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 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 investigate the delay in identification, healthcare-seeking, and definitive diagnosis of tuberculosis among students in Urumqi City from 2010 to 2019, and to identify the influencing factors, so as to provide insights into tuberculosis control among students. Methods: The demographic and diagnosis data of tuberculosis patients in Urumqi City from 2010 to 2019 were captured from the Tuberculosis Information Management System of Chinese Disease Control and Prevention Information System. The delay in identification, healthcare-seeking and definitive diagnosis of tuberculosis was analyzed among students, and the factors affecting the delay in identification, healthcare-seeking and definitive diagnosis of tuberculosis were identified using a multivariable logistic regression model. Results: A total of 996 tuberculosis cases were identified among students in Urumqi City from 2010 to 2019. There were 702 students with delay in identification of tuberculosis (70.48%), 500 students with delay in healthcare-seeking (55.22%) and 534 students with delay in definitive diagnosis (53.61%). Multivariable logistic regression analysis identified active identification (OR=0.116, 95%CI: 0.032-0.420) as a factor affecting delay in identification of tuberculosis, women (OR=1.424, 95%CI: 1.104-1.836), non-local household registration (OR=1.311, 95%CI: 1.016-1.694) and active identification (OR=0.232, 95%CI: 0.064-0.848) as factors affecting delay in healthcare-seeking, and active identification (OR=0.143, 95%CI: 0.032-0.644) as a factor affecting delay in definitive diagnosis of tuberculosis among students. Conclusions There is a high proportion of delay in identification, healthcare-seeking and definitive diagnosis of tuberculosis among students in Urumqi City from 2010 to 2019, and female and non-locally household-registered students were at a high risk of delay in healthcare-seeking for tuberculosis. Active detection and screening of tuberculosis should be reinforced.

Objective: To analyze the association between recombinant solute carrier family 11, member 1 ( SLC11A1 ) rs17235409 polymorphism and treatment failure of pulmonary tuberculosis, so as to provide the basis for the prevention and treatment of pulmonary tuberculosis. Methods: The patients with pulmonary tuberculosis registered for treatment at the Urumqi Center for Disease Control and Prevention in 2019 was recruited and collected demographic, clinical and treatment information from National Infectious Diseases Reporting System. The polymorphism of SLC11A1 rs17235409 was detected by multiple ligase chain reaction and Hardy-Weinberg balance test was performed. The multivariate logistic regression analysis was conducted for the association between rs17235409 and the treatment outcome of tuberculosis. Results: A total of 731 cases of pulmonary tuberculosis patients were enrolled, and 37 cases failed, with a failure rate of 5.06%. The failure rate of the patients with G/A was 8.55%, with G/G was 4.23%. The results of multivariate logistic regression analysis showed that the patients with G/A were more likely to fail in the treatment than those with G/G ( OR=2.213, 95%CI: 1.041-4.702 ). The males with G/A were more likely to fail in the treatment than those with G/G ( OR=2.547, 95%CI: 1.021-6.356 ). Conclusion The rs17235409 polymorphism of SLC11A1 is associated with the failure of tuberculosis treatment, and the patients with G/A are more likely to fail.

Objective To compare the accuracy of the seasonal time series decomposition method and autoregressive integrated moving average (ARIMA) in the prediction of incidence of tuberculosis(TB) in order to facilitate early-warning.Methods The seasonal decomposition model and ARIMA model were constructed by SPSS20.0 software based on time series of monthly TB incidence between January 2005 and December 2014 in Urumqi,China.The obtained models were used to forecast the monthly incidence in 2015 and compared with the actual incidence respectively.Results Between 2005 and 2014,the incidence of TB was higher during March,April and May in Urumqi.A linear fitting model and a cubic curve fitting model were constructed by the time series seasonal decomposition method.The mean absolute percentage error (MAPE) of each predicted monthly incidence in 2015 was 18.75% and 92.25%,respectively.The predicted values of the linear model were lower than actual values and the predicted values of the cubic curve model were higher than actual values.An ARIMA (2,1,1) (1,1,0)12 fitting model was established by ARIMA method.The MAPE of each predicted monthly incidence in 2015 was 9.46% and there were no significant differences between the predicted and actual values.Conclusion The ARIMA method is better than the seasonal decomposition method for predicting the monthly incidence of TB in Urumqi.