Ankylosing spondylitis (AS) is an immune-mediated disease with an unknown etiology, posing challenges in effective treatment. This study aims to investigate the underlying mechanisms and explore the potential of traditional Chinese medicine (TCM) as a treatment avenue. Employing a multiomics analysis and leveraging public databases, we scrutinized AS immune cell subpopulations and associated genes. Gene regulatory mechanisms were dissected, and molecular docking was performed to assess the therapeutic efficacy of TCM. Our findings revealed a significant elevation in effector CD8+ memory T (Tem) cells in AS. Notably, the expression of STAT4 in this cell subpopulation was observed to be down-regulated. This down-regulation might be influenced by multiple circRNAs and miR-574-5p. Intriguingly, components derived from guava leaves exhibited a stable binding affinity to STAT4. Immunohistochemistry and qPCR results confirmed low expression of STAT4 in paraspinal ligament muscle tissue. This comprehensive multiomics analysis sheds light on potential underlying mechanisms of AS and underscores the prospect of traditional Chinese medicine as a viable therapeutic option. The study provides valuable insights for future research endeavors.

ABSTRACT Background Atlantoaxial instability or dislocation requires reliable posterior stabilization and fusion to prevent neurological deterioration. Although conventional Goel–Harms posterior fixation provides effective biomechanical stability, it usually requires extensive posterior cervical muscle dissection and may be associated with substantial blood loss, postoperative axial pain, and soft‐tissue morbidity. Despite the advantages of minimally invasive endoscopic techniques in spinal surgery, clinical evidence regarding their application in the management of AAI/D remains limited. In this study, we report a clinical series of endoscopic‐assisted atlantoaxial lateral mass fusion, provide a detailed description of the key surgical steps, and evaluate its technical feasibility, safety, and early clinical outcomes. Methods Sixteen patients diagnosed with AAI/D underwent endoscopic‐assisted posterior atlantoaxial fusion. The procedure utilized a uniportal endoscopic system through a 2.5–3.0 cm incision, enabling direct visualization of the C1–C2 lateral mass joint, precise screw placement, and intra‐articular bone grafting under continuous irrigation. Radiographic parameters, neurological function (JOA), pain (VAS), and neck disability (NDI) scores were assessed pre‐ and postoperatively. Results All surgeries were successfully completed without intraoperative neurovascular injury or hardware malposition. The mean operative time was 103.06 ± 9.55 min, and the mean blood loss was 40.06 ± 9.28 mL. The atlantodental interval (ADI) decreased from 5.14 ± 1.51 mm preoperatively to 3.37 ± 0.52 mm postoperatively ( p < 0.001), and C1–C2 joint height increased from 2.98 ± 0.27 mm to 4.37 ± 0.49 mm ( p < 0.001). At 3 months, bone fusion was achieved in 93.8% of patients. Clinical outcomes improved compared with preoperative values, including JOA score (8.44 ± 0.89 to 13.31 ± 1.20), VAS score (8.00 ± 0.73 to 2.25 ± 0.45), and NDI score (31.88 ± 1.93 to 12.88 ± 1.26). Conclusion This study introduces a novel minimally invasive technique for managing atlantoaxial instability using uniportal endoscopic‐assisted posterior fusion. The combination of direct endoscopic visualization, accurate screw–rod fixation, and controlled intra‐articular bone grafting offers reliable joint stabilization with reduced tissue disruption. Clinical outcomes suggest this approach may facilitate rapid recovery and improved perioperative safety, offering a valuable addition to current surgical strategies for AAI/D.

This study aims to develop a multi-label classification model based on pelvic X-rays for the diagnosis and assessment of Ankylosing Spondylitis (AS), addressing the challenges of limited expert resources in underdeveloped regions. We propose a deep learning-based multi-label classification model incorporating a prior attention mechanism. This model utilizes predefined bounding boxes to accurately locate key areas, such as the bilateral sacroiliac joints and hip joint, allowing the model to focus on relevant regions during inference. The performance of the model was validated on a multi-center external test set. Experimental results demonstrate that the model achieves significant improvements in diagnostic performance and in the assessment performance of the sacroiliac joints and hip joints. The optimal model’s diagnostic performance achieved an accuracy of 0.875 on a multi-center external test set, surpassing doctors with less experience (0.798) and performing comparably to more experienced human experts (0.880). The accuracy for the assessment of left and right sacroiliac joints and left and right hip joints reached 0.964, 0.970, 0.827, and 0.863, respectively. The proposed multi-label classification model with a prior attention mechanism offers a promising, cost-effective tool for AS diagnosis and assessment, especially in resource-limited settings.

