Platelet-Rich Plasma and Skeletal Muscle Healing: A Molecular Analysis of the Early Phases of the Regeneration Process in an Experimental Animal Model
Bibliographic record
Abstract
Platelet-rich plasma (PRP) has received increasing interest in applied medicine, being widely used in clinical practice with the aim of stimulating tissue healing. Despite the reported clinical success, there is still a lack of knowledge when considering the biological mechanisms at the base of the activity of PRP during the process of muscle healing. The aim of the present study was to verify whether the local delivery of PRP modulates specific molecular events involved in the early stages of the muscle regeneration process. The right flexor sublimis muscle of anesthetized Wistar rats was mechanically injured and either treated with PRP or received no treatment. At day 2 and 5 after surgery, the animals were sacrificed and the muscle samples evaluated at molecular levels. PRP treatment increased significantly the mRNA level of the pro-inflammatory cytokines IL-1β, and TGF-β1. This phenomenon induced an increased expression at mRNA and/or protein levels of several myogenic regulatory factors such as MyoD1, Myf5 and Pax7, as well as the muscular isoform of insulin-like growth factor1 (IGF-1Eb). No effect was detected with respect to VEGF-A expression. In addition, PRP application modulated the expression of miR-133a together with its known target serum response factor (SRF); increased the phosphorylation of αB-cristallin, with a significant improvement in several apoptotic parameters (NF-κB-p65 and caspase 3), indexes of augmented cell survival. The results of the present study indicates that the effect of PRP in skeletal muscle injury repair is due both to the modulation of the molecular mediators of the inflammatory and myogenic pathways, and to the control of secondary pathways such as those regulated by myomiRNAs and heat shock proteins, which contribute to proper and effective tissue regeneration.
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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".