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.

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.

Objective:To explore the impact of air pollution on hospital visits for mental and behavioral disorders among residents in an industrial area in Henan Province from 2016 to 2021.Methods:Daily outpatient visits data for mental and behavioral disorders were collected from Angang General Hospital in Angang Industrial Area at Anyang City between January 2016 and December 2021. And air pollutants and meteorological data during the same period were also collected. A generalized additive model was used for time-series analysis to examine the relationship between daily average concentrations of nitrogen dioxide (NO 2), sulfur dioxide (SO 2), fine particulate matter (PM 2.5), inhalable particulate matter (PM 10), carbon monoxide (CO), and ozone (O 3) with a lag of 0 to 7 days on the number of visits for mental and behavioral disorders among residents. The single-day lag effect (lag0-lag7 d) and cumulative lag effect (lag01-lag07 d) were analyzed. The smooth cubic spline function was used to fit the exposure-response relationship, and subgroup analysis was performed according to different genders, seasons and ages. Results:A total of 26 268 hospital visits for mental and behavioral disorders were collected from the industrial area between 2016 and 2021. The daily average concentrations of SO 2, NO 2, PM 2.5, PM 10, and CO were (27.50±27.33), (43.11±18.33), (73.87±60.30), (134.01±83.81) μg/m 3, and (1.72±1.03) mg/m 3, respectively. The daily maximum 8-hour average concentration of O 3 was (82.18±53.70) μg/m 3. After controlling for long-term trends, temperature, relative humidity, day of the week effects, and holiday effects, the generalized additive model analysis showed that NO 2 had a statistically significant impact on the hospital visits for mental and behavioral disorders at lag0 d, lag2 d and lag01-lag05 d and CO had a statistically significant impact at lag0-lag3 d and lag01-lag06 d (all P<0.05). NO 2 at lag02-lag04 d and CO at lag0-lag2 d and lag01-lag04 d had statistically significant effects on the visits for neurasthenia (both P<0.05). The impacts of NO 2 at lag03-lag04 d, PM 2.5 at lag3 d and lag03-lag04 d, PM 10 at lag3 d and lag03 d, and CO at lag3 d and lag01-lag05 d on visits for generalized anxiety disorder were also statistically significant (all P<0.05). After false discovery rate (FDR) correction, it was shown that for every 10 μg/m 3 increase in NO 2 and every 0.1 mg/m 3 increase in CO, the percentage increase in visits for mental and behavioral disorders and its 95% confidence interval (95% CI) were 3.38% (0.95%-5.87%) and 0.78% (0.38%-1.17%), respectively. For every 0.1 mg/m 3 increase in CO, the visits for neurasthenia increased by 0.78% (0.27%-1.29%). For every 10 μg/m 3 increase in PM 2.5 and every 0.1 mg/m 3 increase in CO, the visits for generalized anxiety disorder increased by 1.07% (0.46%-1.68%) and 1.17% (0.37%-1.97%), respectively (adjusted P<0.05). There was a linear exposure-response relationship between NO 2 and CO and the hospital visits for mental and behavioral disorders, CO and the hospital visits for neurasthenia, and CO and PM 2.5 and the hospital visits for generalized anxiety disorder ( P<0.05 for the overall association test and P>0.05 for the non-linearity test). Stratified analysis showed that air pollutants had an impact on male patients with neurasthenia, female patients with generalized anxiety disorder, individuals aged <45 years with mental and behavioral disorders, and individuals aged ≥65 years with generalized anxiety disorder. The impact of air pollutants was greater during the cold season or winter. Conclusion:Exposure to air pollution can increase hospital visits for mental and behavioral disorders among residents in industrial areas, with a higher risk among those aged<45 years old and during the cold season.

