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:To investigate the risk factors associated with the development of venous thromboembolism with abdominal infection following pancreatic cancer surgery.Methods:A retrospective analysis was conducted on the clinical data of 70 patients with pancreatic cancer who underwent pancreaticoduodenectomy at the Department of Hepatobiliary Surgery of the First Affiliated Hospital of Air Force Medical University from March 2020 to August 2024, of which 31 were male and 39 were female, aged (58.5±6.8) years. Two groups were divided into two groups according to the occurrence of venous thromboembolism with abdominal infection in the postoperative period: the complication group ( n=28) and the non-complication group ( n=42). The general data, laboratory indexes and surgery-related indexes of the two groups were recorded, and logistic regression model was used to analyse the risk factors for the occurrence of venous thromboembolism with abdominal cavity infection after pancreaticoduodenectomy, and receiver operating characteristic (ROC) curve was used to assess the predictive value of the factors for the occurrence of venous thromboembolism with abdominal cavity infection after pancreatic cancer surgery. Results:Multifactorial analysis showed that the combination of diabetes mellitus ( OR=1.216, 95% CI: 1.225-1.657, P=0.035), occurrence of postoperative pancreatic fistula ( OR=1.292, 95% CI: 1.095-1.829, P=0.022), occurrence of postoperative biliary fistula ( OR=1.239, 95% CI: 1.171-2.618, P=0.005), high D-dimer ( OR=137.030, 95% CI: 3.214-5 843.128, P=0.010), prolonged operation time ( OR=1.058, 95% CI: 1.002-1.117, P=0.042), and low serum albumin ( OR=0.711, 95% CI: 0.508-0.995, P=0.046) in pancreatic cancer patients with a high risk of postoperative venous thromboembolism with the development of abdominal infection. ROC curve analysis showed that diabetes mellitus, pancreatic fistula, biliary fistula, D-dimer, duration of surgery, serum albumin, and co-prediction had certain predictive value for the occurrence of postoperative venous thromboembolism with abdominal infection after pancreatic cancer surgery, in which, the area under the curve for co-prediction in assessing the occurrence of postoperative venous thromboembolism with abdominal infection after pancreatic cancer surgery was 0.993 (95% CI: 0.982-1.000), with a sensitivity of 1.000 and a specificity of 0.929. Conclusion:Comorbid diabetes mellitus, pancreatic fistula, biliary fistula, D-dimer, prolonged duration of surgery, and serum albumin are independent risk factors for the development of postoperative venous thromboembolism with abdominal infection in patients with pancreatic cancer, all of which have diagnostic value and a high joint predictive value. The predictive value of co-prediction is relatively high.

Objective:To investigate the risk factors associated with the development of venous thromboembolism with abdominal infection following pancreatic cancer surgery.Methods:A retrospective analysis was conducted on the clinical data of 70 patients with pancreatic cancer who underwent pancreaticoduodenectomy at the Department of Hepatobiliary Surgery of the First Affiliated Hospital of Air Force Medical University from March 2020 to August 2024, of which 31 were male and 39 were female, aged (58.5±6.8) years. Two groups were divided into two groups according to the occurrence of venous thromboembolism with abdominal infection in the postoperative period: the complication group ( n=28) and the non-complication group ( n=42). The general data, laboratory indexes and surgery-related indexes of the two groups were recorded, and logistic regression model was used to analyse the risk factors for the occurrence of venous thromboembolism with abdominal cavity infection after pancreaticoduodenectomy, and receiver operating characteristic (ROC) curve was used to assess the predictive value of the factors for the occurrence of venous thromboembolism with abdominal cavity infection after pancreatic cancer surgery. Results:Multifactorial analysis showed that the combination of diabetes mellitus ( OR=1.216, 95% CI: 1.225-1.657, P=0.035), occurrence of postoperative pancreatic fistula ( OR=1.292, 95% CI: 1.095-1.829, P=0.022), occurrence of postoperative biliary fistula ( OR=1.239, 95% CI: 1.171-2.618, P=0.005), high D-dimer ( OR=137.030, 95% CI: 3.214-5 843.128, P=0.010), prolonged operation time ( OR=1.058, 95% CI: 1.002-1.117, P=0.042), and low serum albumin ( OR=0.711, 95% CI: 0.508-0.995, P=0.046) in pancreatic cancer patients with a high risk of postoperative venous thromboembolism with the development of abdominal infection. ROC curve analysis showed that diabetes mellitus, pancreatic fistula, biliary fistula, D-dimer, duration of surgery, serum albumin, and co-prediction had certain predictive value for the occurrence of postoperative venous thromboembolism with abdominal infection after pancreatic cancer surgery, in which, the area under the curve for co-prediction in assessing the occurrence of postoperative venous thromboembolism with abdominal infection after pancreatic cancer surgery was 0.993 (95% CI: 0.982-1.000), with a sensitivity of 1.000 and a specificity of 0.929. Conclusion:Comorbid diabetes mellitus, pancreatic fistula, biliary fistula, D-dimer, prolonged duration of surgery, and serum albumin are independent risk factors for the development of postoperative venous thromboembolism with abdominal infection in patients with pancreatic cancer, all of which have diagnostic value and a high joint predictive value. The predictive value of co-prediction is relatively high.

Prolonged disorders of consciousness(pDoC)are complex and prolonged conditions that severely impact patient prognosis and remain a clinical treatment challenge.In recent years,neural regulation-based awakening therapies have been widely applied in the assessment and treatment of pDoC patients.Repetitive transcranial magnetic stimulation(rTMS)technology can regulate neural activity and improve patients'consciousness states,demonstrating positive awakening effects for pDoC patients.However,the optimal stimulation parameters and awakening mechanisms of rTMS remain unclear.This article reviews the pathological mechanisms of pDoC,clinical applications of rTMS at different targeting sites and stimulation frequencies,and focuses on exploring how rTMS promotes consciousness recovery through neural mechanisms such as altering neural pathways,reshaping brain networks,promoting synaptic plasticity and neurotransmitter release,regulating neurotrophic factor expression,and modulating cerebral hemodynamics.Based on artificial intelligence,the article also prospects the future clinical research applications of rTMS.

