Circulating Tfh cells are differentially modified by abatacept or TNF blockers and predict treatment response in rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVE: CD4+CXCR5+PD-1hi follicular helper T (Tfh) cells dwell in the germinal centres (GCs) of lymphoid organs and participate in RA pathogenesis. The frequency of their circulating counterparts (cTfh frequency) is expanded in RA and correlates with the pool of GC Tfh cells. Our objective was to study the effect of abatacept (ABT) or TNF blockers (TNFbs) on the cTfh frequency in RA. METHODS: Peripheral blood was drawn from seropositive, long-standing RA patients chronically receiving conventional synthetic DMARDs (csDMARDs; n = 45), TNFb (n = 59) or ABT (n = 34) and healthy controls (HCs; n = 137). Also, patients with an incomplete response to csDMARDs (n = 41) who initiated TNFb (n = 19) or ABT (n = 22) were studied at 0 and 12 months. The cTfh frequency was examined by cytometry. RESULTS: As compared with HCs, an increased cTfh frequency was seen in seropositive, long-standing RA patients chronically receiving csDMARDs or TNFb but not ABT. After changing from csDMARDs, the cTfh frequency did not vary in patients who were given TNFb but decreased to HC levels in those given ABT. In the ABT group, the baseline cTfh frequency was higher for patients who attained 12-month remission (12mr) vs those who remained active (12ma): 0 month cut-off for remission >0.38% [sensitivity 92%, specificity 90%, odds ratio (OR) 25.3]. Conversely, in the TNFb group, the baseline cTfh frequency was lower for 12mr vs 12ma: 0 month cut-off for non-remission >0.44% (sensitivity 67%, specificity 90%, OR 8.5). CONCLUSION: ABT but not TNFb was able to curtail the cTfh frequency in RA. A higher baseline cTfh frequency predicts a good response to ABT but a poor response to TNFb.
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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 | 0.001 |
| 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".