Osteoporosis is a systemic bone disorder characterized by decreased bone mass and deteriorated microarchitecture, leading to an increased risk of fractures. Recent studies have revealed that its pathogenesis involves complex biological processes beyond bone remodeling, including oxidative stress, chronic inflammation, cellular senescence, osteoimmunology, gut microbiota alterations, and epigenetic modifications. Oxidative stress disrupts bone homeostasis by promoting excessive free radical production and osteoclast activity. Chronic inflammation and the accumulation of senescent cells impair skeletal repair mechanisms. Advances in osteoimmunology have highlighted the critical role of immune–bone crosstalk in regulating bone resorption and formation. Moreover, the gut–bone axis, mediated by microbial metabolites, influences bone metabolism through immune and endocrine pathways. Epigenetic changes, such as DNA methylation and histone modification, contribute to gene–environment interactions, affecting disease progression. Multi-omics approaches (genomics, proteomics, and metabolomics) systematically identify molecular networks and comorbid links with diabetes/cardiovascular diseases, revealing pathological feedback loops that exacerbate bone loss. In conclusion, osteoporosis pathogenesis extends beyond bone remodeling to encompass systemic inflammation, immunometabolic dysregulation, and gut microbiota–host interactions. Future research should focus on integrating multi-omics biomarkers with targeted therapies to advance precision medicine strategies for osteoporosis prevention and treatment.

Autoimmune diseases (ADs) exhibit complex heterogeneity and dynamic pathological mechanisms. Traditional biomarkers face numerous challenges in the diagnosis and treatment of ADs. However, the rapid development of multi-omics technologies and bioinformatics has not only deepened the understanding of the pathogenesis of ADs but also identified many novel diagnostic and therapeutic biomarkers with good diagnostic performance. These biomarkers are now beginning to overcome these limitations. This review systematically explores the discovery of novel biomarkers driven by multi-omics technologies such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics, in response to the limitations of traditional biomarkers. It emphasises the significant importance of discovering novel biomarkers through multi-omics in the diagnosis and treatment of ADs, and proposes a concept from omics analysis to solving clinical problems, providing new directions for the diagnosis and treatment of ADs.

Recent advances in immunotherapy have shown remarkable success across multiple solid tumor types, revitalizing interest in immune-modulating strategies for osteosarcoma (OS). Within the complex tumor microenvironment (TME), immune cells exhibit dual regulatory roles-exerting critical regulatory influences on both tumorigenesis and disease progression while simultaneously serving as therapeutic targets. Particularly in OS, the dynamic interplay between malignant cells and the unique bone microenvironment manifests through intricate immune cell-mediated crosstalk. This comprehensive review analyzes the paradoxical roles of immune cell subsets in OS pathophysiology, detailing their tumor-promoting versus tumor-suppressing functions. Furthermore, we systematically evaluate recent progress in immune cell-targeted therapeutic approaches, including adoptive cell therapies and macrophage reprogramming strategies. The review encompasses current clinical applications and emerging preclinical innovations, providing valuable insights for optimizing immunotherapeutic approaches in OS management.

This study explored the proteomic landscape of inflammatory protein dysregulation in ankylosing spondylitis (AS), a chronic inflammatory disorder primarily affecting the axial skeleton and strongly associated with the HLA-B27 allele, particularly the HLA-B2705 and HLA-B2704 subtypes prevalent in Chinese populations. Blood samples from HLA-B27-positive AS patients and normal controls (NC) were analyzed using the Olink Target 96 inflammation panel to profile 92 inflammatory proteins. HLA-B27 subtyping was performed via PCR-SSP. To identify key proteins and stratify AS subtypes, we employed machine learning classifiers, including LightGBM models coupled with SHAP value interpretation, alongside traditional statistical analyses. The proteomic analysis revealed significant dysregulation of pro-inflammatory cytokines, such as IL-6 and IL-17A, in AS patients compared to NC, with CXCL9 and NRTN identified as potential biomarkers associated with disease activity. The combination of LightGBM classifiers and traditional statistical methods demonstrated high accuracy in distinguishing AS from NC and effectively stratifying subtypes. These findings provide valuable insights into the inflammatory mechanisms underlying AS pathogenesis and highlight potential biomarkers and therapeutic targets for improving diagnosis and treatment strategies. Future studies with larger and more diverse cohorts, as well as longitudinal designs, are warranted to validate these biomarkers and elucidate their dynamic changes during disease progression.

