Increasing Intrinsic Hyaluronic Acid and Down-regulation of Inflammation Markers in Synovial Fluid from Patients with Knee Osteoarthritis may be Associated with Symptom Relief after Intra-articular Injection of Hyaluronic Acid
Bibliographic record
Abstract
Abstract Background: Hyaluronic acid (HA) is the most common intra-articular therapy used to treat mild to moderate osteoarthritis (OA). However, the mechanism involved in this treatment is still not fully understood. The aim of the present study was to examine the effect and the possible mechanism of intra-articular HA (IAHA) injection in patients with knee osteoarthritis (OA).Methods: Twenty-eight patients with Kellgren–Lawrence scale II to III were enrolled in this study. All patients underwent ultrasound-guided injection using three consecutive weekly IAHA. Functional ability and pain were determined by the Western Ontario and McMaster University Index (WOMAC) questionnaire and visual analog scale (VAS). Further, the levels of HA, metalloproteinase (MMP)-1, MMP-3, MMP-13, interleukin (IL)-1β and IL-6 in synovial fluid were determined weekly before HA injection. Results: Functional improvement and pain relief were observed 4 weeks after treatment. At week 4, a significant increase of HA concentration was found, and the concentration of inflammatory cytokines including IL-1β, and IL-6, as well as matrix MMP-3 and MMP-13 significantly decreased. However, no significant difference was observed in MMP-1 level. Conclusion: These results suggest that increasing HA accumulation in synovial fluid may be associated with disease relief after weekly IAHA injection in patients with knee 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 | 0.001 |
| 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".