Enhanced Expression of Tissue Inhibitor of Metalloproteinases-4 Gene in Human Osteoarthritic Synovial Membranes and Its Differential Regulation by Cytokines in Chondrocytes
Bibliographic record
Abstract
OBJECTIVE: Tissue inhibitors of metalloproteinases (TIMPs) are multi-functional proteins with matrix metalloproteinases-inhibiting activities. We studied expression of anti-inflammatory, TIMP-4 gene in human joint tissues and its regulation by arthritis-associated cytokines. RESULTS: TIMP-4 RNA expression originating from synovial fibroblasts was significantly (2.4 fold; p<0.001) elevated in 8 osteoarthritic (OA) versus 7 non-arthritic synovial membranes. Non-arthritic and OA femoral head and knee chondrocytes displayed substantial but variably constitutive expression of the TIMP-4 mRNA. In articular chondrocytes, transforming growth factor beta (TGF-β1) and oncostatin M (OSM) upregulated TIMP-4 RNA and protein expression while interleukin-1 (IL-1β) and tumor necrosis factor alpha (TNF-α) did not, suggesting differential regulation by arthritis-associated cytokines. Interleukin 17 (IL-17) mildly induced TIMP-4 mRNA. TGF-β1 induction of TIMP-4 expression was partly inhibited by ERK pathway and Sp1 transcription factor inhibitors. CONCLUSION: Enhanced TIMP-4 gene expression in OA synovial membranes and cartilage may be due to induction by TGF-β1, OSM and IL-17, suggesting its pathophysiological role in tissue remodeling in human joints. TGF-β1 induction of TIMP-4 expression is mediated partly by ERK pathway and Sp1 transcription factor.
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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".