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Title: Reverse transcription PCR to detect low density malaria infections
Authors: Christensen, Peter
Bozdech, Zbynek
Watthanaworawit, Wanitda
Imwong, Mallika
Rénia, Laurent
Malleret, Benoît
Ling, Clare
Nosten, François
Keywords: Science::Biological sciences
Issue Date: 2022
Source: Christensen, P., Bozdech, Z., Watthanaworawit, W., Imwong, M., Rénia, L., Malleret, B., Ling, C. & Nosten, F. (2022). Reverse transcription PCR to detect low density malaria infections. Wellcome Open Research, 6, 39-.
Project: NUHSRO/2018/006/SU/01
Journal: Wellcome Open Research
Abstract: Background: Targeted malaria elimination strategies require highly sensitive tests to detect low density malaria infections (LDMI). Commonly used methods for malaria diagnosis such as light microscopy and antigen-based rapid diagnostic tests (RDTs) are not sensitive enough for reliable identification of infections with parasitaemia below 200 parasites per milliliter of blood. While targeted malaria elimination efforts on the Thailand-Myanmar border have successfully used high sample volume ultrasensitive quantitative PCR (uPCR) to determine malaria prevalence, the necessity for venous collection and processing of large quantities of patient blood limits the widespread tractability of this method. Methods: Here we evaluated a real-time reverse transcription PCR (RT-qPCR) method that reduces the required sample volume compared to uPCR. To do this, 304 samples collected from an active case detection program in Kayin state, Myanmar were compared using uPCR and RT-qPCR. Results: Plasmodium spp. RT-qPCR confirmed 18 of 21 uPCR Plasmodium falciparum positives, while P. falciparum specific RT-qPCR confirmed 17 of the 21 uPCR P. falciparum positives. Combining both RT-qPCR results increased the sensitivity to 100% and specificity was 95.1%. Conclusion: Malaria detection in areas of low transmission and LDMI can benefit from the increased sensitivity of ribosomal RNA detection by RT-PCR, especially where sample volume is limited. Isolation of high quality RNA also allows for downstream analysis of malaria transcripts.
ISSN: 2398-502X
DOI: 10.12688/wellcomeopenres.16564.3
Schools: School of Biological Sciences 
Rights: © 2022 Christensen P et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SBS Journal Articles

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