Objective:To understand the usage situation of e-cigarettes among residents aged 18-65 in Beijing, explore the relationship between e-cigarette use and cigarette smoking as well as smoking cessation behaviors, and provide scientific support for the developing and improving policies and measures related to e-cigarettes.Methods:Using 19 684 residents data from the Beijing Non-communication Chronic Disease and Risk Factors Surveillance in 2022, complex sampling weighted methods were used to estimate proportions, and complex sampling logistic regression analysis was applied to explore the relationship between e-cigarette use, cigarette smoking, and smoking cessation.Results:Among all study participants, the proportion of those who had ever used e-cigarettes was 3.36%, with the current e-cigarette use at 1.26%. The proportion of current e-cigarette users (1.87%) and the former e-cigarette users (3.47%) were higher ( χ2=64.70, P<0.001) among males compared to females (0.60% and 0.64% respectively). The top three reasons for using e-cigarettes were wanting to quit smoking, perceiving e-cigarettes as less harmful, and enjoying the flavors of e-cigarettes. 83.54% of e-cigarette users started with cigarettes. The results of the complex sampling multivariable logistic regression analysis showed that current smoking ( OR=61.35, 95% CI: 36.98-101.76) and former smoking ( OR=31.20, 95% CI: 15.52-62.71) were positively associated with e-cigarette, while current e-cigarette use ( OR=0.13, 95% CI: 0.04-0.39) was negatively associated with quitting cigarette smoking. Conclusions:The proportion of e-cigarette use in Beijing was relatively low. E-cigarette use was associated with cigarette use and was not conducive to smoking cessation. Therefore, stronger regulatory measures and health education campaigns regarding the risks of e-cigarettes should be implemented.

Objective:To analyze the association between remnant cholesterol (RC) and the risk of atherosclerotic cardiovascular disease (ASCVD) in community population in Shanghai.Methods:Using baseline and follow-up data from the Shanghai Suburban Adult Cohort and Biobank, individuals with ASCVD (including coronary heart disease, stroke, myocardial infarction, and peripheral artery disease) at baseline were excluded. A Cox proportional hazards regression model was employed to analyze the relationship between RC and ASCVD risk and the association under different LDL-C levels.Results:A total of 57 281 participants were included, with a median follow-up of 5.61 person-years. During the follow-up, 1 436 ASCVD events (2.51%) were recorded. After adjusting for potential confounders, individuals with moderate ( HR=1.18, 95% CI: 1.03-1.36) or high RC levels ( HR=1.32, 95% CI: 1.15-1.51) had an increased risk of ASCVD. The association was stronger in participants younger than 60 years-old (interaction P=0.048). Participants with RC ≥0.97 mmol/L and LDL-C <3.40 mmol/L demonstrated a 19% ( HR=1.19, 95% CI: 1.06-1.35) increased risk of ASCVD. When RC ≥0.97 mmol/L and LDL-C ≥3.40 mmol/L, ASCVD risk increased by 42% ( HR=1.42, 95% CI: 1.21-1.67). Conclusions:Elevated RC increases ASCVD risk, regardless of LDL-C levels. RC can serve as a valuable predictor and intervention target for ASCVD.

To evaluate the diagnostic performance of a three-gene (GBP5, DUSP3, and TBP) tuberculosis (TB) score in bacteriologically-negative pulmonary tuberculosis, and to develop and validate a discriminative diagnostic model by integrating inflammatory cytokines (IL-2, IL-5, IL-17, and IFN-γ). A prospective cohort study was conducted, a total of 238 patients admitted to the Affiliated Infectious Disease Hospital of Soochow University from May 2023 to May 2024 were enrolled, including 119 pathogen-negative pulmonary tuberculosis patients and 119 patients with other pulmonary diseases (OPD). The GeneXpert MTB-HR kit was used to detect the three-gene TB scores from residual blood routine samples. The diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis. Concurrent data on 12 inflammatory cytokines were collected from patients. Potential biomarkers were screened using univariate analysis and multivariate logistic regression, and selected features were incorporated into the construction of four machine learning models: logistic regression, support vector machine (SVM), K-nearest neighbors (KNN), and adaptive boosting (AdaBoost). The samples were randomly split into a training set (85%) and a test set (15%). The models were trained on the training set, and their diagnostic performance was validated using the test set. The predictive ability of each model was evaluated based on ROC curve parameters. The results showed that the three-gene TB score alone yielded an AUC of 0.539 (sensitivity: 50.94%, specificity: 60.50%) in distinguishing pathogen-negative pulmonary tuberculosis from OPD. Four non-col-linear inflammatory factors (IL-2, IL-5, IL-17, and IFN-γ) were selected and combined with the three-gene TB score to construct machine learning models. The AdaBoost model demonstrated the best performance, achieving an AUC of 0.893 (sensitivity: 85.4%, specificity: 73.0%) in the training set and an AUC of 0.873 (sensitivity: 88.2%, specificity: 72.2%) in the test set. In conclusion,the AdaBoost diagnostic model integrating the three-gene TB score with inflammatory factors (IL-2, IL-5, IL-17, and IFN-γ) exhibits superior discriminating performance for pathogen-negative pulmonary tuberculosis compared to OPD, significantly outperforming the three-gene TB score alone.