Background Ankylosing spondylitis (AS) frequently coexists with other autoimmune diseases, leading to increased clinical heterogeneity and diagnostic complexity. Early identification of autoimmune comorbidity in AS remains challenging in routine practice.Methods A multicenter, retrospective, cross-sectional study was conducted, where clinical and laboratory data were collected from three independent tertiary centers between 2012 and 2025. Patients were classified into three groups: AS alone, autoimmune diseases alone, and AS with autoimmune comorbidities. Routinely available variables, including demographic characteristics, systemic inflammatory indices, hematological parameters, and liver and renal function markers, were analyzed. Multiple machine learning algorithms were developed for two clinically relevant classification tasks: AS alone vs. AS with autoimmune comorbidities, and autoimmune diseases alone vs. AS with autoimmune comorbidities. Model performance was evaluated using AUC, calibration, decision curve analysis, and clinical impact curves. SHapley Additive exPlanations (SHAP) were applied to enhance interpretability.Results Among all models, LightGBM consistently demonstrated superior and stable performance across discrimination, calibration, and clinical utility metrics. In distinguishing AS alone from AS with autoimmune comorbidities, key contributors included age, gender, renal function-related markers (eGFR, CysC, BUN, UA), and protein and hepatobiliary indices (ALB, DBIL). In comparisons between autoimmune diseases alone and AS with autoimmune comorbidities, SHAP highlighted metabolic- and synthesis-related features (GLOB, PREALB, CHE, ALP), acid-base balance (HCO3), and inflammatory activity (ESR). These patterns suggest that AS-associated autoimmune comorbidity represents a distinct systemic inflammatory-metabolic phenotype rather than a simple amplification of inflammation.Conclusions Using routinely available clinical data, an explainable machine learning framework enables accurate identification and characterization of autoimmune comorbidity in AS. This approach has practical potential for early risk stratification and clinical decision support in real-world settings.

Graphical Abstract Abstract Background Previous studies may have overestimated the effect of age at menopause when assessing osteoporosis risk and may have overlooked women with late menopause as a high-risk group. Therefore, we aim to clarify the association between age at menopause and osteoporosis risk, as well as to identify key characteristics of individuals with osteoporosis. Methods This study utilized a nationally representative cross-sectional sample, including 5,804 postmenopausal women aged 55–79 years. Weighted multivariate linear regression and logistic regression models were used to assess the association between age at menopause and femoral neck BMD and osteoporosis. Core characteristics of the osteoporosis population were identified by calculating standardized coefficients and applying SHapley Additive exPlanations (SHAP) analysis. Restricted cubic spline (RCS) analysis and subgroup analysis were conducted on these core characteristics. Results The age at menopause showed no significant association with osteoporosis risk in any model (P > 0.05). The two primary modifiable factors associated with reduced osteoporosis risk were BMI (OR = 0.84, 95% CI: 0.80–0.88) and hormone-use history (OR = 0.50, 95% CI: 0.33–0.74). Furthermore, RCS analyses revealed significant nonlinear relationships between age at menopause and both BMI (P < 0.001) and hormone-use history (P < 0.001). Subgroup analyses indicated that the compensatory effects of these two factors varied across different specific subgroups. Conclusion This study reveals that the initial protective advantage in reducing osteoporosis risk conferred by a later age at menopause diminishes in later life. Maintaining a higher BMI and appropriate hormone therapy can effectively compensate for the initial risk disadvantage.

