Hemodynamic and Cellular Response Feedback in Calcific Aortic Valve Disease
Bibliographic record
Abstract
This review highlights aspects of calcific aortic valve disease that encompass the entire range of aortic valve disease progression from initial cellular changes to aortic valve sclerosis and stenosis, which can be initiated by changes in blood flow (hemodynamics) and pressure across the aortic valve. Appropriate hemodynamics is important for normal valve function and maintenance, but pathological blood velocities and pressure can have profound consequences at the macroscopic to microscopic scales. At the macroscopic scale, hemodynamic forces impart shear stresses on the surface of the valve leaflets and cause deformation of the leaflet tissue. As discussed in this review, these macroscale forces are transduced to the microscale, where they influence the functions of the valvular endothelial cells that line the leaflet surface and the valvular interstitial cells that populate the valve extracellular matrix. For example, pathological changes in blood flow-induced shear stress can cause dysfunction, impairing their homeostatic functions, and pathological stretching of valve tissue caused by elevated transvalvular pressure can activate valvular interstitial cells and latent paracrine signaling cytokines (eg, transforming growth factor-β1) to promote maladaptive tissue remodeling. Collectively, these coordinated and complex interactions adversely impact bulk valve tissue properties, feeding back to further deteriorate valve function and propagate valve cell pathological responses. Here, we review the role of hemodynamic forces in calcific aortic valve disease initiation and progression, with focus on cellular responses and how they feed back to exacerbate aortic valve dysfunction.
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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) | 0.001 | 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) | 0.002 | 0.002 |
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".