20.500.12556/RUL-145236 Evaluation of two broadly used commercial methods for detection of respiratory viruses with a recently added new target for detection of SARS-CoV-2 The clinical symptoms caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are nonspecific and can be associated with most other respiratory viruses that cause acute respiratory tract infections (ARI). Because the clinical differentiation of COVID-19 patients from those with other respiratory viruses is difficult, the evaluation of automated methods to detect important respiratory viruses together with SARS-CoV-2 seems necessary. Therefore, this study compares two molecular assays for the detection of respiratory viruses, including SARS-CoV-2: the Respiratory Viruses 16-Well Assay (AusDiagnostics, Pty Ltd., Mascot, Australia) and the Allplex™ RV Essential Assay coupled with the Allplex™-nCoV Assay (Seegene Inc., Seoul, Korea). The two methods (AusDiagnostics and Allplex™-nCoV Assay SARS-CoV-2) had 98.6% agreement with the reference method, cobas 6800, for the detection of SARS-CoV-2. Agreement between the AusDiagnostics assay and the Allplex™ RV Essential Assay for the detection of seven respiratory viruses was 99%. In our experience, the Respiratory Viruses 16-Well Assay proved to be the most valuable and useful medium-throughput method for simultaneous detection of important respiratory viruses and SARS-CoV-2. The main advantages of the method are high specificity for all targets included and their simultaneous detection and medium throughput with the option of having multiple instruments provide a constant run. respiratory viruses validation molecular assay SARS-CoV-2 COVID-19 respiratorni virusi validacija molekularni test true false true Angleški jezik Slovenski jezik Članek v reviji 2023-04-13 12:55:01 2023-04-13 12:55:05 2023-04-14 03:49:59 0000-00-00 00:00:00 2022 0 0 10 str. iss. 7, art. 1530 Vol. 14 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 616.2 1999-4915 10.3390/v14071530 115279619 RAZ_Jevsnik_Virant_Monika_2022.pdf RAZ_Jevsnik_Virant_Monika_2022.pdf 1 CCDC405825CC55B847F63B5E46CA20C7 991092107817bad6b320e9987690b33dccbe7a22481f5ae820e1496a38947a7c 80477215-d9e9-11ed-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=167961 https://www.mdpi.com/1999-4915/14/7/1530 0 9b53c7ec-d9e9-11ed-a314-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=167962 Medicinska fakulteta 0 0 0