Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/9417
Title: Multicenter study evaluating novel multispecimen pooling assay for the detection of SARS-CoV-2: High sensitivity and high throughput testing
Authors: -Yi Chung, Hsing
Jr Jian, Ming-
Kai Chang, Chih-
Lin, Jung-Chung
-Ming Yeh, Kuo
-Wen Chen, Chien
Keywords: Severe acute respiratory coronavirus 2;
Coronavirus disease 2019;
High throughput;
Pooled specimen
Issue Date: 1-Dec-2022
Publisher: Elsevier Taiwan LLC
Abstract: Abstract Background/purpose: Mass screening for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is important to prevent the spread of coronavirus disease 2019 (COVID-19). Pooling samples can increase the number of tests processed. LabTurbo AIO 48 is an automated platform that allows ribonucleic acid extraction and sample analysis on the sameinstrument. We created a novel pooling assay on this platform for SARS-CoV-2 detection and demonstrated that the pooling strategy increases testing capacity without affecting accuracy and sensitivity. Methods: Comparative limit of detection (LoD) assessment was performed on the LabTurbo AIO 48 platform and the current standard detection system based on real-time reverse transcription polymerase chain reaction (rRT-PCR) using 55 clinically positive samples. An additional 330 primary clinical samples were assessed. Results: Six samples pooled into one reaction tube were detected in approximately 2.5 h using the World Health Organization rRT-PCR protocol. LabTurbo AIO 48 also demonstrated a higher throughput than our reference rRT-PCR assay, with an LoD of 1000 copies/mL. The overall percentage agreement between the methods for the 330 samples was 100%. Conclusion: We created a novel multi-specimen pooling assay using LabTurbo AIO 48 for the robust detection of SARS-CoV-2, allowing high-throughput results; this assay will aid in better control and prevention of COVID-19. The diagnostic assay was cost-effective and timeefficient; thus, the pooling strategy is a practical and effective method for diagnosing large quantities of specimens without compromising precision
URI: http://localhost:8080/xmlui/handle/123456789/9417
ISSN: 1684-1182
Appears in Collections:VOL 55 NO 6 Part 1 2022

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