Assessment of synovial repair in primary knee osteoarthritis after platelet rich plasma (PRP) intra-articular injection
Bibliographic record
Abstract
Primary knee osteoarthritis (KOA) is a persistent condition marked by the gradual deterioration of the joint and cartilage loss on its surfaces. Recently, platelet-rich plasma (PRP) was considered a biological intervention that alleviates symptoms and restricts the advancement of primary KOA in patients. This study aimed to evaluate the effect of intra-articular PRP injections on synovial repair through cytokine assays in 20 patients with primary KOA. Patients received two intra-articular PRP injections, spaced one month apart. The role of PRP was assessed by measuring Transforming growth factor beta (TGF-β) and interleukin-17 (IL-17) levels in synovial fluid before and after the injections. Both visual analogue scale and Western Ontario and McMaster Universities Osteoarthritis index were assessed before and after intervention. IL-17 and TGF-β levels were measured in the synovial fluid using sandwich ELISA technique before the first PRP intra-articular injection and one month after the second injection to assess the synovial repair after PRP injection. Our results showed that the synovial IL-17 levels significantly decreased by 75.21% (p < 0.0001) after intra-articular knee injection, dropping from a range of 102.3-293 (median 173.5: 139.7- 224.5) to 17.86-106 (median 36.38: 23.57- 50.32). In contrast, synovial TGF-β levels significantly increased by 80.3% (p < 0.0001) after intra-articular knee injection, rising from 124-545.5 (mean ± SD: 256.22 ± 123.56) to 693.3-3226 (mean ± SD: 1521.6 ± 765.46). In conclusion, intra-articular PRP administration in primary KOA patients is associated with increased levels of TGF-β and decreased levels of IL-17 in the synovial fluid of the joint. These changes in cytokine levels suggest that PRP treatment effectively reduces inflammation and may contribute to pain relief in primary KOA.
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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) | 0.001 | 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 | 0.000 |
| 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".