Dataset Information

Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2.

ABSTRACT: 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.

SUBMITTER: Samacoits A

PROVIDER: S-EPMC7839157 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2.

Samacoits Aubin A Nimsamer Pattaraporn P Mayuramart Oraphan O Chantaravisoot Naphat N Sitthi-Amorn Pitchaya P Nakhakes Chajchawan C Luangkamchorn Lumrung L Tongcham Phongsakhon P Zahm Ugo U Suphanpayak Suchada S Padungwattanachoke Natta N Leelarthaphin Nutcha N Huayhongthong Hathaichanok H Pisitkun Trairak T Payungporn Sunchai S Hannanta-Anan Pimkhuan P

ACS omega 20210120 4

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 smartpho ...[more]

PMID: 33553890

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Project description:In 2019 the COVID-19 pandemic, caused by SARS-CoV-2, demonstrated the urgent need for rapid, reliable, and portable diagnostics. The COVID-19 pandemic was declared in January 2020 and surges of the outbreak continue to reoccur. It is clear that early identification of infected individuals, especially asymptomatic carriers, plays a huge role in preventing the spread of the disease. The current gold standard diagnostic for SARS-CoV-2 is quantitative reverse transcription polymerase chain reaction (qRT-PCR) test based on the detection of the viral RNA. While RT-PCR is reliable and sensitive, it requires expensive centralized equipment and is time consuming (∼2 h or more); limiting its applicability in low resource areas. The FDA issued Emergency Use Authorizations (EUAs) for several COVID-19 diagnostics with an emphasis on point-of care (PoC) testing. Numerous RT-PCR and serological tests were approved for use at the point of care. Abbott's ID NOW, and Cue Health's COVID-19 test are of particular interest, which use isothermal amplification methods for rapid detection in under 20 min. We look to expand on the range of current PoC testing platforms with a new rapid and portable isothermal nucleic acid detection device. We pair reverse transcription loop mediated isothermal amplification (RT-LAMP) with a particle imaging technique, particle diffusometry (PD), to successfully detect SARS-CoV-2 in only 35 min on a portable chip with integrated heating. A smartphone device is used to image the samples containing fluorescent beads post-RT-LAMP and correlates decreased diffusivity to positive samples. We detect as little as 30 virus particles per μL from a RT-LAMP reaction in a microfluidic chip using a portable heating unit. Further, we can perform RT-LAMP from a diluted unprocessed saliva sample without RNA extraction. Additionally, we lyophilize SARS-CoV-2-specific RT-LAMP reactions that target both the N gene and the ORF1ab gene in the microfluidic chip, eliminating the need for cold storage. Our assay meets specific target product profiles outlined by the World Health Organization: it is specific to SARS-CoV-2, does not require cold storage, is compatible with digital connectivity, and has a detection limit of less than 35 × 104 viral particles per mL in saliva. PD-LAMP is rapid, simple, and attractive for screening and use at the point of care.

Dataset Information

Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2.

Publications

Machine Learning-Driven and Smartphone-Based Fluorescence Detection for CRISPR Diagnostic of SARS-CoV-2.

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