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 observe the characteristics of gastrointestinal tract symptoms in patients with Parkinson′s disease (PD) and analyze the characteristics of these symptoms in patients with different PD subtypes.Methods:A total of 297 PD patients who were admitted to the Neurology Department of the Second Affiliated Hospital of Soochow University from November 2022 to March 2024 were enrolled. The gastrointestinal symptoms of PD patients were evaluated using Drooling Severity and Frequency Scale (DSFS), Sialorrhea Clinical Scale for Parkinson′s disease (SCS-PD), Drooling Rating Scale (DRS), Eating Assessment Tool 10 (EAT-10), Gastroparesis Cardinal Symptom Index (GCSI), and Rome Ⅳ diagnostic criteria. The patients were grouped based on the presence or absence of gastrointestinal symptoms. Additionally, they were stratified according to disease duration (≤2 years, 2-5 years, 5-10 years, and>10 years) and motor symptom subtype [tremor-dominant (TD) vs. postural instability and gait difficulty (PIGD)]. One-way ANOVA and logistic regression analysis were applied to examine between-group differences while Spearman correlation analysis was employed to assess correlations between clinical symptoms.Results:The average age of the patients with PD was 67.0 (60.0, 72.0) years, and 161 (54.2%) were male. The incidence of PD combined with gastrointestinal symptoms was, in descending order: constipation (191, 64.3%), salivation (155, 52.2%), gastroparesis (93, 31.3%), and dysphagia (68, 22.9%). Compared with PD patients without gastrointestinal symptoms, those with symptoms had higher scores in the RBD-HK [12.0 (5.0, 21.5) vs. 5.0 (0.0, 9.0), Z=-3.74, P=0.017], ESS [6.0 (2.0, 12.0) vs. 3.0 (0.0, 6.0), Z=-3.20, P=0.023], and MDS-UPDRS Part Ⅰ [9.0 (5.0, 14.0) vs. 5.0 (2.3, 9.0), Z=-3.61, P=0.014]. The severity of sialorrhea and deglutition disorders, along with the incidence of constipation, all increased with longer disease duration. Patients with the PIGD subtype had higher GCSI scores than those with the TD subtype [0.0 (0.0, 1.9) vs. 0.0 (0.0, 0.0), Z=-3.57, P=0.007]. Across the cohort, sialorrhea, deglutition disorders, gastroparesis, and constipation were positively associated with the H-Y stage, MDS-UPDRS Ⅰ, HAMD, NMSQ, and SCOPA-AUT; EAT-10 scores were negatively correlated with MoCA ( r=-0.171, P<0.05); and GCSI scores were negatively correlated with MMSE and MoCA ( r=-0.154, r=-0.169, both P<0.05). Conclusions:Overall, 84.5% of the patients with PD had one or more gastrointestinal symptoms, and the incidence and severity of gastrointestinal symptoms increased with disease duration. The severity of gastroparesis was higher in the PIGD group than in the TD group. The scores of all gastrointestinal symptoms were positively correlated with the H-Y stage and MDS-UPDRS Ⅰ, while the GCSI scores were negatively correlated with the cognitive scores.

Objective:To identify the relevant factors for the first-course remission of acute myeloid leukemia (AML) and to develop a predictive model as well as assess its predictive capability.Methods:Clinical data of 749 patients newly diagnosed with AML admitted to the Department of Hematology, the First Affiliated Hospital, Zhejiang University, School of Medicine from January 1, 2019, to April 30, 2023, were collected and randomly divided into training and validation sets. Multivariate logistic regression analysis was conducted to determine variables associated with complete remission in the first course of induction therapy, and a predictive model was established based on these variables. The receiver operating characteristic (ROC) curve of the predictive model was plotted, and the area under the curve (AUC) was calculated.Results:The indicators predicting the first remission course included peripheral blood white blood cell count during onset, CBF::MYH11 fusion gene, CEBPA bZIP region mutation, myelodysplastic syndrome-related gene mutation, and induction chemotherapy regimen selection as independent factors for the first remission course. The model’s area under the training and validation curves was 0.738 (95% CI: 0.696-0.780) and 0.726 (95% CI: 0.650-0.801), respectively. The Hosmer-Lemeshow test results yielded P-values of 0.993 and 0.335, respectively. Conclusion:In this study, the developed model demonstrates a strong predictive capability for the efficacy of the first course of patients with AML, providing valuable guidance to clinicians in assessing patient prognosis and selecting appropriate treatment strategies.

