ABSTRACT
The COVID-19 pandemic has spread worldwide, and rapid detection of the SARS-CoV-2 virus is crucial for infection surveillance and epidemic control. This study developed a centrifugal microfluidics-based multiplex reverse transcription recombinase polymerase amplification (RT-RPA) assay for endpoint fluorescence detection of the E, N, and ORF1ab genes of SARS-CoV-2. The microscope slide-shaped microfluidic chip could simultaneously accomplish three target genes and one reference human gene (i.e., ACTB) RT-RPA reactions in 30 min, and the sensitivity was 40 RNA copies/reaction for the E gene, 20 RNA copies/reaction for the N gene, and 10 RNA copies/reaction for the ORF1ab gene. The chip demonstrated high specificity, reproducibility, and repeatability. Chip performance was also evaluated using real clinical samples. Thus, this rapid, accurate, on-site, and multiplexed nucleic acid test microfluidic chip would significantly contribute to detecting patients with COVID-19 in low-resource settings and point-of-care testing (POCT) and, in the future, could be used to detect emerging new variants of SARS-CoV-2.
ABSTRACT
BACKGROUND: Coronavirus disease 2019 (COVID-19) is a highly contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that brings a significant public health challenge. A rapid and simple method is necessary for testing suspected samples and screening the population. METHODS: To better monitor sample effectiveness, this study described a method to detect nucleocapsid protein gene (N gene) of SARS-CoV-2 and human ACTB gene employing real-time duplex reverse transcription multienzyme isothermal rapid amplification (RT-MIRA) assays. RESULTS: The established real-time duplex RT-MIRA assays showed that no cross-reactions were observed to other pathogens and the detection limit was 100 copies/reaction. Using simulated clinical samples to test established assays further and the amplification process took no more than 20 minutes at 42°C. CONCLUSIONS: RT-MIRA assays are faster and easier than reverse transcription real-time polymerase chain reaction (RT-PCR). It is expected to be further optimized and evaluated in the detection of SARS-CoV-2 confirmed cases.
Subject(s)
COVID-19 , Reverse Transcription , Humans , Nucleic Acid Amplification Techniques , SARS-CoV-2 , Sensitivity and SpecificityABSTRACT
BACKGROUND: Corona Virus Disease 2019 (COVID-19) is currently a worldwide pandemic and has a huge impact on public health and socio-economic development. The purpose of this study is to explore the diagnostic value of the quantitative computed tomography (CT) method by using different threshold segmentation techniques to distinguish between patients with or without COVID-19 pneumonia. METHODS: A total of 47 patients with suspected COVID-19 were retrospectively analyzed, including nine patients with positive real-time fluorescence reverse transcription polymerase chain reaction (RT-PCR) test (confirmed case group) and 38 patients with negative RT-PCR test (excluded case group). An improved 3D convolutional neural network (VB-Net) was used to automatically extract lung lesions. Eight different threshold segmentation methods were used to define the ground glass opacity (GGO) and consolidation. The receiver operating characteristic (ROC) curves were used to compare the performance of various parameters with different thresholds for diagnosing COVID-19 pneumonia. RESULTS: The volume of GGO (VOGGO) and GGO percentage in the whole lung (GGOPITWL) were the most effective values for diagnosing COVID-19 at a threshold of - 300 HU, with areas under the curve (AUCs) of 0.769 and 0.769, sensitivity of 66.67 and 66.67%, specificity of 94.74 and 86.84%. Compared with VOGGO or GGOPITWL at a threshold of - 300 Hounsfield units (HU), the consolidation percentage in the whole lung (CPITWL) with thresholds at - 400 HU, - 350 HU, and - 250 HU were statistically different. There were statistical differences in the infection volume and percentage of the whole lung, right lung, and lobes between the two groups. VOGGO, GGOPITWL, and volume of consolidation (VOC) were also statistically different at the threshold of - 300 HU. CONCLUSIONS: Quantitative CT provides an image quantification method for the auxiliary diagnosis of COVID-19 and is expected to assist in confirming patients with COVID-19 pneumonia in suspected cases.