A Colorimetric Test to Differentiate Patients Infected with Influenza from COVID‐19. Issue 8 (6th June 2021)

• Record Type: Journal Article
• Authors: Kozlowski, Hannah N.; Abdou Mohamed, Mohamed A.; Kim, Jisung; Bell, Natalie G.; Zagorovsky, Kyryl; Mubareka, Samira; Chan, Warren C. W.
• Is Part Of: Small structures. Volume 2:Issue 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-06
• Subjects: colorimetric -- diagnostics -- DNAzymes -- gold nanoparticles, influenza A -- respiratory infections, SARS‐CoV‐2; Chemistry -- Periodicals; Science -- Periodicals; Engineering -- Periodicals; 505
• DOI: 10.1002/sstr.202100034
• Languages: English
• ISSNs: 2688-4062

Abstract

Abstract : Patients infected with SARS‐CoV‐2 and influenza display similar symptoms, but treatment requirements are different. Clinicians need to accurately distinguish SARS‐CoV‐2 from influenza to provide appropriate treatment. Here, the authors develope a color‐based technique to differentiate between patients infected with SARS‐CoV‐2 and influenza A using a nucleic acid enzyme‐gold nanoparticle (GNP) molecular test requiring minimal equipment. The MNAzyme and GNP probes are designed to be robust to viral mutations. Conserved regions of the viral genomes are targeted, and two MNAzymes are created for each virus. The ability of the system to distinguish between SARS‐CoV‐2 and influenza A using 79 patient samples is tested. When detecting SARS‐CoV‐2 positive patients, the clinical sensitivity is 90%, and the specificity is 100%. When detecting influenza A, the clinical sensitivity and specificity are 93% and 100%, respectively. The high clinical performance of the MNAzyme‐GNP assay shows that it can be used to help clinicians choose effective treatments. Abstract : Molecular diagnostic tests are needed to differentiate patients infected with COVID‐19 and influenza to guide treatment decisions. A colorimetric test using nucleic acid enzymes and gold nanoparticles is developed to identify these respiratory pathogens. The test has 93–95% sensitivity and 100% specificity in patients. The results support rapid translation of this materials‐based diagnostic approach for use in human patients.