Mathematical Model of Mixed Venous SO <sub>2</sub> Transients at Onset of Exercise in Discrete Capillary Networks
Bibliographic record
Abstract
Increases in muscle O 2 consumption (VO 2 ) result in higher blood flow to accommodate changing metabolic demand. The time required for diffusion of O 2 within a muscle volume to support increased VO 2 causes tissue PO 2 and mixed venous SO 2 to lag behind VO 2 . The purpose of this study was to determine how diffusive transport affects time transients in venous SO 2 following increases in blood flow and VO 2 . A finite difference model was used to simulate O 2 transport in a discrete 3D microvascular network mapped from rat skeletal muscle using intravital video microscopy. Measurements were made in vivo to determine baseline simulation parameters for red blood cell supply rate (RBC SR), capillary inlet SO 2 , and VO 2 . Using the baseline solution as a starting point, exercise was simulated using simultaneous 6X step increases in VO 2 and RBC SR. Tissue PO 2 and capillary venous outflow SO 2 (cvSO 2 ) were recorded at 0.2s intervals until steady‐state (SS) was reached. SS mean tissue PO 2 decreased from 37.2 ± 2.7 at baseline to 18.2 ± 5.9 mmHg in simulated exercise. Figure I shows the cvSO 2 profile of blood as it exits the volume and the relative time course of the step change. This model demonstrates that despite instantaneous step increases in muscle VO 2 and microvascular blood flow, diffusive transport of O 2 in skeletal muscle imposes an observable time transient to cvSO 2 following the onset of exercise. Supported by CIHR MOP 102504 & NIH HL089125
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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) | 0.000 | 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 | 0.001 |
| 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".