Inflammatory Response of Endothelial Cells to Wall Shear Stress in Three Dimensional Stenotic Models
Bibliographic record
Abstract
Endothelial cells (ECs) are believed to respond differentially to hemodynamic forces in the vascular tree. Once atherosclerotic plaque has formed in a vessel, the obstruction creates complex spatial gradients in wall shear stress (WSS). In vitro models have used mostly unrealistic and simplified geometries, which cannot reproduce accurately physiological conditions. The objective of this study was to expose ECs to the complex WSS pattern created by an asymmetric stenosis. Endothelial cells were grown and exposed for different times to physiological steady flows in straight dynamic controls and in idealized asymmetric stenosis models. Cell morphology was noticeably different in the regions with spatial WSS gradients, being more randomly oriented and of cobblestone shape. Inflammatory molecule expression was also altered by exposure to shear and endothelial nitric oxide synthase (eNOS) was upregulated by its presence. A regional response in terms of inflammation was observed through confocal microscopy. This work provides a more realistic model to study endothelial cell response to spatial and temporal WSS 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 | 0.000 |
| 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 | 0.001 |
| 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".