Impairment of elastic properties of the aorta in bicuspid aortic valve: relationship between biomolecular and aortic strain patterns
Bibliographic record
Abstract
Aims: Bicuspid aortic valve (BAV) is associated with aortic wall alterations. We aimed to detect any correlation between aortic elasticity and genetic and biomolecular patterns of elastin. Methods and results: Forty-nine BAV patients (mean age: 38 ± 17.05) were prospectively enrolled. A blood sample was drawn for analysis of a single nucleotide polymorphism of elastin gene (ELN rs2071307) responsible for misfolding of elastin, and for the amount of elastin soluble fragments (ESF) in the plasma. Aortic dimensions and elastic properties were determined by echocardiography, aortic stiffness (AS) by M-mode analysis, and longitudinal strain (LS) of the ascending aorta (AA) by speckle-tracking echocardiography; values of aortic strain were compared with 45 age-matched subjects (mean age: 33 ± 9.67) with tricuspid aortic valve (TAV). BAV patients had greater aortic dimensions [Valsalva sinus (P = 0.004), sinotubular junction (P = 0.013), AA (P < 0.001)] and stiffness (P = 0.002) but lower LS (P = 0.04) than those with TAV. Results from comparisons of mutated genotype patients (AA, n = 10) with heterozygous (GA, n = 21) and wild-types ones (GG, n = 16) revealed that the presence of mutation was associated with increased ESF (P = 0.010 GG vs. GA; P = 0.035 GA vs. AA), larger AA (P = 0.019 GG vs. GA; P = 0.001 GG vs. AA), and lower LS (P = 0.032 GG vs. AA). Patients with a dilated AA showed greater ESF (P < 0.001), greater AS (P = 0.007), and lower LS of the AA (P = 0.002) than those with a normal AA. The same parameters were not significantly different comparing patients with moderate or severe aortic valve disease and patients with less than moderate valve disease. Conclusions: Our results show a close correlation between genetic and biomolecular patterns of elastin and mechanical properties of the aorta in patients with BAV.
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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) | 0.000 | 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) | 0.001 | 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".