Mineralocorticoid receptor activation contributes to intestinal fibrosis through neutrophil gelatinase-associated lipocalin in preclinical models
Bibliographic record
Abstract
Intestinal fibrosis is a common complication in inflammatory bowel diseases with no specific therapy. Because mineralocorticoid receptor antagonism prevented inflammation and fibrosis in extra-intestinal organs, we aimed to evaluate mineralocorticoid receptor antagonism in intestinal fibrosis. Here we show that pharmacological or smooth cell specific deletion mineralocorticoid receptor antagonism prevented colon fibrosis development in male mice. In vitro, spironolactone prevented fibroblast proliferation and endothelial-to-mesenchymal transition. Neutrophil gelatinase-associated lipocalin silencing suppressed aldosterone-induced fibrosis markers and blunted colon fibrosis in mice. Chromatin immunoprecipitation showed mineralocorticoid receptor antagonist inhibits mineralocorticoid receptor binding on the neutrophil gelatinase-associated lipocalin promoter in activated smooth muscle cells. In conclusion, mineralocorticoid receptor antagonism or smooth muscle mineralocorticoid receptor deletion reduced colon fibrosis through the modulation of the neutrophil gelatinase-associated lipocalin pathway. Mineralocorticoid receptor may represent a novel therapeutic target in intestinal fibrosis and may allow the re-positioning in the field of inflammatory bowel diseases of drugs already marketed.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".