Molecular, Viral and Clinical Features of Alcohol- and Non-Alcohol-Induced Liver Injury
Bibliographic record
Abstract
Hepatic cells are sensitive to internal and external signals. Ethanol is one of the oldest and most widely used drugs in the world. The focus on the mechanistic engine of the alcohol-induced injury has been in the liver, which is responsible for the pathways of alcohol metabolism. Ethanol undergoes a phase I type of reaction, mainly catalyzed by the cytoplasmic enzyme, alcohol dehydrogenase (ADH), and by the microsomal ethanol-oxidizing system (MEOS). Reactive oxygen species (ROS) generated by cytochrome (CYP) 2E1 activity and MEOS contribute to ethanol-induced toxicity. We aimed to: (1) Describe the cellular, pathophysiological and clinical effects of alcohol misuse on the liver; (2) Select the biomarkers and analytical methods utilized by the clinical laboratory to assess alcohol exposure; (3) Provide therapeutic ideas to prevent/reduce alcohol-induced liver injury; (4) Provide up-to-date knowledge regarding the Corona virus and its affect on the liver; (5) Link rare diseases with alcohol consumption. The current review contributes to risk identification of patients with alcoholic, as well as non-alcoholic, liver disease and metabolic syndrome. Additional prevalence of ethnic, genetic, and viral vulnerabilities are presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".