Background Long working hours, as a common risk factor for occupational stress, is closely related to the occurrence of depressive symptoms. Understanding how long working hours affect occupational stress and depressive symptoms will inform occupational health interventions. Objective To quantify the impact of long working hours exposure on occupational stress and depressive symptoms among Internet industry employees, translate black-box outputs into actionable insights, and demonstrate the value of interpretable machine learning for early-warning occupational-health surveillance. Methods A dataset was derived from a cross-sectional survey involving 2866 internet industry employees in China. This survey was part of the project Risk Assessment Of Long Working Hour Exposure And Its Adverse Health Effects, conducted by the National Institute for Occupational Health and Poisoning Control, Chinese Center for Disease Control and Prevention, from 2021 to 2023. Working hours, occupational stress and depressive symptoms were quantified with a set of structured questionnaires including the Core Occupational Stress Scale and the Patient Health Questionnaire. Pairwise associations were screened by Mantel tests and variance-inflation factors. Key predictors identified through feature selection were fed into six machine-learning risk-prediction models. Visual interpretation was provided by feature importance, Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), while directed causal effects and intervention impacts of prolonged working hours exposure on occupational stress and depressive symptoms were dissected with causal explanation of features techniques. Results The positive rates of occupational stress and depressive symptoms among internet employees were 12.9% and 77.8% respectively. Twelve core features for occupational stress and nine for depressive symptoms were retained after selection. After these features were supplied to six predictive algorithms and evaluated on five metrics, the Light Gradient Boosting Machine (LGBM) achieved the highest accuracy—0.89 for occupational stress and 0.79 for depressive symptoms on the hold-out test set. The feature-importance rankings converged on fatigue accumulation and life satisfaction as dominant drivers for both outcomes, whereas weekly working hours and daily overtime emerged as the principal exposure-related predictors. The SHAP summary plots revealed that longer weekly hours and daily overtime systematically elevated the probability of occupational stress. The causal feature explanation further quantified that ascending one category in weekly working hours increased the probability of occupational stress by 7.04%. Conclusion Exposure to long working hours is associated with both occupational stress and depressive symptoms among internet industry employees. Interpretable machine-learning frameworks translate these associations into transparent, defensible drivers, enabling precise identification of the pivotal factors and their interplay. This evidence base equips occupational-health practitioners with actionable insights for designing targeted prevention and intervention strategies.

Objective:To analyze the disease burden of colorectal cancer (CRC) among young and middle-aged people in China from 1990 to 2021, and to explore the influence of age, period and cohort on the incidence and mortality of CRC in young and middle-aged people of China.Methods:Data on CRC in patients aged 40-59 years in China from 1990 to 2021 were extracted from the Global Burden of Disease Study 2021 (GBD 2021) database. Statistics such as incidence rate, mortality rate, disability-adjusted life years (DALY), and their corresponding age-standardized rates were calculated to analyze the CRC incidence and mortality in different age and sex groups of young and middle-aged Chinese young people from 1990 to 2021. The Joinpoint regression model was used to analyze the CRC incidence, the mortality and the DALY rate, as well as to calculate the annual percentage change (APC) and the average annual percentage change (AAPC). The effects of three independent factors, namely age, period and cohort, on the incidence and mortality of CRC in young and middle-aged people of China were analyzed and evaluated through the age-period-cohort model.Results:From 1990 to 2021, there was a remarkable upward trend in the incidence, mortality, and DALY of CRC among Chinese young and middle-aged adults. In 2021, the number of incidence cases of CRC among young and middle-aged people in China reached 181 000, and the number of deaths reached 57 900, which were 236.80% and 75.48% higher than those in 1990 (53 800 and 33 000, respectively). During the same period, DALY increased by 62.59%, with the 55-59 age group having the largest increase at 83.35%. From 1990 to 2021, the age-standardized incidence rate (ASIR) increased by 49.04%, rising from 25.51/100 000 to 38.02/100 000, and the age-standardized mortality rate (ASMR) declined by 28.75%, decreasing from 17.01/100 000 to 12.12/100 000, respectively. The increase in ASIR was the greatest among the 40-44 age group, reaching 57.31%, while the decline in ASMR was the most significant among the 50-54 age group, amounting to 30.18%. However, the DALY rate declined by 26.66%, from 673.52/100 000 to 493.94/100 000. The decline in DALY was the greatest among the 50-54 age group, reaching 28.26%. Joinpoint regression model analysis showed that, from 1990 to 2021, the incidence of CRC in Chinese young and middle-aged adults rose on average by 1.32% annually, and the increase was higher in men (1.87%) than that in women (0.36%). The mortality rate showed a downward trend, with an average annual decline of 1.10%, with a higher decline in women than in men (-2.14% vs. -0.50%). The DALY rate showed a downward trend, with an average annual decline of 1.00%, and more decline in women than in men (-2.06% vs. -0.41%). All of these trends were statistically significant (all P<0.001). The age-period-cohort model analysis showed that, the net drift of CRC incidence was 1.21% (1.02%-1.41%) per year among Chinese young and middle-aged adults between 1990 and 2021, while the net drift in mortality was -1.40% (-1.59%--1.21%) per year. The impact of age on CRC incidence and mortality intensified with advancing age. Incidence attributable to age rose from 12.66% (11.90%- 13.46%) in the 40-44 age group to 56.68% (54.37%-59.08%) in the 55-59 age group. Similarly, mortality attributable to age increased from 6.47% (6.12%-6.85%) in the 40-44 age group to 25.74% (24.58%-26.96%) in the 55-59 age group. In all age groups, the role of CRC incidence and mortality attributable to age was higher in men than in women. Period effects on the RR value of CRC incidence showed a declining trend followed by an upward trend, with the highest risk during 2015-2019 ( RR=1.36, 95% CI: 1.28-1.43), using 2000-2004 as the reference. For mortality, period effects exhibited a declining trend, with the highest risk during 1990-1994 ( RR=1.23, 95% CI: 1.15-1.32), using 2000-2004 as the reference. Cohort effects indicated that later birth cohorts had higher incidence risks, with the highest incidence observed in the 1973-1977 birth cohort ( RR=1.30, 95% CI: 1.16-1.45), using the 1953-1957 birth cohort as the reference. Conversely, later birth cohorts had lower mortality risks, with the lowest mortality in the 1973-1977 birth cohort ( RR=0.78, 95% CI: 0.69-0.88), using the 1953-1957 birth cohort as the reference. Conclusions:From 1990 to 2021, the changes in the disease burden of CRC among young and middle-aged people in China are manifested as an increase in standardized incidence rate and a decrease in standardized mortality rate. Meanwhile, there are gender differences in the trend of temporal changes. Age, period and cohort all have a significant impact on the incidence and mortality trends of colorectal cancer in young and middle-aged people. Research on the etiology of CRC should be strengthened, and targeted prevention and control strategies should be formulated.

Objective To investigate biological markers associated with psychotic symptoms in patients with bipolar disorder(BD)based on electronic medical records of patients,and to develop an interpretable risk prediction model that supports the identification of high-risk individuals and that facilitates decision-making for providing clinical intervention in a timely manner.Methods A total of 2 352 patients diagnosed with BD and admitted to West China Hospital,Sichuan University were enrolled using the electronic medical records system of the hospital.The participants were divided into two subgroups,the bipolar disorder depression(BDD)group and the bipolar disorder mania(BDM)group.The logistic regression algorithm was used to train and validate the prediction model,and interpretability methods were used to analyze the contribution of each feature to individuals and the effect of the features on specific target prediction decisions.Results The logistic regression model demonstrated robust predictive performance across the BD,BDD,and BDM cohorts,with areas under the curve(AUC)of the receiver operating characteristic curves always exceeding 81.6%.The core predictive features included platelet distribution width(PDW),fibrinogen(FIB),platelet large cell ratio(P-LCR),activated partial thromboplastin time(APTT),prothrombin time(PT),and triglyceride(TG).The logistic regression model exhibited strong interpretability and was combined with nomograms for intuitive risk quantification and individualized prediction.Conclusion The logistic regression model enables rapid and simple screening of BD patients with psychotic symptoms.Distinct patterns of changes observed in blood biomarkers of BDD and BDM subgroups enrich the understanding of the underlying pathophysiological mechanisms and highlight the importance of considering subtypes in the intervention and management of patients.

