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Malar J. 2019 Mar 19;18(1):85. doi: 10.1186/s12936-019-2719-9.

Detection of Plasmodium falciparum infected Anopheles gambiae using near-infrared spectroscopy.

Author information

1
Swiss Tropical and Public Health Institute, Socinstrasse 57, 4020, Basel, Switzerland. mmaia@kemri-wellcome.org.
2
University of Basel, Petersplatz 1, 4001, Basel, Switzerland. mmaia@kemri-wellcome.org.
3
KEMRI Wellcome Trust Research Programme, P.O. Box 230, Kilifi, 80108, Kenya. mmaia@kemri-wellcome.org.
4
Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Old Road Campus Roosevelt Drive, Oxford, OX3 7FZ, UK. mmaia@kemri-wellcome.org.
5
KEMRI Wellcome Trust Research Programme, P.O. Box 230, Kilifi, 80108, Kenya.
6
Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Old Road Campus Roosevelt Drive, Oxford, OX3 7FZ, UK.
7
Department of Public Health, School of Human and Health Sciences, Pwani University, Kilifi, Kenya.
8
Institute of Biodiversity, Animal Health and Comparative Medicine, University of Glasgow, Graham Kerr Building, Glasgow, G12 8QQ, UK.
9
USDA, Agricultural Research Service, Center for Grain and Animal Health Research, 1515 College Avenue, Manhattan, KS, 66502, USA.

Abstract

BACKGROUND:

Large-scale surveillance of mosquito populations is crucial to assess the intensity of vector-borne disease transmission and the impact of control interventions. However, there is a lack of accurate, cost-effective and high-throughput tools for mass-screening of vectors.

METHODS:

A total of 750 Anopheles gambiae (Keele strain) mosquitoes were fed Plasmodium falciparum NF54 gametocytes through standard membrane feeding assay (SMFA) and afterwards maintained in insectary conditions to allow for oocyst (8 days) and sporozoite development (14 days). Thereupon, each mosquito was scanned using near infra-red spectroscopy (NIRS) and processed by quantitative polymerase chain reaction (qPCR) to determine the presence of infection and infection load. The spectra collected were randomly assigned to either a training dataset, used to develop calibrations for predicting oocyst- or sporozoite-infection through partial least square regressions (PLS); or to a test dataset, used for validating the calibration's prediction accuracy.

RESULTS:

NIRS detected oocyst- and sporozoite-stage P. falciparum infections with 88% and 95% accuracy, respectively. This study demonstrates proof-of-concept that NIRS is capable of rapidly identifying laboratory strains of human malaria infection in African mosquito vectors.

CONCLUSIONS:

Accurate, low-cost, reagent-free screening of mosquito populations enabled by NIRS could revolutionize surveillance and elimination strategies for the most important human malaria parasite in its primary African vector species. Further research is needed to evaluate how the method performs in the field following adjustments in the training datasets to include data from wild-caught infected and uninfected mosquitoes.

KEYWORDS:

Africa; Anopheles gambiae; Malaria; Near infrared spectroscopy; Oocyst; Partial least square regression; Plasmodium falciparum; Sporozoite; Vector

PMID:
30890179
PMCID:
PMC6423776
DOI:
10.1186/s12936-019-2719-9
[Indexed for MEDLINE]
Free PMC Article

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