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.

Purpose: In this study, the neural communication patterns between hypothalamic structures and cortical areas in girls diagnosed with central precocious puberty (CPP) were explored. Endocrine profiles were incorporated to clarify the pathophysiological interactions between cerebral networks and hormonal regulation. The hypothalamus was designated as the key focus area for connectivity analysis. Methods: Fifty-seven girls (37 CPP, 20 non-CPP) were recruited from the Pediatric Development Clinic at the Second Affiliated Hospital of Anhui Medical University. The collected data included demographic information, gonadotropin-releasing hormone stimulation tests, and magnetic resonance imaging scans. Hypothalamic functional connectivity (FC) was analyzed using predefined region of interest coordinates, and correlations between hormone levels and FC values were assessed. Results: Compared to the non-CPP group, the CPP group exhibited elevated baseline follicle-stimulating hormone (FSH), baseline luteinizing hormone (LH), peak FSH, peak LH, and peak LH/FSH ratios. Patients with CPP exhibited enhanced neural synchronization linking the right lateral hypothalamic effector zone to the right superior frontal gyrus (displaying a borderline significant correlation with peak LH concentrations), concurrent with diminished functional coupling of the right lateral hypothalamic efferent to the right fusiform/supramarginal gyri. The left lateral hypothalamic projections demonstrated amplified connectivity with the right cuneus. No FC differences were observed in the medial hypothalamus. Conclusion Abnormal lateral hypothalamus FC patterns were identified in CPP girls and were particularly linked to peak LH levels. The findings offer novel insights into the neuroendocrine mechanisms underlying CPP.

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.

Myopia prevention and control holds significant importance for the cultivation of strategic talents at the national level. In recent years, myopia among Chinese students has shown a trend of increasing prevalence and earlier onset. Once myopia develops, it is difficult to reverse. Schools, as the primary setting for student learning and activities, play a crucial role in mitigating the high incidence of myopia among students. This article provides a review of the progress in school-based myopia prevention and control interventions both domestically and internationally, with a focus on visual tracking exercises, outdoor light exposure, and improvements to classroom lighting environments. The study finds that methods such as visual tracking exercises, outdoor light exposure, and classroom lighting improvement can effectively prevent students from developing myopia and control the progression of myopia. The findings may offer scientific evidence for further improving policy formulation in China.

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.

Objective:To investigate the differences in brain age and brain age gap (BAG) between patients with prolonged disorders of consciousness (pDoC) and healthy controls (HC).Methods:A retrospective cross-sectional study was performed; 43 patients with pDoC admitted to Rehabilitation Medicine Center, Zhujiang Hospital, Southern Medical University from January 2020 to October 2022 were enrolled; 26 healthy volunteers recruited at the same time and 187 healthy subjects from the publicly available healthy control dataset Nathan Kline Institute-Rockland Sample (NKI-RS) were chosen as HC group. The clinical and imaging data of these subjects were collected. A brain age estimation model was constructed based on amplitude of low-frequency fluctuation (ALFF) in resting-state functional magnetic resonance imaging (rs-fMRI) for healthy individuals, and the pDoC group was used as the test set. A two-sample t-test was used to compare the brain age and BAG differences between the pDoC group and HC group. Pearson correlation analysis was used to explore the correlation between BAG and coma recovery scale-revised (CRS-R) in the pDoC group. Results:The chronological age and estimated brain age in the HC group were (41.54±9.61) and (42.32±10.65) years, respectively, without significant difference ( t=-0.254, P=0.801). The chronological age and estimated brain age in the pDoC group were (49.91±12.03) and (62.79±15.00) years, respectively, with significant difference ( t=-4.341, P<0.001). The BAG in the HC and pDoC groups were (0.78±4.59) and (12.88±7.17) years, respectively, with significant difference ( t=-7.822, P<0.001). Correlation analysis results showed that in the pDoC patients, no correlation was noted between BAG and CRS-R score on the day of imaging scan or 6 months after the day of imaging scan ( r=0.090, P=0.738; r=0.205, P=0.674); no correlation was noted between BAG and difference in CRS-R score (difference value of CRS-R score 6 months after the day of imaging scan-CRS-R score on the day of imaging scan, r=0.246, P=0.687). Conclusion:Compared with the HC subjects, patients with pDoC exhibit an abnormal increase in brain age, suggesting that pDoC caused by severe brain injury may lead to accelerated brain aging.

