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 construct a multivariable prediction model for assessing the risk of cardiopulmonary complication after thoracoscopic lobectomy in patients with stage Ⅰ-Ⅱ non-small cell lung cancer(NSCLC).Methods Clinical data of 600 patients with stage Ⅰ-Ⅱ NSCLC who underwent thoracoscopic lobectomy were retrospectively analyzed.Patients were divided into the complication group(84 cases)and the non-complication group(516 cases)based on the occurrence of postoperative complication within 7 days,including atelectasis,pulmonary embolism,respiratory failure,chylothorax,massive pleural effusion,hypoxemia and atrial fibrillation.Demographic characteristics,preoperative pulmonary function assessment,pathological features and perioperative indicators were collected.Multivariate Logistic regression analysis was used to identify independent risk factors influencing postoperative cardiopulmonary complication in patients and construct a predictive model.Internal validation was performed using the Bootstrap resampling method(1 000 iterations)to evaluate the discrimination,calibration and clinical decision-making value of the model.Results Multivariate Logistic regression analysis identified the following independent risk factors of postoperative cardiopulmonary complication,including age(OR=1.832,95%CI:1.537-2.183),history of chronic obstructive pulmonary disease(COPD)(OR=6.782,95%CI:2.685-17.130),Karnofsky performance status(KPS)score(OR=0.926,95%CI:0.888-0.965),the percentage of forced expiratory volume in the first second to the predictive value(FEV1%pred)(OR=0.906,95%CI:0.845-0.972),the percentage of diffusing capacity for carbon monoxide to the expected value(DLCO%pred)(OR=0.901,95%CI:0.832-0.975),intraoperative blood loss(OR=1.025,95%CI:1.014-1.036)and one-lung ventilation time(OR=1.057,95%CI:1.034-1.080).The area under the curve(AUC)of the combined diagnosis was 0.977(95%CI:0.965-0.989),with 96.4%sensitivity and 87.6%specificity.The Hosmer-Lemeshow test indicated excellent calibration(χ2=1.285,P=0.994).Decision curve analysis demonstrated significant clinical net benefit when the risk threshold probability ranged between 20%and 98%.Conclusion The multivariable prediction model for cardiopulmonary complication after thoracoscopic lobectomy in stage Ⅰ-Ⅱ NSCLC patients exhibits strong predictive performance.

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.

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.

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.

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.

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.

Objective:To evaluate the efficacy of the combination of acupuncture and meridian acupoint massage based on theory of meridian and tendon for the treatment of elderly patients with lumbar disc herniation (LDH).Methods:A randomized controlled trial study was carried out. Totally 102 elderly patients with LDH qi stagnation and blood stasis syndrome in our hospital from October 2021 to October 2024 were selected as the observation subjects. They were divided into two groups using computer drawing method, with 51 cases in each group. The control group was treated with conventional therapy+meridian acupoint massage, while the observation group was treated with conventional therapy+meridian acupoint massage+ acupuncture based on the theory of meridian and tendon. Both groups received continuous treatment for 4 weeks. TCM syndrome scoring before and after treatment was performed. A lumbar rotation detection training device was used to detect lumbar flexion, extension, flexion, and rotation range of motion; Enzyme immunoassay was used to detect the levels of prostaglandin E 2 (PGE 2), MMP-2, and IL-6; the recovery of lumbar spine function was evaluated using VAS, Oswestry Disability Index (ODI), and Japan Orthopedic Association (JOA) score; adverse reactions during treatment were recorded to assess clinical efficacy. Results:The total effective rate of the observation group was 92.16% (47/51), while that of the control group was 76.47% (39/51), with statistical significance ( χ2=4.74, P=0.029). After treatment, the observation group had lower scores and total scores for lower back pain, lower back stiffness, fatigue, and tongue purple dullness compared to the control group ( t values were 3.83, 4.28, 4.17, 4.85, 5.46, respectively, P<0.01); Lumbar flexion [(82.13 ± 7.91)° vs. (76.32 ± 7.09)°, t=3.91]; posterior extension [(39.15 ± 5.16)° vs. (34.02 ± 5.03)°, t=5.08], bending [(158.14 ± 14.19)° vs. (142.29 ± 13.31)°, t=5.82], the activity of rotation [(39.04 ± 4.79)° vs. (34.82 ± 4.71)°, t=4.49] were higher than those in the control group ( P<0.01), and the plasma levels of PGE 2, MMP-2, and IL-6 were lower than those in the control group ( t values were 4.60, 4.56, and 4.26, respectively, P<0.01); VAS and ODI scores were lower than those in the control group ( t values were 6.52 and 5.33, respectively, P<0.01); the JOA score was higher than that of the control group ( t=4.94, P<0.01). During the treatment period, the incidence of adverse reactions in the observation group was 9.80% (5/51), while in the control group it was 5.88% (3/51), without statistical significance ( χ2=0.54, P=0.461). Conclusion:The combination of acupuncture and meridian acupoint massage based on theory of meridian and tendon can increase lumbar spine mobility, reduce inflammatory factor levels, improve clinical symptoms, enhance efficacy, and have good treatment safety in elderly LDH patients.