Disease Activity Improvement in Rheumatoid Arthritis Treated with Tumor Necrosis Factor-α Inhibitors Correlates with Increased Soluble Fas Levels
Bibliographic record
Abstract
OBJECTIVE: Rheumatoid arthritis (RA) is characterized by chronic synovial inflammation and hyperplasia. Tumor necrosis factor-α (TNF-α) plays a pivotal role in RA by interfering with the Fas-Fas ligand (FasL) proapoptotic pathway. We investigated the circulating levels of soluble Fas (sFas) and soluble FasL (sFasL), and their possible correlation with disease activity and improvement after anti-TNF-α treatment in RA. METHODS: Serum levels of sFas and sFasL were measured by quantitative ELISA in 52 patients with RA before and after 3 months of anti-TNF-α treatment (adalimumab, n = 32; infliximab, n = 20). Disease activity measures [Disease Activity Score at 28 joints-erythrocyte sedimentation rate (DAS28-ESR), C-reactive protein (CRP)] were recorded before and after treatment. Forty age-matched and sex-matched healthy subjects served as controls. RESULTS: No significant differences in serum sFas levels were detected between anti-TNF-α-naive patients with RA and controls. After anti-TNF-α treatment, serum sFas levels significantly increased in patients with RA compared to both anti-TNF-α-naive patients and controls. Increased sFas levels inversely correlated with disease activity variables (DAS28-ESR: r = -0.739, CRP: r = -0.636, both p < 0.001). No significant differences in sFasL levels were detected in patients with RA before and after anti-TNF-α treatment. CONCLUSION: In RA, an increase in sFas levels closely correlates with improvement in disease activity induced by TNF-α inhibitors, suggesting their ability to modulate Fas-mediated synoviocyte apoptosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".