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Description
Aortic pathologies such as coarctation, dissection, and aneurysm represent a
particularly emergent class of cardiovascular diseases and account for significant cardiovascular morbidity and mortality worldwide. Computational simulations of aortic flows are growing increasingly important as tools for gaining understanding of these pathologies and for planning their surgical repair. In vitro experiments are required to validate these simulations against real world data, and a pulsatile flow pump system can provide physiologic flow conditions characteristic of the aorta.
This dissertation presents improved experimental techniques for in vitro aortic blood flow and the increasingly larger parts of the human cardiovascular system. Specifically, this work develops new flow management and measurement techniques for cardiovascular flow experiments with the aim to improve clinical evaluation and treatment planning of aortic diseases.
The hypothesis of this research is that transient flow driven by a step change in volume flux in a piston-based pulsatile flow pump system behaves differently from transient flow driven by a step change in pressure gradient, the development time being substantially reduced in the former. Due to this difference in behavior, the response to a piston-driven pump can be predicted in order to establish inlet velocity and flow waveforms at a downstream phantom model.
The main objectives of this dissertation were: 1) to design, construct, and validate a piston-based flow pump system for aortic flow experiments, 2) to characterize temporal and spatial development of start-up flows driven by a piston pump that produces a step change from zero flow to a constant volume flux in realistic (finite) tube geometries for physiologic Reynolds numbers, and 3) to develop a method to predict downstream velocity and flow waveforms at the inlet of an aortic phantom model and determine the input waveform needed to achieve the intended waveform at the test section. Application of these newly improved flow management tools and measurement techniques were then demonstrated through in vitro experiments in patient-specific coarctation of aorta flow phantom models manufactured in-house and compared to computational simulations to inform and execute future experiments and simulations.
particularly emergent class of cardiovascular diseases and account for significant cardiovascular morbidity and mortality worldwide. Computational simulations of aortic flows are growing increasingly important as tools for gaining understanding of these pathologies and for planning their surgical repair. In vitro experiments are required to validate these simulations against real world data, and a pulsatile flow pump system can provide physiologic flow conditions characteristic of the aorta.
This dissertation presents improved experimental techniques for in vitro aortic blood flow and the increasingly larger parts of the human cardiovascular system. Specifically, this work develops new flow management and measurement techniques for cardiovascular flow experiments with the aim to improve clinical evaluation and treatment planning of aortic diseases.
The hypothesis of this research is that transient flow driven by a step change in volume flux in a piston-based pulsatile flow pump system behaves differently from transient flow driven by a step change in pressure gradient, the development time being substantially reduced in the former. Due to this difference in behavior, the response to a piston-driven pump can be predicted in order to establish inlet velocity and flow waveforms at a downstream phantom model.
The main objectives of this dissertation were: 1) to design, construct, and validate a piston-based flow pump system for aortic flow experiments, 2) to characterize temporal and spatial development of start-up flows driven by a piston pump that produces a step change from zero flow to a constant volume flux in realistic (finite) tube geometries for physiologic Reynolds numbers, and 3) to develop a method to predict downstream velocity and flow waveforms at the inlet of an aortic phantom model and determine the input waveform needed to achieve the intended waveform at the test section. Application of these newly improved flow management tools and measurement techniques were then demonstrated through in vitro experiments in patient-specific coarctation of aorta flow phantom models manufactured in-house and compared to computational simulations to inform and execute future experiments and simulations.
ContributorsChaudhury, Rafeed Ahmed (Author) / Frakes, David (Thesis advisor) / Adrian, Ronald J (Thesis advisor) / Vernon, Brent (Committee member) / Pizziconi, Vincent (Committee member) / Caplan, Michael (Committee member) / Arizona State University (Publisher)
Created2015
Description
This thesis aims to incorporate exosomes into an electrospun scaffold for tissue engineering applications. The motivation for this work is to develop an implant to regenerate tissue for patients with laryngeal defects. It was determined that it is feasible to incorporate exosomes into an electrospun scaffold. This addition of exosomes does alter the scaffold properties, by decreasing the average fiber diameter by roughly a factor of three and increasing the average modulus by roughly a factor of two. Cells were cultured on a scaffold with exosomes incorporated and were found to proliferate more than on a scaffold alone. This research lays the groundwork for further developing and optimizing an electrospun scaffold with exosomes incorporated to elicit a tissue regenerative response.
ContributorsKennedy, Maeve (Author) / Pizziconi, Vincent (Thesis director) / McPhail, Michael (Committee member) / School of International Letters and Cultures (Contributor) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Statistical process control (SPC) and predictive analytics have been used in industrial manufacturing and design, but up until now have not been applied to threshold data of vital sign monitoring in remote care settings. In this study of 20 elders with COPD and/or CHF, extended months of peak flow monitoring (FEV1) using telemedicine are examined to determine when an earlier or later clinical intervention may have been advised. This study demonstrated that SPC may bring less than a 2.0% increase in clinician workload while providing more robust statistically-derived thresholds than clinician-derived thresholds. Using a random K-fold model, FEV1 output was predictably validated to .80 Generalized R-square, demonstrating the adequate learning of a threshold classifier. Disease severity also impacted the model. Forecasting future FEV1 data points is possible with a complex ARIMA (45, 0, 49), but variation and sources of error require tight control. Validation was above average and encouraging for clinician acceptance. These statistical algorithms provide for the patient's own data to drive reduction in variability and, potentially increase clinician efficiency, improve patient outcome, and cost burden to the health care ecosystem.
ContributorsFralick, Celeste (Author) / Muthuswamy, Jitendran (Thesis advisor) / O'Shea, Terrance (Thesis advisor) / LaBelle, Jeffrey (Committee member) / Pizziconi, Vincent (Committee member) / Shea, Kimberly (Committee member) / Arizona State University (Publisher)
Created2013