Objective:To explore the impact of air pollution on hospital visits for mental and behavioral disorders among residents in an industrial area in Henan Province from 2016 to 2021.Methods:Daily outpatient visits data for mental and behavioral disorders were collected from Angang General Hospital in Angang Industrial Area at Anyang City between January 2016 and December 2021. And air pollutants and meteorological data during the same period were also collected. A generalized additive model was used for time-series analysis to examine the relationship between daily average concentrations of nitrogen dioxide (NO 2), sulfur dioxide (SO 2), fine particulate matter (PM 2.5), inhalable particulate matter (PM 10), carbon monoxide (CO), and ozone (O 3) with a lag of 0 to 7 days on the number of visits for mental and behavioral disorders among residents. The single-day lag effect (lag0-lag7 d) and cumulative lag effect (lag01-lag07 d) were analyzed. The smooth cubic spline function was used to fit the exposure-response relationship, and subgroup analysis was performed according to different genders, seasons and ages. Results:A total of 26 268 hospital visits for mental and behavioral disorders were collected from the industrial area between 2016 and 2021. The daily average concentrations of SO 2, NO 2, PM 2.5, PM 10, and CO were (27.50±27.33), (43.11±18.33), (73.87±60.30), (134.01±83.81) μg/m 3, and (1.72±1.03) mg/m 3, respectively. The daily maximum 8-hour average concentration of O 3 was (82.18±53.70) μg/m 3. After controlling for long-term trends, temperature, relative humidity, day of the week effects, and holiday effects, the generalized additive model analysis showed that NO 2 had a statistically significant impact on the hospital visits for mental and behavioral disorders at lag0 d, lag2 d and lag01-lag05 d and CO had a statistically significant impact at lag0-lag3 d and lag01-lag06 d (all P<0.05). NO 2 at lag02-lag04 d and CO at lag0-lag2 d and lag01-lag04 d had statistically significant effects on the visits for neurasthenia (both P<0.05). The impacts of NO 2 at lag03-lag04 d, PM 2.5 at lag3 d and lag03-lag04 d, PM 10 at lag3 d and lag03 d, and CO at lag3 d and lag01-lag05 d on visits for generalized anxiety disorder were also statistically significant (all P<0.05). After false discovery rate (FDR) correction, it was shown that for every 10 μg/m 3 increase in NO 2 and every 0.1 mg/m 3 increase in CO, the percentage increase in visits for mental and behavioral disorders and its 95% confidence interval (95% CI) were 3.38% (0.95%-5.87%) and 0.78% (0.38%-1.17%), respectively. For every 0.1 mg/m 3 increase in CO, the visits for neurasthenia increased by 0.78% (0.27%-1.29%). For every 10 μg/m 3 increase in PM 2.5 and every 0.1 mg/m 3 increase in CO, the visits for generalized anxiety disorder increased by 1.07% (0.46%-1.68%) and 1.17% (0.37%-1.97%), respectively (adjusted P<0.05). There was a linear exposure-response relationship between NO 2 and CO and the hospital visits for mental and behavioral disorders, CO and the hospital visits for neurasthenia, and CO and PM 2.5 and the hospital visits for generalized anxiety disorder ( P<0.05 for the overall association test and P>0.05 for the non-linearity test). Stratified analysis showed that air pollutants had an impact on male patients with neurasthenia, female patients with generalized anxiety disorder, individuals aged <45 years with mental and behavioral disorders, and individuals aged ≥65 years with generalized anxiety disorder. The impact of air pollutants was greater during the cold season or winter. Conclusion:Exposure to air pollution can increase hospital visits for mental and behavioral disorders among residents in industrial areas, with a higher risk among those aged<45 years old and during the cold season.

AIM: To explore the protective effects of sinomenine (SIN) on oxidative stress and pulmonary fibrosis and its relationship with the Kelch-like ECH-associated protein 1 (Keap1)/nuclear factor erythroid 2-related factor 2 (Nrf2) signaling pathway. METHODS: MRC-5 cells were treated with hydrogen peroxide (H2O2) to establish the oxidative stress injury model, followed by administration with SIN. Cell viability was detected using the CCK-8 method. The biochemical kits were employed to measure malondialdehyde (MDA) content and superoxide dismutase (SOD), glutathione peroxidase (GSH-Px) and catalase (CAT) activities. The protein expression of Keap1 and Nrf2 was examined by western blot. Thirty SD rats were randomly divided into control group, bleomycin A5 (BLM) group and BLM + SIN group, with 10 animals in each group. Bleomycin A5 were intratracheally administered to the rats in BLM group and BLM+SIN group to establish the pulmonary fibrosis model. The rats in control group received the same volume of 9 g/L sodium chloride solution. The second day after model construction, the rats in BLM+SIN group were gavaged with SIN, while the rats in the other two groups were treated with 9 g/L sodium chloride solution. On day 28, all rats were sacrificed. Pulmonary tissue was isolated, and HE and Masson staining was performed to observe the pathological changes. The MDA content and SOD, GSH-Px and CAT activities in pulmonary tissue were evaluated. Western blot was used to assay pulmonary tissues Keap1 and Nrf2 protein expression. RESULTS: When compared with H2O2 group, SIN treatment increased cell viability, decreased MDA content, elevated SOD, GSH-Px and CAT activities, down-regulated Keap1 expression, and promoted nuclear translocation of Nrf2 in MRC-5 cells. In comparison with BLM group, administration of SIN decreased alveolitis and pulmonary fibrosis pathological changes and scores as well as pulmonary tissue MDA content, enhanced pulmonary tissues SOD, GSH-Px and CAT activities, down-regulated pulmonary tissues Keap1 expression, and raised Nrf2 levels in the nucleus. CONCLUSION: SIN alleviates oxidative stress and pulmonary fibrosis possibly by activating the Keap1/Nrf2 signaling pathway.

