Background Work-related musculoskeletal disorders (WMSDs) are a major occupational health concern, particularly among workers exposed to adverse ergonomic conditions. Manganese production involves heavy physical demands, yet research on WMSDs among manganese workers remains limited. Objective To investigate the prevalence and influencing factors of WMSDs among manganese workers in a manganese enterprise in Guangxi. Methods A cross-sectional survey was conducted from May to June 2024 on workers at a manganese factory in Guangxi. The Chinese Musculoskeletal Disorders Questionnaire was used to collect information on demographic characteristics, distribution of musculoskeletal symptoms, and work-related exposures. χ² test was applied to compare differences in positive WMSDs rates across groups, and logistic regression analysis was performed to identify associated factors. Results A total of 1476 workers were enrolled in the study after pre-determined inclusion and exclusion criteria. The overall prevalence of WMSDs was 34.15%. The most commonly affected body regions were the lower back (17.28%), neck (16.67%), and shoulders (13.82%). The results of logistic regression analysis indicated that female, older age, and education level of college or above were associated with a higher risk of WMSDs (P<0.05). Awkward working postures were significantly associated with WMSDs in corresponding body regions; in particular, awkward postures of the neck, upper limbs, trunk, and lower limbs were related to an increased risk of WMSDs in multiple body sites (P<0.05). In addition, poor lighting conditions, high workplace temperature, frequent or sustained arm support during work, and high job demands were associated with an increased risk of overall or site-specific WMSDs (P<0.05). Conclusion The high prevalence of WMSDs among manganese workers is closely associated with demographic characteristics, working postures, and work environment and organizational factors. Targeted ergonomic interventions focusing on high-risk body regions and key ergonomic exposures are warranted to reduce the risk of WMSDs among manganese workers.

Objective To investigate the role of activating transcription factor 4 (ATF4) in benign prostatic hyperplasia (BPH) in order to elucidate the molecular mechanism underlying BPH progression. Methods Firstly, we analyzed the correlation between the expression of ATF4 and the volume of prostate transition zone in BPH tissues using publicly available datasets. Subsequently, we explored the potential association among ATF4, epithelial-mesenchymal transition (EMT) and BPH through single-cell RNA sequencing (scRNA-seq). Finally, we validated the findings by silencing ATF4 and treating BPH cells with ATF4 protein inhibitors, followed by reverse transcription quantitative polymerase chain reaction (RT-qPCR), CCK-8 assay, and Western blot. Results The expression of ATF4 in BPH tissues exhibited a significant positive correlation with the volume of prostate transition zone (r=0.64, P＜0.05). scRNA-seq analysis revealed that ATF4 was significantly upregulated in the prostate epithelial cells of BPH patients (P＜0.05), and its expression demonstrated a significant positive correlation with the Hallmark EMT singscore (r=0.14, P＜0.05). In vitro experiments further indicated that knockdown of ATF4 led to a significant reduction in the proliferative activity of BPH-1 cells (P＜0.05). Similarly, inhibition of ATF4 activity resulted in a marked decrease in the proliferative capacity and EMT of BPH-1 cells (P＜0.05). Conclusion ATF4 may exert a promoting effect on BPH by modulating the EMT signaling pathway;therefore, ATF4 may serve as a novel therapeutic target of BPH.

