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A simple way to identify aging mosquitoes that cause malaria

NatureCommunications (2022). DOI: 10.1038/s41467-022-28980-8″ width=”800″ height=”530″/>

Fig. 1: Experimental setup to capture variation in MIRS caused by the laboratory of origin, individual genetic differences, and the natural environment. To disentangle genetic and environmental effects, mosquitoes were obtained either from colonies reared in the laboratory or from genetically heterogeneous wild larvae; half of the lab larvae were then reared and allowed to grow to adulthood under semi-natural conditions, which provide ecologically realistic conditions while allowing the age of the mosquitoes to be controlled. Credit: Nature Communication (2022). DOI: 10.1038/s41467-022-28980-8

Scientists from the University of Glasgow and partner institutes have developed a cheap, quick and simple way to identify aging mosquitoes that transmit the deadly malaria parasite.

The study, published today in Nature Communication and led by the University of Glasgow-Institute of Biodiversity Animal Health and Comparative Medicine (IBAHCM) and School of Chemistry, and the Ifakara Health Institute (IHI) in Tanzania and the Research Institute of Health Sciences (IRSS) in Burkina Faso—presents a sea change in our ability to accurately identify the age and species of malaria-carrying mosquitoes in wild populations, where it is extremely important to be able to monitor their age, as only old mosquitoes can transmit the disease. disease.

In 2020, there were around 241 million cases of malaria worldwide, according to the WHO, killing around 627,000 people; and although there are vector controls in place to reduce the number of mosquitoes that transmit the disease in some parts of the world, such as insecticides or mosquito nets, the effectiveness of these interventions can be difficult to measure.

In this study, scientists demonstrate a method for identifying the age and species of mosquitoes responsible for malaria in wild populations using infrared spectroscopy and artificial intelligence (AI).

By shining infrared light on individual mosquitoes – which provides information about the chemical composition of the insect’s cuticle – scientists were able to quickly identify chemical changes in aging mosquitoes using an AI algorithm and validate their age predictions on wild mosquitoes with current methods, obtaining similar results.

IHI lead author Doreen Siria said: “Only mosquitoes that live long enough to develop malaria – around ten days – can transmit the disease, so knowing a mosquito’s age can help inform risk. Until now, the only way to tell the age of a mosquito was to go through a complex dissection to assess the age of the ovaries of female mosquitoes, a process that is expensive, time consuming and cannot not be done on a large scale.

Roger Sanou, lead author from IRSS, said, “This AI-based infrared light technology requires a spectrometer that currently costs around $20,000, which can be used for routine vector monitoring of the malaria, and provides a way to quickly establish whether current intervention measures to reduce the number of mosquitoes in the wild are working, which is currently not possible.”

Dr Francesco Baldini of IBAHCM said: “We believe this new method is badly needed in the fight against malaria, a disease that continues to kill many people and children every year. Although vector controls are in place worldwide in In areas with high mosquito populations, it is difficult to measure whether these controls are working effectively.With this infrared technology, we have developed a tool that could be adopted into current mosquito control plans; has the potential to be scaled up for use in different areas; and would greatly help in testing new products and solutions against mosquito-borne diseases.

“We envision that this approach could also be applied to other vectors and vector-borne diseases, from filariasis and chikungunya, to sleeping sickness and Zika; and could be used to evaluate attempts to limit the expansion of invasive mosquito species in Europe and the United States.”

The study used a large dataset of 40,000 genetically and ecologically diverse individual mosquitoes from East and West Africa, covering the three main malaria transmission species at different ages. The study measured mid-infrared spectroscopy signatures reflecting the biochemical signatures of each of these mosquitoes; and used machine learning to correctly identify the age and species of new mosquitoes.

The resulting computer models can be adapted and implemented in the field for vector surveillance.

Simon Babayan, IBAHCM, said: “The versatility of AI combined with the power of infrared spectroscopy opens up huge opportunities for disease surveillance and rapid response. As these technologies become more accessible, we will move towards instant data collection and analysis directly from, and potentially by, the communities that need to act on this information the most.”

Mario Gonzalez-Jimenez, from the School of Chemistry, added: “This work has shown that the same algorithms that allow us to recognize faces and objects in a photo are also able to identify the ways in which chemical compounds show their presence in a spectrum, even in samples as complex as a living being.We see how the use of AI makes possible chemical analyzes that were unimaginable just a few years ago.

The article, “Rapid age-grading and spece identification of natural mosquitoes for malaria surveillance,” is published in Nature Communication.


Researchers use artificial intelligence to identify mosquitoes


More information:
Doreen J. Siria et al, Rapid age classification and species identification of natural mosquitoes for malaria surveillance, Nature Communication (2022). DOI: 10.1038/s41467-022-28980-8

Provided by the University of Glasgow

Quote: A simple way to identify aging mosquitoes that cause malaria (March 21, 2022) retrieved March 21, 2022 from https://medicalxpress.com/news/2022-03-simple-aging-mosquitos-malaria.html

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