had a 1:5 molar ratio of cetirizine-CD and the weakest taste-masking characteristics, was described as being extremely bitter by 11 out of 13 panelists. Despite the fact that all three native CDs exhibit acceptable complexation with cetirizine, only β-CD is suitable for the formulation of oral pharmaceutical dosage forms . Chitosan : In , several gram-scale macromolecular adducts were made by joining chitosan and β- and γ-CDs together through succinyl or maleyl bridges. Their ability to conceal bitter natural extracts (artichoke leaves, aloe, and gentian) was evaluated in a panel test using serial caffeine doses as a reference scale. The highest efficacy was demonstrated by the β-CD-chitosan adduct, and the bitterness mitigation was statistically significant. Allicin : By creating an inclusion with α-CD utilizing a straightforward and environmentally safe manufacturing method, allicin’s disagreeable taste and odor were successfully hidden in . This method of preparation was much easier than the conventional techniques for the preparation of CD inclusion complexes, and it only took 10 min to obtain the desired product. Allicin-α-CD’s stability, which was 33 times better than that of their physical mixture, also experienced significant improvements. The results of molecular dynamics simulations showed that the allicin thiocarbonyl group was partially contained by α-CD, indicating a potential molecular distribution and interaction of allicin-α-CD. It has made a significant improvement in customer compliance and given allicin’s application a fresh, practical approach. Arundic acid : Arundic acid, an oily medication, has a low solubility in water and a strong bitter/irritating taste. To create arundic acid medicinal formulations, these physicochemical qualities must be improved. In , the authors showed how arundic acid interacts with native cyclodextrins (CDs) and hydroxypropyl beta cyclodextrin (HP-β-CD), as well as their ability to powderize, dissolve, and conceal tastes. The most effective CD for arundic acid solubilization was HP-β-CD. Studies using UV and 1 H NMR spectroscopy proved that arundic acid and CDs in solution produced inclusion complexes at a molar ratio of 1:1. The oily form of arundic acid was converted to a solid form through complexation with CDs. According to research on gustatory perception, HP-β-CyD and γ-CD, among other CDs, had the strongest taste-masking effects in solutions and powders, respectively. The response of the electric potential brought on by the adsorption of arundic acid to the taste sensor was greatly diminished by HP-β-CD. These findings imply that multifunctional excipients for creating liquids and powders containing arundic acid may be made from hydrophilic CDs. Bitter gourd extract (BGE) : Although bitter gourd extract (BGE) has several antioxidants and anti-diabetic ingredients that support human health, its bitter flavor makes it difficult to incorporate into cuisine. The authors of examined how carboxymethyl cellulose and β-CD affected the bitterness and other characteristics of BGE. A trained sensory panel assessed the level of bitterness, and the physicochemical characteristics, such as viscosity, total saponin, polyphenol content, antioxidant capacity, and -amylase inhibitory activity, were also identified. The bitterness of BGE with 0.75% w/v β-CD was discovered to have been lowered by more than 90%. Additionally, the creation of a complex between β-CD and components of BGE was confirmed by FTIR, 1 H NMR, and thermogravimetric analysis. The findings of this study also revealed that taste-masking agents did not inhibit the biological activity of BGE. Among the reviewed papers, we have mostly found those in which the application of cyclodextrins as taste-masking excipients was a great success, but also some in which this method was not advantageous. From the successful ones, we can mention the work by Marzouk et al. , who prepared complexes of fluoxetine (FLX) with β-cyclodextrin. A group of six volunteers scored the taste of noncomplexed FLX after 10 s as 3 (bitter) and 4 (very bitter). Additionally, they evaluated the taste of the FLX-β-CD complex as 1 (tasteless), indicating a great taste improvement. Another successful example of such an application is described in the work by Li et al. , who created a complex of atomoxetine hydrochloride with HP-β-CD. While testing the electronic response of HP- β-CD, the authors noticed that only the sensor outputs of AN0 and C00 significantly increased in comparison to those of atomoxetine HCl, which were unchanged. Furthermore, the sensor response was rather slow. As previously stated, each of the three bitterness sensors (AN0, BT0, C00) can successfully identify the bitterness of ionic liquid substances. These results may be explained by the fact that the SA402B taste-detecting system’s chosen sensors are ineffective for neutral substances like HP-β-CD. The taste-sensing system, on the other hand, might be utilized to assess the flavor-masking effectiveness of the formulation with HP-β-CD, given the absence of a reaction to HP-β-CD. Another example of the successful taste-masking properties of cyclodextrins is mentioned in the work by Musuc et al. , who prepared a complex of captopril (CAP) and β-cyclodextrin in a 1:2 molar ratio. A taste evaluation was performed by six healthy volunteers who were asked to rank the following taste characteristics from 1 to 5: 0—tasteless; 1—pleasant; 2—slightly sweet; 3—slightly bitter; 4—moderately bitter; and 5—intensely bitter. Three of them judged CAP-β-CD tablets for oral dispersion as grade 1 (pleasant), two of them judged them as grade 2 (slightly sweet), and one judged them as grade 3 (slightly bitter). All of them assessed the oro-dispersible tablets as having a slightly pleasant taste, and no roughness was reported. On the other hand, the CAP tablets had a burning, metallic flavor. The fourth example of taste-masking success is found in the work by Liu et al. , who created a complex between donepezil hydrochloride (DH) and HP-β-CD. An e-tongue assessment was conducted to evaluate the taste of samples using the taste-sensing system SA402B. It was discovered that samples with similar tastes clustered together. The DH-containing samples, physical mixes, DH-ODF, and the DH- and sucralose-containing film accumulated into a cluster, indicating that the flavor was unappealing or that the taste-masking action was ineffective. It was clear from the clustering of donepezil/HP-β-CD inclusion complexes and donepezil/HP-β-CD ODF that cyclodextrin complexation was significantly effective for taste-masking and that the DP/HP-β-CD inclusion complexes were stable in the film. Also, the work by Al-Gethmy et al. , who created a complex of vardenafil with β-CD, should be mentioned. The disintegration time and taste-masking tests were conducted in the buccal cavity of six healthy human participants in a single-blind study. On a scale of 0 to 3, the human test volunteers were asked to judge how bitter the improved recipe tasted. When the score was less than 1, the taste was tolerable, but when it was greater than 1, the tablet’s flavor was bitter and intolerable. The ratings of the six volunteers were not equal or lower until after the complexation, which shows that the formulation has a respectable taste-masking effect. However, there were also some cases in which the formulation with cyclodextrins did not work as designed; for example, we can mention the work by Lopalco et al. , who created a complex of spironolactone (SPL) and HP-β-CD. They found that although the complex was formed, the taste did not improve, which was confirmed by a group of 24 healthy volunteers. The results obtained are in line with those from brief-access taste aversion (BATA) experiments. In fact, the rats’ preference for SPL solutions with low HP-β-CD concentrations (1%, 2%, and 6%) grew, while the number of licks decreased for the SPL solution that contained 18% HP-β-CD. Overall, taking into account the BATA experiment’s results and human panel results, HP-β-CD’s taste-masking effect on SPL is not very strong. Another example of a partly failed taste evaluation is described in the work by Stojanov, Wimmer, and Larsen , who prepared complexes of cetirizine dihydrochloride with α-, β-, and γ-CDs. The authors proved the good taste-masking properties of α- and β-CDs, but they noticed that γ-CD did not give efficient results. The reason for this is probably due to the relatively low association constant between the API and this CD. The amount of CD employed also had an impact on flavor masking; for instance, solutions with a cetirizine-CD ratio of 1:5 had better taste than those with a 1:2 ratio. This is most likely caused by the extra CD, which makes cetirizine mostly complexed. The best taste-masking effects were provided by the cetirizine-β-CD solutions, which had a molar ratio of 1:5. Of the 13 tasters, 5 and 7 said there was no bitterness or only a mild bitterness, respectively. β-CD’s extraordinary taste-masking ability may be attributed to its sweet flavor and strong connection with the cetirizine molecule, as indicated by its higher association constant than the other two native CDs (α- and γ-CD). The cetirizine-γ-CD solution, which had a 1:5 molar ratio of cetirizine-CD and the weakest taste-masking characteristics, was described as being extremely bitter by 11 out of 13 panelists. Despite the fact that all three native CDs exhibit acceptable complexation with cetirizine, only β-CD is suitable for the formulation of the oral pharmaceutical dosage form. Also, the work by Funasaki et al. presents the varied taste-masking capabilities among studied CDs. The authors investigated the mechanisms of masking the bitter taste of propantheline bromide (PB) and oxyphenonium bromide (OB) by native and modified cyclodextrins: α-, β-, and γ-CDs, 2,6-O-dimethyl-β-CD, HP-β-CD, 6-glucosyl-β-CD, and 6-maltosyl-β-CD. Five volunteers were involved in the sensory test. Native and modified β-CD decreased the bitter taste remarkably better than α- and γ-CDs. Another example of the diverse taste-masking abilities of cyclodextrin is described in the work by Commey et al. . They analyzed the taste-masking abilities of inclusion complexes obtained in solution between 4-phenylbutyrate (PB) and α-, β-, and γ-CDs. The TS-5000Z taste sensor device was used to evaluate the flavor. The fact that all of the CDs, in the order of their stability constants, reduced ( p < 0.001) the response of the taste sensor to PB in the taste assessment shows that the CDs concealed the flavor of PB by creating inclusion complexes with PB. A CD-Povidone K30 ternary system was developed. This matched the taste rating of very mildly bitter given by a human taste panel. However, a strong taste-masking effect is produced when PB is complexed with α-CD at a 1:1 molar ratio, which reduces the bitter flavor of PB by around 30%. Furthermore, it is anticipated that a larger amount of CD would have a poorer taste-masking effect in order to ensure that the majority of the PB is complexed. As a result, the α-CD-PB system is more suited for administration, especially via nasogastric or gastrostomy tubes, due to its superior water solubility and substantially lower API:CD molar ratio, which suggests a less bulky dosage. It is also known that the lower oral solubility of the bad-tasting medication molecule can be used to conceal flavor by forming complexes with CD. The β- and γ-CD complexes might be helpful in this area as well because they are insoluble and tasteless when administered orally, but they can release PB farther down in the gut. The constantly increasing number of works describing the application of cyclodextrins as taste-masking agents highlights the potential of these already widely recognized cyclic oligosaccharides as valuable excipients. 9.1. Study Design and Search Strategy Two independent examiners (Ł.S. and L.A.) were chosen to select the articles. As a result, the examiners performed an extensive literature search in the databases of PubMed, Web of Science, Scopus, and the Cochrane Library. “Cyclodextrin”, “taste masking”, and “taste” were the search phrases. The search technique included a variety of terms and was purposefully wide. Articles that met the criteria were reviewed for any additional pertinent research that may have been cited and included in our analysis. Additionally, a manual search of other relevant articles on this topic was carried out. The same two independent examiners chose and categorized the papers as being included in or removed from the review based on the titles and abstracts. Duplicate articles were removed using the Rayyan for Systematic Reviews program. Following the completion of the eligibility stage, the data were taken from the selected publications. The studies were examined and discussed. Before moving on to the following phases, any disagreements during the procedure were settled by reaching an agreement. 9.2. Study Selection and Criteria Two reviewers (Ł.S. and L.A.) separately went through all of the imported papers in the Rayyan program as part of the screening procedure. The use of cyclodextrins as taste-masking excipients for pharmaceutical purposes was the inclusion criterion for this review. The language and research design (including the inclusion of in vitro and in vivo experiments) were not constrained. Review papers, case studies, letters, comments, and conference abstracts were all excluded. Any discrepancies were settled by the two reviewers’ unanimous agreement once the inclusion and exclusion processes were complete. Two independent examiners (Ł.S. and L.A.) were chosen to select the articles. As a result, the examiners performed an extensive literature search in the databases of PubMed, Web of Science, Scopus, and the Cochrane Library. “Cyclodextrin”, “taste masking”, and “taste” were the search phrases. The search technique included a variety of terms and was purposefully wide. Articles that met the criteria were reviewed for any additional pertinent research that may have been cited and included in our analysis. Additionally, a manual search of other relevant articles on this topic was carried out. The same two independent examiners chose and categorized the papers as being included in or removed from the review based on the titles and abstracts. Duplicate articles were removed using the Rayyan for Systematic Reviews program. Following the completion of the eligibility stage, the data were taken from the selected publications. The studies were examined and discussed. Before moving on to the following phases, any disagreements during the procedure were settled by reaching an agreement. Two reviewers (Ł.S. and L.A.) separately went through all of the imported papers in the Rayyan program as part of the screening procedure. The use of cyclodextrins as taste-masking excipients for pharmaceutical purposes was the inclusion criterion for this review. The language and research design (including the inclusion of in vitro and in vivo experiments) were not constrained. Review papers, case studies, letters, comments, and conference abstracts were all excluded. Any discrepancies were settled by the two reviewers’ unanimous agreement once the inclusion and exclusion processes were complete. Due to the ongoing development of pediatric medications and quick-release oral formulations (ODTs), drug manufacturers are paying an increasing amount of attention to the flavor of drugs. The majority of APIs have an unpleasant taste, which, if unmasked, significantly lowers patient compliance, particularly in children, making the development of taste-masking technology necessary. Due to the variety of existing native and substituted CDs, as well as possible guest–host molar ratios in the inclusion complexes, the application of CDs is not straightforward. Also, sometimes, ternary complexation consisting of an API, cyclodextrin, and an appropriate polymer can be utilized as an alternative approach for effective taste masking. Nevertheless, as the reviewed works suggest, this solution can be an effective, safe, and inexpensive method of taste masking for pharmaceutical purposes. |
Asia‐inclusive global development of pevonedistat: Clinical pharmacology and translational research enabling a phase 3 multiregional clinical trial | 77dc8692-7811-4a22-93f4-1f1c0d6e00ef | 8212745 | Pharmacology[mh] | Multiregional clinical trials (MRCTs) help decrease the lag in drug development and approval that often occurs in Asian countries compared with those in Europe and North America. , For example, delays due to country‐specific requirements for submission of local patient data can be mitigated if the MRCT is designed to enroll local patients. The International Conference on Harmonisation of Technical Requirement for Registration of Pharmaceuticals for Human Use (ICH) E5 guidelines provide a framework for evaluating the impact of ethnic factors on the efficacy and safety of a particular study drug dose and regimen. They also provide guidance on development strategies that assess the effect of ethnic factors while minimizing the duplication of clinical studies and expediting patient access to drugs. Global MRCTs are on the rise, notably in oncology and rare disease/orphan drug development. In 2017, the ICH issued E17 guidelines, which describe general principles for the planning and design of MRCTs with the aim of generating data that are applicable for global regulatory submissions. The key principles of the E17 guidelines are to: conduct well‐designed MRCTs to increase drug development efficiency and support regulatory decision making across regions, understand relevant intrinsic and extrinsic factor effects early in MRCT design, allocate sample size by region to verify consistency in treatment effect while allowing feasibility in recruitment and timely trial conduct, pool prespecified regions based upon similarities, use a single primary analysis supported by structured exploration of consistency, ensure high‐quality trial design and conduct, and encourage efficient communication between sponsors and regulatory authorities during MRCT design. Notably, ICH E17 principles can enable efficient design of MRCTs with regional sample size allocation that is based on scientific justification rather than traditional designs that may be driven by local (i.e., country‐level) regulatory considerations. A key aspect of both the E5 and E17 guidelines is evaluating intrinsic and extrinsic factors as sources of variability in drug response. , This manuscript describes the application of ICH E5 and E17 principles to enable an Asia‐inclusive global development strategy for an investigational anticancer agent under evaluation for the treatment of rare hematologic malignancies. Pevonedistat is the first small‐molecule inhibitor of the NEDD8‐activating enzyme (NAE). NAE facilitates the activation of Cullin‐RING E3 ubiquitin ligases (CRLs) via binding of the small ubiquitin‐like protein NEDD8 (neural precursor cell expressed, developmentally downregulated 8). , Pevonedistat forms an adduct with NEDD8, preventing conjugation of NEDD8 to CRLs and ultimately leading to CRL substrate accumulation and cell death. , Pevonedistat has been investigated as a single agent in multiple studies of hematologic and nonhematologic malignancies and in combination with azacitidine in elderly patients with untreated acute myeloid leukemia (AML). , , , , Based on the promising clinical data of pevonedistat in combination with azacitidine, a multicenter, global, randomized, controlled, open‐label, Phase 3 study (PANTHER, NCT03268954) is currently ongoing to investigate the efficacy and safety of pevonedistat in combination with azacitidine versus single‐agent azacitidine in patients with higher‐risk myelodysplastic syndrome (higher‐risk MDS), higher‐risk chronic myelomonocytic leukemia (higher‐risk CMML), or low‐blast AML (Figure ). To enable timely enrollment of East Asian patients into the PANTHER trial and to support the evaluation of pevonedistat plus azacitidine in both Western and East Asian patients with these rare diseases, we applied MRCT principles of the ICH E17 guidelines informed by ICH E5 to design an Asia‐inclusive study. The design of this MRCT was supported by assessment of drug‐related and disease‐related intrinsic and extrinsic factors, enabling successful reviews during the Clinical Trial Notification/Application (CTN/CTA) process by the Pharmaceuticals and Medical Devices Agency (PMDA, Japan), the Ministry of Food and Drug Safety (MFDS, South Korea), and the National Medical Products Administration (NMPA, China). Furthermore, a pooled East Asian region could be rationalized based on scientific considerations of commonality of intrinsic and extrinsic factors and statistical evaluations to define the number of East Asian patients required to evaluate consistency with the overall data.
Literature review on AML and MDS epidemiology, mutational landscape, efficacy, and molecular pathology We performed a targeted literature review (PubMed; January 2000–November 2019) to identify publications reporting clinical studies of azacitidine in higher‐risk MDS, AML, and CMML conducted in Western (Unites States/European Union) and East Asian regions, and epidemiological studies of incidence and characteristics of higher‐risk MDS, AML, and CMML by region. A comprehensive PubMed literature search was performed from September 2011 to March 2018 to compare the abundance of major mutated genes and cytogenetic abnormality profiles in MDS and AML between patients from Western and East Asian regions. The search terms for azacitidine efficacy were: azacitidine, myelodysplastic, efficacy, Phase 3, Phase 1/2, Korean, Japanese, and Chinese. The search terms for mutation and cytogenetic abnormalities were: myelodysplastic syndrome, acute myeloid leukemia, prognostic, molecular analysis, mutations, next generation sequencing, genetic abnormalities, cytogenetic analysis/abnormalities, karyotype, molecular pathology, and hypomethylation. Population pharmacokinetic analyses Pevonedistat plasma concentration–time data were collected from adult patients with advanced hematologic or nonhematologic malignancies (AML, MDS, multiple myeloma [MM], lymphoma, melanoma, or various solid tumors) who had participated in one of eight clinical studies (Table ) in Western and East Asian regions. , , , , , Patients received single‐agent pevonedistat via a 1‐h i.v. infusion at dose levels of 25–278 mg/m 2 , across 6 different dosing schedules in 21‐day cycles. In the combination study, patients received pevonedistat 10, 20, or 30 mg/m 2 via a 1‐h i.v. infusion in combination with azacitidine (i.v./s.c.) 75 mg/m 2 in 28‐day cycles. All procedures performed in studies involving human participants were in accordance with the ICH Good Clinical Practice guidelines and appropriate regulatory requirements. All patients provided written informed consent. Methods for bioanalysis of pevonedistat plasma concentrations have been previously described. Pevonedistat pharmacokinetic (PK) data were analyzed using nonlinear mixed‐effects modeling (NONMEM version 7.3; ICON Development Solutions) as described previously. Post hoc estimates of pevonedistat clearance in patients according to race (White, Black, and East Asian) were generated and compared. Statistical considerations At the time of calculating the sample size of the East Asian population in the PANTHER study, there were two primary end points: (1) overall response rate (ORR) by cycle 6 and (2) event‐free survival (EFS); overall survival (OS) was the key secondary end point. The protocol was subsequently amended to have one primary end point of EFS and one key secondary end point of OS. The appropriateness of the number of East Asian patients to be enrolled was assessed by calculating the probability of achieving consistent results between the overall population and the East Asian population for these end points. The consistency of the results was defined as follows: hazard ratio (pevonedistat in combination with azacitidine/single‐agent azacitidine) is smaller than one in the East Asian population for both EFS and OS, when the overall results are positive. Two treatment effect scenarios (A: median EFS of 22.2 m vs. 13 m, B: median EFS of 19.6 m vs. 13 m) for EFS that were used to determine minimum and maximum planned event size with adaptive EFS event size re‐estimation. Accordingly, these two scenarios (A and B) were used to calculate the consistency probabilities. The consistency probabilities were calculated by clinical trial simulations with 10,000 iterations per scenario. The simulation data for EFS and OS were randomly generated from an exponential distribution under the assumption that there is no difference in the efficacy of pevonedistat between the East Asian population and the overall population in the study, taking into consideration the enrollment projection and the timing of the analyses. Simulations were conducted using SAS version 9.2.
We performed a targeted literature review (PubMed; January 2000–November 2019) to identify publications reporting clinical studies of azacitidine in higher‐risk MDS, AML, and CMML conducted in Western (Unites States/European Union) and East Asian regions, and epidemiological studies of incidence and characteristics of higher‐risk MDS, AML, and CMML by region. A comprehensive PubMed literature search was performed from September 2011 to March 2018 to compare the abundance of major mutated genes and cytogenetic abnormality profiles in MDS and AML between patients from Western and East Asian regions. The search terms for azacitidine efficacy were: azacitidine, myelodysplastic, efficacy, Phase 3, Phase 1/2, Korean, Japanese, and Chinese. The search terms for mutation and cytogenetic abnormalities were: myelodysplastic syndrome, acute myeloid leukemia, prognostic, molecular analysis, mutations, next generation sequencing, genetic abnormalities, cytogenetic analysis/abnormalities, karyotype, molecular pathology, and hypomethylation.
Pevonedistat plasma concentration–time data were collected from adult patients with advanced hematologic or nonhematologic malignancies (AML, MDS, multiple myeloma [MM], lymphoma, melanoma, or various solid tumors) who had participated in one of eight clinical studies (Table ) in Western and East Asian regions. , , , , , Patients received single‐agent pevonedistat via a 1‐h i.v. infusion at dose levels of 25–278 mg/m 2 , across 6 different dosing schedules in 21‐day cycles. In the combination study, patients received pevonedistat 10, 20, or 30 mg/m 2 via a 1‐h i.v. infusion in combination with azacitidine (i.v./s.c.) 75 mg/m 2 in 28‐day cycles. All procedures performed in studies involving human participants were in accordance with the ICH Good Clinical Practice guidelines and appropriate regulatory requirements. All patients provided written informed consent. Methods for bioanalysis of pevonedistat plasma concentrations have been previously described. Pevonedistat pharmacokinetic (PK) data were analyzed using nonlinear mixed‐effects modeling (NONMEM version 7.3; ICON Development Solutions) as described previously. Post hoc estimates of pevonedistat clearance in patients according to race (White, Black, and East Asian) were generated and compared.
At the time of calculating the sample size of the East Asian population in the PANTHER study, there were two primary end points: (1) overall response rate (ORR) by cycle 6 and (2) event‐free survival (EFS); overall survival (OS) was the key secondary end point. The protocol was subsequently amended to have one primary end point of EFS and one key secondary end point of OS. The appropriateness of the number of East Asian patients to be enrolled was assessed by calculating the probability of achieving consistent results between the overall population and the East Asian population for these end points. The consistency of the results was defined as follows: hazard ratio (pevonedistat in combination with azacitidine/single‐agent azacitidine) is smaller than one in the East Asian population for both EFS and OS, when the overall results are positive. Two treatment effect scenarios (A: median EFS of 22.2 m vs. 13 m, B: median EFS of 19.6 m vs. 13 m) for EFS that were used to determine minimum and maximum planned event size with adaptive EFS event size re‐estimation. Accordingly, these two scenarios (A and B) were used to calculate the consistency probabilities. The consistency probabilities were calculated by clinical trial simulations with 10,000 iterations per scenario. The simulation data for EFS and OS were randomly generated from an exponential distribution under the assumption that there is no difference in the efficacy of pevonedistat between the East Asian population and the overall population in the study, taking into consideration the enrollment projection and the timing of the analyses. Simulations were conducted using SAS version 9.2.
Epidemiology and standards of care Incidences of MDS and AML by region are shown in Table . According to the National Cancer Institute in the United States, the incidence of MDS is estimated to be 4.5 per 100,000 people per year in the overall population, 0.1 per 100,000 in the subpopulation aged less than 40 years, 26.9 per 100,000 in the subpopulation aged 70–79 years, and 55.4 per 100,000 in the subpopulation aged greater than or equal to 80 years, showing increased incidences in higher age groups. In the European Union, the incidence is estimated to be 4.15 per 100,000 people per year in the overall population, and 40–50 per 100,000 in the subpopulation aged greater than or equal to 70 years. Age‐adjusted incidences of MDS in 2008 in Japan, standardized by the world standard population, were 1.6 cases per 100,000 men and 0.8 cases per 100,000 women. In South Korea, age‐standardized incidence rates of MDS in 2008 were 1.3 cases per 100,000 men and 0.8 cases per 100,000 women. In mainland China, no official large‐scale MDS incidence data are available. MDS incidence (based on World Health Organization [WHO] criteria) from 2004 to 2007 in Shanghai was 1.48 per 100,000 men and 1.54 per 100,000 women. These data were based on a survey that randomly selected 6 of 18 districts in Shanghai reflecting the incidence in an urban population in mainland China. Azacitidine 75 mg/m 2 (i.v. or s.c.) administered for 7 days in 28‐day cycles is approved in the United States, the European Union, Japan, South Korea, and China as first‐line treatment for higher‐risk MDS and CMML based on regional prescribing information. Azacitidine is also indicated for the treatment of AML with 20–30% blasts and multilineage dysplasia, according to WHO classification (United States and European Union) and AML with greater than 30% marrow blasts according to the WHO classification (European Union only). , Azacitidine is the standard treatment in these patient populations across the different countries. Treatment response and outcomes A literature search identified a limited number of clinical studies (5 in total) investigating azacitidine in patients with MDS or low bone marrow blast count AML from East Asian or Western countries. A Phase 3 multicenter, randomized, open‐label trial (NCT00071799) conducted in 15 Western countries between February 2004 and August 2006 compared the efficacy of azacitidine 75 mg/m 2 for 7 days in 28‐day cycles with that of conventional care regimens in the treatment of higher‐risk MDS. Median OS for azacitidine was 24.5 months (interquartile range 9.9 was not reached). Median time to AML transformation was 17.8 months (95% confidence interval [CI] 13.6–23.6 months). Approximately one third of patients in this study were classified as AML with low bone marrow blast count and the median OS in this subset of patients was 19.1 months. The ORR across all patients was 29%, with 17% complete remission (CR), 12% partial remission (PR), and 42% stable disease (SD). Similar efficacy was observed in another randomized study conducted in Western countries investigating azacitidine 75 mg/m 2 for 7 days every 28 days versus supportive care in patients with MDS. The ORR was 60% in patients receiving azacitidine including 7% CR, 16% PR, and 37% hematologic improvement ; median time to leukemic transformation or death was 21 months. In a Phase 1/2 study of 53 Japanese patients with MDS who received azacitidine 75 mg/m 2 s.c. or i.v. once daily for 7 consecutive days in a 28‐day cycle, hematologic improvement and hematologic response rates were 55% (28/51) and 28% (15/53; CR 15%, PR 0%, and marrow CR [mCR] 13%), respectively. In a retrospective study of patients with MDS diagnosed and treated with azacitidine in the Korea MDS registry between 2004 and 2011, responses were observed in 46% (93/203) of patients (CR 11%, PR 6%, mCR 11%, and hematologic improvement 17%); median OS was 23.2 months. A Phase 2 study conducted in Chinese patients with higher‐risk MDS reported a disease control rate of 96%, mainly driven by SD (CR 1% + PR 0% + SD 94%), and a hematologic improvement rate of 53% with s.c. azacitidine. The median OS was 22.0 months (95% CI 15.1–not reached) in Chinese patients, comparable with data from Western patients. Based on the studies identified, median OS following azacitidine treatment is generally comparable among patients with MDS from East Asian and Western regions. Mutational landscape and cytogenetic abnormalities Genetic and cytogenetic alterations that are associated with the pathology and prognosis of AML, MDS, and CMML and which may be predictive of response to selected treatment options have been previously described. , , , , , , , It is possible that factors, such as geographic location, environment, lifestyle, genetic background, and others, may influence the mutational and cytogenetic landscape in patients from distinct ethnicities and may affect their sensitivity to specific drugs. In this study, we performed a comprehensive PubMed literature search to retrospectively compare the landscape and abundance of major mutated genes and the frequency of major cytogenetic abnormalities in MDS and AML between patients from Western and East Asian regions. The analysis showed that the mutational landscape and cytogenetic abnormalities of AML and MDS are similar among US, European, Japanese, and Korean populations (Figures , , Table ). , Some differences in percentage mutation frequencies were noted in AML in DNMT3A (26–31, 16.2–23.1, and 7.5–14.9), IDH2 (14–20, 6.1–9, and 9.7), TP53 (8–9, 3.6–8, and 2.2), KRAS (6–12, 2–5.6, and 0–5.7), and ASXL1 (11, 2–2.5, and 8.6), and in MDS in SRSF2 (10–20, 5.2, and 0.9) and STAG2 (5–10, 2.9, and 0.9) between US plus European, and Japanese and Korean populations, respectively. These differences should be considered when correlating genetic and cytogenetic alteration with response and other end points of interest. However, in general, the types and frequencies of molecular alterations in AML and MDS are comparable across Western and East Asian populations, supporting the general conservation of disease‐related intrinsic factors. Pevonedistat dose, safety, and pharmacokinetics Across Western (NCT01814826; conducted in the United States) and East Asian (NCT02782468; conducted in Japan, South Korea, and Taiwan) populations, Phase 1 dose escalation studies determined the same maximum tolerated dose (MTD) of pevonedistat (20 mg/m 2 on days 1, 3, and 5 in 28‐day cycles) in combination with azacitidine (75 mg/m 2 for 7 days in 28‐day cycles). , Population PK analyses were conducted to assess clinically important covariates and compare pevonedistat exposures between East Asian and Western patients. The analysis included data from 2 studies of pevonedistat in Western (NCT02610777) and East Asian (NCT02782468) patient populations in addition to 335 patients across 10 studies in a recently completed population PK analysis. A total of 416 adult patients (receiving pevonedistat 15–278 mg/m 2 ) contributed 4689 observations. Data were adequately described by a two‐compartment PK model. Population PK analysis supported dose‐linear pevonedistat PK with body surface area (BSA) identified as a clinically important covariate on clearance, supporting BSA‐based dosing (Figure ). After individualizing pevonedistat dose based on BSA, systemic pevonedistat clearance was similar when analyzed according to country/race (Figure ). Sample size of East Asian population in the PANTHER trial Assuming that the treatment effects in Japanese/East Asian patients are similar to those in the overall global population, with a total of 30 East Asian patients, the estimated probabilities of achieving consistent efficacy outcomes are ~ 80% (measured by EFS and OS) between East Asian patients and the overall ~ 450 patients in the global, pivotal, Phase 3 PANTHER trial (Table , Figure ).
Incidences of MDS and AML by region are shown in Table . According to the National Cancer Institute in the United States, the incidence of MDS is estimated to be 4.5 per 100,000 people per year in the overall population, 0.1 per 100,000 in the subpopulation aged less than 40 years, 26.9 per 100,000 in the subpopulation aged 70–79 years, and 55.4 per 100,000 in the subpopulation aged greater than or equal to 80 years, showing increased incidences in higher age groups. In the European Union, the incidence is estimated to be 4.15 per 100,000 people per year in the overall population, and 40–50 per 100,000 in the subpopulation aged greater than or equal to 70 years. Age‐adjusted incidences of MDS in 2008 in Japan, standardized by the world standard population, were 1.6 cases per 100,000 men and 0.8 cases per 100,000 women. In South Korea, age‐standardized incidence rates of MDS in 2008 were 1.3 cases per 100,000 men and 0.8 cases per 100,000 women. In mainland China, no official large‐scale MDS incidence data are available. MDS incidence (based on World Health Organization [WHO] criteria) from 2004 to 2007 in Shanghai was 1.48 per 100,000 men and 1.54 per 100,000 women. These data were based on a survey that randomly selected 6 of 18 districts in Shanghai reflecting the incidence in an urban population in mainland China. Azacitidine 75 mg/m 2 (i.v. or s.c.) administered for 7 days in 28‐day cycles is approved in the United States, the European Union, Japan, South Korea, and China as first‐line treatment for higher‐risk MDS and CMML based on regional prescribing information. Azacitidine is also indicated for the treatment of AML with 20–30% blasts and multilineage dysplasia, according to WHO classification (United States and European Union) and AML with greater than 30% marrow blasts according to the WHO classification (European Union only). , Azacitidine is the standard treatment in these patient populations across the different countries.
A literature search identified a limited number of clinical studies (5 in total) investigating azacitidine in patients with MDS or low bone marrow blast count AML from East Asian or Western countries. A Phase 3 multicenter, randomized, open‐label trial (NCT00071799) conducted in 15 Western countries between February 2004 and August 2006 compared the efficacy of azacitidine 75 mg/m 2 for 7 days in 28‐day cycles with that of conventional care regimens in the treatment of higher‐risk MDS. Median OS for azacitidine was 24.5 months (interquartile range 9.9 was not reached). Median time to AML transformation was 17.8 months (95% confidence interval [CI] 13.6–23.6 months). Approximately one third of patients in this study were classified as AML with low bone marrow blast count and the median OS in this subset of patients was 19.1 months. The ORR across all patients was 29%, with 17% complete remission (CR), 12% partial remission (PR), and 42% stable disease (SD). Similar efficacy was observed in another randomized study conducted in Western countries investigating azacitidine 75 mg/m 2 for 7 days every 28 days versus supportive care in patients with MDS. The ORR was 60% in patients receiving azacitidine including 7% CR, 16% PR, and 37% hematologic improvement ; median time to leukemic transformation or death was 21 months. In a Phase 1/2 study of 53 Japanese patients with MDS who received azacitidine 75 mg/m 2 s.c. or i.v. once daily for 7 consecutive days in a 28‐day cycle, hematologic improvement and hematologic response rates were 55% (28/51) and 28% (15/53; CR 15%, PR 0%, and marrow CR [mCR] 13%), respectively. In a retrospective study of patients with MDS diagnosed and treated with azacitidine in the Korea MDS registry between 2004 and 2011, responses were observed in 46% (93/203) of patients (CR 11%, PR 6%, mCR 11%, and hematologic improvement 17%); median OS was 23.2 months. A Phase 2 study conducted in Chinese patients with higher‐risk MDS reported a disease control rate of 96%, mainly driven by SD (CR 1% + PR 0% + SD 94%), and a hematologic improvement rate of 53% with s.c. azacitidine. The median OS was 22.0 months (95% CI 15.1–not reached) in Chinese patients, comparable with data from Western patients. Based on the studies identified, median OS following azacitidine treatment is generally comparable among patients with MDS from East Asian and Western regions.
Genetic and cytogenetic alterations that are associated with the pathology and prognosis of AML, MDS, and CMML and which may be predictive of response to selected treatment options have been previously described. , , , , , , , It is possible that factors, such as geographic location, environment, lifestyle, genetic background, and others, may influence the mutational and cytogenetic landscape in patients from distinct ethnicities and may affect their sensitivity to specific drugs. In this study, we performed a comprehensive PubMed literature search to retrospectively compare the landscape and abundance of major mutated genes and the frequency of major cytogenetic abnormalities in MDS and AML between patients from Western and East Asian regions. The analysis showed that the mutational landscape and cytogenetic abnormalities of AML and MDS are similar among US, European, Japanese, and Korean populations (Figures , , Table ). , Some differences in percentage mutation frequencies were noted in AML in DNMT3A (26–31, 16.2–23.1, and 7.5–14.9), IDH2 (14–20, 6.1–9, and 9.7), TP53 (8–9, 3.6–8, and 2.2), KRAS (6–12, 2–5.6, and 0–5.7), and ASXL1 (11, 2–2.5, and 8.6), and in MDS in SRSF2 (10–20, 5.2, and 0.9) and STAG2 (5–10, 2.9, and 0.9) between US plus European, and Japanese and Korean populations, respectively. These differences should be considered when correlating genetic and cytogenetic alteration with response and other end points of interest. However, in general, the types and frequencies of molecular alterations in AML and MDS are comparable across Western and East Asian populations, supporting the general conservation of disease‐related intrinsic factors.
Across Western (NCT01814826; conducted in the United States) and East Asian (NCT02782468; conducted in Japan, South Korea, and Taiwan) populations, Phase 1 dose escalation studies determined the same maximum tolerated dose (MTD) of pevonedistat (20 mg/m 2 on days 1, 3, and 5 in 28‐day cycles) in combination with azacitidine (75 mg/m 2 for 7 days in 28‐day cycles). , Population PK analyses were conducted to assess clinically important covariates and compare pevonedistat exposures between East Asian and Western patients. The analysis included data from 2 studies of pevonedistat in Western (NCT02610777) and East Asian (NCT02782468) patient populations in addition to 335 patients across 10 studies in a recently completed population PK analysis. A total of 416 adult patients (receiving pevonedistat 15–278 mg/m 2 ) contributed 4689 observations. Data were adequately described by a two‐compartment PK model. Population PK analysis supported dose‐linear pevonedistat PK with body surface area (BSA) identified as a clinically important covariate on clearance, supporting BSA‐based dosing (Figure ). After individualizing pevonedistat dose based on BSA, systemic pevonedistat clearance was similar when analyzed according to country/race (Figure ).
Assuming that the treatment effects in Japanese/East Asian patients are similar to those in the overall global population, with a total of 30 East Asian patients, the estimated probabilities of achieving consistent efficacy outcomes are ~ 80% (measured by EFS and OS) between East Asian patients and the overall ~ 450 patients in the global, pivotal, Phase 3 PANTHER trial (Table , Figure ).
Opportunities to increase efficiency in global drug development are increasing with the availability of the ICH E17 guideline that provides a scientific framework for MRCT design and analysis. Herein, we have demonstrated the application of principles of clinical pharmacology and translational science in enabling the design of an Asia‐inclusive global pivotal Phase 3 trial in rare hematological malignancies for an investigational NAE inhibitor. This was accomplished through formulation of a pooled East Asian region rationalized by assessment of similarity in drug‐related and disease‐related intrinsic and extrinsic factors. The Phase 3 PANTHER trial was designed to investigate the efficacy and safety of pevonedistat plus azacitidine versus single‐agent azacitidine in patients with higher‐risk MDS/CMML and low‐blast AML. We evaluated the regional characteristics of epidemiology, molecular pathology, standard‐of‐care treatment, and associated efficacy in higher‐risk MDS, CMML, and low‐blast AML to assess similarities in disease‐related intrinsic and extrinsic factors across Japanese, Korean, Chinese, and Western patient populations. Similarity in drug‐related intrinsic/extrinsic factors was confirmed based on the safety/tolerability and PKs of pevonedistat across these populations that supported a common global dose. Accordingly, we formulated a pooled East Asian region and determined, based on statistical considerations, that an associated sample size of ~ 30 of 450 randomized patients would be needed from this region to evaluate consistency in efficacy relative to the global population. Epidemiology of the diseases of interest was inferred to be broadly comparable based on published age‐adjusted incidences of MDS across East Asian and Western regions. Consistent with current United States and European Union medical practice guidelines, azacitidine was identified as the standard of care for the treatment of MDS/AML in Japan, South Korea, and China, supporting inclusion of these countries in the common protocol evaluating pevonedistat in addition to azacitidine backbone therapy. Importantly, azacitidine dosage was conserved across these regions. Heterogeneity in efficacy evaluation of azacitidine, notably in selection of response rate end points, was apparent across the reported results of clinical trials. , , , Taken together with the heterogeneity in MDS, direct comparison of response rates to azacitidine across these clinical trials was not straightforward. Nevertheless, clinical survival outcomes were consistent across East Asian countries and the United States/Europe, supporting the inference of lack of clinically meaningful differences in azacitidine treatment outcome. AML and MDS are heterogeneous diseases. Along with age and performance status, mutational profile and cytogenetic abnormalities are key clinical predictors of survival for an individual patient. Currently, cytogenetic status is incorporated into risk categorization schemes to guide treatment in both MDS and AML. The European LeukemiaNet integrates cytogenetics and the mutational status of six genes (FLT3 internal tandem duplication, CEBP, NPM1, RUNX1, ASXL1, and TP53) to classify patients with AML into three prognostic risk groups. However, additional mutations in genes, such as ASXL1 and TET2, have more recently been shown to have prognostic value , and several novel risk‐categorization schemas have been proposed that include mutational status of these and other genes. The Revised International Prognostic Scoring System for MDS incorporates cytogenetics as a way to determine the risk of transformation to AML. Additionally, there are several recurrent somatic mutations, which are drivers of MDS pathogenesis and can be associated with clinical phenotype (e.g., RNA splicing factor mutations in ring‐sideroblasts) , or response to treatment. For example, mutations in TET2 have been shown to be associated with sensitivity to azacitidine, whereas patients with wild‐type TET2 have been shown to be resistant. , , , In MDS, patients with TP53 mutations initially respond well to hypomethylating agents, however, their duration of response is significantly shorter than in TP53 wild‐type patients. In addition, TP53 mutation status has been shown to be the most significant predictor of mortality after hematopoietic stem cell transplantation. In AML, TP53 alterations are the most important prognostic factor in complex karyotype‐AML, outweighing all other variables. In this study, we explored whether the mutational and cytogenetic landscapes in MDS and AML are similar among Western and East Asian populations and whether it would be appropriate to include East Asian patients in the PANTHER trial. AML and MDS mutational landscape and cytogenetic abnormalities were found to be similar among US, European, Japanese, and Korean populations; however, some differences were noted in the frequencies of a few mutations. Compared with Japanese and Korean patients, higher percentage mutation frequencies were found in US and European patients in DNMT3A, IDH2, TP53, KRAS, and ASXL1 in AML, and in SRSF2 and STAG2 in MDS. Given the prognostic value of these genes, their correlation with response and other end points of interest should be carefully addressed while taking into consideration different ethnicities. Nevertheless, the overall concordance in molecular pathologies across East Asian and Western patient populations suggest that disease‐related intrinsic factors are generally preserved. This is further supported by the similarities in the survival outcomes observed in patients with MDS treated with azacitidine across regions. Collectively, these cytogenetic and mutational data support the inclusion of East Asian patients in the PANTHER trial. Regional differences in disease severity, disease progression kinetics, and clinical outcomes, when present, can complicate the design of MRCTs. One such example is the setting of relapsed/refractory MM (RRMM). During global development of the oral proteasome inhibitor ixazomib, clinically and statistically significant progression‐free survival (PFS) benefit was observed in randomized evaluations of ixazomib plus lenalidomide‐dexamethasone versus placebo plus lenalidomide‐dexamethasone in both Chinese and global (primarily Western) populations. However, the absolute PFS achieved with ixazomib‐lenalidomide‐dexamethasone treatment in a China‐continuation study in patients with RRMM was markedly shorter than in a global Phase 3 study due to the later stage of diagnosis of Chinese patients, more advanced or refractory disease, and differences in prior treatments, which resulted in poorer outcomes relative to Western patients. , The global clinical development plan for ixazomib integrated China as a continuation study, allowing robust assessment of the efficacy of ixazomib across both the global and Chinese populations without confounding effects, such as the imbalance of clinical outcomes and prognostic factors in the disease of interest. Nevertheless, this example illustrates the importance of assessing disease‐related intrinsic and extrinsic factors when designing MRCTs across global patient populations. A key aspect of designing MRCTs is to identify early whether ethnicity has any impact on PKs/pharmacodynamics, which might result in regional changes to dose selection. Pevonedistat undergoes oxidative metabolism primarily by CYP3A enzymes (Takeda data on file), although its PKs are not sensitive to CYP3A inhibition or induction based on results of clinical drug‐drug interaction studies. , A mass balance study has been recently completed and subsequent metabolite profiling analyses will shed further light on the clearance mechanisms of pevonedistat in humans. Data from our population PK analysis demonstrated that systemic pevonedistat exposures were similar between East Asian and Western populations and across the major East Asian races after individualizing the pevonedistat dose based on BSA, a key covariate. We clearly demonstrated that fixed dosing resulted in a lower exposure among patients with a larger BSA. This would be expected to translate into regional differences if fixed dosing is used, as populations with smaller average BSA (e.g., East Asians) would be expected to be at risk for higher drug exposures. Our findings of similar exposures across the East Asian and Western populations following BSA‐based dosing of pevonedistat are reflected in a common MTD identified in the Phase 1/1b study of East Asian patients with AML or MDS. The safety profiles of pevonedistat, alone or administrated with azacitidine, were also comparable in East Asian and Western patients in these studies, with the most common grade greater than or equal to 3 adverse events reported in both regions, including anemia, febrile neutropenia, and pneumonia. , Our population PK analysis identified BSA as an important source of PK variability for pevonedistat and illustrates how BSA‐based dosing achieved comparable drug exposures across East Asian and Western populations. This contrasts with drugs that demonstrate regional or race‐related differences in PKs. An example of this is the investigational Aurora A kinase inhibitor alisertib, for which studies have demonstrated a lower MTD in Asian versus Western patients that could be explained by PK differences (i.e., higher dose‐normalized systemic exposures of alisertib in East Asian populations). For an investigational agent like alisertib, an exposure‐matched dosing strategy with a lower regional dose for Asian populations can be considered to preserve benefit/risk due to a clinically relevant difference in PKs between Asian and Western patient populations. A lack of ethnic sensitivity in the clinical pharmacology of pevonedistat was concluded based on the totality of safety and PK data, and the same BSA‐based dose and regimen of pevonedistat in combination with azacitidine can be used across regions in the Phase 3 PANTHER trial. Furthermore, based on the lack of regional differences in disease biology and azacitidine efficacy, as long as the pooled East Asian population is adequately represented in the global population, the totality of the data in the group can be used to substantiate each region, and outcomes can be assessed in combined patient populations. Taken together, based on the totality of data, intrinsic and extrinsic factors relevant to disease (higher‐risk MDS, CMML, and low‐blast AML) and drug (pevonedistat) were inferred to be similar across the planned countries of enrollment in the pooled East Asian region (Japan, South Korea, and China) and between East Asian and Western regions (Figure ). Based on these considerations, clinical efficacy and safety data from the PANTHER trial should therefore be applicable to inform benefit‐risk assessment across Western and East Asian populations. Importantly, pooled data across patients enrolled in East Asia in this global MRCT will enable assessment of consistency in the benefit‐risk profile of pevonedistat in these patients versus the global data based on ICH E17 principles. This analysis is intended to be an exemplar to illustrate the importance of quantitative pharmacological and translational scientific considerations in designing Asia‐inclusive MRCTs. During the course of this effort within our organization, it was evident that cross‐functional and cross‐regional partnerships were central to enabling the design of this MRCT and the synthesis of associated scientific rationale for discussion with Health Authorities worldwide. Viewed from a broader perspective, the framework described here (Figure ) is applicable to the efficient global clinical development of investigational therapies for rare cancers and orphan diseases in Asia‐inclusive MRCTs based on systematic assessments of drug‐related and disease‐related intrinsic and extrinsic factors applying ICH E17 principles.
X.Z., S.F., E.K., F.S., Y.Yu., D.V.F., and S.B. are employees of Millennium Pharmaceuticals, Inc., Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited. S.K., Y.Ya., K.H., K.N., and Y.D. are employees of Takeda Pharmaceutical Company Limited. Z.H. is an employee of Alnylam Pharmaceuticals Inc. K.V. is a former employee of Millennium Pharmaceuticals, Inc., Cambridge, MA, USA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited, and is currently an employee of EMD Serono, Inc.
All authors wrote the manuscript. X.Z., S.F., E.K., Z.H., D.V.F., and K.V. designed the research. All authors performed the research. X.Z., S.F., D.V.F., and K.V. analyzed the data.
Supplementary Material Click here for additional data file.
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Surgery camp for Colostomy reversals at a referral hospital in Lilongwe, Malawi | 564ee32a-fb65-4424-81f8-7b9c326a119e | 11770354 | Surgical Procedures, Operative[mh] | A Hartmann's procedure with end colostomy is a potentially life-saving surgical intervention performed in cases of bowel obstruction or perforation secondary to sigmoid volvulus, diverticulitis, and trauma, among other causes. The postoperative management can be challenging, particularly in a resource-limited setting. The risks of complications related to colostomy placement, such as skin irritation, ischemia, parastomal hernia, retraction, and prolapse may occur in up to half of patients . These complications contribute significantly to decreased quality of life and are exacerbated in settings where pouching supplies are unavailable . Timing of colostomy reversal is often surgeon-dependent, but common practice includes a waiting period of weeks to months for resolution of bowel inflammation and preparation for reversal. However, many colostomy patients may not be evaluated for reversal due to socioeconomic and health system barriers. Several studies in the United States and Europe have demonstrated that advanced age, colon versus ileal ostomy, co-morbid conditions, and lower socioeconomic status were significant predictors of delayed reversal , - . The literature otherwise lacks characterization of specific barriers to and strategies for increasing rates of successful reversals, particularly in sub-Saharan Africa. Kamuzu Central Hospital (KCH) is a 900-bed tertiary care hospital in Lilongwe serving the central region of Malawi with a catchment area of six million people. KCH performed 638 abdominal surgeries between June 2018 and June 2019 for a diagnosis of acute abdomen including bowel obstruction (44.2%), peritonitis (16.9%), perforated viscus (12.2%), incarcerated hernia (9.9%), volvulus (8.6%), and acute appendicitis (8.2%). For patients with colostomies, non-medical barriers to reversal may be substantial, including geographical distance from health care facility, out-of-pocket costs, lack of surgeons and operating theatre staff, and poor healthcare literacy among patients. In addition to these factors, frequent cancellations of elective procedures for emergent cases and a temporary reduction in operating capacity due to renovations in 2017 further exacerbated the burden of potentially reversible colostomies among KCH patients. In response to this burden, an external surgical team was invited to carry out a two-day intensive surgery camp focused on colostomy reversals. The purpose of this camp was to address the local burden of overdue colostomy reversals in addition to training KCH surgery registrars in the procedure. Here we report our center's experience with colostomy reversal, barriers to successful reversals, and a focused colostomy reversal camp to both reduce the burden of colostomies and increase our institution's capacity to perform successful reversals. The Unmet Need for Colostomy Reversal Between June 2018 and June 2019, 54 end colostomies had been completed at KCH for mostly emergent indications including 37 bowel obstructions (68.5%), 12 cases of peritonitis (22.2%), and four cases of sigmoid volvulus (7.4%) However, few colostomy reversals had been carried out. A recent audit in the KCH surgery department revealed that 28 colostomy reversals were done over a 1 year period from January 2019 to January 2020. End colostomy reversals are elective surgical procedures, well within the surgical capacity of Kamuzu Central Hospital. However, emergent procedures on a daily basis routinely take priority over elective cases despite the significant physical, psychological, and socioeconomic consequences of chronic ostomy maintenance , . In addition, the time, personal capital, and opportunity cost of presenting to KCH for a scheduled reversal, particularly for colostomy patients living outside Lilongwe, further emphasizes the need for interventions that prioritize colostomy reversals. Colostomy Reversal Camp The concept of a surgical camp has been deployed at numerous institutions in sub-Saharan Africa to simultaneously address the local burden of surgical disease and increase host institution capacity by training local surgeons and staff - . Camps may adopt a broad scope of practice at the host institution or instead direct efforts at a specific disease or patient population. In some settings, they are regularly used to periodically alleviate high surgical workloads - , and increases in operative caseloads and surgical complexity have been maintained in the absence of the visiting surgery team . The short-term nature of the surgical camp model limits its ability to address structural challenges to surgical capacity at the host institution , such as equipment shortages and limited operating room availability, but has been well-suited to address local training needs when educational goals are built into the camp .
Between June 2018 and June 2019, 54 end colostomies had been completed at KCH for mostly emergent indications including 37 bowel obstructions (68.5%), 12 cases of peritonitis (22.2%), and four cases of sigmoid volvulus (7.4%) However, few colostomy reversals had been carried out. A recent audit in the KCH surgery department revealed that 28 colostomy reversals were done over a 1 year period from January 2019 to January 2020. End colostomy reversals are elective surgical procedures, well within the surgical capacity of Kamuzu Central Hospital. However, emergent procedures on a daily basis routinely take priority over elective cases despite the significant physical, psychological, and socioeconomic consequences of chronic ostomy maintenance , . In addition, the time, personal capital, and opportunity cost of presenting to KCH for a scheduled reversal, particularly for colostomy patients living outside Lilongwe, further emphasizes the need for interventions that prioritize colostomy reversals.
The concept of a surgical camp has been deployed at numerous institutions in sub-Saharan Africa to simultaneously address the local burden of surgical disease and increase host institution capacity by training local surgeons and staff - . Camps may adopt a broad scope of practice at the host institution or instead direct efforts at a specific disease or patient population. In some settings, they are regularly used to periodically alleviate high surgical workloads - , and increases in operative caseloads and surgical complexity have been maintained in the absence of the visiting surgery team . The short-term nature of the surgical camp model limits its ability to address structural challenges to surgical capacity at the host institution , such as equipment shortages and limited operating room availability, but has been well-suited to address local training needs when educational goals are built into the camp .
In response to recognition of the need for prioritization of colostomy reversals, the author VM, a consultant surgeon at KCH, coordinated a partnership with author BH and Access Health Africa, a United States-based NGO with operations in Malawi, to apply the surgical camp model. Access Health Africa was responsible for providing surgeons, anesthetists, and nurses to work pro bono for the duration of the partnership. The visiting surgical team consisted of two surgeons, three anesthetists, six scrub nurses and nurse technicians, all volunteers. Four local consultants and six registrars participated in the camp. The dates of the surgical camp were designated as June 27-28, 2019, and enrollment began four weeks in advance. It was decided that two of the six operating theatres at KCH could be reserved and operationalized for two days for colostomy reversals. Only patients presenting to any of the twice weekly surgery clinics at KCH for temporary Hartmann colostomy follow up during the intervening time were eligible for enrollment in the camp. Enrollment of the colostomy reversal patients began 2 months prior to the time the camp was carried out. Patients with other types of colostomies and those with permanent colostomies were excluded from enrollment, in addition to those unfit for open abdominal surgery. Twenty-one patients presenting to surgery clinic were identified as preliminary candidates, and 16 were ultimately selected for reversal during the camp. Informed consent was given by each one of the patients in this camp. All patients received the prescribed preoperative regimen of oral normal saline (NS) in addition to NS enemas into the stoma and rectal stump for bowel preparation prior to surgery. Coordination on behalf of KCH staff was considerable, and permission was granted by the hospital director and management team prior to undertaking planning. Due to chronic bed shortages in the surgical wards, coordination with ward nursing staff was imperative in preparing to accommodate 16 additional surgical patients requiring several days of inpatient preoperative care as well as postoperative care, in addition to the usual influx of emergency cases. The operating room staff were briefed on the anticipated caseload in order to secure and reserve sufficient sterile supplies and equipment for reversals. KCH surgery registrar education was a priority for the reversal camp, and learning objectives were prepared in advance . Educational materials regarding the procedure and preoperative bowel preparation were secured and provided to registrars prior to the camp. In particular, instruction on stapled bowel anastomosis technique was arranged, as standard practice at KCH had been and is currently hand-sewn anastomosis due to resource limitations. Analysis was performed using basic descriptive statistics tools in Excel 2016 (Microsoft, Redmond, California). Mean and standard deviation (SD) were used to characterize parametric data, and non-parametric data were described by the median and interquartile range (IQR). Due to the small sample size, no statistical comparisons were made in this descriptive study.
Over the course of the reversal camp, thirteen of the 16 selected patients ultimately underwent an end colostomy reversal via exploratory laparotomy under general anesthesia. Of the three patients who did not undergo surgery, one failed to attend surgery, another was deferred due to hypertension, and a third was declared ineligible for surgery due to poor bowel prep and malnutrition. The third patient was planned for follow up in general surgery clinic for rescheduling for surgery when nutritionally fit. Patients were admitted to the inpatient surgery ward for two days preoperatively to undergo bowel preparation . Preoperative bowel preparation consisted of a diet of clear fluids three days prior to surgery, as well as doing a saline enema at both the rectal stump and stoma the night prior to surgery. One gram of ceftriaxone and 500 mg of metronidazole were administered intravenously to all patients within 60 minutes before skin incision. Twelve of 13 patients who underwent surgery were male . The median age was 41 (IQR 27 – 51) and average weight was 57.9±7.7 kg. Sigmoid volvulus was the most common indication for Hartmann's procedure, present in 62% of patients, followed by compound volvulus (23%) and colonic trauma (7.7%). One patients it was noted in theatre, had an ileostomy rather than a colostomy, which was reversed. On average patients lived with their colostomies for an additional 4.3±6.6 months after their initially scheduled reversal. Colostomy reversals are scheduled to have a colostomy reversal three months following the initial surgery. All patients except two underwent a stapled anastomosis, and the remainder had handsewn anastomoses. A KCH consultant surgeon was present and scrubbed for every surgery. The majority of patients were discharged without documented complication (85%) an average of 5.3± 2.8 days after reversal procedure. No patients required postoperative admission to a high-dependency or intensive care unit.
We report our institution's first experience with a surgery camp dedicated to alleviating the burden of overdue colostomy reversals with an emphasis on surgical registrar education. The surgical camp reversed twelve patients' colostomies and one ileostomy, specific pre- and post-operative protocols were more broadly adopted in thesurgicalwards,and KCH achieved proof-of-concept for a surgical camp model. With focus on addressing the described logistics and personnel challenges, we aim to replicate and expand on the surgical camp model at our institution.
The colostomy reversal camp required significant rganizational and operating time from the KCH consultant srgeons, and the effort was met with strong investment on their part. Specifically, consultants advocated for the addition of patients to the camp roster, prepared education materials for registrars, and negotiated with other hospital staff to accommodate the planned influx of surgery patients. Four local consultants and six registrars participated in the camp. The visiting surgical team arrived with a fully operational team consisting of two surgeons, three anesthetists, six scrub nurses and nurse technicians. The local team and the visiting team collaborated for joint efforts in forming the workforce for the camp. The visiting team supplemented operating theatre supplies with EEA staplers for colorectal anastomosis and disposable sterile drapes. The main advantage of EEA staplers was reduction of operating time and anastomosis is quicker. The stapler anastomosis skill is required in the training of surgery registrars, useable throughout their careers, in addition to handsewn anastomosis. Prior to the surgery camp, there was no standard protocol for preoperative preparation for elective bowel surgery at KCH. The author VM collaborated with the deputy head of department of surgery to establish a preoperative protocol based on recommendations from reputable surgical texts modified for the resource constraints of KCH. These orders were printed and distributed to the ward nurses prior to arrival, and all patients received the prescribed preoperative regimen of oral normal saline (NS) in addition to NS enemas into the stoma and rectal stump. Two months after the camp, KCH surgery consultants attest that the new regimen is part of routine preoperative bowel care. The camp was also the first time KCH surgery registrars had exposure to and training with stapled colon anastomoses. From a patient perspective, in-hospital complications were limited. One patient returned to theatre for a laparotomy due to suspected anastomotic leak. Intraoperatively the anastomosis appeared to be healing well, and a diagnosis of postoperative ileus was ultimately made. Surgery may have been avoided in this case with the use of reliable imaging such as CT scan or a barium enema, both of which were unavailable at our institution at the time of the camp. No patient developed a surgical site infection prior to discharge. No planned surgeries were cancelled or delayed due to shortages of supplies or available personnel. The two patients whose reversal operations were cancelled were deemed unfit for surgery at presentation, despite available resources.
Surgical camps carried out in resource-limited settings must expect to confront the challenges of providing care at its host institution, complicated by an unfamiliar surgical team and system. In interprofessional undertakings, it is challenging to foster productive team dynamics between surgeons and anesthetists from the visiting and host institution, particularly in short interval. In our experience, specifically, due to high turnover required for the anticipated case volume and local anesthetist familiarity with theatre resources, limited communication was reported between the visiting and local anesthetist. In contrast, communication between the surgeons and registrars was noted to be productive and involved intraoperative anatomy and technique reviews in addition to new skills training. Interpersonal challenges are not unusual, and a review of the literature concerning neurosurgical surgical camps in such settings found that cultural differences between the visiting and host surgical and anesthesia teams may pose challenges to bilateral exchange and educational objectives . While operating theatre time and supplies were secured in advance through the work of nursing staff in preparation for the camp, encountered additional organizational challenges were encountered. This was the first time our institution had employed the surgical camp model to address a backlog of cases and provide specific training to surgical registrars. As such all protocols, registrar learning objectives and resource allocation plans were devised largely at the personal expense of the lead author. At present, no procedure exists to request and obtain material and administrative support to conduct such an undertaking. Furthermore, staplers for end to end anastomosis are not currently sourced from within Malawi, preventing their more routine use in the absence of an accessible supply chain and appropriate budget. Before and after the operation, the rapid influx of 13 surgical patients was borne heavily by the ward nurses. The patient census in the surgery ward regularly exceeds capacity and nursing staff shortages are often the rule rather than the exception. The specific bowel preparation and post-reversal care, while beneficial to patients, appreciably strained the ancillary staff, particularly in the context of the ward's high volume. Finally, assessment of postoperative complications was possible only until the time of discharge. Patients discharged after surgery at KCH are often asked to return for a 2-week postoperative visit, but there is currently no system that records these patient encounters in the clinic. This surgery camp did not seek additional follow up with patients following discharge, and thus the rate of postoperative complications may underrepresent the true rate.
In order to replicate and expand the camp model at KCH, the aforementioned challenges must be met. First, the nursing staff shortage faced at KCH is representative of the situation at large in Malawi, due to a combination of health worker migration, poor reimbursement, and overwhelming workload , . Anticipatory hiring of locum nurses for short term assignments may allow KCH to absorb the increased workload of a future surgery camp. The past decade in Malawi has seen demands for increases in locum remuneration, and adequate funding would have to be secured from host institution or contributed by the visiting teams. Further assessment of the value of the camp in terms of quality of life gained and avoided ostomy complications may help make the case for financial investment in further surgical camps. In addition to staff shortages, ward space at KCH is severely limited particularly in the surgery wards. Diversion of surgery camp patients to wards with extra capacity may be feasible for the duration of a short camp. Negotiation with ward leaders as well as resource reallocation plans established in advance would be required to estimate the volume of patients that our institution could safely enroll and accommodate in a future surgery camp. Furthermore, establishment of efficient internal protocols for resource requisition and coordination may prevent the concentration of administrative burden on one or two consultant surgeons. Specific needs include support with case allocation, acquiring sufficient sterile gowns and drapes, ensuring maintenance of vital equipment including the autoclave, and coordination of all preoperative anesthesia evaluations, among others. Provided that additional institutional support can be organized, a future surgical camp at this institution would aim to expand both its case volume and inclusion criteria to include ileostomy patients due for reversal. For anastomosis cases a safe option is to conduct them in the tertiary hospital to manage anastomotic leak with critical care in the event of leak occurrence. This first surgical camp was not advertised outside of KCH, and the 21 patients evaluated for inclusion were identified in previously-scheduled clinic appointments. After determining a reasonable volume for a future camp, notification of the camp may be sent to surrounding district facilities and clinics. Indeed, seven of the 13 patients who underwent reversals lived outside the Lilongwe district. Additionally, because the dual mission of the surgical camp included both alleviating disease burden and registrar education, efforts are currently underway to assess the educational value of the experience. A comparison between the frequency of pre-camp reversals and postcamp reversals, extractable from currently maintained KCH surgery registries, may offer insight into the capacity-building potential of the surgery camp.
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Diagnostic policies on nephrolithiasis/nephrocalcinosis of possible genetic origin by Italian nephrologists: a survey by the Italian Society of Nephrology with an emphasis on primary hyperoxaluria | a494ab5f-187b-4e78-9a40-3a1ceccd9c0d | 10393840 | Internal Medicine[mh] | Primary hyperoxaluria (PH) is a rare autosomal recessive genetic disorder affecting the metabolism of glyoxylate, the precursor of oxalate . Three different forms of primary hyperoxaluria are currently known, each characterized by a specific enzymatic defect: type 1 (PH1), type 2 (PH2), and type 3 (PH3) . According to currently available epidemiological data, PH1 is by far the most common form (about 80% of cases), and is caused by a deficiency of the enzyme alanine:glyoxylate aminotransferase (AGT), which is localized in hepatic peroxisomes . PH2 is due to a deficiency of glyoxylate reductase/hydroxypyruvate reductase, an enzyme localized in the cytosol of hepatocytes and leukocytes . Finally, PH3 is associated with a mutation in the HOGA1 gene, which encodes 4-hydroxy-2-oxoglutarate aldolase, a mitochondrial enzyme . Primary hyperoxaluria is clinically characterized by high endogenous production and excessive urinary excretion of oxalate, resulting in the development of calcium oxalate nephrolithiasis, nephrocalcinosis, and progression to chronic renal failure and systemic oxalosis (accumulation of calcium oxalate crystals at various sites in the body, including bone tissue, cardiovascular system, and skin). The presence of nephrolithiasis or nephrocalcinosis, usually very severe, predisposes to the development of kidney failure . A survey on rare forms of nephrolithiasis and nephrocalcinosis with a focus on PH in the setting of Italian Nephrology and Dialysis Centers was recently conducted using an online questionnaire, at the initiative of the Project Group “Rare Forms of Nephrolithiases and Nephrocalcinoses” of the Italian Society of Nephrology (SIN), with the aim of assessing the impact of this disorder on clinical practice and management policies in Italy.
The SIN questionnaire (see Supplementary Material Online, Table 1) was administered to physicians practicing in public and private Centers all over the country. All participating physicians provided informed consent. The first section of the questionnaire, named “Nephrology”, was designed to assess the characteristics of referring Centers, with particular emphasis on the clinical burden in the management of conditions such as nephrolithiasis and nephrocalcinosis, where cases of primary hyperoxaluria may “lurk”; its aim was also to investigate the general diagnostic policies applied by the specialists to whom these patients are referred; and, finally, to investigate the specific diagnostic policies for suspected PH. The second section, named “Dialysis and Transplantation”, aimed to investigate the size of Dialysis Centers and their diagnostic policies for patients reaching end-stage kidney disease (ESKD) with a history of nephrolithiasis and/or nephrocalcinosis. Finally, the aim of the third section, named “Primary Hyperoxaluria”, was to define the specific experience of the specialists responding to the survey, and to obtain information about the “journey” of patients with PH.
Four hundred public and private Centers all over the country, were invited to participate in the SIN Survey (see Supplementary Material Online, Table 1); 45 Centers (11.3%) accepted to participate and completed the questionnaire. Responses to the questionnaire were provided by a total of 54 physicians (1 physician in each of 37 Centers, 2 physicians in each of 7 Centers, and 3 physicians in 1 Center). Among the physicians who responded to the questionnaire, 96% were Nephrologists, while 2 physicians (4%) did not report their specialization. The average age among respondents was 51 years (range 29–70). The responses provided are detailed below. First section (nephrology) Question 1 : “What is the total number of patients with nephrolithiasis you have seen in the last 6 months?” Fifty-four responses were given to this question. The most frequent responses were “Less than 10” ( n = 15), “Between 10 and 20” ( n = 15) , and “Between 20 and 30” ( n = 10). Four physicians reported having seen between 30 and 40 patients with nephrolithiasis, 2 physicians between 40 and 50 patients, and 7 physicians more than 50 patients. Only 1 physician reported not having seen any patients with nephrolithiasis in the past 6 months. Question 2 : “How many of these patients had recurrent nephrolithiasis?” There were 53 responses to this question (1 missing data). The most frequent responses were “Less than 10%” ( n = 11) and “Between 10 and 40%” ( n = 24). Nine physicians responded that between 40 and 80% of the patients seen in the last 6 months had recurrent nephrolithiasis; this percentage was higher than 80% according to 6 physicians. Finally, 3 physicians responded that they had not seen any patients with recurrent nephrolithiasis in the past 6 months. Question 3 : “What is the total number of patients with nephrocalcinosis you have seen in the last 12 months?” There were 53 responses to this question (1 missing data). The most frequent responses were “None” ( n = 10) and “Less than 10” ( n = 29). Seven physicians reported having seen between 10 and 20 patients with nephrocalcinosis in the past 12 months, 4 physicians between 20 and 30 patients, 2 physicians between 30 and 40 patients, and 1 physician between 40 and 50 patients. Question 4 : “By which department(s) (or specialist) were these patients referred?” The majority of patients seen by the surveyed physicians were referred by Urology departments (27%), Primary Care physicians (26%), and Emergency departments (17%). Smaller numbers of patients were referred by other specialty departments including Nephrology (9%), Gastroenterology (7%), Pediatrics (5%), Internal Medicine (5%), Endocrinology (3%), and Medical Genetics (1%). Question 5 : “In your clinical practice, do you perform a metabolic screening in patients with nephrolithiasis?”; and Question 6 : “For what reasons do you perform a metabolic screening in patients with nephrolithiasis?”. Ninety-four percent of physicians routinely carry out metabolic screening for their patients with nephrolithiasis, while only 6% do not consider it necessary. The characteristics of patients with nephrolithiasis undergoing metabolic screening are shown in Fig. . Question 7 : “In your clinical practice, do you perform a metabolic screening or second level tests in patients with nephrocalcinosis?”; and Question 8 : “For what reasons do you perform a metabolic screening or second level tests in patients with nephrocalcinosis?”. Eighty-seven percent of physicians regularly carry out metabolic screening or second level tests in patients with nephrocalcinosis, while 13% do not consider it necessary. The characteristics of patients with nephrocalcinosis undergoing metabolic screening or second level tests are shown in Fig. . Question 9 : “Have you ever requested genetic testing to clarify the diagnosis in a patient with kidney stones or nephrocalcinosis?” Twelve physicians answered “Never”, 15 physicians “Exceptionally”, 13 physicians “Rarely”, and 12 physicians “Often”. Question 10 : “In your experience, what makes (or made) you suspect a primary hyperoxaluria?” Based on the answers given, detailed in Fig. , the most common items of diagnostic suspicion for primary hyperoxaluria were found to be a first episode of lithiasis in childhood/adolescence, and the presence of recurrent lithiasis in childhood/adolescence. Question 11 : “What tests would you order for suspected primary hyperoxaluria?”; and Question 12 : “Which of the following tests are performed in your facility?”. The physicians’ answers to these questions are detailed in Fig. (Boxes A and B , respectively). Question 13 : “In case these tests are not available, if you refer to other Centers, please specify for which tests” The tests for which the surveyed physicians most often had to refer to other Centers, as part of the diagnostic procedure for PH, were genetic analysis ( n = 28), plasma oxalate ( n = 23), and urinary oxalate ( n = 15). Urinary glycolate ( n = 11), urinary glycerate ( n = 10), and liver biopsy with determination of enzyme activity in tissue ( n = 9) were ordered less frequently. Second section (dialysis and transplantation) Question 1 : “Does your Center care for patients on chronic dialysis (hemodialysis, peritoneal dialysis)?”; and Question 2 : “How many dialysis patients does your Center care for?”. Ninety-one percent of physicians reported having patients on chronic dialysis in their Center; the average number of dialysis patients per Center was 124 (range 5–400). Question 3 : “How many dialysis patients have a history of recurrent nephrolithiasis or nephrocalcinosis?”; Question 4 : “How many dialysis patients with a history of nephrolithiasis or nephrocalcinosis have a specific diagnosis of the cause of nephrolithiasis/nephrocalcinosis?”; and Question 5 : “How many dialysis patients with a history of nephrolithiasis or nephrocalcinosis underwent genetic testing?”. Overall, 199 (3.5%) out of 5706 patients on dialysis treatment had a history of recurrent nephrolithiasis/nephrocalcinosis, with an average of 4.5 patients (range 0–22) per Center. Of these, a cause-specific diagnosis of nephrolithiasis/nephrocalcinosis was reported in 30 cases. Genetic testing was performed on a total of 24 dialysis patients with a history of nephrolithiasis/nephrocalcinosis, with an average of 0.6 tests (range 0–5) per Center. Question 5 : “In patients in whom a diagnosis of the cause of nephrolithiasis/ nephrocalcinosis was made, can you specify their diagnoses?” Specific causes of nephrolithiasis/nephrocalcinosis were reported by 18 out of 54 physicians (45%); these diagnoses are shown in Fig. . Third section (primary hyperoxaluria) Question 1 : “How many diagnoses of primary hyperoxaluria have you suspected in your career?” Thirty-nine percent of physicians responded that they had suspected a diagnosis of primary hyperoxaluria in 3 or more cases, 20% in at least 2 cases, and 19% in at least 1 case; finally, 22% of physicians reported that they had never suspected a case of primary hyperoxaluria in their patients. Question 2 : “Does your Center currently manage patients with primary hyperoxaluria?”; and Question 3 : “Did your Center refer patients with primary hyperoxaluria to another Center in the past?”. Out of a total of 45 Centers, 8 answered in the affirmative to Question 2, and 12 to Question 3. Question 4 : “How many patients with primary hyperoxaluria managed at your Center and/or referred to another Center are on dialysis?” This question was answered by 19 physicians (35%). The most common answers were “I don't know” (48%) and “1” (37%), while the answers “2”, “3” , and “ > 3” were less common (5% each). Question 5 : “How many patients with primary hyperoxaluria managed at your Center and/or referred to another Center, received a double liver-kidney transplant (7A) or a single kidney transplant (7B)?”. Physicians’ responses to these questions are detailed in Fig. (Boxes A and B , respectively).
Question 1 : “What is the total number of patients with nephrolithiasis you have seen in the last 6 months?” Fifty-four responses were given to this question. The most frequent responses were “Less than 10” ( n = 15), “Between 10 and 20” ( n = 15) , and “Between 20 and 30” ( n = 10). Four physicians reported having seen between 30 and 40 patients with nephrolithiasis, 2 physicians between 40 and 50 patients, and 7 physicians more than 50 patients. Only 1 physician reported not having seen any patients with nephrolithiasis in the past 6 months. Question 2 : “How many of these patients had recurrent nephrolithiasis?” There were 53 responses to this question (1 missing data). The most frequent responses were “Less than 10%” ( n = 11) and “Between 10 and 40%” ( n = 24). Nine physicians responded that between 40 and 80% of the patients seen in the last 6 months had recurrent nephrolithiasis; this percentage was higher than 80% according to 6 physicians. Finally, 3 physicians responded that they had not seen any patients with recurrent nephrolithiasis in the past 6 months. Question 3 : “What is the total number of patients with nephrocalcinosis you have seen in the last 12 months?” There were 53 responses to this question (1 missing data). The most frequent responses were “None” ( n = 10) and “Less than 10” ( n = 29). Seven physicians reported having seen between 10 and 20 patients with nephrocalcinosis in the past 12 months, 4 physicians between 20 and 30 patients, 2 physicians between 30 and 40 patients, and 1 physician between 40 and 50 patients. Question 4 : “By which department(s) (or specialist) were these patients referred?” The majority of patients seen by the surveyed physicians were referred by Urology departments (27%), Primary Care physicians (26%), and Emergency departments (17%). Smaller numbers of patients were referred by other specialty departments including Nephrology (9%), Gastroenterology (7%), Pediatrics (5%), Internal Medicine (5%), Endocrinology (3%), and Medical Genetics (1%). Question 5 : “In your clinical practice, do you perform a metabolic screening in patients with nephrolithiasis?”; and Question 6 : “For what reasons do you perform a metabolic screening in patients with nephrolithiasis?”. Ninety-four percent of physicians routinely carry out metabolic screening for their patients with nephrolithiasis, while only 6% do not consider it necessary. The characteristics of patients with nephrolithiasis undergoing metabolic screening are shown in Fig. . Question 7 : “In your clinical practice, do you perform a metabolic screening or second level tests in patients with nephrocalcinosis?”; and Question 8 : “For what reasons do you perform a metabolic screening or second level tests in patients with nephrocalcinosis?”. Eighty-seven percent of physicians regularly carry out metabolic screening or second level tests in patients with nephrocalcinosis, while 13% do not consider it necessary. The characteristics of patients with nephrocalcinosis undergoing metabolic screening or second level tests are shown in Fig. . Question 9 : “Have you ever requested genetic testing to clarify the diagnosis in a patient with kidney stones or nephrocalcinosis?” Twelve physicians answered “Never”, 15 physicians “Exceptionally”, 13 physicians “Rarely”, and 12 physicians “Often”. Question 10 : “In your experience, what makes (or made) you suspect a primary hyperoxaluria?” Based on the answers given, detailed in Fig. , the most common items of diagnostic suspicion for primary hyperoxaluria were found to be a first episode of lithiasis in childhood/adolescence, and the presence of recurrent lithiasis in childhood/adolescence. Question 11 : “What tests would you order for suspected primary hyperoxaluria?”; and Question 12 : “Which of the following tests are performed in your facility?”. The physicians’ answers to these questions are detailed in Fig. (Boxes A and B , respectively). Question 13 : “In case these tests are not available, if you refer to other Centers, please specify for which tests” The tests for which the surveyed physicians most often had to refer to other Centers, as part of the diagnostic procedure for PH, were genetic analysis ( n = 28), plasma oxalate ( n = 23), and urinary oxalate ( n = 15). Urinary glycolate ( n = 11), urinary glycerate ( n = 10), and liver biopsy with determination of enzyme activity in tissue ( n = 9) were ordered less frequently.
Question 1 : “Does your Center care for patients on chronic dialysis (hemodialysis, peritoneal dialysis)?”; and Question 2 : “How many dialysis patients does your Center care for?”. Ninety-one percent of physicians reported having patients on chronic dialysis in their Center; the average number of dialysis patients per Center was 124 (range 5–400). Question 3 : “How many dialysis patients have a history of recurrent nephrolithiasis or nephrocalcinosis?”; Question 4 : “How many dialysis patients with a history of nephrolithiasis or nephrocalcinosis have a specific diagnosis of the cause of nephrolithiasis/nephrocalcinosis?”; and Question 5 : “How many dialysis patients with a history of nephrolithiasis or nephrocalcinosis underwent genetic testing?”. Overall, 199 (3.5%) out of 5706 patients on dialysis treatment had a history of recurrent nephrolithiasis/nephrocalcinosis, with an average of 4.5 patients (range 0–22) per Center. Of these, a cause-specific diagnosis of nephrolithiasis/nephrocalcinosis was reported in 30 cases. Genetic testing was performed on a total of 24 dialysis patients with a history of nephrolithiasis/nephrocalcinosis, with an average of 0.6 tests (range 0–5) per Center. Question 5 : “In patients in whom a diagnosis of the cause of nephrolithiasis/ nephrocalcinosis was made, can you specify their diagnoses?” Specific causes of nephrolithiasis/nephrocalcinosis were reported by 18 out of 54 physicians (45%); these diagnoses are shown in Fig. .
Question 1 : “How many diagnoses of primary hyperoxaluria have you suspected in your career?” Thirty-nine percent of physicians responded that they had suspected a diagnosis of primary hyperoxaluria in 3 or more cases, 20% in at least 2 cases, and 19% in at least 1 case; finally, 22% of physicians reported that they had never suspected a case of primary hyperoxaluria in their patients. Question 2 : “Does your Center currently manage patients with primary hyperoxaluria?”; and Question 3 : “Did your Center refer patients with primary hyperoxaluria to another Center in the past?”. Out of a total of 45 Centers, 8 answered in the affirmative to Question 2, and 12 to Question 3. Question 4 : “How many patients with primary hyperoxaluria managed at your Center and/or referred to another Center are on dialysis?” This question was answered by 19 physicians (35%). The most common answers were “I don't know” (48%) and “1” (37%), while the answers “2”, “3” , and “ > 3” were less common (5% each). Question 5 : “How many patients with primary hyperoxaluria managed at your Center and/or referred to another Center, received a double liver-kidney transplant (7A) or a single kidney transplant (7B)?”. Physicians’ responses to these questions are detailed in Fig. (Boxes A and B , respectively).
The results of this survey in the “Nephrology” section, although based on a non-random sample almost exclusively consisting of Nephrologists (96%), appear to be well representative of the broader European Nephrology community . We therefore assume that the data collected on the clinical management of patients with hyperoxaluria are also representative of the standard practice. Since ninety-six percent of the physicians participating in the survey were Nephrologists, we will refer to them as Nephrologists. Only a quarter of the Nephrologists reported that they had seen more than 30 patients with nephrolithiasis in the previous 6 months, while most had seen fewer patients, and 1 physician had seen none. Moreover, only 15 Nephrologists were predominantly seeing patients with recurrent lithiasis. Most patients with stones were referred to the Nephrologist by their primary care physician or urologist. These findings confirm what was reported in a survey recently conducted among European Nephrologists and Urologists . Similar considerations can be drawn for the cases of nephrocalcinosis observed by the participating Nephrologists. Overall, these data suggest that there are no true ‘stone Centers’ with a defined organization and coordination between Urologists and Nephrologists, but rather specialists in Nephrology with diagnostic-therapeutic expertise in nephrolithiasis. The majority of these Nephrologists refer their stone patients for metabolic screening, thus complying with the recommendations of the most important guidelines . This finding, which confirms what has already been observed in Europe , is opposite to what has been observed in the United States, where only 15% of stone patients undergo 24-h urine collection, a 'proxy' for metabolic studies. The difference probably lies in the fact that the U.S. study likely investigated a population seen by a mix of specialists (e.g., not only Nephrologists), unlike our survey, which was conducted only among Nephrologists, and the European survey, in which 78.5% of respondents were Nephrologists. Therefore, it is clear that Nephrologists are involved in the management of this disease to determine its causes, but also probably to initiate preventive treatments for stone recurrence, since recurrence is the main criterion for performing a metabolic study. Other criteria frequently considered by Nephrologists to perform a metabolic study include a family history of stone disease and CKD, and the composition of urinary calculi which may suggest the diagnosis. Nevertheless, as we will see below, a definitive diagnosis is not often achieved, and the results of our study suggest that there is still much room for improvement. A history of recurrent nephrolithiasis/nephrocalcinosis is reported by 3.5% of dialysis patients followed in the Centers of the Nephrologists participating in the survey. This prevalence is similar to that previously found in Italy . We cannot state that recurrent nephrolithiasis/nephrocalcinosis was the cause of ESKD; however, an etiologic diagnosis – that is, a diagnosis that could clarify this point—was made only in a modest number of cases (30/199) (Fig. ). The diagnostic suspicion of PH may arise in the presence of recurrent or early-age onset (usually in the first 20 years of life) calcium oxalate nephrolithiasis. Indeed, our survey confirms that the items of diagnostic suspicion for primary hyperoxaluria that most frequently prompt patients with nephropathy/nephrocalcinosis to undergo metabolic screening or second level tests are the onset of a first stone episode in childhood/adolescence and the presence of recurrent lithiasis during childhood/adolescence. However, other conditions (reduced GFR, nephrocalcinosis, metabolic study findings) also receive sufficient attention as possible indicators of hyperoxaluria. This suggests that the suspicion of primary hyperoxaluria is not ignored in adult patients by the Nephrologists participating in the survey. This is particularly important given the fact that, in a significant proportion (about 20 percent) of patients, the condition can remain asymptomatic or paucisymptomatic until adulthood, occurring even relatively later in life . In the large OxalEurope case series, out of 653 patients with PH1 in whom the date of diagnosis was known, it occurred in adulthood in 197 cases (30.2%) [Metry E, personal communication ]. The first step in the diagnosis of PH is the finding of elevated oxalate levels in the 24-h urine collection, e.g., urine oxalate excretion in excess of 0.46 mmol/1.73 m 2 per day. Markedly higher urine oxalate levels might increase the clinical suspicion , while diagnostic confirmation and differentiation between different types of PH is achieved by biochemical and/or genetic testing . In patients with greatly reduced renal function, plasma oxalate levels are more reliable for diagnosis than urinary levels as well as being predictive of ESKD development . This survey shows that the genetic tests required for the diagnosis of the different forms of PH are still poorly available and underutilized. Only urinary oxalate determination is widely available, while urinary assays of metabolic precursors (glycolate and glycerate) and blood assays of oxalate are even less widely available than genetic testing. It should be remembered that, compared with urinary oxalate levels, the diagnostic utility of serum oxalate concentration has been demonstrated for moderate-to-severe renal impairment, with GFR < 30 mL/min/1.73 m 2 . Furthermore, serum oxalate is elevated in ESKD , although its values are significantly higher in ESKD due to PH . In patients with kidney failure, it has been reported that serum oxalate may increase to levels leading to spontaneous precipitation of calcium oxalate. Thus, one wonders whether the use of genetic testing would be more appropriate when suspecting PH as the cause of CKD/ESKD. The condition of nephrocalcinosis is not always adequately appreciated as a reason for conducting metabolic screening or second level tests in patients with kidney stones. In fact, there are as many as 15 physicians out of 54 who only partially agree or totally disagree to always investigate it. This finding is surprising as nephrocalcinosis is a renal parenchymal disease that may or may not be associated with stones but carries a non-negligible risk of ESKD. Moreover, it is frequently an expression of genetic disorders. Nevertheless, only 12 physicians routinely carry out genetic tests in patients with nephrocalcinosis. It is possible that this is due to the small number of laboratories that perform these tests, and to the fact that until now the search for mutations in the involved genes was only available in some Italian and European laboratories. Only recently have the methods of Exon Sequencing in Gene Panels and Whole Exome or Genome Sequencing, which allow the multiple genes involved to be analyzed in a single laboratory test, become more widely available for diagnostic use . Taken together, these data highlight the need for a service that provides Nephrology Centers with access to comprehensive analysis of a panel of genes for nephrolithiasis/nephrocalcinosis. The results of the SIN survey indicate that a significant number of participating Centers (8 out of 45) currently manage patients with primary hyperoxaluria, mostly on dialysis or with previous hepatorenal or renal transplant. The fact that many Centers refer their patients to other reference Centers is indicative of the need to optimize all steps of case diagnosis and management by referring these patients to physicians with sufficient skills and experience. The limitations of the present study are primarily due to its retrospective nature since the diagnosis of PH is based on the previous experience of the physicians. In addition, the finding that only 1 of the participating physicians reported not having seen any patients with nephrolithiasis in the past 6 months is indicative of a potential 'selection bias ' in the study, as specialists with an above-average interest/experience in this topic may have responded more frequently. The fact that as many as 21 of the Centers participating in this survey have experience in the management of an ultra-rare condition such as PH should also suggest caution in generalizing the survey results, which may not be representative of the practice patterns of average Adult Nephrology Centers. Finally, it should be noted that, given the relatively high degree of heterogeneity across countries in terms of policies in place to investigate potential monogenic disorders, future multicenter studies from different countries would bring valuable information to this important aspect of Nephrology.
The data reported by the survey indicate the need to implement early screening and genetic testing for PH and nephrocalcinosis not only in the setting of dialysis or transplantation, but also to promote early diagnosis of PH in cases of nephrolithiasis and particularly of nephrocalcinosis.
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 26 kb) Supplementary file2 (PDF 96 kb)
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Joint Statement of the Korean Society for Preventive Medicine and the Korean Society of Epidemiology on the response to the COVID-19 outbreak | 9266db12-4408-4da6-87fc-9e2d7347aaa1 | 7285435 | Preventive Medicine[mh] | |
AAPM Medical physics practice guideline 15.A: Peer review in clinical physics | f725cf01-c23d-4188-974f-6532c05ee45f | 10562014 | Internal Medicine[mh] | INTRODUCTION 1.1 Motivation 1.1.1 Practice settings Many medical physicists work as the only physicist in their facility (approximately 17% according to the 2020 AAPM Professional Survey Report ). This includes individuals in all aspects of medical physics. In such practice settings, the medical physicist does not have the continuous feedback of colleagues to refine and improve their skills and performance. Working alone, it is easy to become unaware of deficiencies in one's own work product. While continuing education (CE) is essential for professional development, even with adequate CE it is difficult for one person to keep track of all salient developments in their professional world. Group practices can be subject to similar issues. While physicists in a group practice setting have the benefit of regular access to other physicists, groups can also become isolated from the broader medical physics society. They, too, can fail to evolve as standards of practice change. Additionally, individuals within the group might only have opportunities for growth in specific areas, or the group dynamic may not be conducive to constructive peer input. Peer review (ideally from an external reviewer) can help to mitigate the situations described above. It is an important method for verifying patient safety and quality of care, with origins from the early days of Arab medicine more than a millennium ago. , It is an accepted system for ensuring professionalism and trust by providing an objective evaluation of a practitioner or clinical scientist's performance and professional practice by a qualified colleague. , As stated in the AAPM Task Group 103 report, “The purpose of the peer review process…is to enable a collegial exchange of professional ideas and promote a productive critique of the incumbent's clinical physics program with the aim of enhancing the program while ensuring conformance with regulations, professional guidelines, and established practice patterns.” As such, peer review can be beneficial to all clinical medical physicists. Note that this Practice Guideline focuses on professional peer review (evaluation of a colleague's professional practice), not “clinical peer review” or “medical peer review” (evaluation of medical decision making for specific patients). 1.1.2 Behavioral aspect Studies of human behavior generally agree that humans process information differently depending on the situation, and this can be broadly categorized into a performance mode and a learning mode. We spend most of our working hours in the performance mode, completing tasks efficiently but investing little focus on evaluating how the work is performed. The learning mode allows us to disengage from the daily routine and evaluate the broader context of our practice setting in order to identify opportunities for improvement. Professional peer review offers a mutual opportunity for a constructive learning mode with relevant feedback from a peer benefiting both the incumbent physicist and the reviewer. The American Medical Association recognizes the value of peer review: “The peer review process is intended to balance physicians’ right to exercise medical judgment freely with the obligation to do so wisely and temperately.” [ https://www.ama‐assn.org/delivering‐care/ethics/peer‐review‐due‐process ] 1.1.3 Accreditation and certification Peer review is one approach to meeting Part IV: Assessment of Performance in Practice for the Maintenance of Certification (MOC) program for the American Board of Radiology (ABR), and this applies to therapeutic, diagnostic, and nuclear medical physics. Physicist peer review is an integral component of the American College of Radiology's Radiation Oncology Practice Accreditation (ROPA) as well as the Accreditation Program for Excellence (APEx) practice accreditation program from the American Society for Radiation Oncology (ASTRO). For accredited clinical programs, the most recent accreditation review may inform the physicist peer review process, thereby reducing the overall time commitment for both the reviewer and the incumbent physicist. 1.1.4 Current status It is recognized that peer review is more established in the therapeutic medical physics specialty than in the diagnostic and nuclear medicine specialties, with more than 15 years of experience since the publication of the TG‐103 report and extensive promotion of peer review through therapy practice accreditation programs. Furthermore, practice settings are more diverse in the imaging physics and nuclear medicine physics specialties, with broad utilization of contracted physics services, often coupled with a narrowly defined scope of those services and the attendant resource requirements. Given these realities, professional peer review should be the standard of practice in clinical therapeutic medical physics, whereas peer review in clinical diagnostic and nuclear medical physics will likely remain a distinct characteristic of an exceptional program for the foreseeable future. 1.1.5 Alternative approaches to professional peer review In some practice settings, it may not be feasible to engage an external reviewer for a formal peer review, and alternative approaches may be necessary to accomplish the core objective of providing a productive critique of the incumbent's clinical physics program with the aim of enhancing the program and the incumbent's professional practice. Any alternative peer review approach must use a process that encourages a collegial, peer to peer, non‐punitive review and allows for external guidance to the practice. The following are some considerations for alternative peer review approaches: Documented process: A document describing the alternative peer review process specific to the practice environment. Regular intradisciplinary meetings: Regularly scheduled meetings where all medical physicists in the practice are able to attend, contribute meeting topics and discuss relevant medical physics issues in a non‐punitive environment. Built in review of processes: This can be accomplished via cross‐coverage, rotation of responsibilities, audits of QA reports and/or patient charts, or other methods that cultivate a peer review of medical physics processes. External review: Practice accreditation provides external review of a practice. In addition, a defined process for participation in national meetings (remotely or in person) and for returning knowledge to the group, and/or regularly scheduled journal club meetings to review recent publications or practice guidelines, can provide external perspectives to enable the incumbent physicist(s) to keep track of relevant developments in the profession. 1.2 Principles of constructive professional peer review Peer review is distinct from other types of professional review, such as a compliance audit or an employment annual review. Its focus is on practice improvement, helping the incumbent to be more effective in their role. Peer review should comprise not only a review of professional practice, but should also be a critical assessment of the practice setting: Is there sufficient institutional support? Are appropriate tools and resources available? Is the workload appropriate for thoughtful and thorough work? An approach focusing on systemic aspects of the work environment rather than strictly personal performance is termed “just culture,” one aspect of which recognizes that many errors are not the fault of the individual, but instead reveal weaknesses in the system in which the individual is working. According to the American College of Radiology, “A just culture is an environment in which errors and near‐miss events are evaluated in a deliberately nonpunitive framework, avoiding a culture of blame and responsibility and focusing instead on error prevention and fostering a culture of continuous quality improvement. ” Deficiencies revealed by the peer review process may not necessarily be the fault of the incumbent. If the workload is excessive for one individual, it is easy for that individual to miss important aspects of practice. Therefore, the reviewer should carefully look for systemic causes rather than assuming the worst of the incumbent. An effective peer reviewer remains acutely aware that the incumbent and their practice are the reason for the review process and avoids references to how the reviewer prefers to practice, unless such references are helpful in illustrating specific opportunities for improvement in the incumbent's practice setting. The reviewer should identify commendable aspects of the incumbent's practice and affirm these in the summary report. 1.3 Scope Though therapeutic medical physics has a deeper history of peer review than does diagnostic medical physics and nuclear medicine physics, peer review can be of great benefit to all aspects of clinical medical physics. Therefore, the core recommendations in this document are intended to apply to any clinical physics peer review. The document was developed with external peer reviewers (having a different employer arrangement from the incumbent) as the primary focus. It is recognized that in larger group practices there may be other, equally effective, approaches to peer review. In this document, the principles outlined above will be expanded and implemented. It will cover the process of conducting a peer review, from the initial contact to the final report. Specific applications for both therapy and diagnostic physicists will be presented. In the are included an example survey tool and sample reports. Note that this is not intended to be a guide for self‐audit. While the desired result might be similar, such an audit requires a different approach and different tools.
Motivation 1.1.1 Practice settings Many medical physicists work as the only physicist in their facility (approximately 17% according to the 2020 AAPM Professional Survey Report ). This includes individuals in all aspects of medical physics. In such practice settings, the medical physicist does not have the continuous feedback of colleagues to refine and improve their skills and performance. Working alone, it is easy to become unaware of deficiencies in one's own work product. While continuing education (CE) is essential for professional development, even with adequate CE it is difficult for one person to keep track of all salient developments in their professional world. Group practices can be subject to similar issues. While physicists in a group practice setting have the benefit of regular access to other physicists, groups can also become isolated from the broader medical physics society. They, too, can fail to evolve as standards of practice change. Additionally, individuals within the group might only have opportunities for growth in specific areas, or the group dynamic may not be conducive to constructive peer input. Peer review (ideally from an external reviewer) can help to mitigate the situations described above. It is an important method for verifying patient safety and quality of care, with origins from the early days of Arab medicine more than a millennium ago. , It is an accepted system for ensuring professionalism and trust by providing an objective evaluation of a practitioner or clinical scientist's performance and professional practice by a qualified colleague. , As stated in the AAPM Task Group 103 report, “The purpose of the peer review process…is to enable a collegial exchange of professional ideas and promote a productive critique of the incumbent's clinical physics program with the aim of enhancing the program while ensuring conformance with regulations, professional guidelines, and established practice patterns.” As such, peer review can be beneficial to all clinical medical physicists. Note that this Practice Guideline focuses on professional peer review (evaluation of a colleague's professional practice), not “clinical peer review” or “medical peer review” (evaluation of medical decision making for specific patients). 1.1.2 Behavioral aspect Studies of human behavior generally agree that humans process information differently depending on the situation, and this can be broadly categorized into a performance mode and a learning mode. We spend most of our working hours in the performance mode, completing tasks efficiently but investing little focus on evaluating how the work is performed. The learning mode allows us to disengage from the daily routine and evaluate the broader context of our practice setting in order to identify opportunities for improvement. Professional peer review offers a mutual opportunity for a constructive learning mode with relevant feedback from a peer benefiting both the incumbent physicist and the reviewer. The American Medical Association recognizes the value of peer review: “The peer review process is intended to balance physicians’ right to exercise medical judgment freely with the obligation to do so wisely and temperately.” [ https://www.ama‐assn.org/delivering‐care/ethics/peer‐review‐due‐process ] 1.1.3 Accreditation and certification Peer review is one approach to meeting Part IV: Assessment of Performance in Practice for the Maintenance of Certification (MOC) program for the American Board of Radiology (ABR), and this applies to therapeutic, diagnostic, and nuclear medical physics. Physicist peer review is an integral component of the American College of Radiology's Radiation Oncology Practice Accreditation (ROPA) as well as the Accreditation Program for Excellence (APEx) practice accreditation program from the American Society for Radiation Oncology (ASTRO). For accredited clinical programs, the most recent accreditation review may inform the physicist peer review process, thereby reducing the overall time commitment for both the reviewer and the incumbent physicist. 1.1.4 Current status It is recognized that peer review is more established in the therapeutic medical physics specialty than in the diagnostic and nuclear medicine specialties, with more than 15 years of experience since the publication of the TG‐103 report and extensive promotion of peer review through therapy practice accreditation programs. Furthermore, practice settings are more diverse in the imaging physics and nuclear medicine physics specialties, with broad utilization of contracted physics services, often coupled with a narrowly defined scope of those services and the attendant resource requirements. Given these realities, professional peer review should be the standard of practice in clinical therapeutic medical physics, whereas peer review in clinical diagnostic and nuclear medical physics will likely remain a distinct characteristic of an exceptional program for the foreseeable future. 1.1.5 Alternative approaches to professional peer review In some practice settings, it may not be feasible to engage an external reviewer for a formal peer review, and alternative approaches may be necessary to accomplish the core objective of providing a productive critique of the incumbent's clinical physics program with the aim of enhancing the program and the incumbent's professional practice. Any alternative peer review approach must use a process that encourages a collegial, peer to peer, non‐punitive review and allows for external guidance to the practice. The following are some considerations for alternative peer review approaches: Documented process: A document describing the alternative peer review process specific to the practice environment. Regular intradisciplinary meetings: Regularly scheduled meetings where all medical physicists in the practice are able to attend, contribute meeting topics and discuss relevant medical physics issues in a non‐punitive environment. Built in review of processes: This can be accomplished via cross‐coverage, rotation of responsibilities, audits of QA reports and/or patient charts, or other methods that cultivate a peer review of medical physics processes. External review: Practice accreditation provides external review of a practice. In addition, a defined process for participation in national meetings (remotely or in person) and for returning knowledge to the group, and/or regularly scheduled journal club meetings to review recent publications or practice guidelines, can provide external perspectives to enable the incumbent physicist(s) to keep track of relevant developments in the profession.
Practice settings Many medical physicists work as the only physicist in their facility (approximately 17% according to the 2020 AAPM Professional Survey Report ). This includes individuals in all aspects of medical physics. In such practice settings, the medical physicist does not have the continuous feedback of colleagues to refine and improve their skills and performance. Working alone, it is easy to become unaware of deficiencies in one's own work product. While continuing education (CE) is essential for professional development, even with adequate CE it is difficult for one person to keep track of all salient developments in their professional world. Group practices can be subject to similar issues. While physicists in a group practice setting have the benefit of regular access to other physicists, groups can also become isolated from the broader medical physics society. They, too, can fail to evolve as standards of practice change. Additionally, individuals within the group might only have opportunities for growth in specific areas, or the group dynamic may not be conducive to constructive peer input. Peer review (ideally from an external reviewer) can help to mitigate the situations described above. It is an important method for verifying patient safety and quality of care, with origins from the early days of Arab medicine more than a millennium ago. , It is an accepted system for ensuring professionalism and trust by providing an objective evaluation of a practitioner or clinical scientist's performance and professional practice by a qualified colleague. , As stated in the AAPM Task Group 103 report, “The purpose of the peer review process…is to enable a collegial exchange of professional ideas and promote a productive critique of the incumbent's clinical physics program with the aim of enhancing the program while ensuring conformance with regulations, professional guidelines, and established practice patterns.” As such, peer review can be beneficial to all clinical medical physicists. Note that this Practice Guideline focuses on professional peer review (evaluation of a colleague's professional practice), not “clinical peer review” or “medical peer review” (evaluation of medical decision making for specific patients).
Behavioral aspect Studies of human behavior generally agree that humans process information differently depending on the situation, and this can be broadly categorized into a performance mode and a learning mode. We spend most of our working hours in the performance mode, completing tasks efficiently but investing little focus on evaluating how the work is performed. The learning mode allows us to disengage from the daily routine and evaluate the broader context of our practice setting in order to identify opportunities for improvement. Professional peer review offers a mutual opportunity for a constructive learning mode with relevant feedback from a peer benefiting both the incumbent physicist and the reviewer. The American Medical Association recognizes the value of peer review: “The peer review process is intended to balance physicians’ right to exercise medical judgment freely with the obligation to do so wisely and temperately.” [ https://www.ama‐assn.org/delivering‐care/ethics/peer‐review‐due‐process ]
Accreditation and certification Peer review is one approach to meeting Part IV: Assessment of Performance in Practice for the Maintenance of Certification (MOC) program for the American Board of Radiology (ABR), and this applies to therapeutic, diagnostic, and nuclear medical physics. Physicist peer review is an integral component of the American College of Radiology's Radiation Oncology Practice Accreditation (ROPA) as well as the Accreditation Program for Excellence (APEx) practice accreditation program from the American Society for Radiation Oncology (ASTRO). For accredited clinical programs, the most recent accreditation review may inform the physicist peer review process, thereby reducing the overall time commitment for both the reviewer and the incumbent physicist.
Current status It is recognized that peer review is more established in the therapeutic medical physics specialty than in the diagnostic and nuclear medicine specialties, with more than 15 years of experience since the publication of the TG‐103 report and extensive promotion of peer review through therapy practice accreditation programs. Furthermore, practice settings are more diverse in the imaging physics and nuclear medicine physics specialties, with broad utilization of contracted physics services, often coupled with a narrowly defined scope of those services and the attendant resource requirements. Given these realities, professional peer review should be the standard of practice in clinical therapeutic medical physics, whereas peer review in clinical diagnostic and nuclear medical physics will likely remain a distinct characteristic of an exceptional program for the foreseeable future.
Alternative approaches to professional peer review In some practice settings, it may not be feasible to engage an external reviewer for a formal peer review, and alternative approaches may be necessary to accomplish the core objective of providing a productive critique of the incumbent's clinical physics program with the aim of enhancing the program and the incumbent's professional practice. Any alternative peer review approach must use a process that encourages a collegial, peer to peer, non‐punitive review and allows for external guidance to the practice. The following are some considerations for alternative peer review approaches: Documented process: A document describing the alternative peer review process specific to the practice environment. Regular intradisciplinary meetings: Regularly scheduled meetings where all medical physicists in the practice are able to attend, contribute meeting topics and discuss relevant medical physics issues in a non‐punitive environment. Built in review of processes: This can be accomplished via cross‐coverage, rotation of responsibilities, audits of QA reports and/or patient charts, or other methods that cultivate a peer review of medical physics processes. External review: Practice accreditation provides external review of a practice. In addition, a defined process for participation in national meetings (remotely or in person) and for returning knowledge to the group, and/or regularly scheduled journal club meetings to review recent publications or practice guidelines, can provide external perspectives to enable the incumbent physicist(s) to keep track of relevant developments in the profession.
Principles of constructive professional peer review Peer review is distinct from other types of professional review, such as a compliance audit or an employment annual review. Its focus is on practice improvement, helping the incumbent to be more effective in their role. Peer review should comprise not only a review of professional practice, but should also be a critical assessment of the practice setting: Is there sufficient institutional support? Are appropriate tools and resources available? Is the workload appropriate for thoughtful and thorough work? An approach focusing on systemic aspects of the work environment rather than strictly personal performance is termed “just culture,” one aspect of which recognizes that many errors are not the fault of the individual, but instead reveal weaknesses in the system in which the individual is working. According to the American College of Radiology, “A just culture is an environment in which errors and near‐miss events are evaluated in a deliberately nonpunitive framework, avoiding a culture of blame and responsibility and focusing instead on error prevention and fostering a culture of continuous quality improvement. ” Deficiencies revealed by the peer review process may not necessarily be the fault of the incumbent. If the workload is excessive for one individual, it is easy for that individual to miss important aspects of practice. Therefore, the reviewer should carefully look for systemic causes rather than assuming the worst of the incumbent. An effective peer reviewer remains acutely aware that the incumbent and their practice are the reason for the review process and avoids references to how the reviewer prefers to practice, unless such references are helpful in illustrating specific opportunities for improvement in the incumbent's practice setting. The reviewer should identify commendable aspects of the incumbent's practice and affirm these in the summary report.
Scope Though therapeutic medical physics has a deeper history of peer review than does diagnostic medical physics and nuclear medicine physics, peer review can be of great benefit to all aspects of clinical medical physics. Therefore, the core recommendations in this document are intended to apply to any clinical physics peer review. The document was developed with external peer reviewers (having a different employer arrangement from the incumbent) as the primary focus. It is recognized that in larger group practices there may be other, equally effective, approaches to peer review. In this document, the principles outlined above will be expanded and implemented. It will cover the process of conducting a peer review, from the initial contact to the final report. Specific applications for both therapy and diagnostic physicists will be presented. In the are included an example survey tool and sample reports. Note that this is not intended to be a guide for self‐audit. While the desired result might be similar, such an audit requires a different approach and different tools.
ROLES AND RESPONSIBILITIES 2.1 Reviewer The reviewer must be a Qualified Medical Physicist (QMP) as defined by the AAPM Position Statement and be a peer of the incumbent in the same specialty. Whenever possible, the reviewer should not be a manager, supervisor, or other superior to the incumbent as this may inhibit the peer review process. The reviewer should not have a close personal relationship with the incumbent, though it is recognized that this may be unavoidable in larger group settings. It is the responsibility of the reviewer to ensure that the peer review is done in a way that encourages professional growth for the incumbent in a non‐punitive way. The reviewer must promote a confidential, safe environment in which the incumbent is able to speak freely about workplace challenges and best practices. The reviewer should not have any attachment to or investment in the results of the peer review. It is the responsibility of the reviewer to provide the results of the peer review in a meaningful and constructive manner both verbally in the exit interview and documented in a confidential peer review report. 2.2 Incumbent The incumbent must be a practicing clinical medical physicist. It will benefit the peer review process if the incumbent is seeking to evaluate their clinical practice for potential improvement opportunities. It is the responsibility of the incumbent to be a willing participant in the process with the intent of practice improvement. The incumbent must be honest with the reviewer and forthcoming about limitations and challenges in the workplace. 2.3 Stakeholders Stakeholders are individuals with a vested interest in the peer review process. These may include administrators, supervisors, managers, and department chairs. The responsibility of the stakeholders is to recognize the importance of and support the peer review process as an opportunity for professional growth. The stakeholders must allow for a constructive and open process where the reviewer is able to peer review the incumbent without interference.
Reviewer The reviewer must be a Qualified Medical Physicist (QMP) as defined by the AAPM Position Statement and be a peer of the incumbent in the same specialty. Whenever possible, the reviewer should not be a manager, supervisor, or other superior to the incumbent as this may inhibit the peer review process. The reviewer should not have a close personal relationship with the incumbent, though it is recognized that this may be unavoidable in larger group settings. It is the responsibility of the reviewer to ensure that the peer review is done in a way that encourages professional growth for the incumbent in a non‐punitive way. The reviewer must promote a confidential, safe environment in which the incumbent is able to speak freely about workplace challenges and best practices. The reviewer should not have any attachment to or investment in the results of the peer review. It is the responsibility of the reviewer to provide the results of the peer review in a meaningful and constructive manner both verbally in the exit interview and documented in a confidential peer review report.
Incumbent The incumbent must be a practicing clinical medical physicist. It will benefit the peer review process if the incumbent is seeking to evaluate their clinical practice for potential improvement opportunities. It is the responsibility of the incumbent to be a willing participant in the process with the intent of practice improvement. The incumbent must be honest with the reviewer and forthcoming about limitations and challenges in the workplace.
Stakeholders Stakeholders are individuals with a vested interest in the peer review process. These may include administrators, supervisors, managers, and department chairs. The responsibility of the stakeholders is to recognize the importance of and support the peer review process as an opportunity for professional growth. The stakeholders must allow for a constructive and open process where the reviewer is able to peer review the incumbent without interference.
THE PEER REVIEW PROCESS 3.1 Introduction to the section and topics to be covered In this section, we provide recommendations for the process of conducting peer reviews of clinical medical physicists. The primary focus of these recommendations is to ensure that the peer review is constructive for the incumbent physicist and disconnected from unrelated matters such as personnel decisions (e.g., annual performance evaluations). We provide recommendations for the frequency of review, the initiation of the review process, criteria for reviewer selection, the conduct of the peer review, reporting the findings of the review, and follow up. 3.2 Frequency of the review Peer review should occur at least once every 36 months. However, additional reviews may be indicated for situations such as significant staffing changes in the practice, or the introduction of new technologies or clinical services. Such additional reviews are at the discretion of the incumbent physicist. 3.3 Initiation of the review Consistent with the goal of a peer review that is constructive for the incumbent physicist, we suggest that the most positive review environment is possible when the incumbent clinical physicist voluntarily initiates the review. We recognize that initiation by an employer does happen and is entirely appropriate in many situations. To ensure a constructive review when the employer initiates the review: The incumbent physicist should confirm that the reviewer's experience appropriately overlaps with the clinical scope of the incumbent's responsibilities, and the incumbent and reviewing physicists must manage any potential conflict of interest between the reviewing and incumbent physicists; The employer must verify that the reviewing physicist is a QMP in the specialty being reviewed, and must empower the reviewing physicist to use independent professional judgment; All parties (incumbent, stakeholders, and reviewer) should carefully review guidance documents related to the clinical physics peer review, such as this MPPG, to affirm their mutual understanding of the goals, expectations, and responsibilities. The reviewer must be appropriately compensated for the peer review services. We recognize the potential or perceived bias arising from any payer‐payee relationship. To address potential bias concerns, the employer must empower the reviewer to use independent professional judgment. The employer must not restrict the open communication between the incumbent and reviewer, and the reviewer must ensure that the incumbent receives a copy of the report prior to or at the same time as the employer. [Note: Reviewer compensation models may vary depending on whether the reviewer is external or internal.] 3.4 Selection of reviewer The reviewer must be a QMP according to the AAPM definition. The reviewer should be independent of the incumbent physicist, without a conflict of interest or commitment. Potential or perceived conflicts of interest or bias must be managed in accordance with the AAPM Code of Ethics and any other applicable codes of ethics (e.g., institutional). Avoidable conflicts include not choosing reviewers with the ability to directly affect employment, compensation, promotion, retention, tenure, or funding. Some potential conflicts of interest may be more nuanced. Former colleagues, mentors/mentees, advisors/trainees, and supervisors may have very relevant experience, but may not be entirely impartial. In a larger group practice, it may not be possible to avoid a current or prior professional relationship. Where such relationships exist between the reviewer and incumbent physicist, clear disclosure is appropriate. The reviewer should be experienced with the modalities to be reviewed. Further consideration should be given to potential reviewers who have served as surveyors for nationally recognized accreditation programs. The scope of the clinical physics program must be clearly communicated to the potential reviewer. If the incumbent physicist has concerns about a particular aspect of the physics program, the incumbent and potential reviewer should discuss prioritization of the planned review. Workflows and standard operating procedures may be reviewed remotely prior to the on‐site visit to be more efficient and allow for a cohesive review plan to be formulated prior to the on‐site visit. Prior to conducting peer reviews, the reviewer must become familiar with this MPPG and related examples and templates. An effective peer reviewer must exhibit a number of qualities and abilities that enable the individual to conduct the review in a professional and supportive manner. Examples of these qualities are listed in Table . The reviewer and the incumbent's institution(s) should execute a formal written agreement describing the scope of the review, confidentiality, and other important legal considerations. The incumbent should consider alternating reviewers to provide fresh perspectives as the program evolves. 3.5 Preparation A successful peer review does not happen without careful planning. The agenda should be established, interviewees should be identified and the times for those interviews should be scheduled in advance. Good communication is essential to a rewarding peer review process. Other important aspects of the peer review process are the support from administration for the actual activity and engaged staff who work to maintain a strong safety culture. A recent report on peer review in Radiation Oncology states: “Departmental leadership should emphasize the importance of peer review and encourage others to actively engage in quality and safety initiatives. Otherwise, a major possible pitfall is that peer review becomes an activity that needs to be checked off a to‐do list and staff are distracted and/or uninterested.” Administrators and staff must contribute to an environment where peer review is supportive and considered a part of routine good practice. The incumbent should be afforded appropriate time to prepare for the review, and the day of the on‐site review should be considered a professional development day without other scheduled tasks. In the event of unforeseen clinical priorities, the administration, incumbent and reviewer must coordinate to ensure that a constructive review can be conducted without compromising patient care. This could include rescheduling the time for the on‐site review. The first step in the execution is to select the reviewer. Due to the invasive nature of a review, the incumbent and reviewer must have a professional rapport. The reviewer may therefore be a known colleague. 3.6 Planning phase Early in the planning phase, the scope of the review must be established. Both parties must understand the expectations and agree on the clinical breadth. The parties must mutually decide if there are particular topics that should or should not be included. Note that this should be generally determined by the incumbent physicist and the reviewer, not by the clinic's administration, to ensure that it is a beneficial process for the incumbent. The timeline should be established well before the peer review starts. Items that should be part of the timeline include a date by which the incumbent physicist is to provide preliminary documentation, the date of the review, the expected duration of the review, and the date by which the final report will be provided. It is possible that a review will be performed virtually, and this presents another layer of complexity. For virtual reviews, the technology to be used should be tested prior to the review date, and consideration should be given to providing a visual “tour” of the facility. In both cases, a clear agenda should be created, including who will be interviewed as part of the review process. If available, previous peer review reports should be provided to the reviewer. The reviewer should be aware of the incumbent's practice setting. A rural, solo practice has different needs from a large, urban practice. For example, information technology support and hospital radiation safety programs may be quite different. The reviewer should additionally be sensitive to special circumstances that might impact the incumbent's practice. As the review will most likely include accessing patient records, the incumbent physicist should ensure that any institutional credentialing is completed before the reviewer comes on‐site or is granted remote access. An extremely important part of the review process is maintaining patient confidentiality consistent with the federal HIPAA regulations, as the reviewer will be functioning in the role of a “business associate” as defined under those regulations. 3.7 Review phase The hours of operation of the site should be respected by the reviewer. The review should be kept within normal business hours unless it is specifically arranged otherwise prior to the visit. The review process can be stressful and exhausting for both the incumbent and the reviewer; keeping to normal hours with regular breaks will help to alleviate this issue. The reviewer should be able to give their full attention to the peer review process without interruption. The review should be scheduled at a time that is mutually acceptable to both the incumbent physicist and the reviewer. The reviewer should never show up as a surprise. Instead, the review should be carefully planned with an agreed upon timeline. While most facilities have adequate technology for the peer review, it will be necessary to ensure that the reviewer has adequate access. The reviewer should have easy access to the Internet. Logins to network drives, clinical software applications or other proprietary systems might be needed. When direct access to records or software applications for the reviewer cannot be obtained, the incumbent or a designee must assist the reviewer during the review. It may also be necessary to secure building access to facilitate the review.
Introduction to the section and topics to be covered In this section, we provide recommendations for the process of conducting peer reviews of clinical medical physicists. The primary focus of these recommendations is to ensure that the peer review is constructive for the incumbent physicist and disconnected from unrelated matters such as personnel decisions (e.g., annual performance evaluations). We provide recommendations for the frequency of review, the initiation of the review process, criteria for reviewer selection, the conduct of the peer review, reporting the findings of the review, and follow up.
Frequency of the review Peer review should occur at least once every 36 months. However, additional reviews may be indicated for situations such as significant staffing changes in the practice, or the introduction of new technologies or clinical services. Such additional reviews are at the discretion of the incumbent physicist.
Initiation of the review Consistent with the goal of a peer review that is constructive for the incumbent physicist, we suggest that the most positive review environment is possible when the incumbent clinical physicist voluntarily initiates the review. We recognize that initiation by an employer does happen and is entirely appropriate in many situations. To ensure a constructive review when the employer initiates the review: The incumbent physicist should confirm that the reviewer's experience appropriately overlaps with the clinical scope of the incumbent's responsibilities, and the incumbent and reviewing physicists must manage any potential conflict of interest between the reviewing and incumbent physicists; The employer must verify that the reviewing physicist is a QMP in the specialty being reviewed, and must empower the reviewing physicist to use independent professional judgment; All parties (incumbent, stakeholders, and reviewer) should carefully review guidance documents related to the clinical physics peer review, such as this MPPG, to affirm their mutual understanding of the goals, expectations, and responsibilities. The reviewer must be appropriately compensated for the peer review services. We recognize the potential or perceived bias arising from any payer‐payee relationship. To address potential bias concerns, the employer must empower the reviewer to use independent professional judgment. The employer must not restrict the open communication between the incumbent and reviewer, and the reviewer must ensure that the incumbent receives a copy of the report prior to or at the same time as the employer. [Note: Reviewer compensation models may vary depending on whether the reviewer is external or internal.]
Selection of reviewer The reviewer must be a QMP according to the AAPM definition. The reviewer should be independent of the incumbent physicist, without a conflict of interest or commitment. Potential or perceived conflicts of interest or bias must be managed in accordance with the AAPM Code of Ethics and any other applicable codes of ethics (e.g., institutional). Avoidable conflicts include not choosing reviewers with the ability to directly affect employment, compensation, promotion, retention, tenure, or funding. Some potential conflicts of interest may be more nuanced. Former colleagues, mentors/mentees, advisors/trainees, and supervisors may have very relevant experience, but may not be entirely impartial. In a larger group practice, it may not be possible to avoid a current or prior professional relationship. Where such relationships exist between the reviewer and incumbent physicist, clear disclosure is appropriate. The reviewer should be experienced with the modalities to be reviewed. Further consideration should be given to potential reviewers who have served as surveyors for nationally recognized accreditation programs. The scope of the clinical physics program must be clearly communicated to the potential reviewer. If the incumbent physicist has concerns about a particular aspect of the physics program, the incumbent and potential reviewer should discuss prioritization of the planned review. Workflows and standard operating procedures may be reviewed remotely prior to the on‐site visit to be more efficient and allow for a cohesive review plan to be formulated prior to the on‐site visit. Prior to conducting peer reviews, the reviewer must become familiar with this MPPG and related examples and templates. An effective peer reviewer must exhibit a number of qualities and abilities that enable the individual to conduct the review in a professional and supportive manner. Examples of these qualities are listed in Table . The reviewer and the incumbent's institution(s) should execute a formal written agreement describing the scope of the review, confidentiality, and other important legal considerations. The incumbent should consider alternating reviewers to provide fresh perspectives as the program evolves.
Preparation A successful peer review does not happen without careful planning. The agenda should be established, interviewees should be identified and the times for those interviews should be scheduled in advance. Good communication is essential to a rewarding peer review process. Other important aspects of the peer review process are the support from administration for the actual activity and engaged staff who work to maintain a strong safety culture. A recent report on peer review in Radiation Oncology states: “Departmental leadership should emphasize the importance of peer review and encourage others to actively engage in quality and safety initiatives. Otherwise, a major possible pitfall is that peer review becomes an activity that needs to be checked off a to‐do list and staff are distracted and/or uninterested.” Administrators and staff must contribute to an environment where peer review is supportive and considered a part of routine good practice. The incumbent should be afforded appropriate time to prepare for the review, and the day of the on‐site review should be considered a professional development day without other scheduled tasks. In the event of unforeseen clinical priorities, the administration, incumbent and reviewer must coordinate to ensure that a constructive review can be conducted without compromising patient care. This could include rescheduling the time for the on‐site review. The first step in the execution is to select the reviewer. Due to the invasive nature of a review, the incumbent and reviewer must have a professional rapport. The reviewer may therefore be a known colleague.
Planning phase Early in the planning phase, the scope of the review must be established. Both parties must understand the expectations and agree on the clinical breadth. The parties must mutually decide if there are particular topics that should or should not be included. Note that this should be generally determined by the incumbent physicist and the reviewer, not by the clinic's administration, to ensure that it is a beneficial process for the incumbent. The timeline should be established well before the peer review starts. Items that should be part of the timeline include a date by which the incumbent physicist is to provide preliminary documentation, the date of the review, the expected duration of the review, and the date by which the final report will be provided. It is possible that a review will be performed virtually, and this presents another layer of complexity. For virtual reviews, the technology to be used should be tested prior to the review date, and consideration should be given to providing a visual “tour” of the facility. In both cases, a clear agenda should be created, including who will be interviewed as part of the review process. If available, previous peer review reports should be provided to the reviewer. The reviewer should be aware of the incumbent's practice setting. A rural, solo practice has different needs from a large, urban practice. For example, information technology support and hospital radiation safety programs may be quite different. The reviewer should additionally be sensitive to special circumstances that might impact the incumbent's practice. As the review will most likely include accessing patient records, the incumbent physicist should ensure that any institutional credentialing is completed before the reviewer comes on‐site or is granted remote access. An extremely important part of the review process is maintaining patient confidentiality consistent with the federal HIPAA regulations, as the reviewer will be functioning in the role of a “business associate” as defined under those regulations.
Review phase The hours of operation of the site should be respected by the reviewer. The review should be kept within normal business hours unless it is specifically arranged otherwise prior to the visit. The review process can be stressful and exhausting for both the incumbent and the reviewer; keeping to normal hours with regular breaks will help to alleviate this issue. The reviewer should be able to give their full attention to the peer review process without interruption. The review should be scheduled at a time that is mutually acceptable to both the incumbent physicist and the reviewer. The reviewer should never show up as a surprise. Instead, the review should be carefully planned with an agreed upon timeline. While most facilities have adequate technology for the peer review, it will be necessary to ensure that the reviewer has adequate access. The reviewer should have easy access to the Internet. Logins to network drives, clinical software applications or other proprietary systems might be needed. When direct access to records or software applications for the reviewer cannot be obtained, the incumbent or a designee must assist the reviewer during the review. It may also be necessary to secure building access to facilitate the review.
PEER REVIEW The review must include assessments of the available resources, the incumbent physicist's work product, and professional skills. Recommendations for assessment methodology are provided, along with some tools to facilitate the review process. 4.1 Available resources The reviewer must determine whether instruments (appropriate to the clinical scope) are readily available, with instrument calibrations consistent with current AAPM recommendations and/or applicable regulations. Staffing levels and schedules must be carefully assessed, with consideration for the scope of clinical services, frequency of special procedures requiring physicist support, and other physicist duties such as radiation safety and administrative tasks. The staffing assessment should consider support staff such as medical physicist assistants, medical dosimetrists, administrative support, and information technology. Equipment access for quality control and quality assurance must be assessed, including flexibility of scheduling relative to the incumbent physicist's availability. This is particularly important when the physicist is responsible for multiple clinic locations or is contracted for less than full‐time coverage. 4.2 Work product Core physics conventions and assumptions [such as those used for accelerator calibrations and brachytherapy source strength verification in radiation oncology, or the assumptions and correction factors used in peak skin dose calculations in imaging] must be clearly documented to ensure that other physicists can verify critical findings. The reviewer must assess whether the machine and instrument QC records and templates are appropriate to the scope of the program, and whether the test methodology and action levels are appropriate and consistent with nationally accepted standards, AAPM task group reports, and Medical Physics Practice Guidelines. Key QC templates should be evaluated for accuracy and clarity. The reviewer must assess whether clinical physics quality assurance (e.g., radiation oncology treatment plan reviews, fluoroscopy dose, and pregnant patient radiation safety evaluations) is appropriate to the clinical services being provided, whether the incumbent physicist has conducted risk assessments to justify the existing QA procedures, and whether appropriate tools exist to ensure that such QA is consistently performed. The review must evaluate the program's safety culture and patient safety initiatives (e.g., incident learning, open communication, safety checklists). The review must evaluate the incumbent's supervision of clinical operations and leadership in process improvement, such as overseeing the dosimetric planning process in therapy or overseeing the development and management of imaging protocols and patient shielding practices in imaging. For QC work delegated to support staff, the reviewer must ascertain that instructions and tolerances are unambiguous, that the results are reviewed and co‐signed by the incumbent physicist, and that the work is performed under appropriate supervision. 4.3 Physicist skills The reviewer must assess the incumbent physicist's professional skills relative to the requirements of the position. Examples of these skills are described in Table .
Available resources The reviewer must determine whether instruments (appropriate to the clinical scope) are readily available, with instrument calibrations consistent with current AAPM recommendations and/or applicable regulations. Staffing levels and schedules must be carefully assessed, with consideration for the scope of clinical services, frequency of special procedures requiring physicist support, and other physicist duties such as radiation safety and administrative tasks. The staffing assessment should consider support staff such as medical physicist assistants, medical dosimetrists, administrative support, and information technology. Equipment access for quality control and quality assurance must be assessed, including flexibility of scheduling relative to the incumbent physicist's availability. This is particularly important when the physicist is responsible for multiple clinic locations or is contracted for less than full‐time coverage.
Work product Core physics conventions and assumptions [such as those used for accelerator calibrations and brachytherapy source strength verification in radiation oncology, or the assumptions and correction factors used in peak skin dose calculations in imaging] must be clearly documented to ensure that other physicists can verify critical findings. The reviewer must assess whether the machine and instrument QC records and templates are appropriate to the scope of the program, and whether the test methodology and action levels are appropriate and consistent with nationally accepted standards, AAPM task group reports, and Medical Physics Practice Guidelines. Key QC templates should be evaluated for accuracy and clarity. The reviewer must assess whether clinical physics quality assurance (e.g., radiation oncology treatment plan reviews, fluoroscopy dose, and pregnant patient radiation safety evaluations) is appropriate to the clinical services being provided, whether the incumbent physicist has conducted risk assessments to justify the existing QA procedures, and whether appropriate tools exist to ensure that such QA is consistently performed. The review must evaluate the program's safety culture and patient safety initiatives (e.g., incident learning, open communication, safety checklists). The review must evaluate the incumbent's supervision of clinical operations and leadership in process improvement, such as overseeing the dosimetric planning process in therapy or overseeing the development and management of imaging protocols and patient shielding practices in imaging. For QC work delegated to support staff, the reviewer must ascertain that instructions and tolerances are unambiguous, that the results are reviewed and co‐signed by the incumbent physicist, and that the work is performed under appropriate supervision.
Physicist skills The reviewer must assess the incumbent physicist's professional skills relative to the requirements of the position. Examples of these skills are described in Table .
ASSESSMENT METHODOLOGY AND TOOLS Several different approaches to evaluation are available, and different aspects of the review may lend themselves to different methods. Some aspects of the review may best be evaluated using a performance scale, such as 1−3 or “deficient,” “meets practice expectations,” and “exceeds expectations.” Others may be binary options: yes or no, for example, whether the incumbent has this piece of equipment or does this activity. For some topics, a subjective narrative approach may be most appropriate. Observations and suggestions are best communicated in the narrative form. The reviewer should use a survey tool that covers all of the elements that are to be part of the review and incorporates the various evaluation techniques as appropriate. This will help to keep the review process on track and ensure that the review is complete and that nothing is overlooked. A comprehensive survey tool has been developed by the MPPG 15 task group and is available at https://www.aapm.org/pubs/MPPG .
ORAL EXIT SUMMARY At the conclusion of the review process, the reviewer should provide an informal, oral summary of key observations to the incumbent. The purpose of this summary is to clarify any misinterpretations or omissions by the reviewer, and for the incumbent to provide relevant context to the reviewer's observations before the reviewer prepares the written report. In some cases, it may be appropriate to also seek clarification from other key professionals in the practice, such as the medical director or department administrator.
WRITTEN REPORT The reviewer must provide a written report to the incumbent physicist within one month of the on‐site review. If other stakeholders request a report, a stakeholder report should be provided. A copy of this stakeholder report should also be provided to the incumbent physicist. The reports must be identified as “Privileged and Confidential Peer Review” to clearly express the confidential nature of the peer review for the purpose of continuing professional development. Examples are provided in the . The incumbent physicist report should be organized into major and minor recommendations: Major recommendations are items that, in the reviewer's opinion, do not presently meet applicable regulations or minimum practice guidelines, or scenarios that could potentially result in harm to patients or staff. When appropriate, major recommendations should include a reference to relevant national guidance documents such as AAPM Medical Physics Practice Guidelines and ACR Technical Standards. [Major recommendations are expected to be rare in a well‐managed program.] Minor recommendations relate to items that have no impact on regulatory compliance or generally accepted guidelines but could enhance the physics program's productivity or level of organization/documentation. The report should include a summary page that simplifies the recommendations in a straightforward manner, followed by detailed information gathered during the peer review process to elaborate on the recommendations. As a part of the summary, the reviewer should recommend follow‐up in accordance with their findings. The stakeholder report (if provided) should be an executive summary with information that would be instructive to the stakeholders. Examples include staffing recommendations, equipment needs, or organizational suggestions. A general reference to the physics portion of the peer review is appropriate to include, provided it is productive and does not jeopardize the confidence of the peer‐to‐peer nature of the review. The overall intent of the stakeholder report is to demonstrate where the incumbent physicist may require additional support from the organization.
FOLLOW‐UP Given the work that goes into performing the review and generating the final report, it behooves the incumbent and facility to address the recommendations in the report. It is suggested that a follow‐up plan be developed within one month of receiving the report, providing a path to be followed and time frames in which each follow‐up action should be completed. Templates have been developed to assist with the review process and the generation of the peer review report(s). These templates are available for download from the JACMP article page under the “Supporting documentation” link.
This guideline was reviewed and updated by Medical Physics Practice Guideline Task Group 358 of the Professional Council of the AAPM. Each author reviewed recent literature on the topic and offered opinions on and language for the guideline. They also reviewed and applied comments from the full AAPM membership to the document.
The members of Medical Physics Practice Guideline 15.a: Peer Review in Clinical Physics (TG‐358) listed below attest that they have no potential Conflicts of Interest related to the subject matter or materials presented in this document.
Example Peer Review Reports Click here for additional data file. Peer Review Report Template Click here for additional data file. Peer Review Data Collection Tool Click here for additional data file.
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Pan-cancer analysis and experimental validation identify ndc1 as a potential immunological, prognostic and therapeutic biomarker in pancreatic cancer | 58a850df-0767-4801-996c-45c03d9035ac | 10564436 | Internal Medicine[mh] | Cancer has become the second most critical disease that threatens the health of people, which makes the pathogenesis, development, and treatment of cancer a hot spot in the field of medicine. The Cancer Genome Atlas (TCGA) is a publicly funded project developed by the U.S. National Institutes of Health (NIH) and is a landmark cancer genomics program spanning 33 cancer types . In 2012, the “Pan-Cancer Analysis” project was initiated. The goal of the project is to identify the origin of the tumors, define cancer lineages, and help formulate therapeutic strategies by studying the differences and commonalities between tumors . NDC1 Transmembrane Nucleoporin (NDC1) is a transmembrane nucleoporin, also known as Transmembrane Protein 48 (TMEM48), that contains 656 amino acids involved in 6–7 transmembrane constructs . It participates in cell mitosis by serving as a component of the nuclear pore complex and the spindle. Studies show that NDC1 can control nuclear pore complex (NPC) density and nuclear size in the yeast and early C. elegans embryo . For the past few years, increasing studies have been devoted to the role of NDC1 in tumors. Qiao W et al. found that high expression of NDC1 was associated with higher tumor stage, lymph node metastasis, larger tumor size and shorter survival time in patients with non-small cell lung cancer (NSCLC). He W et al. revealed that NDC1 was a key prognostic factor for esophageal squamous-cell carcinoma. Liu M et al. showed that NDC1 was an independent prognostic factor for colon cancer. Studies have also reported that NDC1 is involved in tumor cell proliferation, migration and invasion. For example, Qiao W et al. reported that suppression of NDC1 expression could lead to apoptosis of NSCLC cells and inhibition of cell adhesion, migration, invasion and tumorigenicity. Akkafa F et al. revealed that miR-421 inhibited NDC1 expression in A549 NSCLC cells, thereby advancing cell apoptosis and increasing the expression of Caspase3, PTEN and TP53. Jiang XY et al. uncovered that NDC1 could advance the initiation and progression of cervical cancer via the Wnt/β-catenin pathway. Qing L et al. showed that NDC1 is a prognostic and immunotherapy predictor of hepatocellular carcinoma and that overexpression of NDC1 can promote the migration and invasion of hepatocellular carcinoma. In the field of oncology, NDC1 has shown its potential as a prognostic marker for multiple tumors. In addition, it is involved in the proliferation, migration and invasion of tumor cells and also affects the chemo-resistance of tumor cells with the activity against apoptosis . The tumor microenvironment (TME) mainly consists of blood vessels, immune cells, fibroblasts, stromal cells, and the extracellular matrix . The interaction between tumor and TME promotes the proliferation, migration and invasion of tumor, and the role of immune cells in TME is particularly important . Although the immune system can eliminate tumors through the cancer immune cycle, tumors appear to ultimately evade immune surveillance by shaping the immunosuppressive microenvironment. Immunotherapy, as a new tumor treatment after surgery, radiotherapy and chemotherapy, can remodel the TME and restore the tumor killing ability of anti-tumor immune cells . Nivolumab, as an inhibitor of programmed death-1, has an objective response rate of 40–45% for non-small cell lung cancer . Therefore, it is particularly important to find tumor immune-related therapeutic targets through TME analysis. In the current study, three databases, TCGA, Genotype-Tissue Expression (GTEx) and Cancer Cell Line Encyclopedia (CCLE), were visited to study the expression of NDC1 in 33 types of cancer and explore its prognostic significance. In the meantime, we also explored the potential association of NDC1 with TME, drug sensitivity, Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), tumor mutational burden (TMB), microsatellite instability (MSI). Notably, we focus on pancreatic cancers to analyze the prognostic significance of NDC1. The results revealed that NDC1 is closely related to the immunity in tumor and has the potential as a target for tumor therapy .
Expression and prognosis of NDC1 in pan-cancer Expression of NDC1 was analyzed in 33 cancer types based on data from TCGA and GTEx. Higher expression of NDC1 was demonstrated in 26 cancer types, including Adrenocortical carcinoma (ACC), Bladder Urothelial Carcinoma (BLCA), Breast invasive carcinoma (BRCA), Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), Cholangiocarcinoma (CHOL), Colon adenocarcinoma (COAD), Esophageal carcinoma (ESCA), Glioblastoma multiforme (GBM), Head and Neck squamous cell carcinoma (HNSC), Kidney Chromophobe (KICH), Acute Myeloid Leukemia (LAML), Brain Lower Grade Glioma (LGG), Liver Hepatocellular carcinoma (LIHC), Lung adenocarcinoma (LUAD), Lung squamous cell carcinoma (LUSC), Ovarian serous cystadenocarcinoma (OV), Pancreatic adenocarcinoma (PAAD), Prostate adenocarcinoma (PRAD), Rectum adenocarcinoma (READ), Sarcoma (SARC), Skin Cutaneous Melanoma (SKCM), Stomach adenocarcinoma (STAD), Testicular Germ Cell Tumors (TGCT), Thyroid carcinoma (THCA), Uterine Corpus Endometrial Carcinoma (UCEC), Uterine Carcinosarcoma (UCS), as compared to the expression in normal tissues . The expression of NDC1 in different cell lines from the CCLE database was shown in . In addition, the expression of NDC1 was associated with the tumor stage in ACC, BRCA, COAD and LIHC . The prognostic significance of NDC1 for overall survival (OS) and progression-free survival (PFS) of cancer patients was analyzed by Univariate Cox regression. It was found that NDC1 expression was closely related to the OS of patients with ACC, COAD, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC), KICH, Kidney renal papillary cell carcinoma (KIRP), LGG, LIHC, MESO, PAAD, READ, SARC, STAD, THYM, UCEC and Uveal Melanoma (UVM) . Notably, high expression of NDC1 was prognostic for poor OS of ACC, LGG, LIHC, LUAD, PAAD and SARC . In the meantime, NDC1 expression was closely related to the PFS of patients with ACC, COAD, KICH, KIRP, LGG, LIHC, LUAD, Mesothelioma (MESO), PAAD, PRAD, SARC, STAD, UCEC, UVM , and high expression of NDC1 was prognostic for poor PFS of KIRP, LGG, LIHC, PAAD, PRAD and UCEC . NDC1 expression and immune infiltration The TME plays an important role in tumor diagnosis, survival outcome and clinical therapeutic sensitivity. Here, we found that the expression of NDC1 was closely associated with the immune infiltration in TME. Specifically, NDC1 was significantly associated with T cells follicular helper in 11 cancer types, with Macrophages M1 in 16 cancer types and with T cells CD4 memory resting in 12 cancer types . In addition, analysis in PAAD also showed significant associations with Antigen_processing_machinery, Mismatch_Repair, Nucleotide_excision_repair, DNA_damage_response, DNA_replication, Base_excision_repair . NDC1 expression and immune-related genes/tumor-regulatory genes Correlation analysis was performed to analyze the associations of NDC1 expression in pan-cancer with multiple immune-related genes, including major histocompatibility complexes (MHC), immunostimulator, immunoinhibitor, chemokine and chemokine receptor, immune checkpoint. The results revealed that the NDC1 expression was significantly associated with almost all the immune-related genes . Additionally, NDC1 was also demonstrated to be associated with the common tumor-regulatory genes involved in TGF BETA SIGNALING, TNFA SIGNALING, hypoxia, pyroptosis, DNA repair, autophagy and ferroptosis . NDC1 expression and TMB/MSI in pan-cancer TMB and MSI are two novel biomarkers for tumor immunotherapy. TMB refers to the number of somatic non-synonymous mutations in a specific genomic region, which can indirectly reflect the ability and degree of neoantigen production of tumors and predict the efficacy of immunotherapy in a variety of tumors . MSI refers to the phenomenon of short, repetitive DNA sequence length changes caused by insertion or deletion mutations during DNA replication, and is often caused by defective MMR function . Correlation analysis revealed that NDC1 expression was significantly associated with TMB in THCA, KICH, ACC, STAD, LAML, READ, SKCM, LUAD, COAD, TGCT, OV, PRAD, UCEC, LGG and evidently associated with MSI in PRAD, THCA, STAD, KIRC, COAD, READ, SARC, LIHC . NDC1 expression and drug sensitivity in pan-cancer The combination of surgery with chemotherapy has significant therapeutic effect in treatment of early tumors, for example, patients with pancreatic cancer who received adjuvant chemotherapy with gemcitabine after surgery had a higher median overall survival than those who received no chemotherapy compared with those who received observation, and dual therapy provided a higher survival benefit than gemcitabine alone . Here, the CellMiner database was visited to analyze the relationship between NDC1 expression and some common anti-tumor drugs. It was found that high NDC1 expression was highly associated with the development of resistance to multiple anti-tumor drugs . More specifically, NDC1 expression was positively associated with the drug resistance of Chelerythrine, Allopurinol, 8-Chloro-adenosine, PX-316, Nelarabine, Parthenolide, but negatively associated with 7-Tert-butyldimethylsilyl-10-hydroxycamptothecin and Elliptinium Acetate . GSVA and GSEA of NDC1 in PAAD To further study the molecular mechanisms of NDC1 in PAAD, PAAD samples were divided into high- and low-expression groups according to the median NDC1 expression. GSVA was performed and the results showed that high NDC1 was mainly involved in E2F_TARGETS, G2M_CHECKPOINT, MTORC1_SIGNALING, UNFOLDED_PROTEIN_RESPONSE and MYC_TARGETS_V1 in PAAD . GSEA was performed and the results showed that high NDC1 was mainly involved in CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION, PATHWAYS_IN_CANCER, REGULATION_OF_ACTIN_CYTOSKELETON, SPLICEOSOME, UBIQUITIN_MEDIATED_PROTEOLYSIS in PAAD . Weighted gene coexpression network analysis (WGCNA) in PAAD We further constructed WGCNA network based on PAAD expression profile data to explore NDC1-related co-expression network in PAAD. The outlier samples were deleted, and the samples with a height below 20000 were retained for subsequent analysis after clustering the samples. The β was set as 6 according to “sft$powerEstimate” function. A total of 16 gene modules were obtained, including MEblack (270), MEblue (504), MEbrown (491), MEcyan (75), MEgreen (365), MEgreenyellow (166), MEgrey (278), MEmagenta (213), MEmidnightblue (68), MEpink (225), MEpurple (179), MEred (319), MEsalmon (85), MEtan (98), MEturquoise (1,280), and MEyellow (384). Module-trait correlation analysis found that the MEgreenyellow module was the highest associated with the clinical traits of PAAD (cor = 0.32, p = (2e-04)) . Genes of the MEgreenyellow module was further analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The most enriched GO terms were response to endoplasmic reticulum stress, response to topologically incorrect protein, response to unfolded protein , and the most enriched KEGG pathways were involved in Protein processing in endoplasmic reticulum, Vibrio cholerae infection, Various types of N−glycan biosynthesis . Prognostic significance of NDC1 in PAAD A nomogram for prognosis of PAAD based on NDC1 expression and several significant clinical characteristics was established. NDC1 expression was found to be significantly prognostic for survival of PAAD . In addition, this study draws correction curves for one year and three years at the same time, and finds that the OS predicted by Nomo chart is in good agreement with the actual observed OS, and is close to the slope, suggesting that Nomo chart has good prediction efficiency. . Chemosensitivity of NDC1 in PAAD Based on the drug sensitivity data of the GDSC database, our study used the R package “pRRophetic” to predict the chemosensitivity of each tumor sample, and further explores the sensitivity of NDC1 to common chemotherapeutic drugs in PAAD. The results showed that the expression of NDC1 in PAAD samples was correlated with the sensitivity of patients to Gemcitabine, Cisplatin, and Sorafenib . Knockdown of NDC1 inhibits the proliferation, migration and apoptosis in PC The effect of NDC1 on pancreatic cancer cell function was determined by loss-of-function experiments expressed in pancreatic cancer (PC) and contributes to drug resistance and poor prognosis. In vitro , we silenced the expression of NDC1 in human pancreatic cancer cell lines BxPC-3 and MIA PaCa-2, respectively, and the interference efficiency was verified by RT-qPCR and Western blot analysis . The results of Our pan-cancer analysis showed that NDC1 is highly MTT and 5-ethynyl-20 deoxyuridine (EdU) experiments showed that silencing NDC1 could significantly inhibit the proliferation of pancreatic cancer cells . Scratch assay and apoptosis assay showed that downregulation of NDC1 expression reduced cell migration ability and promoted cell apoptosis .
Expression of NDC1 was analyzed in 33 cancer types based on data from TCGA and GTEx. Higher expression of NDC1 was demonstrated in 26 cancer types, including Adrenocortical carcinoma (ACC), Bladder Urothelial Carcinoma (BLCA), Breast invasive carcinoma (BRCA), Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), Cholangiocarcinoma (CHOL), Colon adenocarcinoma (COAD), Esophageal carcinoma (ESCA), Glioblastoma multiforme (GBM), Head and Neck squamous cell carcinoma (HNSC), Kidney Chromophobe (KICH), Acute Myeloid Leukemia (LAML), Brain Lower Grade Glioma (LGG), Liver Hepatocellular carcinoma (LIHC), Lung adenocarcinoma (LUAD), Lung squamous cell carcinoma (LUSC), Ovarian serous cystadenocarcinoma (OV), Pancreatic adenocarcinoma (PAAD), Prostate adenocarcinoma (PRAD), Rectum adenocarcinoma (READ), Sarcoma (SARC), Skin Cutaneous Melanoma (SKCM), Stomach adenocarcinoma (STAD), Testicular Germ Cell Tumors (TGCT), Thyroid carcinoma (THCA), Uterine Corpus Endometrial Carcinoma (UCEC), Uterine Carcinosarcoma (UCS), as compared to the expression in normal tissues . The expression of NDC1 in different cell lines from the CCLE database was shown in . In addition, the expression of NDC1 was associated with the tumor stage in ACC, BRCA, COAD and LIHC . The prognostic significance of NDC1 for overall survival (OS) and progression-free survival (PFS) of cancer patients was analyzed by Univariate Cox regression. It was found that NDC1 expression was closely related to the OS of patients with ACC, COAD, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC), KICH, Kidney renal papillary cell carcinoma (KIRP), LGG, LIHC, MESO, PAAD, READ, SARC, STAD, THYM, UCEC and Uveal Melanoma (UVM) . Notably, high expression of NDC1 was prognostic for poor OS of ACC, LGG, LIHC, LUAD, PAAD and SARC . In the meantime, NDC1 expression was closely related to the PFS of patients with ACC, COAD, KICH, KIRP, LGG, LIHC, LUAD, Mesothelioma (MESO), PAAD, PRAD, SARC, STAD, UCEC, UVM , and high expression of NDC1 was prognostic for poor PFS of KIRP, LGG, LIHC, PAAD, PRAD and UCEC .
The TME plays an important role in tumor diagnosis, survival outcome and clinical therapeutic sensitivity. Here, we found that the expression of NDC1 was closely associated with the immune infiltration in TME. Specifically, NDC1 was significantly associated with T cells follicular helper in 11 cancer types, with Macrophages M1 in 16 cancer types and with T cells CD4 memory resting in 12 cancer types . In addition, analysis in PAAD also showed significant associations with Antigen_processing_machinery, Mismatch_Repair, Nucleotide_excision_repair, DNA_damage_response, DNA_replication, Base_excision_repair .
Correlation analysis was performed to analyze the associations of NDC1 expression in pan-cancer with multiple immune-related genes, including major histocompatibility complexes (MHC), immunostimulator, immunoinhibitor, chemokine and chemokine receptor, immune checkpoint. The results revealed that the NDC1 expression was significantly associated with almost all the immune-related genes . Additionally, NDC1 was also demonstrated to be associated with the common tumor-regulatory genes involved in TGF BETA SIGNALING, TNFA SIGNALING, hypoxia, pyroptosis, DNA repair, autophagy and ferroptosis .
TMB and MSI are two novel biomarkers for tumor immunotherapy. TMB refers to the number of somatic non-synonymous mutations in a specific genomic region, which can indirectly reflect the ability and degree of neoantigen production of tumors and predict the efficacy of immunotherapy in a variety of tumors . MSI refers to the phenomenon of short, repetitive DNA sequence length changes caused by insertion or deletion mutations during DNA replication, and is often caused by defective MMR function . Correlation analysis revealed that NDC1 expression was significantly associated with TMB in THCA, KICH, ACC, STAD, LAML, READ, SKCM, LUAD, COAD, TGCT, OV, PRAD, UCEC, LGG and evidently associated with MSI in PRAD, THCA, STAD, KIRC, COAD, READ, SARC, LIHC .
The combination of surgery with chemotherapy has significant therapeutic effect in treatment of early tumors, for example, patients with pancreatic cancer who received adjuvant chemotherapy with gemcitabine after surgery had a higher median overall survival than those who received no chemotherapy compared with those who received observation, and dual therapy provided a higher survival benefit than gemcitabine alone . Here, the CellMiner database was visited to analyze the relationship between NDC1 expression and some common anti-tumor drugs. It was found that high NDC1 expression was highly associated with the development of resistance to multiple anti-tumor drugs . More specifically, NDC1 expression was positively associated with the drug resistance of Chelerythrine, Allopurinol, 8-Chloro-adenosine, PX-316, Nelarabine, Parthenolide, but negatively associated with 7-Tert-butyldimethylsilyl-10-hydroxycamptothecin and Elliptinium Acetate .
To further study the molecular mechanisms of NDC1 in PAAD, PAAD samples were divided into high- and low-expression groups according to the median NDC1 expression. GSVA was performed and the results showed that high NDC1 was mainly involved in E2F_TARGETS, G2M_CHECKPOINT, MTORC1_SIGNALING, UNFOLDED_PROTEIN_RESPONSE and MYC_TARGETS_V1 in PAAD . GSEA was performed and the results showed that high NDC1 was mainly involved in CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION, PATHWAYS_IN_CANCER, REGULATION_OF_ACTIN_CYTOSKELETON, SPLICEOSOME, UBIQUITIN_MEDIATED_PROTEOLYSIS in PAAD .
We further constructed WGCNA network based on PAAD expression profile data to explore NDC1-related co-expression network in PAAD. The outlier samples were deleted, and the samples with a height below 20000 were retained for subsequent analysis after clustering the samples. The β was set as 6 according to “sft$powerEstimate” function. A total of 16 gene modules were obtained, including MEblack (270), MEblue (504), MEbrown (491), MEcyan (75), MEgreen (365), MEgreenyellow (166), MEgrey (278), MEmagenta (213), MEmidnightblue (68), MEpink (225), MEpurple (179), MEred (319), MEsalmon (85), MEtan (98), MEturquoise (1,280), and MEyellow (384). Module-trait correlation analysis found that the MEgreenyellow module was the highest associated with the clinical traits of PAAD (cor = 0.32, p = (2e-04)) . Genes of the MEgreenyellow module was further analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The most enriched GO terms were response to endoplasmic reticulum stress, response to topologically incorrect protein, response to unfolded protein , and the most enriched KEGG pathways were involved in Protein processing in endoplasmic reticulum, Vibrio cholerae infection, Various types of N−glycan biosynthesis .
A nomogram for prognosis of PAAD based on NDC1 expression and several significant clinical characteristics was established. NDC1 expression was found to be significantly prognostic for survival of PAAD . In addition, this study draws correction curves for one year and three years at the same time, and finds that the OS predicted by Nomo chart is in good agreement with the actual observed OS, and is close to the slope, suggesting that Nomo chart has good prediction efficiency. .
Based on the drug sensitivity data of the GDSC database, our study used the R package “pRRophetic” to predict the chemosensitivity of each tumor sample, and further explores the sensitivity of NDC1 to common chemotherapeutic drugs in PAAD. The results showed that the expression of NDC1 in PAAD samples was correlated with the sensitivity of patients to Gemcitabine, Cisplatin, and Sorafenib .
The effect of NDC1 on pancreatic cancer cell function was determined by loss-of-function experiments expressed in pancreatic cancer (PC) and contributes to drug resistance and poor prognosis. In vitro , we silenced the expression of NDC1 in human pancreatic cancer cell lines BxPC-3 and MIA PaCa-2, respectively, and the interference efficiency was verified by RT-qPCR and Western blot analysis . The results of Our pan-cancer analysis showed that NDC1 is highly MTT and 5-ethynyl-20 deoxyuridine (EdU) experiments showed that silencing NDC1 could significantly inhibit the proliferation of pancreatic cancer cells . Scratch assay and apoptosis assay showed that downregulation of NDC1 expression reduced cell migration ability and promoted cell apoptosis .
In this study, the expression level of NDC1 in 33 tumors was evaluated by pan-cancer analysis, and the effects of NDC1 on the proliferation, invasion and migration of pancreatic cancer were experimentally verified for the first time. In the meantime, we also found that NDC1 expression was closely associated with the OS and PFS of multiple cancer types. The most important finding of the study is the significant association with tumor immune infiltration. It is noting that NDC1 expression was profoundly associated with almost all immune-related genes and common tumor-regulatory genes. GSEA and GSVA were performed to analyze the NDC1-mediated signaling pathways. Finally, we analyzed the prognostic significance and chemosensitivity of NDC1 in PAAD, and explored possible molecular mechanisms using WGCNA, GO annotation and KEGG pathway enrichment analysis. At present, studies on NDC1 mainly are involved in lung cancer, colon cancer, cervical cancer, esophageal cancer and hepatocellular carcinoma , and there was no previous report in pancreatic cancers. The latest research revealed that pancreatic cancer is a highly malignant cancer and the 5-year survival rate is reported to 3% only . Given the not obvious clinical symptoms in early stages, pancreatic cancer is readily to be diagnosed at an advanced stage, missing the optimal timing of treatment. Therefore, it is of crucial significance to conduct studies on early diagnosis, prognosis and treatment of the pancreatic cancer. In order to improve the early diagnosis rate and clinical efficacy of pancreatic cancer, some scholars have discovered and verified some biomarkers of the pancreatic cancer in recent years, such as S100 Calcium Binding Protein A6 (S100A6) and Glypican 2 (GPC2) . In the current study, we performed WGCNA, GO annotation and KEGG pathway enrichment analysis to obtain co-expressed genes of NDC1 in PAAD and subsequently explored possible mechanisms. Moreover, corresponding nomograms were established and proved that NDC1 was significantly prognostic for survival of PAAD. Immunotherapy has emerged as an effective treatment for cancer due to the fact that, tumor cells can escape from the attacks by the body’s immune system, which allows them to proliferate with no restrictions . This therapy works with the active and passive tumor targeting strategies , via a variety of approaches, including recombinant vaccine, ICI, adoptive cell transfer (ACT), cytokine therapy and oncolytic virus therapy . In clinical setting, immunotherapy has shown satisfactory effect when combined with the traditional chemotherapy and radiotherapy . Immune cells are important in immunotherapy, and a good understanding on the immune infiltration in TME is key to the improvement of therapeutic outcome and development of new immunotherapeutic strategies . In the present study, CIBERSORT algorithm was applied. The expression of NDC1 is generally associated with the high expression of T cells follicular helper, Neutrophils, T cells CD4 memory activated and the low expression of T cells CD8, NK cells activated and Monocytes in 33 tumor types. This suggests that we should focus on these immune cells in the immunotherapy study of NDC1. In PAAD, NDC1 expression was analyzed to show a significant association with the infiltration of T cells regulatory. These results, to some extent, provide evidence for the development of immunotherapy for pancreatic cancer in clinics. TMB and MSI are recognized biomarkers of response to immune checkpoint inhibitors (ICIs) . The two are independent of each other and also correlated with each other. Despite multiple tumors with high MSI having high TMB as well, there are also some tumors with high TMB do not show MSI deficiency . It is well believed that tumorigenesis is accompanied by gene mutations . Proteins expressed by the mutated genes can be recognized by MHC, which then triggers a series of responses reacted by the body’s immune system . The more mutations, the more antigens and the higher possibility to be recognized by the immune system. Both TMB and MSI can be used to recognize mutated genes. In this context, patients with high TMB and MSI can response better to immunotherapy . Combining the correlation between NDC1 expression and some common tumor-regulatory genes and the enriched pathways identified by GSEA and GSVA, we speculated that NDC1 might be implicated in tumor via other pathways, except immune-related pathways, which requires further exploration. At present, the treatment of tumor includes radiotherapy, chemotherapy, surgery, immunotherapy, etc., but chemotherapy is still the most common treatment. Clinically, patients with long-term chemotherapy are prone to multidrug resistance, resulting in a decline in the treatment effect. Studies have shown that over 90% mortality of cancer patients is attributed to drug resistance . Modified leucovorin, 5-fluorouracil, irinotecan and oxaliplatin (mFOLFIRINOX) are the first-line chemotherapy regimens for pancreatic cancer . Compared with finding new chemotherapeutic drugs, it is more important to solve the problem of chemotherapeutic drug resistance. In this study, it is proposed for the first time that NDC1 is related to tumor chemotherapy resistance, and then we need to analyze the specific drugs targeting NDC1 drug sensitivity to pancreatic cancer through in-depth study, in order to further guide clinical drug use.
To conclude, we identified the importance of NDC1, particularly in pancreatic cancer, by pan-cancer analysis. This study proved that NDC1 could be used as a potential immunological, prognostic and therapeutic target for pancreatic cancer. However, this study has certain limitations, including the lack of in vivo studies on NDC1 in pancreatic cancer and the specific molecular biological mechanism of NDC1 regulation of pancreatic cancer, etc. Further studies are still needed to guide clinical diagnosis and treatment in the future.
Data acquisition and differential analysis TCGA ( https://portal.gdc.cancer.gov/ ) is currently known as the largest database that covers cancer genomics information including gene expression data, copy number variation (CNV) and single nucleotide polymorphism (SNP), etc. Here, mRNA expression data and SNP data of 33 cancer types were downloaded from the TCGA database and performed log2 normalization of gene expression levels for subsequent analysis. Gene expression data of different tissue samples were also obtained from the GTEx database ( https://commonfund.nih.gov/GTEx ). Data from the TCGA and GTEx databases were combined and corrected through the normalize Between Arrays function, after which differential analysis was performed to analyze the differential expression of NDC1 in different cancers. In addition, CCLE database ( https://portals.broadinstitute.org/ccle/ ) was visited to download tumor cell data and then the expression of NDC1 in corresponding tumor tissues was analyzed. The relationship between the NDC1 expression and tumor stage was also explored. According to the expression level of NDC1 gene, the expression difference of NDC1 expression in different stages of tumor was analyzed. The comparison between two groups was conducted by Wilcox test, and the comparison between multiple groups was conducted by Kruskal test. Prognostic analysis Survival data of TCGA samples, including OS and PFS, were downloaded from the Xena database ( https://xenabrowser.net/ ) to study the prognostic significance of NDC1. Kaplan-Meier method was applied for survival analysis with packages “survival” and “survminer”. Univariate Cox regression analysis was conducted to discuss the prognostic significance of NDC1 using packages “survival” and “forestplot” in pan-cancer. Immune cell infiltration analysis CIBERSORT algorithm was adopted to analyze the RNA-seq data of 33 cancer types. The infiltration abundance of immune cells was inferred and the association with NDC1 expression was analyzed. In the meantime, the TISIDB website ( http://cis.hku.hk/TISIDB ) was visited to explore the potential associations of NDC1 expression with immune-related genes, such as chemokine, immunoinhibitor, immunostimulator and MHC. TMB and MSI analysis TMB is defined as the number of somatic, coding, base substitution, and indel mutations per megabase of genome examined. Here, TMB was defined as the ratio of the length of protein-coding region of non-synonymous variants to the total length of protein-coding region, based on the variation frequency and number/exon length of each tumor sample. The MSI of each TCGA sample was derived from a previous published literature . Drug sensitivity analysis The CellMiner database ( https://discover.nci.nih.gov/cellminer/home.do ) is designed for the cancer research community to facilitate integration and study of molecular and pharmacological data for the NCI-60 cancerous cell lines, which are a panel of 60 diverse human cancer cell lines used by the Developmental Therapeutics Program of the U.S. NCI to screen over 100,000 chemical compounds and natural products. The NCI-60 panel is now widely used in anti-cancer drug tests. NCI-60 dataset of drug sensitivity and RNA-seq data were obtained to study the relationship between NDC1 expression and anti-tumor drug sensitivity. GSVA GSVA is a nonparametric, unsupervised method used to perform transcriptome and gene set enrichment analysis. It can confer an enrichment score to a certain gene set of each sample and estimate variation of pathway activity by transforming gene expression variations. Here, related gene sets were obtained from Molecular signatures database (v7.0) to estimate the variation of pathway activity in different samples. GSEA GSEA ranks genes based on their differential expression between two sample types and then uses a pre-defined gene set to study whether the gene set is enriched in the top or the bottom of the rank list. GSEA was performed using packages “ClusterProfiler” and “enrichplot” to analyze the differential signaling pathways between the high- and low-expression groups stratified by the median NDC1 expression, and possible molecular mechanisms were discussed. Weighted correlation network analysis (WGCNA) WGCNA was performed to find co-expressed genes of NDC1 and study the association with clinical traits. The R package “WGCNA” was used to establish a co-expression network based on the top 5,000 genes with differential expression, the “hclust” function was used to cluster samples, and the “pickSoftThreshold” function was used to calculate the soft threshold. The soft-thresholding power (β) was set as 6 for PAAD. The weighted adjacency matrix was transformed to a topological overlap matrix (TOM) to estimate network connectivity, and a clustering tree was generated using the hierarchical clustering method. Each branch represents a gene module and differs by colors. According to the weighted correlation coefficient of each gene, genes of similar expression patterns were clustered into the same module. Enrichment analysis The key gene module was identified. R package “ClusterProfiler” was used to perform functional annotation for the genes in the key module. GO and KEGG were employed to estimate related biological functions. P < 0.05 or q < 0.05 was considered statistically significant. Nomogram establishment Nomogram is generally established for clinical use. It provides a scale according to the contribution (multi-variate regression coefficient) of each variable in prognosis (gene expression and several significant clinical features here). Each variable was conferred a score and the total score was used to estimate the prognosis of patients. Calibration curve is a tool used to evaluate the accuracy of model prediction results. cph function of RMS software package is used to construct Cox proportional risk regression model according to clinical symptoms and NDC1 expression, and the calibration curve of the model is drawn. Chemosensitivity Based on the largest pharmacogenomics database (GDSC Cancer Drug Sensitivity Genomics Database, https://www.cancerrxgene.org/ ), we used the R package “pRRophetic” to predict the chemosensitivity, chemotherapeutic drugs of each tumor sample Includes Paclitaxel, Gemcitabine, Cisplatin, Gefitinib, Dasatinib and Sorafenib. Cell culture and siRNAs transfection The human pancreatic cancer cell lines BxPC-3 and MIA-PaCa2 were procured from the China Center for Type Culture Collection (CCTCC). The cells were grown in Dulbecco’s Modified Eagle Medium (DMEM; HyClone, Cat#SH30022.01, USA) media with 10% Certified Foetal Bovine Serum (FBS; BI, Cat#04-001-1ACS, Israel) and 1% penicillin/streptomycin (Invitrogen, Grand Island, NY, USA). The cells were cultured at 37°C in a 5% CO 2 incubator. The BxPC-3 and MIA-PaCa2 cell lines were transfected by siRNAs designed and synthesized by RiboBio Co., Ltd., (Guangzhou, China) using Opti-MEM (Invitrogen, Cat#31985-070, USA) medium and Lipofectamine™ 3000 Transfection Reagent (Invitrogen, Cat#L3000-008, USA). The cell lines were randomly divided into two groups: the si-NC group transfected with the scrambled siRNA was considered as the negative control, the si-NDC1 group was transfected with the siRNA specific for NDC1. Western blot analysis After the cells were transfected for 48 h, Western blot analysis was performed as previously described . β-actin was used as normalization. All the blots were incubated with the respective primary antibodies, anti-NDC1 (GeneTex, CTX120091, China), anti-E-cadherin (Proteintech Group, Cat#20874-1-AP, China), anti-N-cadherin (Proteintech Group, Cat#22018-1-AP, China), anti-β-actin (Proteintech Group, Cat #81115-1-RR, China). The protein bands were visualized with electrochemiluminescence (Bio-Rad, USA). qRT-PCR According to the manufacturer’s protocol, ChamQ SYBR qPCR Master Mix (Vazyme, Nanjing, China, Cat#Q311-02) on an RT-PCR system (CFX96 Touch; Bio-Rad, USA) are used to verify the interference efficiency of transfection of siRNAs. The primers sequences used for qRT-PCR were obtained from Applied Biosystems (Ribo, Guangzhou, China) as below (5′–3′): CATACTGTGGCGCGTTTTGG (forward primer), GCAGGGCTACCAAAGCTGTTA (reverse primer). Relative gene expression levels were calculated according to the 2−ΔΔCt value method. Cell proliferation, migration and apoptosis assays The effect of NDC1 on the proliferation of BxPC-3 and MIA-PaCa2 cells was assayed using MTT and EdU labelling assays according to the manufacturers’ instructions. The transfected cells were inoculated into 96-well plates with 5 × 10 3 cells per well. At 24, 48, 72, 96 hours, 20 μL MTT solution was added to the medium and cells were incubated for 4 hours. After discarding the medium, 200 μL DMSO was added to the cells and the formazan crystal was dissolved for 15 minutes. The optical density (OD) value at 450 nm was detected by the enzyme-linked immunometric meter (Thermo Fisher Scientific). For the EdU labeling assay, we used an EdU Cell Proliferation Kit with Alexa Fluor 555 (Beyotime, Cat#C0075L, China) to determine cell viability according to the manufacturer’s instructions. BxPC-3 and MIA-PaCa2 cells were seeded in each well of a 6-well plate to form a monolayer overnight. A 200 μL pipette tip was used to create an artificial wound. After washing with phosphate-buffered saline (PBS) twice, cells were cultured in DMEM with 2% FBS for 24 h in a 37°C incubator, and wounds were visualized at 0 and 24 h. The distance of the wounds was measured using Adobe Illustrator CC 2018 software. The apoptotic cells were detected using annexin V-FITC along with the PI solution, by flow cytometry assay according to the manufacturer’s instruction (Annexin V-FITC apoptosis detection kit, Vazyme, Cat# A211-02, China). The experiments were performed three times in triplicate. Statistical analysis R 4.0 was applied to complete all statistical analyses. Univariate Cox regression analysis was performed, and Hazards ratios (HR) along with 95% confidence interval (CI) were calculated. Kaplan-Meier curve was generated to study the survival of patients with high- and low-expression of NDC1. GraphPad Prism 9.5 was used to analyze the data. Two-sided P < 0.05 was considered statistically significant. Data availability statement The data used to support the findings of this study are available from the corresponding authors upon request.
TCGA ( https://portal.gdc.cancer.gov/ ) is currently known as the largest database that covers cancer genomics information including gene expression data, copy number variation (CNV) and single nucleotide polymorphism (SNP), etc. Here, mRNA expression data and SNP data of 33 cancer types were downloaded from the TCGA database and performed log2 normalization of gene expression levels for subsequent analysis. Gene expression data of different tissue samples were also obtained from the GTEx database ( https://commonfund.nih.gov/GTEx ). Data from the TCGA and GTEx databases were combined and corrected through the normalize Between Arrays function, after which differential analysis was performed to analyze the differential expression of NDC1 in different cancers. In addition, CCLE database ( https://portals.broadinstitute.org/ccle/ ) was visited to download tumor cell data and then the expression of NDC1 in corresponding tumor tissues was analyzed. The relationship between the NDC1 expression and tumor stage was also explored. According to the expression level of NDC1 gene, the expression difference of NDC1 expression in different stages of tumor was analyzed. The comparison between two groups was conducted by Wilcox test, and the comparison between multiple groups was conducted by Kruskal test.
Survival data of TCGA samples, including OS and PFS, were downloaded from the Xena database ( https://xenabrowser.net/ ) to study the prognostic significance of NDC1. Kaplan-Meier method was applied for survival analysis with packages “survival” and “survminer”. Univariate Cox regression analysis was conducted to discuss the prognostic significance of NDC1 using packages “survival” and “forestplot” in pan-cancer.
CIBERSORT algorithm was adopted to analyze the RNA-seq data of 33 cancer types. The infiltration abundance of immune cells was inferred and the association with NDC1 expression was analyzed. In the meantime, the TISIDB website ( http://cis.hku.hk/TISIDB ) was visited to explore the potential associations of NDC1 expression with immune-related genes, such as chemokine, immunoinhibitor, immunostimulator and MHC.
TMB is defined as the number of somatic, coding, base substitution, and indel mutations per megabase of genome examined. Here, TMB was defined as the ratio of the length of protein-coding region of non-synonymous variants to the total length of protein-coding region, based on the variation frequency and number/exon length of each tumor sample. The MSI of each TCGA sample was derived from a previous published literature .
The CellMiner database ( https://discover.nci.nih.gov/cellminer/home.do ) is designed for the cancer research community to facilitate integration and study of molecular and pharmacological data for the NCI-60 cancerous cell lines, which are a panel of 60 diverse human cancer cell lines used by the Developmental Therapeutics Program of the U.S. NCI to screen over 100,000 chemical compounds and natural products. The NCI-60 panel is now widely used in anti-cancer drug tests. NCI-60 dataset of drug sensitivity and RNA-seq data were obtained to study the relationship between NDC1 expression and anti-tumor drug sensitivity.
GSVA is a nonparametric, unsupervised method used to perform transcriptome and gene set enrichment analysis. It can confer an enrichment score to a certain gene set of each sample and estimate variation of pathway activity by transforming gene expression variations. Here, related gene sets were obtained from Molecular signatures database (v7.0) to estimate the variation of pathway activity in different samples.
GSEA ranks genes based on their differential expression between two sample types and then uses a pre-defined gene set to study whether the gene set is enriched in the top or the bottom of the rank list. GSEA was performed using packages “ClusterProfiler” and “enrichplot” to analyze the differential signaling pathways between the high- and low-expression groups stratified by the median NDC1 expression, and possible molecular mechanisms were discussed.
WGCNA was performed to find co-expressed genes of NDC1 and study the association with clinical traits. The R package “WGCNA” was used to establish a co-expression network based on the top 5,000 genes with differential expression, the “hclust” function was used to cluster samples, and the “pickSoftThreshold” function was used to calculate the soft threshold. The soft-thresholding power (β) was set as 6 for PAAD. The weighted adjacency matrix was transformed to a topological overlap matrix (TOM) to estimate network connectivity, and a clustering tree was generated using the hierarchical clustering method. Each branch represents a gene module and differs by colors. According to the weighted correlation coefficient of each gene, genes of similar expression patterns were clustered into the same module.
The key gene module was identified. R package “ClusterProfiler” was used to perform functional annotation for the genes in the key module. GO and KEGG were employed to estimate related biological functions. P < 0.05 or q < 0.05 was considered statistically significant.
Nomogram is generally established for clinical use. It provides a scale according to the contribution (multi-variate regression coefficient) of each variable in prognosis (gene expression and several significant clinical features here). Each variable was conferred a score and the total score was used to estimate the prognosis of patients. Calibration curve is a tool used to evaluate the accuracy of model prediction results. cph function of RMS software package is used to construct Cox proportional risk regression model according to clinical symptoms and NDC1 expression, and the calibration curve of the model is drawn.
Based on the largest pharmacogenomics database (GDSC Cancer Drug Sensitivity Genomics Database, https://www.cancerrxgene.org/ ), we used the R package “pRRophetic” to predict the chemosensitivity, chemotherapeutic drugs of each tumor sample Includes Paclitaxel, Gemcitabine, Cisplatin, Gefitinib, Dasatinib and Sorafenib.
The human pancreatic cancer cell lines BxPC-3 and MIA-PaCa2 were procured from the China Center for Type Culture Collection (CCTCC). The cells were grown in Dulbecco’s Modified Eagle Medium (DMEM; HyClone, Cat#SH30022.01, USA) media with 10% Certified Foetal Bovine Serum (FBS; BI, Cat#04-001-1ACS, Israel) and 1% penicillin/streptomycin (Invitrogen, Grand Island, NY, USA). The cells were cultured at 37°C in a 5% CO 2 incubator. The BxPC-3 and MIA-PaCa2 cell lines were transfected by siRNAs designed and synthesized by RiboBio Co., Ltd., (Guangzhou, China) using Opti-MEM (Invitrogen, Cat#31985-070, USA) medium and Lipofectamine™ 3000 Transfection Reagent (Invitrogen, Cat#L3000-008, USA). The cell lines were randomly divided into two groups: the si-NC group transfected with the scrambled siRNA was considered as the negative control, the si-NDC1 group was transfected with the siRNA specific for NDC1.
After the cells were transfected for 48 h, Western blot analysis was performed as previously described . β-actin was used as normalization. All the blots were incubated with the respective primary antibodies, anti-NDC1 (GeneTex, CTX120091, China), anti-E-cadherin (Proteintech Group, Cat#20874-1-AP, China), anti-N-cadherin (Proteintech Group, Cat#22018-1-AP, China), anti-β-actin (Proteintech Group, Cat #81115-1-RR, China). The protein bands were visualized with electrochemiluminescence (Bio-Rad, USA).
According to the manufacturer’s protocol, ChamQ SYBR qPCR Master Mix (Vazyme, Nanjing, China, Cat#Q311-02) on an RT-PCR system (CFX96 Touch; Bio-Rad, USA) are used to verify the interference efficiency of transfection of siRNAs. The primers sequences used for qRT-PCR were obtained from Applied Biosystems (Ribo, Guangzhou, China) as below (5′–3′): CATACTGTGGCGCGTTTTGG (forward primer), GCAGGGCTACCAAAGCTGTTA (reverse primer). Relative gene expression levels were calculated according to the 2−ΔΔCt value method.
The effect of NDC1 on the proliferation of BxPC-3 and MIA-PaCa2 cells was assayed using MTT and EdU labelling assays according to the manufacturers’ instructions. The transfected cells were inoculated into 96-well plates with 5 × 10 3 cells per well. At 24, 48, 72, 96 hours, 20 μL MTT solution was added to the medium and cells were incubated for 4 hours. After discarding the medium, 200 μL DMSO was added to the cells and the formazan crystal was dissolved for 15 minutes. The optical density (OD) value at 450 nm was detected by the enzyme-linked immunometric meter (Thermo Fisher Scientific). For the EdU labeling assay, we used an EdU Cell Proliferation Kit with Alexa Fluor 555 (Beyotime, Cat#C0075L, China) to determine cell viability according to the manufacturer’s instructions. BxPC-3 and MIA-PaCa2 cells were seeded in each well of a 6-well plate to form a monolayer overnight. A 200 μL pipette tip was used to create an artificial wound. After washing with phosphate-buffered saline (PBS) twice, cells were cultured in DMEM with 2% FBS for 24 h in a 37°C incubator, and wounds were visualized at 0 and 24 h. The distance of the wounds was measured using Adobe Illustrator CC 2018 software. The apoptotic cells were detected using annexin V-FITC along with the PI solution, by flow cytometry assay according to the manufacturer’s instruction (Annexin V-FITC apoptosis detection kit, Vazyme, Cat# A211-02, China). The experiments were performed three times in triplicate.
R 4.0 was applied to complete all statistical analyses. Univariate Cox regression analysis was performed, and Hazards ratios (HR) along with 95% confidence interval (CI) were calculated. Kaplan-Meier curve was generated to study the survival of patients with high- and low-expression of NDC1. GraphPad Prism 9.5 was used to analyze the data. Two-sided P < 0.05 was considered statistically significant.
The data used to support the findings of this study are available from the corresponding authors upon request.
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Reinventing the Clinical Audit in a Pediatric Oncology Network | 591a8942-3334-4968-9a98-46f5a00dd231 | 10115487 | Internal Medicine[mh] | The goal of the St. Jude Affiliate Program is to allow more children to receive St. Jude care close to home and to increase access to pediatric oncology clinical research trials developed at St. Jude. The eight St. Jude affiliate clinics serve 9 states in the Southeast and Midwest (Fig. ). The affiliate institutions serve rural and suburban areas with a diverse demographic population. The affiliate clinics provide a substantial resource for patient recruitment for St. Jude clinical trials. During the years of this report, the affiliate clinics in aggregate saw an average of 302 new oncology patients per year of which an average of 38% of patients were enrolled on therapeutic primary clinical trials. Each affiliate clinic is part of a not-for-profit health system. No affiliate clinic is part of an institution that provides pediatric stem cell transplantation, and none has pediatric hematology/oncology fellowship programs. Each clinic ranges in size and capacity. Four clinics had 40 to 60 new oncology patients per year and 4 clinics had 20 to 30 new oncology patients per year during the years of this report. The number of providers in each clinic ranged from 3 to 7. Ensuring high-quality care in smaller programs can be challenging and maintaining equitable high-quality care across a remote network is critical for patient safety and an optimal patient experience. We instituted an on-site clinical audit to assess care in the affiliate clinics. Annually, an audit team composed of 1 physician and 1 nurse observed direct patient care in the clinic over a 2-day to 3-day period. They surveyed central line care, chemotherapy administration, patient teaching, blood product administration, and provider-patient interactions. Patient safety and confidentiality were maintained. The observers looked for adherence to a central line bundle based on national standards. During chemotherapy administration, the observers looked for independent dose calculations, verification of chemotherapy orders with the treatment schema, review of laboratory criteria, patient identification, and proper handling of cytotoxic therapy. Patient and family education, including anticipatory guidance, was reviewed. Thirty-six independent items were evaluated (Supplementary Table 1, Supplemental Digital Content 1, http://links.lww.com/JPHO/A584 ). After the first 2 years, the on-site clinical audit continued to demonstrate deficiencies without improvement. The most common deficiencies noted were inconsistent communication when patients transitioned between St. Jude and the affiliate clinics, delay in the time to antibiotics in febrile immunocompromised patients, inconsistent documentation of oral chemotherapy administration and the lack of adherence to a central line bundle in the ambulatory setting. We then developed a more comprehensive approach to the clinical audit. First, we engaged the clinical team being audited. We began surveys of the clinic staff regarding perceptions of their clinic operations to identify perceived areas of strengths and weaknesses. This component enabled the clinic staff to indicate which aspects of their clinic were working well and which aspects could be improved, setting the tone for continuous quality improvement. We identified provider champions for each clinic who received training in quality improvement using the American Society of Clinical Oncology Quality Training Program (ASCO QTP) ( https://practice.asco.org/quality-improvement/quality-programs/quality-training-program ). Each clinic had a dedicated nurse educator with protected time to lead projects. Joint quality improvement projects were facilitated by the Affiliate Nurse Director (J.M.), with multiple affiliate team members participating from various disciplines, including nursing, pharmacy, and physicians. Each month, the affiliate clinic nurse educator submitted their local data on specific quality indicators to a secure dashboard. The dashboard tracked time to administration of antibiotics in immunocompromised children with fever, central line-associated bloodstream infections in ambulatory patients, patient/parent satisfaction scores, medication adverse events, and laboratory adverse events. We started sharing the individual quality metrics with the clinic team, and the data from each clinic was presented anonymously with team members of the 8 clinics. The quality data from each affiliate clinic was shared annually with the respective hospital leadership, including chief medical officers, chief executive officers, and senior leaders.
The clinical audit structure started as an annual, on-site audit. After the first 2 years 2 clinics showed minimal improvement, however, there was no improvement at the other 6 clinics. After instituting the additional audit components of self-reflection, data sharing, quality training, and engagement of senior leaders the number of findings at every clinic decreased (Fig. ). Building a team approach from the ground-up garnered more engagement with the clinical audit and the quality improvement efforts. The self-reflection surveys gave the clinic team members an opportunity to think about what was working and what needed improvement. The survey was informed by the most common deficiency noted at every clinic, which was inconsistent communication. The survey scored bidirectional communication and asked about barriers to high-quality care and suggestions for improvement. The response rate to the survey ranged from 36% to 38%. We shared the results with all the providers. Rather than a directive being imposed upon them, the clinic staff took ownership of the audit findings, which in some cases led to the development of quality improvement projects. In one case, a clinic assumed the wait times in their clinic were adequate, but audit findings showed otherwise. This clinic initiated a quality improvement project to decrease wait times in the chemotherapy area. Transparent sharing of data with the teams provided opportunities for improvement. Quality metrics shared anonymously between the teams prompted some clinics to ask what higher-performing clinics were doing to improve outcomes. For example, when the central line-associated bloodstream infection rate in implanted catheters was shared between clinics, 1 clinic recognized a potential area for improvement. They learned from another clinic that infections were occurring when lines were accessed outside the pediatric oncology clinic (eg, emergency departments and diagnostic imaging suites). They instituted a teach-back method with parents of children with implanted catheters. Quality metrics were shared with the clinical teams and, importantly, with the hospital leadership of each institution. This step in the comprehensive approach was instrumental to ensure that the teams had resources to achieve the shared goals. For example, decreasing the time to administration of antibiotics in immunocompromised children with fever may not have been possible without senior leadership’s involvement. Because fever often develops in children at nights and on the weekends, when the clinics were closed, the outpatient clinic teams needed support from hospital leadership to ensure that prompt antibiotics administration occurred in emergency departments and on the inpatient hospital units. Training clinic team members as quality champions was critical to implement change. The champions received quality improvement training through the ASCO QTP. Having peers with proficiency in quality improvement increased the commitment to work through the audit findings. One finding noted in the clinical audits was inconsistent documentation of oral chemotherapy administration. With the coaches of the ASCO QTP, a team composed of members of 3 clinics performed a quality improvement project to improve compliance with oral chemotherapy documentation from a baseline of 17.4%. The team developed an aim statement, worked through a process map of the current state, created a cause-and-effect diagram, and studied 3 interventions. Compliance was improved to >85% within 6 months, and the team built a plan for sustainability (unpublished data).
Integrating clinical research trials into the practice of community providers has been proposed as 1 mechanism to increase diversity of research participants. , The St. Jude Affiliate Program is 1 example of this approach. However, ensuring high-quality care in the clinical network of remote sites can be challenging. We developed a comprehensive clinical audit process that decreased deficiencies noted in the clinical audits after implementation. As described in the literature, the effectiveness of audits is not uniformly positive. The nature of an external reviewer giving feedback may appear dictatorial rather than engaging and collaborative. Our initial experience was similar. We found that a simple, yearly clinical audit did not yield continuous improvement. Adding a team-based approach involving all stakeholders was more successful. Because each individual component was not independently evaluated, we do not know the benefit of each component; nonetheless, the comprehensive approach was engaging and provided more sustainable quality improvement. Starting with a bottom-up process, set the tone for a more-inclusive approach to quality improvement. As noted by others, self-reflection is a method to garner engagement with the clinical audit. The sharing of data was key. Rather than the team members making assumptions about how the clinic was operating, examining the data objectively showed how the clinic was functioning. Transparent data sharing can be motivating and reinforces the team approach. , Sharing the data with hospital leadership is a tool to help get support for resources when necessary. The training in quality improvement equipped the teams with knowledge, skills, and attitudes to champion culture change in their clinical practices. Successful quality improvement works when clinicians lead; however, they must know how to design, implement, and evaluate projects. The ASCO QTP provided the tools to practice continuous quality improvement ( https://practice.asco.org/quality-improvement/quality-programs/quality-training-program ). Using a comprehensive approach that involves self-reflection, transparency of data sharing, development of local champions, and engagement of senior leaders, we have been successful in quality improvement across a broad geographic pediatric oncology network. This strategy may be used with other clinical networks working to increase access to clinical research trials in community health care systems.
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The Eastern Québec Telepathology Network: a three-year experience of clinical diagnostic services | 8718451e-0a2f-40a8-b13c-cb0fde6bd192 | 4305967 | Pathology[mh] | The Eastern Quebec Telepathology Network (called "Réseau de Télépathologie de l'Est du Québec" in French) was created in 2004 upon request from the Québec Ministry of Health to develop new telehealth initiatives in the province. It was mainly aimed at providing IOC everywhere and at all times and achieving gains in terms of the speed and quality of surgical services in a territory of 408,760 km 2 with 1.7 million inhabitants where the density, in certain areas, is as low as 0.4 inhabitants/km 2 . In 2007, the Québec Ministry of Health and Canada Health Infoway, a federal telehealth funding agency, agreed to equally fund the project. Following a rigorous selection process, the deployment of the telepathology equipment and software started in 2010 and clinical activities began in January 2011. The Network comprises 24 hospitals providing oncologic surgery, of which 21 are fully operational. Of those 24 sites, 7 have no pathology laboratory, 4 have a pathology laboratory but no pathologist and there is only one practicing pathologist in one fourth of the sites. The other 7 sites have between 2 to 15 pathologists on site. The Network is aimed at covering IOC, expert opinions, primary diagnosis/urgent analyses and macroscopy supervision. The selection of these applications was the result of a survey sent to health professionals of the concerned hospitals. Results of this survey showed that surgeons practicing in remote hospitals without a full-time on-site pathologist needed more consistent pathology coverage. These surgeons complained that without a pathologist on site, pathology coverage depended on the presence of a part-time pathologist or cases needed to be sent to a remote laboratory. They also pointed out that surgeries requiring an IOC had to be grouped on the days when the pathologist was present, thus significantly limiting the flexibility of their operating schedule. The survey also highlighted the challenge of recruiting younger pathologists who felt insecure working alone, mainly because of the difficulty to obtain a quick expert opinion on complex cases. Finally, certain community hospitals did not have enough surgical activities to justify the presence of a full-time pathologist or even of a pathology laboratory. The present article shares the results of our first three-year experience of telepathology diagnostic services.
The equipment deployed in each of the 21 operational sites is shown on Figure and includes a macroscopy station (PathStand 40, Diagnostic Instruments, Sterling Height, USA ) and two videoconferencing devices (PCS-XG80DS Codec, Sony, Minato, Tokyo, Japan ) equipped with a drawing tablet (Bamboo CTE-450K, WACOM, Otone, Saitama, Japan ). Each site was also equipped with either a Nanozoomer 2.0 RS (16 sites) or an HT (8 sites) digital whole-slide scanner (Hamamatsu Photonics, Shizuoka Prefecture, Japan ) and the images are saved on a local dedicated telepathology server. These pieces of equipment were obtained from Olympus Canada Inc. ( Markham , Canada). The WSI are visualized at a 1680 × 1050 pixels resolution with the mScope v.3.6.1 (Aurora Interactive Ltd., Montreal, Canada ) software. An additional server with an academic mScope solution was also included in the package to allow the pathologists of the Network to develop teaching activities. For the IOC and macroscopic supervision, real-time macroscopic evaluation of surgical specimens is required. As shown on Figure , the macroscopy station and videoconferencing device allow the remote pathologist to interact with the surgeon, during an IOC, or with the technician/pathology assistant during a gross description to orient the selection of the area to be microscopically examined. The remote pathologist assists the specimen selection by drawing on the screen, via the drawing tablet. Once the selection of the sample is completed, the technician proceeds to cryosectioning and staining. For the IOC, primary diagnosis/urgent analyses and expert opinions, digital WSI of microscopic slides are obtained by scanning at a 20× or 40× magnification and the images are saved on the local dedicated telepathology server. Through the mScope software, the remote pathologist can read the clinical information and examine the WSI. The pathologist can also either use mScope to dictate or type a final report or use their local laboratory information system. Since the beginning of the clinical activities in January 2011, a number of rigorous evaluations have been performed to: 1. Assess the concordance rate of the diagnosis rendered by telepathology compared to the microscope; 2. Assess the turnaround time of the diagnosis made by telepathology for IOC and expert opinion and 3. Assess the effects of the deployment of telepathology on the health care professionals, patients and on the regional organization and delivery of care. This article summarizes the findings obtained during the first three years of system use.
As per March 2014, 7,440 slides had been scanned for primary diagnosis/urgent analyses; 1,329 for IOC cases and 2,308 for expert opinions. Most IOC were from breast cancers (sentinel lymph nodes, margin close to breast cancer), lung cancer (bronchial margins, mediastinal lymph nodes) and from ovarian, pleural, peritoneal, omental lesions and from stomach and head and neck cancers (Moh's surgery). In addition, a total of 1,260 sessions of macroscopy supervision have been performed. Several smaller laboratories in community hospitals which don't have complete immunohistochemical facilities requested immunohistochemical analyses from larger laboratories. Results were occasionally returned by telepathology to obtain faster results. Although not used extensively yet, telepathology offers an interesting alternative to improve turnaround time in such situation. Teaching cases have also been shared through the mScope academic solution to allow pathologists across the Network to participate to continuing medical education and quality assurance activities. Quality assurance is an important part of the activities of the Network. A steering committee oversees all activities of the Network, including quality assurance. Before any implementation, all potential users are being trained to use the technology. Policies have been developed regarding the indications and contra-indications of telepathology for IOC. A troubleshooting process for both the macroscopy station and the WSI system has been implemented and is being performed every morning before the beginning of IOC. Performance parameters (turnaround time, concordance studies, deferred cases) are documented. The possibility of implementing a systematic process to regularly review a number of telepathology cases is being developed as part of the Québec Quality Assurance plan. A recent quality assurance investigation showed a 98% concordance rate between the diagnosis made on the frozen material of the IOC cases compared to the final diagnosis rendered on paraffin material . This concordance rate compares favorably with the situation when both the surgeon and the pathologist are at the same site . The average turnaround time of IOC cases was 20 minutes and met the College of American Pathologists' recommendation when both the surgeon and the pathologist share the same site . Expert opinion reports were signed out within 24 hours in 68% of cases and within 72 hours in 85%, which is well within the recommendations of the Association of Directors of Anatomic and Surgical Pathology . In other words, telepathology allowed to maintain the same level of quality required in the practice of surgical pathology. Furthermore, a recent multi-method evaluation study of the Network was performed to better understand the expected and unexpected effects of telepathology on health care professionals and patients as well as on the regional organization and delivery of surgical services. Four major benefits of the introduction of telepathology have been identified. First, the interruption of IOC service was clearly prevented in hospitals with no pathologist on site. In two remote pathology laboratories, a pathologist has been on-site for more than 10 years and moved to another laboratory in two months of notice. To maintain the surgical activities requiring IOC, the only option was to obtain support from a remote pathologist by telepathology. This support was provided which allowed the continuation of the surgical activities. Second, surgeons who were interviewed mentioned that two-stage surgeries and patient transfers were prevented by telepathology. This benefit was expected for hospitals which pathology laboratory lacked a pathologist on site but was also wished in the 4 hospitals devoid of pathology laboratory and where the surgeons never had access to this service. In one of the latter, over 98 slides had been scanned for IOC, less than one year after system implementation, demonstrating the existence of such a need. Third, retention and recruitment of surgeons in remote hospitals were both facilitated. Our observations revealed at least one case of staff recruitment and one instance of staff retention in remote hospitals, thanks to the deployment of telepathology. Fourth, professional isolation and insecurity among pathologists working alone was reduced. Over 2,000 slides were submitted for expertise from such pathologists since the launch of the clinical activities in January 2011. Pathologists agreed that wider adoption of telepathology for clinical use would require improvement of current technologies, mainly in connection with response time and the ergonomics of the current software. Furthermore, the sustainability of such a network would need better coordination between the different hospitals of the Network. To be fully operational, a telepathology Network would require the creation of a regional or even a supra-regional organisation which would allow pathologists from any of the participating sites to share urgent and difficult cases. The recent evaluation of the network pointed out the gap between the overall objective of the network to offer consistent pathology coverage in a region and the legal requirement for each institution to prioritize its own in-house cases and to meet defined turnaround times. It seems clear that such technology will force different jurisdictions around the world to redefine the routing and prioritization of most urgent surgical pathology cases and adopt a more integrated and comprehensive pathology coverage at a regional or national level. The Eastern Québec Telepathology Network is currently the most ambitious telepathology project in Canada and ranks among the most important in the world in terms of both the number of sites and geographic coverage . The data collected since the implementation of the Network and summarized in this article confirm that telepathology helps to improve the accessibility and quality of surgical services in remote regions, particularly for oncological surgeries. Our experience also confirms, as reported by others , that the overall diagnostic review by WSI was not inferior to microscope slide review. Furthermore, data reported in the present study reveals that telepathology played a key role to support pathologists working alone and to ensure their retention in remote hospitals. Indeed, it is estimated that 10 to 20% of oncologic cases must be validated by more than one pathologist and we demonstrated that telepathology is a fast and efficient method to reach this objective among pathologists practicing far from academic centers. Finally, our Network also allowed isolated pathologists to participate to online academic seminars and activities organized by academic pathologists. Current literature shows that telemedicine may help to retain physicians in remote regions by contributing to provide better working conditions . The access to expert opinions and continuing medical education activities also ranks among those improved conditions. However, despite the clear advantages of introducing telepathology in the daily pathology practice, there is still resistance from many pathologists and surgeons to adopt the digital technology. We identified a number of barriers to this adoption and several major legal, reimbursement, and licensure issues have already been addressed. It is clear, however, that human factors relating to the fear of using a new technology rank among the most important limitations which explains such inertia in many laboratories, even in academic institutions . However, the key to the success of telepathology requires a strong communication plan and a highly coordinated effort between surgeons, pathologists, stakeholders, laboratory staff, biomedical, administrative and IT support teams working on different sites. In our network, a central coordination center financially supported by the Québec Ministry of Health has been created and each site is being visited regularly or invited to participate to follow-up videoconferences. In the past year, major steps have been completed to improve the adoption of the technology by the pathology community. A guideline on the validation process of WSI for diagnostic purposes in pathology has been recently released by the College of American Pathologists and the Canadian Association of Pathologists mandated a group of Canadian experts to develop a series of guidelines to establish a telepathology service. Image storage and archiving is also a major issue because of the large size of WSI. Initially, since IOC are being systematically controlled on paraffin material shortly after the surgery, it was planned to save WSI for a limited period of time only. However, a legal advice recommended applying the same retention schedule for WSI as for slides and paraffin blocks. Currently, all images are being saved and different alternatives for permanent long-term storage in our Network are under investigation. Digital pathology has been successfully implemented in many countries around the world for education, clinical pathological conferences, and research . Its adoption for diagnostic purposes is increasing, but there are still few examples of structured patient-centered networks largely because of the many barriers that need to be overcome . Canada has been a world leader in the implementation of telepathology and, recently, several companies obtained a Health Canada Class II Medical Device License for creating, managing, storing, annotating, measuring, and viewing digital whole-slide images for routine pathology . Such leadership may be attributed to the initiative of a few leading individuals and to the financial support of provincial governments and Canada Health Infoway. However, it is clear that the demographic, geographic and situational characteristics of Canada, such as its immense territory, its dispersed population and the severe shortage of anatomical pathologists may explain, at least in part, the expansion of telepathology in this country.
In short, our experience demonstrates that the Eastern Quebec Telepathology Network allowed the maintenance of rapid and high quality pathology services in a network of more than 20 sites dispersed across a large territory. A second phase is underway and is aimed at expanding the service to other regions in the province. It is our contention that telepathology provides otherwise unavailable services to remote communities, allows greater flexibility in pathology practice, avoids unnecessary travel and facilitates a better organisation of clinical work in a vast territory with a shortage of pathologists.
IOC: intraoperative consultations; WSI: Whole-slide image
The authors declare that they have no competing interests.
Bernard Têtu coordinated the project as medical director of the Network; Emilie Perron was responsible of the concordance study; Said Louahlia was responsible of coordinating the IOC concordance study: Guy Paré, Marie-Claude Trudel & Julien Meyer conducted the multi-method evaluation study.
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Analysis of pulp histological response to pulpotomy performed with white mineral trioxide aggregate mixed with 2.25% sodium hypochlorite gel in humans: a randomized controlled clinical trial | f5ed9d8b-fa75-46d0-b085-85698fbfbf1b | 11682085 | Dentistry[mh] | Caries are the leading and most common cause of tooth loss in children , in addition to traumatic injuries . Thus, pediatric dentistry seeks to preserve the functional and developmental aspects of primary teeth , . Early loss of primary teeth leads to problems in space maintenance, malocclusion, misalignment of permanent teeth, potential impacts on oral hygiene and aesthetic concerns, and a lack of masticatory function , . Pulpotomy is an effective pulp therapy for pulp-exposed primary teeth due to carious lesions or traumatic injury since coronal pulp tissue usually contains microorganisms and shows signs of inflammation and degeneration , . The coronal affected tissue is removed, and a biocompatible material is applied to the healthy pulp tissue . Recently, there has been increasing interest in vital pulp therapy using different bioactive materials, including biodentine, mineral trioxide aggregate (MTA), calcium-enriched mixture cement, and calcium silicate-based cement , . Pulpotomy is a reliable procedure with a success rate exceeding 90% . MTA is considered the gold standard dressing material for pulpotomy in primary molars . It has been considered a suitable alternative to formocresol pulpotomy as it yielded promising clinical, radiographical, and histological results compared to other dressing materials . However, it has several disadvantages, including difficulty handling the material due to its poor consistency, limited antibacterial activity, short working time, and long setting time when mixed with distilled water. These disadvantages are crucial shortcomings in pediatric dentistry practice . Several in vitro studies suggested mixing the white MTA (WMTA) compound with various additives to improve its properties and reduce the setting time. Among them was sodium hypochlorite (NaOCl) gel, which has good biocompatibility, reduces setting time, and improves physical, chemical, and antimicrobial properties – . Various studies in the literature prove the safety and biocompatibility of the mixture , . According to Jafarnia et al. and Alnezi et al. , MTA mixed with 3% NaOCl gel is biocompatible and could be a possible alternative to distilled water. However, the lack of clinical studies in the consulted literature evaluating the histological success of the mixture in human primary molars – highlights the need for such a study. Therefore, this study aimed to perform a histological evaluation of primary teeth pulpotomy using WMTA mixed with 2.25% NaOCl gel. Additionally, to perform Scanning Electron Microscope (SEM) evaluation, Energy Dispersive X-ray (EDX) analysis, and pH level evaluation of WMTA and 2.25% NaOCl gel mixture. The null hypothesis is that the mix with 2.5% NaOCl gel will not improve the biological and chemical properties of the WMTA in comparison with distilled water.
Part 1—in vivo histologic evaluation of pulp response following pulpotomy Study design and ethics It was a randomized, triple-blinded, split-mouth, active-controlled clinical trial. This trial was conducted in accordance with the Declaration of Helsinki as revised in 2013 and the Consolidated Standards of Reporting Trials (CONSORT) statement . Ethical approval was provided by the Biomedical Research Ethics Committee (1297/2024) and was, retrospectively, registered at the ISRCTN registry (ISRCTN58885533) on July 3, 2024. It was performed at the Department of Pediatric Dentistry and the Department of Oral and Maxillofacial Pathology, Faculty of Dentistry, Damascus University, between August 2023 and January 2024. The treatment plan was explained in detail. Participation was voluntary and confidential. Patients’ legal guardians signed written informed consent before enrollment, and they can withdraw consent at any time. No child was excluded based on their race, gender, and socioeconomic status. Each child received complete other required dental treatments. Sample size calculation The sample size was calculated using G*Power version 3.1.9.4 (G*Power 3.1.9, Heinrich Hein Universität Düsseldorf, Düsseldorf, Germany). A sample size of n = 48 achieved a large effect size f (0.55), 80% Power (1—β err prob), and a significance level of 0.05. A pilot study on 4 samples was performed to determine the effect size . Eligibility criteria and sampling The inclusion criteria were as follows : Children aged 8–10 years. Children with at least 2 bilateral carious first primary molars indicated for pulpotomy. Children require serial extraction of first primary molars for orthodontic reasons. The exclusion criteria were as follows : Children with systematic diseases and/or allergies to the anesthetic agents. Children with clinical and radiographical signs of pulp necrosis in the targeted teeth and/or unrestorable teeth. Children with nocturnal and/or spontaneous pain. The CONSORT flow diagram is illustrated in Fig. . Two experienced pediatric dentists assessed 29 patients who were referred to the Department of Pediatric Dentistry for eligibility. Periapical radiographic image was performed using intraoral periapical sensor (i-sensor, Guilin Woodpecker Medical Instrument Co., LTD., Guilin, China). According to the inclusion criteria, five patients were excluded. A total of 24 patients with 48 first primary molars indicated for pulpotomy were randomly assigned into two groups (n = 24) according to the pulp dressing material used: Group 1 (WMTA + DW): WMTA mixed with distilled water (Rootdent, TehnoDent Co., Belgorod, Russia), this was considered the control group. Group 2 (WMTA + NaOCl gel): WMTA mixed with 2.25% NaOCl gel (LET’S CLEAN Concentrated Chlorine, DTIC®, Damascus, Syria), this was considered the interventional group. Each group was sub-divided into three sub-groups (n = 8) according to the follow-up period : Sub-group I: The serial extraction was scheduled after 7 days. Sub-group II: The serial extraction was scheduled after 30 days. Sub-group III: The serial extraction was scheduled after 90 days. Blinding and randomization It was a triple-blinded trial in which dentists, participants, and outcome assessors were masked to group allocations. A blinded investigator applied a simple randomization technique by flipping a coin for each patient. Then, the first primary molars were randomly allocated to either the control or interventional group in each patient, utilizing a split-mouth model. Procedure Topical anesthetic (Iolite, Dharma Research Inc., Florida, United States) was applied at the site of needle insertion then local anesthetic solution (2% Lidocaine HCL Injection, Huons Co., Ltd, Seongnam, Korea) was deposited using a dental carpule syringe (Dental carpule syringe, Dental Laboratorio, china) and a 27-gauge × 21 mm needle (Disposable Dental Needle, Shanghai Dochem Industries Co., Ltd., Shanghai, China). Rubber dam (Sanctuary®, Perak, Malaysia) and saliva ejector (Disposable Transparent Surgical Dental Saliva Ejectors China, Andent Dental Co., ltd., Hebei) were used for isolation. Caries lesions were removed, and the pulp chamber was deroofed using round tungsten carbide cavity bur (Round E 0123, Dentsply Maillefer, Ballaigues, Switzerland) in an air turbine handpiece (NSK PANA-AIR, NSK Nakanishi Inc., Tochigi-ken, Japan) with copious irrigation. Coronal pulpotomy was performed using a slow–speed endodontic opening cutter carbide bur (Excavabur E123A, Dentsply Maillefer, Ballaigues, Switzerland) in a contra-angle handpiece (NAC-EC, NSK Nakanishi Inc., Tochigi-ken, Japan). The pulp chamber was thoroughly irrigated, and hemostasis was achieved using a moist cotton pellet with normal saline (SODIUM CHLORIDE 0.9% MIAMED, Miamed Pharmaceutical Industry, Damascus, Syria) for 5 m . In the control group, WMTA powder was mixed with distilled water in a 3:1 powder-to-liquid ratio, and then the pulp was stamped with a 3mm thick layer of MTA. In the interventional group, WMTA was mixed with 2.25% NaOCl gel in a 3:1 powder-to-gel ratio . In both study groups, the cavity was sealed with glass ionomer cement (RX Glass lonomer Cement, Stardent Equipment Co., Ltd., Guangdong, China), and then the tooth was restored with a stainless-steel crown (Kids Crown, Shinhung, Seoul, Korea) at the same appointment . Extraction was scheduled after 7, 30, and 90 days for histological evaluation . Once local anesthesia has been administered, a sterile curette (D1086, iM3® Dental Ltd., Country Meath, Ireland) was utilized to detach the gingival attachment. An elevator instrument (E11M, Hu-Friedy Mfg. Co., LLC., Chicago, United States) was employed to loosen the tooth, cut the periodontal ligament, and broaden the alveolar bone. Following sufficient luxation, slight buccal/lingual (palatal) pressure is exerted with the lower (FX2CE, Hu-Friedy Mfg. Co., LLC., Chicago, United States) or upper (FX4CE, Hu-Friedy Mfg. Co., LLC., Chicago, United States) primary molars forceps to widen the alveolar bone and carefully remove the tooth from the socket . Histological evaluation Each sample was stored in 10% buffered formalin solution (10% Neutral Buffered Formalin, Thomas Scientific LLC, New Jersey, United States) for 48 h at room temperature for fixation, and then it was demineralized in Morse’s solution (Morse Solution, FUJIFILM Wako Pure Chemical Co., Hong Kong, China), which is an aqueous solution of 22.5% formic acid and 10% sodium citrate. Each specimen was embedded in a paraffin wax block (Clear Paraffin Block, EverBio Technology INC., New Taipei City, Taiwan), and then the paraffin-embedded wax blocks were sectioned at 5 μm using a semi-motorized rotary microtome (Leica RM2145 Microtome, GMI, New Jersey, United States). The sectioned samples were stained with hematoxylin and eosin (H&E Staining Kit, Abcam, England, United Kingdom), and the histological samples were evaluated using a light microscope (Leica Microscope DM2500, Leica, Hesse, Germany) at 400 × magnification by two blinded operators , . Cohen’s Kappa coefficient values of intra-examiner and inter-examiner reliability were > 0.8. The following primary outcome measures were considered: Odontoblastic integrity. Grade 0 = Normal tissue morphology. Grade 1 = Mild odontoblastic disorganization. Normal morphology of central pulp tissue. Grade 2 = Moderate odontoblastic disorganization. Grade 3 = Severe odontoblastic disorganization. Complete morphological disorganization of pulp tissue. Grade 4 = Pulp necrosis . Pulp tissue hemorrhage. Grade 0 = No hemorrhage. Grade 1 = Mild hemorrhage. A few scattered red blood cells. Grade 2 = Moderate hemorrhage. Some clusters or red blood cells. Grade 3 = Severe hemorrhage. Extensive infiltration of red blood cells . Pulp fibrosis. Grade 0 = No pulp fibrosis. Grade 1 = Mild pulp fibrosis. Thin collagen fibers. Grade 2 = Moderate pulp fibrosis. Grade 3 = Severe pulp fibrosis. Thick collagen fibers . Dentin bridge formation. Grade 0 = No dentin bridge formation. Grade 1 = Initial dentin bridge formation. Dentin bridge extended to < ½ of the exposure site Grade 2 = Partial dentin bridge formation. Dentin bridge extended to > ½ of the exposure site. Grade 3 = Complete dentin bridge formation. Continuity of dentin bridge . Pulp calcification. Grade 0 = No pulp calcification. Grade 1 = Single small calcification. Grade 2 = Multiple small calcifications. Grade 3 = Single large calcification. Grade 4 = Multiple large calcifications . Part 2—in vitro surface morphological and chemical analysis of MTA mixtures Preparation of samples One sample was prepared for each material. Each material was mixed in 3:1 powder-to-liquid ratio and powder-to-gel ratio. Freshly mixed materials were placed into Teflon molds (internal diameter: 3 mm, height: 3.8 mm), and then the samples were immersed in distilled water (Pure Water, Pure Water Co., London, United Kingdom) and stored in an incubator (IN30, Memmert GmbH, Bavaria, Germany) at 37°C for 24 h until complete setting . pH level evaluation The pH level of the solution of each sample was evaluated by immersing a pH microelectrode (WTW—IDS pH Micro Electrode SenTix® Micro 900, Xylem Analytics Germany Sales GmbH & Co. KG., Munich, Germany) after 24 h, and 28 days . Surface morphological evaluation The samples were mounted on SEM sample holders inside the SEM chamber (VEGA3, Tescan, Liberec, Czech Republic). Surface topography was evaluated at 500× , and 5000× magnification and an accelerating voltage of 30 kV after 24 h and after 28 days . Chemical analysis The abundance of elements and their distribution in the samples were mapped using EDX in combination with SEM. EDX device (EDAX Element, EDAX, Inc., California, United States) was connected to SEM, and the spectrum of the compounds was provided by a mean of EDX software (APEX™ EDS, EDAX, Inc., California, United States) after 24 h and 28 days . Statistical analysis Data were analyzed by IBM SPSS software version 24 (IBM SPSS Statistics® version 24, IBM Corp., New York, USA). Descriptive statistics were presented as frequency and percentage. The chi-square test was performed to compare categorical data, and post hoc test was performed when the overall test showed a significant difference. Adjusted residuals were extracted and each one was multiplied by itself to calculate chi-square values, and then chi-square values were transformed to obtain p-values. Statistical significance was adjusted at P < 0.05 .
Study design and ethics It was a randomized, triple-blinded, split-mouth, active-controlled clinical trial. This trial was conducted in accordance with the Declaration of Helsinki as revised in 2013 and the Consolidated Standards of Reporting Trials (CONSORT) statement . Ethical approval was provided by the Biomedical Research Ethics Committee (1297/2024) and was, retrospectively, registered at the ISRCTN registry (ISRCTN58885533) on July 3, 2024. It was performed at the Department of Pediatric Dentistry and the Department of Oral and Maxillofacial Pathology, Faculty of Dentistry, Damascus University, between August 2023 and January 2024. The treatment plan was explained in detail. Participation was voluntary and confidential. Patients’ legal guardians signed written informed consent before enrollment, and they can withdraw consent at any time. No child was excluded based on their race, gender, and socioeconomic status. Each child received complete other required dental treatments. Sample size calculation The sample size was calculated using G*Power version 3.1.9.4 (G*Power 3.1.9, Heinrich Hein Universität Düsseldorf, Düsseldorf, Germany). A sample size of n = 48 achieved a large effect size f (0.55), 80% Power (1—β err prob), and a significance level of 0.05. A pilot study on 4 samples was performed to determine the effect size . Eligibility criteria and sampling The inclusion criteria were as follows : Children aged 8–10 years. Children with at least 2 bilateral carious first primary molars indicated for pulpotomy. Children require serial extraction of first primary molars for orthodontic reasons. The exclusion criteria were as follows : Children with systematic diseases and/or allergies to the anesthetic agents. Children with clinical and radiographical signs of pulp necrosis in the targeted teeth and/or unrestorable teeth. Children with nocturnal and/or spontaneous pain. The CONSORT flow diagram is illustrated in Fig. . Two experienced pediatric dentists assessed 29 patients who were referred to the Department of Pediatric Dentistry for eligibility. Periapical radiographic image was performed using intraoral periapical sensor (i-sensor, Guilin Woodpecker Medical Instrument Co., LTD., Guilin, China). According to the inclusion criteria, five patients were excluded. A total of 24 patients with 48 first primary molars indicated for pulpotomy were randomly assigned into two groups (n = 24) according to the pulp dressing material used: Group 1 (WMTA + DW): WMTA mixed with distilled water (Rootdent, TehnoDent Co., Belgorod, Russia), this was considered the control group. Group 2 (WMTA + NaOCl gel): WMTA mixed with 2.25% NaOCl gel (LET’S CLEAN Concentrated Chlorine, DTIC®, Damascus, Syria), this was considered the interventional group. Each group was sub-divided into three sub-groups (n = 8) according to the follow-up period : Sub-group I: The serial extraction was scheduled after 7 days. Sub-group II: The serial extraction was scheduled after 30 days. Sub-group III: The serial extraction was scheduled after 90 days. Blinding and randomization It was a triple-blinded trial in which dentists, participants, and outcome assessors were masked to group allocations. A blinded investigator applied a simple randomization technique by flipping a coin for each patient. Then, the first primary molars were randomly allocated to either the control or interventional group in each patient, utilizing a split-mouth model. Procedure Topical anesthetic (Iolite, Dharma Research Inc., Florida, United States) was applied at the site of needle insertion then local anesthetic solution (2% Lidocaine HCL Injection, Huons Co., Ltd, Seongnam, Korea) was deposited using a dental carpule syringe (Dental carpule syringe, Dental Laboratorio, china) and a 27-gauge × 21 mm needle (Disposable Dental Needle, Shanghai Dochem Industries Co., Ltd., Shanghai, China). Rubber dam (Sanctuary®, Perak, Malaysia) and saliva ejector (Disposable Transparent Surgical Dental Saliva Ejectors China, Andent Dental Co., ltd., Hebei) were used for isolation. Caries lesions were removed, and the pulp chamber was deroofed using round tungsten carbide cavity bur (Round E 0123, Dentsply Maillefer, Ballaigues, Switzerland) in an air turbine handpiece (NSK PANA-AIR, NSK Nakanishi Inc., Tochigi-ken, Japan) with copious irrigation. Coronal pulpotomy was performed using a slow–speed endodontic opening cutter carbide bur (Excavabur E123A, Dentsply Maillefer, Ballaigues, Switzerland) in a contra-angle handpiece (NAC-EC, NSK Nakanishi Inc., Tochigi-ken, Japan). The pulp chamber was thoroughly irrigated, and hemostasis was achieved using a moist cotton pellet with normal saline (SODIUM CHLORIDE 0.9% MIAMED, Miamed Pharmaceutical Industry, Damascus, Syria) for 5 m . In the control group, WMTA powder was mixed with distilled water in a 3:1 powder-to-liquid ratio, and then the pulp was stamped with a 3mm thick layer of MTA. In the interventional group, WMTA was mixed with 2.25% NaOCl gel in a 3:1 powder-to-gel ratio . In both study groups, the cavity was sealed with glass ionomer cement (RX Glass lonomer Cement, Stardent Equipment Co., Ltd., Guangdong, China), and then the tooth was restored with a stainless-steel crown (Kids Crown, Shinhung, Seoul, Korea) at the same appointment . Extraction was scheduled after 7, 30, and 90 days for histological evaluation . Once local anesthesia has been administered, a sterile curette (D1086, iM3® Dental Ltd., Country Meath, Ireland) was utilized to detach the gingival attachment. An elevator instrument (E11M, Hu-Friedy Mfg. Co., LLC., Chicago, United States) was employed to loosen the tooth, cut the periodontal ligament, and broaden the alveolar bone. Following sufficient luxation, slight buccal/lingual (palatal) pressure is exerted with the lower (FX2CE, Hu-Friedy Mfg. Co., LLC., Chicago, United States) or upper (FX4CE, Hu-Friedy Mfg. Co., LLC., Chicago, United States) primary molars forceps to widen the alveolar bone and carefully remove the tooth from the socket . Histological evaluation Each sample was stored in 10% buffered formalin solution (10% Neutral Buffered Formalin, Thomas Scientific LLC, New Jersey, United States) for 48 h at room temperature for fixation, and then it was demineralized in Morse’s solution (Morse Solution, FUJIFILM Wako Pure Chemical Co., Hong Kong, China), which is an aqueous solution of 22.5% formic acid and 10% sodium citrate. Each specimen was embedded in a paraffin wax block (Clear Paraffin Block, EverBio Technology INC., New Taipei City, Taiwan), and then the paraffin-embedded wax blocks were sectioned at 5 μm using a semi-motorized rotary microtome (Leica RM2145 Microtome, GMI, New Jersey, United States). The sectioned samples were stained with hematoxylin and eosin (H&E Staining Kit, Abcam, England, United Kingdom), and the histological samples were evaluated using a light microscope (Leica Microscope DM2500, Leica, Hesse, Germany) at 400 × magnification by two blinded operators , . Cohen’s Kappa coefficient values of intra-examiner and inter-examiner reliability were > 0.8. The following primary outcome measures were considered: Odontoblastic integrity. Grade 0 = Normal tissue morphology. Grade 1 = Mild odontoblastic disorganization. Normal morphology of central pulp tissue. Grade 2 = Moderate odontoblastic disorganization. Grade 3 = Severe odontoblastic disorganization. Complete morphological disorganization of pulp tissue. Grade 4 = Pulp necrosis . Pulp tissue hemorrhage. Grade 0 = No hemorrhage. Grade 1 = Mild hemorrhage. A few scattered red blood cells. Grade 2 = Moderate hemorrhage. Some clusters or red blood cells. Grade 3 = Severe hemorrhage. Extensive infiltration of red blood cells . Pulp fibrosis. Grade 0 = No pulp fibrosis. Grade 1 = Mild pulp fibrosis. Thin collagen fibers. Grade 2 = Moderate pulp fibrosis. Grade 3 = Severe pulp fibrosis. Thick collagen fibers . Dentin bridge formation. Grade 0 = No dentin bridge formation. Grade 1 = Initial dentin bridge formation. Dentin bridge extended to < ½ of the exposure site Grade 2 = Partial dentin bridge formation. Dentin bridge extended to > ½ of the exposure site. Grade 3 = Complete dentin bridge formation. Continuity of dentin bridge . Pulp calcification. Grade 0 = No pulp calcification. Grade 1 = Single small calcification. Grade 2 = Multiple small calcifications. Grade 3 = Single large calcification. Grade 4 = Multiple large calcifications .
It was a randomized, triple-blinded, split-mouth, active-controlled clinical trial. This trial was conducted in accordance with the Declaration of Helsinki as revised in 2013 and the Consolidated Standards of Reporting Trials (CONSORT) statement . Ethical approval was provided by the Biomedical Research Ethics Committee (1297/2024) and was, retrospectively, registered at the ISRCTN registry (ISRCTN58885533) on July 3, 2024. It was performed at the Department of Pediatric Dentistry and the Department of Oral and Maxillofacial Pathology, Faculty of Dentistry, Damascus University, between August 2023 and January 2024. The treatment plan was explained in detail. Participation was voluntary and confidential. Patients’ legal guardians signed written informed consent before enrollment, and they can withdraw consent at any time. No child was excluded based on their race, gender, and socioeconomic status. Each child received complete other required dental treatments.
The sample size was calculated using G*Power version 3.1.9.4 (G*Power 3.1.9, Heinrich Hein Universität Düsseldorf, Düsseldorf, Germany). A sample size of n = 48 achieved a large effect size f (0.55), 80% Power (1—β err prob), and a significance level of 0.05. A pilot study on 4 samples was performed to determine the effect size .
The inclusion criteria were as follows : Children aged 8–10 years. Children with at least 2 bilateral carious first primary molars indicated for pulpotomy. Children require serial extraction of first primary molars for orthodontic reasons. The exclusion criteria were as follows : Children with systematic diseases and/or allergies to the anesthetic agents. Children with clinical and radiographical signs of pulp necrosis in the targeted teeth and/or unrestorable teeth. Children with nocturnal and/or spontaneous pain. The CONSORT flow diagram is illustrated in Fig. . Two experienced pediatric dentists assessed 29 patients who were referred to the Department of Pediatric Dentistry for eligibility. Periapical radiographic image was performed using intraoral periapical sensor (i-sensor, Guilin Woodpecker Medical Instrument Co., LTD., Guilin, China). According to the inclusion criteria, five patients were excluded. A total of 24 patients with 48 first primary molars indicated for pulpotomy were randomly assigned into two groups (n = 24) according to the pulp dressing material used: Group 1 (WMTA + DW): WMTA mixed with distilled water (Rootdent, TehnoDent Co., Belgorod, Russia), this was considered the control group. Group 2 (WMTA + NaOCl gel): WMTA mixed with 2.25% NaOCl gel (LET’S CLEAN Concentrated Chlorine, DTIC®, Damascus, Syria), this was considered the interventional group. Each group was sub-divided into three sub-groups (n = 8) according to the follow-up period : Sub-group I: The serial extraction was scheduled after 7 days. Sub-group II: The serial extraction was scheduled after 30 days. Sub-group III: The serial extraction was scheduled after 90 days.
It was a triple-blinded trial in which dentists, participants, and outcome assessors were masked to group allocations. A blinded investigator applied a simple randomization technique by flipping a coin for each patient. Then, the first primary molars were randomly allocated to either the control or interventional group in each patient, utilizing a split-mouth model.
Topical anesthetic (Iolite, Dharma Research Inc., Florida, United States) was applied at the site of needle insertion then local anesthetic solution (2% Lidocaine HCL Injection, Huons Co., Ltd, Seongnam, Korea) was deposited using a dental carpule syringe (Dental carpule syringe, Dental Laboratorio, china) and a 27-gauge × 21 mm needle (Disposable Dental Needle, Shanghai Dochem Industries Co., Ltd., Shanghai, China). Rubber dam (Sanctuary®, Perak, Malaysia) and saliva ejector (Disposable Transparent Surgical Dental Saliva Ejectors China, Andent Dental Co., ltd., Hebei) were used for isolation. Caries lesions were removed, and the pulp chamber was deroofed using round tungsten carbide cavity bur (Round E 0123, Dentsply Maillefer, Ballaigues, Switzerland) in an air turbine handpiece (NSK PANA-AIR, NSK Nakanishi Inc., Tochigi-ken, Japan) with copious irrigation. Coronal pulpotomy was performed using a slow–speed endodontic opening cutter carbide bur (Excavabur E123A, Dentsply Maillefer, Ballaigues, Switzerland) in a contra-angle handpiece (NAC-EC, NSK Nakanishi Inc., Tochigi-ken, Japan). The pulp chamber was thoroughly irrigated, and hemostasis was achieved using a moist cotton pellet with normal saline (SODIUM CHLORIDE 0.9% MIAMED, Miamed Pharmaceutical Industry, Damascus, Syria) for 5 m . In the control group, WMTA powder was mixed with distilled water in a 3:1 powder-to-liquid ratio, and then the pulp was stamped with a 3mm thick layer of MTA. In the interventional group, WMTA was mixed with 2.25% NaOCl gel in a 3:1 powder-to-gel ratio . In both study groups, the cavity was sealed with glass ionomer cement (RX Glass lonomer Cement, Stardent Equipment Co., Ltd., Guangdong, China), and then the tooth was restored with a stainless-steel crown (Kids Crown, Shinhung, Seoul, Korea) at the same appointment . Extraction was scheduled after 7, 30, and 90 days for histological evaluation . Once local anesthesia has been administered, a sterile curette (D1086, iM3® Dental Ltd., Country Meath, Ireland) was utilized to detach the gingival attachment. An elevator instrument (E11M, Hu-Friedy Mfg. Co., LLC., Chicago, United States) was employed to loosen the tooth, cut the periodontal ligament, and broaden the alveolar bone. Following sufficient luxation, slight buccal/lingual (palatal) pressure is exerted with the lower (FX2CE, Hu-Friedy Mfg. Co., LLC., Chicago, United States) or upper (FX4CE, Hu-Friedy Mfg. Co., LLC., Chicago, United States) primary molars forceps to widen the alveolar bone and carefully remove the tooth from the socket .
Each sample was stored in 10% buffered formalin solution (10% Neutral Buffered Formalin, Thomas Scientific LLC, New Jersey, United States) for 48 h at room temperature for fixation, and then it was demineralized in Morse’s solution (Morse Solution, FUJIFILM Wako Pure Chemical Co., Hong Kong, China), which is an aqueous solution of 22.5% formic acid and 10% sodium citrate. Each specimen was embedded in a paraffin wax block (Clear Paraffin Block, EverBio Technology INC., New Taipei City, Taiwan), and then the paraffin-embedded wax blocks were sectioned at 5 μm using a semi-motorized rotary microtome (Leica RM2145 Microtome, GMI, New Jersey, United States). The sectioned samples were stained with hematoxylin and eosin (H&E Staining Kit, Abcam, England, United Kingdom), and the histological samples were evaluated using a light microscope (Leica Microscope DM2500, Leica, Hesse, Germany) at 400 × magnification by two blinded operators , . Cohen’s Kappa coefficient values of intra-examiner and inter-examiner reliability were > 0.8. The following primary outcome measures were considered: Odontoblastic integrity. Grade 0 = Normal tissue morphology. Grade 1 = Mild odontoblastic disorganization. Normal morphology of central pulp tissue. Grade 2 = Moderate odontoblastic disorganization. Grade 3 = Severe odontoblastic disorganization. Complete morphological disorganization of pulp tissue. Grade 4 = Pulp necrosis . Pulp tissue hemorrhage. Grade 0 = No hemorrhage. Grade 1 = Mild hemorrhage. A few scattered red blood cells. Grade 2 = Moderate hemorrhage. Some clusters or red blood cells. Grade 3 = Severe hemorrhage. Extensive infiltration of red blood cells . Pulp fibrosis. Grade 0 = No pulp fibrosis. Grade 1 = Mild pulp fibrosis. Thin collagen fibers. Grade 2 = Moderate pulp fibrosis. Grade 3 = Severe pulp fibrosis. Thick collagen fibers . Dentin bridge formation. Grade 0 = No dentin bridge formation. Grade 1 = Initial dentin bridge formation. Dentin bridge extended to < ½ of the exposure site Grade 2 = Partial dentin bridge formation. Dentin bridge extended to > ½ of the exposure site. Grade 3 = Complete dentin bridge formation. Continuity of dentin bridge . Pulp calcification. Grade 0 = No pulp calcification. Grade 1 = Single small calcification. Grade 2 = Multiple small calcifications. Grade 3 = Single large calcification. Grade 4 = Multiple large calcifications .
Preparation of samples One sample was prepared for each material. Each material was mixed in 3:1 powder-to-liquid ratio and powder-to-gel ratio. Freshly mixed materials were placed into Teflon molds (internal diameter: 3 mm, height: 3.8 mm), and then the samples were immersed in distilled water (Pure Water, Pure Water Co., London, United Kingdom) and stored in an incubator (IN30, Memmert GmbH, Bavaria, Germany) at 37°C for 24 h until complete setting . pH level evaluation The pH level of the solution of each sample was evaluated by immersing a pH microelectrode (WTW—IDS pH Micro Electrode SenTix® Micro 900, Xylem Analytics Germany Sales GmbH & Co. KG., Munich, Germany) after 24 h, and 28 days . Surface morphological evaluation The samples were mounted on SEM sample holders inside the SEM chamber (VEGA3, Tescan, Liberec, Czech Republic). Surface topography was evaluated at 500× , and 5000× magnification and an accelerating voltage of 30 kV after 24 h and after 28 days . Chemical analysis The abundance of elements and their distribution in the samples were mapped using EDX in combination with SEM. EDX device (EDAX Element, EDAX, Inc., California, United States) was connected to SEM, and the spectrum of the compounds was provided by a mean of EDX software (APEX™ EDS, EDAX, Inc., California, United States) after 24 h and 28 days . Statistical analysis Data were analyzed by IBM SPSS software version 24 (IBM SPSS Statistics® version 24, IBM Corp., New York, USA). Descriptive statistics were presented as frequency and percentage. The chi-square test was performed to compare categorical data, and post hoc test was performed when the overall test showed a significant difference. Adjusted residuals were extracted and each one was multiplied by itself to calculate chi-square values, and then chi-square values were transformed to obtain p-values. Statistical significance was adjusted at P < 0.05 .
One sample was prepared for each material. Each material was mixed in 3:1 powder-to-liquid ratio and powder-to-gel ratio. Freshly mixed materials were placed into Teflon molds (internal diameter: 3 mm, height: 3.8 mm), and then the samples were immersed in distilled water (Pure Water, Pure Water Co., London, United Kingdom) and stored in an incubator (IN30, Memmert GmbH, Bavaria, Germany) at 37°C for 24 h until complete setting .
The pH level of the solution of each sample was evaluated by immersing a pH microelectrode (WTW—IDS pH Micro Electrode SenTix® Micro 900, Xylem Analytics Germany Sales GmbH & Co. KG., Munich, Germany) after 24 h, and 28 days .
The samples were mounted on SEM sample holders inside the SEM chamber (VEGA3, Tescan, Liberec, Czech Republic). Surface topography was evaluated at 500× , and 5000× magnification and an accelerating voltage of 30 kV after 24 h and after 28 days .
The abundance of elements and their distribution in the samples were mapped using EDX in combination with SEM. EDX device (EDAX Element, EDAX, Inc., California, United States) was connected to SEM, and the spectrum of the compounds was provided by a mean of EDX software (APEX™ EDS, EDAX, Inc., California, United States) after 24 h and 28 days .
Data were analyzed by IBM SPSS software version 24 (IBM SPSS Statistics® version 24, IBM Corp., New York, USA). Descriptive statistics were presented as frequency and percentage. The chi-square test was performed to compare categorical data, and post hoc test was performed when the overall test showed a significant difference. Adjusted residuals were extracted and each one was multiplied by itself to calculate chi-square values, and then chi-square values were transformed to obtain p-values. Statistical significance was adjusted at P < 0.05 .
Part 1—histological findings The mean age of the patients was 8.29 years (SD 0.45; range 8–9 years), and more than half of them were male (n = 15; 62.5%) (Table ). The histological findings were divided according to the following parameters: Odontoblastic integrity. The WMTA + NaOCl gel group showed better odontoblastic integrity with a statistically significant difference ( P = 0.005, P = 0.007, and P < 0.001) after 7, 30, and 90 days, respectively, as found in (Tables , , and ) when compared to the control group. In the WMTA + NaOCl gel, 75% of samples had mild odontoblastic disorganization after 7 days, and 100% of samples showed normal tissue morphology ( P < 0.001) after 90 days (Table ) (Fig. ). Conversely, in the control group, 50% of samples showed normal tissue morphology after 7 days, and 100% of the samples had moderate odontoblastic disorganization ( P < 0.05) after 30 and 90 days (Table ) (Fig. ). 2. Pulp tissue hemorrhage. The control group had no statistically significant difference ( P = 0.096, P = 0.614, and P = 0.131) over the WMTA + NaOCl gel group regarding pulp tissue hemorrhage after 7, 30, and 90 days, respectively, as found in (Tables , , and ). In the WMTA + NaOCl gel group, 75% of samples showed no pulpal hemorrhage after 7 days, but, 100% of samples had a few scattered red blood cells ( P < 0.05) after 30 days (Table ). However, in the control group, 50% of samples had a mild pulp tissue hemorrhage after 7 and 30 days, and then the percentage increased to 75% after 30 days ( P = 0.234) (Table ) (Fig. ). 3. Pulp fibrosis. Both groups showed favorable results regarding pulp fibrosis parameter ( P = 0.522, P = 0.131, P = 0.302) after 7, 30, and 90 days, respectively, as found in (Tables , , and ). In the WMTA + NaOCl gel group, 87.5% of samples had no pulp fibrosis ( P = 0.741) after 7 and 90 days (Table ). In addition, in the control group, 75% of samples had no pulp fibrosis after 7 days, and then the percentage increased to 100% ( P = 0.113) after 90 days (Table ) (Fig. ). 4. Dentin bridge formation. Both materials showed favorable outcomes regarding dentin bridge formation over time (Tables , , and ). In the 7th day assessment, there was a statistically significant difference between the groups ( P = 0.026). In the WMTA + NaOCl gel group, 100% of the samples showed no dentin bridge formation, and 50% of control group the samples showed initial dentin bridge formation (Table ). In the control group, 87.5% of samples achieved complete bridge formation ( P < 0.001) after 90 days (Table ). However, in the WMTA + NaOCl gel group, 87.5% of samples showed partial bridge formation ( P < 0.001) after 90 days (Table ) (Fig. ). 5. Pulp calcification. The control group showed satisfactory results with statistically significant differences ( P = 0.001, P < 0.001, and P < 0.001) regarding pulpal calcification at 7, 30, and 90 days, respectively, as found in (Tables , , and ), when compared to the WMTA + NaOCl gel group. In the control group, 75% of samples had no pulp calcification after 7 days, and 100% had no pulp calcification after 30, and 90 days ( P = 0.113) (Table ). In the WMTA + NaOCl gel group, 87.5% of samples had small multiple calcifications after 7 days. However, 87.5% of samples had a single small calcification ( P = 0.004) after 90 days (Table ) (Fig. ). Part 2—in vitro surface morphological and chemical analysis of MTA mixtures pH level evaluation A discrepancy was observed when monitoring the pH of the samples after 24 h, as the 2.5% NaOCl gel additive contributed to raising the pH value. However, after 28 days, the results were similar. The WMTA + NaOCl gel group had a pH of 9.7 in the first 24 h and increased to 11.6 after 28 days. Similarly, the WMTA + DW group had a pH of 8.5 and increased to 11.1, indicating that both additives produced an alkaline pH. Surface morphological evaluation After 24 h, the WMTA + NaOCl gel group contained products of different sizes, and the surface structure was less homogeneous. It contained pores and is likely the result of the volatilization of oxygen or chlorine gas (Fig. ). The WMTA + DW group showed the beginning of the formation of calcium sulfoaluminate (CSA) crystals resulting from the interaction of calcium silicate cement with water. It is also observed that these crystals accumulate without the presence of large pores on the surface of the sample (Fig. ). After 28 days, the surfaces of the samples in both groups became very similar and consistent and mainly composed of CSA crystals (Fig. ). Chemical analysis After 24 h, EDX analysis showed that the WMTA + NaOCl gel group contains a greater percentage of calcium (59.11%) and silicone (3.29%), compared to the WMTA + DW group (57.38% and 1.84%, respectively) causing greater reactivity (Table ) (Fig. ). After 28 days, EDX analysis confirmed the completion of the setting reaction of both cements since the percentage of calcium increased from 59.11% to 82.82% in the WMTA + NaOCl gel group and from 57.38% to 77.43% in the WMTA + DW group (Table ) (Fig. ).
The mean age of the patients was 8.29 years (SD 0.45; range 8–9 years), and more than half of them were male (n = 15; 62.5%) (Table ). The histological findings were divided according to the following parameters: Odontoblastic integrity. The WMTA + NaOCl gel group showed better odontoblastic integrity with a statistically significant difference ( P = 0.005, P = 0.007, and P < 0.001) after 7, 30, and 90 days, respectively, as found in (Tables , , and ) when compared to the control group. In the WMTA + NaOCl gel, 75% of samples had mild odontoblastic disorganization after 7 days, and 100% of samples showed normal tissue morphology ( P < 0.001) after 90 days (Table ) (Fig. ). Conversely, in the control group, 50% of samples showed normal tissue morphology after 7 days, and 100% of the samples had moderate odontoblastic disorganization ( P < 0.05) after 30 and 90 days (Table ) (Fig. ). 2. Pulp tissue hemorrhage. The control group had no statistically significant difference ( P = 0.096, P = 0.614, and P = 0.131) over the WMTA + NaOCl gel group regarding pulp tissue hemorrhage after 7, 30, and 90 days, respectively, as found in (Tables , , and ). In the WMTA + NaOCl gel group, 75% of samples showed no pulpal hemorrhage after 7 days, but, 100% of samples had a few scattered red blood cells ( P < 0.05) after 30 days (Table ). However, in the control group, 50% of samples had a mild pulp tissue hemorrhage after 7 and 30 days, and then the percentage increased to 75% after 30 days ( P = 0.234) (Table ) (Fig. ). 3. Pulp fibrosis. Both groups showed favorable results regarding pulp fibrosis parameter ( P = 0.522, P = 0.131, P = 0.302) after 7, 30, and 90 days, respectively, as found in (Tables , , and ). In the WMTA + NaOCl gel group, 87.5% of samples had no pulp fibrosis ( P = 0.741) after 7 and 90 days (Table ). In addition, in the control group, 75% of samples had no pulp fibrosis after 7 days, and then the percentage increased to 100% ( P = 0.113) after 90 days (Table ) (Fig. ). 4. Dentin bridge formation. Both materials showed favorable outcomes regarding dentin bridge formation over time (Tables , , and ). In the 7th day assessment, there was a statistically significant difference between the groups ( P = 0.026). In the WMTA + NaOCl gel group, 100% of the samples showed no dentin bridge formation, and 50% of control group the samples showed initial dentin bridge formation (Table ). In the control group, 87.5% of samples achieved complete bridge formation ( P < 0.001) after 90 days (Table ). However, in the WMTA + NaOCl gel group, 87.5% of samples showed partial bridge formation ( P < 0.001) after 90 days (Table ) (Fig. ). 5. Pulp calcification. The control group showed satisfactory results with statistically significant differences ( P = 0.001, P < 0.001, and P < 0.001) regarding pulpal calcification at 7, 30, and 90 days, respectively, as found in (Tables , , and ), when compared to the WMTA + NaOCl gel group. In the control group, 75% of samples had no pulp calcification after 7 days, and 100% had no pulp calcification after 30, and 90 days ( P = 0.113) (Table ). In the WMTA + NaOCl gel group, 87.5% of samples had small multiple calcifications after 7 days. However, 87.5% of samples had a single small calcification ( P = 0.004) after 90 days (Table ) (Fig. ).
A discrepancy was observed when monitoring the pH of the samples after 24 h, as the 2.5% NaOCl gel additive contributed to raising the pH value. However, after 28 days, the results were similar. The WMTA + NaOCl gel group had a pH of 9.7 in the first 24 h and increased to 11.6 after 28 days. Similarly, the WMTA + DW group had a pH of 8.5 and increased to 11.1, indicating that both additives produced an alkaline pH.
After 24 h, the WMTA + NaOCl gel group contained products of different sizes, and the surface structure was less homogeneous. It contained pores and is likely the result of the volatilization of oxygen or chlorine gas (Fig. ). The WMTA + DW group showed the beginning of the formation of calcium sulfoaluminate (CSA) crystals resulting from the interaction of calcium silicate cement with water. It is also observed that these crystals accumulate without the presence of large pores on the surface of the sample (Fig. ). After 28 days, the surfaces of the samples in both groups became very similar and consistent and mainly composed of CSA crystals (Fig. ).
After 24 h, EDX analysis showed that the WMTA + NaOCl gel group contains a greater percentage of calcium (59.11%) and silicone (3.29%), compared to the WMTA + DW group (57.38% and 1.84%, respectively) causing greater reactivity (Table ) (Fig. ). After 28 days, EDX analysis confirmed the completion of the setting reaction of both cements since the percentage of calcium increased from 59.11% to 82.82% in the WMTA + NaOCl gel group and from 57.38% to 77.43% in the WMTA + DW group (Table ) (Fig. ).
Although MTA is considered the gold standard dressing material for pulpotomy in primary molars, the long setting time is a shortcoming of mixing WMTA + DW . Thus, it was recommended to mix MTA with various accelerators, including NaOCl gel. According to Tilakchand et al. and Kogan et al. , mixing WMTA with 3% NaOCl gel improved the handling properties of the material while reducing the setting time. In addition, Jafarnia et al. and Alnezi et al. studied the cytotoxicity of a mixture of WMTA with a 3% NaOCl gel and concluded that it is biocompatible. According to Al Kurdi et al. , adding 2.2% NaOCl gel to white Portland cement improved its antibacterial efficacy since white Portland cement has a similar chemical composition of WMTA cement, in which WMTA consists of 75% white Portland cement. NaOCl gel may be a suitable solution for conducting pulpotomy in primary teeth because of its antimicrobial action . The association with MTA was followed with the aim of leveraging the antimicrobial properties of NaOCl gel while maintaining the sealing and regenerative capabilities of the base material – . Kogan et al. suggested that additional research supports the inclusion of NaOCl gel as an additive to MTA in clinical settings. The literature reviewed did not present any clinical research assessing the histological success of this combination in human primary molars pulpotomy, highlighting the need for such a study. The current study was performed using the split-mouth design because it increases the reliability of the results as it allows comparison of procedures within the same individual, eliminating the effect of confounding factors . There are many methods for evaluating pulp tissue response, including radiographic or histological evaluation. Histological evaluation was selected because it gives more accurate findings about the formation of the dentin bridge and the response of the pulp tissue. In addition, it detects the regenerative behavior of the applied material . The histological evaluation was carried out after 7, 30, and 90 days to assess the immediate and delayed pulp response , . In the current study, an SEM–EDX analysis was performed to reveal the morphological structure of the crystals and their chemical composition , , and the pH level was measured because it directly affects the biological behavior of cement . The previous tests were conducted after 24 h and 28 days of mixing the cement because the concrete usually hardens after 24 h but reaches full strength after 28 days . In the present study, a 2.25% hypochlorite gel was selected over lower concentrations due to its ability to dissolve tissue and its alkaline properties . Furthermore, a 2.5% NaOCl gel was shown to be effective in reducing the counts of E. faecalis . Additionally, as stated by Andrade et al. , combining WMTA with a 1% NaOCl gel did not enhance its antibacterial efficacy compared to a mixture of WMTA and distilled water. Moreover, Al Kurdi et al. study indicated that mixing 2.2% NaOCl gel with white Portland cement increased its antibacterial efficacy since white Portland cement shares a similar chemical composition with WMTA, which is composed of 75% white Portland cement. The results of the present study showed mild odontoblastic disorganization after 7 days in most samples of the WMTA + NaOCl gel group. However, after 90 days, all the samples showed normal tissue morphology. The mild odontoblastic disorganization in the first period can be justified by the high pH of the mixture, as the results of the study showed that the pH was 9.7 after 24 h and rose to 11.6 after 28 days. Dianat et al. indicated that the high alkalinity of the materials stimulates the differentiation of odontoblasts to initiate the regeneration. In addition, the study of Sedek et al. found that when MTA sets, the release of calcium hydroxide, which is irritating to the pulp tissue, decreases, which leads to obtaining a normal tissue morphology, and this is consistent with the findings of the current study after 90 days. The improved odontoblastic integrity in the WMTA + NaOCl group, despite the tissue-dissolving properties of hypochlorite, can be attributed to several factors, including the high pH of the hypochlorite solution. It contributes to an alkaline environment, which leads to the differentiation of odontoblasts and reparative dentinogenesis, as alkaline conditions stimulate pulp stem cells and improve the mineralization process . In addition, releasing calcium ions from MTA in a high-pH environment further facilitates hydroxyapatite formation, providing a scaffold for odontoblast-like cell attachment and activity . Although the initial alkalinity may cause irritation, Sedek et al. suggested that the release of calcium hydroxide decreases over time, reducing cytotoxicity and allowing tissue recovery. This aligns with the findings of complete odontoblastic integrity in the WMTA + NaOCl group at 90 days. During all follow-up periods, no pulp necrosis was observed in either sample, which indicates that they are biocompatible, and this is consistent with the study of Solomon et al. , which reported that MTA stimulates the healing of pulp tissue and prevents long-term necrosis in most cases of pulpotomy. The findings agree with Sedek et al. study, which found a normal tissue morphology in most of the samples of direct pulp capping with MTA after 14 and 45 days. Both study groups showed blood vessel dilation in the early stages, and this could be explained by the angiogenesis event that aims to form a vascular network to transport nutrients, oxygen, and growth factors, as the pulp tissue relies on the blood supply to facilitate healing . Goldberg et al. stated that the tissue healing process begins with moderate inflammation since it is a prerequisite for healing, followed by tissue regeneration. The results of the current study are consistent with the study of Bahammam et al. , which found that blood vessel dilatation was not found after 14 days of direct pulp capping with MTA. In most samples of the WMTA + NaOCl gel group, small multiple calcifications were found after 7 days. After 90 days, calcifications became a single small calcification, while the control group outperformed by a significant difference, as no pulp calcifications were seen in most of the samples after 7 days and disappeared after 90 days. The findings can be justified by the results of the surface morphological characteristics related to this study, which found that the surface of the mixture after 24 h was heterogeneous and contained many pores resulting from the volatilization of oxygen and chlorine gas, which could have played a role in irritating the pulp tissue and causing calcifications, compared to the morphological characteristics of the more homogeneous control group. Satheeshkumar et al. stated that pulp stones are mainly associated with long-term irritation and chronic inflammation, and it is possible that the high alkalinity of the mixture, according to the current study, is a cause of irritation to the pulp tissue. The results are consistent with the Alzoubi et al. study, where no calcifications were observed in most MTA samples after 90 days. However, this study does not correspond to the Srinivasan et al. study, which suggested that MTA causes pulp stones after 180 days. Most of the samples in both groups had no pulp fibrosis during the different follow-up periods. However, mild pulp fibrosis appeared in only some samples, which indicates an incidence of previous irritation during the first period. According to the Kabartai et al. study, when the pulp volume is smaller, the physiological pressure increases, which enhances molecular crowding and, thus, enhances pulp fibrosis. It explains the incidence of pulp fibrosis in the radicular pulp, as in the current study. The findings differ from the study of Bahammam et al. , which found pulp fibrosis in most samples of direct pulp capping with MTA after 60 days. Both materials showed positive results in the dentinal bridge formation over time. After 90 days, most samples of the control group had a complete dentinal bridge, and most samples of the WMTA and + NaOCl gel group had a partial dentinal bridge. The positive results can be explained by the high alkalinity of the two materials. High alkalinity stimulates hard tissue formation by releasing calcium ions, regulating the production of cytokines, and forming an antibacterial environment in contact with living tissue. As a result, it enhances the migration and differentiation of hard tissue-producing cells, leading to a homogeneous dentin bridge . It is consistent with the Song et al. study, which reported that high pH promotes reparative dentin formation. These findings can also be explained by the chemical analysis of the materials conducted in the current study, which concluded that both the control group and WMTA + NaOCl gel mixture group contain a high concentration of surface calcium. This high calcium content promotes the formation of a dentinal bridge. It is consistent with the study of Sedek et al. , which mentioned that MTA is a biocompatible and bioactive material that provides a suitable surface for the adhesion and differentiation of odontoblastic cells. It can be related to the results of the morphological characteristics study, which found that after 28 days, the surfaces of the two samples were homogenous and similar, and it consists mainly of CSA crystals that provide a suitable surface for dentinal bridge adherence. The current results are consistent with the study of Mohamed et al. and Alzoubi et al. , which found a complete dentinal bridge in most MTA samples after 90 days. While both materials create an alkaline environment conducive to dentinal bridge formation, the milder initial pulpal irritation, smoother surface morphology, and consistent calcium availability in the WMTA + DW group likely facilitated the formation of a complete dentinal bridge. In contrast, the initial irritative effects and surface inconsistencies of the WMTA + NaOCl gel may have delayed or disrupted the process, resulting in partial bridge formation in most specimens. The null hypothesis of the current study was partly rejected since the control group outperformed the WMTA + NaOCl gel mixture group in several histological parameters, including dentine bridge formation and pulp calcification, while the WMTA + NaOCl gel mixture group showed better odontoblastic integrity in the short and medium term. In addition, the physical and chemical characteristics of the NaOCl group were worse initially, but after a month, they were similar. The possible advantages of exchanging DW for NaOCl gel in clinical practice are the absence of necrosis, better handling properties, and decreased setting time, a crucial prerequisite for pediatric dentistry practice – . The main limitation of this study is the short-term follow-up and the relatively small sample size. Thus, further studies are recommended to ascertain the findings.
Based on our findings, WMTA + DW group surpassed the WMTA + NaOCl gel group in various histological aspects, such as dentine bridge formation and pulp calcification, while WMTA + NaOCl gel group improved odontoblastic integrity over both the short and medium term evaluations and exhibited, at 28 days, comparable chemical composition, surface morphology, and alkalinity when compared to WMTA + DW.
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Rs205764 and rs547311 in linc00513 may influence treatment responses in multiple sclerosis patients: A pharmacogenomics Egyptian study | 8250bf15-b406-49d8-a2b6-11c42d19875c | 9985893 | Pharmacology[mh] | Introduction Multiple sclerosis (MS) is a disorder of the central nervous system (CNS), causing neurological disabilities in young adults. This complex and multifactorial disease affects more than 2.5 million people globally, with a higher prevalence in females compared to males . Establishing prevalence and estimates of MS in developing countries is yet to be made more feasible, primarily due to the lack of epidemiological studies around this disease. Treatmenst options of MS are aimed at 3 disciplines; the management of acute relapses, symptomatic treatment, and disease modifying treatment (DMT) . DMTs are drugs that are aimed at modulating immune responses. The primary goal of using DMTs is controlling and integrating clinical parameters such as relapses or disease progression, and magnetic resonance imaging (MRI) parameters such as the presence of new lesions. Together, both parameters are combined in a term called no evidence of disease activity (NEDA) . Despite the availability of well-established evidence on the clinical efficacy of these drugs, inconsistent treatment responses still prevail, providing a frequently insurmountable barrier against achieving adequate clinical outcomes and providing a better quality of life for these patients. Personalized therapies for MS are recently gaining a rightful interest, where the integration of parameters beyond MRI scans and disease state has a potential for contributing to better and more efficient treatment choices. Such parameters include accounting for differential epigenetic profiles in patients vs. healthy subjects, an emerging and promising area of research , as well as possibly accounting for genetic variances, or single nucleotide polymorphisms (SNPs), whose downstream effects may ultimately translate into affecting the response to treatment in patients who were typically suited for that given treatment . SNPs accounting for such discrepancies are not uncommon in MS. While accounting for these SNPs would certainly be pivotal in influencing the choice of DMT, a gap would still remain, since all SNPs previously associated with treatment responses were on protein coding elements . Indeed, the insurmountable epigenetic component of MS calls for the imminent bridging between the inconsistent treatment responses and SNPs on both coding and non-coding genetic elements, integrating both the epigenetic component of the disease as well as potential implications of genetic variations. The role of long non-coding RNAs (lncRNAs) is recently emerging in MS, owing to the high regulatory capacity of these elements in the disease pathogenesis . LncRNAs are non-coding species exceeding 200 nucleotides in length, and they can influence the differentiation of oligodendrocytes and the polarization state of macrophages, act as micro-RNA (miRNA) sponges, regulate the levels of immune-modulatory cytokines, as well as influence the activation state of CD4+ cells. It, therefore, comes as no surprise that SNPs occurring on such elements are expected to play important roles in the downstream activity of a given lncRNA, potentially extending to alterations in treatment responses among different patients. Long intergenic non-coding RNA (linc)00513 has been recently reported as a novel regulator of the type 1 interferon (IFN) signaling pathway . Polymorphisms in the promotor region of linc00513 (G for rs205764 and A for rs547311) have also been associated with an overexpression of linc00513 and a subsequent increase in the downstream signaling activity of the type 1 IFN pathway . In MS, no such variances have yet been investigated, and a corresponding role of linc00513 remains elusive. Given the pivotal role that the type 1 IFN signaling pathway plays in MS , investigating the implications of these genetic variations in MS patients seemed of great interest. We therefore aim to provide data on the distribution of genotypes at rs205764 and rs547311 in MS patients of the Egyptian population, and correlate these genotypes with the response to treatment. Other clinical parameters are also included, further asserting the clinical ramifications of these SNPs. Materials and methods 2.1 Study group This study included 144 RRMS patients (115 females and 29 males) with a clinical diagnosis of MS. Clinical parameters of the patients were assessed by the same neurologist at Nasser Institute Hospital MS Unit, Cairo, Egypt. Information was obtained regarding the patients’ response to treatment, which was defined as the lack of clinically documented attacks for at least one year on treatment . Additional information on the age of onset, EDSS, and the annualized relapse rate (ARR) – a parameter reflecting the number of relapses per year, were also obtained. All patients included were older than 18 years of age, diagnosed at the same MS center, and on a given medication for one year at the time of the study. Alternatively, their medical records were retrospectively checked, when applicable, for their status at one year of treatment in order to eliminate the effect of treatment duration on response. The age of onset was defined from the time of symptom onset not from the time of diagnosis, and the EDSS and ARR were assessed and calculated by the neurologist. First, with regards to the response to treatment, patients were considered responsive to a given medication if they experienced no relapses within the first year of treatment initiation. Alternatively, relapses occurring within the first few months of treatment were considered a positive predictor of treatment inefficacy in these particular patients , and they were therefore considered non-responsive to the given medication. All 144 RRMS patients were initially compared for differences in the frequency of responders among the genotype groups to highlight potential genotype-treatment response association. In subsequent subgrouping based on the treatment received, n = 48 patients receiving fingolimod, and n = 19 patients receiving DMF, were analyzed for the frequency of responders among the different genotype groups ; analysis of the response to treatment was done after one year of treatment initiation. For the EDSS, the scores of n = 108 (for rs205764) and n = 110 (for rs547311) RRMS patients were compared with regards to their genotypes to highlight potential genotype-EDSS association . All study participants signed an informed consent, and this study was approved from the ethics committees at the German University in Cairo and Nasser Institute Hospital, Cairo, Egypt. 2.2 Molecular research methodology 2.2.1 Genomic DNA isolation Genomic DNA was isolated from patients’ whole blood using QiAmp DNA extraction kit (Qiagen, USA) according to manufacturer’s protocol. DNA samples were stored at -20 C until downstream processing. DNA was quantified using Nanodrop. 2.2.2 Identification of the polymorphisms of interest Genotyping experiments were performed on StepOne Real Time Quantitative PCR (RT-qPCR) (Applied Biosystems, USA), using TaqMan reagents: TaqMan Genotyping Mastermix and TaqMan SNP Assays with their corresponding unique Assay IDs (rs205764: C:7614549_10; rs547311: C:2595518_10) (Life Technologies, USA). Fluorescence signals detected were VIC and FAM. Preparation of the reaction mixture: Each PCR tube contained a volume of DNA equivalent to at least 20 ng (manufacturer’s recommendation), nuclease-free water to 11.25 ul, 12.5 ul TaqMan Genotyping Mastermix, and finally, 1.25 ul TaqMan SNP Assay. The standard thermal profile was used . 2.3 Statistical analysis Statistical analysis was performed on GraphPad Prism v9.4 using parametric/nonparametric t-test/one-way ANOVA when comparing the age of onset, EDSS, and the ARR, and using Fisher exact when comparing the response to treatment. A p-value < 0.05 was considered as statistically significant. Values on the graphs are expressed as mean ± SEM. In order to determine the exact genotype that correlates to a significant difference in a given clinical parameter (inheritance of one VS two minor alleles), genotypes at the two polymorphisms were analyzed and compared with regards to four different models of inheritance. Initially, all samples were compared with regards to the dominant model of inheritance, where patients carrying the homozygous major genotype were compared to the rest of the patients. If significant differences were found in this model, additional models of inheritance were subsequently examined in order to identify if this difference was due to the inheritance of one or two minor alleles. In the recessive model of inheritance, patients who were homozygous for the minor allele were compared to the rest of the patients. In the overdominant model, patients who were heterozygous were compared to the rest of the patients. Finally, in the codominant model, all three genotypes were compared to each other . Study group This study included 144 RRMS patients (115 females and 29 males) with a clinical diagnosis of MS. Clinical parameters of the patients were assessed by the same neurologist at Nasser Institute Hospital MS Unit, Cairo, Egypt. Information was obtained regarding the patients’ response to treatment, which was defined as the lack of clinically documented attacks for at least one year on treatment . Additional information on the age of onset, EDSS, and the annualized relapse rate (ARR) – a parameter reflecting the number of relapses per year, were also obtained. All patients included were older than 18 years of age, diagnosed at the same MS center, and on a given medication for one year at the time of the study. Alternatively, their medical records were retrospectively checked, when applicable, for their status at one year of treatment in order to eliminate the effect of treatment duration on response. The age of onset was defined from the time of symptom onset not from the time of diagnosis, and the EDSS and ARR were assessed and calculated by the neurologist. First, with regards to the response to treatment, patients were considered responsive to a given medication if they experienced no relapses within the first year of treatment initiation. Alternatively, relapses occurring within the first few months of treatment were considered a positive predictor of treatment inefficacy in these particular patients , and they were therefore considered non-responsive to the given medication. All 144 RRMS patients were initially compared for differences in the frequency of responders among the genotype groups to highlight potential genotype-treatment response association. In subsequent subgrouping based on the treatment received, n = 48 patients receiving fingolimod, and n = 19 patients receiving DMF, were analyzed for the frequency of responders among the different genotype groups ; analysis of the response to treatment was done after one year of treatment initiation. For the EDSS, the scores of n = 108 (for rs205764) and n = 110 (for rs547311) RRMS patients were compared with regards to their genotypes to highlight potential genotype-EDSS association . All study participants signed an informed consent, and this study was approved from the ethics committees at the German University in Cairo and Nasser Institute Hospital, Cairo, Egypt. Molecular research methodology 2.2.1 Genomic DNA isolation Genomic DNA was isolated from patients’ whole blood using QiAmp DNA extraction kit (Qiagen, USA) according to manufacturer’s protocol. DNA samples were stored at -20 C until downstream processing. DNA was quantified using Nanodrop. 2.2.2 Identification of the polymorphisms of interest Genotyping experiments were performed on StepOne Real Time Quantitative PCR (RT-qPCR) (Applied Biosystems, USA), using TaqMan reagents: TaqMan Genotyping Mastermix and TaqMan SNP Assays with their corresponding unique Assay IDs (rs205764: C:7614549_10; rs547311: C:2595518_10) (Life Technologies, USA). Fluorescence signals detected were VIC and FAM. Preparation of the reaction mixture: Each PCR tube contained a volume of DNA equivalent to at least 20 ng (manufacturer’s recommendation), nuclease-free water to 11.25 ul, 12.5 ul TaqMan Genotyping Mastermix, and finally, 1.25 ul TaqMan SNP Assay. The standard thermal profile was used . Genomic DNA isolation Genomic DNA was isolated from patients’ whole blood using QiAmp DNA extraction kit (Qiagen, USA) according to manufacturer’s protocol. DNA samples were stored at -20 C until downstream processing. DNA was quantified using Nanodrop. Identification of the polymorphisms of interest Genotyping experiments were performed on StepOne Real Time Quantitative PCR (RT-qPCR) (Applied Biosystems, USA), using TaqMan reagents: TaqMan Genotyping Mastermix and TaqMan SNP Assays with their corresponding unique Assay IDs (rs205764: C:7614549_10; rs547311: C:2595518_10) (Life Technologies, USA). Fluorescence signals detected were VIC and FAM. Preparation of the reaction mixture: Each PCR tube contained a volume of DNA equivalent to at least 20 ng (manufacturer’s recommendation), nuclease-free water to 11.25 ul, 12.5 ul TaqMan Genotyping Mastermix, and finally, 1.25 ul TaqMan SNP Assay. The standard thermal profile was used . Statistical analysis Statistical analysis was performed on GraphPad Prism v9.4 using parametric/nonparametric t-test/one-way ANOVA when comparing the age of onset, EDSS, and the ARR, and using Fisher exact when comparing the response to treatment. A p-value < 0.05 was considered as statistically significant. Values on the graphs are expressed as mean ± SEM. In order to determine the exact genotype that correlates to a significant difference in a given clinical parameter (inheritance of one VS two minor alleles), genotypes at the two polymorphisms were analyzed and compared with regards to four different models of inheritance. Initially, all samples were compared with regards to the dominant model of inheritance, where patients carrying the homozygous major genotype were compared to the rest of the patients. If significant differences were found in this model, additional models of inheritance were subsequently examined in order to identify if this difference was due to the inheritance of one or two minor alleles. In the recessive model of inheritance, patients who were homozygous for the minor allele were compared to the rest of the patients. In the overdominant model, patients who were heterozygous were compared to the rest of the patients. Finally, in the codominant model, all three genotypes were compared to each other . Results 3.1 Patient characteristics This study group consisted of 79.7% females (n=115) and 20.2% males (n=29). The EDSS and the age of onset were not gender-dependent (p>0.05). Patient characteristics are summarized in . 3.2 Genotyping results The genotype distribution for both polymorphisms were as follows: For rs205764, 70 were homozygous for the allele T (48.6%), 12 were homozygous for the allele G (8.3%), and 62 were heterozygous (43%). For the investigated subset of MS population, T was considered as the major allele and G was considered as the minor allele according to the genotyping results. For rs547311, 76 were homozygous for the allele G (52.7%), 15 were homozygous for the minor A (10.4%), and 52 were heterozygous (36.11%). For the investigated subset of MS population, G was considered as the major allele and A was considered as the minor allele according to the genotyping results. Genotyping results and classification are summarized in . 3.3 Analyzing the response to treatment in different genotype groups for rs205764 and rs547311 The response to treatment was defined as the lack of clinically documented attacks for at least one year on treatment , as previously mentioned. No significant differences were found in the response to treatment in general, between patients carrying polymorphisms at either location and those who do not, either compared as a whole or sub-grouped by gender. When comparing the response of patients to specific DMTs , patients carrying polymorphisms at rs205764 in the dominant model (either one or two G alleles) showed a significantly higher response to fingolimod (p = 0.0362*) with an odds ratio (OR) of 4.72 compared to patients carrying two T alleles . These patients also showed a significantly lower response to DMF (p = 0.0436*) with a relative risk of 0.5 . Upon comparing these patients based on other models of inheritance, starting with the recessive, followed by the overdominant and the codominant, no significant difference was observed, suggesting that the difference in responses to fingolimod or DMF could equally be attributed to inheritance of either one or two G alleles at rs205764. Patients carrying polymorphisms at rs547311 showed no statistically significant differences in their response to fingolimod (p = 0.103), or DMF (p > 0.999). 3.4 Analyzing other clinical parameters in different genotype groups for rs205764 and rs547311 3.4.1 EDSS Patients’ EDSS were assessed by the same consulting neurologist at Nasser Institute Hospital. When comparing the average EDSS of patients carrying different alleles at both positions , patients carrying one or two A alleles at rs547311 showed a significantly higher EDSS (p = 0.0419*) compared to patients carrying two G alleles. Upon comparing these patients based on other models of inheritance, no significant difference was observed, suggesting, again, that the inheritance of one or two A alleles at rs547311 may be equally detrimental for a patient’s EDSS. Different alleles at rs205764, on the other hand, showed no significant association with the patients’ EDSS . 3.4.2 Age of onset The patients’ age of onset was defined from the reported time of onset of symptoms and not the time of diagnosis. The average age of onset of different patient genotype groups were compared for the two polymorphic locations . When comparing the average age of onset between patients carrying one or two G alleles at rs205764, no significant difference was observed (p = 0.7098). This was also the case when comparing patients carrying polymorphisms at rs205764 only (i.e. carrying the major G allele at rs547311) (p = 0.8934). The opposite was also true for rs547311. Additionally, when comparing the average age of onset for patients carrying a polymorphism at either location exclusively without the other , a trend could be seen, yet the difference did not reach significance (p = 0.3683). These results are summarized in . 3.4.3 ARR The patients’ ARR was calculated by the neurologist and compared across the different genotypes in the dominant model with regards to the two polymorphisms. Although non-responders, by definition, experience more relapses than responders, and should be expected to have a higher ARR, no significant difference in the ARR between genotypes at either location was found . This is likely attributed to the fact that with the exception of fingolimod and DMF, there were no significant differences among the different genotypes with regards to the response to MS treatment in this study. However, upon comparing the ARR between genotypes within a given treatment (for both fingolimod and DMF – ), the lack of significant differences persists, presenting the usefulness of assessing treatment responses in terms of more than one analysis in this study. Moreover, the effect of patient genotype on ARR may be better assessed through measuring differential changes in ARR before and after treatment for each genotype. 3.5 Correlation between age and the analyzed parameters In order to ascertain that the analyzed parameters are not influenced by age in our studied patient cohort, a correlation was done between age and each of the EDSS, response to fingolimod, response to DMF, as well as the ARR. Correlations between age and each of EDSS and ARR was done using Pearson correlation, and with the response to treatment using Point-Biserial correlation test. None of the correlations with age appeared to be major nor significant, suggesting that in our cohort, age did not influence any of these parameters. These results are summarized in . Patient characteristics This study group consisted of 79.7% females (n=115) and 20.2% males (n=29). The EDSS and the age of onset were not gender-dependent (p>0.05). Patient characteristics are summarized in . Genotyping results The genotype distribution for both polymorphisms were as follows: For rs205764, 70 were homozygous for the allele T (48.6%), 12 were homozygous for the allele G (8.3%), and 62 were heterozygous (43%). For the investigated subset of MS population, T was considered as the major allele and G was considered as the minor allele according to the genotyping results. For rs547311, 76 were homozygous for the allele G (52.7%), 15 were homozygous for the minor A (10.4%), and 52 were heterozygous (36.11%). For the investigated subset of MS population, G was considered as the major allele and A was considered as the minor allele according to the genotyping results. Genotyping results and classification are summarized in . Analyzing the response to treatment in different genotype groups for rs205764 and rs547311 The response to treatment was defined as the lack of clinically documented attacks for at least one year on treatment , as previously mentioned. No significant differences were found in the response to treatment in general, between patients carrying polymorphisms at either location and those who do not, either compared as a whole or sub-grouped by gender. When comparing the response of patients to specific DMTs , patients carrying polymorphisms at rs205764 in the dominant model (either one or two G alleles) showed a significantly higher response to fingolimod (p = 0.0362*) with an odds ratio (OR) of 4.72 compared to patients carrying two T alleles . These patients also showed a significantly lower response to DMF (p = 0.0436*) with a relative risk of 0.5 . Upon comparing these patients based on other models of inheritance, starting with the recessive, followed by the overdominant and the codominant, no significant difference was observed, suggesting that the difference in responses to fingolimod or DMF could equally be attributed to inheritance of either one or two G alleles at rs205764. Patients carrying polymorphisms at rs547311 showed no statistically significant differences in their response to fingolimod (p = 0.103), or DMF (p > 0.999). Analyzing other clinical parameters in different genotype groups for rs205764 and rs547311 3.4.1 EDSS Patients’ EDSS were assessed by the same consulting neurologist at Nasser Institute Hospital. When comparing the average EDSS of patients carrying different alleles at both positions , patients carrying one or two A alleles at rs547311 showed a significantly higher EDSS (p = 0.0419*) compared to patients carrying two G alleles. Upon comparing these patients based on other models of inheritance, no significant difference was observed, suggesting, again, that the inheritance of one or two A alleles at rs547311 may be equally detrimental for a patient’s EDSS. Different alleles at rs205764, on the other hand, showed no significant association with the patients’ EDSS . 3.4.2 Age of onset The patients’ age of onset was defined from the reported time of onset of symptoms and not the time of diagnosis. The average age of onset of different patient genotype groups were compared for the two polymorphic locations . When comparing the average age of onset between patients carrying one or two G alleles at rs205764, no significant difference was observed (p = 0.7098). This was also the case when comparing patients carrying polymorphisms at rs205764 only (i.e. carrying the major G allele at rs547311) (p = 0.8934). The opposite was also true for rs547311. Additionally, when comparing the average age of onset for patients carrying a polymorphism at either location exclusively without the other , a trend could be seen, yet the difference did not reach significance (p = 0.3683). These results are summarized in . 3.4.3 ARR The patients’ ARR was calculated by the neurologist and compared across the different genotypes in the dominant model with regards to the two polymorphisms. Although non-responders, by definition, experience more relapses than responders, and should be expected to have a higher ARR, no significant difference in the ARR between genotypes at either location was found . This is likely attributed to the fact that with the exception of fingolimod and DMF, there were no significant differences among the different genotypes with regards to the response to MS treatment in this study. However, upon comparing the ARR between genotypes within a given treatment (for both fingolimod and DMF – ), the lack of significant differences persists, presenting the usefulness of assessing treatment responses in terms of more than one analysis in this study. Moreover, the effect of patient genotype on ARR may be better assessed through measuring differential changes in ARR before and after treatment for each genotype. EDSS Patients’ EDSS were assessed by the same consulting neurologist at Nasser Institute Hospital. When comparing the average EDSS of patients carrying different alleles at both positions , patients carrying one or two A alleles at rs547311 showed a significantly higher EDSS (p = 0.0419*) compared to patients carrying two G alleles. Upon comparing these patients based on other models of inheritance, no significant difference was observed, suggesting, again, that the inheritance of one or two A alleles at rs547311 may be equally detrimental for a patient’s EDSS. Different alleles at rs205764, on the other hand, showed no significant association with the patients’ EDSS . Age of onset The patients’ age of onset was defined from the reported time of onset of symptoms and not the time of diagnosis. The average age of onset of different patient genotype groups were compared for the two polymorphic locations . When comparing the average age of onset between patients carrying one or two G alleles at rs205764, no significant difference was observed (p = 0.7098). This was also the case when comparing patients carrying polymorphisms at rs205764 only (i.e. carrying the major G allele at rs547311) (p = 0.8934). The opposite was also true for rs547311. Additionally, when comparing the average age of onset for patients carrying a polymorphism at either location exclusively without the other , a trend could be seen, yet the difference did not reach significance (p = 0.3683). These results are summarized in . ARR The patients’ ARR was calculated by the neurologist and compared across the different genotypes in the dominant model with regards to the two polymorphisms. Although non-responders, by definition, experience more relapses than responders, and should be expected to have a higher ARR, no significant difference in the ARR between genotypes at either location was found . This is likely attributed to the fact that with the exception of fingolimod and DMF, there were no significant differences among the different genotypes with regards to the response to MS treatment in this study. However, upon comparing the ARR between genotypes within a given treatment (for both fingolimod and DMF – ), the lack of significant differences persists, presenting the usefulness of assessing treatment responses in terms of more than one analysis in this study. Moreover, the effect of patient genotype on ARR may be better assessed through measuring differential changes in ARR before and after treatment for each genotype. Correlation between age and the analyzed parameters In order to ascertain that the analyzed parameters are not influenced by age in our studied patient cohort, a correlation was done between age and each of the EDSS, response to fingolimod, response to DMF, as well as the ARR. Correlations between age and each of EDSS and ARR was done using Pearson correlation, and with the response to treatment using Point-Biserial correlation test. None of the correlations with age appeared to be major nor significant, suggesting that in our cohort, age did not influence any of these parameters. These results are summarized in . Discussion Multiple sclerosis is a complex, multifactorial, immune-mediated disease targeting the CNS, causing focal lesions of demyelination, impairing nerve conduction and signal transmission . Treatment strategies of the disease are generally aimed at 3 directions, of particular controversy and importance is the use of drugs that help modulate immune responses, called DMTs, a few examples of which are IFN-β, fingolimod, glatiramer acetate, and dimethyl fumarate . Epigenetic research has garnered rightful interest in its contribution to understanding disease pathology , susceptibility, and development . Several areas of research have recently taken interest in the roles of lncRNAs in immune-mediated diseases in general, and MS in particular, in light of the pre-established epigenetic changes that are observed in the disease pathology . LncRNAs have numerous well-established genetic and epigenetic regulatory roles. Of particular interest in this frame of work is linc00513, since its dysregulation has yet been investigated in a single study conducted on systemic lupus erythematosus (SLE) patients and none is yet known about its functional role in MS. Its overexpression has been shown to positively relate to the activity of the type-1 IFN signaling pathway, contributing to the inflammatory state in SLE patients . Linc00513 has been identified as a risk allele for SLE in the aforementioned study, yet no such correlation has been made with MS as per the most recent MS genetic map . However, due to the previously well-established protective role of the same signaling pathway in MS, drawing a straightforward prediction regarding the population under investigation was not entirely possible, making it all the more intriguing to investigate its correlation to MS disease. Single nucleotide polymorphisms (SNP)s are genetic variations involving a single base-pair. Ample research is available on SNPs involved in the development of MS; however, very little amount of research has yet taken interest in SNPs located on non-coding genetic elements, and the potential influence this may have on downstream regulatory processes, and ultimately the clinical picture of the patients. Regarding linc00513, a study has shown that G allele at rs205764 and A allele at rs547311, located in its promotor region, positively correlate to its expression levels and the subsequent signaling activity of the type-1 IFN pathway . Taking it from there, the aim of this work was to determine the genetic prevalence of rs205764 and rs547311 in MS patients of the Egyptian population, and correlate these genetic variances to several clinical parameters, the primary focus of which was the response to treatment. Blood samples were collected from 144 patients, from which genomic DNA was isolated and analyzed for the genotypes at the positions of interest on linc00513 using RT-qPCR. These polymorphisms were then correlated with the previously obtained clinical parameters of each patient: the response to treatment, onset age, and EDSS. The genotypes were analyzed and compared with regards to 4 different models of inheritance: dominant, recessive, overdominant, and codominant. When analyzing the relationship between these polymorphisms and the patient’s response to treatment, a significant difference was found regarding patients carrying polymorphisms at rs205764, where they showed a significantly higher response to fingolimod compared to patients carrying the major allele, with an OR of 4.7. These patients also showed a significantly lower response to DMF, with an OR of 0.5. When examining additional models of inheritance, no significant differences were found, suggesting that inheritance of either one or two G alleles is equally associated with a difference in the treatment response. For the same variants, there are no reported associations, to date, with the response to treatment in MS or any other autoimmune disease. However, other variants have been studied in the context of response to DMF and fingolimod. Rs6919626, in NADPH oxidase-3 gene, has been significantly associated with a lower response to DMF, but no significant associations have been found with the response to fingolimod yet . For the two remaining clinical parameters, no significant difference was seen in the average age of onset between patients carrying either polymorphism and those who do not. These variants have also not yet been previously associated with the age of onset of MS or any other autoimmune diseases; however, rs10492503 in Glypican-5 gene has previously been significantly associated with an earlier age of onset in male MS patients . In our study, patients carrying polymorphisms at rs547311 showed a significantly higher disability score compared to patients carrying the major allele. No significant differences were seen in the other models of inheritance, suggesting that a single or double A alleles are equally detrimental for a patient’s EDSS. Finally, polymorphisms at rs205764 appear to have no association with the EDSS. These finding appear to be partially consistent, in terms of patient disability, with the study reporting rs205764 and rs547311 as novel regulators of IFN signaling , where the resulting overexpression of linc00513 has been associated with a higher IFN score for SLE patients. Moreover, several other variants have previously been associated with differences in EDSS for MS patients, including rs17445836 in interferon regulatory factor-8 gene , rs3087456 and rs4774 in class-II trans-activator gene , rs1049269 in transferrin gene , and rs1494555 in interleukin-7 receptor gene . Through this work, we intended to assert the relevance of genetic polymorphisms in the clinical course of a complex disease like MS. However, some limitations that ought to be acknowledged in this study include the small number of patients in some of the comparisons, and the lack of available data when it comes to certain clinical parameters; this includes MRI data, in which case, hindering better monitoring of the disease clinical course as well as accounting for sub-clinical disease activity, as well as patient ARR before treatment initiation, which would have been substantially beneficial in assessing the differential treatment efficacies among the different genotypes from a relapse-incidence perspective, further corroborating the significant differences between the number of responders and non-responders found in some of the treatment groups. The allocation of the correct patients to the correct treatment regimens is the ultimate goal in the context of any healthcare specialization. The development of tools, however preliminary, that aid in accomplishing this goal should be regarded with utmost priority. Establishing reliable biomarkers or screening methods for treatment stratification of MS patients is the first stepping stone towards achieving truly personalized MS therapy. This could potentially be achieved through exploring the possibility of constructing a gene panel consisting of all SNPs that are implicated in the inconsistent treatment responses among MS patients, and potentially using it as a guide to direct physicians towards more effective treatment choices, maximizing patient benefits and minimizing the exposure to unnecessary therapies, and possibly untying one of the knots contributing to the complexity of this multifactorial disease. The original contributions presented in the study are included in the article/supplementary materials. Further inquiries can be directed to the corresponding author. The studies involving human participants were reviewed and approved by German university in cairo ethics committee and Nasser Institute hospital ethics committee. The patients/participants provided their written informed consent to participate in this study. HE designed the research framework and methodology. NA carried out sample collection, DNA isolation and genotyping, statistical analysis and manuscript writing. ME-A contributed to sample collection and DNA isolation. RR and MH were the neurologists who provided crucial clinical data including EDSS among other parameters, and also assisted in the ethical approval of this study. All authors contributed to the article and approved the submitted version. |
Interobserver consistency and diagnostic challenges in HER2-ultralow breast cancer: a multicenter study | a12123e6-14af-4df2-9db3-a9db8de69444 | 11841085 | Anatomy[mh] | In recent years, trastuzumab deruxtecan (T-DXd) has demonstrated significant clinical benefits for patients with unresectable and/or metastatic human epidermal growth factor receptor 2 (HER2)-low [immunohistochemistry (IHC) 1+ or IHC 2+/in situ hybridization-negative] breast cancer (BC), following the success of the DESTINY-Breast (DB) series of clinical trials. , , This marks a shift from prior anti-HER2 therapies, such as trastuzumab and ado-trastuzumab emtansine, which required HER2 overexpression to be effective. Preliminary results from the DB-06 clinical trial, announced in June 2024, further demonstrated that T-DXd offers clinical benefits for patients with advanced BC exhibiting HER2-ultralow (defined as HER2 IHC >0 and <1+). Compared with physician-selected single-agent chemotherapy (capecitabine, nab-paclitaxel, or paclitaxel), T-DXd significantly improved progression-free survival (PFS). The PFS results for HER2-ultralow patients [median PFS 13.2 months, hazard ratio 0.78, 95% confidence interval (CI) 0.50-1.21] aligned with those for HER2-low patients (median PFS 13.2 months, hazard ratio 0.62, 95% CI 0.51-0.74). Therefore, precise diagnosis of HER2-null and HER2-ultralow by pathologists is essential to support individualized treatment strategies for BC patients. However, there is inherent variability in HER2 IHC interpretation, both between observers and within the same observer, especially when evaluating low levels of HER2 expression. The 2023 American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) guidelines acknowledge this issue and offer clinical practice recommendations for distinguishing HER2 IHC 0 from IHC 1+. Nonetheless, the assessment of HER2-low cases remains challenging. Zaakouk et al. analyzed the consistency among 16 pathologists evaluating 50 HER2 IHC cases and found moderate overall agreement (Fleiss κ = 0.433), with a high Fleiss κ of 0.803 for HER2 IHC 3+ cases. However, the consistency for HER2 IHC 0 and IHC 1+ cases was only 0.437 and 0.292, respectively. Similarly, a consistency analysis involving 18 pathologists evaluating 170 BC biopsy samples yielded comparable results, with Fleiss κ values of 0.49 and 0.35 for HER2 IHC 0 and IHC 1+ cases, respectively. Few studies have specifically examined pathologists’ ability to distinguish between HER2-null and HER2-ultralow within HER2 IHC 0 cases. Additionally, there is limited direct evidence on pathologists’ capacity to differentiate HER2-null from HER2-non-null, particularly in light of the clinical validation of T-DXd’s efficacy in HER2-ultralow patients. This study aimed to evaluate interobserver consistency among pathologists in distinguishing HER2-null from HER2-ultralow, to analyze the concordance between reassessment scores and historical scores after the expansion of the eligible patient population for T-DXd, and to explore the challenges and promising solutions for accurate diagnosis of HER2-null and HER2-ultralow by pathologists. Sample collection This study retrospectively collected surgical specimens from 50 consecutive patients diagnosed with invasive BC at the Fourth Hospital of Hebei Medical University between February and March 2023, whose pathological reports indicated HER2 IHC 0. The ethics committee of the Fourth Hospital of Hebei Medical University approved the study protocol (approval No. 2021KY124). The study was carried out in accordance with the ethics standards of the participating institutions and the tenets of the Declaration of Helsinki. As this study did not involve interactions with human subjects or the use of personally identifiable information, informed consent was not required for utilizing existing pathology materials, and no identifiable patient information was revealed. Formalin-fixed, paraffin-embedded tissue sections of 4 μm were subjected to HER2 IHC staining using the VENTANA anti-HER2/neu (4B5) rabbit monoclonal primary antibody from Roche (Basel, Switzerland), carried out on the BenchMarK XT automated IHC platform. HER2 IHC interpretation process Thirty-six pathologists from four centers (Xingtai People’s Hospital, Cangzhou People’s Hospital, Hebei University Affiliated Hospital, and the Fourth Hospital of Hebei Medical University), all experienced in evaluating HER2 IHC slides in routine clinical practice, visually assessed the 50 HER2 IHC slides under a microscope. The participating pathologists were blinded to the original HER2 IHC pathological report results and the study’s purpose. All pathologists underwent training based on the 2023 ASCO/CAP guidelines, following their practice recommendations to differentiate between HER2 IHC 0 and 1+ using high-power magnification (×40) for HER2 IHC examination. In addition to providing HER2 IHC scores (HER2 IHC 0/1+/2+/3+), the pathologists were also required to report the percentage of HER2 membrane staining to distinguish HER2-null (no staining observed) from HER2-ultralow (incomplete and faint/barely perceptible membrane staining in ≤10% of tumor cells) within HER2 0 cases. Statistical analysis Statistical analyses were carried out using IBM SPSS Statistics (version 26.0; IBM Corporation, Armonk, NY) and GraphPad Prism (8.01; GraphPad Software, LLC, San Diego, CA). Based on the HER2 IHC interpretation results from the pathologists, three scenarios were analyzed: three categories (HER2 IHC 0/1+/2+), four categories (HER2 IHC null/ultralow/1+/2+), and binary classification (HER2 IHC null/non-null). Fleiss κ was used to assess interobserver consistency in HER2 IHC interpretation. The κ values were interpreted as follows: 0.01-0.20: slight agreement, 0.21-0.40: fair agreement, 0.41-0.60: moderate agreement, 0.61-0.80: substantial agreement, 0.81-1.00: almost perfect agreement. The study defined the degree of case interpretation consistency as follows: high consistency: ≥90% of pathologists (31-34 evaluators) agreed on the HER2 IHC result, moderate consistency: ≥75%-<90% of pathologists (26-30 evaluators) agreed, low consistency: >50%-<75% of pathologists (18-25 evaluators) agreed, and challenging cases: ≤50% of pathologists (≤17 evaluators) agreed. In this study, pathologists’ HER2 IHC assessment results were compared with the consensus scores obtained from the majority of 36 pathologists to analyze their interpretation trends. For each pathologist, the proportion of cases assessed below the consensus score ( P below ), the proportion assessed above the consensus score ( P above ), and the overall agreement rate with the consensus score ( P agreement ) were calculated. The interpretation trends were then classified based on the following criteria: (i) Stable: pathologists whose difference between the proportions of cases assessed below and above the consensus score was <0.1, and whose agreement rate with the consensus score was ≥75%; (ii) Fluctuating: pathologists whose difference between the proportions of cases assessed below and above the consensus score was <0.1, but with an agreement rate <75%; (iii) Conservative: pathologists who assessed a higher proportion of cases below the consensus score by at least 0.1; (iv) Aggressive: pathologists who assessed a higher proportion of cases above the consensus score by at least 0.1. The formulas were as follows: Stable: ∣ P b e l o w − P a b o v e ∣ < 0.1 a n d P agreement ≥ 75 % Fluctuating: ∣ P b e l o w − P a b o v e ∣ < 0.1 a n d P agreement < 75 % Conservative: P b e l o w − P a b o v e ≥ 0.1 Aggressive: P a b o v e − P b e l o w ≥ 0.1 This study retrospectively collected surgical specimens from 50 consecutive patients diagnosed with invasive BC at the Fourth Hospital of Hebei Medical University between February and March 2023, whose pathological reports indicated HER2 IHC 0. The ethics committee of the Fourth Hospital of Hebei Medical University approved the study protocol (approval No. 2021KY124). The study was carried out in accordance with the ethics standards of the participating institutions and the tenets of the Declaration of Helsinki. As this study did not involve interactions with human subjects or the use of personally identifiable information, informed consent was not required for utilizing existing pathology materials, and no identifiable patient information was revealed. Formalin-fixed, paraffin-embedded tissue sections of 4 μm were subjected to HER2 IHC staining using the VENTANA anti-HER2/neu (4B5) rabbit monoclonal primary antibody from Roche (Basel, Switzerland), carried out on the BenchMarK XT automated IHC platform. Thirty-six pathologists from four centers (Xingtai People’s Hospital, Cangzhou People’s Hospital, Hebei University Affiliated Hospital, and the Fourth Hospital of Hebei Medical University), all experienced in evaluating HER2 IHC slides in routine clinical practice, visually assessed the 50 HER2 IHC slides under a microscope. The participating pathologists were blinded to the original HER2 IHC pathological report results and the study’s purpose. All pathologists underwent training based on the 2023 ASCO/CAP guidelines, following their practice recommendations to differentiate between HER2 IHC 0 and 1+ using high-power magnification (×40) for HER2 IHC examination. In addition to providing HER2 IHC scores (HER2 IHC 0/1+/2+/3+), the pathologists were also required to report the percentage of HER2 membrane staining to distinguish HER2-null (no staining observed) from HER2-ultralow (incomplete and faint/barely perceptible membrane staining in ≤10% of tumor cells) within HER2 0 cases. Statistical analyses were carried out using IBM SPSS Statistics (version 26.0; IBM Corporation, Armonk, NY) and GraphPad Prism (8.01; GraphPad Software, LLC, San Diego, CA). Based on the HER2 IHC interpretation results from the pathologists, three scenarios were analyzed: three categories (HER2 IHC 0/1+/2+), four categories (HER2 IHC null/ultralow/1+/2+), and binary classification (HER2 IHC null/non-null). Fleiss κ was used to assess interobserver consistency in HER2 IHC interpretation. The κ values were interpreted as follows: 0.01-0.20: slight agreement, 0.21-0.40: fair agreement, 0.41-0.60: moderate agreement, 0.61-0.80: substantial agreement, 0.81-1.00: almost perfect agreement. The study defined the degree of case interpretation consistency as follows: high consistency: ≥90% of pathologists (31-34 evaluators) agreed on the HER2 IHC result, moderate consistency: ≥75%-<90% of pathologists (26-30 evaluators) agreed, low consistency: >50%-<75% of pathologists (18-25 evaluators) agreed, and challenging cases: ≤50% of pathologists (≤17 evaluators) agreed. In this study, pathologists’ HER2 IHC assessment results were compared with the consensus scores obtained from the majority of 36 pathologists to analyze their interpretation trends. For each pathologist, the proportion of cases assessed below the consensus score ( P below ), the proportion assessed above the consensus score ( P above ), and the overall agreement rate with the consensus score ( P agreement ) were calculated. The interpretation trends were then classified based on the following criteria: (i) Stable: pathologists whose difference between the proportions of cases assessed below and above the consensus score was <0.1, and whose agreement rate with the consensus score was ≥75%; (ii) Fluctuating: pathologists whose difference between the proportions of cases assessed below and above the consensus score was <0.1, but with an agreement rate <75%; (iii) Conservative: pathologists who assessed a higher proportion of cases below the consensus score by at least 0.1; (iv) Aggressive: pathologists who assessed a higher proportion of cases above the consensus score by at least 0.1. The formulas were as follows: Stable: ∣ P b e l o w − P a b o v e ∣ < 0.1 a n d P agreement ≥ 75 % Fluctuating: ∣ P b e l o w − P a b o v e ∣ < 0.1 a n d P agreement < 75 % Conservative: P b e l o w − P a b o v e ≥ 0.1 Aggressive: P a b o v e − P b e l o w ≥ 0.1 Patient characteristics All 50 patients included in the study were female, with a median age of 54 years and an average tumor size of 2.0 cm. Among these cases, 82% (41/50) were diagnosed as having an infiltrating duct carcinoma, not otherwise specified. Estrogen receptor positivity was observed in 76% (38/50) of the cases, and progesterone receptor positivity was seen in 74% (37/50). The Ki67 level was ≤30% in 60% (30/50) of the cases. Tumor-infiltrating lymphocytes showed low infiltration (1%-9%) in 46% (23/50) of cases and moderate infiltration (10%-39%) in 40% (20/50) of cases. Detailed baseline characteristics are presented in , available at https://doi.org/10.1016/j.esmoop.2024.104127 . Interobserver consistency among pathologists Thirty-six pathologists from four centers nationwide conducted microscopic visual evaluations on 50 HER2 IHC slides. The HER2 IHC assessment results from these pathologists are depicted in A. When categorized into three groups (HER2 IHC 0/1+/2+), the overall interobserver consistency among the pathologists was moderate, with a Fleiss κ of 0.344. Consistency within individual centers varied, with Fleiss κ ranging from 0.264 to 0.446 ( B). Further classifying HER2 IHC 0 cases into HER2-null and HER2-ultralow reduced the overall interobserver consistency by approximately 33% under the four-category classification (HER2 IHC null/ultralow/1+/2+), yielding a Fleiss κ of 0.230 (95% CI 0.229-0.230, C). Consistency within individual centers also decreased, with a reduction ranging from 16% to 37%. Giving the positive outcomes of the DB-06 clinical trial, which demonstrated clinical benefits for HER2-ultralow patients, we adjusted the classification to HER2-null versus HER2-non-null. This adjustment improved the overall interobserver consistency among the 36 pathologists compared with the four-category classification (HER2 IHC null/ultralow/1+/2+), with a Fleiss κ of 0.292 (95% CI 0.292-0.293), though the consistency remained suboptimal ( D). Notably, most centers showed improvements in consistency, with center B achieving an approximate 92% increase. Consistency of interpretation for each case As shown in , based on the HER2 scores provided by the 36 pathologists, cases were initially classified into three categories: HER2 IHC 0, IHC 1+, and IHC 2+. Of these cases, 32% (16/50) demonstrated high consistency, 36% (18/50) showed moderate consistency, 30% (15/50) had low consistency, and 2% (1/50) were classified as challenging. When the classification was expanded to four categories (HER2 IHC null/ultralow/1+/2+), the proportion of cases with low consistency increased to 48% (24/50), and the percentage of challenging cases rose significantly from 2% to 28%. Meanwhile, the number of cases with high and moderate consistency decreased by 14 and 8 cases, respectively. Simplifying the classification to two categories (HER2 IHC null/non-null) resulted in 48% (24/50) of the cases remaining in the low-consistency category, while 48% (24/50) achieved high and moderate consistency, and the number of challenging cases decreased. Analysis of trends in pathologists’ interpretation results The evaluation of HER2 IHC scores by different pathologists showed significant variability in the proportion of cases assessed in each category, particularly for HER2-null (median 43%, range 2%-74%) and HER2-ultralow (median 28%, range 0%-78%), followed by HER2 1+ (median 27%, range 8%-46%) ( A). This study also compared each pathologist’s HER2 IHC assessment results with the consensus scores to analyze their interpretation trends, which were categorized into four types: stable, fluctuating, conservative, and aggressive. As shown in B, 42% (15/36) of the pathologists were categorized as aggressive, 25% (9/36) were conservative, 22% (8/36) exhibited fluctuating assessment habits, and only 11% (4/36) were stable in their evaluations. Consistency between reassessment consensus scores and historical scores The consensus scores from the reassessment by the 36 pathologists showed a 72% (36/50) agreement with the historical scores ( , available at https://doi.org/10.1016/j.esmoop.2024.104127 ). When HER2 IHC 0 cases were subdivided into HER2-null and HER2-ultralow categories, the consistency decreased to 54% (27/50). Among these cases, 38% (19/50) were reassessed with scores higher than the historical scores, while 8% (4/50) were reassessed with scores lower than the historical scores ( , available at https://doi.org/10.1016/j.esmoop.2024.104127 ). Retrospective analysis of controversial cases Among the 50 cases analyzed using the four-category interpretation, 14 were identified as challenging . Most of these cases were invasive ductal carcinoma (86%, 12/14), histological grade II (64%, 9/14), and hormone receptor positive (79%, 11/14). Of the controversial cases, six were consensus-scored as HER2-null, five as HER2-ultralow, and three as HER2 IHC 1+. The controversies arose from several factors. HER2 expression exhibited significant heterogeneity, making it challenging for pathologists to consistently interpret the 10% cut-off between HER2-ultralow and HER2 1+. Additionally, some cases showed weak and scattered membrane staining, which pathologists may overlook during microscopic examination. The presence of cytoplasmic staining in tumor cells could also lead to misinterpretation. Furthermore, the degree to which pathologists recognized the clinical significance of differentiating HER2 IHC 0 into HER2-null and HER2-ultralow may have influenced their thoroughness in distinguishing these categories. Finally, there was some disagreement regarding the integrity of weakly stained membranes. These factors collectively contributed to the variability and difficulty in reaching a consensus . All 50 patients included in the study were female, with a median age of 54 years and an average tumor size of 2.0 cm. Among these cases, 82% (41/50) were diagnosed as having an infiltrating duct carcinoma, not otherwise specified. Estrogen receptor positivity was observed in 76% (38/50) of the cases, and progesterone receptor positivity was seen in 74% (37/50). The Ki67 level was ≤30% in 60% (30/50) of the cases. Tumor-infiltrating lymphocytes showed low infiltration (1%-9%) in 46% (23/50) of cases and moderate infiltration (10%-39%) in 40% (20/50) of cases. Detailed baseline characteristics are presented in , available at https://doi.org/10.1016/j.esmoop.2024.104127 . Thirty-six pathologists from four centers nationwide conducted microscopic visual evaluations on 50 HER2 IHC slides. The HER2 IHC assessment results from these pathologists are depicted in A. When categorized into three groups (HER2 IHC 0/1+/2+), the overall interobserver consistency among the pathologists was moderate, with a Fleiss κ of 0.344. Consistency within individual centers varied, with Fleiss κ ranging from 0.264 to 0.446 ( B). Further classifying HER2 IHC 0 cases into HER2-null and HER2-ultralow reduced the overall interobserver consistency by approximately 33% under the four-category classification (HER2 IHC null/ultralow/1+/2+), yielding a Fleiss κ of 0.230 (95% CI 0.229-0.230, C). Consistency within individual centers also decreased, with a reduction ranging from 16% to 37%. Giving the positive outcomes of the DB-06 clinical trial, which demonstrated clinical benefits for HER2-ultralow patients, we adjusted the classification to HER2-null versus HER2-non-null. This adjustment improved the overall interobserver consistency among the 36 pathologists compared with the four-category classification (HER2 IHC null/ultralow/1+/2+), with a Fleiss κ of 0.292 (95% CI 0.292-0.293), though the consistency remained suboptimal ( D). Notably, most centers showed improvements in consistency, with center B achieving an approximate 92% increase. As shown in , based on the HER2 scores provided by the 36 pathologists, cases were initially classified into three categories: HER2 IHC 0, IHC 1+, and IHC 2+. Of these cases, 32% (16/50) demonstrated high consistency, 36% (18/50) showed moderate consistency, 30% (15/50) had low consistency, and 2% (1/50) were classified as challenging. When the classification was expanded to four categories (HER2 IHC null/ultralow/1+/2+), the proportion of cases with low consistency increased to 48% (24/50), and the percentage of challenging cases rose significantly from 2% to 28%. Meanwhile, the number of cases with high and moderate consistency decreased by 14 and 8 cases, respectively. Simplifying the classification to two categories (HER2 IHC null/non-null) resulted in 48% (24/50) of the cases remaining in the low-consistency category, while 48% (24/50) achieved high and moderate consistency, and the number of challenging cases decreased. The evaluation of HER2 IHC scores by different pathologists showed significant variability in the proportion of cases assessed in each category, particularly for HER2-null (median 43%, range 2%-74%) and HER2-ultralow (median 28%, range 0%-78%), followed by HER2 1+ (median 27%, range 8%-46%) ( A). This study also compared each pathologist’s HER2 IHC assessment results with the consensus scores to analyze their interpretation trends, which were categorized into four types: stable, fluctuating, conservative, and aggressive. As shown in B, 42% (15/36) of the pathologists were categorized as aggressive, 25% (9/36) were conservative, 22% (8/36) exhibited fluctuating assessment habits, and only 11% (4/36) were stable in their evaluations. The consensus scores from the reassessment by the 36 pathologists showed a 72% (36/50) agreement with the historical scores ( , available at https://doi.org/10.1016/j.esmoop.2024.104127 ). When HER2 IHC 0 cases were subdivided into HER2-null and HER2-ultralow categories, the consistency decreased to 54% (27/50). Among these cases, 38% (19/50) were reassessed with scores higher than the historical scores, while 8% (4/50) were reassessed with scores lower than the historical scores ( , available at https://doi.org/10.1016/j.esmoop.2024.104127 ). Among the 50 cases analyzed using the four-category interpretation, 14 were identified as challenging . Most of these cases were invasive ductal carcinoma (86%, 12/14), histological grade II (64%, 9/14), and hormone receptor positive (79%, 11/14). Of the controversial cases, six were consensus-scored as HER2-null, five as HER2-ultralow, and three as HER2 IHC 1+. The controversies arose from several factors. HER2 expression exhibited significant heterogeneity, making it challenging for pathologists to consistently interpret the 10% cut-off between HER2-ultralow and HER2 1+. Additionally, some cases showed weak and scattered membrane staining, which pathologists may overlook during microscopic examination. The presence of cytoplasmic staining in tumor cells could also lead to misinterpretation. Furthermore, the degree to which pathologists recognized the clinical significance of differentiating HER2 IHC 0 into HER2-null and HER2-ultralow may have influenced their thoroughness in distinguishing these categories. Finally, there was some disagreement regarding the integrity of weakly stained membranes. These factors collectively contributed to the variability and difficulty in reaching a consensus . The preliminary results of the DB-06 clinical trial confirmed that T-DXd offered clinical benefits to patients with metastatic HER2-ultralow BC, similar to those with HER2-low BC. This finding underscores the importance of pathologists accurately distinguishing between HER2-null and HER2-ultralow within HER2 IHC 0 cases. Our study evaluated the diagnostic consistency of 36 pathologists who reassessed 50 BC cases previously reported as HER2 IHC 0. The results revealed poor consistency in distinguishing between HER2-null and HER2-ultralow or HER2-null and HER2-non-null. This is the first study to directly highlight the significant challenges pathologists encounter when interpreting low HER2 expression levels. Our study demonstrated that when distinguishing between HER2 IHC 0 and 1+, 34% of the cases achieved high consistency, similar to the 26% reported by Fernandez et al. when distinguishing between HER2 IHC 0 and 1+. The reassessed consensus scores showed a 72% agreement with the historical scores, which closely aligns with the 69.9% reported by Viale et al. However, when pathologists further differentiated between HER2-null, -ultralow, and 1+, only 4% of the cases achieved high consistency. Although the proportion of cases with high consistency increased to 18% when distinguishing between HER2-null and HER2-non-null, it remained lower than the results for distinguishing only HER2 IHC 0 and 1+. This indicated that diagnosing HER2-ultralow was more challenging for pathologists. The primary reasons for this challenge could be attributed to two main factors. Firstly, distinguishing between HER2-null and HER2-ultralow depends on the presence or absence of HER2 staining. Due to the principle of microscopy magnification, faint staining was only clearly observable at high magnification (×40). Pathologists may not have accurately selected the appropriate fields of views for further confirmation at high magnification during the initial low magnification scanning, potentially leading to an underestimation of HER2-ultralow cases. Additionally, individual differences in color perception among pathologists, along with potential cytoplasmic staining and non-specific staining, contributed to significant variability in distinguishing HER2-null from HER2-ultralow cases. This variability was evident in our findings, with the greatest discrepancies observed in the assessments of HER2-null cases (range 2%-74%) and HER2-ultralow cases (range 0%-78%). Only 11% of pathologists reported stable HER2 status, with 42% tending to give higher HER2 diagnostic results and 25% tending to give lower results. Secondly, the distinction between HER2-ultralow and HER2 1+ was based on a 10% cut-off value. The semi-quantitative IHC assessment made precise quantification challenging, particularly given the heterogeneity of HER2 expression. Pathologists struggled to provide reproducible evaluations of complex HER2 membrane staining patterns, especially in cases with low-level expression of HER2. Artificial intelligence (AI), with its robust learning capabilities and advantages in quantitative image assessment, holds promise for enhancing HER2 IHC evaluations. Jung et al. found that AI-assisted pathologist assessments of HER2 IHC consistency improved from 49.3% to 74.1%, with a notable increase in the consistency of HER2 IHC 1+ cases from 26.5% to 70.7%. Developing AI algorithms tailored for low HER2 expression levels could help address the challenges pathologists face in distinguishing HER2-null, -ultralow, and 1+ cases. Common AI algorithms used for HER2 IHC evaluation included the HER2-CONNECT algorithm, convolutional neural network algorithm, HER2 4B5 algorithm, and multi-class logistic regression algorithm. , , , , , , These can be employed for automated whole-slide image (WSI) assessment of HER2 status or for AI-assisted evaluation of multiple representative regions of interest selected by pathologists. Given the challenges of accurately identifying and quantifying HER2-ultralow, WSI-based automated assessment might be more suitable for evaluating low HER2 expression cases. Currently, HER2-ultralow detection in clinical practice primarily relies on IHC. However, existing HER2 IHC testing was designed to distinguish HER2 IHC 3+ from other scores and lacks the precision needed to differentiate between HER2-null and HER2-ultralow. Several factors before and during IHC testing can affect the detection of low-level HER2 protein expression. For instance, the type of specimen and the quantity of the sample can both impact HER2 status diagnosis. Na et al. analyzed the consistency of HER2 status between core needle biopsy and surgical specimens in 1387 invasive BC patients, finding a κ value of 0.587 for HER2 classification (HER2 0/low/positive). Bar et al. found that with each additional biopsy, about one-third of patients previously classified as HER2 IHC 0 could be reclassified as HER2-low, with the likelihood of detecting HER2-low increasing with the number of biopsies. Additionally, the selection of antibodies can affect the diagnosis of HER2 status. Rüschoff et al. showed that the HercepTest antibody identified more cases of HER2-low than the VENTANA 4B5 antibody. These findings indicated that several modifiable testing parameters in IHC can influence the detection of HER2-ultralow/low. Therefore, there is a need for more stable, quantifiable, precise, and cost-effective HER2 detection methods. Some studies suggested that RNAScope might be a promising new technique to optimize the detection of HER2-ultralow and HER2-low, although further clinical data were required for validation. Although the DB-06 trial confirmed the clinical benefit of T-DXd for patients with metastatic HER2-ultralow BC, the exact lower limit of T-DXd’s efficacy, particularly the threshold for defining HER2-ultralow, remains unclear. Jung et al. explored the critical threshold for differentiating HER2-null from HER2-ultralow using an AI-based WSI analysis tool. Their results suggested that a 6.0% membrane-specific HER2 score was the optimal threshold to distinguish between HER2-null cells and cells with incomplete and faint/barely perceptible staining. However, this threshold was derived from a model-based analysis and lacks clinical validation. The ongoing DB-15 clinical trial aims to evaluate the efficacy and safety of T-DXd in patients with unresectable and/or metastatic HER2 IHC 0 BC. It will also include patients with HER2 IHC 0 (no staining) to potentially refine the patient population benefiting from T-DXd and help establish a more precise definition of HER2-ultralow. This study evaluated the ability of pathologists from multiple centers to differentiate between HER2-null and HER2-ultralow by reassessing HER2 IHC slides of BC specimens previously diagnosed as HER2 IHC 0. A key strength of this study was the inclusion of a large cohort of pathologists from various centers, which enhanced the reliability of the findings. Additionally, by exclusively focusing on cases previously diagnosed as HER2 IHC 0, the study eliminated potential confounding from other HER2 categories. Besides, in our study, all pathologists reviewed the same HER2 IHC slides, minimizing the influence of pre-analytical and analytical variables and further emphasizing the considerable variability in interpretation. However, the study had some limitations, including the relatively small number of cases reviewed. Nonetheless, the results clearly highlighted the poor interobserver consistency in distinguishing HER2-null from HER2-ultralow. Furthermore, the recent publication of the DB-06 trial results may have influenced the emphasis on accurately differentiating HER2-ultralow from HER2-null, potentially affecting the pathologists’ assessment. However, we did not survey pathologists on this matter, and the increased recognition of the clinical significance of HER2-ultralow might significantly alter the study results. It is also important to note that while the DB06 trial demonstrated the benefit of T-DXd in advanced hormone receptor-positive, HER2-ultralow BC patients, its efficacy in hormone receptor-negative patients remains unclear. This study focused on evaluating pathologists’ ability to identify HER2-ultralow cases without considering hormone receptor status. In conclusion, this study highlighted significant interobserver variability in the precise differentiation between HER2-null, -ultralow, and 1+ categories. It underscored the need for more clinical data to refine the definition of HER2-ultralow. Future efforts should focus on incorporating AI-based algorithms specifically designed for HER2-ultralow assessment, as well as more advanced detection methods, to assist pathologists in improving the accuracy and consistency of HER2 evaluations. |
Development of a Palliative Care Toolkit for the COVID-19 Pandemic | ce692120-140e-4e2d-a921-53ed37b1f979 | 7255186 | Health Communication[mh] | 'Palliative care can play a central role in a health-care institution's response to the Coronavirus disease 2019 (COVID-19) pandemic. The high numbers of critically ill and dying patients create a sharp increase in the need for expert management of dyspnea, delirium, and serious illness conversations, in particular. With the rapid spread of COVID-19 driving surges of infected patients requiring hospitalization, the demand for palliative care consultation can accelerate quickly, putting programs at risk of becoming overwhelmed by the volume of patients, families, and referring teams in need of help. Aware of this possibility, our palliative care program at Dana-Farber Cancer Institute and Brigham and Women's Hospital (BWH) created a compendium of palliative care educational materials over six weeks that could serve as a resource to nonpalliative care clinicians caring for these patients. To build the primary palliative care skillset of these clinicians and to expand our reach, we organized these resources into a Palliative Care Toolkit that we made available to frontline clinicians caring for patients with palliative care needs during the pandemic.
First, we put together an interdisciplinary workgroup comprised of attendings, fellows, nurse practitioners, educators, and informaticians to create the Toolkit. Team members were assigned to work on particular subprojects, with ongoing collaboration and exchange of resources across the larger group. The workgroup met several times a week for several weeks and then weekly thereafter to monitor the progress of projects and strategize about next steps. We identified and followed several principles: 1) Tools should focus on the care of patients with COVID-19, although they may have wider applicability; 2) tools should be clear and concise and reflect palliative care best practices; 3) tools should be accessible to a wide variety of clinicians in a wide variety of settings; and 4) although tools may take different forms and formats, they should have internal consistency in content. As we created the Toolkit, we referenced materials at our institutions , , as well as outside institutions, including materials from the Massachusetts General Hospital and VitalTalk. , We started by writing a detailed palliative care chapter for covidprotocols.org , a comprehensive online resource created by the BWH Division of Pulmonary and Critical Care Medicine to disseminate best practices in treating patients with COVID-19. A subgroup of us subsequently distilled information from that chapter to create one-page palliative care summaries and pocket cards. We gave these to clinicians working in the Emergency Department, COVID intensive care units (ICUs), and COVID Hospital Medicine services and also adapted them for use by bedside nurses. Simultaneously, a separate subgroup built a Web application (Pallicovid.app) to host these resources online and to make them universally accessible by any mobile or desktop device. An additional subgroup scripted and filmed six short communication skills videos modeling techniques to use in goals of care conversations in the emergency department, ICU, and hospital medicine settings. Once our resources were developed, we worked with the leadership of each collaborating clinical service to disseminate our tools by email communication and by posting to their online platforms for clinical resources. In addition, we realized that some clinicians would have questions not covered by the tools, no matter how comprehensive. To meet this need, we created two additional resources: 1) a 24/7 palliative care COVID nurse resource line covered by our nurse practitioners to advise bedside nurses, and 2) daily videoconference office hours staffed by one of our attending physicians available to hospital teams with palliative care management questions.
The Toolkit, available at pallicovid.app, includes a collection of resources described in . Users can access links to the one-page summaries, pocket cards, covidprotocols.org , and the communication skills videos. The combination of physical tools (pocket cards and one-page summaries), online tools (the palliative care section of covidprotocols.org and communications skills videos), real-time support tools (the 24/7 nurse resource line, daily palliative care office hours), and the Pallicovid app has been well received by referring teams. We are able to direct clinicians to these resources to answer straightforward questions, and as a result, we have been better able to focus on more complex consultations that require higher level palliative care expertise. In response to the pandemic, we have also built new clinical programs aligned with the emergency department, ICU, and hospital medicine teams. As we connected with attendings, trainees, nursing leaders, and bedside nurses in those settings, we distributed information about the Toolkit and made the resources available to all. Doing so has strengthened our credibility as helpful partners in the crisis, even if we were not able to perform a consultation for every patient we were called to see. We plan to continue to enhance the Toolkit, including adding a coaching option for referring teams needing more robust help with a particular case, but not a full consult. Meanwhile, we are finding that the current resources are being met with great enthusiasm. Covidprotocols.org had more than 660,000 page views between March 31, when the palliative care chapter went live, and May 4. Similarly, the pocket cards have been positively received; we ran out of the first order of 300 cards within two weeks. In addition, the Pallicovid app was accessed by over 2000 users between April 7 (its launch date) and May 4.
The COVID-19 pandemic is providing surprising opportunities for creativity in the midst of chaos and hardship. Our group's experience creating the Palliative Care Toolkit is one such example, allowing us to pull our varied skills and interests together to rapidly create a suite of helpful resources in anticipation of a surge of seriously ill patients at our hospital. We will continue to track the use of the various resources over time and ask for feedback from referring clinicians to determine which ones are proving especially useful. We will also adapt different parts of the Toolkit for different clinical settings where appropriate; the communication skills videos are one example of this kind of specialization. We anticipate that many clinical and educational strategies developed during the pandemic will continue to be useful long afterward. In creating the Toolkit, our group is discovering new opportunities to expand our program's reach and help referring teams without the need to perform a full consultation in response to every request. We are still experimenting with which requests for consults can be adequately addressed by pointing to the resources in the Toolkit and which requests should result in full consults, and we are in the process of creating algorithms to standardize our triage practice. But we are embracing the possibilities afforded by having an array of specific useful tools to put in the hands of our colleagues to help them care for their patients, especially with real-time backup from the nursing resource line and daily office hours. Our early experience demonstrates that we can provide a high level of support and availability while using our human resources far more efficiently than we have in the past. A more ambitious hope is that the Toolkit will help strengthen the integration of our palliative care program within our hospital. While our team is well supported by the hospital, in the minds of some of our colleagues, our specialty still remains inextricably linked to end-of-life care. As we now help teams care for patients with COVID-19 who sometimes recover from critical illness, it seems possible that the pandemic is creating an opening for real culture change in how palliative care is viewed at our institution. The Palliative Care Toolkit is one tangible demonstration of our intention to be available to teams caring for seriously ill patients regardless of life expectancy or code status. The resources included in the Dana-Farber Cancer Institute/BWH Palliative Care Toolkit can serve as a useful example for other programs facing challenges similar to our own. These tools can be adapted to a wide variety of clinical settings that are anticipating or experiencing higher than usual palliative care needs during the pandemic. We hope other programs find—as we have found—the Toolkit helpful to disseminate best practices in communication and symptom management, allowing palliative care specialists to focus on the highest need consults and increasing acceptance of palliative care across hospital settings.
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Reply to Pfeffer: Macular degeneration clues from comparative anatomy | 9ca2eaa0-a862-4f7b-8d7d-57c8d6d70f92 | 10622899 | Anatomy[mh] | |
Hidden artistic complexity of Peru’s Chancay culture discovered in tattoos by laser-stimulated fluorescence | 2302e4ab-9da4-432b-9cdf-9fd11b213134 | 11789198 | Surgical Procedures, Operative[mh] | This first application of the LSF technique to tattoos on mummified human remains has yielded otherwise hidden results . LSF was able to backlight the pre-Columbian tattoos by making the skin fluoresce brightly, but not the likely carbon-based black ink . With postprocessing for image equalization, saturation, and color balance , the skin becomes white behind the black outlines of the tattoo art. This reveals in these specimens detailed density differences in the ink and virtually eliminates the ink “bleed”, highlighting the precise locations of the original tattoo markings, as seen in . These fine 0.1 to 0.2 mm wide lines are narrower than those produced by the standard #12 modern tattoo needle (0.35 mm) and were only seen in a limited number of mummified individuals out of over 100 inspected specimens. Most specimens showed tattoos that were more amorphous patches with poorly defined edges (e.g., ). LSF was able to more clearly define the features in the artwork by increasing the contrast between the skin and the ink .
The first application of the LSF technique in the study of tattoos on mummified human remains revealed otherwise hidden details not seen using existing techniques like infrared imaging . The 0.1 to 0.2 mm wide linear details reflect the fact that each ink dot was placed deliberately by hand with great skill, creating a variety of exquisite geometric and zoomorphic patterns. We can assume that this technique involved a pointed object finer than a standard #12 modern tattoo needle, probably a single cactus needle or sharpened animal bone based on known materials available to the artists . This suggests that an additional tool was probably unnecessary to tap the point into the skin. The width of the lines corroborates the use of the widely known traditional needle-based tattooing technique, as opposed to “cutting and filling” with ink. In the context of Peruvian archeological cultures, our LSF results indicate the cultural art in the studied tattoos had exceptionally fine scale detail and patterns not seen in other existing Chancay cultural art, e.g., associated pottery, textiles, and rock art , supporting a more partitive decorative organization among the Chancay [ sensu ]. As the most intricate art found in the Chancay culture to date, tattoos were potentially another important category of object—along with textiles— in which aesthetic expectations and performances appear to be concentrated as part of the aesthetic locus of the Chancay . Our investigation found that intricate tattoos were not present on all mummified human remains suggesting that they were restricted to a subset of the population, but future work involving new mummy discoveries would be needed to test this. The study therefore reveals higher levels of artistic complexity in pre-Columbian Peru than previously appreciated, which expands the degree of artistic development found in South America at this time. LSF imaging therefore has the potential to reveal similar milestones in human artistic development through the study of other ancient tattoos, including the evolution of tattooing methods.
Analyses were undertaken on mummified human remains curated at the Arturo Ruiz Estrada Archaeological Museum of the José Faustino Sánchez Carrión National University of Huacho, Peru. These remains were discovered in 1981 during a rescue excavation led by Dr. Arturo Ruiz and his team at the Cerro Colorado cemetery in the Huaura Valley of Peru, an archeological zone located between Puerto de Huacho and Barrio de Amay, near the modern city of Huacho. The first results of radiocarbon dating indicate a chronological affiliation between 1222 and 1282 AD, belonging to the Chancay culture from the pre-Columbian Andes. Mummies, as well as individual limbs, were examined using a handheld UV flashlight as a triage for further imaging under LSF. A 405 nm laser line was scanned across the artwork during a time exposure in a dark room . The images were postprocessed uniformly for equalization, saturation, and color balance in Photoshop . Some mummies were encased and inaccessible for scale bar placement, so in these specimens, the scale was estimated using focus and distance. Consent to the research was granted by the Director of the Arturo Ruiz Estrada Archaeological Museum, as part a research project by Judyta Bąk, which was approved by the José Faustino Sánchez Carrión National University authorities. All ethical implications arising from the research were taken into account including but not limited to remains of a historical person, indigenous people, cultural and religious sensitivity and living descendants. Mummified human remains were handled and studied with care, in strict accordance with the university’s rules and regulations in following with standard archeological practice.
