Orthodontic Tooth Movement in Alveolar Cleft Repaired with a Tissue Engineering Bone: An Experimental Study in Dogs
Bibliographic record
Abstract
Tissue engineering approaches have been successfully used in repairing bone defects and have become a viable alternative to autologous bone. The aim of the present study was to investigate if a construct of porous beta-tricalcium phosphate (β-TCP) combined with osteogenically induced bone marrow stromal cells (bMSCs) could repair alveolar cleft, and allow for subsequent orthodontic tooth movement in a canine model. Twelve alveolar osteotomy surgeries in six animals were made bilaterally and randomly implanted by (1) tissue-engineered bone complex of bMSCs/β-TCP (group A, n=4), (2) β-TCP alone (group B, n=4), and (3) autologous bone obtained from iliac bone (group C, n=4). Contralateral alveolar defects were created in one animal and left untreated to serve as blank control to observe spontaneous healing of the defects. Sequential fluorescent labeling and radiographic observation was used to evaluate new bone formation and mineralization in each defect. Orthodontic tooth movement was initiated 8 weeks after surgical operation for 12 weeks, and then the dogs were sacrificed for histological and histomorphometric analysis. Results indicated that the tissue-engineered complex with bMSCs/β-TCP dramatically promoted new bone formation and mineralization and achieved a favorable height of the repaired alveolar when compared with β-TCP alone, which absorbed severely. The overall effect of the tissue-engineered bone was equivalent to autologous bone; the physiological function of the alveolar bone was restored by allowing the adjacent teeth to move into the newly formed bone in the grafted region. This study demonstrated that the tissue engineering bone from the combination of β-TCP and bMSCs is a feasible clinical approach for patients with alveolar cleft and the subsequent orthodontic tooth movement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".