Cell networks in the mouse liver during partial hepatectomy
Bibliographic record
Abstract
Fetal programming by exposure to high-energy diets increases the susceptibility to type 2 diabetes mellitus (T2DM2) in the offspring. Glucose imbalance during fetal programming might be associated to still unknown selective lipid species and their characterization might be beneficial for T2DM diagnosis and treatment. We aim to characterize the effect of the lipid specie, C24:0 ceramide, on glucose imbalance and metabolic impairment in cellular and murine models. A lipidomic analysis identified accumulation of C24:0 ceramide in plasma of offspring rats exposed to high-energy diets during fetal programing, as well as in obese-T2DM subjects. In vitro experiments in 3T3L-1, hMSC and HUH7 cells and in in vivo models of Wistar rats and C57BL/6 mice demonstrated that C24:0 ceramide disrupted glucose balance, and differentiation and lipid accumulation in adipocytes, whereas promoted liver steatosis. Mechanistically, C24:0 ceramide impaired mitochondrial fatty acid oxidation in adipocytes and hepatic cells, tentatively by favoring reactive oxygen species accumulation and calcium overload in the mitochondria; and also, activates endoplasmic reticulum (ER) stress in hepatocytes. We propose that C24:0 ceramide accumulation in the offspring followed a prenatal diet exposure, impair lipid allocation into adipocytes and enhances liver steatosis associated to mitochondrial dysfunction and ER stress, leading to glucose imbalance.
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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 | 0.001 | 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".