Potential Role of Hyaluronic Acid on Bone in Osteoarthritis: Matrix Metalloproteinases, Aggrecanases, and RANKL Expression are Partially Prevented by Hyaluronic Acid in Interleukin 1-stimulated Osteoblasts
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of hyaluronic acid (HA) on proteolytic enzymes and bone remodeling mediators induced by interleukin 1β (IL-1β) and related to cartilage catabolism in murine osteoblasts. METHODS: Osteoblasts were obtained from Swiss mice and cultured for 3 weeks. HA-treated osteoblasts were incubated with 100 μg/ml HA during the last week of culture, then stimulated with IL-1β (10 ng/ml) for 24 h. The expression of matrix metalloproteinases 3 and 13 (MMP-3 and MMP-13), ADAMTS-4 and ADAMTS-5, tissue inhibitor of metalloproteinases (TIMP), osteoprotegerin, and receptor activator of nuclear factor-κB ligand (RANKL) was determined by real-time polymerase chain reaction. MMP-3 and MMP-13 release was assessed by Western blot analysis. RESULTS: IL-1β increased the mRNA levels of MMP-3 and MMP-13 and ADAMTS-4 and ADAMTS-5 and release of MMP-3 and MMP-13. Seven days of HA treatment significantly prevented the IL-1β-increased mRNA levels of MMP-3 (-61%, p < 0.01), MMP-13 (-56%, p < 0.01), ADAMTS-4 (-58%, p < 0.05), ADAMTS-5 (-52%, p < 0.01), and RANKL (-49%, p < 0.05), but not TIMP. As well, IL-1β-induced production of MMP-3 and MMP-13 was inhibited, by 27% (p < 0.01) and 40% (p < 0.01), respectively. CONCLUSION: In an inflammatory context in murine osteoblasts, HA can inhibit the expression of MMP and ADAMTS. Because HA can counteract the production of these mediators in chondrocytes, its beneficial effect in osteoarthritis may be due to its action on cartilage and subchondral bone.
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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".