Hannanta-Anan, Pimkhuan

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Hannanta-Anan, Pimkhuan
Alternative Name
Hannanta-anan, Pimkhuan
Hannanta-Anan, P.
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Email
pimkhuan.ha@kmitl.ac.th

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    Item type:Publication,
    Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2
    (2021-02-02)
    Samacoits, Aubin
    ;
    Nimsamer, Pattaraporn
    ;
    Mayuramart, Oraphan
    ;
    Chantaravisoot, Naphat
    ;
    Sitthi-Amorn, Pitchaya
    Rapid, accurate, and low-cost detection of SARS-CoV-2 is crucial to contain the transmission of COVID-19. Here, we present a cost-effective smartphone-based device coupled with machine learning-driven software that evaluates the fluorescence signals of the CRISPR diagnostic of SARS-CoV-2. The device consists of a three-dimensional (3D)-printed housing and low-cost optic components that allow excitation of fluorescent reporters and selective transmission of the fluorescence emission to a smartphone. Custom software equipped with a binary classification model has been developed to quantify the acquired fluorescence images and determine the presence of the virus. Our detection system has a limit of detection (LoD) of 6.25 RNA copies/μL on laboratory samples and produces a test accuracy of 95% and sensitivity of 97% on 96 nasopharyngeal swab samples with transmissible viral loads. Our quantitative fluorescence score shows a strong correlation with the quantitative reverse transcription polymerase chain reaction (RT-qPCR) Ct values, offering valuable information of the viral load and, therefore, presenting an important advantage over nonquantitative readouts.
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    Item type:Publication,
    Computer Vision-aided CRISPR Diagnostics for the Detection of COVID-19
    (2021-04-01)
    Nimsamer, Pattaraporn
    ;
    Mayuramart, Oraphan
    ;
    Samacoits, Aubin
    ;
    Chantaravisoot, Naphat
    ;
    Nakhakes, Chajchawan
    Surveillance testing is a key strategy to control the spread of COVID-19. Unlike the gold standard testing method, quantitative reverse transcription polymerase chain reaction (RT-qPCR), CRISPR diagnostics have recently become a more appealing alternative as they are proven to be faster, simpler, and more affordable. However, the current CRISPR diagnostic readouts are typically non-quantitative, making them error-prone and lacking crucial information of viral load. To further improve the CRISPR diagnostic method, we have developed a custom computer vision algorithm that works in complement to common transilluminators to process fluorescence images of the diagnostic samples, quantify their fluorescence signals, and assign the test results. Our analysis showed that the quantified fluorescence intensity was directly correlated to the sample viral load, useful information for transmissibility and disease severity. Verified through laboratory and clinical samples, our algorithm accurately discriminated the samples with the viral RNA as low as 6.25 copies/uL, and correctly classified nasopharyngeal swab (NP swab) samples with 100% accuracy. Our work serves as a potential technique to improve the accuracy of CRISPR diagnostics of COVID-19 and promote rapid testing vital to the containment of the ongoing pandemic.