Biomimetic Tissue‐Engineered Bone Substitutes for Maxillofacial and Craniofacial Repair: The Potential of Cell Sheet Technologies
Bibliographic record
Abstract
Maxillofacial defects are complex lesions stemming from various etiologies: accidental, congenital, pathological, or surgical. A bone graft may be required when the normal regenerative capacity of the bone is exceeded or insufficient. Surgeons have many options available for bone grafting including the "gold standard" autologous bone graft. However, this approach is not without drawbacks such as the morbidity associated with harvesting bone from a donor site, pain, infection, or a poor quantity and quality of bone in some patient populations. This review discusses the various bone graft substitutes used for maxillofacial and craniofacial repair: allografts, xenografts, synthetic biomaterials, and tissue-engineered substitutes. A brief overview of bone tissue engineering evolution including the use of mesenchymal stem cells is exposed, highlighting the first clinical applications of adipose-derived stem/stromal cells in craniofacial reconstruction. The importance of prevascularization strategies for bone tissue engineering is also discussed, with an emphasis on recent work describing substitutes produced using cell sheet-based technologies, including the use of thermo-responsive plates and the self-assembly approach of tissue engineering. Indeed, considering their entirely cell-based design, these natural bone-like substitutes have the potential to closely mimic the osteogenicity, osteoconductivity, osteoinduction, and osseointegration properties of autogenous bone for maxillofacial and craniofacial reconstruction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".