Two Decades of Advances and Limitations in Organ Recellularization
Bibliographic record
Abstract
The recellularization of tissues after decellularization is a relatively new technology in the field of tissue engineering (TE). Decellularization involves removing cells from a tissue or organ, leaving only the extracellular matrix (ECM). This can then be recellularized with new cells to create functional tissues or organs. The first significant mention of recellularization in decellularized tissues can be traced to research conducted in the early 2000s. One of the landmark studies in this field was published in 2008 by Ott, where researchers demonstrated the recellularization of a decellularized rat heart with cardiac cells, resulting in a functional organ capable of contraction. Since then, other important studies have been published. These studies paved the way for the widespread application of recellularization in TE, demonstrating the potential of decellularized ECM to serve as a scaffold for regenerating functional tissues. Thus, although the concept of recellularization was initially explored in previous decades, these studies from the 2000s marked a major turning point in the development and practical application of the technology for the recellularization of decellularized tissues. The article reviews the historical advances and limitations in organ recellularization in TE over the last two decades.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".