Epigenetics and the Developmental Origins of Inflammatory Bowel Diseases
Bibliographic record
Abstract
The gut microbiota, the intestinal mucosa and the host immune system are among the large biological networks involved in the development of inflammatory bowel disease (IBD), which includes Crohn disease (CD) and ulcerative colitis (UC). Host genetics and environmental factors can significantly modulate the interactive relationships among these biological systems and influence predilection toward IBD. High monozygotic twin discordance rates and the rapid rise in the prevalence of IBD indicate that environmental influences may be as important or even more important in their pathogenesis than genetic susceptibility. However, the nature and timing of environmental factors critical for inducing IBD remain largely unknown. The molecular mechanisms and the key biological component(s) that may be affected by such factors are also in question. Epigenetic changes, such as DNA methylation (the methylation of cytosines followed by a guanine in CpG dinucleotides) can be modified by environmental influences during finite developmental periods and have been implicated in the pathogenesis of IBD. Mucosal DNA methylation can also react to changes in the commensal microbiota, underscoring the intercalating relationships among the large biological systems involved in gastrointestinal disorders. Therefore, transient environmental influences during specific periods of development may induce critical change(s) in an isolated or concomitant fashion within the intestinal biomic networks and lead to increased susceptibility to IBD. The present review focuses on the emerging paradigm shift considering IBD to originate from critical environmental effects during pre- and postnatal development.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".