Decreased Circulating CD28-negative T Cells in Patients with Rheumatoid Arthritis Treated with Abatacept Are Correlated with Clinical Response
Bibliographic record
Abstract
OBJECTIVE: To verify the hypothesis that blockade of CD28 costimulation by treatment with abatacept in patients with rheumatoid arthritis (RA) might induce a reduction in the number of CD28- T cells, as well as other effector T cell populations. We evaluated whether these variations correlate with clinical response. METHODS: Peripheral blood T cell subsets were longitudinally evaluated by flow cytometry through the analysis of CD28, CD45RA, and CCR7 expression in 16 patients with RA who were treated with abatacept. RESULTS: After 48 weeks of treatment, the proportion and the absolute number of circulating CD8+CD28- T cells decreased (p = 0.008, p = 0.055, respectively, compared with baseline), as well as the proportion of the CD8+CD45RA+CCR7- cells, thought to represent terminally differentiated effector T cells (p = 0.03). Reductions of percentages of circulating CD4+CD28- and CD8+CD28- T cells, and (CCR7-) CD8+ total effector T cells were directly correlated with the reduction of Disease Activity Score 28 C-reactive protein (r = 0.58, p = 0.014; r = 0.47, p = 0.059; r = 0.59, p = 0.012, respectively). CONCLUSION: After therapy with abatacept, circulating CD28- T cells and other effector populations decrease in patients with RA. This decrease is correlated with clinical response.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".