OBJECTIVES:Ankylosing spondylitis (AS) involves a complex interplay between oxidative stress (OS) and immune dysregulation, yet the precise molecular links involved remain unclear. METHODS:OS-related genes were obtained from the GeneCards database. Differential expression analysis was performed on mRNA microarray (MA) data to identify OS-associated differentially expressed genes (DEGs). A two-sample Mendelian randomisation analysis was conducted to assess the causal relationships between OS-related genes and AS risk at the protein level. Immune cell mediation was further examined using genetic information from 731 immune phenotypes. Additionally, Logistic regression was used to examine the association between immune cell profiles and AS risk. The findings were further validated via experimental study using western blotting (WB) and single-cell RNA-seq (scRNA-seq) data. RESULTS:We found that the MICA protein is a key AS risk modulator via integrative omics analyses, with protein abundance showing a positive association (OR=1.89; PFDR=3.6×10-4). Mediation analyses revealed that the genetic effects of these genes were partially transmitted through specific T cell subsets, particularly CD28+ resting Tregs. The mediated proportion was 82.3%. Clinical validations confirmed lymphocyte-associated protection (OR: 0.64, 95%CI: 0.54-0.76). scRNA-seq analyses demonstrated significant MICA upregulation in AS-drived T cells, confirmed by Western blot analysis. CONCLUSIONS:This integrative multiomics analysis revealed OS-related genes as causal drivers of AS through immune dysregulation pathways. Targeting OS-related immune mechanisms may offer novel precision therapeutic strategies for AS.

Background: Osteosarcoma (OS) is a malignant tumor originating in the bones, predominantly affecting children and adolescents, characterized by high aggressiveness and poor prognosis. Identifying new prognostic biomarkers is crucial for improving the diagnosis and treatment of OS. Methods: In this study, we collected gene expression data from 88 OS samples from the UCSC Xena platform and normal tissue expression data from 396 Genotype-Tissue Expression (GTEx) samples. Prognosis-related genes were first screened by univariate Cox regression and then further selected using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Based on these candidate genes, non-negative matrix factorization (NMF) was used for molecular subtype identification, and the Kaplan-Meier analysis was applied to compare survival among subtypes. Tumor microenvironment and immune cell infiltration analyses were performed to characterize differences between risk groups. In addition, the expression patterns of key genes were validated by quantitative real-time polymerase chain reaction (qRT-PCR), hematoxylin-eosin staining, immunohistochemistry, and immunofluorescence. Results: Pyrroline-5-carboxylate reductase 1 (PYCR1) was consistently upregulated in OS and was associated with poor prognosis. In contrast, glycogen phosphorylase, muscle-associated (PYGM) showed analysis-level-dependent expression patterns: it was downregulated at the bulk transcriptomic and tumor cell levels compared with normal controls, whereas within the OS cohort, relatively higher PYGM expression was observed in the high-risk group. Tumor microenvironment and immune cell infiltration analyses revealed significant immune differences between high- and low-risk groups. Histological and protein-level assays further confirmed the presence and cellular localization of PYCR1 and PYGM in OS tissues. Conclusion: This study systematically identified and validated PYCR1 and PYGM as potential prognostic biomarkers for OS using integrated statistical and machine learning approaches. The PYCR1 showed a consistently tumor-promoting expression pattern, whereas PYGM demonstrated context-dependent expression changes across bulk tissue, risk-stratified tumor samples, and tumor cell lines, highlighting the biological complexity of metabolic biomarkers in OS.

Background: Posterior atlantoaxial arthrodesis is a cornerstone of atlantoaxial instability or dislocation. Open surgery entails extensive soft-tissue dissection, with intraoperative blood loss and postoperative axial pain. Experience from other spinal procedures suggests that one-hole split endoscopy may address these limitations; however, evidence at the craniovertebral junction is lacking. Surgical Technique: This study introduces an innovative, minimally invasive technique for atlantoaxial fusion: one-hole split endoscopy for posterior atlantoaxial lateral mass joint fusion. By delineating the anatomic layers, safety boundaries, and technical points, the technique enables endoscopic lateral mass joint arthrodesis and atlantoaxial screw fixation. Clinical Application: A 12-year-old boy with diagnosed atlantoaxial instability and os odontoideum underwent the one-hole split endoscopy for posterior atlantoaxial lateral mass joint fusion. Early postoperative imaging confirmed anatomic reduction of the joint with stable fixation. At 3 months, computed tomography revealed continuous bony bridging across the lateral mass joint space, indicating early osseous fusion. Conclusion: This study describes the first clinical application of posterior atlantoaxial lateral mass joint fusion using one-hole split endoscopy, demonstrating clinical feasibility and enhanced visualization and suggesting a potential role for endoscopic fusion at the craniovertebral junction. Clinical Relevance: This technique offers an innovative, minimally invasive option for posterior atlantoaxial fusion and supports the broader application of endoscopic procedures at the craniovertebral junction. Level of Evidence: 4.

