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.

Objectives: Osteoporosis is a silent disease with low screening rates in many developing countries. This study aimed to evaluate the feasibility of using an artificial intelligence (AI)-based system to screen osteoporosis from pelvic and hip radiographs in Vietnam. Methods: We conducted a cross-sectional study at a tertiary medical center in Central Vietnam in 2023. A total of 2000 consecutive pelvic and hip radiographs from patients aged ≥ 40 years were collected. After excluding poorquality images, 1987 radiographs were analyzed using an AI-based software designed to estimate bone mineral density (BMD) from plain radiographs and derive T-scores. Osteoporosis was defined as a T-score ≤ − 2.5. Patient characteristics, radiographic findings, and risk factors for osteoporosis were analyzed. Results: Among 1987 patients (mean age 66.4 ± 15.1 years; 41.3% men), osteoporosis was identified in 872 patients (43.9%). The prevalence increased with age and was higher in women than in men (58.7% vs 22.8%, P < 0.001). Osteoporosis was associated with femoral neck (OR = 3.8, 95% CI: 2.7–5.2) and intertrochanteric fractures (OR = 7.0, 95% CI: 4.5–11.0). Patients with lower T-scores had a higher risk of hip fractures, especially those with T-scores ≤ − 3.0 (OR = 11.5, 95% CI: 5.5–24.5). Conclusions AI-based analysis of pelvic and hip radiographs is a feasible and effective tool for osteoporosis screening in Vietnam. The prevalence of osteoporosis in this hospital-based setting was high, particularly among elderly women.AI-assisted screening may offer an accessible strategy for early detection of osteoporosis in resource-limited settings.

Extraintestinal manifestations (EIM) of inflammatory bowel disease (IBD) may affect multiple organ systems, with approximately 50% of IBD patients presenting with at least one EIM during the course of their disease, with cutaneous involvement being particularly common. Cutaneous manifestations can present in various forms. This paper reports a case of ulcerative colitis (UC) complicated with bullous pemphigoid (BP), aiming to enhance clinicians' awareness of skin lesions associated with UC.

Objective To explore the value of support vector machine(SVM)model based on gray matter volume(GMV)for identifying amyotrophic lateral sclerosis(ALS),also to analyze the relevant brain regions.Methods MR 3D T1WI data of 60 ALS patients(ALS group)and 60 healthy volunteers(control group)were retrospectively analyzed.Taken GMV of each brain region obtained by voxel-based morphometry as the input features.F-score analysis was used to select feature with the highest classification accuracy to construct SVM model.Receiver operating characteristic curve was drawn to evaluate the efficacy of SVM model for identifying ALS,and top 10％was used as the weight threshold to obtain gray matter brain regions contributed the most to this model.Results SVM model constructed based on the top 40％GMV features had the highest classification accuracy(82.50％),with sensitivity,specificity and area under the curve(AUG)of 85.05％,80.40％and 0.890,respectively.The left precentral gyrus,left anterior cingulate gyrus and paracingulate gyrus,right middle temporal gyrus,opercular part of left inferior frontal gyrus,right dorsolateral superior frontal gyrus,left temporal pole:middle temporal gyrus,right superior occipital gyrus,orbital part of right middle frontal gyrus,right calcarine fissure and surrounding cortex,right fusiform gyrus were the top 1-10 gray matter brain regions contributed to this model.Conclusion ALS had specific GMV change pattern.SVM model based on GMV could be used to effectively identify ALS,while the left precentral gyrus was the most contributive brain region to this model.