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Classifying Alzheimer’s Disease Neuropathology Using Clinical and MRI Measurements | 84d61567-c32c-440c-976b-468c3865af08 | 11307063 | Pathology[mh] | Alzheimer’s disease (AD) is the most common form of age-related dementia. According to the Alzheimer’s Association report, an estimated 6.5 million Americans are living with AD dementia today, and this number is projected to increase to 13.8 million by 2060 . Without effective treatment, AD will bring tremendous healthcare, economic, social, and emotional burdens to patients, families, communities, and society globally . The recent approvals of two disease modifying therapies are particularly promising in early AD , making accurate and early diagnosis of AD a critical step for potential treatment and to promote public health. Historically, the diagnosis of AD is made based on observable clinical symptoms and the systematic exclusion of other potential dementia etiologies, and with confirmation through postmortem brain autopsy . There exists a discrepancy between the clinical and pathological AD diagnoses, as approximately 10% to 30% clinical AD cases do not exhibit typical AD neuropathological changes at autopsy . As a result, inclusion of AD-specific biomarkers to inform and refine clinical diagnosis has been sought. In the National Institute on Aging and Alzheimer’s Association (NIA-AA) Research Framework, the diagnosis of AD has shifted from syndromal towards biological constructs that are reflective of AD hallmark pathologies . With development and validation of in vivo biomarkers, quantifying the extracellular amyloid-β protein deposition (A), the intraneuronal pathological tau protein accumulation (T), and neurodegeneration (N) has led to formulation of the ATN framework for the biological characterization of AD along a disease continuum. To date, studies have developed computer aided machine learning models to predict AD diagnoses and to identify AD-specific biomarkers . However, relatively fewer efforts have been made to validate these trained models against data from neuropathologically confirmed AD cases. Instead, the neuropathological components used in these models were primarily represented by the qualification of amyloid and tau status using positron emission imaging (PET) with radiolabeled tracers , or more recently, using ultra-sensitive measurements of molecules from central nervous (cerebrospinal, CSF) or peripheral (blood) fluids . The invasive nature of both PET and CSF technologies, the high cost of PET imaging, and the evolving understanding of measurement properties of fluid-based measures, all have motivated the current research to seek non-invasive and clinically available features to facilitate the determination of AD neuropathological status in living persons. Structural magnetic resonance imaging (MRI) is the most common technique for assessing neurodegeneration in AD within the ATN framework, as structural MRI can characterize the severity and progression of brain atrophy throughout the AD continuum . Moreover, MRI is non-invasive, generally well-tolerated, replicable, and widely accessible in many clinical settings and large-scale legacy databases . In contrast to fluid-based biomarkers, MRI provides additional information about affected brain regions and holds the promise of being more AD-specific than some of the emerging fluid biomarkers . Therefore, the primary objectives of this paper are 1) to develop a methodology that could facilitate the classification of the presence or absence of severe AD neuropathology based on autopsy confirmed cases with clinically available MRI features, and 2) to identify clinical features and neurodegeneration patterns that are both sensitive and specific to AD neuropathology. Structural MRI-based estimates of hippocampal and entorhinal cortical volumes were among the first measures of neurodegeneration proposed in AD and have frequently been used in both clinical and research settings . More specifically, structural MRI estimates of hippocampal atrophy are correlated with memory decline in living subjects, and with pathological tau accumulation in postmortem AD subjects . However, hippocampal atrophy is also commonly observed in subjects with other neurodegenerative pathologies, and is not specific to AD . From a technical perspective, volumetric measurements are also biased by the total intracranial volume, and there are multiple quantitative approaches to adjust volumetric estimates for differences in head-size. Thus, despite the ease of use, these limitations undermine the clinical utility of volumetric measurements in quantifying neurodegeneration along the broad AD continuum. Cortical thickness is another biologically meaningful and reliable measure computed from structural MRI. Thickness measurements are less biased by on head size than volume . Cortical thinning in multiple regions of interest (ROI) has been reported across the entire AD continuum, even during the preclinical AD stage ; thus, cortical thinning in these ROIs appears particularly useful in identifying early neurodegenerative changes. In addition, spatial patterns of regional atrophy might also be sensitive to the typical localization of different types of neurodegenerative disorders, providing increased specificity . When methods for automatically quantifying in vivo thickness were introduced (e.g., FreeSurfer; https://surfer.nmr.mgh.harvard.edu/ ), regional thickness values of the entorhinal cortex, medial and inferior temporal gyrus attracted significant attention as measurements of disease severity and progression . Cortical signature regions of AD, encompassing above-mentioned regions in addition to the fusiform gyrus, para-hippocampal gyrus, inferior parietal cortex and precuneus, have been further identified to predict pathological changes, clinical impairment, cognitive declines, and cerebral blood flow variations in subjects along the AD continuum . FreeSurfer derived thicknesses of these regions, which constitute AD meta-ROIs, convey 1) top diagnostic separability between AD and CN subjects, and 2) clinical impairment associations in subjects along the AD continuum . Taken together, cortical thickness measurements of AD-signature meta-ROIs are promising candidates to quantify AD-specific neurodegeneration within the ATN framework, and their predictive ability warrants further exploration. In this study, we aimed to classify AD neuropathological status using clinically accessible features. We hypothesized that structural MRI-derived cortical thickness measurements from AD meta-ROIs, together with the apolipoprotein E ( APOE ) genotype and demographic variables, would accurately classify the presence or absence of severe AD neuropathology, and thus could assist in determining AD neuropathological status in living persons. Using subjects with both postmortem neuropathological data and an antemortem MRI scan, we trained machine learning models to classify the presence or absence of severe AD neuropathology with clinically accessible features. We expected that development of these machine learning models would facilitate the identification of neurodegenerative changes specific to AD neuropathology and could assist in determining AD pathological status in living persons when the clinical etiology is uncertain and other AD biomarkers are unavailable. Primary data set: NACC participants NACC participants. Data from the National Alzheimer’s Coordinating Center (NACC, https://naccdata.org/ ) database were obtained, including the NACC Uniform Data Set (UDS), MRI Data Set and Neuropathology Data Set . The NACC was established in collaboration with more than 42 previous and current Alzheimer’s Disease Research Centers (ADRCs) throughout the U.S. over more than 20 years . Data were collected by each ADRC and the study was approved by each ADRC site’s local Institutional Review Boards. details our NACC sample inclusion/exclusion process. We started with NACC participants that had both postmortem neuropathological data in NACC-neuropathology files and at least one antemortem T1-weighted MRI scan listed under NACC-imaging files. This inclusion criteria led to a sample of 560 participants . AD neuropathological staging in NACC is based on NIA-AA guidelines . From the NACC neuropathological data set, we utilized the NIA-AA Alzheimer’s Disease Neuropathologic Change (ADNC) score to represent the severity/status of participants’ AD neuropathology . Based on ADNC scores (i.e., NPADNC variable in NACC neuropathology data set), participants were staged into 4 groups: no AD neuropathology (ADNC0), low AD neuropathology (ADNC1), intermediate AD neuropathology (ADNC2), and severe AD neuropathology (ADNC3). We focused on participants in the ADNC0, ADNC1, and ADNC3 groups, and excluded participants with Lewy body, frontotemporal lobar degeneration with TPD-43-immunoreactive pathology (FTLD-TDP), and FTLD-tau pathologies to create homogeneous groups to test our hypothesis of identifying the presence or absence of severe AD neuropathology . We did not exclude participants based on vascular changes due to the high prevalence in all ADNC groups (>90%). In addition, because TDP43 pathology was assessed only more recently on a limited number of participants in NACC, we did not exclude any participants based on co-occurrence with TDP-43 pathology. We utilized participants with ADNC3 to represent a group with severe AD neuropathology (ADNC3). We combined ADNC0 and ADNC1 groups (ADNC0&1) to 1) represent a real-world group with no or low AD neuropathology, as both amyloid and tau proteins would accumulate during aging; and 2) to increase our sample-size and boost the statistical power. NACC MRI data collection and process. DICOM images of T1-weighted MRI scans for 560 participants were obtained from the NACC MRI data set . As these T1-weighted MRI scans were collected on a variety of scanners at each ADRC, we obtained scanner field strength, scanner manufacturer, and scanner protocols from the DICOM header of each scan. Information on the implementation inversion recovery (IR) was specifically obtained as it could be the major difference among scans to have an effect on grey (GM) and white matter (WM) contrast that subsequently affects thickness estimations. Scans from 57 participants were T1-weighted 2D spin-echo sequences and therefore were excluded from the following analyses , and the maximum acceptable slice thickness and in-plane resolution for T1-weighted MRI were 1.5 mm and 1.5 mm×1.5 mm, respectively. After preprocessing, T1-weighted MRI images for each participant were analyzed using the FreeSurfer 6.0 processing pipeline . A subject-specific anatomical labeling from the Desikan-Killiany atlas was generated, yielding 68 cortical regions and 12 sub-cortical ROIs for every participant. Thickness measures of 68 cortical regions were calculated for each participant. MRI data quality control. We excluded 31 participants from the study due to failed FreeSurfer processes. Specifically, 18 participants were excluded because their scans lacked orientation information leading to failed Talairach registrations, while 13 participants were excluded since their scan failed FreeSurfer for unknown reasons . For scans that successfully finished the FreeSurfer 6.0 pipeline, we utilized the fsqc toolbox in Python to perform the quality control (QC) step. We focused on fsqc generated 1) signal-to-noise ratio (SNR) for WM and GM on FreeSurfer normalized norm.mgz file (wm_snr_norm and gm_snr_norm), and 2) WM-to-GM contrast SNR ratio in the left and right hemisphere (con_lh_snr and con_rh_snr), as our main FreeSurfer QC matrices. We utilized a data-driven approach to assess the data quality using QC matrices. First, we conducted a repeated measures analysis of variance (rmANOVA) to determine if QC values were significantly different among scanner types and protocols . In addition, to further evaluate if different scanner types and protocols could introduce significant changes to FreeSurfer outputs, we performed the same rmANOVA on cortical thickness measures across different scanner types and protocols in the ADNC0 group alone (i.e., those whose structure was least affected by disease pathology; ). We removed any scans that produced extreme QC values and cortical thickness measures from our analyses. We observed that scanner type or protocol does not significantly affect the WM or GM signal-to-noise ratio (SNR), but significantly affect the WM-to-GM contrast SNR . Therefore, scanner type and scanning protocols were used as covariate features in our following analyses. For the thickness measures, we observed that scans collected on 1.5T Philips scanner generated significantly lower values than other scanners. Therefore, we removed participants with MRI data collected on 1.5T Philips scanner from both groups and included scanner type and scanning protocols as covariates in our analyses. Final samples. Collectively, our final sample included 53 participants with ADNC0&1 and 91 participants with ADNC3 . Their demographics and genetic information including sex, years of education, race APOE genotype, age, and diagnoses at the time of MRI scan, and time differences from MRI scan to postmortem neuropathology were obtained from the NACC and reported in A. Sample characteristics regarding comorbidities are reported in C. Replication data set: CNTN participants We utilized an independent, locally collected, convenience sample from the Center for Neurodegeneration and Translational Neuroscience (CNTN, https://nevadacntn.org/ ) as a validation data set. All CNTN participants were recruited at Cleveland Clinic Lou Ruvo Center for Brain Health Las Vegas, Nevada. The CNTN study was approved by Cleveland Clinic Institutional Review Board and all participants gave written, informed consent. Details of the CNTN cohort has been previously reported . Dring CNTN-COBRE phase I, there were 190 participants enrolled and with MRI data collected (January 2017 to October 2020). Our convenience sample included 144 CNTN participants with a T1-weighted structural MRI scan (FreeSurfer 6.0 successfully and reliably finished), an amyloid PET scan ( 18 F-AV45 scan, standard uptake value ratio (SUVR) computed), and available APOE genotyping. To increase real-world clinical utility and application in vivo , we utilized amyloid positivity status determined from the PET-SUVR as the outcome in this validation data set, which is assumed to reflect underlying AD-pathology. Following the previously published AV45-PET processing pipeline and amyloid positivity criteria on the composite SUVR , 144 CNTN participants were divided into an amyloid positive group (SUVR > 1.11, N = 73) and an amyloid negative group (SUVR≤1.11, N = 71). Participant demographics for both groups are reported in B. Details on MRI and PET image processing steps are included in . Demographic comparisons All statistical and classification analyses were conducted in MATLAB 2018b ( https://www.mathworks.com/ ). Differences between the ADNC0&1 and ADNC3 groups for NACC participants, and differences between amyloid positive and amyloid negative groups for CNTN participants were assessed for demographic and clinical variables including sex, years of education, APOE genotype, race, age at MRI, and diagnosis at MRI. Differences in time intervals between MRI scan and neuropathological data, scanner field strengths, scanner manufacturer, scanning protocol (implementation of IR), and GM-to-WM contrast SNR were further assessed between ADNC0&1 and ADNC3 groups for NACC participants. We performed chi-square tests to examine differences for categorical variables (sex, race, APOE genotype, diagnosis at MRI, scanner field strength, scanner manufacturer and implementation of IR), and used Student’s t -tests to estimate differences among continuous variables (age at MRI, years of education, time intervals between first MRI and neuropathological data, and GM-to-WM contrast SNR). Classify the presence or absence of severe AD neuropathology using clinically available features Using NACC participants with both postmortem neuropathological data and an antemortem MRI scan, we trained both model-based and data-driven machine learning models to classify the presence or absence of severe AD neuropathology using clinically accessible features. shows a schematic representation of our classification analyses. Clinically available features. Four demographic and genetic features (age at MRI, sex, years of education and APOE genotype) and eight structural brain measures from T1-weighted MRI were included as features to classify ADNC group assignments. Race was not included in the feature set because more than 95% NACC participants in this study were White. For the APOE genotype, a categorical variable was created to code APOE4 allele counts (range: 0, 1, and 2). For the T1-weighted MRI measures, we focused on cortical thickness from the FreeSurfer-derived AD-signature meta-ROIs encompassing the bilateral entorhinal, inferior temporal, middle temporal and fusiform . To account for potential variation by MRI scanners, we included 1) a binary vector representing MRI field strengths; 2) a categorical vector representing MRI manufacturer; and 3) a binary vector representing the implementation of IR, as additional features in the classification model, leading to a total of 15 features. Data from all NACC participants were utilized to train the model with a cross-validation schema. Due to the high collinearity among features, especially among cortical thickness features from the eight meta-ROIs, we further incorporated a feature selection step into each classification method (detailed below). Model based method: LASSO logistic regression. A logistic regression classifier with a Least Absolute Shrinkage and Selection Operator (LASSO) was used to evaluate the importance and performance of clinically available features in predicting AD pathology (ADNC0&1 (0) versus ADNC3 (1) groups). Briefly, LASSO parametrically shrinks the logistic regression coefficient of each feature by imposing a penalty term on its absolute value in the objective function . In this case, LASSO ensures that the retained features are the best features that explain the group differences while maintaining a low variance by shrinking the coefficients of all other unexplained features to zero . Since logistic regression assumes sigmoidal relationships between dependent and independent variables, two binary variables were created to separately code subjects with one and two copies of APOE4 alleles, and two binary variables were created to separately code subjects with MRI collected on Siemens and Philips scanners (i.e., GE scanner coded as baseline), resulting in a total of 17 features. Using a 10-fold cross-validation strategy, the feature set that produced the minimum cross-validation error in LASSO-logistic-regression was retained and further utilized in a reduced logistic regression model to classify the probability of assigning a participant to the severe AD neuropathology group (ADNC3). The resulting probability was then compared with the true group assignment using the receiver operating characteristic (ROC) curve method. To offset the population imbalance among two groups (53 versus 91), the threshold used to binarize the probability for final group assignments ( s ) was set as the point on the ROC curve that gives the minimum total false discovery rate (false positive rate (FPR) plus false negative rate (FNR)), instead of the commonly used value of 0.5. Sensitivity, precision, specificity, accuracy, F1-score were reported at this threshold, and area under the ROC curves (AUC) was further used to evaluate the overall classifier performance. The final trained reduced logistic regression model was then tested on the independent CNTN data set to classify amyloid positivity status. The same threshold ( s ) was utilized to binarize the obtained probability of assigning participants to the amyloid positive group, and the same matrices were used to evaluate the classifier performance. Data-driven method: Random Forest . We also utilized the data-driven random forest method for the same classification to take advantage of both linear and nonlinear relationships between the clinically available features and AD neuropathology. The random forest classifier is an ensemble learning method that operates by constructing a large number of decision trees. Each decision tree is constructed using a bootstrapping sample from the original data, and splits participants based on minimum total impurity score criteria computed at each partition . The concluding result of the random forest analysis is determined by counting the majority determination from all decision trees for each sample. In general, approximately 1/3 of participants are left out-of-box (OOB) for each decision tree during bootstrapping. Therefore, the classification of OOB samples were utilized as cross-validation results to evaluate the model performances. We first trained a random forest model with 1000 decision trees using all NACC participants with 15 clinically available features (detailed above). These 15 features were ordered based on the OOB permutation-based feature importance scores. Briefly, this score for a specific feature measures the decrease in mean accuracy when permuting that specific feature in the OOB samples, non-parametrically. We also computed a Gini importance score for each feature. The detailed explanations of both feature importance measures are included in . Next, we performed a recursive feature elimination (RFE), during which we trained 15 individual random forest models, each with 1000 decision trees, by dropping the least important feature in each iteration, respectively. To evaluate each model performance, the predicted probabilities of OOB validation samples were obtained and compared with the true group assignment using the same ROC curve-based method. The model that gave the maximum OOB validation AUCs among all 15 random forest models was selected as the most accurate model, and the features included were considered as the selected features. This most accurate model was further evaluated using the independent CNTN participants. Test the model performance when including participants with ADNC3 and low-level Lewy bodies comorbidities Up to 50% of participants with severe AD neuropathology (i.e., the ADNC3 group) could have some degree of Lewy bodies . Many participants with both severe ADNC and low-level Lewy bodies in the brain stem, amygdala or olfactory bulb, present with AD clinically. Thus, to test the utility of our models with a more comprehensive real-world severe AD group, we further trained and tested our models by including participants with Lewy bodies in brain stem (NPLBOD = 1, Nsub = 5), amygdala, (NPLBOD = 4, Nsub = 32) and olfactory bulb (NPLBOD = 5, Nsub = 4) in the severe ADNC group. NACC participants. Data from the National Alzheimer’s Coordinating Center (NACC, https://naccdata.org/ ) database were obtained, including the NACC Uniform Data Set (UDS), MRI Data Set and Neuropathology Data Set . The NACC was established in collaboration with more than 42 previous and current Alzheimer’s Disease Research Centers (ADRCs) throughout the U.S. over more than 20 years . Data were collected by each ADRC and the study was approved by each ADRC site’s local Institutional Review Boards. details our NACC sample inclusion/exclusion process. We started with NACC participants that had both postmortem neuropathological data in NACC-neuropathology files and at least one antemortem T1-weighted MRI scan listed under NACC-imaging files. This inclusion criteria led to a sample of 560 participants . AD neuropathological staging in NACC is based on NIA-AA guidelines . From the NACC neuropathological data set, we utilized the NIA-AA Alzheimer’s Disease Neuropathologic Change (ADNC) score to represent the severity/status of participants’ AD neuropathology . Based on ADNC scores (i.e., NPADNC variable in NACC neuropathology data set), participants were staged into 4 groups: no AD neuropathology (ADNC0), low AD neuropathology (ADNC1), intermediate AD neuropathology (ADNC2), and severe AD neuropathology (ADNC3). We focused on participants in the ADNC0, ADNC1, and ADNC3 groups, and excluded participants with Lewy body, frontotemporal lobar degeneration with TPD-43-immunoreactive pathology (FTLD-TDP), and FTLD-tau pathologies to create homogeneous groups to test our hypothesis of identifying the presence or absence of severe AD neuropathology . We did not exclude participants based on vascular changes due to the high prevalence in all ADNC groups (>90%). In addition, because TDP43 pathology was assessed only more recently on a limited number of participants in NACC, we did not exclude any participants based on co-occurrence with TDP-43 pathology. We utilized participants with ADNC3 to represent a group with severe AD neuropathology (ADNC3). We combined ADNC0 and ADNC1 groups (ADNC0&1) to 1) represent a real-world group with no or low AD neuropathology, as both amyloid and tau proteins would accumulate during aging; and 2) to increase our sample-size and boost the statistical power. NACC MRI data collection and process. DICOM images of T1-weighted MRI scans for 560 participants were obtained from the NACC MRI data set . As these T1-weighted MRI scans were collected on a variety of scanners at each ADRC, we obtained scanner field strength, scanner manufacturer, and scanner protocols from the DICOM header of each scan. Information on the implementation inversion recovery (IR) was specifically obtained as it could be the major difference among scans to have an effect on grey (GM) and white matter (WM) contrast that subsequently affects thickness estimations. Scans from 57 participants were T1-weighted 2D spin-echo sequences and therefore were excluded from the following analyses , and the maximum acceptable slice thickness and in-plane resolution for T1-weighted MRI were 1.5 mm and 1.5 mm×1.5 mm, respectively. After preprocessing, T1-weighted MRI images for each participant were analyzed using the FreeSurfer 6.0 processing pipeline . A subject-specific anatomical labeling from the Desikan-Killiany atlas was generated, yielding 68 cortical regions and 12 sub-cortical ROIs for every participant. Thickness measures of 68 cortical regions were calculated for each participant. MRI data quality control. We excluded 31 participants from the study due to failed FreeSurfer processes. Specifically, 18 participants were excluded because their scans lacked orientation information leading to failed Talairach registrations, while 13 participants were excluded since their scan failed FreeSurfer for unknown reasons . For scans that successfully finished the FreeSurfer 6.0 pipeline, we utilized the fsqc toolbox in Python to perform the quality control (QC) step. We focused on fsqc generated 1) signal-to-noise ratio (SNR) for WM and GM on FreeSurfer normalized norm.mgz file (wm_snr_norm and gm_snr_norm), and 2) WM-to-GM contrast SNR ratio in the left and right hemisphere (con_lh_snr and con_rh_snr), as our main FreeSurfer QC matrices. We utilized a data-driven approach to assess the data quality using QC matrices. First, we conducted a repeated measures analysis of variance (rmANOVA) to determine if QC values were significantly different among scanner types and protocols . In addition, to further evaluate if different scanner types and protocols could introduce significant changes to FreeSurfer outputs, we performed the same rmANOVA on cortical thickness measures across different scanner types and protocols in the ADNC0 group alone (i.e., those whose structure was least affected by disease pathology; ). We removed any scans that produced extreme QC values and cortical thickness measures from our analyses. We observed that scanner type or protocol does not significantly affect the WM or GM signal-to-noise ratio (SNR), but significantly affect the WM-to-GM contrast SNR . Therefore, scanner type and scanning protocols were used as covariate features in our following analyses. For the thickness measures, we observed that scans collected on 1.5T Philips scanner generated significantly lower values than other scanners. Therefore, we removed participants with MRI data collected on 1.5T Philips scanner from both groups and included scanner type and scanning protocols as covariates in our analyses. Final samples. Collectively, our final sample included 53 participants with ADNC0&1 and 91 participants with ADNC3 . Their demographics and genetic information including sex, years of education, race APOE genotype, age, and diagnoses at the time of MRI scan, and time differences from MRI scan to postmortem neuropathology were obtained from the NACC and reported in A. Sample characteristics regarding comorbidities are reported in C. We utilized an independent, locally collected, convenience sample from the Center for Neurodegeneration and Translational Neuroscience (CNTN, https://nevadacntn.org/ ) as a validation data set. All CNTN participants were recruited at Cleveland Clinic Lou Ruvo Center for Brain Health Las Vegas, Nevada. The CNTN study was approved by Cleveland Clinic Institutional Review Board and all participants gave written, informed consent. Details of the CNTN cohort has been previously reported . Dring CNTN-COBRE phase I, there were 190 participants enrolled and with MRI data collected (January 2017 to October 2020). Our convenience sample included 144 CNTN participants with a T1-weighted structural MRI scan (FreeSurfer 6.0 successfully and reliably finished), an amyloid PET scan ( 18 F-AV45 scan, standard uptake value ratio (SUVR) computed), and available APOE genotyping. To increase real-world clinical utility and application in vivo , we utilized amyloid positivity status determined from the PET-SUVR as the outcome in this validation data set, which is assumed to reflect underlying AD-pathology. Following the previously published AV45-PET processing pipeline and amyloid positivity criteria on the composite SUVR , 144 CNTN participants were divided into an amyloid positive group (SUVR > 1.11, N = 73) and an amyloid negative group (SUVR≤1.11, N = 71). Participant demographics for both groups are reported in B. Details on MRI and PET image processing steps are included in . All statistical and classification analyses were conducted in MATLAB 2018b ( https://www.mathworks.com/ ). Differences between the ADNC0&1 and ADNC3 groups for NACC participants, and differences between amyloid positive and amyloid negative groups for CNTN participants were assessed for demographic and clinical variables including sex, years of education, APOE genotype, race, age at MRI, and diagnosis at MRI. Differences in time intervals between MRI scan and neuropathological data, scanner field strengths, scanner manufacturer, scanning protocol (implementation of IR), and GM-to-WM contrast SNR were further assessed between ADNC0&1 and ADNC3 groups for NACC participants. We performed chi-square tests to examine differences for categorical variables (sex, race, APOE genotype, diagnosis at MRI, scanner field strength, scanner manufacturer and implementation of IR), and used Student’s t -tests to estimate differences among continuous variables (age at MRI, years of education, time intervals between first MRI and neuropathological data, and GM-to-WM contrast SNR). Using NACC participants with both postmortem neuropathological data and an antemortem MRI scan, we trained both model-based and data-driven machine learning models to classify the presence or absence of severe AD neuropathology using clinically accessible features. shows a schematic representation of our classification analyses. Clinically available features. Four demographic and genetic features (age at MRI, sex, years of education and APOE genotype) and eight structural brain measures from T1-weighted MRI were included as features to classify ADNC group assignments. Race was not included in the feature set because more than 95% NACC participants in this study were White. For the APOE genotype, a categorical variable was created to code APOE4 allele counts (range: 0, 1, and 2). For the T1-weighted MRI measures, we focused on cortical thickness from the FreeSurfer-derived AD-signature meta-ROIs encompassing the bilateral entorhinal, inferior temporal, middle temporal and fusiform . To account for potential variation by MRI scanners, we included 1) a binary vector representing MRI field strengths; 2) a categorical vector representing MRI manufacturer; and 3) a binary vector representing the implementation of IR, as additional features in the classification model, leading to a total of 15 features. Data from all NACC participants were utilized to train the model with a cross-validation schema. Due to the high collinearity among features, especially among cortical thickness features from the eight meta-ROIs, we further incorporated a feature selection step into each classification method (detailed below). Model based method: LASSO logistic regression. A logistic regression classifier with a Least Absolute Shrinkage and Selection Operator (LASSO) was used to evaluate the importance and performance of clinically available features in predicting AD pathology (ADNC0&1 (0) versus ADNC3 (1) groups). Briefly, LASSO parametrically shrinks the logistic regression coefficient of each feature by imposing a penalty term on its absolute value in the objective function . In this case, LASSO ensures that the retained features are the best features that explain the group differences while maintaining a low variance by shrinking the coefficients of all other unexplained features to zero . Since logistic regression assumes sigmoidal relationships between dependent and independent variables, two binary variables were created to separately code subjects with one and two copies of APOE4 alleles, and two binary variables were created to separately code subjects with MRI collected on Siemens and Philips scanners (i.e., GE scanner coded as baseline), resulting in a total of 17 features. Using a 10-fold cross-validation strategy, the feature set that produced the minimum cross-validation error in LASSO-logistic-regression was retained and further utilized in a reduced logistic regression model to classify the probability of assigning a participant to the severe AD neuropathology group (ADNC3). The resulting probability was then compared with the true group assignment using the receiver operating characteristic (ROC) curve method. To offset the population imbalance among two groups (53 versus 91), the threshold used to binarize the probability for final group assignments ( s ) was set as the point on the ROC curve that gives the minimum total false discovery rate (false positive rate (FPR) plus false negative rate (FNR)), instead of the commonly used value of 0.5. Sensitivity, precision, specificity, accuracy, F1-score were reported at this threshold, and area under the ROC curves (AUC) was further used to evaluate the overall classifier performance. The final trained reduced logistic regression model was then tested on the independent CNTN data set to classify amyloid positivity status. The same threshold ( s ) was utilized to binarize the obtained probability of assigning participants to the amyloid positive group, and the same matrices were used to evaluate the classifier performance. Data-driven method: Random Forest . We also utilized the data-driven random forest method for the same classification to take advantage of both linear and nonlinear relationships between the clinically available features and AD neuropathology. The random forest classifier is an ensemble learning method that operates by constructing a large number of decision trees. Each decision tree is constructed using a bootstrapping sample from the original data, and splits participants based on minimum total impurity score criteria computed at each partition . The concluding result of the random forest analysis is determined by counting the majority determination from all decision trees for each sample. In general, approximately 1/3 of participants are left out-of-box (OOB) for each decision tree during bootstrapping. Therefore, the classification of OOB samples were utilized as cross-validation results to evaluate the model performances. We first trained a random forest model with 1000 decision trees using all NACC participants with 15 clinically available features (detailed above). These 15 features were ordered based on the OOB permutation-based feature importance scores. Briefly, this score for a specific feature measures the decrease in mean accuracy when permuting that specific feature in the OOB samples, non-parametrically. We also computed a Gini importance score for each feature. The detailed explanations of both feature importance measures are included in . Next, we performed a recursive feature elimination (RFE), during which we trained 15 individual random forest models, each with 1000 decision trees, by dropping the least important feature in each iteration, respectively. To evaluate each model performance, the predicted probabilities of OOB validation samples were obtained and compared with the true group assignment using the same ROC curve-based method. The model that gave the maximum OOB validation AUCs among all 15 random forest models was selected as the most accurate model, and the features included were considered as the selected features. This most accurate model was further evaluated using the independent CNTN participants. Up to 50% of participants with severe AD neuropathology (i.e., the ADNC3 group) could have some degree of Lewy bodies . Many participants with both severe ADNC and low-level Lewy bodies in the brain stem, amygdala or olfactory bulb, present with AD clinically. Thus, to test the utility of our models with a more comprehensive real-world severe AD group, we further trained and tested our models by including participants with Lewy bodies in brain stem (NPLBOD = 1, Nsub = 5), amygdala, (NPLBOD = 4, Nsub = 32) and olfactory bulb (NPLBOD = 5, Nsub = 4) in the severe ADNC group. Demographic comparison ADNC0&1 and ADNC3 groups in NACC did not significantly differ with regard to sex and years of education, and both groups were more than 90% White ( A). There were significantly more APOE4 carriers in the ADNC3 group ( p < 0.001), consistent with the AD neuropathology represented in the ADNC3 group. At their first MRI visit, participants with ADNC0&1 were slightly younger ( p = 0.06) and had a larger age variation, i.e., 68.93±19.69 for ADNC0&1 versus 73.60±9.24 for ADNC3. Participants with ADNC3 had significantly more advanced disease ( p < 0.001). There were no differences in the time intervals between the first MRI visit and the neuropathological data between ADNC3 and ADNC0&1 groups. In addition, a significantly larger number of participants with ADNC3 had their MRI scans collected on 3T scanners ( p = 0.01) and with the implementation of IR ( p < 0.001), as compared to the ADNC0&1 group. Interestingly, WM-to-GM SNRs for these scans did not differ between the two groups. For comorbidities ( C), both groups had a high prevalence of vascular changes (>90%). Around 80% of participants had TDP43 pathologies assessed in the brain, among which, participants with ADNC3 had a relatively higher prevalence of comorbidities with TDP43 neuropathology. Among CNTN participants ( B), when compared to the amyloid negative group, the amyloid positive group had a significantly higher composite SUVR (1.44±0.15 versus 0.99±0.05), a lower proportion of women ( p = 0.02), and more APOE4 carriers ( p < 0.001). Education level did not differ between the two groups. At their first MRI scan, amyloid positive participants were also older ( p = 0.02) and more advanced into disease ( p < 0.001) than amyloid negative participants. Classification performance: LASSO logistic regression model Feature selection. A plots the 10-fold cross-validation error as a function of strengths of the regularization term in the LASSO-logistic-regression model trained to classify AD neuropathological status, i.e., ADNC3 versus ADNC0&1, among NACC participants. As listed in the intersect table in A, six features were selected with the minimum cross-validation error. More specifically, having one copy or two copies of APOE4 alleles were positively associated with severe AD neuropathology, whereas having greater cortical thickness in fusiform and entorhinal ROIs were negatively associated with severe AD neuropathology. Scanner field strength was also selected as an important feature in this model. Model performance with selected features. B shows the cross-validation ROC curve of the reduced logistic regression model trained with six selected features. The cross-validation AUC was 0.88 ( B, intersect table). A threshold of 0.55 was used to binarize the probability for final group assignments, corresponding to the point on the ROC curve with minimum total false rate ( B). Using this threshold to binarize the predicted probability in assigning participants to the ADNC3 group, the cross-validation accuracy, sensitivity, specificity, precision, and F1-score were 77.78%, 72.09%, 87.76%, 91.18%, and 0.81, respectively ( B, intersect table). Independent testing performance. The bottom row in B intersect table lists the performance in applying this reduced logistic regression model to classify amyloid positivity status in the independent CNTN data set (i.e., classifying amyloid positive versus amyloid negative status). Using the same threshold (0.55) to binarize the predicted probability, the independent testing accuracy, sensitivity, specificity, precision, and F1-score were 76.38%, 75.00%, 77.78%, 77.42%, and 0.76. Classification performances: data-driven Random Forest model Feature selection. A plots the feature importance score in the trained random forest model to classify severe AD neuropathology, i.e., ADNC3 versus ADNC0&1, using 15 clinically available features of NACC participants. As shown in A, the OOB permutation-based feature importance score and the Gini impurity index were highly correlated, with a Pearson’s correlation value of 0.86. We next ranked these 15 features based on the OOB permutation-based feature importance score and performed the RFE. lists features included in each model (right), and the corresponding model performances on OOB samples (left). As shown in A, APOE genotype showed the highest feature importance score. Random forest model trained using APOE alone achieved an AUC of 0.63 in classifying ADNC groups on OOB samples ( , last row). The final selected random forest model was trained using APOE genotype, age, and thicknesses of left middle temporal gyrus, left inferior temporal gyrus, left entorhinal cortex, and right fusiform gyrus as features (N-features = 6), which gave the highest AUC of 0.89 on OOB samples ( , 10th row). Model performance with selected features. B plots the ROC curve of the retained random forest model trained with the six selected features. A threshold of 0.59, corresponding to the lowest total false rate on the ROC curve, was used to binarize the predicted probability in assigning participants to the ADNC3 group. As listed in the intersect table in B, the OOB-validation accuracy, sensitivity, specificity, precision, and F1-score were 84.03%, 84.62%, 83.02%, 89.53%, and 0.87, respectively. Independent testing performance. The bottom row in B intersect table shows the performance in applying this model to classify amyloid positivity status in the independent CNTN data set (amyloid positive versus amyloid negative). Using the same threshold (0.59) to binarize the predicted probability, the independent testing accuracy, sensitivity, specificity, precision, and F1-score were 70.14%, 57.75%, 82.19%, 75.93%, and 0.66. Model performances with various starting feature sets. To comprehensively evaluate our model performances, we re-trained our random forest model by 1) removing APOE genotype from the starting feature set; 2) removing both APOE genotype and age from the starting feature set; 3) adding clinical diagnoses to the starting feature set; and 4) adding clinical diagnoses and removing APOE genotype from the starting feature set. lists AUCs on the OOB and independent testing sets in these models trained with different starting feature sets. Without APOE genotype, our model achieved an AUC of 0.82 in classifying the presence or absence of severe AD neuropathology in OOB samples and an AUC of 0.62 in classifying amyloid positivity status in the external validation data set (3rd column ). These AUCs further dropped to 0.80 and 0.59 on the OOB and external validation samples, respectively after removing age from the starting feature set. Without APOE and age, our model selected seven thickness measures from eight meta-ROIs as predictive features (4th column ). On the other hand, adding clinical diagnoses to the model boosted AUCs to 0.93 and 0.77 on the OOB and external validation samples, respectively (5th column in ). Removing APOE4 genotype on top of this model still guaranteed AUCs of 0.92 and 0.71 on the OOB and external validation samples, respectively (6th column in ). Model utilities when including participants with ADNC3 and low-level Lewy body co-pathologies An additional 41 participants with ADNC3 and Lewy bodies in the brain stem, amygdala, or olfactory bulb were included in the analyses. Our lasso logistic regression model selected a similar set of six features as the main analyses with the minimum cross-validation error (having one or two copies of APOE E4 allele, cortical thicknesses encompassing fusiform and entorhinal ROIs, and scanner field strength, ). With these features, the cross-validation accuracy, sensitivity, specificity, precision, F1-score, and AUC were 75.57%, 70.08%, 89.80%, 94.68%, 0.81, and 0.88; and the independent testing accuracy, sensitivity, specificity, precision, F1-score, and AUC were 76.38%, 75.00%, 77.78%, 77.41%, 0.76, and 0.76 . Random Forest model also gave comparable results as our main analyses, with the OOB-validation accuracy, sensitivity, specificity, precision, F1-score, and AUC being 87.70%, 88.63%, 83.02%, 92.86%, 0.91, and 0.90, respectively. The independent testing accuracy, sensitivity, specificity, precision, F1-score, and AUC were 70.83%, 63.38%, 78.08%, 73.77%, 0.69, and 0.71 . ADNC0&1 and ADNC3 groups in NACC did not significantly differ with regard to sex and years of education, and both groups were more than 90% White ( A). There were significantly more APOE4 carriers in the ADNC3 group ( p < 0.001), consistent with the AD neuropathology represented in the ADNC3 group. At their first MRI visit, participants with ADNC0&1 were slightly younger ( p = 0.06) and had a larger age variation, i.e., 68.93±19.69 for ADNC0&1 versus 73.60±9.24 for ADNC3. Participants with ADNC3 had significantly more advanced disease ( p < 0.001). There were no differences in the time intervals between the first MRI visit and the neuropathological data between ADNC3 and ADNC0&1 groups. In addition, a significantly larger number of participants with ADNC3 had their MRI scans collected on 3T scanners ( p = 0.01) and with the implementation of IR ( p < 0.001), as compared to the ADNC0&1 group. Interestingly, WM-to-GM SNRs for these scans did not differ between the two groups. For comorbidities ( C), both groups had a high prevalence of vascular changes (>90%). Around 80% of participants had TDP43 pathologies assessed in the brain, among which, participants with ADNC3 had a relatively higher prevalence of comorbidities with TDP43 neuropathology. Among CNTN participants ( B), when compared to the amyloid negative group, the amyloid positive group had a significantly higher composite SUVR (1.44±0.15 versus 0.99±0.05), a lower proportion of women ( p = 0.02), and more APOE4 carriers ( p < 0.001). Education level did not differ between the two groups. At their first MRI scan, amyloid positive participants were also older ( p = 0.02) and more advanced into disease ( p < 0.001) than amyloid negative participants. Feature selection. A plots the 10-fold cross-validation error as a function of strengths of the regularization term in the LASSO-logistic-regression model trained to classify AD neuropathological status, i.e., ADNC3 versus ADNC0&1, among NACC participants. As listed in the intersect table in A, six features were selected with the minimum cross-validation error. More specifically, having one copy or two copies of APOE4 alleles were positively associated with severe AD neuropathology, whereas having greater cortical thickness in fusiform and entorhinal ROIs were negatively associated with severe AD neuropathology. Scanner field strength was also selected as an important feature in this model. Model performance with selected features. B shows the cross-validation ROC curve of the reduced logistic regression model trained with six selected features. The cross-validation AUC was 0.88 ( B, intersect table). A threshold of 0.55 was used to binarize the probability for final group assignments, corresponding to the point on the ROC curve with minimum total false rate ( B). Using this threshold to binarize the predicted probability in assigning participants to the ADNC3 group, the cross-validation accuracy, sensitivity, specificity, precision, and F1-score were 77.78%, 72.09%, 87.76%, 91.18%, and 0.81, respectively ( B, intersect table). Independent testing performance. The bottom row in B intersect table lists the performance in applying this reduced logistic regression model to classify amyloid positivity status in the independent CNTN data set (i.e., classifying amyloid positive versus amyloid negative status). Using the same threshold (0.55) to binarize the predicted probability, the independent testing accuracy, sensitivity, specificity, precision, and F1-score were 76.38%, 75.00%, 77.78%, 77.42%, and 0.76. Feature selection. A plots the feature importance score in the trained random forest model to classify severe AD neuropathology, i.e., ADNC3 versus ADNC0&1, using 15 clinically available features of NACC participants. As shown in A, the OOB permutation-based feature importance score and the Gini impurity index were highly correlated, with a Pearson’s correlation value of 0.86. We next ranked these 15 features based on the OOB permutation-based feature importance score and performed the RFE. lists features included in each model (right), and the corresponding model performances on OOB samples (left). As shown in A, APOE genotype showed the highest feature importance score. Random forest model trained using APOE alone achieved an AUC of 0.63 in classifying ADNC groups on OOB samples ( , last row). The final selected random forest model was trained using APOE genotype, age, and thicknesses of left middle temporal gyrus, left inferior temporal gyrus, left entorhinal cortex, and right fusiform gyrus as features (N-features = 6), which gave the highest AUC of 0.89 on OOB samples ( , 10th row). Model performance with selected features. B plots the ROC curve of the retained random forest model trained with the six selected features. A threshold of 0.59, corresponding to the lowest total false rate on the ROC curve, was used to binarize the predicted probability in assigning participants to the ADNC3 group. As listed in the intersect table in B, the OOB-validation accuracy, sensitivity, specificity, precision, and F1-score were 84.03%, 84.62%, 83.02%, 89.53%, and 0.87, respectively. Independent testing performance. The bottom row in B intersect table shows the performance in applying this model to classify amyloid positivity status in the independent CNTN data set (amyloid positive versus amyloid negative). Using the same threshold (0.59) to binarize the predicted probability, the independent testing accuracy, sensitivity, specificity, precision, and F1-score were 70.14%, 57.75%, 82.19%, 75.93%, and 0.66. Model performances with various starting feature sets. To comprehensively evaluate our model performances, we re-trained our random forest model by 1) removing APOE genotype from the starting feature set; 2) removing both APOE genotype and age from the starting feature set; 3) adding clinical diagnoses to the starting feature set; and 4) adding clinical diagnoses and removing APOE genotype from the starting feature set. lists AUCs on the OOB and independent testing sets in these models trained with different starting feature sets. Without APOE genotype, our model achieved an AUC of 0.82 in classifying the presence or absence of severe AD neuropathology in OOB samples and an AUC of 0.62 in classifying amyloid positivity status in the external validation data set (3rd column ). These AUCs further dropped to 0.80 and 0.59 on the OOB and external validation samples, respectively after removing age from the starting feature set. Without APOE and age, our model selected seven thickness measures from eight meta-ROIs as predictive features (4th column ). On the other hand, adding clinical diagnoses to the model boosted AUCs to 0.93 and 0.77 on the OOB and external validation samples, respectively (5th column in ). Removing APOE4 genotype on top of this model still guaranteed AUCs of 0.92 and 0.71 on the OOB and external validation samples, respectively (6th column in ). An additional 41 participants with ADNC3 and Lewy bodies in the brain stem, amygdala, or olfactory bulb were included in the analyses. Our lasso logistic regression model selected a similar set of six features as the main analyses with the minimum cross-validation error (having one or two copies of APOE E4 allele, cortical thicknesses encompassing fusiform and entorhinal ROIs, and scanner field strength, ). With these features, the cross-validation accuracy, sensitivity, specificity, precision, F1-score, and AUC were 75.57%, 70.08%, 89.80%, 94.68%, 0.81, and 0.88; and the independent testing accuracy, sensitivity, specificity, precision, F1-score, and AUC were 76.38%, 75.00%, 77.78%, 77.41%, 0.76, and 0.76 . Random Forest model also gave comparable results as our main analyses, with the OOB-validation accuracy, sensitivity, specificity, precision, F1-score, and AUC being 87.70%, 88.63%, 83.02%, 92.86%, 0.91, and 0.90, respectively. The independent testing accuracy, sensitivity, specificity, precision, F1-score, and AUC were 70.83%, 63.38%, 78.08%, 73.77%, 0.69, and 0.71 . In this study, we have developed machine learning models that can classify the presence or absence of severe AD neuropathology using available clinical and MRI features with an accuracy of 84.03%. We further validated these models in an independent data set to classify in vivo amyloid status derived from PET imaging, where we achieved an accuracy of 70.14%. Consistent with our hypothesis, cortical thinning encompassing AD-signature meta-ROIs, together with APOE genotype, are jointly important for identifying severe AD neuropathology. We specifically excluded participants with Lewy body, FTLD-TDP, and FTLD-tau pathologies from our analyses in an effort to ensure the dominance of AD-related neuropathology in our data. Therefore, the retained MRI features might potentially represent an AD-specific neurodegeneration pattern. Major strength Currently, there is a lack of well-established models that could classify postmortem confirmed AD neuropathological status using clinically available and noninvasive features in living persons. Accordingly, there are limited in vivo biomarkers that could directly link neurodegeneration features to AD neuropathology within the ATN framework. Several studies have utilized MRI-derived measures including hippocampal volume, thickness of AD-signature ROIs, and composite atrophy scores as potential candidate markers of neurodegeneration for use in classifying AD-related outcomes. These studies have usually focused on clinically diagnosed subjects along the AD continuum , and only a few reports have confirmed subjects’ neuropathological status with postmortem autopsy data . Without pathological confirmation, the identified neurodegeneration markers in clinically diagnosed AD subjects might not be linked to AD neuropathology, due to the syndromal overlap across various dementias. For example, hippocampal atrophy, which has often been studied in AD , has also been widely reported in various conditions including normal aging, several other neurodegenerative disorders and non-neurodegenerative disorders such as diabetes, sleep apnea, and bipolar disorder . In this regard, the major strength of our study is the inclusion of NACC participants with confirmed no/low (ADNC0&1) or severe (ADNC3) AD neuropathology at autopsy. The ADNC score integrates postmortem assessments of Thal phase for amyloid plaques, Braak stage for neurofibrillary degeneration, and density of neocortical neuritic plaques, and therefore represents a comprehensive evaluation of AD-dominant neuropathology . We excluded participants with confirmed Lewy body, FTLD-TDP, and FTLD-tau pathologies to further establish the dominance of AD-related neuropathology in our ADNC0&1 and ADNC3 groups. Consequently, our ADNC0&1 and ADNC3 groups were dominated by lower and higher stages of A, B, and C scores respectively with minimal overlap , leading to a specific representation of the presence or absence of severe AD neuropathology. We obtained high accuracies on both cross-validation sets with postmortem confirmed AD pathological status and external validation sets with in vivo determined amyloid status ( and ). These results demonstrate that our models can reliably classify AD neuropathology both postmortem and in vivo . Confirmed with neuropathological data, the retained clinical features may be AD-specific and could assist in determining AD neuropathological status in living persons, especially when the etiology is uncertain and other AD biomarkers are unavailable clinically. The inclusion of ADNC stage 1 participants increased our sample size and facilitated the “real world” application of our approach. The external validation against in vivo biomarkers additionally facilitates translation to clinical applications. Classification models For this classification, we trained both a logistic regression model and a data-driven random forest model with features including thicknesses from AD signature meta-ROIs, APOE genotype, age, sex, and years of education . Most previous studies focused on between-group differences of each potential biomarker to define neurodegeneration in AD . The logistic regression model similarly evaluates the predictive ability of individual features and would work well when groups are linearly separable . Random forest, on the other hand, is a data-driven machine learning method that evaluates multivariate predictive abilities among input features towards output variables in a nonlinear manner . Therefore, these two methods complement each other and comprehensively evaluate both linear and nonlinear multivariate relationships among potential neurodegeneration biomarkers and AD neuropathological status. The high classification accuracies obtained with both models additionally support our hypothesis that these included features for classifying confirmatory AD neuropathology, and thus the retained MRI features can be utilized to quantify AD-specific neurodegeneration. Clinically available features Features included and retained in our models are clinically accessible and have already been included in AD legacy databases, which improves the potential clinical utility. Due to high collinearities among meta-ROI thickness measures, feature selection steps are applied in both models. Logistic regression is parsimonious (i.e., fewer but independent predictors could explain the model better than more but collinear predictors). LASSO in logistic regression sets coefficients for non-interesting features to zero automatically by posting a penalty term on the coefficient in the objective function . This parametric feature selection step copes with the collinearity among features by only retaining features that explain the most group differences . Meanwhile, the permutation index in random forest evaluates the decrease in model performances (i.e., classification accuracies) when a given feature is randomly permuted. In our analysis, both parametric (LASSO in logistic regression) and nonparametric (permutation index in random forest) feature selection results indicate that APOE4 allele counts contribute most significantly to classifying the presence or absence of severe AD neuropathology ( A and A). This result may be partially explained by the large overlap between APOE4 carriers and amyloid positive subjects along the AD continuum . After removing the APOE4 allele counts from the starting feature set, our random forest model still achieved reasonable performance on both cross-validation dataset (AUC = 0.82 in classifying ADNC groups) and external validation dataset (AUC = 0.62 in classifying amyloid positivity status, ). Besides APOE4 carrier status, our results consistently show that cortical thinning encompassing AD-signature meta-ROIs contribute to the classification of the presence or absences of severe AD neuropathology. The inclusion of the first cortical thickness measure to the random forest model significantly boosted the AUC from 0.63 to 0.83 (bottom two rows in ). Incorporating additional thickness measures led to an incremental effect on model performances , indicating that cortical thickness measures of meta-ROIs might have comparable impact in classifying confirmatory AD neuropathology. We observed a left-right difference in cortical thickness measures of all four meta-ROIs in the paired t-test . In random forest, thicknesses on the left hemisphere were more frequently selected ( and ), whereas in LASSO logistic regression, two right hemisphere and one left hemisphere features were retained . These observations further suggest that all meta-ROI thicknesses could be important to our models. Nevertheless, our random forest model trained with meta-ROI thicknesses in addition to APOE4 and age could boost the AUC on OOB samples from 0.79 to 0.89, and on external validation samples from 0.58 to 0.70 . These findings demonstrate the additive contribution of MRI-derived thickness measurements to APOE4 and age in classifying AD neuropathological status in living persons, and further validate the predictive ability and potential clinical utility of AD meta-ROIs. In addition to individual thickness measures, we also trained a logistic regression model using average thickness estimated across eight meta-ROIs as the only thickness feature. We obtained AUCs of 0.8723 and 0.7473 on the cross-validation and independent testing samples that were comparable to our main model (0.8751 and 0.7639 in ). These comparable results suggest subtle benefits of using individual regions versus the average measure. One possibility is that our limited and unbalanced sample sizes may hinder significant performance improvement through data-driven feature selections. Thus, future studies with larger samples and more balanced groups may more effectively demonstrate the advantages of integrating measures from individual regions with data-driven methods, compared to relying on composite averages. Nevertheless, our major goal is to train a model that could classify the presence or absence of severe AD neuropathology with clinically available features. To this end, results from models using on average meta-ROI measure further confirm the classification ability and potential clinical utility of AD meta-ROIs. Clinical diagnoses might also be considered as a feature that could assist in the classification of AD neuropathology. Our random forest model with additional clinical diagnoses as features did further increase AUCs to 0.93 and 0.77 on the OOB and external validation samples, respectively (Table 4). Nonetheless, we did not include the clinical diagnoses in our model mainly due to the potential discrepancies between clinical and pathological AD diagnoses. In general, about 10% to 30% clinical AD cases do not display typical AD neuropathological changes at autopsy . Many clinical centers, including our CNTN, are now requiring a positive amyloid status to diagnose AD, which could introduce potential circularities between AD diagnoses and amyloid neuropathology. In addition, despite being a reliable measure in classifying AD neuropathological status, efforts and expertise are required for clinical diagnoses, whereas objective measures combined could possibly achieve a similar performance. To this end, our machine learning model would be helpful in aiding the pathological AD diagnoses in living persons with clinically available features, particularly when handling those 10%–30% cases where an AD diagnosis is not straightforward with overlapping symptoms. Last, to increase the sample size, we included NACC participants with both 1.5T and 3.0T structural MRI scans in training our machine learning models. Previous studies with the same subjects scanned on both 1.5T and 3.0T scanners have shown fair to good between-scanner consistencies for 68 FreeSurfer cortical regions and derived measures . In training our models, we included scanner field strength, scanner manufacturer, and scanning protocols (i.e., implementation of IR) as features. As compared to MRI features, relatively smaller feature importance scores were obtained in our random forest model for these scanner related features ( A). This observation, together with previous reports, demonstrates that structural MRI-derived features could be robust and reliable, and therefore further supports the potential clinical utility of AD meta-ROI thickness measures. Detailed examination of the large age-variance in ADNC0&1 group As shown in A, our ADNC0&1 group had large age variance (68.93±19.69). A detailed examination revealed that this was driven by inclusion of seven participants under the age of 40 at time of the MRI scan and 45 at death, respectively . All seven participants were characterized as ADNC = 0 and without any FTLD-tau, FTLD-TDP, Lewy body, TDP-43, ALS-MND, or trinucleotide diseases pathologies. Five out of seven participants exhibited vascular changes. Clinically, all seven participants were diagnosed with MCI or dementia. We chose to retain these seven participants in our analyses because 1) they met our inclusion/exclusion criteria; 2) our major goal is to train a model that could classify the presence or absence of severe AD neuropathology; and 3) the ADNC3 group included more participants and we sought to avoid introducing further additional sources of bias via more unbalanced group sizes. As a result, in our main random forest model, six out of these seven participants were classified as no or low AD neuropathology group during cross-validation, which further suggest the potential utilities of our model to determine AD neuropathological status in living persons. In addition, we further tested whether model performance was driven by these seven ADNC0 participants by repeating our analyses after excluding them from the ADNC0&1 group . As compared to our main LASSO-logistic regression model , comparable AUCs were obtained on both cross-validation (0.8751 versus 0.8714) and independent testing data sets (0.7639 versus 0.7711). These results confirm that our model results did not appear to be driven by these seven participants. Limitations The utilization of the NACC participants with neuropathology data is a notable strength of our study, but it also introduces limitations that may impact generalizability. The most significant is that our analyses had a relatively small number of participants with no AD neuropathology, as compared to the number of participants with severe AD neuropathology. This group imbalance stems from the fact that the NACC database is heavily enriched for AD neuropathology. This bias could also limit us to comprehensively and impartially evaluate feature importance. To reconcile this bias, we have 1) combined ADNC1 with ADNC0 participants to increase the sample size and represent a “real-world” group of no or low levels of ADNC; 2) utilized thresholds that give minimum total false rates to binarize the probability for group assignments in training our classification models; and 3) validated the trained models using an external dataset with balanced sample sizes. Future replication with a balanced group design would lend additional support to our results. Although we tried to eliminate comorbidities by removing participants with confirmed Lewy body, FTLD-TDP and FTLD-tau pathologies using NPLBOD, NPFTDTDP, and NPFTDTAU variables in NACC Neuropathology Data Set. We were not able to fully exclude vascular changes and TDP43 pathology due to the high prevalence and limited assessments, respectively. Because AD neuropathological levels were low in ADNC0&1 group, it may be possible that these comorbidities could contribute to the clinical symptoms in this group . Our current analyses were limited by sample-size in ADNC0&1 group to further exclude participants based on any clinical or neuropathological criteria related to vascular changes and TDP43 pathology . With an increased sample size, future analyses could benefit from removing participants with a non-normal clinical diagnosis in this group to further refine the classification and prediction of AD neuropathology. Methodologically, we considered it less problematic to include participants with TDP43 pathology and vascular changes in the ADNC3 group, because AD-neuropathology was severe, and was likely to be the strongest pathological contributor to clinical symptoms and patterns of neurodegeneration. In addition, our model was tested by including participants with ADNC3 and low-level Lewy body neuropathology. Given the prevalence of comorbidities between severe AD and Lewy bodies in real-world subjects, the comparable performances further demonstrated our models’ utilities with a more comprehensive representation of real-world severe AD cases. The relatively limited number of participants in the ADNC0&1 group might restrict us from observing any significant differences by including more participants with ADNC3 and Lewy bodies. Additional limitations arise in our use of data from the NACC database, as there is relatively limited representation of racial and ethnic minorities with an overrepresentation of a highly educated and non-Hispanic White population in NACC. It remains unclear whether our results would generalize well to more diverse samples. Future efforts focusing on replicating and validating our models in novel diverse cohorts would be necessary before potential utilities. It is also important to highlight that even though all features included and retained in our machine learning models are clinically accessible, they may not be widely available, especially outside of specialty clinics. For instance, to achieve the best performance of our model, either genotyping or sequencing analyses are required to obtain the APOE genotype. Thickness measurements for meta-ROIs also require detailed processing of structural MRI scans. Therefore, while our models are still optimized for research settings and have the potential for clinical applications, they cannot be deployed currently in non-specialized clinical settings (e.g., primary care). Furthermore, although we demonstrated that thickness measures did not significantly differ among scanner types, we observed a trend of scanner effect on thickness measures in our relatively small samples . For future studies with larger samples, harmonization of thickness measures across scanners might be preferrable, such as those produced by the ComBAT tool . In addition, the current study did not assess the model performance with other features that might be available in specialized clinics, such as neuropsychological measures or blood-based biomarkers. We did not seek access to these features from NACC to train our model, as 1) our validation cohort (CNTN) had limited information on these features, and 2) these features might be invasive, subjective and more variable. Nevertheless, if these features are easily available, future initiatives should be undertaken to develop similar approaches to those presented here. Conclusion We have developed machine learning models to classify the presence or absence of severe AD neuropathology using clinically accessible features. Satisfactory accuracies are obtained in classifying both postmortem confirmed AD neuropathological status on the cross-validation data set and in vivo amyloid status on the external validation data set. Our models further indicate that APOE genotype and cortical thinning encompassing AD meta-ROIs are the most important biological features for classifying AD neuropathological status. Therefore, the retained MRI features may represent an AD-specific neurodegeneration pattern within the ATN framework. Future replications and validations on ethnically and racially diverse samples with balanced pathology groups are necessary before potential clinical utilities. Currently, there is a lack of well-established models that could classify postmortem confirmed AD neuropathological status using clinically available and noninvasive features in living persons. Accordingly, there are limited in vivo biomarkers that could directly link neurodegeneration features to AD neuropathology within the ATN framework. Several studies have utilized MRI-derived measures including hippocampal volume, thickness of AD-signature ROIs, and composite atrophy scores as potential candidate markers of neurodegeneration for use in classifying AD-related outcomes. These studies have usually focused on clinically diagnosed subjects along the AD continuum , and only a few reports have confirmed subjects’ neuropathological status with postmortem autopsy data . Without pathological confirmation, the identified neurodegeneration markers in clinically diagnosed AD subjects might not be linked to AD neuropathology, due to the syndromal overlap across various dementias. For example, hippocampal atrophy, which has often been studied in AD , has also been widely reported in various conditions including normal aging, several other neurodegenerative disorders and non-neurodegenerative disorders such as diabetes, sleep apnea, and bipolar disorder . In this regard, the major strength of our study is the inclusion of NACC participants with confirmed no/low (ADNC0&1) or severe (ADNC3) AD neuropathology at autopsy. The ADNC score integrates postmortem assessments of Thal phase for amyloid plaques, Braak stage for neurofibrillary degeneration, and density of neocortical neuritic plaques, and therefore represents a comprehensive evaluation of AD-dominant neuropathology . We excluded participants with confirmed Lewy body, FTLD-TDP, and FTLD-tau pathologies to further establish the dominance of AD-related neuropathology in our ADNC0&1 and ADNC3 groups. Consequently, our ADNC0&1 and ADNC3 groups were dominated by lower and higher stages of A, B, and C scores respectively with minimal overlap , leading to a specific representation of the presence or absence of severe AD neuropathology. We obtained high accuracies on both cross-validation sets with postmortem confirmed AD pathological status and external validation sets with in vivo determined amyloid status ( and ). These results demonstrate that our models can reliably classify AD neuropathology both postmortem and in vivo . Confirmed with neuropathological data, the retained clinical features may be AD-specific and could assist in determining AD neuropathological status in living persons, especially when the etiology is uncertain and other AD biomarkers are unavailable clinically. The inclusion of ADNC stage 1 participants increased our sample size and facilitated the “real world” application of our approach. The external validation against in vivo biomarkers additionally facilitates translation to clinical applications. For this classification, we trained both a logistic regression model and a data-driven random forest model with features including thicknesses from AD signature meta-ROIs, APOE genotype, age, sex, and years of education . Most previous studies focused on between-group differences of each potential biomarker to define neurodegeneration in AD . The logistic regression model similarly evaluates the predictive ability of individual features and would work well when groups are linearly separable . Random forest, on the other hand, is a data-driven machine learning method that evaluates multivariate predictive abilities among input features towards output variables in a nonlinear manner . Therefore, these two methods complement each other and comprehensively evaluate both linear and nonlinear multivariate relationships among potential neurodegeneration biomarkers and AD neuropathological status. The high classification accuracies obtained with both models additionally support our hypothesis that these included features for classifying confirmatory AD neuropathology, and thus the retained MRI features can be utilized to quantify AD-specific neurodegeneration. Features included and retained in our models are clinically accessible and have already been included in AD legacy databases, which improves the potential clinical utility. Due to high collinearities among meta-ROI thickness measures, feature selection steps are applied in both models. Logistic regression is parsimonious (i.e., fewer but independent predictors could explain the model better than more but collinear predictors). LASSO in logistic regression sets coefficients for non-interesting features to zero automatically by posting a penalty term on the coefficient in the objective function . This parametric feature selection step copes with the collinearity among features by only retaining features that explain the most group differences . Meanwhile, the permutation index in random forest evaluates the decrease in model performances (i.e., classification accuracies) when a given feature is randomly permuted. In our analysis, both parametric (LASSO in logistic regression) and nonparametric (permutation index in random forest) feature selection results indicate that APOE4 allele counts contribute most significantly to classifying the presence or absence of severe AD neuropathology ( A and A). This result may be partially explained by the large overlap between APOE4 carriers and amyloid positive subjects along the AD continuum . After removing the APOE4 allele counts from the starting feature set, our random forest model still achieved reasonable performance on both cross-validation dataset (AUC = 0.82 in classifying ADNC groups) and external validation dataset (AUC = 0.62 in classifying amyloid positivity status, ). Besides APOE4 carrier status, our results consistently show that cortical thinning encompassing AD-signature meta-ROIs contribute to the classification of the presence or absences of severe AD neuropathology. The inclusion of the first cortical thickness measure to the random forest model significantly boosted the AUC from 0.63 to 0.83 (bottom two rows in ). Incorporating additional thickness measures led to an incremental effect on model performances , indicating that cortical thickness measures of meta-ROIs might have comparable impact in classifying confirmatory AD neuropathology. We observed a left-right difference in cortical thickness measures of all four meta-ROIs in the paired t-test . In random forest, thicknesses on the left hemisphere were more frequently selected ( and ), whereas in LASSO logistic regression, two right hemisphere and one left hemisphere features were retained . These observations further suggest that all meta-ROI thicknesses could be important to our models. Nevertheless, our random forest model trained with meta-ROI thicknesses in addition to APOE4 and age could boost the AUC on OOB samples from 0.79 to 0.89, and on external validation samples from 0.58 to 0.70 . These findings demonstrate the additive contribution of MRI-derived thickness measurements to APOE4 and age in classifying AD neuropathological status in living persons, and further validate the predictive ability and potential clinical utility of AD meta-ROIs. In addition to individual thickness measures, we also trained a logistic regression model using average thickness estimated across eight meta-ROIs as the only thickness feature. We obtained AUCs of 0.8723 and 0.7473 on the cross-validation and independent testing samples that were comparable to our main model (0.8751 and 0.7639 in ). These comparable results suggest subtle benefits of using individual regions versus the average measure. One possibility is that our limited and unbalanced sample sizes may hinder significant performance improvement through data-driven feature selections. Thus, future studies with larger samples and more balanced groups may more effectively demonstrate the advantages of integrating measures from individual regions with data-driven methods, compared to relying on composite averages. Nevertheless, our major goal is to train a model that could classify the presence or absence of severe AD neuropathology with clinically available features. To this end, results from models using on average meta-ROI measure further confirm the classification ability and potential clinical utility of AD meta-ROIs. Clinical diagnoses might also be considered as a feature that could assist in the classification of AD neuropathology. Our random forest model with additional clinical diagnoses as features did further increase AUCs to 0.93 and 0.77 on the OOB and external validation samples, respectively (Table 4). Nonetheless, we did not include the clinical diagnoses in our model mainly due to the potential discrepancies between clinical and pathological AD diagnoses. In general, about 10% to 30% clinical AD cases do not display typical AD neuropathological changes at autopsy . Many clinical centers, including our CNTN, are now requiring a positive amyloid status to diagnose AD, which could introduce potential circularities between AD diagnoses and amyloid neuropathology. In addition, despite being a reliable measure in classifying AD neuropathological status, efforts and expertise are required for clinical diagnoses, whereas objective measures combined could possibly achieve a similar performance. To this end, our machine learning model would be helpful in aiding the pathological AD diagnoses in living persons with clinically available features, particularly when handling those 10%–30% cases where an AD diagnosis is not straightforward with overlapping symptoms. Last, to increase the sample size, we included NACC participants with both 1.5T and 3.0T structural MRI scans in training our machine learning models. Previous studies with the same subjects scanned on both 1.5T and 3.0T scanners have shown fair to good between-scanner consistencies for 68 FreeSurfer cortical regions and derived measures . In training our models, we included scanner field strength, scanner manufacturer, and scanning protocols (i.e., implementation of IR) as features. As compared to MRI features, relatively smaller feature importance scores were obtained in our random forest model for these scanner related features ( A). This observation, together with previous reports, demonstrates that structural MRI-derived features could be robust and reliable, and therefore further supports the potential clinical utility of AD meta-ROI thickness measures. As shown in A, our ADNC0&1 group had large age variance (68.93±19.69). A detailed examination revealed that this was driven by inclusion of seven participants under the age of 40 at time of the MRI scan and 45 at death, respectively . All seven participants were characterized as ADNC = 0 and without any FTLD-tau, FTLD-TDP, Lewy body, TDP-43, ALS-MND, or trinucleotide diseases pathologies. Five out of seven participants exhibited vascular changes. Clinically, all seven participants were diagnosed with MCI or dementia. We chose to retain these seven participants in our analyses because 1) they met our inclusion/exclusion criteria; 2) our major goal is to train a model that could classify the presence or absence of severe AD neuropathology; and 3) the ADNC3 group included more participants and we sought to avoid introducing further additional sources of bias via more unbalanced group sizes. As a result, in our main random forest model, six out of these seven participants were classified as no or low AD neuropathology group during cross-validation, which further suggest the potential utilities of our model to determine AD neuropathological status in living persons. In addition, we further tested whether model performance was driven by these seven ADNC0 participants by repeating our analyses after excluding them from the ADNC0&1 group . As compared to our main LASSO-logistic regression model , comparable AUCs were obtained on both cross-validation (0.8751 versus 0.8714) and independent testing data sets (0.7639 versus 0.7711). These results confirm that our model results did not appear to be driven by these seven participants. The utilization of the NACC participants with neuropathology data is a notable strength of our study, but it also introduces limitations that may impact generalizability. The most significant is that our analyses had a relatively small number of participants with no AD neuropathology, as compared to the number of participants with severe AD neuropathology. This group imbalance stems from the fact that the NACC database is heavily enriched for AD neuropathology. This bias could also limit us to comprehensively and impartially evaluate feature importance. To reconcile this bias, we have 1) combined ADNC1 with ADNC0 participants to increase the sample size and represent a “real-world” group of no or low levels of ADNC; 2) utilized thresholds that give minimum total false rates to binarize the probability for group assignments in training our classification models; and 3) validated the trained models using an external dataset with balanced sample sizes. Future replication with a balanced group design would lend additional support to our results. Although we tried to eliminate comorbidities by removing participants with confirmed Lewy body, FTLD-TDP and FTLD-tau pathologies using NPLBOD, NPFTDTDP, and NPFTDTAU variables in NACC Neuropathology Data Set. We were not able to fully exclude vascular changes and TDP43 pathology due to the high prevalence and limited assessments, respectively. Because AD neuropathological levels were low in ADNC0&1 group, it may be possible that these comorbidities could contribute to the clinical symptoms in this group . Our current analyses were limited by sample-size in ADNC0&1 group to further exclude participants based on any clinical or neuropathological criteria related to vascular changes and TDP43 pathology . With an increased sample size, future analyses could benefit from removing participants with a non-normal clinical diagnosis in this group to further refine the classification and prediction of AD neuropathology. Methodologically, we considered it less problematic to include participants with TDP43 pathology and vascular changes in the ADNC3 group, because AD-neuropathology was severe, and was likely to be the strongest pathological contributor to clinical symptoms and patterns of neurodegeneration. In addition, our model was tested by including participants with ADNC3 and low-level Lewy body neuropathology. Given the prevalence of comorbidities between severe AD and Lewy bodies in real-world subjects, the comparable performances further demonstrated our models’ utilities with a more comprehensive representation of real-world severe AD cases. The relatively limited number of participants in the ADNC0&1 group might restrict us from observing any significant differences by including more participants with ADNC3 and Lewy bodies. Additional limitations arise in our use of data from the NACC database, as there is relatively limited representation of racial and ethnic minorities with an overrepresentation of a highly educated and non-Hispanic White population in NACC. It remains unclear whether our results would generalize well to more diverse samples. Future efforts focusing on replicating and validating our models in novel diverse cohorts would be necessary before potential utilities. It is also important to highlight that even though all features included and retained in our machine learning models are clinically accessible, they may not be widely available, especially outside of specialty clinics. For instance, to achieve the best performance of our model, either genotyping or sequencing analyses are required to obtain the APOE genotype. Thickness measurements for meta-ROIs also require detailed processing of structural MRI scans. Therefore, while our models are still optimized for research settings and have the potential for clinical applications, they cannot be deployed currently in non-specialized clinical settings (e.g., primary care). Furthermore, although we demonstrated that thickness measures did not significantly differ among scanner types, we observed a trend of scanner effect on thickness measures in our relatively small samples . For future studies with larger samples, harmonization of thickness measures across scanners might be preferrable, such as those produced by the ComBAT tool . In addition, the current study did not assess the model performance with other features that might be available in specialized clinics, such as neuropsychological measures or blood-based biomarkers. We did not seek access to these features from NACC to train our model, as 1) our validation cohort (CNTN) had limited information on these features, and 2) these features might be invasive, subjective and more variable. Nevertheless, if these features are easily available, future initiatives should be undertaken to develop similar approaches to those presented here. We have developed machine learning models to classify the presence or absence of severe AD neuropathology using clinically accessible features. Satisfactory accuracies are obtained in classifying both postmortem confirmed AD neuropathological status on the cross-validation data set and in vivo amyloid status on the external validation data set. Our models further indicate that APOE genotype and cortical thinning encompassing AD meta-ROIs are the most important biological features for classifying AD neuropathological status. Therefore, the retained MRI features may represent an AD-specific neurodegeneration pattern within the ATN framework. Future replications and validations on ethnically and racially diverse samples with balanced pathology groups are necessary before potential clinical utilities. Xiaowei Zhuang (Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Validation; Visualization; Writing – original draft; Writing – review & editing); Dietmar Cordes (Conceptualization; Formal analysis; Funding acquisition; Investigation; Resources; Supervision; Visualization; Writing – review & editing); Andrew R. Bender (Conceptualization; Investigation; Validation; Visualization; Writing – review & editing); Rajesh Nandy (Methodology; Visualization; Writing – review & editing); Edwin C. Oh (Resources; Supervision; Visualization; Writing – review & editing); Jefferson Kinney (Data curation; Funding acquisition; Resources; Writing – review & editing); Jessica Z.K. Caldwell (Data curation; Funding acquisition; Resources; Writing – review & editing); Jeffrey Cummings (Conceptualization; Data curation; Funding acquisition; Resources; Supervision; Visualization; Writing – review & editing); Justin Miller (Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Resources; Supervision; Validation; Visualization; Writing – review & editing). Supplementary Material |
Comparative Analyses of Medicinal Chemistry and Cheminformatics Filters with Accessible Implementation in Konstanz Information Miner (KNIME) | cc0d6868-981a-4f3a-8841-5fd05981b947 | 9147459 | Pharmacology[mh] | Combinatorial chemistry (CC), novel library design methodologies, and high-throughput screening (HTS) represent the standard approaches for synthesis and evaluation (searching and selecting) of potential lead compounds in drug design efforts . The combined use of chemical libraries and HTS to sift through large libraries and select desired compounds vastly increases the success rate of drug discovery programs . Assays are now performed with libraries consisting of several million compounds: (Pfizer, 4 million ; Novartis 1.7 million ; Astra Zeneca 4 million ). Physical compound libraries and HTS are still regarded as the staple method for identification of leads; however, the advance of computational tools and in silico chemistry means that computer-aided methods have become indispensable in modern drug design efforts. If commercial physical compound libraries include several million molecules, the virtual compound libraries nowadays span from 10 7 to 10 18 molecules. Nevertheless, such expansion of chemical space is a double-edged sword, as on one hand the probability of finding potential leads when screening larger libraries is greater, but on the other hand, screening of entire libraries even with the aid of computational methods may not be economically viable or even accessible in a timely manner. With the identification of the biological target in the early steps of the drug discovery process and the definition of the binding site, the chemical space adequate for further lead design becomes specific, and such information can be used to tailor compound libraries . The specific nature of molecular recognition and interaction combined with the fact that drugs must exhibit additional properties such as bioavailability and acceptable toxicity profiles severely narrows the adequate chemical space making drug design a monumental undertaking. Therefore, in a contemporary VS (virtual screening) or HTVS (high-throughput virtual screening) scenario, the database design is essential for efficient downstream calculations and in vitro testing. In order to achieve success in drug design efforts we need to adhere to certain library design guidelines. Libraries should focus the chemical space on the specific problem at hand, the compounds synthesizable and enriched with molecules that have drug-like properties . The main challenge of library construction is to cover as much diversity of the chemical space as possible, while keeping the total number of compounds low to reduce time and money consumption . Various molecular filters are often used to increase the hit rates of drug development campaigns . With the hit rate of screening being on average as low as 1%, the simplest and most direct way to increase hit rate is to eliminate molecules with a low probability of becoming leads . Filtering removes both unwanted chemical structures and unwanted chemical properties and is used to tailor the molecular libraries in a target focused manner . The work on molecular filters was pioneered by Chris Lipinski and coworkers, who compared early HTS and combinatorial chemistry drug hits at Pfizer (up to 1994) with a subset of 2245 drugs from the World Drug Index . The aim was to understand the common molecular features of orally available drugs and using an efficient version of the QSAR paradigm for structure permeability as suggested by Van de Waterbeemd et al. . They came to several conclusions on the factors affecting poor absorption and permeation . The main principle behind filtering of libraries is based on the term of drug-likeness. Although the term is often used in different ways by different authors, it generally refers to molecules that have properties or contain functional groups that are consistent with the majority of the known drugs . The typical drug-like compounds exhibit desirable properties such as oral bioavailability, low toxicity, membrane permeability, and reasonable clearance rates . Drug-like molecules therefore occupy distinct chemical space described by molecular descriptors and assigned cut-off values derived from experience. The first and to this day the most popular filters in use focused on finding effective and orally absorbable compounds . The main goal of such filters was to address ADME (absorption, distribution, metabolism, and excretion) issues. The research on this topic points towards the fact that certain properties such as logP, MW (molecular weight), and number of hydrogen bonding groups correlate with oral bioavailability. This fact has been used to improve the success of finding lead-like molecules with filters that bias the chemical space of libraries, resulting in filters designed for various drug development applications . Besides filters for drug-like properties, several filters exist that adopt the same knowledge-based approach in their design but expand beyond the scope of classic drug-like filtering. Filters such as the Ro4 (rule-of-4), designed to focus libraries on protein–protein interaction inhibitors, use descriptor cut-offs that are opposite of what is traditionally defined as drug-like and attest to the universal nature of molecular filters . With preparation of molecular libraries, it is not just a question of what to filter out but when. Rules in the form of filters mean that compounds are discriminated on a pass or fail basis—compounds that pass the rules are considered equal, as are all that breach the rules . Typically, filters are employed in the starting steps of a drug discovery campaign. Applying such filters upfront reduces the number of compounds analyzed in successive steps, speeding up the drug development process. However, this comes at the price of eliminating compounds that could show desirable properties in later phases. This is especially true for stringent filters and for the use of compounds that have conformational flexibility . The application of filters in the later stages avoids the problem of eliminating potential leads, but also causes the computationally intensive tasks to be performed on larger libraries, increasing both the financial and time costs. Moreover, we would like to point out that some authors argue against screening out promiscuous compounds in the early drug discovery . Opponents of filtering point out that any rule-based system of filtering ignores the fact that exceptions exist, and that blind use of such restrictive filters would eliminate potential drugs such as cyclosporine and erythromycin, where the majority of the drug-like rules break down . Exceptions such as the aforementioned drugs bring up an important topic of distinction between properties of useful lead-like molecules and drugs. Regardless of whether the screening is done upfront of filtering on more diverse libraries or after filtering on more focused libraries, structural changes for lead optimization will usually be necessary . In general structures, lead compounds exhibit less molecular complexity (less MW, fewer number of rings and rotatable bonds) and are less hydrophobic (lower clogP and logD). This indicates that the process of optimizing simple leads into drugs is favorable, supporting the idea of filtering libraries before screening and optimizing them into drugs later . Filtering out “undesirable” molecular species using computational filters thus forms a key element in library preparation and carries an informed decision in defining “favorable” or “undesirable” properties . Thresholds for such properties are often derived from the experience of the pharmaceutical industry . The criteria of “undesirable” structures should always be considered in their suitable scientific context, e.g., the loss of peptidomimetic molecules employing typical rule-based filters such as the Ro5 (Lipinski’s rule of five) in the development of a protease inhibitor library would result in a poor hit rate . Therefore, we encourage the reader to consider the biological context of the target, the drug discovery campaign, and to employ a plethora of filters to flag compounds for consideration and design in the subsequent drug discovery campaign steps. When using multiple filters in a sequential manner it is generally best to employ the filter that removes the most compounds first to reduce time consumption in later steps. One should also consider which filters will be applied without exceptions and which ones will merely flag the compounds for later assessment. Those that will filter without exceptions should be applied beforehand. A good example of a consecutive filtering protocol is described in the work of Jukič et al., where the library was first filtered for large and small compounds followed by filtering for aggregators, PAINS and REOS . To successfully apply filters in HTVS, the selected compound library must use supported data formats, for example, the string representation SMILES (simplified molecular input line entry specification format) or 3D representations such as SDF (structure-data file format) or MOL (MDL Molfile) . In most cases, 3D conformational data are not required for the use of filters, as these filters are usually referred to as “2D filters”. Despite the widespread adoption of SMILES for storage and interchange of chemical structures no standard for generating SMILES strings exist. The application of canonical SMILES, which use only a single string per molecule, is recommended to avoid duplication and problems in future filtering. To address issues of specifying isotopism and stereochemistry of a molecule the isomeric SMILES was developed and is useful for scoping the library for stereoisomerism duplicates or to generate stereoisomers and expand the chemical space. A SMILES string can be canonical and isomeric at the same time . The SMILES expansion SMARTS (SMILES arbitrary target specification) allows specification of sub-structural patterns and is used for specification of protonation state, hydrogen count, and ionization states. As both the SMILES and SMARTS format are not an open project and are proprietary, this has resulted in the use of different generation algorithms by software developers, resulting in different SMILES versions for the same compounds. Moves towards the open-source string representations of compounds and standardization have been made with OpenSmiles and InChI . However, with the current state of compound libraries the use of standardized chemical forms is not the norm, and care should be taken when combining such libraries for virtual screening . We recommend the use of Konstanz Information Miner (KNIME) software for standardizing the input format before filtering either from the 3D SDF or the string SMILES representation, in an analogous way performed in the filters provided by this article. Many filters for compound library design are present in primary scientific literature with some such as Lipinski’s rule-of-5 enjoying widespread recognition in the scientific community; however, many filters for drug design do not enjoy the same recognition. To bridge the gap between molecular filters and their accessibility to the public, we sought out to implement them in an open-access program that allows visual and dataflow programming through a graphical user interface. We therefore collected data on molecular filters, implemented them into existing open-access software, and compared them side by side to benefit the reader in his/her early drug design steps .
To test and demonstrate the functionality of the filters implemented and their effects on the chemical space, we applied the filtering workflow on a general ZINC database . The database was obtained by accessing the ZINC website ( https://zinc.docking.org/tranches/home/ accessed on 21 June 2021) selecting the following parameters (representation “2D”, reactivity “standard”, purchasability “in-stock”) and downloading the SMILES wget command file. The final downloaded library consisted of 9,216,175 compounds (a large non-specific chemical library). Using the KNIME row sampling node, 1% of the total database was sampled and ran through all the filters implemented in KNIME. We then calculated the average values and standard deviations (SD) of several key molecular descriptors using the statistics KNIME node to assess the change in chemical space after filtering ( , , , , and ). The descriptors chosen were a standard basic set most descriptive for initial chemical space assessment; the partition coefficient as SlogP, molecular refractivity (SMR), total polar surface area (TPSA), molecular weight (MW), No. of rotatable bonds, No. of hydrogen bond acceptors (HBA), No. of hydrogen bond donors (HBD), No. of heavy atoms, No. of rings, and the number of atoms C, N, O present in the compounds. We see that filters impact the chemical space of libraries to various degrees. The more specific the filter, the larger the portion removed, since the chemical space on which they are based is far more defined than with general drug-like filters.
The REOS (rapid elimination of swill) filter removed 32% of the compounds from the tested ZINC database subset but left the chemical space unaffected when compared to the original dataset, as it has no cutoffs on the investigated descriptors. This holds true for the functional group filter for PAINS (pan-assay interference compounds) as well; however, only 7% of compounds were removed. This is due to the fact that the filter is not as broad and tries to remove certain problematic moieties that were not captured in previously developed functional group filters (e.g., REOS). The aggregator filter, designed by Irwin et al. compares similarity of a library to 12.600 known aggregators ( http://advisor.bkslab.org/rawdata/ accessed on 10 February 2022), (set to the most stringent cutoff of “low” similarity to known aggregators) removes ~60% of the database and significantly lowers the SlogP value, as it uses this descriptor as a cutoff to determine the aggregation propensity. The number of rings is also lower after filtering for aggregators implicating that the presence of rings might be involved in aggregation. However, this descriptor is not used as a cutoff, but is indirectly correlated with the properties of aggregators. The average value of rings for the dataset of known aggregators is 3.6 ± 1.03, which is slightly above the average of 3.3 ± 1.2 for the general database, meaning that compounds with rings would likely score higher in the Tanimoto coefficient comparison and get filtered out. The Ro3 (rule of three) and Ro4 (rule of four) filters are the most stringent filters as they define the most specific chemical space, filtering out 97% and 94% of the database, respectively. Despite their similarity in the filtered-out percentage, they operate in opposite ways. The Ro3 represents a strict filter designed to support “hit identification” and “fragment-based” drug research and only accepts molecules with a molecular weight of less than 300. It supports the paradigm that small compounds still capture the desired chemical space yet leave a lot of space for future compound optimization towards leads. The Ro4 attempts to capture the protein–protein interaction inhibitor chemical space and retains molecules with molecular weight above 400, as such larger molecules are able to form multiple interactions. Morelli et al. designed the filter with the aim of establishing guidelines for druggable protein–protein inhibitors, since these most often break traditional property filter rules. Beside the high MW cutoff, Ro4 retains only compounds containing multiple rings and is often above average in the descriptor value graphs ( , and ). The Veber and Egan filters remove a small fraction of molecules with 7.9% and 10.3%, respectively, as they both apply only two filtering rules with a mild cut-off value. The Veber filter tries to capture molecules with good oral bioavailability properties. With just two cut-offs that focus strictly on oral bioavailability, it filters out 8% of the dataset. Another bioavailability and membrane permeability filter is the Egan filter which filters out 10% of the dataset. The molecules score lower in average descriptor values across all the examined descriptors, with both the Egan and Veber filters supporting the notion that smaller compounds are more membrane permeable and show greater bioavailability. The Mozziconacci filter, a filter for drug-like properties, applies five descriptor cutoff rules. All five descriptors used are different from the classical Rule-of-5 descriptors. The Lipinski Rule-of-5 is a set of four rules (logP, MW, and H-bond donor and acceptor cut-offs) for drug-likeness and oral bioavailability derived from a subset of 2245 drugs. It removes a similar share of the data set as well with the Lipinski filter removing 9% and the Mozziconacci filter 10%. Despite both being drug-like filters placing the filters in a chain-like matter, with the Mozziconaci filter placed after Lipinski, we filter out an additional 9% of the total dataset. This means that the drug-like definition of both filters is very different and may be used in conjunction for strict drug-like filtering. Despite only two descriptor rules for the passing of the blood–brain barrier, the Van de Waterbeemd filter removes 35% of the molecules from the database, in large part due to the small TPSA cutoff value, which is reflected in a reasonably low average TPSA descriptor value .The Murcko filter, due to its specificity (determining compounds with central nervous system (CNS) activity), filters out 71% percent of the database using five cut-offs. Low descriptor values for TPSA and molecular weight can also be observed as with the Veber and Egan filters, since these molecules must be smaller in order to pass the blood–brain barrier .
To facilitate open access use of various filters for drug design, we decided to implement the described filters into a single unit, where researchers could access various filters or combine them to a multi-filter to speed up their own drug development efforts. The first step incorporated a thorough search of the literature for information on molecular filters with the aim of defining, implementing, and sorting them as clearly as possible for the end user. Filters described were sorted into one of the two groups; filters that filter out based on the presence of functional groups and filters that filter out based on physiochemical properties. Filters designed to exclude compounds based on the presence of functional groups most often aim to remove compounds that are reactive toward protein targets. The most common such functional groups are Michael acceptors, ketones, aldehydes, and suicide inhibitors. Such compounds would likely be false HTS positives and would increase time and money expenses spent on screening. Removing reactive functionality is based on the premise that covalent interactions are not desired for drug design except for specific cases . Besides filtering for compounds with reactive species, functional group filters aim to remove optically interfering components, aggregators, fluorescent compounds, firefly luciferase inhibitors, redox cycling compounds, oxidizers, cytotoxic compounds, compounds with quenching ability, and surfactant-like compounds, all of which would frequently appear as false positives in the screening tests. Several filters fall under this category, with their properties described in . Some filters, although classified as functional group filters, do possess some additional property filters making them hybrid filters. We collected all filters present in the literature and added a brief description with the cut-off values on which the filter is based ( and ). The other group of filters consists of classical property filters designed to bias the chemical space of filtered libraries into a predetermined and desired direction. As stated above, the majority of such filters aim to define and narrow the scope of the library towards the drug-like paradigm. Property filters eliminate the extrema of undesired properties present in the libraries . The extrema are determined from distributions in databases of desired compounds (e.g., databases of approved drugs). After a careful analysis of the primary filter literature and the implementation of filters in existing bioinformatics software packages, KNIME was chosen as an open and accessible platform for the implementation of examined filters. Its intuitive workflow design, supported by a graphical interface, and its ability for large scale HTVS with the KNIME server makes it perfect for the integration in the established drug design workflows of users, be it ligand or structure-based drug design. KNIME allows users to create visual data flows, or pipelines, where data traverse multiple user-selected nodes. These nodes represent an essential part of KNIME, with each node possessing unique data processing capabilities, where the input and output of each node can transparently be analyzed . The workflows were created using KNIME version 4.2.3 (available at http://knime.org accessed on 17 November 2020). Additional expansion nodes from RDKit, MOE extensions, and Vernalis KNIME were used for the final version of the workflow alongside the default KNIME nodes. All the mentioned nodes are distributed as KNIME community extensions accessible to everyone in their full functionality. All nodes and workflows are open and editable by the user if he/she wishes to change certain parameters or develop novel filters. Experienced users can expand the meta nodes and delete redundant steps in the process (e.g., duplicate generation of the canonical SMILES in the linked workflow) when combining several filters for their drug design, which would result in even faster workflows . The node output can be edited to produce various outputs ranging from text and table formats to chemical library formats suitable for further drug design. We implemented 11 filters (REOS, PAINS, Aggregators, Rule-of-5, Rule-of-4, Rule-of-3, Veber filter, Mozziconacci filter, Egan filter, Van de Waterbeemd filter, Murcko filter) into our multi-filter KNIME workflow accessible at public repository ( https://gitlab.com/Jukic/knime_medchem_filters/ accessed on 15 January 2022). The PAINS and REOS filter are both based on the RDKit substructure counter and compare the substructures present in the input database with a list of problematic functional groups. A rule-based row filter removes the hits from the database. The aggregation propensity detection filter, called the “aggregator filter”, evaluates the aggregation propensity based on the similarity calculated by Tanimoto coefficients of given molecules to a database containing known aggregators . The user can personally control how strict the filter is with the low, medium, and high propensity filters provided. The remaining filters are knowledge-based rule-based filters that, when expanded, can often be modified by the user to suit his or her own needs. The filters are simple property counting filters that firstly calculate descriptor values using the RDKit Descriptor calculator node or the molecule properties (Mozziconacci) and then employ the rule-based row filters. The exception being the Rule-of-5 which allows one rule break, to incorporate the filter consisting of rule engines that assign the value of 1 for each rule break, with the math formula summing up all the values and the final rule-based row filter comparing the value to see. The impact of strict cut-offs that define specific chemical spaces and milder filters such as the Lipinski’s Rule-of-5 which allow a rule break can be seen in and .
After analyzing and implementing several molecular medicinal chemistry filters and testing the created workflows, we conclude that compound filters are essential for modern computer aided drug design (CADD). They provide the researcher with a simple, fast, and robust way to enrich the chemical space and to reduce the time associated with post-filtering methods. They are also easy to use and can be customized to particular preferences of the studied chemical space. However, the user must be aware of the properties used for filtering, as some, such as REOS and PAINS, were not designed with covalent chemistry in mind. In such cases, it is better to flag the compounds for a later evaluation. We firmly believe that this article provides medicinal chemistry community with a handful of useful workflows for novel drug design, identification, and HTVS, as well as with a good initial overview of compound filtering in drug discovery.
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Exploring and Accounting for Genetically Driven Effect Heterogeneity in Mendelian Randomization | 8472a3d7-90fd-4425-99d4-e6f200be7b30 | 11656040 | Pharmacology[mh] | Introduction Confirming or refuting causal relationships is difficult in observational study settings as one can never be sure if all confounders have been identified, appropriately measured and adjusted for. However, one can take advantage of random genetic inheritance from parents to offspring in an observational analysis to help uncover true causal mechanisms and estimate the causal effect of health interventions (Davey Smith, and Ebrahim ). Mendelian randomization (MR) is the formal science of using genetic variants as instrumental variables (IVs) for this purpose (Bowden and Holmes ). Rather than testing the direct association between an exposure and outcome, a genetically predicted exposure is used instead. Under the assumption of random distribution of genetic variants from parents to offspring at conception, an individual's genetically predicted exposure should be far less susceptible to confounding bias. MR requires three core assumptions to hold for a genetic variant, G , to be valid instrument to test for a causal relationship between a modifiable exposure and health outcome (Lawlor et al. ). These are termed the relevance assumption, the independence assumption and the exclusion restriction. To go beyond testing for causality, an additional assumption is required to estimate (or ‘point identify’) the causal effect. The most commonly used fourth assumption is homogeneity. It states that the causal effect an individual experiences is not affected by the value of their genetic instrument. When this is satisfied, an IV analysis can in theory estimate the average causal effect (ACE) of an intervention on the exposure for the entire study population. However, for continuous outcomes, this assumption is often biologically implausible unless a suitable ‘typical’ range for the exposure is defined (Hernán and Robins ). In cases where homogeneity is deemed implausible, an alternative assumption termed monotonicity can instead be applied to enable causal estimation (Bowden et al. ). In the context of an MR study using a genetic variant, G , monotonicity means that there is no individual whose exposure would be higher if they did not carry the exposure raising allele of G than if they did. Such individuals would be ‘Defiers’, and assuming that none exist allows the estimation of the causal effect in the subset of ‘Compliers’—defined as the group of individuals whose exposure level would always be greater with the exposure‐raising allele of G than without. Although homogeneity is typically invoked for interpretation of causal estimates in MR studies, in pharmacogenetic investigations genetic variants are explicitly sought to explain apparent heterogeneity in a treatment's effectiveness. For example, many pharmaceutical interventions are pro‐drugs, which require a specific metabolic process to occur for the patient to experience the full treatment effect. If the patient has a genetic variant that hinders the drug's metabolism (e.g., a ‘Loss‐of‐function’ [LoF] mutation), the treatment effect may be less pronounced in individuals who carry it. For example, Pilling et al. showed that CYP2C19 LoF alleles were associated with higher incidence of ischaemic events among those taking the commonly prescribed anti‐stroke drug, Clopidogrel. National Institute of Health and Care Excellence guidance now recommends genotyping individuals on Clopidogrel who experience an ischaemic event, with a view to altering their medication if the LoF variant is found (Advisory Committee of NICE .). Observational data can be used to quantify the extent of genetically driven treatment effect heterogeneity, but the analysis can be compromised by strong confounding by indication and off‐target genetic effects on the outcome of interest that are independent of any gene–drug interaction. A recently proposed method of pharmacogenetic causal inference using observational data—Triangulation Within a Study (TWIST) (Bowden et al. )—defined the assumptions required to estimate the difference in treatment effect estimates between those with and without a pharmacogenetic variant, as a measure of genetically driven effect heterogeneity. A range of different methods were proposed to estimate this quantity as well as a framework for combining them if sufficiently similar. Although it is a useful tool for estimating this difference, in its most basic form it cannot estimate the causal effect of treatment on the outcome in each genetic group, which is a limitation. Instances of genetically driven effect heterogeneity do exist in mainstream epidemiological investigations of nonpharmaceutical interventions. For example, smoking in pregnancy has been shown to have measurable consequences on offspring birth weight, which is an important marker of long‐term health (Pereira et al. ). Specifically, Freathy et al. show that single‐nucleotide polymorphism (SNP) rs1051730 on chromosome 15 is associated with smoking cessation during pregnancy as well as smoking quantity. However, the same SNP is not associated with smoking initiation. Therefore, mothers with the rs1051730 risk allele are not more likely to smoke than mothers without, but if they do smoke they tend to smoke more heavily than non‐carriers and find it harder to quit, meaning the effect of smoking on birth weight could easily be moderated by rs1051730. In this study, we review the standard MR method, which utilises homogeneity‐respecting genetic instruments, and the TWIST method, which utilises homogeneity‐violating instruments. We highlight the different conceptual starting points for each approach, in terms of their modelling assumptions, and how estimates are biased if these assumptions are violated. Subsequently, we explore the integration of both sets of instruments into a unified analysis to properly characterise the ACEs and genetically driven effect heterogeneity. Using data from the ALSPAC study, we apply our new method to estimate the causal effect of smoking on offspring birth weight in distinct genetic subgroups of pregnant mothers; the magnitude of the effect heterogeneity; and the potential public health impact of genetically targeted treatment going forward. Methods Let S and G be binary variables capturing the exposure and genetic variant of interest. In our applied example, S reflects the smoking status of the mother. We allow for the effect of the exposure on the outcome, Y , to be altered through an interaction with G , denoted as S * = G × S . To motivate the method, we assume the following linear interaction model for the mean outcome Y given S , G , and additionally measured ( Z ) and unmeasured ( U ) confounders of S and Y respectively: (1) E [ Y ∣ S , G , Z , U ] = γ 0 + β 1 S G + β 0 S ( 1 − G ) + γ Y G G + γ Y Z Z + γ Y U U = γ 0 + β 0 S + ( β 1 − β 0 ) S * + γ Y G G + γ Y Z Z + γ Y U U . Figure depicts the directed acyclic graph consistent with the model described in Equation and highlights various key assumptions using coloured arrows. We first consider the traditional set of assumptions required to estimate the ACE of the exposure on the outcome. We can express the ACE as the expected contrast between the potential outcomes of all mothers if they smoked during pregnancy, Y ( S = 1 ) , and if they did not, Y ( S = 0 ) : ACE = E [ Y ( S = 1 ) − Y ( S = 0 ) ] . These assumptions are (Hernán and Robins ): IV1 (relevance) : The genetic instrument G predicts the exposure S (orange arrow); IV2 (independence) : The genetic instrument G is independent of any confounders U ( no yellow arrow); IV3 (exclusion) : The genetic instrument G is independent of the outcome Y given the exposure S and any confounders U ( no green arrow). IV4 (homogeneity) : The effect of the exposure S on the outcome Y is independent of the genetic instrument G ( no grey arrow). Assumptions IV1–4 enable us to extract the ACE via an IV analysis, by turning the general model and causal diagram in Figure into the reduced model and causal diagram in Figure , through the following steps: E [ Y | S , G , Z , U ] = γ 0 + β 0 S + ( β 1 − β 0 ) S * + γ Y G G + γ Y Z Z + γ Y U U , (2) (from IV3) = γ 0 + β 0 S + ( β 1 − β 0 ) S * + γ Y Z Z + γ Y U U , (3) (from IV4) = γ 0 + β S + γ Y Z Z + γ Y U U , (4) (from IV1 and IV2) = γ 0 + β S ˆ + γ Y Z Z + ϵ Y , where S ˆ = E [ S ∣ G ] and ϵ Y is a residual error term that is crucially independent of S ˆ . The reduced causal diagram in Figure is often shown in MR studies. 2.1 What Does an MR Analysis Estimate Under Violation of IV2–4? We now consider what is targeted by the Wald ratio estimate for the causal effect in an MR analysis, assuming the data model described in Equation , when IV1 holds but initially, assumptions IV2–V4 do not. Note that we do not include the measured confounder Z in the following derivations as any bias through Z can be adjusted for. Under our assumed model as described in Equation , the Wald ratio estimate can be expressed as: (5) C o v ( G , Y ) C o v ( G , S ) = β 1 E [ S ∣ G = 1 ] − β 0 E [ S ∣ G = 0 ] E [ S ∣ G = 1 ] − E [ S ∣ G = 0 ] + γ Y G + γ Y U ( E [ U ∣ G = 1 ] − E [ U ∣ G = 0 ] ) E [ S ∣ G = 1 ] − E [ S ∣ G = 0 ] = β 1 E [ S ∣ G = 1 ] − β 0 E [ S ∣ G = 0 ] E [ S ∣ G = 1 ] − E [ S ∣ G = 0 ] + B . IV1 guarantees that the denominator of Equation is nonzero and so the ratio terms are well defined. If homogeneity is violated, but monotonicity holds, we show in Supporting Information that Equation equals the Complier Average Causal Effect (CACE) plus any bias due to violation of IV2 and IV3 (B term). Compliers are defined as individuals that smoke if they have the risk allele ( G = 1 ) and do not smoke if they do not have it ( G = 0 ). 2.2 Genetically Moderated Exposure Effect (GMEE) Genetic instruments that satisfy the homogeneity assumption enable estimation of the ACE. However, in studies into the consequences of smoking versus not smoking, this assumption will be demonstrably false if attempting the analysis with a SNP like rs1051730, since the smoking patterns of people with and without this variant are likely to be different. In this case, a more practical starting point would be to assume the underlying DAG structure in Figure and aim to quantify the magnitude of homogeneity violation as the difference in smoking effects between the two genetic sub‐groups. This ‘genetically moderated exposure effect’ (GMEE) is represented by arrow between S * and the outcome Y . From Equation this is equal to β 1 − β 0 . Bowden et al. discuss various methods for estimating this quantity, which we refer to as the GMEE, but which they referred to as the GMTE (T being for treatment). Each of the methods presented in Bowden et al. relies on a different set of assumptions. For example, when the genetic instrument G is independent of the exposure (i.e., no orange arrow in Figure due to violation of IV1), is independent of any unmeasured confounder (i.e., no yellow arrow in Figure and IV2 satisfied), and only affects the outcome through the moderated exposure variable (i.e., no green arrow in Figure and IV3 satisfied), the GMEE can be estimated in the exposed population only. In our setting, this would be estimated by the difference in mean outcomes across the genetic groups among the population of smokers only: GMEE ( 1 ) = E ˆ [ Y ∣ S = 1 , G = 1 ] − E ˆ [ Y ∣ S = 1 , G = 0 ] . Here the ‘(1)’ notation reminds the analyst that only smoker's data is used and G = 1 ∕ 0 refers to the presence/absence of at least one risk allele of SNP rs1051730. A more robust estimate of the GMEE is the RGMEE = GMEE ( 1 ) − GMEE ( 0 ) , where GMEE ( 0 ) = E ˆ [ Y ∣ S = 0 , G = 1 ] − E ˆ [ Y ∣ S = 0 , G = 0 ] . Here, the R prefix in RGMEE stands for ‘robust’, since it can estimate the GMEE without bias even if IV3 is violated (i.e., G affects the outcome directly as indicated by the green arrow in Figure ). Indeed it is this bias term that is estimated by GMEE(0) before being subtracted out. Bowden et al. state that the RGMEE is unbiased even if the genetic instrument violates IV2, by being associated with the outcome through the unmeasured confounder (yellow arrow in Figure ). Our investigations in this paper have shown this to be incorrect (see Supporting Information: Section ). Nevertheless, this actually makes it more straightforward to verify if the assumptions for the RGEE hold (i.e., a desired violation of IV1 but no violation of IV2), since they imply that G and S are independent. Testing for an association between G and S is therefore an important prerequisite for its use. What Does an MR Analysis Estimate Under Violation of IV2–4? We now consider what is targeted by the Wald ratio estimate for the causal effect in an MR analysis, assuming the data model described in Equation , when IV1 holds but initially, assumptions IV2–V4 do not. Note that we do not include the measured confounder Z in the following derivations as any bias through Z can be adjusted for. Under our assumed model as described in Equation , the Wald ratio estimate can be expressed as: (5) C o v ( G , Y ) C o v ( G , S ) = β 1 E [ S ∣ G = 1 ] − β 0 E [ S ∣ G = 0 ] E [ S ∣ G = 1 ] − E [ S ∣ G = 0 ] + γ Y G + γ Y U ( E [ U ∣ G = 1 ] − E [ U ∣ G = 0 ] ) E [ S ∣ G = 1 ] − E [ S ∣ G = 0 ] = β 1 E [ S ∣ G = 1 ] − β 0 E [ S ∣ G = 0 ] E [ S ∣ G = 1 ] − E [ S ∣ G = 0 ] + B . IV1 guarantees that the denominator of Equation is nonzero and so the ratio terms are well defined. If homogeneity is violated, but monotonicity holds, we show in Supporting Information that Equation equals the Complier Average Causal Effect (CACE) plus any bias due to violation of IV2 and IV3 (B term). Compliers are defined as individuals that smoke if they have the risk allele ( G = 1 ) and do not smoke if they do not have it ( G = 0 ). Genetically Moderated Exposure Effect (GMEE) Genetic instruments that satisfy the homogeneity assumption enable estimation of the ACE. However, in studies into the consequences of smoking versus not smoking, this assumption will be demonstrably false if attempting the analysis with a SNP like rs1051730, since the smoking patterns of people with and without this variant are likely to be different. In this case, a more practical starting point would be to assume the underlying DAG structure in Figure and aim to quantify the magnitude of homogeneity violation as the difference in smoking effects between the two genetic sub‐groups. This ‘genetically moderated exposure effect’ (GMEE) is represented by arrow between S * and the outcome Y . From Equation this is equal to β 1 − β 0 . Bowden et al. discuss various methods for estimating this quantity, which we refer to as the GMEE, but which they referred to as the GMTE (T being for treatment). Each of the methods presented in Bowden et al. relies on a different set of assumptions. For example, when the genetic instrument G is independent of the exposure (i.e., no orange arrow in Figure due to violation of IV1), is independent of any unmeasured confounder (i.e., no yellow arrow in Figure and IV2 satisfied), and only affects the outcome through the moderated exposure variable (i.e., no green arrow in Figure and IV3 satisfied), the GMEE can be estimated in the exposed population only. In our setting, this would be estimated by the difference in mean outcomes across the genetic groups among the population of smokers only: GMEE ( 1 ) = E ˆ [ Y ∣ S = 1 , G = 1 ] − E ˆ [ Y ∣ S = 1 , G = 0 ] . Here the ‘(1)’ notation reminds the analyst that only smoker's data is used and G = 1 ∕ 0 refers to the presence/absence of at least one risk allele of SNP rs1051730. A more robust estimate of the GMEE is the RGMEE = GMEE ( 1 ) − GMEE ( 0 ) , where GMEE ( 0 ) = E ˆ [ Y ∣ S = 0 , G = 1 ] − E ˆ [ Y ∣ S = 0 , G = 0 ] . Here, the R prefix in RGMEE stands for ‘robust’, since it can estimate the GMEE without bias even if IV3 is violated (i.e., G affects the outcome directly as indicated by the green arrow in Figure ). Indeed it is this bias term that is estimated by GMEE(0) before being subtracted out. Bowden et al. state that the RGMEE is unbiased even if the genetic instrument violates IV2, by being associated with the outcome through the unmeasured confounder (yellow arrow in Figure ). Our investigations in this paper have shown this to be incorrect (see Supporting Information: Section ). Nevertheless, this actually makes it more straightforward to verify if the assumptions for the RGEE hold (i.e., a desired violation of IV1 but no violation of IV2), since they imply that G and S are independent. Testing for an association between G and S is therefore an important prerequisite for its use. Enhancing Robustness Through the Integration of MR and GMEE Methods The genetically moderated exposure effect introduced in the previous section proposes an array of methods for estimating the difference β 1 − β 0 under different assumptions, but not the individual values β 1 and β 0 . To address this, we now formally extend the previous framework by incorporating a second variant, G 2 , that is a ‘standard’ instrument for the exposure satisfying assumptions IV1–4. In our case, it therefore influences smoking initiation directly, but does not moderate an individual's smoking habits, thereby violating homogeneity. We now explore two scenarios that expand upon the standard TWIST approach, utilising novel methods that leverage the two available genetic instruments. The DAGs for these two separate methods are shown in Figure . 3.1 Method 1: ( G , G 2 ) are Jointly Valid Instruments for ( S , S * ) We first consider estimation of β 1 and β 0 using genetic instruments G and G 2 within a multivariable model. This can be enacted in a two‐step procedure by using G and G 2 to predict S in stage 1, and then plugging in the predicted values into the linear interaction model in stage 2: 1. Stage 1: Estimate S ˆ with a consistent estimate of E ˆ [ S ∣ G , G 2 ] ; 2. Stage 2: Y = γ 0 + β 1 S ˆ G + β 0 S ˆ ( 1 − G ) , with S ˆ being the fitted values from stage 1. This approach is robust to the case where the (assumed) effect modifying variant G violates IV1 but satisfies IV2 and IV3 (Figure , Left). In Supporting Information, we show through simulation that the true values of β 1 and β 0 can be recovered when these assumptions are satisfied, but violation of the assumptions lead to bias. The standard error for β 1 and β 0 can be obtained directly from the linear model output. As both parameters are estimated in the same model we can use the covariance matrix of β 1 and β 0 to derive the variance of β 1 − β 0 and hence can estimate the standard error for β 1 − β 0 . 3.2 Method 2: Allowing for a Pleiotropic Effect of G on Y We now propose a robust procedure that combines the general MR approach with the RGMEE given in Bowden et al. . We first apply the RGMEE method to consistently estimate the genetically moderated effect β 1 − β 0 . We then define a new variable Y ( S * = 0 ) created by subtracting the genetically moderated effect times the moderated exposure from the original outcome Y . More formally, Y ( S * = 0 ) is a potential outcome in which the treatment effect of S * on Y has been set to zero. It is equal to Y (and therefore observed) for individuals with an S * = 0, but is unobserved for those with S * = 1. Finally, we perform an MR analysis using the genetic instrument G 2 , the exposure S and Y ( S * = 0 ) . This enables estimation of β 0 , which can then be used in combination with the RMGEE to estimate β 1 : 1. Estimate the RGMEE ( β 1 − β 0 ) ^ using G ; 2. Estimate S ˆ with a consistent estimate of E ˆ [ S ∣ G 2 ] ; 3. Estimate β 0 from model E [ Y ( S * = 0 ) ∣ S ˆ ] = γ 0 + β 0 S ˆ , where Y ( S * = 0 ) = Y − ( β 1 − β 0 ) ^ S * . Method 2 delivers consistent estimates if the RGMEE estimate can be consistently estimated using G and β 0 can be consistently determined using G 2 once the GMEE effect has been removed. Compared to Method 1, it allows a direct pleioptropic effect of G on Y (IV3 violation) but requires G to be independent of S (IV1 violated, but IV2 satisfied). When these assumptions are not met, our simulations show that it leads to bias (see Supporting Information). The standard error for β 0 and β 1 − β 0 can be directly taken from the respective model output. We make the assumption that β 1 − β 0 ^ is independent of β ˆ 0 , so that S.E( β 1 ) ≈ V a r ( β 1 − β 0 ^ ) + V a r ( β ˆ 0 ) . In simulations we show that it leads to confidence intervals (CI) with only a slightly conservative coverage. 3.3 What Does the Standard MR Estimate Using G 2 as the IV Target? When including a homogeneity respecting instrument as shown in Figure , a standard MR analysis with G 2 as the IV is possible. Using the two‐stage regression approach means: 1. Stage 1: Estimate S ˆ with a consistent estimate of E ˆ [ S ∣ G 2 ] ; 2. Stage 2: Y = α 0 + α 1 S ˆ + α 2 Z + ϵ Y . Here, α 1 is the ACE on the outcome Y if all mothers where exposed compared to if all mothers were not exposed: E [ Y ( S = 1 ) ] − E [ Y ( S = 0 ) ] . It can be shown that under the model described in Figure and Equation : (6) α 1 = β 0 + ( β 1 − β 0 ) E [ G ∣ S = 1 ] . Method 1: ( G , G 2 ) are Jointly Valid Instruments for ( S , S * ) We first consider estimation of β 1 and β 0 using genetic instruments G and G 2 within a multivariable model. This can be enacted in a two‐step procedure by using G and G 2 to predict S in stage 1, and then plugging in the predicted values into the linear interaction model in stage 2: 1. Stage 1: Estimate S ˆ with a consistent estimate of E ˆ [ S ∣ G , G 2 ] ; 2. Stage 2: Y = γ 0 + β 1 S ˆ G + β 0 S ˆ ( 1 − G ) , with S ˆ being the fitted values from stage 1. This approach is robust to the case where the (assumed) effect modifying variant G violates IV1 but satisfies IV2 and IV3 (Figure , Left). In Supporting Information, we show through simulation that the true values of β 1 and β 0 can be recovered when these assumptions are satisfied, but violation of the assumptions lead to bias. The standard error for β 1 and β 0 can be obtained directly from the linear model output. As both parameters are estimated in the same model we can use the covariance matrix of β 1 and β 0 to derive the variance of β 1 − β 0 and hence can estimate the standard error for β 1 − β 0 . Method 2: Allowing for a Pleiotropic Effect of G on Y We now propose a robust procedure that combines the general MR approach with the RGMEE given in Bowden et al. . We first apply the RGMEE method to consistently estimate the genetically moderated effect β 1 − β 0 . We then define a new variable Y ( S * = 0 ) created by subtracting the genetically moderated effect times the moderated exposure from the original outcome Y . More formally, Y ( S * = 0 ) is a potential outcome in which the treatment effect of S * on Y has been set to zero. It is equal to Y (and therefore observed) for individuals with an S * = 0, but is unobserved for those with S * = 1. Finally, we perform an MR analysis using the genetic instrument G 2 , the exposure S and Y ( S * = 0 ) . This enables estimation of β 0 , which can then be used in combination with the RMGEE to estimate β 1 : 1. Estimate the RGMEE ( β 1 − β 0 ) ^ using G ; 2. Estimate S ˆ with a consistent estimate of E ˆ [ S ∣ G 2 ] ; 3. Estimate β 0 from model E [ Y ( S * = 0 ) ∣ S ˆ ] = γ 0 + β 0 S ˆ , where Y ( S * = 0 ) = Y − ( β 1 − β 0 ) ^ S * . Method 2 delivers consistent estimates if the RGMEE estimate can be consistently estimated using G and β 0 can be consistently determined using G 2 once the GMEE effect has been removed. Compared to Method 1, it allows a direct pleioptropic effect of G on Y (IV3 violation) but requires G to be independent of S (IV1 violated, but IV2 satisfied). When these assumptions are not met, our simulations show that it leads to bias (see Supporting Information). The standard error for β 0 and β 1 − β 0 can be directly taken from the respective model output. We make the assumption that β 1 − β 0 ^ is independent of β ˆ 0 , so that S.E( β 1 ) ≈ V a r ( β 1 − β 0 ^ ) + V a r ( β ˆ 0 ) . In simulations we show that it leads to confidence intervals (CI) with only a slightly conservative coverage. What Does the Standard MR Estimate Using G 2 as the IV Target? When including a homogeneity respecting instrument as shown in Figure , a standard MR analysis with G 2 as the IV is possible. Using the two‐stage regression approach means: 1. Stage 1: Estimate S ˆ with a consistent estimate of E ˆ [ S ∣ G 2 ] ; 2. Stage 2: Y = α 0 + α 1 S ˆ + α 2 Z + ϵ Y . Here, α 1 is the ACE on the outcome Y if all mothers where exposed compared to if all mothers were not exposed: E [ Y ( S = 1 ) ] − E [ Y ( S = 0 ) ] . It can be shown that under the model described in Figure and Equation : (6) α 1 = β 0 + ( β 1 − β 0 ) E [ G ∣ S = 1 ] . Simulation Results 4.1 Data Generation We simulated data consistent with Figure in the following manner: (7) G ~ ℬ ( 0.55 ) , G 2 ~ ℬ ( 0.4 ) , U = γ U G G + N ( 0 , 1 ) , η = − 2 + γ S G G + γ S G 2 G 2 + γ S U U + N ( 0 , 0.5 ) , p S = exp ( η ) 1 + exp ( η ) , S ~ ℬ ( p S ) , Y = 3500 + β 1 S G + β 0 S ( 1 − G ) + γ Y G G + γ Y U U + N ( 0 , 470 ) . The outcome (Y) model in was chosen so that simulated data closely matched real birth weight data (in grams) for mothers with a history of smoking in the Avon Longitudinal Study of Parents and Children (ALSPAC) (Boyd et al. ; Fraser et al. ; Northstone et al. ) which we will subsequently use in our applied analysis. By choosing zero and nonzero values for the parameters γ U G , γ S G and γ Y G , we were able to explore the performance of Methods 1 and 2 in estimating the causal effect parameters β 1 and β 0 . We choose to set β 1 = − 200 and β 0 = − 100 , which assumes a genetically moderated effect of β 1 − β 0 = − 100 g. For all simulations, we made sure that the assumptions of Methods 1 and 2 held. Each simulation was repeated N = 20 , 000 times, which enabled the calculation of bias, coverage and statistical power. For further details, Supporting Information Table provides a summary of the simulated data under all of the explored scenarios. 4.2 Estimation Accuracy With Increasing Sample Size We investigated the sample size needed to unbiasedly estimate β 1 and β 0 using each approach when their respective assumptions were satisfied. Data sets were generated with sample sizes between 100 and 80,000 individuals. Figure shows the mean values of β 1 , β 0 and β 1 − β 0 using Methods 1 and 2 across 20,000 simulations. Shaded areas reflect, for each mean parameter estimate obtained from a given sample size, a 95% CI calculated as ± 1.96 × S D around the mean, S D being the standard deviation of the 20,000 estimates (Morris et al. ). Note that for each estimate we display three subfigures (per column) with a different range on the y ‐axis: one for small sample sizes, one for medium size sample sizes and one for large sample sizes. Details on the parameter values used for each simulation are described in Supporting Information. Estimation of β 1 and β 0 become more precise with shrinking CIs as the sample size increases. Both, Methods 1 and 2 lead to similar results. However, for small sample sizes (below 2000), CIs for Method 1 estimates are wider. The third column of Figure shows the mean estimates for β 1 − β 0 using Methods 1 and 2. Here we can see a distinction in the performance of the methods across all sample sizes. Method 2, which uses the RGMEE method, yields narrower CIs for β 1 − β 0 ^ even for small sample sizes. 4.3 Power and the coverage We estimated the power to reject the null hypothesis that β 1 , β 0 and β 1 − β 0 were statistically different from zero at the 5% significance level, using Methods 1 and 2. For each simulation, we also calculated CIs for the parameter estimates based on estimated standard errors, and reported the coverage of 95% CIs across the 20,000 simulations. The results for power and coverage are shown in Figures and respectively, along with their Monte‐Carlo standard errors (Morris et al. ). Our results show that Method 2 results in a higher power when estimating β 0 and β 1 − β 0 than Method 1 for a given sample size. However, when considering estimation of β 1 , this is reversed. The power to detect β 0 is lower than the power to detect β 1 due to its lower effect size of −100 (compared to β 1 = − 200 ). Figure reveals a near nominal coverage for both methods close to 95%. Crucially, our assumption that β 1 − β 0 ^ and β ˆ 0 are independent leads only to a slightly conservative coverage when estimating a CIs for β 1 with Method 2. Data Generation We simulated data consistent with Figure in the following manner: (7) G ~ ℬ ( 0.55 ) , G 2 ~ ℬ ( 0.4 ) , U = γ U G G + N ( 0 , 1 ) , η = − 2 + γ S G G + γ S G 2 G 2 + γ S U U + N ( 0 , 0.5 ) , p S = exp ( η ) 1 + exp ( η ) , S ~ ℬ ( p S ) , Y = 3500 + β 1 S G + β 0 S ( 1 − G ) + γ Y G G + γ Y U U + N ( 0 , 470 ) . The outcome (Y) model in was chosen so that simulated data closely matched real birth weight data (in grams) for mothers with a history of smoking in the Avon Longitudinal Study of Parents and Children (ALSPAC) (Boyd et al. ; Fraser et al. ; Northstone et al. ) which we will subsequently use in our applied analysis. By choosing zero and nonzero values for the parameters γ U G , γ S G and γ Y G , we were able to explore the performance of Methods 1 and 2 in estimating the causal effect parameters β 1 and β 0 . We choose to set β 1 = − 200 and β 0 = − 100 , which assumes a genetically moderated effect of β 1 − β 0 = − 100 g. For all simulations, we made sure that the assumptions of Methods 1 and 2 held. Each simulation was repeated N = 20 , 000 times, which enabled the calculation of bias, coverage and statistical power. For further details, Supporting Information Table provides a summary of the simulated data under all of the explored scenarios. Estimation Accuracy With Increasing Sample Size We investigated the sample size needed to unbiasedly estimate β 1 and β 0 using each approach when their respective assumptions were satisfied. Data sets were generated with sample sizes between 100 and 80,000 individuals. Figure shows the mean values of β 1 , β 0 and β 1 − β 0 using Methods 1 and 2 across 20,000 simulations. Shaded areas reflect, for each mean parameter estimate obtained from a given sample size, a 95% CI calculated as ± 1.96 × S D around the mean, S D being the standard deviation of the 20,000 estimates (Morris et al. ). Note that for each estimate we display three subfigures (per column) with a different range on the y ‐axis: one for small sample sizes, one for medium size sample sizes and one for large sample sizes. Details on the parameter values used for each simulation are described in Supporting Information. Estimation of β 1 and β 0 become more precise with shrinking CIs as the sample size increases. Both, Methods 1 and 2 lead to similar results. However, for small sample sizes (below 2000), CIs for Method 1 estimates are wider. The third column of Figure shows the mean estimates for β 1 − β 0 using Methods 1 and 2. Here we can see a distinction in the performance of the methods across all sample sizes. Method 2, which uses the RGMEE method, yields narrower CIs for β 1 − β 0 ^ even for small sample sizes. Power and the coverage We estimated the power to reject the null hypothesis that β 1 , β 0 and β 1 − β 0 were statistically different from zero at the 5% significance level, using Methods 1 and 2. For each simulation, we also calculated CIs for the parameter estimates based on estimated standard errors, and reported the coverage of 95% CIs across the 20,000 simulations. The results for power and coverage are shown in Figures and respectively, along with their Monte‐Carlo standard errors (Morris et al. ). Our results show that Method 2 results in a higher power when estimating β 0 and β 1 − β 0 than Method 1 for a given sample size. However, when considering estimation of β 1 , this is reversed. The power to detect β 0 is lower than the power to detect β 1 due to its lower effect size of −100 (compared to β 1 = − 200 ). Figure reveals a near nominal coverage for both methods close to 95%. Crucially, our assumption that β 1 − β 0 ^ and β ˆ 0 are independent leads only to a slightly conservative coverage when estimating a CIs for β 1 with Method 2. Applied Example 5.1 Biological Example for Genetically Driven Exposure Effects Research into the adverse consequences of smoking has been ongoing since the 1950s, up until the present day (Doll and Hill ; U.S. Department of Health and Human Services ). In the specific context of maternal health, it is well established that smoking during pregnancy is associated with lower offspring birth weight, which is itself an important predictor of infant mortality and many later life health outcomes, such as cardiovascular disease, high blood pressure, coronary heart disease and type 2 diabetes (Moen et al. ; Tyrrell et al. ; Warrington et al. ). Attributing the correct proportion of these estimated associations that are due to the causal consequences of smoking is not straightforward, due to strong confounding between smoking and later life outcomes by socioeconomic factors which are very hard to completely control for. Despite this, smoking is viewed as a key modifiable risk factor, and reducing its prevalence during pregnancy remains an important public health target (Cnattingius ; U.S. Department of Health and Human Services ). Unfortunately, NHS digital service statistics indicate that approximately 8.6% of UK mothers were known smokers at the time of delivery in the first half of 2023 (Population Health, Clinical Audit, Team, Specialist Care, & Lead Analyst: Walt Treloar ). Identifying which individuals are at a higher risk of not giving up smoking and therefore might face more severe pregnancy outcomes can be crucial when targeting smoking cessation programmes, to provide support as well as closer monitoring during pregnancy. Recently, genome‐wide association studies (GWAS) have identified genetic variants that are associated with smoking initiation, smoking cessation, the age of starting smoking and smoking quantity (Liu et al. ). Freathy et al. show that rs1051730 on chromosome 15 is associated with smoking cessation during pregnancy as well as smoking quantity. A strong biological rationale for this exists as rs1051730 is in the nicotine acetylcholine receptor gene cluster CHRNA5‐CHRNA3‐CHRNB4 . Rare variant burden associations have implicated all three of these genes as important in influencing smoking quantity (Rajagopal et al. ). However, it has also been shown that rs1051730 is not associated with smoking initiation (Freathy et al. ). The methods we have introduced thus far appear well suited to estimating the causal effect of smoking on birth weight using traditional genetic instruments for smoking initiation, whilst at the same time, quantifying the genetically moderated smoking effect via rs1051730. 5.2 The Effect of Smoking on Birth Weight in the ALSPAC Study The Avon Longitudinal Study of Parents and Children (ALSPAC) (Boyd et al. ; Fraser et al. ; Northstone et al. ) invited pregnant women resident in Avon, UK with expected dates of delivery between 1 April 1991 and 31 December 1992, to take part in the study. The initial number of pregnancies enrolled was 14,541. Of the initial pregnancies, there was a total of 14,676 foetuses, resulting in 14,062 live births and 13,988 children who were alive at 1 year. We restricted our analysis to unrelated mothers with available genetic information. Additionally, we excluded multiple births and preterm births (pregnancy duration ≤ 37 weeks) (Jaitner et al. ). The analysis data set had a sample size of 7752 individual mothers. For the traditional genetic instrument ‘ G 2 ’ we created a weighted genetic risk score (GRS) among the smoking initiation SNPs identified by the latest GWAS (Liu et al. ). The effect sizes from the same GWAS were used as weights. We used rs1051730 as genetic effect‐modifying instrument ‘ G ’, coded as 0 and 1 corresponding to no and at least one risk allele respectively. Various different smoking definitions were used for the exposure outlined in the following sections. The ALSPAC study website contains details of all the data that are available through a fully searchable data dictionary and variable search tool ( http://www.bristol.ac.uk/alspac/researchers/our-data/ ). 5.2.1 Exposure S is Smoking Before Pregnancy Each mother was asked at 16–18 weeks of gestation whether she smoked before pregnancy. We coded mothers that reported ‘yes’ as S = 1 and mothers who reported ‘no’ as S = 0 . Figure displays the assumed DAG for our analysis. We aimed to apply Methods 1 and 2 to estimate the causal effect of pre‐pregnancy smoking on birth weight in the G = 1 group, β 1 , the G =0 group, β 0 , and also the genetically moderated exposure effect β 1 − β 0 . We would expect this latter quantity to be nonzero if the pre‐pregnancy smoking effect persisted differently throughout pregnancy across the two genetic groups. For the first stage of Method 1, we perform a logistic regression of S on the GRS of smoking initiation ( G 2 ) and rs1051730 ( G ). The results are shown in Table . Variant rs1051730 was not associated with smoking before pregnancy, which helpfully means that Method 2 is not ruled out as an analysis option. The GRS is also associated with smoking before pregnancy and we assume it acts as a true IV for this exposure. Two crucial assumptions are that the GRS of smoking initiation has no pleiotropic effect on birth weight and it does not modify the the causal effect between smoking and birth weight in the exposed and the unexposed. To apply Method 1, rs1051730 cannot have a pleiotropic effect on birth weight either but, for Method 2, this assumption is relaxed. The results from applying both methods are shown in Figure . To increase the precision of the estimates we adjust our regression models for different sets of covariates. The model for the genetic prediction of smoking is adjusted for whether the partner of the mother smoked, the mothers' age and the first 10 genetic principal components. The model predicting birth weight is adjusted for offspring sex, mother age, mothers' height, parity, mothers' prepregnancy weight and the first 10 genetic principal components. We viewed these variables as confounders for either smoking before pregnancy or birth weight or both. For mothers who have at least one G risk allele, the ACE of smoking before pregnancy, β 1 , was estimated to be a 168 and 169 g reduction in birth weight using Methods 1 and 2 respectively. On the other hand, the corresponding causal effect ( β 0 ) in smoking mothers without a rs1051730 risk allele is a 159 and 161 g birth weight reduction for Methods 1 and 2 respectively compared to nonsmoking mothers without the risk allele. 5.2.2 Exposure S is Smoking in the First 3 Months of Pregnancy Mothers were asked at 16–18 weeks of gestation whether they smoked in the first 3 months of pregnancy. We coded those that reported ‘yes’ as S = 1 and mothers who reported ‘no’ as S = 0 and proceeded to estimate the causal effect of this exposure at the two levels (no and at least one risk allele) of rs1051730 and the genetically moderated exposure effect. In this analysis, no information about smoking before pregnancy was taken into account and therefore the mothers reporting to be non‐smokers either gave up smoking when getting pregnant or did not smoke before pregnancy. Figure displays the assumed DAG for our analysis. Methods 1 and 2 were applied to derive estimates for β 1 , β 0 and β 1 − β 0 , with results shown in Table and Figure . For these analyses, the logistic model for S given G and G 2 was adjusted for whether the partner of the mother smokes, the mothers' age, parity and the first 10 genetic principal components. The model predicting birth weight was adjusted for offspring sex, mothers' age, mothers' height, parity, mothers' prepregnancy weight and the first 10 genetic principal components. We viewed these variables as confounders for either smoking before pregnancy or birth weight or both. For this analysis, a stronger association was observed between rs1051730 and S , meaning that we were cautious with the interpretation of Method 2 results, given its crucial role in the in estimation of the genetically moderated exposure effect. The causal effect of smoking during pregnancy on birth weight in those with a rs1051730 risk allele, β 1 , was estimated to be −212 and −222 g using Methods 1 and 2 respectively, whereas the effect of smoking in the individuals without the genetic variant on birth weight is −220 and −205 g for Method 1 and 2 respectively. For both smoking exposures, we were unable to identify a difference between the two genetic groups. This could be because the effect of pre‐pregnancy smoking and smoking in the first 3 months does not truly differ in people with or without variant rs1051730. However, a simulation investigation showed that large numbers of individuals would be required to identify a genetically driven effect of smoking with the magnitudes we observed in our analysis. Specifically, we simulated data with sample sizes from 7000 to 500,000 and a true difference between the genetic groups of β 1 − β 0 between −20 and −5 g. To reach 80% power in estimating β 1 − β 0 = − 15 a sample size of 500,000 individuals is required in our simulation when using Method 1. The RGMEE (Method 2), which is more robust to pleiotropy compared to Method 1, is able to estimate β 1 − β 0 = − 15 with a power of over 80% with 200,000 individuals. More details on the results of this are shown in Supporting Information: Section . This provides important guidance on the much larger sample size, way beyond the 7752 mothers in the ALSPAC study, that would be required to detect a difference between the genetic groups in the region of what we observe here. Despite not being able to detect a statistically meaningful genetically moderated exposure effect, overall our results suggest that smoking before pregnancy or smoking in the first 3 months of pregnancy results in a lower birth weight compared to not smoking. This is in line with previous publications (Larsen et al. ; Tyrrell et al. ; Pereira et al. ). 5.3 Observational Analysis and ‘Standard’ MR In addition to applying the new proposed methods to the ALSPAC data set, we also looked at the observational association between smoking and birth weight. A linear regression of S (using both smoking definitions) on birth weight adjusted for partner smoking, mothers' age, mothers' height, mothers' pre‐pregnancy weight, parity and offspring sex yielded negative associational estimates. Although the observational analysis likely suffers from residual confounding, and cannot be interpreted as a causal effect, the direction of effect remains the same compared to estimating β 1 and β 0 (Figure ), albeit of a smaller magnitude. As indicated in Table , rs1051730 ( G ) is not associated with smoking before pregnancy. Therefore, we are unable to perform a standard MR analysis using G as the genetic instrument for S = smoking before pregnancy. However, we did perform a standard MR analysis using individual level data and the two‐stage least squares approach with S = smoking in the first 3 months of pregnancy. We explain in Section that the standard MR with a homogeneity violating instrument like rs1051730 estimates the CACE (while the monotonicity assumption holds). The CACE of smoking in the first 3 months of pregnancy on birth weight is −210 g (95% CI: [−293,−128]). Note that these results potentially suffer from weak instrument bias. Additionally, and as a confirmatory test of our previous genetic analyses, we calculated a standard MR estimate using the GRS for smoking initiation to instrument smoking before and smoking in the first 3 months of pregnancy. For these analyses rs1051730 is not considered. The methodology is described in Section . Using Equation , the estimated β ‐values obtained from applying Method 1 and 2 to the ALSPAC data we derive an ACE of −165 g for smoking before pregnancy. This compares to the ACE of −164 g (Figure ) estimated with standard MR approach. Similarly, for smoking in the first 3 months of pregnancy we obtain an estimate of −213 g using the β ‐values from Methods 1 or 2 outputs and the formula provided in Equation . Biological Example for Genetically Driven Exposure Effects Research into the adverse consequences of smoking has been ongoing since the 1950s, up until the present day (Doll and Hill ; U.S. Department of Health and Human Services ). In the specific context of maternal health, it is well established that smoking during pregnancy is associated with lower offspring birth weight, which is itself an important predictor of infant mortality and many later life health outcomes, such as cardiovascular disease, high blood pressure, coronary heart disease and type 2 diabetes (Moen et al. ; Tyrrell et al. ; Warrington et al. ). Attributing the correct proportion of these estimated associations that are due to the causal consequences of smoking is not straightforward, due to strong confounding between smoking and later life outcomes by socioeconomic factors which are very hard to completely control for. Despite this, smoking is viewed as a key modifiable risk factor, and reducing its prevalence during pregnancy remains an important public health target (Cnattingius ; U.S. Department of Health and Human Services ). Unfortunately, NHS digital service statistics indicate that approximately 8.6% of UK mothers were known smokers at the time of delivery in the first half of 2023 (Population Health, Clinical Audit, Team, Specialist Care, & Lead Analyst: Walt Treloar ). Identifying which individuals are at a higher risk of not giving up smoking and therefore might face more severe pregnancy outcomes can be crucial when targeting smoking cessation programmes, to provide support as well as closer monitoring during pregnancy. Recently, genome‐wide association studies (GWAS) have identified genetic variants that are associated with smoking initiation, smoking cessation, the age of starting smoking and smoking quantity (Liu et al. ). Freathy et al. show that rs1051730 on chromosome 15 is associated with smoking cessation during pregnancy as well as smoking quantity. A strong biological rationale for this exists as rs1051730 is in the nicotine acetylcholine receptor gene cluster CHRNA5‐CHRNA3‐CHRNB4 . Rare variant burden associations have implicated all three of these genes as important in influencing smoking quantity (Rajagopal et al. ). However, it has also been shown that rs1051730 is not associated with smoking initiation (Freathy et al. ). The methods we have introduced thus far appear well suited to estimating the causal effect of smoking on birth weight using traditional genetic instruments for smoking initiation, whilst at the same time, quantifying the genetically moderated smoking effect via rs1051730. The Effect of Smoking on Birth Weight in the ALSPAC Study The Avon Longitudinal Study of Parents and Children (ALSPAC) (Boyd et al. ; Fraser et al. ; Northstone et al. ) invited pregnant women resident in Avon, UK with expected dates of delivery between 1 April 1991 and 31 December 1992, to take part in the study. The initial number of pregnancies enrolled was 14,541. Of the initial pregnancies, there was a total of 14,676 foetuses, resulting in 14,062 live births and 13,988 children who were alive at 1 year. We restricted our analysis to unrelated mothers with available genetic information. Additionally, we excluded multiple births and preterm births (pregnancy duration ≤ 37 weeks) (Jaitner et al. ). The analysis data set had a sample size of 7752 individual mothers. For the traditional genetic instrument ‘ G 2 ’ we created a weighted genetic risk score (GRS) among the smoking initiation SNPs identified by the latest GWAS (Liu et al. ). The effect sizes from the same GWAS were used as weights. We used rs1051730 as genetic effect‐modifying instrument ‘ G ’, coded as 0 and 1 corresponding to no and at least one risk allele respectively. Various different smoking definitions were used for the exposure outlined in the following sections. The ALSPAC study website contains details of all the data that are available through a fully searchable data dictionary and variable search tool ( http://www.bristol.ac.uk/alspac/researchers/our-data/ ). 5.2.1 Exposure S is Smoking Before Pregnancy Each mother was asked at 16–18 weeks of gestation whether she smoked before pregnancy. We coded mothers that reported ‘yes’ as S = 1 and mothers who reported ‘no’ as S = 0 . Figure displays the assumed DAG for our analysis. We aimed to apply Methods 1 and 2 to estimate the causal effect of pre‐pregnancy smoking on birth weight in the G = 1 group, β 1 , the G =0 group, β 0 , and also the genetically moderated exposure effect β 1 − β 0 . We would expect this latter quantity to be nonzero if the pre‐pregnancy smoking effect persisted differently throughout pregnancy across the two genetic groups. For the first stage of Method 1, we perform a logistic regression of S on the GRS of smoking initiation ( G 2 ) and rs1051730 ( G ). The results are shown in Table . Variant rs1051730 was not associated with smoking before pregnancy, which helpfully means that Method 2 is not ruled out as an analysis option. The GRS is also associated with smoking before pregnancy and we assume it acts as a true IV for this exposure. Two crucial assumptions are that the GRS of smoking initiation has no pleiotropic effect on birth weight and it does not modify the the causal effect between smoking and birth weight in the exposed and the unexposed. To apply Method 1, rs1051730 cannot have a pleiotropic effect on birth weight either but, for Method 2, this assumption is relaxed. The results from applying both methods are shown in Figure . To increase the precision of the estimates we adjust our regression models for different sets of covariates. The model for the genetic prediction of smoking is adjusted for whether the partner of the mother smoked, the mothers' age and the first 10 genetic principal components. The model predicting birth weight is adjusted for offspring sex, mother age, mothers' height, parity, mothers' prepregnancy weight and the first 10 genetic principal components. We viewed these variables as confounders for either smoking before pregnancy or birth weight or both. For mothers who have at least one G risk allele, the ACE of smoking before pregnancy, β 1 , was estimated to be a 168 and 169 g reduction in birth weight using Methods 1 and 2 respectively. On the other hand, the corresponding causal effect ( β 0 ) in smoking mothers without a rs1051730 risk allele is a 159 and 161 g birth weight reduction for Methods 1 and 2 respectively compared to nonsmoking mothers without the risk allele. 5.2.2 Exposure S is Smoking in the First 3 Months of Pregnancy Mothers were asked at 16–18 weeks of gestation whether they smoked in the first 3 months of pregnancy. We coded those that reported ‘yes’ as S = 1 and mothers who reported ‘no’ as S = 0 and proceeded to estimate the causal effect of this exposure at the two levels (no and at least one risk allele) of rs1051730 and the genetically moderated exposure effect. In this analysis, no information about smoking before pregnancy was taken into account and therefore the mothers reporting to be non‐smokers either gave up smoking when getting pregnant or did not smoke before pregnancy. Figure displays the assumed DAG for our analysis. Methods 1 and 2 were applied to derive estimates for β 1 , β 0 and β 1 − β 0 , with results shown in Table and Figure . For these analyses, the logistic model for S given G and G 2 was adjusted for whether the partner of the mother smokes, the mothers' age, parity and the first 10 genetic principal components. The model predicting birth weight was adjusted for offspring sex, mothers' age, mothers' height, parity, mothers' prepregnancy weight and the first 10 genetic principal components. We viewed these variables as confounders for either smoking before pregnancy or birth weight or both. For this analysis, a stronger association was observed between rs1051730 and S , meaning that we were cautious with the interpretation of Method 2 results, given its crucial role in the in estimation of the genetically moderated exposure effect. The causal effect of smoking during pregnancy on birth weight in those with a rs1051730 risk allele, β 1 , was estimated to be −212 and −222 g using Methods 1 and 2 respectively, whereas the effect of smoking in the individuals without the genetic variant on birth weight is −220 and −205 g for Method 1 and 2 respectively. For both smoking exposures, we were unable to identify a difference between the two genetic groups. This could be because the effect of pre‐pregnancy smoking and smoking in the first 3 months does not truly differ in people with or without variant rs1051730. However, a simulation investigation showed that large numbers of individuals would be required to identify a genetically driven effect of smoking with the magnitudes we observed in our analysis. Specifically, we simulated data with sample sizes from 7000 to 500,000 and a true difference between the genetic groups of β 1 − β 0 between −20 and −5 g. To reach 80% power in estimating β 1 − β 0 = − 15 a sample size of 500,000 individuals is required in our simulation when using Method 1. The RGMEE (Method 2), which is more robust to pleiotropy compared to Method 1, is able to estimate β 1 − β 0 = − 15 with a power of over 80% with 200,000 individuals. More details on the results of this are shown in Supporting Information: Section . This provides important guidance on the much larger sample size, way beyond the 7752 mothers in the ALSPAC study, that would be required to detect a difference between the genetic groups in the region of what we observe here. Despite not being able to detect a statistically meaningful genetically moderated exposure effect, overall our results suggest that smoking before pregnancy or smoking in the first 3 months of pregnancy results in a lower birth weight compared to not smoking. This is in line with previous publications (Larsen et al. ; Tyrrell et al. ; Pereira et al. ). Exposure S is Smoking Before Pregnancy Each mother was asked at 16–18 weeks of gestation whether she smoked before pregnancy. We coded mothers that reported ‘yes’ as S = 1 and mothers who reported ‘no’ as S = 0 . Figure displays the assumed DAG for our analysis. We aimed to apply Methods 1 and 2 to estimate the causal effect of pre‐pregnancy smoking on birth weight in the G = 1 group, β 1 , the G =0 group, β 0 , and also the genetically moderated exposure effect β 1 − β 0 . We would expect this latter quantity to be nonzero if the pre‐pregnancy smoking effect persisted differently throughout pregnancy across the two genetic groups. For the first stage of Method 1, we perform a logistic regression of S on the GRS of smoking initiation ( G 2 ) and rs1051730 ( G ). The results are shown in Table . Variant rs1051730 was not associated with smoking before pregnancy, which helpfully means that Method 2 is not ruled out as an analysis option. The GRS is also associated with smoking before pregnancy and we assume it acts as a true IV for this exposure. Two crucial assumptions are that the GRS of smoking initiation has no pleiotropic effect on birth weight and it does not modify the the causal effect between smoking and birth weight in the exposed and the unexposed. To apply Method 1, rs1051730 cannot have a pleiotropic effect on birth weight either but, for Method 2, this assumption is relaxed. The results from applying both methods are shown in Figure . To increase the precision of the estimates we adjust our regression models for different sets of covariates. The model for the genetic prediction of smoking is adjusted for whether the partner of the mother smoked, the mothers' age and the first 10 genetic principal components. The model predicting birth weight is adjusted for offspring sex, mother age, mothers' height, parity, mothers' prepregnancy weight and the first 10 genetic principal components. We viewed these variables as confounders for either smoking before pregnancy or birth weight or both. For mothers who have at least one G risk allele, the ACE of smoking before pregnancy, β 1 , was estimated to be a 168 and 169 g reduction in birth weight using Methods 1 and 2 respectively. On the other hand, the corresponding causal effect ( β 0 ) in smoking mothers without a rs1051730 risk allele is a 159 and 161 g birth weight reduction for Methods 1 and 2 respectively compared to nonsmoking mothers without the risk allele. Exposure S is Smoking in the First 3 Months of Pregnancy Mothers were asked at 16–18 weeks of gestation whether they smoked in the first 3 months of pregnancy. We coded those that reported ‘yes’ as S = 1 and mothers who reported ‘no’ as S = 0 and proceeded to estimate the causal effect of this exposure at the two levels (no and at least one risk allele) of rs1051730 and the genetically moderated exposure effect. In this analysis, no information about smoking before pregnancy was taken into account and therefore the mothers reporting to be non‐smokers either gave up smoking when getting pregnant or did not smoke before pregnancy. Figure displays the assumed DAG for our analysis. Methods 1 and 2 were applied to derive estimates for β 1 , β 0 and β 1 − β 0 , with results shown in Table and Figure . For these analyses, the logistic model for S given G and G 2 was adjusted for whether the partner of the mother smokes, the mothers' age, parity and the first 10 genetic principal components. The model predicting birth weight was adjusted for offspring sex, mothers' age, mothers' height, parity, mothers' prepregnancy weight and the first 10 genetic principal components. We viewed these variables as confounders for either smoking before pregnancy or birth weight or both. For this analysis, a stronger association was observed between rs1051730 and S , meaning that we were cautious with the interpretation of Method 2 results, given its crucial role in the in estimation of the genetically moderated exposure effect. The causal effect of smoking during pregnancy on birth weight in those with a rs1051730 risk allele, β 1 , was estimated to be −212 and −222 g using Methods 1 and 2 respectively, whereas the effect of smoking in the individuals without the genetic variant on birth weight is −220 and −205 g for Method 1 and 2 respectively. For both smoking exposures, we were unable to identify a difference between the two genetic groups. This could be because the effect of pre‐pregnancy smoking and smoking in the first 3 months does not truly differ in people with or without variant rs1051730. However, a simulation investigation showed that large numbers of individuals would be required to identify a genetically driven effect of smoking with the magnitudes we observed in our analysis. Specifically, we simulated data with sample sizes from 7000 to 500,000 and a true difference between the genetic groups of β 1 − β 0 between −20 and −5 g. To reach 80% power in estimating β 1 − β 0 = − 15 a sample size of 500,000 individuals is required in our simulation when using Method 1. The RGMEE (Method 2), which is more robust to pleiotropy compared to Method 1, is able to estimate β 1 − β 0 = − 15 with a power of over 80% with 200,000 individuals. More details on the results of this are shown in Supporting Information: Section . This provides important guidance on the much larger sample size, way beyond the 7752 mothers in the ALSPAC study, that would be required to detect a difference between the genetic groups in the region of what we observe here. Despite not being able to detect a statistically meaningful genetically moderated exposure effect, overall our results suggest that smoking before pregnancy or smoking in the first 3 months of pregnancy results in a lower birth weight compared to not smoking. This is in line with previous publications (Larsen et al. ; Tyrrell et al. ; Pereira et al. ). Observational Analysis and ‘Standard’ MR In addition to applying the new proposed methods to the ALSPAC data set, we also looked at the observational association between smoking and birth weight. A linear regression of S (using both smoking definitions) on birth weight adjusted for partner smoking, mothers' age, mothers' height, mothers' pre‐pregnancy weight, parity and offspring sex yielded negative associational estimates. Although the observational analysis likely suffers from residual confounding, and cannot be interpreted as a causal effect, the direction of effect remains the same compared to estimating β 1 and β 0 (Figure ), albeit of a smaller magnitude. As indicated in Table , rs1051730 ( G ) is not associated with smoking before pregnancy. Therefore, we are unable to perform a standard MR analysis using G as the genetic instrument for S = smoking before pregnancy. However, we did perform a standard MR analysis using individual level data and the two‐stage least squares approach with S = smoking in the first 3 months of pregnancy. We explain in Section that the standard MR with a homogeneity violating instrument like rs1051730 estimates the CACE (while the monotonicity assumption holds). The CACE of smoking in the first 3 months of pregnancy on birth weight is −210 g (95% CI: [−293,−128]). Note that these results potentially suffer from weak instrument bias. Additionally, and as a confirmatory test of our previous genetic analyses, we calculated a standard MR estimate using the GRS for smoking initiation to instrument smoking before and smoking in the first 3 months of pregnancy. For these analyses rs1051730 is not considered. The methodology is described in Section . Using Equation , the estimated β ‐values obtained from applying Method 1 and 2 to the ALSPAC data we derive an ACE of −165 g for smoking before pregnancy. This compares to the ACE of −164 g (Figure ) estimated with standard MR approach. Similarly, for smoking in the first 3 months of pregnancy we obtain an estimate of −213 g using the β ‐values from Methods 1 or 2 outputs and the formula provided in Equation . Discussion In this study, we propose a general framework for MR that allows the inclusion of traditional genetic instruments, as well as those that violate the key homogeneity assumption. This enables an analysis that goes beyond estimation of the ACE to consider estimation within specific genetic sub‐groups, with a view to quantifying genetically driven effect heterogeneity. Our approach builds on ideas from the pharmacogenetic TWIST framework proposed by Bowden et al. to a more mainstream epidemiological setting, as well as incorporating the technique of multivariable MR (Sanderson et al. ). Specifically, Method 1 offers a new approach to estimating the genetically moderated exposure effect, which could be triangulated with existing methods. Furthermore, Method 1 allows for a direct effect between the homogeneity‐violating instrument and the exposure. In the presence of unmeasured confounding, the methods proposed in the paper by Bowden et al. require the instrument to be independent of the exposure. To allow for a direct pleiotropic effect between the homogeneity‐violating instrument and the outcome we proposed Method 2. Simulation studies revealed the necessary sample sizes to detect an effect with sufficient power under Methods 1 and 2, considering plausible genetically moderated exposure effect sizes. To motivate the methods, we applied them to data from the ALSPAC cohort to investigate the effect of smoking before and in pregnancy on birth weight using a traditional GRS for smoking and rs1051730 as an effect modifier. Our work could be further extended by considering the incorporation of additional methods to allow for the relaxation of further key assumptions. For example, allowing a genetic variant with a pleiotropic effect that acts through an unmeasured confounder (i.e., correlated pleiotropy). We assumed that the underlying data structure follows a linear interaction model. Future work could explore different data structures and nonlinear effects. For simplicity, and to naturally follow on from the approach proposed in Bowden et al. , we assumed a binary effect modifying instrument through the dichotomisation of the genetic instrument rather than using the number of risk alleles. Future work could relax this assumption. Despite not being able to show a difference between the two genetic groups in our applied example due to a limitation in sample size, our investigation clarifies how large future cohort study samples need to be to estimate effects of the magnitude we observed. We believe our framework is a useful methodological extension to investigate genetically driven heterogeneity. Our methods could, for example, be applied in other setting where larger sample sizes are available, or by meta‐analysing results with additional cohorts. Settings outside of pregnancy research are also possible and would not require mother and child pair information. For example, investigating the genetically driven effect of continuing smoking on lung cancer. Other examples could be using genetic variants associated with reducing alcohol consumption and the effects on various health outcomes. Data sets like the UK Biobank with genetic information available for 500,000 individuals could be used. R code for implementing the methods can be found at https://github.com/AJaitner/paper_heterogeneity_MR as well as code to implement the simulation studies and applied analysis. Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. Informed consent for the use of data collected via questionnaires and clinics was obtained from participants following the recommendations of the ALSPAC Ethics and Law Committee at the time. Jack Bowden is a part‐time employee of Novo Nordisk, however, this work is unrelated to his role at the company. The remaining authors declare no conflicts of interest. Supplementary Information |
Expression patterns of aquaporins 1, 3, 5 in canine mammary gland
carcinomas | dda98cb1-4255-48d3-b268-d4fc4a6031d5 | 10898980 | Anatomy[mh] | Animals Five samples of canine mammary tissues that had been removed surgically and confirmed
pathologically to be normal were used as controls. As tumor samples, 27 formalin-fixed
mammary tumors from female dogs aged 6 to 16 years were used. These cases had been
diagnosed as mammary carcinomas at the Laboratory of Veterinary Pathology, The University
of Tokyo. The breed, sex, and age of dogs examined in this study are shown in . For the evaluation of the specificity of antibodies, we used kidney and lung samples,
both of which had been preserved at the Department of Veterinary Pathology, University of
Miyazaki. Histology Formalin-fixed carcinoma samples were embedded in paraffin blocks and cut into sections 3
μm thick. The sections were deparaffinized and then stained with hematoxylin and eosin
(HE). Images of HE-stained specimens were acquired with a Nano Zoomer 2.0-RS (C10730-13,
Hamamatsu Photonics K.K., Shizuoka, Japan) and observed with NDP. View2 software
(Hamamatsu Photonics K.K.). Classification and grading of the mammary carcinomas Using HE-stained specimens, 27 mammary carcinoma samples were classified according to two
histological classification systems. One system was the WHO classification in which a histological diagnosis was established
on the basis of the type of tumor cells and the structure of the tumor, such as simple
adenocarcinoma, complex carcinoma, carcinoma in mixed tumor, and anaplastic carcinoma. The
other system was tumor grading based on the classification of Goldschmidt et
al . . We evaluated the percentage of
lumen formation in the tumors, the degree of nuclear polymorphism of the tumor cells, and
the number of mitotic figures. A score of 1 was assigned to tumors with a lumen-forming
area accounting for 75% or more of the total tumor area, a score of 2 for 10–75%, and a
score of 3 for less than 10%. Nuclear polymorphism was assigned a score of 1 point if the
nuclei were uniform in size and regular, 2 points if there was moderate atypia in the size
and shape of the nuclei and nucleoli, and 3 points if there was marked nuclear atypia and
distinct nucleoli. For the number of mitoses, 1 point was assigned when 0–9 mitoses were
observed in 10 high-power fields (40x), 2 points for 10–19 mitoses, and 3 points for more
than 20 mitoses. For the total of three evaluation parameters, 3–5 points were considered
as Grade I, 6–7 points as Grade II, and 8–9 points as Grade III. Immunohistochemistry Paraffin sections were deparaffinized and dehydrated. For immunostaining of AQP1, AQP3
and AQP5, the antigens were activated by heat treatment in an autoclave at 121°C for 5 min
in distilled water. Then, the endogenous peroxidase was inactivated by methanol containing
3% H 2 O 2 for 5 min, followed by washing with phosphate buffered
saline (PBS). Primary antibodies used for immunohistochemistry were: rabbit anti-AQP1 antibody
(SC-20810; Santa Cruz Biotechnology Inc., Santa Cruz, CA, USA), rabbit anti-AQP3 antibody
(synthesized at University of Gunma) , rabbit
anti-AQP5 antibody (synthesized at University of Gunma) , mouse anti-p63 antibody (clone# 4A4; Nichirei Biosciences Inc., Tokyo,
Japan). The primary antibodies were diluted with 0.1% bovine serum albumin in PBS
(anti-AQP1, 1:100; anti-AQP3, 1:500; anti-AQP5, 1:1,000, undilution; anti-p63) and reacted
for 1 hr at room temperature. Then, secondary antibodies (EnvisionTM Dual Link System-HRP,
Dako Japan Co., Ltd., Tokyo, Japan) were added for 1 hr at room temperature. After the
reaction, each antibody was reacted with 3,3′-diaminobenzidine for color development, and
the reaction was stopped using distilled water. The samples were reacted with hematoxylin
for 10 sec and washed with running water for 10 min. Tissue specimens for
immunohistochemistry were prepared by dehydration, permeabilization, and sealing according
to conventional methods. Tissue images were acquired using Nano Zoomer 2.0-RS and observed
using NDP. View2 software.
Five samples of canine mammary tissues that had been removed surgically and confirmed
pathologically to be normal were used as controls. As tumor samples, 27 formalin-fixed
mammary tumors from female dogs aged 6 to 16 years were used. These cases had been
diagnosed as mammary carcinomas at the Laboratory of Veterinary Pathology, The University
of Tokyo. The breed, sex, and age of dogs examined in this study are shown in . For the evaluation of the specificity of antibodies, we used kidney and lung samples,
both of which had been preserved at the Department of Veterinary Pathology, University of
Miyazaki.
Formalin-fixed carcinoma samples were embedded in paraffin blocks and cut into sections 3
μm thick. The sections were deparaffinized and then stained with hematoxylin and eosin
(HE). Images of HE-stained specimens were acquired with a Nano Zoomer 2.0-RS (C10730-13,
Hamamatsu Photonics K.K., Shizuoka, Japan) and observed with NDP. View2 software
(Hamamatsu Photonics K.K.).
Using HE-stained specimens, 27 mammary carcinoma samples were classified according to two
histological classification systems. One system was the WHO classification in which a histological diagnosis was established
on the basis of the type of tumor cells and the structure of the tumor, such as simple
adenocarcinoma, complex carcinoma, carcinoma in mixed tumor, and anaplastic carcinoma. The
other system was tumor grading based on the classification of Goldschmidt et
al . . We evaluated the percentage of
lumen formation in the tumors, the degree of nuclear polymorphism of the tumor cells, and
the number of mitotic figures. A score of 1 was assigned to tumors with a lumen-forming
area accounting for 75% or more of the total tumor area, a score of 2 for 10–75%, and a
score of 3 for less than 10%. Nuclear polymorphism was assigned a score of 1 point if the
nuclei were uniform in size and regular, 2 points if there was moderate atypia in the size
and shape of the nuclei and nucleoli, and 3 points if there was marked nuclear atypia and
distinct nucleoli. For the number of mitoses, 1 point was assigned when 0–9 mitoses were
observed in 10 high-power fields (40x), 2 points for 10–19 mitoses, and 3 points for more
than 20 mitoses. For the total of three evaluation parameters, 3–5 points were considered
as Grade I, 6–7 points as Grade II, and 8–9 points as Grade III.
Paraffin sections were deparaffinized and dehydrated. For immunostaining of AQP1, AQP3
and AQP5, the antigens were activated by heat treatment in an autoclave at 121°C for 5 min
in distilled water. Then, the endogenous peroxidase was inactivated by methanol containing
3% H 2 O 2 for 5 min, followed by washing with phosphate buffered
saline (PBS). Primary antibodies used for immunohistochemistry were: rabbit anti-AQP1 antibody
(SC-20810; Santa Cruz Biotechnology Inc., Santa Cruz, CA, USA), rabbit anti-AQP3 antibody
(synthesized at University of Gunma) , rabbit
anti-AQP5 antibody (synthesized at University of Gunma) , mouse anti-p63 antibody (clone# 4A4; Nichirei Biosciences Inc., Tokyo,
Japan). The primary antibodies were diluted with 0.1% bovine serum albumin in PBS
(anti-AQP1, 1:100; anti-AQP3, 1:500; anti-AQP5, 1:1,000, undilution; anti-p63) and reacted
for 1 hr at room temperature. Then, secondary antibodies (EnvisionTM Dual Link System-HRP,
Dako Japan Co., Ltd., Tokyo, Japan) were added for 1 hr at room temperature. After the
reaction, each antibody was reacted with 3,3′-diaminobenzidine for color development, and
the reaction was stopped using distilled water. The samples were reacted with hematoxylin
for 10 sec and washed with running water for 10 min. Tissue specimens for
immunohistochemistry were prepared by dehydration, permeabilization, and sealing according
to conventional methods. Tissue images were acquired using Nano Zoomer 2.0-RS and observed
using NDP. View2 software.
Confirmation of antibodies We confirmed specificities of our antibodies for AQP1, AQP3 and AQP5 in canine tissues.
Firstly, we used normal canine kidney tissue, because AQP1 and AQP3, but not AQP5 is known
to be expressed in the kidney tissues . As shown in , antibody against AQP1 caused immunolabelling of proximal tubules and the
descending limb of Henle, and anti-AQP3 antibody was immunoreacted with basolateral
membrane of collecting duct cells. On the other hand, antibody against AQP5 showed
negative results on the renal epithelial cells . When lung tissue was used, antibody against AQP5 showed an
immunoreactivity in the non-ciliated epithelial cells of the small airway (bronchiole)
, collaborating with previous
data . These data indicate that our antibodies
caused specific immunolabelling with canine tissues. AQPs in normal mammary tissues The patterns of expression of AQP1, AQP3 and AQP5 in normal canine mammary gland are
largely unknown. We performed immunohistochemistry to determine the expression of AQP1,
AQP3, and AQP5 in tissue samples from dogs without mammary tumors. As shown in , AQP1 was not expressed in mammary epithelial cells, but was expressed in
erythrocytes and vascular endothelial cells. AQP3 and AQP5 were expressed in the
basolateral membrane and the luminal membrane of mammary epithelial cells,
respectively. Histology of mammary gland tumors We then examined 27 samples of mammary gland tumors in this study. The breed, age, and
presence of contraceptive surgery in the individual dogs are summarized in . shows histological diagnosis, grade, lymphatic infiltration, lobular
hyperplasia, and results of immunohistochemistry for AQP1, AQP3, and AQP5. Diagnostically,
the present set of tumor samples included 4 simple adenocarcinomas, 15 complex carcinomas,
1 carcinoma in mixed tumor, and 7 anaplastic carcinomas. The histological diagnoses were
based on the WHO classification. In terms of the grading system of Goldschmidt et
al . , 10 mammary carcinomas were
classified as grade I, 12 as grade II, and 5 as grade III. In 8 of the 27 cases, lymphatic
invasion was clearly detected. In mammary tumors, columnar and spindle-shaped cells were reported to be found , and therefore we checked AQP positivity in those
cells in the later section. The spindle-shaped cells have been thought to be classified
into two types of immunological phenotypes, including p63 (a homolog of the tumor
suppressor p53), a marker protein for myoepithelial cells, -positive and -negative cells
.
p63-negative spindle-shaped cells have been thought to include epithelial cells with
altered cell morphology due to epithelial-mesenchymal transition, fibroblasts, and
myofibroblasts . Therefore, we examined the expression of p63 in spindle-shaped cells from 9
samples (#9, #12−#16, #18, #23, and #25 in ), and observed that the immunoreactivity for p63 of these spindle-shaped cells
varied in areas of the same tumor (data not shown). AQP1 in mammary carcinomas As shown in and , in 10 of the grade I
mammary carcinomas, AQP1 expression was not obvious in either columnar or spindle-shaped
cells. Similarly, AQP1 was not expressed in either columnar or spindle-shaped cells in all
the grade II mammary carcinomas. Among the grade III mammary carcinomas, AQP1 was not
expressed in columnar cells (1 sample). However, spindle-shaped cells showed AQP1
positivity in 2 of 5 carcinoma samples, and in case of No. 23, polygonal tumor cells
expressed AQP1 . In that sample, the
tumor cells showed a solid growth pattern, and the neoplastic foci were focally divided by
connective tissue. Each compartment was roughly divided into two parts, including a
compartment with or without AQP1-positive cells. In the compartment containing
AQP1-positive cells, AQP1 was expressed diffusely in the tumor cells. In case of No. 25,
AQP1 was expressed in tumor cells within a scirrhous carcinoma lesion and infiltrating
lymphatic vessels ( and ). Although the exact causes of death are unknown, No. 23 dog
died 44 days after surgery, and No. 25 dog died within 27 days after surgery. In 5 samples , lobular hyperplasia
was observed around the tumor lesion, and AQP1 was not expressed in these hyperplastic
cells. AQP3 in mammary carcinomas The results of AQP3 immunohistochemistry are shown in and . In all samples of the
grade I mammary carcinomas, AQP3 was expressed in columnar cells, and in spindle shaped
cells expression of AQP3 was evident in 2 of 7 samples. Among the 12 grade II mammary
carcinomas, AQP3 was expressed in columnar cells in 10 samples and in spindle-shaped cells
in 2 samples. In the grade III mammary carcinomas, AQP3 was not expressed in columnar
cells. Similarly to AQP1, AQP3 was detected in polygonal cells . As shown in , AQP3 was also expressed in tumor cells infiltrating lymphatic vessels in 2 of the
8 tumor samples. In the lobular hyperplastic region around the tumor, AQP3 expression was increased in the
basolateral membrane of mammary epithelial cells . AQP5 in mammary carcinomas As shown in and , in the grade I and II
mammary carcinomas, AQP5 was expressed in columnar cells in all samples and in
spindle-shaped cells in 8 samples. In terms of subcellular localization, AQP5 was
expressed in the luminal membrane of the columnar cells and in the cell membrane of
spindle cells. In grade III carcinomas, AQP5 was not expressed in either columnar cells or
spindle-shaped cells. In grade I mammary tumor No.10, and in grade II mammary tumor Nos.
21 and 22, tumor cells in the infiltrating lymphatic vessels expressed AQP5 . In contrast to AQP3, AQP5 was not
detected in polygonal cells. In hyperplastic tissue around the tumor, AQP5 was expressed in the luminal, and
basolateral membrane of epithelial cells, the luminal expression being particularly strong
. p63-positivity in AQP-positive regions Finally, we examined p63-positivity in regions including AQP1, AQP3, or AQP5-positive
cells. As shown in , there were no p63-positive cells in regions including AQP1-positive cells. On the
other hand, three samples had p63-positive regions of 4 samples, including regions of
AQP3-positive cells. In terms of AQP5, there was only 1 sample with a p63-positive region
in 7 samples with AQP5-positive regions.
We confirmed specificities of our antibodies for AQP1, AQP3 and AQP5 in canine tissues.
Firstly, we used normal canine kidney tissue, because AQP1 and AQP3, but not AQP5 is known
to be expressed in the kidney tissues . As shown in , antibody against AQP1 caused immunolabelling of proximal tubules and the
descending limb of Henle, and anti-AQP3 antibody was immunoreacted with basolateral
membrane of collecting duct cells. On the other hand, antibody against AQP5 showed
negative results on the renal epithelial cells . When lung tissue was used, antibody against AQP5 showed an
immunoreactivity in the non-ciliated epithelial cells of the small airway (bronchiole)
, collaborating with previous
data . These data indicate that our antibodies
caused specific immunolabelling with canine tissues.
The patterns of expression of AQP1, AQP3 and AQP5 in normal canine mammary gland are
largely unknown. We performed immunohistochemistry to determine the expression of AQP1,
AQP3, and AQP5 in tissue samples from dogs without mammary tumors. As shown in , AQP1 was not expressed in mammary epithelial cells, but was expressed in
erythrocytes and vascular endothelial cells. AQP3 and AQP5 were expressed in the
basolateral membrane and the luminal membrane of mammary epithelial cells,
respectively.
We then examined 27 samples of mammary gland tumors in this study. The breed, age, and
presence of contraceptive surgery in the individual dogs are summarized in . shows histological diagnosis, grade, lymphatic infiltration, lobular
hyperplasia, and results of immunohistochemistry for AQP1, AQP3, and AQP5. Diagnostically,
the present set of tumor samples included 4 simple adenocarcinomas, 15 complex carcinomas,
1 carcinoma in mixed tumor, and 7 anaplastic carcinomas. The histological diagnoses were
based on the WHO classification. In terms of the grading system of Goldschmidt et
al . , 10 mammary carcinomas were
classified as grade I, 12 as grade II, and 5 as grade III. In 8 of the 27 cases, lymphatic
invasion was clearly detected. In mammary tumors, columnar and spindle-shaped cells were reported to be found , and therefore we checked AQP positivity in those
cells in the later section. The spindle-shaped cells have been thought to be classified
into two types of immunological phenotypes, including p63 (a homolog of the tumor
suppressor p53), a marker protein for myoepithelial cells, -positive and -negative cells
.
p63-negative spindle-shaped cells have been thought to include epithelial cells with
altered cell morphology due to epithelial-mesenchymal transition, fibroblasts, and
myofibroblasts . Therefore, we examined the expression of p63 in spindle-shaped cells from 9
samples (#9, #12−#16, #18, #23, and #25 in ), and observed that the immunoreactivity for p63 of these spindle-shaped cells
varied in areas of the same tumor (data not shown).
As shown in and , in 10 of the grade I
mammary carcinomas, AQP1 expression was not obvious in either columnar or spindle-shaped
cells. Similarly, AQP1 was not expressed in either columnar or spindle-shaped cells in all
the grade II mammary carcinomas. Among the grade III mammary carcinomas, AQP1 was not
expressed in columnar cells (1 sample). However, spindle-shaped cells showed AQP1
positivity in 2 of 5 carcinoma samples, and in case of No. 23, polygonal tumor cells
expressed AQP1 . In that sample, the
tumor cells showed a solid growth pattern, and the neoplastic foci were focally divided by
connective tissue. Each compartment was roughly divided into two parts, including a
compartment with or without AQP1-positive cells. In the compartment containing
AQP1-positive cells, AQP1 was expressed diffusely in the tumor cells. In case of No. 25,
AQP1 was expressed in tumor cells within a scirrhous carcinoma lesion and infiltrating
lymphatic vessels ( and ). Although the exact causes of death are unknown, No. 23 dog
died 44 days after surgery, and No. 25 dog died within 27 days after surgery. In 5 samples , lobular hyperplasia
was observed around the tumor lesion, and AQP1 was not expressed in these hyperplastic
cells.
The results of AQP3 immunohistochemistry are shown in and . In all samples of the
grade I mammary carcinomas, AQP3 was expressed in columnar cells, and in spindle shaped
cells expression of AQP3 was evident in 2 of 7 samples. Among the 12 grade II mammary
carcinomas, AQP3 was expressed in columnar cells in 10 samples and in spindle-shaped cells
in 2 samples. In the grade III mammary carcinomas, AQP3 was not expressed in columnar
cells. Similarly to AQP1, AQP3 was detected in polygonal cells . As shown in , AQP3 was also expressed in tumor cells infiltrating lymphatic vessels in 2 of the
8 tumor samples. In the lobular hyperplastic region around the tumor, AQP3 expression was increased in the
basolateral membrane of mammary epithelial cells .
As shown in and , in the grade I and II
mammary carcinomas, AQP5 was expressed in columnar cells in all samples and in
spindle-shaped cells in 8 samples. In terms of subcellular localization, AQP5 was
expressed in the luminal membrane of the columnar cells and in the cell membrane of
spindle cells. In grade III carcinomas, AQP5 was not expressed in either columnar cells or
spindle-shaped cells. In grade I mammary tumor No.10, and in grade II mammary tumor Nos.
21 and 22, tumor cells in the infiltrating lymphatic vessels expressed AQP5 . In contrast to AQP3, AQP5 was not
detected in polygonal cells. In hyperplastic tissue around the tumor, AQP5 was expressed in the luminal, and
basolateral membrane of epithelial cells, the luminal expression being particularly strong
.
Finally, we examined p63-positivity in regions including AQP1, AQP3, or AQP5-positive
cells. As shown in , there were no p63-positive cells in regions including AQP1-positive cells. On the
other hand, three samples had p63-positive regions of 4 samples, including regions of
AQP3-positive cells. In terms of AQP5, there was only 1 sample with a p63-positive region
in 7 samples with AQP5-positive regions.
In the present study, the expression of AQP1, AQP3, and AQP5 in normal canine mammary gland
was examined. AQP1 was not expressed in epithelial cells, but was expressed in erythrocytes
and vascular endothelial cells. AQP3 and AQP5 were expressed in the basolateral membrane and
luminal membrane of mammary epithelial cells, respectively. These observations are similar
to those obtained previously in rodents . Mobasheri et al . have examined the cellular localization of AQP1, AQP3,
and AQP5 in the teat, cistern and secretory portions of the mammary glands in actively
lactating cows . They observed that AQP1 was expressed in capillary endothelia throughout the
gland as well as in myoepithelial cells near teat duct epithelial cells. AQP3 was detected
in some epithelial cells in the teat, cistern and secretory tubuloalveoli, and AQP5 was
markedly expressed in the cistern. AQP3 was also found in smooth muscle bundles in the teat,
secretory epithelial cells and duct epithelial cells. Most of these immunohistochemical
findings agreed with our present observations, except for AQP1 immunopositivity in the
myoepithelial cells. Furthermore, we did not examine mammary gland teat of actively
lactating animals, and therefore AQP1 positivity in this area remained unclear. Since it has
been reported that steroid hormones increase the expression of AQP1 in the small blood
vessels , AQP1 is considered to be related to the
estrogen response. The importance of AQP1 in the mammalian lactation process awaits further
investigation. Lobular hyperplasia has been reported in mammary tissue adjacent to mammary neoplasms
. This hyperplasia consists of the
proliferation of non-neoplastic cells. In the present study, we examined the
immunohistochemical expression of AQP1, AQP3, and AQP5 in this region. As shown in and , although no expression of AQP1 was evident, AQP3 and AQP5 were clearly expressed
in hyperplastic cells. AQP3 was detected in the basolateral membrane, and AQP5 was strongly
expressed in both the basolateral and apical membranes. Expression of AQP3 in the
basolateral membrane and AQP5 in the basolateral and apical membranes has been reported in
mammary glands of actively lactating cows . As
epithelial cell proliferation in the mammary gland is evident during the lactation phase
,
expression of AQP3 and AQP5 might be involved in lactating activity in female animals. In this study, we used a tumor grading according to the classification of Goldschmidt et al . . This canine
classification method is based on the human mammary tumor classification method , and the method is widely used in the veterinary field
. Both gradings are determined by the sum of
three scores, including the percentage of ductal formation within the tumor, the degree of
nuclear atypia, and the number of mitotic figures. Therefore, these two methods are
considered to be homologous. In the present study, AQP1 was not detected in samples of grade I and II mammary
carcinomas. In 2 of 5 grade III samples, AQP1-positive cells were found, and these cells
included polygonal tumor cells and tumor cells within scirrhous carcinoma and infiltrating
lymphatic vessels. These data suggested that expression of AQP1 might be related to
high-grade carcinomas and metastatic activity. In this context, Otterbach et
al . examined the expression of AQP1
in 203 invasive human breast carcinomas (grade I, n=24; grade II, n=87; grade III, n=92).
AQP1 was not detected in any of the grade I cases, but was present in 3 of 87 grade II cases
and 8 of 92 grade III cases. In some of the positive cases, the intensity of immunostaining
was marked at the tumor invasion front. Recently, Guo et al . reported that
AQP1 was a crucial target in local invasion of breast cancer and might be related to
recruitment of ANXA2 and Rab1b . These data
suggest that expression of AQP1 might be related to tumor metastasis in human and canine
mammary gland carcinomas. In the present study, we observed expression of AQP3 in 20 of 22 grade I and II carcinoma
samples, whereas in grade III carcinomas AQP3 was observed in only 1 of 5 samples. AQP5 was
expressed in all grade I and II samples, but not in the grade III carcinoma samples. These
data suggest that AQP3 and AQP5 were minimally expressed in high-grade canine carcinomas,
suggesting that determination of the expression of AQP3 and AQP5 might be helpful in
estimating malignancy in dogs. On the other hand, expression of AQP3 and AQP5 has been reported to some degree in
high-grade human mammary carcinomas. Jung et al ., reported the expression
of AQP5 in 20 patients with breast cancer, AQP5 positivity being evident in 5 of 6 grade III
samples . Lee et al . examined
the expression of AQP5 in 447 patients with early breast cancer and observed strong
expression in 30 of 117 grade III samples . Kang et al . evaluated 447 patients with early breast cancer and found strong
expression of AQP3 in 26 of 67 grade III samples . Furthermore, in an immunohistochemistry study of 96 patients with
triple-negative breast cancer, Zhu et al . found that among histological
grade III cases, 66.7% and 69.4% were positive for AQP3 and AQP5, respectively . Currently, the reason for this discrepancy is
unclear. So far, it has been reported that expression of AQP3 and AQP5 varies considerably
among individual patients . As the present study
included only a limited number of cases, a large-scale cohort study will be required to shed
further light on this issue. The present results revealed that some tumor cells had lower expression of AQP3 and AQP5.
In renal pathophysiology, it has been reported that expression of renal functional proteins
such as transporters and AQPs is down-regulated in growing renal cells, and thus any
increase in the number of proliferative cells might be accompanied by decreased renal
expression of functional proteins . Therefore, in highly proliferative cells such as
mammary gland tumor cells, AQP expression may be decreased. As shown in , we failed to observe the
p63-positive spindle-shaped cells in regions including AQP1-positive cells. On the other
hand, p63-positive cells were found in regions including AQP3-positive cells in three of the
four samples. To our knowledge, AQP3 expression in myoepithelial cells has yet to be
previously reported. In terms of AQP5, there was only 1 sample having a p63-positive region
in 7 samples including AQP5-positive regions. Since it is important to know which
spindle-shaped cells have AQP3- and/or AQP5-positivities, it will be necessary to clarify
this point in future studies. Recently, it has been suggested that AQP7 could be a potential cancer-specific therapeutic
target in breast cancer , based on integrated
metabolomics and gene expression data from breast cancer mouse models and 34 identified gene
hubs. Among them, AQP7 was found to be related to glycerol transport activity and lipid
homeostasis, as well as proliferation and metastasis of breast cancer cells. However, the
role of AQP7 in other animal species is still unclear, and this remains to be investigated
further. The present study had several limitations. A larger sample population would have been
desirable. Furthermore, we did not investigate the functional roles of AQP1, AQP3, and AQP5
in canine mammary tissue. Although we evaluated the expression of AQP1, AQP3, and AQP5 using
immunohistochemistry, more accurate evaluation at the protein level using methods such as
immunoblot analysis would be desirable. Conclusions In conclusion, this study has shown that the expression patterns of AQP3 and AQP5 would
be potentially useful for judging the grading of canine mammary carcinomas. Moreover, AQP1
appears to be related to metastasis, and AQP3 and AQP5 might be involved in lactation in
female dogs.
In conclusion, this study has shown that the expression patterns of AQP3 and AQP5 would
be potentially useful for judging the grading of canine mammary carcinomas. Moreover, AQP1
appears to be related to metastasis, and AQP3 and AQP5 might be involved in lactation in
female dogs.
The authors have no conflicts of interest to disclose.
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Analysis of the Composition of Lyophilisates Obtained from | 739f34c5-5f70-4517-96ed-ecfc7b2dabf0 | 8198272 | Pharmacology[mh] | Plant raw materials contain numerous groups of compounds with a biological effect. Especially the group of secondary metabolites in plant compounds show biological activity . The synergism of the activity of selected metabolites shapes the final pharmacological effect of the plant material. There are many reports in the literature on the subject of the synergism of the action of various groups of secondary metabolites (e.g., Cannabis spp.) , also, of the concept of the so-called entourage, i.e., achieving better pharmacological parameters as a result of the presence of groups of secondary metabolites . Secondary metabolites, as compounds produced as a defense reaction, respond to the variability of many environmental factors, which may modify the composition of secondary metabolites produced by the plant . In the case of relatively constant external factors of perennial medicinal plant cultivation (example of controlled crops), the plant’s age is an essential factor determining its secondary metabolite composition . For almost every medicinal plant, it is important to obtain the raw material in the period of the optimal composition of secondary metabolites. Aloe is a large genus of significant medical, cosmetic, and food importance, consisting of, according to Salehi et al., over 446 species belonging to the Xanthorrhoeaceae family, of which many plants are well known for centuries for their medicinal properties . There are differences in the morphological and anatomical features between species, as these perennials exist in the form of lianas, shrubs, and ‘trees’. However, the gel present in the leaves is characteristic for all Aloe genus plants and is considered a valuable raw material for preparing various forms of herbal drugs, dietary supplements, cosmetics, and functional food. Numerous scientific reports and clinical studies emphasize the multidirectional effect of leaf gel of all Aloe spp. plants on the human body . The most known plants of the genus include Aloe arborescens Mill., Aloe ferox Mill., Aloe artistata Haw., Aloe saponaria Ait., Aloe polyphylla Schonl. ex Pillans ., and—the most famous— Aloe vera L. These plants occur naturally in dry, savannah, and desert conditions in eastern and southern Africa and Madagascar. Over the centuries, Aloe has reached the Mediterranean, warm regions of Europe, Asia, and the Americas, and crops grown in these regions under controlled conditions in greenhouses also provide valuable raw material . A significant species, due to the possibility of obtaining medicinal products, is Aloe arborescens . The rich and diverse composition of Aloe arborescens determines its multidirectional application in herbal medicine . The multiple biological effects of Aloe result from the presence of several classes of biological compounds in the leaves, which are plant raw material. The leaves have different chemical constituents depending on the part—leaf peel, leaf gel . As the most important groups of isolated compounds should be indicated: (i) glycoproteins: exhibit anti-insect, antitumor, immunomodulatory, antimicrobial, and antiviral properties and may inhibit HIV-1 reverse transcriptase activity, (ii) polysaccharides: 1–4 linked glucomannans, linear glucan, and branching arabinose with galactose presenting a potent phagocytosis-enhancing effect; (iii) enzymes: carboxypeptidases which reveal anti-inflammatory and anti-thermal burn action properties, (iv) phenolic compounds: (aloenin, barbaloin, aloe-emodin) may inhibit gastric juice secretion and present anti-inflammatory activity . One of the effects associated with the presence of active compounds in Aloe arborescens is anticancer activity against liver cancer, resulting from the inhibitory effect on induction of preneoplastic glutathione S-transferase positive hepatocyte foci GST-P + − colon cancer (duodenal, intestinal cancer) . Picchietti et al. confirmed the immunomodulatory effect of A. arborescens in bacterial and viral infections . In comparison, Mogale et al. investigated the antidiabetic activity of aqueous extract of A. arborescens leaf gel based on the protection mechanism of pancreatic β-cells . The antioxidant and anti-inflammatory activity should also be pointed out in addition to the mentioned properties of Aloe arborescens . Moreover, similarly to other species of the genus, Aloe arborescens is characterized as a strong laxative agent . A comprehensive analysis of selected Aloe plants carried out by Zapata et al. enables the comparison of Aloe arborescens with other species of the genus. It is characterized by the highest total phenolic concentration and total antioxidant activity. This research team also conducted an analysis of the content of active compounds in Aloe arborescens leaves depending on the harvesting period—the comparison concerned leaves harvested in spring, summer, and winter. In all cases, the highest content was found for leaves harvested in summer . So far, no relationship has been reported between the age of Aloe arborescens (one-, two-, three-, and four-year-old plants) and the biological properties of groups of compounds contained in leaves obtained from plants of subsequent years. Therefore, the aim of this study is to evaluate the composition of one-, two-, three-, and four-year-old Aloe arborescens leaves. Aloe arborescens leaves that were collected from controlled crops are used as the material for the study.
Bearing in mind the need to standardize the raw material from Aloe spp., in our research, at first, the contents of aloin A and aloenin A were determined and compared in lyophilisates obtained from Aloe arborescens leaves of the years one to four. For this purpose, the high-performance liquid chromatography with diode-array detection (HPLC-DAD) method with the detection wavelength λ max = 295 nm and the mobile phase comprised of methanol and water in a gradient was developed and validated . Under the developed chromatographic separation conditions, the following were eluted: aloenin A (R T = 12.7 min) and aloin A (R T =16.5 min) . On the basis of the calibration curves for the reference substances, quantitative analysis was possible . The aloin A content was highest in one-year-old leaves. Similarly, aloenin A also was found in the highest content in one-year-old leaves. It can be noticed that the content of aloin A and aloenin A decreased with the aging of the plant. The high-performance liquid chromatography with tandem mass spectrometric detection (HPLC-MS/MS) analysis was performed to confirm the presence of selected active compounds in the studied extracts . Mass spectrum of aloin A showed [M-H] − at m / z 417 and a daughter ion at m / z 297 , which resulted from the loss of [C 4 H 8 O 4 ] − . Aloenin A displayed [M-H] − at m / z 409 and daughter ions at 247 (corresponding to the loss of [C 6 H 10 O 5 ] − ) and 171 . Moreover, a high concentration of fragment ions of unknown origin at m / z 239, 242, and 266 in the positive ionization mode and at m / z 453 and 480 in the negative ionization mode was noticed. These came from polyphenolic structures, and their sum differed for lyophilisates from individual years. Examples of such compounds include condensates with caffeic acid, whose characteristic base peak, visible in , is at m / z 179. Differences in intensities of the discriminative ions may be caused by the aging of the plant. As the next stage, the content of phenolic compounds in Aloe arborescens leaf lyophilisates obtained from controlled crops for one-, two-, three-, and four-year-old plants was assessed. Obtained results showed the highest content of polyphenols for three-year-old plants. In one- to three-year-olds, the content in the tested extracts increased, and for four-year-old plants, the gel lyophilisates were lower than for three-year-old plants ( a). When it came to phenolic acids content analysis, lyophilisates from three-year-old leaves had by far the highest content of this group of compounds, and it was much higher compared to leaves from other years ( b). All of the tested leaves showed antioxidant properties. During the CUPRAC assay, the determined IC50 values for the examined leaves were between 14.8–43.1 mg/mL. The lowest concentration needed to inhibit the complex of neocuproine’s oxidizing properties and copper ion (II) exhibited in the ethanol extract prepared from the lyophilized gel from the leaves of the one-year-old plant. The IC50 values were higher for ethanol extracts of two- and three-year-olds, and the weakest antioxidative properties characterized the four-year-old Aloe arborescens extract . The aqueous extracts showed such weak antioxidant properties that it was impossible to determine the IC50 values for three- and four-year-old plants. Therefore the results are not included in the figure. The results of the next applied antioxidant assay—DPPH method—indicated three-year-old aloe leaves as the ones with the most potent antioxidant properties, slightly weaker were the properties of two-year-old plants, then four-year-old plants, and the weakest antioxidant properties in the test using the DPPH radical showed in the extracts obtained from one-year-old plants . In the study of the antioxidant properties using the ABTS radical, the most potent antioxidant properties were observed for the studied extract of two-year-old plants, weaker for three- and four-year-old, and the weakest for one-year-old plants . Statistical analysis of the results of antioxidant studies and investigations of phenolic content revealed statistically significant differences in all assays .
Based on literature reports on the differences in the chemical composition of Aloe leaves depending on the harvesting season, A. arborescens leaves collected in late summer were selected as the subject studied . Zapata et al. conducted the research for various Aloe spp. ( A. arborescens , A. aistata , A. claviflora , A. ferox , A. mitriformis , A. saponoaria , A. striata , A. vera ), which resulted in information on the season’s influence (spring, summer, winter) on the content of aloin, free putrescine, spermidine, and phenolic compound, and hydrophilic and lipophilic total antioxidant activity. For the species A. arborescens , the highest concentrations of tested compounds were noted for leaves harvested in summer . Our research aimed to examine the relationship between the age of leaves (one-, two-, three-, four-year-old) of A. arborescens and the contents of specific compounds (aloin A, aloenin A, polyphenols, and phenolic acids) in gel lyophilisates. There are reports in the literature on the therapeutic multidirectional beneficial effects of the Aloe spp. on various body systems: gastrointestinal, respiratory tract, central and peripheral nervous system, skin, and eyes . A. arborescens controlled greenhouse crops are an excellent model for comparing mainly the leaf content variation, due to the plants’ age. Other variables such as soil, hydration, temperature, air humidity, and light exposure become eliminated and are considered constant parameters. As far as the harvesting cycle is concerned, in the case of Phytopharm Klęka crops, the age of the plant is counted from planting the seedling to the ground. In turn, as pharmaceutical raw material, the three-year-old and older leaves are used. In our research, for extract preparation, we used lyophilisates from one-, two-, three-, and four-year-old gel of leaves of A. arborescens . The use and storage of the lyophilisates avoided variations in the content of compounds, resulting from the easy evaporation of solvents commonly used in the preparation of extracts. Freeze-drying and extraction processes can be indicated as stages of storage and preparation of the raw material that does not change its quality. As a result of the conducted research comparing the composition of lyophilisates obtained from the gel of the leaves of different ages, it was found, using the HPLC-DAD method, that the differences in the levels of aloin A were large and amounted to over 50% between individual years. The laxative effect based on hyperemia of pelvic organs may be associated with aloin A content, which in the large intestine under the influence of enzymes—glycosidases—releases the aglycone (aloe-emodin) . Therefore, one-year-old leaves are considered the best plant material for the production of products with this indication. The trend of the aloenin A level was similar—the highest in one-year-old plants. Aloenin A is not a compound found in all species of the genus Aloe . Its presence has been confirmed in 16 Aloe species out of 380 tested. The detection of aloenin has always been associated with the presence of aloin . Taking into account the antioxidant properties of aloenin A and its potential in the stimulation of hair growth, as well as the beneficial effects of Aloe on the skin, one-year-old A. arborescens leaves seem to be the best candidate for testing the possibility of use in skin diseases from the studied years . Additionally, due to the low-molecular structure, stability, and relatively high content of Aloe gel, aloenin A seems to be an excellent marker for the standardization of Aloe arborescens preparations . All of the tested lyophilisates are characterized by antioxidant activity. The compounds present in the lyophilisates have the potential to neutralize free radicals. Polyphenols are biologically active compounds that neutralize free radicals by donating an electron or hydrogen proton, metal chelation, induction of antioxidant enzymes, and action as chain breaker of lipid peroxidation . The polyphenols group includes various groups of compounds that contain a phenolic structure . One of them is a group of phenolic acids. The properties of phenolic acids are well documented with respect to their protective health effects, such as antimicrobial, anticancer, anti-inflammatory, and anti-mutagenic properties . Using the CUPRAC technique, the highest antioxidant potential for lyophilisates was obtained for one-year-old leaves. The analysis of the composition showed that in these lyophilisates, the levels of aloin A and aloenin A (4.18 and 10.31 mg/g d.m., respectively) are the highest, which is 2–3 times more than the lyophilisates obtained from the leaves of older plants. However, it showed the lowest quantity of polyphenols and phenolic acids, so it can be concluded that antioxidant activity presented by lyophilisates in CUPRAC assay is not affected by the presence of these groups of compounds. Therefore, in this case, the antioxidant property of extracts can be connected to the existence of other classes of compounds such as anthraquinones, e.g., aloin A, which are proven to be present in the extract or other groups which were not studied in our case. Moreover, there can be seen a relationship between the content of anthraquinones and CUPRAC assay results; with a decreasing amount of anthraquinones in the extract, there was a drop in the antioxidant potential of lyophilisates. This may suggest that anthraquinones, in the case of Aloe arborescens extracts, are responsible for the antioxidant effect by cupric ion reducing activity. This observation is in line with other studies, where it was confirmed that anthraquinones possess antioxidant activity via radical scavenging effect and ion reducing activity . The potential to act against the oxidation process was also determined by the DPPH technique. The study showed that the lyophilisates obtained from three-year-old leaves have the highest antioxidant potential, which may be associated with a high content of polyphenols (354.88 mg/g d.m.) and phenolic acids (187.43 mg/g d.m.). Higher antioxidant potentials were obtained for ethanol extracts, and the difference was particularly large for extracts obtained from lyophilisates of four-year-old leaves. The last technique for investigating the antioxidant potential was the ABTS method. As in the DPPH technique, ethanol extracts showed the greater potential, regardless of the leaves’ age from which the gel to obtain the lyophilisates came. The highest antioxidant properties were characteristic for the lyophilisates for two- and three-year-old plants. In this case, similar to the DPPH method, taking into account the content of phenolic compounds, it can be suggested that the antioxidant activity is also due to the presence of other compounds, e.g., a synergy between the action of polyphenols and anthranoid compounds can be suggested. Moreover, according to the literature on the subject, it can be seen that the choice of the alcoholic extraction solvent may affect the antioxidant activity of the extract—an example is the use of ethanol which causes the transition to the extract of more compounds with antioxidant activity potential . Due to the potent antioxidant properties of polyphenols, including phenolic acids, it could be expected that their high content in three-year-old leaves would translate into their potentially highest antioxidant activity. However, this relationship can only be applied to the DPPH assay, where extracts from three-year-old leaves showed the best antioxidant activity, which can be connected to the highest amount of polyphenols and phenolic acids present in the raw material. This connection is especially seen in water extracts, where the one from three-year-old leaves revealed a higher quantity of polyphenols and a significantly higher amount of phenolic acids with respect to water extracts obtained from leaves in different ages. The methods used to test the extracts’ antioxidant activity indicated that the one-, two-, and three-year-old leaves showed the highest activity, depending on the type of study (CUPRAC, DPPH, ABTS method, respectively). Therefore, it can be concluded that, besides phenolic acids, other compounds from the polyphenol group and other groups will affect the antioxidant properties. The results of the study may indicate the beneficial usage of one-year-old and two-year-old leaves of Aloe arborescens as a pharmaceutical material, since they are characterized by different quality and quantity properties, especially when it comes to anthranoid compounds composition. It could be advantageous to use the early age leaves as a material in the development of laxative agents.
4.1. Plant Material Aloe arborescens Mill. fresh leaves supplied by Phytopharm Klęka S.A. (Klęka, Poland) were used for the study. The leaves were obtained from the one-, two-, three-, and four-year-old plants growing in controlled crops . 4.2. Crop Conditions Cultivation of Aloe arborescens Mill. was carried out in controlled, inbreed plantations in heated greenhouses. Depending on the season, temperature varies between 10 °C to 30 °C as plants do not withstand temperatures below 0 °C. During cultivation, ventilation and shading are applied in order to regulate temperature and humidity. Aloe arborescens is propagated vegetatively by shoots appearing in the lower part of the plant. Seedlings rooting takes place in pots using a peat substrate and lasts about a year. After that, plants are planted into the ground. The plant material—fresh leaves—is collected manually by cutting off the whole plant; therefore, the harvest means the liquidation of the plantation lot. Since the cultivation engages lots of plants at different age stages, the harvest of plant material of certain age could be throughout the year. The age of the crop is counted from the time of planting seedlings in the ground. The plantation care treatment is limited to watering and weeding. As succulents, Aloe plants are watered moderately, using laboratory-controlled water. Fertilization is controlled, with mineral or organic fertilizers applied in minimal necessary amounts according to the needs and recommendations developed by relevant institutions, based on the results of analyses of soil samples. The risk of contamination is minimal as no pesticides, fungicides, herbicides, or fumigants are used during cultivation. Moreover, the type of plantation (monoculture), cultivation care treatments, and harvest also minimize risks of other contamination types. 4.3. Standards and Reagents Standard substances—aloin A and aloenin A were supplied by PhytoLab. Methanol, Arnov’s reagent, ammonium acetate was supplied by Chempur (Piekary Slaskie, Poland), and methanol HPLC grade by J.T. Baker. Ethanol 96%, formic acid (98–100%), sodium hydroxide, sodium chloride, sodium carbonate, acetate buffer concentrate pH 4.5, and copper (II) chloride 2 hydrate were of analytical grade and were provided by Avantor (Gliwice, Poland), while 2,2-diphenyl-2-picrylhydrazine (DPPH), vitamin C/BHA, neocuproine, Folin- Ciocâlteu reagent, and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) were provided by Sigma Aldrich (Schnelldorf, Germany). High-quality pure water was obtained by applying USFT-801 water purification system (Burlington, VT, USA) and Elix SA 67120 millipore purification system (Molsheim, France). 4.4. Lyophilization A pulp was obtained from the leaves by leaf skin separation. It was stored at −20 °C in a freezer for 24 h, and then frozen pulp was lyophilized using a freeze-dryer HetoPowerDry PL3000 Freeze Dryer (Thermo Fisher Scientific, Waltham, MA, USA). The process lasted for 48 h at a temperature of −50 °C under vacuum conditions. Lyophilisates were stored in the conditions of the minimalized humidity and light access. 4.5. Extracts Preparation The extracts were prepared from the lyophilized leaf gel of particular age plants. Then, 100 mg of the lyophilized milled gel were extracted with ethanol 96% ( v / v ) or water with the use of ultrasounds. After 30 min, the supernatants were decanted, and the lyophilisates were extracted twice more under the mentioned conditions. Finally, the supernatants collected for each lyophilisate were mixed together, filtered through syringe filters, and concentrated to dryness by applying rotary evaporation. The residues were dissolved in 1 mL of ethanol 96% ( v / v ) or water to obtain a 100 mg/mL concentration. 4.6. Chromatographic Analysis 4.6.1. Preparation of the Standard Solutions for HPLC Analyses Two substances were used as standards—aloin A, aloenin A. Their presence in the studied extract was confirmed using mentioned gradient HPLC-DAD method. After setting a baseline, injections of 20 µL volume of extracts were made. Each compound’s identity was confirmed by comparing the retention times of the obtained peaks with the standards. For the purpose of quantitative research, calibration curves were prepared. 4.6.2. Preparation of Extract Solutions for HPLC Analyses Stock extract (100 mg of lyophilisate in 1 mL of ethanol) was filtered through 0.2 µm syringe filters. Then, 50 µL of the filtered extract was diluted with ethanol up to a 1 mL volume, and the prepared solution was injected. The concentrations of standard compounds were calculated on the basis of the obtained HPLC chromatograms and standard curves. 4.6.3. HPLC Analysis The RP-HPLC-DAD method was applied to confirm the identity of the material. For this purpose, two standard substances were used as standards—aloin A and aloenin A. In the study, the HPLC Shimadzu Prominence- i LC-2030C equipped with DAD detector was used (Shimadzu, Tokyo, Japan). The software was LabSolution DB/CS version 6.50 (Shimadzu, Tokyo, Japan). The stationary phase was LiChrospher 100 RP-18e (250 mm × 4.6 mm, 5 µm) HPLC column (Merck, Warsaw, Poland), the mobile phase comprised of methanol (phase A) and water (phase B) in a gradient the injection volume was 20 µL, and a flow rate was 1 mL/min. The detection was performed at the wavelength λ max = 295 nm. 4.6.4. HPLC-MS/MS Analysis HPLC-MS/MS analysis was performed using the Shimadzu Nexera coupled to LC-MS 8030 triple quadrupole mass spectrometer (Shimadzu, Tokyo, Japan) with a Kinetex C18 column (100 mm × 2.1 mm, 2.6 µm) (Phenomenex, Torrance, CA, USA). The mobile phase consisted of 0.1% formic acid in methanol (phase A) and water (phase B). Compounds were eluted into the ESI ion source at the flow rate of 0.35 mL/min. The MS was programmed to carry out a full scan over a range of m / z 100–1000 (MS1) and m / z 100–500 (MS2) in both positive and negative modes. Electrospray needle voltage was maintained at 4.5 kV. MS interface was adjusted with the following parameters: desolvation line 250 °C, heat block temperature 400 °C, interface temperature 350 °C, nitrogen was used as the drying gas and as the nebulizing gas with flow rates of 15 and 2 L/min, respectively. Compounds were characterized by their MS/MS spectra. 4.7. Phenolic Compounds Determination 4.7.1. The Sum of Polyphenolic Compounds The method is based on a color reaction of the Folin- Ciocâlteu reagent with the contained phenolic groups in the presence of sodium carbonate. Therefore, 1 mL of the extract was vortexed with 1 mL of Folin- Ciocâlteu reagent for 3 s, then 1 mL of sodium carbonate and 7 mL of water were added and mixed again by vortex for 3 s. After 90 min of incubation in the dark, the resulting colored complex was determined spectrophotometrically at wavelength 765 nm in the Metertech SP-830 spectrophotometer (Metertech, Taipei, Taiwan). In order to determine the content of compounds reactive with the Folin- Ciocâlteu reagent, a standard curve for gallic acid was established. Then, using the determined curve, it was converted to the content in 1 g of the dry mass of the extract. 4.7.2. Phenolic Acids The presence of phenolic acid compounds was determined by the colorimetric method described in Polish Pharmacopoeia VI . The study was performed by adding to 5 mL of distilled water in a volumetric flask, analyzed extract, 0.5 M solution of hydrochloric acid, Arnov’s reagent, and sodium hydroxide in a volume of 1 mL each. After diluting with distilled water to 10 mL volume, spectroscopy measurement was performed at the wavelength of 490 nm. The content of phenolic acids was converted to caffeic acid on the basis of on the drawn standard curve and expressed in mg per 1 g of dry mass. 4.8. Antioxidant Activity Determination 4.8.1. CUPRAC Method The extract’s antioxidant properties were determined by using CUPRAC (cupric ion reducing antioxidant capacity) assay, which was performed according to Apak et al. with modifications . The study is based on the reduction of the complex of neocuproine and cupric ion (II), which results in the change of the sample’s color from bluish to yellow. The CUPRAC reagent consists of equal volumes of 7.5 mM ethanolic 96% ( v / v ) solution of neocuproine, 10 mM solution of copper chloride (II), and ammonium acetate buffer of Ph = 7.0. The assay was performed by mixing 50 µL of aloe leaf gel extract with 150 µL of CUPRAC reagent. The solution was shaken and incubated in the dark at room temperature for 30 min. After the incubation, absorbance was measured at the wavelength of λ = 450 nm at Multiskan GO plate reader (Thermo Fisher Scientific, Waltham, MA, USA). As a standard, vitamin C was used. The results were presented as the IC 50 (inhibitory concentration), which means the minimum concentration of the extract, which reduces the initial, oxidized form of the complex by 50%. 4.8.2. DPPH Method Determination of activity against DPPH radicals was performed on the basis of the method of Hatano et al. with Amarowicz modifications . The principle of the method was based on the spectrophotometric (Metertech SP-830, Taipei, Taiwan) measurement of the color of the reaction mixture, in which, depending on the antioxidant capacity of the tested extract, free radicals DPPH (1,1-diphenyl-2-pyrrylhydrazyl) were swept away. The absorbance measurement was made at 517 nm after incubation for 30 min at room temperature and protected from light. The calculated percentage of the sweep was put into the standard curve for the Trolox. 4.8.3. ABTS Method Determination of activity against ABTS cation radicals was performed according to the method described by Re et al. . The test solution was introduced into the reaction medium containing the previously generated cation radical ABTS [2,2′-azinobis (3-ethylbenzothiazoline-6-sulfonate)]. The presence of antioxidants caused the reaction mixture to change color. At the same time, in order to prepare the calibration curve, the absorbance was measured in parallel at the same wavelength of 735 nm of samples containing appropriate concentrations of the reference substance—Trolox, and the results were expressed in its equivalents. 4.9. Statistical Analysis The results were analyzed using Statistica software (version 13.0, Stat Soft Inc., Tulsa, OK, USA). An ANOVA test was used to compare results of the measurements in the studied extracts obtained from the plant at different age. The differences were considered to be significant when p ≤ 0.05.
Aloe arborescens Mill. fresh leaves supplied by Phytopharm Klęka S.A. (Klęka, Poland) were used for the study. The leaves were obtained from the one-, two-, three-, and four-year-old plants growing in controlled crops .
Cultivation of Aloe arborescens Mill. was carried out in controlled, inbreed plantations in heated greenhouses. Depending on the season, temperature varies between 10 °C to 30 °C as plants do not withstand temperatures below 0 °C. During cultivation, ventilation and shading are applied in order to regulate temperature and humidity. Aloe arborescens is propagated vegetatively by shoots appearing in the lower part of the plant. Seedlings rooting takes place in pots using a peat substrate and lasts about a year. After that, plants are planted into the ground. The plant material—fresh leaves—is collected manually by cutting off the whole plant; therefore, the harvest means the liquidation of the plantation lot. Since the cultivation engages lots of plants at different age stages, the harvest of plant material of certain age could be throughout the year. The age of the crop is counted from the time of planting seedlings in the ground. The plantation care treatment is limited to watering and weeding. As succulents, Aloe plants are watered moderately, using laboratory-controlled water. Fertilization is controlled, with mineral or organic fertilizers applied in minimal necessary amounts according to the needs and recommendations developed by relevant institutions, based on the results of analyses of soil samples. The risk of contamination is minimal as no pesticides, fungicides, herbicides, or fumigants are used during cultivation. Moreover, the type of plantation (monoculture), cultivation care treatments, and harvest also minimize risks of other contamination types.
Standard substances—aloin A and aloenin A were supplied by PhytoLab. Methanol, Arnov’s reagent, ammonium acetate was supplied by Chempur (Piekary Slaskie, Poland), and methanol HPLC grade by J.T. Baker. Ethanol 96%, formic acid (98–100%), sodium hydroxide, sodium chloride, sodium carbonate, acetate buffer concentrate pH 4.5, and copper (II) chloride 2 hydrate were of analytical grade and were provided by Avantor (Gliwice, Poland), while 2,2-diphenyl-2-picrylhydrazine (DPPH), vitamin C/BHA, neocuproine, Folin- Ciocâlteu reagent, and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) were provided by Sigma Aldrich (Schnelldorf, Germany). High-quality pure water was obtained by applying USFT-801 water purification system (Burlington, VT, USA) and Elix SA 67120 millipore purification system (Molsheim, France).
A pulp was obtained from the leaves by leaf skin separation. It was stored at −20 °C in a freezer for 24 h, and then frozen pulp was lyophilized using a freeze-dryer HetoPowerDry PL3000 Freeze Dryer (Thermo Fisher Scientific, Waltham, MA, USA). The process lasted for 48 h at a temperature of −50 °C under vacuum conditions. Lyophilisates were stored in the conditions of the minimalized humidity and light access.
The extracts were prepared from the lyophilized leaf gel of particular age plants. Then, 100 mg of the lyophilized milled gel were extracted with ethanol 96% ( v / v ) or water with the use of ultrasounds. After 30 min, the supernatants were decanted, and the lyophilisates were extracted twice more under the mentioned conditions. Finally, the supernatants collected for each lyophilisate were mixed together, filtered through syringe filters, and concentrated to dryness by applying rotary evaporation. The residues were dissolved in 1 mL of ethanol 96% ( v / v ) or water to obtain a 100 mg/mL concentration.
4.6.1. Preparation of the Standard Solutions for HPLC Analyses Two substances were used as standards—aloin A, aloenin A. Their presence in the studied extract was confirmed using mentioned gradient HPLC-DAD method. After setting a baseline, injections of 20 µL volume of extracts were made. Each compound’s identity was confirmed by comparing the retention times of the obtained peaks with the standards. For the purpose of quantitative research, calibration curves were prepared. 4.6.2. Preparation of Extract Solutions for HPLC Analyses Stock extract (100 mg of lyophilisate in 1 mL of ethanol) was filtered through 0.2 µm syringe filters. Then, 50 µL of the filtered extract was diluted with ethanol up to a 1 mL volume, and the prepared solution was injected. The concentrations of standard compounds were calculated on the basis of the obtained HPLC chromatograms and standard curves. 4.6.3. HPLC Analysis The RP-HPLC-DAD method was applied to confirm the identity of the material. For this purpose, two standard substances were used as standards—aloin A and aloenin A. In the study, the HPLC Shimadzu Prominence- i LC-2030C equipped with DAD detector was used (Shimadzu, Tokyo, Japan). The software was LabSolution DB/CS version 6.50 (Shimadzu, Tokyo, Japan). The stationary phase was LiChrospher 100 RP-18e (250 mm × 4.6 mm, 5 µm) HPLC column (Merck, Warsaw, Poland), the mobile phase comprised of methanol (phase A) and water (phase B) in a gradient the injection volume was 20 µL, and a flow rate was 1 mL/min. The detection was performed at the wavelength λ max = 295 nm. 4.6.4. HPLC-MS/MS Analysis HPLC-MS/MS analysis was performed using the Shimadzu Nexera coupled to LC-MS 8030 triple quadrupole mass spectrometer (Shimadzu, Tokyo, Japan) with a Kinetex C18 column (100 mm × 2.1 mm, 2.6 µm) (Phenomenex, Torrance, CA, USA). The mobile phase consisted of 0.1% formic acid in methanol (phase A) and water (phase B). Compounds were eluted into the ESI ion source at the flow rate of 0.35 mL/min. The MS was programmed to carry out a full scan over a range of m / z 100–1000 (MS1) and m / z 100–500 (MS2) in both positive and negative modes. Electrospray needle voltage was maintained at 4.5 kV. MS interface was adjusted with the following parameters: desolvation line 250 °C, heat block temperature 400 °C, interface temperature 350 °C, nitrogen was used as the drying gas and as the nebulizing gas with flow rates of 15 and 2 L/min, respectively. Compounds were characterized by their MS/MS spectra.
Two substances were used as standards—aloin A, aloenin A. Their presence in the studied extract was confirmed using mentioned gradient HPLC-DAD method. After setting a baseline, injections of 20 µL volume of extracts were made. Each compound’s identity was confirmed by comparing the retention times of the obtained peaks with the standards. For the purpose of quantitative research, calibration curves were prepared.
Stock extract (100 mg of lyophilisate in 1 mL of ethanol) was filtered through 0.2 µm syringe filters. Then, 50 µL of the filtered extract was diluted with ethanol up to a 1 mL volume, and the prepared solution was injected. The concentrations of standard compounds were calculated on the basis of the obtained HPLC chromatograms and standard curves.
The RP-HPLC-DAD method was applied to confirm the identity of the material. For this purpose, two standard substances were used as standards—aloin A and aloenin A. In the study, the HPLC Shimadzu Prominence- i LC-2030C equipped with DAD detector was used (Shimadzu, Tokyo, Japan). The software was LabSolution DB/CS version 6.50 (Shimadzu, Tokyo, Japan). The stationary phase was LiChrospher 100 RP-18e (250 mm × 4.6 mm, 5 µm) HPLC column (Merck, Warsaw, Poland), the mobile phase comprised of methanol (phase A) and water (phase B) in a gradient the injection volume was 20 µL, and a flow rate was 1 mL/min. The detection was performed at the wavelength λ max = 295 nm.
HPLC-MS/MS analysis was performed using the Shimadzu Nexera coupled to LC-MS 8030 triple quadrupole mass spectrometer (Shimadzu, Tokyo, Japan) with a Kinetex C18 column (100 mm × 2.1 mm, 2.6 µm) (Phenomenex, Torrance, CA, USA). The mobile phase consisted of 0.1% formic acid in methanol (phase A) and water (phase B). Compounds were eluted into the ESI ion source at the flow rate of 0.35 mL/min. The MS was programmed to carry out a full scan over a range of m / z 100–1000 (MS1) and m / z 100–500 (MS2) in both positive and negative modes. Electrospray needle voltage was maintained at 4.5 kV. MS interface was adjusted with the following parameters: desolvation line 250 °C, heat block temperature 400 °C, interface temperature 350 °C, nitrogen was used as the drying gas and as the nebulizing gas with flow rates of 15 and 2 L/min, respectively. Compounds were characterized by their MS/MS spectra.
4.7.1. The Sum of Polyphenolic Compounds The method is based on a color reaction of the Folin- Ciocâlteu reagent with the contained phenolic groups in the presence of sodium carbonate. Therefore, 1 mL of the extract was vortexed with 1 mL of Folin- Ciocâlteu reagent for 3 s, then 1 mL of sodium carbonate and 7 mL of water were added and mixed again by vortex for 3 s. After 90 min of incubation in the dark, the resulting colored complex was determined spectrophotometrically at wavelength 765 nm in the Metertech SP-830 spectrophotometer (Metertech, Taipei, Taiwan). In order to determine the content of compounds reactive with the Folin- Ciocâlteu reagent, a standard curve for gallic acid was established. Then, using the determined curve, it was converted to the content in 1 g of the dry mass of the extract. 4.7.2. Phenolic Acids The presence of phenolic acid compounds was determined by the colorimetric method described in Polish Pharmacopoeia VI . The study was performed by adding to 5 mL of distilled water in a volumetric flask, analyzed extract, 0.5 M solution of hydrochloric acid, Arnov’s reagent, and sodium hydroxide in a volume of 1 mL each. After diluting with distilled water to 10 mL volume, spectroscopy measurement was performed at the wavelength of 490 nm. The content of phenolic acids was converted to caffeic acid on the basis of on the drawn standard curve and expressed in mg per 1 g of dry mass.
The method is based on a color reaction of the Folin- Ciocâlteu reagent with the contained phenolic groups in the presence of sodium carbonate. Therefore, 1 mL of the extract was vortexed with 1 mL of Folin- Ciocâlteu reagent for 3 s, then 1 mL of sodium carbonate and 7 mL of water were added and mixed again by vortex for 3 s. After 90 min of incubation in the dark, the resulting colored complex was determined spectrophotometrically at wavelength 765 nm in the Metertech SP-830 spectrophotometer (Metertech, Taipei, Taiwan). In order to determine the content of compounds reactive with the Folin- Ciocâlteu reagent, a standard curve for gallic acid was established. Then, using the determined curve, it was converted to the content in 1 g of the dry mass of the extract.
The presence of phenolic acid compounds was determined by the colorimetric method described in Polish Pharmacopoeia VI . The study was performed by adding to 5 mL of distilled water in a volumetric flask, analyzed extract, 0.5 M solution of hydrochloric acid, Arnov’s reagent, and sodium hydroxide in a volume of 1 mL each. After diluting with distilled water to 10 mL volume, spectroscopy measurement was performed at the wavelength of 490 nm. The content of phenolic acids was converted to caffeic acid on the basis of on the drawn standard curve and expressed in mg per 1 g of dry mass.
4.8.1. CUPRAC Method The extract’s antioxidant properties were determined by using CUPRAC (cupric ion reducing antioxidant capacity) assay, which was performed according to Apak et al. with modifications . The study is based on the reduction of the complex of neocuproine and cupric ion (II), which results in the change of the sample’s color from bluish to yellow. The CUPRAC reagent consists of equal volumes of 7.5 mM ethanolic 96% ( v / v ) solution of neocuproine, 10 mM solution of copper chloride (II), and ammonium acetate buffer of Ph = 7.0. The assay was performed by mixing 50 µL of aloe leaf gel extract with 150 µL of CUPRAC reagent. The solution was shaken and incubated in the dark at room temperature for 30 min. After the incubation, absorbance was measured at the wavelength of λ = 450 nm at Multiskan GO plate reader (Thermo Fisher Scientific, Waltham, MA, USA). As a standard, vitamin C was used. The results were presented as the IC 50 (inhibitory concentration), which means the minimum concentration of the extract, which reduces the initial, oxidized form of the complex by 50%. 4.8.2. DPPH Method Determination of activity against DPPH radicals was performed on the basis of the method of Hatano et al. with Amarowicz modifications . The principle of the method was based on the spectrophotometric (Metertech SP-830, Taipei, Taiwan) measurement of the color of the reaction mixture, in which, depending on the antioxidant capacity of the tested extract, free radicals DPPH (1,1-diphenyl-2-pyrrylhydrazyl) were swept away. The absorbance measurement was made at 517 nm after incubation for 30 min at room temperature and protected from light. The calculated percentage of the sweep was put into the standard curve for the Trolox. 4.8.3. ABTS Method Determination of activity against ABTS cation radicals was performed according to the method described by Re et al. . The test solution was introduced into the reaction medium containing the previously generated cation radical ABTS [2,2′-azinobis (3-ethylbenzothiazoline-6-sulfonate)]. The presence of antioxidants caused the reaction mixture to change color. At the same time, in order to prepare the calibration curve, the absorbance was measured in parallel at the same wavelength of 735 nm of samples containing appropriate concentrations of the reference substance—Trolox, and the results were expressed in its equivalents.
The extract’s antioxidant properties were determined by using CUPRAC (cupric ion reducing antioxidant capacity) assay, which was performed according to Apak et al. with modifications . The study is based on the reduction of the complex of neocuproine and cupric ion (II), which results in the change of the sample’s color from bluish to yellow. The CUPRAC reagent consists of equal volumes of 7.5 mM ethanolic 96% ( v / v ) solution of neocuproine, 10 mM solution of copper chloride (II), and ammonium acetate buffer of Ph = 7.0. The assay was performed by mixing 50 µL of aloe leaf gel extract with 150 µL of CUPRAC reagent. The solution was shaken and incubated in the dark at room temperature for 30 min. After the incubation, absorbance was measured at the wavelength of λ = 450 nm at Multiskan GO plate reader (Thermo Fisher Scientific, Waltham, MA, USA). As a standard, vitamin C was used. The results were presented as the IC 50 (inhibitory concentration), which means the minimum concentration of the extract, which reduces the initial, oxidized form of the complex by 50%.
Determination of activity against DPPH radicals was performed on the basis of the method of Hatano et al. with Amarowicz modifications . The principle of the method was based on the spectrophotometric (Metertech SP-830, Taipei, Taiwan) measurement of the color of the reaction mixture, in which, depending on the antioxidant capacity of the tested extract, free radicals DPPH (1,1-diphenyl-2-pyrrylhydrazyl) were swept away. The absorbance measurement was made at 517 nm after incubation for 30 min at room temperature and protected from light. The calculated percentage of the sweep was put into the standard curve for the Trolox.
Determination of activity against ABTS cation radicals was performed according to the method described by Re et al. . The test solution was introduced into the reaction medium containing the previously generated cation radical ABTS [2,2′-azinobis (3-ethylbenzothiazoline-6-sulfonate)]. The presence of antioxidants caused the reaction mixture to change color. At the same time, in order to prepare the calibration curve, the absorbance was measured in parallel at the same wavelength of 735 nm of samples containing appropriate concentrations of the reference substance—Trolox, and the results were expressed in its equivalents.
The results were analyzed using Statistica software (version 13.0, Stat Soft Inc., Tulsa, OK, USA). An ANOVA test was used to compare results of the measurements in the studied extracts obtained from the plant at different age. The differences were considered to be significant when p ≤ 0.05.
As a result of the conducted research, we proved that the differences in the content of anthranoid compounds and phenyl pyrone derivatives—aloin A and aloenin A—are significant in lyophilisates obtained from Aloe arborescens leaves of different ages (one- to four-year-old). The developed chromatographic method (HPLC-DAD) can be recommended as suitable for the standardization of lyophilisates for the content of aloin A and aloenin A. The spectrophotometric determination of the sum of polyphenols and phenolic acids revealed the differences in the content of these groups of compounds in lyophilisates obtained from leaves of different ages. All tested lyophilisates exhibited an antioxidant effect, which is worth emphasizing; the technique for determining the potential should depend on the group of secondary metabolites for which we want to monitor the antioxidant effect.
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Guidelines to support the design, and selection and appraisal of multimedia teaching aids for microbiology education | 56038edb-3fec-4501-9b76-102b41b1b4e6 | 11334907 | Microbiology[mh] | Microbes are ubiquitous: they exist in the soil on which we walk, in the air we breathe and in the water we drink. They are involved in uncountable processes throughout nature and utilised by humans to many different ends. Common knowledge of microbiology, often regarded as the ‘language of nature’, is increasingly recognised by scientists as important because microbes are central to various daily activities and are key players in contemporary global crises (Anand et al., ). But, for a variety of reasons, inter alia the invisibility of microbes, their association with disease/germophobia, the relatively recent appreciation of their pervasive positive impacts on humanity and the planet, society has in general little knowledge of microbes and their activities. However, microbiology literacy is key to understanding life and making knowledge‐based decisions on many important issues, including those affecting personal well‐being (Timmis et al., ). To correct this anomaly, the International Microbiology Literacy Initiative (IMiLI) aims to become an enabler of increased microbiology literacy in society by creating appealing and experimental microbiology content, curated foremost for children and young adults (Timmis et al., ). Targeting these specific groups can aid in IMiLI's mission as teaching youngsters a variety of everyday microbiology topics increases their awareness and sense of how these processes relate to broader topics of microbe involvement, for example in climate change. Not only will this help in strengthening informed decision‐making throughout childhood and later adulthood, but also in creating a more widespread acceptance of pertinent decisions, for example, the obligation to wear face masks during the Covid‐19 pandemic, the transition towards bioenergy, or reduction in the use of antibiotics and disinfectants (Damerell et al., ; Timmis et al., ). The challenge for microbiology education is, however, that microbes and microbiology processes are invisible to the naked eye (McGenity et al., ; Timmis et al., ). Multimedia learning can visualise the invisible, create mental connections between what is observed/learned and the actual actors, and support different types of learners. Therefore, the IMiLI is seeking appropriate multimedia teaching aids (MTAs) – both from the web and by creating them de novo – as teaching complements. A major hurdle is the vast amount, and variable quality and primary aims of available content (e.g. the primary aim, and hence content, may be promotional, rather than educational). The world‐wide‐web is full of explanatory videos and other multimedia items that, in principle, can be valuable adjuncts to formal and informal educational activities. However, not all content is suitable for learning. For example, previous research has shown that central educational/cognitive principles are not consistently considered in important educational multimedia – for example, in animated videos for medical education (Yue et al., ). Besides these more formal educational principles, multimedia learning effectiveness is dependent on a range of aspects, such as adequate design and delivery, and focus on different target groups and learning objectives (Almarabeh & Amer, ). Importantly, various types of learners/learning modes (visual, auditory, reading/writing and kinaesthetic) and learning objectives exist, that benefit from the utilisation of different channels of cognitive processing and combinations thereof. Some learners benefit more from audio‐visual presentation of information while others from written information (Flavin, ). Naturally, types of learners/modes of learning are spectra: they are not mutually exclusive, individuals might merely be more effective at learning based on certain learning modes, specific content and learning outcomes will lend themselves more readily to certain learning modes, etc. Multimedia has the capacity to cater to each of the above‐mentioned learning types while enhancing student engagement and, therefore, the learning experience. Videos, animations, comics and video games are particularly good and relevant examples of multimedia that can support and increase the learning experience (Mayer, ; Rolfe & Gray, ). However, guidance for appraising the quality and adequacy of existing, or guiding the design of novel, microbiology MTAs for specific target audiences and learning objectives is lacking. Therefore, this study aimed to create an easy‐to‐use, evidence‐based microbiology education multimedia assessment guideline (checklist) to support microbiology educators in selecting and appraising, or designing, specific multimedia content for their educational objectives.
This communication is based on a joint research project of four master's students as executed by RvB, DJCS, NvdB and BH under the close supervision of JKT and SB. JKT, KNT and PDS ideated the project and KNT and PDS acted as expert advisors throughout. For this study, an exploratory, mixed‐methods (quantitative and qualitative) approach was utilised. We collected secondary data (from literature and multimedia databases, e.g. YouTube, and pertinent websites) and primary data (in semi‐structured expert interviews) and focussed on four types of multimedia for microbiology education: videos, animations, comics and video games. First, two semi‐systematic literature searches based on the PRISMA guidelines (Page et al., ) were executed on Web of Science, Wiley, SCOPUS and ERIC in order to identify quality criteria pertinent to educational theories and multimedia design principles. We sought quality criteria for educational multimedia in general, but also those specific to microbiology education and the four media types listed above. The results were collated and a preliminary microbiology MTA assessment guideline – primarily in a checklist format – was subsequently drafted. The draft guideline consisted of four similarly segmented yet medium‐specific guidelines (specific quality criteria for videos, animations, comics and video games). Second, using a web‐scraping approach (searches based on metadata, for instance for videos on YouTube) and manual searches, we located microbiology‐specific videos ( n = 2327), animations ( n = 198), comics ( n = 89) and video games ( n = 42) on the web, collated our results (deduplicated entries, etc.) in four medium‐specific data tables, and piloted the preliminary guideline by appraising a selection of items each for the four multimedia types. For videos and animations (larger, rather homogeneous samples) we used a quasi‐random selection of 20 items per medium. For comics and video games (smaller, rather heterogeneous samples) we purposively selected, 20 comics and 8 video games. The piloting process was documented regarding adequacy/applicability and formulation of guideline criteria. Based on the results of the pilot appraisals, the guideline was revised. Third, we used semi‐structured interviews to query 13 multimedia and education experts (primarily academics, located in USA, Netherlands, Australia, and Turkey) to learn their views on multimedia appraisal guidelines in general, review our microbiology multimedia specific preliminary guidelines, and discuss our piloting experiences. This led to a further refinement of the guidelines and the creation of the final version. For assessor convenience, instructions regarding utilisation of the guideline and information on a priori categorisation of learning objectives (based on the 7E model) were included in the final version.
Overarching considerations The target audience The most important consideration when selecting multimedia is the target audience . Every target audience is different. It is important to have a clear idea of what is adequate and works best to stimulate interest and the learning process. The main factor to consider is personalization which can be represented in different forms. The first form is to ensure that the phrasing and narration are similar to that of the audience's everyday life. If educators are narrating themselves, it is important that they do not pretend to be someone else, i.e. try to imitate others. Artificial or imitated voices can be off‐putting and distracting, which affects engagement. It can be challenging to find the most adequate type of narration. Second, when including humour , it should be appropriate for the audience and for the context in which it is used, and it should be natural and spontaneous. An excessive or inappropriate use of humour can distract from the important information and can, once again, be off‐putting. Moreover, it should be kept in mind that a natural talent for humour is not a common quality, and that people vary in what they consider to be funny: ‘when in doubt, cut it out’. Third, for audiences less able to self‐regulate, for example primary school children, entertainment can be a useful element to render instructional messages interesting by including story driven elements. By balancing the educational narrative with the instructional narrative, the interest of the audience can be stimulated. Audiences with the ability to self‐regulate, such as young adults, are less dependent on story‐driven elements to remain focussed and interested in topics. Finally, it is important to ensure that content is accessible and inclusive for everyone in target audiences. This refers to inter alia the cultural context, educational background, such as pre‐existing knowledge, or disabilities, all of which can affect how multimedia content is processed and received by target audiences. Learning objectives In this work, multimedia selection is based on the learning objectives as described in the 7E model – Elicit , Engage , Explore , Explain , Elaborate , Evaluate , Extend (the 7Es) – an extension by Eisenkraft of the 5E model as previously described by Bybee . Before selecting multimedia, it is essential to know what the objective of an educational unit is. The 7Es are highly useful in stratifying different learning objectives, see Table : Facilitating learning objectives Teaching methods It is important to select the appropriate teaching methods in order to achieve learning objectives. Examples of teaching methods include, but are not limited to, the flipped classroom approach, game‐based learning (GBL), Interactive Learning System (ILS), and Collaborative Learning Model (CLM). An overview of how these teaching methods correspond to the learning objectives can be found in Table . Selecting multimedia Table shows an overview of suggestions regarding which multimedia can be most suitable to facilitate each of the 7E learning objectives. It is important to bear in mind that these are recommendations, and that, in principle, many different types of multimedia can be suitable for achieving learning objectives. Table also provides a full overview and detailed explanations of the different learning objectives and multimedia tools. It describes when to select certain learning objectives (and when not to), different teaching methods to facilitate the objectives and the multimedia that can facilitate selected objectives, including the corresponding criteria and considerations. Resources When selecting multimedia, it is also important to take into account the availability of resources, such as time, digital infrastructure and funding. Time available regards the contact time in class and the time for teachers to prepare classes. Digital infrastructure covers the technology required to use selected multimedia, such as computers, TVs or mobile phones, or other elements, such as an internet connection. Financial resources can, for example, concern the purchasing of multimedia or other complementary materials/devices. A mismatch might be, for instance, a well‐intended decision such as a recommendation by a centralised education body to use modern technologies, like virtual reality animations to visualise functions of mitochondria, in a situation where resources for virtual reality headsets might not be available in some, or even many, educational settings. This example also demonstrates the importance of assessing a range of options regarding multimedia for similar educational objectives, however, stratified by differentially resourced settings if making centralised decisions. Subjectivity of appraisal and transparency The appraisal of existing multimedia content – even when checklist‐driven – is always somewhat subjective. Whenever possible, it is prudent and insightful to engage a second appraiser and compare and discuss the relevance of appraisal outcomes against envisioned learning objectives. Documentation of executed appraisals also provides a solid basis for transparent educational multimedia decision‐making. Testing multimedia Before implementing multimedia content on a broader scale or including it on a recurring curriculum, it is helpful to run a pilot on a representative target audience/cohort. Using the core outcome criteria of our checklists (see below) to structure a short inquiry with target audiences (or caregivers) can increase the potential success and effectiveness of multimedia learning. The guideline and its main components The guideline consists of four main sections: the overarching considerations, as alluded to above, that provide a top‐level explanation of the rationale for selecting and appraising multimedia for microbiology education focussing on specific audiences and target educational objectives, a 7E model stratified decision tool relating inter alia multimedia selection to learning objectives (Table ), checklists for appraising candidate multimedia, namely videos/animations, comics and video games (Tables , , , videos and animations have been integrated, as the appraisal criteria are mostly identical), detailed background information (Appendices and ) describing the theoretical and practical underpinnings of the multimedia selection and appraisal tools we present here (including a host of references for further reading) and thereby linking all sections. As Tables , , , are readily applicable decision tools, they represent the core of our guidelines. A top‐level example of how to use the guideline is presented at the end of this section (Table ). MTA selection based on the 7E model Table provides guidance on which teaching method(s) and corresponding multimedia – focussing on videos/animations, comics and video games – are well suited for specific learning scenarios/objectives. In consequence, Table should be consulted a priori to contemplate which multimedium (or range thereof) is most adequate – recommendations are provided. MTA quality appraisal Tables , , are based on multimedium‐specific considerations. Table is a joint checklist for both videos and animations – animation‐specific criteria are at the end of the checklist. Table is the appraisal checklist for comics, and Table for video games respectively. The checklists can be used in a tick‐a‐box manner. Videos and animations Note: for animations three additional appraisal items exist, which are located at the end of Table . Comics Note: the Comics appraisal tool also includes a section with items on anthropomorphism (at the end of Table ). Employing anthropomorphism can be highly effective for learner engagement; however, anthropomorphism should be used with caution, as it is highly age and learning‐objective dependent, see comments at the end of Table . Video games An example of using the guideline A top level overview of the sequence of multimedia selection , appraisal and piloting based on the guideline and the 7E learning objective Engage is shown in Table . Video recording of the MTA project Some of this work, including inter alia how to use the guidelines, was presented at SfAM's International Applied Microbiology Conference , a recording of which can be found here: https://youtu.be/6NUNmTCjVv0?feature=shared&t=1410 , The time stamp skips to the application of the appraisal guidelines, although for a more general introduction by the entire research team, the video can be watched from the beginning.
The target audience The most important consideration when selecting multimedia is the target audience . Every target audience is different. It is important to have a clear idea of what is adequate and works best to stimulate interest and the learning process. The main factor to consider is personalization which can be represented in different forms. The first form is to ensure that the phrasing and narration are similar to that of the audience's everyday life. If educators are narrating themselves, it is important that they do not pretend to be someone else, i.e. try to imitate others. Artificial or imitated voices can be off‐putting and distracting, which affects engagement. It can be challenging to find the most adequate type of narration. Second, when including humour , it should be appropriate for the audience and for the context in which it is used, and it should be natural and spontaneous. An excessive or inappropriate use of humour can distract from the important information and can, once again, be off‐putting. Moreover, it should be kept in mind that a natural talent for humour is not a common quality, and that people vary in what they consider to be funny: ‘when in doubt, cut it out’. Third, for audiences less able to self‐regulate, for example primary school children, entertainment can be a useful element to render instructional messages interesting by including story driven elements. By balancing the educational narrative with the instructional narrative, the interest of the audience can be stimulated. Audiences with the ability to self‐regulate, such as young adults, are less dependent on story‐driven elements to remain focussed and interested in topics. Finally, it is important to ensure that content is accessible and inclusive for everyone in target audiences. This refers to inter alia the cultural context, educational background, such as pre‐existing knowledge, or disabilities, all of which can affect how multimedia content is processed and received by target audiences. Learning objectives In this work, multimedia selection is based on the learning objectives as described in the 7E model – Elicit , Engage , Explore , Explain , Elaborate , Evaluate , Extend (the 7Es) – an extension by Eisenkraft of the 5E model as previously described by Bybee . Before selecting multimedia, it is essential to know what the objective of an educational unit is. The 7Es are highly useful in stratifying different learning objectives, see Table : Facilitating learning objectives Teaching methods It is important to select the appropriate teaching methods in order to achieve learning objectives. Examples of teaching methods include, but are not limited to, the flipped classroom approach, game‐based learning (GBL), Interactive Learning System (ILS), and Collaborative Learning Model (CLM). An overview of how these teaching methods correspond to the learning objectives can be found in Table . Selecting multimedia Table shows an overview of suggestions regarding which multimedia can be most suitable to facilitate each of the 7E learning objectives. It is important to bear in mind that these are recommendations, and that, in principle, many different types of multimedia can be suitable for achieving learning objectives. Table also provides a full overview and detailed explanations of the different learning objectives and multimedia tools. It describes when to select certain learning objectives (and when not to), different teaching methods to facilitate the objectives and the multimedia that can facilitate selected objectives, including the corresponding criteria and considerations. Resources When selecting multimedia, it is also important to take into account the availability of resources, such as time, digital infrastructure and funding. Time available regards the contact time in class and the time for teachers to prepare classes. Digital infrastructure covers the technology required to use selected multimedia, such as computers, TVs or mobile phones, or other elements, such as an internet connection. Financial resources can, for example, concern the purchasing of multimedia or other complementary materials/devices. A mismatch might be, for instance, a well‐intended decision such as a recommendation by a centralised education body to use modern technologies, like virtual reality animations to visualise functions of mitochondria, in a situation where resources for virtual reality headsets might not be available in some, or even many, educational settings. This example also demonstrates the importance of assessing a range of options regarding multimedia for similar educational objectives, however, stratified by differentially resourced settings if making centralised decisions. Subjectivity of appraisal and transparency The appraisal of existing multimedia content – even when checklist‐driven – is always somewhat subjective. Whenever possible, it is prudent and insightful to engage a second appraiser and compare and discuss the relevance of appraisal outcomes against envisioned learning objectives. Documentation of executed appraisals also provides a solid basis for transparent educational multimedia decision‐making. Testing multimedia Before implementing multimedia content on a broader scale or including it on a recurring curriculum, it is helpful to run a pilot on a representative target audience/cohort. Using the core outcome criteria of our checklists (see below) to structure a short inquiry with target audiences (or caregivers) can increase the potential success and effectiveness of multimedia learning.
The most important consideration when selecting multimedia is the target audience . Every target audience is different. It is important to have a clear idea of what is adequate and works best to stimulate interest and the learning process. The main factor to consider is personalization which can be represented in different forms. The first form is to ensure that the phrasing and narration are similar to that of the audience's everyday life. If educators are narrating themselves, it is important that they do not pretend to be someone else, i.e. try to imitate others. Artificial or imitated voices can be off‐putting and distracting, which affects engagement. It can be challenging to find the most adequate type of narration. Second, when including humour , it should be appropriate for the audience and for the context in which it is used, and it should be natural and spontaneous. An excessive or inappropriate use of humour can distract from the important information and can, once again, be off‐putting. Moreover, it should be kept in mind that a natural talent for humour is not a common quality, and that people vary in what they consider to be funny: ‘when in doubt, cut it out’. Third, for audiences less able to self‐regulate, for example primary school children, entertainment can be a useful element to render instructional messages interesting by including story driven elements. By balancing the educational narrative with the instructional narrative, the interest of the audience can be stimulated. Audiences with the ability to self‐regulate, such as young adults, are less dependent on story‐driven elements to remain focussed and interested in topics. Finally, it is important to ensure that content is accessible and inclusive for everyone in target audiences. This refers to inter alia the cultural context, educational background, such as pre‐existing knowledge, or disabilities, all of which can affect how multimedia content is processed and received by target audiences.
In this work, multimedia selection is based on the learning objectives as described in the 7E model – Elicit , Engage , Explore , Explain , Elaborate , Evaluate , Extend (the 7Es) – an extension by Eisenkraft of the 5E model as previously described by Bybee . Before selecting multimedia, it is essential to know what the objective of an educational unit is. The 7Es are highly useful in stratifying different learning objectives, see Table :
Teaching methods It is important to select the appropriate teaching methods in order to achieve learning objectives. Examples of teaching methods include, but are not limited to, the flipped classroom approach, game‐based learning (GBL), Interactive Learning System (ILS), and Collaborative Learning Model (CLM). An overview of how these teaching methods correspond to the learning objectives can be found in Table . Selecting multimedia Table shows an overview of suggestions regarding which multimedia can be most suitable to facilitate each of the 7E learning objectives. It is important to bear in mind that these are recommendations, and that, in principle, many different types of multimedia can be suitable for achieving learning objectives. Table also provides a full overview and detailed explanations of the different learning objectives and multimedia tools. It describes when to select certain learning objectives (and when not to), different teaching methods to facilitate the objectives and the multimedia that can facilitate selected objectives, including the corresponding criteria and considerations.
It is important to select the appropriate teaching methods in order to achieve learning objectives. Examples of teaching methods include, but are not limited to, the flipped classroom approach, game‐based learning (GBL), Interactive Learning System (ILS), and Collaborative Learning Model (CLM). An overview of how these teaching methods correspond to the learning objectives can be found in Table .
Table shows an overview of suggestions regarding which multimedia can be most suitable to facilitate each of the 7E learning objectives. It is important to bear in mind that these are recommendations, and that, in principle, many different types of multimedia can be suitable for achieving learning objectives. Table also provides a full overview and detailed explanations of the different learning objectives and multimedia tools. It describes when to select certain learning objectives (and when not to), different teaching methods to facilitate the objectives and the multimedia that can facilitate selected objectives, including the corresponding criteria and considerations.
When selecting multimedia, it is also important to take into account the availability of resources, such as time, digital infrastructure and funding. Time available regards the contact time in class and the time for teachers to prepare classes. Digital infrastructure covers the technology required to use selected multimedia, such as computers, TVs or mobile phones, or other elements, such as an internet connection. Financial resources can, for example, concern the purchasing of multimedia or other complementary materials/devices. A mismatch might be, for instance, a well‐intended decision such as a recommendation by a centralised education body to use modern technologies, like virtual reality animations to visualise functions of mitochondria, in a situation where resources for virtual reality headsets might not be available in some, or even many, educational settings. This example also demonstrates the importance of assessing a range of options regarding multimedia for similar educational objectives, however, stratified by differentially resourced settings if making centralised decisions.
The appraisal of existing multimedia content – even when checklist‐driven – is always somewhat subjective. Whenever possible, it is prudent and insightful to engage a second appraiser and compare and discuss the relevance of appraisal outcomes against envisioned learning objectives. Documentation of executed appraisals also provides a solid basis for transparent educational multimedia decision‐making.
Before implementing multimedia content on a broader scale or including it on a recurring curriculum, it is helpful to run a pilot on a representative target audience/cohort. Using the core outcome criteria of our checklists (see below) to structure a short inquiry with target audiences (or caregivers) can increase the potential success and effectiveness of multimedia learning.
The guideline consists of four main sections: the overarching considerations, as alluded to above, that provide a top‐level explanation of the rationale for selecting and appraising multimedia for microbiology education focussing on specific audiences and target educational objectives, a 7E model stratified decision tool relating inter alia multimedia selection to learning objectives (Table ), checklists for appraising candidate multimedia, namely videos/animations, comics and video games (Tables , , , videos and animations have been integrated, as the appraisal criteria are mostly identical), detailed background information (Appendices and ) describing the theoretical and practical underpinnings of the multimedia selection and appraisal tools we present here (including a host of references for further reading) and thereby linking all sections. As Tables , , , are readily applicable decision tools, they represent the core of our guidelines. A top‐level example of how to use the guideline is presented at the end of this section (Table ).
selection based on the 7E model Table provides guidance on which teaching method(s) and corresponding multimedia – focussing on videos/animations, comics and video games – are well suited for specific learning scenarios/objectives. In consequence, Table should be consulted a priori to contemplate which multimedium (or range thereof) is most adequate – recommendations are provided.
quality appraisal Tables , , are based on multimedium‐specific considerations. Table is a joint checklist for both videos and animations – animation‐specific criteria are at the end of the checklist. Table is the appraisal checklist for comics, and Table for video games respectively. The checklists can be used in a tick‐a‐box manner. Videos and animations Note: for animations three additional appraisal items exist, which are located at the end of Table . Comics Note: the Comics appraisal tool also includes a section with items on anthropomorphism (at the end of Table ). Employing anthropomorphism can be highly effective for learner engagement; however, anthropomorphism should be used with caution, as it is highly age and learning‐objective dependent, see comments at the end of Table . Video games An example of using the guideline A top level overview of the sequence of multimedia selection , appraisal and piloting based on the guideline and the 7E learning objective Engage is shown in Table . Video recording of the MTA project Some of this work, including inter alia how to use the guidelines, was presented at SfAM's International Applied Microbiology Conference , a recording of which can be found here: https://youtu.be/6NUNmTCjVv0?feature=shared&t=1410 , The time stamp skips to the application of the appraisal guidelines, although for a more general introduction by the entire research team, the video can be watched from the beginning.
Note: for animations three additional appraisal items exist, which are located at the end of Table .
Note: the Comics appraisal tool also includes a section with items on anthropomorphism (at the end of Table ). Employing anthropomorphism can be highly effective for learner engagement; however, anthropomorphism should be used with caution, as it is highly age and learning‐objective dependent, see comments at the end of Table .
An example of using the guideline A top level overview of the sequence of multimedia selection , appraisal and piloting based on the guideline and the 7E learning objective Engage is shown in Table . Video recording of the MTA project Some of this work, including inter alia how to use the guidelines, was presented at SfAM's International Applied Microbiology Conference , a recording of which can be found here: https://youtu.be/6NUNmTCjVv0?feature=shared&t=1410 , The time stamp skips to the application of the appraisal guidelines, although for a more general introduction by the entire research team, the video can be watched from the beginning.
A top level overview of the sequence of multimedia selection , appraisal and piloting based on the guideline and the 7E learning objective Engage is shown in Table .
MTA project Some of this work, including inter alia how to use the guidelines, was presented at SfAM's International Applied Microbiology Conference , a recording of which can be found here: https://youtu.be/6NUNmTCjVv0?feature=shared&t=1410 , The time stamp skips to the application of the appraisal guidelines, although for a more general introduction by the entire research team, the video can be watched from the beginning.
Principle findings The aim of this study was to create evidence‐based guidelines for the appraisal (and design) of microbiology teaching aids – to be used by educators perusing the content of the IMiLI curriculum. In addition to creating guidelines for the appraisal of videos/animations, and comics and video games, our study led to six more general findings pertinent to guideline utilisation and multimedia learning. Carefully considering context and target audience is key when selecting and appraising multimedia, as utilising multimedia that conveys educational content at an appropriate level, and in a fashion that resonates with target audiences, maximises learning outcomes. Enhancing students' interest/motivation, for instance by including elements of entertainment/storytelling, can be a more important contributor to improving learning outcomes than strict adherence to cognitive/educational principles. Learning objective frameworks, such as the 7E model, can be invaluable in facilitating the selection of appropriate teaching methods and, in turn, the selection of adequate multimedia complements. Existing resources, and constraints thereof, such as access to the web, devices to view multimedia, subscriptions, etc., must be taken into account when selecting educational multimedia. Appraisal outcomes – even when guideline‐driven – are always more or less subjective and, therefore, should not be readily generalised; other appraisers might come to different conclusions. Multimedia considered adequate post appraisal should be properly piloted and (briefly) evaluated before implementing them in curricula. Videos and animations can be appraised similarly/identically, and the process is rather straightforward. The appraisal of comics and, in particular, video games can, however, be more challenging due to the complexity of considerations (e.g. anthropomorphism in comics and interactive elements in video games). On a practical level, the guidelines can be used by educators to substantiate their decision‐making process during the selection of multimedia to maximise the learning process of their students. The guideline provides guidance and theoretical justification for discriminating between different multimedia in terms of quality and suitability. Naturally, the process of selecting and appraising multimedia can be time‐consuming, so an initial pooling of resources at the education organisation level might helpful, and longer‐term additional funding for the establishment of a centralised assessment body might be a worthwhile policy decision. The mid‐ to long‐term utilisation of the guideline also provides considerable benefits based on economies of scale. If a database with (pre‐)appraised multimedia together with the documentation of appraisals (naturally including target audiences and learning objectives) were to be established, this would facilitate not only rapid selection (and review) of multimedia but also support validation of the guidelines and their potential improvement. The relevance of this study and the development of multimedia selection and appraisal guidelines cannot be more timely: digital applications are becoming increasingly common in education – national virus containment measures introduced during the Covid‐19 pandemic accelerated the transition towards virtual and multimedia learning at an unprecedented rate (Kerssens & van Dijck, ). Due to the complex nature of using multimedia for education, the interpretation of the guidelines cannot be generalised. However, not appraising multimedia at all, nor using guidance to assess multimedia quality, can lead to arbitrary learning outcomes, and education that is potentially ineffective, exclusive, etc. The creation of MTAs will always have a purpose and the purpose will usually define the narrative, which will impact suitability/utility. For example, if the primary purpose is promotion of a research project, a group, an industrial product, it is unlikely to be ideal for education. Many MTAs on the web are promotional. This has important implications for both creators and users of MTAs, because the selection of MTAs for education needs to take this into account. Using this guideline will not guarantee that a medium under consideration is the best in quality for the intended target audience but it is a good vantage point to establish awareness and support the use of more appropriate microbiology MTAs. The mixed‐methods approach provided a thorough basis to, first, create preliminary guidelines, second, empirically test them on specific content and, third, empirically quasi‐validate our findings with experts in the field. The triangulation of data collection methods and results increased the validity and reliability of this research (Abowitz & Michael Toole, ; Anderson, ; Atieno, ). Nevertheless, this study was performed in a limited time of 20 weeks and qualitative research does not aim to produce generalisable results. Therefore, certain perspectives and/or elements might have gone undetected. This limits the degree of certainty regarding the transferability of our results to other settings (McGrath et al., ). Furthermore, potential end‐users of the guideline (educators) were neither directly involved in its development nor testing. Future research should, therefore, aim to extensively test the guideline on a range of multimedia items together with a large sample of educators to better understand how well the guideline works for them, and, if necessary, the guidline modified accordingly. Moreover, the degree of de facto information retention (using different multimedia in a range of settings) – the theoretical assumptions underpinning multimedia learning and, therefore, also our guidelines – must be further tested and evaluated in controlled environments. This means that, although the guidelines are based on solid evidence, the specific learning outcomes of multimedia appraised as adequate and then used for educational purposes, must be validated in future studies. Also, while the guideline has been standardised as well as possible, setting‐ and target audience‐specific needs, as well as assessor subjectivity, require thoughtful utilisation during appraisal of potential multimedia teaching aids. In order to increase the quality of microbiology multimedia learning, educators must be made aware of the existence and application of evidence‐based appraisal tools, such as our guidelines. We propose that teacher university curricula include at least one session on the theoretical underpinnings of multimedia learning and a workshop on the practical utilisation of multimedia selection and appraisal guidelines. In a time when multimedia is omnipresent, it is particularly important that children are introduced to adequate educational multimedia at school – naturally, to maximise learning outcomes but also to provide them with a reference point regarding characteristics of good quality multimedia (Asthana, ; Moursund, ).
The aim of this study was to create evidence‐based guidelines for the appraisal (and design) of microbiology teaching aids – to be used by educators perusing the content of the IMiLI curriculum. In addition to creating guidelines for the appraisal of videos/animations, and comics and video games, our study led to six more general findings pertinent to guideline utilisation and multimedia learning. Carefully considering context and target audience is key when selecting and appraising multimedia, as utilising multimedia that conveys educational content at an appropriate level, and in a fashion that resonates with target audiences, maximises learning outcomes. Enhancing students' interest/motivation, for instance by including elements of entertainment/storytelling, can be a more important contributor to improving learning outcomes than strict adherence to cognitive/educational principles. Learning objective frameworks, such as the 7E model, can be invaluable in facilitating the selection of appropriate teaching methods and, in turn, the selection of adequate multimedia complements. Existing resources, and constraints thereof, such as access to the web, devices to view multimedia, subscriptions, etc., must be taken into account when selecting educational multimedia. Appraisal outcomes – even when guideline‐driven – are always more or less subjective and, therefore, should not be readily generalised; other appraisers might come to different conclusions. Multimedia considered adequate post appraisal should be properly piloted and (briefly) evaluated before implementing them in curricula. Videos and animations can be appraised similarly/identically, and the process is rather straightforward. The appraisal of comics and, in particular, video games can, however, be more challenging due to the complexity of considerations (e.g. anthropomorphism in comics and interactive elements in video games). On a practical level, the guidelines can be used by educators to substantiate their decision‐making process during the selection of multimedia to maximise the learning process of their students. The guideline provides guidance and theoretical justification for discriminating between different multimedia in terms of quality and suitability. Naturally, the process of selecting and appraising multimedia can be time‐consuming, so an initial pooling of resources at the education organisation level might helpful, and longer‐term additional funding for the establishment of a centralised assessment body might be a worthwhile policy decision. The mid‐ to long‐term utilisation of the guideline also provides considerable benefits based on economies of scale. If a database with (pre‐)appraised multimedia together with the documentation of appraisals (naturally including target audiences and learning objectives) were to be established, this would facilitate not only rapid selection (and review) of multimedia but also support validation of the guidelines and their potential improvement. The relevance of this study and the development of multimedia selection and appraisal guidelines cannot be more timely: digital applications are becoming increasingly common in education – national virus containment measures introduced during the Covid‐19 pandemic accelerated the transition towards virtual and multimedia learning at an unprecedented rate (Kerssens & van Dijck, ). Due to the complex nature of using multimedia for education, the interpretation of the guidelines cannot be generalised. However, not appraising multimedia at all, nor using guidance to assess multimedia quality, can lead to arbitrary learning outcomes, and education that is potentially ineffective, exclusive, etc. The creation of MTAs will always have a purpose and the purpose will usually define the narrative, which will impact suitability/utility. For example, if the primary purpose is promotion of a research project, a group, an industrial product, it is unlikely to be ideal for education. Many MTAs on the web are promotional. This has important implications for both creators and users of MTAs, because the selection of MTAs for education needs to take this into account. Using this guideline will not guarantee that a medium under consideration is the best in quality for the intended target audience but it is a good vantage point to establish awareness and support the use of more appropriate microbiology MTAs. The mixed‐methods approach provided a thorough basis to, first, create preliminary guidelines, second, empirically test them on specific content and, third, empirically quasi‐validate our findings with experts in the field. The triangulation of data collection methods and results increased the validity and reliability of this research (Abowitz & Michael Toole, ; Anderson, ; Atieno, ). Nevertheless, this study was performed in a limited time of 20 weeks and qualitative research does not aim to produce generalisable results. Therefore, certain perspectives and/or elements might have gone undetected. This limits the degree of certainty regarding the transferability of our results to other settings (McGrath et al., ). Furthermore, potential end‐users of the guideline (educators) were neither directly involved in its development nor testing. Future research should, therefore, aim to extensively test the guideline on a range of multimedia items together with a large sample of educators to better understand how well the guideline works for them, and, if necessary, the guidline modified accordingly. Moreover, the degree of de facto information retention (using different multimedia in a range of settings) – the theoretical assumptions underpinning multimedia learning and, therefore, also our guidelines – must be further tested and evaluated in controlled environments. This means that, although the guidelines are based on solid evidence, the specific learning outcomes of multimedia appraised as adequate and then used for educational purposes, must be validated in future studies. Also, while the guideline has been standardised as well as possible, setting‐ and target audience‐specific needs, as well as assessor subjectivity, require thoughtful utilisation during appraisal of potential multimedia teaching aids. In order to increase the quality of microbiology multimedia learning, educators must be made aware of the existence and application of evidence‐based appraisal tools, such as our guidelines. We propose that teacher university curricula include at least one session on the theoretical underpinnings of multimedia learning and a workshop on the practical utilisation of multimedia selection and appraisal guidelines. In a time when multimedia is omnipresent, it is particularly important that children are introduced to adequate educational multimedia at school – naturally, to maximise learning outcomes but also to provide them with a reference point regarding characteristics of good quality multimedia (Asthana, ; Moursund, ).
R. Van Beek: Data curation; formal analysis; investigation; methodology; resources; software; validation; visualization; writing – original draft; writing – review and editing. D. J. C. Spijkerman: Data curation; formal analysis; investigation; methodology; resources; software; validation; visualization; writing – original draft; writing – review and editing. N. van der Burgt: Data curation; formal analysis; investigation; methodology; resources; software; validation; visualization; writing – review and editing. B. Hermanns: Data curation; formal analysis; investigation; methodology; resources; software; validation; visualization; writing – review and editing. S. Barendse: Formal analysis; methodology; project administration; resources; supervision; validation; visualization. P. D. Sainsbury: Conceptualization; funding acquisition; resources; supervision; validation. K. N. Timmis: Conceptualization; resources; supervision; validation; writing – review and editing. J. K. Timmis: Conceptualization; formal analysis; methodology; project administration; resources; supervision; validation; visualization; writing – original draft; writing – review and editing.
None to declare.
Appendix S1.
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Adaptation of the National Plan for the Prevention and Fight Against Pandemic Influenza to the 2020 COVID-19 Epidemic in France | ac3a4e18-77f1-4936-9b45-088e5f573b04 | 7170809 | Health Communication[mh] | |
Mapping reactive astrogliosis in Parkinson's brain with astroglial tracers BU99008 and Deprenyl: New insights from a multi‐marker postmortem study | 59636f69-a871-453a-94ea-28810e1016cb | 11848164 | Forensic Medicine[mh] | BACKGROUND Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder and poses a significant clinical challenge due to high heterogeneity and overlapping symptoms with other Parkinsonian disorders. , The disease is characterized primarily by neurodegeneration, the deposition of α‐synuclein Lewy bodies (LBs), and neuroinflammation—processes that contribute to the progression of PD pathology. In the last few years, as the role of glial‐mediated neuroinflammation has become more and more evident in PD and other neurodegenerative disorders, there has been a strong focus on astrocytes. Astrocytes account for 20%–40% of the glial cell subpopulation in the human central nervous system (CNS) and are essential for maintaining brain homeostasis and supporting neuronal function. , , , Under pathological conditions, astrocytes acquire reactive state(s)/phenotype through a process known as reactive astrogliosis and undergo biochemical and morphological remodeling in response to brain injuries or neuroinflammation. , Reactive astrogliosis is already considered a key contributor to the pathogenesis of Alzheimer's disease (AD), with several significant studies cementing its role during the very early phase of the disease. , , , , , , , , However, reactive astrogliosis in PD is still unexplored despite the inherent involvement of astrocytic biology in PD pathogenesis. , Furthermore, neuronal impairment in PD mediated by reactive astrocytes involves other players regulating immune/inflammatory homeostasis. Notably, an increased expression of a specific subpopulation of reactive astrocytes expressing neuroinflammatory protein intercellular adhesion molecule 1 (ICAM‐1) was observed in the substantia nigra of PD patient's postmortem brains, highlighting their significance in PD pathology. Taking into consideration these findings, ICAM‐1‐positive reactive astrocytes could be used as an additional marker of reactive astrogliosis in PD brains. Reactive astrogliosis is often marked by the overexpression of glial fibrillary acidic protein (GFAP) along with other marker proteins such as monoamine oxidase B (MAO‐B) and imidazoline 2 binding sites (I 2 BS), which are targets of astroglial positron emission tomography (PET) tracers Deprenyl and BU99008, respectively. GFAP is the most widely used marker of reactive astrocytes but it is not an absolute marker of reactive astrogliosis because it may not represent entirely the disease severity or the whole population of reactive astrocytes as suggested by a large number of recent studies and consensus statements in the field. , Thus, emphasizing a multi‐marker approach to gain a holistic view of the astrocytic reactivity in diseased conditions is crucial. In this context, MAO‐B and I 2 BS, which are upregulated during reactive astrogliosis, have demonstrated their reliability as an alternative/additional PET biomarker for reactive astrogliosis, specifically in AD, in several studies ranging from in vitro to in vivo. , , , , , , Despite their immense potential these tracers have not been thoroughly studied and validated in PD yet and only tested in two in vivo studies to date. In one study, 11 C‐BU99008 revealed increased reactive astrogliosis in patients with early‐stage PD, whereas a global decline in reactive astrogliosis was observed in moderate/advanced‐stage PD patients in comparison to healthy controls (CN). Although in the second proof‐of‐concept PET‐study, 18 F‐Deprenyl showed moderate global binding (reactive astrogliosis) in two patients with PD. Moreover, all these astrocytic markers are interrelated. , , Taken together, these biomarkers could provide a much‐needed insight into the state of reactive astrogliosis in diseased brains. In this study, we performed radioligand binding (saturation, competition, and regional binding assays) and autoradiography studies using the astroglial tracers Deprenyl and BU99008 alongside GFAP/ICAM‐1 expression analyses to visualize and investigate the signature of reactive astrogliosis in postmortem PD brains to further elucidate the role of reactive astrocytes in PD pathogenesis. MATERIALS AND METHODS 2.1 Chemicals 3 H‐BU99008 (Specific Activity (SA) = 83 Ci/mmol), 3 H‐Deprenyl (SA = 82‐83 Ci/mmol), and unlabeled BU99008 and Deprenyl were custom synthesized by Novandi Chemistry AB (Södertälje, Sweden). Chemicals (sodium chloride, NaCl; potassium chloride, KCl 2 ); calcium chloride, CaCl 2 ; Tris base, magnesium chloride, MgCl 2 ; disodium phosphate, Na 2 HPO 4 ; and potassium dihydrogen phosphate, KH 2 PO 4 ) were purchased from Thermofisher, and Sigma‐Aldrich AB, Sweden. 2.2 Autopsy material Human frozen postmortem brain tissues from PD patients and CNs were obtained from The Netherlands Brain Bank (NBB), Netherlands Institute for Neuroscience, Amsterdam (open access: www.brainbank.nl ). All material has been collected from donors for or from whom written informed consent for a brain autopsy and the use of the material and clinical information for research purposes had been obtained by the NBB. A priori power analyses using G*Power 3.1.9.7 with α = 0.05, Power (1‐ β = 0.95) suggested a sample size ranging from 15 to 51 based on Cohen's d effect size of 1 to 2. At Cohen's effect size d = 0.5, the sample size was ≈200. However, due to limited brain tissue availability, the study was performed on CN ( n = 7) and PD ( n = 4) cases. For PD cases, four regions were selected for all cases: two cortical regions (frontal cortex [FC] and temporal cortex [TC]) and two subcortical regions (caudate nucleus [CAU] and putamen [PUT]). This strategy allowed us to compare different brain regions affected in different stages of the disease development—cortical regions are only affected later/in more advanced stages, whereas subcortical striatum regions are early targets. The clinical diagnosis of all the PD cases was further confirmed on autopsy. Regarding the CNs, we selected the cases that allowed the analysis of the same brain regions from the in‐house inventory of NBB tissues. Factors such as age, sex, amyloid pathology, tau deposits, comorbidities, duration of the disease, medication, and education history were also considered. It is important to note that all the CN and PD cases used in the study had very short postmortem delay (averaging ≈6 h). Table provides a detailed summary of the clinical and demographic information about the PD patients and CNs from which brain tissues were used in this study. RESEARCH IN CONTEXT Systematic review : The authors reviewed the literature using traditional (e.g., PubMed) sources and meeting abstracts and presentations. Reactive astrogliosis is central to the astrocytic response to brain injury and inflammation. Despite heavy astrocytic involvement in Parkinson's disease (PD) pathology, the role of reactive astrogliosis remains unexplored due to the lack of specific positron emission tomography (PET) biomarkers to map these processes (or trajectories) throughout the disease continuum. Recently developed astroglial tracers BU99008 and Deprenyl, which target astrocytic monoamine oxidase (MAO‐B) and imidazoline 2 binding sites (I 2 BS), respectively, hold immense potential for tracking reactive astrogliosis in PD. However, these tracers have not been thoroughly studied and validated in PD yet and have been tested in only a handful of in vivo studies to this date. These relevant citations are appropriately cited. Interpretation : Our findings demonstrated distinct tracer binding behavior with 3 H‐BU99008 and 3 H‐Deprenyl, showing diverse (single or multiple) binding sites with different affinities and % ratios in CN and PD brains along with upregulated expression of I 2 Bs and MAO‐B in a region‐dependent manner. 3 H‐BU99008 and 3 H‐Deprenyl captured reactive astrogliosis at the advanced/end stages of PD and highlighted their potential as surrogate markers of reactive astrogliosis in PD brain. Of interest, our studies also added support to the “ second wave (late)” of reactive astrogliosis at the end stages of the disease by highlighting disease‐stage–dependent dynamic changes in tracer binding in PD brains. Future directions : Further in vivo and postmortem studies in a larger cohort with multiple brain regions (and markers) encompassing different stages of the disease are needed to further confirm these findings and to validate the reliability of astroglial tracers BU99008 and Deprenyl as PET biomarkers for mapping reactive astrogliosis in the PD continuum. 2.3 Brain tissue sectioning and harvesting 20 µm thick frozen brain sections from the FC, TC, CAU, and PUT regions of CN and PD cases were prepared using cryostat (HM550, microm, Thermofisher). The sections were immediately transferred onto superfrost plus slides (25 × 75 mm, Epredia) and kept at −80°C until further use in autoradiography and immunofluorescence binding experiments. The same small frozen tissue sample from each brain region after sectioning was used to prepare brain homogenates, as described in Section 2.4, to minimize inter‐variation between different binding studies. 2.4 Brain homogenates preparation The human brain tissue was homogenized in phosphate‐buffered saline (PBS; pH 7.4) with protease inhibitor cocktail (10 µL/mL; Sigma‐Aldrich #P8340). The brain homogenates (BHs) were stored at −80°C in aliquots until being used in binding experiments. 2.5 Saturation binding assays 3 H‐BU99008 saturation binding assays were performed in two brain regions—FC and CAU—from 2 PD (FC: PD1; CAU: PD1, PD4) and 1 CN case (FC: CN2; CAU: CN1). BHs (0.5 mg tissue) from different brain regions were incubated with increasing concentrations of 3 H‐BU99008 (ranging from 0.6–30 nM) in 50 mM Tris‐HCl binding buffer pH 7.4 (50 mM Tris‐base, 140 mM NaCl, 1.5 mM MgCl 2 , 5 mM KCl, 1.5 mM CaCl 2 ) for 90 min at 37°C. Non‐specific (NSP) binding was determined with 1 µM unlabeled BU99008. 3 H‐Deprenyl saturation binding assays were performed in two brain regions—FC and CAU—from 3 PD (PD1, PD3, PD4) and 1 CN case (CN1). BHs (0.1 mg of tissue) from different brain regions were incubated with increasing concentrations of 3 H‐Deprenyl (from 0.8–40 nM) in Na‐K phosphate buffer pH 7.4 for 60 min at 37°C. NSP binding was determined with 1 µM unlabeled Deprenyl. After the incubation period, the binding reaction was terminated by filtering through glass fiber filters (pre‐soaked for 2–3 h in 0.3% polyethyleneimine), followed by three quick rinses with cold binding buffer and overnight incubation of the filter in the scintillation liquid. The radioactivity in the tubes with reaction filters was counted the next day using a scintillation counter (PerkinElmer Tri‐Carb 2910TR). The binding curves (total, NSP, specific) were fitted and analyzed using the non‐linear regression function of GraphPad Prism 9.0 software to calculate the dissociation constant (Kd) and binding site density (Bmax). Scatchard plots were prepared by transforming the specific binding data with GraphPad Prism 9.0 software. 2.6 Competition binding assays Competitive binding assays were again performed on BHs prepared from two brain regions (FC and CAU) of PD and CN cases. For 3 H‐BU99008 competition binding assays, 1 nM 3 H‐BU99008 was incubated with increasing concentrations (1 × 10 −13 M to 1 × 10 −5 M) of unlabeled BU99008 (FC and CAU: PD1, PD4; FC: CN1, CN2; CAU: CN1, CN3) and Deprenyl (FC and CAU: PD1 and CN3) together with BHs (0.5 mg of tissue) in binding buffer (50 mM Tris‐HCl) for 90 min at 37°C. Competition studies were also performed for 3 H‐Deprenyl by incubating BHs (0.1 mg of tissue) with 3 nM 3 H‐Deprenyl along with increasing concentrations (1 × 10 −13 M to 1 × 10 −4 M) of unlabeled Deprenyl (FC and CAU: PD1, PD4 and CN1, CN3, CN4) and BU99008 (FC and CAU: PD1 and CN3) in binding buffer (50 mM Na‐K phosphate buffer) for 60 min at 37°C. For all the experiments, after incubation, a protocol similar to saturation binding assay was done, and the binding was quantified using the scintillation counter. The half‐maximal inhibitory concentration (IC50) was determined by using the non‐linear regression competitive‐binding model of GraphPad Prism 9.0 software. 2.7 Regional distribution binding assays Regional distribution binding studies were performed using a protocol similar to saturation binding assay on the FC, TC, CAU, and PUT BHs (0.1–0.5 mg tissue) from 4 CNs (CN1–4) and 4 PD patients, at a single concentration of either 1 nM 3 H‐BU99008 or 3 nM 3 H‐Deprenyl, and by incubating for 60 or 90 min at 37°C (depending on the tracer). The binding was quantified with the scintillation counter. NSP binding was determined with 1 µM unlabeled BU99008 or Deprenyl. CAU for CN2 and PUT for CN1 and CN4 were not available when the experiment was planned and performed. The binding data were analyzed using GraphPad Prism 9 software and presented as specific binding (fmol/mg). 2.8 Postmortem autoradiography imaging studies 3 H‐BU99008 and 3 H‐Deprenyl autoradiography studies were performed on small frozen postmortem brain sections from 7 CNs and 4 PD patients, using all available brain regions (FC, TC, CAU, and PUT) from each case. Frozen sections were allowed to dry at room temperature for 20–30 min, followed by 1 h incubation with either 3 H‐BU99008 (1 nM) or 3 H‐Deprenyl (3 nM) alone, or with 1 µM unlabeled BU99008 or Deprenyl, to determine the NSP binding at room temperature. Then the sections were rinsed for 5 min three times in cold buffer (50 mM Tris‐HCl buffer, pH 7.4 for 3 H‐BU99008; 50 mM Na‐K phosphate buffer, pH 7.4 for 3 H‐Deprenyl), followed by a quick dip in cold Milli‐Q water. 3 H‐BU99008 versus unlabeled Deprenyl pre‐blocking autoradiography was performed on small frozen postmortem brain sections from 5 CNs (CN1‐4, CN7) and 4 PD patients, using available brain regions (FC, TC, CAU, and PUT) from each case. The frozen sections were allowed to dry at room temperature for 20–30 min followed by pre‐blocking with unlabeled Deprenyl (1 µM) for 30 min at room temperature. After pre‐blocking, the sections were rinsed twice for 5 min with cold buffer (50 mM Tris‐HCl buffer, pH 7.4 for 3 H‐BU99008) to remove excess/unbound unlabeled Deprenyl. Thereafter, sections were incubated with 1 nM of 3 H‐BU99008 alone or with 1 µM unlabeled BU99008 for 60 min, and finally rinsed for 5 min three times in cold buffer followed by a quick dip in cold Milli‐Q water. All the sections were allowed to dry at room temperature for 24 h and then apposed together with a tritium standard (Larodan Fine Chemicals AB, Mälmo, Sweden) on a phosphor‐plate for 4 and 7 days for 3 H‐Deprenyl and 3 H‐BU99008, respectively, and later imaged using a BAS‐2500 phosphor imager (Fujifilm, Tokyo, Japan). The semi‐quantitative analyses were performed by drawing manually the regions of interest (ROI), using the multigauge software on the autoradiogram and the photo‐stimulated luminescence per square millimeter (PSL/mm 2 ). The obtained values were transformed with the standard curve to fmol/mg, allowing determination of the total, NSP and specific binding of 3 H‐BU99008 and 3 H‐Deprenyl in the ROI. 2.9 GFAP immunoblot analysis Expression of the astrocytic marker GFAP was assessed using immunoblot on BHs from 4 CNs and 4 PD cases, using all available brain regions (FC, TC, CAU, and PUT) from each case. Samples (20 µg of BH) were mixed with NuPAGE Lithium dodecyl sulfate (LDS) Sample buffer (Invitrogen, #NP0007), and incubated for 5 min at 95°C and briefly spun down before loading on the NuPAGE 4%–12% Bis‐Tris Gel (Invitrogen, #NP0322BOX). Samples were electrophoresed at 120–200 V in NuPAGE MES SDS Running buffer (Invitrogen, #NP0002). Nitrocellulose membranes were pre‐equilibrated for 5 min in cold transfer buffer (25 mM Tris, 192 mM glycine, 20% ethanol), before the blot sandwich was assembled. The transfer was performed for 1 h at constant 200 mA at room temperature. After that, membranes were washed using Milli‐Q water and stained with Revert 700 Total Protein Stain (LI‐COR, #926‐11011), and the total protein signal was imaged using LI‐COR Odyssey CLx imaging system followed by destaining as per the manufacturer's instructions. Membranes were blocked for 1 h at room temperature in 5% (w/v) fat‐free milk in Tris‐buffered saline with 0.1% Tween‐20 (TBS‐T) buffer at pH 7.4. After blocking, the membranes were incubated at 4°C overnight with GFAP primary antibody (1:500, Santa Cruz, sc‐58766) in TBS‐T buffer with 5% milk. Next day, membranes were incubated with anti‐mouse secondary antibodies (1:10000 for LI‐COR antibodies) in TBS‐T buffer with 5% milk for 1 h at room temperature. The LICOR Odyssey CLx imaging system was used to visualize the protein bands using the appropriate laser channel. Band intensity was quantified using Empiria Studio 3.0 software (LI‐COR) on the raw images and normalized for the total protein stain. GFAP expression levels are displayed as mean ± SD of a percentage of protein expression of the respective CN. 2.10 Immunofluorescence and microscopy Immunofluorescence staining was performed on frozen brain sections from different brain regions of 2 CNs (CN2, and CN3) and 2 PD cases (PD2, and PD3). Sections were first hydrated with PBS (0.1 M) followed by permeabilization using PBS with 0.2% Tween 20 (PBST) buffer. Next, sections were blocked with bovine serum albumin (BSA; 5%, w/v) for 2 h at room temperature. Next the sections were incubated with primary anti‐GFAP conjugated with Alexa Fluor 488 (Abcam # ab194324, 1:500) and anti‐ICAM‐1 conjugated with coralight594 (CL594‐60299, Proteintech, 1:100) for 24 h in a humid chamber at 2°C–8°C. The excess antibody was removed by washing with PBS (0.1 M, three times, 5 min each). Finally, sections were counterstained with 4′,6‐diamidino‐2‐phenylindole (DAPI) and mounted with glycerin (3%, v/v) using a glass coverslip. To avoid leaking of mounting media, coverslips were sealed with clear nail polish, and signals were viewed in an inverted fluorescence phase contrast microscope (IX71, Olympus), captured with a Hamamatsu camera, and imaged using HCImage software (Hamamatsu corporation, USA). Manual z ‐stack was prepared with an interval of 1µm thickness. Images of high magnification (40X) were further analyzed with Fiji (Image J software, NIH). Reactive astrogliosis processes were measured following standard morphometry in relation to GFAP+/ICAM‐1+ double‐positive cell density, signal intensity, immunoreactivity area, and astrocytic branch length, and diameter of primary projections/rays from GFAP signal were quantified. Two sections per brain area and five non‐overlapping fields at high magnification (40X) were used for morphometric analyses, where double‐positive cells were counted manually in the cell counter plugin on merged/composite images. The reactive area of the markers was drawn manually using the freehand selection tool, whereas the intensity was measured in inverted images for each channel probed with either GFAP or ICAM‐1 in all the fields for all sections, and the data were further analyzed using GraphPad Prism 9 software. Furthermore, for the reconstruction with Sholl rings of the astrocyte primary projections, Neuron J plugin was used to trace on the maximal intensity projections from 1 µm interval z‐stack of GFAP signal from FC, TC, CAU, and PUT brain regions investigated in CN and PD cases. 2.11 Statistical analyses For autoradiography, regional, and immunofluorescence binding studies the mean differences between the CN and PD cases across different brain regions were determined using the Mann–Whitney t ‐test with no correction for multiple comparisons. The correlation analyses between 3 H‐BU99008 and 3 H‐Deprenyl quantitative binding in CN and PD brain regions were determined using the Pearson r correlation coefficient after outlier removal (ROUT method). In addition, the data were also tested with Spearman's r correlation analysis. A p < 0.05 was considered statistically significant. All the analyses were performed with GraphPad Prism 9.0‐9.4 software. Chemicals 3 H‐BU99008 (Specific Activity (SA) = 83 Ci/mmol), 3 H‐Deprenyl (SA = 82‐83 Ci/mmol), and unlabeled BU99008 and Deprenyl were custom synthesized by Novandi Chemistry AB (Södertälje, Sweden). Chemicals (sodium chloride, NaCl; potassium chloride, KCl 2 ); calcium chloride, CaCl 2 ; Tris base, magnesium chloride, MgCl 2 ; disodium phosphate, Na 2 HPO 4 ; and potassium dihydrogen phosphate, KH 2 PO 4 ) were purchased from Thermofisher, and Sigma‐Aldrich AB, Sweden. Autopsy material Human frozen postmortem brain tissues from PD patients and CNs were obtained from The Netherlands Brain Bank (NBB), Netherlands Institute for Neuroscience, Amsterdam (open access: www.brainbank.nl ). All material has been collected from donors for or from whom written informed consent for a brain autopsy and the use of the material and clinical information for research purposes had been obtained by the NBB. A priori power analyses using G*Power 3.1.9.7 with α = 0.05, Power (1‐ β = 0.95) suggested a sample size ranging from 15 to 51 based on Cohen's d effect size of 1 to 2. At Cohen's effect size d = 0.5, the sample size was ≈200. However, due to limited brain tissue availability, the study was performed on CN ( n = 7) and PD ( n = 4) cases. For PD cases, four regions were selected for all cases: two cortical regions (frontal cortex [FC] and temporal cortex [TC]) and two subcortical regions (caudate nucleus [CAU] and putamen [PUT]). This strategy allowed us to compare different brain regions affected in different stages of the disease development—cortical regions are only affected later/in more advanced stages, whereas subcortical striatum regions are early targets. The clinical diagnosis of all the PD cases was further confirmed on autopsy. Regarding the CNs, we selected the cases that allowed the analysis of the same brain regions from the in‐house inventory of NBB tissues. Factors such as age, sex, amyloid pathology, tau deposits, comorbidities, duration of the disease, medication, and education history were also considered. It is important to note that all the CN and PD cases used in the study had very short postmortem delay (averaging ≈6 h). Table provides a detailed summary of the clinical and demographic information about the PD patients and CNs from which brain tissues were used in this study. RESEARCH IN CONTEXT Systematic review : The authors reviewed the literature using traditional (e.g., PubMed) sources and meeting abstracts and presentations. Reactive astrogliosis is central to the astrocytic response to brain injury and inflammation. Despite heavy astrocytic involvement in Parkinson's disease (PD) pathology, the role of reactive astrogliosis remains unexplored due to the lack of specific positron emission tomography (PET) biomarkers to map these processes (or trajectories) throughout the disease continuum. Recently developed astroglial tracers BU99008 and Deprenyl, which target astrocytic monoamine oxidase (MAO‐B) and imidazoline 2 binding sites (I 2 BS), respectively, hold immense potential for tracking reactive astrogliosis in PD. However, these tracers have not been thoroughly studied and validated in PD yet and have been tested in only a handful of in vivo studies to this date. These relevant citations are appropriately cited. Interpretation : Our findings demonstrated distinct tracer binding behavior with 3 H‐BU99008 and 3 H‐Deprenyl, showing diverse (single or multiple) binding sites with different affinities and % ratios in CN and PD brains along with upregulated expression of I 2 Bs and MAO‐B in a region‐dependent manner. 3 H‐BU99008 and 3 H‐Deprenyl captured reactive astrogliosis at the advanced/end stages of PD and highlighted their potential as surrogate markers of reactive astrogliosis in PD brain. Of interest, our studies also added support to the “ second wave (late)” of reactive astrogliosis at the end stages of the disease by highlighting disease‐stage–dependent dynamic changes in tracer binding in PD brains. Future directions : Further in vivo and postmortem studies in a larger cohort with multiple brain regions (and markers) encompassing different stages of the disease are needed to further confirm these findings and to validate the reliability of astroglial tracers BU99008 and Deprenyl as PET biomarkers for mapping reactive astrogliosis in the PD continuum. Systematic review : The authors reviewed the literature using traditional (e.g., PubMed) sources and meeting abstracts and presentations. Reactive astrogliosis is central to the astrocytic response to brain injury and inflammation. Despite heavy astrocytic involvement in Parkinson's disease (PD) pathology, the role of reactive astrogliosis remains unexplored due to the lack of specific positron emission tomography (PET) biomarkers to map these processes (or trajectories) throughout the disease continuum. Recently developed astroglial tracers BU99008 and Deprenyl, which target astrocytic monoamine oxidase (MAO‐B) and imidazoline 2 binding sites (I 2 BS), respectively, hold immense potential for tracking reactive astrogliosis in PD. However, these tracers have not been thoroughly studied and validated in PD yet and have been tested in only a handful of in vivo studies to this date. These relevant citations are appropriately cited. Interpretation : Our findings demonstrated distinct tracer binding behavior with 3 H‐BU99008 and 3 H‐Deprenyl, showing diverse (single or multiple) binding sites with different affinities and % ratios in CN and PD brains along with upregulated expression of I 2 Bs and MAO‐B in a region‐dependent manner. 3 H‐BU99008 and 3 H‐Deprenyl captured reactive astrogliosis at the advanced/end stages of PD and highlighted their potential as surrogate markers of reactive astrogliosis in PD brain. Of interest, our studies also added support to the “ second wave (late)” of reactive astrogliosis at the end stages of the disease by highlighting disease‐stage–dependent dynamic changes in tracer binding in PD brains. Future directions : Further in vivo and postmortem studies in a larger cohort with multiple brain regions (and markers) encompassing different stages of the disease are needed to further confirm these findings and to validate the reliability of astroglial tracers BU99008 and Deprenyl as PET biomarkers for mapping reactive astrogliosis in the PD continuum. Brain tissue sectioning and harvesting 20 µm thick frozen brain sections from the FC, TC, CAU, and PUT regions of CN and PD cases were prepared using cryostat (HM550, microm, Thermofisher). The sections were immediately transferred onto superfrost plus slides (25 × 75 mm, Epredia) and kept at −80°C until further use in autoradiography and immunofluorescence binding experiments. The same small frozen tissue sample from each brain region after sectioning was used to prepare brain homogenates, as described in Section 2.4, to minimize inter‐variation between different binding studies. Brain homogenates preparation The human brain tissue was homogenized in phosphate‐buffered saline (PBS; pH 7.4) with protease inhibitor cocktail (10 µL/mL; Sigma‐Aldrich #P8340). The brain homogenates (BHs) were stored at −80°C in aliquots until being used in binding experiments. Saturation binding assays 3 H‐BU99008 saturation binding assays were performed in two brain regions—FC and CAU—from 2 PD (FC: PD1; CAU: PD1, PD4) and 1 CN case (FC: CN2; CAU: CN1). BHs (0.5 mg tissue) from different brain regions were incubated with increasing concentrations of 3 H‐BU99008 (ranging from 0.6–30 nM) in 50 mM Tris‐HCl binding buffer pH 7.4 (50 mM Tris‐base, 140 mM NaCl, 1.5 mM MgCl 2 , 5 mM KCl, 1.5 mM CaCl 2 ) for 90 min at 37°C. Non‐specific (NSP) binding was determined with 1 µM unlabeled BU99008. 3 H‐Deprenyl saturation binding assays were performed in two brain regions—FC and CAU—from 3 PD (PD1, PD3, PD4) and 1 CN case (CN1). BHs (0.1 mg of tissue) from different brain regions were incubated with increasing concentrations of 3 H‐Deprenyl (from 0.8–40 nM) in Na‐K phosphate buffer pH 7.4 for 60 min at 37°C. NSP binding was determined with 1 µM unlabeled Deprenyl. After the incubation period, the binding reaction was terminated by filtering through glass fiber filters (pre‐soaked for 2–3 h in 0.3% polyethyleneimine), followed by three quick rinses with cold binding buffer and overnight incubation of the filter in the scintillation liquid. The radioactivity in the tubes with reaction filters was counted the next day using a scintillation counter (PerkinElmer Tri‐Carb 2910TR). The binding curves (total, NSP, specific) were fitted and analyzed using the non‐linear regression function of GraphPad Prism 9.0 software to calculate the dissociation constant (Kd) and binding site density (Bmax). Scatchard plots were prepared by transforming the specific binding data with GraphPad Prism 9.0 software. Competition binding assays Competitive binding assays were again performed on BHs prepared from two brain regions (FC and CAU) of PD and CN cases. For 3 H‐BU99008 competition binding assays, 1 nM 3 H‐BU99008 was incubated with increasing concentrations (1 × 10 −13 M to 1 × 10 −5 M) of unlabeled BU99008 (FC and CAU: PD1, PD4; FC: CN1, CN2; CAU: CN1, CN3) and Deprenyl (FC and CAU: PD1 and CN3) together with BHs (0.5 mg of tissue) in binding buffer (50 mM Tris‐HCl) for 90 min at 37°C. Competition studies were also performed for 3 H‐Deprenyl by incubating BHs (0.1 mg of tissue) with 3 nM 3 H‐Deprenyl along with increasing concentrations (1 × 10 −13 M to 1 × 10 −4 M) of unlabeled Deprenyl (FC and CAU: PD1, PD4 and CN1, CN3, CN4) and BU99008 (FC and CAU: PD1 and CN3) in binding buffer (50 mM Na‐K phosphate buffer) for 60 min at 37°C. For all the experiments, after incubation, a protocol similar to saturation binding assay was done, and the binding was quantified using the scintillation counter. The half‐maximal inhibitory concentration (IC50) was determined by using the non‐linear regression competitive‐binding model of GraphPad Prism 9.0 software. Regional distribution binding assays Regional distribution binding studies were performed using a protocol similar to saturation binding assay on the FC, TC, CAU, and PUT BHs (0.1–0.5 mg tissue) from 4 CNs (CN1–4) and 4 PD patients, at a single concentration of either 1 nM 3 H‐BU99008 or 3 nM 3 H‐Deprenyl, and by incubating for 60 or 90 min at 37°C (depending on the tracer). The binding was quantified with the scintillation counter. NSP binding was determined with 1 µM unlabeled BU99008 or Deprenyl. CAU for CN2 and PUT for CN1 and CN4 were not available when the experiment was planned and performed. The binding data were analyzed using GraphPad Prism 9 software and presented as specific binding (fmol/mg). Postmortem autoradiography imaging studies 3 H‐BU99008 and 3 H‐Deprenyl autoradiography studies were performed on small frozen postmortem brain sections from 7 CNs and 4 PD patients, using all available brain regions (FC, TC, CAU, and PUT) from each case. Frozen sections were allowed to dry at room temperature for 20–30 min, followed by 1 h incubation with either 3 H‐BU99008 (1 nM) or 3 H‐Deprenyl (3 nM) alone, or with 1 µM unlabeled BU99008 or Deprenyl, to determine the NSP binding at room temperature. Then the sections were rinsed for 5 min three times in cold buffer (50 mM Tris‐HCl buffer, pH 7.4 for 3 H‐BU99008; 50 mM Na‐K phosphate buffer, pH 7.4 for 3 H‐Deprenyl), followed by a quick dip in cold Milli‐Q water. 3 H‐BU99008 versus unlabeled Deprenyl pre‐blocking autoradiography was performed on small frozen postmortem brain sections from 5 CNs (CN1‐4, CN7) and 4 PD patients, using available brain regions (FC, TC, CAU, and PUT) from each case. The frozen sections were allowed to dry at room temperature for 20–30 min followed by pre‐blocking with unlabeled Deprenyl (1 µM) for 30 min at room temperature. After pre‐blocking, the sections were rinsed twice for 5 min with cold buffer (50 mM Tris‐HCl buffer, pH 7.4 for 3 H‐BU99008) to remove excess/unbound unlabeled Deprenyl. Thereafter, sections were incubated with 1 nM of 3 H‐BU99008 alone or with 1 µM unlabeled BU99008 for 60 min, and finally rinsed for 5 min three times in cold buffer followed by a quick dip in cold Milli‐Q water. All the sections were allowed to dry at room temperature for 24 h and then apposed together with a tritium standard (Larodan Fine Chemicals AB, Mälmo, Sweden) on a phosphor‐plate for 4 and 7 days for 3 H‐Deprenyl and 3 H‐BU99008, respectively, and later imaged using a BAS‐2500 phosphor imager (Fujifilm, Tokyo, Japan). The semi‐quantitative analyses were performed by drawing manually the regions of interest (ROI), using the multigauge software on the autoradiogram and the photo‐stimulated luminescence per square millimeter (PSL/mm 2 ). The obtained values were transformed with the standard curve to fmol/mg, allowing determination of the total, NSP and specific binding of 3 H‐BU99008 and 3 H‐Deprenyl in the ROI. GFAP immunoblot analysis Expression of the astrocytic marker GFAP was assessed using immunoblot on BHs from 4 CNs and 4 PD cases, using all available brain regions (FC, TC, CAU, and PUT) from each case. Samples (20 µg of BH) were mixed with NuPAGE Lithium dodecyl sulfate (LDS) Sample buffer (Invitrogen, #NP0007), and incubated for 5 min at 95°C and briefly spun down before loading on the NuPAGE 4%–12% Bis‐Tris Gel (Invitrogen, #NP0322BOX). Samples were electrophoresed at 120–200 V in NuPAGE MES SDS Running buffer (Invitrogen, #NP0002). Nitrocellulose membranes were pre‐equilibrated for 5 min in cold transfer buffer (25 mM Tris, 192 mM glycine, 20% ethanol), before the blot sandwich was assembled. The transfer was performed for 1 h at constant 200 mA at room temperature. After that, membranes were washed using Milli‐Q water and stained with Revert 700 Total Protein Stain (LI‐COR, #926‐11011), and the total protein signal was imaged using LI‐COR Odyssey CLx imaging system followed by destaining as per the manufacturer's instructions. Membranes were blocked for 1 h at room temperature in 5% (w/v) fat‐free milk in Tris‐buffered saline with 0.1% Tween‐20 (TBS‐T) buffer at pH 7.4. After blocking, the membranes were incubated at 4°C overnight with GFAP primary antibody (1:500, Santa Cruz, sc‐58766) in TBS‐T buffer with 5% milk. Next day, membranes were incubated with anti‐mouse secondary antibodies (1:10000 for LI‐COR antibodies) in TBS‐T buffer with 5% milk for 1 h at room temperature. The LICOR Odyssey CLx imaging system was used to visualize the protein bands using the appropriate laser channel. Band intensity was quantified using Empiria Studio 3.0 software (LI‐COR) on the raw images and normalized for the total protein stain. GFAP expression levels are displayed as mean ± SD of a percentage of protein expression of the respective CN. Immunofluorescence and microscopy Immunofluorescence staining was performed on frozen brain sections from different brain regions of 2 CNs (CN2, and CN3) and 2 PD cases (PD2, and PD3). Sections were first hydrated with PBS (0.1 M) followed by permeabilization using PBS with 0.2% Tween 20 (PBST) buffer. Next, sections were blocked with bovine serum albumin (BSA; 5%, w/v) for 2 h at room temperature. Next the sections were incubated with primary anti‐GFAP conjugated with Alexa Fluor 488 (Abcam # ab194324, 1:500) and anti‐ICAM‐1 conjugated with coralight594 (CL594‐60299, Proteintech, 1:100) for 24 h in a humid chamber at 2°C–8°C. The excess antibody was removed by washing with PBS (0.1 M, three times, 5 min each). Finally, sections were counterstained with 4′,6‐diamidino‐2‐phenylindole (DAPI) and mounted with glycerin (3%, v/v) using a glass coverslip. To avoid leaking of mounting media, coverslips were sealed with clear nail polish, and signals were viewed in an inverted fluorescence phase contrast microscope (IX71, Olympus), captured with a Hamamatsu camera, and imaged using HCImage software (Hamamatsu corporation, USA). Manual z ‐stack was prepared with an interval of 1µm thickness. Images of high magnification (40X) were further analyzed with Fiji (Image J software, NIH). Reactive astrogliosis processes were measured following standard morphometry in relation to GFAP+/ICAM‐1+ double‐positive cell density, signal intensity, immunoreactivity area, and astrocytic branch length, and diameter of primary projections/rays from GFAP signal were quantified. Two sections per brain area and five non‐overlapping fields at high magnification (40X) were used for morphometric analyses, where double‐positive cells were counted manually in the cell counter plugin on merged/composite images. The reactive area of the markers was drawn manually using the freehand selection tool, whereas the intensity was measured in inverted images for each channel probed with either GFAP or ICAM‐1 in all the fields for all sections, and the data were further analyzed using GraphPad Prism 9 software. Furthermore, for the reconstruction with Sholl rings of the astrocyte primary projections, Neuron J plugin was used to trace on the maximal intensity projections from 1 µm interval z‐stack of GFAP signal from FC, TC, CAU, and PUT brain regions investigated in CN and PD cases. Statistical analyses For autoradiography, regional, and immunofluorescence binding studies the mean differences between the CN and PD cases across different brain regions were determined using the Mann–Whitney t ‐test with no correction for multiple comparisons. The correlation analyses between 3 H‐BU99008 and 3 H‐Deprenyl quantitative binding in CN and PD brain regions were determined using the Pearson r correlation coefficient after outlier removal (ROUT method). In addition, the data were also tested with Spearman's r correlation analysis. A p < 0.05 was considered statistically significant. All the analyses were performed with GraphPad Prism 9.0‐9.4 software. RESULTS 3.1 3 H‐BU99008 and 3 H‐Deprenyl saturation binding studies in CN and PD brains To explore the binding properties and kinetics parameters of 3 H‐BU99008 and 3 H‐Deprenyl, we first conducted saturation binding studies in FC and CAU of CN and PD BHs. The saturation binding curves (total, NSP, and specific) including corresponding Scatchard plots ( insets ) are presented in Figures , , and . 3 H‐BU99008 and 3 H‐Deprenyl demonstrated good saturation‐specific binding curves for both FC and CAU brain regions in 0–40 nM concentration range (Figure ). In CN FC, 3 H‐BU99008 saturation occurred at a Bmax of 59.1 fmol/mg with one binding site of Kd 2.97 nM (Figure ). Of interest, in PD FC, two binding sites were observed with Bmax1 = 51.6 fmol/mg, Kd1 = 2.31 nM for the first binding site, whereas the second binding site, which was manually drawn in the Scatchard plot (Figure ; inset‐light red line ), showed higher affinity Kd2 = 0.75 nM and a lower Bmax2 = 25.5 fmol/mg (Figure ). However, in CAU we detected only one binding site in the comparable nM range for both CN and PD (CN Kd‐ 2.77 nM, PD Kd‐ 3.47 nM) with 3‐fold higher Bmax in PD brain (110.0 fmol/mg) as compared to CN (36.3 fmol/mg) (Figure ). 3 H‐Deprenyl binding in CN FC reached saturation at Bmax and Kd values of 190.1 fmol/mg and 6.04 nM, respectively (Figure ). The Bmax and Kd values for PD FC were 237.1 fmol/mg and 5.9 nM, respectively, revealing one binding site with similar nM affinity as CN but a 1.2‐fold higher Bmax (Figure ). For CAU, 3 H‐Deprenyl also showed only one binding site in both CN and PD brains with the following Bmax and Kd values: CN Bmax of 366.6 fmol/mg and Kd of 4.45 nM; and PD Bmax of 321.1 fmol/mg and Kd of 4.08 nM (Figure ). Overall, we observed a different trend with regard to the number of binding sites and densities in both regions for 3 H‐Deprenyl and 3 H‐BU99008. 3.2 3 H‐BU99008 and 3 H‐Deprenyl comparative competition binding studies in CN and PD brains To further characterize the binding behavior of 3 H‐BU99008 and 3 H‐Deprenyl, we performed extensive competition binding studies using unlabeled BU99008 and Deprenyl in FC and CAU brain regions. 3 H‐BU99008 competition binding studies with unlabeled BU99008 in the FC of CN and PD brains showed one binding site with respective IC50 values of 3.06 and 2.97 nM (Figure ), whereas in CAU, two binding sites for CN (IC50 1 = 2.3 pM and IC50 2 = 3.12 nM) and one site for PD with an IC50 value of 3.62 nM were observed (Figure ). Unlabeled Deprenyl competed with 3 H‐BU99008 for one binding site in CN (IC50 = 87.9 nM) and for two sites in PD (IC50 1 = 0.54 pM and IC50 2 = 18.3 nM) in the FC (Figure ), whereas two binding sites for CN with IC50 values 20.2 nM and 13.8 µM were found in CAU (Figure ). Likewise, PD cases also displayed two binding sites in CAU (IC50 1 = 9.67 nM and IC50 2 = 9.55 µM) (Figure ). 3 H‐Deprenyl in competition with unlabeled Deprenyl demonstrated only one binding site in FC with IC50 values of 8.85 and 11.9 nM for CN and PD, respectively (Figure ). Unlabeled Deprenyl versus 3 H‐Deprenyl in CN and PD CAU behaved as in FC and also displayed one site (Figure ; CN IC50 of 10.9 nM, PD IC50 of 11.4 nM). 3 H‐Deprenyl in the presence of competing BU99008 revealed one binding site in both FC and CAU of CN cases with IC50 values of 0.78 µM and 1.08 µM, respectively (Figure ). In contrast, unlabeled BU99008 competed for two binding sites in PD cases for FC (Figure ; IC50 1 = 98.1 pM and IC50 2 = 0.58 µM) and CAU (Figure ; IC50 1 = 9.72 pM and IC50 2 = 0.87 µM). In our previous studies with 3 H‐BU99008 and 3 H‐Deprenyl, , we proposed a binding‐site model where different binding sites were categorized based on their IC50 values/affinities into three groups: Super high‐affinity (SHA; 10 −13 to −11 M), High‐affinity (HA; 10 −10 to −9 M), and Low‐affinity (LA; 10 −7 to −5 M) binding sites . The model was proposed to better understand the number of binding sites, their distribution, and respective proportions in different brain regions. A similar schematic model was also drawn in this study as we observed multiple binding sites ranging from pM to µM in CN and PD brains for unlabeled BU99008 and Deprenyl in competition with 3 H‐BU99008 (Figure ) and 3 H‐Deprenyl (Figure ). Unlabeled BU99008 showed maximum binding (86%–100%) to the HA site in both FC and CAU of CN and PD brains, with a small proportion of SHA site (14%) in CN CAU with 3 H‐BU99008 (Figure ; BU99008). Although unlabeled Deprenyl showed 92%–100% binding to the HA site in FC and rather equally distributed binding between HA (40%–42%) and LA (58%–60%) in CAU of CN and PD brains along with 8% of SHA site in PD FC (Figure ; Deprenyl). Following the same trend as unlabeled BU99008, for 3 H‐Deprenyl, unlabeled Deprenyl showed exclusive and 100% binding to HA site in both FC and CAU of CN and PD brains (Figure ; Deprenyl). Of interest, unlabeled BU99008 showed the majority of binding to LA site ranging from 80%–100% in the FC and CAU of CN and PD brains and a small proportion of SHA sites (5%–20%) in the FC and CAU of PD brains (Figure ; BU99008). 3.3 3 H‐BU99008 and 3 H‐Deprenyl regional distribution binding studies in CN and PD brains 3 H‐BU99008 (1 nM) and 3 H‐Deprenyl (3 nM) regional distribution binding were performed in four brain regions (FC, TC, CAU, and PUT) from 4 CNs and 4 PD cases (Figure ). 3 H‐BU99008 showed almost significantly higher binding (≈95% increase; p = 0.05 with Mann–Whitney test) in PD CAU as compared to CN, whereas in other regions no such distinction was observed in binding between PD cases and CNs (Figure ). 3 H‐Deprenyl also showed slightly higher binding (≈3.7%) in PD CAU but lower binding was observed in PUT, FC, and TC as compared to CNs (Figure ). PD PUT showed the highest decrease in 3 H‐Deprenyl binding (≈48%), whereas the decrease in binding in cortical regions was ≈18% and 28%, for FC and TC, respectively. 3.4 3 H‐BU99008 and 3 H‐Deprenyl comparative small frozen brain sections autoradiography studies in CN and PD brains 3 H‐BU99008 (1 nM) and 3 H‐Deprenyl (3 nM) regional binding autoradiography studies were performed in four brain regions (FC, TC, CAU, PUT) of CN and PD cases using small frozen brain sections (Figure ). Qualitative assessment revealed an overall high total binding in the PD ROI for 3 H‐BU99008 and 3 H‐Deprenyl as compared to CNs. The initial visual assessment demonstrated more intense 3 H‐BU99008 binding in PD cases compared to CNs, with CN1 and PD4 presenting higher general total binding among CN and PD cases (Figure ). The semi‐quantitative analysis confirmed these observations and showed a global increment in the 3 H‐BU99008‐specific binding in all PD ROI compared to CNs (Figure ). The TC and CAU are the two regions that showed the greatest difference in 3 H‐BU99008‐specific binding (81.4% and 78.0% increase; Figure ), and the binding in TC was significantly higher as compared to CNs ( p = 0.015). Moreover, a relatively low non‐specific binding (NSP; ≈0%–3.6%), determined using 1 µM unlabeled BU99008, was observed in both CN and PD ROI (Table ). 3 H‐Deprenyl also showed high specific binding in all the PD ROI as compared to CNs (Figure ), but the extent of the increase was much lower as compared to 3 H‐BU99008, specifically in the CAU and PUT brain regions (compare Figure with ). Once again, NSP determined that the presence of 1 µM unlabeled Deprenyl was insignificant, ranging from ≈0%–4.6% in all PD and CN ROI (Table ). Of interest, we observed a positive association between 3 H‐BU99008 and 3 H‐Deprenyl binding in CN and PD ROI with both Spearman and Pearson r correlation analyses. The association was much stronger and significant in the brain regions of PD cases (Pearson r = 0.96 and p = 0.03) as compared to CN ROI (Pearson r = 0.78 and p = 0.22) (Figure ). The Spearman r value was 0.80 for both CN and PD ROI. Previous studies have shown that MAO B and I 2 BS co‐express in mitochondria and MAO B possesses an additional I 2 B site near its catalytic entrance, which might lead to non‐specific binding of 3 H‐BU99008 to MAO B. , To evaluate the possible contribution of this additional MAO B I 2 BS site in 3 H‐BU99008 regional binding in CN and PD ROI, we designed and performed a pre‐blocking experiment with 1 µM unlabeled Deprenyl. Even after pre‐blocking with unlabeled Deprenyl, 3 H‐BU99008 showed higher binding in all PD ROI as compared to CNs (Figure ). The highest but non‐significant increase was observed in CAU (≈462%; p = 0.05 ). The qualitative comparison of Figure (non‐blocked 3 H‐BU99008 autoradiography) and Figure demonstrated an overall decrease in total binding, with continuing low values for NSP (≈0%–4.6%) (Table ). Of interest, the decrease in 3 H‐BU99008 binding after pre‐blocking was more varied in CN ROI as compared to PD brain regions (compare Figure with ; Table ). In CNs, it ranged from ≈31%–68% in FC, TC, and PUT, with CAU showing the more pronounced decrease of ≈87%. On the contrary, in PD ROI, the decrease in 3 H‐BU99008 binding was consistent at ≈56%–59% (Table ). 3.5 Reactive astrocytes morphology, cellular processes, and markers (GFAP/ICAM‐1 expression) analyses in CN and PD brains Finally, to get a detailed overview of the reactive astrogliosis in the context of 3 H‐BU99008 and 3 H‐Deprenyl binding, we performed in‐depth immunoblot and immunofluorescence analyses to explore the state of astrocytic morphology, processes, and markers expression levels in FC, TC, CAU, and PUT of CN and PD brains. In FC, a significant increase of GFAP+/ICAM‐1+ double‐positive cells in PD was observed as compared to CN ( p = 0.01) brains (Figure ; GFAP+ and ICAM‐1+ co‐localized cells are shown with arrowhead ). However, the principal branch length of astrocytes ( p = 0.86), GFAP reactive area ( p = 0.24), and intensity ( p = 0.79) were not significantly altered between PD and CN brains (Figure , and Figure ; astrocytic projections/rays are highlighted by arrow ). On the contrary, the ICAM‐1 reactive area ( p < 0.0001) and intensity ( p = 0.0007) were increased significantly in PD compared to CN brains (Figure and Figure ) along with the increased diameter of astrocytic principal branches ( p = 0.01), which are shown by the reconstructed traces of representative astrocyte (Figure ). GFAP+/ICAM‐1+ double‐positive cells were also found to be significantly increased ( p = 0.01) together with an increment in the astrocytic principal branch length and diameter ( p < 0.0001) in PD TC as compared to CN brains (Figure , Figure ). However, the reactive area of GFAP ( p = 0.20) or ICAM‐1 puncta ( p = 0.17) signals did not show any changes between groups (Figure ) but the GFAP and ICAM‐1 intensities were significantly higher in PD ( p < 0.0001) compared to CN brains (Figure ). In the subcortical brain region CAU, a significant increase in GFAP+/ICAM‐1+ double‐positive cells ( p = 0.0012), GFAP reactive area ( p < 0.0001), intensity ( p < 0.0001), and astrocytic principal branch length and diameter ( p < 0.0001) was observed as compared to CN brains (Figure , Figure ). For ICAM‐1, the reactive area was unaltered ( p = 0.41), but the intensity ( p = 0.0006) differed significantly between PD and CN brains (Figure and Figure ). However, in PD PUT, a significant decrease in GFAP ( p < 0.0001) and ICAM‐1 ( p = 0.0024) reactive area was observed as compared to CN, but again their intensities were significantly higher ( p < 0.0001) in PD brains (Figure and Figure ). As consistent with other PD brain regions, the GFAP+/ICAM‐1+ double‐positive cells ( p = 0.0079) were also significantly increased in PUT as compared to CN brains (Figure ). However, the astrocytic principal branch length ( p = 0.6654) was not altered between groups, but the diameter ( p < 0.0001) was significantly increased in PD brains in comparison to CN (Figure , Figure ). Overall, ICAM‐1 expression was upregulated extensively in PD brain regions as compared to CNs, and were significantly co‐localized with GFAP‐positive astrocytes together with substantially increased GFAP expression and reactivity plus notably aggravated astrocytic morphology and projections/rays (Figure , Figure , and Figure ). 3 H‐BU99008 and 3 H‐Deprenyl saturation binding studies in CN and PD brains To explore the binding properties and kinetics parameters of 3 H‐BU99008 and 3 H‐Deprenyl, we first conducted saturation binding studies in FC and CAU of CN and PD BHs. The saturation binding curves (total, NSP, and specific) including corresponding Scatchard plots ( insets ) are presented in Figures , , and . 3 H‐BU99008 and 3 H‐Deprenyl demonstrated good saturation‐specific binding curves for both FC and CAU brain regions in 0–40 nM concentration range (Figure ). In CN FC, 3 H‐BU99008 saturation occurred at a Bmax of 59.1 fmol/mg with one binding site of Kd 2.97 nM (Figure ). Of interest, in PD FC, two binding sites were observed with Bmax1 = 51.6 fmol/mg, Kd1 = 2.31 nM for the first binding site, whereas the second binding site, which was manually drawn in the Scatchard plot (Figure ; inset‐light red line ), showed higher affinity Kd2 = 0.75 nM and a lower Bmax2 = 25.5 fmol/mg (Figure ). However, in CAU we detected only one binding site in the comparable nM range for both CN and PD (CN Kd‐ 2.77 nM, PD Kd‐ 3.47 nM) with 3‐fold higher Bmax in PD brain (110.0 fmol/mg) as compared to CN (36.3 fmol/mg) (Figure ). 3 H‐Deprenyl binding in CN FC reached saturation at Bmax and Kd values of 190.1 fmol/mg and 6.04 nM, respectively (Figure ). The Bmax and Kd values for PD FC were 237.1 fmol/mg and 5.9 nM, respectively, revealing one binding site with similar nM affinity as CN but a 1.2‐fold higher Bmax (Figure ). For CAU, 3 H‐Deprenyl also showed only one binding site in both CN and PD brains with the following Bmax and Kd values: CN Bmax of 366.6 fmol/mg and Kd of 4.45 nM; and PD Bmax of 321.1 fmol/mg and Kd of 4.08 nM (Figure ). Overall, we observed a different trend with regard to the number of binding sites and densities in both regions for 3 H‐Deprenyl and 3 H‐BU99008. 3 H‐BU99008 and 3 H‐Deprenyl comparative competition binding studies in CN and PD brains To further characterize the binding behavior of 3 H‐BU99008 and 3 H‐Deprenyl, we performed extensive competition binding studies using unlabeled BU99008 and Deprenyl in FC and CAU brain regions. 3 H‐BU99008 competition binding studies with unlabeled BU99008 in the FC of CN and PD brains showed one binding site with respective IC50 values of 3.06 and 2.97 nM (Figure ), whereas in CAU, two binding sites for CN (IC50 1 = 2.3 pM and IC50 2 = 3.12 nM) and one site for PD with an IC50 value of 3.62 nM were observed (Figure ). Unlabeled Deprenyl competed with 3 H‐BU99008 for one binding site in CN (IC50 = 87.9 nM) and for two sites in PD (IC50 1 = 0.54 pM and IC50 2 = 18.3 nM) in the FC (Figure ), whereas two binding sites for CN with IC50 values 20.2 nM and 13.8 µM were found in CAU (Figure ). Likewise, PD cases also displayed two binding sites in CAU (IC50 1 = 9.67 nM and IC50 2 = 9.55 µM) (Figure ). 3 H‐Deprenyl in competition with unlabeled Deprenyl demonstrated only one binding site in FC with IC50 values of 8.85 and 11.9 nM for CN and PD, respectively (Figure ). Unlabeled Deprenyl versus 3 H‐Deprenyl in CN and PD CAU behaved as in FC and also displayed one site (Figure ; CN IC50 of 10.9 nM, PD IC50 of 11.4 nM). 3 H‐Deprenyl in the presence of competing BU99008 revealed one binding site in both FC and CAU of CN cases with IC50 values of 0.78 µM and 1.08 µM, respectively (Figure ). In contrast, unlabeled BU99008 competed for two binding sites in PD cases for FC (Figure ; IC50 1 = 98.1 pM and IC50 2 = 0.58 µM) and CAU (Figure ; IC50 1 = 9.72 pM and IC50 2 = 0.87 µM). In our previous studies with 3 H‐BU99008 and 3 H‐Deprenyl, , we proposed a binding‐site model where different binding sites were categorized based on their IC50 values/affinities into three groups: Super high‐affinity (SHA; 10 −13 to −11 M), High‐affinity (HA; 10 −10 to −9 M), and Low‐affinity (LA; 10 −7 to −5 M) binding sites . The model was proposed to better understand the number of binding sites, their distribution, and respective proportions in different brain regions. A similar schematic model was also drawn in this study as we observed multiple binding sites ranging from pM to µM in CN and PD brains for unlabeled BU99008 and Deprenyl in competition with 3 H‐BU99008 (Figure ) and 3 H‐Deprenyl (Figure ). Unlabeled BU99008 showed maximum binding (86%–100%) to the HA site in both FC and CAU of CN and PD brains, with a small proportion of SHA site (14%) in CN CAU with 3 H‐BU99008 (Figure ; BU99008). Although unlabeled Deprenyl showed 92%–100% binding to the HA site in FC and rather equally distributed binding between HA (40%–42%) and LA (58%–60%) in CAU of CN and PD brains along with 8% of SHA site in PD FC (Figure ; Deprenyl). Following the same trend as unlabeled BU99008, for 3 H‐Deprenyl, unlabeled Deprenyl showed exclusive and 100% binding to HA site in both FC and CAU of CN and PD brains (Figure ; Deprenyl). Of interest, unlabeled BU99008 showed the majority of binding to LA site ranging from 80%–100% in the FC and CAU of CN and PD brains and a small proportion of SHA sites (5%–20%) in the FC and CAU of PD brains (Figure ; BU99008). 3 H‐BU99008 and 3 H‐Deprenyl regional distribution binding studies in CN and PD brains 3 H‐BU99008 (1 nM) and 3 H‐Deprenyl (3 nM) regional distribution binding were performed in four brain regions (FC, TC, CAU, and PUT) from 4 CNs and 4 PD cases (Figure ). 3 H‐BU99008 showed almost significantly higher binding (≈95% increase; p = 0.05 with Mann–Whitney test) in PD CAU as compared to CN, whereas in other regions no such distinction was observed in binding between PD cases and CNs (Figure ). 3 H‐Deprenyl also showed slightly higher binding (≈3.7%) in PD CAU but lower binding was observed in PUT, FC, and TC as compared to CNs (Figure ). PD PUT showed the highest decrease in 3 H‐Deprenyl binding (≈48%), whereas the decrease in binding in cortical regions was ≈18% and 28%, for FC and TC, respectively. 3 H‐BU99008 and 3 H‐Deprenyl comparative small frozen brain sections autoradiography studies in CN and PD brains 3 H‐BU99008 (1 nM) and 3 H‐Deprenyl (3 nM) regional binding autoradiography studies were performed in four brain regions (FC, TC, CAU, PUT) of CN and PD cases using small frozen brain sections (Figure ). Qualitative assessment revealed an overall high total binding in the PD ROI for 3 H‐BU99008 and 3 H‐Deprenyl as compared to CNs. The initial visual assessment demonstrated more intense 3 H‐BU99008 binding in PD cases compared to CNs, with CN1 and PD4 presenting higher general total binding among CN and PD cases (Figure ). The semi‐quantitative analysis confirmed these observations and showed a global increment in the 3 H‐BU99008‐specific binding in all PD ROI compared to CNs (Figure ). The TC and CAU are the two regions that showed the greatest difference in 3 H‐BU99008‐specific binding (81.4% and 78.0% increase; Figure ), and the binding in TC was significantly higher as compared to CNs ( p = 0.015). Moreover, a relatively low non‐specific binding (NSP; ≈0%–3.6%), determined using 1 µM unlabeled BU99008, was observed in both CN and PD ROI (Table ). 3 H‐Deprenyl also showed high specific binding in all the PD ROI as compared to CNs (Figure ), but the extent of the increase was much lower as compared to 3 H‐BU99008, specifically in the CAU and PUT brain regions (compare Figure with ). Once again, NSP determined that the presence of 1 µM unlabeled Deprenyl was insignificant, ranging from ≈0%–4.6% in all PD and CN ROI (Table ). Of interest, we observed a positive association between 3 H‐BU99008 and 3 H‐Deprenyl binding in CN and PD ROI with both Spearman and Pearson r correlation analyses. The association was much stronger and significant in the brain regions of PD cases (Pearson r = 0.96 and p = 0.03) as compared to CN ROI (Pearson r = 0.78 and p = 0.22) (Figure ). The Spearman r value was 0.80 for both CN and PD ROI. Previous studies have shown that MAO B and I 2 BS co‐express in mitochondria and MAO B possesses an additional I 2 B site near its catalytic entrance, which might lead to non‐specific binding of 3 H‐BU99008 to MAO B. , To evaluate the possible contribution of this additional MAO B I 2 BS site in 3 H‐BU99008 regional binding in CN and PD ROI, we designed and performed a pre‐blocking experiment with 1 µM unlabeled Deprenyl. Even after pre‐blocking with unlabeled Deprenyl, 3 H‐BU99008 showed higher binding in all PD ROI as compared to CNs (Figure ). The highest but non‐significant increase was observed in CAU (≈462%; p = 0.05 ). The qualitative comparison of Figure (non‐blocked 3 H‐BU99008 autoradiography) and Figure demonstrated an overall decrease in total binding, with continuing low values for NSP (≈0%–4.6%) (Table ). Of interest, the decrease in 3 H‐BU99008 binding after pre‐blocking was more varied in CN ROI as compared to PD brain regions (compare Figure with ; Table ). In CNs, it ranged from ≈31%–68% in FC, TC, and PUT, with CAU showing the more pronounced decrease of ≈87%. On the contrary, in PD ROI, the decrease in 3 H‐BU99008 binding was consistent at ≈56%–59% (Table ). Reactive astrocytes morphology, cellular processes, and markers (GFAP/ICAM‐1 expression) analyses in CN and PD brains Finally, to get a detailed overview of the reactive astrogliosis in the context of 3 H‐BU99008 and 3 H‐Deprenyl binding, we performed in‐depth immunoblot and immunofluorescence analyses to explore the state of astrocytic morphology, processes, and markers expression levels in FC, TC, CAU, and PUT of CN and PD brains. In FC, a significant increase of GFAP+/ICAM‐1+ double‐positive cells in PD was observed as compared to CN ( p = 0.01) brains (Figure ; GFAP+ and ICAM‐1+ co‐localized cells are shown with arrowhead ). However, the principal branch length of astrocytes ( p = 0.86), GFAP reactive area ( p = 0.24), and intensity ( p = 0.79) were not significantly altered between PD and CN brains (Figure , and Figure ; astrocytic projections/rays are highlighted by arrow ). On the contrary, the ICAM‐1 reactive area ( p < 0.0001) and intensity ( p = 0.0007) were increased significantly in PD compared to CN brains (Figure and Figure ) along with the increased diameter of astrocytic principal branches ( p = 0.01), which are shown by the reconstructed traces of representative astrocyte (Figure ). GFAP+/ICAM‐1+ double‐positive cells were also found to be significantly increased ( p = 0.01) together with an increment in the astrocytic principal branch length and diameter ( p < 0.0001) in PD TC as compared to CN brains (Figure , Figure ). However, the reactive area of GFAP ( p = 0.20) or ICAM‐1 puncta ( p = 0.17) signals did not show any changes between groups (Figure ) but the GFAP and ICAM‐1 intensities were significantly higher in PD ( p < 0.0001) compared to CN brains (Figure ). In the subcortical brain region CAU, a significant increase in GFAP+/ICAM‐1+ double‐positive cells ( p = 0.0012), GFAP reactive area ( p < 0.0001), intensity ( p < 0.0001), and astrocytic principal branch length and diameter ( p < 0.0001) was observed as compared to CN brains (Figure , Figure ). For ICAM‐1, the reactive area was unaltered ( p = 0.41), but the intensity ( p = 0.0006) differed significantly between PD and CN brains (Figure and Figure ). However, in PD PUT, a significant decrease in GFAP ( p < 0.0001) and ICAM‐1 ( p = 0.0024) reactive area was observed as compared to CN, but again their intensities were significantly higher ( p < 0.0001) in PD brains (Figure and Figure ). As consistent with other PD brain regions, the GFAP+/ICAM‐1+ double‐positive cells ( p = 0.0079) were also significantly increased in PUT as compared to CN brains (Figure ). However, the astrocytic principal branch length ( p = 0.6654) was not altered between groups, but the diameter ( p < 0.0001) was significantly increased in PD brains in comparison to CN (Figure , Figure ). Overall, ICAM‐1 expression was upregulated extensively in PD brain regions as compared to CNs, and were significantly co‐localized with GFAP‐positive astrocytes together with substantially increased GFAP expression and reactivity plus notably aggravated astrocytic morphology and projections/rays (Figure , Figure , and Figure ). DISCUSSION Reactive astrogliosis is a key feature of neuroinflammation and is regarded as a valuable biomarker for multiple neurodegenerative diseases including PD. , , , , Reactive astrogliosis and activated microglia both have been implicated in PD, but the role of microglia is more deeply explored , as compared to reactive astrocytes, despite their crucial role as initiators in the early stages of the PD pathology. , , Recent clinical PET studies with astroglial tracer BU99008 have also shown increased reactive astrogliosis in early PD cases as compared to moderately/advanced cases and controls, further confirming their involvement in the early stages of the disease and progression. However, there is still a significant gap regarding the role and state of reactive astrogliosis throughout the PD continuum. In this study, we performed extensive radioligand binding assays (saturation, competition, and regional distribution) and postmortem brain imaging autoradiography studies on PD and CN brains with astroglial tracers Deprenyl and BU99008, together with astrocytic morphometric and markers (GFAP and ICAM‐1 expression) analyses, to gain deeper insight into the role of reactive astrogliosis at the advanced/end stages of the disease. We also assessed the potential of Deprenyl and BU99008 as astrocytic PET biomarkers for reactive astrogliosis in PD brains. 3 H‐BU99008 and 3 H‐Deprenyl showed high specific and low NSP binding in FC and CAU regions of PD and CN cases in our saturation binding assays. In FC, 3 H‐BU99008 displayed multiple binding sites in PD brains as compared to CNs. The one binding site showed almost comparable Bmax and Kd values as the single CN binding site, whereas the other PD binding site showed an ≈2.3‐fold less Bmax but ≈3.9 times higher affinity as compared to the CN binding site (Bmax CN = 59.1 fmol/mg, Kd = 2.97 nM vs Bmax PD = 25.5 fmol/mg, Kd = 0.75 nM). On the contrary, 3 H‐Deprenyl showed only one binding site in the FC of PD and CN brains with similar Kd values (≈6 nM) and ≈1.2‐fold higher Bmax as compared to CN. In CAU, 3 H‐BU99008 and 3 H‐Deprenyl both showed only one binding site with comparable nM affinity in PD and CN brains. However, the main difference was observed in the PD binding site density (Bmax values); 3 H‐BU99008 showed an ≈3‐fold higher Bmax, whereas 3 H‐Deprenyl presented ≈1.14‐fold lower Bmax as compared to CN. These findings clearly demonstrated the regional differences in tracer binding, with 3 H‐BU99008 and 3 H‐Deprenyl showing distinct (single or multiple) binding sites and upregulated expression of I 2 Bs and MAO B in a region‐dependent manner. Our observations are in line with the well‐established concept of astrocytic heterogeneity by which astrocytes could undergo morphological/pathological transformations and assume multiple subtypes or states (reactive and non‐reactive) that can vary from region to region or in healthy and diseased conditions. , , , To further explore the different binding sites and affinities observed for 3 H‐BU99008 and 3 H‐Deprenyl in CN and PD brains, we performed extensive comparative competition binding studies with unlabeled BU99008 and Deprenyl in FC and CAU brain regions. Our competition binding studies encompassing broad concentration ranges displayed an array of binding sites with different levels of affinity and percentages: SHA, HA, and LA binding sites, and indicated potential interaction between BU99008 and Deprenyl. These findings were consistent with our previous studies in AD brains, , where 3 H‐BU99008 and 3 H‐Deprenyl both showed one specific binding site in the HA range along with several other binding sites in the SHA and LA range in CN and AD brains. This HA binding site for 3 H‐BU99008 and 3 H‐Deprenyl may be permanent and common among pathologies (as we have already shown for AD and PD), whereas the SHA and LA binding sites represent a group of dynamic and transient sites, which might become available due to structural versatility of the protein triggered by multiple factors such as endogenous protein–protein or protein–ligand interactions. , It is also possible that the presence of different endogenous ligands (which are beyond our control) or the binding of a tracer to its specific site, can induce structural changes or transformations in the protein structure, leading to the formation of an allosteric or transient binding site in the target protein. The regional and autoradiography binding studies further complemented these binding differences and indicated that 3 H‐BU99008 and 3 H‐Deprenyl might reflect reactive astrogliosis by targeting different subtypes or a specific population of astrocytes in CN and PD brain regions. 3 H‐BU99008 regional distribution showed comparable binding levels between all CN and PD brain regions, except in CAU, where an ≈1.95‐fold higher binding in PD brains was observed. 3 H‐Deprenyl regional binding distribution data in the PD FC and CAU showed inconsistency as compared to saturation binding findings, which could be attributed to experimental differences because regional binding assays are performed using a single tracer concentration (3 nM), whereas a large concentration range (0–40 nM) is used for saturation binding assays. Overall, 3 H‐BU99008 and 3 H‐Deprenyl both showed similar binding tendencies in regional distribution studies, with the highest binding increase in CAU as compared to CNs, and highlighted potential case‐by‐case variability. 3 H‐BU99008 and 3 H‐Deprenyl frozen brain section autoradiography comparative studies suggested similar overall high specific and very low NSP binding levels in the four brain regions of CN and PD cases. 3 H‐BU99008 showed more intense binding in a regional manner, starting with CAU and TC followed by other regions (CAU and TC > FC > PUT). However, there was a noticeable alteration in all the regions compared to CN brains, revealing an overall increased reactive astrogliosis with prominent differences in CAU and TC. 3 H‐BU99008 regional differences between CN and PD cases were still prominent, even after accounting for possible contributions of BU9908 binding to an additional MAO B I 2 B site, , as we have discussed in the preceding autoradiography studies results section. Moreover, this pre‐blocking experiment with unlabeled Deprenyl demonstrated and confirmed our above‐stated observations: first, a specific astrocytic binding site for BU99008, second, that Deprenyl could interact and interfere with BU99008 binding, and finally, the astrocytic heterogeneity between CN and PD cases if we consider the % decrease in 3 H‐BU99008 specific binding between CN and PD brain regions (Table ). It is possible that in CNs, the different brain regions have different astrocytic populations with variable expression/availability of additional MAO B I 2 BS. Most importantly, the specific I 2 BS expression captured by BU99008 after Deprenyl blockage showed an even greater disparity between CN and PD cases, highlighting a promising capacity to reflect reactive astrogliosis in a pathological context. In contrast, 3 H‐Deprenyl autoradiography studies revealed a different regional binding behaviour with the highest increase in PD cortical regions FC and TC (18% and 27% increase in binding as compared to CNs, respectively) than subcortical regions indicating that the tracers are binding to different subpopulations or specific state of astrocytes, as we also suggested in our previous studies with AD brains. , Despite the differences in binding behavior, 3 H‐BU99008 and 3 H‐Deprenyl showed a significant positive correlation in PD brain regions as compared to CNs, further highlighting their potential to track reactive astrogliosis in the diseased state with high specificity and selectivity. These findings also add support to the “ second wave (late)” of reactive astrogliosis at the end stages of the disease by highlighting disease‐stage–dependent dynamic changes in BU99008 binding in the PD brain. A recent in vivo study by Wilson et al. showed that 11 C‐BU99008 binding is upregulated during the early stages of PD as compared to moderate/advanced stages. We believe that this early upregulation might reflect the “ first wave ” of reactive astrogliosis. We have recently proposed a similar “two‐wave model of reactive astrogliosis” in AD pathology based on the plethora of clinical in vivo and postmortem in vitro studies showing dynamic changes in BU99008 and Deprenyl binding depending on the disease stage. , The reactive astrogliosis visualized by 3 H‐BU99008 and 3 H‐Deprenyl in the PD brains was further evidenced by a significant increase in GFAP+/ICAM‐1+ double‐positive astrocytic subpopulation, along with prominent GFAP reactivity and well‐pronounced changes in astrocytic morphology and cellular processes in all the brain regions as compared to CNs. ICAM‐1 is a cell‐surface cytosolic glycoprotein that is involved in immune response by activation and recruitment of leukocytes to the sites of inflammation. ICAM‐1 has been demonstrated to be involved in the regulation of both pro‐ and anti‐inflammatory responses, , with further studies showing upregulation of ICAM‐1‐positive reactive astrocytes in the substantia nigra of PD patient's postmortem brains and their key role in modulating the astrocyte–microglia neuroinflammatory response in PD pathology, since ICAM‐1 is also a ligand for microglial Lymphocyte function‐associated antigen‐1 (LFA‐1) receptors. , It is possible that these ICAM‐1‐positive astrocytic subpopulations at the end stages of the disease could be disease‐associated astrocytes “DAAs,” as recently demonstrated by Habib et al. in AD mouse model, and might be targeted by 3 H‐BU99008 and 3 H‐Deprenyl in a region‐specific manner based on the autoradiography and immunofluorescence data. However, these speculations warrant further investigation to elucidate the intermediary role of ICAM‐1 in PD astrocyte reactivity and subsequent neuroinflammatory response. In conclusion, our study offers the first comprehensive snapshot of reactive astrogliosis at the advanced/end stages of PD using a multiple astrocytic marker approach. These findings could have implications for both research and potential clinical applications in tracking disease progression. 4.1 Limitations The main limitation of our study is the small sample size and limited brain regions; thus, further validation of our studies in a larger cohort with multiple brain regions encompassing different stages of the disease is highly anticipated. However, it is important to keep in mind that postmortem brain tissues are extremely scarce and difficult to acquire. Despite this limitation, the data presented are novel, conclusive, and consistent with previous findings. , , , Limitations The main limitation of our study is the small sample size and limited brain regions; thus, further validation of our studies in a larger cohort with multiple brain regions encompassing different stages of the disease is highly anticipated. However, it is important to keep in mind that postmortem brain tissues are extremely scarce and difficult to acquire. Despite this limitation, the data presented are novel, conclusive, and consistent with previous findings. , , , Amit Kumar conceptualized and designed the study. Filipa M. Rocha standardized and performed radioligand binding assays, autoradiography, and immunoblot studies. Filipa M. Rocha and Amit Kumar analyzed the results. Mukesh Varshney and Avishek Roy designed and standardized immunolabeling experiments. Avishek Roy performed and analyzed the immunofluorescence experiments. Filipa M. Rocha, Avishek Roy, and Amit Kumar wrote the first draft of the manuscript. All the authors have provided critical input and feedback during the writing of the manuscript. All authors read and approved the final version of the manuscript. The authors declare no conflicts of interest. All experiments on anonymized autopsied human brain tissue obtained from the Netherlands Brain Bank (NBB) were carried out in accordance with ethical permission obtained from the regional human ethics committee in Stockholm (permission number 2011/962/31‐1 and 2024‐05198‐01). All material has been collected from donors for or from whom written informed consent for a brain autopsy and the use of the material and clinical information for research purposes had been obtained by the Netherlands Brain Bank (NBB). Supporting Information Supporting Information |
Targeted panel sequencing of pharmacogenes and oncodrivers in colorectal cancer patients reveals genes with prognostic significance | 912ec888-f9aa-468a-a81f-2689b7e08785 | 11264515 | Pharmacology[mh] | Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of oncology-related deaths with an estimated 1.9 million newly diagnosed patients and 935 thousand deaths per year . The all-stages 5-year survival for both sexes is approximately 65% . There is, therefore, an urgent need for improving preventive measures of all kinds, including reliable biomarkers that would accurately predict the resistance of patients to therapy, potentially extending the patients’ survival. Sporadic CRC, a disease without apparent family history or inherited mutations increasing CRC risk, occurs in about 65% of all cases . On the opposite, hereditary CRC like Lynch syndrome or familial polyposis coli are caused by rare inherited variants in high-penetrance susceptibility genes like MLH1 or APC . Part of the tumors bear also genetic alterations that are either common genetic polymorphisms with low penetrance or their combinations, eventually inherited changes that have not been discovered yet . Therefore, tumors often develop in genetically susceptible individuals by co-inheritance of multiple low-risk variants. CRC is thus a highly heterogeneous malignancy with enormous genetic differences between individuals making the treatment of patients a challenge for current medicine. CRC treatment comprises surgical tumor removal and eventually systemic adjuvant chemotherapy, which depends on tumor staging and risk factors. Patients with stage I disease do not require adjuvant chemotherapy, while stage II patients usually receive chemotherapy only if they are considered high-risk, mostly based on number of evaluated lymph nodes, grade, tumor size, lymphovascular or perineural propagation, mismatch repair status, oncomarkers, ileus, etc. Patients of stage III receive systemic adjuvant and stage IV palliative chemotherapy. Most of the adjuvant regimens constitute chemotherapy based on 5-fluorouracil (5-FU) (de Gramonte regimen or capecitabine) for stage II, or combination regimens with oxaliplatin (FOLFOX or CAPOX) . 5-FU is an anti-cancer drug widely used since 1957 in the treatment of various gastrointestinal cancers. It is an analog of uracil with a structure similar to pyrimidine molecules of DNA and RNA with a fluorine atom at the C-5 position. Due to structural similarity, 5-FU interferes with nucleoside metabolism and can also be incorporated into RNA and DNA , leading to cytotoxicity and cell death. However, the main anticancer mode of action is inhibition of the enzyme thymidylate synthase, which is essential in nucleotide synthesis (reviewed by ). The overall response rates for 5-FU-based chemotherapy for advanced CRC are around 15% . When combined with other anticancer drugs such as oxaliplatin, response rates improve to 40–50% . Despite progress in targeted therapy, 5-FU remains cornerstone of chemotherapy of CRC and other cancers. Although it has been widely used for almost 60 years, some of the mechanisms underlying its toxicity and resistance remain unclear and need further investigation. We thus designed a panel of genes with pharmacogenomics records related to 5-FU and oxaliplatin resistance based on PharmGKB, DGIdb, DrugBank, GDSC (Genomics of Drug Sensitivity in Cancer), and COSMIC (Catalogue Of Somatic Mutations In Cancer) databases. Moreover, we included genes important for sensitivity to other drugs frequently used in CRC patients and major oncodrivers according to the latest tier 1 and 2 CGC and actionable genes in COSMIC. The panel was enriched with principal genes for CRC progression identified in recent whole exome sequencing studies and finally consisted of 558 genes for which the molecular probes have been designed in NimbleDesign. We performed a target enrichment sequencing of DNA from tumors and matched blood samples of CRC patients, and compared the results with patient prognosis stratified by adjuvant chemotherapy. Patients Paired samples of tumor tissue and blood were collected from 83 patients who were diagnosed with sporadic primary CRC tumors at various stages. All patients underwent surgery at the Department of Surgery of the University Hospital in Pilsen between 2015 and 2019. The clinical data were obtained from medical records and contained information about the age at diagnosis, sex, disease stage, tumor grade and location, surgery including resection margins, oncological treatment, recurrence or progression after surgery, and date of last control or death. Table contains a summary demographic and clinical data of patients. The overall survival (OS) was defined as the time elapsed between resection of a primary tumor and death from any cause or patient censoring. The recurrence-free survival (RFS) was defined as the time elapsed between the resection and recurrence of the tumor; death or last control in remission were censored events. DNA isolation and quantification DNA from fresh-frozen tissue samples of primary tumors was isolated using the DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. DNA was eluted into 200 µL of AE buffer, divided into triplicates, and stored at -20 °C until further use. DNA from the whole blood samples collected during the surgery was isolated using BioSprint 15 DNA Blood Kit (Qiagen) combined with an automatized KingFisher mL Purification System (ThermoFisher Scientific, Waltham, MA, USA) using magnetic particles. We modified the manufacturer’s protocol to isolate DNA from 1 mL of human whole blood instead of referenced 100–300 µL. In the modified protocol, we first pipette 90 µL of protease, add 1 mL of whole blood, and vortex sample for 15 s. Then we incubate the sample at 70 °C for 10 min, add 0.9 mL of isopropanol, and shortly spin for 1 min at 1,000 rpm. The obtained lysate is then applied into each column of the KingFisher mL Purification System, other buffers are applied in their respective positions, and the machine is initialized. In the end, isolated DNA is eluted into 300 µL of AE buffer and stored at -20 °C until further use. For DNA quantification, we use Qubit 3.0 Fluorometer and dsDNA Broad Range Assay Kit (both ThermoFisher Scientific). Target capture panel in silico analysis, design, and synthesis We searched several databases, e.g., PharmGKB ( www.pharmgkb.org ), DGIdb ( http://dgidb.genome.wustl.edu/ ), DrugBank ( www.drugbank.ca ), GDSC ( https://www.cancerrxgene.org/ ), and COSMIC v81 ( http://cancer.sanger.ac.uk/cosmic ) for interactions between human genome and sensitivity to 5-FU and oxaliplatin. Through a three-phase search, we prioritized 264 genes. Firstly, mutations in all screened cell line models ( n = 968) in the GDSC have been crosschecked together with sensitivity data for 5-FU and oxaliplatin (IC 50 and area under curve, AUC). The most frequently mutated genes in either the most sensitive or resistant cell line models (each category of “resistant” or “sensitive” cell lines had 20 cell lines with 10 of CRC origin) have been selected. Genes with more than 5% mutations of the total observed in half or more cell lines in each category (to exclude multiple single gene mutations in a single cell line) have been considered as general marks of sensitivity/resistance to drugs and passed to the second phase ( n = 745 for sensitive and n = 101 for resistant). In the second phase, genes were checked by the HGNC server ( http://www.genenames.org/ ) for compliance with HuGo nomenclature and merged ( n = 302 genes). In the third phase, the COSMIC v81 tool was used for the identification of genes somatically mutated in human CRC ( n = 715 samples) with more than 5% frequency ( n = 780 genes) as well as genes without mutations in these samples ( n = 2767 genes). After gene nomenclature check, both databases (cell line and human tumor mutations) have been crosschecked, and the final list of genes fulfilling these criteria: i/ mutated in 50% or more of sensitive or resistant cell lines, ii/ mutated in human CRC at more than 5% frequency, iii/ absent in the list of not mutated genes in CRC has been produced ( n = 264 genes). These genes have been analyzed for molecular function, cellular complement, biological process, and pathway context by the Panther database ( http://pantherdb.org/ ). Binding (GO:0005488), catalytic activity (GO:0003824), structural molecule activity (GO:0005198), receptor activity (GO:0004872), and transporter activity (GO:0005215) comprised more than 85% of cellular functions affected by mutations in both 5-FU sensitive/resistant cell lines and CRC tumors. The list of genes was enriched with an additional 294 genes listed among CGC tier 1 and 2 and “Actionable” in COSMIC v81, together with principal genes for CRC progression or from the latest whole exome sequencing studies . The final list of genes for gene variability consists of 558 genes. The panel was designed in NimbleDesign (Nimblegen, Roche, Basel, Switzerland). All possible transcript variants and RefSeq, Ensembl, and UCSC databases were used to select chromosomal coordinates in genome build hg19. Probes were selected in moderate stringency (preferred close matches 3, maximum close matches 20) and manufactured using NimbleGen SeqCap EZ Choice format (Roche). For the complete list of genes, see Supplementary Table . Library preparation and whole exome sequencing Sequencing libraries were prepared using the NEBNext Ultra II FS DNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, USA) according to the manufacturer’s instructions. Briefly, 100 ng of DNA was enzymatically digested, adaptors were ligated and adaptor-ligated DNA was enriched using 7–8 PCR cycles. The quality of prepared libraries was controlled using TapeStation 2200 (Agilent, Santa Clara, CA, USA) and libraries were quantified using Qubit 3.0 Fluorimeter and dsDNA High Sensitivity Assay Kit (ThermoFisher Scientific). Samples were multiplexed in pooled libraries containing 1000 ng DNA libraries derived either from 11 samples of tumor tissue DNA or 22 samples of blood DNA and hybridized with custom probes using standard NimbleGen SeqCap EZ Library LR protocol (Roche) with the following modifications of hybridization and post-capture PCR steps. In the hybridization reaction, 13.4 µl of Kapa Universal Enhancing Oligos (Roche) were added to the bead-bound DNA Sample instead of SeqCap HE Universal and Index oligos. After performing the capture reaction, the libraries were amplified using Primer 1: 5’-AATGATACGGCGACCACCGAGATCTACAC-3’ and Primer 2: 5’-CAAGCAGAAGACGGCATACGAGAT-3’. For the amplification, Ultra II Q5 PCR master mix (NEB) was used in a total reaction volume of 100 µl. Captured sequences were amplified using 13 PCR cycles. For assessment of the quality and quantity of final libraries, TapeStation 2200 (Agilent) and Qubit 3.0 Fluorometer with dsDNA High Sensitivity Kit (ThermoFisher Scientific), respectively, were used. Samples were pooled into the final pool in a non-equimolar fashion (tumors/blood ratio 3:1) and the final pool was sequenced on the NovaSeq 6000 platform (Illumina, San Diego, CA, USA) using 150 bp pair-end sequencing on one lane of the S4 flow cell. Bioinformatic analysis The pipeline used for bioinformatic processing of raw data has been described elsewhere in detail . Here, we describe the procedure only briefly with relevant references. Adapter and low-quality base trimming was done by Trimmomatic. Reads were aligned to the hg38 human reference genome sequence using Burrows-Wheeler Aligner v0.7.17-r1188 (BWA, Cambridge, UK) with the BWA-maximal exact matches (MEM) algorithm . Base recalibration was done using the Genome Analysis Toolkit v.4.3.0.0 (GATK) (Broad Institute, Cambridge, UK) according to GATK Best Practices . Duplicate reads were identified by MarkDuplicates (Picard). Identification of somatic variants and short indels was performed in paired tumor-normal samples using Mutect2 (GATK). Detected variants were filtered using FilterMutectCalls (GATK) and only variants passing all filters (i.e., somatic variants with filter status PASS) were considered. Variants were filtered on min. variant allele frequency (VAF) 5% and supported by min. three reads. Germline variants were called using HaplotypeCaller and variant recalibration was done by VariantRecalibrator (both GATK). Annotation was performed in Variant Effect Predictor (VEP) v.108 , which assigned one of the following values to each variant: LOW, MODIFIER, MODERATE (missense, in-frame deletions, and insertions), or HIGH (nonsense, frameshift, splice site, transcription start site) functional effect. Variants with HIGH and MODERATE predicted effects were evaluated for clinical associations. Visualization was performed in Maftools or ComplexHeatmap (both R/Bioconductor). CNVs were detected with CNVkit v0.9.9 and VarDict tool v1.8.3 . Tumor purity was estimated using PureCN v.2.0.2 (R/Bioconductor). Significant calls were assessed based on the average read depth a log2 ratio values and B-allele frequencies (BAF) of individual segments. Assuming a theoretical clonal fraction (tumor purity) of 70%, a deletion should have log2 ratio < -0.278 and BAF between 0.325 and 0.675; a duplication should have a log2 ratio > 0.233 and BAF between 0.442 and 0.558. All called segments that contained less than three bins or did not show a statistically significant difference of log2 ratios compared to reference values ( p < 0.05 by the Student’s t-test) were excluded. Microsatellite instability was detected using MSI-sensor2 v0.1 ( https://github.com/niu-lab/msisensor2 ) based on the published 20% threshold . For the detection of indels in homopolymer regions per Mb as a surrogate marker of mismatch repair deficiency (MMR-D) , the homopolymer regions were identified by Vcfpolyx (part of Jvarkit, https://github.com/lindenb/jvarkit ) and were defined as genomic regions with more than four repeat bases. Samples with > 1.5 indels in homopolymer regions per Mb were considered MMR-D . External validation The validation set was downloaded from the USCS Xena Browser . GDA TCGA datasets COAD and READ were merged and only variants in candidate genes with predicted HIGH or MODERATE effect (see Material and section ) were used in statistical analyses. Only primary tumors with adenocarcinoma diagnosis and patients with complete survival follow-up were selected. Statistical analyses Differential analyses were performed in patient subgroups stratified by main clinical data (age, sex, stage, grade, tumor localization, and adjuvant chemotherapy response – remission vs. progression). Analyses of differences in the number of variants or their functional classification between groups of patients divided by the above parameters were performed using Fisher’s exact test. Differences in TMB and CNVs between patients stratified by the above data were compared using the Kruskal-Wallis test and correlations of continuous data such as patient age, CNV size, or CNV counts were assessed using Spearman’s rho test. For the associations of germline variants with survival, Plink v1.9 was used to perform chi-square allelic tests with Monte-Carlo max(T) permutation test. Patients were divided according to RFS ≤ 3 years vs. > 3 years, which is an appropriate end point for adjuvant treatment of regimens based on 5-FU . Manhattan plot was generated using package qqman (R/CRAN). Survival functions for groups of patients divided by genetic data, eventually stratified by chemotherapy, were plotted using the Kaplan-Meier method, and significance was calculated by the Breslow test. All continuous variables were divided by the median. The Benjamini-Hochberg false discovery rate (B-H FDR) test was used for the correction of multiple testing and adjusted p-value (p adj ) < 0.05 was considered significant. For associations between single genes and clinical data, unadjusted p-values (p crude ) are provided to indicate trends. A two-sided p crude <0.05 was used for selection of genes for testing their combinations and associations with p adj < 0.05 were subjected to external validation where possible. All statistical analyses were performed in the SPSS v16 program (SPSS Inc., Chicago, IL, USA) or R package survminer/CRAN (R version 4.3.3). Paired samples of tumor tissue and blood were collected from 83 patients who were diagnosed with sporadic primary CRC tumors at various stages. All patients underwent surgery at the Department of Surgery of the University Hospital in Pilsen between 2015 and 2019. The clinical data were obtained from medical records and contained information about the age at diagnosis, sex, disease stage, tumor grade and location, surgery including resection margins, oncological treatment, recurrence or progression after surgery, and date of last control or death. Table contains a summary demographic and clinical data of patients. The overall survival (OS) was defined as the time elapsed between resection of a primary tumor and death from any cause or patient censoring. The recurrence-free survival (RFS) was defined as the time elapsed between the resection and recurrence of the tumor; death or last control in remission were censored events. DNA from fresh-frozen tissue samples of primary tumors was isolated using the DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. DNA was eluted into 200 µL of AE buffer, divided into triplicates, and stored at -20 °C until further use. DNA from the whole blood samples collected during the surgery was isolated using BioSprint 15 DNA Blood Kit (Qiagen) combined with an automatized KingFisher mL Purification System (ThermoFisher Scientific, Waltham, MA, USA) using magnetic particles. We modified the manufacturer’s protocol to isolate DNA from 1 mL of human whole blood instead of referenced 100–300 µL. In the modified protocol, we first pipette 90 µL of protease, add 1 mL of whole blood, and vortex sample for 15 s. Then we incubate the sample at 70 °C for 10 min, add 0.9 mL of isopropanol, and shortly spin for 1 min at 1,000 rpm. The obtained lysate is then applied into each column of the KingFisher mL Purification System, other buffers are applied in their respective positions, and the machine is initialized. In the end, isolated DNA is eluted into 300 µL of AE buffer and stored at -20 °C until further use. For DNA quantification, we use Qubit 3.0 Fluorometer and dsDNA Broad Range Assay Kit (both ThermoFisher Scientific). We searched several databases, e.g., PharmGKB ( www.pharmgkb.org ), DGIdb ( http://dgidb.genome.wustl.edu/ ), DrugBank ( www.drugbank.ca ), GDSC ( https://www.cancerrxgene.org/ ), and COSMIC v81 ( http://cancer.sanger.ac.uk/cosmic ) for interactions between human genome and sensitivity to 5-FU and oxaliplatin. Through a three-phase search, we prioritized 264 genes. Firstly, mutations in all screened cell line models ( n = 968) in the GDSC have been crosschecked together with sensitivity data for 5-FU and oxaliplatin (IC 50 and area under curve, AUC). The most frequently mutated genes in either the most sensitive or resistant cell line models (each category of “resistant” or “sensitive” cell lines had 20 cell lines with 10 of CRC origin) have been selected. Genes with more than 5% mutations of the total observed in half or more cell lines in each category (to exclude multiple single gene mutations in a single cell line) have been considered as general marks of sensitivity/resistance to drugs and passed to the second phase ( n = 745 for sensitive and n = 101 for resistant). In the second phase, genes were checked by the HGNC server ( http://www.genenames.org/ ) for compliance with HuGo nomenclature and merged ( n = 302 genes). In the third phase, the COSMIC v81 tool was used for the identification of genes somatically mutated in human CRC ( n = 715 samples) with more than 5% frequency ( n = 780 genes) as well as genes without mutations in these samples ( n = 2767 genes). After gene nomenclature check, both databases (cell line and human tumor mutations) have been crosschecked, and the final list of genes fulfilling these criteria: i/ mutated in 50% or more of sensitive or resistant cell lines, ii/ mutated in human CRC at more than 5% frequency, iii/ absent in the list of not mutated genes in CRC has been produced ( n = 264 genes). These genes have been analyzed for molecular function, cellular complement, biological process, and pathway context by the Panther database ( http://pantherdb.org/ ). Binding (GO:0005488), catalytic activity (GO:0003824), structural molecule activity (GO:0005198), receptor activity (GO:0004872), and transporter activity (GO:0005215) comprised more than 85% of cellular functions affected by mutations in both 5-FU sensitive/resistant cell lines and CRC tumors. The list of genes was enriched with an additional 294 genes listed among CGC tier 1 and 2 and “Actionable” in COSMIC v81, together with principal genes for CRC progression or from the latest whole exome sequencing studies . The final list of genes for gene variability consists of 558 genes. The panel was designed in NimbleDesign (Nimblegen, Roche, Basel, Switzerland). All possible transcript variants and RefSeq, Ensembl, and UCSC databases were used to select chromosomal coordinates in genome build hg19. Probes were selected in moderate stringency (preferred close matches 3, maximum close matches 20) and manufactured using NimbleGen SeqCap EZ Choice format (Roche). For the complete list of genes, see Supplementary Table . Sequencing libraries were prepared using the NEBNext Ultra II FS DNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, USA) according to the manufacturer’s instructions. Briefly, 100 ng of DNA was enzymatically digested, adaptors were ligated and adaptor-ligated DNA was enriched using 7–8 PCR cycles. The quality of prepared libraries was controlled using TapeStation 2200 (Agilent, Santa Clara, CA, USA) and libraries were quantified using Qubit 3.0 Fluorimeter and dsDNA High Sensitivity Assay Kit (ThermoFisher Scientific). Samples were multiplexed in pooled libraries containing 1000 ng DNA libraries derived either from 11 samples of tumor tissue DNA or 22 samples of blood DNA and hybridized with custom probes using standard NimbleGen SeqCap EZ Library LR protocol (Roche) with the following modifications of hybridization and post-capture PCR steps. In the hybridization reaction, 13.4 µl of Kapa Universal Enhancing Oligos (Roche) were added to the bead-bound DNA Sample instead of SeqCap HE Universal and Index oligos. After performing the capture reaction, the libraries were amplified using Primer 1: 5’-AATGATACGGCGACCACCGAGATCTACAC-3’ and Primer 2: 5’-CAAGCAGAAGACGGCATACGAGAT-3’. For the amplification, Ultra II Q5 PCR master mix (NEB) was used in a total reaction volume of 100 µl. Captured sequences were amplified using 13 PCR cycles. For assessment of the quality and quantity of final libraries, TapeStation 2200 (Agilent) and Qubit 3.0 Fluorometer with dsDNA High Sensitivity Kit (ThermoFisher Scientific), respectively, were used. Samples were pooled into the final pool in a non-equimolar fashion (tumors/blood ratio 3:1) and the final pool was sequenced on the NovaSeq 6000 platform (Illumina, San Diego, CA, USA) using 150 bp pair-end sequencing on one lane of the S4 flow cell. The pipeline used for bioinformatic processing of raw data has been described elsewhere in detail . Here, we describe the procedure only briefly with relevant references. Adapter and low-quality base trimming was done by Trimmomatic. Reads were aligned to the hg38 human reference genome sequence using Burrows-Wheeler Aligner v0.7.17-r1188 (BWA, Cambridge, UK) with the BWA-maximal exact matches (MEM) algorithm . Base recalibration was done using the Genome Analysis Toolkit v.4.3.0.0 (GATK) (Broad Institute, Cambridge, UK) according to GATK Best Practices . Duplicate reads were identified by MarkDuplicates (Picard). Identification of somatic variants and short indels was performed in paired tumor-normal samples using Mutect2 (GATK). Detected variants were filtered using FilterMutectCalls (GATK) and only variants passing all filters (i.e., somatic variants with filter status PASS) were considered. Variants were filtered on min. variant allele frequency (VAF) 5% and supported by min. three reads. Germline variants were called using HaplotypeCaller and variant recalibration was done by VariantRecalibrator (both GATK). Annotation was performed in Variant Effect Predictor (VEP) v.108 , which assigned one of the following values to each variant: LOW, MODIFIER, MODERATE (missense, in-frame deletions, and insertions), or HIGH (nonsense, frameshift, splice site, transcription start site) functional effect. Variants with HIGH and MODERATE predicted effects were evaluated for clinical associations. Visualization was performed in Maftools or ComplexHeatmap (both R/Bioconductor). CNVs were detected with CNVkit v0.9.9 and VarDict tool v1.8.3 . Tumor purity was estimated using PureCN v.2.0.2 (R/Bioconductor). Significant calls were assessed based on the average read depth a log2 ratio values and B-allele frequencies (BAF) of individual segments. Assuming a theoretical clonal fraction (tumor purity) of 70%, a deletion should have log2 ratio < -0.278 and BAF between 0.325 and 0.675; a duplication should have a log2 ratio > 0.233 and BAF between 0.442 and 0.558. All called segments that contained less than three bins or did not show a statistically significant difference of log2 ratios compared to reference values ( p < 0.05 by the Student’s t-test) were excluded. Microsatellite instability was detected using MSI-sensor2 v0.1 ( https://github.com/niu-lab/msisensor2 ) based on the published 20% threshold . For the detection of indels in homopolymer regions per Mb as a surrogate marker of mismatch repair deficiency (MMR-D) , the homopolymer regions were identified by Vcfpolyx (part of Jvarkit, https://github.com/lindenb/jvarkit ) and were defined as genomic regions with more than four repeat bases. Samples with > 1.5 indels in homopolymer regions per Mb were considered MMR-D . The validation set was downloaded from the USCS Xena Browser . GDA TCGA datasets COAD and READ were merged and only variants in candidate genes with predicted HIGH or MODERATE effect (see Material and section ) were used in statistical analyses. Only primary tumors with adenocarcinoma diagnosis and patients with complete survival follow-up were selected. Differential analyses were performed in patient subgroups stratified by main clinical data (age, sex, stage, grade, tumor localization, and adjuvant chemotherapy response – remission vs. progression). Analyses of differences in the number of variants or their functional classification between groups of patients divided by the above parameters were performed using Fisher’s exact test. Differences in TMB and CNVs between patients stratified by the above data were compared using the Kruskal-Wallis test and correlations of continuous data such as patient age, CNV size, or CNV counts were assessed using Spearman’s rho test. For the associations of germline variants with survival, Plink v1.9 was used to perform chi-square allelic tests with Monte-Carlo max(T) permutation test. Patients were divided according to RFS ≤ 3 years vs. > 3 years, which is an appropriate end point for adjuvant treatment of regimens based on 5-FU . Manhattan plot was generated using package qqman (R/CRAN). Survival functions for groups of patients divided by genetic data, eventually stratified by chemotherapy, were plotted using the Kaplan-Meier method, and significance was calculated by the Breslow test. All continuous variables were divided by the median. The Benjamini-Hochberg false discovery rate (B-H FDR) test was used for the correction of multiple testing and adjusted p-value (p adj ) < 0.05 was considered significant. For associations between single genes and clinical data, unadjusted p-values (p crude ) are provided to indicate trends. A two-sided p crude <0.05 was used for selection of genes for testing their combinations and associations with p adj < 0.05 were subjected to external validation where possible. All statistical analyses were performed in the SPSS v16 program (SPSS Inc., Chicago, IL, USA) or R package survminer/CRAN (R version 4.3.3). Clinical characteristics of the patients The main characteristics of the patients are summarized in Table . The median age of patients at the time of CRC diagnosis is 68 years (range 42–87) and the study group comprises slightly more men (53%) than women (47%). The unequal sex distribution is not intentional and corresponds with the reported higher incidence of CRC in men . The majority of patients have stage II or III disease (40% resp. 47%) and tumors localized in the left colon (60%). One patient at stage IV was excluded from survival analyses and the rest of the analyses included the group of patients with stage III. The vast majority of surgical tumor removal procedures were evaluated as R0, i.e. tumor-free resection margins. About 62% of patients were administered 5-FU-based systemic adjuvant chemotherapy with or without oxaliplatin and the rest of the patients did not receive any chemotherapy due to either poor performance status or lack of risk factors for stage II. The median follow-up is 48 months. RFS of the patients is significantly associated with disease stage ( p = 0.045) and the presence of regional lymph node metastases ( p = 0.021). OS is associated only with the latter ( p = 0.045) ( Supplementary Fig. ). Somatic profile of tumor samples The median number of detected variants per tumor sample was 18.5 (ranging from 0 to 318). The median amount of somatic variants fulfilling the functional classification HIGH (see Materials and Methods) per sample was 4 (0–79) and for MODERATE 14.5 (0–239). APC, TP53 , and KRAS were the most frequently mutated genes (64%, 59%, and 42% of mutated samples, respectively). Additionally, FAT4 (23%), FBXW7 , and PIK3CA (16% for both) belong to the most mutated genes (Fig. a, c and Supplementary Table ). The most common class of somatic variants was the missense mutation (Fig. b). The median TMB per Mb was 3.6 (0–62.3) and seven patients were classified as MSI-high and MMR-D. The median CNV size was 15.48 Mbp (0.048–36.24). The mutation summary for all samples is in Supplementary Table . Germline profile of CRC patients The median number of all detected germline variants per sample was 326 (ranging from 310 to 355) and that of variants with the HIGH predicted effect was 25 (19–30). At least 5% frequency for the sum of all variants with the HIGH effect was observed in 55 genes. Out of these, pathogenic or drug response-connected variants were called by ClinVar or InterVar for SCN1A (88% of patients), DPYD (67%), ZFHX3 (38%) KMT2C (12%), CNTNAP5 (10%), LRP2 (7%) and SMG1 (7%) (Fig. d). SCN1A (rs3812718), ZFHX3 (rs372909378), CNTNAP5 (rs17727261), LRP2 (rs80338754), and SMG1 (rs781029159) were unique polymorphisms, while for KMT2C (rs199504848 and rs763762478) and DPYD (97,883,329, 97,515,839, 97,699,535, 97,305,364) multiple polymorphic loci were found ( Supplementary Table ). Clinical associations of mutational profiles For all analyses, variants with HIGH or MODERATE predicted functional effects counted together were used. We first analyzed associations between individual gene mutation frequencies or functional classification and clinical data including survival. Patients with a higher risk of progression - in stages III or IV ( n = 40) have more frequent mutations in APC (p crude =0.036, Fisher’s exact test), TP53 (p crude =0.040), or KRAS (p crude =0.048) than those in less advanced stages I or II. For KRAS , we also found an increasing trend in mutation counts with grade (G1 < G2 < G3, p crude =0.030). In the case of APC , the association with disease stage was even more pronounced for frameshift type of mutations (p crude =0.015) and KRAS specifically for the 12D mutation (p crude =0.012 for stage and p crude =0.027 for grade), all in the same direction. The response to adjuvant therapy was worse in carriers of frameshift mutations in APC (p crude =0.008) or 12D variant in KRAS (p crude =0.005) (Table ). However, none of these associations passed the FDR adjustment for multiple testing (p adj >0.05). Although the survival analysis did not offer a significant relationship (p crude /p adj >0.05), the trends were very clear. Patients with shorter RFS more often have frameshift mutations in APC (p crude =0.064, Supplementary Fig. a ) or carry the KRAS - 12D variant (p crude =0.057, Supplementary Fig. b ). The relationships with OS were less pronounced, although in the same direction (p crude =0.180 for APC and p crude =0.200 for KRAS - 12D ) ( Supplementary Fig. c, d ). Neither the frequency nor the functional classification of somatic mutations in TP53 had predictive or prognostic significance. When analyzing combinations based on co-mutated APC, KRAS , and TP53 , the combination TP53 co-mutated with KRAS codons 12 or 13 and more specifically subset of KRAS-12D with TP53 co-mutated had worsened OS (p crude =0.024 and p crude =0.047, respectively, Supplementary Fig. e, f ), but not RFS (p crude =0.420 and p crude =0.078, respectively). None of these associations passed the FDR adjustment for multiple testing (p adj >0.05). Several patients had all three genes co-mutated, based on APC frameshift or nonsense type of mutations ( n = 29), but this combination did not significantly modify their survival (p crude >0.05). As for other genes, we found several relationships between mutation spectra and patient survival among genes mutated in at least 10% of samples ( n = 45 genes, Supplementary Table ). The rest of genes was not analyzed in a single gene mode due to small numbers of patients in the compared subgroups. Mutations in ANK2 and SACS were associated with shorter RFS (p crude =0.021 and p crude =0.014, respectively) and those in ABCA13, ANK2, COL7A1, NAV3 , and UNC80 with shorter OS (p crude <0.001, p crude <0.001, p crude =0.002, p crude =0.005, and p crude =0.035, respectively) regardless of treatment. Interestingly, KMT2D showed an inverse relationship to OS, i.e. shorter survival in patients without variants (p crude =0.050) ( Supplementary Fig. a-h). In untreated patients only ( n = 29), no relationship to RFS was found, but carriage of somatic mutations in ABCA13, ANK2, COL7A1, FLG, GLI3 , and UNC80 was associated with OS (p crude <0.001, p crude =0.012, p crude =0.046, p crude =0.006, p crude =0.012, and p crude <0.001, respectively) ( Supplementary Fig. a-f). No association with RFS was also found in patients treated with adjuvant regimens of chemotherapy ( n = 47, one patient with stage IV excluded from survival analyses). On the other hand, we found many relationships with OS, namely for poor OS and carriage of mutations in ABCA13, ANK2, COL6A3, COL7A1, LRP1B, NAV3, RYR1, RYR3, TCHH , and TENM4 (p crude =0.001, p crude =0.029, p crude =0.002, p crude =0.004, p crude =0.003, p crude =0.036, p crude =0.043, p crude =0.027, p crude =0.037, and p crude =0.015, respectively) ( Supplementary Fig. a-j). From the above results, it was apparent that variants in ABCA13, ANK2 , and COL7A1 carry prognostic information regardless of whether adjuvant oncological treatment was administered or the patient was just discharged. Furthermore, FLG, GLI3 , and UNC80 appear to be prognostic in treatment-naïve patients, whereas COL6A3, LRP1B, NAV3, RYR1, RYR3, TCHH , and TENM4 in those treated with adjuvant chemotherapy. We therefore grouped all genes with p crude <0.05 for further analysis. In these analyses, we applied the FDR adjustment for multiple testing to all results. OS of patients with mutations in ABCA13, ANK2, COL7A1 , NAV3 , or UNC80 grouped was highly significantly worse than in patients who did not carry mutations in any of these five genes (p adj =0.015, Fig. a). This five-gene signature was prognostic, in the same direction, also in untreated (p adj =0.007, Fig. b) but not in adjuvantly treated (p crude =0.140, Fig. c) patients. A combination of three genes ABCA13 , ANK2 , or COL7A1 had the same effect (all p adj <0.001, Fig. d-f), but combination of 10 genes ( ABCA13, ANK2, COL7A1, COL6A3, LRP1B, NAV3, RYR1, RYR3, TCHH , and TENM4 ) was not significant (all p crude >0.05) indicating that associations are gene-selective irrespective of just general mutation load. Moreover, the carriage of mutations in any of genes from combination of FLG, GLI3 , or UNC80 was prognostic for worse OS in untreated patients (p adj <0.001, Fig. a), while less significantly also in all patients (p adj =0.007, Fig. b), but not at all in treated ones (p crude =0.700, Fig. c). Finally, worse OS was observed in adjuvantly treated patients with mutations in any of COL6A3, LRP1B, NAV3, RYR1, RYR3, TCHH , or TENM4 and it was even worse in carriers of multiple gene mutations (p adj <0.001, Fig. d). On the contrary, this gene combination was not prognostic in terms of OS for untreated (p crude =0.340, Fig. e) and weakly significant before FDR adjustment in all patients (p crude =0.025/p adj =0.070, Fig. f). None of the above combinations was prognostic for RFS (p crude >0.05). We further divided the gene set according to the occurrence of mutations in oncodriver pathways identified in our previous exome studies as associated with CRC progression (MYC, Hippo, Notch, RTK-RAS, PI3K, HRR, and the immunogenic signature ICB1) ( Supplementary Table ). Although the gene panel was less informative on the complete pathway level as opposed to the exome, the gene selection was broad enough and included the majority of principal genes from the mentioned pathways, as can be judged from the resulting associations with clinical data. Patients with mutations in the MYC, PI3K, RTK-RAS, and ICB1 pathways had more often regionally advanced stage III or generalized IV than locally advanced stages I or II (p crude =0.004, p crude =0.030, p crude =0.003, and p crude =0.033, respectively, Table ) although these associations did not pass the FDR adjustment to multiple testing (p crude >0.05). Despite these associations, we did not observe a prognostic significance for any of the observed pathways. Hippo, Notch, or HRR (homologous recombination repair) pathways did not associate with any of the clinical characteristics (p crude >0.05). The MSI-high status, TMB divided by median, CNV size, or individual copy number alteration types divided by median showed no clinical associations and had no apparent prognostic role ( p > 0.05). We validated the observed prognostic associations of somatic variants with OS using the external dataset TCGA COAD-READ (specification in Materials and methods). We confirmed the association of ABCA13, ANK2, COL7A1 , NAV3 , or UNC80 with OS in all patients regardless of whether patients were treated with adjuvant chemotherapy or not ( p = 0.032, Fig. ). Despite we could still see the trend of longer survival of non-mutated patients the mutation dosage did not yield significant results ( Supplementary Fig. d). Similarly, the rest of the observed associations showed a clear trend although p-values were statistically insignificant. Results of external validation are presented in Supplementary Fig. a-l. From all germline variants, we tested those having more than 5% frequency and either record in ClinVar or InterVar databases (11 variants in 7 genes) or indication of an association with RFS divided by three years on Manhattan plot (7 variants in 4 genes, Supplementary Fig. ). Of these, carriers of heterozygous genotype rs72753407 (intron variant) in NFACS had significantly poorer RFS and OS (p crude <0.001/p adj =0.011 and p crude <0.001/p adj =0.011, respectively) than wild-type patients (Fig. a, b). Additionally, patients carrying heterozygous genotype rs34621071 (intron) in ERBB4 had significantly worse OS and insignificant trend towards worse RFS, after FDR adjustment compared to wild-type carriers (p crude =0.002/p adj =0.018 for RFS and p crude <0.001/p adj =0.011 for OS, Fig. c, d). Although patients with wild-type for rs2444274 (intron) in RIF1 had worse RFS and OS than carriers of heterozygous or variant genotypes, these associations did not pass the FDR adjustment (p crude =0.009/p adj =0.054 for RFS) or remained borderline significant (p crude =0.006/p adj =0.043 for OS) (Fig. e, f). The rest of variants identified by the Manhattan plot ( NFASC -rs2595959, RIF1 -rs16830036 and rs16830047, and SYNE1 -rs9479265) were not significant (p crude >0.05). No pathogenic or drug response-connected variants according to ClinVar or InterVar ( CNTNAP5- rs17727261, DPYD- rs1801265, rs1801160, rs1801159, and rs2297595, LRP2 -rs80338754, KMT2C- rs199504848 and rs763762478, SCN1A- rs3812718, SMG1- rs781029159, and ZFHX3- rs372909378) were associated with survival of patients (p crude >0.05). The main characteristics of the patients are summarized in Table . The median age of patients at the time of CRC diagnosis is 68 years (range 42–87) and the study group comprises slightly more men (53%) than women (47%). The unequal sex distribution is not intentional and corresponds with the reported higher incidence of CRC in men . The majority of patients have stage II or III disease (40% resp. 47%) and tumors localized in the left colon (60%). One patient at stage IV was excluded from survival analyses and the rest of the analyses included the group of patients with stage III. The vast majority of surgical tumor removal procedures were evaluated as R0, i.e. tumor-free resection margins. About 62% of patients were administered 5-FU-based systemic adjuvant chemotherapy with or without oxaliplatin and the rest of the patients did not receive any chemotherapy due to either poor performance status or lack of risk factors for stage II. The median follow-up is 48 months. RFS of the patients is significantly associated with disease stage ( p = 0.045) and the presence of regional lymph node metastases ( p = 0.021). OS is associated only with the latter ( p = 0.045) ( Supplementary Fig. ). The median number of detected variants per tumor sample was 18.5 (ranging from 0 to 318). The median amount of somatic variants fulfilling the functional classification HIGH (see Materials and Methods) per sample was 4 (0–79) and for MODERATE 14.5 (0–239). APC, TP53 , and KRAS were the most frequently mutated genes (64%, 59%, and 42% of mutated samples, respectively). Additionally, FAT4 (23%), FBXW7 , and PIK3CA (16% for both) belong to the most mutated genes (Fig. a, c and Supplementary Table ). The most common class of somatic variants was the missense mutation (Fig. b). The median TMB per Mb was 3.6 (0–62.3) and seven patients were classified as MSI-high and MMR-D. The median CNV size was 15.48 Mbp (0.048–36.24). The mutation summary for all samples is in Supplementary Table . The median number of all detected germline variants per sample was 326 (ranging from 310 to 355) and that of variants with the HIGH predicted effect was 25 (19–30). At least 5% frequency for the sum of all variants with the HIGH effect was observed in 55 genes. Out of these, pathogenic or drug response-connected variants were called by ClinVar or InterVar for SCN1A (88% of patients), DPYD (67%), ZFHX3 (38%) KMT2C (12%), CNTNAP5 (10%), LRP2 (7%) and SMG1 (7%) (Fig. d). SCN1A (rs3812718), ZFHX3 (rs372909378), CNTNAP5 (rs17727261), LRP2 (rs80338754), and SMG1 (rs781029159) were unique polymorphisms, while for KMT2C (rs199504848 and rs763762478) and DPYD (97,883,329, 97,515,839, 97,699,535, 97,305,364) multiple polymorphic loci were found ( Supplementary Table ). For all analyses, variants with HIGH or MODERATE predicted functional effects counted together were used. We first analyzed associations between individual gene mutation frequencies or functional classification and clinical data including survival. Patients with a higher risk of progression - in stages III or IV ( n = 40) have more frequent mutations in APC (p crude =0.036, Fisher’s exact test), TP53 (p crude =0.040), or KRAS (p crude =0.048) than those in less advanced stages I or II. For KRAS , we also found an increasing trend in mutation counts with grade (G1 < G2 < G3, p crude =0.030). In the case of APC , the association with disease stage was even more pronounced for frameshift type of mutations (p crude =0.015) and KRAS specifically for the 12D mutation (p crude =0.012 for stage and p crude =0.027 for grade), all in the same direction. The response to adjuvant therapy was worse in carriers of frameshift mutations in APC (p crude =0.008) or 12D variant in KRAS (p crude =0.005) (Table ). However, none of these associations passed the FDR adjustment for multiple testing (p adj >0.05). Although the survival analysis did not offer a significant relationship (p crude /p adj >0.05), the trends were very clear. Patients with shorter RFS more often have frameshift mutations in APC (p crude =0.064, Supplementary Fig. a ) or carry the KRAS - 12D variant (p crude =0.057, Supplementary Fig. b ). The relationships with OS were less pronounced, although in the same direction (p crude =0.180 for APC and p crude =0.200 for KRAS - 12D ) ( Supplementary Fig. c, d ). Neither the frequency nor the functional classification of somatic mutations in TP53 had predictive or prognostic significance. When analyzing combinations based on co-mutated APC, KRAS , and TP53 , the combination TP53 co-mutated with KRAS codons 12 or 13 and more specifically subset of KRAS-12D with TP53 co-mutated had worsened OS (p crude =0.024 and p crude =0.047, respectively, Supplementary Fig. e, f ), but not RFS (p crude =0.420 and p crude =0.078, respectively). None of these associations passed the FDR adjustment for multiple testing (p adj >0.05). Several patients had all three genes co-mutated, based on APC frameshift or nonsense type of mutations ( n = 29), but this combination did not significantly modify their survival (p crude >0.05). As for other genes, we found several relationships between mutation spectra and patient survival among genes mutated in at least 10% of samples ( n = 45 genes, Supplementary Table ). The rest of genes was not analyzed in a single gene mode due to small numbers of patients in the compared subgroups. Mutations in ANK2 and SACS were associated with shorter RFS (p crude =0.021 and p crude =0.014, respectively) and those in ABCA13, ANK2, COL7A1, NAV3 , and UNC80 with shorter OS (p crude <0.001, p crude <0.001, p crude =0.002, p crude =0.005, and p crude =0.035, respectively) regardless of treatment. Interestingly, KMT2D showed an inverse relationship to OS, i.e. shorter survival in patients without variants (p crude =0.050) ( Supplementary Fig. a-h). In untreated patients only ( n = 29), no relationship to RFS was found, but carriage of somatic mutations in ABCA13, ANK2, COL7A1, FLG, GLI3 , and UNC80 was associated with OS (p crude <0.001, p crude =0.012, p crude =0.046, p crude =0.006, p crude =0.012, and p crude <0.001, respectively) ( Supplementary Fig. a-f). No association with RFS was also found in patients treated with adjuvant regimens of chemotherapy ( n = 47, one patient with stage IV excluded from survival analyses). On the other hand, we found many relationships with OS, namely for poor OS and carriage of mutations in ABCA13, ANK2, COL6A3, COL7A1, LRP1B, NAV3, RYR1, RYR3, TCHH , and TENM4 (p crude =0.001, p crude =0.029, p crude =0.002, p crude =0.004, p crude =0.003, p crude =0.036, p crude =0.043, p crude =0.027, p crude =0.037, and p crude =0.015, respectively) ( Supplementary Fig. a-j). From the above results, it was apparent that variants in ABCA13, ANK2 , and COL7A1 carry prognostic information regardless of whether adjuvant oncological treatment was administered or the patient was just discharged. Furthermore, FLG, GLI3 , and UNC80 appear to be prognostic in treatment-naïve patients, whereas COL6A3, LRP1B, NAV3, RYR1, RYR3, TCHH , and TENM4 in those treated with adjuvant chemotherapy. We therefore grouped all genes with p crude <0.05 for further analysis. In these analyses, we applied the FDR adjustment for multiple testing to all results. OS of patients with mutations in ABCA13, ANK2, COL7A1 , NAV3 , or UNC80 grouped was highly significantly worse than in patients who did not carry mutations in any of these five genes (p adj =0.015, Fig. a). This five-gene signature was prognostic, in the same direction, also in untreated (p adj =0.007, Fig. b) but not in adjuvantly treated (p crude =0.140, Fig. c) patients. A combination of three genes ABCA13 , ANK2 , or COL7A1 had the same effect (all p adj <0.001, Fig. d-f), but combination of 10 genes ( ABCA13, ANK2, COL7A1, COL6A3, LRP1B, NAV3, RYR1, RYR3, TCHH , and TENM4 ) was not significant (all p crude >0.05) indicating that associations are gene-selective irrespective of just general mutation load. Moreover, the carriage of mutations in any of genes from combination of FLG, GLI3 , or UNC80 was prognostic for worse OS in untreated patients (p adj <0.001, Fig. a), while less significantly also in all patients (p adj =0.007, Fig. b), but not at all in treated ones (p crude =0.700, Fig. c). Finally, worse OS was observed in adjuvantly treated patients with mutations in any of COL6A3, LRP1B, NAV3, RYR1, RYR3, TCHH , or TENM4 and it was even worse in carriers of multiple gene mutations (p adj <0.001, Fig. d). On the contrary, this gene combination was not prognostic in terms of OS for untreated (p crude =0.340, Fig. e) and weakly significant before FDR adjustment in all patients (p crude =0.025/p adj =0.070, Fig. f). None of the above combinations was prognostic for RFS (p crude >0.05). We further divided the gene set according to the occurrence of mutations in oncodriver pathways identified in our previous exome studies as associated with CRC progression (MYC, Hippo, Notch, RTK-RAS, PI3K, HRR, and the immunogenic signature ICB1) ( Supplementary Table ). Although the gene panel was less informative on the complete pathway level as opposed to the exome, the gene selection was broad enough and included the majority of principal genes from the mentioned pathways, as can be judged from the resulting associations with clinical data. Patients with mutations in the MYC, PI3K, RTK-RAS, and ICB1 pathways had more often regionally advanced stage III or generalized IV than locally advanced stages I or II (p crude =0.004, p crude =0.030, p crude =0.003, and p crude =0.033, respectively, Table ) although these associations did not pass the FDR adjustment to multiple testing (p crude >0.05). Despite these associations, we did not observe a prognostic significance for any of the observed pathways. Hippo, Notch, or HRR (homologous recombination repair) pathways did not associate with any of the clinical characteristics (p crude >0.05). The MSI-high status, TMB divided by median, CNV size, or individual copy number alteration types divided by median showed no clinical associations and had no apparent prognostic role ( p > 0.05). We validated the observed prognostic associations of somatic variants with OS using the external dataset TCGA COAD-READ (specification in Materials and methods). We confirmed the association of ABCA13, ANK2, COL7A1 , NAV3 , or UNC80 with OS in all patients regardless of whether patients were treated with adjuvant chemotherapy or not ( p = 0.032, Fig. ). Despite we could still see the trend of longer survival of non-mutated patients the mutation dosage did not yield significant results ( Supplementary Fig. d). Similarly, the rest of the observed associations showed a clear trend although p-values were statistically insignificant. Results of external validation are presented in Supplementary Fig. a-l. From all germline variants, we tested those having more than 5% frequency and either record in ClinVar or InterVar databases (11 variants in 7 genes) or indication of an association with RFS divided by three years on Manhattan plot (7 variants in 4 genes, Supplementary Fig. ). Of these, carriers of heterozygous genotype rs72753407 (intron variant) in NFACS had significantly poorer RFS and OS (p crude <0.001/p adj =0.011 and p crude <0.001/p adj =0.011, respectively) than wild-type patients (Fig. a, b). Additionally, patients carrying heterozygous genotype rs34621071 (intron) in ERBB4 had significantly worse OS and insignificant trend towards worse RFS, after FDR adjustment compared to wild-type carriers (p crude =0.002/p adj =0.018 for RFS and p crude <0.001/p adj =0.011 for OS, Fig. c, d). Although patients with wild-type for rs2444274 (intron) in RIF1 had worse RFS and OS than carriers of heterozygous or variant genotypes, these associations did not pass the FDR adjustment (p crude =0.009/p adj =0.054 for RFS) or remained borderline significant (p crude =0.006/p adj =0.043 for OS) (Fig. e, f). The rest of variants identified by the Manhattan plot ( NFASC -rs2595959, RIF1 -rs16830036 and rs16830047, and SYNE1 -rs9479265) were not significant (p crude >0.05). No pathogenic or drug response-connected variants according to ClinVar or InterVar ( CNTNAP5- rs17727261, DPYD- rs1801265, rs1801160, rs1801159, and rs2297595, LRP2 -rs80338754, KMT2C- rs199504848 and rs763762478, SCN1A- rs3812718, SMG1- rs781029159, and ZFHX3- rs372909378) were associated with survival of patients (p crude >0.05). In this study, we designed, with the help of in silico tools, a panel of genes with in vitro records related to sensitivity and resistance of drugs (5-FU and oxaliplatin) most frequently used for treatment of stage II or III CRC patients. Major oncodrivers, actionable genes, and genes identified in recent whole exome sequencing studies were also included to enrich the genetic landscape of patients. The study design enabled the assessment of the contribution of selected genes on both somatic and germline levels. In general, our study identified specific gene sets bearing prognostic relevance for all patients and sets composed of different genes for patients stratified by systemic adjuvant chemotherapy. When considering the whole sample set, patients with somatic mutations in any of the genes ANK2 , ABCA13 , or COL7A1 had highly significantly worse OS than non-carriers. In untreated patients, somatic mutations in FLG , GLI3 , or UNC80 were prognostic towards poor OS, and the same role was seen for somatic mutations in COL6A3 , LRP1B , NAV3 , RYR1 , RYR3 , TCHH , or TENM4 in patients treated with adjuvant therapy based on 5-FU or FOLFOX regimens. Highly interestingly, none of these genes except LRP1B is listed among oncodrivers in CGC or the list of actionable genes in COSMIC. LRP1B is considered a tumor suppressor and its somatic mutability was recently associated with improved OS of CRC patients , higher tumor mutation burden and tumor neoantigen burden connected with benefit from PD-1 blockade in MMR-proficient rectal carcinomas . Thus, our study confirms its relevance for CRC. Taken together, our study demonstrates the power of in silico mining of in vitro data for gene prioritization further empowered by available human tumor data. We identified 13 previously unreported genes with prognostic relevance for CRC. The decision tree for patients with stage II CRC towards systemic adjuvant chemotherapy is still not completely resolved issue. It relies mostly on clinical signs such as pT4 disease, inadequately sampled lymph nodes, the presence of lymphovascular or perineural invasion, poorly differentiated or undifferentiated tumors, positive surgical margins, and eventually high preoperative serum level of tumor markers, mismatch repair deficiency (dMMR), or microsatellite instability-high (MSI-H) status . Nevertheless, there are no established genetic factors in the current therapy selection or patient prognosis estimation processes and our study suggests several candidates with already existing functional in vitro data. Additionally, we speculated whether the prognostic role may be just a proxy to overall tumor mutational load and therefore we evaluated survival for all, untreated, or adjuvantly treated patients divided by the median count of all somatic mutations with HIGH or MODERATE predicted functional effect (defined in Materials and methods). Both this analysis and evaluation of all 13 genes mentioned above grouped together were non-significant. Thus, the observed associations cannot be simply attributed to the overall mutational rate, but rather to mutations in specific gene sets. Moreover, high observed significance after the FDR adjustment for multiple testing for a given gene set in the specific group or subgroup of patients and the lack of it in the other groups/subgroups suggest that these associations are rather causative and not just correlative or by chance results. More importantly, the main oncodrivers in CRC, i.e., APC , KRAS , or TP53 had no prognostic role evaluated either separately as single genes or combined. For KRAS , we evaluated also carriage of mutations 12D, or in codon 12 as an additional factor. For TP53 and APC we divided patients according to functional status of mutations. Except for associations with clinical factors such as disease stage or grade, no prognostic value was found in contrast with data from recent whole exome sequencing of hepatic metastatic loci (12 for KRAS , 13 for KRAS -12D). Interestingly, patients with progression after adjuvant chemotherapy had more frequent KRAS-12D or APC frameshift mutations compared to patients in remission. The sample size and frequency of somatic mutations in these genes allowed also the evaluation of co-mutation effects. Neither the frequently reported for other cancers , the KRAS-TP53 co-mutation with dismal prognostic role, nor the other combinations, including the APC-KRAS-TP53 triple co-mutation, showed associations with survival of patients in the present study. Last, but not least, somatic mutations in MYC, PI3K, RTK-RAS, or immune checkpoint blockade pathway genes are associated with disease stage, but again without prognostic effect. Thus, major oncodrivers did not have a prognostic role in our patient set, but drug sensitivity or resistance-connected genes did. We also utilized main pathway analysis tools (Reactome, WikiPathways, KEGG) to investigate gene enrichment among all 13 genes with prognostic significance, but failed to identify specific pathways for eventual considerations on their targeting. Thus, together with the lack of information in the current literature about the role of specific genes in CRC progression and therapy resistance beyond the previous GDSC in vitro data, more research is necessary for obtaining exploitable functional evidence. Intriguingly, we found prognostic associations for several germline variants prioritized using allele frequency and functional predictions. This stringent approach chosen by us has shown that the carriage of variants NFACS -rs72753407 or ERBB4- rs34621071 was significantly associated with poor RFS and OS. On the other hand, patients with wild-type genotype in RIF1- rs2444274 had significantly poorer RFS and OS, although the former did not pass the FDR test. None of these associations was previously reported, and in contrast with somatic data, the prognostic relevance of germline variants for cancer progression and therapy outcome remains rather neglected. In contrast with somatic variants, external validation is currently unavailable for germline data. Several limitations of the present study cannot be concealed. Firstly, the sample size precludes robust analysis of rare events. Especially, subgroup analyses were underpowered and results that failed to replicate using external dataset need to be interpreted with extreme vigilance. Despite we confirmed the association of ABCA13, ANK2, COL7A1 , NAV3 , or UNC80 with OS in all patients using the external TCGA COAD-READ dataset (Fig. ), we failed for the rest of the associations, although survival trends remained the same ( Supplementary Fig. a, d). It is important to stress that our study was based on targeted panel deep sequencing and thus as such contained a much higher number of mutations, especially indels, than the TCGA dataset, which is based mostly on whole exome or genome sequencing. The composition of variants inevitably differed ( Supplementary Fig. ) and consequently could mask essential mutations. In addition, due to some missing data and the heterogeneous nature of the TCGA datasets, it is, however, quite a difficult task to achieve. Therefore, more studies are necessary in this area. On the other hand, the present study has clear benefits in ethnical homogeneity of the patient population, unified therapy regimen, and long-term complete clinical follow-up. Moreover, 5-FU or FOLFOX regimens remain the cornerstone of systemic adjuvant chemotherapy in CRC and thus, composition of our sample set is highly relevant from this point of view. We provide a proof-of-principle study using a unique design connecting in silico data from several databases containing in vitro functional and ex vivo human tumor datasets that may inspire further research not only on specific genes identified here for CRC but also in a more general fashion aiming exploitation of such already existing resources in future precision oncology concept. Below is the link to the electronic supplementary material. Supplementary Material 1 Supplementary Material 2 |
Association between clinical symptoms and post‐mortem neuropathology in dementia with Lewy bodies | dff5fafd-8a81-48e1-bcf5-4aad690f527f | 7065117 | Pathology[mh] | The authors declare no conflict of interest.