Objective To explore the impact of sinomenine on bleomycin A5-induced pulmonary fibrosis (PF) in rats and the underlying mechanism. Methods MRC-5 cells were cultured and treated with sinomenine to determine its optimal concentration and time through the MTT assay. Subsequently, MRC-5 cells were incubated with 80 μmol/L sinomenine for 48 hours or transfected with miR-21 mimic/a disintegrin-like and metalloproteinase with thrombospondin type 1 motif (ADAMTS-1) siRNA prior to sinomenine treatment. The expression of miR-21, ADAMTS-1, collagen type 1 (Col1) and collagen type 3 (Col3) was detected by quantitative real-time PCR (qRT-PCR) and/or Western blot analysis. Thirty SD rats were randomly divided into control group, sinomenine group and sinomenine combined with miR-21 agomir group, with 10 animals in each group. Bleomycin A5 were intratracheally administered to establish the PF model. Then, rats in control group, sinomenine group and sinomenine +miR-21 agomir group were treated with 9 g/L sodium chloride solution, sinomenine and sinomenine+miR-21 agomir, respectively. On day 28, all rats were sacrificed. HE and Masson staining was performed in pulmonary tissue. The expression of ADAMTS-1, Col1 and Col3 in pulmonary tissue were detected by qRT-PCR and/or Western blot analysis. ELISA was used to measure serum procollagen type 1 carboxyterminal propeptide (P1CP) and procollagen type 3 aminoterminal propeptide (P3NP) levels. Results Administration of sinomenine decreased miR-21 levels, up-regulated ADAMTS-1 expression, and promoted Col1 and Col3 degradation in MRC-5 cells. Importantly, interfering with the miR-21/ADAMTS-1 signaling pathway partially reversed the promotive effect of sinomenine on Col1 and Col3 degradation. Treatment of SD rats with sinomenine reduced alveolitis and PF scores, decreased serum P1CP and P3NP levels, up-regulated pulmonary ADAMTS-1 expression, and down-regulated Col1 and Col3 expression. However, these effects were reversed by miR-21 agomir. Conclusion Sinomenine promotes Col1 and Col3 degradation and inhibits PF in rats by miR-21/ADAMTS-1 pathway.
Rats ; Animals ; Pulmonary Fibrosis/genetics* ; Procollagen/metabolism* ; Rats, Sprague-Dawley ; Signal Transduction ; Bleomycin/adverse effects* ; Collagen Type III/metabolism* ; MicroRNAs/metabolism*

Pulmonary blast injury has become the main type of trauma in modern warfare, characterized by externally mild injuries but internally severe injuries, rapid disease progression, and a high rate of early death. The injury is complicated in clinical practice, often with multiple and compound injuries. Currently, there is a lack of effective protective materials, accurate injury detection instrument and portable monitoring and transportation equipment, standardized clinical treatment guidelines in various medical centers, and evidence-based guidelines at home and abroad, resulting in a high mortality in clinlcal practice. Therefore, the Trauma Branch of Chinese Medical Association and the Editorial Committee of Chinese Journal of Trauma organized military and civilian experts in related fields such as thoracic surgery and traumatic surgery to jointly develop the Clinical treatment guideline for pulmonary blast injury ( version 2023) by combining evidence for effectiveness and clinical first-line treatment experience. This guideline provided 16 recommended opinions surrounding definition, characteristics, pre-hospital diagnosis and treatment, and in-hospital treatment of pulmonary blast injury, hoping to provide a basis for the clinical treatment in hospitals at different levels.

