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Record W2172096126 · doi:10.1109/rbme.2015.2431681

Electrical and Mechanical Strategies to Enable Cardiac Repair and Regeneration

2015· review· en· W2172096126 on OpenAlexaff
Hung Cao, Bong Jin Kang, Chia-An Lee, K. Kirk Shung, Tzung K. Hsiai

Bibliographic record

VenueIEEE Reviews in Biomedical Engineering · 2015
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsÉcole de Technologie Supérieure
FundersNational Institute of Biomedical Imaging and BioengineeringEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsRegeneration (biology)Regenerative medicineInduced pluripotent stem cellEmbryonic stem cellContext (archaeology)Stem cellCoupling (piping)Tissue engineeringHeart failureMyocardial infarctionNeuroscienceBiomedical engineeringCardiologyMedicineBiologyCell biologyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Inadequate replacement of lost ventricular myocardium from myocardial infarction leads to heart failure. Investigating the regenerative capacity of mammalian hearts represents an emerging direction for tissue engineering and cell-based therapy. Recent advances in stem cells hold promise to restore cardiac functions. However, embryonic or induced pluripotent stem cell-derived cardiomyocytes lack functional phenotypes of the native myocardium, and transplanted tissues are not fully integrated for synchronized electrical and mechanical coupling with the host. In this context, this review highlights the mechanical and electrical strategies to promote cardiomyocyte maturation and integration, and to assess the functional phenotypes of regenerating myocardium. Simultaneous microelectrocardiogram and high-frequency ultrasound techniques will also be introduced to assess electrical and mechanical coupling for small animal models of heart regeneration.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.049
GPT teacher head0.344
Teacher spread0.296 · 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 source (direct Gemma or distilled Codex), 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

Citations22
Published2015
Admission routes1
Has abstractyes

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