Alcoholic hepatitis and metabolic disturbance in female mice: a more tractable model than <i>Nrf2</i>−/− animals
Bibliographic record
Abstract
ABSTRACT Alcoholic hepatitis (AH) is the dramatic acute presentation of alcoholic liver disease, with a 15% mortality rate within 28 days in severe cases. Research into AH has been hampered by the lack of effective and reproducible murine models that can be operated under different regulatory frameworks internationally. The liquid Lieber-deCarli (LdC) diet has been used as a means of ad libitum delivery of alcohol but without any additional insult, and is associated with relatively mild liver injury. The transcription factor nuclear factor-erythroid 2-related factor 2 (Nrf2) protects against oxidative stress, and mice deficient in this molecule are suggested to be more sensitive to alcohol-induced injury. We have established a novel model of AH in mice and compared the nature of liver injury in C57/BL6 wild-type (WT) versus Nrf2−/− mice. Our data showed that both WT and Nrf2−/− mice demonstrate robust weight loss, and an increase in serum transaminase, steatosis and hepatic inflammation when exposed to diet and ethanol. This is accompanied by an increase in peripheral blood and hepatic myeloid cell populations, fibrogenic response and compensatory hepatocyte regeneration. We also noted characteristic disturbances in hepatic carbohydrate and lipid metabolism. Importantly, use of Nrf2−/− mice did not increase hepatic injury responses in our hands, and female WT mice exhibited a more-reproducible response. Thus, we have demonstrated that this simple murine model of AH can be used to induce an injury that recreates many of the key human features of AH – without the need for challenging surgical procedures to administer ethanol. This will be valuable for understanding of the pathogenesis of AH, for testing new therapeutic treatments or devising metabolic approaches to manage patients whilst in medical care. This article has an associated First Person interview with the joint first authors of the paper.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".