Inhibition of Wnt/β-catenin signaling ameliorates osteoarthritis in a murine model of experimental osteoarthritis
Bibliographic record
Abstract
Osteoarthritis (OA) is a degenerative joint disease involving both cartilage and synovium. The canonical Wnt/β-catenin pathway, which is activated in OA, is emerging as an important regulator of tissue repair and fibrosis. This study seeks to examine Wnt pathway effects on synovial fibroblasts and articular chondrocytes as well as the therapeutic effects of Wnt inhibition on OA disease severity. Mice underwent destabilization of the medial meniscus surgery and were treated by intra-articular injection with XAV-939, a small-molecule inhibitor of Wnt/β-catenin signaling. Wnt/β-catenin signaling was highly activated in murine synovial fibroblasts as well as in OA-derived human synovial fibroblasts. XAV-939 ameliorated OA severity associated with reduced cartilage degeneration and synovitis in vivo. Wnt inhibition using mechanistically distinct small-molecule inhibitors, XAV-939 and C113, attenuated the proliferation and type I collagen synthesis in synovial fibroblasts in vitro but did not affect human OA-derived chondrocyte proliferation. However, Wnt modulation increased COL2A1 and PRG4 transcripts, which are downregulated in chondrocytes in OA. In conclusion, therapeutic Wnt inhibition reduced disease severity in a model of traumatic OA via promoting anticatabolic effects on chondrocytes and antifibrotic effects on synovial fibroblasts and may be a promising class of drugs for the treatment of OA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".