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 retrospectively analyze the clinical efficacy of Qingchi San in the treatment of mild-to-moderate ul-cerative colitis.METHODS A retrospective analysis was conducted on 221 ulcerative colitis patients treated at the Gastroenterology Department of First Teaching Hospital of Tianjin University of Traditional Chinese Medicine from December 2014 to February 2024.Participants were divided into an observation group(n=121)and a control group(n=100).The control group received oral me-salazine,while the observation group received Qingchi San enema in addition to the control group's treatment.Both groups received a 4-week course of treatment.The two groups were compared in terms of clinical efficacy,clinical remission rate,TCM syndrome score,time to intestinal bleeding remission,and changes in inflammatory markers(C-reactive protein,erythrocyte sedimentation rate,and white blood cells)and coagulation markers(platelets and D-dimer).RESULTS After treatment,the total effective rate and clinical remission rate in the observation group were significantly higher than those in the control group(P<0.05,P<0.01).The TCM syn-drome scores for each item(diarrhea,abdominal pain,bloody stools with mucus,and tenesmus)in the observation group were signifi-cantly improved compared with those in the control group(P<0.01).The time to intestinal bleeding remission in the observation group was significantly shorter than that in the control group(P<0.01).C-reactive protein,erythrocyte sedimentation rate,and white blood cell count in the observation group were significantly decreased compared with those in the control group(P<0.05,P<0.01).There was no statistically significant difference in coagulation markers(platelets and D-dimer)between the two groups(P>0.05).No signif-icant treatment-related adverse reactions,such as liver and kidney damage,were observed during treatment.CONCLUSION Qing-chi San enema can improve the clinical efficacy of patients with mild-to-moderate ulcerative colitis,improve clinical symptoms,short-en bleeding time,and reduce inflammatory markers.

OBJECTIVE To retrospectively analyze the clinical efficacy of Qingchi San in the treatment of mild-to-moderate ul-cerative colitis.METHODS A retrospective analysis was conducted on 221 ulcerative colitis patients treated at the Gastroenterology Department of First Teaching Hospital of Tianjin University of Traditional Chinese Medicine from December 2014 to February 2024.Participants were divided into an observation group(n=121)and a control group(n=100).The control group received oral me-salazine,while the observation group received Qingchi San enema in addition to the control group's treatment.Both groups received a 4-week course of treatment.The two groups were compared in terms of clinical efficacy,clinical remission rate,TCM syndrome score,time to intestinal bleeding remission,and changes in inflammatory markers(C-reactive protein,erythrocyte sedimentation rate,and white blood cells)and coagulation markers(platelets and D-dimer).RESULTS After treatment,the total effective rate and clinical remission rate in the observation group were significantly higher than those in the control group(P<0.05,P<0.01).The TCM syn-drome scores for each item(diarrhea,abdominal pain,bloody stools with mucus,and tenesmus)in the observation group were signifi-cantly improved compared with those in the control group(P<0.01).The time to intestinal bleeding remission in the observation group was significantly shorter than that in the control group(P<0.01).C-reactive protein,erythrocyte sedimentation rate,and white blood cell count in the observation group were significantly decreased compared with those in the control group(P<0.05,P<0.01).There was no statistically significant difference in coagulation markers(platelets and D-dimer)between the two groups(P>0.05).No signif-icant treatment-related adverse reactions,such as liver and kidney damage,were observed during treatment.CONCLUSION Qing-chi San enema can improve the clinical efficacy of patients with mild-to-moderate ulcerative colitis,improve clinical symptoms,short-en bleeding time,and reduce inflammatory markers.

COVID-19 is a novel respiratory infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The continuous emergence of SARS-CoV-2 variants poses a serious threat to the public health. A bedside device with high specificity and sensitivity for real-time and rapid detection is of great value for the prevention and control of COVID-19. Moreover, understanding the transmission behavior of airborne pathogens such as SARS-CoV-2 is also vital for scientific disease prevention. Biosensors are devices that can convert biomolecular reaction signals into detectable physical or chemical signals and have been increasingly used for the detection and analysis of pathogenic microorganisms. Biosensors bases on the conventional detection methods such as colony counting, immunoassays and molecular techniques are often time-consuming, labor-intensive and complicated. Nanomaterial-based biosensors are more portable and can be used for rapid on-site detection. This review summarized the progress in biosensors for pathogen detection and the application of nanomaterial based-biosensors in the surveillance of airborne pathogens.

Objective:To propose an automatic planning approach for Eclipse15.6 planning system based on Eclipse scripting application programming interface (ESAPI) and evaluate its clinical application.Methods:20 patients with nasopharyngeal carcinoma and 20 cases of rectal cancer were selected in the clinical planning. The developed automatic planning script SmartPlan and RapidPlan were used for automatic planning and dosimetric parameters were compared with manual planning. The differences were compared between two groups by using Wilcoxon signed rank test. Results:The dosimetric results of automatic and manual plans could meet clinical requirements. There was no significant difference in target coverage in nasopharyngeal carcinoma planning between two groups ( P>0.05), and automatic plans were superior to manual plans in organs at risk sparing ( P<0.05). Except for the homogeneity index of PTV and the maximum dose of bowel in rectal cancer plans, the other dosimetric parameters of the automatic plans were better than those of the manual plans (all P<0.05). Conclusions:Compared with the manual plans, the automatic plans have the same or similar target coverage, similar or better protection of organs at risk, and more convenient implementation. The developed SmartPlan based on ESAPI has clinical feasibility and effectiveness.

