Micro-PIV and CFD Studies Show Non-Uniform Wall Shear Stress Distributions Over Endothelial Cells
Bibliographic record
Abstract
Wall shear stress acting on arterial walls is an important hemodynamic force determining vessel health. A parallel-plate flow chamber with a 127 μm-thick flow channel is used as an in vitro system to study the fluid mechanics environment. It is essential to know how well this flow chamber performs in emulating physiologic flow regimes especially when cultured cells are present. Hence, the objectives of this work are to computationally and experimentally study the characteristic of the flow chamber in providing a defined flow regime and shear stress to cultured cells and to map wall shear stress distributions in the presence of an endothelial cell layer. Experiments and modeling were performed for the nominal wall shear stresses of 2 and 10 dyn/cm2. Without endothelial cells, the flow field is uniform over 95% of the chamber cross-section and the surfaces are exposed to the target stress level. Using PIV velocity data, the endothelial cell surfaces were re-constructed and flow over these surfaces was then simulated via FLUENT. Once endothelial cells are introduced, local shear variations are large and the velocity profiles are no longer uniform. Due to the velocity distribution between peaks and valleys, the local wall shear stresses range between 47–164% of the nominal values. This study demonstrates the non-uniform shear stress distribution over the cells is non-negligible especially in small vessels or where blockage is important.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".