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 construct a performance management model based on comprehensive evaluation method with probability weight,and to explore its application value in management for emergency equipment.Methods:A performance evaluation system was formulated by performance management model from three dimensions included operation quality,cost investment and social benefit of emergency equipment.Probability weight method was used to assign the index weight,and management countermeasures included engineering technical support,expenditure control and improvement of social benefit were formulated according to the comprehensive evaluation results.A total of 109 emergency equipment in clinical use at Renji Hospital,Shanghai Jiao Tong University School of Medicine from March 2022 to February 2024 were selected.The management method with conventional evaluation model(conventional evaluation management method)was adopted to manage 103 equipment during March 2022 and February 2023,and the management method with performance management model based on comprehensive evaluation method with probability weight(comprehensive evaluation management method)was adopted to manage 104 equipment(included the used 98 equipment that were managed by conventional evaluation management method at the first stage,and 6 new equipment)during March 2023 and February 2024.The operation quality and management cost of the two kinds of management methods for equipment were compared.A self-made questionnaire survey was conducted to investigate the performance evaluation of operators,who used equipment,for the two kinds of management methods in using equipment.Results:The average use rate,data integrity rate and pass rate of quality inspection of clinical emergency equipment of adopting the comprehensive evaluation management method were respectively(96.61±1.52)%,(97.74±1.91)%and(95.96±1.19)%,all of which were higher than those of adopting conventional evaluation management method.The average values of the alarm rate and the damage rate of the component and accessories of the system were respectively(1.24±0.83)%and(1.08±0.56)%,both of which were lower than those of the conventional evaluation management method.The differences of the above indicators were statistically significant(t=3.410,3.292,3.848,2.967,2.825,P<0.05).The average amplification of the management investment of respiratory therapy,diagnosis and treatment of electrocardiogram,rescue treatment,basic treatment and other management investment of emergency equipment were lower than those of the conventional evaluation management method,and the differences were statistically significant(t=3.533,3.487,4.002,3.317,2.726,P<0.05).The scores of operators who used equipment for configurations quantity,configuration quality and management level that adopted comprehensive evaluation management method were all higher than those that adopted conventional evaluation management method,and the differences were statistically significant(t=3.183,3.934,2.363,P<0.05).Conclusion:The performance management model based on the comprehensive evaluation method with probability weight can enhance the operational quality of clinical emergency equipment,and control the management cost of equipment,and improve the service level of equipment.

Objective:To construct a performance management model based on comprehensive evaluation method with probability weight,and to explore its application value in management for emergency equipment.Methods:A performance evaluation system was formulated by performance management model from three dimensions included operation quality,cost investment and social benefit of emergency equipment.Probability weight method was used to assign the index weight,and management countermeasures included engineering technical support,expenditure control and improvement of social benefit were formulated according to the comprehensive evaluation results.A total of 109 emergency equipment in clinical use at Renji Hospital,Shanghai Jiao Tong University School of Medicine from March 2022 to February 2024 were selected.The management method with conventional evaluation model(conventional evaluation management method)was adopted to manage 103 equipment during March 2022 and February 2023,and the management method with performance management model based on comprehensive evaluation method with probability weight(comprehensive evaluation management method)was adopted to manage 104 equipment(included the used 98 equipment that were managed by conventional evaluation management method at the first stage,and 6 new equipment)during March 2023 and February 2024.The operation quality and management cost of the two kinds of management methods for equipment were compared.A self-made questionnaire survey was conducted to investigate the performance evaluation of operators,who used equipment,for the two kinds of management methods in using equipment.Results:The average use rate,data integrity rate and pass rate of quality inspection of clinical emergency equipment of adopting the comprehensive evaluation management method were respectively(96.61±1.52)%,(97.74±1.91)%and(95.96±1.19)%,all of which were higher than those of adopting conventional evaluation management method.The average values of the alarm rate and the damage rate of the component and accessories of the system were respectively(1.24±0.83)%and(1.08±0.56)%,both of which were lower than those of the conventional evaluation management method.The differences of the above indicators were statistically significant(t=3.410,3.292,3.848,2.967,2.825,P<0.05).The average amplification of the management investment of respiratory therapy,diagnosis and treatment of electrocardiogram,rescue treatment,basic treatment and other management investment of emergency equipment were lower than those of the conventional evaluation management method,and the differences were statistically significant(t=3.533,3.487,4.002,3.317,2.726,P<0.05).The scores of operators who used equipment for configurations quantity,configuration quality and management level that adopted comprehensive evaluation management method were all higher than those that adopted conventional evaluation management method,and the differences were statistically significant(t=3.183,3.934,2.363,P<0.05).Conclusion:The performance management model based on the comprehensive evaluation method with probability weight can enhance the operational quality of clinical emergency equipment,and control the management cost of equipment,and improve the service level of equipment.

