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Record W1974388426 · doi:10.1089/ten.teb.2009.0352

Challenges in Cardiac Tissue Engineering

2009· review· en· W1974388426 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTissue Engineering Part B Reviews · 2009
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Toronto
FundersNational Institute of Biomedical Imaging and BioengineeringNational Heart, Lung, and Blood InstituteNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsTissue engineeringCardiac cellCardiac function curveRegeneration (biology)Function (biology)Biomedical engineeringHeart failureNeuroscienceComputer scienceMedicineBiologyCell biologyCardiology

Abstract

fetched live from OpenAlex

Cardiac tissue engineering aims to create functional tissue constructs that can reestablish the structure and function of injured myocardium. Engineered constructs can also serve as high-fidelity models for studies of cardiac development and disease. In a general case, the biological potential of the cell-the actual "tissue engineer"-is mobilized by providing highly controllable three-dimensional environments that can mediate cell differentiation and functional assembly. For cardiac regeneration, some of the key requirements that need to be met are the selection of a human cell source, establishment of cardiac tissue matrix, electromechanical cell coupling, robust and stable contractile function, and functional vascularization. We review here the potential and challenges of cardiac tissue engineering for developing therapies that could prevent or reverse heart failure.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.133
GPT teacher head0.364
Teacher spread0.231 · 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