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
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, an hydrogel made of polyethylene glycol-chitosan (HyCh) and an hybrid core-shell combination of poly(L-lactic acid)/poly(\(\epsilon\)-caprolactone) and HyCh (PLA-PCL-HyCh), seeded with different concentrations of human mesenchymal stem cells (hMSCs) (i.e. 1000, 2000, and 3000 cells/mm3), 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 (2000 and 3000 cells/mm3). The immunohistochemical profile showed significantly higher expression of both VEGF-A, in defects reconstructed with a PLA-PCL-HyCh, and osteopontin, in defects reconstructed with both scaffolds. Native microarchitectural characteristics were not demonstrated in any experimental group.Conclusions 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".