OBJECTIVE To investigate the protective effect of artesunate(Art)against apoptosis and mitophagy induced by NaF in osteocytes MLO-Y4,and to explore the molecular mechanism.METHODS MLO-Y4 cells were treated with NaF(2 mmol·L-1)for 48 h to establish an in vitro model of osteocytes injuries,and the cells were divided into the cell control group,NaF(2 mmol·L-1)group and NaF+Art 0.25,0.50 and 1.00 μmol·L-1 groups.The cells were pretreated for 2 h and NaF was added for 48 h.The cell survival of MLO-Y4 cells was detected by MTT assay.The cell viability of MLO-Y4 cells was measured by Calcein-AM staining.The lactate dehydrogenase(LDH)content in the supernatant was examined by the LDH detection kit.The level of intracellular reactive oxygen species(ROS)was examined by DCFH-DA staining.The malondialdehyde(MDA)content and superoxide dismutase(SOD)activity were detected by chemical colorimetry.Apoptosis was measured by Hoechst33342 staining and Annexin-V/PI staining.The level of mitochondrial membrane potential(MMP)was measured by JC-1 staining.The formation of autophagic vacuoles and morphological mitochondrial changes were observed via Lyso-tracker staining and Mito-Tracker staining.The ATP content was detected with the luciferase method.The expression of microtubule-associated protein light chain 3(LC-3)in mitochon-dria was examined by immunofluorescence staining.Protein expressions of LC-3,P62,E3 ubiquitin-ligase(Parkin)and PTEN-induced putative kinase 1(PINK1)were detected by Western blotting.RESULTS Compared with the cell control group,the cell survival rate and cell viability were significantly reduced in the NaF group(P<0.01),LDH content in the supernatant,the level of intracellular ROS,the MDA content,apoptosis rate and autophagic vesicle formation were remarkably increased(P<0.01),protein levels of Parkin and PINK1,and the conversion of LC-3Ⅱ from LC-3Ⅰ were markedly upregulated along with the elevation of LC-3 in damaged mitochondria(P<0.01),while P62 levels,SOD activity,MMP and ATP contents were reduced in NaF cells(P<0.05,P<0.01).Compared with NaF group,the cell viability and survival rate of MLO-Y4 cells in NaF+Art 0.25,0.50 and 1.00 μmol·L-1 groups were significantly increased(P<0.01);the content of LDH in supernatants was decreased obviously(P<0.01);the levels of intracellular ROS and MDA content were markedly reduced(P<0.05,P<0.01);the apoptosis rate and autophagic vesicle formation were remarkably decreased(P<0.05,P<0.01);protein levels of Parkin and PINK1,and the conversion of LC-3Ⅱ from LC-3Ⅰ were markedly down-regulated along with the accumulation of LC-3 in damaged mitochondria(P<0.01);MMP and ATP content were also reduced(P<0.05,P<0.01);while SOD activityand P62 levelwere significantly increased(P<0.05,P<0.01).CONCLU-SION Art has a protective effect against oxidative damage induced by NaF in MLO-Y4 cells,which might be related to the inhibition of apoptosis and mitophagy.

Inflammatory bowel disease (IBD) is a formidable disease due to its complex pathogenesis. Macrophages, as a major immune cell population in IBD, are crucial for gut homeostasis. However, it is still unveiled how macrophages modulate IBD. Here, we found that LIM domain only 7 (LMO7) was downregulated in pro-inflammatory macrophages, and that LMO7 directly degraded 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) through K48-mediated ubiquitination in macrophages. As an enzyme that regulates glycolysis, PFKFB3 degradation led to the glycolytic process inhibition in macrophages, which in turn inhibited macrophage activation and ultimately attenuated murine colitis. Moreover, we demonstrated that PFKFB3 was required for histone demethylase Jumonji domain-containing protein 3 (JMJD3) expression, thereby inhibiting the protein level of trimethylation of histone H3 on lysine 27 (H3K27me3). Overall, our results indicated the LMO7/PFKFB3/JMJD3 axis is essential for modulating macrophage function and IBD pathogenesis. Targeting LMO7 or macrophage metabolism could potentially be an effective strategy for treating inflammatory diseases.

Loofah seeds ribosome inactivating protein luffin-α was fused with a tumor-targeting peptide NGR to create a recombinant protein, and its inhibitory activity on tumor cells and angiogenesis were assessed. luffin-α-NGR fusion gene was obtained by PCR amplification. The fusion gene was ligated with pGEX-6p-1 vector to create a recombinant plasmid pGEX-6p-1/luffin-α-NGR. The plasmid was transformed into E. coli BL21, and the target protein was isolated and purified by GST affinity chromatography. The luffin-α-NGR fusion gene with a full length of 849 bp was successfully obtained, and the optimal soluble expression of the target protein was achieved under the conditions of 16 ℃, 0.5 mmol/L IPTG after 16 h induction. SDS-PAGE and Western blotting confirmed the recombinant protein has an expected molecular weight of 56.6 kDa. Subsequently, the recombinant protein was de-tagged by precision protease digestion. The inhibitory effects of the recombinant protein on liver tumor cells HepG2 and breast cancer cells MDA-MB-231 were significantly stronger than that of luffin-α. The Transwell and CAM experiment proved that the recombinant protein luffin-α-NGR also had a significant inhibitory effect on tumor cells migration and neovascularization. The inhibitory activity on tumor cells and angiogenesis of the recombinant luffin-α-NGR protein lays a foundation for the development of subsequent recombinant tumor-targeting drugs.
Electrophoresis, Polyacrylamide Gel ; Escherichia coli/metabolism* ; Plasmids ; Recombinant Proteins/pharmacology* ; Saporins/metabolism*

Objective To investigate the genetic diversity differences of human parvoviruse B19 and parvovirus 4 (PRVA4) in Tibet and Han population in China.Methods Phylogenetic analysis was performed on genome fragments of B19 or PARV4 obtained from the blood samples of Tibet and Han population in China by using a PCR followed by sequencing.Results Ten partial VP1 fragments of B19 (2 from Tibet,3 from Sichuan,5 from Zhejiang) and 10 partial ORF1 fragments of PAV4 (2 from Tibet,2 from Sichuan,1 Yunnan,5 from Zhejiang) were obtained.Phylogenetic analysis indicated that different B19 subgroup circulates in Tibet and Han population although they belong to the same 1A subtype.While the gene evolution of PAV4 is very conserved between the Tibet and Han population in China.Conclusion These studies on genetic diversity of B19 in different Chinese population provide a way for detection and prevention of B19 human parvovirus infection.