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.

Bovine rotavirus(BRV)is an important pathogen causing diarrhea in calves.To understand the genomic charac-teristics and genetic variations in bovine rotavirus,and to further enrich data on the biological characteristics of rotavirus,we aimed to amplify 11 gene segments of the isolated and cultured G6P[1]bovine rotavirus BLL strain,perform whole genome se-quencing,and analyze the molecular characteristics.MEGA7.0 and DNAMAN software were used for homology and typing a-nalysis,and the whole genome phylogenetic tree was constructed to analyze genetic evolution relationships.The complete geno-type of the BLL strain was G6-P[1]-I2-R2-C2-M2-A3-N2-T6-E2-H3.Phylogenetic analysis of the VP7 and VP4 genes of the BLL strain showed that the VP7 gene had the highest homology with RVA/Cow-wt/HB01/China/2021,and the VP4 gene of the BLL strain was in the same branch as RVA/Human-tc/ISR/Ro8059/1995.From the sequence alignment of VP8*amino acids,the sialic acid domain of the BLL strain was found to be similar to that in other P[1]strains,but different from those in other types of strains,except for residue 189,which was the same as that in Ro8059 but different from that in other strains.The results suggested that the BLL strain might potentially infect humans.Therefore,continued monitoring and study of the biological characteristics of this strain are necessary to provide more information and evidence supporting further research on the cross-species transmission of group A rotavirus in China.

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.

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: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: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.