Background Long-term occupational health surveillance facilitates the identification of chronic health risks arising from occupational hazard exposures. However, under low-level exposure conditions in chemical enterprises, traditional workplace-centered risk assessment models are insufficient to fully explain the observed health effects among workers, highlighting the need for individual-focused health management strategies that focus on the worker. Objective To characterize long-term health trends among workers in a chemical enterprise and examine the limitations of workplace hazard monitoring and health surveillance under low-level multiple exposure scenarios, thereby providing a scientific basis for more comprehensive worker health protection measures. Methods We analyzed 972 occupational health examination records collected over a 15-year period (2010–2024) from a chemical enterprise, integrating these with enterprise hazard monitoring reports and workshop air monitoring of volatile organic compounds (VOCs). The annual counts and prevalences of abnormalities in liver function tests, metabolic syndrome-related indicators, and liver imaging were summarized. Temporal trends were evaluated using the Cochran-Armitage trend test. Time-related changes in abnormal health indicators among active workers were evaluated by generalized linear mixed-effects models (GLMMs), adjusting for age and sex. Individual longitudinal profiles were described for nine workers with continuous 15-year records. In addition, paired comparisons of health outcomes were conducted for 81 workers with both pre-employment and periodic examinations. Results Workplace hazard monitoring indicated that airborne concentrations of major occupational contaminants including VOCs, acids and bases, dust, and physical factors were below national occupational exposure limits. However, 62 VOC species—including chlorobenzene, chloromethane, and cyclohexane—were detected in workshop air, suggesting complex mixed organic solvent exposure. From 2010 to 2015, occupational health examination records were primarily based on physicians’ qualitative assessment, yielding generally low abnormality detection rates. During 2016–2024, data collection was complete, and the prevalences of abnormalities in liver function, metabolic syndrome-related indicators, and liver imaging among active workers remained relatively high. Trend analysis indicated that only the abnormality rates of total bilirubin (TBIL) and hypertension showed statistically significant temporal trends; other indicators exhibited no significant linear changes despite high prevalences. After adjusting for age and sex, the GLMMs showed that the abnormality rates of aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), TBIL, and hypertension decreased over time, whereas those of total cholesterol, triglycerides, and fatty liver increased. In a subgroup of 9 workers with continuous 15-year follow-up data, the cumulative number of abnormal health indicators increased in several individuals after 2016. Among the 81 workers with both pre-employment and periodic records, abnormalities in total cholesterol, triglycerides, body mass index, and fatty liver were significantly more prevalent after employment over time, whereas changes in liver function enzymes were not statistically significant. Conclusion With increasing age, workers exhibit a gradual deterioration in liver function and metabolic-related health indicators, reaching abnormality rates comparable to those reported in the general population. These findings suggest that both workplace-related and non-occupational factors jointly contribute to the observed health changes. Consequently, occupational health management should shift towards an "individual-centered" approach. Enhancing workers' health literacy, strengthening workplace health promotion, and extending occupational health services beyond the workplace are essential to comprehensively protect workers' health.

Objective: To explore the longitudinal associations between dietary macronutrients and lung function in schoolaged children, so as to provide the nutritional research evidence for promoting children s lung health. Methods: In November 2021, two primary schools located in Chengdu, Sichuan Province were selected from the Southwest China Childhood Nutrition and Growth (SCCNG) cohort by a stratified cluster random sampling method, enrolling a total of 1 112 school aged children aged 8 to 13 years. At baseline, the dietary and sociodemographic characteristics of the children were assessed. One year later, the forced vital capacity (FVC) of the children was measured and converted into Z scores (FVC- Z ), while the vital capacity index (VCI) was also calculated. Generalized linear regression analysis was employed to examine the associations between dietary macronutrients and lung function, considering interactions with gender and age, followed by stratified analysis. Results: After adjusting for confounding factors, the analysis results of the generalized linear regression model showed that the carbohydrate energy ratio was negatively correlated with FVC- Z ( β =-0.02) and VCI ( β =-0.16), while the fat energy ratio showed a positive correlation with FVC- Z ( β =0.03) and VCI ( β =0.23) ( P <0.05). The protein energy ratio was positively correlated with FVC- Z ( β =0.09) and VCI ( β =0.60) specifically in girls ( P <0.05). Additionally, there was an interaction effect of age on the associations between macronutrients and lung function ( P <0.01); in children aged 8-9 and 10-11, the carbohydrate energy supply ratio was negatively correlated with FVC- Z ( β =-0.04, -0.03) and VCI ( β =-0.29, -0.21), and fat energy supply ratio was positively correlated with FVC- Z ( β =0.07, 0.05) and VCI ( β =0.46, 0.32) ( P <0.05). Conclusions There are age and sex differences in the association of dietary macronutrients with lung function, with a low carbohydrate, high fat diet promoting lung function in children. Additionally, protein intake appears to have a positive influence on the lung function of girls. The early school age period may represent a critical window for dietary interventions aimed at promoting lung health.

