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Record W2041087748 · doi:10.1002/ccd.21302

Endothelial progenitor cell therapy for the treatment of coronary disease, acute MI, and pulmonary arterial hypertension: Current perspectives

2007· review· en· W2041087748 on OpenAlexaff
Michael R. Ward, Duncan J. Stewart, Michael J.B. Kutryk

Bibliographic record

VenueCatheterization and Cardiovascular Interventions · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineProgenitor cellRestenosisMyocardial infarctionAngioplastyEndothelial progenitor cellClinical trialCardiologyCell therapyEndotheliumStem cellStem-cell therapyBone marrowEndothelial dysfunctionInternal medicineDiseasePulmonary hypertensionIntensive care medicineTransplantationStent

Abstract

fetched live from OpenAlex

Since their identification in 1997, bone marrow derived endothelial progenitor cells (EPCs) have been studied for their role in the endogenous maintenance and repair of endothelium and their potential regenerative capacity beyond the endothelium. In particular, EPCs have been tested in cell therapy approaches with the aim of developing novel therapies for conditions currently lacking effective treatment options. In this review, we discuss the scientific background and clinical experience using EPC delivery or mobilization for the treatment of post-angioplasty restenosis, acute myocardial infarction and pulmonary arterial hypertension. Although these approaches are safe, efficacy has yet to be proven in large randomized clinical trials. Unfortunately, the biology of EPCs is still poorly understood. The success of future clinical trials depends on a better understanding of EPC biology and intelligent design.

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)0.001
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
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.066
GPT teacher head0.338
Teacher spread0.272 · 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

Citations71
Published2007
Admission routes1
Has abstractyes

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