4.5 Article

Rapid Evaporative Ionization Mass Spectrometry (REIMS): a Potential and Rapid Tool for the Identification of Insecticide Resistance in Mosquito Larvae

期刊

JOURNAL OF INSECT SCIENCE
卷 22, 期 5, 页码 -

出版社

OXFORD UNIV PRESS INC
DOI: 10.1093/jisesa/ieac052

关键词

insecticide resistance; rapid evaporative ionization mass spectrometry; Aedes aegypti; larvae; Colombia

资金

  1. University of Liverpool
  2. Edge Hill University
  3. University of Antioquia
  4. Biological and Biotechnological Sciences Research Council [BB/L014793/1]

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This study aimed to trial the use of rapid evaporative ionization mass spectrometry (REIMS) for identifying insecticide resistance in mosquitoes. The results showed that REIMS had high accuracy in differentiating larvae from different populations, differentiating mosquitoes of field and lab origin, and differentiating susceptible and resistant larvae. Therefore, REIMS may serve as a potential new tool for rapidly identifying resistance in mosquitoes.
Insecticide resistance is a significant challenge facing the successful control of mosquito vectors globally. Bioassays are currently the only method for phenotyping resistance. They require large numbers of mosquitoes for testing, the availability of a susceptible comparator strain, and often insectary facilities. This study aimed to trial the novel use of rapid evaporative ionization mass spectrometry (REIMS) for the identification of insecticide resistance in mosquitoes. No sample preparation is required for REIMS and analysis can be rapidly conducted within hours. Temephos resistant Aedes aegypti (Linnaeus) larvae from Cucuta, Colombia and temephos susceptible larvae from two origins (Bello, Colombia, and the lab reference strain New Orleans) were analyzed using REIMS. We tested the ability of REIMS to differentiate three relevant variants: population source, lab versus field origin, and response to insecticide. The classification of these data was undertaken using linear discriminant analysis (LDA) and random forest. Classification models built using REIMS data were able to differentiate between Ae. aegypti larvae from different populations with 82% (+/- 0.01) accuracy, between mosquitoes of field and lab origin with 89% (+/- 0.01) accuracy and between susceptible and resistant larvae with 85% (+/- 0.01) accuracy. LDA classifiers had higher efficiency than random forest with this data set. The high accuracy observed here identifies REIMS as a potential new tool for rapid identification of resistance in mosquitoes. We argue that REIMS and similar modern phenotyping alternatives should complement existing insecticide resistance management tools.

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