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Record W3213320394 · doi:10.3390/cells10113125

Regenerating Damaged Myocardium: A Review of Stem-Cell Therapies for Heart Failure

2021· review· en· W3213320394 on OpenAlexaff
Dihan Fan, Hanrong Wu, Kaichao Pan, Huashan Peng, Rongxue Wu

Bibliographic record

VenueCells · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsInduced pluripotent stem cellTransdifferentiationHeart failureStem cellMyocardial infarctionMedicineRegenerative medicineProgenitor cellStem-cell therapyMyocyteNeuroscienceRegeneration (biology)CardiologyBioinformaticsCancer researchInternal medicineBiologyCell biologyEmbryonic stem cell

Abstract

fetched live from OpenAlex

Cardiovascular disease (CVD) is one of the contributing factors to more than one-third of human mortality and the leading cause of death worldwide. The death of cardiac myocyte is a fundamental pathological process in cardiac pathologies caused by various heart diseases, including myocardial infarction. Thus, strategies for replacing fibrotic tissue in the infarcted region with functional myocardium have long been a goal of cardiovascular research. This review begins by briefly discussing a variety of somatic stem- and progenitor-cell populations that were frequently studied in early investigations of regenerative myocardial therapy and then focuses primarily on pluripotent stem cells (PSCs), especially induced-pluripotent stem cells (iPSCs), which have emerged as perhaps the most promising source of cardiomyocytes for both therapeutic applications and drug testing. We also describe attempts to generate cardiomyocytes directly from cardiac fibroblasts (i.e., transdifferentiation), which, if successful, may enable the pool of endogenous cardiac fibroblasts to be used as an in-situ source of cardiomyocytes for myocardial repair.

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)0.000
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 score0.682
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
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 head0.033
GPT teacher head0.325
Teacher spread0.292 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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