Beneficial effects of estrogen treatment in the HLA-B27 transgenic rat model of inflammatory bowel disease
Bibliographic record
Abstract
A well-established model of bowel inflammation is the HLA-B27 transgenic rat that exhibits a spontaneous disease phenotype resulting in chronic diarrhea caused by immune cell activation. Estrogens have previously been shown to modulate the immune system, and both estrogen receptors (ERalpha and ERbeta) are present in the intestine and cells of the immune system. Therefore, the ability of estrogen to ameliorate disease progression in the HLA-B27 transgenic rat was determined. HLA-B27 transgenic rats with chronic diarrhea were treated with 17alpha-ethynyl-17beta-estradiol (EE) for 5 days. EE treatment dramatically improved stool scores after only 3 days. Histological scores of the degree of ulceration, inflammatory cell infiltration, fibrosis, and lesion depth of the colon were also improved by EE treatment. Because neutrophil infiltration into the colon is involved in the development and propagation of disease, myeloperoxidase (MPO) activity was measured. MPO levels were reduced by 80% by EE treatment. Cotreatment with the pure ER antagonist ICI-182780 (ICI) blocked the effects of EE on stool character, MPO activity, and histology scores, strongly suggesting that the activity of EE is mediated through ER. Mast cell proteases can promote neutrophil infiltration, and gene expression analysis demonstrated that mast cell protease 1, 3, and 4 mRNA were all decreased in colons from estrogen-treated rats. In addition, a direct effect of estrogen on bone marrow-derived mast cell activity was demonstrated, suggesting that ER-mediated inactivation of mast cells may contribute to the improvement in the clinical sign and histological scores in this model.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".