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.

Objective To explore the value of machine learning(ML)models based on ultrasonic texture features(TF)of coronary artery for predicting incomplete Kawasaki disease(IKD)in children.Methods Forty-eight children with IKD and 48 children without KD(non-KD)were enrolled with propensity score matching and divided into training set(n=67,34 cases of IKD and 33 cases of non-KD)and test set(n=29,14 of IKD and 15 of non-KD)at the ratio of 7∶3.Based on clinic-laboratory indicators(C-L)in training set and TF obtained with texture analysis of coronary artery ultrasound images,the optimal C-L-related features and TF were selected.Based on the optimal C-L correlated features,TF and their combinations,6 ML models,including random forest(RF),support vector machine(SVM),logistic regression(LR),gradient boosting decision tree(GBDT),decision tree(DT)and eXtreme gradient boosting(XGBoost)were respectively constructed for predicting IKD in children.The models were then trained in training set and validated in test set,and the best C-L ML,TF ML and C-L-TF ML models were selected.The area under the curve(AUC)of the best ML models were compared,and the clinical value of the best TF ML model was observed with decision curve analysis(DCA).Results Totally 3 optimal C-L related features and 8 optimal TF were selected.Among the constructed C-L ML,TF ML and C-L-TF ML models,C-L-LR model,TF-LR model and C-L-TF-SVM model were the optimal ones,with AUC in training set of 0.891,0.985 and 0.965,while in test set of 0.676,0.971 and 0.948,respectively.No significant difference of AUC was found between TF-LR model and C-L-TF-SVM model in both training set and test set(both P＞0.05),which were both greater than those of C-L-LR model(all P＜0.05).TF-LR model achieved higher clinical benefits in both training set and test set.Conclusion Ultrasound TF-LR model of coronary artery could be used to effectively predict IKD in children.

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.

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 To explore the value of machine learning(ML)models based on ultrasonic texture features(TF)of coronary artery for predicting incomplete Kawasaki disease(IKD)in children.Methods Forty-eight children with IKD and 48 children without KD(non-KD)were enrolled with propensity score matching and divided into training set(n=67,34 cases of IKD and 33 cases of non-KD)and test set(n=29,14 of IKD and 15 of non-KD)at the ratio of 7∶3.Based on clinic-laboratory indicators(C-L)in training set and TF obtained with texture analysis of coronary artery ultrasound images,the optimal C-L-related features and TF were selected.Based on the optimal C-L correlated features,TF and their combinations,6 ML models,including random forest(RF),support vector machine(SVM),logistic regression(LR),gradient boosting decision tree(GBDT),decision tree(DT)and eXtreme gradient boosting(XGBoost)were respectively constructed for predicting IKD in children.The models were then trained in training set and validated in test set,and the best C-L ML,TF ML and C-L-TF ML models were selected.The area under the curve(AUC)of the best ML models were compared,and the clinical value of the best TF ML model was observed with decision curve analysis(DCA).Results Totally 3 optimal C-L related features and 8 optimal TF were selected.Among the constructed C-L ML,TF ML and C-L-TF ML models,C-L-LR model,TF-LR model and C-L-TF-SVM model were the optimal ones,with AUC in training set of 0.891,0.985 and 0.965,while in test set of 0.676,0.971 and 0.948,respectively.No significant difference of AUC was found between TF-LR model and C-L-TF-SVM model in both training set and test set(both P＞0.05),which were both greater than those of C-L-LR model(all P＜0.05).TF-LR model achieved higher clinical benefits in both training set and test set.Conclusion Ultrasound TF-LR model of coronary artery could be used to effectively predict IKD in children.

Pancreatic cancer, one of the most lethal cancers in the world, has been increasing in incidence and mortality year by year, and the overall prognosis of patients is poor. Early detection and effective treatment are crucial for improving the prognosis and survival rates of pancreatic cancer patients. Unlike traditional imaging, emerging molecular imaging can visualize the abnormalities at the molecular or cellular level in the process of tumor development. At present, multimodality molecular imaging that integrates multiple imaging methods to achieve complementary advantages and multifunctional nanoplatforms with integrated diagnosis and treatment functions have become research hotspots in the field of molecular imaging. Remarkable progress has been made in preclinical research concerning magnetic-optical multimodality molecular imaging probes and their derived multifunctional nanoplatforms, which provides new ideas for early detection, accurate treatment and efficacy evaluation of pancreatic cancer.

Objective:To investigate the occurrence of pain symptoms and risk factors of carpal tunnel syndrome (CTS) in automobile manufacturing workers and provide theoretical basis for the prevention of CTS.Methods:From Nov.5th to Nov.19th, 2017, 446 workers in an automobile factory whose work age was above one year participate in questionnaires by cluster sampling. Chi square test and multifactor logistics regression analysis were used to analyze the factors related to the occurrence of CTS pain symptoms in workers.Results:The incidence of CTS pain among workers in this automobile factory was 20.8%. Working in the same position for a long time ( OR=2.137, 95% CI:1.183-3.862) and unable to work reasonably because of uncomfortable posture ( OR =2.067, 95% CI: 1.075-3.974) were identified as the risk factors of CTS pain symptoms by multifactor logistics regression analysis. Working age ( OR=0.537, 95% CI:0.311-0.926) and work break ( OR= 0.489, 95% CI: 0.282-0.849) were identified as the benefit factors of CTS pain symptoms. Conclusion:The incidence of CTS pain in automobile manufacturing workers is related to the posture in the process of labor . Effective ergonomic interventions should be carried out to prevent the occurrence of CTS pain in automobile manufacturing workers.

Objective:To investigate the occurrence of pain symptoms and risk factors of carpal tunnel syndrome (CTS) in automobile manufacturing workers and provide theoretical basis for the prevention of CTS.Methods:From Nov.5th to Nov.19th, 2017, 446 workers in an automobile factory whose work age was above one year participate in questionnaires by cluster sampling. Chi square test and multifactor logistics regression analysis were used to analyze the factors related to the occurrence of CTS pain symptoms in workers.Results:The incidence of CTS pain among workers in this automobile factory was 20.8%. Working in the same position for a long time ( OR=2.137, 95% CI:1.183-3.862) and unable to work reasonably because of uncomfortable posture ( OR =2.067, 95% CI: 1.075-3.974) were identified as the risk factors of CTS pain symptoms by multifactor logistics regression analysis. Working age ( OR=0.537, 95% CI:0.311-0.926) and work break ( OR= 0.489, 95% CI: 0.282-0.849) were identified as the benefit factors of CTS pain symptoms. Conclusion:The incidence of CTS pain in automobile manufacturing workers is related to the posture in the process of labor . Effective ergonomic interventions should be carried out to prevent the occurrence of CTS pain in automobile manufacturing workers.