Optimizing cardiac energy substrate metabolism: a novel therapeutic intervention for ischemic heart disease
Bibliographic record
Abstract
Ischemic heart diseases, encompassing and ranging from angina pectoris to acute myocardial infarction, have a major impact on both cardiac energy metabolism and cardiac function. In the normal heart, energy metabolism and function are exquisitely matched. However, during and after ischemia there are both a decrease in energy production and disturbances in the balance between use of fatty acid and of glucose by the heart. The dominance of fatty acid oxidation as a source for the generation of ATP at the expense of glucose oxidation during and after ischemia has a negative impact on both cardiac efficiency and cardiac contractile function. Thus optimizing energy substrate metabolism, such that the efficiency of both generating and utilizing ATP is maximized has emerged as a novel therapeutic intervention in various manifestations of ischemic heart disease. For example, the antianginal benefit of trimetazidine can be attributed to the partial inhibition of fatty acid oxidation and the reciprocal increase in glucose oxidation. This optimization of the balance between fatty acid and glucose metabolism results in an improvement in the efficiency of both the generation and utilization of ATP. Other pharmacological agents also exploit this plasticity and interdependence between the pathways of fatty acid and glucose oxidation. This is achieved either by altering flux through these metabolic pathways, or by altering the availability of circulating energy substrates. Thus the multitude of targets available to optimize myocardial energy metabolism may significantly increase the armamentarium of therapeutic interventions for preserving cardiac contractile function and limit the untoward effects of ischemic heart disease. Heart Metab. 2008;38:5–14.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".