Reosseointegration Following Regenerative Therapy of Tissue‐Engineered Bone in a Canine Model of Experimental Peri‐Implantitis
Bibliographic record
Abstract
BACKGROUND: Due to the existence of inflammation and limited osteogenesis on the precontaminated implant surface, reosseointegration is difficult to realize by current therapies. Tissue-engineering strategy has been proved quite effective in intractable bone defect situation. PURPOSE: This study was designed to see whether the adoption of tissue-engineered bone complex of adipose-derived stem cells (ASCs) and bone morphogenetic protein-2 (BMP-2) gene delivery would work efficiently in the correction of experimental peri-implantitis. METHODS: All premolars in both side of mandibular were removed from six beagle canines three months before implant placement. Typical peri-implantitis were then induced by three month ligature placement. After the implementation of identical anti-bacterial and mechanical debridement therapy, the shaped peri-implant defect were stuffed with four groups of constructs, as A: beta tricalcium phosphate (β-TCP); B: β-TCP with ASCs; C: β-TCP with enhanced green fluorescent protein gene transduced ASCs (AdGFP-ASCs); and D: β-TCP with bone morphogenetic protein-2 gene-modified ASCs (AdBMP-2-ASCs). Systematic radiographic, micro-CT, and histomorphometrical assessments were performed. RESULTS: After six months of healing, more bone formation and reosseointegration was found around the implant of groups B and C than group A. And group D further promoted the new bone height and reosseointegration percentage. Moreover, sequential fluorescence labeling tells that group D exhibited the quickest and strongest bone formation on the cleaned implant surface during the entire observation period as compared to the other three groups. CONCLUSIONS: These data demonstrated that tissue engineered bone of ASCs, BMP-2 gene delivery, and β-TCP could exert powerful therapeutic effect on peri-implantitis as expected, which may suggest a feasible way to maintain the stability and masticatory function of dental implant.
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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 | 0.001 | 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) | 0.000 | 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".