Platelet Concentrates: Effects of Calcium and Thrombin on Endothelial Cell Proliferation and Growth Factor Release
Bibliographic record
Abstract
BACKGROUND: Clinical evidence suggests that platelet concentrate (PC) could have beneficial therapeutic effects on hard and soft tissue healing, due to the contents of growth factors (GFs) stored in the platelets. The objectives of this study were: 1) to determine the concentrations of platelet-derived growth factor-BB (PDGF-BB), transforming growth factor-beta1 (TGF-beta1), vascular endothelial growth factor (VEGF), and basic fibroblast growth factor (bFGF) released from PCs and whole blood (WB), before and after the addition of various concentrations of calcium and thrombin, and 2) to assess the physiological importance of the released GFs on angiogenesis. METHODS: WB and PCs were harvested and prepared from three healthy volunteers. Enzyme-linked immunosorbent assay tests, specific for PDGF-BB, TGF-beta1, VEGF, and bFGF, were performed on WB and PC supernatants, collected before and 30 minutes after the addition of various concentrations of calcium and thrombin. The supernatants were also added to human umbilical vein endothelial cell (HUVEC) cultures in order to measure their effects on endothelial cell proliferation. RESULTS: Growth factor concentrations detected in PC supernatants were significantly greater (280% to 800% increase) than concentrations present in WB supernatants. Calcium and thrombin induced immediate GF release from PCs in a dose-dependent fashion. Furthermore, PC supernatants led to greater HUVEC proliferation rates than WB supernatants. However, there was no correlation between the concentrations of specific GFs and HUVEC proliferation rates. CONCLUSION: These results suggest that PCs could stimulate blood vessel formation. They also reinforce the relevance for using PCs in regenerative therapies.
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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 | 0.001 |
| 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 | 0.001 | 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".