A computational model of the effect of capillary density variability on oxygen transport, glucose uptake, and insulin sensitivity in prediabetes
Bibliographic record
Abstract
Abstract Objectives The purpose of this study was to model how CD variability affects tissue oxygenation under resting and exercise conditions. Additionally, we examine how CD impacts glucose and insulin transport in skeletal muscle. Methods We applied an established 3D finite difference model of oxygen transport to predict tissue oxygenation using FCD, hemodynamics, and SO2 measurements from a previous study. A 2D finite element model of glucose transport was applied to predict glucose and insulin uptake in PP and fasting conditions using the same range of CD. Results Control simulations used CD ranging from 562.5 to 781.3 capillaries/mm2, whereas prediabetic densities ranged from 375.0 to 593.8 capillaries/mm2. Mean tissue PO2 was 30.6±4.6 to 40.5±3.6 mm Hg for rest and 19.6±6.7 to 33.27±4.7 mm Hg for control and prediabetic simulations, respectively. Mean PP glucose concentrations were 5.85±1.13 mmol/L in the control group and 5.11±1.28 in the prediabetic simulations. Glucose uptake rates were 35% lower in the lowest capillary CD case compared to the high CD simulation. Conclusions Our simulations predict that CD decreases can have a substantial effect on oxygen delivery and glucose disposal across the observed physiological ranges of capillarization.
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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 | 0.000 |
| 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".