To explore the application of sequential analysis in post-market safety dynamic surveillance of vaccines. Under the dynamic monitoring data of vaccines post-market approval, this research introduces the fundamental principles of maximizing sequential probability ratio test (MaxSPRT) and Bayesian sequential analysis, employing R software. Through an example of dynamic safety monitoring data of vaccines post-market approval, we analyze using the MaxSPRT and Bayesian sequential analysis. The MaxSPRT identified a safety signal in week 4 ( P<0.05), while Bayesian sequential analysis indicated that the 95% highest density interval for the RR value at week 4 is 1.13-3.27, suggesting the first appearance of a safety signal at week 4. The MaxSPRT and Bayesian sequential analysis effectively leverage continuously accumulating dynamic monitoring data, thereby serving as a valuable method for post-market safety surveillance of vaccines.

AIM:To evaluate the application effec-tiveness of a Bayesian mixture model based on the principal stratum strategy for estimating the com-plier average causal effect(CACE)in clinical trials with non-compliance.METHODS:Using a non-infe-riority randomized controlled trial investigating a novel drug for primary type 2 diabetes mellitus(non-inferiority margin:-0.4)as a case study,the primary analysis applied a Bayesian mixture model under the monotonicity assumption to estimate CACE of between-group differences in glycated he-moglobin(HbA1c)changes within the compliant stratum,followed by non-inferiority testing.Sensi-tivity analyses included a Bayesian mixture model relaxing the monotonicity assumption and compar-ing results with per-protocol set(PPS)analysis.RE-SULTS:In the primary analysis,the posterior mean of CACE for HbA1c change in the compliant stratum was 0.081％,with a one-sided 97.5％credible inter-val lower bound of-0.124,exceeding the non-infe-riority margin(-0.4％),supporting the non-inferiori-ty efficacy of the novel drug in the compliant stra-tum(P(H1|Data)=1).Consistent findings were ob-served in PPS analyses(estimated effect:0.136％;one-sided 97.5％credible interval lower bound:-0.069％),further validating methodological robust-ness.CONCLUSION:In clinical trials with noncom-pliance as an intercurrent event,the Bayesian mix-ture model under the principal stratum strategy ef-fectively adjusts for compliance-related bias and yields conservative,robust estimates of causal ef-fects,supporting its value in efficacy evaluation un-der complex compliance scenarios.

Objective:The association between air pollutants and the risk of sudden cardiac death (SCD) remains controversial. This study aimed to investigate the relationship between five air pollutants—PM 2.5, PM 2.5–10, PM 10, NO 2, and NO?—and the risk of SCD. Methods:We analyzed data from 460 862 participants in the UK Biobank cohort, all enrolled between 2006 and 2010, with no baseline SCD. Follow-up continued until the study endpoint. Annual average concentrations of the five pollutants were assessed. Associations between pollutants and SCD were evaluated using Cox proportional hazards models, followed by Mendelian randomization (MR) to assess causality.Results:Over a mean follow-up of 12.4 years, 2 662 SCD cases were recorded. After adjusting for confounders, no significant associations were found between air pollutants and SCD risk: PM 2.5 ( HR 1.03, 95% CI 0.99–1.07, P = 0.14), PM 2.5–10 ( HR 1.04, 95% CI 1.00–1.08, P = 0.08), PM 10 ( HR 1.01, 95% CI 0.99–1.03, P = 0.26), NO? ( HR 1.00, 95% CI 0.99–1.00, P = 0.26), and NO x ( HR 1.00, 95% CI 1.00–1.01, P = 0.19). MR analysis further supported the absence of causal relationships: PM 2.5 ( β = -0.149, P = 0.90), PM 2.5–10 ( β = 0.387, P = 0.62), PM 10 ( β = -0.994, P = 0.62), NO? ( β = –0.005, P = 0.99), and NO 2 ( β = –0.827, P = 0.25). Conclusions:This study found no evidence linking PM 2.5, PM 2.5–10, PM 10, NO?, or NO 2 to an increased risk of SCD. Mendelian randomization confirmed the lack of causal associations between these pollutants and SCD.

