ONE new sensor developed by researchers at Hopkins can detect communicable diseases such as COVID-19, H1N1 and Zika virus in saliva more accurately than traditional rapid tests at approximately the same rate.
The sensor relies on a combination of surface-enhanced Raman spectroscopy (SERS), machine learning and large-area nanoimprint lithography. Researchers believe the technology could potentially increase public health safety measures in crowded places.
The project started about two years ago, near the start of the COVID-19 pandemic. The researchers started by discovering SARS-CoV-2 as their primary target, but their work eventually expanded to include other infectious diseases such as Zika, H1N1 and the Marburg virus. The results were published earlier in the spring of Nano letters.
Ishan Barman is an associate professor of mechanical engineering with joint employment at the Sidney Kimmel Comprehensive Cancer Center and Russell H. Morgan Department of Radiology and Radiological Science. Barman is one of the lead authors of the paper and the primary investigator of the laboratory that created the sensor.
He discussed the beginning of the project in an interview with The newsletter.
“When the first wave [of COVID-19] hit, it was explosive enough; it was a problem of great enough importance that even if it had not lasted as long as it has, it would still have been a problem worth solving, ”he said. “Ultimately, a crucial step in controlling outbreaks is the timely and accurate calculation of new viruses.”
Debadrita Paria is a postdoc in the Barman Lab. In another email The newsletter, Paria noted that the process of manufacturing the sensor was complicated by pandemic restrictions.
“It required rigorous planning and execution. We used to have several Zoom meetings where more ideas came up, and then we went into the lab and tried to implement them and see what worked. When we worked in shifts, we had to to perform the experiments in a limited time frame, ”wrote Paria.
While PCR and rapid antigen testing are currently used for SARS-CoV-2 detection, researchers have pointed out their limitations. PCR testing requires intense sample treatmentincluding the use of fluorescent markers, to detect whether COVID-19 RNA is present in a sample. Rapid antigen testing is lacking accuracy and has been hard to find due to high demand. Another challenge, according to paper published in Nano lettersstores and transports samples to be processed.
Barman described how the sensor aims to address these disadvantages.
“We have always thought that we need better sensory technology that combines the salient features of what we know: RT-PCR, which has incredible sensitivity and specificity, with the convenience and speed of the rapid antigen test,” he said. he.
The new sensor relies on a saliva sample instead of a more invasive nasal swab. In addition, its accuracy in detecting COVID-19 is around 92%, which is comparable to PCR, the current gold standard. Finally, the sensor can work quickly and give a positive or negative test result in about 12 minutes, according to Barman. The laboratory plans to continue the work of reducing that time.
In addition, the sensor has built-in flexibility for SARS-CoV-2 mutations, meaning it will still be able to identify new variants. The team’s next goal is to work on identifying and differentiating these variants and to test real patient samples with the sensor to measure how well it works.
There are three components to how the sensor works: nanoimprint lithography, SERS and machine learning. The nanoimprint lithograph provides a flexible surface for the saliva sample using a field amplifying metal isolator antenna array to amplify the signal for the spectroscopy.
SERS reads the sample based on inelastic scattering of light to characterize how unique molecules vibrate. If COVID-19 or another infectious disease is present in the sample, there will be characteristic vibration patterns on the spectroscopy reading.
The sensor then uses machine learning to determine if new samples are positive or negative based on what previous positive spectroscopy readings looked like. According to papergives the use of machine learning greater sensitivity and specificity to help overcome the noise from other unwanted biological samples in the saliva sample.
According to Barman, it can be placed on door handles, masks and other places to help facilitate quick on-site testing due to the flexibility of the surface used for the sample. He noted that the portable device to be used in these cases is about two shoe boxes high.
Barman highlighted that the new viral sensor has the potential to be used as a mass test technology not only for COVID-19 but also for other pathogens such as influenza, Zika virus and Marburg virus.
“We wanted to create a tool that would be better at dealing with outbreaks in the future. Thinking beyond the pandemic was always a goal,” he said.