Effects of i‐<scp>PRF</scp>, A‐<scp>PRF</scp>+, and <scp>EMD</scp> on Osteogenic Potential of Osteoblasts on Titanium
Bibliographic record
Abstract
OBJECTIVE: The study evaluates three biologically active substances with known bone-inductive potential on previously decontaminated titanium (Ti) discs. MATERIAL AND METHODS: Rough and smooth Ti surfaces were contaminated with a multispecies biofilm and cleaned with a chitosan brush. Discs were treated either with injectable-platelet-rich fibrin (i-PRF), advanced platelet-rich fibrin (A-PRF+), or enamel matrix derivatives (EMDs) before osteoblast seeding. RESULTS: Biocompatibility, adhesion, migration, and gene expression of runt-related transcription factor 2 (RUNX2), collagen Type I Alpha 2 (COL1a2), alkaline phosphatase (ALP), osteocalcin (OC), and osteonectin (ON) were performed. All the tested biologic agents similarly increased cell viability. Specifically, osteoblasts seeded over i-PRF and EMD-treated surfaces showed improvement in adhesion and migration and significantly increased ALP, OC, ON, RUNX-2, and COL1a2 mRNA levels up to 2.8 fold (p < 0.05) with no differences between Ti surfaces. CONCLUSIONS: i-PRF and EMD possess beneficial bioactive properties that enhance tissue healing and promote regeneration on thoroughly sterilized surfaces. Biologically active materials may hold the potential to influence the process of implant re-osseointegration, which warrants more research since sterilization of the affected surfaces under clinical conditions is still not reliably possible and remains one of the greatest challenges.
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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".