Objective To screen ferroptosis genes related to the prognosis of cervical cancer and to construct a prognosis model. Methods Ferroptosis genes were obtained from FerrDb database, and cervical cancer related data were obtained from The Genome-Wide Association Study Catalog database and The Cancer Genome Atlas database. Transcriptome-Wide Association Study, colocalization analysis and differential expression analysis were conducted to screen out candidate ferroptosis genes; Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were conducted on candidate genes. Univariate Cox regression analysis was used to further screen out genes related to the prognosis of cervical cancer. Kaplan-Meier method was used to analyze the relationship between genes and the overall survival of patients. The expression levels of genes in pan-cancer were analyzed through the TIMER database. Two prognostic models were conducted, Model 1 included age and tumor stage, while Model 2 incorporated age, tumor stage, and prognostic genes. The predictive capabilities of the two models were compared. Results A total of 91 candidate genes related to ferroptosis were obtained. Univariate Cox regression analysis showed that 15 genes were associated with the prognosis of cervical cancer. CA9, SCD, TFRC, QSOX1 and CDO1 were risk factors affecting the prognosis of cervical cancer patients (P＜0.05), while PTPN6, ALOXE3, HELLS, IFNG, MIOX, ALOX12B, DUOX1, ALOX15, AQP3 and IDO1 were protective factors (P＜0.05). The mRNA expression levels of the 15 genes showed significant upregulation or downregulation in at least 7 types of cancers, among which TFRC was associated with the largest number of cancer types. Kaplan-Meier analysis showed that HELLS, DUOX1 and ALOXE3 were associated with poor prognosis in cervical cancer. The AUC of the model 1 for predicting 1-year and 3-year overall survival rates of cervical cancer patients was 0.455 and 0.478, and the AUC of Model 2 was 0.854 and 0.595. Model 2 (C-index = 0.727) had better predictive ability than Model 1 (C-index = 0.502). Conclusion The prognostic model composed of 15 prognostic-related genes selected based on bioinformatics has better predictive performance for the survival outcomes of cervical cancer patients, providing important reference value for the prognostic assessment of cervical cancer patients.

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: To investigate the prevalence and associated factors of spinal curvature abnormalities among primary and secondary school students in the Guangxi Zhuang Autonomous Region, so as to provide a scientific basis for the prevention and control of such abnormalities. Methods: From September to November 2023, adopting a stratified cluster random sampling method, spinal curvature screenings and questionnaire surveys were conducted among 168 931 students from grade 4 of primary school to grade 12 of high school in 111 districts and counties across 14 cities in Guangxi. Chi square tests and binary Logistic regression analysis were used to analyze influencing factors of spinal curvature abnormalities. Results: In 2023, the detection rate of poor posture among students above grade 4 in Guangxi was 4.24% , and the detection rate of spinal curvature abnormalities was 2.13%. The detection rate was higher among urban students (2.84%) than rural students (1.66%), boarding students (2.61%) than non-boarding students (1.60%), and high school students (3.16%) than junior high (2.45%) and primary school students (1.15%), and the differences were statistically significant ( χ 2=269.85, 221.44, 565.10, P <0.01). A trend of increasing detection rates with higher grade levels was observed ( χ 2 trend =617.63, P <0.01). Binary Logistic regression analysis indicated that students without boarding at school ( OR =0.82, 95% CI =0.75-0.90), engaging in high-intensity physical activity for over 60 min per day ≥5 days per week ( OR =0.90, 95% CI =0.82-0.98), and adequate sleep ( OR =0.87, 95% CI =0.81-0.94) had lower risks of detecting spinal curvature abnormalities ( P <0.05). Conclusions The prevalence of spinal curvature abnormalities increases with grade level among primary and secondary school students in Guangxi. Regular moderate-to-vigorous physical activity demonstrates protective effects against spinal abnormalities.

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.

ObjectiveTo investigate the diagnostic accuracy and grading capability of ultrasound-derived fat fraction （UDFF）， controlled attenuation parameter （CAP）， and hepatic/renal ratio （HRR） in assessing hepatic steatosis in metabolic associated fatty liver disease （MAFLD） with magnetic resonance imaging-proton density fat fraction （MRI-PDFF） as the gold standard. MethodsA total of 150 patients with MAFLD who attended The First Hospital of Hebei Medical University from January 2023 to December 2024 were enrolled， and 148 healthy volunteers were recruited. All subjects underwent MRI-PDFF， UDFF， CAP， and HRR examinations. Hepatic steatosis was graded based on MRI-PDFF （S0：148 cases； S1：92 cases； S2：21 cases； S3：37 cases）， and the MAFLD patients with different grades of hepatic steatosis were compared in terms of UDFF， CAP， HRR， and clinical features. A one-way analysis of variance was used for comparison of normally distributed continuous data between multiple groups and the Tukey HSD test was used for further comparision between two groups； the Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data between multiple groups， and the Mann-Whitney U test was used for further comparison between two groups； the chi-square test was used for comparison of categorical data between groups. The Spearman correlation analysis was used to investigate the correlation between UDFF， CAP， HRR， and MRI-PDFF in different grades of MAFLD； the receiver operating characteristic （ROC） curve was used to investigate the efficacy of UDFF， CAP， and HRR in the diagnosis of different degrees of hepatic steatosis in MAFLD； the Bland-Altman difference plot was used to analyze the consistency between UDFF and MRI-PDFF in different degrees of hepatic steatosis in MAFLD. ResultsUDFF measurement gradually increased with the increase in the grade of fatty liver （H=201.52，P0.001）. The Spearman correlation analysis showed that there was a strong correlation between any two indicators of UDFF， CAP， HRR， and MRI-PDFF in S1， S2， and S3 MAFLD （all P0.001）， with the strongest correlation between UDFF and MRI-PDFF （r_s1=0.884，r_s2=0.962，r_s3=0.929， all P0.001）. The ROC curve analysis showed that UDFF had a larger area under the ROC curve （AUC） than CAP and HRR in the graded diagnosis of S1 and S3 （all P0.05）， while in the diagnosis of S2 MAFLD， UDFF had a significantly larger AUC than HRR （P0.05） and a similar AUC to CAP （P0.05）. The Bland-Altman difference plot showed good consistency between UDFF and MRI-PDFF in different degrees of hepatic steatosis in MAFLD. ConclusionCompared with CAP and HRR， UDFF can accurately measure liver fat content and has good efficacy in identifying varying degrees of hepatic steatosis in MAFLD.

