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Record W2945960888 · doi:10.1161/circresaha.118.313573

Stimulating Cardiogenesis as a Treatment for Heart Failure

2019· review· en· W2945960888 on OpenAlexfundno aff
Todd R. Heallen, Zachary A. Kadow, Jong H. Kim, Jun Wang, James F. Martin

Bibliographic record

VenueCirculation Research · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsnot available
FundersNational Institute of Dental and Craniofacial ResearchNational Heart, Lung, and Blood InstituteTexas Heart InstituteMcGill UniversityAmerican Heart Association
KeywordsHeart failureEndogenyRegeneration (biology)ReentryMedicineCell growthSignal transductionStem cellmicroRNACell biologyBiologyNeuroscienceInternal medicineCardiology

Abstract

fetched live from OpenAlex

After myocardial injury, cardiomyocyte loss cannot be corrected by using currently available clinical treatments. In recent years, considerable effort has been made to develop cell-based cardiac repair therapies aimed at correcting for this loss. An exciting crop of recent studies reveals that inducing endogenous repair and proliferation of cardiomyocytes may be a viable option for regenerating injured myocardium. Here, we review current heart failure treatments, the state of cardiomyocyte renewal in mammals, and the molecular signals that stimulate cardiomyocyte proliferation. These signals include growth factors, intrinsic signaling pathways, microRNAs, and cell cycle regulators. Animal model cardiac regeneration studies reveal that modulation of exogenous and cell-intrinsic signaling pathways can induce reentry of adult cardiomyocytes into the cell cycle. Using direct myocardial injection, epicardial patch delivery, or systemic administration of growth molecules, these studies show that inducing endogenous cardiomyocytes to self-renew is an exciting and promising therapeutic strategy to treat cardiac injury in humans.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.227
GPT teacher head0.497
Teacher spread0.271 · 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.

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

Citations71
Published2019
Admission routes1
Has abstractyes

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