ObjectiveTo evaluate the clinical efficacy of Zuogui Pill （左归丸，ZGP） decoction on neurological function， motor function， and activities of daily living in patients with ischemic stroke （IS） of liver-kidney yin deficiency syndrome， and to assess its potential mechanisms from the perspectives of cerebral perfusion and corticospinal tract （CST） remodeling. MethodsA total of 108 patients with IS of liver-kidney yin deficiency syndrome and onset time ≤14 days were enrolled and randomly divided into a control group and a treatment group， with 54 cases in each group. The control group received conventional western medicine， while the treatment group received ZGP decoction （one dose per day） in addition to the conventional treatment. Both groups were treated for 90 days. The National Institutes of Health Stroke Scale （NIHSS）， modified Rankin Scale （mRS）， modified Barthel Index （MBI）， Fugl-Meyer Assessment （FMA）， and Chinese medicine syndrome score （CMS） were evaluated before treatment and after （90±3） days of treatment. Three-dimensional arterial spin labeling （3D-ASL） and diffusion tensor imaging （DTI） were performed to obtain ipsilesional cerebral blood flow （CBF） in the unaffected and affected hemispheres， relative CBF （rCBF）， and fractional anisotropy （FA） of the CST. Nodal analysis of FA values along the CST was also conducted. Correlation analysis was used to assess the relationships between post-treatment cerebral perfusion parameters （CBF and rCBF）， and FA values with clinical neurological function scores. ResultsAfter treatment， both groups showed significantly decreased NIHSS， mRS and CMS scores， together with significantly increased FMA and MBI scores （P＜0.01）. Intergroup comparison showed that NIHSS， mRS， and CMS scores in the treatment group were significantly lower than those in the control group ， while MBI and FMA scores were significantly higher （P＜0.01）. The 3D-ASL results revealed that CBF and rCBF values in the affected hemisphere significantly increased in both groups after treatment， and the treatment group had significantly higher values （P＜0.01）. DTI showed that the FA value of the CST on the affected side in the treatment group was significantly higher than that in the control group after treatment （P＜0.05）. Nodal analysis along the CST further demonstrated that the continuity of FA values at lesion-related nodes 45~99 in the treatment group was significantly better than that in the control group （P＜0.01）， with significant increases at nodes 65~71 and 76~81 compared with baseline （P＜0.05 or P＜0.01）， whereas the control group showed a decreasing trend at nodes 80~93 （P＜0.05）. Correlation analysis indicated that CBF and rCBF values in the affected hemisphere were negatively correlated with NIHSS and mRS scores and positively correlated with MBI score （P＜0.05）. FA value of the CST on the affected side， both before and after treatment， were negatively correlated with NIHSS score and positively correlated with FMA score （P＜0.05 or P＜0.01）. ConclusionZGP decoction can effectively improve neurological function， motor function， and activities of daily living in IS patients with liver-kidney yin deficiency syndrome， and its mechanism may be related to enhanced cerebral perfusion in the affected hemisphere and corticospinal tract structural remodeling.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective To analyze the association between lifestyle and the risk of type 2 diabetes(T2D)among adult residents.Methods The data was sourced from the Shanghai Suburban Adult Cohort and Biobank.A total of 42 096 adult residents who had not developed T2D were recruited from four districts of Shanghai(Songjiang,Jiading,Minhang,and Xuhui)between 2016 and 2019.The follow-up ended on Feb 28,2023.A structured questionnaire was used to collect information on six lifestyle-related items,including smoking,alcohol consumption,BMI,waist circumference(WC),physical activity,and diet.The unhealthy lifestyle scores(UHLS)were calculated by counting the number of all the unhealthy lifestyle items,with a range of 0-6.New-onset T2D events diagnosed by physicians were obtained through the medical information system.Cox proportional hazards regression model and restricted cubic spline model were utilized to evaluate the association between unhealthy lifestyles and the risk of T2D incidence.Results About 28.1%of the participants led 4-6 unhealthy lifestyles.A total of 1 752 new T2D cases were identified during 218 513.4 person-years of follow-up.Analysis of single unhealthy lifestyle showed that abnormal WC(HR=1.5,95%CI:1.4-1.7)and abnormal BMI(HR=1.3,95%CI:1.2-1.5)were associated with an increased risk of T2D.Compared with individuals with a UHLS of 0-1,those with a UHLS of 3 and 4-6 had 30%(95%CI:1.1-1.6)and 50%(95%CI:1.2-1.8)higher risks of T2D,respectively.Each additional unhealthy lifestyle was associated with a 10%increase in T2D incidence risk(HR=1.1,95%CI:1.1-1.2).Conclusion The risk of T2D in adult residents increases with the cumulative number of unhealthy lifestyles.Adult residents with abnormal WC or BMI,or have three or more unhealthy lifestyles accumulated,will increase the risk of new-onset T2D.