This comprehensive review systematically explores the multifaceted applications, inherent challenges, and promising future directions of artificial intelligence (AI) within the medical domain. It meticulously examines AI's specific contributions to basic medical research, disease prevention, intelligent diagnosis, treatment, rehabilitation, nursing, and health management. Furthermore, the review delves into AI's innovative practices and pivotal roles in clinical trials, hospital administration, medical education, as well as the realms of medical ethics and policy formulation. Notably, the review identifies several key challenges confronting AI in healthcare, encompassing issues such as inadequate algorithm transparency, data privacy concerns, absent regulatory standards, and incomplete risk assessment frameworks. Looking ahead, the future trajectory of AI in healthcare encompasses enhancing algorithm interpretability, propelling generative AI applications, establishing robust data-sharing mechanisms, refining regulatory policies and standards, nurturing interdisciplinary talent, fostering collaboration among industry, academia, and medical institutions, and advancing inclusive, personalized precision medicine. Emphasizing the synergy between AI and emerging technologies like 5G, big data, and cloud computing, this review anticipates a new era of intelligent collaboration and inclusive sharing in healthcare. Through a multidimensional analysis, it presents a holistic overview of AI's medical applications and development prospects, catering to researchers, practitioners, and policymakers in the healthcare sector. Ultimately, this review aims to catalyze the deep integration and innovative deployment of AI technology in healthcare, thereby driving the sustainable advancement of smart healthcare.

BackgroundObsessive-compulsive disorder （OCD） is a common neuropsychiatric illness and is listed as one of the top ten disabling conditions causing loss of income and reduced quality of life. Psychological distress is an important cause of anhedonia in OCD patients， and is closely related to psychosomatic symptoms. Therefore， exploring the role of anhedonia in the relationship between psychological distress and psychosomatic symptoms is of great significance for optimizing clinical psychological treatment protocols for OCD patients. ObjectiveTo explore the role of anhedonia in the relationship between psychological distress and psychosomatic symptoms in OCD patients， with the aim of providing references for managing psychosomatic symptoms in patients. MethodsA total of 90 patients who met the diagnostic criteria for OCD according to the International Classification of Diseases， tenth edition （ICD-10）， and who visited the Mental Health Center outpatient clinic of General Hospital of Ningxia Medical University from September 2023 to November 2024 were selected as the study objects. The instruments and techniques used for the evaluation were： Dimensional Anhedonia Rating Scale （DARS）， 10-item Kessler Psychological Distress Scale （K10） and Psychosomatic Symptom Scale （PSSS）. Model 4 of the Process for SPSS 26.0 was used to test the mediating role of anhedonia in the relationship between psychological distress and psychosomatic symptoms， with Bootstrapping used to assess the significance of mediating effect. ResultsA total of 84 patients （93.33%） completed the valid questionnaire. K10 score was positively correlated with PSSS total score， psychological symptom score and physical symptom score （r=0.559， 0.460， 0.551， P<0.01）. K10 score was negatively correlated with DARS total score （r=-0.527， P<0.01）. The total score of DARS was negatively correlated with PSSS total score （r=-0.497， P<0.01）. Anhedonia mediated the relationship between psychological distress and psychosomatic symptoms， with an indirect effect value was 0.148 （95% CI： 0.042~0.278）， accounting for 26.48% of the total effect. ConclusionPsychological distress can affect the psychosomatic symptoms in OCD patients both directly and indirectly via anhedonia.

