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    Detection and Quantification of Bacterial Species and Mammalian Cells on a Smartphone Platform Using Fluorescence

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    Author
    Safavinia, Babak
    Issue Date
    2022
    Keywords
    Autofluorescence
    Smartphone imaging
    Advisor
    Yoon, Jeong-Yeol
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Embargo
    Release after 11/19/2022
    Abstract
    Current methods to detect and quantify biological samples, specifically Natural killer cells, E. Coli, S. Typhimurium, and S. aureus, require expensive and specialized equipment, trained personnel, and have a long time-to-result. The aims of this research are to reduce cost, decrease time of detection, and simplify current systems. Two platforms were developed that utilizes fluorescence analysis and a smartphone camera for imaging. Natural killer cell subpopulations, CD56bright and CD56dim, are believed to have a correlation with the overall patient health and immune function. The first platform consists of paper microfluidic chip and a smartphone-based device to identify and quantify NK cell subpopulation. A droplet of buffy coat sample consisting of NK cells are placed onto a single flow lane unit in order to perform special separation of CD56bright and CD56dim cells. Anti-CD56 fluorescent nanoparticles are used as biomarkers due to differential bindings to NK cell subpopulations. A smartphone platform that utilizes a smartphone microscope, 473 nm LED, and a acrylic film to as an optical bandpass filter. The analysis is done using a cloud-based machine learning predictive modeling analyzed NK cell subpopulation differentiation. Cell subpopulation analysis showed 89% accuracy. For the second platform, a smartphone platform was used to detect bacteria presence on laboratory surfaces. Three bacteria species that are commonly found on surfaces E. Coli, S. Typhimurium, and S. aureus, were investigated in this study. The current methods to detect bacteria requires culturing or staining. Autofluorescence from the bacteria was obtained using a 405 nm LED as a light source to excite and cause emission and an acrylic film was used again as an optical bandpass filter. ImageJ was used to analyze the images and quantify the fluorescent intensity signals. The platform was able to detect the presence of all three bacteria species with a limit of detect of 104 CFU/ cm^2 and differentiate the species from controls.
    Type
    text
    Electronic Thesis
    Degree Name
    M.S.
    Degree Level
    masters
    Degree Program
    Graduate College
    Biomedical Engineering
    Degree Grantor
    University of Arizona
    Collections
    Master's Theses

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