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Incidence and risk factors for hospital-acquired infection among paediatric patients in a teaching hospital: a prospective study in southeast Ethiopia | 3fdc7cd8-4150-4458-973e-39749ad1003f | 7747586 | Pediatrics[mh] | There is a ‘perfect storm’ on hospital-acquired infections (HAIs) among hospitalised patients at any point in time throughout the globe. HAI is defined as an infection occurring in a patient during the process of care in a hospital or other healthcare facilities that is not manifested or incubating at the time of admission. Currently, it is a growing public health problem which concerns both the medical and the general community, and a rising issue for patient safety and quality of care in every level. A study by Sheng et al reported that 80% of hospitalised patient deaths were linked to nosocomial infection. Available evidences also showed that financial burden, increased resistance of microorganisms to antimicrobials, prolonged hospital stay and sometimes deaths, are caused by HAIs. Worldwide, it is estimated that hundreds of millions of patients every year in both developed and developing countries are affected by HAIs. In some Australian public hospitals, HAIs affect one in every 74 hospitalisations. In Europe, the total annual number of patients with HAIs in 2011–2012 was estimated around 3.2 million. The prevalence of patients with at least one HAI in acute care hospitals was 6.0% (country range 2.3%–10.8%). Moreover, throughout Europe, HAIs accounted for 16 million additional days, with total costs estimated at approximately €7 billion. In the USA, approximately 2 million patients developed HAIs, and nearly a hundred thousand of these patients were estimated to die annually. This ranked HAIs as the fifth leading cause of death in acute care hospitals, and the risk of acquiring infection is 2–20 times higher in some developing countries. In some developing countries, the magnitude of HAIs remains underestimated and uncertain. There is little information available on the epidemiology of HAIs in African countries. Although data are sparse, evidence suggested that HAIs are considerably adding to the available high burden of infections in some sub-Saharan African countries. A systematic review by Nejad et al reported that hospital-wide HAI prevalence in Africa varied between 2.5% and 14.8%. This review has shown that published studies were only conducted in 10 African countries—emphasised there were paucities of information available among the epidemiology of HAIs in many African countries. In addition to this, a recent review by Irek et al indicated that there was a scarcity of studies on HAIs in Africa—of the 35 eligible articles retrieved, more than half (n=21, 60%) were from East Africa only. In addition, most of the HAIs literature only focused on adults, and the data on HAIs among the paediatric population in sub-Saharan Africa were hardly available. For example, a systematic review conducted by the WHO in the year 2010 identified no reports on paediatric nosocomial bacteraemia in some African countries between 1995 and 2008. In Ethiopia, little is known about the incidence and prevalence of HAIs in the neonatal and paediatric populations. Moreover, previously conducted studies focused only on adults, and many of these were limited to surgical site infections, with an estimated prevalence of 10.9%– 66.5%. The overall cumulative incidence was 35.8 per 100 patients. Furthermore, urinary tract and bloodstream infections were found to be the most common forms of HAIs in Ethiopia. Surgery after admission, underlying medical conditions, patients with catheters, patient on mechanical ventilators, immune-deficient patients, patients’ age, hospital types, the types of ward and prolonged hospitalisations were found to be important factors associated with increased risks of HAIs in Ethiopia. Up to date, there are no surveillance programmes at the regional or national levels which targeted HAIs in Ethiopia. The available evidence on HAIs in the country was originated from primary studies. Moreover, to the best of our knowledge, there is not a single published report on the incidence and risk factors of HAIs among paediatric patients in Ethiopia. In order to maximise the prevention of HAIs and antimicrobial resistance in Ethiopia, epidemiological data on the incidence of HAIs are crucial because without a valid and precise assessment of HAIs, the problem remains unnoticed. Therefore, this study was designed to determine the incidence and risk factors of HAIs among paediatric patients in Goba Referral Hospital, southeast Ethiopia. The current study will help policymakers to improve their decision-making and inputs for healthcare professionals, for the improvement of patient care.
Study design and setting A hospital-based prospective follow-up study was conducted from 1 November 2018 to 30 June 2019, at Madda Walabu University Goba Referral Hospital, southeast Ethiopia. Goba Referral Hospital is the only referral and teaching hospital in the Bale zone, serving over 1 787 575 million people. Goba Referral Hospital is located 445 km far from the capital city of Ethiopia. According to the 2018 annual report of Goba Referral Hospital, the average outpatient flow is over 96 661, and the annual admission is over 7886 patients, of which 1335 were admitted in the paediatric ward and neonatal intensive care unit (NICU). The hospital has a total of 127 inpatient beds—of which 30 and 15 are in the paediatric ward and NICU, respectively. Study population and eligibility criteria All patients (age less than 18 years) admitted to the paediatric ward and NICU were enrolled, and those who at least stayed for 48 hours, were eligible for the study. Enrolled patients who showed signs of infections and/or symptoms of infection within the first 48 hours were excluded from the study. Data collection procedures First, consent was sought from each of the child’s parent/guardian before commencing any study procedures. On admission, all children were evaluated clinically to exclude community-acquired infections by a paediatrician. Afterwards, sociodemographic and clinical data were collected through a structured questionnaire using individual patient chart investigation approach, accordingly, a detailed clinical history of patients were taken and recorded. Patients with no new signs or symptoms of infection after the first 48 hours from admission were included and followed prospectively for the development of HAIs during their stay in the hospital. Data were collected from enrolled patients on a daily basis: children were followed by a paediatrician daily, charts were reviewed, and discussions with nurses and physician caring for the patients were held. HAIs were confirmed by senior paediatrician specialists working in the respective NICU and paediatric ward . Data were collected by trained physicians and one paediatrician. The Centers for Disease Control and Prevention (CDC)/National Health Care Safety Network surveillance definition for HAIs was used. In this study, the usage of any antimicrobials and information on the use of different medical devices at the time of hospital admission and before the diagnosis of HAIs were recorded, respectively (see ). 10.1136/bmjopen-2020-037997.supp1 Supplementary data Data quality control The data collection tool was adapted from different related pieces of literature based on the available evidences of HAIs. To ensure the quality of data, the data collection tool was pretested before the data collection period. The training was given for data collectors on the study procedures, and with practical exercise sessions. Data collection was closely supervised by a principal investigator, and the collected data were checked for completeness, accuracy and consistency. In order to minimise the potential effects of confounder variables, multivariable logistic regression model was used, and analyses were adjusted to known confounder, such as age. In addition, the researchers try to reduce selection bias by including all admitted patients in our follow-ups. Moreover, to reduce the effect of observer bias, the data collectors have no preconceived expectations of what they should find in an examination. Operational definition HAI—a localised or systemic condition that results from an adverse reaction in the presence of an infectious agent or its toxin, and occurring 48 hours or longer after hospital admission, which was not incubating at the time of admission. Severe anaemia—haemoglobin <50 g/L (for patients older than 28 days) or haemoglobin <90 g/L (for neonates). Late-onset neonatal sepsis—infection occurring after birth, but excluding infections known to have been transmitted across the placenta. Study variables The outcome variable of the study was the occurrence of HAIs. The presence of HAIs was confirmed when the patients met the criteria for signs and symptoms determined by the CDC, where the independent variables included: sociodemographic characteristics (age of the child, sex, place of residence and previous hospitalisation), and clinical and other related variables (duration of hospitalisation, insertion of a urinary catheter, presence of peripheral intravenous catheter, received antimicrobial, American Society of Anesthesiology classification, intubation, surgery after admission, underline disease refers to severe acute malnutrition (SAM) presented at the time of admission, mechanical ventilator and HIV status). Data processing and analysis Data were entered into Epi-data V.3.1 and exported to Stata V.14 statistical software for further analysis. Descriptive statistics were computed to present the frequency distribution of important variables. The cumulative incidence (incidence proportion) was calculated as the number of new HAI cases per person in the population over a defined period of time; and it is the probability of developing HAIs over a stated study period (8 months). We estimated the incidence rate as the number of HAI cases per unit of time, and the denominator represents the total amount of time ‘at-risk’ without experiencing HAIs for all children whom were being followed for 8 months. The incidence rate of HAIs was reported per 1000 patient days. Multivariable logistic regression was used to identify factors with an increased risk of HAIs. Variables, that were assumed confounders based on their statistical significant result in the bivariate analysis, were included in the multivariable model. An adjusted risk ratio (ARR) with a 95% CI was used to determine the strength of association. A p value of <0.05 was used to declare statistical significances. Multicollinearity diagnosis was performed between categorical variables by looking at values of variance inflation factors. The final model fitness was assessed by using the Hosmer-Lemeshow goodness of fit test. Patient and public involvement Patients and the public were not involved in the planning, designing and interpreting of these data analyses.
A hospital-based prospective follow-up study was conducted from 1 November 2018 to 30 June 2019, at Madda Walabu University Goba Referral Hospital, southeast Ethiopia. Goba Referral Hospital is the only referral and teaching hospital in the Bale zone, serving over 1 787 575 million people. Goba Referral Hospital is located 445 km far from the capital city of Ethiopia. According to the 2018 annual report of Goba Referral Hospital, the average outpatient flow is over 96 661, and the annual admission is over 7886 patients, of which 1335 were admitted in the paediatric ward and neonatal intensive care unit (NICU). The hospital has a total of 127 inpatient beds—of which 30 and 15 are in the paediatric ward and NICU, respectively.
All patients (age less than 18 years) admitted to the paediatric ward and NICU were enrolled, and those who at least stayed for 48 hours, were eligible for the study. Enrolled patients who showed signs of infections and/or symptoms of infection within the first 48 hours were excluded from the study.
First, consent was sought from each of the child’s parent/guardian before commencing any study procedures. On admission, all children were evaluated clinically to exclude community-acquired infections by a paediatrician. Afterwards, sociodemographic and clinical data were collected through a structured questionnaire using individual patient chart investigation approach, accordingly, a detailed clinical history of patients were taken and recorded. Patients with no new signs or symptoms of infection after the first 48 hours from admission were included and followed prospectively for the development of HAIs during their stay in the hospital. Data were collected from enrolled patients on a daily basis: children were followed by a paediatrician daily, charts were reviewed, and discussions with nurses and physician caring for the patients were held. HAIs were confirmed by senior paediatrician specialists working in the respective NICU and paediatric ward . Data were collected by trained physicians and one paediatrician. The Centers for Disease Control and Prevention (CDC)/National Health Care Safety Network surveillance definition for HAIs was used. In this study, the usage of any antimicrobials and information on the use of different medical devices at the time of hospital admission and before the diagnosis of HAIs were recorded, respectively (see ). 10.1136/bmjopen-2020-037997.supp1 Supplementary data
The data collection tool was adapted from different related pieces of literature based on the available evidences of HAIs. To ensure the quality of data, the data collection tool was pretested before the data collection period. The training was given for data collectors on the study procedures, and with practical exercise sessions. Data collection was closely supervised by a principal investigator, and the collected data were checked for completeness, accuracy and consistency. In order to minimise the potential effects of confounder variables, multivariable logistic regression model was used, and analyses were adjusted to known confounder, such as age. In addition, the researchers try to reduce selection bias by including all admitted patients in our follow-ups. Moreover, to reduce the effect of observer bias, the data collectors have no preconceived expectations of what they should find in an examination.
HAI—a localised or systemic condition that results from an adverse reaction in the presence of an infectious agent or its toxin, and occurring 48 hours or longer after hospital admission, which was not incubating at the time of admission. Severe anaemia—haemoglobin <50 g/L (for patients older than 28 days) or haemoglobin <90 g/L (for neonates). Late-onset neonatal sepsis—infection occurring after birth, but excluding infections known to have been transmitted across the placenta.
The outcome variable of the study was the occurrence of HAIs. The presence of HAIs was confirmed when the patients met the criteria for signs and symptoms determined by the CDC, where the independent variables included: sociodemographic characteristics (age of the child, sex, place of residence and previous hospitalisation), and clinical and other related variables (duration of hospitalisation, insertion of a urinary catheter, presence of peripheral intravenous catheter, received antimicrobial, American Society of Anesthesiology classification, intubation, surgery after admission, underline disease refers to severe acute malnutrition (SAM) presented at the time of admission, mechanical ventilator and HIV status).
Data were entered into Epi-data V.3.1 and exported to Stata V.14 statistical software for further analysis. Descriptive statistics were computed to present the frequency distribution of important variables. The cumulative incidence (incidence proportion) was calculated as the number of new HAI cases per person in the population over a defined period of time; and it is the probability of developing HAIs over a stated study period (8 months). We estimated the incidence rate as the number of HAI cases per unit of time, and the denominator represents the total amount of time ‘at-risk’ without experiencing HAIs for all children whom were being followed for 8 months. The incidence rate of HAIs was reported per 1000 patient days. Multivariable logistic regression was used to identify factors with an increased risk of HAIs. Variables, that were assumed confounders based on their statistical significant result in the bivariate analysis, were included in the multivariable model. An adjusted risk ratio (ARR) with a 95% CI was used to determine the strength of association. A p value of <0.05 was used to declare statistical significances. Multicollinearity diagnosis was performed between categorical variables by looking at values of variance inflation factors. The final model fitness was assessed by using the Hosmer-Lemeshow goodness of fit test.
Patients and the public were not involved in the planning, designing and interpreting of these data analyses.
Sociodemographic characteristics of the study participants A total of 487 paediatric patients were enrolled in this study. However, 39 paediatric patients showed signs of infections and/or symptoms of the infection within the first 48 hours, and were excluded from the study. The remaining 448 paediatric patients were followed up for the occurrence of HAIs until their hospital discharge, referred to other healthcare facilities or death. Of the total patients included in the study, 201 (44.9%) were from the NICU, and the rest were from the paediatric ward. Two hundred forty-eight (55.4%) of the study participants were men with an overall male-to-female ratio of 1.24:1. Also, the median age of the participants was 8 months (IQR: 2–26 months). In addition, the age distribution of the study participants by sex was presented in . Moreover, 390 (71.2%) of the study participants were from rural areas. The median hospital stay of the patients was 6 days (IQR: 3–9 days), and among them, 24 (5.4%) died. The overall incidence density rate of the admitted paediatric mortality was 7.44 per 1000 paediatric days of follow-ups . Clinical characteristics of patients In this study, 46 (10.3%) of the participants had histories of hospitalisations within the last 30 days. Fifty-four (12.1%) of the children were diagnosed with SAM at the time of their admission. Severe anaemia was reported among 41 (9.2%) respondents. Overall, 171 (38.2%) patients received antimicrobials at the time of the study . Incidence and type of HAI During the study period, 448 paediatric patients were followed for a total of 3227 patient days. A total of 57 patients experienced HAIs, and none of the study participants were identified with more than one episode of HAIs. The mean time of diagnosis of HAIs in Goba Referral Hospital is 7.20 (95% CI 6.72 to 7.66) patient days. The overall incidence rate of HAIs was 17.7 per 1000 paediatric days of follow-ups, while the cumulative incidence was 12.7% (95% CI 9.8% to 15.8%) over 8 months. The mean length of stay for the infected paediatric patients was 11.5 days (95% CI 9.5 to 13.4), while it was lower for the remaining patients, at 6.5 days. illustrates the proportion of HAIs among the paediatric patients in Goba Referral Hospital. Hospital-acquired pneumonia (HAP) was the most common type of HAI which was observed among the paediatric patients with a proportion of 56.1% (95% CI 43.9% to 68.4%), followed by late-onset neonatal sepsis 10.5% (95% CI 3.5% to 19.3%), and the least HAIs observed were early onset of neonatal sepsis and surgical site infections, with an overall proportion of 1.8% each. In this study, the stratification of the types of HAIs by ward of admission revealed significant variability (p value=0.007) . Risk factors of HAIs showed the risk factors of HAIs among the paediatric patients in Goba Referral Hospital. Bivariate analysis of RR has indicated that hospital duration (>6 days), patients who received antimicrobial medications, presence of drainage tubes and children diagnosed with SAM were predisposed to HAIs. In the adjusted model, the risk of HAIs was 2.58 times more likely to be higher among children who stayed longer than or equal to 6 days (median day) than those who stayed less (ARR: 2.58, 95% CI 1.72 to 4.38). Patients with SAM conditions had 2.83 times higher risks of developing HAIs compared with its counterparts (ARR: 2.83, 95% CI 1.61 to 4.97). Sociodemographic and some clinically related confounders could not show any statistically significant associations . In this study, we estimated the attributable risk, which estimates the excess risk of disease in those exposed compared with those non-exposed. The excess occurrence of HAIs among children with underlying SAM diseases attributable to their SAM condition is 13 per 100 .
A total of 487 paediatric patients were enrolled in this study. However, 39 paediatric patients showed signs of infections and/or symptoms of the infection within the first 48 hours, and were excluded from the study. The remaining 448 paediatric patients were followed up for the occurrence of HAIs until their hospital discharge, referred to other healthcare facilities or death. Of the total patients included in the study, 201 (44.9%) were from the NICU, and the rest were from the paediatric ward. Two hundred forty-eight (55.4%) of the study participants were men with an overall male-to-female ratio of 1.24:1. Also, the median age of the participants was 8 months (IQR: 2–26 months). In addition, the age distribution of the study participants by sex was presented in . Moreover, 390 (71.2%) of the study participants were from rural areas. The median hospital stay of the patients was 6 days (IQR: 3–9 days), and among them, 24 (5.4%) died. The overall incidence density rate of the admitted paediatric mortality was 7.44 per 1000 paediatric days of follow-ups .
In this study, 46 (10.3%) of the participants had histories of hospitalisations within the last 30 days. Fifty-four (12.1%) of the children were diagnosed with SAM at the time of their admission. Severe anaemia was reported among 41 (9.2%) respondents. Overall, 171 (38.2%) patients received antimicrobials at the time of the study .
During the study period, 448 paediatric patients were followed for a total of 3227 patient days. A total of 57 patients experienced HAIs, and none of the study participants were identified with more than one episode of HAIs. The mean time of diagnosis of HAIs in Goba Referral Hospital is 7.20 (95% CI 6.72 to 7.66) patient days. The overall incidence rate of HAIs was 17.7 per 1000 paediatric days of follow-ups, while the cumulative incidence was 12.7% (95% CI 9.8% to 15.8%) over 8 months. The mean length of stay for the infected paediatric patients was 11.5 days (95% CI 9.5 to 13.4), while it was lower for the remaining patients, at 6.5 days. illustrates the proportion of HAIs among the paediatric patients in Goba Referral Hospital. Hospital-acquired pneumonia (HAP) was the most common type of HAI which was observed among the paediatric patients with a proportion of 56.1% (95% CI 43.9% to 68.4%), followed by late-onset neonatal sepsis 10.5% (95% CI 3.5% to 19.3%), and the least HAIs observed were early onset of neonatal sepsis and surgical site infections, with an overall proportion of 1.8% each. In this study, the stratification of the types of HAIs by ward of admission revealed significant variability (p value=0.007) .
showed the risk factors of HAIs among the paediatric patients in Goba Referral Hospital. Bivariate analysis of RR has indicated that hospital duration (>6 days), patients who received antimicrobial medications, presence of drainage tubes and children diagnosed with SAM were predisposed to HAIs. In the adjusted model, the risk of HAIs was 2.58 times more likely to be higher among children who stayed longer than or equal to 6 days (median day) than those who stayed less (ARR: 2.58, 95% CI 1.72 to 4.38). Patients with SAM conditions had 2.83 times higher risks of developing HAIs compared with its counterparts (ARR: 2.83, 95% CI 1.61 to 4.97). Sociodemographic and some clinically related confounders could not show any statistically significant associations . In this study, we estimated the attributable risk, which estimates the excess risk of disease in those exposed compared with those non-exposed. The excess occurrence of HAIs among children with underlying SAM diseases attributable to their SAM condition is 13 per 100 .
HAIs are current global challenges that increase morbidities, mortality and massive economic cost. Yet, there remain limited data on the occurrences of HAIs in hospitalised paediatric patients in sub-Saharan Africa, including Ethiopia. This study was designed to determine the incidence and risk factors of HAIs among paediatric patients in a teaching hospital, southeast Ethiopia. The overall incidence rate of HAIs was 17.75 per 1000 paediatric days of follow-up while the cumulative incidence was 12.7% (95% CI 9.8% to 15.8%) over 8 months. Children who stayed longer than the median day (6 days) in the hospital, and children with underlying disease conditions (SAM), had higher risks of developing HAIs. In this study, the overall incidence rate of HAIs was 17.7 per 1000 paediatric days of follow-ups. This finding is lower than a related prospective study by Ali et al from southwest Ethiopia, which reported an incidence of HAIs of 28.15 per 1000 patient days. The difference might be associated with the nature of this study which involved only paediatric patients including those in intensive care; whereas, the study by Ali et al included adult study participants. Also, variations in some studies could be attributed to differences in geographical locations and the study settings (as in the case of Ali et al where the study they included a specialised hospital). A previous before-and-after study conducted in a teaching hospital in Indonesia involved children whom were admitted to the paediatric intensive care unit (ICU) and paediatric ward, reported the incidence density rate of HAI 29.1 per 1000 patient days, which is similar to our findings. One of our findings has also revealed that the overall cumulative incidence of HAIs was 12.7%; this is comparable to those reported from a study in the USA (11.9%) which was conducted in the paediatric ICU, and in Poland (13.3%). Also, the present 12.7% of HAIs noted in our study population fell in the ranges of 9.8%–15.8%, and is reported elsewhere, and the WHO pooled estimated for low-income countries 10.1%. Conversely, similar studies from Turkey reported a much higher prevalence of HAIs among children ranging between 22.2% and 68.4%, and in a multicentre prospective study from Europe reported 18.5% among paediatric patients. The present study also demonstrated that the occurrence of HAIs was higher among male participants (52.6%) than women. This result was also supported by other studies conducted elsewhere. In the same vein, one study carried out by Koch et al in Norway reported that men present higher overall HAI prevalence than women. The most common type of HAI observed in this study was HAP, which contributed to a proportion of 56.1% of the total HAIs. It may not be a surprise to see such a high proportion of HAI in the NICU and paediatric ward since most of the patients admitted in intensive care are incapacitated and critical. Moreover, compared with adults, infants and neonates are immunologically immature, and in many cases, vulnerable. The finding was similar to the study done in Tikur Anbessa Hospital, Ethiopia. It is also true for other settings—in Iran 43.7%, India 50%, Vietnam 41.9%, Morocco 34.5%, Saudi Arabia 46.7%, China 52.2% and in a European multicentre prospective study 53%. The high burden of HAP among hospitalised paediatric patients has important implications in terms of length of hospital stay, healthcare cost and mortality. The overall mortality attributed to HAP has been as high as 30%–50%. In this study, ventilator-associated pneumonia (VAP) developed in 9.21% (7/76) of children who underwent mechanical ventilation. Our estimate is in line with studies conducted on children reporting VAP, which occurred in 3%–10% of ventilated paediatric ICU patients. In this study, the risk of developing HAIs was three times higher among children who stayed longer than or equal to the median 6 days than their counterparts. Despite this positive association, this is not a proof that decreasing the length of stay neither increasing admission days increases/decreases the occurrence of HAIs. Possible revered causation may be one of the mechanisms why this prolonged length of stay is associated with HAIs. Moreover, there is evidence that HAIs cause a prolonged length of stay. In our findings, the presence of underlying diseases, such as SAM, was recognised as the main risk factor for HAIs. This was consistent with the finding from another study in Ethiopia, that underlying illnesses increased the susceptibility of patients and predisposed them to infections secondary to the reduction of the patient’s immune response that exacerbated the illnesses through which in many cases, had significant factors that contributed more to the acquisition of HAIs in neonates and paediatric patients. Limitations of the study Several limitations on this prospective study needed to be considered. First, we did not assess the healthcare workers’ infection prevention practices that would have been associated with the prevalence of HAIs. Second, the researchers did not examine the number of HAIs after the patients were discharged. Third, despite that we followed the patients until their discharge, the full burden of HAI could not be captured in this specific study, and is limited to in-hospital assessment only, leaving outpatients whom may have potentially developed HAIs after discharge. Fourth, we focused on a small number of risk factors for HAIs and some important variables were not included. Fifth, the used analysis does not take any time-varying risk into account. Sixth, since there was limited information on the patients’ medical record folders, more social determinant variables were not collected. In addition, this study is not free from the effects of information bias as we do not use ‘blinding’. Another limitation of the study is that we could not adjust the results for the effect of social determinant variables on HAIs because the information on these social determinant variables was not collected in our study. Finally, laboratory cultures to isolate organisms as a guide were not used, in addition to the clinical criteria, to confirm the results of HAIs because of financial constraints, laboratory facilities and expertise. Given the lack of microbiology data, endogenous infections may be misclassified as HAIs. Since the study was conducted in a teaching referral hospital, the generalisation of the study findings was limited to these facilities.
Several limitations on this prospective study needed to be considered. First, we did not assess the healthcare workers’ infection prevention practices that would have been associated with the prevalence of HAIs. Second, the researchers did not examine the number of HAIs after the patients were discharged. Third, despite that we followed the patients until their discharge, the full burden of HAI could not be captured in this specific study, and is limited to in-hospital assessment only, leaving outpatients whom may have potentially developed HAIs after discharge. Fourth, we focused on a small number of risk factors for HAIs and some important variables were not included. Fifth, the used analysis does not take any time-varying risk into account. Sixth, since there was limited information on the patients’ medical record folders, more social determinant variables were not collected. In addition, this study is not free from the effects of information bias as we do not use ‘blinding’. Another limitation of the study is that we could not adjust the results for the effect of social determinant variables on HAIs because the information on these social determinant variables was not collected in our study. Finally, laboratory cultures to isolate organisms as a guide were not used, in addition to the clinical criteria, to confirm the results of HAIs because of financial constraints, laboratory facilities and expertise. Given the lack of microbiology data, endogenous infections may be misclassified as HAIs. Since the study was conducted in a teaching referral hospital, the generalisation of the study findings was limited to these facilities.
The present study revealed that the cumulative incidence of HAIs was 13 per 100 admitted children, and the overall incidence rate of HAIs was 17.75 per 1000 paediatric days. Length of stay in the hospital and patients with SAM conditions were associated with increased risk of HAIs. Further studies are strongly recommended to identify other important factors including isolating of bacterial, fungal and viral agents responsible for HAIs in the region.
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AtlasGrabber: a software facilitating the high throughput analysis of the human protein atlas online database | eacb427f-2174-40a4-bbe6-040f0a2cb716 | 9758778 | Anatomy[mh] | Numerous gene expression datasets are available from homogenized tissues, cell lines, and single cells. However, these sources do not provide conclusive information on cell type, subcellular protein localization, extracellular matrix (ECM) proteins, or the distribution of expression in tissues. Recently, efforts to characterize distinguishing features in whole human cellular populations have been undertaken. Still, these are limited by being based on already established cellular markers . The Human Protein Atlas (HPA) is an online, open-access database that contains over 10 million high-resolution images of tissue microarrays with immunohistochemical (IHC) stainings. It includes both normal and cancerous tissues. In this way, the HPA shows protein expression in the form of images of immunohistochemically stained tissue samples. The current version (version 21.0) covers 44 different normal tissues, the 20 most common cancer forms, and includes more than 87% of the human proteins. There are usually multiple antibodies (1–3) targeting each protein in the database (Fig. A). Each gene is typically targeted with an in-house generated (from the HPA project) and a commercial antibody. This data is freely accessible via the HPA website ( https://www.proteinatlas.org ). The database’s main advantage is that the images contain information on the expression, spatial distribution, and localization of each protein in the different tissues. Thus, the HPA can be used for better spatial localization of protein expression than any other resource can provide. It provides the protein expression patterns in single-cell types and subtypes, localization inside cells, tissues, and ECM and thereby provides a database of such characteristics of biomarker expression. This is particularly important information in cancer tissues, as cellular plasticity and changes in the tumor microenvironment are emerging as key factors in cancer pathogenesis, progression, and cell invasiveness. These tissue features show meaningful connections to most canonical cancer hallmarks . The HPA can be regarded as a hypothesis-generating tool, as a supplement to other high throughput (HTP) expression data, and provides a basis for experimental approaches . The atlas has a freely accessible user-friendly website for exploration. However, it does not directly support direct and fast large-scale HTP analysis, and it doesn’t allow for the easy comparison of the gene expression between normal and cancerous tissues, and between different tissue types. Realizing the potential of exploiting the atlas for extensive HTP analysis, we developed an application designated as AtlasGrabber to enable this. Researchers can use our tool to analyze a set of genes of interest for protein expression in different normal and cancerous tissues, comparing and sorting them into separate sets. The software can also provide information on the number of available genes, specific antibodies, and image links for each normal or tumor tissue in the HPA XML database file . Here we describe the software and demonstrate its usefulness by identifying novel immunohistochemical biomarkers for prostate basal cells, comparing their expression in normal and cancerous tissues. With the expansion of digital pathology, additional IHC based tissue repositories will be established, for which a similar approach can be adopted.
The code was written in the C# programming language as a Windows Desktop application. The code is open source under the Gnu Public License v3 and freely available on GitHub . Users can download the source code to compile it or download and run an available executable. The AtlasGrabber’s intended use is to facilitate the analysis of the protein expression in the HPA from a set of genes. It does so by displaying the images from the HPA in an organized, systematic way, based on a gene list, and allows the saving of genes of interest into new subsets. It is possible to simultaneously analyze a set of predefined genes in up to four different tissue types in normal or cancerous tissues. The gene set analyzed may contain thousands of genes and allows the comparison between stainings with the same antibody in different tissues. An additional feature is the XML parser, which can extract all the gene names, antibodies, and images for a particular tissue from the XML file provided on the HPA website. An initial text file (.txt) that contains a list of Ensembl IDs for the genes the investigator intends to analyze is needed to start using the AtlasGrabber. Such lists can also be generated from the downloadable files from the HPA website ( http://www.proteinatlas.org/about/download ) or by searching keywords in the HPA search field and exporting the file. Detailed step-by-step video instructions can be found in the Readme file on GitHub . The software executable can be downloaded directly or compiled from the source code. No additional setup or installations are required. The software has been tested to run on Windows 8, 10, and 11. We recommend using a high-definition, large-screen monitor (above 20 inches) for the best experience as the software will maximize the usage of the screen area by recalculating the area occupied by each window depending on the screen area. The program uses three different windows: settings, browsers, and analysis windows (Fig. A). Initially, the program opens to the “Settings” window (Fig. ). Here one can load the gene list from the text file (Fig. B). Additional options include the possibility to specify the analysis to all antibodies or to separate commercial and in-house ones, to look at all the image samples, just one, or a random one, and to filter away additional images from the same patient sample for one antibody (typically there are two images per patient sample) (Fig. C). In this window, it is also possible to name different lists for the storage of selected genes (Fig. D). Each list is assigned a key: from 0 to 9. While in the Analysis window, looking through the atlas, the current gene ID is copied to any of the ten lists with the assigned key. If saved, the list will appear in the same folder where the program is located. If the file already exists from a previous analysis, the new gene names will be added to the old ones in case one chooses to do the analysis in multiple runs. The tissues to be analyzed are selected at the top of the screen. One can choose any normal or cancer tissue from the dropdown menu in any of the four menu windows. When a new window is assigned to a tissue, this new window will be added in the Analysis view (Fig. ). To start loading and viewing images, the user selects the “Analysis” window (Fig. ). Images will be displayed for each antibody in the gene list. The mouse enables the spanning of the image. We recommend using the assigned keyboard keys to move through the images, antibodies, and proteins (Fig. B). The scrolling wheel can also be used to move through the images. Pressing any of the keys 0–9 will assign the gene ID to be saved to that list. Returning to the “Settings” window, one can see which gene (ID) is currently being analyzed in the left panel and which gene IDs have been assigned to the different lists. The “Browsers” window will display the HPA website of the particular antibody in a web browser. This window can be used to read a quick summary about the gene or the antibody. For example, if during the analysis the user identifies an interesting antibody candidate, they can quickly access the HPA information on the antibody, (e.g. antibody provider, antibody validation) and protein summary e.g. names, alternative names, description, intracellular location etc.). This window can also be used as a debug mode. The progress bar at the top of the screen will show the progress of the analysis (Fig. B), and the exact gene number from the list is displayed in the application’s title (Fig. A). The Help button links to the Readme file on the GitHub page, where more detailed instructions are available, including tutorials with short clips. The XML parser is available in the Settings window. It can be used to parse the XML database file from the HPA website (Fig. E). Its unzipped format can be loaded and subsets of the data, based on normal or cancer tissues, can be extracted into an easily readable.cvs format that will contain all the available gene IDs, gene names, antibodies and online image locations. The file will be automatically saved to the same folder as the application. To demonstrate the use of the application, we set out to identify new and additional immunohistochemical biomarkers for the basal cells of prostate glands. This cell type surrounds the glands in normal tissues but typically disappears in prostate cancer. In histopathology, three markers are routinely used to identify prostate basal cells: CK14, CK5, and P63. Pathologists use these markers to help diagnose prostate cancer as their absence indicates invasiveness . Using the XML parser functionality, we downloaded the list of Ensembl IDs for normal prostate tissue. As the scope was to demonstrate the usefulness of the software, we selected a subset of the data from the gene list to analyze. We loaded the list into the AtlasGrabber, selected normal prostate tissue to analyze, and went through the list, saving the genes that showed the staining pattern of basal cells. Again using the AtlasGrabber, we compared with their expression in prostate cancer.
We have created an application to enable large-scale, semi-automated analysis of the HPA database. The application was made for the Windows platform. The source code is available on GitHub with a license that allows for free use and further improvements. We have used earlier versions of our software extensively in our research and have identified useful additional refinements, which have been implemented in the current version. The license allows other users to access the source code and implement further functional improvements to their likes. HPASubC is a previously designed software with a similar aim to facilitate the viewing and analysis of HPA images. However, it only runs on the Linux kernel, requires several python scripts to run, and relies on several dependencies to run, many of them outdated. We found it challenging to run this software, even for technically skilled users. In comparison, our application can easily run on Windows. It also allows for the simultaneous comparison of several windows, allowing the comparison of different tissues and tumor types. It is possible to save genes of interest into separate lists and it does not require the download and storage of a large number of images. It also has an additional feature, the XML parser, that can be used to extract data from the raw database file of the HPA. Similar information to the HPA can be obtained by employing single-cell analysis (SCA) of tumor tissues. However, with SCA, information of tissue localization and the microenvironment is lost. Therefore, the HPA provides essential additional information that is unique, and as far as we are aware is offered by no other database. To demonstrate the usefulness of our software, we performed a brief analysis to find proteins expressed in basal cells of normal prostate tissue. We managed to identify six new immunohistochemical biomarkers expressed in these cell types by analyzing a subset of the images from the HPA, including HSPA1B and SRC (Fig. ). Analyzing their expression in cancerous tissue shows that they are absent in prostate cancers, with one exception, EMC8, which is also expressed in prostate cancer, although not in basal cells. The other five markers were specifically only expressed in the basal cells. A limitation to the analysis is the reliability of the antibody specificity, a known challenge in immunohistochemistry. For example, for SRC, two antibodies are targeting the same protein, but only one is specific for basal cells. The other antibody is absent in the normal prostate and expressed in prostate cancer cells. Therefore, we also provide information on the specific antibody that shows the difference we identified for each protein expressed. For each antibody used, the HPA website provides the type of validation used and its overall validation score (supported, validated, or uncertain) . Even though the software greatly increases the ease with which one can analyze a set of genes in the HPA, its limitation is that it still requires the investigator´s input, and the analyses can therefore be time-consuming for larger datasets. Future software should be combined with artificial intelligence approaches to further speed up the analysis using deep learning image classification methods. Prostate cancer is the most common cancer affecting men worldwide . Stratifying the disease into those who should receive treatment and those who should not is particularly important since it is common to have indolent cancer that might not need to be treated . In addition to the TNM score and the PSA levels in the blood, the Gleason score of the cancer histology is used to determine the prognostic risk of the patient. The Gleason Score was established in 1966, and although it has undergone revisions, it remains largely unchanged . In unclear cases, a more detailed analysis is performed using immunohistochemical characterization. Prostate basal cell markers are typically employed, as both the basal cells and the markers are usually downregulated in prostate cancer. Our identified five new markers specific for basal cells in the prostate could potentially be useful in further stratification of prostate cancer to refine and personalize patient treatments.
We have developed a user-friendly software to facilitate large-scale analysis of the Human Protein Atlas database by organizing images for viewing and scrutinizing based on a predefined gene set. Compared to a software with a similar aim that was published previously, the AtlasGrabber is more user-friendly and with additional functionalities. Its main utility is that it allows a user to easily analyze a set of genes for their protein expression in different normal and cancerous tissues, and to easily sort interesting candidate genes into separate subsets that can be saved. This will allow users to identify biomarkers specific to a cell, tumor, or tissue type. To demonstrate its usefulness, we performed an analysis of normal prostate tissues and identified six new biomarkers for basal cells of the prostate. The AtlasGrabber also enables the easy comparison of normal and cancerous tissues. Using this functionality, we compared our identified genes to prostate cancers. This revealed that five of our markers showed no positivity in prostate cancers, while one marker was positive in prostate cancer cells. The HPA is a rich database making available a wealth of data to scientists. It is freely available, but the difficulty in making large-scale analyses has limited its use so far. Our software will enable researchers to exploit and analyze the images in the database at level with its large capacity. With the ongoing expansion of digital pathology, additional HTP, IHC based tissue repositories will emerge, for which a similar approach can be adopted.
Project name: Atlas Grabber. Project homepage and source code: https://github.com/b3nb0z/AtlasGrabber . Operating systems: Windows 8, 10, and 11. Programming language: C#. Other requirements: None. License: GNU GPLv3. Any restrictions to use by non-academics: Users can change or rework the code, but if they distribute these changes/modifications in binary form, they’re also required to release these updates in source code form under the GPL v3 license.
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Impact of artisanal refining activities on bacterial diversity in a Niger Delta fallow land | 50110c79-3965-4f69-b6ba-a7dbc5527c65 | 10873323 | Microbiology[mh] | The Niger Delta, located in Nigeria, is a well-researched area in Africa, renowned for its potential for oil exploration and its diverse ecosystem. It spans 70,000 km 2 , making it the largest wetland in Africa and the third-largest in the world . Furthermore, it holds the most substantial oil reserves (36.2 billion barrels) and gas reserves (185 billion cubic feet) in Africa . The history of commercial oil and gas exploration in the Niger Delta goes back to 1958, which has led to an estimated 3.1 billion barrels of crude oil being discharged, affecting over 5000 sites from 1967 to 2014. This has resulted in the region being identified as one of the most polluted areas globally – . Studies have shown that oil pollution in the region is attributed to inadequate servicing and maintenance facilities, routine oil operations, sabotage, accidental/equipment failures, deliberate release of oil wastes into the environment, and currently, artisanal oil refining activities – . Petroleum pollution releases various types of hydrocarbons (saturates, aromatics, resins, and asphaltenes) into the environment, ultimately impacting the terrestrial ecosystem. This impact arises from their ability to disrupt the healthy ecological balance , terminating sensitive ecological receptor , contaminate groundwater, pose health challenges , as well as reduce, microbial composition , . The shift in microbial structure favours sentinel microorganisms that metabolize those complex hydrocarbons as sources of carbon and energy for growth and other physiological processes , . Petroleum hydrocarbon-degrading bacteria are abundant in contaminated habitats , . Autochthonous bacteria with this specialty are ubiquitous in the terrestrial ecosystem and are key to the natural attenuation and detoxification of such impacted lands . Shen et al. evaluated the bacterial diversity of polluted soil by means of metagenomics and confirmed the dominance of Actinobacteria. A diversity study conducted by Obieze et al. demonstrated that populations of hydrocarbon-utilizing bacteria were highest when the concentration of pollutants were high. Chikere et al. used 16S rRNA technology to show that the shift of bacterial genera moved from a mixed group to Gram-negative bacteria with Betaproteobacteria dominance. Bacterial diversity studies on terrestrial ecosystem impacted by artisanal crude oil refining are an emerging area in the Niger Delta. This necessitates MiSeq-driven metagenomics, which is a reliable Next-Generation Sequencing (NGS) technology for evaluating bacterial compositional structure, diversity, and function. In this study, we investigated bacterial diversity insights in polluted soil using MiSeq sequencing technology to understand how varying hydrocarbon concentrations affect the distribution of bacteria.
Physicochemical analysis The analysis of soil texture indicated that gravel constituted 5.2%, sand comprised 93.2%, clay constituted 4.4%, and silt accounted for 2.4%. Various chemical parameters, encompassing pH, electrical conductivity, total nitrogen, and total organic carbon, were evaluated for both the contaminated soil and control soil samples, with the results summarized in Table . The pH values for the control, mildly polluted, and heavily polluted soils were recorded as 6.50, 6.30, and 7.10, respectively. The electrical conductivity (EC) values for the control, mildly polluted, and heavily polluted soils were 30 µS/cm, 40 µS/cm, and 120 µS/cm, respectively. Similarly, the total nitrogen values were 2.67 mg/kg, 2.02 mg/kg, and 2.00 mg/kg for the control, mildly polluted, and heavily polluted soils, respectively. Additionally, the total organic carbon was 1.25%, 5.25%, and 19.61% for the control, mildly polluted, and heavily polluted soils, respectively. TPH analysis The values of the TPH for the heavily polluted (HP), control sample (CS) and mildly polluted (MP) soil sample are 490,630, 25.98 ppm, and 5398 ppm respectively. Bacterial diversities from unpolluted, mildly polluted and highly polluted soils The final dataset from the three reference samples—heavily polluted (HP), unpolluted (control sample; CS), and mildly polluted (MP)—comprised 20,640 demultiplexed high-quality reads, with an average of 6880 reads per sample. The sequences were clustered into 256 bacterial OTUs, which were further filtered to a minimum count of 4 and 20% prevalence per sample, resulting in a total of 160 retained bacterial OTUs. The dominant bacterial phyla in the heavily polluted soil (HP) samples were Proteobacteria (66%), Firmicutes (27%), Acidobacteria (4%), and Actinobacteria at less than 3%. In the unpolluted soil (CS) sample, the dominant phyla were Proteobacteria (68%), Firmicutes (30%), and Actinobacteria at less than 2%. In the mildly polluted soil (MP), the dominant phyla were Actinobacteria (39%), Proteobacteria (31%), Firmicutes (29%), Acidobacteria (0.67%), and Planctomycetes (0.33%). Figure summarises the bacterial phylum distribution in the three reference points. Dominant genera in the HP sample are Denitratisoma , Clostridium , Alkaliphilus , Diplorickettsia , Methylosinus and Bacillus (in descending order: ranging from 30.56% to 1.99%) . The following genera ( Pseudorhodoplanes , Cohnella , Rhodovastum , Neobacillus , Neomegalonema , Acidomonas , Neobacillus , Salirhabdus ) are duplicated in HP and CS samples, however, at relatively low abundance (< 2.0%) in HP but was dominant genera in CS. These genera are not observed in MP, except for Pseudorhodoplanes (2.7%). The dominant bacterial genera in MP include Spirilospora (34%), Paenibacillus , Swionibacillus , Rhizorhapis , Endobacter , Paraburkholderia , Rhodopila , Mycoavidus , Rummeliibacillus , Quasibacillus , and Phenylobacterium . Genera such as Denitratisoma , Alkaliphilus , Clostridium , and Bacillus were observed in CS but not in MP samples, while Methylosinus was observed in MP and not in CS. Figure a shows the dominant phyla diversity across the three samples, while Fig. b displays the relative abundance of the ten most dominant genera. Figures c–e depict that the bacterial abundance in CS is significantly different from both HP and MP (at p = 0.05). Similarly, as displayed in Table , CS is higher in richness and diversity as compared to the HP and MP samples. Inferred bacterial function by PICRUSt2 A total of 99 KOs (KEGG Orthologues) were predicted, and the superpathway was used for the plot. The predicted KEGG (Kyoto Encyclopedia of Genes and Genomes) orthologues, collapsed into MetaCyc meta-pathways, show abundance values for each sample using the PICRUSt methodology (Fig. ). Six functional modules, comprising cellular processes (3%), environmental information processing (7%), genetic information processing (5%), human diseases (5%), and metabolism (70%), make up almost 90% of the complete dataset in the samples. The higher percentage of metabolism is registered in the order of MP, HP and CS respectively, indicating that hydrocarbon serves as a source of carbon or energy or both. Differentially abundant pathways (at p ≤ 0.05) showed 11 pathways (Fig. ), with oxidative phosphorylation as the most dominant biomarker pathway. Heavily polluted (HP) sample is more pronounced with disease function, with a notable genus such as Diplorickettsia , an agent of tick-borne infection. CS leads in genetic information processing, while MP leads in cellular processes.
The analysis of soil texture indicated that gravel constituted 5.2%, sand comprised 93.2%, clay constituted 4.4%, and silt accounted for 2.4%. Various chemical parameters, encompassing pH, electrical conductivity, total nitrogen, and total organic carbon, were evaluated for both the contaminated soil and control soil samples, with the results summarized in Table . The pH values for the control, mildly polluted, and heavily polluted soils were recorded as 6.50, 6.30, and 7.10, respectively. The electrical conductivity (EC) values for the control, mildly polluted, and heavily polluted soils were 30 µS/cm, 40 µS/cm, and 120 µS/cm, respectively. Similarly, the total nitrogen values were 2.67 mg/kg, 2.02 mg/kg, and 2.00 mg/kg for the control, mildly polluted, and heavily polluted soils, respectively. Additionally, the total organic carbon was 1.25%, 5.25%, and 19.61% for the control, mildly polluted, and heavily polluted soils, respectively.
The values of the TPH for the heavily polluted (HP), control sample (CS) and mildly polluted (MP) soil sample are 490,630, 25.98 ppm, and 5398 ppm respectively.
The final dataset from the three reference samples—heavily polluted (HP), unpolluted (control sample; CS), and mildly polluted (MP)—comprised 20,640 demultiplexed high-quality reads, with an average of 6880 reads per sample. The sequences were clustered into 256 bacterial OTUs, which were further filtered to a minimum count of 4 and 20% prevalence per sample, resulting in a total of 160 retained bacterial OTUs. The dominant bacterial phyla in the heavily polluted soil (HP) samples were Proteobacteria (66%), Firmicutes (27%), Acidobacteria (4%), and Actinobacteria at less than 3%. In the unpolluted soil (CS) sample, the dominant phyla were Proteobacteria (68%), Firmicutes (30%), and Actinobacteria at less than 2%. In the mildly polluted soil (MP), the dominant phyla were Actinobacteria (39%), Proteobacteria (31%), Firmicutes (29%), Acidobacteria (0.67%), and Planctomycetes (0.33%). Figure summarises the bacterial phylum distribution in the three reference points. Dominant genera in the HP sample are Denitratisoma , Clostridium , Alkaliphilus , Diplorickettsia , Methylosinus and Bacillus (in descending order: ranging from 30.56% to 1.99%) . The following genera ( Pseudorhodoplanes , Cohnella , Rhodovastum , Neobacillus , Neomegalonema , Acidomonas , Neobacillus , Salirhabdus ) are duplicated in HP and CS samples, however, at relatively low abundance (< 2.0%) in HP but was dominant genera in CS. These genera are not observed in MP, except for Pseudorhodoplanes (2.7%). The dominant bacterial genera in MP include Spirilospora (34%), Paenibacillus , Swionibacillus , Rhizorhapis , Endobacter , Paraburkholderia , Rhodopila , Mycoavidus , Rummeliibacillus , Quasibacillus , and Phenylobacterium . Genera such as Denitratisoma , Alkaliphilus , Clostridium , and Bacillus were observed in CS but not in MP samples, while Methylosinus was observed in MP and not in CS. Figure a shows the dominant phyla diversity across the three samples, while Fig. b displays the relative abundance of the ten most dominant genera. Figures c–e depict that the bacterial abundance in CS is significantly different from both HP and MP (at p = 0.05). Similarly, as displayed in Table , CS is higher in richness and diversity as compared to the HP and MP samples.
A total of 99 KOs (KEGG Orthologues) were predicted, and the superpathway was used for the plot. The predicted KEGG (Kyoto Encyclopedia of Genes and Genomes) orthologues, collapsed into MetaCyc meta-pathways, show abundance values for each sample using the PICRUSt methodology (Fig. ). Six functional modules, comprising cellular processes (3%), environmental information processing (7%), genetic information processing (5%), human diseases (5%), and metabolism (70%), make up almost 90% of the complete dataset in the samples. The higher percentage of metabolism is registered in the order of MP, HP and CS respectively, indicating that hydrocarbon serves as a source of carbon or energy or both. Differentially abundant pathways (at p ≤ 0.05) showed 11 pathways (Fig. ), with oxidative phosphorylation as the most dominant biomarker pathway. Heavily polluted (HP) sample is more pronounced with disease function, with a notable genus such as Diplorickettsia , an agent of tick-borne infection. CS leads in genetic information processing, while MP leads in cellular processes.
Total petroleum hydrocarbons (TPH) consist of various fractions of petroleum, including polyaromatic hydrocarbons (PAHs). TPH in soil is primarily derived from anthropogenic sources, but some fractions of PAHs are contributed by living organisms such as plants and microorganisms . The major contribution of TPH in the study site was artisanal activities, which include spills, explosions, combustion, deposition, and the burial of the heavy fraction regarded as waste. Intrinsic factors such as soil texture, temperature, biosynthesis, topography, and erosion contribute to the overall concentration of soil TPH. The last two factors play an influential role in the unequal distribution of TPH in the soil. The concentration of the control sample (25.98 ppm) is well below the 100 ppm threshold required as a clean-up standard . The mildly polluted soil (with value of 5398 ppm) and heavily polluted soil (with a value of 490,360 ppm) is quite significant and is comparable to that TPH concentration obtained by Martinez et al. . The mildly polluted soil TPH concentration exceeds the Nigerian regulatory standard, 5000 ppm . The artisanal refining operations in the Niger Delta have a profound impact on the environment as a whole. The pollution that originates from artisanal refining sites is characterized by extremely poor air quality , contamination of surface soil and groundwater , loss of vegetation and mangroves and heavy pollution of the marine ecosystem . The air, consistently laden with hydrocarbons, has the potential to cause and exacerbate respiratory diseases such as reduced lung function, bronchitis, asthma, lung cancer, and chronic obstructive pulmonary disease – . The contamination of surface soil from artisanal refineries leads to a significant loss of vegetation cover and arable land, as well as a drastic alteration of microbial diversity. Contaminated groundwater poses a risk to both animals and humans, serving as a source of unsafe and toxic water. The disposal of the heavy-end fraction of hydrocarbons and other affiliated wastes (as is usually the practice) in water bodies remains a crude method of waste management. Consequently, the marine ecosystem becomes heavily polluted, resulting in a severe loss of mangrove habitats , . Seafood from the marine ecosystem is affected, leading to bioaccumulation and biomagnification , , which ultimately results in cancer and other malignant growths in humans. Overall, artisanal refining activities lead to health challenges, decreased agricultural productivity, reduction in the means of livelihood in local communities, and significant losses in biodiversity and ecosystem services, implicating microorganisms. Microorganisms react to various situations of hydrocarbon pollution in different ways: they either develop resistance or utilize hydrocarbons as a source of carbon and energy. Those that are unable to adapt to the stress of hydrocarbon pollution are eliminated . In all the samples analysed, there were changes in the structure of biodiversity compared to the unpolluted control. The dynamics of the phylum shift, reflecting the dominance of Proteobacteria in the heavily polluted (HP) and control sample (CS) soil samples and not in the mildly polluted (MP) is similar to those observed in the study conducted by Kim et al. . The Firmicutes did not respond significantly to each of the samples as did Proteobacteria, Actinobacteria, and Acidobacteria. The dominance of Proteobacteria in both heavily polluted and unpolluted soils accounts for the relevant roles they play in the biogeochemical cycling of carbon, sulphur, and nitrogen , as well as plant fitness and growth promotion . The shift noticed in the mildly polluted sample favoured the dominance of Actinobacteria. Genera in this group thrive in hydrocarbon-polluted environment, highlighting their physiological and genomic adaptations to challenging conditions . These bacteria play a crucial role in diverse ecological processes, such as the biodegradation of complex molecules , involvement in biogeochemical cycles , deterioration of artefacts, and participation in biological weathering . Actinobacteria demonstrate proficiency in these functions owing to their exceptional capabilities in DNA repair and protection, protein synthesis, biofilm formation, as well as the synthesis of biosurfactants, secondary metabolites, and essential enzymes – . Notably, certain genera such as Geodermatophilus , Modestobacter , and Kocuria , within the Actinobacteria phylum, exhibit resistance to desiccation, heavy metal toxicity, and ionization, as reported by Sayed et al. , Shivlata and Satyanarayana and Guesmi et al. Consequently, Actinobacteria represent a valuable resource for the bioremediation of highly contaminated environments. The trend shows that Acidobacteria diversities decreased significantly in the mildly polluted soil. This study demonstrates that hydrocarbon contamination gradients influence Proteobacteria, Acidobacteria and Actinobacteria as Abena et al. showed. The prevalent genera identified in the heavily polluted (HP) sample include Clostridium (Firmicutes), Methylosinus (Alphaproteobacterial methanotroph), and Bacillus (Firmicutes) among other genera including Denitratisoma (Betaproteobacteria) and Daegula (Alphaproteobacteria). The last two genera are the two most dominant in the HP sample. Clostridium species have the requisite enzyme system , genetic repertoire , and cell surface properties , to access and degrade hydrocarbon (especially halogenated species) under anaerobic condition. Methylosinus is a methanotrophic bacteria that has proven proficiency in degrading methane and chlorinated hydrocarbon , in consortium with other hydrocarbon-degrading bacteria and communities . Bacillus spp. is a widely distributed prolific bacteria known for their ability to metabolise hydrocarbon taking advantage of their ability to form biofilm , genomic capacity , biosurfactant production , metabolic diversity , and favourable redox potentials . Some species are hydrocarbonoclastic , thus effecting an increased population density as they utilise hydrocarbon as source of energy and carbon. While Denitratisoma and Daegula exhibit the highest population density in the heavily polluted soil, there is limited information available about them. However, it is established that Denitratisoma functions as aerobic denitrifiers and plays a crucial role in rhizoremediation . Their reduced presence in the mildly and unpolluted polluted soil suggest that they may have affinity for highly polluted environment. Genera like Neomegalonema , Neobacillus , Acidomonas , Pseudorhodoplanes , Cohnella , Rhodovastum , and Salirhabdus , each comprising less than 2% in the highly polluted sample, exhibited high abundance in the unpolluted sample, indicating their sensitivity to hydrocarbon pollution. However, these genera did not show in the mildly polluted soil, except Pseudorhodoplanes. This suggests that Pseudorhodoplanes is an excellent hydrocarbon-degrading bacteria or an emerging hydrocarbonoclastic bacteria. This bacteria genus has been implicated in hydrocarbon degradation . Tirandaz et al. illustrated that P . sinuspersici exhibits optimal activity at a temperature of 30 °C and a pH of 7. However, it demonstrates tolerance within a pH range of 5.5 to 8 and a temperature range of 15 to 35 °C. It is interesting to note that the study site reflects these optimal parameters, including suitable soil (sandy loamy) type. Other genera that appeared prominent in the unpolluted control sample that has been reported to affiliate with hydrocarbon polluted soil are Cohnella , Rhodovastum and Salirhabdus . Genera that shifted from being rare in the unpolluted sample to becoming more prominent in the polluted samples can be considered as emerging hydrocarbon-degrading bacteria (or tolerant taxa), and they include Spirilospora , Swionibacillus , and Paenibacillus . The catalogue of hydrocarbon-degrading bacteria is abundant with genera like Paenibacillus , Paraburkholderia , Methylosinus , and Phenylobacterium . In the mildly polluted soil examined in this study, these four genera are present, along with the identification of less common bacterial genera, including Spirilospora , which appears to be the most prevalent bacterial genus. Paenibacillus spp. (also abundant in the heavily polluted sample) has been shown to degrade hydrocarbon in consortium with Gordonia , Cupriavidus spp ., is associated with rhizoremediation, produce biosurfactants in contaminated soil, harbour hydrocarbon-degrading pahE genes and other requisite genes . These key biomarkers and other biological factors have positioned Paenibacillus spp. to degrade PAH and transform heavy crude to light oil . Kanwal et al. had indicated that sporogeneisis permits Paenibacillus , Bacillus and other related bacteria to survive inhospitable environments. Some species of Paenibacillus has been described as hydrocarbonoclastic and at the same time diazotrophic in the total environment, highlighting their dual relevance in bioremediation and plant growth promotion. Diazotrophic Paenibacillus polymyxa has been implicated in hydrocarbon degradation specifically polyaromatic hydrocarbon and biodegradation of mixed pesticides . However, there are more diazostrophic Paenibacillus spp. than hydrocarbon-degrading diazotrophic Paenibacillus spp. confirmed by publicly available literature – . From the key hydrocarbon-degrading bacteria examined, it is worthy of note that hydrocarbon degradation is linked to biological factors such as adaptation, metabolic competence, genetics, enzyme system, biomass, biosurfactant production, cell surface property, microbial interaction, biofilm formation and cell’s redox potential. Activation of a considerable number of these attributes has defined Paraburkholderia , Methylosinus , and Phenylobacterium as hydrocarbon-degrading bacteria and possibly Spirilospora and Pseudorhodoplanes , noted as emerging hydrocarbon-degrading bacteria – . Paraburkholderia aromaticivorans BN5 has been reported to degrade aliphatic hydrocarbons, naphthalene and BTEX , while Methylosinus spp. is an obligate methane metabolizer apart from degrading hydrocarbon through cometabolic pathway . In addition, Methylosinus has species that are diazotrophic , and heavy metal detoxifiers. For its part Phenylobacterium spp. has been reported in PAH degradation . Signature sequences in the mildly polluted soil is an indication of hydrocarbon metabolism, some of which may represent emerging hydrocarbon-degraders while some may be affiliated with diazotrophism. The diversity of these signature sequences is critical to the understanding of their structure in relation to the heavily polluted (HP) and control (CS) soil samples. The OTU’s diversity index shows that CS is higher in richness (by observed OTUs), while the Shannon diversity index shows that the CS sample is higher in diversity. The higher diversity index in CS reflects the unpolluted nature of the control sample and the toxicity effect of obvious pollution in HP and MP samples. The consequence of hydrocarbon impact include the shift of broad microbial diversity characterised with broad ecological functions to a narrow microbial diversity with prominent hydrocarbon-degrading and hydrocarbon-tolerant phylotypes. Examination of the mildly and heavily polluted samples reflect a few number of notable and emerging hydrocarbon-degrading bacteria including Paenibacillus , Paraburkholderia , Methylosinus Phenylobacterium , Bacillus , Burkholderia , Alkaliphilus , Pseudorhodoplanes and others. The reason behind this “shift to the left’ phenomenon is the adaptation of keystone species to survive in a stressed ecosystem either as resistant or utilizers of hydrocarbon as source of carbon and energy. Bacteria that can tolerate and degrade moderate hydrocarbons, in their mixed form, can initiate biodegradation process, as much as those that can tolerate and benefit from the process’ metabolites . This phenomenon becomes prominent in ecosystem with long-term history of pollution . Results from most studies in pollution ecology and bioremediation align with the concept of broad-to-narrow concept, confirmed in this study. The trend observed in this study counters that of Benedek et al. which observed positive correlation between TPH (147,000 ppm) and diversity. The reasons, according to Benedek et al. , for this negative results are long-term exposure to hydrocarbon, significant rise of a particular hydrocarbon-degrading bacterial genus, need for alternative carbon source and lack of humus. Recent studies that support positive correlation between TPH and microbial diversity in the Niger Delta are Iturbe-Espinoza et al. , Edet and Antai and in other regional settings are Lee et al. , Mukherjee et al. and Yerulker et al. . These two contradicting research outcome suggests that besides, ecotoxicity effects, other factors play influential roles in diversity profiling . These factors may include soil chemistry, soil’s trophic status, and genera composition in higher taxa. These factors condition a non-uniform trend and responses of microbial taxa to contamination in soil. In pollution-affiliated microbial diversity dynamics, dominance of hydrocarbon-degrading bacteria is a common phenomenon, which is underscored by biological functions such as cellular processes, genetic information and degradation. Hydrocarbons are utilised as an energy source through oxidative phosphorylation, in the inner section of bacteria cytoplasm with the release of ATP for cellular processes: growth, replication, quorum sensing, chemotaxis, and catabolism. The latter is achieved through the use of key signatory enzymes. Though an enzyme profile was not conducted in this study, Obieze et al. reported hydrocarbon-degrading enzymes (through functional prediction) in the same study site, which include (3S,4R)-3,4-dichloroxycyclohexa-1,5-diene-1,4-dicarboxylate dehydrogenase, 2,4-diclorophenol-6-monooxygenase, 3-carboxyethylcatechol-2,3-dioxygenase. Bidja-Abena et al. identified a few functional enzymes (in diesel-polluted soil) specific for xenobiotic metabolisms, chlorobenzene degradation, and polyaromatic hydrocarbon. Protein export and gene repair (indices of genetic information processing) are protective against hydrocarbon toxicity to cells , . Another KEGG functional profile that is connected to metabolism is environmental information processing such as the ABC transporters for mineral/organic ions, amino acids, and lipid transportation . Protein transportation is necessary for uptake of hydrocarbons by bacteria for degradation and metabolism. These functional module is dominated by metabolism with 11 pathways, which support active engagement of hydrocarbon degradation in this study.
This work was undertaken to understand the impact of artisanal refining activities on soil bacterial diversity through metagenomics. Consequently, Ngia Ama was chosen because of its hydrocarbon pollution history of more than six years. Composite samples of heavily (HP) polluted, mildly (MP) polluted and unpolluted (CS) soil were used for the analysis and the results showed a broad-to-narrow bacterial diversity with known and emerging hydrocarbon-degrading bacteria found abundantly in polluted samples. This paradigm shifts in bacteria diversity points to distortion of ecological service at the detriment of the total environmental and its receptors in addition to the vicious impact of artisanal refining activities on vegetation and marine ecosystem in the Niger Delta. However, the study area features as a hub of activated soil critical for ex situ bioremediation programme.
Site description The study was carried out on contaminated fallow land at Ngia Ama (4°47ʹ42ʺ N, 6°51ʹ45ʺE), a community in Tombia Kingdom where illegal refining activities had occurred for over six years, with respect to the sampling year, 2018. The study site is enveloped by mangroves and creeks, featuring moderate lowlands and an average temperature of 25–37 °C. Soil sample collection The heavily (HP) and moderately (MP) polluted portions (7.5 ft away from each other) of the study site were spotted and soil sampling was carried out using a soil auger. Three sub-samples were collected at each point from a depth of ≤ 30 cm and mixed thoroughly to create a composite sample. A similar approach was used to collect a control sample (CS, non-polluted), 23 ft away from the polluted field. The samples were aseptically transferred into sterile plastic containers and preserved at 4 °C and − 20 °C for downstream analysis. Soil’s physicochemical analysis The soil samples collected were initially dried at 25 °C, ground, and then sieved through a 2 mm mesh before analysis, following the method outlined by Durak et al. . The Bouyoucos Hydrometer method was employed for soil texture analysis, following the protocol adopted by Babalola et al. , to determine the content of sand, silt, and clay. In summary, a beaker was filled with 50 g of pre-treated soil and 125 ml of sodium hexametaphosphate (40 g/L) was added. The mixture was stirred until the soil was fully saturated and then left to rest for ten minutes. The resulting soil slurry was moved to a mixer, and distilled water was added until the mixing cup was half full. The solution was then mixed for two minutes. Subsequently, the soil slurry was quickly transferred to an unoccupied sedimentation cylinder, and distilled water was added up to the reference mark. The cylinder was flipped upside down and back 30 times. After placing the cylinder down, the time was noted. The stopper was taken off the cylinder, and a hydrometer was gently inserted. The initial hydrometer reading was taken immediately, followed by a second reading after 15 s. Further hydrometer readings were taken in a doubling pattern until 48 h, resulting in a total of 16 readings. The collected data was then analysed to determine the composition of soil particles. The pH level of the soil was assessed using the method outlined by Adekiya et al. . To measure the soil’s pH, 10 g of soil was placed into a clean 100 mL beaker, to which 20 mL of deionized water was added. A pH tester 20 was then inserted into the resulting suspension in the beaker, with the aim of determining the average pH from three repeated measurements. The analysis of the electrical conductivity (EC) was carried out as previously detailed Oyem and Oyem . This involved adding 10 g of soil to 20 mL of deionized water and allowing it to stand for 30 min. The resulting slurry was then filtered, and the EC was measured using a Hanna digital conductivity meter. The organic carbon content was ascertained using the method reported by Mrayyan and Battikhi introducing 1 g of soil into 10 mL of 1.0 M of K 2 Cr 4 O 7 and the mixture was shaken for homogeneity. Later, 20 mL of 98% H 2 SO 4 was rapidly added using a burette and shaken with vigour for 1 min and left standing on a white tile for 30 min. The mixture was then added with 200 mL of deionized water and later with 10 mL of 85% H 3 PO 4 , 0.2 g NaF and 15 drops of diphenylamine indicator. The ensuing solution was back-titrated with 0.5 N iron(II) sulphate and organic carbon calculated . The Kjeldahl method was used to determine the total nitrogen content. Initially, 10 g of the sample was weighed and placed into a 500 mL Kjeldahl flask. Then, 20 mL of deionized water was added to the flask, which was shaken for a few minutes and left to stand for 30 min. Copper and sodium sulphate (1.5 g each), along with 30 mL of concentrated sulphuric acid, were added and mixed until homogeneous. The contents of the flask were heated until no froth was visible. The mixture was then boiled for 5 h, cooled, and 100 mL of distilled water was added to the flask. A boric acid indicator (50 mL) was used to rinse the sandy residue, which was then added to a conical flask positioned under the condenser of the distillation setup. The Kjeldahl flask, including the digest, was connected to the distillation unit. Sodium hydroxide (150 mL of 10 M NaOH) was added to the distillation flask and distilled until 150 mL of distillate was collected. The nitrogen content/concentration was calculated using a titration technique with a 0.01 M sulphuric acid distillate. The endpoint was indicated by a colour change from green to pink. A blank titration was also performed to obtain the blank titre . The total nitrogen was calculated using Eq. . 1 [12pt]{minimal}
$$\%N=Consumption-Blank 1.4007 n \, size$$ % N = C o n s u m p t i o n - B l a n k × 1.4007 × n × 100 sample s i z e where n represent normality of acid. Determination of total petroleum hydrocarbons (TPH) Two grams of soil sample were heated at 50 °C and crushed well afterwards. Ten millilitres (10 ml) of dichloromethane (Sigma Aldrich, USA) was then added to the finely crushed soil and shaken firmly. To precipitate the soil, it was centrifuged at 3000× g for 10 min . The solvent phase was removed. The TPH analysis was carried out following steps earlier prescribed by . In summary, the hydrocarbon portion was stirred for 5 mins and separated using a Whatman filter paper No. 42. The extracted hydrocarbon was concentrated to 1 mL after being evaporated in a water bath. The TPH was determined using a GC spectrometer (Thermo Scientific™ Nicolet iCS). The samples were run in triplicate. The procedural blank was determined by going through the extraction and clean-up procedures using glass beads instead of a soil sample. Next-generation sequencing for metagenomic analyses Metagenomic DNA extraction The DNA extraction was carried out on the samples using Zymo Research (ZR) Fungi/Bacteria DNA MiniPrep™ (California, USA) supplied by Inqaba Biotec, South Africa according to the manufacturer’s instructions. The summary of the extraction process is illustrated in Fig. . In summary, 0.25 g of soil is added to a ZR BashingBead™ Lysis Tube along with 750 μl of Lysis Solution. The tube is then processed in a bead beater at maximum speed for at least 5 min. Following centrifugation at 10,000× g for 1 min, up to 400 μl of the supernatant is transferred to a Zymo-Spin™ IV Spin Filter, and after centrifuging at 8000× g for 1 min, the filtrate is combined with 1200 μl of Fungal/Bacterial DNA Binding Buffer. Subsequently, 800 μl of this mixture is loaded onto a Zymo-Spin™ IIC Column and centrifuged at 10,000× g for 1 min, with a repeat of the step. The Zymo-Spin™ IIC Column is then treated with 200 μl of DNA Pre-Wash Buffer and centrifuged for 1 min at 10,000× g , followed by the addition of 500 μl Fungal/Bacterial DNA Wash Buffer and another round of centrifugation. The Zymo-Spin™ IIC Column is transferred to a clean 1.5 ml microcentrifuge tube, and 100 μl of DNA Elution Buffer is added directly to the column matrix. The elution is achieved by centrifuging at 10,000× g for 30 s, resulting in the extraction of DNA suitable for downstream analysis . Polymerase chain reaction (PCR) For the PCR analysis, the hypervariable region (V3-V4) of the 16S rRNA was targeted using bacteria-specific primers, namely 341F and 806R. The primers were tagged according to . All the PCRs were done in triplicate (n = 3). Polymerase chain reaction master mix aliquot was dispensed into PCR tubes and the different DNA samples were introduced into each tube alongside a negative control. The PCR reagents in each tube amounted to 50 μl containing: buffer (5 μl: 100 mM), MgCl 2 (1.5 μl: 25 mM), universal primer1 (2 μl forward: 20 μM), primer2 (2 μl reverse: 20 μM), dNTP mix (1 μl: 200 μM), Dream Taq DNA Polymerase (0.25 μl: 1.25 units/50 μl), sterile water (35.25 μl) and DNA samples (3 μl) – . The PCR condition was set at 3 min at 94 °C, followed by 35 cycles comprising 30 s at 94 °C, 30 s at 58 °C, and 1 min at 72 °C . The process concluded with a final extension step of 10 min at 72 °C. The PCRs were performed using an MJ Mini thermal cycler (Bio-Rad, Hercules, CA, USA). The resulting amplicons were separated electrophoretically with 1% agarose gel stained with 0.1 μg/ml ethidium bromide running at 80 V for 60 min, using TAE electrophoresis buffer. The PCR amplicons were visualized by UV fluorescence to determine the amplicon sizes. The PCR products (20 μl each) were later cleaned up using 160 μl of 13% polyethene glycol (PEG) 8000, 20 μl of 5 M NaCl solution and 200 μl of 70% ethanol. MiSeq sequencing and sequence analysis The PCR products (after purification using Omega, Bio-Tek and quantification with Agilent Bioanalyzer 2100) were sequenced with the Miseq platform at the University of South Africa (UNISA), Science Campus, Florida, Roodepoort. This process involved 600 cycles (300 cycles for each paired read and 12 cycles for the barcode sequence) as per the manufacturer’s guidelines. This also involved 600 cycles (300 cycles for each paired read and 12 cycles for the barcode sequence) following the manufacturer’s instructions. The sequence data was analysed using the 16S-based metagenomics workflow provided by MiSeq Reporter v2.3 (Illumina). The 16S rRNA gene, a frequently targeted region, was used for microbial identification, thereby eliminating the need to sequence the entire genome. The Illumina workflow began with purified genomic DNA, where primers were extended with sequences that included indexing barcodes. The samples were then merged into a single library and sequenced on the Illumina MiSeq platform, resulting in paired 230 bp reads , . Bioinformatic analyses Demultiplexed paired-end reads obtained from the sequencing facility were quality-checked using FastQC software version 0.11.5 (Babraham Institute, United Kingdom). Subsequently, Trimmomatic software (version 0.38) was used to quality-trim paired reads, including clipping off any Illumina barcodes and eliminating reads with an average quality score (Phred Q score) lower than 20. Quality-filtered paired reads were then analyzed in the Quantitative Insights into Microbial Ecology (version 2) (QIIME2) software . DADA2 denoiser was used to merge pair-end sequences into full-length sequences as well as remove chimaras. USEARCH version 7 was used to cluster similar sequences into operational taxonomic units (OTUs) at 97% similarity . Taxonomic classification of the clustered OTUs was performed against the RDP classifier . The obtained OTU table was further rarefied to even depths of 7544 sequences. The OTU and sequences of clustered OTUs were used as an input to PICRUSt2 software (installed as a QIIME2 plugin) to predict metabolic functions based on 16S rRNA. PICRUSt2 was developed in 2020 as an improvement over the 2013 version. It is more accurate and features a larger database. PICRUSt2 is a promising tool with the potential for various research applications. For instance, it could be employed to investigate the functional potential of microbial communities in different environments, as demonstrated in this study. The bacterial communities’ relative abundance was visualized at the phylum and genus level to better convey the biological information in these samples. The OTU table with assigned taxonomy was taxonomy was normalized (relative abundance) using MicrobiomeAnalyst ; and used to plot 100% stacked bar graph. Statistical analysis QIIME2 output—OTU table was in text and biom format. OTU table in text format was imported into Rstudio and ranacapa (ranacapa::runRanacapa package was used for rarefication curve, Shannon index and Observed OTUs calculations The biom format of the OTU table was uploaded to MicrobiomeDB—A data-mining platform for interrogating microbiome experiments was used to determine the top 10 abundant genera. These genera were then compared between the samples where significant (q-value > 0.05) features between two groups (HP-MP, HP-CS and CS-MP) calculated using White’s non-parametric test with Benjamini–Hochberg FDR (false discovery rate) in STAMP. The same test was used for PICRUSt2 predicted function.
The study was carried out on contaminated fallow land at Ngia Ama (4°47ʹ42ʺ N, 6°51ʹ45ʺE), a community in Tombia Kingdom where illegal refining activities had occurred for over six years, with respect to the sampling year, 2018. The study site is enveloped by mangroves and creeks, featuring moderate lowlands and an average temperature of 25–37 °C.
The heavily (HP) and moderately (MP) polluted portions (7.5 ft away from each other) of the study site were spotted and soil sampling was carried out using a soil auger. Three sub-samples were collected at each point from a depth of ≤ 30 cm and mixed thoroughly to create a composite sample. A similar approach was used to collect a control sample (CS, non-polluted), 23 ft away from the polluted field. The samples were aseptically transferred into sterile plastic containers and preserved at 4 °C and − 20 °C for downstream analysis.
The soil samples collected were initially dried at 25 °C, ground, and then sieved through a 2 mm mesh before analysis, following the method outlined by Durak et al. . The Bouyoucos Hydrometer method was employed for soil texture analysis, following the protocol adopted by Babalola et al. , to determine the content of sand, silt, and clay. In summary, a beaker was filled with 50 g of pre-treated soil and 125 ml of sodium hexametaphosphate (40 g/L) was added. The mixture was stirred until the soil was fully saturated and then left to rest for ten minutes. The resulting soil slurry was moved to a mixer, and distilled water was added until the mixing cup was half full. The solution was then mixed for two minutes. Subsequently, the soil slurry was quickly transferred to an unoccupied sedimentation cylinder, and distilled water was added up to the reference mark. The cylinder was flipped upside down and back 30 times. After placing the cylinder down, the time was noted. The stopper was taken off the cylinder, and a hydrometer was gently inserted. The initial hydrometer reading was taken immediately, followed by a second reading after 15 s. Further hydrometer readings were taken in a doubling pattern until 48 h, resulting in a total of 16 readings. The collected data was then analysed to determine the composition of soil particles. The pH level of the soil was assessed using the method outlined by Adekiya et al. . To measure the soil’s pH, 10 g of soil was placed into a clean 100 mL beaker, to which 20 mL of deionized water was added. A pH tester 20 was then inserted into the resulting suspension in the beaker, with the aim of determining the average pH from three repeated measurements. The analysis of the electrical conductivity (EC) was carried out as previously detailed Oyem and Oyem . This involved adding 10 g of soil to 20 mL of deionized water and allowing it to stand for 30 min. The resulting slurry was then filtered, and the EC was measured using a Hanna digital conductivity meter. The organic carbon content was ascertained using the method reported by Mrayyan and Battikhi introducing 1 g of soil into 10 mL of 1.0 M of K 2 Cr 4 O 7 and the mixture was shaken for homogeneity. Later, 20 mL of 98% H 2 SO 4 was rapidly added using a burette and shaken with vigour for 1 min and left standing on a white tile for 30 min. The mixture was then added with 200 mL of deionized water and later with 10 mL of 85% H 3 PO 4 , 0.2 g NaF and 15 drops of diphenylamine indicator. The ensuing solution was back-titrated with 0.5 N iron(II) sulphate and organic carbon calculated . The Kjeldahl method was used to determine the total nitrogen content. Initially, 10 g of the sample was weighed and placed into a 500 mL Kjeldahl flask. Then, 20 mL of deionized water was added to the flask, which was shaken for a few minutes and left to stand for 30 min. Copper and sodium sulphate (1.5 g each), along with 30 mL of concentrated sulphuric acid, were added and mixed until homogeneous. The contents of the flask were heated until no froth was visible. The mixture was then boiled for 5 h, cooled, and 100 mL of distilled water was added to the flask. A boric acid indicator (50 mL) was used to rinse the sandy residue, which was then added to a conical flask positioned under the condenser of the distillation setup. The Kjeldahl flask, including the digest, was connected to the distillation unit. Sodium hydroxide (150 mL of 10 M NaOH) was added to the distillation flask and distilled until 150 mL of distillate was collected. The nitrogen content/concentration was calculated using a titration technique with a 0.01 M sulphuric acid distillate. The endpoint was indicated by a colour change from green to pink. A blank titration was also performed to obtain the blank titre . The total nitrogen was calculated using Eq. . 1 [12pt]{minimal}
$$\%N=Consumption-Blank 1.4007 n \, size$$ % N = C o n s u m p t i o n - B l a n k × 1.4007 × n × 100 sample s i z e where n represent normality of acid.
Two grams of soil sample were heated at 50 °C and crushed well afterwards. Ten millilitres (10 ml) of dichloromethane (Sigma Aldrich, USA) was then added to the finely crushed soil and shaken firmly. To precipitate the soil, it was centrifuged at 3000× g for 10 min . The solvent phase was removed. The TPH analysis was carried out following steps earlier prescribed by . In summary, the hydrocarbon portion was stirred for 5 mins and separated using a Whatman filter paper No. 42. The extracted hydrocarbon was concentrated to 1 mL after being evaporated in a water bath. The TPH was determined using a GC spectrometer (Thermo Scientific™ Nicolet iCS). The samples were run in triplicate. The procedural blank was determined by going through the extraction and clean-up procedures using glass beads instead of a soil sample.
Metagenomic DNA extraction The DNA extraction was carried out on the samples using Zymo Research (ZR) Fungi/Bacteria DNA MiniPrep™ (California, USA) supplied by Inqaba Biotec, South Africa according to the manufacturer’s instructions. The summary of the extraction process is illustrated in Fig. . In summary, 0.25 g of soil is added to a ZR BashingBead™ Lysis Tube along with 750 μl of Lysis Solution. The tube is then processed in a bead beater at maximum speed for at least 5 min. Following centrifugation at 10,000× g for 1 min, up to 400 μl of the supernatant is transferred to a Zymo-Spin™ IV Spin Filter, and after centrifuging at 8000× g for 1 min, the filtrate is combined with 1200 μl of Fungal/Bacterial DNA Binding Buffer. Subsequently, 800 μl of this mixture is loaded onto a Zymo-Spin™ IIC Column and centrifuged at 10,000× g for 1 min, with a repeat of the step. The Zymo-Spin™ IIC Column is then treated with 200 μl of DNA Pre-Wash Buffer and centrifuged for 1 min at 10,000× g , followed by the addition of 500 μl Fungal/Bacterial DNA Wash Buffer and another round of centrifugation. The Zymo-Spin™ IIC Column is transferred to a clean 1.5 ml microcentrifuge tube, and 100 μl of DNA Elution Buffer is added directly to the column matrix. The elution is achieved by centrifuging at 10,000× g for 30 s, resulting in the extraction of DNA suitable for downstream analysis . Polymerase chain reaction (PCR) For the PCR analysis, the hypervariable region (V3-V4) of the 16S rRNA was targeted using bacteria-specific primers, namely 341F and 806R. The primers were tagged according to . All the PCRs were done in triplicate (n = 3). Polymerase chain reaction master mix aliquot was dispensed into PCR tubes and the different DNA samples were introduced into each tube alongside a negative control. The PCR reagents in each tube amounted to 50 μl containing: buffer (5 μl: 100 mM), MgCl 2 (1.5 μl: 25 mM), universal primer1 (2 μl forward: 20 μM), primer2 (2 μl reverse: 20 μM), dNTP mix (1 μl: 200 μM), Dream Taq DNA Polymerase (0.25 μl: 1.25 units/50 μl), sterile water (35.25 μl) and DNA samples (3 μl) – . The PCR condition was set at 3 min at 94 °C, followed by 35 cycles comprising 30 s at 94 °C, 30 s at 58 °C, and 1 min at 72 °C . The process concluded with a final extension step of 10 min at 72 °C. The PCRs were performed using an MJ Mini thermal cycler (Bio-Rad, Hercules, CA, USA). The resulting amplicons were separated electrophoretically with 1% agarose gel stained with 0.1 μg/ml ethidium bromide running at 80 V for 60 min, using TAE electrophoresis buffer. The PCR amplicons were visualized by UV fluorescence to determine the amplicon sizes. The PCR products (20 μl each) were later cleaned up using 160 μl of 13% polyethene glycol (PEG) 8000, 20 μl of 5 M NaCl solution and 200 μl of 70% ethanol. MiSeq sequencing and sequence analysis The PCR products (after purification using Omega, Bio-Tek and quantification with Agilent Bioanalyzer 2100) were sequenced with the Miseq platform at the University of South Africa (UNISA), Science Campus, Florida, Roodepoort. This process involved 600 cycles (300 cycles for each paired read and 12 cycles for the barcode sequence) as per the manufacturer’s guidelines. This also involved 600 cycles (300 cycles for each paired read and 12 cycles for the barcode sequence) following the manufacturer’s instructions. The sequence data was analysed using the 16S-based metagenomics workflow provided by MiSeq Reporter v2.3 (Illumina). The 16S rRNA gene, a frequently targeted region, was used for microbial identification, thereby eliminating the need to sequence the entire genome. The Illumina workflow began with purified genomic DNA, where primers were extended with sequences that included indexing barcodes. The samples were then merged into a single library and sequenced on the Illumina MiSeq platform, resulting in paired 230 bp reads , . Bioinformatic analyses Demultiplexed paired-end reads obtained from the sequencing facility were quality-checked using FastQC software version 0.11.5 (Babraham Institute, United Kingdom). Subsequently, Trimmomatic software (version 0.38) was used to quality-trim paired reads, including clipping off any Illumina barcodes and eliminating reads with an average quality score (Phred Q score) lower than 20. Quality-filtered paired reads were then analyzed in the Quantitative Insights into Microbial Ecology (version 2) (QIIME2) software . DADA2 denoiser was used to merge pair-end sequences into full-length sequences as well as remove chimaras. USEARCH version 7 was used to cluster similar sequences into operational taxonomic units (OTUs) at 97% similarity . Taxonomic classification of the clustered OTUs was performed against the RDP classifier . The obtained OTU table was further rarefied to even depths of 7544 sequences. The OTU and sequences of clustered OTUs were used as an input to PICRUSt2 software (installed as a QIIME2 plugin) to predict metabolic functions based on 16S rRNA. PICRUSt2 was developed in 2020 as an improvement over the 2013 version. It is more accurate and features a larger database. PICRUSt2 is a promising tool with the potential for various research applications. For instance, it could be employed to investigate the functional potential of microbial communities in different environments, as demonstrated in this study. The bacterial communities’ relative abundance was visualized at the phylum and genus level to better convey the biological information in these samples. The OTU table with assigned taxonomy was taxonomy was normalized (relative abundance) using MicrobiomeAnalyst ; and used to plot 100% stacked bar graph. Statistical analysis QIIME2 output—OTU table was in text and biom format. OTU table in text format was imported into Rstudio and ranacapa (ranacapa::runRanacapa package was used for rarefication curve, Shannon index and Observed OTUs calculations The biom format of the OTU table was uploaded to MicrobiomeDB—A data-mining platform for interrogating microbiome experiments was used to determine the top 10 abundant genera. These genera were then compared between the samples where significant (q-value > 0.05) features between two groups (HP-MP, HP-CS and CS-MP) calculated using White’s non-parametric test with Benjamini–Hochberg FDR (false discovery rate) in STAMP. The same test was used for PICRUSt2 predicted function.
The DNA extraction was carried out on the samples using Zymo Research (ZR) Fungi/Bacteria DNA MiniPrep™ (California, USA) supplied by Inqaba Biotec, South Africa according to the manufacturer’s instructions. The summary of the extraction process is illustrated in Fig. . In summary, 0.25 g of soil is added to a ZR BashingBead™ Lysis Tube along with 750 μl of Lysis Solution. The tube is then processed in a bead beater at maximum speed for at least 5 min. Following centrifugation at 10,000× g for 1 min, up to 400 μl of the supernatant is transferred to a Zymo-Spin™ IV Spin Filter, and after centrifuging at 8000× g for 1 min, the filtrate is combined with 1200 μl of Fungal/Bacterial DNA Binding Buffer. Subsequently, 800 μl of this mixture is loaded onto a Zymo-Spin™ IIC Column and centrifuged at 10,000× g for 1 min, with a repeat of the step. The Zymo-Spin™ IIC Column is then treated with 200 μl of DNA Pre-Wash Buffer and centrifuged for 1 min at 10,000× g , followed by the addition of 500 μl Fungal/Bacterial DNA Wash Buffer and another round of centrifugation. The Zymo-Spin™ IIC Column is transferred to a clean 1.5 ml microcentrifuge tube, and 100 μl of DNA Elution Buffer is added directly to the column matrix. The elution is achieved by centrifuging at 10,000× g for 30 s, resulting in the extraction of DNA suitable for downstream analysis .
For the PCR analysis, the hypervariable region (V3-V4) of the 16S rRNA was targeted using bacteria-specific primers, namely 341F and 806R. The primers were tagged according to . All the PCRs were done in triplicate (n = 3). Polymerase chain reaction master mix aliquot was dispensed into PCR tubes and the different DNA samples were introduced into each tube alongside a negative control. The PCR reagents in each tube amounted to 50 μl containing: buffer (5 μl: 100 mM), MgCl 2 (1.5 μl: 25 mM), universal primer1 (2 μl forward: 20 μM), primer2 (2 μl reverse: 20 μM), dNTP mix (1 μl: 200 μM), Dream Taq DNA Polymerase (0.25 μl: 1.25 units/50 μl), sterile water (35.25 μl) and DNA samples (3 μl) – . The PCR condition was set at 3 min at 94 °C, followed by 35 cycles comprising 30 s at 94 °C, 30 s at 58 °C, and 1 min at 72 °C . The process concluded with a final extension step of 10 min at 72 °C. The PCRs were performed using an MJ Mini thermal cycler (Bio-Rad, Hercules, CA, USA). The resulting amplicons were separated electrophoretically with 1% agarose gel stained with 0.1 μg/ml ethidium bromide running at 80 V for 60 min, using TAE electrophoresis buffer. The PCR amplicons were visualized by UV fluorescence to determine the amplicon sizes. The PCR products (20 μl each) were later cleaned up using 160 μl of 13% polyethene glycol (PEG) 8000, 20 μl of 5 M NaCl solution and 200 μl of 70% ethanol.
The PCR products (after purification using Omega, Bio-Tek and quantification with Agilent Bioanalyzer 2100) were sequenced with the Miseq platform at the University of South Africa (UNISA), Science Campus, Florida, Roodepoort. This process involved 600 cycles (300 cycles for each paired read and 12 cycles for the barcode sequence) as per the manufacturer’s guidelines. This also involved 600 cycles (300 cycles for each paired read and 12 cycles for the barcode sequence) following the manufacturer’s instructions. The sequence data was analysed using the 16S-based metagenomics workflow provided by MiSeq Reporter v2.3 (Illumina). The 16S rRNA gene, a frequently targeted region, was used for microbial identification, thereby eliminating the need to sequence the entire genome. The Illumina workflow began with purified genomic DNA, where primers were extended with sequences that included indexing barcodes. The samples were then merged into a single library and sequenced on the Illumina MiSeq platform, resulting in paired 230 bp reads , .
Demultiplexed paired-end reads obtained from the sequencing facility were quality-checked using FastQC software version 0.11.5 (Babraham Institute, United Kingdom). Subsequently, Trimmomatic software (version 0.38) was used to quality-trim paired reads, including clipping off any Illumina barcodes and eliminating reads with an average quality score (Phred Q score) lower than 20. Quality-filtered paired reads were then analyzed in the Quantitative Insights into Microbial Ecology (version 2) (QIIME2) software . DADA2 denoiser was used to merge pair-end sequences into full-length sequences as well as remove chimaras. USEARCH version 7 was used to cluster similar sequences into operational taxonomic units (OTUs) at 97% similarity . Taxonomic classification of the clustered OTUs was performed against the RDP classifier . The obtained OTU table was further rarefied to even depths of 7544 sequences. The OTU and sequences of clustered OTUs were used as an input to PICRUSt2 software (installed as a QIIME2 plugin) to predict metabolic functions based on 16S rRNA. PICRUSt2 was developed in 2020 as an improvement over the 2013 version. It is more accurate and features a larger database. PICRUSt2 is a promising tool with the potential for various research applications. For instance, it could be employed to investigate the functional potential of microbial communities in different environments, as demonstrated in this study. The bacterial communities’ relative abundance was visualized at the phylum and genus level to better convey the biological information in these samples. The OTU table with assigned taxonomy was taxonomy was normalized (relative abundance) using MicrobiomeAnalyst ; and used to plot 100% stacked bar graph.
QIIME2 output—OTU table was in text and biom format. OTU table in text format was imported into Rstudio and ranacapa (ranacapa::runRanacapa package was used for rarefication curve, Shannon index and Observed OTUs calculations The biom format of the OTU table was uploaded to MicrobiomeDB—A data-mining platform for interrogating microbiome experiments was used to determine the top 10 abundant genera. These genera were then compared between the samples where significant (q-value > 0.05) features between two groups (HP-MP, HP-CS and CS-MP) calculated using White’s non-parametric test with Benjamini–Hochberg FDR (false discovery rate) in STAMP. The same test was used for PICRUSt2 predicted function.
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The Cancer Survivorship Program at the Abramson Cancer Center of the University of Pennsylvania | 9a60cfcf-4446-472e-a8cb-f39a9592dde6 | 10867042 | Internal Medicine[mh] | Over the past 50 years, there has been substantial progress in cancer care delivery and survival has improved for children and adults diagnosed with cancer. This has resulted in growing populations of cancer survivors with unique needs and with potential risks for significant medical and psychosocial issues over the course of their lives. The past few decades have seen greater emphasis on provision of optimal follow-up care for cancer patients and long-term survivors living with cancer as a chronic illness. Much of this activity can be attributed to the emergence of informed and assertive healthcare consumers who have unified and coordinated the various components of the early cancer survivorship movement and the 2006 Institute of Medicine consensus report From Cancer Patient to Cancer Survivor: Lost in Transition .
The Hospital of the University of Pennsylvania opened in 1874 as the nation’s first teaching hospital for the University’s School of Medicine that was founded in 1765. The Penn Medicine Cancer Center was formally established in 1973 and in June 2002, it was renamed the Abramson Cancer Center (ACC) in recognition of the extraordinary support of Leonard and Madlyn Abramson and family. Located in West Philadelphia, the ACC provides care to those living in Southeastern Pennsylvania and Southern New Jersey and serves the needs of the 12 counties that comprise its catchment area. The population of over 8 million captures approximately 85 percent of patients seen at the ACC, with non-White population comprising approximately 18 percent of cancer patients seen in the West Philadelphia location. This is a matrix cancer center embedded within the University of Pennsylvania Health System, Penn Medicine, and includes community hospitals with in-patient cancer care facilities, in/out-patient rehabilitation, hospice services, and behavioral health (see Table ). Clinical care is documented in EPIC, the electronic health record system at Penn Medicine.
In the 1970s, Anna Meadows, MD, established the first Late Effects Clinic for pediatric cancer survivors at The Children’s Hospital of Philadelphia (CHOP). As a pediatric oncologist and nationally recognized expert in the late effects of cancer treatment, she understood the importance of transitioning survivors into an adult healthcare setting as they aged out of pediatric care. In 2001, in collaboration with the Penn Medicine Cancer Center Director John Glick, MD, Dr. Meadows secured a seed grant (2001-2004) from the Lance Armstrong Foundation (LAF) to establish the first adult cancer survivorship program in the country. In 2001, Linda Jacobs, PhD, CRNP, an oncology and primary care nurse practitioner, was recruited to co-direct the program with Dr. Meadows. When Dr. Meadows retired in 2010, Dr. Jacobs took over as Director. In 2005, a few years after the Penn Medicine Cancer Center was renamed the ACC (2002), The LIVESTRONG™ Survivorship Centers of Excellence Network was created by the LAF to improve the quality of post-treatment cancer care for survivors by expanding knowledge in the field of survivorship. The ACC Survivorship Program joined the Network in 2007 and the seven National Cancer Institute designated Comprehensive Cancer Centers that comprised the Network were supported by the LAF through 2015. These Centers conducted collaborative research projects testing models of care, determining the needs of cancer survivors, and examining the late effects of treatment. Joseph Carver, MD, a cardiologist with an interest in late effects of cancer treatment, joined the ACC in 2001 and established one of the first Cardio-Oncology programs in the country that has evolved into a training program and referral center. Currently, there are over 600 Cardio-Oncology consults placed each year at the ACC. Dava Szalda, MD, a pediatric oncologist (with a Med-Peds training), joined the team in 2014. She assists with transitioning patients from CHOP to the ACC Survivorship Program and sees survivors in clinic one day each week. Adult survivors of pediatric and young adult cancers of all ages who are not transitioning from CHOP are also followed in the program. Survivors refer themselves or are referred to the survivorship program. In 2001, Meadows and Jacobs identified the need for a project manager, a patient navigator, a behavioral scientist, and a research coordinator to assist with the clinical program development, ongoing research, and establishing education goals of the program. Since 2015, when the LAF support ended, support for the program has come from the ACC core grant from the National Cancer Institute (NCI), clinical revenue, philanthropy, as well as other grants funded by the NCI, the Department of Defense, the Susan G. Komen Foundation, the Oncology Nursing Foundation, and the Commonwealth of Pennsylvania. One key funded research initiative was the U54 Translational Research on Energetics and Cancer Survivorship Center Grant (2011-2016), a multi-site, multidisciplinary, multi-specialty collaborative effort by the ACC survivorship program team aimed at promoting exercise among breast and testicular cancer survivors .
The first few years of program development focused on establishing infrastructure engaging with specialty care colleagues to provide survivorship care (see Fig. ). This process resulted in rapid access to specialists with minimal wait time for appointments, a successful endeavor that remains in effect today. A Survivorship Resource Guide was developed by survivorship team members and is revised yearly. This guide outlines services available and contact information for those services. The guide is available in all clinics and easily accessed by patients. Providers and patients use this guide to make referrals to the counseling service, nutrition, financial services, and numerous specialty care providers (e.g., Cardio-Oncology, endocrinology). The ACC survivorship team has conducted numerous clinical pilot projects to determine the clinical care delivery model best suited specifically for adult survivors of pediatric cancers, as well as models for adult testicular and breast cancer survivors, the first populations of survivors seen at the ACC Survivorship Program. Survivorship clinical care programs for genitourinary/prostate, lymphoma, head and neck, gastrointestinal, and thoracic cancers followed, with care being delivered by APPs collaborating with ACC oncologists. Since the start of the program, patient-reported outcomes (PROs) survivorship questionnaires have been administered at each follow-up visit. These revised over the years and are now sent to survivors prior to their follow-up appointments via the patient portal. The Program is currently staffed by medical, surgical, and radiation oncology APPs who self-identified to join the program team. The team also includes administrators and physicians from Penn Medicine, the ACC, and affiliate cancer programs. Regular meetings are held where team members provide input on care delivery models at each site, discuss challenges and solutions, in-services on topics of the choice by the team are provided, and best practices for decreasing the number of follow-up patients in the oncology practices debated. The team is currently working with ACC information technology experts and the EPIC team to develop a process that will streamline referrals from providers to survivorship clinics across the Penn Medicine system by developing an electronic order set. In 2011, a few APP members of the program team met with the ACC EPIC team to develop smart phrases that would improve documentation of survivorship care. Improving the clinical encounter notes assisted the ACC in meeting the Commission on Cancer (CoC) as well as the National Accreditation Program for Breast Centers (NAPBC) standards. All APPs have collaborative practice agreements with every provider to bill for services. In 2017, a financial analysis of the existing breast, gastrointestinal, genitourinary, and thoracic oncology practices was done to determine revenue generated if APPs delivered survivorship care, and more new patient slots were available on physician schedules. Results were impressive in terms of revenue generated from new patient visits and resulting downstream revenue in the Penn Medicine ACC and Health System. Although the profit seen by each disease group was modest, the overall profit to the Health System was upward of 1.9 million dollars per year. The clinics for adult survivors of pediatric and young adult cancers continue to grow, with over 420 patients seen every year. These clinics also function as centers for long-term adult survivors of lymphoma, sarcoma, neuro-oncology, and other cancers, with most survivors coming to the ACC survivorship program through self-referral or referrals from outside providers. In addition, the ACC has disease-based clinics. Among these, the breast cancer team at the ACC has the largest population of patients and survivors. To meet the need to care for long-term survivors, a full-time APP manages this clinic in collaboration with a senior breast oncologist. This program has been very successful, with over 1000 breast cancer survivors seen in 2022. The ACC also has a busy testicular cancer survivorship program which provides care to approximately 300–400 survivors annually. Over the last decade, survivorship programs have developed across disease groups in the ACC and affiliates within the medical, surgical, and radiation oncology departments. For example, there are APP-led prostate, head and neck, lymphoma, and rectal cancer survivorship programs based in the ACC radiation oncology department, a multidisciplinary survivorship program based in the Ear, Nose and Throat department at the Penn Medicine Pennsylvania Hospital location, and Lancaster General Hospital is developing a comprehensive survivorship program also led by an APP in collaboration with their Cancer Center director. Although referrals to specialty services are not formally tracked, patients have expressed overwhelming support for the survivorship program through visit evaluations.
Initial research was conducted in collaboration with the LIVESTRONG™ Survivorship Centers of Excellence Network which included studies evaluating the utility of survivorship care plans , metrics to evaluate treatment summaries and survivorship care plans by use of a scorecard , and the best practice models of survivorship care . In addition, research protocols were developed during the early years of the survivorship program. As mentioned previously, PROs questionnaire data were collected with each follow-up visit on all patients being seen in the breast, testicular, and young adult survivorship clinics. Consequently, the ACC survivorship program databases contain data from over 600 breast, 700 testicular, and 600 young adult survivors. These data have resulted in poster presentations at national meetings . In 2008, the first ACC Survivorship Program retreat was held to assess potential research collaborations across the health system. In 2010, multidisciplinary research findings were presented at a second retreat. In 2022, the multi-disciplinary, multi-specialty scientific retreat sponsored by the ACC Cancer Control Committee presented ongoing research projects and results, including projects on genetic testing, cardiovascular risk factors in cancer survivors, and a study that examined health behaviors and smoking among head and neck cancer survivors . The ACC conducts patient and provider education conferences on a yearly basis on a variety of topics including updates on options for treatment and survivorship care presented by a multidisciplinary faculty. These conferences are open to all disciplines within and outside of the Penn system.
In 2016, the COC released standard 3.3 that included the requirement that a comprehensive care summary and follow-up plan be provided to all individuals receiving curative-intent therapy . The original goal was to facilitate and enhance survivorship care, and an enormous amount of work went into trying to meet the standard. This very challenging effort quickly devolved into focusing more on providing the care plan document and less on delivering the actual cancer survivorship care. However, in 2019, the revised COC standard 4.8 was released and upon review of the requirements, the ACC met the standard requirements with robust programs already in place, a great success for the ACC survivorship program. The importance of survivorship care plans is recognized at the ACC, and there is an ongoing APP campaign to improve encounter documentation. At the ACC, every follow-up post treatment visit is considered a survivorship visit. Recognizing that a care plan cannot be static and must change with each encounter, care plans are ideally outlined in each visit encounter to reflect ongoing care and future recommendations. Patient charts including the encounter notes and the current care plan are electronically shared with patients/survivors after the visit. This endeavor has proven to be very popular with our patients, survivors, and their other providers. Although progress is slow, many providers have adopted this comprehensive mode of documenting a follow-up visit. Sources of program funding remain a significant challenge given that the program relies to a large extent on grants for support. We are currently working with the administration advocating for the ACC Survivorship Program to be funded through the Penn Medicine Health System. The 2023 financial analysis, a repeat of the analysis done in 2017 to determine revenue currently generated by new patients entering the system because of follow-up care being provided by APPs, will provide justification for the Health System supporting the survivorship program.
Over the last 22 years, impressive advances in cancer therapy and the management of late effects have resulted in a growing need for strong cancer survivorship programs. Sustaining the ACC Survivorship Program has been challenging despite patients, their families, and providers’ strong endorsement of the services provided. Challenges include barriers such as cost restraints, changing cancer center priorities, and a reduced oncology workforce. However, these issues are being experienced across the country, problems that must be addressed in the years to come to continue to provide necessary, quality care to the growing number of cancer survivors. The ACC is committed to the survivorship program and key program advocates are working with the program team and the Health System to assure that the program continues to grow. In the meantime, cancer survivorship care is recognized as a critical component of cancer care across the Penn Medicine Health System and the ACC. Although challenges will continue, this segment of the cancer care continuum is here to stay.
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Distribution and Location Stability of the Australian Ophthalmology Workforce: 2014–2019 | eff6444e-8869-4a04-96f2-e3fbe3c7fd43 | 8656490 | Ophthalmology[mh] | Socioeconomic deprivation, lower levels of educational attainment, lifestyle factors and chronic disease risk factors result in the higher prevalence of many eye diseases in rural and remote communities compared with metropolitan communities . Australians living in rural and remote areas have been found to have higher prevalences of cataracts, pterygia and ocular trauma . Indigenous Australians living in remote areas are more likely to be diagnosed with diabetic retinopathy , vision impairment and blindness . Despite higher rates of eye disease and vision loss, people living in rural and remote Australia have poorer access to ophthalmologists and cataract surgery . Low levels of access to ophthalmology services in rural and remote communities are largely a result of the maldistribution of the workforce . Innovative models, such as outreach services and shared care, have been implemented to address the inequities in access to ophthalmic care. Evaluations of these services have found the models reduce barriers to eye screening, treatments and surgery ; however, the overall size and distribution of the ophthalmology outreach workforce is yet to be mapped. Whilst considerable research has been conducted into the distribution and retention of the general practitioner workforce, details about the workforce of specific specialties such as ophthalmology are scant. A 2018 Department of Health report noted that 84% of the ophthalmology workforce is located in metropolitan areas (Modified Monash Model category MMM1), although further detailed analysis of workforce distribution was not provided . The Modified Monash Model (MMM) is how the Australian Department of Health defines whether a location is a city, rural, remote or very remote . The shortage of doctors working in rural areas is an international issue. In Canada, a government report from 2012 found 20% of the population lived in rural areas, yet only 9.3% of doctors worked rurally . In India, the problem of workforce maldistribution is particularly challenging with 66% of the population residing in rural areas yet only 33% of all health workers are located rurally . The issue of workforce maldistribution is most acute amongst specialists, who tend to be more highly concentrated in urban areas than family physicians, as was reported by Barreto et al. in their study of rural counties in the United States . The present study sought to address the paucity of information about the location stability of the ophthalmology workforce in Australia. Specifically, the aim was to investigate workforce distribution and location retention over time according to Modified Monash Model category.
The University of Tasmania Health and Medical Human Research Ethics Committee (Project ID: 23059) and RANZCO Human Research Ethics Committee provided approval for the research. De-identified data extracts listing all medical practitioners registered with the Australian Health Practitioner Registration Agency (AHPRA) were provided by AHPRA to the University of Tasmania Rural Clinical School. These data are stored in a centralised database (GradTRack, HREC reference: H0013913). The GradTrack steering committee provided permission for an extract of the AHPRA data containing ophthalmologists for the research. Longitudinal locations were provided for a six-year period (2014 to 2019). The data set included all ophthalmologists registered with AHPRA for at least one year during this period. Data fields were: registration status, gender, primary training country, year of primary qualification, year of initial AHPRA registration and Primary Place of Practice (PPP) location (suburb and postcode) for each year. Registration status (current, failed to renew, surrendered, withdrawn, or deceased) was available for the years 2014–2018 but AHPRA did not provide this information for 2019. Ophthalmologists who did not have a current registration for at least one of the years were excluded. MMM categories (based on the 2019 iteration) were mapped to postcodes of primary workplace location for each of the six years . The MMM has seven categories of remoteness based on population size and remoteness from capital cities (MMM1—metropolitan, MMM2—regional centres, MMM3—large rural towns, MMM4—medium rural towns, MMM5—small rural towns, MMM6—remote communities and MMM7—very remote communities). Approximately 70% of the Australian population live in MMM1 areas . In this paper, the term regional/rural is used broadly to refer to MMM categories two to seven. The number of ophthalmologists working in each MMM category in each year was investigated and MMM category retention was determined based on MMM category during the six-year study period. The demographic characteristics of those who remained in MMM1 and those who remained in MMM2–MMM7 were investigated. The location stability of the cohort of ophthalmologists who had a known postcode in 2014 was investigated using Kaplan-Meier survival analysis. A ‘failure’ event was defined as leaving MMM category (for any reason) and a censored event was defined as remaining in the same MMM category. The probability of ophthalmologists remaining in the same MMM category continuously for six years was calculated for those practicing in MMM1 and MMM2–MMM7. As AHPRA did not provide registration status for 2019, a secondary competing-risks regression was conducted for the period 2014–2018, where registration status (e.g., current, did not renew, surrendered, deceased) was provided for each year. Registration status provides information on the reason for leaving MMM (e.g., either moving to another location or surrendering registration). Semiparametric competing-risks models, developed by Fine and Grey , were used to calculate the sub-hazards of leaving PPP MMM category to practice in another MMM or leaving for another reason (e.g., failed to renew, surrendered, withdrawn, or deceased, combined into one category). Sex, country of training (Australia/New Zealand vs. other) and years since primary qualification, were covariates in the preliminary models but were removed from the final models as they did not have significant effects on the incidence of moving MMM or leaving MMM for another reason. Stacked cumulative incidence plots (of moving MMM or leaving for another reason) were generated for ophthalmologists in MMM1 compared with MMM2–MMM7.
The AHPRA dataset contained records for 1056 ophthalmologists. After excluding 108 records that did not specify at least one current registration and Australian location between 2014–2019, 948 ophthalmologists were included in the study . The state distribution of ophthalmologists remained stable over the study period, although there was a slight decrease in proportion of ophthalmologists located in South Australia . Analysis by MMM category found the majority of ophthalmologists worked in MMM1 each year. However, there was a smaller proportion of ophthalmologists working in MMM1 in 2017–2019 compared with 2014–2016. While the numbers are small, there was a slight increase in the number of ophthalmologists working in MMM2 in 2019 compared with previous years. There were 655 (69.1%) ophthalmologists who practiced continuously in MMM1 from 2014 to 2019 and 105 (11.1%) who practiced continuously in MMM2–MMM7 . Among those who remained in MMM2–MMM7, 90.5% were male and 49% obtained their primary qualification 31–40 years ago. A total of 12 (1.3%) ophthalmologists moved from MMM1 to MMM2–MMM7. All 12 of these ophthalmologists were Australian graduates. The Kaplan-Meier survival analysis for remaining in the same MMM from 2014–2019 included 896 ophthalmologists, with 4235 person-years at risk for ophthalmologists moving MMM category. presents the Kaplan-Meier survival estimates by MMM1 and MMM2–MMM7. The overall probability of ophthalmologists remaining in the same MMM category continuously from 2014 to 2019 was 0.84 (95% CI 0.82, 0.87), with a probability of 0.85 (95% CI 0.83, 0.88) for those practicing in MMM1 in the baseline year of 2014 and 0.79 (95% CI 0.71, 0.85) for those practicing in MMM2–MMM7. Seven ophthalmologists were notified to be deceased up to 2018, the last year that AHPRA provided registration status. There were 20 ophthalmologists who relocated their primary place of practice to a different MMM category and 82 ophthalmologists who had a registration status of failed to renew, surrendered or deceased from 2014 to 2018. The competing-risks regression found the sub-hazard for moving MMM among ophthalmologists with a primary place of practice in MMM1 in 2014 was 24% (SHR 0.24, 95% CI 0.10, 0.59, p = 0.002) that of ophthalmologists with a primary place of practice in MMM2–MMM7. However, there was no significant difference in leaving MMM due to failure to renew, surrendered or deceased (SHR 1.08, 95% CI 0.58, 2.03, p = 0.80). presents the cumulative incidence plots of moving MMM or leaving MMM for another reason (e.g., failed to renew, surrendered, deceased) between 2014 and 2018, for ophthalmologists in MMM1 compared with MMM2–MMM7.
The results indicate that 84% of ophthalmologists remained working in the same Modified Monash Model category from 2014 to 2019. Largely, these ophthalmologists were working in MMM1 compared to MMM2–MMM7 areas (85% vs. 75%). The competing-risks regression models for the period 2014–2018 (the time period that included registration status data) showed that the hazard of moving MMM was higher among those in regional/rural areas than metropolitan areas. However, there was no difference between regional/rural and metropolitan ophthalmologists for the risk of leaving MMM due to not renewing registration due to temporary overseas relocation or surrendering registration due to retirement. There was a trend for an increasing proportion of ophthalmologists to work outside major cities, from 19% in 2014 to 24% in 2019. This is similar to the finding of a United States study , where there was a mean annual increase of 2.3% in the density of ophthalmologists working in rural areas from 1995 to 2017. Despite the trend for an increasing proportion of ophthalmologists working outside major cities, the workforce remains maldistributed. Unpublished Australian Bureau of Statistics population data show 72% of the Australian population lives in metropolitan (MMM1) areas . This suggests that the concentration of ophthalmologists in major cities does not match the population distribution. This finding contrasts with a recent study conducted in New Zealand which found the number of ophthalmologists in each region was in proportion to the population size . The majority of ophthalmologists working outside MMM1 areas are based in large regional centres (MMM2), not MMM3–MMM7 areas where the current level of ophthalmology services available seems insufficient. The disparity in access to ophthalmologists is evident in the finding by Keefe et al. that 15% of the population living in urban areas had ever seen an ophthalmologist compared with 2% of rural Australians . This is despite higher prevalences of eye diseases and vision loss . Policies that support strategies to increase the number of ophthalmologists in MMM3–MMM7 areas are needed to address the considerable disparity in access to ophthalmologists. The cross-sectional analysis found a larger proportion of male ophthalmologists, compared with female ophthalmologists, stayed in MMM2–MMM7 areas. This finding is similar to that of Lo et al. who reported a lower proportion of female ophthalmologists compared with males working in regional/rural public facilities (18% vs. 27%) or private practices (18% vs. 40%) . This may stem from female ophthalmologists being more likely to have a partner who is employed , which limits flexibility of work location. Indeed, opportunities for partner employment is a key factor in recruiting and retaining specialists in regional/rural areas . Other studies have reported that a lack of suitable educational opportunities for children are a factor in specialist attrition from remote areas . The finding that ophthalmologists who received their primary medical qualification 31–40 years ago were more likely to stay MMM2–MMM6 may reflect this age group being less likely to have young or school-aged children and therefore more content to remain regional/rural. This finding suggests that policies to attract new ophthalmologists to regional/rural areas may not be successful in the longer term, unless policies are implemented to help improve access to educational opportunities and the social integration of new ophthalmologists with young families to rural communities. It is interesting that in the cross-sectional analysis, non-Australian/New Zealand medical graduates were over-represented in the group that remained in MMM2–MMM7 areas. The larger proportion of international graduates remaining in MMM2–MMM7 could be a policy outcome from the District of Workforce Shortage restrictions for international medical practitioners rather than a choice of the individual. Alternatively, it may be that a greater focus on regional/rural training for ophthalmology registrars is helping increase the attractiveness of practicing in regional/rural areas by demonstrating the more diverse and broader scope of practice available . With the widespread availability of high-speed internet throughout Australia, ophthalmologists working in regional/rural areas are not so isolated anymore and have access to similar learning and networking opportunities as their metropolitan colleagues. Study limitations include the combined analysis of MMM2–MMM7 categories. Whilst individual analysis of MMM categories may have allowed for greater insights regarding the rural and remote ophthalmology workforce, the small numbers of ophthalmologists working in discrete MMM3 or higher categories prevented further investigation due to the potential for identification of individuals. Fortunately, the Specialist Training Program (STP), funded by the Australian Commonwealth Government Department of Health, currently funds 15 training posts for ophthalmology in a range of private providers and MMM2 and above areas , therefore the current data provides key insight for this purpose. Other limitations include the absence of data on outreach specialist ophthalmological services provided to remote communities and secondary places of practice. Unfortunately, this workforce information is not recorded by AHPRA. The provision of outreach services means that it is likely that the number of ophthalmologists working (even if on an intermittent basis) in MMM6–MMM7 categories is higher than the number identified in the AHPRA data. The lack of data on the number of years since specialists received their RANZCO fellowship was another limitation. Unfortunately, this was not available, only years since primary qualification. Lastly, the analysis was based on continuity of Modified Monash Model category. Ophthalmologists may have moved within or across states but remained in the same MMM category. These relocations were not considered important as the study was primarily focused on continuity of remoteness category, rather than physical location. Analysis of continuity of suburb location within MMM categories was not possible as the numbers became too small within sub-groups.
While the Australian ophthalmology workforce is skewed toward metropolitan centres, there are early indications of outward migration from metropolitan centres to regional/rural areas by Australian trained ophthalmologists. This reflects trends observed in the United States and New Zealand. Further longitudinal tracking of the primary work location of ophthalmologists is required to confirm the trend recognised in this study. Addressing workforce maldistribution problems will provide opportunities for regional/rural communities to better access ophthalmic services.
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Molecular Profiling
of Glioblastoma Patient-Derived
Single Cells Using Combined MALDI-MSI and MALDI-IHC | 18eac2d4-816b-42d9-95c6-0e4760af69c2 | 11866282 | Biochemistry[mh] | Immunohistochemistry (IHC) has, since its inception, remained the gold standard in the study of cellular structure in histological tissue samples. Using antibodies labeled with fluorescence tags it is possible to identify single cell types in a tissue and visualize specific biomolecules in their native cellular location. , Current microscopy instruments routinely allow for subcellular resolution, enabling precise pathological annotations and IHC is therefore an invaluable tool in clinical biomedicine. Both the research- and diagnostic value of IHC is greatly increased by the fact that fluorescence microscopy allows for visualization of multiple targets in the same experiment. , While multiplexing using fluorescence microscopy is possible, the upper limit of targets is already reached around eight targets, due to spectral overlap and cross-reactivity. Additionally, the overlap in excitation and emission bands between fluorophores, greatly reduces the specificity of the method, counteracting the positive aspects gained from using multiple fluorophores. Other multiplexing methods, such as PerkinElmer’s OPAL multispectral platform, t-CyCIF, and CODEX, rely on iterative workflows that involve the repeated addition and removal of numerous probes. These processes are therefore highly time-consuming and carry the risk of confounding results due to incomplete or unsuccessful cycles. As an alternative, matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is an analytical tool enabling untargeted detection and visualization of biomolecules in their native tissue environment. By altering the matrix used for analyte extraction, MALDI-MSI can be used to detect an array of molecules including lipids, proteins and metabolites and thereby provide multiomics insights into the sample of interest. Recently, targeted analysis using the strengths of MALDI-MSI has gained a lot of focus with the implementation of MALDI-IHC. MALDI-IHC combines traditional immunohistochemistry and MALDI-MSI with the use of Miralys probes, where antibodies are linked to an ionizable photocleavable mass-tag (PC-MT). After the antibody binds to its epitope, the PC-MT can be released via ultraviolet (UV) illumination and measured with MALDI-MSI. Each antibody is conjugated to a PC-MT with a different molecular mass, enabling high multiplexing of proteins or glycans without the drawbacks of other multiplexing methods, by relying on MS detection. Currently, the highest achieved published plexity of MALDI-IHC is 27 different targets. Additionally, Miralys probes also have a fluorophore attached to the antibody, enabling further imaging with fluorescence of the stained samples. In recent years, the achievable spatial resolution of MALDI-MSI, here defined as pixel size, has increased rapidly and measuring pixels down to 5 × 5 μm is now routine. The increased spatial resolution enables analysis of even more biological sample types, as subcellular pixel sizes allow measuring of single cells, , and in some cases even cellular compartments. The ability to measure single cells opens opportunities toward obtaining cell-specific molecular profiles, using MALDI-MSI, depending on specific lipid, peptide or metabolite composition detected. , Increasing amounts of research is being done on dispersed single cell populations which allows for a clear differentiation between single cells. The use of mixed cell type cultures has the ability to mimic simple cell–cell interactions and thereby provide information on the complex dynamics observed in tissues. Furthermore, it was shown that profiles from two-dimensional (2D) cell cultures can be used to build models that are able to detect cell types back in a tissue environment and thereby aid in personalized therapy testing if patient-derived cell lines are used. Untargeted imaging of larger molecules has also become possible, with recent developments in nanospray desorption electrospray ionization enabling detection of different proteoforms on single-cell level. , Spatial omics is the multimodal approach of obtaining spatial tissue information and molecular characteristics at the same time. , Within the field of MSI, this is often achieved by detection of multiple molecular species with MSI or combining the untargeted and targeted approaches through for example MALDI-IHC or even targeted liquid chromatography–mass spectrometry (LC-MS). This combined approach can help enhance our fundamental biological understanding of tissues by simultaneously detecting both predefined analytes and novel molecular species in their native microenvironment and at high spatial resolution. However, a complication that follows with an increase in spatial resolution, is a quadratic reduction in sensitivity due to the pixelated nature of sample ionization in MALDI-MSI. As the pixel size decreases, less material is ablated, resulting in a lower number of total ions generated. Consequently, a tendency to mainly see high-abundant species or species that are more easily ionizable is observed. To mitigate these challenges, sample preparation is of utmost importance to ensure proper interaction between the matrix and the analyte, as well as ensuring that results are stable and reproducible. Here we propose a workflow for measuring single patient-derived cells (PDCL) from glioblastoma (GBM) tumor samples . These cell cultures are grown without induction of specific cell types resulting in a heterogeneous mix of cells ranging from GBM cells, originating from glial cells like astrocytes, to healthy glial cells and neurons. The cells were imaged, first with high-resolution MALDI-MSI to obtain a lipid profile, followed by staining and imaging with MALDI-IHC to visualize cell type specific protein markers. The two modalities were then overlaid to provide cell type specific lipid profiles. Furthermore, we investigated the effect of the initial MSI measurement and the required sample preparation on the subsequent MALDI-IHC measurement. Chemicals Water (HPLC and ULC/MS grade), ethanol, acetone and chloroform were obtained from Biosolve BV (Valkenswaard, The Netherlands). α-Cyano-4-hydroxycinnamic acid (CHCA), phosphate-buffered saline, acetic acid, citric acid, bovine serum albumin, ammonium bicarbonate, 2,5-dihydroxybenzoic acid (DHB) 98% and ammonium phosphate monobasic were obtained from Sigma-Aldrich (St. Louis, MI). Tris-buffered saline, sodium citrate and octyl β- d -glucopyranoside were obtained from Merck KGaA (Darmstadt, Germany). Mouse and rabbit serum was obtained from Jackson immunoresearch (Ely, U.K.). Miralys probes were obtained from Ambergen (Billerica, MA). Samples Fresh tumor tissue was collected from patients undergoing surgical resection at UZ Gasthuisberg, with all patients providing informed consent (S59804). Upon receiving the tissue samples at the LPCM lab, they were immediately processed for establishment of patient-derived GBM stem cell culture (S61081), as previously described. , For the results shown here, cells from one donor (female, age = 45) are highlighted. Approximately 10 6 cells (∼1.5 × 10 5 cells/mL) were grown on slides suitable for MALDI-MSI measurement, indium tin oxide (ITO, CG-40IN-S115, Delta Technologies) glass slides coated with poly- l -lysine, as previously described. N = 4 cell covered ITO slides were prepared and measured with MSI and MALDI-IHC. Cells were frozen in liquid nitrogen and stored at −80 °C until measurement. Mass Spectrometry Imaging Prior to MALDI-MSI, the cells were removed from storage and kept in a desiccator box at room temperature for 30 min to avoid molecular delocalization during thawing. Fiducial markers were applied around the areas to be measured for coregistration. As matrix, 50 mg DHB in 1.5 mL acetone was sublimated onto the slide using an HTX Sublimator (HTX technologies, Chapel Hill) at 160 °C for 200 s. The sublimation tray was preheated to 60 °C. For evaluation of the effect of MALDI sample preparation and MALDI-MSI, respectively, on the subsequent MALDI-IHC measurement, one-third of the slide was covered during matrix application, to have a region which had undergone no prior intervention. The cells were imaged on a rapifleX MALDI Tissuetyper instrument (Bruker Daltonik GmbH, Bremen, Germany) with a pixel size of 10 × 10 μm 2 , in positive ion-mode and a mass range from m / z 600–1340 for lipid detection. Red phosphorus was spotted on the slide for external calibration. MALDI-IHC The methods were based on previous work and adapted for single cell applications. Briefly, the cell slides were prepared for staining by first removing any remaining matrix by washing in −80 °C acetone for 3 min, 2 times. All washing steps were conducted in separate glass Coplin jars. The slides were then dried for 10 min in a desiccator and fixated in 1% PFA for 30 min, followed by a PBS wash for 10 min, an acetone wash for 3 min, 2 times, and a wash in Carnoy’s solution for 3 min. Slides were then rehydrated with an ethanol series of 100% ethanol for 2 min, 2 times, 95% for 3 min, 70% for 3 min and 50% for 3 min. Finally, slides were washed with TBS for 10 min. Next, the cells were prepared for staining by antigen retrieval in citrate buffer at pH = 6, using a Retriever 2100 (Aptum Biologics Ltd., Rownhams, U.K.) for 20 min at 121 °C. The retriever body with slides was removed and cooled in an ice bath for 5 min, after which half of the retrieval buffer was replaced with HPLC grade water and the body was placed back in the ice bath for 5 min. This was repeated 2 more times and slides were then washed with TBS for 10 min. To limit the use of blocking buffer and antibody solution, the region to be stained and measured was surrounded using a hydrophobic PAP pen (Sigma-Aldrich, St. Louis, MI). Each region was then incubated with 150 μL blocking buffer for 1 h. Excess blocking buffer was carefully removed from the slide and cells were then incubated overnight (18–21 h) with 150 μL antibody solution at 4 °C in a humidified dark chamber, to prevent evaporation of solution and bleaching of fluorophores. An overview of the used antibodies can be seen in Supporting Table S1 . From this point, slides were kept covered/in the dark at all times. After staining, the slides were washed in TBS for 5 min, 3 times, ABC for 10 s, and ABC for 2 min, 3 times, all while slightly agitating before drying them completely in a desiccator. The peptide mass tags were cleaved off by UV illumination at 365 nm with a Phrozen UV curing lamp for 10 min (3 mW/cm 2 ) prior to MS imaging. As matrix, 40 mg CHCA in 1.5 mL acetone was sublimated onto the slide using an HTX Sublimator at 180 °C for 360 s. The tray was preheated to 70 °C. Following sublimation, the slide was briefly dipped into an ammonium phosphate monobasic solution (0.5 mM) and dried vertically in a desiccator until fully dry. The stained cell regions were then imaged on a rapifleX MALDI Tissuetyper in positive-ion mode, with a pixel size of 5 × 5 μm 2 and a mass range of m / z 820–1840. Red phosphorus was spotted on the slide for external calibration. Fluorescence Imaging Fluorescence imaging by stimulated emission depletion (STED) microscopy was employed to confirm binding of the Miralys antibody probes to the cells. STED images were obtained using a commercial STED microscope (TCS SP8 STED, Leica Microsystems, Germany), equipped with a UV- and white-light laser. A Fluotar VISIR 25 X /0.95 numeric aperture water immersion objective (Leica Microsystems, Germany) was used for imaging. Images were taken using a 592 nm excitation wavelength, a scan speed of 400 Hz with a 610–675 nm emission detection range respectively using gated hybrid detectors. The pixel size was approximately 0.91 μm (1024 × 1024 pixels), and 2-line averaging was performed. Further, automated staining and imaging was done on the COMET platform. PDCL GBM single cell samples were stained with a GFAP marker. The stainings were performed as reported by Lunaphore in the literature. Data Analysis MALDI-MSI and MALDI-IHC images were visualized and analyzed using SCiLS lab 2024b (SCiLS GmbH, Bremen, Germany). MALDI-MSI images were RMS normalized and MALDI-IHC were TIC normalized. Average spectra from MALDI-MSI images were exported from SCiLS lab and imported into mMass software where peak picking was performed with the following settings: S/N threshold = 3, relative intensity threshold = 0.5%, picking height = 75, with baseline correction and deisotoping functions enabled. One-way ANOVA and t test calculations were done in R (Version 4.1). Cells were selected for analysis by creating ROIs around signals which were clearly separated out from other signals indicating that it is a lone standing cell. The cells selected contained between 5 and 15 pixels to avoid selecting too small areas but also too big areas which could represent more than one cell. The cutoff at 10 cells was selected based on the number of cells present per probe. For some of the low abundant probes (pTau, CD163, PVALB) it was difficult to select more than 10 cells. Lipid masses were matched and identified based on previous results with LC-MS/MS from GBM tissue, as described in. The classification model was created using the “training” and “classification” options in SCiLS lab 2024b. Between five and 12 cellular ROIs, corresponding with a specific class based on MALDI-IHC, were selected per class with repeated random subsampling at 15% used as cross validation parameter. Two separate MALDI-IHC measurements were needed to cover the entire MALDI-MSI measurement region, due to the difference in pixel-size. The classification model was trained on one of these MALDI-IHC measurement regions, while the classification itself was carried out on the other region. Water (HPLC and ULC/MS grade), ethanol, acetone and chloroform were obtained from Biosolve BV (Valkenswaard, The Netherlands). α-Cyano-4-hydroxycinnamic acid (CHCA), phosphate-buffered saline, acetic acid, citric acid, bovine serum albumin, ammonium bicarbonate, 2,5-dihydroxybenzoic acid (DHB) 98% and ammonium phosphate monobasic were obtained from Sigma-Aldrich (St. Louis, MI). Tris-buffered saline, sodium citrate and octyl β- d -glucopyranoside were obtained from Merck KGaA (Darmstadt, Germany). Mouse and rabbit serum was obtained from Jackson immunoresearch (Ely, U.K.). Miralys probes were obtained from Ambergen (Billerica, MA). Fresh tumor tissue was collected from patients undergoing surgical resection at UZ Gasthuisberg, with all patients providing informed consent (S59804). Upon receiving the tissue samples at the LPCM lab, they were immediately processed for establishment of patient-derived GBM stem cell culture (S61081), as previously described. , For the results shown here, cells from one donor (female, age = 45) are highlighted. Approximately 10 6 cells (∼1.5 × 10 5 cells/mL) were grown on slides suitable for MALDI-MSI measurement, indium tin oxide (ITO, CG-40IN-S115, Delta Technologies) glass slides coated with poly- l -lysine, as previously described. N = 4 cell covered ITO slides were prepared and measured with MSI and MALDI-IHC. Cells were frozen in liquid nitrogen and stored at −80 °C until measurement. Prior to MALDI-MSI, the cells were removed from storage and kept in a desiccator box at room temperature for 30 min to avoid molecular delocalization during thawing. Fiducial markers were applied around the areas to be measured for coregistration. As matrix, 50 mg DHB in 1.5 mL acetone was sublimated onto the slide using an HTX Sublimator (HTX technologies, Chapel Hill) at 160 °C for 200 s. The sublimation tray was preheated to 60 °C. For evaluation of the effect of MALDI sample preparation and MALDI-MSI, respectively, on the subsequent MALDI-IHC measurement, one-third of the slide was covered during matrix application, to have a region which had undergone no prior intervention. The cells were imaged on a rapifleX MALDI Tissuetyper instrument (Bruker Daltonik GmbH, Bremen, Germany) with a pixel size of 10 × 10 μm 2 , in positive ion-mode and a mass range from m / z 600–1340 for lipid detection. Red phosphorus was spotted on the slide for external calibration. The methods were based on previous work and adapted for single cell applications. Briefly, the cell slides were prepared for staining by first removing any remaining matrix by washing in −80 °C acetone for 3 min, 2 times. All washing steps were conducted in separate glass Coplin jars. The slides were then dried for 10 min in a desiccator and fixated in 1% PFA for 30 min, followed by a PBS wash for 10 min, an acetone wash for 3 min, 2 times, and a wash in Carnoy’s solution for 3 min. Slides were then rehydrated with an ethanol series of 100% ethanol for 2 min, 2 times, 95% for 3 min, 70% for 3 min and 50% for 3 min. Finally, slides were washed with TBS for 10 min. Next, the cells were prepared for staining by antigen retrieval in citrate buffer at pH = 6, using a Retriever 2100 (Aptum Biologics Ltd., Rownhams, U.K.) for 20 min at 121 °C. The retriever body with slides was removed and cooled in an ice bath for 5 min, after which half of the retrieval buffer was replaced with HPLC grade water and the body was placed back in the ice bath for 5 min. This was repeated 2 more times and slides were then washed with TBS for 10 min. To limit the use of blocking buffer and antibody solution, the region to be stained and measured was surrounded using a hydrophobic PAP pen (Sigma-Aldrich, St. Louis, MI). Each region was then incubated with 150 μL blocking buffer for 1 h. Excess blocking buffer was carefully removed from the slide and cells were then incubated overnight (18–21 h) with 150 μL antibody solution at 4 °C in a humidified dark chamber, to prevent evaporation of solution and bleaching of fluorophores. An overview of the used antibodies can be seen in Supporting Table S1 . From this point, slides were kept covered/in the dark at all times. After staining, the slides were washed in TBS for 5 min, 3 times, ABC for 10 s, and ABC for 2 min, 3 times, all while slightly agitating before drying them completely in a desiccator. The peptide mass tags were cleaved off by UV illumination at 365 nm with a Phrozen UV curing lamp for 10 min (3 mW/cm 2 ) prior to MS imaging. As matrix, 40 mg CHCA in 1.5 mL acetone was sublimated onto the slide using an HTX Sublimator at 180 °C for 360 s. The tray was preheated to 70 °C. Following sublimation, the slide was briefly dipped into an ammonium phosphate monobasic solution (0.5 mM) and dried vertically in a desiccator until fully dry. The stained cell regions were then imaged on a rapifleX MALDI Tissuetyper in positive-ion mode, with a pixel size of 5 × 5 μm 2 and a mass range of m / z 820–1840. Red phosphorus was spotted on the slide for external calibration. Fluorescence imaging by stimulated emission depletion (STED) microscopy was employed to confirm binding of the Miralys antibody probes to the cells. STED images were obtained using a commercial STED microscope (TCS SP8 STED, Leica Microsystems, Germany), equipped with a UV- and white-light laser. A Fluotar VISIR 25 X /0.95 numeric aperture water immersion objective (Leica Microsystems, Germany) was used for imaging. Images were taken using a 592 nm excitation wavelength, a scan speed of 400 Hz with a 610–675 nm emission detection range respectively using gated hybrid detectors. The pixel size was approximately 0.91 μm (1024 × 1024 pixels), and 2-line averaging was performed. Further, automated staining and imaging was done on the COMET platform. PDCL GBM single cell samples were stained with a GFAP marker. The stainings were performed as reported by Lunaphore in the literature. MALDI-MSI and MALDI-IHC images were visualized and analyzed using SCiLS lab 2024b (SCiLS GmbH, Bremen, Germany). MALDI-MSI images were RMS normalized and MALDI-IHC were TIC normalized. Average spectra from MALDI-MSI images were exported from SCiLS lab and imported into mMass software where peak picking was performed with the following settings: S/N threshold = 3, relative intensity threshold = 0.5%, picking height = 75, with baseline correction and deisotoping functions enabled. One-way ANOVA and t test calculations were done in R (Version 4.1). Cells were selected for analysis by creating ROIs around signals which were clearly separated out from other signals indicating that it is a lone standing cell. The cells selected contained between 5 and 15 pixels to avoid selecting too small areas but also too big areas which could represent more than one cell. The cutoff at 10 cells was selected based on the number of cells present per probe. For some of the low abundant probes (pTau, CD163, PVALB) it was difficult to select more than 10 cells. Lipid masses were matched and identified based on previous results with LC-MS/MS from GBM tissue, as described in. The classification model was created using the “training” and “classification” options in SCiLS lab 2024b. Between five and 12 cellular ROIs, corresponding with a specific class based on MALDI-IHC, were selected per class with repeated random subsampling at 15% used as cross validation parameter. Two separate MALDI-IHC measurements were needed to cover the entire MALDI-MSI measurement region, due to the difference in pixel-size. The classification model was trained on one of these MALDI-IHC measurement regions, while the classification itself was carried out on the other region. Single Cell MALDI-IHC Method Optimization A number of adaptations were made to the recommended staining protocol to optimize the MALDI-IHC method for high-resolution, single cell measurements. First, to reduce potential delocalization or diffusion of molecules, all washes were performed at lower temperatures in ice-cold solutions. For measurements with pixel sizes down to 5 × 5 μm 2 of single cells, any form of delocalization can be detrimental to the experiments and should be minimized, especially if the images are to be correlated across multiple modalities. Furthermore, when working with fresh frozen tissue, efforts to minimize delocalization are crucial as proteins in the tissue are left in their native state when unfrozen, compared to formalin-fixed paraffin-embedded tissue, where proteins are cross-linked in place by the formalin fixation. Previous work has shown that performing washing steps at freezing temperatures would reduce delocalization as compared to room-temperature washes. Second, the matrix application method was changed to achieve sufficient signal at 5 × 5 μm 2 spatial resolution. Initial experiments were carried out with the recommended matrix application methods of either 2,5-DHB sublimation or automated CHCA spraying, both followed by recrystallization for increased analyte integration with the matrix and reduced crystal size. , These experiments resulted in insufficient intensity, as only 2 out of 14 PC-MTs were detected at 5 × 5 μm 2 pixel size (data not shown). To remove most of the matrix signal, which is usually obtained when sublimating CHCA, the slides were briefly dipped in ammonium phosphate monobasic after successful sublimation. This step significantly reduced signal from matrix clusters and resulted in high intensities for peptides even at 5 × 5 μm 2 pixel size. Using this alternative matrix application method to visualize the PC-MT peptides improved the signal intensity enough to detect almost all stained markers and single cells from 10 out of 14 markers. Effect of Pretreatment on MALDI-IHC Staining The same, identical cells needed to be measured twice, first with MALDI-MSI (lipids) and then with MALDI-IHC (cell types) to determine cell-specific lipid spectra of the specific single cell types. On the subsequent MALDI-IHC measurements, three different conditions were prepared on the same ITO slide containing PDCL GBM single cells. This enables the investigation of the effect of prior MALDI-MSI sample preparation and measurements. One-third of the cells were stained with the Miralys probes without prior treatment (no prep), one-third went through the MALDI-MSI sample preparation treatment of matrix application but without the subsequent MSI measurement (MALDI prep) and the final third was prepared for and analyzed with MALDI-MSI (MSI1). An overview of the experimental setup can be seen in Supporting Figure S1 . This setup would ensure that the compared sample preparations and cells were always the same between conditions. The results from the MALDI-IHC measurements can be seen in . shows data from the PDCL GBM single cells stained with MALDI-IHC. Using the optimized sample preparation workflow, single cells were able to be visualized and differentially stained with cell-specific antibody markers. A shows the visualization of three PC-MTs, corresponding to antibodies targeting GLUT1 for GBM cells ( m / z 856.67, red), NF-L for neurofilaments ( m / z 1345.75, yellow) and SYN-I as a synaptic marker ( m / z 1482.77, blue), with their respective single pixel mass spectra shown in 2B. Under normal conditions, GLUT1 is typically found in the blood-brain barrier as the transporter of glucose into the brain but has been found to be overexpressed in GBM cells likely due to the increased demand for glucose, and is therefore used as a marker for GBM cells here. − An overview of all detected markers and corresponding average mass spectrum can be seen in Supporting Figures S3 and S4 . Electron microscopy image and fluorescence image from immunostaining with GFAP can be seen in Supporting Figure S5 . Each area of high intensity represents a single cell or cluster of cells. Note that each of the cell specific markers are present in different areas as expected by the cellular specificity of the antibodies in a heterogeneous single cell culture. C–E shows images from test of pretreatment effects on the subsequent MALDI-IHC measurement. C shows cells, untreated before MALDI-IHC, 2D shows cells that have undergone MALDI-MSI sample preparation, but no MALDI-MSI measurement, and 2E shows cells that have been prepared for and measured by MALDI-MSI. Imaging experiments were imported into the same data file, normalized to the root-mean-square, and visualized side-by-side with the same intensity for all data sets to compare any effects. No differences in intensity of MALDI-IHC probes were found between the three conditions. This was confirmed on a cell-by-cell basis by examining peak areas. For each condition, mean peak areas, per single cell, were compared between all measured MALDI-IHC targets, based on 10 cells per target and condition and showed no significant difference in intensity between conditions, F. For the PC-MTs corresponding with NeuN, IBA-1, nicastrin and MAP2, the intensity was not sufficient to identify 10 single cells in the measured area in any of the three conditions. These results demonstrate how MALDI-MSI measurements prior to any MALDI-IHC imaging has no negative impact on the results, thus reinforcing how the two modalities can be easily combined to gain extra valuable information from a sample. Multimodal Single Cell Correlation and Molecular Profiling The fluorescence capabilities of the Miralys probes were utilized to get high-resolution images of the stained cells and confirm that the high-intensity ion clusters were in fact single cells. This approach aids in the correlation of the different molecular imaging modalities, as demonstrated in . showcases MALDI-IHC images corresponding with the PC-MT marker for neurofilaments ( m / z 1345.74, NF-L) and therefore specifically the axons and dendrites in neurons ( A,C). Utilizing the fluorophore, also present on the Miralys probes, fluorescence images were also obtained of the same single cells as can be seen in B. The cells were first imaged by MALDI-IHC and then by fluorescence imaging, allowing for precise overlay of the two modalities and it was therefore possible to find back many of the cells observed in MALDI-IHC, and in the fluorescence image as well, as indicated by the white arrows ( A,B). The fluorescence images obtained were not cell type-specific, as the fluorophore present on the Miralys probes used in the selected kit were the same for every probe. This meant that the fluorescence imaging could be used as a confirmation of a given antibody being bound to a single cell and that the signals observed in the corresponding MALDI-IHC images showed single-cell specificity. Furthermore, the MALDI-IHC images were correlated with the previously obtained MALDI-MSI lipid images of the same single cells ( C,D). The single cell-specific MALDI-IHC images were imported into the MALDI-MSI SCiLS datafile to precisely coregister the two measurements. This allowed for visualization of multiple lipids across specific cell types. C shows an image correlating with the Miralys antibody for NF-L and D visualizes the PC 34:4 at m / z 754.5, with some of the single cells that are recognizable in both images highlighted with green arrows. Comparing C, D, it is also clear that not all cells show up in both modalities with cells being uniquely present in both the MALDI-MSI and the MALDI-IHC images, respectively. When correlating the two modalities in two separate MALDI-IHC measurements, 54 of 396 (13.64%) and 17 of 144 (11.8%) of MALDI-MSI cells had no corresponding MALDI-IHC marker. A corresponding MALDI-IHC marker was included only when four or more pixels of high intensity were joined together. Furthermore, there are many cells where the intensity of lipids detected is not high enough to pick them out from the background, this is visualized in Supporting Figure S6 . Finally, the apparent “streaking” of signal observed in larger clusters in the MALDI-IHC image ( C, right side) is likely due to insufficient drying following matrix sublimation. Supporting Figure S7 shows that cells are still intact postmeasurement, with only some MALDI-IHC markers presenting with streaks. Differences in lipid abundances between single cells of varying cell types, as classified by the MALDI-IHC data, were investigated, and are visualized in . shows how correlating MALDI-IHC and MALDI-MSI data can help with molecular profiling of single cells. Two MALDI-MSI lipid images from the same region are presented and show a discrepancy of the cell-distribution between different m / z -images. ( A,B). For the two cells/cell clusters highlighted, different lipid distributions are observed. A MALDI-IHC image of the same area shows the distribution of GLUT1 expressing cells (green) and neurofilaments in neurons (purple), with the same two single cells/cell clusters being highlighted by arrows ( C). While lipid PC 36:4 at m / z 782.6 is detected in both the GBM cell and the neuron, the other lipid, PC 36:5 at m / z 780.5, seems to only be present in the neuronal cell cluster and is not present in the GBM cell. This pattern can be seen in other areas in the MALDI-MSI images as well, with PC 36:5 not being present in all cells where PC 34:1 is present. The mass spectra from both selected cells are shown in D, again showcasing the different lipid profile observed in both cell types and clearly highlights the intensity difference observed for PC 36:5, as well as differences observed in TG 48:2 and TG 48:1 between the two cells. Additionally, it can be seen that fewer cell-signals are present in the MALDI-MSI images, compared to the MALDI-IHC images. This can be further accentuated when considering that only two out of 10 detected markers are visualized to reduce visual noise. This could indicate that the highly localized signals produced in MALDI-IHC, allows for higher sensitivity in smaller areas, compared to the complex lipid spectra that is present in each single cell and measured with MALDI-MSI. Furthermore, for these experiments, MALDI-MSI measurements were carried out with a pixel size of 10 × 10 μm 2 whereas the MALDI-IHC measurements were done at 5 × 5 μm 2 . This could potentially result in loss of specificity for different lipids within the cells. Reproducing these experiments with both modalities at 5 × 5 μm 2 would be interesting for a more direct comparison of images. Cellular Recognition Modeling A cellular recognition model was created based on the molecular profiles obtained by correlating the MALDI-MSI lipid data with the MALDI-IHC cell type analysis. Since each cell type showed a different lipid profile, these could be extracted on a per-cell basis to create a recognition model which automatically classified the cell type for a given MALDI-MSI spectrum. In this case, the model was built in SCiLS Lab on spectra associated with GLUT1 (GBM cells), GFAP (astrocytes), NF-L (neurons) and poly- l -lysine as a background. An overview of the recognition model overlaid on the corresponding MALDI-MSI measurement can be seen in . The overlay demonstrates that each pixel within a cell gets assigned to one of the four classes included in the model. For a cell to be classified to either one of the cell type classes, a threshold of 50% was chosen for class-associated pixels in a cellular ROI. The cell type assignment from the model was then compared to the cell type indicated by the MALDI-IHC staining. In total, 142 cellular ROIs were indicated in the MALDI-MSI lipid data set and used for the recognition model and 55 of these showed a clear correlation with a specific cellular marker in both the model and the corresponding MALDI-IHC image ( A). The rest of the ROIs did either not have a specific class with more than 50% pixels associated with it or did not correlate with any markers in the MALDI-IHC image. For each class, there was a false prediction rate of 22% (GLUT1), 17% (GFAP) and 6% (NF-L) on a per-cell basis, where a class was assigned to a cell based on the model, but the MALDI-IHC staining indicated a contrasting classification. Further investigation of the model showed that cells classified as neurons (>50% NF-L classified pixels), showed, on average, a higher percentage of NF-L specific pixels per cell (83%) versus GFAP assigned pixels for cells classified as astrocytes (57%) and GLUT1 assigned pixels for GBM cells (58%), based on five randomly selected cellular ROIs per class. B–D shows the different lipid profiles observed between each cell type classification. The top ten contributing lipid m / z values for each class, based on the corresponding PCA, can be seen in Supporting Table S4 . Notably, PC 36:4 is the third highest contributor to the GLUT1 class, while PC 36:5 is the third highest contributor to the NF-L class, in correspondence with the data shown in . This model works as a proof of concept for a cellular recognition model based on MSI lipid spectra alone, validated by MALDI-IHC cell-typing. Potentially, if a model was trained on enough robust data sets, the cell-typing obtained in this project could be done without the help of MALDI-IHC or alternative methods, thus reducing the overall work time. However, a number of points need to be considered beforehand. First, 62% of cells were classified as “mixed” by the model, with no specific class-associated pixel being more than 50% abundant. This could be due to the model-building software in SCiLS Lab, where there is no outlier option for pixels and all pixels are therefore forced into one of the included classes in the model. This will eventually lead to more false classifications. Another reason for this could be the overlap of expression between GBM cells and astrocytes. Some GBM cells originate from astrocytes, and the two cell types therefore share protein expression patterns and could be a potential explanation for the mixed cell classification. Moreover, the exact specificity and correlation with ion intensity of the probes used for MALDI-IHC is unknown. Knowing the total number of cells versus the number of cells to which the antibodies bound would give an indication of how effective the staining is. Additionally, when working with single cells, it is a point of discussion, when a positive signal correlates with a cell. On average the cells used in this project have a diameter of 30 μm, so when imaging with a spot size of 5 × 5 μm 2 , a positive signal could indicate that the antibody has bound to its target on a specific area of the cell, however that is also hard to conclude from one single pixel of high intensity. Finally, the cellular ROIs detected in the MALDI-MSI measurements are very large, when compared to the corresponding MALDI-IHC images and the expected size of the single cells. This can be explained by cells clustering on top of each other, thus appearing larger, or the difference in pixel-size between measurements. The larger pixel-size used in the MALDI-MSI measurement is more likely to pick up ions from multiple cells, and thereby indicate a larger area of ion intensity. Also, many cellular ROIs are present in the image at very low intensities, suggesting that the sensitivity of the method is not high enough to detect every single cell on the slide. However, the MALDI-MSI images also show a “lipid discharge” surrounding each cell, potentially masking smaller cells in close proximity of each other ( Figure S8 ). In the future, conducting both the MALDI-MSI and the MALDI-IHC measurements with a pixel-size of 5 × 5 μm would potentially provide a better understanding of the cell-to-cell correlation between the two modalities. Method Optimization A number of adaptations were made to the recommended staining protocol to optimize the MALDI-IHC method for high-resolution, single cell measurements. First, to reduce potential delocalization or diffusion of molecules, all washes were performed at lower temperatures in ice-cold solutions. For measurements with pixel sizes down to 5 × 5 μm 2 of single cells, any form of delocalization can be detrimental to the experiments and should be minimized, especially if the images are to be correlated across multiple modalities. Furthermore, when working with fresh frozen tissue, efforts to minimize delocalization are crucial as proteins in the tissue are left in their native state when unfrozen, compared to formalin-fixed paraffin-embedded tissue, where proteins are cross-linked in place by the formalin fixation. Previous work has shown that performing washing steps at freezing temperatures would reduce delocalization as compared to room-temperature washes. Second, the matrix application method was changed to achieve sufficient signal at 5 × 5 μm 2 spatial resolution. Initial experiments were carried out with the recommended matrix application methods of either 2,5-DHB sublimation or automated CHCA spraying, both followed by recrystallization for increased analyte integration with the matrix and reduced crystal size. , These experiments resulted in insufficient intensity, as only 2 out of 14 PC-MTs were detected at 5 × 5 μm 2 pixel size (data not shown). To remove most of the matrix signal, which is usually obtained when sublimating CHCA, the slides were briefly dipped in ammonium phosphate monobasic after successful sublimation. This step significantly reduced signal from matrix clusters and resulted in high intensities for peptides even at 5 × 5 μm 2 pixel size. Using this alternative matrix application method to visualize the PC-MT peptides improved the signal intensity enough to detect almost all stained markers and single cells from 10 out of 14 markers. Effect of Pretreatment on MALDI-IHC Staining The same, identical cells needed to be measured twice, first with MALDI-MSI (lipids) and then with MALDI-IHC (cell types) to determine cell-specific lipid spectra of the specific single cell types. On the subsequent MALDI-IHC measurements, three different conditions were prepared on the same ITO slide containing PDCL GBM single cells. This enables the investigation of the effect of prior MALDI-MSI sample preparation and measurements. One-third of the cells were stained with the Miralys probes without prior treatment (no prep), one-third went through the MALDI-MSI sample preparation treatment of matrix application but without the subsequent MSI measurement (MALDI prep) and the final third was prepared for and analyzed with MALDI-MSI (MSI1). An overview of the experimental setup can be seen in Supporting Figure S1 . This setup would ensure that the compared sample preparations and cells were always the same between conditions. The results from the MALDI-IHC measurements can be seen in . shows data from the PDCL GBM single cells stained with MALDI-IHC. Using the optimized sample preparation workflow, single cells were able to be visualized and differentially stained with cell-specific antibody markers. A shows the visualization of three PC-MTs, corresponding to antibodies targeting GLUT1 for GBM cells ( m / z 856.67, red), NF-L for neurofilaments ( m / z 1345.75, yellow) and SYN-I as a synaptic marker ( m / z 1482.77, blue), with their respective single pixel mass spectra shown in 2B. Under normal conditions, GLUT1 is typically found in the blood-brain barrier as the transporter of glucose into the brain but has been found to be overexpressed in GBM cells likely due to the increased demand for glucose, and is therefore used as a marker for GBM cells here. − An overview of all detected markers and corresponding average mass spectrum can be seen in Supporting Figures S3 and S4 . Electron microscopy image and fluorescence image from immunostaining with GFAP can be seen in Supporting Figure S5 . Each area of high intensity represents a single cell or cluster of cells. Note that each of the cell specific markers are present in different areas as expected by the cellular specificity of the antibodies in a heterogeneous single cell culture. C–E shows images from test of pretreatment effects on the subsequent MALDI-IHC measurement. C shows cells, untreated before MALDI-IHC, 2D shows cells that have undergone MALDI-MSI sample preparation, but no MALDI-MSI measurement, and 2E shows cells that have been prepared for and measured by MALDI-MSI. Imaging experiments were imported into the same data file, normalized to the root-mean-square, and visualized side-by-side with the same intensity for all data sets to compare any effects. No differences in intensity of MALDI-IHC probes were found between the three conditions. This was confirmed on a cell-by-cell basis by examining peak areas. For each condition, mean peak areas, per single cell, were compared between all measured MALDI-IHC targets, based on 10 cells per target and condition and showed no significant difference in intensity between conditions, F. For the PC-MTs corresponding with NeuN, IBA-1, nicastrin and MAP2, the intensity was not sufficient to identify 10 single cells in the measured area in any of the three conditions. These results demonstrate how MALDI-MSI measurements prior to any MALDI-IHC imaging has no negative impact on the results, thus reinforcing how the two modalities can be easily combined to gain extra valuable information from a sample. Multimodal Single Cell Correlation and Molecular Profiling The fluorescence capabilities of the Miralys probes were utilized to get high-resolution images of the stained cells and confirm that the high-intensity ion clusters were in fact single cells. This approach aids in the correlation of the different molecular imaging modalities, as demonstrated in . showcases MALDI-IHC images corresponding with the PC-MT marker for neurofilaments ( m / z 1345.74, NF-L) and therefore specifically the axons and dendrites in neurons ( A,C). Utilizing the fluorophore, also present on the Miralys probes, fluorescence images were also obtained of the same single cells as can be seen in B. The cells were first imaged by MALDI-IHC and then by fluorescence imaging, allowing for precise overlay of the two modalities and it was therefore possible to find back many of the cells observed in MALDI-IHC, and in the fluorescence image as well, as indicated by the white arrows ( A,B). The fluorescence images obtained were not cell type-specific, as the fluorophore present on the Miralys probes used in the selected kit were the same for every probe. This meant that the fluorescence imaging could be used as a confirmation of a given antibody being bound to a single cell and that the signals observed in the corresponding MALDI-IHC images showed single-cell specificity. Furthermore, the MALDI-IHC images were correlated with the previously obtained MALDI-MSI lipid images of the same single cells ( C,D). The single cell-specific MALDI-IHC images were imported into the MALDI-MSI SCiLS datafile to precisely coregister the two measurements. This allowed for visualization of multiple lipids across specific cell types. C shows an image correlating with the Miralys antibody for NF-L and D visualizes the PC 34:4 at m / z 754.5, with some of the single cells that are recognizable in both images highlighted with green arrows. Comparing C, D, it is also clear that not all cells show up in both modalities with cells being uniquely present in both the MALDI-MSI and the MALDI-IHC images, respectively. When correlating the two modalities in two separate MALDI-IHC measurements, 54 of 396 (13.64%) and 17 of 144 (11.8%) of MALDI-MSI cells had no corresponding MALDI-IHC marker. A corresponding MALDI-IHC marker was included only when four or more pixels of high intensity were joined together. Furthermore, there are many cells where the intensity of lipids detected is not high enough to pick them out from the background, this is visualized in Supporting Figure S6 . Finally, the apparent “streaking” of signal observed in larger clusters in the MALDI-IHC image ( C, right side) is likely due to insufficient drying following matrix sublimation. Supporting Figure S7 shows that cells are still intact postmeasurement, with only some MALDI-IHC markers presenting with streaks. Differences in lipid abundances between single cells of varying cell types, as classified by the MALDI-IHC data, were investigated, and are visualized in . shows how correlating MALDI-IHC and MALDI-MSI data can help with molecular profiling of single cells. Two MALDI-MSI lipid images from the same region are presented and show a discrepancy of the cell-distribution between different m / z -images. ( A,B). For the two cells/cell clusters highlighted, different lipid distributions are observed. A MALDI-IHC image of the same area shows the distribution of GLUT1 expressing cells (green) and neurofilaments in neurons (purple), with the same two single cells/cell clusters being highlighted by arrows ( C). While lipid PC 36:4 at m / z 782.6 is detected in both the GBM cell and the neuron, the other lipid, PC 36:5 at m / z 780.5, seems to only be present in the neuronal cell cluster and is not present in the GBM cell. This pattern can be seen in other areas in the MALDI-MSI images as well, with PC 36:5 not being present in all cells where PC 34:1 is present. The mass spectra from both selected cells are shown in D, again showcasing the different lipid profile observed in both cell types and clearly highlights the intensity difference observed for PC 36:5, as well as differences observed in TG 48:2 and TG 48:1 between the two cells. Additionally, it can be seen that fewer cell-signals are present in the MALDI-MSI images, compared to the MALDI-IHC images. This can be further accentuated when considering that only two out of 10 detected markers are visualized to reduce visual noise. This could indicate that the highly localized signals produced in MALDI-IHC, allows for higher sensitivity in smaller areas, compared to the complex lipid spectra that is present in each single cell and measured with MALDI-MSI. Furthermore, for these experiments, MALDI-MSI measurements were carried out with a pixel size of 10 × 10 μm 2 whereas the MALDI-IHC measurements were done at 5 × 5 μm 2 . This could potentially result in loss of specificity for different lipids within the cells. Reproducing these experiments with both modalities at 5 × 5 μm 2 would be interesting for a more direct comparison of images. Cellular Recognition Modeling A cellular recognition model was created based on the molecular profiles obtained by correlating the MALDI-MSI lipid data with the MALDI-IHC cell type analysis. Since each cell type showed a different lipid profile, these could be extracted on a per-cell basis to create a recognition model which automatically classified the cell type for a given MALDI-MSI spectrum. In this case, the model was built in SCiLS Lab on spectra associated with GLUT1 (GBM cells), GFAP (astrocytes), NF-L (neurons) and poly- l -lysine as a background. An overview of the recognition model overlaid on the corresponding MALDI-MSI measurement can be seen in . The overlay demonstrates that each pixel within a cell gets assigned to one of the four classes included in the model. For a cell to be classified to either one of the cell type classes, a threshold of 50% was chosen for class-associated pixels in a cellular ROI. The cell type assignment from the model was then compared to the cell type indicated by the MALDI-IHC staining. In total, 142 cellular ROIs were indicated in the MALDI-MSI lipid data set and used for the recognition model and 55 of these showed a clear correlation with a specific cellular marker in both the model and the corresponding MALDI-IHC image ( A). The rest of the ROIs did either not have a specific class with more than 50% pixels associated with it or did not correlate with any markers in the MALDI-IHC image. For each class, there was a false prediction rate of 22% (GLUT1), 17% (GFAP) and 6% (NF-L) on a per-cell basis, where a class was assigned to a cell based on the model, but the MALDI-IHC staining indicated a contrasting classification. Further investigation of the model showed that cells classified as neurons (>50% NF-L classified pixels), showed, on average, a higher percentage of NF-L specific pixels per cell (83%) versus GFAP assigned pixels for cells classified as astrocytes (57%) and GLUT1 assigned pixels for GBM cells (58%), based on five randomly selected cellular ROIs per class. B–D shows the different lipid profiles observed between each cell type classification. The top ten contributing lipid m / z values for each class, based on the corresponding PCA, can be seen in Supporting Table S4 . Notably, PC 36:4 is the third highest contributor to the GLUT1 class, while PC 36:5 is the third highest contributor to the NF-L class, in correspondence with the data shown in . This model works as a proof of concept for a cellular recognition model based on MSI lipid spectra alone, validated by MALDI-IHC cell-typing. Potentially, if a model was trained on enough robust data sets, the cell-typing obtained in this project could be done without the help of MALDI-IHC or alternative methods, thus reducing the overall work time. However, a number of points need to be considered beforehand. First, 62% of cells were classified as “mixed” by the model, with no specific class-associated pixel being more than 50% abundant. This could be due to the model-building software in SCiLS Lab, where there is no outlier option for pixels and all pixels are therefore forced into one of the included classes in the model. This will eventually lead to more false classifications. Another reason for this could be the overlap of expression between GBM cells and astrocytes. Some GBM cells originate from astrocytes, and the two cell types therefore share protein expression patterns and could be a potential explanation for the mixed cell classification. Moreover, the exact specificity and correlation with ion intensity of the probes used for MALDI-IHC is unknown. Knowing the total number of cells versus the number of cells to which the antibodies bound would give an indication of how effective the staining is. Additionally, when working with single cells, it is a point of discussion, when a positive signal correlates with a cell. On average the cells used in this project have a diameter of 30 μm, so when imaging with a spot size of 5 × 5 μm 2 , a positive signal could indicate that the antibody has bound to its target on a specific area of the cell, however that is also hard to conclude from one single pixel of high intensity. Finally, the cellular ROIs detected in the MALDI-MSI measurements are very large, when compared to the corresponding MALDI-IHC images and the expected size of the single cells. This can be explained by cells clustering on top of each other, thus appearing larger, or the difference in pixel-size between measurements. The larger pixel-size used in the MALDI-MSI measurement is more likely to pick up ions from multiple cells, and thereby indicate a larger area of ion intensity. Also, many cellular ROIs are present in the image at very low intensities, suggesting that the sensitivity of the method is not high enough to detect every single cell on the slide. However, the MALDI-MSI images also show a “lipid discharge” surrounding each cell, potentially masking smaller cells in close proximity of each other ( Figure S8 ). In the future, conducting both the MALDI-MSI and the MALDI-IHC measurements with a pixel-size of 5 × 5 μm would potentially provide a better understanding of the cell-to-cell correlation between the two modalities. A number of adaptations were made to the recommended staining protocol to optimize the MALDI-IHC method for high-resolution, single cell measurements. First, to reduce potential delocalization or diffusion of molecules, all washes were performed at lower temperatures in ice-cold solutions. For measurements with pixel sizes down to 5 × 5 μm 2 of single cells, any form of delocalization can be detrimental to the experiments and should be minimized, especially if the images are to be correlated across multiple modalities. Furthermore, when working with fresh frozen tissue, efforts to minimize delocalization are crucial as proteins in the tissue are left in their native state when unfrozen, compared to formalin-fixed paraffin-embedded tissue, where proteins are cross-linked in place by the formalin fixation. Previous work has shown that performing washing steps at freezing temperatures would reduce delocalization as compared to room-temperature washes. Second, the matrix application method was changed to achieve sufficient signal at 5 × 5 μm 2 spatial resolution. Initial experiments were carried out with the recommended matrix application methods of either 2,5-DHB sublimation or automated CHCA spraying, both followed by recrystallization for increased analyte integration with the matrix and reduced crystal size. , These experiments resulted in insufficient intensity, as only 2 out of 14 PC-MTs were detected at 5 × 5 μm 2 pixel size (data not shown). To remove most of the matrix signal, which is usually obtained when sublimating CHCA, the slides were briefly dipped in ammonium phosphate monobasic after successful sublimation. This step significantly reduced signal from matrix clusters and resulted in high intensities for peptides even at 5 × 5 μm 2 pixel size. Using this alternative matrix application method to visualize the PC-MT peptides improved the signal intensity enough to detect almost all stained markers and single cells from 10 out of 14 markers. The same, identical cells needed to be measured twice, first with MALDI-MSI (lipids) and then with MALDI-IHC (cell types) to determine cell-specific lipid spectra of the specific single cell types. On the subsequent MALDI-IHC measurements, three different conditions were prepared on the same ITO slide containing PDCL GBM single cells. This enables the investigation of the effect of prior MALDI-MSI sample preparation and measurements. One-third of the cells were stained with the Miralys probes without prior treatment (no prep), one-third went through the MALDI-MSI sample preparation treatment of matrix application but without the subsequent MSI measurement (MALDI prep) and the final third was prepared for and analyzed with MALDI-MSI (MSI1). An overview of the experimental setup can be seen in Supporting Figure S1 . This setup would ensure that the compared sample preparations and cells were always the same between conditions. The results from the MALDI-IHC measurements can be seen in . shows data from the PDCL GBM single cells stained with MALDI-IHC. Using the optimized sample preparation workflow, single cells were able to be visualized and differentially stained with cell-specific antibody markers. A shows the visualization of three PC-MTs, corresponding to antibodies targeting GLUT1 for GBM cells ( m / z 856.67, red), NF-L for neurofilaments ( m / z 1345.75, yellow) and SYN-I as a synaptic marker ( m / z 1482.77, blue), with their respective single pixel mass spectra shown in 2B. Under normal conditions, GLUT1 is typically found in the blood-brain barrier as the transporter of glucose into the brain but has been found to be overexpressed in GBM cells likely due to the increased demand for glucose, and is therefore used as a marker for GBM cells here. − An overview of all detected markers and corresponding average mass spectrum can be seen in Supporting Figures S3 and S4 . Electron microscopy image and fluorescence image from immunostaining with GFAP can be seen in Supporting Figure S5 . Each area of high intensity represents a single cell or cluster of cells. Note that each of the cell specific markers are present in different areas as expected by the cellular specificity of the antibodies in a heterogeneous single cell culture. C–E shows images from test of pretreatment effects on the subsequent MALDI-IHC measurement. C shows cells, untreated before MALDI-IHC, 2D shows cells that have undergone MALDI-MSI sample preparation, but no MALDI-MSI measurement, and 2E shows cells that have been prepared for and measured by MALDI-MSI. Imaging experiments were imported into the same data file, normalized to the root-mean-square, and visualized side-by-side with the same intensity for all data sets to compare any effects. No differences in intensity of MALDI-IHC probes were found between the three conditions. This was confirmed on a cell-by-cell basis by examining peak areas. For each condition, mean peak areas, per single cell, were compared between all measured MALDI-IHC targets, based on 10 cells per target and condition and showed no significant difference in intensity between conditions, F. For the PC-MTs corresponding with NeuN, IBA-1, nicastrin and MAP2, the intensity was not sufficient to identify 10 single cells in the measured area in any of the three conditions. These results demonstrate how MALDI-MSI measurements prior to any MALDI-IHC imaging has no negative impact on the results, thus reinforcing how the two modalities can be easily combined to gain extra valuable information from a sample. The fluorescence capabilities of the Miralys probes were utilized to get high-resolution images of the stained cells and confirm that the high-intensity ion clusters were in fact single cells. This approach aids in the correlation of the different molecular imaging modalities, as demonstrated in . showcases MALDI-IHC images corresponding with the PC-MT marker for neurofilaments ( m / z 1345.74, NF-L) and therefore specifically the axons and dendrites in neurons ( A,C). Utilizing the fluorophore, also present on the Miralys probes, fluorescence images were also obtained of the same single cells as can be seen in B. The cells were first imaged by MALDI-IHC and then by fluorescence imaging, allowing for precise overlay of the two modalities and it was therefore possible to find back many of the cells observed in MALDI-IHC, and in the fluorescence image as well, as indicated by the white arrows ( A,B). The fluorescence images obtained were not cell type-specific, as the fluorophore present on the Miralys probes used in the selected kit were the same for every probe. This meant that the fluorescence imaging could be used as a confirmation of a given antibody being bound to a single cell and that the signals observed in the corresponding MALDI-IHC images showed single-cell specificity. Furthermore, the MALDI-IHC images were correlated with the previously obtained MALDI-MSI lipid images of the same single cells ( C,D). The single cell-specific MALDI-IHC images were imported into the MALDI-MSI SCiLS datafile to precisely coregister the two measurements. This allowed for visualization of multiple lipids across specific cell types. C shows an image correlating with the Miralys antibody for NF-L and D visualizes the PC 34:4 at m / z 754.5, with some of the single cells that are recognizable in both images highlighted with green arrows. Comparing C, D, it is also clear that not all cells show up in both modalities with cells being uniquely present in both the MALDI-MSI and the MALDI-IHC images, respectively. When correlating the two modalities in two separate MALDI-IHC measurements, 54 of 396 (13.64%) and 17 of 144 (11.8%) of MALDI-MSI cells had no corresponding MALDI-IHC marker. A corresponding MALDI-IHC marker was included only when four or more pixels of high intensity were joined together. Furthermore, there are many cells where the intensity of lipids detected is not high enough to pick them out from the background, this is visualized in Supporting Figure S6 . Finally, the apparent “streaking” of signal observed in larger clusters in the MALDI-IHC image ( C, right side) is likely due to insufficient drying following matrix sublimation. Supporting Figure S7 shows that cells are still intact postmeasurement, with only some MALDI-IHC markers presenting with streaks. Differences in lipid abundances between single cells of varying cell types, as classified by the MALDI-IHC data, were investigated, and are visualized in . shows how correlating MALDI-IHC and MALDI-MSI data can help with molecular profiling of single cells. Two MALDI-MSI lipid images from the same region are presented and show a discrepancy of the cell-distribution between different m / z -images. ( A,B). For the two cells/cell clusters highlighted, different lipid distributions are observed. A MALDI-IHC image of the same area shows the distribution of GLUT1 expressing cells (green) and neurofilaments in neurons (purple), with the same two single cells/cell clusters being highlighted by arrows ( C). While lipid PC 36:4 at m / z 782.6 is detected in both the GBM cell and the neuron, the other lipid, PC 36:5 at m / z 780.5, seems to only be present in the neuronal cell cluster and is not present in the GBM cell. This pattern can be seen in other areas in the MALDI-MSI images as well, with PC 36:5 not being present in all cells where PC 34:1 is present. The mass spectra from both selected cells are shown in D, again showcasing the different lipid profile observed in both cell types and clearly highlights the intensity difference observed for PC 36:5, as well as differences observed in TG 48:2 and TG 48:1 between the two cells. Additionally, it can be seen that fewer cell-signals are present in the MALDI-MSI images, compared to the MALDI-IHC images. This can be further accentuated when considering that only two out of 10 detected markers are visualized to reduce visual noise. This could indicate that the highly localized signals produced in MALDI-IHC, allows for higher sensitivity in smaller areas, compared to the complex lipid spectra that is present in each single cell and measured with MALDI-MSI. Furthermore, for these experiments, MALDI-MSI measurements were carried out with a pixel size of 10 × 10 μm 2 whereas the MALDI-IHC measurements were done at 5 × 5 μm 2 . This could potentially result in loss of specificity for different lipids within the cells. Reproducing these experiments with both modalities at 5 × 5 μm 2 would be interesting for a more direct comparison of images. A cellular recognition model was created based on the molecular profiles obtained by correlating the MALDI-MSI lipid data with the MALDI-IHC cell type analysis. Since each cell type showed a different lipid profile, these could be extracted on a per-cell basis to create a recognition model which automatically classified the cell type for a given MALDI-MSI spectrum. In this case, the model was built in SCiLS Lab on spectra associated with GLUT1 (GBM cells), GFAP (astrocytes), NF-L (neurons) and poly- l -lysine as a background. An overview of the recognition model overlaid on the corresponding MALDI-MSI measurement can be seen in . The overlay demonstrates that each pixel within a cell gets assigned to one of the four classes included in the model. For a cell to be classified to either one of the cell type classes, a threshold of 50% was chosen for class-associated pixels in a cellular ROI. The cell type assignment from the model was then compared to the cell type indicated by the MALDI-IHC staining. In total, 142 cellular ROIs were indicated in the MALDI-MSI lipid data set and used for the recognition model and 55 of these showed a clear correlation with a specific cellular marker in both the model and the corresponding MALDI-IHC image ( A). The rest of the ROIs did either not have a specific class with more than 50% pixels associated with it or did not correlate with any markers in the MALDI-IHC image. For each class, there was a false prediction rate of 22% (GLUT1), 17% (GFAP) and 6% (NF-L) on a per-cell basis, where a class was assigned to a cell based on the model, but the MALDI-IHC staining indicated a contrasting classification. Further investigation of the model showed that cells classified as neurons (>50% NF-L classified pixels), showed, on average, a higher percentage of NF-L specific pixels per cell (83%) versus GFAP assigned pixels for cells classified as astrocytes (57%) and GLUT1 assigned pixels for GBM cells (58%), based on five randomly selected cellular ROIs per class. B–D shows the different lipid profiles observed between each cell type classification. The top ten contributing lipid m / z values for each class, based on the corresponding PCA, can be seen in Supporting Table S4 . Notably, PC 36:4 is the third highest contributor to the GLUT1 class, while PC 36:5 is the third highest contributor to the NF-L class, in correspondence with the data shown in . This model works as a proof of concept for a cellular recognition model based on MSI lipid spectra alone, validated by MALDI-IHC cell-typing. Potentially, if a model was trained on enough robust data sets, the cell-typing obtained in this project could be done without the help of MALDI-IHC or alternative methods, thus reducing the overall work time. However, a number of points need to be considered beforehand. First, 62% of cells were classified as “mixed” by the model, with no specific class-associated pixel being more than 50% abundant. This could be due to the model-building software in SCiLS Lab, where there is no outlier option for pixels and all pixels are therefore forced into one of the included classes in the model. This will eventually lead to more false classifications. Another reason for this could be the overlap of expression between GBM cells and astrocytes. Some GBM cells originate from astrocytes, and the two cell types therefore share protein expression patterns and could be a potential explanation for the mixed cell classification. Moreover, the exact specificity and correlation with ion intensity of the probes used for MALDI-IHC is unknown. Knowing the total number of cells versus the number of cells to which the antibodies bound would give an indication of how effective the staining is. Additionally, when working with single cells, it is a point of discussion, when a positive signal correlates with a cell. On average the cells used in this project have a diameter of 30 μm, so when imaging with a spot size of 5 × 5 μm 2 , a positive signal could indicate that the antibody has bound to its target on a specific area of the cell, however that is also hard to conclude from one single pixel of high intensity. Finally, the cellular ROIs detected in the MALDI-MSI measurements are very large, when compared to the corresponding MALDI-IHC images and the expected size of the single cells. This can be explained by cells clustering on top of each other, thus appearing larger, or the difference in pixel-size between measurements. The larger pixel-size used in the MALDI-MSI measurement is more likely to pick up ions from multiple cells, and thereby indicate a larger area of ion intensity. Also, many cellular ROIs are present in the image at very low intensities, suggesting that the sensitivity of the method is not high enough to detect every single cell on the slide. However, the MALDI-MSI images also show a “lipid discharge” surrounding each cell, potentially masking smaller cells in close proximity of each other ( Figure S8 ). In the future, conducting both the MALDI-MSI and the MALDI-IHC measurements with a pixel-size of 5 × 5 μm would potentially provide a better understanding of the cell-to-cell correlation between the two modalities. In conclusion, we developed an optimized workflow for multimodal imaging single cell samples using a 14-plex MALDI-IHC antibody panel—successfully detecting 10 out of 14 targets. Altering the matrix application technique and including a dip in ammonium phosphate monobasic, greatly increased sensitivity for detection of the MALDI-IHC probes at 5 × 5 μm 2 spatial resolution. This allowed for cell type characterization and molecular profiling by correlating corresponding single cell MALDI-MSI measurements. Furthermore, we show that conducting MALDI-MSI on the single cell samples prior to MALDI-IHC staining and measurement, does not alter the observed intensity of the MALDI-IHC probes. Using this molecular profiling workflow, basic differences in lipid profiles between GBM cells and neurons were shown. The added information on altered lipidomic profiles obtained with MALDI-MSI could potentially help improve cell differentiation by adding the metabolic state to the suite of methods used for cell-typing. The ability of MALDI-MSI to distinguish single-cell-specific mass spectra across different cell types, further enabled the generation of a classification model which was successful in cell-typing of three different cell types using MALDI-MSI data alone. This proof-of-concept study shows how multiple imaging modalities can be used to extract single-cell data and eventually build large-scale recognition models for cell-typing by fully utilizing the strengths of MALDI-MSI. |
Understanding polypharmacy for people receiving home care services: a scoping review of the evidence | 5992f81f-4590-4f91-b971-814f1802b022 | 11837856 | Health Literacy[mh] | This review is the first to explore polypharmacy for individuals receiving home care services, which provide support with activities of daily living, including personal care. Polypharmacy is common amongst older adults, and those living with frailty and multiple long-term conditions. This is the population most likely to need homecare support. Home care workers are not health care professionals but are well placed to help older adults with their medications. Interprofessional interventions, and enhanced health literacy can potentially reduce inappropriate polypharmacy. Future studies should seek to explore the experiences people providing, and receiving, home care, particularly in the context of polypharmacy and managing medications. Multimorbidity, the occurrence of two or more diseases or conditions , is prevalent in older adults and is frequently accompanied by polypharmacy (concurrent use of five or more medications) . Polypharmacy can be ‘appropriate’ when medications align with evidence-based care for complex conditions , or ‘problematic’ when risks outweigh the intended benefits . Problematic polypharmacy, resulted from over-prescribing or prescription of potentially inappropriate medications (PIMs) , poses a significant financial burden on healthcare systems and is associated with negative health outcomes, including developing adverse drug reactions (ADRs), drug–drug interactions, medication errors, healthcare utilisation and increased rates of mortality . Polypharmacy has been extensively studied across various health and social care settings. In United Kingdom (UK) care homes, researchers have reported a polypharmacy prevalence rate of 62% . Factors associated with polypharmacy include increasing age, lower wealth, multimorbidity and obesity . One area where polypharmacy has seldom been explored is in individuals who are receiving social care support in their own homes—a type of support known as home or domiciliary care. In the UK, there are ~12 500 officially registered home care organisations offering services to an estimated 959 000 people, predominantly older people , and that number is expected to increase in the future . There is currently no global definition for home care, but it typically involves help with tasks such as personal care, administration of medications and other individual or household activities . Using home care providers to provide medication support adds an additional layer of complexity in already fragmented health and social care systems. This could potentially increase the risk of patient safety events, such as medication errors. Moreover, there are international differences in the provision of home care , with implications for medication support. Countries such as Germany, predominantly employ healthcare professionals (e.g. registered nurses) , whilst others, such as the UK, have no degree-level educational requirements , and <10% of home care staff having a nursing degree . People who receive support with daily activities are also likely to have one or multiple long-term health conditions that may require treatment with a range of medications. As older people prefer to remain in their own homes as they age, any adverse reactions or consequences of complex medication regimens will be experienced there. It will be essential to understand the specific challenges and opportunities for optimising polypharmacy management in the home care setting. No published or ‘in-progress’ systematic reviews or scoping reviews have specifically sought to investigate polypharmacy in individuals receiving home care services. Previous reviews have explored the extent of drug-related problems , medication management , and adverse events experienced by individuals receiving home care services , with only one identifying polypharmacy as a key concern . Conducting a scoping review that is focused on polypharmacy in the home care setting will improve understanding of the specific challenges faced by individuals experiencing polypharmacy, as well as the potential impact of interventions designed to address these challenges. The aim of this scoping review was to explore the extent and type of evidence in relation to individuals receiving home care services experiencing polypharmacy. Protocol and registration The review followed the Preferred Reporting of Items for Systematic Reviews and Meta-Analyses—extension for scoping reviews (PRISMA-ScR) framework (see , Supplementary Data) . The scoping review protocol was registered with Open Science Framework (OSF) (registration: nu3w6) . Eligibility criteria and search strategy The review sought published material exploring the extent of polypharmacy in the home care setting, in addition to studies examining the effectiveness of interventions in reducing polypharmacy in individuals receiving home care services. Eligible studies for inclusion consisted of adults (any age) taking five or more medications, receiving home care services or staff providing home care to adults experiencing polypharmacy. Home care was defined as a service that enables individuals with physical, mental or cognitive impairment to live at home . Studies that did not provide an explicit definition of home care, but reported on ‘home care patients’ or ‘patients receiving home care services’, were discussed by the research team to reach a consensus decision on inclusion. Randomised controlled trials, non-randomised controlled trials, before and after studies, interrupted time-series studies, prospective and retrospective cohort studies, case–control studies, analytical cross-sectional studies, case series, individual case reports and descriptive cross-sectional studies were all eligible for inclusion. Studies published in English, irrespective of their geographical location and year of publication, were included. Articles were excluded if they were not conducted in home care settings or populations that were outside the scope of our study (e.g. conducted in home health care, end-of-life care, hospice care and home medical care), were not in English, did not report polypharmacy, or involved people aged under 18 years. A pilot search of MEDLINE, Embase and CINAHL was undertaken to identify relevant articles on the topic. Moreover, online databases such as Cochrane library and PROSPERO were explored to ensure the review question had not been addressed in any published or ‘in-progress’ reviews. A broader search strategy was developed under the headings of ‘polypharmacy’ and ‘home care’ for MEDLINE (Ovid), Embase (Ovid) and CINAHL (EBSCO) (see , Supplementary Data) (December 2023). The reference list of all included sources of evidence was then hand searched to identify additional studies. Selection of sources of evidence Following the search, all identified citations were collated and uploaded to EndNote 21, then Rayyan QCRI, to remove duplicates. Titles and abstracts were screened by two independent reviewers for assessment against the inclusion criteria for the review. The full text articles were assessed in detail against the inclusion criteria by two independent reviewers (RK and VD). Any disagreements between the reviewers at each stage of the selection process were resolved through discussion; if consensus could not be reached, a discussion was had with the wider review team (AR-B, DS, BH and AT). The Cohen’s kappa statistic was used to test interrater reliability. Charting the data A data extraction tool developed by the authors ( , Supplementary Data) was used to report the date and country of study’s conduct, definition of home care, definition of polypharmacy, age, sample size, setting and key findings. As this was a scoping review, no quality appraisal of the included studies was conducted . Collating, summarising and synthesis of results The extracted information was tabulated, and a narrative synthesis summarising the findings was undertaken. Included studies were categorised according to their objectives, methodologies and outcomes. Trends and evidence gaps within the literature were then identified. The review followed the Preferred Reporting of Items for Systematic Reviews and Meta-Analyses—extension for scoping reviews (PRISMA-ScR) framework (see , Supplementary Data) . The scoping review protocol was registered with Open Science Framework (OSF) (registration: nu3w6) . The review sought published material exploring the extent of polypharmacy in the home care setting, in addition to studies examining the effectiveness of interventions in reducing polypharmacy in individuals receiving home care services. Eligible studies for inclusion consisted of adults (any age) taking five or more medications, receiving home care services or staff providing home care to adults experiencing polypharmacy. Home care was defined as a service that enables individuals with physical, mental or cognitive impairment to live at home . Studies that did not provide an explicit definition of home care, but reported on ‘home care patients’ or ‘patients receiving home care services’, were discussed by the research team to reach a consensus decision on inclusion. Randomised controlled trials, non-randomised controlled trials, before and after studies, interrupted time-series studies, prospective and retrospective cohort studies, case–control studies, analytical cross-sectional studies, case series, individual case reports and descriptive cross-sectional studies were all eligible for inclusion. Studies published in English, irrespective of their geographical location and year of publication, were included. Articles were excluded if they were not conducted in home care settings or populations that were outside the scope of our study (e.g. conducted in home health care, end-of-life care, hospice care and home medical care), were not in English, did not report polypharmacy, or involved people aged under 18 years. A pilot search of MEDLINE, Embase and CINAHL was undertaken to identify relevant articles on the topic. Moreover, online databases such as Cochrane library and PROSPERO were explored to ensure the review question had not been addressed in any published or ‘in-progress’ reviews. A broader search strategy was developed under the headings of ‘polypharmacy’ and ‘home care’ for MEDLINE (Ovid), Embase (Ovid) and CINAHL (EBSCO) (see , Supplementary Data) (December 2023). The reference list of all included sources of evidence was then hand searched to identify additional studies. Following the search, all identified citations were collated and uploaded to EndNote 21, then Rayyan QCRI, to remove duplicates. Titles and abstracts were screened by two independent reviewers for assessment against the inclusion criteria for the review. The full text articles were assessed in detail against the inclusion criteria by two independent reviewers (RK and VD). Any disagreements between the reviewers at each stage of the selection process were resolved through discussion; if consensus could not be reached, a discussion was had with the wider review team (AR-B, DS, BH and AT). The Cohen’s kappa statistic was used to test interrater reliability. A data extraction tool developed by the authors ( , Supplementary Data) was used to report the date and country of study’s conduct, definition of home care, definition of polypharmacy, age, sample size, setting and key findings. As this was a scoping review, no quality appraisal of the included studies was conducted . The extracted information was tabulated, and a narrative synthesis summarising the findings was undertaken. Included studies were categorised according to their objectives, methodologies and outcomes. Trends and evidence gaps within the literature were then identified. The search retrieved 8560 articles for title and abstract screening after de-duplication. Upon checking 296 full-text articles and two abstracts, 23 studies were included in the review . Interrater reliability between the authors was perfect, with a Cohen’s Kappa of 1.0. Studies were excluded at the full text stage for the following reasons: (i) not reporting polypharmacy or the use of five or more medications by an individual, (ii) not reporting home care services, (iii) studies not focusing on home care setting (e.g. home health care, end-of-life care, hospice care and home medical care), and (iv) not being in the English language. Characteristics of the included studies Included studies were published from 2000 to 2021, with 14 of the 23 studies being published after 2015. Studies were conducted across 14 different countries, including Canada , Finland , Netherlands , Norway , Germany , Italy and Iceland . The study designs included cross-sectional studies, retrospective cohort studies, longitudinal cohort studies and randomised controlled trials. Eight studies included participants aged 65 years and older , three studies included participants aged 75 years and over , whilst two studies included participants aged 18 years and over . Six studies reported mean age of participants, ranging from 71.5 to 83 years . The population size reported in the studies ranged from 45 in a pilot study , to 438,114 in a retrospective cohort study . A summary of the characteristics of the included studies is illustrated in . Definitions of home care Six studies provided a definition or offered any explanation of how home care services were defined . Three studies were from Canada, in which home care was defined as ‘a service that helps individuals of all ages (birth to extreme old age) live in their home and community, by providing support with medical, nursing, physiotherapy, social and therapeutic treatments, and assistance with activities of daily living’ . The two studies from Netherlands defined home care as ‘a service that provides care in individuals’ own homes by nurses of varying educational levels (such as nurse aides, registered nurses and licenced practical nurses), with the aim of assisting and educating individuals of all ages in activities of daily living including help with getting dressed and bathing, pharmacotherapy and medication intake, wound care and disease treatment to promote well-being and greater independence, in order to prevent hospital admission or admission to long-term care organisations’ . A similar definition was used by the study from Finland; defining home care as ‘services that provide support with activities of daily living, home nursing, rehabilitation and end-of-life care’ . Reviewing the topic at the centre of the included studies resulted in identification of the following groups: (i) prevalence of polypharmacy in individuals receiving home care services, (ii) interventions to reduce inappropriate polypharmacy, (iii) perceived role of home care workers in management of medication, (iv) assessment of health literacy in individuals experiencing polypharmacy and (v) factors associated with polypharmacy and PIMs in individuals receiving home care services. Prevalence of polypharmacy in individuals receiving home care services Out of 23 included studies, 16 studies reported the prevalence of polypharmacy or number of participants taking five or more medications. This ranged from 41.1% (reported by Doran et al . for one region of the study where polypharmacy was defined as ≥9 medications) to 89.5% (reported by Schneider et al ., where polypharmacy was defined as ≥5 medications), with eight studies reporting prevalence rates of 80% or higher . Moreover, six studies reported on excessive polypharmacy (≥10 medications) , which ranged from 21.0% to 54.9% . Overall, polypharmacy was found to be common in the home care setting (see ). Whilst no studies specifically examined the prevalence of problematic polypharmacy, several investigated the prescription of PIMs . The reported prevalence of PIMs in home care settings ranged from 13.8% to 27.0% ; one study found an overall prevalence of 19.8%, with significant regional variation; the Czech Republic had the highest prevalence at 41.1%, compared to just 5.8% in Denmark . In the UK, the prevalence of PIMs in home care settings was 14.2% . Interventions to reduce inappropriate polypharmacy One study reported the effects of an intervention to reduce inappropriate polypharmacy and improve medication quality in the context of home care. The study, by Auvinen et al ., examined the effects of an interprofessional medication assessment on medication quality amongst home care patients . The interprofessional team consisted of a pharmacist, physician and registered nurse who reviewed patients’ medications and health conditions, and implemented recommendations regarding prevention of drug–drug interactions, risks of drug-induced renal impairment, medication-related risk loads and PIMs . During the six-month follow up period, the intervention reduced inappropriate polypharmacy by decreasing the risk of anticholinergic burden, ADRs (e.g. renal impairment, bleeding and constipation) and the use of PIMs . Perceived role of home care workers in management of medication Three studies explored the role of home care workers. The study, by Dijkstra et al. , explored medication adherence support provided by home care nurses , and identified the most common support provided to individuals receiving home care services which included ‘noticing when I don’t take medications as prescribed’, ‘helping to find solutions to overcome problems with using medications’, ‘helping with taking medications’ and ‘explaining the importance of taking medications at the right moment’ . This study also suggested that individuals receiving home care services had negative experiences of care, which stemmed from inadequate timing of home visits, rushed visits from providers and insufficient expertise of home care workers regarding side effects and medication administration . Moreover, Sino et al . explored home care workers’ ability to detect potential ADRs using a standardised observation list . Home care workers reported signs or symptoms in 80% of patients . Similarly, in the study by Dimitrow et al. , 82% of staff’s drug-related problem recommendations were validated by a geriatrician, with ADR-related symptoms being the key risk factors . Assessment of health literacy in individuals experiencing polypharmacy Health literacy refers to possession of the appropriate knowledge, skills and understanding to obtain, comprehend, evaluate and navigate health and social care information and services . Three studies explored aspects of health literacy for individuals experiencing polypharmacy receiving home care services. Medication knowledge and the ability to take medication were reviewed, where it was shown that increasing the number of prescribed medications was associated with decreased medication knowledge by individuals taking six or more medications . A significant number of participants lacked the knowledge and skills to independently manage their medication, with a considerable proportion of participants being unable to state the names of their medications and having problems with opening packaging . Moreover, in a study by Sun et al. , which explored the impact of therapeutic self-care on the types and frequency of adverse events, low therapeutic self-care ability was associated with an increase in adverse events such as falls, unplanned hospital visits and unintended weight loss . Factors associated with polypharmacy and potentially inappropriate medication Several studies explored factors associated with polypharmacy and PIMs in individuals receiving home care services. PIM use was found to be associated with a patient’s poor economic situation, polypharmacy, anxiolytic drug use, age 85 years and over, living alone, female sex and depression . Polypharmacy was associated with chronic disease, multimorbidity, female sex, old age, dyspnoea and falls . In a study by Huang et al. , polypharmacy and excessive polypharmacy were associated with lower risk of mortality comparing to non-polypharmacy , whilst Larsen et al. report that polypharmacy was associated with increased frailty . Moreover, studies found polypharmacy to be associated with increased hospitalisation due to cardiovascular events , reduced therapeutic self-care ability , and a decreased probability of dementia-related hospitalisation . Similar trends were seen regarding the association of excessive polypharmacy and increased rates of inaccuracies within patient medical records . A treemap highlighting the most common conditions, PIMs, adverse events and ADRs reported in individuals receiving home care services is shown in . Common conditions included dementia, cardiovascular and respiratory diseases and diabetes. PIMs (as reported in the studies ) included benzodiazepines, tricyclic antidepressants, antiarrhythmics and opioids. Common adverse events experienced by individuals receiving home care services, included falls, pressure ulcers, psychosocial (e.g. delirium and suicide thoughts) and unintended weight loss, whilst frequent ADRs included arrhythmias, bleeding, renal failure and falls. Included studies were published from 2000 to 2021, with 14 of the 23 studies being published after 2015. Studies were conducted across 14 different countries, including Canada , Finland , Netherlands , Norway , Germany , Italy and Iceland . The study designs included cross-sectional studies, retrospective cohort studies, longitudinal cohort studies and randomised controlled trials. Eight studies included participants aged 65 years and older , three studies included participants aged 75 years and over , whilst two studies included participants aged 18 years and over . Six studies reported mean age of participants, ranging from 71.5 to 83 years . The population size reported in the studies ranged from 45 in a pilot study , to 438,114 in a retrospective cohort study . A summary of the characteristics of the included studies is illustrated in . Six studies provided a definition or offered any explanation of how home care services were defined . Three studies were from Canada, in which home care was defined as ‘a service that helps individuals of all ages (birth to extreme old age) live in their home and community, by providing support with medical, nursing, physiotherapy, social and therapeutic treatments, and assistance with activities of daily living’ . The two studies from Netherlands defined home care as ‘a service that provides care in individuals’ own homes by nurses of varying educational levels (such as nurse aides, registered nurses and licenced practical nurses), with the aim of assisting and educating individuals of all ages in activities of daily living including help with getting dressed and bathing, pharmacotherapy and medication intake, wound care and disease treatment to promote well-being and greater independence, in order to prevent hospital admission or admission to long-term care organisations’ . A similar definition was used by the study from Finland; defining home care as ‘services that provide support with activities of daily living, home nursing, rehabilitation and end-of-life care’ . Reviewing the topic at the centre of the included studies resulted in identification of the following groups: (i) prevalence of polypharmacy in individuals receiving home care services, (ii) interventions to reduce inappropriate polypharmacy, (iii) perceived role of home care workers in management of medication, (iv) assessment of health literacy in individuals experiencing polypharmacy and (v) factors associated with polypharmacy and PIMs in individuals receiving home care services. Out of 23 included studies, 16 studies reported the prevalence of polypharmacy or number of participants taking five or more medications. This ranged from 41.1% (reported by Doran et al . for one region of the study where polypharmacy was defined as ≥9 medications) to 89.5% (reported by Schneider et al ., where polypharmacy was defined as ≥5 medications), with eight studies reporting prevalence rates of 80% or higher . Moreover, six studies reported on excessive polypharmacy (≥10 medications) , which ranged from 21.0% to 54.9% . Overall, polypharmacy was found to be common in the home care setting (see ). Whilst no studies specifically examined the prevalence of problematic polypharmacy, several investigated the prescription of PIMs . The reported prevalence of PIMs in home care settings ranged from 13.8% to 27.0% ; one study found an overall prevalence of 19.8%, with significant regional variation; the Czech Republic had the highest prevalence at 41.1%, compared to just 5.8% in Denmark . In the UK, the prevalence of PIMs in home care settings was 14.2% . One study reported the effects of an intervention to reduce inappropriate polypharmacy and improve medication quality in the context of home care. The study, by Auvinen et al ., examined the effects of an interprofessional medication assessment on medication quality amongst home care patients . The interprofessional team consisted of a pharmacist, physician and registered nurse who reviewed patients’ medications and health conditions, and implemented recommendations regarding prevention of drug–drug interactions, risks of drug-induced renal impairment, medication-related risk loads and PIMs . During the six-month follow up period, the intervention reduced inappropriate polypharmacy by decreasing the risk of anticholinergic burden, ADRs (e.g. renal impairment, bleeding and constipation) and the use of PIMs . Three studies explored the role of home care workers. The study, by Dijkstra et al. , explored medication adherence support provided by home care nurses , and identified the most common support provided to individuals receiving home care services which included ‘noticing when I don’t take medications as prescribed’, ‘helping to find solutions to overcome problems with using medications’, ‘helping with taking medications’ and ‘explaining the importance of taking medications at the right moment’ . This study also suggested that individuals receiving home care services had negative experiences of care, which stemmed from inadequate timing of home visits, rushed visits from providers and insufficient expertise of home care workers regarding side effects and medication administration . Moreover, Sino et al . explored home care workers’ ability to detect potential ADRs using a standardised observation list . Home care workers reported signs or symptoms in 80% of patients . Similarly, in the study by Dimitrow et al. , 82% of staff’s drug-related problem recommendations were validated by a geriatrician, with ADR-related symptoms being the key risk factors . Health literacy refers to possession of the appropriate knowledge, skills and understanding to obtain, comprehend, evaluate and navigate health and social care information and services . Three studies explored aspects of health literacy for individuals experiencing polypharmacy receiving home care services. Medication knowledge and the ability to take medication were reviewed, where it was shown that increasing the number of prescribed medications was associated with decreased medication knowledge by individuals taking six or more medications . A significant number of participants lacked the knowledge and skills to independently manage their medication, with a considerable proportion of participants being unable to state the names of their medications and having problems with opening packaging . Moreover, in a study by Sun et al. , which explored the impact of therapeutic self-care on the types and frequency of adverse events, low therapeutic self-care ability was associated with an increase in adverse events such as falls, unplanned hospital visits and unintended weight loss . Several studies explored factors associated with polypharmacy and PIMs in individuals receiving home care services. PIM use was found to be associated with a patient’s poor economic situation, polypharmacy, anxiolytic drug use, age 85 years and over, living alone, female sex and depression . Polypharmacy was associated with chronic disease, multimorbidity, female sex, old age, dyspnoea and falls . In a study by Huang et al. , polypharmacy and excessive polypharmacy were associated with lower risk of mortality comparing to non-polypharmacy , whilst Larsen et al. report that polypharmacy was associated with increased frailty . Moreover, studies found polypharmacy to be associated with increased hospitalisation due to cardiovascular events , reduced therapeutic self-care ability , and a decreased probability of dementia-related hospitalisation . Similar trends were seen regarding the association of excessive polypharmacy and increased rates of inaccuracies within patient medical records . A treemap highlighting the most common conditions, PIMs, adverse events and ADRs reported in individuals receiving home care services is shown in . Common conditions included dementia, cardiovascular and respiratory diseases and diabetes. PIMs (as reported in the studies ) included benzodiazepines, tricyclic antidepressants, antiarrhythmics and opioids. Common adverse events experienced by individuals receiving home care services, included falls, pressure ulcers, psychosocial (e.g. delirium and suicide thoughts) and unintended weight loss, whilst frequent ADRs included arrhythmias, bleeding, renal failure and falls. Polypharmacy is a major public health issue . The management of polypharmacy in the home care setting will be an important future issue, as an increasing proportion of the ageing population prefer to receive care within their own homes, rather than move into care homes . This scoping review has identified five categories of evidence on polypharmacy and home care services. Existing research describes the extent of polypharmacy and associated factors, potential interventions, the perceived role of home care staff, and health literacy amongst care recipients. Notable gaps in the literature include exploration of the experiences people providing, and receiving, home care, particularly in relation to polypharmacy and medication management. In this review, polypharmacy was found to be common in individuals receiving home care services, ranging from 41.1% to 89.5% across studies. Individuals receiving home care services found interventions provided by the home care workers helpful in adhering to their medication regimens. However, inadequate timing of home visits, and insufficient expertise of home care workers regarding administration and side effects of medications were highlighted as potential areas for improvement. Given the challenges faced by individuals with multimorbidity, advanced medical treatments at home, and the risks associated with handling medications by lone home care workers, it is crucial that appropriate training is in place to minimise medication errors and risk of harm to individuals receiving home care services . Medication training for home care nurses has been shown to considerably reduce the number of medication errors, and increase their awareness of polypharmacy and the necessity of deprescribing within the home care setting . Whether training for home care workers who are not nurses would be feasible, safe or acceptable is unknown. There is currently a gap in the literature regarding the perceptions of home care and support workers in relation to medication management issues. In this review, individuals with polypharmacy were found to be at risk of experiencing ADRs and adverse events such as falls. Around a third of people aged 65 and over fall at least once a year . Home care recipients are likely to be at greater than average risk and vulnerable to adverse consequences, including increased mortality . The cause of falls in the home care setting can be multifactorial; however, it can be directly linked to common PIMs reported in this setting, such as benzodiazepines and amitriptyline . Benzodiazepines and amitriptyline have been shown to significantly increase the risk of falls for several reasons, including the effects of increasing anticholinergic burden, cognitive impairment, muscle relaxation and central nervous system depression . Pharmacist-led medication reviews in residential aged care settings and primary care have been shown to considerably reduce the prescription of PIMs and promote appropriate polypharmacy . In this review, an interprofessional medication assessment intervention carried out by a team of pharmacists, physicians and nurses was shown to improve medication quality by reducing the anticholinergic burden and prescription of PIMs in individuals receiving home care services. In addition to medication reviews, interventions such as home safety assessments to identify potential hazards, exercise programmes to improve strength and balance, and educational programmes regarding the use of assistive devices (e.g. canes and walkers) have been effective in prevention of falls in individuals receiving home health care . Home care workers are not in a position to deprescribe, but they are uniquely positioned to observe care recipients daily, monitor for signs of ADRs or missed doses, and escalate concerns to healthcare professionals. Enhancing communication pathways between home care workers and healthcare professionals could ensure that potential issues are addressed promptly, thereby improving medication safety. Studies in this review reported that a significant number of individuals receiving home care services do not have the ability to independently manage their own medications. Taking a higher number of medications was linked to reduced knowledge and understanding of medications. Individuals who lacked knowledge about their medications and the ability to manage their treatment were more likely to experience adverse events, such as falls and unplanned hospital visits. Low levels of health literacy have been shown to cause challenges regarding medication management, ultimately resulting in increased rates of polypharmacy . Digital health interventions, such as using apps to access and apply health information , as well as health literacy training , have been shown to improve health literacy for people in the home care setting. Further research could explore the feasibility of implementing digital health interventions to improve health literacy in individuals experiencing polypharmacy in the home care setting, and the appropriate involvement of home care workers. This is the first review to explore the nature of existing evidence in relation to polypharmacy in individuals receiving home care services—specifically defined as a service that enables individuals to live at home, within their own communities. There are important differences in the definitions of home care used by previous reviews compared to this review, which included referring to home care as services that address the treatment of health conditions in the patient’s home , or services that enable patients to live at home with the support of professional caregivers (mostly nursing professionals) . This review recognises that home care can also be provided for purposes of rehabilitative and supportive care. By defining home care as a service that enables people with mental, physical and cognitive to live at home, this review provides a clear overview of evidence regarding challenges faced by individuals experiencing polypharmacy, in addition to possible interventions that could positively impact medication management in this setting. Our findings have several practise and policy implications. Firstly, the different services described as home care highlight the need for the adoption of a standard definition. This may help to ensure that the heterogeneity and breadth of clients and services are appreciated when planning and delivering medication related interventions . Identification of common PIMs may be an important preventive initiative, as they are predictors of inappropriate polypharmacy and adverse events. An adequately resourced home care service could be well placed to have a role in the detection of ADRs, which calls for interventions such as medication reviews to improve medication safety in this setting. In the UK, pharmacists have recently been given greater roles in care homes . However, the situation in home care is more complex, involving multiple prescribers from different primary care organisations. Future research could explore optimal models for pharmacist involvement in home care, and the role of preventative strategies aimed at care staff, families and care recipients, to minimise the risk of adverse events. This scoping review adds to the literature by identifying the most common PIMs and adverse events experienced by individuals with polypharmacy who receive home care services. We also highlight challenges faced by these individuals, emphasising the role that home care workers may play in the safer management of polypharmacy in this setting, juxtaposed by concerns over inadequate levels of expertise and time to do so. Limitations of this review include the requirements of studies being published in the English language, only using three databases and lack of universal definition for home care and polypharmacy. Studies exploring home health care, end-of-life care, home hospice care and home medical care were not included as they did not fit our definition of home care. Broadening the definition of home care to encompass all variations of home care across different countries could result potentially in the identification of more studies. Furthermore, extending the definition of polypharmacy, for instance, to the concurrent use of two or more medications, could result in the inclusion of more studies. This review provides a comprehensive overview of evidence on polypharmacy in individuals receiving home care services. Whilst suggestions such as improving health literacy, better utilisation of home care workers, and interprofessional medication assessment interventions have the potential to positively impact polypharmacy, their feasibility needs to be explored further. Key gaps identified through this review around the experiences of care staff and recipients in managing medications and polypharmacy need to be addressed by future studies. aa-24-2206-File002_afaf031(1) |
Palivizumab and prevention of childhood respiratory syncytial viral infection: protocol for a systematic review and meta-analysis of breakthrough infections | 04a3234a-7ccf-43e4-b652-8044dbd131c3 | 6661690 | Preventive Medicine[mh] | Respiratory syncytial virus in children Human respiratory syncytial virus (RSV) infects almost all children within the first 2 years of life. Typically, RSV infection manifests as the common cold; however, some children develop acute lower respiratory infections (ALRI), a leading cause of childhood morbidity and mortality. Globally, RSV accounts for approximately 33.1 million annual ALRI episodes in children. Across North America, the rates of RSV-associated paediatric hospitalisations remain high. Although supportive care is commonly used to manage RSV symptoms, no existing treatment for RSV infection has been demonstrated to be effective. Palivizumab Palivizumab is an expensive monoclonal antibody that binds to a RSV surface glycoprotein, the fusion protein and inhibits virus-cell membrane fusion; thereby inhibiting RSV replication. Despite its promising mechanism of action, the clinical evidence for palivizumab is limited to demonstrated effectiveness within select population groups in developed countries, where palivizumab is affordable and available. Moreover, some animal and human studies have demonstrated RSV resistance to palivizumab. In an effort to mitigate the burden of childhood RSV, attention has been directed towards preventative strategies. Current clinical recommendations from the American Academy of Paediatrics (AAP), which may differ from other jurisdictions, indicate for all high risk infants to receive palivizumab: preterm infants with chronic lung disease (CLD), preterm infants without CLD of prematurity or congenital heart disease (CHD), infants with haemodynamically significant CHD, children with anatomic pulmonary abnormalities or neuromuscular disorder and profoundly immunocompromised children. Why is it important to do this review? To date, there have been two reviews on the effectiveness of palivizumab ; however, both are limited in scope. The 2013 review includes only randomised controlled trials, and synthesised findings from a very small number of studies. The 2014 review excluded prospective studies and registries with end dates in 2013. Moreover, both existing reviews limited the study population to AAP-defined high risk infants. Care of infants with prematurity has changed remarkably since the original palivizumab trials were conducted with far less use of invasive ventilation and complete repair of CHD now occurs at a much younger age. Therefore, recent retrospective studies are of potential value to assessing real-world effectiveness in the modern era. At this time, an updated evidence base of palivizumab effectiveness with an inclusive paediatric population is needed. Given the evidence for potential resistance to palivizumab, there is a need to determine whether there is new evidence demonstrating decreasing effectiveness over time. This evidence is important to justify continued use of palivizumab for the current indications, or to inform changes to clinical guidelines and practice. Coupled with its implications on health outcomes, confirming palivizumab effectiveness is also foundational to a real assessment of economic benefit and implications for emerging RSV vaccines. Objective To evaluate the incidence of palivizumab breakthrough RSV infections in children. Human respiratory syncytial virus (RSV) infects almost all children within the first 2 years of life. Typically, RSV infection manifests as the common cold; however, some children develop acute lower respiratory infections (ALRI), a leading cause of childhood morbidity and mortality. Globally, RSV accounts for approximately 33.1 million annual ALRI episodes in children. Across North America, the rates of RSV-associated paediatric hospitalisations remain high. Although supportive care is commonly used to manage RSV symptoms, no existing treatment for RSV infection has been demonstrated to be effective. Palivizumab is an expensive monoclonal antibody that binds to a RSV surface glycoprotein, the fusion protein and inhibits virus-cell membrane fusion; thereby inhibiting RSV replication. Despite its promising mechanism of action, the clinical evidence for palivizumab is limited to demonstrated effectiveness within select population groups in developed countries, where palivizumab is affordable and available. Moreover, some animal and human studies have demonstrated RSV resistance to palivizumab. In an effort to mitigate the burden of childhood RSV, attention has been directed towards preventative strategies. Current clinical recommendations from the American Academy of Paediatrics (AAP), which may differ from other jurisdictions, indicate for all high risk infants to receive palivizumab: preterm infants with chronic lung disease (CLD), preterm infants without CLD of prematurity or congenital heart disease (CHD), infants with haemodynamically significant CHD, children with anatomic pulmonary abnormalities or neuromuscular disorder and profoundly immunocompromised children. To date, there have been two reviews on the effectiveness of palivizumab ; however, both are limited in scope. The 2013 review includes only randomised controlled trials, and synthesised findings from a very small number of studies. The 2014 review excluded prospective studies and registries with end dates in 2013. Moreover, both existing reviews limited the study population to AAP-defined high risk infants. Care of infants with prematurity has changed remarkably since the original palivizumab trials were conducted with far less use of invasive ventilation and complete repair of CHD now occurs at a much younger age. Therefore, recent retrospective studies are of potential value to assessing real-world effectiveness in the modern era. At this time, an updated evidence base of palivizumab effectiveness with an inclusive paediatric population is needed. Given the evidence for potential resistance to palivizumab, there is a need to determine whether there is new evidence demonstrating decreasing effectiveness over time. This evidence is important to justify continued use of palivizumab for the current indications, or to inform changes to clinical guidelines and practice. Coupled with its implications on health outcomes, confirming palivizumab effectiveness is also foundational to a real assessment of economic benefit and implications for emerging RSV vaccines. To evaluate the incidence of palivizumab breakthrough RSV infections in children. This systematic review protocol has been registered with the PROSPERO International Prospective Register of Systematic Reviews ( http://www.crd.york.ac.uk/prospero ), as CRD42019122120. We prepared this protocol in accordance with the Preferred Reporting Items for Systematic Review and Meta-analysis Protocol (PRISMA-P) checklist. Study design We will undertake a PRISMA-compliant systematic review and meta-analysis guided by the Cochrane Collaboration and Centre for Reviews and Dissemination. Inclusion and exclusion criteria Types of studies We will include primary population-based research studies that report on the effectiveness of palivizumab use for prevention of RSV-confirmed hospitalisation in children (ie, randomised controlled trials, cohort studies and case-control studies) and will screen the reference lists of systematic reviews identified by the search for relevant studies. Full-text studies from databases and other sources will be included, however abstracts without full text will be excluded. There will be no language restrictions. We will include studies published from 1997 to present, as marketing of palivizumab for paediatric prophylaxis was approved by the US Federal Drug Administration in June 1998. Population Studies on children who received palivizumab for any indication will be included. Infants or children with cystic fibrosis will be excluded as a recent RSV systematic review was published on this population. Intervention/comparison We will include studies that report the effectiveness of palivizumab to prevent RSV hospitalisation, with or without a comparison group. Studies that include historical RSV cohorts for comparison will be treated as a single arm study, as RSV hospitalisation rates vary from year to year. We will exclude studies that involve palivizumab use for RSV outbreak control in hospitals and studies that retrospectively determine palivizumab use from a population of patients hospitalised for RSV infection. Outcome measures Primary outcomes measures will include hospitalisation due to RSV, hospital length of stay, intensive care unit (ICU) admission, ICU length of stay and the need for mechanical ventilation. The secondary outcome will be mortality due to RSV infection. Search methods Electronic databases We will search the following electronic databases: Ovid MEDLINE (1947-), Ovid Embase (1974-), Wiley Cochrane Library (inception-) and Web of Science (All databases) via Clarivate Analytics (1864-). Our search strategy will combine index terms (eg, MeSH) and text words for palivizumab, or monoclonal antibodies for RSV. The MEDLINE search strategy will be peer-reviewed by a second research librarian and then translated into additional databases. Search results will be limited to human studies published since 1997. No language restrictions will be applied and any studies included based on their English title and/or abstract will be translated. See online for the MEDLINE search strategy. 10.1136/bmjopen-2019-029832.supp1 Supplementary data Additional sources We will also search the trial registry ClinicalTrials.gov for relevant drug trials on palivizumab, and the regulatory agency website Drugs@FDA for unpublished reports. Conference proceedings will be included in our search of Embase and Web of Science (which includes the Conference Proceedings Citation Index). Finally, we will hand-search reference lists of relevant systematic reviews and included studies. Search results will be exported to Endnote X7 for primary screening. Data collection Study selection Two review authors will independently screen all studies identified from the search in two phases, primary and secondary screening, assessing study eligibility using predetermined inclusion and exclusion criteria. During primary screening, the authors will review the title and abstracts of all unique records retrieved during the search using EndNote X7, and classify each as ‘include/unsure’ or ‘exclude’. For secondary screening, full texts of all ‘include/unsure’ records will be retrieved and the two authors will independently review and select studies that meet the inclusion criteria. If disagreements between the authors cannot be resolved by discussion, a third review author will be consulted. Data extraction Data from included studies will be extracted using piloted and standardised electronic data forms (Microsoft Excel 2010) by one author, with partial verification by a second author to ensure accuracy and completeness. Discrepancies will be resolved through discussion or third party consultation. The data extraction form will capture study characteristics (ie, publication year, study design, funding support, sample size, palivizumab prophylaxis regimen), patient characteristics (ie, age, sex, comorbid disease), type of RSV testing performed and outcomes (ie, RSV-confirmed hospital admission, length of stay, need for ventilation, ICU admission and RSV-associated mortality). Data analysis and synthesis Primary analyses Descriptive analysis will be used for study and patient characteristics. Pooled effectiveness data will undergo meta-analysis in RevMan (RevMan V.5.3 Cochrane) using relative risk ratios for binary events and mean differences for continuous data. Due to expected variations between the included studies we will use a random effects model and heterogeneity will be measured using the I 2 statistic, with values greater than 25%, 50% and 75% considered moderate, high and very high heterogeneity, respectively. If possible publication bias will be determined using a funnel plot and Egger’s test. Secondary analyses If appropriate with the generated data, we will calculate the number needed to treat, using pooled risk ratios and ranges of control event rates from our primary analysis, to enhance the clinical meaningfulness of our report. Subgroup and sensitivity analyses Subgroup analyses will be conducted based on patient groups of clinical interest and intervention compliance. The following subgroups have been identified: preterm infants, children with CLD, children with haemodynamically significant CHD, mixed risk population (data were not divided according to a specific risk) and two subgroups based on compliance with the dosing schedule—children who received either <80% or ≥80% of the recommended number of palivizumab doses. We will perform sensitivity analyses based on study design (ie, prospective vs retrospective), type of RSV testing performed and risk of bias (see section ‘Quality Assessment’). Missing data For the meta-analysis, we will calculate missing parameters from the provided data if possible and exclude studies that do not report or provide enough information to calculate effect estimates. We will not attempt to contact authors regarding missing or unreported data. Quality assessment Individual studies Two authors will independently assess the methodological quality of each primary study based on the study design. For randomised control trials we will apply the Cochrane Risk of Bias tool, using the high, low or unclear categories to define risk. For cohort and case-control studies we will apply the Newcastle-Ottawa Scale with a score of 3 or less considered poor quality with a high risk of bias, a score between 4 and 6 considered fair quality with a medium risk of bias and a score of 7 or greater considered good quality with a low risk of bias. Discrepancies will be resolved through discussion or third party consultation. Analyses will be informed by assessment of risk of bias, and when necessary, we will down-weigh studies with high risk of bias. Body of evidence Two authors will independently assess the certainty of the body of evidence for each outcome using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool using direction from the GRADE Handbook. Assessments will be made over five domains: risk of bias, inconsistency, indirectness, imprecision and publication bias, with quality scored as high, moderate, low or very low. Ethics and dissemination There is no ethics protocol to be approved or reported. For methodological transparency, our protocol will be submitted for peer-reviewed publication; and likewise, our final manuscript with study data will be disseminated through peer-reviewed publication. Study findings will be presented at academic conferences and scientific meetings engaging researchers in the field and paediatric healthcare providers. If findings from this review necessitate updates to current clinical practice guidelines, we plan to establish a clinician working group to develop an evidence-based report targeted to health administrators and decision makers. We will undertake a PRISMA-compliant systematic review and meta-analysis guided by the Cochrane Collaboration and Centre for Reviews and Dissemination. Types of studies We will include primary population-based research studies that report on the effectiveness of palivizumab use for prevention of RSV-confirmed hospitalisation in children (ie, randomised controlled trials, cohort studies and case-control studies) and will screen the reference lists of systematic reviews identified by the search for relevant studies. Full-text studies from databases and other sources will be included, however abstracts without full text will be excluded. There will be no language restrictions. We will include studies published from 1997 to present, as marketing of palivizumab for paediatric prophylaxis was approved by the US Federal Drug Administration in June 1998. Population Studies on children who received palivizumab for any indication will be included. Infants or children with cystic fibrosis will be excluded as a recent RSV systematic review was published on this population. Intervention/comparison We will include studies that report the effectiveness of palivizumab to prevent RSV hospitalisation, with or without a comparison group. Studies that include historical RSV cohorts for comparison will be treated as a single arm study, as RSV hospitalisation rates vary from year to year. We will exclude studies that involve palivizumab use for RSV outbreak control in hospitals and studies that retrospectively determine palivizumab use from a population of patients hospitalised for RSV infection. Outcome measures Primary outcomes measures will include hospitalisation due to RSV, hospital length of stay, intensive care unit (ICU) admission, ICU length of stay and the need for mechanical ventilation. The secondary outcome will be mortality due to RSV infection. We will include primary population-based research studies that report on the effectiveness of palivizumab use for prevention of RSV-confirmed hospitalisation in children (ie, randomised controlled trials, cohort studies and case-control studies) and will screen the reference lists of systematic reviews identified by the search for relevant studies. Full-text studies from databases and other sources will be included, however abstracts without full text will be excluded. There will be no language restrictions. We will include studies published from 1997 to present, as marketing of palivizumab for paediatric prophylaxis was approved by the US Federal Drug Administration in June 1998. Studies on children who received palivizumab for any indication will be included. Infants or children with cystic fibrosis will be excluded as a recent RSV systematic review was published on this population. We will include studies that report the effectiveness of palivizumab to prevent RSV hospitalisation, with or without a comparison group. Studies that include historical RSV cohorts for comparison will be treated as a single arm study, as RSV hospitalisation rates vary from year to year. We will exclude studies that involve palivizumab use for RSV outbreak control in hospitals and studies that retrospectively determine palivizumab use from a population of patients hospitalised for RSV infection. Primary outcomes measures will include hospitalisation due to RSV, hospital length of stay, intensive care unit (ICU) admission, ICU length of stay and the need for mechanical ventilation. The secondary outcome will be mortality due to RSV infection. Electronic databases We will search the following electronic databases: Ovid MEDLINE (1947-), Ovid Embase (1974-), Wiley Cochrane Library (inception-) and Web of Science (All databases) via Clarivate Analytics (1864-). Our search strategy will combine index terms (eg, MeSH) and text words for palivizumab, or monoclonal antibodies for RSV. The MEDLINE search strategy will be peer-reviewed by a second research librarian and then translated into additional databases. Search results will be limited to human studies published since 1997. No language restrictions will be applied and any studies included based on their English title and/or abstract will be translated. See online for the MEDLINE search strategy. 10.1136/bmjopen-2019-029832.supp1 Supplementary data Additional sources We will also search the trial registry ClinicalTrials.gov for relevant drug trials on palivizumab, and the regulatory agency website Drugs@FDA for unpublished reports. Conference proceedings will be included in our search of Embase and Web of Science (which includes the Conference Proceedings Citation Index). Finally, we will hand-search reference lists of relevant systematic reviews and included studies. Search results will be exported to Endnote X7 for primary screening. We will search the following electronic databases: Ovid MEDLINE (1947-), Ovid Embase (1974-), Wiley Cochrane Library (inception-) and Web of Science (All databases) via Clarivate Analytics (1864-). Our search strategy will combine index terms (eg, MeSH) and text words for palivizumab, or monoclonal antibodies for RSV. The MEDLINE search strategy will be peer-reviewed by a second research librarian and then translated into additional databases. Search results will be limited to human studies published since 1997. No language restrictions will be applied and any studies included based on their English title and/or abstract will be translated. See online for the MEDLINE search strategy. 10.1136/bmjopen-2019-029832.supp1 Supplementary data We will also search the trial registry ClinicalTrials.gov for relevant drug trials on palivizumab, and the regulatory agency website Drugs@FDA for unpublished reports. Conference proceedings will be included in our search of Embase and Web of Science (which includes the Conference Proceedings Citation Index). Finally, we will hand-search reference lists of relevant systematic reviews and included studies. Search results will be exported to Endnote X7 for primary screening. Study selection Two review authors will independently screen all studies identified from the search in two phases, primary and secondary screening, assessing study eligibility using predetermined inclusion and exclusion criteria. During primary screening, the authors will review the title and abstracts of all unique records retrieved during the search using EndNote X7, and classify each as ‘include/unsure’ or ‘exclude’. For secondary screening, full texts of all ‘include/unsure’ records will be retrieved and the two authors will independently review and select studies that meet the inclusion criteria. If disagreements between the authors cannot be resolved by discussion, a third review author will be consulted. Data extraction Data from included studies will be extracted using piloted and standardised electronic data forms (Microsoft Excel 2010) by one author, with partial verification by a second author to ensure accuracy and completeness. Discrepancies will be resolved through discussion or third party consultation. The data extraction form will capture study characteristics (ie, publication year, study design, funding support, sample size, palivizumab prophylaxis regimen), patient characteristics (ie, age, sex, comorbid disease), type of RSV testing performed and outcomes (ie, RSV-confirmed hospital admission, length of stay, need for ventilation, ICU admission and RSV-associated mortality). Two review authors will independently screen all studies identified from the search in two phases, primary and secondary screening, assessing study eligibility using predetermined inclusion and exclusion criteria. During primary screening, the authors will review the title and abstracts of all unique records retrieved during the search using EndNote X7, and classify each as ‘include/unsure’ or ‘exclude’. For secondary screening, full texts of all ‘include/unsure’ records will be retrieved and the two authors will independently review and select studies that meet the inclusion criteria. If disagreements between the authors cannot be resolved by discussion, a third review author will be consulted. Data from included studies will be extracted using piloted and standardised electronic data forms (Microsoft Excel 2010) by one author, with partial verification by a second author to ensure accuracy and completeness. Discrepancies will be resolved through discussion or third party consultation. The data extraction form will capture study characteristics (ie, publication year, study design, funding support, sample size, palivizumab prophylaxis regimen), patient characteristics (ie, age, sex, comorbid disease), type of RSV testing performed and outcomes (ie, RSV-confirmed hospital admission, length of stay, need for ventilation, ICU admission and RSV-associated mortality). Primary analyses Descriptive analysis will be used for study and patient characteristics. Pooled effectiveness data will undergo meta-analysis in RevMan (RevMan V.5.3 Cochrane) using relative risk ratios for binary events and mean differences for continuous data. Due to expected variations between the included studies we will use a random effects model and heterogeneity will be measured using the I 2 statistic, with values greater than 25%, 50% and 75% considered moderate, high and very high heterogeneity, respectively. If possible publication bias will be determined using a funnel plot and Egger’s test. Secondary analyses If appropriate with the generated data, we will calculate the number needed to treat, using pooled risk ratios and ranges of control event rates from our primary analysis, to enhance the clinical meaningfulness of our report. Subgroup and sensitivity analyses Subgroup analyses will be conducted based on patient groups of clinical interest and intervention compliance. The following subgroups have been identified: preterm infants, children with CLD, children with haemodynamically significant CHD, mixed risk population (data were not divided according to a specific risk) and two subgroups based on compliance with the dosing schedule—children who received either <80% or ≥80% of the recommended number of palivizumab doses. We will perform sensitivity analyses based on study design (ie, prospective vs retrospective), type of RSV testing performed and risk of bias (see section ‘Quality Assessment’). Missing data For the meta-analysis, we will calculate missing parameters from the provided data if possible and exclude studies that do not report or provide enough information to calculate effect estimates. We will not attempt to contact authors regarding missing or unreported data. Descriptive analysis will be used for study and patient characteristics. Pooled effectiveness data will undergo meta-analysis in RevMan (RevMan V.5.3 Cochrane) using relative risk ratios for binary events and mean differences for continuous data. Due to expected variations between the included studies we will use a random effects model and heterogeneity will be measured using the I 2 statistic, with values greater than 25%, 50% and 75% considered moderate, high and very high heterogeneity, respectively. If possible publication bias will be determined using a funnel plot and Egger’s test. If appropriate with the generated data, we will calculate the number needed to treat, using pooled risk ratios and ranges of control event rates from our primary analysis, to enhance the clinical meaningfulness of our report. Subgroup analyses will be conducted based on patient groups of clinical interest and intervention compliance. The following subgroups have been identified: preterm infants, children with CLD, children with haemodynamically significant CHD, mixed risk population (data were not divided according to a specific risk) and two subgroups based on compliance with the dosing schedule—children who received either <80% or ≥80% of the recommended number of palivizumab doses. We will perform sensitivity analyses based on study design (ie, prospective vs retrospective), type of RSV testing performed and risk of bias (see section ‘Quality Assessment’). For the meta-analysis, we will calculate missing parameters from the provided data if possible and exclude studies that do not report or provide enough information to calculate effect estimates. We will not attempt to contact authors regarding missing or unreported data. Individual studies Two authors will independently assess the methodological quality of each primary study based on the study design. For randomised control trials we will apply the Cochrane Risk of Bias tool, using the high, low or unclear categories to define risk. For cohort and case-control studies we will apply the Newcastle-Ottawa Scale with a score of 3 or less considered poor quality with a high risk of bias, a score between 4 and 6 considered fair quality with a medium risk of bias and a score of 7 or greater considered good quality with a low risk of bias. Discrepancies will be resolved through discussion or third party consultation. Analyses will be informed by assessment of risk of bias, and when necessary, we will down-weigh studies with high risk of bias. Body of evidence Two authors will independently assess the certainty of the body of evidence for each outcome using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool using direction from the GRADE Handbook. Assessments will be made over five domains: risk of bias, inconsistency, indirectness, imprecision and publication bias, with quality scored as high, moderate, low or very low. Two authors will independently assess the methodological quality of each primary study based on the study design. For randomised control trials we will apply the Cochrane Risk of Bias tool, using the high, low or unclear categories to define risk. For cohort and case-control studies we will apply the Newcastle-Ottawa Scale with a score of 3 or less considered poor quality with a high risk of bias, a score between 4 and 6 considered fair quality with a medium risk of bias and a score of 7 or greater considered good quality with a low risk of bias. Discrepancies will be resolved through discussion or third party consultation. Analyses will be informed by assessment of risk of bias, and when necessary, we will down-weigh studies with high risk of bias. Two authors will independently assess the certainty of the body of evidence for each outcome using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool using direction from the GRADE Handbook. Assessments will be made over five domains: risk of bias, inconsistency, indirectness, imprecision and publication bias, with quality scored as high, moderate, low or very low. There is no ethics protocol to be approved or reported. For methodological transparency, our protocol will be submitted for peer-reviewed publication; and likewise, our final manuscript with study data will be disseminated through peer-reviewed publication. Study findings will be presented at academic conferences and scientific meetings engaging researchers in the field and paediatric healthcare providers. If findings from this review necessitate updates to current clinical practice guidelines, we plan to establish a clinician working group to develop an evidence-based report targeted to health administrators and decision makers. This systematic review will address a priority health topic, childhood RSV infection, and contribute to the advancement of evidence-based management practices. Based on studies to date, the evidence for palivizumab effectiveness in different subgroups after licensure remains unclear. Moreover, prior reviews have excluded retrospective studies in their analyses of palivizumab effectiveness. This exclusion limits our present understanding of palivizumab, as retrospective studies provide valuable ‘real world’ data that is often not captured with controlled trials. Our proposed systematic review and meta-analysis will build a comprehensive evidence base on palivizumab effectiveness in children, taking into consideration clinically important ‘real world’ factors, such as patient comorbidities and compliance. Findings from this review will inform clinical decision-making with regards to expanded indications for palivizumab prophylaxis, and potentially guide vaccination strategies. Potential limitations We anticipate potential limitations to this review. Methods for RSV testing have rapidly evolved since palivizumab was introduced in 1998. As such, in recent years, molecular assays used to detect RSV antigen have greater sensitivity, which may lead to an underestimation of breakthrough RSV admission in earlier studies. In contrast, some ‘Choosing Wisely’ campaigns suggest that routine viral testing is not indicated when children are admitted with lower respiratory tract infections, potentially underestimating the number of breakthrough admissions in more recent studies. We anticipate potential limitations to this review. Methods for RSV testing have rapidly evolved since palivizumab was introduced in 1998. As such, in recent years, molecular assays used to detect RSV antigen have greater sensitivity, which may lead to an underestimation of breakthrough RSV admission in earlier studies. In contrast, some ‘Choosing Wisely’ campaigns suggest that routine viral testing is not indicated when children are admitted with lower respiratory tract infections, potentially underestimating the number of breakthrough admissions in more recent studies. RSV is the most common cause of paediatric respiratory hospitalisations. This systematic review of the available evidence on the effectiveness of palivizumab has the potential to change how palivizumab is used and may influence the economic evaluation of RSV vaccines. Amendments to the protocol will be documented and reported in PROSPERO, detailing the changes made, date, timing within review conduct and purpose. All amendments will be reported in the final manuscript. Reviewer comments Author's manuscript |
An Isolated Hydatid Cyst of the Spleen with High Serum Levels of CA 19-9—A Meaningful Association or Just a Challenge for Diagnosis? A Case Report | c9c9b5f6-0342-46ea-907a-8f19d6a81d80 | 11857084 | Surgical Procedures, Operative[mh] | Isolated hydatid cysts of the spleen are exceptional, and often reported as a case presentation or a small series of patients . Surgery represents the main therapeutic option for these patients , with an emerging role of minimally invasive, parenchyma-sparing procedures ; however, total splenectomy is indicated for large cysts . High serum levels of the CA 19-9 tumor marker are usually associated with malignancies, mainly pancreatic and biliary tract cancers . Although high serum levels of CA 19-9 are also reported in benign pathologies , there are only a few reports on patients with benign non-parasitic splenic cysts with elevated CA 19-9 serum levels . Furthermore, the clinical value of high serum levels of CA 19-9 in the context of benign spleen pathology remains unclear . In light of this, the case of a patient with an isolated hydatid cyst of the spleen, with preoperative high serum levels of CA 19-9 in the absence of other pathologies is presented, with normalization of CA 19-9 serum levels after surgery.
A 25-year-old female non-smoker, non-drinker patient was admitted to Fundeni Clinical Institute in Bucharest for left upper quadrant abdominal pain. The symptoms appeared a few days before the presentation and became more intense, making the patient present at the hospital in an emergency setting. The medical history of the patient was unremarkable. The clinical examination revealed no pathological changes, including a gynecological clinical examination that did not identify any significant genital pathology (corroborated by further imaging explorations). The laboratory findings were within normal limits (including serum levels of amylase and lipase), except for very high serum levels of CA 19-9—700 UI/mL (normal: <37 UI/mL). An initial abdominal ultrasound examination identified a large cystic lesion in the left upper quadrant of the abdomen. However, it did not identify a particular point of origin (suggested pancreas vs. spleen). Further contrast-enhanced computed tomography revealed a large cystic lesion of 9.7/13/10.6 cm occupying almost the entire spleen, with linear calcifications of the wall. The cystic lesion was well circumscribed, with iodophil thin septum inside and fluid content without any solid component inside. The abovementioned cystic lesion compressed without invading the surrounding organs such as the stomach, left hemi-liver, distal pancreas, left kidney, jejunal loops, and the celiac trunk , and no other abnormalities were observed, particularly at the level of the pancreas, biliary tract, or genital organs. After the administration of non-steroidal anti-inflammatory parenteral drugs, the abdominal pain was relieved, allowing for a planned surgery. With a preoperative diagnosis of a large symptomatic cyst of the spleen, the patient was submitted to surgery in November 2023. At laparotomy, an isolated hydatid cyst of the spleen was discovered, and a total splenectomy was performed , with an uneventful postoperative outcome; the patient was discharged on postoperative day 5. The pathology examination confirmed the diagnosis of a hydatid cyst . One month after surgery, the serum levels of CA 19-9 decreased to 41.8 UI/mL and normalized at two months (CA 19-9 serum level = 10.8 UI/mL). The patient underwent postoperative administration of oral anti-parasitic drugs (albendazole 400 mg per day, three weeks with one week pause, for three months). The patient’s clinical examination, laboratory tests (including serum level of CA 19-9), and ultrasound examination at 6 and 12 months after surgery did not reveal any pathological findings.
Elevated serum levels of CA 19-9 in hydatic disease have been previously reported in a limited number of patients. Thus, two studies analyzing the data of 39 to 40 patients with liver hydatid disease identified an elevated serum level of CA 19-9 in up to 28.2% of the patients; however, only mildly elevated values were observed in these studies . A few other case report papers showed patients with very high CA 19-9 serum levels associated with a hydatid cyst of the breast or liver . However, the reported cases of liver hydatid cysts and elevated serum levels of CA 19-9 have had associated jaundice with cholangitis for the most significant part of the patients ; high serum levels of CA 19-9 are not uncommon in patients with benign etiologies of jaundice, albeit the mechanism remains largely unclear. Remarkably, our patient presented very high levels of CA 19-9 in the absence of jaundice or other pathologies. Pfister et al. propose, as an explanation for high serum levels of CA 19-9 in hydatid disease, the presence of a few substances that possibly bear the Lewis-a antigen, or with closely related structures that are recognized by the anti-CA 19-9 antibodies; these substances are presumed to originate either from the parasite, or as a response of the host to the infection (particularly from Echinococcus multilocularis) . As was the case in our reported patient, resection of the hydatid cysts was followed by normalization of CA 19-9 serum levels , a situation that might sustain the relationship between the hydatid disease and high CA 19-9 serum levels, albeit there are no shreds of evidence to sustain the Pfister theory. It was suggested that the epithelial cells of the splenic cysts might produce tumor makers within the cysts that may further be secreted into the blood . High serum levels of CA 19-9 were previously reported in patients with non-parasitic benign spleen cysts, particularly epidermoid cysts . This association may increase the complexity of the diagnosis, complicating the distinction between benign and malignant spleen pathology or even malignant pancreatic pathology, which may impact the therapeutic strategy. In almost all cases reported in the literature, the normalization of serum CA 19-9 levels was obtained after surgery . In the above-presented case, the clinical differential diagnosis included any other cause of intense abdominal pain in a young woman. Thus, a gynecological pathology was excluded by the gynecological clinical examination, corroborated the imaging explorations. This aspect is exciting, because a few frequent gynecological pathologies in young women, such as cystic teratomas, ovarian abscesses, or endometriosis, may be associated with increased CA 19-9 serum levels . Furthermore, a biliary and pancreatic inflammatory pathology was excluded, given the absence of risk factors from the anamnesis, the normal serum levels of amylase and lipase, and the normal appearance of the pancreas and gallbladder at ultrasound examination and computed tomography. The initial ultrasound examination identified a sizeable cystic lesion without a precise point of origin, suggesting it was in the pancreas or the spleen. Thus, the patient was considered to be tested for serum CA 19-9 levels before the computer tomography exploration, as a cystic pancreatic neoplasm could not be excluded at that time. Cystic pancreatic neoplasms, such as mucinous cysts, solid pseudopapillary tumors, or even neuroendocrine tumors with cystic transformation should be included in the imaging differential diagnosis of a pancreatic cystic lesion in a young woman. A meta-analysis published in 2016 has shown that serum levels of CA 19-9 are valuable in differentiating benign and malignant cystic neoplasms . The imaging differential diagnosis of a pancreatic cyst with parietal calcifications may include the following benign and malignant pancreatic pathologies: mucinous pancreatic cystic neoplasms , solid pseudopapillary tumors , neuroendocrine pancreatic tumors with cystic transformation , calcified pseudocyst in the context of chronic pancreatitis , or even pancreatic adenocarcinoma with cystic changes . The imaging differential diagnosis of a splenic cyst with parietal calcifications may include other benign pathologies besides hydatid cysts, such as epidermoid cysts, splenic pseudocyst, calcified granulomas of infectious etiologies, or even malignant splenic pathologies: calcified splenic metastasis, lymphoma, epithelioid hemangioendothelioma, and splenic sarcoma . However, a recent meta-analysis exploring imaging predictors of malignancies in splenic lesions associated calcifications with higher chances of benign pathology . Other benign causes of elevated serum levels of CA 19-9 may include hepatic diseases (alcoholic liver cirrhosis or hepatitis, drug-induced hepatitis, or chronic hepatitis B virus, etc.), pulmonary diseases, rheumatoid arthritis, polycystic renal disease, or endocrine diseases, including diabetes mellitus . A recent large populational study of young adults has associated elevated serum levels of CA 19-9 with an increased risk of developing type 2 diabetes in men, but not in women . Interestingly, patients with an epidermoid cyst of the left diaphragm or primary retroperitoneal mucinous cystadenoma associated with high serum levels of CA 19-9 were previously described and could be included in the imaging differential diagnosis of the above-presented case. The normalization of the CA 19-9 serum levels after splenectomy in our patient, without any other further pathologies identified in the postoperative surveillance, makes the hydatid cyst of the spleen the most likely cause of its high preoperative CA 19-9 serum levels. However, the patient should be closely clinically monitored, including using bioumoral markers and imaging.
High serum levels of CA 19-9 in the context of a splenic cyst, including a hydatid one, may complicate diagnosis and challenge the therapeutic strategy. It may be difficult to distinguish between a benign and malignant spleen pathology, or even a malignant pancreatic pathology. The source and clinical value of high serum levels of CA 19-9 in hydatid cysts of the spleen remain unclear.
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Human Decedent Identification Unit: identifying the deceased at a South African medico-legal mortuary | 6efc0d9b-8483-4ebf-ade4-a50b13d572c7 | 9510341 | Pathology[mh] | The Forensic Pathology Services (FPS) is legally mandated to perform postmortem investigations of all cases of unnatural death in South Africa . An unnatural death is legally defined as any death due to unnatural causes as contemplated in the Inquests Act 1959 (Act No. 58 of 1959) , which includes any death due to physical or chemical influence, death as a result of an act of commission or omission, procedure-related deaths and any death which is sudden and unexpected, or unexplained, or where the cause of death is not apparent. South Africa’s most populated province is Gauteng, which has an estimated population of over 12 million . The province is serviced by 11 FPS medico-legal mortuaries, which are divided into two clusters. The Northern Cluster is comprised of three FPS facilities, and the Southern Cluster is comprised of eight FPS facilities. In the 2006–2020 period, these two clusters investigated an average of 15,702.5 cases of unnatural deaths per annum. The Southern Cluster processed approximately ± 17.5% of all unnatural forensic postmortem investigations in South Africa. The most recent available statistics provided by to the authors by the FPS indicate that the eight Southern Cluster facilities performed 183,783 postmortem investigations between the years 2006 and 2020, which constitutes 78% of all unnatural deaths in Gauteng. The Southern Cluster FPS mortuaries are associated with the University of the Witwatersrand’s (Wits) Department of Forensic Medicine and Pathology. The academic seat of the department and the Southern Cluster is located at the Johannesburg FPS medico-legal mortuary, which services the greater Johannesburg area. The city of Johannesburg has a population of 4.9 million , making it South Africa’s largest and most populous city. Consequently, the Johannesburg FPS Medico-legal Laboratory is one of the largest and busiest FPS facilities in South Africa. The Johannesburg FPS performed 42,681 postmortem investigations between the years 2006 and 2020, averaging 2845.4 cases per annum. The city of Johannesburg houses a large migrant population; drawn from remote national and foreign regions, largely the result of the perceived notion that the city provides many economic opportunities . Many of these migrants are not documented. Undocumented migrants include individuals who have entered the country through irregular means without the required, official documentation established by the country for entry, habitation, and economic activity in the country. The exact number of undocumented migrants in South Africa is unknown; however, estimates range between 2 and 5 million . This affects the service delivery of the FPS because the bodies of undocumented migrants remain unidentified and unclaimed. The Johannesburg FPS mortuary constantly receives a disproportionally large number of unidentified cases relative to the other Gauteng facilities. In 2016, the Johannesburg FPS investigated 3106 cases of unnatural death, of which 319 cases were not identified—constituting 10.3% of the Johannesburg cases—a trend that has remains relatively consistent. South African legislation and regulations direct that the inquest into unnatural deaths by the Forensic Pathology Services not only determine and record the cause of death but that the FPS must also assist in the process of identification of the deceased person. The Regulations outline that at admission of a body, a clear photograph of the face of the deceased must be taken. Visual identification is the most common method of identification. The deceased may be visually identified by a spouse, partner, major child, parent, guardian, major brother, major sister, caregiver, or any person with personal knowledge of the deceased. Visual identification is considered an acceptable method of identification and is the most commonly used method internationally . This is due to the pragmatic and time-saving benefits of visual identification. If a decedent has not been identified within 7 days, the body must be moved to a freezer after a set of fingerprints have been taken (Fig. ). The fingerprints are compared to those recorded in the Home Affairs National Identification System (HANIS) (which are added to the system when individuals receive their identity document from the age of 16 years), and when this is unsuccessful, the prints are compared to the electronic SAPS’s criminal fingerprint database using the automated fingerprint identification system (AFIS). Where visual identification is not possible, such as in cases where there is trauma to the face and body or no presumptive identification is possible, and/or fingerprint identification is unsuccessful, other means of identification must be instituted . Although the identification process is the responsibility of the South African Police Services (SAPS), the identification process must be facilitated by the FPS who is the custodian of the bodies . These methods may be instituted by the authorized medical practitioner, who is defined as including, but not limited to, forensic pathology officers, forensic pathology specialist investigators, scientists, and or any other person appointed in the Forensic Pathology Service to work in a support capacity in a medico-legal mortuary or designated Forensic Pathology Service facility. Prescribed methods of identification include dental and radiological examinations and DNA samples which are submitted to the South African Police Services Forensic Science Laboratory for analysis . INTERPOL has determined that the most reliable methods of identification, termed primary identifiers, include friction ridge analysis (fingerprints), comparative dental analysis, and DNA analysis . Secondary means of identification, termed secondary identifiers, serve to support primary identifiers. Secondary identifiers include demographic descriptions, tattoos, property, and clothing recovered on the body . Primary methods of identification are accepted on their own for a positive identification; however, these are not always available (such as in severely decomposed cases and partial remains) (Fig. ). Secondary identifiers can be used for identification purposes if primary identifiers are not available. Ideally, a combination of primary and secondary identifiers is used to make a positive identification when numerous identifiers are available . If the decedent remains unidentified for 30 days and all prescribed methods of identification have been followed, the local authority (municipality) arranges for the state-sponsored burial of the decedent . Unfortunately, this process is not always implemented according to the mandated time-frames and can be drawn out over months or even years (Fig. ). The decedent’s level of decomposition determines which identification processes are employed. Skeletonized remains are referred for forensic anthropological analysis by the Human Variation and Identification Research Unit (HVIRU) in the Wits School of Anatomical Sciences. Decedents that are not yet in the advanced stages of decomposition are referred to the Human Decedent Identification Unit (ID Unit) for analysis. The purpose of the present article is to describe the creation and processes of the ID Unit. The ID Unit was created in 2016 to help better address the growing issue of unidentified decedents in Johannesburg. This unit was created as a humanitarian effort to support the FPS and SAPS in the identification of the unidentified deceased. The unit’s standard operating procedures and documentation were developed in a working group that involved the Forensic Pathology Services, University of the Witwatersrand, and University of Amsterdam. The postmortem data collection forms were adapted from the INTERPOL DVI postmortem Form for Unidentified Human Remains (“pink form”) . The ID Unit currently runs as a collaborative effort between the Wits Department of Forensic Pathology Services, the Gauteng Southern Cluster Forensic Pathology Services, the International Committee of the Red Cross (as part of their Missing and Deceased Migrant Pilot Project), and the Victim Identification Centre (VIC) of the SAPS. The unit is a pilot program, which aims to expand in the future to other FPS facilities in South Africa and Africa. Although the primary aim of the ID Unit is to perform secondary postmortem examinations for identification purposes, a secondary aim of the ID Unit is to provide practical training to postgraduate forensic students in the collection and recording of primary and secondary identifiers from unidentified decedents. The unit includes academic and technical staff of the Wits Department of Forensic Medicine and Pathology (including forensic anthropologists, forensic entomologists, and a forensic photographer) and their postgraduate students. The unit is supported by the forensic pathologists, medical officers, and forensic pathology officers of the Johannesburg FPS. Bodies that have not been identified 7 days after admission to the JHB FPS mortuary and/or cases whose fingerprints, which were taken at admission, came back with inconclusive results are referred to the ID Unit for further processing (Fig. ). Each body is first scanned using a LODOX StatScan X-ray machine to identify any internal features that could facilitate identification (e.g., healed fractures, surgical interventions or implants, etc.). Photographs of the body are taken, which includes the full body, upper body, lower body, facial profiles (profile and portrait), and the teeth. An external examination of the body is performed, whereby potential identification features are described, recorded, and photographed, including the individual’s height, weight, demographics, hair, scars, tattoos, and skin markings. DNA samples are collected in the forms of femoral blood, toenail, and pubic hair samples. Additional fingerprints are taken regardless of if fingerprints were already taken at admission or during the initial postmortem investigation because fingerprints taken at autopsy sometimes fail because of decomposition or poor-quality prints. The ID Unit employs additional fingerprinting methods not employed during autopsy, including degloving of the skin , photography of desiccated finger pads , and the use of rehydration and tissue filler techniques . Basic odonatological descriptions are recorded and photographed. These include simple descriptions of dental features that a family member might report (such as cosmetic modifications, gaps, missing teeth, and rotated teeth). There is no practicing forensic odontologist in South Africa due to the lack of dental records and the low socio-economic status of most citizens who do not have dental work and relay on traditional herbal remedies. For this reason, only simple dental descriptions and photographs are recorded. All clothing and personal effects are also photographed and recorded. Digital copies of the photographs and the JHB PM ID forms, including copies of the case file forms (SAPS 180/death scene form and autopsy report), and the DNA samples are collected by the SAPS VIC for processing (Fig. ). Publications on the identification of the deceased in South Africa, within a medico-legal context, are lacking. One published study has provided a general overview of the unidentified cases in Cape Town, South Africa . The present study aimed to provide an overview of the unidentified cases at the Johannesburg Forensic Pathology Services medico-legal mortuary and report on the success rate of the positive identifications made subsequent to processing through the ID Unit. This allows for the analysis and review of the identification methods used by the ID Unit. It will allow for comparison to other foreign identification units and facilitate discussion on the feasibility of identification methods used in different national and foreign contexts. The records of the ID Unit were reviewed for a 31-month period from January 2018 to July 2020. Descriptive data was collected from the records including the demographics of the decedents, the type and frequency of fingerprint, and other identifiers collected (blood, hair, and nail). The frequency of positive identifications, their method of identification, and their nationality were recorded. Descriptive statistics were used to compare the data. Approval for the study was granted by the Human Research Ethics Committee – Medical (clearance number: M210235). Over the 31-month period between January 2018 and July 2020, 8560 cases of unnatural death were received at the Johannesburg FPS ( [12pt]{minimal} $$$$ x ¯ = 276 cases per month). Unidentified individuals constituted 8.1% of all cases ( n = 693; [12pt]{minimal} $$$$ x ¯ = 22.4 unidentified cases per month). The ID Unit processed 55.6% ( n = 385) of all unidentified cases (Table ). The demographics of the 385 cases processed by the ID Unit were comprised mostly of adult (100%; n = 385), Black (94.5%; n = 364), males (91.7%; n = 353) (Table ). Samples for DNA analysis were successfully collected from most cases in the form of hair (96.4%; n = 371), blood (92.2%; n = 355), and nail samples (90.1%; n = 347). Fingerprints were successfully collected in 65.5% of cases (n = 252) (Table ). Positive identifications (at the time of writing this article) were subsequently made in 87 cases (22.6% of cases processed by the ID Unit), with 77.4% ( n = 298) of cases remaining unidentified at the time of publication of this study. The most successful method of identification was through fingerprinting (98.9% of cases; n = 86). A single positive identification was facilitated through DNA analysis (1.1%; n = 1) (Table ). The nationalities of the positively identified decedents were confirmed to be from South Africa (52.9%; n = 46), Zimbabwe (5.7%), Uganda (1.1%), Mozambique (1.1%), Malawi (1.1%), South Sudan (1.1%), and undisclosed in 36.8% of cases (Table ). Unidentified decedents constituted 8.1% of all cases received at the Johannesburg FPS and have in the past reached as high as 10% of all cases. This is similar to that reported at the Salt River Mortuary in Cape Town (9.2%) . A similar trend has been reported internationally, such as in Australia’s Victorian Institute of Forensic Medicine who reported that 9% of their cases require primary and secondary methods of identification, since visual identification was not possible . The Department of Forensic Medicine and Pathology (University Hospital R. Poincaré, Garches, France), who performs forensic autopsies of the west area of Paris, reported 9.1% of their cases as unidentified or of “dubious identity” . These rates are higher than that reported in other international regions such as Milan (3.1%) and Fulton County (Georgia, USA), which recorded a rate of 44 unidentified decedents per 1000 deaths reported) . The unidentified cases at each of these facilities is likely a reflection of the population density and the population dynamics of their catchment areas. Despite these few publications, there is a lack of reporting on unidentified decedent rates—globally. Additionally, reviews and analyses of context-specific medico-legal identification processes also remain sparse . The ID Unit in Johannesburg, South Africa has facilitated the identification of 87 individuals over the stipulated research period. Were it not for the supplementary, humanitarian efforts of the ID Unit, these individuals would have remained unidentified and buried by the municipality. Most of the unidentified decedents were adult Black males. This population and sex group was disproportionally higher than the population statistics of the catchment area . The unidentified cohort is most likely a representation of the migrant worker population that resides in the greater Johannesburg region. Many national and foreign migrants are drawn to the city due to the perception that there are greater opportunities and economic prospects . Johannesburg houses a large migrant population with foreigners making up the majority of some Johannesburg neighborhoods . A 2001 census reported that 35.2% of the Johannesburg population were South African internal migrants born outside of the Gauteng province, with 6.7% of the population were foreign migrants . Unfortunately, this census is outdated and may not reflect the current migrant population and the census cannot account for the number of undocumented migrants. It is estimated that South Africa has between 2 and 5 million undocumented migrants; however, the exact number is unknown . Cross-border migrants, refugees, and asylum seekers are more likely to be male . These individuals often travel before their families to establish themselves before the family joins them . This could also account for why females were underrepresented in the unidentified decedent cohort . The other population groups categorized by the South African government—White, Asian (includes Indian), and Colored populations—are also underrepresented in the unidentified decedent cohort . This is likely a result of socio-economic status. Census data suggests that the incomes and living conditions of documented cross-border migrants are similar to South Africans; however, undocumented migrants are often more vulnerable and poorer . Their lower socioeconomic status potentially increases their vulnerability to death by unnatural causes and certainly increases, if not guarantees, their chance of not being identified. Most cases (77.4%) were not positively identified by their fingerprints, which strongly suggests that they are undocumented migrants, further highlighting the vulnerability of the undocumented migrant community . A large proportion (48%) of the subsequently identified decedents in this study were documented foreign nationals originating from countries that historically and presently continue to migrate to South Africa either permanently or temporarily in large numbers . Fingerprinting was the most successful method of identification—in all cases except one. The greatest obstacles to taking complete fingerprints, and subsequently the identification of the remains, include severe desiccation of the fingertips and scavenging of the fingers by vertebrate animals . This study highlights the limitations of DNA analysis as a means of identifying unidentified decedents in South Africa. A presumptive identity is necessary for the deceased’s DNA to be compared to possible biological relatives. DNA was used to identify a decedent in only one case in this sample. This emphasizes the rare occurrence of this in the South African context. The case involved an unidentified elderly female. The investigating officer registered her as a missing person and regularly checked unidentified female bodies at hospitals and morgues. Based on her last known location and the clothing worn at the time she was last seen alive; a presumptive identification was made when compared to the data collected by the ID Unit. DNA was matched to her daughter. Successful DNA analysis for identification of the deceased is not common in the ID Unit due to the large migrant population (local and international migrants) in Johannesburg. DNA analysis relies on presumptive identifications, which is not always possible because the deceased’s family is unaware of their location and the lack of regular communication between the parties. However, there is a positive outcome to collecting DNA samples. Matrices for DNA analysis were successfully collected from all cases in the form of hair (96.4%), blood (92.2%), and/or nail samples (90.1%). Currently, the VIC analyzes these samples and stores an electronic profile of each DNA sample in a database. When a presumptive identification is made, the SAPS will compare the potential family member’s DNA to the digital profile or, with the acquiescence of the SAPS, the digital DNA profile can be provided to a private laboratory for comparison (at the request and cost of the possible family), which speeds up the identification process. As part of their Missing and Deceased Migrant Pilot Project, the ICRC is interviewing family members of migrants who have gone missing and collecting antemortem data. In the event that their antemortem data matches the postmortem data recorded by the ID Unit, the DNA samples could be employed for analysis. The main limitation of the ID Unit is that it is structured as a supplementary service that assists the Johannesburg FPS in the identification of decedents, in addition to the fingerprinting and visual identification processes already employed at the medico-legal mortuary. The average turnaround time for primary identifier processing is approximately 3 to 6 months. The lag in processing is due to administrative steps required for the samples to be handed over to the VIC and then incorporated into the SAPS system and then processed. Since this laboratory also processes police evidence and is one of the busiest forensic science laboratory systems in the country, there is very high pressure on their systems. The ID Unit team is staffed and run by forensic science academic and technical staff and postgraduate students from the Wits Department of Forensic Medicine and Pathology. As a result, the time afforded to the ID Unit to function depends on the availability of the staff and students and the accessibility to the decedents and facilities afforded by the Johannesburg Forensic Pathology Services’ mortuary manager and forensic pathology officers. This is why only 55.6% of unidentified decedents were processed by the ID Unit. Since the ID Unit is a pilot project, such limitations are important to highlight on review when the project is rolled out to other FPS facilities in South Africa. Ideally, the processes of the ID Unit will, in the future, be absorbed into the standard protocols of the FPS. This will increase the positive identification rates and the number of cases that are duly processed. Since the ID Unit is an ongoing pilot project, the standard operating procedures (SOPs) are under constant review and modification. An annual review of the SOP and documentation is required to ensure that the processes are up to date, modified according to present procedures of the collaborating entities. Presently the processes that are being refined include the fingerprinting processes (since they are currently the most successful process of identification), the regularity of ID Unit sessions (impacted by the COVID-19 pandemic and the availability of postgraduate student participation) and the potential future linking of the data collected by the ID Unit directly to the SAPS Missing Persons database. This pilot project also aims to expand to other FPS facilities in South Africa and then to other countries in Africa, such as Zimbabwe. In addition to the regular processes of the ID Unit, the unit also hosts annual training workshops. The aim of the workshop is to improve the identification processes at the various Forensic Pathology Services medico-legal mortuaries in South Africa. These workshops have been held since 2016. To date, the workshops have trained 115 individuals who are involved with the identification of deceased. This includes 38 forensic pathology officers stationed at 38 Forensic Pathology Services medico-legal mortuaries in four South African provinces (Gauteng, Kwa-Zulu Natal, Free State, and the Western Cape), 61 postgraduate forensic science students from five South African universities and 16 forensic scientists from South Africa, Nigeria, and Canada. Previous workshop presenters include African forensic specialists and international forensic specialists who present on their contexts in Canada, the United Kingdom, and France. This provides attendees with current and international standards and has facilitated international collaborations on active forensic cases who are unidentified. The ID Unit is a unique supplementary service, which acts as a humanitarian collaborative effort between police, forensic pathology services, university and humanitarian aid organizations who all have a mandate and role in the identification of the deceased. The ID Unit provides a unique nexus between these organizations which facilitates the sharing of vital information that assists in the identification of the deceased in Johannesburg. The ID Unit also provides invaluable practical experience for postgraduate forensic science students and training for forensic practitioners in South Africa. The ID Unit has facilitated the identification of 87 individuals who would have otherwise remained unidentified and buried by the municipality. Collecting as much postmortem data, to aid in an eventual identification, ensures that unidentified decedents are treated with dignity and respect. Through the collaborative efforts of all the agencies involved, the impact of the work of this unit is vast—not only for South African authorities but most importantly for the decedents and their families. This is a vital service and pilot program that aims to expand to and be adapted by other FPS facilities in South Africa in the future. |
Content-based histopathology image retrieval using CometCloud | a2378ab4-b3dd-4c3a-a9b7-559b80fcc469 | 4161917 | Pathology[mh] | A growing number of leading institutions now routinely utilize digital imaging technologies to support investigative research and routine diagnostic procedures. The exponential rate at which images and videos are being generated has resulted in a significant need for efficient content-based image retrieval (CBIR) methods, which allow one to quickly characterize and locate images in large collections based upon the features of a given query image. CBIR has been one of the most active research areas in a wide spectrum of imaging informatics fields over the past few decades
. Several domains stand to benefit from the use of CBIR including cinematography, education, investigative basic and clinical research, and the practice of medicine. CBIR has been successfully utilized in applications spanning radiology
, pathology
, dermatology
, and cytology
. There have been several successful CBIR systems that have been developed for medical applications since the 1980’s. Several approaches utilize simple features such as color histograms
, shape
, texture
, or fuzzy features
to characterize the content of images while allowing higher level diagnostic abstractions based on systematic queries
. The recent adoption and popularity of case-based reasoning
and evidence-based medicine
has created a compelling need for more reliable image retrieval strategies to support diagnostic decisions. In fact, a number of state-of-the-art CBIR systems
have been designed to support the processing of queries across imaging modalities. With the advent of whole-slide imaging technology, the size and scale of image-based data has grown tremendously, making it impractical to perform matching operations across an entire image dataset using traditional methods. To meet this challenge, a new family of strategies are being developed, which enable investigators to perform sub-region searching to automatically identify image patches that exhibit patterns that are consistent with a given query patch. In practice, this approach makes it possible to select a region or object of interest within a digitized specimen as a query while the algorithm systematically identifies regions exhibiting similar characteristics in either the same specimen or across disparate specimens. The results can then be used to draw comparisons among patient samples in order to make informed decisions regarding likely prognoses and most appropriate treatment regimens. To perform a region-of-interest (ROI) query, Vu et al.
presented a Sam Match framework-based similarity model. The use of a part-based approach was later reported in
to solve the CBIR problem by synthesizing a DoG detector, and a local hashing table search algorithm. The primary limitation of this approach, however, was the time cost of the large number of features that need to be computed. Intra-expansion and inter-expansion strategies were later developed to boost the hash-based search quality based on a bag-of-features model which could more accurately represent the images. Recently, a structured visual search method was developed to perform CBIR in medical image datasets
. The primary advantage of this framework is that it is flexible and can be quickly extended to other modalities. Most CBIR algorithms rely on content localization, feature extraction, and user feedback steps
. The retrieved results are then ranked by some criteria, such as appearance similarity or diagnostic relevance, which can also serve as a measure of the practical usability of the algorithm. Typically the retrieved images only include those cases with the most similar appearance to a given query image whereas introducing relevance feedback
to CBIR provides a practical means for addressing the semantic gap between visual and semantic similarity. Large-scale image retrieval applications are generally computationally expensive. In this paper, we present the use of the CometCloud
to execute CBIR in a parallel fashion on multiple high performance computing (HPC) and cloud resources as a means for reducing computational time significantly. CometCloud is an autonomic cloud framework that allows dynamic, on-demand federation of distributed infrastructures. It also provides an effective programming platform that supports MapReduce, Workflow, and Master-Worker/BOT models making it possible for investigators to quickly develop applications that can run across the federated resources
. The algorithm that our team developed exploits the parallelism of CBIR by combining the HPC assets at Rutgers University with external cloud resources. Moreover, our solution uses cloud abstractions to federate resources elastically to achieve acceleration, while hiding infrastructure and deployment details. In this way, the CBIR algorithm can be made available as accessible services to end users. The contributions of this paper are: 1) a novel CBIR algorithm based on a newly developed coarse-to-fine searching criteria which is coupled with a novel feature called hierarchical annular histogram (HAH); 2) a CBIR refinement schema based on dual-similarity relevance feedback; and 3) a reliable parallel implementation of the CBIR algorithm based on Cloud computing.
Research design After discussing the needs and requirements of pathologists from their perspective, the CBIR study is designed to quickly and accurately find images exhibiting similar morphologic and staining characteristics throughout a single or collection of imaged specimens. Our team specifically choose to use Giemsa stained peripheral blood smear and hematoxylin and eosin (H&E) stained renal glomeruli datasets to systematically test the algorithms since these are two routine use case scenarios that our clinical colleagues indicated might benefit from the proposed technology. Leukocytes are often differentiated based on traditional morphological characteristics, however the subtle visible differences exhibited by some lymphomas and leukemias result in a significant number of false negative during routine screenings. In many cases, the diagnosis is only rendered after conducting immunophenotyping and a range of other molecular or cytogenetic studies. The additional studies are expensive, time consuming, and usually require fresh tissues that may not be readily available
. Pre-transplantation biopsies of kidney grafts have become a routine means for selecting organs which are suitable for transplantation from marginal donors. The main histopathology characteristics that are routinely evaluated by pathologists are percentage of glomerulosclerosis, interstitial fibrosis, and degree of vascular pathology
. The central incentive for developing the CBIR algorithms is to determine a reliable means for assisting pathologists when they are called upon to render diagnostic decisions based on whole-slide scanned specimens. In this paper, we present a novel content-based image retrieval (CBIR) algorithm that is systematically tested on both imaged Giemsa stained peripheral blood smears and digitized H&E stained renal glomeruli specimens. Because of the intense computational requirements of the algorithms, our team systematically investigate the use of high performance computing solutions based on CometCloud to distribute the tasks of performing CBIR to significantly reduce the overall running time. The details of datasets, the relevant CBIR algorithms, and the CometCloud implementation of the methods are explained in detail in the following sections. In the case of Giemsa stained peripheral blood smear datasets, the algorithms operate on a given query patch to quickly and reliably detect other leukocytes of the same class throughout the imaged specimen in support of diagnostic decisions. These hematopathology datasets were acquired using a 20× objective to provide a gross overview of the specimen while also supplying sufficient resolution to distinguish among different classes of leukocytes. The dataset consisted of 925 imaged blood smears (1000×1000 pixels). In the case of the H&E stained renal glomeruli datasets, the algorithms are used to process any given query patch to discriminate necrotic glomeruli and normal glomeruli throughout imaged kidney tissue specimens. In these experiments, our team cropped 32 images (5024×3504 pixels) from within eight whole-slide renal specimens using a 20× objective. Quality control of all datasets was conducted by an experienced pathologist (Dr. Zhong) whereas query image patches and ground-truth classification were determined by two pathologists (Dr. Zhong and Dr. Goodell). The retrieved results were evaluated by both pathologists through a completely independent and blinded process. During the peripheral blood smear experiments, pathologists were asked to assign each leukocyte retrieved using the CBIR algorithm to either the relevant or non-relevant class as a means for judging the appropriateness of each returned patch. In all, there were five different classes of leukocytes used in the studies. During the renal glomeruli studies, either a relevant or non-relevant assignment was made to judge the performance of the algorithms in distinguishing between necrotic glomeruli and normal glomeruli. The CBIR algorithms consist of four major steps: 1) regions of interest (ROIs) localization, 2) hierarchical three-stage searching, 3) retrieval refinement based on dual-similarity relevance feedback, and 4) high performance computing using CometCloud
. Figure illustrates the actual workflow of the process. Step 1: regions of interest localization The first step is to locate the regions of interest (ROIs) throughout the imaged specimens by excluding the background regions from the candidate objects. Using color-decomposition and morphology
based preprocessing, the algorithm identifies application-specific ROIs. These regions serve as candidate searching regions in the subsequent stages of hierarchical searching. Candidate image patches are generated using a sliding window approach with an overlapping ratio within the range of [ 50 % ,90 % ]. Step 2: hierarchical three-stage searching The hierarchical three-stage searching method includes: coarse searching, fine searching, and mean-shift clustering. Coarse searching Let Q represents a query image patch and P serves the candidate image patches. Each patch is divided into consecutive concentric rectangular bin regions (termed as rings) as shown in Figure (a-b). As the number of rings, r , increases, more detailed image characteristics are captured and while the computational time increases accordingly. r is determined based on cross-validation. Figure (b) illustrates the process of coarse searching. Given a query image patch, the algorithm computes local features from the innermost ring. Based on a similarity measure between candidate image patches, P , and the query image, Q , retrieved image patches, P , are ranked from high to low, and only the top 50 % ranked candidates are reserved at each step. This procedure continues until the outermost ring is reached. This cascade structure significantly reduces the computational time, as 50% of the image patches are eliminated in the very first stage of processing by simply evaluating features in the innermost ring. Fine searching After the coarse searching stage has been completed, each rectangular annular ring from both the query and candidate patches are equally subdivided into eight segments, and local features are calculated in each segment. The final candidates are chosen based on a similarity measure of a concatenated feature vector corresponding to the eight segments. Figure (c) illustrates the process of the fine searching. This stage is designed to capture the spatial configuration of the local features. Due to the limited number of candidates passing through the coarse searching stage, the computational time for completing this stage is dramatically reduced. Mean-shift clustering In order to assemble the final retrieval results, mean-shift (MS) clustering
is applied to the top ranked candidate patches, which have survived both the coarse and fine searching stages. The bandwidth b for the mean-shift clustering is calculated as , where w is the width of the query image and h is the height of the query image. In this way, the final CBIR results are obtained. HAH Feature and feature comparison HAH feature To implement the hierarchical searching framework, we develop a hierarchical annular histogram (HAH). The intensity color histograms of consecutive concentric rectangular rings are calculated and concatenated together to form a coarse searching feature vector, H c =( h 1 , h 2 ,…, h r ), where h i is the intensity color histogram of the i th ring, i ∈ [ 1, r ] and r is the number of rings selected for the HAH feature. For fine searching, each rectangular annular ring is equally divided into eight segments, and the color histogram is calculated from each segment sequentially and then concatenated together to form the fine searching feature vector, H f =( h 1,1 ,…, h 1,8 , h 2,1 ,…, h 2,8 ,…, h r ,1 ,…, h r ,8 ), where h i , j is the intensity color histogram of the i th ring within the j th segment, j ∈ [ 1,8]. Here superscript c represents coarse searching and f represents fine searching. Throughout the CBIR study, we use Euclidean distance as the similarity measure. The distance D i , between the i th candidate patch v i and the query patch q in coarse searching and fine searching are defined as and , respectively: where . Here H c ( q c ), H f ( q f ) are the feature vector of query image during coarse searching and fine searching stages, respectively, and are the feature vector of the i th candidate patch in the coarse searching and fine searching stages, respectively. Figure (a) and (b) illustrate the calculation of the HAH from the innermost rectangle and the fourth ring from the center. Figure (c) and (d) show an example of two image patches with similar traditional color histogram (d), but completely different HAH (c). This demonstrates the capacity of the HAH to differentiate among image patches exhibiting similar total color distributions, but different spatial configurations. In order to compare the performance of the HAH feature in CBIR, the Gabor wavelet feature
and co-occurrence texture feature
were compared with the HAH feature with respect to both speed and accuracy using both imaged peripheral blood smear and renal glomeruli datasets. For the purpose of the studies, precision and recall were used to measure the performance of the CBIR algorithm. Precision is defined as the ratio between the number of retrieved relevant images and the total number of retrieved images. Recall is defined as the ratio between the number of retrieved relevant images and the total number of relevant images in the datasets. The Gabor wavelet feature The Gabor wavelet feature is used to describe the image patterns at a range of different directions and scales. Throughout the experiments, we utilize a Gabor filter with 8 directions and 5 scales, ( M =5, N =8), and the mean value and standard deviation of each filtered image are concatenated to form a feature vector: f =( μ 1,1 , σ 1,1 , μ 1,2 , σ 1,2 ,…, μ 5,8 , σ 5,8 ), in which μ m , n and σ m , n represent the mean value and standard deviation of the filtered image using Gabor filter at the m th scale and n th direction, m ∈ [ 1, M ], n ∈ [ 1, N ]. The distance D i between the i th candidate patch v i , and the query patch q , is defined as where . COOC texture feature Co-occurrence (COOC) matrices, also called spatial gray-level dependence matrices, were first proposed by Haralick et al.
. COOC matrices are calculated from an estimation of the second-order joint conditional probability of the image intensity with various distances and four specific orientations (0 0 , 45 0 , 90 0 , 135 0 ). COOC texture feature using the COOC matrices quantifies the distribution of gray-level values within an image. For the feature comparison experiment, COOC texture feature including contrast, correlation, energy, and homogeneity
, is calculated from the COOC matrices within the candidate ROIs and the query image. The distance, D i , between the i th candidate patch v i , and the query patch q , is defined as where , and F = {contrast, correlation, energy, homogeneity}. Stage 3: CBIR retrieval refinement using a dual-similarity relevance feedback Relevance feedback is an interactive procedure which is used to refine the initial retrieval results. Upon completion of the initial retrieval, top ranked retrieval images were reviewed by two pathologists with consensus to label them as relevant or non-relevant as previously described. These responses are used as users’ feedback to re-rank the retrieval results accordingly. Two types of similarities are used in the above retrieval and feedback procedure: similarity in visual appearance as measured by image feature distance and similarity in semantic category as measured as relevant or non-relevant. Current relevance feedback algorithms typically only consider the second similarity. In our algorithm, we develop a dual-similarity schema that combines both types of similarity measures. This is achieved by rebuilding the initial distributions of training samples in an on-line manner. For each top ranked retrieved image, a 256×3× r dimension feature vector is constructed, where r is the number of rings defined in the hierarchical searching process. Dimension reduction using principal component analysis (PCA) is applied to the original HAH feature space, and the top principal components accounting for 90% of the total variance are used as inputs for the following relevance feedback procedure. Adaboost
is utilized to train an ensemble classifier composed of a set of weak learners. Given a training dataset, a strong classifier is built as a weighted sum of weak learners by minimizing the misclassification errors. Define weight W i , to be measured by a normalized Euclidean distance D i , representing the image appearance similarity between a pair of retrieved image and the original query. The initial distribution of the training samples is recalculated to update the classifier to place more weights on the visually similar cases following the relevance feedback step. The algorithm is summarized as follows. Step 4: accelerating CBIR using CometCloud Due to the data-independence property of the CBIR algorithm, we can formulate our problem as a set of heterogeneous and independent or loosely couple tasks. In this way, we can parallelize and solve our problem using the aggregated computational power of distributed resources. Our team has designed and developed a framework that enables the execution of CBIR across distributed, federated resources. Our framework uses cloud abstractions to present the underlying infrastructure as a single elastic pool of resources regardless of their physical location or specific particularities. In this way, computational resources are dynamically provisioned on-demand to meet the application’s requirements. These resources can be high performance computing grids, clouds, or supercomputers. In the current application, the framework is built on top of CometCloud
. CometCloud is purposely chosen for this application since it enables dynamic and on-demand federation of advanced cyber-infrastructures (ACIs). It also provides a flexible application programming interface (API), for developing applications that can take advantage of federated ACIs. Furthermore, it provides fault-tolerance in the resulting infrastructure. The framework used to run the CBIR algorithm across federated resources is implemented using the master/worker paradigm. In this scenario, the CBIR software serves as a computational engine, while CometCloud orchestrates the entire execution. The master/worker model is suitable for problems with a large pool of independent tasks, where both the tasks and the resources are heterogeneous. Using this approach, the master component generates tasks, collects results, and verifies that tasks are properly executed. Each task contains the description of the images to be processed. All tasks are automatically placed in the CometCloud-managed distributed task space for execution. Workers are dedicated to carry out tasks pulled from the CometCloud task space and send results back to the master. The implementation that we have presented has several important and highly desirable properties. From the user’s perspective, the framework creates a cloud abstraction on top of the resources that hides infrastructure details and offers the CBIR software as a readily accessible service. In this way, one can query the database using different algorithms via a simple interface without consideration of how and where queries are executed. On the other hand, from the developer’s perspective, the integration of the existing CBIR software with the CometCloud framework does not require any adjustments on the application side. Additionally, the resulting framework completely operates within the limits of the end-user space. This means that it is possible to aggregate computational resources without special privileges, which is very important when using external resources.
