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Subject: microfluidics

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    Label-Free CD8+ T-cell Purification and Electroporation in Relation to CAR T-cell Therapy
    (North Dakota State University, 2020) Ringwelski, Beth Anne
    Immunotherapy is becoming recognized as a superior treatment for cancer. In recent years, chimeric antigen receptor (CAR) therapy is among the immunotherapies that has had growing success rates. CAR T-cell therapy takes patient’s T-cells and encodes them with a CAR expressing gene, which can then target their cancer cells. However, there are some dangers associated with this therapy. If a cancer cell is mistakenly transfected with the CAR molecule, it can become resistant to the therapy. Using the electric properties of the cells, we have created a technique that can purify the T-cells from the remaining cancer cells using microfluidics and dielectrophoresis (DEP). Then, to further improve the therapy, the sample is electroporated following being patterned using DEP forces, which transfects the cells without using viral vectors and provides longer CD19 expression.
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    On the Feasibility of Machine Learning Algorithms Towards Low-Cost Flow Cytometry
    (North Dakota State University, 2023-08-01) Vandal, Noah
    Utilization low cost, scalable architectures for detection of specific cells for both mass flow and minute incidence analysis is something that is attractive for the clinical researcher, in order to expand access to otherwise costly devices. We demonstrate the use of a low-cost microfluidics device that performs detection of beads and cells, both for cell counting and for discrete cell type identification. This was accomplished using polymer technology via implementation of polydimethylsiloxane microfluidics, which were created by using a 3-D printed mold, and machine learning technologies with algorithms that can inference and track analyte particles within the microfluidic of interest. Our demonstration of our microfluidics device is proof that creating low cost instruments for analyte detection using current machine learning models and hardware is possible. We foresee the scalability of this design to be immense, in terms of throughput rate, inexpensiveness of product, and multiple different parameters and classes that can be searched for.