Cytokines and Regulatory T Cells in Rheumatoid Arthritis and Their Relationship with Response to Corticosteroids
Bibliographic record
Abstract
OBJECTIVE: To analyze circulating cytokines and regulatory T cells (Treg) in patients with rheumatoid arthritis (RA) of different durations, and their association with functional interleukin 10 (IL-10) and tumor necrosis factor-α (TNF-α) genotypes in patients treated with corticosteroids. METHODS: Serum levels of IL-6, IL-10, IL-17, IL-18, TNF-α, and transforming growth factor-ß (TGF-ß) were quantified in 196 patients and 61 healthy controls. Percentage of CD4+CD25high cells was determined by flow cytometry and Foxp3 expression by real-time reverse-transcription polymerase chain reaction. Data were related to clinical measurements and presence of the genotype -1082GG IL-10/-308GG TNF-α, previously associated with good response to corticosteroids. RESULTS: Levels of TNF-α, IL-6, and IL-18 were significantly higher in patients compared to controls, while TGF-ß and IL-10 were lower. Serum samples of patients at disease onset (n = 32) had increased IL-6 and decreased TGF-ß, but there were no differences in other cytokines. These patients also presented a higher percentage of CD4+CD25high cells than those with established disease, although no significant differences were detected in Foxp3. Patients under corticosteroid treatment who were carriers of the good responder genotype had higher levels of TGF-ß, Foxp3, and Treg compared to patients with other genotypes, while relatively lower levels of TNF-α and IL-17 were observed. CONCLUSION: Patients at onset of RA present fewer alterations in cytokine levels and Treg than those with longer disease duration, supporting the role of disease progression in subsequent changes. The antiinflammatory balance observed in high IL-10/low TNF-α patients treated with prednisone supports the use of these genetic polymorphisms as predictors of response to corticosteroid therapy.
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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 | 0.001 |
| 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".