After discussing the needs and requirements of pathologists from their perspective, the CBIR study is designed to quickly and accurately find images exhibiting similar morphologic and staining characteristics throughout a single or collection of imaged specimens. Our team specifically choose to use Giemsa stained peripheral blood smear and hematoxylin and eosin (H&E) stained renal glomeruli datasets to systematically test the algorithms since these are two routine use case scenarios that our clinical colleagues indicated might benefit from the proposed technology. Leukocytes are often differentiated based on traditional morphological characteristics, however the subtle visible differences exhibited by some lymphomas and leukemias result in a significant number of false negative during routine screenings. In many cases, the diagnosis is only rendered after conducting immunophenotyping and a range of other molecular or cytogenetic studies. The additional studies are expensive, time consuming, and usually require fresh tissues that may not be readily available
. Pre-transplantation biopsies of kidney grafts have become a routine means for selecting organs which are suitable for transplantation from marginal donors. The main histopathology characteristics that are routinely evaluated by pathologists are percentage of glomerulosclerosis, interstitial fibrosis, and degree of vascular pathology
. The central incentive for developing the CBIR algorithms is to determine a reliable means for assisting pathologists when they are called upon to render diagnostic decisions based on whole-slide scanned specimens. In this paper, we present a novel content-based image retrieval (CBIR) algorithm that is systematically tested on both imaged Giemsa stained peripheral blood smears and digitized H&E stained renal glomeruli specimens. Because of the intense computational requirements of the algorithms, our team systematically investigate the use of high performance computing solutions based on CometCloud to distribute the tasks of performing CBIR to significantly reduce the overall running time. The details of datasets, the relevant CBIR algorithms, and the CometCloud implementation of the methods are explained in detail in the following sections. In the case of Giemsa stained peripheral blood smear datasets, the algorithms operate on a given query patch to quickly and reliably detect other leukocytes of the same class throughout the imaged specimen in support of diagnostic decisions. These hematopathology datasets were acquired using a 20× objective to provide a gross overview of the specimen while also supplying sufficient resolution to distinguish among different classes of leukocytes. The dataset consisted of 925 imaged blood smears (1000×1000 pixels). In the case of the H&E stained renal glomeruli datasets, the algorithms are used to process any given query patch to discriminate necrotic glomeruli and normal glomeruli throughout imaged kidney tissue specimens. In these experiments, our team cropped 32 images (5024×3504 pixels) from within eight whole-slide renal specimens using a 20× objective. Quality control of all datasets was conducted by an experienced pathologist (Dr. Zhong) whereas query image patches and ground-truth classification were determined by two pathologists (Dr. Zhong and Dr. Goodell). The retrieved results were evaluated by both pathologists through a completely independent and blinded process. During the peripheral blood smear experiments, pathologists were asked to assign each leukocyte retrieved using the CBIR algorithm to either the relevant or non-relevant class as a means for judging the appropriateness of each returned patch. In all, there were five different classes of leukocytes used in the studies. During the renal glomeruli studies, either a relevant or non-relevant assignment was made to judge the performance of the algorithms in distinguishing between necrotic glomeruli and normal glomeruli. The CBIR algorithms consist of four major steps: 1) regions of interest (ROIs) localization, 2) hierarchical three-stage searching, 3) retrieval refinement based on dual-similarity relevance feedback, and 4) high performance computing using CometCloud
. Figure illustrates the actual workflow of the process.
The first step is to locate the regions of interest (ROIs) throughout the imaged specimens by excluding the background regions from the candidate objects. Using color-decomposition and morphology
based preprocessing, the algorithm identifies application-specific ROIs. These regions serve as candidate searching regions in the subsequent stages of hierarchical searching. Candidate image patches are generated using a sliding window approach with an overlapping ratio within the range of [ 50 % ,90 % ].
The hierarchical three-stage searching method includes: coarse searching, fine searching, and mean-shift clustering. Coarse searching Let Q represents a query image patch and P serves the candidate image patches. Each patch is divided into consecutive concentric rectangular bin regions (termed as rings) as shown in Figure (a-b). As the number of rings, r , increases, more detailed image characteristics are captured and while the computational time increases accordingly. r is determined based on cross-validation. Figure (b) illustrates the process of coarse searching. Given a query image patch, the algorithm computes local features from the innermost ring. Based on a similarity measure between candidate image patches, P , and the query image, Q , retrieved image patches, P , are ranked from high to low, and only the top 50 % ranked candidates are reserved at each step. This procedure continues until the outermost ring is reached. This cascade structure significantly reduces the computational time, as 50% of the image patches are eliminated in the very first stage of processing by simply evaluating features in the innermost ring. Fine searching After the coarse searching stage has been completed, each rectangular annular ring from both the query and candidate patches are equally subdivided into eight segments, and local features are calculated in each segment. The final candidates are chosen based on a similarity measure of a concatenated feature vector corresponding to the eight segments. Figure (c) illustrates the process of the fine searching. This stage is designed to capture the spatial configuration of the local features. Due to the limited number of candidates passing through the coarse searching stage, the computational time for completing this stage is dramatically reduced. Mean-shift clustering In order to assemble the final retrieval results, mean-shift (MS) clustering
is applied to the top ranked candidate patches, which have survived both the coarse and fine searching stages. The bandwidth b for the mean-shift clustering is calculated as , where w is the width of the query image and h is the height of the query image. In this way, the final CBIR results are obtained.
Let Q represents a query image patch and P serves the candidate image patches. Each patch is divided into consecutive concentric rectangular bin regions (termed as rings) as shown in Figure (a-b). As the number of rings, r , increases, more detailed image characteristics are captured and while the computational time increases accordingly. r is determined based on cross-validation. Figure (b) illustrates the process of coarse searching. Given a query image patch, the algorithm computes local features from the innermost ring. Based on a similarity measure between candidate image patches, P , and the query image, Q , retrieved image patches, P , are ranked from high to low, and only the top 50 % ranked candidates are reserved at each step. This procedure continues until the outermost ring is reached. This cascade structure significantly reduces the computational time, as 50% of the image patches are eliminated in the very first stage of processing by simply evaluating features in the innermost ring.
After the coarse searching stage has been completed, each rectangular annular ring from both the query and candidate patches are equally subdivided into eight segments, and local features are calculated in each segment. The final candidates are chosen based on a similarity measure of a concatenated feature vector corresponding to the eight segments. Figure (c) illustrates the process of the fine searching. This stage is designed to capture the spatial configuration of the local features. Due to the limited number of candidates passing through the coarse searching stage, the computational time for completing this stage is dramatically reduced.
In order to assemble the final retrieval results, mean-shift (MS) clustering
is applied to the top ranked candidate patches, which have survived both the coarse and fine searching stages. The bandwidth b for the mean-shift clustering is calculated as , where w is the width of the query image and h is the height of the query image. In this way, the final CBIR results are obtained.
HAH feature To implement the hierarchical searching framework, we develop a hierarchical annular histogram (HAH). The intensity color histograms of consecutive concentric rectangular rings are calculated and concatenated together to form a coarse searching feature vector, H c =( h 1 , h 2 ,…, h r ), where h i is the intensity color histogram of the i th ring, i ∈ [ 1, r ] and r is the number of rings selected for the HAH feature. For fine searching, each rectangular annular ring is equally divided into eight segments, and the color histogram is calculated from each segment sequentially and then concatenated together to form the fine searching feature vector, H f =( h 1,1 ,…, h 1,8 , h 2,1 ,…, h 2,8 ,…, h r ,1 ,…, h r ,8 ), where h i , j is the intensity color histogram of the i th ring within the j th segment, j ∈ [ 1,8]. Here superscript c represents coarse searching and f represents fine searching. Throughout the CBIR study, we use Euclidean distance as the similarity measure. The distance D i , between the i th candidate patch v i and the query patch q in coarse searching and fine searching are defined as and , respectively: where . Here H c ( q c ), H f ( q f ) are the feature vector of query image during coarse searching and fine searching stages, respectively, and are the feature vector of the i th candidate patch in the coarse searching and fine searching stages, respectively. Figure (a) and (b) illustrate the calculation of the HAH from the innermost rectangle and the fourth ring from the center. Figure (c) and (d) show an example of two image patches with similar traditional color histogram (d), but completely different HAH (c). This demonstrates the capacity of the HAH to differentiate among image patches exhibiting similar total color distributions, but different spatial configurations. In order to compare the performance of the HAH feature in CBIR, the Gabor wavelet feature
and co-occurrence texture feature
were compared with the HAH feature with respect to both speed and accuracy using both imaged peripheral blood smear and renal glomeruli datasets. For the purpose of the studies, precision and recall were used to measure the performance of the CBIR algorithm. Precision is defined as the ratio between the number of retrieved relevant images and the total number of retrieved images. Recall is defined as the ratio between the number of retrieved relevant images and the total number of relevant images in the datasets. The Gabor wavelet feature The Gabor wavelet feature is used to describe the image patterns at a range of different directions and scales. Throughout the experiments, we utilize a Gabor filter with 8 directions and 5 scales, ( M =5, N =8), and the mean value and standard deviation of each filtered image are concatenated to form a feature vector: f =( μ 1,1 , σ 1,1 , μ 1,2 , σ 1,2 ,…, μ 5,8 , σ 5,8 ), in which μ m , n and σ m , n represent the mean value and standard deviation of the filtered image using Gabor filter at the m th scale and n th direction, m ∈ [ 1, M ], n ∈ [ 1, N ]. The distance D i between the i th candidate patch v i , and the query patch q , is defined as where . COOC texture feature Co-occurrence (COOC) matrices, also called spatial gray-level dependence matrices, were first proposed by Haralick et al.
. COOC matrices are calculated from an estimation of the second-order joint conditional probability of the image intensity with various distances and four specific orientations (0 0 , 45 0 , 90 0 , 135 0 ). COOC texture feature using the COOC matrices quantifies the distribution of gray-level values within an image. For the feature comparison experiment, COOC texture feature including contrast, correlation, energy, and homogeneity
, is calculated from the COOC matrices within the candidate ROIs and the query image. The distance, D i , between the i th candidate patch v i , and the query patch q , is defined as where , and F = {contrast, correlation, energy, homogeneity}.
To implement the hierarchical searching framework, we develop a hierarchical annular histogram (HAH). The intensity color histograms of consecutive concentric rectangular rings are calculated and concatenated together to form a coarse searching feature vector, H c =( h 1 , h 2 ,…, h r ), where h i is the intensity color histogram of the i th ring, i ∈ [ 1, r ] and r is the number of rings selected for the HAH feature. For fine searching, each rectangular annular ring is equally divided into eight segments, and the color histogram is calculated from each segment sequentially and then concatenated together to form the fine searching feature vector, H f =( h 1,1 ,…, h 1,8 , h 2,1 ,…, h 2,8 ,…, h r ,1 ,…, h r ,8 ), where h i , j is the intensity color histogram of the i th ring within the j th segment, j ∈ [ 1,8]. Here superscript c represents coarse searching and f represents fine searching. Throughout the CBIR study, we use Euclidean distance as the similarity measure. The distance D i , between the i th candidate patch v i and the query patch q in coarse searching and fine searching are defined as and , respectively: where . Here H c ( q c ), H f ( q f ) are the feature vector of query image during coarse searching and fine searching stages, respectively, and are the feature vector of the i th candidate patch in the coarse searching and fine searching stages, respectively. Figure (a) and (b) illustrate the calculation of the HAH from the innermost rectangle and the fourth ring from the center. Figure (c) and (d) show an example of two image patches with similar traditional color histogram (d), but completely different HAH (c). This demonstrates the capacity of the HAH to differentiate among image patches exhibiting similar total color distributions, but different spatial configurations. In order to compare the performance of the HAH feature in CBIR, the Gabor wavelet feature
and co-occurrence texture feature
were compared with the HAH feature with respect to both speed and accuracy using both imaged peripheral blood smear and renal glomeruli datasets. For the purpose of the studies, precision and recall were used to measure the performance of the CBIR algorithm. Precision is defined as the ratio between the number of retrieved relevant images and the total number of retrieved images. Recall is defined as the ratio between the number of retrieved relevant images and the total number of relevant images in the datasets.
The Gabor wavelet feature is used to describe the image patterns at a range of different directions and scales. Throughout the experiments, we utilize a Gabor filter with 8 directions and 5 scales, ( M =5, N =8), and the mean value and standard deviation of each filtered image are concatenated to form a feature vector: f =( μ 1,1 , σ 1,1 , μ 1,2 , σ 1,2 ,…, μ 5,8 , σ 5,8 ), in which μ m , n and σ m , n represent the mean value and standard deviation of the filtered image using Gabor filter at the m th scale and n th direction, m ∈ [ 1, M ], n ∈ [ 1, N ]. The distance D i between the i th candidate patch v i , and the query patch q , is defined as where .
Co-occurrence (COOC) matrices, also called spatial gray-level dependence matrices, were first proposed by Haralick et al.
. COOC matrices are calculated from an estimation of the second-order joint conditional probability of the image intensity with various distances and four specific orientations (0 0 , 45 0 , 90 0 , 135 0 ). COOC texture feature using the COOC matrices quantifies the distribution of gray-level values within an image. For the feature comparison experiment, COOC texture feature including contrast, correlation, energy, and homogeneity
, is calculated from the COOC matrices within the candidate ROIs and the query image. The distance, D i , between the i th candidate patch v i , and the query patch q , is defined as where , and F = {contrast, correlation, energy, homogeneity}.
Relevance feedback is an interactive procedure which is used to refine the initial retrieval results. Upon completion of the initial retrieval, top ranked retrieval images were reviewed by two pathologists with consensus to label them as relevant or non-relevant as previously described. These responses are used as users’ feedback to re-rank the retrieval results accordingly. Two types of similarities are used in the above retrieval and feedback procedure: similarity in visual appearance as measured by image feature distance and similarity in semantic category as measured as relevant or non-relevant. Current relevance feedback algorithms typically only consider the second similarity. In our algorithm, we develop a dual-similarity schema that combines both types of similarity measures. This is achieved by rebuilding the initial distributions of training samples in an on-line manner. For each top ranked retrieved image, a 256×3× r dimension feature vector is constructed, where r is the number of rings defined in the hierarchical searching process. Dimension reduction using principal component analysis (PCA) is applied to the original HAH feature space, and the top principal components accounting for 90% of the total variance are used as inputs for the following relevance feedback procedure. Adaboost
is utilized to train an ensemble classifier composed of a set of weak learners. Given a training dataset, a strong classifier is built as a weighted sum of weak learners by minimizing the misclassification errors. Define weight W i , to be measured by a normalized Euclidean distance D i , representing the image appearance similarity between a pair of retrieved image and the original query. The initial distribution of the training samples is recalculated to update the classifier to place more weights on the visually similar cases following the relevance feedback step. The algorithm is summarized as follows.
Due to the data-independence property of the CBIR algorithm, we can formulate our problem as a set of heterogeneous and independent or loosely couple tasks. In this way, we can parallelize and solve our problem using the aggregated computational power of distributed resources. Our team has designed and developed a framework that enables the execution of CBIR across distributed, federated resources. Our framework uses cloud abstractions to present the underlying infrastructure as a single elastic pool of resources regardless of their physical location or specific particularities. In this way, computational resources are dynamically provisioned on-demand to meet the application’s requirements. These resources can be high performance computing grids, clouds, or supercomputers. In the current application, the framework is built on top of CometCloud
. CometCloud is purposely chosen for this application since it enables dynamic and on-demand federation of advanced cyber-infrastructures (ACIs). It also provides a flexible application programming interface (API), for developing applications that can take advantage of federated ACIs. Furthermore, it provides fault-tolerance in the resulting infrastructure. The framework used to run the CBIR algorithm across federated resources is implemented using the master/worker paradigm. In this scenario, the CBIR software serves as a computational engine, while CometCloud orchestrates the entire execution. The master/worker model is suitable for problems with a large pool of independent tasks, where both the tasks and the resources are heterogeneous. Using this approach, the master component generates tasks, collects results, and verifies that tasks are properly executed. Each task contains the description of the images to be processed. All tasks are automatically placed in the CometCloud-managed distributed task space for execution. Workers are dedicated to carry out tasks pulled from the CometCloud task space and send results back to the master. The implementation that we have presented has several important and highly desirable properties. From the user’s perspective, the framework creates a cloud abstraction on top of the resources that hides infrastructure details and offers the CBIR software as a readily accessible service. In this way, one can query the database using different algorithms via a simple interface without consideration of how and where queries are executed. On the other hand, from the developer’s perspective, the integration of the existing CBIR software with the CometCloud framework does not require any adjustments on the application side. Additionally, the resulting framework completely operates within the limits of the end-user space. This means that it is possible to aggregate computational resources without special privileges, which is very important when using external resources.
CBIR results and feature comparison A dual-processor system based on Intel Xeon [email protected] GHz with 24 GB RAM and 64-bit operating system was used for the CBIR study. Initial CBIR results using two pathology image datasets and different feature comparison are presented below. Figure shows an example of the CBIR three-stage hierarchical searching results using one neutrophil as a query image in a peripheral blood smear dataset acquired using 20× magnification objective. Green box labeled regions represent the candidate patches that are similar to the query image patch. Figure shows CBIR results using different classes of leukocytes as query images, including basophil, eosinophil, lymphocyte, monocyte, and neutrophil, respectively. Green box labeled regions represent the candidate patches that are similar to the query image patch. Each box has a number to indicate the ranking order of every candidate patch in the dataset. Figure shows an example of CBIR results for a necrotic glomeruli query image using a testing dataset containing multi-scale regions at 1/2, 1, 2, 3, and 4 times of the original size of the query image. Red box labeled regions indicate the query image. Blue box labeled regions represent the healthy glomeruli for comparison. Green box labeled regions represent the top-ranked 10 % of retrieval patches of the 32 randomly selected regions (5024×3504 pixels) cropped from whole-slide scanned images. By varying the number of rings ∈ [ 2,3,5,10,15] in the hierarchical searching, the performance of CBIR is summarized as follows. For imaged peripheral blood smears, all five classes of leukocytes were correctly retrieved using three inner rings of the HAH. For imaged renal glomeruli, as the number of rings increased to 10, all necrotic glomeruli were correctly retrieved. With an increase of the number of the rings, the computational time also increased. The number of rings was shown to be dependent upon the complexity of the dataset. For local feature comparison, image retrieval was performed on the same datasets with the same query images using HAH, Gabor wavelet, and COOC texture features. Figure (a) and (b) show precision-recall curves and average of feature calculation times using peripheral blood smear images, respectively. Figure (c) and (d) show precision-recall curves and average of feature calculation times using renal glomeruli images, respectively. The area under a curve (AUC) value of each feature for peripheral blood smear images and renal glomeruli images are shown in Figure (a) and (c), respectively. The average of feature computation times are shown in Figure (b) and (d). Based on these experiments, it is clear that HAH feature outperforms Gabor wavelet and COOC texture features with respect to both speed and accuracy. Validation of relevance feedback To evaluate the performance of the dual-similarity relevance feedback algorithm, both peripheral blood smear and multi-scale renal datasets were used. Table summarizes the numbers of relevant/non-relevant images within initial top retrieved 100 images for peripheral blood smear and renal glomeruli datasets, which were labeled by two pathologists with consensus. In general, the percentages of basophils and eosinophils in a given specimen are quite small (e. g., less than 1 % and 4 % in our dataset as shown in Table ). In addition, they can be accurately retrieved as we show in Table . Due to this reason, only neutrophils, monocytes, and lymphocytes were utilized for relevance feedback analysis. In those experiments, we applied relevance feedback on the first 100 initial retrieved image patches because this number was sufficient to retrieve all similar cases in the datasets. The original query images, initial top retrieval results, and re-ranked results after relevance feedback are showed in Figures and for blood smear and renal datasets. In both figures, image patches with red rectangles represent the incorrect results (negative examples), and the blue ones represent the correct results (positive examples), which were re-assigned to higher ranking after relevance feedback. For the retrieval results of leukocyte image datasets, the ranking of many correct patches were increased from their initial ranking after relevance feedback. Relevance feedback corrected for 5/6 of the incorrect retrieval patches and increased the ranking for 7 patches from the lower ranking (with initial ranking between 41 and 100) in the neutrophil dataset. This procedure also amended all 10 incorrect patches, and increased ranking for 23 patches in the monocyte dataset. This procedure eliminated all 4 incorrect patches, and increased ranking for 35 patches in the lymphocyte dataset. For the renal dataset, the relevance feedback procedure successfully increased the ranking for all of the 9 correct patches of multi-scale renal dataset shown in Figure . Ten-fold cross-validation was applied to evaluate the performance of the proposed dual-similarity relevance feedback with receiver operating characteristic (ROC) curves for both peripheral blood smear and renal datasets. The ROC curves after applying relevance feedback on the peripheral blood smear and multi-scale renal datasets are shown in Figure . Another measures of performance for the proposed relevance feedback are the recall rate and processing speed. The relevance feedback (RF) calculation time includes feature vector dimension reduction and Adaboost classifier training. The numbers of training samples were 20, 50, and 90, and the training samples were randomly selected from the datasets. Based on Figure , the values of area under recall curves increased as the number of training samples increased for three leukocytes ((a) neutrophil, (b) monocyte, and (c) lymphocyte), and (d) renal glomeruli. The recall rate after RF for neutrophils (a) using 20 training samples was no better than the result before RF. This was because the original retrieval process already provided a good performance. As the value of area under recall curve before RF was already 76.902, which was much higher than the rest of cases ((b) monocyte, (c) lymphocyte, and (d) renal glomeruli). In this specific case, there was no significant improvement using RF in a small training set (e.g., 20 training samples). However, RF significantly improved the recall rate in larger training sets (e. g., 50 and 90 training samples). In general, the values of area under recall curves were significantly increased after RF with the number of training samples increased. Acceleration of CBIR using CometCloud We conducted experiments to test the performance of CBIR using CometCloud. For HAH, we evaluated two leukocytes query images against a dataset of 925 peripheral blood smear images. In the case of CBIR using multi-scale image candidate patches, we evaluated two different renal glomeruli query images against a dataset of 32 renal images. All the experiments were repeated three times to obtain average results. During the experiments, the input data were initially located on a single site, the required files were transferred as needed. However, once a file was transferred to a remote site, it was locally staged to minimize the amount of data transferred across sites, especially when multiple tasks require the same input data. To address this issue, a pull model was used where workers request tasks when they become idle. In this way, the workload was uniformly distributed across all workers to address the load imbalance. To accommodate the CBIR algorithms, we federated various resources including HPC clusters and clouds. In particular, we federated a HPC cluster at Rutgers (a Dell Power Edge system with 256 cores in 8-core nodes - “Dell” hereafter), a SMP machine at Rutgers (64 cores - “Snake” hereafter), and 40 large instances from OpenStack
(“FutureGrid”, hereafter), which is a cloud similar to Amazon EC2. Currently we are exploiting the inherent task parallelism of the problem, which means that we can divide the algorithm into smaller sub-modules and execute each module independently. This provides a linear scalability as long as we have more tasks than computational cores. Figure presents a summary of the execution time of the proposed hierarchical searching algorithm using two representative peripheral blood smears and a multi-scale renal glomeruli dataset while varying the parameters, respectively. The results illustrate average values, including error bars showing their associated variabilities. Please note that the Y -axes in the sub-figures represent different scales. The figure also demonstrates the execution time of each stage and the time required to transfer the images for processing. Since the image transfer time represents a small fraction of the total execution time (i.e., from a few seconds to a 2–3 minutes depending on the configuration), in our current implementation we copy the images sequentially from a central repository. The execution time varies depending on the algorithm we used, the query and dataset images, and the configuration (e.g., 90 % overlapping takes longer than 50 % overlapping). The fraction of time spent on each stage of the hierarchical searching is shown in Figure . Figure compares the execution time of different configurations using a single system and federated cyber-infrastructure. We observe an average acceleration of 70-fold with a maximum of 96-fold. This is achieved by elastically using multiple resources as discussed below. Figure shows the contribution of the FutureGrid cloud to the execution of the multi-scale algorithm. Cloud resources significantly accelerate the execution of the algorithm. During stages with lower parallelism (e.g., last minutes of the execution), computation can be performed using local HPC resources and cloud resources can be released to reduce operational costs. The variability of the execution time of different tasks is shown in Figures and . Figure shows the average task execution time and variability using different configurations. The variability of task execution time is heterogeneous and depends on the configurations and the machine. In general, the longer the execution takes, the larger the variability. Figure shows that the execution time of individual task is relatively heterogeneous. It also demonstrates that the distribution of tasks among different federated resources depends on the number of cores available in each platform (e.g., one of the cores, snake, runs only a few tasks). The results show that the parallelization of CBIR at the image level can dramatically reduce the overall computational time.
A dual-processor system based on Intel Xeon [email protected] GHz with 24 GB RAM and 64-bit operating system was used for the CBIR study. Initial CBIR results using two pathology image datasets and different feature comparison are presented below. Figure shows an example of the CBIR three-stage hierarchical searching results using one neutrophil as a query image in a peripheral blood smear dataset acquired using 20× magnification objective. Green box labeled regions represent the candidate patches that are similar to the query image patch. Figure shows CBIR results using different classes of leukocytes as query images, including basophil, eosinophil, lymphocyte, monocyte, and neutrophil, respectively. Green box labeled regions represent the candidate patches that are similar to the query image patch. Each box has a number to indicate the ranking order of every candidate patch in the dataset. Figure shows an example of CBIR results for a necrotic glomeruli query image using a testing dataset containing multi-scale regions at 1/2, 1, 2, 3, and 4 times of the original size of the query image. Red box labeled regions indicate the query image. Blue box labeled regions represent the healthy glomeruli for comparison. Green box labeled regions represent the top-ranked 10 % of retrieval patches of the 32 randomly selected regions (5024×3504 pixels) cropped from whole-slide scanned images. By varying the number of rings ∈ [ 2,3,5,10,15] in the hierarchical searching, the performance of CBIR is summarized as follows. For imaged peripheral blood smears, all five classes of leukocytes were correctly retrieved using three inner rings of the HAH. For imaged renal glomeruli, as the number of rings increased to 10, all necrotic glomeruli were correctly retrieved. With an increase of the number of the rings, the computational time also increased. The number of rings was shown to be dependent upon the complexity of the dataset. For local feature comparison, image retrieval was performed on the same datasets with the same query images using HAH, Gabor wavelet, and COOC texture features. Figure (a) and (b) show precision-recall curves and average of feature calculation times using peripheral blood smear images, respectively. Figure (c) and (d) show precision-recall curves and average of feature calculation times using renal glomeruli images, respectively. The area under a curve (AUC) value of each feature for peripheral blood smear images and renal glomeruli images are shown in Figure (a) and (c), respectively. The average of feature computation times are shown in Figure (b) and (d). Based on these experiments, it is clear that HAH feature outperforms Gabor wavelet and COOC texture features with respect to both speed and accuracy.
To evaluate the performance of the dual-similarity relevance feedback algorithm, both peripheral blood smear and multi-scale renal datasets were used. Table summarizes the numbers of relevant/non-relevant images within initial top retrieved 100 images for peripheral blood smear and renal glomeruli datasets, which were labeled by two pathologists with consensus. In general, the percentages of basophils and eosinophils in a given specimen are quite small (e. g., less than 1 % and 4 % in our dataset as shown in Table ). In addition, they can be accurately retrieved as we show in Table . Due to this reason, only neutrophils, monocytes, and lymphocytes were utilized for relevance feedback analysis. In those experiments, we applied relevance feedback on the first 100 initial retrieved image patches because this number was sufficient to retrieve all similar cases in the datasets. The original query images, initial top retrieval results, and re-ranked results after relevance feedback are showed in Figures and for blood smear and renal datasets. In both figures, image patches with red rectangles represent the incorrect results (negative examples), and the blue ones represent the correct results (positive examples), which were re-assigned to higher ranking after relevance feedback. For the retrieval results of leukocyte image datasets, the ranking of many correct patches were increased from their initial ranking after relevance feedback. Relevance feedback corrected for 5/6 of the incorrect retrieval patches and increased the ranking for 7 patches from the lower ranking (with initial ranking between 41 and 100) in the neutrophil dataset. This procedure also amended all 10 incorrect patches, and increased ranking for 23 patches in the monocyte dataset. This procedure eliminated all 4 incorrect patches, and increased ranking for 35 patches in the lymphocyte dataset. For the renal dataset, the relevance feedback procedure successfully increased the ranking for all of the 9 correct patches of multi-scale renal dataset shown in Figure . Ten-fold cross-validation was applied to evaluate the performance of the proposed dual-similarity relevance feedback with receiver operating characteristic (ROC) curves for both peripheral blood smear and renal datasets. The ROC curves after applying relevance feedback on the peripheral blood smear and multi-scale renal datasets are shown in Figure . Another measures of performance for the proposed relevance feedback are the recall rate and processing speed. The relevance feedback (RF) calculation time includes feature vector dimension reduction and Adaboost classifier training. The numbers of training samples were 20, 50, and 90, and the training samples were randomly selected from the datasets. Based on Figure , the values of area under recall curves increased as the number of training samples increased for three leukocytes ((a) neutrophil, (b) monocyte, and (c) lymphocyte), and (d) renal glomeruli. The recall rate after RF for neutrophils (a) using 20 training samples was no better than the result before RF. This was because the original retrieval process already provided a good performance. As the value of area under recall curve before RF was already 76.902, which was much higher than the rest of cases ((b) monocyte, (c) lymphocyte, and (d) renal glomeruli). In this specific case, there was no significant improvement using RF in a small training set (e.g., 20 training samples). However, RF significantly improved the recall rate in larger training sets (e. g., 50 and 90 training samples). In general, the values of area under recall curves were significantly increased after RF with the number of training samples increased.
We conducted experiments to test the performance of CBIR using CometCloud. For HAH, we evaluated two leukocytes query images against a dataset of 925 peripheral blood smear images. In the case of CBIR using multi-scale image candidate patches, we evaluated two different renal glomeruli query images against a dataset of 32 renal images. All the experiments were repeated three times to obtain average results. During the experiments, the input data were initially located on a single site, the required files were transferred as needed. However, once a file was transferred to a remote site, it was locally staged to minimize the amount of data transferred across sites, especially when multiple tasks require the same input data. To address this issue, a pull model was used where workers request tasks when they become idle. In this way, the workload was uniformly distributed across all workers to address the load imbalance. To accommodate the CBIR algorithms, we federated various resources including HPC clusters and clouds. In particular, we federated a HPC cluster at Rutgers (a Dell Power Edge system with 256 cores in 8-core nodes - “Dell” hereafter), a SMP machine at Rutgers (64 cores - “Snake” hereafter), and 40 large instances from OpenStack
(“FutureGrid”, hereafter), which is a cloud similar to Amazon EC2. Currently we are exploiting the inherent task parallelism of the problem, which means that we can divide the algorithm into smaller sub-modules and execute each module independently. This provides a linear scalability as long as we have more tasks than computational cores. Figure presents a summary of the execution time of the proposed hierarchical searching algorithm using two representative peripheral blood smears and a multi-scale renal glomeruli dataset while varying the parameters, respectively. The results illustrate average values, including error bars showing their associated variabilities. Please note that the Y -axes in the sub-figures represent different scales. The figure also demonstrates the execution time of each stage and the time required to transfer the images for processing. Since the image transfer time represents a small fraction of the total execution time (i.e., from a few seconds to a 2–3 minutes depending on the configuration), in our current implementation we copy the images sequentially from a central repository. The execution time varies depending on the algorithm we used, the query and dataset images, and the configuration (e.g., 90 % overlapping takes longer than 50 % overlapping). The fraction of time spent on each stage of the hierarchical searching is shown in Figure . Figure compares the execution time of different configurations using a single system and federated cyber-infrastructure. We observe an average acceleration of 70-fold with a maximum of 96-fold. This is achieved by elastically using multiple resources as discussed below. Figure shows the contribution of the FutureGrid cloud to the execution of the multi-scale algorithm. Cloud resources significantly accelerate the execution of the algorithm. During stages with lower parallelism (e.g., last minutes of the execution), computation can be performed using local HPC resources and cloud resources can be released to reduce operational costs. The variability of the execution time of different tasks is shown in Figures and . Figure shows the average task execution time and variability using different configurations. The variability of task execution time is heterogeneous and depends on the configurations and the machine. In general, the longer the execution takes, the larger the variability. Figure shows that the execution time of individual task is relatively heterogeneous. It also demonstrates that the distribution of tasks among different federated resources depends on the number of cores available in each platform (e.g., one of the cores, snake, runs only a few tasks). The results show that the parallelization of CBIR at the image level can dramatically reduce the overall computational time.
In this paper, we present a set of newly developed CBIR algorithms and demonstrate its application on two different pathology applications, which are regularly evaluated in the practice of pathology. The experimental results suggest that the proposed CBIR algorithm using sequential HAH searching follows a progression which parallels to the same logical steps as ever invoked when physicians review digital pathology images. During the review process, the pathologist typically begins by first identifying gross locations of potential regions of interest (coarse searching in the proposed algorithm) before executing the more refined stages (fine searching in the proposed algorithm) to examine the detailed morphometric characteristics. For the peripheral blood smear study, we tested performance using a range of different leukocytes and experimentally showed the reliable performance of the CBIR algorithm. The success of the proposed CBIR algorithm in identifying neutrophils suggests further exploration of the HAH feature in detecting abnormal or hypersegmented neutrophils, which are indicators of megaloblastic anemia and potential risk of gastric cancer. Similarly, a pathologist’s assessment of normal vs. diseased glomeruli in renal biopsies is often used as an indicator of overall kidney health, such as, the determination of graft function from pre-transplantation biopsies
. Assisted by the proposed CBIR algorithm, physicians and researchers can quickly review a digital biopsy to evaluate the proportion of ischemic or necrotic glomeruli within a given field to quickly assess whether an incoming specimen is suitable for transplantation or not. This type of review can have multiple applications, such as, determining whether a rejection of the organ might occur by identifying areas of focal and segmental glomerulosclerosis
. Currently, our algorithm requires some external feedback to optimize the search. We are exploring different ways of automatizing this process by applying machine learning techniques. On the other hand, although the proposed hierarchical searching has significantly improved the retrieval speed, it is still a computational demanding procedure. Therefore, we are exploring new ways of exploiting parallelism to speed-up this process. We present a generalizable cloud-enabled CBIR algorithm that can be extended to a wide variety of applications. Because of the computational requirements needed for retrieving whole-slide scanned images, we explore the use of federated high performance computing (HPC) cyber-infrastructures and clouds using CometCloud. Comparative results of HPC versus standard computation time demonstrate that the CBIR process can be dramatically accelerated, from weeks to minutes, making real-time clinical practice feasible. Moreover, the proposed framework hides infrastructure and deployment details and offers end-users the CBIR functionality in a readily accessible manner. We are currently working on improving the utilization of resources by exploit the particular capabilities and capacities of each heterogeneous resource, e.g., switching between the usage of the original CBIR implementation in MATLAB (The MathWorks, Natick, MA) when licenses are available or a parallel implementation using graphic processing unit (GPU) and many-core architectures in cases where resources with accelerators are available.
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Alternative Models in Neuropharmacology: The Zebrafish ( | c5cd60cb-1acd-4162-8070-4254d245d3e7 | 9608224 | Pharmacology[mh] | |
Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture | 48eb1656-95fc-4db0-b942-fe35bcd10c2f | 7146561 | Physiology[mh] | Recently, the demand for human movement estimation based on soft sensors has increased in the field of human motion capture. Human motion is widely utilized for the natural user interface/experience (NUI/NUX) in humanized computing environments , which needs advanced technology of human motion capture and estimation. Two kinds of sensory devices are developed for capturing motion: graphical data-based devices and time series data-based devices. Graphical data-based devices provide a means for end-users to interact with computers with the aid of one or more cameras. One typical graphical data-based sensory device is the Microsoft Kinect motion-sensing input device . Kinect is popular because it creates a novel way for end-users to interact with computers. End-users can control the virtual characters directly through their body movements, without any other attached sensor . However, it is difficult for Kinect to estimate subtle movements, particularly the movements that need sufficient operational and smooth sensory feedbacks. Time series data-based sensory devices provide a means for end-users to interact with computers using one or more sensor-based controllers. HTC VIVE is a powerful time series data-based sensory device (consisting of one headset and two controllers) that has been developed as a naturally interacting system. Users’ head and hand positions can be estimated accurately with the headset and controllers as the latter are directly measured by sensors in the former. Therefore, HTC VIVE is better suited to capture motions with accurate control . The time series data-based sensory devices have limitations in capturing the end-user’s expression, such as the movement of arms and legs. To overcome this, multiple sensors can be attached to the user’s limbs to enable accurate measurement. However, this makes it inconvenient for the end-users to move, and the collected sensory movements may be unnatural. Therefore, it is preferable to estimate the arm and leg movements with soft sensors. Previously, the arm movements were estimated based on Bayesian probability . One HTC VIVE controller was utilized to collect the sensory value including the hand positions, and one Myo gesture-control sensory armband (Myo armband) was attached to an arm to collect its orientation. Bayesian probabilities were then calculated, considering the movements of the hand and arm. Arm movements were estimated by the corresponding movements of the highest Bayesian probabilities. However, for calculating Bayesian probability, estimative movements should be defined in advance. As there has been much research on the diverse kinds of domains applicable to deep learning networks , it is preferable that this method can estimate movements without the need for predefining movements in advance. This paper proposes a framework to estimate the orientations of a paired upper arm and forearm of a single arm using a two-stream bidirectional two-layer long short-term memory (LSTM)-based framework (TBTLF), based on two-stream bidirectional two-layer long short-term memory (LSTM) fusion by combining Bi-LSTM and two-layer LSTM. Using Bi-LSTM, multiple consecutive sensory movements obtained from sensors can be analyzed. Using two-layer LSTM, high-level features can be analyzed, which also increases the accuracy of estimated movements. Using the proposed framework, sensory movements can be estimated without relying on pre-defined motions, thereby making the motion estimation process more flexible as well. This paper is organized as follows: introduces related works. presents the proposed movement estimation framework. validates the proposed framework experimentally and compares its performance with the traditional framework. discusses the issues and limitations associated with the proposed framework and presents the conclusions of the study.
Recently, movement estimation has been widely studied in human motion capture fields. This chapter introduces Bayesian-based and deep learning-based approaches for movement estimation. 2.1. Bayesian-Based Movement Estimation Due to the current substantial cost of wearable sensors, it is desirable to reduce the number of sensors required to estimate movements. Some traditional algorithms, such as Bayesian probability and K-means, are utilized to estimate the movements of the unmonitored parts of a body by considering the movements measured directly using sensors . Bayesian probability was first used to estimate the arm movement by Kim et al. . Arm movements were measured using two Myo armbands (Thalmic Labs) and the estimations were presented as coordinate values of arm orientations. As the measured data differed among themselves despite presenting the same orientation, they were sorted into angles ranging from −180° to 180° at 30° intervals. The upper arm movement was estimated based on the maximum Bayesian probability between the movement orientation angles of the forearm and upper arm. Therefore, the movement of one arm (an upper arm and a forearm) was represented by one Myo armbands instead of two. This Bayesian probability-based approach was then improved by Lee et al. using a MinMax movement estimation framework. In this approach, rather than using a fixed angle range of −180° to 180°, the angle range was determined by the minimum and maximum values of the measured data and thereby provided more accurate movement estimation. Choi et al. proposed a Bayesian probability approach to estimate forearm orientations based on hand positions . Forearm orientations were still measured by Myo armband, while the hand positions were collected using VIVE controllers. The unmeasured orientations of a forearm were estimated using the measured positions of a hand and the calculated Bayesian probability between the orientations of the forearm and the positions of the hand. Bayesian-based approaches perform well for movement estimation with pre-defined motions. In such approaches, large amounts of data are collected using sensor-based wearable devices. However, only a small proportion of these data match pre-defined motions. Consequently, these rich data sets do not provide any benefits for improving the performance of movement estimation using Bayesian-based movement estimation approaches. However, deep learning has recently been widely used in many domains due to its excellent capability to deal with large amounts of data, and thereby offers an enhanced method for improving the performance of movement estimation. 2.2. Deep Learning-Based Movement Estimation Approaches Technological improvements enable large amounts of movement data to be analyzed. Deep learning is the most popular approach for dealing with large amounts of data for movement estimation. State-of-the-art performances have been reported in many human motion capture tasks based on deep learning algorithms . One previous study proposed a deep neural network (DNN)-based framework to accurately estimate 3D poses from multi-view images . MoDeep, developed by Arjun et al. , is a deep learning framework for estimating the two-dimensional (2D) locations of human joints based on the movement features in a video. A convolutional network architecture deals with color and movement features based on a sliding-window architecture. The input is a three-dimensional (3D) tensor, which is a combination of an RGB image and its corresponding movement features in optical flow, and the output is a 3D tensor comprising one response map for each joint. Aria et al. trained a Convolutional Neural Network (CNN) for the estimation of unsupervised movements. The input for this network is a pair of images, and a dense motion field can be produced at its output layer. This network is a fully convolutional neural network with 12 convolutional layers that could be regarded as two parts. In the first part, CNN makes a compact representation of the movement information, which involves four down samplings. In the second part, the compact representation is used to reconstruct the motion field; this involves four upsamplings. Then, the movement of the motion can be estimated. However, MoDeep estimated human poses using the FLIC-motion dataset , which comprises 5003 images collected from Hollywood movies, augmented with movement features. Aria et al. trained a CNN using pairs of consecutive frames from the UCF101 dataset . Both these two approaches estimated movements based on the visual information of human movements contained in the video. The goal of these approaches was to estimate the movements in the video frame sequences. For using the sensory data, Hu et al. proposed a method to investigate the performance of the deep learning network with long short-term memory (LSTM) units to deal with the sensory value of an inertial motion unit (IMU). They verified that machine-learning approaches are able to detect the surface conditions of the road and age-group of the subjects from the sensory data collected from the walking behavior of the subjects. Therefore, a deep learning network should be proposed for estimating the movement based on the sensory movement values measured by wearable devices. 2.3. Comparison of the Bayesian-Based and Deep Learning-Based Movement Estimation The Bayesian-based and deep learning-based movement estimation methods mentioned above are analyzed and compared with the framework proposed in this paper in . From , it can be seen that there are mainly two types of conventional and widespread motion capture methods. These methods can be classified into image-based methods , which estimate the movement based on convolutional neural networks (CNNs) , and sensor-based methods, which use Bayesian probability and LSTM . In , the movements were estimated using Bayesian probability, whereas in , the surface conditions of the road and age-group of the subjects were detected based on the sensor values and an LSTM network. Owing to the significant contribution of deep learning methods in the field of motion capture, this study is expected to bring forth a deep learning-based framework, instead of the traditional methods , to improve the performance of VR applications using soft sensors. 2.4. Consideration of Deep Learning Frameworks This section introduces the most commonly used deep learning frameworks. A convolutional neural network was first designed for image recognition. A traditional CNN comprises three structures: convolution, activation, and pooling. The output of the CNN is the specific feature space of each image. CNN deals well with the image inputs due to its excellent ability in extracting the spatial features of the inputs. However, it is not widely used to deal with time-related sequence data. Another popular deep learning neural network is the recurrent neural network (RNN) . Compared with CNN, RNN provides better advantages in the processing of time-related sequence information, but its training architecture causes long-term dependency problems. LSTM is used to solve the issue of long-term dependency through its special cell structure with several gates . Like RNN, LSTM retains the ability to deal with long-term sequence data; however, only data before the current time can be used to train its relative parameters. Therefore, bidirectional LSTM (Bi-LSTM) is used, because it has an excellent ability to process two-directional data. In traditional LSTM, the state of the LSTM cell is transmitted forward to backward, while in bidirectional LSTM, the outputs of the current time are decided considering not only the previous states but also the subsequent ones. Traditional Bi-LSTM contains two LSTM layers: forward LSTM layer and backward LSTM. The method proposed in this paper is useful for dealing with the time-related sequence sensory data, which are collected by HTC VIVE controllers and Myo armbands. Each single layer (forward LSTM layer and backward LSTM layer) of a traditional Bi-LSTM can only utilize the primitive features of inputs. For estimating the movements of a single arm, the high-level features can be utilized to improve the accuracy of the estimated results. Therefore, the framework proposed herein adds a two-layer LSTM as a sub-layer of the Bi-LSTM to enhance the ability of expression for the entire learning model.
Due to the current substantial cost of wearable sensors, it is desirable to reduce the number of sensors required to estimate movements. Some traditional algorithms, such as Bayesian probability and K-means, are utilized to estimate the movements of the unmonitored parts of a body by considering the movements measured directly using sensors . Bayesian probability was first used to estimate the arm movement by Kim et al. . Arm movements were measured using two Myo armbands (Thalmic Labs) and the estimations were presented as coordinate values of arm orientations. As the measured data differed among themselves despite presenting the same orientation, they were sorted into angles ranging from −180° to 180° at 30° intervals. The upper arm movement was estimated based on the maximum Bayesian probability between the movement orientation angles of the forearm and upper arm. Therefore, the movement of one arm (an upper arm and a forearm) was represented by one Myo armbands instead of two. This Bayesian probability-based approach was then improved by Lee et al. using a MinMax movement estimation framework. In this approach, rather than using a fixed angle range of −180° to 180°, the angle range was determined by the minimum and maximum values of the measured data and thereby provided more accurate movement estimation. Choi et al. proposed a Bayesian probability approach to estimate forearm orientations based on hand positions . Forearm orientations were still measured by Myo armband, while the hand positions were collected using VIVE controllers. The unmeasured orientations of a forearm were estimated using the measured positions of a hand and the calculated Bayesian probability between the orientations of the forearm and the positions of the hand. Bayesian-based approaches perform well for movement estimation with pre-defined motions. In such approaches, large amounts of data are collected using sensor-based wearable devices. However, only a small proportion of these data match pre-defined motions. Consequently, these rich data sets do not provide any benefits for improving the performance of movement estimation using Bayesian-based movement estimation approaches. However, deep learning has recently been widely used in many domains due to its excellent capability to deal with large amounts of data, and thereby offers an enhanced method for improving the performance of movement estimation.
Technological improvements enable large amounts of movement data to be analyzed. Deep learning is the most popular approach for dealing with large amounts of data for movement estimation. State-of-the-art performances have been reported in many human motion capture tasks based on deep learning algorithms . One previous study proposed a deep neural network (DNN)-based framework to accurately estimate 3D poses from multi-view images . MoDeep, developed by Arjun et al. , is a deep learning framework for estimating the two-dimensional (2D) locations of human joints based on the movement features in a video. A convolutional network architecture deals with color and movement features based on a sliding-window architecture. The input is a three-dimensional (3D) tensor, which is a combination of an RGB image and its corresponding movement features in optical flow, and the output is a 3D tensor comprising one response map for each joint. Aria et al. trained a Convolutional Neural Network (CNN) for the estimation of unsupervised movements. The input for this network is a pair of images, and a dense motion field can be produced at its output layer. This network is a fully convolutional neural network with 12 convolutional layers that could be regarded as two parts. In the first part, CNN makes a compact representation of the movement information, which involves four down samplings. In the second part, the compact representation is used to reconstruct the motion field; this involves four upsamplings. Then, the movement of the motion can be estimated. However, MoDeep estimated human poses using the FLIC-motion dataset , which comprises 5003 images collected from Hollywood movies, augmented with movement features. Aria et al. trained a CNN using pairs of consecutive frames from the UCF101 dataset . Both these two approaches estimated movements based on the visual information of human movements contained in the video. The goal of these approaches was to estimate the movements in the video frame sequences. For using the sensory data, Hu et al. proposed a method to investigate the performance of the deep learning network with long short-term memory (LSTM) units to deal with the sensory value of an inertial motion unit (IMU). They verified that machine-learning approaches are able to detect the surface conditions of the road and age-group of the subjects from the sensory data collected from the walking behavior of the subjects. Therefore, a deep learning network should be proposed for estimating the movement based on the sensory movement values measured by wearable devices.
The Bayesian-based and deep learning-based movement estimation methods mentioned above are analyzed and compared with the framework proposed in this paper in . From , it can be seen that there are mainly two types of conventional and widespread motion capture methods. These methods can be classified into image-based methods , which estimate the movement based on convolutional neural networks (CNNs) , and sensor-based methods, which use Bayesian probability and LSTM . In , the movements were estimated using Bayesian probability, whereas in , the surface conditions of the road and age-group of the subjects were detected based on the sensor values and an LSTM network. Owing to the significant contribution of deep learning methods in the field of motion capture, this study is expected to bring forth a deep learning-based framework, instead of the traditional methods , to improve the performance of VR applications using soft sensors.
This section introduces the most commonly used deep learning frameworks. A convolutional neural network was first designed for image recognition. A traditional CNN comprises three structures: convolution, activation, and pooling. The output of the CNN is the specific feature space of each image. CNN deals well with the image inputs due to its excellent ability in extracting the spatial features of the inputs. However, it is not widely used to deal with time-related sequence data. Another popular deep learning neural network is the recurrent neural network (RNN) . Compared with CNN, RNN provides better advantages in the processing of time-related sequence information, but its training architecture causes long-term dependency problems. LSTM is used to solve the issue of long-term dependency through its special cell structure with several gates . Like RNN, LSTM retains the ability to deal with long-term sequence data; however, only data before the current time can be used to train its relative parameters. Therefore, bidirectional LSTM (Bi-LSTM) is used, because it has an excellent ability to process two-directional data. In traditional LSTM, the state of the LSTM cell is transmitted forward to backward, while in bidirectional LSTM, the outputs of the current time are decided considering not only the previous states but also the subsequent ones. Traditional Bi-LSTM contains two LSTM layers: forward LSTM layer and backward LSTM. The method proposed in this paper is useful for dealing with the time-related sequence sensory data, which are collected by HTC VIVE controllers and Myo armbands. Each single layer (forward LSTM layer and backward LSTM layer) of a traditional Bi-LSTM can only utilize the primitive features of inputs. For estimating the movements of a single arm, the high-level features can be utilized to improve the accuracy of the estimated results. Therefore, the framework proposed herein adds a two-layer LSTM as a sub-layer of the Bi-LSTM to enhance the ability of expression for the entire learning model.
The proposed framework estimates the orientations of a single arm, comprising a pair of an upper arm and a forearm, according to the movements of two hands (left and right hands). This chapter provides an overview of the movement estimation processes and the structure of TBTLF. 3.1. Overview TBTLF is realized based on the newly proposed two-stream bidirectional two-layer LSTM (TBTL). TBTL is a combination of Bi-LSTM and two-layer LSTM and is built to deal with sensory movements, which are defined as those represented by combinations of sensory values. The proposed framework comprises two stages as shown in : pre-processing and movement estimation. In the pre-processing stage, the positions of the left and right hands and the orientations of one arm are collected by two time-series-data-based devices and two gesture-based devices, respectively. The proposed framework in the movement estimation stage contains a two-stream architecture with bidirectional two-layer LSTMs and fully connected layers. Finally, the outputs of these two streams are combined with a fusion layer, and the fused outputs are provided as the final estimated orientation of a single arm. A dataset was collected as the ground truth using two Myo armbands and two HTC VIVE controllers. The Myo armbands measure the orientations of an upper arm and a forearm, and the HTC measures the locations of the two hands. Subsequently, the proposed framework was used to estimate the orientations of a single arm, which could either be a left arm or right arm. An example of the placement of the two Myo armbands and two HTC VIVEs is shown in . However, if two other Myo armbands are placed on the not-attached arm to collect the data of the corresponding arm, the orientations of both the left arm and right arm can be estimated by training the proposed framework twice using the left-arm dataset and right-arm dataset, respectively. 3.2. Pre-Processing Stage The sensory movement, m t , measured at time t by two-time series data-based devices and two gesture-based devices is defined by the sensory values of the pair of the arm movement m t A and the hand movement m t H , as shown in Equation (1). (1) m t = [ m t A , m t H ] The arm movement m t A consists of the upper arm movement m t U and forearm movement m t F , as shown in Equation (2). (2) m t A = [ m t U , m t F ] The upper arm movement m t U and the forearm movement m t F are defined as the corresponding orientations expressed by Equations (3) and (4), and they are measured by two gesture-based devices that collect the orientations as the motion quaternions (orientation coordinates, x, y, z, and w). (3) m t U = [ x t U , y t U , z t U , w t U ] (4) m t F = [ x t F , y t F , z t F , w t F ] The hand movement m t H is defined by the left-hand movement m t L and the right-hand movement m t R , as shown in Equation (5). (5) m t H = [ m t L , m t R ] The left-hand movement m t L and the right-hand movement m t R are defined as the positions from time series data-based devices, as shown in Equations (6) and (7). (6) m t L = [ x t L , y t L , z t L ] (7) m t R = [ x t R , y t R , z t R ] The differences between the two hand positions obtained consecutively are used as the short-term information on the corresponding movement to improve the accuracy of the proposed framework. In this study, the difference in each hand position, d t H = [ d t L , d t R ] , was calculated by a difference calculator. The left-hand position difference d t L and the right-hand position difference d t R are as shown in Equations (8) and (9), respectively. (8) d t L = [ d t L , X , d t L , X , d t L , X ] (9) d t R = [ d t R , X , d t R , X , d t R , X ] where d t L , X = x t L , X - x t − 1 L , X and so on. In the results, low-level features consist of the arm movement, hand movement, and hand position differences, where l t = [ m t A , m t H , d t H ] . For training relative parameters, the arm movement m t A and hand movement m t H are used as inputs to the first stream of the TBTL, whereas m t A and d t H are used as the inputs to the second stream of the TBTL. 3.3. Movement Estimation Stage The movement estimation stage includes two parts: a TBTL network and a fusion layer, as shown in . The proposed framework is based on two-stream structures. Given that a single bidirectional two-layer LSTM (BTL) stream is not able to capture the hierarchy of features in its entirety , another BTL is added to consider the hand position differences. The differences between the hand positions provide the short-term movement features between two consecutive movements, which aid the estimation of movements by combining the advantage of Bi-LSTM for the long-term features of inputs with the advantage of the short-term movement features. Two streams are applied to deal with low-level features. Then, two preliminary arm movements are estimated by the forward propagation and back-propagation of each BTL layer. The structures of the BTL for each stream in the TBTL are shown in , considering time sequences. The two arm movements estimated by the TBTL network are concatenated and input to a fully connected layer. The secondary estimated arm movement is m t , k A ″ , as shown in Equation (10), and is generated by the k th stream. (10) m t , k A ″ = [ m t , k U ″ , m t , k F ″ ] where m t , k U ″ and m t , k F ″ are the secondary estimated upper arm movements and forearm movements. They consist of the secondary estimated orientations of the upper arm and the forearm, as shown in Equations (11) and (12). (11) m t , k U ″ = [ x t , k U ″ , y t , k U ″ , z t , k U ″ , w t , k U ″ ] (12) m t , k F ″ = [ x t , k F ″ , y t , k F ″ , z t , k F ″ , w t , k F ″ ] where x t , k U ″ , y t , k U ″ , z t , k U ″ , w t , k U ″ and x t , k F ″ , y t , k F ″ , z t , k F ″ , w t , k F ″ are the coordinates of the secondary estimated orientations of the upper arm and the forearm. The secondary estimated arm movements of both streams are concatenated and input to a fusion layer, which is another fully connected structure. Therefore, the final estimated arm movement m t A ∗ is generated as shown in Equation (13). (13) m t A ∗ = [ m t U ∗ , m t F ∗ ] where m t U ∗ and m t F ∗ are the final estimated upper arm movement and forearm movement, respectively. They consist of the final estimated orientations of the upper arm and the forearm as shown in Equations (14) and (15). (14) m t , k U ∗ = [ x t , k U ∗ , y t U ∗ , z t U ∗ , w t U ∗ ] (15) m t F ″ = [ x t F ∗ , y t F ∗ , z t F ∗ , w t F ∗ ] where x t , k U ∗ , y t U ∗ , z t U ∗ , w t U ∗ and x t F ∗ , y t F ∗ , z t F ∗ , w t F ∗ are the coordinates of the final estimated orientations of the upper arm and the forearm.
TBTLF is realized based on the newly proposed two-stream bidirectional two-layer LSTM (TBTL). TBTL is a combination of Bi-LSTM and two-layer LSTM and is built to deal with sensory movements, which are defined as those represented by combinations of sensory values. The proposed framework comprises two stages as shown in : pre-processing and movement estimation. In the pre-processing stage, the positions of the left and right hands and the orientations of one arm are collected by two time-series-data-based devices and two gesture-based devices, respectively. The proposed framework in the movement estimation stage contains a two-stream architecture with bidirectional two-layer LSTMs and fully connected layers. Finally, the outputs of these two streams are combined with a fusion layer, and the fused outputs are provided as the final estimated orientation of a single arm. A dataset was collected as the ground truth using two Myo armbands and two HTC VIVE controllers. The Myo armbands measure the orientations of an upper arm and a forearm, and the HTC measures the locations of the two hands. Subsequently, the proposed framework was used to estimate the orientations of a single arm, which could either be a left arm or right arm. An example of the placement of the two Myo armbands and two HTC VIVEs is shown in . However, if two other Myo armbands are placed on the not-attached arm to collect the data of the corresponding arm, the orientations of both the left arm and right arm can be estimated by training the proposed framework twice using the left-arm dataset and right-arm dataset, respectively.
The sensory movement, m t , measured at time t by two-time series data-based devices and two gesture-based devices is defined by the sensory values of the pair of the arm movement m t A and the hand movement m t H , as shown in Equation (1). (1) m t = [ m t A , m t H ] The arm movement m t A consists of the upper arm movement m t U and forearm movement m t F , as shown in Equation (2). (2) m t A = [ m t U , m t F ] The upper arm movement m t U and the forearm movement m t F are defined as the corresponding orientations expressed by Equations (3) and (4), and they are measured by two gesture-based devices that collect the orientations as the motion quaternions (orientation coordinates, x, y, z, and w). (3) m t U = [ x t U , y t U , z t U , w t U ] (4) m t F = [ x t F , y t F , z t F , w t F ] The hand movement m t H is defined by the left-hand movement m t L and the right-hand movement m t R , as shown in Equation (5). (5) m t H = [ m t L , m t R ] The left-hand movement m t L and the right-hand movement m t R are defined as the positions from time series data-based devices, as shown in Equations (6) and (7). (6) m t L = [ x t L , y t L , z t L ] (7) m t R = [ x t R , y t R , z t R ] The differences between the two hand positions obtained consecutively are used as the short-term information on the corresponding movement to improve the accuracy of the proposed framework. In this study, the difference in each hand position, d t H = [ d t L , d t R ] , was calculated by a difference calculator. The left-hand position difference d t L and the right-hand position difference d t R are as shown in Equations (8) and (9), respectively. (8) d t L = [ d t L , X , d t L , X , d t L , X ] (9) d t R = [ d t R , X , d t R , X , d t R , X ] where d t L , X = x t L , X - x t − 1 L , X and so on. In the results, low-level features consist of the arm movement, hand movement, and hand position differences, where l t = [ m t A , m t H , d t H ] . For training relative parameters, the arm movement m t A and hand movement m t H are used as inputs to the first stream of the TBTL, whereas m t A and d t H are used as the inputs to the second stream of the TBTL.
The movement estimation stage includes two parts: a TBTL network and a fusion layer, as shown in . The proposed framework is based on two-stream structures. Given that a single bidirectional two-layer LSTM (BTL) stream is not able to capture the hierarchy of features in its entirety , another BTL is added to consider the hand position differences. The differences between the hand positions provide the short-term movement features between two consecutive movements, which aid the estimation of movements by combining the advantage of Bi-LSTM for the long-term features of inputs with the advantage of the short-term movement features. Two streams are applied to deal with low-level features. Then, two preliminary arm movements are estimated by the forward propagation and back-propagation of each BTL layer. The structures of the BTL for each stream in the TBTL are shown in , considering time sequences. The two arm movements estimated by the TBTL network are concatenated and input to a fully connected layer. The secondary estimated arm movement is m t , k A ″ , as shown in Equation (10), and is generated by the k th stream. (10) m t , k A ″ = [ m t , k U ″ , m t , k F ″ ] where m t , k U ″ and m t , k F ″ are the secondary estimated upper arm movements and forearm movements. They consist of the secondary estimated orientations of the upper arm and the forearm, as shown in Equations (11) and (12). (11) m t , k U ″ = [ x t , k U ″ , y t , k U ″ , z t , k U ″ , w t , k U ″ ] (12) m t , k F ″ = [ x t , k F ″ , y t , k F ″ , z t , k F ″ , w t , k F ″ ] where x t , k U ″ , y t , k U ″ , z t , k U ″ , w t , k U ″ and x t , k F ″ , y t , k F ″ , z t , k F ″ , w t , k F ″ are the coordinates of the secondary estimated orientations of the upper arm and the forearm. The secondary estimated arm movements of both streams are concatenated and input to a fusion layer, which is another fully connected structure. Therefore, the final estimated arm movement m t A ∗ is generated as shown in Equation (13). (13) m t A ∗ = [ m t U ∗ , m t F ∗ ] where m t U ∗ and m t F ∗ are the final estimated upper arm movement and forearm movement, respectively. They consist of the final estimated orientations of the upper arm and the forearm as shown in Equations (14) and (15). (14) m t , k U ∗ = [ x t , k U ∗ , y t U ∗ , z t U ∗ , w t U ∗ ] (15) m t F ″ = [ x t F ∗ , y t F ∗ , z t F ∗ , w t F ∗ ] where x t , k U ∗ , y t U ∗ , z t U ∗ , w t U ∗ and x t F ∗ , y t F ∗ , z t F ∗ , w t F ∗ are the coordinates of the final estimated orientations of the upper arm and the forearm.
4.1. Experimental Goals The proposed framework focuses on the movement estimation of a single arm, which could either be a left arm or a right arm, including its corresponding upper arm and forearm, based on the positions of the two hands. In the experiments, since the positions of the two arms are required to be estimated, the experiments were repeated twice with the proposed framework: once for the right arm, and the second time for the left arm. The performance of the proposed framework was then compared with those of the Bayesian-based approach . 4.2. Experimental Environments Two types of experiments were conducted. First, in the TBTLF-based experiments, movements were estimated by the proposed framework trained with 2000, 20,000, and 200,000 episodes. In these experiments, an episode is the time taken to repeat the training data during the training of the framework. Then, comparative trials based on the Bayesian-based movement estimation approach were conducted with 50, 100, and 1000 intervals, respectively. In the Bayesian-based experiments, an interval is the number of subsections left after all the training data are divided uniformly . Therefore, the best performances of each type of experiment were compared based on the distance calculated by dynamic time warping (DTW) , which is widely used to compare the similarity of two sequences. All experiments were conducted on a computer running the Windows 10 Pro operating system with an Intel i7-7700 3.6 GHz processor, NVIDIA GeForce GTX-1050-2GB graphics card, and 16G of DDR4 RAM. Hand positions were measured with HTC VIVE controllers and arm orientations were measure with two Myo armbands . The dataset was collected by a Unity 3D project, which was developed based on HTC VIVE SDK (software development kit) and Myo SDK using C# programming language. All experiments were carried out using the python programming language based on the TensorFlow deep learning architecture. The ground truth of the proposed framework comprises the measured values of the two hand positions and single-arm orientations that are used for comparison with the estimated arm orientations. The ground truth was collected based on a VR game called “Rise of the Tomb Raider” . Fifteen gestures represented by sensory values collected from two HTC VIVEs and two Myo armbands were used to train the proposed framework. The gestures in are the gesture commands used for training and evaluation. The gestures were combined with several consecutive motions. There are 11 motions in total such as running, shooting, and jumping. Each motion is defined by multiple movements, consecutive combinations of the orientations of arms and the positions of hands. The collected arm orientation and hand position for running and jumping are shown in and . Considering the playing of the game , the gestures are predefined. Every motion was performed 10 times. Seven times of the performed motions (70%) were used as the dataset for training the proposed framework, which is referred to as the training data. Three times of the motions (30%) were used as the dataset for validating the proposed framework, which is referred to as the validation data. To demonstrate the performance of the proposed framework in experiments on different subjects, the data collected from three subjects were used to validate the proposed framework. The corresponding anthropomorphic information is shown in . Both the training data and the validation data contained the measured arm orientations and hand positions measured simultaneously by Myo armbands and HTC VIVE controllers. The training data was used for training the parameters in the proposed framework. The measured hand positions in the validation data were used to generate the estimated arm orientations using the proposed framework or Bayesian-based framework , while the measured arm orientations were used to calculate the similarity to the estimated arm orientations by DTW. To train the TBTL network, several sets of hyper-parameters were adjusted. Finally, hidden_size was set to 256, time_steps to 160, and batch_size to 128. 4.3. Dataset Collection To illustrate the performed motions, some of the data collected for jumping motions are shown in , and . The values in were used as the input of the proposed framework, and the those in and were used as the labels when training the frameworks for the left and right arm, respectively. In addition, they were also used as the ground truth to perform the evaluation experiments. In these figures, Frame is defined to describe one set of data that was collected at the same time. Orientation is defined as the collected orientation of arm with a range of –1 to 1. Position is defined as the collected position of the hand, which is represented by the distance between base stations and controllers of HTC VIVE. 4.4. Experimental Results The measured data of the gestures with Indexes 1-15 was used to perform the evaluation experiments with its order as the ground truth. All gestures are performed by three subjects, one by one. Therefore, the ground truth data includes 11 motions, walking, running, picking up, shaking tree, jumping, avoiding, shooting, towing, opening door, sneaking, and attacking. The best performance by the proposed framework was achieved with 200,000 episodes, while that by Bayesian-based approach was achieved with 50 intervals. The comparisons between the two performances are illustrated in for Subject #1, for Subject #2, and for Subject #3. Given that only forearm (both a left and a right forearm) orientations were estimated in the Bayesian-based experiments, only the performances of the estimated movement of the forearm were compared. The movements estimated by the TBTLF-based experiments showed a great regularity, revealing the feature and discipline between the motions and subjects. Meanwhile, the movements estimated by the Bayesian-based experiments were chaotic; consequently, this method could not estimate the consecutive movements to show an entire motion. The estimated movements of the left upper arm and right upper arm for Subjects #1, #2, and #3 when TBTLF-based experiments achieved the best performance with 200,000 episodes are depicted in , and , respectively. The loss of the left and right arms during the training of the proposed frameworks with 200,000 episodes are as shown in . At first, the loss began at ~0.2; it then dropped to ~0.125. Afterward, there was a sharp decrease from 0.125 to 0.025 before 25,000 episodes for both left and right hands. Following this, a stable and slight decrease occurred until 200,000 episodes for the left hand while for the right, another slightly stronger decrease was observed from 25,000 to 110,000 episodes. DTW distance was used to calculate the distance among every estimated and measured arm movements to compare the similarity among them. For example, the DTW distance of the left upper arm was calculated with estimated coordinates of the left upper arm and measured coordinates of left upper arm, in which the estimated coordinates of the left upper arm were the estimated results of the proposed framework and the measured coordinates of left upper arm are the label data of the dataset. The higher the DTW distance is, the less similar the estimated movement is to the measured movement. In order to make a more intuitive comparison, the sum of DTW distances of the three subjects was used for experimental verification. The DTW distances obtained from the TBTLF-based and Bayesian-based experiments are shown in and , respectively. Bayesian-based experiments were performed according to , which only estimated the x, y, and z coordinate values of the arm orientations to represent the arm movement. compares the DTW distances for orientations x, y, z, and w among 2000, 20,000, and 200,000 episodes in the TBTLF-based experiments. According to and , the best performance in the Bayesian-based experiments was obtained with 50 intervals, and that in the TBTLF-based experiments was obtained with 200,000 episodes. The Bayesian-based framework only focuses on the x, y, and z coordinates of the forearm orientations of left and right arms. However, the proposed framework estimated x, y, z, and w coordinates of both forearm orientations of the left and right arms and the upper arm orientations of left and right arms. Consequently, the reduction rate of the DTW distance R is only calculated for the estimated forearm orientations of the left and right arms in the TBTLF-based experiment and Bayesian-based experiment, according to Equation (16). The results are given in . (16) R = D B − D T D B where D B is the DTW distance of Bayesian-based experiments and D T is the DTW distance of TBTLF-based experiments. The results show that the framework proposed by us can estimate the arm orientation with an average of 73.90% reduction rate of the DTW distance compared to the traditional framework, confirming that the proposed framework can estimate movements much more accurately.
The proposed framework focuses on the movement estimation of a single arm, which could either be a left arm or a right arm, including its corresponding upper arm and forearm, based on the positions of the two hands. In the experiments, since the positions of the two arms are required to be estimated, the experiments were repeated twice with the proposed framework: once for the right arm, and the second time for the left arm. The performance of the proposed framework was then compared with those of the Bayesian-based approach .
Two types of experiments were conducted. First, in the TBTLF-based experiments, movements were estimated by the proposed framework trained with 2000, 20,000, and 200,000 episodes. In these experiments, an episode is the time taken to repeat the training data during the training of the framework. Then, comparative trials based on the Bayesian-based movement estimation approach were conducted with 50, 100, and 1000 intervals, respectively. In the Bayesian-based experiments, an interval is the number of subsections left after all the training data are divided uniformly . Therefore, the best performances of each type of experiment were compared based on the distance calculated by dynamic time warping (DTW) , which is widely used to compare the similarity of two sequences. All experiments were conducted on a computer running the Windows 10 Pro operating system with an Intel i7-7700 3.6 GHz processor, NVIDIA GeForce GTX-1050-2GB graphics card, and 16G of DDR4 RAM. Hand positions were measured with HTC VIVE controllers and arm orientations were measure with two Myo armbands . The dataset was collected by a Unity 3D project, which was developed based on HTC VIVE SDK (software development kit) and Myo SDK using C# programming language. All experiments were carried out using the python programming language based on the TensorFlow deep learning architecture. The ground truth of the proposed framework comprises the measured values of the two hand positions and single-arm orientations that are used for comparison with the estimated arm orientations. The ground truth was collected based on a VR game called “Rise of the Tomb Raider” . Fifteen gestures represented by sensory values collected from two HTC VIVEs and two Myo armbands were used to train the proposed framework. The gestures in are the gesture commands used for training and evaluation. The gestures were combined with several consecutive motions. There are 11 motions in total such as running, shooting, and jumping. Each motion is defined by multiple movements, consecutive combinations of the orientations of arms and the positions of hands. The collected arm orientation and hand position for running and jumping are shown in and . Considering the playing of the game , the gestures are predefined. Every motion was performed 10 times. Seven times of the performed motions (70%) were used as the dataset for training the proposed framework, which is referred to as the training data. Three times of the motions (30%) were used as the dataset for validating the proposed framework, which is referred to as the validation data. To demonstrate the performance of the proposed framework in experiments on different subjects, the data collected from three subjects were used to validate the proposed framework. The corresponding anthropomorphic information is shown in . Both the training data and the validation data contained the measured arm orientations and hand positions measured simultaneously by Myo armbands and HTC VIVE controllers. The training data was used for training the parameters in the proposed framework. The measured hand positions in the validation data were used to generate the estimated arm orientations using the proposed framework or Bayesian-based framework , while the measured arm orientations were used to calculate the similarity to the estimated arm orientations by DTW. To train the TBTL network, several sets of hyper-parameters were adjusted. Finally, hidden_size was set to 256, time_steps to 160, and batch_size to 128.
To illustrate the performed motions, some of the data collected for jumping motions are shown in , and . The values in were used as the input of the proposed framework, and the those in and were used as the labels when training the frameworks for the left and right arm, respectively. In addition, they were also used as the ground truth to perform the evaluation experiments. In these figures, Frame is defined to describe one set of data that was collected at the same time. Orientation is defined as the collected orientation of arm with a range of –1 to 1. Position is defined as the collected position of the hand, which is represented by the distance between base stations and controllers of HTC VIVE.
The measured data of the gestures with Indexes 1-15 was used to perform the evaluation experiments with its order as the ground truth. All gestures are performed by three subjects, one by one. Therefore, the ground truth data includes 11 motions, walking, running, picking up, shaking tree, jumping, avoiding, shooting, towing, opening door, sneaking, and attacking. The best performance by the proposed framework was achieved with 200,000 episodes, while that by Bayesian-based approach was achieved with 50 intervals. The comparisons between the two performances are illustrated in for Subject #1, for Subject #2, and for Subject #3. Given that only forearm (both a left and a right forearm) orientations were estimated in the Bayesian-based experiments, only the performances of the estimated movement of the forearm were compared. The movements estimated by the TBTLF-based experiments showed a great regularity, revealing the feature and discipline between the motions and subjects. Meanwhile, the movements estimated by the Bayesian-based experiments were chaotic; consequently, this method could not estimate the consecutive movements to show an entire motion. The estimated movements of the left upper arm and right upper arm for Subjects #1, #2, and #3 when TBTLF-based experiments achieved the best performance with 200,000 episodes are depicted in , and , respectively. The loss of the left and right arms during the training of the proposed frameworks with 200,000 episodes are as shown in . At first, the loss began at ~0.2; it then dropped to ~0.125. Afterward, there was a sharp decrease from 0.125 to 0.025 before 25,000 episodes for both left and right hands. Following this, a stable and slight decrease occurred until 200,000 episodes for the left hand while for the right, another slightly stronger decrease was observed from 25,000 to 110,000 episodes. DTW distance was used to calculate the distance among every estimated and measured arm movements to compare the similarity among them. For example, the DTW distance of the left upper arm was calculated with estimated coordinates of the left upper arm and measured coordinates of left upper arm, in which the estimated coordinates of the left upper arm were the estimated results of the proposed framework and the measured coordinates of left upper arm are the label data of the dataset. The higher the DTW distance is, the less similar the estimated movement is to the measured movement. In order to make a more intuitive comparison, the sum of DTW distances of the three subjects was used for experimental verification. The DTW distances obtained from the TBTLF-based and Bayesian-based experiments are shown in and , respectively. Bayesian-based experiments were performed according to , which only estimated the x, y, and z coordinate values of the arm orientations to represent the arm movement. compares the DTW distances for orientations x, y, z, and w among 2000, 20,000, and 200,000 episodes in the TBTLF-based experiments. According to and , the best performance in the Bayesian-based experiments was obtained with 50 intervals, and that in the TBTLF-based experiments was obtained with 200,000 episodes. The Bayesian-based framework only focuses on the x, y, and z coordinates of the forearm orientations of left and right arms. However, the proposed framework estimated x, y, z, and w coordinates of both forearm orientations of the left and right arms and the upper arm orientations of left and right arms. Consequently, the reduction rate of the DTW distance R is only calculated for the estimated forearm orientations of the left and right arms in the TBTLF-based experiment and Bayesian-based experiment, according to Equation (16). The results are given in . (16) R = D B − D T D B where D B is the DTW distance of Bayesian-based experiments and D T is the DTW distance of TBTLF-based experiments. The results show that the framework proposed by us can estimate the arm orientation with an average of 73.90% reduction rate of the DTW distance compared to the traditional framework, confirming that the proposed framework can estimate movements much more accurately.
According to the experimental results presented in , the performance of the Bayesian-based experiments remained stable and no obvious progress was observed even with more intervals, while in the TBTLF-based experiments, a significant improvement was achieved between 20,000 episodes and 200,000 episodes. That is, the performance of the TBTLF-based experiment with 200,000 episodes was found to be much better than that of any of the Bayesian-based experiment. In addition, the Bayesian-based experiments can only estimate the arm movement according to the hand movement within the range of the training data due to the limitation of the Bayesian probability. However, in the TBTLF-based experiments, the arm movement could be estimated even when the validation data was not in the range of the training data, which shows the better flexibility of the TBTLF-based movement estimation.
This paper proposed a deep learning approach for human movement estimations. Firstly, movements were collected by HTC VIVE and Myo armbands, and the collected data were analyzed, wherein the movements were represented by arm orientations and hand positions. The proposed TBTLF-based framework estimated the movements of one upper arm and one forearm based on left- and right-hand movements. The TBTLF-based experiments showed significant improvements when using 200,000 episodes than when using 2000 episodes and 20,000 episodes, and also compared to the Bayesian-based experiments with 50, 100, and 1000 intervals. The effectiveness of the proposed framework was verified by several experiments, showing an average 73.90% reduction in DTW. The proposed framework requires large amounts of training data to achieve good performance in movement estimation. Therefore, in future work, we plan to enhance the framework to reduce the size of the dataset required for accurate movement estimation.
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Person-centered care content in medicine, occupational therapy, nursing, and physiotherapy education programs | 962bc0a1-b2cd-4944-8f17-d87bd739fc9e | 9229130 | Preventive Medicine[mh] | Patients, caregivers, and the World Health Organization have all called for a shift towards person-centered care (PCC) , moving away from care based on New Public Management principles to care based on mutual respect and collaboration between the patient and the clinician. In Canada , Eastern Mediterranean Region , England , Indonesia , Sweden , and USA , discussions are in progress about what implementing PCC may mean in the context of their respective health care systems. Several frameworks exist for PCC , including a recently presented European standard: ‘Patient involvement in health care—Minimum requirements for PCC to ensure quality improvement in PCC’ . Even if these frameworks differ somewhat, they all share the same foundational principle that the patient is not a disease or diagnosis, but a person who has the ability and resources to express will, needs, and desires and who wants and can take responsibility for her/his own life. Several meta-analyses have pointed out convincing evidence for the effect of PCC on a variety of outcomes, such as improved blood pressure and psychological health. In addition, there are multiple randomized controlled trials that have shown an effect on and reduction of health care costs . A successful implementation strategy for PCC requires that several actors from different health and social care sectors work together in a unified approach to mutually drive forward change . This requirement also implicates educational institutions as important enablers of PCC given their roles in training future health and social care workers . In several countries, PCC is being introduced into the university and college education systems, including Australia , Scotland , and Sweden . Some studies have explored PCC in medical and nursing undergraduate curricula . However, to the best of our knowledge, no studies have examined the emphasis on or inclusion of PCC concomitantly across multiple national education programs in different clinical areas. Aim The overall aim was to explore the PCC content in four Swedish national study programs: medicine, nursing, occupational therapy, and physiotherapy. Design This study used a qualitative document analysis design . Setting Swedish national study programs are funded publicly, and the education system is governed legally by the Swedish Higher Education Act and Higher Education Ordinance . In 2007, Swedish higher education was adapted to the European Bologna process . For this study we will refer to three different levels: level I, national study programs; level II, local program syllabuses; and level III, local course syllabuses. The national study programs (level I) are to have local program syllabuses (level II) and be accompanied by local course syllabuses (level III). These steering documents must contain information about included courses and requirements for special eligibility. The mandatory information in the local course syllabuses is a description of the level, the number of higher education credits (HECs) earned with course completion, intended learning outcomes, special eligibility, and forms of assessment. Optional information includes associated literature and compulsory elements. An independent government agency, the Swedish Higher Education Authority, regularly assesses the quality of Swedish higher education . Description of context In spring 2020, the Swedish national study program (level I) in medicine comprised 330 HECs, which corresponds to 11 semesters of full-time studies provided at seven higher education institutions (HEIs). The national study programs in nursing, occupational therapy, and physiotherapy (level 1) comprise 180 HECs or six semesters of full-time studies. The national nursing program is offered at 25 HEIs and the national programs in occupational therapy and physiotherapy at 8 institutions each. In spring 2020, a total of 10,895 students were admitted to these four national study programs: 2155 to the program in medicine, 7218 to the program in nursing, 693 to the program in occupational therapy, and 829 to the program in physical therapy . Data extraction To extract data from national steering documents, such as national study programs (level 1), local program syllabues (level II), and local course syllabuses (level III), a protocol was developed in three phases. Phase 1. Setting up a steering committee A steering committee consisting of three researchers (CF, IB, CW). In terms of the researchers’ prior understanding, all had previous research experience with PCC and were affiliated with University of Gothenburg Centre for Person-Centred Care (GPCC). Moreover, they all had previous teaching experience and knowledge and understanding of steering documents at the three levels. Their task was to develop a protocol to examine the steering documents. Phase 2. Developing a protocol A crucial step in developing a protocol was to a priori agree upon the conceptual definitions of PCC to include in the study. PCC is described within several frameworks and, even though the authors adhered to the description of PCC by Ekman et al. , our research goal was to apply a broad understanding of PCC in the search process. This broad approach was chosen for two reasons. First, information in the literature suggests that the choice of terms affects the professional approach and execution in clinical practice . Second, there are multiple descriptions of PCC . As a result, the research group concluded that documents that have content referring to actors (person, patient, client) and are linked to context (care, rehabilitation), actions (listening, documenting), and relationships (co-creation, approach) should be accepted as having content equivalent to PCC as described in previous work . A syllabus governs and controls the content and learning outcomes of education programs; thus, it highlights the values of a specific society . According to the Swedish National Agency for Higher Education (UKÄ) , the national study program (level I), local program syllabuses (level II), and local course syllabuses (level III) must account for which courses are included in the program and which content and intended learning outcomes are in each level. According to Swedish law , each steering document needs to have a short description of its contents. This content consists of subject content on which the teaching must focus. The intended learning outcomes should be classified into three categories: knowledge/comprehensibility, skills/ability, and judgment/attitude. In cases of unclear classification of the intended learning outcomes, Bloom’s taxonomy could be used to guide the examination. We chose to use Bloom’s taxonomy in unclear situations because of its history as a fruitful tool for developing and evaluating levels of knowledge in documents used in higher education . Thus, the steering group wanted to examine the total number of intended learning outcomes referring to actors and linked to context, actions, and relationships in the national study plan (level I), a total number of included local program syllabuses (level II) and local course syllabuses (level III), intended learning outcomes, courses, and course titles. Furthermore, the total distribution of the intended learning outcomes by semester in the study programs and how they were distributed around what students should have achieved at the end of the course regarding knowledge/comprehension and skills judgment/attitude was calculated. Finally, there was an interest in examining the contents describing PCC and variants of this term. In the first level of the protocol constructed based on the above choices, one question was crafted to examine the national study plans for the four programs. In the second level of the protocol, four questions were crafted to examine local program syllabuses ( n = 48), and in the third level of the protocol five questions to examine the local course syllabuses ( n = 799; Table ). Phase 3. Evaluation of steering documents The appointed researchers (CF, IB, CW) retrieved data on the national study programs and syllabuses at all three levels from the university and college websites between January and May 2020. Thereafter, the researchers separately examined the national study program (level I), local program syllabuses (level II), and local course syllabuses (level III) using the protocol described in Table . The researchers checked the results of the protocol review and any differences discussed until consensus was reached . Data analysis According to previous authors , document analysis is intended to identify, select, evaluate, and synthesize the content in the documents, which in this study involves content referring to actors and linked to context, actions, and relationships. We used content analysis to analyze the steering documents in three steps: reading of documents, coding and categorizing, and calculating frequencies and percentages. Reading of documents The steering documents were read (CF, IB, CW) several times to achieve overall familiarity and a picture of their manifest content (i.e., phenomenon – content referring to actors and linked to context, actions, and relationships). Coding and building categories Meaning units (words, sentences, paragraphs) were selected by the researchers (CF, IB, CW) using a deductive approach. PCC context was searched for in the the local program (level II) and local course syllabuses (level III) (questions 2 and 6), as well as intended learning outcomes (questions 1, 3, and 7), courses (question 4), course titles (question 5), semesters (question 8), headings of learning levels (question 9), and terms connected to actors, context, actions, and relationships (question 10). Identified meaning units were then extracted, condensed, and labeled with a code. Finally, the codes were sorted and abstracted into categories. Deductive coding was used to assign an appropriate heading for an intended learning outcome (question 9). The unassigned headings were coded into a code map based on Bloom’s taxonomy . Unassigned headings were sorted into three categories: knowledge/comprehensibility, skills/ability, and judgment/attitude. Finally, all of the codes were sorted and abstracted into categories. Thus, in the deductive analyses, each word or sentence was coded, condensed, and grouped to describe the explicit content. Any differences between the researchers (CF, IB, CW) in the data interpretation were discussed until consensus was reached . Calculating frequencies and percentages Frequencies and percentages were calculated to describe the total number of local program and course syllabuses (questions 2 and 6), intended learning outcomes (question 1, 3, and 7), courses (question 4), course titles (question 5), semesters (question 8), headings of learning levels (question 9), and terms connected to actors, context, actions, and relationships (question 10) . Trustworthiness To ensure trustworthiness, several actions were planned a priori. Credibility was ensured by the fact that all researchers (CF, IB, CW) who collected data have experience reading and interpreting steering documents in higher education (reflexivity). The data collection period lasted for 5 months (prolonged engagement). All researchers in the study participated in the discussion on how the results should be interpreted and understood (member checking). To account for the study's dependability, there was a description of how the steering documents were retrieved, identified, analyzed, and described (investigator triangulation). Confirmability was secured by describing how the study protocol was created and what phenomenon (i.e., content referring to actors and linked to context, actions, and relationships) we looked for in the steering documents (audit trail). Confirmability was ensured by describing what the study's phenomenon was, which control steering documents constituted data, what the study context was, and how data were analyzed (audit trail). Authenticity was ensured by all researchers using the study protocol (audit trail) and all research group members participating in the discussions of the results (member checking) . The overall aim was to explore the PCC content in four Swedish national study programs: medicine, nursing, occupational therapy, and physiotherapy. This study used a qualitative document analysis design . Swedish national study programs are funded publicly, and the education system is governed legally by the Swedish Higher Education Act and Higher Education Ordinance . In 2007, Swedish higher education was adapted to the European Bologna process . For this study we will refer to three different levels: level I, national study programs; level II, local program syllabuses; and level III, local course syllabuses. The national study programs (level I) are to have local program syllabuses (level II) and be accompanied by local course syllabuses (level III). These steering documents must contain information about included courses and requirements for special eligibility. The mandatory information in the local course syllabuses is a description of the level, the number of higher education credits (HECs) earned with course completion, intended learning outcomes, special eligibility, and forms of assessment. Optional information includes associated literature and compulsory elements. An independent government agency, the Swedish Higher Education Authority, regularly assesses the quality of Swedish higher education . In spring 2020, the Swedish national study program (level I) in medicine comprised 330 HECs, which corresponds to 11 semesters of full-time studies provided at seven higher education institutions (HEIs). The national study programs in nursing, occupational therapy, and physiotherapy (level 1) comprise 180 HECs or six semesters of full-time studies. The national nursing program is offered at 25 HEIs and the national programs in occupational therapy and physiotherapy at 8 institutions each. In spring 2020, a total of 10,895 students were admitted to these four national study programs: 2155 to the program in medicine, 7218 to the program in nursing, 693 to the program in occupational therapy, and 829 to the program in physical therapy . To extract data from national steering documents, such as national study programs (level 1), local program syllabues (level II), and local course syllabuses (level III), a protocol was developed in three phases. A steering committee consisting of three researchers (CF, IB, CW). In terms of the researchers’ prior understanding, all had previous research experience with PCC and were affiliated with University of Gothenburg Centre for Person-Centred Care (GPCC). Moreover, they all had previous teaching experience and knowledge and understanding of steering documents at the three levels. Their task was to develop a protocol to examine the steering documents. A crucial step in developing a protocol was to a priori agree upon the conceptual definitions of PCC to include in the study. PCC is described within several frameworks and, even though the authors adhered to the description of PCC by Ekman et al. , our research goal was to apply a broad understanding of PCC in the search process. This broad approach was chosen for two reasons. First, information in the literature suggests that the choice of terms affects the professional approach and execution in clinical practice . Second, there are multiple descriptions of PCC . As a result, the research group concluded that documents that have content referring to actors (person, patient, client) and are linked to context (care, rehabilitation), actions (listening, documenting), and relationships (co-creation, approach) should be accepted as having content equivalent to PCC as described in previous work . A syllabus governs and controls the content and learning outcomes of education programs; thus, it highlights the values of a specific society . According to the Swedish National Agency for Higher Education (UKÄ) , the national study program (level I), local program syllabuses (level II), and local course syllabuses (level III) must account for which courses are included in the program and which content and intended learning outcomes are in each level. According to Swedish law , each steering document needs to have a short description of its contents. This content consists of subject content on which the teaching must focus. The intended learning outcomes should be classified into three categories: knowledge/comprehensibility, skills/ability, and judgment/attitude. In cases of unclear classification of the intended learning outcomes, Bloom’s taxonomy could be used to guide the examination. We chose to use Bloom’s taxonomy in unclear situations because of its history as a fruitful tool for developing and evaluating levels of knowledge in documents used in higher education . Thus, the steering group wanted to examine the total number of intended learning outcomes referring to actors and linked to context, actions, and relationships in the national study plan (level I), a total number of included local program syllabuses (level II) and local course syllabuses (level III), intended learning outcomes, courses, and course titles. Furthermore, the total distribution of the intended learning outcomes by semester in the study programs and how they were distributed around what students should have achieved at the end of the course regarding knowledge/comprehension and skills judgment/attitude was calculated. Finally, there was an interest in examining the contents describing PCC and variants of this term. In the first level of the protocol constructed based on the above choices, one question was crafted to examine the national study plans for the four programs. In the second level of the protocol, four questions were crafted to examine local program syllabuses ( n = 48), and in the third level of the protocol five questions to examine the local course syllabuses ( n = 799; Table ). The appointed researchers (CF, IB, CW) retrieved data on the national study programs and syllabuses at all three levels from the university and college websites between January and May 2020. Thereafter, the researchers separately examined the national study program (level I), local program syllabuses (level II), and local course syllabuses (level III) using the protocol described in Table . The researchers checked the results of the protocol review and any differences discussed until consensus was reached . According to previous authors , document analysis is intended to identify, select, evaluate, and synthesize the content in the documents, which in this study involves content referring to actors and linked to context, actions, and relationships. We used content analysis to analyze the steering documents in three steps: reading of documents, coding and categorizing, and calculating frequencies and percentages. The steering documents were read (CF, IB, CW) several times to achieve overall familiarity and a picture of their manifest content (i.e., phenomenon – content referring to actors and linked to context, actions, and relationships). Meaning units (words, sentences, paragraphs) were selected by the researchers (CF, IB, CW) using a deductive approach. PCC context was searched for in the the local program (level II) and local course syllabuses (level III) (questions 2 and 6), as well as intended learning outcomes (questions 1, 3, and 7), courses (question 4), course titles (question 5), semesters (question 8), headings of learning levels (question 9), and terms connected to actors, context, actions, and relationships (question 10). Identified meaning units were then extracted, condensed, and labeled with a code. Finally, the codes were sorted and abstracted into categories. Deductive coding was used to assign an appropriate heading for an intended learning outcome (question 9). The unassigned headings were coded into a code map based on Bloom’s taxonomy . Unassigned headings were sorted into three categories: knowledge/comprehensibility, skills/ability, and judgment/attitude. Finally, all of the codes were sorted and abstracted into categories. Thus, in the deductive analyses, each word or sentence was coded, condensed, and grouped to describe the explicit content. Any differences between the researchers (CF, IB, CW) in the data interpretation were discussed until consensus was reached . Frequencies and percentages were calculated to describe the total number of local program and course syllabuses (questions 2 and 6), intended learning outcomes (question 1, 3, and 7), courses (question 4), course titles (question 5), semesters (question 8), headings of learning levels (question 9), and terms connected to actors, context, actions, and relationships (question 10) . To ensure trustworthiness, several actions were planned a priori. Credibility was ensured by the fact that all researchers (CF, IB, CW) who collected data have experience reading and interpreting steering documents in higher education (reflexivity). The data collection period lasted for 5 months (prolonged engagement). All researchers in the study participated in the discussion on how the results should be interpreted and understood (member checking). To account for the study's dependability, there was a description of how the steering documents were retrieved, identified, analyzed, and described (investigator triangulation). Confirmability was secured by describing how the study protocol was created and what phenomenon (i.e., content referring to actors and linked to context, actions, and relationships) we looked for in the steering documents (audit trail). Confirmability was ensured by describing what the study's phenomenon was, which control steering documents constituted data, what the study context was, and how data were analyzed (audit trail). Authenticity was ensured by all researchers using the study protocol (audit trail) and all research group members participating in the discussions of the results (member checking) . Level I. National study programs In the national study programs ( n = 4), we found no content referring to actors and linked to context, actions, and relationships in the intended learning outcomes. Level II. Local program syllabuses Of the 48 approved local program syllabuses, 7 (15%) included nine local intended learning outcomes of content referring to actors and linked to context, actions, and relationships. For example, one local program syllabus in medicine had the local intended learning outcome, ‘To use a patient-centered approach in clinical work,’ one local study program in occupational therapy had the local intended learning outcome, ‘To apply person-centered and reflective approach,’ and one local study program in nursing used the local intended learning outcome, ‘Have the competence to implement PCC.’ Of the 799 local course syllabuses identified in the local program syllabuses ( n = 48), 8 (1%) had course titles referring to actors and linked to context, actions, and relationships. All identified course titles were in the nursing program. For example, two HEIs in nursing used the titles, ‘Person-centered care for mental illness’ and ‘Person-centered nursing, caring approach, and communication.’ Level III. Local course syllabuses Of the 799 local course syllabuses, 101 (13%) included 142 intended learning outcomes with content referring to actors and linked to context, actions, and relationships. For example, one of the intended learning outcomes in medical education was given as, ‘Using patient-centered conversation methodology, initiate the conversation and clarify the reason for the visit, including thought, concern, and desire.’ Another example, from the physiotherapist program, was, ‘Reflect on the importance of good communication and collaboration for effective person-centered care.’ Fourteen of the 48 HEIs (29%) did not have any content referring to actors and linked to context, actions, and relationships in their intended learning outcomes (Table ). In addition, across the 48 HEIs, there was a difference when content referring to actors and linked to context, actions, and relationships in the intended learning outcome was introduced and taught (Table ). In the national study programs in medicine, HEIs ( n = 7) had most intended learning outcomes with content referring to actors and linked to context, actions, and relationships in semesters 4, 5, and 6 (each 17%). Furthermore, this study program had 4% intended learning outcomes with content referring to actors and linked to context, action, and relationship in semesters 7, 8, 9, and 11. However, HEIs of nursing ( n = 25) had most intended learning outcomes with content referring to actors and linked to context, action and relationship (23.5%) in the first semester. HEIs of occupational therapy ( n = 8) had most of their intended learning outcomes with content referring to actors and linked to context, actions, and relationships (33.3%) in semester 4 and HEIs of physiotherapy ( n = 8) in semesters 4 and 6 (each 30%). Overall, the HEIs ( n = 48) had most of their intended learning outcomes with content referring to actors and linked to context, actions and relationship in the sixth semester (28.2%). Of the 142 intended learning outcomes, 22 in nursing, 8 in occupational therapy, and 6 in physiotherapy needed to be assessed against Bloom’s taxonomy . A total of 52 intended learning outcomes were connected to knowledge/comprehensibility. For example, in a medicine program, one was given as, ‘Describe the different parts of a person-centered patient–doctor conversation.’ A total of 71 intended learning outcomes were distributed under skills/ability. In the nursing program, one example was, ‘To prepare and conduct conversations with the individual and relatives in difficult and vulnerable situations based on a person-centered approach.’ Another intended learning outcome was in the physiotherapy program: ‘Be able to explain what person/family/child centering means in physiotherapeutic intervention.’ Finally, there were 19 intended learning outcomes distributed under judgment/attitude (Fig. ). For example, in occupational therapy, an intended learning outcome was, ‘To further develop knowledge, skills, and values in applying occupational therapy based on a client-centered approach in collaboration with the client, relatives, and other professional groups involved.’ Twenty-one terms connected to content referring to actors and linked to context, actions, and relationships were found in the intended learning outcomes. These 21 terms were used 96 times in the 142 identified outcomes. The most frequently used terms in the nursing study program were ‘person-centered approach’ (23 times) and ‘person-centered care’ (23 times). The most common terms in the medical study program were ‘patient-centered consultation’ (4 times), whereas the physiotherapy study program used ‘person-centered approach’ (2 times), ‘person-centered care’ (2 times), and ‘person-centered goal’ (2 times). In the occupational therapy study program, the term ‘client-centered practice’ (5 times) was most commonly used (Fig. .) In the national study programs ( n = 4), we found no content referring to actors and linked to context, actions, and relationships in the intended learning outcomes. Of the 48 approved local program syllabuses, 7 (15%) included nine local intended learning outcomes of content referring to actors and linked to context, actions, and relationships. For example, one local program syllabus in medicine had the local intended learning outcome, ‘To use a patient-centered approach in clinical work,’ one local study program in occupational therapy had the local intended learning outcome, ‘To apply person-centered and reflective approach,’ and one local study program in nursing used the local intended learning outcome, ‘Have the competence to implement PCC.’ Of the 799 local course syllabuses identified in the local program syllabuses ( n = 48), 8 (1%) had course titles referring to actors and linked to context, actions, and relationships. All identified course titles were in the nursing program. For example, two HEIs in nursing used the titles, ‘Person-centered care for mental illness’ and ‘Person-centered nursing, caring approach, and communication.’ Of the 799 local course syllabuses, 101 (13%) included 142 intended learning outcomes with content referring to actors and linked to context, actions, and relationships. For example, one of the intended learning outcomes in medical education was given as, ‘Using patient-centered conversation methodology, initiate the conversation and clarify the reason for the visit, including thought, concern, and desire.’ Another example, from the physiotherapist program, was, ‘Reflect on the importance of good communication and collaboration for effective person-centered care.’ Fourteen of the 48 HEIs (29%) did not have any content referring to actors and linked to context, actions, and relationships in their intended learning outcomes (Table ). In addition, across the 48 HEIs, there was a difference when content referring to actors and linked to context, actions, and relationships in the intended learning outcome was introduced and taught (Table ). In the national study programs in medicine, HEIs ( n = 7) had most intended learning outcomes with content referring to actors and linked to context, actions, and relationships in semesters 4, 5, and 6 (each 17%). Furthermore, this study program had 4% intended learning outcomes with content referring to actors and linked to context, action, and relationship in semesters 7, 8, 9, and 11. However, HEIs of nursing ( n = 25) had most intended learning outcomes with content referring to actors and linked to context, action and relationship (23.5%) in the first semester. HEIs of occupational therapy ( n = 8) had most of their intended learning outcomes with content referring to actors and linked to context, actions, and relationships (33.3%) in semester 4 and HEIs of physiotherapy ( n = 8) in semesters 4 and 6 (each 30%). Overall, the HEIs ( n = 48) had most of their intended learning outcomes with content referring to actors and linked to context, actions and relationship in the sixth semester (28.2%). Of the 142 intended learning outcomes, 22 in nursing, 8 in occupational therapy, and 6 in physiotherapy needed to be assessed against Bloom’s taxonomy . A total of 52 intended learning outcomes were connected to knowledge/comprehensibility. For example, in a medicine program, one was given as, ‘Describe the different parts of a person-centered patient–doctor conversation.’ A total of 71 intended learning outcomes were distributed under skills/ability. In the nursing program, one example was, ‘To prepare and conduct conversations with the individual and relatives in difficult and vulnerable situations based on a person-centered approach.’ Another intended learning outcome was in the physiotherapy program: ‘Be able to explain what person/family/child centering means in physiotherapeutic intervention.’ Finally, there were 19 intended learning outcomes distributed under judgment/attitude (Fig. ). For example, in occupational therapy, an intended learning outcome was, ‘To further develop knowledge, skills, and values in applying occupational therapy based on a client-centered approach in collaboration with the client, relatives, and other professional groups involved.’ Twenty-one terms connected to content referring to actors and linked to context, actions, and relationships were found in the intended learning outcomes. These 21 terms were used 96 times in the 142 identified outcomes. The most frequently used terms in the nursing study program were ‘person-centered approach’ (23 times) and ‘person-centered care’ (23 times). The most common terms in the medical study program were ‘patient-centered consultation’ (4 times), whereas the physiotherapy study program used ‘person-centered approach’ (2 times), ‘person-centered care’ (2 times), and ‘person-centered goal’ (2 times). In the occupational therapy study program, the term ‘client-centered practice’ (5 times) was most commonly used (Fig. .) This study aimed to explore the PCC content in four Swedish national study programs in medicine, nursing, occupational therapy, and physiotherapy. No content referring to a person, patient, or client and linked to context, actions, and relationships was found in the level I steering documents but mainly in the level III documentation. In addition, there was an uneven representation and distribution of content referring to a person, patient, or client and linked to context, actions, and relationships between and within programs. We identified local intended learning outcomes with PCC in three of the four national study programs at level II, and all examined national study programs had intended learning outcomes with PCC in their local course syllabuses (level III). The implication is that changes in the four Swedish national study programs are driven more often by the university lecturer, given that the local course syllabuses (level III; n = 101) contained more PCC references than the national study program (level I; n = 0) and local program syllabuses (level II; n = 7). On the one hand, our results could be interpreted as a lack of governance from authorities and leading politicians regarding the national study program. There is also a lack of governance of the faculties and departments at the universities when it comes to the local program syllabuses. On the other hand, the results could be seen as a good example of an implementation process driven by a bottom-up process in which the local teachers are the ones taking lead on the change. In this study, we did not explore study guidelines, but it is not unreasonable to assume, as other studies have shown , that the identified terms regarding PCC used in Swedish national study programs are inconsistently described. Bowden pointed out that documents are rarely developed for research and, therefore, often contain few detailed descriptions. To gain a deeper understanding of the assumptions behind PCC, supplementing with other data, such as interviews, is recommended . For this reason, we have started an interview study with program directors to obtain a better understanding of factors that promote or enable implementation of PCC in the four national study programs. In this study, 21 different terms were used in reference to PCC, which is in line with earlier results . The most commonly used terms in the present study were ‘person-centered approach’ and ‘person-centered care.’ In this study, we used a broad conceptual definition of PCC because we wanted to apply an inclusive approach. A problem identified by Sharma et al. in 2015 was the lack of a universal definition. However, they identified several common components in the examined terms. Today, there is a European standard that describes minimal patient involvement in PCC and is recommended as a tool for planning, implementing, and evaluating PCC in clinical practice and research . Based on our results demonstrating a large diversity of terms related to PCC, the standard could also be used for clarity and to plan, implement, and evaluate pedagogical and educational initiatives. The results also show that most intended learning outcomes in the local course syllabuses (level III) could be classified within the skills/ability heading. It is reasonable that HEIs promote skills/ability if person-centered ethics is the theoretical starting point. This ethical premise rewards actions directed at other humans (i.e., a form of applied ethics) . However, 25% (36/142) of the local intended learning outcomes did not clearly state the level of knowledge that they reference. This lack is problematic because learning needs to be transparent so that the student knows what they know after completing a course or education program. Moreover, the content of a course should be able to communicate to the surrounding community. According to the Swedish Higher Education Act , ‘[T]he mandate of higher education shall include third stream activities and the provision of their activities, as well as ensuring that benefit is derived from their research findings.’ The uneven representation and distribution of PCC between and within programs calls for a consistent implementation strategy. Implementation requires that several actors from different societal systems work together, take a unified approach, and mutually drive the change forward . Educational institutions are important facilitators by educating the next generation of professionals in PCC . Therefore, it would be of interest to apply a strategy for implementation, and one available framework that can guide such implementation may be the ADDIE model . The implementation of PCC is ongoing in the health care and social care sectors, but educational bodies have not yet been included in the strategy. Organizational culture was previously suggested to be regarded as an essential starting point before any change is implemented . Limitations Many factors affect the quality of research when reviewing documents , and this study is no exception. Here, we explored documents, such as national study programs, local program syllabuses, and local course syllabuses, and conducted the study as a single look at these documents at a single point in time. Thus, it is possible that the included documents have been revised since the study was conducted. The explored documents also describe only the overall content of a program or course and contain few details, which can contribute to misinterpretations. Another limitation is that we only used documents as a data source in this study. This means that the result risks offering a one-sided and unvarnished picture of the content of the various documents. Another limitation is that we needed to assess 36 of 142 intended learning outcome levels of knowledge using Bloom’s taxonomy , so it is possible that we assessed them differently from what their developers intended. Many factors affect the quality of research when reviewing documents , and this study is no exception. Here, we explored documents, such as national study programs, local program syllabuses, and local course syllabuses, and conducted the study as a single look at these documents at a single point in time. Thus, it is possible that the included documents have been revised since the study was conducted. The explored documents also describe only the overall content of a program or course and contain few details, which can contribute to misinterpretations. Another limitation is that we only used documents as a data source in this study. This means that the result risks offering a one-sided and unvarnished picture of the content of the various documents. Another limitation is that we needed to assess 36 of 142 intended learning outcome levels of knowledge using Bloom’s taxonomy , so it is possible that we assessed them differently from what their developers intended. The change towards more PCC within the educational system is driven by local course leaders and teachers. Most PCC content found within the study programs was at a local level in intended course-learning outcomes. Increasing the inclusion of PCC instruction within and between the national study programs, local program syllabuses, and local course syllabuses requires action from politicians, authorities, faculty, and departments of higher education (Fig. ). There is a need to further explore health care professionals’ education programs. More specifically, the content of study guides, offered learning activities, examination forms, and literature choices needs to be studied in detail. Furthermore, interviews with students and teachers will help us understand their learning of PCC in the context of higher education. There is also a need to support faculties to develop their knowledge and skills by offering in-service education to improve their practices regarding PCC. |
How stressful was the COVID-19 pandemic for residents specializing in family practice?. A study of stressors and psychological well-being of physicians in further training specializing in family practice (GP trainees) within a pandemic context | 736e718b-d48f-403a-80c8-87e79baeddf0 | 9713726 | Family Medicine[mh] | Residents specializing in family practice (GP trainees) experienced complex and problematic situations in their training practices/clinics as a result of the COVID-19 pandemic. Working in primary care offices, which are considered the first point of contact for many people when they become ill, held a high potential for spread and personal infection with SARS-CoV-2 at the onset of the 2020 pandemic. In this context, many medical personnel reported psychological symptoms or disorders that were fostered due to stressors related to the COVID-19 pandemic . Holton et al. highlighted significant negative effects of the COVID-19 pandemic on the psychological well-being of clinical professionals . Effects reported were stress, anxiety, depressive symptoms . Zerbini et al. also reported more stress, exhaustion, and depressed mood among medical staff, even independent of regular Covid-19 contact. The most common causes of stress were job overload and uncertainty about the future . Studies have indicated that the number of positive COVID-19 cases in an area may be less important than the evaluation of the situation, a phenomenon which is related to social cognitive processes . This is, according to the view of a transactional understanding of stress (theory according to Lazarus & Folkman ), in which especially dangerous, delicate or very challenging transactions between the individuals and their environment are evaluated as stressful, decisive for the occurrence and occurrence of psychological symptoms . In this context, the perception of stress was influenced by a variety of stressors that could increase the risk for symptoms and thereby reduce the quality of life . Stress factors such as worries about one’s own health and that of family members played an important role in stress perception . In this context, work-family conflict may have a strong negative impact on psychological well-being . It can be assumed that the Corona pandemic favored the work-family conflict, since working as a physician in direct contact with the Corona virus also exposed the family to a higher and uncontrollable risk. Knowing that pre-existing conditions increased the risk of a life-threatening course of COVID-19 may have acted as an additional stress factor among affected medical personnel . In addition, a certain degree of discomfort, e.g., due to the lack or insufficiency of protective clothing/materials, as well as the care and treatment of (unstable) patients, were also circumstances that had a negative impact on the psychological well-being of medical personnel . Another condition was job satisfaction and well-being at work . Studies have found a link between the Corona pandemic and depression with effects within the workplace. For example, Obrenovic et al. analyzed using a cross-sectional study the impact of the Corona pandemic on depression and consequently decreasing job security in the United States, in their study on “The threat of COVID-19 and job insecurity impact on depression and anxiety” . According to the results, there is a positive and highly significant impact of job insecurity on depression. Job insecurity thus leads to depression and to anxiety, according to Obrenovic et al. Both symptoms can be traced back to the Corona pandemic in the past year. Particularly discouraging in this regard had been the lack of knowledge, the inability to make reliable predictions, and the lack of coherent and accurate information about COVID-19 . These findings could be noted or confirmed internationally. Khudaykulov et al. conducted a very similar study in Jiangsu province in China . Again, the results show a significant association between the pandemic and economic deterioration with consequences on job insecurity and on psychological well-being, especially anxiety and depression . Further extensive empirical research confirms these findings and demonstrates the stress and depression caused by job insecurity during COVID-19 . Determinants of well-being include autonomy, control, mastery of the environment, social connectedness, self-efficacy, and a meaningful existence . In the context of many new situations caused by the COVID-19 pandemic, Brose et al. found that especially untrained or inexperienced staff showed a higher risk for the development of psychological symptoms . This correlation also seems conclusive for physicians in training, because as untrained or inexperienced employees, they have not yet developed the previously mentioned determinants of well-being (autonomy, control, mastery of the environment, social connectedness, self-efficacy) to the same extent as physicians with many years of experience and a well-integrated work structure within their lives. The COVID-19 pandemic had a negative impact on self-confidence at work . Since GP trainees do not yet have years of experience as practicing physicians, this can also be hypothesized to occur for them. In order to counter such challenging situations and to reduce psychological strain and stress, increased social support should be made possible . According to Guberina et al. the workplace acts as a buffer against fear, panic and anxiety. If this buffer falls away during a critical time, such as the Corona pandemic, the workplace is perceived as a threat and becomes a source of psychological stress . This means that not only the Corona virus itself and the increased occurrence of the virus in medical facilities can lead to psychological stress, but additionally the perceived stability or instability at the place of work. Therefore, especially in a crisis, behavior on the part of managers/professional support persons plays a special role . The provision of critical resources and positive incentives, such as orientation, training, motivation, psychological and social support, guidance, awards, and praise are indispensable for positive well-being. If these aspects are not adequately met, an increase in psychological unwellness could results . These supportive aspects play a particularly important role for physicians in further training, as they are still inexperienced personnel and thus do not yet have their own sufficiently large repertoire of points of orientation to counter such a crisis due to their limited work experience. As a result, physicians in further training are at a higher risk of developing psychological symptoms than physicians and medical staff with years of experience. In Germany, among others, the trainers and competence centers have the task of providing these positive impulses. For physicians in further training, they are the immediate reference persons and points of contact for uncertainties and questions. So how was this Corona pandemic crisis and support for physicians in training perceived? Was the pandemic particularly stressful for this group? Were there factors that mitigated the stress of the Corona pandemic? Much of the literature to date refers generally to medical professionals and makes only limited distinctions between staff with many years of experience and those who are still in training. Differences between workplaces, such as intensive care units and ambulatory physician practices, have been examined to some extent, including between physicians and nurses. However, a specific look at physicians in training, who are particularly exposed to more uncertainties due to their training status and less experience in autonomy and dealing with patients, has been overlooked so far. This study will attempt to take a closer look at the conditions for this particular group and thus fill research gaps. The results presented here will show to what influence various stress factors have on well-being of trainees in general practice. In addition, it will also be discussed to what extent social support existed at the training site through instructors, mentors and contact persons within the residency training and at which points more support should be given in the future.
Design and target group The KWASa is a state-supported institution for strengthening the continuing education of physicians in training during their residency. Within Germany, there are a total of 16 competence centers for general medicine. The KWASa is responsible for the federal state of Saxony in the east of Germany and consists of the two locations Dresden and Leipzig. As part of the assessment program of KWA Sa , a survey was conducted with the aim of identifying support needs of future GPs in a pandemic situation. For this purpose, an online questionnaire with Limesurvey was conducted during the first phase of the COVID-19 pandemic over a period of 4 weeks (from May 5, 2020 to June 4, 2020). The target group was the GP trainees enrolled in KWA Sa since 2018 ( n = 316, ca. 150 of whom are regular active members). A total of 73 GP trainees participated in the survey . .Thus, the study participants were composed of physicians in further training from the area of Saxony, mainly from the urban areas of Dresden and Leipzig. The study was divided into two different topics: emotional well-being of GP trainees, which is the topic of this article, and infomation level about COVID-19 of GP trainees, which has been published elsewhere . Recruitment and ethics The link to the questionnaire was sent to the members registered in KWA Sa by e-mail. Before the survey was started, written information on data protection and an electronic declaration of consent were obtained. Only after confirmation of the data protection conditions could the survey be started. Participation was voluntary, anonymous and the questionnaire could be cancelled at any time. Answers already given could be deleted at any time during the survey . Measurement tools In addition to questions on sociodemographics, the questionnaire consisted of items assessing individual psychological well-being and stress characteristics due to the pandemic situation. Specifically, the following topics were queried: (1) fears in everyday work related to the COVID-19 pandemic; (2) worries and stresses caused by the pandemic situation; (3) evaluations of the protective measures implemented; and (4) feelings of support at the training site . The survey was designed using a mixed-methods approach. Quantitative survey techniques and qualitative survey techniques both were used. In this study, it is a matter of methodological triangulation, or more precisely, embedded design: this design means that either quantitative or qualitative method predominates and the respective other method is used in a complementary manner to answer sub-questions already during data collection. In our study, the quantitative design predominated, while the qualitative design was used as a supplement to support the standardized surveys with individual statements. The quantitative design consisted of various standardized items. In addition to multiple-choice questions and yes/no questions, various likert scales were used to answer some items (depending on the question type). In addition to the standardized questions, some open-ended questions were used in the qualitative design. The goal of the open-ended questions was to gain insights beyond the specific research subject and standardized results. A total of seven open-ended questions related to stress and mental health were used. Due to the rapid pace of the Corona pandemic and lack of time resources, we did not use face-to-face interviews and asked the open-ended questions in writing. The specific open-ended questions can be found in Table . Due to the current topic, there were further studies of other research networks at the time of the survey, which referred to other, but situationally similar target groups. To compare some results with the study results of other target groups, 3 items from already existing projects of other research networks were used for the topic “stressors and psychological well-being” of our study. This also had the advantage that the existing items had already been tested. On the one hand, items from the research project “COVI-Prim - Accompanying monitoring of primary care in family practices during the COVID-19 pandemic” of the Institute of General Medicine of the Goethe University Frankfurt am Main were used. In this project, the challenges that family physicians face during this pandemic and how they deal with them were recorded and analyzed. The target group here was family physicians. Two items from this study were used for our study. Second, an item from the study survey of the Applied Medical Psychology and Medical Sociology Research Group of the Department of Psychosocial Medicine and Developmental Neurosciences of the Carl Gustav Carus University Hospital at the Technical University of Dresden were used. The study analyzed how psychological well-being develops during such a pandemic and which factors affect it. Here, the target group was medical students. By using the items from these two research projects, better comparisons can be made between physicians in training and general practitioners or students. The specific items used and references to the research groups can be found in the Additional file : Appendix under “Appendix B - Items used from other projects”. The questionnaire was tested by a cognitive pretest procedure. Since the quality of the collected data depends primarily on the comprehensibility of the questionnaire design, the cognitive pretest is a particularly suitable test method, since the response process of the respondents is actively scrutinized and examined for comprehension problems. This approach attempts to gain insights into the cognitive processes involved in answering questionnaires . Cognitive pretesting is intended to bring out the non-functional parts or design flaws of the survey instrument, such as question problems, in order to counteract the unintended effects and to be able to improve the questionnaire. Various techniques are used in cognitive pretesting. The most important and most used cognitive techniques are probing, confidence rating, paraphrasing, card sorting, think-aloud and response latency . In this study, card sorting was not used due to the corona pandemic situation. The methods used were: Comprehension-Probing: Inquiry meaning or their understanding about certain terms or word groups, inquiry follows immediately after answering the question Categoryselection-Probing: Inquiries about the choice of answer categories General-Probing: direct questioning about problems in answering the questionnaire ConfidenceRating: Questioning subjectively assumed reliability of an answer (certainty of answer) (How certain are you about your answer?) Paraphrasing: Read question aloud - reproduce content in own words Think-Aloud: Prompt to speak out loud thoughts during reflections A test design was created in preparation for the test. For this purpose, the research team selected items that might be difficult or problematic and assigned them to different tests according to the methodological guidelines. The test design can be found in the Additional file : Appendix under “Appendix A - Test design: cognitive pretest KWA Sa survey: how stressful was the COVID-19 pandemic for family practice residents?” The cognitive test was conducted using this test design by means of three observational interviews. These lasted between 1 and 1.5 hours each. The interviews were recorded auditorily. Subsequently, a transcript and an observation protocol were created, which were qualitatively evaluated. Based on the qualitative evaluations, problems in the questionnaire were subsequently highlighted and rectified. Both the preparation of the test as well as the implementation and evaluations were carried out by a social scientist who was familiar with and experienced in cognitive pretesting. In addition to the very time-consuming cognitive pretesting, the questionnaire was reviewed by experienced scientific employees of the Department of General Medicine at the University Hospital of the Technical University of Dresden and colleagues of the research team. Analysis methods Subsequent to the survey, data analysis of the standardized survey sections was carried out by means of the csv values read out and using Excel and SPSS Statistics 27 (IBM). First, descriptive statistics were performed. Then, the open-ended questions were analyzed using the principle of qualitative content analysis according to Mayring using the software MAXQDA (VERBI). In the following, the qualitative analysis method of the free-text responses will be explained in more detail. As described above, seven open-ended questions were asked in the survey, in which participants had the opportunity to describe their feelings and thoughts as well as motivations and backgrounds on topics related to stress caused by the Corona pandemic by means of free-text answers (Table ). In preparation for the asessment, the responses were recorded and sorted within a transcript. Since these were written rather than oral interviews, the responses could be transcribed directly and verbatim. The transcript can be found in the Additional file Appendix under “Appendix C - Transcripts of the open questions”. Since many of the answers were very detailed, qualitative content analysis according to Mayring was selected for more detailed analysis. This analysis method is a recognized method for qualitative data evaluation in Germany and originates from the field of empirical social research. The aim of the method is the organization and structuring of qualitative, i.e. latent and manifest data in the form of transcripts, observation protocols, video or image recordings . Content analysis according to Mayring provides for three different methods of analysis: Summary, Explication, and Structuring. For this research project the summarizing content analysis was chosen, which is useful if the content level of the data is to be analyzed. Here, the data material was structured according to certain criteria by means of an inductive coding procedure. This means that the transcripts are first paraphrased and then bundled into categories . MAXQDA was used as a tool for inductive categorization. This is a highly recognized software for computer-assisted qualitative data and text analysis, which facilitates the technical procedure and written recording of the coding. Based on the qualitative content analysis, a total of nine categories with a total of 69 subcategories were identified in relation to the stresses and the handling of the Covid 19 pandemic. The elaborated code system can be found in the Additional file : Appendix under “Appendix D - Code system of qualitative content analysis according to Mayring”. The results of the content analysis are presented in the following chapters.
The KWASa is a state-supported institution for strengthening the continuing education of physicians in training during their residency. Within Germany, there are a total of 16 competence centers for general medicine. The KWASa is responsible for the federal state of Saxony in the east of Germany and consists of the two locations Dresden and Leipzig. As part of the assessment program of KWA Sa , a survey was conducted with the aim of identifying support needs of future GPs in a pandemic situation. For this purpose, an online questionnaire with Limesurvey was conducted during the first phase of the COVID-19 pandemic over a period of 4 weeks (from May 5, 2020 to June 4, 2020). The target group was the GP trainees enrolled in KWA Sa since 2018 ( n = 316, ca. 150 of whom are regular active members). A total of 73 GP trainees participated in the survey . .Thus, the study participants were composed of physicians in further training from the area of Saxony, mainly from the urban areas of Dresden and Leipzig. The study was divided into two different topics: emotional well-being of GP trainees, which is the topic of this article, and infomation level about COVID-19 of GP trainees, which has been published elsewhere .
The link to the questionnaire was sent to the members registered in KWA Sa by e-mail. Before the survey was started, written information on data protection and an electronic declaration of consent were obtained. Only after confirmation of the data protection conditions could the survey be started. Participation was voluntary, anonymous and the questionnaire could be cancelled at any time. Answers already given could be deleted at any time during the survey .
In addition to questions on sociodemographics, the questionnaire consisted of items assessing individual psychological well-being and stress characteristics due to the pandemic situation. Specifically, the following topics were queried: (1) fears in everyday work related to the COVID-19 pandemic; (2) worries and stresses caused by the pandemic situation; (3) evaluations of the protective measures implemented; and (4) feelings of support at the training site . The survey was designed using a mixed-methods approach. Quantitative survey techniques and qualitative survey techniques both were used. In this study, it is a matter of methodological triangulation, or more precisely, embedded design: this design means that either quantitative or qualitative method predominates and the respective other method is used in a complementary manner to answer sub-questions already during data collection. In our study, the quantitative design predominated, while the qualitative design was used as a supplement to support the standardized surveys with individual statements. The quantitative design consisted of various standardized items. In addition to multiple-choice questions and yes/no questions, various likert scales were used to answer some items (depending on the question type). In addition to the standardized questions, some open-ended questions were used in the qualitative design. The goal of the open-ended questions was to gain insights beyond the specific research subject and standardized results. A total of seven open-ended questions related to stress and mental health were used. Due to the rapid pace of the Corona pandemic and lack of time resources, we did not use face-to-face interviews and asked the open-ended questions in writing. The specific open-ended questions can be found in Table . Due to the current topic, there were further studies of other research networks at the time of the survey, which referred to other, but situationally similar target groups. To compare some results with the study results of other target groups, 3 items from already existing projects of other research networks were used for the topic “stressors and psychological well-being” of our study. This also had the advantage that the existing items had already been tested. On the one hand, items from the research project “COVI-Prim - Accompanying monitoring of primary care in family practices during the COVID-19 pandemic” of the Institute of General Medicine of the Goethe University Frankfurt am Main were used. In this project, the challenges that family physicians face during this pandemic and how they deal with them were recorded and analyzed. The target group here was family physicians. Two items from this study were used for our study. Second, an item from the study survey of the Applied Medical Psychology and Medical Sociology Research Group of the Department of Psychosocial Medicine and Developmental Neurosciences of the Carl Gustav Carus University Hospital at the Technical University of Dresden were used. The study analyzed how psychological well-being develops during such a pandemic and which factors affect it. Here, the target group was medical students. By using the items from these two research projects, better comparisons can be made between physicians in training and general practitioners or students. The specific items used and references to the research groups can be found in the Additional file : Appendix under “Appendix B - Items used from other projects”. The questionnaire was tested by a cognitive pretest procedure. Since the quality of the collected data depends primarily on the comprehensibility of the questionnaire design, the cognitive pretest is a particularly suitable test method, since the response process of the respondents is actively scrutinized and examined for comprehension problems. This approach attempts to gain insights into the cognitive processes involved in answering questionnaires . Cognitive pretesting is intended to bring out the non-functional parts or design flaws of the survey instrument, such as question problems, in order to counteract the unintended effects and to be able to improve the questionnaire. Various techniques are used in cognitive pretesting. The most important and most used cognitive techniques are probing, confidence rating, paraphrasing, card sorting, think-aloud and response latency . In this study, card sorting was not used due to the corona pandemic situation. The methods used were: Comprehension-Probing: Inquiry meaning or their understanding about certain terms or word groups, inquiry follows immediately after answering the question Categoryselection-Probing: Inquiries about the choice of answer categories General-Probing: direct questioning about problems in answering the questionnaire ConfidenceRating: Questioning subjectively assumed reliability of an answer (certainty of answer) (How certain are you about your answer?) Paraphrasing: Read question aloud - reproduce content in own words Think-Aloud: Prompt to speak out loud thoughts during reflections A test design was created in preparation for the test. For this purpose, the research team selected items that might be difficult or problematic and assigned them to different tests according to the methodological guidelines. The test design can be found in the Additional file : Appendix under “Appendix A - Test design: cognitive pretest KWA Sa survey: how stressful was the COVID-19 pandemic for family practice residents?” The cognitive test was conducted using this test design by means of three observational interviews. These lasted between 1 and 1.5 hours each. The interviews were recorded auditorily. Subsequently, a transcript and an observation protocol were created, which were qualitatively evaluated. Based on the qualitative evaluations, problems in the questionnaire were subsequently highlighted and rectified. Both the preparation of the test as well as the implementation and evaluations were carried out by a social scientist who was familiar with and experienced in cognitive pretesting. In addition to the very time-consuming cognitive pretesting, the questionnaire was reviewed by experienced scientific employees of the Department of General Medicine at the University Hospital of the Technical University of Dresden and colleagues of the research team.
Subsequent to the survey, data analysis of the standardized survey sections was carried out by means of the csv values read out and using Excel and SPSS Statistics 27 (IBM). First, descriptive statistics were performed. Then, the open-ended questions were analyzed using the principle of qualitative content analysis according to Mayring using the software MAXQDA (VERBI). In the following, the qualitative analysis method of the free-text responses will be explained in more detail. As described above, seven open-ended questions were asked in the survey, in which participants had the opportunity to describe their feelings and thoughts as well as motivations and backgrounds on topics related to stress caused by the Corona pandemic by means of free-text answers (Table ). In preparation for the asessment, the responses were recorded and sorted within a transcript. Since these were written rather than oral interviews, the responses could be transcribed directly and verbatim. The transcript can be found in the Additional file Appendix under “Appendix C - Transcripts of the open questions”. Since many of the answers were very detailed, qualitative content analysis according to Mayring was selected for more detailed analysis. This analysis method is a recognized method for qualitative data evaluation in Germany and originates from the field of empirical social research. The aim of the method is the organization and structuring of qualitative, i.e. latent and manifest data in the form of transcripts, observation protocols, video or image recordings . Content analysis according to Mayring provides for three different methods of analysis: Summary, Explication, and Structuring. For this research project the summarizing content analysis was chosen, which is useful if the content level of the data is to be analyzed. Here, the data material was structured according to certain criteria by means of an inductive coding procedure. This means that the transcripts are first paraphrased and then bundled into categories . MAXQDA was used as a tool for inductive categorization. This is a highly recognized software for computer-assisted qualitative data and text analysis, which facilitates the technical procedure and written recording of the coding. Based on the qualitative content analysis, a total of nine categories with a total of 69 subcategories were identified in relation to the stresses and the handling of the Covid 19 pandemic. The elaborated code system can be found in the Additional file : Appendix under “Appendix D - Code system of qualitative content analysis according to Mayring”. The results of the content analysis are presented in the following chapters.
Sample description and response behavior Before presenting the interpretation of the results, we will take a look at the sample description and the response behavior of the participants. A total of 73 physicians in continuing education participated in the survey. The survey took approximately 20 minutes to complete. To understand the participants’responses in the context of their current life situation, some socio-biographical data were collected, such as gender, age, workplace, etc. Table shows the results of the sample description. In addition, the response behavior of the open-ended questions will now be discussed. As described in the methodology, in addition to the standardized questions, some open questions were also asked. Here the participants could describe their answers in a free text field. It can be noted that this type of response option was very well used. Table shows the number of responses per open question. Within the responses to the open-ended items, the comprehensiveness and amount of information were a little mixed. A very large number of answers consisting of at least two longer sentences or bullet points can be found, as well as a very large number of answers consisting of four or more longer sentences or bullet points. Very short answers, consisting of one short sentence or one short bullet point, appear less frequently. Since the open-ended question types were items embedded in the online questionnaire, the answers given are not comparable with an interview conducted face-to-face. However, considering this written form of open-ended responses, a surprising amount of very detailed information can be found. The participants showed a pronounced and response behavior in terms of content, gave detailed and substantial answers, which indicates the importance of the topic queried here. The participants felt heard and made extensive use of the open questions to express their opinions. The response rate for the individual items in relation to the total number of participants is also largely satisfactory. However, it must be mentioned that the response rate decreased towards the end of the questionnaire. In relation to the content and the abundance of the individual responses, however, the responses did not lose quality. Mental well-being during the coronavirus pandemic The time during the first phase of the COVID-19 pandemic in spring 2020 was perceived as stressful by the majority of GP trainees. Let’s first take a look at the statistical analyses of the anxiety or stress caused by the Corona pandemic. To the statement “COVID-19 worries me,” 61% of respondents gave an affirmative response (13% indicated “strongly agree” and 48% indicated “somewhat agree”). A negative selection was made by 26% of GP trainees surveyed (21% “rather disagree” and 5% “strongly disagree”) and 13% abstained from a clear position with the answer option “neither”. Fig. shows how pronounced the existing concerns were due to the Corona pandemic. Concerns due to the corona pandemic were also evident within the open-ended free text responses. Worries and fears were openly addressed here: “ Fear for the future for me and my children, fear, how the occupation can look in the future at all , fear that especially the children are to be socialized at a distance and masks, fear, for the education of my and all children. The own opportunities for further education are currently impossible for me. And much more ” (Additional file : Appendix C - Transcripts of the open questions; item no. 2; line 4–9). “ Fear of self-infection, fear of unnoticed infection of my partner and my children by me. The patients’ severe psychological problems, which have clearly come to the fore in recent weeks, are also burdensome. The feeling of being abandoned by politics. Seeing how all around small businesses are struggling to survive and many of them will not survive the crisis, but of course the state wants to support the car industry ” (Additional file : Appendix C - Transcripts of the open questions; item no. 8; lines 23–28). “Concern about an outbreak, overburdening of the health- and economicsystem. Social isolation of the family, 1 school child of ours has to stay at home alone, every day. Worry of contagion, worry of chaos and anarchy.” (Additional file : Appendix C - Transcripts of the open questions; item no. 22; lines 61–63). These exemplary citations make it clear that during the Corona pandemic there were challenges perceived as stressful, such as those mentioned above: the political handling of the situation, anxiety about the impact of the Corona pandemic on the socialization of children, overburdening of the healthsystem and psychological problems of patients. Perceptions of emotional challenges were also statistically collected. When asked about emotional challenges triggered by the coronavirus pandemic, the majority of GP trainees surveyed also gave an affirmative response, with 50% of respondents indicating that they had experienced emotional challenges. In contrast, 27% of GP trainee respondents indicated that they had not experienced any emotional challenges, and 22% abstained from responding. Overall, it appeared that at the onset of the coronavirus pandemic, the majority of GP trainees questioned were concerned about the SARS-CoV-2 virus and experienced emotional challenges as a result of the pandemic. Stress aspects caused by the coronavirus pandemic Let us now turn to elaborating on the specific emotional stresses and challenges caused by the Corona pandemic. First, the statistical results of the survey showed that the existing worries related to the SARS-CoV-2 virus were mainly in the professional environment. Of the general practice trainees surveyed, 71% said that the work environment was a greater source of stress than the home environment. In this context, standardized questionnaires initially showed that about half of the GP trainees surveyed felt burdened by the prospect of unknowing infection due to working in a GP practice: 55% of the GP trainees surveyed felt burdened by the dilemma of wanting to provide good care for patients on the one hand and not wanting to endanger their families by working in a GP practice and the associated potential for increased risk of COVID-19 infection on the other. Of the trainees interviewed, 53% felt burdened by the possibility of unknowingly infecting patients. Feeling burdened by the increased potential to unknowingly infect family was reported by 62% of GP trainees surveyed. Fig. shows the expression of these three aspects of stress during the Corona pandemic within the surveyed group of general practice trainees: An affirmative response (strongly agree to somewhat agree) was given by 50% of GP trainees to the item “I feel I have little control over whether or not I contract COVID-19.” In addition, the results of open-ended questions (using free-text responses) revealed further aspects of stress in the professional and private environment (Table ). Within the qualitative evaluation of the free-text answers, various stress factors could be identified in relation to both the working and the private context. Table shows in columns 1 and 2 the identified stress factors of both environments. In the professional environment (column 1), aspects that make patient treatment more difficult (Uncontrollable patient behavior, Increased effort per patient, Uncertainty in patient care) are particularly noticeable. These aspects appear to be the most important stress factors within the field of work. The following citations are intended to provide some insight into the perceived stress: “Panicked and overwhelmed patients despairing between homeschooling, toddler care, and job in three-shift system” (Additional file : Appendix C - Transcripts of the open questions; item no. 17; lines 50–51). “ […] in addition I have the feeling that I cannot help my patients, most of whom are mentally ill, as much as I would like to at the moment due to the considerable restrictions in their daily lives, since aftercare services, support groups and the like do not take place for an indefinite period of time, or only to a very limited extent.” (Additional file : Appendix C - Transcripts of the open questions; item no. 23; lines 64–68). “[…] The patients’ severe psychological problems, which have clearly come to the fore in recent weeks, are also a burden. […]” (Additional file : Appendix C - Transcripts of the open questions; item no. 8; lines 24). “[…] patients who visit the practice with suspected cases despite all indications and only become concrete in the consulting room and want to be tested […]” (Additional file : Appendix C - Transcripts of the open questions; item no. 3; lines 13–14). In addition, a lack of protective equipment and too few instructions for procedures also play a role in increased stress and strain. “[…] Endangerment of high-risk patients due to lack of protective equipment […]” (Additional file : Appendix C - Transcripts of the open questions; item no. 13; lines 43). “[…] In some cases, the clinic management was also overtaxed, which led to uncertainty and a lack of a clear line.” (Additional file : Appendix C - Transcripts of the open questions; item no. 28; lines 75–76). Within the private environment, it is above all the fear of infecting the family, the own infection as well as the unclear political situation within the Corona pandemic, aspects which promote emotional stress. “Above all, I am worried about infecting or endangering my children, my husband or our parents and my grandmother […]” (Additional file : Appendix C - Transcripts of the open questions; item no. 63; lines 134–135). “it is not clear how severe the course would be for me and how much I would infect my family and they would suffer from it” (Additional file : Appendix C - Transcripts of the open questions; item no. 74; lines 168–169). Participants were also asked about successful approaches they had used to manage aspects of stress (see Table , column 3). Here, communication and optimization of patient care played the most important role Protective measures to reduce the feeling of insecurity In order to be able to ensure safety in everyday professional life, some protective measures were recommended for GP practices at the beginning of the pandemic . The results of the standardized survey of the actual use of the recommended protective measures showed a heterogeneous picture (Table ). A few physicians in further education even saw no solution or way out at all, which was especially shown by the open free text answers: „ The problems cannot be solved, or they increasingly show already existing structural problems.” (Additional file : Appendix C - Transcripts of the open questions; item no. 38; lines 91–92). “There is no solution. I felt like I was on the verge of a nervous breakdown and now I’m on vacation and trying to distract myself.” (Additional file : Appendix C - Transcripts of the open questions; item no. 39; lines 93–94). Sense of support at the training site Another aspect that can play a role in reducing uncertainties regarding the COVID-19 pandemic is “job satisfaction” - the situation directly at the workplace in connection with the cooperation with the respective continuing education instructor(s) and colleagues. The evaluations showed positive results at this point the majority of the GP trainees felt supported at their place of continuing education. Specifically, 64% of the respondents stated that there was mutually supportive communication with their colleagues during the first pandemic phase, 60% of the GP trainees surveyed were asked by their respective continuing educator(s) how they felt, and 66% felt protected or reassured by the behavior of the continuing educator(s). The results can also be traced in Fig. . These data show that in about two-thirds of the cases, the mutual interaction within the continuing education location was able to convey a feeling of security and support during this very uncertain time. However, despite some support from the training and continuing education site, as the results here showed, there was strong anxiety and worry about the pandemic period (see Fig. and the open free text responses described above). Fear for family and major uncertainties/stresses in patient care seem to play the largest role, as shown above. Nevertheless, 1/3 of respondents did not feel safe or supported at the training site by the behavior of supervisors and caregivers. While this is not the majority, 1/3 of all respondents are a variable that cannot be ignored. Worry in the workplace was also confirmed in the open free text responses: “challenge in dealing with my boss, who perceived the corona crisis as of little importance and I have few options for action on my own.” (Additional file : Appendix C - Transcripts of the open questions; item no. 4; lines 16–17). “In some cases, the clinic management was also overtaxed, which led to uncertainty and a lack of a clear line.” (Additional file : Appendix C - Transcripts of the open questions; item no. 28; lines 75–76). This shows the need for supportive communication in the workplace. The results of the standardized questions also confirm this. As a safety-giving measure, 49% of the GP trainees surveyed wished for more offers of emotional support (50% did not). Summary of results: stressors among physicians in training during the Corona pandemic The above results indicate that the Corona pandemic had a negative effect on the well-being of physicians in training. Sixty-one percent of GP trainees surveyed indicated that they were concerned about the coronavirus. Most of the GP trainees surveyed also gave an affirmative response regarding coronavirus-related emotional challenges. Regarding the emotional challenges experienced, various stress factors could be identified within both their professional and personal environments. These stressors have been presented so far in the chapters above and will now be summarized here in a final way to the most prominent characteristics. There were four stress factors that particularly stood out. These include: (1) Anxiety/ fear: the fear of infection of the family as well as of the patients with the SARS-CoV-2 virus; (2) low protective measures : lack of or insufficient protective measures (protective equipment and protective handling instructions); (3) difficult patient care : an increased need for counseling due to unpredictable or uncontrollable patient behavior and uncertainty in patient care; and. (4) insufficient social support : a need for more communication and experienced social support within the collegial environment and in the context of continuing education programs. Summary and discussion How was the Corona pandemic crisis and support perceived by physicians in training? Was the pandemic particularly stressful for this group? Are there factors that may mitigate the stress of the Corona pandemic? Overall, the results presented here confirm previous research on medical personnel with contacts to the SARS-CoV-2 virus. Concerns about one’s own health and that of others, insufficient social support, lack of information (regarding current guidelines, adequate treatment of COVID-19) as well as lack of protective equipment seemed to be conditions in all medical fields worldwide that increased the perception of stress and thus represent a risk for psychological well-being . Symptoms resulting from the pandemic were already identified by Zerbini et al. These are increased stress, fatigue and depressive mood . The psychological stresses are closely related to the workplace. Again, the results of this study confirm previous research literature by Obrenovic et al. and also Khudaykulov et al. . A lower sense of control and little mastery of the social environment are determinants of well-being according to Guberina et al. . The results of this study show that a loss of control of patient behavior, especially impulsive and moody patients, and the feeling of being able to help the pateints little to not at all were very stressful for the physicians in training (stress factor 3). Thus, this study confirms that the determinants of well-being mentioned by Guberina et al. broke down during the Corona crisis, and psychological unwellness resulted as a consequence. The feeling of loss of control in patient care is still a poorly studied phenomenon in the literature. With regard to the group of physicians in training, who, as novices in the medical field, still have very little experience with such loss-of-control experiences, no knowledge exists to date. The results of this study show that, especially for newcomers to the profession, the aspect of loss of control and mastery of situations in the Corona pandemic had a particularly stressful effect. Especially the connection between family and work can lead to stress and unhappiness, as Obrenovic et al. pointed out . This was shown to be the main problem in the results of this study (stress factor 1). The fear of endangering the family through professional contact with Covid-19, the dilemma of caring for patients and thereby endangering the family higher as well as the poor compatibility of professional duties (increased working hours) with the care of children were the main responsible characteristics for psychological stress. Thus, the results confirm the theses of Obrenovic et al. Moreover, this characteristic turns out to be the main reason for stress during the Corona pandemic among physicians in training. The conflict between family and work due to the pandemic situation led to a higher stress than the virus itself, because the fear of one’s own infection for one’s own health played a smaller role than family security. This is particularly problematic for young physicians in continuing education, as they are often in the early stages of family time with their comparatively young years, unlike older colleagues. Many studies indicate that job satisfaction played an important role in relation to stress during the coronavirus pandemic and that anxiety and stress are negatively related to job satisfaction . The protective measures in place play an important role (stress factor 2). But Tracy et al. also pointed out that a lack of supportive social environment acts as a major stressor . The study by Suryavanshi et al., which was conducted with a total of 197 healthcare professionals (doctors, nurses, doctors in training/internship) in India, also showed the relevance of work environment to the risk of combined depression and anxiety In this context, Guberina et al. describe a very important factor: the place of work as a buffer in times of crisis . Thus, a supportive, safe and satisfied feeling at the workplace seems to be an aspect that contributes significantly to the positive perception of the situation within the coronavirus pandemic and, consequently, to the sense of stress, which is why this point should be of particular concern for the centers of excellence and continuing education in their missions. The “buffer in times of crisis” is an aspect that is of particularly high importance for the competence centers, trainers and contact persons of physicians in continuing education (stress factor 4), since the competence centers and trainers carry a certain responsibility for the quality of this support. Lack of communication and mutual support in the professional environment and in continuing education can bring the danger of strain, such as collegial disagreements with instructors here. On the other hand, good communication within the team was indicated as a solution strategy to improve the situation. This shows the importance of communication and support. The responses to the standardized items on the feeling of support also showed the relevance of positive communication and support from the continuing education program. Most of the physicians surveyed stated that they felt supported at the training site and protected by the behavior of the continuing education instructors. Nevertheless, for about one third of the GP trainees surveyed, there was no supportive environment. Also, within the results of our study it can be stated that the communication and the feeling of support at the place of continuing education can have both mitigating and negative or stressful effects on the psychological well-being of the GP trainees. Therefore, it seems even more important to strengthen the education and training of GP trainees, for example by preparing trainers for crisis-specific and challenging communication. As described in the literature, the number of positive COVID-19 cases in the area was less important than the evaluation of the situation [1; 5]. It should be possible for the professional environment of physicians in training (the competence evaluationcenters and trainers) to support this crisis situation in such a way that the evaluation of the situation is more positive and thus there are fewer burdens. As Brose et al. pointed out, support is especially important for untrained staff, as they are at higher risk for developing psychological symptoms . The studies by Tracy et al. and Labrague and de los Santos also confirmed that untrained medical staff and lack of training contribute to an increase in feelings of stress and 49% of the GP trainees surveyed in the present study also wished for more offers of support. Competence centers can and should therefore respond with support services specifically designed to address this issue, such as crisis-specific education and training. Initial studies have shown the success of such crisis-specific trainings: Khan & Kiani presented in their work on “Impact of multi-professional simulation-based training on perceptions of safety and preparedness among health workers caring for coronavirus disease” the success of first simulation trainings concerning the handling and treatment of COVID-19 patients . By training typical procedures, such as in this case the testing of COVID-19 patients, blood sampling, cleaning and hygiene measures, etc., the perception of risks and the (treatment) preparedness of health care workers could be improved, and the feeling of safety increased . Supportive aspects play a particularly important role for physicians in further training. Due to their limited professional experience, they do not yet have a sufficiently large repertoire of orientation points of their own to counter such a crisis. Physicians in further training therefore have a higher risk of developing psychological symptoms than physicians and medical staff with many years of experience. A specific look at physicians in postgraduate training, who are particularly exposed to many uncertainties due to their level of training and less experience in working independently and with patients, has not been strongly considered in previous research. This study shows that this group has special support needs and that there are also opportunities to better support physicians in continuing education in times of crisis. Strengths and weaknesses A strength of this publication is that it presents data on stress factors experienced by GP trainees during a pandemic situation. Previous literature on the corona pandemic often differentiates between physicians and nurses or the different areas, such as the intensive care unit or corona outpatient clinic. However, a specific look at physicians in training is lacking. This study will advance research for this specific group. The stress factors can be considered in the future for support needs in residency education and training and can be used for the design of continuing education programs for GP trainees. In Germany, centers of excellence in general practice are still very young (the call for proposals started in 2017). Therefore, research that advances the work of the centers of excellence and trainers for physicians in continuing education in general practice is important. A limitation is that the results presented here are attributable to a specific and small target group with a rather low response rate. KWA Sa refers specifically to Saxony, a federal state in Germany. To generalize the results nationwide, further studies in other German states are necessary.
Before presenting the interpretation of the results, we will take a look at the sample description and the response behavior of the participants. A total of 73 physicians in continuing education participated in the survey. The survey took approximately 20 minutes to complete. To understand the participants’responses in the context of their current life situation, some socio-biographical data were collected, such as gender, age, workplace, etc. Table shows the results of the sample description. In addition, the response behavior of the open-ended questions will now be discussed. As described in the methodology, in addition to the standardized questions, some open questions were also asked. Here the participants could describe their answers in a free text field. It can be noted that this type of response option was very well used. Table shows the number of responses per open question. Within the responses to the open-ended items, the comprehensiveness and amount of information were a little mixed. A very large number of answers consisting of at least two longer sentences or bullet points can be found, as well as a very large number of answers consisting of four or more longer sentences or bullet points. Very short answers, consisting of one short sentence or one short bullet point, appear less frequently. Since the open-ended question types were items embedded in the online questionnaire, the answers given are not comparable with an interview conducted face-to-face. However, considering this written form of open-ended responses, a surprising amount of very detailed information can be found. The participants showed a pronounced and response behavior in terms of content, gave detailed and substantial answers, which indicates the importance of the topic queried here. The participants felt heard and made extensive use of the open questions to express their opinions. The response rate for the individual items in relation to the total number of participants is also largely satisfactory. However, it must be mentioned that the response rate decreased towards the end of the questionnaire. In relation to the content and the abundance of the individual responses, however, the responses did not lose quality.
The time during the first phase of the COVID-19 pandemic in spring 2020 was perceived as stressful by the majority of GP trainees. Let’s first take a look at the statistical analyses of the anxiety or stress caused by the Corona pandemic. To the statement “COVID-19 worries me,” 61% of respondents gave an affirmative response (13% indicated “strongly agree” and 48% indicated “somewhat agree”). A negative selection was made by 26% of GP trainees surveyed (21% “rather disagree” and 5% “strongly disagree”) and 13% abstained from a clear position with the answer option “neither”. Fig. shows how pronounced the existing concerns were due to the Corona pandemic. Concerns due to the corona pandemic were also evident within the open-ended free text responses. Worries and fears were openly addressed here: “ Fear for the future for me and my children, fear, how the occupation can look in the future at all , fear that especially the children are to be socialized at a distance and masks, fear, for the education of my and all children. The own opportunities for further education are currently impossible for me. And much more ” (Additional file : Appendix C - Transcripts of the open questions; item no. 2; line 4–9). “ Fear of self-infection, fear of unnoticed infection of my partner and my children by me. The patients’ severe psychological problems, which have clearly come to the fore in recent weeks, are also burdensome. The feeling of being abandoned by politics. Seeing how all around small businesses are struggling to survive and many of them will not survive the crisis, but of course the state wants to support the car industry ” (Additional file : Appendix C - Transcripts of the open questions; item no. 8; lines 23–28). “Concern about an outbreak, overburdening of the health- and economicsystem. Social isolation of the family, 1 school child of ours has to stay at home alone, every day. Worry of contagion, worry of chaos and anarchy.” (Additional file : Appendix C - Transcripts of the open questions; item no. 22; lines 61–63). These exemplary citations make it clear that during the Corona pandemic there were challenges perceived as stressful, such as those mentioned above: the political handling of the situation, anxiety about the impact of the Corona pandemic on the socialization of children, overburdening of the healthsystem and psychological problems of patients. Perceptions of emotional challenges were also statistically collected. When asked about emotional challenges triggered by the coronavirus pandemic, the majority of GP trainees surveyed also gave an affirmative response, with 50% of respondents indicating that they had experienced emotional challenges. In contrast, 27% of GP trainee respondents indicated that they had not experienced any emotional challenges, and 22% abstained from responding. Overall, it appeared that at the onset of the coronavirus pandemic, the majority of GP trainees questioned were concerned about the SARS-CoV-2 virus and experienced emotional challenges as a result of the pandemic.
Let us now turn to elaborating on the specific emotional stresses and challenges caused by the Corona pandemic. First, the statistical results of the survey showed that the existing worries related to the SARS-CoV-2 virus were mainly in the professional environment. Of the general practice trainees surveyed, 71% said that the work environment was a greater source of stress than the home environment. In this context, standardized questionnaires initially showed that about half of the GP trainees surveyed felt burdened by the prospect of unknowing infection due to working in a GP practice: 55% of the GP trainees surveyed felt burdened by the dilemma of wanting to provide good care for patients on the one hand and not wanting to endanger their families by working in a GP practice and the associated potential for increased risk of COVID-19 infection on the other. Of the trainees interviewed, 53% felt burdened by the possibility of unknowingly infecting patients. Feeling burdened by the increased potential to unknowingly infect family was reported by 62% of GP trainees surveyed. Fig. shows the expression of these three aspects of stress during the Corona pandemic within the surveyed group of general practice trainees: An affirmative response (strongly agree to somewhat agree) was given by 50% of GP trainees to the item “I feel I have little control over whether or not I contract COVID-19.” In addition, the results of open-ended questions (using free-text responses) revealed further aspects of stress in the professional and private environment (Table ). Within the qualitative evaluation of the free-text answers, various stress factors could be identified in relation to both the working and the private context. Table shows in columns 1 and 2 the identified stress factors of both environments. In the professional environment (column 1), aspects that make patient treatment more difficult (Uncontrollable patient behavior, Increased effort per patient, Uncertainty in patient care) are particularly noticeable. These aspects appear to be the most important stress factors within the field of work. The following citations are intended to provide some insight into the perceived stress: “Panicked and overwhelmed patients despairing between homeschooling, toddler care, and job in three-shift system” (Additional file : Appendix C - Transcripts of the open questions; item no. 17; lines 50–51). “ […] in addition I have the feeling that I cannot help my patients, most of whom are mentally ill, as much as I would like to at the moment due to the considerable restrictions in their daily lives, since aftercare services, support groups and the like do not take place for an indefinite period of time, or only to a very limited extent.” (Additional file : Appendix C - Transcripts of the open questions; item no. 23; lines 64–68). “[…] The patients’ severe psychological problems, which have clearly come to the fore in recent weeks, are also a burden. […]” (Additional file : Appendix C - Transcripts of the open questions; item no. 8; lines 24). “[…] patients who visit the practice with suspected cases despite all indications and only become concrete in the consulting room and want to be tested […]” (Additional file : Appendix C - Transcripts of the open questions; item no. 3; lines 13–14). In addition, a lack of protective equipment and too few instructions for procedures also play a role in increased stress and strain. “[…] Endangerment of high-risk patients due to lack of protective equipment […]” (Additional file : Appendix C - Transcripts of the open questions; item no. 13; lines 43). “[…] In some cases, the clinic management was also overtaxed, which led to uncertainty and a lack of a clear line.” (Additional file : Appendix C - Transcripts of the open questions; item no. 28; lines 75–76). Within the private environment, it is above all the fear of infecting the family, the own infection as well as the unclear political situation within the Corona pandemic, aspects which promote emotional stress. “Above all, I am worried about infecting or endangering my children, my husband or our parents and my grandmother […]” (Additional file : Appendix C - Transcripts of the open questions; item no. 63; lines 134–135). “it is not clear how severe the course would be for me and how much I would infect my family and they would suffer from it” (Additional file : Appendix C - Transcripts of the open questions; item no. 74; lines 168–169). Participants were also asked about successful approaches they had used to manage aspects of stress (see Table , column 3). Here, communication and optimization of patient care played the most important role
In order to be able to ensure safety in everyday professional life, some protective measures were recommended for GP practices at the beginning of the pandemic . The results of the standardized survey of the actual use of the recommended protective measures showed a heterogeneous picture (Table ). A few physicians in further education even saw no solution or way out at all, which was especially shown by the open free text answers: „ The problems cannot be solved, or they increasingly show already existing structural problems.” (Additional file : Appendix C - Transcripts of the open questions; item no. 38; lines 91–92). “There is no solution. I felt like I was on the verge of a nervous breakdown and now I’m on vacation and trying to distract myself.” (Additional file : Appendix C - Transcripts of the open questions; item no. 39; lines 93–94).
Another aspect that can play a role in reducing uncertainties regarding the COVID-19 pandemic is “job satisfaction” - the situation directly at the workplace in connection with the cooperation with the respective continuing education instructor(s) and colleagues. The evaluations showed positive results at this point the majority of the GP trainees felt supported at their place of continuing education. Specifically, 64% of the respondents stated that there was mutually supportive communication with their colleagues during the first pandemic phase, 60% of the GP trainees surveyed were asked by their respective continuing educator(s) how they felt, and 66% felt protected or reassured by the behavior of the continuing educator(s). The results can also be traced in Fig. . These data show that in about two-thirds of the cases, the mutual interaction within the continuing education location was able to convey a feeling of security and support during this very uncertain time. However, despite some support from the training and continuing education site, as the results here showed, there was strong anxiety and worry about the pandemic period (see Fig. and the open free text responses described above). Fear for family and major uncertainties/stresses in patient care seem to play the largest role, as shown above. Nevertheless, 1/3 of respondents did not feel safe or supported at the training site by the behavior of supervisors and caregivers. While this is not the majority, 1/3 of all respondents are a variable that cannot be ignored. Worry in the workplace was also confirmed in the open free text responses: “challenge in dealing with my boss, who perceived the corona crisis as of little importance and I have few options for action on my own.” (Additional file : Appendix C - Transcripts of the open questions; item no. 4; lines 16–17). “In some cases, the clinic management was also overtaxed, which led to uncertainty and a lack of a clear line.” (Additional file : Appendix C - Transcripts of the open questions; item no. 28; lines 75–76). This shows the need for supportive communication in the workplace. The results of the standardized questions also confirm this. As a safety-giving measure, 49% of the GP trainees surveyed wished for more offers of emotional support (50% did not).
The above results indicate that the Corona pandemic had a negative effect on the well-being of physicians in training. Sixty-one percent of GP trainees surveyed indicated that they were concerned about the coronavirus. Most of the GP trainees surveyed also gave an affirmative response regarding coronavirus-related emotional challenges. Regarding the emotional challenges experienced, various stress factors could be identified within both their professional and personal environments. These stressors have been presented so far in the chapters above and will now be summarized here in a final way to the most prominent characteristics. There were four stress factors that particularly stood out. These include: (1) Anxiety/ fear: the fear of infection of the family as well as of the patients with the SARS-CoV-2 virus; (2) low protective measures : lack of or insufficient protective measures (protective equipment and protective handling instructions); (3) difficult patient care : an increased need for counseling due to unpredictable or uncontrollable patient behavior and uncertainty in patient care; and. (4) insufficient social support : a need for more communication and experienced social support within the collegial environment and in the context of continuing education programs.
How was the Corona pandemic crisis and support perceived by physicians in training? Was the pandemic particularly stressful for this group? Are there factors that may mitigate the stress of the Corona pandemic? Overall, the results presented here confirm previous research on medical personnel with contacts to the SARS-CoV-2 virus. Concerns about one’s own health and that of others, insufficient social support, lack of information (regarding current guidelines, adequate treatment of COVID-19) as well as lack of protective equipment seemed to be conditions in all medical fields worldwide that increased the perception of stress and thus represent a risk for psychological well-being . Symptoms resulting from the pandemic were already identified by Zerbini et al. These are increased stress, fatigue and depressive mood . The psychological stresses are closely related to the workplace. Again, the results of this study confirm previous research literature by Obrenovic et al. and also Khudaykulov et al. . A lower sense of control and little mastery of the social environment are determinants of well-being according to Guberina et al. . The results of this study show that a loss of control of patient behavior, especially impulsive and moody patients, and the feeling of being able to help the pateints little to not at all were very stressful for the physicians in training (stress factor 3). Thus, this study confirms that the determinants of well-being mentioned by Guberina et al. broke down during the Corona crisis, and psychological unwellness resulted as a consequence. The feeling of loss of control in patient care is still a poorly studied phenomenon in the literature. With regard to the group of physicians in training, who, as novices in the medical field, still have very little experience with such loss-of-control experiences, no knowledge exists to date. The results of this study show that, especially for newcomers to the profession, the aspect of loss of control and mastery of situations in the Corona pandemic had a particularly stressful effect. Especially the connection between family and work can lead to stress and unhappiness, as Obrenovic et al. pointed out . This was shown to be the main problem in the results of this study (stress factor 1). The fear of endangering the family through professional contact with Covid-19, the dilemma of caring for patients and thereby endangering the family higher as well as the poor compatibility of professional duties (increased working hours) with the care of children were the main responsible characteristics for psychological stress. Thus, the results confirm the theses of Obrenovic et al. Moreover, this characteristic turns out to be the main reason for stress during the Corona pandemic among physicians in training. The conflict between family and work due to the pandemic situation led to a higher stress than the virus itself, because the fear of one’s own infection for one’s own health played a smaller role than family security. This is particularly problematic for young physicians in continuing education, as they are often in the early stages of family time with their comparatively young years, unlike older colleagues. Many studies indicate that job satisfaction played an important role in relation to stress during the coronavirus pandemic and that anxiety and stress are negatively related to job satisfaction . The protective measures in place play an important role (stress factor 2). But Tracy et al. also pointed out that a lack of supportive social environment acts as a major stressor . The study by Suryavanshi et al., which was conducted with a total of 197 healthcare professionals (doctors, nurses, doctors in training/internship) in India, also showed the relevance of work environment to the risk of combined depression and anxiety In this context, Guberina et al. describe a very important factor: the place of work as a buffer in times of crisis . Thus, a supportive, safe and satisfied feeling at the workplace seems to be an aspect that contributes significantly to the positive perception of the situation within the coronavirus pandemic and, consequently, to the sense of stress, which is why this point should be of particular concern for the centers of excellence and continuing education in their missions. The “buffer in times of crisis” is an aspect that is of particularly high importance for the competence centers, trainers and contact persons of physicians in continuing education (stress factor 4), since the competence centers and trainers carry a certain responsibility for the quality of this support. Lack of communication and mutual support in the professional environment and in continuing education can bring the danger of strain, such as collegial disagreements with instructors here. On the other hand, good communication within the team was indicated as a solution strategy to improve the situation. This shows the importance of communication and support. The responses to the standardized items on the feeling of support also showed the relevance of positive communication and support from the continuing education program. Most of the physicians surveyed stated that they felt supported at the training site and protected by the behavior of the continuing education instructors. Nevertheless, for about one third of the GP trainees surveyed, there was no supportive environment. Also, within the results of our study it can be stated that the communication and the feeling of support at the place of continuing education can have both mitigating and negative or stressful effects on the psychological well-being of the GP trainees. Therefore, it seems even more important to strengthen the education and training of GP trainees, for example by preparing trainers for crisis-specific and challenging communication. As described in the literature, the number of positive COVID-19 cases in the area was less important than the evaluation of the situation [1; 5]. It should be possible for the professional environment of physicians in training (the competence evaluationcenters and trainers) to support this crisis situation in such a way that the evaluation of the situation is more positive and thus there are fewer burdens. As Brose et al. pointed out, support is especially important for untrained staff, as they are at higher risk for developing psychological symptoms . The studies by Tracy et al. and Labrague and de los Santos also confirmed that untrained medical staff and lack of training contribute to an increase in feelings of stress and 49% of the GP trainees surveyed in the present study also wished for more offers of support. Competence centers can and should therefore respond with support services specifically designed to address this issue, such as crisis-specific education and training. Initial studies have shown the success of such crisis-specific trainings: Khan & Kiani presented in their work on “Impact of multi-professional simulation-based training on perceptions of safety and preparedness among health workers caring for coronavirus disease” the success of first simulation trainings concerning the handling and treatment of COVID-19 patients . By training typical procedures, such as in this case the testing of COVID-19 patients, blood sampling, cleaning and hygiene measures, etc., the perception of risks and the (treatment) preparedness of health care workers could be improved, and the feeling of safety increased . Supportive aspects play a particularly important role for physicians in further training. Due to their limited professional experience, they do not yet have a sufficiently large repertoire of orientation points of their own to counter such a crisis. Physicians in further training therefore have a higher risk of developing psychological symptoms than physicians and medical staff with many years of experience. A specific look at physicians in postgraduate training, who are particularly exposed to many uncertainties due to their level of training and less experience in working independently and with patients, has not been strongly considered in previous research. This study shows that this group has special support needs and that there are also opportunities to better support physicians in continuing education in times of crisis.
A strength of this publication is that it presents data on stress factors experienced by GP trainees during a pandemic situation. Previous literature on the corona pandemic often differentiates between physicians and nurses or the different areas, such as the intensive care unit or corona outpatient clinic. However, a specific look at physicians in training is lacking. This study will advance research for this specific group. The stress factors can be considered in the future for support needs in residency education and training and can be used for the design of continuing education programs for GP trainees. In Germany, centers of excellence in general practice are still very young (the call for proposals started in 2017). Therefore, research that advances the work of the centers of excellence and trainers for physicians in continuing education in general practice is important. A limitation is that the results presented here are attributable to a specific and small target group with a rather low response rate. KWA Sa refers specifically to Saxony, a federal state in Germany. To generalize the results nationwide, further studies in other German states are necessary.
GP trainees faced emotional and psychologically stressful challenges during the initial pandemic phase due to certain factors described here. A distinctive feature for the target group addressed in this study is that these GP trainees are still in the training phase and should therefore be provided with a specific level of support. At the same time, centers of excellence share responsibility for this support and can interpret the results to some extent as an evaluation of the quality of support provided by continuing education. The results show the importance of communication and support by the respective trainers. It is the task of the competence centers to support them in this respect and to teach the trainers how to deal with their GP trainees in a supportive way. However, crisis-specific training should also be included and implemented in continuing education. Within KWA Sa , we have already conducted seminars on topics related to the COVID-19 pandemic. We also want to contribute in the future to the communication of supportive measures for such uncertain experiences as a pandemic brings with it.
Additional file 1.
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Reflexiones sobre el papel del profesional de la salud-profesor en la educación en áreas de la salud | 251b3403-9acd-4675-8a3f-a0b507d9ebd2 | 10419871 | Gynaecology[mh] | |
Dissecting the pathobiology of altered MRI signal in amyotrophic lateral sclerosis: A | d6bcb0bf-ad9b-475a-8e64-7d9711226b02 | 5848544 | Pathology[mh] | Amyotrophic lateral sclerosis (ALS) is a typically rapidly progressive, fatal neurodegenerative disorder that is genetically and phenotypically heterogeneous. It is primarily characterised by selective degeneration of upper and lower motor neurons . A significant proportion of ALS patients develop cognitive impairment within the spectrum of frontotemporal dementia (FTD) . The diagnosis is essentially clinical . Conventional magnetic resonance imaging (MRI) is generally used during diagnosis as a tool to exclude ALS mimics, but advanced techniques such as functional MRI and diffusion tensor imaging (DTI) have enabled investigation of both structural and functional connectivity in vivo . Diffusion tensor imaging in particular has highlighted the widespread cerebral pathology associated with ALS . Furthermore, quantitative susceptibility mapping has demonstrated potential as a biomarker of upper motor neuron dysfunction in ALS . However, these MRI markers are non-specific and generally known to be influenced by several aspects of tissue related to neuropathology. It is therefore crucial to define the microstructural and molecular pathologic correlates of these MRI measures in ALS. Therefore, MRI-histology correlation analysis in post mortem tissue offers a platform to discover the histological underpinnings of MRI signal changes. Limited studies have explored the relationship between post mortem MRI and histology in ALS with assessment restricted to a segment of the primary motor cortex of a hemisphere in small number of cases. Reported changes include R2* hyperintensity in the middle and deep layers of the grey matter and signal alteration in the subcortical white matter of the motor cortex . Histopathological evaluation of the corresponding cortical region showed microglial iron accumulation and myelin pallor in subcortical white matter . Similarly, diminished contrast between grey and white matter has been observed, with altered T1 relaxation rate ratio in the motor cortex corresponding to reduced neuronal and axonal density and increased astroglial density and reactivity . The study also identified comparable differences in T1 relaxation rate ratio in other cortical regions anterior and posterior to the motor cortex as a gradient suggestive of contiguous pathology spread , though with limited histological evaluation. Interestingly, no significant changes in average T2 relaxation rate ratio were detected . These MRI modalities are sensitive to more than one aspect of tissue related to neuropathology. For accurate interpretation of specificity and validation of the sensitivity of such findings, multiple MRI modalities accompanied with a range of histological parameters must be considered, extending to other regions involved in disease progression. Combining whole brain post mortem MRI with systematic histological evaluation representing ALS-FTD pathology propagation is a pathway to determining and validating disease specific changes in structural organisation and connectivity. Whole fixed brain MRI confer additional benefits over MRI of small segmented regions, including reduced artefacts related to MRI, preserved landmarks for topographical identification of regions of interest, and a single scan that can cover the whole brain . Given the growing interest, studies have established direct correlation between post mortem whole brain MRI in situ with large-scale digital histology on corresponding coronal slices for evaluation of pathology and structural connectivity . However, whole slice digital histology is costly and labour-intensive, therefore routine implementation is not feasible on a large number of cases. Interestingly, precise comparison of high-resolution whole brain post mortem MRI with histological analysis on strategically sampled pathologically relevant regions of interest in multiple sclerosis have also been demonstrated . This article outlines a universal whole brain sampling strategy suitable for systematic evaluation of differential microstructural and molecular changes associated with pathology spread in cortical, subcortical and deep white matter regions of ALS and FTD to ultimately enable accurate post mortem MRI-histology correlation. The proposed methodology, modified from a standard brain banking protocol, was carefully designed to selectively sample pathologically relevant regions guided by anatomical landmarks and tractography. The protocol takes into consideration various aspects of downstream co-registration requirements and quantitative histological analyses to facilitate accurate depiction of changes within the regions of interest.
Methodological considerations Identification of neuroanatomical regions relevant to pathology spread Identification of neuroanatomical regions that depict ALS and FTD pathology spread is crucial to establishing a universal whole brain sampling strategy by identifying clinical phenotypes and accompanying neuropathological characterisation. Clinical phenotypes of ALS are heterogeneous and the focality of symptom onset and subsequent contiguous spread of motor dysfunction are common to all diagnosed patients and indicative of a disease continuum . Neuropathologically, ALS is characterised by a degree of upper and lower motor neuron loss concomitant with degeneration of their respective axonal projections, glial activation and the pathological aggregation of 43-kDa TAR DNA-binding protein (TDP-43) . The unique molecular feature of TDP-43 proteinopathy is reported in almost all cases of ALS and in the largest subset of FTD supporting the concept of ALS-FTD as a pathological spectrum . Although neuropathological evaluation is made at the “end-stage” of disease, recent studies have defined a pathological staging system based on systematic neuroanatomical distribution of TDP-43 pathology that correlated with severity of clinical phenotypes . In a system of ALS pathological ‘stages’ based on regional patterns of post mortem phosphorylated TDP-43 (pTDP-43) pathology, Stage 1 was defined as involving the upper motor neurons of the primary motor cortex and the lower motor neurons of the brainstem and the spinal cord . From the motor cortex, Stage 2 involves pathology extended rostrally to prefrontal cortical regions of the middle frontal gyrus and caudally to the reticular formation of the brainstem, red nucleus and precerebellar nuclei. Stage 3 was defined as pTDP-43 lesions extending into the gyrus rectus and orbital gyri of the basal prefrontal cortex, post-central sensory areas of the parietal lobe, the temporal lobe and the striatum. Stage 4 involves pathological burden in the anteromedial regions of the temporal lobe and the hippocampus. Distinct pTDP-43 distribution patterns have also been proposed for behavioural variant FTD (bvFTD) , the most common form of FTD overlapping with ALS . In bvFTD, pTDP-43 lesions first appear in the basal prefrontal cortex (orbital gyri and gyrus rectus) and amygdala in Pattern I. Involvement caudally into the prefrontal cortical regions (middle frontal gyrus, insular cortex and anterior cingulate), temporal lobe, including hippocampus, and subcortical regions striatum, thalamus, red nucleus and precerebellar nuclei of pons and medulla defines Pattern II. In Pattern III, motor and parietal cortical regions and lower motor neurons of the brainstem and spinal cord are involved. In contrast to ALS, Pattern IV in bvFTD involves pTDP-43 within the occipital pole . The concept of anterograde corticofugal propagation of pTDP-43 is supported by these patterns and the presence of pTDP-43 pathology in the subcortical white matter of the affected cortical areas and the involvement of subcortical regions with substantial neocortical afferents . Diffusion tensor imaging has been utilised to analyse white matter tracts that are likely to be involved in corresponding pTDP-43-based stages of ALS in vivo . The study observed substantial differences in corticospinal tract (Stage 1), corticorubral and corticopontine tracts (Stage 2), corticostriatal pathway (Stage 3) and proximal portion of perforant pathway (Stage 4) in ALS in comparison to the control group and tract involvement corresponded with disease duration. Experimental evidence in vivo also supports involvement of axonal pathways in FTD. Diffusion tensor imaging of bvFTD showed significant degree of white matter damage to the uncinate fasciculus, inferior and superior longitudinal fasciculus, genu of corpus callosum, forceps minor and cingulum bundle . Furthermore, the fornix was identified as a key locus of damage in bvFTD . However, a more limited number of studies have validated histopathological changes in deep white matter tracts with demonstrated involvement in ALS pathology in vivo. Independent neuropathological validation demonstrated a lack of pTDP-43 pathology in corticospinal tract, corpus callosum and cingulum bundle in ALS staging . Interestingly, other distinctive pathological features such as glial activation and axonal degeneration in corticospinal tract and corpus callosum have been reported previously , and callosal involvement is one of the most consistent MRI findings in ALS . These findings suggest that the underlying pathobiological features of neuroimaging modalities remain unclear highlighting the need for validation of MRI modalities with their respective histologic correlates at cellular and subcellular levels. Validation of microstructural changes in regions representing pTDP-43 spreading pattern in post mortem brains against whole brain MRI signals requires systematic sampling of pathologically relevant regions of interest. Presented in Table are the key neuroanatomical regions (cortical and subcortical grey matter; subcortical and deep white matter tracts) that (1) represent ALS and bvFTD pathology spread, and (2) are topographically recognisable with defined anatomical landmarks to facilitate systematic sampling and MRI-histology co-registration. Systematic sampling and MRI-histology co-registration requirements Standardised systematic sampling should be implemented for making valid statements about regions of interest, however, challenged by the highly folded nature of the cortex, large number of areas with variable sizes and undeniable inter-individual variability . Nevertheless, maintaining reproducibility with neuroanatomical accuracy is crucial to a standardised sampling strategy in a disease specific cohort. The proposed sampling strategy considers two approaches to minimise inter-individual variability whilst maintaining neuroanatomical accuracy: (1) the use of neuroanatomical landmarks, e.g. gyri, sulci and subcortical grey matter that can be robustly identified in all brains on MRI and during cut-up of the corresponding brain, for identifying cortical and subcortical regions of interest and (2) the use of diffusion MRI tractography to guide identification and sampling of white matter tracts. These approaches enabled implementation of systematic sampling for regions of interest to provide accurate estimates of changes in them and are aimed to facilitate multi-modal alignment and co-registration of 2-dimensional (2-D) histological images on 3-dimensional (3-D) MR volumes . Direct MRI-histology correlation, in vivo and ex vivo, is generally made difficult by the heterogeneity in characteristics on images from the two modalities in addition to the deformation and damage caused by histological processing . Although substantial distortions are likely to occur at the time of fresh brain extraction and during fixation, geometric deformations also occur during MRI scanning due to the brain being stabilised in a close-fitting container resulting in twisting or compression. Subsequent serial sampling of the fixed brain following scanning, histological sectioning of the brain regions into 2-D slices and staining would often introduce tears, rips, folds, missing pieces of tissue, artefacts, debris, uneven staining gradient and displacement of anatomical landmarks such as gyri and sulci from the original 3-D geometry . In addition, tissue shrinkage as a result of chemical processing for paraffin embedding needs to be taken into account. Several studies have explored registration of histology to MR with digital photographs of block faces acquired during sampling, in addition to stained sections, to serve as an intermediate modality for alignment and reconstruction on a 3-D volume . To enable accurate co-registration whilst taking into consideration structural deformations and damage, the proposed sampling strategy is coupled with digital photography pipeline (Fig. ) at various stages of block face sampling and histological processing: (1) coronal slices (2) sampled regions of interest and their remnants, and (3) trimmed surfaces of paraffin blocks with (4) stained serial histological sections. Tissue preparation and MR imaging Post mortem tissues were obtained from the Oxford Brain Bank. Fixed whole brains were drained of formalin and immersed in 3 M™ Fluorinert™ (FC-3283) for susceptibility matching (Fluorinert has a similar susceptibility to tissue, however, no signal) in a closed fitting plastic mould and imaged with a Siemens 7 T scanner using 32-channel receive, single-channel transmit RF coil for 48 h. Magnetic resonance imaging acquisition consisted of a set of protocols that provides a variety of contrasts (Table ), including structural scans, relaxography (T1, T2), susceptibility-weighted contrast and diffusion weighted steady-state free precession (DW-SSFP) for more specific measures of white matter . Following MRI, brain drained of Fluorinert™ and returned to formalin in preparation for sampling. MR image processing pipelines Structural datasets obtained with the 3-D – TRUFI protocol were averaged by calculating the root-mean-square of the individual phase-cycled scans. The T2-mapping protocol utilised a voxel-wise, signal-weighted pseudoinverse of the linearised temporal signal evolution from the T2 turbo spin-echo protocol. An identical process was adopted to generate T2*-maps, fitting to the magnitude component of signal evolution from the susceptibility weighted imaging (SWI) protocol. The T1-mapping protocol utilised a non-linear least-squares fit to the signal evolution from the T1 turbo spin-echo protocol. All fitting was performed using the NumPy and SciPy toolboxes in Python . Processing of the diffusion data obtained via the DW-SSFP imaging protocol utilised modified forms of DTIFIT and BEDPOSTX from the FSL toolbox , to account for the DW-SSFP , dual flip-angle datasets. All co-registration between and within imaging modalities was performed using FSL-FLIRT . Phase datasets from the SWI protocol were processed to generate quantitative susceptibility maps in MATLAB (The MathWorks, Inc.), original phase images were first unwrapped using a Laplacian-based method , the unwrapped phase images were subsequently filtered using V-SHARP algorithm to remove the background field, quantitative susceptibility maps were finally generated using STAR-QSM algorithm from the STI Suite toolbox . Sampling strategy for regions of interest Brains were prepared separately for sampling initially by carefully removing the meninges, photographed at several planes and dimensions were measured prior to dissection. Brain stem and cerebellum were removed from the cerebrum by knife section across the cerebral peduncle at the level of the 3rd cranial nerve (oculomotor nerve) in a plane perpendicular to the brainstem and aqueduct. Specific regions of interest were then sampled in both hemispheres as described in the following sections. Motor and somatosensory cortex The motor cortex (ALS Stage 1) on the precentral gyrus is demarcated by the anatomical borders the precentral sulcus (anteriorly) and the central sulcus (posteriorly) on the dorsolateral surface. Somatosensory cortex was identified in the post-central gyrus posterior to the central sulcus. Sampling of the motor and somatosensory cortex was restricted to clinicopathologically relevant regions defined by the anatomical landmarks (Fig. ): Leg area was represented on the medial surface of the paracentral lobule at the banks of the interhemispheric fissure. Motor hand area was distinguished by the hand knob (shaped as an inverted omega). Face area was identified on the precentral gyrus lateral to the intersection of inferior frontal sulcus and precentral sulcus. Approximately 1 cm blocks of tissue perpendicular to the central sulcus were dissected out at the leg area, middle hand knob region and face area to include pre-central gyrus, corresponding post-central gyrus, central sulcus and subcortical white matter (Fig. ). Care was taken to ensure that blocks were sampled at a reasonable depth to maintain integrity of pre- and post-central gyri together with the central sulcus on the same block (to facilitate the identification of the M1/S1 border, which is in the depth of the sulcus). Coronal slices Following extraction of the primary motor and somatosensory cortical regions, brains were sliced in a coronal plane through the mammillary bodies perpendicular to the longitudinal axis of the forebrain. Subsequent coronal slices, at 1 cm intervals, were made anterior and posterior to the initial cut (Fig. ). Anterior and posterior surfaces of each slice were photographed prior to sampling of the following regions of interest. Middle frontal gyrus The middle frontal gyrus is involved in Stage 2 and Pattern II of ALS and bvFTD, respectively. It forms part of the dorsolateral prefrontal cortex involved in regulation of attention, executive function and working memory that have been reported to be deficient in ALS patients without dementia . The middle frontal gyrus is one of the largest regions in the prefrontal cortex located between the superior frontal sulcus, inferior frontal sulcus and pre-central sulcus . It cytoarchitectonically constitutes Brodmann areas (BA) 6, 8, 9, 46 and 10 along its caudal-rostral axis . The middle frontal gyrus was identified in coronal slices lateral to the anatomical landmark superior frontal sulcus and sampled at a reasonable depth (~ 2 cm) to include subcortical white matter, and the adjacent superior frontal gyrus and sulcus (Fig. ). Sampling was carried out in serial coronal slices rostral to the precentral gyrus extending to the anterior most slice that clearly defines the superior frontal sulcus. Orbital gyri and gyrus rectus The orbital gyri and gyrus rectus, involved in the later stages of ALS (Stage 3) and in the earliest stage of bvFTD (Pattern I), lie in the basal surface of the frontal lobe. The gyrus rectus extends longitudinally in the medial border of the basal surface. The medial orbital gyrus is situated immediately lateral to the gyrus rectus and demarcated by the olfactory sulcus. The gyrus rectus and the medial orbital gyrus were sampled in rostral-caudal axis in serial coronal slices (Fig. ). Inferior frontal gyrus (Broca’s area) Impairment in language and syntactic processing has been implicated in ALS patients . Pathological changes in Broca’s area, associated with speech production and a wide range of language and communication related functions , have been reported in ALS with and without other FTD features . Broca’s motor speech area and its homologue in the right hemisphere occupy the caudal part of the inferior frontal gyrus demarcated by the precentral sulcus posteriorly, inferior frontal sulcus superiorly and the lateral fissure inferiorly . The area is further delineated macroscopically by the opercular and triangular gyri corresponding to BA 44 and 45 . Broca’s area was sampled from the inferior frontal gyrus, including the depth of the circular insular sulcus in two coronal slices (~ 2 cm) rostral to the precentral sulcus (Fig. ). Corticospinal tract, thalamus and striatum Identification of the corticospinal tract was guided by diffusion tractography of fibres originating from the leg area of paracentral lobule, and hand and face regions of the precentral gyrus (Fig. ). The corticospinal projections that descend through the posterior limb of the internal capsule were sampled with the surrounding thalamus and the lentiform nucleus. Following sampling of the corticospinal tract, the remaining basal ganglia and internal capsule were sampled in anterior coronal slices. Hippocampal formation The hippocampal formation of the dorsomedial temporal lobe, involved in the final stage of ALS and representing Pattern II of bvFTD, constitutes the hippocampus with dentate and adjacent cortical regions extending to the parahippocampal gyrus. The parahippocampal gyrus is located inferior to the hippocampus between the hippocampal fissure and collateral sulcus. The hippocampal formation, identified by its unique topographical organisation, was sampled in consecutive coronal slices superior to the collateral sulcus. Corpus callosum, cingulate gyrus and paracallosal cingulum The cingulate gyrus is situated on the medial surface of the brain extending from the rostral genu area and dorsally to the body of the corpus callosum and terminating ventral to the splenium . The white matter underlying the cingulate cortex presents the paracallosal cingulum bundle. The cingulate gyrus, inferior to the cingulate sulcus and subparietal sulcus, was sampled serially in the coronal plane together with the corpus callosum. Fornix The fornix, which lies immediately inferior to the corpus callosum, is an arch shaped white matter tract carrying efferent fibres from the hippocampus primarily into the mammillary bodies. The fornix is divided into three regions named crus, body and column along the caudal-rostral axis . The crus of the fornix, an extension of the fimbria of the hippocampus, from both hemispheres arches below the splenium of the corpus callosum forming the body of the fornix. The fibre bundle then descends at the rostral end, at the level of the anterior commissure, dividing into the columns of the fornix. Given the shape and compact nature of the fornix, sampling is restricted to macroscopically distinguishable regions such as the body of the fornix (Fig. ), which can be sampled together with the thalamus or the corpus callosum (refer to previous sections). Presentation of the crus and column of the fornix is dependent on the plane and the level of coronal cut and therefore sampled only when the regions can be macroscopically recognised on the coronal plane. Brainstem and cerebellum The brainstem was sampled perpendicular to its longitudinal axis to include multiple levels of the midbrain, pons and medulla at 5 mm intervals. Cerebellar folial pattern and the anatomy of the lobules are best studied in serial parasagittal sections . The cerebellum was bisected sagittally in the midline through the vermis and subsequent slices were cut through the cerebellar hemispheres at 5–7 mm thickness (Fig. ). Whole sagittal slices were sampled from vermis to cerebellar hemisphere, including the dentate nucleus. Internal controls In addition to regions that are involved in ALS and associated FTD, regions predicted to have little or no involvement in the disease process were sampled to serve as internal controls. This is a crucial part of the protocol because in our experience any prospective post mortem ultra-high-field MRI study of the human brain is affected by a number of interindividual variables that are difficult to control. However, the strength of a whole brain MRI approach with extensive histological sampling means that an intraindividual calibration of both MRI and histology data is possible via the inclusion of areas that remain unaffected by disease. The primary visual cortex (BA 17), situated in the medial surface of the occipital lobe, which can be clearly identified by the stria Gennarii at the banks of the calcarine fissure, was sampled with the adjacent secondary visual area (BA 18) and subcortical white matter. The forceps major are radiations of commissural fibres arising from the splenium of the corpus callosum that connects the occipital lobes. Guided by tractography, the forceps major was sampled along the medial wall of the posterior horn of the lateral ventricle and superior to calcar avis on a coronal slice posterior to splenium. Other regions Representative blocks of regions that are not described here were sampled as per standard brain banking protocol for diagnostic purposes. Histological processing, immunohistochemistry and quantitative digital image analysis All sampled tissues were processed for paraffin embedding. Briefly, formalin fixed samples were dehydrated in a series of graded ethanol solutions (70–100%), cleared in xylene and embedded in paraffin wax. Subsequently, paraffin block surfaces were trimmed and photographed prior to acquiring serial sections at 6–10 μm thickness for histology (10 μm thickness for pTDP-43 and 6 μm for other stains). Immunohistochemistry was performed with a range of primary antibodies to detect myelin, inflammation (microglia and astroglia activation), iron, neurofilaments and pTDP-43 (Table ), and visualised using DAKO EnVision Systems and counterstained with haematoxylin. Whole slides were digitised at high resolution (×40 objective for pTDP-43 and ×20 objective for all other stains) with Aperio ScanScope®AT Turbo (Leica Biosystems) high-throughput slide scanner. The relative burden of pathology for immunohistochemical staining in regions of interest was analysed in digital images using Aperio Colour Deconvolution algorithm (version 9.1, Leica Biosystems). Colour deconvolution facilitates stain separation by calibration of colour vectors for each stain (brown immunostain and blue haematoxylin) to generate measurements of selected positive colour channel and intensity thresholding to exclude non-specific background staining. Algorithm input parameters were calibrated and separate threshold for each stain was established by finding a threshold that yielded robust results in at least 10 randomly selected structurally distinct regions . Regions of interests were outlined manually on each image in Aperio ImageScope based on general cytoarchitectural morphology and density differences (Fig. ), while histological artefacts such as staining artefacts, debris, folds, tears and air bubbles in regions of interest were outlined to exclude from analysis. The colour deconvolution algorithm was applied to quantify the stained area fraction (area of positive staining over total analysis area, Fig. ) for myelin proteolipid protein (PLP), glial activation (ionised calcium binding adaptor molecule 1: Iba1, cluster of differentiation 68: CD68, glial fibrillary acidic protein: GFAP), ferritin and neurofilaments (SMI-311 and SMI-312), and validated by a neuropathologist. Aperio image analysis algorithms have been used in previous studies to quantify the amount of immunostaining underlying neuropathological changes and the fraction of positively stained pixels in an area were reported with demonstrated sensitivity to packing density, size and number of cells and their processes . The colour deconvolution algorithm, as described above, was utilised to quantify the burden of pTDP-43 in manually outlined regions and reported as stained area fraction. Similar algorithms have been used previously for quantification of pathological TDP-43 load . Classification of pTDP-43 phenotype was based on cellular localisation and cortical distribution and categorised into neuronal cytoplasmic inclusions, dystrophic neurites, neuronal intranuclear inclusions, and/or glial inclusions by a neuropathologist according to the accepted criteria . Registration As illustrated in Fig. , the strategy for registration involves several separate stages. All of these are encapsulated in our actively developed software tool (EMMA for Efficient Microscopy–MRI Alignment) that we ultimately intend to release publicly. The first stage in the registration process involves separating the foreground and background for the photographs (Fig. a–c). In the second stage the photographed tissue block (or block face) is located within the photograph of the cut-out slice, which we call block face insertion (Fig. f). The insertion step itself is a rigid-body registration between the block face (Fig. d) and the photograph of the intact brain slice (Fig. e). It is informed by the photograph of the cut-out slice (Fig. b, after background removal) to minimise the chance of an erroneous insertion. The third stage involves registering the histology images (Fig. g) with the block face photograph (Fig. d), which are likely to involve distortions and tears associated with slicing sections from the block, but have the same edges. To overcome the resolution gap (0.5 μm/pixel vs. 50 μm/pixel), histological images are first sub-sampled to the resolution of the photographs. Tissue shrinkage and large-scale distortions are addressed by an initial affine transformation of the histological images. Finally, small-scale distortions are gradually compensated by smoothly deforming the histological images (Fig. g) to follow the edges of the block face (Fig. d). To make the registration work across modalities, both the histological images (Fig. g) and the block face (Fig. d) are represented using the Modality Independent Neighbourhood Descriptor (MIND) . MIND was developed for the purpose of cross-modality image registration, and its robustness has already been demonstrated in multiple applications , including the registration of brain MR images to histology . The fourth stage involves registering the digital photograph of the intact coronal section (Fig. e) to the 3-D MR image of the hemisphere prior to slicing. This registration needs to deal with the relative deformations involved with the brain being placed into the scanner and then cut into slices, meaning that the cut face need not be a simple plane in the 3-D image but would in general be a slightly curved surface. Therefore the MR volume is first re-sampled (Fig. h) parallel to a (curvilinear) surface that best represents the anatomical features in the photograph, then grey-white matter boundary information is obtained from a tissue-type segmentation (Fig. i to drive the boundary-based registration (BBR) process . The final stage then combines these various stages together, to obtain a non-linear registration of the histology image to the appropriate portion of the 3-D MR image (Fig. j). Each stage involves specific challenges but is a lot simpler than a direct registration of the histology image to the 3-D MR image, as a direct registration has far fewer anatomical features to work with as well as very large changes in the type of intensity contrast.
Identification of neuroanatomical regions relevant to pathology spread Identification of neuroanatomical regions that depict ALS and FTD pathology spread is crucial to establishing a universal whole brain sampling strategy by identifying clinical phenotypes and accompanying neuropathological characterisation. Clinical phenotypes of ALS are heterogeneous and the focality of symptom onset and subsequent contiguous spread of motor dysfunction are common to all diagnosed patients and indicative of a disease continuum . Neuropathologically, ALS is characterised by a degree of upper and lower motor neuron loss concomitant with degeneration of their respective axonal projections, glial activation and the pathological aggregation of 43-kDa TAR DNA-binding protein (TDP-43) . The unique molecular feature of TDP-43 proteinopathy is reported in almost all cases of ALS and in the largest subset of FTD supporting the concept of ALS-FTD as a pathological spectrum . Although neuropathological evaluation is made at the “end-stage” of disease, recent studies have defined a pathological staging system based on systematic neuroanatomical distribution of TDP-43 pathology that correlated with severity of clinical phenotypes . In a system of ALS pathological ‘stages’ based on regional patterns of post mortem phosphorylated TDP-43 (pTDP-43) pathology, Stage 1 was defined as involving the upper motor neurons of the primary motor cortex and the lower motor neurons of the brainstem and the spinal cord . From the motor cortex, Stage 2 involves pathology extended rostrally to prefrontal cortical regions of the middle frontal gyrus and caudally to the reticular formation of the brainstem, red nucleus and precerebellar nuclei. Stage 3 was defined as pTDP-43 lesions extending into the gyrus rectus and orbital gyri of the basal prefrontal cortex, post-central sensory areas of the parietal lobe, the temporal lobe and the striatum. Stage 4 involves pathological burden in the anteromedial regions of the temporal lobe and the hippocampus. Distinct pTDP-43 distribution patterns have also been proposed for behavioural variant FTD (bvFTD) , the most common form of FTD overlapping with ALS . In bvFTD, pTDP-43 lesions first appear in the basal prefrontal cortex (orbital gyri and gyrus rectus) and amygdala in Pattern I. Involvement caudally into the prefrontal cortical regions (middle frontal gyrus, insular cortex and anterior cingulate), temporal lobe, including hippocampus, and subcortical regions striatum, thalamus, red nucleus and precerebellar nuclei of pons and medulla defines Pattern II. In Pattern III, motor and parietal cortical regions and lower motor neurons of the brainstem and spinal cord are involved. In contrast to ALS, Pattern IV in bvFTD involves pTDP-43 within the occipital pole . The concept of anterograde corticofugal propagation of pTDP-43 is supported by these patterns and the presence of pTDP-43 pathology in the subcortical white matter of the affected cortical areas and the involvement of subcortical regions with substantial neocortical afferents . Diffusion tensor imaging has been utilised to analyse white matter tracts that are likely to be involved in corresponding pTDP-43-based stages of ALS in vivo . The study observed substantial differences in corticospinal tract (Stage 1), corticorubral and corticopontine tracts (Stage 2), corticostriatal pathway (Stage 3) and proximal portion of perforant pathway (Stage 4) in ALS in comparison to the control group and tract involvement corresponded with disease duration. Experimental evidence in vivo also supports involvement of axonal pathways in FTD. Diffusion tensor imaging of bvFTD showed significant degree of white matter damage to the uncinate fasciculus, inferior and superior longitudinal fasciculus, genu of corpus callosum, forceps minor and cingulum bundle . Furthermore, the fornix was identified as a key locus of damage in bvFTD . However, a more limited number of studies have validated histopathological changes in deep white matter tracts with demonstrated involvement in ALS pathology in vivo. Independent neuropathological validation demonstrated a lack of pTDP-43 pathology in corticospinal tract, corpus callosum and cingulum bundle in ALS staging . Interestingly, other distinctive pathological features such as glial activation and axonal degeneration in corticospinal tract and corpus callosum have been reported previously , and callosal involvement is one of the most consistent MRI findings in ALS . These findings suggest that the underlying pathobiological features of neuroimaging modalities remain unclear highlighting the need for validation of MRI modalities with their respective histologic correlates at cellular and subcellular levels. Validation of microstructural changes in regions representing pTDP-43 spreading pattern in post mortem brains against whole brain MRI signals requires systematic sampling of pathologically relevant regions of interest. Presented in Table are the key neuroanatomical regions (cortical and subcortical grey matter; subcortical and deep white matter tracts) that (1) represent ALS and bvFTD pathology spread, and (2) are topographically recognisable with defined anatomical landmarks to facilitate systematic sampling and MRI-histology co-registration. Systematic sampling and MRI-histology co-registration requirements Standardised systematic sampling should be implemented for making valid statements about regions of interest, however, challenged by the highly folded nature of the cortex, large number of areas with variable sizes and undeniable inter-individual variability . Nevertheless, maintaining reproducibility with neuroanatomical accuracy is crucial to a standardised sampling strategy in a disease specific cohort. The proposed sampling strategy considers two approaches to minimise inter-individual variability whilst maintaining neuroanatomical accuracy: (1) the use of neuroanatomical landmarks, e.g. gyri, sulci and subcortical grey matter that can be robustly identified in all brains on MRI and during cut-up of the corresponding brain, for identifying cortical and subcortical regions of interest and (2) the use of diffusion MRI tractography to guide identification and sampling of white matter tracts. These approaches enabled implementation of systematic sampling for regions of interest to provide accurate estimates of changes in them and are aimed to facilitate multi-modal alignment and co-registration of 2-dimensional (2-D) histological images on 3-dimensional (3-D) MR volumes . Direct MRI-histology correlation, in vivo and ex vivo, is generally made difficult by the heterogeneity in characteristics on images from the two modalities in addition to the deformation and damage caused by histological processing . Although substantial distortions are likely to occur at the time of fresh brain extraction and during fixation, geometric deformations also occur during MRI scanning due to the brain being stabilised in a close-fitting container resulting in twisting or compression. Subsequent serial sampling of the fixed brain following scanning, histological sectioning of the brain regions into 2-D slices and staining would often introduce tears, rips, folds, missing pieces of tissue, artefacts, debris, uneven staining gradient and displacement of anatomical landmarks such as gyri and sulci from the original 3-D geometry . In addition, tissue shrinkage as a result of chemical processing for paraffin embedding needs to be taken into account. Several studies have explored registration of histology to MR with digital photographs of block faces acquired during sampling, in addition to stained sections, to serve as an intermediate modality for alignment and reconstruction on a 3-D volume . To enable accurate co-registration whilst taking into consideration structural deformations and damage, the proposed sampling strategy is coupled with digital photography pipeline (Fig. ) at various stages of block face sampling and histological processing: (1) coronal slices (2) sampled regions of interest and their remnants, and (3) trimmed surfaces of paraffin blocks with (4) stained serial histological sections.
Identification of neuroanatomical regions that depict ALS and FTD pathology spread is crucial to establishing a universal whole brain sampling strategy by identifying clinical phenotypes and accompanying neuropathological characterisation. Clinical phenotypes of ALS are heterogeneous and the focality of symptom onset and subsequent contiguous spread of motor dysfunction are common to all diagnosed patients and indicative of a disease continuum . Neuropathologically, ALS is characterised by a degree of upper and lower motor neuron loss concomitant with degeneration of their respective axonal projections, glial activation and the pathological aggregation of 43-kDa TAR DNA-binding protein (TDP-43) . The unique molecular feature of TDP-43 proteinopathy is reported in almost all cases of ALS and in the largest subset of FTD supporting the concept of ALS-FTD as a pathological spectrum . Although neuropathological evaluation is made at the “end-stage” of disease, recent studies have defined a pathological staging system based on systematic neuroanatomical distribution of TDP-43 pathology that correlated with severity of clinical phenotypes . In a system of ALS pathological ‘stages’ based on regional patterns of post mortem phosphorylated TDP-43 (pTDP-43) pathology, Stage 1 was defined as involving the upper motor neurons of the primary motor cortex and the lower motor neurons of the brainstem and the spinal cord . From the motor cortex, Stage 2 involves pathology extended rostrally to prefrontal cortical regions of the middle frontal gyrus and caudally to the reticular formation of the brainstem, red nucleus and precerebellar nuclei. Stage 3 was defined as pTDP-43 lesions extending into the gyrus rectus and orbital gyri of the basal prefrontal cortex, post-central sensory areas of the parietal lobe, the temporal lobe and the striatum. Stage 4 involves pathological burden in the anteromedial regions of the temporal lobe and the hippocampus. Distinct pTDP-43 distribution patterns have also been proposed for behavioural variant FTD (bvFTD) , the most common form of FTD overlapping with ALS . In bvFTD, pTDP-43 lesions first appear in the basal prefrontal cortex (orbital gyri and gyrus rectus) and amygdala in Pattern I. Involvement caudally into the prefrontal cortical regions (middle frontal gyrus, insular cortex and anterior cingulate), temporal lobe, including hippocampus, and subcortical regions striatum, thalamus, red nucleus and precerebellar nuclei of pons and medulla defines Pattern II. In Pattern III, motor and parietal cortical regions and lower motor neurons of the brainstem and spinal cord are involved. In contrast to ALS, Pattern IV in bvFTD involves pTDP-43 within the occipital pole . The concept of anterograde corticofugal propagation of pTDP-43 is supported by these patterns and the presence of pTDP-43 pathology in the subcortical white matter of the affected cortical areas and the involvement of subcortical regions with substantial neocortical afferents . Diffusion tensor imaging has been utilised to analyse white matter tracts that are likely to be involved in corresponding pTDP-43-based stages of ALS in vivo . The study observed substantial differences in corticospinal tract (Stage 1), corticorubral and corticopontine tracts (Stage 2), corticostriatal pathway (Stage 3) and proximal portion of perforant pathway (Stage 4) in ALS in comparison to the control group and tract involvement corresponded with disease duration. Experimental evidence in vivo also supports involvement of axonal pathways in FTD. Diffusion tensor imaging of bvFTD showed significant degree of white matter damage to the uncinate fasciculus, inferior and superior longitudinal fasciculus, genu of corpus callosum, forceps minor and cingulum bundle . Furthermore, the fornix was identified as a key locus of damage in bvFTD . However, a more limited number of studies have validated histopathological changes in deep white matter tracts with demonstrated involvement in ALS pathology in vivo. Independent neuropathological validation demonstrated a lack of pTDP-43 pathology in corticospinal tract, corpus callosum and cingulum bundle in ALS staging . Interestingly, other distinctive pathological features such as glial activation and axonal degeneration in corticospinal tract and corpus callosum have been reported previously , and callosal involvement is one of the most consistent MRI findings in ALS . These findings suggest that the underlying pathobiological features of neuroimaging modalities remain unclear highlighting the need for validation of MRI modalities with their respective histologic correlates at cellular and subcellular levels. Validation of microstructural changes in regions representing pTDP-43 spreading pattern in post mortem brains against whole brain MRI signals requires systematic sampling of pathologically relevant regions of interest. Presented in Table are the key neuroanatomical regions (cortical and subcortical grey matter; subcortical and deep white matter tracts) that (1) represent ALS and bvFTD pathology spread, and (2) are topographically recognisable with defined anatomical landmarks to facilitate systematic sampling and MRI-histology co-registration.
Standardised systematic sampling should be implemented for making valid statements about regions of interest, however, challenged by the highly folded nature of the cortex, large number of areas with variable sizes and undeniable inter-individual variability . Nevertheless, maintaining reproducibility with neuroanatomical accuracy is crucial to a standardised sampling strategy in a disease specific cohort. The proposed sampling strategy considers two approaches to minimise inter-individual variability whilst maintaining neuroanatomical accuracy: (1) the use of neuroanatomical landmarks, e.g. gyri, sulci and subcortical grey matter that can be robustly identified in all brains on MRI and during cut-up of the corresponding brain, for identifying cortical and subcortical regions of interest and (2) the use of diffusion MRI tractography to guide identification and sampling of white matter tracts. These approaches enabled implementation of systematic sampling for regions of interest to provide accurate estimates of changes in them and are aimed to facilitate multi-modal alignment and co-registration of 2-dimensional (2-D) histological images on 3-dimensional (3-D) MR volumes . Direct MRI-histology correlation, in vivo and ex vivo, is generally made difficult by the heterogeneity in characteristics on images from the two modalities in addition to the deformation and damage caused by histological processing . Although substantial distortions are likely to occur at the time of fresh brain extraction and during fixation, geometric deformations also occur during MRI scanning due to the brain being stabilised in a close-fitting container resulting in twisting or compression. Subsequent serial sampling of the fixed brain following scanning, histological sectioning of the brain regions into 2-D slices and staining would often introduce tears, rips, folds, missing pieces of tissue, artefacts, debris, uneven staining gradient and displacement of anatomical landmarks such as gyri and sulci from the original 3-D geometry . In addition, tissue shrinkage as a result of chemical processing for paraffin embedding needs to be taken into account. Several studies have explored registration of histology to MR with digital photographs of block faces acquired during sampling, in addition to stained sections, to serve as an intermediate modality for alignment and reconstruction on a 3-D volume . To enable accurate co-registration whilst taking into consideration structural deformations and damage, the proposed sampling strategy is coupled with digital photography pipeline (Fig. ) at various stages of block face sampling and histological processing: (1) coronal slices (2) sampled regions of interest and their remnants, and (3) trimmed surfaces of paraffin blocks with (4) stained serial histological sections.
Post mortem tissues were obtained from the Oxford Brain Bank. Fixed whole brains were drained of formalin and immersed in 3 M™ Fluorinert™ (FC-3283) for susceptibility matching (Fluorinert has a similar susceptibility to tissue, however, no signal) in a closed fitting plastic mould and imaged with a Siemens 7 T scanner using 32-channel receive, single-channel transmit RF coil for 48 h. Magnetic resonance imaging acquisition consisted of a set of protocols that provides a variety of contrasts (Table ), including structural scans, relaxography (T1, T2), susceptibility-weighted contrast and diffusion weighted steady-state free precession (DW-SSFP) for more specific measures of white matter . Following MRI, brain drained of Fluorinert™ and returned to formalin in preparation for sampling.
Structural datasets obtained with the 3-D – TRUFI protocol were averaged by calculating the root-mean-square of the individual phase-cycled scans. The T2-mapping protocol utilised a voxel-wise, signal-weighted pseudoinverse of the linearised temporal signal evolution from the T2 turbo spin-echo protocol. An identical process was adopted to generate T2*-maps, fitting to the magnitude component of signal evolution from the susceptibility weighted imaging (SWI) protocol. The T1-mapping protocol utilised a non-linear least-squares fit to the signal evolution from the T1 turbo spin-echo protocol. All fitting was performed using the NumPy and SciPy toolboxes in Python . Processing of the diffusion data obtained via the DW-SSFP imaging protocol utilised modified forms of DTIFIT and BEDPOSTX from the FSL toolbox , to account for the DW-SSFP , dual flip-angle datasets. All co-registration between and within imaging modalities was performed using FSL-FLIRT . Phase datasets from the SWI protocol were processed to generate quantitative susceptibility maps in MATLAB (The MathWorks, Inc.), original phase images were first unwrapped using a Laplacian-based method , the unwrapped phase images were subsequently filtered using V-SHARP algorithm to remove the background field, quantitative susceptibility maps were finally generated using STAR-QSM algorithm from the STI Suite toolbox .
Brains were prepared separately for sampling initially by carefully removing the meninges, photographed at several planes and dimensions were measured prior to dissection. Brain stem and cerebellum were removed from the cerebrum by knife section across the cerebral peduncle at the level of the 3rd cranial nerve (oculomotor nerve) in a plane perpendicular to the brainstem and aqueduct. Specific regions of interest were then sampled in both hemispheres as described in the following sections. Motor and somatosensory cortex The motor cortex (ALS Stage 1) on the precentral gyrus is demarcated by the anatomical borders the precentral sulcus (anteriorly) and the central sulcus (posteriorly) on the dorsolateral surface. Somatosensory cortex was identified in the post-central gyrus posterior to the central sulcus. Sampling of the motor and somatosensory cortex was restricted to clinicopathologically relevant regions defined by the anatomical landmarks (Fig. ): Leg area was represented on the medial surface of the paracentral lobule at the banks of the interhemispheric fissure. Motor hand area was distinguished by the hand knob (shaped as an inverted omega). Face area was identified on the precentral gyrus lateral to the intersection of inferior frontal sulcus and precentral sulcus. Approximately 1 cm blocks of tissue perpendicular to the central sulcus were dissected out at the leg area, middle hand knob region and face area to include pre-central gyrus, corresponding post-central gyrus, central sulcus and subcortical white matter (Fig. ). Care was taken to ensure that blocks were sampled at a reasonable depth to maintain integrity of pre- and post-central gyri together with the central sulcus on the same block (to facilitate the identification of the M1/S1 border, which is in the depth of the sulcus). Coronal slices Following extraction of the primary motor and somatosensory cortical regions, brains were sliced in a coronal plane through the mammillary bodies perpendicular to the longitudinal axis of the forebrain. Subsequent coronal slices, at 1 cm intervals, were made anterior and posterior to the initial cut (Fig. ). Anterior and posterior surfaces of each slice were photographed prior to sampling of the following regions of interest. Middle frontal gyrus The middle frontal gyrus is involved in Stage 2 and Pattern II of ALS and bvFTD, respectively. It forms part of the dorsolateral prefrontal cortex involved in regulation of attention, executive function and working memory that have been reported to be deficient in ALS patients without dementia . The middle frontal gyrus is one of the largest regions in the prefrontal cortex located between the superior frontal sulcus, inferior frontal sulcus and pre-central sulcus . It cytoarchitectonically constitutes Brodmann areas (BA) 6, 8, 9, 46 and 10 along its caudal-rostral axis . The middle frontal gyrus was identified in coronal slices lateral to the anatomical landmark superior frontal sulcus and sampled at a reasonable depth (~ 2 cm) to include subcortical white matter, and the adjacent superior frontal gyrus and sulcus (Fig. ). Sampling was carried out in serial coronal slices rostral to the precentral gyrus extending to the anterior most slice that clearly defines the superior frontal sulcus. Orbital gyri and gyrus rectus The orbital gyri and gyrus rectus, involved in the later stages of ALS (Stage 3) and in the earliest stage of bvFTD (Pattern I), lie in the basal surface of the frontal lobe. The gyrus rectus extends longitudinally in the medial border of the basal surface. The medial orbital gyrus is situated immediately lateral to the gyrus rectus and demarcated by the olfactory sulcus. The gyrus rectus and the medial orbital gyrus were sampled in rostral-caudal axis in serial coronal slices (Fig. ). Inferior frontal gyrus (Broca’s area) Impairment in language and syntactic processing has been implicated in ALS patients . Pathological changes in Broca’s area, associated with speech production and a wide range of language and communication related functions , have been reported in ALS with and without other FTD features . Broca’s motor speech area and its homologue in the right hemisphere occupy the caudal part of the inferior frontal gyrus demarcated by the precentral sulcus posteriorly, inferior frontal sulcus superiorly and the lateral fissure inferiorly . The area is further delineated macroscopically by the opercular and triangular gyri corresponding to BA 44 and 45 . Broca’s area was sampled from the inferior frontal gyrus, including the depth of the circular insular sulcus in two coronal slices (~ 2 cm) rostral to the precentral sulcus (Fig. ). Corticospinal tract, thalamus and striatum Identification of the corticospinal tract was guided by diffusion tractography of fibres originating from the leg area of paracentral lobule, and hand and face regions of the precentral gyrus (Fig. ). The corticospinal projections that descend through the posterior limb of the internal capsule were sampled with the surrounding thalamus and the lentiform nucleus. Following sampling of the corticospinal tract, the remaining basal ganglia and internal capsule were sampled in anterior coronal slices. Hippocampal formation The hippocampal formation of the dorsomedial temporal lobe, involved in the final stage of ALS and representing Pattern II of bvFTD, constitutes the hippocampus with dentate and adjacent cortical regions extending to the parahippocampal gyrus. The parahippocampal gyrus is located inferior to the hippocampus between the hippocampal fissure and collateral sulcus. The hippocampal formation, identified by its unique topographical organisation, was sampled in consecutive coronal slices superior to the collateral sulcus. Corpus callosum, cingulate gyrus and paracallosal cingulum The cingulate gyrus is situated on the medial surface of the brain extending from the rostral genu area and dorsally to the body of the corpus callosum and terminating ventral to the splenium . The white matter underlying the cingulate cortex presents the paracallosal cingulum bundle. The cingulate gyrus, inferior to the cingulate sulcus and subparietal sulcus, was sampled serially in the coronal plane together with the corpus callosum. Fornix The fornix, which lies immediately inferior to the corpus callosum, is an arch shaped white matter tract carrying efferent fibres from the hippocampus primarily into the mammillary bodies. The fornix is divided into three regions named crus, body and column along the caudal-rostral axis . The crus of the fornix, an extension of the fimbria of the hippocampus, from both hemispheres arches below the splenium of the corpus callosum forming the body of the fornix. The fibre bundle then descends at the rostral end, at the level of the anterior commissure, dividing into the columns of the fornix. Given the shape and compact nature of the fornix, sampling is restricted to macroscopically distinguishable regions such as the body of the fornix (Fig. ), which can be sampled together with the thalamus or the corpus callosum (refer to previous sections). Presentation of the crus and column of the fornix is dependent on the plane and the level of coronal cut and therefore sampled only when the regions can be macroscopically recognised on the coronal plane. Brainstem and cerebellum The brainstem was sampled perpendicular to its longitudinal axis to include multiple levels of the midbrain, pons and medulla at 5 mm intervals. Cerebellar folial pattern and the anatomy of the lobules are best studied in serial parasagittal sections . The cerebellum was bisected sagittally in the midline through the vermis and subsequent slices were cut through the cerebellar hemispheres at 5–7 mm thickness (Fig. ). Whole sagittal slices were sampled from vermis to cerebellar hemisphere, including the dentate nucleus. Internal controls In addition to regions that are involved in ALS and associated FTD, regions predicted to have little or no involvement in the disease process were sampled to serve as internal controls. This is a crucial part of the protocol because in our experience any prospective post mortem ultra-high-field MRI study of the human brain is affected by a number of interindividual variables that are difficult to control. However, the strength of a whole brain MRI approach with extensive histological sampling means that an intraindividual calibration of both MRI and histology data is possible via the inclusion of areas that remain unaffected by disease. The primary visual cortex (BA 17), situated in the medial surface of the occipital lobe, which can be clearly identified by the stria Gennarii at the banks of the calcarine fissure, was sampled with the adjacent secondary visual area (BA 18) and subcortical white matter. The forceps major are radiations of commissural fibres arising from the splenium of the corpus callosum that connects the occipital lobes. Guided by tractography, the forceps major was sampled along the medial wall of the posterior horn of the lateral ventricle and superior to calcar avis on a coronal slice posterior to splenium. Other regions Representative blocks of regions that are not described here were sampled as per standard brain banking protocol for diagnostic purposes.
The motor cortex (ALS Stage 1) on the precentral gyrus is demarcated by the anatomical borders the precentral sulcus (anteriorly) and the central sulcus (posteriorly) on the dorsolateral surface. Somatosensory cortex was identified in the post-central gyrus posterior to the central sulcus. Sampling of the motor and somatosensory cortex was restricted to clinicopathologically relevant regions defined by the anatomical landmarks (Fig. ): Leg area was represented on the medial surface of the paracentral lobule at the banks of the interhemispheric fissure. Motor hand area was distinguished by the hand knob (shaped as an inverted omega). Face area was identified on the precentral gyrus lateral to the intersection of inferior frontal sulcus and precentral sulcus. Approximately 1 cm blocks of tissue perpendicular to the central sulcus were dissected out at the leg area, middle hand knob region and face area to include pre-central gyrus, corresponding post-central gyrus, central sulcus and subcortical white matter (Fig. ). Care was taken to ensure that blocks were sampled at a reasonable depth to maintain integrity of pre- and post-central gyri together with the central sulcus on the same block (to facilitate the identification of the M1/S1 border, which is in the depth of the sulcus).
Following extraction of the primary motor and somatosensory cortical regions, brains were sliced in a coronal plane through the mammillary bodies perpendicular to the longitudinal axis of the forebrain. Subsequent coronal slices, at 1 cm intervals, were made anterior and posterior to the initial cut (Fig. ). Anterior and posterior surfaces of each slice were photographed prior to sampling of the following regions of interest.
The middle frontal gyrus is involved in Stage 2 and Pattern II of ALS and bvFTD, respectively. It forms part of the dorsolateral prefrontal cortex involved in regulation of attention, executive function and working memory that have been reported to be deficient in ALS patients without dementia . The middle frontal gyrus is one of the largest regions in the prefrontal cortex located between the superior frontal sulcus, inferior frontal sulcus and pre-central sulcus . It cytoarchitectonically constitutes Brodmann areas (BA) 6, 8, 9, 46 and 10 along its caudal-rostral axis . The middle frontal gyrus was identified in coronal slices lateral to the anatomical landmark superior frontal sulcus and sampled at a reasonable depth (~ 2 cm) to include subcortical white matter, and the adjacent superior frontal gyrus and sulcus (Fig. ). Sampling was carried out in serial coronal slices rostral to the precentral gyrus extending to the anterior most slice that clearly defines the superior frontal sulcus.
The orbital gyri and gyrus rectus, involved in the later stages of ALS (Stage 3) and in the earliest stage of bvFTD (Pattern I), lie in the basal surface of the frontal lobe. The gyrus rectus extends longitudinally in the medial border of the basal surface. The medial orbital gyrus is situated immediately lateral to the gyrus rectus and demarcated by the olfactory sulcus. The gyrus rectus and the medial orbital gyrus were sampled in rostral-caudal axis in serial coronal slices (Fig. ).
Impairment in language and syntactic processing has been implicated in ALS patients . Pathological changes in Broca’s area, associated with speech production and a wide range of language and communication related functions , have been reported in ALS with and without other FTD features . Broca’s motor speech area and its homologue in the right hemisphere occupy the caudal part of the inferior frontal gyrus demarcated by the precentral sulcus posteriorly, inferior frontal sulcus superiorly and the lateral fissure inferiorly . The area is further delineated macroscopically by the opercular and triangular gyri corresponding to BA 44 and 45 . Broca’s area was sampled from the inferior frontal gyrus, including the depth of the circular insular sulcus in two coronal slices (~ 2 cm) rostral to the precentral sulcus (Fig. ).
Identification of the corticospinal tract was guided by diffusion tractography of fibres originating from the leg area of paracentral lobule, and hand and face regions of the precentral gyrus (Fig. ). The corticospinal projections that descend through the posterior limb of the internal capsule were sampled with the surrounding thalamus and the lentiform nucleus. Following sampling of the corticospinal tract, the remaining basal ganglia and internal capsule were sampled in anterior coronal slices.
The hippocampal formation of the dorsomedial temporal lobe, involved in the final stage of ALS and representing Pattern II of bvFTD, constitutes the hippocampus with dentate and adjacent cortical regions extending to the parahippocampal gyrus. The parahippocampal gyrus is located inferior to the hippocampus between the hippocampal fissure and collateral sulcus. The hippocampal formation, identified by its unique topographical organisation, was sampled in consecutive coronal slices superior to the collateral sulcus.
The cingulate gyrus is situated on the medial surface of the brain extending from the rostral genu area and dorsally to the body of the corpus callosum and terminating ventral to the splenium . The white matter underlying the cingulate cortex presents the paracallosal cingulum bundle. The cingulate gyrus, inferior to the cingulate sulcus and subparietal sulcus, was sampled serially in the coronal plane together with the corpus callosum.
The fornix, which lies immediately inferior to the corpus callosum, is an arch shaped white matter tract carrying efferent fibres from the hippocampus primarily into the mammillary bodies. The fornix is divided into three regions named crus, body and column along the caudal-rostral axis . The crus of the fornix, an extension of the fimbria of the hippocampus, from both hemispheres arches below the splenium of the corpus callosum forming the body of the fornix. The fibre bundle then descends at the rostral end, at the level of the anterior commissure, dividing into the columns of the fornix. Given the shape and compact nature of the fornix, sampling is restricted to macroscopically distinguishable regions such as the body of the fornix (Fig. ), which can be sampled together with the thalamus or the corpus callosum (refer to previous sections). Presentation of the crus and column of the fornix is dependent on the plane and the level of coronal cut and therefore sampled only when the regions can be macroscopically recognised on the coronal plane.
The brainstem was sampled perpendicular to its longitudinal axis to include multiple levels of the midbrain, pons and medulla at 5 mm intervals. Cerebellar folial pattern and the anatomy of the lobules are best studied in serial parasagittal sections . The cerebellum was bisected sagittally in the midline through the vermis and subsequent slices were cut through the cerebellar hemispheres at 5–7 mm thickness (Fig. ). Whole sagittal slices were sampled from vermis to cerebellar hemisphere, including the dentate nucleus.
In addition to regions that are involved in ALS and associated FTD, regions predicted to have little or no involvement in the disease process were sampled to serve as internal controls. This is a crucial part of the protocol because in our experience any prospective post mortem ultra-high-field MRI study of the human brain is affected by a number of interindividual variables that are difficult to control. However, the strength of a whole brain MRI approach with extensive histological sampling means that an intraindividual calibration of both MRI and histology data is possible via the inclusion of areas that remain unaffected by disease. The primary visual cortex (BA 17), situated in the medial surface of the occipital lobe, which can be clearly identified by the stria Gennarii at the banks of the calcarine fissure, was sampled with the adjacent secondary visual area (BA 18) and subcortical white matter. The forceps major are radiations of commissural fibres arising from the splenium of the corpus callosum that connects the occipital lobes. Guided by tractography, the forceps major was sampled along the medial wall of the posterior horn of the lateral ventricle and superior to calcar avis on a coronal slice posterior to splenium.
Representative blocks of regions that are not described here were sampled as per standard brain banking protocol for diagnostic purposes.
All sampled tissues were processed for paraffin embedding. Briefly, formalin fixed samples were dehydrated in a series of graded ethanol solutions (70–100%), cleared in xylene and embedded in paraffin wax. Subsequently, paraffin block surfaces were trimmed and photographed prior to acquiring serial sections at 6–10 μm thickness for histology (10 μm thickness for pTDP-43 and 6 μm for other stains). Immunohistochemistry was performed with a range of primary antibodies to detect myelin, inflammation (microglia and astroglia activation), iron, neurofilaments and pTDP-43 (Table ), and visualised using DAKO EnVision Systems and counterstained with haematoxylin. Whole slides were digitised at high resolution (×40 objective for pTDP-43 and ×20 objective for all other stains) with Aperio ScanScope®AT Turbo (Leica Biosystems) high-throughput slide scanner. The relative burden of pathology for immunohistochemical staining in regions of interest was analysed in digital images using Aperio Colour Deconvolution algorithm (version 9.1, Leica Biosystems). Colour deconvolution facilitates stain separation by calibration of colour vectors for each stain (brown immunostain and blue haematoxylin) to generate measurements of selected positive colour channel and intensity thresholding to exclude non-specific background staining. Algorithm input parameters were calibrated and separate threshold for each stain was established by finding a threshold that yielded robust results in at least 10 randomly selected structurally distinct regions . Regions of interests were outlined manually on each image in Aperio ImageScope based on general cytoarchitectural morphology and density differences (Fig. ), while histological artefacts such as staining artefacts, debris, folds, tears and air bubbles in regions of interest were outlined to exclude from analysis. The colour deconvolution algorithm was applied to quantify the stained area fraction (area of positive staining over total analysis area, Fig. ) for myelin proteolipid protein (PLP), glial activation (ionised calcium binding adaptor molecule 1: Iba1, cluster of differentiation 68: CD68, glial fibrillary acidic protein: GFAP), ferritin and neurofilaments (SMI-311 and SMI-312), and validated by a neuropathologist. Aperio image analysis algorithms have been used in previous studies to quantify the amount of immunostaining underlying neuropathological changes and the fraction of positively stained pixels in an area were reported with demonstrated sensitivity to packing density, size and number of cells and their processes . The colour deconvolution algorithm, as described above, was utilised to quantify the burden of pTDP-43 in manually outlined regions and reported as stained area fraction. Similar algorithms have been used previously for quantification of pathological TDP-43 load . Classification of pTDP-43 phenotype was based on cellular localisation and cortical distribution and categorised into neuronal cytoplasmic inclusions, dystrophic neurites, neuronal intranuclear inclusions, and/or glial inclusions by a neuropathologist according to the accepted criteria .
As illustrated in Fig. , the strategy for registration involves several separate stages. All of these are encapsulated in our actively developed software tool (EMMA for Efficient Microscopy–MRI Alignment) that we ultimately intend to release publicly. The first stage in the registration process involves separating the foreground and background for the photographs (Fig. a–c). In the second stage the photographed tissue block (or block face) is located within the photograph of the cut-out slice, which we call block face insertion (Fig. f). The insertion step itself is a rigid-body registration between the block face (Fig. d) and the photograph of the intact brain slice (Fig. e). It is informed by the photograph of the cut-out slice (Fig. b, after background removal) to minimise the chance of an erroneous insertion. The third stage involves registering the histology images (Fig. g) with the block face photograph (Fig. d), which are likely to involve distortions and tears associated with slicing sections from the block, but have the same edges. To overcome the resolution gap (0.5 μm/pixel vs. 50 μm/pixel), histological images are first sub-sampled to the resolution of the photographs. Tissue shrinkage and large-scale distortions are addressed by an initial affine transformation of the histological images. Finally, small-scale distortions are gradually compensated by smoothly deforming the histological images (Fig. g) to follow the edges of the block face (Fig. d). To make the registration work across modalities, both the histological images (Fig. g) and the block face (Fig. d) are represented using the Modality Independent Neighbourhood Descriptor (MIND) . MIND was developed for the purpose of cross-modality image registration, and its robustness has already been demonstrated in multiple applications , including the registration of brain MR images to histology . The fourth stage involves registering the digital photograph of the intact coronal section (Fig. e) to the 3-D MR image of the hemisphere prior to slicing. This registration needs to deal with the relative deformations involved with the brain being placed into the scanner and then cut into slices, meaning that the cut face need not be a simple plane in the 3-D image but would in general be a slightly curved surface. Therefore the MR volume is first re-sampled (Fig. h) parallel to a (curvilinear) surface that best represents the anatomical features in the photograph, then grey-white matter boundary information is obtained from a tissue-type segmentation (Fig. i to drive the boundary-based registration (BBR) process . The final stage then combines these various stages together, to obtain a non-linear registration of the histology image to the appropriate portion of the 3-D MR image (Fig. j). Each stage involves specific challenges but is a lot simpler than a direct registration of the histology image to the 3-D MR image, as a direct registration has far fewer anatomical features to work with as well as very large changes in the type of intensity contrast.
Preliminary results from each processed MRI modality for a brain are displayed in Fig. . The individual modalities display distinctive anatomical contrast and unique information about the underlying tissue composition and microstructure. Furthermore, an example of comparison between multiple histological stains and MRI modalities on a mapped plane from the normal appearing orbitofrontal cortex and its subcortical white matter is demonstrated in Fig. . Serial histology sections stained for axonal myelin (PLP) and neurofilament content (SMI-312) were mapped to the corresponding MRI plane (structural and diffusion weighted) that represents the region of interest, spared from pathology, from an ALS patient (ALS 1) (Fig. ). Qualitative evaluation of the stains demonstrates high myelin and neurofilament stain density in the white matter compared to the grey matter and distinct grey/white matter boundary (Fig. ). The grey scale structural, fractional anisotropy (FA) and mean diffusivity (MD) images showed comparable contrast in signal intensity that corresponded with histology. Similar observations were made in quantitative FA and MD maps (Fig. ) indicating sensitivity of these MRI modalities to multiple components of axonal microstructure. Further validation is required with additional stains that represent other microstructural features and should be compared with other MRI modalities for accurate interpretation of these findings. Proof of concept examining the influence of disease specific microstructural pathological changes on multiple MRI modalities was demonstrated in the post mortem spinal cord from an ALS patient (ALS 2). The MRI acquisition of the spinal cord consisted of a set of protocols (Additional file ) for structural scan, T1- and T2-mapping, and DW-SSFP. The structural MRI showed lateral corticospinal tract hyperintensity along the scanned segment of the spinal cord (Additional file ). The latter MRI signal change was concomitant with the degeneration of the lateral corticospinal tract histologically demonstrated with marked axonal myelin and neurofilament loss together with heightened inflammatory response (CD68) on a segment of the cervical cord (Fig. and Table ). Quantitative evaluation of the same region on the corresponding MRI plane demonstrated significant decrease in average FA value ( P < 0.001) and significant increase in average MD, T1 and T2 values ( P < 0.001) in comparison to the normal appearing white matter region (Table ). The burden of pathology in the hand knob area of the primary motor cortex grey matter was compared between 2 ALS patients, with varying degree of pathology, and a control (Fig. ). Qualitative comparisons were made in MRI signal changes in R2* and QSM maps that have been previously demonstrate to be sensitive microglial iron accumulation . Compared to the control, ALS 3 shows moderate diffused hyperintense signal changes in both R2* and susceptibility maps. ALS 4 shows the strongest hyperintense signal change in R2* and susceptibility maps. Upon qualitative and quantitative histological evaluation of the same region, the intensity in signal changes corresponds to the relative burden of CD68 pathology in all cases, with lowest in the control and the highest in ALS 4 (Fig. and Additional file ). Quantitative pTDP-43 burden appears to be slightly higher in ALS 4 (Additional file ), however its relationship to CD68 pathology is unclear and further evaluations are warranted. Interestingly, cortical myelin content in the ALS cases was similar to that of the control (Fig. and Additional file ). Patient data for ALS and control cases reported are presented in Table . The pTDP-43 staging for each ALS case was classified according to the accepted criteria . These findings from the lateral corticospinal tract degeneration of the spinal cord demonstrate multiple MRI modalities that are influenced by several candidate features of microstructural pathological changes. The findings from the motor cortex indicate that this protocol is suitable for the investigation of subtle microstructural changes. However, more robust validation of specificity and sensitivity of these MRI modalities should be extended to a larger cohort and are ideally demonstrated in numerous brain regions with heterogeneous degree of pathological changes. Therefore, the proposed methodology for systematic histological evaluation of pathology propagation in post mortem whole brain in ALS and validating the pathophysiological correlates with MRI signal changes would be ideal to uncover the specificity and sensitivity of MRI measures (Fig. ).
The extensive landmark based systematic whole brain sampling strategy is aimed for the accurate study of pathological propagation in ALS through identification of regions that represent the proposed four stage pTDP-43 spreading pattern with neuroanatomical accuracy. In addition to the sampling of cortical and subcortical grey matter, tractography can be used to guide accurate sampling and histological analysis of white matter tracts of interest, particularly where these tracts are not demarcated by an anatomical landmark. Systematic sampling of regions of interest was combined with a digital photography pipeline of sampling and histological processing to facilitate alignment and registration required for validation of sensitivity and specificity of MRI modalities to microstructural changes. One of the attributes of the proposed protocol is sequential sampling of whole regions of interest with distinct topographical features and neuroanatomical landmarks. However, partial sampling was considered for several regions due to size limitations, lack of defined landmarks and adequate representation of pathology spread. Specific examples of the latter include the large middle frontal gyrus, without defined anterior boundary, where sampling was restricted to its dorsal portion immediately lateral to the superior frontal sulcus. In addition, only the main clinicopathologically relevant segments of the primary motor and somatosensory cortex (pre-central and post-central gyri) were sampled. Sampling of the fornix was limited to the body and fimbria, whereas crus and column region extraction on the coronal plane was restricted by slice thickness and variation in cutting angle of coronal slices for macroscopic identification. In such events, accurate MRI-histology correlation in regions of interest may be challenged by the limited availability of histology. Therefore, findings should be interpreted with careful consideration and reported with neuroanatomical accuracy. Defining white matter tracts on histological images are generally guided by anatomical boundaries derived from location-based histological atlases. Unlike DTI tractography, which facilitates 3-D delineation and visualisation of multiple white matter tracts simultaneously , histological delineation of multiple white matter tracts of interest with a common anatomical trajectory on 2-D plane is difficult. For example, the posterior limb of the internal capsule located between the pallidum and thalamus contain corticospinal, corticobulbar and corticopontine projections and thalamic radiations along the superior-inferior axis . Tractography can be utilised for identification of the histological slice that best depict the tract of interest whilst taking into consideration the other adjacent tracts that may or may not be involved in the disease process. If more than one tract of interest is identified on a single stained histology section, they should be clearly demarcated prior to histological analysis using tractography-derived data. Furthermore, interpretation and reporting of findings should consider the possibility of multiple tracts being represented on analytical planes. The visual cortex and forceps major are regions where ALS associated degenerative changes are not anticipated; they have been used previously as internal controls or reference regions . Conversely, some in vivo imaging studies have reported changes in functional connectivity , grey matter atrophy, cortical thinning and altered glucose metabolism in occipital cortex of individuals with ALS independent of visual impairment . These findings have not been validated ex vivo, although it has been reported that pTDP-43 pathology may propagate into occipital lobe in the final stages of bvFTD . Growing evidence recognise ALS as a multisystem disorder and given the complex connectivity within the brain, identification of regions that are entirely spared of pathological involvement is challenging. Therefore, identification of internal control regions should be based on the likelihood of no or minimal involvement in the disease process, however their validity should be assessed individually for each case with histological analysis. It is further essential that molecular and morphological features of the internal control regions are comparable with aged-matched controls with no known neurological disease. Quantitative characterisation of digital histology images has been recognised as important for advances in high-throughput pathological diagnosis and research, and minimises subjective biases generally expected from traditional qualitative or semi-quantitative (arbitrary rating-scale-based) assessments. Such analytical outputs from histology images provide a comparable platform to correlate against quantitative MR analyses, with the potential to provide an interpretation of MRI-based measures with improved biological specificity. However, the proposed quantitative digital image analysis tool is limited by its inability to discern false positive staining intensity gradient, due methodological challenges, from true positive staining. It is therefore critical to ensure that staining quality is consistently maintained (via batch processing) and all images should be assessed manually to recognise any false positive staining and overall quality prior to analysis. Accurate alignment of a histology region of interest on the MRI plane is necessary for making direct correlations between the modalities. However, variation in slice plane orientations and spatial resolution in histology and MRI images can have impact on alignment. A typical histology section of 6 µm thickness imaged at ×20 objective magnification has an in-plane resolution of 0.502 µm/pixel. By comparison, our MR images are of lower resolution with voxel size ranging from 0.25 mm × 0.25 mm × 0.27 mm for the structural scans to 1.00 mm × 1.00 mm × 1.00 mm for the T1-and T2-maps. Alignment of smaller regions of interest that are clearly identifiable within a histology image may not be feasible on an MRI plane. Conversely, it is also important to recognise that MRI signal from larger 3-D volume may not always be reflected on a single 2-D histology section . The feasibility of the proposed methodology to better interpret MRI measurements with biological specificity is supported by our preliminary findings in a small number of ALS cases. The lateral corticospinal tract degeneration in the spinal cord of an ALS patient accompanied statistically significant changes in multiple MRI modalities that corresponded with clear axonal degeneration, myelin loss, microglial activation and iron accumulation. Furthermore, the intensity of signal change in R2* and susceptibility maps of the grey matter of the primary motor cortex corresponded primarily with the degree of microglial activation in two ALS cases and a control with comparative changes to pTDP-43 burden. However, the relationship between the CD68 and pTDP-43 expression and their influence (combined and individual) on MR signal is unclear. No difference was observed in myelin content. Previous studies have reported individual microstructural features that can strongly influence a specific MR signal , however validation of these modalities against multiple molecular and structural features were generally not considered. Recent evidence support the concept that changes to neural microstructure are almost always never isolated to one structural feature and are generally accompanied with several molecular and microstructural changes that can influence the MR signal to a lesser degree . Therefore, it is the ultimate aim of our project to identify which MRI modality best maps onto which specific neuropathological feature (see Fig. ). We aim to achieve this through extensive evaluation of multiple regions involved in ALS and FTD pathology spread coupled with histological analysis of multiple microstructural changes that can influence the MR signal in a large cohort.
The proposed landmark based systematic whole brain sampling strategy of pathologically relevant regions of interest is feasible for routine implementation in a high-throughput manner for the study of disease propagation and direct MRI-histology correlation in ALS. Together with quantitative image analysis and robust registration, this sampling approach facilitates acquisition of large-scale histology datasets for accurate comparisons with quantitative MRI data. This protocol aims to elucidate the relationship of MRI signal changes with underlying pathophysiology. Furthermore, the general principles of this protocol such as identification of regions of interest, systematic sampling, digital photography pipeline for registration and histology image analysis, can be extended to other ex vivo neuroimaging studies with histology correlation.
Additional file 1. Spinal cord MRI protocol parameters. Additional file 2. Structural MRI from thoracic to cervical region of the spinal cord from an ALS patient. Additional file 3. Quantitative histological data for the primary motor cortex grey matter at the hand knob.
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Training in cytopathology in times of social distancing: a comparison of remote vs. traditional learning | 9f9332c1-fa72-48ae-b2cc-eece242286c4 | 8414736 | Pathology[mh] | Residency and fellowship training in cytopathology unexpectedly became challenging during this past year due to the novel coronavirus 2019 (COVID-19) pandemic. Not much was known at the onset but soon the genome of coronavirus SARS-CoV-2 was sequenced and public health measures such as “social distancing”, mask wearing, and hand washing were implemented to curb the transmission of disease. Social distancing was defined as a distance of at least 6 feet be maintained between any 2 individuals. This prevented gatherings and led to closures of many non-essential services and institutions. Hospitals and health care facilities maintained essential health care work and reduced elective procedures. These measures led to a marked decline in the volume of specimens received by anatomic pathology/cytopathology laboratories and decreased the number of fine-needle aspiration (FNA) procedures performed. The trainees in pathology—both residents and fellows—were required to stay at home either entirely or partly during the early period of the pandemic under the recommendation of the department of academic affairs. As a result, programs had to redesign their cytopathology fellowship and residency training programs, complying with local directives and regulations while maintaining high-quality education without risking trainee health. Herein we describe our department’s remote cytopathology training program developed in response to the COVID-19 pandemic. The issues identified due to the “stay at home” order were screening slides, case sign-out with faculty, 1-on-1 microscopic teaching sessions with cytotechnologists, performing FNAs, attending rapid onsite evaluation (ROSE), reviewing radiology images with radiologists, participating in multi-headed microscopic consensus conferences, sharing cases among peers, taking entry and exit slide tests, learning cytology preparation techniques in the wet laboratory, and any activity that was attended by more than 10 people congregating in a room such as quality- and management-related meetings or didactic lectures in conference rooms. Self-study by trainees played a major role in developing diagnostic skills and medical knowledge during remote training. To aide this, digital study sets were built utilizing the Aperio AT2 (Buffalo Grove, IL) whole slide scanner. Access was given to rotating residents and the cytology fellow using the Aperio E-slide manager to review whole slides. Trainees developed digital screening skills for gynecologic (GYN) and non-gynecologic (Non-GYN) slides. Sets of GYN and Non-GYN slides were scanned in and utilized as entry and exit tests. Online resources were provided covering a wide variety of topics in cytopathology. Our trainees availed the free webinars and lecture series that The American Society of Cytopathology made available during the pandemic. These lectures, having question-and-answer sessions, were immensely helpful with high educational value. The local cytology continuing medical education lecture series was conducted via Microsoft Teams (Microsoft, Redmond, WA) that participants joined remotely. Remote video conferencing via Microsoft Teams and desktop sharing helped in maintaining day-to-day interactions between trainees, cytotechnologists, and faculty. The rotation began with remote a video entry interview with the Senior Director of Cytopathology to discuss goals, expectations, structure of the changed program, ideas for projects, and the trainee’s end of rotation presentation. The trainee took the entry GYN and Non-GYN slide tests remotely, wherein they screened ten GYN and ten Non-GYN digital slides virtually via the E-slide manager. These were graded and reviewed via Microsoft Teams video conferencing and desktop sharing of live microscopic slide images with the Specialist Technologist of Education and trainee. Introductory microscopic slide sessions were given to the trainees via Teams and telecytology to review GYN and Non-GYN criteria for cytologic interpretations by the Specialist Technologist of Education. These microscopic sessions were continued by a team of cytotechnologists throughout the training on a case-by-case and on-demand basis. The trainee's responsibility included contacting assigned faculty to discuss tasks for the day. This involved signing out cases via telecytology, as well as discussion of the structured question of the day. A microscopic slide session of interesting cases was given daily by the Cytology Fellowship Program Director with trainees participating remotely. The daily cytopathology consensus conference could not be held at the multi-headed microscope. It was maintained via Teams and telecytology. Trainees joined in the discussions remotely to learn from these interesting and difficult cases. They learned the utility of ancillary studies, radiologic correlation and findings, importance of history and clinical impression, and developed interpersonal communication skills. The trainees were assigned 3 online “mock” board exams that were previously developed to help prepare for board examinations. We utilized older American Society for Clinical Pathology (ASCP) GYN and Non-GYN Digital Image Programs by converting the CD-ROM images of unknown cases and histories into a cloud format with worksheets to correspond to the photos. These were submitted by the residents weekly. To overcome the lack of hands-on training and performance of FNAs, didactic lectures were given by the Director of the FNA clinic on the basics of the ultrasound-guided FNA performance and interpretation of ultrasound images. Web sites to access videos of FNA techniques and simulations of other cytology procedures were provided . A simulated ROSE FNA experience was created through video conferencing and desktop sharing of live microscopic slides. Cytotechnologists performed real-time screening of known DiffQuik-stained FNA cytology slides that were projected to trainees. Trainees performed an evaluation of the case with determination of adequacy, triaging the specimen, and other pertinent questions particular to the case as if they were attending a ROSE telecytology procedure. Activities of the Cytology Preparatory Laboratory were covered via virtual tours, lectures, video conferencing, and telephone meetings with the laboratory supervisors and managers. Telephone sessions and video chats were also held by the Cytology Management Team to discuss quality metrics, quality assurance, quality improvement, laboratory regulations, and lab management. At the end of the rotation, the residents presented a half-hour lecture on a predetermined topic of interest to the Cytopathology faculty and cytotechnologists via Teams. They took an exit exam like the entry exam, which was then reviewed with them via Teams and telecytology. Grades for entry test, exit test, and the ASCP GYN and Non-GYN digital image workbooks were noted and submitted to the Senior Director for an exit interview. The trainees filled out a survey at the end of their rotation to give feedback on their experience with remote learning in comparison to in person learning. Overall, our experience was similar to that reported by others. , , , Eight trainees (4 postgraduate year [PGY]-4, 3 PGY-3 and 1 PGY-2 residents) participated in evaluating the remote learning program. The GYN and Non- GYN exit exam results for the virtual slide vs. real slide for the most promising of the PGY-4 residents were 75% and 70% vs. 77.5% and 85%, showing a slight decrease in scores. The online assignments showed the performance of the same resident to remain at the same level. In tallying the surveys we found that many trainees felt that the amount of work received was comparable to that of pre-remote learning, that they learned about the same to more than previous in-person learning, and that they would like to continue to receive their work virtually. They found the workload easier to manage with having both of the options that is to work remotely and in-person learning. Advantages of the remote learning experience were that it allowed trainees to have more control of their learning experience, improved time management, and allowed more time for studying and research. Trainees could concentrate on projects and academic activities as they did not have their daily commute or non-academic activities. One of the major limitations experienced throughout the remote learning process was screening whole-slide images. Scanning cytology slides that require multilayer focusing led to prolonged screening. As case volumes had markedly diminished at the beginning of the pandemic, trainees became heavily dependent on online resources. Internet access was limited due to carrier coverage or access limitations placed on personal computers by the hospital. Communication issues via email and online conferencing posed another hurdle when Teams was first introduced. It took some time for users to become familiar with the system, set up meetings, chat, and participate in remote conferencing. Lack of physical screening, no access to the physical patient, and lack of experience with radiologists were disadvantages to the trainees, hindering their ability to appreciate and understand cell morphology and radiologic images. It was difficult for trainees to receive immediate feedback or mark digital slides for clarifications and questions that often come up while screening slides. Lack of face-to-face communication poses a barrier to building interpersonal skills, such as reading body language and facial expressions. We found that remote learning allowed our institution to continue teaching trainees without severely compromising the education of the trainees when compared to traditional learning, while allowing for appropriate social distancing and the ability to adhere to public health mandates, at the same time forcing faculty and trainees to embrace the virtual space and online educational content. Virtual learning allowed trainees to learn at their own pace, focus on areas of weakness, and increase academic productivity by working on projects and reading. Our residents and fellow were able to develop and improve skills in screening digital slides, evaluating images for determination of adequacy via telecytology, and reviewing online images. Screening whole digitalized cytology slides remains challenging. Some institutions that are converting to an all-digital workflow mention digitalizing cytology cell block slides rather than smear slides. Tumor boards continue to be held remotely, with our trainees sharing live slides or projecting scanned digital images of cases that are discussed, thus saving time in not having to prepare elaborate presentations. Scanning slides led to the creation of an online digital slide library to be utilized not only for study purposes by trainees but also as a resource for cytotechnologists and faculty. Consensus conferences are held both in person and virtually, allowing offsite residents and faculty to participate. Inter- and intradepartmental meetings continue to be virtual with increased participation at all levels. Resident end of rotation presentations are held in the virtual platform. Residents not rotating is cytopathology are able to participate without leaving their rotations. As a result of our feedback from our evaluations and our experiences, we have partially instituted remote learning into our academic curricula and developed a hybrid program. Although remote learning cannot replace physical learning, it can be used in conjunction with physical learning, opening more doors for expanded learning techniques and help in keeping us prepared for the next unprecedented challenge. Through this experience, we have developed patience, understanding, compassion, and empathy for each other, which we intend to continue to put into practice after the pandemic ends. |
Radiomics in precision oncology: hype or | 5059b1a2-cf3f-4377-a785-8b1c6180eddb | 10683192 | Internal Medicine[mh] | Precision oncology has emerged as the next frontier of therapeutic strategies in the treatment of human cancers. The idea is simple yet appealing: to decipher the genotypes and phenotypes of cancer, so that risk prediction and therapies can be individualised for every patient. To this end, next-generation sequencing (NGS) tools have been developed to characterise the genomics of tumours. While there have been successes with this approach in terms of matching effective therapies to genotypes, testing is however restricted by the availability of tumour tissue. Consequently, research has explored using imaging as a data source for deeper phenotyping, given that radiological scans, e.g. computed tomography and magnetic resonance imaging (MRI), are routinely performed for cancer diagnosis, assessment of treatment response, and disease surveillance. Radiomics thus represents a domain of science that involves extraction of quantitative imaging features pertaining to shapes, grey-level textures, and intensities that are non-perceivable to the human eye from specific regions of interest (ROI) delineated on radiological images. Radiomics tools for cancer diagnosis and clinical stratification Over the years, several studies have reported on the promise of radiomics for diagnosis and clinical stratification of phenotypes for treatment intensification or de-intensification . These published radiomics models purport to prognosticate outcomes of cancer patients and/or predict their response to a specific drug . Nonetheless, the implementation of radiomics in the clinic remains challenging, partly due to poor model reproducibility. The Image Biomarker Standardisation Initiative (IBSI) was thus launched to standardise the radiomics workflow . Separately, the radiomics quality score (RQS) was introduced to assist clinicians with evaluating the quality of radiomics studies . It is in this background that we appraise the study by Liu and colleagues who developed a radiomics signature to predict post-radiation nasopharyngeal necrosis (PRNN) in patients with locoregionally-recurrent nasopharyngeal carcinoma (lrNPC). For model development, the investigators utilised pre-treatment MRI images (T1-weighted with and without contrast-enhancement, and T2-weighted sequences) of 761 patients (split into 420 for training, and 341 for validation) enrolled from four hospitals. Using a random forest model, they built a 6-feature signature consisting of 1 first-order statistic, 2 shape features, and 3 texture features that could discretise patients into low- and high-risk for PRNN. The signature achieved AUCs of 0.722 in the training dataset, and 0.713 and 0.756 for the internal and external validation cohorts, respectively. The signature outperformed known prognostic clinical predictors, including gross tumour volume, age, disease-free interval, sex, and re-irradiation dose . It was also generalised across different centres, imaging parameters, and patient subgroups (e.g., different ages and rT-categories), with AUCs ranging from 0.671 to 0.888. To provide explanability for the model, the investigators correlated the radiomics features with somatic transcriptomic profiles of 29 patients. From gene set enrichment analyses, they associated the 6 radiomics features with fibrosis and vascularity signalling pathways. Strengths and limitations Overall, there are several strengths of this study. First, PRNN is an important and clinically relevant outcome in lrNPC patients who are being planned for re-irradiation; for these patients, soft tissue necrosis following re-irradiation is a common and potentially debilitating toxicity . Thus, having a tool that can assist with patient selection is advantageous from the clinical perspective. Second, the study investigators asserted the reliability of their radiomics model by undertaking comprehensive steps for validation, which included proving its generalisability across different disease states and institutions. That said, do we anticipate the deployment of this radiomics tool in the clinic tomorrow? Some notable limitations deserve mention. First, the AUCs of ~ 0.7 for prediction are modest at best. Second, it is uncertain if the samples used for the transcriptomic profiling were spatially correlated with the ROI from which the radiomics features were extracted. This is a crucial consideration when interpreting the robustness of the radio-transcriptomic analyses. Third, the lack of comprehensive documentation, provision of open-source codes, and data availability pose substantial challenges to assess model reproducibility. Translation of radiomics tools from research to clinic Ultimately, what are the radical steps needed to bridge the deployment of radiomics tools from research to the clinic? Standardisation of radiomics workflow: Harmonising the processes from image acquisition to model validation is a key step. To promote adherence, the IBSI working group had derived a set of guidelines for benchmarking of future radiomics studies . Automation of ROI segmentation: This step is important since radiomics feature extraction is highly sensitive to subtle variations in segmentation methods . Ensuring data quality: For external validation of radiomics models, we propose the need for benchmarking criteria to appraise the quality of datasets relating to the accuracy of clinical annotation and the extent of data missingness, given that these parameters can skew model performance. Transparency of study results and validation: Instead of solely relying on the investigators, efforts must be made to encourage validation studies by independent groups within a defined window period. The results of these studies should be made transparent regardless of their outcomes (positive or negative validation), and journals should commit to publishing them. To achieve this, detailed reports, source codes, and anonymised data from the original study must be made available. Explanability of the radiomics model: We surmise it would be best practice for radiomics models to include clinical and biological associations that underpin their development. This could be achieved by either spatially correlating the radiomics indices to molecular profiles or treatment response within the ROI . Spatial-level resolution of radiomics features: Distinct regions within a ROI may exhibit differential treatment responses. Thus, interrogating spatial-level radiomics may enhance its explanability compared with bulk-level radiomics, and represents an exciting direction for the field.
Over the years, several studies have reported on the promise of radiomics for diagnosis and clinical stratification of phenotypes for treatment intensification or de-intensification . These published radiomics models purport to prognosticate outcomes of cancer patients and/or predict their response to a specific drug . Nonetheless, the implementation of radiomics in the clinic remains challenging, partly due to poor model reproducibility. The Image Biomarker Standardisation Initiative (IBSI) was thus launched to standardise the radiomics workflow . Separately, the radiomics quality score (RQS) was introduced to assist clinicians with evaluating the quality of radiomics studies . It is in this background that we appraise the study by Liu and colleagues who developed a radiomics signature to predict post-radiation nasopharyngeal necrosis (PRNN) in patients with locoregionally-recurrent nasopharyngeal carcinoma (lrNPC). For model development, the investigators utilised pre-treatment MRI images (T1-weighted with and without contrast-enhancement, and T2-weighted sequences) of 761 patients (split into 420 for training, and 341 for validation) enrolled from four hospitals. Using a random forest model, they built a 6-feature signature consisting of 1 first-order statistic, 2 shape features, and 3 texture features that could discretise patients into low- and high-risk for PRNN. The signature achieved AUCs of 0.722 in the training dataset, and 0.713 and 0.756 for the internal and external validation cohorts, respectively. The signature outperformed known prognostic clinical predictors, including gross tumour volume, age, disease-free interval, sex, and re-irradiation dose . It was also generalised across different centres, imaging parameters, and patient subgroups (e.g., different ages and rT-categories), with AUCs ranging from 0.671 to 0.888. To provide explanability for the model, the investigators correlated the radiomics features with somatic transcriptomic profiles of 29 patients. From gene set enrichment analyses, they associated the 6 radiomics features with fibrosis and vascularity signalling pathways.
Overall, there are several strengths of this study. First, PRNN is an important and clinically relevant outcome in lrNPC patients who are being planned for re-irradiation; for these patients, soft tissue necrosis following re-irradiation is a common and potentially debilitating toxicity . Thus, having a tool that can assist with patient selection is advantageous from the clinical perspective. Second, the study investigators asserted the reliability of their radiomics model by undertaking comprehensive steps for validation, which included proving its generalisability across different disease states and institutions. That said, do we anticipate the deployment of this radiomics tool in the clinic tomorrow? Some notable limitations deserve mention. First, the AUCs of ~ 0.7 for prediction are modest at best. Second, it is uncertain if the samples used for the transcriptomic profiling were spatially correlated with the ROI from which the radiomics features were extracted. This is a crucial consideration when interpreting the robustness of the radio-transcriptomic analyses. Third, the lack of comprehensive documentation, provision of open-source codes, and data availability pose substantial challenges to assess model reproducibility.
Ultimately, what are the radical steps needed to bridge the deployment of radiomics tools from research to the clinic? Standardisation of radiomics workflow: Harmonising the processes from image acquisition to model validation is a key step. To promote adherence, the IBSI working group had derived a set of guidelines for benchmarking of future radiomics studies . Automation of ROI segmentation: This step is important since radiomics feature extraction is highly sensitive to subtle variations in segmentation methods . Ensuring data quality: For external validation of radiomics models, we propose the need for benchmarking criteria to appraise the quality of datasets relating to the accuracy of clinical annotation and the extent of data missingness, given that these parameters can skew model performance. Transparency of study results and validation: Instead of solely relying on the investigators, efforts must be made to encourage validation studies by independent groups within a defined window period. The results of these studies should be made transparent regardless of their outcomes (positive or negative validation), and journals should commit to publishing them. To achieve this, detailed reports, source codes, and anonymised data from the original study must be made available. Explanability of the radiomics model: We surmise it would be best practice for radiomics models to include clinical and biological associations that underpin their development. This could be achieved by either spatially correlating the radiomics indices to molecular profiles or treatment response within the ROI . Spatial-level resolution of radiomics features: Distinct regions within a ROI may exhibit differential treatment responses. Thus, interrogating spatial-level radiomics may enhance its explanability compared with bulk-level radiomics, and represents an exciting direction for the field.
The oncology community remains in limbo about the relevance of radiomics in precision oncology, even though studies continue to report on its promise. Looking ahead, there needs to be a pivot in focus from reporting another “ hyped ” radiomics model to showcasing scientific robustness for clinical implementation. This would entail adopting some of our proposed measures and to eventually test these models in prospective radiomics-directed clinical trials. Only then, will radiomics fulfil its promise as a “ ludum mutante ” in precision oncology.
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GLUT1 targeting and hypoxia-activating polymer-drug conjugate-based micelle for tumor chemo-thermal therapy | 6d8976ce-b257-4238-9f41-5e31a1bcf95b | 8530487 | Pharmacology[mh] | Introduction Mitochondria are known as the powerhouse of the cell and the source of important mediators of apoptosis (Yamada et al., ). Recent studies discovered that it also played a central role in the occurrence and development of tumor metastasis. On one hand, the mitochondria produced excess superoxide free radical that initiated the formation of metastatic foci (Denisenko et al., ). On the other hand, excess lactic acid produced by glycolysis of mitochondria activated the proteolytic enzyme and angiogenesis that accelerated the degradation of extra-cellular matrix and paved escaping channel for metastasis (Deng et al., ; Gandhi & Das, ). Although mitochondria seem to be an appealing target for the development of anti-tumor metastasis pharmaceutics, this area is still far from being covered. To specifically deliver drug to mitochondria, various nanocarriers such as liposomes (Yamada et al., ), nanoparticles (Gisbert-Garzaran et al., ), and PAMAM dendrimer (Ma et al., ) have been designed by the incorporation of mitochondria tropic agents. However, there exist some intrinsic limitations for these nanocarriers, such as low-drug loading, burst drug release, nanocarrier-associated toxicity, and poor stability (Ghosh et al., ). The polyprodrug-based micelle seems to be an alternative strategy to overcome these deficiencies (Lin et al., ; Liu et al., ). When the functional groups of polymers conjugated with targeting ligand or therapeutic agents through stimuli-responsive linkers, the resulting amphiphilic polymeric prodrugs tend to self-assemble into micelles with tumor-targeting ability, high-drug loading, and low immunogenicity. Particularly, they are stable and inactive under normal conditions, but can realize controllable drug release, size shrink, or surface change when stimuli are applied (Deng & Liu, ). The abnormal energy metabolism results in the over-expression of glucose transporter 1 (GLUT1) and glutathione (GSH) as well as lack of oxygen (Vander Heiden & DeBerardinis, ). Although pH, glutathione (GSH), and enzymes have been widely exploited to construct tumor microenvironment responsive drug delivery systems, the hypoxia is still far from being covered (Yin et al., ; Zhou et al., ). Moreover, the mitochondria are more susceptible to hyperthermia under hypoxic tumor microenvironment (TME). Interestingly, IR808, a photothermal agent, also exhibited excellent mitochondrial targeting ability and has been used for mitochondria chemo-photothermal therapy to conquer tumors in recent studies. Therefore, hypoxia-activated mitochondria targeting chemo-thermal therapy might be worth exploring for tumor therapy. Based on this, we constructed a GLUT1 targeting and tumor micro-environment responsive polyprodrug-based micelle for tumor therapy. As shown in , the prodrug IR808-S-S-PTX was served as the hydrophobic block and modified with the glycosylated PEG as the hydrophilic shell through a hypoxia sensitive linker p-aminoazobenzene (Azo) to obtain the amphiphilic polyprodrug conjugate glucose-PEG-Azo-IR808-S-S-PTX. When dissolved in water, it self-assembled into nanosized micelle. Following intravenous administration, the micelle could be transported by the over-expressing GLUT1 of tumor cell and hydrophilic PEG shell detached under the hypoxic TME. For the prodrug IR808-S-S-PTX, it ruptured into paclitaxel (PTX) and IR808 under the GSH reductive TME, which acted on the tubulin and mitochondria, respectively. With laser irradiation, the mitochondria were destroyed by photothermal effect, and thus tumor proliferation and metastasis were inhibited.
Materials and methods 2.1. Materials and reagents Paclitaxel (PTX), p-aminoazobenzene (Azo), and IR808 were purchased from Abmole Bioscience Inc. (Shanghai, China). PEG polymers (MW 1500 Da) were purchased from Jiankai Technology Co., Ltd (Beijing, China). Dulbecco’s modified eagle’s medium (DMEM), fetal bovine serum (FBS), and 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) were purchased from Gibco Life Technology Company (Grand Island, NY). RIPA lysis buffer and BCA protein assay kit were purchased from Aladdin Co., Ltd (Beijing, China). 3-[(2-Aminoethyl)dithio]propionic Acid (AEDP), Mitotracker green, Hoechst, JC-1 dyeing working solution, ATP assay kit, and other chemical reagents were purchased from Thermo Fisher Scientific Inc. (Waltham, MA. USA). Methanol, methylene chloride (DCM) and other chemical reagents were obtained from Sinopharm Chemical Reagent Co., Ltd (Shanghai, China). 2.2. Synthesis of Glu-PEG-Azo-IR808-S-S-PTX conjugate Glucose-PEG-COOH and excess Azo were dissolved in DCM, added with HOBT and EDCI, and stirred overnight. The reaction solution was purified by silica gel column chromatography with mobile phase DCM/MeOH (50:1). Then, the GLU-PEG-Azo and IR808 were dissolved in DCM, added with HOBT and EDCI, and stirred overnight. The reaction mixture was dialyzed against ultrapure water. Following freeze-drying, the product GLU-PEG-Azo-IR808 was dissolved in DMF containing 1% triethylamine, added with AEDP, stirred for 6 h at 90 °C. The reaction was purified by dialysis. The GLU-PEG-Azo-IR808 and PTX were dissolved in DCM and reacted for 24 h with DMAP and EDCI as catalysts. After evaporating the DCM by reduced pressure distillation, the residue was re-dissolved in water and dialyzed against ultrapure water to obtain the final product GLU-PEG-Azo-IR808-S-S-PTX conjugate. At the same time, the mPEG-Azo-IR808-S-S-PTX and GLU-PEG-Azo-IR808-C-C-PTX conjugates were also synthesized as control. The chemical structures of all conjugates were analyzed using 1 HNMR, UV-Vis and fluorescence spectra. The hypoxia sensitivity of conjugates was evaluated using the sodium dithionite as hypoxia activator (Hofstetter et al., ). The GLU-PEG-Azo-IR808-S-S-PTX conjugate (1 mg/mL) was incubated with different concentration of sodium dithionite (0.05, 0.075, and 0.1 mg/mL) for 10 min, and then it was incubated with sodium dithionite (0.1 mg/mL) for different time (2, 4, and 6 h). The absorption spectrum of the mixture solution was scanned by an UV-Vis spectrophotometer (Shimadzu UV-3600i Plus, Tokyo, Japan). 2.3. Micelle construction and characterization The dialysis method was employed to prepare micelles (Shen et al., ). Briefly, 10 mg conjugates were dissolved in 2.0 mL DMF and dropwisely added into 4.0 mL H 2 O, then the mixture was transferred into a dialysis bag (MWCO 1000) and dialyzed against water for 24 h, and the polymer-drug conjugates-based micelles were obtained by lyophilization. The critical micelle concentration (CMC) was determined using the surface tension method as described elsewhere (Scholz et al., ). The particle size and zeta potential of micelles were determined using a DLS at the concentration of 1 mg/mL (Nicomp 380 Zeta Potential/Particle Sizer, Santa Barbara, CA). The morphology of micelles was observed by the TEM (JEM-F200, JEOL, Japan). The photothermal conversion efficiency was determined with an 808 nm laser (laserwave LWIRPD-5F, Beijing, China) at different power density and micelle concentrations, and the temperature was recorded by a platinum resistance thermometer (YOWEX A YET710 YET-710, Shenzhen, China). The drug release profile of micelles was studied using dialysis method. The micelles containing 1 mg paclitaxel were sealed into dialysis bags, immersed into 30 mL PBS containing Tween-80 (0.5%) and GSH (0, 1 or 10 mM), and shaken in a 37 °C water bather at 100 rpm. About 1 mL release medium was sampled at predefined time and the PTX concentration was determined using HPLC at 230 nm with acetonitrile/water (50:50, v/v) as the mobile phase. 2.4. Cytotoxicity The MTT method was used to evaluate the cytotoxicity of micelles. Briefly, A549 cells were seeded in 96-well plates at a concentration of 1 × 10 4 cells/well and maintained for 24 h at 37 °C. Then, the cells were treated with micelles for 48 h at the concentration gradient of 1.5 × 1 0 −3 –1.5 × 10 3 nM (represented as PTX). About 20 μL MTT (5 mg/mL) was added in each well and incubated with cells for 4 h. Following discarding cell culture medium, 200 μL DMSO was added to dissolve the formazan crystal and the absorbance at 570 nm was recorded using a microplate reader (BioRad, Model 680, Hercules, CA). 2.5. Cell apoptosis Annexin V-FITC/PI double staining was used to detect the apoptosis effect (Ma et al., ). A549 cells were planted in 6-well plates with 1 × 10 6 cells/well. After reaching a confluence of 70–80%, the cells were treated with 3 mL drug solutions at the concentration of 5 nM for 24 h. Then, the A549 cells were washed with pre-cooled PBS and cultured for another 24 h. After that, the cells were digested with trypsin without EDTA, collected, and resuspended in 100 μL of pre-cooled PBS. Then, the cells were stained with 5 μL Annexin V-FITC and 5 μL PI, and incubated in dark for 15 min. Diluting with 400 μL PBS, the fluorescence intensity of cells was measured by a flow cytometry (CytoFLEX, Suzhou, China). 2.6. Cell metastasis Both wound scratch assay and transwell migration assay were utilized to evaluate anti-metastasis effect of micelles (Lee et al., ). For wound scratch assay, cells were seeded in 12-well plates for 24 h at 37 °C. The cell monolayer was scratched using a 200 μL pipette tip and washed twice with fresh PBS. Micelles were added into each well at a concentration of 10 μg/mL and co-incubated with cells for 24 h at 37 °C. Discarding culture media, the scratched area was observed using an optical microscope. For transwell migration assay, cells were dispersed in FBS-free culture media and seeded onto the upper surface of transwell chamber at a density of 1 × 10 5 cells/mL, and the outside chamber was filled with 600 μL complete medium as chemoattractant. Following co-incubated with micelles for 12 h, the cells on the bottom of upper chamber were immobilized and stained with 0.1% crystal violet. The cells were observed by an optical microscope and the absorbance at 470 nm was also determined. 2.7. Mitochondria targeting The mitochondria targeting ability of micelles was conducted under both hypoxic and normoxic environments. The A549 cells were seeded in 24-well plates at a concentration of 1 × 10 5 cells/mL for 24 h at 37 °C. Micelles were added into each well at a concentration of 5 μg/mL and co-incubated with cells for 12 h at 37 °C. The hypoxia-activated mitochondria targeting was evaluated using the AnaeroPack as described before (Wen et al., ). The drug solution was replaced with fresh cell culture media, and then the nucleus and mitochondria were stained with Hoechst 33342 and Mito-Tracker Green, respectively. The colocalization of micelles was observed using a laser scanning confocal microscope (LSCM, Olympus FV1000, Tokyo, Japan). 2.8. In vivo fluorescence imaging The A549 tumor-bearing animal model was established as described before (Ehlerding et al., ). Briefly, A549 cells were inoculated into the right axillary of nude mice. When the tumor grew to 100 mm 3 , the mice were randomly divided into five groups and administrated with micelles at a concentration of 5 mg/kg (represented as PTX). The biodistribution of micelles were observed using an in vivo imaging system (CRi Maestro, MA, USA) at predefined time 0.5 h, 1 h, 2 h, 4 h, 8 h, and 12 h. After 12 h, the organs (heart, liver, spleen, lung, kidney and tumor) were harvested for observation. Besides, frozen sections of tumor tissues were prepared, the nucleus and mitochondria were stained with Hoechst 33342 and Mito-Tracker Green, respectively. The distribution of micelles in mitochondria was observed using the LSCM. All animal studies were performed according to the Guide for the Care and Use of Laboratory Animals (China), and the Animal Care and Use Committee of Beijing University of Chinese Medicine approved the animal study protocols. 2.9. Anti-tumor efficacy and safety The tumor-bearing mice were randomly divided into four groups and administrated with micelles via tail vein injection at a dose of 5 mg/kg every 2 days for 16 days. The body weight and tumor volume were recorded every two days. At the last day, the mice were sacrificed by pick of eyeball and blood was collected for routine biochemistry assays (ALT, AST, BUN, and CER). 2.10. ATP content and mitochondria membrane potential assay For mitochondria membrane potential (MPP) assay, the cells were treated with micelles under different conditions and then co-incubated with 1 mL JC-10 staining solution for 20 min at 37 °C. Following washed with JC-10 staining buffer, the cells were observed using the LSCM. For ATP content assay, the A549 cells in logarithmic growth phase were treated with micelles for 24 h under hypoxic/normoxic or ±808 nm laser conditions. After trypsinization, the cells were collected at 1500 r/min, added with 1 mL extracting solution, and subjected to ultrasonication for 1 min. Following centrifugation at 10,000 rpm for 10 min, the supernatant was collected, added with 0.5 mL chloroform, and centrifugated at 10,000 rpm for 3 min. The supernatant was placed on ice and processed with the specification of ATP content determination kit. 2.11. Statistical analysis All data were expressed as mean ± SD ( n = 6). Data were processed with SPSS statistical software (SPSS, Chicago, IL). Statistically significant differences between groups were determined with Student’s t -test. When p < 0.05, it was considered as statistically significant.
Materials and reagents Paclitaxel (PTX), p-aminoazobenzene (Azo), and IR808 were purchased from Abmole Bioscience Inc. (Shanghai, China). PEG polymers (MW 1500 Da) were purchased from Jiankai Technology Co., Ltd (Beijing, China). Dulbecco’s modified eagle’s medium (DMEM), fetal bovine serum (FBS), and 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) were purchased from Gibco Life Technology Company (Grand Island, NY). RIPA lysis buffer and BCA protein assay kit were purchased from Aladdin Co., Ltd (Beijing, China). 3-[(2-Aminoethyl)dithio]propionic Acid (AEDP), Mitotracker green, Hoechst, JC-1 dyeing working solution, ATP assay kit, and other chemical reagents were purchased from Thermo Fisher Scientific Inc. (Waltham, MA. USA). Methanol, methylene chloride (DCM) and other chemical reagents were obtained from Sinopharm Chemical Reagent Co., Ltd (Shanghai, China).
Synthesis of Glu-PEG-Azo-IR808-S-S-PTX conjugate Glucose-PEG-COOH and excess Azo were dissolved in DCM, added with HOBT and EDCI, and stirred overnight. The reaction solution was purified by silica gel column chromatography with mobile phase DCM/MeOH (50:1). Then, the GLU-PEG-Azo and IR808 were dissolved in DCM, added with HOBT and EDCI, and stirred overnight. The reaction mixture was dialyzed against ultrapure water. Following freeze-drying, the product GLU-PEG-Azo-IR808 was dissolved in DMF containing 1% triethylamine, added with AEDP, stirred for 6 h at 90 °C. The reaction was purified by dialysis. The GLU-PEG-Azo-IR808 and PTX were dissolved in DCM and reacted for 24 h with DMAP and EDCI as catalysts. After evaporating the DCM by reduced pressure distillation, the residue was re-dissolved in water and dialyzed against ultrapure water to obtain the final product GLU-PEG-Azo-IR808-S-S-PTX conjugate. At the same time, the mPEG-Azo-IR808-S-S-PTX and GLU-PEG-Azo-IR808-C-C-PTX conjugates were also synthesized as control. The chemical structures of all conjugates were analyzed using 1 HNMR, UV-Vis and fluorescence spectra. The hypoxia sensitivity of conjugates was evaluated using the sodium dithionite as hypoxia activator (Hofstetter et al., ). The GLU-PEG-Azo-IR808-S-S-PTX conjugate (1 mg/mL) was incubated with different concentration of sodium dithionite (0.05, 0.075, and 0.1 mg/mL) for 10 min, and then it was incubated with sodium dithionite (0.1 mg/mL) for different time (2, 4, and 6 h). The absorption spectrum of the mixture solution was scanned by an UV-Vis spectrophotometer (Shimadzu UV-3600i Plus, Tokyo, Japan).
Micelle construction and characterization The dialysis method was employed to prepare micelles (Shen et al., ). Briefly, 10 mg conjugates were dissolved in 2.0 mL DMF and dropwisely added into 4.0 mL H 2 O, then the mixture was transferred into a dialysis bag (MWCO 1000) and dialyzed against water for 24 h, and the polymer-drug conjugates-based micelles were obtained by lyophilization. The critical micelle concentration (CMC) was determined using the surface tension method as described elsewhere (Scholz et al., ). The particle size and zeta potential of micelles were determined using a DLS at the concentration of 1 mg/mL (Nicomp 380 Zeta Potential/Particle Sizer, Santa Barbara, CA). The morphology of micelles was observed by the TEM (JEM-F200, JEOL, Japan). The photothermal conversion efficiency was determined with an 808 nm laser (laserwave LWIRPD-5F, Beijing, China) at different power density and micelle concentrations, and the temperature was recorded by a platinum resistance thermometer (YOWEX A YET710 YET-710, Shenzhen, China). The drug release profile of micelles was studied using dialysis method. The micelles containing 1 mg paclitaxel were sealed into dialysis bags, immersed into 30 mL PBS containing Tween-80 (0.5%) and GSH (0, 1 or 10 mM), and shaken in a 37 °C water bather at 100 rpm. About 1 mL release medium was sampled at predefined time and the PTX concentration was determined using HPLC at 230 nm with acetonitrile/water (50:50, v/v) as the mobile phase.
Cytotoxicity The MTT method was used to evaluate the cytotoxicity of micelles. Briefly, A549 cells were seeded in 96-well plates at a concentration of 1 × 10 4 cells/well and maintained for 24 h at 37 °C. Then, the cells were treated with micelles for 48 h at the concentration gradient of 1.5 × 1 0 −3 –1.5 × 10 3 nM (represented as PTX). About 20 μL MTT (5 mg/mL) was added in each well and incubated with cells for 4 h. Following discarding cell culture medium, 200 μL DMSO was added to dissolve the formazan crystal and the absorbance at 570 nm was recorded using a microplate reader (BioRad, Model 680, Hercules, CA).
Cell apoptosis Annexin V-FITC/PI double staining was used to detect the apoptosis effect (Ma et al., ). A549 cells were planted in 6-well plates with 1 × 10 6 cells/well. After reaching a confluence of 70–80%, the cells were treated with 3 mL drug solutions at the concentration of 5 nM for 24 h. Then, the A549 cells were washed with pre-cooled PBS and cultured for another 24 h. After that, the cells were digested with trypsin without EDTA, collected, and resuspended in 100 μL of pre-cooled PBS. Then, the cells were stained with 5 μL Annexin V-FITC and 5 μL PI, and incubated in dark for 15 min. Diluting with 400 μL PBS, the fluorescence intensity of cells was measured by a flow cytometry (CytoFLEX, Suzhou, China).
Cell metastasis Both wound scratch assay and transwell migration assay were utilized to evaluate anti-metastasis effect of micelles (Lee et al., ). For wound scratch assay, cells were seeded in 12-well plates for 24 h at 37 °C. The cell monolayer was scratched using a 200 μL pipette tip and washed twice with fresh PBS. Micelles were added into each well at a concentration of 10 μg/mL and co-incubated with cells for 24 h at 37 °C. Discarding culture media, the scratched area was observed using an optical microscope. For transwell migration assay, cells were dispersed in FBS-free culture media and seeded onto the upper surface of transwell chamber at a density of 1 × 10 5 cells/mL, and the outside chamber was filled with 600 μL complete medium as chemoattractant. Following co-incubated with micelles for 12 h, the cells on the bottom of upper chamber were immobilized and stained with 0.1% crystal violet. The cells were observed by an optical microscope and the absorbance at 470 nm was also determined.
Mitochondria targeting The mitochondria targeting ability of micelles was conducted under both hypoxic and normoxic environments. The A549 cells were seeded in 24-well plates at a concentration of 1 × 10 5 cells/mL for 24 h at 37 °C. Micelles were added into each well at a concentration of 5 μg/mL and co-incubated with cells for 12 h at 37 °C. The hypoxia-activated mitochondria targeting was evaluated using the AnaeroPack as described before (Wen et al., ). The drug solution was replaced with fresh cell culture media, and then the nucleus and mitochondria were stained with Hoechst 33342 and Mito-Tracker Green, respectively. The colocalization of micelles was observed using a laser scanning confocal microscope (LSCM, Olympus FV1000, Tokyo, Japan).
In vivo fluorescence imaging The A549 tumor-bearing animal model was established as described before (Ehlerding et al., ). Briefly, A549 cells were inoculated into the right axillary of nude mice. When the tumor grew to 100 mm 3 , the mice were randomly divided into five groups and administrated with micelles at a concentration of 5 mg/kg (represented as PTX). The biodistribution of micelles were observed using an in vivo imaging system (CRi Maestro, MA, USA) at predefined time 0.5 h, 1 h, 2 h, 4 h, 8 h, and 12 h. After 12 h, the organs (heart, liver, spleen, lung, kidney and tumor) were harvested for observation. Besides, frozen sections of tumor tissues were prepared, the nucleus and mitochondria were stained with Hoechst 33342 and Mito-Tracker Green, respectively. The distribution of micelles in mitochondria was observed using the LSCM. All animal studies were performed according to the Guide for the Care and Use of Laboratory Animals (China), and the Animal Care and Use Committee of Beijing University of Chinese Medicine approved the animal study protocols.
Anti-tumor efficacy and safety The tumor-bearing mice were randomly divided into four groups and administrated with micelles via tail vein injection at a dose of 5 mg/kg every 2 days for 16 days. The body weight and tumor volume were recorded every two days. At the last day, the mice were sacrificed by pick of eyeball and blood was collected for routine biochemistry assays (ALT, AST, BUN, and CER).
ATP content and mitochondria membrane potential assay For mitochondria membrane potential (MPP) assay, the cells were treated with micelles under different conditions and then co-incubated with 1 mL JC-10 staining solution for 20 min at 37 °C. Following washed with JC-10 staining buffer, the cells were observed using the LSCM. For ATP content assay, the A549 cells in logarithmic growth phase were treated with micelles for 24 h under hypoxic/normoxic or ±808 nm laser conditions. After trypsinization, the cells were collected at 1500 r/min, added with 1 mL extracting solution, and subjected to ultrasonication for 1 min. Following centrifugation at 10,000 rpm for 10 min, the supernatant was collected, added with 0.5 mL chloroform, and centrifugated at 10,000 rpm for 3 min. The supernatant was placed on ice and processed with the specification of ATP content determination kit.
Statistical analysis All data were expressed as mean ± SD ( n = 6). Data were processed with SPSS statistical software (SPSS, Chicago, IL). Statistically significant differences between groups were determined with Student’s t -test. When p < 0.05, it was considered as statistically significant.
Results and discussion 3.1. Characterization of conjugates The glycosylated derivative of PEG (glucose-PEG) and IR808 were firstly conjugated by p-aminoazobenzene via amidation reaction (Glu-PEG-Azo), and the chemical structure was confirmed by 1 HNMR spectra. As shown in , the peaks at δ (ppm) 0.5–2.0 could be assigned to aliphatic protons of glucose, and the peak at δ (ppm) 3.6 was the characteristic peak of methylene, which could be assigned to PEG. Then, the IR808 also conjugated with Azo via amidation reaction (Glu-PEG-Azo-IR808). Following the AEDP nucleophilic substitution of the chloride of IR808, PTX was conjugated with AEDP via esterification reaction. The peaks at δ (ppm) 7.0–8.0 were the characteristic peaks of phenyl groups belonging to Azo and IR808. The chemical structures were also confirmed by UV-Vis and fluorescence spectra. As shown in , the characteristic peaks of Azo and IR808 could be found at 365 nm, 495 nm, and 808 nm, respectively. Besides, the peak intensity at 808 nm decreased after the chlorine atom of IR808 was substituted by the AEDP. And the emission ray peak of IR808, Glu-PEG-Azo-IR808, and Glu-PEG-Azo-IR808-S-S-PTX was at wavelength of 808 nm when excited by 660 nm, whereas the peak intensity at 808 nm decreased and a new peak at 620 nm appeared when the chlorine atom of IR808 was substituted by the AEDP. These results could demonstrate that the Glu-PEG-Azo-IR808-S-S-PTX conjugate was successfully synthesized. Furthermore, the hypoxia-sensitivity of the Glu-PEG-Azo-IR808-S-S-PTX conjugate was evaluated with sodium dithionite (Na 2 S 2 O 4 ) as an exogenous generator of hypoxia. When the incubation concentration and time increased, the characteristic peak of Azo (365 nm) decreased, while the characteristic peak of benzene (220 nm) increased , which was due to the Azo bond breaking. However, for the Azo non-containing conjugate, its spectra had no change after treatment with Na 2 S 2 O 4. So, the hypoxia sensitivity of the Glu-PEG-Azo-IR808-S-S-PTX conjugate was concentration and time dependent. 3.2. Characterization of conjugate-based micelle The amphiphilic conjugates tend to self-assemble into micelles when dissolved in water. As shown in , the critical micelle concentration (CMC) of the Glu-PEG-Azo-IR808-S-S-PTX conjugate was about 5.0 × 10 −3 nM, which indicated that it could easily assemble into micelle. As the micelle was composed of conjugates, its drug loading could be calculated by the chemical formula of conjugates, and the drug loading of the Glu-PEG-Azo-IR808-S-S-PTX micells calculated to be about 19%. The particle size of micelles ranged from 50–100 nm, and the zeta potential was negatively charged , which was beneficial for its in vivo long circulation (Suchaoin et al., ). The morphology was further confirmed by the TEM, and the micelles were approximate to sphere particles with particle size about 100 nm . The photothermal conversion efficiency was both concentration dependent and irradiation dependent. At the highest concentration, the temperature increased from 20 °C to 50 °C (Δt 30 °C) within 5 min , which was requisite for ablating the tumor (Liu et al., ). The in vitro drug release of disulfide bond containing micelle was more quick than the control micelle, and the drug release speed was GSH concentration dependent . So, the micelle could quickly release drug after entering into tumor cell and avoiding drug release in normal cells. 3.3. In vitro pharmacodynamics 3.3.1. Cytotoxicity The anti-tumor potential of micelles was firstly evaluated by the cytotoxicity. As shown in , the Glu-PEG-Azo-IR808-S-S-PTX micelle showed stronger inhibition ability than the mPEG-Azo-IR808-S-S-PTX micelle, indicating the glucose modification could promote cellular uptake thus enhancing the cellular drug concentration. Under hypoxic environment, the cytotoxicity of micelles significantly increased and had higher cytotoxicity compared with that under normoxic environment. The possible reason might be due to the IR808-S-S-PTX conjugate which would release and act on tumor cells when the PEG detached from PAMAM under hypoxia condition. Due to the positive charge of IR808, the IR808-S-S-PTX conjugate was more easily adsorbed and endocytosed by the cell, which induced higher cytotoxicity. When irradiation with 808 nm laser, the cytotoxicity of micelles further enhanced, the IC50 of the Glu-PEG-Azo-IR808-S-S-PTX micelle decreased by 7 folders reaching to 1.30 nM. Thus, the photothermal effect induced by IR808 could strengthen the chemotherapeutic effect of PTX. Figure 4. Pharmacodynamics evaluation on the A549 cells. (a) Cytotoxicity of micelles on A549 cells; (b) Flow cytometric analyses of A549 cells using annexin V-FITC/PI dual staining after treatment with micelles; (c) Microscopic images and quantitative analysis of the wound-healing after treatment with micelles; (d) Microscopic images and quantitative analysis of the transwell migration after treatment with micelles. * p < 0.05. 3.3.2. Cell apoptosis The anti-tumor potential of micelles was then assessed by cell apoptosis. As shown in , all administration groups had pro-apoptoxic effect. For the Glu-PEG-Azo-IR808-S-S-PTX micelle, its cell apoptotic ratio increased after treatment with laser irradiation both under normoxic and hypoxic conditions ( p < 0.05), which demonstrated that the phtotothermal effect could induce cell apoptosis. Especially under hypoxia condition, the apoptotic ratio increased from 17.65% to 31.23%, and the result might be due to the mitochondria which was more sensitive to thermal under hypoxia condition (Tan et al., ). 3.3.3. Cell metastasis The inhibition of cell metastasis was evaluated by wound healing and transwell migration. As shown in , the area of scratch for the control group almost completely recovered after 12 h, whereas for micelle groups, they exhibited migration inhibition effect with varying degrees. The migration rate of Glu-PEG-Azo-IR808-S-S-PTX micelle group was 57.0%. After irradiation with laser, the migration rate further decreased to 32.89%, indicating the synergistic effect of PTX and IR808. Likewise, compared with control group, the number of cells for micelle groups that migrated through the membrane was less . These results demonstrated that the Glu-PEG-Azo-IR808-S-S-PTX micelle could efficiently inhibit the metastasis of tumor cells. 3.4. Mitochondria targeting evaluation on cells Due to the fluorescence of IR808, the cellular uptake of micelles could be observed directly without modifying with other fluorescent reagents. As shown in , under normoxic environment, the fluorescence intensity of the Glu-PEG-Azo-IR808-S-S-PTX micelle was much higher than the mPEG-Azo-IR808-S-S-PTX micelle group, and it could be blocked by pre-treating with glucose, which demonstrated that the cellular uptake was mediated by GLUT1. Furthermore, the co-localization experiments confirmed that the Glu-PEG-Azo-IR808-S-S-PTX exhibited stronger cellular uptake than the mPEG-Azo-IR808-S-S-PTX micelle under normoxic environment . And they tend to target the nucleus. Although under hypoxic environment, the red fluorescence of IR808 co-localized with the green fluorescence of mitochondria quite well, which further demonstrated that the IR808 could effectively targeting the to the mitochondria following the Azo and disulfide bond got broken. 3.5. In vivo tumor targeting evaluation The in vivo tumor targeting was evaluated through observing the distribution of micelles in the tumor-bearing nude mice. As shown in , all micelles rapidly distributed over the whole body after intravenous injection. The Glu-PEG-Azo-IR808-S-S-PTX micelle began to accumulate at tumor for 2 h and last to 12 h. Meanwhile, the fluorescence intensity of the Glu-PEG-Azo-IR808-S-S-PTX micelle was much higher than the mPEG-Azo-IR808-S-S-PTX micelle. The in vitro tissue observation further confirmed that the fluorescence intensity of the Glu-PEG-Azo-IR808-S-S-PTX micelle was 5-fold compared to the mPEG-Azo-IR808-S-S-PTX micelle . Then, the mitochondria-targeting evaluation was conducted by preparing cryostat sections of tumors. As shown in , the red fluorescence of the Glu-PEG-Azo-IR808-S-S-PTX micelle was stronger than the mPEG-Azo-IR808-S-S-PTX micelle and colocalized well with the green fluorescence of the mitochondria. These results demonstrated that the Glu-PEG-Azo-IR808-S-S-PTX micelle could target both tumor and its mitochondria. 3.6. Anti-tumor efficacy on animals In vivo therapeutic efficacy was evaluated on the A549 tumor-bearing nude mice. As shown in , the tumor volume of administration groups was less than the saline group, which indicated that the tumor growth was inhibited after drug administration. Meanwhile, compared with the PTX group, all micelle groups exhibited obvious higher tumor inhibition efficacy. The Glu-PEG-Azo-IR808-S-S-PTX micelle showed superior anti-tumor ability compared with the mPEG-Azo-IR808-S-S-PTX micelle that might be due to its better tumor-targeting ability. Interestingly, when the Glu-PEG-Azo-IR808-S-S-PTX micelle irradiated by laser, the tumor almost disappeared. Moreover, there was no obvious body weights loss in mice injected with different formulations. The temperature of tumor was higher than 45 °C following irradiation with 808 nm laser at 1.5 W/cm 2 for 4 min , indicating the photothermal effect could efficiently ablate the tumor. To further investigate the tumor metastasis and in vivo toxicity, the major organs of the treated mice were sliced and stained by H&E staining for histology analysis . For micelle groups, no tumor metastasis forci could be observed, whereas there were obvious metastatic adenocarcinomas in lung or spleen tissues for the PTX and saline groups. And there was no pathological variation on the H&E-stained sections of the main organs in all micelle groups. These results suggested that no apparent systemic or tissue toxicity was induced by these micelles in the treated animals. The low toxicity of micelles could be attributed to their tumor-specific bio-distribution and reduced drug release in the normal tissues. 3.7. Anti-tumor mechanism on cells The potential antitumor mechanism of micelles was illustrated by exploring its effect on cell mitochondria. The TEM observation of tumor cell mitochondria demonstrated that the mitochondria were seriously destroyed by the Glu-PEG-Azo-IR808-S-S-PTX micelle . The mitochondria were swollen and expanded, and the crista disappeared. After treatment with NIR laser irradiation, some mitochondria appeared vacuolation with a large number of microfilaments and microtubules. Besides, the MMP of Glu-PEG-Azo-IR808-S-S-PTX micelle groups was significantly lower than the PTX group, indicating that the IR808 could induce mitochondrial depolarization after targeting to mitochondria . The similar changing trend also could be found in the ATP content , further indicating that the micelle could disturb the mitochondrial function and reduce intracellular ATP production. It is noteworthy that both the MMP and ATP content extremely decreased following irradiation with laser, which illustrated that the photothermal effect had stronger effect on mitochondria compared with the chemotherapeutic effect.
Characterization of conjugates The glycosylated derivative of PEG (glucose-PEG) and IR808 were firstly conjugated by p-aminoazobenzene via amidation reaction (Glu-PEG-Azo), and the chemical structure was confirmed by 1 HNMR spectra. As shown in , the peaks at δ (ppm) 0.5–2.0 could be assigned to aliphatic protons of glucose, and the peak at δ (ppm) 3.6 was the characteristic peak of methylene, which could be assigned to PEG. Then, the IR808 also conjugated with Azo via amidation reaction (Glu-PEG-Azo-IR808). Following the AEDP nucleophilic substitution of the chloride of IR808, PTX was conjugated with AEDP via esterification reaction. The peaks at δ (ppm) 7.0–8.0 were the characteristic peaks of phenyl groups belonging to Azo and IR808. The chemical structures were also confirmed by UV-Vis and fluorescence spectra. As shown in , the characteristic peaks of Azo and IR808 could be found at 365 nm, 495 nm, and 808 nm, respectively. Besides, the peak intensity at 808 nm decreased after the chlorine atom of IR808 was substituted by the AEDP. And the emission ray peak of IR808, Glu-PEG-Azo-IR808, and Glu-PEG-Azo-IR808-S-S-PTX was at wavelength of 808 nm when excited by 660 nm, whereas the peak intensity at 808 nm decreased and a new peak at 620 nm appeared when the chlorine atom of IR808 was substituted by the AEDP. These results could demonstrate that the Glu-PEG-Azo-IR808-S-S-PTX conjugate was successfully synthesized. Furthermore, the hypoxia-sensitivity of the Glu-PEG-Azo-IR808-S-S-PTX conjugate was evaluated with sodium dithionite (Na 2 S 2 O 4 ) as an exogenous generator of hypoxia. When the incubation concentration and time increased, the characteristic peak of Azo (365 nm) decreased, while the characteristic peak of benzene (220 nm) increased , which was due to the Azo bond breaking. However, for the Azo non-containing conjugate, its spectra had no change after treatment with Na 2 S 2 O 4. So, the hypoxia sensitivity of the Glu-PEG-Azo-IR808-S-S-PTX conjugate was concentration and time dependent.
Characterization of conjugate-based micelle The amphiphilic conjugates tend to self-assemble into micelles when dissolved in water. As shown in , the critical micelle concentration (CMC) of the Glu-PEG-Azo-IR808-S-S-PTX conjugate was about 5.0 × 10 −3 nM, which indicated that it could easily assemble into micelle. As the micelle was composed of conjugates, its drug loading could be calculated by the chemical formula of conjugates, and the drug loading of the Glu-PEG-Azo-IR808-S-S-PTX micells calculated to be about 19%. The particle size of micelles ranged from 50–100 nm, and the zeta potential was negatively charged , which was beneficial for its in vivo long circulation (Suchaoin et al., ). The morphology was further confirmed by the TEM, and the micelles were approximate to sphere particles with particle size about 100 nm . The photothermal conversion efficiency was both concentration dependent and irradiation dependent. At the highest concentration, the temperature increased from 20 °C to 50 °C (Δt 30 °C) within 5 min , which was requisite for ablating the tumor (Liu et al., ). The in vitro drug release of disulfide bond containing micelle was more quick than the control micelle, and the drug release speed was GSH concentration dependent . So, the micelle could quickly release drug after entering into tumor cell and avoiding drug release in normal cells.
In vitro pharmacodynamics 3.3.1. Cytotoxicity The anti-tumor potential of micelles was firstly evaluated by the cytotoxicity. As shown in , the Glu-PEG-Azo-IR808-S-S-PTX micelle showed stronger inhibition ability than the mPEG-Azo-IR808-S-S-PTX micelle, indicating the glucose modification could promote cellular uptake thus enhancing the cellular drug concentration. Under hypoxic environment, the cytotoxicity of micelles significantly increased and had higher cytotoxicity compared with that under normoxic environment. The possible reason might be due to the IR808-S-S-PTX conjugate which would release and act on tumor cells when the PEG detached from PAMAM under hypoxia condition. Due to the positive charge of IR808, the IR808-S-S-PTX conjugate was more easily adsorbed and endocytosed by the cell, which induced higher cytotoxicity. When irradiation with 808 nm laser, the cytotoxicity of micelles further enhanced, the IC50 of the Glu-PEG-Azo-IR808-S-S-PTX micelle decreased by 7 folders reaching to 1.30 nM. Thus, the photothermal effect induced by IR808 could strengthen the chemotherapeutic effect of PTX. Figure 4. Pharmacodynamics evaluation on the A549 cells. (a) Cytotoxicity of micelles on A549 cells; (b) Flow cytometric analyses of A549 cells using annexin V-FITC/PI dual staining after treatment with micelles; (c) Microscopic images and quantitative analysis of the wound-healing after treatment with micelles; (d) Microscopic images and quantitative analysis of the transwell migration after treatment with micelles. * p < 0.05. 3.3.2. Cell apoptosis The anti-tumor potential of micelles was then assessed by cell apoptosis. As shown in , all administration groups had pro-apoptoxic effect. For the Glu-PEG-Azo-IR808-S-S-PTX micelle, its cell apoptotic ratio increased after treatment with laser irradiation both under normoxic and hypoxic conditions ( p < 0.05), which demonstrated that the phtotothermal effect could induce cell apoptosis. Especially under hypoxia condition, the apoptotic ratio increased from 17.65% to 31.23%, and the result might be due to the mitochondria which was more sensitive to thermal under hypoxia condition (Tan et al., ). 3.3.3. Cell metastasis The inhibition of cell metastasis was evaluated by wound healing and transwell migration. As shown in , the area of scratch for the control group almost completely recovered after 12 h, whereas for micelle groups, they exhibited migration inhibition effect with varying degrees. The migration rate of Glu-PEG-Azo-IR808-S-S-PTX micelle group was 57.0%. After irradiation with laser, the migration rate further decreased to 32.89%, indicating the synergistic effect of PTX and IR808. Likewise, compared with control group, the number of cells for micelle groups that migrated through the membrane was less . These results demonstrated that the Glu-PEG-Azo-IR808-S-S-PTX micelle could efficiently inhibit the metastasis of tumor cells.
Cytotoxicity The anti-tumor potential of micelles was firstly evaluated by the cytotoxicity. As shown in , the Glu-PEG-Azo-IR808-S-S-PTX micelle showed stronger inhibition ability than the mPEG-Azo-IR808-S-S-PTX micelle, indicating the glucose modification could promote cellular uptake thus enhancing the cellular drug concentration. Under hypoxic environment, the cytotoxicity of micelles significantly increased and had higher cytotoxicity compared with that under normoxic environment. The possible reason might be due to the IR808-S-S-PTX conjugate which would release and act on tumor cells when the PEG detached from PAMAM under hypoxia condition. Due to the positive charge of IR808, the IR808-S-S-PTX conjugate was more easily adsorbed and endocytosed by the cell, which induced higher cytotoxicity. When irradiation with 808 nm laser, the cytotoxicity of micelles further enhanced, the IC50 of the Glu-PEG-Azo-IR808-S-S-PTX micelle decreased by 7 folders reaching to 1.30 nM. Thus, the photothermal effect induced by IR808 could strengthen the chemotherapeutic effect of PTX. Figure 4. Pharmacodynamics evaluation on the A549 cells. (a) Cytotoxicity of micelles on A549 cells; (b) Flow cytometric analyses of A549 cells using annexin V-FITC/PI dual staining after treatment with micelles; (c) Microscopic images and quantitative analysis of the wound-healing after treatment with micelles; (d) Microscopic images and quantitative analysis of the transwell migration after treatment with micelles. * p < 0.05.
Cell apoptosis The anti-tumor potential of micelles was then assessed by cell apoptosis. As shown in , all administration groups had pro-apoptoxic effect. For the Glu-PEG-Azo-IR808-S-S-PTX micelle, its cell apoptotic ratio increased after treatment with laser irradiation both under normoxic and hypoxic conditions ( p < 0.05), which demonstrated that the phtotothermal effect could induce cell apoptosis. Especially under hypoxia condition, the apoptotic ratio increased from 17.65% to 31.23%, and the result might be due to the mitochondria which was more sensitive to thermal under hypoxia condition (Tan et al., ).
Cell metastasis The inhibition of cell metastasis was evaluated by wound healing and transwell migration. As shown in , the area of scratch for the control group almost completely recovered after 12 h, whereas for micelle groups, they exhibited migration inhibition effect with varying degrees. The migration rate of Glu-PEG-Azo-IR808-S-S-PTX micelle group was 57.0%. After irradiation with laser, the migration rate further decreased to 32.89%, indicating the synergistic effect of PTX and IR808. Likewise, compared with control group, the number of cells for micelle groups that migrated through the membrane was less . These results demonstrated that the Glu-PEG-Azo-IR808-S-S-PTX micelle could efficiently inhibit the metastasis of tumor cells.
Mitochondria targeting evaluation on cells Due to the fluorescence of IR808, the cellular uptake of micelles could be observed directly without modifying with other fluorescent reagents. As shown in , under normoxic environment, the fluorescence intensity of the Glu-PEG-Azo-IR808-S-S-PTX micelle was much higher than the mPEG-Azo-IR808-S-S-PTX micelle group, and it could be blocked by pre-treating with glucose, which demonstrated that the cellular uptake was mediated by GLUT1. Furthermore, the co-localization experiments confirmed that the Glu-PEG-Azo-IR808-S-S-PTX exhibited stronger cellular uptake than the mPEG-Azo-IR808-S-S-PTX micelle under normoxic environment . And they tend to target the nucleus. Although under hypoxic environment, the red fluorescence of IR808 co-localized with the green fluorescence of mitochondria quite well, which further demonstrated that the IR808 could effectively targeting the to the mitochondria following the Azo and disulfide bond got broken.
In vivo tumor targeting evaluation The in vivo tumor targeting was evaluated through observing the distribution of micelles in the tumor-bearing nude mice. As shown in , all micelles rapidly distributed over the whole body after intravenous injection. The Glu-PEG-Azo-IR808-S-S-PTX micelle began to accumulate at tumor for 2 h and last to 12 h. Meanwhile, the fluorescence intensity of the Glu-PEG-Azo-IR808-S-S-PTX micelle was much higher than the mPEG-Azo-IR808-S-S-PTX micelle. The in vitro tissue observation further confirmed that the fluorescence intensity of the Glu-PEG-Azo-IR808-S-S-PTX micelle was 5-fold compared to the mPEG-Azo-IR808-S-S-PTX micelle . Then, the mitochondria-targeting evaluation was conducted by preparing cryostat sections of tumors. As shown in , the red fluorescence of the Glu-PEG-Azo-IR808-S-S-PTX micelle was stronger than the mPEG-Azo-IR808-S-S-PTX micelle and colocalized well with the green fluorescence of the mitochondria. These results demonstrated that the Glu-PEG-Azo-IR808-S-S-PTX micelle could target both tumor and its mitochondria.
Anti-tumor efficacy on animals In vivo therapeutic efficacy was evaluated on the A549 tumor-bearing nude mice. As shown in , the tumor volume of administration groups was less than the saline group, which indicated that the tumor growth was inhibited after drug administration. Meanwhile, compared with the PTX group, all micelle groups exhibited obvious higher tumor inhibition efficacy. The Glu-PEG-Azo-IR808-S-S-PTX micelle showed superior anti-tumor ability compared with the mPEG-Azo-IR808-S-S-PTX micelle that might be due to its better tumor-targeting ability. Interestingly, when the Glu-PEG-Azo-IR808-S-S-PTX micelle irradiated by laser, the tumor almost disappeared. Moreover, there was no obvious body weights loss in mice injected with different formulations. The temperature of tumor was higher than 45 °C following irradiation with 808 nm laser at 1.5 W/cm 2 for 4 min , indicating the photothermal effect could efficiently ablate the tumor. To further investigate the tumor metastasis and in vivo toxicity, the major organs of the treated mice were sliced and stained by H&E staining for histology analysis . For micelle groups, no tumor metastasis forci could be observed, whereas there were obvious metastatic adenocarcinomas in lung or spleen tissues for the PTX and saline groups. And there was no pathological variation on the H&E-stained sections of the main organs in all micelle groups. These results suggested that no apparent systemic or tissue toxicity was induced by these micelles in the treated animals. The low toxicity of micelles could be attributed to their tumor-specific bio-distribution and reduced drug release in the normal tissues.
Anti-tumor mechanism on cells The potential antitumor mechanism of micelles was illustrated by exploring its effect on cell mitochondria. The TEM observation of tumor cell mitochondria demonstrated that the mitochondria were seriously destroyed by the Glu-PEG-Azo-IR808-S-S-PTX micelle . The mitochondria were swollen and expanded, and the crista disappeared. After treatment with NIR laser irradiation, some mitochondria appeared vacuolation with a large number of microfilaments and microtubules. Besides, the MMP of Glu-PEG-Azo-IR808-S-S-PTX micelle groups was significantly lower than the PTX group, indicating that the IR808 could induce mitochondrial depolarization after targeting to mitochondria . The similar changing trend also could be found in the ATP content , further indicating that the micelle could disturb the mitochondrial function and reduce intracellular ATP production. It is noteworthy that both the MMP and ATP content extremely decreased following irradiation with laser, which illustrated that the photothermal effect had stronger effect on mitochondria compared with the chemotherapeutic effect.
Conclusion In this study, we fabricated a GLUT1 targeting, hypoxia, and GSH responsive polymer-drug conjugate-based micelle to combine chemotherapy and photo-thermal therapy. The micelle not only could be specifically transported by the GLUT1 of tumor cells, but also efficiently delivered PTX and IR808 to the tubulin and mitochondria under tumor microenvironment, ultimately leading to cell apoptosis through destroying mitochondria and depleting ATP production. In vivo assays also revealed that the micelle mainly accumulated in tumor tissues and its mitochondria exhibited super high suppression efficiency on tumor growth and metastasis as well as no serious toxic effects toward the whole body. Therefore, this work provided a concise and promising nanomedicine for tumor therapy.
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Proteomic profiling of the local and systemic immune response to pediatric respiratory viral infections | 255348d0-4f8b-44bc-af47-4ff636ec60e7 | 11748518 | Biochemistry[mh] | Respiratory viral infections are the most common cause of pediatric illness worldwide . Although often mild and self-limited, a substantial number of children progress to severe viral lower respiratory tract infection (vLRTI) requiring hospital admission and mechanical ventilation (MV), often further complicated by acute respiratory distress syndrome (ARDS) and/or bacterial coinfections. In a global epidemiological study of children under five, severe LRTI was the leading cause of mortality outside of the neonatal period, contributing to an estimated 760,000 deaths . The marked heterogeneity in vLRTI clinical outcomes, driven in large part by differential host responses, remains poorly understood . Deeply profiling the host immune response to vLRTI can offer insights into pathophysiology and also enable novel diagnostic test development . Prior work evaluating the host response in LRTI using systems biology approaches has mainly focused on adult populations, and the few pediatric LRTI studies predominantly utilized transcriptomic or metabolomic approaches . Proteomics, or the large-scale study of the protein composition within a biologic sample, has the potential to complement studies of the transcriptome, as protein expression is influenced by post-transcriptional regulation and may be a more direct reflection of cellular and immunologic processes . The limited number of proteomic pediatric LRTI studies published to date have profiled plasma or urine samples , which provide useful insights into the systemic response to LRTI and offer candidate diagnostic biomarkers but may not reflect biological processes at the site of active infection. The local host proteomic response to severe viral infection in the lower respiratory tract remains poorly understood in children, as does the compartmentalization of proteomic responses in the blood versus airway. To address these questions, we perform high-dimensional proteomic profiling of paired tracheal aspirate (TA) and plasma samples in a prospective multicenter cohort of critically ill children with acute respiratory failure, specifically comparing vLRTI to non-infectious etiologies. We hypothesized that there would be a distinct proteomic signature of vLRTI, more pronounced in the airway than blood, and that exploring proteomic correlations with bacterial-viral coinfection and viral load would yield valuable biological insights. Description of cohort Children in this study represent a subset of those enrolled in a previously described prospective cohort of 454 mechanically ventilated children admitted to eight pediatric intensive care units in the National Institute of Child Health and Human Development’s (NICHD) Collaborative Pediatric Critical Care Research Network (CPCCRN) from February 2015 to December 2017 . See supplementary material for enrollment criteria. IRB approval was granted for TA sample collection prior to consent, as endotracheal suctioning is standard-of-care. Specimens of children for whom consent was not obtained were destroyed. The study was approved by University of Utah IRB #00088656. Sample collection and processing TA specimens collected within 24 h of intubation were processed for proteomic analysis, with centrifugation at 4°C at 15,000 × g for 5 min and freezing of supernatant at −80°C in a microvial within 30 min. Some patients did not have TA samples available for proteomic analysis due to inadequate processing. Plasma samples collected within 24 h of MV were frozen at −80°C. Some patients did not have plasma collected because consent was not obtained within the timeframe. Adjudication of LRTI status Adjudication was carried out retrospectively by study-site physicians who reviewed all clinical, laboratory, and imaging data following hospital discharge, with specific criteria detailed in the supplementary material. Standard-of-care microbiological testing, including multiplex respiratory pathogen polymerase chain reaction (PCR) and semiquantitative bacterial respiratory cultures, was considered in the adjudication process. In addition, microbes detected by TA metagenomic next-generation sequencing (mNGS), as previously described , were considered for pathogen identification. Patients were assigned a diagnosis of “vLRTI” if clinicians made a diagnosis of LRTI, and the patient had a respiratory virus detected by PCR and/or mNGS. Within the vLRTI group, subjects were subcategorized as viral infection alone or bacterial coinfection, based on whether a bacterial respiratory pathogen was detected by bacterial culture, PCR, and/or mNGS. Patients alternatively were assigned a diagnosis of “No LRTI” if clinicians identified a clear, non-infectious cause of respiratory failure without clinical or microbiologic evidence of bacterial or viral LRTI. Subject selection for proteomic analysis Subjects that were clinically adjudicated as vLRTI and No LRTI were selected for proteomics analysis, in an approximately 2:1 ratio. This subset of subjects represented a convenience sample of the larger cohort, with the goal of maximizing the number of subjects with both TA and plasma samples to allow comparative proteomic analysis, although not all subjects had all both samples available. Proteomic analysis The SomaScan 1.3 k assay (SomaLogic) was utilized to quantify the protein expression in plasma and TA samples. The assay, described and validated elsewhere , utilizes 1,305 single-stranded DNA aptamers that bind specific proteins, which are quantified on a customized Agilent hybridization assay. Aptamer measurement is therefore a surrogate of protein expression. The assay outputs fluorescence units that are relative but quantitatively proportional to the protein concentration in the sample. Statistical analysis Relative fluorescence units (RFUs) for each of the 1,305 protein aptamers were log-transformed for analysis. Differential expression was calculated between groups for each aptamer using limma , a R package that facilitates simultaneous comparisons between numerous targets . Age-adjusted and age-unadjusted differential protein analyses were performed. Biological pathways were interrogated against the Reactome database with the R package WebGestaltR using a functional class scoring approach . Specifically, the input list included the full set of 1,305 proteins and the corresponding log2-fold change between the conditions of interest, ranked by T-statistic. P values for protein and pathway analyses were adjusted for multiple comparisons using the Benjamini-Hochberg procedure; adjusted P value (p adj ) <0.05 was considered statistically significant. A parsimonious proteomic classifier was generated using LASSO logistic regression on TA samples with the cv.glmnet function in R, setting family = “binomial” and leaving other parameters as default . LASSO was used for both feature selection and classification. The model was generated using 5-fold cross-validation, where a model was trained on ~80% of samples and tested on ~20% of samples to generate vLRTI probabilities for each of the subjects in the cohort. To keep the fold composition comparable, we required at least 3 No LRTI subjects in each fold. Area under the receiver operator curve (AUC) was calculated using the pROC package, and confidence intervals were generated with bootstrapping . Correlation for each protein between TA and plasma samples was calculated using Pearson correlation for all paired samples in bulk and then subdivided by group (vLRTI vs No LRTI). Correlation coefficients were considered strong if the absolute value was >0.5, moderate if 0.3–0.5, and weak if 0–0.3. Correlation between specific proteins and viral load was calculated similarly. Viral load was extrapolated from mNGS reads-per-million, and if multiple viruses were detected, the viral loads were summed. Children in this study represent a subset of those enrolled in a previously described prospective cohort of 454 mechanically ventilated children admitted to eight pediatric intensive care units in the National Institute of Child Health and Human Development’s (NICHD) Collaborative Pediatric Critical Care Research Network (CPCCRN) from February 2015 to December 2017 . See supplementary material for enrollment criteria. IRB approval was granted for TA sample collection prior to consent, as endotracheal suctioning is standard-of-care. Specimens of children for whom consent was not obtained were destroyed. The study was approved by University of Utah IRB #00088656. TA specimens collected within 24 h of intubation were processed for proteomic analysis, with centrifugation at 4°C at 15,000 × g for 5 min and freezing of supernatant at −80°C in a microvial within 30 min. Some patients did not have TA samples available for proteomic analysis due to inadequate processing. Plasma samples collected within 24 h of MV were frozen at −80°C. Some patients did not have plasma collected because consent was not obtained within the timeframe. Adjudication was carried out retrospectively by study-site physicians who reviewed all clinical, laboratory, and imaging data following hospital discharge, with specific criteria detailed in the supplementary material. Standard-of-care microbiological testing, including multiplex respiratory pathogen polymerase chain reaction (PCR) and semiquantitative bacterial respiratory cultures, was considered in the adjudication process. In addition, microbes detected by TA metagenomic next-generation sequencing (mNGS), as previously described , were considered for pathogen identification. Patients were assigned a diagnosis of “vLRTI” if clinicians made a diagnosis of LRTI, and the patient had a respiratory virus detected by PCR and/or mNGS. Within the vLRTI group, subjects were subcategorized as viral infection alone or bacterial coinfection, based on whether a bacterial respiratory pathogen was detected by bacterial culture, PCR, and/or mNGS. Patients alternatively were assigned a diagnosis of “No LRTI” if clinicians identified a clear, non-infectious cause of respiratory failure without clinical or microbiologic evidence of bacterial or viral LRTI. Subjects that were clinically adjudicated as vLRTI and No LRTI were selected for proteomics analysis, in an approximately 2:1 ratio. This subset of subjects represented a convenience sample of the larger cohort, with the goal of maximizing the number of subjects with both TA and plasma samples to allow comparative proteomic analysis, although not all subjects had all both samples available. The SomaScan 1.3 k assay (SomaLogic) was utilized to quantify the protein expression in plasma and TA samples. The assay, described and validated elsewhere , utilizes 1,305 single-stranded DNA aptamers that bind specific proteins, which are quantified on a customized Agilent hybridization assay. Aptamer measurement is therefore a surrogate of protein expression. The assay outputs fluorescence units that are relative but quantitatively proportional to the protein concentration in the sample. Relative fluorescence units (RFUs) for each of the 1,305 protein aptamers were log-transformed for analysis. Differential expression was calculated between groups for each aptamer using limma , a R package that facilitates simultaneous comparisons between numerous targets . Age-adjusted and age-unadjusted differential protein analyses were performed. Biological pathways were interrogated against the Reactome database with the R package WebGestaltR using a functional class scoring approach . Specifically, the input list included the full set of 1,305 proteins and the corresponding log2-fold change between the conditions of interest, ranked by T-statistic. P values for protein and pathway analyses were adjusted for multiple comparisons using the Benjamini-Hochberg procedure; adjusted P value (p adj ) <0.05 was considered statistically significant. A parsimonious proteomic classifier was generated using LASSO logistic regression on TA samples with the cv.glmnet function in R, setting family = “binomial” and leaving other parameters as default . LASSO was used for both feature selection and classification. The model was generated using 5-fold cross-validation, where a model was trained on ~80% of samples and tested on ~20% of samples to generate vLRTI probabilities for each of the subjects in the cohort. To keep the fold composition comparable, we required at least 3 No LRTI subjects in each fold. Area under the receiver operator curve (AUC) was calculated using the pROC package, and confidence intervals were generated with bootstrapping . Correlation for each protein between TA and plasma samples was calculated using Pearson correlation for all paired samples in bulk and then subdivided by group (vLRTI vs No LRTI). Correlation coefficients were considered strong if the absolute value was >0.5, moderate if 0.3–0.5, and weak if 0–0.3. Correlation between specific proteins and viral load was calculated similarly. Viral load was extrapolated from mNGS reads-per-million, and if multiple viruses were detected, the viral loads were summed. Cohort characteristics and microbiology From the prospective multi-center cohort ( n = 454), samples from 62 subjects underwent proteomic analysis, including 40 with vLRTI and 22 with No LRTI . Those with vLRTI were further subdivided into viral infection alone ( n = 16) or viral-bacterial coinfection ( n = 24). The demographic characteristics did not differ between the vLRTI and No LRTI groups, with the exception of age, which was higher in No LRTI than vLRTI (median 10.2 years [IQR 1.1–14.9] vs 0.9 [0.3–1.6]) . Diagnoses in the No LRTI group included trauma, neurologic conditions, ingestion, and anatomic airway abnormalities, with many having abnormal chest radiographs and meeting ARDS criteria. Within the vLRTI group, respiratory syncytial virus (RSV) was the most common pathogen, and 15 subjects had more than one virus . Haemophilus influenzae , Moraxella catarrhalis , and Streptococcus pneumoniae were the most common coinfecting bacterial pathogens. Defining a lower respiratory tract proteomic signature of vLRTI We first compared protein expression in TA samples between the vLRTI ( n = 37) and No LRTI ( n = 18) groups. Two hundred proteins (15.3% of all proteins assayed) were differentially expressed at p adj <0.05 . Among the 80 proteins upregulated in vLRTI were interferon-stimulated ubiquitin-like protein ISG15 and oligoadenylate synthase protein OAS1, which are central to type I interferon signaling, and Granzyme B and Granulysin, proteins present in granules of cytotoxic T cells and natural killer (NK) cells. Among the 120 proteins downregulated in vLRTI were fatty acid-binding protein FABP, macrophage inhibitory protein MIP-5, and neutrophil-activating protein NAP-2. Pathway analysis confirmed interferon signaling as the primary pathway upregulated in the vLRTI group, although only the “influenza infection” pathway achieved p adj <0.05 . Having identified a strong host proteomic signature of vLRTI, we hypothesized that a parsimonious number of TA proteins could accurately differentiate vLRTI from No LRTI subjects. Utilizing LASSO logistic regression and employing five-fold cross-validation, we built parsimonious proteomic classifiers (ranging in size from 9 to 15 proteins) that accurately distinguished vLRTI and No LRTI with an AUC of 0.96 (95% CI: 0.90–1.00) ( ; Table S1). The proteins with consistently positive coefficients (i.e., increasing vLRTI probability) were Granulysin, Granzyme B, and ISG-15 as well as cyclin-dependent kinase protein CDK2 and kinesin-like protein KIF23. The proteins with consistently negative coefficients (i.e., decreasing probability of vLRTI) were FABP and NAP-2. Since age was statistically different between the two groups, we added age as a continuous covariate in our differential expression model (Fig. S1). The results overall were similar, with 176 differentially expressed proteins (58 upregulated and 118 downregulated with vLRTI). There was considerable overlap (80%) in the top 10 most differentially expressed proteins between the two models. Comparison of plasma proteomics between vLRTI and No LRTI groups We next compared plasma protein concentrations between vLRTI ( n = 33) and No LRTI ( n = 22) groups. The age-unadjusted differential expression analysis yielded 56 statistically significant proteins (4.3% of all proteins assayed)–45 upregulated in vLRTI and 11 downregulated in vLRTI . However, adjusting for age, only one protein, ISG15, remained significant (Fig. S2). ISG15, a type 1 interferon-stimulated protein, showed promise in distinguishing vLRTI and No LRTI groups in both plasma and TA (p adj for both <0.0001), and ISG15 expression was strongly correlated between paired TA and plasma samples (correlation coefficient 0.79, P < 0.0001) . ISG-15 alone implemented as a diagnostic test exhibited strong performance with AUCs of 0.95 (95% CI: 0.89–1.00) and 0.91 (95% CI: 0.83–0.99) in TA and plasma, respectively (Fig. S3). Comparative analysis of plasma and respiratory tract proteomics Comparing the differentially expressed proteins between vLRTI and No LRTI groups in TA and plasma (using the age-unadjusted analyses), only 15 proteins were differentially expressed in both compartments, with seven proteins upregulated in vLRTI in both, four proteins downregulated in vLRTI in both, and four proteins with opposite directionality . We further investigated protein correlation utilizing our paired samples ( n = 48 total paired TA and plasma samples from the same subject, including n = 30 paired vLRTI samples and n = 18 paired No LRTI samples). Correlation in expression between the lower airway and systemic circulation was weak for the majority of proteins (Pearson correlation coefficient −0.3 to + 0.3) , although there were exceptions, namely ISG-15. Lower respiratory tract proteomic differences in bacterial-viral coinfection Within the vLRTI group, subjects were categorized as either viral infection ( n = 16) or bacterial-viral coinfection ( n = 24) based on clinical microbiology and respiratory mNGS. Differential protein expression in TA between these two groups did not yield any statistically significant proteins at p adj <0.05, but we did note an absolute increase in the expression of TSG-6, a tumor-necrosis factor-stimulated protein (p adj = 0.07), and C-reactive protein (CRP) (p adj = 0.10) in coinfection . Pathway analysis showed heightened interferon signaling in coinfection compared with viral infection alone. Pathways associated with cell turnover and division were preferentially upregulated in viral infection compared with coinfection . Lower respiratory tract protein correlations with viral load For the vLRTI subjects that tested positive for a virus by mNGS, the expression of TA proteins was correlated with viral load, measured as reads-per-million . Interferon-related proteins, such as interferon-lambda 1 and ISG-15, were positively correlated with viral load, as well as monocyte chemotactic protein MCP-2. Conversely, platelet receptor GI-24, TFG-β superfamily protein Activin AB, and neutrophil-activating glycoprotein CD177 were inversely correlated with viral load. From the prospective multi-center cohort ( n = 454), samples from 62 subjects underwent proteomic analysis, including 40 with vLRTI and 22 with No LRTI . Those with vLRTI were further subdivided into viral infection alone ( n = 16) or viral-bacterial coinfection ( n = 24). The demographic characteristics did not differ between the vLRTI and No LRTI groups, with the exception of age, which was higher in No LRTI than vLRTI (median 10.2 years [IQR 1.1–14.9] vs 0.9 [0.3–1.6]) . Diagnoses in the No LRTI group included trauma, neurologic conditions, ingestion, and anatomic airway abnormalities, with many having abnormal chest radiographs and meeting ARDS criteria. Within the vLRTI group, respiratory syncytial virus (RSV) was the most common pathogen, and 15 subjects had more than one virus . Haemophilus influenzae , Moraxella catarrhalis , and Streptococcus pneumoniae were the most common coinfecting bacterial pathogens. We first compared protein expression in TA samples between the vLRTI ( n = 37) and No LRTI ( n = 18) groups. Two hundred proteins (15.3% of all proteins assayed) were differentially expressed at p adj <0.05 . Among the 80 proteins upregulated in vLRTI were interferon-stimulated ubiquitin-like protein ISG15 and oligoadenylate synthase protein OAS1, which are central to type I interferon signaling, and Granzyme B and Granulysin, proteins present in granules of cytotoxic T cells and natural killer (NK) cells. Among the 120 proteins downregulated in vLRTI were fatty acid-binding protein FABP, macrophage inhibitory protein MIP-5, and neutrophil-activating protein NAP-2. Pathway analysis confirmed interferon signaling as the primary pathway upregulated in the vLRTI group, although only the “influenza infection” pathway achieved p adj <0.05 . Having identified a strong host proteomic signature of vLRTI, we hypothesized that a parsimonious number of TA proteins could accurately differentiate vLRTI from No LRTI subjects. Utilizing LASSO logistic regression and employing five-fold cross-validation, we built parsimonious proteomic classifiers (ranging in size from 9 to 15 proteins) that accurately distinguished vLRTI and No LRTI with an AUC of 0.96 (95% CI: 0.90–1.00) ( ; Table S1). The proteins with consistently positive coefficients (i.e., increasing vLRTI probability) were Granulysin, Granzyme B, and ISG-15 as well as cyclin-dependent kinase protein CDK2 and kinesin-like protein KIF23. The proteins with consistently negative coefficients (i.e., decreasing probability of vLRTI) were FABP and NAP-2. Since age was statistically different between the two groups, we added age as a continuous covariate in our differential expression model (Fig. S1). The results overall were similar, with 176 differentially expressed proteins (58 upregulated and 118 downregulated with vLRTI). There was considerable overlap (80%) in the top 10 most differentially expressed proteins between the two models. We next compared plasma protein concentrations between vLRTI ( n = 33) and No LRTI ( n = 22) groups. The age-unadjusted differential expression analysis yielded 56 statistically significant proteins (4.3% of all proteins assayed)–45 upregulated in vLRTI and 11 downregulated in vLRTI . However, adjusting for age, only one protein, ISG15, remained significant (Fig. S2). ISG15, a type 1 interferon-stimulated protein, showed promise in distinguishing vLRTI and No LRTI groups in both plasma and TA (p adj for both <0.0001), and ISG15 expression was strongly correlated between paired TA and plasma samples (correlation coefficient 0.79, P < 0.0001) . ISG-15 alone implemented as a diagnostic test exhibited strong performance with AUCs of 0.95 (95% CI: 0.89–1.00) and 0.91 (95% CI: 0.83–0.99) in TA and plasma, respectively (Fig. S3). Comparing the differentially expressed proteins between vLRTI and No LRTI groups in TA and plasma (using the age-unadjusted analyses), only 15 proteins were differentially expressed in both compartments, with seven proteins upregulated in vLRTI in both, four proteins downregulated in vLRTI in both, and four proteins with opposite directionality . We further investigated protein correlation utilizing our paired samples ( n = 48 total paired TA and plasma samples from the same subject, including n = 30 paired vLRTI samples and n = 18 paired No LRTI samples). Correlation in expression between the lower airway and systemic circulation was weak for the majority of proteins (Pearson correlation coefficient −0.3 to + 0.3) , although there were exceptions, namely ISG-15. Within the vLRTI group, subjects were categorized as either viral infection ( n = 16) or bacterial-viral coinfection ( n = 24) based on clinical microbiology and respiratory mNGS. Differential protein expression in TA between these two groups did not yield any statistically significant proteins at p adj <0.05, but we did note an absolute increase in the expression of TSG-6, a tumor-necrosis factor-stimulated protein (p adj = 0.07), and C-reactive protein (CRP) (p adj = 0.10) in coinfection . Pathway analysis showed heightened interferon signaling in coinfection compared with viral infection alone. Pathways associated with cell turnover and division were preferentially upregulated in viral infection compared with coinfection . For the vLRTI subjects that tested positive for a virus by mNGS, the expression of TA proteins was correlated with viral load, measured as reads-per-million . Interferon-related proteins, such as interferon-lambda 1 and ISG-15, were positively correlated with viral load, as well as monocyte chemotactic protein MCP-2. Conversely, platelet receptor GI-24, TFG-β superfamily protein Activin AB, and neutrophil-activating glycoprotein CD177 were inversely correlated with viral load. In this study, we identified the proteomic signature of severe pediatric vLRTI in both the lower respiratory tract and systemic circulation, leveraging results to understand compartment-specific host responses, host-viral dynamics, and viral-bacterial coinfection, as well as identify specific proteins with diagnostic potential. This work represents the first simultaneous proteomic profiling of both TA and plasma samples from children with severe vLRTI. As hypothesized, the proteomic response to vLRTI was most robust at the local site of infection, with approximately 15% of assayed proteins differentially expressed in TA. This lower airway proteomic vLRTI signature was dominated by interferon-related proteins, which are well-known innate mediators of host defense and immunologic injury in viral infection , and importantly also shown in vitro to be structurally altered during certain viral infections . In addition, this signature was enriched in proteins contained in cytotoxic lymphocytes that in turn secrete interferons . The list of upregulated proteins and pathways share significant overlap with a smaller study that performed proteomic profiling on nasal swab samples from adults with influenza, suggesting overlapping immune responses across the upper and lower respiratory tract . Notable downregulated proteins were macrophage inhibitory protein-5 (MIP-5) and fatty acid binding protein (FABP), which have a diverse array of functions including macrophage regulation, suggesting that macrophage dynamics play an important role in response to vLRTI . Interestingly, in the subanalysis of bacterial-viral coinfection, the expression of interferon-related proteins was even greater than in viral infection alone. Prior work, mostly in influenza infections, has suggested that type 1 interferons can suppress key neutrophil and macrophage defenses, increasing susceptibility to bacterial coinfection, which may explain this finding . By integrating viral load measurements, we identified host TA proteins exhibiting proportional changes in expression based on viral load. Interferon-related proteins, including ISG-15 and interferon-lambda 1, and MCP-2, a chemokine-induced by interferon signaling, exhibited the strongest induction in expression with viral load, underscoring the central role of interferons in innate antiviral defense. In contrast, the levels of CD177 (a glycoprotein involved in neutrophil activation) , Activin AB (a TGF-β family protein implicated in ARDS inflammatory remodeling) , and GI-24 (a platelet aggregation receptor) all decreased in response to higher viral loads. As previously noted, impaired neutrophil responses have been implicated in the pathophysiology of post-viral bacterial pneumonia , and our results suggest that this may occur in a viral load-dependent manner. Complementing these findings, a longitudinal transcriptomic study in adults hospitalized with severe influenza infection demonstrated the initial upregulation of interferon pathways, followed by inflammatory neutrophil activation and cell-stress patterns , and a study of severe pediatric influenza infection found that early upregulation of genes associated with neutrophil degranulation was associated with multi-organ dysfunction and mortality . Although we observed a robust protein signature of vLRTI in the lower airways, the findings in the peripheral blood were more subtle, and the correlation between plasma and TA proteins was generally weak. Furthermore, we observed a greater impact of age on the blood proteomic signature of vLRTI, potentially because the signal in the peripheral blood was weaker and thus more susceptible to confounding. Understanding the systemic response to a local infection is certainly useful and practical, as peripheral blood samples and urine samples are less invasive to collect than lower respiratory samples and would allow for application in a broader population of children who do not require MV. However, to obtain the most informative and potent proteomic signal of infection, our findings suggest that sampling the site of infection has the highest yield. Supporting this intuitive finding is a comparative adult proteomic study assaying both serum and bronchoalveolar lavage in interstitial lung diseases that similarly found a much higher number of differentially expressed proteins in the lower respiratory tract compared with the blood . Increasingly, high-dimensional proteomic assays including mass spectrometry and antibody-based methods like SomaScan have been employed in different sample types to identify novel biomarkers for a wide range of disease states . In addition to contributing insights into the pathophysiology of vLRTI, our study also highlights the utility of proteomic approaches in diagnostic biomarker discovery specific to respiratory infections. Standard-of-care multiplexed PCR assays only evaluate a limited subset of respiratory viruses and cannot detect novel emerging viruses or differentiate asymptomatic carriage from true infection . Host response-based assays agnostic to viral species could be invaluable for pandemic preparedness and infection prevention in congregate settings. When employed as a diagnostic test to distinguish vLRTI from non-infectious respiratory failure, our nine-protein TA classifier achieved excellent performance with an AUC of 0.96. The single protein ISG15 also showed potential for use as a diagnostic biomarker in both TA and plasma. Type 1 interferons have previously been proposed as an accurate diagnostic screening test for pediatric viral infection . Another diagnostic challenge in vLRTI is identifying bacterial coinfection, as standard respiratory bacterial cultures do not distinguish between coinfection and colonization and are often negative in the context of prior antibiotic administration. Our subanalysis of bacterial coinfection highlighted two TA proteins, TSG-6 (a tumor necrosis factor-inducible protein) and CRP (an inflammatory protein with modest specificity for bacterial LRTI in blood) , that may be useful respiratory biomarkers of secondary bacterial infection. Our study has several strengths including our multi-center enrollment, clinical sampling at early time points, evaluation of protein expression in multiple compartments, and integration of respiratory mNGS for comprehensive pathogen evaluation. It also has several important limitations, including a small sample size, which may have limited our ability to detect more subtle but clinically important differences in protein expression. Additionally, the version of the SomaScan assay utilized does not encompass the entire human proteome, and we likely missed some important differentially expressed proteins and pathways. Evaluation of a larger number of proteins using mass spectrometry, OLINK, or the newer SomaScan 11 k would enable a more comprehensive evaluation of the proteome . From the diagnostic biomarker standpoint, our findings are more preliminary in nature and warrant further optimization and validation in larger cohorts with all relevant classes of infection (bacterial infection, viral infection, coinfection, and non-infectious controls) and a wider range of severity represented to rigorously understand performance. Finally, we recognize that there is no gold standard for LRTI diagnosis in children, and our reliance on the best practical methodology of combining retrospective clinical adjudication and microbiology results may have resulted in classification errors. Taken together, we present a comprehensive proteomic characterization of severe pediatric vLRTI, highlighting pathophysiologic insights in both viral infection and bacterial-viral coinfection and deepening our understanding of compartmentalization of the human host response to LRTI. Validation of the present findings in larger external cohorts is needed with more in-depth analysis to determine whether new therapeutic targets can be identified and whether proteomic biomarkers may augment current standard-of-care pathogen-based diagnostic testing. Looking forward, multi-omic approaches combining proteomics and transcriptomics as well as integration with microbiology hold promise for advancing understanding of the heterogeneity of pediatric LRTI, modernizing diagnostics, and personalizing treatment. Reviewer comments |
Comprehensive evaluation of the antibacterial and antibiofilm activities of NiTi orthodontic wires coated with silver nanoparticles and nanocomposites: an in vitro study | d527a9fd-ea63-41fd-8470-56adc7ed8f0f | 11539822 | Dentistry[mh] | The oral microbiota contains cariogenic acid-producing bacteria that adhere to the tooth surface via biofilm formation and cause demineralization of tooth enamel. Orthodontic arch-wires and brackets placed under the influence of the oral cavity environment which includes the presence of saliva, ingested food, and temperature, in addition to friction or biocorrosion processes. Studies have explained the effect of fixed orthodontic appliances, where they act as plaque-retentive niches by creating stagnant areas with irregular surfaces for brackets, bands, and wires that cause limitations to the cleaning process of the oral cavity; leading to the accumulation of bacterial plaque and aciduric bacterial infections, triggering cavitated lesion periodontal diseases, and the formation of white spot lesions (WSLs) . Studies have shown that 50% of orthodontic patients develop WSL within four weeks or less during their course of treatment . Furthermore, plaque accumulation around orthodontic bands is a slippery slope toward periodontal diseases, where decalcification occurs, followed by the formation of WSLs and gingival inflammation at high precedents reaching 34.4% and 56.8% increases in gingivitis in adult and adolescent patients, respectively . During the course of dental treatment, patients are under various types of influences. Specially from Non-oral bacteria that may have access to the oral cavity and take residence in oral cavity and on dental appliances . These microorganisms may be opportunistic pathogens in healthy individual without apparent infections, but can contribute in the drug resistant biofilm formation . Furthermore, some bacterial isolates can be acquired during the course of treatment and dental assessment . To prevent the development of various infections, lesions and their consequential diseases, various methods ranging from modifying dietary habits; to applying topical fluoride, forming fluorapatite crystals, and stimulating remineralization and maintaining oral hygiene, have been employed. However, these methods are not effective, and other alternatives have been investigated . The coating of orthodontic appliances can modify the material surface improving the mechanical and biological properties of metallic materials utilized in orthodontics . Different coating methods involving the application of a thin film coating via various techniques, have been proposed. These layers applied to orthodontic wires; may be helpful in enhancing the surface properties of these wires. Coating orthodontic wires will affect their surface roughness, thickness, frictional properties, and ultimately, bacterial adhesion ability. Other methods used fluoride (F)-releasing adhesives and fluoride-releasing elastomeric ligatures. They initially reported that the release of fluoride ions can reduce biofilm formation and control dental caries development . However, further studies showed that fluoridated elastomers were ineffective for long-term use, and their release rate vanished after one week of treatment. Therefore, the need for orthodontic appliances with antibacterial properties is inevitable to lower bacterial adhesion to orthodontic appliances . Nanotechnological solutions were proposed, by providing materials with antibacterial and anti-caries properties that can be used on dental appliances. Nanomaterials, such as silver compounds and nanoparticle (NP) metals, can cause a significant decrease in biofilm formation and therefore inhibit enamel demineralization by acid-producing bacteria . Nickel–titanium (NiTi) arch-wires are an ideal orthodontic appliance for the early stage of comprehensive orthodontic treatment because they generate a light force for dental alignment and levelling. They combine excellent properties, such as a super elastic state with a shape memory effect, making them suitable for biomedical applications. The surfaces of these materials are rough due to their high friction coefficient, resulting in elevated frictional resistance and greater orthodontic forces are needed to overcome this sliding resistance. The surface roughness (SR) of arch-wires is highly important because it determines the surface area in contact and influences frictional, corrosion, and biocompatibility properties . However, the use of NiTi wires is often limited by unstable long-term use; due to erosion by saliva and the release of Ni ions into the oral cavity which can cause health problems . The outstanding properties were determined from the coating materials that have been used, specifically, nanoparticles, where inorganic fullerene-like tungsten disulfide (WS 2 ), zinc oxide (ZnO), chitosan (CS) and carbone Nitride (CNx) nanoparticles significantly decrease the friction coefficient . Silver nanoparticles (AgNPs) are among the most promising antimicrobial agents with a wide range of applications such as wound and burn healing, as well as in bone and dental implants, benefiting from their antibacterial activities . The synthesis of AgNPs using bacterial isolates as biofactors, which mediate the nucleation and growth processes of AgNPs provided an effective metabolic pathway for the bio-formation of nanoparticles with well-defined shapes, high reactivity, and water solubility properties. These findings qualify biogenically, i.e., using bacteria as bio-factory and eco-friendly synthesized AgNPs as the favored choice for various biomedical applications. This route is more favored than physical or chemical methods because substantial amounts of energy and toxic solvents used for nanoparticles extraction, which limits its use in biomedical applications. The biogenic pathway provides nanoparticles with a controlled shape, low aggregation rates, high homogeneity, with a low polydispersity index (PI), and high stability . These AgNPs have broad antibacterial activity against Gram-positive and Gram-negative bacteria. Beside the well-known oral bacterial inhabitants such as Staphylococcus spp , Streptococcus spp and Enterococcus spp , other microbial non oral pathogens have used the oral cavity as a reservoir . Coating dental appliances with nanoparticles such as AgNPs is critical part of recent research and focusing on evaluating this new advancement against drug resistant bacterial isolates and aggressive biofilm forming bacterial such as Acinetobacter baumannii and Pseudomonas aeruginosa. Other studies have stated these microbial infections in the oral cavity as code blue alert, to be focused on in dental research, as the emergence of non-oral pathogens is gaining a lot of research interest due to their effect and refractory to endodontic and periodontal treatments . Nanostructured silver particles are highly accessible to bacterial cells due to their nano-size range and high surface-to-volume ratio, leading to elevated efficacy in anchoring to the microorganisms’ cell structure and penetrating the cell membrane, forming free radicals, damaging DNA, causing structural changes, and ultimately causing cell death. AgNPs can release Ag ions from silver clusters at continuous rates, ensuring antimicrobial durability . These nanoparticles can bind to polymers to form nanocomposites that gain antimicrobial and antibiofilm properties from the encapsulated particles. Polyvinyl alcohol (PVA) and chitosan (CS) have been integrated in other applications as they are biodegradable and biocompatible. PVA is FDA approved for use in the food industry and medical applications without side effects. CS is a natural biodegradable, nontoxic polymer that can help in buffering the acidic oral environment and reducing tooth demineralization. Both PVA and CS can be used as biological scaffold for nanoparticles, leading to a wider range of applications . Here, NiTi orthodontic wires were separately coated with AgNPs and nanocomposites, using the dip coating method. This procedure is part of sol-gel process that is best known for their stoichiometry stability, purity, and ensures homogeneity, which makes it suitable for coating materials at low cost by only changing the solution composition. Furthermore, it aims to ensure the binding and stability of the synthesized nanoparticles and nanocomposites on the tested surface . This study aimed to evaluate the antimicrobial and antibiofilm efficacy of coating NiTi orthodontic wires with five materials, CS, PVA, AgNPs, CS-Ag, and PVA-Ag, on wire surfaces using the sol-gel thin-film dip coating method. The effects of these coated and uncoated control wires on multidrug-resistant and oral flora bacteria were tested. Furthermore, the topography and surface roughness fluctuation of these coated vs. uncoated wires were assessed and the release of silver and nickel ions from these wires was calculated.
