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Record W2109360124 · doi:10.2174/138161211798357773

Optimization of Cardiac Metabolism in Heart Failure

2011· review· en· W2109360124 on OpenAlexaff
Tomohisa Nagoshi, Michihiro Yoshimura, Giuseppe Rosano, Gary D. Lopaschuk, Seibu Mochizuki

Bibliographic record

VenueCurrent Pharmaceutical Design · 2011
Typereview
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsUniversity of Alberta
FundersMinistry of Education, Culture, Sports, Science and TechnologyUehara Memorial Foundation
KeywordsHeart failureMetabolismInsulin resistanceCarbohydrate metabolismPopulationSubstrate (aquarium)Internal medicineCardiologyMedicineBiochemistryChemistryBiologyInsulin

Abstract

fetched live from OpenAlex

The derangement of the cardiac energy substrate metabolism plays a key role in the pathogenesis of heart failure. The utilization of non-carbohydrate substrates, such as fatty acids, is the predominant metabolic pathway in the normal heart, because this provides the highest energy yield per molecule of substrate metabolized. In contrast, glucose becomes an important preferential substrate for metabolism and ATP generation under specific pathological conditions, because it can provide greater efficiency in producing high energy products per oxygen consumed compared to fatty acids. Manipulations that shift energy substrate utilization away from fatty acids toward glucose can improve the cardiac function and slow the progression of heart failure. However, insulin resistance, which is highly prevalent in the heart failure population, impedes this adaptive metabolic shift. Therefore, the acceleration of the glucose metabolism, along with the restoration of insulin sensitivity, would be the ideal metabolic therapy for heart failure. This review discusses the therapeutic potential of modifying substrate utilization to optimize cardiac metabolism in heart failure.

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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.165
GPT teacher head0.401
Teacher spread0.236 · 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

Citations150
Published2011
Admission routes1
Has abstractyes

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