Osteosarcoma is a highly malignant tumor with poor prognosis. Current CAR-T cell therapies for osteosarcoma are predominantly designed with single targets, but their efficacy remains unsatisfactory. In this study, a novel bispecific CAR-T cell was developed to provide an experimental basis for improving the therapeutic outcome of osteosarcoma. Single-cell RNA sequencing (scRNA-seq) identified two antigens highly expressed in osteosarcoma cells, ANXA2 and CD147, whose expression was further validated at the tissue level by qRT-PCR, flow cytometry, and immunohistochemistry. Based on a second-generation CAR backbone, a bispecific ANXA2/CD147 CAR-T construct was generated using magnetic bead sorting, primary T-cell culture, and lentiviral transduction, achieving a transduction efficiency of 47.1%. LDH release assays demonstrated that bispecific CAR-T cells exhibited significantly greater cytotoxicity against tumor cells than single-target and control groups. ELISA confirmed that bispecific CAR-T cells released higher levels of effector molecules, including GZMB and TNFα. In a subcutaneous CDX model, bispecific CAR-T cells displayed superior antitumor activity and greater T-cell infiltration. In a paw pad xenograft model, mice treated with bispecific CAR-T cells exhibited the smallest tumor volumes, lowest tumor weights, and reduced rates of lymph node metastasis. Furthermore, PDX models confirmed that bispecific CAR-T cells effectively suppressed osteosarcoma growth. ScRNA-seq of tumors derived from CDX models and immunohistochemistry revealed markedly increased infiltration of M1 macrophages in the bispecific group. Collectively, this study successfully generated a bispecific ANXA2/CD147 CAR-T cell with robust antitumor activity, providing a promising new strategy for the immunotherapy of osteosarcoma.

Objective To solve the problems of insufficient model generalization ability and unclear influence of anatomical regional differences in the intelligent diagnosis of spinal tuberculosis, and to construct a multi-center CT deep learning model.Method This study adopted a retrospective multi-center design and collected CT images of 1025 patients with spinal tuberculosis from 4 hospitals in the southwest region from 2012 to 2024. In the preprocessing stage, DICOM is converted to 224x224 RGB format and marked by senior radiologist. A binary classification model was constructed based on ResNet50 transfer learning and trained for 100 rounds using the Adam optimizer (lr = 1e-4, batch = 8). The verification framework is divided into three-stage validation framework: Center 1, 80% training of data/verification within 20%; Centers 2 to 4 were combined into the external test set and subdivided by cervical vertebrae/thoracic vertebrae/lumbar vertebrae/sacrum vertebrae. The evaluation indicators include AUC, accuracy, precision, recall and F1 score, supplemented by Grad-CAM visualization.Result The internal validation performance of the model was excellent (AUC = 0.90), but the external test decreased (AUC = 0.88). Anatomical stratification showed that the cervical vertebrae were the weakest (AUC = 0.70), the lumbar vertebrae were the most stable (AUC = 0.91), the sacral vertebrae had high sensitivity (recall rate = 0.756), and the thoracic vertebrae had a high false positive rate.Conclusion The anatomical dependence of diagnostic performance was revealed for the first time, and tiered deployment strategy is suggested: priority deployment of high-stability segments (lumbar vertebrae/sacrum), low-efficiency regions (cervical vertebrae) combined with MRI verification, and hierarchical data standards were established to optimize the algorithm.

Purpose To establish a segmentation model and classification model of tuberculosis lesions based on CT images to improve the accuracy of early diagnosis of spinal tuberculosis. Methods This study adopted multicenter retrospective data (n = 1025). Firstly, the vertebral body region of the spine was extracted through the U-Net segmentation model. Then, the segmented images were input into the improved ResNet50 network. Combined with the CT bone window gradient attention mechanism, an end-to-end deep learning diagnostic model was constructed. Results In the internal validation datasets, the model achieved an AUC of 0.920, accuracy of 0.874, and sensitivity of 0.876. For External test datasets 1, the AUC was 0.867, accuracy 0.801, and sensitivity 0.794; for External test datasets 2, the AUC was 0.866, accuracy 0.769, and sensitivity 0.883; and for External test datasets 3, the AUC was 0.941, accuracy 0.843, and sensitivity 0.790. Conclusion The multi-center study built up a deep learning model for spinal tuberculosis diagnosis with the assist of the CT bone window gradient attention mechanism. The model achieved a good internal verification ability (AUC = 0.920, accuracy rate = 0.874) and external verification ability (AUC = 0.866–0.941,accuracy rate = 0.769–0.843) which showed the wide applicability of the model to different medical institutions.The main developments of this work are good performances for features that extract relevant information about trabecular micro-fractures and calcification contours’ gradients.