Objective:To investigate the mechanism of natural antigen B(nAgB)to protect Immune thrombocytopenia(ITP)mouse model by influencing autophagy.Methods:Twenty-eight female BALB/c mice aged 8-10 weeks were randomly divided into four groups.7 mice of each group were immunized intraperitoneally,the control group was treated with PBS as the control group;ITP group was treated with anti-CD41 monoclonal antibody(anti-CD41Ab)only;nAgB group was treated with nAgB intraperitoneal injection for 5d;nAgB+ITP group was treated with nAgB intraperitoneal injection for 5d,then treated with anti-CD41 Ab.The peripheral blood platelet count in each group was tested;and the spleen and liver should be isolated and weighed,the organ index was calculated;qRT-PCR was used to detect spleen microtubule-associated protein 1 light chain 3(LC3),p62,Beclin-1 mRNA expression levels.Western blot was used to detect the protein expression level of spleen LC3 Ⅱ/LC3 Ⅰ,p62,Beclin-1.Results:Compared with the control group,mice in the ITP group showed a significant decrease in blood PLT count[(102.1±17.9)× 109/L vs(485.4±185.2)×109/L,P＜0.01],a significant increase in spleen index(P＜0.01),mice in the nAgB group showed a significant increase in blood PLT count,rising to(1051±127.6)× 109/L on the 3 day after modeling.Compared with the ITP group,mice in the nAgB+ITP group showed a significant increase in PLT count on the 1 day of anti-CD41 Ab administration[(428.6±131.6)× 109/L vs(102.1±17.9)×109/L,P＜0.05],however,the spleen index was significantly decreased(P＜0.05).qRT-PCR and Western blot results showed that compared with the control group,the mRNA and protein expression levels of spleen LC3,p62 and Beclin-1 were increased in the ITP group of mice(P＜0.05,P＜0.01).Compared with the ITP group,the nAgB+ITP group could significantly decrease mRNA levels of spleen LC3,p62 and Beclin-1(P＜0.05,P＜0.01),and also significantly decrease the protein expression levels of LC3 Ⅱ/LC3 Ⅰ,p62 and Beclin-1(P＜0.05,P＜0.01).Conclusion:nAgB inhibits the transcription and expression levels of autophagy-related genes and regulates immune intolerance,thereby protecting ITP mouse models.

To explore an effective health management model for obese students,a comprehensive intervention was carried out for obese students with cardiovascular and metabolic risks,and the effectiveness of this intervention model was evaluated.From Jan to Apr 2024(excluding the winter vacation),300 students were selected from 6 primary schools in Qibao Community,Minhang District,Shanghai,to participate in the study(28 students dropped out during the study period).The study subjects were divided into two intervention groups(pilot intervention group:equipped with a health vice principal;general intervention group:not equipped with a health vice principal)and a control group.The intervention group received comprehensive intervention measures such as science popularization,diet,exercise and psychology,while the control group received daily health management.The post-intervention results showed that the intervention group had significant improvements in healthy diet,scientific exercise and positive psychology,with significant differences compared to the control group(P<0.05).At the same time,the intervention group had a reduced detection rate of obesity(BMI≥P95),and a decreased detection rate of abnormal metabolic indicators such as blood pressure,fasting blood glucose and triglyceride,especially significant differences in fasting blood glucose and triglyceride compared with the control group(P<0.001).In addition,the pilot intervention group under the operation of health vice principal showed better effects in changing healthy behaviors and improving some metabolic indicators compared with the general intervention group.The implementation of this project provided a scientific basis for the promotion of a comprehensive intervention model for student health under the oprtation of health vice principle.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.