Based on the core collection retrieval of the Web of Science database,researches related to the medicinal field of plant-derived vesicles(PDVs)were retrieved.The research hotspots and their changes in the pharmaceutical field of PDVs are visually analyzed by using bibliometric software VOSviewer and CiteSpace.A detailed discussion is held around the author,institution,country,key research hotspots and annual develop-ment hotspots,revealing the current research status of PDVs in the pharmaceutical field and predicting future trends,which provides valuable perspectives for researchers to understand the current research status of PDVs in the pharmaceutical field and discover possible unexplored areas in this field.

To evaluate the diagnostic performance of a three-gene (GBP5, DUSP3, and TBP) tuberculosis (TB) score in bacteriologically-negative pulmonary tuberculosis, and to develop and validate a discriminative diagnostic model by integrating inflammatory cytokines (IL-2, IL-5, IL-17, and IFN-γ). A prospective cohort study was conducted, a total of 238 patients admitted to the Affiliated Infectious Disease Hospital of Soochow University from May 2023 to May 2024 were enrolled, including 119 pathogen-negative pulmonary tuberculosis patients and 119 patients with other pulmonary diseases (OPD). The GeneXpert MTB-HR kit was used to detect the three-gene TB scores from residual blood routine samples. The diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis. Concurrent data on 12 inflammatory cytokines were collected from patients. Potential biomarkers were screened using univariate analysis and multivariate logistic regression, and selected features were incorporated into the construction of four machine learning models: logistic regression, support vector machine (SVM), K-nearest neighbors (KNN), and adaptive boosting (AdaBoost). The samples were randomly split into a training set (85%) and a test set (15%). The models were trained on the training set, and their diagnostic performance was validated using the test set. The predictive ability of each model was evaluated based on ROC curve parameters. The results showed that the three-gene TB score alone yielded an AUC of 0.539 (sensitivity: 50.94%, specificity: 60.50%) in distinguishing pathogen-negative pulmonary tuberculosis from OPD. Four non-col-linear inflammatory factors (IL-2, IL-5, IL-17, and IFN-γ) were selected and combined with the three-gene TB score to construct machine learning models. The AdaBoost model demonstrated the best performance, achieving an AUC of 0.893 (sensitivity: 85.4%, specificity: 73.0%) in the training set and an AUC of 0.873 (sensitivity: 88.2%, specificity: 72.2%) in the test set. In conclusion,the AdaBoost diagnostic model integrating the three-gene TB score with inflammatory factors (IL-2, IL-5, IL-17, and IFN-γ) exhibits superior discriminating performance for pathogen-negative pulmonary tuberculosis compared to OPD, significantly outperforming the three-gene TB score alone.

Objective:To understand the usage situation of e-cigarettes among residents aged 18-65 in Beijing, explore the relationship between e-cigarette use and cigarette smoking as well as smoking cessation behaviors, and provide scientific support for the developing and improving policies and measures related to e-cigarettes.Methods:Using 19 684 residents data from the Beijing Non-communication Chronic Disease and Risk Factors Surveillance in 2022, complex sampling weighted methods were used to estimate proportions, and complex sampling logistic regression analysis was applied to explore the relationship between e-cigarette use, cigarette smoking, and smoking cessation.Results:Among all study participants, the proportion of those who had ever used e-cigarettes was 3.36%, with the current e-cigarette use at 1.26%. The proportion of current e-cigarette users (1.87%) and the former e-cigarette users (3.47%) were higher ( χ2=64.70, P<0.001) among males compared to females (0.60% and 0.64% respectively). The top three reasons for using e-cigarettes were wanting to quit smoking, perceiving e-cigarettes as less harmful, and enjoying the flavors of e-cigarettes. 83.54% of e-cigarette users started with cigarettes. The results of the complex sampling multivariable logistic regression analysis showed that current smoking ( OR=61.35, 95% CI: 36.98-101.76) and former smoking ( OR=31.20, 95% CI: 15.52-62.71) were positively associated with e-cigarette, while current e-cigarette use ( OR=0.13, 95% CI: 0.04-0.39) was negatively associated with quitting cigarette smoking. Conclusions:The proportion of e-cigarette use in Beijing was relatively low. E-cigarette use was associated with cigarette use and was not conducive to smoking cessation. Therefore, stronger regulatory measures and health education campaigns regarding the risks of e-cigarettes should be implemented.

