New insights into the molecular basis of alcohol abstinence and relapse in alcohol-associated liver disease
Bibliographic record
Abstract
Alcohol use disorder remains a significant public health concern, affecting around 5% of adults worldwide. Novel pathways of damage have been described during the last years, providing insight into the mechanism of injury due to alcohol misuse beyond the direct effect of ethanol byproducts on the liver parenchyma and neurobehavioral mechanisms. Thus, the gut-liver-brain axis and immune system involvement could be therapeutic targets for alcohol use disorder. In particular, changes in gut microbiota composition and function, and bile acid homeostasis, have been shown with alcohol consumption and cessation. Alcohol can also directly disrupt intestinal and blood-brain barriers. Activation of the immune system can be triggered by intestinal barrier dysfunction and translocation of bacteria, pathogen-associated molecular patterns (such as lipopolysaccharide), cytokines, and damage-associated molecular patterns. These factors, in turn, promote liver and brain inflammation and the progression of liver fibrosis. Other involved mechanisms include oxidative stress, apoptosis, autophagy, and the release of extracellular vesicles and miRNA from hepatocytes. Potential therapeutic targets include gut microbiota (probiotics and fecal microbiota transplantation), neuroinflammatory pathways, as well as neuroendocrine pathways, for example, the ghrelin system (ghrelin receptor blockade), incretin mimetics (glucagon-like peptide-1 analogs), and the mineralocorticoid receptor system (spironolactone). In addition, support with psychological and behavioral treatments is essential to address the multiple dimensions of alcohol use disorder. In the future, a personalized approach considering these novel targets can contribute to significantly decreasing the alcohol-associated burden of disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".