Peri‐Implant Bone Regeneration Using <scp>rhPDGF‐BB</scp>, <scp>BMSCs</scp>, and <scp>β‐TCP</scp> in a Canine Model
Bibliographic record
Abstract
BACKGROUND: The presence of insufficient bone volume remains a major clinical problem for dental implant placement to restore oral function. Tissue engineering provides a promising approach for inducing bone regeneration and enhancing osseointegration in dental implants. PURPOSE: The tissue-engineered bone consisting of recombinant human platelet-derived growth factor (rhPDGF-BB), bone marrow stem cells (BMSCs), and beta-tricalcium phosphate (β-TCP) particles was validated for the first time in a preclinical large animal canine model in terms of its ability to promote new bone formation around the implants, as well as osseointegration between the tissue-engineered bone and dental implants. MATERIALS AND METHODS: Proliferation and osteogenic differentiation of canine BMSCs treated with rhPDGF-BB were evaluated with an MTT, alkaline phosphatase (ALP) activity, Alizarin Red staining, and real-time quantitative PCR (RT-qPCR) analysis of osteogenic genes. The therapeutic potential of tissue-engineered bone consisting of rhPDGF-BB/BMSCs/β-TCP in bone repair was evaluated in mesial-implant defects of immediate postextraction implants in the canine mandible. RESULTS: rhPDGF-BB treatment significantly increased proliferation and osteogenic differentiation of canine BMSCs. Furthermore, the tissue-engineered bone consisting of rhPDGF-BB/BMSCs/β-TCP significantly enhanced bone formation and osseointegration. CONCLUSION: This study provides important evidence that supports the potential application of rhPDGF-BB/BMSCs/β-TCP tissue-engineered bone in immediate implantation for oral function restoration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".