Tissue and organ decellularization in regenerative medicine
Bibliographic record
Abstract
The advancement and improvement in decellularization methods can be attributed to the increasing demand for tissues and organs for transplantation. Decellularized tissues and organs, which are free of cells and genetic materials while retaining the complex ultrastructure of the extracellular matrix (ECM), can serve as scaffolds to subsequently embed cells for transplantation. They have the potential to mimic the native physiology of the targeted anatomic site. ECM from different tissues and organs harvested from various sources have been applied. Many techniques are currently involved in the decellularization process, which come along with their own advantages and disadvantages. This review focuses on recent developments in decellularization methods, the importance and nature of detergents used for decellularization, as well as on the role of the ECM either as merely a physical support or as a scaffold in retaining and providing cues for cell survival, differentiation and homeostasis. In addition, application, status, and perspectives on commercialization of bioproducts derived from decellularized tissues and organs are addressed. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:1494-1505, 2018.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".