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Record W2060038044 · doi:10.2967/jnumed.109.069732

Comparison of Initial Cell Retention and Clearance Kinetics After Subendocardial or Subepicardial Injections of Endothelial Progenitor Cells in a Canine Myocardial Infarction Model

2010· article· en· W2060038044 on OpenAlexafffund
Andrea J. Mitchell, Eric Sabondjian, Jane Sykes, Lela Deans, Wendy Zhu, Xiangru Lu, Qingping Feng, Frank S. Prato, Gerald Wisenberg

Bibliographic record

VenueJournal of Nuclear Medicine · 2010
Typearticle
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsLawson Health Research InstituteWestern University
FundersCanadian Institutes of Health ResearchBoston Scientific CorporationOntario Ministry of Research and InnovationHeart and Stroke Foundation of Canada
KeywordsMyocardial infarctionProgenitor cellCardiologyKineticsInternal medicineChemistryMedicineStem cellCell biologyBiology

Abstract

fetched live from OpenAlex

UNLABELLED: Neither intravenous nor intracoronary routes provide targeted stem cell delivery to recently infarcted myocardium in sufficient quantities. Direct routes appear preferable. However, most prior studies have used epicardial injections, which are not practical for routine clinical use. The objective of this study was to compare cell retention and clearance kinetics between a subepicardial and a subendocardial technique. METHODS: We evaluated 7 dogs with each technique, using (111)In-tropolone-labeled endothelial progenitor cells and serial SPECT/CT for 15 d after injection. RESULTS: In vivo indium imaging demonstrated comparable degrees of retention: 57% +/- 15% for the subepicardial injections and 54% +/- 26% for the subendocardial injections. Clearance half-lives were also similar at 69 +/- 26 and 60 +/- 21 h, respectively. CONCLUSION: This study demonstrates that subendocardial injections, clinically more practical, show clearance kinetics comparable to those of subepicardial injections and will facilitate the ultimate clinical use of this treatment modality.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.571

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.364
Teacher spread0.325 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations40
Published2010
Admission routes2
Has abstractyes

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