Modulation of TIMP-1 synthesis by antiinflammatory cytokines and prostaglandin E2 in interleukin 17 stimulated human monocytes/macrophages.
Bibliographic record
Abstract
OBJECTIVE: To examine the regulation of tissue inhibitor of metalloproteinase 1 (TIMP-1) synthesis by interleukin 17 (IL-17) stimulated human monocytes/macrophages in primary culture in the presence of prostaglandin E2 (PGE2) and antiinflammatory cytokines, and to compare this with the regulation of matrix metalloproteinase (MMP-9) production. METHODS: IL-17 stimulated human monocytes isolated from the peripheral blood of healthy donors were cultured in the presence of PGE2, cyclic adenosine monophosphate (cAMP) mimetics (IBMX, cAMP, forskolin, cholera toxin), or antiinflammatory cytokines (IL-4, IL-10, IL-13), or with protein kinase inhibitors of diverse specificity. MMP-9 and TIMP-1 were measured using specific ELISA, while expression of specific messenger RNA was determined by Northern blotting. RESULTS: IL-17 stimulated an increased level of MMP-9 production relative to TIMP-1 production in monocytes/macrophages. Stimulation was accompanied by upregulation of specific MMP-9 mRNA expression relative to TIMP-1 mRNA. Exogenous PGE2, cAMP, and cAMP-mimetics completely inhibited both basal and IL-17 induced MMP-9 synthesis, while only IL-17 induced TIMP-1 synthesis was abrogated. The same effect was found for the antiinflammatory cytokines. Both basal and IL-17 induced production of TIMP-1 involved p42/44 and p38 kinases and nuclear factor kappaB signaling pathways. CONCLUSION: The excess of MMP-9 over TIMP-1 production, and decreased inhibition of MMP-9 activity in chronic rheumatoid diseases, may result in cartilage degradation and 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) | 0.002 | 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".