Given that ovarian stimulation is vital for assisted reproductive technology (ART) and results in elevated serum estrogen levels, exploring the impact of elevated estrogen exposure on oocytes and embryos is necessary. We investigated the effects of various ovarian stimulation treatments on oocyte and embryo morphology and gene expression using a mouse model and estrogen-treated mouse embryonic stem cells (mESCs). Female C57BL/6J mice were subjected to two types of conventional ovarian stimulation and ovarian hyperstimulation; mice treated with only normal saline served as controls. Hyperstimulation resulted in high serum estrogen levels, enlarged ovaries, an increased number of aberrant oocytes, and decreased embryo formation. The messenger RNA (mRNA)‍-sequencing of oocytes revealed the dysregulated expression of lysine-specific demethylase 6b (Kdm6b), which may be a key factor indicating hyperstimulation-induced aberrant oocytes and embryos. In vitro, Kdm6b expression was downregulated in mESCs treated with high-dose estrogen; treatment with an estrogen receptor antagonist could reverse this downregulated expression level. Furthermore, treatment with high-dose estrogen resulted in the upregulated expression of histone H3 lysine 27 trimethylation (H3K27me3) and phosphorylated H2A histone family member X (γ-H2AX). Notably, knockdown of Kdm6b and high estrogen levels hindered the formation of embryoid bodies, with a concomitant increase in the expression of H3K27me3 and γ-H2AX. Collectively, our findings revealed that hyperstimulation-induced high-dose estrogen could impair the demethylation of H3K27me3 by reducing Kdm6b expression. Accordingly, Kdm6b could be a promising marker for clinically predicting ART outcomes in patients with ovarian hyperstimulation syndrome.
Female ; Mice ; Demethylation/drug effects* ; Embryonic Stem Cells ; Estrogens/administration & dosage* ; Gene Expression/drug effects* ; Histones/metabolism* ; Jumonji Domain-Containing Histone Demethylases/metabolism* ; Mice, Inbred C57BL ; Oocytes ; Ovary/drug effects* ; Reproductive Techniques, Assisted ; Animals

OBJECTIVE: To reveal the molecular basis of knee osteoarthritis (KOA) with Yang deficiency and blood stasis syndrome by analyzing the gene expression profiles in synovial fluid and blood of KOA patients with this syndrome. METHODS: A total of 80 KOA patients were recruited from October 2022 to June 2024, including 40 cases in the non-Yang deficiency and blood stasis group (27 males and 13 females), with an average age of (61.75±3.45) years old;and 40 cases in the Yang deficiency and blood stasis group (22 males and 18 females), with an average age of (62.00±2.76) years old. The levels of body mass index (BMI), high-density lipoprotein (HDL), low-density lipoprotein (LDL), fibrinogen, total cholesterol, and D-dimer were recorded and summarized. Blood and synovial fluid samples from patients were collected for gene expression profile microarray sequencing, and then PCR and immunohistochemistry were used for clinical verification on the patients' synovial fluid and cartilage samples. RESULTS: Logistic regression analysis showed that compared with KOA patients with non-Yang deficiency and blood stasis syndrome, those with Yang deficiency and blood stasis syndrome had increased BMI, LDL, fibrinogen, total cholesterol, and D-dimer, and decreased HDL, with a clear correlation between the two groups. There were 562 differential genes in the blood, among which 322 were up-regulated and 240 were down-regulated;755 differential genes were found in the synovial fluid, with 350 up-regulated and 405 down-regulated. KEGG signaling pathway analysis of synovial fluid revealed changes in lipid metabolism-related pathways, including cholesterol metabolism, fatty acid metabolism, and PPARG signaling pathway. Analysis of the involved differential genes identified 6 genes in synovial fluid that were closely related to lipid metabolism, namely LRP1, LPL, ACOT6, TM6SF2, DGKK, and PPARG. Subsequently, PCR and immunohistochemical verification were performed using synovial fluid and cartilage samples, and the results were consistent with those of microarray sequencing. CONCLUSION This study explores the clinical and genomic correlation between traditional Chinese medicine syndromes and knee osteoarthritis from the perspective of lipid metabolism, and proves that abnormal lipid metabolism is closely related to KOA with Yang deficiency and blood stasis syndrome from both clinical and basic aspects.
Humans ; Male ; Female ; Middle Aged ; Synovial Fluid/metabolism* ; Osteoarthritis, Knee/metabolism* ; Yang Deficiency/complications* ; Aged

