Objective: To explore the interaction between school bullying and insomnia in relation to depression-anxiety-stress emotions among primary and secondary school students,so as to provide a basis for preventing negative emotional states in adolescents. Methods: In October 2024, a stratified cluster sampling method was used to select 3 058 students in grade 5-6 of primary, junior and senior high school in Sheyang County of Jiangsu Province. The Delaware Bullying Victimization Scale, Insomnia Severity Index, Depression-Anxiety-Stress Scale-21, and Study Condition Questionnaire were employed to investigate school bullying, insomnia, depression-anxiety-stress emotions, and academic performance. The χ 2 test and Logistic regression were used to analyze the association between school bullying and insomnia interactions and depression-anxiety-stress emotions among primary and secondary school students, multiplicative interaction analysis was conducted, and additive interaction analysis was performed using R software. Results: The detection rates of depression-anxiety-stress emotions among primary and secondary school students were 21.6%, 28.4% and 10.8%, respectively. The detection rates of physical bullying, relationship bullying, verbal bullying and cyberbullying in school bullying were 10.6%, 14.0%, 22.3%, and 6.2%, respectively. The detection rate for insomnia was 23.1%. Results from Logistic regression analysis showed that, after adjusting for relevant factors, physical, relational, verbal, and cyberbullying and insomnia were positively correlated with the detection rates of depression ( OR = 5.72- 10.93), anxiety ( OR =6.35-12.17), and stress emotions ( OR =5.97-14.52) among primary and secondary school students (all P <0.01). The multiplicative interaction between physical, relational, verbal, and cyberbullying and insomnia was positively correlated with the detection rates of depression ( OR =8.00-18.01), anxiety ( OR =11.35-17.76), and stress emotions ( OR =7.64-9.12) in primary and secondary school students (all P <0.01). Additive interactions were observed between physical, relational, verbal, and cyberbullying and insomnia in relation to the detection rates of depression, anxiety, and stress emotions among primary and secondary school students (both RERI and AP >0 and the credible interval excluded 0, SI >1 and the credible interval excluded 1). Conclusion School bullying and insomnia are associated with depression, anxiety, and stress emotions among primary and secondary school students, and they exhibit both multiplicative and additive interactions.

Objective: To explore the developmental trajectories of depression in children and adolescents and their association with family types, and to analyze the role of being an only child in the context, so as to provide a basis for early identification of mental health issues in children and adolescents. Methods: The study was a secondary analysis based on the existing database of the Jiangsu Provincial Student Common Diseases and Health Influencing Factors Monitoring and Intervention Project. A total of 11 502 students who had completed at least two measurements using the Chinese version of the Center for Epidemiologic Studies Depression Scale (CES-D) between 2022 and 2024, and had complete information on family type, gender, and age, were selected as the study subjects. Latent class trajectory modeling was used to identify depression developmental trajectories. Multinomial Logistic regression was applied to analyze the association between family type and depression developmental trajectories, and the interaction effect of family type and being an only child was tested. Results: Three types of depression developmental trajectories were identified among children and adolescents: low stable type (91.3%, 10 504 students), moderate rising type (4.3%, 500 students), and high declining type (4.3%, 498 students). Significant differences were observed among the different trajectory groups in terms of gender, age, parental education level, family type, baseline CES-D score, and baseline school type ( χ 2/H=17.48, 139.97, 19.72 , 30.77, 1 081.35, 220.81, all P <0.05). Multinomial Logistic regression analysis showed that, using the low stable type as the reference trajectory group and the nuclear family as the reference family type, after adjusting for confounding factors such as gender, age, and parental education level, single parent families ( OR=1.87, 95%CI= 1.16-3.03) and grandparent headed families ( OR=1.83, 95%CI =1.04-3.21) were significantly associated with the high declining type trajectory (both P <0.05). No significant association was found between family type and the moderate rising type trajectory (all P >0.05). The interaction effect between family type and being an only child was not statistically significant ( LRT=7.71, df=8, P =0.46). Conclusions Depressive symptoms in children and adolescents show heterogeneous developmental patterns during school age. Children and adolescents from single parent and intergenerational families are more likely to follow the high decreasing trajectory.

