Determination of Unsteady LDL Transport through Patient-specific Multi-layer Left Coronary Artery | Chapter 04 | New Visions in Science and Technology Vol. 11

 The goal of this work is to use computational fluid dynamic analysis to investigate the transport and distribution of Low Density Lipoprotein (LDL) within a patient-specific multi-layer artery wall model under unsteady flow.

In order to study the LDL transport and d, the performed analysis takes into account the specific geometry (properly modified with a constant wall intima layer thickness whereas the media layer has variable wall thickness derived by the IVUS Angio tool), the interfacial coupling (lumen-media) achieved by the Kedem-Katchalsky equation, the volume flux, the pressure drop across the endothelium, and the physical values of the multi-layer arterial materials.

Methods: A patient-specific Left Coronary Artery (LCA) model was used. Numerical analysis is performed on both flow-mass transport equations in the lumen and flow-mass transport equations within the patient-specific multi-layer artery wall.

The concentration polarisation of the lumen-side LDL concentration occurs primarily at the concave geometry portions denoting concentration polarisation. The lumen-side LDL concentration is not only determined by the Average Wall Shear Stress (AWSS). The proximal segment flow parts create increased time-averaged luminal concentration more than the distal segment flow parts. At the endothelium/intima interface, LDL concentration is significantly lower than at the lumen/endothelium interface (almost 90 times). The concentration drop across the intima layer is modest, whereas the drop across the Internal Elastic Layer (IEL) is significant. The concentrations of LDL found at the IEL/media interface are one order of magnitude lower than those found in the intima layer.

Conclusions: The flow pattern, the arterial wall thickness, and the physical parameters of the layers all influence the transport of LDL across the multi-layer artery wall.

 
Author(S) Details

Dimitrios G. Mpairaktaris
Fluid Mechanics Division, School of Engineering, Democritus University of Thrace, Xanthi, Greece.

Johannes V. Soulis
Fluid Mechanics Division, School of Engineering, Democritus University of Thrace, Xanthi, Greece.

George D. Giannoglou
Cardiovascular Engineering and Atherosclerosis Laboratory, 1st Cardiology Department, AHEPA University Hospital, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece.


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The Intricacies of Computational Medical Research: An Advanced Study Approach | Chapter 9 | Recent Developments in Medicine and Medical Research Vol. 4

 Computational simulations of biological systems are an important part of medical engineering because they allow doctors to see the pathophysiology of a disease. Fluid–structure interaction evaluations are required in many biomedical applications. The development of computational fluid–structure interaction models is required to achieve a quantitative understanding of their structure and function in both health and disease due to the inherent complexity of biological applications, which frequently include fluids and structures interacting together. Furthermore, the interaction of biological structures with the fluid that surrounds them is crucial to their function. As a result, we believe that using fluid–structure interaction models in computational analyses of biological systems is feasible, if not essential. The goal is to create computational models that can anticipate all biological events in humans. These models are designed to help us navigate the wide range of diseases that can impact our organs and to lead to more effective disease diagnosis, risk stratification, medical management, and therapy strategies. This chapter delves into the complexities of computational medical research, particularly in regards to patient specificity and the validation of computational approaches.

Author(S) Details

Milan Toma
New York Institute of Technology College of Osteopathic Medicine, Serota Academic Center (room 138), Northern Boulevard, P.O. Box 8000, Old Westbury, NY 11568, USA.

David Addepalli
New York Institute of Technology College of Osteopathic Medicine at Arkansas State, P.O. Box 119, State University, AR 72467, USA.

Rosalyn Chan-Akeley
Theresa and Eugene M. Lang Center for Research and Education, NewYork-Presbyterian/Queens, 56-45 Main St., Flushing, NY 11355, USA.

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