A Microvascular Wall Shear Rate Function Derived From <i>In Vivo</i> Hemodynamic and Geometric Parameters in Continuously Branching Arterioles
Bibliographic record
Abstract
OBJECTIVES: Conventional approaches to WSR estimation in the microcirculation involve assumptions that may result in under-/over-estimation of WSR. Therefore, our objectives were: (i) calculate WSR from RBC velocity profiles for a wide range of arteriolar diameters, (ii) provide an experimentally derived and straightforward WSR estimation function, and (iii) compare calculated to conventional WSR estimations. METHODS: We characterized RBC velocity profiles in arterioles (n = 39) of branching networks (21-115 μm) in the rat gluteus maximus muscle (n = 6). Measures included mean and maximum velocities, CFL thickness, and RBC column edge velocity, and an experiment-based WSR function was derived. RESULTS: CFL thickness (1-4.3 μm) positively correlated with arteriolar diameter (r(2) = 0.64). Results from the WSR equation were similar to values from edge RBC velocities/CFL. Experimental WSRs (1317-4334/sec) were independent of arteriolar diameter, and were greater than pseudoshear rates (for VRatio of 1.6, 2, or diameter-dependent VRatio function) (p < 0.05). CONCLUSION: A WSR equation was derived from experimental hemodynamic parameters, and is adaptable to other velocity measurement techniques in order to obtain WSR and stress (when plasma viscosity is known). These findings provide insight on the nature of conventional WSR calculation methods in underestimating microvascular WSR values.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".