Aortic and Cardiac Structure and Function Using High-Resolution Echocardiography and Optical Coherence Tomography in a Mouse Model of Marfan Syndrome
Bibliographic record
Abstract
Marfan syndrome (MFS) is an autosomal-dominant disorder of connective tissue caused by mutations in the fibrillin-1 (FBN1) gene. Mortality is often due to aortic dissection and rupture. We investigated the structural and functional properties of the heart and aorta in a [Fbn1C1039G/+] MFS mouse using high-resolution ultrasound (echo) and optical coherence tomography (OCT). Echo was performed on 6- and 12-month old wild type (WT) and MFS mice (n = 8). In vivo pulse wave velocity (PWV), aortic root diameter, ejection fraction, stroke volume, left ventricular (LV) wall thickness, LV mass and mitral valve early and atrial velocities (E/A) ratio were measured by high resolution echocardiography. OCT was performed on 12-month old WT and MFS fixed mouse hearts to measure ventricular volume and mass. The PWV was significantly increased in 6-mo MFS vs. WT (366.6 ± 19.9 vs. 205.2 ± 18.1 cm/s; p = 0.003) and 12-mo MFS vs. WT (459.5 ± 42.3 vs. 205.3 ± 30.3 cm/s; p< 0.0001). PWV increased with age in MFS mice only. We also found a significantly enlarged aortic root and decreased E/A ratio in MFS mice compared with WT for both age groups. The [Fbn1C1039G/+] mouse model of MFS replicates many of the anomalies of Marfan patients including significant aortic dilation, central aortic stiffness, LV systolic and diastolic dysfunction. This is the first demonstration of the direct measurement in vivo of pulse wave velocity non-invasively in the aortic arch of MFS mice, a robust measure of aortic stiffness and a critical clinical parameter for the assessment of pathology in the 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".