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.

As the relationship between exposure to chemical carcinogens and human health risks becomes increasingly clear, identifying effective biomarkers to reveal early exposure and prevent the occurrence and development of tumors has become particularly critical. Ribosomal DNA (rDNA), a cluster of genes encoding ribosomal RNA, has emerged as a focal point in chemical carcinogen exposure research in recent years, due to its high copy number within cells and sensitivity to environmental stimuli. The dynamic changes in rDNA copy numbers, especially the significant alterations observed following exposure to environmental carcinogens, have rendered it a promising potential biomarker. This paper aims to systematically review the theoretical underpinnings and research progress of rDNA copy numbers as a biomarker for exposure to chemical carcinogens, and further discuss the challenges and prospects in future research and applications.

As the relationship between exposure to chemical carcinogens and human health risks becomes increasingly clear, identifying effective biomarkers to reveal early exposure and prevent the occurrence and development of tumors has become particularly critical. Ribosomal DNA (rDNA), a cluster of genes encoding ribosomal RNA, has emerged as a focal point in chemical carcinogen exposure research in recent years, due to its high copy number within cells and sensitivity to environmental stimuli. The dynamic changes in rDNA copy numbers, especially the significant alterations observed following exposure to environmental carcinogens, have rendered it a promising potential biomarker. This paper aims to systematically review the theoretical underpinnings and research progress of rDNA copy numbers as a biomarker for exposure to chemical carcinogens, and further discuss the challenges and prospects in future research and applications.

Objective: To explore the changes in ribosomal DNA copy number in peripheral blood among patients with pneumoconiosis and its influencing factors, so as to provide insights into prevention and treatment of pneumoconiosis. Methods: Eighty-eight patients with pneumoconiosis who visited a designated hospital and 71 community residents with no history of pneumoconiosis or dust exposure were selected as the pneumoconiosis group and control group, and age, smoking history, drinking history and cumulative years of exposure to dust were collected through questionnaire surveys. The copy number of 45S rDNA and 5S rDNA was detected using real-time fluorescence quantitative PCR, and the differences between the two groups were compared. Factors affecting the copy number of 45S rDNA and 5S rDNA were identified by a multiple linear regression model. Results: The pneumoconiosis group had a median age of 56.00 (interquartile range, 15.25) and a mean cumulative dust exposure duration of (12.40±8.08) years, with 56.82% smoking and 62.50% drinking. The control group had a median age of 64.00 (interquartile range, 37.00) years, with 32.39% smoking and 26.76% drinking. The median copy number of 45S rDNA in the pneumoconiosis group was 1.29 (interquartile range, 0.59), which was lower than 2.10 (interquartile range, 1.88) in the control group; the median copy number of 5S rDNA in the pneumoconiosis group was 5.33 (interquartile range, 0.85), which was higher than 4.66 (1.34) in the control group (both P<0.05). Multiple linear regression analysis identified age (β=-0.034) and pneumoconiosis (β=-1.595) as factors affecting 45S rDNA copy number, age (β=-0.013) as a factor affecting 5S rDNA copy number, and age (β=0.018) as a factor affecting 5S rDNA copy number in the pneumoconiosis group (all P<0.05). Conclusions Compared with community residents with no history of pneumoconiosis or dust exposure, the copy number of 45S rDNA in peripheral blood among patients with pneumoconiosis is reduced and the copy number of 5S rDNA is increased.

Objective: To analyze the semen quality parameters of sperm donor volunteers, so as to provide insights into male infertility control and related research. Methods: A total of 155 sperm donors were recruited from the Human Sperm Bank of Zhejiang Province using the convenience sampling method from January to March 2021. Demographic information were collected through questionnaire surveys. Semen were collected, and parameters including semen volume, sperm concentration, total number of sperm, forward motility rate and total sperm viability were measured. Semen quality was evaluated according to the WHO Laboratory Manual for the Examination and Processing of Human Semen. : Results Conclusion There were 20.65% of the 155 sperm donors with unqualified semen, and the unqualified rates of forward motility rate and total sperm viability were relatively high.

Objective To isolate and culture neural crest cells(NCCs)from lung tissue of mice and to identify the characteristics of the cells in order to provide a new cell model for studying lung injury and injure repair.Methods The mT/mG dual-fluorescence reporter mice and Wnt1-Cre transgenic mice were hybridized,and mT/mG;Wnt1-Cre transgenic mice were screened to obtain enhanced green fluorescent protein(EGFP)permanently labeled NCCs.Cell suspension of mouse lung tissue was prepared by enzymolysis.EGFP+cells(namely NCCs)were har-vested by flow cytometry.Primary culture was performed with DMEM/F12 culture medium optimized in the labora-tory,NCCs was characterized by immunofluorescence microscopy.Then NCCs differentiation was directed by mouse bone marrow mesenchymal stem cells osteogenic induction.Results The mT/mG of EGFP permanently labeled NCCs was successfully obtained by hybridization and high-purity NCCs were isolated from Wnt1-Cre transgenic mice lung tissue.They can be cultured in vitro and with spindle morphology which was,similar to fibroblast adherent proliferation.NCCs expressed the neural crest stem cell marker Sox10 and induced to differentiate into osteoblasts.Conclusions NCCs isolated and cultured from lung tissue of mT/mG;Wnt1-Cre transgenic mice show stable prolif-eration and have the characteristics of neural crest stem cells,which may function as a potential cell model for re-search on lung tissue injury and the mechanism of repair.

