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.

ObjectiveTo investigate the association between noninvasive liver fibrosis markers and carotid plaque （CP） in patients with lean metabolic dysfunction-associated fatty liver disease （MAFLD）， and to provide a basis for screening high-risk populations. MethodsA total of 957 patients with lean MAFLD who underwent physical examination in Subei People’s Hospital from January 2021 to June 2023 was enrolled as the observation cohort， with the presence or absence of CP as the outcome， and fibrosis-4 （FIB-4） index and nonalcoholic fatty liver disease fibrosis score （NFS） were used to assess liver fibrosis degree. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical data between two groups. The multivariate logistic regression analysis， the restricted cubic spline analysis， the receiver operating characteristic curve， and the mediation effect analysis were used to investigate the association between liver fibrosis degree and CP. ResultsThe prevalence rate of CP was 36.6% in the lean MAFLD population. Compared with the non-CP group（n=607）， the CP group （n=350） had a significantly higher proportion of male patients， a significantly higher proportion of patients with smoking/diabetes/hypertension， and significantly higher levels of age， creatinine， blood urea nitrogen， triglycerides， fasting blood glucose， aspartate aminotransferase， aspartate aminotransferase/alanine aminotransferase ratio， NFS， and FIB-4 index， as well as significantly lower levels of platelet count and albumin （all P<0.05）. The multivariate logistic regression analysis showed that after adjustment for confounding factors， FIB-4 index （odds ratio［OR］=2.979， 95% confidence interval［CI］：2.141 — 4.219， P<0.001） and NFS （OR=1.747， 95%CI： 1.499 — 2.046， P<0.001） were positively correlated with CP. Both FIB-4 index and NFS had a good value in predicting CP. Hypertension had a significant indirect effect on the prevalence rate of CP through its impact on liver fibrosis markers， and its mediating effect accounted for 39.5% — 40.8% of the total effect （P<0.001）. ConclusionIn patients with lean MAFLD， NFS and FIB-4 index are significantly positively correlated with the prevalence rate of CP， and they can be used as potential epidemiological predictive indicators. Liver fibrosis markers may play a mediating role in the association between hypertension and CP. Interventions targeting hypertension and liver fibrosis markers may help to prevent and delay the progression of CP.

ObjectiveTo investigate the association of liver fibrosis scores and inflammation markers with gallstones in patients with metabolic dysfunction-associated fatty liver disease （MAFLD）， as well as the mediating role of liver fibrosis scores in the relationship between inflammation markers and gallstones. MethodsA total of 14 567 patients who received physical examination and were diagnosed with MAFLD in Subei People’s Hospital from January 2014 to June 2023 were enrolled in this study， and according to the results of abdominal color Doppler ultrasound， they were divided into gallstone group with 1 724 patients and non-gallstone group with 12 843 patients. Related clinical data were collected from all patients， including demographic data， medical history， family history， physical examination， Color Doppler ultrasound， and biochemical parameters. The biomarkers associated with metabolic disorders and insulin resistance included triglyceride-glucose index （TyG）， TyG-body mass index （BMI） index， atherogenic index of plasma （AIP）， and non-high-density lipoprotein cholesterol-to-high-density lipoprotein cholesterol ratio （NHHR）； the biomarkers associated with inflammation and nutritional status included neutrophil-to-lymphocyte ratio （NLR）， neutrophil percentage-to-albumin ratio （NPAR）， and monocyte-to-lymphocyte ratio （MLR）； the biomarkers for assessing liver fibrosis degree and liver function included albumin-bilirubin （ALBI） score， NAFLD fibrosis score （NFS）， fibrosis-4 （FIB-4） index， and aspartate aminotransferase-to-platelet ratio index （APRI）. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， while the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical data between two groups. Multivariate Logistic regression analysis， restricted cubic spline analysis， and mediating effect analysis were used to assess the association of liver fibrosis markers and inflammation markers with the risk of gallstones. ResultsThe prevalence rate of gallstones was 11.8% among the MAFLD patients. There were significant differences between the gallstone group and the non-gallstone group in sex， age， smoking history， diabetes， hypertension， lymphocytes， platelets， glucose， albumin， serum uric acid， alanine aminotransferase， aspartate aminotransferase， red blood cell， NLR， NPAR， MLR， NFS， FIB-4 index， and ALBI score （all P<0.05）. The multivariate Logistic regression analysis showed that NLR （odds ratio ［OR］=1.091， 95% confidence interval ［CI］： 1.028　—　1.160， P<0.05）， NPAR （OR=1.073， 95%CI： 1.042　—　1.105， P<0.05）， MLR （OR=1.142， 95%CI： 1.057　—　1.232， P<0.05）， NFS （OR=1.239， 95%CI： 1.190　—　1.291， P<0.05）， and FIB-4 index （OR=1.326， 95%CI： 1.241　—　1.417， P<0.05） were influencing factors for the prevalence rate of gallstones. The restricted cubic spline analysis showed a significant non-linear association between NFS/FIB-4 index and the risk of gallstone （non-linear P<0.05）. The mediating effect analysis further showed that the association of NLR， MLR， and NPAR with gallstones was partially mediated by NFS or FIB-4 index， with a mediating effect accounting for 36.79%、28.09%、29.67% and 18.31%、17.70、11.57%， respectively. ConclusionNFS and FIB-4 index have a non-linear association with the prevalence rate of gallstones in MAFLD patients， and they also mediate the association of NLR， NPAR， and MLR with the risk of gallstone.

