Bibliographic record
Abstract
The human gut microbiome is thought to have a major role in contributing to the health and disease. In particular, evidence has arisen regarding the pathophysiology of liver disease and the microbial flora of the intestines. Chronic alcohol ingestion may perturb the gut flora and cause increased gut permeability leading to the translocation of inflammatory bacterial components to the liver through the portal system. This inflammatory effect is in addition to the direct effects of ethanol on the liver. Additionally, altered gut flora and known to be associated with obesity, which is the major risk factor for non-alcoholic fatty liver disease (NAFLD). These changes are thought to increase the amount of energy extracted from the diet, contributing to obesity. The altered microbiota are also thought to produce alcohol and induce liver damage in NAFLD as well. There are specific changes in the gut flora associated with cirrhosis including the upregulation of bacterial enzymatic pathways for the metabolism of ammonia and GABA. Using a set of only 15 bacterial genes, researchers were able to distinguish cirrhotic patients from controls. Further research into the connection between the gut microbiota and liver disease may lead to new diagnostic and targeted therapeutics means.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".