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.

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.

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 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.

ObjectiveTo investigate the acute effects of compound air pollution on children’s respiratory function. MethodsUsing panel group study design， 223 students in five classes of grade 4 from two primary schools （a， b） in Xuhui and Hongkou districts of Shanghai were randomly selected to measure pulmonary function and exhaled nitric oxide （FeNO）. The first three tests were carried out from May to June in 2020， and the fourth test was carried out from September to December in 2021. At the same time， the daily and hourly mean values of PM_2.5， PM₁₀， SO₂， NO₂， O₃ and CO was collected from the nearby air quality monitoring points of the two schools during the same period ， as well as meteorological monitoring data （temperature， humidity， wind speed and atmospheric pressure）. The linear mixed effect model was used to analyze the effects of air pollution on pulmonary function and respiratory inflammation in the summer. ResultsThe results of single pollutant model showed that PM_2.5， PM_10， SO₂ and NO₂ were positively correlated with FeNO， and the effect was reflected in lag0， lag1 and lag3 （P<0.05）. PM_2.5， PM₁₀ and NO₂ were negatively correlated with the changes of lung function FEF_25%， FEF_50%， FEF_75%， FeF_25%-75%， PEF， FVC， FEV1 and FEV1/FVC， and the effect was reflected in lag0 to lag3 days （P<0.05）. The results of the dual pollutant model showed that the concentration changes of SO₂ and NO₂ were significantly correlated with the decrease of FEV1 when combined with O₃ or PM_2.5 （P<0.01）， and the concentration changes of PM_2.5 was significantly correlated with the increase of FeNO when O₃， SO₂ and NO₂ were combined respectively （P<0.01）. The effects of the dual pollutant model were greater than the effect of PM_2.5 single pollutant model. ConclusionThe health effects of different air pollutants on children’s respiratory tract function indexes in summer are different. The combined effects of two pollutants on the lung function of children increased to different degrees. Although air pollution is light in summer， it still has an impact on children’s respiratory tract function index and inflammation index， and the combined effect of dual pollutants is more significant than that of single pollutant.

Humans ; Nephrotic Syndrome ; Hematopoietic Stem Cell Transplantation ; Tissue Donors ; Transplantation Conditioning ; Graft vs Host Disease

Child ; Humans ; Granulomatous Disease, Chronic/complications* ; Pneumonia

Objective:To evaluate the current status of alpha-fetoprotein (AFP) detection, a comparability analysis was conducted on the results measured by eight automated immunoassay systems, incorporating external quality assessment (EQA) data from the Beijing Center for Clinical Laboratories (BCCL) for the years 2020, 2021, and 2023.Methods:Methodological evaluation. Abbott Architect i2000, Beckman DxI 800, Roche Cobas E601, Diasorin Liaison XL, Maccura IS1200, Autolumo A2000, Leadman CI1000, and Mindray CL-2000i were used to detect 40 individual AFP serum samples that were collected from the laboratory of Beijing Chaoyang Hospital in 2019. The AFP results from eight different systems were compared with the median cohort. Passing-Bablok regression was used to evaluate the correlation between methods, and the concordance correlation coefficient was used to analyse the consistency between methods. Taking the optimal biological variability (±5.90%) as the criterion for bias evaluation, the bias between systems was evaluated using Bland-Altman analysis. The EQA results for AFP from BCCL over the past three years were statistically analysed to calculate the robust mean, robust coefficient of variation ( CV), and standard uncertainty within groups. The acceptance limit is based on the requirement of desirable biological variability (±21.87%) of allowable total error, and the pass rates were calculated for instrument or method groups, respectively. Results:The CVs of the eight detection systems were all≤1/3 allowable total error (±8.3%), passing the precision verification. The average relative biases between two detection systems (Roche Cobas E601 and Maccura IS1200) and the median cohort were>±5.90%, while the other six detection systems were<±5.90%. The eight detection systems showed good correlation and consistency with the median cohort (both R2 and concordance correlation coefficients>0.95). The results of EQA showed that there were no statistically significant differences in the robust means within each instrument or method group ( P>0.05). In the instrument group, except for Siemens and two other groups, the robust CVs of other groups were within 9%. The pass rates of most instruments and methods after being grouped were higher than the total pass rate, but that of the enzyme immunoassay chemiluminescence method was relatively low. Conclusions:The eight automated AFP immunoassay systems show a good correlation with the median cohort, and the consistency of AFP detection results is satisfactory among most detection systems. However, the comparability of AFP detection results for certain systems needs further improvement.