Objective:To explore the prognostic value of BRCA1-associated protein 1 (BAP1) expression loss in patients with malignant mesothelioma (MM) .Methods:A total of 82 MM patients from January 1998 to December 2017 in Zhejiang Province were selected to detect the expression of BAP1 protein by immunohistochemical analysis. Kaplan-Meier method was used to draw the survival curve, and multivariate Cox proportional risk model was used to analyze the factors affecting the survival rate.Results:Among 82 MM patients, 61 (74.4%) were female, aged (57±11) years. BAP1 protein expression was deficient in 39 patients (47.6%). The survival rate was correlated with the loss of BAP1 protein expression and age (χ 2=5.27, 5.66, P=0.022, 0.017). Subgroup analysis showed that loss of BAP1 protein expression was associated with better prognosis in MM patients <57 years of age, female, pleural MM, epithelial MM, and treated with drugs or surgery ( P<0.05). Multivariate model results showed that positive expression of BAP1 protein ( HR=3.75, 95% CI: 2.23-6.30, P<0.001) and age ≥57 years ( HR=1.66, 95% CI: 1.01-2.72, P=0.049) were risk factors for survival in patients with MM. Conclusion:Loss of BAP1 protein expression may be an independent prognostic factor in patients with MM, which is associated with longer survival.

Objective:To explore the prognostic value of BRCA1-associated protein 1 (BAP1) expression loss in patients with malignant mesothelioma (MM) .Methods:A total of 82 MM patients from January 1998 to December 2017 in Zhejiang Province were selected to detect the expression of BAP1 protein by immunohistochemical analysis. Kaplan-Meier method was used to draw the survival curve, and multivariate Cox proportional risk model was used to analyze the factors affecting the survival rate.Results:Among 82 MM patients, 61 (74.4%) were female, aged (57±11) years. BAP1 protein expression was deficient in 39 patients (47.6%). The survival rate was correlated with the loss of BAP1 protein expression and age (χ 2=5.27, 5.66, P=0.022, 0.017). Subgroup analysis showed that loss of BAP1 protein expression was associated with better prognosis in MM patients <57 years of age, female, pleural MM, epithelial MM, and treated with drugs or surgery ( P<0.05). Multivariate model results showed that positive expression of BAP1 protein ( HR=3.75, 95% CI: 2.23-6.30, P<0.001) and age ≥57 years ( HR=1.66, 95% CI: 1.01-2.72, P=0.049) were risk factors for survival in patients with MM. Conclusion:Loss of BAP1 protein expression may be an independent prognostic factor in patients with MM, which is associated with longer survival.

Objective: To establish a convenient non-invasive tracheal perfusion method for constructing a mouse model of silicosis-induced pulmonary fibrosis. Methods: The specific pathogen-free C57BL/6 mice were randomly divided into control group and model group, with 15 mice in each group. After anesthesia, a 22G arteriovenous indwelling needle was used to inset into the trachea through the mice's mouth. The model group mice were perfused with 0.1 mL of silica suspension with a mass concentration of 25 g/L, and the mice in the control group were perfused with an equal volume of 0.9% sodium chloride solution. On the 7th, 14th, and 30th day after modeling, the body weight of the mice was measured, and the lung tissue morphology and pathological changes were observed. The expression of α-smooth muscle actin (α-SMA) and collagen type 1 alpha 1 (COL1A1) protein in lung tissue of mice was detected by immunofluorescence on the 30th day after modeling. Results: There was no death of mice in the two groups during the experiment. There was no significant difference in body weight between the two groups (P>0.05). The lung tissues of the mice in the model group were pinkish-gray and uneven in color on the 7th and 14th days after dust exposure. On the 30th day after dust exposure, the lung tissue of the mice in the model group was gray and hard, and unevenly distributed silicon nodules were visible by the naked eyes. The histopathology results of lung tissue showed that compared with the mice in control group, the model group mice exhibited persistent aggravation of pulmonary inflammation, thickening of alveolar septum, infiltration of inflammatory cells gradually clustering into clumps, and an increasing number of fibrous foci.On the 30th day after dust exposure, the relative expression of α-SMA and COL1A1 proteins in the lung of the model group was higher than those in the control group (median： 72.59 vs 5.91, 35.62 vs 10.07, both P<0.05). Conclusion: The method of tracheal perfusion silica suspension of mice using 22G arteriovenous indwelling needle can successfully construct an animal model of silicosis fibrosis. This method is convenient, safe and effective, and is worth promoting.