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 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"><mml:mrow><mml:mi>ε</mml:mi></mml:mrow></mml:math> -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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".