Biomechanics of the Aortic Root and Ascending Aorta in Patients With Marfan Syndrome
Bibliographic record
Abstract
OBJECTIVES: How Marfan syndrome (MFS) translates to alterations in ex vivo biomechanical properties of aortic tissue is not well defined. We aimed to characterize the biomechanical properties of the aortic root and ascending aorta in MFS and compare them to properties of normal, dissected, and non-MFS aneurysmal aortas. METHODS: Biaxial tensile and delamination testing were performed on aortic tissue from patients with MFS (root = 10, ascending = 6), aneurysms with bicuspid aortic valves (BAVs) (root = 19, ascending = 88), aneurysms with tricuspid aortic valves (root = 25, ascending = 90), type A dissections (ascending = 17), and normal aortas (root = 12, ascending = 28). Differences in energy loss, low strain modulus, and delamination strength were investigated between patient groups. RESULTS: In the aortic root, energy loss, low strain modulus, and delamination strength were not statistically different in MFS compared to all other groups. In the ascending aorta, delamination strength was lower in MFS than normal ascending aortas (31.9 ± 8.5 mN/mm vs 57.1 ± 17.0 mN/mm, P = 0.0003). The MFS group (0.071 ± 0.017) also had higher energy loss than normal (0.049 ± 0.011, P = 0.04), and lower low strain modulus (75.0 ± 11.0 kPa) than BAV (113.5 ± 33.6 kPa, P = 0.02). CONCLUSIONS: The root and ascending aorta in MFS proved to have distinct biomechanical characteristics compared to normal aortas and other risk groups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".