Mechanisms of increased gluconeogenesis from alanine in rat isolated hepatocytes after endurance training
Bibliographic record
Abstract
This work aimed at further investigating the mechanisms by which liver gluconeogenic capacity from alanine is improved after training in rats, with an isolated hepatocyte model. Compared with controls in hepatocytes from trained rats incubated with gluconeogenic precursors (20 mM), the glucogenic flux (J(glucose)) was increased by 64% from alanine (vs. 21% for glycerol, 18% for lactate-pyruvate 10:1, and 10% for dihydroxyacetone). Maximal intracellular alanine accumulation capacity was also increased by 50%. Further experiments conducted on perifused hepatocytes showed that the putative adaptation at the level of the phosphoenolpyruvate-pyruvate cycle, which could be involved in the increased J(glucose) from lactate-pyruvate, was not involved in the increased J(glucose) from alanine after training. For alanine concentration higher than approximately 1 mM, an increased flux through alanine aminotransferase appeared responsible for the increased J(glucose). This could, in turn, depend on an increased supply of cytosolic 2-oxoglutarate because of the higher mitochondrial respiration observed in hepatocytes from trained rats and the activation of the malate-aspartate shuttle. At lower alanine concentration, the increase in J(glucose) appeared to be entirely due to the improved transport capacity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".