To explore the application of sequential analysis in post-market safety dynamic surveillance of vaccines. Under the dynamic monitoring data of vaccines post-market approval, this research introduces the fundamental principles of maximizing sequential probability ratio test (MaxSPRT) and Bayesian sequential analysis, employing R software. Through an example of dynamic safety monitoring data of vaccines post-market approval, we analyze using the MaxSPRT and Bayesian sequential analysis. The MaxSPRT identified a safety signal in week 4 ( P<0.05), while Bayesian sequential analysis indicated that the 95% highest density interval for the RR value at week 4 is 1.13-3.27, suggesting the first appearance of a safety signal at week 4. The MaxSPRT and Bayesian sequential analysis effectively leverage continuously accumulating dynamic monitoring data, thereby serving as a valuable method for post-market safety surveillance of vaccines.

AIM:To evaluate the application effec-tiveness of a Bayesian mixture model based on the principal stratum strategy for estimating the com-plier average causal effect(CACE)in clinical trials with non-compliance.METHODS:Using a non-infe-riority randomized controlled trial investigating a novel drug for primary type 2 diabetes mellitus(non-inferiority margin:-0.4)as a case study,the primary analysis applied a Bayesian mixture model under the monotonicity assumption to estimate CACE of between-group differences in glycated he-moglobin(HbA1c)changes within the compliant stratum,followed by non-inferiority testing.Sensi-tivity analyses included a Bayesian mixture model relaxing the monotonicity assumption and compar-ing results with per-protocol set(PPS)analysis.RE-SULTS:In the primary analysis,the posterior mean of CACE for HbA1c change in the compliant stratum was 0.081％,with a one-sided 97.5％credible inter-val lower bound of-0.124,exceeding the non-infe-riority margin(-0.4％),supporting the non-inferiori-ty efficacy of the novel drug in the compliant stra-tum(P(H1|Data)=1).Consistent findings were ob-served in PPS analyses(estimated effect:0.136％;one-sided 97.5％credible interval lower bound:-0.069％),further validating methodological robust-ness.CONCLUSION:In clinical trials with noncom-pliance as an intercurrent event,the Bayesian mix-ture model under the principal stratum strategy ef-fectively adjusts for compliance-related bias and yields conservative,robust estimates of causal ef-fects,supporting its value in efficacy evaluation un-der complex compliance scenarios.

At present, there are still some problems in the standardized residency training. Some standardized training bases often regard the resident trainees as ordinary practitioners, and devote their time and energy to the daily medical procedural work without giving enough training and teaching, ignoring the basic skills training of resident trainees. Therefore, this study constructed an active knowledge push system based on business scenarios. The system mainly includes three parts: sensitive operation identification layer, knowledge index layer and resource push layer in order to cultivate the norms of diagnosis and treatment of standardized training students and reasonably solve the problem of inconvenient resource acquisition in clinical work. Through the preliminary application, it was found that the system has effectively improved the mini-clinical evaluation exercise (Mini-CEX) score of the trainees and achieved good results.

Objective:To evaluate the possible mediation effect of smoking and healthy diet score on the association between educational level and the risk of lung cancer incidence.Methods:After excluding individuals with missing educational levels and cancer information at baseline, 446?772 participants in the UK Biobank (UKB) prospective cohort study were included. Cox regression models were used to investigate the associations of educational level and smoking and healthy diet score with the incidence of lung cancer. Mediating effect analysis was conducted to analyze the mediating effect of smoking and healthy diet score on the correlation between educational level and lung cancer.Results:During a median follow-up of 7.13 years, 1?994 new- onset lung cancer cases were observed. Per 1 standard deviation (5 years) increase in educational level was associated with a 12% lower risk of lung cancer ( HR=0.88, 95% CI: 0.84-0.92). The corresponding level 1-5 in the International Standard Classification for Education (ISCED) were mapped to UKB self‐report highest qualification to estimate the educational level. A higher rank means a higher educational level. Compared with level ISCED-1, the HR(95% CI) of level ISCED-2, ISCED-3, ISCED-4 and ISCED-5 were respectively 0.83 (0.72-0.94), 0.67 (0.53-0.85), 0.76 (0.65-0.89) and 0.72 (0.64-0.80) for lung cancer. Education years were negatively correlated with smoking, with β coefficients (95% CI) being -0.079 (-0.081- -0.077), but positively correlated with healthy diet score ( β=0.042, 95% CI: 0.039-0.045). Analysis of mediating effect indicated that the association of educational level with lung cancer risk was mediated by smoking and healthy diet score, the proportions of mediating effect were 38.952% (95% CI: 31.802%-51.659%) and 1.784% (95% CI: 0.405%-3.713%), respectively. Conclusion:Smoking and healthy diet score might mediate the effect of educational level on the incidence of lung cancer, indicating that improving the level of education can reduce the risk of lung cancer by changing lifestyles such as smoking and diet.

