Regulatory Impact of Intra-Hepatic Carbohydrate and Lipid Metabolism
Bibliographic record
Abstract
The first evidence that the liver can afferently contribute to regulatory activities comes from studies on regulation of food intake. The hepatic afferent pathway has been shown to be responsive to glucoprivic as well as lipoprivic stimuli. Similarly to regulation of food intake, it has been reported that the liver may afferently contribute to the metabolic regulation of exercise. The best reported evidence of this view is the observation that the decrease in insulin and the increase in glucagon and noradrenaline levels during exercise are diminished in hepatic vagotomized rats (Lavoie et al., 1989). The concept behind these observations is that the liver, through the existence of hepatic glucoreceptors, is responsive to a decrease in glycogen content or to some metabolites of the glycolytic chain related to liver glycogen content. There is also some evidence that lipids in the liver may have some regulatory impact inside and outside the liver. Recent interest in looking at lipid metabolism in liver has been spurred by the observation that the increased flux of lipids through the hepatic portal vein has been associated with increased risks of metabolic and cardiovascular abnormalities. To explore this avenue, a 10% triglyceride emulsion was infused into either the portal or a peripheral vein of rats for 48 hrs while another group of rats was acutely infused for 2 hrs into the portal vein. The results indicate that all of these lipid infusions resulted in an increase in liver lipid infiltration, which may be associated with the development of a state of hepatic and peripheral insulin resistance.
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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) | 0.000 | 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) | 0.003 | 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".