The Microvascular Lattice: An Updated Paradigm of Flow Distribution Through Capillary Networks
Bibliographic record
Abstract
Background Capillaries, as the smallest vessels in the microcirculation, represent the most essential site for oxygen delivery; yet, given their overwhelming density and complicated geometry, understanding how they integrate within the broader microvascular network has been challenging. Classical microvascular flow modelling has used linear branching networks such that each capillary bundle (CB) corresponds to a unique arteriolar‐venular pair with one‐directional pathway of flow. However, it has long been observed in vivo that terminal arterioles supply multiple CBs and similarly, post‐capillary venules collect from multiple CBs. Such a network forms a lattice‐like structure with an alternating pattern of arterioles, CBs, and venules. Objective We aim to develop a mathematical model for the microvascular lattice that can be applied to study blood flow properties through capillary networks in skeletal muscle. Methods/Results Microvascular lattice networks are generated from our own rodent intravital videomicroscopy data and compared to data reported in the literature. Distinct CBs are selected and their anatomical features are delineated with 3‐D reconstruction software. Capillary hemodynamics (RBC velocity, RBC supply rate, hematocrit) are extracted from video data and used to characterize the flow within and between multiple CBs. The generalized architecture of a microvascular lattice is then extrapolated from our experimental data and applied to a dual‐phase steady‐state blood flow model. Flow properties of the lattice are compared to those of a classical linear branching network ‐ both containing the same number of CBs. Conclusions/Impact The microvascular lattice represents an updated paradigm for describing blood flow through capillary networks. This lattice structure raises important questions regarding how flow can be regulated to support local oxygen demand and how larger microvessels interact with capillaries within the microcirculation. Furthermore, CB functional data for a variety of microvascular conditions can be inputted into this lattice model to determine how alterations to capillary architecture affect overall flow through the microvascular network. This abstract is from the Experimental Biology 2018 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".