Inteins are unique protein insertion sequences capable of self-excision, enabling the covalent ligation of flanking extein peptides via amide bond formation. This process proceeds spontaneously without requiring external enzymes, cofactors, or chemical reagents, granting inteins exceptional biocompatibility and traceless performance in protein engineering applications. Split inteins represent a specialized and versatile subclass whose splicing domains are encoded by two separate gene fragments rather than a single continuous open reading frame. These fragments, known as the N-terminal (IntN) and C-terminal (IntC) split inteins, associate through non-covalent interactions including hydrophobic forces, hydrogen bonds, and van der Waals forces to assemble into an active three-dimensional structure, which then drives efficient extein ligation and enables protein trans-splicing. Protein trans-splicing mediated by split inteins has become a cornerstone for traceless protein ligation owing to its high specificity and irreversibility, fundamentally reshaping strategies for protein modification, assembly, and functional regulation. Compared with traditional chemical ligation methods, split intein systems require no complex chemical derivatization of peptide fragments and can operate efficiently at micromolar concentrations under physiological conditions, thus avoiding structural and functional damage caused by organic reagents. In contrast to enzymatic ligation tools such as sortase, split inteins eliminate the need for additional enzymes or cofactors, simplifying reaction systems, reducing costs, and minimizing non-specific side products. These distinctive advantages render split inteins highly promising for applications in chemical biology, synthetic biology, and biopharmaceutical development. In recent years, deepened mechanistic understanding has established structure-guided rational design as the primary approach to overcoming key limitations of split inteins, including intrinsic aggregation propensity, strict extein sequence dependence, and limited splicing efficiency. Bioinformatic tools have been used to identify aggregation-prone regions in the IntN fragment, and site-directed mutagenesis of hydrophobic residues, relocation of split sites, or removal of misfolding-prone sequences has substantially reduced in vitro aggregation and improved soluble expression and assembly activity. Rational engineering of catalytic residues and adjacent flexible loops has relaxed strict amino acid preferences at extein junctions, enhancing sequence tolerance and reducing the risk of functional impairment in target proteins. Consensus design based on multiple sequence alignments has yielded ultra-fast splicing variants such as Cfa DnaE and Cat-TerL, which exhibit significantly accelerated kinetics and improved tolerance to denaturing conditions. Meanwhile, advances in structural biology have further clarified the conformational dynamics and catalytic mechanisms of splicing, supporting the precise design of high-performance intein modules. On this basis, electrostatic interaction tuning and metagenomic screening have yielded multiple mutually orthogonal split intein pairs, enabling selective multi-fragment protein ligation and providing new routes for the efficient synthesis of large multi-domain functional proteins. With these engineered split inteins offering continuously improved performance and expanded applicability, protein trans-splicing has been widely applied in numerous cutting-edge areas of protein research and biomedicine. In gene delivery, split intein-based systems overcome the packaging limit of adeno-associated viral vectors, enabling the accurate reconstitution of large therapeutic proteins and base editors in target cells, thereby enhancing the efficacy and scope of gene therapy for genetic diseases. In internal protein sequence editing, split inteins mediate precise sequence replacement and modification in flexible regions or loops of target proteins, without the need for complex multi-step ligation and protein refolding involved in traditional protein semisynthesis. In protein-protein interaction studies, intein-mediated splicing covalently captures transient and weak intracellular complexes, enabling sensitive, high-throughput interaction detection and drug screening. In synthetic biology, conditionally controllable splicing systems support the construction of diverse intracellular and cell-surface biological logic gates for the precise regulation of cellular behavior. In mechanistic biochemical research, split inteins enable photocatalytic proximity labeling and site-specific tagging, allowing the preparation of homogeneous protein samples carrying precise post-translational modifications such as ubiquitination and polyglutamylation for chromatin interactome analysis and epigenetic studies. Moreover, covalent trapping strategies using split inteins stabilize transient enzymatic intermediates, providing unprecedented insights into molecular mechanisms such as nucleosome ubiquitination that are difficult to elucidate using conventional methods. This review systematically summarizes key technological advances in split inteins over the past decade, highlighting engineering strategies, mechanistic insights, and the development of orthogonal components. It comprehensively surveys emerging applications at the frontiers of protein research, analyzes current core challenges, and proposes future directions, particularly emphasizing artificial intelligence-driven de novo design and novel splicing pathways to break existing technical bottlenecks. By enabling traceless, efficient, and versatile protein manipulation, split inteins continue to serve as indispensable tools that drive innovation in protein engineering and fundamental life science research.

