Efficacy of Parallel Capillary Arrays in Modelling Oxygen Transport in Discrete Microvascular Networks
Bibliographic record
Abstract
Parallel Capillary Array (PCA) models have frequently been used to model oxygen transport in the microvasculature. Our objective was to compare digitally Reconstructed Microvascular Networks (RMN) to PCA models under several simulated physiological conditions. Two discrete networks were reconstructed from intra-vital video microscopy of rat skeletal muscle (volumes of interest 84x168x342 μm and 70x157x268 μm). Blood flow in individual capillaries was analyzed and measurements for velocity, hematocrit and oxygen saturation were recorded in the majority of vessels. Flow was modeled for each RMN and adjusted to match velocity profiles measured experimentally. Geometric analysis of the RMNs was used to create equivalent PCAs that were matched in volumetric dimension, vascular density and mean capillary diameter. A computational model of O2 transport was used to compare RMN to PCA models under 3 conditions (Baseline, 2X Increased Flow & O2 Consumption, and 37% Functional Capillary Density Loss). O2 supply rate was equalized between the RMN and paired PCA in each of the three conditions. Mean percent tissue pO2 difference between the RMN and PCA was −11.9 ± 0.9% at baseline, −13.5 ± 0.4% with increased flow and consumption and −50.8 ± 34.9% with perfusion loss. This suggests that under some conditions PCA models do not suitably represent microvascular geometry when applied to oxygen transport modeling. Funded by CIHR
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".