Defense Date

3-22-2021

Graduation Date

Spring 5-8-2021

Availability

One-year Embargo

Submission Type

thesis

Degree Name

MS

Department

Biomedical Engineering

School

Rangos School of Health Sciences

Committee Chair

Jelena Janjic

Committee Member

John Viator

Committee Member

Kimberly Williams

Committee Member

Lauren Sugden

Keywords

Machine Learning, ARDS, ALI, Nanoemulsion, Perfluorocarbon

Abstract

Acute Respiratory distress Syndrome (ARDS) and Acute Lung Injury (ALI) are inflammatory lung pathologies consisting of non-hydrostatic pulmonary edema leading to hypoxia and impaired gas exchange in the lungs. ARDS/ALI is both difficult to study and treat as it is not in itself a specific pathology but rather a syndrome consisting of many pathologies that vary case by case. It is, however, consistently characterized by an explosive acute inflammatory response in the lung parenchyma leading to hypoxia. Although time has seen to an increase in the understanding of ARDS/ALI, the mortality rate remains in the range of 30-50%. For these reasons, nanomedicine may offer solutions to the diagnosis and treatment of ARDS/ALI. Nanomedicine, by definition, utilizes nanoscale materials to address various disease states in the hopes of being more effective than traditional medicine. Especially in cases of imaging, nanomedicine seeks to redress some of the issues seen in traditional imaging such as with more targeted delivery platform. This can be achieved through macrophage targeted nanomedicine platforms, therefore focus of this paper will be on macrophage targeted perfluorocarbon(PFC) nanoemulsions. We believe that nanoemulsions aimed for macrophage imaging in severely ill patients require the highest quality possible. We will understand the current state of the art through machine learning to determine what manufacturing parameters impact the performance of perfluorinated Nanoemulsions. Machine learning will be used to analyze what parameters of production are critical to the various colloidal attributes (size, zeta potential, and PDI) to the performance of emulsion-based drug delivery platforms.

Language

English

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