Effects of Steady Spatial Wall Shear Stress Gradients on Endothelial Cell Morphology in Three-Dimensional Models
Bibliographic record
Abstract
Endothelial cell (EC) dysfunction has been linked to atherosclerosis through their response to hemodynamic forces. Flow in stenotic vessels creates complex spatial gradients in wall shear stress. In vitro studies examining the effect of shear stress on endothelial cells have used unrealistic and simplified models, which cannot reproduce physiological conditions. The objective of this study was to expose endothelial cells to the complex shear shear pattern created by an asymmetric stenosis. Endothelial cells were grown and exposed for different times to physiological steady flow in straight dynamic controls and in idealized asymmetric stenosis models. Cells subjected to 1D flow aligned with flow direction and had a spindle-like shape when compared to static controls. Endothelial cell morphology was noticeable different in the regions with a spatial gradient in wall shear stress, being more randomly oriented and of cobblestone shape. This occurred despite the presence of an increased magnitude in shear stress. No other study to date has described this morphology in the presence of a positive wall shear stress gradient or gradient of significant shear magnitude. This technique provides a more realistic model to study endothelial cell response to spatial and temporal shear stress gradients that are present in vivo and is an important advancement towards a better understanding of the mechanisms involved in coronary artery disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".