Background Due to the unique working environment and numerous occupational disease hazards, workers in mining industry are particularly susceptible to psychological problems such as occupational stress. Objective To understand the current status of occupational stress, depressive symptoms, anxiety symptoms and sleep quality of workers in ferrous and non-ferrous metal mining industry in Gansu Province, and to explore the effects of occupational stress on depressive symptoms, anxiety symptoms, and sleep. Methods From April to December 2022, the workers of 25 large, medium, and small and micro enterprises were selected by stratified cluster random sampling and surveyed in ferrous and non-ferrous metal mining industry in Gansu Province. The Occupational Health Literacy Questionnaire of National Key Population, Core Occupational Stress Scale, Patient Health Questionnaire-q, Generalized Anxiety Disorder, and Self-administer Sleep Questionnaire were used to collect basic information, occupational stress, depressive symptoms, anxiety symptoms, and sleep quality of the workers. Chi-square test was used to compare occupational stress, depressive symptoms, anxiety symptoms and sleep disorders among different categories. Logistic regression model was used to study the effects of occupational stress on depressive symptoms, anxiety symptoms, and sleep quality. Results In this study, 2192 workers in mining industry were surveyed, of which 2087 returned valid questionnaires, and the recovery rate of valid questionnaires was 95.2%. Among them, 728 (38.4%) reported occupational stress. There were statistically significant differences in the positive rate of occupational stress among workers by gender, age, marital status, education level, average monthly income, length of service, enterprise size, and night shifts (P<0.001). The positive rates of depressive symptoms, anxiety symptoms, and sleep disorders in the mining industry workers were 85.6%, 49.8%, and 47.3%, respectively. The positive rates of depressive symptoms, anxiety symptoms, and sleep disorders among workers with higher occupational stress were higher than those among workers without occupational stress (P<0.001). The logistic regression analysis showed that occupational stress was a common risk factor for depressive symptoms (OR=3.29, 95%CI: 2.26, 4.79), anxiety symptoms (OR=2.87, 95%CI: 2.33, 3.53), and sleep disorder (OR=1.78, 95%CI: 1.46, 2.16). In addition, length of service and enterprise-scale were also risk factors for depressive symptoms, anxiety symptoms, and sleep disorders. In the 5−<10 and 10−<20 years of service groups, the risks of reporting depressive symptoms were 1.80 (95%CI: 1.18, 2.74) and 1.73 (95%CI: 1.10, 2.71) times higher than that of workers with less than 1 year of service, respectively; the risks of reporting anxiety symptoms were 1.98 (95%CI: 1.42, 2.76) and 2.11 (95%CI: 1.48, 3.01) times higher than that with less than 1 year of service, respectively; the risks of reporting sleep disorders were 1.58 (95%CI: 1.15, 2.16) and 1.78 (95%CI: 1.29, 2.45) times higher than that with less than 1 year of service, respectively. Workers in large enterprises were inclined to report more depressive symptoms, anxiety symptoms, and sleep disturbances than those in micro and small enterprises (OR=3.53, 95%CI: 2.41, 5.16; OR=2.49, 95%CI: 1.98, 3.13; OR=2.10, 95%CI: 1.67, 2.62). Conclusion The occupational stress level of mining industry workers in Gansu Province is high, and reporting occupational stress, 5−<10 and 10−<20 working age groups, and large enterprises are important risk factors affecting workers' mental health and sleep. It is necessary to increase the attention and develop intervention programs for mental health of miners in the working age group of 5−20 years old and in large enterprises.

Traditional Chinese medicine (TCM) is an ancient medical system distinctive and effective in treating cancer, depression, coronavirus disease 2019 (COVID-19), and other diseases. However, the relatively abstract diagnostic methods of TCM lack objective measurement, and the complex mechanisms of action are difficult to comprehend, which hinders the application and internationalization of TCM. Recently, while breakthroughs have been made in utilizing methods such as network pharmacology and virtual screening for TCM research, the rise of machine learning (ML) has significantly enhanced their integration with TCM. This article introduces representative methodological cases in quality control, mechanism research, diagnosis, and treatment processes of TCM, revealing the potential applications of ML technology in TCM. Furthermore, the challenges faced by ML in TCM applications are summarized, and future directions are discussed.

