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.

OBJECTIVE The emergence of evolving variants of Coronavirus disease 2019(COVID-19)has fostered the need for change of newer and adaptive treatments for these infections.During the COVID-19 pandemic and persists,traditional Chinese medicine(TCM)herbs exhibit significant bioactivity and therapeutic effect.This study is aimed to evaluate the efficacy of four TCM preparations on 28-day mortality risk of patients and changes of the laboratory indicators.METHODS The retrospective cohort study included patients with COVID-19 who were admitted to the Jiangsu Province Hospital of Chinese Medicine from December 15,2022 to January 15,2023,and those died within 48 hours of admission or cannot be tracked for outcomes were excluded.The pri-mary outcome was survival status in 28 days(death or survival)starting from the day of admission.The second outcomes were labora-tory indicators,including absolute lymphocyte count,lactate dehydrogenase,creatinine,and blood urea nitrogen.Binary logistic re-gressions were used to estimate the effect of TCM preparations on the primary and secondary outcomes in main analysis.Meanwhile,heterogeneity and robustness of results from main analysis were assessed by subgroup analyses and multiple sensitivity analyses.RESULTS 1 816 eligible patients were included in analysis dataset,including 573 patients received standard care(control group)and 1 243 patients received TCM preparations(hospital preparation group).The 28-day mortality rate of hospital preparation group was lower than that of control group(4.75%vs.14.83%),and the difference was statistically significant(χ2=54.666,P<0.001).The risk of 28-day mortality was 0.535 times lower in the hospital preparation group as compared with the control group(OR=0.46,95%CI:0.305-0.708,P<0.001)showed by multivariable binary logistic regressions.Subgroup analyses showed that taking TCM preparations reduced the 28-day mortality risk.Sensitivity analyses demonstrated that the results of the main analysis for primary outcomes were robust.For secondary outcomes,the risk of abnormal absolute lymphocyte counts at discharge in the hospital prepara-tion group decreased by 0.284 times(OR=0.703,95%CI:0.515-0.961,P=0.027).CONCLUSION Compared with standard of care,taking four hospital preparations including Kanggan Heji,Feining Heji,Qishen Gubiao Keli,and Qianghuo Qushi Qingwen Heji decreased risk of 28-day mortality among hospitalized COVID-19 patients.TCM therapy achieves adequate therapeutic effects in COVID-19.

Medical ultrasound technology is widely used for diagnosis and therapy in clinical practice.Ultrasound probes,which are directly contact with patients,pose a potential risk of pathogen transmission.This expert consen-sus was developed by a multidisciplinary team based on international guidelines,standards in China,and the results of a national survey,aiming to reduce the risk of healthcare-associated infection through standardizing reprocessing of medical ultrasound probes,and formulating consensus recommendations with the Delphi method.The consensus clarifies the reprocessing principles for three types of ultrasound probes of different infection risks:external-use ul-trasound probes,interventional percutaneous ultrasound probes,and internal-use ultrasound probes,puts forward systematic suggestions on the reprocessing standards and disinfection levels of ultrasound probe isolation covers and coupling agents,the reprocessing procedures and methods of ultrasound probes,as well as architectural layout and management of reprocessing,so as to provide a scientific prevention and control framework for ensuring ultrasound diagnosis and therapy safety.

Heatstroke,especially in high-temperature and high-humidity environments,is a life-threatening acute heat-injury disease that seriously endangers human health.Timely and effective on-site treatment is crucial for patients'survival and prognosis.Early recognition,rapid assessment,and on-site cooling are the core of pre-hospital treatment of heatstroke.Currently,there is a lack of standardized application procedures for pre-hospital emergency care of heatstroke.Therefore,the"Expert Consensus on Pre-hospital Emergency Management of Heatstroke(2024 edition)"was initiated by the Expert Group on Heatstroke Prevention of the People's Liberation Army and developed in collaboration with experts from local pre-hospital emergency care,emergency departments,and intensive care units.This consensus focuses on heatstroke prevention,on-site and ambulance-based treatment,and early emergency room interventions,and puts forward 10 evidence-based recommendations,aiming to provide a reference for scientific and standardized pre-hospital emergency care of heatstroke.

