Insights on Microvascular Flow Regulation in Microvascular Units: A Computational Modeling Study
Bibliographic record
Abstract
Background Regulation of RBC oxygen delivery and plasma flow is a critical function of the microcirculation. Given the complexity of microvascular networks, mathematical modeling has been an essential adjunct for understanding physiological principles. Many studies have simulated flow through branching arteriolar networks or isolated groups of capillaries, but the completed microvascular units (MVU) ‐ from terminal arteriole, through a capillary bundle, and into a post‐capillary venule ‐ has rarely been studied. Modeling this fundamental microvascular structure will help describe how capillary networks interface with the broader microcirculation and provide insight into properties of flow regulation on this scale. Methods We constructed an idealized MVU and applied a dual‐phase steady‐state blood flow model to solve for RBC and plasma flow. We incorporated physiologic parameters that were varied individually while keeping all of the other variables constant: (i) number of parallel capillaries in a bundle (4–10 capillaries), (ii) capillary length (50–600 micron), (iii) arteriolar inflow hematocrit (0.1–0.5), (iv) arteriolar diameter (6–18 micron), (v) venular diameter (6–18 micron), and (vi) driving pressure across the MVU. Mean and coefficient of variation (CV) were calculated for RBC flow, plasma flow, and tube hematocrit (HT) for all parallel capillaries. Results Plasma flow is significantly more variable than RBC flow in capillaries for all test cases in this study. Increasing the number of capillaries per bundle decreased the mean RBC and plasma flow but increased total flow through the MVU; plasma flow CV (17%–54%) and HT CV (19%–52%) increased substantially while RBC flow CV was much less affected (4%–9%). Increasing capillary length reduced mean RBC flow, plasma flow, and HT nonlinearly with an inflection point occurring at capillary lengths of 200 microns or greater. Increasing arteriolar inflow hematocrit reduced RBC flow CV (16% vs 2%) and increased plasma flow CV (26%–36%). Increases to arteriolar and venular diameter above 10 microns had little effect on the magnitude or distribution of RBC and plasma flow through the MVU. Changes to the driving pressure across the MVU had a linear effect on RBC and plasma flow with no effect on the relative distribution between capillaries. Conclusions This study provides insight into how the biophysical properties of the microcirculation may influence flow regulation through completed microvascular units. Pre‐ and post‐capillary microvessels appear optimized for diameters less than 10 microns. Modifications to driving pressure provide a much more straight forward method of flow regulation than alterations to vessel diameter. Future work will compare these results against in vivo capillary measurements with heterogeneous spatial geometry and explore modeling approaches for multiple interconnected MVU. Example of one idealized microvascular unit included in the study; this microvascular unit has 6 parallel capillaries with length 300 microns. image Example of one idealized microvascular unit included in the study; this microvascular unit has 6 parallel capillaries with length 300 microns. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".