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p. berghei infection

Prediction of malaria transmission drivers in Anopheles mosquitoes using artificial intelligence coupled to MALDI-TOF mass spectrometry

July 13, 2020 - 16:42 -- Open Access
Author(s): 
Nabet C, Chaline A, Franetich JF, Brossas JY, Shahmirian N, Silvie O, Tannier X, Piarroux R
Reference: 
Sci Rep. 2020 Jul 9;10(1):11379

Vector control programmes are a strategic priority in the fight against malaria. However, vector control interventions require rigorous monitoring. Entomological tools for characterizing malaria transmission drivers are limited and are difficult to establish in the field. To predict Anopheles drivers of malaria transmission, such as mosquito age, blood feeding and Plasmodium infection, we evaluated artificial neural networks (ANNs) coupled to matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry (MS) and analysed the impact on the proteome of laboratory-reared Anopheles stephensi mosquitoes.

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