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.

Objective To enhance the gamma spectrometry analysis and detection capabilities of personnel in measuring radionuclides in bio-ash, and ensure the accuracy and precision of gamma spectrometer in our laboratory for the analysis of radionuclides in bio-ash. Methods The laboratory participated in the 2024 national interlaboratory comparison for radionuclide measurement in bio-ash. Radionuclides ²⁴¹Am, ¹³⁷Cs, and ⁴⁰K in four different bio-ash samples were measured. Based on the measurement methods specified in GB/T 16145—2022, sample measurement, result calculation, and uncertainty evaluation were performed using spectrometry analysis software and sourceless efficiency calibration software. The measurement results and performance evaluation of the laboratory in the comparison were analyzed, and factors affecting gamma-spectrometry measurement and result interpretation were discussed. Results The relative deviation (D) of the measurement results for radionuclides ²⁴¹Am and ¹³⁷Cs in the four bio-ash samples ranged from −10.2% to 2.0% and −1.7% to 0.3%, respectively. The P statistic ranged from 7.0% to 12.5% and 5.0% to 10.8%, respectively. These results met the criteria of |D| ≤ 20% (MARB), |D| ≤ 2.58P, and P ≤ 20% (MARB). The ζ-scores for the measurement of ⁴⁰K in the four bio-ash samples ranged from 0.8 to 1.3, satisfying the criterion of |ζ| < 2. All measurement results in this laboratory in the comparison were satisfactory. Conclusion The laboratory uses a high-purity germanium gamma spectrometer to measure radionuclides in bio-ash. The self-absorption correction method, analysis method, and uncertainty evaluation are appropriate, which can ensure the accuracy and precision of the measurement results. The measurement results are accurate and reliable.

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).

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Objective:To develop PixelSIFT-UNet, a novel semantic segmentation model that integrates deep learning with scale-invariant feature transform (SIFT) algorithm to improve the segmentation accuracy of oral mucosal lesions.Methods:This investigation utilized 838 standard clinical white light images of oral mucosal diseases acquired from January 2020 to December 2022 at the Stomatology Hospital Zhejiang University School of Medicine. Randomization was achieved through Python′s random.seed function implementation. The random sample function was subsequently applied for sampling distribution. The dataset was stratified into three subsets with a 6∶2∶2 ratio: training ( n=506), validation ( n=166), and testing ( n=166). Lesion boundaries were annotated using Labelme software, and a PixelSIFT-UNet-based deep learning model was developed with VGG-16 and ResNet-50 backbone networks. Model parameters were optimized using the validation set, and performance metrics [including Dice coefficient, mean intersection over union (mIoU), mean pixel accuracy (mPA), and Precision] were assessed on the test set. The model′s performance was benchmarked against conventional semantic segmentation frameworks (U-Net and PSPNet). Results:The developed PixelSIFT-UNet model could achieve precise segmentation of three common oral mucosal lesions: oral lichen planus, oral leukoplakia, and oral submucous fibrosis. Utilizing VGG-16 as the backbone network, the model achieved Dice coefficient, mIoU, mPA, and Precision values of 0.642, 0.699, 0.836, and 0.792, respectively. Implementation with ResNet-50 backbone network yielded metrics of 0.668, 0.733, 0.872 and 0.817, demonstrating significant improvements across all performance indicators compared to conventional U-Net model (relevant metrics: 0.662, 0.717, 0.861 and 0.809) and PSPNet model (relevant metrics: 0.671, 0.721, 0.858 and 0.813).Conclusions:The proposed PixelSIFT-UNet architecture demonstrates superior performance in oral mucosal lesion segmentation tasks, surpassing conventional semantic segmentation models and providing robust quantitative improvements in segmentation accuracy.

This study was aimed at determining the molecular characteristics of Yersinia pestis in the natural plague foci around Qinghai Lake through single nucleotide polymorphism technology,to lay a foundation for molecular epidemiological and source-tracing analysis of Y.pestis in this area.Using the whole genome sequencing technology,we obtained the whole genome sequences of 84 representative Y.pestis strains.Using the sequences of Y.pestis and Yersinia pseudotuberculosis IP32953 from the NCBI database as references,we compared and analyzed the 2 298 SNP loci of these strains.From 1957 to 2020,84 representative strains of Y.pestis from the natural plague foci around Qinghai Lake were divided into two clades:1.IN2 and 3.ANT1.The 1.IN2 clade was the characteristic population of Y.pestis throughout all epidemic years in this area.Additionally,analysis of the SNP distribution and hosts in the region indicated that the 1.IN2 clade was located in five counties except Wulan,whereas the 3.ANT1 clade was isolated from Himalayan marmot and dog in two counties.In conclusion,the population structure of SNP of Y.pestis in the natural plague foci around Qinghai Lake is relatively simple,and SNP analysis of Y.pestis provided a scientific basis for tracing plague epidemic sources and formulating plague prevention and control measures in this area.