Preparation and Use of Decellularized Extracellular Matrix for Tissue Engineering
Bibliographic record
Abstract
The multidisciplinary fields of tissue engineering and regenerative medicine have the potential to revolutionize the practise of medicine through the abilities to repair, regenerate, or replace tissues and organs with functional engineered constructs. To this end, tissue engineering combines scaffolding materials with cells and biologically active molecules into constructs with the appropriate structures and properties for tissue/organ regeneration, where scaffolding materials and biomolecules are the keys to mimic the native extracellular matrix (ECM). For this, one emerging way is to decellularize the native ECM into the materials suitable for, directly or in combination with other materials, creating functional constructs. Over the past decade, decellularized ECM (or dECM) has greatly facilitated the advance of tissue engineering and regenerative medicine, while being challenged in many ways. This article reviews the recent development of dECM for tissue engineering and regenerative medicine, with a focus on the preparation of dECM along with its influence on cell culture, the modification of dECM for use as a scaffolding material, and the novel techniques and emerging trends in processing dECM into functional constructs. We highlight the success of dECM and constructs in the in vitro, in vivo, and clinical applications and further identify the key issues and challenges involved, along with a discussion of future research directions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".