Hepatic steatosis associated with decreased β-oxidation and mitochondrial function contributes to cell damage in obese mice after thermal injury
Bibliographic record
Abstract
Severely burned patients who are morbidly obese have poor clinical outcomes with aggravated metabolic consequences, a higher incidence of multiple organ dysfunction/failure, and significantly increased morbidity and mortality. The underlying mechanisms of these adverse outcomes are essentially unknown. Since the liver is one of the central metabolic organs, we hypothesized that thermal injury in obese patients leads to substantially increased lipolysis, hepatic fat infiltration, resulting in profound hepatic cellular and organellar alterations, consequently causing liver damage and severely augmented metabolic dysfunction. We tested this hypothesis using an obese mouse model subjected to a 20% total body surface area burn injury. C57BL/6 mice were randomly divided into low-fat diet (LFD) and high-fat diet (HFD) sham and burn groups (n = 6 per group) and fed for 16 weeks. 7 days after the thermal injury portal and cardiac blood were taken separately and liver tissue was collected for western blotting and immunohistochemical analysis. Gross examination of the liver showed apparent lipid infiltration in HFD fed and burned mice. We confirmed that augmented ER stress and inhibition of Akt-mTOR signaling dysregulated calcium homeostasis, contributed to the decrease of ER-mitochondria contact, and reduced mitochondrial β-oxidation in HFD fed and burned mice, leading to profound hepatic fat infiltration and substantial liver damage, hence increased morbidity and mortality. We conclude that obesity contributes to hepatic fat infiltration by suppressing β-oxidation, inducing cell damage and subsequent organ dysfunction after injury.
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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 | 0.001 |
| 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".