The use of biologically synthesized AgNPs and Ag nanocomposite as a coating layer on the surface of NiTi wires was evaluated and depicted in a flowchart showing the steps of this study (Fig. ). This power analysis used the adhesion of bacteria to the wire as the primary outcome. Based on the results of Gonçalves, et al. 2020 the mean and standard deviation (SD) values were 0.174 (0.04) and 0.118 (0.007) for the uncoated and coated wires, respectively. The effect size (d) was 1.95. Using an alpha (α) level of 5% and beta (β) level of (20%) i.e. power = 80%; the minimum estimated sample size was 6 wires per group. Sample size calculation was performed using G*Power Version 3.1.9.2. The wires were divided into six groups, each with six wires: an uncoated control group (wires with no treatment), a CS coated group, a PVA coated group, a AgNPs coated group, a CS-Ag nanocomposite-coated group, and a PVA-Ag nanocomposite-coated group. Synthesis of AgNPs and nanocomposites Biologically synthesized AgNPs were prepared in our laboratory using Enterobacter cloacae Ism 26 (KP988024) . Briefly, 100 mL of nutrient broth medium was inoculated with 100 µL (10 8 CFU) of KP988024 and incubated at 35 °C and 180 rpm for 24 h. The bacterial culture was then centrifuged at 7000 rpm for 10 min. The bacterial pellets were collected, washed, and sonicated after which the bacterial cell lysate supernatant was mixed with 1 mM AgNO 3 solution and incubated at 35 °C for 24 h. the synthesized AgNPs solution was obtained in powder form by lyophilization using an Edwards model RV5 (England). Different solutions were prepared at three concentrations (0.1% (C1), 0.2% (C2) and 0.3% (C3) (w/v)) in deionized sterilized water and used for further experiments. Briefly, for the synthesis of PVA-Ag and CS-Ag nanocomposites, PVA (alpha cheimeka, India) (2 g) was added to 18 ml of deionized water and magnetically stirred at 100 rev/ min on a hot plate at 90 °C for 3 h. Then, different concentrations of the biosynthesized AgNPs were added and stirred for another 4 h (0.1% (C1), 0.2% (C2) and 0.3% (C3) (w/v)). Chitosan (alpha cheimeka, India) (0.4 g) was added to 20 ml of acetic acid (1%) and magnetically stirred for 1 h at 60 °C; then, 0.1%, 0.2% and 0.3% (w/v) AgNPs were added, and the mixture was further stirred for 2 h. These nanocomposite solutions were used for further experiments and pure PVA and CS solutions were used as controls . Characterization of biogenically synthesized AgNPs UV-Vis spectroscopy Analysis was carried out to assess the presence of AgNPs by using 1 mL of brown-colored solution sample in a quartz cell where the presence of the specific peaks (400–450 nm) for AgNPs was assessed using an Evolution 201 Scan UV-visible spectrophotometer (Thermo Scientific). Measurements were performed between 200 and 800 nm in the range of 1 nm. To set up the baseline we used double distilled water as a blank. Dynamic light scattering (DLS) The particle size and charge of the AgNPs in the solution were measured by DLS using a PSS-NICOMP particle sizer 380ZLS and zeta sizer (Malvern Instruments Ltd.). Transmission electron microscopy (TEM) TEM was used to accurately determine the shape and size in nm of the synthesized AgNPs. JOEL JEM-1010, Transmission electron microscopy at 80 KV was used at the Regional Centre for Mycology and Biotechnology (RCMB) of Al-Azhar University. Coating NiTi wires with AgNPs and ag nanocomposites Here, we used the sol-gel thin film dip coating method, where round 0.016 × 0.022-inch orthodontic NiTi wires (OrthoPro, FL, USA) were cleaned using an ethyl alcohol solution. NiTi wires were dip-coated in the previously prepared solutions with different AgNPs concentrations (AgNPs, PVA-Ag, CS-Ag) and pure solutions of PVA and CS and left for 8 h. Then all the samples were grasped with sterilized tweezers and left to air dry in a laminar flow hood overnight. Finally, the coated wires were placed in an oven at 40 °C for 10 min as a final step before use in further experiments. Uncoated wires were used as control. All procedure steps were conducted in a laminar flow, and all samples were kept separate and sealed in sterilized Eppendorf tubes until the next experiment . Antibacterial activity The antibacterial activity of all the coated and uncoated samples was evaluated using an agar diffusion test according to the Clinical and Laboratory Standards Institute (CLSI). Mueller-Hinton agar (MHA) (Oxoid Ltd., England) was sterilized and inoculated with the bacterial culture. This test was performed against clinically relevant Gram-positive and Gram-negative bacterial microorganisms commonly used as standards (three multidrug-resistant bacteria, Acinetobacter baumannii (A1), Acinetobacter baumannii (A2), and Pseudomonas aeruginosa (P1)), and three other common bacterial oral flora, Staphylococcus aureus (S1), Streptococcus mutans ( ATCC 25175) (St1), and Enterococcus faecalis (ATCC 19433) (E1) ). These bacterial strains were obtained from Ain Shams University Hospital, Cairo, Egypt; these strains are endemic to society and present as oral microbial flora . MHA plates were inoculated with 10 6 CFU/ml bacterial cultures using a sterile cotton swab, and each tested bacterial isolate was uniformly spread on culture media, separately and under aseptic conditions. Six orthodontic wires were placed on each plate (uncoated control, PVA-coated, CS-coated, AgNPs-coated, PVA-Ag coated, and CS-Ag coated) then incubated at 37 °C for 24 h. The antimicrobial assay was performed in triplicate, and the diameter of the inhibition zone (mm) was measured; the results are reported as the mean ± standard deviation . Antibiofilm activity The ability of the coated wires to inhibit biofilm formation was evaluated against two strong biofilm-forming bacteria (A1 and P1). Briefly, bacterial isolates were grown in sterile (10 ml) tryptic soy broth (TSB) (Merck, Germany). Bacterial inoculum of 0.5 McFarland standard (1.5 × 10 8 CFU/ml ) was used and diluted by 1:100, then inoculated into 96-well microtiter plates that previously contained coated and uncoated wires. The plates were incubated at 37 °C for 24 h and then washed and air dried at room temperature. Crystal violet solution (200 µl of 0.1% w/v) was added, and the mixture was left for 30 min and then washed and dried. Finally, 100 µl of ethanol (96%) was added to extract the stained bound biofilm. The absorbance was measured at 490 nm by microplate reader (ELx808™ Absorbance, Biotek, USA). Biofilm reduction was measured and compared to that of the control and the percentage of biofilm inhibition was calculated using the following equation: [12pt]{minimal}
$$ {OD\,ofnegativecontrol}}} .-} {OD\,ofnegativecontrol}}} ) 100 }$$ The OD of the tested wire is the optical density of the sample. The OD of the negative control is the optical density of the control biofilm-forming bacteria. Characterization of NiTi coated wires Atomic force microscopy (AFM) The surface characteristics of the coated wires were assessed by AFM (NanoSurf C3000, Gräubernstrasse, Liestal, Switzerland) operating in phase contrast mode. AFM provides measurements of surface roughness, and variability in defined measured areas by providing 3D images. The average coated layer thickness, roughness (Ra), and maximum roughness depth (Rq) were calculated for the coated wires using image processing and data analysis software supplied with the AFM. Scanning electron microscopy (SEM)/ energy dispersive X-ray spectroscopy (EDX) Wire specimens were examined by SEM/EDX (QUANTA, FEG 250, Thermo Scientific) operating at an accelerating voltage of 20 kV to visualize the surface changes and detect the coating extent and its efficacy on wires for all tested coating materials vs. uncoated control wire samples EDX analysis was used to identify the composition and elemental analysis the specimen surfaces. The wire specimens were mounted on metallic copper stubs and fixed with carbon conductive tape at a standard tilt angle, and SEM photomicrographs were taken from the surface at various magnifications . Release of Ag and Ni ions Nickel and Ag ion release analysis was performed by immersing the NiTi arch-wires (uncoated and coated) in 5 mL of phosphate-buffered saline (PBS) in a sterilized tube for a period of 192 h. The released Ag and Ni ions were measured using Atomic Absorption Spectrometer (AAS) (Perkin Elmer 3100) at 24, 48, 96 and 192 h for each wire sample. Statistical analysis The SPSS standard software package was used for data analysis. One-way analysis of variance (ANOVA) with Tukey’s post-hoc test was used to compare the effects between the groups ( n = 6) for antibacterial and antibiofilm tests. The data are represented as the mean ± standard deviation (SD). The level of significant difference was set at p < 0.05.
Biologically synthesized AgNPs were prepared in our laboratory using Enterobacter cloacae Ism 26 (KP988024) . Briefly, 100 mL of nutrient broth medium was inoculated with 100 µL (10 8 CFU) of KP988024 and incubated at 35 °C and 180 rpm for 24 h. The bacterial culture was then centrifuged at 7000 rpm for 10 min. The bacterial pellets were collected, washed, and sonicated after which the bacterial cell lysate supernatant was mixed with 1 mM AgNO 3 solution and incubated at 35 °C for 24 h. the synthesized AgNPs solution was obtained in powder form by lyophilization using an Edwards model RV5 (England). Different solutions were prepared at three concentrations (0.1% (C1), 0.2% (C2) and 0.3% (C3) (w/v)) in deionized sterilized water and used for further experiments. Briefly, for the synthesis of PVA-Ag and CS-Ag nanocomposites, PVA (alpha cheimeka, India) (2 g) was added to 18 ml of deionized water and magnetically stirred at 100 rev/ min on a hot plate at 90 °C for 3 h. Then, different concentrations of the biosynthesized AgNPs were added and stirred for another 4 h (0.1% (C1), 0.2% (C2) and 0.3% (C3) (w/v)). Chitosan (alpha cheimeka, India) (0.4 g) was added to 20 ml of acetic acid (1%) and magnetically stirred for 1 h at 60 °C; then, 0.1%, 0.2% and 0.3% (w/v) AgNPs were added, and the mixture was further stirred for 2 h. These nanocomposite solutions were used for further experiments and pure PVA and CS solutions were used as controls .
UV-Vis spectroscopy Analysis was carried out to assess the presence of AgNPs by using 1 mL of brown-colored solution sample in a quartz cell where the presence of the specific peaks (400–450 nm) for AgNPs was assessed using an Evolution 201 Scan UV-visible spectrophotometer (Thermo Scientific). Measurements were performed between 200 and 800 nm in the range of 1 nm. To set up the baseline we used double distilled water as a blank. Dynamic light scattering (DLS) The particle size and charge of the AgNPs in the solution were measured by DLS using a PSS-NICOMP particle sizer 380ZLS and zeta sizer (Malvern Instruments Ltd.). Transmission electron microscopy (TEM) TEM was used to accurately determine the shape and size in nm of the synthesized AgNPs. JOEL JEM-1010, Transmission electron microscopy at 80 KV was used at the Regional Centre for Mycology and Biotechnology (RCMB) of Al-Azhar University.
Analysis was carried out to assess the presence of AgNPs by using 1 mL of brown-colored solution sample in a quartz cell where the presence of the specific peaks (400–450 nm) for AgNPs was assessed using an Evolution 201 Scan UV-visible spectrophotometer (Thermo Scientific). Measurements were performed between 200 and 800 nm in the range of 1 nm. To set up the baseline we used double distilled water as a blank.
The particle size and charge of the AgNPs in the solution were measured by DLS using a PSS-NICOMP particle sizer 380ZLS and zeta sizer (Malvern Instruments Ltd.).
TEM was used to accurately determine the shape and size in nm of the synthesized AgNPs. JOEL JEM-1010, Transmission electron microscopy at 80 KV was used at the Regional Centre for Mycology and Biotechnology (RCMB) of Al-Azhar University.
Here, we used the sol-gel thin film dip coating method, where round 0.016 × 0.022-inch orthodontic NiTi wires (OrthoPro, FL, USA) were cleaned using an ethyl alcohol solution. NiTi wires were dip-coated in the previously prepared solutions with different AgNPs concentrations (AgNPs, PVA-Ag, CS-Ag) and pure solutions of PVA and CS and left for 8 h. Then all the samples were grasped with sterilized tweezers and left to air dry in a laminar flow hood overnight. Finally, the coated wires were placed in an oven at 40 °C for 10 min as a final step before use in further experiments. Uncoated wires were used as control. All procedure steps were conducted in a laminar flow, and all samples were kept separate and sealed in sterilized Eppendorf tubes until the next experiment .
The antibacterial activity of all the coated and uncoated samples was evaluated using an agar diffusion test according to the Clinical and Laboratory Standards Institute (CLSI). Mueller-Hinton agar (MHA) (Oxoid Ltd., England) was sterilized and inoculated with the bacterial culture. This test was performed against clinically relevant Gram-positive and Gram-negative bacterial microorganisms commonly used as standards (three multidrug-resistant bacteria, Acinetobacter baumannii (A1), Acinetobacter baumannii (A2), and Pseudomonas aeruginosa (P1)), and three other common bacterial oral flora, Staphylococcus aureus (S1), Streptococcus mutans ( ATCC 25175) (St1), and Enterococcus faecalis (ATCC 19433) (E1) ). These bacterial strains were obtained from Ain Shams University Hospital, Cairo, Egypt; these strains are endemic to society and present as oral microbial flora . MHA plates were inoculated with 10 6 CFU/ml bacterial cultures using a sterile cotton swab, and each tested bacterial isolate was uniformly spread on culture media, separately and under aseptic conditions. Six orthodontic wires were placed on each plate (uncoated control, PVA-coated, CS-coated, AgNPs-coated, PVA-Ag coated, and CS-Ag coated) then incubated at 37 °C for 24 h. The antimicrobial assay was performed in triplicate, and the diameter of the inhibition zone (mm) was measured; the results are reported as the mean ± standard deviation .
The ability of the coated wires to inhibit biofilm formation was evaluated against two strong biofilm-forming bacteria (A1 and P1). Briefly, bacterial isolates were grown in sterile (10 ml) tryptic soy broth (TSB) (Merck, Germany). Bacterial inoculum of 0.5 McFarland standard (1.5 × 10 8 CFU/ml ) was used and diluted by 1:100, then inoculated into 96-well microtiter plates that previously contained coated and uncoated wires. The plates were incubated at 37 °C for 24 h and then washed and air dried at room temperature. Crystal violet solution (200 µl of 0.1% w/v) was added, and the mixture was left for 30 min and then washed and dried. Finally, 100 µl of ethanol (96%) was added to extract the stained bound biofilm. The absorbance was measured at 490 nm by microplate reader (ELx808™ Absorbance, Biotek, USA). Biofilm reduction was measured and compared to that of the control and the percentage of biofilm inhibition was calculated using the following equation: [12pt]{minimal}
$$ {OD\,ofnegativecontrol}}} .-} {OD\,ofnegativecontrol}}} ) 100 }$$ The OD of the tested wire is the optical density of the sample. The OD of the negative control is the optical density of the control biofilm-forming bacteria.
Atomic force microscopy (AFM) The surface characteristics of the coated wires were assessed by AFM (NanoSurf C3000, Gräubernstrasse, Liestal, Switzerland) operating in phase contrast mode. AFM provides measurements of surface roughness, and variability in defined measured areas by providing 3D images. The average coated layer thickness, roughness (Ra), and maximum roughness depth (Rq) were calculated for the coated wires using image processing and data analysis software supplied with the AFM. Scanning electron microscopy (SEM)/ energy dispersive X-ray spectroscopy (EDX) Wire specimens were examined by SEM/EDX (QUANTA, FEG 250, Thermo Scientific) operating at an accelerating voltage of 20 kV to visualize the surface changes and detect the coating extent and its efficacy on wires for all tested coating materials vs. uncoated control wire samples EDX analysis was used to identify the composition and elemental analysis the specimen surfaces. The wire specimens were mounted on metallic copper stubs and fixed with carbon conductive tape at a standard tilt angle, and SEM photomicrographs were taken from the surface at various magnifications . Release of Ag and Ni ions Nickel and Ag ion release analysis was performed by immersing the NiTi arch-wires (uncoated and coated) in 5 mL of phosphate-buffered saline (PBS) in a sterilized tube for a period of 192 h. The released Ag and Ni ions were measured using Atomic Absorption Spectrometer (AAS) (Perkin Elmer 3100) at 24, 48, 96 and 192 h for each wire sample.
The surface characteristics of the coated wires were assessed by AFM (NanoSurf C3000, Gräubernstrasse, Liestal, Switzerland) operating in phase contrast mode. AFM provides measurements of surface roughness, and variability in defined measured areas by providing 3D images. The average coated layer thickness, roughness (Ra), and maximum roughness depth (Rq) were calculated for the coated wires using image processing and data analysis software supplied with the AFM.
Wire specimens were examined by SEM/EDX (QUANTA, FEG 250, Thermo Scientific) operating at an accelerating voltage of 20 kV to visualize the surface changes and detect the coating extent and its efficacy on wires for all tested coating materials vs. uncoated control wire samples EDX analysis was used to identify the composition and elemental analysis the specimen surfaces. The wire specimens were mounted on metallic copper stubs and fixed with carbon conductive tape at a standard tilt angle, and SEM photomicrographs were taken from the surface at various magnifications .
Nickel and Ag ion release analysis was performed by immersing the NiTi arch-wires (uncoated and coated) in 5 mL of phosphate-buffered saline (PBS) in a sterilized tube for a period of 192 h. The released Ag and Ni ions were measured using Atomic Absorption Spectrometer (AAS) (Perkin Elmer 3100) at 24, 48, 96 and 192 h for each wire sample.
The SPSS standard software package was used for data analysis. One-way analysis of variance (ANOVA) with Tukey’s post-hoc test was used to compare the effects between the groups ( n = 6) for antibacterial and antibiofilm tests. The data are represented as the mean ± standard deviation (SD). The level of significant difference was set at p < 0.05.
Characterization of AgNPs Enterobacter cloacae Ism 26 (KP988024) was able to synthesize AgNPs by forming a reddish-brown solution which further showed characteristic peak of the AgNPs at 430 nm. TEM micrographs showed nanoparticle sizes ranging from 30 to 13 nm. The AgNPs were spherical in shape, with an average size of 20 nm and negatively charged, with zeta potential of (-42 mV) (Fig. ) . Antibacterial activity The antimicrobial activity of the coated and uncoated NiTi wires (uncoated control, PVA-coated, CS-coated, AgNPs-coated, PVA-Ag coated, and CS-Ag coated) at different concentrations of AgNPs (C1, C2 and C3) was assessed against MDR bacteria using an agar diffusion assay. CS-Ag coated NiTi wires showed the most significant antimicrobial activity at different concentrations, where C3 showed the largest inhibition zones, with diameters ranging between 20.33 ± 0.816 and 26.16 ± 1.329 mm, against both Gram-positive and Gram-negative bacteria, followed by PVA-Ag coated at C3, with inhibition zones ranging from 18.5 ± 0.816 to 25.33 ± 0.516 mm. Both PVA-Ag coated, and CS-Ag coated showed the maximum activity against S1, and the minimum activity against P1 and St1. Furthermore, the relationship between the concentration of AgNPs and the inhibition zone was directly proportional for both Gram-positive and Gram-negative bacteria. The uncoated control and PVA-coated NiTi wires showed zero inhibition zones against all the evaluated bacterial isolates. However, the CS-coated wires showed similar, sometimes even greater inhibition zone values than did the AgNPs coated wires (Fig. ; Table ). Antibiofilm activity Coated and uncoated NiTi wires were evaluated for their antibiofilm activity against biofilm-forming bacterial isolates (A1 and P1). In this test we have calculated the dead and inhibited cells not the metabolic activity of the biofilm. Both bacterial isolates showed high absorbance at 490 nm with biofilm formation against control uncoated NiTi wires; furthermore, this absorption increased with PVA-coated wires, showing the same low range of biofilm inhibition even as the concentration of AgNPs increased. Although the presence of CS as a coating material caused a slight decrease in biofilm formation, augmenting CS with AgNPs to form a nanocomposite coating layer has caused significant biofilm inhibition which was significantly observed as the concentration of AgNPs increased at C3, reaching 83 ± 3.5% inhibition against (A1) Acinetobacter baumannii biofilm-forming bacteria (Fig. ). Characterization of the NiTi coated wires Atomic force microscopy (AFM) The AFM 3D image pseudocolor graphs show the different coating material attachments and surface differences between thickness and appearance of the three coated NiTi wires (Fig. ). The observed coated layers showed variability in thickness, where the PVA-Ag layer showed the lowest thickness at an average of 10 nm, while the AgNPs coating layer showed an intermediate line fit thickness at an average of 178 nm. The CS-Ag coating layer showed the greatest thickness at an average of 193 nm. The average Ra values for the PVA-Ag, CS-Ag, and AgNPs coated NiTi wires were 40.06 nm, 139.99 nm, and 40.33 nm, respectively. Furthermore, the average Rq for the PVA-Ag, CS-Ag, and AgNPs coated wires were 46.3 nm, 164.12 nm, and 49.58 nm, respectively. Scanning electron microscope equipped with energy dispersive X-ray spectroscopy (SEM/EDX) SEM images of the coated and uncoated wire surfaces revealed the topography and demonstrated homogeneity with an almost equal distribution of the prepared coating layers. EDX spectroscopy analysis is used to determine the essential elemental composition and percentile of a substance (Figs. and ). This analysis confirmed the presence of Ag within the coated orthodontic arch-wire segments compared with the control groups, indicating the attachment and presence of Ag coating the specimens in the nanocomposite matrices of CS and PVA along with carbon (C), oxygen (O 2 ), Ni and Ti. The presence of AgNPs was indicated by the presence of dark spots detected on the NiTi wires. The CS-Ag coated NiTi wires showed fewer surface irregularities than did the PVA-Ag coated wires. The CS-Ag coated NiTi wires exhibited a very homogeneous, dense layer surrounding the wire, ensuring that the AgNPs attached to the wire surface with a smooth, fully surrounding layer, resulting in more pigmented spots around the wire. After intentional scraping, SEM images revealed a well-defined continuous layer of nanocomposite layer with dark spots adhered to the wires. In contrast, the AgNPs and PVA-Ag coated wires had a thinner layer with lighter spots. Release of Ni and Ag ions The release of Ag and Ni ions from the coated wires was calculated after 24, 48, 96 and 192 h for each NiTi wire sample. AAS results showed that the Ag ions in all the AgNPs and nanocomposites coated samples were the highest after 24 h at a maximum of 0.18 ppm and were inversely proportional to time. The release rates ranged between 0.03 and 0.18 ppm, while the Ni ions in all the samples ranged between 0.12 and 0.33 ppm, with a continuous decrease in the release rates of both elements up to 192 h (8 days).
Enterobacter cloacae Ism 26 (KP988024) was able to synthesize AgNPs by forming a reddish-brown solution which further showed characteristic peak of the AgNPs at 430 nm. TEM micrographs showed nanoparticle sizes ranging from 30 to 13 nm. The AgNPs were spherical in shape, with an average size of 20 nm and negatively charged, with zeta potential of (-42 mV) (Fig. ) .
The antimicrobial activity of the coated and uncoated NiTi wires (uncoated control, PVA-coated, CS-coated, AgNPs-coated, PVA-Ag coated, and CS-Ag coated) at different concentrations of AgNPs (C1, C2 and C3) was assessed against MDR bacteria using an agar diffusion assay. CS-Ag coated NiTi wires showed the most significant antimicrobial activity at different concentrations, where C3 showed the largest inhibition zones, with diameters ranging between 20.33 ± 0.816 and 26.16 ± 1.329 mm, against both Gram-positive and Gram-negative bacteria, followed by PVA-Ag coated at C3, with inhibition zones ranging from 18.5 ± 0.816 to 25.33 ± 0.516 mm. Both PVA-Ag coated, and CS-Ag coated showed the maximum activity against S1, and the minimum activity against P1 and St1. Furthermore, the relationship between the concentration of AgNPs and the inhibition zone was directly proportional for both Gram-positive and Gram-negative bacteria. The uncoated control and PVA-coated NiTi wires showed zero inhibition zones against all the evaluated bacterial isolates. However, the CS-coated wires showed similar, sometimes even greater inhibition zone values than did the AgNPs coated wires (Fig. ; Table ).
Coated and uncoated NiTi wires were evaluated for their antibiofilm activity against biofilm-forming bacterial isolates (A1 and P1). In this test we have calculated the dead and inhibited cells not the metabolic activity of the biofilm. Both bacterial isolates showed high absorbance at 490 nm with biofilm formation against control uncoated NiTi wires; furthermore, this absorption increased with PVA-coated wires, showing the same low range of biofilm inhibition even as the concentration of AgNPs increased. Although the presence of CS as a coating material caused a slight decrease in biofilm formation, augmenting CS with AgNPs to form a nanocomposite coating layer has caused significant biofilm inhibition which was significantly observed as the concentration of AgNPs increased at C3, reaching 83 ± 3.5% inhibition against (A1) Acinetobacter baumannii biofilm-forming bacteria (Fig. ).
Atomic force microscopy (AFM) The AFM 3D image pseudocolor graphs show the different coating material attachments and surface differences between thickness and appearance of the three coated NiTi wires (Fig. ). The observed coated layers showed variability in thickness, where the PVA-Ag layer showed the lowest thickness at an average of 10 nm, while the AgNPs coating layer showed an intermediate line fit thickness at an average of 178 nm. The CS-Ag coating layer showed the greatest thickness at an average of 193 nm. The average Ra values for the PVA-Ag, CS-Ag, and AgNPs coated NiTi wires were 40.06 nm, 139.99 nm, and 40.33 nm, respectively. Furthermore, the average Rq for the PVA-Ag, CS-Ag, and AgNPs coated wires were 46.3 nm, 164.12 nm, and 49.58 nm, respectively. Scanning electron microscope equipped with energy dispersive X-ray spectroscopy (SEM/EDX) SEM images of the coated and uncoated wire surfaces revealed the topography and demonstrated homogeneity with an almost equal distribution of the prepared coating layers. EDX spectroscopy analysis is used to determine the essential elemental composition and percentile of a substance (Figs. and ). This analysis confirmed the presence of Ag within the coated orthodontic arch-wire segments compared with the control groups, indicating the attachment and presence of Ag coating the specimens in the nanocomposite matrices of CS and PVA along with carbon (C), oxygen (O 2 ), Ni and Ti. The presence of AgNPs was indicated by the presence of dark spots detected on the NiTi wires. The CS-Ag coated NiTi wires showed fewer surface irregularities than did the PVA-Ag coated wires. The CS-Ag coated NiTi wires exhibited a very homogeneous, dense layer surrounding the wire, ensuring that the AgNPs attached to the wire surface with a smooth, fully surrounding layer, resulting in more pigmented spots around the wire. After intentional scraping, SEM images revealed a well-defined continuous layer of nanocomposite layer with dark spots adhered to the wires. In contrast, the AgNPs and PVA-Ag coated wires had a thinner layer with lighter spots. Release of Ni and Ag ions The release of Ag and Ni ions from the coated wires was calculated after 24, 48, 96 and 192 h for each NiTi wire sample. AAS results showed that the Ag ions in all the AgNPs and nanocomposites coated samples were the highest after 24 h at a maximum of 0.18 ppm and were inversely proportional to time. The release rates ranged between 0.03 and 0.18 ppm, while the Ni ions in all the samples ranged between 0.12 and 0.33 ppm, with a continuous decrease in the release rates of both elements up to 192 h (8 days).
The AFM 3D image pseudocolor graphs show the different coating material attachments and surface differences between thickness and appearance of the three coated NiTi wires (Fig. ). The observed coated layers showed variability in thickness, where the PVA-Ag layer showed the lowest thickness at an average of 10 nm, while the AgNPs coating layer showed an intermediate line fit thickness at an average of 178 nm. The CS-Ag coating layer showed the greatest thickness at an average of 193 nm. The average Ra values for the PVA-Ag, CS-Ag, and AgNPs coated NiTi wires were 40.06 nm, 139.99 nm, and 40.33 nm, respectively. Furthermore, the average Rq for the PVA-Ag, CS-Ag, and AgNPs coated wires were 46.3 nm, 164.12 nm, and 49.58 nm, respectively.
SEM images of the coated and uncoated wire surfaces revealed the topography and demonstrated homogeneity with an almost equal distribution of the prepared coating layers. EDX spectroscopy analysis is used to determine the essential elemental composition and percentile of a substance (Figs. and ). This analysis confirmed the presence of Ag within the coated orthodontic arch-wire segments compared with the control groups, indicating the attachment and presence of Ag coating the specimens in the nanocomposite matrices of CS and PVA along with carbon (C), oxygen (O 2 ), Ni and Ti. The presence of AgNPs was indicated by the presence of dark spots detected on the NiTi wires. The CS-Ag coated NiTi wires showed fewer surface irregularities than did the PVA-Ag coated wires. The CS-Ag coated NiTi wires exhibited a very homogeneous, dense layer surrounding the wire, ensuring that the AgNPs attached to the wire surface with a smooth, fully surrounding layer, resulting in more pigmented spots around the wire. After intentional scraping, SEM images revealed a well-defined continuous layer of nanocomposite layer with dark spots adhered to the wires. In contrast, the AgNPs and PVA-Ag coated wires had a thinner layer with lighter spots.
The release of Ag and Ni ions from the coated wires was calculated after 24, 48, 96 and 192 h for each NiTi wire sample. AAS results showed that the Ag ions in all the AgNPs and nanocomposites coated samples were the highest after 24 h at a maximum of 0.18 ppm and were inversely proportional to time. The release rates ranged between 0.03 and 0.18 ppm, while the Ni ions in all the samples ranged between 0.12 and 0.33 ppm, with a continuous decrease in the release rates of both elements up to 192 h (8 days).
Orthodontic wires are essential tools for the correction of dental malocclusions in a process associated with bacterial accumulation and biofilm formation. Surface coatings are a simple means to augment the chemical and biological activities of dental appliances. Antibacterial and, more attractively, the antibiofilm activities are the most targeted properties, where these coatings can sustain the release of antibacterial agents for the desired period after implantation. These active coatings ensure the release of encapsulated bactericidal nanoparticles at effective concentrations that can inhibit bacterial colonization and biofilm formation. Providing the ultimate required niche for orthodontic treatment without the disadvantages and obstacles traditionally faced during orthodontic procedures . Coating these wires with antimicrobial agents, to decrease microbial colonization is the main aim of this study. Here, we augmented the biological activity of NiTi orthodontic wires (antimicrobial and antibiofilm) by coating them with AgNPs and Ag nanocomposites (PVA-Ag and CS-Ag). The recorded antimicrobial activity of these wires confirms the significance of the added layer in the fight against microbial attachment and colonization. These results showed the superiority of CS-Ag coated wires, as they retained AgNPs, where they acted as an encapsulated form of Ag nanocomposite with maximum rates of inhibition against Gram-positive bacteria such as Staphylococcus aureus , Streptococcus mutans and Enterococcus faecalis , which are the most predominant bacterial species in the oral cavity. Similar results against Gram-positive bacteria were reported in multiple studies . Other studies, such as Nafarrate-Valdez et al., 2022, have tested the antimicrobial activity of AgNPs and CuNPs coated stainless steel (SS) wires against Streptococcus mutans . Other studies have shown the ability of AgNPs coated orthodontic bands to cause significantly greater antimicrobial activity than ZnO coated bands . In this study, we explored the effects of nanoparticles and nanocomposite coatings on Gram-negative bacteria and demonstrated their significant microbial inhibitory effects on Pseudomonas spp. and Acinetobacter spp. We have noted that studies have not tested the antibacterial activity of NiTi coated wires against Gram-negative bacteria, under the assumption that Gram-positive are the most dominate and represent the highest rates of recorded infections. However, Gram-negative infections are present in hospitalised individuals, patients with poor oral hygiene and patients with chronic periodontal infection . The results confirmed the ability of these coated NiTi wires to cause significant biofilm inhibition. CS-Ag coated wires showed the most meaningful results against Gram-negative biofilm-former multidrug-resistant bacteria. Our results confirmed that the attachment of AgNPs to wires can be achieved and the Ag nanocomposite (CS-Ag) inhibited bacterial attachment, growth, and colonization. In this study, we highlighted the efficacy of these coated NiTi wires against Gram-negative bacteria, as this is the current and upcoming critical category of microorganisms as stated by the WHO. This type of infection has become more prevalent after the emergence of COVID-19 and the dominance of antibiotic resistance, with a higher susceptibility of individuals to infections, especially Gram- negative infections. This antibacterial and antibiofilm activity of AgNPs can be attributed to several factors. These factors include the following: synthesis route by using an environmentally friendly pathway, a small particle size less than 50 nm, a spherical shape form of nanoparticles with a well-defined zeta potential, high penetration power and accessibility to bacterial cell (Gram-positive and Gram-negative) that are able to inhibit bacterial growth by causing bacterial cell disturbance and death . In our study, the significant bacterial growth inhibition can be attributed to the small sized AgNPs, average size of 20 nm, which have acted against all tested bacterial isolates. Furthermore, AgNPs have shown more antimicrobial affinity against Gram-negative than Gram-positive bacteria. This can be explained by the difference in bacterial structure and the thin LPS layer in Gram-negative bacteria that may function as attraction sites for nanoparticles. On the other hand, Gram-positive bacteria have thick layer of peptidoglycan that act as barricades against the flow of AgNPs towards the bacterial cell . The antimetabolic activity of synthesized nanoparticles and coated orthodontic wires against formed biofilms need to be assessed in future research to be able to have a better understanding to the mechanism of action of nanoparticles and coated material on the biofilm metabolic activity. Using carriers and scaffolds for these biologically synthesized nanoparticles has enhanced the sustainability and survival of these particles, increasing the effectiveness and durability of antimicrobial and antibiofilm activity . This superior characters for coated NiTi wires using biologically synthesized AgNPs and Ag nanocomposite further confirm the availability of an eco-friendly, alternative against multi drug resistant bacteria and for immunocompromised patients. A similar deduction was reached by other studies emphasizing the augmentation effect resulting from coating orthodontic wires and their ability to prevent dental plaque formation and caries during orthodontic treatments . An early study showed the coating effect of adding vancomycin to a thin sol-gel film, which showed antimicrobial activity against Staphylococcus aureus . On the other hand, some studies failed to show any change in bacterial growth using wires coated with epoxy resin and polytetrafluoroethylene (PTFE), where no antimicrobial agent was added . However, the addition of AgNPs in combination with PTFE and chitosan nanoparticles with polyethylene glycol (PEG) as a coating layer has achieved significant antimicrobial activity confirming the essential role of AgNPs as antimicrobial agents. We can hypothesize that the combination of nanoparticles with a polymer, increases the reachability of the nanoparticles and their sustainability at the targeted site, pushing the antimicrobial and antibiofilm activities a further step by augmenting their results. In this study we demonstrated the antimicrobial and antibiofilm activity of the nanoparticles and coated wires against normal oral flora such as Staphylococcus spp , Streptococcus spp and Enterococcus spp and the emerging drug resistant and biofilm forming Acinetobacter baumannii and Pseudomonas aeruginosa . This study has focused on the highly alarming emerging drug resistant and opportunistic bacterial species by developing new dental appliances, with the capability to withstand the emergence of multi drug resistance bacteria and other transient bacteria from other body sites to the oral cavity, during the long course of treatment, providing orthodontic wires with antimicrobial and antibiofilm activities against various number of bacterial infections and act as a shield against the susceptibility of the dental wires to be a niche for transient or normal oral microbial inhabitant . In this study, we used a sol-gel thin film dipping process, which ensures high starting material purity and provides 3D structural homogeneity. This process can also be conducted at low temperatures and using chemicals with a low toxicity index. Furthermore, as a nanocomposite, this form of encapsulation can protect the NiTi wires from oxidation by the nanoparticles themselves . Other studies used higher temperature reaching 380 0 C to achieve similar results, of higher resistance to microbial colonization and lower friction and coloration properties using different polymers as carrier or nanocomposite such as PTFE , 2-methacryloyloxyethyl phosphorylcholine , parylene , and epoxy . The AFM results revealed that the AgNPs, PVA-Ag and CS-Ag coatings significantly reduced the surface roughness of the NiTi wires. When uncoated NiTi wires have high surface roughness (0.1 and 1.3 micrometres) , nanocoating with AgNPs alone or with Ag nanocomposites decreases these values to the nanometer scale and decreases the surface roughness significantly. Similar results were obtained by previous studies using various nanomaterials as coatings , confirming the effectiveness of coating on future improvements targeted by research to increase the surface quality, corrosion resistance and biocompatibility of NiTi wires. Here, we must indicate that surface roughness is directly proportional to the friction coefficient, where coating NiTi wires not only decreased the surface roughness but also impacts the friction of the wires. This can be explained by the ability of nanoparticles to function as a buffer or a separation layer that can minimize contact and decrease surface sharpness. Another aspect that can be discussed is the continuous release of the nanomaterial, which acts as a lubricant that has been washed and provides a slippery route, with a positive impact on the friction forces . Furthermore, SEM images and EDX analysis confirmed a uniform coating layer on the NiTi wire surface of the AgNPs and Ag nanocomposites with available Ag, especially in the CS-Ag coated NiTi wires, indicating the ability of this coating material to retain Ag and consequently explain the highest antimicrobial and antibiofilm activities against all tested bacterial isolates. Similar results were presented by previous studies using either AgNPs or CS as a coating material for orthodontic wires and similar results were recorded using nano-ZnO as a coating material . Furthermore, we can speculate that coating orthodontic wires can impact their corrosion resistance and limit metal ions release. This need to be further studied, where normally formed oxide layer can be affected by orthodontic sliding mechanics and the acidic pH created by colonizing bacteria compromises this protective oxide layer and accelerates the corrosion process. Previous studies have suggest that coating materials can delay and optimistically prevent the corrosion process and limit the release of Ni ions release . By performing an in vitro study, we acknowledge the controlled experimental conditions and results provided in the laboratory in comparison to in vivo studies; however, we pave the way for guidelines and preliminary conclusive results for the upcoming animal and clinical studies that should address the advantages of coating by metal nanoparticle encapsulation to reduce all metal-ion-related allergies and the consequential clinical effects of the coating in clinical practice and its effects on microbial growth, tooth surface demineralization, and periodontal diseases. Finally, during the long course of orthodontic treatment, that may last for many months, these coated wires will provide antibacterial and antibiofilm capabilities with a prophylactic edge, if and when patients are exposed to antibiotic resistant non-oral bacterial species that may present as transient unwelcome guests or an opportunistic pathogens.
The AgNPs and nanocomposite coated NiTi wires showed significantly greater antibacterial and antibiofilm activities, especially the CS-Ag coated wires, which exhibited the highest rates of bacterial and biofilm inhibition against both Gram-positive and Gram-negative bacteria. All three types of coatings impacted the surface roughness, topography, and release of ions, with positive implications for future in vivo studies using coated NiTi wires.
Below is the link to the electronic supplementary material. Supplementary Material 1
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ECHO OEM virtual community of learning for primary care | 83d89ddc-7928-4348-918b-fd57a115f8d9 | 11444376 | Preventive Medicine[mh] | Patients presenting with occupational health issues are common in primary care . Healthcare providers (HCPs) must be able to take an effective occupational history to determine if an exposure in the workplace (chemical, biological, physical, ergonomic or psychological) is causing or aggravating a patient’s disease or injury . HCPs must also be able to advise patients with disabling health conditions, who want to return to or remain at work, about appropriate workplace adjustments that may overcome obstacles to work arising from ill health. Despite the need for HCPs to have knowledge of occupational health, HCPs receive limited training in occupational and environmental medicine in undergraduate and graduate programmes, and there is also a paucity of continuing education opportunities for HCPs in this area . HCP confidence in assessing and facilitating return-to-work (RTW) for their patients is particularly important. A cross-sectional study conducted in Ontario demonstrated that HCPs are key players in the RTW process . In general, positive and encouraging messages from physicians, and provision of a date when a patient can expect to RTW, are associated with better RTW outcomes. Patients who receive information about injury prevention, pain management and work accommodation are also more likely to RTW . A need for interdisciplinary working is recognized to achieve optimal outcomes , and evidence indicates interprofessional teams achieve better vocational outcomes for chronic pain , mental illness and severe trauma such as burns . To fulfil their role in supporting the occupational health of patients, HCPs need to develop skills and learn about resources that enable them to work collaboratively. It is important to ensure the teaching of HCPs does not unduly increase demands on those who are already overburdened by increasing caseloads and demands on their time, especially in rural, remote and underserved areas. Project ECHO (Extensions for Community Healthcare Outcomes) connects HCPs in primary care with experts using weekly videoconference sessions to discuss cases and deliver didactic presentations. Project ECHO was developed in 2003 for the treatment of individuals with Hepatitis C at the University of New Mexico ( https://hsc.unm.edu/echo/ ) and has expanded beyond healthcare outcomes in the USA (e.g. climate change, education and policing) and to other health conditions ). ECHO combines several medical education methods to enhance practice in primary care. There are four pillars of ECHO to break down the walls between specialists and primary HCPs through regular videoconferencing sessions connecting rural and remote HCPs, where de-identified patient cases are presented to an academic expert interprofessional team who then provide guidance to enable the HCPs to treat their own patients: To provide a channel where specialist mentors can share best practices with primary HCPs, reducing variation in care to improve health outcomes; to use a case-based learning process, similar to the supervised apprenticeship characteristics of medical training, as opposed to a purely didactic approach; and to use continuous outcome monitoring for quality improvement and programme evaluation. In 2020, our team was funded by the Ontario Workplace Safety and Insurance Board (WSIB) for a 2-year pilot study. The WSIB is the provincial worker’s compensation agency providing wage-loss benefits, medical coverage and support to help people RTW after a work-related injury or illness. The goal of the ECHO OEM is to increase capacity among HCPs in primary care to manage cases related to occupational and environmental medicine. The aim of this pilot project was to successfully develop, implement and evaluate this ECHO OEM. We followed the recommendations of the ECHO Institute in New Mexico and the ECHO Ontario Superhub for ECHO implementation and evaluation. We employed an observational pre–post study design to assess changes in self-efficacy and knowledge about OEM topics, as well as attitudes and beliefs related to WSIB interactions among participants who attended ECHO OEM. The study was approved by the Research Ethics Board at the University of Toronto (Protocol 40747). Participating HCPs were recruited from September 2021 to November 2021 for cycle 1 and from April 2022 to June 2022 for cycle 2. Eligibility for this study included any HCP working in primary care in Ontario in a regulated profession (physicians, physician assistants, nurse practitioners, registered nurses, pharmacists, psychologists, social workers, chiropractors, registered massage therapists, and physical and occupational therapists), working in any type of practice (solo or team practice), and having the ability to present at least one case in English. We also included any professional with a role in occupational health and safety, and students in healthcare programmes in Ontario. We offered two cycles of 12 sessions each, the first in the Fall of 2021 and the second in the Spring of 2022. The topics were the same for both cycles. The topics and presenters’ areas of expertise for cycle 2 are shown in . One slight modification was made to cycle 2 based on feedback received in cycle 1, that is, we invited a person with live experience of chronic pain and workplace injury to co-present in one session with the expert hub member. The ECHO sessions were held once a week for 12 consecutive weeks and of 90-minute duration. Each included introductions and announcements (5 minutes), didactic with questions and answers (35 minutes), and case presentation and discussion (50 minutes). Sessions were held using Zoom software and moderated by a hub member. Case presenters submitted their de-identified patient cases using a case presentation form before the sessions. We limited the cases to working-age individuals who had a health condition caused or exacerbated by work or where the health condition was impacting RTW or stay-at-work independent of the cause of the health condition. We excluded cases where the patient did not reside in Ontario. Data were collected using online questionnaires in Qualtrics. Pre-ECHO questionnaires were administered after registration in the ECHO programme, but prior to commencement of the cycle. Post-ECHO questionnaires were administered at the end of each cycle. Participants who attended both cycles were assessed before cycle 1 and after cycle 2. Participant demographics and practice characteristics were measured using eight items in the pre-ECHO questionnaire only. Information included age, gender, years in practice, primary profession, country where professional training was completed, number of patients in practice, number of sick notes written per month for patients off work, and estimated number of patients seen per month with injuries or illnesses caused by or worsened by work. Attendance and participation data were collected at every session and included individual attendance and presentation of cases. Acceptability and satisfaction with ECHO were measured using 11 items, post-ECHO only. Participants indicated their level of agreement with statements adapted from ECHO Institute in New Mexico to measure the satisfaction and impact of ECHO OEM. Statements were rated using a 6-point Likert scale from 1 (strongly disagree) to 5 (strongly agree), with 6 representing ‘not applicable’ ( , available as Supplementary data at Occupational Medicine Online) . Self-efficacy was measured pre- and post-ECHO using a 21-item scale adapted from the ECHO Institute in New Mexico . Participants indicated their level of agreement with statements about their skills, knowledge or competence in the 12 ECHO OEM curriculum topics. Statements began with the phrase, ‘I am confident in my ability to ...’ (e.g. I am confident in my ability to make modified work recommendations for my patients). Items were assessed on a 6-point Likert scale from 1 (strongly disagree) to 5 (strongly agree), with 6 representing ‘not applicable’ ( , available as Supplementary data at Occupational Medicine Online). Knowledge was assessed pre- and post-ECHO using 23 items developed by the ECHO OEM expert hub members. Knowledge test questions included statements related to the information covered in the 12 curriculum didactics. Some questions were also adapted from Braeckman et al . . The response options for the first 10 of 23 questions were ‘true’ or ‘false’ (e.g. An activity limitation describes the difficulties a worker may have in executing the job tasks‘). The last 13 knowledge questions included multiple choice response options (‘a’, ‘b’, ‘c’ or ‘d’; e.g. ‘Which of the following questions is most likely to provide the best information about a worker’s exposure to workplace hazards?’. A score was assigned for correct answers only. Total knowledge scores could range from 0 to 23 ( , available as Supplementary data at Occupational Medicine Online). Attitudes and beliefs related to the WSIB and the management of conditions that affect a patient’s ability to RTW was measured using a 10-item scale adapted from other Project ECHO hubs . However, one item was dropped from analysis (‘I believe that multiple chemical sensitivity is a valid diagnosis to obtain compensation benefits’) as this topic was not discussed either in the didactic or case presentations of any ECHO session. Participants reported attitudes and beliefs using a 5-point Likert scale from 1 ‘strongly disagree’ to 5 ‘strongly agree’ (e.g. I believe that most workers’ compensation recipients fake their injuries because they want to get paid not to work”). Data were analysed using parametric and non-parametric statistics. We only included participants who completed both pre- and post-ECHO evaluations. Self-efficacy and knowledge tests were aggregated as scores for each participant. The knowledge test was analysed in two subgroups participants with some expertise (‘informed participants’) and participants without expertise in OEM. Repeated-measures analysis of variance (ANOVA) was performed to assess differences in knowledge before and after ECHO participation within and between informed and non-informed groups. In total, 229 participants registered for the programme: 65 for cycle 1 and 164 for cycle 2 . Seventy-nine registrants never attended any sessions: 16 in cycle 1 and 63 in cycle 2. Of the remaining 150 people who attended at least one session, more than 50% attended six or fewer sessions . A total of 124 people completed at least one of the questionnaires (pre- or post-ECHO). Of these, 67 completed both pre- and post-ECHO questionnaires. The subsample of the 67 people with complete data was similar, in demographics and practice characteristics, to the total sample of 124 participants who completed at least one questionnaire (data available in , available as Supplementary data at Occupational Medicine Online). Although the target audience of ECHO programmes are non-experts, some registrants had experience in occupational health, such as non-academic occupational physicians and occupational therapists who work full time with injured workers. There were 25 cases presented over the course of the two cycles: 13 cases in cycle 1 and 12 in cycle 2. The types of cases presented included musculoskeletal injuries (6), post-traumatic stress disorders (4), chemical exposures (2), concussions (2), mental health conditions (2), caregiver strain (2), pain (2), non-adherence to treatment (2), occupational dust exposure risk (1), low visual acuity (1) and pregnancy (1). Respondents reported high levels of acceptability and satisfaction with ECHO OEM on 11 measures post-ECHO . Measures relating to acceptability and personal satisfaction (worthwhile experience, learning, benefit to patient care and professional satisfaction) were rated highest (≥80%), and questions related to general care considerations in the community (access, variations in care) were rated lower (59–78%). Self-efficacy significantly increased in the post-ECHO compared to pre-ECHO, F (1, 64) = 64.11, P < 0.001 . There was no difference in the average self-efficacy score between informed and non-informed participants, F (1, 64) = 3.03, P non-significant . Knowledge was assessed in the subgroup of non-informed participants ( n = 48) and in the informed subgroup ( n = 18) . On average, there was no difference in knowledge scores between informed and non-informed, F (1, 64) = 8.9, P non-significant . The within-subject main effect test was significant ( F (1, 64) = 8.9, P < 0.01); therefore, we can conclude that the knowledge score increased significantly after ECHO. Since the interaction term of informed participant by time was not significant ( F (1, 64) = 1.36, P non-significant ), there was no significant difference in knowledge change between informed and non-informed . presents individual pre- and post-test responses to the 10 items assessing attitudes and beliefs. Of the 10 items assessed before and after ECHO, only 2 items significantly changed pre- and post-ECHO. Both items were related to perceptions and the role of the WSIB. For item 6 ‘I believe that the Workplace Safety and Insurance Board (WSIB) is not doing enough to help patients’, participants showed an overall improvement in confidence in the WSIB (i.e. WSIB working to help patients) and for item 7 ‘I believe that the WSIB is always on the side of the employer’, participants showed an average decrease in belief that WSIB was biased towards employers. The results of this pilot study demonstrated the challenges to implement the first ECHO OEM in Canada. We delivered two cycles of 12 sessions each and discussed 25 cases of people with work-related health issues. Although we initially recruited 229 HCPs, only 150 (66%) attended at least one session, suggesting improvements are necessary to achieve greater participation from primary healthcare providers. Importantly, individuals who participated and completed all questionnaires reported moderate to high acceptability and satisfaction with the programme and increased self-efficacy. Increases in knowledge scores about OEM, while statistically significant, were small and only two of nine valid items improved in the attitudes and beliefs questionnaire. Our results are similar to those from other ECHO programmes in Ontario for chronic pain , COVID-19 and mental health . In the pilot evaluation of the ECHO chronic pain, 296 people registered to the programme, 170 (64%) completed the pre-ECHO questionnaires, there were 51 dropouts (30%) and 119 (70%) who completed both pre- and post-ECHO questionnaires. There was a significant increase in self-efficacy and knowledge. Self-efficacy improvement was significantly higher among physicians, physician assistants and nurse practitioners than non-prescribers group. On average, 96% of participants were satisfied with ECHO chronic pain, and satisfaction was higher among those who presented cases and attended more sessions . There is a need to employ qualitative research methods to understand the reasons and intentions of the ECHO participants when they register. Our study did not collect patient data, and we cannot determine if the changes in knowledge, self-efficacy and beliefs demonstrated by participants in ECHO OEM also translated into changes in patients’ outcomes. The 25 cases presented were de-identified, and we did not follow up with the recommendations made during the session. The main goal of ECHO OEM was to increase capacity among primary healthcare providers to manage patients with occupational and environmental health issues in Ontario. This was the first ECHO OEM in Canada, and the first ECHO in the world to consider all forms of occupational injury and diseases. We demonstrated that it is possible to implement this type of programme. Although there was considerable interest in the programme, with 229 healthcare providers registering to participate, participation and retention in the programme were poor. Future studies need to employ principles of implementation science to understand the barriers to participation and retention in continuing education programmes like ECHO. The time commitment of 90 minutes per week for 12 weeks might need to be re-evaluated. A secondary goal of ECHO OEM was to identify strategies to improve physician engagement with the WSIB. To assess if we had achieved this goal, we included a series of questions on the WSIB in the knowledge, behaviours and attitudes questionnaires. Although WSIB is the main funder, our project was conducted under the auspices of a broader Advisory Committee, and the WSIB had no influence on the questionnaires or data collection. Participants in the programme also reported acceptability of and satisfaction with ECHO OEM. However, the magnitude of the changes in self-efficacy, knowledge, and attitudes and beliefs were small. This may reflect the fact that the programme seemed to have attracted some participants with some pre-existing expertise in occupational and environmental health, as demonstrated by the high baseline scores in these measures. This type of continuing medical education programme may also be more likely to be attended by those interested in the topic. As such, there may have been less room for improvement in scores. The attitudes and beliefs questions will need to be revised in future offers of this programme, and they need to reflect content that is discussed during the ECHO sessions. This ECHO OEM provided knowledge and skills to HCP in primary care to manage patients with work-related problems, occupational illnesses and RTW strategies. Although the intended audience was primary HCPs with minimal occupational and environmental medicine knowledge, our programme attracted participants with some expertise and experience in this topic area. Future offerings of this programme should focus on recruiting participants in primary care with minimal expertise and experience. Levels of knowledge and self-efficacy were assessed immediately after the end of the ECHO cycle, and we do not know if any changes to these measures were sustained after the programme ended. It is also important to assess if there is a spill-over effect on other patients seen by the participating HCPs, and if they share their knowledge with other HCPs. Diffusion and penetration of ECHO are defined as the influence on the treatment of other patients seen by the same HCP, transfer of knowledge to other HCPs within the same team, and transfer of knowledge to other HCPs in the same geographical area. Qualitative research methods are also important to assess if there is a better understanding of the role of WSIB after participants attended ECHO OEM. Key learning points What is already known about this subject: Health conditions caused and/or exacerbated by work are commonly seen in primary care settings. Primary care providers receive little to no training in occupational or environmental medicine. As a result, primary care clinicians lack knowledge of their role in return to work or how to engage with other partners in the return-to-work process. What this study adds: Project ECHO OEM (Extensions for Community Healthcare Outcomes Occupational and Environmental Medicine) is a virtual programme that was developed to disseminate knowledge from specialists in OEM to primary care providers in rural, remote and underserved areas. Healthcare providers who attended ECHO and completed pre- and post-questionnaires reported gains in knowledge and self-efficacy in OEM. What impact this may have on practice or policy: There is a need to understand barriers to participation in programmes that teach primary care providers about occupational medicine. Health conditions caused and/or exacerbated by work are commonly seen in primary care settings. Primary care providers receive little to no training in occupational or environmental medicine. As a result, primary care clinicians lack knowledge of their role in return to work or how to engage with other partners in the return-to-work process. Project ECHO OEM (Extensions for Community Healthcare Outcomes Occupational and Environmental Medicine) is a virtual programme that was developed to disseminate knowledge from specialists in OEM to primary care providers in rural, remote and underserved areas. Healthcare providers who attended ECHO and completed pre- and post-questionnaires reported gains in knowledge and self-efficacy in OEM. There is a need to understand barriers to participation in programmes that teach primary care providers about occupational medicine. kqae067_suppl_Supplementary_Appendix_1 kqae067_suppl_Supplementary_Appendix_2 kqae067_suppl_Supplementary_Appendix_3 kqae067_suppl_Supplementary_Appendix_4 |
Systematic assessment of HER2 status in ductal carcinoma in situ of the breast: a perspective on the potential clinical relevance | e43d5226-2aeb-4475-81d9-f9c7d4ce413a | 11348742 | Anatomy[mh] | Ductal carcinoma in situ (DCIS) is regarded as a non-obligate precursor lesion of invasive breast carcinoma (IBC), with marked heterogeneity at the morphological, immunohistochemical and molecular level . Histopathological grading of DCIS is prone to substantial interobserver variability, with kappa statistics ranging from 0.27 to 0.67, irrespective of the classification system used . Notwithstanding the histological grade, most DCIS patients are uniformly treated, either by lumpectomy and radiotherapy or by mastectomy, depending on the tumor size and the breast size, and ultimately, the patients’ preferences. In several countries, national guidelines recommend hormone receptor status assessment since adjuvant treatment with tamoxifen or aromatase inhibitors in hormone receptor-positive DCIS reduces both the ipsilateral recurrence risk and the contralateral breast cancer risk . However, systematic immunohistochemistry (IHC) for estrogen receptor (ER), progesterone receptor (PR) and HER2 for so-called ‘surrogate molecular subtyping’ is currently only performed for IBC. In particular, the HER2 status in DCIS is not routinely assessed yet, because its role in tumor biology is unclear and there seems to be no substantial clinical impact so far. In the present ‘perspective’, we discuss why it could be useful to add HER2 assessment to hormone receptor status assessment in the pre-operative DCIS work-up. Figure provides an overview of all potential advantages and disadvantages of systematic IHC of ER, PR and HER2 in DCIS. We address this issue through several questions, most of them still debated, which could help stimulate research efforts in these different fields, and pave the way towards a DCIS subtype-dependent treatment. This perspective article does not comprise an exhaustive systematic review nor meta-analysis, but we aimed to provide an evidence-based plea for routine implementation of HER2 IHC in DCIS. HER2 gene amplification and its associated HER2 protein overexpression occur in around 14% of IBC , wherein it correlates with aggressive behavior and poor prognosis in the absence of targeted therapy. As such, HER2-positivity constitutes an important predictive marker for anti-HER2 drugs . Paradoxically, HER2-positivity is much more common in DCIS (Fig. ), with a prevalence ranging from 27 to 35% in several large study cohorts . If DCIS would be an obligate precursor for IBC, one would expect a similar prevalence of HER2-positivity in both invasive and in situ carcinoma, since HER2-positivity embodies a survival benefit for cancer cells . The marked difference in HER2-positivity rates between DCIS and IBC implies that HER2 amplification is an early oncogenic event, acting as a driver of neoplastic cell proliferation rather than as an instigator of transition from in situ to invasive carcinoma . An accumulation of other (yet unknown) oncogenic events, possibly in association with tumor microenvironmental factors, might subsequently trigger the transition to invasion . An additional argument favoring the ‘driver theory’ is the high rate (> 90%) of HER2-positivity in mammary Paget’s disease, suggesting that HER2 is essential for intraductal and intraepithelial spread of the neoplastic cells . An alternative theory suggests that HER2-negative subclones containing other oncogenic drivers outgrow the HER2-positive DCIS cells, eventually resulting in HER2-negative IBC. This ‘negative selection’ phenomenon was observed in patients with clonally related HER2-positive DCIS and HER2-negative ipsilateral invasive recurrence within a genomic analysis by the Grand Challenge PRECISION Consortium . The explanation of the HER2 paradox in breast cancer is limited to theories deduced from observational, mostly retrospective data. Further studies are required to fully elucidate this discrepancy. The natural history of DCIS is poorly understood, as most patients undergo surgery after its diagnosis, which prevents to study its natural course . Data on the progression risk of DCIS to IBC are retrospectively derived from women who were diagnosed with DCIS but who did not undergo (immediate) surgery for variable reasons, such as the Forget-Me-Not 1 and 2 studies . This specific patient population, often presenting comorbidities, renders such studies prone to bias. Interestingly, the 10 year cumulative risk of ipsilateral IBC in the Forget-Me-Not 2 study was not substantially different between intermediate and high grade DCIS, although it was significantly lower for low grade DCIS . The HER2 status was not available in this study, but larger DCIS size was associated with increased ipsilateral IBC risk, regardless of DCIS grade . As HER2-positive DCIS are generally larger than HER2-negative DCIS (Table ) , it could be worthwhile to retrospectively determine the HER2 status in the Forget-Me-Not 2 study, to investigate its association with subsequent ipsilateral IBC. Interestingly, retrospective HER2 IHC within the patient cohort of the UK/ANZ DCIS randomized trial demonstrated that HER2-positive DCIS were more frequently associated with ipsilateral recurrence than HER2-negative DCIS (30.2% versus 15.2%, respectively), but these recurrences were less often IBC (28.4% versus 46.5%, respectively) . In other words, the risk of ipsilateral recurrence is much higher in HER2-positive DCIS, but once ipsilateral recurrence occurs, it is less likely to be IBC . As for the use of HER2 status as a prognostic marker for the overall ipsilateral recurrence risk after surgery, the presently available data are contradictory . Some studies identified a correlation between HER2-positive DCIS and increased ipsilateral in situ recurrence risk , whereas others observed an association between HER2-positive DCIS and increased ipsilateral invasive recurrence risk (Table ) . Available literature on this topic has been recently reviewed by Garg and Thorat , and by Akrida and Mulita . The lack of a significant association between HER2 status and ipsilateral recurrence risk is likely due to lack of power, as many retrospective studies were performed on cohorts of limited size. Many studies were therefore unable to perform reliable multivariable analysis. However, treatment-related confounding in real-world cohorts, outside the clinical trial setting, probably plays an important role as well, given the important radiotherapy benefit observed in HER2-positive DCIS . When adjuvant therapy is not randomly allocated, HER2-positive DCIS are much more likely to be irradiated, since these lesions more frequently present with unfavorable histopathological characteristics, such as high nuclear grade, large size, and necrosis . Without knowledge of the pretreatment HER2 status, real-world DCIS patient cohorts, either prospectively or retrospectively investigated, suffer from a substantial treatment bias . Future routine HER2 assessment could lower the threshold for adjuvant radiotherapy, as a retrospective analysis of the UK/ANZ DCIS randomized trial population showed a higher benefit from adjuvant radiotherapy in HER2-positive DCIS than in HER2-negative DCIS . Vice versa, de-escalation of the current DCIS treatment by omitting radiotherapy could be considered in ER-positive, HER2-negative low grade DCIS , resulting in more personalized treatment. An alternative method to analyze the spontaneous progression to IBC, is to study only those DCIS patients who developed an ipsilateral recurrence after breast-conserving surgery . Most ipsilateral recurrences in the randomized EORTC-10853 trial developed within the same quadrant as the primary DCIS . A substantial percentage of these primary and recurrent lesions showed similar histo-morphological and immunohistochemical profiles, suggesting that most ipsilateral recurrences represent outgrowths of residual, initially incompletely removed DCIS . In a series of 266 DCIS patients with ipsilateral recurrences, invasive recurrences were more often preceded by ER-positive, HER2-negative DCIS, whereas in situ recurrences were more often preceded by ER-negative, HER2-positive DCIS . Discordant HER2 status occurred only in 10,5% of cases and was more frequently observed in invasive recurrences . According to Visser et al., around one in three HER2-positive DCIS with an ipsilateral invasive recurrence shows a discordant HER2 status . Similar discordant HER2 status rates were observed by Gennaro et al. . Although histomorphology and immunohistochemical profiles can hint at clonality between primary DCIS and recurrent tumors, extensive molecular analysis is required to establish a strong conclusion. Gorringe et al. used copy number analysis to study the clonal relationship between eight primary DCIS and their ipsilateral recurrences, and six tumors showed clear copy number events suggesting clonality . The most extensive genomic analysis so far was performed by the Grand Challenge PRECISION Consortium, comprising 34 DCIS with in situ recurrence and 95 DCIS with invasive recurrence . Clonality between primary DCIS and its recurrence was formally established for approximately 75% of patients, and despite this clear clonal relationship, some recurrences showed a discordant HER2 status . It would be interesting to investigate whether HER2-positive primary DCIS is more frequently observed in patients with ipsilateral clonally related invasive recurrences, regardless of the HER2 status of this recurrence, since the main purpose of surgical DCIS treatment is to prevent IBC development, and thus, risk of systemic disease and death . At present, HER2 status cannot be used as a predictive marker for response to anti-HER2 targeted therapies in DCIS, due to lack of sufficient evidence. So far, only one randomized controlled clinical trial has investigated the effect of trastuzumab in a large cohort of DCIS patients: the NSABP B-43 trial did not show a significant benefit from two doses of adjuvant trastuzumab in pure DCIS patients treated with breast-conserving surgery and adjuvant radiotherapy . There was a statistically nonsignificant reduction of 19% of the ipsilateral recurrences in favor of trastuzumab, but the foreseen objective of a 36% reduction was not met . This observed difference could be due to a lack of power whereas a clinical effect is present, due to insufficient follow-up, or due to chance. Longer follow-up within the NSABP B-43 trial cohort would be interesting to investigate late treatment effects. An open-label phase 2 trial, including 24 patients with HER2-positive DCIS, investigated whether preoperative single-dose intravenous trastuzumab could evoke a therapy response . Despite the absence of a histopathological response, treated patients showed higher numbers of CD56-positive natural killer cells, hinting at increased antibody-dependent cell-mediated cytotoxicity . These non-significant results might be due to the limited number of doses of trastuzumab administered, as DCIS admixed with HER2-positive IBC often shows substantial regression after neoadjuvant treatment . Future studies could explore whether prolonged preoperative monotherapy with trastuzumab could downsize pure DCIS, aiming to reduce both the ipsilateral recurrence risk and the resected volume during breast-conserving surgery, with potentially better cosmetic outcome. On the other hand, the use of systemic anti-HER2 treatment is questionable, since DCIS is only a non-obligate precursor of invasive breast cancer, resulting in potential overtreatment for the majority of HER2-positive DCIS patients. HER2 status could be used as a predictive marker for response to radiotherapy . A retrospective analysis, performed on available tissue samples within the prospective UK/ANZ DCIS Randomized Trial, is the only large-scale study to date which performed HER2 IHC on a patient cohort with random allocation to adjuvant radiotherapy . Thorat et al. demonstrated that HER2-positive DCIS patients substantially benefited from adjuvant radiotherapy in comparison with HER2-negative DCIS patients, with a greater reduction in in situ recurrences, but not in invasive recurrences . Ipsilateral in situ recurrence was reduced by 84% by adjuvant radiotherapy in the HER2-positive DCIS patient group, whereas this reduction amounted only to 42% in the HER2-negative DCIS patients . Radiotherapy resulted in similar ten-year ipsilateral recurrence rates in HER2-positive (11.0%) and HER2-negative (9.6%) DCIS patients, whereas omission of radiotherapy resulted in much higher ten-year ipsilateral recurrence rates in HER2-positive (42.1%) than HER2-negative (17.5%) DCIS patients, mainly due to a substantial increase in in situ recurrences . This observation fuels the hypothesis that adjuvant radiotherapy could be omitted in small hormone receptor-positive, HER2-negative DCIS, especially when margin width is at least 2 mm . Given the high number of in situ recurrences in HER2-positive DCIS treated with lumpectomy alone, and given its excellent response to irradiation, it seems desirable to offer radiotherapy to all HER2-positive DCIS patients treated with breast-conserving surgery, to optimize local control. Patient age at diagnosis, as well as any comorbidities, should likely be taken into account too, since adjuvant radiotherapy after breast-conserving surgery for DCIS does not affect overall survival. Nevertheless, systematic HER2 IHC in daily practice seems therefore helpful to offer personalized therapy to DCIS patients . Several studies have shown that HER2-positivity in pure DCIS is strongly associated with larger DCIS size (Table ) . Similarly, HER2-positive IBC is more often associated with a DCIS component than HER2-negative IBC, and this DCIS component is significantly larger and more frequently associated with positive margins . Interestingly, Zhou et al. reported that larger (> 15 mm) primary DCIS lesions were more frequently associated with an in situ recurrence than with an invasive recurrence . All these observations indirectly corroborate the underlying cause of the HER2 paradox, i.e. HER2 is a potent driver of cancer cell proliferation instead of cancer cell invasion . Such powerful neoplastic proliferation can then colonize and involve a complete breast lobe, supporting the ‘sick lobe theory’ described by Tibor Tot . If HER2-positive DCIS is larger, the risk of positive margins is higher, and therefore, the risk of incompletely surgically removed DCIS is higher. The residual DCIS in the breast can then continue to proliferate, slowly but steadily spreading throughout the affected ‘sick lobe’. This could explain why several retrospective studies observed a significant association between HER2-positivity and increased ipsilateral in situ recurrence risk . The number of in situ recurrences in the NSABP B-43 trial doubled the number of invasive recurrences , which provides further indirect support for this theory. Preoperative assessment of the HER2 status in biopsy-diagnosed pure DCIS could encourage breast surgeons to perform wider local excisions for HER2-positive DCIS, thereby aiming to reduce the risk of involved margins and ipsilateral (in situ) recurrence risk. Once the results of four ongoing active surveillance trials will be available, watchful waiting might even become a legitimate option for ER-positive, HER2-negative non-high grade DCIS patients . In addition, HER2-positivity in pure biopsy-diagnosed DCIS is associated with increased upstaging to invasive carcinoma after subsequent surgery . Pre-operative knowledge of the HER2 status of DCIS could therefore help in the selection of patients with a potential benefit from axillary staging by sentinel node procedure. It is a commonly acknowledged fact that grading of DCIS is subject to substantial interobserver variability . During the past decades, DCIS grading was entirely based upon histo-morphological evaluation of cytonuclear atypia . Some classification systems also included a particular architecture and/or comedonecrosis, but grosso modo , their main histopathological constituents are similar. As pathologists are not computers, it is challenging to objectively categorize the biological continuum of cytonuclear atypia into three categories . Interestingly, the majority-based opinion regarding DCIS grade among 38 pathologists is associated with the risk of ipsilateral IBC development . Since HER2 protein overexpression in DCIS is strongly associated with high grade atypia (Table ) , Van Seijen et al. investigated the addition of HER2 IHC to the reproducibility of histopathological grading . Low grade DCIS is unlikely to present with HER2-positivity. For example, the NSABP B-43 cohort of 2.014 HER2-positive DCIS contained only twenty low grade DCIS (1%) and 317 intermediate grade DCIS (16%) . Although not all high grade DCIS present with HER2 protein overexpression, a 3 + HER2-positive score is very suggestive of high grade (Table ) . HER2 IHC is also prone to a certain degree of interobserver variability, but this appears to be mainly an issue for the distinction of 0 scores versus 1 + /2 + scores, whereas the identification of HER2 3 + cases is more reproducible . The systematic use of HER2 IHC in the histopathological work-up of DCIS could therefore improve the reproducibility of grading, which is an important prognostic factor to identify those patients at risk of developing a second ipsilateral breast tumor, either in situ or invasive. This practice is already standard in currently ongoing active surveillance trials LORD and COMET . The addition of HER2 IHC to the histopathological work-up of DCIS calls for new guidelines. We propose an integration of morphological features and ER and HER2 IHC in Fig. , reflecting the workflow of the currently ongoing COMET trial . The feasibility of this integration likely requires prospective validation before routine implementation. As shown in the SWOT analysis (Fig. ), routine IHC for ER, PR and HER2 in DCIS will increase the working costs for pathology labs. However, these immunohistochemical profiles have the potential to lead towards personalized treatment. If some DCIS patients could forego adjuvant radiotherapy, or even surgery by opting for active surveillance, the routine implementation of IHC could perhaps reduce therapy-related costs. At present, it is difficult to provide a detailed cost/benefit analysis, as we did not yet obtain the data of ongoing active surveillance trials to decide how many patients could forego surgery . Once these data are available, such a health economic analysis could be undertaken. Nevertheless, this remains a difficult financial assessment, as reimbursement of health care-related costs differs between countries. Moreover, it is yet unknown how active surveillance needs to be performed: which type of investigation is required and what is its frequency? From a health economic point of view, it might even be cheaper to perform upfront surgery, instead of offering regular medical imaging with associated biopsies. This question needs to be addressed in future clinical and health economic studies. Additionally, the question remains whether HER2 2 + DCIS need to undergo complementary analysis by in situ hybridization (ISH), as is currently performed for invasive breast cancer . Performing ISH additionally increases the cost for histopathology labs. The only large-scale randomized clinical trial on DCIS wherein HER2 IHC has been performed in a systematic way in a central laboratory is the NSABP B-43 study . Here, ISH was performed on all centrally stained HER2 1 + and 2 + DCIS. In total, 1424 out of 5645 DCIS were 1 + (25,2%) of which only one was amplified) . It therefore seems not necessary to perform ISH testing on HER2 1 + cases. In NSABP B-43, 437 patients out of 5645 (7,7%) had a HER2 2 + score, of which 91 DCIS were amplified (20,8%). In other words, the IHC 2 + amplified DCIS represented only 1,6% of that total DCIS population . It remains to be investigated whether the HER2 amplification in these HER2 2 + DCIS has an important biological and clinical consequence, but until we have large-scale studies that can reliably provide these data, we could extrapolate the ASCO/CAP algorithm for HER2 assessment in invasive breast cancer to DCIS, and perform ISH on all DCIS with a HER2 2 + score. Lastly, the question remains whether IHC for PR is required. In several countries, patients with hormone receptor-positive DCIS are eligible for endocrine therapy, and IHC for ER and PR is often performed simultaneously. There is no strong evidence available to support this practice in DCIS; it is mainly based on extrapolation of the ASCO/CAP algorithm for hormonal receptor status assessment in invasive breast cancer patients, although the ASCO/CAP expert panel considers PR IHC as optional . Patients with ER-positive, PR-positive invasive breast cancer tend to respond better to hormonal therapy than patients with an ER-positive, PR-negative invasive breast cancer , but there is no proof of such benefit in DCIS. A retrospective analysis of DCIS samples of patients enrolled in the NSABP B-24 trial showed no added value of PR IHC to ER IHC . Patient stratification by PR status alone or by combined ER and PR status was not more predictive for response to endocrine therapy than ER status alone . We propose thus to follow the ASCO/CAP expert panel consensus, which considers PR IHC as optional but not obligatory in DCIS. Hormone receptor status in pure DCIS is already routinely assessed in many countries, but the evaluation of HER2 status is generally omitted. We believe that systematic implementation of immunohistochemistry for ER, PR and HER2 could substantially improve the diagnostic work-up of pure DCIS, at the very least in clinical trials, but preferentially in routine practice too. HER2 immunohistochemistry (and if required, HER2 in situ hybridization for equivocal cases) signify an additional cost and increased workload for pathologists, but there are also several advantages (Fig. ) . Firstly, the HER2 status in DCIS seems to be associated with ipsilateral recurrence risk. Secondly, HER2-positive DCIS tends to be larger, with a higher risk of involved margins after breast-conserving surgery, and a higher benefit from adjuvant radiotherapy. HER2-positivity in pure biopsy-diagnosed DCIS is associated with increased upstaging to invasive carcinoma after subsequent surgery. Thirdly, immunohistochemistry could reduce the present interobserver variability in morphological DCIS grading among pathologists, as HER2-positivity strongly correlates with high grade. Reproducible grading will become more important in the future, if active surveillance would enter routine practice as a legitimate alternative for surgery in low-risk DCIS patients. Routine assessment of ER, PR and HER2 status in pure DCIS is therefore a promising instrument that could facilitate the development of evidence-based and DCIS subtype-dependent guidelines, aiming to de-escalate therapy in low-risk patients. |
Recommendations for diagnosis and treatment of Atypical Hemolytic Uremic Syndrome (aHUS): an expert consensus statement from the Rare Diseases Committee of the Brazilian Society of Nephrology (COMDORA-SBN) | bdb8e53b-b744-42ac-b24a-52615b876ca1 | 11804885 | Internal Medicine[mh] | Atypical hemolytic uremic syndrome (aHUS) is an ultra-rare cause of thrombotic microangiopathy (TMA), characterized by non-immune hemolytic anemia, thrombocytopenia, and systemic manifestations including renal involvement, frequently manifested as acute kidney injury (AKI). Typically, an abnormality in the regulatory proteins of the alternative complement pathway leads to an excessive formation of the membrane attack complex (C5b-9), causing endothelial cell damage and microthrombi formation throughout the body . Disease-related variants in complement regulatory genes or presence of complement Factor H (CFH) autoantibodies are found in 60–70% of patients . While there is a shift towards using the term complement-mediated HUS, we chose to adhere to aHUS in this consensus, as defined in pivotal trials of complement inhibitors and by the Food and Drug Administration (FDA). The epidemiology of aHUS is influenced by genetic background and population traits , . Global data is limited due to the rarity of aHUS. A 2020 systematic review provided initial consistent epidemiological insights . Data from Norway, France, Italy, and Australia estimated the prevalence and incidence of aHUS. Prevalence among individuals aged 20 years or younger ranged from 2.2 to 9.4 per million, with an overall prevalence of 4.9 per million . Annual incidence rates for those older than 20 years varied from 0.26 to 0.75 per million and for all ages from 0.23 to 1.9 per million . The diverse genetic ancestry of the Brazilian population and its high admixture rate render its population ideal for broadening the genetic spectrum of aHUS , . The Brazilian aHUS Registry, coordinated by the Rare Diseases Committee of the Brazilian Society of Nephrology (COMDORA-SBN), revealed a unique disease profile . Predominantly affecting women and young adults, a high rate of renal involvement was observed. Pediatric patients had lower hemoglobin and platelet levels on presentation, and higher LDH levels compared to adults. Common genetic variants, notably in the CFH gene and a large CFHR1-3 deletion, were found across age groups , which has implications for the choice of genetic testing methods. Clinical manifestations depend on the severity of ischemia in affected organs . Associated with the hematological condition, kidney involvement is often observed, manifesting as acute renal lesion, edema, oligoanuria, proteinuria, hematuria, and systemic arterial hypertension. Additionally, there may be central nervous system involvement (mental confusion, lethargy, seizures, coma), gastrointestinal tract disorders (diarrhea, liver disorders, pancreatitis), pulmonary involvement leading to alveolar hemorrhage, ocular complications (amaurosis), cutaneous ischemia (which can lead to necrosis of the extremities), and cardiac involvement , . It is important to emphasize that in some cases, a subacute presentation may occur with renal impairment and arterial hypertension with signs of TMA on renal biopsy, but no systemic signs of hemolysis and thrombocytopenia. Therefore, the differential diagnosis of TMA should be considered in any patient presenting kidney injury and low-grade hemolysis (grade 1B) . aHUS is very heterogeneous in its clinical manifestation, resulting in difficulties in diagnosis and treatment . To address these issues, a group of experts presents the first Brazilian consensus document for the diagnosis and management of patients with aHUS. Although similar articles have been previously published worldwide, the Brazilian population is unique and these particularities along with the difficulty of accessing all exams and treatments, justify the development of a national consensus document. Goals of the Brazilian Consensus for aHUS This consensus document was developed as part of an initiative coordinated by COMDORA-SBN to standardize the diagnosis and management of aHUS in Brazil. A panel of Brazilian experts developed this document based on literature review, data from the aHUS Brazilian Registry, and their own experience with these patients. A meeting was held in São Paulo on August 19 and 20 (2023) to define key points for the document. Literature review was performed on the following databases: PubMed, Scielo, LILACS (Latin American Research Review), and Cochrane Library. The keywords used were: “Atypical Hemolytic Uremic Syndrome” OR “aHUS” AND “Diagnosis” OR “Treatment”. The inclusion criteria used were articles published up to August 2023 in English, Portuguese, or Spanish. The quality of evidence was determined based on the literature review. In rare diseases, obtaining high-quality evidence is challenging due to the small number of patients and clinical heterogeneity. As randomized controlled trials are scarce, recommendations were derived from systematic reviews, randomized clinical trials, previously published guidelines, case series, cohort studies, and registry data reflecting real-world data . Moreover, meta-analyses of individual trials may help address this issue . In addition, the personal experience of the panelists was considered, especially in controversial issues. The GRADE system was used to classify the strength of the recommendations and the quality of the evidence , . This consensus document was developed as part of an initiative coordinated by COMDORA-SBN to standardize the diagnosis and management of aHUS in Brazil. A panel of Brazilian experts developed this document based on literature review, data from the aHUS Brazilian Registry, and their own experience with these patients. A meeting was held in São Paulo on August 19 and 20 (2023) to define key points for the document. Literature review was performed on the following databases: PubMed, Scielo, LILACS (Latin American Research Review), and Cochrane Library. The keywords used were: “Atypical Hemolytic Uremic Syndrome” OR “aHUS” AND “Diagnosis” OR “Treatment”. The inclusion criteria used were articles published up to August 2023 in English, Portuguese, or Spanish. The quality of evidence was determined based on the literature review. In rare diseases, obtaining high-quality evidence is challenging due to the small number of patients and clinical heterogeneity. As randomized controlled trials are scarce, recommendations were derived from systematic reviews, randomized clinical trials, previously published guidelines, case series, cohort studies, and registry data reflecting real-world data . Moreover, meta-analyses of individual trials may help address this issue . In addition, the personal experience of the panelists was considered, especially in controversial issues. The GRADE system was used to classify the strength of the recommendations and the quality of the evidence , . Recognizing Thrombotic Microangiopathy (TMA) Diagnosis relies on histopathological features, but renal biopsy is often challenging due to thrombocytopenia and severe clinical presentation . The histopathological findings are complex and diverse and can be summarized as shown in . Suspected aHUS starts with the TMA triad: microangiopathic hemolytic anemia (MAHA), thrombocytopenia (absolute or signs of progressive platelet consumption), and organ damage (kidneys, heart, brain, gastrointestinal tract, and others) . Renal involvement, observed in all Brazilian aHUS population, is common . This syndrome can manifest at any age, regardless of whether it is inherited or acquired . It is important to keep in mind that there are some conditions that may mimic TMA, such as prosthetic heart valves or the use of cardiopulmonary bypass , sickle cell crisis in patients with sickle cell anemia, and even emboli of metastatic neoplasia. These conditions can also manifest with MAHA, which are often associated with thrombocytopenia and organ dysfunction, although they are not classified as TMA. In 2017, the Kidney Disease Improving Global Outcomes (KDIGO) initiative listed all known causes of TMA . Traditionally, TMA is divided into primary and secondary . Primary TMA: The primary causes of TMA have a well-known pathophysiological mechanism and an established treatment. Classically, these include thrombotic thrombocytopenic purpura (TTP) – a severe deficiency of a disintegrin and metalloproteinase with a thrombospondin type 1 motif, member 13 (ADAMTS13, also known as von Willebrand factor-cleaving protease), and aHUS . The other patients with TMA are classified as having secondary TMA. Secondary TMA: Secondary causes of thrombotic microangiopathy typically occur in the context of systemic diseases, and TMA often resolves with treatment or removal of the underlying cause. Classic secondary causes include TMA associated with Shiga toxin (ST) produced by Escherichia coli (EC), known as typical hemolytic uremic syndrome (HUS) or STEC-HUS, HUS associated with other infections such as Streptococcus pneumoniae -related HUS ( Sp -related HUS), pregnancy-related TMA, solid organ (especially kidney) and hematopoietic stem cells transplantations, malignancies, autoimmune diseases, drugs, and malignant hypertension , . They are more frequent than primary TMA. An analysis of 500 patients from four French centers revealed that 94% of cases were secondary to pregnancy (35%), infection (33%), drugs (26%), neoplasia (19%), transplantation (17%), autoimmune diseases (9%), malignant hypertension (4%), and other factors (6%) , . The diagnosis of aHUS is only established after ruling out other causes of TMA, such as TTP, STEC-HUS , and secondary TMA conditions , . However, as our understanding of TMA advances and underlying mechanisms are elucidated, the classification and nomenclature of TMA continue to evolve. One of the practical schemes suggested by Genest et al. offers a new TMA classification approach. In the present document, the authors have modified the proposal of Genest et al. and classify TMA into the following categories: 1) TTP, congenital or acquired; 2) aHUS, a complement-mediated TMA caused by variants in complement-associated genes (congenital) or by antibody-mediated complement dysregulation, such as anti-CFH autoantibodies (auto-immune); 3) TMA associated with variants in non-complement genes, such as those involved in the coagulation system (e.g., DGKE , THBD ) or metabolic defects, such as cobalamin metabolism disturbances; 4) Infection-associated TMA, including STEC-HUS and others; and 5) TMA secondary to systemic disease or drug exposure. This revised classification is illustrated in . Determinig the Etiology of TMA Once TMA has been identified, the challenge is to establish the correct cause to start a customized treatment immediately. Anamnesis, physical examination, and family health history help identify the etiology of TMA. A positive family history raises the suspicion of a genetic-related disease. Furthermore, recognizing symptoms like those seen in STEC-HUS helps determining the etiology . The next step is to assess the severity of organ damage, which determines the clinical presentation and is crucial for managing life-threatening situations , . A systematic approach to identify the underlying cause is essential to reassess targeted therapy . Diagnostic Criteria of aHUS The diagnosis of aHUS is clinical and is established after ruling out other causes of TMA, such as TTP and STEC-HUS, and secondary TMA conditions . Recommendations for diagnostic tests are shown in . The recommended diagnostic criteria are shown in and . aHUS is suspected in patients with TMA after ruling out secondary causes, i.e. ADAMTS13 activity is above 10% ruling out TTP and tests for STEC-HUS are negative (grade 1B). Whenever available, complement activation should be investigated based on local resources, although measuring plasma C5b-9 is not yet available in clinical practice. Plasma C3 levels can be assessed - low levels are found in less than 20% of patients and normal levels do not rule out aHUS (grade 1B). There is still no consensus on complement tests in aHUS. If ADAMTS-13 activity test is unavailable or while awaiting results, the PLASMIC score is a helpful bedside tool to diagnose TTP, allowing for an early treatment of this lethal disease. The sensitivity and specificity of a PLASMIC score equal or above 6 was 0.85 (confidence interval 0.67–0.94) and 0.89 (95% confidence interval 0.81–0.94) . The PLASMIC score is shown in and online calculators can be helpful ( www.mdcalc.com ). Only 5% of patients with TTP present with the classic pentad: fever, hemolytic anemia, thrombocytopenia, neurologic manifestations, and kidney injury. The interpretation of PLASMIC scores is : Total points = 0 to 4 – low risk for severe ADAMTS-13 deficiency Total points= 5 – intermediate risk for severe ADAMTS-13 deficiency Total points= 6 or 7 – high risk for severe ADAMTS-13 deficiency Special Issues on aHUS Diagnosis A) Role of the renal biopsy in aHUS The main histopathological features of aHUS are: endothelial cell edema, subendothelial expansion due to edema or increase in matrix components and basement membrane detachment, accumulation of debris in the subendothelial space, and increased Von Willebrand factor expression, which attracts platelets and leads to the formation of microthrombi - which partially or completely occlude the lumen of vessels in the microvasculature. This occlusion leads to the mechanical destruction of erythrocytes by shear stress, which explains the intravascular anemia (intravascular hemolysis), platelet adhesion with thrombocytopenia, fragmented red blood cells (schistocytes) in the peripheral blood, and variable ischemia in the tissue. Renal biopsy is not mandatory to diagnose TMA since there is a clinical correspondence with the triad MAHA, thrombocytopenia, and organ injury (particularly renal) . However, it is recommended in special situations such as renal graft dysfunction in which the histological findings can discriminate between TMA and graft rejection, define the presence of underlying glomerulonephritis, and determine chronicity index to manage treatment expectations (grade 1B) . , , , show some examples of histological diagnostic criteria of TMA. B) Role of genetic testing in aHUS There is a known genetic basis for nearly two-thirds of aHUS cases, most of which are related to an inactivating mutation of the proteins that inhibit the alternative pathway: Factor H ( CFH ), Factor I ( CFI ), membrane cofactor protein ( MCP or CD46), thrombomodulin ( THBD ), proteins related to Factor H 1 to 5 ( CFHR1-5 ) or a gain-of-function mutation of activating factors of this complement pathway, C3 or Factor B ( CFB ) . The formation of anti-CFH IgG antibodies has been found mostly in pediatric patients and is associated with genetic rearrangements (homozygous large deletions) in CFH-related proteins 1 and 3 ( CFHR1 - CFHR3 deletion) in 87% of cases , , . In the Global Registry of aHUS , approximately 40% of the 851 studied patients had no mutations or risk variants identified in complement genes. This may be due to alterations in other complement or coagulation genes, as demonstrated in an exome sequencing study conducted in 10 pediatric patients with aHUS . In Brazil, 33.5% of patients who underwent genetic analysis were found to lack genetic variants , . There is great variation among the groups and laboratories that carry out genetic analysis of aHUS, with the most common method being a next generation sequencing (NGS) panel containing genes from the alternative complement pathway ( CFH , CFI , CFB , C3 , MCP , THBD ). Other laboratories also analyze coagulation genes ( PLG , DGKe ), large deletions or rearrangements of genes related to Factor H ( CFHR1 to 5 ), and lectin pathway genes ( MASP2 ). There is still no consensus regarding which genes should compose the ideal NGS panel. In this context, findings from the aHUS Brazilian Registry largely coincide with those of the Global Registry, revealing a predominance of CHF variants across all age groups and an absence of CFI variants in pediatric patients . However, a higher proportion of variants were identified in genes encoding Factor H-related proteins ( CFRH ) compared with other cohorts in Brazil , . The CFHR1 - CFHR3 large deletion was also detected in a high proportion of Brazilian patients. This finding suggests that Multiplex Ligation-Dependent Probe Amplification (MLPA), a gold standard for DNA copy number determination, should be performed in these patients, especially when no disease-related variant (grade 1B) has been detected by NGS , . Patients often exhibit mutations in more than one gene or polymorphisms, potentially showing an additive effect of various genetic factors. Despite advancements, questions remain regarding genetic basis of aHUS, as the genotype-phenotype correlation may involve modifier genes, epigenetic events, and environmental factors. Some asymptomatic carriers have genetic alterations, while others with severe disease yield inconclusive genetic study results. While genetic analysis helps to understand the pathogenesis, negative findings do not rule out aHUS and the diagnosis relies on clinical markers . C) Overlap of aHUS-related genetic variants and other causes of TMA aHUS-related genetic variants have already been described in patients with STEC-HUS , pregnancy-associated TMA , treatment-refractory autoimmune diseases , hematopoietic cell transplantation , and monoclonal gammopathy . Therefore, if TMA persists after treating the underlying disease or secondary TMA, concurrent aHUS or TTP should be explored, which affect therapeutic strategies and patient prognosis. Although a study of 110 patients with secondary TMA detected genetic findings like those of the general population of TMA patients , other studies showed that many of the patients with secondary TMA refractory to treatment of the underlying disease responded to eculizumab, which was used only temporarily, with no TMA recurrence after withdrawal . Diagnosis relies on histopathological features, but renal biopsy is often challenging due to thrombocytopenia and severe clinical presentation . The histopathological findings are complex and diverse and can be summarized as shown in . Suspected aHUS starts with the TMA triad: microangiopathic hemolytic anemia (MAHA), thrombocytopenia (absolute or signs of progressive platelet consumption), and organ damage (kidneys, heart, brain, gastrointestinal tract, and others) . Renal involvement, observed in all Brazilian aHUS population, is common . This syndrome can manifest at any age, regardless of whether it is inherited or acquired . It is important to keep in mind that there are some conditions that may mimic TMA, such as prosthetic heart valves or the use of cardiopulmonary bypass , sickle cell crisis in patients with sickle cell anemia, and even emboli of metastatic neoplasia. These conditions can also manifest with MAHA, which are often associated with thrombocytopenia and organ dysfunction, although they are not classified as TMA. In 2017, the Kidney Disease Improving Global Outcomes (KDIGO) initiative listed all known causes of TMA . Traditionally, TMA is divided into primary and secondary . Primary TMA: The primary causes of TMA have a well-known pathophysiological mechanism and an established treatment. Classically, these include thrombotic thrombocytopenic purpura (TTP) – a severe deficiency of a disintegrin and metalloproteinase with a thrombospondin type 1 motif, member 13 (ADAMTS13, also known as von Willebrand factor-cleaving protease), and aHUS . The other patients with TMA are classified as having secondary TMA. Secondary TMA: Secondary causes of thrombotic microangiopathy typically occur in the context of systemic diseases, and TMA often resolves with treatment or removal of the underlying cause. Classic secondary causes include TMA associated with Shiga toxin (ST) produced by Escherichia coli (EC), known as typical hemolytic uremic syndrome (HUS) or STEC-HUS, HUS associated with other infections such as Streptococcus pneumoniae -related HUS ( Sp -related HUS), pregnancy-related TMA, solid organ (especially kidney) and hematopoietic stem cells transplantations, malignancies, autoimmune diseases, drugs, and malignant hypertension , . They are more frequent than primary TMA. An analysis of 500 patients from four French centers revealed that 94% of cases were secondary to pregnancy (35%), infection (33%), drugs (26%), neoplasia (19%), transplantation (17%), autoimmune diseases (9%), malignant hypertension (4%), and other factors (6%) , . The diagnosis of aHUS is only established after ruling out other causes of TMA, such as TTP, STEC-HUS , and secondary TMA conditions , . However, as our understanding of TMA advances and underlying mechanisms are elucidated, the classification and nomenclature of TMA continue to evolve. One of the practical schemes suggested by Genest et al. offers a new TMA classification approach. In the present document, the authors have modified the proposal of Genest et al. and classify TMA into the following categories: 1) TTP, congenital or acquired; 2) aHUS, a complement-mediated TMA caused by variants in complement-associated genes (congenital) or by antibody-mediated complement dysregulation, such as anti-CFH autoantibodies (auto-immune); 3) TMA associated with variants in non-complement genes, such as those involved in the coagulation system (e.g., DGKE , THBD ) or metabolic defects, such as cobalamin metabolism disturbances; 4) Infection-associated TMA, including STEC-HUS and others; and 5) TMA secondary to systemic disease or drug exposure. This revised classification is illustrated in . Once TMA has been identified, the challenge is to establish the correct cause to start a customized treatment immediately. Anamnesis, physical examination, and family health history help identify the etiology of TMA. A positive family history raises the suspicion of a genetic-related disease. Furthermore, recognizing symptoms like those seen in STEC-HUS helps determining the etiology . The next step is to assess the severity of organ damage, which determines the clinical presentation and is crucial for managing life-threatening situations , . A systematic approach to identify the underlying cause is essential to reassess targeted therapy . The diagnosis of aHUS is clinical and is established after ruling out other causes of TMA, such as TTP and STEC-HUS, and secondary TMA conditions . Recommendations for diagnostic tests are shown in . The recommended diagnostic criteria are shown in and . aHUS is suspected in patients with TMA after ruling out secondary causes, i.e. ADAMTS13 activity is above 10% ruling out TTP and tests for STEC-HUS are negative (grade 1B). Whenever available, complement activation should be investigated based on local resources, although measuring plasma C5b-9 is not yet available in clinical practice. Plasma C3 levels can be assessed - low levels are found in less than 20% of patients and normal levels do not rule out aHUS (grade 1B). There is still no consensus on complement tests in aHUS. If ADAMTS-13 activity test is unavailable or while awaiting results, the PLASMIC score is a helpful bedside tool to diagnose TTP, allowing for an early treatment of this lethal disease. The sensitivity and specificity of a PLASMIC score equal or above 6 was 0.85 (confidence interval 0.67–0.94) and 0.89 (95% confidence interval 0.81–0.94) . The PLASMIC score is shown in and online calculators can be helpful ( www.mdcalc.com ). Only 5% of patients with TTP present with the classic pentad: fever, hemolytic anemia, thrombocytopenia, neurologic manifestations, and kidney injury. The interpretation of PLASMIC scores is : Total points = 0 to 4 – low risk for severe ADAMTS-13 deficiency Total points= 5 – intermediate risk for severe ADAMTS-13 deficiency Total points= 6 or 7 – high risk for severe ADAMTS-13 deficiency A) Role of the renal biopsy in aHUS The main histopathological features of aHUS are: endothelial cell edema, subendothelial expansion due to edema or increase in matrix components and basement membrane detachment, accumulation of debris in the subendothelial space, and increased Von Willebrand factor expression, which attracts platelets and leads to the formation of microthrombi - which partially or completely occlude the lumen of vessels in the microvasculature. This occlusion leads to the mechanical destruction of erythrocytes by shear stress, which explains the intravascular anemia (intravascular hemolysis), platelet adhesion with thrombocytopenia, fragmented red blood cells (schistocytes) in the peripheral blood, and variable ischemia in the tissue. Renal biopsy is not mandatory to diagnose TMA since there is a clinical correspondence with the triad MAHA, thrombocytopenia, and organ injury (particularly renal) . However, it is recommended in special situations such as renal graft dysfunction in which the histological findings can discriminate between TMA and graft rejection, define the presence of underlying glomerulonephritis, and determine chronicity index to manage treatment expectations (grade 1B) . , , , show some examples of histological diagnostic criteria of TMA. B) Role of genetic testing in aHUS There is a known genetic basis for nearly two-thirds of aHUS cases, most of which are related to an inactivating mutation of the proteins that inhibit the alternative pathway: Factor H ( CFH ), Factor I ( CFI ), membrane cofactor protein ( MCP or CD46), thrombomodulin ( THBD ), proteins related to Factor H 1 to 5 ( CFHR1-5 ) or a gain-of-function mutation of activating factors of this complement pathway, C3 or Factor B ( CFB ) . The formation of anti-CFH IgG antibodies has been found mostly in pediatric patients and is associated with genetic rearrangements (homozygous large deletions) in CFH-related proteins 1 and 3 ( CFHR1 - CFHR3 deletion) in 87% of cases , , . In the Global Registry of aHUS , approximately 40% of the 851 studied patients had no mutations or risk variants identified in complement genes. This may be due to alterations in other complement or coagulation genes, as demonstrated in an exome sequencing study conducted in 10 pediatric patients with aHUS . In Brazil, 33.5% of patients who underwent genetic analysis were found to lack genetic variants , . There is great variation among the groups and laboratories that carry out genetic analysis of aHUS, with the most common method being a next generation sequencing (NGS) panel containing genes from the alternative complement pathway ( CFH , CFI , CFB , C3 , MCP , THBD ). Other laboratories also analyze coagulation genes ( PLG , DGKe ), large deletions or rearrangements of genes related to Factor H ( CFHR1 to 5 ), and lectin pathway genes ( MASP2 ). There is still no consensus regarding which genes should compose the ideal NGS panel. In this context, findings from the aHUS Brazilian Registry largely coincide with those of the Global Registry, revealing a predominance of CHF variants across all age groups and an absence of CFI variants in pediatric patients . However, a higher proportion of variants were identified in genes encoding Factor H-related proteins ( CFRH ) compared with other cohorts in Brazil , . The CFHR1 - CFHR3 large deletion was also detected in a high proportion of Brazilian patients. This finding suggests that Multiplex Ligation-Dependent Probe Amplification (MLPA), a gold standard for DNA copy number determination, should be performed in these patients, especially when no disease-related variant (grade 1B) has been detected by NGS , . Patients often exhibit mutations in more than one gene or polymorphisms, potentially showing an additive effect of various genetic factors. Despite advancements, questions remain regarding genetic basis of aHUS, as the genotype-phenotype correlation may involve modifier genes, epigenetic events, and environmental factors. Some asymptomatic carriers have genetic alterations, while others with severe disease yield inconclusive genetic study results. While genetic analysis helps to understand the pathogenesis, negative findings do not rule out aHUS and the diagnosis relies on clinical markers . C) Overlap of aHUS-related genetic variants and other causes of TMA aHUS-related genetic variants have already been described in patients with STEC-HUS , pregnancy-associated TMA , treatment-refractory autoimmune diseases , hematopoietic cell transplantation , and monoclonal gammopathy . Therefore, if TMA persists after treating the underlying disease or secondary TMA, concurrent aHUS or TTP should be explored, which affect therapeutic strategies and patient prognosis. Although a study of 110 patients with secondary TMA detected genetic findings like those of the general population of TMA patients , other studies showed that many of the patients with secondary TMA refractory to treatment of the underlying disease responded to eculizumab, which was used only temporarily, with no TMA recurrence after withdrawal . The main histopathological features of aHUS are: endothelial cell edema, subendothelial expansion due to edema or increase in matrix components and basement membrane detachment, accumulation of debris in the subendothelial space, and increased Von Willebrand factor expression, which attracts platelets and leads to the formation of microthrombi - which partially or completely occlude the lumen of vessels in the microvasculature. This occlusion leads to the mechanical destruction of erythrocytes by shear stress, which explains the intravascular anemia (intravascular hemolysis), platelet adhesion with thrombocytopenia, fragmented red blood cells (schistocytes) in the peripheral blood, and variable ischemia in the tissue. Renal biopsy is not mandatory to diagnose TMA since there is a clinical correspondence with the triad MAHA, thrombocytopenia, and organ injury (particularly renal) . However, it is recommended in special situations such as renal graft dysfunction in which the histological findings can discriminate between TMA and graft rejection, define the presence of underlying glomerulonephritis, and determine chronicity index to manage treatment expectations (grade 1B) . , , , show some examples of histological diagnostic criteria of TMA. There is a known genetic basis for nearly two-thirds of aHUS cases, most of which are related to an inactivating mutation of the proteins that inhibit the alternative pathway: Factor H ( CFH ), Factor I ( CFI ), membrane cofactor protein ( MCP or CD46), thrombomodulin ( THBD ), proteins related to Factor H 1 to 5 ( CFHR1-5 ) or a gain-of-function mutation of activating factors of this complement pathway, C3 or Factor B ( CFB ) . The formation of anti-CFH IgG antibodies has been found mostly in pediatric patients and is associated with genetic rearrangements (homozygous large deletions) in CFH-related proteins 1 and 3 ( CFHR1 - CFHR3 deletion) in 87% of cases , , . In the Global Registry of aHUS , approximately 40% of the 851 studied patients had no mutations or risk variants identified in complement genes. This may be due to alterations in other complement or coagulation genes, as demonstrated in an exome sequencing study conducted in 10 pediatric patients with aHUS . In Brazil, 33.5% of patients who underwent genetic analysis were found to lack genetic variants , . There is great variation among the groups and laboratories that carry out genetic analysis of aHUS, with the most common method being a next generation sequencing (NGS) panel containing genes from the alternative complement pathway ( CFH , CFI , CFB , C3 , MCP , THBD ). Other laboratories also analyze coagulation genes ( PLG , DGKe ), large deletions or rearrangements of genes related to Factor H ( CFHR1 to 5 ), and lectin pathway genes ( MASP2 ). There is still no consensus regarding which genes should compose the ideal NGS panel. In this context, findings from the aHUS Brazilian Registry largely coincide with those of the Global Registry, revealing a predominance of CHF variants across all age groups and an absence of CFI variants in pediatric patients . However, a higher proportion of variants were identified in genes encoding Factor H-related proteins ( CFRH ) compared with other cohorts in Brazil , . The CFHR1 - CFHR3 large deletion was also detected in a high proportion of Brazilian patients. This finding suggests that Multiplex Ligation-Dependent Probe Amplification (MLPA), a gold standard for DNA copy number determination, should be performed in these patients, especially when no disease-related variant (grade 1B) has been detected by NGS , . Patients often exhibit mutations in more than one gene or polymorphisms, potentially showing an additive effect of various genetic factors. Despite advancements, questions remain regarding genetic basis of aHUS, as the genotype-phenotype correlation may involve modifier genes, epigenetic events, and environmental factors. Some asymptomatic carriers have genetic alterations, while others with severe disease yield inconclusive genetic study results. While genetic analysis helps to understand the pathogenesis, negative findings do not rule out aHUS and the diagnosis relies on clinical markers . aHUS-related genetic variants have already been described in patients with STEC-HUS , pregnancy-associated TMA , treatment-refractory autoimmune diseases , hematopoietic cell transplantation , and monoclonal gammopathy . Therefore, if TMA persists after treating the underlying disease or secondary TMA, concurrent aHUS or TTP should be explored, which affect therapeutic strategies and patient prognosis. Although a study of 110 patients with secondary TMA detected genetic findings like those of the general population of TMA patients , other studies showed that many of the patients with secondary TMA refractory to treatment of the underlying disease responded to eculizumab, which was used only temporarily, with no TMA recurrence after withdrawal . Supportive Treatment Supportive care follows AKI management principles: addressing volume/electrolyte balance, controlling hypertension, adjusting nephrotoxic drugs, initiating dialysis if indicated, and ensuring adequate nutrition. Severe anemia (Hb <7g/dL) requires blood transfusions, while platelet transfusions are reserved for active bleeding or surgical needs. Blood samples for direct Coombs test should be obtained before any transfusion. Additional supportive measures include dialysis, plasma exchange, and plasmapheresis/plasma infusion , (grade 1B). Specific Treatment Before the era of terminal complement inhibitors, aHUS management with supportive measures was considered ineffective, with 50% of patients requiring chronic dialysis and up to 25% of deaths occurring in the acute phase of the disease . After the approval of the C5 inhibitor, eculizumab, by the FDA and the European Medicine Agency in 2011 among other agencies worldwide, including the Agência Nacional de Vigilância Sanitária (ANVISA, Brazilian Health Agency), eculizumab became the first-line therapy for this disease . In the next section, we will discuss specific therapies for aHUS. Use of C5 Inhibitors – The Post-Eculizumab Era All aHUS patients are eligible for C5 inhibitor therapy , recommended as first line treatment (grade 1A). Initiation during the acute phase improves kidney function recovery . Eculizumab, a recombinant humanized monoclonal antibody targeting factor C5, blocks the complement system’s terminal portion, preventing C5b-9 formation, which damages endothelial cells . Two-year prospective studies on eculizumab efficacy and safety demonstrated improvement of hemolysis, thrombocytopenia, and renal function. Patients with end-stage kidney disease (ESKD) treated with eculizumab showed fewer extrarenal manifestations and improved quality of life , , , , . Each Soliris ® vial (eculizumab’s commercial name) by Alexion Pharmaceuticals contains 300 mg in 30 mL solution for intravenous infusion over 35 minutes minimum , , , . Dosing and schedule are shown in . The side effects of this drug are associated with increased vulnerability to infections by encapsulated germs, especially Neisseria meningitidis. In addition to the use of prophylactic antibiotics, vaccination with tetravalent conjugate vaccine (MenACW135Y) and meningococcus B are recommended for all patients to protect against most meningococcal serotypes (at least 15 days before initiation of therapy). Other vaccines are also recommended, such as Pn13, Pn23, Hib, and influenza (grade 1A) , , , , , , . We also recommend updating the vaccination schedule with booster doses. Although the manufacturer recommends antibiotics only for 15 days after vaccination, if vaccination was not possible before, we recommend using prophylactic antibiotics (against meningococcal disease) while the patient is under C5 inhibitor treatment (grade 1A). Ravulizumab is a newly approved C5 inhibitor with a longer half-life that allows the maintenance dose to be extended to once every 4 weeks (for patients under 20 kg) or once every 8 weeks (for patients over 20 kg). The safety and efficacy of the medication in adults and children (over 10 kg) were confirmed in prospective trials , . Ultomiris ® (ravulizumab’s commercial name) from Alexion Pharmaceuticals provides vials of 300 mg in 3 mL, 1100 mg in 11 mL, and 300 mg in 30 mL. In Brazil, only the 300 mg/3 mL option is available. Following dilution, the final concentration should be 50 mg/mL. Treatment comprises a loading dose followed by a maintenance phase two weeks later administered via intravenous infusion according to . Patients transitioning from eculizumab to ravulizumab should receive a loading dose of ravulizumab 2 weeks post-eculizumab’s final dose, followed by maintenance doses every 4 or 8 weeks based on weight, as previously outlined. When ravulizumab is used, longer intervals between infusions improve quality of life by minimizing punctures and displacements , . Ravulizumab was found to be more cost-effective than eculizumab, with further savings possible if a concentrated 100 mg/mL is used , . The recommended diagnostic and treatment criteria are shown in . Monitoring Recommendation Studies indicate that monitoring eculizumab complement blockade with CH50 levels can adjust infusion intervals for patients without disease recurrence. Jodele et al. found that serum eculizumab levels correlated with CH50 in 365 paired samples from 18 bone marrow transplant patients, noting that a blood level above 99 μ g/mL suppressed CH50 . Monitoring of complement blockade through CH50 inhibition for eculizumab is recommended (grade 1A). For ravulizumab, CH50’s reliability has not been proven; hence, clinical monitoring coupled with serum drug level measurement is advised (grade 1A). Dose Spacing Ardissino et al. proposed that dose spacing should be monitored for patients maintaining CH50 lower than 30% without disease recurrence and/or organ damage. They suggested that a 0.75 mg/kg/day eculizumab dose maintains complement blockade for 4 weeks. Volokhina et al. evaluated 11 aHUS patients and their treatment spacing. With a 1200 mg maintenance at 4-5 week intervals, 80% had serum eculizumab levels higher than 50 μ g/mL. All patients with levels > 50 μ g/mL exhibited complete complement system blockage (CH50 lower than 10%) . Individualizing treatment with eculizumab serum levels between 50–100 μ g/mL and monitoring complement blockade via CH50 may be feasible . Gatault et al. analyzed 7 patients who used eculizumab, and found that those under 90 kg had dosing intervals of 4 weeks and those under 70 kg had dosing intervals of up to 6 weeks . Dose spacing should be adjusted according to patient profile, comorbidities, treatment adherence, and available CH50 and/or serum eculizumab dosing (grade 1B). For ravulizumab, there are no studies that recommend dose spacing beyond the aHUS indications on the drug label. In Brazilian clinical practice, due to the challenges of measuring serum anti-C5 drug level, it is recommended by this consensus that dose spacing should not be reviewed until 3 months of therapy onset, after hematological, renal, and systemic parameter improvement, with no sign of disease activity. Patient assessment should consider comorbidities, renal function, age, treatment adherence, commitment, and available genetic analysis (grade 2A). CH50 monitoring is essential, and without it, dose spacing is not recommended (grade 1B). Moreover, dose spacing is not recommended for kidney transplant patients (grade 1A). See for eculizumab dose spacing criteria. Suggested management of eculizumab dose spacing: CH50 must be measured the day before the subsequent dose. If CH50 is below 30%, the dose should be spaced by 3 weeks, with infusion normally administered on the third week. If the patient maintains normal test results (markers of TMA) and is asymptomatic, CH50 should be measured the day before the subsequent dose (the third week infusion). If CH50 is below 30%, spacing could be extended to the fourth week. Although some studies suggest spacing up to 6 weeks based on patient factors and weight, this consensus does not recommend intervals longer than 4 weeks (grade 2A). For ravulizumab, the manufacturer recommends considering treatment interruption based on medical observation and patient profile after 6 months of treatment, in the absence of disease activity. A study on patients with paroxysmal nocturnal hemoglobinuria revealed efficacy in serum level assessment and longer infusion intervals of up to 10 weeks, reducing treatment costs by 37% . Discontinuation of Therapy The high cost of therapy, risk of potentially serious side effects (increased risk of meningococcal infection), and biweekly intravenous infusions in the maintenance phase, motivated studies on the discontinuation of eculizumab treatment , . Many observational studies on eculizumab discontinuation emerged in the past decade. An Italian cohort of 16 aHUS patients who discontinued the drug reported 31.2% experiencing recurrence within 180 days, three of whom had a CFH variant , . In a French cohort, 31% of 38 patients relapsed within 22 months after therapy cessation, with CFH mutation correlating with more severe manifestation and early recurrence . A Dutch cohort study on restricted eculizumab use in 20 aHUS patients observed a 25% recurrence rate over 1460 days . The researchers developed a mathematical tool for individualized eculizumab dosing and spacing during maintenance, guided by therapeutic drug monitoring. With this approach, equivalent therapeutic outcomes and cost-effectiveness were achieved, reducing therapy costs by up to 13% , . Additionally, early eculizumab initiation (within 3 months) in aHUS patients with native kidney involvement yielded a 19% recurrence rate, with cost savings of up to 30% . The first prospective cohort study was published by Fakhouri et al . in 2021 and involved 55 patients from different French centers. It had a recurrence rate of 23% and only 3 patients had kidney transplant . In a systematic review, Macia et al. analyzed published cases, unpublished data, clinical studies, and data from the Global aHUS Registry. Recurrence episodes were found in 4 (66.6%) of the 6 patients in unpublished case reports and 16 (30.7%) of 52 patients in published case reports. In clinical studies, recurrence occurred in 12 (19.6%) of 61 patients, 5 (41.6%) of whom had a CFH mutation. Finally, the global registry showed 12 (15.7%) recurrences in 76 patients who discontinued eculizumab therapy . A Brazilian cohort of aHUS patients who had unplanned eculizumab discontinuation found a cumulative recurrence incidence of 58% in almost 400 days of follow-up. Patients with native kidney, transplant recipients, and dialysis patients were included , . While there are no definitive guidelines on discontinuing therapy and timing in the literature, this consensus recommends planned discontinuation if genetic testing, complement system component evaluation (e.g., CH50 and C5b9), or therapeutic drug level are available. Furthermore, the immediate availability of the drug for reintroduction in the event of a relapse is mandatory , , , , (grade 1C). We recommend shared decision making between the medical team and the patient regarding eculizumab discontinuation (grade 1A). Safety data on discontinuation remains inconclusive for determining patient eligibility and timing. Whenever possible, we recommend laboratory evaluation of drug therapeutic levels and components of the complement system, at least serum CH50 dosage (grade 1A). In addition, we recommend immediate access to drugs to treat patients with recurrence (grade 1A). Supportive care follows AKI management principles: addressing volume/electrolyte balance, controlling hypertension, adjusting nephrotoxic drugs, initiating dialysis if indicated, and ensuring adequate nutrition. Severe anemia (Hb <7g/dL) requires blood transfusions, while platelet transfusions are reserved for active bleeding or surgical needs. Blood samples for direct Coombs test should be obtained before any transfusion. Additional supportive measures include dialysis, plasma exchange, and plasmapheresis/plasma infusion , (grade 1B). Before the era of terminal complement inhibitors, aHUS management with supportive measures was considered ineffective, with 50% of patients requiring chronic dialysis and up to 25% of deaths occurring in the acute phase of the disease . After the approval of the C5 inhibitor, eculizumab, by the FDA and the European Medicine Agency in 2011 among other agencies worldwide, including the Agência Nacional de Vigilância Sanitária (ANVISA, Brazilian Health Agency), eculizumab became the first-line therapy for this disease . In the next section, we will discuss specific therapies for aHUS. All aHUS patients are eligible for C5 inhibitor therapy , recommended as first line treatment (grade 1A). Initiation during the acute phase improves kidney function recovery . Eculizumab, a recombinant humanized monoclonal antibody targeting factor C5, blocks the complement system’s terminal portion, preventing C5b-9 formation, which damages endothelial cells . Two-year prospective studies on eculizumab efficacy and safety demonstrated improvement of hemolysis, thrombocytopenia, and renal function. Patients with end-stage kidney disease (ESKD) treated with eculizumab showed fewer extrarenal manifestations and improved quality of life , , , , . Each Soliris ® vial (eculizumab’s commercial name) by Alexion Pharmaceuticals contains 300 mg in 30 mL solution for intravenous infusion over 35 minutes minimum , , , . Dosing and schedule are shown in . The side effects of this drug are associated with increased vulnerability to infections by encapsulated germs, especially Neisseria meningitidis. In addition to the use of prophylactic antibiotics, vaccination with tetravalent conjugate vaccine (MenACW135Y) and meningococcus B are recommended for all patients to protect against most meningococcal serotypes (at least 15 days before initiation of therapy). Other vaccines are also recommended, such as Pn13, Pn23, Hib, and influenza (grade 1A) , , , , , , . We also recommend updating the vaccination schedule with booster doses. Although the manufacturer recommends antibiotics only for 15 days after vaccination, if vaccination was not possible before, we recommend using prophylactic antibiotics (against meningococcal disease) while the patient is under C5 inhibitor treatment (grade 1A). Ravulizumab is a newly approved C5 inhibitor with a longer half-life that allows the maintenance dose to be extended to once every 4 weeks (for patients under 20 kg) or once every 8 weeks (for patients over 20 kg). The safety and efficacy of the medication in adults and children (over 10 kg) were confirmed in prospective trials , . Ultomiris ® (ravulizumab’s commercial name) from Alexion Pharmaceuticals provides vials of 300 mg in 3 mL, 1100 mg in 11 mL, and 300 mg in 30 mL. In Brazil, only the 300 mg/3 mL option is available. Following dilution, the final concentration should be 50 mg/mL. Treatment comprises a loading dose followed by a maintenance phase two weeks later administered via intravenous infusion according to . Patients transitioning from eculizumab to ravulizumab should receive a loading dose of ravulizumab 2 weeks post-eculizumab’s final dose, followed by maintenance doses every 4 or 8 weeks based on weight, as previously outlined. When ravulizumab is used, longer intervals between infusions improve quality of life by minimizing punctures and displacements , . Ravulizumab was found to be more cost-effective than eculizumab, with further savings possible if a concentrated 100 mg/mL is used , . The recommended diagnostic and treatment criteria are shown in . Studies indicate that monitoring eculizumab complement blockade with CH50 levels can adjust infusion intervals for patients without disease recurrence. Jodele et al. found that serum eculizumab levels correlated with CH50 in 365 paired samples from 18 bone marrow transplant patients, noting that a blood level above 99 μ g/mL suppressed CH50 . Monitoring of complement blockade through CH50 inhibition for eculizumab is recommended (grade 1A). For ravulizumab, CH50’s reliability has not been proven; hence, clinical monitoring coupled with serum drug level measurement is advised (grade 1A). Ardissino et al. proposed that dose spacing should be monitored for patients maintaining CH50 lower than 30% without disease recurrence and/or organ damage. They suggested that a 0.75 mg/kg/day eculizumab dose maintains complement blockade for 4 weeks. Volokhina et al. evaluated 11 aHUS patients and their treatment spacing. With a 1200 mg maintenance at 4-5 week intervals, 80% had serum eculizumab levels higher than 50 μ g/mL. All patients with levels > 50 μ g/mL exhibited complete complement system blockage (CH50 lower than 10%) . Individualizing treatment with eculizumab serum levels between 50–100 μ g/mL and monitoring complement blockade via CH50 may be feasible . Gatault et al. analyzed 7 patients who used eculizumab, and found that those under 90 kg had dosing intervals of 4 weeks and those under 70 kg had dosing intervals of up to 6 weeks . Dose spacing should be adjusted according to patient profile, comorbidities, treatment adherence, and available CH50 and/or serum eculizumab dosing (grade 1B). For ravulizumab, there are no studies that recommend dose spacing beyond the aHUS indications on the drug label. In Brazilian clinical practice, due to the challenges of measuring serum anti-C5 drug level, it is recommended by this consensus that dose spacing should not be reviewed until 3 months of therapy onset, after hematological, renal, and systemic parameter improvement, with no sign of disease activity. Patient assessment should consider comorbidities, renal function, age, treatment adherence, commitment, and available genetic analysis (grade 2A). CH50 monitoring is essential, and without it, dose spacing is not recommended (grade 1B). Moreover, dose spacing is not recommended for kidney transplant patients (grade 1A). See for eculizumab dose spacing criteria. Suggested management of eculizumab dose spacing: CH50 must be measured the day before the subsequent dose. If CH50 is below 30%, the dose should be spaced by 3 weeks, with infusion normally administered on the third week. If the patient maintains normal test results (markers of TMA) and is asymptomatic, CH50 should be measured the day before the subsequent dose (the third week infusion). If CH50 is below 30%, spacing could be extended to the fourth week. Although some studies suggest spacing up to 6 weeks based on patient factors and weight, this consensus does not recommend intervals longer than 4 weeks (grade 2A). For ravulizumab, the manufacturer recommends considering treatment interruption based on medical observation and patient profile after 6 months of treatment, in the absence of disease activity. A study on patients with paroxysmal nocturnal hemoglobinuria revealed efficacy in serum level assessment and longer infusion intervals of up to 10 weeks, reducing treatment costs by 37% . The high cost of therapy, risk of potentially serious side effects (increased risk of meningococcal infection), and biweekly intravenous infusions in the maintenance phase, motivated studies on the discontinuation of eculizumab treatment , . Many observational studies on eculizumab discontinuation emerged in the past decade. An Italian cohort of 16 aHUS patients who discontinued the drug reported 31.2% experiencing recurrence within 180 days, three of whom had a CFH variant , . In a French cohort, 31% of 38 patients relapsed within 22 months after therapy cessation, with CFH mutation correlating with more severe manifestation and early recurrence . A Dutch cohort study on restricted eculizumab use in 20 aHUS patients observed a 25% recurrence rate over 1460 days . The researchers developed a mathematical tool for individualized eculizumab dosing and spacing during maintenance, guided by therapeutic drug monitoring. With this approach, equivalent therapeutic outcomes and cost-effectiveness were achieved, reducing therapy costs by up to 13% , . Additionally, early eculizumab initiation (within 3 months) in aHUS patients with native kidney involvement yielded a 19% recurrence rate, with cost savings of up to 30% . The first prospective cohort study was published by Fakhouri et al . in 2021 and involved 55 patients from different French centers. It had a recurrence rate of 23% and only 3 patients had kidney transplant . In a systematic review, Macia et al. analyzed published cases, unpublished data, clinical studies, and data from the Global aHUS Registry. Recurrence episodes were found in 4 (66.6%) of the 6 patients in unpublished case reports and 16 (30.7%) of 52 patients in published case reports. In clinical studies, recurrence occurred in 12 (19.6%) of 61 patients, 5 (41.6%) of whom had a CFH mutation. Finally, the global registry showed 12 (15.7%) recurrences in 76 patients who discontinued eculizumab therapy . A Brazilian cohort of aHUS patients who had unplanned eculizumab discontinuation found a cumulative recurrence incidence of 58% in almost 400 days of follow-up. Patients with native kidney, transplant recipients, and dialysis patients were included , . While there are no definitive guidelines on discontinuing therapy and timing in the literature, this consensus recommends planned discontinuation if genetic testing, complement system component evaluation (e.g., CH50 and C5b9), or therapeutic drug level are available. Furthermore, the immediate availability of the drug for reintroduction in the event of a relapse is mandatory , , , , (grade 1C). We recommend shared decision making between the medical team and the patient regarding eculizumab discontinuation (grade 1A). Safety data on discontinuation remains inconclusive for determining patient eligibility and timing. Whenever possible, we recommend laboratory evaluation of drug therapeutic levels and components of the complement system, at least serum CH50 dosage (grade 1A). In addition, we recommend immediate access to drugs to treat patients with recurrence (grade 1A). Pegcetacoplan Pegcetacoplan is a new complement inhibitor approved by the FDA in 2021 for paroxysmal nocturnal hemoglobinuria. This drug binds to the C3 component of the complement system, preventing its cleavage and activation. The recommended dose is based on weight, and for adults is subcutaneous administration of 1080 mg twice a week. It is being studied for C3 glomerulopathy, macular degeneration, and autoimmune hemolytic anemia, with good results . There are still no studies for aHUS, but as it is a proximal complement blocker, it is believed to be beneficial . Iptacopan Iptacopan is a potent CFB inhibitor that acts on the complement alternative pathway . There are some studies evaluating this drug in complement dysregulation disease such as C3 glomerulopathy , demonstrating improvement in proteinuria . Also, a phase II clinical trial is currently underway to evaluate Iptacopan in patients with aHUS, but no results are yet available. However, this could be another possibility for this treatment. Crovalimab Crovalimab (RO7112689 or SKY59; marketed by Chugai Pharmaceutical) is a novel anti-C5 sequential monoclonal antibody recycling technology (SMART) antibody that combines isoelectric point, neonatal Fc receptor, and pH-dependent affinity engineering . This results in efficient C5 binding, enhanced uptake of C5-bound crovalimab by endothelial cells, disposal of C5 in the endosome, and efficient neonatal Fc receptor-mediated recycling of crovalimab. Furthermore, crovalimab is highly soluble, allowing for small injection volumes . Crovalimab binds to the C5 β-chain and prevents cleavage of the wild-type and SNP C5 by the C5 convertase. Two clinical trials are under way for aHUS patients (NCT04958265 and NCT04861259), and are recruiting pediatric, adolescent, and adult patients. This medication has great potential for a good response in aHUS patients . Eculizumab Biosimilars (Elizaria) Elizaria, developed by IBC Generium, Russia, is the world’s first registered biosimilar of eculizumab (Soliris ® , marketed by Alexion Pharmaceuticals) . A multitude of analyses revealed that the amino acid sequence is identical and higher-order structures, post-translational modifications, purity, and product variants are highly similar between Elizaria ® DP and Eculizumab RP, except for minor differences in the relative abundance of the charge variants and glycan moieties, which are not considered clinically significant . However, due to the limited experience with this drug worldwide, this consensus recommends the use of reference anti-C5 inhibitors such as eculizumab or ravulizumab instead of biosimilars. Narsoplimab Narsoplimab is a humanized anti-MASP2 monoclonal antibody. MASP2 is a serine protease associated with the mannose pathway that binds to the complement lectin pathway. It is believed that the hyperactivation of MASP2 stimulates the lectin pathway, mainly in autoimmune diseases, TMA associated with bone marrow transplantation (BMT), and infections . This medication is indicated for TMA related to BMT, following evidence from a phase II study . There is no relevant evidence for use in patients with aHUS. Pegcetacoplan is a new complement inhibitor approved by the FDA in 2021 for paroxysmal nocturnal hemoglobinuria. This drug binds to the C3 component of the complement system, preventing its cleavage and activation. The recommended dose is based on weight, and for adults is subcutaneous administration of 1080 mg twice a week. It is being studied for C3 glomerulopathy, macular degeneration, and autoimmune hemolytic anemia, with good results . There are still no studies for aHUS, but as it is a proximal complement blocker, it is believed to be beneficial . Iptacopan is a potent CFB inhibitor that acts on the complement alternative pathway . There are some studies evaluating this drug in complement dysregulation disease such as C3 glomerulopathy , demonstrating improvement in proteinuria . Also, a phase II clinical trial is currently underway to evaluate Iptacopan in patients with aHUS, but no results are yet available. However, this could be another possibility for this treatment. Crovalimab (RO7112689 or SKY59; marketed by Chugai Pharmaceutical) is a novel anti-C5 sequential monoclonal antibody recycling technology (SMART) antibody that combines isoelectric point, neonatal Fc receptor, and pH-dependent affinity engineering . This results in efficient C5 binding, enhanced uptake of C5-bound crovalimab by endothelial cells, disposal of C5 in the endosome, and efficient neonatal Fc receptor-mediated recycling of crovalimab. Furthermore, crovalimab is highly soluble, allowing for small injection volumes . Crovalimab binds to the C5 β-chain and prevents cleavage of the wild-type and SNP C5 by the C5 convertase. Two clinical trials are under way for aHUS patients (NCT04958265 and NCT04861259), and are recruiting pediatric, adolescent, and adult patients. This medication has great potential for a good response in aHUS patients . Elizaria, developed by IBC Generium, Russia, is the world’s first registered biosimilar of eculizumab (Soliris ® , marketed by Alexion Pharmaceuticals) . A multitude of analyses revealed that the amino acid sequence is identical and higher-order structures, post-translational modifications, purity, and product variants are highly similar between Elizaria ® DP and Eculizumab RP, except for minor differences in the relative abundance of the charge variants and glycan moieties, which are not considered clinically significant . However, due to the limited experience with this drug worldwide, this consensus recommends the use of reference anti-C5 inhibitors such as eculizumab or ravulizumab instead of biosimilars. Narsoplimab is a humanized anti-MASP2 monoclonal antibody. MASP2 is a serine protease associated with the mannose pathway that binds to the complement lectin pathway. It is believed that the hyperactivation of MASP2 stimulates the lectin pathway, mainly in autoimmune diseases, TMA associated with bone marrow transplantation (BMT), and infections . This medication is indicated for TMA related to BMT, following evidence from a phase II study . There is no relevant evidence for use in patients with aHUS. Pediatric Establishing the diagnosis and etiology of TMA in children is important for immediate disease management. Although there is an overlap of TMA etiologies in adults and children, some of the diseases are more common in children, while others only occur children . The main cause of TMA in children is STEC-HUS, followed by aHUS and Sp-HUS . Especially in children under 2 years of age, there are rare conditions such as congenital TTP (caused by variants in the gene ADAMTS13) , cobalamin metabolic disturbances (caused by variants in the gene MMAHC, C cobalamin defects or MTA, G cobalamin defects ), and coagulation disorders that must be ruled out before the diagnosis of aHUS . In neonates, perinatal asphyxia is a critical differential diagnosis that can confirm TMA. Perinatal abnormalities (due to fetal, maternal, or placental reasons) can impair fetal or neonatal gas exchange, triggering TMA (MAHA, thrombocytopenia, and several organ injuries, mainly renal) . Delayed treatment can result in severe organ compromise, including cardiac, hepatic, and renal insufficiency, vascular lesions, and encephalopathy . Signs of disseminated intravascular coagulopathy (DIC) are critical in asphyxiated newborns , indicating consumption coagulopathy due to ischemia/hypoxia . Perinatal asphyxia markers include low Apgar score, metabolic acidosis (detected early in umbilical cord blood) and multiple organ failure , (grade 1B). However, the clinical overlap between neonatal aHUS and perinatal asphyxia complicates diagnosis. aHUS can also lead to asphyxia and cerebral damage in newborns, making identification of the primary event difficult , . Maternal and gestational history, placental appearance, birth conditions, Apgar score, and early metabolic acidosis are crucial in clinical practice. Low plasma C3 levels suggest hyperactivation of the alternative complement pathway. aHUS is the main diagnosis in cases of TMA recurrence , more severe neurological involvement , and an accelerated and not-consumptive disease . Clinicians should be vigilant for TMA development in asphyxiated newborns, initiating appropriate treatment to reverse TMA. However, persistent TMA warrants consideration of neonatal aHUS (grade 1B). Clinical Manifestations and Particularities of aHUS Therapy in Pediatrics Children exhibit significantly lower levels of hemoglobin and platelets and higher LDH compared to adults , , indicating a potentially more severe hemolytic effect in childhood. Moreover, children have a higher mortality rate than adults , . The anti-C5 monoclonal antibody (mAb) eculizumab is the first line therapy for aHUS in children , , , , , and it has been demonstrated to be safe and effective by many clinical trials, cohort studies, and case reports. Especially in children, eculizumab has promoted TMA remission and it is frequently associated with complete recovery of the renal function , , . If the anti-C5 mAb is not immediately available at the emergency department, plasma therapy should be initiated, including plasmapheresis or plasma infusion (grade 1B); the choice depends on the appropriate conditions of the service, professional experience, clinical status, and child size. Although plasma therapy has not been shown to be effective in maintaining long-term remission and promoting renal function recovery, it may transiently improve TMA by providing complement regulatory proteins and, in the case of plasmapheresis, it is possible to remove CFH antibodies . However, it is important to emphasize the morbidity associated with this procedure, especially in children, linked to venous central catheterization complications and hypervolemia . Hydroxycobalamin can be administered in an emergency, while test results are not available. Although cobalamin disturbances leading to TMA are rare, they are treatable and there is no severe adverse event . Currently, other anti-C5 blockers have been studied in children. Ravulizumab is now approved and there are pediatric clinical trials showing its efficacy and safety . Other options are now under investigation, with better posology and the possibility of subcutaneous (crovalimab) or oral (iptacoplan) administration. Pregnancy Pregnancy-associated TMA is a rare disorder with an estimated incidence of approximately 1 in 25,000 pregnancies and it is associated with significant perinatal and maternal morbidity and mortality . Pregnancy and postpartum have long been recognized as high-risk conditions for TMA. There are three main differential diagnoses for pregnancy-associated TMA: (1) Pre-eclampsia/hemolysis, elevated liver function tests, low platelet syndrome (PE/ HELLP); (2) TTP; and (3) aHUS. Pregnancy is a known trigger for TTP and aHUS, and the presence of these disorders increases the risk of PE/HELLP syndrome. For TMA markers, some experts propose a lower platelet count threshold for clinical diagnosis, considering that in normal pregnancy platelets decrease. Approximately 10% of uncomplicated pregnancies have platelet counts below 150,000/mm at delivery. Hence, a threshold of 100,000/mm appears to be appropriate for diagnosing pregnancy-associated TMA . Other parameters such as anemia, elevated LDH, reduced haptoglobin, presence of schistocytes, and organ damage align with recommendations for other TMA forms. AKI is frequently found in most types of pregnancy-associated TMA, except TTP. Although there is no universally accepted definition of AKI during pregnancy, the various definitions available refer to the KDIGO guidelines . Other publications are based on a serum creatinine above 0.90 mmol/L and/ or a 0.25% increase from baseline . aHUS in Pregnancy Pregnancy is a condition of increased activity of all pathways of the complement system, including classical, lectin, and alternative pathways. The aim is to clear the maternal circulation of immune complexes and, on the other hand, of regulatory proteins for complement control (mainly MCP and CD59). Also, studies have identified variants in complement system genes in more than 50% of pregnancy-associated TMA . aHUS, the rarest form of TMA in pregnancy, often arises in late third trimester or postpartum. Cases outside these periods complicate differential diagnosis with PE/HELLP , . Renal impairment is common, while platelet count is usually not critically reduced, and neurological involvement, unlike TTP, is infrequent , , . Currently, the recommended treatment is a C5 inhibitor (grade 1B). Without this treatment, renal outcomes are dismal, with 76% of patients progressing to end-stage kidney disease (ESKD) despite receiving plasmapheresis , . Another study showed a 50% risk of ESKD in pregnant women with aHUS, regardless of whether they underwent plasmapheresis or not . Despite the high cost of the medication, it generally does not exceed the cost of intensive care treatment, plasmapheresis, hemodialysis, probable kidney failure, and transplantation . Anti-C5 mAb can cross the placenta, but data limited to the number of pregnancies exposed to eculizumab (fewer than 300 pregnancy outcomes) indicate that there is no increased risk of fetal malformation or fetal-neonatal toxicity , . No controlled clinical study has been carried out to evaluate the efficacy of anti-C5 in pregnancy-associated aHUS. Despite this, more than 35 cases have been reported in the literature in which eculizumab was administered during or after pregnancy, with approximately 90% showing hematological response and remission of kidney disease . Treatment duration is uncertain, and discontinuation of anti-C5 treatment should be personalized. Complement gene variants increase the risk of recurrence. Terminal complement blockade must be monitored since pregnancy may require higher dose/frequency due to volume changes, increased C5 synthesis, or proteinuria. Despite eculizumab, prior aHUS history elevates risk of recurrence in subsequent pregnancies, requiring vigilant monitoring . According to the label, ravulizumab is considered Category C during pregnancy (pregnant women should not use this medication without medical advice). There are no clinical data on exposure in pregnancy. However, recent studies report the effectiveness and safety of ravulizumab in postpartum aHUS . Establishing the diagnosis and etiology of TMA in children is important for immediate disease management. Although there is an overlap of TMA etiologies in adults and children, some of the diseases are more common in children, while others only occur children . The main cause of TMA in children is STEC-HUS, followed by aHUS and Sp-HUS . Especially in children under 2 years of age, there are rare conditions such as congenital TTP (caused by variants in the gene ADAMTS13) , cobalamin metabolic disturbances (caused by variants in the gene MMAHC, C cobalamin defects or MTA, G cobalamin defects ), and coagulation disorders that must be ruled out before the diagnosis of aHUS . In neonates, perinatal asphyxia is a critical differential diagnosis that can confirm TMA. Perinatal abnormalities (due to fetal, maternal, or placental reasons) can impair fetal or neonatal gas exchange, triggering TMA (MAHA, thrombocytopenia, and several organ injuries, mainly renal) . Delayed treatment can result in severe organ compromise, including cardiac, hepatic, and renal insufficiency, vascular lesions, and encephalopathy . Signs of disseminated intravascular coagulopathy (DIC) are critical in asphyxiated newborns , indicating consumption coagulopathy due to ischemia/hypoxia . Perinatal asphyxia markers include low Apgar score, metabolic acidosis (detected early in umbilical cord blood) and multiple organ failure , (grade 1B). However, the clinical overlap between neonatal aHUS and perinatal asphyxia complicates diagnosis. aHUS can also lead to asphyxia and cerebral damage in newborns, making identification of the primary event difficult , . Maternal and gestational history, placental appearance, birth conditions, Apgar score, and early metabolic acidosis are crucial in clinical practice. Low plasma C3 levels suggest hyperactivation of the alternative complement pathway. aHUS is the main diagnosis in cases of TMA recurrence , more severe neurological involvement , and an accelerated and not-consumptive disease . Clinicians should be vigilant for TMA development in asphyxiated newborns, initiating appropriate treatment to reverse TMA. However, persistent TMA warrants consideration of neonatal aHUS (grade 1B). Children exhibit significantly lower levels of hemoglobin and platelets and higher LDH compared to adults , , indicating a potentially more severe hemolytic effect in childhood. Moreover, children have a higher mortality rate than adults , . The anti-C5 monoclonal antibody (mAb) eculizumab is the first line therapy for aHUS in children , , , , , and it has been demonstrated to be safe and effective by many clinical trials, cohort studies, and case reports. Especially in children, eculizumab has promoted TMA remission and it is frequently associated with complete recovery of the renal function , , . If the anti-C5 mAb is not immediately available at the emergency department, plasma therapy should be initiated, including plasmapheresis or plasma infusion (grade 1B); the choice depends on the appropriate conditions of the service, professional experience, clinical status, and child size. Although plasma therapy has not been shown to be effective in maintaining long-term remission and promoting renal function recovery, it may transiently improve TMA by providing complement regulatory proteins and, in the case of plasmapheresis, it is possible to remove CFH antibodies . However, it is important to emphasize the morbidity associated with this procedure, especially in children, linked to venous central catheterization complications and hypervolemia . Hydroxycobalamin can be administered in an emergency, while test results are not available. Although cobalamin disturbances leading to TMA are rare, they are treatable and there is no severe adverse event . Currently, other anti-C5 blockers have been studied in children. Ravulizumab is now approved and there are pediatric clinical trials showing its efficacy and safety . Other options are now under investigation, with better posology and the possibility of subcutaneous (crovalimab) or oral (iptacoplan) administration. Pregnancy-associated TMA is a rare disorder with an estimated incidence of approximately 1 in 25,000 pregnancies and it is associated with significant perinatal and maternal morbidity and mortality . Pregnancy and postpartum have long been recognized as high-risk conditions for TMA. There are three main differential diagnoses for pregnancy-associated TMA: (1) Pre-eclampsia/hemolysis, elevated liver function tests, low platelet syndrome (PE/ HELLP); (2) TTP; and (3) aHUS. Pregnancy is a known trigger for TTP and aHUS, and the presence of these disorders increases the risk of PE/HELLP syndrome. For TMA markers, some experts propose a lower platelet count threshold for clinical diagnosis, considering that in normal pregnancy platelets decrease. Approximately 10% of uncomplicated pregnancies have platelet counts below 150,000/mm at delivery. Hence, a threshold of 100,000/mm appears to be appropriate for diagnosing pregnancy-associated TMA . Other parameters such as anemia, elevated LDH, reduced haptoglobin, presence of schistocytes, and organ damage align with recommendations for other TMA forms. AKI is frequently found in most types of pregnancy-associated TMA, except TTP. Although there is no universally accepted definition of AKI during pregnancy, the various definitions available refer to the KDIGO guidelines . Other publications are based on a serum creatinine above 0.90 mmol/L and/ or a 0.25% increase from baseline . Pregnancy is a condition of increased activity of all pathways of the complement system, including classical, lectin, and alternative pathways. The aim is to clear the maternal circulation of immune complexes and, on the other hand, of regulatory proteins for complement control (mainly MCP and CD59). Also, studies have identified variants in complement system genes in more than 50% of pregnancy-associated TMA . aHUS, the rarest form of TMA in pregnancy, often arises in late third trimester or postpartum. Cases outside these periods complicate differential diagnosis with PE/HELLP , . Renal impairment is common, while platelet count is usually not critically reduced, and neurological involvement, unlike TTP, is infrequent , , . Currently, the recommended treatment is a C5 inhibitor (grade 1B). Without this treatment, renal outcomes are dismal, with 76% of patients progressing to end-stage kidney disease (ESKD) despite receiving plasmapheresis , . Another study showed a 50% risk of ESKD in pregnant women with aHUS, regardless of whether they underwent plasmapheresis or not . Despite the high cost of the medication, it generally does not exceed the cost of intensive care treatment, plasmapheresis, hemodialysis, probable kidney failure, and transplantation . Anti-C5 mAb can cross the placenta, but data limited to the number of pregnancies exposed to eculizumab (fewer than 300 pregnancy outcomes) indicate that there is no increased risk of fetal malformation or fetal-neonatal toxicity , . No controlled clinical study has been carried out to evaluate the efficacy of anti-C5 in pregnancy-associated aHUS. Despite this, more than 35 cases have been reported in the literature in which eculizumab was administered during or after pregnancy, with approximately 90% showing hematological response and remission of kidney disease . Treatment duration is uncertain, and discontinuation of anti-C5 treatment should be personalized. Complement gene variants increase the risk of recurrence. Terminal complement blockade must be monitored since pregnancy may require higher dose/frequency due to volume changes, increased C5 synthesis, or proteinuria. Despite eculizumab, prior aHUS history elevates risk of recurrence in subsequent pregnancies, requiring vigilant monitoring . According to the label, ravulizumab is considered Category C during pregnancy (pregnant women should not use this medication without medical advice). There are no clinical data on exposure in pregnancy. However, recent studies report the effectiveness and safety of ravulizumab in postpartum aHUS . Pre-Transplant Investigation Stage 5 CKD patients with unknown cause, post- pregnancy cases, lupus nephritis, TMA histology, and malignant hypertension should be considered potential aHUS cases. Pre-transplant assessment should include blood count with schistocytes, LDH, Coombs test, haptoglobin, autoantibodies and complement levels (C3 and C4) . If aHUS is likely and hemolysis evident (active aHUS), a 6-month course of anti-C5 mAb before transplantation should be considered to evaluate potential kidney function recovery (grade 1C). Genetic Analysis in Transplantation Genetic analysis of all potentially linked genes helps medical teams and patients in devising strategies to prevent post-transplant aHUS recurrence (grade 1C). The risk of recurrence of aHUS in kidney graft correlates with genetic variant type. Kidney transplantation in aHUS and ESKD patients is intricate, with relapse rates of 50–80% , resulting in graft loss in up to 91.6% of cases 42,43,98 . Transplant recipients are at TMA risk from factors damaging the endothelium, including immunosuppressive drugs (calcineurin inhibitors and mTOR inhibitors), ischemia-reperfusion injury, rejection, and post-transplant infections . After the genetic tests, patients must be stratified into recurrence risk groups (grade 1A), and the best prophylactic regimen should be addressed before the surgery. High-risk patients are those with previous transplant recurrence, disease-related variants in CFH , or gain-of-function variants in CFB or C3 . Moderate-risk patients have anti-factor H antibodies, CFI variants, uncertain significance variants, or CFH polymorphisms. Low-risk patients have MCP mutations, persistently negative factor H antibodies, or no mutations/polymorphisms and one can observe transplant outcomes of these patients using eculizumab, if needed. Using living related donors is not recommended for aHUS patients due to potential donor variant risks after nephrectomy (grade 1B). If considering a related donor, genetic analysis should ensure no complement gene variants. Discussing post-nephrectomy aHUS risks with the potential donor is crucial (grade 1B). Additionally, it is recommended to avoid mTOR inhibitors with calcineurin inhibitors, high calcineurin inhibitors doses, anti-donor antibodies in transplantation, expanded criteria donors, and prolonged cold ischemia times (grade 1B). These strategies aim to mitigate graft endothelial stress, reduce ischemia-reperfusion injury, and potentially decrease activation of the alternative complement pathway. Diagnosis of Post-Transplant aHUS The diagnosis of post-transplant aHUS is similar to that in the general context. However, some special secondary causes should be ruled out, such as those induced by calcineurin/mTOR inhibitors as well as antibody-mediated rejection and autoimmune and viral diseases . Daily laboratory tests are advised until normal hematological parameters are obtained and renal function improves. Hemolytic anemia tests include blood count, platelet count, peripheral blood smear (for schistocytes), LDH, and haptoglobin. Renal function monitoring involves serum creatinine and urinary protein/creatinine ratio measurements (grade 1B). Treatment Recommendations After Transplantation Eculizumab is effective in post-kidney transplantation cases of aHUS 101,103–105 . Ravulizumab also is also effective and safe in transplant patients, as per case reports. The recommended dose of these drugs for kidney transplant patients is the same as that for other patients. Immunosuppression with calcineurin inhibitors is advised with careful monitoring to prevent overexposure (grade 1B), while mTOR inhibitors should be avoided in aHUS patients undergoing kidney transplantation (grade 2B). Long-term belatacept can be used to avoid calcineurin inhibitors, but this drug in not regularly available in Brazil. Stage 5 CKD patients with unknown cause, post- pregnancy cases, lupus nephritis, TMA histology, and malignant hypertension should be considered potential aHUS cases. Pre-transplant assessment should include blood count with schistocytes, LDH, Coombs test, haptoglobin, autoantibodies and complement levels (C3 and C4) . If aHUS is likely and hemolysis evident (active aHUS), a 6-month course of anti-C5 mAb before transplantation should be considered to evaluate potential kidney function recovery (grade 1C). Genetic analysis of all potentially linked genes helps medical teams and patients in devising strategies to prevent post-transplant aHUS recurrence (grade 1C). The risk of recurrence of aHUS in kidney graft correlates with genetic variant type. Kidney transplantation in aHUS and ESKD patients is intricate, with relapse rates of 50–80% , resulting in graft loss in up to 91.6% of cases 42,43,98 . Transplant recipients are at TMA risk from factors damaging the endothelium, including immunosuppressive drugs (calcineurin inhibitors and mTOR inhibitors), ischemia-reperfusion injury, rejection, and post-transplant infections . After the genetic tests, patients must be stratified into recurrence risk groups (grade 1A), and the best prophylactic regimen should be addressed before the surgery. High-risk patients are those with previous transplant recurrence, disease-related variants in CFH , or gain-of-function variants in CFB or C3 . Moderate-risk patients have anti-factor H antibodies, CFI variants, uncertain significance variants, or CFH polymorphisms. Low-risk patients have MCP mutations, persistently negative factor H antibodies, or no mutations/polymorphisms and one can observe transplant outcomes of these patients using eculizumab, if needed. Using living related donors is not recommended for aHUS patients due to potential donor variant risks after nephrectomy (grade 1B). If considering a related donor, genetic analysis should ensure no complement gene variants. Discussing post-nephrectomy aHUS risks with the potential donor is crucial (grade 1B). Additionally, it is recommended to avoid mTOR inhibitors with calcineurin inhibitors, high calcineurin inhibitors doses, anti-donor antibodies in transplantation, expanded criteria donors, and prolonged cold ischemia times (grade 1B). These strategies aim to mitigate graft endothelial stress, reduce ischemia-reperfusion injury, and potentially decrease activation of the alternative complement pathway. The diagnosis of post-transplant aHUS is similar to that in the general context. However, some special secondary causes should be ruled out, such as those induced by calcineurin/mTOR inhibitors as well as antibody-mediated rejection and autoimmune and viral diseases . Daily laboratory tests are advised until normal hematological parameters are obtained and renal function improves. Hemolytic anemia tests include blood count, platelet count, peripheral blood smear (for schistocytes), LDH, and haptoglobin. Renal function monitoring involves serum creatinine and urinary protein/creatinine ratio measurements (grade 1B). Eculizumab is effective in post-kidney transplantation cases of aHUS 101,103–105 . Ravulizumab also is also effective and safe in transplant patients, as per case reports. The recommended dose of these drugs for kidney transplant patients is the same as that for other patients. Immunosuppression with calcineurin inhibitors is advised with careful monitoring to prevent overexposure (grade 1B), while mTOR inhibitors should be avoided in aHUS patients undergoing kidney transplantation (grade 2B). Long-term belatacept can be used to avoid calcineurin inhibitors, but this drug in not regularly available in Brazil. The COMDORA-SBN expert group provides recommendations for the diagnosis and treatment of aHUS in the Brazilian population. These guidelines aim to improve, rather than restrict, current clinical practices. This consensus will be regularly updated with new information and data as needed. |
Prolific authors in ophthalmology and vision science | 8aefce18-3cb8-42a5-a0dd-f71959f033bc | 11884359 | Ophthalmology[mh] | |
Impacto de la intervención educativa en una población con Diabetes Mellitus tipo 2
| 8ee29f8b-69ac-4e70-8bee-6c33d5a5cef7 | 11905787 | Patient Education as Topic[mh] | Qué se sabe sobre el tema. El paciente diabético tipo 2 presenta simultáneamente diversos factores de riesgo cardiovascular
(FRCV) dislipemia, hipertensión y obesidad que junto con aspectos, como la no adherencia
terapéutica, el desconocimiento de la enfermedad y sus complicaciones pueden dificultar el
control
metabólico óptimo de los mismos, llevandolos incluso a hospitalizaciones. Qué aporta este trabajo. Proporciona a los pacientes diabéticos educación terapéutica del conococimiento de la enfermedad
hasta pautas para el autocuidado, evitando posibles complicaciones.
El paciente diabético tipo 2 presenta simultáneamente diversos factores de riesgo cardiovascular
(FRCV) dislipemia, hipertensión y obesidad que junto con aspectos, como la no adherencia
terapéutica, el desconocimiento de la enfermedad y sus complicaciones pueden dificultar el
control
metabólico óptimo de los mismos, llevandolos incluso a hospitalizaciones.
Proporciona a los pacientes diabéticos educación terapéutica del conococimiento de la enfermedad
hasta pautas para el autocuidado, evitando posibles complicaciones.
La intervención educativa diabetológica es un complemento terapéutico del paciente diabético, una
herramienta fundamental costo-efectiva, que incluye procesos de aprendizaje en automonitoreo,
autocuidado y cambios en el estilo de vida del paciente, promoviendo la prevención de
complicaciones. Este estudio evidenció cambios en el control metabólico, ya que los pacientes
intervenidos mostraron porcentajes más bajos de IMC, grasa visceral y no visceral en la
posintervención, junto con los niveles de hemoglobina glicosilada.
Según, la Federación Internacional de Diabetes (IDF), durante el año 2021 la diabetes mellitus tipo 2
(DM2) afectó 73,6 millones de personas en todo el mundo, es decir uno de cada 10 adultos presento esta
enfermedad.Para ese mismo año, Colombia se ubicó entre los 5 países con el mayor número de
personas con DM2 de 20 a 79 años, aproximadamente 3,4 millones . El Programa Nacional de Educación sobre la Diabetes (NDEP),
indico que la DM2 representa
un problema de salud pública, conduce a complicaciones microvasculares como retinopatía, nefropatía,
neuropatía diabética, y macrovasculares como cardiopatía isquémica, insuficiencia cardíaca, enfermedad
cerebrovascular e insuficiencia arterial periférica entre otras . La organización Mundial de la Salud (OMS) para el año 2021, lanzo el Pacto Mundial contra la DM, que
tiene por objetivo mejoras en la prevención y el cuidado, especialmente en países de ingresos bajos y
medianos, considera que la educación en salud es fundamental en el tratamiento de la enfermedad . La educación diabetológica, es una herramienta efectiva que
permite a los pacientes
adquirir un papel responsable en el control y prevención de complicaciones vasculares, lo que puede
reducir la dependencia médica y terapéutica . Entre los efectos positivos de los programas en educación
diabetológica, está la
alfabetización sanitaria, conocimientos y habilidades en el paciente para cambiar su comportamiento en
hábitos saludables y aumentar su motivación para mejorar su calidad de vida por medio del autocuidado , . El Cuestionario de Conocimientos sobre la Diabetes (DKQ-24), es un cuestionario de 24 ítems, que ha sido
desarrollado por el estudio de educación sobre diabetes . Considerado un instrumento válido y confiable que contribuye a
la evaluación del
conocimiento y autocuidado de pacientes con DM, incluye aspectos metabólicos, como el control
terapéutico y nutricional .Se ha sugerido, que los centros hospitalarios de mediana y alta
complejidad, en sus
servicios de hospitalización busquen la integración, promoción y mantenimiento de la salud, garantizando
información en hábitos de vida saludable, que contribuyan en el manejo de la DM2, sin limitarse al
control farmacológico de las enfermedades crónicas . El objetivo del presente estudio fue evaluar el impacto de la
aplicación de un modelo de
educación terapéutica diabetológica hospitalaria (autocuidado y conocimiento) a largo plazo.
Diseño de estudio y participantes Se realizó un estudio longitudinal prospectivo, con un muestreo por conveniencia en 60 pacientes con
diagnóstico de DM2, mayores de edad, hospitalizados en una institución de salud de tercer nivel en
la
ciudad de Neiva, a causa de las complicaciones agudas y/o crónicas durante el periodo de marzo del
2022
hasta febrero del 2023, que cumplieran con los criterios de inclusión, definidos como; pacientes con
prescripción de insulina de Novo, capacidad de autogestión, pacientes con hemoglobina glicosilada
(HbA1c) >10% y participación voluntaria en el estudio. Fueron excluidos aquellos pacientes que
consultaron a control con nutricionista en los últimos 3 meses, control por obesidad, afiliados a
programas de fisioterapia y rehabilitación, pie diabético, enfermedades crónicas en estado terminal,
diagnóstico de Covid-19 y no residentes en la ciudad. Después de la firma voluntaria del consentimiento informado, los participantes fueron entrevistados
solicitándoles información sociodemográfica. Posteriormente, se procedió a la toma de las variables
biométricas; peso, talla, índice de masa corporal (IMC), perímetro abdominal, porcentaje de grasa
visceral y de grasa no visceral usando una báscula de bioimpedancia y plicómetro. Se utilizó el
Cuestionario de conocimiento de la diabetes (DKQ-24) como evaluación previa y posterior a 90 días se
aplicó de nuevo junto con la toma de las variables biométricas. Se consideró como adecuado en el
DKQ-24,
un porcentaje igual o mayor al 70% y como inadecuado por debajo de este, de acuerdo con lo reportado
por
la literatura , . Intervención La intervención consta de 5 sesiones educativas basados en el modelo de educación terapéutica
diabetológica en ámbitos hospitalarios, sobre los lineamientos y recomendaciones de la Asociación
Americana de Educadores de Diabetes (AADE), adaptado de múltiples estudios de conocimiento en
diabetes,
insulinización, hipoglicemia, glucometría, dieta y ejercicio . Se realizó de forma individual y coordinada con cada uno
de los pacientes, con énfasis en
educación nutricional, proactiva y cognitiva. Para garantizar la adecuada adherencia a cada una de
las
sesiones, se realizaron preguntas de retroalimentación durante cada sesión. Todos los procedimientos y protocolos utilizados en el estudio fueron revisados y aprobados por el
comité
de Ética para la investigación científica, adoptando los principios bioéticos establecidos en la
Declaración de Helsinki . Los cuestionarios y las variables registradas fueron
codificados para proteger la
confidencialidad de los participantes. Análisis estadístico El análisis de datos se realizó con el paquete estadístico SPSS versión 25 (SPSS Inc.,
Chicago, IL, EUA). Se analizaron las variables métricas (perímetro abdominal, peso, talla, IMC,
porcentaje de grasa visceral y no visceral) mediante distribución de frecuencias, promedios, media,
desviación estándar y medidas de variabilidad. Así mismo, se llevaron a cabo las tabulaciones de
estas
variables, clasificadas en preintervención y posintervención. Las comparaciones para muestras
relacionadas tanto para las variables cuantitativas como cualitativas se realizaron mediante las
pruebas
T de Student y McNemar, respectivamente. Para aquellas variables cuantitativas que no presentaron
una
distribución normal se utilizó la prueba de Wilcoxon. Se calculó la confiabilidad test/retest (a
través
de coeficientes de correlación intraclase-ICC) del DKQ-24. Se consideraron valores de ICC > 0.75
como
aceptables en este caso. Se analizaron los parámetros descritos anteriormente después de realizar la
prueba de Shapiro-Wilk para la normalidad. Los datos se expresan como media y desviación estándar
(DE),
con un nivel de significancia (valor p ) de 0,05.
Se realizó un estudio longitudinal prospectivo, con un muestreo por conveniencia en 60 pacientes con
diagnóstico de DM2, mayores de edad, hospitalizados en una institución de salud de tercer nivel en
la
ciudad de Neiva, a causa de las complicaciones agudas y/o crónicas durante el periodo de marzo del
2022
hasta febrero del 2023, que cumplieran con los criterios de inclusión, definidos como; pacientes con
prescripción de insulina de Novo, capacidad de autogestión, pacientes con hemoglobina glicosilada
(HbA1c) >10% y participación voluntaria en el estudio. Fueron excluidos aquellos pacientes que
consultaron a control con nutricionista en los últimos 3 meses, control por obesidad, afiliados a
programas de fisioterapia y rehabilitación, pie diabético, enfermedades crónicas en estado terminal,
diagnóstico de Covid-19 y no residentes en la ciudad. Después de la firma voluntaria del consentimiento informado, los participantes fueron entrevistados
solicitándoles información sociodemográfica. Posteriormente, se procedió a la toma de las variables
biométricas; peso, talla, índice de masa corporal (IMC), perímetro abdominal, porcentaje de grasa
visceral y de grasa no visceral usando una báscula de bioimpedancia y plicómetro. Se utilizó el
Cuestionario de conocimiento de la diabetes (DKQ-24) como evaluación previa y posterior a 90 días se
aplicó de nuevo junto con la toma de las variables biométricas. Se consideró como adecuado en el
DKQ-24,
un porcentaje igual o mayor al 70% y como inadecuado por debajo de este, de acuerdo con lo reportado
por
la literatura , .
La intervención consta de 5 sesiones educativas basados en el modelo de educación terapéutica
diabetológica en ámbitos hospitalarios, sobre los lineamientos y recomendaciones de la Asociación
Americana de Educadores de Diabetes (AADE), adaptado de múltiples estudios de conocimiento en
diabetes,
insulinización, hipoglicemia, glucometría, dieta y ejercicio . Se realizó de forma individual y coordinada con cada uno
de los pacientes, con énfasis en
educación nutricional, proactiva y cognitiva. Para garantizar la adecuada adherencia a cada una de
las
sesiones, se realizaron preguntas de retroalimentación durante cada sesión. Todos los procedimientos y protocolos utilizados en el estudio fueron revisados y aprobados por el
comité
de Ética para la investigación científica, adoptando los principios bioéticos establecidos en la
Declaración de Helsinki . Los cuestionarios y las variables registradas fueron
codificados para proteger la
confidencialidad de los participantes.
El análisis de datos se realizó con el paquete estadístico SPSS versión 25 (SPSS Inc.,
Chicago, IL, EUA). Se analizaron las variables métricas (perímetro abdominal, peso, talla, IMC,
porcentaje de grasa visceral y no visceral) mediante distribución de frecuencias, promedios, media,
desviación estándar y medidas de variabilidad. Así mismo, se llevaron a cabo las tabulaciones de
estas
variables, clasificadas en preintervención y posintervención. Las comparaciones para muestras
relacionadas tanto para las variables cuantitativas como cualitativas se realizaron mediante las
pruebas
T de Student y McNemar, respectivamente. Para aquellas variables cuantitativas que no presentaron
una
distribución normal se utilizó la prueba de Wilcoxon. Se calculó la confiabilidad test/retest (a
través
de coeficientes de correlación intraclase-ICC) del DKQ-24. Se consideraron valores de ICC > 0.75
como
aceptables en este caso. Se analizaron los parámetros descritos anteriormente después de realizar la
prueba de Shapiro-Wilk para la normalidad. Los datos se expresan como media y desviación estándar
(DE),
con un nivel de significancia (valor p ) de 0,05.
Un total de 60 pacientes con DM2 fueron reclutados (as) para este estudio. La indica las
características sociodemográficas de la población estudio. La edad promedio fue 56,32 ± 7,60 años, 40%
eran adultos mayores (>56 años). El 57% fueron mujeres, procedentes del área urbana en un 88%. En
cuanto al nivel educativo el 48% tenían un nivel educativo secundaria y el 35% primaria. Adicionalmente,
el 53% y el 43% eran pertenecientes al estrato social 1 y 2 respectivamente. Perfil biométrico: La , indica las características del perfil biométrico de la población. La media
de la hemoglobina glicosilada inicialmente fue de 8,07 ± 0,84, en la posintervención fue de 7,26 ± 0,90,
con diferencias estadísticamente significativas (p=0,000). La media del IMC fue de 30,15±3,82 (preint)
el cual disminuyó a 28,13±3,58 en la posintervención, indicando diferencias estadísticamente
significativas (p=0,000). El perímetro abdominal obtuvo una media inicial de 100,43±9,84, mejoró
significativamente con una media de 96,47±9,26 al finalizar la intervención (p=0,000). La mediana de la
presión sistólica inicialmente fue de 130 (125-136), la cual disminuyó en la posintervención a 128 (125
-130) (p=0,001). La bioimpedancia obtuvo una media de 26,35 ± 8,45, se redujo a 21,93 ± 6,69 posterior a
la intervención, indicando diferencias estadísticamente significativas (p=0,000). La plicometría
presentó una media de 37,90 ± 7,09, posterior a la intervención diabetológica se redujo, con diferencias
estadísticamente significativas (p=0,000). En cuanto a las consultas al servicio de nutrición y la
actividad física, estas mejoraron en la posintervención con diferencias estadísticamente significativas
respectivamente (p=0,000). Diabetes Knowledge Questionnaire (DKQ-24): La , indica los resultados según el cuestionario DKQ-24
pre (10,13±3,28) y posintervención (20,13±2,77) indicando diferencias significativas entre los grupos
(p=0,000). En cuanto al conocimiento de la enfermedad, durante la preintervención solo el 13% tenían un
conocimiento adecuado de su enfermedad, el 27% tenían un conocimiento adecuado sobre el control de la DM
y solo el 7% conocían acerca de las complicaciones agudas y crónicas de su enfermedad. En la
posintervención el 92% de los pacientes tuvo un adecuado conocimiento sobre su enfermedad, el 97%
respondió de manera adecuada sobre el control de la glicemia, y el 78% reconocieron adecuadamente las
complicaciones de la enfermedad, indicando diferencias estadísticamente significativas (p=0,000). El índice alfa de Cronbach se calculó en 0,860, cuyo valor fue adecuado y se puede caracterizar como
"bueno". La , presenta los coeficientes de correlación intraclase (ICC), con un
Intervalo de Confianza del 95% = 0,803-0,90 del ICC de medidas promedio 0,860, donde el nivel de
confiabilidad podría considerarse como "bueno" a "excelente" (p<0.000).
La DM2 es una enfermedad crónica que requiere un control adecuado, adherencia a los tratamientos y
conocimiento de la enfermedad, con el fin de prevenir y reducir las complicaciones macro y
microvasculares . Diferentes estudios han demostrado que la alfabetización en
salud funcional puede influir
en el autocontrol del paciente, la comunicación con el médico y el progreso en los tratamientos . Este estudio evaluó el impacto de la educación terapéutica
diabetológica, en
pacientes en estancia hospitalaria con DM2, aplicando la prueba de DKQ-24. Nuestros resultados mostraron que la puntuación media de DKQ-24 en la preintervención fue de 10,13,
indicando una falta de conocimiento sobre la enfermedad. Resultados similares fueron reportados por
Chrysi et al., en el 2022 en Grecia y por Formosa et al., en 2016, en Malta , con una baja puntuación media, revelando la brecha existente
en el autocuidado de la
diabetes. Sin embargo, en la posintervención nuestro estudio mostro una media de 20,12, indicando un
conocimiento adecuado de la DM. Un estudio realizado en México, indico cambios positivos en los niveles
de conocimiento, complicaciones e información básica durante la posintervención, con un impacto positivo
en la calidad de vida de los pacientes , . De igual forma, un estudio realizado en Lima- Peru, reporto datos similares a los encontrados, indicando cambios positivos. La población estudio se caracterizó por encontrarse entre los 50 y 65 años (adultos), Chrysi et al.,
2022, señalo que los grupos de edad entre 41-50 años, pueden tener un mejor conocimiento de la diabetes,
asociado a los programas educativos para autocontrol de la DM .En nuestro
estudio el 48% de los participantes indicaron un nivel educativo bajo (≤secundaria), esto puede estar
asociado a una baja compresión sobre los conocimientos acerca de la enfermedad en la preintervención,
puesto que se ha determinado que el nivel de alfabetización en salud funcional puede afectar el
autocontrol de los pacientes, la comunicación con los médicos y presentar resultados desfavorables, como
reingresos hospitalarios . Nuestro análisis del índice alfa de Cronbach del DKQ-24 fue de 0,860 y el ICC fue de 0,860. Estos
resultados son consistentes con otros estudios de diversas poblaciones. Por ejemplo, Ahmad et al., 2010
aplicaron el DKQ-24 para la población indígena en Malasia, donde el alfa de Cronbach fue de 0,806;
hicieron una comparación del lugar de residencia (rural y urbana) destacando el valor de alfa de
Cronbach aceptable con valores válidos . Por otro lado, un estudio en población griega evaluó un grupo
de 40 personas obteniendo un
valor alfa de Cronbach del DKQ-24 adecuado de 0,845 y una consistencia interna (IC) de 0,830 muy
similares a nuestros datos calculados. Concluyeron que el DKQ-24 es una herramienta válida y confiable
para evaluar el conocimiento sobre la diabetes en la población griega . Con respecto, a la aplicación del DKQ-24 en población latina,
los resultados muestran
coherencia con los obtenidos en nuestro estudio, según lo reportado por López - López et al; 2016 al
obtener un índice alfa de Cronbach del DKQ-24 de 0,78 en un grupo de 17 pacientes mexicanos
diagnosticados de diabetes . Adicionalmente, la media de hemoglobina glicosilada inicial fue del 8,07, mientras que en la
posintervención fue de 7,26 ( p =0,000). Datos similares fueron reportados por Roselló
Araya et al., 2020, en una población de Costa Rica, encontró niveles de hemoglobina glicosilada de 8,8
en la preintervención y de 7,8 al final de la intervención, indicando que la educación terapéutica es
parte del tratamiento de la diabetes para lograr objetivos favorables . En nuestro estudio, la mayoría de los pacientes tenían
sobrepeso u obesidad, lo que
aumenta el riesgo de complicaciones e induce un impacto fisiopatológico significativo en varios estadios
de la enfermedad . Sin embargo, durante la posintervención, el IMC disminuyó,
estos datos son consistentes a
los reportados por Cabrera-Pivaral et al., 2004 en una población Mexicana, indicando que la intervención
educativa participativa contribuye a mejorar el nivel de IMC en los diabéticos obesos, favoreciendo el
autocuidado y la conciencia social en salud . Se ha demostrado, que la educación en salud está asociada
inversamente proporcional al
sobrepeso y la obesidad . La media del perímetro abdominal en la preintervención fue de 100,43 cm la cual disminuyo significante a
96,47 cm en la posintervención. Vázquez et al., 2007 indicó que la obesidad abdominal es un predictor
más fuerte de DM, que de obesidad . Adicionalmente, estudios recientes han demostrado que la
bioimpedancia es más precisa para
determinar la masa magra en humanos, ofreciendo resultados confiables al evaluar la condición clínica
del paciente . Según, Zaharia OP, et al., 2021 la combinación de una dieta
baja en calorías, actividad
física, junto a un programa educativo guiado en pacientes con DM, reduce clínicamente la pérdida de
peso, masa grasa y el control glucémico . En nuestro estudio, la posintervención indico una reducción
significativa de estas
variables, al igual que la reducción de la presión sistólica. Diferentes estudios en intervención han
indicado que la pérdida de peso, reduce la presión sistólica y por lo tanto ofrece protección
cardiometabólica, disminuyendo el riesgo de morbilidad y mortalidad cardiovascular . Los resultados de este estudio son de vital importancia para diferentes profesionales en el área de
salud, puesto que al evaluar el grado de conocimiento sobre la DM2 de cada individuo, permite orientar
estrategias terapéuticas e intervenciones en el curso de la enfermedad, con el fin de lograr metas de
control metabólico y perfil de riesgo cardiovascular. Sin embargo, este estudio presento algunas
limitaciones, como el tamaño de la muestra, el cual puede influir en la potencia estadística del
estudio. Adicionalmente, el estudio se centra principalmente en el conocimiento y las medidas clínicas,
sin abordar factores como la motivación del paciente, el apoyo social o las barreras psicológicas que
pueden influir en la adherencia a los tratamientos. Si bien, mencionamos la educación de los
participantes, no se profundiza en cómo las intervenciones podrían adaptarse a diferentes niveles de
alfabetización en salud, teniendo en cuenta, que los programas de educación diabetológica podrían variar
según el nivel educativo de los pacientes. Finalmente, este estudio de investigación concluye que alcanzar un control metabólico no resulta ser una
práctica sencilla. Sin embargo, el conocimiento de la enfermedad contribuye a mejorar a largo plazo el
autocuidado del paciente, mediante hábitos saludables junto a la terapia farmacológica. Por lo tanto,
las intervenciones educativas continuas son indispensables desde la atención primaria en salud, puesto
que pueden disminuir las complicaciones de la DM2, mejorando el perfil biométrico del paciente, su
control glicémico y por lo tanto su calidad de vida.
|
Risk factors for surgical site infection in patients undergoing obstetrics and gynecology surgeries: A meta-analysis of observational studies | 66e288dd-b9a0-4232-843e-d2fc25759ae4 | 10917295 | Gynaecology[mh] | Hysterectomy is one of the three most commonly performed procedures in gynecology , while cesarean section is the most commonly performed procedure in obstetrics and constitutes approximately 40% of all deliveries in China . Incisions in obstetrics and gynecology surgery are often placed on the skin, vulva, vagina, and other places where a large number of microorganisms exist. These incisions are extremely susceptible to infection. At the same time, infection is associated with increased hospitalization time and elevated health care costs . Of the infections, SSI, which affects surgical therapeutic outcomes, is the most prevalent hospital-based infection . In China, the incidence of SSI after obstetric and gynecologic surgery is 4.62% . The incidence of SSI after hysterectomy ranged from 2.3% to 8.1% , and the incidence of SSI after cesarean section ranged from 3% to 16% . However, the risk factors for SSI are complex and difficult to identify. Current findings on risk factors in the literature are often limited by small sample sizes and weak statistical power. The aim of this study was to provide an evidence-based theoretical basis as well as scientific recommendations for the prevention of surgical site infections in obstetrics and gynecological surgery by combining and analyzing the outcome data from several related publications. Search strategy Eight databases were searched in CBM, Wanfang, CNKI, VIP, Pubmed, WOS, Cochrane Library, and Embase according to the search strategy (inclusion date:May 12, 2023). The search terms will follow the standard PICO guideline (population, intervention, comparator, outcome) and were developed according to disease category (gynecological surgery or obstetric surgery) and study purpose (surgical site infection). The search formula was developed by combining free words with subject terms, and the Medical Subject Headings (MeSH) terms were searched in the Pubmed database . Inclusion criteria and exclusion criteria The selection of studies was first performed on the basis of titles and abstracts. Then two authors (Yin Liu and Dong Wang) independently screened the full text of the identified papers using the following inclusion criteria: (1) studies must meet the National Healthcare Safety Network’s definition of SSI: a wound infection that occurs within 30 days of an operative procedure or within a year if an implant is left in place and the infection is thought to be secondary to surgery ; (2) Statistics must be included in the multi-factor analysis after univariate analysis of significant indicators; (3) studies providing effect estimates of the relative risks (RRs) or odds ratios (ORs) with 95% confidence intervals (CIs); (4) case-control or cohort studies. Review articles, conference abstracts, animal experiments, meta-analyses, and studies with insufficient or overlapping data were excluded from this study. Mediolateral episiotomy, vulval surgeries, repair of perineal tears, hysteroscopic surgery and cervical surgery, were also excluded at the same time. Quality assessment The quality of all the included studies was evaluated by NOS based on the three modules: the selection of case group and control group (0–4 points), inter-comparability of groups (0–2 points), exposure and outcomes (0–3 points), with a maximum score of 9. The studies with NOS scores ≥ 6 were considered relatively higher quality . Data extraction For all eligible studies, the following variables were extracted by two authors (Yin Liu and Dong Wang): (1) the first author’s name; (2) publication year; (3) year of the study; (4) country; (5) risk factors; (6) surgical types; (7) study type; (8) statistical methods; (9) numbers of cases and controls; and (10) estimates of odds ratios (ORs) or relative risks (RRs). Any disagreement was settled by the third reviewer (Zhan Yang). Statistical analysis All the statistical analyses were performed with RevMan 5.3 (The Nordic Cochrane Centre, Copenhagen, Denmark) and Stata 14.0 (Stata Corporation, College Station, TX). For all risk factors in our study, adjusted ORs and 95% CIs were extracted from the original studies. A two-tail P value less than 0.05 was considered significant. Heterogeneity was tested by the Q-test (with significance set at P < 0.10) and I 2 statistics (with I 2 > 50% implying heterogeneity). In the case of significant heterogeneity, we use sensitivity analysis to recognize the potential contribution of each study to the heterogeneity by removing one study at a time. If heterogeneity still existed, random-effects models were used; otherwise, fixed-effects models were used.The outcomes of the meta-analysis were summarized by the forest plot. Eight databases were searched in CBM, Wanfang, CNKI, VIP, Pubmed, WOS, Cochrane Library, and Embase according to the search strategy (inclusion date:May 12, 2023). The search terms will follow the standard PICO guideline (population, intervention, comparator, outcome) and were developed according to disease category (gynecological surgery or obstetric surgery) and study purpose (surgical site infection). The search formula was developed by combining free words with subject terms, and the Medical Subject Headings (MeSH) terms were searched in the Pubmed database . The selection of studies was first performed on the basis of titles and abstracts. Then two authors (Yin Liu and Dong Wang) independently screened the full text of the identified papers using the following inclusion criteria: (1) studies must meet the National Healthcare Safety Network’s definition of SSI: a wound infection that occurs within 30 days of an operative procedure or within a year if an implant is left in place and the infection is thought to be secondary to surgery ; (2) Statistics must be included in the multi-factor analysis after univariate analysis of significant indicators; (3) studies providing effect estimates of the relative risks (RRs) or odds ratios (ORs) with 95% confidence intervals (CIs); (4) case-control or cohort studies. Review articles, conference abstracts, animal experiments, meta-analyses, and studies with insufficient or overlapping data were excluded from this study. Mediolateral episiotomy, vulval surgeries, repair of perineal tears, hysteroscopic surgery and cervical surgery, were also excluded at the same time. The quality of all the included studies was evaluated by NOS based on the three modules: the selection of case group and control group (0–4 points), inter-comparability of groups (0–2 points), exposure and outcomes (0–3 points), with a maximum score of 9. The studies with NOS scores ≥ 6 were considered relatively higher quality . For all eligible studies, the following variables were extracted by two authors (Yin Liu and Dong Wang): (1) the first author’s name; (2) publication year; (3) year of the study; (4) country; (5) risk factors; (6) surgical types; (7) study type; (8) statistical methods; (9) numbers of cases and controls; and (10) estimates of odds ratios (ORs) or relative risks (RRs). Any disagreement was settled by the third reviewer (Zhan Yang). All the statistical analyses were performed with RevMan 5.3 (The Nordic Cochrane Centre, Copenhagen, Denmark) and Stata 14.0 (Stata Corporation, College Station, TX). For all risk factors in our study, adjusted ORs and 95% CIs were extracted from the original studies. A two-tail P value less than 0.05 was considered significant. Heterogeneity was tested by the Q-test (with significance set at P < 0.10) and I 2 statistics (with I 2 > 50% implying heterogeneity). In the case of significant heterogeneity, we use sensitivity analysis to recognize the potential contribution of each study to the heterogeneity by removing one study at a time. If heterogeneity still existed, random-effects models were used; otherwise, fixed-effects models were used.The outcomes of the meta-analysis were summarized by the forest plot. Study selection and evaluation A total of 11429 potentially eligible studies were identified by the initial database search, of which 3701 were included after excluding those published before 2004, duplicates, reviews, animal studies, patents, and commentaries. After screening titles and abstracts, 42 articles were included. After reading the full article carefully, 13 retrospective case control studies that were published between 2017 and 2023 were included . The outcomes of the NOS score for these 13 articles were as follows: One study scored 8; five studies scored 7; and seven studies scored 6. Literature with an assessment score of 5 or more was included in the meta-analysis, and all 13 papers were included in the meta-analysis. Detailed information about those 13 studies is presented in . Meta-analysis Combined analysis of effect sizes A fixed model was selected to analyze anemia, BMI, malignant lesions, surgery time, intraoperative bleeding, diabetes, retained urinary catheter, and vaginal digital examination, and the results are shown sequentially in Figs – . As can be seen from Figs – : BMI, intraoperative bleeding, retained urinary catheter, and vaginal digital examination were independent risk factors for surgical site infection after obstetrical and gynecological surgery ( p < 0.05); there was significant heterogeneity in the results of the surgery time, malignant lesion, diabetes and anemia (I 2 > 50%, p < 0.1), and sensitivity analysis needs to be continued. Sensitivity analysis The heterogeneity of surgery time was significantly reduced after removing Weng YR 2018. Xie ZY 2019 in the malignant lesion study and Ye Q 2019 in the diabetes study were the main causes of heterogeneity, and the meta-analysis was performed again after removing the literature that caused heterogeneity, which yielded: surgery time and malignant lesions were independent risk factors for SSI in obstetrical and gynecological surgery ( p < 0.05). In studies involving anemia, the result was I 2 ≥ 99%, regardless of which study was deleted. Bias test Separate tests of bias for BMI, operative time, intraoperative bleeding, retained urinary catheter, vaginal digital examination, and malignant lesions yielded: operative time ( t = 1.99, P > | t | = 0.094 > 0.05), intraoperative bleeding ( t = 1.19, P > |t| = 0.320 > 0.05), retained urinary catheter ( t = -0.47, P > | t | = 0.721 > 0.05), vaginal digital examination ( t = 1.13, P > |t | = 0.461 > 0.05), and malignant lesions ( z = 1.00, Pr > |z| = 0.317 > 0.05) were not publication biased. There was a mild publication bias in the BMI funnel plot ( t = 3.72, P > | t| = 0.01 < 0.05). The asymmetric funnel plot was processed by the cut-and-patch method, and the symmetry of the funnel plot could be ensured and publication bias eliminated by the three points of the square in , indicating the need to include future effect size studies with results close to those of Arakaki Y 2019, Xu CW 2020, and Xie ZY 2017 . A total of 11429 potentially eligible studies were identified by the initial database search, of which 3701 were included after excluding those published before 2004, duplicates, reviews, animal studies, patents, and commentaries. After screening titles and abstracts, 42 articles were included. After reading the full article carefully, 13 retrospective case control studies that were published between 2017 and 2023 were included . The outcomes of the NOS score for these 13 articles were as follows: One study scored 8; five studies scored 7; and seven studies scored 6. Literature with an assessment score of 5 or more was included in the meta-analysis, and all 13 papers were included in the meta-analysis. Detailed information about those 13 studies is presented in . Combined analysis of effect sizes A fixed model was selected to analyze anemia, BMI, malignant lesions, surgery time, intraoperative bleeding, diabetes, retained urinary catheter, and vaginal digital examination, and the results are shown sequentially in Figs – . As can be seen from Figs – : BMI, intraoperative bleeding, retained urinary catheter, and vaginal digital examination were independent risk factors for surgical site infection after obstetrical and gynecological surgery ( p < 0.05); there was significant heterogeneity in the results of the surgery time, malignant lesion, diabetes and anemia (I 2 > 50%, p < 0.1), and sensitivity analysis needs to be continued. Sensitivity analysis The heterogeneity of surgery time was significantly reduced after removing Weng YR 2018. Xie ZY 2019 in the malignant lesion study and Ye Q 2019 in the diabetes study were the main causes of heterogeneity, and the meta-analysis was performed again after removing the literature that caused heterogeneity, which yielded: surgery time and malignant lesions were independent risk factors for SSI in obstetrical and gynecological surgery ( p < 0.05). In studies involving anemia, the result was I 2 ≥ 99%, regardless of which study was deleted. Bias test Separate tests of bias for BMI, operative time, intraoperative bleeding, retained urinary catheter, vaginal digital examination, and malignant lesions yielded: operative time ( t = 1.99, P > | t | = 0.094 > 0.05), intraoperative bleeding ( t = 1.19, P > |t| = 0.320 > 0.05), retained urinary catheter ( t = -0.47, P > | t | = 0.721 > 0.05), vaginal digital examination ( t = 1.13, P > |t | = 0.461 > 0.05), and malignant lesions ( z = 1.00, Pr > |z| = 0.317 > 0.05) were not publication biased. There was a mild publication bias in the BMI funnel plot ( t = 3.72, P > | t| = 0.01 < 0.05). The asymmetric funnel plot was processed by the cut-and-patch method, and the symmetry of the funnel plot could be ensured and publication bias eliminated by the three points of the square in , indicating the need to include future effect size studies with results close to those of Arakaki Y 2019, Xu CW 2020, and Xie ZY 2017 . A fixed model was selected to analyze anemia, BMI, malignant lesions, surgery time, intraoperative bleeding, diabetes, retained urinary catheter, and vaginal digital examination, and the results are shown sequentially in Figs – . As can be seen from Figs – : BMI, intraoperative bleeding, retained urinary catheter, and vaginal digital examination were independent risk factors for surgical site infection after obstetrical and gynecological surgery ( p < 0.05); there was significant heterogeneity in the results of the surgery time, malignant lesion, diabetes and anemia (I 2 > 50%, p < 0.1), and sensitivity analysis needs to be continued. The heterogeneity of surgery time was significantly reduced after removing Weng YR 2018. Xie ZY 2019 in the malignant lesion study and Ye Q 2019 in the diabetes study were the main causes of heterogeneity, and the meta-analysis was performed again after removing the literature that caused heterogeneity, which yielded: surgery time and malignant lesions were independent risk factors for SSI in obstetrical and gynecological surgery ( p < 0.05). In studies involving anemia, the result was I 2 ≥ 99%, regardless of which study was deleted. Separate tests of bias for BMI, operative time, intraoperative bleeding, retained urinary catheter, vaginal digital examination, and malignant lesions yielded: operative time ( t = 1.99, P > | t | = 0.094 > 0.05), intraoperative bleeding ( t = 1.19, P > |t| = 0.320 > 0.05), retained urinary catheter ( t = -0.47, P > | t | = 0.721 > 0.05), vaginal digital examination ( t = 1.13, P > |t | = 0.461 > 0.05), and malignant lesions ( z = 1.00, Pr > |z| = 0.317 > 0.05) were not publication biased. There was a mild publication bias in the BMI funnel plot ( t = 3.72, P > | t| = 0.01 < 0.05). The asymmetric funnel plot was processed by the cut-and-patch method, and the symmetry of the funnel plot could be ensured and publication bias eliminated by the three points of the square in , indicating the need to include future effect size studies with results close to those of Arakaki Y 2019, Xu CW 2020, and Xie ZY 2017 . SSI is one of the most common complications after obstetric and gynecologic surgery . Previous studies have identified many risk factors, including BMI, operating time, vaginal digital examination, intraoperative bleeding, diabetes, obesity, and malignant lesions . However, these studies usually focused on only some of the risk factors and lacked a comprehensive quantitative summary of all the risk factors for SSI in obstetric and gynecologic surgery. A total of 13 articles were included in this study, including 860 cases in the case group and 13574 cases in the control group. Eventually, our meta-analysis showed that BMI≥24 (OR = 2.66; P < 0.0001), malignant lesions (OR = 4.65; P < 0.0001), operating time≥60min (OR = 2.58; P < 0.0001), intraoperative bleeding≥300ml (OR = 2.54; P < 0.0001), retained urinary catheter (OR = 4.45; P < 0.0001), and vaginal digital examination≥3times (OR = 2.52; P < 0.0001) were independent risk factors for SSIs in obstetrics and gynecology surgery. The greatest risk factor for SSI in obstetric and gynecologic surgery is malignant lesions (OR = 4.65), which increase the likelihood of SSI by 365%. Malignant lesions have long been recognized as a major source of postoperative infections . The immune system is generally compromised in patients with malignant tumors . This impairment in the primary immune function directly results from the tumor’s pervasive influence on the natural defense mechanisms . In addition, standard therapeutic interventions for tumors, including surgery, chemotherapy, and radiation therapy, also lead to weakened immune function . The second largest risk factor is retained urinary catheter (OR = 4.45), which has a 355% increased likelihood of SSI. Retaining a urinary catheter is, on the one hand, an invasive operation itself, and on the other hand, the friction of the tube in the urethra can cause inflammation . The study by Li Jing et al. also concluded that BMI, operating time, vaginal digital examination, and intraoperative bleeding were risk factors for SSI in obstetric and gynecologic surgery, but that article did not give specific values for BMI, operating time, or intraoperative bleeding. The results of this study showed that anemia and diabetes mellitus were not risk factors for SSI in obstetric and gynecologic surgery, which is inconsistent with the findings of Li Runrong et al. The possible reason is that BMI and diabetes may be interlinked, and obesity-induced insulin resistance is one of the major sources of type 2 diabetes . Therefore, most of the studies selected only one of the two factors for analysis. Only three of the 13 articles included in this study analyzed diabetes, and more data support is needed for a more scientific conclusion. To ensure the reliability of the conclusions of the analysis and the homogeneity of the study outcomes, three aspects of the included literature, namely, clinical research direction, experimental design methodology, and statistics , were strictly controlled during the literature screening process in this study . In terms of clinical study orientation, confounding factors such as perineal surgeries were excluded from this study because the female lower genital tract is connected to the outside world and hosts a variety of colonizing bacteria, mycoplasma, chlamydia, and pseudofilamentous yeasts . Although surgical sites were excluded, there are many different types of obstetric and gynecologic surgery, including total laparoscopic hysterectomy, abdominal hysterectomy, and total vaginal hysterectomy, etc. The type of surgery may also be an influencing factor for SSI , and this point was not explored in this article. In this study, BMI≥24, intraoperative bleeding≥300ml, malignant lesions, operating time≥60min, retained urinary catheter, and vaginal digital examination≥3times were considered as independent risk factors for SSI in obstetrics and gynecology surgery.According to the results of this study, in order to reduce the incidence of SSI in obstetrics and gynecology surgery, the medical staff should carry out a comprehensive assessment of the patient before the surgery and formulate a reasonable surgical program. In patients undergoing planned obstetric and gynecologic surgery, weight management should be done. Rational prophylactic use of antimicrobials before performing surgery for patients with malignant lesions. Surgical methods, surgical instruments, and experienced medical staff should be rationally selected to minimize the surgical incision in order to shorten the operation time and reduce intraoperative bleeding. When estimating the progress of labor, focus on the observation of the mother’s condition, such as the contraction of the uterus, the heartbeat of the fetus, etc., and reduce the number of vaginal digital examinations. Post-operative observation of the patient should be strengthened, and the catheter should be removed as early as possible. S1 Checklist PRISMA 2009 checklist. (DOCX) S1 File Search strategy. (DOCX) |
Optimized workflow for digitalized FISH analysis in pathology | 68e93533-b64e-40aa-a723-8a3290eaeb15 | 8114497 | Pathology[mh] | Interphase fluorescence in situ hybridization (FISH) has gained importance as diagnostic and predictive examination in pathology . Together with its cost effectiveness, it allows for a rapid target-oriented analysis providing results within a day. In most instances, FISH slides are analyzed by an epi-fluorescent microscope, with or without a motorized scanning platform. Signal counting is done either manually at the microscope or at a computer screen or automatically by software-supported algorithms. Bright field whole-slide imaging (WSI) for Hematoxylin and Eosin (H&E) stained slides and immunohistochemistry is already used in many routine diagnostic laboratories. However, scanning FISH slides is not widely used yet. Our aim was to introduce the WSI for FISH slides into routine diagnostics. A further goal was to accelerate and standardize the FISH analysis process by introducing a rapid hybridization protocol and an automated cell-signal counting program. Herein, we report our experiences in establishing an optimal workflow for our digitalized FISH technique. Furthermore, we discuss the pros and cons of different scanning profiles and the value of an automated cell counting software. Pre-analytical workflow First, a representative tumor area was encircled on the H&E-slide by a pathologist. Second, the corresponding area was marked with a diamond pen on the back of the slide to be hybridized. This narrowed down the tissue surface to be scanned since the diamond scratches remain visible on the scan-preview. FISH technique A standard protocol was established for FISH on formalin fixed, paraffin embedded (FFPE) specimens. A tissue-micro-array (TMA) with ten cores (at 3mm 2 diameter) for the probe set up and 42 diagnostic samples including core needle and excisional biopsies were analyzed. Adapted to the tissue type, the pretreatment time varied from 30 to 40 min. The specific probes (Zytovision, Germany or Vysis, Abbott Molecular, USA) were hybridized at 37 °C for 4 h in the presence of the IntelliFISH Hybridization buffer (Vysis, Abbott Molecular, USA). As nuclear stain and mounting medium the DAPI (4′,6 diamidino-2-phenylindole) VECTASHIELD® HardSet™ (Vector laboratories, CA, USA) with a minimum hardening time of 30 min was used. Slide imaging and analyzing The optical system of the Pannoramic 250 Flash II Scanner (3DHISTECH, Sysmex, Switzerland) contains two Zeiss (Jena, Germany) Plan-Apochromat dry objectives (20x and 40x with a numerical aperture of 0.80 and 0.95 respectively) allowing also bright field scanning. In a motorized software-controlled wheel, three fluorescence filters are incorporated: FITC (green light, 459 nm), TRITC (red light, 544 nm) and DAPI (autofluorescence, 360 nm). The “SPECTRA light (Lumencor, USA) engine 6” switches the filters fast without fading. Images are acquired by the 16-bit scientific CMOS pco.edge 4.2 camera. We scanned all slides with the 40x objective with a resolution of 0.25 um/pixel. To circumvent inherent tissue-quality fluctuations, two main scanning profiles (low (LP) and high (HP)) were generated. The profiles differ in their exposure time (ET) (LP: 150 ms vs HP: 2000 ms) for the FITC and TRITC channels and their digital gain (LP: 3–4 vs HP: 0–2). For both profiles, the Z-stack function was activated using five to seven layers with a layer distance of 0.4 μm. The scanning time (min), the file size (MB) and the fields of view (FOV) of the two profiles were compared (Table B). The area-scanning technology of the current scanner is FOV based. The FOV corresponds to the square image of the camera sensor. The larger the area to be scanned, the greater the number of the FOV required. When using different profiles with the identical area to be scanned, the number of FOV remains the same. Manual counting Digitalized images were visualized in the CaseViewer (3DHISTEC, Sysmex, Switzerland), a digital microscope application software. As a control step, the pre-selected areas of the corresponding H&E- and FISH-slides were viewed in parallel. Thereafter, FISH signals of a hundred of nuclei were counted manually at the computer screen. Cut-off levels were assessed as described earlier . A signal was counted as abnormal, when the green and the red signal were two diameters of one signal apart. Software counting FISHQuant (3DHISTECH, Sysmex, Switzerland), is an IVD approved module allowing to automatically quantify structural and numerical FISH signal abnormalities in solid tumors and neoplasias of the hematopoietic system. Since automated classification is error prone due to tissue inherent artefacts like overlapping of nuclei, manual editing is mandatory before signing out final reports. First, a representative tumor area was encircled on the H&E-slide by a pathologist. Second, the corresponding area was marked with a diamond pen on the back of the slide to be hybridized. This narrowed down the tissue surface to be scanned since the diamond scratches remain visible on the scan-preview. A standard protocol was established for FISH on formalin fixed, paraffin embedded (FFPE) specimens. A tissue-micro-array (TMA) with ten cores (at 3mm 2 diameter) for the probe set up and 42 diagnostic samples including core needle and excisional biopsies were analyzed. Adapted to the tissue type, the pretreatment time varied from 30 to 40 min. The specific probes (Zytovision, Germany or Vysis, Abbott Molecular, USA) were hybridized at 37 °C for 4 h in the presence of the IntelliFISH Hybridization buffer (Vysis, Abbott Molecular, USA). As nuclear stain and mounting medium the DAPI (4′,6 diamidino-2-phenylindole) VECTASHIELD® HardSet™ (Vector laboratories, CA, USA) with a minimum hardening time of 30 min was used. The optical system of the Pannoramic 250 Flash II Scanner (3DHISTECH, Sysmex, Switzerland) contains two Zeiss (Jena, Germany) Plan-Apochromat dry objectives (20x and 40x with a numerical aperture of 0.80 and 0.95 respectively) allowing also bright field scanning. In a motorized software-controlled wheel, three fluorescence filters are incorporated: FITC (green light, 459 nm), TRITC (red light, 544 nm) and DAPI (autofluorescence, 360 nm). The “SPECTRA light (Lumencor, USA) engine 6” switches the filters fast without fading. Images are acquired by the 16-bit scientific CMOS pco.edge 4.2 camera. We scanned all slides with the 40x objective with a resolution of 0.25 um/pixel. To circumvent inherent tissue-quality fluctuations, two main scanning profiles (low (LP) and high (HP)) were generated. The profiles differ in their exposure time (ET) (LP: 150 ms vs HP: 2000 ms) for the FITC and TRITC channels and their digital gain (LP: 3–4 vs HP: 0–2). For both profiles, the Z-stack function was activated using five to seven layers with a layer distance of 0.4 μm. The scanning time (min), the file size (MB) and the fields of view (FOV) of the two profiles were compared (Table B). The area-scanning technology of the current scanner is FOV based. The FOV corresponds to the square image of the camera sensor. The larger the area to be scanned, the greater the number of the FOV required. When using different profiles with the identical area to be scanned, the number of FOV remains the same. Digitalized images were visualized in the CaseViewer (3DHISTEC, Sysmex, Switzerland), a digital microscope application software. As a control step, the pre-selected areas of the corresponding H&E- and FISH-slides were viewed in parallel. Thereafter, FISH signals of a hundred of nuclei were counted manually at the computer screen. Cut-off levels were assessed as described earlier . A signal was counted as abnormal, when the green and the red signal were two diameters of one signal apart. FISHQuant (3DHISTECH, Sysmex, Switzerland), is an IVD approved module allowing to automatically quantify structural and numerical FISH signal abnormalities in solid tumors and neoplasias of the hematopoietic system. Since automated classification is error prone due to tissue inherent artefacts like overlapping of nuclei, manual editing is mandatory before signing out final reports. FISH technique Introducing the IntelliFISH Hybridization buffer substantially shortened the hybridization process from 18 to 4 h and resulted in good signal to noise ratios with strong and distinct signals (Figs. and ). The DAPI hardening mounting media VECTASHIELD® HardSet™ proved to be the fastest option of the several types of media tested. Slide imaging and analyzing The handling of the scanner software turned out to be intuitive and rather easy. Both established profiles (LP and HP) provided signals of good quality, however, the HP translated a better signal to noise ratio (Fig. ). The scanning time of a TMA core (3mm 2 ) varied between 5 to 7 min with a LP and 15 to 20 min with a HP. The scanning time for FISH depended on the FOV reflecting the size of the selected area and the exposure time (ET) per fluorescence channel. In 16/42 samples we applied an LP (150 ms ET) and in 26/42 samples a HP (2000 ms ET) (Table B). With this approach, the scanning time was more than ten times longer for the HP (mean 170 min) than for the LP (mean 15 min). Moreover, the file size was 2.5 times larger for the HP than for the LP (Table B) while the mean file size per FOV remained comparable for all approaches (low: 0.32; high: 0.34) as expected. In four cases (one with LP and three with HP) the FOV was > 10′000 resulting in high data volumes (min 2390 MB, max 7620 MB, mean 4453 MB) leading to the longest scanning time with the LP (72 min) and HP (949 min). Whereas the LP showed strong enough signals to be successfully analyzed in most instances, HP improved picture quality in cases with weak signals or high background (Figs. b and ). Manual vs automated counting In three out of the 42 diagnostic samples and the TMA, the FISHQuant software automatically classified the signals and the nuclei correctly. Compared to manual counting FISHQuant provided similar results within seconds (e.g. 7% vs 6% for ETVS1). However, in the remaining samples, especially those containing lymphatic tissue, the nuclei were too densely packed to be correctly identified by the automatic algorithm, leading to a high number of erroneously classified signals. Introducing the IntelliFISH Hybridization buffer substantially shortened the hybridization process from 18 to 4 h and resulted in good signal to noise ratios with strong and distinct signals (Figs. and ). The DAPI hardening mounting media VECTASHIELD® HardSet™ proved to be the fastest option of the several types of media tested. The handling of the scanner software turned out to be intuitive and rather easy. Both established profiles (LP and HP) provided signals of good quality, however, the HP translated a better signal to noise ratio (Fig. ). The scanning time of a TMA core (3mm 2 ) varied between 5 to 7 min with a LP and 15 to 20 min with a HP. The scanning time for FISH depended on the FOV reflecting the size of the selected area and the exposure time (ET) per fluorescence channel. In 16/42 samples we applied an LP (150 ms ET) and in 26/42 samples a HP (2000 ms ET) (Table B). With this approach, the scanning time was more than ten times longer for the HP (mean 170 min) than for the LP (mean 15 min). Moreover, the file size was 2.5 times larger for the HP than for the LP (Table B) while the mean file size per FOV remained comparable for all approaches (low: 0.32; high: 0.34) as expected. In four cases (one with LP and three with HP) the FOV was > 10′000 resulting in high data volumes (min 2390 MB, max 7620 MB, mean 4453 MB) leading to the longest scanning time with the LP (72 min) and HP (949 min). Whereas the LP showed strong enough signals to be successfully analyzed in most instances, HP improved picture quality in cases with weak signals or high background (Figs. b and ). In three out of the 42 diagnostic samples and the TMA, the FISHQuant software automatically classified the signals and the nuclei correctly. Compared to manual counting FISHQuant provided similar results within seconds (e.g. 7% vs 6% for ETVS1). However, in the remaining samples, especially those containing lymphatic tissue, the nuclei were too densely packed to be correctly identified by the automatic algorithm, leading to a high number of erroneously classified signals. FISH has become an important theranostic auxiliary method in surgical pathology over the years . To meet the current needs of shortening turn-around-times while maintaining high quality and cost-effectiveness, we accelerated the hybridization process by introducing the IntelliFISH hybridization buffer. Thereby, we shortened the duration of the experimental process by more than 12 h while preserving an excellent signal quality. The overall time from the entry of the order to the hybridized slide was cut to approximatively 6 h with around 30 min hands on time. The Pannoramic 250 Flash II Scanner equipped with a fluorescent module has proven to be a reliable and efficient tool for routine diagnostics of break-apart and enumeration probes. Our observations are in line with a previous report regarding the same system and a second one dealing with a different scanning system . One main difference and advantage compared to conventional fluorescence microscopes is the lack of fading of the fluorescent probes during the scanning process. Additional major advantages of digitalizing FISH slides are the preview and the alignment of the hybridized slides with their corresponding H&E or immunohistochemical stain on the CaseViewer, allowing a more precise as well as fast, identification and analysis of the diseased area (Fig. ) . Other benefits for the examiner compared to the use of a traditional fluorescence microscope were the larger fields of view and wider zoom-ranges. Both could be easily and continuously adjusted on the CaseViewer without losing the area of interest. This simplified analysis and the optimized FISH protocols might be reasons for the lower cut-off values for our probes as compared to those described in the literature (Table A) . However, the methods used for the assessment of the thresholds were not indicated in all reports . Based on our experience, the establishment of two different scanning profiles is sufficient to enable a routine diagnostic FISH laboratory to easily scan and analyze tissue samples of different origin. A mean scanning time of 15 min for the majority of samples applying the LP seems reasonable. Hence, the HP can be reserved for more demanding probes. In our hands, the automated FISHQuant software is promising and provides graphically represented results of break-apart probes within seconds. However, the ability to discriminate nuclei and to correctly assign the signals to them is limited by the algorithm, necessitating an elaborate manual editing compared to the manual counting by means of the CaseViewer. Therefore, the FISHQuant software is not yet ideal for certain tissues, especially not for lymphomatous tissue, since the algorithm is only able to correctly classify a minority of nuclei. A further refinement into a self-learning system would be desirable. In conclusion, in our view the advantages of scanning FISH slides far outweigh the conventional analysis by fluorescence microscopes. Particularly storage, sharing and remote diagnostics open up new opportunities. The development of tissue adapted self-scoring software would be desirable. |
Sensitive detection of mitochondrial DNA variants for analysis of mitochondrial DNA-enriched extracts from frozen tumor tissue | dfc944bb-5e0f-4e27-a54b-a9ecdd3dd465 | 5797170 | Pathology[mh] | The past decades, extensive genomic analysis of tumor specimens using massive parallel sequencing by large sequencing consortia (e.g. https://www.icgc.org/icgc and http://cancergenome.nih.gov/ ) have revealed the major somatic drivers of human cancer, that have been reported in numerous studies. However, the small circular genome of the mitochondria has been largely ignored in such analyses. The human mitochondrial DNA (mtDNA) consists of ~16,569 base pairs encoding 37 genes: two rRNAs and twenty-two tRNAs functioning in the mitochondrial translation apparatus and thirteen proteins essential for oxidative phosphorylation. The total number of mtDNA molecules per cell varies between cell types from a few up to several thousand, and depends on both the number of mitochondria per cell as well as the number of mtDNA molecules per mitochondrion – . Similar to chromosomal DNA in the nucleus (nDNA), mtDNA may contain rare or polymorphic variants. Currently nearly 10,000 variable positions within mtDNA are reported in public databases . When variation is acquired, genetically different mtDNA molecules can reside within a single cell, referred to as heteroplasmy (that is, >0% and <100% allele frequency per cell). Importantly, heteroplasmic patterns can differ within an individual across tissues – . Despite inherited and somatically acquired variants in mtDNA being associated with multiple human diseases , the exact significance of somatic mtDNA variants in cancer remains controversial , . Recently, taking advantage of publically available data from the large sequencing consortia, a handful of papers reported on the catalog of somatic mitochondrial variants in multiple tumor types – . However, a complicating issue in the genomic analysis of mtDNA is the presence of sequences of mitochondrial origin in the nDNA (termed nuclear insertions of mitochondrial origin, NUMTs). NUMTs have likely originated from joining mtDNA/RNA fragments to nDNA ends during double strand break repair , and are found in nearly all eukaryotes that contain mtDNA. This process may occur at any moment during lifetime as well as during tumor evolution . There are fixed NUMTs present in virtually every human genome–and thus reported in the human reference genome–inserted millions of years ago, but also more recent NUMT insertions have been described . Unfortunately, due to their sequence similarity to mtDNA, NUMTs can interfere with accurate variant detection and thus investigation of mitochondrial heteroplasmy , – . Estimations based on the human reference genome indicate that for each 175 base pairs mtDNA segment an average of 9.5 NUMT copies are present in the human nDNA , but this number may likely be higher . In addition, since the insertion of the mitochondrial genome is an ongoing process, this number is even larger in tumor cells since they also contain all somatic insertions events of NUMTs . In addition, in tumor cells the processes shaping nDNA , are substantially different from the one that shapes the mtDNA , resulting in somatic variants in NUMTs and complicating accurate mtDNA heteroplasmy detection even further for tumor cells. Consequently, the large variation in mtDNA between and within individuals as well as the presence of NUMTs demands a highly specific and sensitive detection of mtDNA variants, especially for low-frequent tumor-specific variants. In the study described here, we aimed to develop a sensitive procedure to detect low-frequent single-nucleotide mtDNA variants in frozen tumor tissue. Multiple efforts in developing methods for extraction of pure mtDNA exist – . These include methods using commercial kits or (laborious) ultracentrifugation to obtain pure mitochondria, and techniques to enrich for mtDNA by either the isolation technique or enzymatic degradation of nDNA. Unfortunately, the majority of previous studies focused on either cultured cells or cells from the blood and not on more physically and biochemically complex structures formed by tissue specimens. Thus, the application of these techniques to frozen tumor tissue specimens–an important source to assess tumor cell characteristics–has not been shown to date. Therefore, we compared four easily implementable procedures to extract mtDNA as pure as possible from frozen tumor tissue. Also, we evaluated three state-of-the-art techniques for the detection of low-frequent mtDNA-specific variants: Pacific Biosciences’ SMRT sequencing , UltraSEEK chemistry and digital PCR.
Procedure to obtain mtDNA-enriched DNA extracts from frozen tumor tissue To obtain mtDNA as pure as possible from frozen tumor tissue, our first focus was on the most optimal isolation procedure to extract mtDNA with minimal carry-over of nDNA. For this, we extracted DNA from fresh frozen primary tumor specimens using four easily implementable methods, and compared the yields via quantification of the percentage of mtDNA ( Fig. ) and total amount of dsDNA ( Fig. ). A silica-based total cellular DNA extraction method (I) used as reference for yield resulted in median 863 ng (interquartile range IQR 94 ng) dsDNA of which 0.1% (IQR 0.0%) mtDNA. A method (II) based on alkaline extraction–commonly used to extract plasmid DNA and thus designed to extract circular DNA , , , –yielded median 144 ng (IQR 140 ng) dsDNA with 0.5% (IQR 0.6%) mtDNA. Extracting DNA from isolated mitochondria (III) yielded median 825 ng (IQR 529 ng) dsDNA with 0.2% (IQR 0.1%) mtDNA. A selective lysis method (IV) that starts with the disruption of the plasma membrane to release the cellular components , followed by sedimentation of cell nuclei, and DNA extracted from the remaining cytosol fraction yielded median 403 ng (IQR 321 ng) dsDNA with 1.0% (IQR 0.8%) mtDNA. Note that a similar trend was obtained by these methods using frozen cultured cells as input (Supplementary Figure ). From these results, it is evident that the best isolation procedure to extract mtDNA from frozen tumor tissue is method IV–DNA from cytosol fractions–with the highest mtDNA percentage and sufficient dsDNA yield. To increase the mtDNA fraction, we applied an enzymatic exonuclease reaction to degrade specifically linear nDNA. This greatly increased the percentage of mtDNA in DNA extracts from cytosol fractions, from median 1% (IQR 0.8%) to median 27% (IQR 40%) (Fig. ) . This result was also obtained when using DNA from frozen cultured cells as input material (Supplementary Figure ). Exonuclease treatment on total cellular DNA extracts increased the percentage of mtDNA as well, but not to the same extent as for DNA extracts from cytosol fractions, and total dsDNA yield was lower (Supplementary Figure ). Concluding, the preferred procedure to obtain mtDNA as pure as possible from fresh frozen tumor tissue is to extract DNA from cytosol fractions followed by exonuclease treatment. Approach for sequencing of mtDNA Next we explored sequencing methods for the detection of mtDNA variants. First, whole genome sequencing-by-synthesis (SBS) was applied to total cellular DNA extracts (method I) and DNA extracts from cytosol fractions (method IV), both without and with additional enrichment for mtDNA by exonuclease treatment. As expected, the cell line DNA extract from cytosol fraction treated with exonuclease yielded the highest percentage of aligned reads to mtDNA (86%), whereas the other methods yielded much lower percentages (<25%) (Supplementary Table ) . The DNA extract from cytosol fraction treated with exonuclease derived from fresh frozen tumor tissue yielded a percentage of aligned reads to mtDNA in line with the PCR-based mtDNA percentage (respectively 12% and 10%). Thus, despite the relatively high fraction of 10% mtDNA, a major proportion of reads were derived from nuclear DNA. The observed spread in mtDNA percentage in exonuclease treated method IV extracts from frozen tumor tissue (Fig. ) will therefore lead to a variable proportion of mtDNA reads using whole genome SBS. To circumvent this variability, we decided to explore a targeted approach for sequencing mtDNA. For this, nine primer sets covering the complete mtDNA were evaluated for their specificity to mtDNA, as in silico BLAST search showed that the primers did not match to known NUMT sequences in the reference genome. Specificity of the nine primer sets was confirmed by the absence of PCR products in two mtDNA-depleted cell lines (Supplementary Figure ), allowing mtDNA-specific sequencing of the nine amplicons using single-molecule real-time (SMRT) sequencing. This method is able to generate long reads, covering each amplicon in a single read. To obtain an estimate of sequencing output and to evaluate variants detected by the whole genome SBS and targeted SMRT sequencing approaches, we compared for the two approaches the sequencing output of MDA-MB-231 DNA extracts from cytosol fraction treated with exonuclease. Whole genome SBS generated a total of 800,504 reads of 100 nucleotides (of which 87% duplicated reads) and after alignment resulted in an evenly distributed coverage of median 201x (IQR 2, range 13–404). The 2,727 reads of 1,738–2,836 base pairs by targeted SMRT sequencing displayed more variable coverage among the amplicons with median 282x (IQR 132, range 87–761) (Supplementary Figure ). The more variable coverage in targeted SMRT sequencing was mainly due to regions where amplicons overlapped, causing an increase in coverage (Supplementary Figure ). Both sequencing approaches detected all 29 positions with a documented alternative allele in MDA-MB-231 against rCRS at homoplasmic levels (>99% allele frequency). Also additional heteroplasmic variants were detected, with no major differences observed between the two sequencing approaches (Supplementary File). Given the lower output in read depth per number of generated reads by whole genome SBS sequencing–due to a loss of reads which map to the nuclear genome–and the risk of introducing NUMTs hampering downstream analysis, we continued sequencing experiments using the targeted SMRT sequencing approach. Sensitive detection of low-frequent mtDNA variants To detect low-frequent single-nucleotide variants in mtDNA, we evaluated three approaches: SMRT sequencing, UltraSEEK chemistry and digital PCR. As a source of mtDNA we used breast cancer cell lines MDA-MB-231 and MCF-7. A total of respectively 29 and 13 variants alternative to rCRS have been documented in the mtDNA of MDA-MB-231 (also see above) and MCF-7, with a total of 28 positions containing a different allele between the two cell lines. To determine detection limits empirically, we prepared mixtures of the cell lines–considering MDA-MB-231 as the mutant variant–to generate samples with allele frequencies of 0%, 0.001%, 0.01%, 0.1%, 1% and 10% variant. The mixture samples were subjected to the three detection methods, and we evaluated their ability to detect the mutant variant. By SMRT sequencing, we obtained a median coverage of 4,060x per sample (IQR 4,842x, range 648–34,263x) (see Supplementary Table for coverage per sample per amplicon). In the 0% variant allele sample (pure MCF-7), we confirmed all 13 positions with an alternative allele against rCRS at >95% allele frequency. At 5/28 positions known to be different between the two cell lines, heteroplasmic variants were observed in all mixture samples (Supplementary Table ), prompting us to omit these positions in further analysis for limit of detection. Thus, we explored 23 positions by SMRT sequencing and confirmed all variant alleles, with a detection limit of 0.1% for 21 positions and a detection limit of 1% for 2 positions (Table and Supplementary Figure ). The UltraSEEK method employs amplification of the region(s) of interest by PCR and subsequent detection of the variant(s)-of-interest via a single-base extension using chain terminators labeled with a moiety for solid phase capture, allowing enrichment of product, and identification of the product using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry . By UltraSEEK, we explored 7 positions and detected all variant alleles at those positions, with a detection limit of 0.1% for 5 positions and a detection limit of 1% for 2 positions (Table and Supplementary Figure ). In digital PCR, a sample is partitioned into many individual parallel probe-based PCR reactions, each reaction contains either one target molecule or none, allowing a “yes” or “no” answer for the target molecule containing the mutant and wildtype allele in each reaction. By digital PCR 2 positions were evaluated for the variant allele, and one variant allele was detected ≥0.01% allele frequency and the other ≥0.1% allele frequency (Table and Supplementary Figure ). Detection of de novo mtDNA variants by SMRT sequencing Since by SMRT sequencing the entire mtDNA was sequenced, we explored all alternative alleles that were called in the dataset of the six sample mixtures containing 0%, 0.001%, 0.01%, 0.1%, 1% and 10% mutant variant frequency. A total of 132 variants were called at 126 positions (some positions contained more than one alternative allele, Supplementary Table ). Besides the documented homoplasmic variants for these two cell lines (35 variants, including the 28 differing alleles described above and 7 concordant alleles), 97 de novo variants were detected. Of those, 55 appeared as false positive calls in Integrative Genomics Viewer since they were associated with homopolymer regions or were in close proximity to homoplasmic alternative variants (Supplementary Figure ). Of the remaining 42 de novo variants, the allele frequency ranged from 0.01% to 24.8% (Table ). To evaluate if those de novo variants are true positive variants or potential false positives, we assessed their validation within the dataset: independent observations of a variant in multiple mixtures, or independent observations of a variant in overlapping regions of the sequenced amplicons. Of the 42 de novo variants, 20 were present in multiple mixtures, whereas 22 were present in one mixture only (Table ). Also, 5 had been detected in the mutant-only sample (100% MDA-MB-231) that was sequenced at lower depth by both SMRT and SBS sequencing (see Supplementary File). Ten de novo variants were detected in overlapping regions of the sequenced amplicons, and thus represent two independent observations within one sample (Table ). This resulted in 26 de novo variants that could be validated in our dataset, and thus true positive calls. A total of 16 de novo variants were detected in only a single amplicon in a single sample (Table ), and can in theory be false positive calls (i.e. PCR errors or sequencing errors). These potential false positive variants had an allele frequency between 0.03% and 0.34%. Based on this, if validation of variants in either multiple samples or multiple amplicons is not possible, a conservative threshold on allele frequency for de novo variant detection of the SMRT sequencing approach would be ≥1.0% allele frequency. Allelic phasing of mtDNA variants detected by SMRT sequencing The long read length of SMRT sequencing enables to phase variants i.e. determine if they are present on the same read or on separate reads and thus if they originated from the same or another mtDNA molecule (Fig. ). By this, we could evaluate if variants phased together with the known homoplasmic variants of the wildtype (MCF-7) or of the mutant (MDA-MB-231) genotype. Of the 42 de novo variants, a total of 32 variants phased together with the wildtype genotype and not with the mutant genotype (Table ) . The variants with an allele frequency ≥0.5% in the wildtype-only mixture (0% mutant) were typically detected in all mixtures, whereas variants ≤0.5% allele frequency in the wildtype-only mixture were typically detected in the mixtures with only low mutant fractions (Table ), hence the detection limit of the method. The remaining 10 de novo variants phased together with the mutant genotype and not with the wildtype genotype. Among those 10 variants that phased together with the mutant genotype, were the five that had also been detected in the mutant-only sample (100% MDA-MB-231) sequenced at lower depth by both SMRT and SBS sequencing (see Supplementary File). Also here, variants with a higher allele frequency in the mutant-only sample were typically detected in the mixtures with high mutant fractions (Table ), hence the detection limit of the method. Thus, by SMRT sequencing we were able to evaluate the origin of the 42 de novo variants, phased to either the wildtype or mutant genotype (Table ).
To obtain mtDNA as pure as possible from frozen tumor tissue, our first focus was on the most optimal isolation procedure to extract mtDNA with minimal carry-over of nDNA. For this, we extracted DNA from fresh frozen primary tumor specimens using four easily implementable methods, and compared the yields via quantification of the percentage of mtDNA ( Fig. ) and total amount of dsDNA ( Fig. ). A silica-based total cellular DNA extraction method (I) used as reference for yield resulted in median 863 ng (interquartile range IQR 94 ng) dsDNA of which 0.1% (IQR 0.0%) mtDNA. A method (II) based on alkaline extraction–commonly used to extract plasmid DNA and thus designed to extract circular DNA , , , –yielded median 144 ng (IQR 140 ng) dsDNA with 0.5% (IQR 0.6%) mtDNA. Extracting DNA from isolated mitochondria (III) yielded median 825 ng (IQR 529 ng) dsDNA with 0.2% (IQR 0.1%) mtDNA. A selective lysis method (IV) that starts with the disruption of the plasma membrane to release the cellular components , followed by sedimentation of cell nuclei, and DNA extracted from the remaining cytosol fraction yielded median 403 ng (IQR 321 ng) dsDNA with 1.0% (IQR 0.8%) mtDNA. Note that a similar trend was obtained by these methods using frozen cultured cells as input (Supplementary Figure ). From these results, it is evident that the best isolation procedure to extract mtDNA from frozen tumor tissue is method IV–DNA from cytosol fractions–with the highest mtDNA percentage and sufficient dsDNA yield. To increase the mtDNA fraction, we applied an enzymatic exonuclease reaction to degrade specifically linear nDNA. This greatly increased the percentage of mtDNA in DNA extracts from cytosol fractions, from median 1% (IQR 0.8%) to median 27% (IQR 40%) (Fig. ) . This result was also obtained when using DNA from frozen cultured cells as input material (Supplementary Figure ). Exonuclease treatment on total cellular DNA extracts increased the percentage of mtDNA as well, but not to the same extent as for DNA extracts from cytosol fractions, and total dsDNA yield was lower (Supplementary Figure ). Concluding, the preferred procedure to obtain mtDNA as pure as possible from fresh frozen tumor tissue is to extract DNA from cytosol fractions followed by exonuclease treatment.
Next we explored sequencing methods for the detection of mtDNA variants. First, whole genome sequencing-by-synthesis (SBS) was applied to total cellular DNA extracts (method I) and DNA extracts from cytosol fractions (method IV), both without and with additional enrichment for mtDNA by exonuclease treatment. As expected, the cell line DNA extract from cytosol fraction treated with exonuclease yielded the highest percentage of aligned reads to mtDNA (86%), whereas the other methods yielded much lower percentages (<25%) (Supplementary Table ) . The DNA extract from cytosol fraction treated with exonuclease derived from fresh frozen tumor tissue yielded a percentage of aligned reads to mtDNA in line with the PCR-based mtDNA percentage (respectively 12% and 10%). Thus, despite the relatively high fraction of 10% mtDNA, a major proportion of reads were derived from nuclear DNA. The observed spread in mtDNA percentage in exonuclease treated method IV extracts from frozen tumor tissue (Fig. ) will therefore lead to a variable proportion of mtDNA reads using whole genome SBS. To circumvent this variability, we decided to explore a targeted approach for sequencing mtDNA. For this, nine primer sets covering the complete mtDNA were evaluated for their specificity to mtDNA, as in silico BLAST search showed that the primers did not match to known NUMT sequences in the reference genome. Specificity of the nine primer sets was confirmed by the absence of PCR products in two mtDNA-depleted cell lines (Supplementary Figure ), allowing mtDNA-specific sequencing of the nine amplicons using single-molecule real-time (SMRT) sequencing. This method is able to generate long reads, covering each amplicon in a single read. To obtain an estimate of sequencing output and to evaluate variants detected by the whole genome SBS and targeted SMRT sequencing approaches, we compared for the two approaches the sequencing output of MDA-MB-231 DNA extracts from cytosol fraction treated with exonuclease. Whole genome SBS generated a total of 800,504 reads of 100 nucleotides (of which 87% duplicated reads) and after alignment resulted in an evenly distributed coverage of median 201x (IQR 2, range 13–404). The 2,727 reads of 1,738–2,836 base pairs by targeted SMRT sequencing displayed more variable coverage among the amplicons with median 282x (IQR 132, range 87–761) (Supplementary Figure ). The more variable coverage in targeted SMRT sequencing was mainly due to regions where amplicons overlapped, causing an increase in coverage (Supplementary Figure ). Both sequencing approaches detected all 29 positions with a documented alternative allele in MDA-MB-231 against rCRS at homoplasmic levels (>99% allele frequency). Also additional heteroplasmic variants were detected, with no major differences observed between the two sequencing approaches (Supplementary File). Given the lower output in read depth per number of generated reads by whole genome SBS sequencing–due to a loss of reads which map to the nuclear genome–and the risk of introducing NUMTs hampering downstream analysis, we continued sequencing experiments using the targeted SMRT sequencing approach.
To detect low-frequent single-nucleotide variants in mtDNA, we evaluated three approaches: SMRT sequencing, UltraSEEK chemistry and digital PCR. As a source of mtDNA we used breast cancer cell lines MDA-MB-231 and MCF-7. A total of respectively 29 and 13 variants alternative to rCRS have been documented in the mtDNA of MDA-MB-231 (also see above) and MCF-7, with a total of 28 positions containing a different allele between the two cell lines. To determine detection limits empirically, we prepared mixtures of the cell lines–considering MDA-MB-231 as the mutant variant–to generate samples with allele frequencies of 0%, 0.001%, 0.01%, 0.1%, 1% and 10% variant. The mixture samples were subjected to the three detection methods, and we evaluated their ability to detect the mutant variant. By SMRT sequencing, we obtained a median coverage of 4,060x per sample (IQR 4,842x, range 648–34,263x) (see Supplementary Table for coverage per sample per amplicon). In the 0% variant allele sample (pure MCF-7), we confirmed all 13 positions with an alternative allele against rCRS at >95% allele frequency. At 5/28 positions known to be different between the two cell lines, heteroplasmic variants were observed in all mixture samples (Supplementary Table ), prompting us to omit these positions in further analysis for limit of detection. Thus, we explored 23 positions by SMRT sequencing and confirmed all variant alleles, with a detection limit of 0.1% for 21 positions and a detection limit of 1% for 2 positions (Table and Supplementary Figure ). The UltraSEEK method employs amplification of the region(s) of interest by PCR and subsequent detection of the variant(s)-of-interest via a single-base extension using chain terminators labeled with a moiety for solid phase capture, allowing enrichment of product, and identification of the product using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry . By UltraSEEK, we explored 7 positions and detected all variant alleles at those positions, with a detection limit of 0.1% for 5 positions and a detection limit of 1% for 2 positions (Table and Supplementary Figure ). In digital PCR, a sample is partitioned into many individual parallel probe-based PCR reactions, each reaction contains either one target molecule or none, allowing a “yes” or “no” answer for the target molecule containing the mutant and wildtype allele in each reaction. By digital PCR 2 positions were evaluated for the variant allele, and one variant allele was detected ≥0.01% allele frequency and the other ≥0.1% allele frequency (Table and Supplementary Figure ).
de novo mtDNA variants by SMRT sequencing Since by SMRT sequencing the entire mtDNA was sequenced, we explored all alternative alleles that were called in the dataset of the six sample mixtures containing 0%, 0.001%, 0.01%, 0.1%, 1% and 10% mutant variant frequency. A total of 132 variants were called at 126 positions (some positions contained more than one alternative allele, Supplementary Table ). Besides the documented homoplasmic variants for these two cell lines (35 variants, including the 28 differing alleles described above and 7 concordant alleles), 97 de novo variants were detected. Of those, 55 appeared as false positive calls in Integrative Genomics Viewer since they were associated with homopolymer regions or were in close proximity to homoplasmic alternative variants (Supplementary Figure ). Of the remaining 42 de novo variants, the allele frequency ranged from 0.01% to 24.8% (Table ). To evaluate if those de novo variants are true positive variants or potential false positives, we assessed their validation within the dataset: independent observations of a variant in multiple mixtures, or independent observations of a variant in overlapping regions of the sequenced amplicons. Of the 42 de novo variants, 20 were present in multiple mixtures, whereas 22 were present in one mixture only (Table ). Also, 5 had been detected in the mutant-only sample (100% MDA-MB-231) that was sequenced at lower depth by both SMRT and SBS sequencing (see Supplementary File). Ten de novo variants were detected in overlapping regions of the sequenced amplicons, and thus represent two independent observations within one sample (Table ). This resulted in 26 de novo variants that could be validated in our dataset, and thus true positive calls. A total of 16 de novo variants were detected in only a single amplicon in a single sample (Table ), and can in theory be false positive calls (i.e. PCR errors or sequencing errors). These potential false positive variants had an allele frequency between 0.03% and 0.34%. Based on this, if validation of variants in either multiple samples or multiple amplicons is not possible, a conservative threshold on allele frequency for de novo variant detection of the SMRT sequencing approach would be ≥1.0% allele frequency.
The long read length of SMRT sequencing enables to phase variants i.e. determine if they are present on the same read or on separate reads and thus if they originated from the same or another mtDNA molecule (Fig. ). By this, we could evaluate if variants phased together with the known homoplasmic variants of the wildtype (MCF-7) or of the mutant (MDA-MB-231) genotype. Of the 42 de novo variants, a total of 32 variants phased together with the wildtype genotype and not with the mutant genotype (Table ) . The variants with an allele frequency ≥0.5% in the wildtype-only mixture (0% mutant) were typically detected in all mixtures, whereas variants ≤0.5% allele frequency in the wildtype-only mixture were typically detected in the mixtures with only low mutant fractions (Table ), hence the detection limit of the method. The remaining 10 de novo variants phased together with the mutant genotype and not with the wildtype genotype. Among those 10 variants that phased together with the mutant genotype, were the five that had also been detected in the mutant-only sample (100% MDA-MB-231) sequenced at lower depth by both SMRT and SBS sequencing (see Supplementary File). Also here, variants with a higher allele frequency in the mutant-only sample were typically detected in the mixtures with high mutant fractions (Table ), hence the detection limit of the method. Thus, by SMRT sequencing we were able to evaluate the origin of the 42 de novo variants, phased to either the wildtype or mutant genotype (Table ).
In this research, we aimed to develop a sensitive procedure to detect low-frequent single-nucleotide mtDNA variants from frozen tumor tissue. In assessing tumor cell characteristics, tissue specimens are an important source to detect tumor-specific variants. Especially when the focus is on low-frequent variants, frozen tissue is more suitable than formalin-fixed paraffin-embedded tissue since the latter is prone to deamination artefacts . We started by establishing an extraction procedure to obtain mtDNA as pure as possible from frozen tumor tissue. The optimal method was DNA from cytosol fractions (method IV) treated with exonuclease, and resulted in a 270-fold mtDNA enrichment when compared to total cellular DNA extraction (27% versus 0.1% mtDNA yield, Fig. ). The method based on alkaline extraction that is normally applied to extract plasmid DNA has also been described by others for preparation of mtDNA-enriched samples , , , . In line with the work by Quispe-Tintaya et al . , we find for frozen cultured cells a good mtDNA enrichment compared to total cellular DNA extraction (158-fold, Supplementary Figure ). However, application to frozen tumor tissue resulted in only a 5-fold mtDNA enrichment (Fig. ) indicating that this method is less suited for frozen specimens. The method that extracts DNA from isolated mitochondria has also been described by others , for which we find for frozen cultured cells similar mtDNA enrichment levels compared to total cellular DNA extraction (3-fold, Supplementary Figure ). However, again for frozen tumor tissue we observe lower mtDNA enrichment (2-fold, Fig. ). Note that, although the alkaline-based and mitochondria-based extraction methods were equivalent, different methods were applied to extract total cellular DNA in the above mentioned studies, and even among silica-based extraction methods mtDNA yield can be different , . Importantly, DNA from cytosol fractions either with or without exonuclease treatment compared to total cellular DNA extraction did also show better results for cultured cells (resp. 33-fold and 760-fold enrichment, Supplementary Figure ). Thus, generally, extraction methods that significantly enrich for mtDNA from frozen cultured cells (and possibly also blood cells) do not guarantee a proper enrichment for mtDNA from frozen tissue. A high fraction of mtDNA obtained within the DNA extract is vital to minimize the presence of NUMTs, which may lead to misinterpretation of mtDNA variants. Due to the variable number of mtDNA molecules per cell and the variable frequency of NUMTs, estimating the potential misinterpretation with NUMTs is difficult and unique for each position in each individual. Since the generation of NUMTs is an ongoing process – estimating NUMT frequency is even more difficult for tumor cells since, they contain all private and all somatic NUMT events that have occurred during tumorigenesis and before that time. This is why we have chosen–and recommend–to analyze a mtDNA extract as pure as possible in SMRT sequencing. Exemplifying, in the case of 20x abundance of a NUMT (which is the case for numerous mtDNA regions ) in a cell type with 500 mtDNA molecules, it is possible to misinterpret the NUMT as a mtDNA variant with 8% heteroplasmy (2 × 20/500) in a total cellular DNA extract. Indeed, misinterpretation of non-identical mtDNA and NUMT positions is not a rare event and multiple examples have been highlighted in the literature , – . Therefore, obtaining a high mtDNA fraction corresponds to obtaining a high number of mtDNA molecules as opposed to nDNA molecules, decreasing the variant allele frequency of the NUMTs, thus diminishing the likelihood for misinterpretation: a 270-fold increase in mtDNA for the example mentioned above would result in suppressing the NUMT variant to 0.03% heteroplasmy (2 × 20/270 × 500). To detect low-frequent variants in mtDNA, we compared three state-of-the-art approaches. All three methods–SMRT sequencing, UltraSEEK, digital PCR–obtained 100% sensitivity at 1% variant allele frequency (Table ). Specifically, SMRT shows a sensitivity of 100% at 1% allele frequency, 91% at 0.1% allele frequency and 0% at 0.01% allele frequency. SMRT sensitivity mainly depends on the read depth: positions 6221 and 6371 were sequenced less deep and had a detection limit of 1% (Supplementary Table ). UltraSEEK shows a sensitivity of 100% at 1% allele frequency, 71% at 0.1% allele frequency and 0% at 0.01% allele frequency. Digital PCR shows a sensitivity of 100% at 0.1% allele frequency, of 50% at 0.01% allele frequency and 0% at 0.001% allele frequency. Notably, whereas UltraSEEK and digital PCR are limited to the positions chosen beforehand, the SMRT sequencing approach is able to evaluate the entire mtDNA. Since to date no mutational hotspot regions have been described for mtDNA in primary tumor specimens – , this is a valuable feature to study tumor-specific mtDNA variants. A limitation of all three methods is that they start with PCR amplification, and due to the large variation in mtDNA between and within individuals, primer binding sites can encounter variants that can bias PCR amplification. A whole genome sequencing method would enable a more unbiased approach, where a DNA sample is fragmented and subsequently sequenced independent of variants present in the sample. However–as shown by our results using whole-genome sequencing-by-synthesis (SBS)–this method requires deeper sequencing since a substantial part of the reads will be derived from nDNA. A bioinformatics approach would also be needed to filter reads originating from known NUMTs. In addition, the observed spread in mtDNA percentage in DNA extracts from frozen tumor tissue (Fig. ) will lead to variability in the proportion of mtDNA reads between specimens when using a whole genome sequencing approach. This variability is likely due to biological variability in the number of mtDNA molecules within a cell or biochemical differences (e.g. fat or stromal content) between specimens, or due to technical variability in the multiplex qPCR assay. Samples with an extreme high mtDNA:nDNA ratio (and thus those greatly enriched for mtDNA) will have their mtDNA Ct value at the upper end whereas the nDNA Ct will be at the lower end, making the ratio estimation more variable because Ct estimations are less reliable. Also, the observed number of duplicated reads in SBS (87%) is within the expected range for single-end sequencing of the mitochondrial genome. Due to its small size, it contains only 16,569 starting positions for the 776,959 generated reads (Supplementary Table ). When no variants or sequencing errors would be present within the reads, this would result in 97.9% of the reads appearing as duplicate reads. One could also use a targeted approach prior to SBS sequencing. Amplification of the complete mitochondrial genome in a single amplicon has been applied in SBS approaches, obtaining an error rate of 0.33% at a read depth of 20,000x . Sequencing such an amplicon by SMRT is not feasible with the current chemistry, since it would require a read length >80,000 base pairs (5 passes of ~16,569 base pairs). Our targeted approach to amplify mtDNA by primer sets to generate amplicons between 1,700 base pairs and 3,000 base pairs does allow for high quality SMRT reads (≥5 passes to create a consensus sequence, minimizing sequencing errors) covering the complete amplicon, and simultaneously minimizes the risk of NUMT amplification (87% of known NUMTs are mtDNA fragments ≤1,500 base pairs ). In addition, the used primer sets did not generate an amplification product in mtDNA-depleted counterparts of two cell lines (Supplementary Figure ) nor products by in silico BLAST, affirming that known NUMTs are unlikely to interfere. A drawback is that template amplification by PCR can introduce errors that may result in false positive calls. To decrease this, the PCR used a high fidelity polymerase (error rate of ~10 −7 ) and the number of PCR cycles was limited (15 + 5 cycli). This would theoretically mean that 98.5–97.5% of the generated products per amplicon are entirely error-free, or that each product contains 0.02 random errors. By setting alternative allelic calls to at least 5 independent high-quality reads we intent to minimize calling PCR errors. An alternative would be to employ molecular barcodes prior to PCR amplification, which will allow tracing PCR duplicates and thus yield more confident calls of the original molecules. Note that five of the de novo variants detected by SMRT present in only a single sample appeared on two amplicons and are thus independent observations and unlikely to be PCR errors (Table ). For the de novo variants that appear in only one sample on one amplicon ( n = 16) we cannot rule out that they are not PCR errors, despite their phasing with a particular genotype (Table ). All those were low-frequent variants (allele frequency between 0.03% and 0.34%). Thus, given the 100% sensitivity at 1% allele frequency, the SMRT approach is able to call variants reliable ≥1% allele frequency. To ascertain that variants below 1% allele frequency are true variants, validation is necessary by either independent re-sequencing (an additional sample, or in some cases in overlapping regions of amplicons within the same sample) or an orthogonal method. Both UltraSEEK and digital PCR prove suitable as orthogonal methods to confirm allelic calls, since they are both able to detect low-frequent variants. Analysis by UltraSEEK can be performed in multiplex (up to hundreds): the region(s) of interest are PCR amplified and subsequently the variant(s)-of-interest are detected via a single-base extension using chain terminators labeled with a moiety for solid phase capture, enrichment of product, and identification using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. However, both UltraSEEK and digital PCR are not suitable for de novo variant detection because they do need information on the variants-of-interest beforehand. Also, primer design has to be done for each variant separately, which can be limiting due to design constraints. The sensitivity of UltraSEEK mainly depends on the number of molecules analyzed, where 3 variant copies would suffice for detection (corresponding to at least 3,000 total copies for a 0.1% allele frequency). Analysis by digital PCR can be performed in multiplex (up to 4–8), with for each DNA molecule the region of interest is PCR-amplified and subsequently detected by specific probes on the variant-of-interest. Also in here, sensitivity mainly depends on the number of input molecules (minimal 2 variant copies of ≤20,000 total copies). The SMRT sequencing approach is as performant in terms of sensitivity (dependent on minimal 5 alternative reads) compared to these two methods, but is not limited to the necessity of knowing positions of variants-of-interest beforehand. To conclude, our sensitive procedure to detect low-frequent single-nucleotide mtDNA variants from frozen tumor tissue is based on the extraction of DNA from cytosol fractions followed by exonuclease treatment to obtain high mtDNA yield, and subsequent SMRT sequencing for ( de novo ) detection and allelic phasing of variants. Orthogonal validation of variants can be done by either UltraSEEK (in the case of numerous variants) or digital PCR (in the case of a few variants). We conclude that the presented approach enables mtDNA-specific detection of de novo variants ≥1% allele frequency.
Specimens Cell lines MDA-MB-231 and MCF-7 were cultured using RPMI ( Invitrogen ) supplemented with FBS (10%) ( Lonza ), 100 U/mL penicillin ( Invitrogen ), 100 µg/mL streptomycin ( Invitrogen ) and 0.05 mg/mL gentamycin ( Invitrogen ). A mtDNA-depleted MDA-MB-231 breast cancer cell line (MDA-MB-231-ρ0) was established by culturing MDA-MB-231 cells in the presence of 50 ng/µL ethidium bromide for 100 days in medium supplemented with uridine (0.05 mg/mL) ( Sigma-Aldrich ) and pyruvate (1 mM) ( Invitrogen) . Frozen 143B and 143B-ρ0 osteosarcoma cell line pellets were kindly provided by dr. W.N.M. Dinjens (Department of Pathology, Erasmus MC). Fresh frozen primary breast tumor tissue specimens (resection material) were selected from the tumor biobank at the Erasmus MC (n = 10, stored in liquid nitrogen). The use of these patient materials was approved by the medical ethics committee of the Erasmus MC (MEC 02.953) and in accordance to the code of conduct of Federation of Medical Scientific Societies in the Netherlands. In the Netherlands, according to the Code of Conduct, informed consent is not required for retrospective analysis of bio-specimens retrieved during standard of care procedures. DNA extraction and mtDNA enrichment Input for frozen tumor tissue was standardized at 20 cryosections of 30 µm thickness, which resulted in an average input of 19.2 mg (range of 5.9–33.4 mg) tumor tissue per extraction. Input for cultured cells was standardized at 1 million frozen cells per extraction. Total cellular DNA was extracted using the NucleoSpin Tissue kit ( Macherey-Nagel ) according to the supplier’s protocol (method I). Alkaline-based extraction was performed using the QIAprep Spin Miniprep kit ( Qiagen ), according to the supplier’s protocol (method II). Mitochondria were extracted using the Qproteome mitochondria isolation kit ( Qiagen ) according to the supplier’s protocol, and subsequently DNA was extracted using the NucleoSpin Tissue kit (above) (method III). To remove cell nuclei, samples were lysed using detergent that dissolves the cellular membrane (1 mL of 0.5x TBE containing 0.5% (v/v) Triton X-100 ) for 10 minutes, followed by sedimentation of the nuclei at 1,020 × g for 10 minutes. From the remaining supernatant–the cytosol fraction–DNA was extracted using the QIAamp Circulating Nucleic Acid Kit ( Qiagen ) according to the suppliers’ protocol (method IV). In experiments to remove linear DNA, extracts (max. 100 ng DNA) were treated with 40 units of the ATP-dependent exonuclease PlasmidSafe ( Epicentre ) for 3 hours at 37 °C. Exonuclease was heat-inactivated (30 minutes 70 °C) and the circular DNA was purified using ethanol precipitation (70% ethanol). DNA quantification and mtDNA purity assessment All DNA extracts were quantified using the Qubit dsDNA HS assay kit ( Life Technologies ) according to the suppliers’ protocol. Purity of mtDNA was assessed in duplicate runs of a multiplex qPCR assay targeting a nuclear and a mitochondrial encoded gene to calculate the ratio of mtDNA molecules opposed to nDNA molecules by the relative quantitation method (2 ΔCq ) as described before . The percentage of mtDNA in the DNA extract was quantified (eq. ) based on the ratio mtDNA:nDNA molecules and the sizes of the mitochondrial reference genome (16,569 base pairs, NC_012920) and complete reference genome (haploid 3,088,269,805 base pairs, GRCh38). If no amplification signal for the nuclear encoded gene was obtained, the ratio mtDNA:nDNA was set to 20,000,000 corresponding to a mtDNA percentage of 99%. 1 [12pt]{minimal}
$$mtDNA\,percentage= {m}{i}{t}{o}{c}{h}{o}{n}{d}{r}{i}{a}{l}\,genome\,size}{({r}{a}{t}{i}{o} {m}{i}{t}{o}{c}{h}{o}{n}{d}{r}{i}{a}{l}\,genome\,size)+nuclear\,genome\,size} 100$$ m t D N A p e r c e n t a g e = r a t i o ∗ m i t o c h o n d r i a l g e n o m e s i z e ( r a t i o ∗ m i t o c h o n d r i a l g e n o m e s i z e ) + n u c l e a r g e n o m e s i z e ∗ 100 Whole genome sequencing-by-synthesis (SBS) Input DNA was mechanically sheared using focused-ultrasonicator ( Covaris ) to yield fragments of ~300 base pairs in length, which required the following shearing-time for different DNA extracts: 90 seconds for total cellular DNA, 120 seconds for total cellular DNA treated with exonuclease, 90 seconds for cytosol fraction DNA, 50 seconds for cytosol fraction DNA treated with exonuclease. Sequence library was created using the Thruplex DNA-seq sample preparation kit ( Rubicon Genomics ), using 0.1–7.7 ng sheared input DNA. Sequencing was performed on an Illumina HiSeq2500 sequencer using HiSeq Rapid v2 chemistry and yielding 100 nucleotides single-end reads. UltraSEEK UltraSEEK assays were designed using the AgenaCx online assay design software which automatically selects the PCR and extension primers (Supplementary Table ), and adds to each reaction control assays for PCR and capturing. All oligonucleotides were obtained from Integrated DNA Technologies and control oligos from Agena Bioscience GmbH. Reactions were performed as described before , using reagents obtained from Agena Bioscience. Briefly, PCR (45 cycles) was followed by shrimp alkaline phosphatase treatment and single base primer extension using biotinylated ddNTPs specific for the mutant alleles. After capture of the extended primers using streptavidin-coated magnetic beads, a cation-exchange resin was added for cleaning and 10-15 nl of the reaction was transferred to a SpectroCHIP® Array (a silicon chip with pre-spotted matrix crystals) using an RS1000 Nanodispenser ( Agena Bioscience ). Data were acquired via matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a MassARRAY Analyzer 4 ( Agena Bioscience ). After data processing, a spectrum was produced with relative intensity on the y-axis and mass/charge on the x-axis. Typer Analyzer software was used for data analysis and report generation. Digital PCR Custom assays for two alternative variants were performed on the Quantstudio 3D digital PCR system ( Thermo Fisher ) according to the supplier’s protocol, with an adaption to the DNA input due to high mtDNA copy number. Reactions contained 20 pg of DNA in 1x dPCR mastermix v2, 0.9 µM of each primer ( Invitrogen ) and 0.2 µM of each probe ( Sigma ) (Supplementary Table ). After initial denaturation for 10 minutes at 96 °C, the 40-cycle two-step PCR was performed at 30 seconds denaturation (98 °C) and 120 seconds annealing/extension (56 °C), and followed by a final 2 minute extension (56 °C). To calculate a variant frequency of the alternative variant, the threshold for signal dots was set to at least two dots. Single Molecule Real-Time (SMRT) sequencing Amplicons covering the complete mtDNA , (Supplementary Table ) were generated in singleplex PCR reactions with initial denaturation for 3 minutes at 98 °C, 15 cycles of a three-step PCR with 10 seconds denaturation (98 °C), 30 seconds annealing (67 °C) and 90 seconds extension (72 °C), and final extension (72 °C) for 5 minutes. Each 50 µL reaction contained 2.5 ng of template DNA and 1 unit of Hot-Start Q5 High Fidelity DNA polymerase ( NEB ) in 1x Q5 reaction buffer, 200 µM dNTPs and 0.5 µM of each 5′-M13 tailed primer ( Invitrogen ) (Supplementary Table ). Specificity of the generated products was confirmed using microchip electrophoresis (DNA-12000 reagent kit, Shimadzu ). Amplicons were equimolar pooled per sample and purified using AMPure PB paramagnetic beads ( Pacific Biosciences ) with a 0.6 beads:sample ratio according to the SMRTbell Template Prep Kit protocol and eluted in 10 mM Tris-HCl pH 8.5. The 5′-M13 universal sequence tail of the primers allowed barcoding of each sample by performing 5 amplification cycles of the three-step PCR as described above but with an annealing temperature of 58 °C. Specificity of the generated products was confirmed using microchip electrophoresis (BioAnalyzer, DNA12000 or High Sensitivity DNA kit, Agilent ). A final mix of barcoded fragments of all samples was obtained by equimolar pooling and subsequently purified using AMPure PB paramagnetic beads with a 0.6 beads:sample ratio. Concentration of the final mix was determined using the Qubit dsDNA HS assay kit, and SMRTbell library was generated according to the Amplicon Template Preparation and Sequencing guide ( Pacific Biosciences ). Sequencing was performed on Pacific Biosciences RSII with P6-C4 sequencing chemistry and 360 minutes movie-time or Sequel platforms with version 2 sequencing chemistry and 600 minutes movie-time. A total of twenty-two RSII and two Sequel SMRT cells were used to reach a read depth estimated at 3,000x per sample. In addition, two RSII SMRT cells were used to reach an estimated 5,000x for one sample (cell line mixture with 0.1% mutant allele frequency). Bioinformatics Whole genome sequencing-by-synthesis (SBS) reads were trimmed and aligned using hisat2 against the human reference genome GRCh38, after which the percentage of mtDNA was calculated (eq. ). In addition, for evaluation of detected variants (Supplementary File), SBS reads were aligned against an extended version of rCRS (BWA-MEM version 0.7.15 default parameters ) and duplicate reads marked (Picard MarkDuplicates default parameters http://broadinstitute.github.io/picard/ ). We aligned the data against extended versions of rCRS (Supplementary Table ) to compensate for mapping bias due to circularity of the mitochondrial genome. 2 [12pt]{minimal}
$${percentage}\,{reads}\,{of}\,{mitochondrail}\,{origin}=\,{reads}\,{on}\,{chrM}}{{aligned}\,{reads}\,{on}\,{GRCh38}} 100$$ percentage reads of mitochondrail origin = aligned reads on chrM aligned reads on GRCh38 ∗ 100 Single Molecule Real-Time (SMRT) sequencing RS bax.h5 files were converted to Sequel BAM files, of which circular consensus reads (CCS) were generated using the CCS2 algorithm for each sample-specific barcode . Next, a minimum quality threshold of 99% and at least five passes of the SMRTbell were applied to select for highly accurate single-molecule reads. Selected CCS reads were trimmed (Cutadapt for primers-tails) and subsequently aligned against an extended rCRS (BWA- MEM version 0.7.15 parameters -k17 -W40 -r10 -A1 -B1 -O1 -E1 -L0 ). We aligned the data against extended versions of rCRS (Supplementary Table ) to compensate for mapping bias due to circularity of the mitochondrial genome. For the comparison between SBS and SMRT sequencing methods (Supplementary File), pileup files were generated (Bioconductor Rsamtools 1.26.2 pileup function with pileupParam min_base_quality = 30, min_mapq = 0, min_nucleotide_depth = 0, min_minor_allele_depth = 0, distinguish_strands = TRUE, distinguish_nucleotides = TRUE, ignore_query_Ns = TRUE, include_deletions = FALSE, include_insertions = FALSE and in the case of SBS data flag isDuplicate = FALSE) and converted back to rCRS positions. For evaluation of detection limit and de novo variant detection for SMRT data, pileup files were generated as described above but with a more stringent threshold on the minimal number of alternative allele reads (min_nucleotide_depth = 5) to minimize detection of potential PCR errors (see Supplementary File). All detected variants were manually inspected in the Integrative Genomics Viewer (IGV, Broad Institute ) . Phasing of variants was done by manual inspection of every read containing the detected alternative variant and evaluating the other detected alternative variants present on that read. MDA-MB-231 and MCF-7 mitochondrial sequences were obtained from the NCBI GenBank (resp. AB626609.1 and AB626610.1, deposited after resequencing by Imanishi et al . ) and blasted against rCRS to obtain the homoplasmic mtDNA positions alternative to the reference sequence for these two cell lines (NCBI’s nucleotide web blast, https://blast.ncbi.nlm.nih.gov ). Data availability Sequencing datasets can be accessed as BAM files (.bam) from the European Nucleotide Archive under accession number PRJEB23243.
Cell lines MDA-MB-231 and MCF-7 were cultured using RPMI ( Invitrogen ) supplemented with FBS (10%) ( Lonza ), 100 U/mL penicillin ( Invitrogen ), 100 µg/mL streptomycin ( Invitrogen ) and 0.05 mg/mL gentamycin ( Invitrogen ). A mtDNA-depleted MDA-MB-231 breast cancer cell line (MDA-MB-231-ρ0) was established by culturing MDA-MB-231 cells in the presence of 50 ng/µL ethidium bromide for 100 days in medium supplemented with uridine (0.05 mg/mL) ( Sigma-Aldrich ) and pyruvate (1 mM) ( Invitrogen) . Frozen 143B and 143B-ρ0 osteosarcoma cell line pellets were kindly provided by dr. W.N.M. Dinjens (Department of Pathology, Erasmus MC). Fresh frozen primary breast tumor tissue specimens (resection material) were selected from the tumor biobank at the Erasmus MC (n = 10, stored in liquid nitrogen). The use of these patient materials was approved by the medical ethics committee of the Erasmus MC (MEC 02.953) and in accordance to the code of conduct of Federation of Medical Scientific Societies in the Netherlands. In the Netherlands, according to the Code of Conduct, informed consent is not required for retrospective analysis of bio-specimens retrieved during standard of care procedures.
Input for frozen tumor tissue was standardized at 20 cryosections of 30 µm thickness, which resulted in an average input of 19.2 mg (range of 5.9–33.4 mg) tumor tissue per extraction. Input for cultured cells was standardized at 1 million frozen cells per extraction. Total cellular DNA was extracted using the NucleoSpin Tissue kit ( Macherey-Nagel ) according to the supplier’s protocol (method I). Alkaline-based extraction was performed using the QIAprep Spin Miniprep kit ( Qiagen ), according to the supplier’s protocol (method II). Mitochondria were extracted using the Qproteome mitochondria isolation kit ( Qiagen ) according to the supplier’s protocol, and subsequently DNA was extracted using the NucleoSpin Tissue kit (above) (method III). To remove cell nuclei, samples were lysed using detergent that dissolves the cellular membrane (1 mL of 0.5x TBE containing 0.5% (v/v) Triton X-100 ) for 10 minutes, followed by sedimentation of the nuclei at 1,020 × g for 10 minutes. From the remaining supernatant–the cytosol fraction–DNA was extracted using the QIAamp Circulating Nucleic Acid Kit ( Qiagen ) according to the suppliers’ protocol (method IV). In experiments to remove linear DNA, extracts (max. 100 ng DNA) were treated with 40 units of the ATP-dependent exonuclease PlasmidSafe ( Epicentre ) for 3 hours at 37 °C. Exonuclease was heat-inactivated (30 minutes 70 °C) and the circular DNA was purified using ethanol precipitation (70% ethanol).
All DNA extracts were quantified using the Qubit dsDNA HS assay kit ( Life Technologies ) according to the suppliers’ protocol. Purity of mtDNA was assessed in duplicate runs of a multiplex qPCR assay targeting a nuclear and a mitochondrial encoded gene to calculate the ratio of mtDNA molecules opposed to nDNA molecules by the relative quantitation method (2 ΔCq ) as described before . The percentage of mtDNA in the DNA extract was quantified (eq. ) based on the ratio mtDNA:nDNA molecules and the sizes of the mitochondrial reference genome (16,569 base pairs, NC_012920) and complete reference genome (haploid 3,088,269,805 base pairs, GRCh38). If no amplification signal for the nuclear encoded gene was obtained, the ratio mtDNA:nDNA was set to 20,000,000 corresponding to a mtDNA percentage of 99%. 1 [12pt]{minimal}
$$mtDNA\,percentage= {m}{i}{t}{o}{c}{h}{o}{n}{d}{r}{i}{a}{l}\,genome\,size}{({r}{a}{t}{i}{o} {m}{i}{t}{o}{c}{h}{o}{n}{d}{r}{i}{a}{l}\,genome\,size)+nuclear\,genome\,size} 100$$ m t D N A p e r c e n t a g e = r a t i o ∗ m i t o c h o n d r i a l g e n o m e s i z e ( r a t i o ∗ m i t o c h o n d r i a l g e n o m e s i z e ) + n u c l e a r g e n o m e s i z e ∗ 100
Input DNA was mechanically sheared using focused-ultrasonicator ( Covaris ) to yield fragments of ~300 base pairs in length, which required the following shearing-time for different DNA extracts: 90 seconds for total cellular DNA, 120 seconds for total cellular DNA treated with exonuclease, 90 seconds for cytosol fraction DNA, 50 seconds for cytosol fraction DNA treated with exonuclease. Sequence library was created using the Thruplex DNA-seq sample preparation kit ( Rubicon Genomics ), using 0.1–7.7 ng sheared input DNA. Sequencing was performed on an Illumina HiSeq2500 sequencer using HiSeq Rapid v2 chemistry and yielding 100 nucleotides single-end reads.
UltraSEEK assays were designed using the AgenaCx online assay design software which automatically selects the PCR and extension primers (Supplementary Table ), and adds to each reaction control assays for PCR and capturing. All oligonucleotides were obtained from Integrated DNA Technologies and control oligos from Agena Bioscience GmbH. Reactions were performed as described before , using reagents obtained from Agena Bioscience. Briefly, PCR (45 cycles) was followed by shrimp alkaline phosphatase treatment and single base primer extension using biotinylated ddNTPs specific for the mutant alleles. After capture of the extended primers using streptavidin-coated magnetic beads, a cation-exchange resin was added for cleaning and 10-15 nl of the reaction was transferred to a SpectroCHIP® Array (a silicon chip with pre-spotted matrix crystals) using an RS1000 Nanodispenser ( Agena Bioscience ). Data were acquired via matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a MassARRAY Analyzer 4 ( Agena Bioscience ). After data processing, a spectrum was produced with relative intensity on the y-axis and mass/charge on the x-axis. Typer Analyzer software was used for data analysis and report generation.
Custom assays for two alternative variants were performed on the Quantstudio 3D digital PCR system ( Thermo Fisher ) according to the supplier’s protocol, with an adaption to the DNA input due to high mtDNA copy number. Reactions contained 20 pg of DNA in 1x dPCR mastermix v2, 0.9 µM of each primer ( Invitrogen ) and 0.2 µM of each probe ( Sigma ) (Supplementary Table ). After initial denaturation for 10 minutes at 96 °C, the 40-cycle two-step PCR was performed at 30 seconds denaturation (98 °C) and 120 seconds annealing/extension (56 °C), and followed by a final 2 minute extension (56 °C). To calculate a variant frequency of the alternative variant, the threshold for signal dots was set to at least two dots.
Amplicons covering the complete mtDNA , (Supplementary Table ) were generated in singleplex PCR reactions with initial denaturation for 3 minutes at 98 °C, 15 cycles of a three-step PCR with 10 seconds denaturation (98 °C), 30 seconds annealing (67 °C) and 90 seconds extension (72 °C), and final extension (72 °C) for 5 minutes. Each 50 µL reaction contained 2.5 ng of template DNA and 1 unit of Hot-Start Q5 High Fidelity DNA polymerase ( NEB ) in 1x Q5 reaction buffer, 200 µM dNTPs and 0.5 µM of each 5′-M13 tailed primer ( Invitrogen ) (Supplementary Table ). Specificity of the generated products was confirmed using microchip electrophoresis (DNA-12000 reagent kit, Shimadzu ). Amplicons were equimolar pooled per sample and purified using AMPure PB paramagnetic beads ( Pacific Biosciences ) with a 0.6 beads:sample ratio according to the SMRTbell Template Prep Kit protocol and eluted in 10 mM Tris-HCl pH 8.5. The 5′-M13 universal sequence tail of the primers allowed barcoding of each sample by performing 5 amplification cycles of the three-step PCR as described above but with an annealing temperature of 58 °C. Specificity of the generated products was confirmed using microchip electrophoresis (BioAnalyzer, DNA12000 or High Sensitivity DNA kit, Agilent ). A final mix of barcoded fragments of all samples was obtained by equimolar pooling and subsequently purified using AMPure PB paramagnetic beads with a 0.6 beads:sample ratio. Concentration of the final mix was determined using the Qubit dsDNA HS assay kit, and SMRTbell library was generated according to the Amplicon Template Preparation and Sequencing guide ( Pacific Biosciences ). Sequencing was performed on Pacific Biosciences RSII with P6-C4 sequencing chemistry and 360 minutes movie-time or Sequel platforms with version 2 sequencing chemistry and 600 minutes movie-time. A total of twenty-two RSII and two Sequel SMRT cells were used to reach a read depth estimated at 3,000x per sample. In addition, two RSII SMRT cells were used to reach an estimated 5,000x for one sample (cell line mixture with 0.1% mutant allele frequency).
Whole genome sequencing-by-synthesis (SBS) reads were trimmed and aligned using hisat2 against the human reference genome GRCh38, after which the percentage of mtDNA was calculated (eq. ). In addition, for evaluation of detected variants (Supplementary File), SBS reads were aligned against an extended version of rCRS (BWA-MEM version 0.7.15 default parameters ) and duplicate reads marked (Picard MarkDuplicates default parameters http://broadinstitute.github.io/picard/ ). We aligned the data against extended versions of rCRS (Supplementary Table ) to compensate for mapping bias due to circularity of the mitochondrial genome. 2 [12pt]{minimal}
$${percentage}\,{reads}\,{of}\,{mitochondrail}\,{origin}=\,{reads}\,{on}\,{chrM}}{{aligned}\,{reads}\,{on}\,{GRCh38}} 100$$ percentage reads of mitochondrail origin = aligned reads on chrM aligned reads on GRCh38 ∗ 100 Single Molecule Real-Time (SMRT) sequencing RS bax.h5 files were converted to Sequel BAM files, of which circular consensus reads (CCS) were generated using the CCS2 algorithm for each sample-specific barcode . Next, a minimum quality threshold of 99% and at least five passes of the SMRTbell were applied to select for highly accurate single-molecule reads. Selected CCS reads were trimmed (Cutadapt for primers-tails) and subsequently aligned against an extended rCRS (BWA- MEM version 0.7.15 parameters -k17 -W40 -r10 -A1 -B1 -O1 -E1 -L0 ). We aligned the data against extended versions of rCRS (Supplementary Table ) to compensate for mapping bias due to circularity of the mitochondrial genome. For the comparison between SBS and SMRT sequencing methods (Supplementary File), pileup files were generated (Bioconductor Rsamtools 1.26.2 pileup function with pileupParam min_base_quality = 30, min_mapq = 0, min_nucleotide_depth = 0, min_minor_allele_depth = 0, distinguish_strands = TRUE, distinguish_nucleotides = TRUE, ignore_query_Ns = TRUE, include_deletions = FALSE, include_insertions = FALSE and in the case of SBS data flag isDuplicate = FALSE) and converted back to rCRS positions. For evaluation of detection limit and de novo variant detection for SMRT data, pileup files were generated as described above but with a more stringent threshold on the minimal number of alternative allele reads (min_nucleotide_depth = 5) to minimize detection of potential PCR errors (see Supplementary File). All detected variants were manually inspected in the Integrative Genomics Viewer (IGV, Broad Institute ) . Phasing of variants was done by manual inspection of every read containing the detected alternative variant and evaluating the other detected alternative variants present on that read. MDA-MB-231 and MCF-7 mitochondrial sequences were obtained from the NCBI GenBank (resp. AB626609.1 and AB626610.1, deposited after resequencing by Imanishi et al . ) and blasted against rCRS to obtain the homoplasmic mtDNA positions alternative to the reference sequence for these two cell lines (NCBI’s nucleotide web blast, https://blast.ncbi.nlm.nih.gov ).
Sequencing datasets can be accessed as BAM files (.bam) from the European Nucleotide Archive under accession number PRJEB23243.