Objective: To explore the independent and interactive effects of school green space exposure and outdoor activity duration on screening myopia among primary and secondary school students, so as to provide theoretical support for the prevention and control of screening myopia in children and adolescents. Methods: From September to November 2023, 117 487 primary and secondary school students from 497 schools were selected using a cluster random sampling method, covering 98 counties (cities, districts) in Jiangsu Province. Data on the students screening myopia status and associated health influencing factors were collected and analyzed. School green space exposure was quantified using the normalized difference vegetation index (NDVI), which was extracted with ArcGIS Pro software; meanwhile, information on students outdoor activity duration was gathered through self reported questionnaires. Multivariate Logistic regression was applied to assess the independent and interactive effects of green space exposure and outdoor activity duration on screening myopia among primary and secondary school students. Results: Univariate analysis showed that there were statistically significant differences in screening myopia detection rates among primary and secondary school students of different genders, NDVI groups, every outdoor activity duration, monitoring points, school stages, parents educational level, and whether they lived on campus or had parents with screening myopia ( χ 2=88.91-1 950.08, all P <0.05); as the school age and sedentary time increased, the detection rate of screening myopia in primary and secondary school students also increased ( χ 2 trend =8 410.15, 2 028.91, both P <0.05). Independent effects showed that compared to the low NDVI group, the medium and high NDVI groups had lower risks of screening myopia ( OR =0.93, 0.95, both P <0.05). Compared to those with outdoor activity duration<2 h/d, students with outdoor activity duration≥2 h/d had a lower risk of screening myopia ( OR =0.96, P <0.05). When stratified by school level, compared to the low NDVI group, the medium NDVI group had lower risks of screening myopia in primary and junior high schools (primary school: OR =0.91; junior high school: OR =0.88, both P <0.05). Compared to those with outdoor activity duration<2 h/d, junior high school students with outdoor activity duration≥2 h/d had a lower risk of screening myopia ( OR = 0.90, P <0.05). When stratified by monitoring site, urban primary and secondary school students in the medium and high NDVI groups and those with outdoor activity duration≥2 h/d had lower risks of screening myopia ( OR =0.92, 0.92, 0.93, all P <0.05). Interactive effects showed that when medium or high NDVI was combined with outdoor activity duration≥2 h/d, the risks of screening myopia among primary and secondary school students were lower (medium NDVI×≥2 h/d: OR =0.89; high NDVI×≥ 2 h/d : OR =0.89, both P <0.05), and the combined effect was superior to that of a single factor. Conclusion Green space exposure and outdoor activity duration have negative correlations with screening myopia among primary and secondary students, and the combined effect is better than that of a single factor.

