CT02 - CARD-1
Senate Chamber (#2145) in The Ohio Union

CARD Subgroup Contributed Talks

Tuesday, July 18 at 2:30pm

SMB2023 SMB2023 Follow Tuesday during the "CT02" time block.
Room assignment: Senate Chamber (#2145) in The Ohio Union.
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Elisa Serafini

Houston Methodist Research Institute
"An Agent-Based Model of Cardiac Allograft Vasculopathy: towards a Cost-Effective Platform to Better Understanding Chronic Rejection Dynamics"
Cardiac allograft vasculopathy (CAV) is a coronary artery disease affecting 50% of heart transplant recipients, and it is the major cause of chronic graft rejection. CAV is driven by the interplay of immunological and non-immunological factors with the infiltration of macrophages as one of the main pathological triggers, setting off a cascade of events promoting endothelial damage and vascular cell dysfunction. Since etiology and evolution of the pathology are still largely unknown, disease management remains challenging and re-transplantation is today the only long-term solution to CAV. A deep understanding of the pathology mechanobiology is fundamental to improve prevention, diagnosis, and treatment of CAV. So far, in vivo models, mostly mouse-based, have been widely used to study CAV, but they are resource-consuming, pose many ethical issues, and allow limited time points of investigation during experimental follow-up. Recently, agent-based models (ABMs) proved to be valid computational tools for capturing and deciphering processes at cell/tissue level, augmenting cost-effectively in vivo lab-based experiments, i.e., guaranteeing richness in observation time points while maintaining low resource consumptions. We hypothesize that integrating ABMs with lab-based experiments can aid classic pre-clinical research by overcoming its limitations. Accordingly, we present a bidimensional ABM of CAV in a mouse-like coronary artery cross-section, simulating the arterial wall response to two distinct stimuli: inflammation and hemodynamic disturbances, the latter in terms of low wall-shear stress (WSS), which together trigger macrophage response activation and exacerbate vascular cell activities. In addition, we performed an extensive analysis to investigate the ABM working mechanisms and gain insight on the driving parameters and the stimuli influences. The ABM replicates with high fidelity a 4-week CAV initiation and progression, well highlighting lumen area decreasing due to progressive intimal thickening in regions exposed to high inflammation and low WSS. The sensitivity analysis remarked that the inflammatory-related events, rather than the WSS, predominantly drive CAV, corroborating the inflammatory nature of the vasculopathy. This proof-of-concept model offers to the scientific community an agile computational platform to deepen CAV understanding and to support the in vivo analysis of CAV in a cost-effective fashion.
Additional authors: Anna Corti1, Diego Gallo2, Claudio Chiastra2, Xian C. Li3,4,5, Stefano Casarin5,6,7 1Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering ‘Giulio Natta’, Politecnico di Milano, Milan, Italy 2PoliToBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy 3Immunobiology and Transplant Science Center, Houston Methodist Hospital, Houston, TX, USA 4Department of Surgery, Weill Cornell Medical College of Cornell University, New York, NY, USA 5Department of Surgery, Houston Methodist Hospital, Houston, TX 6Center for Precision Surgery, Houston Methodist Research Institute, Houston, Texas, USA 7LaSIE, UMR 7356 CNRS, La Rochelle Université, La Rochelle, France

Pak-Wing Fok

University of Delaware
"Shear stress regulation in cylindrical arteries through medial growth and nitric oxide release"
The mechanisms employed by blood vessels in order to adapt to their hemodynamic environment are important for our general understanding of disease and development. Changes in arterial geometry are generally induced by two effects: vasodilation and/or constriction; and growth and remodeling (“G&R”). The first can occur over short periods of a few minutes, while the second usually occurs over timescales of weeks or months. The free radical Nitric oxide (NO) is one of the few biological signaling molecules that is gaseous. When smooth muscle cells internalize NO, they lengthen and ultimately induce a relaxation of the artery. In addition, Platelet-Derived Growth Factor (PDGF) is a growth factor released by smooth muscle cells and platelets that regulates cell growth and division. In this talk, we present a single-layered, axisymmetric hyperelastic model for a deforming, growing artery in which the opening angle is regulated by NO and growth is induced by PDGF. Our model describes vasodilation and G&R in a long cylindrical artery regulated by a steady-state Poiseuille flow. The transport of NO released by the endothelium is governed by a diffusion equation with a shear-stress dependent flux boundary condition. Arterial opening angle is assumed to be a Hill function of the wall-averaged NO concentration. We find that both growth and NO help to regulate shear stress with respect to the flow rate, but regulation through growth occurs only at large times. In contrast, regulation through NO is immediate but can only occur as long as the opening angle is able to continually decrease as a function of flow rate. Our model is calibrated using experimental data from ligated, control, and anastomosed carotid arteries of adult and weanling rabbits. Our results generate shear stress/flow rate and lumen radius/flow rate curves that agree with experimental data from control and NO-inhibited rabbit carotid arteries.

Shake Ibna Abir

Western Kentucky University
"Deep Learning Application of Long Short-Term Memory (LSTM) to predict the risk factors of etiology cardiovascular disease."
Cardiovascular disease (CVD) is presently one of the leading causes of death, with an estimated 24.1 million people expected to be affected by 2025. Therefore, the establishment of the health care industry's objective is to gather a vast amount of data on cardiovascular disease and utilize Deep Learning (DL) algorithms to analyze the information to assist doctors in early detection and identification of potential risk factors for CVD. DL algorithms can help to discover potential patterns of diseases and symptoms based on this structured and unstructured case information. In epidemiology, this is the first prospective study on cardiovascular disease in the community free movement population, and the related risk factors can be recognized. The prediction method of cardiovascular disease based on LSTM is proposed, and the connection between LSTM and unit state is tried to ensure the correct data acquisition during operation, and the prediction method based on LSTM is realized. The original medical data of 4434 participants in the data set with 11628 observations are verified by experiments. The algorithm has an accuracy of nearly 94% and a 0.96 Matthews correlation coefficient (MCC) score.
Additional authors: Dr. Richard Schugart ; Dr. Jing Feng GUO ; Shaharina Shoha

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Annual Meeting for the Society for Mathematical Biology, 2023.