Comprehensive Geometric and Hemodynamic Analysis of Complete Microvascular Networks in Rat Gluteus Maximus Muscle: An Integrated Model Derived from Experimental Data
Bibliographic record
Abstract
Skeletal muscle microcirculatory networks consist of sets of branching arterioles, which terminate at capillary beds, which subsequently give rise to a branching array of collecting venules. Generally, microcirculationists study each component of the microcirculation in isolation and provide hemodynamic analyses based on their discrete component of study. However, the interconnectivity of arterioles, capillaries, and venules suggests that measuring and predicting accurate hemodynamic outcomes requires collecting data from complete microvascular networks. Having studied blood flow in arteriolar networks both experimentally and computationally, we are now beginning to consider the roles of capillary resistance and venular network geometry. In particular, we are considering how capillaries and venules alter both overall microvascular network resistance and the flow pattern in terminal arterioles (TA's) supplying capillary beds. The venular networks used were reconstructed from intravital videomicroscopy (IVVM) data used previously to reconstruct complete arteriolar geometries, hence there was an exact correspondence between the arteriolar and venular networks. Preliminary work used an arteriolar network consisting of 1657 nodes, ~200 unbranched vessels, and 89 terminal arterioles, as well as an established steady‐state model of plasma and red blood cell (RBC) flow distribution. When resistive elements were added to the TA's to represent downstream capillary beds, overall flow resistance increased by 32% and the coefficient of variation (CV TA , standard deviation/mean) of TA RBC flow decreased by 9%. When the corresponding reconstructed venular network was added to form a complete arteriolar‐venular unit (2589 nodes), flow resistance increased by 69% and CV TA decreased by 10% compared to the arteriolar network alone. Not only was heterogeneity of TA RBC flow altered by adding capillaries and venules, but the ordering of TA's in terms of amount of RBC flow was also affected. Thus, these results imply the importance of considering complete microvascular networks when seeking to understand blood flow resistance and distribution. We believe entire networks will also be key in understanding regulation of local blood flow and its dysregulation in diseases such as type 2 diabetes and the metabolic syndrome. Support or Funding Information Natural Sciences and Engineering Research Council of Canada (NSERC) grants R4218A03 (DNJ) and R4081A03 (DG)
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".