Objective To observe the value of cervical lymph node ultrasound network model based on deep learning(DL)for diagnosing cervical lymph node metastasis of papillary thyroid carcinoma(PTC).Methods Totally 444 PTC patients with suspected cervical lymph node enlargement on ultrasonography were retrospectively enrolled and divided into metastasis group(n=253)and non-metastasis group(n=191)based on fine needle aspiration pathology and thyroglobulin detection.And 1 754 cervical lymph node ultrasonic images were divided into training set(n=1 404),validation set(n=175)and test set(n=175)at the ratio of 8∶1∶1.Ultrasonic features of cervical lymph nodes were extracted,then ultrasound network model was established using DL,and the diagnostic efficacy of this model for diagnosing lymph node metastasis was analyzed.Results The sensitivity,specificity,accuracy and area under the curve of ultrasound network model for diagnosing cervical lymph node metastasis of PTC was 93.06％,97.05％,95.04％and 0.858 in validation set,which was 86.14％,78.67％,88.49％and 0.828 in test set,respectively.Conclusion Cervical lymph node ultrasound network model based on DL was helpful for diagnosing cervical lymph node metastasis of PTC.

According to the recent WHO mortality statistics, suicide accounted for approximately 720 000 deaths worldwide in 2021, representing a critical public health challenge.The implementation of effective prevention strategies for suicide is essential for reducing the suicide rate which substantially contributes to the global burden of disease. This article systematically examines core intervention strategies from the WHO's suicide prevention implementation guidelines, alongside national suicide prevention policies and practical experiences from some representative countries. Drawing upon China's current suicide intervention context, tailored recommendations are proposed for a suicide prevention framework in China. Successful implementation requires a whole-of-society approach, characterized by coordinated cross-disciplinary collaboration and integrated community-based interventions. Professional workers in medical, educational, mental health and public health should assume leadership roles by leveraging their clinical expertise, thereby supporting the achievement of the mental health promotion targets outlined in the " Healthy China 2030" national health strategy, as well as reaching the target of reducing global suicide rates by 1/3 from 2015 to 2030, in accordance with the UN Sustainable Development Goals 2030.

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.

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a refractory condition characterized by chronic inflammation of the bladder wall, disruption of the urothelial barrier, and neural sensitization. Current therapies, such as oral pentosan polysulfate sodium (PPS) or intravesical hyaluronic acid instillations, offer limited efficacy due to transient effects and an inability to reverse tissue fibrosis. Platelet-rich plasma (PRP), a regenerative medicine approach, has demonstrated significant therapeutic potential in urological disorders through the synergistic actions of its multiple growth factors. This review summarizes the latest advances in PRP therapy for IC/BPS, revealing that the underlying mechanisms primarily involve the release of diverse growth factors, suppression of inflammatory responses, restoration of the urothelial barrier, and modulation of nerve axonal regeneration. Clinically, PRP therapy significantly alleviates symptoms including pelvic/bladder pain, urinary frequency, nocturia episodes, and improves patients'quality of life. Furthermore, it offers advantages such as convenient administration, a favorable safety profile, and strong feasibility, presenting new therapeutic methods and options for the clinical treatment of IC/BPS.

According to the recent WHO mortality statistics, suicide accounted for approximately 720 000 deaths worldwide in 2021, representing a critical public health challenge.The implementation of effective prevention strategies for suicide is essential for reducing the suicide rate which substantially contributes to the global burden of disease. This article systematically examines core intervention strategies from the WHO's suicide prevention implementation guidelines, alongside national suicide prevention policies and practical experiences from some representative countries. Drawing upon China's current suicide intervention context, tailored recommendations are proposed for a suicide prevention framework in China. Successful implementation requires a whole-of-society approach, characterized by coordinated cross-disciplinary collaboration and integrated community-based interventions. Professional workers in medical, educational, mental health and public health should assume leadership roles by leveraging their clinical expertise, thereby supporting the achievement of the mental health promotion targets outlined in the " Healthy China 2030" national health strategy, as well as reaching the target of reducing global suicide rates by 1/3 from 2015 to 2030, in accordance with the UN Sustainable Development Goals 2030.

Objective To observe the value of cervical lymph node ultrasound network model based on deep learning(DL)for diagnosing cervical lymph node metastasis of papillary thyroid carcinoma(PTC).Methods Totally 444 PTC patients with suspected cervical lymph node enlargement on ultrasonography were retrospectively enrolled and divided into metastasis group(n=253)and non-metastasis group(n=191)based on fine needle aspiration pathology and thyroglobulin detection.And 1 754 cervical lymph node ultrasonic images were divided into training set(n=1 404),validation set(n=175)and test set(n=175)at the ratio of 8∶1∶1.Ultrasonic features of cervical lymph nodes were extracted,then ultrasound network model was established using DL,and the diagnostic efficacy of this model for diagnosing lymph node metastasis was analyzed.Results The sensitivity,specificity,accuracy and area under the curve of ultrasound network model for diagnosing cervical lymph node metastasis of PTC was 93.06％,97.05％,95.04％and 0.858 in validation set,which was 86.14％,78.67％,88.49％and 0.828 in test set,respectively.Conclusion Cervical lymph node ultrasound network model based on DL was helpful for diagnosing cervical lymph node metastasis of PTC.