Therapeutic implications for interferon-alpha in arthritis: a pilot study.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the therapeutic potential of interferon-a (IFN-a) in osteoarthritis (OA) and rheumatoid arthritis (RA) by examining regulation of cytokine antagonist expression. METHODS: Expression of interleukin 1 receptor antagonist (IL-1Ra) and soluble tumor necrosis factor receptor (sTNFR) was examined by ELISA in cells from freshly isolated synovial fluids (SF) and synovial tissues (ST) from patients with OA or RA, either left untreated or treated with IFN-a. Single (7) and paired (5) SF and ST cells from OA and RA patients were examined. As well, the ability of IFN-a to regulate gene expression levels for osteoprotegerin (OPG) and osteoprotegerin ligand (OPGL) was examined in freshly isolated SF cells from patients with RA, by reverse transcriptase polymerase chain reaction. RESULTS: IL-1Ra and sTNFR were found to be constitutively expressed in OA and RA SF and ST cells. IFN-a treatment resulted in an increase in both IL 1Ra and sTNFR production. Freshly isolated RA SF cells exhibited constitutive OPGL gene expression in both the non-T and T cell fractions of the SF. In contrast, OPG gene expression levels were undetectable or low. IFN-a treatment of RA SF cells resulted in upregulation of OPG gene expression in the T cell fraction of the RA SF cells, whereas OPGL gene expression remained unaffected. CONCLUSION: These in vitro data suggest a therapeutic role for IFN-a in the treatment of arthritis through upregulation of critical cytokine antagonists.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".