Efficacy of Combining PRP and MMP Inhibitors in Treating Moderately Damaged Tendons Ex Vivo
Bibliographic record
Abstract
Platelet-rich plasma (PRP) and broad-spectrum matrix metalloproteinase inhibitors (MMPIs) have been used as therapeutic options for tendinopathy. However, mixed results have been reported regarding their efficacy. We posited that the combination of these two treatment strategies would be more beneficial for healing tendons than each treatment alone. Rat tail tendons were harvested and cultured without mechanical stress for 0, 4, or 10 days. Single and combination treatment with PRP and MMPIs with either broad- or narrow-spectrum (MMP-13 selective), was administered to 4-day stress-deprived (SD) tendons, an ex vivo model for moderate tendinopathy. This treatment was applied to the damaged tendons over 6 days. At the end of their culture time, the tendons were subjected to traction testing and pathohistology, immunohistochemistry, and viability assays. The results showed better histological features for the PRP + narrow-spectrum MMPI group compared with all individual treatment modalities. Moreover, higher fiber density, more elongated nucleus shape, smaller space between fibers, and a trend toward higher mechanical strength were noted for PRP + narrow-spectrum MMPI group compared with 10-day SD tendons. This study shows that the combination of PRP + narrow-spectrum MMPI is a potentially effective treatment approach for tendinopathy. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:1838-1847, 2019.
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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 | 0.001 | 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) | 0.001 | 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".