With the rapid development of artificial intelligence and machine learning algorithms in the med-ical field,as well as the high-quality development requirements put forward by the state for hospitals and vari-ous departments,the development of laboratory medicine shows a new trend.The development of clinical labo-ratory has evolved from the initial test suite to the test pathway based on clinical pathway and diagnosis-relat-ed groups payment,and then to clinical laboratory omics.Clinical laboratory omics refers to the use of high-throughput methods to obtain a large number of laboratory project results data.Combined with the clinical characteristics of patients,statistical methods and machine learning algorithms are commonly used to reveal the information behind a large number of medical data to assist clinicians in diagnosis and treatment.Clinical laboratory omics may be a new trend in the future development of laboratory medicine,and it is likely to play an important role in the field of laboratory medicine.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by persistent inflammation and joint damage, accompanied by the accumulation of plasma cells, which contributes to its pathogenesis. Understanding the genetic alterations occurring during plasma cell differentiation in RA can deepen our comprehension of its pathogenesis and guide the development of targeted therapeutic interventions. Here, our study elucidates the intricate molecular mechanisms underlying plasma cell differentiation by demonstrating that PRDX1 interacts with DOK3 and modulates its degradation by the autophagy-lysosome pathway. This interaction results in the inhibition of plasma cell differentiation, thereby alleviating the progression of collagen-induced arthritis. Additionally, our investigation identifies Salvianolic acid B (SAB) as a potent small molecular glue-like compound that enhances the interaction between PRDX1 and DOK3, consequently impeding the progression of collagen-induced arthritis by inhibiting plasma cell differentiation. Collectively, these findings underscore the therapeutic potential of developing chemical stabilizers for the PRDX1-DOK3 complex in suppressing plasma cell differentiation for RA treatment and establish a theoretical basis for targeting PRDX1-protein interactions as specific therapeutic targets in various diseases.

Objective: To analyze the changes in homologous immunity after RhCE-matched transfusion in positive patients with RhCE blood group antibodies, and to provide precise transfusion strategies for chronic anemia patients. Methods: Patients with chronic anemia in our hospital from January 2020 to March 2024 (continuously receiving blood transfusions for more than 6 months) were enrolled, and 63 cases of unexpected antibody screening positive and identified as RhCE blood group antibodies were selected as the research subjects. The changes in unexpected antibody yield rate after ABO and RhCcDEe isotype blood transfusion were observed. Patients with MNS, Kidd, or Lewis blood group antibodies were screened for corresponding negative donors using monoclonal antibodies for extended typing transfusion based on RhCcEe typing, and the changes in unexpected antibody yield rate after transfusion were observed. Blood group genotyping was performed when serological techniques failed to resolve discrepancies or detect abnormal antigen expression. Results: After RhCcDEe-matched transfusions, RhCE antibodies disappeared in 62 patients, while 1 patient developed anti-Ce. The latter did not develop blood type isotype immunity after receiving RhccEE donor blood. Among the 62 patients, 9 developed unexpected antibodies against other systems: anti-M (4 cases), anti-Mur (2), anti-S (1), anti-Jka (1), and anti-Lea (1). No additional alloimmunization occurred after extended antigen-matched transfusions. A patient with serologically weak e phenotype was genotyped as DCe/DcE, with gene sequencing revealing an 827C>A mutation in exon 6 of the RHCE gene, forming the RHCE 01.31 allele. Conclusion: Precise transfusion strategies incorporating RhCE, MNS, Kidd, and Lewis blood group antigen typing can reduce the probability of blood group homologous immunity. RhCE complex antibodies and RhCE variants pose difficulties for clinical RhCE typing transfusion, which can be addressed through cross-matching and genetic analysis.

