The role of cytochrome P4502E1 in ethanol mediated diseases: a narrative update
Bibliographic record
Abstract
Cytochrome P450 (CYPs) superfamily of enzymes metabolize thousands of endogenous and exogenous substrates including ethanol. Results: Cytochrome P4502E1 (CYP2E1) is involved in ethanol metabolism as part of the so-called microsomal ethanol metabolizing system, in the metabolism of fatty acids and some drugs such as acetaminophen and isoniazid, and in the activation of a variety of procarcinogens (PCs). Chronic ethanol consumption induces CYP2E1 which may result in an enhanced metabolism of these drugs to their toxic intermediates, and in the generation of carcinogens. In addition, ethanol oxidation increases and is associated with the generation of reactive oxygen species (ROS). This oxidative stress is an important driver for the development of alcohol-associated liver disease (AALD) and alcohol-mediated cancer (AMC). ROS may bind directly to proteins and to DNA. ROS may also lead to lipid peroxidation (LPO) with the generation of LPO products. These LPO products may bind to DNA forming etheno-DNA adducts. Cell culture studies as well as animal experiments have shown that CYP2E1 knock-out animals or the inhibition of CYP2E1 by chemicals results in a significant improvement of liver histology. CYP2E1 is also involved in pathogenesis of hepatic steatosis and fibrosis. More recent studies in patients with AALD have demonstrated an improvement of serum transaminase activities when CYP2E1 was inhibited by clomethiazole. In addition to its role in the generation of ROS, CYP2E1 also enhances the activation of PCs and decreases the level of retinol and retinoic acid in the liver. Conclusion: Inhibition of CYP2E1 may improve AALD and may inhibit AMC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".