Objective: To investigate the association between outdoor light at night (LAN) exposure and sleep quality among primary and secondary school students, and to examine the pathways of negative affects including depressive, stress and anxiety symptoms, so as to provide a theoretical basis for optimizing the school environment and enhancing the physical and mental well being of students. Methods: In December 2024, a total of 36 885 students from 154 primary and secondary schools in Suzhou, Nantong, and Changzhou were included via a stratified cluster sampling method. Sleep quality and negative affect were assessed by using the Pittsburgh Sleep Quality Index (PSQI), Center for Epidemiologic Studies Depression Scale (CES-D), Generalized Anxiety Disorder-7 (GAD-7), and Depression, Anxiety and Stress Scale-21 (DASS-21), respectively. Outdoor LAN exposure data were obtained from the national polar orbiting partnership visible infrared imaging radiometer suite nighttime light(NPP-VIIRS NTL) dataset provided by the National Earth System Science Data Center. Multivariate Logistic regression model was employed to analyze the association between LAN exposure and sleep quality across different regions, with stratification by monitoring site. Spearman correlation analysis was used to examine the relationships between LAN, negative affect, and sleep quality. The mediating role of negative affect was tested by using Model 4 of the PROCESS macro. Results: The detection rates among students were 13.95%( n =5 147) for depressive symptom, 16.72%( n =6 166) for stress symptom, and 17.49%( n =6 451) for anxiety symptom. The median outdoor LAN exposure was 28.85(19.10, 41.44)nW/(cm 2 · ( sr). After adjusting for confounders, multivariate Logistic regression analysis showed that high LAN exposure ( Q 4) was positively associated with sleep problems (urban areas: OR =1.28, 95% CI = 1.17- 1.41; rural areas: OR =1.21, 95% CI =1.07-1.36; both P <0.05). Spearman correlation analysis revealed significant positive correlations between LAN exposure, depressive symptoms, stress symptoms, anxiety symptoms, and sleep quality ( r =0.03-0.75, all P < 0.01). The mediation analysis indicated that all dimensions of negative affect significantly mediated the relationship between high LAN exposure and poor sleep quality (all P <0.01). Specifically, the indirect effects were 0.03 (95% CI =0.02-0.05) for depressive symptom, 0.05(95% CI =0.03-0.08) for stress symptom, and 0.07(95% CI =0.05-0.09) for anxiety symptom. Overall, 31.9% of the total effect was mediated by negative affect, with anxiety (14.89%) being the strongest mediator, followed by stress (10.64%) and depression(6.38%). Conclusion Reducing high levels of outdoor LAN exposure and implementing interventions targeting negative affect may contribute to improved sleep quality among primary and secondary school students.

Objective:To analyze the association between unhealthy lifestyles and depressive symptoms, anxiety symptoms and stress among secondary school students in Jiangsu Province.Methods:From September to November 2023, a multistage stratified cluster random sampling method was used to select secondary school students from 13 districts and cities in Jiangsu Province. A questionnaire survey was conducted on their unhealthy lifestyles (low physical activity, smoking, drinking, internet addiction, poor sleep quality, and unhealthy diet), as well as their depressive symptoms, anxiety symptoms and stress. The multivariate logistic regression model and mixed graph model were used to construct a network and analyze the association between unhealthy lifestyles and depressive symptoms, anxiety symptoms and stress.Results:A total of 81 414 secondary school students were finally included in this study, including 39 725 (48.79%) female students and 41 689 (51.21%) male students. The prevalence of depressive symptoms, anxiety symptoms and stress were 18.55%, 32.09% and 12.91%, respectively. The multivariate logistic regression model showed that after adjusting for age, gender, urban-rural status, residential status, and family type, compared with students without unhealthy lifestyles, students with low physical activity, smoking, alcohol consumption, internet addiction, poor sleep quality, and unhealthy diet had a significantly increased risk of depressive symptoms ( OR=1.12, 95% CI:1.07-1.17; OR=1.60, 95% CI: 1.49-1.72; OR=1.79, 95% CI: 1.71-1.88; OR=3.05, 95% CI: 2.77-3.36; OR=6.66, 95% CI: 6.40-6.93; OR=1.29, 95% CI: 1.24-1.34) and a significantly increased risk of anxiety symptoms ( OR=1.09, 95% CI: 1.05-1.13; OR=1.42, 95% CI: 1.33-1.52; OR=1.76, 95% CI: 1.69-1.83; OR=2.40, 95% CI: 2.17-2.65; OR=5.79, 95% CI: 5.59-6.00; OR=1.16, 95% CI: 1.12-1.21). Students who smoked, drank alcohol, had internet addiction, and had poor sleep quality had a significantly increased risk of stress ( OR=1.49, 95% CI: 1.38-1.61; OR=1.79, 95% CI: 1.70-1.89; OR=2.25, 95% CI: 2.04-2.48; OR=6.74, 95% CI: 6.43-7.06). The node with poor sleep quality (bridge strength=0.48) in the network constructed by the mixed graph model had the strongest centrality of the bridge connecting unhealthy lifestyles with depressive symptoms, anxiety symptoms and stress. Conclusion:Low physical activity, smoking, alcohol consumption, internet addiction, poor sleep quality, and unhealthy diet increase the risk of depressive symptoms and anxiety symptoms among Jiangsu Province secondary school students. Smoking, alcohol consumption, internet addiction, and poor sleep quality increase the risk of stress among Jiangsu Province secondary school students. Sleep quality is an important intervention target for Jiangsu Province secondary school students to alleviate their negative emotions.

