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miRNAs in Cardiac Metabolism

2010· book-chapter· en· W2177400294 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBENTHAM SCIENCE PUBLISHERS eBooks · 2010
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsHeart failureGLUT4Internal medicineCardiac function curveEndocrinologyGlycolysisGlucose transporterBiologyGLUT1Energy homeostasisFlux (metallurgy)Diabetic cardiomyopathyMetabolismCardiomyopathyCardiologyMedicineChemistryObesityInsulin

Abstract

fetched live from OpenAlex

This chapter aims to provide an implicit introduction to the role of miRNAs in regulating cardiac metabolism. The homeostasis of glucose, lipid, protein, and energy, which is critical for normal cardiovascular function, is maintained by cellular metabolism. Metabolic perturbation occurs in various types of cardiac disease, including myocardial ischemia, cardiac hypertrophy, heart failure, diabetic cardiomyopathy, atherosclerosis, etc. The depletion of high-energy-phosphate metabolites may contribute to heart failure, and a decreased PCr/ATP ratio has been found in cardiac muscle of heart failure patients and animal models of heart failure. A major determinant of glycolytic flux is glucose transport; glucose enters cardiac cells via the facilitative glucose transporters GLUT1 and GLUT4. Several miRNAs have been demonstrated to produce regulatory effects on GLUT4, cellular ATP level, and the pleiotropic factor IGF-1. A succinct summary on these studies is given in this chapter.

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.

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: Other · Consensus signal: Other
Teacher disagreement score0.918
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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