Abstract 249: Thoracic Aortic Dilation in Patients with Bicuspid Aortic Valves is Marked by Accelerated Vascular Smooth Muscle Cell Aging
Bibliographic record
Abstract
Individuals with a bicuspid aortic valve (BAV) are at increased risk for ascending aortic dilation and dissection. Loss of aortic medial smooth muscle cells (SMCs) and disruption of the extracellular matrix are well-recognized pathologies, but the underlying cellular mechanisms remain elusive. We tested the hypothesis that the dilated aorta in patients with BAV was marked by accelerated cellular aging. Samples of human ascending aorta were obtained from individuals with BAV undergoing thoracic aorta replacement (n=37, age 54.7±2.2, aortic diameter 4.8±0.9 cm) or patients with a tricuspid aortic valve and non-dilated aorta undergoing heart transplantation or coronary bypass procedures (n=6, age 55.3±8.1, aortic diameter 3.1±0.3 cm). Assessment of fresh aortic samples for senescence-associated β-galactosidase revealed evidence for rare medial cell senescence that was 4.2-fold more prevalent in dilated aortas (0.83±0.10%) than in non-dilated aortas (0.20±0.10%, p=0.048). Expression of p16 was abundantly detected in medial SMCs within dilated aortas (27.0±2.1%) and 3-fold more abundant than in non-dilated aortas (8.9±1.8%, p<0.0001). Interestingly, immunostaining for γH2A.X (phosphorylated Ser139) revealed discrete nuclear DNA double-strand breakage signals in 25.7±3.8% of medial cells in dilated aortas from patients with BAV, which was 2.3-fold higher than that found in non-dilated aortas (11.0±4.9, p=0.03). CONCLUSION: These findings identify a previously unrecognized phenomenon of accelerated SMC aging in the aortas of patients with BAV, with cellular senescence and unresolved DNA breaks. Accelerated cell aging could thus be a driver of aortic wall degeneration in these patients and a potential therapeutic target.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".