Objective To investigate the effects of liraglutide on the gut microbiota of db/db diabetic mice and the predictive value of the microbial structure for the hypoglycemic efficacy.Methods The db/db mice were randomly divided into control group and liraglutide group(0.4 μg/g).Fasting blood glucose levels were measured in the mice,and fecal samples were collected to determine the structure of the gut microbiota.Results Compared with the control group,the liraglutide group exhibi-ted a significant increase in the average number of operational taxonomic unit(OTU)(P＜0.01).The number of observed species,Chao1 index and ACE index were all significantly higher in the liraglutide group than those in the control group(P＜0.05).Additionally,the liraglutide group showed a signifi-cant increase in the relative abundance of Firmicutes and a significant decrease in the abundance of Actinobacteria compared with the control group(P＜0.01).The percentage decrease in blood glucose levels in db/db mice was positively correlated with the abundance of Pseudomonadaceae and negatively correlated with the abundances of Clostridiaceae and Peptostreptococcaceae.Conclusion Liraglutide treatment can modulate the structure of the gut microbiota,increasing the number of OTU and diversity,particularly enhancing the abundance of beneficial bacteria such as Clostridium,Lachnospira and Oscillospira.The gut microbiota structure in mice serves as a predictive factor for the hypoglycemic efficacy of liraglutide,with diabetic mice exhibiting a higher abundance of Pseudomonas being more likely to benefit from liraglutide-induced hypoglycemic therapy.

ObjectiveTo investigate the chemical hazards in the production line of lithium batteries, so as to provide a scientific basis for the management of occupational-health risk and to promote the healthy and sustainable development of the lithium battery industry. MethodsAn on-site survey on the process flow of the production of lithium battery was conducted in an enterprise. Volatile organic compounds (VOCs) in the occupational environment were collected by Summa canisters, carbonates and N-methyl pyrrolidone (NMP) were collected using activated carbon tubes, and airborne metals were collected using filter membranes. VOCs, carbonates and NMP were detected by gas chromatography-mass spectrometry (GC-MS), and airborne metal elements in the dust samples were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). ResultsNon-targeted environmental monitoring results indicated that NMP was detected in the negative /positive electrode coating, assembly and drying filling workstations, dimethyl carbonate (DMC) was detected in the assembly, drying and electrolyte injection workstations, and ethyl methyl carbonate (EMC) was detected solely in the electrolyte injection workstation. Semi-quantitative analyses of VOCs identified 136 pollutants, including acrylonitrile and halohydrocarbons. Quantitative targeted environmental monitoring results revealed the highest geometric mean (GM) concentration of EMC (31.450 mg·m^-3) was found in the assembly and drying workstations, diethyl carbonate (DEC) was detected in all workstations. While vinylene carbonate (VC) and ethylene carbonate (EC) were detected only in electrolyte injection, assembly and drying workstations. NMP was detected in all positive electrode coating samples, with a GM concentration of 5.68 mg·m^-3 (concentration range: 4.0‒ 7.4 mg·m^-³). Lithium was exclusively detected in dust samples from the liquid injection workstation (GM: 0.014 μg·m^-³). ConclusionNMP, EMC, DEC, and other chemicals are identified at the key workstations such as the positive electrode coating, electrolyte injection, assembly and drying in the lithium production line. Furthermore, semi-quantitative VOCs analyses identified 136 pollutants, demonstrating a characteristic of multicomponent chemical exposure.

