Hannanta-Anan, Pimkhuan

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

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    Item type:Publication,
    Fabrications of Low-Cost Poly(methyl methacrylate) Microfluidic Devices for Precise Passive Flow Control
    (2024-01-01)
    Kaewtae, Wattana
    ;
    Tanasapsakul, Warinthorn
    ;
    Pattamang, Pattaraluck
    ;
    Ranron, Norabadee
    ;
    Sripumkhai, Witsaroot
    Microfluidics serve as effective platforms for conducting point-of-care diagnostics and biochemical assays which require precise control over flow rates and incubation times. To enable their uses in resource-limited settings, a pump-free approach is essential for driving fluid flow within the microfluidics. In this study, we introduced cost-effective methodologies for fabricating poly(methyl methacrylate) (PMMA)-based microfluidic devices driven passively by capillary pressure. We explored three fabrication techniques including two-layer CNC micro-milling, two-layer laser engraving, and three-layer laser cutting. Among these methods, three-layer laser cutting proved to be the most reproducible method, yielding devices with consistent channel dimensions and contact angles. Consequently, this fabrication technique enables better control over flow rates within microfluidic systems. Our findings demonstrate the feasibility of fabricating low-cost, pump-free microfluidic devices for precise flow control, with potential applications in disease detection and biomedical research.
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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.