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Expanding Translational Potential of Quantitative Phase Imaging: Machine Learning and Live Cell Interferometry Applications for Cancer Immunotherapy


Quantitative phase imaging (QPI) has seen tremendous growth in the past decade. As the field progresses, the focus is shifting from technological development of new QPI technologies to development of new biological and clinically relevant applications. Increased translational application has driven integration of QPI platforms with other technological platforms, such as microfluidics and machine learning. This dissertation presents a collection of studies that enable and advance application of live cell interferometry (LCI), an established QPI technique, to probe and answer important biological questions. First, a novel approach to correcting phase wrapping issues in QPI measurements is discussed to increase accuracy of interferometric QPI measurements. Next, easily adaptable soft lithography protocol for a new application of low refractive index polymer is presented to enable integration of useful microfluidic systems with QPI. Finally, an approach to integrate machine learning to efficiently and accurately identify tumor reactive T cell killing, a biological event with great clinical significance for cancer immunotherapies, is introduced. These studies underline the translational potential of QPI and ways to accelerate QPI adaptation into biological and clinical application

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