Background: To prove that serum vitamin D (VD) levels are strongly associated with ankylosing spondylitis (AS) disease activity, the association between serum VD levels and key monitoring indicators of AS disease activity has been analyzed, such as the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP). Methods: Studies published in PubMed, Cochrane Library, EMBASE, and China National Knowledge Infrastructure by August 30, 2022 were searched, and 6 studies finally met the selection criteria. Serum 25-hydroxyvitamin D (25(OH)D), ESR, CRP levels, and correlation coefficients between serum VD and BASDAI, ESR, CRP in AS, and control in these studies were extracted for the meta-analysis. Results: When compared to controls, patients with AS had considerably lower blood 25(OH)D levels (MD = −7.53 ng/mL, 95% CI, −9.78 to −5.28, P < .001) and significantly higher ESR and CRP levels (ESR: MD = 11.75 mm/h, 95% CI, 4.20 to 19.31, P = .002; CRP: MD = 15.36 mg/L, 95% CI, 4.95 to 25.77, P = .004). Additionally, a negative correlation was discovered between serum VD levels and BASDAI, ESR, and CRP (Fisher’ Z = −0.34, −0.38, −0.35, respectively). Conclusion: The findings of our meta-analysis demonstrated a negative correlation between serum VD levels and the main monitoring indices of disease activity in patients with AS and verified that the differences in the continent and ethnicity may be one of the major contributors to this finding.

To explore the molecular mechanism of Simiao Decoction (SMD) intervening atherosclerosis (AS). The main components and potential mechanisms of SMD remain unknown. This study aims to initially clarify the potential mechanism of SMD in the treatment of AS based on network pharmacology and molecular docking techniques. The principal components and corresponding protein targets of SMD were searched on Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and the compound-target network was constructed by Cytoscape3.9.1. AS targets were searched on DrugBank, OMIM, and TTD databases. The intersection of compound target and disease target was obtained and the coincidence target was imported into STRING database to construct a protein–protein interaction network. We further performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis on the targets. The molecular docking method was used to verify the interaction between core components of SMD and targets. We created the active compounds-targets network and the active compounds-AS-targets network based on the network database containing Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, DrugBank, OMIM, and TTD. We discovered that the therapy of AS with SMD involves 3 key substances—quercetin, kaempferol, and luteolin—as well as 5 crucial targets—ALB, AKT1, TNF, IL6, and TP53. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that the shared targets involved a number of signaling pathways, including the advanced glycosylation end product-receptor of AGE signaling pathway in diabetic complications, Hepatitis B, Lipid and atherosclerosis, Chemical Carcinogenesis-Receptor Activation, and Pathways in Cancer. The molecular docking demonstrated that the binding energies of quercetin, kaempferol, and luteolin with 5 important targets were favorable. This study reveals the active ingredients and potential molecular mechanism of SMD in the treatment of AS, and provides a reference for subsequent basic research.

This study aimed to construct an artificial neural network (ANN) diagnostic model for rheumatoid arthritis (RA) based on a neutrophil inflammation- and cell death-associated gene signature derived from a previously reported neutrophil extracellular traps (NETs)-related gene set, and to explore its association with immune infiltration and inflammatory pathways. The GEO dataset GSE110169 was used as the training dataset to identify differentially expressed genes between RA patients and healthy controls. Genes derived from a previously published NETs-related gene set were used as the initial candidate genes. Candidate genes were further screened using a random forest algorithm, and an ANN diagnostic model was constructed using the selected feature genes. Model performance was assessed by 10-fold cross-validation and externally validated in GSE93272. Cell Type Identification By Estimating Relative Samples Of RNA Transcripts was used to estimate immune infiltration, and weighted gene co-expression network analysis, gene ontology, and Kyoto encyclopedia of genes and genomes analyses were performed to explore related biological functions. Eleven selected feature genes were retained for model construction: Wiskott–Aldrich syndrome protein-like actin nucleation promoting factor, MFN1, enolase-1, optic atrophy 1, CLEC7A, interleukin-8, S100A8, ACTN4, RIPK1, CASP1, and integrin-linked kinase. The ANN model achieved an area under the curve of 0.923 in the training dataset and 0.722 in the external validation dataset. Immune infiltration analysis suggested that these genes were associated with gamma delta T cells, macrophages M0, memory B cells, resting dendritic cells, and activated dendritic cells. Functional enrichment analysis indicated involvement in lysosome, osteoclast differentiation, hematopoietic cell lineage, chemokine signaling pathway, and Fc gamma R-mediated phagocytosis. This study developed an ANN diagnostic model based on an neutrophil inflammation- and cell death-associated gene signature with potential diagnostic value for RA. However, because direct NET markers were not experimentally evaluated, the selected genes should not be interpreted as NETs-specific biomarkers. Further experimental validation is required.