India’s National Health Authority Develops Public Dashboard for Health Data – OpenGov Asia

A COVID-19 sensor developed by Johns Hopkins University could revolutionize virus testing by improving accuracy and speeding up a process that many people have found frustrating during the pandemic. The sensor, the researchers say, combines PCR-like accuracy with the speed of rapid antigen testing and could be used for mass testing at airports, schools and hospitals.

The sensor, which requires no sample preparation and little operator knowledge, offers a significant advantage over conventional testing methods, especially for population-scale testing, the researchers say.

“The technique is as simple as putting a drop of saliva on our device and getting a negative or positive result,” said the study’s lead authors, Associate Professor of Mechanical Engineering Ishan Barman and Professor of chemical and biomolecular engineering, David. Thanks.

According to Barman, the new method, which is not yet on the market, addresses the shortcomings of the two most widely used COVID-19 tests – PCR and rapid tests. The approach is unique in that it is label-free, requiring no further chemical modification such as molecular tagging or antibody functionalization. As a result, the sensor could be used in wearable gadgets in the future.

PCR tests are extremely accurate, but they require time-consuming sample preparation, with results taking hours or even days to analyze in the lab. Rapid tests, on the other hand, which check for the presence of antigens, are less effective at detecting early infections and asymptomatic cases and can lead to incorrect results.

The sensor is almost as sensitive as a PCR test and as fast as a rapid antigen test. The sensor achieved 92% accuracy in detecting SARS-COV-2 in saliva samples in the first test, which is comparable to PCR techniques. The sensor was also extremely good at detecting the presence of other viruses, such as H1N1 and Zika.

Large-area nanoimprint lithography, surface-enhanced Raman spectroscopy (SERS), and machine learning are used to create the sensor. It can be used for mass testing on rigid or flexible surfaces in disposable chip formats.

Another important feature of the technology is its use of modern machine learning algorithms to detect very small signs in spectroscopic data, allowing researchers to identify the presence and concentration of the virus.

The flexible wide-field metal-insulating antenna array (FEMIA) is the essential component of the method. In this table, a saliva sample is applied to the material and analyzed using surface-enhanced Raman spectroscopy. This technique uses laser light to study how molecules in the sample being examined vibrate.

The nanostructured FEMIA greatly enhances the Raman signal of the virus, allowing the system to detect the presence of virus even if only minor traces exist in the sample. From door handles and building entrances to masks and textiles, the sensor material can be applied to any surface. It can also be used with a handheld test device for quick checks in congested areas such as airports or stadiums.

Researchers are still working to improve the technique and test it with patient samples. The associated intellectual property has been patented by Johns Hopkins Technology Ventures, and the team is pursuing licensing and commercialization prospects.

The study was funded by the National Science Foundation’s Early-concept Grants for Exploratory Research (EAGER) program and the National Institute of Health’s Director’s New Innovator Award.

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