Treatment With a Growth Factor–Protein Mixture Inhibits Formation of Mineralized Nodules in Osteogenic Cell Cultures Grown on Titanium
Bibliographic record
Abstract
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. This study aimed to evaluate the effects of a well-defined PRP-like mixture containing platelet-derived growth factor-BB, transforming growth factor (TGF)-beta1, TGF-beta2, albumin, fibronectin, and thrombospondin [growth factors (GFs) + proteins] on the development of the osteogenic phenotype on titanium (Ti) in vitro. Human alveolar bone-derived osteoblastic 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 primary 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 cycling cells at days 1 and 4 and of total cells at days 4 and 7, significantly reduced alkaline phosphatase (ALP) activity at days 4, 7, and 10, and no Alizarin red-stained areas (calcium deposits) at day 14, indicating an impairment in osteoblast differentiation. Although the 1:10 and 1:100 dilutions of the mixture restored the proliferative activity of rat-derived osteogenic cells to control levels and promoted a significant increase in ALP activity at day 10 compared with GFs + proteins, mineralized nodule formation was only observed with the 1:100 dilution ( approximately 50% of the control). These results showed that a PRP-like protein mixture inhibits development of the osteogenic phenotype in both human and rat osteoblastic cell cultures grown on Ti.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".