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WVU biomedical engineer will receive $1.2 million to improve early detection of tick-borne infections
A team led by a West Virginia University biomedical engineer is working to rethink and rethink the way medical professionals diagnose tick-borne infections like Lyme disease. Soumya Srivastava, an assistant professor in the Benjamin M. Statler College of Engineering and Mineral Resources, is developing a tool that detects tick-borne diseases more quickly using a blood sample on a single chip. Srivastava's model aims to detect illness within one to two weeks of an infection onset, while existing approaches rely on a symptom-based questionnaire - which may ask whether a person has a fever or a...
WVU biomedical engineer will receive $1.2 million to improve early detection of tick-borne infections
A team led by a West Virginia University biomedical engineer is working to rethink and rethink the way medical professionals diagnose tick-borne infections like Lyme disease.
Soumya Srivastava, an assistant professor in the Benjamin M. Statler College of Engineering and Mineral Resources, is developing a tool that detects tick-borne diseases more quickly using a blood sample on a single chip. Srivastava's model aims to detect disease within one to two weeks of an infection emerging, while existing approaches rely on a symptom-based questionnaire - which may ask whether a person has a fever or a rash - and tests that are only reliable at least several weeks after infection.
Srivastava's project was recently awarded $1.2 million as a joint initiative of the National Science Foundation and the National Institutes of Health.
Tick-borne pathogens can be transmitted to humans through the bite of infected ticks. These ticks can transmit bacteria, viruses or parasites. Srivastava's efforts could produce a much-needed tool in the fight against tick-borne diseases, which have surged in recent years. There are now about 30,000 cases of Lyme disease annually in the U.S., up from 22,000 in 2010, according to the Centers for Disease Control and Prevention.
Tick-borne diseases can cause serious morbidity and mortality and have increased significantly in the United States over the past 15 to 20 years. This project will create a rapid, sensitive and label-free diagnostic tool to improve early detection and their co-infections to reduce complications and deaths from undiagnosed and late-diagnosed diseases.”
Soumya Srivastava, assistant professor, Benjamin M. Statler College of Engineering and Mineral Resources
Srivastava's research will involve the interdisciplinary use of microfluidics, sensors and machine learning. These factors will enable improved diagnosis of tick-borne infections via a non-invasive, affordable, rapid and user-friendly tool.
After taking a blood sample from a patient, the device analyzes the cells. All cells have a set of dielectric properties, such as permittivity and conductivity, that are unique to the cell membrane and cell cytoplasm, Srivastava explained. These properties depend heavily on the condition of the cell, such as whether it is normal or abnormal.
The unique properties depend on the shape and size of the cell; if the membrane is rough, smooth or leaking; and what happens inside the cell.
"Basically, we are measuring these properties on our microfluidic chip," she said, "and the electrical signal coming from the sensor will help us determine whether there is an infection or not. This technique is known as dielectrophoresis."
As soon as a few drops of blood enter the device, they are sorted by an electric field according to the condition, size and shape of the cells. The sorted cells will have a baseline capacitance value that will be displayed by the sensor, and this will allow us to infer the type of infection, Srivastava said.
“Machine learning will be used to make this tool robust and sensitive, detecting multiple infections within minutes.”
What makes the project even more unique is its ability to detect multiple tick-borne infections simultaneously and in a timely manner.
“In addition, our platform will non-invasively detect anaplasmosis, babesiosis and Lyme disease at an early stage, compared to the other available techniques that test four to six weeks after the infection develops,” said Srivastava. "Most tests currently available are symptom-based and symptoms appear four to six weeks after a tick bite. Our platform can detect these diseases early using a portable diagnostic tool within one to two weeks and in less than 30 minutes. If successful, this." The tool can be useful for a variety of health applications beyond tick-borne diseases.
“Rapid detection could reduce the risk of hospitalizations and doctor visits and prevent the disease from developing into a chronic, lifelong condition.”
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