A platelet‐rich plasma‐like growth factor‐protein mixture inhibits development of the osteogenic phenotype in osteoblastic cell cultures grown on titanium
Bibliographic record
Abstract
The efficacy of platelet‐rich plasma (PRP) for enhancing osseointegration of titanium (Ti) implants is still subject of debate. The present study evaluated the effects of a well‐defined PRP‐like mixture containing PDGF‐BB, TGF‐β1, TGF‐β2, albumin, fibronectin, and thrombospondin (GFs + proteins) on the development of the osteogenic phenotype on Ti. Human alveolar bone‐derived cells were subcultured on Ti discs and exposed during the first 7 days to osteogenic medium supplemented with GFs + proteins and to osteogenic medium alone thereafter up to 14 days. Control cultures were exposed to only osteogenic medium. Dose‐response experiments were carried out using rat calvarial cells exposed to GFs + proteins and 1:10 or 1:100 dilutions of the mixture. Treated human‐derived cell cultures exhibited a significantly higher number of cells and of Ki‐67‐positive cells, significantly reduced alkaline phosphatase (ALP) activity, and no Alizarin red staining. Although the 1:10 and 1:100 dilutions of the mixture restored the proliferative activity of rat‐derived cells to control levels and promoted a significant increase in ALP activity compared to GFs + proteins, calcified nodule formation was only observed with the 1:100 dilution. The present results demonstrated that a PRP‐like protein mixture inhibits development of the osteogenic phenotype in both human and rat osteoblast cell cultures grown on Ti. FAPESP and CNPq .
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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) | 0.000 | 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 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".