Myocardial Metabolism and Improved OutcomesAfter High Risk Heart Surgery
Bibliographic record
Abstract
The healthy heart relies primarily upon the oxidation of fatty acids for energy, with the remaining coming from the oxidation of glucose and lactate. Changes in energy requirements are met by altering the balance of fuels depending upon the hormonal milieu as well as upon the availability of oxygen and substrates. The use of carbohydrates for fuel is metabolically more efficient and may improve the coupling between glycolysis and pyruvate oxidation. Therefore, promoting a shift in metabolic fuel substrate use during times of reduced oxygen availability may represent a cardioprotective strategy. Subsequently, there has been interest in pharmacologic strategies such insulin or drugs like ranolazine and dichloroacetate that stimulate carbohydrate oxidation either by enhancing oxidation at the pyruvate dehydrogenase complex or by limiting fatty acid oxidation. There is evidence that nutrients may also be able to stimulate carbohydrate oxidation. Previous studies by our group suggest that a combination of nutrients (carnitine, coenzyme Q10, and taurine) may work together, resulting in pleiotropic cardioprotective effects. Our current studies are investigating the potential of nutrients as both a preventative and adjunctive treatment before and after an ischemic event. These investigations will determine the role of nutritional supplementation in the care of patients with ischemic injury.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".