The aryl hydrocarbon receptor: A promising target for intestinal fibrosis therapy
Bibliographic record
Abstract
Intestinal fibrosis, a severe complication of inflammatory bowel disease, leads to intestinal stenosis. Effective therapies for this condition are lacking. The aryl hydrocarbon receptor (AhR), a highly conserved nuclear transcription factor activated by diverse ligands, plays dual roles in fibrogenesis, but its relationship to intestinal fibrosis has not been comprehensively reviewed. This review explores the pathogenesis of intestinal fibrosis, and places particular focus on the mechanistic role of AhR signaling pathways, which may be mediated by dietary, microbial, and environmental ligands. We propose a new strategy for the targeting of AhR-related dietary ligands to prevent intestinal fibrosis. Dietary AhR ligands, such as glucobrassicin, flavonoids, and curcumin, exert anti-fibrotic effects by modulating the gut microbiota, suppressing collagen deposition, and inhibiting transforming growth factor-β pathways. Conversely, environmental pollutants (e.g., polycyclic aromatic hydrocarbons, microplastics, and propiconazole) exacerbate fibrosis via AhR activation. In multiple disease models, 16S rRNA sequencing has revealed positive and negative linear relationships between the gut microbiota and fibrosis. Intestinal microbiota-derived metabolites also affect fibrosis, including via immune cell regulation to indirectly reduce collagen deposition and direct action on extracellular matrix-related proteins to relieve intestinal fibrosis. The interaction among the AhR, microbiota, and diet suggests new therapeutic strategies, such as dietary interventions and fecal microbiota transplantation, to restore the microbial balance and inhibit fibrosis. The promotion of intestinal fibrosis by AhR agonists in environmental pollutants further emphasizes the need to reduce exposure to environmental toxins while following a plant-based diet rich in AhR agonists.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".