Background Under intensive dairy farming conditions, Enterococcus spp. can be transmitted between animals, farm workers, and the environment via multiple vectors such as feces, soil, water, air, and farming equipment, posing a potential threat to public health. Objective To elucidate the prevalence, distribution, and antimicrobial resistance profiles of Enterococcus faecalis (E. faecalis) and Enterococcus faecium (E. faecium) among farm workers, dairy cattle, and the farm environment in Xinjiang, and to assess the risk of their cross-host transmission. Methods From May 2024 to January 2025, a total of 317 samples were collected from 11 large-scale dairy farms in Xinjiang, China, including feces from farm workers (n=130) and dairy cattle (n=154), and environmental samples (n=33). E. faecalis and E. faecium were isolated and identified, followed by antimicrobial susceptibility testing and multilocus sequence typing (MLST) to analyze their molecular characteristics. Results A total of 183 Enterococcus isolates were obtained (66 E. faecalis and 117 E. faecium isolated). The isolation rates of both species showed statistically significant differences among the three sources (χ²=29.21, P=0.003). Antimicrobial resistance analysis revealed that E. faecalis generally exhibited higher resistance rates across multiple antibiotic classes than E. faecium. High resistance to rifampicin was observed across all sources (50.00%–81.25%), with statistical variation among origins (χ²=8.03, P=0.024). Multidrug-resistant strains accounted for 69.10% of the isolates. Multidrug resistance patterns in E. faecium varied significantly by source (χ²=27.19, P=0.014), and one isolate displayed resistance to eight antibiotic classes. MLST indicated high genetic diversity; E. faecalis was dominated by ST472 and ST227 of which the distrubution was significantly different among sources, while E. faecium primarily clustered into clonal complexes CC94 (centered on ST94) and CC17 (centered on ST22). Conclusion Resistant Enterococcus strains exhibit cross-transmission among farm workers, animals, and the environment. Under the "One Health" framework, standardized farming protocols and prudent antimicrobial use are essential to disrupt the transmission chain of resistant clones and mitigate the spread of antimicrobial resistance at its source.

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.

With industrial restruction, workplace transformation, and the emergence of new materials, technologies, and processes, mental health issues among working population have become a pressing concern in occupational health. Digital interventions, supported by their accessibility and personalization capabilities, may offer a promising solution to this challenge. This article reviewed key concepts and the developmental history of digital interventions and occupational health psychology, and systematically examined representative intervention approaches and their effectiveness in addressing workplace mental health concerns. It further explored the challenges and corresponding strategies in this field, including strengthening the evidence-based foundation, integrating theoretical models, incorporating organizational and ecological factors, and addressing technological issues. In the future, strengthening interdisciplinary and cross-sector collaboration should be enhanced to optimize and promote the application of digital mental health services for working population, thereby promoting workplace well-being and advancing the sustainable development of digital interventions.

Background Under intensive dairy farming conditions, Enterococcus spp. can be transmitted between animals, farm workers, and the environment via multiple vectors such as feces, soil, water, air, and farming equipment, posing a potential threat to public health. Objective To elucidate the prevalence, distribution, and antimicrobial resistance profiles of Enterococcus faecalis (E. faecalis) and Enterococcus faecium (E. faecium) among farm workers, dairy cattle, and the farm environment in Xinjiang, and to assess the risk of their cross-host transmission. Methods From May 2024 to January 2025, a total of 317 samples were collected from 11 large-scale dairy farms in Xinjiang, China, including feces from farm workers (n=130) and dairy cattle (n=154), and environmental samples (n=33). E. faecalis and E. faecium were isolated and identified, followed by antimicrobial susceptibility testing and multilocus sequence typing (MLST) to analyze their molecular characteristics. Results A total of 183 Enterococcus isolates were obtained (66 E. faecalis and 117 E. faecium isolated). The isolation rates of both species showed statistically significant differences among the three sources (χ²=29.21, P=0.003). Antimicrobial resistance analysis revealed that E. faecalis generally exhibited higher resistance rates across multiple antibiotic classes than E. faecium. High resistance to rifampicin was observed across all sources (50.00%–81.25%), with statistical variation among origins (χ²=8.03, P=0.024). Multidrug-resistant strains accounted for 69.10% of the isolates. Multidrug resistance patterns in E. faecium varied significantly by source (χ²=27.19, P=0.014), and one isolate displayed resistance to eight antibiotic classes. MLST indicated high genetic diversity; E. faecalis was dominated by ST472 and ST227 of which the distrubution was significantly different among sources, while E. faecium primarily clustered into clonal complexes CC94 (centered on ST94) and CC17 (centered on ST22). Conclusion Resistant Enterococcus strains exhibit cross-transmission among farm workers, animals, and the environment. Under the "One Health" framework, standardized farming protocols and prudent antimicrobial use are essential to disrupt the transmission chain of resistant clones and mitigate the spread of antimicrobial resistance at its source.