Supplementary Figures Supplementary Tables Supplementary Information File
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Interpretation of molecular autopsy findings in 45 sudden unexplained death cases: from coding region to untranslated region | 7d160cdf-fd7e-4cd2-a99c-c534aabec05f | 11732962 | Forensic Medicine[mh] | Unexpected sudden natural death in young individuals can very often be the first reported manifestation and most severe outcome of an undiagnosed disease. For the majority of cases, the cause of death turns out to be congenital cardiovascular diseases . However, there is still around one third of the cases that remain elusive and are termed as sudden unexplained death (SUD). In these cases, no or only mild histological changes can be identified through a comprehensive medico-legal investigation . In the past, a growing number of studies have demonstrated that primary cardiac arrhythmias, such as Brugada syndrome (BrS), long QT syndrome (LQTS), and short QT syndrome (SQTS), account for the majority of SUD cases. In addition, inherited cardiomyopathies, such as dilated cardiomyopathy, hypertrophic cardiomyopathy, and restrictive cardiomyopathy, could also lead to lethal arrhythmias in the early stages when only non-specific morphological features can be observed . Currently, effective prediction algorithms for the functional annotation of variants located in coding regions or canonical splice sites have greatly facilitated the application of molecular autopsy in the multidisciplinary management of SUD . Nevertheless, due to a variety of regulatory mechanisms behind the non-coding sequences and the poor understanding of dosage-sensitive genes, the interpretation of rare variants in the non-coding region of known SUD susceptibility genes remains challenging. To better elucidate the genetic background of SUD, there is a need for the development of specialized guidelines that allow appropriate prioritization and functional annotation of non-coding variants. Since the untranslated regions (UTRs) of mRNAs contain various regulatory elements, rare variants in the 5’ UTR and 3’ UTR are usually considered an important class of non-coding variants with significant impact on post-transcriptional and translational processes . In a recent study, Griesemer et al. applied a massively parallel reporter assay to systematically evaluate the functional effects of genetic variations in 3’ UTRs. Their work nominated hundreds of novel 3’ UTR causal variants with genetically fine-mapped phenotype associations, once again highlighting the strong association between UTR variants and human disease . However, only a handful of SUD studies have taken UTR variants into consideration. Therefore, compared to coding variants, a much smaller number of UTR variants have been reported to modify the risk of cardiac arrhythmia or SUD . In this study, we re-evaluated the whole-exome sequencing (WES) data in a SUD cohort of 45 individuals by investigating the functional effects of variants in the UTRs of 244 genes associated with cardiac diseases. Subsequently, updated criteria for variant screening were applied, where ACMG/AMP rules for the pathogenicity assessment and functional prediction for the recognition of regulatory elements-involved regions were both taken into consideration. The impact of variants that met our requirements on gene transcriptional activity was validated by an additional functional assay. In order to establish a direct connection between our candidate variants and the genetic predisposition to SUD, we have further estimated the consequences of aberrant gene expression based on the constraint metrics, intolerance indexes, and dosage sensitivity scores. WES data re-analysis The WES data of 45 SUD cases from our previous work were re-evaluated for the purpose of this study. Since the Sure Select All Exon V5 + UTR kit (Agilent Technologies AG, Basel, Switzerland) was used for library preparation, identification of variants in the UTRs of target genes was available. The inclusion criteria, autopsy findings and sequencing procedures have been described previously. The SUD cohort consisted of 45 cases with a mean age (± SD) of 30.2 (± 14.5) years (range: 1–63 years of age) and a mean body mass index (± SD) of 24.9 (± 4.9) (range: 13.7–35.5). 34 (76%) of the deceased were males and most of them were of European origin (89%). Genetic investigation of the WES data was confined to a target gene panel consisting of 244 genes associated with cardiac diseases (Supplementary Table ). This list of candidate genes is a combination of genes reported to be associated with heart diseases in various SCD/SUD studies and is based on genes listed in the recommendations and guidelines for genetic testing in sudden cardiac death cases . Since the pathogenicity assessment of coding region/splice site variants identified in these genes was conducted previously , we herein investigated the functional effects of variants in the UTRs of these genes. Variant screening was performed according to the following rules: (1) allele frequency-based filtration retains only rare variants with a global minor allele frequency (MAF) < 0.1% (including variants with unknown MAF) according to the Genome Aggregation Database (gnomAD) , (2) pathogenicity assessment-based filtration retains only variants classified as being pathogenic, likely pathogenic, or variants of uncertain significance (VUS) following the ACMG/AMP guidelines , and (3) functional prediction-based filtration retains only variants with likely regulatory effect. In this third step, we used Ensembl Variant Effect Predictor (VEP) , miRDB , SRAMP , and rSNPBase to identify variants located on predicted regulatory elements, including upstream premature start codon (uAUG), Kozak consensus sequence, miRNA binding site, m6A site, and other functional-related regions. In addition, circVIS was used to visualize the overlapping sequences between the variants and known human circRNAs . Plasmid construction and dual-luciferase reporter assay The full length 5’ UTR of SCO2 , 3’ UTR of CALM2 , and 3’ UTR of TBX3 containing either the mutant type (MT) sequence with the candidate variants or wild type (WT) sequences were directly synthesized by GENEWIZ (Azenta Life Sciences) and subcloned into the BglII and HindIII sites (for 5’ UTR), or the XbaI and FseI sites (for 3’ UTR) of pGL3-Basic plasmid (Promega), generating the WT and MT plasmids (Supplementary Table ). In-vitro experiments were conducted on two different cell lines (HEK293 and AC16) in parallel to increase the validity of our results. Both cell lines were cultured in DMEM supplemented with 10% FBS at 37℃ in a cell incubator supplemented with 5% CO 2 . Transfection of the plasmids was performed in 24-well plates (Corning) using Lipofectamin 2000 (Invitrogen) according to the manufacturer’s protocol. In each well, 450 ng of the reconstructed pGL3-Basic plasmid was co-transfected with 50 ng of pRL-TK plasmid (Promega). In addition, an empty pGL3-Basic/pRL-TK co-transfected group was used as the negative control. Twenty-four hours after transfection, cells were harvested immediately with the addition of 100 µL passive lysis buffer per well. The firefly luciferase activity normalized by the renilla luciferase activity was measured with the Synergy H4 microplate reader (BioTek) using the dual-luciferase reporter assay System (Promega). All experiments were repeated three times and each plasmid group was triplicated in three wells. Estimating the consequence of aberrant gene expression Pathogenic variants in the UTRs usually play a critical role in regulating the expression of genes, resulting in similar outcomes of gene dosage effect caused by loss-of-function (LOF) and gain-of-function (GOF) variants. We have therefore investigated the consequence of gene expression change observed in the dual-luciferase reporter assay results. To achieve a comprehensive assessment of the association between known LOF and GOF variants in these genes and selective pressure, several disease-related properties of the three genes affected ( SCO2 , CALM2 and TBX3 ) were evaluated, including the constraint metrics (pLI and LOEUF) , intolerance indexes (ncRVIS and ncGERP) , and dosage sensitivity scores obtained from the ClinGen Genome Dosage Map ( http://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/ ). Statistical analysis Statistical analyses were implemented with GraphPad Prism software v 8.3.0. The relative luciferase activities were presented as mean ± standard error of mean (SEM). Student’s t test was used to examine the differences of the relative luciferase activities between WT and MT. All statistical tests were two-sided and a p value < 0.05 was considered statistically significant. The WES data of 45 SUD cases from our previous work were re-evaluated for the purpose of this study. Since the Sure Select All Exon V5 + UTR kit (Agilent Technologies AG, Basel, Switzerland) was used for library preparation, identification of variants in the UTRs of target genes was available. The inclusion criteria, autopsy findings and sequencing procedures have been described previously. The SUD cohort consisted of 45 cases with a mean age (± SD) of 30.2 (± 14.5) years (range: 1–63 years of age) and a mean body mass index (± SD) of 24.9 (± 4.9) (range: 13.7–35.5). 34 (76%) of the deceased were males and most of them were of European origin (89%). Genetic investigation of the WES data was confined to a target gene panel consisting of 244 genes associated with cardiac diseases (Supplementary Table ). This list of candidate genes is a combination of genes reported to be associated with heart diseases in various SCD/SUD studies and is based on genes listed in the recommendations and guidelines for genetic testing in sudden cardiac death cases . Since the pathogenicity assessment of coding region/splice site variants identified in these genes was conducted previously , we herein investigated the functional effects of variants in the UTRs of these genes. Variant screening was performed according to the following rules: (1) allele frequency-based filtration retains only rare variants with a global minor allele frequency (MAF) < 0.1% (including variants with unknown MAF) according to the Genome Aggregation Database (gnomAD) , (2) pathogenicity assessment-based filtration retains only variants classified as being pathogenic, likely pathogenic, or variants of uncertain significance (VUS) following the ACMG/AMP guidelines , and (3) functional prediction-based filtration retains only variants with likely regulatory effect. In this third step, we used Ensembl Variant Effect Predictor (VEP) , miRDB , SRAMP , and rSNPBase to identify variants located on predicted regulatory elements, including upstream premature start codon (uAUG), Kozak consensus sequence, miRNA binding site, m6A site, and other functional-related regions. In addition, circVIS was used to visualize the overlapping sequences between the variants and known human circRNAs . The full length 5’ UTR of SCO2 , 3’ UTR of CALM2 , and 3’ UTR of TBX3 containing either the mutant type (MT) sequence with the candidate variants or wild type (WT) sequences were directly synthesized by GENEWIZ (Azenta Life Sciences) and subcloned into the BglII and HindIII sites (for 5’ UTR), or the XbaI and FseI sites (for 3’ UTR) of pGL3-Basic plasmid (Promega), generating the WT and MT plasmids (Supplementary Table ). In-vitro experiments were conducted on two different cell lines (HEK293 and AC16) in parallel to increase the validity of our results. Both cell lines were cultured in DMEM supplemented with 10% FBS at 37℃ in a cell incubator supplemented with 5% CO 2 . Transfection of the plasmids was performed in 24-well plates (Corning) using Lipofectamin 2000 (Invitrogen) according to the manufacturer’s protocol. In each well, 450 ng of the reconstructed pGL3-Basic plasmid was co-transfected with 50 ng of pRL-TK plasmid (Promega). In addition, an empty pGL3-Basic/pRL-TK co-transfected group was used as the negative control. Twenty-four hours after transfection, cells were harvested immediately with the addition of 100 µL passive lysis buffer per well. The firefly luciferase activity normalized by the renilla luciferase activity was measured with the Synergy H4 microplate reader (BioTek) using the dual-luciferase reporter assay System (Promega). All experiments were repeated three times and each plasmid group was triplicated in three wells. Pathogenic variants in the UTRs usually play a critical role in regulating the expression of genes, resulting in similar outcomes of gene dosage effect caused by loss-of-function (LOF) and gain-of-function (GOF) variants. We have therefore investigated the consequence of gene expression change observed in the dual-luciferase reporter assay results. To achieve a comprehensive assessment of the association between known LOF and GOF variants in these genes and selective pressure, several disease-related properties of the three genes affected ( SCO2 , CALM2 and TBX3 ) were evaluated, including the constraint metrics (pLI and LOEUF) , intolerance indexes (ncRVIS and ncGERP) , and dosage sensitivity scores obtained from the ClinGen Genome Dosage Map ( http://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/ ). Statistical analyses were implemented with GraphPad Prism software v 8.3.0. The relative luciferase activities were presented as mean ± standard error of mean (SEM). Student’s t test was used to examine the differences of the relative luciferase activities between WT and MT. All statistical tests were two-sided and a p value < 0.05 was considered statistically significant. Variants with likely regulatory effects Among the 17,027 variants identified in the UTRs of 244 genes investigated, there were only 21 rare variants (MAF < 0.1%) that met our requirements of ACMG/AMP classification. Through functional annotation, three variants were further found to overlap with potential regulatory elements (Fig. ). As shown in Table , the three heterozygous variants were all classified as VUS with a conservation score ranging from 1.378 to 7.385. The variants were located in the 5’ UTR of one gene ( SCO2 ) and in the 3’ UTR of another two genes ( CALM2 and TBX3 ). One of the variants identified in SUDS038 ( SCO2 , (NM_001169109.1):c.-135G > C) was extremely rare and has previously not been reported in the gnomAD database. The other two variants identified in SUDS026 ( CALM2 , (NM_001743.6):c.*343_*345del) and SUDS068 ( TBX3 , (NM_005996.4):c.*889_*891del) were also found to be ultra-rare (MAF < 0.01%). Although no records could be found for these variants in the NCBI ClinVar database, functional prediction revealed that the SCO2 variant could lead to an uAUG loss, while the two 3’ UTR variants were both found to be located on predicted miRNA binding sites. As shown in Fig. A and Fig. B, hsa-miR-421 and hsa-miR-4795-3p were the miRNAs with the highest target scores for CALM2 and TBX3 , respectively, while the introduction of the candidate variants could either disrupt base pairing or lead to a change in binding site type. In addition, the CALM2 variant was found to overlap with the formation regions of two circRNAs (Fig. C and Supplementary Table ). Among two of the three abovementioned SUD cases, several variants with likely functional effects in the coding regions of these 244 genes were previously identified . Specifically, a likely pathogenic variant in exon 3 of ACADS ( ACADS , (NM_000017.4):c.320G > A) and a VUS in exon 38 of CACNA1C ( CACNA1C , (NM_199460.3):c.4604 C > G) was found in SUDS038, while another VUS in exon 326 of TTN ( TTN , (NM_001267550.2):c.70349 A > T) was found in SUDS068. However, in SUDS026 no pathogenic variant or VUS was found in the coding region. The impact of candidate variants on gene transcriptional activity In general, similar trends were observed in the two different cell lines (HEK293 and AC16) regarding the impact of candidate variants on gene transcriptional activity. As shown in Fig. , the relative luciferase activity of cells transfected with pGL3- SCO2 -MT (HEK293: 2.31 ± 0.32; AC16: 2.26 ± 0.20) was significantly lower compared to that of cells transfected with pGL3- SCO2 -WT (HEK293: 2.92 ± 0.39; AC16: 3.06 ± 0.33), suggesting that the candidate variant in the 5’ UTR of SCO2 induced a reduction in its downstream gene expression. In addition, cells transfected with pGL3- CALM2 -MT (HEK293: 1.99 ± 0.20; AC16: 0.95 ± 0.07) or pGL3- TBX3 -MT (HEK293: 1.11 ± 0.07; AC16: 1.00 ± 0.05) showed a significant increase in terms of the relative luciferase activity compared to cells transfected with pGL3- CALM2 -WT (HEK293: 1.65 ± 0.14; AC16: 0.70 ± 0.04) or pGL3- TBX3 -WT (HEK293: 0.91 ± 0.05; AC16: 0.69 ± 0.04), indicating that the candidate variants in the 3’ UTR of CALM2 and TBX3 could disrupt certain regulatory elements of their upstream genes. Intolerance of genes to dosage effect Based on the constraint metrics (Table ), only pLI values for CALM2 (0.921) and TBX3 (0.989) were greater than 0.9. Furthermore, the LOEUF values for these two genes were close to or less than 0.35, indicating that CALM2 and TBX3 are likely to be intolerant to LOF variation, while SCO2 is not. Regarding the intolerance of regulatory sequence variation, CALM2 is in the upper 25th percentile of ncRVIS, suggesting that this gene is more intolerant to variation in the regulatory sequence compared to most other genes. Besides, CALM2 and TBX3 are both in the upper 25th percentile of ncGERP, indicating that these two genes have relatively strong dosage sensitivities due to their highly conserved non-coding sequences compared to the rest of the genome. In addition, the dosage sensitivity scores further verified that TBX3 is very likely to exhibit haploinsufficiency. However, since no available data for SCO2 and CALM2 could be obtained from the ClinGen Genome Dosage Map, we were not able to judge whether these two genes are subject to triplosensitivity. Among the 17,027 variants identified in the UTRs of 244 genes investigated, there were only 21 rare variants (MAF < 0.1%) that met our requirements of ACMG/AMP classification. Through functional annotation, three variants were further found to overlap with potential regulatory elements (Fig. ). As shown in Table , the three heterozygous variants were all classified as VUS with a conservation score ranging from 1.378 to 7.385. The variants were located in the 5’ UTR of one gene ( SCO2 ) and in the 3’ UTR of another two genes ( CALM2 and TBX3 ). One of the variants identified in SUDS038 ( SCO2 , (NM_001169109.1):c.-135G > C) was extremely rare and has previously not been reported in the gnomAD database. The other two variants identified in SUDS026 ( CALM2 , (NM_001743.6):c.*343_*345del) and SUDS068 ( TBX3 , (NM_005996.4):c.*889_*891del) were also found to be ultra-rare (MAF < 0.01%). Although no records could be found for these variants in the NCBI ClinVar database, functional prediction revealed that the SCO2 variant could lead to an uAUG loss, while the two 3’ UTR variants were both found to be located on predicted miRNA binding sites. As shown in Fig. A and Fig. B, hsa-miR-421 and hsa-miR-4795-3p were the miRNAs with the highest target scores for CALM2 and TBX3 , respectively, while the introduction of the candidate variants could either disrupt base pairing or lead to a change in binding site type. In addition, the CALM2 variant was found to overlap with the formation regions of two circRNAs (Fig. C and Supplementary Table ). Among two of the three abovementioned SUD cases, several variants with likely functional effects in the coding regions of these 244 genes were previously identified . Specifically, a likely pathogenic variant in exon 3 of ACADS ( ACADS , (NM_000017.4):c.320G > A) and a VUS in exon 38 of CACNA1C ( CACNA1C , (NM_199460.3):c.4604 C > G) was found in SUDS038, while another VUS in exon 326 of TTN ( TTN , (NM_001267550.2):c.70349 A > T) was found in SUDS068. However, in SUDS026 no pathogenic variant or VUS was found in the coding region. In general, similar trends were observed in the two different cell lines (HEK293 and AC16) regarding the impact of candidate variants on gene transcriptional activity. As shown in Fig. , the relative luciferase activity of cells transfected with pGL3- SCO2 -MT (HEK293: 2.31 ± 0.32; AC16: 2.26 ± 0.20) was significantly lower compared to that of cells transfected with pGL3- SCO2 -WT (HEK293: 2.92 ± 0.39; AC16: 3.06 ± 0.33), suggesting that the candidate variant in the 5’ UTR of SCO2 induced a reduction in its downstream gene expression. In addition, cells transfected with pGL3- CALM2 -MT (HEK293: 1.99 ± 0.20; AC16: 0.95 ± 0.07) or pGL3- TBX3 -MT (HEK293: 1.11 ± 0.07; AC16: 1.00 ± 0.05) showed a significant increase in terms of the relative luciferase activity compared to cells transfected with pGL3- CALM2 -WT (HEK293: 1.65 ± 0.14; AC16: 0.70 ± 0.04) or pGL3- TBX3 -WT (HEK293: 0.91 ± 0.05; AC16: 0.69 ± 0.04), indicating that the candidate variants in the 3’ UTR of CALM2 and TBX3 could disrupt certain regulatory elements of their upstream genes. Based on the constraint metrics (Table ), only pLI values for CALM2 (0.921) and TBX3 (0.989) were greater than 0.9. Furthermore, the LOEUF values for these two genes were close to or less than 0.35, indicating that CALM2 and TBX3 are likely to be intolerant to LOF variation, while SCO2 is not. Regarding the intolerance of regulatory sequence variation, CALM2 is in the upper 25th percentile of ncRVIS, suggesting that this gene is more intolerant to variation in the regulatory sequence compared to most other genes. Besides, CALM2 and TBX3 are both in the upper 25th percentile of ncGERP, indicating that these two genes have relatively strong dosage sensitivities due to their highly conserved non-coding sequences compared to the rest of the genome. In addition, the dosage sensitivity scores further verified that TBX3 is very likely to exhibit haploinsufficiency. However, since no available data for SCO2 and CALM2 could be obtained from the ClinGen Genome Dosage Map, we were not able to judge whether these two genes are subject to triplosensitivity. It is well known that UTRs have comparable genomic footprints similar to coding regions and important regulatory roles in human diseases . So far, a variety of different regulatory elements are known to be distributed in the UTR or upstream/downstream regions of gene coding sequences, such as uAUG sites , miRNA binding sites , and methylated CpG sites . Regarding the identification of such regulatory elements, some could be predicted with specific algorithms based on characteristic sequences (e.g. the AUG initiation codon sequences or sequences matching to known miRNA seed regions), while some still lack a recognizable pattern and are difficult to identify by in-silico prediction. Therefore, functional assays remain an important way to confirm the potential effects of a given genetic variant on gene regulation. Since UTR variants usually cause disease through gene dosage effects, when looking for pathogenic UTR variants, it is important to focus on sequences that fall into regulatory elements that have well-established or functionally validated links to target genes. Besides, there should be well-documented associations between the target genes and phenotypes of interest. For SUD, the phenotypes could be short/prolonged QT intervals, ventricular fibrillation, conduction block, etc. Currently, functional assays widely used to test the impact of non-coding variants on gene regulation include RNA sequencing, luciferase reporter assays, and chromosome conformation capture (3C) approaches. However, each assay has its own advantages and disadvantages. For example, RNA sequencing is the most direct way to detect aberrant gene expression, but fresh human tissue from exactly the same anatomical site of different individuals is not always available, not to mention that most forensic samples are highly degraded. Previous studies investigating human post-mortem gene expression have shown that mRNA degradation occurs in a tissue-specific manner and is associated with gene-specific properties . Salzmann et al. also demonstrated in their study on body fluids that some gene transcripts are more prone to degradation than others after deposition . The stability discrepancies among different transcripts in degraded samples will make the comparison of RNA sequencing results even more difficult. In contrast, the luciferase reporter assay can avoid the influence of external factors, such as postmortem interval and individual variation. Nevertheless, the disadvantage of this method is that the assay system is artificial, which might not be able to accurately simulate in-vivo conditions. Regarding cell lines for the luciferase reporter assays, HEK293 and AC16 were used in parallel to increase the validity of our results. Due to the fact that the expression pattern of some regulatory RNAs are tissue specific , a representative cell line that shares the common expression regulatory mechanism with the studied tissue/organ is recommended. AC16 is a cell line derived from adult human ventricular cardiomyocytes and thus usually used to study developmental regulation of cardiomyocytes. In addition, HEK293 is a cell line derived from human embryonic kidney cells and currently widely used for functional studies due to its well-performance regarding reproducibility and transfection amenability. Previous studies have demonstrated that the combination of these two cell lines could improve the reliability of functional studies focusing on regulatory elements . Among our candidate variants, only the one in the 5’ UTR of SCO2 induced a reduction in gene expression, which is similar to the effect of LOF variants in the coding region of this gene. Previous studies have shown that uAUG-generating variants are under strong negative selection , suggesting a correlation between such variants and aberrant gene expression. As the encoding gene for a metallochaperone involved in the biogenesis of cytochrome c oxidase (COX) subunit, SCO2 dysfunction could cause COX deficiency, resulting in reduced mitochondrial oxidative ATP production capacity . Previous studies have proposed that mutations in SCO2 are associated with infantile encephalocardiomyopathy and hypertrophic cardiomyopathy . However, a comprehensive assessment of this gene’s constraint metrics, intolerance indexes, and dosage sensitivity scores indicated that the aberrant expression of SCO2 will not likely cause fatal disease alone. Thus, we assume that the SCO2 variant identified in this study is at most a contributing factor rather than the main cause of SUD, even though it could lead to a moderate expression change in SCO2 . SUDS038 was previously found to also carry a likely pathogenic variant in the coding region of ACADS and a VUS in the coding region of CACNA1C . Therefore, it is possible that the coding variants and UTR variants have all contributed to the death of this individual to varying degrees, as suggested by the multifactorial model of cardiac genetic diseases . The 3’ UTR variants identified in CALM2 and TBX3 could disrupt their binding to certain miRNAs, thereby up-regulating gene expression, leading to a similar effect as GOF variants in coding regions. However, the constraint metrics could only provide evidence that CALM2 and TBX3 are intolerant to LOF variation. Subsequent ncRVIS and ncGERP analysis revealed that CALM2 and TBX3 are intolerant to variation in their regulatory sequences and might have strong dosage sensitivities. The dosage sensitivity scores further confirmed that TBX3 is subject to haploinsufficiency, while it remains questionable whether these two genes also exhibit triplosensitivity. Taken together, these data highlight the strong deleterious effect of aberrant expression of CALM2 and TBX3 . As a calmodulin encoding gene, CALM2 is associated with severe early-onset LQTS, which can cause life-threatening ventricular arrhythmias . According to a summary of published pathogenic missense variants in this gene , both variants that increase and decrease its affinity for Ca 2+ have been identified in LQTS cohorts, suggesting that either LOF or GOF variants in CALM2 are phenotype-related. Therefore, variants that lead to an overexpression of this gene, as shown in this study, are also very likely to be pathogenic. More importantly, our previous study focusing on the coding region did not identify any pathogenic variant or VUS in SUDS026, further strengthening the significance of this single positive finding. However, it remains unclear whether this UTR variant can be considered the sole cause of death until we better understand to what extent the decrease of CALM2 is severe enough to have fatal consequences. The TBX3 gene is a well-known member of the ancient T-box gene family, which is conserved across a wide range of species. It is a critical developmental regulator of several structures, including the heart . Moreover, Frank et al. have shown that TBX3 is required for functional maturation and post-natal homoeostasis of the conduction system in a highly dosage-sensitive manner . Taken together with our findings, it is reasonable to assume that in this case (SUDS068), a joint effect of this variant and another previously identified VUS in the TTN coding region could be responsible for the lethal arrhythmia. It is noteworthy that the ACMG/AMP guidelines were applied for the initial variant screening of the massive UTR variants identified in our SUD cohort, even though many of these existing rules relate specifically to coding region variants. As proposed by Ellingford et al. , some of the rules from Richard et al. can be applied directly to variants in non-coding regions, without the need for additional considerations. These include the use of frequency information (BA1, BS1, BS2, and PM2), up-weighting of confirmed de novo variants (PM6 and PS2), and incorporation of co-segregation evidence (PP1 and BS4). But some other rules from Richard et al. require modifications in terms of strength of evidence and manual curation of triggering conditions (Table ). In our study, PM2 (absent from controls or at extremely low frequency) was met for the three candidate variants due to their low frequencies in the general population. For computational prediction, PP3 (multiple lines of computational evidence support a deleterious effect on the gene or gene product) was met for SCO2 (NM_001169109.1):c.-135G > C based on in-silico prediction (MetaRNN score = 0.82, which is within the range of pathogenic supportive evidence), while the other two variants were not assigned with a high deleterious score by the prediction tools integrated in Varsome. A possible explanation could be that many pathogenic mutations that act via dominant-negative or GOF mechanisms are likely to be missed by current variant prioritization strategies . In addition to computational prediction, functional evidence is also important in assessing whether a non-coding variant is pathogenic or benign. As our results from the dual-luciferase reporter assay suggest that the three variants all have significant effects on gene expression, manual curation to activate PS3 (well-established in-vitro or in-vivo functional studies supportive of a damaging effect on the gene or gene product) should be acceptable. There are also some limitations in our study. First, not all currently available in-silico prediction tools were applied for the functional annotation of the UTR variants, since some are open source scripts that can only run on a specific programming language or environment. Therefore, some regulatory variants with functional effects may have been missed in this study. However, as new prediction algorithms continue to emerge rapidly through iterative improvements, the in-silico tools we used are also likely to be outdated quickly. With this in mind, periodic re-analysis of molecular autopsy results is recommended, as there may be significant updates in variant interpretation in the future . Second, although the candidate variants seem to cause aberrant gene expression, we were unable to confirm their actual contribution to SUD due to the lack of conclusive evidence in terms of gene dosage effect. In future studies, more explicit associations between up/down-regulation of previously proposed SUD susceptibility genes and specific phenotypes deserve to be better clarified to identify the dosage-sensitive genes that are intolerant to variation in their regulatory sequences. In conclusion, by functional analysis of the UTR variants from the molecular autopsy findings in our SUD cohort, we identified three variants with high confidence of pathogenicity in the UTRs of SCO2 , CALM2 and TBX3 . Our strategies for UTR variant prioritization and functional annotation could be considered as a practical way of interpreting molecular autopsy findings in the non-coding sequences, which will improve the understanding of the pathogenicity of a significant proportion of VUS identified in SUD cohorts. Below is the link to the electronic supplementary material. Supplementary Material 1 |
Società Italiana di Medicina del LavoroPosition Paper Amianto | 6d8948bf-c40c-4ce7-948e-6c0aae93a6ab | 7809933 | Preventive Medicine[mh] | La Società Italiana di Medicina del Lavoro (SIML) ritiene necessario sintetizzare lo stato dell’arte sul tema dell’amianto. Molteplici gli aspetti di interesse, che spaziano dall’eziologia di patologie neoplastiche e non-neoplastiche alla sorveglianza sanitaria di esposti ed ex-esposti. Per perseguire tale finalità, la Società ha incaricato alcuni Soci della redazione di un Position Paper (documento di posizione) da divulgare attraverso i canali propri della Società e mediante pubblicazione su riviste scientifiche. Come coordinatore del gruppo di lavoro, ho ritenuto necessario richiedere un contributo sia al mondo accademico, rappresentato, nel gruppo di lavoro, da ricercatori che hanno fornito un apporto di livello internazionale sul tema delle patologie asbesto-correlate, sia a quello clinico, in quanto ai lavori hanno partecipato Medici del Lavoro con ampia esperienza nella sorveglianza sanitaria di esposti ed ex-esposti ad amianto. Ho ritenuto che la nostra prestigiosa Società, riferimento per la pratica della Medicina del Lavoro nel nostro Paese, dovesse, in primo luogo, utilizzare risorse interne, ossia assicurare la partecipazione dei Soci che da anni contribuiscono alle molteplici attività predisposte da SIML. Successivamente, per alcuni specifici aspetti tecnici, sono stati coinvolti stimati professionisti esterni alla Società, che hanno prestato opera altamente qualificata, volontaria e scientificamente indipendente. L’apporto di ogni membro del gruppo di lavoro è stato espresso nei documenti finali nel rispetto dei criteri di authorship e contributorship attualmente vigenti in ambito scientifico. Per propria natura, il Position Paper , pur commissionato da SIML, si è basato sul parere degli esperti che hanno partecipato volontariamente ai lavori. La Società, nella mia persona, in qualità di Presidente e coordinatore del gruppo di lavoro, ha valutato la presenza di eventuali conflitti di interesse che precludessero la stesura di un documento redatto secondo scienza e coscienza; ovviamente, a tale fase di controllo “interno”, si è aggiunto il pieno adempimento delle procedure richieste dalla rivista La Medicina del Lavoro . Come coordinatore del gruppo di lavoro, ho garantito a tutti i partecipanti e firmatari del documento finale la possibilità di esprimere, circostanziare e illustrare eventuali posizioni discordanti. Occorre ricordare che i contenuti di un Position Paper , una volta accettato dalla Società, diventano posizione ufficiale; questa caratteristica differenzia il Position Paper dal documento di consenso, che può limitarsi ad esprimere l’opinione di una maggioranza (solitamente qualificata). Pubblicare un Position Paper rappresenta quindi un esercizio più difficile (e coraggioso) rispetto alla diffusione di un semplice documento di consenso; SIML ha ritenuto indispensabile prendere una posizione ben definita laddove le evidenze scientifiche lo permettessero. Tuttavia, in considerazione del dibattito pubblico e scientifico che ancora interessa alcuni dei temi propri dell’amianto, i membri del gruppo di lavoro hanno concordato sulla necessità di: presentare chiaramente, nei documenti finali, l’eventuale grado di incertezza che accompagna ogni specifica posizione; illustrare chiaramente le posizioni dei più noti e qualificati enti sovranazionali, laddove disponibili; indicare, in calce ai documenti pubblicati, una data di retrazione; il documento dovrà essere riconsiderato, aggiornato o retratto entro tale data. In considerazione dell’importanza che SIML attribuisce a questo documento, come Presidente, ho deciso di aggiungere ulteriori livelli di discussione e controllo rispetto alle procedure comunemente adottate in ambito scientifico. La struttura ed i contenuti della prima stesura del documento sono stati presentati e discussi pubblicamente al Congresso Nazionale SIML di Padova. È stata quindi redatta la versione del documento da inviare ad esperti nominati dalla Società per fornire un parere critico sui contenuti del testo. Sono stati incaricati due revisori esterni al gruppo di lavoro. I membri del gruppo di lavoro hanno agito secondo le prassi del processo di peer-review , predisponendo una risposta ai revisori, ottemperando alle loro richieste, laddove condivise, e fornendo un razionale scientifico in caso di discordanza. Solo a questo punto, il Position Paper , nella sua versione rivista ed approvata da tutti i membri del gruppo di lavoro, è stato inviato all’Editore per essere considerato per la pubblicazione. Tutto il materiale prodotto nel corso dei lavori, dai moduli per la dichiarazione del conflitto di interesse (redatti in ottemperanza alle procedure interne recentemente riviste dalla Società) alle versioni preliminari del presente documento, inclusi i commenti dei revisori interni, sono disponibili presso la Segreteria. Sono convinto che il presente Position Paper sarà cosa gradita ai Nostri Soci e arricchirà il corpo di letteratura tecnico-scientifica che la Società predispone, grazie al contributo volontario e scientificamente indipendente dei membri dei gruppi di lavoro, per facilitare e promuovere una buona pratica della Medicina del Lavoro nel nostro Paese. Considerazioni generali La stesura di un “ Position Paper ” dedicato all’amianto potrebbe apparire come scelta anacronistica, dato che si tratta sicuramente di un importante fattore di rischio occupazionale ma con una particolare rilevanza in decadi remote. Il progressivo incremento di politiche di prevenzione ha portato alla scomparsa del quadro classico attribuibile a questo agente, ovvero la fibrosi polmonare di tipo interstiziale diffuso (asbestosi). Stante che, nel nostro Paese, dal 1992 ne è stato bandito l’impiego, in tutte le sue forme e in tutte le lavorazioni, permangono come attività a rischio gli interventi di rimozione di prodotti contenenti amianto avvenuta nel corso degli anni successivi, prevalentemente in edilizia. Ciò nonostante negli ultimi decenni è cresciuto l’interesse della comunità scientifica su particolari aspetti connessi all’esposizione ad amianto ed è aumentata, anche nel pubblico, l’attenzione per condizioni di esposizione, anche non professionale, con elevata percezione del rischio circa la possibilità di insorgenza di gravi forme neoplastiche (il mesotelioma) il cui sviluppo può essere associato a livelli di esposizione molto bassi, quali quelli che si possono riscontrare negli ambienti generali di vita . Oggetto del presente “ Position Paper ” non è solo l’esame di alcuni aspetti controversi, in particolare riguardo al mesotelioma, ma si è ritenuto opportuno ampliare il documento estendendolo anche alla definizione delle attuali procedure di valutazione dell’esposizione e della diagnosi. Parrebbe pleonastico discutere oggi di criteri diagnostici per l’asbestosi, dato che dal 1992 è in vigore il bando dell’asbesto ma, a fronte dei numeri che ricaviamo dall’Istituto Assicuratore Nazionale, riteniamo che questo approccio possa risultare utile. Dalla banca dati INAIL risulta infatti che in anni recenti (2012-2016) sono stati denunciati complessivamente 3688 casi di asbestosi (oltre 700, in media, per anno), di cui 1543 riconosciuti (con un massimo di 431 nel 2013 ed un minimo di 204 nel 2016). Sono numeri importanti che richiedono verifiche anche (e forse soprattutto) sull’aggiornamento dei criteri diagnostici. Definizioni (si veda testo) Scopi del documento Il documento si propone di offrire uno strumento che consenta al Medico del Lavoro un rapido orientamento, ovviamente suscettibile di approfondimenti, sui criteri diagnostici attuali e sui risultati della ricerca clinica ed epidemiologica con le relative implicazioni preventive, di diagnosi e di interventi terapeutici precoci e di valutazione in contesti medico-legali e assicurativi: il documento si propone, quindi, di offrire una equilibrata sintesi di tutti i principali aspetti medico-scientifici delle patologie da amianto. Destinatari Medici del Lavoro, Medici Competenti e Operatori della Prevenzione. La stesura di un “ Position Paper ” dedicato all’amianto potrebbe apparire come scelta anacronistica, dato che si tratta sicuramente di un importante fattore di rischio occupazionale ma con una particolare rilevanza in decadi remote. Il progressivo incremento di politiche di prevenzione ha portato alla scomparsa del quadro classico attribuibile a questo agente, ovvero la fibrosi polmonare di tipo interstiziale diffuso (asbestosi). Stante che, nel nostro Paese, dal 1992 ne è stato bandito l’impiego, in tutte le sue forme e in tutte le lavorazioni, permangono come attività a rischio gli interventi di rimozione di prodotti contenenti amianto avvenuta nel corso degli anni successivi, prevalentemente in edilizia. Ciò nonostante negli ultimi decenni è cresciuto l’interesse della comunità scientifica su particolari aspetti connessi all’esposizione ad amianto ed è aumentata, anche nel pubblico, l’attenzione per condizioni di esposizione, anche non professionale, con elevata percezione del rischio circa la possibilità di insorgenza di gravi forme neoplastiche (il mesotelioma) il cui sviluppo può essere associato a livelli di esposizione molto bassi, quali quelli che si possono riscontrare negli ambienti generali di vita . Oggetto del presente “ Position Paper ” non è solo l’esame di alcuni aspetti controversi, in particolare riguardo al mesotelioma, ma si è ritenuto opportuno ampliare il documento estendendolo anche alla definizione delle attuali procedure di valutazione dell’esposizione e della diagnosi. Parrebbe pleonastico discutere oggi di criteri diagnostici per l’asbestosi, dato che dal 1992 è in vigore il bando dell’asbesto ma, a fronte dei numeri che ricaviamo dall’Istituto Assicuratore Nazionale, riteniamo che questo approccio possa risultare utile. Dalla banca dati INAIL risulta infatti che in anni recenti (2012-2016) sono stati denunciati complessivamente 3688 casi di asbestosi (oltre 700, in media, per anno), di cui 1543 riconosciuti (con un massimo di 431 nel 2013 ed un minimo di 204 nel 2016). Sono numeri importanti che richiedono verifiche anche (e forse soprattutto) sull’aggiornamento dei criteri diagnostici. Scopi del documento Il documento si propone di offrire uno strumento che consenta al Medico del Lavoro un rapido orientamento, ovviamente suscettibile di approfondimenti, sui criteri diagnostici attuali e sui risultati della ricerca clinica ed epidemiologica con le relative implicazioni preventive, di diagnosi e di interventi terapeutici precoci e di valutazione in contesti medico-legali e assicurativi: il documento si propone, quindi, di offrire una equilibrata sintesi di tutti i principali aspetti medico-scientifici delle patologie da amianto. Medici del Lavoro, Medici Competenti e Operatori della Prevenzione. Con il termine generale di Amianto (o asbesto, asbestos = indistruttibile, inestinguibile ) si intende un insieme di minerali naturali (silicati idrati) che cristallizzano e formano fibre lunghe e sottili che a loro volta possono separarsi longitudinalmente in fibrille ancora più sottili; è proprio quest’ultima particolarità, tipica degli amianti, che li distingue dagli altri silicati, caratterizzandone la pericolosità. Per la normativa italiana solo sei minerali sono considerati amianti, suddivisi in due categorie mineralogiche: serpentino ed anfiboli. Il crisotilo, conosciuto anche come amianto bianco, costituisce la varietà di asbesto più diffusa, è un silicato di magnesio Mg 3 Si 2 O 5 (OH) 4 appartenente al gruppo dei serpentini, soffice e setoso, ha una struttura cilindrica. Gli altri silicati fibrosi considerati amianti appartengono al gruppo degli anfiboli e si differenziano per il catione aggiuntivo: la crocidolite Na 2 (Mg,Fe) 6 Si 8 O 22 (OH) 2 , conosciuta anche come amianto blu, si presenta sotto forma di fibre diritte e flessibili con una resistenza meccanica e una tenuta agli agenti acidi superiori a quelle degli altri tipi di amianto; l’amosite (Mg,Fe) 7 Si 8 O 22 (OH) 2 , conosciuta anche come amianto bruno, ha fibre lunghe, diritte e fragili, particolarmente stabili al calore, ed è stata utilizzata prevalentemente come isolante termico; la tremolite Ca 2 (Mg,Fe) 5 Si 8 O 22 (OH) 2 e l’actinolite Ca 2 (Mg,Fe) 5 Si 8 O 22 (OH) 2 sono anfiboli di calcio; l’antofillite (Mg,Fe) 7 Si 8 O 22 (OH) 2 ha composizione chimica analoga all’amosite. Gli amianti che sono stati sfruttati da un punto di vista industriale sono essenzialmente crisotilo, crocidolite, amosite ed antofillite in quanto erano presenti in giacimenti utilizzabili a questo scopo; non si è, invece, a conoscenza di depositi di tremolite ed actinolite che sono considerati solo degli inquinanti naturali. Monitoraggio ambientale Fino a tutti gli anni ’70 le misure di fibre di amianto venivano condotte quasi esclusivamente nelle aziende in cui l’amianto veniva utilizzato come materia prima (cemento amianto, tessitura, …); all’epoca non vi erano metodi standardizzati e i campionamenti venivano prelevati con sistemi molto imprecisi (Conimetri, Tyndallometri, Pompe a clessidra) che presentavano numerosi inconvenienti, soprattutto relativamente ai tempi di campionamento che spesso, come nel caso dei Conimetri, erano di brevissima durata, dell’ordine di secondi o minuti, con volumi di campionamento da 2,5 a 5 millilitri, quindi difficilmente utilizzabili per un confronto con un qualsivoglia valore assunto a limite di soglia. Questi campionatori, non adatti alla misura personale, venivano utilizzati in posizioni fisse, solitamente vicino alle sorgenti di aerodispersione. Per stimare poi la concentrazione sulle otto ore (Time Weighted Average, TWA) i dati misurati venivano associati a ipotetici tempi di permanenza dei lavoratori presso le singole sorgenti. Spesso, inoltre, i campionamenti venivano eseguiti in condizioni rappresentative di specifiche situazioni a potenziale maggior esposizione (“caso peggiore”) . È evidente l’elevata possibilità di errore nella stima delle esposizioni derivante dall’utilizzo di concentrazioni ottenute con questi strumenti, in queste condizioni e utilizzando tempi di esposizione poco rappresentativi. Il primo metodo codificato che prevede il campionamento su membrana e il successivo conteggio in microscopia ottica a contrasto di fase (MOCF) è del 1979 [ Method for the determination of airborne asbestos fibres and other inorganic fibres by phase-contrast optical microscope AIA-RTM1 ] e quello che prevede la lettura in microscopia elettronica è del 1984 [ Method for the determination of airborne asbestos fibres and other inorganic fibres by scanning electron microscope AIA-RTM2 ]. La normativa Europea adotterà di fatto queste metodiche. In Italia i sistemi di campionamento e analisi sono inseriti nel Decreto Legislativo 277 del 1991 e nel Decreto del Ministro della Sanità di concerto con quello dell’Industria del Commercio e dell’Artigianato del 6 Settembre 1994, poi ripresi dal DLgs 81/08 (artt. 246, 247, 253, 254). Il metodo da adottarsi per il controllo dell’esposizione consiste nella misurazione della concentrazione delle fibre di amianto nell’aria, espressa come media ponderata in rapporto ad un periodo di riferimento di otto ore (TWA); ai fini della misurazione si prendono in considerazione unicamente le fibre che hanno una lunghezza superiore a 5 µm, un diametro inferiore a 3 µm e con un rapporto lunghezza/diametro uguale o superiore a 3 (fibre abitualmente definite, pertanto, “normate”). Tuttavia, è stato osservato che fibre di dimensioni diverse, anche molto più piccole, potrebbero avere effetti patogeni. Infatti, anche le fibre corte e sottili misurabili in microscopia elettronica a trasmissione (TEM) potrebbero essere cancerogene per il polmone ; Loomis et al hanno rilevato una possibile significativa associazione tra fibre di asbesto ≤1,5 µm in lunghezza e <0,25 µm in diametro e cancro. La tecnica di campionamento prevista dalla norma prevede la filtrazione di un volume noto d’aria su una membrana che trattiene le fibre aspirate. Le membrane possono essere di diverso tipo (esteri misti di cellulosa o policarbonato), con porosità di 0,8 μm e diametro di 25 mm o 47 mm a seconda del campionamento (personale o ambientale). Trattandosi di un conteggio di fibre con un valore di concentrazione presumibilmente basso (almeno nelle decadi più recenti), è preferibile utilizzare membrane filtranti con un basso valore di sostanze interferenti. Particolare attenzione è riservata anche al supporto di filtrazione, dove è alloggiata la membrana durante il prelievo (porta membrana). Generalmente si utilizza un porta membrana metallico o in plastica conduttrice dal diametro corrispondente al filtro utilizzato, corredato di cappuccio metallico cilindrico davanti al filtro, lungo tra 33 mm e 44 mm; durante l’uso il cappuccio è rivolto verso il basso. La scelta dei substrati da utilizzare e del volume d’aria da campionare è in relazione all’analisi che deve essere effettuata. Nella sono riportati alcuni esempi. Per la scelta del metodo analitico occorre considerare in primo luogo il tipo di ambiente e il contesto in cui è effettuata la misurazione: in quelli di lavoro (rimozione o manutenzione manufatti), in cui si suppone ci siano concentrazioni relativamente elevate di fibre di amianto aerodisperse, la tecnica Microscopia ottica a contrasto di fase (MOCF) fornisce dati sufficienti, anche se non certi, per valutare l’esposizione dei lavoratori. Nei luoghi confinati in cui, in genere, la presenza di fibre è bassa, è preferibile ricorrere alla microscopia elettronica, anche se le norme prevedono la possibilità di utilizzare entrambe le tecniche. Nel caso della valutazione del rilascio di fibre in un ambiente in cui sono presenti manufatti contenenti amianto, i valori di riferimento sono diversi a seconda della tecnica adoperata: con la tecnica della MOCF si considera un ambiente con inquinamento in atto quando il conteggio delle fibre è superiore a 20ff/l, mentre con la microscopia elettronica il limite considerato è di 2 ff/l. Per gli ambienti di vita è necessaria l’analisi in Microsopia Elettronica a Scansione (SEM). Qualunque sia la metodica scelta, non bisogna dimenticare che, in generale, tutti i metodi microscopici sono affetti da ampi margini di errore; in nessun caso è possibile attribuire un valore di concentrazione pari a ZERO; il valore minimo è pari o inferiore al Limite di Rilevabilità che dipende dal volume d’aria campionato e dalla superficie della membrana esplorata. Microscopia ottica È la tecnica più diffusa ed accessibile, anche se presenta limiti maggiori perché non permette di determinare con certezza la natura della fibra (errore di sovrastima) o di rilevare le fibre di dimensioni più piccole (errore di sottostima). Solo nel caso di campioni massivi la normativa prevede l’identificazione qualitativa delle fibre mediante la tecnica della dispersione cromatica in microscopia ottica. Microscopia elettronica a scansione L’uso della Microscopia elettronica a scansione corredata di microanalisi fornisce una caratterizzazione molto affidabile degli aspetti morfologici delle fibre e della loro composizione chimica. È un’analisi che permette la determinazione quali/quantitativa delle fibre di amianto aerodisperse regolamentate, ed il risultato è espresso in concentrazione (fibre/volume); può essere effettuata su tutte le matrici: aria, acqua, suolo, rifiuti. È il metodo di elezione per la determinazione dell’amianto, in quanto consente l’attribuzione certa delle fibre di amianto rispetto ad altre tipologie di fibre grazie al sistema di microanalisi; può essere indicato per determinazione quantitativa in campioni massivi (MCA); in caso di presenza di amianto <1% in peso è il metodo analitico di riferimento previsto dalla normativa. Tecniche per la determinazione ponderale in campioni massivi Le tecniche per la determinazione ponderale dell’amianto sono la diffrattometria a raggi X (DRX) e la spettroscopia infrarossa in trasformata di Fourier (FTIR). Ambedue le tecniche necessitano di una preparazione del campione che prevede un trattamento fisico o chimico per ridurre al minimo l’effetto della matrice in cui si trova disperso l’amianto; questo è tanto più importante quanto più la concentrazione del materiale da misurare è vicina alla sensibilità delle tecniche analitiche (>1%). L’interferenza della matrice, in particolare con la tecnica FTIR, può determinare una incertezza nei risultati. Per concentrazioni inferiori al Limite di Rilevabilità si ricorre alla SEM, peraltro con risultati piuttosto incerti in quanto, per la determinazione ponderale dell’amianto, occorre calcolare il volume delle fibre osservate e moltiplicarlo per la densità dell’amianto. Ad oggi la soluzione al problema analitico per la determinazione dell’amianto in campioni in massa non esiste; è però sempre consigliabile accoppiare alla determinazione ponderale un esame microscopico che permetta di effettuare un riscontro morfologico delle fibre analizzate. Fino a tutti gli anni ’70 le misure di fibre di amianto venivano condotte quasi esclusivamente nelle aziende in cui l’amianto veniva utilizzato come materia prima (cemento amianto, tessitura, …); all’epoca non vi erano metodi standardizzati e i campionamenti venivano prelevati con sistemi molto imprecisi (Conimetri, Tyndallometri, Pompe a clessidra) che presentavano numerosi inconvenienti, soprattutto relativamente ai tempi di campionamento che spesso, come nel caso dei Conimetri, erano di brevissima durata, dell’ordine di secondi o minuti, con volumi di campionamento da 2,5 a 5 millilitri, quindi difficilmente utilizzabili per un confronto con un qualsivoglia valore assunto a limite di soglia. Questi campionatori, non adatti alla misura personale, venivano utilizzati in posizioni fisse, solitamente vicino alle sorgenti di aerodispersione. Per stimare poi la concentrazione sulle otto ore (Time Weighted Average, TWA) i dati misurati venivano associati a ipotetici tempi di permanenza dei lavoratori presso le singole sorgenti. Spesso, inoltre, i campionamenti venivano eseguiti in condizioni rappresentative di specifiche situazioni a potenziale maggior esposizione (“caso peggiore”) . È evidente l’elevata possibilità di errore nella stima delle esposizioni derivante dall’utilizzo di concentrazioni ottenute con questi strumenti, in queste condizioni e utilizzando tempi di esposizione poco rappresentativi. Il primo metodo codificato che prevede il campionamento su membrana e il successivo conteggio in microscopia ottica a contrasto di fase (MOCF) è del 1979 [ Method for the determination of airborne asbestos fibres and other inorganic fibres by phase-contrast optical microscope AIA-RTM1 ] e quello che prevede la lettura in microscopia elettronica è del 1984 [ Method for the determination of airborne asbestos fibres and other inorganic fibres by scanning electron microscope AIA-RTM2 ]. La normativa Europea adotterà di fatto queste metodiche. In Italia i sistemi di campionamento e analisi sono inseriti nel Decreto Legislativo 277 del 1991 e nel Decreto del Ministro della Sanità di concerto con quello dell’Industria del Commercio e dell’Artigianato del 6 Settembre 1994, poi ripresi dal DLgs 81/08 (artt. 246, 247, 253, 254). Il metodo da adottarsi per il controllo dell’esposizione consiste nella misurazione della concentrazione delle fibre di amianto nell’aria, espressa come media ponderata in rapporto ad un periodo di riferimento di otto ore (TWA); ai fini della misurazione si prendono in considerazione unicamente le fibre che hanno una lunghezza superiore a 5 µm, un diametro inferiore a 3 µm e con un rapporto lunghezza/diametro uguale o superiore a 3 (fibre abitualmente definite, pertanto, “normate”). Tuttavia, è stato osservato che fibre di dimensioni diverse, anche molto più piccole, potrebbero avere effetti patogeni. Infatti, anche le fibre corte e sottili misurabili in microscopia elettronica a trasmissione (TEM) potrebbero essere cancerogene per il polmone ; Loomis et al hanno rilevato una possibile significativa associazione tra fibre di asbesto ≤1,5 µm in lunghezza e <0,25 µm in diametro e cancro. La tecnica di campionamento prevista dalla norma prevede la filtrazione di un volume noto d’aria su una membrana che trattiene le fibre aspirate. Le membrane possono essere di diverso tipo (esteri misti di cellulosa o policarbonato), con porosità di 0,8 μm e diametro di 25 mm o 47 mm a seconda del campionamento (personale o ambientale). Trattandosi di un conteggio di fibre con un valore di concentrazione presumibilmente basso (almeno nelle decadi più recenti), è preferibile utilizzare membrane filtranti con un basso valore di sostanze interferenti. Particolare attenzione è riservata anche al supporto di filtrazione, dove è alloggiata la membrana durante il prelievo (porta membrana). Generalmente si utilizza un porta membrana metallico o in plastica conduttrice dal diametro corrispondente al filtro utilizzato, corredato di cappuccio metallico cilindrico davanti al filtro, lungo tra 33 mm e 44 mm; durante l’uso il cappuccio è rivolto verso il basso. La scelta dei substrati da utilizzare e del volume d’aria da campionare è in relazione all’analisi che deve essere effettuata. Nella sono riportati alcuni esempi. Per la scelta del metodo analitico occorre considerare in primo luogo il tipo di ambiente e il contesto in cui è effettuata la misurazione: in quelli di lavoro (rimozione o manutenzione manufatti), in cui si suppone ci siano concentrazioni relativamente elevate di fibre di amianto aerodisperse, la tecnica Microscopia ottica a contrasto di fase (MOCF) fornisce dati sufficienti, anche se non certi, per valutare l’esposizione dei lavoratori. Nei luoghi confinati in cui, in genere, la presenza di fibre è bassa, è preferibile ricorrere alla microscopia elettronica, anche se le norme prevedono la possibilità di utilizzare entrambe le tecniche. Nel caso della valutazione del rilascio di fibre in un ambiente in cui sono presenti manufatti contenenti amianto, i valori di riferimento sono diversi a seconda della tecnica adoperata: con la tecnica della MOCF si considera un ambiente con inquinamento in atto quando il conteggio delle fibre è superiore a 20ff/l, mentre con la microscopia elettronica il limite considerato è di 2 ff/l. Per gli ambienti di vita è necessaria l’analisi in Microsopia Elettronica a Scansione (SEM). Qualunque sia la metodica scelta, non bisogna dimenticare che, in generale, tutti i metodi microscopici sono affetti da ampi margini di errore; in nessun caso è possibile attribuire un valore di concentrazione pari a ZERO; il valore minimo è pari o inferiore al Limite di Rilevabilità che dipende dal volume d’aria campionato e dalla superficie della membrana esplorata. È la tecnica più diffusa ed accessibile, anche se presenta limiti maggiori perché non permette di determinare con certezza la natura della fibra (errore di sovrastima) o di rilevare le fibre di dimensioni più piccole (errore di sottostima). Solo nel caso di campioni massivi la normativa prevede l’identificazione qualitativa delle fibre mediante la tecnica della dispersione cromatica in microscopia ottica. L’uso della Microscopia elettronica a scansione corredata di microanalisi fornisce una caratterizzazione molto affidabile degli aspetti morfologici delle fibre e della loro composizione chimica. È un’analisi che permette la determinazione quali/quantitativa delle fibre di amianto aerodisperse regolamentate, ed il risultato è espresso in concentrazione (fibre/volume); può essere effettuata su tutte le matrici: aria, acqua, suolo, rifiuti. È il metodo di elezione per la determinazione dell’amianto, in quanto consente l’attribuzione certa delle fibre di amianto rispetto ad altre tipologie di fibre grazie al sistema di microanalisi; può essere indicato per determinazione quantitativa in campioni massivi (MCA); in caso di presenza di amianto <1% in peso è il metodo analitico di riferimento previsto dalla normativa. Le tecniche per la determinazione ponderale dell’amianto sono la diffrattometria a raggi X (DRX) e la spettroscopia infrarossa in trasformata di Fourier (FTIR). Ambedue le tecniche necessitano di una preparazione del campione che prevede un trattamento fisico o chimico per ridurre al minimo l’effetto della matrice in cui si trova disperso l’amianto; questo è tanto più importante quanto più la concentrazione del materiale da misurare è vicina alla sensibilità delle tecniche analitiche (>1%). L’interferenza della matrice, in particolare con la tecnica FTIR, può determinare una incertezza nei risultati. Per concentrazioni inferiori al Limite di Rilevabilità si ricorre alla SEM, peraltro con risultati piuttosto incerti in quanto, per la determinazione ponderale dell’amianto, occorre calcolare il volume delle fibre osservate e moltiplicarlo per la densità dell’amianto. Ad oggi la soluzione al problema analitico per la determinazione dell’amianto in campioni in massa non esiste; è però sempre consigliabile accoppiare alla determinazione ponderale un esame microscopico che permetta di effettuare un riscontro morfologico delle fibre analizzate. La grande maggioranza degli studi epidemiologici sul rischio da amianto si basa su una valutazione dell’esposizione con approccio retrospettivo. La ricostruzione dell’esposizione pregressa ad una qualsiasi sostanza in ambienti di lavoro, per sua natura retrospettiva, comporta sempre notevoli difficoltà, anche quando fossero disponibili i risultati di misurazioni ambientali. Metodi standardizzati per il campionamento dell’amianto sono disponibili, come già riportato, solo dall’inizio degli anni ’80. In precedenza, nelle industrie dove venivano eseguiti campionamenti ambientali (in genere quelle dove l’amianto era utilizzato come materia prima), venivano utilizzati strumenti che fornivano valori istantanei o relativi a pochi minuti ed i risultati di queste misure venivano spesso forniti in particelle per centimetro cubo (pp/cc) e non in fibre per centimetro cubo (ff/cc). Anche le tecniche di scelta del campione e della sua numerosità risalgono alla fine degli anni ’70 [NIOSH 1978] e, per l’Europa, agli anni ’90 [UNI-EN 689:1997]. I dati di esposizione più risalenti nel tempo devono essere trattati con prudenza in quanto risentono di una serie di errori derivanti dalla scelta della numerosità campionaria, dalla scelta del campione, dal sistema di campionamento e dal sistema di analisi; errori che determinano una maggiore o minore rappresentatività della situazione reale e introducono un limite interpretativo nella stima retrospettiva di esposizione. Nel caso in cui non siano disponibili misure ambientali riferibili alla situazione in esame, si può far ricorso a dati di letteratura o a dati misurati in situazioni simili a quella considerata che possono riferirsi a categorie o a valori puntuali di concentrazioni di fibre nell’atmosfera. Anche in questo caso è necessario prestare una particolare attenzione alla qualità ed alla rappresentatività dei dati per poterli applicare alla situazione in studio. In genere le informazioni che si possono trarre sono relative all’ordine di grandezza e non al valore puntuale. Utilizzando i dati di letteratura per stimare esposizioni individuali è comunque necessario ricostruire nel modo più accurato possibile la frequenza delle operazioni e la loro durata. È inoltre necessario acquisire informazioni sugli impianti, sui processi e sulle mansioni. In questo caso è possibile formulare ipotesi ragionevoli seppure non precise; si potrà, ad esempio, definire un range di esposizione più o meno largo, anche se non si potrà con ragionevole certezza definire uno specifico valore di esposizione. Spesso alla ricerca della letteratura viene affiancata la ricerca tramite banche dati che raccolgono e sistematizzano dati di letteratura di misurazioni di igiene industriale; spesso, tuttavia, in tali banche dati non sono riportate le informazioni necessarie alla valutazione della qualità della misura. Alcune banche dati forniscono anche una stima del range di esposizione per mansione (Ev@lutil , DatAmiant . A volte, per sopperire alla carenza di dati, vengono usate matrici mansione-esposizione che possono essere costruite in diversi modi (qualitativi/quantitativi) a seconda degli obiettivi che ci si pone e di conseguenza della loro utilizzazione. Le matrici mansione-esposizione possono essere specifiche per una data azienda o industria, ed in questo caso sono solitamente utilizzate in studi di coorte, ovvero possono essere costruite a priori sulla base di codifiche occupazionali ed industriali, e quindi generalizzabili nella loro applicazione in studi di popolazione, solitamente condotti con il disegno dello studio caso-controllo. Le stime di esposizione applicate si avvalgono di categorie di intensità, probabilità e frequenza, desumibili da risposte a specifiche domande da questionario, nozioni di tecnologia industriale e pubblicazioni e/o banche dati di igiene industriale. Le variabili di esposizione suddette possono essere combinate tra loro per produrre stime di tipo dicotomico (assente/presente), ordinale (assente, bassa, media, alta), score di esposizione cumulativa semi-quantitativa (valori numerici che non esprimono una concentrazione), o quantitativa (concentrazione in fibre/ml). L’assegnazione di un livello di esposizione ad una categoria non dovrebbe essere utilizzata per i casi singoli, ma, al più, collettivamente per gli addetti ad una mansione specifica in un determinato contesto. Infatti, l’esame dei casi singoli non può prescindere da una accurata raccolta anamnestica dei tempi di esecuzione di ciascuna operazione e della loro frequenza, suffragata, ogniqualvolta sia possibile, da dati obiettivi registrati. In particolare, le stime quantitative non basate su misure reali, sul campo, solo ottenute con valutazioni derivate per analogia, anche da altri contesti. Per questo motivo devono essere considerate con estrema cautela. È comunque opportuno che ad ogni categoria venga associato un ambito di variabilità numerico. Valutazioni di questo tipo possono essere usate in campo epidemiologico ma non possono essere ritenute valide per la definizione dell’esposizione nei singoli casi, in quanto espressione di stime su gruppi all’interno dei quali l’esposizione potrebbe essere stata eterogenea, anche non compresa nella stima fornita con l’ambito di variabilità. È utile ricordare che l’utilizzo di dati basati su surrogati della misura dell’esposizione, come la mansione, possono produrre misclassificazioni delle esposizioni stesse, provocando errori di associazione causale (sovrastima o sottostima del rischio relativo), e pertanto possono trovare applicazione in ambito epidemiologico ma non nella valutazione di casi individuali. La valutazione dell’esposizione retrospettiva non è basata su metodi standardizzati, perché il metodo di stima appropriata per uno studio dipende dal tipo di informazioni che sono disponibili, che varia da caso a caso. Tale valutazione dell’esposizione può essere soggetta ad errore (misclassificazione). È opinione diffusa che, negli studi caso controllo, l’eventuale misclassificazione dell’esposizione possa differire sistematicamente tra casi e controlli, con il risultato di generare una sovrastima o una sottostima dei rischi. Inoltre, anche nel caso di misclassificazione non-differenziale, il bias è nella direzione di una sottostima nel caso di variabile binaria (esposto/non esposto), ma può avere degli effetti più complessi nel caso di variabili categoriche o continue. Nel caso di indicatori derivati di esposizione (matrici o valutazioni di esperti), in particolare quelli usati in studi di popolazione generale, l’errore si distribuisce in maniera non omogenea, in quanto la validità dell’informazione varia nel tempo (con stime migliori per periodi più recenti), così come in riferimento alle diverse industrie e mansioni nelle quali i dati ambientali sono più frequentemente disponibili nei luoghi di lavoro di medie-grandi dimensioni e, in queste, per le mansioni ad esposizione più elevata, mentre sono più frequentemente carenti nei luoghi di lavoro di piccole-medie dimensioni, dove le esposizioni potrebbero essere meno controllate e verosimilmente più elevate. L’errore che ne risulta a livello delle stime per i soggetti in studio è quindi complesso, e di difficile valutazione. Un ulteriore tipo di errore nella valutazione dell’esposizione è quello che risulta da stime quantitative che non siano basate su affidabili misure ambientali, ma poggino su modelli e valutazioni da parte di esperti. Il valore assoluto assegnato alle diverse categorie di esposizione (mansioni, o episodi lavorativi individuali) può essere sottostimato a causa del fatto che le misure ambientali sono disponibili da periodi relativamente recenti, quando le esposizioni erano in generale più basse. Una sottostima delle esposizioni nel passato comporta (in presenza di un’associazione) una sovrastima della relazione dose-risposta, e viceversa come illustrato in . A fronte di queste incertezze nella stima quantitativa (o semi-quantitativa) dell’esposizione, i dati temporali (tempo dalla prima esposizione, tempo dalla cessazione dell’esposizione, durata di esposizione) potrebbero apparire più precisi e validi. Tuttavia, 1) tali dati temporali dovrebbero essere riferiti a specifiche mansioni ed operazioni, evitando di accomunare categorie espositive diverse (ad esempio capisquadra, operatori di impianti e manutentori meccanici); 2) in numerosi studi di coorte i dati temporali mancano o sono incompleti; e 3) in studi caso-controllo anche questa informazione può anch’essa essere soggetta a bias di ricordo. In altre parole, se i dati sui livelli di esposizione sono di bassa qualità, in particolare per quanto riguarda le esposizioni più vecchie, è ragionevole limitarsi ad una valutazione del rischio sulla base delle variabili temporali di esposizione. Occorre infine ricordare che la valutazione dell’esposizione, per quanto importante, è solo una delle possibili sorgenti di bias negli studi epidemiologici sugli effetti dell’esposizione ad amianto. Altre fonti potenziali di bias comprendono: bassa rispondenza di soggetti, con differenze tra casi e controlli o tra esposti e non esposti (soprattutto in studi in popolazione generale); perdita elevata al follow-up (in studi di coorte); misclassificazione della malattia: questa fonte di bias è particolarmente importante negli studi su esposti ad amianto a causa della presenza di malattie e condizioni che sono patognomoniche (asbestosi) o fortemente associate all’esposizione (mesotelioma, placche pleuriche). La conoscenza di una condizione di esposizione presente o passata può influenzare la diagnosi; confondimento, per esempio da fumo di tabacco o da altre esposizioni professionali nel caso del tumore del polmone; pubblicazione selettiva di risultati positivi o statisticamente significativi ( bias di pubblicazione). Pertanto, pur sottolineando la grande importanza scientifica delle indagini epidemiologiche, si ricorda che si tratta sempre di studi osservazionali in cui manca la possibilità del controllo preciso dei valori e della durata dell’esposizione negli esposti relativamente alle popolazioni di riferimento e a quelle di controllo. Nonostante questi problemi la ricerca epidemiologica ha contribuito nel passato e può ancora contribuire al progresso delle conoscenze sull’origine delle patologie umane ed all’identificazione di strategie di prevenzione. Le classiche patologie da amianto, sia benigne non neoplastiche (asbestosi, placche ed ispessimenti pleurici, versamenti pleurici benigni, atelettasie rotonde) che neoplastiche (neoplasie polmonari e dei mesoteli) sono ormai ben conosciute, per cui non si ritiene opportuno, in questa sede, ritornare sulle loro definizioni, rimandando alla trattatistica consolidata più recente . In questo capitolo si darà, invece, spazio alla diagnostica di talune di queste patologie, anche e soprattutto per le implicazioni terapeutiche e medico-legali che le medesime sottendono e che, di conseguenza, comportano la inderogabile necessità di addivenire ad una diagnosi di certezza. Analogamente verranno approfonditi i criteri di attribuzione all’esposizione ad amianto di quelle patologie che riconoscono cause multifattoriali, ciascuna delle quali di per sé sufficiente a generare la patologia. La valutazione del rischio nell’esposizione occupazionale e/o ambientale ad agenti chimici, ed in particolare ai cancerogeni, per ampio consenso della comunità scientifica si basa non solo sugli effetti sull’uomo (epidemiologia) ma anche e in taluni casi in maniera preponderante o anche esclusiva sugli studi sperimentali in vitro e in vivo sull’animale (tossicologia). Questi ultimi infatti sono l’unico strumento per capire come la sostanza agisce a livello molecolare, cellulare e tissutale nel determinare un dato effetto avverso (meccanismo d’azione). La conoscenza del meccanismo d’azione, quando disponibile, è inoltre fondamentale per costruire la curva dose-risposta e definire (o quantomeno ipotizzare) la presenza o meno di una dose-soglia. Questi due fattori sono fondamentali per poter scegliere il modello più appropriato di estrapolazione del rischio dalle alte alle basse dosi. Ovvero dai livelli di esposizione ai quali gli effetti sono misurabili direttamente a quelli per i quali non lo sono e devono pertanto essere inferiti. Nel caso dei cancerogeni, in particolare, le conoscenze sul meccanismo d’azione della sostanza sono importanti inoltre per individuare le modalità con cui definire il valore limite di esposizione da adottare. Le principali agenzie o comitati internazionali concordano, pur con differenze apprezzabili, su due diversi tipi di meccanismi di cancerogenesi: uno generalmente indicato come “genotossico” per il quale non vi è evidenza dell’esistenza di una dose-soglia e l’altro definito “epigenetico” per il quale invece è ipotizzabile, o talora anche osservabile, una dose-soglia. Tale dicotomia, ancorché semplicistica, è utile dal punto di vista pratico nel decidere se un valore limite debba essere calcolato a partire da una dose-soglia (NOAEL, LOAEL, BMD) o piuttosto in base al livello di rischio “accettabile”. Nel caso dell’amianto, nonostante gli innumerevoli studi in vitro e in vivo svolti nell’arco di vari decenni, la questione del meccanismo rimane aperta. Sono stati infatti proposti diversi meccanismi di interazione tra le fibre di amianto e i suoi bersagli cellulari e tissutali ritenuti alla base del processo di cancerogenesi sul polmone e sul mesotelio. Tra questi ve ne sono sia di tipo genotossico che epigenetico, sia diretti sul DNA che indiretti, attraverso formazione di specie reattive dell’ossigeno, infiammazione cronica e aumento della proliferazione cellulare. L’evidenza disponibile suggerisce che meccanismi diversi contribuiscano a livelli diversi, interagendo tra loro e sui bersagli, all’innesco e sviluppo degli effetti cancerogeni dell’amianto. La questione, pertanto, di come sia meglio estrapolare i dati dalle alte alle basse dosi, non risolvibile sulla base dei soli studi epidemiologici, rimane anche sulla base dell’evidenza sperimentale tuttora aperta. • Asbestosi Una delle principali difficoltà che si riscontra nel procedere alla diagnosi di asbestosi risiede nel fatto che si tratta di una patologia dal punto di vista clinico, della diagnosi per immagini e di quella istologica, indistinguibile da interstiziopatie di altra natura. Nell’iter diagnostico il Gold Standard è rappresentato sicuramente dall’esame istologico che deve dimostrare la contestuale presenza di fibrosi e corpuscoli dell’asbesto (due o più in una sezione di tessuto polmonare di 1 cm 2 ) . La riassume la grande difficoltà dal punto di vista istologico di per sé di procedere a una diagnosi differenziale fra Usual Interstitial Pneumonia (UIP) o, più genericamente, fibrosi polmonare idiopatica, e asbestosi, secondo quanto affermato dall’ Asbestosis Committee of the College of American Pathologists and Pulmonary Pathology Society . Sul medesimo documento viene riportato uno schema di grading istologico dell’asbestosi, riportato in . I Criteri di Helsinki 2014 concordano sull’utilizzo di tale schema di grading al fine di garantire una comparabilità fra referti e studi clinici. Sempre i Criteri di Helsinki affermano che “ Una diagnosi istologica di asbestosi richiede l’identificazione di una fibrosi interstiziale diffusa in campioni polmonari tecnicamente adeguati oltre alla presenza di 2 o più corpuscoli di amianto, in un tessuto con sezione di 1 cm 2 , o un conteggio di fibre di amianto libere o rivestite che rientri nel range registrato per asbestosi da quello stesso laboratorio. Si segnala inoltre che, in rari casi, l’asbestosi può verificarsi senza la presenza di corpuscoli di amianto e che tali casi sono riconoscibili solo dalla quantità di fibre non rivestite ” e consigliano il riferimento al “ sistema CAP-NIOSH modificato secondo Roggli-Pratt ”. L’approccio seguito dagli estensori della revisione dei Criteri di Helsinki è senz’altro condivisibile anche se non mancano Autori che manifestano posizioni critiche relativamente alla obbligatorietà del rilievo dei corpuscoli dell’asbesto . Un altro strumento importante nell’iter diagnostico dell’asbestosi è rappresentato dall’esame radiologico del torace, eseguito secondo i criteri fissati dalla Classificazione radiologica delle pneumoconiosi dell’ILO 1980 e successive modifiche . Le prime alterazioni individuabili nei radiogrammi sono rappresentate da fini opacità irregolari e/o lineari alle basi polmonari. Nelle fasi successive di malattia tali opacità tendono alla coalescenza fino a determinare quadri di fibrosi a “vetro smerigliato” e/o a “nido d’ape” ( honey combing ). Queste alterazioni risultano tuttavia essere aspecifiche, e comuni a tutte le fibrosi polmonari interstiziali; nel caso particolare in cui si riscontri in aggiunta la presenza di placche pleuriche, la probabilità di trovarsi di fronte a un quadro di asbestosi aumenta. Un esame radiologico di secondo livello, caratterizzato da maggior sensibilità, ma non specificità, è la tomografia computerizzata (CT) e, in particolare, la CT ad alta risoluzione (HRCT). Nell’ultimo decennio è stata introdotta e validata la Classificazione Internazionale delle HRCT, ICOERD , allo scopo di standardizzare (e, quindi, rendere comparabile) la refertazione della HRCT delle interstiziopatie polmonari occupazionali. Il modello ICOERD, analogamente alla classificazione ILO per i radiogrammi standard del torace, prevede come elementi classificativi principali per il parenchima polmonare: le piccole opacità nodulari o irregolari/lineari, le grandi opacità, le aree a vetro smerigliato e di honeycombing , l’enfisema e l’atelettasia rotonda. La profusione delle piccole opacità è stratificata in quattro gradi (0-3), da assegnare a ciascuna delle tre regioni di ogni singolo polmone (con un possibile range da 0 a 18). È poi prevista la registrazione delle anomalie pleuriche, distinte fra parietali e viscerali. Per la classificazione ICOERD l’ imaging HRCT tipico dell’asbestosi comprende l’ispessimento interstiziale intralobulare (cui corrisponde, istologicamente, la fibrosi peribronchiolare), l’ispessimento dei setti interlobulari, le lesioni puntiformi sub-pleuriche, le strie curvilinee sub-pleuriche, le bande parenchimali (che, peraltro, rifletterebbero un ispessimento pleurico viscerale piuttosto che una fibrosi) . L’uso della classificazione ICOERD è raccomandata nel Documento di consenso di Helsinki 2014 , secondo il quale può essere posta diagnosi di asbestosi in presenza di una somma dei punteggi relativi alle opacità irregolari bilaterali ≥2-3 nelle aree inferiori o di honeycombing bilaterale di grado ≥2. Questa posizione appare in contrasto con quanto affermato nelle premesse del documento ICOERD in cui è riportato che “ Il Sistema di codifica deve essere applicato come un sistema strettamente descrittivo e non è diagnostico. È stato definito per coprire tutti gli aspetti delle patologie occupazionali ed ambientali che comportano anomalie parenchimali e pleuriche. Per quanto alcuni dei termini descrittivi siano classicamente associati alle pneumoconiosi, ad esempio le opacità tondeggianti alla silicosi, le linee settali interlobulari o non settali intralobulari e l’honeycombing all’asbestosi, queste sono immagini radiografiche che devono essere prese in considerazione con cautela per pervenire ad una corretta diagnosi differenziale ”. Ed ancora: “ Lo scopo della Classificazione HRCT è quello di descrivere e codificare le manifestazioni parenchimali e pleuriche delle malattie respiratorie non maligne occupazionali ed ambientali. La Classificazione fornisce uno strumento semi quantitativo per la scoperta precoce di cambiamenti fibrotici indotti dalla esposizione a polveri in ambito occupazionale ed ambientale. Un risultato positivo ottenuto dalla Classificazione HRCT non sempre significa la presenza di una pneumoconiosi ”. Sono quindi opportune valutazioni puntuali su ogni singolo caso, consapevoli degli intendimenti degli estensori del documento ICOERD. Per quanto riguarda la diagnosi clinica di asbestosi (con ciò intendendo una diagnosi non confermata istologicamente), sono stati proposti nel tempo diversi protocolli. Si riporta qui di seguito quello proposto da Roggli et al : storia di esposizione ad amianto, da moderata a forte, tipicamente, ma non sempre, professionale e spesso protratta per molti anni. Tuttavia, l’asbestosi non è il risultato univoco di significative o anche forti esposizioni ad amianto. In generale, quando l’esposizione cumulativa è stata da significativa a pesante, la probabilità di sviluppare un quadro clinico di asbestosi e la sua gravità sono maggiori, con un più breve intervallo di latenza tra l’inizio dell’esposizione e la conseguente insorgenza dei sintomi della patologia; segni clinici di fibrosi interstiziale in forma di crepitii teleinspiratori all’auscultazione dei campi polmonari, soprattutto a livello delle basi; riscontro di opacità diffuse reticolo-lineari a livello dei campi polmonari inferiori all’esame radiologico del torace; classicamente, riscontro di deficit funzionale restrittivo alle prove di funzionalità respiratoria o di riduzione della diffusione del monossido di carbonio (CO); generalmente, ma non sempre, presenza di placche pleuriche e/o di fibrosi pleurica diffusa. I criteri 1 e 3 sono obbligatori per la diagnosi clinica, che viene ulteriormente supportata dal criterio 5. Quando uno o più dei criteri 5, 2, o 4 (in ordine di importanza decrescente) non sono soddisfatti, l’indice di fiducia per la diagnosi declina corrispondentemente. • Placche pleuriche La diagnosi “in vita” è, di norma, radiologica. L’esame diagnostico più sensibile è rappresentato dalla HRCT del torace senza mezzo di contrasto. Nell’eseguire una diagnosi radiologica il rischio di “falsi positivi” alla radiografia del torace può essere determinato dai muscoli extrapleurici e dal grasso sub-pleurico , mentre nella CT il medesimo rischio è legato alla tecnica di esecuzione; è noto, infatti, che se l’esame viene eseguito in posizione supina possono comparire anomalie pleuriche focali e che la ripetizione delle scansioni stesse in posizione prona può portare alla scomparsa delle medesime . • Atelettasia rotonda La diagnosi di atelettasia rotonda viene posta quando le immagini HRCT dimostrano un’opacità tondeggiante o ellittica concomitante ad anomalie pleuriche, a sede periferica con un contatto significativo con la superficie pleurica anomala, associata ad incurvamento di vasi o bronchi nel bordo della lesione (il cosiddetto segno della cometa), e perdita di volume del lobo affetto . • Mesotelioma pleurico La diagnosi di mesotelioma, oggi più che mai indispensabile in considerazione delle nuove possibilità terapeutiche , presenta tutt’oggi notevoli difficoltà anche in relazione al fatto che tutti i tumori, con l’eccezione per le neoplasie cerebrali, possono potenzialmente metastatizzare a livello pleurico. Dati recenti documentano un rapporto metastasi pleuriche/mesoteliomi pari a 130:1 ; nei Paesi industrializzati solo l’1% dei versamenti pleurici maligni è causato dal mesotelioma maligno diffuso . Inoltre, nel procedere alla diagnosi di mesotelioma pleurico, bisogna tener conto anche dell’esistenza di altre neoplasie primitive della pleura, benigne e maligne, così come illustrato nella . Pertanto, le difficoltà che si riscontrano nell’iter diagnostico del Mesotelioma Pleurico Maligno (MPM) hanno condotto alla stesura di linee guida e documenti di consenso da parte di numerose società scientifiche internazionali e nazionali con lo scopo di fornire uno strumento utile per raggiungere diagnosi di certezza e di conseguenza guidare il paziente a sottoporsi a trattamenti mirati e specifici. Fra questi si ricordano (in quanto più recenti e più autorevoli): • Il documento di consenso dell’ International Mesothelioma Interest Group , in cui si afferma che: - l’anatomopatologo non deve essere in alcun modo influenzato nel suo percorso diagnostico, e pertanto non deve essere informato circa eventuali pregresse esposizioni ad amianto; - la distinzione fra le proliferazioni mesoteliali benigne e maligne si basa sull’analisi delle caratteristiche istologiche e sui pannelli immunoistochimici che devono essere scelti e utilizzati in funzione delle diverse ipotesi di diagnosi differenziale; - per la diagnosi sono di norma sufficienti due marcatori positivi per il mesotelioma e due marcatori tipici di altre neoplasie potenzialmente confondenti; - in presenza di risultati dubbi o discordanti, devono essere impiegati ulteriori marcatori - sono risultati essere di limitata utilità diagnostica: la citologia, le colorazioni istochimiche, la microscopia elettronica, e i marcatori molecolari. Nelle tabelle 5 e 6, riprese a titolo di esempio dal citato documento, sono riassunti i marcatori fondamentali per la diagnosi differenziale fra mesotelioma pleurico epitelioide, adenocarcinoma polmonare e carcinoma polmonare squamoso metastatizzato alla pleura. La scelta è stata determinata dal fatto che la necessità di porre diagnosi differenziale trova questa condizione come la più frequente in assoluto. • Il Terzo Consensus Document italiano sul mesotelioma , le cui conclusioni sono riassunte nella . Si sottolinea come la scelta dei marcatori sia estremamente complessa e costituisca materia in costante divenire. Il concetto fondamentale è che la scelta del pannello di marcatori deve essere guidata dagli orientamenti diagnostici differenziali più probabili (su base istomorfologica ed anche citomorfologica, se sono soddisfatti i criteri base . Si rimarca inoltre che il numero di marcatori richiesto è di due positivi per mesotelioma e due negativi ma che il numero debba essere aumentato nei casi di maggior complessità e, comunque, ogni qual volta si voglia aumentare il valore predittivo di accuratezza diagnostica. Si sottolinea come la scelta del tipo e del numero dei marcatori sia tutt’oggi particolarmente complessa e difficilmente standardizzabile nel caso delle forme sarcomatoidi (ed in particolare della variante desmoplastica). Le già citate Linee Guida dell’ International Mesothelioma Interest Group indicano che “Un pannello immunoistochimico iniziale che può essere utile per escludere un sarcoma fuso cellulare comprende gli anticorpi AE1/3, OSCAR, KL1, CK18 o CAM 5.2. Marcatori positivi utilizzati nella valutazione dei mesoteliomi epitelioidi come WT1 e CK5/6, così come marcatori positivi negli adenocarcinomi come claudina 4, MOC31, BER-Ep4 e CEA, non sono utili nei tumori sarcomatoidi e dovrebbero essere evitati, in particolare quanto il tessuto è scarso. Podoplanina (D2-40) e calretinina possono essere espressi nei mesoteliomi sarcomatoidi in una percentuale variabile di casi: la calretinina è il marcatore più frequentemente positivo. Circa il 30% dei mesoteliomi sarcomatoidi esprime la calretinina, che può essere estremamente focale. Quando positiva, la podoplanina (D2-40) dimostra una più elevata sensibilità e specificità nella diagnosi differenziale fra il mesotelioma pleurico sarcomatoide ed il carcinoma sarcomatoide polmonare. Tuttavia, la falsa positività costituisce il problema maggiore e può occorrere a seguito di una cattiva interpretazione di una reattività positiva della podoplanina (D2-40) nell’ambito di elementi linfatici benigni o elementi reattivi mesoteliali e fibrosi. Un tumore sarcomatoide istologicamente maligno che esprime in maniera forte e diffusa le citocheratine normalmente restringe la diagnosi differenziale al mesotelioma sarcomatoide, al carcinoma sarcomatoide del polmone e, talora, al sarcoma sinoviale, all’angiosarcoma o ad altri tumori sarcomatoidi extrapolmonari … In particolare in caso di assenza di convincente positività delle citocheratine, la isolata positività di calretinina e/o podoplanina (D2-40) non dovrebbe essere interpretata come evidenza di differenziazione mesoteliomatosa. Questi marcatori sono variabilmente positivi in molti tipi differenti di sarcomi, per cui si rende necessario l’impiego di ulteriori marcatori ”. Nel Terzo Consensus Document italiano sul mesotelioma si raccomanda che, per il mesotelioma sarcomatoide a sede pleurica, il panel di marcatori immunoistochimici debba prevedere citocheratine ad ampio spettro, calretinina, WT-1 e D2-40. Inoltre, è di fondamentale importanza, per raggiungere una diagnosi di elevata probabilità, prossima alla certezza, che nella formulazione del referto immunoistochimico sia specificato il grado di positività dei marcatori utilizzati tra quelli considerati “positivi per mesotelioma”, e che venga inoltre definita la sede cellulare di colorazione (nucleare, citoplasmatica, entrambe). L’attribuzione di talune patologie alla esposizione all’amianto • Le neoplasie polmonari La Consensus Conference di Helsinki del 1997 e l’aggiornamento del 2014 hanno definito i criteri di attribuzione di un carcinoma polmonare all’esposizione all’asbesto che sono di seguito riassunti: 1) tutti e sei i tipi istologici principali (squamoso, adenocarcinoma, carcinomi a grandi cellule e piccole cellule, adenosquamoso e sarcomatoide) possono essere correlati all’amianto; 2) la sede di localizzazione del tumore nell’ambito dei polmoni non è considerata importante per determinarne l’attribuibilità; 3) l’esposizione cumulativa, su una base di probabilità, dovrebbe essere considerata il criterio principale per l’attribuzione di un contributo sostanziale da parte dell’amianto al rischio di cancro al polmone. Anche molta letteratura scientifica conferma questo indirizzo . Nel Documento di consenso si ritiene che, affinché sia possibile attribuire un carcinoma polmonare all’asbesto, deve essere dimostrata un’esposizione all’inalazione di fibre d’asbesto di almeno 10 anni. Gli Autori del Documento ritengono, inoltre, che un’esposizione cumulativa di 25 fibre/cc-anni 2 consenta di apprezzare un eventuale rischio relativo di circa 2 nella popolazione esposta. Studi recenti suggeriscono possibili effetti anche ad esposizioni cumulative inferiori; tali studi, tuttavia, non sono basati su misurazioni dell’esposizione ma su ricostruzioni derivate da stime retrospettive di esposizione e, come tali, meno precise. In generale, storie lavorative attendibili, raccolte da personale esperto, forniscono lo strumento più pratico e utile per valutare l’esposizione lavorativa ad amianto. A titolo di esempio, secondo i Criteri di Helsinki, si considera “ pesante ” l’esposizione associata ad attività quali “ manifattura dei prodotti in amianto, spruzzatura dell’amianto, coibentazione con amianto, demolizione di vecchi edifici ”, e“ moderata ” l’esposizione derivante da attività di “ costruzione o cantieristica navale ”. Nella revisione dei Criteri di Helsinki del 2014 le citate indicazioni non sono state modificate; 4) ai livelli suddetti di esposizione possono verificarsi casi clinici di asbestosi. La presenza di asbestosi è un indicatore di alta esposizione. Tuttavia anche tra i non fumatori esposti all’amianto in assenza di asbestosi il rischio di cancro ai polmoni può risultare aumentato. Questo concetto è espresso anche nella maggior parte della letteratura scientifica ; 5) L’analisi, nel tessuto polmonare, di fibre nude e corpuscoli di amianto può fornire dati utili a integrare la storia lavorativa. A fini clinici, i Criteri di Helsinki forniscono le seguenti indicazioni al fine di identificare le persone che con elevata probabilità abbiano avuto un’esposizione di tipo professionale ad amianto: oltre 0,1 milioni di fibre di anfibolo (>5 µm) per grammo di tessuto polmonare secco (gps) o oltre 1 milione di fibre di anfibolo (>1 µm) per gps misurate mediante microscopia elettronica in un laboratorio qualificato o più di 1000 corpuscoli di amianto per gps (100 corpuscoli di amianto per grammo di tessuto umido) o oltre 1 corpuscolo di amianto per millilitro di liquido derivante da lavaggio bronco-alveolare misurati mediante microscopia ottica in un laboratorio qualificato; 6) ai fini dell’attribuzione causale di un cancro polmonare all’esposizione ad amianto, sempre nell’ottica del raddoppio del rischio, vengono indicati invece i seguenti valori: oltre 2 milioni di fibre di anfibolo (>5 µm) per gps o oltre 5 milioni di fibre di anfibolo (>1 µm) per gps misurate mediante microscopia elettronica o 5000-15000 corpuscoli di amianto per gps (100 corpuscoli di amianto per grammo di tessuto umido) o 5-15 corpuscoli di amianto per millilitro di liquido derivante da lavaggio bronco-alveolare misurati mediante microscopia ottica in un laboratorio qualificato (quando la concentrazione di corpuscoli è inferiore a 10000 per gps viene raccomandata un’analisi in microscopia elettronica); 7) le placche pleuriche sono indicatori di esposizione alle fibre di amianto, in particolare se bilaterali e/o diaframmatiche. Poiché le placche pleuriche possono essere associate a bassi livelli di esposizione, le placche pleuriche da sole, senza un’importante storia di esposizione occupazionale, sono insufficienti per l’attribuzione del cancro del polmone all’amianto; 8) l’ispessimento pleurico diffuso bilaterale è spesso associato a un’esposizione moderata o elevata, come si è visto nei casi di asbestosi, e dovrebbe essere considerato di conseguenza in termini di attribuzione. Non tutti i criteri di esposizione devono essere soddisfatti contemporaneamente ai fini dell’attribuzione. Ad esempio dovrebbe essere considerato l’elevato numero di fibre di amianto nel tessuto polmonare anche con una storia di lavoro incerta. Quando si considera il crisotilo, la ricostruzione anamnestica del lavoro svolto (fibre/cc-anni di esposizione) rappresenta probabilmente l’indicatore migliore del rischio di tumore del polmone rispetto all’analisi del contenuto polmonare in fibre in considerazione di un possibile maggior rateo di eliminazione polmonare del crisotilo. Questo argomento è stato oggetto di discussione in una recente pubblicazione . Spesso viene utilizzato, specie in campo di controversie civili, lo standard legale del “più probabile che non” (concetto peraltro in discussione anche a livello epidemiologico , che, sulla base dei Criteri di Helsinki del 1997, equivaleva ad un rischio relativo (RR) di 2, come valore soglia per l’attribuzione di causalità di una malattia in soggetti esposti a rischio. Il Consensus 2014 indica che la soglia del RR da utilizzare per l’attribuzione individuale possa essere stabilita anche a livelli inferiori cui peraltro corrisponde una frazione attribuibile inferiore al 50%. In termini generali, per le patologie neoplastiche, la formula suggerita per il calcolo della frazione attribuibile è AF = (RR-1) / RR. Ricordiamo che il concetto di “più probabile che non” ricorre costantemente nella criteriologia medico-legale per l’attribuzione del nesso di causa ed è accettato nella giurisprudenza di merito in ambito civilistico. Ne consegue, a maggior ragione, la sua non applicabilità in contesti di giudizio penale nei quali vige il criterio dell’elevata probabilità logica e credibilità razionale, di fatto prossima alla certezza. Per tutte si veda la sentenza Franzese delle Sezioni Unite della Cassazione Penale (sentenza n° 30328 dell’11/9/2002), costantemente ripresa per la sua universale applicabilità nei diversi contesti del giudizio penale. Al punto 6 del paragrafo “Considerato in Diritto” della sentenza si legge: “Tutto ciò significa che il giudice, pur dovendo accertare ex post, inferendo dalle suddette generalizzazioni causali e sulla base dell'intera evidenza probatoria disponibile, che la condotta dell'agente ‘è’ (non ‘può essere’) condizione necessaria del singolo evento lesivo, è impegnato nell’operazione ermeneutica alla stregua dei comuni canoni di ‘certezza processuale’, conducenti conclusivamente, all’esito del ragionamento probatorio di tipo largamente induttivo, ad un giudizio di responsabilità caratterizzato da ‘alto grado di credibilità razionale’ o ‘conferma’ dell'ipotesi formulata sullo specifico fatto da provare: giudizio enunciato dalla giurisprudenza anche in termini di ‘elevata probabilità logica’ o ‘probabilità prossima alla - confinante con la certezza’.” Anche se il fumo di tabacco influenza il rischio totale di cancro ai polmoni, questo effetto non esclude il rischio di un cancro polmonare associabile all’esposizione all’amianto. Nei Criteri di Helsinki del 1997 e del 2014 non è stato fatto alcun tentativo di ripartire i relativi contributi dell’esposizione all’amianto e del fumo di tabacco. Si parla di interazione sinergica fra due agenti quando l’effetto della loro azione congiunta è differente dalla semplice somma delle singole azioni considerate separatamente, ovvero da come sarebbe previsto da un modello additivo di effetto congiunto (nel caso di fumo e asbesto: RR fa ≠ 1+ (RR f -1) + (RR a -1), dove RR fa , RR f , e RR a si riferiscono rispettivamente al rischio relativo in esposti ad asbesto fumatori, fumatori non esposti, e esposti non fumatori, usando i non esposti non fumatori come riferimento). Un caso particolare di interazione è il modello moltiplicativo (RR fa = RR f x RR a ), in cui l’effetto congiunto dei due agenti è pari al prodotto degli effetti di ciascuno di essi. Gli iniziali studi epidemiologici che hanno condotto a ipotizzare una interazione con modello moltiplicativo tra fumo e asbesto erano stati condotti in soggetti con rilevante esposizione lavorativa: i due studi citati, infatti, erano riferiti a una popolazione di coibentisti americani, con livelli di esposizione particolarmente elevati ad amianti di anfibolo. L’interazione fra asbesto e fumo di sigaretta è stata più volte sistematicamente rivalutata sulla base di studi più recenti . I risultati di tali lavori, basati su studi di coorte, sono compatibili con un’interazione intermedia tra il modello additivo e il modello moltiplicativo. D’altra parte una recente analisi pooled di vari studi caso-controllo ha prodotto risultati compatibili con il modello moltiplicativo negli uomini, e intermedi tra il modello additivo e quello moltiplicativo nelle donne. Ai livelli di esposizione considerati significativi per l’associazione causale tra amianto e tumore del polmone, come quelli definiti in precedenza, la presenza di una interazione più che additiva fra abitudine al fumo ed esposizione ad amianto implica l’occorrenza di casi aggiuntivi a causa del sinergismo tra i due fattori. Tumori della laringe IARC 2012: “ Evidenza sufficiente per ritenere l’amianto causalmente associato nell’uomo al tumore della laringe ” Criteri di Helsinki 2014: “ il tumore della laringe deve essere considerato come una patologia causata dall’amianto ” Tumori della faringe IARC 2012: “ Evidenza limitata, negli studi epidemiologici, sulla presenza di una associazione nell’uomo tra esposizione ad amianto e tumore della faringe ” Criteri di Helsinki 2014: Il tumore della faringe non viene discusso Tumori dell’ovaio IARC 2012: “ Evidenza sufficiente per ritenere l’amianto causalmente associato nell’uomo al tumore dell’ovaio ” Criteri di Helsinki 2014: “ Il tumore dell’ovaio deve essere considerato come una patologia causata dall’amianto. Un mesotelioma peritoneale deve essere considerato tra le diagnosi differenziali in un sospetto tumore ovarico in donne che siano state esposte ad amianto, dato che le due condizioni possono presentare similitudini ed essere misclassificate. Sono necessarie ulteriori ricerche per valutare gli specifici tipi istopatologici di tumore ovarico causati dall’esposizione all’amianto ” Tumori dello stomaco IARC 2012: “Evidenza limitata, negli studi epidemiologici, sulla presenza di una associazione nell’uomo tra esposizione ad amianto e tumore dello stomaco” Criteri di Helsinki 2014: “Il tumore dello stomaco non può al momento attuale essere considerato con certezza una patologia causata dall’amianto” Tumori del colon-retto IARC 2012: “Evidenza limitata negli studi epidemiologici per valutare l’associazione nell’uomo tra l’esposizione ad amianto e tumore del colon-retto” Criteri di Helsinki 2014: “Il tumore del colon-retto non può essere al momento attuale considerato con certezza una patologia causata da amianto” Gli studi basati su coorti industriali possono fornire dati di esposizione anche quantitativi e quindi permettere di derivare risultati più affidabili sulla funzione di rischio. Gli studi nella popolazione generale (caso-controllo) non sono basati su misure quantitative di esposizione, ma spesso su informazioni autodichiarate o su matrici: questi studi sono certamente utili per identificare situazioni di rischio difficili da studiare con l’approccio di coorte (per es. i settori informali dell’economia o l’industria edilizia o delle costruzioni), per fornire stime a livello di popolazione generale (per es. di rischio attribuibile) e per il controllo dei fattori di confondimento e modificatori di effetto ma possono presentare maggiori criticità nello studio delle funzioni di rischio e delle relazioni dose-risposta. Il rapporto dell’Health and Safety Executive (HSE) di Doll e Peto ha fornito una preziosa sintesi dei dati disponibili in letteratura sulla funzione di rischio di asbestosi, tumore del polmone e mesotelioma in seguito all’esposizione ad amianto. Molte delle conclusioni di questo rapporto sono state rinforzate dai risultati di studi pubblicati negli ultimi 30 anni e mantengono la loro validità. In alcuni casi, in particolare per ciò che attiene al mesotelioma, per il quale è stato necessario attendere la maturazione delle coorti storiche disponibili, le conclusioni (riconosciute dagli autori come preliminari) sono state integrate da risultati più recenti. • Asbestosi Per quanto riguarda l’asbestosi, esiste un ragionevole consenso che il rischio è funzione dell’esposizione cumulativa al di sopra di 100 ff/cc-anni. La presenza di una soglia di esposizione al di sotto della quale una forma clinicamente rilevante di fibrosi polmonare non sembra essere presente si situa intorno a 25 ff/cc-anni. La funzione dose-risposta nell’intervallo tra 25 e 100 ff/cc-anni è soggetta a incertezza, a causa della possibilità che il riconoscimento dei sintomi e la diagnosi della malattia siano condizionati dalla conoscenza della pregressa esposizione . L’incertezza sulla funzione del rischio è destinata probabilmente a rimanere irrisolta a causa della difficoltà a organizzare nuovi studi reclutando soggetti esposti a livelli superiori a 25 ff/cc-anni. • Tumore del polmone La funzione di rischio per il tumore del polmone, secondo Doll e Peto, sembra essere principalmente determinata in maniera lineare dall’esposizione cumulativa ad amianto, con un coefficiente per l’eccesso di rischio dell’ordine di 0.01 per ff/cc-anni. I risultati di alcune coorti sembrano tuttavia suggerire un effetto più importante della dose rispetto a quello della durata, ed in generale non si nota un aumento del rischio durante i primi 10-20 anni dall’inizio dell’esposizione. Inoltre, in molte coorti, non è stato osservato un aumento al di sotto di un valore di esposizione cumulativa dell’ordine di 25 ff/cc-anni. Lo studio della funzione di rischio per il tumore del polmone è complicato dal ruolo predominante del fumo di tabacco come agente eziologico della malattia e dalla presenza di altri cancerogeni occupazioni o ambientali. In molte coorti occupazionali i dati sul fumo di tabacco erano mancanti o molto limitati e, pertanto, la funzione dose-risposta del rischio da amianto potrebbe essere sovrastimata negli studi che non hanno preso in considerazione le abitudini al fumo. L’esposizione contemporanea o in epoche successive ad amianto e ad altri cancerogeni polmonari potrebbe dar luogo a modificazioni dell’associazione tra esposizione ad amianto e tumore polmonare, in conseguenza dei quali potrebbero essersi determinate, almeno in parte, le differenze tra i risultati osservati in diverse coorti. Un effetto della cessazione dell’esposizione sembra essere presente per quanto riguarda il tumore del polmone, con una diminuzione del rischio dopo 15 o 20 anni dalla cessazione (differenze nell’effetto della cessazione tra coorti diverse possono essere spiegate dalla diversa composizione in tipo di fibra, o dalla presenza di altri cancerogeni). • Mesotelioma I casi che si verificano negli esposti ad amianto sono molto più numerosi rispetto agli attesi, calcolati a partire dai tassi nella popolazione generale, con rischi relativi spesso superiori a 20 volte, e quelli in eccesso costituiscono pertanto la stragrande maggioranza (>95%) e non si sarebbero mai verificati in assenza dell’esposizione ad amianto. Il modello classico della funzione di rischio di mesotelioma in esposti ad amianto è quello proposto da Peto all’inizio degli anni ’80, sulla base dei risultati di una coorte americana di coibentisti e di una coorte inglese di lavoratori tessili . Questo modello identifica il tempo dall’inizio dell’esposizione come determinante dell’incidenza di mesotelioma con una relazione alla terza o quarta potenza, il livello di esposizione con una relazione lineare, e un coefficiente specifico per il tipo di fibra, derivato empiricamente una volta fissati gli altri parametri. Il modello è stato ampliato per comprendere un secondo termine temporale per l’effetto del tempo dalla cessazione dell’esposizione, anch’esso elevato alla terza o quarta potenza: poiché però il tempo dalla cessazione è relativamente breve rispetto al tempo dall’inizio dell’esposizione, il contributo di questo secondo termine nel definire l’incidenza è relativamente modesto. Nel caso di episodi di esposizione a livelli diversi, questi vengono sommati per ricavare la stima complessiva di esposizione. In letteratura, come indicatore di esposizione, sono stati utilizzati o la dose media o la durata di attività o l’esposizione cumulativa. In particolare sono diversificate le interpretazioni su utilizzo e significato dell’indicatore “dose-cumulativa”. Bisogna inoltre tenere presente che il modello di rischio di Peto e colleghi è stato derivato sulla base dei risultati di coorti di lavoratori esposti a dosi molto elevate di amianto, ed errori nella stima del livello dell’esposizione possono aver portato ad una sovrastima dell’importanza dell’effetto della latenza rispetto a quello della dose stessa. Alcuni studi hanno riportato un effetto della durata di esposizione sul rischio di mesotelioma. Tuttavia, una recente analisi combinata di sei coorti occupazionali e di due coorti con alta esposizione ambientale ha mostrato una relazione log-lineare tra incidenza di mesotelioma pleurico e tempo dall’inizio dell’esposizione (almeno per i primi 45/50 anni; l’effetto sembra attenuarsi per periodi più lunghi) e la mancanza di un effetto della durata di esposizione . Analoghi risultati per tempo dall’inizio dell’esposizione emergono da una recente analisi “pooled” di alcune coorti italiane nella quale un eccesso di rischio è presente in tutte le classi di durata, anche se il rischio relativo negli uomini non aumenta dopo i primi 10 anni di durata. Altre interpretazioni suggeriscono che l’incidenza o il rischio relativo per mesotelioma tendano ad aumentare in funzione della durata di esposizione o della dose cumulativa indipendentemente dal tempo dall’inizio dell’esposizione . I risultati di alcune coorti suggeriscono un effetto del tempo dalla cessazione dell’esposizione, con una diminuzione del rischio dopo 20-30 anni; altri risultati tuttavia non sembrano confermare questo fenomeno ; questa discrepanza può essere spiegata dal numero relativamente piccolo di osservazioni con un tempo prolungato dalla cessazione dell’esposizione, dalla presenza di mortalità competitiva o dall’effetto biologico diverso del crisotilo rispetto agli anfiboli. Una recente analisi dei dati internazionali di mortalità per mesotelioma mostra che continua a crescere la mortalità nei gruppi di soggetti che hanno avuto esposizioni in periodi remoti mentre si osserva l’opposto per i soggetti entrati più di recente nel modo del lavoro con conseguente drastica riduzione della probabilità di esposizione . Il minore effetto di misclassificazione che caratterizza le variabili temporali (ed in particolare il tempo dall’inizio della esposizione) rispetto alle variabili che esprimono il livello di esposizione può avere influenzato la formulazione della funzione di rischio. Raccomandazioni La revisione dei risultati disponibili sulla forma della funzione di rischio ha messo in evidenza le carenze della letteratura disponibile. Per aspetti importanti non esistono in letteratura revisioni sistematiche recenti condotte secondo metodologie consolidate: si possono citare in particolare (i) la relazione tra livello di esposizione e rischio di mesotelioma (tenendo conto degli aspetti temporali), (ii) la presenza di interazione tra tipo di fibra e altre variabili di esposizione, in particolare per il mesotelioma, (iii) la quantificazione del rischio di tumore del polmone per esposizioni cumulative inferiori a 100 ff/cc-anni, (iv) la presenza di un pericolo ( hazard ) per altre malattie, quali il tumore del colon-retto. Tali revisioni sistematiche, alcune delle quali già in corso, potrebbero essere condotte da gruppi di lavoro specifici sotto l’egida della SIML. Questo tema ha richiesto un’ampia sezione dedicata in quanto rappresenta, nel nostro Paese, un argomento assai dibattuto in cui, sostanzialmente, si confrontano opinioni opposte. Nella III Conferenza di Consenso sul Mesotelioma, nella sezione dedicata all’Epidemiologia e Sanità Pubblica, è proposto un approfondimento epidemiologico del problema , trattando il tema della anticipazione degli eventi in un paragrafo che ha come titolo “ L’esposizione influisce sulla latenza? ” e che fa parte di un più generale capitolo (“ Relazione temporale fra esposizione ad asbesto e rischio di mesotelioma maligno ”) nel quale vengono discussi diversi elementi della relazione temporale tra esposizione ad amianto e rischio di insorgenza del mesotelioma. Il testo della III Conferenza di Consenso inizia introducendo una distinzione tra anticipazione degli eventi e abbreviazione della latenza: “ Un certo numero di studi sono stati pianificato per fornire una risposta alla seguente domanda: un incremento dell’esposizione determina un’anticipazione dell’occorrenza del mesotelioma maligno o un aumento dell’incidenza fra i soggetti esposti? Comunemente, tuttavia, tale domanda è stata confusa e sostituita che sembra identica ma che, come dimostreremo, è completamente differente: Un aumento dell’esposizione accorcia la latenza?” . Tale distinzione è necessaria perché si tratta di dare risposta a due diverse domande, anche se tali domande sembrano (ma solo in apparenza) riguardare lo stesso tema. Sono di seguito entrambe discusse. Un aumento dell’esposizione accorcia la latenza? Come noto la latenza (convenzionale) è la distanza tra l’inizio dell’esposizione e la data di incidenza (o di morte). Nel caso del mesotelioma la latenza è molto lunga, con una mediana di circa 48 anni e con valori che variano tra 4 e 89 anni : la quota di casi con latenza inferiore a 25 anni, nel registro dei mesoteliomi italiano, è inferiore al 5% . Studiando la latenza media di alcune casistiche qualche autore ha suggerito l’esistenza di una relazione tra esposizione e latenza, supportando l’idea che l’aumento della esposizione avrebbe l’effetto di ridurre la latenza; altri autori (sempre studiando alcune casistiche) hanno invece negato l’esistenza di questa relazione. Entrambe le analisi sono state largamente criticate. La III Italian Consensus Conference on malignant mesothelioma of the pleura , anche alla luce di alcuni articoli comparsi in letteratura (si rimanda in proposito, per i dettagli, alla bibliografia citata nel Documento di Consenso), ha concluso la discussione nel modo che segue: “L’esposizione influenza la latenza? Anche se l’analisi della latenza dei casi di mesotelioma maligno è attraente nell’ottica di una latenza minore per i maggiormente esposti, la stessa è errata in quanto i suoi risultati non dipendono dalla relazione fra esposizione e malattia ma dai termini temporali dell’osservazione … In sintesi: l’idea che l’accelerazione del tempo all’evento possa essere stimata utilizzando la latenza media è forse intuitivamente attraente ma errata. Parimenti è errato inferire che quando non si osserva un cambiamento nella latenza non si verifica un’accelerazione del tempo all’evento ”. In conclusione, lo studio della latenza convenzionale media calcolata nei soli casi (malati o deceduti) non consente di trarre conclusioni sulla presenza/assenza di un effetto di anticipazione. In aggiunta, non ha senso cercare un qualche segno di anticipazione correlando durata di esposizione e latenza in singoli lavoratori: alla motivazione appena citata si aggiunge l’errore insito nel fatto che la durata è una parte della latenza (durata di esposizione + tempo dall’ultima esposizione), quindi una correlazione positiva è attesa su basi puramente matematiche. Un aumento dell’esposizione determina un’anticipazione dell’occorrenza del Mesotelioma Maligno (MM) fra i soggetti esposti così come un aumento dell’incidenza? Come indicato nel titolo del paragrafo è bene evidenziare che il tema della anticipazione dell’occorrenza (non solo nel caso del MM) presenta due aspetti distinti ma complementari: da una parte l’anticipazione dell’evento nel singolo individuo e dall’altra l’anticipazione di una misura di frequenza della malattia nel gruppo di individui. Poiché nel singolo individuo non si può accertare direttamente la presenza di un effetto di anticipazione del momento dell’incidenza della patologia, il tema della anticipazione ha dato luogo sostanzialmente a considerazioni epidemiologico-statistiche. In statistica il concetto di accelerazione (della comparsa della malattia e/o della morte) è abbastanza antico, mentre in ambito epidemiologico questo tema ha ricevuto nuovo impulso nell’ambito della discussione sulla frazione attribuibile (attributable fraction), con lo scopo di considerare tutti i casi di malattia attribuibili a una certa esposizione, non solo i casi in eccesso (cioè che non si sarebbero verificati in assenza di esposizione), ma anche i casi in cui la malattia potrebbe essere stata anticipata dall’esposizione stessa . Semplificando, ed omettendo alcuni passaggi e dettagli tecnici piuttosto complessi, l’idea di fondo dell’accelerazione è quella di esprimere in modo diverso dal rischio relativo l’impatto di una esposizione nociva, ad esempio ai fini di una migliore comunicazione al pubblico. Sono stati quindi introdotti (o ripresi) concetti quali gli “anni di vita persi” (“ years of life lost ”) e i “periodi di anticipazione di rischi e tassi” (“ risk and rate advancement periods ”, RAP), come modi di comunicare con una diversa misura epidemiologica l’impatto di un’esposizione . In quest’ottica, un rischio aumentato (esempio: rischio relativo maggiore di 1) può essere interpretato come un effetto di accelerazione/anticipazione della frequenza di malattia. Il legame, però, tra aspetti individuali ed aspetti epidemiologici nella tematica della anticipazione-accelerazione (come indicato nel titolo del paragrafo) segnala che sono presenti due differenti posizioni: i sostenitori della esistenza di una relazione tra esposizione (ad amianto) ed anticipazione della patologia ed i critici della esistenza di tale relazione. I sostenitori della esistenza di una relazione tra esposizione (ad amianto) ed anticipazione della patologia si appoggiano principalmente a tre considerazioni: da una parte sull’esistenza di una relazione epidemiologica tra incidenza, esposizione e latenza convenzionale; in secondo luogo sull’assunto che tutti i casi tra gli esposti (o più esposti) sono anticipati; ed infine sull’utilizzo dell’approccio proposto nel 2007 da Berry come strumento per calcolare il valore numerico della anticipazione (in media e nei singoli casi). Oltre a quello di Berry sono presenti in letteratura anche altri approcci nel calcolare l’anticipazione. Il testo della III Conferenza di Consenso presenta , con l’aiuto della , gli argomenti che costituiscono il cuore del sostegno alla tesi della esistenza della anticipazione. In sintesi, gli argomenti sono i seguenti: lo stesso tasso di incidenza negli esposti (o nei più esposti) avviene prima (anticipazione) rispetto ai non esposti (o meno esposti); tra gli esposti (o più esposti) ci sono più casi, e tutti i casi negli esposti (o più esposti) sono anticipati; aumento di incidenza e anticipazione di incidenza sono indistinguibili, e sono presenti contemporaneamente; un aumento di incidenza implica necessariamente una anticipazione degli eventi che può essere calcolata. I critici della esistenza di una relazione tra esposizione (ad amianto) ed anticipazione della patologia si appoggiano alle seguenti considerazioni. Aumento di incidenza e anticipazione di incidenza sono indistinguibili; si tratta di due letture alternative (e non utilizzabili contemporaneamente) per rappresentare l’effetto di una esposizione nociva. Sta al ricercatore scegliere quale strada sia preferibile per descrivere l’effetto della esposizione (o della maggiore esposizione), ben sapendo che con l’epidemiologia non si potrà distinguere tra le due spiegazioni; Dai dati epidemiologici non è dato conoscere cosa succede ai singoli casi, che potrebbero essere anticipati, non anticipati o anticipati solo in parte; La dimostrazione della esistenza di anticipazione nei casi richiede la postulazione di una teoria biologica a supporto: nel caso del mesotelioma una tale teoria non esiste; Il modello di Berry (e così le altre proposte di letteratura) non dimostrano l’esistenza della anticipazione, ma assumono che l’anticipazione sia presente per tutti i casi e ne calcolano un valore medio; La anticipazione della incidenza non implica una accelerazione del processo biologico. Le argomentazioni dei critici si trovano, in particolare, nei lavori di Zocchetti (e nel dibattito che ne è seguito . Come noto la latenza (convenzionale) è la distanza tra l’inizio dell’esposizione e la data di incidenza (o di morte). Nel caso del mesotelioma la latenza è molto lunga, con una mediana di circa 48 anni e con valori che variano tra 4 e 89 anni : la quota di casi con latenza inferiore a 25 anni, nel registro dei mesoteliomi italiano, è inferiore al 5% . Studiando la latenza media di alcune casistiche qualche autore ha suggerito l’esistenza di una relazione tra esposizione e latenza, supportando l’idea che l’aumento della esposizione avrebbe l’effetto di ridurre la latenza; altri autori (sempre studiando alcune casistiche) hanno invece negato l’esistenza di questa relazione. Entrambe le analisi sono state largamente criticate. La III Italian Consensus Conference on malignant mesothelioma of the pleura , anche alla luce di alcuni articoli comparsi in letteratura (si rimanda in proposito, per i dettagli, alla bibliografia citata nel Documento di Consenso), ha concluso la discussione nel modo che segue: “L’esposizione influenza la latenza? Anche se l’analisi della latenza dei casi di mesotelioma maligno è attraente nell’ottica di una latenza minore per i maggiormente esposti, la stessa è errata in quanto i suoi risultati non dipendono dalla relazione fra esposizione e malattia ma dai termini temporali dell’osservazione … In sintesi: l’idea che l’accelerazione del tempo all’evento possa essere stimata utilizzando la latenza media è forse intuitivamente attraente ma errata. Parimenti è errato inferire che quando non si osserva un cambiamento nella latenza non si verifica un’accelerazione del tempo all’evento ”. In conclusione, lo studio della latenza convenzionale media calcolata nei soli casi (malati o deceduti) non consente di trarre conclusioni sulla presenza/assenza di un effetto di anticipazione. In aggiunta, non ha senso cercare un qualche segno di anticipazione correlando durata di esposizione e latenza in singoli lavoratori: alla motivazione appena citata si aggiunge l’errore insito nel fatto che la durata è una parte della latenza (durata di esposizione + tempo dall’ultima esposizione), quindi una correlazione positiva è attesa su basi puramente matematiche. Come indicato nel titolo del paragrafo è bene evidenziare che il tema della anticipazione dell’occorrenza (non solo nel caso del MM) presenta due aspetti distinti ma complementari: da una parte l’anticipazione dell’evento nel singolo individuo e dall’altra l’anticipazione di una misura di frequenza della malattia nel gruppo di individui. Poiché nel singolo individuo non si può accertare direttamente la presenza di un effetto di anticipazione del momento dell’incidenza della patologia, il tema della anticipazione ha dato luogo sostanzialmente a considerazioni epidemiologico-statistiche. In statistica il concetto di accelerazione (della comparsa della malattia e/o della morte) è abbastanza antico, mentre in ambito epidemiologico questo tema ha ricevuto nuovo impulso nell’ambito della discussione sulla frazione attribuibile (attributable fraction), con lo scopo di considerare tutti i casi di malattia attribuibili a una certa esposizione, non solo i casi in eccesso (cioè che non si sarebbero verificati in assenza di esposizione), ma anche i casi in cui la malattia potrebbe essere stata anticipata dall’esposizione stessa . Semplificando, ed omettendo alcuni passaggi e dettagli tecnici piuttosto complessi, l’idea di fondo dell’accelerazione è quella di esprimere in modo diverso dal rischio relativo l’impatto di una esposizione nociva, ad esempio ai fini di una migliore comunicazione al pubblico. Sono stati quindi introdotti (o ripresi) concetti quali gli “anni di vita persi” (“ years of life lost ”) e i “periodi di anticipazione di rischi e tassi” (“ risk and rate advancement periods ”, RAP), come modi di comunicare con una diversa misura epidemiologica l’impatto di un’esposizione . In quest’ottica, un rischio aumentato (esempio: rischio relativo maggiore di 1) può essere interpretato come un effetto di accelerazione/anticipazione della frequenza di malattia. Il legame, però, tra aspetti individuali ed aspetti epidemiologici nella tematica della anticipazione-accelerazione (come indicato nel titolo del paragrafo) segnala che sono presenti due differenti posizioni: i sostenitori della esistenza di una relazione tra esposizione (ad amianto) ed anticipazione della patologia ed i critici della esistenza di tale relazione. I sostenitori della esistenza di una relazione tra esposizione (ad amianto) ed anticipazione della patologia si appoggiano principalmente a tre considerazioni: da una parte sull’esistenza di una relazione epidemiologica tra incidenza, esposizione e latenza convenzionale; in secondo luogo sull’assunto che tutti i casi tra gli esposti (o più esposti) sono anticipati; ed infine sull’utilizzo dell’approccio proposto nel 2007 da Berry come strumento per calcolare il valore numerico della anticipazione (in media e nei singoli casi). Oltre a quello di Berry sono presenti in letteratura anche altri approcci nel calcolare l’anticipazione. Il testo della III Conferenza di Consenso presenta , con l’aiuto della , gli argomenti che costituiscono il cuore del sostegno alla tesi della esistenza della anticipazione. In sintesi, gli argomenti sono i seguenti: lo stesso tasso di incidenza negli esposti (o nei più esposti) avviene prima (anticipazione) rispetto ai non esposti (o meno esposti); tra gli esposti (o più esposti) ci sono più casi, e tutti i casi negli esposti (o più esposti) sono anticipati; aumento di incidenza e anticipazione di incidenza sono indistinguibili, e sono presenti contemporaneamente; un aumento di incidenza implica necessariamente una anticipazione degli eventi che può essere calcolata. I critici della esistenza di una relazione tra esposizione (ad amianto) ed anticipazione della patologia si appoggiano alle seguenti considerazioni. Aumento di incidenza e anticipazione di incidenza sono indistinguibili; si tratta di due letture alternative (e non utilizzabili contemporaneamente) per rappresentare l’effetto di una esposizione nociva. Sta al ricercatore scegliere quale strada sia preferibile per descrivere l’effetto della esposizione (o della maggiore esposizione), ben sapendo che con l’epidemiologia non si potrà distinguere tra le due spiegazioni; Dai dati epidemiologici non è dato conoscere cosa succede ai singoli casi, che potrebbero essere anticipati, non anticipati o anticipati solo in parte; La dimostrazione della esistenza di anticipazione nei casi richiede la postulazione di una teoria biologica a supporto: nel caso del mesotelioma una tale teoria non esiste; Il modello di Berry (e così le altre proposte di letteratura) non dimostrano l’esistenza della anticipazione, ma assumono che l’anticipazione sia presente per tutti i casi e ne calcolano un valore medio; La anticipazione della incidenza non implica una accelerazione del processo biologico. Le argomentazioni dei critici si trovano, in particolare, nei lavori di Zocchetti (e nel dibattito che ne è seguito . Nell’ambito del programma di attività del Centro Nazionale per la Prevenzione ed il Controllo delle Malattie (CCM) del Ministero della Salute, per l’anno 2012, è stato individuato un ambito operativo inerente l’area “ Sostegno alle Regioni per l’implementazione del Piano nazionale della Prevenzione e di Guadagnare Salute ”. In tale ambito è stato approvato dai comitati CCM un progetto denominato “ Sperimentazione e validazione di un protocollo di sorveglianza sanitaria dei lavoratori ex-esposti ad amianto, ai sensi dell’art. 259, DLgs 81/08 ”, che ha come obiettivo quello di definire una proposta di protocollo di sorveglianza sanitaria dei lavoratori ex-esposti ad amianto. Sono state coinvolte 19 Regioni italiane, il dipartimento di Medicina del Lavoro dell’INAIL ed il Dipartimento di Scienze Cardiologiche, Toraciche e Vascolari dell’Università di Padova. Sono stati valutati dati provenienti da 14 Regioni; ognuna ha trasmesso informazioni in merito a nuovi atti o delibere, dati epidemiologici, azioni intraprese e protocolli sanitari in essere che hanno riguardato circa 25.000 soggetti ex esposti ad amianto. La Conferenza Permanente per i Rapporti tra lo Stato, le Regioni e le Province Autonome di Trento e Bolzano, sulla base delle esperienze maturate a livello regionale (in particolare Friuli Venezia Giulia e Toscana) ha recentemente (22/2/2018) approvato le procedure da attivare per il controllo dei lavoratori ex esposti all’amianto . Tale protocollo, da applicarsi in esenzione di spesa, è articolato in due fasi. La prima, di controllo generale, consta di: a) anamnesi fisiologica, familiare, patologica prossima e remota, finalizzata a raccogliere informazioni su altri possibili fattori di rischio, occupazionali e non; b) anamnesi lavorativa: per ricostruire l’esposizione lavorativa e ottenere la massima integrazione delle informazioni disponibili, tali da permettere un’adeguata valutazione del livello di esposizione realizzatasi nel corso dell’attività lavorativa; c) visita medica ed esame clinico con particolare riguardo all’apparato respiratorio; d) esame spirometrico basale; e) accertamento radiologico (Rx torace refertato, preferibilmente accompagnato da lettura e classificazione ILO-BIT eseguita da un B-reader), se non effettuato negli ultimi tre anni o non leggibile per la classificazione ILO-BIT o se giustificato in relazione al sospetto Le attività c), d) ed e) vengono offerte solo qualora venga accertato lo stato di ex esposto in sede di anamnesi e vengono ripetute, con cadenza almeno triennale fino a 30 anni dalla cessazione dell’esposizione per i soggetti negativi dal punto di vista strumentale o con placche pleuriche minime. I soggetti affetti da asbestosi o placche pleuriche diffuse devono effettuare con cadenza annuale controlli radiografici e DLCO. La visita medica deve completarsi con attività di Counseling breve per la riduzione dei rischi da esposizioni occupazionali e voluttuarie (fumo), fornendo informazioni sulle patologie legate all’esposizione ad asbesto e sull’opportunità di sospendere l’esposizione a polveri o irritanti delle vie respiratorie, sull’importanza di stili di vita salutari e, in particolare, ai soggetti con asbestosi, sull’importanza di sottoporsi a vaccinazione contro l’influenza e lo pneumococco. In relazione ai riscontri emersi nella prima fase e alla necessità di approfondimento diagnostico di sospetta patologia amianto correlata, sono effettuati ulteriori esami, che devono poter essere eseguiti con percorsi di accettazione facilitati, sempre in esenzione di spesa per l’interessato, adottando il follow up previsto per la specifica malattia quali: f) l’esame della diffusione alveolo-capillare del CO, ove si sospetti la sussistenza di danni a carico della membrana alveolo capillare; g) visite specialistiche (pneumologica, chirurgica, oncologica) o accertamenti radiologici (TAC, PET-TC, Eco addome ecc.) se giustificati da una precisa indicazione clinica (sintomi e/o obiettività positiva per problemi amianto correlati a carico dell’apparato respiratorio o di altri organi o apparati) e dalle evidenze di esposizione emerse dall’analisi dell’anamnesi occupazionale. Secondo quanto indicato nel documento della Conferenza Stato Regioni i contenuti del protocollo sono da intendersi quali contenuti minimi e da riferirsi allo stato attuale delle conoscenze: qualora si rendessero disponibili nuove evidenze scientifiche a livello nazionale e internazionale è prevista la revisione delle procedure previste. L’organizzazione di una campagna di sorveglianza sanitaria rivolta agli ex esposti ad amianto presenta numerose criticità. Un aspetto cruciale è senz’altro rappresentato dalla metodologia di identificazione degli ex esposti in quanto non sembrerebbe necessario applicare una qualsiasi forma di sorveglianza indiscriminata a chi non ha conosciuto esposizioni professionali rilevanti ma che abbia ottenuto comunque, in qualche modo, il riconoscimento dei benefici previdenziali ex 257/92. Infatti, a fronte di una previsione di un numero massimo di circa 30000 lavoratori che avrebbero potuto accedere al beneficio previdenziale in quanto venivano dismesse le attività del settore, alla data del 15/6/05 le domande erano 607764 e si è registrato, negli anni sucessivi, un progressivo incremento del numero dei soggetti che hanno ottenuto tali benefici. Fra gli approcci proposti in Italia può essere preso in considerazione quello che compare nel “ Documento Programmatico di proposta di un protocollo di sorveglianza sanitaria dei lavoratori ex esposti ad amianto, ai sensi dell’art. 259 D.Lgs 81/08 ” prodotto dalla Regione Veneto nell’ambito del “ Progetto CCM. Sperimentazione e validazione di un protocollo di sorveglianza sanitaria dei lavoratori ex esposti ad amianto, ai sensi dell’art. 259 D.Lgs 81/08 ”, in cui l’assistenza di primo livello “ È offerta al lavoratore che viene valutato ex esposto avvalendosi sia dei codici ATECO dell’azienda presso cui il lavoratore ha svolto la sua attività, sia dei dati forniti dal rapporto ReNaM per valutare possibili attività svolte che presentano, anche se solo a livello territoriale, alta incidenza di mesoteliomi ”. Va quindi considerato che: le esposizioni del passato nel settore amiantiero vero e proprio sono cessate dal 1992 e che, dalla fine degli anni ‘70, le esposizioni, anche in quegli ambienti di lavoro, si erano significativamente ridotte; l’utilizzo di materiali contenenti amianto è comunque cessato per effetto della Legge 257/92, pur se non in maniera rapida ed uniforme su tutto il territorio nazionale (a causa dello smaltimento delle scorte di magazzino concesso dalla norma); un’esposizione a 100 ff/l protratta oltre un arco temporale di 10 anni, non comporta ipotesi di rischio per la patologia che maggiormente si gioverebbe di campagne di screening , ossia il tumore del polmone. Un secondo aspetto critico è rappresentato dall’utilità di predisporre una sorveglianza sanitaria anche per soggetti che, pur avendo lavorato in condizioni di esposizione rilevante nel passato, non possano giovarsi oggi degli effetti predittivi di campagne di screening a causa di un’età ormai molto avanzata, che non consentirebbe approcci aggressivi di tipo chirurgico per il miglioramento prognostico di patologie neoplastiche del polmone: le campagne di screening per le neoplasie polmonari non coinvolgono, di regola, soggetti con oltre 75 anni di età e, comunque, non oltre gli 80 anni . In parziale contrasto con tale assunto è la posizione della Consensus Conference di Helsinki che afferma “ Noi proponiamo che il follow-up dei lavoratori altamente esposti ad amianto debba essere proseguita per almeno 30 anni dal termine dell’esposizione ”, affermazione che pone l’accento sulla obbligatorietà di definire l’entità dell’esposizione, riservando di fatto la sorveglianza sanitaria ai lavoratori “altamente esposti” e non indiscriminatamente a tutti (e si ritorna quindi al primo problema esposto in precedenza). In tema di screening del cancro polmonare negli ex esposti ad amianto il Protocollo di sorveglianza sanitaria approvato dalla Conferenza Stato-Regioni afferma “ Ad oggi non esistono programmi validati di screening/diagnosi precoce del polmone a cui far afferire i soggetti ex-esposti ad amianto sottoposti a sorveglianza sanitaria. Qualora screening per il tumore del polmone fossero resi disponibili nell’ambito del SSN sarà valutata l’eleggibilità di adulti con esposizione all’amianto per tali screening ”. Sul punto, l’aggiornamento 2014 del Consensus di Helsinki conclude “ Ad oggi vi è limitata evidenza relativamente alla stima del rischio ed allo screening con LDCT nei lavoratori ad alto rischio di tumore del polmone a causa dell’esposizione all’amianto, fumatori o meno. Tuttavia, sulla base dei risultati favorevoli degli studi relativi allo screening con LDCT sul tumore del polmone, del rischio dose-risposta di tumore del polmone negli esposti ad amianto, e della relazione ben documentata di un contributo più che additivo al rischio negli adulti esposti sia all’amianto, sia al fumo di tabacco, è ragionevole raccomandare che gli adulti esposti ad amianto debbano essere sottoposti a screening per il tumore del polmone. I soggetti con precedente esposizione ad amianto che si trovano in ragionevolmente buona salute e che si trovano al livello o sopra il livello di rischio per l’ammissione allo studio NLST per quanto riguarda la storia tabagica [ndr: età 55-74, almeno 30 pacchi/anni di abitudine tabagica in fumatori o ex fumatori da meno di 15 anni] , con la combinazione dell’esposizione all’amianto e l’abitudine tabagica o con la sola esposizione all’asbesto, dovrebbero essere presi in considerazione per lo screening del cancro polmonare ”. Pertanto, riassumendo, un approccio logico potrebbe essere, almeno per quanto riguarda lo specifico obiettivo di ridurre la mortalità per cancro polmonare negli ex esposti ad amianto, quello di avviare a specifici programmi di sorveglianza sanitaria i soggetti con esposizioni medio alte (in altri termini, con esposizioni iniziate prima del 1985) e con età non superiore ai 75 anni. Considerati i diversi approcci proposti dalle Regioni ed il recente documento della Conferenza Stato-Regioni, SIML auspicherebbe la promulgazione di una legge nazionale di indirizzo che consenta di uniformare protocolli e procedure ed avviare al contempo una valutazione epidemiologica sui soggetti arruolati in campagne di sorveglianza al fine di valutare ad oggi l’efficacia di un approccio indiscriminato e comprendere se esistono differenze negli outcome a seconda del settore di attività di provenienza. Nella eventuale stesura di tale provvedimento sarebbe auspicabile l’attività di consulenza diretta da parte della SIML. Si tratta, sostanzialmente, degli addetti alla bonifica ed alla rimozione dell’asbesto. Anche in questo caso sono stati proposti diversi protocolli operativi, peraltro, tutti molto simili fra loro. A titolo di esempio, si riporta in quello previsto dalla Regione Friuli Venezia Giulia nel 2013 . Dichiarazioni conflitto di interesse PA dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico di parti, in procedimenti civili e penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. PB dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico di parti, in procedimenti penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. MB dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura o di parti, in procedimenti civili e penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. PLC dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura o di parti, in procedimenti penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. DC dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura, in procedimenti penali aventi per oggetto esposizioni ad amianto e patologie a queste attribuite. AC dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura o di parti, in procedimenti civili e penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. GD dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura inquirente e di Collegi di difesa, in procedimenti civili e penali aventi per oggetto effetti sulla salute da esposizione ad amianto. AF dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico delle parti, in procedimenti penali aventi per oggetto neoplasie di sospetta origine occupazionale o ambientale. MM dichiara di non avere conflitti d’interesse in relazione a consulenze tecniche o di altro genere su lavoratori o pazienti con esposizioni ad amianto o patologie a queste attribuite. SM dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura o di parti, in procedimenti civili e penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. EP Dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura giudicante ed inquirente, di Collegi di difesa e di Parti Civili, in procedimenti civili e penali aventi per oggetto effetti sulla salute da esposizione ad amianto. LS dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura o di parti, in procedimenti civili e penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. GT dichiara di non avere conflitti d’interesse in relazione a consulenze tecniche o di altro genere su lavoratori o pazienti con esposizioni ad amianto o patologie a queste attribuite. FSV dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura o di parti, in procedimenti civili e penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. CZ dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico di parti, in procedimenti penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. Fonti di finanziamento: non finanziamenti dedicati PA dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico di parti, in procedimenti civili e penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. PB dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico di parti, in procedimenti penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. MB dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura o di parti, in procedimenti civili e penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. PLC dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura o di parti, in procedimenti penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. DC dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura, in procedimenti penali aventi per oggetto esposizioni ad amianto e patologie a queste attribuite. AC dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura o di parti, in procedimenti civili e penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. GD dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura inquirente e di Collegi di difesa, in procedimenti civili e penali aventi per oggetto effetti sulla salute da esposizione ad amianto. AF dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico delle parti, in procedimenti penali aventi per oggetto neoplasie di sospetta origine occupazionale o ambientale. MM dichiara di non avere conflitti d’interesse in relazione a consulenze tecniche o di altro genere su lavoratori o pazienti con esposizioni ad amianto o patologie a queste attribuite. SM dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura o di parti, in procedimenti civili e penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. EP Dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura giudicante ed inquirente, di Collegi di difesa e di Parti Civili, in procedimenti civili e penali aventi per oggetto effetti sulla salute da esposizione ad amianto. LS dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura o di parti, in procedimenti civili e penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. GT dichiara di non avere conflitti d’interesse in relazione a consulenze tecniche o di altro genere su lavoratori o pazienti con esposizioni ad amianto o patologie a queste attribuite. FSV dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico della magistratura o di parti, in procedimenti civili e penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. CZ dichiara di aver svolto attività di consulenza tecnica in giudizio, su incarico di parti, in procedimenti penali aventi per oggetto esposizioni ad amianto o patologie a queste attribuite. Fonti di finanziamento: non finanziamenti dedicati |
Digital pathology and artificial intelligence in translational medicine and clinical practice | 13a17527-dd16-48ce-baa6-4e8a51cac46a | 8491759 | Pathology[mh] | Pathology has historically played a crucial role in the drug development process, including preclinical research to facilitate target identification, define drug mechanism of action and pharmacodynamics, and enable toxicology assessments , . More recently, pathology has formed a bridge between drug discovery, translational, and clinical research programs that are striving to decipher disease pathophysiology in the context of the mechanism of action, patient selection, or patient stratification (Fig. ) , . Such insights form the basis of novel hypotheses that can further be explored in drug discovery programs or applied to inform clinical trial design, thereby improving the probability of technical and regulatory success. Pathology-based assessments have been used to classify disease and determine efficacy in drug development across a variety of disease areas – . For example, during phase 2 trials for drug development in non-alcoholic steatohepatitis, the US Food and Drug Administration (FDA) considers evidence of efficacy on a histological endpoint to support initiation of phase 3 trials . Additionally, pathological complete response (pCR) has been studied as a surrogate endpoint in patients with cancer for the prediction of long-term clinical benefit and favorable prognosis with the administration of neoadjuvant therapy – . More recently, pCR was associated with improved long-term efficacy in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer treated with chemotherapy plus either intravenous or subcutaneous trastuzumab . In the immuno-oncology (I-O) arena, immune-related pathologic response criteria have been applied retrospectively to surgical specimens from patients treated with immunotherapy in the neoadjuvant or advanced disease setting to predict survival in several tumor types , . Immunohistochemistry (IHC) has been used to characterize biomarkers, such as programmed cell death-ligand 1 (PD-L1), and their association with clinical benefit. Traditional pathology techniques present several advantages, such as low cost, widespread availability, and application on formalin-fixed, paraffin-embedded (FFPE) tissue samples , but challenges pertaining to differences in laboratory methods and subjective interpretation, particularly with the evaluation of immune cell staining, may lead to inter-observer variability . This can produce inconsistency in diagnoses, which may impact treatment decisions – . While the use of IHC assays has led to better identification of patients who respond to I-O therapy – , there remains a need to more accurately quantify complex immune markers, including cell phenotypes in a spatial context, that require advanced quantitative tools to maximize the amount of information yielded from individual samples , . Artificial intelligence (AI) applications in pathology improve quantitative accuracy and enable the geographical contextualization of data using spatial algorithms. Adding spatial metrics to IHC can improve the clinical value of biomarker identification approaches. For example, in a recent meta-analysis, the addition of spatial context to IHC, achieved using multiplex IHC and immunofluorescence (IF), was significantly better at predicting objective response to immune checkpoint inhibitors (ICIs) compared with gene expression profiling (GEP) or IHC alone , indicating the need for more complex computational approaches to decipher the underlying biology and enhance clinical utility. The development and integration of digital pathology and AI–based approaches provide substantive advantages over traditional methods, such as enabling spatial analysis while generating highly precise, unbiased, and consistent readouts that can be accessed remotely by pathologists . Efforts to overcome some of the challenges seen with traditional pathology methods have led to the development and adoption of complex, novel imaging systems and whole slide image (WSI) scanners that have enabled the transition of pathology into the digital era, also known as digital pathology. Within minutes, WSI scanners capture multiple images of entire tissue sections on the slide, which are digitally stitched together to generate a WSI that can be reviewed by a pathologist on a computer monitor (Fig. ) , . Two scanners, Philips IntelliSite Pathology Solution (PIPS) (Philips, Amsterdam, Netherlands) and Leica Aperio AT2 DX System (Leica Biosystems, Buffalo Grove, Illinois, USA), are approved by the FDA for review and interpretation of digital surgical pathology slides prepared from biopsied tissue , . There are many practical advantages to using these digital pathology image systems and solutions that would bring substantial benefits to translational and clinical research. These include the organization and storage of large amounts of data in a centralized location, integration of digital workflow software to help streamline processes and improve efficiency, convenient sharing of image data to enable cross-specialty worldwide remote communication, reduced testing turnaround time, and the generation of precise and highly reproducible tissue-derived readouts reducing inter-pathologist variability , – . The increased speed and efficiency gained in image acquisition can enhance the downstream utilization options of traditional techniques such as hematoxylin and eosin (H&E), IHC, and in situ hybridization. These slides can be converted into a remotely available image within minutes and centrally reviewed by multiple pathologists from various sites , with applications including education, research, consultation, and diagnostics . Recently, due to ongoing disruptions in relation to the COVID-19 pandemic, including remote working and restricted travel, digital pathology has been crucial in the continuation of clinical and academic research, as well as routine pathology services . Without the need to transport glass slides and the ensuing logistical and safety concerns, central pathology review enables secure remote working . Additionally, the utilization of digital images allows the generation of pixel-level pattern information, leading to expanded use of computational approaches that enable a quantitative analysis of WSIs , . Improvements gained from digital pathology: quantitative analysis of the WSI The use of digital image analysis in pathology can identify and quantify specific cell types quickly and accurately and can quantitatively evaluate histological features, morphological patterns, and biologically relevant regions of interest (e.g., tumoral or peritumoral areas, relationships between different immune cell populations, areas of expression, presence of metastasis) , . Quantitative image analysis tools also enable the capturing of data from tissue slides that may not be accessible during manual assessment via routine microscopy. Additionally, performing similar tasks manually can require significant time investment and can be prone to human error, such as counting fatigue , . Expanding data capabilities: multiplex and multispectral imaging Quantitative image analysis can also be used to generate high-content data through application to a technique known as multiplexing, which allows co-expression and co-localization analysis of multiple markers in situ with respect to the complex spatial context of tissue regions, including the stroma, tumor parenchyma, and invasive margin , . Current imaging metrics can utilize multispectral unmixing strategies to reveal co-expression patterns that define unique cell phenotypes and spatial relationships (Fig. ) . Automated classification of epithelial and immune cells and simultaneous marker analysis at the single-cell level has been conducted using prostate cancer, pancreatic adenocarcinoma, and melanoma tissue samples , , . Application of this technique allowed identification of distinct T-cell populations and their spatial distributions and underscored the potential of immune markers to identify patients who may benefit from immunotherapy , . While a highly multiplexed imaging platform can be used to understand intra- and inter-cellular signaling pathways by examining how phenotypically distinct cell populations are spatially distributed relative to one another, it is a time-consuming process applicable to a predefined region of interest . However, as technology quickly advances, allowing digital evaluation of entire tissue slides, we are no longer confined to a region of interest , . The wealth of new information provided by these techniques has created a need for more consistent and reproducible interpretation of large and complex datasets, along with defining the interaction patterns between cell types and spatial context found in pathological images that define biological underpinnings , , . Advances in computational approaches: AI and machine learning The need for data reproducibility and the increasing complexity of the analyses described above has led to the application of AI in pathology , , . AI refers to a broad scientific discipline that involves using algorithms to train machines to extract information or features beyond human visual perception , , . AI approaches are built to initially extract appropriate image representations and then to train a machine classifier for a particular segmentation, diagnostic, or prognostic task using a supervised or unsupervised approach , , . The power of AI to analyze large amounts of data quickly can significantly speed up the discovery of novel histopathology features that may aid our understanding of or ability to predict how a patient’s disease will progress and how the patient will likely respond to a specific treatment , , . In breast cancer, for example, unsupervised learning models have been used to generate histologic scores that can differentiate between low- and high-grade tumors and evaluate prognostically relevant morphological features from the epithelium and stroma of tissue samples to provide a score associated with the probability of overall survival , . The success of these AI-based approaches relies on the quality and quantity of the data used to train the algorithm, limiting the generalizability of these image analysis algorithms to larger or more complex datasets . Taking it further: deep learning networks Deep learning takes machine learning a step further, using sophisticated, multilevel deep or convolutional neural networks (DNN or CNN) to create systems that perform feature classification from large datasets , , , . Figure highlights key differences between machine learning and deep learning. The impact of applications of deep learning algorithms to IHC- and H&E-stained specimens have been well documented across many tumor types. These include grading prostate cancer , identifying biomarkers for disease-specific survival in early-stage melanoma , detection of invasive breast cancer regions on WSIs , , predicting response to chemoradiotherapy in locally advanced rectal cancer , and identifying morphological features (nuclear shape, nuclear orientation, texture, tumor architecture, etc.) to predict recurrence in early-stage non-small cell lung cancer (NSCLC) from H&E slides . Deep learning has also been used to construct entity-graph-based tissue representations, where cell morphology and topology are embedded within each node to effectively describe the phenotypical and structural properties of tissues and can be processed by graph neural networks (GNNs). GNNs therefore enhance the interpretability of pathological assessments gleaned from neural networks , . It is important to compare AI-based interpretations with those of the pathologist to define the associated algorithm’s performance characteristics and utility. For example, when a CNN trained to classify melanoma samples was compared against manual scoring by histopathologists, the CNN was significantly superior in classifying images as malignant melanoma or benign nevi compared with manual assessment by histopathologists . In the CAMELYON16 challenge, deep learning algorithms to detect breast cancer metastases in H&E-stained WSIs of lymph node sections performed similarly to the best performing pathologists under time constraints in detecting macrometastases and were better in detecting micrometastases . However, it should be noted that the performance of any algorithm will depend on the task, due to the degree of accuracy required and the quality of the samples to be assessed . Another application of machine learning in the preclinical space is the assessment of tumor purity (TP). TP estimation, currently evaluated visually by pathologists, is used to ensure a signal is derived from cancer cells rather than other noncancerous cells that may be present in the TME based on tissue morphology when tissue is used to generate orthogonal data such as transcriptome or exome , . In a comparison of TP determined using AI (using deep learning algorithms generated on the PathAI platform) and manual estimates by pathologists, AI-assessed TP was found to be more accurate than visual assessment by pathologists . Previous evidence has shown that immunosuppressive pathways are upregulated in patients with low TP, suggesting that low TP is associated with poor prognosis in some tumor types, including gastric cancer. Therefore, improved methods of evaluating TP may also aid in the identification of patients who may be suitable for immunotherapy . Given the amount of additional detail and insights that can be gained from combining WSI with machine learning algorithms, this technology can be readily applied to translational research. However, one major limitation of machine learning is the large amount of high-quality data required to develop these algorithms . Data used for training need to be accurate and as complete as possible in order to maximize predictability and utility . This can be challenging when histological data are obtained from various laboratories, leading to some variability due to factors such as differences in slide preparation (sectioning, fixation, staining, and mounting) , scoring algorithms , and inherent inter-observer variability . These challenges become more apparent when more complex computational analytics methods are used for multiplexed imaging. Although AI could be used to overcome inter-reader variability across multiple institutions with the development of robust algorithms that take specific histological features of various tumors and subtypes into account , further research is needed to fully understand the impact of these factors on the quality of AI data. The use of digital image analysis in pathology can identify and quantify specific cell types quickly and accurately and can quantitatively evaluate histological features, morphological patterns, and biologically relevant regions of interest (e.g., tumoral or peritumoral areas, relationships between different immune cell populations, areas of expression, presence of metastasis) , . Quantitative image analysis tools also enable the capturing of data from tissue slides that may not be accessible during manual assessment via routine microscopy. Additionally, performing similar tasks manually can require significant time investment and can be prone to human error, such as counting fatigue , . Quantitative image analysis can also be used to generate high-content data through application to a technique known as multiplexing, which allows co-expression and co-localization analysis of multiple markers in situ with respect to the complex spatial context of tissue regions, including the stroma, tumor parenchyma, and invasive margin , . Current imaging metrics can utilize multispectral unmixing strategies to reveal co-expression patterns that define unique cell phenotypes and spatial relationships (Fig. ) . Automated classification of epithelial and immune cells and simultaneous marker analysis at the single-cell level has been conducted using prostate cancer, pancreatic adenocarcinoma, and melanoma tissue samples , , . Application of this technique allowed identification of distinct T-cell populations and their spatial distributions and underscored the potential of immune markers to identify patients who may benefit from immunotherapy , . While a highly multiplexed imaging platform can be used to understand intra- and inter-cellular signaling pathways by examining how phenotypically distinct cell populations are spatially distributed relative to one another, it is a time-consuming process applicable to a predefined region of interest . However, as technology quickly advances, allowing digital evaluation of entire tissue slides, we are no longer confined to a region of interest , . The wealth of new information provided by these techniques has created a need for more consistent and reproducible interpretation of large and complex datasets, along with defining the interaction patterns between cell types and spatial context found in pathological images that define biological underpinnings , , . The need for data reproducibility and the increasing complexity of the analyses described above has led to the application of AI in pathology , , . AI refers to a broad scientific discipline that involves using algorithms to train machines to extract information or features beyond human visual perception , , . AI approaches are built to initially extract appropriate image representations and then to train a machine classifier for a particular segmentation, diagnostic, or prognostic task using a supervised or unsupervised approach , , . The power of AI to analyze large amounts of data quickly can significantly speed up the discovery of novel histopathology features that may aid our understanding of or ability to predict how a patient’s disease will progress and how the patient will likely respond to a specific treatment , , . In breast cancer, for example, unsupervised learning models have been used to generate histologic scores that can differentiate between low- and high-grade tumors and evaluate prognostically relevant morphological features from the epithelium and stroma of tissue samples to provide a score associated with the probability of overall survival , . The success of these AI-based approaches relies on the quality and quantity of the data used to train the algorithm, limiting the generalizability of these image analysis algorithms to larger or more complex datasets . Taking it further: deep learning networks Deep learning takes machine learning a step further, using sophisticated, multilevel deep or convolutional neural networks (DNN or CNN) to create systems that perform feature classification from large datasets , , , . Figure highlights key differences between machine learning and deep learning. The impact of applications of deep learning algorithms to IHC- and H&E-stained specimens have been well documented across many tumor types. These include grading prostate cancer , identifying biomarkers for disease-specific survival in early-stage melanoma , detection of invasive breast cancer regions on WSIs , , predicting response to chemoradiotherapy in locally advanced rectal cancer , and identifying morphological features (nuclear shape, nuclear orientation, texture, tumor architecture, etc.) to predict recurrence in early-stage non-small cell lung cancer (NSCLC) from H&E slides . Deep learning has also been used to construct entity-graph-based tissue representations, where cell morphology and topology are embedded within each node to effectively describe the phenotypical and structural properties of tissues and can be processed by graph neural networks (GNNs). GNNs therefore enhance the interpretability of pathological assessments gleaned from neural networks , . It is important to compare AI-based interpretations with those of the pathologist to define the associated algorithm’s performance characteristics and utility. For example, when a CNN trained to classify melanoma samples was compared against manual scoring by histopathologists, the CNN was significantly superior in classifying images as malignant melanoma or benign nevi compared with manual assessment by histopathologists . In the CAMELYON16 challenge, deep learning algorithms to detect breast cancer metastases in H&E-stained WSIs of lymph node sections performed similarly to the best performing pathologists under time constraints in detecting macrometastases and were better in detecting micrometastases . However, it should be noted that the performance of any algorithm will depend on the task, due to the degree of accuracy required and the quality of the samples to be assessed . Another application of machine learning in the preclinical space is the assessment of tumor purity (TP). TP estimation, currently evaluated visually by pathologists, is used to ensure a signal is derived from cancer cells rather than other noncancerous cells that may be present in the TME based on tissue morphology when tissue is used to generate orthogonal data such as transcriptome or exome , . In a comparison of TP determined using AI (using deep learning algorithms generated on the PathAI platform) and manual estimates by pathologists, AI-assessed TP was found to be more accurate than visual assessment by pathologists . Previous evidence has shown that immunosuppressive pathways are upregulated in patients with low TP, suggesting that low TP is associated with poor prognosis in some tumor types, including gastric cancer. Therefore, improved methods of evaluating TP may also aid in the identification of patients who may be suitable for immunotherapy . Given the amount of additional detail and insights that can be gained from combining WSI with machine learning algorithms, this technology can be readily applied to translational research. However, one major limitation of machine learning is the large amount of high-quality data required to develop these algorithms . Data used for training need to be accurate and as complete as possible in order to maximize predictability and utility . This can be challenging when histological data are obtained from various laboratories, leading to some variability due to factors such as differences in slide preparation (sectioning, fixation, staining, and mounting) , scoring algorithms , and inherent inter-observer variability . These challenges become more apparent when more complex computational analytics methods are used for multiplexed imaging. Although AI could be used to overcome inter-reader variability across multiple institutions with the development of robust algorithms that take specific histological features of various tumors and subtypes into account , further research is needed to fully understand the impact of these factors on the quality of AI data. Deep learning takes machine learning a step further, using sophisticated, multilevel deep or convolutional neural networks (DNN or CNN) to create systems that perform feature classification from large datasets , , , . Figure highlights key differences between machine learning and deep learning. The impact of applications of deep learning algorithms to IHC- and H&E-stained specimens have been well documented across many tumor types. These include grading prostate cancer , identifying biomarkers for disease-specific survival in early-stage melanoma , detection of invasive breast cancer regions on WSIs , , predicting response to chemoradiotherapy in locally advanced rectal cancer , and identifying morphological features (nuclear shape, nuclear orientation, texture, tumor architecture, etc.) to predict recurrence in early-stage non-small cell lung cancer (NSCLC) from H&E slides . Deep learning has also been used to construct entity-graph-based tissue representations, where cell morphology and topology are embedded within each node to effectively describe the phenotypical and structural properties of tissues and can be processed by graph neural networks (GNNs). GNNs therefore enhance the interpretability of pathological assessments gleaned from neural networks , . It is important to compare AI-based interpretations with those of the pathologist to define the associated algorithm’s performance characteristics and utility. For example, when a CNN trained to classify melanoma samples was compared against manual scoring by histopathologists, the CNN was significantly superior in classifying images as malignant melanoma or benign nevi compared with manual assessment by histopathologists . In the CAMELYON16 challenge, deep learning algorithms to detect breast cancer metastases in H&E-stained WSIs of lymph node sections performed similarly to the best performing pathologists under time constraints in detecting macrometastases and were better in detecting micrometastases . However, it should be noted that the performance of any algorithm will depend on the task, due to the degree of accuracy required and the quality of the samples to be assessed . Another application of machine learning in the preclinical space is the assessment of tumor purity (TP). TP estimation, currently evaluated visually by pathologists, is used to ensure a signal is derived from cancer cells rather than other noncancerous cells that may be present in the TME based on tissue morphology when tissue is used to generate orthogonal data such as transcriptome or exome , . In a comparison of TP determined using AI (using deep learning algorithms generated on the PathAI platform) and manual estimates by pathologists, AI-assessed TP was found to be more accurate than visual assessment by pathologists . Previous evidence has shown that immunosuppressive pathways are upregulated in patients with low TP, suggesting that low TP is associated with poor prognosis in some tumor types, including gastric cancer. Therefore, improved methods of evaluating TP may also aid in the identification of patients who may be suitable for immunotherapy . Given the amount of additional detail and insights that can be gained from combining WSI with machine learning algorithms, this technology can be readily applied to translational research. However, one major limitation of machine learning is the large amount of high-quality data required to develop these algorithms . Data used for training need to be accurate and as complete as possible in order to maximize predictability and utility . This can be challenging when histological data are obtained from various laboratories, leading to some variability due to factors such as differences in slide preparation (sectioning, fixation, staining, and mounting) , scoring algorithms , and inherent inter-observer variability . These challenges become more apparent when more complex computational analytics methods are used for multiplexed imaging. Although AI could be used to overcome inter-reader variability across multiple institutions with the development of robust algorithms that take specific histological features of various tumors and subtypes into account , further research is needed to fully understand the impact of these factors on the quality of AI data. Enhancing our understanding of the TME Tumor evolution and progression involve many complex cellulars and molecular interactions that are spatially and temporally regulated within the TME . IHC can be used to gain insights into the composition of the TME by facilitating the identification of different cell types expressing a protein of interest and assessing the density and spatial distribution of specific biomarkers . Digital pathology approaches, such as quantitative analysis of TILs, present an opportunity to gain greater insight into intra-tumor heterogeneity, spatial patterns of cell phenotypes, and the complex interactions between cancer and the immune system within the TME , . Image-based techniques can be used to determine immune cell responses to immunotherapy such as macrophage activation or lymphocyte infiltration by regulatory T cells (Tregs) into core tumor regions in solid tumors , which may in turn have value as a predictive indicator for the effectiveness of ICIs. Favorable cancer prognosis has also been associated with factors in the TME, including high CD8 + TIL rates , . Recently, image analysis and AI methods have contributed to the development of novel approaches to concurrently assess multiple biomarkers in preclinical and exploratory studies, revealing complex interactions within the TME and providing the potential to improve cancer diagnosis and the selection of treatment regimens. Combining multiple techniques, such as multiplex IF, with image analysis has yielded important insights into specific immune cell populations, such as those in the TME of classical Hodgkin lymphoma, and their associations with PD-1/CTLA-4 +/− T cells . These studies require multiple large cohorts to add the scale and robustness necessary to gain these important insights, to elucidate relationships that may not be apparent to the human eye, and to help overcome observer bias that may mask potential biomarker signals. Assessing treatment response: immune cell interactions in the TME Digital pathology can also be used to gain insights into a receptor-ligand binding, as proximity may be indicative of receptor engagement and activation. For example, lymphocyte-activation gene 3 (LAG-3), expressed on exhausted T cells, principally interacts with major histocompatibility-II (MHC-II) molecules, expressed on the surface of antigen-presenting and tumor cells , . Spatial analysis in bladder and gastric cancer tumor cells has demonstrated that the density and proximity of LAG-3 + were significantly greater when associated with MHC II + vs. MHC II − tumor cells, suggesting that LAG-3–expressing TILs may be preferentially located in proximity to MHC II + tumor cells, allowing for LAG-3 activation and the inhibition of antitumor immunity . The insights provided by digital pathology into the number and location of immune cells relative to tumor cells may provide information on immune response , , which could guide future treatment strategies. AI has also been used to quantify immune cells within the TME to define T-cell abundance and associated geographic localization in the tumor stroma, parenchyma, parenchyma-stromal interface, and invasive margin, which are then associated with transcriptomic factors to define underlying biological associations . Identifying genomic features Additionally, AI-based approaches may find applications in translational medicine and clinical practice by predicting gene mutations from routine histopathology slides. With genomic tests being associated with high costs and high rates of failure due to stringent sample requirements , , AI may be particularly useful for evaluating genomic instability and the mutational landscape, with the possibility to assess pathologic and genomic features in conjunction with one another. A CNN trained with WSIs of H&E-stained hepatocellular carcinoma (HCC) tissue was used to predict the ten most common prognostic and mutated genes in HCC, with four of these ( CTNNB1 , FMN2 , TP53 , and ZFX4 ) correctly identified by the model . Similar results were obtained when a DNN was trained to predict the most commonly mutated genes in lung adenocarcinoma, with 6 ( STK11 , EGFR , FAT1 , SETBP1 , KRAS , and TP53 ) being predicted from WSIs . Deep learning has also been used to predict microsatellite instability (MSI) status from tumor tissue . A CNN trained to classify MSI versus microsatellite stability was able to robustly distinguish features predictive of MSI in gastric and colorectal cancer samples . However, there are limitations to using AI for molecular classification. For example, current imaging techniques can only identify genetic variants when they directly impact tissue morphology, as described previously . At the same time, AI algorithms cannot be applied in cases where actual variant allele frequencies of selected mutations can impact the classification and prognosis of individual diseases, such as hematologic myeloid neoplasms . Tumor evolution and progression involve many complex cellulars and molecular interactions that are spatially and temporally regulated within the TME . IHC can be used to gain insights into the composition of the TME by facilitating the identification of different cell types expressing a protein of interest and assessing the density and spatial distribution of specific biomarkers . Digital pathology approaches, such as quantitative analysis of TILs, present an opportunity to gain greater insight into intra-tumor heterogeneity, spatial patterns of cell phenotypes, and the complex interactions between cancer and the immune system within the TME , . Image-based techniques can be used to determine immune cell responses to immunotherapy such as macrophage activation or lymphocyte infiltration by regulatory T cells (Tregs) into core tumor regions in solid tumors , which may in turn have value as a predictive indicator for the effectiveness of ICIs. Favorable cancer prognosis has also been associated with factors in the TME, including high CD8 + TIL rates , . Recently, image analysis and AI methods have contributed to the development of novel approaches to concurrently assess multiple biomarkers in preclinical and exploratory studies, revealing complex interactions within the TME and providing the potential to improve cancer diagnosis and the selection of treatment regimens. Combining multiple techniques, such as multiplex IF, with image analysis has yielded important insights into specific immune cell populations, such as those in the TME of classical Hodgkin lymphoma, and their associations with PD-1/CTLA-4 +/− T cells . These studies require multiple large cohorts to add the scale and robustness necessary to gain these important insights, to elucidate relationships that may not be apparent to the human eye, and to help overcome observer bias that may mask potential biomarker signals. Digital pathology can also be used to gain insights into a receptor-ligand binding, as proximity may be indicative of receptor engagement and activation. For example, lymphocyte-activation gene 3 (LAG-3), expressed on exhausted T cells, principally interacts with major histocompatibility-II (MHC-II) molecules, expressed on the surface of antigen-presenting and tumor cells , . Spatial analysis in bladder and gastric cancer tumor cells has demonstrated that the density and proximity of LAG-3 + were significantly greater when associated with MHC II + vs. MHC II − tumor cells, suggesting that LAG-3–expressing TILs may be preferentially located in proximity to MHC II + tumor cells, allowing for LAG-3 activation and the inhibition of antitumor immunity . The insights provided by digital pathology into the number and location of immune cells relative to tumor cells may provide information on immune response , , which could guide future treatment strategies. AI has also been used to quantify immune cells within the TME to define T-cell abundance and associated geographic localization in the tumor stroma, parenchyma, parenchyma-stromal interface, and invasive margin, which are then associated with transcriptomic factors to define underlying biological associations . Additionally, AI-based approaches may find applications in translational medicine and clinical practice by predicting gene mutations from routine histopathology slides. With genomic tests being associated with high costs and high rates of failure due to stringent sample requirements , , AI may be particularly useful for evaluating genomic instability and the mutational landscape, with the possibility to assess pathologic and genomic features in conjunction with one another. A CNN trained with WSIs of H&E-stained hepatocellular carcinoma (HCC) tissue was used to predict the ten most common prognostic and mutated genes in HCC, with four of these ( CTNNB1 , FMN2 , TP53 , and ZFX4 ) correctly identified by the model . Similar results were obtained when a DNN was trained to predict the most commonly mutated genes in lung adenocarcinoma, with 6 ( STK11 , EGFR , FAT1 , SETBP1 , KRAS , and TP53 ) being predicted from WSIs . Deep learning has also been used to predict microsatellite instability (MSI) status from tumor tissue . A CNN trained to classify MSI versus microsatellite stability was able to robustly distinguish features predictive of MSI in gastric and colorectal cancer samples . However, there are limitations to using AI for molecular classification. For example, current imaging techniques can only identify genetic variants when they directly impact tissue morphology, as described previously . At the same time, AI algorithms cannot be applied in cases where actual variant allele frequencies of selected mutations can impact the classification and prognosis of individual diseases, such as hematologic myeloid neoplasms . Potential for patient stratification As a further application in translational medicine, digital pathology approaches have been used to predict response and identify patients most likely to respond to treatment. For example, studies have used spatial analysis to determine the response of patients with NSCLC to nivolumab therapy. These included training machine learning models to extract morphological details, such as the spatial arrangement of tumor nuclei and variance in shape and chromatin structure , as well as the area and density of TILs and the proximity of TILs to each other and to tumor cells . The features extracted from these models were able to distinguish patients who responded to nivolumab therapy , . In another example, digital image analysis was used to quantify CD8 and PD-L1 positive cell densities from patients treated with durvalumab across multiple tumor types . Patients defined as positive for the CD8xPD-L1 composite signature had longer median survival compared with signature-negative patients, demonstrating the potential predictive value of digitally defined composite biomarkers. AI and machine learning can also assist in classification and staging across various tumor types. A new approach to tumor subtyping has been developed based on a DNN (MesoNet) to predict OS of patients with mesothelioma from hematoxylin, eosin, and saffron stained WSIs, without any pathologist-provided annotations . Results demonstrated that the model was more accurate in predicting patient survival than using current pathology practices and was able to identify regions contributing to patient outcomes , suggesting that deep learning models can identify new features predictive of patient survival and potentially lead to new biomarker discoveries. Application of digital pathology and AI algorithms in diagnostics Biomarker research has been an area of particular interest in the I-O space due to its potential predictive value in some solid tumors , , – . ICIs, such as anti–PD-(L)1 and anti–cytotoxic T lymphocyte antigen-4, have been studied in multiple clinical trials, leading to improved prognosis for patients across various solid tumors . Evidence has shown that PD-L1 expression may be indicative of response to ICI therapy in some tumor types , , – , while other studies have shown that patients demonstrated durable responses to ICIs regardless of PD-L1 expression , – . Given the widespread clinical use of ICIs, predictive assays are needed to help stratify patients to determine who may benefit from such treatments. While the use of these assays can help determine whether a patient will benefit from ICI therapy, biomarker identification, such as PD-L1 status, using tumor biopsies is challenging. Even when used by experienced pathologists, visual interpretation of PD-L1 using IHC is subjective and prone to error, which may contribute to inaccurate patient stratification. Digital scoring of PD-L1 expression can assist pathologists in overcoming these barriers by providing standardized metrics for biomarker assessment at single-cell resolution across whole tissue sections . Multiple studies have evaluated PD-L1 assessment using digital scoring and AI algorithms and have shown that digital-based techniques can perform better than or equal to manual pathological evaluation across various tumor types. A high correlation between AI and manual assessment of PD-L1 expression on tumor and immune cells has been observed in multiple CheckMate trials with samples from NSCLC, urothelial carcinoma, melanoma, and gastric cancer – . Furthermore, similar associations between PD-L1 expression and response to nivolumab have been reported between manual and digital scoring , . Using the combined positive score to assess PD-L1 expression on tumor and immune cells, digital image analyses and pathologists’ interpretations on stained slides (using the 22C3 pharmDx assay [Dako, Denmark]) demonstrated 33 (84.6%) of 39 cases had concordant results, and statistical analyses indicated that PD-L1 expression interpreted by pathologists or digital image analysis did not differ significantly for predicting responses to pembrolizumab . Prospective clinical trials in colorectal cancer and NSCLC are also using digital image analyses to identify potential immune cell biomarkers within the TME. The role of AI and machine learning in biomarker identification has been evaluated in studies outside of immunotherapy. For example, a DNN model (ConvNets) trained to automatically recognize cancer cell types were compared with conventional machine learning techniques. ConvNets achieved significantly higher accuracy than conventional algorithms, suggesting a role for computer-aided diagnosis to facilitate clinical decision-making . Beyond oncology, AI and machine learning have been studied in the context of a morphological assessment of nonalcoholic steatohepatitis/nonalcoholic fatty liver disease and liver allograft fibrosis , . In these cases, AI-based methods were able to correctly reflect markers of steatotic severity and assess liver allograft fibrosis progression over time . Various platforms have been developed for the purpose of quantitative image analysis. Several have received FDA approval, including those used to detect HER2 . The goal of a HER2-directed image analysis platform is to detect and quantify HER2 membranous IHC staining of invasive breast cancer cells and to provide an accurate, precise, and reproducible quantitative HER2 result that can then be used to guide treatment decisions . Digital image analysis has also been used to classify biological subtypes beyond HER2, including ER- and progesterone receptor (PR)–positive subtypes. Ahern et al demonstrated considerable overlap between unsupervised and supervised computational pathology platforms using image analysis to measure ER and PR expression in breast tumors between positive and negative groups, as classified by a pathologist . While the supervised platform had a marginally higher performance than the unsupervised platform, both platforms provided meaningful results and may have important roles in future molecular epidemiology studies . Addressing consistency issues for application in clinical practice There are several published resources for pathologists as well as for physicians, including guidelines, position papers, and directives relating to digital pathology , , , – . These include detailed information on the handling of digital images in nonclinical and clinical settings, technical aspects and performance standards for WSI devices – , validation and quality assurance of digital pathology systems for nonclinical and clinical use , , , AI concepts and best practices , , tutorials on using deep learning frameworks for image analysis , and reimbursement considerations . For example, the College of American Pathologists provides comprehensive guidelines to laboratories on validating their own WSI systems for clinical use, including emulation of the real-world environment, sample set size, establishing concordance using intra-observer variability, and documentation, among others . The performance of AI applications in digital pathology is largely dependent on the size and quality of the dataset used to train an algorithm . Digital images used for training purposes should be obtained from multiple staining batches, scanners, and institutions to ensure generalizability. Such datasets should be curated by pathologists, ensuring that representative images have been obtained at an appropriate magnification and that all regions of interest are comprehensively annotated depending on the diagnostic application . Crucially, the validation of AI algorithms developed for clinical purposes increases the concordance between manual and digital pathology interpretations. The role of pathologists in the validation step is equally important in order to ensure that datasets represent the sample type of interest (e.g., H&E-stained FFPE section), encompass the entirety of a glass slide, and are big enough to reveal potential interpretational discrepancies, as well as to evaluate the accuracy and performance of the algorithm , . As a further application in translational medicine, digital pathology approaches have been used to predict response and identify patients most likely to respond to treatment. For example, studies have used spatial analysis to determine the response of patients with NSCLC to nivolumab therapy. These included training machine learning models to extract morphological details, such as the spatial arrangement of tumor nuclei and variance in shape and chromatin structure , as well as the area and density of TILs and the proximity of TILs to each other and to tumor cells . The features extracted from these models were able to distinguish patients who responded to nivolumab therapy , . In another example, digital image analysis was used to quantify CD8 and PD-L1 positive cell densities from patients treated with durvalumab across multiple tumor types . Patients defined as positive for the CD8xPD-L1 composite signature had longer median survival compared with signature-negative patients, demonstrating the potential predictive value of digitally defined composite biomarkers. AI and machine learning can also assist in classification and staging across various tumor types. A new approach to tumor subtyping has been developed based on a DNN (MesoNet) to predict OS of patients with mesothelioma from hematoxylin, eosin, and saffron stained WSIs, without any pathologist-provided annotations . Results demonstrated that the model was more accurate in predicting patient survival than using current pathology practices and was able to identify regions contributing to patient outcomes , suggesting that deep learning models can identify new features predictive of patient survival and potentially lead to new biomarker discoveries. Biomarker research has been an area of particular interest in the I-O space due to its potential predictive value in some solid tumors , , – . ICIs, such as anti–PD-(L)1 and anti–cytotoxic T lymphocyte antigen-4, have been studied in multiple clinical trials, leading to improved prognosis for patients across various solid tumors . Evidence has shown that PD-L1 expression may be indicative of response to ICI therapy in some tumor types , , – , while other studies have shown that patients demonstrated durable responses to ICIs regardless of PD-L1 expression , – . Given the widespread clinical use of ICIs, predictive assays are needed to help stratify patients to determine who may benefit from such treatments. While the use of these assays can help determine whether a patient will benefit from ICI therapy, biomarker identification, such as PD-L1 status, using tumor biopsies is challenging. Even when used by experienced pathologists, visual interpretation of PD-L1 using IHC is subjective and prone to error, which may contribute to inaccurate patient stratification. Digital scoring of PD-L1 expression can assist pathologists in overcoming these barriers by providing standardized metrics for biomarker assessment at single-cell resolution across whole tissue sections . Multiple studies have evaluated PD-L1 assessment using digital scoring and AI algorithms and have shown that digital-based techniques can perform better than or equal to manual pathological evaluation across various tumor types. A high correlation between AI and manual assessment of PD-L1 expression on tumor and immune cells has been observed in multiple CheckMate trials with samples from NSCLC, urothelial carcinoma, melanoma, and gastric cancer – . Furthermore, similar associations between PD-L1 expression and response to nivolumab have been reported between manual and digital scoring , . Using the combined positive score to assess PD-L1 expression on tumor and immune cells, digital image analyses and pathologists’ interpretations on stained slides (using the 22C3 pharmDx assay [Dako, Denmark]) demonstrated 33 (84.6%) of 39 cases had concordant results, and statistical analyses indicated that PD-L1 expression interpreted by pathologists or digital image analysis did not differ significantly for predicting responses to pembrolizumab . Prospective clinical trials in colorectal cancer and NSCLC are also using digital image analyses to identify potential immune cell biomarkers within the TME. The role of AI and machine learning in biomarker identification has been evaluated in studies outside of immunotherapy. For example, a DNN model (ConvNets) trained to automatically recognize cancer cell types were compared with conventional machine learning techniques. ConvNets achieved significantly higher accuracy than conventional algorithms, suggesting a role for computer-aided diagnosis to facilitate clinical decision-making . Beyond oncology, AI and machine learning have been studied in the context of a morphological assessment of nonalcoholic steatohepatitis/nonalcoholic fatty liver disease and liver allograft fibrosis , . In these cases, AI-based methods were able to correctly reflect markers of steatotic severity and assess liver allograft fibrosis progression over time . Various platforms have been developed for the purpose of quantitative image analysis. Several have received FDA approval, including those used to detect HER2 . The goal of a HER2-directed image analysis platform is to detect and quantify HER2 membranous IHC staining of invasive breast cancer cells and to provide an accurate, precise, and reproducible quantitative HER2 result that can then be used to guide treatment decisions . Digital image analysis has also been used to classify biological subtypes beyond HER2, including ER- and progesterone receptor (PR)–positive subtypes. Ahern et al demonstrated considerable overlap between unsupervised and supervised computational pathology platforms using image analysis to measure ER and PR expression in breast tumors between positive and negative groups, as classified by a pathologist . While the supervised platform had a marginally higher performance than the unsupervised platform, both platforms provided meaningful results and may have important roles in future molecular epidemiology studies . There are several published resources for pathologists as well as for physicians, including guidelines, position papers, and directives relating to digital pathology , , , – . These include detailed information on the handling of digital images in nonclinical and clinical settings, technical aspects and performance standards for WSI devices – , validation and quality assurance of digital pathology systems for nonclinical and clinical use , , , AI concepts and best practices , , tutorials on using deep learning frameworks for image analysis , and reimbursement considerations . For example, the College of American Pathologists provides comprehensive guidelines to laboratories on validating their own WSI systems for clinical use, including emulation of the real-world environment, sample set size, establishing concordance using intra-observer variability, and documentation, among others . The performance of AI applications in digital pathology is largely dependent on the size and quality of the dataset used to train an algorithm . Digital images used for training purposes should be obtained from multiple staining batches, scanners, and institutions to ensure generalizability. Such datasets should be curated by pathologists, ensuring that representative images have been obtained at an appropriate magnification and that all regions of interest are comprehensively annotated depending on the diagnostic application . Crucially, the validation of AI algorithms developed for clinical purposes increases the concordance between manual and digital pathology interpretations. The role of pathologists in the validation step is equally important in order to ensure that datasets represent the sample type of interest (e.g., H&E-stained FFPE section), encompass the entirety of a glass slide, and are big enough to reveal potential interpretational discrepancies, as well as to evaluate the accuracy and performance of the algorithm , . Despite the advantages of incorporating digital pathology into the clinical setting, challenges remain (Table ). Value determination and reimbursement structures for digital pathology are lacking. This leaves value interpretation, investment, and cost savings considerations up to individual laboratories, which is difficult and a substantial hinderance to widespread adoption. Image analysis platforms have been shown to provide prognostic value, such as risk classification in patients with colon cancer . However, these are offered as single-site, standalone tests, thereby limiting their applicability to the wider pathology community. Studies that have evaluated the adoption of complete digital pathology workflows have shown increases in efficiency and operational utility . Technical concerns related to reproducibility, interpretability, the accuracy of competing devices, financial costs of processing hardware, and regulatory approvals that must accompany studies of clinical utility all represent barriers to adoption . Some level of error with digital pathology is anticipated to be present at this point, and approaches that combine algorithm performance with manual validation, with margins of error similar to or stricter than those used for manual pathology, are likely to be the standard moving forward. This approach has already been tested in routine diagnostics, whereby pathologists interacted directly with an AI platform to conduct IHC-based intrinsic subtyping of breast cancers. The AI platform, both alone and working in consort with pathologists, was significantly more accurate in determining subtypes . Additionally, translation and adoption into clinical practice will depend on algorithms being validated across many patient cohorts utilizing data not included in the training set. This will require large amounts of data to be acquired from multiple laboratories in order to assure the broad applicability required in a clinical setting , . While there have been instances of AI being used in the clinical trial setting, most have been observational studies . Techniques that take into account variations in real-world practice and can influence decision-making need to be evaluated in interventional studies to ascertain true clinical value . Although a protocol for the development of a reporting guideline and risk of bias tool has been published , no official guidelines are available yet on the numbers of annotations, images, and laboratories needed to capture the variation seen in the real-world. Additional statistical studies will be required for application to properly determine the optimal processes and workflows to ensure full implementation of this technology in clinical practice . Algorithms would also be subject to periodic quality assurance (eg, when a new staining protocol is introduced), similar to how assays are revalidated when there is a change in workflow or procedure . Various quality control (QC) techniques can be used to overcome preanalytical issues such as variations in slide preparation, origin, and scanner type. One approach is to train individual models of the same architecture to recognize specific variables . Other approaches, such as combining image metrics in a QC application , or transformation of image patches with synthetically generated artifacts , can be used to train an algorithm to recognize different types of histological artifacts. Other unforeseen hurdles may exist once these systems are in place, including unfamiliarity with a new system and associated need for training, technical support, security, monitoring, and software integration , . In the US, software solutions should be developed under the FDA’s Quality System Regulation and Good Machine Learning Practices. However, artificial neural networks have been described as “black boxes”, whereby data can be difficult to interpret, which may lead to regulatory concerns, as image features are extracted in ways that are difficult for a human to understand , . Despite the challenges, the efficiency gains, such as faster results and higher throughput, are key motivators for pathologists to adopt digital pathology. The benefits of AI can be seen across all stages of the drug development process and in the clinical setting . One of the first applications of AI in the clinical setting is likely to be assessing multiple IHC I-O markers within a single tissue section. Application of image analysis to multiplexed IHC–stained samples offers accelerated scan times while increasing accuracy and productivity by automatically measuring parameters that may be hard to reliably achieve by eye . In the evolving field of digital pathology, a strategy towards the implementation of digital pathology may involve several phases culminating in the adoption of digitized images and AI technology in the clinic. A first step involves demonstrating the reliability of digital pathology with a biomarker that has shown clinical utility with manual pathology, such as approved complementary diagnostics. For example, using PD-L1 expression, which has demonstrated clinical utility across a range of tumor types , , , would allow digital pathology readouts to be compared directly with manual pathology data and clinical outcomes. In this phase, pathologists would maintain a role in QC, but with improved efficiency. Data from the evaluation of such biomarkers with digital pathology could then be used in applications to the FDA for companion diagnostic status. Subsequent steps would introduce digital pathology as a diagnostic with novel biomarkers, with the aim of demonstrating the clinical utility of the biomarker with digital quantification. This phase would require the development of AI-based software for use in prospective clinical trials to evaluate the selected biomarker for patient stratification or selection. The next phase, and the long-term goal of digital pathology, would be to establish deep learning AI models trained using large quantities of data that can predict patient response and stratify patients using only WSIs. The current advances in digital pathology offer practical advantages over manual pathology, including enhanced accuracy and precision, the ability for digital images to be uploaded and reviewed remotely by multiple pathologists, and the acquisition and processing of large and complex datasets. Within immuno-oncology, a deeper understanding of the complexity and underlying mechanisms of the TME can be achieved with the help of AI and machine learning, where datasets can be consistently analyzed and validated for application across many large cohorts, which may have implications for drug development and clinical trial design. AI and machine learning can then be utilized within the clinic to describe clinical and pathologic features across multiple patient samples. These advances will not only facilitate the entry of more precise I-O therapies, but also ultimately improve diagnostic, prognostic, and predictive clinical decision-making in cancer treatment. |
Here I Am, Despite Myself | 85ac8f9f-56fb-49f3-9b69-8f81ac42065d | 4565686 | Pathology[mh] | |
Measuring concern about smile appearance among adults | ae657388-02c7-4ace-a034-5b16f9ec52eb | 11491515 | Dentistry[mh] | Physical beauty has a broad conceptual value and can be described by evolutionary, psychosocial, or cultural theories. However, one’s first impression of another person is typically based on physical appearance, including the body and the face . The face is directly related to expressiveness and communication between individuals. It is important to establish individual identity and contribute to social interactions and integration, which may explain why people are concerned about appearance . Orofacial appearance (OA) is one of the components of general body appearance that can impact social and subjective well-being . OA may also affect the perception of attractiveness, both by oneself and by others . Therefore, self-perception of OA is psychosocially important, regardless of whether there is a relevant aesthetic or functional clinical impairment . For this reason, the self-perception of OA is one of the four dimensions of oral health-related quality of life . In this context, OA refers to the overall appearance of the face, including the dental, oral, and facial features. Smile appearance is a subset of OA and focuses on the smile components, encompassing the lips, teeth, and gums. Teeth appearance refers solely to the aesthetic aspects of the teeth themselves. The self-perception of OA and its components are subjective aspects and, therefore, may differ, for example, between dentist and patient . Thus, the dentist should attempt to identify the patient’s perception so that the treatment plan can be better aligned with their demands and expectations. This approach can enhance patient satisfaction with the treatment , especially in orthodontics, which involves tooth movement to improve the smile appearance. However, objective and clinical assessments by the dentist do not always align with the patient’s expectations. Psychometric scales, known as dental patient-reported outcome measures (dPROMs), are an interesting alternative to measure the patient’s perception . Several dPROMs for measuring OA can be found in the literature. These include Psychosocial Impact of Dental Aesthetics Questionnaire (PIDAQ) and Orofacial Esthetic Scale (OES) . PIDAQ measures the impact of dental aesthetics on an individual’s life and consists of four factors (dental self-confidence, social impact, psychological impact, and aesthetic concerns) . OES was originally proposed as a single-factor scale to measure satisfaction with general OA . Both PIDAQ and OES have proven to be adequate dPROMs for measuring these latent constructs: psychosocial impact and satisfaction, respectively . Even so, there is still a need for dPROMs that measure other latent constructs, such as concern. They can provide an exploratory means to gather information about patient demand in treatments focusing on smile appearance, including orthodontics and rehabilitation. Because the self-perception of OA is a patient-reported outcome (PRO), it is important to use short and simple scales that do not overwhelm respondents and are applicable to clinical practice, research settings, and epidemiological studies . Thus, there is room for the inclusion of new measurement tools that are easy to use and measure concern about smile appearance. Utrecht Questionnaire for esthetic outcome assessment in rhinoplasty (OAR) is a psychometric scale with similar characteristics. This scale measures the concern about the nose appearance as an aesthetic outcome of rhinoplasty . Although it focuses on the nose and is applied to individuals who have undergone rhinoplasty, its adaptation to smile appearance can provide the dentist with information that may contribute to a more individualized and patient-centered treatment plan. Adapting a psychometric scale requires rigorous methodological procedures that demonstrate the validity and reliability of the dPROM for a specific context and sample . Demographic characteristics may influence the perception of OA. Campos et al . observed that people with lower economic status, who wear dental prostheses, and dislike their smile have a greater psychosocial impact of dental aesthetics on their lives. These authors also found no significant association between self-perception of OA and sex or age, although other studies have shown that OA had more influence on the lives of women and younger people . Therefore, identifying demographic characteristics that influence concern about one’s smile may also be relevant information for planning more targeted treatments that meet patients’ demands and expectations. This study aimed to adapt OAR to measure concerns about smile appearance, named Questionnaire for Outcome Assessment of Smile Aesthetic (OA-Smile), and estimate the psychometric properties of OA-Smile when applied to adults. In addition, the influence of demographic characteristics on the concern about smile appearance was investigated.
Study design and sampling This was a cross-sectional observational study with a nonprobability sampling design conducted in two phases: convenience sampling and snowball sampling. Adults of both sexes between the ages of 18 and 40 years participated in the study. The age was limited to 40 years to minimize the effect of this variable on the results. The perception of OA can differ between young and mature adults, with the literature reporting that young people may experience a greater impact of OA on their lives due to their OA . The sample size was estimated according to the proposal of Hair et al . , who recommend 5 to 10 participants per parameter to be tested in the factor model of the psychometric scale. The model to be tested had 10 parameters (five items and five errors). Thus, the minimum sample size was 50 to 100 participants. However, this is the first time the psychometric properties of the adapted version of OAR to measure concerns about smile appearance (OA-Smile) were being studied. To test the measurement invariance of the factor model and the differential functioning of the items, the analyses were conducted in several independent subsamples (by sex, marital status, use of dental prostheses, dental treatment, liking the own smile, and economic level). Each subsample was therefore planned to have the minimum estimated size. In addition, since the influence of demographic characteristics on smile concern was to be evaluated, it was decided to recruit the largest possible number of participants for the study . Procedures and ethical aspects The study was approved by the Research Ethics Committee and included only individuals who consented to participate in the study and signed the informed consent. Because of the Coronavirus Disease 2019 (COVID-19) pandemic, data was collected online from April to September 2021 using a self-reported survey created with LimeSurvey program (LimeSurvey GmbH, Hamburg, Germany; http://www.limesurvey.org ). The invitation to participate in the study was initially sent by email to members of the academic community of a University in Brazil (convenience sample). The invitation email contained the aims of the study and a link to the online survey. The first page of the survey presented the informed consent that the participant needed to agree to proceed. The next page was the demographic questionnaire, which collected information on age, sex, marital status, use of dental prostheses, dental treatment, liking the own smile, and economic level. The economic level was classified as E (family monthly income < R$1255), D (R$1255├ 2005), C (R$2005├ 8641), B (R$8641├ 11 262), and A (≥ R$11 262). The exchange rate was U$1.00 = R$5.74 on 2 August 2024, according to the Central Bank of Brazil. The demographic questionnaire was followed by the psychometric scales presented in random order. Subsequently, a snowball sampling strategy was adopted, and participants were asked to forward the link to the online survey to their personal contacts, specifically those not related to academia/university, via email, social networks, or messages. Adaptation of OAR to OA-Smile OAR was originally developed by Lohuis et al. to measure the self-perception of the nose appearance aesthetic after rhinoplasty and its impact on the individual’s life. It consists of five items with a 5-point Likert-type response scale (1: not at all to 5: very much) arranged in a unifactorial model. OAR also includes an item in which the participant rates the nose appearance of the nose on an 11-point response scale (0: very ugly to 10: very beautiful). However, this item is used only to determine how the patient rates their nose appearance and is not included in the factor structure. The Portuguese version of OAR was used in the present study . Permission to use the scale was obtained from the original researchers via email before the beginning of the study. The 11-point numerical rating scale result was used to split the participants into two samples according to whether they perceived their smile as ‘beautiful’ (scores ranging from > 5.5 to 10) or ‘ugly’ (scores ranging from 0 to ≤ 5.5). The adaptation of OAR to measure concern about smile appearance was carried out by a panel of three expert judges, consisting of dentists with clinical experience in smile aesthetics and research experience using psychometric scales. They independently reviewed the original scientific articles , the content of each item, the response scale, and the instructions of OAR. They provided suggestions for adapting the scale to measure smile concerns. All three judges agreed that replacing the word ‘nose’ with ‘smile’ was sufficient for this purpose. The researchers adapted the scale based on the judges’ comments . This preliminary version of the adapted scale was named Questionnaire for Outcome Assessment of Smile Aesthetic (OA-Smile). Psychometric properties of OA-Smile The evidence of validity and reliability of data collected using OA-Smile was verified following the Standards for Educational and Psychological Testing . This study evaluated the content validity, validity based on internal structure, validity based on relationships with external measures, discriminant validity, and validity based on the response process, as described below. Content validity Content validity verifies the adequacy of the grammatical, semantic, and idiomatic terms of the item content and how well the latent construct (concern with smile appearance) is theoretically reflected in the set of items. First, the same panel of three expert judges who participated in the scale adaptation process evaluated the clarity of the items, their practical relevance, and the theoretical appropriateness of the content of the preliminary version of OA-Smile. They considered the content relevant for measuring concern about smile appearance and its impact on individuals’ lives. A pretest was then conducted on a sample of the target population to verify the incomprehensibility index (II) of the items. II aims to determine whether the participants adequately understood the meaning of the instructions and the content (words and phrases) of the items. II below 20% indicated that the scale was appropriate for participants’ comprehension. Validity based on internal structure Validity based on internal structure verifies how well the relationships among the scale items reflect the latent construct of interest . This analysis aims to ensure that the scale accurately measures what it is intended to measure. First, the psychometric sensitivity of the items was assessed using summary measures [mean, median, and standard deviation (SD)] and the distribution of data (skewness and kurtosis). The absolute values of skewness < 3 and kurtosis < 10 indicated the absence of a severe violation of the normal distribution . Then, factorial validity was assessed using confirmatory factor analysis with the robust Weighted Least Squares Mean and Variance Adjusted (WLSMV). The choice of estimation method was based on the number of points on the response scale (1–5). The fit of the model to the data was assessed using the comparative fit index (CFI), Tucker-Lewis index (TLI), and the standardized root mean residual square (SRMR) . The local fit was also assessed by the factor loadings of the items (λ). Model fit was considered appropriate when CFI and TLI > 0.90, SRMR < 0.08, and λ ≥ 0.50 . The convergent construct validity was estimated using the average variance extracted (AVE), which was considered adequate if AVE ≥ 0.50 . The reliability, which refers to the consistency of the measure by the scale, was estimated using the ordinal coefficient alpha (α) and omega (ω). Values of α and ω ≥ 0.70 were considered adequate . The measurement invariance of OA-Smile was tested to verify if the factor model solution remains consistent across independent samples (random division of the total sample into a test sample and a validation sample) and within subsamples based on demographic variables (sex, marital status, use of dental prostheses, being in dental treatment, liking the own smile, and economic level). Multigroup analysis with CFI difference (∆ CFI) was conducted between two increasingly constrained models. The CFI values of the configural (M0), thresholds (M1), factor loadings (M2), and residuals (M3) models were considered. Invariance was confirmed when the CFI reduction (∆ CFI) between models (M1–M0, M2–M1, and M3–M2) was less than 0.01 . Analyses were performed in the R software using the ‘lavaan’ and ‘semTools’ packages. Validity based on the response process The validity based on the response process was evaluated using Item Response Theory. This analysis examines the probability of a participant endorsing an item based on their level of the latent trait and the difficulty of the item. The information-weighted mean square value (infit: individuals with a latent trait level equal to the item difficulty do not respond as expected) and the unweighted mean square value (outfit: individuals with a latent trait level different from the item difficulty do not respond as expected) were estimated for each subsample given above (in Validity based on internal structure—measurement invariance) using the ‘eRm’ package in the R software and considering the partial credit model (PCM). INFIT and OUTFIT values between 0.5 and 1.5 indicate a reasonable fit of the item to the PCM and were considered productive for the measurement. Differential item functioning (DIF) was conducted to verify if the items of the scale function similarly across those subsamples. In other words, DIF assesses whether individuals with the same level of the latent trait, but from different subsamples, respond differently to the OA-Smile items. DIF was estimated using ordinal logistic regression based on the likelihood ratio chi-square statistic, with a significance level of 1%. Items with ‘total DIF effect’ ( P < .01) were considered nonequivalent. McFadden’s and Nagelkerke’s pseudoR 2 were used and effect sizes < 0.13 were considered negligible , i.e. they were not used in the model. DIF and pseudoR 2 analyses were performed in the ‘lordif’ package of the R software . Validity based on relationships with external measures This validity examined how the OA-Smile is related to other established measures of similar or different latent constructs . For that, the Portuguese versions of OES and the OAR were used. OES measures the individual’s satisfaction with their OA. It is an unifactorial scale and consists of seven items with an 11-point response scale (0: very dissatisfied; 10: very satisfied). OA-Smile is expected to have a negative and significant correlation with OES (negative convergent validity), i.e. the lower the satisfaction with the appearance of orofacial aesthetics, the greater the concern about the smile appearance. OAR and OA-Smile were used to estimate discriminant validity, with a low-to-moderate correlation expected between the two variables, as the scales assess independent physical components of the face and are not necessarily strongly correlated. Discriminant criterion validity Discriminant criterion validity was evaluated to estimate whether the scale can identify different groups defined by a given demographic criterion. We defined the groups (subsamples) based on denture use (0 = no; 1 = yes), current dental treatment (0 = no; 1 = yes), and liking the own smile (0 = no, 1 = yes). After checking the measurement invariance of OA-Smile for the different groups, the comparison of smile appearance concern scores between them was performed using analysis of variance (ANOVA). The assumptions of normality and homoscedasticity were tested. The data of all groups presented non-severe violations of normality (skewness < 3, kurtosis < 10) and heteroscedasticity (Levene’s test: P < .05). Therefore, Welch’s ANOVA was conducted. A significance level of 5% was adopted. Discriminant criterion validity was established if the difference between group scores was statistically significant. Structural model A structural model was elaborated to estimate the influence of demographic characteristics on concern about the smile appearance. The variables sex (0 = male, 1 = female), marital status (0 = single, 1 = married), economic level (0 = C/D/E, 1 = B/A), use of dental prostheses (0 = no; 1 = yes), being in a dental treatment (0 = no, 1 = yes), and liking the own smile (0 = no, 1 = yes) were the independent variables. The concern about smile appearance (OA-Smile) was the dependent variable of the model. The fit of the structural model was evaluated using the WLSMV estimator, and the goodness of fit was assessed based on the CFI, TLI, and SRMR indices. The hypothetical path estimates (standardized β) were estimated and evaluated using the z-test and a significance level of 5%. This analysis was performed in the R software using the ‘lavaan’ and ‘semTools’ packages.
This was a cross-sectional observational study with a nonprobability sampling design conducted in two phases: convenience sampling and snowball sampling. Adults of both sexes between the ages of 18 and 40 years participated in the study. The age was limited to 40 years to minimize the effect of this variable on the results. The perception of OA can differ between young and mature adults, with the literature reporting that young people may experience a greater impact of OA on their lives due to their OA . The sample size was estimated according to the proposal of Hair et al . , who recommend 5 to 10 participants per parameter to be tested in the factor model of the psychometric scale. The model to be tested had 10 parameters (five items and five errors). Thus, the minimum sample size was 50 to 100 participants. However, this is the first time the psychometric properties of the adapted version of OAR to measure concerns about smile appearance (OA-Smile) were being studied. To test the measurement invariance of the factor model and the differential functioning of the items, the analyses were conducted in several independent subsamples (by sex, marital status, use of dental prostheses, dental treatment, liking the own smile, and economic level). Each subsample was therefore planned to have the minimum estimated size. In addition, since the influence of demographic characteristics on smile concern was to be evaluated, it was decided to recruit the largest possible number of participants for the study .
The study was approved by the Research Ethics Committee and included only individuals who consented to participate in the study and signed the informed consent. Because of the Coronavirus Disease 2019 (COVID-19) pandemic, data was collected online from April to September 2021 using a self-reported survey created with LimeSurvey program (LimeSurvey GmbH, Hamburg, Germany; http://www.limesurvey.org ). The invitation to participate in the study was initially sent by email to members of the academic community of a University in Brazil (convenience sample). The invitation email contained the aims of the study and a link to the online survey. The first page of the survey presented the informed consent that the participant needed to agree to proceed. The next page was the demographic questionnaire, which collected information on age, sex, marital status, use of dental prostheses, dental treatment, liking the own smile, and economic level. The economic level was classified as E (family monthly income < R$1255), D (R$1255├ 2005), C (R$2005├ 8641), B (R$8641├ 11 262), and A (≥ R$11 262). The exchange rate was U$1.00 = R$5.74 on 2 August 2024, according to the Central Bank of Brazil. The demographic questionnaire was followed by the psychometric scales presented in random order. Subsequently, a snowball sampling strategy was adopted, and participants were asked to forward the link to the online survey to their personal contacts, specifically those not related to academia/university, via email, social networks, or messages.
OAR was originally developed by Lohuis et al. to measure the self-perception of the nose appearance aesthetic after rhinoplasty and its impact on the individual’s life. It consists of five items with a 5-point Likert-type response scale (1: not at all to 5: very much) arranged in a unifactorial model. OAR also includes an item in which the participant rates the nose appearance of the nose on an 11-point response scale (0: very ugly to 10: very beautiful). However, this item is used only to determine how the patient rates their nose appearance and is not included in the factor structure. The Portuguese version of OAR was used in the present study . Permission to use the scale was obtained from the original researchers via email before the beginning of the study. The 11-point numerical rating scale result was used to split the participants into two samples according to whether they perceived their smile as ‘beautiful’ (scores ranging from > 5.5 to 10) or ‘ugly’ (scores ranging from 0 to ≤ 5.5). The adaptation of OAR to measure concern about smile appearance was carried out by a panel of three expert judges, consisting of dentists with clinical experience in smile aesthetics and research experience using psychometric scales. They independently reviewed the original scientific articles , the content of each item, the response scale, and the instructions of OAR. They provided suggestions for adapting the scale to measure smile concerns. All three judges agreed that replacing the word ‘nose’ with ‘smile’ was sufficient for this purpose. The researchers adapted the scale based on the judges’ comments . This preliminary version of the adapted scale was named Questionnaire for Outcome Assessment of Smile Aesthetic (OA-Smile).
The evidence of validity and reliability of data collected using OA-Smile was verified following the Standards for Educational and Psychological Testing . This study evaluated the content validity, validity based on internal structure, validity based on relationships with external measures, discriminant validity, and validity based on the response process, as described below.
Content validity verifies the adequacy of the grammatical, semantic, and idiomatic terms of the item content and how well the latent construct (concern with smile appearance) is theoretically reflected in the set of items. First, the same panel of three expert judges who participated in the scale adaptation process evaluated the clarity of the items, their practical relevance, and the theoretical appropriateness of the content of the preliminary version of OA-Smile. They considered the content relevant for measuring concern about smile appearance and its impact on individuals’ lives. A pretest was then conducted on a sample of the target population to verify the incomprehensibility index (II) of the items. II aims to determine whether the participants adequately understood the meaning of the instructions and the content (words and phrases) of the items. II below 20% indicated that the scale was appropriate for participants’ comprehension.
Validity based on internal structure verifies how well the relationships among the scale items reflect the latent construct of interest . This analysis aims to ensure that the scale accurately measures what it is intended to measure. First, the psychometric sensitivity of the items was assessed using summary measures [mean, median, and standard deviation (SD)] and the distribution of data (skewness and kurtosis). The absolute values of skewness < 3 and kurtosis < 10 indicated the absence of a severe violation of the normal distribution . Then, factorial validity was assessed using confirmatory factor analysis with the robust Weighted Least Squares Mean and Variance Adjusted (WLSMV). The choice of estimation method was based on the number of points on the response scale (1–5). The fit of the model to the data was assessed using the comparative fit index (CFI), Tucker-Lewis index (TLI), and the standardized root mean residual square (SRMR) . The local fit was also assessed by the factor loadings of the items (λ). Model fit was considered appropriate when CFI and TLI > 0.90, SRMR < 0.08, and λ ≥ 0.50 . The convergent construct validity was estimated using the average variance extracted (AVE), which was considered adequate if AVE ≥ 0.50 . The reliability, which refers to the consistency of the measure by the scale, was estimated using the ordinal coefficient alpha (α) and omega (ω). Values of α and ω ≥ 0.70 were considered adequate . The measurement invariance of OA-Smile was tested to verify if the factor model solution remains consistent across independent samples (random division of the total sample into a test sample and a validation sample) and within subsamples based on demographic variables (sex, marital status, use of dental prostheses, being in dental treatment, liking the own smile, and economic level). Multigroup analysis with CFI difference (∆ CFI) was conducted between two increasingly constrained models. The CFI values of the configural (M0), thresholds (M1), factor loadings (M2), and residuals (M3) models were considered. Invariance was confirmed when the CFI reduction (∆ CFI) between models (M1–M0, M2–M1, and M3–M2) was less than 0.01 . Analyses were performed in the R software using the ‘lavaan’ and ‘semTools’ packages.
The validity based on the response process was evaluated using Item Response Theory. This analysis examines the probability of a participant endorsing an item based on their level of the latent trait and the difficulty of the item. The information-weighted mean square value (infit: individuals with a latent trait level equal to the item difficulty do not respond as expected) and the unweighted mean square value (outfit: individuals with a latent trait level different from the item difficulty do not respond as expected) were estimated for each subsample given above (in Validity based on internal structure—measurement invariance) using the ‘eRm’ package in the R software and considering the partial credit model (PCM). INFIT and OUTFIT values between 0.5 and 1.5 indicate a reasonable fit of the item to the PCM and were considered productive for the measurement. Differential item functioning (DIF) was conducted to verify if the items of the scale function similarly across those subsamples. In other words, DIF assesses whether individuals with the same level of the latent trait, but from different subsamples, respond differently to the OA-Smile items. DIF was estimated using ordinal logistic regression based on the likelihood ratio chi-square statistic, with a significance level of 1%. Items with ‘total DIF effect’ ( P < .01) were considered nonequivalent. McFadden’s and Nagelkerke’s pseudoR 2 were used and effect sizes < 0.13 were considered negligible , i.e. they were not used in the model. DIF and pseudoR 2 analyses were performed in the ‘lordif’ package of the R software .
This validity examined how the OA-Smile is related to other established measures of similar or different latent constructs . For that, the Portuguese versions of OES and the OAR were used. OES measures the individual’s satisfaction with their OA. It is an unifactorial scale and consists of seven items with an 11-point response scale (0: very dissatisfied; 10: very satisfied). OA-Smile is expected to have a negative and significant correlation with OES (negative convergent validity), i.e. the lower the satisfaction with the appearance of orofacial aesthetics, the greater the concern about the smile appearance. OAR and OA-Smile were used to estimate discriminant validity, with a low-to-moderate correlation expected between the two variables, as the scales assess independent physical components of the face and are not necessarily strongly correlated.
Discriminant criterion validity was evaluated to estimate whether the scale can identify different groups defined by a given demographic criterion. We defined the groups (subsamples) based on denture use (0 = no; 1 = yes), current dental treatment (0 = no; 1 = yes), and liking the own smile (0 = no, 1 = yes). After checking the measurement invariance of OA-Smile for the different groups, the comparison of smile appearance concern scores between them was performed using analysis of variance (ANOVA). The assumptions of normality and homoscedasticity were tested. The data of all groups presented non-severe violations of normality (skewness < 3, kurtosis < 10) and heteroscedasticity (Levene’s test: P < .05). Therefore, Welch’s ANOVA was conducted. A significance level of 5% was adopted. Discriminant criterion validity was established if the difference between group scores was statistically significant.
A structural model was elaborated to estimate the influence of demographic characteristics on concern about the smile appearance. The variables sex (0 = male, 1 = female), marital status (0 = single, 1 = married), economic level (0 = C/D/E, 1 = B/A), use of dental prostheses (0 = no; 1 = yes), being in a dental treatment (0 = no, 1 = yes), and liking the own smile (0 = no, 1 = yes) were the independent variables. The concern about smile appearance (OA-Smile) was the dependent variable of the model. The fit of the structural model was evaluated using the WLSMV estimator, and the goodness of fit was assessed based on the CFI, TLI, and SRMR indices. The hypothetical path estimates (standardized β) were estimated and evaluated using the z-test and a significance level of 5%. This analysis was performed in the R software using the ‘lavaan’ and ‘semTools’ packages.
Pretest Thirty individuals participated in the pretest [56.7% women, mean age=28.2 (SD = 4.6) years]. No item or word/concept was reported as incomprehensible by participants (II = 0%), so there was no need to rephrase the content. Therefore, participants’ understanding of the items was considered adequate, confirming the content validity of OA-Smile for measuring the concern about smile appearance. The preliminary version used in the pretest was considered the final version of OA-Smile . Psychometric properties of the OA-Smile A total of 2523 individuals participated in the study (age: mean = 32.86, SD = 11.39 years; 68.1% female), of whom 58.4% were single and 36.8% married. In terms of economic level, 21.3% belong to level A, 16.2% to level B, 44.7% to C, 10% to D, and 7.8% to E. Most participants reported not undergoing dental treatment (80.4%) and liking their smile (77.9%). shows the distribution of participants based on responses to the 11-point numerical rating scale of OA-Smile. Most participants (83.1%, n = 2.091) rated their smile as ‘beautiful’ (> 5.5 to 10) and 16.9% ( n = 425) as ‘ugly’ (scores ranging from 0 to ≤ 5.5). Validity based on internal structure The data indicated that items of OA-Smile presented adequate psychometric sensitivity for each subsample. The model had adequate fit, convergent validity, and reliability for all subsamples . Strict invariance was found between models for all characteristics assessed . Validity based on response process shows the item fit statistics of OA-Smile. The fit of OA-Smile items was appropriate for the subsamples according to sex, marital status, use of dental prostheses, dental treatment, liking of own smile, and economic level. Items 2 and 4 presented different responses (DIF) according to sex, but the effect sizes were small (pseudoR² = 0.003 and 0.005, respectively). For marital status, items 1, 4, and 5 had DIF with a low effect size (pseudoR² = 0.004–0.010). DIF was confirmed in items 1 and 5 between groups based on dental treatment and economic level, respectively. The effect sizes were also small (pseudoR² = 0.003 and 0.004). For liking the own smile, most items had DIF, with effect sizes ranging from 0.003 to 0.019. Regarding the use of dental prostheses, the items did not show any DIF. Validity based on relationships with external measures Correlation analysis showed adequate negative convergent validity (OA-Smile factor vs OES factor: r = −0.686; P < .001) and discriminant validity (OA-Smile factor vs OAR factor: r = 0.371; P < .001). Discriminant criterion validity Individuals who use dental prostheses (mean score = 1.98, SD = 1.15, 95% CI = 1.79–2.16), do not like their smile (mean score = 2.61, SD = 1.05, 95% CI = 2.52–2.70), or are undergoing dental treatment (mean score = 2.01, SD = 1.02, 95% CI = 1.91–2.10) showed greater concern about smile appearance compared to those who do not use prostheses (mean score = 1.67, SD = 0.84, 95% CI = 1.64–1.71), like their smile (mean score = 1.43, SD = 0.57, 95% CI = 1.41–1.46), or are not undergoing dental treatment (mean score = 1.61, SD = 0.80, 95% CI = 1.58–1.65). The comparisons between subsamples according to the independent variables revealed significant differences, confirming the discriminant criterion validity of OA-Smile (prostheses use—Welch’s ANOVA: F = 10.05, P = .002; like their own smile—Welch’s ANOVA: F = 641.68, P < .001; and dental treatment—Welch’s ANOVA: F = 62.47, P < .001). Structural model The structural model with all independent variables showed an acceptable fit (CFI = 0.974; TLI = 0.991, and SRMR = 0.053) with a statistically significant contribution of all independent variables to smile appearance concern ( P < .05) . Women, those who were younger, single, had lower income, used dental prostheses, were undergoing dental treatment, and disliked their smile were more concerned about the appearance of their smile.
Thirty individuals participated in the pretest [56.7% women, mean age=28.2 (SD = 4.6) years]. No item or word/concept was reported as incomprehensible by participants (II = 0%), so there was no need to rephrase the content. Therefore, participants’ understanding of the items was considered adequate, confirming the content validity of OA-Smile for measuring the concern about smile appearance. The preliminary version used in the pretest was considered the final version of OA-Smile .
A total of 2523 individuals participated in the study (age: mean = 32.86, SD = 11.39 years; 68.1% female), of whom 58.4% were single and 36.8% married. In terms of economic level, 21.3% belong to level A, 16.2% to level B, 44.7% to C, 10% to D, and 7.8% to E. Most participants reported not undergoing dental treatment (80.4%) and liking their smile (77.9%). shows the distribution of participants based on responses to the 11-point numerical rating scale of OA-Smile. Most participants (83.1%, n = 2.091) rated their smile as ‘beautiful’ (> 5.5 to 10) and 16.9% ( n = 425) as ‘ugly’ (scores ranging from 0 to ≤ 5.5).
The data indicated that items of OA-Smile presented adequate psychometric sensitivity for each subsample. The model had adequate fit, convergent validity, and reliability for all subsamples . Strict invariance was found between models for all characteristics assessed .
shows the item fit statistics of OA-Smile. The fit of OA-Smile items was appropriate for the subsamples according to sex, marital status, use of dental prostheses, dental treatment, liking of own smile, and economic level. Items 2 and 4 presented different responses (DIF) according to sex, but the effect sizes were small (pseudoR² = 0.003 and 0.005, respectively). For marital status, items 1, 4, and 5 had DIF with a low effect size (pseudoR² = 0.004–0.010). DIF was confirmed in items 1 and 5 between groups based on dental treatment and economic level, respectively. The effect sizes were also small (pseudoR² = 0.003 and 0.004). For liking the own smile, most items had DIF, with effect sizes ranging from 0.003 to 0.019. Regarding the use of dental prostheses, the items did not show any DIF.
Correlation analysis showed adequate negative convergent validity (OA-Smile factor vs OES factor: r = −0.686; P < .001) and discriminant validity (OA-Smile factor vs OAR factor: r = 0.371; P < .001).
Individuals who use dental prostheses (mean score = 1.98, SD = 1.15, 95% CI = 1.79–2.16), do not like their smile (mean score = 2.61, SD = 1.05, 95% CI = 2.52–2.70), or are undergoing dental treatment (mean score = 2.01, SD = 1.02, 95% CI = 1.91–2.10) showed greater concern about smile appearance compared to those who do not use prostheses (mean score = 1.67, SD = 0.84, 95% CI = 1.64–1.71), like their smile (mean score = 1.43, SD = 0.57, 95% CI = 1.41–1.46), or are not undergoing dental treatment (mean score = 1.61, SD = 0.80, 95% CI = 1.58–1.65). The comparisons between subsamples according to the independent variables revealed significant differences, confirming the discriminant criterion validity of OA-Smile (prostheses use—Welch’s ANOVA: F = 10.05, P = .002; like their own smile—Welch’s ANOVA: F = 641.68, P < .001; and dental treatment—Welch’s ANOVA: F = 62.47, P < .001).
The structural model with all independent variables showed an acceptable fit (CFI = 0.974; TLI = 0.991, and SRMR = 0.053) with a statistically significant contribution of all independent variables to smile appearance concern ( P < .05) . Women, those who were younger, single, had lower income, used dental prostheses, were undergoing dental treatment, and disliked their smile were more concerned about the appearance of their smile.
This study aimed to adapt and assess the psychometric properties of Utrecht Questionnaire (OAR), originally developed to study the aesthetics of rhinoplasty , to measure concerns about smile appearance. These processes were conducted in line with international standards , and OA-Smile was introduced, serving as an initial tracking tool to better understand these concerns in clinical and research settings. The study was initiated because of the need to consider patients’ perspectives about their smile appearance in dental practice. This is especially important given the increasing demand for aesthetic treatments that affect the smile, such as orthodontics. Although often neglected, adapting a psychometric scale following international recommendations requires rigorous methodological care to ensure that it can measure what it intends to measure when applied to different samples . The validity and reliability of the data obtained by OA-Smile were confirmed when applied to Brazilian adults. This evidence supports recommending this scale for measuring concerns about smile appearance. We hope this study will stimulate further studies using OA-Smile in diverse contexts, e.g. in other cultures and countries. This would facilitate comparisons and the establishment of correlations to promote discussions about OA and, in particular about smile appearance from the patients’ perspective. This is an important topic given the high demand in dental practices focused on aesthetic treatments. The findings of the present study demonstrated that OA-Smile provides valid and reliable data when applied to different subsamples according to sex, marital status, use of dental prostheses, dental treatment or not, liking the own smile, and economic level. In addition, measurement invariance of OA-Smile factor model was observed across the subsamples. These findings suggest that the scale works similarly in different subsamples to measure the concern about smile appearance. Measurement invariance between groups must be verified whenever sample characteristics change, as comparisons between groups are only possible when invariance is confirmed . Although some items of OA-Smile had DIF between subsamples (IRT analyses), the practical significance was low (pseudoR²). This indicates that the items are generally compatible with the latent trait across each subsample, complementing the results from measurement invariance and suggesting that the scale is suitable for use with subsamples having these different characteristics. Our second aim was to study the potential influences of demographic characteristics on the concern about smile appearance. Structural Equation Modeling analysis revealed that demographic characteristics have a significant impact on how individuals perceive their smile appearance. The investigation of how self-perception of OA varies between individuals of different sexes and ages is a topic of interest in the literature . Our findings corroborate previous studies which indicate that women and younger individuals perceive the body differently and are more conscious of the appearance of their teeth. In contrast, Campos et al . found no significant contribution of age and sex to perceived OA. These authors explained that the scale used in their study measures the psychosocial impact of appearance rather than other latent constructs like satisfaction or concern, which could explain the differing results between studies. The influence of economic level on OA is consistent with previous studies that found that people with lower economic status place more importance on the appearance of their teeth. This may be related to factors such as oral health status and access to dental products and services . Since these characteristics were not examined in our study, any association between them and the observed results is speculative and should be interpreted with caution. In a related study, Campos et al . found that Brazilian adults with lower income experienced a greater psychosocial impact of OA and had less access to aesthetic dental treatment, supporting the latter argument. Our study also found a significant difference in OA based on marital status, which contrasts with the findings of Alhajj et al . . These authors reported that single individuals rated their OA more favorably than married individuals, though there were no significant differences in satisfaction with their smiles between the two groups. While Alhajj et al . did not find significant differences, they attributed potential variations in self-perception of OA to differing life priorities between married and single people. In terms of demographic characteristics related to dental practice, participants who underwent dental treatment, used dental prostheses, and reported disliking their own smiles were more concerned about their smile appearance. It suggests that dental treatments, whether prosthetic or otherwise, may significantly impact patients’ psychological well-being . Therefore, professionals should consider the patient’s clinical demands and expectations to achieve treatment success and be aware that professionals’ perceptions may differ from those of the patient . The cross-sectional design is a limitation of the study which does not allow establishing a causal association between the dependent and independent variables. However, this study design is commonly adopted in observational studies on the psychometric properties of scales . Other limitations of the study are the convenience sample and online data collection, in which only participants with internet access were initially recruited at a university in one district state (São Paulo) in Brazil, followed by a snowball strategy. Brazil is a country of continental proportions, with cultural variations across its regions and high social inequality, which makes internet access non-democratic. Thus, the generalization of the results to the entire population may be hindered and should be interpreted with caution. Future national studies using probability sampling, considering different regions of Brazil and socioeconomic levels, are necessary. We clarify that our initial aim was not to obtain results that could be extrapolated to the Brazilian population. Our primary objective was to adapt an existing psychometric scale and assess its psychometric properties to verify its potential applicability in both clinical and research contexts in dentistry. Therefore, the sample and analyses of the study, including measurement invariance analysis across independent samples, were sufficient to address this objective. In addition, we did not conduct a detailed dental clinical examination of the participants, which could provide even more interesting data. Unfortunately, a clinical examination was not possible because of online data collection, due to the COVID-19 pandemic, a time when social distancing was a public health recommendation that limited clinical care only to emergencies or for a specific clinical indication. Therefore, we suggest that further studies also include clinical examinations to determine associations with smile appearance concerns. Furthermore, we recognize that certain grammatical structures and wording in OA-Smile may introduce biases in the interpretation and response pattern. However, we decided to retain this language structure to align with the original scale and follow the results of the adaptation steps. This decision was based firstly on the strong evidence of construct validity obtained in the psychometric analyses. Secondly, the study aimed to adapt OAR rather than develop a new scale, which would require a different analytical approach. Lastly, none of the expert judges and study participants provided feedback regarding this issue. Nevertheless, we encourage researchers to consider a thorough investigation of potential interpretability biases, such as social desirability. Future studies conducting both qualitative and quantitative analyses using OA-Smile and other dPROMs are important for identifying relevant constructs and physical components (such as dental characteristics, lips, gum exposure, nose, etc.) related to OA. These studies may help in developing a comprehensive dPROM with minimal response bias. Despite these limitations, the findings presented should promote the use of the OA-Smile in different contexts and samples and contribute to future research and clinical dental practice.
The OA-Smile is a short, easy-to-use scale that has been shown to produce valid and reliable data when applied to adults and be suitable for use in epidemiologic studies, national surveys, and dental practices to assess specific components of OA, especially in areas such as orthodontics, which works on dental aesthetics. Demographic and clinical characteristics should be considered when assessing smile appearance concerns to tailor treatment to patients’ demands and expectations.
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Glitches in the utilization of telehealth in pediatric rheumatology patients during the COVID-19 pandemic | 272ec4bb-fbf4-4999-bf19-7daabfd427db | 7558240 | Pediatrics[mh] | The workforce shortage of pediatric rheumatology practitioners in the United States has been well documented with many states remaining underserved without full time pediatric rheumatologists or a severely limited work force . This has led to the adaptation of shared care models utilizing adult rheumatologists or community generalists, the use of mid-level practitioners, and most recently, the introduction of telehealth. Despite demonstrating feasibility and acceptability in studies, the uptake of telehealth within pediatric rheumatology has been slow due to a variety of reimbursement related concerns, and technical and regulatory obstacles . The Coronavirus Disease 2019 (COVID-19) pandemic is a global medical emergency that has radically changed the way healthcare workers have been able to deliver care. Beginning in March, the Centers for Medicare and Medicaid Services in the US have increased resources to expand telehealth services and eliminated former barriers and requirements that would usually require in-person visits . Similar changes have been noted internationally in an effort to triage potential COVID-19 patients and to provide convenient access to routine care while allowing for social distancing . What this rapid surge in utilization of telehealth has shown the medical community is that this modality is effective as an emergency response and is a feasible option for patient monitoring. Therefore, the integration of telehealth into standard healthcare practices is likely to become permanent. Despite a myriad of positives supporting utilization of telehealth there are important concerns to consider as pediatric practitioners, both general and subspecialists, strive to provide comprehensive patient and family centered care. Most patients who are referred to a pediatric rheumatologist and are subsequently followed by this subspecialty over long periods of time fit into the chronically ill pediatric care model. These complex patients are at risk to receive fragmented care without proper coordination . For this reason, the pediatric rheumatologist often acts as the medical home and has become an expert in patient centered care and the coordination of the multidisciplinary approach. Our current telehealth system is not uniformly built to accurately and carefully manage and coordinate the care for the chronically ill pediatric patient . Within this population, the poor, the uninsured, and the minority children may be at increased risk for inferior coordination of services and may also have decreased access to telehealth services. Psychosocial risk evaluation, such as the validated HEADSS exam , are an important cornerstone of the pediatric visit and may be challenging to perform remotely. Parents are required to sign consent for televisits and may expect to be present for the entire exam making it difficult to discuss sensitive topics . Depression and anxiety are common with pediatric rheumatologic diseases and are associated with poor adherence, quality of life, and long-term outcomes . Studies have shown that pediatric patients with systemic lupus erythematosus and mixed connective tissue disease have a trend towards increased depressive symptoms and statistically significant increase of suicidal ideation compared to healthy pediatric controls . Despite such high prevalence of mental health concerns within this population, there are poor rates of prior mental health treatment and therefore an assumed lack of integration of mental health centered telemedicine. During the COVID-19 pandemic, it has been noted that children and adolescents with disabilities, already existing mental health problems, and low socioeconomic status have an increased risk of worsening suicidal ideation, depression, and anxiety . With sole utilization of telehealth, patients might be less likely to divulge worsening of acute on chronic mental health symptoms. Patients with pediatric rheumatologic diseases have been noted to have increased risk of smoking, alcohol use, and illicit drug use and dependence . Due to the COVID-19 pandemic and need for social distancing, there has been a reported increase in substance use . People who already have increased tendencies for use of substances as an aid to help with stress may be at increased risk for abuse due to the increased stress, anxieties, and feelings of isolation due to social distancing . Although there is availability of telehealth services for substance use disorders, there is a lack of providers in rural areas and those who specialize in pediatrics. It can be difficult over a telehealth visit to provide an environment where a patient may feel comfortable speaking about these issues and to properly conduct a physical exam and read nonverbal cues which may increase a clinician’s suspicion for further evaluation for substance use disorder. Importantly, the necessary social distancing measures needed to control the spread of COVID-19 are causing a “secondary pandemic” of neglect and abuse of children . Similar patterns of increased violence towards children have been noted during periods of school closures associated with health emergencies such as Ebola . children have been noted during periods of school closures associated with health emergencies such as Ebola . In Illinois, it has been noted that child abuse hotline reports have dropped by 50% but it is believed to be due to children’s decreased access to mandated reporters, not a decrease in abuse . Despite increasing screening for child abuse and neglect, it may be more difficult for children to disclose on a telehealth session if they are in the same environment with their abuser or are worried that they might be overheard .
The American Academy of Pediatrics estimates that about a quarter of children with rheumatic disease live 80 miles or more from a pediatric rheumatologist . Telehealth has done wonders for expanding the reach of pediatric rheumatology within the United States to care for our vulnerable population. During this current pandemic, we have seen the incredible capabilities of medical technology and the importance of telehealth in helping with triage, ensuring that patients have access to care, and aiding the medical community to economically withstand the financial impact of COVID-19. However, given the complicated psychosocial complexities of chronically ill children that are further exacerbated by the pandemic, hopefully we will be able to utilize telehealth as an adjunct to in-person multidisciplinary team approach visits and not as a sole means to care. As subspecialists but also medical home providers, it is our duty to not only focus on the medical pathology and pharmaceutical treatment of disease, but also the important comorbidities that impact the short term and long term well-being and safety of our patients.
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Pharmacogenomics on the Treatment Response in Patients with Psoriasis: An Updated Review | 4bcec6b3-1692-4c99-846b-a66f4b2edf5d | 10138383 | Pharmacology[mh] | Psoriasis is a chronic, immune-mediated, inflammatory skin disease concomitant with other systemic complications. Environmental, behavioral, and genetic factors play a role in the etiology of the disease. Especially, genetic predisposition is thought to be a key contributor to psoriasis through involvement in immune pathophysiology , and about 40% of patients diagnosed with psoriasis or psoriatic arthritis have a related family history . To date, almost 100 psoriasis susceptibility loci have been identified through selective candidate genes or genome-wide association studies (GWAS) . The pharmacogenetic issue of psoriasis struck a chord after the immunogenetics of psoriasis were outlined gradually, and the need for personalized medicine increased when more and more anti-psoriatic drugs were available and showed variable efficacy among different drugs and individuals. This study aimed to overview the current findings of possible genetically predictive markers for treatment outcomes of psoriasis under the use of systemic and topical medicine. Regards to pathogenesis and immunogenetics of psoriasis , the disease results from an aberrant innate or adaptive immune response associated with T lymphocytes that leads to inflammation, angiogenesis, and epidermal hyperplasia . Genetic or environmental factors can trigger immune-mediated damage for keratinocytes in psoriasis patients. The key pathomechanism of psoriasis is that dendritic cells or macrophages can secrete IL-23 and then stimulate CD4 + Th17 polarization, resulting in the secretion of cytokines, such as IL-17, IL22, TNF-α, etc. Moreover, IL-12 can activate the differentiation of CD4 + Th1 cells, which induces INF-γ, IL-2, and TNF-α synthesis; CD8 + T cells are also known to be activated and can release pro-inflammatory cytokines, including TNF-α and INF-γ. The abundant cytokines lead to epidermal overgrowth, immune over-activation, and neovascularization. Consequently, the positive feedback loop of immune reaction leads to the development and maintenance of psoriatic lesions. The initiation of psoriasis lesion is when antigenic or auto-antigenic stimuli induced by damaged or stressed skin activate antigen-presenting cells (APCs), including dendritic cells (DCs) and macrophages. The process results in producing pro-inflammatory cytokines such as interferon (IFN)-α, tumor necrosis factor (TNF)-α, interleukin (IL)-12, IL-20, and IL-23, and initiates the early phase of cutaneous inflammation in psoriasis . The pro-inflammatory cytokines released from activated APCs promote T cell-mediated immunity through nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway and Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway. In addition, engagement of the T cell receptor (TCR) with major histocompatibility complex (MHC)-presenting antigen of APCs activates the calcium–calcineurin–nuclear factor of activated T cells (NFAT) pathway. Thus, these signals result in the migration, differentiation, and activation of naïve effector T cells. In particular, IL-23 stimulates CD4 + T helper 17 (Th17) polarization, which releases IL-17A/F, IL-22, and TNF-α. On the other hand, IL-12 activates the differentiation of the Th1 subset of CD4+ cells, which induces INF-γ, IL-2, and TNF-α synthesis . The inflammatory cytokines secreted from T cells, especially IL-17A, attract many more immune cells, such as neutrophils, enhance angiogenesis, facilitate hyperproliferation of keratinocytes, and promote the further release of cytokines. Additionally, keratinocytes activated by IL-17, IL-22, and IL-20 through JAK-STAT, NF-κB, and calcium–calcineurin–NFAT pathways release C-C motif ligand 20 (CCL20), antimicrobial peptides (AMP), and cytokines; hence, they contribute to the pro-inflammatory environment and amplify the inflammatory response . In brief, the over-activated innate immunity induces exaggerative T cell-mediated autoimmune activation, epidermal overgrowth, and neovascularization. Consequently, a positive feedback loop leads to the development and maintenance of psoriatic lesions. The psoriasis susceptibility genes were found to involve in the entire immunopathogenesis from antigen presentation, cytokines and receptors, signal transductions, and transcription factors to regulators of immune responses ; at the same time, whether these susceptibility genes are potential predictors of treatment response has been investigated. In the following context, we discuss the response-related genes in psoriasis treatment ( , , , , , and , ) and present levels of evidence of the pharmacogenomic association by the PharmGKB annotation scoring system. According to PharmGKB, six levels from 1A to 4 represent high, moderate, and low to unsupported evidence, respectively. 3.1. Methotrexate Methotrexate (MTX) is an antagonist of the enzymes dihydrofolate reductase (DHFR) and thymidylate synthase (TYMS). It is commonly used as a first-line systemic immunosuppressive therapy for moderate to severe psoriasis. However, significant variations in its efficacy and toxicity exist among individuals. Therefore, several studies have identified potential pharmacogenetic factors that can be used to predict the clinical response of MTX . 3.1.1. ABCC1, ABCC2, ABCG2 The genes encoding the efflux transporters of MTX are ATP-binding cassette (ABC) subfamily C member 1 (ABCC1) , ABCC member 2 (ABCC2) , and ABC subfamily G member 2 (ABCG2) . Overexpression of these genes can lead to multidrug resistance by extruding drugs out of the cell through various mechanisms . In regard to psoriasis, a cohort study of 374 British patients found significant positive associations between methotrexate responder, two of ABCG2 (rs17731538, rs13120400), and three SNPs of ABCC1 (rs35592, rs28364006, rs2238476) with rs35592 being the most significant (PASI75 at 3 months, p = 0.008). One cohort study from Slovenia demonstrated that polymorphism of ABCC2 (rs717620) presented an insufficient response to MTX treatment (75% reduction from baseline PASI score (PASI75) at 6 months, p = 0.039) . About toxicity, a British cohort study has noted that the major allele of six SNPs in ABCC1 (rs11075291, rs1967120, rs3784862, rs246240, rs3784864, and rs2238476) was significantly associated with the onset of adverse events, with rs246240 showing the strongest association ( p = 0.0006) . 3.1.2. ADORA2A Adenosine receptors A2a (ADORA2a) is responsible for mediating the metabolic product of methotrexate. One SNP, rs5760410 of ADORA2A , was weakly associated with the onset of toxicity ( p = 0.03) . 3.1.3. ATIC MTX inhibits 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase (ATIC) , which leads to the accumulation of adenosine, a potent anti-inflammatory agent . Campalani et al. analyzed 188 patients in the United Kingdom (UK) with psoriasis under methotrexate therapy and revealed that allele frequency of ATIC (rs2372536) was significantly increased in patients who discontinued methotrexate owing to intolerable side effects ( p = 0.038) . Another British cohort study found that two SNPs in ATIC (rs2372536 and rs4672768) were associated with the onset of MTX toxicity ( p = 0.01). However, these associations did not remain significant after adjusting for folic acid supplementation . 3.1.4. BHMT Betaine-homocysteine S-methyltransferase (BHMT) is a zinc-containing metalloenzyme responsible for folate-independent remethylation of homocysteine using betaine as the methyl donor . A genotype analysis identified that the BHMT genotype was significantly associated with MTX hepatotoxicity ( p = 0.022) . 3.1.5. DNMT3b DNA methyltransferase 3β (DNMT3b) is a methyltransferase that is involved in de-novo DNA methylation, and its polymorphism is supposed to be associated with increased promoter activity . At least one copy of the variant DNMT3b rs242913 allele has been found to be associated with an insufficient response to MTX when compared to the wild-type ( p = 0.005) . 3.1.6. FOXP3 Forkhead box P3 (FOXP3) appears to function as a master regulator of the regulatory pathway in the development and function of regulatory T cells (Tregs) . A study on a population of 189 southern Indian patients who had used methotrexate for 12 weeks found a significant difference in genotype frequencies of FOXP3 (rs3761548) between responders and non-responders (PASI75 at 3 months, p = 0.003) . 3.1.7. GNMT Glycine N-methyltransferase (GNMT) is a methyltransferase that converts S-adenosylmethionine to S-adenosylhomocysteine and is also a folate-binding protein. The rs10948059 polymorphism is associated with increased expression of the GNMT gene and reduces cell sensitivity to MTX . The patients with at least one variant GNMT allele were more likely to be non-responders to MTX treatment than the reference allele (PASI75 at 6 months, p = 0.0004) . 3.1.8. HLA-Cw6 The human leukocyte antigen (HLA) , known as the human MHC system, regulates the immune system by encoding cell-surface proteins. HLA-Cw6 is a psoriasis susceptibility allele that has been strongly linked to the disease. It was reported that carriers of HLA-Cw6 from southern India had a higher response rate to methotrexate (PASI75 at 3 months, p = 0.003) . A Scotland cohort study with 70 HLA-tested patients demonstrated that more proportion of HLA-Cw6 positive patients was carried on beyond 12 months, as compared to the HLA-Cw6 negative group ( p = 0.05) . 3.1.9. MTHFR The Methylenetetrahydrofolate reductase (MTHFR) enzyme is responsible for catalyzing the formation of 5-methyl-tetrahydrofolic acid, which acts as a methyl donor for the synthesis of methionine from homocysteine. This enzyme is indirectly inhibited by MTX. According to Zhu et al., the PASI 90 response rates to MTX were significantly higher in Han Chinese patients who had the MTHFR rs1801133 TT genotype as compared to those who had the CT and CC genotype (PASI90 at 3 months, p = 0.006). Furthermore, patients with the MTHFR rs1801131 CT genotype had lower PASI 75 response rates to MTX in Han Chinese population (PASI75 at 3 months, p = 0.014). They also had a lower risk of ALT elevation ( p = 0.04) . However, three studies have demonstrated that no significant association was detected between clinical outcomes in individuals with psoriasis treated with methotrexate and SNPs in the MTHFR gene . 3.1.10. SLC19A1 The Solute carrier family 19 , member 1 (SLC19A1) gene encodes the reduced folate carrier (RFC) protein, which actively transports MTX into cells. Multiple point mutations have been identified in SLC19A1 to be associated with impaired MTX transport and resistance to MTX . SLC19A1 (rs1051266) was associated with MTX-induced toxicity instead of efficacy in patients with psoriasis . 3.1.11. SLCO1B1 The encoded protein of solute carrier organic anion transporter family member 1B1 (SLO1B1) is a transmembrane receptor that transports drug compounds into cells. Genetic variations in SLCO1B1 have been linked to delayed MTX clearance and increased toxicity . The haplotype variants have been classified into two groups based on their reported transporter activity: the high-activity group and the low-activity group. Patients with low-activity haplotypes of SLCO1B1 (SLCO1B1*5 and SLCO1B1*15) were less likely to be MTX non-responders as compared to patients with high-activity haplotypes (SLCO1B1*1a and SLCO1B1*1b) (PASI75 at 6 months, p = 0.027) . 3.1.12. TNIP1 TNFAIP3 interacting protein 1 (TNIP1) , as one of the psoriasis susceptibility genes, is related to the immune response IL-23 signaling pathway. A Chinese study mentioned that in 221 patients with psoriasis, the TT genotype of TNIP1 rs10036748 showed a better response to MTX (PASI75 at 3 months, p = 0.043) . 3.1.13. TYMS Thymidylate synthase (TS), encoded by the thymidylate synthase gene (TYMS) , is a critical protein for pyrimidine synthesis and responsible for DNA synthesis and repair, which could be inhibited by MTX . The association of polymorphisms of TYMS , TS levels, and MTX response was found in several diseases . For example, polymorphism rs34743033 is a 28-base pair (bp) with double or triple tandem repeat (2R or 3R) located on the 5′ untranslated region (UTR) . A study performed in European adults with psoriasis found that the rs34743033 3R allele was more frequent in patients with poor therapeutic response to methotrexate, but the loss of significance was noted after the exclusion of palmoplantar pustulosis patients. In addition, this allele was significantly associated with an increased incidence of MTX-induced toxicity in patients who did not receive folic acid ( p = 0.0025). Another TS polymorphism, 3′-UTR 6bp del of rs11280056, was significantly more frequent in patients with an adverse event irrespective of folic acid supplementation ( p = 0.025) . In short, positive genotypic associations were detected with methotrexate responders in ten genes ( ABCC1 , ABCC2 , ABCG2 , DNMT3b , FOXP3 , GNMT , HLA-Cw , MTHFR , SLCO1B1 , TNIP1 ) while the development of methotrexate-related toxicity in five genes ( ABCC1 , ATIC , ADORA2A , BHMT , MTHFR , SLC19A1 , TYMS ). Nonetheless, three British studies seemed to believe that toxicity has overlapped populations; hence, several replicated results may also be owing to similar databases . 3.2. Acitretin Acitretin is an oral vitamin A derivative that is used to treat psoriasis by inhibiting epidermal proliferation, inflammatory processes, and angiogenesis. lists the genetic polymorphisms that have been associated with the response of acitretin in patients with psoriasis. 3.2.1. ApoE Apolipoprotein E (ApoE) is a glycoprotein component of chylomicrons and VLDL. It has a crucial role in regulating lipid profiles and metabolism . The lipid and lipoprotein abnormalities as a consequence of ApoE gene polymorphism are close to the side effects during acitretin therapy. In addition, ApoE levels have been linked with clinical improvement in psoriasis, indicating a potential role of the gene in acitretin treatment for psoriasis . However, according to Campalani, E, et al., while ApoE gene polymorphisms are associated with psoriasis, they do not determine the response of the disease to acitretin . 3.2.2. ANKLE1 Ankyrin repeat and LEM domain containing 1 (ANKLE1) enables endonuclease activity and plays a role in positively regulating the response to DNA damage stimulus and protein export from the nucleus. ANKLE1 rs11086065 AG/GG was associated with an ineffective response compared to the GG genotype in 166 Chinese patients (PASI75 at 3 months, p = 0.003) . 3.2.3. ARHGEF3 Rho guanine nucleotide exchange factor 3 (ARHGEF3) activates Rho GTPase, which involve in bone cell biology. ARHGEF3 rs3821414 CT was associated with a more effective response compared to the TT genotype (PASI75 at 3 months, p = 0.01) . 3.2.4. CRB2 Crumbs cell polarity complex component 2 (CRB2) encodes proteins that are components of the Crumbs cell polarity complex, which plays a crucial role in apical-basal epithelial polarity and cellular adhesion. CRB2 rs1105223 TT/CT was also associated with acitretin efficacy compared to the CC genotype (PASI75 at 3 months, p = 0.048) . 3.2.5. HLA-DQA1*02:01 HLA-DQA1*0201 alleles may act as psoriasis susceptibility genes or may be closely linked to the susceptibility genes in Han Chinese . Among 100 Chinese individuals, those who were positive for the DQA10201 allele demonstrated a more favorable response to acitretin compared to those who were negative for the same allele. (PASI75 at 2 months, p = 0.001) . 3.2.6. HLA-DQB1*02:02 HLA-DQB1 alleles have been mentioned to involve in genetic predisposition to psoriasis vulgaris in the Slovak population . In 100 Chinese patients, the DQB1*0202 -positive patients showed a better response to acitretin than the DQB1*0202 -negative patients (PASI75 at 2 months, p = 0.005) . 3.2.7. HLA-G HLA-G is a nonclassical class I MHC molecule that plays a role in suppressing the immune system by inhibiting natural killer cells and T cells . Among patients treated with acitretin, Borghi, Alessandro, et al. observed a significantly increased frequency of the 14 bp sequence deletion in the exon 8 of the HLA-G allele, functioning as a modification of mRNA stability, in responder patients, in comparison to the non-responders (PASI75 at 4 months, p = 0.008) . 3.2.8. IL-12B Patients with the IL-12B rs3212227 genotype of TG were more responsive to acitretin in the treatment of psoriasis in 43 Chinese patients (PASI50, p = 0.035) . 3.2.9. IL-23R Acitretin was found to improve the secondary non-response to TNFα monoclonal antibody in patients who were homozygous for the AA genotype at the SNP rs112009032 in the IL-23R gene (PASI75, p = 0.02) . 3.2.10. SFRP4 Secreted frizzled-related protein 4 (SFRP4) is a negative regulator of the Wnt signaling pathway, and the downregulation of SFRP4 is a possible mechanism contributing to the hyperplasia of the epidermis of psoriasis . The GG/GT variation of SFRP4 rs1802073 has been found to be associated with a more effective response to acitretin compared to the TT genotype (PASI75 at 3 months, p = 0.007) . 3.2.11. VEGF Vascular endothelial growth factor (VEGF) promotes angiogenesis in the pathophysiology of psoriasis, and the variant of the VEGF gene is supposed to affect the ability of acitretin to downregulate VEGF production . The TT genotype of the VEGF rs833061 was associated with non-response to oral acitretin, whereas the TC genotype was associated with a significant response to acitretin (PASI75 at 3 months, p = 0.01) . However, the result of VEGF polymorphism was not replicated in the population of southern China . 3.3. Cyclosporin Cyclosporine, a calcineurin inhibitor, is commonly used to treat moderate to severe psoriasis. However, clinical studies investigating the pharmacogenetics of cyclosporine in psoriasis patients are currently lacking . 3.3.1. ABCB1 One Greek study enrolled 84 patients revealed that ATP-binding cassette subfamily B member 1 (ABCB1) rs1045642 had statistically significant association with a negative response of cyclosporin (PASI < 50 at 3 months, p = 0.0075) . In 168 Russian patients with psoriasis receiving cyclosporine therapy, a strongly negative association was observed for the TT/CT genotype of ABCB1 rs1045642 (PASI75 at 3 months, p < 0.001), the TT/CT genotype of ABCB1 rs1128503 (PASI75 at 3 months, p = 0.027), and the TT/GT genotype of ABCB1 rs2032582 (PASI75 at 3 months, p = 0.048), respectively. Additionally, the TGC haplotype was significantly linked to a negative response (PASI75 at 3 months, p < 0.001) . 3.3.2. CALM1 Calmodulin (CALM1) is known as a calcium-dependent protein and is related to cell proliferation and epidermal hyperplasia in psoriasis . In 200 Greek patients, the allele T of CALM1 rs12885713 displayed a significantly better response to cyclosporin (PASI75 at 3 months, p = 0.011) . 3.3.3. MALT1 MALT1 encodes MALT1 paracaspase, a potent activator of the transcription factors NF-κB and AP-1, and hence has a role in psoriasis . MALT1 rs287411 allele G was associated with the effective response compared to allele A (PASI75 at 3 months, p < 0.001) . 3.4. Tumor Necrosis Factor Antagonist There are four FDA-approved TNF antagonists for plaque psoriasis, including etanercept, adalimumab, infliximab, and certolizumab pegol. According to our review of the literature, pharmacogenetic research has been mainly focused on the first three drugs. Etanercept is a recombinant fusion protein comprising two extracellular parts of the human tumor necrosis factor receptor 2 (TNFR2) coupled to a human immunoglobulin 1 (IgG1) Fc. Adalimumab is a fully human monoclonal antibody with human TNF binding Fab and human IgG1 Fc backbone, whereas infliximab is a chimeric IgG1 monoclonal antibody composed of a human constant and a murine variable region binding to TNFα . Despite their unique pharmacological profile from each other, TNF antagonists act on the same pathologic mechanism to achieve therapeutic outcomes. Therefore, some pharmacogenetic researchers regarded all TNF antagonists as one category to analyze potential predictive genetic markers under a large-scale population, while some discussed each TNF antagonist separately . 3.4.1. Nonspecific TNF Antagonist Better Response of Efficacy In 144 Spanish patients, carriers of the CT/CC allele in MAP3K1 rs96844 and the CT/TT allele in HLA-C rs12191877 achieved a better PASI75 response at 3 months. The study also found significantly better results for carriers of MAP3K1 polymorphism and CT/TT in CDKAL1 rs6908425 at 6 months . Another study enrolled 70 patients in Spain implicated that patients harboring high-affinity alleles, FCGR2A-H131R (rs1801274) and FCGR 3A-V158F (rs396991), contribute to better mean BSA improvement but not PASI improvement at 6–8 weeks after anti-TNF treatment of psoriasis . The result between FCGR 3A-V158F (rs396991) and response to anti-TNFα therapy (PASI75 at 6 months, p = 0.005), especially etanercept (PASI75 at 6 months, p = 0.01), was replicated in 100 Caucasian patients from Greece, while FCGR2A-H131R (rs1801274) was found to be no association . A study conducted in 199 Greek patients found an association between carriers of CT/CC in HLA-C rs10484554 and a good response to anti-TNF agents (PASI 75 at 6 months, p = 0.0032), especially adalimumab ( p = 0.0007) . In 238 Caucasian adults in Spain, the rs4819554 promoter SNP allele A of the IL17RA gene was significantly more prevalent among responders at week 12 . Moreover, several genetic variants exert favorable effects at 6 months of treatment in 109 patients with psoriasis from Spain, including GG genotype of IL23R rs11209026 (PASI90 p = 0.006), GG genotype of TNF-a-238 rs361525 (PASI75, p = 0.049), CT/TT genotypes of TNF-a-857 rs1799724 (PASI75, p = 0.006, ΔPASI, p = 0.004; BSA, p = 0.009), and TT genotype of TNF-a-1031 rs1799964 (PASI75, p = 0.038; ΔPASI, p = 0.041; at 3 months, PASI75, p = 0.047) . Poor Response of Efficacy In 144 Spanish patients, four SNPs were associated with the inability to achieve PASI75 at three months, including AG/GG allele in PGLYRP4-24 rs2916205, CC allele in ZNF816A rs9304742, AA allele in CTNNA2 rs11126740, and AG/GG allele in IL12B rs2546890. Additionally, the results for polymorphisms in the IL12B gene were replicated at six months and one year. The study also obtained significant results for the FCGR2A and HTR2A polymorphism at 6 months . Notably, the result of the FCGR2A polymorphism showed variability between studies . In 376 Danish patients, five SNPs, which are IL1B (rs1143623, rs1143627), LY96 (rs11465996), and TLR2 (rs11938228, rs4696480), were all associated with nonresponse to treatment . One study found a higher frequency of G-carriers of the TNFRSF1B rs1061622 among non-responders (PASI < 50) compared to cases achieving PASI75 to TNF blockers in 90 Caucasians from Spain . Toxicity Among the 161 Caucasian patients, the polymorphism rs10782001 in FBXL19 and rs11209026 in IL23R may contribute to an increased risk of the secondary development of psoriasiform reactions owing to TNF blocking. In addition, in 70 Spanish patients, the copy number variation (CNV) harboring three genes (ARNT2, LOC101929586, and MIR5572) was related to the occurrence of paradoxical psoriasiform reactions at 3 and 6 months ( p = 0.006) . In contrast, the presence of rs3087243 in CTLA4 , rs651630 in SLC12A8 , or rs1800453 in TAP1 was related to protection against psoriasiform lesions . Interestingly, the IL23R rs11209026 polymorphism was reported as having a protective role reported in classical psoriasis. 3.4.2. Etanercept (ETA/ETN) CD84 Cluster of Differentiation 84 (CD84) gene encodes a membrane glycoprotein, which enhances IFN-γ secretion in activated T cells . In 161 patients from the Netherlands, the GA genotype in CD84 (rs6427528) had a more sensitive response to etanercept than the referential GG genotype (ΔPASI at 3 months, p = 0.025) . FCGR3A This gene encodes a receptor for the Fc portion of immunoglobulin G, where the TNF antagonist binds specifically. In 100 psoriasis patients in Greece, the study showed an association with FCGR3A-V158F (rs396991) and better response to etanercept (PASI75 at 6 months, p = 0.01) . TNFAIP3 TNFα induced protein 3 (TNFAIP3) plays a protective role against the harmful effects of inflammation and is involved in immune regulation . Rs610604 in TNFAIP3 showed associations with good responses to etanercept (PASI75 at 6 months, p = 0.007) . TNF, TNFRSF1B TNFα transmits signals through TNF receptor superfamily member 1B (TNFRSF1B) , which exhibits predominantly on Tregs and is responsible for initiating immune modulation . Carriage of TNF-857C (rs1799724) or TNFRSF1B-676T (rs1061622) alleles was associated with a positive response to drug treatment in patients treated with etanercept (PASI75 at 6 months, p = 0.002 and p = 0.001, respectively) . 3.4.3. Adalimumab (ADA) & Infliximab (IFX/INF) CPM CPM (Carboxypeptidase M) is involved in the maturation of macrophages in psoriasis pathogenesis . The CNV of the CPM gene was significantly associated with adalimumab response among 70 Spanish patients (PASI75 at 3 and 6 months, p < 0.05) . HLA The rs9260313 in the HLA-A gene was found to be associated with more favorable responses to adalimumab (PASI75 at 6 months, p = 0.05) . Among 169 Spanish patients, HLA-Cw06 positivity had a better response to adalimumab. (PASI75 at 6 months, p = 0.018) . IL17F IL-17F , activated by IL23/Th17, is recognized as having a critical role in the pathogenesis of psoriasis. In a cohort study in Spain, carriers of TC genotype in IL-17F rs763780 were associated with a lack of response to adalimumab ( n = 67, PASI75 at weeks 24–28, p = 0.0044) while interestingly, with better response to infliximab ( n = 37, PASI at weeks 12–16, p = 0.023; PASI at weeks 24–28, p = 0.02). NFKBIZ The nuclear factor of kappa light polypeptide gene enhancer in B cells inhibitor , zeta (NFKBIZ) gene encodes an atypical inhibitor of nuclear factor κB (IκB) protein, involved in inflammatory signaling of psoriasis . Among 169 Spanish patients, the deletion of NFKBIZ rs3217713 had a better response to adalimumab (PASI75 at 6 months, p = 0.015) . TNF, TNFRSF1B None of the genotyped SNPs of TNF , TNFRSF1A , and TNFRSF1B genes were associated with responsiveness to treatment with infliximab or adalimumab . TRAF3IP2 TNF receptor-associated factor 3 interacting protein 2 (TRAF3IP2) involves in IL-17 signaling and interacts with members of the Rel/NF-κB transcription factor family . The rs13190932 in the TRAF3IP2 gene showed associations with a favorable response to infliximab (PASI75 at 6 months, p = 0.041) . 3.5. IL-12/IL-23 Antagonist Ustekinumab, as an IL12/IL23 antagonist, targets the p40 subunit that is shared by IL-12 and IL-23, whereas guselkumab, tildrakizumab, and risankizumab target the p19 subunit of IL-23. These four drugs are efficacious in treating moderate to severe plaque psoriasis . While ustekinumab is the earliest commercially available drug among IL23 antagonists, relatively abundant studies of the association between the response and gene status have been conducted. In contrast, there is limited research on the genetic predictors of clinical response to guselkumab, tildrakizumab, and risankizumab . 3.5.1. Ustekinumab (UTK) Better Response of Efficacy In a Spanish study enrolled 69 patients, good responders at 4 months were associated with CC genotype in ADAM33 rs2787094 ( p = 0.015), CG/CC genotype in HTR2A rs6311 ( p = 0.037), GT/TT genotype in IL-13 rs848 ( p = 0.037), CC genotype in NFKBIA rs2145623 ( p = 0.024), and CT/CC genotype in TNFR1 rs191190 . Rs151823 and rs26653 in the ERAP1 gene showed associations with a favorable response to anti-IL-12/23 therapy among 22 patients from the UK. Several studies exhibited that the presence of the HLA-Cw*06 or Cw*06:02 allele may serve as a predictor of faster response and better response to ustekinumab in Italian, Dutch, Belgian, American, and Chinese patients . A recent meta-analysis study confirmed that HLA-C*06:02 -positive patients had higher response rates (PASI76 at 6 months, p < 0 .001) . In addition, the presence of the GG genotype on the IL12B rs6887695 SNP and the absence of the AA genotype on the IL12B rs3212227 or the GG genotype on the IL6 rs1800795 SNP significantly increased the probability of therapeutic success in HLA-Cw6 -positive patients . Rs10484554 in the HLA-Cw gene did not show an association with a good response to ustekinumab in a Greek population . Patients with heterozygous genotype (CT) in the IL12B rs3213094 showed better PASI improvement to ustekinumab than the reference genotype (CC) (∆PASI at 3 months, p = 0.017), but the result was not replicated with regard to PASI75 . The genetic polymorphism of TIRAP rs8177374 and TLR5 rs5744174 were associated with a better response in the Danish population (PASI75 at 3 months, p = 0.0051 and p = 0.0012, respectively) . Poor Response of Efficacy In a Spanish study that enrolled 69 patients treating psoriasis with ustekinumab, poor responders at 4 months were associated with CG/CC genotype in CHUK rs11591741 ( p = 0.029), CT/CC genotype in C9orf72 rs774359 ( p = 0.016), AG/GG in C17orf51 rs1975974 ( p = 0.012), CT genotype in SLC22A4 rs1050152 ( p = 0.037), GT/TT genotype in STAT4 rs7574865 ( p = 0.015) and CT/CC genotype in ZNF816A rs9304742 ( p = 0.012) . Among 376 Danish patients, genetic variants of IL1B rs1143623 and rs1143627 related to increased IL-1β levels may be unfavorable outcomes (PASI75 at 3 months, p = 0.0019 and 0.0016, respectively), similar results with anti-TNF agents . An association between the TC genotype of IL-17F rs763780 and no response to ustekinumab was found in 70 Spanish (PASI75 at 3 and 6 months, p = 0.022 and p = 0.016, respectively) . Patients with homozygous (GG) for the rs610604 SNP in TNFAIP3 showed a worse PASI improvement to ustekinumab ( p = 0.031) than the TT genotype . Carriers of allele G in TNFRSF1B rs1061622 under anti-TNF or anti-IL-12/IL-23 treatment tended to be non-responders in 90 patients from Spain (PASI < 50 at 6 months, p = 0.05) . 3.6. IL-17 Antagonist Secukinumab and ixekizumab are human monoclonal antibodies that bind to the protein interleukin IL-17A, while brodalumab is a human monoclonal antibody of IL17R, which means a pan inhibitor of IL-17A, IL-17F, and IL-25. The three IL-17 antagonists are currently used in the treatment of moderate-to-severe psoriasis . 3.6.1. Secukinumab (SCK) and Ixekizumab (IXE) and Brodalumab (BDL) HLA-Cw6 The responses to SCK were comparable up to 18 months between HLA-Cw*06 -positive and -negative patients, as it is highly effective regardless of the HLA-Cw6 status in Italy and Switzerland . IL-17 No associations were found between the five genetic variants of IL-17 (rs2275913, rs8193037, rs3819025, rs7747909, and rs3748067) and ΔPASI, PASI75, or PASI90 after 12 and 24 weeks of anti-IL-17A agents, including SCK and IXE in European . The lack of pharmacogenetic data for BDL was noted during the review. 3.7. PDE4 Antagonist Apremilast, a selective phosphodiesterase 4 (PDE4) inhibitor, is used to treat plaque psoriasis. A Russian study identified 78 pre-selected single-nucleotide polymorphisms, increased minor allele of IL1β (rs1143633), IL4 (IL13) (rs20541), IL23R (rs2201841), and TNFα (rs1800629) genes that are associated with the better outcome in 34 patients (PASI75 at 6.5 months, p = 0.05, p = 0.04, p = 0.03, p = 0.03, respectively) . 3.8. Topical Agents Globally used topical therapies for psoriasis include retinoids, vitamin D analogs, corticosteroids, and coal tar. Lack of evidence emphasizes the association between treatment response and pharmacogenetics of corticosteroids, retinoids, and coal tar. The link between VDR genes, encoding the nuclear hormone receptor for vitamin D3, and the response to calcipotriol has been discussed but remained controversial in different populations . Lindioil is another topical medicine refined from Chinese herbs and is effective in treating plaque psoriasis . It has been reported that HLA-Cw*06:02 positivity showed a better response (PASI75 at 3 months, p = 0.033) while HLA-Cw*01:02 positivity showed a poor response in 72 patients (PASI 75 at 2.5 months, p = 0.019) . Methotrexate (MTX) is an antagonist of the enzymes dihydrofolate reductase (DHFR) and thymidylate synthase (TYMS). It is commonly used as a first-line systemic immunosuppressive therapy for moderate to severe psoriasis. However, significant variations in its efficacy and toxicity exist among individuals. Therefore, several studies have identified potential pharmacogenetic factors that can be used to predict the clinical response of MTX . 3.1.1. ABCC1, ABCC2, ABCG2 The genes encoding the efflux transporters of MTX are ATP-binding cassette (ABC) subfamily C member 1 (ABCC1) , ABCC member 2 (ABCC2) , and ABC subfamily G member 2 (ABCG2) . Overexpression of these genes can lead to multidrug resistance by extruding drugs out of the cell through various mechanisms . In regard to psoriasis, a cohort study of 374 British patients found significant positive associations between methotrexate responder, two of ABCG2 (rs17731538, rs13120400), and three SNPs of ABCC1 (rs35592, rs28364006, rs2238476) with rs35592 being the most significant (PASI75 at 3 months, p = 0.008). One cohort study from Slovenia demonstrated that polymorphism of ABCC2 (rs717620) presented an insufficient response to MTX treatment (75% reduction from baseline PASI score (PASI75) at 6 months, p = 0.039) . About toxicity, a British cohort study has noted that the major allele of six SNPs in ABCC1 (rs11075291, rs1967120, rs3784862, rs246240, rs3784864, and rs2238476) was significantly associated with the onset of adverse events, with rs246240 showing the strongest association ( p = 0.0006) . 3.1.2. ADORA2A Adenosine receptors A2a (ADORA2a) is responsible for mediating the metabolic product of methotrexate. One SNP, rs5760410 of ADORA2A , was weakly associated with the onset of toxicity ( p = 0.03) . 3.1.3. ATIC MTX inhibits 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase (ATIC) , which leads to the accumulation of adenosine, a potent anti-inflammatory agent . Campalani et al. analyzed 188 patients in the United Kingdom (UK) with psoriasis under methotrexate therapy and revealed that allele frequency of ATIC (rs2372536) was significantly increased in patients who discontinued methotrexate owing to intolerable side effects ( p = 0.038) . Another British cohort study found that two SNPs in ATIC (rs2372536 and rs4672768) were associated with the onset of MTX toxicity ( p = 0.01). However, these associations did not remain significant after adjusting for folic acid supplementation . 3.1.4. BHMT Betaine-homocysteine S-methyltransferase (BHMT) is a zinc-containing metalloenzyme responsible for folate-independent remethylation of homocysteine using betaine as the methyl donor . A genotype analysis identified that the BHMT genotype was significantly associated with MTX hepatotoxicity ( p = 0.022) . 3.1.5. DNMT3b DNA methyltransferase 3β (DNMT3b) is a methyltransferase that is involved in de-novo DNA methylation, and its polymorphism is supposed to be associated with increased promoter activity . At least one copy of the variant DNMT3b rs242913 allele has been found to be associated with an insufficient response to MTX when compared to the wild-type ( p = 0.005) . 3.1.6. FOXP3 Forkhead box P3 (FOXP3) appears to function as a master regulator of the regulatory pathway in the development and function of regulatory T cells (Tregs) . A study on a population of 189 southern Indian patients who had used methotrexate for 12 weeks found a significant difference in genotype frequencies of FOXP3 (rs3761548) between responders and non-responders (PASI75 at 3 months, p = 0.003) . 3.1.7. GNMT Glycine N-methyltransferase (GNMT) is a methyltransferase that converts S-adenosylmethionine to S-adenosylhomocysteine and is also a folate-binding protein. The rs10948059 polymorphism is associated with increased expression of the GNMT gene and reduces cell sensitivity to MTX . The patients with at least one variant GNMT allele were more likely to be non-responders to MTX treatment than the reference allele (PASI75 at 6 months, p = 0.0004) . 3.1.8. HLA-Cw6 The human leukocyte antigen (HLA) , known as the human MHC system, regulates the immune system by encoding cell-surface proteins. HLA-Cw6 is a psoriasis susceptibility allele that has been strongly linked to the disease. It was reported that carriers of HLA-Cw6 from southern India had a higher response rate to methotrexate (PASI75 at 3 months, p = 0.003) . A Scotland cohort study with 70 HLA-tested patients demonstrated that more proportion of HLA-Cw6 positive patients was carried on beyond 12 months, as compared to the HLA-Cw6 negative group ( p = 0.05) . 3.1.9. MTHFR The Methylenetetrahydrofolate reductase (MTHFR) enzyme is responsible for catalyzing the formation of 5-methyl-tetrahydrofolic acid, which acts as a methyl donor for the synthesis of methionine from homocysteine. This enzyme is indirectly inhibited by MTX. According to Zhu et al., the PASI 90 response rates to MTX were significantly higher in Han Chinese patients who had the MTHFR rs1801133 TT genotype as compared to those who had the CT and CC genotype (PASI90 at 3 months, p = 0.006). Furthermore, patients with the MTHFR rs1801131 CT genotype had lower PASI 75 response rates to MTX in Han Chinese population (PASI75 at 3 months, p = 0.014). They also had a lower risk of ALT elevation ( p = 0.04) . However, three studies have demonstrated that no significant association was detected between clinical outcomes in individuals with psoriasis treated with methotrexate and SNPs in the MTHFR gene . 3.1.10. SLC19A1 The Solute carrier family 19 , member 1 (SLC19A1) gene encodes the reduced folate carrier (RFC) protein, which actively transports MTX into cells. Multiple point mutations have been identified in SLC19A1 to be associated with impaired MTX transport and resistance to MTX . SLC19A1 (rs1051266) was associated with MTX-induced toxicity instead of efficacy in patients with psoriasis . 3.1.11. SLCO1B1 The encoded protein of solute carrier organic anion transporter family member 1B1 (SLO1B1) is a transmembrane receptor that transports drug compounds into cells. Genetic variations in SLCO1B1 have been linked to delayed MTX clearance and increased toxicity . The haplotype variants have been classified into two groups based on their reported transporter activity: the high-activity group and the low-activity group. Patients with low-activity haplotypes of SLCO1B1 (SLCO1B1*5 and SLCO1B1*15) were less likely to be MTX non-responders as compared to patients with high-activity haplotypes (SLCO1B1*1a and SLCO1B1*1b) (PASI75 at 6 months, p = 0.027) . 3.1.12. TNIP1 TNFAIP3 interacting protein 1 (TNIP1) , as one of the psoriasis susceptibility genes, is related to the immune response IL-23 signaling pathway. A Chinese study mentioned that in 221 patients with psoriasis, the TT genotype of TNIP1 rs10036748 showed a better response to MTX (PASI75 at 3 months, p = 0.043) . 3.1.13. TYMS Thymidylate synthase (TS), encoded by the thymidylate synthase gene (TYMS) , is a critical protein for pyrimidine synthesis and responsible for DNA synthesis and repair, which could be inhibited by MTX . The association of polymorphisms of TYMS , TS levels, and MTX response was found in several diseases . For example, polymorphism rs34743033 is a 28-base pair (bp) with double or triple tandem repeat (2R or 3R) located on the 5′ untranslated region (UTR) . A study performed in European adults with psoriasis found that the rs34743033 3R allele was more frequent in patients with poor therapeutic response to methotrexate, but the loss of significance was noted after the exclusion of palmoplantar pustulosis patients. In addition, this allele was significantly associated with an increased incidence of MTX-induced toxicity in patients who did not receive folic acid ( p = 0.0025). Another TS polymorphism, 3′-UTR 6bp del of rs11280056, was significantly more frequent in patients with an adverse event irrespective of folic acid supplementation ( p = 0.025) . In short, positive genotypic associations were detected with methotrexate responders in ten genes ( ABCC1 , ABCC2 , ABCG2 , DNMT3b , FOXP3 , GNMT , HLA-Cw , MTHFR , SLCO1B1 , TNIP1 ) while the development of methotrexate-related toxicity in five genes ( ABCC1 , ATIC , ADORA2A , BHMT , MTHFR , SLC19A1 , TYMS ). Nonetheless, three British studies seemed to believe that toxicity has overlapped populations; hence, several replicated results may also be owing to similar databases . The genes encoding the efflux transporters of MTX are ATP-binding cassette (ABC) subfamily C member 1 (ABCC1) , ABCC member 2 (ABCC2) , and ABC subfamily G member 2 (ABCG2) . Overexpression of these genes can lead to multidrug resistance by extruding drugs out of the cell through various mechanisms . In regard to psoriasis, a cohort study of 374 British patients found significant positive associations between methotrexate responder, two of ABCG2 (rs17731538, rs13120400), and three SNPs of ABCC1 (rs35592, rs28364006, rs2238476) with rs35592 being the most significant (PASI75 at 3 months, p = 0.008). One cohort study from Slovenia demonstrated that polymorphism of ABCC2 (rs717620) presented an insufficient response to MTX treatment (75% reduction from baseline PASI score (PASI75) at 6 months, p = 0.039) . About toxicity, a British cohort study has noted that the major allele of six SNPs in ABCC1 (rs11075291, rs1967120, rs3784862, rs246240, rs3784864, and rs2238476) was significantly associated with the onset of adverse events, with rs246240 showing the strongest association ( p = 0.0006) . Adenosine receptors A2a (ADORA2a) is responsible for mediating the metabolic product of methotrexate. One SNP, rs5760410 of ADORA2A , was weakly associated with the onset of toxicity ( p = 0.03) . MTX inhibits 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase (ATIC) , which leads to the accumulation of adenosine, a potent anti-inflammatory agent . Campalani et al. analyzed 188 patients in the United Kingdom (UK) with psoriasis under methotrexate therapy and revealed that allele frequency of ATIC (rs2372536) was significantly increased in patients who discontinued methotrexate owing to intolerable side effects ( p = 0.038) . Another British cohort study found that two SNPs in ATIC (rs2372536 and rs4672768) were associated with the onset of MTX toxicity ( p = 0.01). However, these associations did not remain significant after adjusting for folic acid supplementation . Betaine-homocysteine S-methyltransferase (BHMT) is a zinc-containing metalloenzyme responsible for folate-independent remethylation of homocysteine using betaine as the methyl donor . A genotype analysis identified that the BHMT genotype was significantly associated with MTX hepatotoxicity ( p = 0.022) . DNA methyltransferase 3β (DNMT3b) is a methyltransferase that is involved in de-novo DNA methylation, and its polymorphism is supposed to be associated with increased promoter activity . At least one copy of the variant DNMT3b rs242913 allele has been found to be associated with an insufficient response to MTX when compared to the wild-type ( p = 0.005) . Forkhead box P3 (FOXP3) appears to function as a master regulator of the regulatory pathway in the development and function of regulatory T cells (Tregs) . A study on a population of 189 southern Indian patients who had used methotrexate for 12 weeks found a significant difference in genotype frequencies of FOXP3 (rs3761548) between responders and non-responders (PASI75 at 3 months, p = 0.003) . Glycine N-methyltransferase (GNMT) is a methyltransferase that converts S-adenosylmethionine to S-adenosylhomocysteine and is also a folate-binding protein. The rs10948059 polymorphism is associated with increased expression of the GNMT gene and reduces cell sensitivity to MTX . The patients with at least one variant GNMT allele were more likely to be non-responders to MTX treatment than the reference allele (PASI75 at 6 months, p = 0.0004) . The human leukocyte antigen (HLA) , known as the human MHC system, regulates the immune system by encoding cell-surface proteins. HLA-Cw6 is a psoriasis susceptibility allele that has been strongly linked to the disease. It was reported that carriers of HLA-Cw6 from southern India had a higher response rate to methotrexate (PASI75 at 3 months, p = 0.003) . A Scotland cohort study with 70 HLA-tested patients demonstrated that more proportion of HLA-Cw6 positive patients was carried on beyond 12 months, as compared to the HLA-Cw6 negative group ( p = 0.05) . The Methylenetetrahydrofolate reductase (MTHFR) enzyme is responsible for catalyzing the formation of 5-methyl-tetrahydrofolic acid, which acts as a methyl donor for the synthesis of methionine from homocysteine. This enzyme is indirectly inhibited by MTX. According to Zhu et al., the PASI 90 response rates to MTX were significantly higher in Han Chinese patients who had the MTHFR rs1801133 TT genotype as compared to those who had the CT and CC genotype (PASI90 at 3 months, p = 0.006). Furthermore, patients with the MTHFR rs1801131 CT genotype had lower PASI 75 response rates to MTX in Han Chinese population (PASI75 at 3 months, p = 0.014). They also had a lower risk of ALT elevation ( p = 0.04) . However, three studies have demonstrated that no significant association was detected between clinical outcomes in individuals with psoriasis treated with methotrexate and SNPs in the MTHFR gene . The Solute carrier family 19 , member 1 (SLC19A1) gene encodes the reduced folate carrier (RFC) protein, which actively transports MTX into cells. Multiple point mutations have been identified in SLC19A1 to be associated with impaired MTX transport and resistance to MTX . SLC19A1 (rs1051266) was associated with MTX-induced toxicity instead of efficacy in patients with psoriasis . The encoded protein of solute carrier organic anion transporter family member 1B1 (SLO1B1) is a transmembrane receptor that transports drug compounds into cells. Genetic variations in SLCO1B1 have been linked to delayed MTX clearance and increased toxicity . The haplotype variants have been classified into two groups based on their reported transporter activity: the high-activity group and the low-activity group. Patients with low-activity haplotypes of SLCO1B1 (SLCO1B1*5 and SLCO1B1*15) were less likely to be MTX non-responders as compared to patients with high-activity haplotypes (SLCO1B1*1a and SLCO1B1*1b) (PASI75 at 6 months, p = 0.027) . TNFAIP3 interacting protein 1 (TNIP1) , as one of the psoriasis susceptibility genes, is related to the immune response IL-23 signaling pathway. A Chinese study mentioned that in 221 patients with psoriasis, the TT genotype of TNIP1 rs10036748 showed a better response to MTX (PASI75 at 3 months, p = 0.043) . Thymidylate synthase (TS), encoded by the thymidylate synthase gene (TYMS) , is a critical protein for pyrimidine synthesis and responsible for DNA synthesis and repair, which could be inhibited by MTX . The association of polymorphisms of TYMS , TS levels, and MTX response was found in several diseases . For example, polymorphism rs34743033 is a 28-base pair (bp) with double or triple tandem repeat (2R or 3R) located on the 5′ untranslated region (UTR) . A study performed in European adults with psoriasis found that the rs34743033 3R allele was more frequent in patients with poor therapeutic response to methotrexate, but the loss of significance was noted after the exclusion of palmoplantar pustulosis patients. In addition, this allele was significantly associated with an increased incidence of MTX-induced toxicity in patients who did not receive folic acid ( p = 0.0025). Another TS polymorphism, 3′-UTR 6bp del of rs11280056, was significantly more frequent in patients with an adverse event irrespective of folic acid supplementation ( p = 0.025) . In short, positive genotypic associations were detected with methotrexate responders in ten genes ( ABCC1 , ABCC2 , ABCG2 , DNMT3b , FOXP3 , GNMT , HLA-Cw , MTHFR , SLCO1B1 , TNIP1 ) while the development of methotrexate-related toxicity in five genes ( ABCC1 , ATIC , ADORA2A , BHMT , MTHFR , SLC19A1 , TYMS ). Nonetheless, three British studies seemed to believe that toxicity has overlapped populations; hence, several replicated results may also be owing to similar databases . Acitretin is an oral vitamin A derivative that is used to treat psoriasis by inhibiting epidermal proliferation, inflammatory processes, and angiogenesis. lists the genetic polymorphisms that have been associated with the response of acitretin in patients with psoriasis. 3.2.1. ApoE Apolipoprotein E (ApoE) is a glycoprotein component of chylomicrons and VLDL. It has a crucial role in regulating lipid profiles and metabolism . The lipid and lipoprotein abnormalities as a consequence of ApoE gene polymorphism are close to the side effects during acitretin therapy. In addition, ApoE levels have been linked with clinical improvement in psoriasis, indicating a potential role of the gene in acitretin treatment for psoriasis . However, according to Campalani, E, et al., while ApoE gene polymorphisms are associated with psoriasis, they do not determine the response of the disease to acitretin . 3.2.2. ANKLE1 Ankyrin repeat and LEM domain containing 1 (ANKLE1) enables endonuclease activity and plays a role in positively regulating the response to DNA damage stimulus and protein export from the nucleus. ANKLE1 rs11086065 AG/GG was associated with an ineffective response compared to the GG genotype in 166 Chinese patients (PASI75 at 3 months, p = 0.003) . 3.2.3. ARHGEF3 Rho guanine nucleotide exchange factor 3 (ARHGEF3) activates Rho GTPase, which involve in bone cell biology. ARHGEF3 rs3821414 CT was associated with a more effective response compared to the TT genotype (PASI75 at 3 months, p = 0.01) . 3.2.4. CRB2 Crumbs cell polarity complex component 2 (CRB2) encodes proteins that are components of the Crumbs cell polarity complex, which plays a crucial role in apical-basal epithelial polarity and cellular adhesion. CRB2 rs1105223 TT/CT was also associated with acitretin efficacy compared to the CC genotype (PASI75 at 3 months, p = 0.048) . 3.2.5. HLA-DQA1*02:01 HLA-DQA1*0201 alleles may act as psoriasis susceptibility genes or may be closely linked to the susceptibility genes in Han Chinese . Among 100 Chinese individuals, those who were positive for the DQA10201 allele demonstrated a more favorable response to acitretin compared to those who were negative for the same allele. (PASI75 at 2 months, p = 0.001) . 3.2.6. HLA-DQB1*02:02 HLA-DQB1 alleles have been mentioned to involve in genetic predisposition to psoriasis vulgaris in the Slovak population . In 100 Chinese patients, the DQB1*0202 -positive patients showed a better response to acitretin than the DQB1*0202 -negative patients (PASI75 at 2 months, p = 0.005) . 3.2.7. HLA-G HLA-G is a nonclassical class I MHC molecule that plays a role in suppressing the immune system by inhibiting natural killer cells and T cells . Among patients treated with acitretin, Borghi, Alessandro, et al. observed a significantly increased frequency of the 14 bp sequence deletion in the exon 8 of the HLA-G allele, functioning as a modification of mRNA stability, in responder patients, in comparison to the non-responders (PASI75 at 4 months, p = 0.008) . 3.2.8. IL-12B Patients with the IL-12B rs3212227 genotype of TG were more responsive to acitretin in the treatment of psoriasis in 43 Chinese patients (PASI50, p = 0.035) . 3.2.9. IL-23R Acitretin was found to improve the secondary non-response to TNFα monoclonal antibody in patients who were homozygous for the AA genotype at the SNP rs112009032 in the IL-23R gene (PASI75, p = 0.02) . 3.2.10. SFRP4 Secreted frizzled-related protein 4 (SFRP4) is a negative regulator of the Wnt signaling pathway, and the downregulation of SFRP4 is a possible mechanism contributing to the hyperplasia of the epidermis of psoriasis . The GG/GT variation of SFRP4 rs1802073 has been found to be associated with a more effective response to acitretin compared to the TT genotype (PASI75 at 3 months, p = 0.007) . 3.2.11. VEGF Vascular endothelial growth factor (VEGF) promotes angiogenesis in the pathophysiology of psoriasis, and the variant of the VEGF gene is supposed to affect the ability of acitretin to downregulate VEGF production . The TT genotype of the VEGF rs833061 was associated with non-response to oral acitretin, whereas the TC genotype was associated with a significant response to acitretin (PASI75 at 3 months, p = 0.01) . However, the result of VEGF polymorphism was not replicated in the population of southern China . Apolipoprotein E (ApoE) is a glycoprotein component of chylomicrons and VLDL. It has a crucial role in regulating lipid profiles and metabolism . The lipid and lipoprotein abnormalities as a consequence of ApoE gene polymorphism are close to the side effects during acitretin therapy. In addition, ApoE levels have been linked with clinical improvement in psoriasis, indicating a potential role of the gene in acitretin treatment for psoriasis . However, according to Campalani, E, et al., while ApoE gene polymorphisms are associated with psoriasis, they do not determine the response of the disease to acitretin . Ankyrin repeat and LEM domain containing 1 (ANKLE1) enables endonuclease activity and plays a role in positively regulating the response to DNA damage stimulus and protein export from the nucleus. ANKLE1 rs11086065 AG/GG was associated with an ineffective response compared to the GG genotype in 166 Chinese patients (PASI75 at 3 months, p = 0.003) . Rho guanine nucleotide exchange factor 3 (ARHGEF3) activates Rho GTPase, which involve in bone cell biology. ARHGEF3 rs3821414 CT was associated with a more effective response compared to the TT genotype (PASI75 at 3 months, p = 0.01) . Crumbs cell polarity complex component 2 (CRB2) encodes proteins that are components of the Crumbs cell polarity complex, which plays a crucial role in apical-basal epithelial polarity and cellular adhesion. CRB2 rs1105223 TT/CT was also associated with acitretin efficacy compared to the CC genotype (PASI75 at 3 months, p = 0.048) . HLA-DQA1*0201 alleles may act as psoriasis susceptibility genes or may be closely linked to the susceptibility genes in Han Chinese . Among 100 Chinese individuals, those who were positive for the DQA10201 allele demonstrated a more favorable response to acitretin compared to those who were negative for the same allele. (PASI75 at 2 months, p = 0.001) . HLA-DQB1 alleles have been mentioned to involve in genetic predisposition to psoriasis vulgaris in the Slovak population . In 100 Chinese patients, the DQB1*0202 -positive patients showed a better response to acitretin than the DQB1*0202 -negative patients (PASI75 at 2 months, p = 0.005) . HLA-G is a nonclassical class I MHC molecule that plays a role in suppressing the immune system by inhibiting natural killer cells and T cells . Among patients treated with acitretin, Borghi, Alessandro, et al. observed a significantly increased frequency of the 14 bp sequence deletion in the exon 8 of the HLA-G allele, functioning as a modification of mRNA stability, in responder patients, in comparison to the non-responders (PASI75 at 4 months, p = 0.008) . Patients with the IL-12B rs3212227 genotype of TG were more responsive to acitretin in the treatment of psoriasis in 43 Chinese patients (PASI50, p = 0.035) . Acitretin was found to improve the secondary non-response to TNFα monoclonal antibody in patients who were homozygous for the AA genotype at the SNP rs112009032 in the IL-23R gene (PASI75, p = 0.02) . Secreted frizzled-related protein 4 (SFRP4) is a negative regulator of the Wnt signaling pathway, and the downregulation of SFRP4 is a possible mechanism contributing to the hyperplasia of the epidermis of psoriasis . The GG/GT variation of SFRP4 rs1802073 has been found to be associated with a more effective response to acitretin compared to the TT genotype (PASI75 at 3 months, p = 0.007) . Vascular endothelial growth factor (VEGF) promotes angiogenesis in the pathophysiology of psoriasis, and the variant of the VEGF gene is supposed to affect the ability of acitretin to downregulate VEGF production . The TT genotype of the VEGF rs833061 was associated with non-response to oral acitretin, whereas the TC genotype was associated with a significant response to acitretin (PASI75 at 3 months, p = 0.01) . However, the result of VEGF polymorphism was not replicated in the population of southern China . Cyclosporine, a calcineurin inhibitor, is commonly used to treat moderate to severe psoriasis. However, clinical studies investigating the pharmacogenetics of cyclosporine in psoriasis patients are currently lacking . 3.3.1. ABCB1 One Greek study enrolled 84 patients revealed that ATP-binding cassette subfamily B member 1 (ABCB1) rs1045642 had statistically significant association with a negative response of cyclosporin (PASI < 50 at 3 months, p = 0.0075) . In 168 Russian patients with psoriasis receiving cyclosporine therapy, a strongly negative association was observed for the TT/CT genotype of ABCB1 rs1045642 (PASI75 at 3 months, p < 0.001), the TT/CT genotype of ABCB1 rs1128503 (PASI75 at 3 months, p = 0.027), and the TT/GT genotype of ABCB1 rs2032582 (PASI75 at 3 months, p = 0.048), respectively. Additionally, the TGC haplotype was significantly linked to a negative response (PASI75 at 3 months, p < 0.001) . 3.3.2. CALM1 Calmodulin (CALM1) is known as a calcium-dependent protein and is related to cell proliferation and epidermal hyperplasia in psoriasis . In 200 Greek patients, the allele T of CALM1 rs12885713 displayed a significantly better response to cyclosporin (PASI75 at 3 months, p = 0.011) . 3.3.3. MALT1 MALT1 encodes MALT1 paracaspase, a potent activator of the transcription factors NF-κB and AP-1, and hence has a role in psoriasis . MALT1 rs287411 allele G was associated with the effective response compared to allele A (PASI75 at 3 months, p < 0.001) . One Greek study enrolled 84 patients revealed that ATP-binding cassette subfamily B member 1 (ABCB1) rs1045642 had statistically significant association with a negative response of cyclosporin (PASI < 50 at 3 months, p = 0.0075) . In 168 Russian patients with psoriasis receiving cyclosporine therapy, a strongly negative association was observed for the TT/CT genotype of ABCB1 rs1045642 (PASI75 at 3 months, p < 0.001), the TT/CT genotype of ABCB1 rs1128503 (PASI75 at 3 months, p = 0.027), and the TT/GT genotype of ABCB1 rs2032582 (PASI75 at 3 months, p = 0.048), respectively. Additionally, the TGC haplotype was significantly linked to a negative response (PASI75 at 3 months, p < 0.001) . Calmodulin (CALM1) is known as a calcium-dependent protein and is related to cell proliferation and epidermal hyperplasia in psoriasis . In 200 Greek patients, the allele T of CALM1 rs12885713 displayed a significantly better response to cyclosporin (PASI75 at 3 months, p = 0.011) . MALT1 encodes MALT1 paracaspase, a potent activator of the transcription factors NF-κB and AP-1, and hence has a role in psoriasis . MALT1 rs287411 allele G was associated with the effective response compared to allele A (PASI75 at 3 months, p < 0.001) . There are four FDA-approved TNF antagonists for plaque psoriasis, including etanercept, adalimumab, infliximab, and certolizumab pegol. According to our review of the literature, pharmacogenetic research has been mainly focused on the first three drugs. Etanercept is a recombinant fusion protein comprising two extracellular parts of the human tumor necrosis factor receptor 2 (TNFR2) coupled to a human immunoglobulin 1 (IgG1) Fc. Adalimumab is a fully human monoclonal antibody with human TNF binding Fab and human IgG1 Fc backbone, whereas infliximab is a chimeric IgG1 monoclonal antibody composed of a human constant and a murine variable region binding to TNFα . Despite their unique pharmacological profile from each other, TNF antagonists act on the same pathologic mechanism to achieve therapeutic outcomes. Therefore, some pharmacogenetic researchers regarded all TNF antagonists as one category to analyze potential predictive genetic markers under a large-scale population, while some discussed each TNF antagonist separately . 3.4.1. Nonspecific TNF Antagonist Better Response of Efficacy In 144 Spanish patients, carriers of the CT/CC allele in MAP3K1 rs96844 and the CT/TT allele in HLA-C rs12191877 achieved a better PASI75 response at 3 months. The study also found significantly better results for carriers of MAP3K1 polymorphism and CT/TT in CDKAL1 rs6908425 at 6 months . Another study enrolled 70 patients in Spain implicated that patients harboring high-affinity alleles, FCGR2A-H131R (rs1801274) and FCGR 3A-V158F (rs396991), contribute to better mean BSA improvement but not PASI improvement at 6–8 weeks after anti-TNF treatment of psoriasis . The result between FCGR 3A-V158F (rs396991) and response to anti-TNFα therapy (PASI75 at 6 months, p = 0.005), especially etanercept (PASI75 at 6 months, p = 0.01), was replicated in 100 Caucasian patients from Greece, while FCGR2A-H131R (rs1801274) was found to be no association . A study conducted in 199 Greek patients found an association between carriers of CT/CC in HLA-C rs10484554 and a good response to anti-TNF agents (PASI 75 at 6 months, p = 0.0032), especially adalimumab ( p = 0.0007) . In 238 Caucasian adults in Spain, the rs4819554 promoter SNP allele A of the IL17RA gene was significantly more prevalent among responders at week 12 . Moreover, several genetic variants exert favorable effects at 6 months of treatment in 109 patients with psoriasis from Spain, including GG genotype of IL23R rs11209026 (PASI90 p = 0.006), GG genotype of TNF-a-238 rs361525 (PASI75, p = 0.049), CT/TT genotypes of TNF-a-857 rs1799724 (PASI75, p = 0.006, ΔPASI, p = 0.004; BSA, p = 0.009), and TT genotype of TNF-a-1031 rs1799964 (PASI75, p = 0.038; ΔPASI, p = 0.041; at 3 months, PASI75, p = 0.047) . Poor Response of Efficacy In 144 Spanish patients, four SNPs were associated with the inability to achieve PASI75 at three months, including AG/GG allele in PGLYRP4-24 rs2916205, CC allele in ZNF816A rs9304742, AA allele in CTNNA2 rs11126740, and AG/GG allele in IL12B rs2546890. Additionally, the results for polymorphisms in the IL12B gene were replicated at six months and one year. The study also obtained significant results for the FCGR2A and HTR2A polymorphism at 6 months . Notably, the result of the FCGR2A polymorphism showed variability between studies . In 376 Danish patients, five SNPs, which are IL1B (rs1143623, rs1143627), LY96 (rs11465996), and TLR2 (rs11938228, rs4696480), were all associated with nonresponse to treatment . One study found a higher frequency of G-carriers of the TNFRSF1B rs1061622 among non-responders (PASI < 50) compared to cases achieving PASI75 to TNF blockers in 90 Caucasians from Spain . Toxicity Among the 161 Caucasian patients, the polymorphism rs10782001 in FBXL19 and rs11209026 in IL23R may contribute to an increased risk of the secondary development of psoriasiform reactions owing to TNF blocking. In addition, in 70 Spanish patients, the copy number variation (CNV) harboring three genes (ARNT2, LOC101929586, and MIR5572) was related to the occurrence of paradoxical psoriasiform reactions at 3 and 6 months ( p = 0.006) . In contrast, the presence of rs3087243 in CTLA4 , rs651630 in SLC12A8 , or rs1800453 in TAP1 was related to protection against psoriasiform lesions . Interestingly, the IL23R rs11209026 polymorphism was reported as having a protective role reported in classical psoriasis. 3.4.2. Etanercept (ETA/ETN) CD84 Cluster of Differentiation 84 (CD84) gene encodes a membrane glycoprotein, which enhances IFN-γ secretion in activated T cells . In 161 patients from the Netherlands, the GA genotype in CD84 (rs6427528) had a more sensitive response to etanercept than the referential GG genotype (ΔPASI at 3 months, p = 0.025) . FCGR3A This gene encodes a receptor for the Fc portion of immunoglobulin G, where the TNF antagonist binds specifically. In 100 psoriasis patients in Greece, the study showed an association with FCGR3A-V158F (rs396991) and better response to etanercept (PASI75 at 6 months, p = 0.01) . TNFAIP3 TNFα induced protein 3 (TNFAIP3) plays a protective role against the harmful effects of inflammation and is involved in immune regulation . Rs610604 in TNFAIP3 showed associations with good responses to etanercept (PASI75 at 6 months, p = 0.007) . TNF, TNFRSF1B TNFα transmits signals through TNF receptor superfamily member 1B (TNFRSF1B) , which exhibits predominantly on Tregs and is responsible for initiating immune modulation . Carriage of TNF-857C (rs1799724) or TNFRSF1B-676T (rs1061622) alleles was associated with a positive response to drug treatment in patients treated with etanercept (PASI75 at 6 months, p = 0.002 and p = 0.001, respectively) . 3.4.3. Adalimumab (ADA) & Infliximab (IFX/INF) CPM CPM (Carboxypeptidase M) is involved in the maturation of macrophages in psoriasis pathogenesis . The CNV of the CPM gene was significantly associated with adalimumab response among 70 Spanish patients (PASI75 at 3 and 6 months, p < 0.05) . HLA The rs9260313 in the HLA-A gene was found to be associated with more favorable responses to adalimumab (PASI75 at 6 months, p = 0.05) . Among 169 Spanish patients, HLA-Cw06 positivity had a better response to adalimumab. (PASI75 at 6 months, p = 0.018) . IL17F IL-17F , activated by IL23/Th17, is recognized as having a critical role in the pathogenesis of psoriasis. In a cohort study in Spain, carriers of TC genotype in IL-17F rs763780 were associated with a lack of response to adalimumab ( n = 67, PASI75 at weeks 24–28, p = 0.0044) while interestingly, with better response to infliximab ( n = 37, PASI at weeks 12–16, p = 0.023; PASI at weeks 24–28, p = 0.02). NFKBIZ The nuclear factor of kappa light polypeptide gene enhancer in B cells inhibitor , zeta (NFKBIZ) gene encodes an atypical inhibitor of nuclear factor κB (IκB) protein, involved in inflammatory signaling of psoriasis . Among 169 Spanish patients, the deletion of NFKBIZ rs3217713 had a better response to adalimumab (PASI75 at 6 months, p = 0.015) . TNF, TNFRSF1B None of the genotyped SNPs of TNF , TNFRSF1A , and TNFRSF1B genes were associated with responsiveness to treatment with infliximab or adalimumab . TRAF3IP2 TNF receptor-associated factor 3 interacting protein 2 (TRAF3IP2) involves in IL-17 signaling and interacts with members of the Rel/NF-κB transcription factor family . The rs13190932 in the TRAF3IP2 gene showed associations with a favorable response to infliximab (PASI75 at 6 months, p = 0.041) . Better Response of Efficacy In 144 Spanish patients, carriers of the CT/CC allele in MAP3K1 rs96844 and the CT/TT allele in HLA-C rs12191877 achieved a better PASI75 response at 3 months. The study also found significantly better results for carriers of MAP3K1 polymorphism and CT/TT in CDKAL1 rs6908425 at 6 months . Another study enrolled 70 patients in Spain implicated that patients harboring high-affinity alleles, FCGR2A-H131R (rs1801274) and FCGR 3A-V158F (rs396991), contribute to better mean BSA improvement but not PASI improvement at 6–8 weeks after anti-TNF treatment of psoriasis . The result between FCGR 3A-V158F (rs396991) and response to anti-TNFα therapy (PASI75 at 6 months, p = 0.005), especially etanercept (PASI75 at 6 months, p = 0.01), was replicated in 100 Caucasian patients from Greece, while FCGR2A-H131R (rs1801274) was found to be no association . A study conducted in 199 Greek patients found an association between carriers of CT/CC in HLA-C rs10484554 and a good response to anti-TNF agents (PASI 75 at 6 months, p = 0.0032), especially adalimumab ( p = 0.0007) . In 238 Caucasian adults in Spain, the rs4819554 promoter SNP allele A of the IL17RA gene was significantly more prevalent among responders at week 12 . Moreover, several genetic variants exert favorable effects at 6 months of treatment in 109 patients with psoriasis from Spain, including GG genotype of IL23R rs11209026 (PASI90 p = 0.006), GG genotype of TNF-a-238 rs361525 (PASI75, p = 0.049), CT/TT genotypes of TNF-a-857 rs1799724 (PASI75, p = 0.006, ΔPASI, p = 0.004; BSA, p = 0.009), and TT genotype of TNF-a-1031 rs1799964 (PASI75, p = 0.038; ΔPASI, p = 0.041; at 3 months, PASI75, p = 0.047) . Poor Response of Efficacy In 144 Spanish patients, four SNPs were associated with the inability to achieve PASI75 at three months, including AG/GG allele in PGLYRP4-24 rs2916205, CC allele in ZNF816A rs9304742, AA allele in CTNNA2 rs11126740, and AG/GG allele in IL12B rs2546890. Additionally, the results for polymorphisms in the IL12B gene were replicated at six months and one year. The study also obtained significant results for the FCGR2A and HTR2A polymorphism at 6 months . Notably, the result of the FCGR2A polymorphism showed variability between studies . In 376 Danish patients, five SNPs, which are IL1B (rs1143623, rs1143627), LY96 (rs11465996), and TLR2 (rs11938228, rs4696480), were all associated with nonresponse to treatment . One study found a higher frequency of G-carriers of the TNFRSF1B rs1061622 among non-responders (PASI < 50) compared to cases achieving PASI75 to TNF blockers in 90 Caucasians from Spain . Toxicity Among the 161 Caucasian patients, the polymorphism rs10782001 in FBXL19 and rs11209026 in IL23R may contribute to an increased risk of the secondary development of psoriasiform reactions owing to TNF blocking. In addition, in 70 Spanish patients, the copy number variation (CNV) harboring three genes (ARNT2, LOC101929586, and MIR5572) was related to the occurrence of paradoxical psoriasiform reactions at 3 and 6 months ( p = 0.006) . In contrast, the presence of rs3087243 in CTLA4 , rs651630 in SLC12A8 , or rs1800453 in TAP1 was related to protection against psoriasiform lesions . Interestingly, the IL23R rs11209026 polymorphism was reported as having a protective role reported in classical psoriasis. In 144 Spanish patients, carriers of the CT/CC allele in MAP3K1 rs96844 and the CT/TT allele in HLA-C rs12191877 achieved a better PASI75 response at 3 months. The study also found significantly better results for carriers of MAP3K1 polymorphism and CT/TT in CDKAL1 rs6908425 at 6 months . Another study enrolled 70 patients in Spain implicated that patients harboring high-affinity alleles, FCGR2A-H131R (rs1801274) and FCGR 3A-V158F (rs396991), contribute to better mean BSA improvement but not PASI improvement at 6–8 weeks after anti-TNF treatment of psoriasis . The result between FCGR 3A-V158F (rs396991) and response to anti-TNFα therapy (PASI75 at 6 months, p = 0.005), especially etanercept (PASI75 at 6 months, p = 0.01), was replicated in 100 Caucasian patients from Greece, while FCGR2A-H131R (rs1801274) was found to be no association . A study conducted in 199 Greek patients found an association between carriers of CT/CC in HLA-C rs10484554 and a good response to anti-TNF agents (PASI 75 at 6 months, p = 0.0032), especially adalimumab ( p = 0.0007) . In 238 Caucasian adults in Spain, the rs4819554 promoter SNP allele A of the IL17RA gene was significantly more prevalent among responders at week 12 . Moreover, several genetic variants exert favorable effects at 6 months of treatment in 109 patients with psoriasis from Spain, including GG genotype of IL23R rs11209026 (PASI90 p = 0.006), GG genotype of TNF-a-238 rs361525 (PASI75, p = 0.049), CT/TT genotypes of TNF-a-857 rs1799724 (PASI75, p = 0.006, ΔPASI, p = 0.004; BSA, p = 0.009), and TT genotype of TNF-a-1031 rs1799964 (PASI75, p = 0.038; ΔPASI, p = 0.041; at 3 months, PASI75, p = 0.047) . In 144 Spanish patients, four SNPs were associated with the inability to achieve PASI75 at three months, including AG/GG allele in PGLYRP4-24 rs2916205, CC allele in ZNF816A rs9304742, AA allele in CTNNA2 rs11126740, and AG/GG allele in IL12B rs2546890. Additionally, the results for polymorphisms in the IL12B gene were replicated at six months and one year. The study also obtained significant results for the FCGR2A and HTR2A polymorphism at 6 months . Notably, the result of the FCGR2A polymorphism showed variability between studies . In 376 Danish patients, five SNPs, which are IL1B (rs1143623, rs1143627), LY96 (rs11465996), and TLR2 (rs11938228, rs4696480), were all associated with nonresponse to treatment . One study found a higher frequency of G-carriers of the TNFRSF1B rs1061622 among non-responders (PASI < 50) compared to cases achieving PASI75 to TNF blockers in 90 Caucasians from Spain . Among the 161 Caucasian patients, the polymorphism rs10782001 in FBXL19 and rs11209026 in IL23R may contribute to an increased risk of the secondary development of psoriasiform reactions owing to TNF blocking. In addition, in 70 Spanish patients, the copy number variation (CNV) harboring three genes (ARNT2, LOC101929586, and MIR5572) was related to the occurrence of paradoxical psoriasiform reactions at 3 and 6 months ( p = 0.006) . In contrast, the presence of rs3087243 in CTLA4 , rs651630 in SLC12A8 , or rs1800453 in TAP1 was related to protection against psoriasiform lesions . Interestingly, the IL23R rs11209026 polymorphism was reported as having a protective role reported in classical psoriasis. CD84 Cluster of Differentiation 84 (CD84) gene encodes a membrane glycoprotein, which enhances IFN-γ secretion in activated T cells . In 161 patients from the Netherlands, the GA genotype in CD84 (rs6427528) had a more sensitive response to etanercept than the referential GG genotype (ΔPASI at 3 months, p = 0.025) . FCGR3A This gene encodes a receptor for the Fc portion of immunoglobulin G, where the TNF antagonist binds specifically. In 100 psoriasis patients in Greece, the study showed an association with FCGR3A-V158F (rs396991) and better response to etanercept (PASI75 at 6 months, p = 0.01) . TNFAIP3 TNFα induced protein 3 (TNFAIP3) plays a protective role against the harmful effects of inflammation and is involved in immune regulation . Rs610604 in TNFAIP3 showed associations with good responses to etanercept (PASI75 at 6 months, p = 0.007) . TNF, TNFRSF1B TNFα transmits signals through TNF receptor superfamily member 1B (TNFRSF1B) , which exhibits predominantly on Tregs and is responsible for initiating immune modulation . Carriage of TNF-857C (rs1799724) or TNFRSF1B-676T (rs1061622) alleles was associated with a positive response to drug treatment in patients treated with etanercept (PASI75 at 6 months, p = 0.002 and p = 0.001, respectively) . Cluster of Differentiation 84 (CD84) gene encodes a membrane glycoprotein, which enhances IFN-γ secretion in activated T cells . In 161 patients from the Netherlands, the GA genotype in CD84 (rs6427528) had a more sensitive response to etanercept than the referential GG genotype (ΔPASI at 3 months, p = 0.025) . This gene encodes a receptor for the Fc portion of immunoglobulin G, where the TNF antagonist binds specifically. In 100 psoriasis patients in Greece, the study showed an association with FCGR3A-V158F (rs396991) and better response to etanercept (PASI75 at 6 months, p = 0.01) . TNFα induced protein 3 (TNFAIP3) plays a protective role against the harmful effects of inflammation and is involved in immune regulation . Rs610604 in TNFAIP3 showed associations with good responses to etanercept (PASI75 at 6 months, p = 0.007) . TNFα transmits signals through TNF receptor superfamily member 1B (TNFRSF1B) , which exhibits predominantly on Tregs and is responsible for initiating immune modulation . Carriage of TNF-857C (rs1799724) or TNFRSF1B-676T (rs1061622) alleles was associated with a positive response to drug treatment in patients treated with etanercept (PASI75 at 6 months, p = 0.002 and p = 0.001, respectively) . CPM CPM (Carboxypeptidase M) is involved in the maturation of macrophages in psoriasis pathogenesis . The CNV of the CPM gene was significantly associated with adalimumab response among 70 Spanish patients (PASI75 at 3 and 6 months, p < 0.05) . HLA The rs9260313 in the HLA-A gene was found to be associated with more favorable responses to adalimumab (PASI75 at 6 months, p = 0.05) . Among 169 Spanish patients, HLA-Cw06 positivity had a better response to adalimumab. (PASI75 at 6 months, p = 0.018) . IL17F IL-17F , activated by IL23/Th17, is recognized as having a critical role in the pathogenesis of psoriasis. In a cohort study in Spain, carriers of TC genotype in IL-17F rs763780 were associated with a lack of response to adalimumab ( n = 67, PASI75 at weeks 24–28, p = 0.0044) while interestingly, with better response to infliximab ( n = 37, PASI at weeks 12–16, p = 0.023; PASI at weeks 24–28, p = 0.02). NFKBIZ The nuclear factor of kappa light polypeptide gene enhancer in B cells inhibitor , zeta (NFKBIZ) gene encodes an atypical inhibitor of nuclear factor κB (IκB) protein, involved in inflammatory signaling of psoriasis . Among 169 Spanish patients, the deletion of NFKBIZ rs3217713 had a better response to adalimumab (PASI75 at 6 months, p = 0.015) . TNF, TNFRSF1B None of the genotyped SNPs of TNF , TNFRSF1A , and TNFRSF1B genes were associated with responsiveness to treatment with infliximab or adalimumab . TRAF3IP2 TNF receptor-associated factor 3 interacting protein 2 (TRAF3IP2) involves in IL-17 signaling and interacts with members of the Rel/NF-κB transcription factor family . The rs13190932 in the TRAF3IP2 gene showed associations with a favorable response to infliximab (PASI75 at 6 months, p = 0.041) . CPM (Carboxypeptidase M) is involved in the maturation of macrophages in psoriasis pathogenesis . The CNV of the CPM gene was significantly associated with adalimumab response among 70 Spanish patients (PASI75 at 3 and 6 months, p < 0.05) . The rs9260313 in the HLA-A gene was found to be associated with more favorable responses to adalimumab (PASI75 at 6 months, p = 0.05) . Among 169 Spanish patients, HLA-Cw06 positivity had a better response to adalimumab. (PASI75 at 6 months, p = 0.018) . IL-17F , activated by IL23/Th17, is recognized as having a critical role in the pathogenesis of psoriasis. In a cohort study in Spain, carriers of TC genotype in IL-17F rs763780 were associated with a lack of response to adalimumab ( n = 67, PASI75 at weeks 24–28, p = 0.0044) while interestingly, with better response to infliximab ( n = 37, PASI at weeks 12–16, p = 0.023; PASI at weeks 24–28, p = 0.02). The nuclear factor of kappa light polypeptide gene enhancer in B cells inhibitor , zeta (NFKBIZ) gene encodes an atypical inhibitor of nuclear factor κB (IκB) protein, involved in inflammatory signaling of psoriasis . Among 169 Spanish patients, the deletion of NFKBIZ rs3217713 had a better response to adalimumab (PASI75 at 6 months, p = 0.015) . None of the genotyped SNPs of TNF , TNFRSF1A , and TNFRSF1B genes were associated with responsiveness to treatment with infliximab or adalimumab . TNF receptor-associated factor 3 interacting protein 2 (TRAF3IP2) involves in IL-17 signaling and interacts with members of the Rel/NF-κB transcription factor family . The rs13190932 in the TRAF3IP2 gene showed associations with a favorable response to infliximab (PASI75 at 6 months, p = 0.041) . Ustekinumab, as an IL12/IL23 antagonist, targets the p40 subunit that is shared by IL-12 and IL-23, whereas guselkumab, tildrakizumab, and risankizumab target the p19 subunit of IL-23. These four drugs are efficacious in treating moderate to severe plaque psoriasis . While ustekinumab is the earliest commercially available drug among IL23 antagonists, relatively abundant studies of the association between the response and gene status have been conducted. In contrast, there is limited research on the genetic predictors of clinical response to guselkumab, tildrakizumab, and risankizumab . 3.5.1. Ustekinumab (UTK) Better Response of Efficacy In a Spanish study enrolled 69 patients, good responders at 4 months were associated with CC genotype in ADAM33 rs2787094 ( p = 0.015), CG/CC genotype in HTR2A rs6311 ( p = 0.037), GT/TT genotype in IL-13 rs848 ( p = 0.037), CC genotype in NFKBIA rs2145623 ( p = 0.024), and CT/CC genotype in TNFR1 rs191190 . Rs151823 and rs26653 in the ERAP1 gene showed associations with a favorable response to anti-IL-12/23 therapy among 22 patients from the UK. Several studies exhibited that the presence of the HLA-Cw*06 or Cw*06:02 allele may serve as a predictor of faster response and better response to ustekinumab in Italian, Dutch, Belgian, American, and Chinese patients . A recent meta-analysis study confirmed that HLA-C*06:02 -positive patients had higher response rates (PASI76 at 6 months, p < 0 .001) . In addition, the presence of the GG genotype on the IL12B rs6887695 SNP and the absence of the AA genotype on the IL12B rs3212227 or the GG genotype on the IL6 rs1800795 SNP significantly increased the probability of therapeutic success in HLA-Cw6 -positive patients . Rs10484554 in the HLA-Cw gene did not show an association with a good response to ustekinumab in a Greek population . Patients with heterozygous genotype (CT) in the IL12B rs3213094 showed better PASI improvement to ustekinumab than the reference genotype (CC) (∆PASI at 3 months, p = 0.017), but the result was not replicated with regard to PASI75 . The genetic polymorphism of TIRAP rs8177374 and TLR5 rs5744174 were associated with a better response in the Danish population (PASI75 at 3 months, p = 0.0051 and p = 0.0012, respectively) . Poor Response of Efficacy In a Spanish study that enrolled 69 patients treating psoriasis with ustekinumab, poor responders at 4 months were associated with CG/CC genotype in CHUK rs11591741 ( p = 0.029), CT/CC genotype in C9orf72 rs774359 ( p = 0.016), AG/GG in C17orf51 rs1975974 ( p = 0.012), CT genotype in SLC22A4 rs1050152 ( p = 0.037), GT/TT genotype in STAT4 rs7574865 ( p = 0.015) and CT/CC genotype in ZNF816A rs9304742 ( p = 0.012) . Among 376 Danish patients, genetic variants of IL1B rs1143623 and rs1143627 related to increased IL-1β levels may be unfavorable outcomes (PASI75 at 3 months, p = 0.0019 and 0.0016, respectively), similar results with anti-TNF agents . An association between the TC genotype of IL-17F rs763780 and no response to ustekinumab was found in 70 Spanish (PASI75 at 3 and 6 months, p = 0.022 and p = 0.016, respectively) . Patients with homozygous (GG) for the rs610604 SNP in TNFAIP3 showed a worse PASI improvement to ustekinumab ( p = 0.031) than the TT genotype . Carriers of allele G in TNFRSF1B rs1061622 under anti-TNF or anti-IL-12/IL-23 treatment tended to be non-responders in 90 patients from Spain (PASI < 50 at 6 months, p = 0.05) . Better Response of Efficacy In a Spanish study enrolled 69 patients, good responders at 4 months were associated with CC genotype in ADAM33 rs2787094 ( p = 0.015), CG/CC genotype in HTR2A rs6311 ( p = 0.037), GT/TT genotype in IL-13 rs848 ( p = 0.037), CC genotype in NFKBIA rs2145623 ( p = 0.024), and CT/CC genotype in TNFR1 rs191190 . Rs151823 and rs26653 in the ERAP1 gene showed associations with a favorable response to anti-IL-12/23 therapy among 22 patients from the UK. Several studies exhibited that the presence of the HLA-Cw*06 or Cw*06:02 allele may serve as a predictor of faster response and better response to ustekinumab in Italian, Dutch, Belgian, American, and Chinese patients . A recent meta-analysis study confirmed that HLA-C*06:02 -positive patients had higher response rates (PASI76 at 6 months, p < 0 .001) . In addition, the presence of the GG genotype on the IL12B rs6887695 SNP and the absence of the AA genotype on the IL12B rs3212227 or the GG genotype on the IL6 rs1800795 SNP significantly increased the probability of therapeutic success in HLA-Cw6 -positive patients . Rs10484554 in the HLA-Cw gene did not show an association with a good response to ustekinumab in a Greek population . Patients with heterozygous genotype (CT) in the IL12B rs3213094 showed better PASI improvement to ustekinumab than the reference genotype (CC) (∆PASI at 3 months, p = 0.017), but the result was not replicated with regard to PASI75 . The genetic polymorphism of TIRAP rs8177374 and TLR5 rs5744174 were associated with a better response in the Danish population (PASI75 at 3 months, p = 0.0051 and p = 0.0012, respectively) . Poor Response of Efficacy In a Spanish study that enrolled 69 patients treating psoriasis with ustekinumab, poor responders at 4 months were associated with CG/CC genotype in CHUK rs11591741 ( p = 0.029), CT/CC genotype in C9orf72 rs774359 ( p = 0.016), AG/GG in C17orf51 rs1975974 ( p = 0.012), CT genotype in SLC22A4 rs1050152 ( p = 0.037), GT/TT genotype in STAT4 rs7574865 ( p = 0.015) and CT/CC genotype in ZNF816A rs9304742 ( p = 0.012) . Among 376 Danish patients, genetic variants of IL1B rs1143623 and rs1143627 related to increased IL-1β levels may be unfavorable outcomes (PASI75 at 3 months, p = 0.0019 and 0.0016, respectively), similar results with anti-TNF agents . An association between the TC genotype of IL-17F rs763780 and no response to ustekinumab was found in 70 Spanish (PASI75 at 3 and 6 months, p = 0.022 and p = 0.016, respectively) . Patients with homozygous (GG) for the rs610604 SNP in TNFAIP3 showed a worse PASI improvement to ustekinumab ( p = 0.031) than the TT genotype . Carriers of allele G in TNFRSF1B rs1061622 under anti-TNF or anti-IL-12/IL-23 treatment tended to be non-responders in 90 patients from Spain (PASI < 50 at 6 months, p = 0.05) . In a Spanish study enrolled 69 patients, good responders at 4 months were associated with CC genotype in ADAM33 rs2787094 ( p = 0.015), CG/CC genotype in HTR2A rs6311 ( p = 0.037), GT/TT genotype in IL-13 rs848 ( p = 0.037), CC genotype in NFKBIA rs2145623 ( p = 0.024), and CT/CC genotype in TNFR1 rs191190 . Rs151823 and rs26653 in the ERAP1 gene showed associations with a favorable response to anti-IL-12/23 therapy among 22 patients from the UK. Several studies exhibited that the presence of the HLA-Cw*06 or Cw*06:02 allele may serve as a predictor of faster response and better response to ustekinumab in Italian, Dutch, Belgian, American, and Chinese patients . A recent meta-analysis study confirmed that HLA-C*06:02 -positive patients had higher response rates (PASI76 at 6 months, p < 0 .001) . In addition, the presence of the GG genotype on the IL12B rs6887695 SNP and the absence of the AA genotype on the IL12B rs3212227 or the GG genotype on the IL6 rs1800795 SNP significantly increased the probability of therapeutic success in HLA-Cw6 -positive patients . Rs10484554 in the HLA-Cw gene did not show an association with a good response to ustekinumab in a Greek population . Patients with heterozygous genotype (CT) in the IL12B rs3213094 showed better PASI improvement to ustekinumab than the reference genotype (CC) (∆PASI at 3 months, p = 0.017), but the result was not replicated with regard to PASI75 . The genetic polymorphism of TIRAP rs8177374 and TLR5 rs5744174 were associated with a better response in the Danish population (PASI75 at 3 months, p = 0.0051 and p = 0.0012, respectively) . In a Spanish study that enrolled 69 patients treating psoriasis with ustekinumab, poor responders at 4 months were associated with CG/CC genotype in CHUK rs11591741 ( p = 0.029), CT/CC genotype in C9orf72 rs774359 ( p = 0.016), AG/GG in C17orf51 rs1975974 ( p = 0.012), CT genotype in SLC22A4 rs1050152 ( p = 0.037), GT/TT genotype in STAT4 rs7574865 ( p = 0.015) and CT/CC genotype in ZNF816A rs9304742 ( p = 0.012) . Among 376 Danish patients, genetic variants of IL1B rs1143623 and rs1143627 related to increased IL-1β levels may be unfavorable outcomes (PASI75 at 3 months, p = 0.0019 and 0.0016, respectively), similar results with anti-TNF agents . An association between the TC genotype of IL-17F rs763780 and no response to ustekinumab was found in 70 Spanish (PASI75 at 3 and 6 months, p = 0.022 and p = 0.016, respectively) . Patients with homozygous (GG) for the rs610604 SNP in TNFAIP3 showed a worse PASI improvement to ustekinumab ( p = 0.031) than the TT genotype . Carriers of allele G in TNFRSF1B rs1061622 under anti-TNF or anti-IL-12/IL-23 treatment tended to be non-responders in 90 patients from Spain (PASI < 50 at 6 months, p = 0.05) . Secukinumab and ixekizumab are human monoclonal antibodies that bind to the protein interleukin IL-17A, while brodalumab is a human monoclonal antibody of IL17R, which means a pan inhibitor of IL-17A, IL-17F, and IL-25. The three IL-17 antagonists are currently used in the treatment of moderate-to-severe psoriasis . 3.6.1. Secukinumab (SCK) and Ixekizumab (IXE) and Brodalumab (BDL) HLA-Cw6 The responses to SCK were comparable up to 18 months between HLA-Cw*06 -positive and -negative patients, as it is highly effective regardless of the HLA-Cw6 status in Italy and Switzerland . IL-17 No associations were found between the five genetic variants of IL-17 (rs2275913, rs8193037, rs3819025, rs7747909, and rs3748067) and ΔPASI, PASI75, or PASI90 after 12 and 24 weeks of anti-IL-17A agents, including SCK and IXE in European . The lack of pharmacogenetic data for BDL was noted during the review. HLA-Cw6 The responses to SCK were comparable up to 18 months between HLA-Cw*06 -positive and -negative patients, as it is highly effective regardless of the HLA-Cw6 status in Italy and Switzerland . IL-17 No associations were found between the five genetic variants of IL-17 (rs2275913, rs8193037, rs3819025, rs7747909, and rs3748067) and ΔPASI, PASI75, or PASI90 after 12 and 24 weeks of anti-IL-17A agents, including SCK and IXE in European . The lack of pharmacogenetic data for BDL was noted during the review. The responses to SCK were comparable up to 18 months between HLA-Cw*06 -positive and -negative patients, as it is highly effective regardless of the HLA-Cw6 status in Italy and Switzerland . No associations were found between the five genetic variants of IL-17 (rs2275913, rs8193037, rs3819025, rs7747909, and rs3748067) and ΔPASI, PASI75, or PASI90 after 12 and 24 weeks of anti-IL-17A agents, including SCK and IXE in European . The lack of pharmacogenetic data for BDL was noted during the review. Apremilast, a selective phosphodiesterase 4 (PDE4) inhibitor, is used to treat plaque psoriasis. A Russian study identified 78 pre-selected single-nucleotide polymorphisms, increased minor allele of IL1β (rs1143633), IL4 (IL13) (rs20541), IL23R (rs2201841), and TNFα (rs1800629) genes that are associated with the better outcome in 34 patients (PASI75 at 6.5 months, p = 0.05, p = 0.04, p = 0.03, p = 0.03, respectively) . Globally used topical therapies for psoriasis include retinoids, vitamin D analogs, corticosteroids, and coal tar. Lack of evidence emphasizes the association between treatment response and pharmacogenetics of corticosteroids, retinoids, and coal tar. The link between VDR genes, encoding the nuclear hormone receptor for vitamin D3, and the response to calcipotriol has been discussed but remained controversial in different populations . Lindioil is another topical medicine refined from Chinese herbs and is effective in treating plaque psoriasis . It has been reported that HLA-Cw*06:02 positivity showed a better response (PASI75 at 3 months, p = 0.033) while HLA-Cw*01:02 positivity showed a poor response in 72 patients (PASI 75 at 2.5 months, p = 0.019) . Psoriasis has been proven to be genetically affected over half a century . With the breakthrough of the technique of genetic analysis, more and more psoriasis susceptibility genes have been widely detected and analyzed as predictive markers of treatment response when unexplained and unsatisfied treatment responses and side effects have been recorded . In addition, several reviews have highlighted the findings of pharmacogenomics in psoriasis in the last ten years . In the review, regarding efficacy, carriers of HLA-Cw*06 positivity implied a more favorable response in the treatment of methotrexate and ustekinumab. HLA-Cw6 status was not indicative of treatment response to adalimumab, etanercept, and secukinumab. Polymorphism of ABCB1 rs1045642 may indicate poor responses in Greek and Russian. However, there are some limitations in the current review. First, the relevant data of anti-IL17 agents were lacking, which reflects that it is relatively novel to the market and shows outstanding responses irrespective of genotype. Further genetic analysis of acitretin, cyclosporin, and apremilast is worth exploring. Secondly, the majority of the included pharmacogenomic studies of psoriasis were from Europe and America. This implies the limited application to Asians and Africans. It may reflect that Europe and America have more clinical trial studies or drug options, resulting in interest in studying treatment responses for psoriasis than in other areas . In addition, the accessibility of gene-analysis resources may affect the development of pharmacogenomic studies. Thirdly, the protocol to identify the related gene varies between studies. A generalized and standardized method would facilitate the utilization and replication of the pharmacogenomic studies. Fourthly, pharmGKB is a comprehensive resource that curates knowledge about the impact of genetic variation on drug responses for clinicians . The level of evidence of the pharmacogenetic results in this database mostly remains low (level three) due to conflicted results, small cases, or a single study. Whereas biomarkers must show a relatively strong effect in order to be of use in clinical decision-making, replicated large cohort studies of each medical therapy are required in different ethnic groups. The use of the global polygenic risk score allowed for the prediction of onset psoriasis in Chinese and Russians . The establishment of the polygenic score for psoriasis treatment response may be developed in the future. In addition, tofacitinib, a kind of Janus kinase (JAK) inhibitor, was approved by FDA for psoriatic arthritis in 2017. Although no indication of psoriasis alone is approved, pharmacogenetic research of JAK inhibitor is expected considering its potential cardiovascular and cancer risk in patients with rheumatoid arthritis . This review article updates the current pharmacogenomic studies of treatment outcomes for psoriasis. A standardized protocol could be established for utilization and comparison worldwide. Currently, high-throughput whole exome sequencing (WES) or whole genome sequencing (WGS) can rapidly obtain comprehensive genetic information for individuals . Genetic basic research promotes the progress of personalized medicine. Its development contributes to the precision of the effective treatment individually, providing alternatives when treatment fails, preventing adverse effects, and reducing the economic burden of treating psoriasis. |
Characterization and metabolomic profiling of endophytic bacteria isolated from | 2d418ba5-e4a4-4e92-bd4b-e728ec65ce51 | 11698722 | Biochemistry[mh] | Endophytes, known as the largest unexplored reservoirs forming a “ware house of natural bioactive compounds on the earth” . Endophytes are micro-organisms (bacteria and fungi) colonizing themselves in living, interior tissues of host plants without causing any overt negative effects to the host . Endophytes are ubiquitous, found in every plant and shows vast biodiversity . Endophytes show a great biodiversity of adaptations, developed in special and sequestered environments . This endophytic diversity may be similar with the diversity of their host plant. The most common endophytes are fungi and bacteria, but comparatively fungi are the more commonly isolated and studied endophytes . Medicinal plants are gaining global attention owing to the fact that the herbal drugs are cost-effective, easily available and with negligible side effects. These plants harbour an untapped source of bioactive metabolites and large number of microorganisms, more specifically bacteria and fungi, called endophytes. Endophytes are the microorganisms that reside within the plant tissue without showing any overt negative effect. Nearly all plant species that exist in the earth harbour one or more endophytic microorganisms . To survive in the plant tissue, these endophytes decompose plant metabolites and obtain nutrition and energy. Due to long-term co-evolution of endophytes with their host plant, these endophytes have adapted to the special microenvironments by uptake of plant genome through genetic variation . Endophytes are also an important source of pharmaceutical bioactive metabolites such as antitumor, antioxidant, antibacterial, antifungal, antiviral, anti-inflammatory, immunosuppressive drugs, and many related compounds. Endophytes, also known for the production of different natural products and exhibit a wide range of biological activity and classified into numerous categories, which include steroids, terpenoids, phenolic compounds, lactones alkaloids quinines, lignans . Wide range of reports on numerous new endophytic species are evident which may exist in medicinal plants . Moringa oleifera , the most widely found species of the Moringaceae family thrives in tropical insular climate. The tree ranges from 5–10 m in height . Phytochemical analyses have revealed that Moringa leaves are a rich source of potassium, calcium, phosphorous, iron, vitamins A and D and essential amino acids . The leaf extracts of Moringa contain antioxidant properties and inhibit the peroxidation of linoleic acid. Extracts of leaves were also shown to prevent the bleaching of carotene and scavenge radicals in the DPPH radical scavenging assay . Piper betel is the most important and useful asexually propagated cash crop having various cultivars . It belongs to Piperaceae family and is a shed loving plant. It has a perennial creeper and bears leaves that are 4–7 inch long and 2–4 inch broad. It bears both male and female flowers. It is originated from Malaysia but is distributed extensively in South and Southwest China .
Isolation of endophytic bacteria from medicinal plants samples Nutrient agar media has been used for the isolation of endophytic bacteria and different colonies have been recovered after pure culture plate. Moringa oleifera isolate code S5 was identified, and from Piper betel isolate code N3 was obtained. Cultural characterization In vitro multiplications of bacterial isolates were carried out on nutrient agar plates and. colonial characteristics were recorded in size, shape, elevation, margin, texture, opacity and pigment. Isolate S5, from Moringa oleifera , exhibits medium-sized, circular colonies that are opaque with a white pigment, entire margin, convex elevation, and a buttery texture. In contrast, isolate N3, from Piper betel, forms small, circular colonies that are opaque with a yellow pigment, undulate margin, crateriform elevation, and a dry texture. These findings are similar to the findings of . Microscopic characterization Microscopic characterization of the isolates was carried out by gram’s staining for cellular morphological characterization. Different isolates displayed different cell sizes and morphologies when viewed under the microscope. Isolate S5 is a Gram-negative short rod that does not form spores, while isolate N3 is a Gram-positive coccus that forms spores. Molecular identification by 16s rRNA gene amplification The endophytic bacterial isolates under study were identified using 16S rRNA gene sequencing. A fragment of the 16S rRNA gene was amplified using 16S universal primers listed in Table S6. The sequencing was carried out on capillary sequencer (Applied Bio Systems 3130). Partial 16S rRNA gene sequence of studied bacteria were analysed with nucleotide BLAST search in GenBank of NCBI. Scanning electron microscopic characterization of isolates The scanning electron microscopic characterization of the two isolates also showed that that isolate S5, a short rod-shaped bacterium, measures 1.612–6.783 μm in length and 0.707–0.833 μm in width, and exhibits motility. Isolate N3, a cocci-shaped bacterium, measures 0.848–1.559 μm in length and 0.638–0.646 μm in width, and is also motile (Fig. ). Untargeted metabolomics profiling by GC/MS Untargeted metabolomics is a valuable method for simultaneously analysing a large number of molecules without prior information. GC–MS chromatogram displaying different peaks of the compounds shown in – in in supplementary file. The chromatogram displays peak intensity plotted against retention time (min). This investigation found six chemicals in M. Oleifera leaf extract and fourteen compounds in the S5 isolate. This study revealed six compounds were present in P. betel leaves extract and fourteen compounds were present in N3 isolate (Tables and ). Untargeted metabolomics profiling by LC–MS Untargeted metabolomics is a valuable method for simultaneously analysing a large number of molecules without prior information. LC–MS chromatogram displaying different peaks of the compounds shown in S5-S8 in supplementary file. The chromatogram illustrates the relationship between peak intensity and retention time (min). This investigation found 41 chemicals in M. Oleifera leaf extract and fourteen compounds in the S5 isolate. This investigation found 43 chemicals in P. betel leaf extract and fourteen compounds in the N3 isolate.The heat map displays the expression levels of various compounds, with a focus on comparing samples labelled N3 B and N3 L. The color gradient ranges from blue (low expression) to red (high expression), indicating the relative abundance of each compound. In the N3 B sample, several compounds are notably upregulated, as indicated by the intense red coloration (Tables and ).
Nutrient agar media has been used for the isolation of endophytic bacteria and different colonies have been recovered after pure culture plate. Moringa oleifera isolate code S5 was identified, and from Piper betel isolate code N3 was obtained.
In vitro multiplications of bacterial isolates were carried out on nutrient agar plates and. colonial characteristics were recorded in size, shape, elevation, margin, texture, opacity and pigment. Isolate S5, from Moringa oleifera , exhibits medium-sized, circular colonies that are opaque with a white pigment, entire margin, convex elevation, and a buttery texture. In contrast, isolate N3, from Piper betel, forms small, circular colonies that are opaque with a yellow pigment, undulate margin, crateriform elevation, and a dry texture. These findings are similar to the findings of .
Microscopic characterization of the isolates was carried out by gram’s staining for cellular morphological characterization. Different isolates displayed different cell sizes and morphologies when viewed under the microscope. Isolate S5 is a Gram-negative short rod that does not form spores, while isolate N3 is a Gram-positive coccus that forms spores.
The endophytic bacterial isolates under study were identified using 16S rRNA gene sequencing. A fragment of the 16S rRNA gene was amplified using 16S universal primers listed in Table S6. The sequencing was carried out on capillary sequencer (Applied Bio Systems 3130). Partial 16S rRNA gene sequence of studied bacteria were analysed with nucleotide BLAST search in GenBank of NCBI.
The scanning electron microscopic characterization of the two isolates also showed that that isolate S5, a short rod-shaped bacterium, measures 1.612–6.783 μm in length and 0.707–0.833 μm in width, and exhibits motility. Isolate N3, a cocci-shaped bacterium, measures 0.848–1.559 μm in length and 0.638–0.646 μm in width, and is also motile (Fig. ). Untargeted metabolomics profiling by GC/MS Untargeted metabolomics is a valuable method for simultaneously analysing a large number of molecules without prior information. GC–MS chromatogram displaying different peaks of the compounds shown in – in in supplementary file. The chromatogram displays peak intensity plotted against retention time (min). This investigation found six chemicals in M. Oleifera leaf extract and fourteen compounds in the S5 isolate. This study revealed six compounds were present in P. betel leaves extract and fourteen compounds were present in N3 isolate (Tables and ). Untargeted metabolomics profiling by LC–MS Untargeted metabolomics is a valuable method for simultaneously analysing a large number of molecules without prior information. LC–MS chromatogram displaying different peaks of the compounds shown in S5-S8 in supplementary file. The chromatogram illustrates the relationship between peak intensity and retention time (min). This investigation found 41 chemicals in M. Oleifera leaf extract and fourteen compounds in the S5 isolate. This investigation found 43 chemicals in P. betel leaf extract and fourteen compounds in the N3 isolate.The heat map displays the expression levels of various compounds, with a focus on comparing samples labelled N3 B and N3 L. The color gradient ranges from blue (low expression) to red (high expression), indicating the relative abundance of each compound. In the N3 B sample, several compounds are notably upregulated, as indicated by the intense red coloration (Tables and ).
Untargeted metabolomics is a valuable method for simultaneously analysing a large number of molecules without prior information. GC–MS chromatogram displaying different peaks of the compounds shown in – in in supplementary file. The chromatogram displays peak intensity plotted against retention time (min). This investigation found six chemicals in M. Oleifera leaf extract and fourteen compounds in the S5 isolate. This study revealed six compounds were present in P. betel leaves extract and fourteen compounds were present in N3 isolate (Tables and ).
Untargeted metabolomics is a valuable method for simultaneously analysing a large number of molecules without prior information. LC–MS chromatogram displaying different peaks of the compounds shown in S5-S8 in supplementary file. The chromatogram illustrates the relationship between peak intensity and retention time (min). This investigation found 41 chemicals in M. Oleifera leaf extract and fourteen compounds in the S5 isolate. This investigation found 43 chemicals in P. betel leaf extract and fourteen compounds in the N3 isolate.The heat map displays the expression levels of various compounds, with a focus on comparing samples labelled N3 B and N3 L. The color gradient ranges from blue (low expression) to red (high expression), indicating the relative abundance of each compound. In the N3 B sample, several compounds are notably upregulated, as indicated by the intense red coloration (Tables and ).
The 16S rRNA gene sequence can be used to identify the genus and species of isolates. The biochemically most potent isolates are listed in Table along with their accession ID and percentage of identity with the query sequence. to in supplementary file present the gene sequence and gel image obtained from the sequencing of the isolates’ 16S rRNA gene. Figures and represent phylogenetic tree based on the 16S rRNA gene sequencing of isolate S5 and N3 compared with the most closely related organisms. The heat map (Figs. and ) provided illustrates the relative abundance of six distinct chemical compounds: Dodecyl acrylate, n-Hexadecanoic acid, Pyrrolo[1,2-a]pyrazine-1,4-dione, hexahydro-3-(2-methylpropyl)-, 1,2-Benzenedicarboxylic acid, bis(2-methylpropyl) ester, Oleic acid, and Methyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl)propionate. The intensity of the colours on the heat map serves as an indicator of the abundance levels, with red denoting the highest abundance and blue representing the lowest abundance. Notably, the compound n-Hexadecanoic acid demonstrates the highest relative abundance, evidenced by its prominent red coloration on the map. As a saturated fatty acid, it is a key component of membrane lipids, contributing to the structural integrity and fluidity of cell membranes. This is essential for maintaining proper cell function and facilitating the transport of molecules across the membrane . Palmitic acid is also involved in energy storage, serving as a major component of triacylglycerols, which are stored in lipid droplets within the cell. In contrast, Methyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl) propionate is characterized by the lowest relative abundance, marked by its blue shading. This visual representation allows for a clear comparative analysis of the abundance levels of these compounds, providing valuable insights into their relative concentrations within the sample.Dodecyl acrylate and Pyrrolo [1,2-a]pyrazine-1,4-dione, hexahydro-3-(2-methylpropyl)- are the anozther abundant compound in N3 isolate as compared to plant leaf sample. Dodecyl acrylate, a fatty acid ester, can be involved in the formation of plant cuticles, providing a protective barrier against water loss, pathogens, and environmental stress. It may also play a role in signalling pathways related to stress responses . On the other hand, Pyrrolo [1,2-a]pyrazine-1,4-dione, hexahydro-3-(2-methylpropyl), is an antioxidant compound that helps protect plant cells from oxidative damage caused by reactive oxygen species (ROS). This compound aids in maintaining cellular health and preventing oxidative stress, which can impair cell function and lead to cellular damage . The higher abundance of these compounds in the N3 (Fig. ) isolate sample suggests an enhanced protective and stress-responsive capacity. Moreover, Methyl 3-(3,5-di-tert-butyl-4-hydroxyphenyl) propionate, which is slightly upregulated in N3 isolates broth, plays a pivotal role in N3 isolates by mitigating autoxidation and enhancing the frictional stability of materials subjected to stress . By studying the upregulated and downregulated compounds in S5 broth compared to compounds identified from Moringa leaves. The upregulated compounds in S5 isolate (Fig. ) are Pyrrolo [1,2-a]pyrazine-1,4-dione, hexahydro-3-(2-methylpropyl);1,2-Benzendicarboxylic acid, buty2-ethylhexyester; Propanedioic acid, phenyl, n-Hexadecanoic acid and Dodecyl acetate. Our research identified a significant upregulation of Pyrrolo[1,2-a]pyrazine-1,4-dione, hexahydro-3-(2-methylpropyl) (hereafter referred to as Pyrrolo[1,2-a]pyrazine-1,4-dione, hexahydro-3-(2-methylpropyl)) in the broth of isolate N3. This compound possesses well-established antioxidant properties, as demonstrated by its ability to scavenge free radicals in a reducing power assay. Free radicals are highly reactive molecules that can damage cells and contribute to the development of various chronic diseases. Therefore, the upregulation of Pyrrolo [1,2-a]pyrazine-1,4-dione, hexahydro-3-(2-methylpropyl) in isolate N3 suggests a potential protective mechanism against oxidative stress . Other than Pyrrolo [1,2-a]pyrazine-1,4-dione, hexahydro-3-(2-methylpropyl)-, n-Hexadecanoic acid is upregulated in broth of isolate which function as major component of plant cell membranes. N-hexadecanoic acid contributes significantly to membrane fluidity and stability, two crucial properties for various cellular processes in plants. These processes include the transport of essential ions required for growth, signal transduction pathways that allow plants to respond to their environment, and maintaining the integrity of the cell itself . Potentially influencing these cellular processes, the S5 isolate may contribute positively to plant health and function. The S5 isolate’s broth reveals not only n-hexadecanoic acid, but also upregulated levels of propanedioic acid (malonic acid) and dodecyl acrylate, suggesting a multi-faceted contribution to plant health (Fig. ). Malonic acid plays significant roles in plant development and stress responses. It acts as a precursor in the synthesis of various important compounds within plants, and it also contributes to plant defence mechanisms against biotic (e.g., fungal or insect attack) and abiotic (e.g., drought or temperature extremes) stresses . Dodecyl acrylate, on the other hand, exhibits antibacterial activity against multidrug-resistant human pathogens . The most abundant compound identified through LC–MS (Fig. ) in N3 broth is Dihydrocapsaicin, which significantly contributes to plant defence. Dihydrocapsaicin plays a crucial role in inhibiting the growth of fungal pathogens that affect plants, thereby enhancing the plant’s defence mechanisms against fungal diseases. This inhibition not only helps in protecting the plants from pathogenic invasions but also promotes overall plant health and resilience. The presence of Dihydrocapsaicin in N3 broth underscores its importance in bolstering the plant’s natural defence system .Another abundant compound identified in N3 broth is-16(17)-Epoxy-4Z,7Z,10Z,13Z,19Z-docosapentaenoic acid(DPA).Like eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), DPA serves as a substrate for the synthesis of specialized pro-resolving mediators (SPMs) such as resolving and protections, which possess significant biological activity. These SPMs play a critical role in resolving inflammation and promoting tissue repair, highlighting DPA’s potential in modulating inflammatory responses and supporting overall health . Several bioactive compounds identified in N3 broth demonstrate diverse and significant roles in plant health and defence, (3,4-Dihydroxyphenyl) ethanol acts as a potent biological antioxidant in cell culture experimental models, providing essential protection against oxidative stress . γ-Hexalactone exhibits strong antimicrobial activity against Phytophthora infestans , a notorious plant pathogen, thereby enhancing plant disease resistance . 7 β-Hydroxy Wortmannin plays a crucial role in vesicular trafficking and organelle dynamics within plant cells, essential processes for maintaining cellular function and integrity . Additionally, Pinolenic acid ethyl ester interacts with phytohormones such as abscisic acid (ABA) and jasmonic acid (JA), which are pivotal for plant growth, development, and stress responses . These compounds collectively contribute to the robust defence mechanisms and physiological regulation in plants, highlighting their potential applications in agriculture.One of the most abundant compounds (Fig. ) in S5 broth as compared to moringa’s leaf sample is D-erythro-Sphinganine, prominently marked by a red bar, signifying a high concentration. D-erythro-Sphinganine, a significant sphingolipid metabolite, plays a crucial role in various cellular processes in plants. Notably, sphingolipid-mediated signalling pathways, in which D-erythro-Sphinganine is a key component, are fundamental in regulating programmed cell death (PCD). This process is essential for plant development and defence mechanisms, ensuring the removal of damaged or infected cells and maintaining cellular homeostasis . Other abundant compounds found in the S5 broth include β-Homoproline, which plays a pivotal role in plant physiology. β-Homoproline is implicated in cell wall signal transduction cascades, which are crucial for maintaining the structural integrity and functionality of the plant cell wall. These signal transduction pathways are essential for the plant’s ability to perceive and respond to various environmental stimuli, thereby contributing to stress tolerance mechanisms . Another abundant compound identified in S5 broth is 1,2-Di-(9Z,12Z,15Z-octadecatrienoyl)-sn-glycero-3-phosphocholine, which plays a crucial role in seed biology. This compound is involved in providing polyunsaturated fatty acids (PUFAs) for the synthesis of triacylglycerols (TAGs) in seeds. PUFAs are essential components of plant oils and are vital for seed germination, storage, andenergy metabolism . Among the abundant compounds identified in S5 broth (Fig. ), Ala-Trp-Arg stands out for its multifaceted role in plant physiology and signalling pathways. This tripeptide not only participates in protein synthesis but also exerts profound effects on plant growth, defence mechanisms, and responses to environmental stressors . D-Pipecolinic acid stands out for its significant role in regulating plant systemic acquired resistance (SAR) and basal immunity against bacterial pathogens. SAR is a crucial defence mechanism in plants that confers broad-spectrum resistance following an initial exposure to a pathogen . Among the abundant compounds identified in S5 broth are Trp-Ala-Arg and Vigabatrin, which exemplify the diverse roles of bioactive peptides in plants. Trp-Ala-Arg is recognized for its potential in growth regulation and stress responses, underscoring its importance in enhancing plant resilience and adaptation to environmental challenges. Meanwhile, Vigabatrin demonstrates multifaceted bioactivities including antioxidant, antimicrobial, and anti-inflammatory properties .
The investigation was carried out at the “Department of Biotechnology, College of Agriculture, Junagadh Agricultural University, Junagadh” during 2023–2024. Sample collection The fresh and healthy leaves ofmedicinal plants: M. oleifera and P. betel were collected from Department of Biotechnology, Junagadh Agricultural University, Junagadh. Each sample was tagged and placed in separate polythene bags and processed within 24 h of collection. Fresh plant materials were used for isolation of endophytic bacteria to reduce the chance of contamination. Sterilization procedure of sample For the pre-treatment of leaf samples and isolation of endophytic bacteria all the leaf samples were excised and subjected to a surface sterilization procedure described by . The procedure for sample pre-treatment is shown below; the efficiency of surface sterilization was checked by imprint method . The samples were also washed with distilled water and inoculated into nutrient broth containing bavistin (30 µg/ml) media as a control to check growth in liquid medium. Sample inoculum Leaves were crushed in sterile distilled water using mortar and pestle and plant extracts were prepared. About 1 ml of crushed samples were serially diluted and 0.1 ml was spread onto nutrient agar (NA) medium. Plates were incubated at 35 °C for 2–3 days. Characterization of isolates Isolated endophytic bacteria were phenotypically characterized for growth characteristics on Nutrient agar, colony morphology and Grams reaction by using standard procedures following . Molecular identification by 16 s rRNA gene amplification Primers for 16S rRNA genes were selected from standard scientific literature . Table shows sequences of the primers used to amplify and detect 16S rRNA genes. A fragment of the 16S rRNA gene was amplified using 16S universal primers. Each pair of primers were highly specific and gave a PCR product of known size that was easily identified by electrophoresis on agarose gel. Lab procedure Uncontaminated QC passed culture plates were used to obtain 1–2 well isolated colonies which were suspended in 50μL of Molecular Biology Grade Water mixed well. The suspension was incubated for 10 min at 95 °C and then centrifuged briefly. The supernatant of the lysate solution was used as template for PCR amplification using the universal primers 16S27F (5’-CCA GAG TTT GAT CMT GGC TCA G-3’) and 16S1492R (5’- TAC GGY TAC CTT GTT ACG ACT T-3’) . The amplified PCR product was further purified by salt-precipitation, then subjected to cycle sequencing using BDT v3.1 chemistry and subsequently sequenced on an ABI 3500XL Genetic Analyser. Additional internal primers were used to obtain near-full length sequence to generate good quality base reads covering the target. Bioinformatics method The .ab1 trace files obtained after sequencing were manually curated, converted into a fasta read file and then assembled into a contiguous sequence and exported as a FASTA file. The consensus sequence was subsequently subjected to database search against the SILVA database v138 . using the BLAST tool . For the phylogenetic analysis, upto 10 closest-neighbour sequences belonging to different taxa from amongst the top 1000 hits with highest similarity in the search results were retrieved from the database and aligned using the MUSCLE aligner . The multiple sequence alignment was manually inspected and used to produce a consensus phylogram using maximum likelihood or neighbour joining algorithm with 1000 iterations using MEGA11 (Molecular Evolutionary Genetic Analysis, version 11) software. Scanning electron microscopy A loopful of the bacterial isolates colony was picked from their respective fresh culture plates and a light smear was made on the aluminium stub with the help of inoculating needle. The smeared stub was then flooded with 4% glutaraldehyde and kept in a fridge at 4 °C for 24 h. The following day the smeared samples were dehydrated by using gradient dilution of acetone in a concentration ranging from 30, 50, 70, 80, 90 and 100% and treating each sample by dipping into dilution of each respective concentrations in the order of 30% to 100% for 15 min. The samples treated by dipping in acetone concentration of 100% was repeated for second time for another 15 min for each sample. The dehydrated samples were then coated in a spotter coater having gold palladium mixture plate and observed under scanning electron microscope. Metabolomics study Untargeted metabolomic profiling by GC/MS Untargeted metabolomics profiling by GC/MS was conducted on leaves and bacterial cultures of M. oleifera and P. betel , using powdered and broth samples stored at 4 °C. For the extraction of metabolites, 100–200 mg of leaf samples were crushed in liquid nitrogen, followed by the addition of 1.4 to 1.5 ml methanol. The extract was sonicated for 5–10 min and then heated in a water bath at 70 °C for 10 min. After adding 1 ml of M. Q. water and vortexing for 30 s, 1 ml of chloroform was added, vortexed, and centrifuged at 7000 rpm for 10 min at 4 °C. The polar (methanol and water) and non-polar (chloroform) phases were separated and dried in a vacuum concentrator at 42–45 °C, then stored at − 20 °C. For derivatization, the next day, the samples were dried in a microwave at 40 °C for 8:30 min, cooled to room temperature, and mixed with 50 µl pyridine. Methoxylamine hydrochloride (20 mg/ml in pyridine) was added, vortexed, sonicated, and incubated at 37 °C for 90 min. Then, 100 µl MSTFA was added, and the samples were incubated again at 37 °C for 60 min. Both phases were mixed, centrifuged, and 400–600 µl of the upper supernatant was taken for GC/MS analysis. Two µl of the sample was injected into the GC/MS, and the data obtained were analysed using MS libraries for identification, followed by statistical analysis using GraphPad Prism 10.0. The GC/MS analysis was performed using a Shimadzu GC-2010 plus (Plate: 3.1) equipped with a DB17MS column (30 m × 0.25 mm). The system’s oven temperature could reach up to 450 °C, with the injector port set at 280 °C and utilizing a split injection mode. The AFC pressure ranged from 0 to 970 kPa, while the column oven was maintained at 50 °C, and helium was used as the carrier gas at a flow rate of 1.0 ml/min. The MS interface had a direct connection with the capillary column, set at 250 °C. The ion source operated in EI mode, with options for PCI and NCI, maintaining an ion source temperature of 250 °C. The mass analyser, a metal quadrupole mass filter with a pre-rod, covered a mass range of m/z 1.5–1090, and the detector voltage was set at 1.3 kV. The software operated in Full Scan mode, with optional access to various mass spectrum libraries, including NIST, Wiley, Pesticide Library, FFNSC Library (Flavor and Fragrance), and Drug Library. Untargeted metabolomic profilingby LC/MS Untargeted metabolomic profiling by LC/MS involved extracting metabolites following the method described with minor modifications. The workflow started with 0.6 gm of powdered plant material placed in a 15 ml centrifuge tube, to which 10 ml of a chloroform:methanol (1:2) solvent was added. The mixture was left at room temperature for 2 days. After this period, the extract was centrifuged at 7000 rpm for 10 min and filtered using Whatman no. 1 filter paper. The solvent was evaporated using a water bath at 65 °C, and then 200 μl of methanol was added (for leaf samples, 200 μl extract was dissolved in 1 ml methanol). This extract was briefly vortexed for proper mixing and transferred to LC vials with 250 μl glass inserts for LC–MS analysis. The LC/MS parameters were set as required, and 10 μl of the sample was injected into the LC for observation. After the sample run, the data were analyzed using MS libraries for identification, followed by statistical analysis using GraphPad Prism 10.0. The LC/MS analysis was performed using a ZORBAX Eclipse Plus C-18 column (3.0 × 100 mm, 1.8-micron) maintained at 25 °C, with an injection volume of 10 µl. The mobile phase was set in positive ionization mode with A being 0.1% formic acid in water and B being 0.1% formic acid in acetonitrile, flowing at 0.3 ml/min. The gradient started with B at 5% at 0 min, increasing to 35% at 2 min, 95% at 8.50 min, remaining at 95% until 15 min, then returning to 5% at 16 min and held until 20 min, with a total run time of 20 min. The QTOF conditions were set to Auto Ms/Ms mode, with specific parameters including positive ESI ionization mode, a drying gas temperature of 300 °C, drying gas flow of 8 L/min, nebulizer pressure at 35 psig, vaporizer/sheath gas temperature of 350 °C, and sheath gas flow at 11 L/min. The Vcap was set to 3500 V, fragmentor to 140 V, skimmer to 65 V, and Oct 1 RF Vpp to 750 V. Collision energies were set at 10, 30, 40, and 60 V for m/z values of 100, 300, 500, and 700, respectively. The precursor per cycle maximum was 1, with an absolute threshold of 200 counts and a relative threshold of 0.01%. The mass range for MS was 100–1700, and for MS/MS it was 40–1000. Statistical analysis The above experiments were carried out in triplicate replications. The data obtained from their mean values were used for statistical analysis of variance (ANOVA) using a Completely Randomized Design (CRD) for the interpretation of results. Consent to participate/publish As the corresponding author of this work, I hereby declare that all involved authors have provided their appropriate consent for the entire research to be conducted, and that all involved authors have approved of the effort to be published.
The fresh and healthy leaves ofmedicinal plants: M. oleifera and P. betel were collected from Department of Biotechnology, Junagadh Agricultural University, Junagadh. Each sample was tagged and placed in separate polythene bags and processed within 24 h of collection. Fresh plant materials were used for isolation of endophytic bacteria to reduce the chance of contamination.
For the pre-treatment of leaf samples and isolation of endophytic bacteria all the leaf samples were excised and subjected to a surface sterilization procedure described by . The procedure for sample pre-treatment is shown below; the efficiency of surface sterilization was checked by imprint method . The samples were also washed with distilled water and inoculated into nutrient broth containing bavistin (30 µg/ml) media as a control to check growth in liquid medium.
Leaves were crushed in sterile distilled water using mortar and pestle and plant extracts were prepared. About 1 ml of crushed samples were serially diluted and 0.1 ml was spread onto nutrient agar (NA) medium. Plates were incubated at 35 °C for 2–3 days.
Isolated endophytic bacteria were phenotypically characterized for growth characteristics on Nutrient agar, colony morphology and Grams reaction by using standard procedures following .
Primers for 16S rRNA genes were selected from standard scientific literature . Table shows sequences of the primers used to amplify and detect 16S rRNA genes. A fragment of the 16S rRNA gene was amplified using 16S universal primers. Each pair of primers were highly specific and gave a PCR product of known size that was easily identified by electrophoresis on agarose gel. Lab procedure Uncontaminated QC passed culture plates were used to obtain 1–2 well isolated colonies which were suspended in 50μL of Molecular Biology Grade Water mixed well. The suspension was incubated for 10 min at 95 °C and then centrifuged briefly. The supernatant of the lysate solution was used as template for PCR amplification using the universal primers 16S27F (5’-CCA GAG TTT GAT CMT GGC TCA G-3’) and 16S1492R (5’- TAC GGY TAC CTT GTT ACG ACT T-3’) . The amplified PCR product was further purified by salt-precipitation, then subjected to cycle sequencing using BDT v3.1 chemistry and subsequently sequenced on an ABI 3500XL Genetic Analyser. Additional internal primers were used to obtain near-full length sequence to generate good quality base reads covering the target. Bioinformatics method The .ab1 trace files obtained after sequencing were manually curated, converted into a fasta read file and then assembled into a contiguous sequence and exported as a FASTA file. The consensus sequence was subsequently subjected to database search against the SILVA database v138 . using the BLAST tool . For the phylogenetic analysis, upto 10 closest-neighbour sequences belonging to different taxa from amongst the top 1000 hits with highest similarity in the search results were retrieved from the database and aligned using the MUSCLE aligner . The multiple sequence alignment was manually inspected and used to produce a consensus phylogram using maximum likelihood or neighbour joining algorithm with 1000 iterations using MEGA11 (Molecular Evolutionary Genetic Analysis, version 11) software. Scanning electron microscopy A loopful of the bacterial isolates colony was picked from their respective fresh culture plates and a light smear was made on the aluminium stub with the help of inoculating needle. The smeared stub was then flooded with 4% glutaraldehyde and kept in a fridge at 4 °C for 24 h. The following day the smeared samples were dehydrated by using gradient dilution of acetone in a concentration ranging from 30, 50, 70, 80, 90 and 100% and treating each sample by dipping into dilution of each respective concentrations in the order of 30% to 100% for 15 min. The samples treated by dipping in acetone concentration of 100% was repeated for second time for another 15 min for each sample. The dehydrated samples were then coated in a spotter coater having gold palladium mixture plate and observed under scanning electron microscope.
Uncontaminated QC passed culture plates were used to obtain 1–2 well isolated colonies which were suspended in 50μL of Molecular Biology Grade Water mixed well. The suspension was incubated for 10 min at 95 °C and then centrifuged briefly. The supernatant of the lysate solution was used as template for PCR amplification using the universal primers 16S27F (5’-CCA GAG TTT GAT CMT GGC TCA G-3’) and 16S1492R (5’- TAC GGY TAC CTT GTT ACG ACT T-3’) . The amplified PCR product was further purified by salt-precipitation, then subjected to cycle sequencing using BDT v3.1 chemistry and subsequently sequenced on an ABI 3500XL Genetic Analyser. Additional internal primers were used to obtain near-full length sequence to generate good quality base reads covering the target.
The .ab1 trace files obtained after sequencing were manually curated, converted into a fasta read file and then assembled into a contiguous sequence and exported as a FASTA file. The consensus sequence was subsequently subjected to database search against the SILVA database v138 . using the BLAST tool . For the phylogenetic analysis, upto 10 closest-neighbour sequences belonging to different taxa from amongst the top 1000 hits with highest similarity in the search results were retrieved from the database and aligned using the MUSCLE aligner . The multiple sequence alignment was manually inspected and used to produce a consensus phylogram using maximum likelihood or neighbour joining algorithm with 1000 iterations using MEGA11 (Molecular Evolutionary Genetic Analysis, version 11) software.
A loopful of the bacterial isolates colony was picked from their respective fresh culture plates and a light smear was made on the aluminium stub with the help of inoculating needle. The smeared stub was then flooded with 4% glutaraldehyde and kept in a fridge at 4 °C for 24 h. The following day the smeared samples were dehydrated by using gradient dilution of acetone in a concentration ranging from 30, 50, 70, 80, 90 and 100% and treating each sample by dipping into dilution of each respective concentrations in the order of 30% to 100% for 15 min. The samples treated by dipping in acetone concentration of 100% was repeated for second time for another 15 min for each sample. The dehydrated samples were then coated in a spotter coater having gold palladium mixture plate and observed under scanning electron microscope.
Untargeted metabolomic profiling by GC/MS Untargeted metabolomics profiling by GC/MS was conducted on leaves and bacterial cultures of M. oleifera and P. betel , using powdered and broth samples stored at 4 °C. For the extraction of metabolites, 100–200 mg of leaf samples were crushed in liquid nitrogen, followed by the addition of 1.4 to 1.5 ml methanol. The extract was sonicated for 5–10 min and then heated in a water bath at 70 °C for 10 min. After adding 1 ml of M. Q. water and vortexing for 30 s, 1 ml of chloroform was added, vortexed, and centrifuged at 7000 rpm for 10 min at 4 °C. The polar (methanol and water) and non-polar (chloroform) phases were separated and dried in a vacuum concentrator at 42–45 °C, then stored at − 20 °C. For derivatization, the next day, the samples were dried in a microwave at 40 °C for 8:30 min, cooled to room temperature, and mixed with 50 µl pyridine. Methoxylamine hydrochloride (20 mg/ml in pyridine) was added, vortexed, sonicated, and incubated at 37 °C for 90 min. Then, 100 µl MSTFA was added, and the samples were incubated again at 37 °C for 60 min. Both phases were mixed, centrifuged, and 400–600 µl of the upper supernatant was taken for GC/MS analysis. Two µl of the sample was injected into the GC/MS, and the data obtained were analysed using MS libraries for identification, followed by statistical analysis using GraphPad Prism 10.0. The GC/MS analysis was performed using a Shimadzu GC-2010 plus (Plate: 3.1) equipped with a DB17MS column (30 m × 0.25 mm). The system’s oven temperature could reach up to 450 °C, with the injector port set at 280 °C and utilizing a split injection mode. The AFC pressure ranged from 0 to 970 kPa, while the column oven was maintained at 50 °C, and helium was used as the carrier gas at a flow rate of 1.0 ml/min. The MS interface had a direct connection with the capillary column, set at 250 °C. The ion source operated in EI mode, with options for PCI and NCI, maintaining an ion source temperature of 250 °C. The mass analyser, a metal quadrupole mass filter with a pre-rod, covered a mass range of m/z 1.5–1090, and the detector voltage was set at 1.3 kV. The software operated in Full Scan mode, with optional access to various mass spectrum libraries, including NIST, Wiley, Pesticide Library, FFNSC Library (Flavor and Fragrance), and Drug Library. Untargeted metabolomic profilingby LC/MS Untargeted metabolomic profiling by LC/MS involved extracting metabolites following the method described with minor modifications. The workflow started with 0.6 gm of powdered plant material placed in a 15 ml centrifuge tube, to which 10 ml of a chloroform:methanol (1:2) solvent was added. The mixture was left at room temperature for 2 days. After this period, the extract was centrifuged at 7000 rpm for 10 min and filtered using Whatman no. 1 filter paper. The solvent was evaporated using a water bath at 65 °C, and then 200 μl of methanol was added (for leaf samples, 200 μl extract was dissolved in 1 ml methanol). This extract was briefly vortexed for proper mixing and transferred to LC vials with 250 μl glass inserts for LC–MS analysis. The LC/MS parameters were set as required, and 10 μl of the sample was injected into the LC for observation. After the sample run, the data were analyzed using MS libraries for identification, followed by statistical analysis using GraphPad Prism 10.0. The LC/MS analysis was performed using a ZORBAX Eclipse Plus C-18 column (3.0 × 100 mm, 1.8-micron) maintained at 25 °C, with an injection volume of 10 µl. The mobile phase was set in positive ionization mode with A being 0.1% formic acid in water and B being 0.1% formic acid in acetonitrile, flowing at 0.3 ml/min. The gradient started with B at 5% at 0 min, increasing to 35% at 2 min, 95% at 8.50 min, remaining at 95% until 15 min, then returning to 5% at 16 min and held until 20 min, with a total run time of 20 min. The QTOF conditions were set to Auto Ms/Ms mode, with specific parameters including positive ESI ionization mode, a drying gas temperature of 300 °C, drying gas flow of 8 L/min, nebulizer pressure at 35 psig, vaporizer/sheath gas temperature of 350 °C, and sheath gas flow at 11 L/min. The Vcap was set to 3500 V, fragmentor to 140 V, skimmer to 65 V, and Oct 1 RF Vpp to 750 V. Collision energies were set at 10, 30, 40, and 60 V for m/z values of 100, 300, 500, and 700, respectively. The precursor per cycle maximum was 1, with an absolute threshold of 200 counts and a relative threshold of 0.01%. The mass range for MS was 100–1700, and for MS/MS it was 40–1000.
Untargeted metabolomics profiling by GC/MS was conducted on leaves and bacterial cultures of M. oleifera and P. betel , using powdered and broth samples stored at 4 °C. For the extraction of metabolites, 100–200 mg of leaf samples were crushed in liquid nitrogen, followed by the addition of 1.4 to 1.5 ml methanol. The extract was sonicated for 5–10 min and then heated in a water bath at 70 °C for 10 min. After adding 1 ml of M. Q. water and vortexing for 30 s, 1 ml of chloroform was added, vortexed, and centrifuged at 7000 rpm for 10 min at 4 °C. The polar (methanol and water) and non-polar (chloroform) phases were separated and dried in a vacuum concentrator at 42–45 °C, then stored at − 20 °C. For derivatization, the next day, the samples were dried in a microwave at 40 °C for 8:30 min, cooled to room temperature, and mixed with 50 µl pyridine. Methoxylamine hydrochloride (20 mg/ml in pyridine) was added, vortexed, sonicated, and incubated at 37 °C for 90 min. Then, 100 µl MSTFA was added, and the samples were incubated again at 37 °C for 60 min. Both phases were mixed, centrifuged, and 400–600 µl of the upper supernatant was taken for GC/MS analysis. Two µl of the sample was injected into the GC/MS, and the data obtained were analysed using MS libraries for identification, followed by statistical analysis using GraphPad Prism 10.0. The GC/MS analysis was performed using a Shimadzu GC-2010 plus (Plate: 3.1) equipped with a DB17MS column (30 m × 0.25 mm). The system’s oven temperature could reach up to 450 °C, with the injector port set at 280 °C and utilizing a split injection mode. The AFC pressure ranged from 0 to 970 kPa, while the column oven was maintained at 50 °C, and helium was used as the carrier gas at a flow rate of 1.0 ml/min. The MS interface had a direct connection with the capillary column, set at 250 °C. The ion source operated in EI mode, with options for PCI and NCI, maintaining an ion source temperature of 250 °C. The mass analyser, a metal quadrupole mass filter with a pre-rod, covered a mass range of m/z 1.5–1090, and the detector voltage was set at 1.3 kV. The software operated in Full Scan mode, with optional access to various mass spectrum libraries, including NIST, Wiley, Pesticide Library, FFNSC Library (Flavor and Fragrance), and Drug Library.
Untargeted metabolomic profiling by LC/MS involved extracting metabolites following the method described with minor modifications. The workflow started with 0.6 gm of powdered plant material placed in a 15 ml centrifuge tube, to which 10 ml of a chloroform:methanol (1:2) solvent was added. The mixture was left at room temperature for 2 days. After this period, the extract was centrifuged at 7000 rpm for 10 min and filtered using Whatman no. 1 filter paper. The solvent was evaporated using a water bath at 65 °C, and then 200 μl of methanol was added (for leaf samples, 200 μl extract was dissolved in 1 ml methanol). This extract was briefly vortexed for proper mixing and transferred to LC vials with 250 μl glass inserts for LC–MS analysis. The LC/MS parameters were set as required, and 10 μl of the sample was injected into the LC for observation. After the sample run, the data were analyzed using MS libraries for identification, followed by statistical analysis using GraphPad Prism 10.0. The LC/MS analysis was performed using a ZORBAX Eclipse Plus C-18 column (3.0 × 100 mm, 1.8-micron) maintained at 25 °C, with an injection volume of 10 µl. The mobile phase was set in positive ionization mode with A being 0.1% formic acid in water and B being 0.1% formic acid in acetonitrile, flowing at 0.3 ml/min. The gradient started with B at 5% at 0 min, increasing to 35% at 2 min, 95% at 8.50 min, remaining at 95% until 15 min, then returning to 5% at 16 min and held until 20 min, with a total run time of 20 min. The QTOF conditions were set to Auto Ms/Ms mode, with specific parameters including positive ESI ionization mode, a drying gas temperature of 300 °C, drying gas flow of 8 L/min, nebulizer pressure at 35 psig, vaporizer/sheath gas temperature of 350 °C, and sheath gas flow at 11 L/min. The Vcap was set to 3500 V, fragmentor to 140 V, skimmer to 65 V, and Oct 1 RF Vpp to 750 V. Collision energies were set at 10, 30, 40, and 60 V for m/z values of 100, 300, 500, and 700, respectively. The precursor per cycle maximum was 1, with an absolute threshold of 200 counts and a relative threshold of 0.01%. The mass range for MS was 100–1700, and for MS/MS it was 40–1000.
The above experiments were carried out in triplicate replications. The data obtained from their mean values were used for statistical analysis of variance (ANOVA) using a Completely Randomized Design (CRD) for the interpretation of results.
As the corresponding author of this work, I hereby declare that all involved authors have provided their appropriate consent for the entire research to be conducted, and that all involved authors have approved of the effort to be published.
Endophytes are microorganisms that enter the inside tissues of host plants without causing any negative effects. Endophytes, which may be found in nearly every plant species, have a huge biodiversity and have evolved to unique microenvironments as a result of long-term coexistence with their host plants. Among these adaptations include increased resistance to diseases, herbivores, and other environmental challenges. Endophytes produce a wide range of natural products, including steroids, terpenoids, phenolic compounds, lactones, alkaloids, quinones, and lignans, and are a significant source of pharmaceutical bioactive metabolites such as antitumor, antioxidant, antibacterial, antifungal, and antiviral compounds. Medicinal plants, such as Moringa oleifera and Piper betel , are particularly valuable for studying endophytes due to their rich nutrient profiles and therapeutic properties. M. oleifera leaves are known for their high content of potassium, calcium, phosphorous, iron, vitamins A and D, and essential amino acids, as well as their antioxidant properties. P. betel , a perennial creeper, is widely used for its medicinal benefits and harbours a diverse range of endophytic microorganisms. The isolation and characterization of endophytic bacteria from these plants involve surface sterilization, sample inoculation, and various analyses, including molecular identification by 16S rRNA gene amplification and phenotypic characterization. These processes help identify specific endophytes, such as Priestia aryabhattai from M. oleifera and Kocuria rhizophila from P. betel , which have shown potential in promoting plant growth, nutrient solubilisation, and antagonistic activity against pathogens.
Below is the link to the electronic supplementary material. Supplementary Material 1
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Commentary: What the eye sees, Let’s make the world see - Smart evolution of teleophthalmology | 8d1e480c-eb86-4793-9e6d-61d038f2b596 | 9940584 | Ophthalmology[mh] | Launched during the COVID-19 pandemic, the e-Sanjeevani platform provides multifaceted benefits. As observed by the authors, there is a myriad of benefits and specific challenges in the field of ophthalmology. Under the Ayushman Bharat scheme of the Government of India, e-Sanjeevani is the first successful attempt at digitalizing health care in the country. Currently, this platform encapsulates a team of one lakh doctors and paramedics and over one crore consultations done in core specialities like medicine, obstetrics and gynecology, psychiatry, dermatology, orthopedics, and ophthalmology. The authors of the accompanying article have elaborated well on the spectrum of patients seeking treatment for ocular complaints via e-Sanjeevani. As noted by the authors, the vast majority of common anterior segment pathologies have been successfully managed via teleconsultations. It is relevant to mention here that anterior segment photos (using a smartphone camera) at the site aided the diagnosis via teleconsultations in most cases. A provisional diagnosis could not be reached in only less than 10% of cases. The main strength of teleophthalmology lies in the ability to take standard eye care to the primary and secondary health centre level, which is lacking in most states. The provision for video conferencing with the doctor in real-time is an added benefit that may be utilized increasingly in the coming years.
However, it is prudent to highlight two significant challenges this platform must overcome. First and foremost, the success of telemedicine rests mainly on infrastructure. Many rural areas have yet to receive the digital revolution’s benefits, despite India’s booming telecom network. Another primordial observation made by the authors is poor network connectivity as a hurdle. Second, due to the unavailability of a fundus camera, the lacunae in posterior segment examination may prove to be a significant limiting factor to the success of e-Sanjeevani. Evaluation of the fundus is often overlooked in the rural setting, and incorporating this into this platform will be a significant boon. Numerous authors have cited low-cost do-it-yourself fundus imaging with smartphones. This is where such an innovation may be game-changing.
Since outreach services are quite financially burdening, sending experts for such programs becomes a daunting task. Hence, doctors or health care workers (HCWs) of junior cadres are mostly sought for these services. While they are exposed to a newer domain, most often the process of learning remains incomplete. This is primarily due to the gap between what they see and their inability to express it to their peers and seniors. To bridge the gap, a recent innovation called the Anterior Segment Photography with an Intraocular Lens (ASPI) by Gosalia et al . yields detailed and high-quality images that can be used by residents and HCWs alike in storing and learning from them. A similar smartphone technology for fundus capture was the Trash to Treasure (T3) Retcam invented by Chandrakanth et al . IOL scope is a game-changing innovation in visualizing the microorganisms implicated in corneal ulcers. It can reduce the delay caused by the transport of the sample and avoid unnecessary contamination in the chain of custody. This also aids in quick opinion from microbiological experts across renowned institutes, which can significantly reduce the patient’s morbidity through timely intervention.
Registering and capturing details regarding a patient’s condition was never a simple task in the earlier days in teleophthalmology, even with reasonable connectivity. But lately, the teleophthalmological document has witnessed a few innovations in this area, starting with simple smartphone photography to document images of grossly visible lesions. Akkara et al . described simple methods and gadgets to facilitate slit lamp photography and videography, which can be maintained well in a cloud-based system for easier access later by people across the state.
Lastly, documentation of the findings on a virtual platform can help prevent data loss, ensure timely expert opinions, and deliver appropriate services to the patients at the point of care. The new in-vogue with augmented reality and fascinating holograms designed by Ramesh et al. can be a vital tool in documenting the various ocular pathologies encountered at the point of care. With its simple and easy-to-use interface, it can be a five-finger exercise for healthcare workers to use the technology on a routine basis. It can also be a great teaching tool for young minds to understand the anatomic intricacies of the eye and how they are altered with different diseases.
To conclude, teleophthalmology has been booming beyond perceptible limits in the recent past. It shall continue to open new frontiers in taking eye care to the nooks and corners of this country. While it has its own set of drawbacks and challenges, multiple efforts are being made by researchers and enthusiasts from all spheres to overcome it cost-effectively. Thus, embracing them as we grow both individually and as a fraternity in the future is necessary. On that note, e-Sanjeevani is a step forward in the right direction, providing teleophthalmology with a much-needed boost.
