Real-time, accurate virus detection method could help fight next pandemic

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Electron microscopy shows carbon nanotubes (purple) effectively catching the flu virus (bright circular objects). Radiation spectroscopy and machine learning of these similar pathogens are studied and can be detected with accuracy> 95%. Credit: Elizabeth Floresgomez and Yin-Ting Yeh

High-quality and effective virus detection using Raman spectroscopy, a scanning machine and machine learning can allow periodic virus detection and detection to help fight future pandemics, in according to a team of researchers led by Penn State.

Shengxi Huang, assistant professor of electrical engineering and biochemistry and co-author of the study, said “This method of detecting viruses is not labeled and does not refer to any specific virus, so it allows us to detect new types of viruses.” today (June 2) in Submissions of the National College of Science. “It is also fast, so it is convenient to do a quick test in a crowded place. In addition, the rich Raman feature along with machine learning studies allows for an understanding of the bacterial system.”

Radiation spectroscopy detects specific stimuli in the cells by taking up motion when the laser beam triggers these vibrations. To catch viruses, a tool known as a microfluidic device will be used to trap viruses between forests of carbon nanotubes adjustable.

Microfluidic devices use small amounts of fluid on the microchip to perform medical and laboratory tests. Such a device can use bacterial culture, soup, nasal wash, or even exhale, as well as products collected on site during an explosion. Forests of carbon nanotubes will filter out any external or organic matter from the environment or the atmosphere that can make it more difficult to obtain accurate readings.

Mauricio said “The fact that we use carbon nanotubes to enrich the products has been very useful because in this way we enrich the product of bacteria and eliminate other organic matter that you do not want to find while looking for a virus,” says Mauricio. Terrones, Professor Evan Pugh University and Professor Verne M. Willaman Professor of Physics and author.

As soon as the products were captured and Raman’s vision machine examined them, the field of machine learning came into play. The researchers collected Raman holes for three different types of viruses: human respiratory viruses, Avian viruses, and enteroviruses. This information is used to train a machine learning producta vascular network, which detects viruses.

Sharon Huang, assistant professor of information science and technology and author Sharon Huang said “After the machine model is trained, and then given to an unknown type of Raman virus, our machine learning model can automatically detect the type of virus.” reading. “This includes, for example, the flu, which is a type, or influenza A or influenza B, and the product can detect types of viruses, such as H1N1 or H3N2.”

The benefits of such a device are enormous, according to researchers, especially in the immediate aftermath of an outbreak.

Yin-Ting Yeh, assistant professor of research at Eberly College of Science and said “By providing a fast and unobtrusive virus detection device to monitor the virus, this method will allow public health officials to closely monitor the evolution of the virus.” co-author of the study.

Together with researchers from Penn State, George Washington University and Johns Hopkins University, researchers from the National Institutes of Health (NIH) participated in the study. The next steps of the research team will include the collection of additional strains of different strains of different strains of humans and animals, including DNA strands to enhance viral spectra data. This will allow for more practical training of machine learning and upgrade them all with the ability to detect new types of viruses. In addition, they will work to improve the efficiency of the Raman in the device to allow improved signal and lower noise levels.

“While engineering is used for Raman signal processing It is not a story in itself, “said Elodie Ghedin, senior researcher, department of genomics, NIH and author in the study. “The reason for this new story is the combination of a virus detector, a collection of Raman spectra from. catch viruses on this deviceand the rapid distribution of germs by the use of a machine learning role model. This virus detection in real time is particularly convenient to deal with current and future outbreaks.”

A fast and inexpensive device for detecting and detecting viruses

Learn more:
Jiarong Ye et al. Submissions of the National College of Science (2022). DOI: 10.1073 / pnas.2118836119

hint: real-time, effective way to detect viruses can help fight the next pandemic (2022, June 3) restored June 3, 2022 from -accurate-virus-method-pandemic .html

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