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 systematically evaluate the current financing status of Beijing's urban and rural residents'basic medical insurance,analyze equity disparities among different groups under the existing flat-rate financing policy.By simulating the equity changes of various financing schemes with different contribution rates,this study aims to provide foundations for advancing medical insurance financing system reform.Methods:Based on the per capita disposable income and number of insured residents in Beijing's 16 districts from 2018 to 2023,we separately calculated the Gini coefficient,concentration index,and Kakwani index,along with their changes before and after financing adjustments,to assess the funding burden among different insured groups.Using the geometric mean method,we projected per capita disposable income and insured populations for each district in Beijing from 2024 to 2035,simulating various financing schemes under different premium rate systems.Results:From 2018 to 2023,the Gini coefficient of net income after financing consistently exceeded that of original income before financing.The concentration index remained positive and showed an upward trend,while the Kakwani index was negative for all periods.The Gini coefficient after financing slightly decreased for the elderly and working populations with fiscal subsidy support,whereas it remained higher than that for students and children.Under the simulated differentiated rate system,the post-funding Gini coefficient for 2024-2035 was lower than the original value,and the Kakwani index was positive.Conclusions:The current fixed-amount financing mechanism for urban and rural residents'basic medical insurance exhibits regressive characteristics and insufficient fairness,with disparities in equity among different insured groups.It is necessary to establish differentiated financing standards based on differential rates,particularly implementing a financing mechanism with dynamic adjustments according to regional and group income levels,to enhance the fairness of financing for urban and rural residents'basic medical insurance.

Aim To investigate the im-munomodulatory effects of Ononin on Th17/Treg bal-ance and inflammatory responses,and to evaluate its neuroinmune regulatory role in ischemic stroke.Methods A Foxp3 promoter-driven luciferase reporter assay was used to assess the effect of Ononin on Foxp3 transcriptional activity.The effect of Ononin on the dif-ferentiation of na?ve CD4+T cells into Th 17 and Treg subsets was evaluated by flow cytometry.A transient middle cerebral artery occlusion(tMCAO)model was established in mice to evaluate the effects of Ononin on infarct size,neurological recovery,body weight resto-ration,and survival rate.Flow cytometry and RT-PCR were conducted to elucidate the immunological mechanisms underlying Ononin's effects.Results On-onin significantly enhanced Foxp3 promoter activity,promoted the differentiation of na?ve CD4+T cells into Treg cells,and suppressed Th17 polarization.In the tMCAO model,Ononin significantly reduced acute in-farct size,improved survival,ameliorated long-term neurological deficits,and increased Treg cell propor-tions in ischemic brain tissue.Conclusions Ononin modulates peripheral and central inflammation by resto-ring the Th17/Treg immune balance,thereby exerting significant immunomodulatory effects.Its therapeutic benefit in ischemic stroke is closely linked to immune balance restoration.

Objective To explore the feasibility and safety of non-anticoagulation veno-venous extracorporeal membrane oxygenation(VV-ECMO)in patients with severe chest trauma.Methods A retrospective cohort study method was used.A total of 19 patients with severe chest trauma who received VV-ECMO with a delayed anticoagulation strategy at Zhengzhou Central Hospital Affiliated to Zhengzhou University from January 2018 to October 2021 were included in the delayed anticoagulation group,and 20 patients with severe chest trauma who received VV-ECMO with a non-anticoagulation strategy from November 2021 to October 2024 were included in the non-anticoagulation group.The overall clinical characteristics of the patients were statistically analyzed,including gender,age,injury severity score(ISS),acute physiology and chronic health evaluationⅡ(APACHEⅡ),reason for VV-ECMO,use of vasoactive drugs,oxygenation index(PaO2/FiO2),and interval from injury to VV-ECMO.The primary outcomes were hemorrhagic and thrombotic complications.The secondary outcomes were blood transfusion during VV-ECMO,VV-ECMO time,mechanical ventilation time,intensive care unit(ICU)length of stay,and 28-day mortality.Results There was no significant difference in gender,age,ISS score,APACHEⅡscore,reason for VV-ECMO,use of vasoactive drugs,PaO2/FiO2,and interval from injury to VV-ECMO between the non-anticoagulation group and the delayed anticoagulation group.There was no significant difference in overall incidence of hemorrhagic and thrombotic between the two groups[incidence of hemorrhagic complications:15.0%(3/20)vs.31.6%(6/19),incidence of thrombotic:15.0%(3/20)vs.5.3%(1/19),both P>0.05].The infusion rate of 4 or more paked red blood cell(PRBC)within 24 hours during VV-ECMO in the non-anticoagulation group was significantly lower than that in the delayed anticoagulation group[5.0%(1/20)vs.31.6%(6/19),P<0.05].The amount of PRBC and platelet transfusion and the time on VV-ECMO in the non-anticoagulation group during VV-ECMO were significantly lower than those in the delayed anticoagulation group[PRBC(U):5.8±3.8 vs.8.1±3.1,platelets(U):1(0,1)vs.2(1,3),time on VV-ECMO(hours):71.55±24.37 vs.114.21±34.08,all P<0.05].There were no statistically significant differences in the amount of plasma and cryoprecipitate transfusion during VV-ECMO,mechanical ventilation time,ICU hospitalization time,and 28-day mortality between the two groups.Conclusion For patients with severe chest trauma receiving VV-ECMO withholding routine systemic anticoagulation did not result in thrombotic complications or higher mortality and required less PRBC and platelet transfusions.Non-anticoagulant VV-ECMO is safe and feasible for patients with severe chest trauma with high risk of bleeding.

