Hydrogel-chitosan and polylactic acid-polycaprolactone bioengineered scaffolds for reconstruction of mandibular defects: a preclinical in vivo study with assessment of translationally relevant aspects
Bibliographic record
Abstract
Background: Reconstruction of mandibular bone defects is a surgical challenge, and microvascular reconstruction is the current gold standard. The field of tissue bioengineering has been providing an increasing number of alternative strategies for bone reconstruction. Methods: In this preclinical study, the performance of two bioengineered scaffolds, a hydrogel made of polyethylene glycol-chitosan (HyCh) and a hybrid core-shell combination of poly (L-lactic acid)/poly ( ε -caprolactone) and HyCh (PLA-PCL-HyCh), seeded with different concentrations of human mesenchymal stromal cells (hMSCs), has been explored in non-critical size mandibular defects in a rabbit model. The bone regenerative properties of the bioengineered scaffolds were analyzed by in vivo radiological examinations and ex vivo radiological, histomorphological, and immunohistochemical analyses. Results: The relative density increase (RDI) was significantly more pronounced in defects where a scaffold was placed, particularly if seeded with hMSCs. The immunohistochemical profile showed significantly higher expression of both VEGF-A and osteopontin in defects reconstructed with scaffolds. Native microarchitectural characteristics were not demonstrated in any experimental group. Conclusion: Herein, we demonstrate that bone regeneration can be boosted by scaffold- and seeded scaffold-reconstruction, achieving, respectively, 50% and 70% restoration of presurgical bone density in 120 days, compared to 40% restoration seen in spontaneous regeneration. Although optimization of the regenerative performance is needed, these results will help to establish a baseline reference for future experiments.
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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 | 0.000 |
| 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 | 0.001 | 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".