Infrared spectroscopy (NIRS and ATR-FTIR) together with multivariate classification for non-destructive differentiation between female mosquitoes of Aedes aegypti recently infected with dengue vs. uninfected females

Santos, Marfran C. D.; Viana, Jorge L.S.; Monteiro, Joelma D.; Freire, Renato C.M.; Freitas, Daniel Lucas Dantas de; Câmara, Ingrid M.; Silva, Gigliane J.S. da; Gama, Renata Antonaci; Araújo, Josélio Maria Galvão de; Lima, Kássio M. G.

doi:10.1016/j.actatropica.2022.106633

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…aegypti female mosquitoes single infected with either ZIKV or DENV, coinfected with both ZIKV and DENV and uninfected samples. NIRS has previously been used to detect single infections of ZIKV [ 32 ], CHIKV [ 37 ], and DENV [ 47 ] in laboratory-reared Ae. aegypti mosquitoes.…”

Section: Discussionmentioning

confidence: 99%

Rapid and Non-Invasive Detection of Aedes aegypti Co-Infected with Zika and Dengue Viruses Using Near Infrared Spectroscopy

Garcia

Lord

Santos

et al. 2022

Viruses

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The transmission of dengue (DENV) and Zika (ZIKV) has been continuously increasing worldwide. An efficient arbovirus surveillance system is critical to designing early-warning systems to increase preparedness of future outbreaks in endemic countries. The Near Infrared Spectroscopy (NIRS) is a promising high throughput technique to detect arbovirus infection in Ae. aegypti with remarkable advantages such as cost and time effectiveness, reagent-free, and non-invasive nature over existing molecular tools for similar purposes, enabling timely decision making through rapid detection of potential disease. Our aim was to determine whether NIRS can differentiate Ae. aegypti females infected with either ZIKV or DENV single infection, and those coinfected with ZIKV/DENV from uninfected ones. Using 200 Ae. aegypti females reared and infected in laboratory conditions, the training model differentiated mosquitoes into the four treatments with 100% accuracy. DENV-, ZIKV-, and ZIKV/DENV-coinfected mosquitoes that were used to validate the model could be correctly classified into their actual infection group with a predictive accuracy of 100%, 84%, and 80%, respectively. When compared with mosquitoes from the uninfected group, the three infected groups were predicted as belonging to the infected group with 100%, 97%, and 100% accuracy for DENV-infected, ZIKV-infected, and the co-infected group, respectively. Preliminary lab-based results are encouraging and indicate that NIRS should be tested in field settings to evaluate its potential role to monitor natural infection in field-caught mosquitoes.

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Section: Discussionmentioning

confidence: 99%

Rapid and Non-Invasive Detection of Aedes aegypti Co-Infected with Zika and Dengue Viruses Using Near Infrared Spectroscopy

Garcia

Lord

Santos

et al. 2022

Viruses

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“…The use of computational genetic algorithms (GA) and linear discriminant analysis (LDA) improved to 100% sensitivity and specificity in DENV detection in Ae. aegypti with recent infection [ 139 ].…”

Section: Nondestructive Approaches To Pathogen Detection In Mosquitoesmentioning

confidence: 99%

Nondestructive Methods of Pathogen Detection: Importance of Mosquito Integrity in Studies of Disease Transmission and Control

et al. 2023

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Mosquitoes are vectors of many pathogens, including viruses, protozoans, and helminths, spreading these pathogens to humans as well as to wild and domestic animals. As the identification of species and the biological characterization of mosquito vectors are cornerstones for understanding patterns of disease transmission, and the design of control strategies, we conducted a literature review on the current use of noninvasive and nondestructive techniques for pathogen detection in mosquitoes, highlighting the importance of their taxonomic status and systematics, and some gaps in the knowledge of their vectorial capacity. Here, we summarized the alternative techniques for pathogen detection in mosquitoes based on both laboratory and field studies. Parasite infection and dissemination by mosquitoes can also be obtained via analyses of saliva- and excreta-based techniques or of the whole mosquito body, using a near-infrared spectrometry (NIRS) approach. Further research should be encouraged to seek strategies for detecting target pathogens while preserving mosquito morphology, especially in biodiversity hotspot regions, thus enabling the discovery of cryptic or new species, and the determination of more accurate taxonomic, parasitological, and epidemiological patterns.

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“…Molecular vibrations can absorb speci c frequencies of the infrared light, which will be speci c to the various functional groups inside the molecule; thus providing a ngerprint of the analyte 37-39 . MIRS has been used to identify biological traits in Anopheles to identify blood meals 40 and predict the age and species of laboratory and eld populations of Anopheles and Aedes 36, [41][42][43] . In these studies, the most common method for collecting spectra is attenuated total re ection (ATR) spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Diffuse reflectance spectroscopy for predicting age, species, and insecticide resistance of the malaria mosquito Anopheles gambiae s.l

Betancourth

Ochoa-Gutierrez

Ferguson

et al. 2023

Preprint

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Mid-infrared spectroscopy (MIRS) combined with machine learning analysis has shown potential for quick and efficient identification of mosquito species and age groups. However, current technology to collect spectra is destructive to the sample and does not allow targeting specific tissues of the mosquito, limiting the identification of other important biological traits such as insecticide resistance. Here, we assessed the use of a non-destructive approach of MIRS for vector surveillance, micro diffuse reflectance spectroscopy (µDRIFT) using mosquito legs to identify species, age and cuticular insecticide resistance within the Anopheles gambiae s.l. complex. These mosquitoes are the major vectors of malaria in Africa and the focus on surveillance in malaria control programs. Legs required significantly less scanning time and showed more spectral consistence compared to other mosquito tissues. Machine learning models were able to identify An. gambiae and An. coluzzii with an accuracy of 0.73, two ages groups (3 and 10 days old) with 0.77 accuracy and we obtained accuracy of 0.75 when identifying cuticular insecticide resistance. Our results highlight the potential of different mosquito tissues and µDRIFT as tools for biological trait identification on mosquitoes that transmit malaria. These results can guide new ways of identifying mosquito traits which can help the creation of innovative surveillance programs by adapting new technology into mosquito surveillance and control tools.

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Infrared spectroscopy (NIRS and ATR-FTIR) together with multivariate classification for non-destructive differentiation between female mosquitoes of Aedes aegypti recently infected with dengue vs. uninfected females

Cited by 6 publications

References 27 publications

Rapid and Non-Invasive Detection of Aedes aegypti Co-Infected with Zika and Dengue Viruses Using Near Infrared Spectroscopy

Rapid and Non-Invasive Detection of Aedes aegypti Co-Infected with Zika and Dengue Viruses Using Near Infrared Spectroscopy

Nondestructive Methods of Pathogen Detection: Importance of Mosquito Integrity in Studies of Disease Transmission and Control

Diffuse reflectance spectroscopy for predicting age, species, and insecticide resistance of the malaria mosquito Anopheles gambiae s.l

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