Long‐term effects of losartan on structure and function of the thoracic aorta in a mouse model of Marfan syndrome
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: During development of thoracic aortic aneurysms in a mouse model of Marfan syndrome, upregulation of matrix metalloproteinase (MMP)-2 and -9 was accompanied by compromised aortic constriction and endothelium-dependent relaxation. Losartan has been proposed for the prevention of thoracic aortic aneurysm. We hypothesized that losartan would suppress MMP-2/-9 activation and improve aortic vasomotor function in this model. EXPERIMENTAL APPROACH: A well-characterized mouse model of Marfan syndrome (Fbn1(C1039G/+)) was used. Starting at 6 weeks old, Marfan mice were untreated or given losartan (0.6 g.L(-1) in drinking water, n= 30). The littermate Fbn1(+/+) mice served as control. Thoracic aortas were studied at 3, 6 and 9 months by histology and by contractility assays in isolated segments in vitro. KEY RESULTS: Losartan improved elastic fibre organization and increased aortic breaking stress. Losartan reduced the activity and protein expression of MMP-2 and MMP-9 at all ages. Aortic constriction in response to membrane depolarization or phenylephrine was increased by losartan at 3 and 9 months by 100-200%. Active force of aortic smooth muscle was also increased at 6 and 9 months. Acetylcholine-induced endothelium-dependent relaxation was improved by 30% after 3 months of losartan treatment, but such improvement disappeared with longer duration of treatment, accompanied by reduced phosphorylation of endothelial nitric oxide (NO) synthase(Ser1177), Akt(Thr308) and Akt(Ser473), compared with the control. CONCLUSIONS AND IMPLICATIONS: Losartan improved the contractile function of aorta and reduced MMP activation. However, the endothelial NO pathway remained suppressed in the thoracic aorta during losartan treatment, which might limit its long-term benefits in Marfan syndrome.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".