We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

LabMedica

Download Mobile App
Recent News Expo Clinical Chem. Molecular Diagnostics Hematology Immunology Microbiology Pathology Technology Industry Focus

Sensor for Faster, More Accurate COVID-19 Tests Could Revolutionize Virus Testing

By LabMedica International staff writers
Posted on 30 Mar 2022
Print article
Image: Sensor combines accuracy of PCR testing with speed of rapid antigen tests (Photo courtesy of Johns Hopkins University)
Image: Sensor combines accuracy of PCR testing with speed of rapid antigen tests (Photo courtesy of Johns Hopkins University)

PCR tests are highly accurate, but require complicated sample preparation, with results taking hours or even days to process in a laboratory. On the other hand, rapid tests, which look for the existence of antigens, are less successful at detecting early infections and asymptomatic cases and can lead to erroneous results. Now, a new technology addresses the limitations of these two most widely used types of COVID-19 tests.

A COVID-19 sensor developed at Johns Hopkins University (Baltimore, MD, USA) could revolutionize virus testing by adding accuracy and speed to a process that frustrated many during the pandemic. The sensor, which requires no sample preparation and minimal operator expertise, offers a strong advantage over existing testing methods, especially for population-wide testing.

The sensor is nearly as sensitive as a PCR test and as convenient as a rapid antigen test. During initial testing, the sensor demonstrated 92% accuracy at detecting SARS-COV-2 in saliva samples - comparable to that of PCR tests. The sensor was also highly successful at rapidly determining the presence of other viruses, including H1N1 and Zika. The sensor is based on large area nanoimprint lithography, surface enhanced Raman spectroscopy (SERS), and machine learning. It can be used for mass testing in disposable chip formats or on rigid or flexible surfaces.

Key to the method is the large-area, flexible field enhancing metal insulator antenna (FEMIA) array developed by the researchers. The saliva sample is placed on the material and analyzed using surface-enhanced Raman spectroscopy, which employs laser light to examine how molecules of the examined specimen vibrate. Because the nanostructured FEMIA strengthens the virus's Raman signal significantly, the system can rapidly detect the presence of a virus, even if only small traces exist in the sample. Another major innovation of the system is the use of advanced machine learning algorithms to detect very subtle signatures in the spectroscopic data that allow researchers to pinpoint the presence and concentration of the virus. The sensor material can be placed on any type of surface, from doorknobs and building entrances to masks and textiles. The sensor could potentially be integrated with a hand-held testing device for fast screenings at crowded places like airports or stadiums, according to the researchers. The team continues working to further develop and test the technology with patient samples.

"Our platform goes beyond the current COVID-19 pandemic," added Barman. "We can use this for broad testing against different viruses, for instance, to differentiate between SARS-CoV-2 and H1N1, and even variants. This is a major issue that can't be readily addressed by current rapid tests," said Ishan Barman, an associate professor of mechanical engineering, and one of the senior authors of the study.

"Using state of the art nanoimprint fabrication and transfer printing we have realized highly precise, tunable, and scalable nanomanufacturing of both rigid and flexible COVID sensor substrates, which is important for future implementation not just on chip-based biosensors but also wearables," said David Gracias, a professor of chemical and biomolecular engineering, and one of the senior authors of the study.

Related Links:
Johns Hopkins University 

Platinum Member
COVID-19 Rapid Test
OSOM COVID-19 Antigen Rapid Test
Magnetic Bead Separation Modules
MAG and HEATMAG
Anti-Cyclic Citrullinated Peptide Test
GPP-100 Anti-CCP Kit
New
Gold Member
COVID-19 Rapid Test
AQ+ COVID-19 Ag Rapid Test

Print article

Channels

Clinical Chemistry

view channel
Image: The 3D printed miniature ionizer is a key component of a mass spectrometer (Photo courtesy of MIT)

3D Printed Point-Of-Care Mass Spectrometer Outperforms State-Of-The-Art Models

Mass spectrometry is a precise technique for identifying the chemical components of a sample and has significant potential for monitoring chronic illness health states, such as measuring hormone levels... Read more

Hematology

view channel
Image: The CAPILLARYS 3 DBS devices have received U.S. FDA 510(k) clearance (Photo courtesy of Sebia)

Next Generation Instrument Screens for Hemoglobin Disorders in Newborns

Hemoglobinopathies, the most widespread inherited conditions globally, affect about 7% of the population as carriers, with 2.7% of newborns being born with these conditions. The spectrum of clinical manifestations... Read more

Immunology

view channel
Image: The AI predictive model identifies the most potent cancer killing immune cells for use in immunotherapies (Photo courtesy of Shutterstock)

AI Predicts Tumor-Killing Cells with High Accuracy

Cellular immunotherapy involves extracting immune cells from a patient's tumor, potentially enhancing their cancer-fighting capabilities through engineering, and then expanding and reintroducing them into the body.... Read more

Microbiology

view channel
Image: The T-SPOT.TB test is now paired with the Auto-Pure 2400 liquid handling platform for accurate TB testing (Photo courtesy of Shutterstock)

Integrated Solution Ushers New Era of Automated Tuberculosis Testing

Tuberculosis (TB) is responsible for 1.3 million deaths every year, positioning it as one of the top killers globally due to a single infectious agent. In 2022, around 10.6 million people were diagnosed... Read more

Pathology

view channel
Image: The new AI tool can help beat brain tumors (Photo courtesy of Crystal Light/Shutterstock)

New AI Tool Classifies Brain Tumors More Quickly and Accurately

Precision in diagnosing and categorizing tumors is essential for delivering effective treatment to patients. Currently, the gold standard for identifying various types of brain tumors involves DNA methylation-based... Read more