Objective To observe the changes in the serum level of tumor necrosis factor-α(TNF-α)during pegylated interferon-alpha(PEG-IFN-α)treatment in inactive HBsAg carriers(IHCs),to investigate the association between the dynamic changes of TNF-α and HBsAg clearance,and to assess the value of TNF-α as a potential biomarker for predicting the therapeutic efficacy of PEG-IFN-α.Methods A prospective study was conducted among 455 IHCs who attended our hospital from January 2018 to March 2023,and they were divided into treatment group and IHC control group.The 210 IHCs in the treatment group voluntarily received PEG-IFNα-2b treatment for 48 weeks,followed by follow-up for 24 weeks,and the 245 IHCs in the IHC control group were followed up for 72 weeks without treatment.The serum level of TNF-α was measured at baseline(week 0)and at weeks 12,24,48,and 72,and at week 72,the treatment group was further divided into HBsAg clearance group and non-clearance group.The serum level of TNF-α at different time points was compared between groups.The logistic regression analysis was used to assess the value of TNF-α in predicting HBsAg clearance.The t-test was used for comparison of normally distributed continuous data between two groups,and a one-way analysis of variance used for comparison between multiple groups;the repeated measures analysis of variance was used for comparison of normally distributed repeated measurement data within each group and between groups;the chi-square test or the Fisher's exact test was used for comparison of categorical data between groups.Univariate and multivariate logistic regression analyses were used to investigate the predictive factors for HBsAg clearance,and the receiver operating characteristic(ROC)curve was used to determine the cut-off value of TNF-α in predicting HBsAg clearance.Results At week 72,compared with the IHC control group,the treatment group had significantly higher HBsAg clearance rate(46.2%vs 1.2%,χ2=133.333,P<0.001)and seroconversion rate(34.8%vs 0.8%,χ2=94.650,P<0.001).The HBsAg clearance group and the non-clearance group had a significant increase in the serum level of TNF-α during treatment,which gradually returned to the baseline level after drug withdrawal(F=351.733 and 76.434,both P<0.001).Comparisons between groups showed that the HBsAg clearance group had the highest serum level of TNF-α at weeks 12,24,and 48,followed by the non-clearance group and the IHC control group(all P<0.001).The multivariate logistic regression analysis showed that baseline HBsAg level(odds ratio[OR]=0.329,95%confidence interval[CI]:0.189-0.571,P<0.001),baseline HBV DNA<20 IU/mL(OR=1.414,95%CI:1.057-1.787,P=0.045),ALT≥2×upper limit of normal at week 12(OR=1.127,95%CI:1.028-1.722,P=0.043),TNF-α level at week 12(OR=1.336,95%CI:1.018-1.754,P=0.037),and TNF-α level at week 24(OR=1.879,95%CI:1.477-2.391,P<0.001)were independent predictive factors for HBsAg clearance.The ROC analysis showed that TNF-α level at week 12 had an area under the ROC curve(AUC)of 0.846(95%CI:0.814-0.889)in predicting HBsAg clearance at week 72,with a sensitivity of 76.3%and a specificity of 81.0%,while TNF-α level at week 24 had an AUC of 0.912(95%CI:0.758-0.972),with a sensitivity of 81.4%and a specificity of 96.2%.Conclusion PEG-IFN-α can increase the serum level of TNF-α in IHCs,and the serum level of TNF-α at weeks 12 and 24 can effectively predict HBsAg clearance induced by PEG-IFN-α.

Extraintestinal manifestations (EIM) of inflammatory bowel disease (IBD) may affect multiple organ systems, with approximately 50% of IBD patients presenting with at least one EIM during the course of their disease, with cutaneous involvement being particularly common. Cutaneous manifestations can present in various forms. This paper reports a case of ulcerative colitis (UC) complicated with bullous pemphigoid (BP), aiming to enhance clinicians' awareness of skin lesions associated with UC.

Knee osteoarthritis(KOA)is a chronic degenerative joint disease,which poses a major challenge particularly among the elderly population due to its high incidence and high disability.Imaging examination has been used commonly to diagnose KOA.However,it faces imitations in predicting disease progression due to the lack of prior information and constraints in manpower and time.With the rapid evolution of big data and computational technologies,artificial intelligence(AI)is progressively integrating into various healthcare domains.Therefore,the integration of artificial intelligence(AI)into healthcare holds promise for revolutionizing KOA diagnosis and treatment.AI-assisted diagnostic models have demonstrated the potential to automate diagnosis,classify disease severity,and predict disease progression with improved efficiency and accuracy.In addition,these models provide personalized diagnosis and treatment options,as well as accurate disease progression risk assessment.Despite these promising outcomes,challenges such as high costs associated with data annotation and limitations in model generalization capabilities persist.This paper reviews recent advancements in AI applications and summarizes the potential value of utilizing AI applications for KOA.To further enhance the utilization of AI in KOA management to overcome current limitations,future efforts should focus on standardizing clinical sample databases,optimizing AI algorithms,and enhancing external verification sets.