Inhibition of mTOR improves malnutrition induced hepatic metabolic dysfunction
Bibliographic record
Abstract
Severe malnutrition accounts for half-a-million deaths annually in children under the age of five. Despite improved WHO guidelines, inpatient mortality remains high and is associated with metabolic dysfunction. Previous studies suggest a correlation between hepatic metabolic dysfunction and impaired autophagy. We aimed to determine the role of mTORC1 inhibition in a murine model of malnutrition-induced hepatic dysfunction. Wild type weanling C57/B6 mice were fed a 18 or 1% protein diet for two weeks. A third low-protein group received daily rapamycin injections, an mTORC1 inhibitor. Hepatic metabolic function was assessed by histology, immunofluorescence, gene expression, metabolomics and protein levels. Low protein-fed mice manifested characteristics of severe malnutrition, including weight loss, hypoalbuminemia, hypoglycemia, hepatic steatosis and cholestasis. Low protein-fed mice had fewer mitochondria and showed signs of impaired mitochondrial function. Rapamycin prevented hepatic steatosis, restored ATP levels and fasted plasma glucose levels compared to untreated mice. This correlated with increased content of LC3-II, and decreased content mitochondrial damage marker, PINK1. We demonstrate that hepatic steatosis and disturbed mitochondrial function in a murine model of severe malnutrition can be partially prevented through inhibition of mTORC1. These findings suggest that stimulation of autophagy could be a novel approach to improve metabolic function in severely malnourished children.
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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 | 0.001 |
| 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".