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.

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 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 Animal farming may affect the structure and diversity of gut microbiota of farm workers, but it needs more studies to provide solid evidence. Objective To analyze the diversity characteristics of gut microbiota in dairy farm workers, dairy cows, and the control population (non-animal contact occupational group), and to assess the impact of dairy farming on the gut microbiota of workers. Methods The 16S rRNA full-length amplicon sequencing technology was used to sequence 60 fecal samples from dairy farm workers, 89 from dairy cows, and 50 from the general population. The gut microbiota structure characteristics, including operational taxonomic units (OTUs), alpha diversity, beta diversity, and the composition of species at the phylum, family, and genus levels were analyzed. The differences in gut microbiota among the three groups of samples were compared to explore the impact of occupational exposure on the gut microbiota structure of dairy farm workers. Results A total of 50478 OTUs were identified in the gut microbiota of farm workers, dairy cows, and the general population, with 23613, 42723, and 16592 OTUs, respectively. The number of shared OTUs between the dairy cows and the general population was 12674 (27.17%), between the general population and the farm workers was 10099 (33.54%), and between the dairy cows and the farm workers was 17690 (36.36%). Compared with the general population, the farm workers and dairy cows showed more shared gut microbiota (P<0.01). The abundance of Firmicutes in the gut of the farm workers was higher than that of the general population (P<0.01), while Actinobacteriota and Proteobacteria were the opposite (P<0.01). The alpha diversity of gut microbiota in the farm workers was different from that of the general population. The ACE index of the farm workers was slightly lower than that of the general population and much lower than that of dairy cows (P<0.05), while their Shannon index was higher than that of the general population but lower than that of dairy cows (P<0.01). The linear discriminant analysis effect size (LefSe) showed that the abundances of Lachnospiraceae and Ruminococcaceae in the gut microbiota of the farm workers were significantly higher than those of the general population, while Streptococcaceae, Lactobacillaceae, Bifidobacteriaceae, Enterobacteriaceae, and Erysipelatoclostridiaceae were lower than those of the general population. Conclusion The dairy farm workers shares a certain proportion of common bacterial communities with dairy cows. Dairy farming operations can affect the structure and diversity of the gut microbiota of workers by increasing the abundances of Lachnospiraceae and Ruminococcaceae, while decreasing the abundances of Streptococcaceae, Lactobacillaceae, Bifidobacteriaceae, Enterobacteriaceae, and Erysipelatoclostridiaceae. The study results provide reference data for further exploring the cross-host transmission of pathogens related to occupational exposure in dairy farming.

OBJECTIVE To evaluate the cost-effectiveness of dapagliflozin and empagliflozin in the treatment of type 2 diabetic kidney disease from the perspective of healthcare system in China. METHODS Based on the data from the two multicenter clinical trials， DECLARE-TIMI 58 and EMPA-REG OUTCOME， a Markov model was constructed according to the urinary albumin-to-creatinine ratio （UACR） of the patients with type 2 diabetic kidney disease with a cycle of 1 year， simulating until 99% of patients died. The model outputs were total costs and quality-adjusted life year （QALY）. The cost-effectiveness of the two treatment regimens was assessed by comparing their incremental cost-effectiveness ratio （ICER） and the willingness-to-pay threshold （WTP，set at three times China’s 2023 per capita gross domestic product， i.e.， 268 074 yuan/QALY）. Additionally， oneway sensitivity analysis and probabilistic sensitivity analysis were performed to test the robustness of the base analysis results. RESULTS Compared with dapagliflozin， the ICER for empagliflozin regimen was 44 334.82 yuan/QALY， which was below the WTP ， indicating its cost-effectiveness. The results of the oneway sensitivity analysis indicated that the incidence of non-fatal myocardial infarction in both groups and the utility values associated with the microalbuminuria state had the most significant impact on the outcomes， but did not change the base-case conclusion. The probabilistic sensitivity analysis indicated that the results of the base-case analysis were robust. CONCLUSIONS With a WTP of three times China’s per capita gross domestic product in 2023， empagliflozin is more cost-effective than dapagliflozin in treating type 2 diabetic kidney disease.

