The Intricate Link among Gut “Immunological Niche,” Microbiota, and Xenobiotics in Intestinal Pathology
Bibliographic record
Abstract
Inflammatory bowel diseases (IBDs) are diseases characterized by various degrees of inflammation involving the gastrointestinal tract. Ulcerative colitis and Crohn's disease are characterized by a dysregulated immune response leading to structural gut alterations in genetically predisposed individuals. Diverticular disease is characterized by abnormal immune response to normal gut microbiota. IBDs are linked to a lack of physiological tolerance of the mucosal immune system to resident gut microbiota and pathogens. The disruption of immune tolerance involves inflammatory pathways characterized by an unbalance between the anti-inflammatory regulatory T cells and the proinflammatory Th1/Th17 cells. The interaction among T cell subpopulations and their related cytokines, mediators of inflammation, gut microbiota, and the intestinal mucosa constitute the gut "immunological niche." Several evidences have shown that xenobiotics, such as rifaximin, can positively modulate the inflammatory pathways at the site of gut immunological niche, acting as anti-inflammatory agents. Xenobiotics may interfere with components of the immunological niche, leading to activation of anti-inflammatory pathways and inhibition of several mediators of inflammation. In summary, xenobiotics may reduce disease-related gut mucosal alterations and clinical symptoms. Studying the complex interplay between gut immunological niche and xenobiotics will certainly open new horizons in the knowledge and therapy of intestinal pathologies.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".