Objective:To build an internet hospital cost-benefit evaluation index system based on a large public tertiary hospital, for references for improving the operation and management of internet hospitals.Methods:From May to October 2024, this study identified the elements of cost-benefit through on-site investigation, literature analysis and expert discussion, and built an initial evaluation index system of cost-benefit of internet hospitals; Delphi method and Pareto chart method were used to determine indicators and their weights; This evaluation index system was used to quantitatively evaluate an internet hospital since its operation for two years (from May 2022 to April 2024).Results:Five profit entities and 26 cost-benefit components had been identified; The expert authority coefficient of the two rounds of Delphi method was 0.73, and the Kendall coefficient was 0.80 ( P<0.001). The costs and benefits of an internet hospital since its operation for two years were 14.06 million yuan and 134.95 million yuan, respectively, with a benefit cost ratio of 9.60. Conclusions:The cost-benefit evaluation index system of internet hospitals built in this study was suitable for these relying on physical hospitals. This system was scientific and practical, and could provide references for cost-benefit evaluation of other Internet hospitals.

Multiple sclerosis(MS)is a chronic central nervous system inflammatory demyelinating disease caused by autoim-mune diseases.Intermittent fasting(IF)is a dietary pattern with periodic energy limitation.Studies have shown that IF can delay the occurrence and development of MS and experimental allergic encephalomyelitis(EAE).Its mechanism of action includes repairing nerve damage,promoting myelin regeneration,regulating gut microbiota,and reversing immune inflammation.The use of IF can re-duce the severity,recurrence time,and lesion size of MS patients clinically,which brings new ideas for the treatment and prevention of MS.This article provides a review of the effects and mechanisms of IF in treating MS,with the aim of gaining new insights into the treatment of MS and providing reference for the application of IF in the treatment of MS patients.

Objective:To analyze the association between unhealthy lifestyles and depressive symptoms, anxiety symptoms and stress among secondary school students in Jiangsu Province.Methods:From September to November 2023, a multistage stratified cluster random sampling method was used to select secondary school students from 13 districts and cities in Jiangsu Province. A questionnaire survey was conducted on their unhealthy lifestyles (low physical activity, smoking, drinking, internet addiction, poor sleep quality, and unhealthy diet), as well as their depressive symptoms, anxiety symptoms and stress. The multivariate logistic regression model and mixed graph model were used to construct a network and analyze the association between unhealthy lifestyles and depressive symptoms, anxiety symptoms and stress.Results:A total of 81 414 secondary school students were finally included in this study, including 39 725 (48.79%) female students and 41 689 (51.21%) male students. The prevalence of depressive symptoms, anxiety symptoms and stress were 18.55%, 32.09% and 12.91%, respectively. The multivariate logistic regression model showed that after adjusting for age, gender, urban-rural status, residential status, and family type, compared with students without unhealthy lifestyles, students with low physical activity, smoking, alcohol consumption, internet addiction, poor sleep quality, and unhealthy diet had a significantly increased risk of depressive symptoms ( OR=1.12, 95% CI:1.07-1.17; OR=1.60, 95% CI: 1.49-1.72; OR=1.79, 95% CI: 1.71-1.88; OR=3.05, 95% CI: 2.77-3.36; OR=6.66, 95% CI: 6.40-6.93; OR=1.29, 95% CI: 1.24-1.34) and a significantly increased risk of anxiety symptoms ( OR=1.09, 95% CI: 1.05-1.13; OR=1.42, 95% CI: 1.33-1.52; OR=1.76, 95% CI: 1.69-1.83; OR=2.40, 95% CI: 2.17-2.65; OR=5.79, 95% CI: 5.59-6.00; OR=1.16, 95% CI: 1.12-1.21). Students who smoked, drank alcohol, had internet addiction, and had poor sleep quality had a significantly increased risk of stress ( OR=1.49, 95% CI: 1.38-1.61; OR=1.79, 95% CI: 1.70-1.89; OR=2.25, 95% CI: 2.04-2.48; OR=6.74, 95% CI: 6.43-7.06). The node with poor sleep quality (bridge strength=0.48) in the network constructed by the mixed graph model had the strongest centrality of the bridge connecting unhealthy lifestyles with depressive symptoms, anxiety symptoms and stress. Conclusion:Low physical activity, smoking, alcohol consumption, internet addiction, poor sleep quality, and unhealthy diet increase the risk of depressive symptoms and anxiety symptoms among Jiangsu Province secondary school students. Smoking, alcohol consumption, internet addiction, and poor sleep quality increase the risk of stress among Jiangsu Province secondary school students. Sleep quality is an important intervention target for Jiangsu Province secondary school students to alleviate their negative emotions.