OBJECTIVE To analyze the relationship between interleukin-35(IL-35)and serine/arginine-rich splicing factor 6(SRSF6)levels and pleural thickness in patients with tuberculous pleurisy,as well as their values of joint assessment for the risk of pleural tuberculoma.METHODS A total of 100 patients with tuberculous pleurisy admit-ted to the Second Affiliated Hospital of Zhejiang University School of Medicine,Linping Campus,from Jan.2022 to Jul.2024 were selected.The IL-35 and SRSF6 levels in pleural effusion and pleural thickness were measured.The relationship between IL-35,SRSF6 levels and pleural thickness were analyzed.After a one-year follow-up,patients with tuberculosis were divided into the occurrence group and non-occurrence group according to whether pleurotuberculoma developed.The risk factors for developing pleurotuberculoma and the value of IL-35 and SRSF6 in accessing the risk of developing pleurotuberculoma were analyzed.RESULTS In patients with tuberculous pleuri-sy,IL-35 and SRSF6 in pleural effusion was(529.78±146.85)ng/L and(1.44±0.43)ng/ml,respectively,and the pleural thickness was(4.07±0.53)mm.IL-35 and SRSF6 levels in pleural effusion were positively correlated with pleural thickness(P<0.05).Logistic analysis showed that the risk of pleural tuberculoma significantly in-creased in patients with tuberculous pleurisy when there was pleural fluid separation,abnormal pleural thickness and elevated levels of IL-35 and SRSF6 in pleural effusion(P<0.05).Receiver operating characteristic(ROC)curve analysis showed that the area under the curve(AUC),specificity and sensitivity of combined IL-35 and SRSF6 in predicting the occurrence of pleural tuberculoma in patients with tuberculous pleurisy were significantly higher than those of either marker alone(P<0.05).CONCLUSION IL-35 and SRSF6 levels are positively correla-ted with the pleural thickness of tuberculous pleurisy,and the combined detection of IL-35 and SRSF6 can effec-tively assess the risk of pleural tuberculoma in tuberculous pleurisy patients.

Objective:To investigate the current status of insomnia symptoms and executive function (EF) impairments among adolescents from regions with different economic development levels, and to analyze their relationship with depressive symptoms, so as to provide clues for improved depressive symptoms screening practices.Methods:This population-based cross-sectional study employed a multistage, stratified cluster random sampling method. During November 2017 to January 2018 and December 2018 to January 2019, a total of 2 495 adolescents aged 11 to 18 years were selected from Shanghai, representing a highly developed economic region, and 2 704 adolescents aged 11 to 18 years were selected from Shangrao city, Jiangxi province, representing a less developed economic region. The depressive symptoms were assessed using the short version of the 21-item depression, anxiety, and stress scale, based on which participants were categorized into groups with or without depressive symptoms. Insomnia symptoms and EF impairments were measured using a self-designed insomnia scale and the behavior rating inventory of executive function, respectively. Participants were further classified into 4 subgroups: neither insomnia nor EF impairment, EF impairment only, insomnia only, and comorbid insomnia and EF impairment. Chi-square test was used to compare the differences in basic information of adolescents from different regions. Multivariate Logistic regression models were applied to examine the associations between insomnia, EF