Objective To evaluate the effects of transcatheter aortic valve replacement(TAVR)on left ventricular reverse remodeling(LVRR)and outcomes in patients with mixed aortic valve disease(MAVD)and predominant aortic stenosis(AS).Methods Patients undergoing TAVR at our center between January 2020 and December 2022 were enrolled consecutively.Propensity score matching(PSM)(1∶1 ratio)was used to reduce selection bias.Transthoracic echocardiography(TTE)was used to monitor left ventricular ejection fraction(LVEF)and other structural parameters over time.The study outcome was a composite of cardiovascular death and rehospitalization due to cardiovascular causes.Linear mixed-effects models and logistic regression were utilized for comparing echocardiographic changes across groups and identifying independent risk factors for no-LVRR,respectively.Results After PSM,126 patients were included.MAVD group exhibited larger structural parameters(left ventricular end-systolic/end-diastolic diameter and volume,left ventricular mass index)and a lower left ventricular ejection fraction(LVEF)(all P＜0.05).However,more pronounced improvements in left ventricular structure and hemodynamics were observed during follow-up.Multivariate logistic regression analysis indicated that the left ventricular mass index(LVMI)was an independent predictor of left ventricular reverse remodeling(LVRR)after TAVR,whereas persistent moderate or greater mitral regurgitation(MR)and paravalvular leak(PVL)significantly reduced the incidence of LVRR.During a median follow-up period of 23 months,a total of 31 endpoint events occurred,and there was no statistically significant difference in long-term prognosis between the two groups(Log-rank P=0.330).Conclusions Compared to patients in the AS group,those in the MAVD group exhibited more severe left ventricular remodeling before TAVR.However,more significant LVRR was observed during postoperative follow-up.Additionally,the long-term prognosis was comparable between the two groups.

Objective:To assess the relationship between basophil levels and mortality in patients with intra-abdominal infection.Methods:Information on patients with intraperitoneal infection admitted to the intensive care unit were extracted from the MIMIC database.A time-dependent Cox regression model was used to adjust for confounders associated with 28-day mortality.Propensity score matching(PSM)was used to balance the baseline differences be-tween groups with different basophil levels,and a restricted cube chart(RCS)was used to show the relationship between basophil count and 28-day mortality in patients with intra-abdominal infection.Results:A total of 4403 patients with intra-abdominal infection were enrolled in the MIMIC database.Patients with high basophil levels have lower mortality than those with low basophil levels.There was an L-shaped curve between basophil level and 28-day mortality,with a cut-off value of 0.47×109/L.Cox regression analysis showed that basophil levels were an independent protective factor for mortal-ity in patients with intra-abdominal infection after adjusting for potential confounders(HR=0.586,95%CI:0.443-0.769).Protective factors for death at basophil levels remained after PSM adjusted for potential confounders(HR=0.628,95%CI:0.470-0.832).Conclusion:Basophil level is an independent protective factor for mortality in patients with intra-abdominal infection,and basophil levels should be dynamically monitored to better evaluate the prognosis of patients.