Background: Ankylosing spondylitis often presents with nonspecific symptoms, making the identification of high-risk individuals challenging in clinical practice.Objective: This study aimed to utilize blood cell indices to construct interpretable machine learning models to assist in clinical triage and the identification of patients at high risk for ankylosing spondylitis.Methods: A retrospective case-control study was conducted involving 17,504 participants, comprising 4903 patients with ankylosing spondylitis and 12,601 controls with low back pain. Recursive feature elimination was applied to identify key variables, and six machine learning models were developed to diagnose ankylosing spondylitis using blood cell indices. The best-performing model was identified and compared with established biomarkers through receiver operating characteristic curve analysis. External validation was carried out using data from the Fourth People's Hospital of Nanning. The SHapley Additive Explanations method was applied to interpret the model and evaluate the contribution of individual indices to diagnostic predictions. In addition, to examine the independent associations between blood cell indices and ankylosing spondylitis risk while minimizing selection bias, propensity score matching was conducted, followed by binary logistic regression on the matched cohort.Results: Among the diagnostic models, the light gradient boosting machine model demonstrated the best performance, with areas under the curve of 0.866 in the test set and 0.872 in the external validation set. Several blood cell indices showed significant associations with ankylosing spondylitis.Conclusion: The light gradient boosting machine model exhibited reliable diagnostic performance for ankylosing spondylitis, and interpretable machine learning approaches provided insights into the contributions of specific hematologic parameters. These findings suggest that blood cell indices, as inexpensive and widely available markers, may serve as a tool for clinical triage and prioritizing high-risk individuals for further diagnostic evaluation.

Background:Immune cells are involved in rheumatoid arthritis (RA), but the link between other blood cell indices and the disease activity of RA, along with the underlying mechanisms, is unclear. Objective:This study aimed to develop an interpretable machine learning model based on blood cell parameters to assess RA disease severity and assist in personalized treatment decisions. Methods:A retrospective case-control study was conducted with blood routine and biochemical detection data from 4401 patients at the First Affiliated Hospital of Guangxi Medical University, spanning from January 1, 2018, to January 1, 2024. The primary outcome was disease severity stratification. Recursive feature elimination was applied to identify key variables, and 10 machine learning algorithms were benchmarked on 55 clinical features with internal validation. Model interpretability was assessed with SHAP, while logistic regression and restricted cubic spline models were used to examine associations between blood cell indices and disease severity. In addition, Mendelian randomization analysis was performed to explore potential causal relationships. Design:This was a retrospective case-control study. Results:Blood cell indices were identified as the primary factors associated with RA severity. In model evaluation, the Random Forest achieved the best performance, with test set AUCs of 0.870 and 0.874. Mendelian randomization supported a causal relationship between blood cell indices and RA risk. Conclusion:These results reinforce the associations between blood cell indices and RA severity. The machine learning model demonstrates good predictive capabilities for RA severity and may assist clinicians in developing personalized treatment strategies.

To elucidate the mechanism underlying micro-arc oxidation (MAO) coatings in bone regeneration, biomimetic coatings with different surface roughness were fabricated on titanium alloy substrates via MAO, and their effects on osteogenic differentiation and mechanotransductive signaling were investigated. The prepared hydroxyapatite (HA)-containing MAO coating exhibited a porous micro-nano structure, with porosity and micro-roughness ranging from 28.1% to 38.6% and 9.3 to 15.8 µm, respectively. Cell experiments demonstrated that the coating with a porosity of 35.6% and a roughness of 15.8 µm significantly promoted cell adhesion, proliferation, and osteogenic differentiation (with a 76.0% increase in alkaline phosphatase activity relative to the NC group), while upregulating the expression of downstream target genes YAP1, Runx2 and OCN. Micro-CT results revealed that the 500 V coating group exhibited a 30.9% increase in bone volume fraction and an 81.3% elevation in trabecular thickness relative to the NC group, with the bone-implant contact ratio showing a corresponding 44.6% enhancement. Histological analysis further confirmed that the elevated porosity and surface roughness facilitated the tight encapsulation of the porous coating structure by newly formed bone tissue. Bond strength testing revealed that the 500 V coating group achieved an interfacial bond strength of 33.2 N after 42 days in vivo, representing a 55.87% increase compared with the NC group. This study elucidated the influence of surface roughness on osseointegration, providing a theoretical basis for optimising the surface microstructure of implant materials.

