Estrogen receptor-β signaling modulates epithelial barrier function
Bibliographic record
Abstract
Impaired epithelial barrier function and estrogens are recognized as factors influencing inflammatory bowel disease (IBD) pathology and disease course. Estrogen receptor-β (ERβ) is the most abundant estrogen receptor in the colon and a complete absence of ERβ expression is associated with disrupted tight-junction formation and abnormal colonic architecture. The aim of this study was to determine whether ERβ signaling has a role in the maintenance of epithelial permeability in the colon. ERβ mRNA levels and colonic permeability were assessed in IL-10-deficient mice and HLA-B27 rats by RT-PCR and Ussing chambers. ERβ expression and monolayer resistance were measured in HT-29 and T84 colonic epithelial monolayers by RT-PCR and electric cell-substrate impedance sensing. The effect of 17β-estradiol and an estrogen agonist [diarylpropionitrile (DPN)] and antagonist (ICI 182780) on epithelial resistance in T84 cells was measured. Expression of ERβ and proinflammatory cytokines was investigated in colonic biopsies from IBD patients. Levels of ERβ mRNA were decreased, whereas colonic permeability was increased, in IL-10-deficient mice and HLA-B27 transgenic rats prior to the onset of colitis. T84 cells demonstrated higher resistance and increased levels of ERβ mRNA compared with HT-29 cells. 17β-estradiol and DPN induced increased epithelial resistance in T84 cells, whereas an ERβ blocker prevented the increased resistance. Decreased ERβ mRNA levels were observed in colonic biopsies from IBD patients. This study suggests a potential role for ERβ signaling in the modulation of epithelial permeability and demonstrates reduced ERβ mRNA in animal models of colitis and colon of patients with 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".