Nonactivated versus Thrombin-Activated Platelets on Wound Healing and Fibroblast-to-Myofibroblast Differentiation In Vivo and In Vitro
Bibliographic record
Abstract
BACKGROUND: Platelet preparations for tissue healing are usually preactivated before application to deliver concentrated growth factors. In this study, the authors investigated the differences between nonactivated and thrombin-activated platelets in wound healing. METHODS: The healing effects (i.e., wound closure, myofibroblast formation, and angiogenesis) of nonactivated and thrombin-activated platelets were compared in experimental wounds in diabetic (db/db) animals. In vitro, fibroblast phenotype and function were tested in response to platelets and activated platelets. No treatment served as a negative control. RESULTS: Wounds treated with platelets reached 90 percent closure after 15 days, faster than activated platelets (26 days), and with higher levels of myofibroblasts and angiogenesis. In vitro, platelets enhanced cell migration and induced two-fold higher myofibroblast differentiation and contraction compared with activated platelets. CONCLUSIONS: Platelets stimulate wound healing more efficiently compared with activated platelets by enhancing fibroblast differentiation and contractile function. Similar levels of growth factors may induce different biological effects when delivered "on demand" rather than in an initial bolus.
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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 | 0.000 |
| 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) | 0.001 | 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".