The Gut Barrier, Intestinal Microbiota, and Liver Disease: Molecular Mechanisms and Strategies to Manage
Bibliographic record
Abstract
Liver disease encompasses pathologies as non-alcoholic fatty liver disease, non-alcoholic steatohepatitis, alcohol liver disease, hepatocellular carcinoma, viral hepatitis, and autoimmune hepatitis. Nowadays, underlying mechanisms associating gut permeability and liver disease development are not well understood, although evidence points to the involvement of intestinal microbiota and their metabolites. Animal studies have shown alterations in Toll-like receptor signaling related to the leaky gut syndrome by the action of bacterial lipopolysaccharide. In humans, modifications of the intestinal microbiota in intestinal permeability have also been related to liver disease. Some of these changes were observed in bacterial species belonging Roseburia, Streptococcus, and Rothia. Currently, numerous strategies to treat liver disease are being assessed. This review summarizes and discusses studies addressed to determine mechanisms associated with the microbiota able to alter the intestinal barrier complementing the progress and advancement of liver disease, as well as the main strategies under development to manage these pathologies. We highlight those approaches that have shown improvement in intestinal microbiota and barrier function, namely lifestyle changes (diet and physical activity) and probiotics intervention. Nevertheless, knowledge about how such modifications are beneficial is still limited and specific mechanisms involved are not clear. Thus, further in-vitro, animal, and human studies are needed.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 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".