Exposição a fatores de crescimento e proteínas típicos de plasma rico em plaquetas inibe a formação de nódulos de mineralização de culturas de células osteogênicas crescidas sobre titânio
Bibliographic record
Abstract
Background: Despite wide clinical application, the efficacy of platelet-rich plasma (PRP) for repairing bone defects and enhancing osseointegration of metal implants is still subject of debate. The objective of the present study was to evaluate the effects of a well-defined mixture of growth factors (GFs) and proteins (GFs+proteins) on the development of the osteogenic phenotype on titanium (Ti) in vitro. The composition of the mixture was based on the major components found in PRP preparations. Methods: The PRP-like mixture contained PDGF-BB, TGF-β1, TGF-β2, albumin, fibronectin, and thrombospondin. Osteoblastic cells were obtained by enzymatic digestion of human alveolar bone and cultured under standard osteogenic condition until subconfluence. They were then subcultured on Ti discs up to 14 days. Treated cultures were exposed during the first 7 days to osteogenic medium supplemented with GFs+proteins and to osteogenic medium alone thereafter. Control cultures were exposed to only osteogenic medium throughout the culture interval. Dose-response experiments were carried out using rat primary calvarial cells exposed to GFs+proteins and 1:10 or 1:100 dilutions of the mixture. Results: Treated human-derived cell cultures exhibited a significantly higher number of cells from day 4 on and of cycling cells at days 1 and 4, significantly reduced levels for alkaline phosphatase (ALP) activity, and no Alizarin red stained areas at day 14. Although the 1:10 and 1:100 dilutions restored the proliferative activity of rat calvaria-derived osteogenic cells to control levels, mineralized bone-like nodule formation was only observed with the 1:100 dilution. Conclusions: The present results demonstrated that a PRP-like protein mixture inhibits development of the osteogenic phenotype in both human and rat osteoblastic cell cultures grown
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".