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 investigate the distribution of carbapenem-resistant genes and their drug susceptibility in vitro on carbapen-em-resistant Enterobacter cloacae(CRECC)in Taizhou area,and provide evidence for effective anti-infective treatment in clinical prac-tice.Methods Forty-seven strains of CRECC isolated from Enze Hospital,Taizhou Enze Medical Center(Group)and Luqiao Reha-bilitation Hospital during January 2015 and November 2022 were retrospectively analyzed.The enzyme types and resistance genes of carbapenemase were detected by the NG-Test Carba 5 and Carba-R Xpert,respectively,and the susceptibility of CERCC to common drugs was tested in vitro.Results Among 47 strains of CRECC,27 were detected to produce carbapenemase,including 24 producing New Delhi metallo-β-lactamase(NDM)type,1 producing both Klebsiella pneumoniae carbapenemase(KPC)and NDM types,and 2 producing imipenemase(IMP)type.One strain belonged to NDM genotype but no NDM enzyme type was detected.The CRECC strains had the highest sensitivity to polymyxin B(95.7%),followed by tigecycline(93.6%),fosfomycin(61.7%),and ceftazidime/avibac-tam(40.4%).In addition,the CRECC strains producing carbapenemase were more sensitive to polymyxin B,fosfomycin and aztreo-nam than those without producing carbapenemase.Conclusion The CRECC strains in Taizhou area are mainly NDM type,which has high sensitivity to polycolistin B,tigecycline and fosfomycin.NG-Test Carba 5 can not cover some strains that do not produce carbapen-emase or carry mutations in carbapenemase.

Objective:To investigate the dosimetric impact of dwell position spacing in the design of three-dimensional (3D) interstitial brachytherapy plans for cervical cancer, and to provide a reference for selecting dwell spacing in clinical planning.Methods:A total of 15 patients with cervical cancer who underwent 3D interstitial brachytherapy at Tianjin Medical University Cancer Institute & Hospital between March 2022 and March 2024 were selected using simple random sampling. For each patient, 10 brachytherapy plans were generated with different dwell position spacings set at 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 mm, respectively. Key parameters among different dwell spacings compared included D 90%, V 100%, V 200%, and V 300% for the high-risk clinical target volume (HRCTV); D 90% for the intermediate-risk clinical target volume (IRCTV); D 2 cm3 for organs at risk (OARs) (bladder, small intestine, colon, and rectum); and the total dwell time. Statistical analyses were performed using repeated measurement ANOVA or the Friedman test. Results:Among different dwell spacings, there were no statistically significant differences in HRCTV D 90%, HRCTV V 100%, bladder D 2 cm3, and rectum D 2 cm3 among different dwell spacings ( P=0.075, 0.061, 0.480, 0.639). All plans with dwell spacings ≤ 3 mm met clinical dose requirements. When the dwell spacing was set to 1 mm, HRCTV V 200% and V 300% had the smallest mean values, while IRCTV D 90% and total dwell time had the largest mean values; all differences were statistically significant ( P<0.05). When the dwell spacing was ≥6 mm, an increase in spacing led to a decrease in mean small intestine D 2 cm3, and total dwell time, but an increase in HRCTV V 200% and a decrease in IRCTV D 90%, with statistically significant differences compared to spacings of 1-4 mm ( P<0.05). When the dwell spacing was ≥8 mm, the median colon D 2 cm3 decreased, with statistically significant differences compared to spacings of 1-3 mm ( P<0.05). Conclusions:For 3D interstitial brachytherapy planning in cervical cancer, dwell position spacings ≤ 3 mm can meet clinical dose requirements, with 1 mm providing optimal target coverage. Spacings ≥6 mm / ≥8 mm can reduce radiation dose to the small intestine and colon, respectively, while also shortening dwell time.

Prostate cancer is the most prevalent malignant tumor among men, ranking first in incidence and second in mortality globally. Novel hormone therapies (NHT) targeting the androgen receptor (AR) pathway have become the standard of care for metastatic prostate cancer. This review offers a comprehensive overview of NHT, including abiraterone, enzalutamide, apalutamide, darolutamide, and rezvilutamide, which have demonstrated efficacy in delaying disease progression and improving patient survival and quality of life. Nevertheless, resistance to NHT remains a critical challenge. The mechanisms underlying resistance are complex, involving AR gene amplification, mutations, splice variants, increased intratumoral androgens, and AR-independent pathways such as the glucocorticoid receptor, neuroendocrine differentiation, DNA repair defects, autophagy, immune evasion, and activation of alternative signaling pathways. This review discusses these resistance mechanisms and examines strategies to counteract them, including sequential treatment with novel AR-targeted drugs, chemotherapy, poly ADP-ribose polymerase inhibitors, radionuclide therapy, bipolar androgen therapy, and approaches targeting specific resistance pathways. Future research should prioritize elucidating the molecular basis of NHT resistance, optimizing existing therapeutic strategies, and developing more effective combination regimens. Additionally, advanced sequencing technologies and resistance research models should be leveraged to identify novel therapeutic targets and improve drug delivery efficiencies. These advancements hold the potential to overcome NHT resistance and significantly enhance the management and prognosis of patients with advanced prostate cancer.