ObjectiveStroke poses a heavy burden due to its high mortality and morbidity rates. Accurate and real-time detection of lesions is pivotal for prompt clinical intervention and favorable prognosis. Electrical impedance tomography (EIT) and microwave tomography (MWT) have emerged as compelling alternatives for stroke screening, owing to their non-ionizing, non-invasive and portable nature. EIT provides information on tissue conductivity, and MWT offers high sensitivity to changes in dielectric properties. However, single-modality imaging is inherently limited, EIT suffers from low sensitivity to deep-seated tissues and severe ill-posedness of inverse problems, whereas MWT is challenged by strong nonlinearity in inverse scattering and susceptibility to modeling errors. Consequently, the clinical utility of standalone EIT or MWT for stroke diagnosis remains constrained by poor spatial resolution and imaging artifacts. To improve the accuracy and robustness of stroke imaging, a dual-modality fusion conditional denoising diffusion probabilistic model (DM-DDPM) was proposed for high-precision dual-modality image reconstruction. MethodsA dual-encoder network with a symmetric architecture and independently trained parameters was constructed to extract heterogeneous features separately from EIT boundary voltage measurements and MWT scattered field signals. Attentional feature fusion (AFF) is employed to integrate complementary information from the two modalities adaptively, generating robust fused priors that suppress redundant noise while preserving key physical characteristics. Subsequently, the fused priors are embedded into a Transformer-based diffusion model via a cross attention mechanism to guide the reverse denoising process. This approach effectively reduces artifacts and enhances the stability of conductivity distribution reconstruction. Time step embedding is introduced to enable the network to perceive the diffusion stage and further improve the accuracy of noise prediction. ResultsSimulated experiments demonstrated that DM-DDPM significantly outperforms single-modality and multi-modality networks under various noise levels. A head model simulation dataset was constructed based on COMSOL Multiphysics, and tests were carried out under 50 dB, 40 dB and 30 dB signal-to-noise ratio levels. At 30 dB, the average relative error (RE) was below 0.20, while the structural similarity index measure (SSIM) and correlation coefficient (CC) remained above 0.90 and 0.89, respectively. Compared with single-modality and multi-modality networks, artifacts were significantly reduced, lesion edges were clearer, and localization was more accurate. The model maintains high reconstruction quality and strong robustness for single, double, and triple lesions simultaneously. Furthermore, physical experiments were conducted using a 16-electrode EIT system and a 16-antenna MWT system with asynchronous data acquisition. These experiments confirmed the feasibility of the method in real-world scenarios and demonstrated that it can robustly reconstruct simulated lesions despite environmental interference and measurement noise, validating its reliability for practical clinical applications. ConclusionThe proposed method effectively combines complementary dual-modality information with a conditional diffusion model. Low accuracy and poor noise resistance in single-modality imaging were effectively addressed, while the noise amplification issue caused by direct multimodal data fusion was avoided. The proposed algorithm exhibits strong anti-noise interference ability and high imaging stability in both simulation and physical experiments. Precise localization of stroke lesions with different quantities was achieved, providing a high-precision, and practical technical support for clinical stroke detection.