Objective:To analyze the synergistic relationship between financing and payment systems in China's urban employee basic medical insurance under the background of aging,providing decision-making basis for promoting coordinated development of these two systems.Methods:Based on statistical yearbook data from medical security systems,the Delphi method was employed to identify collaborative analysis indicators for urban employee medical insurance financing and payment under the aging population context.A collaborative theory-based model was constructed to measure the coordination degree of China's urban employee medical insurance financing and payment composite system,while simultaneously assessing the orderliness of the financing and payment system and the coordination degree of the composite system.Results:The order degree of the financing system ranged from 0.324 in 2019 to 0.517 in 2023;the order degree of the payment system ranged from 0.454 in 2019 to 0.517 in 2023;the synergy degree of both systems reached-0.084 in 2023.Conclusions:Influenced by aging,the development speed and scale of China's urban employee basic medical insurance financing system and payment system are asynchronous,with the payment system being more affected than the financing system,resulting in a non-coordinated and disordered development state of the composite financing-payment system.

Objective:To analyze the synergistic relationship between financing and payment systems in China's urban employee basic medical insurance under the background of aging,providing decision-making basis for promoting coordinated development of these two systems.Methods:Based on statistical yearbook data from medical security systems,the Delphi method was employed to identify collaborative analysis indicators for urban employee medical insurance financing and payment under the aging population context.A collaborative theory-based model was constructed to measure the coordination degree of China's urban employee medical insurance financing and payment composite system,while simultaneously assessing the orderliness of the financing and payment system and the coordination degree of the composite system.Results:The order degree of the financing system ranged from 0.324 in 2019 to 0.517 in 2023;the order degree of the payment system ranged from 0.454 in 2019 to 0.517 in 2023;the synergy degree of both systems reached-0.084 in 2023.Conclusions:Influenced by aging,the development speed and scale of China's urban employee basic medical insurance financing system and payment system are asynchronous,with the payment system being more affected than the financing system,resulting in a non-coordinated and disordered development state of the composite financing-payment system.

Objective:To systematically evaluate the current financing status of Beijing's urban and rural residents'basic medical insurance,analyze equity disparities among different groups under the existing flat-rate financing policy.By simulating the equity changes of various financing schemes with different contribution rates,this study aims to provide foundations for advancing medical insurance financing system reform.Methods:Based on the per capita disposable income and number of insured residents in Beijing's 16 districts from 2018 to 2023,we separately calculated the Gini coefficient,concentration index,and Kakwani index,along with their changes before and after financing adjustments,to assess the funding burden among different insured groups.Using the geometric mean method,we projected per capita disposable income and insured populations for each district in Beijing from 2024 to 2035,simulating various financing schemes under different premium rate systems.Results:From 2018 to 2023,the Gini coefficient of net income after financing consistently exceeded that of original income before financing.The concentration index remained positive and showed an upward trend,while the Kakwani index was negative for all periods.The Gini coefficient after financing slightly decreased for the elderly and working populations with fiscal subsidy support,whereas it remained higher than that for students and children.Under the simulated differentiated rate system,the post-funding Gini coefficient for 2024-2035 was lower than the original value,and the Kakwani index was positive.Conclusions:The current fixed-amount financing mechanism for urban and rural residents'basic medical insurance exhibits regressive characteristics and insufficient fairness,with disparities in equity among different insured groups.It is necessary to establish differentiated financing standards based on differential rates,particularly implementing a financing mechanism with dynamic adjustments according to regional and group income levels,to enhance the fairness of financing for urban and rural residents'basic medical insurance.

Aim To investigate the im-munomodulatory effects of Ononin on Th17/Treg bal-ance and inflammatory responses,and to evaluate its neuroinmune regulatory role in ischemic stroke.Methods A Foxp3 promoter-driven luciferase reporter assay was used to assess the effect of Ononin on Foxp3 transcriptional activity.The effect of Ononin on the dif-ferentiation of na?ve CD4+T cells into Th 17 and Treg subsets was evaluated by flow cytometry.A transient middle cerebral artery occlusion(tMCAO)model was established in mice to evaluate the effects of Ononin on infarct size,neurological recovery,body weight resto-ration,and survival rate.Flow cytometry and RT-PCR were conducted to elucidate the immunological mechanisms underlying Ononin's effects.Results On-onin significantly enhanced Foxp3 promoter activity,promoted the differentiation of na?ve CD4+T cells into Treg cells,and suppressed Th17 polarization.In the tMCAO model,Ononin significantly reduced acute in-farct size,improved survival,ameliorated long-term neurological deficits,and increased Treg cell propor-tions in ischemic brain tissue.Conclusions Ononin modulates peripheral and central inflammation by resto-ring the Th17/Treg immune balance,thereby exerting significant immunomodulatory effects.Its therapeutic benefit in ischemic stroke is closely linked to immune balance restoration.

With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.

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.