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 investigate the efficacy of radiotherapy in patients with advanced esophageal cancer receiving first-line chemoimmunotherapy.Methods:A retrospective analysis was conducted on the data of 137 patients with Stage Ⅳ esophageal squamous cell carcinoma (ESCC) treated at our hospital from January 2018 to May 2023. These patients were divided into two groups: a group treated with first-line chemoimmunotherapy combined with radiotherapy (chemoimmunotherapy + radiotherapy group, n = 43) and a group treated with only chemoimmunotherapy ( n = 94). Inverse probability of treatment weighting (IPTW) was applied to balance baseline characteristics between the groups. With overall survival (OS) and progression-free survival (PFS) as study endpoints, the survival data were analyzed using the Kaplan-Meier method, the log-rank test, and the Cox regression method. Results:Before calibration, the chemoimmunotherapy + radiotherapy group significantly outperformed the sole chemoimmunotherapy group in median PFS (13.6 months vs. 7.0 months; HR: 0.501, 95% CI: 0.309-0.811, P = 0.005). After calibration using the COX proportional-hazards model for age, gender, Eastern Cooperative Oncology Group (ECOG) performance status, smoking history, T/N/M stage, and tumor location, the chemoimmunotherapy + radiotherapy group still had significant advantages in PFS (14.7 months vs. 7.0 months; HR: 0.441, 95% CI: 0.261-0.745, P = 0.002). IPTW analysis further confirmed this trend (13.9 months vs. 7.0 months; HR: 0.492, 95% CI: 0.304-0.795, P < 0.001). Specifically, the median OS of the chemoimmunotherapy + radiotherapy group demonstrated significant improvement in all analyses: pre-calibration (29.5 months vs. 18.0 months; HR: 0.507, 95% CI: 0.297-0.867, P = 0.013), after calibration using the Cox model (27.5 months vs. 16.7 months; HR: 0.470, 95% CI: 0.266-0.830, P = 0.009), and after calibration using IPTW (29.5 months vs. 16.9 months; HR: 0.448, 95% CI: 0.262-0.764, P < 0.001). Conclusions:The combination of radiotherapy and first-line chemoimmunotherapy can significantly improve survival outcomes of patients with advanced ESCC, suggesting its potential as a standard treatment strategy.

Galectin-9 (Gal-9), a member of the β-galactoside-binding lectin family, is widely expressed in various tissues and cells. It can specifically bind to multiple glycoprotein receptors, including the receptors of Tim-3, CD44, 4-1BB/CD137, and Dectin-1, thereby regulating the activity of immune cells and participating in crucial physiological and pathological processes such as immune regulation and tumor development. Given its role in immunomodulation, Gal-9 is considered a potential target for immunotherapy, showing promising prospects in the treatment of various diseases, including autoimmune disorders, transplantation rejection, pregnancy complications, inflammation, infection, and cancer. This review summarizes the biological effects mediated by Gal-9 upon binding to its receptors, which may help to explore the potential application value of Gal-9 in clinical diagnosis and therapy.

Objective:The Social Isolation Scale for people with type 2 diabetes mellitus (T2DM) was developed and tested for reliability and validity, which provided an effective tool for measuring the social isolation level of patients with T2DM.Methods:The initial scale was developed through literature review, qualitative interviews, and expert consultation. Convenience sampling method was used to select T2DM patients who met the inclusion and exclusion criteria for questionnaire survey. The item analysis method was used to select the items of the scale. Exploratory and confirmatory factor analyses were used to evaluate construct validity. The content validity of the scale was evaluated by the scale-level content validity index and the item-level content validity index. The criterion validity was verified by using the Lubben Social Network Scale-6 and the De Jong Gierveld Loneliness Scale. Reliability was tested through internal consistency and test-retest reliability.Results:Through literature review and qualitative interviews, 30 items of the scale were selected to form the first version scale. After two rounds of inquiries from 16 experts (expert authority coefficient = 0.897), the second version of the scale was formed. A total of 407 questionnaires were distributed in two stages using the second version scale. Among the 407 patients, there were 214 males and 193 females. Exploratory factor analysis extracted 4 common factors, with a cumulative variance contribution rate of 61.338%. The final scale was determined to include 4 dimensions and 16 items, with the dimensions being "social support network", "frequency of social participation", "satisfaction with interpersonal relationships", and "diabetes-related sense of isolation".The average content validity index at the scale level was 0.929, the content validity index at the item level was 0.830 - 1.000, and the criterion validity was - 0.647 and 0.681. The Cronbach′s α coefficient of the total scale was 0.822, and the test-retest reliability was 0.858.Conclusions:The Social Isolation Scale for people with T2DM has good reliability and validity. It is a reliable and valid tool for assessing social isolation in T2DM patients.

