Tumor Necrosis Factor Blockade Differentially Affects Innate Inflammatory and Th17 Cytokines in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of a tumor necrosis factor-α (TNF-α) inhibitor (etanercept) on innate inflammatory and Th17 cytokines in patients with rheumatoid arthritis (RA). METHODS: Serum samples were collected from 40 patients with active RA refractory to conventional disease-modifying antirheumatic drugs who initiated therapy with etanercept plus methotrexate (MTX). Treatment response was assessed at Week 24 according to the European League Against Rheumatism response criteria. Serum levels of interleukin 6 (IL-6), TNF-α, IL-32, IL-23, IL-17A, IL-21, and IL-22 were measured in patients with RA and 25 healthy controls. RESULTS: Patients with RA had increased levels of IL-6 (p < 0.001), IL-32 (p < 0.001), IL-23 (p < 0.001), and a trend toward increased IL-21 in the sera compared to controls. At 24 weeks' posttreatment, followup serum samples of etanercept responders had decreased levels of IL-6 (p < 0.001) and increased IL-21 (p < 0.05) and IL-32 (p < 0.001), while there were no differences in cytokine levels in non-responders. Serum IL-6 levels were positively correlated with levels of erythrocyte sedimentation rate (r = 0.458, p < 0.01), C-reactive protein (r = 0.593, p < 0.01), and 28-joint Disease Activity Score (r = 0.432, p < 0.01) at baseline. Serum IL-21 levels were positively correlated with levels of rheumatoid factor (r = 0.513, r = 0.633, both p < 0.01) and antimutated citrullinated vimentin antibodies (r = 0.515, p < 0.01; r = 0.428, p < 0.05) at baseline and after 24 weeks of treatment with etanercept. CONCLUSION: Multiple inflammatory pathways contribute to persistent chronic inflammation in RA. In contrast to nonresponders, etanercept therapy modulated serum cytokine levels and caused a marked decrease of IL-6 levels in responders. IL-21 might be involved in the regulation of autoantibody production in RA.
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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 | 0.000 |
| 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".