impairment, and their combination with depressive symptoms as well as the differences in gender and school-stage among each subgroup.Results:A total of 2 305 adolescents were recruited from Shanghai (1 192 boys and 1 113 girls, 1 266 junior high school students and 1 039 senior high school students) and 2 250 adolescents from Shangrao (1 126 boys and 1 124 girls, 1 146 junior high school students and 1 104 senior high school students). The numbers of adolescents with depressive symptoms, insomnia symptoms and EF impairment in Shanghai were 460 adolescents (20.0%), 907 adolescents (39.3%), and 411 adolescents (17.8%), respectively, all of which were fewer than those in Shangrao, which were 616 adolescents (27.4%), 1 251 adolescents (55.6%), and 524 adolescents (23.3%), respectively (all P<0.001). In Shanghai, the numbers of adolescents with EF impairment only, insomnia only, and comorbid insomnia and EF impairment were 219 adolescents (9.5%), 670 adolescents (29.1%), and 237 adolescents (10.3%), respectively. And in Shangrao, the corresponding numbers were 193 adolescents (8.6%), 865 adolescents (38.4%), and 386 adolescents (17.2%), respectively. Compared to adolescents in Shanghai with neither EF impairment nor insomnia, the risk of depressive symptoms was all higher in adolescents with EF impairment only, insomnia only, and comorbid EF impairment-insomnia ( OR=2.86, 6.48, 20.10; 95% CI 1.57-5.22, 5.09-8.26, 13.66-29.58; all P<0.01). Similar results were observed in adolescents in Shangrao ( OR=3.22, 4.82, 10.91; 95% CI 1.66-6.28, 3.09-7.51, 7.26-16.40; all P<0.01). The analysis of gender and educational stage differences showed that, compared to the group neither EF impairment nor insomnia, the risk of depressive symptoms all higher in the groups with EF impairment only, insomnia only (all P<0.05), and comorbid EF impairment-insomnia, and the risk in comorbid EF impairment-insomnia group was the highest (all P<0.05). Conclusions:Compared with adolescents in regions with underdeveloped economies, those in economically developed regions had lower rates of insomnia, EF impairment, and depression. Both insomnia and EF impairment significantly increase the risk of depressive symptoms. Their coexistence confers the highest risk and therefore warrants particular attention for prevention and intervention efforts.

Objective Based on multi-dimensional bioinformatics technology,the molecular pathological mechanism of acute hypotension is systematically analyzed.Methods Integrate the gene expression profile of the Gene Expression Omnibus database(GSE2401),use limma package(R software)to screen differential genes,and optimize targets through data dimensionality reduction(log2(FC)＞1,P＜0.05);Further combine Gene Ontology,Kyoto Encyclopedia of Genes and Genomes and gene set enrichment analysis pathway enrichment and STRING-Cytoscape(MCODE,CytoHubba plug-in)to build a protein-protein interaction(PPI)network to mine core genes;realize the full-chain analysis of"differential gene-functional pathway-PPI network".Results 676 differential genes(304 upregulated,372 downregulated),ribosome structural components(Rps8,Rps27,Rpl35,etc.)and multiple pathways such as forkhead box protein and cyclic adenosine monophosphate were found to coordinate the regulation of acute hypotension.Conclusion This study uses a low-cost and efficient bioinformatics analysis framework to reveal the association between ribosomal dysfunction,changes in insulin signal efficacy and blood pressure regulation,providing new ideas for targeted therapy.

