Bibliographic record
Abstract
BACKGROUND/AIM: It is now clear that intestinal microbes are involved in many aspects of inflammatory bowel diseases (IBD) and that understanding how microbes lead to disease could present novel opportunities for diagnosis and treatment. Microbes are linked to most disease-associated genetic polymorphisms and are critical mediators of environmental effects (through food, hygiene, and infection). This paper reviews recent findings and future implications for targeting microbes in IBD. METHODS: A comprehensive review of the literature is presented, with specific focus on how treating microbes could alter patient care in the future. RESULTS: Human and animal-based research supports the central role of microbes in IBD pathogenesis at multiple levels. Antibiotics, probiotics, diet, and potentially fecal transplantation are all potential treatments for IBD. Animal models of IBD only develop in the presence of microbes and co-housing mice genetically susceptible to gut inflammation with normal mice can lead to the development of bowel injury. Key papers have used microbial sequencing and metagenomics to study the role of microbes in IBD and we are now on the cusp of expanding into clinically relevant fields, such as diagnosis and therapeutics. However, many challenges still remain in understanding how microbes can be manipulated to prevent or treat disease. CONCLUSIONS: In the future, 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, microbial products to microbe-tailored diets, may replace current IBD treatments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".