Malonyl CoA: A promising target for the treatment of cardiac disease
Bibliographic record
Abstract
Alterations in cardiac energy metabolism are an important contributor to the high incidence and severity of heart disease in the world. These alterations can include an impairment of the production of ATP necessary to meet the high energy demands of the heart, as well as adverse switches in energy substrate preference by the heart. With regard to this latter point, evidence suggests that a decrease in cardiac efficiency, caused by a rise in cardiac fatty acid oxidation and/or an increase in the uncoupling of glycolysis from glucose oxidation, impairs cardiac function and is a contributing factor to cardiac disease. In support of this concept, therapeutic strategies that modulate these metabolic pathways and increase cardiac efficiency produce beneficial results in the setting of heart disease. One such strategy is to increase cardiac malonyl CoA levels, an important inhibitor of mitochondrial fatty acid uptake. This includes malonyl CoA decarboxylase (MCD) inhibition that results in increased cardiac malonyl CoA levels, decreased cardiac fatty acid oxidation rates, and improved cardiac efficiency. Preclinical studies have shown that MCD inhibition can improve cardiac function in various forms of heart disease. Here, we focus on the importance of malonyl CoA in the regulation of cardiac energy metabolism and function in the normal and diseased heart and discuss the evidence that suggests that inhibition of fatty acid oxidation especially via regulation of malonyl CoA, through MCD inhibition, is a promising strategy to treat cardiac disease. © 2014 IUBMB Life, 66(3):139-146, 2014.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".