Extracellular Matrices as Bioactive Materials for In Situ Tissue Regeneration
Bibliographic record
Abstract
Bioactive materials based on a nature-derived extracellular matrix (NECM) represent a category of biomedical devices with versatile therapeutic applications in the realms of tissue repair and engineering. With advancements in decellularization technique, the inherent bioactive molecules and the innate nano-structural and mechanical properties are preserved in three-dimensional scaffolds mainly composed of collagens. Techniques such as electrospinning, three-dimensional printing, and the intricate fabrication of hydrogels are developed to mimic the physical structures, biosignalling and mechanical cues of ECM. Until now, there has been no approach that can fully account for the multifaceted properties and diverse applications of NECM. In this review, we introduce the main proteins composing NECMs and explicate the importance of them when used as therapeutic devices in tissue repair. Nano-structural features of NECM and their applications regarding tissue repair are summarized. The origins, degradability, and mechanical property of and immune responses to NECM are also introduced. Furthermore, we review their applications, and clinical features thereof, in the repair of acute and chronic wounds, abdominal hernia, breast deformity, etc. Some typical marketed devices based on NECM, their indications, and clinical relevance are summarized.
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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 | 0.001 |
| 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".