Background: Adrenocortical hormones, particularly 11-oxygenated androgens, are pivotal in female reproductive health and fertility. Standardized detection kits and population-specific reference intervals are lacking in China, hindering related clinical applications. Methods: A HPLC-tandem mass spectrometry (HPLC-MS/MS) pipeline was developed, rigorously validated, and applied to simultaneously quantify corticosterone, cortisone, cortisol, 18-OH cortisol, androstenedione (A4), 11β-hydroxyandrostenedione (11-OH A4), dehydroepiandrosterone, and dehydroepiandrosterone sulfate in serum samples from 455 reproductive-aged women (18–45 yrs) in Guangxi, China. Age-dependent concentration trends were analyzed, and reference intervals stratified by age (2.5th to 97.5th percentiles) were established. Correlations with body-composition metrics, ethnicity, and the menstrual cycle were investigated. Results: The HPLC-MS/MS method demonstrated high precision (intra- and inter-assay CVs < 15%), accuracy, and sensitivity. All eight hormones exhibited significant age-related declines (P < 0.001 for seven hormones; P = 0.001 for 11-OH A4). Notably, 11-OH A4 levels were significantly lower in the 35–45-yr (3.05 nmol/L) and 25–34-yr (3.09 nmol/L) age groups than in the 18–24-yr (3.57 nmol/L) age group, whereas no significant difference was observed between the 35–45-yr and 25–34-yr age groups. Weak negative correlations were observed between the body mass index and corticosterone and cortisone levels, whereas ethnicity and the menstrual cycle showed no significant associations with hormone levels. Conclusions We developed an HPLC-MS/MS-based method for simultaneously quantifying eight adrenocortical hormones, including 11-OH A4, and defined age-specific reference intervals for reproductive-aged Chinese women. These findings advance the clinical utility of adrenocortical hormones in diagnosing and managing reproductive disorders.

BACKGROUND:Rapid and accurate segmentation of brain tissue in medical images is of great significance for three-dimensional biomechanical modeling and diagnosis of craniocerebral injuries.Currently,artificial intelligence(AI)-based baseline models exhibit excellent generalization capabilities on large-scale datasets.However,due to the specificity and complexity of craniocerebral tissues,these models have certain limitations in their application to craniocerebral tissue segmentation.Additionally,the scarcity of craniocerebral tissue samples makes it difficult for baseline models to achieve precise segmentation results through fine-tuning.OBJECTIVE:To construct a craniocerebral tissue segmentation model based on texture feature retrieval enhancement to improve segmentation accuracy under a small number of samples.METHODS:Segment Anything in Medical Images(MedSAM)model was selected as the basic framework,and texture features were combined with deep learning to build a brain tissue segmentation model based on texture feature retrieval enhancement(DP-MedSAM).Dice Coefficient and mean intersection over union(MIoU)were selected to evaluate the efficiency of image segmentation results.In comparison with the original MedSAM model,the ablation experiment systematically evaluated the influence of key components on the model performance.The sensitivities of MedSAM,the Segment Anything Model(SAM)for medical image segmentation(SAM-Med2D)and DP-MedSAM in the mandible,left optic nerve,and left parotid gland were compared.RESULTS AND CONCLUSION:(1)By verifying the impact of the number of point prompts on segmentation results on the HaN-Seg dataset,the experimental results indicated that the optimal Dice score was achieved with the addition of three points.(2)DP-MedSAM demonstrated performance improvements compared with MedSAM and SAM-Med2D on two datasets(HaN and Public Domain Database for Computational Anatomy).Especially on the Public Domain Database for Computational Anatomy dataset,in terms of the MIoU metric,DP-MedSAM outperformed MedSAM by 6.59％and SAM-Med2D by 37.35％;in terms of the Dice metric,DP-MedSAM outperformed MedSAM and SAM-Med2D by 4.34％and 25.32％,respectively.(3)The ablation experiment results showed that removing the texture feature extraction module in the DP-MedSAM model,relying solely on original image features,led to a significant decrease in results on the test set.Furthermore,removing the vector cache database and its retrieval enhancement function from the model,which deprived the ability of the model to perform similarity retrieval using an external knowledge base,further reduced model performance.(4)Under conditions of limited data resources,the DP-MedSAM model outperformed the other two models in all evaluation metrics.The DP-MedSAM model performed excellently when processing simple and moderately difficult samples,demonstrating a clear advantage over the other two models and indicating good generalization ability.Processing the fine structures of difficult samples placed higher demands on the model's segmentation capabilities.Although the performance of the DP-MedSAM model declined slightly,it still outperformed the other two models.(5)This study proposes an innovative craniocerebral tissue segmentation model,DP-MedSAM,which improves the baseline model's performance in capturing local details and global structural information in medical images by introducing target region texture feature extraction.Through vector similarity retrieval technology,DP-MedSAM can retrieve the feature vector most similar to the current target region from a pre-constructed vector database,providing more precise guiding information for the segmentation process.

