Increased Levels of Interleukin 33 in Sera and Synovial Fluid from Patients with Active Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine levels of interleukin 33 (IL-33) in serum and synovial fluid (SF) and their clinical associations in patients with rheumatoid arthritis (RA). To evaluate the ability of activated peripheral blood mononuclear cells (PBMC) and fibroblast-like synoviocytes (FLS) from RA patients to release IL-33. METHODS: Sera were obtained from 59 patients with RA, 10 patients with infectious diseases, and 42 healthy volunteers. SF samples were obtained from 15 patients with RA and 13 with osteoarthritis. IL-33 levels were measured using a sandwich ELISA after removal of rheumatoid factor with protein A-Sepharose beads. FLS were stimulated with IL-1beta and tumor necrosis factor, and treated with or without chemical damage. PBMC were stimulated with anti-CD3/CD28 antibodies. The levels of IL-33 were measured in the culture supernatants and cell lysates by ELISA or immunoblotting. RESULTS: Serum IL-33 levels were significantly higher in RA patients, especially in the high disease activity group compared to the moderate or low activity group. IL-33 levels in SF were elevated in all 15 RA patients measured. IL-33 levels were higher in SF samples than in sera in 7 RA patients measured simultaneously. The 30-kDa IL-33 precursor was detected in the culture supernatants of damaged FLS but was not detected in those of activated PBMC and non-damaged FLS. CONCLUSION: IL-33 levels were elevated in sera and SF samples from patients with RA, and correlated with disease activity. IL-33 was produced mainly in inflamed joints; IL-33/ST2L signaling might play an important role in joint inflammation of human 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".