Objective:To investigate the difference and relationship between synaptophysin, vimentin in positive or negative schizophrenia.Methods:Fifty-nine patients with schizophrenia mainly presented positive syndrome(positive case group), 41 patients with schizophrenia mainly presented negative syndrome(negative case group), 100 individuals without schizophrenia were enrolled.Patients with schizophrenia were assessed with positive and negative symptom scale (PANSS), then the patients were divided into positive symptom group (positive group, n=59) and negative symptom group (negative group, n=41). The serum levels of synaptophysin and vimentin were measured by enzyme-linked immunosorbent assay (Elisa). The levels of serum synaptophysin and vimentin were compared among the groups by independent t test.The correlation between positive and negative symptoms and serum levels of synaptophysin and vimentin were analyzed by Pearson correlation analysis. Results:In the positive case group: the synaptophysin level after treatment ((80.34±9.94)μg/L)was significantly lower than that before treatment ((102.89±12.03)μg/L). The vimentin level after treatment ((10.81±1.98)μg/L)was significantly lower than that before treatment((13.96±2.10)μg/L). The differences were statistically significant(both P<0.01). In the negative case group: the synaptophysin level after treatment ((80.42±10.61)μg/L) was significantly lower than that before treatment((102.98±11.04)μg/L). The vimentin level after treatment ((11.18±1.74)μg/L)was significantly lower than that before treatment((14.14±1.82)μg/L). The differences were statistically significant(both P<0.01). The synaptophysin levels in the positive case group and the negative case group were significantly higher than those in the control group((64.29±11.26)μg/L). The vimentin levels in the positive case group and the negative case group were significantly higher than those in the control group((8.33±1.62)μg/L). The differences were statistically significant(all P<0.01). The PANSS score of the positive and negative case group were significantly decreased after treatment compared with before treatment, the differences were statistically significant( P<0.05). Pearson correlation analysis suggested that synaptophysin was positively correlated with positive scale score in positive case group( r=0.650, P<0.01). Vimentin was positively correlated with negative scale score in negative case group( r=0.629, P<0.01). Conclusion:There may be different pathological mechanisms in schizophrenia with positive and negative symptoms.

OBJECTIVE: To investigate the metabolic profiles of urine from mice with early-stage Alzheimer's disease (AD). METHODS: Urine samples were collected from 13 mice of 16 weeks and 15 wild-type mice. H-NMR spectroscopy was acquired with a one-dimensional NOESY pulse sequence, and the integral values were imported to SIMCA-P+12.0 software for analysis. RESULTS: The metabonomic analysis showed that the metabolic profiles of the mice were significantly different from that of age-matched wild-type mice. The levels of 3-hydroxybutyrate, 2-hydroxybutyrate, succinic acid, 2-ketoglutaric acid, citric acid, cis-aconitic acid, fumaric acid decreased, and those of acetic acid, trimethylamine, taurine, creatinine, hippuric acid, formic acid, trigonelline, urea increased (all <0.05). CONCLUSIONS Metabolic pathways including glucose metabolism and methylamine metabolism may be involved in the pathogenesis of early AD.
Alzheimer Disease ; urine ; Animals ; Disease Models, Animal ; Magnetic Resonance Spectroscopy ; Metabolome ; Metabolomics ; Mice ; Mice, Inbred C57BL

Objectives To investigate the occipital cortex metabolite alterations in repetitive and severe neonatal hypoglycemia rats treated with sodium pyruvate and to reveal the protective role of sodium pyruvate using high resolution 1H nuclear magnetic resonance spectroscopy.Methods Thirty-six 2-dayold Sprague-Dawley rats were randomly divided into hypoglycemia group and pyruvate group with 18 rats in each group.Rats in both groups received intraperitoneal injections of insulin (40 U/kg body weight) at 2,4 and 6 days of age to induce severe hypoglycemia (blood glucose value ≤ 1.4 mmol/L).In the hypoglycemia group,2.5 hours after insulin injection,intraperitoneal injection of 50％ glucose (2 ml/kg) was administered to terminate hypoglycemia,while in the pyruvate group,50％ glucose (2 ml/kg) and sodium pyruvate solution 2.5 ml/kg (500 mg/kg) were injected.Terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling assay was used to observe the status of injured neurons in six neonatal rats,and metabolite changes in occipital cortex of the other 12 rats were detected by 1H nuclear magnetic resonance spectroscopy.The difference between the two groups was compared by independent-samples t test.Results Neonatal rats of both groups reached severe hypoglycemia level 2.5 hours after insulin injection.Compared with hypoglycemia group,pyruvate group had fewer injured neurons (45±5 vs 113 ± 12,t=0.782,P=0.013) and lower injured index in the occipital cortex (0.15 ± 0.03 vs 0.36 ± 0.06,t=l.143,P=0.020).Pyruvate group showed significant decreases in the concentration of taurine [(13.31 ± 2.06) vs (18.44 ± 3.86) mol/kg,t=8.231],glutamine[(1.50 ± 0.24) vs (2.02 ± 0.40) mol/kg,t=3.137],glutamate[(7.04 ± 0.95) vs (9.40 ± 1.73) mol/kg,t=6.449],aspartate[(1.51 ± 0.28) vs (2.15 ± 0.58) mol/kg,t=2.561] and creatine [(6.37±0.99) vs (8.46± 1.77) mol/kg,t =4.226] in the occipital cortex (all P'＜0.017).Conclusions Simultaneous use of glucose and sodium pyruvate to terminate hypoglycemia in repetitive and severe neonatal hypoglycemia rats can effectively alleviate severe hypoglycemia-induced occipital lobe damage via regulating excitatory amino acid neurotransmitters,energy metabolism and other metabolic pathways.