Institut de Recherche en Sciences de la Santé, Direction Régionale, 399 avenue de la liberté, 01 BP 545, Bobo-Dioulasso 01, Burkina Faso.
MRC Centre for Global Infectious Disease Analysis, Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK.
Sci Rep. 2021 May 13;11(1):10289. doi: 10.1038/s41598-021-89715-1.
There is an urgent need for high throughput, affordable methods of detecting pathogens inside insect vectors to facilitate surveillance. Near-infrared spectroscopy (NIRS) has shown promise to detect arbovirus and malaria in the laboratory but has not been evaluated in field conditions. Here we investigate the ability of NIRS to identify Plasmodium falciparum in Anopheles coluzzii mosquitoes. NIRS models trained on laboratory-reared mosquitoes infected with wild malaria parasites can detect the parasite in comparable mosquitoes with moderate accuracy though fails to detect oocysts or sporozoites in naturally infected field caught mosquitoes. Models trained on field mosquitoes were unable to predict the infection status of other field mosquitoes. Restricting analyses to mosquitoes of uninfectious and highly-infectious status did improve predictions suggesting sensitivity and specificity may be better in mosquitoes with higher numbers of parasites. Detection of infection appears restricted to homogenous groups of mosquitoes diminishing NIRS utility for detecting malaria within mosquitoes.
迫切需要高通量、经济实惠的方法来检测昆虫媒介中的病原体，以促进监测。近红外光谱（NIRS）已显示出在实验室中检测虫媒病毒和疟疾的潜力，但尚未在野外条件下进行评估。在这里，我们研究了 NIRS 识别斑氏疟原虫在致倦库蚊中的能力。在实验室饲养的感染野生疟原虫的蚊子上训练的 NIRS 模型可以以中等准确度检测寄生虫，但无法检测到自然感染的野外捕获蚊子中的卵囊或孢子。在野外蚊子上训练的模型无法预测其他野外蚊子的感染状况。将分析仅限于无感染和高感染状态的蚊子可以提高预测能力，表明在寄生虫数量较高的蚊子中，敏感性和特异性可能更好。感染的检测似乎仅限于感染程度一致的蚊子群，这降低了 NIRS 在蚊子中检测疟疾的实用性。
