Evidence map›Paper›PMID 39913530›Full record

ArticlePLoS neglected tropical diseases2025

Improved limit of detection for zoonotic Plasmodium knowlesi and P. cynomolgi surveillance using reverse transcription for total nucleic acid preserved samples or dried blood spots.

Kamil A Braima, Kim A Piera, Inke N D Lubis, Rintis Noviyanti, Giri S Rajahram, Pinkan Kariodimedjo, Irbah R A Nainggolan, Ranti Permatasari, Leily Trianty, Ristya Amalia and 15 more

Abstract readEvaluation Study
In one paragraph

Article in PLoS neglected tropical diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

25 authors.

Kamil A BraimaGlobal and Tropical Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Northern Territory, Australia.
Kim A PieraGlobal and Tropical Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Northern Territory, Australia.
Inke N D LubisGlobal and Tropical Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Northern Territory, Australia.ORCID 0000-0001-7075-9491
Rintis NoviyantiEijkman Research Center for Molecular Biology, BRIN, Indonesia.
Giri S RajahramExeins Health Initiative, Jakarta, Indonesia.
Pinkan KariodimedjoSchool of Medicine and Health Sciences, Monash University Malaysia, Kuala Lumpur, Malaysia.
Irbah R A NainggolanFaculty of Medicine, Universitas Sumatera Utara, Medan, Sumatera Utara, Indonesia.
Ranti PermatasariFaculty of Medicine, Universitas Sumatera Utara, Medan, Sumatera Utara, Indonesia.
Leily TriantyEijkman Research Center for Molecular Biology, BRIN, Indonesia.
Ristya AmaliaExeins Health Initiative, Jakarta, Indonesia.
Sitti Saimah SakamInfectious Diseases Society Kota Kinabalu Sabah-Menzies School of Health Research 6 Clinical Research Unit, Kota Kinabalu, Sabah, Malaysia.
Angelica F TanGlobal and Tropical Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Northern Territory, Australia.
Timothy WilliamInfectious Diseases Society Kota Kinabalu Sabah-Menzies School of Health Research 6 Clinical Research Unit, Kota Kinabalu, Sabah, Malaysia.
Jacob A F WestawayGlobal and Tropical Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Northern Territory, Australia.
PingChin LeeBiotechnology Research Institute, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia.
Sylvia DaimFaculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia.
Henry SurendraMonash University Indonesia, Tangerang, Indonesia.
Nathaniel ChristyU.S. Naval Medical Research Unit INDO PACIFIC, Singapore.
Andrew G LetiziaU.S. Naval Medical Research Unit INDO PACIFIC, Singapore.
Christopher L PeateyDrug Resistance and Diagnostics, Australian Defence Force Malaria and Infectious Disease Institute, Brisbane, Queensland, Australia.
Mohd Arshil MoideenFaculty of Medicine & Defence Health, National Defence University of Malaysia.
Bridget E BarberGlobal and Tropical Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Northern Territory, Australia.
Colin J SutherlandDepartment of Infection Biology, London School of Hygiene & Tropical Medicine, London, United Kingdom.
Nicholas M AnsteyGlobal and Tropical Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Northern Territory, Australia.
Matthew J GriggGlobal and Tropical Health Division, Menzies School of Health Research and Charles Darwin University, Darwin, Northern Territory, Australia.ORCID 0000-0001-9914-8352

Funding

Emerging zoonotic malaria in Malaysia: strengthening surveillance and evaluating population genetic structure to improve regional risk prediction toolsR01AI160457 · NIAID · INFECTIOUS DISEASE SOCIETY KOTA KINABALU · PI RAJAHRAM, GIRI · 2021 to 2025
$674k
NIAID NIH HHS R01 AI160457
6 · The paper itself

Abstract

backgroundZoonotic P. knowlesi and P. cynomolgi symptomatic and asymptomatic infections occur across endemic areas of Southeast Asia. Most infections are low-parasitemia, with an unknown proportion below routine microscopy detection thresholds. Molecular surveillance tools optimizing the limit of detection (LOD) would allow more accurate estimates of zoonotic malaria prevalence. METHODOLOGY/PRINCIPAL

findingsAn established ultra-sensitive Plasmodium genus quantitative-PCR (qPCR) assay targeting the 18S rRNA gene underwent LOD evaluation with and without reverse transcription (RT) for P. knowlesi, P. cynomolgi and P. vivax using total nucleic acid preserved (DNA/RNA Shield) isolates and archived dried blood spots (DBS). LODs for selected P. knowlesi-specific assays, and reference P. vivax- and P. cynomolgi-specific assays were determined with reverse transcription (RT). Assay specificities were assessed using clinical malaria samples and malaria-negative controls. The use of reverse transcription improved Plasmodium species detection by up to 10,000-fold (Plasmodium genus), 2759-fold (P. knowlesi) and 1000-fold (P. vivax and P. cynomolgi). The Kamau et al. Plasmodium genus RT-qPCR assay was highly sensitive for P. knowlesi detection with a median LOD of ≤0.0002 parasites/μL compared to 0.002 parasites/μL for P. cynomolgi and P. vivax. The LODs with RT for P. knowlesi-specific PCRs were enhanced for the Imwong et al. 18S rRNA (0.0007 parasites/μL) and Divis et al. real-time 18S rRNA (0.0002 parasites/μL) assays, but not for the Lubis et al. hemi-nested SICAvar (1.1 parasites/μL) and Lee et al. nested 18S rRNA (11 parasites/μL). The LOD for P. vivax- and P. cynomolgi-specific assays with RT were moderately improved at 0.02 and 0.002 parasites/μL, respectively (1000-fold change). For DBS P. knowlesi samples the use of RT also markedly improved the Plasmodium genus qPCR LOD from 19.89 to 0.08 parasites/μL (249-fold change); no LOD improvement was demonstrated in DBS archived beyond 6 years. The Plasmodium genus and P. knowlesi-assays were 100% specific for Plasmodium species and P. knowlesi detection, respectively, from 190 clinical infections and 48 healthy controls. Reference P. vivax-specific primers demonstrated known cross-reactivity with P. cynomolgi. CONCLUSIONS/SIGNIFICANCE: Our findings support the use of an 18S rRNA Plasmodium genus qPCR and species-specific nested PCR protocol with RT for highly-sensitive surveillance of zoonotic and human Plasmodium species infections.

Indexed as

Dried Blood Spot TestingMalariaPlasmodium cynomolgiPlasmodium knowlesiZoonosesAnimalsAsia, SoutheasternDNA, ProtozoanHumansLimit of DetectionReal-Time Polymerase Chain ReactionReverse TranscriptionRNA, Ribosomal, 18SSensitivity and SpecificityDNA, ProtozoanRNA, Ribosomal, 18S

Identifiers

PMID39913530
PMCPMC11838868

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.