Objective To develop a risk assessment scale for immune checkpoint inhibitor (ICI)-associated myocarditis based on multidisciplinary collaboration, and to evaluate its diagnostic performance. Methods Based on multidisciplinary cooperation, integrating clinical experience from oncology and cardiology, literature data, and patient conditions, a risk assessment scale for ICI-associated myocarditis was developed. A total of 101 patients with malignancies who received immunotherapy at Zhongshan Hospital, Fudan University, from October 2020 to October 2024 were included as the validation cohort. Patients were stratified into low-risk (0-1 point), medium-risk (2-4 points), and high-risk (≥5 points) groups based on their scale scores. The association between pretictive risk stratifications and actual assessment results was assessed using the Cox proportional hazards regression model. The predictive value of the scale for ICI-associated myocarditis was evaluated using receiver operating characteristic (ROC) curve. Agreement between the scale scores and actual assessment results was assessed using Cohen’s Kappa coefficient. Results Based on the scale pretictive results, 28(27.7%), 8(7.9%), 65(64.4%) patients were at low risk, medium risk, and high risk for ICI-related myocarditis, respectively; however, 46(45.5%), 8(7.9%), 47(46.5%) were at low risk, medium risk, and high risk actually. Kaplan-Meier survival analysis showed that the cumulative incidence of ICI-related myocarditis in the high-risk group was significantly higher than that in the medium- and low-risk groups (P＜0.05). In the multivariable-adjusted Cox proportional hazards model, the ICI-related myocarditis risk in high-risk group was about 4 times that in the low-risk group. ROC curve analysis demonstrated that the average area under the curve (AUC) for predicting ICI-related myocarditis was 0.81, with an accuracy of 0.74. The Cohen’s Kappa coefficient was 0.55, indicating moderate agreement. In the actual high-risk group, no patient was predicted to be at low risk; in the actual low-risk group, 16 patients were predicted to be at high risk. Conclusions This risk assessment scale for ICI-associated myocarditis shows high predictive performance. It provides oncologists with a simple yet effective multidisciplinary diagnostic reference tool, potentially enhancing early identification of ICI-associated myocarditis.

Objective A Nomogram model of cognitive frailty was constructed and validated in hospitalized older adults,providing a reference for early screening,intervention and personalized management of cognitive frailty.Methods A convenience sampling approach was employed to recruit 322 elderly inpatients from a tertiary hospital in Beijing between October 2024 and February 2025 as study participants,and data were collected using the General Information Questionnaire,the Short Form-Mini-Nutritional Assessment,the Asens Insomnia Scale,the Activity of Daily Living Rating,the Self-Rating Anxiety Scale,the Geriatric Depression Scale-15,the Social Support Rating Scale,the Frailty Phenotype scale,the Subjective Cognitive Decline Questionnaire-9,the Mini-Mental State Examination,and the Clinical Dementia Rating.Lasso-Logistic regression was used to screen the variables,R software was used to draw the nomogram model;Bootstrap method was used for internal validation.Results Lasso-Logistic regression screened 8 predictors of age,depression,anxiety,support utilization,nutritional status,literacy,physical activity,and chronic pain,with an area under the subject operating characteristic curve of 0.830(95％CI:0.787-0.873),a sensitivity of 0.764,a specificity of 0.730,an accuracy of 0.748,and a calibrated curve,Brier score,and Hosmer-Lemeshow test(P=0.774)all showed that the model fit was good.Conclusion The Lasso-Logistic regression-based nomogram model of cognitive frailty in hospitalized older adults has good predictive performance and clinical utility,and can be used as a reference for early identification and intervention of cognitive decline in hospitalized older adults.

Objective:To explore the risk factors for bronchiolitis obliterans (BO) after Mycoplasma pneumoniae bronchiolitis in children. Methods:A retrospective cohort study was conducted on 122 children diagnosed with Mycoplasma pneumoniae bronchiolitis in Department No.2 of Respiratory Medicine of Beijing Children′s Hospital, Capital Medical University, from March 2017 to December 2024. Clinical data, including general information, clinical manifestations, imaging findings, laboratory tests, and outcomes, were analyzed. Patients were divided into BO and non-BO groups based on the presence of BO. Differences between groups were assessed using Mann-Whitney U test, χ2 test, or Fisher exact test. Logistic regression and receiver operating characteristic (ROC) curve analysis were employed to identify risk factors and evaluate predictive performance. Results:Among 122 children (73 males, 49 females), the age at onset was 5.0 (2.4, 7.1) years. The BO group included 21 patients, and the non-BO group 101. The BO group exhibited significantly longer durations of persistent high fever and higher peak levels of C-reactive protein, lactate dehydrogenase, and D-dimer compared to the non-BO group (9 (7, 11) vs. 4 (2, 6) d, 19 (7, 35) vs. 10 (7, 18) mg/L, 438 (337, 498) vs. 315 (274, 351) U/L, 0.36 (0.27, 0.91) vs. 0.21 (0.15, 0.29) mg/L, U=295.00, 743.50, 463.50, 470.50, all P<0.05). The BO group also had higher proportions of resting oxygen saturation <0.95 on room air (100.0% (21/21) vs. 43.6% (44/101)), inspiratory retractions (57.1% (12/21) vs. 18.8% (19/101), χ2=11.53), and adenovirus co-infection (38.1% (8/21) vs. 5.0% (5/101)) (all P<0.05). Multivariate Logistic regression identified prolonged high fever ( OR=1.83, 95% CI 1.31-2.58, P<0.001), inspiratory retractions ( OR=10.48, 95% CI 1.72-63.85, P=0.011), and adenovirus co-infection ( OR=42.47, 95% CI 4.04-446.87, P=0.002) as independent risk factors for BO. ROC curve analysis revealed that a fever duration cutoff of 7.5 days predicted BO with 0.71 sensitivity and 0.92 specificity. Conclusions:Prolonged high fever (≥7.5 days), inspiratory retractions, and adenovirus co-infection are significant predictors of BO after Mycoplasma pneumoniae bronchiolitis in children, which are helpful for early clinical identification.

