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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".