Diagnostic accuracy and limit of detection of ten malaria parasite lactate dehydrogenase-based rapid tests for Plasmodium knowlesi and P. falciparum

Tan, Angelica Fiona; Sakam, Sitti Saimah binti; Rajahram, Giri Shan; William, Timothy; Isnadi, Mohammad Faruq Abd Rachman; Daim, Sylvia; Barber, Bridget E.; Kho, Steven; Sutherland, Colin J.; Anstey, Nicholas M.; Yerlikaya, Seda; Schalkwyk, Donelly A. van; Grigg, Matthew J.

doi:10.3389/fcimb.2022.1023219

Cited by 10 publications

(14 citation statements)

References 51 publications

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“…Regular screening as well as awareness programs shall be conducted for communities in these areas. Specifically, in high-risk areas with a lack of accessible routes, the development and distribution of highly sensitive, mobile, and affordable tools such as novel rapid diagnostic test kits will enhance public health outreach ( Tan et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Predicting Plasmodium knowlesi transmission risk across Peninsular Malaysia using machine learning-based ecological niche modeling approaches

Phang

Hamid²,

Jelip³

et al. 2023

Front. Microbiol.

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The emergence of potentially life-threatening zoonotic malaria caused by Plasmodium knowlesi nearly two decades ago has continued to challenge Malaysia healthcare. With a total of 376 P. knowlesi infections notified in 2008, the number increased to 2,609 cases in 2020 nationwide. Numerous studies have been conducted in Malaysian Borneo to determine the association between environmental factors and knowlesi malaria transmission. However, there is still a lack of understanding of the environmental influence on knowlesi malaria transmission in Peninsular Malaysia. Therefore, our study aimed to investigate the ecological distribution of human P. knowlesi malaria in relation to environmental factors in Peninsular Malaysia. A total of 2,873 records of human P. knowlesi infections in Peninsular Malaysia from 1st January 2011 to 31st December 2019 were collated from the Ministry of Health Malaysia and geolocated. Three machine learning-based models, maximum entropy (MaxEnt), extreme gradient boosting (XGBoost), and ensemble modeling approach, were applied to predict the spatial variation of P. knowlesi disease risk. Multiple environmental parameters including climate factors, landscape characteristics, and anthropogenic factors were included as predictors in both predictive models. Subsequently, an ensemble model was developed based on the output of both MaxEnt and XGBoost. Comparison between models indicated that the XGBoost has higher performance as compared to MaxEnt and ensemble model, with AUCROC values of 0.933 ± 0.002 and 0.854 ± 0.007 for train and test datasets, respectively. Key environmental covariates affecting human P. knowlesi occurrence were distance to the coastline, elevation, tree cover, annual precipitation, tree loss, and distance to the forest. Our models indicated that the disease risk areas were mainly distributed in low elevation (75–345 m above mean sea level) areas along the Titiwangsa mountain range and inland central-northern region of Peninsular Malaysia. The high-resolution risk map of human knowlesi malaria constructed in this study can be further utilized for multi-pronged interventions targeting community at-risk, macaque populations, and mosquito vectors.

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Section: Discussionmentioning

confidence: 99%

Predicting Plasmodium knowlesi transmission risk across Peninsular Malaysia using machine learning-based ecological niche modeling approaches

Phang

Hamid²,

Jelip³

et al. 2023

Front. Microbiol.

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“…Recent improvements in lateral flow point-of-care diagnostics for P. knowlesi have been based on either nonspecific Plasmodium species parasite lactate dehydrogenase (pLDH) targets, or cross-reactivity with epitopes for P. vivax-specific pLDH 36,37 . However, adapting technology for P. knowlesi detection designed for more common human malaria species requires trade-offs between test accuracy, complexity, and cost.…”

Section: Discussionmentioning

confidence: 99%

“…However, adapting technology for P. knowlesi detection designed for more common human malaria species requires trade-offs between test accuracy, complexity, and cost. The Gazelle™ was tested on a similar parasite density distribution sample set of clinical P. knowlesi infections as a previously published large evaluation of 10 pLDH-based malaria rapid diagnostic tests 37 . The best performing Plasmodium species pan-pLDH and P. vivax-pLDH tests for P. knowlesi detection reported sensitivities of 87.0% and 92.0%, respectively 37 , comparable to the overall Gazelle™ performance in the current study.…”

Section: Discussionmentioning

confidence: 99%

“…The Gazelle™ was tested on a similar parasite density distribution sample set of clinical P. knowlesi infections as a previously published large evaluation of 10 pLDH-based malaria rapid diagnostic tests 37 . The best performing Plasmodium species pan-pLDH and P. vivax-pLDH tests for P. knowlesi detection reported sensitivities of 87.0% and 92.0%, respectively 37 , comparable to the overall Gazelle™ performance in the current study. Other antigen-and antibody-based novel diagnostic assays are being designed to detect multiple infectious causes of acute febrile illness, including the DPP® Fever Panel II Asia for diagnosis of falciparum and non-falciparum malaria, dengue and melioidosis 38 .…”

Section: Discussionmentioning

confidence: 99%

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Evaluation of a point-of-care haemozoin assay (Gazelle device) for rapid detection of Plasmodium knowlesi malaria

