Insights on Microvascular Flow Regulation in Microvascular Units: A Computational Modeling Study
Notice bibliographique
Résumé
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 .
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».