Microbiome Regulation of Autoimmune, Gut and Liver Associated Diseases
Bibliographic record
Abstract
Extensive analysis of the complexity and diversity of microbiota using metagenomics in the gut and other body sites has provided evidence that dysbiosis occurs in many disease states. With the application of next generation sequencing technology this research is starting to uncover the impact of microbiota on metabolic, physiological and immunological pathways and elucidate the cellular and molecular mechanisms involved. To highlight these advances we have focused on autoimmunity and gut and liver related diseases and discuss the opportunities and challenges of translating microbiome research towards its application in humans. Towards this goal we discuss the application of fecal microbiome transplantation (FMT) for the treatment of multiple chronic gut associated inflammatory diseases such as Clostridium difficile infection (CDI) and inflammatory bowel disease (IBD). The potential role of human migration across continents and cultures leading to alteration in their microbiome and its implication in health and disease is also discussed. Keywords: Autoimmune diseases, fecal microbiota transplantation, gut microbiota, human migration and microbiome, inflammatory gut diseases, immune system, liver diseases, microbiome, next generation sequencing.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".