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High-fidelity simulation versus case-based discussion for training undergraduate medical students in pediatric emergencies: a quasi-experimental study | e2e9f27f-fda6-4307-86bb-4bc6c131d5f8 | 11331236 | Pediatrics[mh] | Since the 1980s, the use of realistic simulation as a training and evaluation tool in the health area has gained significant attention and has been widely adopted. It is a teaching strategy that reproduces real situations, allowing the student to use the concepts necessary for understanding and solving problems actively. , Realistic simulation is particularly valuable in pediatrics, as severe acute events occur infrequently. Consequently, students and residents are less exposed to training in these clinical situations. , , , , Simulation fills this gap, becoming an essential educational tool, especially in technical skills training, resuscitation, crisis management, and teamwork. The simulation tries to achieve a level of fidelity sufficient to convince users that they are involved in situations that mimic real life and can be categorized as low, medium, or high fidelity. The high-fidelity simulation incorporates a full-body computerized simulator that can be programmed to provide a realistic physiological response to students' actions. , , A systematic review reported that using technology-enhanced simulation for health professional education showed a consistent association with large effects on knowledge, skills, and behavior outcomes and moderate effects on patient-related outcomes. Many studies evaluating the effectiveness of high-fidelity simulation for pediatric training involve graduate and post-graduate professionals. , , , , At graduation, studies in the area of nursing predominate. , , Few studies evaluated high-fidelity simulation's effect on training medical students in pediatric emergencies. , , , This study aims to evaluate the effect of high-fidelity simulation training compared to case-based discussion in pediatric emergencies. Self-confidence, theoretical knowledge, and skills related to clinical reasoning, communication, attitude, and leadership in undergraduate medical students were the main variables studied. Study design, setting, and population This is a quasi-experimental study, conducted in a private medical school in Brazil. The simulation laboratory where the study was conducted has a physical area of 400 m2, with offices with one-way glass for simultaneous observation, six training rooms for pediatric, obstetric, clinical, and surgical emergencies, a home care training room, one for semiology training, and two rooms for debriefing. The Realistic Simulation in Pediatrics team is composed of eight professors (two PhDs and four MSc in Pediatrics), with extensive experience in pediatric emergencies. Thirty-three medical undergraduate internship students eligible for rotation in the pediatric emergency course during the second semester of 2020 were allocated to one of two teaching methods (interventions) according to their time availability: high-fidelity simulation training (HFS, n = 18) or case-based discussion (CBD, n = 15). The students were distributed into the two groups according to the schedule convenience of their other curricular activities. Ethics approval and consent to participate This study was approved by the Research Ethics Committee (CAAE: No.83366618.1.00005245), on 03/04/2018. All students gave written informed consent. Study procedures Before the start of the teaching methods, all students underwent self-confidence and theoretical knowledge tests. Then, during the first three weeks of the course, the following seven pediatric emergency topics were addressed for both groups: wheezing infants, hypovolemic shock, pneumonia/septic shock, anaphylaxis, neonatal hypoglycemia, seizures, and organophosphate poisoning All topics were based on the consensus and guidelines of the Brazilian Society of Pediatrics and the guidelines of the Pediatric Advanced Life Support program of the America Heart Association. The students were distributed into the two groups according to the schedule convenience of their other curricular activities. Group 1 was trained on a high-fidelity patient simulator (PediaSIM) in the Simulation Laboratory, and Group 2 was submitted to the CBD method. After the end of the intervention, students from both groups experienced the same self-confidence and knowledge tests applied at the beginning of the course. In addition, they were submitted to an Objective Structured Clinical Examination (OSCE)-type simulation activity in two randomly chosen scenarios among the seven topics covered in the course, all considered with the same degree of difficulty. An overview of the study procedures is presented in a flowchart in Supplement 1. Two independent raters assessed their performance in this activity with a specific checklist. Eight different teachers worked in pairs scoring the checklist. High-fidelity simulation Five to ten students participated in each simulated scenario, two of them as active players and the others as observers. The students’ roles changed with each scenario so that all students had the opportunity to be active players or observers. Three teachers participated in the simulation activity: two played the patient parents and members of the health team, and one commanded the PediaSIM responses. The training began with the case presentation, followed by the simulation of emergencies with the high-fidelity mannequin, lasting about 15 to 20 min. After that, a 40-minute debriefing took place. The students’ performances were discussed with teachers, pointing out adequate and inadequate actions and procedures. Each student participated in the simulation activity of seven different topics, and each session of HFS lasted approximately 1 hour. The total hours of HFS per student was 7 h. Case-based discussion Case-based learning (CBD) is a long-established pedagogic method that usually occurs via small group discussions of patient cases in healthcare. The CBD group discussed pediatric emergency topics in interactive activities. The same clinical scenarios were presented to the students, and they were challenged to answer on a theoretical base how to conduct anamnesis, diagnostic, and therapeutic procedures in emergencies. Each topic had outlined and structured objectives. A gamified strategy (pedagogical methodology based on games), with elements of peer-to-peer competition and teamwork was used to motivate the students through healthy competition. Adequate and inadequate responses and actions were discussed. The activity had the same duration as the simulation methodology and lasted around 60 min per theme. Each student participated in the discussion of seven topics, and each CBD lasted approximately 1 hour. The total hours of CBD per student was 7 h. Assessment tools When the idea of studying the impact of HFS training on developing clinical skills emerged, a major challenge was ensuring a robust assessment of the desired outcomes. The team of teachers devoted a lot of time judiciously reviewing and discussing the literature to develop and improve the assessment tools. Several meetings were held with experts and the teachers involved in the course until a consensus was reached on the content validity of all the clinical scenarios and the assessment instruments, as they had to contain specific items about the emergency pediatric topics addressed. Self-Confidence test - The self-confidence test was a 36-item self-reported scale with 5-level Likert-type responses (0 = no confidence to 4 = full confidence) to affirmative sentences about feeling confident to provide medical care in different pediatric emergency scenarios. The total score was given by the sum of the item scores and could vary from 0 to 144 (Supplement 2A). Knowledge test - The knowledge test comprised 24 multiple-choice items with specific questions about pediatric emergencies. The test result was given by the percentual of corrected answers (Supplement 2B). Simulation checklist – The HFS has been used to teach pediatric emergencies to undergraduate medical students in the study medical school since 2014. The simulation checklists were already used by the Pediatrics Curricular Unit of the educational institution. They have been developed and refined over the years (since 2014). For this study, the teachers involved conducted a detailed review of the checklists based on previous experience and pediatric consensus to standardize the objectives of the different pediatric emergency topics. The simulation checklists were applied after previous training of all evaluators, showing moderate to almost perfect inter-observer and intra-observer reliability in all evaluated domains (Supplement 3A and 3B). The standardized simulation checklists were comprised of several items grouped into eight domains. Items from the domains of anamnesis, physical exam, and treatment were specific to each scenario. Items from the domains of diagnosis, systematization, communication, attitude, and leadership were common in all the scenarios (Supplement 2C). The domains of “diagnosis” and “systematization” had objective binary responses (Yes/No). The other domains were objectively scored as the percentage of correct answers to their items and subjectively scored as a 5-level Likert-type scale (very poor, poor, fair, good, and very good), depending on the rater's general impression of the student's performance in each domain. A total score was calculated as the percentage of correct answers to the items of all domains. Variables and data collection The variables collected were biological sex, age, ranking order in the class (based on the student performance in medical school), the self-confidence and knowledge scores obtained before and after the interventions, and the simulation checklist scores assigned by the two evaluators after the interventions. All data were entered into Excel spreadsheets. Statistical analysis Continuous variables were presented as means or medians and their measures of variation (standard deviations or interquartile ranges). Categorical variables were presented as proportions. Baseline students’ characteristics were compared between groups using the student's t -test or the Wilcoxon test for continuous variables and the chi-square test or Fisher's test for categorical variables. To assess the effects of the intervention on self-confidence and knowledge, longitudinal analyses were performed using linear mixed-effect models (PROC MIXED, a procedure from the statistical software SAS OnDemand for Academics, SAS Inc., Cary, NC, USA). This analysis tests differences between groups on changes in outcomes (gains for individual students) from pre-intervention (T0) to post-intervention time (T1), accounting for correlations between the repeated measures over time and incomplete data. The simulation checklist scores had only post-intervention measures given by two raters on two scenarios. Therefore, the average scores assigned by the two raters for each scenario (total and domain scores) in both groups were compared to assess the intervention effects on the checklist scores. Student t -tests for independent samples and chi-square tests were performed to compare the scores of both groups (HFS x CDB) in all dimensions. Linear and logistic regressions were also performed with the total and domain scores as dependent variables, the group as an independent variable, and student ranking as a covariate. Effect sizes were calculated using the Hedges'g formula for continuous outcomes, with a correction for small samples, according to the “What Works Clearinghouse Procedures Handbook version 5. Hegdes'g was interpreted as follows: 0.2 (small effect size), 0.5 (medium effect size), and 0.8 (large effect size). Statistical significance was set at a two-tailed type 1 error of < 0.05 and a confidence interval of 95 %. Descriptive and regression analyses were performed using SAS OnDemand for Academics. Inter and intra-rater reliability of the simulation checklist measurements were estimated using the Stata version 9.0 (Stata Corp, College Station, Texas, USA). More details on the statistical analysis are available in the Supplement 4. This is a quasi-experimental study, conducted in a private medical school in Brazil. The simulation laboratory where the study was conducted has a physical area of 400 m2, with offices with one-way glass for simultaneous observation, six training rooms for pediatric, obstetric, clinical, and surgical emergencies, a home care training room, one for semiology training, and two rooms for debriefing. The Realistic Simulation in Pediatrics team is composed of eight professors (two PhDs and four MSc in Pediatrics), with extensive experience in pediatric emergencies. Thirty-three medical undergraduate internship students eligible for rotation in the pediatric emergency course during the second semester of 2020 were allocated to one of two teaching methods (interventions) according to their time availability: high-fidelity simulation training (HFS, n = 18) or case-based discussion (CBD, n = 15). The students were distributed into the two groups according to the schedule convenience of their other curricular activities. This study was approved by the Research Ethics Committee (CAAE: No.83366618.1.00005245), on 03/04/2018. All students gave written informed consent. Before the start of the teaching methods, all students underwent self-confidence and theoretical knowledge tests. Then, during the first three weeks of the course, the following seven pediatric emergency topics were addressed for both groups: wheezing infants, hypovolemic shock, pneumonia/septic shock, anaphylaxis, neonatal hypoglycemia, seizures, and organophosphate poisoning All topics were based on the consensus and guidelines of the Brazilian Society of Pediatrics and the guidelines of the Pediatric Advanced Life Support program of the America Heart Association. The students were distributed into the two groups according to the schedule convenience of their other curricular activities. Group 1 was trained on a high-fidelity patient simulator (PediaSIM) in the Simulation Laboratory, and Group 2 was submitted to the CBD method. After the end of the intervention, students from both groups experienced the same self-confidence and knowledge tests applied at the beginning of the course. In addition, they were submitted to an Objective Structured Clinical Examination (OSCE)-type simulation activity in two randomly chosen scenarios among the seven topics covered in the course, all considered with the same degree of difficulty. An overview of the study procedures is presented in a flowchart in Supplement 1. Two independent raters assessed their performance in this activity with a specific checklist. Eight different teachers worked in pairs scoring the checklist. High-fidelity simulation Five to ten students participated in each simulated scenario, two of them as active players and the others as observers. The students’ roles changed with each scenario so that all students had the opportunity to be active players or observers. Three teachers participated in the simulation activity: two played the patient parents and members of the health team, and one commanded the PediaSIM responses. The training began with the case presentation, followed by the simulation of emergencies with the high-fidelity mannequin, lasting about 15 to 20 min. After that, a 40-minute debriefing took place. The students’ performances were discussed with teachers, pointing out adequate and inadequate actions and procedures. Each student participated in the simulation activity of seven different topics, and each session of HFS lasted approximately 1 hour. The total hours of HFS per student was 7 h. Case-based discussion Case-based learning (CBD) is a long-established pedagogic method that usually occurs via small group discussions of patient cases in healthcare. The CBD group discussed pediatric emergency topics in interactive activities. The same clinical scenarios were presented to the students, and they were challenged to answer on a theoretical base how to conduct anamnesis, diagnostic, and therapeutic procedures in emergencies. Each topic had outlined and structured objectives. A gamified strategy (pedagogical methodology based on games), with elements of peer-to-peer competition and teamwork was used to motivate the students through healthy competition. Adequate and inadequate responses and actions were discussed. The activity had the same duration as the simulation methodology and lasted around 60 min per theme. Each student participated in the discussion of seven topics, and each CBD lasted approximately 1 hour. The total hours of CBD per student was 7 h. Assessment tools When the idea of studying the impact of HFS training on developing clinical skills emerged, a major challenge was ensuring a robust assessment of the desired outcomes. The team of teachers devoted a lot of time judiciously reviewing and discussing the literature to develop and improve the assessment tools. Several meetings were held with experts and the teachers involved in the course until a consensus was reached on the content validity of all the clinical scenarios and the assessment instruments, as they had to contain specific items about the emergency pediatric topics addressed. Self-Confidence test - The self-confidence test was a 36-item self-reported scale with 5-level Likert-type responses (0 = no confidence to 4 = full confidence) to affirmative sentences about feeling confident to provide medical care in different pediatric emergency scenarios. The total score was given by the sum of the item scores and could vary from 0 to 144 (Supplement 2A). Knowledge test - The knowledge test comprised 24 multiple-choice items with specific questions about pediatric emergencies. The test result was given by the percentual of corrected answers (Supplement 2B). Simulation checklist – The HFS has been used to teach pediatric emergencies to undergraduate medical students in the study medical school since 2014. The simulation checklists were already used by the Pediatrics Curricular Unit of the educational institution. They have been developed and refined over the years (since 2014). For this study, the teachers involved conducted a detailed review of the checklists based on previous experience and pediatric consensus to standardize the objectives of the different pediatric emergency topics. The simulation checklists were applied after previous training of all evaluators, showing moderate to almost perfect inter-observer and intra-observer reliability in all evaluated domains (Supplement 3A and 3B). The standardized simulation checklists were comprised of several items grouped into eight domains. Items from the domains of anamnesis, physical exam, and treatment were specific to each scenario. Items from the domains of diagnosis, systematization, communication, attitude, and leadership were common in all the scenarios (Supplement 2C). The domains of “diagnosis” and “systematization” had objective binary responses (Yes/No). The other domains were objectively scored as the percentage of correct answers to their items and subjectively scored as a 5-level Likert-type scale (very poor, poor, fair, good, and very good), depending on the rater's general impression of the student's performance in each domain. A total score was calculated as the percentage of correct answers to the items of all domains. Five to ten students participated in each simulated scenario, two of them as active players and the others as observers. The students’ roles changed with each scenario so that all students had the opportunity to be active players or observers. Three teachers participated in the simulation activity: two played the patient parents and members of the health team, and one commanded the PediaSIM responses. The training began with the case presentation, followed by the simulation of emergencies with the high-fidelity mannequin, lasting about 15 to 20 min. After that, a 40-minute debriefing took place. The students’ performances were discussed with teachers, pointing out adequate and inadequate actions and procedures. Each student participated in the simulation activity of seven different topics, and each session of HFS lasted approximately 1 hour. The total hours of HFS per student was 7 h. Case-based learning (CBD) is a long-established pedagogic method that usually occurs via small group discussions of patient cases in healthcare. The CBD group discussed pediatric emergency topics in interactive activities. The same clinical scenarios were presented to the students, and they were challenged to answer on a theoretical base how to conduct anamnesis, diagnostic, and therapeutic procedures in emergencies. Each topic had outlined and structured objectives. A gamified strategy (pedagogical methodology based on games), with elements of peer-to-peer competition and teamwork was used to motivate the students through healthy competition. Adequate and inadequate responses and actions were discussed. The activity had the same duration as the simulation methodology and lasted around 60 min per theme. Each student participated in the discussion of seven topics, and each CBD lasted approximately 1 hour. The total hours of CBD per student was 7 h. When the idea of studying the impact of HFS training on developing clinical skills emerged, a major challenge was ensuring a robust assessment of the desired outcomes. The team of teachers devoted a lot of time judiciously reviewing and discussing the literature to develop and improve the assessment tools. Several meetings were held with experts and the teachers involved in the course until a consensus was reached on the content validity of all the clinical scenarios and the assessment instruments, as they had to contain specific items about the emergency pediatric topics addressed. Self-Confidence test - The self-confidence test was a 36-item self-reported scale with 5-level Likert-type responses (0 = no confidence to 4 = full confidence) to affirmative sentences about feeling confident to provide medical care in different pediatric emergency scenarios. The total score was given by the sum of the item scores and could vary from 0 to 144 (Supplement 2A). Knowledge test - The knowledge test comprised 24 multiple-choice items with specific questions about pediatric emergencies. The test result was given by the percentual of corrected answers (Supplement 2B). Simulation checklist – The HFS has been used to teach pediatric emergencies to undergraduate medical students in the study medical school since 2014. The simulation checklists were already used by the Pediatrics Curricular Unit of the educational institution. They have been developed and refined over the years (since 2014). For this study, the teachers involved conducted a detailed review of the checklists based on previous experience and pediatric consensus to standardize the objectives of the different pediatric emergency topics. The simulation checklists were applied after previous training of all evaluators, showing moderate to almost perfect inter-observer and intra-observer reliability in all evaluated domains (Supplement 3A and 3B). The standardized simulation checklists were comprised of several items grouped into eight domains. Items from the domains of anamnesis, physical exam, and treatment were specific to each scenario. Items from the domains of diagnosis, systematization, communication, attitude, and leadership were common in all the scenarios (Supplement 2C). The domains of “diagnosis” and “systematization” had objective binary responses (Yes/No). The other domains were objectively scored as the percentage of correct answers to their items and subjectively scored as a 5-level Likert-type scale (very poor, poor, fair, good, and very good), depending on the rater's general impression of the student's performance in each domain. A total score was calculated as the percentage of correct answers to the items of all domains. The variables collected were biological sex, age, ranking order in the class (based on the student performance in medical school), the self-confidence and knowledge scores obtained before and after the interventions, and the simulation checklist scores assigned by the two evaluators after the interventions. All data were entered into Excel spreadsheets. Continuous variables were presented as means or medians and their measures of variation (standard deviations or interquartile ranges). Categorical variables were presented as proportions. Baseline students’ characteristics were compared between groups using the student's t -test or the Wilcoxon test for continuous variables and the chi-square test or Fisher's test for categorical variables. To assess the effects of the intervention on self-confidence and knowledge, longitudinal analyses were performed using linear mixed-effect models (PROC MIXED, a procedure from the statistical software SAS OnDemand for Academics, SAS Inc., Cary, NC, USA). This analysis tests differences between groups on changes in outcomes (gains for individual students) from pre-intervention (T0) to post-intervention time (T1), accounting for correlations between the repeated measures over time and incomplete data. The simulation checklist scores had only post-intervention measures given by two raters on two scenarios. Therefore, the average scores assigned by the two raters for each scenario (total and domain scores) in both groups were compared to assess the intervention effects on the checklist scores. Student t -tests for independent samples and chi-square tests were performed to compare the scores of both groups (HFS x CDB) in all dimensions. Linear and logistic regressions were also performed with the total and domain scores as dependent variables, the group as an independent variable, and student ranking as a covariate. Effect sizes were calculated using the Hedges'g formula for continuous outcomes, with a correction for small samples, according to the “What Works Clearinghouse Procedures Handbook version 5. Hegdes'g was interpreted as follows: 0.2 (small effect size), 0.5 (medium effect size), and 0.8 (large effect size). Statistical significance was set at a two-tailed type 1 error of < 0.05 and a confidence interval of 95 %. Descriptive and regression analyses were performed using SAS OnDemand for Academics. Inter and intra-rater reliability of the simulation checklist measurements were estimated using the Stata version 9.0 (Stata Corp, College Station, Texas, USA). More details on the statistical analysis are available in the Supplement 4. At baseline, the response rate was 97 % to the self-confidence test (one student from group 1 [HFS] did not respond) and 91 % to the knowledge test (two from group 1 and one from group 2 [CBD] did not respond). At the end of the intervention, the response rate to both tests and the checklist was 100 %. Of the 33 students, 61 % were female, the mean age was 24, and the mean student ranking was 48.8 (for a total of 119 students in the same medical class). The mean pre-intervention scores on the self-confidence and knowledge tests were 55.1 and 44.3 %, respectively, no differences between groups were observed . Descriptive statistics for each outcome are available in Supplement 4. The percentage distribution of responses for each item on the self-confidence and knowledge tests is available in Supplements 5A and 5B Self-Confidence scores improved significantly after interventions in both groups (HFS 59.1 × 93.6, p < 0.001; CDB 50.5 × 88.2, p < 0.001), without differences between the two groups ( p = 0.659) ( A). Knowledge scores improved significantly after interventions in both groups (HFS 45.1 × 63.2, p = 0.001; CDB 43.5 × 56.7, p-value < 0.01), without differences between the two groups ( p = 0.272) ( B and Supplement 4). Simulation checklist post-intervention scores were significantly higher in the HFS group compared to the CBD group in both scenarios and all dimensions, except for correct diagnosis in the first scenario and anamnesis in the second scenario ( and Supplement 4). represents graphically the results of the mixed-effect models to test the intervention's main effect on self-confidence and knowledge outcomes adjusted for the student ranking. The time vs. group effect is the critical test of the group on score gains from pre to post-test. No differences between groups were observed regarding changes in the scores of both tests over time ( p = 0.6565 for the self-confidence test; p = 0.3331 for the knowledge test). The time effect was significant for both groups in the self-confidence test ( p < 000.1 {HFS] and p < 0.001 [CDB]) and in the knowledge test ( p = 0.001 {HFS] and p < 0.01 ([CBD]). Table S6.1 in Supplement 6 shows the results of the mixed-effect models. The results of linear and logistic regression models to test the effect of the HFS on the student's performance in the simulation checklist, adjusting for student ranking are available in Table S6.2 in Supplement 6. The HFS group performed significantly better than the CBD group in all dimensions with large effect sizes (Hedges g varying from 1.15 to 2.20), except in correct diagnosis in both scenarios and anamnesis in the second scenario. This study evaluated the effect of high-fidelity simulation (HFS) of pediatric emergencies on different domains of knowledge, attitude, and behavior of medical internship students compared to structured case-based discussions (CBD) applied with gamified methodology. Gamification in education involves the use of game-based elements such as peer-to-peer competition, teamwork, and scoreboards to drive engagement, help students assimilate new information, and test their knowledge. This method develops an environment conducive to learning, with great student adherence, establishing itself as an active motivating methodology. The results revealed that students who participated in the HFS training performed better in clinical reasoning, communication, attitude, and leadership than those trained with CBD. Both groups showed an increase in self-confidence and theoretical knowledge scores, but there was no statistical difference between the two groups. Self-confidence is considered a predictor of behavior in the face of emergency care, even in the case of competent physicians. Health professionals with low self-confidence in managing critically ill children can cause a severe delay in starting immediate care, leading to severe consequences for the patient. On the other hand, previous research indicated no relationship between the self-reported confidence level and students' formally assessed performance in pediatric emergency procedures. One Brazilian study showed that high fidelity simulation improves knowledge, leads the student to feel more challenged and more self-confident in recognizing the severity of the clinical case, including memory retention, and showed benefits regarding self-confidence in recognizing respiratory distress and failure in pediatric cases. In the present study, the two active methodologies increased self-confidence scores with no difference between groups. A study comparing the effect of high-fidelity versus medium-fidelity simulation in pediatrics revealed that medical students improved self-confidence scores with both methods. HFS was superior in the knowledge of Pediatric Advanced Life Support (PALS) algorithms compared to simulation on traditional low-fidelity (non-computerized) mannequins. Coolen et al. compared three training methods for acute pediatric emergencies – high-fidelity video-assisted real-time simulation (VARS), problem-based learning (PBL), and Pediatric Advanced Life Support (PALS). Although the authors found no statistical differences in the self-confidence scores between groups, they observed a slightly lesser increase in the VARS group compared to the other groups. They argue that the stress associated with real-time actions could help recognize the difficulty of conducting a structured approach during stressful circumstances. Both groups showed an increase in the theoretical knowledge test scores, with no difference between groups. Literature findings are divergent. One study revealed that the simulation of intensive care topics resulted in higher scores on multiple-choice tests for knowledge evaluation and was considered more enjoyable than lectures by final-year medical students. Couto et al. found results similar to ours when comparing HFS with CBD for teaching pediatric emergencies to medical students. No difference was observed regarding acquiring and retaining knowledge, but HFS was superior in terms of student satisfaction. On the other hand, according to a study by Avabratha et al. with final-year medical students, both lectures and high-fidelity simulation improved learning outcomes. However, knowledge scores were significantly higher after lectures than simulation. Finally, a study by Besbes et al. showed that both HFS and video-based training are effective educational strategies for septic shock training of internship students, with HFS appearing to be superior in short-term knowledge retention. In the present study, the HFS proved superior to CBD in assessing student performance through simulation checklists. A point that draws attention and corroborates the role of simulation is that only the items that evaluated the correctness of the diagnosis (in the first scenario) and the anamnesis (in the second scenario) exhibited no difference between the groups. From the authors’ point of view, these items depend more on theoretical knowledge about the topics addressed than on practical skills and attitudes, which are the pillars of simulated activities. Indeed, when comparing HSF with CBD in the present study, the effect sizes of HSF training on communication, attitude, and leadership were large. The present findings support the need for training technical and non-technical skills related to behavior, attitude, leadership, and communication during undergraduate degrees. Another study revealed that interns who participate in pediatric traumatic brain injury training with HFS compared to clinical case discussion better understood, and applied pre-established rules for traumatic brain injury, and retained them longer. Limitations This study has limitations. The main one, imposed by the COVID-19 pandemic, was the sample size. The plan was to include the 119 students who would rotate in the pediatric emergency course during 2020 based on a sample size calculation. The non-significant statistics of this study may be due to a lack of power. Another limitation is the non-randomized design. The students were distributed into the two groups according to the schedule convenience of their other curricular activities. Despite this, demographic characteristics and pre-intervention scores were similar in both groups. In addition, the authors used the covariate “student ranking” to adjust the regression models. The use of variable pairs of scenarios in the final assessment can also be pointed out as a limitation. It was a necessary strategy to avoid prior knowledge of the topics by the students, given the impossibility of evaluating all students on the same day. However, the scenarios were carefully designed with similar degrees of difficulty by the team of teachers. Finally, the study was conducted in a single educational institution with a specific physical structure and human resources, limiting its generalization to other institutions with different characteristics. Despite these limitations, the results of this study corroborated with the empirical perception that HFS in pediatrics is necessary to improve the technical and non-technical skills of undergraduate medical students. The positive impact of this strategy resulted in the expansion and earlier introduction of the method in the curriculum. Currently, it has been inserted since the pre-internship in pediatrics. The present study contributes evidence on the positive effect of using high-fidelity simulation on the acquisition of competencies, skills, and attitudes in undergraduate students in pediatric emergency settings. This study has limitations. The main one, imposed by the COVID-19 pandemic, was the sample size. The plan was to include the 119 students who would rotate in the pediatric emergency course during 2020 based on a sample size calculation. The non-significant statistics of this study may be due to a lack of power. Another limitation is the non-randomized design. The students were distributed into the two groups according to the schedule convenience of their other curricular activities. Despite this, demographic characteristics and pre-intervention scores were similar in both groups. In addition, the authors used the covariate “student ranking” to adjust the regression models. The use of variable pairs of scenarios in the final assessment can also be pointed out as a limitation. It was a necessary strategy to avoid prior knowledge of the topics by the students, given the impossibility of evaluating all students on the same day. However, the scenarios were carefully designed with similar degrees of difficulty by the team of teachers. Finally, the study was conducted in a single educational institution with a specific physical structure and human resources, limiting its generalization to other institutions with different characteristics. Despite these limitations, the results of this study corroborated with the empirical perception that HFS in pediatrics is necessary to improve the technical and non-technical skills of undergraduate medical students. The positive impact of this strategy resulted in the expansion and earlier introduction of the method in the curriculum. Currently, it has been inserted since the pre-internship in pediatrics. The present study contributes evidence on the positive effect of using high-fidelity simulation on the acquisition of competencies, skills, and attitudes in undergraduate students in pediatric emergency settings. The authors declare no conflicts of interest. |
Intraspecific plant–soil feedback in four tropical tree species is inconsistent in a field experiment | 48b14de0-bdfe-42f4-88d0-77aeca2444f0 | 11659945 | Microbiology[mh] | Study site and species Our study focused on four canopy tree species on Barro Colorado Island (BCI), Republic of Panama (9°09′N, 79°51′W). The island is a 15.6‐km 2 moist tropical lowland forest (Croat, ), receiving ~2600 mm of rainfall per year, with a distinct dry season from ~January to May (Windsor, ). The species represent a range of tropical tree families on BCI: (1) Lacmellea panamensis (Woodson) Markgf. (Apocynaceae), (2) Ormosia macrocalyx Ducke (Fabaceae), (3) Tetragastris panamensis (Engl.) Kuntze (Burseraceae), and (4) V. surinamensis (Myristicaceae). Ormosia macrocalyx , T. panamensis , and V. surinamensis are native to much of Central America and northern Amazonia, while L. panamensis has a more limited distribution spanning Panama, Costa Rica, and Belize (Croat, ). On BCI, the species vary in relative abundance: T. panamensis and V. surinamensis are common, L. panamensis is occasional, while O. macrocalyx is rare (Croat, ). Seedlings of each species are shade tolerant (Howe, ; Gilbert et al., ; Myers and Kitajima, ; Krause et al., ) but vary in drought sensitivity: L. panamensis and T. panamensis are drought tolerant; V. surinamensis is drought sensitive (Kursar et al., ; drought tolerance for O. macrocalyx is unknown). Seeds of each species are medium‐ or large‐sized (~1–2 cm) and animal‐dispersed, with seed production peaking during March in T. panamensis and L. panamensis , June in V. surinamensis , and September–November and April in O. macrocalyx (Croat, ; Zimmerman et al., ; S. J. Wright, Smithsonian Tropical Research Institute, personal communication). Virola surinamensis and T. panamensis are dioecious, while L. panamensis and O. macrocalyx are hermaphroditic (Croat, ). These species were chosen because their seeds were available at the time of collection and germinated in sufficient quantities in the shadehouse. Field experiment To test whether seedling performance differed beneath maternal conspecific trees compared to beneath nonmaternal conspecific trees, we conducted a field experiment in the BCI forest. We collected seeds from beneath the canopy of six fruiting V. surinamensis , three fruiting T. panamensis , seven fruiting L. panamensis , and three fruiting O. macrocalyx on BCI during late June–early August 2015. We assigned the tree that a seed was collected beneath as the seed's putative parent. Seeds from each parental source were surface‐sterilized (10% v/v bleach for 1 min, rinse, 70% v/v ethanol for 30 s, rinse) and air‐dried. Ormosia macrocalyx seeds were scarified and submerged in water for 24 h before planting to encourage germination (Sautu et al., ). All seeds were germinated in a shadehouse in autoclaved BCI soil (collected from the forest edge near the shadehouse) under two layers of 80% shadecloth. One month after germination, 145 V. surinamensis seedlings, 130 O. macrocalyx seedlings, 68 L. panamensis seedlings, and 30 T. panamensis seedlings were selected for inclusion in the experiment (for a total of 373 experimental seedlings). The number of seedlings per species and seed source reflected availability of seeds collected and healthy seedlings available at the time of transplantation (see Table for an overview of the experimental design). Seedlings of each species were randomly assigned to one of two treatments: maternal field environment or nonmaternal conspecific field environment. Seedlings in the first group were transplanted beneath their own maternal tree in the field. Seedlings in the second group were randomly assigned to be transplanted beneath one of the other seed source trees of their same species (other than their maternal tree). Thus, the experimental treatment signified the putative relationship between an experimental seedling and the conspecific tree it was transplanted beneath during the experiment. Both putative offspring and non‐offspring conspecific seedlings were transplanted beneath each maternal seed source. In the hermaphroditic species, nonmaternal conspecific trees cannot be ruled out as potential pollen donors to the seedlings. To limit this possibility, in the dioecious species, only female trees were included in the experiment. To increase the number of nonmaternal conspecific field environments in T. panamensis and O. macrocalyx , we selected one additional fruiting T. panamensis and two additional fruiting O. macrocalyx as seedling transplant sites. All focal trees in the experiment were located by exploring a ~5.5‐km 2 area of BCI with the aid of a mapped 25‐ha plot (provided by S. J. Wright, Smithsonian Tropical Research Institute, personal communication). Focal trees were located ~100 m to ~2 km apart (see Figure for map). All met or exceeded the minimum reproductive diameter for their species (Croat, ; Wright et al., ). The experiment was set up one species at a time during late August–October 2015. All experimental seedlings were transplanted at ~1 mo of age into their field treatments. Each seedling was randomly transplanted into one of several seedling plots (1 m 2 ) beneath the canopy of their assigned conspecific tree. The number of focal plots per tree (1–7 plots) and the density of experimental seedlings within each plot was determined by seedling availability: Species with more experimental seedlings available had more plots per focal tree and more seedlings per plot (Table ). Small ranges in the number of conspecific seedlings per plot (2–5 seedlings) were used to minimize the potential impact of conspecific seedling neighbors on seedling survival or survival. Plot locations were randomized with respect to direction and distance from the base of the focal tree (1–4 m). Each seedling was transplanted into a randomly selected 25‐cm 2 position within a plot. Seedlings roots were not rinsed before transplant. At the time of transplant, each seedling was stem‐tagged with a unique identification number (see Figure for an example photograph). Stem height, the number of leaves, and the length and width of each leaf were also measured for each seedling at the time of transplant. To estimate initial oven‐dried biomass for each experimental seedling, we used species‐specific allometric equations. We generated these equations from models built using measurements of the stem height, leaf area (measured with a leaf area scanner), and total oven‐dried biomass of a randomly harvested sample of potential experimental seedlings of each species at the beginning of the experiment ( V. surinamensis : F 2,47 = 496.5, P < 0.001, R 2 = 0.95; O. macrocalyx : F 2,37 = 154.7, P < 0.001, R 2 = 0.89; L. panamensis : F 2,26 = 183.7, P < 0.001, R 2 = 0.93; T. panamensis : F 2,7 = 19.7, P = 0.001, R 2 = 0.81). All field experimental seedlings were censused every 1–2 mo, during which time survival was recorded, and the stem height and number of leaves were measured for all surviving seedlings. We also recorded instances of seedling stem breakage or uprooting (which we refer to as clipping), likely caused by mammalian herbivores (e.g., agoutis). Seedlings were not watered or enclosed during the field experiment and received only ambient rainfall. Seedlings that were not found during a census (with or without their tags being found) were recorded as dead. After ~7 mo, all surviving seedlings were harvested ( N = 21, V. surinamensis seedlings; N = 84, O. macrocalyx seedlings; N = 12, L. panamensis seedlings, and N = 9, T. panamensis seedlings). Seedlings were harvested one species at a time between late March and early May 2016. Total oven‐dried biomass (including aboveground and belowground portions), stem height, and total leaf area (measured with a scanner) were measured at harvest for each surviving seedling. The final months of the experiments overlapped with a severe dry season due to the strong 2015–2016 El Niño event: signs of wilting were observed in some seedlings (particularly V. surinamensis ). Statistical analyses To test whether seedling performance in the field experiment differed in maternal conspecific field environments relative to nonmaternal conspecific field environments, we built a series of mixed‐effects models. Because we expected pathogen effects to be stronger in the wet season and because the experiment extended into an unusually severe dry season, we analyzed seedling performance at two time points during the experiment: (1) shortly after the end of the wet season (early‐mid January, ~4 mo into the experiment), to exclude dry season effects, and (2) at the end of the 7‐mo experiment (which coincided with the dry season). Seedling performance at these time points was analyzed using data on seedling survival and seedling relative growth rate (RGR, which was based on measurements of seedling stem height and leaf number during the experiment, or biomass at the end of the experiment). First, we constructed models examining seedling survival at each time point using data from all four species combined. Seedling survival was modelled as a binary response variable using generalized linear mixed‐models with binomial errors. Field environment (i.e., maternal conspecific or nonmaternal conspecific) and species were included as fixed effects in each model. Because we expected that patterns of seedling performance in the field environments could vary among species, we also included an interaction between field environment and species as a fixed effect in these models. Other fixed effects in these models included estimated initial seedling biomass and seedling clipping (i.e., whether the seedling had experienced clipping up until the time of analysis). The identity of the maternal seed source tree, the identity of the conspecific tree under which the seedling was transplanted (i.e., soil source), and field plot were included as random effects in each model. Initial seedling biomass did not vary significantly among field environments in any focal species. To look more deeply at patterns of survival in each species, we then built separate survival models for each of the four species in the experiment during each season. The list of fixed and random effects in these models differed from the model described above in the following ways: (1) species and its interaction term with field environment were necessarily excluded; (2) due to a limited number of seed sources in O. macrocalyx and T. panamensis , seed source was included in these species’ models as a fixed effect rather than a random effect; and (3) for the same reason, soil source was included as a fixed effect rather than a random effect in the T. panamensis models. In addition, clipped seedlings and the term denoting clipping were removed from the O. macrocalyx and T. panamensis models, because clipping was infrequent in these species (4.6% of seedlings in O. macrocalyx and 6.6% in T. panamensis ) and including the term prevented convergence in some models. In species where clipping was more frequent, i.e., V. surinamensis (31.7% of seedlings) and L. panamensis (16.2% of seedlings), we built two versions of each model: The first excluded clipped seedlings from the model, while the other retained clipped seedlings but included clipping as a fixed effect. Because including clipped seedlings did not affect model results in V. surinamensis and L. panamensis , we present the results of the models that include clipped seedlings to utilize data on all available seedlings in these species. We also tested for differences in seedling RGRs between the field environments during the experiment. Seedling RGRs were calculated based on field measurements of stem height and counts of leaves for each living seedling during each census interval as RGR = ln ( S 2 ) − ln ( S 1 ) t 2 − t 1 , where S 1 = size at time one (i.e., estimated dry biomass in grams), S 2 = size at time two, t 1 = time one (in days elapsed since the seedling was transplanted), and t 2 = time two. This resulted in a total of 959 RGR observations from 260 seedling individuals across five census intervals. The RGR data were analyzed at the end of the wet season and at the end of the experiment using linear mixed‐effects models that included data from all four species. The RGR models included the same set of fixed and random effects specified for the all‐species survival model. In addition, the RGR models included seedling ID (to account for repeated measurements of seedlings over time) and census interval as random effects. Because the number of surviving seedlings was small in some species (Figure ), we did not construct separate RGR models for each species. All statistical analyses in our study were conducted in the R statistical environment version 4.3.1 (R Core Team, ). Linear mixed‐effects models in our study were constructed using the R package lme4 (Bates et al., ). P values for mixed‐effect model predictors were obtained using R packages lmerTest (Kuznetsova et al., ) and car (Fox and Weisberg, ). Long‐format data for the RGR analyses were obtained using R package tidyr (Wickham et al., ). Plots were generated using R packages ggplot2 (Wickham, ) and interactions (Long, ). Our study focused on four canopy tree species on Barro Colorado Island (BCI), Republic of Panama (9°09′N, 79°51′W). The island is a 15.6‐km 2 moist tropical lowland forest (Croat, ), receiving ~2600 mm of rainfall per year, with a distinct dry season from ~January to May (Windsor, ). The species represent a range of tropical tree families on BCI: (1) Lacmellea panamensis (Woodson) Markgf. (Apocynaceae), (2) Ormosia macrocalyx Ducke (Fabaceae), (3) Tetragastris panamensis (Engl.) Kuntze (Burseraceae), and (4) V. surinamensis (Myristicaceae). Ormosia macrocalyx , T. panamensis , and V. surinamensis are native to much of Central America and northern Amazonia, while L. panamensis has a more limited distribution spanning Panama, Costa Rica, and Belize (Croat, ). On BCI, the species vary in relative abundance: T. panamensis and V. surinamensis are common, L. panamensis is occasional, while O. macrocalyx is rare (Croat, ). Seedlings of each species are shade tolerant (Howe, ; Gilbert et al., ; Myers and Kitajima, ; Krause et al., ) but vary in drought sensitivity: L. panamensis and T. panamensis are drought tolerant; V. surinamensis is drought sensitive (Kursar et al., ; drought tolerance for O. macrocalyx is unknown). Seeds of each species are medium‐ or large‐sized (~1–2 cm) and animal‐dispersed, with seed production peaking during March in T. panamensis and L. panamensis , June in V. surinamensis , and September–November and April in O. macrocalyx (Croat, ; Zimmerman et al., ; S. J. Wright, Smithsonian Tropical Research Institute, personal communication). Virola surinamensis and T. panamensis are dioecious, while L. panamensis and O. macrocalyx are hermaphroditic (Croat, ). These species were chosen because their seeds were available at the time of collection and germinated in sufficient quantities in the shadehouse. To test whether seedling performance differed beneath maternal conspecific trees compared to beneath nonmaternal conspecific trees, we conducted a field experiment in the BCI forest. We collected seeds from beneath the canopy of six fruiting V. surinamensis , three fruiting T. panamensis , seven fruiting L. panamensis , and three fruiting O. macrocalyx on BCI during late June–early August 2015. We assigned the tree that a seed was collected beneath as the seed's putative parent. Seeds from each parental source were surface‐sterilized (10% v/v bleach for 1 min, rinse, 70% v/v ethanol for 30 s, rinse) and air‐dried. Ormosia macrocalyx seeds were scarified and submerged in water for 24 h before planting to encourage germination (Sautu et al., ). All seeds were germinated in a shadehouse in autoclaved BCI soil (collected from the forest edge near the shadehouse) under two layers of 80% shadecloth. One month after germination, 145 V. surinamensis seedlings, 130 O. macrocalyx seedlings, 68 L. panamensis seedlings, and 30 T. panamensis seedlings were selected for inclusion in the experiment (for a total of 373 experimental seedlings). The number of seedlings per species and seed source reflected availability of seeds collected and healthy seedlings available at the time of transplantation (see Table for an overview of the experimental design). Seedlings of each species were randomly assigned to one of two treatments: maternal field environment or nonmaternal conspecific field environment. Seedlings in the first group were transplanted beneath their own maternal tree in the field. Seedlings in the second group were randomly assigned to be transplanted beneath one of the other seed source trees of their same species (other than their maternal tree). Thus, the experimental treatment signified the putative relationship between an experimental seedling and the conspecific tree it was transplanted beneath during the experiment. Both putative offspring and non‐offspring conspecific seedlings were transplanted beneath each maternal seed source. In the hermaphroditic species, nonmaternal conspecific trees cannot be ruled out as potential pollen donors to the seedlings. To limit this possibility, in the dioecious species, only female trees were included in the experiment. To increase the number of nonmaternal conspecific field environments in T. panamensis and O. macrocalyx , we selected one additional fruiting T. panamensis and two additional fruiting O. macrocalyx as seedling transplant sites. All focal trees in the experiment were located by exploring a ~5.5‐km 2 area of BCI with the aid of a mapped 25‐ha plot (provided by S. J. Wright, Smithsonian Tropical Research Institute, personal communication). Focal trees were located ~100 m to ~2 km apart (see Figure for map). All met or exceeded the minimum reproductive diameter for their species (Croat, ; Wright et al., ). The experiment was set up one species at a time during late August–October 2015. All experimental seedlings were transplanted at ~1 mo of age into their field treatments. Each seedling was randomly transplanted into one of several seedling plots (1 m 2 ) beneath the canopy of their assigned conspecific tree. The number of focal plots per tree (1–7 plots) and the density of experimental seedlings within each plot was determined by seedling availability: Species with more experimental seedlings available had more plots per focal tree and more seedlings per plot (Table ). Small ranges in the number of conspecific seedlings per plot (2–5 seedlings) were used to minimize the potential impact of conspecific seedling neighbors on seedling survival or survival. Plot locations were randomized with respect to direction and distance from the base of the focal tree (1–4 m). Each seedling was transplanted into a randomly selected 25‐cm 2 position within a plot. Seedlings roots were not rinsed before transplant. At the time of transplant, each seedling was stem‐tagged with a unique identification number (see Figure for an example photograph). Stem height, the number of leaves, and the length and width of each leaf were also measured for each seedling at the time of transplant. To estimate initial oven‐dried biomass for each experimental seedling, we used species‐specific allometric equations. We generated these equations from models built using measurements of the stem height, leaf area (measured with a leaf area scanner), and total oven‐dried biomass of a randomly harvested sample of potential experimental seedlings of each species at the beginning of the experiment ( V. surinamensis : F 2,47 = 496.5, P < 0.001, R 2 = 0.95; O. macrocalyx : F 2,37 = 154.7, P < 0.001, R 2 = 0.89; L. panamensis : F 2,26 = 183.7, P < 0.001, R 2 = 0.93; T. panamensis : F 2,7 = 19.7, P = 0.001, R 2 = 0.81). All field experimental seedlings were censused every 1–2 mo, during which time survival was recorded, and the stem height and number of leaves were measured for all surviving seedlings. We also recorded instances of seedling stem breakage or uprooting (which we refer to as clipping), likely caused by mammalian herbivores (e.g., agoutis). Seedlings were not watered or enclosed during the field experiment and received only ambient rainfall. Seedlings that were not found during a census (with or without their tags being found) were recorded as dead. After ~7 mo, all surviving seedlings were harvested ( N = 21, V. surinamensis seedlings; N = 84, O. macrocalyx seedlings; N = 12, L. panamensis seedlings, and N = 9, T. panamensis seedlings). Seedlings were harvested one species at a time between late March and early May 2016. Total oven‐dried biomass (including aboveground and belowground portions), stem height, and total leaf area (measured with a scanner) were measured at harvest for each surviving seedling. The final months of the experiments overlapped with a severe dry season due to the strong 2015–2016 El Niño event: signs of wilting were observed in some seedlings (particularly V. surinamensis ). To test whether seedling performance in the field experiment differed in maternal conspecific field environments relative to nonmaternal conspecific field environments, we built a series of mixed‐effects models. Because we expected pathogen effects to be stronger in the wet season and because the experiment extended into an unusually severe dry season, we analyzed seedling performance at two time points during the experiment: (1) shortly after the end of the wet season (early‐mid January, ~4 mo into the experiment), to exclude dry season effects, and (2) at the end of the 7‐mo experiment (which coincided with the dry season). Seedling performance at these time points was analyzed using data on seedling survival and seedling relative growth rate (RGR, which was based on measurements of seedling stem height and leaf number during the experiment, or biomass at the end of the experiment). First, we constructed models examining seedling survival at each time point using data from all four species combined. Seedling survival was modelled as a binary response variable using generalized linear mixed‐models with binomial errors. Field environment (i.e., maternal conspecific or nonmaternal conspecific) and species were included as fixed effects in each model. Because we expected that patterns of seedling performance in the field environments could vary among species, we also included an interaction between field environment and species as a fixed effect in these models. Other fixed effects in these models included estimated initial seedling biomass and seedling clipping (i.e., whether the seedling had experienced clipping up until the time of analysis). The identity of the maternal seed source tree, the identity of the conspecific tree under which the seedling was transplanted (i.e., soil source), and field plot were included as random effects in each model. Initial seedling biomass did not vary significantly among field environments in any focal species. To look more deeply at patterns of survival in each species, we then built separate survival models for each of the four species in the experiment during each season. The list of fixed and random effects in these models differed from the model described above in the following ways: (1) species and its interaction term with field environment were necessarily excluded; (2) due to a limited number of seed sources in O. macrocalyx and T. panamensis , seed source was included in these species’ models as a fixed effect rather than a random effect; and (3) for the same reason, soil source was included as a fixed effect rather than a random effect in the T. panamensis models. In addition, clipped seedlings and the term denoting clipping were removed from the O. macrocalyx and T. panamensis models, because clipping was infrequent in these species (4.6% of seedlings in O. macrocalyx and 6.6% in T. panamensis ) and including the term prevented convergence in some models. In species where clipping was more frequent, i.e., V. surinamensis (31.7% of seedlings) and L. panamensis (16.2% of seedlings), we built two versions of each model: The first excluded clipped seedlings from the model, while the other retained clipped seedlings but included clipping as a fixed effect. Because including clipped seedlings did not affect model results in V. surinamensis and L. panamensis , we present the results of the models that include clipped seedlings to utilize data on all available seedlings in these species. We also tested for differences in seedling RGRs between the field environments during the experiment. Seedling RGRs were calculated based on field measurements of stem height and counts of leaves for each living seedling during each census interval as RGR = ln ( S 2 ) − ln ( S 1 ) t 2 − t 1 , where S 1 = size at time one (i.e., estimated dry biomass in grams), S 2 = size at time two, t 1 = time one (in days elapsed since the seedling was transplanted), and t 2 = time two. This resulted in a total of 959 RGR observations from 260 seedling individuals across five census intervals. The RGR data were analyzed at the end of the wet season and at the end of the experiment using linear mixed‐effects models that included data from all four species. The RGR models included the same set of fixed and random effects specified for the all‐species survival model. In addition, the RGR models included seedling ID (to account for repeated measurements of seedlings over time) and census interval as random effects. Because the number of surviving seedlings was small in some species (Figure ), we did not construct separate RGR models for each species. All statistical analyses in our study were conducted in the R statistical environment version 4.3.1 (R Core Team, ). Linear mixed‐effects models in our study were constructed using the R package lme4 (Bates et al., ). P values for mixed‐effect model predictors were obtained using R packages lmerTest (Kuznetsova et al., ) and car (Fox and Weisberg, ). Long‐format data for the RGR analyses were obtained using R package tidyr (Wickham et al., ). Plots were generated using R packages ggplot2 (Wickham, ) and interactions (Long, ). Seedling survival in the field experiment Patterns of seedling survival in maternal field environments versus nonmaternal conspecific field environments varied among species at the end of the wet season (Figure ; Appendix : Table ; P = 0.03, N = 373 seedlings), but were similar among species by the end of the experiment (Figure ; Appendix : Table ; P = 0.32, N = 373 seedlings). At the end of the wet season, seedlings of V. surinamensis had higher survival when growing beneath their maternal tree versus beneath another female conspecific tree (Figure ; Appendix : Table ; P = 0.02, N = 145 seedlings). In contrast, seedlings of O. macrocalyx had lower survival at the end of the wet season when growing beneath their maternal tree (Figure ; Appendix : Table ; P < 0.05, N = 124 seedlings). Differences in seedling survival between field environments in these species disappeared during the dry season (Figures ; Appendix : Tables , ). Seedling survival was similar between field environments in L. panamensis (Appendix : Table ) and T. panamensis (Appendix : Table ) at the end of the wet season and the end of the experiment. Rates of seedling survival also varied among species (Appendix : Table ; P < 0.01, N = 373 seedlings). Seedlings with higher initial biomass were more likely to survive (Appendix : Table ; P < 0.01, N = 373 seedlings), while clipped seedlings were less likely to survive (Appendix : Table ; P = 0.02, N = 373 seedlings). Seedling growth in the field experiment Seedling RGRs did not vary between field environments and did not change over time throughout the experiment (Figure ; Appendix : Table ). Relative growth rates varied among species throughout the experiment (Appendix : Table ; p < 0.01, n = 959 observations). In addition, seedlings with higher initial biomass had higher RGRs during the experiment (Appendix : Table ; P < 0.01, N = 959 observations), while clipping reduced seedling RGRs (Appendix : Table ; P < 0.01, N = 959 observations). Patterns of seedling survival in maternal field environments versus nonmaternal conspecific field environments varied among species at the end of the wet season (Figure ; Appendix : Table ; P = 0.03, N = 373 seedlings), but were similar among species by the end of the experiment (Figure ; Appendix : Table ; P = 0.32, N = 373 seedlings). At the end of the wet season, seedlings of V. surinamensis had higher survival when growing beneath their maternal tree versus beneath another female conspecific tree (Figure ; Appendix : Table ; P = 0.02, N = 145 seedlings). In contrast, seedlings of O. macrocalyx had lower survival at the end of the wet season when growing beneath their maternal tree (Figure ; Appendix : Table ; P < 0.05, N = 124 seedlings). Differences in seedling survival between field environments in these species disappeared during the dry season (Figures ; Appendix : Tables , ). Seedling survival was similar between field environments in L. panamensis (Appendix : Table ) and T. panamensis (Appendix : Table ) at the end of the wet season and the end of the experiment. Rates of seedling survival also varied among species (Appendix : Table ; P < 0.01, N = 373 seedlings). Seedlings with higher initial biomass were more likely to survive (Appendix : Table ; P < 0.01, N = 373 seedlings), while clipped seedlings were less likely to survive (Appendix : Table ; P = 0.02, N = 373 seedlings). Seedling RGRs did not vary between field environments and did not change over time throughout the experiment (Figure ; Appendix : Table ). Relative growth rates varied among species throughout the experiment (Appendix : Table ; p < 0.01, n = 959 observations). In addition, seedlings with higher initial biomass had higher RGRs during the experiment (Appendix : Table ; P < 0.01, N = 959 observations), while clipping reduced seedling RGRs (Appendix : Table ; P < 0.01, N = 959 observations). In our in situ field experiment with four tropical tree species in Panama, we did not find evidence that intraspecific plant–soil feedbacks play a significant role in shaping seedling mortality in the field. Though interspecific PSFs are well documented (e.g., Crawford et al., ), relatively few studies have tested for intraspecific PSFs, especially under ecologically relevant field conditions. In our experiment, patterns of seedling mortality indicative of intraspecific PSFs were not maintained over time and were detected (temporarily) only in two of the four focal species. At the end of the wet season, seedling survival was higher in maternal conspecific field environments relative to nonmaternal conspecific field environments for V. surinamensis (consistent with positive intraspecific PSF), but lower for O. macrocalyx (consistent with negative intraspecific PSF). In both species, these patterns were no longer present by the end of the experiment. Thus, our field experiment suggests that intraspecific PSFs may play a limited role in determining seedling performance in tropical tree communities. In a prior shadehouse experiment with the same population of V. surinamensis on BCI, we found that seedling growth and colonization by arbuscular mycorrhizal fungi were reduced in the soil microbial community from beneath maternal trees relative to those in the soil microbial community from beneath other female conspecific trees (Eck et al., ). Thus, we found patterns of seedling growth that were consistent with negative intraspecific PSF in the shadehouse but did not find similar patterns consistent with intraspecific PSF in the field in this species. In the field, patterns of seedling survival in V. surinamensis matched those expected due to positive intraspecific PSF at the end of the wet season, but these effects were not maintained over time, and we found no effect of intraspecific PSF overall. In general, differences within species in the direction of intraspecific PSFs for growth versus survival could indicate a growth‐defense trade‐off for seedlings in the soil microbial communities near their maternal tree. This trade‐off could occur, for example, if the accumulation of genotype‐specific pathogens in the soil near adult trees activates seedling defenses, reducing the resources available for seedling growth but increasing the seedlings’ chance of survival. In our field experiment, seedling mortality rates were high but growth rates were low, indicating the challenges of measuring these metrics in field experiments in tropical forests. Conflicting findings between these studies may reflect differences in conditions in the shadehouse versus field. Soil microbial effects that are easily isolated in controlled conditions in the shadehouse may be modified by other ecological variables in the field. In our prior shadehouse experiment, seedlings were grown in a common sterilized soil medium that was inoculated with a small volume of field soil. Thus, the negative intraspecific plant–soil feedback we observed in that study could be attributed entirely to soil microbes. In contrast, in the present study, seedling performance may have been affected not only by conditioned soil microbes, but by differences in soil nutrients or biochemistry, microclimate, herbivore composition, or other unmeasured factors caused by the adult trees. In addition, it is possible that other environmental factors such as topography, light levels, soil type or moisture, and vegetation composition varied near the conspecific trees. Environmental variables such as soil conditions (Wei et al., ) and plant litter (Veen et al., ) have been shown to play a role in determining PSFs (reviewed by van der Putten et al., ; De Long et al., ), and environmental factors other than soil microbes could have affected seedling survival (Johnson et al., ). In addition, the cause of mortality was not usually known for the seedlings in our study. Ultimately, it is unsurprising for different patterns to arise in shadehouse versus field studies because several factors can potentially modify or override the effects of soil microbes in the field; thus, one may not necessarily predict the other (Beckman et al., ). Variation in the incidence, direction, and magnitude of interspecific plant–soil feedbacks among species within plant communities has often been documented (e.g., Mangan et al., ; Reinhart, ; Bennett et al., ). In our study, V. surinamensis and O. macrocalyx briefly demonstrated opposite patterns of intraspecific PSF for seedling survival. Variation in intraspecific PSFs among plant species might be expected due to variation in species’ genetic resources, growth and defensive traits, soil microbial associations, and life history. However, characteristics of interspecific PSF might not correlate with characteristics of intraspecific PSF within plant species. In another previous shadehouse experiment with three of our focal species on BCI, V. surinamensis and L. panamensis exhibited negative interspecific PSFs, while T. panamensis did not exhibit interspecific PSF (Mangan et al., ). In our study, these species did not exhibit intraspecific PSF by the end of the experiment. Simulation studies have shown that negative intraspecific PSF can help maintain species diversity (albeit, less strongly than negative interspecific PSF) and is most effective at maintaining species richness when genetic diversity is relatively low (Eck et al., ). Genetic relatedness between seedlings and conspecific adult plants has been shown to predict seedling survival in the soil near those adults: soil microbial communities promoted the survival of seedlings from more genetically distant populations (Liu et al., ). Variation in seedling performance in conspecific field soils has also been demonstrated to favor the survival of seedlings of locally rare genotypes (Browne and Karubian, ) and promote genetic diversity within plant populations (Browne and Karubian, ). Together, these studies suggest a role for plant genotype in determining patterns of seedling performance and diversity in the field. Our findings also indicate that patterns of intraspecific plant–soil feedback could vary over time, potentially due to several factors. Interspecific PSFs are also thought to vary temporally (Packer and Clay, ; Hawkes et al., ; reviewed by Kardol et al., ; Chung, ), to occur more often in stable relative to variable environments (Duell et al., ), and to be more negative in temporally stable patches (Chung et al., ). Changing environmental conditions in the field could drive changes in intraspecific PSF. In our experiment, patterns of intraspecific PSF emerged during the wet season, but disappeared by the end of the following dry season. Our experiment coincided with the 2015–2016 El Niño event that resulted in a severe dry season, which was linked to elevated seedling mortality in our study region (Browne et al., ). Drought has been shown to influence PSFs (Kaisermann et al., ; Fry et al., ; reviewed by Hassan et al., ; de Vries et al., ). In addition, intraspecific PSF effects could be strongest during certain seedling developmental phases (reviewed by Kardol et al., ). Our study would miss any effects that might impact seed survival, germination, very early seedling mortality, or later‐stage seedling performance. Additional studies are needed to disentangle the effects of environmental variation versus developmental age on changes in intraspecific PSFs. Plant–soil feedbacks are one mechanism by which soil microbes may drive patterns of species diversity in plant communities, but how consistently PSFs occur among genotypes within plant species is unclear, especially under variable field conditions. Intraspecific variability in biotic interactions is often overlooked in community ecology but can greatly affect ecological patterns (reviewed by Freckleton and Lewis, ; Bolnick et al., ). To date, few studies have tested for evidence of intraspecific PSFs in the field, where seedlings are exposed to their full set of natural enemies (see also Browne and Karubian, ; Kirchoff et al., ). Intraspecific variation in seedling performance near conspecific adults, if it occurs, could be consequential because of its potential to influence species and/or genetic diversity in plant communities (Stump and Chesson, ; Browne and Karubian, ; Eck et al., ). Future studies are needed to elucidate the environmental, genetic, or trait‐based factors that determine the incidence and direction of intraspecific plant–soil feedbacks in plant communities. Understanding the causes and consequences of variability in plant–microbe interactions and plant–soil feedbacks is a key challenge in plant biology. We demonstrated via a field experiment with four tropical tree species in Panama that the patterns of seedling performance indicative of intraspecific plant–soil feedback do not occur consistently among species and erode over time. Thus, our study suggests a limited role of intraspecific plant–soil feedback in determining patterns of seedling performance in tropical tree communities. J.L.E. and L.S.C. designed the experiments. J.L.E. and L.H.H. set up the experiments and collected the data. J.L.E. analyzed the data with help from L.S.C. J.L.E. wrote the first draft of the manuscript. All authors contributed to and approved the final version of the manuscript. Table S1 . Effect of maternal conspecific versus other conspecific field environments on seedling survival in four species. Table S2 . Effect of maternal conspecific versus nonparent female conspecific field environments on Virola surinamensis seedling survival. Table S3 . Effect of maternal conspecific versus other conspecific field environments on Ormosia macrocalyx seedling survival. Table S4 . Effect of maternal conspecific versus other conspecific field environments on Lacmellea panamensis seedling survival. Table S5 . Effect of maternal conspecific versus nonparent female conspecific field environments on Tetragastris panamensis seedling survival. Table S6 . Effect of maternal conspecific versus other conspecific field environments on seedling relative growth rates in four species. |
Implementation and evaluation of a mentorship program in clinical master in family medicine during the COVID-19 pandemic at the Arabian Gulf University: a longitudinal study | ec0b25b5-59b1-4531-bf46-54b9cd0fe7a2 | 11241932 | Family Medicine[mh] | Mentorship is an insightful process in which guidance is ensured from an experienced and trusted advisor in a supportive relationship, that requires active participation from both the mentor and the mentee . The mentor role was recognized historically to be responsible for the mentees’ education, shaping their character, and supporting the overall growth and development of the individual at a critical point . Mentoring is identified to be an important asset in academic medicine, which impacts and helps shape the careers of the future generation of healthcare providers . Mentoring programs are crucial in fostering a learner-centered environment for promoting professionalism and humanistic values while maintaining a work-life balance . Mentors are role models who can support in tracking and supporting the individual academic and personal progress, making links over time, and helping the mentee identify areas of improvement in a safe and non-judgmental relationship . Mentorship in academic medicine has also been recognized to have an important impact on personal growth and development, increased academic productivity, career guidance, job satisfaction, networking in the field of interest, and research productivity and publication . In postgraduate medical education, formal mentorship programs were distinguished to provide an effective teaching–learning strategy that is strongly associated with passing board exams and career preparation and satisfaction . Mentored medical residents were nearly twice as likely to describe excellent career preparation and highlighted the importance of mentoring to career advancement and identity formation . Early academic mentoring impacted positively career development in cardiothoracic surgery specialization as 24% of the mentored students and trainees have completed or are enrolled in higher research degrees, 18.9% were enrolled or have completed doctoral degrees, and 81% of participants have published at least one journal article . The Mentoring programs established in several medical schools worldwide vary in their goals and objectives . The process of implementation of these programs is adapted to fit specific institutional or target group needs . While some mentoring programs are designed for medical students in all years, or at specific stages of training, others are tailored to postgraduate medical training . Medical school mentoring programs are usually based on successful initiatives at other organizations and adapted to the context and feedback from different stakeholders. Needs analysis and program piloting are rarely conducted to ensure adequate design and effectiveness before implementation . Mentoring programs in different contexts vary in how mentors are assigned mentees, the mentor’s role, frequency, and format of meetings. While some programs allow mentees to choose their mentors, others are allocated randomly . Interestingly, students’ peer mentoring is integrated into some initiatives to support physician mentoring . Mentoring meetings are often held in person, but other modes of communication, such as email and phone, are increasingly being employed . The frequency of meetings varies according to the aims of the specific program and meetings might take place in a clinical setting, university, or outside the workplace . Mentoring activities in various programs tend to take place over a considerable period to enable the cultivation of successful mentor relationships . Finally, the topics covered during mentoring meetings may include diverse areas of discussion such as motivation, clinical supervision, discussion of specific mentee-selected topics, feedback, ethics, personal development plans, and career planning . Mentoring benefits are of value to mentees, mentors, medical programs, and institutions . Mentoring has been identified as fundamental to the retention and recruitment of trainees in different specialties, advancement in clinical care, as well as enhancement of research outputs and academia . Mentoring has been reported to support the personal and professional development of students and junior doctors through constructive feedback and observing positive role models as well as helping in developing insight into subspecialty training and career guidance, enhancing self-esteem, satisfaction, and stress management . Despite their benefits, mentoring programs might face several challenges. This is especially recognized when mentoring is informal and lacks structures and standards for consistency. Such challenges might arise when mentors are not trained and prepared for this role and lack a protected time for this function . Furthermore, mentee engagement with mentoring can be a challenge, when students’ engagement and perceived benefits are low . The literature review about mentorship programs revealed that they rarely rely on mixed methods to grasp a comprehensive understanding of the needs of trainees and the impact of the program. Furthermore, most of these programs are not based on standardized guidelines before their implementation. The longitudinal follow-up of the effectiveness of the program and multi-level control of the smooth running is lacking. This study builds on these previous experiences and attempts to address these gaps. It highlights the specific features of the mentoring program in The Clinical Master in Family Medicine (CMFM), at Arabian Gulf University and evaluates its effectiveness, especially during the critical period of the Coronavirus disease 2019 (COVID-19) pandemic. Study objectives This study aims to: i) highlight the innovative features of the mentorship program of the Clinical Master in Family Medicine (CMFM) at the Arabian Gulf University (AGU); ii) derive the major challenges faced by trainees and related corrective actions; iii) Evaluate the impact of this mentorship program (short term represented in trainees’ performance). Study design and population We conducted a longitudinal study design using a mixed-method approach for data collection. The study population includes two cohorts of 80 CMFM trainees enrolled and graduated between 2020 and 2023. Study settings and process of the mentorship program The CMFM program is a two-year clinically oriented postgraduate program at AGU which was established in April 2020 during the COVID-19 pandemic. The program combines different modalities of training, mainly clinical training in primary and secondary care, theoretical group activities, and quality improvement research projects. During this period, trainees are evaluated longitudinally through formative workplace-based assessments and summative end-of-each-year written (Multiple Choice Questions) and Objective Structured Clinical Examinations (OSCE). The program's intensity, implementation within the COVID-19 period, and the diversity of learning modalities within a short period required a contextualized mentorship program. Therefore, the academic committee conceived a mentoring program after an extensive literature review based on the program's needs and desired outcomes. The program was further adapted based on program piloting and stakeholders' feedback. Through this program, we aimed to ensure real-time monitoring of professional growth and optimal academic progress and well-being of trainees during each rotation. In addition, to identify struggling trainees timely who require personalized interventions. Recruitment of mentors, training, and mentoring meetings process Mentors were recruited based on their clinical experience in training in family medicine (more than five years), dedication, and motivation for the mentor role. All trainees enrolled in the CMFM program were enrolled in the mentoring program, and every five trainees were assigned a mentor by the program coordinator, however, there was flexibility in assigning mentees to specific mentors based on their request. A mentoring guide developed by the program committee was shared with mentors and mentees to provide an orientation and details about the process and outcomes of the program. Furthermore, induction workshops for both mentors and mentees were conducted to discuss details related to the mentoring program and the mentor's role. One-to-one meetings between the mentor and each trainee take place every twelve weeks but can be requested according to the mentee’s needs. For convenience and sustainability, the mentors and mentees had the option to conduct the meetings either face-to-face, virtual, or through phone calls. As part of the continuous monitoring and evaluation of the mentorship program, continuous meetings were conducted after each phase of training with both mentors and mentees separately, to obtain feedback and highlight areas for improvement. During mentoring meetings, mentors and mentees were encouraged to reflect on and discuss achievements, feedback, and potential problems (physical, mental, or social) that could affect training. Mentors also have access to the trainees' electronic portfolio (E-portfolio) documentation summarized in a dashboard for each mentee for each rotation. The academic committee considers five Key performance indicators (KPIs) in the dashboard of high priority. They are monitored in an electronic dashboard extracted from the learning platform “Moodle”. They include attendance, daily cases encountered (coded according to the international classification of primary care), skills, and procedures performed or observed by the trainee, and participation in educational activities. In addition, the mentor is encouraged to review and discuss with the trainee detailed documentation and reflection on selected submitted clinical cases to ensure deep learning and self-confidence. By the end of the meeting, the mentor and the mentee identify the gaps and agree on a plan for the coming period. The mentor can decide if the mentee needs a meeting with the academic committee in case of a serious issue that could hinder the trainee’s personal well-being and academic progress. To ensure a standardized process of information, the program’s committee conceived a structured electronic mentoring report form on the Moodle platform to guide discussion and probe areas that need follow-up and specific interventions by the program’s committee. High-risk trainees who were identified to have sub-optimal performance or health/well-being issues are subject to more extensive mentoring by the program’s committee until resolving the identified problems. Figure illustrates the multi-level framework and the cycle of the mentorship program during its implementation. The mentoring domains and related challenges are derived during every rotation at three levels: the mentors, the mentorship program coordinator, and the academic committee. At each level, specific tasks and tools are utilized to ensure a deep, comprehensive, and real-time assessment of the trainees’ progress and identify the threats. A personalized intervention plan is timely deployed and monitored at all levels until resolved. Data collection All trainees are enrolled in the mentorship program and included in this study (80 trainees, 40 for each cohort). All submitted mentoring reports by all the assigned mentors between September 2020 to April 2023 were included. In addition, to findings of academic committee meetings with the trainees. We extracted the data from, the electronic mentoring report form submitted by the mentors, and academic committee reports of meetings with trainees. Information collected from the mentoring reports includes the type, frequency of meetings, duration of meetings in minutes, and dashboard KPIs generated from the E-portfolio. The latter includes attendance (a percentage), daily cases encountered (a number with access to a Word file for every case), detailed reflective cases (a number with access to a Word file for every case), skills and procedures practiced (a number with a word file for every procedure), and educational activities (a number with word file for every activity). The type of challenges (available as a list of categories: health, psychological, social, time management, others, and the recommended action plan (available as a list of categories: specific topic reading/online course, adapt learning approaches, time management, engaging in team learning activities, others). The qualitative data collected from the mentors' documentation is provided in the open-text sections and academic committee meetings’ reports. It includes details related to training progress in primary and secondary care courses, progress in quality improvement projects, challenges faced by trainees, interventions/ recommendations agreed between mentors and mentees, and corrective actions implemented by the academic committee. The thematic analysis was conducted manually following the steps of the one sheet of paper (OSOP) technique of Ziebland. We integrated the transcripts from the mentorship text file, the academic committee file, and the review report file for every trainee. Two members of the academic committee read all the transcripts for every trainee and merged them into one transcript and then we extracted themes that emerged from the data. A second iteration of reading the transcripts permitted to linking of subthemes and quotes to the most likely related theme and subthemes as illustrated in Fig. . Data related to mentees’ GPAs were provided by the program’s officials and the university registration unit. Case definitions Cohort 1 refers to the first 40 trainees enrolled in the program in April 2020 and Cohort 2 refers to the group of 40 trainees who started the program in May 2021. Graduation with a master's degree requires the completion of the two-year curriculum and obtaining a cumulative GPA of a minimum of 3 out of 4. Trainees who had an accumulative GPA between 2 and 3 were entitled to a Diploma degree unless they wished to repeat a certain number of courses based on the university regulations to improve their GPA to obtain the master’s degree. A trainee is considered high risk if having one of the following situations: Not able to accomplish the required level of achievement in the KPIs (related to attendance, number of documented daily cases encountered, skills and procedures, continuous medical education activities, and quality of documentation in the reflective detailed clinical cases of the E-portfolio. If exposed to any health or psychosocial threat that prevents normal progress in the training. Having low academic performance in formative and/or summative assessments. Statistical analysis plan Statistical Analysis relies on a mixed method approach to calculate proportions and means for continuous quantitative variables, as well as thematic analysis for the qualitative textual information. The cohort and time effect were assessed using the T-test or the Mann–Whitney U test to account for the lack of normality (based on the significance of Kolmogorov–Smirnov). All quantitative analyses are performed using SPSS V28. The thematic analysis was conducted manually following the one sheet of paper (OSOP) technique of Ziebland . This study aims to: i) highlight the innovative features of the mentorship program of the Clinical Master in Family Medicine (CMFM) at the Arabian Gulf University (AGU); ii) derive the major challenges faced by trainees and related corrective actions; iii) Evaluate the impact of this mentorship program (short term represented in trainees’ performance). We conducted a longitudinal study design using a mixed-method approach for data collection. The study population includes two cohorts of 80 CMFM trainees enrolled and graduated between 2020 and 2023. The CMFM program is a two-year clinically oriented postgraduate program at AGU which was established in April 2020 during the COVID-19 pandemic. The program combines different modalities of training, mainly clinical training in primary and secondary care, theoretical group activities, and quality improvement research projects. During this period, trainees are evaluated longitudinally through formative workplace-based assessments and summative end-of-each-year written (Multiple Choice Questions) and Objective Structured Clinical Examinations (OSCE). The program's intensity, implementation within the COVID-19 period, and the diversity of learning modalities within a short period required a contextualized mentorship program. Therefore, the academic committee conceived a mentoring program after an extensive literature review based on the program's needs and desired outcomes. The program was further adapted based on program piloting and stakeholders' feedback. Through this program, we aimed to ensure real-time monitoring of professional growth and optimal academic progress and well-being of trainees during each rotation. In addition, to identify struggling trainees timely who require personalized interventions. Mentors were recruited based on their clinical experience in training in family medicine (more than five years), dedication, and motivation for the mentor role. All trainees enrolled in the CMFM program were enrolled in the mentoring program, and every five trainees were assigned a mentor by the program coordinator, however, there was flexibility in assigning mentees to specific mentors based on their request. A mentoring guide developed by the program committee was shared with mentors and mentees to provide an orientation and details about the process and outcomes of the program. Furthermore, induction workshops for both mentors and mentees were conducted to discuss details related to the mentoring program and the mentor's role. One-to-one meetings between the mentor and each trainee take place every twelve weeks but can be requested according to the mentee’s needs. For convenience and sustainability, the mentors and mentees had the option to conduct the meetings either face-to-face, virtual, or through phone calls. As part of the continuous monitoring and evaluation of the mentorship program, continuous meetings were conducted after each phase of training with both mentors and mentees separately, to obtain feedback and highlight areas for improvement. During mentoring meetings, mentors and mentees were encouraged to reflect on and discuss achievements, feedback, and potential problems (physical, mental, or social) that could affect training. Mentors also have access to the trainees' electronic portfolio (E-portfolio) documentation summarized in a dashboard for each mentee for each rotation. The academic committee considers five Key performance indicators (KPIs) in the dashboard of high priority. They are monitored in an electronic dashboard extracted from the learning platform “Moodle”. They include attendance, daily cases encountered (coded according to the international classification of primary care), skills, and procedures performed or observed by the trainee, and participation in educational activities. In addition, the mentor is encouraged to review and discuss with the trainee detailed documentation and reflection on selected submitted clinical cases to ensure deep learning and self-confidence. By the end of the meeting, the mentor and the mentee identify the gaps and agree on a plan for the coming period. The mentor can decide if the mentee needs a meeting with the academic committee in case of a serious issue that could hinder the trainee’s personal well-being and academic progress. To ensure a standardized process of information, the program’s committee conceived a structured electronic mentoring report form on the Moodle platform to guide discussion and probe areas that need follow-up and specific interventions by the program’s committee. High-risk trainees who were identified to have sub-optimal performance or health/well-being issues are subject to more extensive mentoring by the program’s committee until resolving the identified problems. Figure illustrates the multi-level framework and the cycle of the mentorship program during its implementation. The mentoring domains and related challenges are derived during every rotation at three levels: the mentors, the mentorship program coordinator, and the academic committee. At each level, specific tasks and tools are utilized to ensure a deep, comprehensive, and real-time assessment of the trainees’ progress and identify the threats. A personalized intervention plan is timely deployed and monitored at all levels until resolved. All trainees are enrolled in the mentorship program and included in this study (80 trainees, 40 for each cohort). All submitted mentoring reports by all the assigned mentors between September 2020 to April 2023 were included. In addition, to findings of academic committee meetings with the trainees. We extracted the data from, the electronic mentoring report form submitted by the mentors, and academic committee reports of meetings with trainees. Information collected from the mentoring reports includes the type, frequency of meetings, duration of meetings in minutes, and dashboard KPIs generated from the E-portfolio. The latter includes attendance (a percentage), daily cases encountered (a number with access to a Word file for every case), detailed reflective cases (a number with access to a Word file for every case), skills and procedures practiced (a number with a word file for every procedure), and educational activities (a number with word file for every activity). The type of challenges (available as a list of categories: health, psychological, social, time management, others, and the recommended action plan (available as a list of categories: specific topic reading/online course, adapt learning approaches, time management, engaging in team learning activities, others). The qualitative data collected from the mentors' documentation is provided in the open-text sections and academic committee meetings’ reports. It includes details related to training progress in primary and secondary care courses, progress in quality improvement projects, challenges faced by trainees, interventions/ recommendations agreed between mentors and mentees, and corrective actions implemented by the academic committee. The thematic analysis was conducted manually following the steps of the one sheet of paper (OSOP) technique of Ziebland. We integrated the transcripts from the mentorship text file, the academic committee file, and the review report file for every trainee. Two members of the academic committee read all the transcripts for every trainee and merged them into one transcript and then we extracted themes that emerged from the data. A second iteration of reading the transcripts permitted to linking of subthemes and quotes to the most likely related theme and subthemes as illustrated in Fig. . Data related to mentees’ GPAs were provided by the program’s officials and the university registration unit. Cohort 1 refers to the first 40 trainees enrolled in the program in April 2020 and Cohort 2 refers to the group of 40 trainees who started the program in May 2021. Graduation with a master's degree requires the completion of the two-year curriculum and obtaining a cumulative GPA of a minimum of 3 out of 4. Trainees who had an accumulative GPA between 2 and 3 were entitled to a Diploma degree unless they wished to repeat a certain number of courses based on the university regulations to improve their GPA to obtain the master’s degree. A trainee is considered high risk if having one of the following situations: Not able to accomplish the required level of achievement in the KPIs (related to attendance, number of documented daily cases encountered, skills and procedures, continuous medical education activities, and quality of documentation in the reflective detailed clinical cases of the E-portfolio. If exposed to any health or psychosocial threat that prevents normal progress in the training. Having low academic performance in formative and/or summative assessments. Statistical Analysis relies on a mixed method approach to calculate proportions and means for continuous quantitative variables, as well as thematic analysis for the qualitative textual information. The cohort and time effect were assessed using the T-test or the Mann–Whitney U test to account for the lack of normality (based on the significance of Kolmogorov–Smirnov). All quantitative analyses are performed using SPSS V28. The thematic analysis was conducted manually following the one sheet of paper (OSOP) technique of Ziebland . Participants’ characteristics All trainees are enrolled in the mentorship program and included in this study (80 trainees, 40 for each cohort). Most of the trainees were female (93.75%) and the mean age was 30.00 ± 2.19 years. This reflects the national statistics of primary care physicians in The Kingdom of Bahrain where most family physicians are female (77.7%). A total of 16 mentors were involved with a ratio of 5 trainees per mentor. The monitoring meetings were conducted either through phone calls (62%), virtually (29.7%), or face-to-face (8.3%). The mean number of meetings during the evaluation period (20 months) was 3.88 ± 2.31 and the mean duration for the meetings was 20.08 min ± 9.50. Data related to the mentorship program indicators are presented in Table . Challenges identified from the mentorship program and related interventions The analysis of the quantitative data related to the challenges reported by mentors revealed that time management was the most reported issue affecting the progress of the trainees (41.3%), followed by health-related (7.6%), social (4.6%), and psychological issues (3%). Interestingly, 43.6% of other types of challenges were reported, and they are detailed in the qualitative part of the study. Figure shows the presentation of different challenges as reported by mentors. The qualitative part of the study permitted to obtain very important information from struggling trainees, particularly sensitive information such as health and mental or psychosocial related issues. We extracted four main themes, such as challenges, and related subthemes data from the mentoring meeting as well as the academic committee face-to-face meeting reports. The main themes are related to trainees, training setting, E-portfolio, and COVID-19 challenges and are detailed below. Trainees’ related challenges Trainee-related challenges included five main sub-themes: health-related, psychological, social, learning style, and time management. Some of the trainees had chronic medical problems such as multiple sclerosis, systemic lupus erythematosus, sickle cell disease, and migraine as expressed by other trainees “ My migraine attacks are occurring more frequently and it affects my study” and “ “ I am afraid that my admission in the hospital will affect my training and need guidance on how to catch up”. In addition, others suffered from pre-existing mental health problems such as depression and anxiety as expressed “. Being diagnosed with medical and mental health problems and exposed to the additional stress caused by the intensive nature of the curriculum in the COVID-19 pandemic context, some trainees had low self-confidence and psychological challenges in their capacity to pursue the program as expressed by one of the trainees: “ I am not sure if I can cover all the training requirements, and study, and feel lost”. Some other trainees were facing some social challenges such as the death of a close relative, the birth of a new child, and being a caregiver of young children, as expressed by a trainee “ I feel that my study progress is slow since I need to manage between my study in the program and taking care of my two little daughters and their requirements”. The intensive character of the program generated serious challenges to some trainees related to time management and a fair balance between the program requirements and their other life aspects “ I am not sure if I am studying correctly….it is difficult to manage my time”. A group of trainees struggled to adapt their learning style to the primary care approach and setting which favors a learning based on clinical presentations. The latter challenge was very difficult to overcome particularly for freshly graduated trainees who are still influenced by the undergraduate learning mindsets. Trainees facing such challenges were considered by the academic committee at higher risk and required intensive mentoring. They were entitled to close monitoring to overcome a stressful period. Training setting challenges The training takes place in the regular primary care setting in which the trainers are primary care physicians assigned to train besides managing their scheduled patients’ lists. This situation permitted to immerse the trainees in a real context of family practice but created the challenge of trainers' dedication toward training and facing the problem of unavailability of training rooms in some health centers on some occasions busy trainers who were challenged to manage their role as trainers and other duties as stated by trainees sometimes: “There are no vacant consultation rooms and we alternate with the trainers in conducting the consultation…” and “The trainer is not available all the time to provide detailed feedback on my performance since she is busy as well with other tasks”. The study also permitted, through probing, “deviant cases” such as students suffering from mental health or chronic diseases that required special care such as a placement in a more psychologically- safe training environment (a more compassionate clinical trainer). Some other unexpected findings emerged from the probing such as the rejection of an experienced trainer because of a perceived “autocratic” vertical approach in training as well as loading the trainees by contents rather than best approaches in learning as expressed “My trainer is treating us in a very rigid way which makes me feel uncomfortable in my training and doubt if I’m not doing well”. Surprisingly, the same trainer was highly appreciated by other trainees. These conflicting patterns discovered through in-depth interviews might reflect different personality traits and cultural frameworks in the study group. E-portfolio-related challenges E-portfolio identified challenges were the suboptimal entry of cases and procedures encountered by some trainees and poor quality of documentation. The trainees did not consider electronic documentation of the daily activities as a high priority. It was usually left for a later time in the week, which increased the recall bias and incompleteness of information as expressed by a trainee “ Sometimes, I do not have the time to document in the E-portfolio and I have a lot of pending work related to my E-portfolio….I try to do it in the weakened”. This difficulty was reduced through feedback meetings and the improvement of E-portfolio forms in the Moodle platform. COVID-19 related challenges The program started during the first period of the COVID-19 pandemic when the social distancing precautions and regulations that included primary health care centers were strictly enforced. This resulted in a limitation in terms of the number and variety of clinical cases encountered especially in preventive and non-communicable diseases as expressed by a trainee “ There is a very low flow of patients and I am concerned that it will affect my learning”. In addition, we faced a gap in training for minor surgical procedures including those in primary care settings as expressed by a trainee “ I was not trained in any minor surgical procedures during this rotation….all minor non-urgent procedures were withheld due to the COVID-19 regulations”. Figure illustrates themes and sub-themes for these challenges. Triangulating the information from the mixed method approach permitted the CMFM program academic committee to obtain a comprehensive situation analysis of the progress of every trainee. Consequently, it allowed the timely implementation of relevant personalized corrective measures by the academic committee, to support the trainees and pursue their normal academic progress. These interventions are described in Table . Evaluation of the performance of the mentorship program Out of the 80 trainees, 12 (15%) were identified as high-risk trainees, 6 of the 12 (50% of the high-risk) graduated on time while the remaining had to repeat some courses to pass the exit assessment and obtain the master's degree. When we consider the mean global GPA of high-risk trainees ( n = 12) during year one, it was 3.22 (SE = 0.16) versus 3.41 (SE = 0.05) for low-risk trainees ( n = 68), ( P = 0.33). During year two the mean GPA for the low-risk trainees was 3.29 (SE = 0.07) versus 3.06 (SE = 0.03) for high-risk trainees, ( P = 0.04). Interestingly, the overall mean cumulative GPA was 3.35 (SE = 0.03) for the low-risk trainees, versus 3.14 (SE = 0.08) for the high-risk trainees, ( P = 0.043). These findings suggest that trainees are mainly challenged in the second year, but the discrepancy between the high-risk and low-risk trainees significantly reduced at the final cumulative GPA implying the effectiveness of the corrective action plans resulting from the mentorship program. The academic committee considered the GPA of trainees as one of the main outcomes reflecting the effect of monitoring, including the mentorship program, of progress of trainees, and the implementation of timely corrective actions. When we consider the mean GPA trends over time and cohorts, we realize that the mean GPA in year 1 (GPA 1) for Cohort 1 (40 trainees) was 3.43 (SE = 0.06) versus 3.45 (SE = 0.07) for Cohort 2 (40 trainees) ( P = 0.022). On the other hand, the mean GPA in year 2 (GPA2) for cohort 1 was 3.18 (SE = 0.04) versus 3.33 (SE = 0.04) for cohort 2, ( P = 0.02). Despite this slight significant difference, the two cohorts achieved equivalent successful GPAs in both years when we consider the cut-off of a minimum of 3 out of 4 required for the master’s degree. This was corroborated by the final cumulative mean GPA of 3.30 (SE = 0.03), versus 3.34 (SE = 0.05) for cohorts 1 and 2 respectively, ( P = 0.40). This reflects the stability of the performance of the program over time and cohorts. All trainees are enrolled in the mentorship program and included in this study (80 trainees, 40 for each cohort). Most of the trainees were female (93.75%) and the mean age was 30.00 ± 2.19 years. This reflects the national statistics of primary care physicians in The Kingdom of Bahrain where most family physicians are female (77.7%). A total of 16 mentors were involved with a ratio of 5 trainees per mentor. The monitoring meetings were conducted either through phone calls (62%), virtually (29.7%), or face-to-face (8.3%). The mean number of meetings during the evaluation period (20 months) was 3.88 ± 2.31 and the mean duration for the meetings was 20.08 min ± 9.50. Data related to the mentorship program indicators are presented in Table . The analysis of the quantitative data related to the challenges reported by mentors revealed that time management was the most reported issue affecting the progress of the trainees (41.3%), followed by health-related (7.6%), social (4.6%), and psychological issues (3%). Interestingly, 43.6% of other types of challenges were reported, and they are detailed in the qualitative part of the study. Figure shows the presentation of different challenges as reported by mentors. The qualitative part of the study permitted to obtain very important information from struggling trainees, particularly sensitive information such as health and mental or psychosocial related issues. We extracted four main themes, such as challenges, and related subthemes data from the mentoring meeting as well as the academic committee face-to-face meeting reports. The main themes are related to trainees, training setting, E-portfolio, and COVID-19 challenges and are detailed below. Trainee-related challenges included five main sub-themes: health-related, psychological, social, learning style, and time management. Some of the trainees had chronic medical problems such as multiple sclerosis, systemic lupus erythematosus, sickle cell disease, and migraine as expressed by other trainees “ My migraine attacks are occurring more frequently and it affects my study” and “ “ I am afraid that my admission in the hospital will affect my training and need guidance on how to catch up”. In addition, others suffered from pre-existing mental health problems such as depression and anxiety as expressed “. Being diagnosed with medical and mental health problems and exposed to the additional stress caused by the intensive nature of the curriculum in the COVID-19 pandemic context, some trainees had low self-confidence and psychological challenges in their capacity to pursue the program as expressed by one of the trainees: “ I am not sure if I can cover all the training requirements, and study, and feel lost”. Some other trainees were facing some social challenges such as the death of a close relative, the birth of a new child, and being a caregiver of young children, as expressed by a trainee “ I feel that my study progress is slow since I need to manage between my study in the program and taking care of my two little daughters and their requirements”. The intensive character of the program generated serious challenges to some trainees related to time management and a fair balance between the program requirements and their other life aspects “ I am not sure if I am studying correctly….it is difficult to manage my time”. A group of trainees struggled to adapt their learning style to the primary care approach and setting which favors a learning based on clinical presentations. The latter challenge was very difficult to overcome particularly for freshly graduated trainees who are still influenced by the undergraduate learning mindsets. Trainees facing such challenges were considered by the academic committee at higher risk and required intensive mentoring. They were entitled to close monitoring to overcome a stressful period. The training takes place in the regular primary care setting in which the trainers are primary care physicians assigned to train besides managing their scheduled patients’ lists. This situation permitted to immerse the trainees in a real context of family practice but created the challenge of trainers' dedication toward training and facing the problem of unavailability of training rooms in some health centers on some occasions busy trainers who were challenged to manage their role as trainers and other duties as stated by trainees sometimes: “There are no vacant consultation rooms and we alternate with the trainers in conducting the consultation…” and “The trainer is not available all the time to provide detailed feedback on my performance since she is busy as well with other tasks”. The study also permitted, through probing, “deviant cases” such as students suffering from mental health or chronic diseases that required special care such as a placement in a more psychologically- safe training environment (a more compassionate clinical trainer). Some other unexpected findings emerged from the probing such as the rejection of an experienced trainer because of a perceived “autocratic” vertical approach in training as well as loading the trainees by contents rather than best approaches in learning as expressed “My trainer is treating us in a very rigid way which makes me feel uncomfortable in my training and doubt if I’m not doing well”. Surprisingly, the same trainer was highly appreciated by other trainees. These conflicting patterns discovered through in-depth interviews might reflect different personality traits and cultural frameworks in the study group. E-portfolio identified challenges were the suboptimal entry of cases and procedures encountered by some trainees and poor quality of documentation. The trainees did not consider electronic documentation of the daily activities as a high priority. It was usually left for a later time in the week, which increased the recall bias and incompleteness of information as expressed by a trainee “ Sometimes, I do not have the time to document in the E-portfolio and I have a lot of pending work related to my E-portfolio….I try to do it in the weakened”. This difficulty was reduced through feedback meetings and the improvement of E-portfolio forms in the Moodle platform. The program started during the first period of the COVID-19 pandemic when the social distancing precautions and regulations that included primary health care centers were strictly enforced. This resulted in a limitation in terms of the number and variety of clinical cases encountered especially in preventive and non-communicable diseases as expressed by a trainee “ There is a very low flow of patients and I am concerned that it will affect my learning”. In addition, we faced a gap in training for minor surgical procedures including those in primary care settings as expressed by a trainee “ I was not trained in any minor surgical procedures during this rotation….all minor non-urgent procedures were withheld due to the COVID-19 regulations”. Figure illustrates themes and sub-themes for these challenges. Triangulating the information from the mixed method approach permitted the CMFM program academic committee to obtain a comprehensive situation analysis of the progress of every trainee. Consequently, it allowed the timely implementation of relevant personalized corrective measures by the academic committee, to support the trainees and pursue their normal academic progress. These interventions are described in Table . Out of the 80 trainees, 12 (15%) were identified as high-risk trainees, 6 of the 12 (50% of the high-risk) graduated on time while the remaining had to repeat some courses to pass the exit assessment and obtain the master's degree. When we consider the mean global GPA of high-risk trainees ( n = 12) during year one, it was 3.22 (SE = 0.16) versus 3.41 (SE = 0.05) for low-risk trainees ( n = 68), ( P = 0.33). During year two the mean GPA for the low-risk trainees was 3.29 (SE = 0.07) versus 3.06 (SE = 0.03) for high-risk trainees, ( P = 0.04). Interestingly, the overall mean cumulative GPA was 3.35 (SE = 0.03) for the low-risk trainees, versus 3.14 (SE = 0.08) for the high-risk trainees, ( P = 0.043). These findings suggest that trainees are mainly challenged in the second year, but the discrepancy between the high-risk and low-risk trainees significantly reduced at the final cumulative GPA implying the effectiveness of the corrective action plans resulting from the mentorship program. The academic committee considered the GPA of trainees as one of the main outcomes reflecting the effect of monitoring, including the mentorship program, of progress of trainees, and the implementation of timely corrective actions. When we consider the mean GPA trends over time and cohorts, we realize that the mean GPA in year 1 (GPA 1) for Cohort 1 (40 trainees) was 3.43 (SE = 0.06) versus 3.45 (SE = 0.07) for Cohort 2 (40 trainees) ( P = 0.022). On the other hand, the mean GPA in year 2 (GPA2) for cohort 1 was 3.18 (SE = 0.04) versus 3.33 (SE = 0.04) for cohort 2, ( P = 0.02). Despite this slight significant difference, the two cohorts achieved equivalent successful GPAs in both years when we consider the cut-off of a minimum of 3 out of 4 required for the master’s degree. This was corroborated by the final cumulative mean GPA of 3.30 (SE = 0.03), versus 3.34 (SE = 0.05) for cohorts 1 and 2 respectively, ( P = 0.40). This reflects the stability of the performance of the program over time and cohorts. We conducted a longitudinal study using mixed methods to describe the implementation of a mentorship program and evaluate its effectiveness in the context of an intensive two-year CMFM curriculum that started during the COVID-19 pandemic. We obtained a real-time comprehensive evaluation of the progress of trainees through parsimonious quantitative indicators and qualitative analysis of challenges they are facing, which was instrumental in designing real-time, personalized corrective actions. The mentorship program proved to be effective for the smooth academic progress of trainees and reduced the risk of failure in graduation. It supported trainees’ overall well-being while maintaining work-life balance and minimizing burnout. The CMFM mentorship program helped to ensure that the trainees’ progress was meeting curriculum standards and certain key performance indicators related to the training. Mentors provided a longitudinal and holistic evaluation of training that helped to bridge the gap between theoretical learning and clinical practice and suggested recommendations to ensure progress and attainment of appropriate skills and program standards on time. Our study confirmed that the COVID-19 pandemic threatened postgraduate medical trainees’ academic advancement by constraining opportunities for knowledge and skill acquisition, scholar productivity, and networking. On the other hand, the pandemic has created new opportunities. The exerted challenges of the pandemic-era circumstances required extra efforts and innovative solutions aimed at enhancing trainees’ academic progress while supporting work-life balance . In agreement with others, the consistent approach to mentoring, oversight continuous monitoring, and feedback facilitated oversight and regulation of the mentoring processes . Findings in the literature highlight that several health science faculties could avoid mentoring due to numerous factors including the lack of knowledge about the mentoring process, lack of confidence, and the fear of managing ‘challenging situations’, including problems of a personal nature . Indeed, to standardize the mentorship program and facilitate its implementation, the CMFM program committee provided a mentoring program guide and workshops targeting both mentors and mentees before implementation. Our findings confirm the importance of these preparatory aspects before launching the mentorship program. In addition, mentors attended several longitudinal workshops while the program was running to build their capacities as mentors, receive their feedback, and provide them with guidance and support. Similarly, we conducted periodic meetings with the trainees to identify any issue affecting the mentoring process and relationship that needs timely interventions. Trainees who were unsatisfied or faced any challenges related to the mentor–mentee relationship were assigned to other mentors to ensure the accomplishment of outcomes through academic and personal support. One of the innovative and strong aspects related to the CMFM mentorship program is structuring an electronic mentoring meeting report with a mixed structure in data collection that helped to harmonize mentor–mentee discussions during the meetings without compromising the specific needs of trainees. Since mentors are composed of family physician consultants and some of them are involved as trainers, we expected the possibility for mentors to focus more on trainee-related issues compared to other factors. Indeed, mentors with integrated physician and mentor identities can embrace liminality and develop as mentors, this was addressed through guidance and support . This tendency is reflected in the frequency of challenges listed in the quantitative study. However, this was fixed by the findings of the qualitative section in the form that permitted to grasp a richer understanding of the trainees' global progress and constraints for pertinent and customized corrective actions as detailed in the findings. This mixed format provided a standardized structure without compromising flexibility in areas of discussion, rapid interventions, and follow-up . Another strength related to the CMFM mentorship program is the formal one-to-one mentoring providing a safe and non-judgmental environment for discussions and personalized advice and guidance. This contrasts with other mentorship programs where the mentor conducts group meetings with mentees . The various options, face-to-face, virtual, and phone calls, for mentoring meetings, eased continuity in meetings, especially during the period of COVID-19. Another strength of our mentorship program is a cascade of checkpoints and interventions at the level of mentors, students’ feedback meetings, and academic committee oversight particularly for trainees at high risk. This permitted a large consensus about corrective plans and strong governance of a complex and intensive program. The identified challenges through the CMFM mentoring program are consistent with those reported in other postgraduate clinical training programs. The major reduction in the volume of inpatients and outpatients encountered during the pandemic affected the number and diversity of clinical exposure and mitigated drastically the opportunities for trainees to perform physical examinations and essential procedures, which can be mastered mostly during clinical training . It was reported similarly in other studies, that the ongoing pandemic has added new stressors while aggravating the existing ones for students and trainees . On the contrary, the pandemic has created new opportunities for the CMFM program academic committee, trainers, and mentors, to sustain and enhance training outcomes. Trainers had more time dedicated to interactions and discussions around selected clinical cases leading to deep clinical learning and high self-confidence. The role of teleconsultation, underutilized in the pre-COVID era, was integrated to ensure continuity of healthcare delivery during the current pandemic by the healthcare system, which offered an opportunity for integrating training on telemedicine and teleconsultation. These skills are nowadays necessary to continue with safe, high-quality delivery of services and increase this modality of care integration in healthcare systems . Another innovative aspect of the CMFM program was the utilization of trainees' E-portfolio entries related to the clinical cases encountered, skills and procedures that they were exposed to, and mentoring meeting reports to identify gaps related to clinical exposure, mainly during the COVID-19 pandemic. This allowed the implementation of pioneering interventions such as engaging learners in experiences that simulate reality and compensate for cases of fewer encounters. Trainers with the help of program administrators also integrated simulated scenarios followed by constructive feedback discussions during clinical training and on weekly educational activities. We also highlight the big added value of on-campus skills and procedures training in the Medical Skills and Simulation Center using high-fidelity mannequins. Simulation is a useful modality to supplement training in real clinical situations because it allows control over the sequence of tasks offered to learners, provides opportunities to offer support and guidance to learners, prevents unsafe and dangerous situations, and creates tasks that rarely occur in the real world. It is also an excellent form that supports inter-professional and communication skills education . We also integrated team-based learning to promote active learning and enhance inter-professional skills development . In addition, trainees whose training was disrupted for any reason (birth of a new child, contact with COVID-19 cases, health-related), and whose situation allowed distance learning were provided with a distance learning toolkit containing clinical scenarios followed by smart questions and recommended online courses related to the ongoing clinical rotation. They received more intense mentorship and administrative support to overcome their challenges. The monitoring of smart few KPIs during the mentorship program permitted, in our context, the early detection of struggling trainees before the summative exam, allowing timely corrective actions to be implemented. The CMFM program is system-centered and integrated into primary health care, which increased the ownership; by health authorities and preparedness to facilitate any action needed to better prepare the training environment for an optimized outcome and increased the recruitment of graduated trainees. The provision of a protected time for the trainers to discussions around clinical cases of high educational value and the availability of independent consultation lists and rooms under the supervision of the trainers was particularly instrumental in facilitating deep learning and enhancing the level of self-confidence, safety of trainees and their immersion in a real context of practice. The learning environment, an important dimension in our mentorship program, was adapted to promote changes in students' thinking strategies as well as their development as flexible, reflective learners . These endeavors require support from mentors and program administrators with rigorous expectations and good facilitation skills. The mentorship program was successful and effective, particularly when coupled with longitudinal meetings and feedback from trainees, trainers, and mentors to get the best comprehensive analysis of the situation and to implement the most appropriate intervention plans. Students’ voices and perspectives as important stakeholders in the process of learning are essential to providing emotional and cognitive support that enhances learning and prevents burnout . In addition to the mentioned benefits for all trainees, the mentorship program identified 12 trainees (5%) at high risk for failure. Six of them (50%) achieved high scores and obtained their degrees at the end of the program due to early identification, extensive follow-up, and support by the program’s committee. Five out of the remaining six trainees obtained their degrees after repeating a few courses to ensure the needed level of safety and competency. Only one trainee out of the two cohorts (80 trainees) graduated with a diploma because the final GPA was less than the threshold of 3/4 as required by AGU regulations. On the other hand, when we consider the overall GPA of high-risk trainees and the rest of the cohort, the difference is not significant which reflects the effectiveness of corrective plans. In addition, analysis of GPA through years and cohorts demonstrates the stability of the performance of the CMFM program over time, partly due to the mentorship program. This study highlights the importance of a mentorship program in supporting and monitoring postgraduate training in family medicine practice. The lessons learned here lay the foundation for the design of formal and contextualized mentorship programs that align with the training context and curriculum, and the importance of engaging both mentors and mentees in the mentoring process through several aspects including training, guidance, and longitudinal monitoring. All of these aspects, in addition to setting specific key performance indicators, are essential for sustainability, robustness, and meeting intended outcomes. Our recommendations are in alignment with the literature findings regarding the value of designing a customized, holistic, longitudinal mentoring assessment tool in facilitating mentoring and providing timely and specific support to the evolving needs of mentees as they develop their clinical competencies . In addition to the importance of institutional support, adapting programs to local needs and resources, and mentors’ engagement and training for sustainability and performance . Mentorship programs can be instrumental, as we found in this study, in identifying challenges associated with postgraduate clinical training and executing promptly corrective measures. These challenges can be trainee-related (time management, study style, and physical, mental, and social well-being issues), training-setting-related, and implementation phase-related (COVID-19 pandemic in our situation). Mentorship was reported to be positively associated with specific academic performance, attitudes, and minimizing psychological stress . Addressing these challenges and facilitating identification by triangulating findings from different stakeholders supported the timely implementation of appropriate interventions and the optimization of results. The mentorship program has proven to be beneficial in ensuring trainees' smooth academic development and lowering the risks of failure to graduate. It improved trainees' overall well-being while also promoting work-life balance and reducing burnout. The CMFM program was a success story, in our hands, due to the inclusiveness of all stakeholders and the robustness of design, process, and monitoring despite the constraints of the COVID-19 pandemic. To the best of our knowledge, the format of the contextualized formal mentoring program and the mixed-method approach as well as the multiple levels of oversight are novel in this study. Despite its originality and the significance of its findings, this study has some limitations. The mentors’ reports might be prone to subjectivity. The COVID-19 pandemic and other confounders might affect trainees’ performance represented in GPA. However, this is out of the scope of this study. As a future perspective, more detailed qualitative studies targeting mentees and mentors probing their experience are highly recommended, particularly after the COVID-19 pandemic. Evaluating the level of satisfaction and the mentoring experience from their perspectives will provide another insight that we have not formally measured in the current study. A mentorship program implemented in the CMFM program of Arabian Gulf University integrated key performance indicators extracted from a parsimonious e-portfolio and mixed data from mentorship forms as well as periodic face-to-face meetings with different stakeholders. Triangulating longitudinal information using mixed methods design and analyzing at multiple levels permitted timely personalized pertinent interventions. |
Venomous snakes elicit stronger fear than nonvenomous ones: Psychophysiological response to snake images | 35348885-b25f-44b1-aec8-85fe7f815d94 | 7437868 | Physiology[mh] | 1.1. Snakes as evolutionary threat Ever since their appearance, primates and early hunter-gatherers have been subject to life-threatening risks from snakes. As a consequence, primates including contemporary humans developed improved visual abilities and superior pre-attentive attention specifically for detecting snakes and other stimuli representing an evolutionary threat . Although this predation pressure has left no trace in the fossil record, some circumstantial evidence is available. In an attempt to assess the hazards that snakes pose to primates, McGrew observed a group of chimpanzees in Senegal. Within four years, as many as 142 encounters of snakes belonging to 14 species were recorded. Headland and Greene showed that local populations in some parts of the world have been regularly exposed to predatory attacks by giant constrictor snakes in the recent past. Over the course of four decades, a quarter of Agta Negritos men, a tribe of hunter-gatherers from the Philippines, were attacked by the reticulated python ( Malayopython reticulatus ), resulting in six fatalities . Even today, snakebite envenoming remains a significant health concern. In total, 3 709 snake species are currently being recognized and around 35% of them use venom to kill their prey . In fact, the number of reptile species capable of producing toxins in their saliva may be up as high as 2 000 . Out of these, 250 are listed by the World Health Organization as being medically important , especially members of the Elapidae and Viperidae families that possess a very potent venom delivery system . Every year, 4.5–5.4 million people are bitten by snakes worldwide and the estimated death toll ranges from 81,000 to 138,000 . Another 400,000 victims suffer major disabilities such as amputations . Therefore, snakebites have been recently claimed the world’s biggest and grossly underestimated hidden health crisis . 1.2. Risk of envenoming in different regions The risk of snake envenomation is the highest in South and Southeast Asia and Sub-Saharan Africa . Southeast Asia is inhabited by several deadly venomous viperid and elapid snakes, e.g., the Russell’s viper ( Daboia russelii ), saw-scaled viper ( Echis carinatus ), Indian cobra ( Naja naja ), monocled cobra ( Naja kaouthia ), and common krait ( Bungarus caeruleus ). Africa is home to some of the most dangerous snakes, e.g., the West African carpet viper ( Echis ocellatus ), Roman's saw-scaled viper ( E . leucogaster ), and puff adder ( Bitis arietans ) from Viperidae and the forest cobra ( Naja melanoleuca ), black-necked spitting cobra ( N . nigricollis ), and mambas ( Dendroaspis spp.) from Elapidae. The majority of fatal snakebites in Europe and the Middle East is caused by the Levant viper ( Macrovipera lebetina ), coastal viper ( Montivipera xantina ), and Palestine viper ( Daboia palaestinae ). Rattle snakes (Crotalinae, Viperidae) are the most dangerous snakes in North America, mainly the western diamondback rattlesnake ( Crotalus atrox ) and eastern diamondback rattlesnake ( C . adamanteus ). Several viperid snakes pose a significant threat also in South America, e.g., the South American rattlesnake ( C . terrificus ), common lancehead ( Bothrops atrox ), jararaca ( B . jararaca ), jararacussu ( B . jararacussu ), and South American bushmaster ( Lachesis muta) , as well as deadly venomous elapids, the coral snakes ( Micrurus spp.) . Viperids are absent from Australia, while many elapids occur there, with the most venomous being the brown snakes ( Pseudonaja spp.), tiger snake ( Notechis scutatus ), taipans ( Oxyuranus spp.), and death adders ( Acanthophis spp.) . To summarize, snakes of the Viperidae family in particular present a major health risk for humans over much of the world except Australia. Consequently, vipers elicit significant fear response and therefore can be used as a salient evolutionarily relevant stimulus in emotion research . 1.3. Fear module It has been hypothesized that because of the risk presented by venomous snakes, human ancestors have evolved a complex adaptive system of interconnected fear responses manifested on the psychological, behavioral, physiological, and neural level . This system, according to some authors, has been encapsulated in a specific brain structure, the so-called module of fear localized in the amygdala (but see Rosen and Donely who failed to observe amygdala activation in rodents experiencing unconditioned fear). Many years of extensive research have demonstrated that the fear module is particularly triggered by snakes. In contrast to other animals, snakes are associated with a fearful human voice already in infants as young as 9 months . Snakes also capture pre-attentive attention, so they can be spotted much faster than, for example, flowers or mushrooms on a background of distractors . And finally, the psychophysiological fear response elicited by snakes compared to other animate objects is stronger, longer-lasting , and can be triggered even unconsciously [ – , cf. ]. 1.4. Variable snake appearance may trigger distinct emotions of fear and disgust So far, research has been treating snakes as a uniform stimulus category supposedly activating the evolved fear module , although different snake taxa are likely to elicit different levels of fear. Although venomous snakes show great pattern and morphological variation, only certain morphotypes are perceived as dangerous and highly fear-evoking, specifically snakes of the Viperidae family ( Crotalinae , Viperinae , and Azemiopinae ). It has been shown that large body size, conspicuous scales with contrasting patterns, and bright coloration contribute to fear perception . This is congruent with results of a cross-cultural comparison of human fear responses to various venomous and nonvenomous snake species commonly occurring in Europe, Middle East, and North Africa. Both Czech and Azerbaijani respondents rated various species of vipers as the most fear-eliciting. These snakes have characteristic features such as a thick short body, wide distinct neck, and prominent eyes. Interestingly, the Egyptian cobra ( Naja haje ), which is a slender-bodied elapid, was evaluated by the respondents from both countries as the most fear-eliciting and dangerous only when presented in a threatening (in contrast to resting) position , highlighting the importance of context (and possibly movement) in fear perception. Appearance and dangerousness to humans is even more variable across the whole suborder of snakes, including the non-venomous ones. Consequently, some snakes may be perceived as not frightening but highly disgusting . Mainly harmless subterranean (fossorial) snakes from the group of blind snakes called Typhlopoidea ( Xenotyphlopidae , Typhlopidae , Leptotyphlopidae , Gerrhopilidae , and Anomalepididae ) are less fear-evoking, but highly repulsive . These findings raise an intriguing question: do snake species advertise the danger they present to humans, for example, their toxicity, through their appearance? From the functional perspective, fear and disgust are biologically adaptive and genetically fixed intense responses to potentially life-threatening situations . Although both negative emotions should lead to avoidance/withdrawal , they can be clearly distinguished on the physiological, psychological, and behavioral level . While fear is elicited by the presence of a predator (e.g., a snake) or other imminent threat posing a direct risk of physical harm or even death , disgust has originally developed as a food-rejection emotion. Its main function is to prevent the transmission of illness or disease through ingestion of contaminated objects . Thus, it triggers disease-avoidance behavior as a part of the “behavioral immune system” . However, understanding of the psychophysiological differences between emotions in general is still insufficient , particularly between fear and disgust. The fear response involves activation of the sympathetic nervous system, which initiates the “fight or flight” reaction characterized by heart rate acceleration . Disgusting stimuli, on the other hand, have highly variable physiological effects on heart and respiratory rates . Skin conductance, which is determined by the activity of sympathetically innervated sweat glands, is reported to increase with both fear and disgust . However, some authors have challenged the view of basic emotions as biologically fixed, universally shared, discrete entities that serve a specific function in survival each having a distinct facial expression, physiological pattern and neural substrate. For example, Russell conceptualized emotions as simple affective states called the core affects, which can be either good or bad and energized or enervated and were attributed to some internal or external cause. Similarly, Barrett highlighted that despite people’s belief of being able to recognize their own emotions, research has not yet identified clear criteria for the presence of a certain emotion. Therefore, according to her model, an emotional experience arises when affective feeling is cognitively categorized based on our knowledge. 1.5. Autonomic electrodermal and heart rate response to snakes Since the 1970s, an extensive series of experiments has demonstrated that snakes, compared with other stimuli, selectively trigger a stronger and longer-lasting physiological fear response, particularly an increase in heart rate, blood pressure, and skin conductance, which is more resistant to extinction . Its main purpose is to mobilize energy reserves and prepare the body for rapid action [cf. 51]. The majority of studies did not measure a spontaneous response to snakes but rather used a within-subject controlled differential conditioning paradigm in which some fear-relevant (snakes and spiders) and fear-irrelevant (flowers and mushrooms) stimuli were followed by an electric shock (CS+), while others were not paired with any shocks (CS-). Most often, the dependent variable was the skin conductance response (SCR), alternatively also heart rate (HR). It was argued that the difference in SCR to the CS+ vs CS- stimulus should be bigger for fear-relevant than for fear-irrelevant stimuli, which was then supported by several studies , for a review see . For example, Soares and Öhman reported that both fearful and non-fearful control subjects had significantly larger differential electrodermal responses to pictures of snakes and spiders than to pictures of flowers and mushrooms. It was also shown that SCR triggered by fear-relevant compared with fear-irrelevant stimuli was more resistant to extinction, however, no effect of fear-relevance on HR was found . Interestingly, a backwardly masked presentation (stimulus appears on the screen for only about 30 ms) or an instruction that no shock will follow may wipe out differential SCR to neutral stimuli, but has no effect on differential SCR to fear-relevant stimuli . Öhman and Soares demonstrated on SCR changes that subjects can be non-consciously conditioned by electric shocks to fear snakes and spiders but not flowers and mushrooms even when these are masked during acquisition. Others have used a different approach and instead of conditioning normal subjects to fear snakes, they directly measured spontaneous physiological responses of respondents with low vs high (phobic) fear of snakes. It has been repeatedly shown that high-fear individuals display larger SCR and increased HR when exposed to snakes compared to control stimuli and, moreover, their SCR in response to snakes is elevated compared to low-fear individuals under both conscious and unconscious (masked) presentation conditions . In regards to autonomous fear responses to snakes in particular, there is an ongoing debate in the literature whether these are acquired through direct aversive experiences (or observation of others’ behavior) during development , as the studies using classical Pavlovian fear conditioning might suggest, or snake fear is rather biologically prepared (genetically fixed), universal trait in humans and other animal species that can be manifested even without any prior negative experience (humans: , birds: , geckos: , primates: ). 1.6. Study aims Even though there is a substantial body of literature dealing with psychophysiological responses to snakes (live or on pictures), those studies fail to reflect the immense morphological and pattern variety of different snake taxa that can trigger various emotions. Often researchers use the snake as a uniform stimulus prototypically eliciting fear and no attention is being paid to characteristics of that particular species (in a majority of studies the species is not even specified) such as its body size, color pattern, posture, toxicity, etc. However, from our previous research, we know that this is crucial as it can significantly affect our responses . Moreover, as mentioned above, most of earlier studies measured physiological parameters using a differential conditioning paradigm, which is a qualitatively different phenomenon than spontaneous reactions of unconditioned individuals and therefore, such an approach is not ideal for studying traits that might be biologically prepared. Viperid snakes are unique stimuli for humans in several aspects: 1) many species of viperid snakes currently pose a serious risk of venomous snakebite on all the continents except Australia and Antarctica and thus exert an important selective pressure on the human ability to perceive, emotionally evaluate, and avoid these snakes; and 2) it was demonstrated cross-culturally that humans perceive the specific viperid morphotype (including the families Crotalinae , Viperinae , and Azemiopinae) as highly fear-eliciting . Thus, viperid snakes present an ideal model group for studying the effect of fear response when spotting a snake. In this study, we focused on psychophysiological responses of human subjects elicited by 20 species of snakes belonging to two distinct groups differing in their morphology, ecology, behavior, toxicity (dangerousness), and fear/disgust-evoking properties. Although autonomous bodily responses (mostly skin conductance and heart rate) to snakes have been already well-explored in previous research, to our knowledge, no one has ever focused on interstimulus variability within the category of snakes. There is evidence that people distinguish between different snake species emotionally by experiencing either fear or disgust. It is thus reasonable to expect that the same distinction is reflected in physiological responses as well. For the first time, this would show that snakes are an emotionally ambivalent category, which might have significant implications for future research by highlighting the importance of careful stimulus selection. Moreover, it would also be beneficial to the clinical practice as it might tailor treatment of snake phobics to their specific needs of better emotional regulation. In our previous study, we demonstrated a difference in fear and disgust evaluation of various snake species between people with low and high fear of snakes and disgust propensity . Thus, it would be interesting to see whether these interindividual differences also manifest in physiological parameters. Finally, comparing two distinct categories of snakes as stimuli might reveal an adaptive pattern of specific physiological response targeted to venomous viperid snakes. First, we aimed to examine the autonomous physiological response to venomous viperid snakes eliciting high fear but low disgust compared to non-venomous fossorial snakes eliciting low fear but high disgust. This exploratory question has never been studied, although based on the research using other fear- and disgust-eliciting animal stimuli, one might assume that while fear-eliciting snakes should trigger the sympathetic (predatory defense) nervous system activating the body energy resources and leading to increased skin conductance and heart rate, disgust-eliciting worm-like snakes should activate the parasympathetic (behavioral immune) nervous system causing increased skin conductance but decreased heart rate. Second, we will study how physiological responses might be affected by different levels of snake fear and disgust propensity. Again, although a few studies have compared physiological responses of snake phobics versus healthy controls, no one has incorporated disgust into the model, which might shed more light on psychopathological dynamics of snake phobia development. Previous psychophysiological research on snake fear has used various methodology which makes any comparisons difficult. Therefore, we have chosen to study the differences in physiological responses; first, between stimulus categories and then between subjects using two experimental designs, i.e., sequential (presentation of an individual picture stimulus followed by an interstimulus) and block presentation (presentation of 10 pictures one right after the other with no interstimulus in-between) that might provoke an emotional response of different intensity. Specifically, we assume that stimulation in a block design should trigger a more intense physiological response compared to stimulation in a sequential design. Finally, as the current literature is not consistent regarding correspondence between self-reported emotions and physiological parameters, we will investigate the link between evaluation of snake stimuli and physiological responses.
Ever since their appearance, primates and early hunter-gatherers have been subject to life-threatening risks from snakes. As a consequence, primates including contemporary humans developed improved visual abilities and superior pre-attentive attention specifically for detecting snakes and other stimuli representing an evolutionary threat . Although this predation pressure has left no trace in the fossil record, some circumstantial evidence is available. In an attempt to assess the hazards that snakes pose to primates, McGrew observed a group of chimpanzees in Senegal. Within four years, as many as 142 encounters of snakes belonging to 14 species were recorded. Headland and Greene showed that local populations in some parts of the world have been regularly exposed to predatory attacks by giant constrictor snakes in the recent past. Over the course of four decades, a quarter of Agta Negritos men, a tribe of hunter-gatherers from the Philippines, were attacked by the reticulated python ( Malayopython reticulatus ), resulting in six fatalities . Even today, snakebite envenoming remains a significant health concern. In total, 3 709 snake species are currently being recognized and around 35% of them use venom to kill their prey . In fact, the number of reptile species capable of producing toxins in their saliva may be up as high as 2 000 . Out of these, 250 are listed by the World Health Organization as being medically important , especially members of the Elapidae and Viperidae families that possess a very potent venom delivery system . Every year, 4.5–5.4 million people are bitten by snakes worldwide and the estimated death toll ranges from 81,000 to 138,000 . Another 400,000 victims suffer major disabilities such as amputations . Therefore, snakebites have been recently claimed the world’s biggest and grossly underestimated hidden health crisis .
The risk of snake envenomation is the highest in South and Southeast Asia and Sub-Saharan Africa . Southeast Asia is inhabited by several deadly venomous viperid and elapid snakes, e.g., the Russell’s viper ( Daboia russelii ), saw-scaled viper ( Echis carinatus ), Indian cobra ( Naja naja ), monocled cobra ( Naja kaouthia ), and common krait ( Bungarus caeruleus ). Africa is home to some of the most dangerous snakes, e.g., the West African carpet viper ( Echis ocellatus ), Roman's saw-scaled viper ( E . leucogaster ), and puff adder ( Bitis arietans ) from Viperidae and the forest cobra ( Naja melanoleuca ), black-necked spitting cobra ( N . nigricollis ), and mambas ( Dendroaspis spp.) from Elapidae. The majority of fatal snakebites in Europe and the Middle East is caused by the Levant viper ( Macrovipera lebetina ), coastal viper ( Montivipera xantina ), and Palestine viper ( Daboia palaestinae ). Rattle snakes (Crotalinae, Viperidae) are the most dangerous snakes in North America, mainly the western diamondback rattlesnake ( Crotalus atrox ) and eastern diamondback rattlesnake ( C . adamanteus ). Several viperid snakes pose a significant threat also in South America, e.g., the South American rattlesnake ( C . terrificus ), common lancehead ( Bothrops atrox ), jararaca ( B . jararaca ), jararacussu ( B . jararacussu ), and South American bushmaster ( Lachesis muta) , as well as deadly venomous elapids, the coral snakes ( Micrurus spp.) . Viperids are absent from Australia, while many elapids occur there, with the most venomous being the brown snakes ( Pseudonaja spp.), tiger snake ( Notechis scutatus ), taipans ( Oxyuranus spp.), and death adders ( Acanthophis spp.) . To summarize, snakes of the Viperidae family in particular present a major health risk for humans over much of the world except Australia. Consequently, vipers elicit significant fear response and therefore can be used as a salient evolutionarily relevant stimulus in emotion research .
It has been hypothesized that because of the risk presented by venomous snakes, human ancestors have evolved a complex adaptive system of interconnected fear responses manifested on the psychological, behavioral, physiological, and neural level . This system, according to some authors, has been encapsulated in a specific brain structure, the so-called module of fear localized in the amygdala (but see Rosen and Donely who failed to observe amygdala activation in rodents experiencing unconditioned fear). Many years of extensive research have demonstrated that the fear module is particularly triggered by snakes. In contrast to other animals, snakes are associated with a fearful human voice already in infants as young as 9 months . Snakes also capture pre-attentive attention, so they can be spotted much faster than, for example, flowers or mushrooms on a background of distractors . And finally, the psychophysiological fear response elicited by snakes compared to other animate objects is stronger, longer-lasting , and can be triggered even unconsciously [ – , cf. ].
So far, research has been treating snakes as a uniform stimulus category supposedly activating the evolved fear module , although different snake taxa are likely to elicit different levels of fear. Although venomous snakes show great pattern and morphological variation, only certain morphotypes are perceived as dangerous and highly fear-evoking, specifically snakes of the Viperidae family ( Crotalinae , Viperinae , and Azemiopinae ). It has been shown that large body size, conspicuous scales with contrasting patterns, and bright coloration contribute to fear perception . This is congruent with results of a cross-cultural comparison of human fear responses to various venomous and nonvenomous snake species commonly occurring in Europe, Middle East, and North Africa. Both Czech and Azerbaijani respondents rated various species of vipers as the most fear-eliciting. These snakes have characteristic features such as a thick short body, wide distinct neck, and prominent eyes. Interestingly, the Egyptian cobra ( Naja haje ), which is a slender-bodied elapid, was evaluated by the respondents from both countries as the most fear-eliciting and dangerous only when presented in a threatening (in contrast to resting) position , highlighting the importance of context (and possibly movement) in fear perception. Appearance and dangerousness to humans is even more variable across the whole suborder of snakes, including the non-venomous ones. Consequently, some snakes may be perceived as not frightening but highly disgusting . Mainly harmless subterranean (fossorial) snakes from the group of blind snakes called Typhlopoidea ( Xenotyphlopidae , Typhlopidae , Leptotyphlopidae , Gerrhopilidae , and Anomalepididae ) are less fear-evoking, but highly repulsive . These findings raise an intriguing question: do snake species advertise the danger they present to humans, for example, their toxicity, through their appearance? From the functional perspective, fear and disgust are biologically adaptive and genetically fixed intense responses to potentially life-threatening situations . Although both negative emotions should lead to avoidance/withdrawal , they can be clearly distinguished on the physiological, psychological, and behavioral level . While fear is elicited by the presence of a predator (e.g., a snake) or other imminent threat posing a direct risk of physical harm or even death , disgust has originally developed as a food-rejection emotion. Its main function is to prevent the transmission of illness or disease through ingestion of contaminated objects . Thus, it triggers disease-avoidance behavior as a part of the “behavioral immune system” . However, understanding of the psychophysiological differences between emotions in general is still insufficient , particularly between fear and disgust. The fear response involves activation of the sympathetic nervous system, which initiates the “fight or flight” reaction characterized by heart rate acceleration . Disgusting stimuli, on the other hand, have highly variable physiological effects on heart and respiratory rates . Skin conductance, which is determined by the activity of sympathetically innervated sweat glands, is reported to increase with both fear and disgust . However, some authors have challenged the view of basic emotions as biologically fixed, universally shared, discrete entities that serve a specific function in survival each having a distinct facial expression, physiological pattern and neural substrate. For example, Russell conceptualized emotions as simple affective states called the core affects, which can be either good or bad and energized or enervated and were attributed to some internal or external cause. Similarly, Barrett highlighted that despite people’s belief of being able to recognize their own emotions, research has not yet identified clear criteria for the presence of a certain emotion. Therefore, according to her model, an emotional experience arises when affective feeling is cognitively categorized based on our knowledge.
Since the 1970s, an extensive series of experiments has demonstrated that snakes, compared with other stimuli, selectively trigger a stronger and longer-lasting physiological fear response, particularly an increase in heart rate, blood pressure, and skin conductance, which is more resistant to extinction . Its main purpose is to mobilize energy reserves and prepare the body for rapid action [cf. 51]. The majority of studies did not measure a spontaneous response to snakes but rather used a within-subject controlled differential conditioning paradigm in which some fear-relevant (snakes and spiders) and fear-irrelevant (flowers and mushrooms) stimuli were followed by an electric shock (CS+), while others were not paired with any shocks (CS-). Most often, the dependent variable was the skin conductance response (SCR), alternatively also heart rate (HR). It was argued that the difference in SCR to the CS+ vs CS- stimulus should be bigger for fear-relevant than for fear-irrelevant stimuli, which was then supported by several studies , for a review see . For example, Soares and Öhman reported that both fearful and non-fearful control subjects had significantly larger differential electrodermal responses to pictures of snakes and spiders than to pictures of flowers and mushrooms. It was also shown that SCR triggered by fear-relevant compared with fear-irrelevant stimuli was more resistant to extinction, however, no effect of fear-relevance on HR was found . Interestingly, a backwardly masked presentation (stimulus appears on the screen for only about 30 ms) or an instruction that no shock will follow may wipe out differential SCR to neutral stimuli, but has no effect on differential SCR to fear-relevant stimuli . Öhman and Soares demonstrated on SCR changes that subjects can be non-consciously conditioned by electric shocks to fear snakes and spiders but not flowers and mushrooms even when these are masked during acquisition. Others have used a different approach and instead of conditioning normal subjects to fear snakes, they directly measured spontaneous physiological responses of respondents with low vs high (phobic) fear of snakes. It has been repeatedly shown that high-fear individuals display larger SCR and increased HR when exposed to snakes compared to control stimuli and, moreover, their SCR in response to snakes is elevated compared to low-fear individuals under both conscious and unconscious (masked) presentation conditions . In regards to autonomous fear responses to snakes in particular, there is an ongoing debate in the literature whether these are acquired through direct aversive experiences (or observation of others’ behavior) during development , as the studies using classical Pavlovian fear conditioning might suggest, or snake fear is rather biologically prepared (genetically fixed), universal trait in humans and other animal species that can be manifested even without any prior negative experience (humans: , birds: , geckos: , primates: ).
Even though there is a substantial body of literature dealing with psychophysiological responses to snakes (live or on pictures), those studies fail to reflect the immense morphological and pattern variety of different snake taxa that can trigger various emotions. Often researchers use the snake as a uniform stimulus prototypically eliciting fear and no attention is being paid to characteristics of that particular species (in a majority of studies the species is not even specified) such as its body size, color pattern, posture, toxicity, etc. However, from our previous research, we know that this is crucial as it can significantly affect our responses . Moreover, as mentioned above, most of earlier studies measured physiological parameters using a differential conditioning paradigm, which is a qualitatively different phenomenon than spontaneous reactions of unconditioned individuals and therefore, such an approach is not ideal for studying traits that might be biologically prepared. Viperid snakes are unique stimuli for humans in several aspects: 1) many species of viperid snakes currently pose a serious risk of venomous snakebite on all the continents except Australia and Antarctica and thus exert an important selective pressure on the human ability to perceive, emotionally evaluate, and avoid these snakes; and 2) it was demonstrated cross-culturally that humans perceive the specific viperid morphotype (including the families Crotalinae , Viperinae , and Azemiopinae) as highly fear-eliciting . Thus, viperid snakes present an ideal model group for studying the effect of fear response when spotting a snake. In this study, we focused on psychophysiological responses of human subjects elicited by 20 species of snakes belonging to two distinct groups differing in their morphology, ecology, behavior, toxicity (dangerousness), and fear/disgust-evoking properties. Although autonomous bodily responses (mostly skin conductance and heart rate) to snakes have been already well-explored in previous research, to our knowledge, no one has ever focused on interstimulus variability within the category of snakes. There is evidence that people distinguish between different snake species emotionally by experiencing either fear or disgust. It is thus reasonable to expect that the same distinction is reflected in physiological responses as well. For the first time, this would show that snakes are an emotionally ambivalent category, which might have significant implications for future research by highlighting the importance of careful stimulus selection. Moreover, it would also be beneficial to the clinical practice as it might tailor treatment of snake phobics to their specific needs of better emotional regulation. In our previous study, we demonstrated a difference in fear and disgust evaluation of various snake species between people with low and high fear of snakes and disgust propensity . Thus, it would be interesting to see whether these interindividual differences also manifest in physiological parameters. Finally, comparing two distinct categories of snakes as stimuli might reveal an adaptive pattern of specific physiological response targeted to venomous viperid snakes. First, we aimed to examine the autonomous physiological response to venomous viperid snakes eliciting high fear but low disgust compared to non-venomous fossorial snakes eliciting low fear but high disgust. This exploratory question has never been studied, although based on the research using other fear- and disgust-eliciting animal stimuli, one might assume that while fear-eliciting snakes should trigger the sympathetic (predatory defense) nervous system activating the body energy resources and leading to increased skin conductance and heart rate, disgust-eliciting worm-like snakes should activate the parasympathetic (behavioral immune) nervous system causing increased skin conductance but decreased heart rate. Second, we will study how physiological responses might be affected by different levels of snake fear and disgust propensity. Again, although a few studies have compared physiological responses of snake phobics versus healthy controls, no one has incorporated disgust into the model, which might shed more light on psychopathological dynamics of snake phobia development. Previous psychophysiological research on snake fear has used various methodology which makes any comparisons difficult. Therefore, we have chosen to study the differences in physiological responses; first, between stimulus categories and then between subjects using two experimental designs, i.e., sequential (presentation of an individual picture stimulus followed by an interstimulus) and block presentation (presentation of 10 pictures one right after the other with no interstimulus in-between) that might provoke an emotional response of different intensity. Specifically, we assume that stimulation in a block design should trigger a more intense physiological response compared to stimulation in a sequential design. Finally, as the current literature is not consistent regarding correspondence between self-reported emotions and physiological parameters, we will investigate the link between evaluation of snake stimuli and physiological responses.
2.1. Participants We recruited respondents with different levels of fear of snakes and general disgust propensity as measured by commonly used psychometric questionnaires–the Snake Questionnaire (SNAQ , in a Czech translation ) and the Disgust Scale-Revised (DS-R , in a Czech translation ). The respondents were selected so that the dataset would be balanced with comparable numbers of respondents with high and low scores from each of the above-mentioned questionnaires. The high-fear/disgust participants were defined as those scoring above the upper quartile on the SNAQ/DS-R scales (upper quartiles were computed for Czech population: SNAQ score ≥ 8 ; DS-R score ≥ 52 ). The respondents also completed the Emotion Reactivity Scale (ERS ) and provided information about their gender, age, and field of study. In total, 161 individuals were included in the study. Out of these, 143 respondents performed the sequential design experiment (139 of them completed all the questionnaires; 75 high-fear, 64 low-fear, 59 high-disgust, 80 low-disgust; 116 females, 27 males; 46 biological education, 97 non-biological education; mean age 28.12 ± 10.65) and 143 the block design experiment (all of them completed the questionnaires; 82 high-fear, 61 low-fear; 59 high-disgust, 86 low-disgust; 118 females, 25 males; 46 biological education, 97 non-biological education; mean age 28.0 ± 10.18). Both experimental designs were performed by 125 respondents with the second experiment being carried out after at least a month-long period, so that the effect of habituation would be minimized. The sample size was based on both, previous studies (for the comparison, see ) and a statistical a priori power analysis computed in G*Power 3 . This analysis was conducted to test the difference in physiological responses to three categories of stimuli (fear/disgust/control, see below) between two main categories of respondents (high/low fear of snakes) using an ANOVA, a medium effect size (f = 0.25) and an alpha of 0.05. The result showed that a total sample of 158 participants in one experimental design was required to achieve a power of 0.80. However, the prevalence of people with high fear of snakes who are willing to attend an experiment with snakes is rather low, thus, we compromised on having 143 respondents in each design. The sample is not perfectly balanced especially in terms of gender; however, our main aim was to keep the ratio of respondents with high and low fear of snakes balanced. As the prevalence of snake fear is higher in women , our study included more women than men. 2.2. Stimuli We selected 20 photographs of snake species evoking a strong and distinct emotional response based on their morphotype according to the self-reported evaluation (15)– 10 dangerous (highly venomous) viperid snakes evoking strong fear (for their venom characteristics, see ) and 10 disgust-eliciting harmless (nonvenomous) fossorial snakes, evoking only a weak fear response (see also Fig). On a 7-point Likert scale of fear (1 = no fear, 7 = extreme fear), the fear-eliciting snakes scored high (mean score 5.15 ± 1.95), while the disgust-eliciting ones scored much lower (mean score 3.24 ± 2.00) . As emotionally neutral controls, we used 20 photographs of tree leaves (see also for examples of the tested species in each category depicting their morphological variety). According to a preliminary study with 135 respondents, leaves do not elicit any fear (mean score 1.09 ± 0.53), nor disgust (mean score 1.09 ± 0.54; 7-point scale, 1 = no fear/disgust). The photos used in the study were either taken by the authors themselves or downloaded from the Internet; in this case they were licensed under the Creative Commons and/or a written permission for scientific use was obtained from their authors. For the full list of included snake species, see . The photos were standardized by placing the stimuli on a unified grey background and resized to assume a similar relative size in a 2:3 ratio of the picture. To analyze the effect of morphologic characteristics of the examined snake species on the human responses, we included the following measures and color characteristics with considerable variability within the two snake categories (fear- and disgust-eliciting) as explanatory variables: body length, neck width, eye size, proportion of white, black, grey, red, brown, and blue colors, mean saturation and standard deviation of saturation (for more information on the measurement and extraction of these variables, see Rádlová et al. . We also included three venom characteristics of the fear-eliciting snakes: LD50 (50% lethal dose, intravenous), index of dangerousness as retrieved from the Clinical Toxinology Resources , and a ratio of venom volume to body length . 2.3. Procedure and apparatus To fulfill the aims, we examined several skin resistance (SR) and heart rate (HR) parameters (see ) in response to images of fear-eliciting venomous viperids, disgust-eliciting non-venomous fossorial snakes, and leaves as control stimuli. We also adopted two experimental designs to examine different intensities of visual stimulation. In the first one, further referred to as the sequential design, the pictures of snakes and leaves were presented individually in an alternating order starting with a control stimulus (i.e. leaf–venomous snake–leaf–disgusting snake and so on repeated through the entire presentation of 40 images), each presented for 5 seconds and separated with a black screen (interstimulus) presented for 5 seconds or until the participant calmed down, whichever lasted longer. In the second experimental design, further referred to as the block design, the pictures were presented in blocks consisting of 10 pictures from a single category (fear/disgust/control). This design, commonly used in fMRI and EEG studies, is hypothesized to present stronger stimulation compared to individually presented stimuli (also called event-related design in fMRI/EEG studies). We applied it to physiological measurement to compare the effect of these two designs of visual stimulation on the physiological response and we plan to compare the results of the block design with a subsequent fMRI experiment. The pictures in the block design were presented one right after the other (with no black screen in-between) and each picture from the specific category appeared on the screen for 2.5 seconds only, i.e. the entire block was shown for 25 seconds. This was followed by a black screen presented for at least 5 seconds or more if necessary, for the respondent to calm down (see ). Snake illustrations in this preview have been made by Pavel Procházka, photos of leaves taken by Petra Frýdlová and Eva Landová. Please note that during the experiment, photos of real snakes were used. High-fear participants were presented with the fear-evoking block at the end of the presentation, and similarly, high-disgust participants viewed the disgust-evoking block as the last one. This was mainly due to methodological reasons to ensure that the physiological response we were most interested in would not be compromised by object novelty. Additionally, this design was also more suitable for the high-fear/disgust subjects as they were exposed to the strongest stimuli at the end of the trial. Low-fear and low-disgust participants viewed these two presentations in a random order (in total, 70 respondents started with the fear-evoking snakes and 73 with the disgust-evoking ones). Respondents, who attended both experiments (n = 125) did so in a random, counter-balanced order. Moreover, 111 respondents from the main sample (59 high-fear, 52 low-fear, 49 high-disgust, 62 low-disgust, 93 females, 18 males, 40 with biological education, mean age 27.89 ± 8.41) rated all depicted snake species for fear and disgust. We adopted a well-established method used in a number of previous studies . The photographs of snakes (360 x 540 pixels) were presented one by one on a computer screen in a random order. The respondent was asked to score fear or disgust elicited by each species on a 7-point Likert scale (1 –not disgusting/fear-evoking at all, 7 –the most disgusting/fear-evoking) in two separate tasks, the first scored emotion was chosen randomly. The rating was performed after the main experiment to minimize the effect of habituation. For measuring physiological responses, we used Multifunction Biotelemetry Support System for Psychophysiology Monitoring VLV3 , which enables measuring and evaluating multiple physiological variables in real time during the stimuli presentation. Skin resistance (SR) was measured using dry sensors attached to the second phalanx of the index and middle fingers of the non-dominant hand. Heart rate (HR) was measured with a pair of standard Ag/AgCl electrodes attached by adhesive collars to the skin under the right collarbone and the left fifth intercostals. To analyze the reactions, we measured length (from the beginning of the SR change curve to the peak of the curve) and amplitude of the SR change curve, which corresponds to the intensity of the emotional reaction. The heart activity was recorded as mean HR (in beats per minute) in the given time period. The pictures (1772 x 1181 pixels, 300 DPI resolution) were presented on a computer screen (26", 2560 x 1440 resolution, full screen presentation) placed 55 cm from the edge of the table. The respondents were asked to leave their hands with attached sensors on the table and to watch the screen during the whole presentation without unnecessary movements. This study was carried out in accordance with the approval of the Ethical Committee of the National Institute of Mental Health no. 55/16, with the written informed consent from all subjects in accordance with the Declaration of Helsinki. 2.4. Statistical analysis For the variables used to characterize physiological responses, see . They were used as raw data when possible and were transformed for use of linear models, using either logarithmic or square root transformations to approximate to the normal distribution. The distribution of model residuals was visually inspected for both deviations from normality and variance heterogeneity. The Spearman’s correlation coefficient was computed to compare the self-reported evaluation and physiological responses. To test the differences in physiological responses to individual snake species and to different stimulus categories, we performed a Friedman test and post hoc Nemenyi test as implemented in the R package PMCMR . A Mann-Whitney U test was used to compare the physiological reactions of high- and low- fear/disgust respondents. The above-mentioned tests were used as a non-parametric alternative for raw data deviating from normality, as we aimed to maintain extreme values of highly fearful participants. Two analyses were used to examine the contribution of respondent’s characteristics (gender, age, education, SNAQ, DS-R, and ERS scores) to the physiological responses; these were used as explanatory variables in linear mixed effects models (LME; implemented in R package nlme), which allowed for inclusion of the effects of respondent’s characteristics accounted for the individual identity using it as a random factor. An ANOVA was applied to test the effect of explanatory variables. We also performed an exploratory redundancy analysis (RDA; implemented in the R package vegan ), which is a multivariate direct gradient method. It extracts and summarizes the variation in a set of response variables (parameters of physiological reactions) that can be explained by a set of explanatory variables. This analysis permits to plot both response and explanatory variables to a space defined by the extracted gradients and enables detection of redundancy (i.e., shared variability) between sets of response and explanatory variables. Statistical significance of the gradients was confirmed by permutation tests. Repeatability was computed as another exploratory analysis to test the intra-individual consistency between respondents performing both tasks using the R package rptR . Repeatability allowed us to establish the relative contribution of between-individual variation to the overall variation . Calculations were performed in R and Statistica .
We recruited respondents with different levels of fear of snakes and general disgust propensity as measured by commonly used psychometric questionnaires–the Snake Questionnaire (SNAQ , in a Czech translation ) and the Disgust Scale-Revised (DS-R , in a Czech translation ). The respondents were selected so that the dataset would be balanced with comparable numbers of respondents with high and low scores from each of the above-mentioned questionnaires. The high-fear/disgust participants were defined as those scoring above the upper quartile on the SNAQ/DS-R scales (upper quartiles were computed for Czech population: SNAQ score ≥ 8 ; DS-R score ≥ 52 ). The respondents also completed the Emotion Reactivity Scale (ERS ) and provided information about their gender, age, and field of study. In total, 161 individuals were included in the study. Out of these, 143 respondents performed the sequential design experiment (139 of them completed all the questionnaires; 75 high-fear, 64 low-fear, 59 high-disgust, 80 low-disgust; 116 females, 27 males; 46 biological education, 97 non-biological education; mean age 28.12 ± 10.65) and 143 the block design experiment (all of them completed the questionnaires; 82 high-fear, 61 low-fear; 59 high-disgust, 86 low-disgust; 118 females, 25 males; 46 biological education, 97 non-biological education; mean age 28.0 ± 10.18). Both experimental designs were performed by 125 respondents with the second experiment being carried out after at least a month-long period, so that the effect of habituation would be minimized. The sample size was based on both, previous studies (for the comparison, see ) and a statistical a priori power analysis computed in G*Power 3 . This analysis was conducted to test the difference in physiological responses to three categories of stimuli (fear/disgust/control, see below) between two main categories of respondents (high/low fear of snakes) using an ANOVA, a medium effect size (f = 0.25) and an alpha of 0.05. The result showed that a total sample of 158 participants in one experimental design was required to achieve a power of 0.80. However, the prevalence of people with high fear of snakes who are willing to attend an experiment with snakes is rather low, thus, we compromised on having 143 respondents in each design. The sample is not perfectly balanced especially in terms of gender; however, our main aim was to keep the ratio of respondents with high and low fear of snakes balanced. As the prevalence of snake fear is higher in women , our study included more women than men.
We selected 20 photographs of snake species evoking a strong and distinct emotional response based on their morphotype according to the self-reported evaluation (15)– 10 dangerous (highly venomous) viperid snakes evoking strong fear (for their venom characteristics, see ) and 10 disgust-eliciting harmless (nonvenomous) fossorial snakes, evoking only a weak fear response (see also Fig). On a 7-point Likert scale of fear (1 = no fear, 7 = extreme fear), the fear-eliciting snakes scored high (mean score 5.15 ± 1.95), while the disgust-eliciting ones scored much lower (mean score 3.24 ± 2.00) . As emotionally neutral controls, we used 20 photographs of tree leaves (see also for examples of the tested species in each category depicting their morphological variety). According to a preliminary study with 135 respondents, leaves do not elicit any fear (mean score 1.09 ± 0.53), nor disgust (mean score 1.09 ± 0.54; 7-point scale, 1 = no fear/disgust). The photos used in the study were either taken by the authors themselves or downloaded from the Internet; in this case they were licensed under the Creative Commons and/or a written permission for scientific use was obtained from their authors. For the full list of included snake species, see . The photos were standardized by placing the stimuli on a unified grey background and resized to assume a similar relative size in a 2:3 ratio of the picture. To analyze the effect of morphologic characteristics of the examined snake species on the human responses, we included the following measures and color characteristics with considerable variability within the two snake categories (fear- and disgust-eliciting) as explanatory variables: body length, neck width, eye size, proportion of white, black, grey, red, brown, and blue colors, mean saturation and standard deviation of saturation (for more information on the measurement and extraction of these variables, see Rádlová et al. . We also included three venom characteristics of the fear-eliciting snakes: LD50 (50% lethal dose, intravenous), index of dangerousness as retrieved from the Clinical Toxinology Resources , and a ratio of venom volume to body length .
To fulfill the aims, we examined several skin resistance (SR) and heart rate (HR) parameters (see ) in response to images of fear-eliciting venomous viperids, disgust-eliciting non-venomous fossorial snakes, and leaves as control stimuli. We also adopted two experimental designs to examine different intensities of visual stimulation. In the first one, further referred to as the sequential design, the pictures of snakes and leaves were presented individually in an alternating order starting with a control stimulus (i.e. leaf–venomous snake–leaf–disgusting snake and so on repeated through the entire presentation of 40 images), each presented for 5 seconds and separated with a black screen (interstimulus) presented for 5 seconds or until the participant calmed down, whichever lasted longer. In the second experimental design, further referred to as the block design, the pictures were presented in blocks consisting of 10 pictures from a single category (fear/disgust/control). This design, commonly used in fMRI and EEG studies, is hypothesized to present stronger stimulation compared to individually presented stimuli (also called event-related design in fMRI/EEG studies). We applied it to physiological measurement to compare the effect of these two designs of visual stimulation on the physiological response and we plan to compare the results of the block design with a subsequent fMRI experiment. The pictures in the block design were presented one right after the other (with no black screen in-between) and each picture from the specific category appeared on the screen for 2.5 seconds only, i.e. the entire block was shown for 25 seconds. This was followed by a black screen presented for at least 5 seconds or more if necessary, for the respondent to calm down (see ). Snake illustrations in this preview have been made by Pavel Procházka, photos of leaves taken by Petra Frýdlová and Eva Landová. Please note that during the experiment, photos of real snakes were used. High-fear participants were presented with the fear-evoking block at the end of the presentation, and similarly, high-disgust participants viewed the disgust-evoking block as the last one. This was mainly due to methodological reasons to ensure that the physiological response we were most interested in would not be compromised by object novelty. Additionally, this design was also more suitable for the high-fear/disgust subjects as they were exposed to the strongest stimuli at the end of the trial. Low-fear and low-disgust participants viewed these two presentations in a random order (in total, 70 respondents started with the fear-evoking snakes and 73 with the disgust-evoking ones). Respondents, who attended both experiments (n = 125) did so in a random, counter-balanced order. Moreover, 111 respondents from the main sample (59 high-fear, 52 low-fear, 49 high-disgust, 62 low-disgust, 93 females, 18 males, 40 with biological education, mean age 27.89 ± 8.41) rated all depicted snake species for fear and disgust. We adopted a well-established method used in a number of previous studies . The photographs of snakes (360 x 540 pixels) were presented one by one on a computer screen in a random order. The respondent was asked to score fear or disgust elicited by each species on a 7-point Likert scale (1 –not disgusting/fear-evoking at all, 7 –the most disgusting/fear-evoking) in two separate tasks, the first scored emotion was chosen randomly. The rating was performed after the main experiment to minimize the effect of habituation. For measuring physiological responses, we used Multifunction Biotelemetry Support System for Psychophysiology Monitoring VLV3 , which enables measuring and evaluating multiple physiological variables in real time during the stimuli presentation. Skin resistance (SR) was measured using dry sensors attached to the second phalanx of the index and middle fingers of the non-dominant hand. Heart rate (HR) was measured with a pair of standard Ag/AgCl electrodes attached by adhesive collars to the skin under the right collarbone and the left fifth intercostals. To analyze the reactions, we measured length (from the beginning of the SR change curve to the peak of the curve) and amplitude of the SR change curve, which corresponds to the intensity of the emotional reaction. The heart activity was recorded as mean HR (in beats per minute) in the given time period. The pictures (1772 x 1181 pixels, 300 DPI resolution) were presented on a computer screen (26", 2560 x 1440 resolution, full screen presentation) placed 55 cm from the edge of the table. The respondents were asked to leave their hands with attached sensors on the table and to watch the screen during the whole presentation without unnecessary movements. This study was carried out in accordance with the approval of the Ethical Committee of the National Institute of Mental Health no. 55/16, with the written informed consent from all subjects in accordance with the Declaration of Helsinki.
For the variables used to characterize physiological responses, see . They were used as raw data when possible and were transformed for use of linear models, using either logarithmic or square root transformations to approximate to the normal distribution. The distribution of model residuals was visually inspected for both deviations from normality and variance heterogeneity. The Spearman’s correlation coefficient was computed to compare the self-reported evaluation and physiological responses. To test the differences in physiological responses to individual snake species and to different stimulus categories, we performed a Friedman test and post hoc Nemenyi test as implemented in the R package PMCMR . A Mann-Whitney U test was used to compare the physiological reactions of high- and low- fear/disgust respondents. The above-mentioned tests were used as a non-parametric alternative for raw data deviating from normality, as we aimed to maintain extreme values of highly fearful participants. Two analyses were used to examine the contribution of respondent’s characteristics (gender, age, education, SNAQ, DS-R, and ERS scores) to the physiological responses; these were used as explanatory variables in linear mixed effects models (LME; implemented in R package nlme), which allowed for inclusion of the effects of respondent’s characteristics accounted for the individual identity using it as a random factor. An ANOVA was applied to test the effect of explanatory variables. We also performed an exploratory redundancy analysis (RDA; implemented in the R package vegan ), which is a multivariate direct gradient method. It extracts and summarizes the variation in a set of response variables (parameters of physiological reactions) that can be explained by a set of explanatory variables. This analysis permits to plot both response and explanatory variables to a space defined by the extracted gradients and enables detection of redundancy (i.e., shared variability) between sets of response and explanatory variables. Statistical significance of the gradients was confirmed by permutation tests. Repeatability was computed as another exploratory analysis to test the intra-individual consistency between respondents performing both tasks using the R package rptR . Repeatability allowed us to establish the relative contribution of between-individual variation to the overall variation . Calculations were performed in R and Statistica .
3.1. Sequential design of individual stimuli presentation 3.1.1. Differences in the physiological response to fear- and disgust-eliciting snakes We pooled the data for individual stimuli and performed further analyses with the mean SR parameters for each stimulus category (fear-eliciting snakes, disgust-eliciting snakes, and leaves as control stimuli). To fully compare the physiological responses to a given category, we computed the number of reactions (NR), mean amplitude/duration of reaction per stimulus (MAS and MDS, respectively; i.e. the sum of the amplitudes/durations divided by the number of stimuli in each category), and mean amplitude/duration per actual reaction (MAR and MDR, respectively; i.e., the sum of the amplitudes/durations divided by the number of reactions in each category). NR describes the frequency of any SR reaction regardless its intensity or overall respondent’s responsiveness, MAR and MDR describe the quality of the response (e.g. intensity) and MAS and MDS combine both quantity and quality in one parameter. We included the respondents with no skin resistance reaction (n = 9), too, because this represents a relevant result for people with no fear of snakes. Mean number of reactions was 14.04 ± 9.37. We also computed the mean HR slope for each stimulus category (fear/disgust/control), i.e., the slope of the linear regression line through the data points, which describes the change in heart activity in time. It was computed for the five-second interval of each stimulus presentation and subsequently as a mean for all stimuli in a given category. Positive slope values indicate an increase in HR in time, while negative values indicate a decrease, and zero slope indicates no change (for overview of the variables, see ).The Friedman test revealed that the effect of category on all tested SR parameters was highly significant (Friedman NR χ 2 = 74.711, MAS χ 2 = 58.682, MDS χ 2 = 74.652, MAR χ 2 = 27.106, MDR χ 2 = 25.879, all df = 2, all p < 0.0001; for the visualization, see ). Furthermore, we performed a pairwise comparison of the stimulus categories using the post-hoc Nemenyi test. All of the comparisons were significant (p values from 0.0210 to < 0.0001) except for MAR and MDR, where the disgust vs control comparison was not significant (p > 0.05). Therefore, the SR responses to fear-eliciting snakes are significantly differentiated from those to the control stimuli in all examined parameters. However, when responses to disgust-eliciting snakes are compared to controls, the differences lie rather in the response frequency rather than in their amplitude or length. For the HR slope, the result of the Friedman test was not significant, however, the visualization shows there is a slight tendency for higher HR in response to fear-eliciting snakes and lower HR in response to disgust-eliciting snakes compared to controls . When we performed the analysis separately for high-fear respondents, the result was marginally significant (Friedman χ 2 = 5.5254, df = 2, p-value = 0.0631). The subsequent post-hoc Nemenyi test was also marginally significant for the comparison of fear-evoking snakes and controls (p = 0.056) and not significant in other cases (for more analyses of high-fear respondents, see below). 3.1.2. Effect of respondents’ fear of snakes and disgust propensity Next, we analyzed the effect of respondents’ scores on the Snake Questionnaire (SNAQ, a measure of snake fear), Disgust Scale-Revised (DS-R, a disgust propensity measure), and Emotion Reactivity Scale (ERS, a measure of emotional sensitivity, intensity, and persistence) and other characteristics, e.g., the gender, age, biological or non-biological education, stimulus category, and interactions of the respondent’s characteristics with the stimulus category on the SR response. We used an LME model that allows to include the effects of respondent’s characteristics accounted for individual identity. Regarding the amplitude (MAS), five explanatory variables remained in the final reduced model: category, DS-R, age, ERS and ERS*category interaction. The ANOVA revealed that only the effects of stimulus category (F 2,274 = 42.7670, p < 0.0001) and ERS*category interaction (F 2,274 = 8.4200, p = 0.0003) were significant. In the case of duration (MDS), seven explanatory variables remained in the final reduced model: stimulus category, SNAQ, DS-R, ERS, gender, SNAQ*category and ERS*category interactions. The ANOVA revealed that the effects of category (F 2,272 = 58.5345, p < 0.0001), SNAQ (F 1,134 = 7.6997, p = 0.0063), SNAQ*category interaction (F 2,272 = 9.9585, p = 0.0001) and ERS*category interaction (F 2,272 = 5.4564, p = 0.0047) were significant. Furthermore, we employed an RDA with the same explanatory variables except the stimulus category. The analysis generated three constrained axes that explained 9.3% of the full variability. The sequential "Type I" ANOVA (n permutations = 20 000) revealed that the effect of SNAQ scores (F 1,135 = 5.2362, p = 0.0097), ERS (F 1,135 = 3.7564, p = 0.0344), and age (F 1,135 = 4.8459, p = 0.0191) on the mean physiological parameters (MAS, MDS, and NR) were significant. Thus, the examined individual characteristics have a significant effect on the SR response, however, they explain only a small portion of the full variability. On the other hand, the effect of stimulus category on the HR slope was not significant. Five explanatory variables remained in the final reduced model: stimulus category, SNAQ, gender, age and SNAQ*category interaction. The ANOVA revealed that only the effects of SNAQ (F 1,111 = 17.4560, p = 0.0001), age (F 1,111 = 7.2836, p = 0.0080) and SNAQ*category interaction (F 2,226 = 6.6595, p = 0.0015) remained significant. We then analyzed differences in the examined SR parameters between high- and low-fear and high- and low-disgust respondents using the Mann-Whitney U tests. As for high- and low-fear respondents, the comparisons were significant in the case of number and duration of reactions to fear-eliciting snakes (NR p = 0.0187, MDS p = 0.0129 and MDR p = 0.0103; ), but nonsignificant in the case of amplitude and reactions to other categories of stimuli. As for high- and low-disgust respondents, the comparisons were significant for all the examined parameters in responses to both the fear- and disgust-eliciting snakes (for fear-eliciting snakes all p < 0.05, for disgust-eliciting snakes all p < 0.01; ), except for MDR, which was significant only for disgust-eliciting snakes (p < 0.01). Thus, respondents differing in the disgust propensity level demonstrate not only different reactions to disgust- but also fear-eliciting snakes. Furthermore, the Mann-Whitney U test revealed a significant difference in the HR slope for fear-evoking snakes when comparing low- vs high-fear respondents (p < 0.0001), but no significant difference when comparing high- vs low-disgust respondents. 3.2. Block design of stimuli presentation 3.2.1. Differences in the physiological response to fear- and disgust-eliciting snakes To compare the physiological response not only between the blocks of stimuli of discrete categories (fear/disgust/control), but also between both designs (intra-individual consistency, see below in section 3.3.), we computed the same mean variables for both designs: NR, MAS, MDS, MAR, MDR, and HR slope for the whole block of each stimulus category (see ). The mean number of reactions was 12.29 ± 7.77 and respondents with no skin resistance reaction were included (n = 7). Similarly to the sequential design, the Friedman test revealed a highly significant effect of stimulus category on all the measured SR parameters (Friedman NR χ 2 = 24.852, MAS χ 2 = 43.758, MDS χ 2 = 40.950, MAR χ 2 = 22.535, MDR χ 2 = 17.112, all df = 2, all p < 0.0001, except for MDR: p = 0.0002; for the visualization, see ). However, based on the post-hoc Nemenyi test, both snake categories significantly differed from controls (p values ranged from 0.0030 to < 0.0001), but the difference in the SR response to fear- and disgust-eliciting snakes in the block design was not significant in any of the comparisons. Thus, compared to the sequential design, there is always a significant difference between disgust and control stimuli, but not between fear and disgust stimuli. The result of the Friedman test was also significant for the HR slope (Friedman chi-squared = 11.529, df = 2, p-value = 0.0031). However, unlike for SR, the only significant difference in the HR slope was between the fear-eliciting snakes and controls as revealed by the post-hoc Nemenyi test (p = 0.002). 3.2.2. Effect of respondent’s fear of snakes and disgust propensity Subsequently, we analyzed the effect of respondent’s individual characteristics (SNAQ, DS-R, and ERS score, gender, age, and biological vs non-biological education), the stimulus category, and their interactions on the SR response using the LME models. The results (see below) supported the crucial effect of stimulus category on the SR changes. In the case of amplitude (MAS), five explanatory variables remained in the final reduced model: category, SNAQ, gender, age, and SNAQ*category interaction. The ANOVA subsequently revealed that only the effect of stimulus category (F 2,282 = 22.3322, p < 0.0001), SNAQ (F 1,139 = 8.6516, p = 0.0038), and SNAQ*category interaction (F 2,282 = 5.7753, p = 0.0035) were significant. In case of duration (MDS), five explanatory variables remained in the final reduced model, four of them were significant: category (ANOVA, F 2,280 = 23.2492, p < 0.0001), SNAQ (F 1,140 = 12.1516, p = 0.0007), SNAQ*category (F 2,280 = 5.1285, p = 0.0065), and DS-R*category interaction (F 2,280 = 4.6166, p = 0.0107), while the effect of DS-R was not significant. We also performed an RDA, which generated 3 constrained axes that explained 12.6% of the full variability. The sequential "Type I" ANOVA (n permutations = 20 000) revealed a significant effect of SNAQ scores (F 1,103 = 6.7917, p = 0.0013), age (F 1,103 = 3.1672, p = 0.0407), and gender (F 1,103 = 4.8827, p = 0.0115) on the mean SR parameters (NR, MAS, and MDS). However, we did not find a significant effect of stimulus category on the HR slope. Seven explanatory variables remained in the final reduced model: category, DS-R, education, age, ERS, age*category interaction, and ERS*category interaction. The ANOVA revealed that only the effect of age*category interaction was significant (F 2,266 = 5.6566, p = 0.0039). Thus, in both experimental designs, the effect of stimulus category on the HR change was not significant. We also performed Mann-Whitney U tests to analyze the differences in SR responses between pre-defined groups of respondents with high and low fear of snakes and disgust propensity. As for high- and low-fear respondents, the comparisons were significant for MDS (p = 0.0322) and MAR (p = 0.0386) in response to fear-eliciting snakes, as well as for all the parameters in response to disgust-eliciting snakes (p values from 0.0419 to 0.0022; ). Thus, in the block design, different levels of fear of snakes affected more the reactions to disgust- but not fear-eliciting snakes. Unlike in the sequential design, there were no significant differences between respondents with high and low disgust propensity in the block design. Furthermore, the Mann-Whitney U test revealed no significant difference in the HR slope when comparing low- and high-fear respondents. For the comparison of high- vs low-disgust respondents, the only significant difference was in the reactions to fear-evoking snakes (p = 0.0407). 3.3. Comparison of the two designs 3.3.1. Effect of experimental design From the above-presented results, we concluded that the effect of experimental design was not negligible and examined it further employing LME models. The experimental design and respondent’s characteristics (SNAQ, DS-R, and ERS scores, gender, age, and biological or non-biological education) were used as explanatory variables. The ANOVA revealed that in the case of MAS for all stimuli, the effects of design (F 1,124 = 9.0677, p = 0.0032), SNAQ (F 1,118 = 5.7467, p = 0.0181), and age (F 1,118 = 4.2450, p = 0.0416) were significant. For MDS, the effects of design (F 1,124 = 7.1368, p = 0.0086) and SNAQ (F 1,118 = 10.9151, p = 0.0013) were significant. And for MDR, only the effect of SNAQ (F 1,118 = 8.3484, p = 0.0046) was significant. The results were comparable when computed for mean reactions to all stimuli and to fear- and disgust-eliciting snakes separately. However, in the case of MAR, the results were different when computed for all stimuli and both snake categories separately. For all stimuli, the effects of gender (F 1,118 = 6.7606, p = 0.0105) and age (F 1,118 = 5.7223, p = 0.0183) were significant, in the case of fear-eliciting snakes the effects of design (F 1,124 = 12.2090, p = 0.0007), gender (F 1,118 = 4.5649, p = 0.0347), and age (F 1,118 = 5.3629, p = 0.0223) were significant. Conversely, there was no significant effect on the reactions to disgust-eliciting snakes. Thus, the experimental design affects especially the mean reactions per stimulus, which corresponds to the reaction frequency rather than its amplitude or duration and is higher in the sequential design. The amplitude of the SR reaction (MAR) is higher in the sequential design only in response to fear-eliciting snakes. 3.3.2. Intra-individual consistency We moreover examined intra-individual consistency of SR responses in respondents who performed both experimental designs (n = 125) by computing repeatability. The results were highly significant for NR (R values from 0.392 for control stimuli to 0.563 for all stimuli, all p < 0.0001), MAS (R values from 0.384 for disgust-eliciting stimuli to 0.454 for all stimuli), and MDS (R values from 0.351 for disgust-eliciting stimuli to 0.491 for all stimuli), except for MAS in response to fear-eliciting stimuli, which was significant at p = 0.0003 (R = 0.301). As for MAR and MDR, the results were highly significant in the case of MDR in response to control stimuli (R = 0.337, p < 0.0001) and significant at the p level from 0.0067 to 0.0004 in all other cases (MAR: R values from 0.222 for disgust-eliciting to 0.3 for control stimuli; MDR: R values from 0.224 for disgust-eliciting to 0.241 for fear-eliciting stimuli). We then computed repeatability for high-fear respondents (n = 70). Overall, the results were significant except for MDR in response to disgust-eliciting and control stimuli. For the control stimuli, the repeatability R level was lower in all parameters and higher in the case of amplitude in response to snake stimuli and all stimuli. Thus, despite a significant effect of the design, the SR responses were relatively highly intra-individually consistent across both designs in most examined cases. For the complete repeatability results, see . 3.4. Correlation of self-reported emotions and physiological response In the current study, we measured the physiological response to 10 venomous fear-eliciting snakes and 10 harmless disgust-eliciting snakes (see for more details on the snake species in both categories). To examine the relationship between the self-reported evaluation and physiological response, we computed Spearman’s correlations. Mean fear or disgust score of each snake species reported by the respondents was highly correlated with the mean SR amplitude of the response to the respective snake (fear: Spearman’s r = 0.7729, p = 0.0001; disgust: Spearman’s r = - 0.6827, p = 0.0009; ). 3.5. Testing homogeneity of physiological responses within categories of fear- and disgust-eliciting snakes—exploratory analysis As most analyses were based on a comparison of means of distinct stimulus categories, we furthermore explored the homogeneity within the categories of snake stimuli defined by Rádlová et al. , whether the SR responses correspond to the same distinct categories of two types of snakes. The Friedman test showed that the differences in the SR amplitude between snakes within the pre-defined categories were significant (fear: Friedman χ2 = 73.02, df = 9, p < 0.0001; disgust: Friedman χ2 = 45.435, df = 9, p < 0.0001). However, the post-hoc Nemenyi test revealed that only one species from each snake category significantly differed from the others in the mean SR amplitudes: in the case of fear-eliciting snakes, the Sochurek's saw-scaled viper differed from all the other snake species except the Sahara sand viper ( Cerastes vipera ), thus, only 8 out of 45 comparisons were significant (p < 0.05). In the case of disgust-eliciting snakes, the brahminy blind snake ( Indotyphlops braminus ) differed from the other snake species except the northern blind snake ( Anilios diversus ), Eurasian blind snake ( Xerotyphlops vermicularis ), rotund blind snake ( Anilios pinguis ), and the northern rubber boa ( Charina botae ), thus, only 5 out of 45 comparisons were significant (p < 0.05). The results calculated for SR duration were in this case almost identical to those for the amplitude and therefore will not be further mentioned in the text. The Friedman test comparing the responses to individual species was significant. Therefore, we used a redundancy analysis (RDA) to further examine the contribution of nine morphological and three venom characteristics of relatively more diverse fear-eliciting snakes (treated as explanatory variables—constraints) to the SR amplitude. However, the model showed no constrained component. Thus, these analyses supported the hypothesis that the selected snake species present a homogenous category based on both the self-reported evaluation and physiological measures, regardless of their morphological or venom variability.
3.1.1. Differences in the physiological response to fear- and disgust-eliciting snakes We pooled the data for individual stimuli and performed further analyses with the mean SR parameters for each stimulus category (fear-eliciting snakes, disgust-eliciting snakes, and leaves as control stimuli). To fully compare the physiological responses to a given category, we computed the number of reactions (NR), mean amplitude/duration of reaction per stimulus (MAS and MDS, respectively; i.e. the sum of the amplitudes/durations divided by the number of stimuli in each category), and mean amplitude/duration per actual reaction (MAR and MDR, respectively; i.e., the sum of the amplitudes/durations divided by the number of reactions in each category). NR describes the frequency of any SR reaction regardless its intensity or overall respondent’s responsiveness, MAR and MDR describe the quality of the response (e.g. intensity) and MAS and MDS combine both quantity and quality in one parameter. We included the respondents with no skin resistance reaction (n = 9), too, because this represents a relevant result for people with no fear of snakes. Mean number of reactions was 14.04 ± 9.37. We also computed the mean HR slope for each stimulus category (fear/disgust/control), i.e., the slope of the linear regression line through the data points, which describes the change in heart activity in time. It was computed for the five-second interval of each stimulus presentation and subsequently as a mean for all stimuli in a given category. Positive slope values indicate an increase in HR in time, while negative values indicate a decrease, and zero slope indicates no change (for overview of the variables, see ).The Friedman test revealed that the effect of category on all tested SR parameters was highly significant (Friedman NR χ 2 = 74.711, MAS χ 2 = 58.682, MDS χ 2 = 74.652, MAR χ 2 = 27.106, MDR χ 2 = 25.879, all df = 2, all p < 0.0001; for the visualization, see ). Furthermore, we performed a pairwise comparison of the stimulus categories using the post-hoc Nemenyi test. All of the comparisons were significant (p values from 0.0210 to < 0.0001) except for MAR and MDR, where the disgust vs control comparison was not significant (p > 0.05). Therefore, the SR responses to fear-eliciting snakes are significantly differentiated from those to the control stimuli in all examined parameters. However, when responses to disgust-eliciting snakes are compared to controls, the differences lie rather in the response frequency rather than in their amplitude or length. For the HR slope, the result of the Friedman test was not significant, however, the visualization shows there is a slight tendency for higher HR in response to fear-eliciting snakes and lower HR in response to disgust-eliciting snakes compared to controls . When we performed the analysis separately for high-fear respondents, the result was marginally significant (Friedman χ 2 = 5.5254, df = 2, p-value = 0.0631). The subsequent post-hoc Nemenyi test was also marginally significant for the comparison of fear-evoking snakes and controls (p = 0.056) and not significant in other cases (for more analyses of high-fear respondents, see below). 3.1.2. Effect of respondents’ fear of snakes and disgust propensity Next, we analyzed the effect of respondents’ scores on the Snake Questionnaire (SNAQ, a measure of snake fear), Disgust Scale-Revised (DS-R, a disgust propensity measure), and Emotion Reactivity Scale (ERS, a measure of emotional sensitivity, intensity, and persistence) and other characteristics, e.g., the gender, age, biological or non-biological education, stimulus category, and interactions of the respondent’s characteristics with the stimulus category on the SR response. We used an LME model that allows to include the effects of respondent’s characteristics accounted for individual identity. Regarding the amplitude (MAS), five explanatory variables remained in the final reduced model: category, DS-R, age, ERS and ERS*category interaction. The ANOVA revealed that only the effects of stimulus category (F 2,274 = 42.7670, p < 0.0001) and ERS*category interaction (F 2,274 = 8.4200, p = 0.0003) were significant. In the case of duration (MDS), seven explanatory variables remained in the final reduced model: stimulus category, SNAQ, DS-R, ERS, gender, SNAQ*category and ERS*category interactions. The ANOVA revealed that the effects of category (F 2,272 = 58.5345, p < 0.0001), SNAQ (F 1,134 = 7.6997, p = 0.0063), SNAQ*category interaction (F 2,272 = 9.9585, p = 0.0001) and ERS*category interaction (F 2,272 = 5.4564, p = 0.0047) were significant. Furthermore, we employed an RDA with the same explanatory variables except the stimulus category. The analysis generated three constrained axes that explained 9.3% of the full variability. The sequential "Type I" ANOVA (n permutations = 20 000) revealed that the effect of SNAQ scores (F 1,135 = 5.2362, p = 0.0097), ERS (F 1,135 = 3.7564, p = 0.0344), and age (F 1,135 = 4.8459, p = 0.0191) on the mean physiological parameters (MAS, MDS, and NR) were significant. Thus, the examined individual characteristics have a significant effect on the SR response, however, they explain only a small portion of the full variability. On the other hand, the effect of stimulus category on the HR slope was not significant. Five explanatory variables remained in the final reduced model: stimulus category, SNAQ, gender, age and SNAQ*category interaction. The ANOVA revealed that only the effects of SNAQ (F 1,111 = 17.4560, p = 0.0001), age (F 1,111 = 7.2836, p = 0.0080) and SNAQ*category interaction (F 2,226 = 6.6595, p = 0.0015) remained significant. We then analyzed differences in the examined SR parameters between high- and low-fear and high- and low-disgust respondents using the Mann-Whitney U tests. As for high- and low-fear respondents, the comparisons were significant in the case of number and duration of reactions to fear-eliciting snakes (NR p = 0.0187, MDS p = 0.0129 and MDR p = 0.0103; ), but nonsignificant in the case of amplitude and reactions to other categories of stimuli. As for high- and low-disgust respondents, the comparisons were significant for all the examined parameters in responses to both the fear- and disgust-eliciting snakes (for fear-eliciting snakes all p < 0.05, for disgust-eliciting snakes all p < 0.01; ), except for MDR, which was significant only for disgust-eliciting snakes (p < 0.01). Thus, respondents differing in the disgust propensity level demonstrate not only different reactions to disgust- but also fear-eliciting snakes. Furthermore, the Mann-Whitney U test revealed a significant difference in the HR slope for fear-evoking snakes when comparing low- vs high-fear respondents (p < 0.0001), but no significant difference when comparing high- vs low-disgust respondents.
We pooled the data for individual stimuli and performed further analyses with the mean SR parameters for each stimulus category (fear-eliciting snakes, disgust-eliciting snakes, and leaves as control stimuli). To fully compare the physiological responses to a given category, we computed the number of reactions (NR), mean amplitude/duration of reaction per stimulus (MAS and MDS, respectively; i.e. the sum of the amplitudes/durations divided by the number of stimuli in each category), and mean amplitude/duration per actual reaction (MAR and MDR, respectively; i.e., the sum of the amplitudes/durations divided by the number of reactions in each category). NR describes the frequency of any SR reaction regardless its intensity or overall respondent’s responsiveness, MAR and MDR describe the quality of the response (e.g. intensity) and MAS and MDS combine both quantity and quality in one parameter. We included the respondents with no skin resistance reaction (n = 9), too, because this represents a relevant result for people with no fear of snakes. Mean number of reactions was 14.04 ± 9.37. We also computed the mean HR slope for each stimulus category (fear/disgust/control), i.e., the slope of the linear regression line through the data points, which describes the change in heart activity in time. It was computed for the five-second interval of each stimulus presentation and subsequently as a mean for all stimuli in a given category. Positive slope values indicate an increase in HR in time, while negative values indicate a decrease, and zero slope indicates no change (for overview of the variables, see ).The Friedman test revealed that the effect of category on all tested SR parameters was highly significant (Friedman NR χ 2 = 74.711, MAS χ 2 = 58.682, MDS χ 2 = 74.652, MAR χ 2 = 27.106, MDR χ 2 = 25.879, all df = 2, all p < 0.0001; for the visualization, see ). Furthermore, we performed a pairwise comparison of the stimulus categories using the post-hoc Nemenyi test. All of the comparisons were significant (p values from 0.0210 to < 0.0001) except for MAR and MDR, where the disgust vs control comparison was not significant (p > 0.05). Therefore, the SR responses to fear-eliciting snakes are significantly differentiated from those to the control stimuli in all examined parameters. However, when responses to disgust-eliciting snakes are compared to controls, the differences lie rather in the response frequency rather than in their amplitude or length. For the HR slope, the result of the Friedman test was not significant, however, the visualization shows there is a slight tendency for higher HR in response to fear-eliciting snakes and lower HR in response to disgust-eliciting snakes compared to controls . When we performed the analysis separately for high-fear respondents, the result was marginally significant (Friedman χ 2 = 5.5254, df = 2, p-value = 0.0631). The subsequent post-hoc Nemenyi test was also marginally significant for the comparison of fear-evoking snakes and controls (p = 0.056) and not significant in other cases (for more analyses of high-fear respondents, see below).
Next, we analyzed the effect of respondents’ scores on the Snake Questionnaire (SNAQ, a measure of snake fear), Disgust Scale-Revised (DS-R, a disgust propensity measure), and Emotion Reactivity Scale (ERS, a measure of emotional sensitivity, intensity, and persistence) and other characteristics, e.g., the gender, age, biological or non-biological education, stimulus category, and interactions of the respondent’s characteristics with the stimulus category on the SR response. We used an LME model that allows to include the effects of respondent’s characteristics accounted for individual identity. Regarding the amplitude (MAS), five explanatory variables remained in the final reduced model: category, DS-R, age, ERS and ERS*category interaction. The ANOVA revealed that only the effects of stimulus category (F 2,274 = 42.7670, p < 0.0001) and ERS*category interaction (F 2,274 = 8.4200, p = 0.0003) were significant. In the case of duration (MDS), seven explanatory variables remained in the final reduced model: stimulus category, SNAQ, DS-R, ERS, gender, SNAQ*category and ERS*category interactions. The ANOVA revealed that the effects of category (F 2,272 = 58.5345, p < 0.0001), SNAQ (F 1,134 = 7.6997, p = 0.0063), SNAQ*category interaction (F 2,272 = 9.9585, p = 0.0001) and ERS*category interaction (F 2,272 = 5.4564, p = 0.0047) were significant. Furthermore, we employed an RDA with the same explanatory variables except the stimulus category. The analysis generated three constrained axes that explained 9.3% of the full variability. The sequential "Type I" ANOVA (n permutations = 20 000) revealed that the effect of SNAQ scores (F 1,135 = 5.2362, p = 0.0097), ERS (F 1,135 = 3.7564, p = 0.0344), and age (F 1,135 = 4.8459, p = 0.0191) on the mean physiological parameters (MAS, MDS, and NR) were significant. Thus, the examined individual characteristics have a significant effect on the SR response, however, they explain only a small portion of the full variability. On the other hand, the effect of stimulus category on the HR slope was not significant. Five explanatory variables remained in the final reduced model: stimulus category, SNAQ, gender, age and SNAQ*category interaction. The ANOVA revealed that only the effects of SNAQ (F 1,111 = 17.4560, p = 0.0001), age (F 1,111 = 7.2836, p = 0.0080) and SNAQ*category interaction (F 2,226 = 6.6595, p = 0.0015) remained significant. We then analyzed differences in the examined SR parameters between high- and low-fear and high- and low-disgust respondents using the Mann-Whitney U tests. As for high- and low-fear respondents, the comparisons were significant in the case of number and duration of reactions to fear-eliciting snakes (NR p = 0.0187, MDS p = 0.0129 and MDR p = 0.0103; ), but nonsignificant in the case of amplitude and reactions to other categories of stimuli. As for high- and low-disgust respondents, the comparisons were significant for all the examined parameters in responses to both the fear- and disgust-eliciting snakes (for fear-eliciting snakes all p < 0.05, for disgust-eliciting snakes all p < 0.01; ), except for MDR, which was significant only for disgust-eliciting snakes (p < 0.01). Thus, respondents differing in the disgust propensity level demonstrate not only different reactions to disgust- but also fear-eliciting snakes. Furthermore, the Mann-Whitney U test revealed a significant difference in the HR slope for fear-evoking snakes when comparing low- vs high-fear respondents (p < 0.0001), but no significant difference when comparing high- vs low-disgust respondents.
3.2.1. Differences in the physiological response to fear- and disgust-eliciting snakes To compare the physiological response not only between the blocks of stimuli of discrete categories (fear/disgust/control), but also between both designs (intra-individual consistency, see below in section 3.3.), we computed the same mean variables for both designs: NR, MAS, MDS, MAR, MDR, and HR slope for the whole block of each stimulus category (see ). The mean number of reactions was 12.29 ± 7.77 and respondents with no skin resistance reaction were included (n = 7). Similarly to the sequential design, the Friedman test revealed a highly significant effect of stimulus category on all the measured SR parameters (Friedman NR χ 2 = 24.852, MAS χ 2 = 43.758, MDS χ 2 = 40.950, MAR χ 2 = 22.535, MDR χ 2 = 17.112, all df = 2, all p < 0.0001, except for MDR: p = 0.0002; for the visualization, see ). However, based on the post-hoc Nemenyi test, both snake categories significantly differed from controls (p values ranged from 0.0030 to < 0.0001), but the difference in the SR response to fear- and disgust-eliciting snakes in the block design was not significant in any of the comparisons. Thus, compared to the sequential design, there is always a significant difference between disgust and control stimuli, but not between fear and disgust stimuli. The result of the Friedman test was also significant for the HR slope (Friedman chi-squared = 11.529, df = 2, p-value = 0.0031). However, unlike for SR, the only significant difference in the HR slope was between the fear-eliciting snakes and controls as revealed by the post-hoc Nemenyi test (p = 0.002). 3.2.2. Effect of respondent’s fear of snakes and disgust propensity Subsequently, we analyzed the effect of respondent’s individual characteristics (SNAQ, DS-R, and ERS score, gender, age, and biological vs non-biological education), the stimulus category, and their interactions on the SR response using the LME models. The results (see below) supported the crucial effect of stimulus category on the SR changes. In the case of amplitude (MAS), five explanatory variables remained in the final reduced model: category, SNAQ, gender, age, and SNAQ*category interaction. The ANOVA subsequently revealed that only the effect of stimulus category (F 2,282 = 22.3322, p < 0.0001), SNAQ (F 1,139 = 8.6516, p = 0.0038), and SNAQ*category interaction (F 2,282 = 5.7753, p = 0.0035) were significant. In case of duration (MDS), five explanatory variables remained in the final reduced model, four of them were significant: category (ANOVA, F 2,280 = 23.2492, p < 0.0001), SNAQ (F 1,140 = 12.1516, p = 0.0007), SNAQ*category (F 2,280 = 5.1285, p = 0.0065), and DS-R*category interaction (F 2,280 = 4.6166, p = 0.0107), while the effect of DS-R was not significant. We also performed an RDA, which generated 3 constrained axes that explained 12.6% of the full variability. The sequential "Type I" ANOVA (n permutations = 20 000) revealed a significant effect of SNAQ scores (F 1,103 = 6.7917, p = 0.0013), age (F 1,103 = 3.1672, p = 0.0407), and gender (F 1,103 = 4.8827, p = 0.0115) on the mean SR parameters (NR, MAS, and MDS). However, we did not find a significant effect of stimulus category on the HR slope. Seven explanatory variables remained in the final reduced model: category, DS-R, education, age, ERS, age*category interaction, and ERS*category interaction. The ANOVA revealed that only the effect of age*category interaction was significant (F 2,266 = 5.6566, p = 0.0039). Thus, in both experimental designs, the effect of stimulus category on the HR change was not significant. We also performed Mann-Whitney U tests to analyze the differences in SR responses between pre-defined groups of respondents with high and low fear of snakes and disgust propensity. As for high- and low-fear respondents, the comparisons were significant for MDS (p = 0.0322) and MAR (p = 0.0386) in response to fear-eliciting snakes, as well as for all the parameters in response to disgust-eliciting snakes (p values from 0.0419 to 0.0022; ). Thus, in the block design, different levels of fear of snakes affected more the reactions to disgust- but not fear-eliciting snakes. Unlike in the sequential design, there were no significant differences between respondents with high and low disgust propensity in the block design. Furthermore, the Mann-Whitney U test revealed no significant difference in the HR slope when comparing low- and high-fear respondents. For the comparison of high- vs low-disgust respondents, the only significant difference was in the reactions to fear-evoking snakes (p = 0.0407).
To compare the physiological response not only between the blocks of stimuli of discrete categories (fear/disgust/control), but also between both designs (intra-individual consistency, see below in section 3.3.), we computed the same mean variables for both designs: NR, MAS, MDS, MAR, MDR, and HR slope for the whole block of each stimulus category (see ). The mean number of reactions was 12.29 ± 7.77 and respondents with no skin resistance reaction were included (n = 7). Similarly to the sequential design, the Friedman test revealed a highly significant effect of stimulus category on all the measured SR parameters (Friedman NR χ 2 = 24.852, MAS χ 2 = 43.758, MDS χ 2 = 40.950, MAR χ 2 = 22.535, MDR χ 2 = 17.112, all df = 2, all p < 0.0001, except for MDR: p = 0.0002; for the visualization, see ). However, based on the post-hoc Nemenyi test, both snake categories significantly differed from controls (p values ranged from 0.0030 to < 0.0001), but the difference in the SR response to fear- and disgust-eliciting snakes in the block design was not significant in any of the comparisons. Thus, compared to the sequential design, there is always a significant difference between disgust and control stimuli, but not between fear and disgust stimuli. The result of the Friedman test was also significant for the HR slope (Friedman chi-squared = 11.529, df = 2, p-value = 0.0031). However, unlike for SR, the only significant difference in the HR slope was between the fear-eliciting snakes and controls as revealed by the post-hoc Nemenyi test (p = 0.002).
Subsequently, we analyzed the effect of respondent’s individual characteristics (SNAQ, DS-R, and ERS score, gender, age, and biological vs non-biological education), the stimulus category, and their interactions on the SR response using the LME models. The results (see below) supported the crucial effect of stimulus category on the SR changes. In the case of amplitude (MAS), five explanatory variables remained in the final reduced model: category, SNAQ, gender, age, and SNAQ*category interaction. The ANOVA subsequently revealed that only the effect of stimulus category (F 2,282 = 22.3322, p < 0.0001), SNAQ (F 1,139 = 8.6516, p = 0.0038), and SNAQ*category interaction (F 2,282 = 5.7753, p = 0.0035) were significant. In case of duration (MDS), five explanatory variables remained in the final reduced model, four of them were significant: category (ANOVA, F 2,280 = 23.2492, p < 0.0001), SNAQ (F 1,140 = 12.1516, p = 0.0007), SNAQ*category (F 2,280 = 5.1285, p = 0.0065), and DS-R*category interaction (F 2,280 = 4.6166, p = 0.0107), while the effect of DS-R was not significant. We also performed an RDA, which generated 3 constrained axes that explained 12.6% of the full variability. The sequential "Type I" ANOVA (n permutations = 20 000) revealed a significant effect of SNAQ scores (F 1,103 = 6.7917, p = 0.0013), age (F 1,103 = 3.1672, p = 0.0407), and gender (F 1,103 = 4.8827, p = 0.0115) on the mean SR parameters (NR, MAS, and MDS). However, we did not find a significant effect of stimulus category on the HR slope. Seven explanatory variables remained in the final reduced model: category, DS-R, education, age, ERS, age*category interaction, and ERS*category interaction. The ANOVA revealed that only the effect of age*category interaction was significant (F 2,266 = 5.6566, p = 0.0039). Thus, in both experimental designs, the effect of stimulus category on the HR change was not significant. We also performed Mann-Whitney U tests to analyze the differences in SR responses between pre-defined groups of respondents with high and low fear of snakes and disgust propensity. As for high- and low-fear respondents, the comparisons were significant for MDS (p = 0.0322) and MAR (p = 0.0386) in response to fear-eliciting snakes, as well as for all the parameters in response to disgust-eliciting snakes (p values from 0.0419 to 0.0022; ). Thus, in the block design, different levels of fear of snakes affected more the reactions to disgust- but not fear-eliciting snakes. Unlike in the sequential design, there were no significant differences between respondents with high and low disgust propensity in the block design. Furthermore, the Mann-Whitney U test revealed no significant difference in the HR slope when comparing low- and high-fear respondents. For the comparison of high- vs low-disgust respondents, the only significant difference was in the reactions to fear-evoking snakes (p = 0.0407).
3.3.1. Effect of experimental design From the above-presented results, we concluded that the effect of experimental design was not negligible and examined it further employing LME models. The experimental design and respondent’s characteristics (SNAQ, DS-R, and ERS scores, gender, age, and biological or non-biological education) were used as explanatory variables. The ANOVA revealed that in the case of MAS for all stimuli, the effects of design (F 1,124 = 9.0677, p = 0.0032), SNAQ (F 1,118 = 5.7467, p = 0.0181), and age (F 1,118 = 4.2450, p = 0.0416) were significant. For MDS, the effects of design (F 1,124 = 7.1368, p = 0.0086) and SNAQ (F 1,118 = 10.9151, p = 0.0013) were significant. And for MDR, only the effect of SNAQ (F 1,118 = 8.3484, p = 0.0046) was significant. The results were comparable when computed for mean reactions to all stimuli and to fear- and disgust-eliciting snakes separately. However, in the case of MAR, the results were different when computed for all stimuli and both snake categories separately. For all stimuli, the effects of gender (F 1,118 = 6.7606, p = 0.0105) and age (F 1,118 = 5.7223, p = 0.0183) were significant, in the case of fear-eliciting snakes the effects of design (F 1,124 = 12.2090, p = 0.0007), gender (F 1,118 = 4.5649, p = 0.0347), and age (F 1,118 = 5.3629, p = 0.0223) were significant. Conversely, there was no significant effect on the reactions to disgust-eliciting snakes. Thus, the experimental design affects especially the mean reactions per stimulus, which corresponds to the reaction frequency rather than its amplitude or duration and is higher in the sequential design. The amplitude of the SR reaction (MAR) is higher in the sequential design only in response to fear-eliciting snakes. 3.3.2. Intra-individual consistency We moreover examined intra-individual consistency of SR responses in respondents who performed both experimental designs (n = 125) by computing repeatability. The results were highly significant for NR (R values from 0.392 for control stimuli to 0.563 for all stimuli, all p < 0.0001), MAS (R values from 0.384 for disgust-eliciting stimuli to 0.454 for all stimuli), and MDS (R values from 0.351 for disgust-eliciting stimuli to 0.491 for all stimuli), except for MAS in response to fear-eliciting stimuli, which was significant at p = 0.0003 (R = 0.301). As for MAR and MDR, the results were highly significant in the case of MDR in response to control stimuli (R = 0.337, p < 0.0001) and significant at the p level from 0.0067 to 0.0004 in all other cases (MAR: R values from 0.222 for disgust-eliciting to 0.3 for control stimuli; MDR: R values from 0.224 for disgust-eliciting to 0.241 for fear-eliciting stimuli). We then computed repeatability for high-fear respondents (n = 70). Overall, the results were significant except for MDR in response to disgust-eliciting and control stimuli. For the control stimuli, the repeatability R level was lower in all parameters and higher in the case of amplitude in response to snake stimuli and all stimuli. Thus, despite a significant effect of the design, the SR responses were relatively highly intra-individually consistent across both designs in most examined cases. For the complete repeatability results, see .
From the above-presented results, we concluded that the effect of experimental design was not negligible and examined it further employing LME models. The experimental design and respondent’s characteristics (SNAQ, DS-R, and ERS scores, gender, age, and biological or non-biological education) were used as explanatory variables. The ANOVA revealed that in the case of MAS for all stimuli, the effects of design (F 1,124 = 9.0677, p = 0.0032), SNAQ (F 1,118 = 5.7467, p = 0.0181), and age (F 1,118 = 4.2450, p = 0.0416) were significant. For MDS, the effects of design (F 1,124 = 7.1368, p = 0.0086) and SNAQ (F 1,118 = 10.9151, p = 0.0013) were significant. And for MDR, only the effect of SNAQ (F 1,118 = 8.3484, p = 0.0046) was significant. The results were comparable when computed for mean reactions to all stimuli and to fear- and disgust-eliciting snakes separately. However, in the case of MAR, the results were different when computed for all stimuli and both snake categories separately. For all stimuli, the effects of gender (F 1,118 = 6.7606, p = 0.0105) and age (F 1,118 = 5.7223, p = 0.0183) were significant, in the case of fear-eliciting snakes the effects of design (F 1,124 = 12.2090, p = 0.0007), gender (F 1,118 = 4.5649, p = 0.0347), and age (F 1,118 = 5.3629, p = 0.0223) were significant. Conversely, there was no significant effect on the reactions to disgust-eliciting snakes. Thus, the experimental design affects especially the mean reactions per stimulus, which corresponds to the reaction frequency rather than its amplitude or duration and is higher in the sequential design. The amplitude of the SR reaction (MAR) is higher in the sequential design only in response to fear-eliciting snakes.
We moreover examined intra-individual consistency of SR responses in respondents who performed both experimental designs (n = 125) by computing repeatability. The results were highly significant for NR (R values from 0.392 for control stimuli to 0.563 for all stimuli, all p < 0.0001), MAS (R values from 0.384 for disgust-eliciting stimuli to 0.454 for all stimuli), and MDS (R values from 0.351 for disgust-eliciting stimuli to 0.491 for all stimuli), except for MAS in response to fear-eliciting stimuli, which was significant at p = 0.0003 (R = 0.301). As for MAR and MDR, the results were highly significant in the case of MDR in response to control stimuli (R = 0.337, p < 0.0001) and significant at the p level from 0.0067 to 0.0004 in all other cases (MAR: R values from 0.222 for disgust-eliciting to 0.3 for control stimuli; MDR: R values from 0.224 for disgust-eliciting to 0.241 for fear-eliciting stimuli). We then computed repeatability for high-fear respondents (n = 70). Overall, the results were significant except for MDR in response to disgust-eliciting and control stimuli. For the control stimuli, the repeatability R level was lower in all parameters and higher in the case of amplitude in response to snake stimuli and all stimuli. Thus, despite a significant effect of the design, the SR responses were relatively highly intra-individually consistent across both designs in most examined cases. For the complete repeatability results, see .
In the current study, we measured the physiological response to 10 venomous fear-eliciting snakes and 10 harmless disgust-eliciting snakes (see for more details on the snake species in both categories). To examine the relationship between the self-reported evaluation and physiological response, we computed Spearman’s correlations. Mean fear or disgust score of each snake species reported by the respondents was highly correlated with the mean SR amplitude of the response to the respective snake (fear: Spearman’s r = 0.7729, p = 0.0001; disgust: Spearman’s r = - 0.6827, p = 0.0009; ).
As most analyses were based on a comparison of means of distinct stimulus categories, we furthermore explored the homogeneity within the categories of snake stimuli defined by Rádlová et al. , whether the SR responses correspond to the same distinct categories of two types of snakes. The Friedman test showed that the differences in the SR amplitude between snakes within the pre-defined categories were significant (fear: Friedman χ2 = 73.02, df = 9, p < 0.0001; disgust: Friedman χ2 = 45.435, df = 9, p < 0.0001). However, the post-hoc Nemenyi test revealed that only one species from each snake category significantly differed from the others in the mean SR amplitudes: in the case of fear-eliciting snakes, the Sochurek's saw-scaled viper differed from all the other snake species except the Sahara sand viper ( Cerastes vipera ), thus, only 8 out of 45 comparisons were significant (p < 0.05). In the case of disgust-eliciting snakes, the brahminy blind snake ( Indotyphlops braminus ) differed from the other snake species except the northern blind snake ( Anilios diversus ), Eurasian blind snake ( Xerotyphlops vermicularis ), rotund blind snake ( Anilios pinguis ), and the northern rubber boa ( Charina botae ), thus, only 5 out of 45 comparisons were significant (p < 0.05). The results calculated for SR duration were in this case almost identical to those for the amplitude and therefore will not be further mentioned in the text. The Friedman test comparing the responses to individual species was significant. Therefore, we used a redundancy analysis (RDA) to further examine the contribution of nine morphological and three venom characteristics of relatively more diverse fear-eliciting snakes (treated as explanatory variables—constraints) to the SR amplitude. However, the model showed no constrained component. Thus, these analyses supported the hypothesis that the selected snake species present a homogenous category based on both the self-reported evaluation and physiological measures, regardless of their morphological or venom variability.
In the current research using two experimental designs, we directly compared autonomous physiological responses of human subjects exposed to snake pictures of two distinct emotional and zoological categories. One composed of viperid snakes that are all venomous, dangerous to humans and evoke intense fear, the other one including fossorial snakes that are non-venomous, harmless, and evoke mainly disgust and repulsion. We have demonstrated that the fear-eliciting venomous snakes trigger a significantly more pronounced physiological response as evidenced by higher SR amplitude compared with the disgust-eliciting snakes, while no significant difference could be found in HR. Furthermore, we provide evidence that the individual level of snake fear greatly effects bodily responses as high-fear subjects show more increased response in both SR and HR parameters compared with low-fear subjects. Although people demonstrate a measurable emotional response in both the SR and HR channels upon seeing a snake picture, measuring SR was a much more sensitive and robust method in the current study. By analyzing the curve of SR changes, we can reliably discriminate between reactions to fear-eliciting viperids and disgust-eliciting fossorial snakes. In other studies, the measurement of electrodermal activity was sensitive enough to detect differences in reactions to phobia-triggering animal stimuli (snakes and spiders) compared to controls even in a masked condition, when the stimuli were presented only for 30 ms . Fredrikson and Öhman in their detailed study of fear conditioning likewise measured both parameters, i.e., the SR as well as HR response to snakes and spiders (fear-relevant stimuli). They found that electrodermal responses conditioned to fear-relevant stimuli, once learned, showed a resistance to extinction compared to neutral stimuli. However, this was not the case for HR, nor did they find differences in HR during acquisition or habituation phases, in contrast to SR. Similarly, Bradley, Cuthbert, & Lang showed that HR after the stimulus onset first increased (1 s), then decreased (2–3 s), and then increased again (4–5 s), but no such pattern was detected for SR. This phasic HR response is also typical for anticipated threat situations . For all these reasons, HR as a psychophysiological response parameter may be highly dependent not only on intensity of the stimulus, but also the details of a particular experimental design, e.g., the time for which the stimulus is presented to the subject or whether the subject has a chance to somehow predict when the stimulus is going to appear. It is noteworthy that the way of visual emotional stimulation plays a significant role . It can be demonstrated that stimuli presented sequentially elicit a different level of emotional response compared to a stronger visual stimulation by 10 consecutive stimuli in a block design. When using a single stimulus presentation (sequential design), we can better differentiate responses to fear- and disgust-eliciting snakes and at the same time, the reactions to fear-eliciting are stronger in a sequential than in a block design. The question remains as to why the psychophysiological responses measured in the block design are lower. It can be argued that the block design facilitates habituation and this effect is even more pronounced in the category of fear-eliciting snakes. However, it has been shown that repeated presentation of pictures of similar affective valence does not lead to habituation and the emotional response measured by corrugator electromyographic (EMG) activity is maintained across more than twenty trials . Surprisingly, in our experiment a stronger visual stimulation in the block design did not necessarily lead to a stronger emotional response, particularly if fear-eliciting snakes are shown. This may have considerable implications for fMRI or PET studies where similar picture block designs are commonly used to study the emotional response . There have already been numerous psychophysiological studies using snakes as emotionally relevant stimuli. However, not all of them could be easily compared with the present work due to substantial methodological differences (e.g., conditioned electrodermal responses to masked stimuli , which is not the same as unconditioned spontaneous responses examined here). For a more detailed comparison, we have selected 11 studies meeting at least one of the following criteria: 1) measurement of electrodermal activity or heart rate in response to snakes compared to fear-irrelevant control stimuli or 2) comparison of responses to snakes in respondents with high and low fear of snakes (see , which compares mean changes in the physiological response between given categories of stimuli or respondents). Most of the studies found higher or more frequent changes in electrodermal activity in response to snake stimuli compared to neutral controls and stronger reactions in snake fearful participants, which is consistent with our results. The results of HR changes were not as robust, however, there was a trend for HR acceleration in reaction to fear-eliciting snakes compared to controls (significant in the block design) and higher HR acceleration in fearful respondents (significant in the sequential design), which is also in agreement with results of the previous studies. 4.1. Effect of individual characteristics on the emotional response to fear- and disgust-evoking snakes Further intensification of the emotional reaction can be attributed to variable sensitivity of subjects to emotions in general (ERS), their physiological reactivity (repeatability in different parameters of physiological response), and specifically increased snake fear (corresponding to the SNAQ score). Even when filtering out the individual variability in LME models, the intensity and duration of emotional response (SR) is still affected by the subject’s emotional reactivity as measured by the ERS, especially in response towards fear-eliciting snakes. Duration of the SR response is also specifically influenced by the subject’s preexisting snake fear. Individuals experiencing higher levels of snake fear tend to demonstrate longer reactions and increased HR in response to fear-eliciting viperid snake images. These effects apply when we measure reactions to a single stimulus, however, when presented in the block design, there is no measurable effect of emotional reactivity (ERS) either on SR, or on HR. In either case, snake fear still significantly influences the intensity (amplitude) and duration of SR response. As snake fear seems to be a crucial variable affecting a range of measured psychophysiological parameters which is also supported by the literature , it is necessary to examine the differences between individuals with low and high snake fear in more detail. Subjects with high fear of snakes experience more frequent and longer SR reactions (NR, MDS, MDR; see for the abbreviations’ explanation) to individually presented viperid snakes, but there is no difference in intensity (amplitude). Snake fearful respondents also show increased HR. In the block design, individuals with high snake fear demonstrated longer SR reactions (MDS) and a higher mean amplitude per reaction (MAR). In the block design, differences in the measured emotional reaction between individuals with low and high snake fear were smaller, but the latter ones demonstrated longer SR reactions (MDS) and a higher mean amplitude per reaction (MAR). In responses to the disgust-evoking fossorial snakes, we only found differences in the block design, where high-fear respondents reacted more strongly in all the parameters of SR (the number of reactions, their amplitude and duration). A similar effect has been observed in snake phobics or participants with a high level of snake fear, where snake pictures provoked an increased number of SR reactions or a larger skin conductance response . These subjects also showed HR acceleration during exposure to snake pictures . When analyzing differences in reactions of individuals with low and high disgust propensity, it seems that the latter ones tend to react more intensely to snakes in general. They show increased SR (the number of reactions, their amplitude and duration) in response not only to the fear-eliciting viperids, but also the repulsive fossorial snakes. It can be argued that people with higher disgust propensity react more strongly to any snake picture irrespective of its morphotype. Individuals with high disgust propensity also show increased HR, but only in response to viperid snakes presented in a block design. However, it is noteworthy that the association between disgust (presumably activating mainly the parasympathetic nervous system) and HR is a complex one often with opposite effects (for example disgust-associated decrease in HR in blood-injection phobia ). However, even if these individual characteristics, i.e., snake fear or emotional reactivity, are associated with the measured psychophysiological parameters, the overall explained variability remains as little as 9–12% (based on the RDA). The largest effect is attributable to the stimulus category, i.e., whether the subject is looking at a fear-eliciting viperid or a disgust-eliciting fossorial snake (or alternatively a leaf as a control stimulus). As the variability of physiological parameters might be caused not only by external factors (in this case the experimental design), but also intrinsic inter-individual differences, we calculated repeatability of the SR parameters to establish the relative contribution of respondent’s individuality to the overall variation (see ). In our results, repeatability was the highest for responses to all the stimuli pooled together irrespective of specific variables, while NR, MAS, and MDS were the most individually repeatable variables. It has been previously shown that HR has an exceptionally good repeatability when measured twice in the same design . In our study, we found relatively high repeatability of SR parameters as well, even across different experimental designs of stimuli presentation (single stimulus vs. block of stimuli). This is fairly surprising given the fact that the mean repeatability of behavioral traits in animals is r = 0.37 ; no meta-analysis for humans was found. The fact that there is significant repeatability even across very different experimental designs shows that there are consistent inter-individual differences in SR, which account for 30–50% of the overall variability. For people with high fear of snakes, the intensive reactions to both snake groups (amplitudes) were more repeatable as opposed to reactions to control stimuli. In general, fear-eliciting stimuli (viperids) evoke more repeatable individual reactions (SR) in many parameters reflecting the intensity of emotional response (MDR, MAR) than fossorial snakes, despite the fact that fear-eliciting snakes often evoke more extreme fear responses. 4.2. Psychophysiological response to viperid snakes and how it might affect venom activity Based on our previous study, all viperids clearly belong to the fear-eliciting group of snakes . Here, we demonstrate that they evoke a fear response of similar intensity on the physiological level too, irrespective of their toxicity or relative threat presented to humans. Here we hypothesize that the observed higher psychophysiological response to viperid snakes is a result of ancestral prioritization in terms of early recognition as well as associated emotion of fear. Furthermore, this autonomous bodily response might be adaptive in a specific interaction with the main components of snake venom. Conversely, it might be argued that the distinct physiological response to viperids is not driven by higher fear, but is merely based on specific low-level visual features that differ between the two studied groups of snakes (i.e., size of scales, head shape, tail shape, body posture, etc.). However, people still report significantly higher fear of vipers. Moreover, some of our subjects demonstrated an increased physiological response to fossorial snakes that do not possess those visual cues. Therefore, it seems that both attention and emotion play a key role. To separate their influence, another experimental design using artificially created rather than natural stimuli would have to be employed. Besides venom characteristics, there are additional factors contributing to the level of dangerousness of a particular snake species to humans, mainly its body size (which also corresponds to the venom expenditure ), level of defensiveness (aggression), as well as the species’ abundance and distribution that influences the probability of encounter (see ). Toxicity of the fear-eliciting snakes from the Viperidae family that we tested is highly variable. The most dangerous snakes for humans are vipers of the genus Bitis ( B . gabonica a B . rhinoceros ) and Echis ( E . carinatus multisquamata and E . carinatus sochureki ) with cardiotoxins directly affecting heart activity. Specifically, they cause a decrease in myocardial contractility, as well as disturbances in atrio-ventricular conduction and reduction in amplitude and duration of the action potential . Their venom has a systemic effect on the body, is more toxic and, consequently, causes significantly more fatalities (about 10–20% of envenomings may be fatal). Another very dangerous snake from this subfamily is the eastern diamondback rattlesnake that alongside to the above-mentioned possesses also myotoxins, which can directly affect heart activity (via non-enzymatic destruction of the cardiac muscle ). On the other hand, even though the venom of the Sahara sand viper ( Cerastes vipera ) has similar effects (procoagulants, hemorrhagins), its bite does not pose such a high risk of lethality . The Sahara sand viper is a small-sized snake that releases a low amount of venom, which only has a local impact of low efficacy and can resolve even without medical intervention. Similarly, the remaining species (i.e. the Orlov’s viper Vipera orlovi , Fea’s viper Azemiops feae , and variable bush viper Atheris squamigera ) are rather smaller snakes producing less venom that predominantly specialize in feeding on amphibians, reptiles, and small-sized mammals . It can be argued that alteration of heart rate activity is a parameter common to both snake venom action and the corresponding psychophysiological emotional response. Fear in general (not only of snakes) operates through activation of the sympathetic nervous system and hypothalamic–pituitary–adrenal (HPA) axis, which leads to a significant increase in heart rate . In the case of disgust evoked by other types of snakes, which is probably mediated by the parasympathetic nervous system, we might expect considerably smaller or even opposite effects . The interaction between venom of viperid snakes and psychophysiological changes might therefore vary depending on the underlying emotion, i.e., fear or disgust. It has long been known that some snake venoms dramatically lower the blood pressure in human victims and experimental animals . This effect could either be caused directly by specific hypotensive agents present in the venom or indirectly through pulmonary vascular obstruction and coronary ischemia . As venom of viperid snakes may affect HR , which is also affected by intense fear of snakes, we propose a hypothetical interaction between elicited fear and venom spreading and action following a snakebite. However, it is necessary to distinguish between different timeframes when emotional state of the patient and efficacy of venom may interact. Immobilization is used as first aid immediately after a snakebite to reduce spreading of venom of viperids and elapids . In this early stage, high fear that increases HR might lead to snake venom spreading faster in the body with negative consequences for the victim’s survival. Experiencing disgust, on the other hand, might have an opposite effect by decreasing HR through parasympathetic activation. However, in later stages, the interaction between fear or disgust, their associated physiological changes, and the venom might depend more specifically on the particular composition of toxins. Interestingly, there are various hypotensive agents in toxins contained in venom of viperid snakes which have been extracted to develop drugs for treating hypertension . When these hypotensive compounds take effect, the blood pressure drastically drops. Therefore, we may hypothesize that the counter-effect of fear increasing the blood pressure might potentially improve the physiological response to envenoming by these snakes. However, no clear-cut prediction for the interaction with disgust can be made, due to its highly variable effect on heart rate. This phenomenon is worth studying. Alternatively, the stronger physiological response to venomous snakes found in our study might as well be explained by the need of activating energetic resources in dangerous situations, which is necessary for a fast and effective defense (fight-or-flight) response before a snakebite can be delivered, i.e., eliminate the source of threat or rather withdraw oneself from the snake’s presence. However, in their latest review on presumed preparedness of fear of snakes, Coelho et al. argue, that most snakebites happen at very close vicinity and are extremely fast, so the victim usually has no chance to effectively respond.
Further intensification of the emotional reaction can be attributed to variable sensitivity of subjects to emotions in general (ERS), their physiological reactivity (repeatability in different parameters of physiological response), and specifically increased snake fear (corresponding to the SNAQ score). Even when filtering out the individual variability in LME models, the intensity and duration of emotional response (SR) is still affected by the subject’s emotional reactivity as measured by the ERS, especially in response towards fear-eliciting snakes. Duration of the SR response is also specifically influenced by the subject’s preexisting snake fear. Individuals experiencing higher levels of snake fear tend to demonstrate longer reactions and increased HR in response to fear-eliciting viperid snake images. These effects apply when we measure reactions to a single stimulus, however, when presented in the block design, there is no measurable effect of emotional reactivity (ERS) either on SR, or on HR. In either case, snake fear still significantly influences the intensity (amplitude) and duration of SR response. As snake fear seems to be a crucial variable affecting a range of measured psychophysiological parameters which is also supported by the literature , it is necessary to examine the differences between individuals with low and high snake fear in more detail. Subjects with high fear of snakes experience more frequent and longer SR reactions (NR, MDS, MDR; see for the abbreviations’ explanation) to individually presented viperid snakes, but there is no difference in intensity (amplitude). Snake fearful respondents also show increased HR. In the block design, individuals with high snake fear demonstrated longer SR reactions (MDS) and a higher mean amplitude per reaction (MAR). In the block design, differences in the measured emotional reaction between individuals with low and high snake fear were smaller, but the latter ones demonstrated longer SR reactions (MDS) and a higher mean amplitude per reaction (MAR). In responses to the disgust-evoking fossorial snakes, we only found differences in the block design, where high-fear respondents reacted more strongly in all the parameters of SR (the number of reactions, their amplitude and duration). A similar effect has been observed in snake phobics or participants with a high level of snake fear, where snake pictures provoked an increased number of SR reactions or a larger skin conductance response . These subjects also showed HR acceleration during exposure to snake pictures . When analyzing differences in reactions of individuals with low and high disgust propensity, it seems that the latter ones tend to react more intensely to snakes in general. They show increased SR (the number of reactions, their amplitude and duration) in response not only to the fear-eliciting viperids, but also the repulsive fossorial snakes. It can be argued that people with higher disgust propensity react more strongly to any snake picture irrespective of its morphotype. Individuals with high disgust propensity also show increased HR, but only in response to viperid snakes presented in a block design. However, it is noteworthy that the association between disgust (presumably activating mainly the parasympathetic nervous system) and HR is a complex one often with opposite effects (for example disgust-associated decrease in HR in blood-injection phobia ). However, even if these individual characteristics, i.e., snake fear or emotional reactivity, are associated with the measured psychophysiological parameters, the overall explained variability remains as little as 9–12% (based on the RDA). The largest effect is attributable to the stimulus category, i.e., whether the subject is looking at a fear-eliciting viperid or a disgust-eliciting fossorial snake (or alternatively a leaf as a control stimulus). As the variability of physiological parameters might be caused not only by external factors (in this case the experimental design), but also intrinsic inter-individual differences, we calculated repeatability of the SR parameters to establish the relative contribution of respondent’s individuality to the overall variation (see ). In our results, repeatability was the highest for responses to all the stimuli pooled together irrespective of specific variables, while NR, MAS, and MDS were the most individually repeatable variables. It has been previously shown that HR has an exceptionally good repeatability when measured twice in the same design . In our study, we found relatively high repeatability of SR parameters as well, even across different experimental designs of stimuli presentation (single stimulus vs. block of stimuli). This is fairly surprising given the fact that the mean repeatability of behavioral traits in animals is r = 0.37 ; no meta-analysis for humans was found. The fact that there is significant repeatability even across very different experimental designs shows that there are consistent inter-individual differences in SR, which account for 30–50% of the overall variability. For people with high fear of snakes, the intensive reactions to both snake groups (amplitudes) were more repeatable as opposed to reactions to control stimuli. In general, fear-eliciting stimuli (viperids) evoke more repeatable individual reactions (SR) in many parameters reflecting the intensity of emotional response (MDR, MAR) than fossorial snakes, despite the fact that fear-eliciting snakes often evoke more extreme fear responses.
Based on our previous study, all viperids clearly belong to the fear-eliciting group of snakes . Here, we demonstrate that they evoke a fear response of similar intensity on the physiological level too, irrespective of their toxicity or relative threat presented to humans. Here we hypothesize that the observed higher psychophysiological response to viperid snakes is a result of ancestral prioritization in terms of early recognition as well as associated emotion of fear. Furthermore, this autonomous bodily response might be adaptive in a specific interaction with the main components of snake venom. Conversely, it might be argued that the distinct physiological response to viperids is not driven by higher fear, but is merely based on specific low-level visual features that differ between the two studied groups of snakes (i.e., size of scales, head shape, tail shape, body posture, etc.). However, people still report significantly higher fear of vipers. Moreover, some of our subjects demonstrated an increased physiological response to fossorial snakes that do not possess those visual cues. Therefore, it seems that both attention and emotion play a key role. To separate their influence, another experimental design using artificially created rather than natural stimuli would have to be employed. Besides venom characteristics, there are additional factors contributing to the level of dangerousness of a particular snake species to humans, mainly its body size (which also corresponds to the venom expenditure ), level of defensiveness (aggression), as well as the species’ abundance and distribution that influences the probability of encounter (see ). Toxicity of the fear-eliciting snakes from the Viperidae family that we tested is highly variable. The most dangerous snakes for humans are vipers of the genus Bitis ( B . gabonica a B . rhinoceros ) and Echis ( E . carinatus multisquamata and E . carinatus sochureki ) with cardiotoxins directly affecting heart activity. Specifically, they cause a decrease in myocardial contractility, as well as disturbances in atrio-ventricular conduction and reduction in amplitude and duration of the action potential . Their venom has a systemic effect on the body, is more toxic and, consequently, causes significantly more fatalities (about 10–20% of envenomings may be fatal). Another very dangerous snake from this subfamily is the eastern diamondback rattlesnake that alongside to the above-mentioned possesses also myotoxins, which can directly affect heart activity (via non-enzymatic destruction of the cardiac muscle ). On the other hand, even though the venom of the Sahara sand viper ( Cerastes vipera ) has similar effects (procoagulants, hemorrhagins), its bite does not pose such a high risk of lethality . The Sahara sand viper is a small-sized snake that releases a low amount of venom, which only has a local impact of low efficacy and can resolve even without medical intervention. Similarly, the remaining species (i.e. the Orlov’s viper Vipera orlovi , Fea’s viper Azemiops feae , and variable bush viper Atheris squamigera ) are rather smaller snakes producing less venom that predominantly specialize in feeding on amphibians, reptiles, and small-sized mammals . It can be argued that alteration of heart rate activity is a parameter common to both snake venom action and the corresponding psychophysiological emotional response. Fear in general (not only of snakes) operates through activation of the sympathetic nervous system and hypothalamic–pituitary–adrenal (HPA) axis, which leads to a significant increase in heart rate . In the case of disgust evoked by other types of snakes, which is probably mediated by the parasympathetic nervous system, we might expect considerably smaller or even opposite effects . The interaction between venom of viperid snakes and psychophysiological changes might therefore vary depending on the underlying emotion, i.e., fear or disgust. It has long been known that some snake venoms dramatically lower the blood pressure in human victims and experimental animals . This effect could either be caused directly by specific hypotensive agents present in the venom or indirectly through pulmonary vascular obstruction and coronary ischemia . As venom of viperid snakes may affect HR , which is also affected by intense fear of snakes, we propose a hypothetical interaction between elicited fear and venom spreading and action following a snakebite. However, it is necessary to distinguish between different timeframes when emotional state of the patient and efficacy of venom may interact. Immobilization is used as first aid immediately after a snakebite to reduce spreading of venom of viperids and elapids . In this early stage, high fear that increases HR might lead to snake venom spreading faster in the body with negative consequences for the victim’s survival. Experiencing disgust, on the other hand, might have an opposite effect by decreasing HR through parasympathetic activation. However, in later stages, the interaction between fear or disgust, their associated physiological changes, and the venom might depend more specifically on the particular composition of toxins. Interestingly, there are various hypotensive agents in toxins contained in venom of viperid snakes which have been extracted to develop drugs for treating hypertension . When these hypotensive compounds take effect, the blood pressure drastically drops. Therefore, we may hypothesize that the counter-effect of fear increasing the blood pressure might potentially improve the physiological response to envenoming by these snakes. However, no clear-cut prediction for the interaction with disgust can be made, due to its highly variable effect on heart rate. This phenomenon is worth studying. Alternatively, the stronger physiological response to venomous snakes found in our study might as well be explained by the need of activating energetic resources in dangerous situations, which is necessary for a fast and effective defense (fight-or-flight) response before a snakebite can be delivered, i.e., eliminate the source of threat or rather withdraw oneself from the snake’s presence. However, in their latest review on presumed preparedness of fear of snakes, Coelho et al. argue, that most snakebites happen at very close vicinity and are extremely fast, so the victim usually has no chance to effectively respond.
The psychophysiological response to images of fear-eliciting venomous snakes from the family Viperidae is higher than the response evoked by images of fossorial, disgust-eliciting snakes. Interestingly, more intensive visual stimulation (i.e., presented longer in a block of ten subsequent images) does not lead to a stronger emotional response than less intensive stimulation (presentation of single images). It would be interesting to explore the effect of different modes of visual stimulation (e.g., comparing the effect of pictures, videos, and live snakes) on the emotional response of human subjects to fear-eliciting snakes. Our study showed that various parameters of skin resistance reflect changes in the emotional response evoked by snake pictures while heart rate activity increases only when watching pictures of venomous snakes. Various analyses revealed that the respondents’ increased general emotional reactivity, disgust propensity, and specific sensitivity to snake fear measured by psychological questionnaires (ERS, DS-R, and SNAQ) predict the psychophysiological response. High-fear respondents (as compared to low-fear respondents) show a stronger, longer, and more frequent skin resistance response and higher heart rate when watching images of venomous, fear-eliciting snakes. As physiological mechanisms underlying this response may modify the effects of snakebite envenoming, we suggest paying attention not only to the venom itself, but also to the particular species delivering the bite and the victim’s individual sensitivity. It should become an integral part of studies quantifying the effects of envenomation, including studies on animal models.
S1 Table List of species and picture sources used in the study as visual stimuli. The columns D and E show mean ratings of fear and disgust based on self-reported answers by 112 respondents. (XLSX) Click here for additional data file.
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Cutoff CT value can identify upper gastrointestinal bleeding on postmortem CT: Development and validation study | 4387d741-9fde-4553-bb7c-369134c4db60 | 11161085 | Forensic Medicine[mh] | Computed tomography (CT) is a medical imaging technique that uses X-rays and computer processing to create detailed cross-sectional images of the body. Postmortem computed tomography (PMCT) is an important non-invasive method for examining the cause of death in the field of forensic medicine . PMCT requires less cost and time, is more acceptable to bereaved families than conventional autopsies, and provides helpful information for subsequent conventional autopsies. Moreover, it is a substitute for conventional autopsies in certain situations . Upper gastrointestinal bleeding (UGIB) is one of the most common disease entities, with a mortality rate of up to 10% . Findings on noncontrast antemortem CT, such as clotted hematoma and elevated CT density of the gastrointestinal contents, are useful in identifying the gastrointestinal bleeding site . However, distinguishing hematomas from other high-density gastrointestinal contents (e.g., food residues and medications) remains challenging . As the endoscopic examination is not practical after death and autopsy is not performed in all cases of death, previous case reports indicated the capability of noncontrast-enhanced or contrast-enhanced PMCT to identify the gastrointestinal bleeding site . The role of an increased CT density in gastrointestinal contents has been emphasized in UGIB diagnosis. Therefore, in the present case-control study, we aimed to investigate and validate the diagnostic power of CT density and other findings of the upper gastrointestinal tract on noncontrast PMCT in the diagnosis of UGIB. This case-control study was approved by the Ethical Committee of the participating institution (Ethical Committee No. 2076, June 9, 2008). The protocol complied with the 1964 Declaration of Helsinki and its later amendments (or with comparable ethical standards). Written informed consent for the use of cadavers in our study was obtained from all families of the deceased participants. The data were accessed between April 2021 and March 2022. Participants Cases of nontraumatic in-hospital deaths at our university hospital between April 2009 and December 2020 were included if the patients were aged 18 years or older at death and had undergone both noncontrast PMCT and conventional autopsy; otherwise, they were excluded. A total of 593 patients satisfied these criteria. All cadavers were stored in the supine position at room temperature from the time of death until the PMCT examination and subsequent autopsy. Using the results of the pathological autopsy as a reference standard, the patients were divided into two groups: 27 with UGIB and 566 without UGIB. We estimated the minimum sample size for representing cases without UGIB to be 83, using the conditions of a 95% confidence level and a 10% margin of error. Using a random number generator, we reduced the number of cases without UGIB to 200 and allocated all cases evenly into the derivation and validation sets. Finally, a derivation set, including 13 patients with UGIB and 100 patients without UGIB, and a validation set, including 14 patients with UGIB and 100 patients without UGIB, were generated. Cases with insufficient upper gastrointestinal content on PMCT were excluded after image analysis. summarizes the patient inclusion process. For patients in the derivation and validation sets, a history of anticoagulation or antiplatelet therapy, the time interval between the last meal (via oral or nasogastric tube) and death, and the time interval between death and PMCT were recorded. CT scanning PMCT was performed using ROBUSTO (Hitachi Medical, Japan) or Aquillion (Toshiba Medical, Japan) multidetector CT devices. The scan parameters were as follows: scanning mode, helical; slice thickness, 2.5 mm; slice interval, 1.25 mm; rotation time, 0.5 s; tube voltage, 120 kVp; tube current, 250 mA. Image reconstruction was performed at a 5-mm thickness with a 350-mm field of view and a 512 × 512 image matrix. No contrast medium was administrated. All data were transferred to an image server in Digital Imaging and Communication in Medicine format. Image analysis Images were analyzed using an open-source medical image viewer, Horos (version 4.0.0, Horos Project, https://horosproject.org/ ). As for the derivation set, all images were interpreted by a board-certified radiologist (N.O.) with 5 years of experience in PMCT, supervised by another board-certified radiologist (W.G.) with 16 years of experience, who were blinded to the participants’ clinical information and autopsy results. The interpreter placed regions of interest approximately 100 mm 2 in size on the upper gastrointestinal contents, including the highest CT value area but avoiding streak artifacts, and recorded the mean CT value (Hounsfield Unit, HU; a standardized scale used in CT scanning to measure and compare the radiodensity of various substances: -1000 HU for air, 0 HU for water, and +1000 HU and beyond for dense materials like bone). If the area of the upper gastrointestinal contents was insufficient and a 100-mm 2 -size region of interest was not drawable at any place, the case was excluded. The optimal CT cutoff value to diagnose UGIB was determined through a receiver operating characteristic (ROC) analysis with the Youden index. To elucidate CT findings characteristic of true-positive and false-positive cases, that is, cases with a higher CT value of upper gastrointestinal content than the optimal CT cutoff value, an additional assessment was conducted. The following findings of the high-density upper gastrointestinal contents were recorded: location (ventral, dorsal, homogeneous, or heterogeneous); expected nature of the high-density contents considering the shape of food residue or feces, the presence/absence of fluid-air level, and homogeneity/inhomogeneity of the content (liquid or solid); presence of bubbles inside or close (≤10 mm) to the high-density contents (present or absent); the relationship of the bubbles mentioned above to the high-density contents if present (inside or outside); the maximum size of the bubbles mentioned above if present (mm); and presence of CT value >100 HU because the CT value of the hematoma rarely exceed 100 HU (yes or no) . Among these findings, candidates for useful imaging findings to differentiate true-positives and false-positives were extracted statistically, as mentioned later. For the validation set, all images were assessed by another radiologist (M.I.) with 15 years of experience in PMCT, who was blinded to the participant’s clinical information and autopsy results. The interpreter measured the CT values of the upper gastrointestinal contents in the same manner as for the derivation set. Patients with insufficient upper gastrointestinal contents were excluded. Autopsy technique Conventional autopsies, including investigations of the upper gastrointestinal tract, were performed by board-certified pathologists immediately after PMCT (within 1 h) in all cases. The pathologists were informed of the patient’s clinical histories but were blinded to the radiologists’ PMCT reports. Gross inspection and histological analyses were performed for each organ, and the cause of death was determined for each case. UGIB was diagnosed when hemorrhagic contents or hematomas were macroscopically observed in the upper gastrointestinal tract. A minute or microscopic hemorrhage was not recorded as UGIB as it was unlikely to cause death or critical conditions. Statistical analysis Statistical analysis was performed using EZR software (Saitama Medical Center, Jichi Medical University) . Fisher’s exact test and the Mann–Whitney U test were used to examine the statistical significance of differences. Statistical significance was set at a P value < 0.05. The ROC curve with the Youden index was used to define the optimal CT cutoff values for the upper gastrointestinal contents. The area under the curve (AUC), sensitivity, and specificity were also calculated . Cases of nontraumatic in-hospital deaths at our university hospital between April 2009 and December 2020 were included if the patients were aged 18 years or older at death and had undergone both noncontrast PMCT and conventional autopsy; otherwise, they were excluded. A total of 593 patients satisfied these criteria. All cadavers were stored in the supine position at room temperature from the time of death until the PMCT examination and subsequent autopsy. Using the results of the pathological autopsy as a reference standard, the patients were divided into two groups: 27 with UGIB and 566 without UGIB. We estimated the minimum sample size for representing cases without UGIB to be 83, using the conditions of a 95% confidence level and a 10% margin of error. Using a random number generator, we reduced the number of cases without UGIB to 200 and allocated all cases evenly into the derivation and validation sets. Finally, a derivation set, including 13 patients with UGIB and 100 patients without UGIB, and a validation set, including 14 patients with UGIB and 100 patients without UGIB, were generated. Cases with insufficient upper gastrointestinal content on PMCT were excluded after image analysis. summarizes the patient inclusion process. For patients in the derivation and validation sets, a history of anticoagulation or antiplatelet therapy, the time interval between the last meal (via oral or nasogastric tube) and death, and the time interval between death and PMCT were recorded. PMCT was performed using ROBUSTO (Hitachi Medical, Japan) or Aquillion (Toshiba Medical, Japan) multidetector CT devices. The scan parameters were as follows: scanning mode, helical; slice thickness, 2.5 mm; slice interval, 1.25 mm; rotation time, 0.5 s; tube voltage, 120 kVp; tube current, 250 mA. Image reconstruction was performed at a 5-mm thickness with a 350-mm field of view and a 512 × 512 image matrix. No contrast medium was administrated. All data were transferred to an image server in Digital Imaging and Communication in Medicine format. Images were analyzed using an open-source medical image viewer, Horos (version 4.0.0, Horos Project, https://horosproject.org/ ). As for the derivation set, all images were interpreted by a board-certified radiologist (N.O.) with 5 years of experience in PMCT, supervised by another board-certified radiologist (W.G.) with 16 years of experience, who were blinded to the participants’ clinical information and autopsy results. The interpreter placed regions of interest approximately 100 mm 2 in size on the upper gastrointestinal contents, including the highest CT value area but avoiding streak artifacts, and recorded the mean CT value (Hounsfield Unit, HU; a standardized scale used in CT scanning to measure and compare the radiodensity of various substances: -1000 HU for air, 0 HU for water, and +1000 HU and beyond for dense materials like bone). If the area of the upper gastrointestinal contents was insufficient and a 100-mm 2 -size region of interest was not drawable at any place, the case was excluded. The optimal CT cutoff value to diagnose UGIB was determined through a receiver operating characteristic (ROC) analysis with the Youden index. To elucidate CT findings characteristic of true-positive and false-positive cases, that is, cases with a higher CT value of upper gastrointestinal content than the optimal CT cutoff value, an additional assessment was conducted. The following findings of the high-density upper gastrointestinal contents were recorded: location (ventral, dorsal, homogeneous, or heterogeneous); expected nature of the high-density contents considering the shape of food residue or feces, the presence/absence of fluid-air level, and homogeneity/inhomogeneity of the content (liquid or solid); presence of bubbles inside or close (≤10 mm) to the high-density contents (present or absent); the relationship of the bubbles mentioned above to the high-density contents if present (inside or outside); the maximum size of the bubbles mentioned above if present (mm); and presence of CT value >100 HU because the CT value of the hematoma rarely exceed 100 HU (yes or no) . Among these findings, candidates for useful imaging findings to differentiate true-positives and false-positives were extracted statistically, as mentioned later. For the validation set, all images were assessed by another radiologist (M.I.) with 15 years of experience in PMCT, who was blinded to the participant’s clinical information and autopsy results. The interpreter measured the CT values of the upper gastrointestinal contents in the same manner as for the derivation set. Patients with insufficient upper gastrointestinal contents were excluded. Conventional autopsies, including investigations of the upper gastrointestinal tract, were performed by board-certified pathologists immediately after PMCT (within 1 h) in all cases. The pathologists were informed of the patient’s clinical histories but were blinded to the radiologists’ PMCT reports. Gross inspection and histological analyses were performed for each organ, and the cause of death was determined for each case. UGIB was diagnosed when hemorrhagic contents or hematomas were macroscopically observed in the upper gastrointestinal tract. A minute or microscopic hemorrhage was not recorded as UGIB as it was unlikely to cause death or critical conditions. Statistical analysis was performed using EZR software (Saitama Medical Center, Jichi Medical University) . Fisher’s exact test and the Mann–Whitney U test were used to examine the statistical significance of differences. Statistical significance was set at a P value < 0.05. The ROC curve with the Youden index was used to define the optimal CT cutoff values for the upper gastrointestinal contents. The area under the curve (AUC), sensitivity, and specificity were also calculated . Subject characteristics in the derivation set Of the 113 cases, one with UGIB and 15 without UGIB were excluded due to insufficient upper gastrointestinal contents for measurement on the PMCT images. As a result, 12 patients with UGIB and 85 without UGIB comprised the derivation set (97 cases). The causes of death in the derivation set were as follows (number of cases with UGIB vs. without UGIB): respiratory failure (1 vs. 32), liver failure (0 vs. 6), renal failure (0 vs.1), sepsis (1 vs. 6), multiorgan failure (0 vs. 2), heart disease (2 vs. 12), upper gastrointestinal hemorrhage (5 vs. 0), extra-gastrointestinal hemorrhage (1 vs. 2), malignant tumor (1 vs. 11), intracranial lesion (1 vs. 8), peritonitis (0 vs. 2), and unidentified (0 vs.3). The clinical characteristics of the other participants are summarized in . Determining the CT cutoff value using the derivation set In the derivation set, the mean CT value of the upper gastrointestinal contents was 48.2 HU (standard deviation, 19.3 HU) in cases with UGIB and 22.8 HU (standard deviation, 19.1 HU) in cases without UGIB (Mann–Whitney U test, p < 0.001). The ROC curve for the CT values of the upper gastrointestinal contents is illustrated in . According to the ROC curve analysis using the Youden index, the optimal CT cutoff value was 27.7 HU (sensitivity, 91.7%; specificity, 81.2%; AUC, 0.898). PMCT findings in cases with high CT value content in the derivation set A total of 27 cases in the derivation set had upper gastrointestinal contents of ≥27.7 HU. Among them, 11 had UGIB, and 16 did not have UGIB. The results of the additional assessments for these cases are summarized in . In cases with UGIB, the high-density contents were more solid, and the size of the bubbles was greater than that in cases without UGIB. All bubbles in cases with UGIB were > 4 mm, while it were < 4 mm in all cases without UGIB. Differences between cases with and without UGIB in other PMCT findings were not significant. After using the CT cutoff value of ≥27.7 HU, the sensitivity and specificity increased if the additional criteria were used as follows: solid nature of the high-density contents among cases with a CT cutoff value ≥27.7 HU, with a sensitivity of 63.6% and specificity of 87.5%, and existence of bubbles ≥4 mm inside or close to the high-density contents among cases with a CT cutoff value ≥27.7 HU and bubbles, with a sensitivity of 100% and specificity of 100%. Additionally, in four of the five patients who died from UGIB, the CT values of the upper gastrointestinal contents were above the optimal cutoff value. Subject characteristics of the validation set In the validation set of 114 cases, one with UGIB and 15 without UGIB were excluded because of insufficient upper gastrointestinal contents. Consequently, 13 patients with UGIB and 85 patients without UGIB (98 cases) were included in the validation set. The causes of death were as follows (number of cases with UGIB vs. without UGIB): respiratory failure (5 vs. 40), liver failure (1 vs. 3), renal failure (0 vs. 1), sepsis (0 vs. 5), multiorgan failure (0 vs. 1), heart disease (1 vs. 4), upper gastrointestinal hemorrhage (5 vs. 0), extra-gastrointestinal hemorrhage (0 vs. 4), malignant tumor (0 vs. 17), intracranial lesion (0 vs. 8), peritonitis (1 vs. 1), and unidentified (0 vs. 1). The clinical characteristics of the other participants are summarized in . Testing the CT cutoff value and additional PMCT findings in the validation set In the validation set, the mean CT value of the upper gastrointestinal contents was 51.8 HU (standard deviation, 21.4 HU) in cases with UGIB and 40.9 HU (standard deviation, 104.7 HU) in cases without UGIB (Mann–Whitney U test, p < 0.001). Thirty of 98 cases in the validation set had ≥27.7 HU gastrointestinal content, comprising 11 cases with UGIB and 19 without UGIB. In diagnosing UGIB, the sensitivity and specificity for the CT cutoff value ≥27.7 HU were 84.6% and 77.6%, respectively. Using the criteria of both the CT cutoff value ≥27.7 HU and the solid nature of the high-density contents, overall sensitivity and specificity in the validation set changed to 61.5% and 96.5%, respectively. Using the criteria of both the CT cutoff value ≥27.7 HU and the existence of bubbles ≥4 mm inside or close to the high-density contents, sensitivity and specificity were 38.5% and 98.8%, respectively. Representative true-positive, false-positive, false-negative, and true-negative cases of UGIB are shown in . As a supplement, for all five patients who died from UGIB, the CT values of the upper gastrointestinal contents were above the optimal cutoff value. Of the 113 cases, one with UGIB and 15 without UGIB were excluded due to insufficient upper gastrointestinal contents for measurement on the PMCT images. As a result, 12 patients with UGIB and 85 without UGIB comprised the derivation set (97 cases). The causes of death in the derivation set were as follows (number of cases with UGIB vs. without UGIB): respiratory failure (1 vs. 32), liver failure (0 vs. 6), renal failure (0 vs.1), sepsis (1 vs. 6), multiorgan failure (0 vs. 2), heart disease (2 vs. 12), upper gastrointestinal hemorrhage (5 vs. 0), extra-gastrointestinal hemorrhage (1 vs. 2), malignant tumor (1 vs. 11), intracranial lesion (1 vs. 8), peritonitis (0 vs. 2), and unidentified (0 vs.3). The clinical characteristics of the other participants are summarized in . In the derivation set, the mean CT value of the upper gastrointestinal contents was 48.2 HU (standard deviation, 19.3 HU) in cases with UGIB and 22.8 HU (standard deviation, 19.1 HU) in cases without UGIB (Mann–Whitney U test, p < 0.001). The ROC curve for the CT values of the upper gastrointestinal contents is illustrated in . According to the ROC curve analysis using the Youden index, the optimal CT cutoff value was 27.7 HU (sensitivity, 91.7%; specificity, 81.2%; AUC, 0.898). A total of 27 cases in the derivation set had upper gastrointestinal contents of ≥27.7 HU. Among them, 11 had UGIB, and 16 did not have UGIB. The results of the additional assessments for these cases are summarized in . In cases with UGIB, the high-density contents were more solid, and the size of the bubbles was greater than that in cases without UGIB. All bubbles in cases with UGIB were > 4 mm, while it were < 4 mm in all cases without UGIB. Differences between cases with and without UGIB in other PMCT findings were not significant. After using the CT cutoff value of ≥27.7 HU, the sensitivity and specificity increased if the additional criteria were used as follows: solid nature of the high-density contents among cases with a CT cutoff value ≥27.7 HU, with a sensitivity of 63.6% and specificity of 87.5%, and existence of bubbles ≥4 mm inside or close to the high-density contents among cases with a CT cutoff value ≥27.7 HU and bubbles, with a sensitivity of 100% and specificity of 100%. Additionally, in four of the five patients who died from UGIB, the CT values of the upper gastrointestinal contents were above the optimal cutoff value. In the validation set of 114 cases, one with UGIB and 15 without UGIB were excluded because of insufficient upper gastrointestinal contents. Consequently, 13 patients with UGIB and 85 patients without UGIB (98 cases) were included in the validation set. The causes of death were as follows (number of cases with UGIB vs. without UGIB): respiratory failure (5 vs. 40), liver failure (1 vs. 3), renal failure (0 vs. 1), sepsis (0 vs. 5), multiorgan failure (0 vs. 1), heart disease (1 vs. 4), upper gastrointestinal hemorrhage (5 vs. 0), extra-gastrointestinal hemorrhage (0 vs. 4), malignant tumor (0 vs. 17), intracranial lesion (0 vs. 8), peritonitis (1 vs. 1), and unidentified (0 vs. 1). The clinical characteristics of the other participants are summarized in . In the validation set, the mean CT value of the upper gastrointestinal contents was 51.8 HU (standard deviation, 21.4 HU) in cases with UGIB and 40.9 HU (standard deviation, 104.7 HU) in cases without UGIB (Mann–Whitney U test, p < 0.001). Thirty of 98 cases in the validation set had ≥27.7 HU gastrointestinal content, comprising 11 cases with UGIB and 19 without UGIB. In diagnosing UGIB, the sensitivity and specificity for the CT cutoff value ≥27.7 HU were 84.6% and 77.6%, respectively. Using the criteria of both the CT cutoff value ≥27.7 HU and the solid nature of the high-density contents, overall sensitivity and specificity in the validation set changed to 61.5% and 96.5%, respectively. Using the criteria of both the CT cutoff value ≥27.7 HU and the existence of bubbles ≥4 mm inside or close to the high-density contents, sensitivity and specificity were 38.5% and 98.8%, respectively. Representative true-positive, false-positive, false-negative, and true-negative cases of UGIB are shown in . As a supplement, for all five patients who died from UGIB, the CT values of the upper gastrointestinal contents were above the optimal cutoff value. We elucidated that measuring the CT values of the upper gastrointestinal contents on noncontrast PMCT is useful for diagnosing UGIB in deceased patients in this case-control study. The proposed criterion of ≥27.7 HU was robustly validated owing to its high diagnostic power and reproducibility. Previous antemortem CT studies have reported that the CT value of extravascular blood was 20–40 HU in the unclotted type and 40–70 HU in the clotted type . Thus, UGIB observed in the present study may be a combination of unclotted and clotted blood samples. An ex vivo study reported that the higher the hematocrit, the higher the density of blood to above 100 HU . While the cutoff CT value proposed in the present study differentiated UGIB and non-UGIB adequately, there were some false-positive cases. These false-positive cases may have high-density gastrointestinal contents, such as food residue and medication, rendering a clear differential diagnosis difficult . In the present study, we explored additional candidate imaging findings to enhance the accuracy in distinguishing true-positive UGIB from false-positive UGIB. One was the solid nature of the high-density contents in the upper gastrointestinal tract, which improved the specificity by 18.9% but decreased the sensitivity by 23.1%. As reported previously, the solidity of the hematoma is associated with a high degree of clot contraction, and blood concentrates (or coagulates) rapidly compared to nonbiological gastrointestinal contents. Therefore, the solid components may distinguish UGIB from other liquid-like gastrointestinal contents. Another distinguishing candidate imaging finding was the existence of bubbles ≥4 mm inside or close to the high-density contents where measurement was conducted. We first hypothesized that food residues would include small amounts of air and bubbles caused by mastication and swallowing, whereas hematomas would not. However, in the present study, a larger bubble size was more likely to be observed in the UGIB cases than in the non-UGIB cases. The mechanism by which this phenomenon occurs could be explained as follows: the patient would start fasting on developing UGIB. Therefore, hematomas caused by UGIB are fresher than food residues and have less time to release gas. Otherwise, the relatively larger bubbles in the hemorrhage group may have been due to the higher viscosity of the blood clots. The present study had several limitations. First, the cohort comprised in-hospital death cases from a single institute, and PMCT was performed immediately after death. Therefore, direct application of these results to putrid forensic cases is not possible. Second, as the aim of the study was to identify the presence of UGIB, the cause of death in patients with UGIB might not necessarily be UGIB. Third, a detailed examination was not conducted on the CT values for each subject’s oral medication as it was exceedingly challenging to assess the CT values for all oral medications. In conclusion, in diagnosing UGIB on noncontrast PMCT, a CT cutoff value of ≥27.7 HU was a reproducible diagnostic criterion. S1 File (XLSX) |
Immunohistochemistry in the pathologic diagnosis and management of thyroid neoplasms | 7bb50e61-81de-4bb8-8849-81f972a19b5d | 10266214 | Anatomy[mh] | Introduction Thyroid carcinoma is the most common malignancy of endocrine organs and accounts for approximately 1% of all cancers. As per the new WHO classification scheme, the neoplasms of the thyroid gland are stratified into the following main categories: follicular cell-derived neoplasms, C-cell derived neoplasms, mixed medullary and follicular cell-derived neoplasms, salivary gland type carcinomas, thyroid tumors of uncertain histogenesis, thymic tumors within the thyroid, and embryonal thyroid neoplasms. Even though the majority of thyroid neoplasms can be diagnosed on the basis of cellular and architectural features, difficulties in the diagnosis can occur due to overlapping histomorphologic features between primary and secondary thyroid neoplasms and partial or complete loss of differentiation . The well-differentiated thyroid carcinomas originating from the thyroid follicular cells show either follicular or papillary growth patterns or an admixture of both. The presence of colloid within follicles and complex papillary structures with diagnostic nuclear cytology in these neoplasms facilitate the diagnosis of these neoplasms . The solid and “insular” growth pattern of poorly differentiated carcinoma, especially in cases with an inconspicuous or lack of a well-differentiated component, can be mistaken for C-cell-derived medullary thyroid carcinoma or metastatic neuroendocrine neoplasm arising at other body sites. Anaplastic/undifferentiated carcinoma can show varying cytologic features and growth patterns mimicking lymphoma, mesenchymal tumors, and secondary tumors of the thyroid gland . In the abovementioned scenarios, employing a panel of immunostains can help solve diagnostic conundrums. In addition, immunohistochemistry (IHC) has proven to be helpful in the diagnosis of the following rare tumors: mixed follicular and medullary thyroid carcinoma, salivary gland type carcinomas, tumors of uncertain histogenesis, and intra-thyroidal thymic neoplasms . Employing mutation-specific antibodies can serve to distinguish between papillary carcinomas harboring the BRAFV600E mutation from RAS-like neoplasms. The utility of proliferation markers such as Ki67 cannot be underestimated in the grading of thyroid carcinoma, which has been shown to be a predictor of clinical behavior in both follicular and C-cell-derived neoplasms .
Immunochemistry: basic concepts Immunostaining is an easy, cheap, and widely available technique for selectively identifying specific molecules in tissue sections and cytological preparations. This technique is based on the use of antibodies (also called immunoglobulins) that are Y‐shaped globular proteins formed by two light chains and two heavy chains, held together by disulphide bonds . The molecular recognition abilities of the antibodies allow for various applications in diagnostic pathology. Each antibody is capable of binding only to a specific antigen ; therefore, they are currently applied in pathology to identify the cell lineage, examine the expression of biomarkers, characterise tumors, and more recently, determine the expression of targets for tailored therapies. For diagnostic purposes, antibodies are labelled, directly or with a multistep chain, with a visible molecule that allows the recognition of their binding reaction in tissue sections or cytological samples. The immunostaining protocol is mostly automated in many laboratories, which improves the reproducibility of the reaction product, although this standardization is usually developed for IHC on histological sections, whereas dedicated recommendations and practice paradigms are still lacking for cytological samples. This is probably because of the large variability in cellular preparations (conventional smears, thin layer cytology, and cell-blocks), different treatment of the specimens for immunostaining, and interpretative cutoffs . As stated above, there are three main reasons for the application of immunocytochemistry in thyroid pathology: determining cell and site of origin, differentiating benign from malignant neoplasms, and influencing clinical management.
Determining cell and site of origin IHC is an indispensable tool that complements routine histologic techniques for elucidating differential diagnosis in histologic and cytologic preparations. The use of IHC in thyroid pathology is based on knowledge regarding cell of origin and further characterization. It is mainly suggested for lesions that are suspected of non-follicular or non-thyroidal origin (e.g., parathyroid, medullary thyroid carcinoma, lymphoma, metastases from other organs—secondary tumors, etc.) . 3.1 Thyroid follicular cell lineage markers 3.1.1 Thyroid follicular cell origin Is usually confirmed by a panel of immunohistochemical markers that can identify metastasis to the thyroid gland from other organs, thyroid cancer metastasis to extra-thyroidal sites, and thyroid carcinoma arising in ectopic thyroid tissue. The most important markers of thyroid follicular cell derivation are thyroglobulin (TG), thyroid transcription factor 1 (TTF1), and paired-box gene 8 (PAX8); antibodies against these are often used in a panel to overcome the limits of a single antibody . It is relevant to know some details about these antibodies . 3.1.2 Thyroglobulin Is the most specific marker of thyroid follicular cell derivation. It is a glycoprotein manufactured by thyrocytes, from which it is secreted into thyroid follicles, forming a major constituent of colloid. Normal thyrocytes show diffuse cytoplasmic staining by TG; this staining pattern is maintained in well-differentiated follicular-derived thyroid carcinomas, such as papillary thyroid carcinoma (PTC) and follicular thyroid carcinoma (FTC), and is completely absent in medullary thyroid carcinoma (MTC) and metastasis to the thyroid gland . Focal expression of TG in the follicular component is seen in cases of mixed medullary and follicular thyroid carcinoma. Focal diffuse staining has been reported in >50% of high-grade follicular-cell-derived non-anaplastic carcinomas and is often lost in the foci of necrotic tumor and anaplastic thyroid carcinoma (ATC). In oncocytic lesions, TG usually shows punctate and dot-like perinuclear staining pattern . The low or absent production of thyroglobulin by some tumors can lead to diagnostic conundrums, especially in the following clinical scenarios: the use of TG FNA washout evaluation for regional and distant metastasis and the role of serum TG measurement for the follow up of patients with thyroid carcinomas . 3.1.3 Thyroid transcription factor 1 Also termed as thyroid-specific enhancer binding protein (NKx2.1), belongs to the family of homologous transcription factors in the NKx2 gene, and is located in the q12–q21 region of chromosome 14. The TTF-1 gene translates a nuclear protein with an approximate mass of 38 kDa, comprising a single polypeptide of 371 amino acid polypeptides. TTF-1 expression is regulated during embryonic development and appears early in the foregut endoderm and then in the tracheal precursor cells. After birth, TTF-1 expression is confined to the pulmonary type II alveolar cells . In the thyroid gland, TTF-1 expression occurs earlier than the expression of genes related to follicular cell differentiation, such as TG, thyroid peroxidase (TPO), and thyrotropin receptor (TSHR) . TTF-1 shows nuclear expression by IHC in thyroid follicular and parafollicular cells and lung. TTF-1 is diffusely expressed in PTC, FTC, high-grade follicular-derived non-anaplastic thyroid carcinoma and MTC. TTF-1 expression is retained in less than 20% of ATCs . TTF-1 is expressed in more than 80% of lung adenocarcinomas, a subset of squamous cell carcinoma of pulmonary origin, small cell carcinoma, neuroendocrine carcinomas, and also rarely in adenocarcinoma of genitourinary and gastrointestinal tracts and breast . TTF-1 can be useful in confirming the diagnosis of a thyroid primary lacking a well-differentiated growth pattern (papillary or follicular) or unusual cytology, such as poorly differentiated thyroid carcinoma, mucoepidermoid carcinoma, and secondary tumors . 3.1.4 Paired box gene 8 Is a transcription factor that belongs to the paired-box family of genes; it plays a critical role in the development of the thyroid gland, kidney, and Mullerian tract . With IHC, its expression is seen in thyroid, renal, and urinary bladder neoplasms and malignancies of Mullerian origin, including ovarian primaries. Several studies have added the following to the repertoire of PAX8 positive tumors: carcinomas of the breast, lung, prostate, gastrointestinal tract, liver and pancreas, testicular tumors, mesothelioma, melanoma, and rhabdomyosarcoma . 3.1.5 PAX8 Gives a nuclear staining in normal and neoplastic thyrocytes, and usually maintains this expression pattern also in cases of high-grade follicular-cell-derived carcinoma, anaplastic carcinoma, and its squamous subtype . Among thyroid tumors, a majority of non-follicular cell-derived thyroid carcinomas stain negative for the PAX8 antibody . Rare cases of medullary thyroid carcinoma can show PAX8 expression. Intrathyroid thymic carcinoma (ITC) shows a nuclear positive reaction with polyclonal PAX8 antibody but does not react with the monoclonal form. Therefore, monoclonal PAX8 antibody is more specific for thyroid follicular cell origin . As noted above, as PAX8 is also expressed in a wide variety of neoplasms from other organs, an initial panel of TTF-1, TG, and PAX8 is needed to confirm or exclude distant metastases from a thyroid primary . The other markers used to confirm thyroid follicular cell differentiation include TTF-2 (FOXE1) and thyroid peroxidase . 3.2 Parafollicular C-cell specific markers Medullary thyroid carcinoma (MTC) originates from parafollicular C-cells of the thyroid gland. The C-cells mainly secrete calcitonin hormone, which plays a minor role in calcium metabolism compared with parathyroid hormone (PTH) . Most MTCs (>95%) secrete calcitonin and show patchy to diffuse cytoplasmic expression of this biomarker with IHC . It is well-known that MTC, in addition to its typical nesting growth, tumor cells with nuclear chromatin typical of neuroendocrine tumors (salt and pepper), and amyloid rich tumor stroma, can demonstrate a variety of architectures and cellular features that can be mistaken for other primary thyroid tumors . In such cases, IHC for calcitonin in pathologic preparations confirms the diagnosis of MTC. This also holds true for rare cases of mixed medullary and follicular thyroid carcinoma, in which calcitonin specifically highlights the MTC components . Owing to its architecture and cellular features, MTC can be difficult to distinguish from metastases to the thyroid from neuroendocrine carcinoma arising in other organs, especially the lung and gastrointestinal tracts . It is well known that calcitonin is also expressed in other neuroendocrine tumors besides MTC. In cases in which the diagnostic differential includes MTC and metastatic neuroendocrine carcinoma, clinical correlation and serum calcitonin level, which is often quite increased in MTC, can help determine the correct diagnosis. MTC also shows cytoplasmic expression of monoclonal carcinoembryonic antigen (mCEA), which can also serve as biomarker for disease surveillance in addition to calcitonin . This proves to be helpful in rare cases of calcitonin negative MTC . Additionally, MTC shows expression of other neuroendocrine markers, such as chromogranin, synaptophysin, and rarely CD56. The second-generation neuroendocrine markers insulinoma-associated protein 1 (INSM1), ISL1, and secretagogin show high sensitivity and specificity for neuroendocrine differentiation and maintain the expression even in poorly differentiated neuroendocrine carcinomas. In particular, INSM1 has been reported to be a highly sensitive and specific neuroendocrine marker and is useful in the diagnosis of MTC and C-cell hyperplasia . 3.2.1 Rare thyroid neoplasms The value of IHC cannot be underemphasized in the diagnosis of uncommon thyroid neoplasms and those of uncertain histogenesis (see ).
Thyroid follicular cell lineage markers 3.1.1 Thyroid follicular cell origin Is usually confirmed by a panel of immunohistochemical markers that can identify metastasis to the thyroid gland from other organs, thyroid cancer metastasis to extra-thyroidal sites, and thyroid carcinoma arising in ectopic thyroid tissue. The most important markers of thyroid follicular cell derivation are thyroglobulin (TG), thyroid transcription factor 1 (TTF1), and paired-box gene 8 (PAX8); antibodies against these are often used in a panel to overcome the limits of a single antibody . It is relevant to know some details about these antibodies . 3.1.2 Thyroglobulin Is the most specific marker of thyroid follicular cell derivation. It is a glycoprotein manufactured by thyrocytes, from which it is secreted into thyroid follicles, forming a major constituent of colloid. Normal thyrocytes show diffuse cytoplasmic staining by TG; this staining pattern is maintained in well-differentiated follicular-derived thyroid carcinomas, such as papillary thyroid carcinoma (PTC) and follicular thyroid carcinoma (FTC), and is completely absent in medullary thyroid carcinoma (MTC) and metastasis to the thyroid gland . Focal expression of TG in the follicular component is seen in cases of mixed medullary and follicular thyroid carcinoma. Focal diffuse staining has been reported in >50% of high-grade follicular-cell-derived non-anaplastic carcinomas and is often lost in the foci of necrotic tumor and anaplastic thyroid carcinoma (ATC). In oncocytic lesions, TG usually shows punctate and dot-like perinuclear staining pattern . The low or absent production of thyroglobulin by some tumors can lead to diagnostic conundrums, especially in the following clinical scenarios: the use of TG FNA washout evaluation for regional and distant metastasis and the role of serum TG measurement for the follow up of patients with thyroid carcinomas . 3.1.3 Thyroid transcription factor 1 Also termed as thyroid-specific enhancer binding protein (NKx2.1), belongs to the family of homologous transcription factors in the NKx2 gene, and is located in the q12–q21 region of chromosome 14. The TTF-1 gene translates a nuclear protein with an approximate mass of 38 kDa, comprising a single polypeptide of 371 amino acid polypeptides. TTF-1 expression is regulated during embryonic development and appears early in the foregut endoderm and then in the tracheal precursor cells. After birth, TTF-1 expression is confined to the pulmonary type II alveolar cells . In the thyroid gland, TTF-1 expression occurs earlier than the expression of genes related to follicular cell differentiation, such as TG, thyroid peroxidase (TPO), and thyrotropin receptor (TSHR) . TTF-1 shows nuclear expression by IHC in thyroid follicular and parafollicular cells and lung. TTF-1 is diffusely expressed in PTC, FTC, high-grade follicular-derived non-anaplastic thyroid carcinoma and MTC. TTF-1 expression is retained in less than 20% of ATCs . TTF-1 is expressed in more than 80% of lung adenocarcinomas, a subset of squamous cell carcinoma of pulmonary origin, small cell carcinoma, neuroendocrine carcinomas, and also rarely in adenocarcinoma of genitourinary and gastrointestinal tracts and breast . TTF-1 can be useful in confirming the diagnosis of a thyroid primary lacking a well-differentiated growth pattern (papillary or follicular) or unusual cytology, such as poorly differentiated thyroid carcinoma, mucoepidermoid carcinoma, and secondary tumors . 3.1.4 Paired box gene 8 Is a transcription factor that belongs to the paired-box family of genes; it plays a critical role in the development of the thyroid gland, kidney, and Mullerian tract . With IHC, its expression is seen in thyroid, renal, and urinary bladder neoplasms and malignancies of Mullerian origin, including ovarian primaries. Several studies have added the following to the repertoire of PAX8 positive tumors: carcinomas of the breast, lung, prostate, gastrointestinal tract, liver and pancreas, testicular tumors, mesothelioma, melanoma, and rhabdomyosarcoma . 3.1.5 PAX8 Gives a nuclear staining in normal and neoplastic thyrocytes, and usually maintains this expression pattern also in cases of high-grade follicular-cell-derived carcinoma, anaplastic carcinoma, and its squamous subtype . Among thyroid tumors, a majority of non-follicular cell-derived thyroid carcinomas stain negative for the PAX8 antibody . Rare cases of medullary thyroid carcinoma can show PAX8 expression. Intrathyroid thymic carcinoma (ITC) shows a nuclear positive reaction with polyclonal PAX8 antibody but does not react with the monoclonal form. Therefore, monoclonal PAX8 antibody is more specific for thyroid follicular cell origin . As noted above, as PAX8 is also expressed in a wide variety of neoplasms from other organs, an initial panel of TTF-1, TG, and PAX8 is needed to confirm or exclude distant metastases from a thyroid primary . The other markers used to confirm thyroid follicular cell differentiation include TTF-2 (FOXE1) and thyroid peroxidase .
Thyroid follicular cell origin Is usually confirmed by a panel of immunohistochemical markers that can identify metastasis to the thyroid gland from other organs, thyroid cancer metastasis to extra-thyroidal sites, and thyroid carcinoma arising in ectopic thyroid tissue. The most important markers of thyroid follicular cell derivation are thyroglobulin (TG), thyroid transcription factor 1 (TTF1), and paired-box gene 8 (PAX8); antibodies against these are often used in a panel to overcome the limits of a single antibody . It is relevant to know some details about these antibodies .
Thyroglobulin Is the most specific marker of thyroid follicular cell derivation. It is a glycoprotein manufactured by thyrocytes, from which it is secreted into thyroid follicles, forming a major constituent of colloid. Normal thyrocytes show diffuse cytoplasmic staining by TG; this staining pattern is maintained in well-differentiated follicular-derived thyroid carcinomas, such as papillary thyroid carcinoma (PTC) and follicular thyroid carcinoma (FTC), and is completely absent in medullary thyroid carcinoma (MTC) and metastasis to the thyroid gland . Focal expression of TG in the follicular component is seen in cases of mixed medullary and follicular thyroid carcinoma. Focal diffuse staining has been reported in >50% of high-grade follicular-cell-derived non-anaplastic carcinomas and is often lost in the foci of necrotic tumor and anaplastic thyroid carcinoma (ATC). In oncocytic lesions, TG usually shows punctate and dot-like perinuclear staining pattern . The low or absent production of thyroglobulin by some tumors can lead to diagnostic conundrums, especially in the following clinical scenarios: the use of TG FNA washout evaluation for regional and distant metastasis and the role of serum TG measurement for the follow up of patients with thyroid carcinomas .
Thyroid transcription factor 1 Also termed as thyroid-specific enhancer binding protein (NKx2.1), belongs to the family of homologous transcription factors in the NKx2 gene, and is located in the q12–q21 region of chromosome 14. The TTF-1 gene translates a nuclear protein with an approximate mass of 38 kDa, comprising a single polypeptide of 371 amino acid polypeptides. TTF-1 expression is regulated during embryonic development and appears early in the foregut endoderm and then in the tracheal precursor cells. After birth, TTF-1 expression is confined to the pulmonary type II alveolar cells . In the thyroid gland, TTF-1 expression occurs earlier than the expression of genes related to follicular cell differentiation, such as TG, thyroid peroxidase (TPO), and thyrotropin receptor (TSHR) . TTF-1 shows nuclear expression by IHC in thyroid follicular and parafollicular cells and lung. TTF-1 is diffusely expressed in PTC, FTC, high-grade follicular-derived non-anaplastic thyroid carcinoma and MTC. TTF-1 expression is retained in less than 20% of ATCs . TTF-1 is expressed in more than 80% of lung adenocarcinomas, a subset of squamous cell carcinoma of pulmonary origin, small cell carcinoma, neuroendocrine carcinomas, and also rarely in adenocarcinoma of genitourinary and gastrointestinal tracts and breast . TTF-1 can be useful in confirming the diagnosis of a thyroid primary lacking a well-differentiated growth pattern (papillary or follicular) or unusual cytology, such as poorly differentiated thyroid carcinoma, mucoepidermoid carcinoma, and secondary tumors .
Paired box gene 8 Is a transcription factor that belongs to the paired-box family of genes; it plays a critical role in the development of the thyroid gland, kidney, and Mullerian tract . With IHC, its expression is seen in thyroid, renal, and urinary bladder neoplasms and malignancies of Mullerian origin, including ovarian primaries. Several studies have added the following to the repertoire of PAX8 positive tumors: carcinomas of the breast, lung, prostate, gastrointestinal tract, liver and pancreas, testicular tumors, mesothelioma, melanoma, and rhabdomyosarcoma .
PAX8 Gives a nuclear staining in normal and neoplastic thyrocytes, and usually maintains this expression pattern also in cases of high-grade follicular-cell-derived carcinoma, anaplastic carcinoma, and its squamous subtype . Among thyroid tumors, a majority of non-follicular cell-derived thyroid carcinomas stain negative for the PAX8 antibody . Rare cases of medullary thyroid carcinoma can show PAX8 expression. Intrathyroid thymic carcinoma (ITC) shows a nuclear positive reaction with polyclonal PAX8 antibody but does not react with the monoclonal form. Therefore, monoclonal PAX8 antibody is more specific for thyroid follicular cell origin . As noted above, as PAX8 is also expressed in a wide variety of neoplasms from other organs, an initial panel of TTF-1, TG, and PAX8 is needed to confirm or exclude distant metastases from a thyroid primary . The other markers used to confirm thyroid follicular cell differentiation include TTF-2 (FOXE1) and thyroid peroxidase .
Parafollicular C-cell specific markers Medullary thyroid carcinoma (MTC) originates from parafollicular C-cells of the thyroid gland. The C-cells mainly secrete calcitonin hormone, which plays a minor role in calcium metabolism compared with parathyroid hormone (PTH) . Most MTCs (>95%) secrete calcitonin and show patchy to diffuse cytoplasmic expression of this biomarker with IHC . It is well-known that MTC, in addition to its typical nesting growth, tumor cells with nuclear chromatin typical of neuroendocrine tumors (salt and pepper), and amyloid rich tumor stroma, can demonstrate a variety of architectures and cellular features that can be mistaken for other primary thyroid tumors . In such cases, IHC for calcitonin in pathologic preparations confirms the diagnosis of MTC. This also holds true for rare cases of mixed medullary and follicular thyroid carcinoma, in which calcitonin specifically highlights the MTC components . Owing to its architecture and cellular features, MTC can be difficult to distinguish from metastases to the thyroid from neuroendocrine carcinoma arising in other organs, especially the lung and gastrointestinal tracts . It is well known that calcitonin is also expressed in other neuroendocrine tumors besides MTC. In cases in which the diagnostic differential includes MTC and metastatic neuroendocrine carcinoma, clinical correlation and serum calcitonin level, which is often quite increased in MTC, can help determine the correct diagnosis. MTC also shows cytoplasmic expression of monoclonal carcinoembryonic antigen (mCEA), which can also serve as biomarker for disease surveillance in addition to calcitonin . This proves to be helpful in rare cases of calcitonin negative MTC . Additionally, MTC shows expression of other neuroendocrine markers, such as chromogranin, synaptophysin, and rarely CD56. The second-generation neuroendocrine markers insulinoma-associated protein 1 (INSM1), ISL1, and secretagogin show high sensitivity and specificity for neuroendocrine differentiation and maintain the expression even in poorly differentiated neuroendocrine carcinomas. In particular, INSM1 has been reported to be a highly sensitive and specific neuroendocrine marker and is useful in the diagnosis of MTC and C-cell hyperplasia . 3.2.1 Rare thyroid neoplasms The value of IHC cannot be underemphasized in the diagnosis of uncommon thyroid neoplasms and those of uncertain histogenesis (see ).
Rare thyroid neoplasms The value of IHC cannot be underemphasized in the diagnosis of uncommon thyroid neoplasms and those of uncertain histogenesis (see ).
Differentiating benign from malignant thyroid neoplasms Most thyroid neoplasms are diagnosed based on architectural and cellular features and a lack or presence of invasive features. However, in some instances, it may be difficult to distinguish between follicular adenoma and non-invasive follicular tumor with papillary-like nuclear features (NIFTP), an encapsulated follicular variant of papillary thyroid carcinoma, follicular carcinoma, and follicular adenoma with papillary architecture from papillary thyroid carcinoma. The diagnosis of follicular carcinoma and the encapsulated follicular variant of papillary thyroid carcinoma requires the evaluation of the tumor-capsule-thyroid interface. Invasion of the capsule, invasion through the capsule, and invasion into veins in or beyond the capsule represent the diagnostic criteria for carcinoma in a follicular-patterned thyroid neoplasm. To this date, what constitutes “ capsular ” invasion in a follicular-patterned thyroid neoplasm remains controversial. Some require penetration through the capsule of the tumor and others invasion into the capsule to render a diagnosis of either follicular carcinoma or an encapsulated follicular variant of papillary thyroid carcinoma, while others believe that the diagnosis of minimally invasive follicular carcinoma should only be rendered when vascular invasion is present. However, studies have shown that metastatic disease can occur in cases of follicular carcinoma in which only capsular invasion occurred. Thus, “capsular invasion is a sufficient criterion to diagnose malignancy” . Despite the controversy regarding capsular invasion as a criterion for malignancy, all agree that angioinvasion is a definite feature of malignancy. It has been shown that encapsulated angioinvasive follicular-patterned tumors carry a significant propensity for clinically malignant behavior. The following histomorphologic criteria have been proposed for the diagnosis of angioinvasion: the invasive tumor should form a plug or polyp in a subendothelial location, enveloping of tumor thrombus by the endothelium, and the tumor thrombus does not have to be attached to the vessel wall to be accepted as an invasion . Immunostaining for Factor VIII–related antigen and other endothelial markers, such as CD31, CD34, and ERG, can confirm the foci of angioinavsion. Rarely, histiocytes intermixed with fibrin and inflammatory cells within capsular vessels can mimic foci of angioinvasion. In such instances, macrophage markers, such as CD68 or CD163, and markers for follicular cell lineage, TTF-1, PAX8, and thyroglobulin, can help to confirm the presence of tumor cells within a vessel lumen . The diagnostic conundrum of differentiating a benign from a malignant thyroid lesion is often encountered in limited cellularity fine-needle aspiration (FNA) and core-biopsy specimens. The use of immunohistochemical markers for differentiating benign from malignant thyroid neoplasms in FNA specimens classified as indeterminate is often debated in the literature . The combination of HBME-1, GAL-3, and CK19 is by far the most common panel for distinguishing benign from malignant thyroid neoplasms, as no individual biomarker has sufficient sensitivity or specificity to accomplish this task. Combined immunopositivity for Gal-3, CK19, and HBME-1 shows high sensitivity (95%) and specificity (97%) for the diagnosis of papillary thyroid carcinoma . Combined immunoexpression of Gal-3 and CK19 had 92% sensitivity and 99% specificity while combined positivity for Gal-3 and HBME-1 had 95% sensitivity and 95% specificity for papillary thyroid carcinoma . It should be noted that the expression of HBME-1, GAL-3, and CK19 is not predictive of the clinical aggressiveness of the tumor and cannot be used to guide the surgical excision . Galectin 3 (Gal-3) has received significant attention for its utility as a diagnostic marker for thyroid cancer, being positive in thyroid carcinoma and negative in benign neoplasms and normal thyroid tissue . In a meta-analysis of 8,172 thyroid nodules with histologic evaluation, Gal-3 IHC was reported to be positive in 87% of thyroid cancers, confirmed by histopathologic follow-up. This information confirms that many thyroid carcinomas have overexpression of this marker. A Gal-3 test on thyroid FNA samples (cellblock preparation) has a sensitivity lower than that observed in histologic preparations (pooled histologic sensitivity of Gal-3 was 96%, while sensitivity with FNA was 90%); mainly due to the different methods used for Gal-3 evaluation in thyroid cytological specimens, technical variability in antibody clones and immunostaining protocols, and relevant differences in staining interpretation (i.e. nuclear, cytoplasmic, or membranous positivity) . In summary, the use of Gal-3 in cellblock preparation from thyroid FNA may support a diagnosis of malignancy in thyroid nodules classified as indeterminate. In addition to galectin-3, other markers such as Hector Battifora mesothelial cell-1 (HBME-1), cytokeratin-19 (CK19), and cluster differentiation antigen 56 (CD56) can facilitate the diagnosis of thyroid carcinoma in both histologic and cytologic preparations . Ki-67 is the protein product of the gene MKI67 and is a commonly used IHC marker for cell proliferation. Recently, the Ki-67 index has been proposed for the stratification of PTC, FTC, and MTC into different risk categories. The proposed Ki-67 indices show that differentiated thyroid carcinomas can be stratified into low-, moderate-, and high-risk groups using the cutoff values of <5%, 5–10%, and 10–30% . A two-tiered system is recommended for Ki67 evaluation in medullary thyroid carcinoma, employing a cutoff of 5% . With light microscopy, at least one of three features, mitotic index of ≥5 per 2 mm 2 , Ki67 proliferative index of ≥5%, or tumor necrosis, is required to define high-grade MTCs, and these criteria have been integrated in the 5th edition of the World Health Organization classification of thyroid tumors . Additionally, the use of Ki67 IHC with cytological samples from thyroid FNA has been investigated. In a recent study, the authors applied a scoring evaluation for calculating the percentage of positive cells by counting at least 200 tumor cells; they concluded that the Ki-67 index determined in cytology specimens significantly correlates with the Ki-67 index obtained by immunohistochemical analyses of histologic specimens . This analysis was performed on air-dried smears that were formalin fixed before immunostaining. IgG4-thyroid-related disease (TRD), although uncommon, is a spectrum of diseases with a clinical presentation that can often mimic malignancy. The threshold to confirm increased IgG4-positive plasma cells ranges from more than 20 to more than 30 IgG4-positive plasma cells per high-power field by microscopic examination . The FNA specimens show lymphoplasmacytic infiltrates and oncocytes and the cytological features are usually classified as benign . Clinical history, radiological characteristics, and cytological features, such as abundant plasma cells, fibroblast, and epithelial atypia, should raise the suspicion of IgG4-related disease . If IgG4 TRD is clinically suspected at the time of FNA, IHC might confirm the predominance of IgG4-secreting plasma cells in the cytological sample, leading to additional clinical workup.
Molecular immunohistochemistry Molecular profiling of thyroid carcinomas with aggressive clinical behavior has become a standard of care. Modern immunohistochemistry has proven to be an easily practiced approach in the everyday practice of histopathology to triage advanced tumors for further mutation testing. Specific IHC is available for BRAFV600E mutation, RASQ61R mutation, NTRK rearrangement, and ALK rearrangement. Of note, among the IHC for these altered proteins derived from molecular changes, only IHC for BRAFV600E is approved to be of value in the clinical management of malignant thyroid neoplasms; other mutation-specific IHC only confirms the presence or absence of mutation or rearrangement . IHC using mutation-specific antibodies against BRAFV600E (VE1 clone, Spring Bioscience, Pleasanton, CA) provides an alternative inexpensive method for the rapid identification of BRAFV600E mutation-positive thyroid tumors. The overall reported sensitivity and specificity of BRAF p.V600E immunostaining with cellblock preparation is 94.4% and 100%, respectively; however, this approach is not recommended for FNA smears and monolayer preparations . Consensus guidelines drawn up by an international expert panel do not recommend IHC for NTRK fusion confirmation; however, in some cases, IHC can be used for preliminary screening . Similarly, ALK IHC is suggested as a screening procedure, and FISH analysis is recommended for the final confirmation of ALK rearrangement . The IHC for RET should not be considered as an option for pre-screening . IHC also allows the characterization of tumor microenvironment (PD-L1 and CD markers) and its role in predicting the response of thyroid cancer to immunotherapy . Different scoring systems for PD-L1 immunostaining have been approved by the FDA as companion diagnostic tests for patient selection for the treatment of various tumors, such as melanoma and lung cancer. PD-L1 expression in thyroid cancer has been shown to be similar to that in other solid tumors. A study of 407 primary thyroid cancers showed PD-L1 expression in 6.1% of papillary thyroid carcinomas, 7.6% of follicular thyroid carcinomas, and 22.2% of anaplastic thyroid carcinomas at a threshold of 1% . In a recent meta-analysis, the frequency of PD-L1 positivity in thyroid tumor cells for different histological types ranged from 7% to 90%. This study also demonstrates the role of PD-L1 expression as a potential prognostic marker of disease recurrence in patients with papillary thyroid carcinoma . The clinical utility of determining PD-L1 expression supports the use of PD-L1 immunotherapy as a part of combination therapy in metastatic and RAI-refractory thyroid cancer . The optimal cutoff value for immunohistochemical positivity of PD-L1 immunostaining has not yet been validated in thyroid cancer, and the variability in different studies depends on the selected clone, the immunostaining method, and the morphological interpretation. When PD-L1 immunoreaction is used for treatment purposes, it is mandatory that only membranous staining of PD-L1 is considered positive and not the cytoplasmic expression . A recent review has shown that cytological samples constitute a reliable source for PDL-1 IHC analysis, as evidenced by the tumor-rich specimens and concordant results between cytological and histological specimens . This study emphasizes that the fixatives used in today’s cytology laboratories do not compromise PD-L1 staining, attesting to the utility of cytological specimens for PD-L1 testing in routine clinical practice. PD-L1 IHC may predict the success of PD-1 blockade therapy in a subset of patients with an anaplastic carcinoma PD-1 tumor proportion score of >=1% . 5.1 Technical considerations for immunohistochemistry Destained smear slides prepared during the rapid onsite evaluation of FNA specimens have been used for IHC in cytology. This technique only allows the use of either one or two immunostains and helps the characterization of the cell of origin (follicular, parafollicular/C-cells, metastatic disease, and hematologic neoplasms). In specimens with limited cellularity, areas of interest on the smear can be circled with a glass pen on the reverse slide to easily locate the cell cluster after immunostaining . As a general rule, international guidelines recommend that cellblock is the cytology preparation of choice for performing a panel of immunostains . Cytology cell blocks are made by employing different fixatives and paraffin to obtain morphology similar to histologic preparations and can be routinely used for IHC. Different techniques can be used to prepare cellblocks either by automatic instruments or ready to use gels and matrices. Quality control assessment for immunostaining with cytological samples is a mandatory requirement for each laboratory. UK NEQAS ICC for the quality of immunocytochemical staining reported that cellblock sections achieved the highest score . Cellblock preparations are also recommended for “molecular immunohistochemistry”, in which markers are designed to recognize the presence of altered proteins from mutated genes .
Technical considerations for immunohistochemistry Destained smear slides prepared during the rapid onsite evaluation of FNA specimens have been used for IHC in cytology. This technique only allows the use of either one or two immunostains and helps the characterization of the cell of origin (follicular, parafollicular/C-cells, metastatic disease, and hematologic neoplasms). In specimens with limited cellularity, areas of interest on the smear can be circled with a glass pen on the reverse slide to easily locate the cell cluster after immunostaining . As a general rule, international guidelines recommend that cellblock is the cytology preparation of choice for performing a panel of immunostains . Cytology cell blocks are made by employing different fixatives and paraffin to obtain morphology similar to histologic preparations and can be routinely used for IHC. Different techniques can be used to prepare cellblocks either by automatic instruments or ready to use gels and matrices. Quality control assessment for immunostaining with cytological samples is a mandatory requirement for each laboratory. UK NEQAS ICC for the quality of immunocytochemical staining reported that cellblock sections achieved the highest score . Cellblock preparations are also recommended for “molecular immunohistochemistry”, in which markers are designed to recognize the presence of altered proteins from mutated genes .
Conclusion Currently, immunohistochemical and molecular analysis are integral to the diagnosis and management of thyroid neoplasms. Accurate diagnosis and classification of thyroid tumors according to the recent classification scheme can be achieved by employing specific immunostains in both histologic and cytologic specimens . New multiplex chromogenic and multiplex fluorescent IHC are emerging technologies that enable the simultaneous detection of multiple biomarkers in a single tissue section. Their development for preclinical research and clinical application has increased extraordinarily in the last 5 years, paving the way for a better understanding of tumorigenesis and clinical behavior, and they are expected to improve the personalized treatment of patients with malignant tumors of the thyroid gland .
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
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Assessment of Skimmed Milk Flocculation for Bacterial Enrichment from Water Samples, and Benchmarking of DNA Extraction and 16S rRNA Databases for Metagenomics | 2be2b824-c335-4f07-b186-368c7c2d7e42 | 11477342 | Microbiology[mh] | Freshwater and marine ecosystems are vital for human survival and evolution and are commonly studied using targeted or shotgun metagenomics . Metagenomics studies of these environments involve extracting nucleic acids from samples to analyze the microbial communities and their functional characteristics . Regardless of methods, study designs, and goals, samples of various water volumes are collected, transported to laboratories, and optimally processed immediately to avoid biased results . The initial step of most bacterial metagenomics studies on water samples involves extracting biomass (also called enrichment) from the water content . Most often, water samples include small particles such as soil, rocks, and plant residues to which microorganisms and/or extracellular DNA may adhere. To capture all microbial content, these particles are ideally retained in the final sample , including those present as planktonic forms. For this purpose, several techniques have been developed and used to varying degrees, namely, vacuum filtration (VF), ultracentrifugation (UC), skimmed milk flocculation (SMF), and polyethylene glycol (PEG) precipitation. UC and PEG precipitation are applied almost exclusively for viral and phage metagenomics . VF is mainly used for the characterization of bacterial and fungal communities and is by far considered the gold standard . It is a straightforward size exclusion or inclusion method used in metagenomics to separate microbial cells and sample debris from the water content by vacuum as a pressure-driven factor. However, there are numerous factors that influence the filterability during vacuum filtration, such as membrane protein-binding affinity, surface charge, hydrophobicity, pore size and structure, and roughness . Additionally, the size, shape, flexibility, charge, and hydrophobicity of cells also influence the potential of bacteria to flow through the filter, and lastly, the particulate nature of the sample suspension itself . For these reasons, research on filtration methods is still evolving and alternative approaches are being sought. The SMF might be considered a viable option for bacterial enrichment. It was originally developed to concentrate viruses from coastal waters , later widely applied to SARS-CoV-2 , as well as others . By design, it is straightforward, affordable, and quick to perform, and requires simple laboratory equipment. The flocculation is achieved at 3.5–4.0 pH, where casein proteins (net positive charge) interact with viral particles from water samples, which carry a net negative charge due to their functional surface groups, such as carboxylates and phosphates . The reaction solution is usually agitated at low rpm for approx. 2 h, during which, through electrostatic interactions, flocs (virus-protein complexes or aggregates) are formed and settle out of the solution either naturally or facilitated through centrifugation at 3500× g for 30 min . Previously, SMF has been used for simultaneous concentration and quantification of waterborne viruses, bacteria, and protozoa mainly for water control purposes and microbial risk assessment studies. However, there is a literature gap on its applicability in bacterial microbiome studies. Therefore, we used both 16S rRNA and shotgun metagenomics data from water samples to assess the applicability of SMF for bacterial metagenomics. Another critical step is the extraction of DNA from filters or precipitates. Numerous studies have assessed the impact of various DNA extraction protocols on the quantitative analysis of bacterial biomass , highlighting the need for a standardized protocol that yields reproducible results to facilitate cross-study comparisons, especially in research areas with future health diagnostic perspectives such as the profiling of the human gut microbiota. For instance, according to the largest current comparative study based on shotgun sequencing evaluating the bacterial extraction performance of 21 fecal DNA extraction protocols , Protocol Q, which is a slightly modified version of Qiagen’s QIAamp DNA Stool Mini Kit, has been proposed as the standard protocol providing the best results for bacterial DNA extraction from human feces. To date, there is no equivalent DNA extraction protocol for aqueous environmental samples . Next, in the case of targeted bacterial metagenomics, after sequencing and initial quality control of sequencing data, 16S rRNA databases (16S-DBs) are utilized to infer the bacterial taxonomic composition. While the de novo clustering approach is often the choice for initial observation as the composition is not influenced by 16S-DBs, it is not optimal for cross-study comparisons . On the other hand, closed or open-reference clustering approaches are still widely preferred , although they utilize 16S-DBs for clustering, which introduces bias. Several studies benchmarked their performance previously , but they undergo constant updates that prompt additional evaluation. To address the last two problems, we optimized an in-house protocol that incorporates elements from other published protocols and applied it to a 10-species microbial community standard (MCS) alongside four other commercial DNA extraction kits. The MCS samples were then subjected to 16S rRNA metagenomics, and the resulting sequence data were used to benchmark the DNA extraction kits and six well-established as well as more recently published 16S-DBs. For effective comparison, we used the measurement integrity quotient (MIQ) score, which quantifies the difference between the observed and the expected composition . This study covers three critical aspects of metagenomics workflows: the applicability of SMF for bacterial metagenomics on real samples, the evaluation of our in-house DNA extraction method, and the comparison of 16S-DBs for taxonomic assignments.
First, we evaluated the bias introduced by different DNA extraction protocols and the 16S-DBs on the 16S metagenomics MCS datasets ( n = 8). In total, we scored factors such as DNA yield, A 260/280nm , A 260/230nm , species-level taxon accuracy rate (TAR), genus-level taxon detection rate (TDR), MIQ, and the percent of reads that mapped to the reference sequences, failed quality filter, failed to merge, or were chimeric . The in-house method yielded the highest amount of DNA, while the highest purity was achieved with the EurX and EZNA kits. Based on the overall results, the EurX kit was used for the shotgun studies and the evaluation of SMF. 2.1. Variability of MIQ Score in DNA Extraction Protocols The miqScore16SPublic tool (2.6.) provided by Zymo was used to establish the bias introduced by DNA extraction kits. It was designed to generate amplicon sequence variants (ASVs) instead of operational taxonomic units (OTUs) and calculate an MIQ score for each MCS, assigning a value between 0 (indicating bias) and 100 (indicating no bias) based on the comparison of observed versus expected composition. Complete reports are available in and only the radar plots were provided in . Results indicate that the samples isolated by the FastSpin Soil kit showed the least biased score (88 MIQ), followed by EurX and the in-house method, whereas the worst MIQ scores were in the Zymo kit and its datasets with varying annealing temperatures. 2.2. Bias Introduced by 16S rRNA DBs and/or DNA Extraction Kits on MCS Samples Herein, we assessed to what extent the 16S-DBs might introduce bias in taxonomic composition. Closed-reference OTU clustering at 99% was applied to the MCS samples. Unfortunately, the tool miqScore16SPublic is incompatible with taxonomic tables generated from external sources. As a workaround, we implemented its intrinsic MIQ score formula in a simple Python script, allowing the calculation to be applied to taxonomic tables regardless of their origin. Taxonomic composition plots of all MCS samples are visualized in . The GTDB-full and Silva DBs generated more OTUs compared to the remaining 16S-DBs. This could be considered both beneficial and negative depending on the study’s purpose. However, in this case, the additional OTU Citrobacter_B in GTDB-full that should not be present in the MCS sample skewed the results, resulting in lower MIQ scores ( and ). We compared the 16S-DBs and DNA extraction protocols using MIQ scores in parallel, and the results are shown in . First, 16S-DBs were compared ( A), and the best compositions with the least bias were yielded by GG_13.8, followed by GSR and GTDB-full. Interestingly, the GG_13.8 DB failed to differentiate between Escherichia coli and Salmonella enterica , combining them into a single family-level OTU group ( Enterobacteriaceae ). Unfortunately, there was no adequate approach to separate this 99% clustered OTU group for the MIQ calculator to precisely quantify the bias for each microorganism. Therefore, for GG_13.8 specifically, we treated both E. coli and S. enterica as a single organism and adjusted the reference expected composition in the MIQ score script. As a result, all the MIQ scores from GG_13.8 are elevated compared to the remaining 16S-DBs and should be interpreted with caution. Next, we compared the performance of different DNA extraction kits ( B), regardless of the 16S-DBs used. Except for the ZymoBIOMICs with a primer annealing temperature of 55 °C, all the Zymo variants performed worse compared to the other kits. The FastSpin kit produced the highest MIQ scores (82.6 on average) with all 16S-DBs. The second-best results were scored by our in-house protocol. Interestingly, all the 16S-DBs aside from GG_13.8 yielded comparable results across the DNA extraction kits, with scores ranging from 59 to 71 in the worst-performing sample (Zymo-62C) and 80 to 83 in the best sample (FastSpin). These results suggest that the choice of DNA extraction kits had a greater impact on the final MCS composition. The bias observed in MCS samples could not be compensated by using a better-performing 16S-DB (as seen with both repetitions of Zymo-62C in C). Conversely, a sample treated with a good-performing DNA extraction kit yielded a taxonomic composition resembling the expected outcome, regardless of the 16S-DB used. 2.3. Taxa Identification Efficiency of 16S rRNA DBs on De Novo Clustered MCS Samples While taxonomic composition is the first criterion to assess the bias in MCS samples, TAR and TDR are two important factors that essentially evaluate how 16S-DBs perform the taxonomical identification. Although we presented the results as values for each 16S-DB, one should note that they are heavily influenced by the amplified region, and the choice of the OTUs vs. ASVs approach, and should not be considered entirely as drawbacks to the 16S-DBs. The results of TAR and TDR were calculated with data from 99% de novo clustered OTUs as the taxonomic composition of all resulting OTUs is the same regardless of the DB, which provides an equal basis for comparison. The 99% OTU-generated results tend to underrepresent Enterococcus faecalis by splitting it into two separate OTUs g_Enterococcus and s_Enterococcus faecalis with similar relative frequency values in all extraction kits, thus skewing the total microbial composition. For a small MCS with only eight bacterial strains, results did not vary drastically, and also the TAR and TDR scores at each taxonomic level were identical. At the species level, accurate identification varied from 1/8 bacteria for Silva, 2/8 for GSR, and up to 4/8 for Ezbio, while for the genus level, it was between 6/8 and 8/8 . GG_13.8 and GTDB were designed for genus-level identification; therefore, species-level resolution was not possible and not discussed. Only one case of misclassification was recorded, namely, Listeria monocytogenes identified as Listeria ivanovii by Ezbio. Underclassifications by two taxonomic levels were observed for Salmonella enterica by GTDB-full, GTDB-less, and Silva and for Escherichia coli by GG_13.8 and GSR. Additionally, P. aeruginosa was underclassified by two ranks by GG_13.8 . 2.4. Evaluation of Skimmed Milk Flocculation SMF and VF were compared only on real samples as the MCS by Zymo is not designed to be pre-treated before DNA extraction as cells are stored in DNA/RNA Shield TM and are partially lysed. The relative taxonomic composition of both types of datasets was presented in A,B. In all sample pairs (a pair being VF and SMF-treated), noticeable separation of SMF- and VF-treated samples was observed in the PCoA plots (PERMANOVA: F = 26.6, R 2 = 0.6, p < 0.001), as shown in While the significant separation based on the Bray–Curtis beta diversity index indicated that the taxonomic composition of VF and SMF-treated samples differed, differential abundance analysis (DAA) was employed to identify which taxa were the driving factors of this effect. The results are available in C,D and the full-length plots in . In the 16S rRNA amplicon datasets (River Perlovska), the genera Polaromonas and Agitococcus and an OTU identified at high taxonomic rank were observed to be the highest overrepresented in the SMF-treated samples. The genus Polaromonas was identified by GG_13.8 and the GSR at the species level as P. naphtalenivorans . The third OTU was either domain Bacteria , order Bacteroidales , or identified as family Saprospiraceae by EzbioCloud (both the free DB v2018 and their website non-free latest DB v2023.08.23). All three taxa and a few more were also shown to be significantly enriched ( p < 0.5) with log fold change (LFC) of 2.0 or greater in all DNA extraction kits, as shown by the DAA in C. A similar trend was observed in the shotgun metagenomics datasets (River Iskar) but with different profiles of enriched taxa. Not all enriched taxa were visible on the bars except for the genera Streptococcus (brown) and Lactococcus (pale yellow) in SMF-S2, SMF-S3, and SMF-S4 in B. Sankey plots are provided for better visualization of all taxa in . According to the DAA, the lactic acid bacteria members Lactococcus , Leuconostoc , Streptococcus , Enterococcus , and Lactobacillus were significantly enriched ( p < 0.5) with LFC between 1.75 and 5.0 in the SMF-treated samples. Lastly, the genus Macrococcus was also significantly overrepresented and Bracken species-level hits were mostly Macrococcus caseolyticus. Interestingly, the genus Streptococcus was also detected in the amplicon metagenomics samples but was not overrepresented. While the profiles of these enriched taxa varied across different samples and methodologies, the enrichment effect was clear and significantly influenced the final taxonomic composition. This consistency was observed despite variations in DNA extraction kits used for 16S and different sampling dates in shotgun metagenomics. Therefore, this effect appeared independently of methodology or sample type. The complete nonfiltered taxonomy tables are available as .
The miqScore16SPublic tool (2.6.) provided by Zymo was used to establish the bias introduced by DNA extraction kits. It was designed to generate amplicon sequence variants (ASVs) instead of operational taxonomic units (OTUs) and calculate an MIQ score for each MCS, assigning a value between 0 (indicating bias) and 100 (indicating no bias) based on the comparison of observed versus expected composition. Complete reports are available in and only the radar plots were provided in . Results indicate that the samples isolated by the FastSpin Soil kit showed the least biased score (88 MIQ), followed by EurX and the in-house method, whereas the worst MIQ scores were in the Zymo kit and its datasets with varying annealing temperatures.
Herein, we assessed to what extent the 16S-DBs might introduce bias in taxonomic composition. Closed-reference OTU clustering at 99% was applied to the MCS samples. Unfortunately, the tool miqScore16SPublic is incompatible with taxonomic tables generated from external sources. As a workaround, we implemented its intrinsic MIQ score formula in a simple Python script, allowing the calculation to be applied to taxonomic tables regardless of their origin. Taxonomic composition plots of all MCS samples are visualized in . The GTDB-full and Silva DBs generated more OTUs compared to the remaining 16S-DBs. This could be considered both beneficial and negative depending on the study’s purpose. However, in this case, the additional OTU Citrobacter_B in GTDB-full that should not be present in the MCS sample skewed the results, resulting in lower MIQ scores ( and ). We compared the 16S-DBs and DNA extraction protocols using MIQ scores in parallel, and the results are shown in . First, 16S-DBs were compared ( A), and the best compositions with the least bias were yielded by GG_13.8, followed by GSR and GTDB-full. Interestingly, the GG_13.8 DB failed to differentiate between Escherichia coli and Salmonella enterica , combining them into a single family-level OTU group ( Enterobacteriaceae ). Unfortunately, there was no adequate approach to separate this 99% clustered OTU group for the MIQ calculator to precisely quantify the bias for each microorganism. Therefore, for GG_13.8 specifically, we treated both E. coli and S. enterica as a single organism and adjusted the reference expected composition in the MIQ score script. As a result, all the MIQ scores from GG_13.8 are elevated compared to the remaining 16S-DBs and should be interpreted with caution. Next, we compared the performance of different DNA extraction kits ( B), regardless of the 16S-DBs used. Except for the ZymoBIOMICs with a primer annealing temperature of 55 °C, all the Zymo variants performed worse compared to the other kits. The FastSpin kit produced the highest MIQ scores (82.6 on average) with all 16S-DBs. The second-best results were scored by our in-house protocol. Interestingly, all the 16S-DBs aside from GG_13.8 yielded comparable results across the DNA extraction kits, with scores ranging from 59 to 71 in the worst-performing sample (Zymo-62C) and 80 to 83 in the best sample (FastSpin). These results suggest that the choice of DNA extraction kits had a greater impact on the final MCS composition. The bias observed in MCS samples could not be compensated by using a better-performing 16S-DB (as seen with both repetitions of Zymo-62C in C). Conversely, a sample treated with a good-performing DNA extraction kit yielded a taxonomic composition resembling the expected outcome, regardless of the 16S-DB used.
While taxonomic composition is the first criterion to assess the bias in MCS samples, TAR and TDR are two important factors that essentially evaluate how 16S-DBs perform the taxonomical identification. Although we presented the results as values for each 16S-DB, one should note that they are heavily influenced by the amplified region, and the choice of the OTUs vs. ASVs approach, and should not be considered entirely as drawbacks to the 16S-DBs. The results of TAR and TDR were calculated with data from 99% de novo clustered OTUs as the taxonomic composition of all resulting OTUs is the same regardless of the DB, which provides an equal basis for comparison. The 99% OTU-generated results tend to underrepresent Enterococcus faecalis by splitting it into two separate OTUs g_Enterococcus and s_Enterococcus faecalis with similar relative frequency values in all extraction kits, thus skewing the total microbial composition. For a small MCS with only eight bacterial strains, results did not vary drastically, and also the TAR and TDR scores at each taxonomic level were identical. At the species level, accurate identification varied from 1/8 bacteria for Silva, 2/8 for GSR, and up to 4/8 for Ezbio, while for the genus level, it was between 6/8 and 8/8 . GG_13.8 and GTDB were designed for genus-level identification; therefore, species-level resolution was not possible and not discussed. Only one case of misclassification was recorded, namely, Listeria monocytogenes identified as Listeria ivanovii by Ezbio. Underclassifications by two taxonomic levels were observed for Salmonella enterica by GTDB-full, GTDB-less, and Silva and for Escherichia coli by GG_13.8 and GSR. Additionally, P. aeruginosa was underclassified by two ranks by GG_13.8 .
SMF and VF were compared only on real samples as the MCS by Zymo is not designed to be pre-treated before DNA extraction as cells are stored in DNA/RNA Shield TM and are partially lysed. The relative taxonomic composition of both types of datasets was presented in A,B. In all sample pairs (a pair being VF and SMF-treated), noticeable separation of SMF- and VF-treated samples was observed in the PCoA plots (PERMANOVA: F = 26.6, R 2 = 0.6, p < 0.001), as shown in While the significant separation based on the Bray–Curtis beta diversity index indicated that the taxonomic composition of VF and SMF-treated samples differed, differential abundance analysis (DAA) was employed to identify which taxa were the driving factors of this effect. The results are available in C,D and the full-length plots in . In the 16S rRNA amplicon datasets (River Perlovska), the genera Polaromonas and Agitococcus and an OTU identified at high taxonomic rank were observed to be the highest overrepresented in the SMF-treated samples. The genus Polaromonas was identified by GG_13.8 and the GSR at the species level as P. naphtalenivorans . The third OTU was either domain Bacteria , order Bacteroidales , or identified as family Saprospiraceae by EzbioCloud (both the free DB v2018 and their website non-free latest DB v2023.08.23). All three taxa and a few more were also shown to be significantly enriched ( p < 0.5) with log fold change (LFC) of 2.0 or greater in all DNA extraction kits, as shown by the DAA in C. A similar trend was observed in the shotgun metagenomics datasets (River Iskar) but with different profiles of enriched taxa. Not all enriched taxa were visible on the bars except for the genera Streptococcus (brown) and Lactococcus (pale yellow) in SMF-S2, SMF-S3, and SMF-S4 in B. Sankey plots are provided for better visualization of all taxa in . According to the DAA, the lactic acid bacteria members Lactococcus , Leuconostoc , Streptococcus , Enterococcus , and Lactobacillus were significantly enriched ( p < 0.5) with LFC between 1.75 and 5.0 in the SMF-treated samples. Lastly, the genus Macrococcus was also significantly overrepresented and Bracken species-level hits were mostly Macrococcus caseolyticus. Interestingly, the genus Streptococcus was also detected in the amplicon metagenomics samples but was not overrepresented. While the profiles of these enriched taxa varied across different samples and methodologies, the enrichment effect was clear and significantly influenced the final taxonomic composition. This consistency was observed despite variations in DNA extraction kits used for 16S and different sampling dates in shotgun metagenomics. Therefore, this effect appeared independently of methodology or sample type. The complete nonfiltered taxonomy tables are available as .
This is the first study to evaluate the applicability of SMF in bacterial metagenomics. Despite the small scale of the study design, with the use of DAA analysis, it was clearly shown that SMF skewed the taxonomic composition of real water samples, therefore rendering this SMF protocol inapplicable for bacterial enrichment in metagenomics. Interestingly, not all taxa were altered, rather only specific ones. On the contrary, a previous study on the concentration of specific species such as Escherichia coli and Helicobacter pylori SMF concluded that it could be used for the qualitative detection of those pathogens . Although they proved that both species could be effectively recovered from water samples by using SMF, it remains unknown if their actual concentration was affected, as observed for other species in this study. Skimmed milk primarily consists of protein (casein and whey) and lactose, in addition to other nutrients and minerals that could act as growth factors for bacteria, and is commonly supplied in culture media. In this regard, the genus Polaromonas has previously been shown to be enriched in dairy products removal tanks , while other studies have identified it as the third most abundant genus in mixed-species dairy biofilm within biofilters . The genus Agitococcus was also significantly enriched in SMF-treated samples and, while no species-level identification was achieved, Agitococcus lubricus , a species first described in 1981, tested positive for skimmed milk proteolysis . It is likely that other members of the genus Agitococcus would also be capable of proteolysis. Unfortunately, the most abundant significantly enriched OTU group in the sample was identified at a high taxonomic level with all 16S-DBs. This level of identification is too general and possibly unreliable, making it difficult to draw any meaningful conclusions about its potential role in SMF utilization. Similarly, in the shotgun datasets, all the enriched lactic acid bacteria are generally found in decomposing plants and milk products, which produce lactic acid as the main metabolic end product of carbohydrate fermentation by utilizing the lactose from the skimmed milk. The acidification of the samples (pH = 3.5) during SMF, which facilitates the flocculation process, might be advantageous to their replication. Lastly, according to Bracken’s reports, most of the Macrococcus read hits were Macrococcus caseolyticus , which has again been shown to efficiently hydrolyze casein and is a natural component of the secondary microflora in cheeses and sausages . The skimmed milk was highly likely metabolized during the 2 h incubation protocol resulting in the replication of specific taxa. DNA extraction is a critical step in a metagenomics workflow and is known to be influenced by numerous parameters, which are challenging to evaluate comprehensively. The choice of the DNA extraction method strongly affects the detection and composition of bacterial communities . In-house protocols and commercial products are constantly being developed and widely used, making cross-study comparisons difficult. As a result, either updated benchmarking studies or standardization efforts are required. While we developed a well-performing DNA extraction protocol, further improvements are needed to match the performance of the EurX kit (with ASVs) or the FastSpin (with OTUs). However, our in-house method could be a viable option for cost-effective research or where other protocols are unavailable. Surprisingly, the EZNA Universal Pathogen kit is not designed for metagenomics and is certainly not optimized to extract DNA equally from Gram-positive and Gram-negative bacteria, as the cell wall of Gram-positive bacteria contains a thick layer of peptidoglycan. Despite this, it performed similarly to other kits, such as ZymoBIOMICs and EurX, without a bead-beating step, which is currently widely adopted and recommended to facilitate balanced lysis . Next, our optimized 16S rRNA amplicon library sequencing protocol yielded good results, producing MIQ scores > 80 with most DNA extraction kits including the in-house method, which classifies them as good. Since MCS is used as a control for DNA extraction, running it in parallel with real samples helps confirm that there is minimal or no bias in the extraction process. However, benchmarking sequencing datasets from simultaneous 16S amplicon library generating protocols or commercial kits is required to fully evaluate the applicability of the 16S protocol. In regard to the 16S-DBs comparison, we aimed to present the most sample- and primer pair-specific taxonomic identification by first truncating the reference sequences to the primer regions and then building a classifier. By doing so, the detection and identification accuracy of each 16S-DB were specifically adjusted to the primer pair used, allowing for a standardized comparison. The TAR and TDR values were not as informative as initially perceived, mainly due to the small number of bacteria included in the MCS. The results of the 16S-DB comparison presented here should be interpreted alongside the amplified region, as identification is also heavily influenced by this factor. The TAR of the resulting OTUs, clustered at a threshold equal to or below 99%, usually suffered from identification bias . As anticipated, no eight out of eight TAR was achieved with the OTUs. While the most reliable identification is typically achieved with ASVs, a large portion of studies still rely on OTU clustering . A few limitations of the study can be listed. The SMF protocol applied in this study was originally optimized for virus concentration. In the literature, SMF protocols adopted or adjusted for bacteria are lacking and additional pre-treatment steps could be implemented to inhibit bacteria growth. Ideally, MCS with a higher number of bacteria (20+) would provide more insightful results compared to the eight-bacteria MCS used here.
4.1. Samples Two different sets of water samples were collected and processed separately. For shotgun metagenomic sequencing, four composite water samples (1 L each) were collected in pairs along the River Iskar from the two locations (42.367698, 23.555463—Dragushinovo village and 42.431095, 23.531900—villa area “Mechkata”) with an automatic sampler (Bühler 2000 Portable automatic water sampler, Hach UK, Manchester, UK) for a 24 h period to avoid day/night fluctuations bias on 3 November 2022 and 17 November 2022. They were transferred to the laboratory within 6 h and immediately processed. For 16S rRNA amplicon sequencing, one non-composite water sample (1 L) was collected in a sterile HDPE plastic container from a small urban River Perlovska at location 42.692164, 23.343892 and transported within 30 min to the laboratory. All samples were divided into two equal parts of 500 mL for SMF and VF treatment. The portions from the Perlovska River were further split into five sub-portions, each subjected to a different DNA extraction method, resulting in a total of ten DNA samples . The MCS used here was (cat. D6300, Zymo Research, Irvine, CA, USA). The MCS mimics a mixed microbial community of 10 members (8 bacteria and 2 fungi) of a well-defined composition. 4.2. Skimmed Milk Flocculation A previously described SMF protocol was used . In brief, 5% skimmed milk (HiMedia Laboratories, Mumbai, Maharashtra, India) was autoclaved for 15 min at 115 °C, 18 psi. Then, 5 mL of the 5% preflocculated skimmed milk solution was added to the 500 mL sample to achieve 0.05% final skimmed milk concentration. The sample pH was adjusted to 3.5–4.0 with 1M HCl, placed on a horizontal shaker, and agitated at 200 rpm for 2 h at room temperature. It was then distributed into 50 mL conical tubes and centrifuged at 3500× g for 30 min at 4 °C. The supernatant was decanted and the tubes were left upside down to drain residual water for 5 min. Pellets were used for DNA extraction (see ). 4.3. Vacuum Filtration VF was conducted with 47 mm diameter 0.2 µm pore size nylon filters (Cytiva, Marlborough, MA, USA), using Lafil 400-LF 30 Filtration System (Rocker, Kaohsiung City, Taiwan). The sample of River Perlovska was further divided among five filters as shown in , while the entire volume of 500 mL from River Iskar samples was filtered through one filter. Regardless of the sample sets, each filter was cut sterilely into two equal halves to be further extracted in pairs. Each half was cut into smaller pieces for better homogenization and directly added to extraction tubes (see ). 4.4. DNA Extraction DNA was extracted in duplicates from Perlovska River (16S metanogemics) and MCS samples with the following kits by adhering to the manufacturer’s instructions: (1) E.Z.N.A. Universal Pathogen Kit (OMEGA Bio-Tek, Inc., Norcross, GA, USA); (2) ZymoBIOMICS DNA Miniprep Kit (Zymo Research, Irvine, CA, USA); (3) FastDNA Spin Kit for Soil (MP Biomedicals, Santa Ana, CA, USA); (4) Environmental DNA & RNA Purification Kit (EURx Sp. z o.o., Gdańsk, Poland); and (5) the in-house protocol. The in-house protocol was developed by combining elements from other published protocols with slight modifications and optimization performed locally. The complete detailed protocol and required reagents are provided in . DNA from the Iskar River samples was extracted with the Environmental DNA & RNA Purification Kit (EURx Sp. z o.o., Gdańsk, Poland). 4.5. 16S rRNA and “Shotgun” Metagenomics The 16S rRNA V3-V4 region was amplified with previously published primer pairs Pro341F and Pro805R and with an optimized 16S rRNA amplification protocol described in . Sequencing was carried out on Illumina MiSeq V3 (2 × 300 bp). Additionally, for the Zymo miniprep DNA kit, we used the same DNA sample in three additional amplification reactions with primer annealing temperature gradient (55 °C, 58.5 °C, 62 °C). Shotgun sequencing libraries were constructed with Illumina DNA Prep kit (Illumina, Inc., San Diego, CA, USA) with 50 ng genomic DNA input. The libraries were pooled and sequenced on NextSeq 550 with the V2.5 mid-output kit (2 × 150 bp) (Illumina, San Diego, CA, USA). 4.6. Bioinformatic Analysis For comparison of DNA extraction kits and 16S-DBs on MCS samples, we used the Qiime2 platform and miqScore16SPublic tool ( https://github.com/Zymo-Research/miqScore16SPublic , accessed on 22 July 2024) to determine which DNA extraction kit yields microbial composition closest to the expected. Raw reads were automatically demultiplexed and trimmed from adapters in BaseSpace. The cleaned reads were submitted to the miqScore16SPublic tool to calculate MIQ scores, and the results were considered as references. Next, cutadapt v4.6 was used to remove 16S rRNA primers and remove low-quality bases (<20 Q at the 5′end, and <15 Q at the 3′end). We used six 16S-DBs and trained classifiers ( n = 6) locally. First, full-length 16S-DBs: Silva 99%-OTU (Silva) , GSR-99%-OTU (GSR) , GTDB-214.1-99%-OTU-less (GTDB-less) , GTDB-214.1-99%-OTU-full (GTDB-full) , GreenGenes-13_8-99%-OTU (GG_13.8) , and EzBio-Cloud-v.2018 were downloaded. Reference sequences were extracted based on in silico PCR with the 16S primer pairs, and used as input for the Naive Bayes algorithm within sk-learn Python package v1.5.2 to train personalized classifiers with default parameters, which were later used for taxonomic assignments with a confidence index of 0.7 (default). Both de novo and closed-reference clustered OTUs (for each 16S rRNA DB) were obtained at 99%. De novo clustered OTUs were processed with the “evaluate-composition” plugin in Qiime2 to compare TAR and TDR. Closed-reference OTUs were scored with a custom Python script ( https://github.com/maddne/MIQ-calc-from-OTU-tables (accessed on 4 October 2024)), which uses the intrinsic formula for MIQ score calculation by the tool miqScore16SPublic. Next, for a comparison of SMF and VF, we used DAA. The taxonomic assignment to raw reads was performed with Kraken2 v2.1.2 with the PlusPF DB (standard plus Refeq protozoa and fungi), built in January 2023, followed by Bracken v2.8 as described in protocol . The resulting OTU count tables were imported into the Qiime2 platform, filtered from low-count taxa ( n = 20 in at least 3 samples), and used for differential abundance analysis with the ANCOM-BC2 plugin .
Two different sets of water samples were collected and processed separately. For shotgun metagenomic sequencing, four composite water samples (1 L each) were collected in pairs along the River Iskar from the two locations (42.367698, 23.555463—Dragushinovo village and 42.431095, 23.531900—villa area “Mechkata”) with an automatic sampler (Bühler 2000 Portable automatic water sampler, Hach UK, Manchester, UK) for a 24 h period to avoid day/night fluctuations bias on 3 November 2022 and 17 November 2022. They were transferred to the laboratory within 6 h and immediately processed. For 16S rRNA amplicon sequencing, one non-composite water sample (1 L) was collected in a sterile HDPE plastic container from a small urban River Perlovska at location 42.692164, 23.343892 and transported within 30 min to the laboratory. All samples were divided into two equal parts of 500 mL for SMF and VF treatment. The portions from the Perlovska River were further split into five sub-portions, each subjected to a different DNA extraction method, resulting in a total of ten DNA samples . The MCS used here was (cat. D6300, Zymo Research, Irvine, CA, USA). The MCS mimics a mixed microbial community of 10 members (8 bacteria and 2 fungi) of a well-defined composition.
A previously described SMF protocol was used . In brief, 5% skimmed milk (HiMedia Laboratories, Mumbai, Maharashtra, India) was autoclaved for 15 min at 115 °C, 18 psi. Then, 5 mL of the 5% preflocculated skimmed milk solution was added to the 500 mL sample to achieve 0.05% final skimmed milk concentration. The sample pH was adjusted to 3.5–4.0 with 1M HCl, placed on a horizontal shaker, and agitated at 200 rpm for 2 h at room temperature. It was then distributed into 50 mL conical tubes and centrifuged at 3500× g for 30 min at 4 °C. The supernatant was decanted and the tubes were left upside down to drain residual water for 5 min. Pellets were used for DNA extraction (see ).
VF was conducted with 47 mm diameter 0.2 µm pore size nylon filters (Cytiva, Marlborough, MA, USA), using Lafil 400-LF 30 Filtration System (Rocker, Kaohsiung City, Taiwan). The sample of River Perlovska was further divided among five filters as shown in , while the entire volume of 500 mL from River Iskar samples was filtered through one filter. Regardless of the sample sets, each filter was cut sterilely into two equal halves to be further extracted in pairs. Each half was cut into smaller pieces for better homogenization and directly added to extraction tubes (see ).
DNA was extracted in duplicates from Perlovska River (16S metanogemics) and MCS samples with the following kits by adhering to the manufacturer’s instructions: (1) E.Z.N.A. Universal Pathogen Kit (OMEGA Bio-Tek, Inc., Norcross, GA, USA); (2) ZymoBIOMICS DNA Miniprep Kit (Zymo Research, Irvine, CA, USA); (3) FastDNA Spin Kit for Soil (MP Biomedicals, Santa Ana, CA, USA); (4) Environmental DNA & RNA Purification Kit (EURx Sp. z o.o., Gdańsk, Poland); and (5) the in-house protocol. The in-house protocol was developed by combining elements from other published protocols with slight modifications and optimization performed locally. The complete detailed protocol and required reagents are provided in . DNA from the Iskar River samples was extracted with the Environmental DNA & RNA Purification Kit (EURx Sp. z o.o., Gdańsk, Poland).
The 16S rRNA V3-V4 region was amplified with previously published primer pairs Pro341F and Pro805R and with an optimized 16S rRNA amplification protocol described in . Sequencing was carried out on Illumina MiSeq V3 (2 × 300 bp). Additionally, for the Zymo miniprep DNA kit, we used the same DNA sample in three additional amplification reactions with primer annealing temperature gradient (55 °C, 58.5 °C, 62 °C). Shotgun sequencing libraries were constructed with Illumina DNA Prep kit (Illumina, Inc., San Diego, CA, USA) with 50 ng genomic DNA input. The libraries were pooled and sequenced on NextSeq 550 with the V2.5 mid-output kit (2 × 150 bp) (Illumina, San Diego, CA, USA).
For comparison of DNA extraction kits and 16S-DBs on MCS samples, we used the Qiime2 platform and miqScore16SPublic tool ( https://github.com/Zymo-Research/miqScore16SPublic , accessed on 22 July 2024) to determine which DNA extraction kit yields microbial composition closest to the expected. Raw reads were automatically demultiplexed and trimmed from adapters in BaseSpace. The cleaned reads were submitted to the miqScore16SPublic tool to calculate MIQ scores, and the results were considered as references. Next, cutadapt v4.6 was used to remove 16S rRNA primers and remove low-quality bases (<20 Q at the 5′end, and <15 Q at the 3′end). We used six 16S-DBs and trained classifiers ( n = 6) locally. First, full-length 16S-DBs: Silva 99%-OTU (Silva) , GSR-99%-OTU (GSR) , GTDB-214.1-99%-OTU-less (GTDB-less) , GTDB-214.1-99%-OTU-full (GTDB-full) , GreenGenes-13_8-99%-OTU (GG_13.8) , and EzBio-Cloud-v.2018 were downloaded. Reference sequences were extracted based on in silico PCR with the 16S primer pairs, and used as input for the Naive Bayes algorithm within sk-learn Python package v1.5.2 to train personalized classifiers with default parameters, which were later used for taxonomic assignments with a confidence index of 0.7 (default). Both de novo and closed-reference clustered OTUs (for each 16S rRNA DB) were obtained at 99%. De novo clustered OTUs were processed with the “evaluate-composition” plugin in Qiime2 to compare TAR and TDR. Closed-reference OTUs were scored with a custom Python script ( https://github.com/maddne/MIQ-calc-from-OTU-tables (accessed on 4 October 2024)), which uses the intrinsic formula for MIQ score calculation by the tool miqScore16SPublic. Next, for a comparison of SMF and VF, we used DAA. The taxonomic assignment to raw reads was performed with Kraken2 v2.1.2 with the PlusPF DB (standard plus Refeq protozoa and fungi), built in January 2023, followed by Bracken v2.8 as described in protocol . The resulting OTU count tables were imported into the Qiime2 platform, filtered from low-count taxa ( n = 20 in at least 3 samples), and used for differential abundance analysis with the ANCOM-BC2 plugin .
In this study, we systematically evaluated the effectiveness of SMF for bacterial metagenomics, introduced and benchmarked an adopted in-house DNA extraction method against four commercial kits, and assessed the performance of six 16S-DBs for taxonomic identification. The findings reveal that skimmed milk flocculation is not suitable for bacterial microbiome studies as it significantly alters the microbial composition due to the proliferation of lactic acid or casein utilizing bacteria, leading to an increased relative abundance compared to the traditional vacuum filtration method. Our in-house DNA extraction protocol demonstrated competitive performance, particularly in comparison to the commercial kits, which were optimized for minimal bias. This in-house protocol provides a cost-effective alternative for researchers with limited access to commercial kits, offering reliable results for metagenomic studies. Lastly, the evaluation of 16S-DBs showed that while there are variances in taxonomic assignments, the choice of DNA extraction protocol has a more pronounced impact on the microbial composition than the choice of the 16S-DB. This underscores the importance of selecting an appropriate DNA extraction method to minimize biases in metagenomic studies. This comprehensive benchmarking study offers insights for the design of future water metagenomic studies, emphasizing the importance of method selection at various stages to ensure accurate and reliable microbial community profiling.