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 To explore the correlation between serum hypoxia-inducible factor-1α(HIF-1α)levels and cerebral microbleeds(CMBs)and cognitive impairment and to assess the predictive value of HIF-1α for CSVD-related cognitive impairment.Methods A total of 104 patients with CSVD who attended the Department of Neurology,First Affiliated Hospital of Xinxiang Medical University from June 2022 to November 2023 were enrolled.All enrolled patients were subjected to basic statistics,cranial nuclear magnetic resonance examination,cognitive function assessment,and serum HIF-1α test,and the number and location of CMBs were counted.Based on the above data the enrolled patients were grouped.The correlation between HIF-1α and cognitive function and CMBs was studied the influencing factors of CMBs and cognitive impairment were analyzed,and the predictive value of HIF-1α on the occurrence of cognitive impairment was evaluated.Results There were statistically significant differences in HIF-1α levels and cognitive function among different CMBs groups.Serum HIF-1α levels were significantly negatively correlated with overall cognitive function,visuospatial and executive function,attention,and delayed recall,and serum HIF-1α was positively correlated with the number of CMBs.HIF-1α may be a risk factor for CMBs and cognitive impairment associated with CSVD,and serum HIF-1α has potential in predict the cognitive impairment caused by CSVD.Conclusion Serum levels of HIF-1α were associated with the number of CMB and CSVD-related cognitive impairment,and serum levels of HIF-1α may have a predictive value for CSVD-related cognitive impairment.

Objective:To investigate the differences in brain age and brain age gap (BAG) between patients with prolonged disorders of consciousness (pDoC) and healthy controls (HC).Methods:A retrospective cross-sectional study was performed; 43 patients with pDoC admitted to Rehabilitation Medicine Center, Zhujiang Hospital, Southern Medical University from January 2020 to October 2022 were enrolled; 26 healthy volunteers recruited at the same time and 187 healthy subjects from the publicly available healthy control dataset Nathan Kline Institute-Rockland Sample (NKI-RS) were chosen as HC group. The clinical and imaging data of these subjects were collected. A brain age estimation model was constructed based on amplitude of low-frequency fluctuation (ALFF) in resting-state functional magnetic resonance imaging (rs-fMRI) for healthy individuals, and the pDoC group was used as the test set. A two-sample t-test was used to compare the brain age and BAG differences between the pDoC group and HC group. Pearson correlation analysis was used to explore the correlation between BAG and coma recovery scale-revised (CRS-R) in the pDoC group. Results:The chronological age and estimated brain age in the HC group were (41.54±9.61) and (42.32±10.65) years, respectively, without significant difference ( t=-0.254, P=0.801). The chronological age and estimated brain age in the pDoC group were (49.91±12.03) and (62.79±15.00) years, respectively, with significant difference ( t=-4.341, P<0.001). The BAG in the HC and pDoC groups were (0.78±4.59) and (12.88±7.17) years, respectively, with significant difference ( t=-7.822, P<0.001). Correlation analysis results showed that in the pDoC patients, no correlation was noted between BAG and CRS-R score on the day of imaging scan or 6 months after the day of imaging scan ( r=0.090, P=0.738; r=0.205, P=0.674); no correlation was noted between BAG and difference in CRS-R score (difference value of CRS-R score 6 months after the day of imaging scan-CRS-R score on the day of imaging scan, r=0.246, P=0.687). Conclusion:Compared with the HC subjects, patients with pDoC exhibit an abnormal increase in brain age, suggesting that pDoC caused by severe brain injury may lead to accelerated brain aging.