A new device that identifies various virus strains very quickly was developed by a group of researchers at Pennsylvania State University and New York University. The problem of virus identification is one of the main ones regarding the transmission of diseases from animals to people.
According to virologists, there are still 1.67 million unknown viruses that can be transmitted from animals to humans. We are talking about viruses, to name but a few, such as Zika, Ebola, H5N1, etc., which can cause even serious diseases and death. Early diagnosis is only possible by knowing the virus a priori in order to identify it in time.
This is why Mauricio Terrones, professor of physics, chemistry and materials science as well as engineering at Penn State, has developed together with his colleagues a device that he himself defines as portable, fast and cheap, which can identify viruses according to their size: “Our device uses nanotube arrays designed to be comparable in size to a wide range of viruses. So we use Raman spectroscopy (a spectroscopic technique to identify molecules, n.d.r.) to identify viruses based on their individual vibration”.
The device, which has been given the name VIRRION, can however be used not only in the context of medicine and the detection of infections in the human body. For example, it can also be used in agriculture to detect a plant virus in time and in all those fields where there is a need to detect these small life forms that can have a parasitic approach. Due to its low cost and small size, this device can therefore also be used in medical practices in remote locations and in all those remote locations where epidemics occur.
As Terrones himself explains, in fact, the device is only a few centimeters wide. It works thanks to gold nanoparticles with which it improves the Raman signal in order to detect the virus molecules faster but with the same degree of precision as the more “traditional” but also more complex methods.
It is then possible to create a sort of library of all types of viruses identified with this method and to use modern machine learning techniques to perform very fast detection and “better management of virus evolution in real time,” as stated by Elodie Ghedin, biologist who participated in the project together with Ying-Ting Yeh, the main author of the study published in Proceedings of the National Academy of Science.