Implications of Microbiota and Immune System in Development and Progression of Metabolic Dysfunction-Associated Steatotic Liver Disease
Bibliographic record
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent type of liver disease worldwide. The exact pathophysiology behind MASLD remains unclear; however, it is thought that a combination of factors or "hits" act as precipitants for disease onset and progression. Abundant evidence supports the roles of diet, genes, metabolic dysregulation, and the intestinal microbiome in influencing the accumulation of lipids in hepatocytes and subsequent progression to inflammation and fibrosis. Currently, there is no cure for MASLD, but lifestyle changes have been the prevailing cornerstones of management. Research is now focusing on the intestinal microbiome as a potential therapeutic target for MASLD, with the spotlight shifting to probiotics, antibiotics, and fecal microbiota transplantation. In this review, we provide an overview of how intestinal microbiota interact with the immune system to contribute to the pathogenesis of MASLD and metabolic dysfunction-associated steatohepatitis (MASH). We also summarize key microbial taxa implicated in the disease and discuss evidence supporting microbial-targeted therapies in its management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".