Metabolic therapy for the treatment of ischemic heart disease: reality and expectations
Bibliographic record
Abstract
Ischemic heart disease is a major cause of morbidity and mortality in the world. Most of the existing therapeutic strategies used to treat ischemic heart disease aim at either increasing the oxygen supply to the heart (thrombolysis, revascularization, angiotensin converting enzyme inhibition and antiplatelet therapy) or decreasing the oxygen demand of the heart (beta-blockers and nitrates). Despite the fact that a compromised energy supply to the heart muscle is central to the pathology of ischemic heart disease, therapeutic approaches that focus on altering cardiac energy metabolism have not seen major clinical use. Therapeutic strategies in which the efficiency of oxygen utilization by the heart is enhanced could theoretically benefit the ischemic heart, and could have an additive benefit to existing therapeutic strategies. The energy supply for the heart (in the form of ATP) is normally provided by the balanced metabolism of both fatty acids (major part) and carbohydrates (minor part) oxidation. During reperfusion, this balance is broken by the dramatic enhancement of fatty acid oxidation and attenuation of carbohydrate oxidation, which results in intracellular H(+) accumulation and Ca(2+) overload. This article reviews the alterations in cardiac energy metabolism that occur in the ischemic heart, and discusses the existing and proposed pharmacologic therapies to optimize the balance of fatty acids and carbohydrate oxidation for the treatment of ischemic heart diseases.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
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".