Microvascular Flow Modeling using <i>In Vivo</i> Hemodynamic Measurements in Reconstructed 3D Capillary Networks
Bibliographic record
Abstract
OBJECTIVE: We describe a systematic approach to modeling blood flow using reconstructed capillary networks and in vivo hemodynamic measurements. Our goal was to produce flow solutions that represent convective O(2) delivery in vivo. METHODS: Two capillary networks, I and II (84 × 168 × 342 and 70 × 157 × 268 μm(3)), were mapped using custom software. Total network red blood cell supply rate (SR) was calculated from in vivo data and used as a target metric for the flow model. To obtain inlet hematocrits, mass balances were applied recursively from downstream vessels. Pressure differences across the networks were adjusted to achieve target SR. Baseline flow solutions were used as inputs to existing O(2) transport models. To test the impact of flow redistribution, asymmetric flow solutions (Asym) were generated by applying a ± 20% pressure change to network outlets. RESULTS: Asym solutions produced a mean absolute difference in SR per capillary of 27.6 ± 33.3% in network I and 33.2 ± 40.1% in network II vs. baseline. The O(2) transport model calculated mean tissue PO(2) of 28.2 ± 4.8 and 28.1 ± 3.5 mmHg for baseline and 27.6 ± 5.2 and 27.7 ± 3.7 mmHg for Asym. CONCLUSIONS: This outcome illustrates that moderate changes in flow distribution within a capillary network have little impact on tissue PO(2) provided that total SR remains unchanged.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".