Bicuspid aortic valve-associated aortopathy
Bibliographic record
Abstract
PURPOSE OF REVIEW: Bicuspid aortic valve (BAV)-associated aortopathy is common and its progression for individual patients is difficult to predict. The present review aims to identify recent developments using biomarkers for the determination of risk and progression of disease in patients with BAV aortopathy. RECENT FINDINGS: Novel rare genetic variants and epigenetic methylation signatures affecting non-cytosine phosphate guanine (non-CpG) and CpG sites, nicotinamide phosphoribosyltransferase and Sod expression may lead to improved prediction of the aortopathy phenotype. Circulating transforming growth factor β-1/endoglin and miRNA signatures are found to be indicative of aortic dilation. Aortic miRNA, sphingomyelin and oxidative stress levels are linked to aortopathy progression and aortic dilation. Further evidence is shown that the pattern of cusp fusion in BAV may influence the location and extent of aortopathy. SUMMARY: The clinical phenotypic variability seen in BAV patients suggests complex interactions between genetic variants, epigenetic regulation modifications and the variable effect of valve-mediated hemodynamic flow disturbances on the aorta and its secreted markers. Emerging biomarkers may serve along with advanced noninvasive imaging modalities to precisely identify risk of aortic complications and identify those patients who are in need of surgical intervention.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".