Role of oral hyaluronic acid for joint health: insights from rat models and clinical trials
Bibliographic record
Abstract
Background: Early studies have demonstrated the significant potential of hyaluronic acid (HA) in alleviating osteoarthritis (OA); however, the relationship between different molecular weights (MWs) and efficacy remains unclear. Methods: The rat model was used to evaluate the effects of different MWs of HA on OA and to identify the MW that was most effective in alleviating OA. Based on this, a clinical trial was conducted to verify the selected HA's clinical efficacy. Results: The results showed that HA significantly reduced joint swelling in rats, dramatically increased HA content in the serum and joint synovial fluid, decreased serum and joint synovial fluid levels of pro-inflammatory cytokines, and reduced the expression of matrix metalloproteinases (MMPs), inducible nitric oxide synthase (iNOS), and cyclooxygenase-2 (COX-2) when compared with the OA group, especially high-MW HA. Importantly, these protective roles may be attributed to the immune regulation of HA. Clinical trial results indicated that HA significantly decreased pain, stiffness, and physical function of Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores and had no significant impact on blood and urine indices. Conclusion: Our findings suggest that oral supplementation with HA can reduce the progression of arthritis, pain, and cartilage damage, and can be a new strategy to relieve joint discomfort.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".