Seno-destructive smooth muscle cells in the ascending aorta of patients with bicuspid aortic valve disease
Bibliographic record
Abstract
BACKGROUND: Ascending aortic aneurysms constitute an important hazard for individuals with a bicuspid aortic valve (BAV). However, the processes that degrade the aortic wall in BAV disease remain poorly understood. METHODS: and p21 expression, and double-strand DNA breaks. The senescence-associated secretory phenotype (SASP) of cultured-aged BAV aortic smooth muscle cells (SMCs) was evaluated by transcript profiling and consequences probed by combined immunofluorescence and circular polarization microscopy. The contribution of p38 MAPK signaling was assessed by immunostaining and blocking strategies. FINDINGS: We uncovered SMCs at varying depths of cellular senescence within BAV- and tricuspid aortic valve (TAV)-associated aortic aneurysms. Senescent SMCs were also abundant in non-aneurysmal BAV aortas but not in non-aneurysmal TAV aortas. Multivariable analysis revealed that BAV disease independently associated with SMC senescence. Furthermore, SMC senescence was heightened at the convexity of aortas associated with right-left coronary cusp fusion. Aged BAV SMCs had a pronounced collagenolytic SASP. Moreover, senescent SMCs in the aortic wall were enriched with surface-localized MMP1 and surrounded by weakly birefringent collagen fibrils. The senescent-collagenolytic SMC phenotype depended on p38 MAPK signaling, which was chronically activated in BAV aortas. INTERPRETATION: We have identified a cellular senescence-collagen destruction axis in at-risk ascending aortas. This novel "seno-destructive" SMC phenotype could open new opportunities for managing BAV aortopathy. FUND: Canadian Institutes of Health Research, Lawson Health Research Institute, Heart and Stroke Foundation of Ontario/Barnett-Ivey Chair.
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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".