Control of lipid oxidation at the mitochondrial levelThis paper article is one of a selection of papers published in this Special Issue, entitled 14th International Biochemistry of Exercise Conference – Muscles as Molecular and Metabolic Machines, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
The rate of lipid oxidation during exercise is controlled at several sites, and there is a reciprocal dependency between oxidation of lipids and carbohydrates (CHO). It is well known that the proportion of the 2 fuels oxidized is influenced by substrate availability and exercise intensity, but the mechanisms regulating fuel preferences remain unclear. During intense exercise, oxidation of long-chain fatty acids (LCFAs) decreases, and the major control is likely to be at the mitochondrial level. Potential mitochondrial sites for control of lipid oxidation include transport of LCFAs into mitochondrial matrix, beta-oxidation, the tricarboxylic acid cycle, and the electron transport chain (ETC). CHO catabolism may impair lipid oxidation by interfering with the transfer of LCFAs into mitochondria and by competing for mutual cofactors (i.e., nicotinamide adenine dinucleotide and (or) coenzyme A (CoA)). The different effect of energy state on the catabolism of CHO and lipids is likely to be of major importance in explaining the shift in fuel utilization during intensive exercise. Formation of acetyl-CoA from CHO is activated by a low energy state, and will lead to accumulation of products that are inhibitory to lipid oxidation. In contrast, beta-oxidation of LCFAs to acetyl-CoA is not stimulated by a low energy state. Further interaction between CHO and LCFAs may occur by substrate competition for electron carriers at ETC, due to provisions of electrons through different complexes. Feedback inhibition of beta-oxidation by redox state is thought to be an important mechanism for the slowing of lipid oxidation during intensive exercise.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".