Objective This study examines the factors influencing oral frailty in the elderly community,develops a risk prediction model,and validates its efficacy,so as to provide references for identifying and preventing oral weakness in the elderly.Methods 556 elderly individuals from 4 communities were selected by convenience sampling from June to August 2024 in Zigong City Sichuan Province.They were randomly divided into a training group(383 cases)and a validation group(165 cases).Data were collected by a general information questionnaire,Social Frailty Scale,Geriatric Depression Scale,and the Oral Frailty Index-8 screening tool.Logistic regression was used to determine the influencing factors,and R software was used to establish a nomogram model for predicting the risk of oral frailty.Bootstrap method and the validation group were used for internally validation of the model.Calibration curve was used to evaluate the prediction performance of the model.Results 548 valid questionnaires were collected.The final model variables included whether the age ≥80 years,wearing removable dentures,reduced frequency of going out,brushing teeth less than twice a day,frequent dry mouth,increased difficulty in eating hard foods,and choking.The area under the receiver operating characteristic curve of the training group was 0.95(95％CI:0.93～0.97),and the best cutoff value was 0.687.The model achieved an accuracy of 87％,sensitivity of 91％,specificity of 85％,positive predictive value of 0.75,and negative predictive value of 0.95.The Hosmer-Lemeshow fitting test show that x2=3.036,P=0.932,indicating a good model fit.Conclusion The oral frailty prediction model demonstrated a good discrimination,calibration,and clinical utility,which can provide a scientific basis for the prevention and early screening of oral frailty in the elderly.

Objective This study examines the factors influencing oral frailty in the elderly community,develops a risk prediction model,and validates its efficacy,so as to provide references for identifying and preventing oral weakness in the elderly.Methods 556 elderly individuals from 4 communities were selected by convenience sampling from June to August 2024 in Zigong City Sichuan Province.They were randomly divided into a training group(383 cases)and a validation group(165 cases).Data were collected by a general information questionnaire,Social Frailty Scale,Geriatric Depression Scale,and the Oral Frailty Index-8 screening tool.Logistic regression was used to determine the influencing factors,and R software was used to establish a nomogram model for predicting the risk of oral frailty.Bootstrap method and the validation group were used for internally validation of the model.Calibration curve was used to evaluate the prediction performance of the model.Results 548 valid questionnaires were collected.The final model variables included whether the age ≥80 years,wearing removable dentures,reduced frequency of going out,brushing teeth less than twice a day,frequent dry mouth,increased difficulty in eating hard foods,and choking.The area under the receiver operating characteristic curve of the training group was 0.95(95％CI:0.93～0.97),and the best cutoff value was 0.687.The model achieved an accuracy of 87％,sensitivity of 91％,specificity of 85％,positive predictive value of 0.75,and negative predictive value of 0.95.The Hosmer-Lemeshow fitting test show that x2=3.036,P=0.932,indicating a good model fit.Conclusion The oral frailty prediction model demonstrated a good discrimination,calibration,and clinical utility,which can provide a scientific basis for the prevention and early screening of oral frailty in the elderly.