Objective To construct a predictive model for high-grade pathological components of early invasive lung adenocarcinoma(ILAC)based on radiomics.Methods Collecting information on total 495 patients who underwent radical operation and were pathologically diagnosed as stage Ⅰ in the cardiothoracic surgery of Zhoushan Hospital from January 2015 to December 2019,including gender,age,pathological findings,tumor markers and preoperative chest CT images.The micropapillary and solid components in postoperative pathology were defined as"high-grade pathological components",while those without high-grade pathological components were classified into the low-grade group and those with high-grade pathological components were classified into the high-grade group.And patients were randomly divided into the training set(343 cases)and the validation set(152 cases)with a ratio of 7∶3 using the simple randomization grouping method.The region of interest of nodules on CT images were delineated layer by layer by scientific research platform and 1950 radiomics features were extracted.And then those features were filtrated by F-test,Pearson correlation coefficient,and L1 based feature selection.A model was built by using Logistic regression machine learning classifier,named mod 2,and radscore was also obtained.Differences between general information and CT features were analyzed.Binary Logistic regression analysis was used to construct a model for statistically significant variables,named mod 1.At the same time,Radscore was added to build the mod and named comb mod.The area under the curve(AUC),sensitivity and specificity of the three models were calculated.A nomogram was also drawn.Results A total of 495 patients were divided into the training set(n=343)and the validation set(n=152).Gender,carcinoma embryonic antigen(CEA),nodule,and maximum diameter were screened out in clinical features and involved in constructing the mod 1.Twelve features were selected from the radiomics features to build mod 2.Comb mod performed best,training set AUC:0.887,validation set AUC:0.875,and had good clinical practicability.Conclusion The model composed of general feature,CT feature and radiomics features could accurately predict high-grade pathological components in early ILAC,and provide references for clinicians to choose surgical method for patients.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

[Objective]To explore the academic characteristics of SHI Fa's On Continuation of Simple Formulas,the backbone physician of Yongjia medical school,and enrich the research on the academic thought of Yongjia medical school.[Methods]By using the method of literature research,through the study of On Continuation of Simple Formulas and other works of Yongjia medical school,combined with modern related research literature,this paper analyzes and sorts out the background,compilation principles and academic characteristics of On Continuation of Simple Formulas,and verifies it with medical cases.[Results]SHI Fa inherited the academic thoughts of CHEN Yan,the founder of Yongjia medical school,and his great disciple WANG Shuo,and corrected the oversimplification of Simple Formulas,"There is no difference between deficiency,excess,cold and heat syndromes,and one party governs one disease".Starting from clinical application,he not only pursued simplicity,but also pursued practicality.He abided by the principle of syndrome differentiation and treatment,adhered to the principle of pulse first and combination of pulse and disease,emphasized the protection of stomach Qi and the treatment of three causes,and advocated familiarity with medicinal properties and medication according to syndrome.The attached medical cases fully verified the fallacy of WANG Shuo's"one party governs all the syndromes"and the correctness of the academic characteristics of syndrome differentiation and treatment and addition and subtraction with the syndrome.[Conclusion]On Continuation of Simple Formulas attaches importance to the academic characteristics of syndrome differentiation,combination of pulse and disease,careful medication and protection of stomach Qi,which enriches the academic thought of Yongjia medical school and promotes the inheritance and development of Yongjia medical school.It also has enlightening significance for today's clinical diagnosis and treatment.

Objective To investigate the current situation of off-label use of immune checkpoint inhibitors(ICIs)in China and the cognition of medical staff.Methods From August 31 to September 9,2022,a nationwide survey questionnaire was sent to medical staff in the form of electronic questionnaire.The questionnaire included 13 questions,covering four dimensions:Drug allocation,current situation of medication beyond the instructions,cognition of medication beyond the instructions and current situation of medication beyond the instructions.Results A total of 745 questionnaires were collected.75.70％of respondents reported off-label use of ICIs in their hospitals,with the most common type being off-label indications.The primary reasons for such practices included support from authoritative domestic and international guidelines,clinical research data validation,and approved indications in foreign regulatory documents.85.37％of respondents believed off-label use could offer new hope for patients,while 68.86％considered it unlikely to increase adverse reactions.44.97％of respondents' hospitals had not established off-label use registration systems for ICIs.88.86％of respondents emphasized the need for stricter regulations governing off-label use of immunotherapeutic agents.Conclusion Off-label use of ICIs is common,and there is a lack of unified guidance in clinical practice.It is urgent to form norms and consensus on the management of off-label use of ICIs.