Objective To explore the role of regulatory T cells and exosome programmed death-ligand 1(PD-L1)in the immunotherapy of advanced non-small cell lung carcinoma(NSCLC).Methods Sixty patients with advanced NSCLC who visited China Coast Guard Hospital of the People's Armed Police Force from March 2022 to June 2024 were selected and included in lung carcinoma group.Among them,there were 30 patients squamous cell carcinoma and 30 patients with non-squamous cell carcinoma.Sixty healthy individuals undergoing physical examinations during the same period were selected as control group.Patients in squamous cell carcinoma group were treated with sintilimab+paclitaxel+cisplatin,while patients in non-squamous cell carcinoma group were treated with sintilimab+pemetrexed+carboplatin.Therapeutic effects and blood index results of two groups of patients were evaluated.Results The red blood cell count,hemoglobin,white blood cell count,platelets,CD3+,CD4+,CD4/CD8 and PD-L1 of patients in lung carcinoma group were significantly lower than those in control group(P＜0.05).The remission rate of patients in squamous cell carcinoma group was significantly higher than that in non-squamous cell carcinoma group(P＜0.05).After treatment,white blood cell count,CD8+,and exosome PD-L1 of patients in squamous cell carcinoma group were significantly lower than those in non-squamous cell carcinoma group,while red blood cell count,hemoglobin,platelets,CD3+,CD4+,and CD4/CD8 were significantly higher than those in non-squamous cell carcinoma group(P＜0.05).Conclusion There are differences in immune responses among different subtypes of lung carcinoma,and targeted treatment plans should be formulated based on pathological types.

Objective To explore the role of regulatory T cells and exosome programmed death-ligand 1(PD-L1)in the immunotherapy of advanced non-small cell lung carcinoma(NSCLC).Methods Sixty patients with advanced NSCLC who visited China Coast Guard Hospital of the People's Armed Police Force from March 2022 to June 2024 were selected and included in lung carcinoma group.Among them,there were 30 patients squamous cell carcinoma and 30 patients with non-squamous cell carcinoma.Sixty healthy individuals undergoing physical examinations during the same period were selected as control group.Patients in squamous cell carcinoma group were treated with sintilimab+paclitaxel+cisplatin,while patients in non-squamous cell carcinoma group were treated with sintilimab+pemetrexed+carboplatin.Therapeutic effects and blood index results of two groups of patients were evaluated.Results The red blood cell count,hemoglobin,white blood cell count,platelets,CD3+,CD4+,CD4/CD8 and PD-L1 of patients in lung carcinoma group were significantly lower than those in control group(P＜0.05).The remission rate of patients in squamous cell carcinoma group was significantly higher than that in non-squamous cell carcinoma group(P＜0.05).After treatment,white blood cell count,CD8+,and exosome PD-L1 of patients in squamous cell carcinoma group were significantly lower than those in non-squamous cell carcinoma group,while red blood cell count,hemoglobin,platelets,CD3+,CD4+,and CD4/CD8 were significantly higher than those in non-squamous cell carcinoma group(P＜0.05).Conclusion There are differences in immune responses among different subtypes of lung carcinoma,and targeted treatment plans should be formulated based on pathological types.

Objective To investigate the distribution of carbapenem-resistant genes and their drug susceptibility in vitro on carbapen-em-resistant Enterobacter cloacae(CRECC)in Taizhou area,and provide evidence for effective anti-infective treatment in clinical prac-tice.Methods Forty-seven strains of CRECC isolated from Enze Hospital,Taizhou Enze Medical Center(Group)and Luqiao Reha-bilitation Hospital during January 2015 and November 2022 were retrospectively analyzed.The enzyme types and resistance genes of carbapenemase were detected by the NG-Test Carba 5 and Carba-R Xpert,respectively,and the susceptibility of CERCC to common drugs was tested in vitro.Results Among 47 strains of CRECC,27 were detected to produce carbapenemase,including 24 producing New Delhi metallo-β-lactamase(NDM)type,1 producing both Klebsiella pneumoniae carbapenemase(KPC)and NDM types,and 2 producing imipenemase(IMP)type.One strain belonged to NDM genotype but no NDM enzyme type was detected.The CRECC strains had the highest sensitivity to polymyxin B(95.7%),followed by tigecycline(93.6%),fosfomycin(61.7%),and ceftazidime/avibac-tam(40.4%).In addition,the CRECC strains producing carbapenemase were more sensitive to polymyxin B,fosfomycin and aztreo-nam than those without producing carbapenemase.Conclusion The CRECC strains in Taizhou area are mainly NDM type,which has high sensitivity to polycolistin B,tigecycline and fosfomycin.NG-Test Carba 5 can not cover some strains that do not produce carbapen-emase or carry mutations in carbapenemase.

