Unknown

Dataset Information

0

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 |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC8743278 | biostudies-literature
| S-EPMC7235268 | biostudies-literature
| S-EPMC9107629 | biostudies-literature
| S-EPMC8905050 | biostudies-literature
| S-EPMC8457901 | biostudies-literature
| S-EPMC7737895 | biostudies-literature
| S-EPMC7703567 | biostudies-literature
| S-EPMC8294595 | biostudies-literature
2022-06-03 | E-MTAB-11261 | biostudies-arrayexpress
| S-EPMC8574161 | biostudies-literature