Objective:To evaluate the effectiveness of T-piece resuscitator as a respiratory support strategy during the transport of critically ill premature infants,and to provide a scientific basis for clinical decision-making.Methods:A total of 280 critically ill premature newborns hospitalized in the NICU of Fifth Medical Center of Chinese People's Liberation Army General Hospital from January 2017 to December 2023 were included.Infants were categorized into three groups based on the respiratory support method given during transport: the ventilator group(108 cases),the T-piece group(102 cases),and the resuscitation sac group(70 cases).The transport distance,general condition at birth,prenatal conditions,dyspnea symptoms at admission,blood gas analysis results,clinical diagnosis,clinical intervations,and related treatment among the three groups were retrospectively analyzed.Results:There were no significant differences in the transport distance,the number of endotrached intubations during transport,the main complications during pregnancy,the general condition at birth,and the history of asphyxia among the three groups(all P>0.05).The incidence of triple-concave sign at admission in T-piece group was significantly lower than that in resuscitation sac group (41.7% vs.62.9%, P=0.005),and the arterial carbon dioxide tension(PaCO 2) at admission was also significantly lower in T-piece group than that in resuscitation sac group[(41.194±8.720) mmHg vs.(45.360±13.998) mmHg, P=0.034].Furthermore,the T-piece group had significantly lower rates of type II respiratory failure(0.9% vs.22.9%),respiratory acidosis(9.3% vs.27.1%),hypoxemia(7.4% vs.28.6%),hyperoxygen partial pressure(1.9% vs.28.6%),neonatal respiratory distress syndrome(66.7% vs.87.1%),and intracranial hemorrhage(18.5% vs.38.6%) during hospitalization compared to the resuscitation sac group (all P<0.05).The proportion of tracheal intubations(63.9% vs.87.1%) and the time of using non-invasive ventilator[1.0(1.0,2.0)d vs.1.0(1.0,6.0)d] were also significantly lower in T-piece group compared to the resuscitation sac group(both P<0.05).Compared with the respiratory group,there were no statistically significant differences in the aforementioned indicators for the T-piece group. Conclusion:The T-piece resuscitator can provide stable and adjustable positive end-inspiratory pressure and positive expiratory pressure,as well as a stable inspired oxygen flow rate,without increasing the risk of invasive procedures and severe complications.Its application during the transport and treatment of critically ill premature infants has definite clinical value.

Objective To explore the associations of dietary patterns with bone mineral density(BMD)and osteoporosis(OP)in patients with knee osteoarthritis(KOA)in south China,identify the dietary patterns that could reduce the risk of OP,and provide a reference for prevention of OP.Methods Data from 600 KOA patients who were enrolled in the Pearl River Osteoarthritis Cohort(PROC)between April 2020 and April 2024 were extracted,including general characteristics,dietary questionnaire and BMD test.Factor analysis was employed to identify dietary patterns.Scores of dietary pattern were treated as continuous or categorical variables(quartile-based groups).Multivariable linear regression was used to assess associations between scores of dietary pattern and BMD,while the relationships with OP were analysed by binary logistic regression.Results The prevalence of OP in KOA patients was 19.8%.Factor analysis extracted eight dietary patterns:seafood-nuts,livestock-poultry meats,coarse grains-fruits,wheat-eggs,fish-dairy-vegetables,legumes-fruits,rice-wheat flour,and soy-dairy.After adjusting for confounders,it was found that the pattern of coarse grains-fruits was positively correlated with the BMD of left femoral neck and lumbar spine(P<0.01)and the fish-dairy-vegetables pattern was positively correlated with the BMD of lumbar spine(P<0.01).When pattern scores were discretized into quartiles,the legumes-fruits pattern(Q3 vs.Q1)was found in association with a 68.2%reduction in the risk of OP(P<0.05).Conclusion This study has identified that the pattern of legumes-fruits may lower a risk of OP,while the patterns of coarse grains-fruits and fish-dairy-vegetables correlate with higher BMD.The diet patterns are potentially beneficial to prevention of OP.For bone health,a balanced diet rich in legumes,fruits,vegetables,coarse grains,fish,nuts and dairy is recommended.

