Pathogenesis of Alcoholic Fatty Liver a Narrative Review
Bibliographic record
Abstract
Alcohol effect hepatic lipid metabolism through various mechanisms, leading synergistically to an accumulation of fatty acids (FA) and triglycerides. Obesity, as well as dietary fat (saturated fatty acids (FA) versus poly-unsaturated fatty acids (PUFA)) may modulate the hepatic fat. Alcohol inhibits adenosine monophosphate activated kinase (AMPK). AMPK activates peroxisome proliferator activated receptor a (PPARα) and leads to a decreased activation of sterol regulatory element binding protein 1c (SRABP1c). The inhibition of AMPK, and thus of PPARα, results in an inhibition of FA oxidation. This ß-oxidation is further reduced due to mitochondrial damage induced through cytochrome P4502E1 (CYP2E1)-driven oxidative stress. Furthermore, the synthesis of FAs is stimulated through an activation of SHREP1. In addition, alcohol consumption leads to a reduced production of adiponectin in adipocytes due to oxidative stress and to an increased mobilization of FAs from adipose tissue and from the gut as chylomicrons. On the other side, the secretion of FAs via very-low-density lipoproteins (VLDL) from the liver is inhibited by alcohol. Alcohol also affects signal pathways such as early growth response 1 (Egr-1) associated with the expression of tumour necrosis factor α (TNF α), and the mammalian target of rapamycin (mTOR) a key regulator of autophagy. Both have influence the pathogenesis of alcoholic fatty liver. Alcohol-induced gut dysbiosis contributes to the severity of ALD by increasing the metabolism of ethanol in the gut and promoting intestinal dysfunction. Moreover, pathogen-associated molecular patterns (PAMPS) via specific Toll-like receptor (TLR) bacterial overgrowth leads to the translocation of bacteria. Endotoxins and toxic ethanol metabolites enter the enterohepatic circulation, reaching the liver and inducing the activation of the nuclear factor kappa-B (NFκB) pathway. Pro-inflammatory cytokines released in the process contribute to inflammation and fibrosis. In addition, cellular apoptosis is inhibited in favour of necrosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".