Ankylosing Spondylitis (AS) and Systemic Sclerosis (SSc) are both autoimmune diseases, albeit with distinct anatomical targets. AS primarily affects the spine and sacroiliac joints, triggering inflammation and eventual fusion of the vertebrae. SSc predominantly impacts the skin and connective tissues, leading to skin fibrosis, thickening, and potential damage to vital organs such as the lungs, heart, and kidneys. Despite their differing anatomical manifestations, inflammation serves as a pivotal factor in both conditions. Exploring the causes of the different pathogenesis of inflammation in AS and SSc could provide new insights into their treatment. We selected RNA-seq profiles of peripheral blood mononuclear cells (PBMCs) from the GEO datasets GSE73754 and GSE19617. DEGs were identified using the Limma R package with an adjusted p-value cutoff of < 0.05. Gene Ontology pathway analysis, SVM recursive feature elimination, and Gene Set Enrichment Analysis (GSEA) were conducted to analyze the DEGs. CIBERSORT was applied to estimate immune cell composition and its correlation with hub genes. Single-cell RNA sequencing data from peripheral blood mononuclear cells in the GSE194315 dataset were included to support differential expression analysis and biomarker identification. Additionally, single-cell RNA sequencing data from bone marrow blood samples were utilized to further validate these findings, offering complementary insights into biomarker expression across distinct sample types. A total of 762 DEGs were identified between AS patients and controls, and 441 DEGs between SSc patients and controls. Both conditions showed enrichment in the Natural killer cell mediated cytotoxicity pathway. ZSWIM6 and CCL3L3 were identified as potential biomarkers in AS and SSc, with significant diagnostic capabilities demonstrated by ROC analysis. Correlation analysis revealed associations between these biomarkers and specific immune cell types. The study utilizing ZSWIM6 and CCL3L3 as potential biomarkers provides deep insights into the distinct molecular mechanisms of SSc and AS. These findings lay the foundation for future research on targeted therapies and enhance our understanding of immune cell interactions in these autoimmune diseases.

BACKGROUND:To evaluate the efficacy of low screw density constructs versus high screw density constructs in adolescent idiopathic scoliosis (AIS) surgery. METHODS:Data were collected from AIS patients who underwent pedicle screw fixation surgery at two medical centers. Patients were stratified into low and high screw density groups, calculated as pedicle screws per fused vertebral level. The dataset comprised demographics, radiological parameters, surgical outcomes and postoperative complications. RESULTS:Of 213 AIS patients analyzed, 114 and 99 comprised low- and high-density groups respectively. Compared with high-density constructs, low-density constructs demonstrated shorter operative time (median: 17.93 min, IQR: 14.66-20.50 vs. 22.15 min, IQR: 16.80-24.77; p = 6.30e-07), lower intraoperative blood loss (median: 762.29 ml, IQR: 600-900 vs. 954.19 ml, IQR: 800-1030; p = 7.70e-07), fewer postoperative pain (median: 4.719, IQR: 3-7 vs. 5.505, IQR: 3.5-7; p = 0.009), and shorter hospital stays (median: 11.15 days, IQR: 7-13.5 vs. 12.48 days, IQR: 8-15; p = 0.04). Both groups had equivalent Cobb angle correction (median: 67.31%, IQR: 60.50% -76.84% vs. 67.97%, IQR: 62.54%-73.52%; p = 0.90). CONCLUSION:Optimizing screw density may minimize intraoperative blood loss and postoperative pain without affecting correction efficacy in AIS; however, longitudinal studies are needed to assess long-term functional and quality-of-life outcomes.

