Androgen Receptor Promotes Abdominal Aortic Aneurysm Development via Modulating Inflammatory Interleukin-1α and Transforming Growth Factor-β1 Expression
Bibliographic record
Abstract
Sex difference is a risk factor for abdominal aortic aneurysm (AAA) formation yet the reason for male predominance remains unclear. Androgen and the androgen receptor (AR) influence the male sex difference, indicating that AR signaling may affect AAA development. Using angiotensin II–induced AAA in apolipoprotein E null mouse models (82.4% AAA incidence), we found that mice lacking AR failed to develop AAA and aorta had dramatically reduced macrophages infiltration and intact elastic fibers. These findings suggested that AR expression in endothelial cells, macrophages, or smooth muscle cells might play a role in AAA development. Selective knockout of AR in each of these cell types further demonstrated that mice lacking AR in macrophages (20% AAA incidence) or smooth muscle cells (12.5% AAA incidence) but not in endothelial cells (71.4% AAA incidence) had suppressed AAA development. Mechanism dissection showed that AR functioned through modulation of interleukin-1α (IL-1α) and transforming growth factor-β1 signals and by targeting AR with the AR degradation enhancer ASC-J9 led to significant suppression of AAA development. These results demonstrate the underlying mechanism by which AR influences AAA development is through IL-1α and transforming growth factor-β1, and provides a potential new therapy to suppress/prevent AAA by targeting AR with ASC-J9.
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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".