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Cholesterol-Lowering Bioactive Foods and Nutraceuticals in Pediatrics: Clinical Evidence of Efficacy and Safety | c3bc46a8-b2b2-435c-8d6b-f83af8385004 | 11123713 | Pediatrics[mh] | Optimal low-density lipoprotein cholesterol and/or triglyceride plasma levels significantly protect against cardiovascular disease (CVD) development, and higher levels are associated with an increased risk . The optimal values are defined based on age and ethnicity. Dyslipidemias are largely prevalent disorders of lipoprotein metabolism that can result in abnormal lipid and lipoprotein values. The prevalence of dyslipidaemias is increasing all over the world, even in Mediterranean countries where the diet should be qualitatively healthier. A recent Italian study showed a prevalence of suboptimal cholesterolemia (total cholesterol–TC > 170 mg/dL) in 78% of children and adolescents . Recent studies suggest that long-term mild exposure during childhood to even a mild suboptimal LDL-C level is associated with an increased risk of coronary artery disease. International guidelines suggest screening children and adolescents for lipid levels, especially in families with dyslipidemia and/or atherosclerotic CVD (ASCVD) . In a large Australian cohort, subjects who had incident non-high density lipoprotein-cholesterol (non-HDL-C) dyslipidemia from childhood to adulthood and those with persistent dyslipidemia had dramatic increased risks of cardiovascular events (Hazard Ratio 2.17 [95% Confidence Interval (CI) 1.00–4.69] and Hazard Ratio 5.17 [95% CI 2.80–9.56], respectively), when compared with those whose non-HDL-C levels remained within the guideline-recommended range in childhood and adulthood. Then, participants who had high non-HDL-C in childhood but whose non-HDL-C levels were within the guideline-recommended range in adulthood did not have a significantly increased risk (Hazard Ratio 1.13 (95%CI 0.50–2.56)) . Thus, improvements in physical activity and some dietary behaviors that ensure all necessary nutrients for adequate growth during childhood are advisable , although specific lipid-lowering pharmacological treatment is needed to manage secondary and severe genetic dyslipidemias . Some nutraceuticals have clearly demonstrated cholesterol-lowering activity in adults. Experts recommend their utilization in managing individuals who have a low estimated risk of developing ASCVD, but who show an insufficient metabolic response to dietary changes. Additionally, nutraceuticals may be considered for some low-risk patients with statin-intolerance when combined with ezetimibe . According to the American Academy of Pediatrics (AAP) and European Atherosclerosis Society (EAS), the first step in the management of adult and pediatric patients with hypercholesterolemia is nutritional approach and life-style management, followed by drug therapy . Functional foods or “nutraceuticals” have been used as adjunct treatment for adult patients, and are suggested for pediatric patients with familial hypercholesterolaemia (FH) . Studies on cholesterol-lowering nutraceuticals in children and adolescents are relatively limited, as only a few short-term, randomized, controlled studies have been performed. These are mainly concerned with the administration of (soluble) fibers and plant sterols/stanols, whereas sporadic reports have tested the efficacy and tolerability of standardized red yeast rice extract, soy proteins, probiotics, and Omega-3 and Omrega-6 polyunsaturated fatty acids (PUFAs). This review article aimed to critically summarize the scientific evidence supporting cholesterol-lowering dietary supplements and nutraceuticals in managing children and adolescents with dyslipidemia. We focused on the most widely used nutraceuticals in dyslipidemia management, especially those that have shown evidence of modifying plasma levels of LDL-C and triglycerides. Additionally, we also examined the impact of these nutraceuticals on TC and HDL-C levels, to provide clinicians comprehensive objective information to guide their decision-making process. A detailed literature search was performed in this narrative review on PubMed using the following keywords: “Children”, “Adolescent”, “Pediatric”, “Hypercholesterolemia”, “Hypertriglyceridemia”, “Dyslipidemia”, “Dietary supplement”, “Nutraceutical”, “Efficacy”, “Tolerability”, “Safety”, “Cholesterol-lowering” and “Lipid-lowering”. Preference was given to placebo-controlled randomized clinical trials. The collected articles underwent independent review by two authors. Inclusion criteria were established prior to article review, and were as follows: (i) all published scientific papers that describe dietary supplement, nutraceuticals and lipid-lowering agents in pediatric population; and (ii) high-quality systematic research, randomized control trials, study cohorts and cross-sectional studies that were published in English. We excluded studies that were not reported in English, and those that exclusively focused on adults (age ≥ 19 years). Findings were classified by the main mechanisms of action (i.e., cholesterol absorption inhibitors from the bowel, LDL synthesis inhibition by the liver, and a mixed mechanism of action), and summarized in tables and figures. The review tables included nutraceutical, study type, study aim, participants, intervention, intolerance, compliance and observed effects. Several natural compounds can interfere with cholesterol absorption, significantly improving cholesterolemia in children and adolescents. 3.1. Soluble Fibers Fibers are edible parts of plants that pass through the small intestine relatively unchanged in humans, and include complex carbohydrates such as non-starch polysaccharides (pectins, gums, cellulose, hemicellulose, oat bran and wheat bran), oligosaccharides (inulin and fructooligosaccharides), and lignin (i.e., the non-carbohydrate fraction of the dietary fiber). Fibers intake is linked to positive health outcomes, including a lower risk of developing ASCVD and obesity . Fibers naturally contained in cereals, vegetables and fruits (as part of a balanced dietary pattern) ameliorate the lipid profile in adults , and reduce concentrations of TC and Low Density Lipoprotein-Cholesterol (LDL-C) by 5–15% and 9–22%, respectively . These beneficial effects have led regulatory agencies to issue health claims for the intake of fibers , oat β-glucan and its LDL-C lowering effect or ASCVD risk reduction . A large cohort study on 5873 Japanese children (10–11 years) highlighted the presence of an inverse association between the consumption of dietary fibers and plasma concentrations of TC, and the presence of overweight and obesity, confirming data from clinical trials in adults . Although dietary fibers help maintain good health, their quantitative need in children has not been defined yet . Food and Drug Administration (FDA) guidelines relate it to the need for calorie intake (12 g/1000 calories), while the American Academy of Pediatrics guidelines relate it to weight or age . Pediatric recommendations widely vary across countries, being also influenced by the available evidence . In general practice, many pediatricians follow a formula that involves adding 5 g/day to the child’s age (for children older than 3 years) . Even if this guideline is often considered when advocating for a Mediterranean diet or a diet rich in vegetables, it is seldom met in clinical practice. Children living in Western countries typically consume fewer vegetables and fruits, contributing to a lower dietary fiber intake, high calorie dense food, and highly refined high-fat diet . The consequences of such an incorrect dietary intake include metabolic changes and hyperlipidemia, which are sometimes associated with overweight/obesity. In this context, a positive effect of dietary fibers on blood lipids with monounsaturated FAs (MUFAs) was shown in children in the Healthy Start Preschool Study of Cardiovascular Disease Risk Factors and Diet . Soluble fibers include psyllium (viscous and non-fermentable fiber), glucomannan, oat, pectin, and guar gum (viscous and fermentable fiber) and are commercially available as unprocessed fibers that can be added to food or used as flavored powders or capsules. Psyllium, derived from the seed husk of Plantago ovata , is one of the richest sources of soluble mucilaginous dietary fiber, acting as a gel-forming polysaccharide, similarly to pectin and guar gum . Locust bean gum (that is, a galactomannan from the carob tree) is a white and odorless powder extracted from the endosperm of beans without a distinctive taste. Pectin is less viscous than other fibers, but similar in ash and more palatable . Glucomannan, the main polysaccharide obtained from the Asian tuber Amorphophallus konjac, is a palatable, highly viscous soluble fiber. Its chemical structure consists of a mannose (8): glucose (5) ratio linked by b-glycosidic bonds. Glucomannan has the highest molecular weight and viscosity among all dietary fibers . Oat seeds are also an important source of the viscous soluble fiber beta-glucan . The ability of fibers to lower plasma lipids relies on their physicochemical properties and viscosity. Soluble fiber acts mainly to form viscous solutions that slow gastric emptying and reduce fat absorption, thereby modulating lipoprotein metabolism. In the small intestine, the gelling process binds to dietary fats and hinders the absorption of cholesterol, and the reabsorption of bile acids increases their excretion in feces. It follows that there is a reduced uptake of intestinal cholesterol and a reduced circulation of chylomicrons. Of consequence is that the synthesis of bile in the liver increases and LDL-C levels decrease. Another cholesterol-lowering mechanism involves bacterial fermentation in the colon (except for lignin), which leads to production of short-chain FAs (acetate, propionate, and butyrate). Propionate inhibits cholesterol synthesis . A relatively small number of short-term randomized clinical trials have investigated the cholesterol-lowering effect of fibers in children and adolescents with largely variable results, ranging from no effect to a 30% reduction in LDL-C plasma levels. The most frequently studied fiber is psyllium, followed by glucomannan, oats, and gum, which are usually added to STEP I (daily fat intake < 30%, saturated FAs < 10%, cholesterol < 300 mg) or STEP II (saturated FAs 7%, cholesterol < 200 mg), now indicated as CHILD I and CHILD II diet . Balancing the fiber intake from food and nutraceuticals or fiber-added food is relevant to compliance and outcomes. A very restricted diet—as required by the STEP II diet—even if safe, is not always well accepted by children . Combining the STEP I diet with food-enriched or capsule-containing fiber is often more effective in reducing LDL-C levels. Many studies have demonstrated the efficacy of psyllium . A 12-week randomized controlled study on 50 children with mild hypercholesterolemia on a STEP I diet supplemented with psyllium (3.2 g/daily) showed an additional 8.9% LDL-C decrease compared with the controls . Further, in 36 children with familial combined hyperlipoproteinemia, supplementation with psyllium (2.5–10 g, depending on the age) to a STEP I diet led to a TC and LDL-C level reduction of 11.9% and 13.8%, respectively . Psyllium supplementation also improved the LDL-C lowering effect of the STEP II diet in children with hyperlipidemia , different from what was previously observed in a randomized, double-blinded, placebo-controlled, cross-over study employing 6 g/day of psyllium in 20 children with mild hypercholesterolemia, already on the STEP II diet . Glucomannan supplementation was successfully tested in 36 children with hyperlipidemia who underwent a double-blinded, randomized, placebo-controlled cross-over trial that lasted 24 weeks. This cohort, affected by primary dyslipidemia, was fed a CHILD I diet for ≥1 month. Capsules containing glucomannan (500 mg) were administered at a dose of 1000–1500 mg/day depending on the proband’s weight. TC, LDL-C, and non-HDL-C levels decreased significantly by 5.1%, 7.3%, and 7.2%, respectively . Consistent with previous findings, these results were more pronounced in females than males . However, two meta-analyses of the LDL-C-lowering effect of glucomannan supplementation in children did not confirm any positive effect of this fiber on LDL-C levels . Among other fibers, oat bran supplementation has been tested in children in several clinical trials . For instance, oat bran significantly increased HDL-C levels and reduced LDL-C levels after 7 months of consumption (dosage: 1 g/kg body weight/day) compared with soy derivatives in 20 children with hypercholesterolemia (5–12 years) . Furthermore, locust bean gum (Carruba) showed a significant 11–19% LDL-C level decrease when comparing active and placebo groups in a 16-week cross-over controlled trial, including 11 children with familial combined hyperlipidemia, 10 controls, and 17 adults who consumed locust bean gum (8–30 g/daily) . Among these interventions , psyllium consistently showed the highest reduction in LDL-C levels, ranging from 6.8% to 23%. This was followed by gum interventions, which resulted in a reduction of LDL-C levels ranging from 11% to 19%. Pectin interventions demonstrated a significant reduction of 17% in LDL-C levels. Lastly, Glucomannan interventions combined with Chromium polynicotinate or policosanols showed moderate reductions, ranging from 7.3% to 16% in LDL-C levels. It is important to note that these interventions were effective in lowering LDL-C levels in pediatric populations, but the extent of reduction varied across studies. The compliance was overall good, even if some children refused to follow the prescribed diet or take the capsules. Even if a fiber rich diet is always to be preferred, when the intake is not sufficient, supplemented fibers are usually safe and well tolerated. Mild intestinal discomfort has been reported in clinical trials. 3.2. Plant Sterols Plant sterols, also known as phytosterols or non-cholesterol sterols, are natural compounds found in plants, and are commonly consumed through foods like vegetable oils and nuts. These compounds are ingested in amounts comparable to cholesterol intake (200–400 mg/day), which cannot be synthesized by the human body. Plant sterols effectively and safely lower serum cholesterol levels by hindering cholesterol absorption . Since 2001, plant sterol-enriched foods have been recommended by the National Cholesterol Education Program Guidelines as part of dietary strategies to reduce LDL-C levels . Non-cholesterol sterols, or stanols (in the form of sterol esters), are available commercially, and are added to various foods such as bread, cereals, salad dressings, milk, margarine, and yogurt, often with different flavors and a good taste . Studies have shown that, in adults, incorporating stanols into the milk matrix yielded better results compared to cereals, with LDL-C levels decreasing by 15.9% versus 5.4%, respectively . While stanols were more effective in reducing cholesterol levels compared to sterols, most studies administered sterols at varying doses ranging from 1.6 to 2 g daily. Plant sterols work by inhibiting cholesterol absorption in the intestines, leading to a reduction in serum cholesterol concentration . Phytosterols, particularly sitostanol, compete with cholesterol for absorption in the intestines and displace cholesterol from micelles . Phytosterols are more hydrophobic than cholesterol, making them more susceptible to mixed micelles. Cholesterol and phytosterols rely on Niemann–Pick C1-Like 1 (NPC1L1) protein for absorption into enterocytes. Once absorbed, non-esterified cholesterol and phytosterols are transported back into the intestinal lumen through the action of the ABCG5/G8 gene. Approximately 50% of the cholesterol, but less than 5% of plant sterols, is ultimately absorbed . Phytosterols, when in their free form, are absorbed at low rates (less than 10%), while stanols are not absorbed physiologically . The reduced uptake of intestinal cholesterol and its transport via chylomicrons to the liver result in decreased levels of intermediate-density lipoproteins in addition to LDL-C . In several randomized clinical trials, plant sterols significantly decreased cholesterolemia in children with mild hypercholesterolemia and FH . Dietary supplementation with 1.2–2.0 g/day sterols has been mainly tested in children with FH who had already been on STEP I or II, showing a further LDL-C lowering effect of ~10% in 2–12 months . In children with FH, a daily intake of 2.3 g phytosterols significantly decreased TC (−11%) and LDL-C (−14%) levels compared with placebo spread , whereas higher decreases were observed in children undergoing stanol-added diet (3 g/day) . Apolipoprotein B (Apo-B) levels were also significantly reduced by plant sterols (7–10%) . The efficacy of phytosterols was further demonstrated in non-FH children on a STEP II diet and with mild hypercholesterolemia (mean TC > 197 mg and LDL-C > 125 mg/dL). The daily intake of 1.2 g plant sterol in two doses reduced TC (from −7% to −11%) and LDL-C (from −9% to −14%) levels, respectively, compared with the control group . Margarine containing 1.6 g/day plant sterols or plant stanol ester reduced TC (−9%) and LDL-C (−12%) in children with FH after for 5–6 weeks , while in the STRIP study, 6-year-olds with mild hypercholesterolemia significantly decreased TC and LDL-C, respectively, by −5.4% and −7.5% . Then, plant sterol supplementation could safely reduce LDL-C by roughly 10% and without significantly affecting other lipoprotein levels . Remarkably, the Apo E4 or E3 genotypes were not reported to influence the biochemical effects of sterol addiction in children . The administration of milk, yogurt and margarine frequently could influence the cholesterol-lowering effect of phytosterols , whereas the lipid drop seems independent of baseline levels, being the maximum effect usually reached in a short time (2 weeks, usually) . An additive benefit of the above-mentioned changes is the significant decrease in small dense LDL-C levels after the daily dietary supplementation of 2 g plant sterols in children and adolescents with dyslipidemia . It must, however, be recognized that TG and HDL-C concentrations in plasma are usually unaffected by phytosterol supplementation , as well as the endothelial function . Children undergoing statin therapy show homeostatic changes characterized by increased cholesterol absorption and plant sterol levels. Phytosterol supplementation reverses these changes, and should be considered advantageous . Phytosterols are usually safe and well-tolerated. Variations in carotenoids and fat-soluble vitamins have been reported by studies that used plant sterol- or stanol ester-enriched spreads in adults and children. In children with FH, lipid-adjusted lycopene levels decreased by 8.1% ( p = 0.015) during the stanol period; however, this reduction was not significant at the 6-month follow-up. In addition, alfa- and beta-carotene levels significantly decreased by 17.4% and 10.9%, respectively, in children with FH after the daily consumption of 1.2 g plant sterols for 2 months, recovering at the 6-month follow-up . In the Special Turku Coronary Risk Factor Intervention Project for children (STRIP study), the dietary supplementation of 1.5 g phytosterols in children with mild hypercholesterolemia was associated to a decrease (−19%; p = 0.003) in serum beta-carotene to LDL-C ratio, while the alpha-tocopherol to LDL-C ratio remained unchanged . Moreover, no changes were observed in the levels of the other carotenoids or fat-soluble vitamins . To the extent that there is little data, improving vegetables and fruits intake in children should be suggested as add-on to phytosterol-added dietary regimen, to compensate for any possible reduction in carotenoid also related to seasonal dietary variations. Long-term safety was questioned as phytosterol plasma levels increased the incidence of atherosclerosis , and should be related to an increased risk of cardiovascular events, as described in the large epidemiological cohorts of the PROCAM and MONICA/KORA studies . Premature atherosclerosis has also been observed in the rare autosomal recessive familial form of sitosterolemia . However, campesterol and sitosterol under physiological conditions do not exceed 1% of the total serum sterols, whereas cholesterol accounts for >99% of serum sterols. Moreover, lathosterol was not modified over a 12-week period, proving that the inhibition of cholesterol absorption by phytosterols does not cause an increased cholesterol synthesis . 3.3. Probiotics Probiotics have a limited evidence of cholesterol-lowering effects in adults. This effect results from cholesterol absorption and bile salts hydrolysis (BSH) . The first mechanism, activated by lactic acid bacteria, suppresses the reabsorption of cholesterol in the intestines, while the second mechanism affects the balance of bile salts, resulting in a decrease in plasma LDL-C levels. Moreover, certain strains of bifidobacteria improve blood lipid levels by converting linoleic acid (LA) into conjugated linoleic acid (CLA) . A recent umbrella systematic review of 38 meta-analyses concluded that the probiotics supplementation was effective in reducing TC (effect size [ES], −0.46 mg/dL; 95%CI, −0.61, −0.30; p < 0.001), TG (ES, −0.13 mg/dL; 95%CI, −0.23, −0.04; p = 0.006), and LDL-C levels (ES, −0.29 mg/dL; 95%CI, −0.40, −0.19; p < 0.001), without affecting HDL-C The evidence in children is rare. A 32-week-long, double-blinded, randomized, placebo-controlled, cross-over trial was conducted involving children whose TC levels exceeded the 90th percentile for their age and sex. Administering a mixture of three bifidobacterium strains, selected for characteristics that ameliorated the lipid profile, such as BSH activity, cholesterol adsorption, and CLA production, mildly but significantly improved TC (3.4%), LDL-C (3.8%), and TG (1.9%) levels, and increased HDL-C (1.7%) levels . The effect seems less impressive than that in adults; however, the effect could depend on the tested probiotic formulation, as the probiotic action depends on the strain, strain mix, dosage, and administration medium. Probiotic supplementation is usually safe and well-tolerated. The available clinical data regarding probiotics in adults is inconclusive, and there is limited information available regarding their effects in children. Therefore, it would be premature to definitively state that probiotics have a significant lipid-lowering effect, given the limited evidence available. Fibers are edible parts of plants that pass through the small intestine relatively unchanged in humans, and include complex carbohydrates such as non-starch polysaccharides (pectins, gums, cellulose, hemicellulose, oat bran and wheat bran), oligosaccharides (inulin and fructooligosaccharides), and lignin (i.e., the non-carbohydrate fraction of the dietary fiber). Fibers intake is linked to positive health outcomes, including a lower risk of developing ASCVD and obesity . Fibers naturally contained in cereals, vegetables and fruits (as part of a balanced dietary pattern) ameliorate the lipid profile in adults , and reduce concentrations of TC and Low Density Lipoprotein-Cholesterol (LDL-C) by 5–15% and 9–22%, respectively . These beneficial effects have led regulatory agencies to issue health claims for the intake of fibers , oat β-glucan and its LDL-C lowering effect or ASCVD risk reduction . A large cohort study on 5873 Japanese children (10–11 years) highlighted the presence of an inverse association between the consumption of dietary fibers and plasma concentrations of TC, and the presence of overweight and obesity, confirming data from clinical trials in adults . Although dietary fibers help maintain good health, their quantitative need in children has not been defined yet . Food and Drug Administration (FDA) guidelines relate it to the need for calorie intake (12 g/1000 calories), while the American Academy of Pediatrics guidelines relate it to weight or age . Pediatric recommendations widely vary across countries, being also influenced by the available evidence . In general practice, many pediatricians follow a formula that involves adding 5 g/day to the child’s age (for children older than 3 years) . Even if this guideline is often considered when advocating for a Mediterranean diet or a diet rich in vegetables, it is seldom met in clinical practice. Children living in Western countries typically consume fewer vegetables and fruits, contributing to a lower dietary fiber intake, high calorie dense food, and highly refined high-fat diet . The consequences of such an incorrect dietary intake include metabolic changes and hyperlipidemia, which are sometimes associated with overweight/obesity. In this context, a positive effect of dietary fibers on blood lipids with monounsaturated FAs (MUFAs) was shown in children in the Healthy Start Preschool Study of Cardiovascular Disease Risk Factors and Diet . Soluble fibers include psyllium (viscous and non-fermentable fiber), glucomannan, oat, pectin, and guar gum (viscous and fermentable fiber) and are commercially available as unprocessed fibers that can be added to food or used as flavored powders or capsules. Psyllium, derived from the seed husk of Plantago ovata , is one of the richest sources of soluble mucilaginous dietary fiber, acting as a gel-forming polysaccharide, similarly to pectin and guar gum . Locust bean gum (that is, a galactomannan from the carob tree) is a white and odorless powder extracted from the endosperm of beans without a distinctive taste. Pectin is less viscous than other fibers, but similar in ash and more palatable . Glucomannan, the main polysaccharide obtained from the Asian tuber Amorphophallus konjac, is a palatable, highly viscous soluble fiber. Its chemical structure consists of a mannose (8): glucose (5) ratio linked by b-glycosidic bonds. Glucomannan has the highest molecular weight and viscosity among all dietary fibers . Oat seeds are also an important source of the viscous soluble fiber beta-glucan . The ability of fibers to lower plasma lipids relies on their physicochemical properties and viscosity. Soluble fiber acts mainly to form viscous solutions that slow gastric emptying and reduce fat absorption, thereby modulating lipoprotein metabolism. In the small intestine, the gelling process binds to dietary fats and hinders the absorption of cholesterol, and the reabsorption of bile acids increases their excretion in feces. It follows that there is a reduced uptake of intestinal cholesterol and a reduced circulation of chylomicrons. Of consequence is that the synthesis of bile in the liver increases and LDL-C levels decrease. Another cholesterol-lowering mechanism involves bacterial fermentation in the colon (except for lignin), which leads to production of short-chain FAs (acetate, propionate, and butyrate). Propionate inhibits cholesterol synthesis . A relatively small number of short-term randomized clinical trials have investigated the cholesterol-lowering effect of fibers in children and adolescents with largely variable results, ranging from no effect to a 30% reduction in LDL-C plasma levels. The most frequently studied fiber is psyllium, followed by glucomannan, oats, and gum, which are usually added to STEP I (daily fat intake < 30%, saturated FAs < 10%, cholesterol < 300 mg) or STEP II (saturated FAs 7%, cholesterol < 200 mg), now indicated as CHILD I and CHILD II diet . Balancing the fiber intake from food and nutraceuticals or fiber-added food is relevant to compliance and outcomes. A very restricted diet—as required by the STEP II diet—even if safe, is not always well accepted by children . Combining the STEP I diet with food-enriched or capsule-containing fiber is often more effective in reducing LDL-C levels. Many studies have demonstrated the efficacy of psyllium . A 12-week randomized controlled study on 50 children with mild hypercholesterolemia on a STEP I diet supplemented with psyllium (3.2 g/daily) showed an additional 8.9% LDL-C decrease compared with the controls . Further, in 36 children with familial combined hyperlipoproteinemia, supplementation with psyllium (2.5–10 g, depending on the age) to a STEP I diet led to a TC and LDL-C level reduction of 11.9% and 13.8%, respectively . Psyllium supplementation also improved the LDL-C lowering effect of the STEP II diet in children with hyperlipidemia , different from what was previously observed in a randomized, double-blinded, placebo-controlled, cross-over study employing 6 g/day of psyllium in 20 children with mild hypercholesterolemia, already on the STEP II diet . Glucomannan supplementation was successfully tested in 36 children with hyperlipidemia who underwent a double-blinded, randomized, placebo-controlled cross-over trial that lasted 24 weeks. This cohort, affected by primary dyslipidemia, was fed a CHILD I diet for ≥1 month. Capsules containing glucomannan (500 mg) were administered at a dose of 1000–1500 mg/day depending on the proband’s weight. TC, LDL-C, and non-HDL-C levels decreased significantly by 5.1%, 7.3%, and 7.2%, respectively . Consistent with previous findings, these results were more pronounced in females than males . However, two meta-analyses of the LDL-C-lowering effect of glucomannan supplementation in children did not confirm any positive effect of this fiber on LDL-C levels . Among other fibers, oat bran supplementation has been tested in children in several clinical trials . For instance, oat bran significantly increased HDL-C levels and reduced LDL-C levels after 7 months of consumption (dosage: 1 g/kg body weight/day) compared with soy derivatives in 20 children with hypercholesterolemia (5–12 years) . Furthermore, locust bean gum (Carruba) showed a significant 11–19% LDL-C level decrease when comparing active and placebo groups in a 16-week cross-over controlled trial, including 11 children with familial combined hyperlipidemia, 10 controls, and 17 adults who consumed locust bean gum (8–30 g/daily) . Among these interventions , psyllium consistently showed the highest reduction in LDL-C levels, ranging from 6.8% to 23%. This was followed by gum interventions, which resulted in a reduction of LDL-C levels ranging from 11% to 19%. Pectin interventions demonstrated a significant reduction of 17% in LDL-C levels. Lastly, Glucomannan interventions combined with Chromium polynicotinate or policosanols showed moderate reductions, ranging from 7.3% to 16% in LDL-C levels. It is important to note that these interventions were effective in lowering LDL-C levels in pediatric populations, but the extent of reduction varied across studies. The compliance was overall good, even if some children refused to follow the prescribed diet or take the capsules. Even if a fiber rich diet is always to be preferred, when the intake is not sufficient, supplemented fibers are usually safe and well tolerated. Mild intestinal discomfort has been reported in clinical trials. Plant sterols, also known as phytosterols or non-cholesterol sterols, are natural compounds found in plants, and are commonly consumed through foods like vegetable oils and nuts. These compounds are ingested in amounts comparable to cholesterol intake (200–400 mg/day), which cannot be synthesized by the human body. Plant sterols effectively and safely lower serum cholesterol levels by hindering cholesterol absorption . Since 2001, plant sterol-enriched foods have been recommended by the National Cholesterol Education Program Guidelines as part of dietary strategies to reduce LDL-C levels . Non-cholesterol sterols, or stanols (in the form of sterol esters), are available commercially, and are added to various foods such as bread, cereals, salad dressings, milk, margarine, and yogurt, often with different flavors and a good taste . Studies have shown that, in adults, incorporating stanols into the milk matrix yielded better results compared to cereals, with LDL-C levels decreasing by 15.9% versus 5.4%, respectively . While stanols were more effective in reducing cholesterol levels compared to sterols, most studies administered sterols at varying doses ranging from 1.6 to 2 g daily. Plant sterols work by inhibiting cholesterol absorption in the intestines, leading to a reduction in serum cholesterol concentration . Phytosterols, particularly sitostanol, compete with cholesterol for absorption in the intestines and displace cholesterol from micelles . Phytosterols are more hydrophobic than cholesterol, making them more susceptible to mixed micelles. Cholesterol and phytosterols rely on Niemann–Pick C1-Like 1 (NPC1L1) protein for absorption into enterocytes. Once absorbed, non-esterified cholesterol and phytosterols are transported back into the intestinal lumen through the action of the ABCG5/G8 gene. Approximately 50% of the cholesterol, but less than 5% of plant sterols, is ultimately absorbed . Phytosterols, when in their free form, are absorbed at low rates (less than 10%), while stanols are not absorbed physiologically . The reduced uptake of intestinal cholesterol and its transport via chylomicrons to the liver result in decreased levels of intermediate-density lipoproteins in addition to LDL-C . In several randomized clinical trials, plant sterols significantly decreased cholesterolemia in children with mild hypercholesterolemia and FH . Dietary supplementation with 1.2–2.0 g/day sterols has been mainly tested in children with FH who had already been on STEP I or II, showing a further LDL-C lowering effect of ~10% in 2–12 months . In children with FH, a daily intake of 2.3 g phytosterols significantly decreased TC (−11%) and LDL-C (−14%) levels compared with placebo spread , whereas higher decreases were observed in children undergoing stanol-added diet (3 g/day) . Apolipoprotein B (Apo-B) levels were also significantly reduced by plant sterols (7–10%) . The efficacy of phytosterols was further demonstrated in non-FH children on a STEP II diet and with mild hypercholesterolemia (mean TC > 197 mg and LDL-C > 125 mg/dL). The daily intake of 1.2 g plant sterol in two doses reduced TC (from −7% to −11%) and LDL-C (from −9% to −14%) levels, respectively, compared with the control group . Margarine containing 1.6 g/day plant sterols or plant stanol ester reduced TC (−9%) and LDL-C (−12%) in children with FH after for 5–6 weeks , while in the STRIP study, 6-year-olds with mild hypercholesterolemia significantly decreased TC and LDL-C, respectively, by −5.4% and −7.5% . Then, plant sterol supplementation could safely reduce LDL-C by roughly 10% and without significantly affecting other lipoprotein levels . Remarkably, the Apo E4 or E3 genotypes were not reported to influence the biochemical effects of sterol addiction in children . The administration of milk, yogurt and margarine frequently could influence the cholesterol-lowering effect of phytosterols , whereas the lipid drop seems independent of baseline levels, being the maximum effect usually reached in a short time (2 weeks, usually) . An additive benefit of the above-mentioned changes is the significant decrease in small dense LDL-C levels after the daily dietary supplementation of 2 g plant sterols in children and adolescents with dyslipidemia . It must, however, be recognized that TG and HDL-C concentrations in plasma are usually unaffected by phytosterol supplementation , as well as the endothelial function . Children undergoing statin therapy show homeostatic changes characterized by increased cholesterol absorption and plant sterol levels. Phytosterol supplementation reverses these changes, and should be considered advantageous . Phytosterols are usually safe and well-tolerated. Variations in carotenoids and fat-soluble vitamins have been reported by studies that used plant sterol- or stanol ester-enriched spreads in adults and children. In children with FH, lipid-adjusted lycopene levels decreased by 8.1% ( p = 0.015) during the stanol period; however, this reduction was not significant at the 6-month follow-up. In addition, alfa- and beta-carotene levels significantly decreased by 17.4% and 10.9%, respectively, in children with FH after the daily consumption of 1.2 g plant sterols for 2 months, recovering at the 6-month follow-up . In the Special Turku Coronary Risk Factor Intervention Project for children (STRIP study), the dietary supplementation of 1.5 g phytosterols in children with mild hypercholesterolemia was associated to a decrease (−19%; p = 0.003) in serum beta-carotene to LDL-C ratio, while the alpha-tocopherol to LDL-C ratio remained unchanged . Moreover, no changes were observed in the levels of the other carotenoids or fat-soluble vitamins . To the extent that there is little data, improving vegetables and fruits intake in children should be suggested as add-on to phytosterol-added dietary regimen, to compensate for any possible reduction in carotenoid also related to seasonal dietary variations. Long-term safety was questioned as phytosterol plasma levels increased the incidence of atherosclerosis , and should be related to an increased risk of cardiovascular events, as described in the large epidemiological cohorts of the PROCAM and MONICA/KORA studies . Premature atherosclerosis has also been observed in the rare autosomal recessive familial form of sitosterolemia . However, campesterol and sitosterol under physiological conditions do not exceed 1% of the total serum sterols, whereas cholesterol accounts for >99% of serum sterols. Moreover, lathosterol was not modified over a 12-week period, proving that the inhibition of cholesterol absorption by phytosterols does not cause an increased cholesterol synthesis . Probiotics have a limited evidence of cholesterol-lowering effects in adults. This effect results from cholesterol absorption and bile salts hydrolysis (BSH) . The first mechanism, activated by lactic acid bacteria, suppresses the reabsorption of cholesterol in the intestines, while the second mechanism affects the balance of bile salts, resulting in a decrease in plasma LDL-C levels. Moreover, certain strains of bifidobacteria improve blood lipid levels by converting linoleic acid (LA) into conjugated linoleic acid (CLA) . A recent umbrella systematic review of 38 meta-analyses concluded that the probiotics supplementation was effective in reducing TC (effect size [ES], −0.46 mg/dL; 95%CI, −0.61, −0.30; p < 0.001), TG (ES, −0.13 mg/dL; 95%CI, −0.23, −0.04; p = 0.006), and LDL-C levels (ES, −0.29 mg/dL; 95%CI, −0.40, −0.19; p < 0.001), without affecting HDL-C The evidence in children is rare. A 32-week-long, double-blinded, randomized, placebo-controlled, cross-over trial was conducted involving children whose TC levels exceeded the 90th percentile for their age and sex. Administering a mixture of three bifidobacterium strains, selected for characteristics that ameliorated the lipid profile, such as BSH activity, cholesterol adsorption, and CLA production, mildly but significantly improved TC (3.4%), LDL-C (3.8%), and TG (1.9%) levels, and increased HDL-C (1.7%) levels . The effect seems less impressive than that in adults; however, the effect could depend on the tested probiotic formulation, as the probiotic action depends on the strain, strain mix, dosage, and administration medium. Probiotic supplementation is usually safe and well-tolerated. The available clinical data regarding probiotics in adults is inconclusive, and there is limited information available regarding their effects in children. Therefore, it would be premature to definitively state that probiotics have a significant lipid-lowering effect, given the limited evidence available. Other dietary and food components with cholesterol-lowering actions beyond inhibiting cholesterol absorption from the bowel have also been tested in children and/or adolescents. These trials were usually small, short-term, and limited to specific settings , suggesting the need for further confirming larger and long-term studies. 4.1. Nuts Nuts (i.e., almonds, hazelnuts, pistachios, walnuts, macadamia nuts and peanuts) are classified as dry fruits. In recent decades, the cardioprotective and health-promoting qualities of nuts—particularly walnuts—have been extensively demonstrated in epidemiological studies. These positive effects are due to their composition rich in bioactives, such as monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs), tocopherols (vitamin E), antioxidants, phytosterols, fibers and polyphenols, with hazelnuts being especially noteworthy in this regard . Additionally, hazelnuts stand out for having the highest content of MUFAs among all nuts . Regular hazelnut intake also seems to improve microbiota composition and reduce the intestinal concentration of short-chain fatty acids (SCFAs) . Moreover, polyphenols influence cholesterol absorption, TG synthesis and secretion and exert antioxidant effects . To the best of our knowledge, only a few small trials until now have investigated the effects of hazelnuts on plasma lipids in children. Hazelnuts peeled or with the skin (0.43 g/kg of body weight, 15–30 g portions) were shown to significantly reduce LDL-C, while the ratio HDL-C/LDL-C increased compared to a control group receiving the STEP I diet. Moreover, their intake increased the MUFA/saturated fatty acids (SFA) ratio in red blood cells and lowered the endogenous and oxidative induced DNA damage . 4.2. Soy Soy contains several bioactive compounds that are supposed to improve plasma lipid levels in humans (i.e., isoflavones, phytosterols and specific peptides, which can promote LDL-C receptor expression in liver cells) . A recent meta-analysis showed that a median intake of 25 g/day of soy proteins during a median follow-up of 6 weeks decreased LDL-C plasma levels by 3–4% in adults (−4.8 mg/dL; 95%CI −6.7, −2.8 mg/dL, p < 0.0001) . However, only few studies involving children exist. In a pilot study testing the cholesterol-lowering effect of soy or milk protein in children with FH, TC and LDL-C levels did not significantly change though TG and HDL-C improved . Different results were obtained in a prospective study conducted on 16 children with FH. TC, LDL-C, and ApoB significantly improved (−7.7%, −6.4% and −12.6%, respectively) after a 3-month period in which soy proteins were incorporated into the Step I diet, being administered in the form of soy-based dairy-free milk at a dosage of 0.25–0.5 g/kg body weight . This study represents an extended examination of soy protein in pediatric populations. Interestingly, even if children were generally adherent to the program, not all participants (4 out of 16) exhibited the desired response, despite having similar characteristics at entry. Overall, most studies concur on the efficacy of soy protein; however, safety concerns—such as allergic reactions or the potential effects of dietary phytoestrogens (i.e., isoflavones)—remain debatable, especially in the youngest participants . A 13-week randomized controlled clinical trial has been recently launched to address the effect of a soy-rich diet compared to a low-fat diet and a control diet in children with FH. After 7 weeks from randomization, the reduction in LDL-C levels was notably greater in the soy group (155 ± 29 mg/dL) compared to the control group (176 ± 28 mg/dL; P for comparison = 0.038), with a similar trend observed at 13-week follow-up (LDL-C = 180 ± 42 mg/dL in the control group and 155 ± 30 mg/dL in the soy group; P for comparison = 0.089). The relative decrease in LDL-C levels was significantly associated with plasma isoflavone concentrations (specifically daidzein and genistein), as measured at week 7 . Moreover, it must be acknowledged that soy proteins are usually well tolerated, and the occurrence of acute reactions depends on the individual hypersensitivity. However, total protein intake must be balanced with soy intake, and safety of the phytoestrogens must be confirmed on the long term. 4.3. Polyunsaturated Fatty Acids (PUFAs) The dietary fats composition is a crucial determinant of lipid concentrations in plasma . Nonetheless, the dietary intake of polyunsaturated fatty acids (PUFAs)—including Omega-3 and Omega-6—is frequently insufficient in children . Dietary supplementation of PUFAs have shown to impact cardiovascular risk markers, such as TG, LDL-C and adhesion molecules, while also possessing anti-inflammatory properties . In a randomized double-blinded placebo-controlled clinical trial involving 107 healthy children, PUFAs and low saturated fatty acids were able to significantly reduce markers of endothelial cell activation (i.e., adhesion molecules, E-selectin, ICAM-1 and lymphocyte levels), while increasing plasma concentration of Docosahexaenoic acid (DHA) after receiving a 5-month daily intake of a milk enriched product . Another functional food that has been studied in this context is the hempseed oil, which is notably rich in essential fatty acids, including Omega-3 PUFA α-linolenic acid (ALA) and Omega-6 PUFA linoleic acid (LA), with an LA/ALA ratio ranging between 2:1 and 3:1 . The cholesterol-lowering effect of hempseed oil has been extensively assessed in animals and adult humans. However, a recent 8-week randomized controlled trial showed that HSO increases the content of total n-3 and n-6 PUFAs in red blood cells and improves the Omega-3 index, even in children with hyperlipidemia. Moreover, according to the findings of the study, hempseed oil exerted significant reductions in LDL-C (−14%) compared to the control . Overall, emerging observations offer valuable insights for enhancing and complementing food intake with PUFAs. However, further studies are warranted to validate preliminary data. 4.4. Red Yeast Rice Red yeast rice is a widely used and clinically tested cholesterol-lowering nutraceutical derived from the fermentation of standard rice by specific mycelia (usually Monascus purpureus Went) with the production of a pigment (making the rice red) and some bioactive compounds, among which monacolins are reversible inhibitors of 3-hydroxy-3-methyl-glutaril Coenzyme A reductase . The most bioactive compound was monacolin K, which is chemically analogous to lovastatin. In adults, monacolin K significantly reduces cholesterolemia while maintaining an acceptable safety profile . To date, only one study has been conducted in children at increased cardiovascular risk, including those with familial hypercholesterolemia (FH) and familial combined hyperlipidemia, while following a step II diet . The study, designed as a double-blinded, randomized cross-over trial, spanned 6 months in total, during which all participants completed the trial without experiencing any notable adverse effects. After 8 weeks of treatment, there was a significant reduction in TC, LDL-C, and ApoB levels by −18.5%, −25.1%, and −25.3%, respectively, while HDL-C remained unchanged. These findings were remarkable in terms of compliance, tolerability, and efficacy. Notably, the administered dose of Monacolin K was 3 mg/day, demonstrating impressive results comparable to those achieved with pravastatin (10 mg/day or more), indicating a potential synergistic effect with other bioactive components. A recent change in European Union (EU) regulations forbids the use of red yeast rice in children and adolescents because of the presumed (but not demonstrated) risk to health . Nuts (i.e., almonds, hazelnuts, pistachios, walnuts, macadamia nuts and peanuts) are classified as dry fruits. In recent decades, the cardioprotective and health-promoting qualities of nuts—particularly walnuts—have been extensively demonstrated in epidemiological studies. These positive effects are due to their composition rich in bioactives, such as monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs), tocopherols (vitamin E), antioxidants, phytosterols, fibers and polyphenols, with hazelnuts being especially noteworthy in this regard . Additionally, hazelnuts stand out for having the highest content of MUFAs among all nuts . Regular hazelnut intake also seems to improve microbiota composition and reduce the intestinal concentration of short-chain fatty acids (SCFAs) . Moreover, polyphenols influence cholesterol absorption, TG synthesis and secretion and exert antioxidant effects . To the best of our knowledge, only a few small trials until now have investigated the effects of hazelnuts on plasma lipids in children. Hazelnuts peeled or with the skin (0.43 g/kg of body weight, 15–30 g portions) were shown to significantly reduce LDL-C, while the ratio HDL-C/LDL-C increased compared to a control group receiving the STEP I diet. Moreover, their intake increased the MUFA/saturated fatty acids (SFA) ratio in red blood cells and lowered the endogenous and oxidative induced DNA damage . Soy contains several bioactive compounds that are supposed to improve plasma lipid levels in humans (i.e., isoflavones, phytosterols and specific peptides, which can promote LDL-C receptor expression in liver cells) . A recent meta-analysis showed that a median intake of 25 g/day of soy proteins during a median follow-up of 6 weeks decreased LDL-C plasma levels by 3–4% in adults (−4.8 mg/dL; 95%CI −6.7, −2.8 mg/dL, p < 0.0001) . However, only few studies involving children exist. In a pilot study testing the cholesterol-lowering effect of soy or milk protein in children with FH, TC and LDL-C levels did not significantly change though TG and HDL-C improved . Different results were obtained in a prospective study conducted on 16 children with FH. TC, LDL-C, and ApoB significantly improved (−7.7%, −6.4% and −12.6%, respectively) after a 3-month period in which soy proteins were incorporated into the Step I diet, being administered in the form of soy-based dairy-free milk at a dosage of 0.25–0.5 g/kg body weight . This study represents an extended examination of soy protein in pediatric populations. Interestingly, even if children were generally adherent to the program, not all participants (4 out of 16) exhibited the desired response, despite having similar characteristics at entry. Overall, most studies concur on the efficacy of soy protein; however, safety concerns—such as allergic reactions or the potential effects of dietary phytoestrogens (i.e., isoflavones)—remain debatable, especially in the youngest participants . A 13-week randomized controlled clinical trial has been recently launched to address the effect of a soy-rich diet compared to a low-fat diet and a control diet in children with FH. After 7 weeks from randomization, the reduction in LDL-C levels was notably greater in the soy group (155 ± 29 mg/dL) compared to the control group (176 ± 28 mg/dL; P for comparison = 0.038), with a similar trend observed at 13-week follow-up (LDL-C = 180 ± 42 mg/dL in the control group and 155 ± 30 mg/dL in the soy group; P for comparison = 0.089). The relative decrease in LDL-C levels was significantly associated with plasma isoflavone concentrations (specifically daidzein and genistein), as measured at week 7 . Moreover, it must be acknowledged that soy proteins are usually well tolerated, and the occurrence of acute reactions depends on the individual hypersensitivity. However, total protein intake must be balanced with soy intake, and safety of the phytoestrogens must be confirmed on the long term. The dietary fats composition is a crucial determinant of lipid concentrations in plasma . Nonetheless, the dietary intake of polyunsaturated fatty acids (PUFAs)—including Omega-3 and Omega-6—is frequently insufficient in children . Dietary supplementation of PUFAs have shown to impact cardiovascular risk markers, such as TG, LDL-C and adhesion molecules, while also possessing anti-inflammatory properties . In a randomized double-blinded placebo-controlled clinical trial involving 107 healthy children, PUFAs and low saturated fatty acids were able to significantly reduce markers of endothelial cell activation (i.e., adhesion molecules, E-selectin, ICAM-1 and lymphocyte levels), while increasing plasma concentration of Docosahexaenoic acid (DHA) after receiving a 5-month daily intake of a milk enriched product . Another functional food that has been studied in this context is the hempseed oil, which is notably rich in essential fatty acids, including Omega-3 PUFA α-linolenic acid (ALA) and Omega-6 PUFA linoleic acid (LA), with an LA/ALA ratio ranging between 2:1 and 3:1 . The cholesterol-lowering effect of hempseed oil has been extensively assessed in animals and adult humans. However, a recent 8-week randomized controlled trial showed that HSO increases the content of total n-3 and n-6 PUFAs in red blood cells and improves the Omega-3 index, even in children with hyperlipidemia. Moreover, according to the findings of the study, hempseed oil exerted significant reductions in LDL-C (−14%) compared to the control . Overall, emerging observations offer valuable insights for enhancing and complementing food intake with PUFAs. However, further studies are warranted to validate preliminary data. Red yeast rice is a widely used and clinically tested cholesterol-lowering nutraceutical derived from the fermentation of standard rice by specific mycelia (usually Monascus purpureus Went) with the production of a pigment (making the rice red) and some bioactive compounds, among which monacolins are reversible inhibitors of 3-hydroxy-3-methyl-glutaril Coenzyme A reductase . The most bioactive compound was monacolin K, which is chemically analogous to lovastatin. In adults, monacolin K significantly reduces cholesterolemia while maintaining an acceptable safety profile . To date, only one study has been conducted in children at increased cardiovascular risk, including those with familial hypercholesterolemia (FH) and familial combined hyperlipidemia, while following a step II diet . The study, designed as a double-blinded, randomized cross-over trial, spanned 6 months in total, during which all participants completed the trial without experiencing any notable adverse effects. After 8 weeks of treatment, there was a significant reduction in TC, LDL-C, and ApoB levels by −18.5%, −25.1%, and −25.3%, respectively, while HDL-C remained unchanged. These findings were remarkable in terms of compliance, tolerability, and efficacy. Notably, the administered dose of Monacolin K was 3 mg/day, demonstrating impressive results comparable to those achieved with pravastatin (10 mg/day or more), indicating a potential synergistic effect with other bioactive components. A recent change in European Union (EU) regulations forbids the use of red yeast rice in children and adolescents because of the presumed (but not demonstrated) risk to health . Multiple nutraceuticals have shown efficacy in reducing lipid levels, as evidenced by the available clinical trials. However, it is important to note that no single dietary supplement can replace the importance of proper dietary counseling. Lifestyle changes and adherence to a correct Mediterranean diet showed a mean decrease of 9.5% in TC, 13.5% in LDL-C, and −10.9% in non-HDL-C plasma levels in a recent large retrospective study conducted on children with polygenic and familial hypercholestremia . This may be sufficient to manage mild polygenic hypercholesterolemia. Phytosterols and fiber-enriched foods are usually shown to be effective in reducing TC and LDL-C levels in children with FH or polygenic hypercholesterolemia or children affected by other secondary dyslipidaemias, especially when combined with a STEP I (daily fat intake < 30%, saturated FAs < 10%, cholesterol < 300 mg) or STEP II (saturated FAs 7%, cholesterol < 200 mg) diet. ApoB is also ameliorated after phytosterol intake, while contrasting results were observed after fiber intake. In contrast, no favorable variations have been observed in the endothelial function of phytosterol/sterol addition, and only two studies concerning this topic are inconclusive. Plant sterol/stanol and fiber were well received, with high compliance observed, particularly in the short term. Some gastrointestinal side effects would be expected if phytosterols are assumed with addition of artificial sweeteners. Noteworthy is the absence of significant adverse effects; nonetheless, abdominal discomfort or diarrhea were commonly reported symptoms with fiber supplementation. They could be even more frequent and severe when fibers are assumed with addition of artificial sweeteners. Functional foods incorporating phytosterols/stanols were associated with a notable decline in carotenoids, but not other vitamins, highlighting the importance of maintaining an adequate intake of vegetables and fruits to prevent nutritional deficiencies. While randomized and controlled studies focusing on robust endpoints in children are lacking and infrequent, certain benefits have been noted, particularly in the short term, primarily relating to plant sterol/stanol and fibers. Many other bioactive compounds have demonstrated efficacy as cholesterol-lowering agents in adults (red yeast rice, berberine, bergamot polyphenol fraction, and artichoke extracts), but not in children. Presently, no dietary supplements have been shown to significantly reduce lipoprotein (a) levels, beyond L-carnitine and Coenzyme Q10, but this effect has never tested in children . The European Atherosclerosis Society Consensus Panel recommended that functional foods containing phytosterols to be considered for children with familial hypercholesterolemia, and as dietary additive supplementation rather than independent pharmaceutical treatment, especially with lifestyle modification . Despite the limitation on the available evidence of dyslipidemia in pediatric age group, guidelines and evidence suggest that nutraceuticals, particularly fibers and phytosterols, can be utilized when combined with appropriate diet in children with genetic dyslipidemias starting from the age of 6 years old . However, it could make sense if the treatment with cholesterol-lowering nutraceuticals are adequately dosed, long-term and effective. Lifestyle and dietary modifications are recommended for any child or adolescent presenting with mild to moderate dyslipidemia. Phytosterols and fibers are deemed safe, while the other mentioned nutraceuticals may serve as possible efficacious additions to dietary treatment when combined with appropriate diet regimen. It is important to note that nutraceuticals should not be viewed as substitutes for diet or statins when they are medically indicated, as advised by the main international guidelines. |
The Triangulation WIthin a STudy (TWIST) framework for causal inference within pharmacogenetic research | c2ab2915-472f-4f45-a57c-2205d105b72c | 8452063 | Pharmacology[mh] | Over the last 20 years the field of Epidemiology has embraced the exploitation of random genetic inheritance to help uncover causal mechanisms of disease using the technique of Mendelian randomization (MR). . The basic premise of MR is illustrated by the causal diagrams in : Genetic variants, usually Single Nucleotide Polymorphisms or SNPs, G , are found which robustly explain variation in a modifiable risk factor, X , where X is typically continuous (for example a person’s body mass index). The association between the exposure and an outcome, Y , hypothesised to be a downstream consequence of X , may be contributed to in observational data by unobserved confounding, U . If present, such confounding would mean that the naive association between X and Y would not reflect the causal effect of X on Y . If the important confounders could be appropriately measured and adjusted for, and no systematic selection bias or loss to follow up was present in the data this last assumption, then individuals with the same exposure level would be exchangeable and observational associations could be interpreted causally. An MR analysis aims to circumvent any potential confounding by instead measuring the association between the outcome and the portion of the exposure that can be genetically predicted by the SNPs. Provided that the SNPs are independent of the confounders, are not associated with the outcome through any other pathway except the exposure, and the causal effect of a unit increase in the exposure is the same across individuals, then MR can consistently estimate the average causal effect of intervening on the exposure (e.g. to reduce or increase it) on the outcome. The Instrumental Variable (IV) assumptions of the MR approach are denoted IV1-IV4 in . Genetic variants can also play an important role in helping to explain treatment effect heterogeneity in pharmacogenetics research. A canonical example is Clopidogrel: the primary drug for ischemic stroke prevention in the UK and many other countries (see https://cks.nice.org.uk/antiplatelet-treatment ). It requires CYP2C19 enzyme activation in order to be properly metabolised into the active form of the drug and thus work to its fullest extent. However, it has long been known that both common loss-of-function and gain-of-function variants within the CYP2C19 gene region can massively impact each patient’s ability to metabolise the drug . Consequently, when prescribed in a primary care setting its effectiveness is heterogeneous, working well for some and not for others. Estimating the true effectiveness of a treatment from observational data is challenging due to ‘confounding by indication’ (see ). For example, Clopidogrel use will, quite rightly, strongly depend on an individual’s underlying risk of stroke. However, unmeasured socio-economic factors may also influence both an individual’s ability to access appropriate healthcare and their underlying stroke risk . Use of the drug for a sustained period may additionally depend on whether it can be tolerated without side-effects. Whilst these confounding factors could in principle be directly accounted for in a statistical analysis if complete information on a patient’s clinical state were available, this is seldom the case. A well known example (albeit in a non-pharmacogenetic context) where confounder adjustment failed is hormone replacement therapy, which was linked to increased cancer risk in observational data but not in subsequent randomized trials . Thankfully, the need for adjustment can be circumvented if the purpose of the analysis is instead to compare the relative effectiveness of a treatment across genetic groups (see ). In the case of Clopidogrel and CYP2C19 , typically one might assume that CYPC219 variants G : do not predict whether an individual receives Clopidogrel T ; are not associated with any confounders predicting Clopidogrel use and stroke, Y ; and only affects stroke risk through their interaction with Clopidogrel. We will refer to these as the ‘Pharmacogenetic’ (PG) assumptions as a counterpart to the IV assumptions utilised by MR. A key difference exists between the role of the gene in MR and the role of the gene in pharmacogenetics: In MR, genes are assumed to directly influence a modifiable exposure. In Pharmacogenetics we can think of treatment as the exposure, and the genes are hypothesised to alter treatment once it is taken. In we denote the genetically altered treatment with the symbol T *. In recent work, Pilling et al use data from GP-linked UK Biobank participants on Clopidogrel treatment to estimate its effect in different CYP2C19 genetic subgroups and from this the number of strokes that could potentially be avoided if all individuals could experience the same benefit as the group with the most favourable genotype (through either dose modification or through switching to an alternative drug). In this paper we review the methodological underpinnings of the general PG approach, which utilises only individuals who are treated and relies on fairly strong baseline PG assumptions to estimate what we refer to as the ‘Genetically Moderated Treatment Effect’ (GMTE). When the PG assumptions are violated, we show that a robust but less efficient estimate of the GMTE that incorporates information on the population of untreated individuals can instead be used. In cases of partial violation, we clarify when Mendelian randomization and traditional confounder adjustment can also yield consistent estimates for the GMTE. A decision framework is then described to decide when a particular estimation strategy is most appropriate and how specific estimators can be combined to further improve efficiency. Triangulation of evidence from different data sources, each with their inherent biases and limitations, is becoming a well established principle for strengthening causal analysis . We call this framework ‘ T riangulation WI thin a ST udy’ (TWIST)’ in order to emphasise that an analysis in this spirit is also possible within a single data set, using causal estimates that are approximately uncorrelated and reliant on different sets of assumptions. This makes their estimates easy to quantitatively combine if sufficiently similar to improve the precision and robustness of any findings. More broadly, it enables estimates to be qualitatively compared and contrasted, with expert judgement used to assess whether their assumptions are likely to have been met, in order to come to an overall conclusion about the totality of evidence. We illustrate these approaches by re-analysing primary-care-linked UK Biobank data relating to CYP2C19 genetic variants, Clopidogrel use and stroke risk, and data relating to APOE genetic variants, statin use and Coronary Artery Disease (CAD).
Suppose that we are interested in evaluating the maximal effectiveness of a treatment T on an outcome Y using observational data. For simplicity we will assume initially that T is a binary treatment indicator so that, if prescribed, it is taken in full, that Y is a continuous or binary outcome variable and we are interested in estimating the treatment effect as a mean or risk difference contrast. A simple but naive way of estimating this effect would be to compare outcomes across those who are treated and those who are untreated. Borrowing terminology from the clinical trials literature, we refer to this as the ‘As treated’ (AT) estimate ( top right). The AT estimate may not directly address our research needs for two reasons. The first reason is that, although we may understand many of the factors which influence whether an eligible individual is prescribed treatment by their doctor, there may be unmeasured variables, U , which influence both the decision to prescribe treatment and the outcome. Indeed, even if the treatment is truly effective in reducing the severity or risk of Y , it is highly likely that the population of treated individuals may still experience worse outcomes than those who are untreated. This would mean that the sign of the AT estimate could be positive, and thus qualitatively different than the true causal effect. This is classic confounding by indication. The second reason is that a pharmacogenetic investigation may suggest that the treatment does not in fact work for a certain proportion of the population at all, has a markedly reduced effectiveness, or increases the risk of side-effects. We would like to estimate the difference in patient outcomes if all patients who take treatment experienced the ‘full’ effect, as experienced by those with the treatment-enabling genotype versus the reduced (or possibly zero) effect experienced by those with a treatment-inhibiting genotype. To realise such a benefit in practice, we could switch patients with the treatment-inhibiting genotype to an alternative medication which then works to the same extent as the ‘full’ effect of the original treatment. We will call this hypothetical quantity (or ‘estimand’) the Genetically Moderated Treatment Effect (GMTE). 2.1 The causal estimand and key identifying assumptions To make the target of our analysis more explicit we will assume a simple model with a binary genotype G , where G = 1 denotes the treatment-enabling genotype and G = 0 denotes the treatment-inhibiting genotype. We now define a new treatment-moderating variable T * which is equal to the product or interaction T × G . We consider the following simple linear interaction model for the expectation (or mean) of outcome Y given treatment, T , genetic variant G , measured confounder Z and unmeasured confounder U : E [ Y | T , G , U ] = γ Y 0 + β 1 T G + β 0 T ( 1 - G ) + γ Y G G + γ Y Z Z + γ Y U U (1) = γ Y 0 + β 0 T + ( β 1 - β 0 ) T * + γ Y G G + γ Y Z Z + γ Y U U (2) Under model , β 1 and β 0 reflect the treatment effect experienced by those with genotype G = 1 and G = 0 respectively and thus allows for genetically driven treatment effect heterogeneity. The parameter γ YG represents the direct effect of G on Y and γ YU represents the direct effect of U on Y . To clarify the causal estimand of interest we re-write model as model . Using potential outcomes notation, we can express the GMTE estimand as the average causal effect if everyone could receive moderated treatment level T * = 1 (i.e. the full or enhanced effect) versus if everyone could receive treatment level T * = 0 (i.e. no enhanced effect): β G M T E ( Y ) = E [ Y i ( T * = 1 ) - Y i ( T * = 0 ) ] (3) This is equal to β 1 − β 0 , the coefficient of T * in model . We now define the key assumptions that will be leveraged by the various methods proposed in this paper. These assumptions are also represented by the causal diagram and corresponding association parameters in : Homogeneity (Hom) : Individuals who take treatment with genotype level G = 0 experience no treatment effect all ( β 0 = 0). Note that this is subtly different to Homogeneity assumption IV4 made in Mendelian randomization, which in this context would state that β 1 = β 0 ; PG1 : An individual’s genotype G is independent of the decision to take treatment, T , given all unmeasured confounders, U , of T and outcome Y ( γ TG = 0); PG2 : An individual’s genotype G is independent of confounders Z , U ( γ UG = 0); PG3 : An individual’s genotype G is independent of their outcome Y given treatment T and all unmeasured confounders U ( γ YG = 0); No unmeasured confounding (NUC) : All confounding variables U that predict T and Y have been measured and adjusted for ( γ YU or γ TU = 0). As previously stated, we will assume that the NUC assumption implies exchangeability between treatment groups, which rules out the presence of systematic selection bias or systematic loss to follow up in the data. 2.2 Estimating the GMTE by correcting the As-Treated estimate The As-Treated estimate suffers in general from confounding by indication, and a lack of specificity to the genetic variant driving the mechanistic interaction with the treatment. However, if the Hom and NUC assumptions are satisfied and either PG1 or PG3 are satisfied, then, the ‘Corrected’ As Treated estimate (CAT) can consistently estimate the GMTE. Put simply, if confounding bias can be addressed and the population treatment effect is driven entirely by the G = 1 subgroup, then the correct quantity can be estimated by scaling the treatment-outcome association by the proportion of treated individuals with the G = 1 genotype. 2.3 Estimating the GMTE in the treated population only We next consider estimation of the GMTE in the general case where only assumptions PG1-PG3 hold. Together they imply that G is jointly independent of treatment and any unmeasured confounders, and they only affect the outcome through the treatment moderator variable T *. Among the population of treated individuals, we can think of an individual’s genotype as randomly allocating them to either moderated treatment level T * = 1 or T * = 0. This means we can calculate the GMTE using only treated individuals via the ‘GMTE(1)’ estimate. We use the additional subscript ‘(1)’ in the estimate’s nomenclature to denote that it conditions on T = 1. 2.4 A robust GMTE estimator We next consider estimation of the GMTE under violations of PG1-PG3. Violation of PG1 implies that an individual’s genotype directly influences the likelihood that they receive treatment. For example, it could be that those with a G = 0 genotype have an increased risk of side effects on treatment and choose to immediately come off the drug. An alternative explanation could be genetic population stratification : e.g the allele frequency of the genetic variant G and the rate of treatment could simultaneously vary across individuals from different ethnic groups. An example of PG2 violation would be if the genetic variant increases the likelihood of an unmeasured risk factor for the outcome, and this risk factor also increases their likelihood of being treated. An example of PG3 violation would be if an individual’s genotype directly affects the outcome through a pathway completely independent of either treatment or any confounding factor, which could be viewed as horizontal pleiotropy . When any of these assumptions are violated the GMTE(1) estimate will reflect the genetically moderated effect of treatment plus the bias due to PG1–3 violation. Specifically, this bias would be the sum of: b PG 1 via the G → T ← U → Y pathway (due to violation of PG1); b PG 2 via the G → U → Y pathway (due to violation of PG2); b PG 3 via the G → Y pathway (due to violation of PG3). Whilst the bias contributions b PG 2 and b PG 3 are clear, bias contribution b PG 1 is perhaps less so; it occurs because the GMTE(1) estimate explicitly conditions on treatment T , the presence of an association between G and T makes T a ‘collider’ . This is one example of broader point: the RGMTE estimate is not robust to effect modification of any variable associated with T. Thankfully, when assumption PG1 is satisfied and no other effect modification is present, bias terms b PG 2 and b PG 3 can be consistently estimated and removed by incorporating information on the untreated population. This is achieved by calculating the equivalent GMTE(1) estimate for the untreated group (we call this the GMTE(0) estimate) and then subtracting this estimate from that in the treated group. We call this the ‘Robust’ genetically moderated treatment effect (RGMTE) estimate. Although assumption PG1 is key for the RGMTE estimate, we show that it can work if PG1 is violated but the NUC assumption is satisfied. 2.5 A ‘Mendelian randomization’ estimate Given data on both treated and untreated individuals, it is possible to obtain an estimate for the GMTE by using the genetic variant G as an instrumental variable for the treatment moderator variable T * directly, as in Mendelian randomization. In the context of a single gene, G , the MR estimate is the ratio of the gene-outcome association and the the gene- T * association. The MR estimate is consistent for the GMTE if PG2-PG3 hold and either PG1 holds, or the Hom assumption holds. In we provide a formal justification of when the CAT, GMTE(1), RGMTE and MR estimates are consistent for the GMTE assuming outcome model . 2.6 Method summary and implementation In we: (i) give statistical formulae for the GMTE(1), GMTE(0), RGMTE, MR and CAT estimates; (ii) provide more detailed information on the sufficient assumptions each one relies upon to consistently estimate the GMTE (or in the case of the GMTE(0) estimate, zero); (iii) show how to test whether potential measured confounders of the treatment and outcome could bias each estimate; and (iv) give generic R psuedocode to obtain each estimate. To further clarify point (iii), take the GMTE(1) estimate as an example. In order to assess whether a potential confounder Z 1 could meaningfully bias its estimate, we calculate the GMTE(1) estimate but use Z 1 as the outcome in place of the true outcome Y . If this GMTE(1) estimate is significantly non-zero then it indicates a meaningful bias in the GMTE estimate with respect to the outcome, unless the confounder is adjusted for by treating Z 1 as an additional component of Z . This principle holds for all other GMTE estimates as well. Unlike the GMTE(1) and RGMTE estimators, the MR and CAT estimators both have a ratio form with the denominator dependent on G . For this reason they are more susceptible to bias and imprecision when the sample size is small and G has a low allele frequency. When the outcome is continuous, the approaches can be implemented using linear regression to estimate the GMTE as a mean difference. With a binary outcome, we recommend estimating risk differences directly using either a linear probability model, or using a logistic regression model to furnish estimates on the risk difference scale as an average marginal effect. With time-to-event data, we recommend analysing the data under an additive hazards model. Further details are provided . We suggest to estimate mean difference, risk differences or additive hazard differences in order to obtain estimates for the GMTE from different estimators on the same scale, because these measures are collapsible. That is, they should remain constant when marginalised over unobserved confounders . This is especially important for being able to effectively combine methods, as described in the next section. 2.7 Which estimates can be combined? When two estimates are highly correlated, we gain little knowledge when they are observed to be similar. However, when two uncorrelated or weakly correlated estimates are similar, it gives credence to the hypothesis that they are estimating the same underlying quantity, and there is the potential to combine them into a single, more precise estimate. In we show that the RGMTE and MR estimates are asymptotically uncorrelated. We also show that the CAT estimate is mutually uncorrelated with the GMTE(1) and RGMTE estimates, and uncorrelated with the MR estimate when G is independent of T . In cases where G and T are not perfectly independent, but G is a modest predictor of T (a highly plausible scenario in most pharmcogenetic contexts), the correlation between the MR and CAT estimate will be non-zero but practically negligible. The fact that most estimate-pairs are uncorrelated makes them easy to combine via a simple inverse variance weighted average or meta-analysis. In order to decide whether two uncorrelated estimates can be combined, we propose the use of a simple heterogeneity statistic. This procedure is illustrated in taking the GMTE(1) and CAT estimates as an example. Using each estimate we calculate their inverse variance weighted average and from this the heterogeneity statistic, Q GMTE (1), CAT . If this statistic is less than the 1- α quantile of a χ 1 2 density (where α is the pre-specified significance threshold) then we judge the GMTE(1) and CAT estimates to be sufficiently similar to combine into a single estimate more efficient estimate. If Q GMTE (1), CAT is greater than 1- α threshold then the two estimates should be left separate. Along with single estimates, combined estimates that meet this heterogeneity statistic are colour coded blue (e.g. case (i)) and those which do not will be colour coded black (e.g. case (ii)). We stick to this convention for the remainder of the paper. The RMGTE and MR estimates are in general highly correlated with the GMTE(1) estimate, and should therefore not be combined. we show that the MR and RMGTE estimates can be viewed as complementary functions of the GMTE(1) and GMTE(0) estimates, and that the combined RGMTE/MR estimate is exactly equivalent to the GMTE(1) estimate when G is independent of T and the proportion of treated and untreated participants in the data is the same. shows a pictorial diagram of all single and combined estimates that can be derived using the above heterogeneity statistic criteria. This comprises four original estimates (CAT,GMTE1,RMGTE,MR), four ‘paired estimates (CAT/GMTE1, CAT/RGMTE, CAT/MR, RGMTE/MR) and one ‘triplet’ estimate (CAT/RGMTE/MR), making nine in total. One possible analysis option would be to report all single and valid combined estimates which are sufficiently homogeneous according to a particular significance threshold. Another option would be to allow the GMTE(0) estimate to initially guide the analysis towards either the GMTE(1) estimate (and its possible combination with the CAT estimate) or the RMGTE estimate (and its possible combination with either MR estimate, the CAT estimates or both). Alternatively, some may be more comfortable with a qualitative assessment of the totality of evidence gleaned across the four distinct analysis procedures, using prior scientific knowledge to weigh up their individual importance after careful consideration given the plausibility of their key assumptions.
To make the target of our analysis more explicit we will assume a simple model with a binary genotype G , where G = 1 denotes the treatment-enabling genotype and G = 0 denotes the treatment-inhibiting genotype. We now define a new treatment-moderating variable T * which is equal to the product or interaction T × G . We consider the following simple linear interaction model for the expectation (or mean) of outcome Y given treatment, T , genetic variant G , measured confounder Z and unmeasured confounder U : E [ Y | T , G , U ] = γ Y 0 + β 1 T G + β 0 T ( 1 - G ) + γ Y G G + γ Y Z Z + γ Y U U (1) = γ Y 0 + β 0 T + ( β 1 - β 0 ) T * + γ Y G G + γ Y Z Z + γ Y U U (2) Under model , β 1 and β 0 reflect the treatment effect experienced by those with genotype G = 1 and G = 0 respectively and thus allows for genetically driven treatment effect heterogeneity. The parameter γ YG represents the direct effect of G on Y and γ YU represents the direct effect of U on Y . To clarify the causal estimand of interest we re-write model as model . Using potential outcomes notation, we can express the GMTE estimand as the average causal effect if everyone could receive moderated treatment level T * = 1 (i.e. the full or enhanced effect) versus if everyone could receive treatment level T * = 0 (i.e. no enhanced effect): β G M T E ( Y ) = E [ Y i ( T * = 1 ) - Y i ( T * = 0 ) ] (3) This is equal to β 1 − β 0 , the coefficient of T * in model . We now define the key assumptions that will be leveraged by the various methods proposed in this paper. These assumptions are also represented by the causal diagram and corresponding association parameters in : Homogeneity (Hom) : Individuals who take treatment with genotype level G = 0 experience no treatment effect all ( β 0 = 0). Note that this is subtly different to Homogeneity assumption IV4 made in Mendelian randomization, which in this context would state that β 1 = β 0 ; PG1 : An individual’s genotype G is independent of the decision to take treatment, T , given all unmeasured confounders, U , of T and outcome Y ( γ TG = 0); PG2 : An individual’s genotype G is independent of confounders Z , U ( γ UG = 0); PG3 : An individual’s genotype G is independent of their outcome Y given treatment T and all unmeasured confounders U ( γ YG = 0); No unmeasured confounding (NUC) : All confounding variables U that predict T and Y have been measured and adjusted for ( γ YU or γ TU = 0). As previously stated, we will assume that the NUC assumption implies exchangeability between treatment groups, which rules out the presence of systematic selection bias or systematic loss to follow up in the data.
The As-Treated estimate suffers in general from confounding by indication, and a lack of specificity to the genetic variant driving the mechanistic interaction with the treatment. However, if the Hom and NUC assumptions are satisfied and either PG1 or PG3 are satisfied, then, the ‘Corrected’ As Treated estimate (CAT) can consistently estimate the GMTE. Put simply, if confounding bias can be addressed and the population treatment effect is driven entirely by the G = 1 subgroup, then the correct quantity can be estimated by scaling the treatment-outcome association by the proportion of treated individuals with the G = 1 genotype.
We next consider estimation of the GMTE in the general case where only assumptions PG1-PG3 hold. Together they imply that G is jointly independent of treatment and any unmeasured confounders, and they only affect the outcome through the treatment moderator variable T *. Among the population of treated individuals, we can think of an individual’s genotype as randomly allocating them to either moderated treatment level T * = 1 or T * = 0. This means we can calculate the GMTE using only treated individuals via the ‘GMTE(1)’ estimate. We use the additional subscript ‘(1)’ in the estimate’s nomenclature to denote that it conditions on T = 1.
We next consider estimation of the GMTE under violations of PG1-PG3. Violation of PG1 implies that an individual’s genotype directly influences the likelihood that they receive treatment. For example, it could be that those with a G = 0 genotype have an increased risk of side effects on treatment and choose to immediately come off the drug. An alternative explanation could be genetic population stratification : e.g the allele frequency of the genetic variant G and the rate of treatment could simultaneously vary across individuals from different ethnic groups. An example of PG2 violation would be if the genetic variant increases the likelihood of an unmeasured risk factor for the outcome, and this risk factor also increases their likelihood of being treated. An example of PG3 violation would be if an individual’s genotype directly affects the outcome through a pathway completely independent of either treatment or any confounding factor, which could be viewed as horizontal pleiotropy . When any of these assumptions are violated the GMTE(1) estimate will reflect the genetically moderated effect of treatment plus the bias due to PG1–3 violation. Specifically, this bias would be the sum of: b PG 1 via the G → T ← U → Y pathway (due to violation of PG1); b PG 2 via the G → U → Y pathway (due to violation of PG2); b PG 3 via the G → Y pathway (due to violation of PG3). Whilst the bias contributions b PG 2 and b PG 3 are clear, bias contribution b PG 1 is perhaps less so; it occurs because the GMTE(1) estimate explicitly conditions on treatment T , the presence of an association between G and T makes T a ‘collider’ . This is one example of broader point: the RGMTE estimate is not robust to effect modification of any variable associated with T. Thankfully, when assumption PG1 is satisfied and no other effect modification is present, bias terms b PG 2 and b PG 3 can be consistently estimated and removed by incorporating information on the untreated population. This is achieved by calculating the equivalent GMTE(1) estimate for the untreated group (we call this the GMTE(0) estimate) and then subtracting this estimate from that in the treated group. We call this the ‘Robust’ genetically moderated treatment effect (RGMTE) estimate. Although assumption PG1 is key for the RGMTE estimate, we show that it can work if PG1 is violated but the NUC assumption is satisfied.
Given data on both treated and untreated individuals, it is possible to obtain an estimate for the GMTE by using the genetic variant G as an instrumental variable for the treatment moderator variable T * directly, as in Mendelian randomization. In the context of a single gene, G , the MR estimate is the ratio of the gene-outcome association and the the gene- T * association. The MR estimate is consistent for the GMTE if PG2-PG3 hold and either PG1 holds, or the Hom assumption holds. In we provide a formal justification of when the CAT, GMTE(1), RGMTE and MR estimates are consistent for the GMTE assuming outcome model .
In we: (i) give statistical formulae for the GMTE(1), GMTE(0), RGMTE, MR and CAT estimates; (ii) provide more detailed information on the sufficient assumptions each one relies upon to consistently estimate the GMTE (or in the case of the GMTE(0) estimate, zero); (iii) show how to test whether potential measured confounders of the treatment and outcome could bias each estimate; and (iv) give generic R psuedocode to obtain each estimate. To further clarify point (iii), take the GMTE(1) estimate as an example. In order to assess whether a potential confounder Z 1 could meaningfully bias its estimate, we calculate the GMTE(1) estimate but use Z 1 as the outcome in place of the true outcome Y . If this GMTE(1) estimate is significantly non-zero then it indicates a meaningful bias in the GMTE estimate with respect to the outcome, unless the confounder is adjusted for by treating Z 1 as an additional component of Z . This principle holds for all other GMTE estimates as well. Unlike the GMTE(1) and RGMTE estimators, the MR and CAT estimators both have a ratio form with the denominator dependent on G . For this reason they are more susceptible to bias and imprecision when the sample size is small and G has a low allele frequency. When the outcome is continuous, the approaches can be implemented using linear regression to estimate the GMTE as a mean difference. With a binary outcome, we recommend estimating risk differences directly using either a linear probability model, or using a logistic regression model to furnish estimates on the risk difference scale as an average marginal effect. With time-to-event data, we recommend analysing the data under an additive hazards model. Further details are provided . We suggest to estimate mean difference, risk differences or additive hazard differences in order to obtain estimates for the GMTE from different estimators on the same scale, because these measures are collapsible. That is, they should remain constant when marginalised over unobserved confounders . This is especially important for being able to effectively combine methods, as described in the next section.
When two estimates are highly correlated, we gain little knowledge when they are observed to be similar. However, when two uncorrelated or weakly correlated estimates are similar, it gives credence to the hypothesis that they are estimating the same underlying quantity, and there is the potential to combine them into a single, more precise estimate. In we show that the RGMTE and MR estimates are asymptotically uncorrelated. We also show that the CAT estimate is mutually uncorrelated with the GMTE(1) and RGMTE estimates, and uncorrelated with the MR estimate when G is independent of T . In cases where G and T are not perfectly independent, but G is a modest predictor of T (a highly plausible scenario in most pharmcogenetic contexts), the correlation between the MR and CAT estimate will be non-zero but practically negligible. The fact that most estimate-pairs are uncorrelated makes them easy to combine via a simple inverse variance weighted average or meta-analysis. In order to decide whether two uncorrelated estimates can be combined, we propose the use of a simple heterogeneity statistic. This procedure is illustrated in taking the GMTE(1) and CAT estimates as an example. Using each estimate we calculate their inverse variance weighted average and from this the heterogeneity statistic, Q GMTE (1), CAT . If this statistic is less than the 1- α quantile of a χ 1 2 density (where α is the pre-specified significance threshold) then we judge the GMTE(1) and CAT estimates to be sufficiently similar to combine into a single estimate more efficient estimate. If Q GMTE (1), CAT is greater than 1- α threshold then the two estimates should be left separate. Along with single estimates, combined estimates that meet this heterogeneity statistic are colour coded blue (e.g. case (i)) and those which do not will be colour coded black (e.g. case (ii)). We stick to this convention for the remainder of the paper. The RMGTE and MR estimates are in general highly correlated with the GMTE(1) estimate, and should therefore not be combined. we show that the MR and RMGTE estimates can be viewed as complementary functions of the GMTE(1) and GMTE(0) estimates, and that the combined RGMTE/MR estimate is exactly equivalent to the GMTE(1) estimate when G is independent of T and the proportion of treated and untreated participants in the data is the same. shows a pictorial diagram of all single and combined estimates that can be derived using the above heterogeneity statistic criteria. This comprises four original estimates (CAT,GMTE1,RMGTE,MR), four ‘paired estimates (CAT/GMTE1, CAT/RGMTE, CAT/MR, RGMTE/MR) and one ‘triplet’ estimate (CAT/RGMTE/MR), making nine in total. One possible analysis option would be to report all single and valid combined estimates which are sufficiently homogeneous according to a particular significance threshold. Another option would be to allow the GMTE(0) estimate to initially guide the analysis towards either the GMTE(1) estimate (and its possible combination with the CAT estimate) or the RMGTE estimate (and its possible combination with either MR estimate, the CAT estimates or both). Alternatively, some may be more comfortable with a qualitative assessment of the totality of evidence gleaned across the four distinct analysis procedures, using prior scientific knowledge to weigh up their individual importance after careful consideration given the plausibility of their key assumptions.
Trial data comprising a binary genotype G , treatment indicator T , observed covariate Z and a continuous outcome Y are simulated for n = 10,000 patients using the following data generating model which is consistent with the causal diagram in : G i ∼ B e r n ( p G ) , p G = 0 . 3 Z i ∼ N ( 0 , 1 ) U i ∼ N ( 0 , 1 ) + γ U G G i η T i ∼ γ T 0 + γ T U U i + γ T G G i + γ T Z Z i + ϵ T i P r ( T i = 1 | U i , G i ) = expit ( η T i ) Y i | T i , G i , Z i , U i = γ Y 0 + β 1 T i G i + β 0 T i ( 1 - G i ) + γ Y G G i + γ Y Z Z i + γ Y U U i + ϵ Y i Under this model, assumptions PG1-PG3 are violated if γ TG , γ UG and γ YG are non-zero respectively. The Hom assumption is violated when β 0 is non-zero. Finally the NUC assumption is violated if either γ TU or γ YU (or both) are non-zero. For simplicity we keep γ YU fixed and non-zero and vary only γ TU . Note that if the NUC assumption holds, then PG2 is in a sense automatically satisfied because U is no longer a confounder. However, in this case there may still be a path from G to Y via U . This would then form all or part of any PG3 violation. shows the distribution of estimates for the GMTE obtained across 500 independent data sets and six simulation scenarios, using the CAT, GMTE(1), RGMTE and MR estimators. We also show the distribution of the GMTE(0) estimate in each case, as a helpful guide to understand the extent of bias that can be estimated from the data. In all scenarios the true GMTE is fixed at -0.5. shows the mean point estimates, standard errors and 95% confidence interval coverage corresponding to the same six scenarios. For the five combined estimators, shows: mean point estimates, mean standard errors, 95% confidence interval coverage and the proportion of times each combined estimator passes the heterogeneity test using a significance threshold of α = 0.05. In Scenario 1 of assumption PG3 is violated but all others (PG1, PG2, Hom, NUC) are satisfied. In this case both the CAT and RMGTE estimators are unbiased, with the RGMTE having the smallest standard error. In we see that the combined RGMTE/CAT estimate is consequently unbiased with a standard error of 0.085, which is smaller than either the RMGTE or CAT estimates. In Scenario 2 of the NUC assumption is violated but all others (PG1, PG2, PG3 Hom) are satisfied. In this case the GMTE(1), RGMTE and MR estimators are unbiased, with the GMTE(1) estimate being the most precise. In we see that the combined RGMTE/MR estimate is consequently unbiased with a standard error near-identical to the GMTE(1) estimate, in line with the theoretical prediction outlined in . In Scenario 3 of assumption PG1 is violated and (PG2, PG3, Hom, NUC) are satisfied. In this case all estimators are unbiased. In we show in this case that the most efficient unbiased estimate of all comes from combining the RGMTE, CAT and MR estimates. In Scenario 4 of PG1 and NUC are violated but the remaining assumptions (PG2, PG3, Hom) are satisfied. In this case only the MR estimate is unbiased. Consequently, no combined estimator is unbiased although the bias in the RGMTE/MR estimate is small. In Scenario 5 of , PG2 and Hom are violated but (PG1, PG3, NUC) are satisfied. In this case the GMTE(1) and RGMTE estimators are unbiased, with the GMTE(1) estimator being the most efficient. No combined estimate is unbiased. In Scenario 6 of all assumptions except PG1 are violated. In this case only the RGMTE estimate is unbiased and, again, no combined estimate is unbiased. In order to gauge the sensitivity of each estimator to the minor allele frequency of G , we repeat simulation Scenario 3 of for six values of p G between 0.02 and 0.3. plots the mean standard error of the estimates in each case. We see clearly that the precision of all estimates is an increasing function of minor allele frequency. However, the loss in precision at low allele frequencies is strongest for the MR and CAT estimates. This is because they are both ratio estimates, with the denominator depending heavily on G . In conclusion, our simulation study provides an empirical verification of the strengths and limitations of each approach, and when any two uncorrelated estimates can be effectively combined via a simple inverse variance weighted meta-analysis. Although the standard error of any estimate that combines the CAT and MR estimates requires G to be independent of T to be strictly valid (since this implies a zero correlation between their estimates) when this assumption is violated in Scenario 3 of it only induces a modest loss of coverage (e.g. 91% for the CAT/MR estimate and 92% for the CAT/RGMTE/MR estimate). Across the simulations the RGMTE is emerges as the most robust estimate.
4.1 Clopidogrel, CYPC219 & Stroke risk Clopidogrel is a widely used anti-platelet therapy that impairs platelet aggregation with consequent reductions in risk of atherothrombotic events such as myocardial infarctions and ischemic strokes . Clopidogrel is a pro-drug that requires activation by liver enzymes, primarily CYP2C19. Genetic variants in CYP2C19 impair function with subsequently reduced Clopidogrel active plasma levels , and we have previously shown using primary care linked data on UK Biobank participants that carriers of these variants have increased risks of ischemic stroke and myocardial infarction (MI) whilst prescribed Clopidogrel . In this work we calculated the population attributable fraction using established methods by analysing data on only those who were treated with Clopidogrel, but we revisit the analysis and apply the full TWIST decision framework proposed in this paper. The UK Biobank study recruited 503,325 volunteers from the community who attended one of 22 assessment centres in England, Wales or Scotland between 2006 and 2010 . Participants were aged 40 to 70 years at the time of assessment, and baseline assessment included extensive questionnaires on demographic, health, and lifestyle information. Blood samples were taken, allowing analysis of participant genetics. Ethical approval for the UK Biobank study was obtained from the North West Multi-Centre Research Ethics Committee. This research was conducted under UK Biobank application 14631 (PI: DM). Linked electronic medical records from primary care are available for 230,096 (45.7%) of participants, which includes >57 million prescribing events between 1998 and 2017. Detailed description of the data extracted and limitations are available from UK Biobank. For this analysis we excluded 5,353 participants missing any genetics data, then 14,856 of non-European genetic ancestry, then 555 missing any CYP2C19 loss of function genotype data, leaving 209,333 participants with sufficient primary care and genetic data. N = 198,868 never received a Clopidogrel prescription. N = 938 only ever received one prescription, so did not have sufficient exposure time for study. Of the 9,527 participants remaining, in 2,044 the prescribing frequency was less than once every 2 months, and these were also excluded. This left 7,483 participants with at least two Clopidogrel prescriptions for analysis. Baseline information on the included participants is shown in . CYP2C19 loss-of-function (LoF) carriers (any *2-*8 alleles) had significantly increased ischemic stroke risk (Hazard Ratio (HR) 1.53: 95% CIs 1.04 to 2.26, p = 0.031) and separately MI (HR 1.14: 1.04 to 1.26, p = 0.008) whilst on Clopidogrel, compared to non-LoF carriers in Cox’s proportional hazards regression models adjusted for age at first Clopidogrel prescription, sex, and the first 10 genetic principal components of ancestry. For this analysis non-LoF carriers constituted those with a ‘normal’ CYPC219 genotype and those with the CYP2C19*17 gain-of-function genotype. An in-depth analysis in our companion paper (Supplementary Table 3 in ) showed that normal and * 17 individuals had a near-identical risk of stroke (HR = 0.99, p = 0.97) and that removing * 17 individuals had little impact on the analysis estimates other than a loss in precision, since they constitute 22% of the population. For this reason we chose to keep the binary LoF/non-LoF genetic classification for the full TWIST analysis in the next section. 4.1.1 Estimating the GMTE To estimate the GMTE in this case we modelled the time to stroke using an Aalen additive hazards model, as described in Section 2.4 and . All models were adjusted for age at recruitment or first Clopidogrel prescription, sex, and the first 10 genetic principal components of ancestry. and show the results for this analysis, which reflect the genetically moderated effect of Clopidogrel treatment on the hazard of stroke per year, expressed as a percentage. The GMTE(1) estimate suggests that being a CYP2C19 LoF carrier ( G = 1) increases the risk of stroke by 0.28% (p = 0.048) compared to those without the LoF variant ( G = 0). To put this figure in context, if we could reduce the LoF carrier’s risk by this amount then, when multiplied by the 5264 LoF carrier patient years in the data, it would lead to an expected 13.2% reduction in the total number of strokes (or a reduction of 15 strokes from 110 to 95). To test for potential bias in the GMTE(1) estimate, we calculate the GMTE(0) estimate in the untreated population. Thankfully, it is close to zero (Hazard diff = -0.0039%, p = 0.61), although slightly negative. Taken at face value, this suggest LoF carriers have a slightly reduced risk of stroke through pathways other than Clopidogrel use. Next we calculate the Corrected As Treated (CAT) estimate. As discussed, the validity of this method rests strongly on being able to identify all confounders of Clopidogrel use and stroke. With the data available, it was only possible to adjust for age, sex and genetic principal components and perhaps unsurprisingly, the CAT estimate is an order of magnitude larger (Hazard diff = 2.2%, p ≤ 2 × 10 −16 ). Consequently, the Q CAT , GMTE (1) statistic detects large heterogeneity and suggests that the CAT and GMTE(1) estimates should not be combined. For completeness, we next calculate the RGMTE estimate for the GMTE hazard difference. Since this is itself the difference between the GMTE(1) and GMTE(0) estimates, and given they are of opposite sign, the RMGTE estimate is slightly larger at 0.33% (p = 0.037), suggesting 17 strokes could have been avoided. The MR estimate for the GMTE hazard difference is similar at 0.29% (p = 0.008). Heterogeneity analysis reveals that the MR and RGMTE estimates are sufficiently similar to combine into a more precise single estimate of the GMTE ( Q MR , RGMTE = 0.8). The combined estimate is 0.3 (p = 7.5 × 10 −04 ), or that 16 strokes could have been avoided. No other combination of estimates are sufficiently similar to combine. 4.2 Statins, APOE & CAD We now apply our framework to estimate the extent to which genetic variation at the APOE locus modulates the risk of coronary artery disease (CAD) due to statin treatment using UK Biobank data. Our full data comprises 155,409 unrelated participants of European descent, with primary care data available (updated to March 2017) and up-to-date hospital admission data as of December 2020. Of this sample, we excluded: n = 11 participants with missing APOE genotypes; n = 6,456 non-regular statin users with less than four prescriptions per year or residuals from the linear regression for total statin prescriptions on years of statin treatment greater than 3 or less than -3; n = 1,273 non-statin users diagnosed with CAD at baseline (or prior to baseline); n = 4,566 participants starting statin after a doctor’s diagnosis of coronary artery disease (CAD) based on the hospital admission records. Among the included samples (n = 143,103), 57,682 (59.5%) were female. Of these, 46,179 (32.3%) were statin users, with a median of 9.4 (inter-quartile range: 6.6 to 13.5) statin prescriptions per year and a median of 5.6 (inter-quartile range: 1.2 to 9.9) years of statin treatment. Several SNPs were associated with LDL cholesterol response to statins based on a genome-wide association study, where the APOE e2 defining SNP rs7412 showed a larger LDL cholesterol lowering response to statins compared to e3e3s . APOE genotypes (diplotypes essentially) were determined based on genotypes at rs7412 and rs429358. Inspecting the APOE genotype distribution, the majority of participants were classed as e 3 e 3 (n = 83,813, 58.6%), followed by e 3 e 4 (n = 33,597, 23.5%), e 2 e 3 s (n = 17,811, 12.4%), e 2 e 4 s (n = 3,616, 2.5%), e 4 e 4 s (n = 3,366, 2.4%), and e 2 e 2 s (n = 900, 0.6%). These groups are mutually exclusive. Summary statistics for statin users and non-statin users are presented in . 4.2.1 Results Using the e 3 e 3 group as a reference, we fitted Aalen additive hazard models within each mutually exclusive genetic group additionally adjusting for sex, age on statin or age at recruitment, and the top 10 genetic principal components. For brevity, we focus on the results of the e2e3 versus e3e3 and e4e4 versus e3e3 analyses, which are shown in and account for approximately 72% of the patient data. Estimates reflect the hazard or risk difference of a CAD event per year, expressed as a percentage. Only results of combined estimates that pass a heterogeneity test at the 5% level are shown. Equivalent estimates for the remaining genetic groups showed no evidence of a non-zero genetically moderated effect. Results for all genetic groups are given in . 4.2.2 e 4 e 4 versus e 3 e 3 Inspecting the e 4 e 4 genetic subgroup first, the GMTE(1) estimate suggests that the risk of CAD could be reduced by 0.031% per year if e 3 e 3 patients experienced the same treatment effect as e 4 e 4 patients (p = 0.043). This estimate is valid if the e4e4 genotype only affects the risk of CAD through modulating the effectiveness of statins (i.e. assumptions PG1-PG3 hold). In order to probe this we calculate the equivalent GMTE(0) estimate in non-statin users. The e 4 e 4 group is now seen to have a 0.011% larger risk of CAD than e 3 e 3 (p = 0.07), which suggests that PG1-PG3 violation is possible. Furthermore, shows clear differences in the allele frequencies between treatment groups. Since the RMGTE(0) and RGMTE(1) estimates are of opposite signs, the RGMTE estimate, which is robust to PG2-PG3 violation, infers the risk difference between e4e4 and e3e3’s is larger at -0.037% per year (p = 0.046). The MR estimate of the GMTE is also positive (0.0069%), but very close to zero (p = 0.69). This is, however, sufficiently similar to the RGMTE estimate at the 5% threshold for it to be combined with the MR estimate (despite being qualitatively different), and the combined value suggests a hazard difference of -0.014% per year (p = 0.28). The CAT estimate for the hazard difference in these data is a 3.9% increase per year. Its magnitude is so large compared to the other estimates that we could reasonably assume that adjustment for age, sex and genetic PCs is not sufficient to remove unmeasured confounding by indication. Consequently, no other estimate is sufficiently similar in order to combine with the CAT estimate, as shown in . In the final column of we translate the hazard difference estimate per year implied by the GMTE1, MR, RGMTE and combined RMGTE/MR estimate to give an expected number of CAD events that could be avoided if all 26,938 e3e3 statin user patients could receive the same benefit as the e4e4 patients, by multiplying the per-year risk reduction over the relevant 278,409 patients-years in the data. Using the RGMTE estimate for this risk reduction gives a figure of 103. The GMTE1 and combined RGMTE/MR estimates imply more modest reductions of 85 and 39 CAD events respectively. 4.2.3 e 2 e 3 versus e 3 e 3 Turning our attention to the e2e3 subgroup in and , we again see a large, non-credible CAT hazard difference estimate for the GMTE of a 1.2% per year between e2e3 and e3e3 groups. The GMTE(1), GMTE(0) and RGMTE estimates for the GMTE are all close to zero and non-significant at the 5% level. In contrast, the MR estimate for the GMTE suggests that e2e3’s have a 0.046% reduced risk of CAD per year (p = 3 × 10 −5 ). Using this estimate, the expected number of CAD events that could be avoided if all 26,938 e3e3 patients could receive the same benefit as the e2e3 patients is 128. This is valid if we believe that assumptions PG2-PG3 hold, but either PG1 or the Hom assumption are violated. The GMTE1 and RGMTE estimates imply more modest reductions of 28 and 15 CAD events respectively. In this example, no two single uncorrelated estimates are sufficiently similar in order to combine.
CYPC219 & Stroke risk Clopidogrel is a widely used anti-platelet therapy that impairs platelet aggregation with consequent reductions in risk of atherothrombotic events such as myocardial infarctions and ischemic strokes . Clopidogrel is a pro-drug that requires activation by liver enzymes, primarily CYP2C19. Genetic variants in CYP2C19 impair function with subsequently reduced Clopidogrel active plasma levels , and we have previously shown using primary care linked data on UK Biobank participants that carriers of these variants have increased risks of ischemic stroke and myocardial infarction (MI) whilst prescribed Clopidogrel . In this work we calculated the population attributable fraction using established methods by analysing data on only those who were treated with Clopidogrel, but we revisit the analysis and apply the full TWIST decision framework proposed in this paper. The UK Biobank study recruited 503,325 volunteers from the community who attended one of 22 assessment centres in England, Wales or Scotland between 2006 and 2010 . Participants were aged 40 to 70 years at the time of assessment, and baseline assessment included extensive questionnaires on demographic, health, and lifestyle information. Blood samples were taken, allowing analysis of participant genetics. Ethical approval for the UK Biobank study was obtained from the North West Multi-Centre Research Ethics Committee. This research was conducted under UK Biobank application 14631 (PI: DM). Linked electronic medical records from primary care are available for 230,096 (45.7%) of participants, which includes >57 million prescribing events between 1998 and 2017. Detailed description of the data extracted and limitations are available from UK Biobank. For this analysis we excluded 5,353 participants missing any genetics data, then 14,856 of non-European genetic ancestry, then 555 missing any CYP2C19 loss of function genotype data, leaving 209,333 participants with sufficient primary care and genetic data. N = 198,868 never received a Clopidogrel prescription. N = 938 only ever received one prescription, so did not have sufficient exposure time for study. Of the 9,527 participants remaining, in 2,044 the prescribing frequency was less than once every 2 months, and these were also excluded. This left 7,483 participants with at least two Clopidogrel prescriptions for analysis. Baseline information on the included participants is shown in . CYP2C19 loss-of-function (LoF) carriers (any *2-*8 alleles) had significantly increased ischemic stroke risk (Hazard Ratio (HR) 1.53: 95% CIs 1.04 to 2.26, p = 0.031) and separately MI (HR 1.14: 1.04 to 1.26, p = 0.008) whilst on Clopidogrel, compared to non-LoF carriers in Cox’s proportional hazards regression models adjusted for age at first Clopidogrel prescription, sex, and the first 10 genetic principal components of ancestry. For this analysis non-LoF carriers constituted those with a ‘normal’ CYPC219 genotype and those with the CYP2C19*17 gain-of-function genotype. An in-depth analysis in our companion paper (Supplementary Table 3 in ) showed that normal and * 17 individuals had a near-identical risk of stroke (HR = 0.99, p = 0.97) and that removing * 17 individuals had little impact on the analysis estimates other than a loss in precision, since they constitute 22% of the population. For this reason we chose to keep the binary LoF/non-LoF genetic classification for the full TWIST analysis in the next section. 4.1.1 Estimating the GMTE To estimate the GMTE in this case we modelled the time to stroke using an Aalen additive hazards model, as described in Section 2.4 and . All models were adjusted for age at recruitment or first Clopidogrel prescription, sex, and the first 10 genetic principal components of ancestry. and show the results for this analysis, which reflect the genetically moderated effect of Clopidogrel treatment on the hazard of stroke per year, expressed as a percentage. The GMTE(1) estimate suggests that being a CYP2C19 LoF carrier ( G = 1) increases the risk of stroke by 0.28% (p = 0.048) compared to those without the LoF variant ( G = 0). To put this figure in context, if we could reduce the LoF carrier’s risk by this amount then, when multiplied by the 5264 LoF carrier patient years in the data, it would lead to an expected 13.2% reduction in the total number of strokes (or a reduction of 15 strokes from 110 to 95). To test for potential bias in the GMTE(1) estimate, we calculate the GMTE(0) estimate in the untreated population. Thankfully, it is close to zero (Hazard diff = -0.0039%, p = 0.61), although slightly negative. Taken at face value, this suggest LoF carriers have a slightly reduced risk of stroke through pathways other than Clopidogrel use. Next we calculate the Corrected As Treated (CAT) estimate. As discussed, the validity of this method rests strongly on being able to identify all confounders of Clopidogrel use and stroke. With the data available, it was only possible to adjust for age, sex and genetic principal components and perhaps unsurprisingly, the CAT estimate is an order of magnitude larger (Hazard diff = 2.2%, p ≤ 2 × 10 −16 ). Consequently, the Q CAT , GMTE (1) statistic detects large heterogeneity and suggests that the CAT and GMTE(1) estimates should not be combined. For completeness, we next calculate the RGMTE estimate for the GMTE hazard difference. Since this is itself the difference between the GMTE(1) and GMTE(0) estimates, and given they are of opposite sign, the RMGTE estimate is slightly larger at 0.33% (p = 0.037), suggesting 17 strokes could have been avoided. The MR estimate for the GMTE hazard difference is similar at 0.29% (p = 0.008). Heterogeneity analysis reveals that the MR and RGMTE estimates are sufficiently similar to combine into a more precise single estimate of the GMTE ( Q MR , RGMTE = 0.8). The combined estimate is 0.3 (p = 7.5 × 10 −04 ), or that 16 strokes could have been avoided. No other combination of estimates are sufficiently similar to combine.
To estimate the GMTE in this case we modelled the time to stroke using an Aalen additive hazards model, as described in Section 2.4 and . All models were adjusted for age at recruitment or first Clopidogrel prescription, sex, and the first 10 genetic principal components of ancestry. and show the results for this analysis, which reflect the genetically moderated effect of Clopidogrel treatment on the hazard of stroke per year, expressed as a percentage. The GMTE(1) estimate suggests that being a CYP2C19 LoF carrier ( G = 1) increases the risk of stroke by 0.28% (p = 0.048) compared to those without the LoF variant ( G = 0). To put this figure in context, if we could reduce the LoF carrier’s risk by this amount then, when multiplied by the 5264 LoF carrier patient years in the data, it would lead to an expected 13.2% reduction in the total number of strokes (or a reduction of 15 strokes from 110 to 95). To test for potential bias in the GMTE(1) estimate, we calculate the GMTE(0) estimate in the untreated population. Thankfully, it is close to zero (Hazard diff = -0.0039%, p = 0.61), although slightly negative. Taken at face value, this suggest LoF carriers have a slightly reduced risk of stroke through pathways other than Clopidogrel use. Next we calculate the Corrected As Treated (CAT) estimate. As discussed, the validity of this method rests strongly on being able to identify all confounders of Clopidogrel use and stroke. With the data available, it was only possible to adjust for age, sex and genetic principal components and perhaps unsurprisingly, the CAT estimate is an order of magnitude larger (Hazard diff = 2.2%, p ≤ 2 × 10 −16 ). Consequently, the Q CAT , GMTE (1) statistic detects large heterogeneity and suggests that the CAT and GMTE(1) estimates should not be combined. For completeness, we next calculate the RGMTE estimate for the GMTE hazard difference. Since this is itself the difference between the GMTE(1) and GMTE(0) estimates, and given they are of opposite sign, the RMGTE estimate is slightly larger at 0.33% (p = 0.037), suggesting 17 strokes could have been avoided. The MR estimate for the GMTE hazard difference is similar at 0.29% (p = 0.008). Heterogeneity analysis reveals that the MR and RGMTE estimates are sufficiently similar to combine into a more precise single estimate of the GMTE ( Q MR , RGMTE = 0.8). The combined estimate is 0.3 (p = 7.5 × 10 −04 ), or that 16 strokes could have been avoided. No other combination of estimates are sufficiently similar to combine.
APOE & CAD We now apply our framework to estimate the extent to which genetic variation at the APOE locus modulates the risk of coronary artery disease (CAD) due to statin treatment using UK Biobank data. Our full data comprises 155,409 unrelated participants of European descent, with primary care data available (updated to March 2017) and up-to-date hospital admission data as of December 2020. Of this sample, we excluded: n = 11 participants with missing APOE genotypes; n = 6,456 non-regular statin users with less than four prescriptions per year or residuals from the linear regression for total statin prescriptions on years of statin treatment greater than 3 or less than -3; n = 1,273 non-statin users diagnosed with CAD at baseline (or prior to baseline); n = 4,566 participants starting statin after a doctor’s diagnosis of coronary artery disease (CAD) based on the hospital admission records. Among the included samples (n = 143,103), 57,682 (59.5%) were female. Of these, 46,179 (32.3%) were statin users, with a median of 9.4 (inter-quartile range: 6.6 to 13.5) statin prescriptions per year and a median of 5.6 (inter-quartile range: 1.2 to 9.9) years of statin treatment. Several SNPs were associated with LDL cholesterol response to statins based on a genome-wide association study, where the APOE e2 defining SNP rs7412 showed a larger LDL cholesterol lowering response to statins compared to e3e3s . APOE genotypes (diplotypes essentially) were determined based on genotypes at rs7412 and rs429358. Inspecting the APOE genotype distribution, the majority of participants were classed as e 3 e 3 (n = 83,813, 58.6%), followed by e 3 e 4 (n = 33,597, 23.5%), e 2 e 3 s (n = 17,811, 12.4%), e 2 e 4 s (n = 3,616, 2.5%), e 4 e 4 s (n = 3,366, 2.4%), and e 2 e 2 s (n = 900, 0.6%). These groups are mutually exclusive. Summary statistics for statin users and non-statin users are presented in . 4.2.1 Results Using the e 3 e 3 group as a reference, we fitted Aalen additive hazard models within each mutually exclusive genetic group additionally adjusting for sex, age on statin or age at recruitment, and the top 10 genetic principal components. For brevity, we focus on the results of the e2e3 versus e3e3 and e4e4 versus e3e3 analyses, which are shown in and account for approximately 72% of the patient data. Estimates reflect the hazard or risk difference of a CAD event per year, expressed as a percentage. Only results of combined estimates that pass a heterogeneity test at the 5% level are shown. Equivalent estimates for the remaining genetic groups showed no evidence of a non-zero genetically moderated effect. Results for all genetic groups are given in . 4.2.2 e 4 e 4 versus e 3 e 3 Inspecting the e 4 e 4 genetic subgroup first, the GMTE(1) estimate suggests that the risk of CAD could be reduced by 0.031% per year if e 3 e 3 patients experienced the same treatment effect as e 4 e 4 patients (p = 0.043). This estimate is valid if the e4e4 genotype only affects the risk of CAD through modulating the effectiveness of statins (i.e. assumptions PG1-PG3 hold). In order to probe this we calculate the equivalent GMTE(0) estimate in non-statin users. The e 4 e 4 group is now seen to have a 0.011% larger risk of CAD than e 3 e 3 (p = 0.07), which suggests that PG1-PG3 violation is possible. Furthermore, shows clear differences in the allele frequencies between treatment groups. Since the RMGTE(0) and RGMTE(1) estimates are of opposite signs, the RGMTE estimate, which is robust to PG2-PG3 violation, infers the risk difference between e4e4 and e3e3’s is larger at -0.037% per year (p = 0.046). The MR estimate of the GMTE is also positive (0.0069%), but very close to zero (p = 0.69). This is, however, sufficiently similar to the RGMTE estimate at the 5% threshold for it to be combined with the MR estimate (despite being qualitatively different), and the combined value suggests a hazard difference of -0.014% per year (p = 0.28). The CAT estimate for the hazard difference in these data is a 3.9% increase per year. Its magnitude is so large compared to the other estimates that we could reasonably assume that adjustment for age, sex and genetic PCs is not sufficient to remove unmeasured confounding by indication. Consequently, no other estimate is sufficiently similar in order to combine with the CAT estimate, as shown in . In the final column of we translate the hazard difference estimate per year implied by the GMTE1, MR, RGMTE and combined RMGTE/MR estimate to give an expected number of CAD events that could be avoided if all 26,938 e3e3 statin user patients could receive the same benefit as the e4e4 patients, by multiplying the per-year risk reduction over the relevant 278,409 patients-years in the data. Using the RGMTE estimate for this risk reduction gives a figure of 103. The GMTE1 and combined RGMTE/MR estimates imply more modest reductions of 85 and 39 CAD events respectively. 4.2.3 e 2 e 3 versus e 3 e 3 Turning our attention to the e2e3 subgroup in and , we again see a large, non-credible CAT hazard difference estimate for the GMTE of a 1.2% per year between e2e3 and e3e3 groups. The GMTE(1), GMTE(0) and RGMTE estimates for the GMTE are all close to zero and non-significant at the 5% level. In contrast, the MR estimate for the GMTE suggests that e2e3’s have a 0.046% reduced risk of CAD per year (p = 3 × 10 −5 ). Using this estimate, the expected number of CAD events that could be avoided if all 26,938 e3e3 patients could receive the same benefit as the e2e3 patients is 128. This is valid if we believe that assumptions PG2-PG3 hold, but either PG1 or the Hom assumption are violated. The GMTE1 and RGMTE estimates imply more modest reductions of 28 and 15 CAD events respectively. In this example, no two single uncorrelated estimates are sufficiently similar in order to combine.
Using the e 3 e 3 group as a reference, we fitted Aalen additive hazard models within each mutually exclusive genetic group additionally adjusting for sex, age on statin or age at recruitment, and the top 10 genetic principal components. For brevity, we focus on the results of the e2e3 versus e3e3 and e4e4 versus e3e3 analyses, which are shown in and account for approximately 72% of the patient data. Estimates reflect the hazard or risk difference of a CAD event per year, expressed as a percentage. Only results of combined estimates that pass a heterogeneity test at the 5% level are shown. Equivalent estimates for the remaining genetic groups showed no evidence of a non-zero genetically moderated effect. Results for all genetic groups are given in .
e 4 e 4 versus e 3 e 3 Inspecting the e 4 e 4 genetic subgroup first, the GMTE(1) estimate suggests that the risk of CAD could be reduced by 0.031% per year if e 3 e 3 patients experienced the same treatment effect as e 4 e 4 patients (p = 0.043). This estimate is valid if the e4e4 genotype only affects the risk of CAD through modulating the effectiveness of statins (i.e. assumptions PG1-PG3 hold). In order to probe this we calculate the equivalent GMTE(0) estimate in non-statin users. The e 4 e 4 group is now seen to have a 0.011% larger risk of CAD than e 3 e 3 (p = 0.07), which suggests that PG1-PG3 violation is possible. Furthermore, shows clear differences in the allele frequencies between treatment groups. Since the RMGTE(0) and RGMTE(1) estimates are of opposite signs, the RGMTE estimate, which is robust to PG2-PG3 violation, infers the risk difference between e4e4 and e3e3’s is larger at -0.037% per year (p = 0.046). The MR estimate of the GMTE is also positive (0.0069%), but very close to zero (p = 0.69). This is, however, sufficiently similar to the RGMTE estimate at the 5% threshold for it to be combined with the MR estimate (despite being qualitatively different), and the combined value suggests a hazard difference of -0.014% per year (p = 0.28). The CAT estimate for the hazard difference in these data is a 3.9% increase per year. Its magnitude is so large compared to the other estimates that we could reasonably assume that adjustment for age, sex and genetic PCs is not sufficient to remove unmeasured confounding by indication. Consequently, no other estimate is sufficiently similar in order to combine with the CAT estimate, as shown in . In the final column of we translate the hazard difference estimate per year implied by the GMTE1, MR, RGMTE and combined RMGTE/MR estimate to give an expected number of CAD events that could be avoided if all 26,938 e3e3 statin user patients could receive the same benefit as the e4e4 patients, by multiplying the per-year risk reduction over the relevant 278,409 patients-years in the data. Using the RGMTE estimate for this risk reduction gives a figure of 103. The GMTE1 and combined RGMTE/MR estimates imply more modest reductions of 85 and 39 CAD events respectively.
e 2 e 3 versus e 3 e 3 Turning our attention to the e2e3 subgroup in and , we again see a large, non-credible CAT hazard difference estimate for the GMTE of a 1.2% per year between e2e3 and e3e3 groups. The GMTE(1), GMTE(0) and RGMTE estimates for the GMTE are all close to zero and non-significant at the 5% level. In contrast, the MR estimate for the GMTE suggests that e2e3’s have a 0.046% reduced risk of CAD per year (p = 3 × 10 −5 ). Using this estimate, the expected number of CAD events that could be avoided if all 26,938 e3e3 patients could receive the same benefit as the e2e3 patients is 128. This is valid if we believe that assumptions PG2-PG3 hold, but either PG1 or the Hom assumption are violated. The GMTE1 and RGMTE estimates imply more modest reductions of 28 and 15 CAD events respectively. In this example, no two single uncorrelated estimates are sufficiently similar in order to combine.
In this paper we propose the general TWIST framework for estimating the genetically moderated treatment effect that combines several distinct but complementary causal inference techniques. We propose a rudimentary decision framework for choosing when to combine approaches based on heterogeneity statistics. In practice, expert knowledge and prior evidence should also be leveraged to decide whether the particular assumptions of the causal estimation strategy are likely to be met, in order to put more or less weight on their findings. For example, if a variant is known to be associated with the outcome through another mechanistic pathway, then the PG3 assumption required for the GMTE(1) and MR estimates is likely violated, and the RGMTE estimate should be favored. Or, if it is known that those with the metabolically unfavourable genotype ( G = 0) still benefit from treatment, then the homogeneity assumption is likely violated. This would then rule out the CAT estimate completely and one would need to be sure the PG1 assumption was satisfied when using the MR estimate. In we provide R code for fitting the TWIST framework for continuous, binary and time-to-event data as well as code used in the simulation study. Work is underway at https://github.com/lukepilling/twistR to produce a single R package to apply TWIST and visualise its results. Our inverse variance meta-analysis procedure for combining estimates is very simple, and simulations showed that it exhibited good statistical properties even when small correlations between constituent estimates were present. As future work, we plan to develop a more sophisticated procedure to explicitly account for this correlation within TWIST based on a Mahalanobis distance statistic, and to further develop the framework in several directions to address current limitations, some of which are now described. 5.1 Limitations and further work We chose to illustrate the utility of the TWIST framework for combining similar estimates by demonstrating that it can increase precision. An alternative strategy would be to use multiple estimates to improve the robustness of any inference due to possible violations of variety of assumptions. For example, given a prior null hypothesis about the specific value of the GMTE, we would not reject the hypothesis it if was not rejected by any individual analysis. On the other hand, we could reject a proposed value of the estimand with increased confidence if it is rejected by multiple independent analyses that depend on assumptions that do not completely overlap. In future work we plan to develop a rigorous sequential testing procedure for TWIST that can control family wise error or false discovery rates. Since the majority of estimates reported within a TWIST analysis are statistically uncorrelated by design, multiplicity correction will be vital for this approach going forward. We thank reviewer 1 and 4 for these helpful suggestions. The TWIST framework offers a means to combine statistically uncorrelated estimates that rely on overlapping sets of assumptions. If two estimates are similar enough to warrant combining into a single estimate, one hopes that this represents a more precise estimate of the true GMTE. However, there is always the possibility that both estimates are instead systematically biased in the same direction when there is a degree of overlap in their identifying assumptions and these assumptions are violated. In this case, combining them could give a more precise estimate of the wrong answer. Although we saw little evidence of this in simulation Scenarios 4–6 of , further research is needed to understand the extent of this issue more clearly. We thank reviewer 4 for raising this important point. In our analysis of the statin data, we estimated the GMTE in several mutually exclusive genetic groups, which resulted in an inevitable loss of precision. Efficiency could potentially be regained by collapsing genetic subsets together if they give similar estimates, or by making a linearity assumption about the magnitude of effect across genotypic groups (e.g. between e 3 e 3 , e 3 e 4 and e 4 e 4 ). This would not be defensible if the genetic groups were ordered with respect to the magnitude of their causal estimate, but would be defensible if genetic groups could be ordered by their effect on increasing drug metabolism. In the case of 3 genetic groups, G i and T i * could take a value in {0,1,2}. This would enable the data to be pooled in order to target a combined estimand β G M T E ( Y ) = E [ Y i ( T i * ( m ) ) ] - E [ Y i ( T i * ( m - 1 ) ) ] , (4) for all m in {1, 2}. If such a model were correct, it opens up the possibility of making the analysis even more robust to violations of the PG assumptions, because an additional causal parameter could be jointly estimated alongside the GMTE to reflect, for example the direct effect of G on Y . This is an important avenue for further research. Although the CAT estimate can in principle consistently estimate the GMTE estimand, it relies heavily on the NUC assumption. In both applied analyses we were not able to sufficiently control for confounding by indication to deliver an estimate close to any other GMTE estimate, due to a lack of relevant covariate data. In future work we plan to revisit both analyses after collecting a much larger set of relevant information. More-sophisticated approaches such as Propensity Scores, matching methods and inverse probability weighting may then offer some utility . So too may methods for multi-variable Mendelian randomization, where instead of directly adjusting for confounders of treatment and outcome, we instead adjust for their genetically predicted value. This latter approach could be more robust to collider bias . The TWIST framework has parallels with the general theory of ‘Evidence Factors’ for combining two or more observational associations estimates gleaned from the same data, which are susceptible to different biases. As far as we are aware, this approach has not been applied within the context of pharmacogenetics before, but a more detailed investigation of the connection between TWIST and Evidence Factors is an interesting topic for further research.
We chose to illustrate the utility of the TWIST framework for combining similar estimates by demonstrating that it can increase precision. An alternative strategy would be to use multiple estimates to improve the robustness of any inference due to possible violations of variety of assumptions. For example, given a prior null hypothesis about the specific value of the GMTE, we would not reject the hypothesis it if was not rejected by any individual analysis. On the other hand, we could reject a proposed value of the estimand with increased confidence if it is rejected by multiple independent analyses that depend on assumptions that do not completely overlap. In future work we plan to develop a rigorous sequential testing procedure for TWIST that can control family wise error or false discovery rates. Since the majority of estimates reported within a TWIST analysis are statistically uncorrelated by design, multiplicity correction will be vital for this approach going forward. We thank reviewer 1 and 4 for these helpful suggestions. The TWIST framework offers a means to combine statistically uncorrelated estimates that rely on overlapping sets of assumptions. If two estimates are similar enough to warrant combining into a single estimate, one hopes that this represents a more precise estimate of the true GMTE. However, there is always the possibility that both estimates are instead systematically biased in the same direction when there is a degree of overlap in their identifying assumptions and these assumptions are violated. In this case, combining them could give a more precise estimate of the wrong answer. Although we saw little evidence of this in simulation Scenarios 4–6 of , further research is needed to understand the extent of this issue more clearly. We thank reviewer 4 for raising this important point. In our analysis of the statin data, we estimated the GMTE in several mutually exclusive genetic groups, which resulted in an inevitable loss of precision. Efficiency could potentially be regained by collapsing genetic subsets together if they give similar estimates, or by making a linearity assumption about the magnitude of effect across genotypic groups (e.g. between e 3 e 3 , e 3 e 4 and e 4 e 4 ). This would not be defensible if the genetic groups were ordered with respect to the magnitude of their causal estimate, but would be defensible if genetic groups could be ordered by their effect on increasing drug metabolism. In the case of 3 genetic groups, G i and T i * could take a value in {0,1,2}. This would enable the data to be pooled in order to target a combined estimand β G M T E ( Y ) = E [ Y i ( T i * ( m ) ) ] - E [ Y i ( T i * ( m - 1 ) ) ] , (4) for all m in {1, 2}. If such a model were correct, it opens up the possibility of making the analysis even more robust to violations of the PG assumptions, because an additional causal parameter could be jointly estimated alongside the GMTE to reflect, for example the direct effect of G on Y . This is an important avenue for further research. Although the CAT estimate can in principle consistently estimate the GMTE estimand, it relies heavily on the NUC assumption. In both applied analyses we were not able to sufficiently control for confounding by indication to deliver an estimate close to any other GMTE estimate, due to a lack of relevant covariate data. In future work we plan to revisit both analyses after collecting a much larger set of relevant information. More-sophisticated approaches such as Propensity Scores, matching methods and inverse probability weighting may then offer some utility . So too may methods for multi-variable Mendelian randomization, where instead of directly adjusting for confounders of treatment and outcome, we instead adjust for their genetically predicted value. This latter approach could be more robust to collider bias . The TWIST framework has parallels with the general theory of ‘Evidence Factors’ for combining two or more observational associations estimates gleaned from the same data, which are susceptible to different biases. As far as we are aware, this approach has not been applied within the context of pharmacogenetics before, but a more detailed investigation of the connection between TWIST and Evidence Factors is an interesting topic for further research.
S1 Text Document containing the important technical details on the TWIST framework, including consistency proofs for the linear case, and the implementation of TWIST with binary and time-to-event data. (PDF) Click here for additional data file. S1 Code Zip file containing code to implement the TWIST framework with continous, binary and time-to-event data. (ZIP) Click here for additional data file.
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Comment on “Mutagenic damage among bronchiectasis patients attending in the pulmonology sector of a hospital in southern Brazil” | 97e9dc5e-6813-4a57-a281-9b9aa5ee9ee3 | 10176658 | Internal Medicine[mh] | The data that support the findings of this study are available from the corresponding author upon reasonable request.
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The PRESIDE (PhaRmacogEnomicS In DEpression) Trial: a double-blind randomised controlled trial of pharmacogenomic-informed prescribing of antidepressants on depression outcomes in patients with major depressive disorder in primary care | b0fc7fa8-99f8-4cb4-9085-ca0de7e146b6 | 10197047 | Pharmacology[mh] | Background and rationale {6a} Prevalence of depression worldwide and in Australia Depression affects at least 264 million people worldwide and is a leading cause of non-fatal burden of disease . Australia ranks second in the prevalence of depression worldwide . It causes significant costs to individuals and to society through medical costs and loss of productivity . The majority of people with depression are identified, treated and followed up by general practitioners (GPs), managing patients across the spectrum of disease severity . Therefore, interventions to improve the effectiveness and cost-effectiveness of managing depression have the greatest chance of impact when focused on primary care. Treatment of major depressive disorder Depression, clinically referred to as major depressive disorder (MDD), is primarily treated with a combination of antidepressant medication and psychological interventions . First-line antidepressant medications are selective serotonin reuptake inhibitors (SSRIs) and serotonin-noradrenaline reuptake inhibitors (SNRIs). SSRIs are the most common medications prescribed for MDD as they have fewer reported side effects . Many patients do not respond to these classes of medication or experience intolerable side effects. Up to a half of patients with MDD do not respond to their first antidepressant , and remission rates are as low as 37.5% . This leads to prolonged duration of symptoms, increased burden of side-effects for limited benefit and greater medical costs . While the antidepressant response is multifactorial, genetic factors contribute 42% of this variance in drug response . This has led to the development of international pharmacogenomic-based guidelines which use an individual’s genetic information to inform the selection and dosing of antidepressants . Pharmacogenomic-informed prescribing of antidepressants Pharmacogenomic (PGx) testing for variants in genes that encode key proteins involved in pharmacokinetics and pharmacodynamics can guide drug and dose selection with the aim of improving efficacy and decreasing adverse effects . To ensure standardised and evidence-based implementation of PGx testing results, the Clinical Pharmacogenetics Implementation Consortium (CPIC) and Royal Dutch Pharmacogenetics Working Group (DPWG) have produced guidelines to inform the use of genotyping for prescribing 14 antidepressant medications, including SSRIs, SNRIs and tricyclic antidepressants (TCA) . Recommendations are based on genotype-predicted metaboliser phenotypes of the cytochrome P450 genes CYP2D6 , CYP2C19 and/or CYP2B6 . At the time this trial was designed, these guidelines included recommendations for 13 antidepressants incorporating predicted metaboliser phenotypes of CYP2D6 and CYP2C19 . Previous randomised controlled trials of PGx testing for antidepressant prescribing have shown an almost 50% relative increase in the proportion of remission of major depression compared to usual care (risk ratio = 1.46, 95% CI 1.13–1.88, p = 0.003) . However, participants and treating clinicians were not blinded to intervention allocation in these trials, leading to possible information bias. Additionally, most participants were recruited from psychiatric settings, with a minority from primary care, where 86% of antidepressant prescribing occurs , limiting the generalisability of results. Finally, studies rarely followed patients beyond 12 weeks and did not measure improvement in long-term depressive symptoms. Recent primary care data has shown the potential clinical utility of PGx testing for antidepressants in this setting, with 45–84% of prescribed antidepressants in an Australian cohort having an associated pharmacogenetic guideline that could guide dose . Sixty-six per cent had combined CYP2D6 and CYP2C19 genotype-predicted metaboliser phenotypes which would be considered actionable by CPIC or DPWG antidepressant prescribing guidelines. Furthermore, one-quarter of patients were taking an antidepressant medication which would not be recommended based on their CYP2D6 and/or CYP2C19 genotype-predicted metaboliser phenotype . Although there is significant literature highlighting the utility of CYP2D6 and CYP2C19 genotyping in reducing gene-antidepressant mismatches, a knowledge gap exists about its clinical application in primary care. Therefore, the PRESIDE (PhaRmacogEnomicS In DEpression) Trial aims to fill that gap, through a randomised double-blinded controlled trial. Prevalence of depression worldwide and in Australia Depression affects at least 264 million people worldwide and is a leading cause of non-fatal burden of disease . Australia ranks second in the prevalence of depression worldwide . It causes significant costs to individuals and to society through medical costs and loss of productivity . The majority of people with depression are identified, treated and followed up by general practitioners (GPs), managing patients across the spectrum of disease severity . Therefore, interventions to improve the effectiveness and cost-effectiveness of managing depression have the greatest chance of impact when focused on primary care. Treatment of major depressive disorder Depression, clinically referred to as major depressive disorder (MDD), is primarily treated with a combination of antidepressant medication and psychological interventions . First-line antidepressant medications are selective serotonin reuptake inhibitors (SSRIs) and serotonin-noradrenaline reuptake inhibitors (SNRIs). SSRIs are the most common medications prescribed for MDD as they have fewer reported side effects . Many patients do not respond to these classes of medication or experience intolerable side effects. Up to a half of patients with MDD do not respond to their first antidepressant , and remission rates are as low as 37.5% . This leads to prolonged duration of symptoms, increased burden of side-effects for limited benefit and greater medical costs . While the antidepressant response is multifactorial, genetic factors contribute 42% of this variance in drug response . This has led to the development of international pharmacogenomic-based guidelines which use an individual’s genetic information to inform the selection and dosing of antidepressants . Pharmacogenomic-informed prescribing of antidepressants Pharmacogenomic (PGx) testing for variants in genes that encode key proteins involved in pharmacokinetics and pharmacodynamics can guide drug and dose selection with the aim of improving efficacy and decreasing adverse effects . To ensure standardised and evidence-based implementation of PGx testing results, the Clinical Pharmacogenetics Implementation Consortium (CPIC) and Royal Dutch Pharmacogenetics Working Group (DPWG) have produced guidelines to inform the use of genotyping for prescribing 14 antidepressant medications, including SSRIs, SNRIs and tricyclic antidepressants (TCA) . Recommendations are based on genotype-predicted metaboliser phenotypes of the cytochrome P450 genes CYP2D6 , CYP2C19 and/or CYP2B6 . At the time this trial was designed, these guidelines included recommendations for 13 antidepressants incorporating predicted metaboliser phenotypes of CYP2D6 and CYP2C19 . Previous randomised controlled trials of PGx testing for antidepressant prescribing have shown an almost 50% relative increase in the proportion of remission of major depression compared to usual care (risk ratio = 1.46, 95% CI 1.13–1.88, p = 0.003) . However, participants and treating clinicians were not blinded to intervention allocation in these trials, leading to possible information bias. Additionally, most participants were recruited from psychiatric settings, with a minority from primary care, where 86% of antidepressant prescribing occurs , limiting the generalisability of results. Finally, studies rarely followed patients beyond 12 weeks and did not measure improvement in long-term depressive symptoms. Recent primary care data has shown the potential clinical utility of PGx testing for antidepressants in this setting, with 45–84% of prescribed antidepressants in an Australian cohort having an associated pharmacogenetic guideline that could guide dose . Sixty-six per cent had combined CYP2D6 and CYP2C19 genotype-predicted metaboliser phenotypes which would be considered actionable by CPIC or DPWG antidepressant prescribing guidelines. Furthermore, one-quarter of patients were taking an antidepressant medication which would not be recommended based on their CYP2D6 and/or CYP2C19 genotype-predicted metaboliser phenotype . Although there is significant literature highlighting the utility of CYP2D6 and CYP2C19 genotyping in reducing gene-antidepressant mismatches, a knowledge gap exists about its clinical application in primary care. Therefore, the PRESIDE (PhaRmacogEnomicS In DEpression) Trial aims to fill that gap, through a randomised double-blinded controlled trial. Depression affects at least 264 million people worldwide and is a leading cause of non-fatal burden of disease . Australia ranks second in the prevalence of depression worldwide . It causes significant costs to individuals and to society through medical costs and loss of productivity . The majority of people with depression are identified, treated and followed up by general practitioners (GPs), managing patients across the spectrum of disease severity . Therefore, interventions to improve the effectiveness and cost-effectiveness of managing depression have the greatest chance of impact when focused on primary care. Depression, clinically referred to as major depressive disorder (MDD), is primarily treated with a combination of antidepressant medication and psychological interventions . First-line antidepressant medications are selective serotonin reuptake inhibitors (SSRIs) and serotonin-noradrenaline reuptake inhibitors (SNRIs). SSRIs are the most common medications prescribed for MDD as they have fewer reported side effects . Many patients do not respond to these classes of medication or experience intolerable side effects. Up to a half of patients with MDD do not respond to their first antidepressant , and remission rates are as low as 37.5% . This leads to prolonged duration of symptoms, increased burden of side-effects for limited benefit and greater medical costs . While the antidepressant response is multifactorial, genetic factors contribute 42% of this variance in drug response . This has led to the development of international pharmacogenomic-based guidelines which use an individual’s genetic information to inform the selection and dosing of antidepressants . Pharmacogenomic (PGx) testing for variants in genes that encode key proteins involved in pharmacokinetics and pharmacodynamics can guide drug and dose selection with the aim of improving efficacy and decreasing adverse effects . To ensure standardised and evidence-based implementation of PGx testing results, the Clinical Pharmacogenetics Implementation Consortium (CPIC) and Royal Dutch Pharmacogenetics Working Group (DPWG) have produced guidelines to inform the use of genotyping for prescribing 14 antidepressant medications, including SSRIs, SNRIs and tricyclic antidepressants (TCA) . Recommendations are based on genotype-predicted metaboliser phenotypes of the cytochrome P450 genes CYP2D6 , CYP2C19 and/or CYP2B6 . At the time this trial was designed, these guidelines included recommendations for 13 antidepressants incorporating predicted metaboliser phenotypes of CYP2D6 and CYP2C19 . Previous randomised controlled trials of PGx testing for antidepressant prescribing have shown an almost 50% relative increase in the proportion of remission of major depression compared to usual care (risk ratio = 1.46, 95% CI 1.13–1.88, p = 0.003) . However, participants and treating clinicians were not blinded to intervention allocation in these trials, leading to possible information bias. Additionally, most participants were recruited from psychiatric settings, with a minority from primary care, where 86% of antidepressant prescribing occurs , limiting the generalisability of results. Finally, studies rarely followed patients beyond 12 weeks and did not measure improvement in long-term depressive symptoms. Recent primary care data has shown the potential clinical utility of PGx testing for antidepressants in this setting, with 45–84% of prescribed antidepressants in an Australian cohort having an associated pharmacogenetic guideline that could guide dose . Sixty-six per cent had combined CYP2D6 and CYP2C19 genotype-predicted metaboliser phenotypes which would be considered actionable by CPIC or DPWG antidepressant prescribing guidelines. Furthermore, one-quarter of patients were taking an antidepressant medication which would not be recommended based on their CYP2D6 and/or CYP2C19 genotype-predicted metaboliser phenotype . Although there is significant literature highlighting the utility of CYP2D6 and CYP2C19 genotyping in reducing gene-antidepressant mismatches, a knowledge gap exists about its clinical application in primary care. Therefore, the PRESIDE (PhaRmacogEnomicS In DEpression) Trial aims to fill that gap, through a randomised double-blinded controlled trial. Primary objective The primary objective is to determine the efficacy of a PGx-informed antidepressant prescribing report on depressive symptoms at 12 weeks after GP receipt of the prescribing report, when delivered in primary care for patients aged 18 to 65 years old with moderate to severe depressive symptoms, compared to a prescribing report based on the current Australian Therapeutic Guidelines (Psychotropic) for antidepressant prescribing (i.e. standard of care) . Secondary objectives The secondary objective is to determine the effect of PGx-informed antidepressant prescribing, compared to the control prescribing report on the following: Change in depressive symptoms at 4, 8 and 26 weeks Depressive symptom remission at 12 weeks Depressive symptom response at 12 weeks Side effect frequency at 4, 8, 12 and 26 weeks Medication adherence at 4, 8, 12 and 26 weeks Quality of life at 4, 8, 12 and 26 weeks Number of antidepressant medication changes within 26 weeks Cost-effectiveness within 26 weeks The primary objective is to determine the efficacy of a PGx-informed antidepressant prescribing report on depressive symptoms at 12 weeks after GP receipt of the prescribing report, when delivered in primary care for patients aged 18 to 65 years old with moderate to severe depressive symptoms, compared to a prescribing report based on the current Australian Therapeutic Guidelines (Psychotropic) for antidepressant prescribing (i.e. standard of care) . The secondary objective is to determine the effect of PGx-informed antidepressant prescribing, compared to the control prescribing report on the following: Change in depressive symptoms at 4, 8 and 26 weeks Depressive symptom remission at 12 weeks Depressive symptom response at 12 weeks Side effect frequency at 4, 8, 12 and 26 weeks Medication adherence at 4, 8, 12 and 26 weeks Quality of life at 4, 8, 12 and 26 weeks Number of antidepressant medication changes within 26 weeks Cost-effectiveness within 26 weeks The PRESIDE Trial is a multi-site, double-blinded, individually randomised controlled superiority trial with a 1:1 allocation of participants to the experimental (PGx-informed) and control (Australian TG-informed) prescribing interventions. This protocol is reported in accordance with the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) guidance . Study setting {9} In general practice clinics across Victoria, Australia, practices are recruited from areas representing a broad range of sociodemographic backgrounds to reflect the wider population of Victoria, Australia. Eligibility criteria {10} Eligibility criteria for general practice clinics General practices are approached to participate in the study if they have two or more full-time equivalent GPs to ensure a sufficient volume of potential participants. General practices are excluded if they do not have at least one private room for researchers to conduct recruitment activities. Individual GPs within clinics are consented to the study, allowing for researchers to approach their patients to participate and at least two GPs must consent to be a part of the trial. Inclusion criteria for participants Participants are eligible if they are: (i) Aged between 18 and 65 years old, inclusive (ii) Have an upcoming appointment with a consented GP within 2 days of being approached for participation in the trial (iii) Score a total of 10 on the Patient Health Questionnaire 9 (PHQ-9) indicating at least moderate depressive symptoms in the past two weeks (iv) Are able to read and understand English (v) Are competent to give informed consent Exclusion criteria for participants Participants are ineligible if they: (i) Are currently taking antipsychotic medication, except if taking quetiapine ≤ 100 mg PRN for sleep, with no history of psychosis (ii) Are pregnant (iii) Report that they have had suicidal thoughts ‘nearly every day’, as per question 9 on the PHQ-9 (iv) Have a current diagnosis of dementia (v) Have an active diagnosis of COVID-19 (vi) Are unavailable over the next 6 months for study follow-up The first exclusion criterion was stipulated to omit potential participants who have a history of psychosis, given the additional complexity of the management of depressive symptoms in this group, which often also occurs outside of primary care. The original wording of this exclusion criterion was “currently taking antipsychotic medication”. On 22 July 2022, after the 204th participant was recruited, this was amended to allow low-dose use of quetiapine as several unnecessary exclusions were made of potentially eligible participants who were taking low doses of quetiapine for sleep disturbance, without any history of psychosis. The fifth exclusion criterion was added on 17 November 2021 as a safety precaution for researchers handling the study DNA samples. Who will take informed consent? {26a} General practitioner informed consent Members of the research team provide the study rationale and participant recruitment processes to interested GP clinics then invite discussion about the study. This includes information about PGx testing, its potential utility in guiding antidepressant prescribing and the prescribing reports they will receive for each of their patients recruited to the study. It is emphasised that GPs should use their clinical judgement when discussing and determining what, if any, treatment to commence for their patient’s depressive symptoms. GPs are reminded of the clinical guidelines for the management of depression that state patients should be followed up every 4 weeks. Each GP is provided a GP information sheet about the study, given the opportunity to ask questions and individually consented to the study to allow recruitment of their patients. Patient informed consent for trial participation Trained research assistants provide individuals who have appointments with consented GPs within 2 days of approach with verbal and written information about the trial, check their eligibility and answer any questions about the study. A second research assistant obtains written informed consent if they agree to participate in the trial. Due to COVID-19 and resulting government restrictions, both face-to-face and teletrial methods are used for approach, eligibility assessment and informed consent discussions with potential participants. Interested and eligible participants are provided with the study information sheet prior to consenting to the study and given the opportunity to ask questions. Participants recruited via teletrial complete an online e-consent form through the study’s REDCap database . A copy (either hard or electronic) of their study consent form is provided to participants. Patient informed consent for release of administrative health service use and prescribing data (optional) Additional and optional written consent is also sought for the release of participants’ Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) data via Services Australia. Initial approvals for the collection of administrative data on health service use and prescription medication dispensing from our government-funded health service (administered by Services Australia) were substantially delayed. Therefore, it was decided by the study steering committee that recruitment for the trial should begin prior to this approval being provided. Upon approval, these participants were retrospectively contacted to obtain this consent. Furthermore, participants not entitled to government-funded healthcare (e.g. they are foreign citizens and not permanent residents of Australia) do not have any Services Australia data available; however, we are collecting additional data directly from the GP record of participants, as well as self-reported use of health services and medications. Additional consent provisions for collection and use of participant data and biological specimens {26b} Participants give specific consent for the study, in that their data and biological sample will not be used for future studies. Any excess DNA is securely disposed of by the laboratory conducting the PGx test (Sonic Healthcare, Sydney, Australia). If a sample fails to yield a result, the sample is tested once more before it is securely discarded. In general practice clinics across Victoria, Australia, practices are recruited from areas representing a broad range of sociodemographic backgrounds to reflect the wider population of Victoria, Australia. Eligibility criteria for general practice clinics General practices are approached to participate in the study if they have two or more full-time equivalent GPs to ensure a sufficient volume of potential participants. General practices are excluded if they do not have at least one private room for researchers to conduct recruitment activities. Individual GPs within clinics are consented to the study, allowing for researchers to approach their patients to participate and at least two GPs must consent to be a part of the trial. Inclusion criteria for participants Participants are eligible if they are: (i) Aged between 18 and 65 years old, inclusive (ii) Have an upcoming appointment with a consented GP within 2 days of being approached for participation in the trial (iii) Score a total of 10 on the Patient Health Questionnaire 9 (PHQ-9) indicating at least moderate depressive symptoms in the past two weeks (iv) Are able to read and understand English (v) Are competent to give informed consent Exclusion criteria for participants Participants are ineligible if they: (i) Are currently taking antipsychotic medication, except if taking quetiapine ≤ 100 mg PRN for sleep, with no history of psychosis (ii) Are pregnant (iii) Report that they have had suicidal thoughts ‘nearly every day’, as per question 9 on the PHQ-9 (iv) Have a current diagnosis of dementia (v) Have an active diagnosis of COVID-19 (vi) Are unavailable over the next 6 months for study follow-up The first exclusion criterion was stipulated to omit potential participants who have a history of psychosis, given the additional complexity of the management of depressive symptoms in this group, which often also occurs outside of primary care. The original wording of this exclusion criterion was “currently taking antipsychotic medication”. On 22 July 2022, after the 204th participant was recruited, this was amended to allow low-dose use of quetiapine as several unnecessary exclusions were made of potentially eligible participants who were taking low doses of quetiapine for sleep disturbance, without any history of psychosis. The fifth exclusion criterion was added on 17 November 2021 as a safety precaution for researchers handling the study DNA samples. General practices are approached to participate in the study if they have two or more full-time equivalent GPs to ensure a sufficient volume of potential participants. General practices are excluded if they do not have at least one private room for researchers to conduct recruitment activities. Individual GPs within clinics are consented to the study, allowing for researchers to approach their patients to participate and at least two GPs must consent to be a part of the trial. Participants are eligible if they are: (i) Aged between 18 and 65 years old, inclusive (ii) Have an upcoming appointment with a consented GP within 2 days of being approached for participation in the trial (iii) Score a total of 10 on the Patient Health Questionnaire 9 (PHQ-9) indicating at least moderate depressive symptoms in the past two weeks (iv) Are able to read and understand English (v) Are competent to give informed consent Participants are ineligible if they: (i) Are currently taking antipsychotic medication, except if taking quetiapine ≤ 100 mg PRN for sleep, with no history of psychosis (ii) Are pregnant (iii) Report that they have had suicidal thoughts ‘nearly every day’, as per question 9 on the PHQ-9 (iv) Have a current diagnosis of dementia (v) Have an active diagnosis of COVID-19 (vi) Are unavailable over the next 6 months for study follow-up The first exclusion criterion was stipulated to omit potential participants who have a history of psychosis, given the additional complexity of the management of depressive symptoms in this group, which often also occurs outside of primary care. The original wording of this exclusion criterion was “currently taking antipsychotic medication”. On 22 July 2022, after the 204th participant was recruited, this was amended to allow low-dose use of quetiapine as several unnecessary exclusions were made of potentially eligible participants who were taking low doses of quetiapine for sleep disturbance, without any history of psychosis. The fifth exclusion criterion was added on 17 November 2021 as a safety precaution for researchers handling the study DNA samples. General practitioner informed consent Members of the research team provide the study rationale and participant recruitment processes to interested GP clinics then invite discussion about the study. This includes information about PGx testing, its potential utility in guiding antidepressant prescribing and the prescribing reports they will receive for each of their patients recruited to the study. It is emphasised that GPs should use their clinical judgement when discussing and determining what, if any, treatment to commence for their patient’s depressive symptoms. GPs are reminded of the clinical guidelines for the management of depression that state patients should be followed up every 4 weeks. Each GP is provided a GP information sheet about the study, given the opportunity to ask questions and individually consented to the study to allow recruitment of their patients. Patient informed consent for trial participation Trained research assistants provide individuals who have appointments with consented GPs within 2 days of approach with verbal and written information about the trial, check their eligibility and answer any questions about the study. A second research assistant obtains written informed consent if they agree to participate in the trial. Due to COVID-19 and resulting government restrictions, both face-to-face and teletrial methods are used for approach, eligibility assessment and informed consent discussions with potential participants. Interested and eligible participants are provided with the study information sheet prior to consenting to the study and given the opportunity to ask questions. Participants recruited via teletrial complete an online e-consent form through the study’s REDCap database . A copy (either hard or electronic) of their study consent form is provided to participants. Patient informed consent for release of administrative health service use and prescribing data (optional) Additional and optional written consent is also sought for the release of participants’ Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) data via Services Australia. Initial approvals for the collection of administrative data on health service use and prescription medication dispensing from our government-funded health service (administered by Services Australia) were substantially delayed. Therefore, it was decided by the study steering committee that recruitment for the trial should begin prior to this approval being provided. Upon approval, these participants were retrospectively contacted to obtain this consent. Furthermore, participants not entitled to government-funded healthcare (e.g. they are foreign citizens and not permanent residents of Australia) do not have any Services Australia data available; however, we are collecting additional data directly from the GP record of participants, as well as self-reported use of health services and medications. Members of the research team provide the study rationale and participant recruitment processes to interested GP clinics then invite discussion about the study. This includes information about PGx testing, its potential utility in guiding antidepressant prescribing and the prescribing reports they will receive for each of their patients recruited to the study. It is emphasised that GPs should use their clinical judgement when discussing and determining what, if any, treatment to commence for their patient’s depressive symptoms. GPs are reminded of the clinical guidelines for the management of depression that state patients should be followed up every 4 weeks. Each GP is provided a GP information sheet about the study, given the opportunity to ask questions and individually consented to the study to allow recruitment of their patients. Trained research assistants provide individuals who have appointments with consented GPs within 2 days of approach with verbal and written information about the trial, check their eligibility and answer any questions about the study. A second research assistant obtains written informed consent if they agree to participate in the trial. Due to COVID-19 and resulting government restrictions, both face-to-face and teletrial methods are used for approach, eligibility assessment and informed consent discussions with potential participants. Interested and eligible participants are provided with the study information sheet prior to consenting to the study and given the opportunity to ask questions. Participants recruited via teletrial complete an online e-consent form through the study’s REDCap database . A copy (either hard or electronic) of their study consent form is provided to participants. Additional and optional written consent is also sought for the release of participants’ Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) data via Services Australia. Initial approvals for the collection of administrative data on health service use and prescription medication dispensing from our government-funded health service (administered by Services Australia) were substantially delayed. Therefore, it was decided by the study steering committee that recruitment for the trial should begin prior to this approval being provided. Upon approval, these participants were retrospectively contacted to obtain this consent. Furthermore, participants not entitled to government-funded healthcare (e.g. they are foreign citizens and not permanent residents of Australia) do not have any Services Australia data available; however, we are collecting additional data directly from the GP record of participants, as well as self-reported use of health services and medications. Participants give specific consent for the study, in that their data and biological sample will not be used for future studies. Any excess DNA is securely disposed of by the laboratory conducting the PGx test (Sonic Healthcare, Sydney, Australia). If a sample fails to yield a result, the sample is tested once more before it is securely discarded. Explanation for the choice of comparators {6b} Participants are randomised to receive either PGx-informed prescribing (experimental intervention) or Australian Therapeutic Guidelines-informed prescribing (control intervention). The sole difference between the experimental and control interventions is the method used to make dosing recommendations in the report the GP receives. The dosing recommendations offered in the control intervention are based on the Australian Therapeutic Guidelines, whereas the dosing recommendations offered in the experimental intervention are based on the participant’s CYP2D6 and CYP2C19 genotype-predicted metaboliser phenotypes. Previous clinical trials of PGx-informed antidepressant prescribing have exclusively used treatment as usual (i.e. standard prescribing) as the comparator . However, this comparator does not allow blinding of the treating clinician and results in a higher risk of performance bias as well as attention and ancillary treatment biases. As such, the comparator in the PRESIDE Trial is Australian Therapeutic Guideline-informed prescribing, delivered using a prescribing report that is formatted identically to the experimental intervention. Intervention description {11a} Determination of PGx genotype and phenotype Prior to randomisation, all participants provide a saliva sample using the ORAcollect®-DNA OCR100 kit (DNA Genotek, Ottawa, Canada). Saliva samples are sent via Melbourne Pathology to Douglass Hanly Moir Pathology (Sonic Healthcare Australia Pathology) for testing. Genomic DNA is isolated, and pharmacogenomic genotyping is performed using either the iPLEX® PGx74 or VeriDose® Core panel (Agena Biosciences, San Diego, USA), which includes eight CYP2C19 alleles (*2, *3, *4, *5, *6, *7, *8, *17) and 18 CYP2D6 alleles (*2, *3, *4, *6, *7,*8, *9, *10, *11, *12, *14, *15, *17, *18, *19, *29, *41, *114). In addition, an in-house digital droplet PCR copy number assay is conducted to detect CYP2D6 gene deletions (*5) and duplications (*XN). Genotype to metaboliser phenotype translation is performed according to the CPIC guidelines by the Translational Software. Incorporation of interaction with concomitant medications into PGx phenotype CYP2D6 and CYP2C19 metaboliser phenotypes are adjusted for participant-reported concomitant medications known to induce or inhibit these enzymes using the Sequence2Script tool . In the presence of an inducer, the genotype-predicted phenotype is converted to the next higher activity phenotype (e.g. an intermediate metaboliser is converted to a normal metaboliser). In the presence of a moderate inhibitor, the genotype-predicted phenotype is converted to the next lower activity phenotype (e.g. a normal metaboliser is converted to an intermediate metaboliser), whereas in the presence of a strong inhibitor, the phenotype is converted to a poor metaboliser, regardless of the genotype-predicted phenotype. Determination of algorithm of actionable recommendations Table shows the algorithm for drug selection based on concomitant medication-adjusted PGx phenotype. The algorithm of recommendations includes contraindicated drugs and those where a dose alteration is recommended and was based on CPIC and DPWG antidepressant guidelines . Selection of medications for inclusion in the antidepressant prescribing report The selection of medications and dosages to be included in the report is generated in R using the algorithm in Table . Firstly, the number of medications selected for the report (between four and six) is randomly determined using the base R function sample. Variation in the number of drugs included in the report (four to six) was chosen to maintain blinding and allow the potential to include all genotype-based actionable recommendations in an intervention report. Drugs which are contraindicated according to the participant’s phenotype (‘not recommended’) are never included in the report. Medications with actionable recommendations (bold texted cells) are prioritised in the report, i.e. they are always included and listed at the top of the report. If there are more medications with actionable recommendations than the number of medications to be included in the report, then a random selection is taken. To fill the remaining medications in the report, a random selection of medications with no recommendations (non-bold texted cells) is taken. Return of report to GPs Reports are returned to the GP clinic via hard copy or secure file transfer. The GP clinic staff are asked to treat the report as per their standard procedures for receiving and actioning pathology test reports. GP clinic staff are asked to upload the report to the participant’s GP medical record. Ultimately, any clinical decisions regarding the pharmacological or non-pharmacological treatment of depressive symptoms are at the discretion of the participants and their GP. This means that GPs are asked to employ their clinical decision-making as per usual clinical practice but are equipped with the antidepressant prescribing report to consider antidepressant treatment options. Responsibility for all aspects of participant care is the GP’s, as per standard of care. Criteria for discontinuing or modifying allocated interventions {11b} The trial intervention only contains the provision of the prescribing report to the GP clinic, and therefore, no substantive modifications are anticipated. Participants, in collaboration with their GP, are free to take up their treatment recommendation or not and may discontinue treatment at any time. Strategies to improve adherence to interventions {11c} Upon recruitment, participants are informed that the prescribing report will be provided to their GP after 2–3 weeks and that they should make an appointment with their GP at this time to discuss its recommendation and the management of their depressive symptoms. In the event of any substantial delay to the PGx results and therefore the antidepressant prescribing report, participants and GPs are informed of this delay and when to expect the report. Relevant concomitant care permitted or prohibited during the trial {11d} Those who are taking antipsychotic medication at baseline are ineligible for the trial, as described above; however, those who begin treatment with antipsychotic medication during their study participation are not excluded. There are no other exclusions based on concomitant care. Provisions for post-trial care {30} There are no anticipated harms associated with the intervention, given that the participant and their GP have final responsibility for any clinical decisions and care and all antidepressant medication recommendations are taken from the Australian Therapeutic Guidelines. GPs are under no obligation to use the recommendations on the provided prescribing report. All participants are invited to discuss their participation with their GP and any mental health symptoms they may be experiencing. Therefore, there are no provisions for post-trial care. Outcomes {12} Outcome measures are collected at baseline prior to randomisation and then at 4, 8, 12 and 26 weeks after the GP’s receipt of the antidepressant prescribing report. Further details about measures can be found in item 18a (Plans for assessment and collection of outcomes) below. Primary outcome measures Difference between the experimental and control interventions in the mean change of depressive symptom score from baseline to 12 weeks from the GP’s receipt of the antidepressant prescribing report. The depressive symptoms score is the sum of the nine items measured using the Patient Health Questionnaire 9 (PHQ-9) . Secondary outcomes measures Difference between the experimental and control interventions in the: (i) mean change in PHQ-9 depressive symptom scores from baseline to 4, 8 and 26 weeks from the GP’s receipt of the antidepressant prescribing report (ii) Proportion of participants in remission from depressive symptoms (defined as PHQ-9 score < 5) at 12 weeks (iii) Proportion of participants who respond to treatment, defined as > 50% decrease in PHQ-9 score from baseline, at 12 weeks (iv) The mean side effect score due to antidepressant medications at 4, 8, 12 and 26 weeks, measured using the FIBSER scale , that includes the domains of frequency, intensity and burden of side effects (v) Quality of life, measured as the mean AQoL-4D utility score, at 12 and 26 weeks (exploratory analyses of the sub-domains of his scale, including the mental health dimension, will also be undertaken) (vi) The mean self-reported adherence score to antidepressants prescribed, measured using the MARS-5 scale , at 4, 8, 12 and 26 weeks (vii) Adherence to antidepressants prescribed, measured using the medication possession ratio , derived from prescription and PBS data (viii) Number of antidepressant medication changes, derived from GP record audit and PBS data (ix) Proportion of participants where the GP prescribing was concordant with the medication recommendations in the antidepressant prescribing report Economic evaluation Health economic outcomes will be measured as the difference between the experimental and control interventions in the following: (i) Quality-adjusted life years (QALYs) calculated using AQoL-4D utility values and the area under the curve method (ii) Health service use, measured using a fit-for-purpose resource use questionnaire , MBS and PBS data, and GP record audit, at 12 and 26 weeks (iii) Lost productivity from paid and unpaid work and presenteeism (time working but at a reduced capacity) measured with questions in the resource use questionnaire (iv) Total health sector costs calculated by adding the cost of intervention delivery to participant health care service use (v) Total partial societal costs calculated by adding the total cost of lost productivity to total health sector costs Process evaluation A process evaluation, based on a logic model of how the intervention is designed to affect the outcome, will also be conducted to further explore how elements of the trial intervention influenced potential outcomes. This will be measured using qualitative data from semi-structured interviews from a subset of general practitioners and participants enrolled in the trial, as well as documented participant and GP interactions regarding mental health throughout the trial period, obtained from GP electronic medical record audit. Participant timeline {13} Table shows the participant timeline from the time of enrolment and the timing of the different assessments. Twenty-six weeks post-allocation (i.e. after the final endpoint of the study), all participants’ GPs receive a full clinical PGx report that outlines PGx-guided prescribing recommendations for a range of commonly prescribed medications. Sample size {14} Sample size estimates were informed by two large randomised trials in which 1868 participants (Target-D ) and 1671 participants (Link-me ) with depressive symptoms attending general practice. We would require a sample size of 672 eligible patients to be randomised (336 patients in each experimental and control intervention) to detect a between-arm difference of 0.3 standard deviation (SD) for the primary outcome, with 90% power and a 5% significance level (2-sided test), after allowing for 30% attrition over 12 weeks. This is equivalent to a difference in the mean change of PHQ-9 score of 1.8 at 12 weeks (measured from baseline) between the two study interventions, assuming conservatively the standard deviation is 6 . A reduction of at least 0.3 SD in the mean PHQ-9 depressive symptom score at 12 weeks between study interventions is considered a clinically important reduction in the primary care setting. From our previous trials and experience , we expected that 40% of all patients approached would complete the PHQ-9, of whom 32% would be eligible due to moderate to severe depressive symptoms. Of these eligible patients, we expected 40% would consent to enter the trial. Therefore, we predicted 13,125 patients will need to be approached to reach the required sample size. Recruitment {15} Identification of potential participants Patients from appointment lists of consented GPs are sequentially approached. The approach occurs either via telephone up to two days prior to their scheduled GP appointment or in person in the waiting room immediately before their scheduled GP appointment. Telephone approach Potential participants are first approached via SMS text to notify in advance that a researcher based in their GP clinic will be calling them to discuss the study. They are then phoned to introduce the study and screen them for eligibility, including completing the PHQ-9 over the telephone. Research assistants attempt to call potential participants a maximum of two times. A voicemail may be left after the first attempt. If the potential participant is eligible and interested in hearing more about the study, they are then asked to attend their GP appointment 30 min early (in person or virtually for telehealth appointments) to meet with a second researcher to discuss further what the study involves. After the initial phone call, they are emailed a copy of the study information sheet. Face-to-face approach Potential participants are first approached in the waiting room immediately prior to their GP appointment. If they are willing, they are provided with a tablet to complete the PHQ-9 questionnaire and other eligibility questions. If they are eligible for the study, they are invited to a private consulting room with another researcher to confirm their eligibility and discuss the study further. Participant recruitment and consent Given the sensitive nature of the topic being discussed with participants, the recruitment appointment can only be scheduled on the day of the participant’s existing GP appointment, ideally immediately prior to the GP appointment. Recruitment can occur either face-to-face at the participant’s GP clinic or via teletrial. Face-to-face recruitment and consent Interested potential participants meet with the researcher before their scheduled GP appointment, in a private consulting room. After confirmation of eligibility, the trial is explained and the potential participant is given the opportunity to ask questions, followed by informed consent to participate. During this appointment, the participant signs the hard copy study consent form, as well as the optional Services Australia consent form (for access to Medical Benefits Scheme and Pharmaceutical Benefits Scheme data), provides a sample of DNA using the saliva collection kit, completes the baseline questionnaire and is provided with an alert card to give their GP to inform the GP they are part of the trial. Participants are informed they will be required to schedule a follow-up appointment after 2–3 weeks to discuss the antidepressant prescribing report with their GP. Teletrial recruitment and consent Interested potential participants who cannot attend their clinic (either for convenience reasons or due to COVID-19 lockdowns) can consent to the trial via teletrial, using online videoconferencing software. Confirmation of eligibility and obtaining informed consent are as per face-to-face protocols. The study consent form is completed as an e-consent form via the study’s REDCap database, as is the baseline questionnaire. After this initial consent appointment, the participant is express-posted a hard copy of the Services Australia consent form (which cannot be completed electronically) and the DNA saliva collection kit. Once received by the participant, a researcher has another videoconferencing appointment with the participant to witness them complete the DNA collection (to ensure its correct identity and sample integrity). The Services Australia consent form and DNA sample are then express-posted back to the research team for processing and logging. Ineligible patients and patients who do not wish to participate in the trial An electronic recruitment log containing age and gender is kept throughout recruitment. Reasons for ineligibility or refusal (if provided) are recorded in REDCap. No identifying data is kept for this group. This recruitment log is maintained to track the representativeness of the trial sample. Participants are randomised to receive either PGx-informed prescribing (experimental intervention) or Australian Therapeutic Guidelines-informed prescribing (control intervention). The sole difference between the experimental and control interventions is the method used to make dosing recommendations in the report the GP receives. The dosing recommendations offered in the control intervention are based on the Australian Therapeutic Guidelines, whereas the dosing recommendations offered in the experimental intervention are based on the participant’s CYP2D6 and CYP2C19 genotype-predicted metaboliser phenotypes. Previous clinical trials of PGx-informed antidepressant prescribing have exclusively used treatment as usual (i.e. standard prescribing) as the comparator . However, this comparator does not allow blinding of the treating clinician and results in a higher risk of performance bias as well as attention and ancillary treatment biases. As such, the comparator in the PRESIDE Trial is Australian Therapeutic Guideline-informed prescribing, delivered using a prescribing report that is formatted identically to the experimental intervention. Determination of PGx genotype and phenotype Prior to randomisation, all participants provide a saliva sample using the ORAcollect®-DNA OCR100 kit (DNA Genotek, Ottawa, Canada). Saliva samples are sent via Melbourne Pathology to Douglass Hanly Moir Pathology (Sonic Healthcare Australia Pathology) for testing. Genomic DNA is isolated, and pharmacogenomic genotyping is performed using either the iPLEX® PGx74 or VeriDose® Core panel (Agena Biosciences, San Diego, USA), which includes eight CYP2C19 alleles (*2, *3, *4, *5, *6, *7, *8, *17) and 18 CYP2D6 alleles (*2, *3, *4, *6, *7,*8, *9, *10, *11, *12, *14, *15, *17, *18, *19, *29, *41, *114). In addition, an in-house digital droplet PCR copy number assay is conducted to detect CYP2D6 gene deletions (*5) and duplications (*XN). Genotype to metaboliser phenotype translation is performed according to the CPIC guidelines by the Translational Software. Incorporation of interaction with concomitant medications into PGx phenotype CYP2D6 and CYP2C19 metaboliser phenotypes are adjusted for participant-reported concomitant medications known to induce or inhibit these enzymes using the Sequence2Script tool . In the presence of an inducer, the genotype-predicted phenotype is converted to the next higher activity phenotype (e.g. an intermediate metaboliser is converted to a normal metaboliser). In the presence of a moderate inhibitor, the genotype-predicted phenotype is converted to the next lower activity phenotype (e.g. a normal metaboliser is converted to an intermediate metaboliser), whereas in the presence of a strong inhibitor, the phenotype is converted to a poor metaboliser, regardless of the genotype-predicted phenotype. Determination of algorithm of actionable recommendations Table shows the algorithm for drug selection based on concomitant medication-adjusted PGx phenotype. The algorithm of recommendations includes contraindicated drugs and those where a dose alteration is recommended and was based on CPIC and DPWG antidepressant guidelines . Selection of medications for inclusion in the antidepressant prescribing report The selection of medications and dosages to be included in the report is generated in R using the algorithm in Table . Firstly, the number of medications selected for the report (between four and six) is randomly determined using the base R function sample. Variation in the number of drugs included in the report (four to six) was chosen to maintain blinding and allow the potential to include all genotype-based actionable recommendations in an intervention report. Drugs which are contraindicated according to the participant’s phenotype (‘not recommended’) are never included in the report. Medications with actionable recommendations (bold texted cells) are prioritised in the report, i.e. they are always included and listed at the top of the report. If there are more medications with actionable recommendations than the number of medications to be included in the report, then a random selection is taken. To fill the remaining medications in the report, a random selection of medications with no recommendations (non-bold texted cells) is taken. Return of report to GPs Reports are returned to the GP clinic via hard copy or secure file transfer. The GP clinic staff are asked to treat the report as per their standard procedures for receiving and actioning pathology test reports. GP clinic staff are asked to upload the report to the participant’s GP medical record. Ultimately, any clinical decisions regarding the pharmacological or non-pharmacological treatment of depressive symptoms are at the discretion of the participants and their GP. This means that GPs are asked to employ their clinical decision-making as per usual clinical practice but are equipped with the antidepressant prescribing report to consider antidepressant treatment options. Responsibility for all aspects of participant care is the GP’s, as per standard of care. Prior to randomisation, all participants provide a saliva sample using the ORAcollect®-DNA OCR100 kit (DNA Genotek, Ottawa, Canada). Saliva samples are sent via Melbourne Pathology to Douglass Hanly Moir Pathology (Sonic Healthcare Australia Pathology) for testing. Genomic DNA is isolated, and pharmacogenomic genotyping is performed using either the iPLEX® PGx74 or VeriDose® Core panel (Agena Biosciences, San Diego, USA), which includes eight CYP2C19 alleles (*2, *3, *4, *5, *6, *7, *8, *17) and 18 CYP2D6 alleles (*2, *3, *4, *6, *7,*8, *9, *10, *11, *12, *14, *15, *17, *18, *19, *29, *41, *114). In addition, an in-house digital droplet PCR copy number assay is conducted to detect CYP2D6 gene deletions (*5) and duplications (*XN). Genotype to metaboliser phenotype translation is performed according to the CPIC guidelines by the Translational Software. CYP2D6 and CYP2C19 metaboliser phenotypes are adjusted for participant-reported concomitant medications known to induce or inhibit these enzymes using the Sequence2Script tool . In the presence of an inducer, the genotype-predicted phenotype is converted to the next higher activity phenotype (e.g. an intermediate metaboliser is converted to a normal metaboliser). In the presence of a moderate inhibitor, the genotype-predicted phenotype is converted to the next lower activity phenotype (e.g. a normal metaboliser is converted to an intermediate metaboliser), whereas in the presence of a strong inhibitor, the phenotype is converted to a poor metaboliser, regardless of the genotype-predicted phenotype. Table shows the algorithm for drug selection based on concomitant medication-adjusted PGx phenotype. The algorithm of recommendations includes contraindicated drugs and those where a dose alteration is recommended and was based on CPIC and DPWG antidepressant guidelines . The selection of medications and dosages to be included in the report is generated in R using the algorithm in Table . Firstly, the number of medications selected for the report (between four and six) is randomly determined using the base R function sample. Variation in the number of drugs included in the report (four to six) was chosen to maintain blinding and allow the potential to include all genotype-based actionable recommendations in an intervention report. Drugs which are contraindicated according to the participant’s phenotype (‘not recommended’) are never included in the report. Medications with actionable recommendations (bold texted cells) are prioritised in the report, i.e. they are always included and listed at the top of the report. If there are more medications with actionable recommendations than the number of medications to be included in the report, then a random selection is taken. To fill the remaining medications in the report, a random selection of medications with no recommendations (non-bold texted cells) is taken. Reports are returned to the GP clinic via hard copy or secure file transfer. The GP clinic staff are asked to treat the report as per their standard procedures for receiving and actioning pathology test reports. GP clinic staff are asked to upload the report to the participant’s GP medical record. Ultimately, any clinical decisions regarding the pharmacological or non-pharmacological treatment of depressive symptoms are at the discretion of the participants and their GP. This means that GPs are asked to employ their clinical decision-making as per usual clinical practice but are equipped with the antidepressant prescribing report to consider antidepressant treatment options. Responsibility for all aspects of participant care is the GP’s, as per standard of care. The trial intervention only contains the provision of the prescribing report to the GP clinic, and therefore, no substantive modifications are anticipated. Participants, in collaboration with their GP, are free to take up their treatment recommendation or not and may discontinue treatment at any time. Upon recruitment, participants are informed that the prescribing report will be provided to their GP after 2–3 weeks and that they should make an appointment with their GP at this time to discuss its recommendation and the management of their depressive symptoms. In the event of any substantial delay to the PGx results and therefore the antidepressant prescribing report, participants and GPs are informed of this delay and when to expect the report. Those who are taking antipsychotic medication at baseline are ineligible for the trial, as described above; however, those who begin treatment with antipsychotic medication during their study participation are not excluded. There are no other exclusions based on concomitant care. There are no anticipated harms associated with the intervention, given that the participant and their GP have final responsibility for any clinical decisions and care and all antidepressant medication recommendations are taken from the Australian Therapeutic Guidelines. GPs are under no obligation to use the recommendations on the provided prescribing report. All participants are invited to discuss their participation with their GP and any mental health symptoms they may be experiencing. Therefore, there are no provisions for post-trial care. Outcome measures are collected at baseline prior to randomisation and then at 4, 8, 12 and 26 weeks after the GP’s receipt of the antidepressant prescribing report. Further details about measures can be found in item 18a (Plans for assessment and collection of outcomes) below. Primary outcome measures Difference between the experimental and control interventions in the mean change of depressive symptom score from baseline to 12 weeks from the GP’s receipt of the antidepressant prescribing report. The depressive symptoms score is the sum of the nine items measured using the Patient Health Questionnaire 9 (PHQ-9) . Secondary outcomes measures Difference between the experimental and control interventions in the: (i) mean change in PHQ-9 depressive symptom scores from baseline to 4, 8 and 26 weeks from the GP’s receipt of the antidepressant prescribing report (ii) Proportion of participants in remission from depressive symptoms (defined as PHQ-9 score < 5) at 12 weeks (iii) Proportion of participants who respond to treatment, defined as > 50% decrease in PHQ-9 score from baseline, at 12 weeks (iv) The mean side effect score due to antidepressant medications at 4, 8, 12 and 26 weeks, measured using the FIBSER scale , that includes the domains of frequency, intensity and burden of side effects (v) Quality of life, measured as the mean AQoL-4D utility score, at 12 and 26 weeks (exploratory analyses of the sub-domains of his scale, including the mental health dimension, will also be undertaken) (vi) The mean self-reported adherence score to antidepressants prescribed, measured using the MARS-5 scale , at 4, 8, 12 and 26 weeks (vii) Adherence to antidepressants prescribed, measured using the medication possession ratio , derived from prescription and PBS data (viii) Number of antidepressant medication changes, derived from GP record audit and PBS data (ix) Proportion of participants where the GP prescribing was concordant with the medication recommendations in the antidepressant prescribing report Economic evaluation Health economic outcomes will be measured as the difference between the experimental and control interventions in the following: (i) Quality-adjusted life years (QALYs) calculated using AQoL-4D utility values and the area under the curve method (ii) Health service use, measured using a fit-for-purpose resource use questionnaire , MBS and PBS data, and GP record audit, at 12 and 26 weeks (iii) Lost productivity from paid and unpaid work and presenteeism (time working but at a reduced capacity) measured with questions in the resource use questionnaire (iv) Total health sector costs calculated by adding the cost of intervention delivery to participant health care service use (v) Total partial societal costs calculated by adding the total cost of lost productivity to total health sector costs Process evaluation A process evaluation, based on a logic model of how the intervention is designed to affect the outcome, will also be conducted to further explore how elements of the trial intervention influenced potential outcomes. This will be measured using qualitative data from semi-structured interviews from a subset of general practitioners and participants enrolled in the trial, as well as documented participant and GP interactions regarding mental health throughout the trial period, obtained from GP electronic medical record audit. Difference between the experimental and control interventions in the mean change of depressive symptom score from baseline to 12 weeks from the GP’s receipt of the antidepressant prescribing report. The depressive symptoms score is the sum of the nine items measured using the Patient Health Questionnaire 9 (PHQ-9) . Difference between the experimental and control interventions in the: (i) mean change in PHQ-9 depressive symptom scores from baseline to 4, 8 and 26 weeks from the GP’s receipt of the antidepressant prescribing report (ii) Proportion of participants in remission from depressive symptoms (defined as PHQ-9 score < 5) at 12 weeks (iii) Proportion of participants who respond to treatment, defined as > 50% decrease in PHQ-9 score from baseline, at 12 weeks (iv) The mean side effect score due to antidepressant medications at 4, 8, 12 and 26 weeks, measured using the FIBSER scale , that includes the domains of frequency, intensity and burden of side effects (v) Quality of life, measured as the mean AQoL-4D utility score, at 12 and 26 weeks (exploratory analyses of the sub-domains of his scale, including the mental health dimension, will also be undertaken) (vi) The mean self-reported adherence score to antidepressants prescribed, measured using the MARS-5 scale , at 4, 8, 12 and 26 weeks (vii) Adherence to antidepressants prescribed, measured using the medication possession ratio , derived from prescription and PBS data (viii) Number of antidepressant medication changes, derived from GP record audit and PBS data (ix) Proportion of participants where the GP prescribing was concordant with the medication recommendations in the antidepressant prescribing report Economic evaluation Health economic outcomes will be measured as the difference between the experimental and control interventions in the following: (i) Quality-adjusted life years (QALYs) calculated using AQoL-4D utility values and the area under the curve method (ii) Health service use, measured using a fit-for-purpose resource use questionnaire , MBS and PBS data, and GP record audit, at 12 and 26 weeks (iii) Lost productivity from paid and unpaid work and presenteeism (time working but at a reduced capacity) measured with questions in the resource use questionnaire (iv) Total health sector costs calculated by adding the cost of intervention delivery to participant health care service use (v) Total partial societal costs calculated by adding the total cost of lost productivity to total health sector costs Process evaluation A process evaluation, based on a logic model of how the intervention is designed to affect the outcome, will also be conducted to further explore how elements of the trial intervention influenced potential outcomes. This will be measured using qualitative data from semi-structured interviews from a subset of general practitioners and participants enrolled in the trial, as well as documented participant and GP interactions regarding mental health throughout the trial period, obtained from GP electronic medical record audit. Health economic outcomes will be measured as the difference between the experimental and control interventions in the following: (i) Quality-adjusted life years (QALYs) calculated using AQoL-4D utility values and the area under the curve method (ii) Health service use, measured using a fit-for-purpose resource use questionnaire , MBS and PBS data, and GP record audit, at 12 and 26 weeks (iii) Lost productivity from paid and unpaid work and presenteeism (time working but at a reduced capacity) measured with questions in the resource use questionnaire (iv) Total health sector costs calculated by adding the cost of intervention delivery to participant health care service use (v) Total partial societal costs calculated by adding the total cost of lost productivity to total health sector costs A process evaluation, based on a logic model of how the intervention is designed to affect the outcome, will also be conducted to further explore how elements of the trial intervention influenced potential outcomes. This will be measured using qualitative data from semi-structured interviews from a subset of general practitioners and participants enrolled in the trial, as well as documented participant and GP interactions regarding mental health throughout the trial period, obtained from GP electronic medical record audit. Table shows the participant timeline from the time of enrolment and the timing of the different assessments. Twenty-six weeks post-allocation (i.e. after the final endpoint of the study), all participants’ GPs receive a full clinical PGx report that outlines PGx-guided prescribing recommendations for a range of commonly prescribed medications. Sample size estimates were informed by two large randomised trials in which 1868 participants (Target-D ) and 1671 participants (Link-me ) with depressive symptoms attending general practice. We would require a sample size of 672 eligible patients to be randomised (336 patients in each experimental and control intervention) to detect a between-arm difference of 0.3 standard deviation (SD) for the primary outcome, with 90% power and a 5% significance level (2-sided test), after allowing for 30% attrition over 12 weeks. This is equivalent to a difference in the mean change of PHQ-9 score of 1.8 at 12 weeks (measured from baseline) between the two study interventions, assuming conservatively the standard deviation is 6 . A reduction of at least 0.3 SD in the mean PHQ-9 depressive symptom score at 12 weeks between study interventions is considered a clinically important reduction in the primary care setting. From our previous trials and experience , we expected that 40% of all patients approached would complete the PHQ-9, of whom 32% would be eligible due to moderate to severe depressive symptoms. Of these eligible patients, we expected 40% would consent to enter the trial. Therefore, we predicted 13,125 patients will need to be approached to reach the required sample size. Identification of potential participants Patients from appointment lists of consented GPs are sequentially approached. The approach occurs either via telephone up to two days prior to their scheduled GP appointment or in person in the waiting room immediately before their scheduled GP appointment. Telephone approach Potential participants are first approached via SMS text to notify in advance that a researcher based in their GP clinic will be calling them to discuss the study. They are then phoned to introduce the study and screen them for eligibility, including completing the PHQ-9 over the telephone. Research assistants attempt to call potential participants a maximum of two times. A voicemail may be left after the first attempt. If the potential participant is eligible and interested in hearing more about the study, they are then asked to attend their GP appointment 30 min early (in person or virtually for telehealth appointments) to meet with a second researcher to discuss further what the study involves. After the initial phone call, they are emailed a copy of the study information sheet. Face-to-face approach Potential participants are first approached in the waiting room immediately prior to their GP appointment. If they are willing, they are provided with a tablet to complete the PHQ-9 questionnaire and other eligibility questions. If they are eligible for the study, they are invited to a private consulting room with another researcher to confirm their eligibility and discuss the study further. Participant recruitment and consent Given the sensitive nature of the topic being discussed with participants, the recruitment appointment can only be scheduled on the day of the participant’s existing GP appointment, ideally immediately prior to the GP appointment. Recruitment can occur either face-to-face at the participant’s GP clinic or via teletrial. Face-to-face recruitment and consent Interested potential participants meet with the researcher before their scheduled GP appointment, in a private consulting room. After confirmation of eligibility, the trial is explained and the potential participant is given the opportunity to ask questions, followed by informed consent to participate. During this appointment, the participant signs the hard copy study consent form, as well as the optional Services Australia consent form (for access to Medical Benefits Scheme and Pharmaceutical Benefits Scheme data), provides a sample of DNA using the saliva collection kit, completes the baseline questionnaire and is provided with an alert card to give their GP to inform the GP they are part of the trial. Participants are informed they will be required to schedule a follow-up appointment after 2–3 weeks to discuss the antidepressant prescribing report with their GP. Teletrial recruitment and consent Interested potential participants who cannot attend their clinic (either for convenience reasons or due to COVID-19 lockdowns) can consent to the trial via teletrial, using online videoconferencing software. Confirmation of eligibility and obtaining informed consent are as per face-to-face protocols. The study consent form is completed as an e-consent form via the study’s REDCap database, as is the baseline questionnaire. After this initial consent appointment, the participant is express-posted a hard copy of the Services Australia consent form (which cannot be completed electronically) and the DNA saliva collection kit. Once received by the participant, a researcher has another videoconferencing appointment with the participant to witness them complete the DNA collection (to ensure its correct identity and sample integrity). The Services Australia consent form and DNA sample are then express-posted back to the research team for processing and logging. Ineligible patients and patients who do not wish to participate in the trial An electronic recruitment log containing age and gender is kept throughout recruitment. Reasons for ineligibility or refusal (if provided) are recorded in REDCap. No identifying data is kept for this group. This recruitment log is maintained to track the representativeness of the trial sample. Patients from appointment lists of consented GPs are sequentially approached. The approach occurs either via telephone up to two days prior to their scheduled GP appointment or in person in the waiting room immediately before their scheduled GP appointment. Telephone approach Potential participants are first approached via SMS text to notify in advance that a researcher based in their GP clinic will be calling them to discuss the study. They are then phoned to introduce the study and screen them for eligibility, including completing the PHQ-9 over the telephone. Research assistants attempt to call potential participants a maximum of two times. A voicemail may be left after the first attempt. If the potential participant is eligible and interested in hearing more about the study, they are then asked to attend their GP appointment 30 min early (in person or virtually for telehealth appointments) to meet with a second researcher to discuss further what the study involves. After the initial phone call, they are emailed a copy of the study information sheet. Face-to-face approach Potential participants are first approached in the waiting room immediately prior to their GP appointment. If they are willing, they are provided with a tablet to complete the PHQ-9 questionnaire and other eligibility questions. If they are eligible for the study, they are invited to a private consulting room with another researcher to confirm their eligibility and discuss the study further. Potential participants are first approached via SMS text to notify in advance that a researcher based in their GP clinic will be calling them to discuss the study. They are then phoned to introduce the study and screen them for eligibility, including completing the PHQ-9 over the telephone. Research assistants attempt to call potential participants a maximum of two times. A voicemail may be left after the first attempt. If the potential participant is eligible and interested in hearing more about the study, they are then asked to attend their GP appointment 30 min early (in person or virtually for telehealth appointments) to meet with a second researcher to discuss further what the study involves. After the initial phone call, they are emailed a copy of the study information sheet. Potential participants are first approached in the waiting room immediately prior to their GP appointment. If they are willing, they are provided with a tablet to complete the PHQ-9 questionnaire and other eligibility questions. If they are eligible for the study, they are invited to a private consulting room with another researcher to confirm their eligibility and discuss the study further. Given the sensitive nature of the topic being discussed with participants, the recruitment appointment can only be scheduled on the day of the participant’s existing GP appointment, ideally immediately prior to the GP appointment. Recruitment can occur either face-to-face at the participant’s GP clinic or via teletrial. Face-to-face recruitment and consent Interested potential participants meet with the researcher before their scheduled GP appointment, in a private consulting room. After confirmation of eligibility, the trial is explained and the potential participant is given the opportunity to ask questions, followed by informed consent to participate. During this appointment, the participant signs the hard copy study consent form, as well as the optional Services Australia consent form (for access to Medical Benefits Scheme and Pharmaceutical Benefits Scheme data), provides a sample of DNA using the saliva collection kit, completes the baseline questionnaire and is provided with an alert card to give their GP to inform the GP they are part of the trial. Participants are informed they will be required to schedule a follow-up appointment after 2–3 weeks to discuss the antidepressant prescribing report with their GP. Teletrial recruitment and consent Interested potential participants who cannot attend their clinic (either for convenience reasons or due to COVID-19 lockdowns) can consent to the trial via teletrial, using online videoconferencing software. Confirmation of eligibility and obtaining informed consent are as per face-to-face protocols. The study consent form is completed as an e-consent form via the study’s REDCap database, as is the baseline questionnaire. After this initial consent appointment, the participant is express-posted a hard copy of the Services Australia consent form (which cannot be completed electronically) and the DNA saliva collection kit. Once received by the participant, a researcher has another videoconferencing appointment with the participant to witness them complete the DNA collection (to ensure its correct identity and sample integrity). The Services Australia consent form and DNA sample are then express-posted back to the research team for processing and logging. Interested potential participants meet with the researcher before their scheduled GP appointment, in a private consulting room. After confirmation of eligibility, the trial is explained and the potential participant is given the opportunity to ask questions, followed by informed consent to participate. During this appointment, the participant signs the hard copy study consent form, as well as the optional Services Australia consent form (for access to Medical Benefits Scheme and Pharmaceutical Benefits Scheme data), provides a sample of DNA using the saliva collection kit, completes the baseline questionnaire and is provided with an alert card to give their GP to inform the GP they are part of the trial. Participants are informed they will be required to schedule a follow-up appointment after 2–3 weeks to discuss the antidepressant prescribing report with their GP. Interested potential participants who cannot attend their clinic (either for convenience reasons or due to COVID-19 lockdowns) can consent to the trial via teletrial, using online videoconferencing software. Confirmation of eligibility and obtaining informed consent are as per face-to-face protocols. The study consent form is completed as an e-consent form via the study’s REDCap database, as is the baseline questionnaire. After this initial consent appointment, the participant is express-posted a hard copy of the Services Australia consent form (which cannot be completed electronically) and the DNA saliva collection kit. Once received by the participant, a researcher has another videoconferencing appointment with the participant to witness them complete the DNA collection (to ensure its correct identity and sample integrity). The Services Australia consent form and DNA sample are then express-posted back to the research team for processing and logging. An electronic recruitment log containing age and gender is kept throughout recruitment. Reasons for ineligibility or refusal (if provided) are recorded in REDCap. No identifying data is kept for this group. This recruitment log is maintained to track the representativeness of the trial sample. Sequence generation {16a} Participants are randomly allocated 1:1 to the experimental and control intervention. The allocation sequence is computer-generated, stratified by general practice and current antidepressant use using permuted blocks of random sizes. To ensure concealment, the block sizes are not disclosed until after recruitment of trial participants is completed. Concealment mechanism {16b} The random allocation schedule is embedded within a secure online web database (REDCap ) which automatically randomises participants to either experimental or control intervention after their DNA results have been returned to the investigator team. Implementation {16c} A statistician not involved in the recruitment of participants or data collection generates the randomisation schedule and upload it to the trial online database. Researchers randomise participants upon receipt of their PGx test result, immediately prior to generating the antidepressant prescribing report, using the randomisation function in the REDCap database . Allocated intervention is hidden from this researcher using the Hide Randomisation Module v1.0.4 in REDCap. Protocol modification Until 26 August 2021 and the 65th randomised participant, randomisation occurred upon receipt of the study consent form. However, seven of the initial PGx test samples failed genotyping in the laboratory, which then resulted in the withdrawal of four of these participants who did not wish to provide a blood sample for repeat testing. At this point, the decision was made by the trial steering committee on 26 August 2021 to randomise participants only on receipt of complete PGx results, due to concerns of a large number of patients not able to receive the antidepressant prescribing report at all that may lead to an attenuation in the intervention effect. Although there are some pragmatic elements in the design of this trial, we wanted to maximise the chance of demonstrating an effect of the experimental intervention compared to the control intervention. For this aspect of the trial, the design is more explanatory . Any participants who were recruited and randomised prior to this will all be included in the final analysis, under the intention-to-treat principle, regardless of if they received the full intervention. Participants are randomly allocated 1:1 to the experimental and control intervention. The allocation sequence is computer-generated, stratified by general practice and current antidepressant use using permuted blocks of random sizes. To ensure concealment, the block sizes are not disclosed until after recruitment of trial participants is completed. The random allocation schedule is embedded within a secure online web database (REDCap ) which automatically randomises participants to either experimental or control intervention after their DNA results have been returned to the investigator team. A statistician not involved in the recruitment of participants or data collection generates the randomisation schedule and upload it to the trial online database. Researchers randomise participants upon receipt of their PGx test result, immediately prior to generating the antidepressant prescribing report, using the randomisation function in the REDCap database . Allocated intervention is hidden from this researcher using the Hide Randomisation Module v1.0.4 in REDCap. Protocol modification Until 26 August 2021 and the 65th randomised participant, randomisation occurred upon receipt of the study consent form. However, seven of the initial PGx test samples failed genotyping in the laboratory, which then resulted in the withdrawal of four of these participants who did not wish to provide a blood sample for repeat testing. At this point, the decision was made by the trial steering committee on 26 August 2021 to randomise participants only on receipt of complete PGx results, due to concerns of a large number of patients not able to receive the antidepressant prescribing report at all that may lead to an attenuation in the intervention effect. Although there are some pragmatic elements in the design of this trial, we wanted to maximise the chance of demonstrating an effect of the experimental intervention compared to the control intervention. For this aspect of the trial, the design is more explanatory . Any participants who were recruited and randomised prior to this will all be included in the final analysis, under the intention-to-treat principle, regardless of if they received the full intervention. Until 26 August 2021 and the 65th randomised participant, randomisation occurred upon receipt of the study consent form. However, seven of the initial PGx test samples failed genotyping in the laboratory, which then resulted in the withdrawal of four of these participants who did not wish to provide a blood sample for repeat testing. At this point, the decision was made by the trial steering committee on 26 August 2021 to randomise participants only on receipt of complete PGx results, due to concerns of a large number of patients not able to receive the antidepressant prescribing report at all that may lead to an attenuation in the intervention effect. Although there are some pragmatic elements in the design of this trial, we wanted to maximise the chance of demonstrating an effect of the experimental intervention compared to the control intervention. For this aspect of the trial, the design is more explanatory . Any participants who were recruited and randomised prior to this will all be included in the final analysis, under the intention-to-treat principle, regardless of if they received the full intervention. Who will be blinded {17a} Only researchers who randomise participants and generate the antidepressant prescribing reports are unblinded to participants allocated intervention. All others involved in the trial are masked to participants’ study intervention allocation. This includes trial participants, GPs (who act upon the antidepressant prescribing report), other researchers who recruit and follow-up participant questionnaires, all other researchers in the trial steering committee overseeing the conduct and running of the trial and the trial statistician. The trial online database (REDCap ) restricts access to participants’ study intervention allocation for all researchers who do require it, using the user rights options. Masking at the time of trial results, analyses will be maintained by randomly designating an uninformative code to each of the study interventions. The results of the trial will initially be presented to the trial steering committee using the uninformative code to maintain masking and will be revealed after the results have been interpreted. Procedure for unblinding if needed {17b} On trial While on the trial, there will be no unblinding, given the clinical care of participants is always at the discretion of GPs and any antidepressant recommendations after within the Australian Therapeutic Guidelines. On completion of the trial Explicit unblinding of participants will not occur at the completion of the trial. However, the full clinical PGx report is sent directly to GPs at the completion of the participant’s involvement in the trial. Review and discussion of these reports is the responsibility of the GP, as per a regular pathology report. At this stage, it may be possible to determine the participant’s study intervention by looking for discrepancies between the trial and more extensive clinical report. Unblinding of researchers and investigators not involved in the participant recruitment, including the statistician responsible for the analyses, will occur after the primary statistical analyses have been completed and results interpreted. Only researchers who randomise participants and generate the antidepressant prescribing reports are unblinded to participants allocated intervention. All others involved in the trial are masked to participants’ study intervention allocation. This includes trial participants, GPs (who act upon the antidepressant prescribing report), other researchers who recruit and follow-up participant questionnaires, all other researchers in the trial steering committee overseeing the conduct and running of the trial and the trial statistician. The trial online database (REDCap ) restricts access to participants’ study intervention allocation for all researchers who do require it, using the user rights options. Masking at the time of trial results, analyses will be maintained by randomly designating an uninformative code to each of the study interventions. The results of the trial will initially be presented to the trial steering committee using the uninformative code to maintain masking and will be revealed after the results have been interpreted. On trial While on the trial, there will be no unblinding, given the clinical care of participants is always at the discretion of GPs and any antidepressant recommendations after within the Australian Therapeutic Guidelines. On completion of the trial Explicit unblinding of participants will not occur at the completion of the trial. However, the full clinical PGx report is sent directly to GPs at the completion of the participant’s involvement in the trial. Review and discussion of these reports is the responsibility of the GP, as per a regular pathology report. At this stage, it may be possible to determine the participant’s study intervention by looking for discrepancies between the trial and more extensive clinical report. Unblinding of researchers and investigators not involved in the participant recruitment, including the statistician responsible for the analyses, will occur after the primary statistical analyses have been completed and results interpreted. While on the trial, there will be no unblinding, given the clinical care of participants is always at the discretion of GPs and any antidepressant recommendations after within the Australian Therapeutic Guidelines. Explicit unblinding of participants will not occur at the completion of the trial. However, the full clinical PGx report is sent directly to GPs at the completion of the participant’s involvement in the trial. Review and discussion of these reports is the responsibility of the GP, as per a regular pathology report. At this stage, it may be possible to determine the participant’s study intervention by looking for discrepancies between the trial and more extensive clinical report. Unblinding of researchers and investigators not involved in the participant recruitment, including the statistician responsible for the analyses, will occur after the primary statistical analyses have been completed and results interpreted. Plans for assessment and collection of outcomes {18a} Questionnaire measures Questionnaire data from participants is collected using a dedicated REDCap database . At baseline, this is completed by the researcher in person, or via a teletrial video call. Participants are then asked for their preference to complete subsequent questionnaires (4, 8, 12 and 26 weeks) via email (sent on the due date, with a link to the REDCap survey), via a post on hardcopy (sent 5 days prior to the due date) or via phone (where the researcher calls the participant on the due date and enters data directly into the REDCap survey). Demographics Participants’ demographics are collected directly from participants at baseline, including gender, age, language mainly spoken at home, ethnicity, highest level of education, employment status and living arrangements. Additionally, smoking status, alcohol intake and cannabis use are collected. Categories provided for these questions are derived from the Australian census . Medication use Current medications (prescribed and over-the-counter) are collected via self-report from participants at baseline and current antidepressant use (yes/no) at all questionnaire time points. PHQ-9 The Patient Health Questionnaire 9 (PHQ-9) measures the severity of depressive symptoms . The PHQ-9 assesses the nine symptoms of depression, outlined in the Diagnostic and Statistical Manual of Mental Disorders, over the last 2 weeks using a 4-point Likert scale. Total scores calculated by adding the 9 items range between 0 and 27 with cut-points of 5, 10, 15 and 20 indicating mild, moderate, moderately severe and severe depressive symptoms, respectively. The PHQ-9 is a validated, self-reported diagnostic measure in primary care with demonstrated efficacy and sensitivity as an outcome measure for treatment trials with a recommended Reliable Change Index . FIBSER The FIBSER (Frequency, Intensity, Burden of Side Effects Rating) is a 3-item validated self-reported symptom checklist . Participants rate how often they experience side effects they attribute to their medication and how severe these side effects are and the degree to which they interfere with daily functioning. Each domain (frequency, intensity and burden) is rated on a 7-point scale and assessed separately. MARS-5 The Medication Adherence Report Scale (MARS-5) measures patient adherence to antidepressant prescribing . It is a 5-item self-reported scale, with each item indicating elements of non-adherence rated as never (5), rarely (4), sometimes (3), often (2) and always (1). Scores are summed to give a total score ranging between 5 and 25, with higher scores indicating higher levels of reported adherence. The MARS-5 is a validated scale and has been shown to have good reliability and validity across health conditions . AQoL-4D The Assessment of Quality of Life 4 Dimension (AQoL-4D) is a 12-item scale that measures four domains of health-related quality of life: independent living, mental health, relationships and senses . The AQoL-4D is validated and is scored using a preference-weighted scoring algorithm to derive a utility score between 0 and 1 used to calculate quality-adjusted life years (QALYs) for cost-effectiveness analyses. COVID-19 impact scale The PRESIDE Trial commenced during the COVID-19 pandemic (first participant recruited in May 2021) and some of the participant recruitment occurred during strict lockdown restrictions in Victoria, Australia. The impact of the pandemic and associated public health interventions on the mental health of the population is now well documented . Given some participants were recruited during lockdowns and some when public movement and social interaction were restricted, we hypothesised that this could potentially impact the proportion of potential participants who were eligible for the study (i.e. scored ≥ 10 on the PHQ-9 scale), or the nature of depressive symptoms in those eligible (i.e. situational-based depression versus long-standing, potentially refractory depression). It could also affect GP and patient decisions to take antidepressants or use psychological therapies instead. Therefore, on 25 November 2021, it was proposed that an additional measure should be collected to determine the perceived impact of the pandemic and lockdowns on the mental health of trial participants and the effect it may have had on their depressive symptoms and their treatment. The COV19 – Impact on Quality of Life measure was selected as it asks participants to reflect on the impact COVID-19 has had on their mental health. It is a six-item scale that has been validated in a European general population and clinical sample. Each item is answered on a scale of strongly disagree (1) to strongly agree (5) regarding the impact that the spread of coronavirus has had on aspects of participants’ mental health. Scores are averaged to determine a total score. A higher score indicates a greater perceived impact of the pandemic on one’s quality of life. This measure was added to the 26-week questionnaire, as at the time of making this decision (25 November 2021), no participants had reached this time point, facilitating the collection of these data for the entire study cohort. Resource use questionnaire A fit-for-purpose resource use questionnaire, used in our previous studies , has been included in the study measures. This questionnaire covers access to medical and mental health professionals; self-help measures, such as the use of mobile apps or internet support; and impact of mental health symptoms on paid and unpaid work and presenteeism. Administrative health service use data collection Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) data collection MBS and PBS data are administrative datasets that contain information on services and medications that qualify for a benefit under the Australian Health Insurance Act and for which a claim has been processed. These datasets for study participants will be requested from Services Australia for all participants providing consent for the period of 1 year prior to and 1 year after their consent date. This data includes services provided by doctors and allied health professionals (i.e. general practitioners, psychiatrists, psychologists), diagnostic tests (i.e. pathology and imaging) and prescription medications dispensed. GP record audit Participants’ GP records are audited by researchers blinded to the trial allocation of the participants for all consultations where their mental health was discussed from the date of consent to 26 weeks after the GP’s receipt of the antidepressant prescribing report (the final endpoint of the study). The audit collects the date of consultations; any mental health diagnoses; discussion of the antidepressant prescribing report; discussion of the use of antidepressants, including current antidepressants and their effect on symptoms and side effects; discussions of commencement or change of antidepressants; and final prescription of antidepressants. These data are entered by researchers into the study REDCap database while at general practices. Process evaluation The process evaluation, based on the logic model of the proposed effect of the intervention on the outcome, aims to explore the barriers and facilitators of pharmacogenomic testing for antidepressant use in primary care. This evaluation will also explore the underlying assumptions of the proposed logic model. It will be conducted using data on consultations between the participant and their GP regarding their mental health in the study follow-up period, as well as semi-structured interviews for qualitative responses from general practitioners and participants enrolled in the study through individual interviews. The interviews will be conducted with 15–20 GPs and 15–20 participants. A purposive sample of participants is being recruited according to age, gender, time point in the trial and whether they were newly prescribed an antidepressant within the trial or had their treatment altered. Participants do not need to have finished their trial period to participate as they are blinded to trial group allocation. We are also interviewing a purposive sample of GPs whose patients are in the trial, to explore their perspectives on the use of pharmacogenomic testing to inform their antidepressant prescribing. This covers their understanding of the test, preferences for reporting and recommendations, their use of the trial prescribing reports and impact on their prescribing decisions, potential impact on the therapeutic alliance with their patients and future models of implementation into routine practice. GPs must not have active participants in their trial period to participate, all the pharmacogenomic test results must have been obtained by the GP for their patients. Consent to be contacted for these interviews is indicated in the study consent form and additional recorded verbal consent for the interview is recorded. Interviews are undertaken either in person or via videoconferencing software. All interviews are transcribed by an automated programme with researcher review, or by a professional transcription service. Interviews are informed by a topic guide (i.e. interview schedule) based on relevant literature and revised based on emerging findings from the iterative analytic process. Interviews are audio recorded (if videoconferencing software is used, video is used during the interviews so that the interviewer can respond to non-verbal cues, but video recordings are not stored). Interviews will be analysed using thematic analysis. Themes arising from the interviews will be organised and coded using a qualitative data analysis software (e.g. NVivo ). At least two researchers will be involved in the coding and analysis. Plans to promote participant retention and complete follow-up {18b} During the baseline appointment, participants are asked for a preferred format for their follow-up questionnaire (email/post/telephone). In the case of participants who do not complete the follow-up questionnaire at 4, 8, 12 and 26 weeks, a further three attempts are made to contact them via phone, email or SMS. If no response is obtained within 2 weeks (for the 4- and 8-week questionnaires) or 5 weeks (for the 12- and 26-week questionnaires), the participant is considered a non-responder for that questionnaire. Participants that do not respond to a specific questionnaire are still invited to complete their subsequent follow-up questionnaires, unless they explicitly have withdrawn from the trial. GP and participant withdrawal Participants and GPs can withdraw from the study at any time without giving a reason, as stated to them prior to informed consent and within the consent form. Participants can withdraw from further contact from the trial team (i.e. from questionnaire follow-up). They also have the option to withdraw any unprocessed data at the time of withdrawal. If a participant withdraws prior to the saliva sample being analysed, their DNA sample is destroyed and they do not receive the full PGx report at the conclusion of their 26 weeks of participation. Participants who withdraw after their sample has been analysed and are randomised have their full PGx report sent to their GP, unless they opt not to, or they opt to remove all their unprocessed data. If participants withdraw from contact only, their objective health service use data is still collected as this does not require contact with the participant. If participants opt to withdraw their data, all their unprocessed data is destroyed. Data management {19} Data are collected, managed and stored according to the study’s data management plan, developed in accordance with the University of Melbourne’s (UoM) Research Data Management Policy and Research Code of Conduct. A REDCap online database is used to collect and store data, only accessible by authorised and trained researchers. REDCap is a password-protected online database that has mandatory data entry fields to reduce missing data, range checks for the data values and branching questions . Before randomisation, REDCap provides a pop-up for researchers to double-check data entry of the variables used for stratifying randomisation. All paper-based data is entered directly into REDCap by researchers blinded to arm allocation and these are stored securely in an office within UoM offices, under the responsibility of the study principal investigator (JE) in a locked file cabinet. All data is only accessible to researchers listed on ethical approvals. Confidentiality {27} Prior to consent, any identifiable information about potential participants does not leave their general practice and is not retained by researchers. Participant confidentiality is strictly held in trust by the principal and study investigators, research staff and the sponsoring institution and their agents. This confidentiality is extended to cover testing of biological samples and genetic tests in addition to the clinical information relating to participants. To preserve confidentiality and reduce the risk of identification during the collection, analyses and storage of data, the following are undertaken: Minimal sensitive and health information is collected on participants. The data collected is limited to that required to address the primary and secondary objectives. Participant identifiers are stored securely with restricted access using REDCap’s permission control functionality. Where possible, participant data is identified through the use of a unique participant study ID assigned to the participant (“re-identifiable”). The study coordinator is responsible for the management of REDCap’s permission control functionality and restricting access to participant identifiers to those who a directly involved in participant follow-up. The trial statistician conducting the analyses will be provided with anonymised data using a unique participant trial ID. All DNA sample specimens and associated forms are transported to the testing laboratory through Melbourne Pathology (Sonic Healthcare), using a courier. Upon receipt by Melbourne Pathology, a unique identifier (episode ID) is allocated to each sample. This episode ID, along with the participant trial ID, then accompanies all data through the genotyping and phenotyping process, including return to researchers at UoM. These two unique identifiers then allow for reidentification of the data by UoM researchers, without the need to send personal identifiers. All data is managed according to UoM’s Research Data Management Policy and Research Code of Conduct, including security protocols such as two-factor authentication and storage on secure servers. This research involves the linkage of data sets with the consent of participants. Participants are advised that identifying data is collected and provided to respective government agencies and departments to facilitate linkage. Participants provide separate written informed consent for the team to access MBS and PBS data. The extent to which identifying information is shared to each agency and department is outlined in the consent process. Plans for collection, laboratory evaluation and storage of biological specimens for genetic or molecular analysis in this trial/future use {33} DNA is collected with ORAcollect®-DNA OCR100 saliva collection kits (DNA Genotek, Ottawa, ON, Canada). DNA samples are logged on the REDCap database by the UoM team and then sent to Melbourne Pathology (Sonic Healthcare Australia Pathology) by courier. Sample management at Sonic Healthcare is according to their standard approved protocols, given the clinical nature of the sample and test (NATA accredited). DNA samples are disposed of by Sonic Healthcare’s standard operating procedures. Samples are not returned to UoM for storage. If the original DNA saliva sample does not yield enough quantity or quality of DNA, then another DNA sample is required. This second sample is a blood sample, given the much smaller chance of insufficient DNA. Questionnaire measures Questionnaire data from participants is collected using a dedicated REDCap database . At baseline, this is completed by the researcher in person, or via a teletrial video call. Participants are then asked for their preference to complete subsequent questionnaires (4, 8, 12 and 26 weeks) via email (sent on the due date, with a link to the REDCap survey), via a post on hardcopy (sent 5 days prior to the due date) or via phone (where the researcher calls the participant on the due date and enters data directly into the REDCap survey). Demographics Participants’ demographics are collected directly from participants at baseline, including gender, age, language mainly spoken at home, ethnicity, highest level of education, employment status and living arrangements. Additionally, smoking status, alcohol intake and cannabis use are collected. Categories provided for these questions are derived from the Australian census . Medication use Current medications (prescribed and over-the-counter) are collected via self-report from participants at baseline and current antidepressant use (yes/no) at all questionnaire time points. PHQ-9 The Patient Health Questionnaire 9 (PHQ-9) measures the severity of depressive symptoms . The PHQ-9 assesses the nine symptoms of depression, outlined in the Diagnostic and Statistical Manual of Mental Disorders, over the last 2 weeks using a 4-point Likert scale. Total scores calculated by adding the 9 items range between 0 and 27 with cut-points of 5, 10, 15 and 20 indicating mild, moderate, moderately severe and severe depressive symptoms, respectively. The PHQ-9 is a validated, self-reported diagnostic measure in primary care with demonstrated efficacy and sensitivity as an outcome measure for treatment trials with a recommended Reliable Change Index . FIBSER The FIBSER (Frequency, Intensity, Burden of Side Effects Rating) is a 3-item validated self-reported symptom checklist . Participants rate how often they experience side effects they attribute to their medication and how severe these side effects are and the degree to which they interfere with daily functioning. Each domain (frequency, intensity and burden) is rated on a 7-point scale and assessed separately. MARS-5 The Medication Adherence Report Scale (MARS-5) measures patient adherence to antidepressant prescribing . It is a 5-item self-reported scale, with each item indicating elements of non-adherence rated as never (5), rarely (4), sometimes (3), often (2) and always (1). Scores are summed to give a total score ranging between 5 and 25, with higher scores indicating higher levels of reported adherence. The MARS-5 is a validated scale and has been shown to have good reliability and validity across health conditions . AQoL-4D The Assessment of Quality of Life 4 Dimension (AQoL-4D) is a 12-item scale that measures four domains of health-related quality of life: independent living, mental health, relationships and senses . The AQoL-4D is validated and is scored using a preference-weighted scoring algorithm to derive a utility score between 0 and 1 used to calculate quality-adjusted life years (QALYs) for cost-effectiveness analyses. COVID-19 impact scale The PRESIDE Trial commenced during the COVID-19 pandemic (first participant recruited in May 2021) and some of the participant recruitment occurred during strict lockdown restrictions in Victoria, Australia. The impact of the pandemic and associated public health interventions on the mental health of the population is now well documented . Given some participants were recruited during lockdowns and some when public movement and social interaction were restricted, we hypothesised that this could potentially impact the proportion of potential participants who were eligible for the study (i.e. scored ≥ 10 on the PHQ-9 scale), or the nature of depressive symptoms in those eligible (i.e. situational-based depression versus long-standing, potentially refractory depression). It could also affect GP and patient decisions to take antidepressants or use psychological therapies instead. Therefore, on 25 November 2021, it was proposed that an additional measure should be collected to determine the perceived impact of the pandemic and lockdowns on the mental health of trial participants and the effect it may have had on their depressive symptoms and their treatment. The COV19 – Impact on Quality of Life measure was selected as it asks participants to reflect on the impact COVID-19 has had on their mental health. It is a six-item scale that has been validated in a European general population and clinical sample. Each item is answered on a scale of strongly disagree (1) to strongly agree (5) regarding the impact that the spread of coronavirus has had on aspects of participants’ mental health. Scores are averaged to determine a total score. A higher score indicates a greater perceived impact of the pandemic on one’s quality of life. This measure was added to the 26-week questionnaire, as at the time of making this decision (25 November 2021), no participants had reached this time point, facilitating the collection of these data for the entire study cohort. Resource use questionnaire A fit-for-purpose resource use questionnaire, used in our previous studies , has been included in the study measures. This questionnaire covers access to medical and mental health professionals; self-help measures, such as the use of mobile apps or internet support; and impact of mental health symptoms on paid and unpaid work and presenteeism. Questionnaire data from participants is collected using a dedicated REDCap database . At baseline, this is completed by the researcher in person, or via a teletrial video call. Participants are then asked for their preference to complete subsequent questionnaires (4, 8, 12 and 26 weeks) via email (sent on the due date, with a link to the REDCap survey), via a post on hardcopy (sent 5 days prior to the due date) or via phone (where the researcher calls the participant on the due date and enters data directly into the REDCap survey). Demographics Participants’ demographics are collected directly from participants at baseline, including gender, age, language mainly spoken at home, ethnicity, highest level of education, employment status and living arrangements. Additionally, smoking status, alcohol intake and cannabis use are collected. Categories provided for these questions are derived from the Australian census . Medication use Current medications (prescribed and over-the-counter) are collected via self-report from participants at baseline and current antidepressant use (yes/no) at all questionnaire time points. PHQ-9 The Patient Health Questionnaire 9 (PHQ-9) measures the severity of depressive symptoms . The PHQ-9 assesses the nine symptoms of depression, outlined in the Diagnostic and Statistical Manual of Mental Disorders, over the last 2 weeks using a 4-point Likert scale. Total scores calculated by adding the 9 items range between 0 and 27 with cut-points of 5, 10, 15 and 20 indicating mild, moderate, moderately severe and severe depressive symptoms, respectively. The PHQ-9 is a validated, self-reported diagnostic measure in primary care with demonstrated efficacy and sensitivity as an outcome measure for treatment trials with a recommended Reliable Change Index . FIBSER The FIBSER (Frequency, Intensity, Burden of Side Effects Rating) is a 3-item validated self-reported symptom checklist . Participants rate how often they experience side effects they attribute to their medication and how severe these side effects are and the degree to which they interfere with daily functioning. Each domain (frequency, intensity and burden) is rated on a 7-point scale and assessed separately. MARS-5 The Medication Adherence Report Scale (MARS-5) measures patient adherence to antidepressant prescribing . It is a 5-item self-reported scale, with each item indicating elements of non-adherence rated as never (5), rarely (4), sometimes (3), often (2) and always (1). Scores are summed to give a total score ranging between 5 and 25, with higher scores indicating higher levels of reported adherence. The MARS-5 is a validated scale and has been shown to have good reliability and validity across health conditions . AQoL-4D The Assessment of Quality of Life 4 Dimension (AQoL-4D) is a 12-item scale that measures four domains of health-related quality of life: independent living, mental health, relationships and senses . The AQoL-4D is validated and is scored using a preference-weighted scoring algorithm to derive a utility score between 0 and 1 used to calculate quality-adjusted life years (QALYs) for cost-effectiveness analyses. COVID-19 impact scale The PRESIDE Trial commenced during the COVID-19 pandemic (first participant recruited in May 2021) and some of the participant recruitment occurred during strict lockdown restrictions in Victoria, Australia. The impact of the pandemic and associated public health interventions on the mental health of the population is now well documented . Given some participants were recruited during lockdowns and some when public movement and social interaction were restricted, we hypothesised that this could potentially impact the proportion of potential participants who were eligible for the study (i.e. scored ≥ 10 on the PHQ-9 scale), or the nature of depressive symptoms in those eligible (i.e. situational-based depression versus long-standing, potentially refractory depression). It could also affect GP and patient decisions to take antidepressants or use psychological therapies instead. Therefore, on 25 November 2021, it was proposed that an additional measure should be collected to determine the perceived impact of the pandemic and lockdowns on the mental health of trial participants and the effect it may have had on their depressive symptoms and their treatment. The COV19 – Impact on Quality of Life measure was selected as it asks participants to reflect on the impact COVID-19 has had on their mental health. It is a six-item scale that has been validated in a European general population and clinical sample. Each item is answered on a scale of strongly disagree (1) to strongly agree (5) regarding the impact that the spread of coronavirus has had on aspects of participants’ mental health. Scores are averaged to determine a total score. A higher score indicates a greater perceived impact of the pandemic on one’s quality of life. This measure was added to the 26-week questionnaire, as at the time of making this decision (25 November 2021), no participants had reached this time point, facilitating the collection of these data for the entire study cohort. Resource use questionnaire A fit-for-purpose resource use questionnaire, used in our previous studies , has been included in the study measures. This questionnaire covers access to medical and mental health professionals; self-help measures, such as the use of mobile apps or internet support; and impact of mental health symptoms on paid and unpaid work and presenteeism. Participants’ demographics are collected directly from participants at baseline, including gender, age, language mainly spoken at home, ethnicity, highest level of education, employment status and living arrangements. Additionally, smoking status, alcohol intake and cannabis use are collected. Categories provided for these questions are derived from the Australian census . Current medications (prescribed and over-the-counter) are collected via self-report from participants at baseline and current antidepressant use (yes/no) at all questionnaire time points. The Patient Health Questionnaire 9 (PHQ-9) measures the severity of depressive symptoms . The PHQ-9 assesses the nine symptoms of depression, outlined in the Diagnostic and Statistical Manual of Mental Disorders, over the last 2 weeks using a 4-point Likert scale. Total scores calculated by adding the 9 items range between 0 and 27 with cut-points of 5, 10, 15 and 20 indicating mild, moderate, moderately severe and severe depressive symptoms, respectively. The PHQ-9 is a validated, self-reported diagnostic measure in primary care with demonstrated efficacy and sensitivity as an outcome measure for treatment trials with a recommended Reliable Change Index . The FIBSER (Frequency, Intensity, Burden of Side Effects Rating) is a 3-item validated self-reported symptom checklist . Participants rate how often they experience side effects they attribute to their medication and how severe these side effects are and the degree to which they interfere with daily functioning. Each domain (frequency, intensity and burden) is rated on a 7-point scale and assessed separately. The Medication Adherence Report Scale (MARS-5) measures patient adherence to antidepressant prescribing . It is a 5-item self-reported scale, with each item indicating elements of non-adherence rated as never (5), rarely (4), sometimes (3), often (2) and always (1). Scores are summed to give a total score ranging between 5 and 25, with higher scores indicating higher levels of reported adherence. The MARS-5 is a validated scale and has been shown to have good reliability and validity across health conditions . The Assessment of Quality of Life 4 Dimension (AQoL-4D) is a 12-item scale that measures four domains of health-related quality of life: independent living, mental health, relationships and senses . The AQoL-4D is validated and is scored using a preference-weighted scoring algorithm to derive a utility score between 0 and 1 used to calculate quality-adjusted life years (QALYs) for cost-effectiveness analyses. The PRESIDE Trial commenced during the COVID-19 pandemic (first participant recruited in May 2021) and some of the participant recruitment occurred during strict lockdown restrictions in Victoria, Australia. The impact of the pandemic and associated public health interventions on the mental health of the population is now well documented . Given some participants were recruited during lockdowns and some when public movement and social interaction were restricted, we hypothesised that this could potentially impact the proportion of potential participants who were eligible for the study (i.e. scored ≥ 10 on the PHQ-9 scale), or the nature of depressive symptoms in those eligible (i.e. situational-based depression versus long-standing, potentially refractory depression). It could also affect GP and patient decisions to take antidepressants or use psychological therapies instead. Therefore, on 25 November 2021, it was proposed that an additional measure should be collected to determine the perceived impact of the pandemic and lockdowns on the mental health of trial participants and the effect it may have had on their depressive symptoms and their treatment. The COV19 – Impact on Quality of Life measure was selected as it asks participants to reflect on the impact COVID-19 has had on their mental health. It is a six-item scale that has been validated in a European general population and clinical sample. Each item is answered on a scale of strongly disagree (1) to strongly agree (5) regarding the impact that the spread of coronavirus has had on aspects of participants’ mental health. Scores are averaged to determine a total score. A higher score indicates a greater perceived impact of the pandemic on one’s quality of life. This measure was added to the 26-week questionnaire, as at the time of making this decision (25 November 2021), no participants had reached this time point, facilitating the collection of these data for the entire study cohort. A fit-for-purpose resource use questionnaire, used in our previous studies , has been included in the study measures. This questionnaire covers access to medical and mental health professionals; self-help measures, such as the use of mobile apps or internet support; and impact of mental health symptoms on paid and unpaid work and presenteeism. Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) data collection MBS and PBS data are administrative datasets that contain information on services and medications that qualify for a benefit under the Australian Health Insurance Act and for which a claim has been processed. These datasets for study participants will be requested from Services Australia for all participants providing consent for the period of 1 year prior to and 1 year after their consent date. This data includes services provided by doctors and allied health professionals (i.e. general practitioners, psychiatrists, psychologists), diagnostic tests (i.e. pathology and imaging) and prescription medications dispensed. GP record audit Participants’ GP records are audited by researchers blinded to the trial allocation of the participants for all consultations where their mental health was discussed from the date of consent to 26 weeks after the GP’s receipt of the antidepressant prescribing report (the final endpoint of the study). The audit collects the date of consultations; any mental health diagnoses; discussion of the antidepressant prescribing report; discussion of the use of antidepressants, including current antidepressants and their effect on symptoms and side effects; discussions of commencement or change of antidepressants; and final prescription of antidepressants. These data are entered by researchers into the study REDCap database while at general practices. Process evaluation The process evaluation, based on the logic model of the proposed effect of the intervention on the outcome, aims to explore the barriers and facilitators of pharmacogenomic testing for antidepressant use in primary care. This evaluation will also explore the underlying assumptions of the proposed logic model. It will be conducted using data on consultations between the participant and their GP regarding their mental health in the study follow-up period, as well as semi-structured interviews for qualitative responses from general practitioners and participants enrolled in the study through individual interviews. The interviews will be conducted with 15–20 GPs and 15–20 participants. A purposive sample of participants is being recruited according to age, gender, time point in the trial and whether they were newly prescribed an antidepressant within the trial or had their treatment altered. Participants do not need to have finished their trial period to participate as they are blinded to trial group allocation. We are also interviewing a purposive sample of GPs whose patients are in the trial, to explore their perspectives on the use of pharmacogenomic testing to inform their antidepressant prescribing. This covers their understanding of the test, preferences for reporting and recommendations, their use of the trial prescribing reports and impact on their prescribing decisions, potential impact on the therapeutic alliance with their patients and future models of implementation into routine practice. GPs must not have active participants in their trial period to participate, all the pharmacogenomic test results must have been obtained by the GP for their patients. Consent to be contacted for these interviews is indicated in the study consent form and additional recorded verbal consent for the interview is recorded. Interviews are undertaken either in person or via videoconferencing software. All interviews are transcribed by an automated programme with researcher review, or by a professional transcription service. Interviews are informed by a topic guide (i.e. interview schedule) based on relevant literature and revised based on emerging findings from the iterative analytic process. Interviews are audio recorded (if videoconferencing software is used, video is used during the interviews so that the interviewer can respond to non-verbal cues, but video recordings are not stored). Interviews will be analysed using thematic analysis. Themes arising from the interviews will be organised and coded using a qualitative data analysis software (e.g. NVivo ). At least two researchers will be involved in the coding and analysis. MBS and PBS data are administrative datasets that contain information on services and medications that qualify for a benefit under the Australian Health Insurance Act and for which a claim has been processed. These datasets for study participants will be requested from Services Australia for all participants providing consent for the period of 1 year prior to and 1 year after their consent date. This data includes services provided by doctors and allied health professionals (i.e. general practitioners, psychiatrists, psychologists), diagnostic tests (i.e. pathology and imaging) and prescription medications dispensed. Participants’ GP records are audited by researchers blinded to the trial allocation of the participants for all consultations where their mental health was discussed from the date of consent to 26 weeks after the GP’s receipt of the antidepressant prescribing report (the final endpoint of the study). The audit collects the date of consultations; any mental health diagnoses; discussion of the antidepressant prescribing report; discussion of the use of antidepressants, including current antidepressants and their effect on symptoms and side effects; discussions of commencement or change of antidepressants; and final prescription of antidepressants. These data are entered by researchers into the study REDCap database while at general practices. The process evaluation, based on the logic model of the proposed effect of the intervention on the outcome, aims to explore the barriers and facilitators of pharmacogenomic testing for antidepressant use in primary care. This evaluation will also explore the underlying assumptions of the proposed logic model. It will be conducted using data on consultations between the participant and their GP regarding their mental health in the study follow-up period, as well as semi-structured interviews for qualitative responses from general practitioners and participants enrolled in the study through individual interviews. The interviews will be conducted with 15–20 GPs and 15–20 participants. A purposive sample of participants is being recruited according to age, gender, time point in the trial and whether they were newly prescribed an antidepressant within the trial or had their treatment altered. Participants do not need to have finished their trial period to participate as they are blinded to trial group allocation. We are also interviewing a purposive sample of GPs whose patients are in the trial, to explore their perspectives on the use of pharmacogenomic testing to inform their antidepressant prescribing. This covers their understanding of the test, preferences for reporting and recommendations, their use of the trial prescribing reports and impact on their prescribing decisions, potential impact on the therapeutic alliance with their patients and future models of implementation into routine practice. GPs must not have active participants in their trial period to participate, all the pharmacogenomic test results must have been obtained by the GP for their patients. Consent to be contacted for these interviews is indicated in the study consent form and additional recorded verbal consent for the interview is recorded. Interviews are undertaken either in person or via videoconferencing software. All interviews are transcribed by an automated programme with researcher review, or by a professional transcription service. Interviews are informed by a topic guide (i.e. interview schedule) based on relevant literature and revised based on emerging findings from the iterative analytic process. Interviews are audio recorded (if videoconferencing software is used, video is used during the interviews so that the interviewer can respond to non-verbal cues, but video recordings are not stored). Interviews will be analysed using thematic analysis. Themes arising from the interviews will be organised and coded using a qualitative data analysis software (e.g. NVivo ). At least two researchers will be involved in the coding and analysis. During the baseline appointment, participants are asked for a preferred format for their follow-up questionnaire (email/post/telephone). In the case of participants who do not complete the follow-up questionnaire at 4, 8, 12 and 26 weeks, a further three attempts are made to contact them via phone, email or SMS. If no response is obtained within 2 weeks (for the 4- and 8-week questionnaires) or 5 weeks (for the 12- and 26-week questionnaires), the participant is considered a non-responder for that questionnaire. Participants that do not respond to a specific questionnaire are still invited to complete their subsequent follow-up questionnaires, unless they explicitly have withdrawn from the trial. Participants and GPs can withdraw from the study at any time without giving a reason, as stated to them prior to informed consent and within the consent form. Participants can withdraw from further contact from the trial team (i.e. from questionnaire follow-up). They also have the option to withdraw any unprocessed data at the time of withdrawal. If a participant withdraws prior to the saliva sample being analysed, their DNA sample is destroyed and they do not receive the full PGx report at the conclusion of their 26 weeks of participation. Participants who withdraw after their sample has been analysed and are randomised have their full PGx report sent to their GP, unless they opt not to, or they opt to remove all their unprocessed data. If participants withdraw from contact only, their objective health service use data is still collected as this does not require contact with the participant. If participants opt to withdraw their data, all their unprocessed data is destroyed. Data are collected, managed and stored according to the study’s data management plan, developed in accordance with the University of Melbourne’s (UoM) Research Data Management Policy and Research Code of Conduct. A REDCap online database is used to collect and store data, only accessible by authorised and trained researchers. REDCap is a password-protected online database that has mandatory data entry fields to reduce missing data, range checks for the data values and branching questions . Before randomisation, REDCap provides a pop-up for researchers to double-check data entry of the variables used for stratifying randomisation. All paper-based data is entered directly into REDCap by researchers blinded to arm allocation and these are stored securely in an office within UoM offices, under the responsibility of the study principal investigator (JE) in a locked file cabinet. All data is only accessible to researchers listed on ethical approvals. Prior to consent, any identifiable information about potential participants does not leave their general practice and is not retained by researchers. Participant confidentiality is strictly held in trust by the principal and study investigators, research staff and the sponsoring institution and their agents. This confidentiality is extended to cover testing of biological samples and genetic tests in addition to the clinical information relating to participants. To preserve confidentiality and reduce the risk of identification during the collection, analyses and storage of data, the following are undertaken: Minimal sensitive and health information is collected on participants. The data collected is limited to that required to address the primary and secondary objectives. Participant identifiers are stored securely with restricted access using REDCap’s permission control functionality. Where possible, participant data is identified through the use of a unique participant study ID assigned to the participant (“re-identifiable”). The study coordinator is responsible for the management of REDCap’s permission control functionality and restricting access to participant identifiers to those who a directly involved in participant follow-up. The trial statistician conducting the analyses will be provided with anonymised data using a unique participant trial ID. All DNA sample specimens and associated forms are transported to the testing laboratory through Melbourne Pathology (Sonic Healthcare), using a courier. Upon receipt by Melbourne Pathology, a unique identifier (episode ID) is allocated to each sample. This episode ID, along with the participant trial ID, then accompanies all data through the genotyping and phenotyping process, including return to researchers at UoM. These two unique identifiers then allow for reidentification of the data by UoM researchers, without the need to send personal identifiers. All data is managed according to UoM’s Research Data Management Policy and Research Code of Conduct, including security protocols such as two-factor authentication and storage on secure servers. This research involves the linkage of data sets with the consent of participants. Participants are advised that identifying data is collected and provided to respective government agencies and departments to facilitate linkage. Participants provide separate written informed consent for the team to access MBS and PBS data. The extent to which identifying information is shared to each agency and department is outlined in the consent process. DNA is collected with ORAcollect®-DNA OCR100 saliva collection kits (DNA Genotek, Ottawa, ON, Canada). DNA samples are logged on the REDCap database by the UoM team and then sent to Melbourne Pathology (Sonic Healthcare Australia Pathology) by courier. Sample management at Sonic Healthcare is according to their standard approved protocols, given the clinical nature of the sample and test (NATA accredited). DNA samples are disposed of by Sonic Healthcare’s standard operating procedures. Samples are not returned to UoM for storage. If the original DNA saliva sample does not yield enough quantity or quality of DNA, then another DNA sample is required. This second sample is a blood sample, given the much smaller chance of insufficient DNA. Statistical methods for primary and secondary outcomes {20a} Descriptive statistics will be used to summarise the baseline characteristics of participants by the experimental and control interventions. Primary analyses will include all randomised participants using an intention-to-treat principle, where they will be analysed in the study intervention that they were assigned, regardless of whether they received all, part, or none of the intended intervention. For the primary outcome, a linear mixed-effects model using restricted maximum likelihood with random intercepts for individuals will be used to estimate the mean difference between experimental and control interventions in the mean change of depressive symptoms from baseline at each follow-up time point. The model will adjust for general practice, antidepressant use at baseline, ancestry (if imbalanced as CYP2C19 and CYP2D6 , phenotype frequency varies by ancestry) and time (4, 8, 12 and 26 weeks), with a two-way interaction between study intervention and time. The model will also adjust for baseline depressive symptoms which will be constrained to be equal between the two study interventions. Estimates of the intervention effect will be reported as the mean difference between the experimental and control interventions, with 95% confidence intervals and p -value. There will be no adjustment for handling the multiplicity of testing and control for the final type I error rate. The same approach will be undertaken using a linear mixed-effects model between experimental and control interventions for continuous secondary endpoints. Similar regression analyses appropriate to the data type (e.g. logistic for binary, Poisson for count data) will be performed on other secondary endpoints. Analyses for the secondary endpoints will be described in detail in a statistical analysis plan (SAP), which will be made available on the trial registry prior to the primary analysis. Analyses will be conducted in Stata 17.0 and R . Economic evaluation The economic evaluation will be undertaken from the health sector and partial societal perspectives. The health sector perspective includes costs borne by the government as a third-party payer in addition to out-of-pocket costs incurred by patients when accessing health care. This includes the estimated cost to deliver the PGx-informed antidepressant prescribing combined with the cost of additional health services used by participants over the time period of the trial. The partial societal perspective adds the cost of lost productivity (absenteeism and presenteeism) for study participants to health sector costs. Incremental cost-effectiveness ratios (ICERs) will be calculated as the difference in the average total cost between the randomised arms, divided by the difference in the average outcome. The outcomes used in these analyses will include the primary outcome of the PHQ-9 score and QALYs calculated from AQoL-4D utility values using the area under the curve method. ICERs using other secondary study outcomes (e.g. cost per remitted case) will also be explored. Confidence intervals around ICERs will be calculated using a nonparametric bootstrap procedure, with 1000 iterations to reflect sampling uncertainty. The bootstrapped ICERs and the CIs will be graphically represented on cost-effectiveness planes. A cost-effectiveness plane is a plot of the 1000 bootstrapped incremental costs and outcomes across four quadrants. Acceptability curves will be used to graphically present the proportion of bootstrapped iterations falling below a specific willingness to pay threshold. The Productivity Commissions range of willingness to pay thresholds will be used to assess cost-effectiveness . Ratios under $33,000/QALY are deemed very cost-effective, between $33,000 and $64,000 per QALY gained cost-effective and between $64,000 and $96,000 per QALY gained marginally cost. Ratios greater than $96,000 per QALY gained are not considered cost-effective. Sensitivity analyses will be used to determine the impact of changes to important study parameters (e.g. unit cost price variation including the cost of genotyping in this trial). A modelled budget impact analysis using the results of this trial will be undertaken to estimate the costs of implementing the PGx-informed antidepressant prescribing at a state or national level. Interim analyses {21b} No interim analyses are planned. Methods for additional analyses (e.g. subgroup analyses) {20b} Sensitivity analyses will also test the robustness of the result to variations in the underlying assumptions and inputs to the health economic analysis. Further supplementary analyses, including sensitivity analyses and pre-planned sub-group analyses, will be described in the SAP. Methods in analysis to handle protocol non-adherence and any statistical methods to handle missing data {20c} Details of the compliance-adjusted analysis and appropriate methods for dealing with missing endpoint data will also be provided in the SAP. Plans to give access to the full protocol, participant-level data and statistical code {31cI} To assist with reproducible research, the full protocol, non-identifiable participant-level data and statistical code will be made available to external researchers upon reasonable request. The trial steering committee will manage external requests for these materials. Descriptive statistics will be used to summarise the baseline characteristics of participants by the experimental and control interventions. Primary analyses will include all randomised participants using an intention-to-treat principle, where they will be analysed in the study intervention that they were assigned, regardless of whether they received all, part, or none of the intended intervention. For the primary outcome, a linear mixed-effects model using restricted maximum likelihood with random intercepts for individuals will be used to estimate the mean difference between experimental and control interventions in the mean change of depressive symptoms from baseline at each follow-up time point. The model will adjust for general practice, antidepressant use at baseline, ancestry (if imbalanced as CYP2C19 and CYP2D6 , phenotype frequency varies by ancestry) and time (4, 8, 12 and 26 weeks), with a two-way interaction between study intervention and time. The model will also adjust for baseline depressive symptoms which will be constrained to be equal between the two study interventions. Estimates of the intervention effect will be reported as the mean difference between the experimental and control interventions, with 95% confidence intervals and p -value. There will be no adjustment for handling the multiplicity of testing and control for the final type I error rate. The same approach will be undertaken using a linear mixed-effects model between experimental and control interventions for continuous secondary endpoints. Similar regression analyses appropriate to the data type (e.g. logistic for binary, Poisson for count data) will be performed on other secondary endpoints. Analyses for the secondary endpoints will be described in detail in a statistical analysis plan (SAP), which will be made available on the trial registry prior to the primary analysis. Analyses will be conducted in Stata 17.0 and R . Economic evaluation The economic evaluation will be undertaken from the health sector and partial societal perspectives. The health sector perspective includes costs borne by the government as a third-party payer in addition to out-of-pocket costs incurred by patients when accessing health care. This includes the estimated cost to deliver the PGx-informed antidepressant prescribing combined with the cost of additional health services used by participants over the time period of the trial. The partial societal perspective adds the cost of lost productivity (absenteeism and presenteeism) for study participants to health sector costs. Incremental cost-effectiveness ratios (ICERs) will be calculated as the difference in the average total cost between the randomised arms, divided by the difference in the average outcome. The outcomes used in these analyses will include the primary outcome of the PHQ-9 score and QALYs calculated from AQoL-4D utility values using the area under the curve method. ICERs using other secondary study outcomes (e.g. cost per remitted case) will also be explored. Confidence intervals around ICERs will be calculated using a nonparametric bootstrap procedure, with 1000 iterations to reflect sampling uncertainty. The bootstrapped ICERs and the CIs will be graphically represented on cost-effectiveness planes. A cost-effectiveness plane is a plot of the 1000 bootstrapped incremental costs and outcomes across four quadrants. Acceptability curves will be used to graphically present the proportion of bootstrapped iterations falling below a specific willingness to pay threshold. The Productivity Commissions range of willingness to pay thresholds will be used to assess cost-effectiveness . Ratios under $33,000/QALY are deemed very cost-effective, between $33,000 and $64,000 per QALY gained cost-effective and between $64,000 and $96,000 per QALY gained marginally cost. Ratios greater than $96,000 per QALY gained are not considered cost-effective. Sensitivity analyses will be used to determine the impact of changes to important study parameters (e.g. unit cost price variation including the cost of genotyping in this trial). A modelled budget impact analysis using the results of this trial will be undertaken to estimate the costs of implementing the PGx-informed antidepressant prescribing at a state or national level. The economic evaluation will be undertaken from the health sector and partial societal perspectives. The health sector perspective includes costs borne by the government as a third-party payer in addition to out-of-pocket costs incurred by patients when accessing health care. This includes the estimated cost to deliver the PGx-informed antidepressant prescribing combined with the cost of additional health services used by participants over the time period of the trial. The partial societal perspective adds the cost of lost productivity (absenteeism and presenteeism) for study participants to health sector costs. Incremental cost-effectiveness ratios (ICERs) will be calculated as the difference in the average total cost between the randomised arms, divided by the difference in the average outcome. The outcomes used in these analyses will include the primary outcome of the PHQ-9 score and QALYs calculated from AQoL-4D utility values using the area under the curve method. ICERs using other secondary study outcomes (e.g. cost per remitted case) will also be explored. Confidence intervals around ICERs will be calculated using a nonparametric bootstrap procedure, with 1000 iterations to reflect sampling uncertainty. The bootstrapped ICERs and the CIs will be graphically represented on cost-effectiveness planes. A cost-effectiveness plane is a plot of the 1000 bootstrapped incremental costs and outcomes across four quadrants. Acceptability curves will be used to graphically present the proportion of bootstrapped iterations falling below a specific willingness to pay threshold. The Productivity Commissions range of willingness to pay thresholds will be used to assess cost-effectiveness . Ratios under $33,000/QALY are deemed very cost-effective, between $33,000 and $64,000 per QALY gained cost-effective and between $64,000 and $96,000 per QALY gained marginally cost. Ratios greater than $96,000 per QALY gained are not considered cost-effective. Sensitivity analyses will be used to determine the impact of changes to important study parameters (e.g. unit cost price variation including the cost of genotyping in this trial). A modelled budget impact analysis using the results of this trial will be undertaken to estimate the costs of implementing the PGx-informed antidepressant prescribing at a state or national level. No interim analyses are planned. Sensitivity analyses will also test the robustness of the result to variations in the underlying assumptions and inputs to the health economic analysis. Further supplementary analyses, including sensitivity analyses and pre-planned sub-group analyses, will be described in the SAP. Details of the compliance-adjusted analysis and appropriate methods for dealing with missing endpoint data will also be provided in the SAP. To assist with reproducible research, the full protocol, non-identifiable participant-level data and statistical code will be made available to external researchers upon reasonable request. The trial steering committee will manage external requests for these materials. Composition of the coordinating centre and trial steering committee {5d} All meetings, including of the trial steering committee (SS, PC, CM, MLC, JG, TC, TP, ED, MG, CD, JE) and trial management (SS, PC, CM, MLC, TC, TP, ED, LH, NM, TS, MG, CB, JE) group, will be organised, recorded (as appropriate) and minuted by the coordinating centre. ED is a lived-experience researcher and brings the stakeholder and public perspective to the trial steering committee and trial management group. Coordinating centre The coordinating centre primarily comprises the research team who liaises with general practice clinics and oversees the day-to-day management of the trial (SS, AA, RB, LS, PA, GR, ZS, JL, RS, PL and JE). The research team is supervised by the trial coordinator (SS), and the overall responsibility and decision-making is with the chief investigator (JE). The research team, including the trial coordinator and chief investigator, is responsible for implementing and executing the trial including general practice recruitment, patient recruitment, governance and administration, data collection, management of adverse events and document management. Trial management group The chief investigator is responsible for supervising any individual or party to whom they have delegated tasks for the trial. Delegated tasks and roles will be recorded on a delegation log. They provide continuous supervision and documentation of their oversight. To meet this GCP requirement, a small group will be responsible for the day-to-day management of the trial, led by the trial coordinator who will delegate and provide daily supervision to the research team. The research team at the coordinating centre meet 4–6 weekly with external researchers and laboratory staff (CB, MG, Sonic Healthcare, Translational Software) for oversight of the day-to-day trial. The group closely reviews all aspects of the conduct and progress of the trial, ensuring that there is a forum for identifying and addressing issues. Meetings are minuted with attendees listed, pertinent emails retained, and phone calls documented. Trial steering committee A trial steering committee has been established to provide expert advice and overall supervision and ensure that the trial is conducted to the required standards. The steering committee includes the chief investigators, associate investigators and the research team. The steering committee meets quarterly, with more frequent meetings added as required throughout the duration of the trial set-up, recruitment and post-recruitment analysis phase. All meetings are minuted and digitally stored with all trial documentation. Composition of the data monitoring committee, its role and reporting structure {21a} We do not expect significant adverse effects arising from the trial itself, as clinical management of all participants is the responsibility of their GP and treating team. We have therefore decided not to have a separate data monitoring committee. Oversight of the trial will be managed by the trial steering committee. Adverse event reporting and harms {22} All protocol deviations are recorded in the participant record and reported to the study coordinator and lead investigator (SS and JE), who will assess for seriousness. Those deviations deemed to affect to a significant degree the rights of a trial participant or the reliability and robustness of the data generated in the clinical trial are reported as serious breaches. Reporting is done in a timely manner (within 72 h to the study coordinator and lead investigator) and within 7 days to the site’s Research Governance Office. The study coordinator and lead investigator must review and report serious breaches to the approving Human Research Ethics Committee (HREC) within 7 days. Where non-compliance significantly affects participant protection or the reliability of results, a root cause analysis will be undertaken, and a corrective and preventative action plan prepared. Where protocol deviations or serious breaches identify protocol-related issues, the protocol is reviewed and, where indicated, amended. Frequency and plans for auditing trial conduct {23} Researchers in the coordinating centre meet at least weekly with the chief investigator to discuss and review the trial progress. The chief investigator is contactable for prompt reporting of adverse events. The steering committee meets quarterly, with more frequent meetings added as required throughout the duration of the trial set-up, recruitment and post-recruitment analysis phase. Minutes of all meetings are digitally stored with all trial documentation. Progress is reported to the trial funder every 12 months. There is no independent auditing of trial conduct. Plans for communicating important protocol amendments to relevant parties (e.g. trial participants, ethical committees) {25} This trial is conducted in compliance with the current version of the protocol. Any change to the protocol document or informed consent form that affects the scientific intent, trial design, participant safety, or may affect a participant’s willingness to continue participation in the trial is considered an amendment and therefore is written and filed as an amendment to this protocol and/or informed consent form. All such amendments are submitted to the HREC for approval prior to being implemented. Dissemination plans {31a} Data from this trial will be disseminated in several ways. Informal dissemination of results will occur with participants, participating GPs and other collaborators. Participants in the study are given the option at the time of consent to receive a plain language, one-page summary of the study findings after statistical analyses are completed. Other collaborators will receive a similar summary, tailored to their position and interests (i.e. consumers will receive a lay summary). The results of this research will be published in peer-reviewed journals. Upon publication of the results of the trial, we will generate media releases to health professionals and general outlets, generate Twitter and other social media content, and engage with health professionals and general podcasts. We will use all these approaches to promote the trial results. The chief investigators of the study hold primary responsibility for the publications of the results of the trial. All meetings, including of the trial steering committee (SS, PC, CM, MLC, JG, TC, TP, ED, MG, CD, JE) and trial management (SS, PC, CM, MLC, TC, TP, ED, LH, NM, TS, MG, CB, JE) group, will be organised, recorded (as appropriate) and minuted by the coordinating centre. ED is a lived-experience researcher and brings the stakeholder and public perspective to the trial steering committee and trial management group. Coordinating centre The coordinating centre primarily comprises the research team who liaises with general practice clinics and oversees the day-to-day management of the trial (SS, AA, RB, LS, PA, GR, ZS, JL, RS, PL and JE). The research team is supervised by the trial coordinator (SS), and the overall responsibility and decision-making is with the chief investigator (JE). The research team, including the trial coordinator and chief investigator, is responsible for implementing and executing the trial including general practice recruitment, patient recruitment, governance and administration, data collection, management of adverse events and document management. Trial management group The chief investigator is responsible for supervising any individual or party to whom they have delegated tasks for the trial. Delegated tasks and roles will be recorded on a delegation log. They provide continuous supervision and documentation of their oversight. To meet this GCP requirement, a small group will be responsible for the day-to-day management of the trial, led by the trial coordinator who will delegate and provide daily supervision to the research team. The research team at the coordinating centre meet 4–6 weekly with external researchers and laboratory staff (CB, MG, Sonic Healthcare, Translational Software) for oversight of the day-to-day trial. The group closely reviews all aspects of the conduct and progress of the trial, ensuring that there is a forum for identifying and addressing issues. Meetings are minuted with attendees listed, pertinent emails retained, and phone calls documented. Trial steering committee A trial steering committee has been established to provide expert advice and overall supervision and ensure that the trial is conducted to the required standards. The steering committee includes the chief investigators, associate investigators and the research team. The steering committee meets quarterly, with more frequent meetings added as required throughout the duration of the trial set-up, recruitment and post-recruitment analysis phase. All meetings are minuted and digitally stored with all trial documentation. The coordinating centre primarily comprises the research team who liaises with general practice clinics and oversees the day-to-day management of the trial (SS, AA, RB, LS, PA, GR, ZS, JL, RS, PL and JE). The research team is supervised by the trial coordinator (SS), and the overall responsibility and decision-making is with the chief investigator (JE). The research team, including the trial coordinator and chief investigator, is responsible for implementing and executing the trial including general practice recruitment, patient recruitment, governance and administration, data collection, management of adverse events and document management. The chief investigator is responsible for supervising any individual or party to whom they have delegated tasks for the trial. Delegated tasks and roles will be recorded on a delegation log. They provide continuous supervision and documentation of their oversight. To meet this GCP requirement, a small group will be responsible for the day-to-day management of the trial, led by the trial coordinator who will delegate and provide daily supervision to the research team. The research team at the coordinating centre meet 4–6 weekly with external researchers and laboratory staff (CB, MG, Sonic Healthcare, Translational Software) for oversight of the day-to-day trial. The group closely reviews all aspects of the conduct and progress of the trial, ensuring that there is a forum for identifying and addressing issues. Meetings are minuted with attendees listed, pertinent emails retained, and phone calls documented. A trial steering committee has been established to provide expert advice and overall supervision and ensure that the trial is conducted to the required standards. The steering committee includes the chief investigators, associate investigators and the research team. The steering committee meets quarterly, with more frequent meetings added as required throughout the duration of the trial set-up, recruitment and post-recruitment analysis phase. All meetings are minuted and digitally stored with all trial documentation. We do not expect significant adverse effects arising from the trial itself, as clinical management of all participants is the responsibility of their GP and treating team. We have therefore decided not to have a separate data monitoring committee. Oversight of the trial will be managed by the trial steering committee. All protocol deviations are recorded in the participant record and reported to the study coordinator and lead investigator (SS and JE), who will assess for seriousness. Those deviations deemed to affect to a significant degree the rights of a trial participant or the reliability and robustness of the data generated in the clinical trial are reported as serious breaches. Reporting is done in a timely manner (within 72 h to the study coordinator and lead investigator) and within 7 days to the site’s Research Governance Office. The study coordinator and lead investigator must review and report serious breaches to the approving Human Research Ethics Committee (HREC) within 7 days. Where non-compliance significantly affects participant protection or the reliability of results, a root cause analysis will be undertaken, and a corrective and preventative action plan prepared. Where protocol deviations or serious breaches identify protocol-related issues, the protocol is reviewed and, where indicated, amended. Researchers in the coordinating centre meet at least weekly with the chief investigator to discuss and review the trial progress. The chief investigator is contactable for prompt reporting of adverse events. The steering committee meets quarterly, with more frequent meetings added as required throughout the duration of the trial set-up, recruitment and post-recruitment analysis phase. Minutes of all meetings are digitally stored with all trial documentation. Progress is reported to the trial funder every 12 months. There is no independent auditing of trial conduct. This trial is conducted in compliance with the current version of the protocol. Any change to the protocol document or informed consent form that affects the scientific intent, trial design, participant safety, or may affect a participant’s willingness to continue participation in the trial is considered an amendment and therefore is written and filed as an amendment to this protocol and/or informed consent form. All such amendments are submitted to the HREC for approval prior to being implemented. Data from this trial will be disseminated in several ways. Informal dissemination of results will occur with participants, participating GPs and other collaborators. Participants in the study are given the option at the time of consent to receive a plain language, one-page summary of the study findings after statistical analyses are completed. Other collaborators will receive a similar summary, tailored to their position and interests (i.e. consumers will receive a lay summary). The results of this research will be published in peer-reviewed journals. Upon publication of the results of the trial, we will generate media releases to health professionals and general outlets, generate Twitter and other social media content, and engage with health professionals and general podcasts. We will use all these approaches to promote the trial results. The chief investigators of the study hold primary responsibility for the publications of the results of the trial. The PRESIDE Trial aims to determine if personalised antidepressant prescribing in primary care based on pharmacogenomic testing decreases depressive symptoms and increases remission from depression, reduces side effects and therefore improves adherence to antidepressants. Given that the vast majority of antidepressant prescribing occurs in primary care, evidence of the clinical utility of pharmacogenomics for antidepressants from tertiary psychiatric settings may not be sufficient to justify its routine use in general practice. Additionally, this trial will provide data on longer-term effects and impact on health service use, providing evidence on potential cost-effectiveness. This trial began recruitment during the COVID-19 pandemic, and the recruitment period has included long periods of stay-at-home restrictions. This meant that both researchers and potential participants were subject to movement restrictions. The teletrial recruitment methods described above were employed, in part, to ensure recruitment to the study could continue during these lockdowns. These teletrial methods were designed to ensure that teletrial recruitment mirrored face-to-face recruitment as closely as possible, including witnessing via videoconferencing software the self-collection of the DNA samples. The PRESIDE Trial was initially designed and funded prior to the emergence of the COVID-19 pandemic. As well as the global burden on mortality and morbidity from the disease itself, we now know that it has had a substantial impact on the mental health of many . We do not know how this impact will affect the results of this trial, over and above the effects of the intervention. Upon discussion with the trial steering committee, we included the impact of the COVID-19 scale partway through the study, which will allow for exploration of whether there is an effect modification of the intervention between those whose depressive symptoms may be a result of the pandemic and those with other aetiology. It is difficult to hypothesise whether this may be the case and if so, in what direction this effect may go, as we do not yet understand whether antidepressant prescribing patterns may be different in this group (i.e. whether GPs may have been more or less likely to prescribe antidepressants to those experiencing depressive symptoms due to the social isolation or anxiety resulting from the pandemic) and the general efficacy of antidepressants on symptoms in this group. Regardless, the collection of this specific impact questionnaire will allow for the exploration of these hypotheses. The collection of secondary quantitative outcomes using questionnaires, as well as the process evaluation including in-depth qualitative interviews, will allow for a thorough examination of the elements effect of this complex intervention. The development of an intervention logic model maps the potential points of the effect of the intervention and this mixed-method collection of process data will assist in the interpretation of the results of the trial. This trial will provide evidence as to whether PGx-informed antidepressant prescribing is clinically efficacious and cost-effective. It will inform national and international policy and guidelines about the use of PGx to select antidepressants for people with moderate to severe depressive symptoms presenting in primary care. Protocol Version 1.2, July 2022. The first participant was recruited on 26 May 2021. Trial recruitment is estimated to be completed in July 2023. |
Do women accurately predict their odds of having a child following planned oocyte cryopreservation? | 6eb2c62f-e3a2-4ef2-ba06-d7eaa6bf382d | 11896684 | Surgical Procedures, Operative[mh] | Planned oocyte cryopreservation (POC) has become widely available, allowing women to preserve their fertility in an attempt to circumvent age-related fertility decline . The main reasons for women to pursue POC are increasing age and the absence of a male partner committed to start a family . When patients consider how many cycles of POC they should do, they need to receive clear counseling, including an accurate estimation of the chance to achieve a live birth. Overestimating the chance of a live birth might result in a sense of ‘false hope’ and the cryopreservation of an insufficient number of oocytes. On the other hand, underestimating the chance of a live birth might result in a sense of urgency and unjustified financial, physical and psychological burdens . Previous studies have consistently shown that the chance of having a child with cryopreserved oocytes depends on two major factors: age at oocyte retrieval and the number of cryopreserved oocytes . In 2017, published an evidence-based model to predict the probability of a woman to achieve a live birth based on her age at oocyte retrieval and the number of cryopreserved oocytes. That model was derived from a surrogate population of ICSI patients with uncompromised ovarian reserve. Previous surveys on women undergoing POC have focused on their clinical characteristics, motivations and satisfaction levels . However, little is known about how accurately they perceive their odds of having a child with their cryopreserved oocytes. In the current study, we conducted a telephone survey among women who underwent POC at a single fertility unit, focusing on their self-estimation of the odds to achieve at least one live birth by utilizing their cryopreserved oocytes in the future. The aim of our study was to examine whether women who have undergone POC were able to correctly predict the chance of having a child with their cryopreserved oocytes and to identify factors that are associated with over- and underestimation.
In this cross-sectional study, we conducted a telephone survey among women who underwent one or more cycles of POC between January 2017 and December 2023 at a single university-affiliated fertility unit. The survey was conducted between February 2024 and May 2024. Women were eligible for participation if they underwent planned (elective, nonmedical) oocyte cryopreservation for age-related fertility decline. Those who had undergone medically indicated fertility preservation (e.g., before gonadotoxic treatment) and those who had undergone embryo cryopreservation were excluded. The study was approved by the Institutional Review Board (IRB, approval number 0118-23-WOMC, November 26th, 2023). Women gave verbal consent for participation, which was documented as required by the IRB for telephone surveys. POC was approved in Israel in 2011 for women aged 30–41, with a limit of up to four ovarian stimulation cycles, which was later expanded to six cycles. Most POC treatments in Israel are not covered by national health insurance and are self-funded. Only a small percentage of women with low ovarian reserve, according to baseline follicle stimulating hormone (FSH) level, anti-Müllerian hormone (AMH) level and antral follicle count, are entitled for reimbursement. A 13-item questionnaire was created by the research group. The content of the questionnaire items was derived from the up-to-date literature on POC, including questions addressing demographics, satisfaction, perceptions and estimation of the chances of usability and success. The two main questions were: ‘how do you estimate the chances that you will use your cryopreserved oocytes in the future?’ and ‘if you do decide to use your eggs, what do you think are your chances of having at least one successful childbirth?’. The survey was conducted by five fertility specialists after training in order to achieve consistency and uniformity in the way the questions were presented. The primary outcome of the study was the ‘estimated’ chance of having at least one live birth, as predicted by the participants, in case they would use their cryopreserved oocytes. For each participant, the ‘calculated’ chance of achieving at least one live birth was also determined based on her age at the last oocyte retrieval and the number of cryopreserved oocytes, according to . The estimated chance was considered accurate if the difference between the estimated and calculated chances was no greater than ±10%. Overestimation was defined when the estimated chance was >10% higher than the calculated chance. Underestimation was defined when the estimated chance was >10% lower than the calculated chance. Secondary outcomes included personal satisfaction and future reproductive plans. Statistical analysis was performed using SPSS version 28.0 (IBM Corp., USA; https://www.ibm.com/products/spss-statistics ). Continuous variables are presented as mean ± standard deviation (SD) of median (range), as appropriate. Categorical variables are presented as numbers and percentages. The Wilcoxon test for paired nonparametric variables was used for the comparison of the estimated and calculated chances of a live birth. The characteristics of women who overestimated their chances of having a child with their cryopreserved oocytes were compared with those who underestimated their chances. Comparison of non-paired continuous variables was performed by Student’s t -test and comparison of categorical variables was performed by the chi-squared test or Fischer’s exact test, as appropriate. We anticipated that women would estimate the chance of having a live birth as around 50%. Based on that, a sample size of 267 participants would be sufficient to detect a 12% difference between the estimated and calculated chance of live birth, with a power of 80% and an alpha of 0.05. A P -value <0.05 was considered statistically significant.
During the study period (2017–2023), 435 women underwent POC at our unit. We managed to contact 310 women via phone call, out of whom 260 (83.9%) consented to participate in the study. The median follow-up time was 15 months (range: 4–84). The mean age of the participants at the time of their last ovum pick-up (OPU) was 35.3 ± 2.3 years (range: 30–41). Baseline characteristics are presented in . About half of the participants (51.2%) underwent a single oocyte retrieval. The mean total number of cryopreserved MII oocytes was 17.6 ± 9.7 (range: 2–67). The treatment was self-funded in most (93.5%) patients. In terms of education level, most patients had an academic degree while undergoing POC. The main reason for choosing to freeze oocytes was the absence of a partner to start a family with (88.5%). At the time of the survey, three patients (1.2%) had already used their cryopreserved oocytes in order to conceive, seven patients (2.7%) had conceived with a new ART treatment and 28 patients (10.8%) had conceived naturally. presents the responses to questions related to satisfaction and future reproductive plans. Most participants (85.4%) announced that they were satisfied with their decision to freeze oocytes, and 96.5% said they would recommend POC to a friend. More than half (55.4%) of the respondents said they would rather perform the POC at an earlier age. Overall, 170 (65.4%) participants estimated their chances of using their cryopreserved oocytes in the future as moderate or high. In the advanced age group (>35 yeas), more women estimated the chance of future use to be moderate or high compared to the younger age group (72.6 vs 60.4%, P = 0.041). Most women (61.2%) said they would consider sperm donation in case they do not have a partner. Using the Wilcoxon test for paired nonparametric variables, we found that patients underestimated their chance of having a child with their cryopreserved oocytes. Their median estimated chance was 50%, whereas the median calculated chance was 75.0% ( P < 0.001). This trend remained significant among women who underwent POC at the age of 30–35. In contrast, in women who underwent POC at age >35, the difference between the estimated and the calculated chances did not reach statistical significance . Only 73 respondents (28.1%) accurately predicted their chance of having a child with their cryopreserved oocytes, whereas 138 (53.1%) respondents underestimated their chances and 44 (16.9%) overestimated their chances. Five respondents replied that they did not know what their chances were. Compared to women who overestimated their chances, women who underestimated their chances were younger at the time of their last oocyte retrieval and at the time of the survey. They also had a higher number of cryopreserved oocytes .
In this study, we found that only 28.1% of women who underwent POC accurately predicted their chances of having a child using their cryopreserved oocytes in the future. The majority of women underestimated their chances, with underestimation being more pronounced among those who underwent POC at a younger age and had a high number of oocytes cryopreserved. In the last decades, women have been having children at older ages. This trend is driven by greater gender equality and expanded opportunities for women and is further facilitated by the availability of contraception and assisted reproductive technologies . Along with improvements in oocyte cryopreservation techniques, POC has become widely available, enabling individuals to extend their reproductive window. A major increase in the number of POC cycles was reported in the USA, Australia and New Zealand , as well as in Europe . While the technology is promising, POC may be involved with substantial financial, psychological and physical difficulties . Therefore, providers must ensure that women requesting POC are informed about its efficacy, safety, costs, benefits and risks. It is important that clinicians provide counseling about the probability that an individual patient will attempt to use her cryopreserved oocytes and the chance of achieving a live birth if she does. A realistic estimation will allow each patient to decide on her target number of cryopreserved oocytes and, accordingly, the number of POC cycles she should opt for . In our study, the majority of respondents did not estimate their chance of having a child correctly. It appears that women did not comprehend the large impact that age has on success rates. Numerous studies have demonstrated that a patient’s age at cryopreservation greatly affects the chance of achieving a live birth. A fifteen-years follow-up of 543 patients who attempted to use their cryopreserved oocytes found that the final live birth rate (FLBR) was 51% in women who were <38 years old at cryopreservation. In contrast, the FLBR was 34% in women aged 38–40 and only 23% in women aged ≥41 at oocyte cryopreservation . Similarly, a recent systematic review and meta-analysis including 1,517 women who attempted to conceive with their cryopreserved oocytes found that the live birth rate per patient was 52% in women aged ≤35, 34% in women aged 36–39 and only 19% in women aged >40 at the time of oocyte cryopreservation. These data emphasize the importance of a patient’s age at cryopreservation and should be discussed with women considering how many POC cycles they should undergo. Evidence regarding women’s estimation of the chances to have a child with their cryopreserved oocytes is limited. conducted a survey among 133 women who underwent POC. Participants were asked, ‘what is the pregnancy chance per one frozen egg in women between 38 and 40 years?’. Similar to our study, only 28% of patients estimated the chance correctly. In a cohort of 85 women who underwent POC in the UK , the majority of women (83%) knew there was a chance of treatment failure in the future and that a live birth could not be guaranteed. To the best of our knowledge, in previous studies, women were not asked to quantify their own chances. Previous studies have shown that only a small portion of women return to use their cryopreserved oocytes. A retrospective review from over a decade of POC at a single large center in the US found that only 7.4% of patients returned to use their cryopreserved oocytes . Similarly, a systematic review including ten studies conducted between 1999 and 2020 found a return rate of 11.1 ± 4.7% . While these rates might increase with longer follow-up, they still indicate that most patients will not return to utilize their oocytes. The most common reasons for not using the cryopreserved oocytes are achieving spontaneous pregnancy or preferring not to have a child without a partner . In our study, 65.4% of the participants estimated the chances of using their cryopreserved oocytes in the future as moderate or high. A previous survey from Belgium also reported that two-thirds of women who had POC anticipated using their oocytes at some point in the future . This is, of course in contrast, with the above reports of the return rate, reflecting a misunderstanding of the actual chance of natural conception. The misconceptions that they will likely return to use their cryopreserved oocytes and that the chances of having a child with these oocytes are low might prompt women to undergo additional unnecessary treatments, thereby increasing their financial, physical and psychological burdens . Indeed, most respondents said that the treatment involved some difficulties. On the other hand, a small number of older patients (16.9%) overestimated their chances of a live birth. This might generate a sense of ‘false hope’, resulting in the cryopreservation of an insufficient number of oocytes and delaying pregnancy to a point where women might not be able to conceive with their own oocytes. Both types of misconceptions should be avoided by providing clear and comprehensive counseling. Our study demonstrated high levels of satisfaction among women who chose to undergo POC. Moreover, the vast majority of participants indicated they would recommend POC to a friend. These results suggest that women who opt for oocyte cryopreservation understand the biological rationale behind the procedure and are highly satisfied with their decision to invest time and money in extending their fertility window. Our results are in agreement with previous surveys also showing high levels of satisfaction . The strengths of our study include a large cohort of patients and a high response rate. Furthermore, we addressed the question of whether women accurately estimate their chances of success if they will use their cryopreserved oocytes in the future, a topic that has been scarcely examined in previous research. Nevertheless, some limitations should be acknowledged. The main limitation of our study arises from the fact that the calculation of the probability of a live birth was based on the model of Goldman et al. which is purely theoretical and was derived from a specific IVF program with patients undergoing treatment for infertility. We, like Goldman et al. , suggest that the best outcome data to develop a predictive model should be obtained from women who have undergone POC and then returned to use their oocytes. However, at the present time, there is a paucity of such validation data available. In addition, we approached women who underwent POC at a single fertility unit, which might affect the generalizability of our results. However, the baseline characteristics of the participants in our cohort are similar to those of previous cohorts of women undergoing POC. In addition, physicians’ counseling and other resources of information may differ between different clinics and countries. In conclusion, women who undergo POC exhibit very high satisfaction rates. However, many underestimated their probability of achieving a live birth using their cryopreserved oocytes. Improved counseling is essential to provide comprehensive information and prevent women from undergoing unnecessary treatments.
The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the work reported.
This research did not receive any specific grant from any funding agency in the public, commercial or not-for-profit sector.
M Friedman contributed to the writing of the manuscript, as well as to data collection and analysis. N Jaffe assisted with study design, data collection and curation. D Tairy and M Torem assisted with data collection and analysis. M Finkelstein, E Horowitz, A Weissman and A Raziel helped design the study, write and review the manuscript while providing valuable input for improvement. Y Mizrachi contributed to designing the study, writing the manuscript, collecting data and conducting the statistical analysis.
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The future of ophthalmology and vision science with the Apple Vision Pro | 83b19634-c82d-478b-b36c-f6d1f5e06f76 | 10810972 | Ophthalmology[mh] | |
Evaluation of Combined p57KIP2 Immunohistochemistry and Fluorescent | 52d531dd-cef4-445d-b2be-3e85d2b97070 | 11332376 | Anatomy[mh] | Ethical Statement This study was conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the institutional ethical committee of the Graduate School of Medicine, Chiba University, Japan (approval numbers 1198 and 2113). All participants provided written informed consent. Participants and Samples In this study, 153 patients who had undergone evacuation of a POC due to suspected molar pregnancy at Chiba University Hospital between 2009 and 2015 were included. The POCs were fixed in formalin neutral buffer solution for 24 to 48 h and then embedded in paraffin for histologic diagnosis, as per the routine procedure. The specimens were diagnosed by certified pathologists at the Department of Pathology of Chiba University Hospital. Genetic Diagnosis of POCs Molecular diagnostic analysis was performed on blood and fresh POC samples from 152 patients. Villous tissues were isolated from blood clots and decidua tissues under a stereomicroscope when villous tissues were scarce. Genomic DNA was extracted from both blood and tissue samples, and STR polymorphic alleles were amplified with the PowerPlex 16 HS Kit (Promega, Madison). The amplified fragments were electrophoresed on the ABI Prism 310 Genetic Analyzer (Applied Biosystems, Foster City) and analyzed through the GeneMapper 4.0 software (Applied Biosystems). The classification of HM and non-molar villi was based on previous research and definitions from other institutions , . Cases with one or more villous loci without maternal alleles were considered androgenetic and categorized as genetic CHM; otherwise, they were identified as biparental conceptuses. The triploid or diploid classification of biparental concepts was based on parental contribution. The peak heights of 2 allelic loci were evaluated in all loci. A biparental disomic chromosome was indicated by an even peak height, whereas a trisomic chromosome was indicated by a peak height ratio of 2:1 (Fig. ). Cases estimated to be trisomic in almost all loci with 2 paternal and 1 maternal parental contribution were assigned as diandric monogynic PHM (genetic PHM). Cases estimated to be disomic in almost all loci were assigned as genetically confirmed non-molar diploid conceptus. Study Flow Of the 153 POCs, genetic assessment through STR analysis was performed on 150 villous tissues, as 3 samples could not be successfully analyzed. The analysis of the 150 POCs revealed that 111 were androgenetic CHMs, 20 were diandric monogynic PHMs, 18 were biparental diploid abortions, and 1 was a monoandric digynic triploid abortion. Because the number of androgenetic CHMs was significantly higher compared with the other groups, we selected 78 samples of intrauterine conceptuses comprising 44 CHMs, 20 PHMs, and 14 non-molar abortions for further analysis. In addition, 2 villous samples, including an aborted monoandric digynic triploid villous specimen and a vaginal specimen of low-risk GTN after CHM, were included as validation controls. Consequently, the analysis included a total of 80 samples. Transfer of Formalin-fixed Paraffin-embedded Samples and Pathologic Diagnosis The 80 FFPE blocks selected for the study (1 block per sample) were anonymized and sent to the Pathologic Laboratory of Kotobiken Medical Laboratories, Inc., without revealing any clinical information or diagnosis. The pathologists were only informed that the specimens were villous tissues, including CHM, PHM, and non-molar villi. All pathologic evaluations and FISH analyses were performed independently of Chiba University Hospital at the Pathological Laboratory of Kotobiken Medical Laboratories. FFPE sections (3 μm thick) were deparaffinized with xylene, rehydrated with a graded ethanol series, and stained with HE for histologic analyses. Immunostaining was performed using an Autostainer Link 48 (Dako, Glostrup, Denmark). Immunohistochemistry of p57KIP2 was conducted using a rabbit polyclonal antibody (p57Kip2 Ab-7 #RB-1637-R7, 1:10; Thermo Fisher Scientific, Waltham, MA, USA). Antigen retrieval was performed using Target Retrieval Solution (pH 9) on PT Link 200 (Dako). Endogenous peroxidase was quenched with 3% H 2 O 2 in distilled water for 5 min, and then the slides were incubated with the primary antibody for 30 min at room temperature. The sections were stained using EnVision FLEX (Dako), according to the manufacturer’s protocol, and counterstained with hematoxylin. Positive immunohistochemical staining of p57KIP2 was indicated by diffuse and distinct nuclear staining of villous cytotrophoblasts and stromal cells. Lack of staining or <10% nuclear staining was considered negative. Stained extravillous trophoblasts and maternal decidua were used as positive internal controls. The 2 pathologists independently evaluated the specimens using the combination of HE and p57KIP2 staining without access to clinical information or primary pathologic diagnosis from Chiba University Hospital. Subsequently, the specimens were diagnosed as CHM, PHM, or non-molar abortus, including hydropic abortion. Fluorescent in situ Hybridization The ploidy pattern of specimens from all 80 patients was analyzed. To minimize confounding factors, we selected the centromeric FISH probe for chromosome 17, as trisomy of this chromosome is exceedingly rare , . The FFPE tissue specimens were cut into 4 µm-thick sections and placed on coated slides. The slides were treated with hydrochloric acid and a pre-treatment solution, followed by proteolytic digestion with pepsin. FISH was then performed for chromosomes 17 and XY using Vysis CEP17 (D17Z1) and CEPX Spectrum Orange/Y Spectrum Green DNA Probe Kits (Abbott Molecular, Chicago), respectively. After hybridization, the tissue specimens on the slides were counterstained with 4′,6-diamidino-2-phenylindole, and examined under a fluorescence microscope (Olympus, Tokyo). To ensure objectivity, we counted at least 50 non-overlapping nuclei in interphase and metaphase chromosomes to determine the number of signals. The three-signal rate of CEP17 was calculated by dividing the number of cells presenting 3 signals by the total number of counted cells. For CEPX/Y probes, cells presenting 3 signals were defined as those with 3 signals on CEPX and none on CEPY, 2 signals on CEPX and 1 on CEPY, 1 signal on CEPX and 2 on CEPY, or no signal on CEPX and 3 on CEPY. Statistical Analysis Statistical analyses and data visualization were conducted using the R software (version 4.2.2: https://www.R-project.org ).
This study was conducted in accordance with the Declaration of Helsinki. The study protocol was approved by the institutional ethical committee of the Graduate School of Medicine, Chiba University, Japan (approval numbers 1198 and 2113). All participants provided written informed consent.
In this study, 153 patients who had undergone evacuation of a POC due to suspected molar pregnancy at Chiba University Hospital between 2009 and 2015 were included. The POCs were fixed in formalin neutral buffer solution for 24 to 48 h and then embedded in paraffin for histologic diagnosis, as per the routine procedure. The specimens were diagnosed by certified pathologists at the Department of Pathology of Chiba University Hospital.
Molecular diagnostic analysis was performed on blood and fresh POC samples from 152 patients. Villous tissues were isolated from blood clots and decidua tissues under a stereomicroscope when villous tissues were scarce. Genomic DNA was extracted from both blood and tissue samples, and STR polymorphic alleles were amplified with the PowerPlex 16 HS Kit (Promega, Madison). The amplified fragments were electrophoresed on the ABI Prism 310 Genetic Analyzer (Applied Biosystems, Foster City) and analyzed through the GeneMapper 4.0 software (Applied Biosystems). The classification of HM and non-molar villi was based on previous research and definitions from other institutions , . Cases with one or more villous loci without maternal alleles were considered androgenetic and categorized as genetic CHM; otherwise, they were identified as biparental conceptuses. The triploid or diploid classification of biparental concepts was based on parental contribution. The peak heights of 2 allelic loci were evaluated in all loci. A biparental disomic chromosome was indicated by an even peak height, whereas a trisomic chromosome was indicated by a peak height ratio of 2:1 (Fig. ). Cases estimated to be trisomic in almost all loci with 2 paternal and 1 maternal parental contribution were assigned as diandric monogynic PHM (genetic PHM). Cases estimated to be disomic in almost all loci were assigned as genetically confirmed non-molar diploid conceptus.
Of the 153 POCs, genetic assessment through STR analysis was performed on 150 villous tissues, as 3 samples could not be successfully analyzed. The analysis of the 150 POCs revealed that 111 were androgenetic CHMs, 20 were diandric monogynic PHMs, 18 were biparental diploid abortions, and 1 was a monoandric digynic triploid abortion. Because the number of androgenetic CHMs was significantly higher compared with the other groups, we selected 78 samples of intrauterine conceptuses comprising 44 CHMs, 20 PHMs, and 14 non-molar abortions for further analysis. In addition, 2 villous samples, including an aborted monoandric digynic triploid villous specimen and a vaginal specimen of low-risk GTN after CHM, were included as validation controls. Consequently, the analysis included a total of 80 samples.
The 80 FFPE blocks selected for the study (1 block per sample) were anonymized and sent to the Pathologic Laboratory of Kotobiken Medical Laboratories, Inc., without revealing any clinical information or diagnosis. The pathologists were only informed that the specimens were villous tissues, including CHM, PHM, and non-molar villi. All pathologic evaluations and FISH analyses were performed independently of Chiba University Hospital at the Pathological Laboratory of Kotobiken Medical Laboratories. FFPE sections (3 μm thick) were deparaffinized with xylene, rehydrated with a graded ethanol series, and stained with HE for histologic analyses. Immunostaining was performed using an Autostainer Link 48 (Dako, Glostrup, Denmark). Immunohistochemistry of p57KIP2 was conducted using a rabbit polyclonal antibody (p57Kip2 Ab-7 #RB-1637-R7, 1:10; Thermo Fisher Scientific, Waltham, MA, USA). Antigen retrieval was performed using Target Retrieval Solution (pH 9) on PT Link 200 (Dako). Endogenous peroxidase was quenched with 3% H 2 O 2 in distilled water for 5 min, and then the slides were incubated with the primary antibody for 30 min at room temperature. The sections were stained using EnVision FLEX (Dako), according to the manufacturer’s protocol, and counterstained with hematoxylin. Positive immunohistochemical staining of p57KIP2 was indicated by diffuse and distinct nuclear staining of villous cytotrophoblasts and stromal cells. Lack of staining or <10% nuclear staining was considered negative. Stained extravillous trophoblasts and maternal decidua were used as positive internal controls. The 2 pathologists independently evaluated the specimens using the combination of HE and p57KIP2 staining without access to clinical information or primary pathologic diagnosis from Chiba University Hospital. Subsequently, the specimens were diagnosed as CHM, PHM, or non-molar abortus, including hydropic abortion.
in situ Hybridization The ploidy pattern of specimens from all 80 patients was analyzed. To minimize confounding factors, we selected the centromeric FISH probe for chromosome 17, as trisomy of this chromosome is exceedingly rare , . The FFPE tissue specimens were cut into 4 µm-thick sections and placed on coated slides. The slides were treated with hydrochloric acid and a pre-treatment solution, followed by proteolytic digestion with pepsin. FISH was then performed for chromosomes 17 and XY using Vysis CEP17 (D17Z1) and CEPX Spectrum Orange/Y Spectrum Green DNA Probe Kits (Abbott Molecular, Chicago), respectively. After hybridization, the tissue specimens on the slides were counterstained with 4′,6-diamidino-2-phenylindole, and examined under a fluorescence microscope (Olympus, Tokyo). To ensure objectivity, we counted at least 50 non-overlapping nuclei in interphase and metaphase chromosomes to determine the number of signals. The three-signal rate of CEP17 was calculated by dividing the number of cells presenting 3 signals by the total number of counted cells. For CEPX/Y probes, cells presenting 3 signals were defined as those with 3 signals on CEPX and none on CEPY, 2 signals on CEPX and 1 on CEPY, 1 signal on CEPX and 2 on CEPY, or no signal on CEPX and 3 on CEPY.
Statistical analyses and data visualization were conducted using the R software (version 4.2.2: https://www.R-project.org ).
Short Tandem Repeats Polymorphism Analysis Fig. shows electropherograms of representative multiplex STR-PCR fragments. Fig. A–C show genetic CHM, PHM, and non-molar conceptuses, respectively. As shown in Fig. A, only paternal alleles were detected at 3 loci, leading to the classification of the case as a genetic CHM. The triploid diagnosis was based on the almost equal peak heights of 3 allelic loci (D13S317, D7S820, and Penta D in Fig. B). Moreover, the longer peak height of the 2 allelic loci (D5S818, D16S539, and CSF1PO in Fig. B) was almost twice the height of the shorter one, consistent with diandric monogynic triploid as genetic PHM , . In Fig. C, the peak heights of 2 allelic loci (D13S317, D21S11, D18S51, and Penta E) were almost equal, indicating non-molar conceptus. Finally, 78 enrolled samples were classified as 44 genetic CHMs, 20 genetic PHMs, and 14 genetically confirmed non-molar conceptuses. In addition, 1 invasive HM (genetic CHM) of a vaginal specimen and 1 monoandric digynic triploid villous specimen were further analyzed. Pathologic Diagnosis Combined with Hematoxylin-eosin Staining and p57KIP2 Immunostaining Table summarizes the results of the independent diagnosis of specimens by 2 pathologists using HE staining and p57KIP2 immunostaining. Representative images of HE staining and p57KIP2 immunostaining are presented in Fig. . The villous cytotrophoblasts and stromal cells in androgenetic CHM specimens were negative for p57KIP2 (Fig. D and G). Conversely, villous cytotrophoblasts and stromal cells were positive for p57KIP2 in diandric monogynic PHM and non-molar villous specimens (Fig. E, F, H, and I), which contained the maternal chromosomes. For CHM cases, extravillous trophoblasts were typically stained as an internal positive control (Fig. G). The villous samples were classified as CHM, PHM, or non-molar conceptus based on their HE staining and p57KIP2 immunostaining patterns (Table ). Among the 18 diandric genetic PHM cases (cases #45–62), at least 1 pathologist determined 7 cases to be non-molar hydropic abortions but not PHMs. Two cases of genetically confirmed abortus (cases #77 and #78) were identified as PHM. Fluorescent in situ Hybridization Analysis All cases underwent successful FISH analysis using CEP17 and CEPX/Y probes, including the oldest FFPE blocks that were up to 5 years old. Fig. shows representative FISH results where CHM and PHM cases displayed 2 and 3 FISH signals of CEP17, respectively (Fig. A and B). Similarly, CHM, non-molar hydropic abortion, PHM, and PHM cases showed 2, 2, 3, and 3 FISH signals of CEPX/Y, respectively (Fig. C-F). Supplementary Table S1, Supplemental Digital Content 1, http://links.lww.com/IJGP/A157 details the signal counts of 50 nuclei, and Fig. illustrates the distribution of the three-signal ratios in the diploid and triploid samples, which showed a bimodal pattern with a recognizable boundary zone (Fig. A). The threshold for the three-signal ratio was determined as 0.15, although 2 triploid cases (cases #63 and #64) diagnosed by STR analysis were in the diploid region with 2 FISH signals, where the three-signal ratios counting 50 cells were 0.00 and 0.00 on CEP17 and 0.04 and 0.02 on CEPX/Y probes, respectively (Fig. B and Supplementary Table S1, Supplemental Digital Content 1, http://links.lww.com/IJGP/A157 ). The median and range of the three-signal ratio of CEP17 in diploid and triploid samples were 0.00 [0.00–0.08] and 0.31 [0.18–0.44], respectively, whereas the median and range of the three-signal ratio of CEPX/Y in diploid and triploid samples were 0.04 [0.00–0.10] and 0.32 [0.20–0.68], respectively. In addition the vaginal specimen of low-risk GTN after CHM showed slightly higher three-signal ratios of 0.10 on CEP17 and 0.14 on CEPX/Y, as depicted in Fig. A, although the ratios were within the diploid range. Evaluation of the Diagnostic Workflow Involving p57KIP2 Immunostaining and FISH We evaluated the results of the diagnostic workflow along with those of p57KIP2 immunostaining and FISH analysis, which are presented in Table . We identified a single androgenetic heterozygous CHM case (case #44) as a non-molar hydropic abortion due to the diploid signal in the FISH analysis and retained expression of p57KIP2 (Fig. A and B). We observed discordant p57KIP2 immunostaining patterns, with positive staining in the villous cytotrophoblasts and negative staining in villous stromal cells. Although single nucleotide polymorphism array data, in addition to STR analysis, indicated no possibility of genetic mosaicism, we noted a mosaic pattern of p57KIP2 staining, as previously detailed . Initially, 8 genetically confirmed PHM cases were diagnosed as non-molar abortions (cases #54–62) based on morphology and immunohistochemistry by at least 1 pathologist but were later identified as PHM based on the FISH ploidy information (Fig. C). In addition, 2 genetically confirmed non-molar abortion cases (cases #77 and #78) were initially misdiagnosed as PHM but later corrected to abortion (Fig. D). We classified 1 exceptional case of monoandric digynic triploidy as PHM because of the triploid signal in the FISH analysis and positive staining of p57KIP2 (case #80). Two cases of diandric monogynic triploidy (cases #63 and #64) classified by STR analysis (Fig. A) were indeterminate. All loci showed 1 or 2, but not 3, alleles. Both cases showed a swollen villus and a discordant p57KIP2 staining pattern, with positive staining observed in the villous cytotrophoblasts and negative staining in the stromal cells (Fig. B). The peak heights on the STR electropherogram were not uniform in 2 allelic loci but were roughly in the ratio of 2:1 or 1:2 (Fig. A, middle lane), which could be consistent with triploid PHM. However, we observed 2 clear FISH signals for CEP17 and CEPX (Fig. C and D). Therefore, we concluded that these 2 cases were androgenetic/biparental mosaic (MP1/P1P1) (Fig. E) but not diandric monogyny triploid PHM (MP1P1, paternally homozygous) (Fig. F).
Fig. shows electropherograms of representative multiplex STR-PCR fragments. Fig. A–C show genetic CHM, PHM, and non-molar conceptuses, respectively. As shown in Fig. A, only paternal alleles were detected at 3 loci, leading to the classification of the case as a genetic CHM. The triploid diagnosis was based on the almost equal peak heights of 3 allelic loci (D13S317, D7S820, and Penta D in Fig. B). Moreover, the longer peak height of the 2 allelic loci (D5S818, D16S539, and CSF1PO in Fig. B) was almost twice the height of the shorter one, consistent with diandric monogynic triploid as genetic PHM , . In Fig. C, the peak heights of 2 allelic loci (D13S317, D21S11, D18S51, and Penta E) were almost equal, indicating non-molar conceptus. Finally, 78 enrolled samples were classified as 44 genetic CHMs, 20 genetic PHMs, and 14 genetically confirmed non-molar conceptuses. In addition, 1 invasive HM (genetic CHM) of a vaginal specimen and 1 monoandric digynic triploid villous specimen were further analyzed.
Table summarizes the results of the independent diagnosis of specimens by 2 pathologists using HE staining and p57KIP2 immunostaining. Representative images of HE staining and p57KIP2 immunostaining are presented in Fig. . The villous cytotrophoblasts and stromal cells in androgenetic CHM specimens were negative for p57KIP2 (Fig. D and G). Conversely, villous cytotrophoblasts and stromal cells were positive for p57KIP2 in diandric monogynic PHM and non-molar villous specimens (Fig. E, F, H, and I), which contained the maternal chromosomes. For CHM cases, extravillous trophoblasts were typically stained as an internal positive control (Fig. G). The villous samples were classified as CHM, PHM, or non-molar conceptus based on their HE staining and p57KIP2 immunostaining patterns (Table ). Among the 18 diandric genetic PHM cases (cases #45–62), at least 1 pathologist determined 7 cases to be non-molar hydropic abortions but not PHMs. Two cases of genetically confirmed abortus (cases #77 and #78) were identified as PHM.
in situ Hybridization Analysis All cases underwent successful FISH analysis using CEP17 and CEPX/Y probes, including the oldest FFPE blocks that were up to 5 years old. Fig. shows representative FISH results where CHM and PHM cases displayed 2 and 3 FISH signals of CEP17, respectively (Fig. A and B). Similarly, CHM, non-molar hydropic abortion, PHM, and PHM cases showed 2, 2, 3, and 3 FISH signals of CEPX/Y, respectively (Fig. C-F). Supplementary Table S1, Supplemental Digital Content 1, http://links.lww.com/IJGP/A157 details the signal counts of 50 nuclei, and Fig. illustrates the distribution of the three-signal ratios in the diploid and triploid samples, which showed a bimodal pattern with a recognizable boundary zone (Fig. A). The threshold for the three-signal ratio was determined as 0.15, although 2 triploid cases (cases #63 and #64) diagnosed by STR analysis were in the diploid region with 2 FISH signals, where the three-signal ratios counting 50 cells were 0.00 and 0.00 on CEP17 and 0.04 and 0.02 on CEPX/Y probes, respectively (Fig. B and Supplementary Table S1, Supplemental Digital Content 1, http://links.lww.com/IJGP/A157 ). The median and range of the three-signal ratio of CEP17 in diploid and triploid samples were 0.00 [0.00–0.08] and 0.31 [0.18–0.44], respectively, whereas the median and range of the three-signal ratio of CEPX/Y in diploid and triploid samples were 0.04 [0.00–0.10] and 0.32 [0.20–0.68], respectively. In addition the vaginal specimen of low-risk GTN after CHM showed slightly higher three-signal ratios of 0.10 on CEP17 and 0.14 on CEPX/Y, as depicted in Fig. A, although the ratios were within the diploid range.
We evaluated the results of the diagnostic workflow along with those of p57KIP2 immunostaining and FISH analysis, which are presented in Table . We identified a single androgenetic heterozygous CHM case (case #44) as a non-molar hydropic abortion due to the diploid signal in the FISH analysis and retained expression of p57KIP2 (Fig. A and B). We observed discordant p57KIP2 immunostaining patterns, with positive staining in the villous cytotrophoblasts and negative staining in villous stromal cells. Although single nucleotide polymorphism array data, in addition to STR analysis, indicated no possibility of genetic mosaicism, we noted a mosaic pattern of p57KIP2 staining, as previously detailed . Initially, 8 genetically confirmed PHM cases were diagnosed as non-molar abortions (cases #54–62) based on morphology and immunohistochemistry by at least 1 pathologist but were later identified as PHM based on the FISH ploidy information (Fig. C). In addition, 2 genetically confirmed non-molar abortion cases (cases #77 and #78) were initially misdiagnosed as PHM but later corrected to abortion (Fig. D). We classified 1 exceptional case of monoandric digynic triploidy as PHM because of the triploid signal in the FISH analysis and positive staining of p57KIP2 (case #80). Two cases of diandric monogynic triploidy (cases #63 and #64) classified by STR analysis (Fig. A) were indeterminate. All loci showed 1 or 2, but not 3, alleles. Both cases showed a swollen villus and a discordant p57KIP2 staining pattern, with positive staining observed in the villous cytotrophoblasts and negative staining in the stromal cells (Fig. B). The peak heights on the STR electropherogram were not uniform in 2 allelic loci but were roughly in the ratio of 2:1 or 1:2 (Fig. A, middle lane), which could be consistent with triploid PHM. However, we observed 2 clear FISH signals for CEP17 and CEPX (Fig. C and D). Therefore, we concluded that these 2 cases were androgenetic/biparental mosaic (MP1/P1P1) (Fig. E) but not diandric monogyny triploid PHM (MP1P1, paternally homozygous) (Fig. F).
The diagnostic methodology proposed in this study, which incorporates p57KIP2 immunostaining with FISH analysis, can be a useful tool for distinguishing diploid and triploid conceptuses. Our results indicated that although p57KIP2 immunostaining correctly identified almost all androgenetic CHM cases, almost half of the diandric monogynic PHMs were incorrectly diagnosed as abortion based on morphology and p57KIP2 staining information by at least 1 pathologist. This study demonstrates that FISH analysis can overcome the limitations of p57KIP2 immunostaining; in cases where the differential diagnosis between PHM and non-molar conceptuses presents difficulty, FISH ploidy information can aid in assigning cases to PHM, except for exceptional cases, such as monoandric digynic triploid cases. The incidence of digynic triploidy accounts for one-third of all triploid cases observed during the first trimester . Therefore, it is important to acknowledge the occurrence of monoandric digynic triploid cases. The availability and reproducibility of diagnostic procedures are essential factors to consider when selecting a clinically applicable diagnostic method. Numerous institutions subject genomic DNA, extracted from chorionic villous tissues and decidua (maternal) tissues using FFPE blocks, to STR analysis , , . However, in many pathologic departments, FISH analysis can be performed more easily than STR analysis, as it is technically available. Although FISH probes are expensive, FISH analysis has been widely used in pathologic departments and laboratories to diagnose breast cancer subtypes and some types of sarcoma or leukemia using FISH probes for human epidermal growth factor receptor 2 and fusion genes, respectively – . In this study, we used only 2 FISH probes, chromosomes 17 and XY, to determine the ploidies of POCs. The result obtained from 1 FISH probe does not strictly represent the ploidy but represents the disomy or trisomy of the selected probe chromosome. Moreover, aneuploidy is the main factor contributing to early pregnancy loss. Therefore, caution is required when using single-probe FISH assays to determine ploidy in the villous tissues of POCs, which may include numerous trisomy cases. In this study, we selected the CEP17 probe because it rarely harbors trisomy of chromosome 17, which has a reported frequency below 1% , . Thus, a single CEP17 probe is one of the best choices for evaluating ploidy. Furthermore, the three-signal ratio of FISH was found to be a critical parameter for determining the ploidy (diploid vs. triploid) of the samples. When histologic sections are prepared for examination by FISH analysis, some parts of the cells are inevitably lost during sectioning, leading to artificial deletions . The frequency of occurrence of this artificial effect, known as “truncation artifacts,” may be high. We overcame this problem by using the three-signal ratio as an objective indicator. When the boundary zones are sufficiently large, suspected molar conceptuses can be directly categorized as diploid or triploid (Fig. ). However, the conditions for performing FISH, including section thickness at each laboratory, must be adjusted to achieve reproducible FISH results. Owing to its availability, ease, and reproducibility, FISH may be a feasible diagnostic tool in clinical settings and pathologic departments. In the present study, we have uncovered a caveat regarding STR analysis; although STR analysis offers a significant advantage in distinguishing between PHM and non-molar abortus, it has limitations. The significance of STR analysis was introduced in the book “World Health Organization Classification of Tumours, Female Genital Tumours,” which presented the STR electropherogram of genetic PHM . However, in our study, the 2 cases (case #63 and #64) previously diagnosed as genetic PHM without three allelic loci (diandric monogynic triploid) using STR analysis were found to be androgenetic/biparental mosaic diploid cases using FISH. The STR patterns of our androgenetic/biparental mosaic diploid cases were similar to those of previously reported diandric monogynic triploids without three allelic loci , , , . Therefore, based on the STR analysis, biparental and androgenetic diploid cells in the cases of biparental/androgenetic conceptus could include one paternal haploid in both cell populations. The discrepancy in the p57KIP2 immunohistochemistry pattern and the absence of three allelic loci in the villi by STR analysis suggest the presence of androgenetic/biparental mosaic diploid, as mentioned by Xing et al . In cases where biparental/androgenetic mosaic gestation is suspected, accurate diagnosis can be facilitated through STR analysis, employing laser microdissection, which separately collects villous trophoblasts and stromal cells . Nevertheless, FISH can still be helpful for precise diagnosis. The limitation of the diagnostic workflow in this study is its propensity to generate perplexing outcomes in exceptional cases involving paradoxical p57KIP2 immunostaining. The most notable limitation of this workflow is its inability to distinguish between monoandric digynic triploid (non-molar) (case #80) and diandric monogynic triploid (PHM). STR analysis is deemed more specific in diagnosing PHM than p57KIP2 and FISH analysis, as up to 1/3 of triploid abortions were digynic, non-molar conceptuses , . However, three signals in FISH analysis strongly support the decision to diagnose PHM. In addition, androgenetic diploid CHMs that retain maternal chromosome 11 have been reported to exhibit positive p57KIP2 immunostaining. Previous studies have indicated that 1% to 2% of androgenetic CHM cases could manifest sustained expression of p57KIP2 , , , . In addition to p57KIP2-positive outcomes, 2 FISH signals could be interpreted as non-molar abortus. Moreover, a diandric monogynic triploid PHM or uniparental disomy case with loss of maternal chromosome 11 was reported to be negative in p57KIP2 immunostaining , . In the present study, vaginal villous specimens of invasive moles displayed a slightly elevated three-signal ratio (Fig. A), attributable to their high proliferation potential. However, our diagnostic workflow is not applicable if the initial histologic (HE) examination did not suspect HM. For example, in this study, 2 independent pathologists evaluated case #60 (diandric monogynic triploid PHM) as a non-molar hydropic abortion. However, this case would not have been diagnosed as an HM if it had not been included for analysis using the diagnostic workflow; case #60 was referred to our hospital based on sonographic findings suggesting a molar pregnancy. Therefore, clinical information is a critical determinant for applying the diagnostic workflow. In conclusion, this study demonstrates that a comprehensive analysis using p57KIP2 immunostaining and FISH is helpful in the pathologic diagnosis of POC. Further validation in multiple laboratories and with multiple pathologists is required to confirm our findings. Reliable differentiation between molar and non-molar pregnancies is crucial for accurate patient management, early detection of GTN, and ensuring the safety of subsequent pregnancies.
SUPPLEMENTARY MATERIAL
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null | d5b74266-7ded-41d4-a6ae-77227f7ee1f9 | 11448420 | Internal Medicine[mh] | Equine piroplasmosis (EP) is a tick-borne disease affecting equines (horses, donkeys, mules, and zebras) and is caused by the intra-erythrocytic protozoa Babesia caballi and Theileria equi . Clinical signs of EP include fever, hemolytic anemia, red urine, jaundice, edema, and potentially death . However, these clinical signs are often non-specific, and most infected horses show no clinical signs, becoming lifelong carriers and serving as reservoirs for transmission to naïve horses in endemic regions . These parasites are widely distributed throughout the world, including in Europe, Asia, Africa, and South America, with approximately 90% of equids worldwide living in areas endemic for EP . Due to the socioeconomic impact of the disease, EP is listed as one of the notifiable diseases of equids by the Terrestrial Animal Health Code of the World Organization for Animal Health (WOAH) . Many countries also require EP inspections for international and, sometimes, intra-country transfers of horses . Laboratory diagnostic methods for EP rely on the identification of parasites and specific antibodies . Common serological tests include the indirect immunofluorescence assay (IFAT), complement fixation test, and competitive enzyme-linked immunosorbent assay (ELISA), which are used to identify chronically infected horses. Specifically, IFAT and cELISA are the primary tests used for qualifying horses for importation . While IFAT is sensitive, it is time-consuming and highly subjective, particularly in interpreting fluorescence, and requires a large quantity of antigens. Conversely, cELISA, recommended by the United States Department of Agriculture (USDA) and WOAH for international horse transport, uses purified recombinant antigens, RAP1 and EMA1/EMA2, for B. caballi and T. equi , respectively. However, challenges arise due to the genetic variability of rap-1 gene in B. caballi isolates leading to detection failures in some cases with USDA-approved cELISA . Also, recent studies have reported that false negative results might be associated with cELISA for T. equi due to the absence of the EMA1 gene in Theileria haneyi , formerly known as T. equi clade C . Moreover, the necessity for separate tests for B. caballi and T. equi makes serological tests more laborious. Furthermore, early-stage infections may yield false-negative results until the antibodies reach levels detectable by these serodiagnostic methods . For an accurate diagnosis of EP, microscopic examination of blood smears and/or PCR in combination with serological techniques is recommended in the WOAH manual . Microscopic examination of Giemsa-stained thin blood smears, being simple and useful for demonstrating parasites in red blood cells (RBCs), is still used today in most diagnostic laboratories and clinical settings. However, this method is labor intensive and has low sensitivity, and its success is dependent on the experience and expertise of the examiner. It particularly struggles with the detection of low parasitemia levels in subclinical and chronic infections. In contrast, molecular diagnostic methods, such as PCR , nested PCR , and real-time PCR , are more sensitive but expensive and require skilled laboratory techniques. In addition, these tests take 3–6 hours to yield results, limiting their utility for large-scale use, such as screening assays. Therefore, there is a need for a sensitive, user-friendly, and field-deployable diagnostic test for EP. An innovative assay has recently been developed to meet these requirements, using the automated hematology analyzer XN-31 (Sysmex Corp., Kobe, Japan). This CE-marked platform was developed to support the diagnosis of human malaria in clinical settings. The XN-31 analyzer has the same physical characteristics as the XN-30 developed for research use. It operates on the principle of fluorescence flow cytometry, which counts and classifies cells by irradiating them with a 405 nm laser beam, analyzing the resultant forward-scattered light (FSC) to reflect the size of infected RBCs and side fluorescent light (SFL) indicating nucleic acid content . The XN-31 analyzer is also able to distinguish between the malarial species Plasmodium falciparum and Plasmodium vivax and detect their gametocytes with good sensitivity and specificity. A previous study has reported that the limit of detection (LoD) is 5.9 infected cells/µL . The parasitemia levels determined by the XN-31 analyzer show a strong correlation with those obtained from microscopic analysis of cultured malaria and mouse/human blood samples . This innovative, user-friendly, and rapid testing method, using a blood cell counter, is expected to serve as an alternative to PCR and microscopy in diagnosing human malaria. Due to these attributes, the XN-31 analyzer should be potentially capable of detecting RBCs infected with B. caballi and T. equi . The objective of this study was to evaluate the performance of the XN-31 analyzer in the rapid diagnosis of EP. Parasite culture B. caballi (USDA strain) and T. equi (USDA strain) were cultured in vitro in an atmosphere of 5% O 2 and 5% CO 2 at 37°C, as previously described . The culture medium for B. caballi and T. equi was RPMI1640 medium (Sigma-Aldrich, Tokyo, Japan) and Medium 199 (Sigma-Aldrich), respectively, each supplemented with 40% horse serum, 13.6 µg/mL of hypoxanthine, 1% GlutaMAX-I (all from Sigma-Aldrich), and horse RBCs at a 10% hematocrit. The horse serum and RBCs were collected from healthy horses and prepared as previously described . The collected whole blood was defibrinated immediately using glass beads and centrifuged at 500 × g for 20 minutes at 4°C. The serum was decanted and stored at −80°C until use. The buffy coat was discarded, and the remaining erythrocytes were suspended in the appropriate culture medium and stored at 4°C until future use. This study was approved by the Institutional Animal Care and Use Committee (Permission number: 21–12) and carried out according to the Equine Research Institute Animal Experimentation Regulations. Measurement by automated hematology analyzer XN-31 and data analysis In vitro cultured B. caballi , T. equi , and their mixed samples were analyzed using the XN-31 analyzer (Sysmex Corp., Kobe, Japan) modified for horse blood testing (XN-31m) and set to low malaria (LM) mode . For the measurements, dedicated reagents (CELLPACK DCL, SULFOLYSER, Lysercell M, and Fluorocell M; Sysmex Corp.) were used, according to the manufacturer’s instructions. The RBC count (RBC#) was determined using the sheath flow direct current detection method, and the corresponding count of infected RBCs (MI-RBC#; reported as infected RBC per microliter) was measured using the XN-31 fluorescence flow cytometry-based technique. For manual analysis, flow cytometry standard data exported from the XN-31m analyzer were analyzed using FlowJo version 10.8.1 (BD Biosciences, Ashland, OR, USA). Areas of infected RBCs and non-infected RBCs were designated on the scattergram (SFL-FSC), and the dots representing infected RBCs in the gated area were counted. The scattergram presented dots corresponding to 0.953 µL of sample volume, whereas the analyzer reported total RBC counts per 1 µL. This difference was compensated in the calculation of parasitemia, as shown in the following equation: P a r a s i t e m i a = i n f e c t e d R B C # ( c o u n t s ) / 0.953 / 3 / t o t a l R B C # ( c o u n t s / μ L ) × 100 Precision (within-run repeatability) To evaluate the precision of the XN-31m analyzer, samples with varying levels of parasitemia for B. caballi and T. equi were tested. These samples were categorized into four groups based on their parasitemia levels: high (>1.5%), medium (1.0%–1.5%), low (0.5%–1.0%), and very low (<0.5%). Each group was tested 10 consecutive times. For the total RBC counts, infected RBC counts, and parasitemia, the mean, SD, and coefficient of variation (CV%) were calculated. Linearity Linearity in diagnostic testing is the ability to provide results that are directly proportional to the concentration of the analyte being measured over a specific range. The linearity of the XN-31m analyzer was evaluated by analyzing three repeats of a 10-point (0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%) serial dilution of known parasitemia samples with a diluent solution (CELLPACK DCL) . Carryover Carryover was evaluated according to the CLSI H26-A2 guideline, which covers the assessment of high parasitemia (H1, H2, and H3) and low parasitemia (L1, L2, and L3) samples ( B. caballi and T. equi , respectively) in a sequence of three consecutive tests . First, the high parasitemia samples were analyzed, followed by the low parasitemia samples, to observe the influence of preceding high parasitemia measurements on subsequent low parasitemia readings. We assessed the percentage of carryover using the formula: [ ( L 1 − L 3 ) / ( H 3 − L 3 ) ] × 100 Limit of blank, limit of detection, and limit of quantification To determine the lower detection threshold of the XN-31m analyzer, we evaluated the limit of blank (LoB), LoD, and limit of quantification (LoQ). The LoB was estimated using blank samples (CELLPACK DCL) and determined as the 95th percentile of the blank samples’ distribution, calculated non-parametrically based on 70 values. The LoD was assessed by measuring seven standard samples with different parasitemia levels. Each standard sample was measured five consecutive times daily, over a 3-day period. The LoD was calculated as LoD = LoB + Cp × SD, where Cp is the coefficient corresponding to the 95th percentile of a normal distribution. The LoQ was determined based on the point where the CV of measurements from the same dilution series did not exceed 20%. Comparison of microscopic examinations For microscopic examination, thin blood smears were prepared, stained with May-Grünwald reagent (Muto Pure Chemicals, Tokyo, Japan) at room temperature for 5 minutes, incubated with PBS (pH 6.4) for 10 minutes, and then incubated with 1:20 diluted Giemsa solution (Muto Pure Chemicals) at room temperature for 20 minutes. The stained slides were examined under a model BX53 light microscope (Olympus, Tokyo, Japan) at 1,000× magnification. The percentage of infected RBCs (parasitemia) was determined by counting infected RBCs in at least 10,000 erythrocytes by an experienced pathologist. Statistical analyses Statistical analyses were conducted to assess the reliability and precision of the data collected in this study. Key statistical metrics calculated included the coefficient of determination ( R 2 ), mean, SD, and coefficient of variation (CV% = SD/mean × 100). These calculations were performed using Excel software for Mac 2019 (Microsoft, Redmond, WA, USA). B. caballi (USDA strain) and T. equi (USDA strain) were cultured in vitro in an atmosphere of 5% O 2 and 5% CO 2 at 37°C, as previously described . The culture medium for B. caballi and T. equi was RPMI1640 medium (Sigma-Aldrich, Tokyo, Japan) and Medium 199 (Sigma-Aldrich), respectively, each supplemented with 40% horse serum, 13.6 µg/mL of hypoxanthine, 1% GlutaMAX-I (all from Sigma-Aldrich), and horse RBCs at a 10% hematocrit. The horse serum and RBCs were collected from healthy horses and prepared as previously described . The collected whole blood was defibrinated immediately using glass beads and centrifuged at 500 × g for 20 minutes at 4°C. The serum was decanted and stored at −80°C until use. The buffy coat was discarded, and the remaining erythrocytes were suspended in the appropriate culture medium and stored at 4°C until future use. This study was approved by the Institutional Animal Care and Use Committee (Permission number: 21–12) and carried out according to the Equine Research Institute Animal Experimentation Regulations. In vitro cultured B. caballi , T. equi , and their mixed samples were analyzed using the XN-31 analyzer (Sysmex Corp., Kobe, Japan) modified for horse blood testing (XN-31m) and set to low malaria (LM) mode . For the measurements, dedicated reagents (CELLPACK DCL, SULFOLYSER, Lysercell M, and Fluorocell M; Sysmex Corp.) were used, according to the manufacturer’s instructions. The RBC count (RBC#) was determined using the sheath flow direct current detection method, and the corresponding count of infected RBCs (MI-RBC#; reported as infected RBC per microliter) was measured using the XN-31 fluorescence flow cytometry-based technique. For manual analysis, flow cytometry standard data exported from the XN-31m analyzer were analyzed using FlowJo version 10.8.1 (BD Biosciences, Ashland, OR, USA). Areas of infected RBCs and non-infected RBCs were designated on the scattergram (SFL-FSC), and the dots representing infected RBCs in the gated area were counted. The scattergram presented dots corresponding to 0.953 µL of sample volume, whereas the analyzer reported total RBC counts per 1 µL. This difference was compensated in the calculation of parasitemia, as shown in the following equation: P a r a s i t e m i a = i n f e c t e d R B C # ( c o u n t s ) / 0.953 / 3 / t o t a l R B C # ( c o u n t s / μ L ) × 100 To evaluate the precision of the XN-31m analyzer, samples with varying levels of parasitemia for B. caballi and T. equi were tested. These samples were categorized into four groups based on their parasitemia levels: high (>1.5%), medium (1.0%–1.5%), low (0.5%–1.0%), and very low (<0.5%). Each group was tested 10 consecutive times. For the total RBC counts, infected RBC counts, and parasitemia, the mean, SD, and coefficient of variation (CV%) were calculated. Linearity in diagnostic testing is the ability to provide results that are directly proportional to the concentration of the analyte being measured over a specific range. The linearity of the XN-31m analyzer was evaluated by analyzing three repeats of a 10-point (0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%) serial dilution of known parasitemia samples with a diluent solution (CELLPACK DCL) . Carryover was evaluated according to the CLSI H26-A2 guideline, which covers the assessment of high parasitemia (H1, H2, and H3) and low parasitemia (L1, L2, and L3) samples ( B. caballi and T. equi , respectively) in a sequence of three consecutive tests . First, the high parasitemia samples were analyzed, followed by the low parasitemia samples, to observe the influence of preceding high parasitemia measurements on subsequent low parasitemia readings. We assessed the percentage of carryover using the formula: [ ( L 1 − L 3 ) / ( H 3 − L 3 ) ] × 100 To determine the lower detection threshold of the XN-31m analyzer, we evaluated the limit of blank (LoB), LoD, and limit of quantification (LoQ). The LoB was estimated using blank samples (CELLPACK DCL) and determined as the 95th percentile of the blank samples’ distribution, calculated non-parametrically based on 70 values. The LoD was assessed by measuring seven standard samples with different parasitemia levels. Each standard sample was measured five consecutive times daily, over a 3-day period. The LoD was calculated as LoD = LoB + Cp × SD, where Cp is the coefficient corresponding to the 95th percentile of a normal distribution. The LoQ was determined based on the point where the CV of measurements from the same dilution series did not exceed 20%. For microscopic examination, thin blood smears were prepared, stained with May-Grünwald reagent (Muto Pure Chemicals, Tokyo, Japan) at room temperature for 5 minutes, incubated with PBS (pH 6.4) for 10 minutes, and then incubated with 1:20 diluted Giemsa solution (Muto Pure Chemicals) at room temperature for 20 minutes. The stained slides were examined under a model BX53 light microscope (Olympus, Tokyo, Japan) at 1,000× magnification. The percentage of infected RBCs (parasitemia) was determined by counting infected RBCs in at least 10,000 erythrocytes by an experienced pathologist. Statistical analyses were conducted to assess the reliability and precision of the data collected in this study. Key statistical metrics calculated included the coefficient of determination ( R 2 ), mean, SD, and coefficient of variation (CV% = SD/mean × 100). These calculations were performed using Excel software for Mac 2019 (Microsoft, Redmond, WA, USA). Detection of non-infected and infected RBCs To apply the XN-31m analyzer for the diagnosis of EP, we first analyzed peripheral blood samples from healthy horses. A malaria (M) scattergram is generated for each sample and consists of SFL intensity (corresponding to the amount of nucleic acid) on the horizontal axis (x-axis) and FSC intensity (indicating the size of cells) on the vertical axis (y-axis; ). The XN-31m analyzer detected white blood cells, non-infected RBCs, and RBCs containing Howell-Jolly bodies (HJB-RBCs) on the M scattergram . Non-infected RBCs were formed a cluster on the left side of the M scattergram, along the vertical axis. White blood cells are larger in size and have a higher nucleic acid content, forming a cluster in the top right-hand corner of the M scattergram. In the evaluation of cultured samples infected with B. caballi and T. equi , the XN-31m analyzer distinguished infected RBCs (red dots) from non-infected RBCs (blue dots) on the M scattergram in approximately 1 minute. Moreover, the XN-31m distinguished B. caballi -infected RBCs from T. equi -infected RBCs, representing them on the M scattergrams as separate clusters ( ; Fig. S1). HJB-RBCs were often misidentified as EP-infected RBCs, as in a previous study on rodents , although they formed a distinct cluster from the infected RBCs . The low prevalence of HJB-RBCs did not affect the detection of infected RBCs using the XN-31m. However, these findings suggest a limitation in accurately counting infected RBCs by the XN-31m due to the presence of HJB-RBCs, indicating a need to re-analyze the data to calculate the number of infected RBCs. We used manual gating to count infected and uninfected RBCs on the scattergrams, as described in the Materials and methods. Precision (within-run repeatability) The precision of infected RBC counts, total RBC counts, and parasitemia measurements for samples infected with B. caballi and T. equi is shown in . For samples infected with B. caballi and T. equi , the CV%s of infected RBC counts ranged from 0.82% to 2.26% and 0.61% to 1.72%, respectively. The CV%s of other parameters were also acceptable (<2.5%). Linearity The linearity was evaluated by serially diluting in vitro cultured samples infected with B. caballi and T. equi . Using this approach, the correlation coefficients were excellent ( R 2 > 0.999) between expected theoretical concentrations and obtained values of parasitemia for samples infected with B. caballi and T. equi . Carryover Carryover is defined as the amount of analyte carried by the analyzer from one sample measurement into the subsequent measurement. The results showed that carryover never exceeded 0.2% . Limit of blank, limit of detection, and limit of quantification The detection limits of the XN-31m analyzer for EP are summarized in . The LoB was 0.70 infected RBCs/µL. A dilution series of in vitro cultured parasites was used to determine the LoD and LoQ. The LoD was 4.54 infected RBCs/µL for B. caballi and 5.80 infected RBCs/µL for T. equi . The LoQ was 14.10 infected RBCs/µL for B. caballi and 11.44 infected RBCs/µL for T. equi (Fig. S2). Comparison between the XN-31 analyzer and microscopic examination To investigate the reliability of the XN-31m, its ability to measure parasitemia was compared with traditional microscopic examination of Giemsa-stained smears. The correlation analysis showed a strong coefficient of determination for B. caballi ( R 2 = 0.986) and T. equi ( R 2 = 0.990; ). To apply the XN-31m analyzer for the diagnosis of EP, we first analyzed peripheral blood samples from healthy horses. A malaria (M) scattergram is generated for each sample and consists of SFL intensity (corresponding to the amount of nucleic acid) on the horizontal axis (x-axis) and FSC intensity (indicating the size of cells) on the vertical axis (y-axis; ). The XN-31m analyzer detected white blood cells, non-infected RBCs, and RBCs containing Howell-Jolly bodies (HJB-RBCs) on the M scattergram . Non-infected RBCs were formed a cluster on the left side of the M scattergram, along the vertical axis. White blood cells are larger in size and have a higher nucleic acid content, forming a cluster in the top right-hand corner of the M scattergram. In the evaluation of cultured samples infected with B. caballi and T. equi , the XN-31m analyzer distinguished infected RBCs (red dots) from non-infected RBCs (blue dots) on the M scattergram in approximately 1 minute. Moreover, the XN-31m distinguished B. caballi -infected RBCs from T. equi -infected RBCs, representing them on the M scattergrams as separate clusters ( ; Fig. S1). HJB-RBCs were often misidentified as EP-infected RBCs, as in a previous study on rodents , although they formed a distinct cluster from the infected RBCs . The low prevalence of HJB-RBCs did not affect the detection of infected RBCs using the XN-31m. However, these findings suggest a limitation in accurately counting infected RBCs by the XN-31m due to the presence of HJB-RBCs, indicating a need to re-analyze the data to calculate the number of infected RBCs. We used manual gating to count infected and uninfected RBCs on the scattergrams, as described in the Materials and methods. The precision of infected RBC counts, total RBC counts, and parasitemia measurements for samples infected with B. caballi and T. equi is shown in . For samples infected with B. caballi and T. equi , the CV%s of infected RBC counts ranged from 0.82% to 2.26% and 0.61% to 1.72%, respectively. The CV%s of other parameters were also acceptable (<2.5%). The linearity was evaluated by serially diluting in vitro cultured samples infected with B. caballi and T. equi . Using this approach, the correlation coefficients were excellent ( R 2 > 0.999) between expected theoretical concentrations and obtained values of parasitemia for samples infected with B. caballi and T. equi . Carryover is defined as the amount of analyte carried by the analyzer from one sample measurement into the subsequent measurement. The results showed that carryover never exceeded 0.2% . The detection limits of the XN-31m analyzer for EP are summarized in . The LoB was 0.70 infected RBCs/µL. A dilution series of in vitro cultured parasites was used to determine the LoD and LoQ. The LoD was 4.54 infected RBCs/µL for B. caballi and 5.80 infected RBCs/µL for T. equi . The LoQ was 14.10 infected RBCs/µL for B. caballi and 11.44 infected RBCs/µL for T. equi (Fig. S2). To investigate the reliability of the XN-31m, its ability to measure parasitemia was compared with traditional microscopic examination of Giemsa-stained smears. The correlation analysis showed a strong coefficient of determination for B. caballi ( R 2 = 0.986) and T. equi ( R 2 = 0.990; ). Increased international transport of horses has created a need for more accurate and rapid EP testing methods. The purpose of EP testing is usually to declare an animal free from infection, to confirm suspicion of infection, or to confirm the efficacy of treatment. EP diagnosis largely relies on serological tests, blood smears, and molecular methods; however, despite their effectiveness, these methods are time-consuming and require highly trained examiners and laboratory equipment for accurate testing . The increasing demand for highly accurate and rapid EP testing has created an unmet need. In this study, we demonstrated that the XN-31m analyzer can detect RBCs infected with B. caballi and T. equi in a rapid and automated manner with high sensitivity. Given the excellent performance and practicability of the XN-31m, this automated analyzer emerges as a promising novel diagnostic method for EP. Rapid and accurate diagnosis of EP is essential for successful control and prevention of the disease. The WOAH recommends a combined approach of serological tests and PCR/blood smear for the diagnosis of EP . Serological methods, such as IFAT and cELISA, are widely used to identify chronic cases. However, during the early stage of the infection, horses may not have detectable levels of antibodies for serological tests to be positive, although they might still test positive by blood smears and PCR. The diagnosis of EP involves serodiagnosis and agent identification tests, which are time-consuming. Our data suggest that the XN-31 would be a game changer as a novel diagnostic method for EP. The LoD of the XN-31m is less than 10 infected RBC/µL, suggesting its high sensitivity as a method for diagnosing EP. Microscopic examination of blood smears has been the most conventional method for confirming Babesia spp. and Theileria spp. infections in animals and malaria in humans . The sensitivity of blood smear analysis for detecting babesiosis ranges from 10 −5 % to 10 −6 % parasitemia (single-infected RBC per 10 5 –10 6 RBCs) . In the case of human malaria, the LoD of a blood smear is at about 1–2 × 10 −3 % parasitemia (50–100 infected RBCs/μL, assuming an average total RBC count of 5 × 10 6 cells/µL of blood in humans) under optimum conditions by experienced examiners . Molecular methods for diagnosing EP are more sensitive than microscopic examinations. The highest analytical sensitivity values have been reported for nested PCR and qPCR methods. Specifically, nested PCR can detect fewer than 10 parasites of T. equi and a single infected cell of B. caballi . Moreover, real-time PCR assays for T. equi have demonstrated the capability to detect as few as 2.5 infected RBCs/μL (at least 2–3.8 × 10 −5 % parasitemia) . The parasitemia during clinical infections caused by B. caballi does not exceed 1% and may be less than 0.1% (1,000 infected RBCs/µL assuming a total RBC count of 10 6 cells/µL of blood), while during clinical diseases caused by T. equi , the parasitemia is usually between 1% and 5% (1–5 × 10 4 infected RBCs/µL) . Taken together, the XN-31 analyzer is considered sensitive enough to detect such acute cases of EP. However, in chronic or subclinical EP cases, the parasitemia is usually too low for reliable detection by blood smears. Although this study showed that the XN-31m analyzer has high sensitivity, further in vivo evaluations are required to determine whether it can detect chronic cases of EP. To evaluate the instrument’s ability to detect RBCs infected with T. equi and B. caballi , we first validated fluorescence and light-scattering parameters between samples infected with the parasites. The fluorescence and light scattering properties of T. equi and B. caballi were distinct on the M scattergrams, allowing for species identification ( ; Fig. S1). Within erythrocytes, B. caballi merozoites are pear-shaped, 2–5 µm in length and 1.3–3.0 µm in diameter, and sometimes occur in pairs. In contrast, T. equi merozoites in RBCs are relatively smaller, less than 2–3 µm, and are round or oval in shape, forming one to four separate merozoites . Considering that the XN-31 analyzer distinguishes the Plasmodium spp. by measuring DNA amount and internal cell structures , the differences in parasite size, genome size (12.8 Mbp for B. caballi and 11.6 Mbp for T. equi ) , and number of parasites in RBCs may be responsible for the differences observed in the M scattergrams. The default algorithm of the XN-31m analyzer was insufficient to accurately calculate the parasitemia of B. caballi and T. equi . This can be attributed to the tendency of the analyzer to misidentify HJB-RBCs as infected RBCs, as previously reported in mouse blood samples . HJBs are nuclear remnants and can occasionally be observed in healthy horses. These findings suggest that HJB-RBCs prevent accurate quantification of infected RBCs using the algorithm equipped in the XN-31m analyzer. This algorithm automatically gates a specific region of the M-scattergram, known as the M-gating area, and detects and counts RBCs within this region as malaria-infected RBCs. In this study, for accurate calculation of parasitemia, the infected RBC regions on M scattergrams were manually gated for every sample. Therefore, consideration regarding an automated gating method will be required to improve the performance of diagnosis for EP. It is recommended that other testing methods should be used in combination with the XN-31m when a suspicious scattergram is obtained. The XN-31 analyzer detects malaria parasites using flow cytometry technology . This technology has been used to detect malaria and babesia parasites since the 1970s, as it provides a more accurate and low-labor alternative to manual cell counting through microscopy. Most flow cytometry-based methods use DNA/RNA staining dyes, such as SYBR green and SYTO16, to distinguish infected RBCs from their uninfected counterparts . Although flow cytometry has high sensitivity, its implementation in the field would require well-trained technicians, and the methods can be time-consuming due to the processing of specimens and the setup required for detection, in addition to expensive equipment. Consequently, flow cytometry-based methods are not yet practical for diagnosing babesiosis/theileriosis in animals. In contrast, the XN-31 analyzer aspirates approximately 60 µL of blood directly from a blood collection tube and analyzes it automatically. The entire testing process of the XN-31m analyzer takes approximately 1 minute. No sample processing is required, allowing for high-throughput testing of targeted horses. These performances are supported by a combination of two dedicated reagents, Lysercell M (a lysate containing a non-ionic surfactant) and Fluorocell M (a solution containing the nucleic acid stain Hoechst dye) with a blue laser. Owing to its property of directly detecting parasites in RBCs, the XN-31 analyzer has the potential to detect blood protozoa other than T. equi and B. caballi. T. haneyi , a newly identified piroplasm affecting equids, has recently been reported in many countries , complicating the diagnostic workflow for EP. The XN-31 analyzer might simultaneously detect T. haneyi, other intra-erythrocytic parasites, and Trypanosoma. Another important advantage of the XN-31 analyzer is that it also provides a conventional complete blood count with each analysis, which is not offered by other diagnostic tools for EP. This provides clinical veterinarians important information regarding the hematological status of the animal during infection and treatment, in addition to the parasitemia. In conclusion, the XN-31m analyzer holds promise as a rapid and sensitive diagnostic method for EP. It can be used to detect horses with low-density parasitemia and is a useful screening tool in EP control and prevention programs. Deployment of this device at strategic locations could lead to improved disease management, treatment, and screening, ultimately contributing to more effective containment and mitigation of EP. |
PredCMB: predicting changes in microbial metabolites based on the gene–metabolite network analysis of shotgun metagenome data | daff30d3-4397-45ae-86eb-668e9cff3a96 | 11771765 | Biochemistry[mh] | The microbiome, residing in various organs of the host body, plays a critical role in the host’s biological response to biochemical stimuli . From this perspective, various studies have been carried out to investigate the interaction between microbiome and host biology including that of human. Recent studies have highlighted the significant influence of microbial metabolites on host health . For instance, metabolic products of intestinal microorganisms have been linked to metabolic diseases such as obesity and diabetes, cardiovascular diseases, and neuropsychiatric disorders . Given their potential impact on host biology and pathogenicity, accurately identifying microbiome-derived metabolites is of great importance. Conventional strategies for analyzing microbial metabolites include direct metabolomic profiling and analytical predictions based on metagenome sequencing data . Techniques like mass spectrometry (MS) and liquid chromatography (LC) used for direct metabolomic profiling face limitations in metabolite coverage and high costs, as these technologies often only detect a limited range of metabolites. Analytical prediction tools, such as HUMAnN , infer the abundances of functional or metabolic pathways from microbial gene abundances. However, these methods typically estimate pathway abundances rather than metrics specific to metabolites. Recently, new approaches were presented to predict disease phenotypes or microbial metabolic features based on training data, where predictive models of metabolites have been developed based on a paired dataset of metagenome and metabolome . Despite their promise, these machine learning-based predictive methods are constrained by the limited availability of high-quality training datasets of paired microbiome and metabolome that comprehensively cover diverse biological and experimental conditions. The performance of such models heavily depends on the quantity and quality of training data, which is often restricted to specific conditions. Another approach involves predicting microbial metabolite dynamics using genome-scale metabolic models (GEMs) of the microbiome, where flux-based metabolic dynamics can be assessed based on GEMs [see review articles on microbial GEM studies for more detailed information]. GEM-based analysis on specific microbial strains can be conducted with pre-built GEMs and constraint information for target biological contexts , where pre-built GEMs can be publicly available in model repositories . However, GEM-based analysis on microbial communities is not straightforward, as an integrated analysis of GEMs from microorganisms in the community needs to be conducted or a new pan-genome GEMs needs to be built from the metagenome data . Both approaches demand substantial effort to build and optimize models for specific contexts. Without such needs for target context-specific resources of training data or computational models like GEMs, utilizing known metabolic reaction information like the reporter metabolites method can be another choice of approach. However, this method has not been applied to metagenome data from microbial communities due to the lack of curated microbial metabolic reaction data suitable for such analyses. In this study, we propose PredCMB, a novel method for quantitatively predicting changes in individual microbial metabolites between two conditions solely using shotgun metagenome sequencing data. PredCMB estimates metabolite changes based on the abundance changes of enzymatic gene families that are involved in the production and consumption of the metabolite as well as the concept of reaction abundance weight. To show the validity of our proposed method, we used two publicly available paired datasets of shotgun metagenomics and metabolomics from inflammatory bowel diseases (IBDs) cohorts and cohorts of gastrectomy for gastric cancer . We evaluated the concordance between PredCMB’s predicted metabolite changes and experimentally measured changes. The comparative evaluation shows that our proposed method can predict correlated changes of metabolites with actual measurements, while concordantly predicting the metabolite classes that show major changes between given conditions. The main contributions of this study are 2-fold: (i) the development of curated microbial metabolic reaction data suitable for network-based prediction methods, and (ii) the enhancement of network-based analysis with PredCMB, which accounts for both production and consumption reactions as well as reaction abundance weight, unlike previous methods that consider only production reactions. PredCMB provides a metagenome-based method for predicting metabolic changes, paving the way for novel microbiome research opportunities. It enables rapid identification of major metabolic shifts in the microbiome and offers directional insights for downstream metabolomic analyses.
PredCMB uses shotgun metagenome sequencing data of two conditions (control and experiment) as input and gives predicted changes of metabolites through the enzymatic gene–metabolite network analysis based on differential abundances of enzymatic gene families between two conditions. The overall workflow of PredCMB is illustrated in . First, the abundance of each known enzymatic gene family is profiled by mapping the shotgun metagenome sequencing data to the enzymatic gene families. Then, the differentiality of each enzymatic gene family is computed as a P -value by statistically comparing the abundances of the enzymatic gene family between the two conditions. Finally, the enzymatic gene-metabolic network analysis predicts the relative change in each metabolite between conditions, by summarizing the differentiality statistics based on P -values of enzymatic gene families contributing to the production and consumption of each metabolite, while incorporating the reaction abundance weight into the calculation. 2.1 Profiling the abundances of enzymatic gene families from microbiome From shotgun metagenome sequencing data, the abundances of known enzymatic gene families are profiled by aligning sequencing reads to the UniRef90 protein family sequence database using the Biobakery3 pipeline, including Kneaddata, MetaPhlAn , and HUMAnN3 . UniRef90 defines protein families as groups of evolutionarily related protein-coding sequences that perform the same function, serving as a foundation for describing microbial gene family structures. Through the process of HUMAnN3 within Biobakery3, the gene family abundance is weighted based on the alignment quality and normalized by the length of both gene and alignment, then further normalized to copies per million (CoPM) units. Since PredCMB focuses exclusively on enzymatic gene families, aligning sequence reads only to enzymatic genes can significantly reduce HUMAnN3’s computational runtime. To achieve this, we created a custom Enzyme Commission (EC)-filtered ChocoPhlAn database by filtering reference sequences in the UniRef90 database with assigned EC numbers. This custom EC-filtered database enabled a roughly 3-fold reduction in HUMAnN3’s runtime (data not shown). However, some performance degradation was observed in downstream metabolite change predictions ( Section SA). For this study, we opted to use the conventional HUMAnN3 pipeline with the full reference database to ensure optimal performance in the preparation of input data for subsequent analyses. 2.2 Identifying differentially abundant enzymatic gene families The differentiality P -value of each enzymatic gene family can be computed by utilizing existing tools to evaluate differential expressions of next-generation sequencing data, and the PyDESeq2 python package was used in this study to evaluate the differentiality based on the negative binomial model for sequence read-count information. Before evaluating differentiality P -values, the CoPM-normalized enzymatic gene family abundances are rounded to nearest integer values to mimic conventional integer-valued count data. Gene families that show zero abundances in >75% of samples are omitted from further analysis . The identification of differentially abundant enzymatic gene families (DAGs) can be performed with different thresholds of fold-changes in abundances and differentiality P -values. In our study, we declared DAGs as enzymatic gene families that show >2-fold changes in abundances (|log 2 (abundance ratio)| > 1) and the differentiality P -value < 0.05. 2.3 Construction of enzymatic gene–metabolite networks The enzymatic gene–metabolite interaction data were compiled from the metabolic reactions cataloged in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database . Enzymes in KEGG reactions were mapped to their corresponding enzymatic gene families in UniRef90 using EC and KO numbers. For bidirectional metabolic reactions, the directionality was simplified by assigning each reaction a single direction for ease of analysis. The eQuilibrator tool was used to determine the direction of bidirectional KEGG metabolic reactions. This determination was based on Gibbs free energy calculations, which leveraged the thermodynamic properties of biochemical compounds and reactions. Enzymatic gene–metabolite networks were constructed from the compiled interactions through these processes, where a metabolite-centric network component is built for each metabolite compound by connecting enzymatic gene families that contribute to the production and consumption of the metabolite. The entire process of constructing enzymatic gene–metabolite networks is illustrated in . In total, 8298 metabolic reactions from KEGG were compiled, resulting in the construction of metabolite-centric network components for 3360 metabolite compounds. 2.4 Estimating metabolite changes and statistical significance using network information To estimate the change of each metabolite abundance from the change of the microbial genome, the metabolite-centric network component is used along with the P -values of differentiality statistics of DAGs and their reaction abundance weights around the metabolite. In the metabolite-centric network, each metabolite has incoming interactions from enzymatic genes contributing to its production and outgoing interactions to enzymatic genes involved in its consumption. We assume that changes in metabolite abundance can be determined by summarizing the differentiality statistics of the relevant enzymatic genes, accounting for their roles in production or consumption. In this study, the differentiality statistic of each enzymatic gene is represented by its P -value. To quantify these changes, the P -values of enzymatic gene families are converted to Z-scores, which reflect the magnitude and direction of abundance changes relative to controls. In this process of P -value conversion to Z-scores, we convert the P -values of increased gene families to positive Z-scores and the P -values of decreased gene families to negative Z-scores. Specifically, the P -value P i of the i th gene family is modified as follows: P i /2 if the abundance of the gene family is increased. P i /2 if the abundance of the gene family is decreased. This makes increased gene families have modified P -values with the range between 0 and 0.5, and decreased gene families have modified P -values with the range between 0.5 and 1. The modified P -values are then converted to Z-scores that matches the modified P -values from the inverse normal cumulative distribution, eventually making positive Z-scores for increased gene families and negative Z-scores for decreased gene families. Based on the Z-scores of enzymatic gene families, the Z-score Z metabolite that represents the relative change of a metabolite is calculated as follows: (1) Z metabolite = 1 k ∑ i = 1 k R ( i ) · w i · Z i , where k is the number of enzymatic gene families associated with the metabolite, R ( i ) is a function that returns 1 for an enzymatic gene families producing the metabolite and −1 for those consuming it. w i is a reaction abundance weight for the enzymatic gene family i , calculated as the relative abundance of the gene family in control samples as follows: (2) w i = abundance i avr ∑ j = 1 E abundance j avr , where E is the total number of enzymatic gene families and abundance i avr represents the average abundance of an enzymatic gene family i in control samples. This weight reflects the relative contribution of a gene family’s reaction under the baseline (control) condition. To evaluate the statistical significance of Z metabolite , we use the normal distribution to obtain the corresponding P -value. However, the distribution of Z metabolite may not match the exact normal distribution. Thus, Z metabolite values are adjusted first based on the null distribution of Z metabolite values that is obtained by randomly building 1000 metabolite-centric network components with randomly selected k enzymatic gene families as follows while preserving the original count of producing and consuming gene families: (3) Z NullAdj = ( Z metabolite - μ k ) σ k , where μ k and σ k indicate the mean and standard deviation of the null distribution Z-scores from random building of metabolite-centric network components. Finally, the statistical significance P -value for Z metabolite is determined by the right-tail P -value that corresponds to Z NullAdj from the standard normal distribution if it is positive, and by the left-tail P -value if Z NullAdj is negative. 2.5 Estimating metabolite changes and statistical significance using network information To identify metabolite classes with significant increase or decrease, we performed enrichment analysis based on the Wilcoxon rank sum test by ranking Z metabolite values of individual metabolites. A metabolite class was considered significantly increased or decreased if its metabolites consistently ranked particularly high or low in the rank-based comparison between metabolites within the class and those outside the class. Two-tailed nominal P -values were evaluated and corrected using the Benjamini-Hochberg (BH) method, and the false discovery rate (FDR) of 0.05 was used as significance threshold in this study. We used the categorization of metabolite classes based on the chemical classes level information from Human Metabolome Database (HMDB) , and metabolite classes with <5 metabolites were excluded in this study.
From shotgun metagenome sequencing data, the abundances of known enzymatic gene families are profiled by aligning sequencing reads to the UniRef90 protein family sequence database using the Biobakery3 pipeline, including Kneaddata, MetaPhlAn , and HUMAnN3 . UniRef90 defines protein families as groups of evolutionarily related protein-coding sequences that perform the same function, serving as a foundation for describing microbial gene family structures. Through the process of HUMAnN3 within Biobakery3, the gene family abundance is weighted based on the alignment quality and normalized by the length of both gene and alignment, then further normalized to copies per million (CoPM) units. Since PredCMB focuses exclusively on enzymatic gene families, aligning sequence reads only to enzymatic genes can significantly reduce HUMAnN3’s computational runtime. To achieve this, we created a custom Enzyme Commission (EC)-filtered ChocoPhlAn database by filtering reference sequences in the UniRef90 database with assigned EC numbers. This custom EC-filtered database enabled a roughly 3-fold reduction in HUMAnN3’s runtime (data not shown). However, some performance degradation was observed in downstream metabolite change predictions ( Section SA). For this study, we opted to use the conventional HUMAnN3 pipeline with the full reference database to ensure optimal performance in the preparation of input data for subsequent analyses.
The differentiality P -value of each enzymatic gene family can be computed by utilizing existing tools to evaluate differential expressions of next-generation sequencing data, and the PyDESeq2 python package was used in this study to evaluate the differentiality based on the negative binomial model for sequence read-count information. Before evaluating differentiality P -values, the CoPM-normalized enzymatic gene family abundances are rounded to nearest integer values to mimic conventional integer-valued count data. Gene families that show zero abundances in >75% of samples are omitted from further analysis . The identification of differentially abundant enzymatic gene families (DAGs) can be performed with different thresholds of fold-changes in abundances and differentiality P -values. In our study, we declared DAGs as enzymatic gene families that show >2-fold changes in abundances (|log 2 (abundance ratio)| > 1) and the differentiality P -value < 0.05.
The enzymatic gene–metabolite interaction data were compiled from the metabolic reactions cataloged in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database . Enzymes in KEGG reactions were mapped to their corresponding enzymatic gene families in UniRef90 using EC and KO numbers. For bidirectional metabolic reactions, the directionality was simplified by assigning each reaction a single direction for ease of analysis. The eQuilibrator tool was used to determine the direction of bidirectional KEGG metabolic reactions. This determination was based on Gibbs free energy calculations, which leveraged the thermodynamic properties of biochemical compounds and reactions. Enzymatic gene–metabolite networks were constructed from the compiled interactions through these processes, where a metabolite-centric network component is built for each metabolite compound by connecting enzymatic gene families that contribute to the production and consumption of the metabolite. The entire process of constructing enzymatic gene–metabolite networks is illustrated in . In total, 8298 metabolic reactions from KEGG were compiled, resulting in the construction of metabolite-centric network components for 3360 metabolite compounds.
To estimate the change of each metabolite abundance from the change of the microbial genome, the metabolite-centric network component is used along with the P -values of differentiality statistics of DAGs and their reaction abundance weights around the metabolite. In the metabolite-centric network, each metabolite has incoming interactions from enzymatic genes contributing to its production and outgoing interactions to enzymatic genes involved in its consumption. We assume that changes in metabolite abundance can be determined by summarizing the differentiality statistics of the relevant enzymatic genes, accounting for their roles in production or consumption. In this study, the differentiality statistic of each enzymatic gene is represented by its P -value. To quantify these changes, the P -values of enzymatic gene families are converted to Z-scores, which reflect the magnitude and direction of abundance changes relative to controls. In this process of P -value conversion to Z-scores, we convert the P -values of increased gene families to positive Z-scores and the P -values of decreased gene families to negative Z-scores. Specifically, the P -value P i of the i th gene family is modified as follows: P i /2 if the abundance of the gene family is increased. P i /2 if the abundance of the gene family is decreased. This makes increased gene families have modified P -values with the range between 0 and 0.5, and decreased gene families have modified P -values with the range between 0.5 and 1. The modified P -values are then converted to Z-scores that matches the modified P -values from the inverse normal cumulative distribution, eventually making positive Z-scores for increased gene families and negative Z-scores for decreased gene families. Based on the Z-scores of enzymatic gene families, the Z-score Z metabolite that represents the relative change of a metabolite is calculated as follows: (1) Z metabolite = 1 k ∑ i = 1 k R ( i ) · w i · Z i , where k is the number of enzymatic gene families associated with the metabolite, R ( i ) is a function that returns 1 for an enzymatic gene families producing the metabolite and −1 for those consuming it. w i is a reaction abundance weight for the enzymatic gene family i , calculated as the relative abundance of the gene family in control samples as follows: (2) w i = abundance i avr ∑ j = 1 E abundance j avr , where E is the total number of enzymatic gene families and abundance i avr represents the average abundance of an enzymatic gene family i in control samples. This weight reflects the relative contribution of a gene family’s reaction under the baseline (control) condition. To evaluate the statistical significance of Z metabolite , we use the normal distribution to obtain the corresponding P -value. However, the distribution of Z metabolite may not match the exact normal distribution. Thus, Z metabolite values are adjusted first based on the null distribution of Z metabolite values that is obtained by randomly building 1000 metabolite-centric network components with randomly selected k enzymatic gene families as follows while preserving the original count of producing and consuming gene families: (3) Z NullAdj = ( Z metabolite - μ k ) σ k , where μ k and σ k indicate the mean and standard deviation of the null distribution Z-scores from random building of metabolite-centric network components. Finally, the statistical significance P -value for Z metabolite is determined by the right-tail P -value that corresponds to Z NullAdj from the standard normal distribution if it is positive, and by the left-tail P -value if Z NullAdj is negative.
To identify metabolite classes with significant increase or decrease, we performed enrichment analysis based on the Wilcoxon rank sum test by ranking Z metabolite values of individual metabolites. A metabolite class was considered significantly increased or decreased if its metabolites consistently ranked particularly high or low in the rank-based comparison between metabolites within the class and those outside the class. Two-tailed nominal P -values were evaluated and corrected using the Benjamini-Hochberg (BH) method, and the false discovery rate (FDR) of 0.05 was used as significance threshold in this study. We used the categorization of metabolite classes based on the chemical classes level information from Human Metabolome Database (HMDB) , and metabolite classes with <5 metabolites were excluded in this study.
3.1 Configuration for evaluating PredCMB with benchmark datasets For the benchmark evaluation of the proposed method PredCMB, we used two publicly available paired datasets of shotgun metagenome and metabolome from the PRISM IBD cohort study and the cohort of gastrectomy for gastric cancer . The IBD cohort dataset was generated from the stool samples of the 68 patients with Crohn’s disease (CD), 53 patients with Ulcerative colitis (UC), and 34 subjects of non-IBD controls. The dataset of Gastrectomy cohort was generated from the stool samples of the 42 subjects with the history of gastrectomy for gastric cancer and 54 healthy control subjects. The metabolomics data for the IBD cohort were derived from untargeted metabolomics, encompassing 8848 metabolic features. However, only a subset of these features was annotated with known metabolites or metabolite classes: 3829 features were annotated with 474 metabolite classes, and 466 features were annotated with 386 metabolites. The Gastrectomy metabolome data are based on targeted metabolomics, where 524 metabolic features are included. All 524 metabolic features are annotated with known 524 metabolite compounds as they are targeted, and 384 of them are annotated with 76 metabolite classes. In the subsequent benchmark analysis, we compared the predicted changes in metabolites and metabolite classes by PredCMB with the experimentally measured changes. These comparisons were made for three contrasts: CD versus controls, UC versus controls, and gastrectomy versus controls. For comparative analysis, the reporter metabolite method was also used to predict metabolic changes for these benchmarks. The analysis using the reporter metabolite method was conducted using the Piano Bioconductor R package , which was built by the developers of the reporter metabolite method. 3.2 Differentially abundant microbial enzymatic gene families in benchmark data We evaluated the abundances of enzymatic gene families in all the benchmark metagenome samples using HUMAnN3. From evaluating the differential abundances of the enzymatic gene families between CD versus control, the abundances of 18 549 enzymatic gene families were identified to be significantly increased and 58 656 enzymatic gene families showed significantly decreased abundances. Between UC and control, the abundances of 5312 enzymatic gene families were increased while 45 729 enzymatic gene families showed decreased abundances . Out of all the identified DAGs, the majority (76% in CD, 90% in UC) of DAGs showed significantly decreased abundances in IBD-subtypes relative to control. This can be relevant with the result of the reference study where the majority (71%) of significantly changed metabolites in IBD cases showed decreased abundances compared to controls. This can imply that the changes in stool metabolites have certain level of consistency with the changes in relevant microbial enzymatic genes. The number of DAGs from the Gastrectomy cohort was relatively smaller than that of the IBD cohort (4513 increased DAGs and 3217 decreased DAGs), and the numbers of increased DAGs and decreased DAGs did not show such huge difference from the IBD cohort. Evaluation of metabolite class changes in the Gastrectomy cohort revealed no statistically significant increases or decreases in any metabolite class , although some classes demonstrated a modest decrease. The smaller number of DAGs in the gastrectomy cohort may explain the mild changes observed in metabolite abundances. 3.3 Concordance between predicted and measured metabolite changes We compared the Z-scores of predicted metabolite changes generated by PredCMB with the t-statistics of metabolite changes derived from actual measurements . For each benchmark cohort data, only the commonly identified metabolites were compared, where 103, 61, and 221 metabolites were common for CD, UC, and Gastrectomy cohorts accordingly. The predicted metabolite changes using PredCMB show correlation coefficients from 0.395 to 0.522 from these three comparisons, where all correlations satisfy statistical significance P -value < 0.05. We also compared the predicted changes of metabolites using the reporter metabolites method with their measured changes, where 90, 52, and 165 metabolites were common and compared for CD, UC, and Gastrectomy cohorts accordingly . The reporter metabolite method also shows meaningful correlations from 0.367 to 0.438 across benchmarks, where they also satisfy statistical significance with low P -values. When comparing the results of PredCMB and the reporter metabolites method, it is notable that PredCMB consistently achieved higher correlation coefficients across all benchmark datasets. 3.4 Concordance between predicted and measured changes in metabolite classes The predicted changes in metabolite classes using PredCMB and the reporter metabolites method were compared with the actual measurements to evaluate their concordance. Only the metabolite classes common across predictions and measurements were analyzed. illustrates scatter plots of the median change statistics of metabolite classes (based on their member metabolites) relative to controls, comparing predictions with actual measurements. Predicted changes and the changes from the actual measurements show positive correlations for both methods across all benchmark datasets. All predictions show certain level of correlations from 0.446 to 0.575, while statistically significant P -values (<0.05) were observed only from the CD cohort of the IBD benchmark data. This can be partly due to the small number of comparable metabolite classes between predictions and the actual measurements. It is notable that PredCMB showed higher correlations than the reporter metabolites method across all benchmark datasets. The results also highlighted the concordance in metabolite classes with substantial changes. The class of sphingolipids showed the largest increase, while the class of cholesterol and its derivatives showed the largest decrease among the commonly identified metabolite classes in both predictions and actual measurements . For the Gastrectomy cohort, the class of pterins and derivatives exhibited the largest decrease in actual measurements among the common metabolite classes. PredCMB successfully identified this class as the most decreased, unlike the reporter metabolites method . In addition, the class of benzoic acids and derivatives, identified only by PredCMB, showed a median change statistic lower than that of pterins and derivatives, consistent with the actual measurement . illustrates the boxplots for the change statistics of metabolite classes based on their individual metabolites. For the CD benchmark, sphingolipids as well as cholesterols and derivatives showed the largest increase and decrease (based on median values) among the common metabolite classes from both predictions and the actual measurement, while satisfying statistical significance for their changes. For the UC group, sphingolipids showed the largest increase with statistical significance from both predictions and measurement. Cholesterols and derivatives showed a statistically significant decrease only from the actual measurement, but it showed the largest decrease from both predictions and measurement among these metabolite classes. For the Gastrectomy cohort data, no metabolite class showed statistically significant changes in the actual measurements or predictions. However, PredCMB identified pterins and derivatives as the most decreased class among these common classes, consistent with the actual measurements. There can be inherent discrepancies between the change statistics of measured metabolites and our metagenome-based change predictions due to environmental factors. The measured metabolites from subjects’ stool samples can contain variations from subjects’ heterogeneity such as different diets, lifestyles, and health conditions. However, the metagenome-based prediction estimates the metabolic changes that are caused only by the constitution of microbiome, based on their DNA contents. In addition, the coverage of metabolites from experimental measurements and computational predictions based on curated metabolic reactions can be largely different as mentioned in the previous section where relatively small fractions of metabolites are comparable for validation. Despite these challenges, our results demonstrate that metagenome-based predictions of metabolite changes using PredCMB are correlated with actual measurements. PredCMB showed especially strong concordance for metabolite classes with substantial changes. Moreover, additional considerations of metabolite-consuming reactions and reaction abundance weights in PredCMB enabled predictions with further improved correlations with actual measurements than the previous method that considers only the metabolite-producing reactions. Thus, our proposed method can be a beneficial tool in predicting the changes in microbial metabolites, especially when the environment consists of complex microbial communities and the change in microbial contents is a major factor that causes metabolic changes.
For the benchmark evaluation of the proposed method PredCMB, we used two publicly available paired datasets of shotgun metagenome and metabolome from the PRISM IBD cohort study and the cohort of gastrectomy for gastric cancer . The IBD cohort dataset was generated from the stool samples of the 68 patients with Crohn’s disease (CD), 53 patients with Ulcerative colitis (UC), and 34 subjects of non-IBD controls. The dataset of Gastrectomy cohort was generated from the stool samples of the 42 subjects with the history of gastrectomy for gastric cancer and 54 healthy control subjects. The metabolomics data for the IBD cohort were derived from untargeted metabolomics, encompassing 8848 metabolic features. However, only a subset of these features was annotated with known metabolites or metabolite classes: 3829 features were annotated with 474 metabolite classes, and 466 features were annotated with 386 metabolites. The Gastrectomy metabolome data are based on targeted metabolomics, where 524 metabolic features are included. All 524 metabolic features are annotated with known 524 metabolite compounds as they are targeted, and 384 of them are annotated with 76 metabolite classes. In the subsequent benchmark analysis, we compared the predicted changes in metabolites and metabolite classes by PredCMB with the experimentally measured changes. These comparisons were made for three contrasts: CD versus controls, UC versus controls, and gastrectomy versus controls. For comparative analysis, the reporter metabolite method was also used to predict metabolic changes for these benchmarks. The analysis using the reporter metabolite method was conducted using the Piano Bioconductor R package , which was built by the developers of the reporter metabolite method.
We evaluated the abundances of enzymatic gene families in all the benchmark metagenome samples using HUMAnN3. From evaluating the differential abundances of the enzymatic gene families between CD versus control, the abundances of 18 549 enzymatic gene families were identified to be significantly increased and 58 656 enzymatic gene families showed significantly decreased abundances. Between UC and control, the abundances of 5312 enzymatic gene families were increased while 45 729 enzymatic gene families showed decreased abundances . Out of all the identified DAGs, the majority (76% in CD, 90% in UC) of DAGs showed significantly decreased abundances in IBD-subtypes relative to control. This can be relevant with the result of the reference study where the majority (71%) of significantly changed metabolites in IBD cases showed decreased abundances compared to controls. This can imply that the changes in stool metabolites have certain level of consistency with the changes in relevant microbial enzymatic genes. The number of DAGs from the Gastrectomy cohort was relatively smaller than that of the IBD cohort (4513 increased DAGs and 3217 decreased DAGs), and the numbers of increased DAGs and decreased DAGs did not show such huge difference from the IBD cohort. Evaluation of metabolite class changes in the Gastrectomy cohort revealed no statistically significant increases or decreases in any metabolite class , although some classes demonstrated a modest decrease. The smaller number of DAGs in the gastrectomy cohort may explain the mild changes observed in metabolite abundances.
We compared the Z-scores of predicted metabolite changes generated by PredCMB with the t-statistics of metabolite changes derived from actual measurements . For each benchmark cohort data, only the commonly identified metabolites were compared, where 103, 61, and 221 metabolites were common for CD, UC, and Gastrectomy cohorts accordingly. The predicted metabolite changes using PredCMB show correlation coefficients from 0.395 to 0.522 from these three comparisons, where all correlations satisfy statistical significance P -value < 0.05. We also compared the predicted changes of metabolites using the reporter metabolites method with their measured changes, where 90, 52, and 165 metabolites were common and compared for CD, UC, and Gastrectomy cohorts accordingly . The reporter metabolite method also shows meaningful correlations from 0.367 to 0.438 across benchmarks, where they also satisfy statistical significance with low P -values. When comparing the results of PredCMB and the reporter metabolites method, it is notable that PredCMB consistently achieved higher correlation coefficients across all benchmark datasets.
The predicted changes in metabolite classes using PredCMB and the reporter metabolites method were compared with the actual measurements to evaluate their concordance. Only the metabolite classes common across predictions and measurements were analyzed. illustrates scatter plots of the median change statistics of metabolite classes (based on their member metabolites) relative to controls, comparing predictions with actual measurements. Predicted changes and the changes from the actual measurements show positive correlations for both methods across all benchmark datasets. All predictions show certain level of correlations from 0.446 to 0.575, while statistically significant P -values (<0.05) were observed only from the CD cohort of the IBD benchmark data. This can be partly due to the small number of comparable metabolite classes between predictions and the actual measurements. It is notable that PredCMB showed higher correlations than the reporter metabolites method across all benchmark datasets. The results also highlighted the concordance in metabolite classes with substantial changes. The class of sphingolipids showed the largest increase, while the class of cholesterol and its derivatives showed the largest decrease among the commonly identified metabolite classes in both predictions and actual measurements . For the Gastrectomy cohort, the class of pterins and derivatives exhibited the largest decrease in actual measurements among the common metabolite classes. PredCMB successfully identified this class as the most decreased, unlike the reporter metabolites method . In addition, the class of benzoic acids and derivatives, identified only by PredCMB, showed a median change statistic lower than that of pterins and derivatives, consistent with the actual measurement . illustrates the boxplots for the change statistics of metabolite classes based on their individual metabolites. For the CD benchmark, sphingolipids as well as cholesterols and derivatives showed the largest increase and decrease (based on median values) among the common metabolite classes from both predictions and the actual measurement, while satisfying statistical significance for their changes. For the UC group, sphingolipids showed the largest increase with statistical significance from both predictions and measurement. Cholesterols and derivatives showed a statistically significant decrease only from the actual measurement, but it showed the largest decrease from both predictions and measurement among these metabolite classes. For the Gastrectomy cohort data, no metabolite class showed statistically significant changes in the actual measurements or predictions. However, PredCMB identified pterins and derivatives as the most decreased class among these common classes, consistent with the actual measurements. There can be inherent discrepancies between the change statistics of measured metabolites and our metagenome-based change predictions due to environmental factors. The measured metabolites from subjects’ stool samples can contain variations from subjects’ heterogeneity such as different diets, lifestyles, and health conditions. However, the metagenome-based prediction estimates the metabolic changes that are caused only by the constitution of microbiome, based on their DNA contents. In addition, the coverage of metabolites from experimental measurements and computational predictions based on curated metabolic reactions can be largely different as mentioned in the previous section where relatively small fractions of metabolites are comparable for validation. Despite these challenges, our results demonstrate that metagenome-based predictions of metabolite changes using PredCMB are correlated with actual measurements. PredCMB showed especially strong concordance for metabolite classes with substantial changes. Moreover, additional considerations of metabolite-consuming reactions and reaction abundance weights in PredCMB enabled predictions with further improved correlations with actual measurements than the previous method that considers only the metabolite-producing reactions. Thus, our proposed method can be a beneficial tool in predicting the changes in microbial metabolites, especially when the environment consists of complex microbial communities and the change in microbial contents is a major factor that causes metabolic changes.
We introduce a novel method, PredCMB, which predicts changes in individual microbial metabolites based on shotgun metagenome data from two different conditions. PredCMB utilizes networks of microbial enzymatic genes and their interactions with metabolites to infer these changes. To evaluate its predictive capabilities, we used publicly available paired datasets of shotgun metagenomics and metabolomics from IBD studies and a gastrectomy cohort as benchmarks. We compared the predicted changes of metabolites with the changes from actual measurements based on the benchmarks. Comparisons of predicted metabolite changes with measured changes demonstrated positive and statistically significant correlations, with PredCMB achieving higher correlations than a previous method. In addition, predicted changes in metabolite classes, derived by summarizing individual metabolite predictions, were compared with measured changes. From the comparison, the predicted changes of metabolite classes showed positive correlations with their measured changes. Notably, metabolite classes exhibiting the largest increases or decreases in predictions were largely consistent with those from measurements. PredCMB was able to identify an additional metabolite class with further decreased metabolites, underscoring its improved performance. These findings suggest that PredCMB provides concordant predictions of metabolic changes with actual measurements, offering improved concordance over previous methods. Using PredCMB can provide us meaningful opportunities in microbial studies by enabling the prediction of metabolic changes solely based on microbial genomic contents. For example, we can adjust the coverage of metabolomics measurements based on the prediction using PredCMB for more effective experiments with lower costs. Another benefit of PredCMB is that it works solely based on shotgun metagenome data, and it does not require reference metabolomics measurements or computational models of target biological contexts as other approaches based on machine learning predictions or GEM approaches do. A limitation of our study is that PredCMB relies on the statistical analysis of differentially abundant enzymatic gene families, which require multiple samples per group for statistical testing. As such, it cannot provide predictions for individual samples. A more inherent limitation is that prediction of metabolic changes based on metagenome information cannot reflect other contributing factors in stool samples than microbiome. In addition, the limited coverage of common metabolites between predictions and measurements may have constrained the completeness of our evaluations. Future studies will address this issue by incorporating improved metabolomics technologies and additional benchmark datasets as they become available. Many recent studies reveal the effects of microbiome activities in host pathogenesis and phenotypic changes, and we believe that our method can provide certain advances in studying the biological mechanisms of microbial metabolites.
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