Host–microbe interactions in the gut: lessons learned from models of inflammatory bowel diseases
Bibliographic record
Abstract
The mammalian gut is the richest immune organ in the body and serves as a central location for immune system development, processing, and education. Inflammatory bowel diseases (IBD) provide excellent models for studying both innate and adaptive responses to gut microbes and the host-immune system – microbe interactions in the gut. Microbes are linked to almost all of the known disease-associated genetic polymorphisms in IBD and are critical mediators of environmental effects (through food, hygiene, and infection). Human and animal-based research supports the central role of microbes in IBD pathogenesis at multiple levels. Animal models of IBD only develop in the presence of microbes, and co-housing mice that are genetically susceptible to gut inflammation with normal mice can lead to the development of bowel injury. Recent advances in research technologies, such as deep-sequencing that enables detailed compositional analyses, have revolutionized the study of host–microbe interactions in the gut; however, knowing which bacteria are present in the bowel is likely not sufficient. The function of the microbiota as a community is recognized as a critical factor for gut homeostasis. Animal models of IBD have provided critical insight into basic biology and disease pathogenesis, especially regarding the role of microbes in IBD pathogenesis. Although many of these recent discoveries on host–microbe interactions are not yet applied to patient care, these basic observations will certainly revolutionize patient care in the future. Using such data, we may be able to predict risk of disease, define biological subtypes, establish tools for prevention, and even cure IBD using microbes or their products. A broad spectrum of therapeutic tools spanning from fecal transplantation, probiotics, prebiotics, and microbial products to microbe-tailored diets may supplement current IBD treatments.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.013 |
| 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".