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 analyze the prognostic factors of patients with liver cirrhosis and portal vein thrombosis (PVT), and to construct a prognostic prediction model based on machine learning methods.Methods:The clinical data of 388 patients with liver cirrhosis and PVT admitted to the Fifth Medical Center of PLA General Hospital from January 2022 to April 2024 were retrospectively collected and analyzed, including 243 males and 145 females, aged (56.9±10.9) years. A total of 388 patients were randomly divided into the training set ( n=310) and the testing set ( n=78) in a 4∶1 ratio. The Boruta algorithm was used to screen the key features in the training set, and then four machine learning algorithms, including random forest, support vector machine, generalized linear model and Bayesian, were used to establish a survival prediction model. Model performance was evaluated by the receiver operating characteristic (ROC) curves of the test set and the training set. The patients were followed up for 1 year for survival. Sort the importance of features based on the SHAP value. Results:There were 250 patients (80.6%) who survived and 60 (19.4%) who died. The model for end-stage liver disease score, total bilirubin, serum creatinine, prothrombin time, international normalized ratio, D-dimer, white blood cell count, severe ascites ratio, and Child-Pugh grade C ratio of liver function in the death group were higher than those in the survival group, and the red blood cell count and hematocrit were lower than those in the survival group, and the differences were statistically significant (all P<0.05). The areas under the ROC curve for predicting survival by random forest, support vector machine, generalized linear model and Bayesian model were 0.92, 0.78, 0.81 and 0.71 in the training set, and the area under the ROC curve in the testing set were 0.81, 0.72, 0.67 and 0.68, respectively. Random forest had the best prediction performance, with an accuracy of 81.7%, a sensitivity of 84.6%, and a specificity of 76.9% in the testing set. In the analysis of the importance of characteristic parameters of the random forest model, total bilirubin, red blood cells, hematocrit, serum creatinine, ascites classification, etc. had a relatively high contribution to the model. Conclusion:In the survival prediction model of patients with liver cirrhosis and PVT based on machine learning algorithm, the random forest model had high prediction performance, and total bilirubin may be the most important factor affecting the survival prognosis of patients.

Objective lt aims to identify key issues in the current informatization development of internal control over revenue and expenditure in public hospitals.The findings are intended to serve as a reference for deepening this informatization effort.Methods Following the steps and requirements of content analysis method,it involved a semi-quantitative analysis of policies and expert interviews to establish an analytical framework.Two researchers uti-lized NVivo 15 software to analyze the policy and articles.The analysis results were quantified statistically,and key is-sues were summarized.Results lt developed a content analysis framework comprising 5 primary categories and 17 secondary categories.Based on this framework,326 analytical units extracted from 81 articles were categorized and statistically analyzed.Key problems corresponding to each secondary category were summarized.Conclusion To ad-dress five key issues,it proposes a five-dimensional solution including:building a digital foundation to integrate busi-ness systems;establishing a Master Data Management platform to dismantle data silos;developing flexible ap-proval mechanismsfor medical emergencies;deploying an Al risk control hub for precise payment interception,and implementing zero-trust architecture to enhance e-bill anti-counterfeiting-ultimately forming a business-finance-in-tegrated,data-and-Al-driven,proactively secured internal control system covering budget formulation to fund su-pervision.

Galectin-9 (Gal-9), a member of the β-galactoside-binding lectin family, is widely expressed in various tissues and cells. It can specifically bind to multiple glycoprotein receptors, including the receptors of Tim-3, CD44, 4-1BB/CD137, and Dectin-1, thereby regulating the activity of immune cells and participating in crucial physiological and pathological processes such as immune regulation and tumor development. Given its role in immunomodulation, Gal-9 is considered a potential target for immunotherapy, showing promising prospects in the treatment of various diseases, including autoimmune disorders, transplantation rejection, pregnancy complications, inflammation, infection, and cancer. This review summarizes the biological effects mediated by Gal-9 upon binding to its receptors, which may help to explore the potential application value of Gal-9 in clinical diagnosis and therapy.

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 investigate the efficacy of radiotherapy in patients with advanced esophageal cancer receiving first-line chemoimmunotherapy.Methods:A retrospective analysis was conducted on the data of 137 patients with Stage Ⅳ esophageal squamous cell carcinoma (ESCC) treated at our hospital from January 2018 to May 2023. These patients were divided into two groups: a group treated with first-line chemoimmunotherapy combined with radiotherapy (chemoimmunotherapy + radiotherapy group, n = 43) and a group treated with only chemoimmunotherapy ( n = 94). Inverse probability of treatment weighting (IPTW) was applied to balance baseline characteristics between the groups. With overall survival (OS) and progression-free survival (PFS) as study endpoints, the survival data were analyzed using the Kaplan-Meier method, the log-rank test, and the Cox regression method. Results:Before calibration, the chemoimmunotherapy + radiotherapy group significantly outperformed the sole chemoimmunotherapy group in median PFS (13.6 months vs. 7.0 months; HR: 0.501, 95% CI: 0.309-0.811, P = 0.005). After calibration using the COX proportional-hazards model for age, gender, Eastern Cooperative Oncology Group (ECOG) performance status, smoking history, T/N/M stage, and tumor location, the chemoimmunotherapy + radiotherapy group still had significant advantages in PFS (14.7 months vs. 7.0 months; HR: 0.441, 95% CI: 0.261-0.745, P = 0.002). IPTW analysis further confirmed this trend (13.9 months vs. 7.0 months; HR: 0.492, 95% CI: 0.304-0.795, P < 0.001). Specifically, the median OS of the chemoimmunotherapy + radiotherapy group demonstrated significant improvement in all analyses: pre-calibration (29.5 months vs. 18.0 months; HR: 0.507, 95% CI: 0.297-0.867, P = 0.013), after calibration using the Cox model (27.5 months vs. 16.7 months; HR: 0.470, 95% CI: 0.266-0.830, P = 0.009), and after calibration using IPTW (29.5 months vs. 16.9 months; HR: 0.448, 95% CI: 0.262-0.764, P < 0.001). Conclusions:The combination of radiotherapy and first-line chemoimmunotherapy can significantly improve survival outcomes of patients with advanced ESCC, suggesting its potential as a standard treatment strategy.