Orthopedic wound infection is a difficult problem in the clinic. Accurate and rapid microbiological test results are essential for case management, antibiotic therapy, and infection control. We retrospectively evaluated 1285 specimens (puncture fluid, catheter, secretions, joint fluid, lavage fluid, extraction fluid, blood culture, drainage fluid, cerebrospinal fluid, bone, prosthesis tissue, etc.) from 739 patients who received orthopedic diagnosis and treatment, using routine conventional method (RCM)s as a reference method to evaluate the performance of multiplex PCR combined with capillary electrophoresis (mPCR-CE) for detecting pathogens and antibiotic resistance genes associated with bone infection. Among the 1285 samples analyzed, 1045 samples were consistent with the results of the RCM, with an agreement rate of 81.32%. Among the 155 inconsistent results, 13 (1.01%) were mPCR-CE negative but RCM positive, 142 (11.05%) was mPCR-CE positive but RCM negative. Compared with RCM, mPCR-CE demonstrated positive percentage and negative percentage agreement values of 65.37% and 98.35%, respectively. Moreover, the detection rate of multidrug-resistant bacteria by the mPCR-CE method was generally better than that by the RCM method. The detection rate of methicillin-resistant Staphylococcus aureus (MRSA) by the mPCR-CE method is relatively high. The traditional drug-sensitive culture method is more inclined to detect extended-spectrum β-lactamases (ESBLs). The mPCR-CE method has obvious advantages in terms of timeliness. This study revealed that mPCR-CE is a new and effective diagnostic method that can significantly reduce the identification time of bacterial identification and drug resistance, and has the potential to improve the management of orthopedic infections.

Introduction: Red meat is an important dietary component, with global consumption rising substantially in recent decades. Over the past decades, growing attention has been paid to the potential adverse health effects associated with red meat consumption. Despite numerous epidemiological studies investigating the relationship between red meat consumption and stroke, the impact of red meat consumption on stroke incidence remains inconclusive, and existing meta-analyses synthesizing the evidence for this association are limited. This meta-analysis aimed to summarize the evidence for an association between red meat consumption and stroke risk. Methods: We searched all prospective studies that provided effect estimates for the potential association between red meat consumption and stroke incidence by conducting a comprehensive search of multiple databases up to Sept 2025, including Web of Science, MEDLINE, EMBASE, and by checking the references of relevant articles. All prospective studies that reported relative risks (RR) and 95% confidence intervals (CI) between red meat consumption and stroke risk were included in our study. A random effects model was used to combine the results. Results: Overall, 15 studies were included in the meta-analyses, including 1,294,166 subjects and 26,000 stroke cases. Substantial heterogeneity ( I ² = 72.5%, p < 0.001) was observed across the included studies, and subgroup analyses were performed to explore the potential sources of heterogeneity. We found a modest but significant positive association between red meat consumption and stroke risk (pooled RR = 1.05, 95% CI: 1.00–1.11), robust to sensitivity and publication bias correction. Subgroup analyses showed stronger associations for processed red meat (RR = 1.12), ischemic stroke, and follow-up≥20 years, with heterogeneity by sex and region; no significant associations were observed for unprocessed red meat or hemorrhagic stroke. Discussion: Our findings support moderate red meat consumption (limiting processed red meat) and provide evidence for stroke prevention dietary guidelines. Systematic review registration: CRD420261331748.