Ankylosing spondylitis (AS) and rheumatoid arthritis (RA) are closely related autoimmune diseases with shared mechanisms that remain unclear. This study aims to identify shared molecular signatures and hub genes underlying the co-occurrence of AS and RA using clinical and transcriptomic data, focusing on immune dysregulation pathways. The CBC data of 23,289 patients were collected, and six machine learning algorithms were applied to develop disease prediction models for AS and RA. Using permutation feature importance and Shapley Additive Explanations (SHAP) based on the optimal model, the top 10 features most influential for AS and RA prediction were identified, followed by selecting their intersections. Bioinformatics analysis was conducted to identify key immune cells associated with AS and RA and to evaluate the correlation between these immune cells and the hub gene. Clinical data, hematoxylin-eosin (H&E) staining, and immunohistochemical analysis were used to validate the findings. Neutrophils and lymphocytes emerged as key predictors in AS and RA models. Bioinformatics identified MYO1F as a hub gene, significantly upregulated in both diseases, with a strong correlation to neutrophil infiltration (p < 0.05). Clinical data, H&E staining of histological sections of interspinous ligaments from AS patients and synovial tissue from RA patients, and immunohistochemistry confirmed elevated neutrophil counts and MYO1F expression (p < 0.05), supporting their roles in immune dysregulation. This study is the first to identify MYO1F as a hub gene in AS and RA co-occurrence, highlighting neutrophil infiltration as a critical factor in their pathogenesis. Our integrative approach combining machine learning, transcriptomics, and clinical validation, provides novel insights into shared mechanisms, positioning MYO1F and neutrophils as potential diagnostic and therapeutic targets.

BACKGROUND AND OBJECTIVES:Ankylosing spondylitis (AS) is a chronic immune-mediated inflammatory disease primarily affecting the axial skeleton. Despite significant advances, its pathogenic mechanisms remain unclear, posing challenges to early diagnosis and effective treatment. This study aims to elucidate the pathogenic pathways of AS and explore potential therapeutic strategies. METHODS:Blood routine test results from AS and non-AS patients were collected, and t-tests and logistic regression analyses were performed on blood cell count parameters. Key findings from the blood tests were validated using GEO transcriptomic datasets. Single-cell data from GEO were then used to conduct in-depth analyses of immune cell subsets and their functions. To validate findings, single-cell sequencing was performed on bone marrow samples collected from AS and fracture control patients, followed by pathway analysis through GSEA. Finally, upstream regulatory mechanisms and potential therapeutic agents were investigated. RESULTS:This study identified a classical monocyte-macrophage-inflammatory macrophage differentiation trajectory in AS, demonstrating that monocytes/macrophages play a critical role in AS pathogenesis via the NOD-like receptor signaling pathway, primarily mediated by NLRP3. Several regulatory factors, including hsa-miR-3682-3p, AR, IRF4, MYB, RUNX1, and TAL1, were found to modulate NLRP3 expression. Additionally, various chemical compounds, anticancer drugs, and cinnamaldehyde were identified as potential therapeutic agents targeting NLRP3. CONCLUSION:In AS, the classical monocyte-macrophage-inflammatory macrophage differentiation pathway is enhanced, with monocyte/macrophage-derived NLRP3 driving disease progression via the NOD-like receptor signaling pathway. Regulatory factors and potential therapeutic agents targeting NLRP3 were identified, offering new insights into AS pathogenesis and therapeutic strategies.

Ankylosing spondylitis (AS), a chronic inflammatory disease affecting the spine and sacroiliac joints, is influenced by both genetic and environmental factors. This study investigates the impact of air pollution and meteorological conditions on AS incidence in 14 cities across Southwest China from 2014 to 2021, focusing on immune-environmental interactions. Using a two-step approach, we applied the Fixed Effects Model to city-level data to assess the influence of air pollutants (PM10, PM2.5, SO2, NO2, CO, O3_8H) and meteorological factors (humidity, temperature, precipitation, sunshine hours) on AS incidence, and the Distributed Lag Non-Linear Model (DLNM) to individual patient data to explore non-linear and delayed effects on immune parameters. Results indicate a significant association between PM2.5 exposure and increased AS incidence, with a potential two-year lag effect and a correlation with humidity. No consistent relationships were found with other pollutants or meteorological factors. In male AS patients, PM2.5 exposure was linked to elevated monocyte counts, suggesting heightened inflammatory activity. These findings highlight PM2.5 as a potential environmental risk factor for AS, underscoring the need for targeted public health interventions.