AUTHOR=Pandey Mragnayani , Xess Immaculata , Sachdev Janya , Yadav Usha , Singh Gagandeep , Pradhan Dibyabhaba , Xess Ashit Bhushan , Rana Bhaskar , Dar Lalit , Bakhshi Sameer , Seth Rachna , Mahapatra Manoranjan , Jyotsna Viveka P. , Jain Arun Kumar , Kumar Rakesh , Agarwal Reshu , Mani Prashant TITLE=Development of a Sensitive and Specific Novel qPCR Assay for Simultaneous Detection and Differentiation of Mucormycosis and Aspergillosis by Melting Curve Analysis JOURNAL=Frontiers in Fungal Biology VOLUME=2 YEAR=2022 URL=https://www.frontiersin.org/journals/fungal-biology/articles/10.3389/ffunb.2021.800898 DOI=10.3389/ffunb.2021.800898 ISSN=2673-6128 ABSTRACT=

Molecular diagnostic assays can expedite the diagnosis of fungal infections, and subsequently help in early interventions and appropriate management of patients. The aim of this study was to develop a single set of primers for a real-time quantitative polymerase chain reaction (qPCR) assay to detect and identify commonly reported, clinically relevant molds i.e., Aspergillus spp, Mucorales and Fusarium spp., up to genus level by melting curve analysis. This assay was evaluated in whole blood from patients with suspected invasive aspergillosis (IA), and in tissue biopsy, bronchoalveolar lavage (BAL) fluid and other site-specific samples from patients with suspected invasive mucormycosis (IM). The limit of detection (LoD) was determined as 10 copies/μl for all three molds. The mean coefficient of variation (CV) across all sets of intra- and inter-assay data was 0.63% (ranging from 0.42 to 1.56%), showing high reproducibility of the assay. Sensitivity and specificity of the assay were 93.3 and 97.1% respectively for diagnosis of IA, and 99.29 and 83.84% respectively for diagnosis of IM. Fusarium was not detected in any of the clinical samples included and the few laboratory confirmed cases of fusariosis did not meet the inclusion criteria of the study. Hence no ROC curve or cutoff value could be generated for the same. This newly developed qPCR assay therefore appears to be a promising tool in detection of IA and IM.