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 effects of joint function screening and correction on intervention efficacy of prevention and assessment score of training injuries in new recruits.Methods A randomized controlled study was conducted on 265 new recruits subjected from two organizational units of an army unit with cluster sampling.Based on entire organizational unit,the participants were randomly divided into a control group(n=132)and an experimental group(n=133).The experimental group received joint function screening and corrective movement training,which was subsequently applied in the new recruit training,while the control group underwent training according to conventional methods.Joint function were collected before and after training.The demographic data,assessment score of training,and incidence of training injuries were collected through the participant's own organizational unit.Receiver operating characteristic(ROC)curve was plotted to evaluate the efficacy of joint function screening in predicting training injuries,and binary logistic regression and general linear regression analyses were applied to verify the correlation of joint function screening score with training injuries and assessment score of training.Results After new training,the score of joint function screening was significantly higher in the experimental group than the control group(16.62±1.87 vs 14.92±2.58,P<0.001).And the score was obviously increased in the experimental group(16.62±1.87 vs 12.82±1.98,P<0.001)and the control group(14.92±2.58 vs 12.95±1.81,P<0.001)when compared with the corresponding score before training.The area under the ROC curve(AUC)of joint function screening in predicting training injuries was 0.762(95%CI:0.694～0.830),indicating good predictive efficacy.During the new training process,the incidence of training injuries in the experimental group(13.53%)was significantly lower than that in the control group(24.24%,Chi-square=4.963,P=0.026).Binary logistic regression analysis showed that the pre-training assessment score of joint function screening was an important influencing factor for training injuries in new recruits(OR=0.552,95%CI:0.413～0.660,P<0.001).The experimental group obtained notably higher mean assessment score than the control group[733.00(716.00,752.75)vs 728.79(710.46,744.28),P=0.027].Linear regression analysis revealed a correlation between post-training score of joint function screening and the assessment score of newly trained personnel(P<0.001).Conclusion Joint function screening and correction for newly trained personnel can effectively prevent training-related injuries during the new training period,and correcting joint function through training can effectively improve the assessment score of newly trained personnel.

Ketamine is a dissociative anesthetic.It is clinically used as a surgical anesthetic or anes-thetic inducer and has a certain degree of mental dependence.Its abuse can lead to nerve damage,ad-verse emotional reactions and other toxic side effects.The primary mechanism by which ketamine exerts its pharmacological effects is to block N-methyl-D-aspartate receptors(NMDAR).It also functions through pathways such as α-amino-3-hydroxy-5-methyl-4-isox-azolepropionic acid receptors(AMPAR),opioid receptors,γ-aminobutyric acid(GABA)receptors,monoaminergic receptors,cholinergic recep-tors,hyperpolarization-activated cyclic nucleotide-gated(HCN)channels,voltage-gated sodium channels,and L-type voltage-dependent calcium channels(VDCC).This article summarizes the molecular mecha-nism and toxic effects of ketamine's pharmacological functions,in order to provide a basis for foren-sic applications such as the identification of symptomatic phenotypes of ketamine toxic effects and the identification of ketamine abuse.

This study analyzed the epidemiological characteristics and strain genotypic distribution of varicella in Fengtai District, Beijing, utilizing population-based surveillance data from March 2024 to February 2025 combined with laboratory nucleic acid detection and genotyping.We reported 522 varicella cases with male predominance (282 cases, 54.41%) and a majority aged >15 years (358 cases, 68.58%).A bimodal incidence pattern peaked in May and November, while 161 breakthrough infections (30.8%) occurred predominantly among students aged 6-20 years.The multivariate logistic regression model analysis showed that, compared to the 0-5 years age group, individuals aged 6-14 years ( OR: 2.729, 95% CI: 1.083-6.88), 15-20 years ( OR: 2.495, 95% CI: 1.158-5.378), and>20 years ( OR: 5.382, 95% CI: 2.478-11.689) exhibited progressively higher odds of oderate-to-severe rash; females demonstrated a lower risk compared to males ( OR: 0.485, 95% CI: 0.286-0.822); regarding vaccination status, recipients of one vaccine dose ( OR: 0.301, 95% CI: 0.161-0.564) and two doses ( OR: 0.203, 95% CI: 0.086-0.48) showed significantly reduced risks relative to unvaccinated individuals. Genotyping of 174 specimens identified 161 VZV-positive samples, with successful ORF22/38/62 sequencing in 142 samples confirming Clade 2 predominance (141 strains, 99.3%) and one Clade 5 strain; local isolates exhibited high vaccine-strain homology (ORF22 nucleotide:99.5%-100%, amino acid:99.3%-100%) with mutation sites partially overlapping other Chinese regions.