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miRNAs in Cardiac Metabolism

2010· book-chapter· en· W2177400294 on OpenAlexaff
Zhiguo Wang

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.

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)0.000
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 score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.007

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

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

Citations0
Published2010
Admission routes1
Has abstractyes

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