Tan

Thota²,

Sakam³

et al. 2023

Sci Rep

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Plasmodium knowlesi is the major cause of zoonotic malaria in Southeast Asia. Rapid and accurate diagnosis enables effective clinical management. A novel malaria diagnostic tool, Gazelle (Hemex Health, USA) detects haemozoin, a by-product of haem metabolism found in all Plasmodium infections. A pilot phase refined the Gazelle haemozoin identification algorithm, with the algorithm then tested against reference PCR in a larger cohort of patients with P. knowlesi mono-infections and febrile malaria-negative controls. Limit-of-detection analysis was conducted on a subset of P. knowlesi samples serially diluted with non-infected whole blood. The pilot phase of 40 P. knowlesi samples demonstrated 92.5% test sensitivity. P. knowlesi-infected patients (n = 203) and febrile controls (n = 44) were subsequently enrolled. Sensitivity and specificity of the Gazelle against reference PCR were 94.6% (95% CI 90.5–97.3%) and 100% (95% CI 92.0–100%) respectively. Positive and negative predictive values were 100% and 98.8%, respectively. In those tested before antimalarial treatment (n = 143), test sensitivity was 96.5% (95% CI 92.0–98.9%). Sensitivity for samples with ≤ 200 parasites/µL (n = 26) was 84.6% (95% CI 65.1–95.6%), with the lowest parasitaemia detected at 18/µL. Limit-of-detection (n = 20) was 33 parasites/µL (95% CI 16–65%). The Gazelle device has the potential for rapid, sensitive detection of P. knowlesi infections in endemic areas.

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“…Similarly, P. cynomolgi microscopically resembles P. vivax in human infections (12,13). Current malaria rapid diagnostic tests which detect circulating Plasmodium species antigens also remain insufficiently sensitive for P. knowlesi passive case detection at the low parasite counts able to produce symptomatic infections (14–16). Multiple molecular methods to detect P. knowlesi have been developed, including both quantitative and conventional qualitative PCR assays (7,17).…”

Section: Introductionmentioning

confidence: 99%

Improved limit of detection for zoonoticPlasmodium knowlesiandP. cynomolgisurveillance using reverse transcription for total nucleic acid preserved samples or dried blood spots

Braima,

Piera,

Lubis

et al. 2024

Preprint

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Background:ZoonoticP. knowlesiandP. cynomolgisymptomatic 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.Methods:An established ultra-sensitivePlasmodiumgenus quantitative-PCR (qPCR) assay targeting the 18S rRNA gene underwent LOD evaluation with and without reverse transcription (RT) forP. knowlesi, P. cynomolgiandP. vivaxusing total nucleic acid preserved (DNA/RNA ShieldTM) isolates and archived dried blood spots (DBS). LODs for selectedP. knowlesi-specific assays, and referenceP. vivax- andP. cynomolgi-specific assays were determined with RT. Assay specificities were assessed using clinical malaria samples and malaria-negative controls.Results:The use of reverse transcription improvedPlasmodiumspecies detection by up to 10,000-fold (Plasmodiumgenus), 2759-fold (P. knowlesi), 1000-fold (P. vivax) and 10-fold (P. cynomolgi). The median LOD with RT for the Kamau et al.Plasmodiumgenus RT-qPCR assay was ≤0.0002 parasites/µL forP. knowlesiand 0.002 parasites/µL for bothP. cynomolgiandP. vivax. The LODs with RT forP. knowlesi-specific PCRs were: Imwong et al. 18S rRNA (0.0007 parasites/µL); Divis et al. real-time 18S rRNA (0.0002 parasites/µL); Lubis et al. hemi-nested SICAvar (1.1 parasites/µL) and Lee et al. nested 18S rRNA (11 parasites/µL). The LOD forP. vivax- andP. cynomolgi-specific assays with RT were 0.02 and 0.20 parasites/µL respectively. For DBSP. knowlesisamples the median LOD for thePlasmodiumgenus qPCR with RT was 0.08, and without RT was 19.89 parasites/uL (249-fold change); no LOD improvement was demonstrated in DBS archived beyond 6 years. ThePlasmodiumgenus andP. knowlesi-assays were 100% specific forPlasmodiumspecies andP. knowlesidetection, respectively, from 190 clinical infections and 48 healthy controls. ReferenceP. vivax-specific primers demonstrated known cross-reactivity withP. cynomolgi.Conclusion:Our findings support the use of an 18S rRNAPlasmodiumgenus qPCR and species-specific nested PCR protocol with RT for highly-sensitive surveillance of zoonotic and humanPlasmodiumspecies infections.

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Diagnostic accuracy and limit of detection of ten malaria parasite lactate dehydrogenase-based rapid tests for Plasmodium knowlesi and P. falciparum

Cited by 10 publications

References 51 publications

Predicting Plasmodium knowlesi transmission risk across Peninsular Malaysia using machine learning-based ecological niche modeling approaches

Predicting Plasmodium knowlesi transmission risk across Peninsular Malaysia using machine learning-based ecological niche modeling approaches

Evaluation of a point-of-care haemozoin assay (Gazelle device) for rapid detection of Plasmodium knowlesi malaria

Improved limit of detection for zoonoticPlasmodium knowlesiandP. cynomolgisurveillance using reverse transcription for total nucleic acid preserved samples or dried blood spots

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