Although genome-wide association studies have identified more than eighty genetic variants associated with non-small cell lung cancer (NSCLC) risk, biological mechanisms of these variants remain largely unknown. By integrating a large-scale genotype data of 15 581 lung adenocarcinoma (AD) cases, 8350 squamous cell carcinoma (SqCC) cases, and 27 355 controls, as well as multiple transcriptome and epigenomic databases, we conducted histology-specific meta-analyses and functional annotations of both reported and novel susceptibility variants. We identified 3064 credible risk variants for NSCLC, which were overrepresented in enhancer-like and promoter-like histone modification peaks as well as DNase I hypersensitive sites. Transcription factor enrichment analysis revealed that USF1 was AD-specific while CREB1 was SqCC-specific. Functional annotation and gene-based analysis implicated 894 target genes, including 274 specifics for AD and 123 for SqCC, which were overrepresented in somatic driver genes (ER = 1.95, P = 0.005). Pathway enrichment analysis and Gene-Set Enrichment Analysis revealed that AD genes were primarily involved in immune-related pathways, while SqCC genes were homologous recombination deficiency related. Our results illustrate the molecular basis of both well-studied and new susceptibility loci of NSCLC, providing not only novel insights into the genetic heterogeneity between AD and SqCC but also a set of plausible gene targets for post-GWAS functional experiments.
Adenocarcinoma of Lung/genetics* ; Carcinoma, Non-Small-Cell Lung/genetics* ; Carcinoma, Squamous Cell/genetics* ; Genetic Heterogeneity ; Genetic Predisposition to Disease ; Genome-Wide Association Study ; Humans ; Lung Neoplasms/genetics* ; Polymorphism, Single Nucleotide

Objective:To analyze the correlation between the changes of lung function and serum proinflammatory cytokines in workers occupationally exposed to toluene diisocyanate (TDI), and to explore the evaluation index of respiratory toxicity of TDI.Methods:In October 2014, 61 male workers engaged in TDI synthesis process, purification process, packaging process and the above production process in a TDI factory in western China were selected as TDI exposure group; 62 male enterprise managers who were not exposed to TDI and other known allergenic chemicals were selected as control group, which were matched at the age of workers in exposure group. The questionnaire survey obtained information such as gender, length of service, age, occupational history, exposed length of service and so on. The lung function indexes [forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), FEV1/FVC] and serum levels of interleukin (IL)-1 β, IL-6, IL-8, tumor necrosis factor (TNF)-α, macrophage inflammatory factor-1 β, monocyte chemoattractant factor-1 and vascular endothelial growth factor were measured. The urine was collected after the weekend shift, and the concentration of (TDA), the metabolite of TDI, was determined as the index of internal exposure. Spearman rank correlation was used to analyze the correlation between cytokines and lung function indexes, and multivariate linear regression was used to analyze the changes of lung function indexes and cytokines with TDI exposure concentration and time.Results:The median age ( P5- P95) of the exposed group and the control group was 36.5 (24.0-51.0) and 38.0 (24.0-50.0) years, respectively. In the exposed group, the median length of service ( P5- P95) was 6.94 (0.97-26.33) years, and the median concentration of TDA in urine was 15.56 (2.28-112.16) ng/ml. The three indexes of lung function, FVC, FEV1, FEV1/FVC and the levels of serum IL-8 and TNF-α were significantly lower than those in the control group ( P<0.01). With the increase of exposure concentration and exposure time, the level of serum TNF-α, FVC and FEV1 decreased, and showed a good dose-effect and time-effect relationship (all Ptrend values< 0.05). Serum IL-8 and TNF-α were positively correlated with FVC, FEV1 and FEV1/FVC (all P values<0.01). Conclusion:The levels of serum inflammatory factors IL-8 and TNF-α in worker exposed to TDI are related to lung function indexes, which can be used as early evaluation indexes of respiratory toxicity induced by TDI.