<sup/> Extracellular Matrices for Bone Regeneration: A Literature Review
Bibliographic record
Abstract
The gold standard material for bone regeneration is still autologous bone, a mesenchymal tissue that consists mainly of extracellular matrix (ECM) (90% v/v) and little cellular content (10% v/v). However, the fact that decellularized allogenic bone grafts often present a clinical performance comparable to autologous bone grafts demonstrates the crucial role of ECM in bone regeneration. For long, the mechanism by which bone allografts function was not clear, but recent research has unveiled many unique characteristics of ECM that seem to play a key role in tissue regeneration. This is further confirmed by the fact that synthetic biomaterials with composition and properties resembling bone ECM present excellent bone regeneration properties. In this context, ECM molecules such as glycosaminoglycans (GAGs) and self-assembly peptides (SAPs) can improve the performance of bone regeneration biomaterials. Moreover, decellularized ECM derived either from native tissues such as bone, cartilage, skin, and tooth germs or from cells such as osteoblasts, chondrocytes, and stem cells has shown promising results in bone regeneration applications. Understanding the role of ECM in bone regeneration is crucial for the development of the next generation of biomaterials for bone tissue engineering. In this sense, this review addresses the state-of-the-art on this subject matter.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".