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Record W2727069163 · doi:10.1080/14712598.2017.1346080

Cellular mechanisms underlying cardiac engraftment of stem cells

2017· review· en· W2727069163 on OpenAlexafffund
Pushpinder Kanda, Darryl R. Davis

Bibliographic record

VenueExpert Opinion on Biological Therapy · 2017
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Ottawa
FundersInstitute of Circulatory and Respiratory HealthCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsStem cellEx vivoRegeneration (biology)Cell therapyTransplantationCellLimitingCancer researchCell biologyBiologyNeuroscienceMedicineImmunologyIn vivoInternal medicineBiotechnology

Abstract

fetched live from OpenAlex

INTRODUCTION: Over the past decade, it has become clear that long-term engraftment of any ex vivo expanded cell product transplanted into injured myocardium is modest and all therapeutic regeneration is mediated by stimulation of endogenous repair rather than differentiation of transplanted cells into working myocardium. Given that increasing the retention of transplanted cells boosts myocardial function, focus on the fundamental mechanisms limiting retention and survival of transplanted cells may enable strategies to help to restore normal cardiac function. Areas covered: This review outlines the challenges confronting cardiac engraftment of ex vivo expanded cells and explores means of enhancing cell-mediated repair of injured myocardium. Expert opinion: Stem cell therapy has already come a long way in terms of regenerating damaged hearts though the poor retention of transplanted cells limits the full potential of truly cardiotrophic cell products. Multifaceted strategies directed towards fundamental mechanisms limiting the long-term survival of transplanted cells will be needed to enhance transplanted cell retention and cell-mediated repair of damaged myocardium for cardiac cell therapy to reach its full potential.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.423
GPT teacher head0.444
Teacher spread0.021 · 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 designOther design
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

Citations33
Published2017
Admission routes2
Has abstractyes

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