Comprehensive Evaluation of Cryopreserved Bone‐Derived Osteoblasts for the Repair of Segmental Mandibular Defects in Canines
Bibliographic record
Abstract
BACKGROUND: The repair of segmental mandibular defects remains challenging in the clinic. Previous studies have shown that cryopreserved bone-derived osteoblasts (CBOs) have good proliferation and osteogenicity. However, whether these cells can be used in the repair of segmental mandibular defects is largely unknown. PURPOSE: In this study, we applied CBOs combined with beta-tricalcium phosphate (β-TCP) to repair a segmental mandibular defect in canines and thus established the feasibility of using this type of tissue-bank cell for the repair of large bone defects in the future. MATERIAL AND METHODS: Sixteen segmental mandibular defects in 16 animals were made on the right side. Sequential radiographs, computer tomography, polychrome fluorescent labeling, immunohistochemical staining, and histological analysis were used to evaluate the effects of tissue-engineered bone for segmental mandibular defects. RESULTS: Our results demonstrated that CBOs combined with β-TCP promoted bone mineralization and deposition at the early stage, and bony union was achieved in the CBO and fresh bone-derived osteoblast (FBO) groups. However, nonunion and minimal callus were present in the β-TCP group. Furthermore, there was a large amount of newly formed bone in the CBO and FBO groups and in the autogenous bone group. Additionally, osteocalcin immunohistochemistry showed intensive osteocalcin immunoreactivity in the bone matrix of the CBO and FBO groups. CONCLUSIONS: These data indicate that CBOs implanted in a scaffold can promote new bone formation, and this tissue-engineered bone can repair critically sized segmental mandibular defects in canines. The use of CBOs combined with β-TCP may be an effective approach for the reconstruction of segmental mandibular defects in the clinic.
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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) | 0.000 | 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".