How Do Host MicroRNAs Impact the Gut Epithelial Barrier During Inflammatory Bowel Disease?
Bibliographic record
Abstract
Introduction: Inflammatory bowel disease (IBD) is a condition characterised by chronic inflammation of the gastrointestinal tract, a dysfunctional immune system and dysbiosis. Very little research explains the mechanisms underpinning the pathogenesis of IBD, but many studies have looked at the role of microRNAs and their impact on the epithelial barrier in which they increase intestinal permeability leading to an increase in inflammatory development in the barrier. The aim is to investigate the role of microRNAs on proteins that make up the tight junctions of the epithelial barrier. Methods: This literature review will examine a range of review and primary studies and summarise how the microRNAs contribute to the degradation of the occludin protein, damaging of the epithelial gut barrier and the pathogenesis of inflammatory bowel disease. Results: The author expects to have a thorough understanding of the effect of microRNA on occludin and how their relationship leads to the pathogenesis of IBD. Discussion: Different primary studies examined different types of microRNAs and tested their effect on the occludin protein in the epithelial barrier, many of which caused its degradation and resulted in intestinal permeability. Conclusion: Intestinal permeability can potentially lead to intestinal inflammation and the pathogenesis of inflammatory bowel disease. Future research can examine how microRNAs can affect other aspects of the intestine such as the microbiome and also look into potential therapeutic agents that can alleviate the symptoms or cure inflammatory bowel disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".