Objective:To evaluate the clinical efficacy of TCM acupoint application combined with Fuyang cupping in the treatment of chronic bronchitis with lung and kidney yin deficiency syndrome.Methods:A randomized controlled trial study was conducted. From July 2022 to July 2024, 96 patients with chronic bronchitis in our hospital were selected as the observation subjects and divided into two groups using a random number table method, with 48 cases in each group. The control group received treatment with Fuyang cupping, while the study group received treatment with Fuyang cupping and acupoint application. Both groups were treated continuously for 14 days. TCM syndrome scores were performed before and after treatment, and serum TNF-α, CRP, and procalcitonin (PCT) levels were detected using ELISA. Arterial oxygen partial pressure (PaO 2) and carbon dioxide partial pressure (PaCO 2) were measured using a blood gas analyzer. FVC, FEV1, and FEV1 percentage of predicted values (FEV1% predicted value) and maximum expiratory flow (PEF) were measured using a pulmonary function analyzer. The relief time of clinical symptoms such as cough, sputum production, and wheezing during the treatment period were observed and recorded, and the clinical efficacy was evaluated. Results:The total effective rate of the research group was 95.83% (46/48), while that of the control group was 79.16% (38/48), with statistical significance ( χ2=6.10, P=0.013). After treatment, the scores of cough, sputum, wheezing, wheezing, and dry throat and mouth in the study group were lower than those in the control group ( t values were 4.86, 5.89, 5.23, 4.10, 5.49, respectively, P<0.01); PaO 2 level [(95.63 ± 6.45) mmHg vs. (88.44 ± 5.76) mmHg, t=5.76] was higher than of the control group ( P<0.01), and the PaCO 2 level [(40.12 ± 4.28) mmHg vs. (45.63 ± 4.36) mmHg, t=6.25] was lower than of the control group ( P<0.01); the levels of FEV1 [(2.64 ± 0.69) L vs. (1.97 ± 0.74) L, t=4.59], FEV1% predicted value (79.25 ± 11.27 vs. 62.55 ± 10.56, t=7.49), FVC [(3.05 ± 1.04) L vs. (2.06 ± 0.96) L, t=4.85], and PEF [(241.54 ± 19.85) L/min vs. (221.36 ± 19.65) L/min, t=5.01] were higher than those in the control group ( P<0.01); the levels of TNF-α, CRP and PCT in the serum were lower than those in the control group ( t values were 4.39, 6.51 and 18.59, respectively, P<0.001). Conclusion:TCM acupoint application combined with Fuyang cupping can effectively improve the TCM syndrome score and lung function, inhibit inflammatory factor levels, and improve clinical efficacy in chronic bronchitis patients with lung and kidney yin deficiency syndrome.

Inflammatory bowel disease (IBD) is an autoimmune disorder involving complex immune regulation, where balancing localized and systemic immunosuppression is a key challenge. This study aimed to enhance the therapeutic efficacy by engineering the probiotic Escherichia coli Nissle 1917 (EcN). We removed endogenous plasmids pMUT1 and pMUT2 from wild-type EcN and expressed the mPD-L1 (19‒238 aa)-mFc fusion protein on the bacterial surface using a cytolysin A (ClyA) fragment. This modification stabilized mPD-L1 (19‒238 aa) protein expression and promoted its recruitment to outer membrane vesicles (OMVs). The engineered strain, EcNΔ_pMUT1/2-ClyA-mPD-L1-mFc (EcN-ePD-L1-mFc), features conditional ePD-L1-mFc expression under the araBAD promoter, enhancing gut-targeted release and reducing systemic side effects. This strain improved treatment targeting and efficiency by enabling direct ePD-L1-mFc interaction with immune cells at inflammation sites. OMVs from this strain induced Treg proliferation, inhibited effector T cell proliferation in vitro, and significantly improved intestinal inflammation and colonic epithelial barrier repair in vivo. Additionally, the bacterium restored intestinal microbiota balance, increasing Lactobacillaceae and reducing Bacteroides. This study highlights the engineered bacterium's potential for targeted intestinal immune modulation and offers a novel local IBD treatment approach with promising clinical prospects.

Traditional Chinese medicine(TCM)is an ancient medical system distinctive and effective in treating cancer,depression,coronavirus disease 2019(COVID-19),and other diseases.However,the relatively abstract diagnostic methods of TCM lack objective measurement,and the complex mechanisms of action are difficult to comprehend,which hinders the application and internationalization of TCM.Recently,while breakthroughs have been made in utilizing methods such as network pharmacology and virtual screening for TCM research,the rise of machine learning(ML)has significantly enhanced their inte-gration with TCM.This article introduces representative methodological cases in quality control,mechanism research,diagnosis,and treatment processes of TCM,revealing the potential applications of ML technology in TCM.Furthermore,the challenges faced by ML in TCM applications are summarized,and future directions are discussed.