Objective:To provide a scoping review of domestic and international studies on the influencing factors of frailty in stroke inpatients, and to provide guidance for interventional studies on debility in stroke inpatients in China.Methods:A systematic search of domestic and international literature databases, including China National Knowledge Infrastructure, WanFang, VIP, China Biomedical Literature Database, PubMed, Web of Science, Embase, Cochrane Library, CINAHL was conducted about the status and its influencing factors of frailty in stroke inpatients. The retrieval time limit was from database establishment to November 23, 2023. The included literature was extracted, analyzed, and summarized according to the reporting specifications of the scope review guidelines.Results:Thirty-six articles were included, all of the 36 papers reported the prevalence of frailty in patients with stroke. The top three countries with the highest prevalence were China (15.3%-86.9%), the United Kingdom (28%-78%), and Italy (31.37%). The risk factors of stroke frailty were categorized mainly into 4 themes: social demographic factors, physical health factors, disease-related factors, and psychological and social background factors.Conclusions:The confidence of frailty in stroke patients of a higher level at home and abroad, and the risk factors involved are complex and diverse. It is suggested that future studies need to conduct large-sample, multicenter longitudinal studies to clarify the causal relationship, strengthen the exploration of controversial factors of stroke debility, and carry out more targeted intervention studies, so as to reduce the incidence of frailty and improve the recovery process of patients.

Background Occupational stress has emerged as a critical public health concern affecting the physical and mental well-being of workers in the manufacturing sector. However, researchers typically evaluate its core driver—cumulative fatigue—using a crude binary “present/absent” variable, thereby overlooking the high-dimensional complexity and heterogeneity inherent in fatigue characteristics. This oversimplification constrains both the precision and predictive performance of occupational stress risk assessment model. Objective Leveraging a data-driven approach, to survey data on cumulative fatigue among manufacturing employees, and then use this new classification to develop and validate an occupational stress prediction model, with an ultimate aim of enhancing the accuracy and effectiveness of occupational stress assessment. Methods A set of cross-sectional survey data on 3871 manufacturing employees in 2021 were derived from the “Long working hours exposure and its adverse health effect risk assessment” program of the National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention. Occupational stress was assessed using the Core Occupational Stress Measurement Scale, while cumulative fatigue was evaluated via the Worker’s Self-Diagnostic Questionnaire for Fatigue Accumulation. Boruta method was applied to screen core variables from 20 occupational stress influencing factors. Dimensionality reduction of cumulative fatigue features was performed using a Bayesian sparse autoencoder (BASE), followed by comparison and application of clustering methods to achieve multi-class classification of the reduced features. Six machine learning classification models were then selected including logistic regression, support vector machine, decision tree, random forest, adaptive boosting, and lightweight gradient boosting machine (LightGBM) to construct and compare two occupational stress prediction models involving cumulative fatigue combined with ten other core variables: one utilizing the original binary cumulative fatigue label as a core variable, and another employing the novel fatigue classification proposed herein as a core variable. Results The positive rate of occupational stress among the manufacturing employees was 38.9%. The Boruta method identified 11 core variables of occupational stress: depressive symptoms, cumulative fatigue, age, length of service, tenure in current position, average weekly working hours, average daily overtime hours, average monthly income, low levels of exercise, life satisfaction, and sleep quality. The BSAE reduced the original cumulative fatigue factors to 12 dimensions, while the K-means clustering grouped the reduced fatigue features into three categories: no fatigue, moderate fatigue, and sever fatigue. Six occupational stress prediction models were constructed using the three-category labels of cumulative fatigue and ten additional factors. The results indicated that the LightGBM model performed best, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, and an F1 score of 0.72. Compared with the model incorporating the traditional binary labels for cumulative fatigue, the best prediction AUC (0.72), accuracy (0.66), and F1 score (0.65) improved by 6%, 11%, and 7%, respectively. Conclusion Cumulative fatigue is a significant predictor of occupational stress among manufacturing employees. Applying data dimensionality reduction combined with three-class cluster analysis to characterize cumulative fatigue improves the performance of occupational stress prediction models. From a data-driven perspective, machine learning methods demonstrate strengths in processing complex datasets, capturing nonlinear relationships, and generating predictions based on key variables. This study therefore confirms that refined processing of core factors substantially enhances the predictive capability of machine learning models in assessing occupational stress risk in the manufacturing workforce.