Gut microbiome in primary sclerosing cholangitis: A review
Bibliographic record
Abstract
Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease characterized by biliary inflammation and stricturing. Exploration of the pathogenesis of PSC in light of its association with inflammatory bowel disease (IBD) and the "gut-liver" axis is an emerging area of interest. A growing number of studies have begun to elucidate the role of the gut microbiota, its metabolites and its influence on host immune responses in the development of PSC and PSC-IBD. Studies of the fecal microbiota have highlighted enriched levels of certain species, including Veillonella, Streptococcus and Enterococcus, among others. A heightened immune response to enteric dysbiosis and bacterial translocation have also been implicated. For example, Klebsiella pneumoniae strains derived from gnotobiotic mice transplanted with PSC-IBD microbiota were found to induce pore formation in human intestinal epithelial cells and enhanced Th17 responses. Gut microbes have additionally been hypothesized to be implicated in PSC pathogenesis through their role in the synthesis of various metabolites, including bile acids (BAs), which function as signaling molecules with important gut and hepatic effects. An expanded knowledge of the gut microbiome as it relates to PSC offers critical insight into the development of microbe-altering therapeutic interventions, such as antibiotics, nutritional interventions and fecal microbial transplantation. Some of these have already shown some preliminary evidence of benefit. Despite exciting progress in the field, much work remains to be done; areas that are particularly lacking include functional characterization of the microbiome and examination of pediatric populations. In this review, we summarize studies that have investigated the microbiome in PSC and PSC-IBD as well as putative mechanisms, including the potential role of metabolites, such as BAs. We then briefly review the evidence for interventions with microbe-altering properties for treating PSC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".