A Simple “Streak Length Method” for Quantifying and Characterizing Red Blood Cell Velocity Profiles and Blood Flow in Rat Skeletal Muscle Arterioles
Bibliographic record
Abstract
Please cite this paper as: Al‐Khazraji BK, Novielli NM, Goldman D, Medeiros PJ, Jackson DN. A simple “Streak Length Method” for quantifying and characterizing red blood cell velocity profiles and blood flow in rat skeletal muscle arterioles. Microcirculation 19: 327–335, 2012. Abstract Objectives: To develop a valid experimental method for quantifying blood flow in continuously branching skeletal muscle arterioles, and to derive an empirical relationship between velocity ratio (VMax/VMean) and arteriolar diameter. Methods: We evaluated arteriolar trees using IVVM of rat gluteus maximus muscle and developed a method to acquire single fluorescent‐labeled RBC velocities across arteriolar lumens to create velocity profiles. These data were used to calculate the blood flow for 37 vessel segments (diameters: 21–115 μm). Results: Mass balance at arteriolar bifurcations had 0.6 ± 3.2% error. Velocity ratios ranged from 1.35 to 1.98 and were positively correlated with diameter (p < 0.0001), and VRBC profiles were blunted with decreasing diameter. Conclusions: We present a means for quantifying blood flow in continuously branching skeletal muscle arterioles. Further, we provide an equation for calculating velocity ratios based on arteriolar diameter, which may be used by others for blood flow calculations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".