BACKGROUND:Inter-limb asymmetry is a common phenomenon observed during human growth and development.Prolonged specialized training can lead to specific adaptations in inter-limb asymmetry among athletes.OBJECTIVE:To review the formation causes,manifestations,and impacts of inter-limb asymmetry on sports performance,and provide an overview of the relevant assessment methods and intervention strategies.METHODS:A literature search was conducted in the CNKI,WanFang,PubMed,and Web of Science databases from their inception to September 2024.The search terms included"asymmetry,asymmetries,asymmetric,asymmetrical,imbalance,strength,power,force,jump,sprint,athletic performance,anthropometry,injury"in English and Chinese.After excluding duplicate publications,irrelevant content,and conference papers,a total of 131 articles were finally included for analysis.RESULTS AND CONCLUSION:(1)Inter-limb asymmetry can be influenced by various factors including genetics,task demands,training regimens,injuries,fatigue,and limb preference.These factors lead to being primarily manifested in anatomical structure,strength performance,and task-specific asymmetry.(2)An increase in inter-limb asymmetry can result in impaired performance in bilateral in-phase symmetric movements.However,the relationship between increased inter-limb asymmetry and bilateral out-of-phase symmetric movements remains unclear and requires further investigation.(3)Training interventions have been shown to effectively mitigate inter-limb asymmetry,with unilateral training demonstrating superior outcomes compared with bilateral training.The choice of training methods and content should be tailored to meet the specific demands of the sport.(4)To further clarify the relationship between inter-limb asymmetry and athletic performance,it is recommended that future research adopt the concept of"task specificity"in inter-limb asymmetry.This includes standardizing study designs,selecting sensitive testing methods and indicators,unifying calculation methods to provide more high-quality evidence,and establishing categorized warning threshold standards for inter-limb asymmetry in different sports.