Objective:To analyze the association between remnant cholesterol (RC) and the risk of atherosclerotic cardiovascular disease (ASCVD) in community population in Shanghai.Methods:Using baseline and follow-up data from the Shanghai Suburban Adult Cohort and Biobank, individuals with ASCVD (including coronary heart disease, stroke, myocardial infarction, and peripheral artery disease) at baseline were excluded. A Cox proportional hazards regression model was employed to analyze the relationship between RC and ASCVD risk and the association under different LDL-C levels.Results:A total of 57 281 participants were included, with a median follow-up of 5.61 person-years. During the follow-up, 1 436 ASCVD events (2.51%) were recorded. After adjusting for potential confounders, individuals with moderate ( HR=1.18, 95% CI: 1.03-1.36) or high RC levels ( HR=1.32, 95% CI: 1.15-1.51) had an increased risk of ASCVD. The association was stronger in participants younger than 60 years-old (interaction P=0.048). Participants with RC ≥0.97 mmol/L and LDL-C <3.40 mmol/L demonstrated a 19% ( HR=1.19, 95% CI: 1.06-1.35) increased risk of ASCVD. When RC ≥0.97 mmol/L and LDL-C ≥3.40 mmol/L, ASCVD risk increased by 42% ( HR=1.42, 95% CI: 1.21-1.67). Conclusions:Elevated RC increases ASCVD risk, regardless of LDL-C levels. RC can serve as a valuable predictor and intervention target for ASCVD.

Stroke is one of the most common and serious neurological complications associated with extracorporeal membrane oxygenation （ECMO） therapy， and close monitoring， early recognition， and timely intervention can reduce the incidence rate of stroke and the mortality rate of patients. Transcranial Doppler （TCD） and transcranial color Doppler ultrasonography （TCCD） have emerged as the preferred modalities for monitoring cerebral blood flow in ECMO patients due to their bedside applicability， ability to provide real-time dynamic assessments， and noninvasive safety. This article summarizes the spectral characteristics of cerebral blood flow in patients undergoing various ECMO modalities and elaborates on the latest research advances and clinical significance of TCD/TCCD in predicting stroke events， monitoring cerebral microembolic signals， reflecting the change in intracranial pressure， confirming brain death， and providing prognostic evaluation.
Stroke

Objective: To understand the trends in malnutrition among primary and middle school students of Han nationality in Hainan Province from 2005 to 2019, so as to provide a basis for improving nutrition intervention measures for children and adolescents. Methods: A sample of 32 949 Han nationality primary and middle school students aged 7-18 years old in Hainan Province were investigated in 2005, 2010, 2014 and 2019 based on national survey on student physical fitness and health. The Malnutrition Screening Standard of Schoolage Children and Adolescents was used to screen malnutrition. Statistical analysis was performed using the χ2 test and the χ2trend test. Results: In the four surveys conducted during 2005 to 2019, the prevalence of malnutrition among primary and middle school students were 22.12%, 18.80%, 15.89% and 9.56%, respectively, with an increase of -12.56% and an average annual increase of -5.82%. The decreasing trend of malnutrition by year was statistically significant (χ2trend=600.72, P<0.01), and the proportion of emaciation type was the highest (8.87%-20.15%). The detection rates of malnutrition among all students aged 7 to 18 showed a decreasing trend from 2005 to 2019 (χ2trend=56.44, 60.04, 61.48, 42.49, 51.81, 50.81, 72.86, 101.34, 86.38, 24.81, 17.72, 10.38, P<0.01). From 2005 to 2019, the detection rates of malnutrition in boys were higher than that of girls (in 4 surveys), and that in rural students from 2005 to 2014 of 3 surveys were higher than that in towns (χ2=92.07, 35.16, 25.29, 29.98; 64.35, 4.26, 6.32, P<0.05). Conclusions The malnutrition of Han nationality primary and middle school students aged 7-18 years in Hainan Province show a trend of improvement year by year from 2005 to 2019, despite the overall high detection rate. Wasting is the most common type of malnutrition. The epidemic of malnutrition varies by age, sex and areas. Further targeted measures should be taken to strengthen intervention in the diet of primary and middle school students, to improve the nutritional status of children and adolescents.