Bibliographic record
Abstract
Several presentations at the recent International Liver Congress™ (ILC), held in London, UK, from 22nd–26th of June 2022, addressed the role of the gut microbiome in chronic liver disease. Debbie L. Shawcross from the Department of Inflammation Biology, School of Immunology and Microbial Sciences, Institute of Liver Studies, King’s College London, UK, outlined the role of the gut-liver axis in the pathogenesis of cirrhosis, and how existing and novel therapies manipulate gut microbes. Emina Halilbasic from the Medical University of Vienna, Austria, and Benjamin H. Mullish from the Division of Digestive Diseases, Imperial College London, UK. Focused on the use of gut-based therapies in cholestatic liver disease. They explained the current understanding of the interplay between bile acids, microbiota, and the mucosal immune system, and the ways in which this may be manipulated for therapeutic gain. The role of gut barrier impairment in alcohol-related liver disease (ArLD) was presented by Shilpa Chokshi from the Roger Williams Institute of Hepatology, Foundation for Liver Research, London, UK, and School of Immunology and Microbial Sciences, Faculty of Life Sciences and Medicine, King’s College London, UK. Charlotte Skinner from the Department of Metabolism, Digestion, and Reproduction, Division of Digestive Diseases, Imperial College London, UK, described the role of gut proteases in this process, while Jasmohan S. Bajaj from the Virginia Commonwealth University, Richmond, USA, and Central Virginia Veterans Healthcare System, Richmond, USA, illustrated new therapies that target the gut-liver axis in this condition. Yue Shen from Zhongshan Hospital, Fudan University, Shanghai, China, and the Department of Gastroenterology and Hepatology, Shanghai Institute of Liver Diseases, China, described a combined microbiome-metabolome study to characterise the gut microbiome in hepatitis B virus infection-associated liver diseases (HBV-CLD), and how specific microbes might impact peripheral immunity. Finally, Bajaj outlined why the gut is a major target for hepatic encephalopathy (HE) treatment and described cutting edge research into therapies that show promise in this arena, such as soluble solid dispersion rifaximin, faecal microbiota transplantation (FMT), and rationally defined bacterial consortia. Overall, these presentations highlight an expanding knowledge of the gut-liver axis and promise an exciting future in liver treatment.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".