Background Methicillin-resistant Staphylococcus aureus (MRSA) is an important pathogen for both human bloodstream infections and mastitis in cows. However, little attention has been paid to the cross-host transmission of MRSA from cows to high-risk groups in China. Objective To determine the MRSA colonization rates among dairy cows and dairy farm workers in Xinjiang, identify the antibiotic resistance profiles and molecular characteristics of the isolates, and provide scientific evidence for the formulation of targeted infection control strategies. Method A cross-sectional survey combined with laboratory pathogen analysis was conducted. From June to August 2024, large-scale dairy farms in Xinjiang region were selected as study sites. Nasal swabs (n=96) and skin swabs (n=39) were collected from workers, and bovine nasal swab samples (n=109) were collected simultaneously. All samples were subjected to MRSA isolation, cultivation, and identification, followed by antibiotic susceptibility testing to characterize resistance phenotypes. Staphylococcus aureus protein A (Spa) typing was performed to determine strain genotypes and elucidate MRSA colonization rates and molecular epidemiological patterns. Results A total of 35 MRSA strains was successfully isolated from 244 samples. The MRSA colonization rates among dairy farm workers and dairy cows were 20.83% (20/96) and 12.84% (14/109), respectively, with an overall isolation rate of 14.34% (35/244). Among the workers, the nasal colonization rate was 16.67% (16/96), and the skin colonization rate was 12.82% (5/39). One worker exhibited MRSA colonization at multiple body sites. All MRSA strains were resistant to cefoxitin (100%, 35/35). The resistance rates to erythromycin and clindamycin were 42.86% (15/35) and 34.29% (12/35), respectively. Thirteen strains showed a multidrug-resistant phenotype, whereas all strains were susceptible to vancomycin. The MRSA isolates exhibited high genetic diversity, with 13 Spa types identified, among which t441 was the most prevalent (8 strains). Both t441 and t034 types were detected in samples from both the dairy cows and their handlers. These two Spa types also carried and stably inherited specific resistance combinations, including erythromycin–clindamycin–cefoxitin and ciprofloxacin–erythromycin–clindamycin–gentamicin–cefoxitin–tetracycline, and a statistically significant association was also observed between the two resistance profiles and the bacterial types (P < 0.001). In addition, one novel Spa type strain was identified. Conclusion MRSA colonization rates among dairy cows and dairy farm workers in Xinjiang are relatively high, with evidence of multi-site colonization. The isolates exhibit high levels of multidrug resistance and genetic diversity, indicating a potential risk of cross-host transmission.

Objective: To systematically evaluate the effect of exercise intervention on lipid metabolism among overweight or obese children and adolescents, providing evidence for improving lipid metabolism. Methods: Literature were retrieved and screened from PubMed, Web of Science, Cochrane Library, EBSCO, Embase, CNKI, Wanfang, and VIP databases regarding the impact of exercise intervention on lipid metabolism among overweight or obese children and adolescents, with a search period from 1,January 2000 to 30,April 2025. Data extraction and quality assessment of included studies were performed, and network Meta analysis was conducted by using RevMan 5.3 and Stata 18.0 software, with standardized mean difference (SMD) and 95% CI as the effect measures. Results: A total of 15 eligible randomized controlled trials were included, with 13 reporting outcomes for total cholesterol (TC) and low density lipoprotein cholesterol (LDL-C), and 14 for triglycerides (TG) and high density lipoprotein cholesterol (HDL-C). Network Meta analysis results showed that compared with the control group, aerobic exercise (AE) ( SMD = -0.51, 95% CI =-0.97 to -0.06) and combined aerobic and resistance exercise (COM) ( SMD =-0.54, 95% CI =-1.03 to -0.05) effectively reduced TC levels; AE ( SMD =-0.98, 95% CI =-1.64 to -0.32) and high intensity interval training (HIIT) ( SMD =-1.58, 95% CI = -2.52 to -0.64) effectively reduced TG levels; AE ( SMD =-0.60, 95% CI =-1.05 to -0.14), COM( SMD =-1.10, 95% CI =-1.72 to -0.48) and HIIT ( SMD =-1.05, 95% CI =-1.68 to -0.43) all effectively reduced LDL-C levels; only HIIT ( SMD = 1.03 , 95% CI =0.05-2.00) significantly increased HDL-C levels(all P <0.05). Conclusions Exercise intervention is beneficial to improve the dyslipidemia among children and adolescents, and the intervention effects of different exercise on different types of dyslipidemia are different.

Gorham-Stout disease(GSD) is a rare osteolytic disorder characterized by spontaneous and progressive osteolysis, along with abnormal angiogenesis and lymphangiogenesis, with no new bone formation. We present a case of a 15-year-old female admitted due to " recurrent right leg pain for 5 years, 11 months after undergoing right femoral fracture surgery". Through comprehensive integration of the patient's clinical phenotype, laboratory tests, imaging findings, pathological examinations, and molecular biological test results, GSD was considered highly likely. A multidisciplinary treatment approach was conducted, including a combination of zoledronic acid and sirolimus to inhibit osteolysis, along with rehabilitation training and orthopedic intervention, providing a personalized and comprehensive treatment strategy.