Objective A joint Poisson-logistic model in a Bayesian framework is proposed to constructed using tuberculosis(TB)reporting data from 14 prefectures in Xinjiang from 2014 to 2020 in combination with relevant social,economic,and environmental factors affecting the reported incidence rate of TB to explore potential underreporting areas of the TB reporting data,and to provide a strong evidence－based support for the subsequent decision－making on the precision prevention and control of TB.Methods Relevant factors affecting the reporting process and disease process of TB were collected,and important covariates were screened for inclusion in the model using the factor detector in the Geo-detector method,and the reported incidence model of TB and the expected incidence model of TB in Xinjiang were constructed separately,which together constituted a hybrid model of underreporting of TB(Poisson-logistic joint model).The mixed model was used to estimate the risk of TB underreporting in each prefecture of Xinjiang,and to explore the regional distribution of the potential risk of TB underreporting.Results Factor detector result pairs showed that GDP per capita was associated with the largest contribution to the risk of TB underreporting(0.5481);goodness－of－fit test showed that the data were well fitted(Bayesian P－value<0.001),and the Bayesian Poisson-logistic joint model could be applied to the study of the risk of underreporting of TB reporting data in Xinjiang from 2014 to 2020.The results showed that the risk of underreporting of TB The risk of underreporting of reported data was concentrated in the four southern Xinjiang prefectures,with the greatest risk of underreporting of TB reported data in Kashgar 0.1426(0.1403,0.1445).The lower risk of underreporting was concentrated in the eastern and central parts of Xinjiang,with the lowest risk of underreporting in the city of Karamay[0.1017(0.9983,0.1034)].In a joint Bayesian Poisson-logistic model,it was found that population density(IRR＝1.0060,95%CI:1.0059~1.0061)and average annual temperature(IRR＝1.0087,95%CI:1.0086~1.0088)were risk factors for underreporting of TB,and GDP per capita(IRR＝0.9385,95%CI:0.9365~0.9394)and an increase in the number of registered nurses(IRR＝0.9916,95%CI:0.9913 to 0.9920)reduced the risk of TB underreporting.Conclusion The Bayesian Poisson-logistic joint model estimated the potential incidence of TB in Xinjiang Uygur Autonomous Region and revealed significant discrepancies between reported and true TB incidence rates.It identified underreporting trends and localized potential underreporting risk areas,providing a theoretical basis for tailored and precise TB prevention and control strategies in Xinjiang.

Objective To investigate the effects of high-frequency repetitive transcranial magnetic stimulation(HF-rTMS)applied to the supplementary motor area(SMA)or primary motor cortex(M1)on upper limb function in stroke patients in terms of motor sequence learning.Methods From April,2024 to February,2025,60 inpatients were recruited from the First Affiliated Hospital with Nan-jing Medical University.They were randomly assigned into the control group,SMA group and M1 group,with 20 patients in each group.All the groups received medication and conventional rehabilitation.On this basis,SMA group underwent HF-rTMS on the affected side's SMA,while M1 group received HF-rTMS on the affected side's M1 for two weeks.All the groups were measured with motor evoked potentials(MEP),the serial reaction time(RT)task,Fugl-Meyer Assessment-Upper Extremities(FMA-UE)and modified Barthel Index(MBI)before and after intervention.Results The SMA and M1 groups dropped one case respectively.MEP elicitation rate of the affected side's increased in SMA and M1 groups(P<0.05),and it was better than that in the control group(χ2>4.792,P<0.05).The intra-group effects of RTsequential sequence,FMA-UE and MBI scores were significant(|F|>81.546,P<0.05).The inter-group effects of RTrandom sequence,RTsequential sequence,?RT,and MBI scores were significant(F>3.228,P<0.05).The in-teractive effects of RTrandom sequence,RTsequential sequence,?RT,FMA-UE and MBI scores were significant(|F|>3.520,P>0.05).After intervention,RTsequential sequence,?RT,FMA-UE and MBI scores improved(P<0.05).RTrandom sequence was lower in SMA group than in the control group(P<0.017),RTsequential sequence,?RT,FMA-UE and MBI scores im-proved more in SMA and M1 groups than in the control group(P<0.05),but no significant difference was found between the SMA group and the M1 group(P>0.05).Conclusion HF-rTMS applied to the affected SMA or M1 can activate motor sequence learning and promote the recov-ery of upper limb function in stroke patients.

