Experimental model for the study of the effects of platelet-rich plasma on the early phases of muscle healing.
Bibliographic record
Abstract
BACKGROUND: There is abundant evidence suggesting that growth factors may play a key role in the healing process, especially in the early stages of inflammation. Despite the reported clinical successes with the use of growth factors there is still a lack of knowledge on the biological mechanism underlying the activity of platelet-rich plasma during the process of muscle healing. The aim of this study was to analyse the early effects of platelet- rich plasma in an easily reproducible animal model. MATERIALS AND METHODS: Wistar male adult rats (n=102) were used in this study. The muscle lesion was created with a scalpel in the flexor sublimis muscles. Platelet-rich plasma was applied immediately after surgery. Treated, untreated and contralateral muscles were analysed by morphological evaluation and western blot assay. RESULTS: Leucocyte infiltration was significantly greater in muscles treated with platelet-rich plasma than in both untreated and contralateral muscles. The latter showed greater leucocyte infiltration when compared to the untreated muscles. Platelet-rich plasma treatment also modified the cellular composition of the leucocyte infiltration leading to increased expression of CD3, CD8, CD19 and CD68 and to decreased CD4 antigen expression in both platelet-rich plasma treated and contralateral muscles. Blood vessel density and blood vessel diameters were not statistically significantly different between the three groups analysed. DISCUSSION: The results of this study showed that treatment with platelet-rich plasma magnified the physiological early inflammatory response following a muscle injury, modifying the pattern of cellular recruitment. Local platelet-rich plasma treatment may exert a direct or, more plausibly, indirect systemic effect on healing processes, at least in the earliest inflammatory phase.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".