Bibliographic record
Abstract
There have been numerous recent advances in the understanding of the mechanisms of alcoholic liver disease pathogenesis. Endotoxin-induced Kupffer cell activation plays a role in cytokine-mediated inflammatory changes in the liver, and this can be blocked by a diet high in saturated fat, by a diet containing lactobacillus, which does not produce endotoxin, by neomycin antibiotic sterilization of the gut, by eliminating Kupffer cells, or by removing tumour necrosis factor-alpha with antibody or by using tumour necrosis factor-alpha knockout mice. The fatty liver component is mainly the result of the nicotinamide adenine dinucleotide/reduced nicotinamide adenine dinucleotide redox shift to the reduced state by ethanol oxidation generation of reduced nicotinamide adenine dinucleotide, although this too can be blocked by a diet high in saturated fat. Hepatocytic enlargement occurs due to ethanol-induced inhibition of the ubiquitin-proteasome pathway of cytoplasmic protein degradation and the retention of oxidized proteins in hepatocytes. The liver is scarred by stellate cells that have been activated by inflammatory cytokines and growth factors produced by activated Kupffer cells, and by bile ductule metaplasia. Mallory bodies and balloon cell degeneration develop through the ethanol-induced oxidative stress-protein kinase activation pathway, inhibition of phosphatase activity and inhibition of the ubiquitin-proteasome pathway.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".