Objective:To investigate the dosimetric impact of dwell position spacing in the design of three-dimensional (3D) interstitial brachytherapy plans for cervical cancer, and to provide a reference for selecting dwell spacing in clinical planning.Methods:A total of 15 patients with cervical cancer who underwent 3D interstitial brachytherapy at Tianjin Medical University Cancer Institute & Hospital between March 2022 and March 2024 were selected using simple random sampling. For each patient, 10 brachytherapy plans were generated with different dwell position spacings set at 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 mm, respectively. Key parameters among different dwell spacings compared included D 90%, V 100%, V 200%, and V 300% for the high-risk clinical target volume (HRCTV); D 90% for the intermediate-risk clinical target volume (IRCTV); D 2 cm3 for organs at risk (OARs) (bladder, small intestine, colon, and rectum); and the total dwell time. Statistical analyses were performed using repeated measurement ANOVA or the Friedman test. Results:Among different dwell spacings, there were no statistically significant differences in HRCTV D 90%, HRCTV V 100%, bladder D 2 cm3, and rectum D 2 cm3 among different dwell spacings ( P=0.075, 0.061, 0.480, 0.639). All plans with dwell spacings ≤ 3 mm met clinical dose requirements. When the dwell spacing was set to 1 mm, HRCTV V 200% and V 300% had the smallest mean values, while IRCTV D 90% and total dwell time had the largest mean values; all differences were statistically significant ( P<0.05). When the dwell spacing was ≥6 mm, an increase in spacing led to a decrease in mean small intestine D 2 cm3, and total dwell time, but an increase in HRCTV V 200% and a decrease in IRCTV D 90%, with statistically significant differences compared to spacings of 1-4 mm ( P<0.05). When the dwell spacing was ≥8 mm, the median colon D 2 cm3 decreased, with statistically significant differences compared to spacings of 1-3 mm ( P<0.05). Conclusions:For 3D interstitial brachytherapy planning in cervical cancer, dwell position spacings ≤ 3 mm can meet clinical dose requirements, with 1 mm providing optimal target coverage. Spacings ≥6 mm / ≥8 mm can reduce radiation dose to the small intestine and colon, respectively, while also shortening dwell time.

Objective To investigate the clinical features of of patients with Staphylococcus aureus blood-stream infection and risk factors for septic shock.Methods A total of 51 patients diagnosed with Staphylococ-cus aureus bloodstream infection in the hospital from January 2018 to March 2023 were enrolled in the study.According to whether the patients developed septic shock,they were divided into septic shock group and non-septic shock group.The clinical data of the patients were collected,and the clinical laboratory indicators were detected on the day of blood culture samples were collected.Bacteria isolated from blood culture specimens of patients were identified and tested for drug sensitivity.The clinical data and clinical laboratory test indicators of the two groups were compared.Multivariate Logistic regression was used to analyze the independent risk factors of septic shock in patients with Staphylococcus aureus bloodstream infection.The receiver operating characteristic(ROC)curve was used to analyze the predictive value of clinical laboratory test indicators for septic shock in patients with Staphylococcus aureus bloodstream infection.Results Septic shock occurred in 12 of 51 patients,with an incidence of 23.5％.The proportion of patients with diabetes,gouty arthritis,the proportion of patients with long-term glucocorticoid use,the proportion of patients with respiratory tract in-fection,the proportion of patients who died,and the hospitalization cost in the septic shock group were higher than those in the non-septic shock group,and the differences were statistically significant(P＜0.05).Long-term glucocorticoid use was an independent risk factor for septic shock in Staphylococcus aureus bloodstream infection(P＜0.05).The combination of C-reactive protein(CRP),albumin(Alb),neutrophil/lymphocyte ratio(NLR)and procalcitonin(PCT)had high value in predicting septic shock in patients with Staphylococ-cus aureus bloodstream infection,and the area under the ROC curve was 0.983.Conclusion Long-term use of glucocorticoids can lead to an increased risk of septic shock in patients with Staphylococcus aureus blood-stream infection.The combined detection of CRP,Alb,NLR and PCT has a higher predictive value than single detection for septic shock in patients with Staphylococcus aureus bloodstream infection.