Defective liver glycogen autophagy related to hyperinsulinemia in intrauterine growth-restricted newborn wistar rats
Bibliographic record
Abstract
Maternal malnutrition plays a critical role in the developmental programming of later metabolic diseases susceptibility in the offspring, such as obesity and type 2 diabetes. Because the liver is the major organ that produces and supplies blood glucose, we aimed at defining the potential role of liver glycogen autophagy in the programming of glucose metabolism disturbances. To this end, newborns were obtained from pregnant Wistar rats fed ad libitum with a standard diet or 65% food-restricted during the last week of gestation. We found that newborns from undernourished mothers showed markedly high basal insulin levels whereas those of glucagon were decreased. This unbalance led to activation of the mTORC1 pathway and inhibition of hepatic autophagy compromising the adequate handling of glycogen in the very early hours of extrauterine life. Restoration of autophagy with rapamycin but not with glucagon, indicated no defect in autophagy machinery per se, but in signals triggered by glucagon. Taken together, these results support the notion that hyperinsulinemia is an important mechanism by which mobilization of liver glycogen by autophagy is defective in food-restricted animals. This early alteration in the hormonal control of liver glycogen autophagy may influence the risk of developing metabolic diseases later in life.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".