Objective A joint Poisson-logistic model in a Bayesian framework is proposed to constructed using tuberculosis(TB)reporting data from 14 prefectures in Xinjiang from 2014 to 2020 in combination with relevant social,economic,and environmental factors affecting the reported incidence rate of TB to explore potential underreporting areas of the TB reporting data,and to provide a strong evidence－based support for the subsequent decision－making on the precision prevention and control of TB.Methods Relevant factors affecting the reporting process and disease process of TB were collected,and important covariates were screened for inclusion in the model using the factor detector in the Geo-detector method,and the reported incidence model of TB and the expected incidence model of TB in Xinjiang were constructed separately,which together constituted a hybrid model of underreporting of TB(Poisson-logistic joint model).The mixed model was used to estimate the risk of TB underreporting in each prefecture of Xinjiang,and to explore the regional distribution of the potential risk of TB underreporting.Results Factor detector result pairs showed that GDP per capita was associated with the largest contribution to the risk of TB underreporting(0.5481);goodness－of－fit test showed that the data were well fitted(Bayesian P－value<0.001),and the Bayesian Poisson-logistic joint model could be applied to the study of the risk of underreporting of TB reporting data in Xinjiang from 2014 to 2020.The results showed that the risk of underreporting of TB The risk of underreporting of reported data was concentrated in the four southern Xinjiang prefectures,with the greatest risk of underreporting of TB reported data in Kashgar 0.1426(0.1403,0.1445).The lower risk of underreporting was concentrated in the eastern and central parts of Xinjiang,with the lowest risk of underreporting in the city of Karamay[0.1017(0.9983,0.1034)].In a joint Bayesian Poisson-logistic model,it was found that population density(IRR＝1.0060,95%CI:1.0059~1.0061)and average annual temperature(IRR＝1.0087,95%CI:1.0086~1.0088)were risk factors for underreporting of TB,and GDP per capita(IRR＝0.9385,95%CI:0.9365~0.9394)and an increase in the number of registered nurses(IRR＝0.9916,95%CI:0.9913 to 0.9920)reduced the risk of TB underreporting.Conclusion The Bayesian Poisson-logistic joint model estimated the potential incidence of TB in Xinjiang Uygur Autonomous Region and revealed significant discrepancies between reported and true TB incidence rates.It identified underreporting trends and localized potential underreporting risk areas,providing a theoretical basis for tailored and precise TB prevention and control strategies in Xinjiang.

Objective To analyze risk factors of acute cardio-cerebrovascular accidents in elderly patients with lower extremity fracture,and to construct a risk prediction diagram model.Methods A retrospective analysis was conducted on the clinical data of 510 elderly patients with lower extremity fractures,and the patients were divided into the occurrence group and the non-occurrence group based on the occurrence of acute cardiovascular and cerebrovascular accidents during the postoperative hospitalization.Univariate and multivariate Logistic regression analysis was used to analyze risk factors affecting acute cardiovascular and cerebrovascular accidents in elderly patients with lower extremity fracture,and the risk prediction model was constructed and verified.Results A total of 52 cases of acute cardio-cerebrovascular accidents occurred during hospitalization in 510 patients(10.20%).Age,ASA grade Ⅲ ratio,preoperative concurrent cerebrovascular disease,postoperative pneumonia ratio and postoperative bed time≥5 days ratio were higher in the occurrence group than those in the non-occurrence group(P＜0.05).Multivariate Logistic regression analysis showed that age,ASA grade Ⅲ,preoperative concurrent cardiovascular and cerebrovascular disease,and postoperative pneumonia were risk factors for acute cardio-cerebrovascular accidents in elderly patients with lower limb fracture(P＜0.05).Multivariate Logistic regression analysis was used to screen risk factors as predictive variables to build a risk early warning histogram model for acute cardio-cerebrovascular accidents.The total score ranged from 103 to 168 points,corresponding to a risk range of 0.1 to 0.9 points.Receiver operating characteristic curve(ROC)curve results showed that the area under the curve(AUC)of this model for predicting acute cardiovascular and cerebrovascular accidents was 0.980(95%CI:0.964-1.000),and discrete choice approach(DCA)showed that when the risk threshold was 0.2-0.6,the prediction model of this column graph had a good clinical advantage.Conclusion The risk diagram model constructed based on risk factors has good predictive value for the occurrence of acute cardio-cerebrovascular accidents after lower extremity fracture in elderly patients.

