Epidermal growth factor released from platelet-rich plasma promotes endothelial cell proliferation<i>in vitro</i>
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The therapeutic benefits of platelet-rich plasma (PRP) for the promotion of healing and regeneration of periodontal tissues are thought to result from enrichment in growth factors released from platelets. The aim of this study was to evaluate the effects of specific growth factors released from PRP on endothelial cell proliferation. MATERIAL AND METHODS: The levels of vascular endothelial growth factor (VEGF), platelet-derived growth factor BB (PDGF-BB), basic fibroblast growth factor (bFGF) and epidermal growth factor (EGF) in supernatants of calcium- and thrombin-activated PRP samples from five donors were quantified by enzyme-linked immunosorbent assay. Supernatants were treated with neutralizing antibodies specific to each growth factor, and the effects of these treatments on human umbilical vein endothelial cell (HUVEC) proliferation in vitro were determined. The effect of removing EGF from PRP supernatants with antibody-coated beads on HUVEC proliferation was also tested. RESULTS: Average concentrations of VEGF, PDGF-BB, bFGF and EGF in PRP supernatants were 189, 27,190, 39.5 and 513 pg/mL, respectively. The addition of EGF neutralizing antibodies to the PRP supernatants significantly reduced HUVEC proliferation (up to 40%), while such an inhibition was not observed following neutralization of the other growth factors. Removal of EGF from PRP supernatants by treatment with antibody-coated beads also resulted in a significant decrease in HUVEC proliferation. Recombinant EGF increased HUVEC proliferation in vitro in a dose-dependent manner. CONCLUSION: This study showed that PRP supernatants are highly mitogenic for endothelial cells and provided evidence that this effect may be due, at least in part, to the presence of EGF. In vivo experiments are needed to confirm the roles of specific growth factors released from PRP in the healing of oral surgical and/or periodontal wounds.
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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".