Metabolic-associated fatty liver disease (MAFLD), which is previously known as non-alcoholic fatty liver disease (NAFLD), represents a major health concern worldwide with limited therapy. Here, we provide evidence that ferroptosis, a novel form of regulated cell death characterized by iron-driven lipid peroxidation, was comprehensively activated in liver tissues from MAFLD patients. The canonical-GPX4 (cGPX4), which is the most important negative controller of ferroptosis, is downregulated at protein but not mRNA level. Interestingly, a non-canonical GPX4 transcript-variant is induced (inducible-GPX4, iGPX4) in MAFLD condition. The high fat-fructose/sucrose diet (HFFD) and methionine/choline-deficient diet (MCD)-induced MAFLD pathologies, including hepatocellular ballooning, steatohepatitis and fibrosis, were attenuated and aggravated, respectively, in cGPX4-and iGPX4-knockin mice. cGPX4 and iGPX4 isoforms also displayed opposing effects on oxidative stress and ferroptosis in hepatocytes. Knockdown of iGPX4 by siRNA alleviated lipid stress, ferroptosis and cell injury. Mechanistically, the triggered iGPX4 interacts with cGPX4 to facilitate the transformation of cGPX4 from enzymatic-active monomer to enzymatic-inactive oligomers upon lipid stress, and thus promotes ferroptosis. Co-immunoprecipitation and nano LC-MS/MS analyses confirmed the interaction between iGPX4 and cGPX4. Our results reveal a detrimental role of non-canonical GPX4 isoform in ferroptosis, and indicate selectively targeting iGPX4 may be a promising therapeutic strategy for MAFLD.

Influenza B virus (IBV) is more likely to cause complications than influenza A virus (IAV) and even causes higher disease burden than IAV in a certain season, but IBV has received less attention. In order to analyze the genetic evolution characteristics of the clinical strain IBV (B/Guangxi-Jiangzhou/1352/2018), we constructed genetic evolution trees and analyzed the homology and different amino acids of hemagglutinin and neuraminidase referring to the vaccine strains recommended by World Health Organization (WHO). We found that strain B/Guangxi-Jiangzhou/1352/2018 was free of interlineage reassortment and poorly matched with the vaccine strain B/Colorado/06/2017 of the same year. We also determined the median lethal dose (LD₅₀) and the pathogenicity of strain B/Guangxi-Jiangzhou/1352/2018 in mice. The results showed that the LD₅₀ was 10^5.9 TCID₅₀ (median tissue culture infective dose), the IBV titer in the lungs reached peak 1 d post infection and the mRNA level of the most of inflammatory cytokines in the lungs reached peak 12 h post infection. The alveoli in the lungs were severely damaged and a large number of inflammatory cells were infiltrated post infection. The study demonstrated that the clinical strain IBV (B/Guangxi-Jiangzhou/1352/2018) could infect mice and induce typical lung inflammation. This will facilitate the research on the pathogenesis and transmission mechanism of IBV, and provide an ideal animal model for evaluation of new vaccines, antiviral and anti-inflammatory drug.
Amino Acids/genetics* ; Animals ; Antiviral Agents/pharmacology* ; China ; Cytokines/metabolism* ; Hemagglutinins/metabolism* ; Humans ; Influenza B virus/pathogenicity* ; Influenza, Human/virology* ; Mice ; Neuraminidase/genetics* ; Orthomyxoviridae Infections/virology* ; Phylogeny ; RNA, Messenger/metabolism* ; Virulence/genetics*

Objective:To explore the potential of dosiomics in predicting the incidence of radiation pneumonitis by extracting dosiomic features of definitive radiotherapy for lung cancer, and building a machine learning model.Methods:The clinical data, dose files of radiotherapy, planning CT and follow-up CT of 314 patients with lung cancer undergoing definitive radiotherapy were collected retrospectively. According to the clinical data and follow-up CT, the radiation pneumonia was graded, and the dosiomic features of the whole lung were extracted to establish a machine learning model. Dosiomic features associated with radiation pneumonia by LASSO-LR with 1000 bootstrap and AIC backward method with 1000 bootstraps were selected. Training cohort and validation cohort were randomly divided on the basis of 7:3.Logistic regression was used to establish the prediction model, and ROC curve and calibration curve were adopted to evaluate the performance of the model.Results:A total of 120 dosiomic features were extracted. After LASSO-LR dimensionality reduction, 12 features were selected into the "feature pool".After AIC, 6 dosiomic features were finally selected for model construction. The AUC of training cohort was 0.77(95% CI: 0.65 to 0.87), and the AUC of validation cohort was 0.72 (95% CI: 0.64 to 0.81). Conclusion:The dosiomics prediction model has the potential to predict the incidence of radiation pneumonia, but it still needs to include multicenter data and prospective data.