MétaCan
Menu
Back to cohort
Record W4401905866 · doi:10.3390/cimb46080543

Two Decades of Advances and Limitations in Organ Recellularization

2024· review· en· W4401905866 on OpenAlexaff
Alina Stoian, Aisha Adil, Felor Biniazan, Siba Haykal

Bibliographic record

VenueCurrent Issues in Molecular Biology · 2024
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of TorontoToronto General HospitalTed Rogers Centre for Heart ResearchUniversity Health Network
Fundersnot available
KeywordsDecellularizationExtracellular matrixScaffoldRegenerative medicineMedicineBiologyCell biologyBiomedical engineeringStem cell

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.436
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations14
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Issues in Molecular BiologySame topicTissue Engineering and Regenerative MedicineFrench-language works237,207