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Record W1974452584 · doi:10.1139/y2012-115

Cellular therapy promotes endogenous stem cell repair

2012· review· en· W1974452584 on OpenAlexvenueno aff
Forum Kamdar, Mohammad Nurulqadr Jameel, Paul R. Score, Jianyi Zhang

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2012
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteU.S. Public Health ServiceAmerican Heart Association
KeywordsStem cellParacrine signallingRegeneration (biology)Myocardial infarctionVentricular remodelingTransplantationHeart failureCardiac function curveEndogenyMedicinePopulationGenetic enhancementCardiologyStem-cell therapyInternal medicineBiologyCell biologyReceptorGene

Abstract

fetched live from OpenAlex

Cellular transplantation for cardiac repair has emerged as an exciting treatment option for patients with myocardial infarction (MI) and heart failure. Animal models of post-infarction left ventricular remodeling have demonstrated an improvement in left ventricular (LV) function, decrease in scar size, and amelioration of adverse cardiac remodeling after stem cell transplantation. These beneficial effects occur despite minimal engraftment and negligible differentiation of transplanted cells. Evidence of the heart capability to self-renew continues to mount; however, the extent to which this occurs is still unclear. Although there is a specific population of cardiac stem cells capable of differentiating into cardiomyocytes, they alone are not capable of fully regenerating tissue damaged by MI. Therefore, paracrine mechanisms may be responsible for activating endogenous stem cells to promote regeneration and prevent apoptosis. These structural beneficial effects may reduce regional wall stresses, consequently leading to long-term host myocardium gene/protein expression changes, which may subsequently result in improvement in LV function.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.308
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

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

Citations5
Published2012
Admission routes1
Has abstractyes

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