Objective: To investigate the association between the physical fitness index (PFI) and symptoms of depressive, anxiety and stress symptoms among college students, providing a reference for mental health interventions. Methods: From June to September 2025, combined convenience and cluster random sampling approach was used to administer questionnaire surveys and perform physical fitness tests on 2 712 college students from Zhejiang Chinese Medical University. The Depression Anxiety Stress Scales-21 Items (DASS-21) was used to assess mental health status. Chi square test and multivariate Logistic regression analysis were used to determine the associations between the PFI and the PFI component indicators with depressive, anxiety and stress symptoms. Results: The prevalence of depressive, anxiety and stress among college students were 24.26%, 33.22% and 13.68%, respectively. Statistically significant differences in the prevalence of these symptoms were detected across groups differing in sleep quality, physical activity, weekly breakfast frequency, and history of low back or neck pain ( χ 2=9.33-151.83, all P <0.05). After adjusting for confounding factors, Logistic regression revealed that the moderate and high PFI groups had significantly reduced risks of depressive and anxiety compared to the low PFI group ( OR =0.73, 0.63; 0.61, 0.72, all P <0.05). Poor speed (50 m run) and lower body strength (standing long jump) emerged as common risk factors affecting anxiety and depressive symptoms in both male and female college students (all P <0.05). Increased muscle strength (sit up for 1 min) in female students reduced the risk of depressive ( OR =0.81), anxiety ( OR =0.85), and stress symptoms ( OR =0.79) (all P <0.05). Enhanced lung capacity in male students decreased the risk of depressive ( OR =0.84) and anxiety symptoms ( OR =0.85) (both P <0.05). Conclusions The PFI is negatively correlated with depressive and anxiety symptoms among college students with notable gender differences. Insufficient speed and lower body explosive power represent common risk factors for mental health among male and female college students.

BACKGROUND: The urban-rural disparities in overweight and obesity among children and adolescents are narrowing, and there is a need for long-term and updated data to explain this inequality, understand the underlying mechanisms, and identify priority groups for interventions. METHODS: We analyzed data from seven rounds of the Chinese National Survey on Students Constitution and Health (CNSSCH) conducted from 1985 to 2019, focusing on school-age children and adolescents aged 7-18 years. Joinpoint regression was used to identify inflection points (indicating a change in the trend) in the prevalence of overweight and obesity during the study period, stratified by urban/rural areas and sex. Annual percent change (APC), average annual percent change (AAPC), and 95% confidence interval (CI) were used to describe changes in the prevalence of overweight and obesity. Polynomial regression models were used to predict the prevalence of overweight and obesity among children and adolescents in 2025 and 2030, considering urban/rural areas, sex, and age groups. RESULTS: The prevalence of overweight and obesity in urban boys and girls showed an inflection point of 2000, with AAPC values of 10.09% (95% CI: 7.33-12.92%, t = 7.414, P <0.001) and 8.67% (95% CI: 6.10-11.30%, t = 6.809, P <0.001), respectively. The APC for urban boys decreased from 18.31% (95% CI: 4.72-33.67%, t = 5.926, P = 0.027) to 4.01% (95% CI: 1.33-6.75%, t = 6.486, P = 0.023), while the APC for urban girls decreased from 13.88% (95% CI: 1.82-27.38%, t = 4.994, P = 0.038) to 4.72% (95% CI: 1.43-8.12%, t = 6.215, P = 0.025). However, no inflection points were observed in the best-fit models for rural boys and girls during the period 1985-2019. The prevalence of overweight and obesity for both urban and rural boys is expected to converge at 35.76% by approximately 2027. A similar pattern is observed for urban and rural girls, with a prevalence of overweight and obesity reaching 20.86% in 2025. CONCLUSIONS The prevalence of overweight and obesity among Chinese children and adolescents has been steadily increasing from 1985 to 2019. A complete reversal in urban-rural prevalence is expected by 2027, with a higher prevalence of overweight and obesity in rural areas. Urgent action is needed to address health inequities and increase investments, particularly policies targeting rural children and adolescents.
Humans ; Child ; Adolescent ; Female ; Male ; Rural Population/statistics & numerical data* ; Overweight/epidemiology* ; Prevalence ; China/epidemiology* ; Pediatric Obesity/epidemiology* ; Obesity/epidemiology* ; Urban Population