To explore the clinical value of a multimodal predictive model based on multiparametric magnetic resonance imaging(MRI) radiomics combined with deep learning(DL) features for the preoperative noninvasive assessment of mismatch repair-deficient(MMRd) status in endometrial cancer(EC).

BACKGROUND:The guinea pig is considered to be the most useful spontaneous model for evaluating primary osteoarthritis in humans because of its similar knee joint structure and close histopathologic features to those of humans. OBJECTIVE:To investigate the pathological process of spontaneous knee osteoarthritis in guinea pigs by analyzing the histopathology of the total knee joint of guinea pigs aged 1 to 18 months. METHODS:Eight healthy female Hartley guinea pigs in each age group of 1,6,10,14,16,and 18 months old were selected.The quadriceps femoris was taken for hematoxylin-eosin staining,and the total knee joint was stained with hematoxylin-eosin and toluidine blue.The histopathology of the cartilage,subchondral bone,synovium,meniscus,and muscles were observed under light microscope.Mankin's score and synovitis score were compared,and the correlation analysis was conducted. RESULTS AND CONCLUSION:As the guinea pig age increased,the Mankin's score increased(P<0.05),and the pathological score of synovitis also gradually increased(P<0.05),and there was a significant positive correlation between the two(r=0.641,P<0.001).The incidence rate of subchondral bone marrow lesion in 18-month-old guinea pigs was 50%,and the incidence of meniscus injury was 37.5%.In addition,osteophyte and narrowing of the joint space were observed,and only a few guinea pigs had inflammation in the quadriceps femoris.To conclude,guinea pigs develop significant cartilage defects,synovial inflammation,subchondral bone lesions,meniscus injury,osteophyte formation,and joint space narrowing as they age,all of which are similar to the pathological processes of primary knee osteoarthritis in humans,making it an ideal model of spontaneous knee osteoarthritis.

ObjectiveThis study aims to develop and validate a novel multimodal predictive model， termed criss-cross network（CCNet）-directed graph neural network（DGNN）（CGN）， for accurate assessment of pulmonary nodule progression in high-risk individuals for lung cancer， by integrating longitudinal chest computed tomography（CT） imaging with both traditional Chinese and western clinical evaluation data. MethodsA cohort of 4 432 patients with pulmonary nodules was retrospectively analyzed. A twin CCNet was employed to extract spatiotemporal representations from paired sequential CT scans. Structured clinical assessment and imaging-derived features were encoded via a multilayer perceptron， and a similarity-based alignment strategy was adopted to harmonize multimodal imaging features across temporal dimensions. Subsequently， a DGNN was constructed to integrate heterogeneous features， where nodes represented modality-specific embeddings and edges denoted inter-modal information flow. Finally， model optimization was performed using a joint loss function combining cross-entropy and cosine similarity loss， facilitating robust classification of nodule progression status. ResultsThe proposed CGN model demonstrated superior predictive performance on the held-out test set， achieving an area under the receiver operating characteristic curve（AUC） of 0.830， accuracy of 0.843， sensitivity of 0.657， specificity of 0.712， Cohen's Kappa of 0.417， and F₁ score of 0.544. Compared with unimodal baselines， the CGN model yielded a 36%-48% relative improvement in AUC. Ablation studies revealed a 2%-22% increase in AUC when compared to simplified architectures lacking key components， substantiating the efficacy of the proposed multimodal fusion strategy and modular design. Incorporation of traditional Chinese medicine （TCM）-specific symptomatology led to an additional 5% improvement in AUC， underscoring the complementary value of integrating TCM and western clinical data. Through gradient-weighted activation mapping visualization analysis， it was found that the model's attention predominantly focused on nodule regions and effectively captured dynamic associations between clinical data and imaging-derived features. ConclusionThe CGN model， by synergistically combining cross-attention encoding with directed graph-based feature integration， enables effective alignment and fusion of heterogeneous multimodal data. The incorporation of both TCM and western clinical information facilitates complementary feature enrichment， thereby enhancing predictive accuracy for pulmonary nodule progression. This approach holds significant potential for supporting intelligent risk stratification and personalized surveillance strategies in lung cancer prevention.

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.

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.