Background Personal protection is crucial for reducing the risk of zoonotic pathogen infection among livestock workers. Investigating the current status of its implementation and associated influencing factors can provide empirical evidence for developing more effective intervention measures. Objective To investigate the current status of personal protection implementation among livestock workers in Xinjiang, China and its influencing factors, providing a reference for formulating targeted intervention measures. Methods This study was conducted in Bayingolin Mongol Autonomous Prefecture, Kashgar region, and the First and Eighth Divisions of Xinjiang Production and Construction Corps. We selected large-scale cattle and sheep farms, cooperatives, individual livestock households, livestock trading markets, slaughterhouses, and retail markets. Using cluster sampling, we recruited all livestock workers (1074 participants) at sampled sites. During the period from April to July 2024, we conducted face-to-face surveys with a self-designed questionnaire among livestock workers, collecting general demographic information, occupational exposure history, and information related to the use of personal protective equipment, resulting in 939 valid questionnaires. EpiData 3.1 was used for data entry and SPSS 26.0 for statistical analysis. We employed logistic regression to compare the implementation of personal protection among workers by selected categories. Results Among the study subjects, there were 300 workers from large-scale farms (31.95%) and 583 self-employed livestock farmers (62.09%). The sample included 600 males (63.90%) and 339 females (36.10%). The majority of the study subjects was farmers, breeders, or aged 18-60 years (87.75%), auxiliary workers (87.97%) and had an education level of junior high school or below (81.36%). The proportion of workers who consistently and correctly implemented all five personal protective measures (washing hands after work, wearing gloves, wearing masks, wearing dedicated work clothes, and using rubber boots) was 35.14% (330/939). The implementation rates for these five protective measures were 93.61% (879/939), 58.68% (551/939), 57.93% (544/939), 46.01% (432/939), and 42.81% (402/939), respectively. There were statistically significant differences in the implementation rates of differen personal protective measures among the workers by occupation and education level (P<0.05). Among them, workers with lower education levels had lower implementation rates for various protective measures. The results of logistic regression indicated that, compared to male workers, female workers (OR=2.26, 95%CI: 1.68, 3.04) showed a higher rate of non-standard implementation of personal protection. Using the age group of 18 to less than 36 years as a reference, those aged 36 to less than 60 years (OR=1.82, 95%CI: 1.33, 2.50) and 60 years or older (OR=4.45, 95%CI: 2.56, 7.73) reported higher rates of non-standard implementation of personal protection. Compared to those with an education level of primary school or below, those with a junior high school education (OR=0.64, 95%CI: 0.47, 0.88) and a high school education or above (OR=0.13, 95%CI: 0.19, 0.29) showed lower rates of non-standard implementation of personal protection. Compared to farmers, veterinarians (OR=0.03, 95%CI: 0.02, 0.07) reported the lowest rate of non-standard implementation of personal protection, followed by auxiliary workers (OR=0.08, 95%CI: 0.05, 0.12), breeders (OR=0.10, 95%CI: 0.07, 0.15), and other occupational groups (OR=0.28, 95%CI: 0.15, 0.52). Using large-scale farms as a reference, self-employed farmers (OR=13.5, 95%CI: 9.65, 18.88) reported a higher rate of non-standard implementation of personal protection, followed by workers from other workplaces (OR=3.63, 95%CI: 2.01, 6.54). Conclusion The implementation of personal protective measures among livestock workers in Xinjiang is generally satisfactory. However, significant disparities in the execution of these measures exist among different types of workers, indicating the need to enhance occupational health promotion to reduce the risk of zoonotic pathogen infection among the workers.