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Abnormalities in the catabolic fate of long‐chain fatty acids and glucose in compensated murine cardiac hypertrophy

2008· article· en· W2263860435 on OpenAlexaff
Richard B. Wambolt, Hannah L. Parsons, Lubos Bohunek, Michael F. Allard

Bibliographic record

VenueThe FASEB Journal · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsCatabolismInternal medicineEndocrinologyMuscle hypertrophyFatty acidMetabolismChemistryIn vivoBiologyIsletBeta oxidationInsulinBiochemistryMedicine

Abstract

fetched live from OpenAlex

Reduced oxidative catabolism of fatty acids (FAO) and accelerated rates of non‐oxidative glucose catabolism (NOG) are recognized metabolic abnormalities in genetic and acquired models of cardiac hypertrophy (H) in rats, but are less well characterized in mice. We determined catabolic fates of long‐chain fatty acid (palmitate – P) and glucose (G) in compensated non‐hypertrophied (C) and hypertrophied (H) hearts from sham‐operated and abdominal aortic‐constricted CD1 mice. Hearts were perfused with either 1.2mM P, 5.5mM G, 0.5mM lactate and 20mU/L insulin (Series 1) or 0.6mM P, 5.5mM G, 1.5mM lactate, 0.5mM pyruvate and 20mU/L insulin (Series 2 ‐ reflects in vivo substrate concentrations in mice). No differences in heart function, expressed as cardiac power (ml·mmHg·min −1 ), were observed between C and H in either Series 1 (10.9 ± 0.6 vs. 10.4 ± 0.3) or Series 2 (12.8 ± 0.4 vs. 13.1 ± 0.6). Results: Substrate catabolism in a murine model of acquired compensated H resembles that in models of H in rats. Abnormalities in NOG persist in hearts exposed to a physiologically relevant substrate mixture, even though significant differences in FAO disappear, suggesting that defects in NOG are a key metabolic abnormality occurring early in the development of H.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.019
GPT teacher head0.232
Teacher spread0.214 · 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 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

Citations0
Published2008
Admission routes1
Has abstractyes

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