Swiss Tropical and Public Health Institute, Socinstrasse 57, 4020, Basel, Switzerland.
University of Basel, Petersplatz 1, 4001, Basel, Switzerland.
Malar J. 2019 Mar 19;18(1):85. doi: 10.1186/s12936-019-2719-9.
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.
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.
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.
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.
大规模监测蚊群对于评估虫媒传染病传播的强度和控制干预措施的效果至关重要。然而，目前缺乏用于大规模筛选媒介的准确、经济高效且高通量的工具。
通过标准膜喂养试验（SMFA）共给 750 只冈比亚按蚊（基利株）喂食恶性疟原虫 NF54 配子体，然后在昆虫饲养条件下饲养，以允许卵囊（8 天）和子孢子（14 天）发育。之后，用近红外光谱（NIRS）扫描每只蚊子，并通过定量聚合酶链反应（qPCR）进行处理，以确定感染和感染载量的存在。收集的光谱随机分配到训练数据集或测试数据集中，用于通过偏最小二乘回归（PLS）为预测卵囊或子孢子感染建立校准，或用于验证校准的预测准确性。
NIRS 检测到卵囊和子孢子阶段的恶性疟原虫感染的准确率分别为 88%和 95%。本研究证明了近红外光谱技术能够快速识别非洲蚊媒中的人类疟疾实验室株感染，这是一种概念验证。
NIRS 实现了对蚊群的准确、低成本、无试剂筛选，这可能会彻底改变针对非洲主要疟原虫在其主要非洲媒介物种中的监测和消除策略。需要进一步研究如何在调整训练数据集以包括野外捕获的感染和未感染蚊子的数据后，该方法在现场的表现。
