Oncostatin M in combination with tumor necrosis factor α induces cartilage damage and matrix metalloproteinase expression in vitro and in vivo
Bibliographic record
Abstract
OBJECTIVE: To determine the effects of the proinflammatory cytokine combination of oncostatin M (OSM) and tumor necrosis factor alpha (TNFalpha) on cartilage destruction in both in vitro and in vivo model systems. METHODS: The release of collagen and proteoglycan was assessed in bovine cartilage explant cultures, while messenger RNA (mRNA) from bovine chondrocytes was analyzed by Northern blotting. Immunohistochemistry was performed on sections prepared from murine joints following injection of adenovirus vectors encoding murine OSM and/or murine TNFalpha. RESULTS: The combination of OSM + TNFalpha induced significant collagen release from bovine cartilage, accompanied by high levels of active collagenolytic activity. Northern blot analysis indicated that this cytokine combination synergistically induced matrix metalloproteinase 1 (MMP-1), MMP-3, and MMP-13 mRNA. The in vivo data clearly indicated that OSM + TNFalpha overexpression increased MMP levels and decreased levels of tissue inhibitor of metalloproteinases 1 (TIMP-1). Specifically, OSM + TNFalpha induced marked synovial hyperplasia, inflammation, and cartilage and bone destruction with a concomitant increase in MMP expression in both cartilage and synovium and decreased TIMP-1 expression in the articular cartilage. These effects were markedly greater than those seen with either cytokine alone. CONCLUSION: This study demonstrates that OSM + TNFalpha represents a potent proinflammatory cytokine combination that markedly induces MMP production in both cartilage and synovium, thus promoting joint destruction.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".