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COVID 19 Sensors

Hopkins developed saliva sensor improves speed and accuracy of COVID detection

David Gracias and Ishan Barman at Johns Hopkins have developed a COVID 19 sensor which provides fast and accurate results using a drop of saliva placed on a device. No additional chemical modifications like molecular labeling or antibody functionalization are required, which could allow the sensor to be used in wearable devices.

Current PCR tests are highly accurate, but require complicated sample preparation, with results taking hours or even days to process in a laboratory. Rapid tests are less successful at detecting early infections and asymptomatic cases and can lead to erroneous results.

The Gracias/Barman developed sensor is nearly as sensitive as a PCR test and as convenient as a rapid antigen test. In a study, the sensor demonstrated 92% accuracy at detecting SARS-COV-2 in saliva samples—comparable to that of PCR tests. It was also highly successful at rapidly determining the presence of other viruses, including H1N1 and Zika.

The sensor material can be placed on any type of surface, from doorknobs and building entrances to masks and textiles, or potentially be integrated with a hand-held testing device for fast screenings at crowded places like airports or stadiums.


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Brain Sensors

Computer modeling may help soldiers, athletes, avoid concussions

http://hub.jhu.edu/2013/03/07/concussion-research-impact

Johns Hopkins engineers have developed a powerful new computer-based process that helps identify the dangerous conditions that lead to concussion-related brain injuries.

Professor K.T. Ramesh led a team that used a technique called diffusion tensor imaging, together with a computer model of the head, to identify injured axons, which are tiny but important fibers that carry information from one brain cell to another. These axons are concentrated in a kind of brain tissue known as “white matter,” and they appear to be injured during the so-called mild traumatic brain injury associated with concussions. Ramesh’s team has shown that the axons are injured most easily by strong rotations of the head, and the researchers’ process can calculate which parts of the brain are most likely to be injured during a specific event.