Quantification of the regional bioarchitecture in the human aorta.
J ConcannonPeter DockeryA BlackSherif SultanN HynesP E McHughKevin M MoermanJ Patrick McGarryPublished in: Journal of anatomy (2019)
Regional variance in human aortic bioarchitecture responsible for the elasticity of the vessel is poorly understood. The current study quantifies the elements responsible for aortic compliance, namely, elastin, collagen and smooth muscle cells, using histological and stereological techniques on human tissue with a focus on regional heterogeneity. Using donated cadaveric tissue, a series of samples were excised between the proximal ascending aorta and the distal abdominal aorta, for five cadavers, each of which underwent various staining procedures to enhance specific constituents of the wall. Using polarised light microscopy techniques, the orientation of collagen fibres was studied for each location and each tunical layer of the aorta. Significant transmural and longitudinal heterogeneity in collagen fibre orientations were uncovered throughout the vessel. It is shown that a von Mises mixture model is required accurately to fit the complex collagen fibre distributions that exist along the aorta. Additionally, collagen and smooth muscle cell density was observed to increase with increasing distance from the heart, whereas elastin density decreased. Evidence clearly demonstrates that the aorta is a highly heterogeneous vessel which cannot be simplistically represented by a single compliance value. The quantification and fitting of the regional aortic bioarchitectural data, although not without its limitations, including mean cohort age of 77.6 years, facilitates the development of next-generation finite element models that can potentially simulate the influence of regional aortic composition and microstructure on vessel biomechanics.
Keyphrases
- aortic valve
- pulmonary artery
- aortic dissection
- endothelial cells
- coronary artery
- pulmonary hypertension
- pulmonary arterial hypertension
- smooth muscle
- single cell
- induced pluripotent stem cells
- left ventricular
- pluripotent stem cells
- tissue engineering
- machine learning
- white matter
- stem cells
- ultrasound guided
- mesenchymal stem cells
- multiple sclerosis
- big data