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

Nabet, Cécile; Chaline, Aurélien; Franetich, Jean‐François; Brossas, Jean-Yves; Shahmirian, Noémie; Silvie, Olivier; Tannier, Xavier; Piarroux, Renaud

doi:10.1038/s41598-020-68272-z

Cited by 24 publications

(31 citation statements)

References 40 publications

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“…aegypti , allowing a robust classification according to their infectious status. Similarly, other authors have shown the ability of artificial intelligence to predict Anopheles aging, their blood feeding status and Plasmodium infection when using MALDI-TOF MS spectral profile 25 . These proofs of concept extend the field of applications of MALDI-TOF MS coupled with artificial intelligence in medical entomology.…”

Section: Discussionmentioning

confidence: 81%

“…Current advances in machine learning approaches could complement and enhance performance of the MALDI-TOF MS to analyze mosquito spectra. For example, it was used to recognize MALDI-TOF MS spectral patterns associated with Anopheles ages, blood-feeding and Plasmodium infection 25 . Machine learning methods is a domain includes all supervise classification methods.…”

Section: Introductionmentioning

confidence: 99%

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Wolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence

Rakotonirina

Caruzzo

Ballan

et al. 2021

Sci Rep

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The mosquito Aedes aegypti is the major vector of arboviruses like dengue, Zika and chikungunya viruses. Attempts to reduce arboviruses emergence focusing on Ae. aegypti control has proven challenging due to the increase of insecticide resistances. An emerging strategy which consists of releasing Ae. aegypti artificially infected with Wolbachia in natural mosquito populations is currently being developed. The monitoring of Wolbachia-positive Ae. aegypti in the field is performed in order to ensure the program effectiveness. Here, the reliability of the Matrix‑Assisted Laser Desorption Ionization‑Time Of Flight (MALDI‑TOF) coupled with the machine learning methods like Convolutional Neural Network (CNN) to detect Wolbachia in field Ae. aegypti was assessed for the first time. For this purpose, laboratory reared and field Ae. aegypti were analyzed. The results showed that the CNN recognized Ae. aegypti spectral patterns associated with Wolbachia-infection. The MALDI-TOF coupled with the CNN (sensitivity = 93%, specificity = 99%, accuracy = 97%) was more efficient than the loop-mediated isothermal amplification (LAMP), and as efficient as qPCR for Wolbachia detection. It therefore represents an interesting method to evaluate the prevalence of Wolbachia in field Ae. aegypti mosquitoes.

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Section: Discussionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Wolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence

Rakotonirina

Caruzzo

Ballan

et al. 2021

Sci Rep

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“…These factors could be promising target for detecting the insecticide resistance through proteomics. In Anopheles mosquitoes, with the help of artificial neural networks (ANNs) and MALDI-TOF/MS, the effect of insecticide resistance on these factors were described [76]. Proteomics detected the contribution of age as one of significant factor to insecticide resistance by using matrix-assisted laser desorption ionization tandem time-of-flight mass spectrometry or capillary high-pressure liquid chromatography with linear ion-trap (LTQ )-Orbitrap XL hybrid mass spectrometer which was further quantified by Western Blot leading to detection of protein biomarkers [77].…”

Section: Proteomics Assaysmentioning

confidence: 99%

Insecticide Resistance in Vectors of Medically Important Parasitic Infections

Kaura¹,

Sharma²,

Mewara³

2022

Insecticides - Impact and Benefits of Its Use for Humanity

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Insecticide resistance is a major threat to vector control programmes as insecticides still remain the most effective method to control the vector-borne diseases. For effective management of insecticide resistance, a knowledge of the insecticides used along with their mode of action is a prerequisite to optimize their use. Nowadays, different detection methods, viz., phenotypic, genotypic and proteomic assays are used for assessment of insecticide resistance in vectors. An understanding of the phenotypic and genotypic variations present in the vectors help in implementation of these techniques to evaluate the usefulness of insecticides in an area and to determine the efficacy of an ongoing vector control programmes. The understanding of different factors involved in emergence of insecticide resistance and the alternative solutions to control this problem by the use of rotational, mixture of insecticides and use of piperonyl butoxide to increase the efficacy of indoor residual spray and insecticide treated bed nets are some of the steps taken to tackle the problem of insecticide resistance in vectors.

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“…For instance, Wang et al developed machine-learning models permitting the screening for heterogeneous vancomycin-intermediate Staphylococcus aureus 10 or the recognition of different MLST types of methicillin-resistant Staphylococcus aureus 11 . In a recent study dealing with the use of mass spectra in entomology 12 , Nabet et al . succeeded in extracting information that predicts age, blood-meal status and Plasmodium carriage from female Anopheles MALDI-TOF mass spectra.…”

Section: Introductionmentioning

confidence: 99%

Identification of a clonal population of Aspergillus flavus by MALDI-TOF mass spectrometry using deep learning

Normand

Chaline

Mohammad

et al. 2022

Sci Rep

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The spread of fungal clones is hard to detect in the daily routines in clinical laboratories, and there is a need for new tools that can facilitate clone detection within a set of strains. Currently, Matrix Assisted Laser Desorption-Ionization Time-of-Flight Mass Spectrometry is extensively used to identify microbial isolates at the species level. Since most of clinical laboratories are equipped with this technology, there is a question of whether this equipment can sort a particular clone from a population of various isolates of the same species. We performed an experiment in which 19 clonal isolates of Aspergillus flavus initially collected on contaminated surgical masks were included in a set of 55 A. flavus isolates of various origins. A simple convolutional neural network (CNN) was trained to detect the isolates belonging to the clone. In this experiment, the training and testing sets were totally independent, and different MALDI-TOF devices (Microflex) were used for the training and testing phases. The CNN was used to correctly sort a large portion of the isolates, with excellent (> 93%) accuracy for two of the three devices used and with less accuracy for the third device (69%), which was older and needed to have the laser replaced.

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

Cited by 24 publications

References 40 publications

Wolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence

Wolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence

Insecticide Resistance in Vectors of Medically Important Parasitic Infections

Identification of a clonal population of Aspergillus flavus by MALDI-TOF mass spectrometry using deep learning

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