Cell-derived matrices for tissue engineering and regenerative medicine applications
Bibliographic record
Abstract
The development and application of decellularized extracellular matrices (ECM) has grown rapidly in the fields of cell biology, tissue engineering and regenerative medicine in recent years. Similar to decellularized tissues and whole organs, cell-derived matrices (CDMs) represent bioactive, biocompatible materials consisting of a complex assembly of fibrillar proteins, matrix macromolecules and associated growth factors that often recapitulate, at least to some extent, the composition and organization of native ECM microenvironments. The unique ability to engineer CDMs de novo based on cell source and culture methods makes them an attractive alternative to conventional allogeneic and xenogeneic tissue-derived matrices that are currently harvested from cadaveric sources, suffer from inherent heterogeneity, and have limited ability for customization. Although CDMs have been investigated for a number of biomedical applications, including adhesive cell culture substrates, synthetic scaffold coatings, and tissue engineered products, such as heart valves and vascular grafts, the state of the field is still at a relatively nascent stage of development. In this review, we provide an overview of the various applications of CDM and discuss successes to date, current limitations and future 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".