Gut Microbiota and Metagenomic Advancement in Digestive Disease
Bibliographic record
Abstract
Recent studies have made significant advances in understanding the mechanisms of gut microbiota involved in human health and disease [1,2].Now the gut microbiota has been recognized as a key player in a broad spectrum of human diseases from obesity associated liver and cardiovascular diseases to mental development and psychiatric diseases [3,4].Accordingly, modulations of gut microbial diversity and composition are expected to improve human health and to provide novel therapeutic modalities for human disease.The gut microbial modulators can be simply specific diets and drinks, natural tea and Chinese herbs, or specialized prebiotics and probiotics.In this special issue, authors presented a number of very interesting studies on changes of gut microbiota in digestive diseases.These review and original articles of research and clinical studies cover a range of topics, including the pathogenesis of alcoholic and nonalcoholic fatty liver diseases (NAFLD), the outcome of intestinal bacterial translocation in advanced cirrhosis, the gut microbiota changes in an animal colitis model after treatment with a monoclonal antibody, and fecal microbiota transplantation (FMT) in elderly patients with refractory Clostridium difficile infection.In these articles, authors have described mechanisms of disease development as well as therapeutic effects of specific antibodies, probiotics, and FMT.While we know the simple cause of alcoholic fatty liver disease (AFLD), its pathogenesis is complicated and it
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".