CD4+CD25<sup>high</sup>CD127<sup>low/-</sup> Treg Cell Frequency from Peripheral Blood Correlates with Disease Activity in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To investigate whether the frequency of peripheral blood (PB) regulatory T cells (Treg) correlates with the clinical disease activity of rheumatoid arthritis (RA). METHODS: PB Treg cells, defined as the CD4+CD25(high)CD127(low/-) population, were examined by flow cytometry in 48 patients with RA, including 13 who had never received disease-modifying antirheumatic drugs (DMARD), 19 with active disease who were receiving (n = 14) or had received (n = 5) DMARD, and 16 receiving DMARD whose disease was in remission. The clinical disease activity of the patients was defined by the 28-joint Disease Activity Score (DAS28). The association of DAS28, C-reactive protein (CRP), or erythrocyte sedimentation rate (ESR) with the frequency of PB Treg cells was examined. RESULTS: The frequency of PB Treg cells in patients with RA was significantly low compared with that of healthy controls (n = 14). Among the 3 populations of patients with RA, Treg cell frequency was lowest in patients with active RA. In contrast, the Treg cell frequency of patients with RA in remission was similar to that of healthy controls. Accordingly, the frequency of CD4+CD25(high)CD127(low/-) Treg cells negatively correlated with DAS28, CRP, and ESR in patients with RA. CONCLUSION: The data suggest that Treg cells, defined as the CD4+CD25(high)CD127(low/-) population, may contribute to the pathogenesis of RA and be an indicator of disease activity.
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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 | 0.001 | 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".