Author: Juang, Duane S.; Juang, Terry D.; Dudley, Dawn M.; Newman, Christina M.; Accola, Molly A.; Rehrauer, William M.; Friedrich, Thomas C.; O’Connor, David H.; Beebe, David J.
                    Title: Oil immersed lossless total analysis system for integrated RNA extraction and detection of SARS-CoV-2  Cord-id: nbw96ar3  Document date: 2021_7_14
                    ID: nbw96ar3
                    
                    Snippet: The COVID-19 pandemic exposed difficulties in scaling current quantitative PCR (qPCR)-based diagnostic methodologies for large-scale infectious disease testing. Bottlenecks include lengthy multi-step processes for nucleic acid extraction followed by qPCR readouts, which require costly instrumentation and infrastructure, as well as reagent and plastic consumable shortages stemming from supply chain constraints. Here we report an Oil Immersed Lossless Total Analysis System (OIL-TAS), which integra
                    
                    
                    
                     
                    
                    
                    
                    
                        
                            
                                Document: The COVID-19 pandemic exposed difficulties in scaling current quantitative PCR (qPCR)-based diagnostic methodologies for large-scale infectious disease testing. Bottlenecks include lengthy multi-step processes for nucleic acid extraction followed by qPCR readouts, which require costly instrumentation and infrastructure, as well as reagent and plastic consumable shortages stemming from supply chain constraints. Here we report an Oil Immersed Lossless Total Analysis System (OIL-TAS), which integrates RNA extraction and detection onto a single device that is simple, rapid, cost effective, and requires minimal supplies and infrastructure to perform. We validated the performance of OIL-TAS using contrived SARS-CoV-2 viral particle samples and clinical nasopharyngeal swab samples. OIL-TAS showed a 93% positive predictive agreement (n = 57) and 100% negative predictive agreement (n = 10) with clinical SARS-CoV-2 qPCR assays in testing clinical samples, highlighting its potential to be a faster, cheaper, and easier-to-deploy alternative for infectious disease testing.
 
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