Objective:To evaluate the clinical utility of machine learning model based radiomics in predicting high-risk molecular subtypes of lower-grade gliomas(LrGGs).Methods:This was a cross-sectional study. A total of 287 patients diagnosed with LrGGs in the First Hospital of Shanxi Medical University, Shanxi Provincial People′s Hospital, and the Third Hospital of Shanxi Medical University from January 2011 to September 2023 were retrospectively collected, including 166 males and 121 females; 114 cases of high-risk molecular subtypes and 173 cases of non-high-risk molecular subtypes. All patients were divided into 201 cases in the training set and 86 cases in the test set according to 7∶3 in simple randomized grouping method. All patients underwent contrast-enhanced T 1WI (CE-T 1WI) and T 2-weighted fluid-attenuated inversion recovery sequence imaging (T 2-FLAIR), and the imaging features of high-risk and non-high-risk molecular subtypes were analyzed. Analysis of variance, recursive feature elimination, and Kruskal-Wallis were used for radiomics feature screening, and a support vector machine (SVM) classifier was used to construct a radiomics-based classifier model. Univariate and multivariate logistic regression were used to analyze clinical variables independently influencing high-risk molecular subtypes of LrGGs to construct a clinical model; a combined model was developed by integrating radiomics labels and clinical variables. Receiver operating characteristic curve and area under the curve (AUC), calibration curve, and decision curve were used to compare the predictive performance of different models. Results:The patient′s age ( OR=1.042, 95% CI 1.018-1.068, P=0.001), pathological grade ( OR=2.270, 95% CI 1.212-4.311, P=0.011), MGMT methylation status ( OR=0.456, 95% CI 0.238-0.866, P=0.017), and ependymal involvement ( OR=7.335, 95% CI 2.929-18.370, P<0.001) were independent influencing factors for the high-risk molecular subtype of LrGGs, and a clinical model was developed based on these factors. An SVM model was constructed based on 12 radiomics features (3 radiomics features based on CE-T 1WI and 9 radiomics features based on T 2-FLAIR). The radiomics score of the probability output by the SVM model was combined with age, pathological grade, MGMT methylation status, and ependymal involvement to develop a combined model. The AUC values of the SVM model for predicting the high-risk molecular subtype of LrGGs were 0.824 and 0.859 in the training set and test set, respectively; the AUC values of the clinical model in the training set and test set were 0.759 and 0.721, respectively; and the AUC values of the combined model in the training set and test set were 0.823 and 0.815, respectively. The combined model had a high clinical net benefit. Conclusion:The machine learning MRI radiomics model can preoperatively predict high risk molecular subtypes of LGGrs, assist in individualized treatment decisions.

Objective:To explore the clinical features, histopathological morphology, and differential diagnosis of lymphoepithelioma-like carcinoma with abnormal expression of follicular dendritic cell markers.Methods:From 2020 to 2021, 4 cases of lymphoepithelioma-like carcinoma with abnormal expression of follicular dendritic cell markers diagnosed in Fujian Cancer Hospital (2 cases) and the Second Affiliated Hospital of Fujian Medical University (2 cases) were collected. Different ancillary procedures such as HE, special stains, immunohistochemistry, and in situ hybridization techniques were used to assess the histopathological features and immunophenotypes. The clinical data were collected and literature was reviewed.Results:All 4 cases of lymphoepithelioma-like carcinoma with abnormal expression of follicular dendritic cell markers were male. They were 32, 45, 67 and 39 years old, respectively. The main clinical manifestations were bloody phlegm, abdominal pain, fatigue and anorexia. The clinical stages at diagnosis were stage Ⅳ (3 cases) and stage Ⅱ (1 case). Cases 2 and 3 had two pathological examinations at different sites, with a total of six pathological examinations. The histomorphology showed singly scattered or nests of tumor cells in a background of abundant small lymphocytes. The tumor cells were enlarged and pleomorphic, some appeared polygonal with inconspicuous cell borders, and they were arranged in a syncytial pattern. There were megakaryocytes, multinucleated tumor cells, and a few spindle-shaped cells seen. Atypical mitosis was commonly noted. By immunohistochemistry, the tumor cells were positive for CKpan(5/6), CK8/18(4/4), CAM5.2(2/5), CK-H(0/4), CK-L(3/4), EMA(4/5), CK5/6(3/6), p63(1/6), p40(1/6), E-cadherin (4/6), SSTR2(6/6), PD-L1(5/5), LCA(0/6), vimentin(5/6), CD2 (6/6), CD23(6/6), CD35(5/6), CXCL-13(4/5) and D2-40(1/5). The Ki-67 proliferative index was 60%-95%. In situ hybridization for EBER were all positive (6/6). Special stain for reticulin showed positive staining surrounding nests of tumor cells.Conclusions:The expression of follicular dendritic cell markers in lymphoepithelioma-like carcinoma is very rare, which may be related to EBV infection. Occasionally, it can overlap with follicular dendritic cell sarcoma by morphology and immunophenotype, which can lead to misdiagnosis. Only by combining clinical information, morphological characteristics and immunophenotype can an appropriate diagnosis be made.