Cell-free layer and wall shear stress variation in microvessels
Bibliographic record
Abstract
In this study, we simulated multiple red blood cells flowing through straight microvessels with the immersed-boundary lattice-Boltzmann model to examine the shear stress variation on the microvessel surface and its relation to the properties of cell-free layer. Significant variation in shear stress has been observed due to the irregular configuration of blood cells flowing near the microvessel wall. A low shear stress is typically found at locations where there is a cell flowing close to the wall, and a large shear stress at locations with a relatively wide gap between cell and wall. This relationship between the shear stress magnitude and the distance between cell and wall has been attributed to the reverse pressure difference developed between the front and rear sides of a cell flowing near the vessel wall. We further studied the effects of several hemodynamic factors on the variation of shear stress, including the cell deformability, the flow rate, and the aggregation among red blood cells. These simulations show that the shear stress variation is less profound in situations with wider cell-free layers, since the reverse pressure difference around the edge cells is less evident, and the influence of this pressure difference on wall shear stress becomes weaker. This study also demonstrates the complexity of the flow field in the gap between cell and wall. More precise experimental techniques are required accurately measure such shear stress variation in microcirculation.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".