Oct 2026· Israa University Journal for Applied Science· 0 citations
Rheumatoid Arthritis Research and Therapies
Abstract
Background: Rheumatoid Arthritis constitutes a long-lasting autoimmune inflammatory condition characterized by persistent synovial inflammation and progressive joint deterioration. Dysregulated immunological responses involved in the development and progression of the disease may be the result of a combination of hereditary predisposition and environmental variables, according to mounting evidence.
Amis : This study set out to compare female RA patients to healthy controls in terms of Toll-like receptor-2 (TLR-2) mRNA expression and disease progression.
Methods: From January to May of 2025, one hundred female participants took part in the study. Fifty of them were diagnosed with rheumatoid arthritis according to the 1987 American College of Rheumatology standards, and the other fifty were considered healthy controls. Laboratory tests and a rheumatological evaluation verified the clinical diagnosis. For the purpose of measuring TLR-2 transcript levels, blood samples were taken from each individual. RNA extraction, cDNA synthesis, and real-time PCR analysis were performed. Clinicopathological characteristics, including erythrocyte sedimentation rate (ESR), rheumatoid factor (RF), C-reactive protein (CRP), anti-cyclic citrullinated peptide (anti-CCP) antibody, and Disease Activity Score-28 (DAS28), were evaluated.
Results: In contrast to controls, RA patients' TLR-2 gene expression was significantly upregulated (fold change: 2.52) compared to the former (1.01). A higher ESR, RF titer, CRP, anti-CCP antibody level, or DAS28 score was substantially linked to an increase in TLR-2 expression.
Conclusions: There was a positive correlation between TLR-2 expression and inflammatory and disease activity indicators, and TLR-2 expression was considerably higher in RA patients. Based on these results, TLR-2 might be a useful biomarker for RA activity and development.
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Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.