United States: A neglected disease that affect millions of people around the globe, particularly in the poorest regions, has not even been studied enough as they should.
Schistosomiasis is one such disease – a chronic parasitic disease that is estimated to be prevalent among 250 million people in 78 nations, with most of the burden being borne by peoples of Africa and Latin America.
Due to the comparatively low level of scientific attention paid to schistosomiasis, advancing diagnostics and therapies are frequently an adjunct.
The current diagnosis available to diagnose and manage schistosomiasis fails to identify the disease at times when the disease is at its initial stage or the infection is still mild.
Often, blood tests can not differentiate between acute and chronic disease. If left untreated, schistosomiasis can progress to major complications of the bladder and liver, msn.com reported.
Researchers and study
Now, a team of researchers, including Jessica Fairley, an infectious disease specialist in the Donald Hopkins Professor of Medicine at Emory University’s School of Medicine, has identified the ways to identify schistosomiasis when other less sensitive tests are unable to do that led to early treatment and better long term outcomes.
Their findings, which are reported in the Science Translational Medicine, demonstrate the possibility of coming up with a clinical antibody test that could quickly and easily diagnose even a mild form of the current infection.
Sarkar, the co-author, is a biomedical engineer at Emory and the Georgia Institute of Technology, primarily investigating the behavior of the fluid at the micro and nanoscale in electronic chips.
As all biology takes place in fluids and electronic chips perform at the same microscopic scale at which cells and biomolecules exist, Sarkar partnered with Fairley to adapt his expertise “to probe biological processes at a very basic level,” as he puts it.
Fairley and Sarkar used new approaches by integrating their interest in infectious disease and biological data analysis, which in this case was previously unrecognized in the diagnosis of schistosomiasis.
Fairley said, “The traditional gold standard is microscopic visualization of the eggs of the Schistosoma parasite,” and, “You look under a microscope, and it’s very time-intensive. It also can cause infection.”
Sarkar had the opportunity to work with machine learning specialist Jishnu Das at the University of Pittsburgh, and they both, as well as another author of the paper, were able to build the machine learning platform capable of finding the groups of biomarkers for schistosomiasis that would provide the most information about its development in a specific patient.
Sarkar also mentioned that basing the diagnosis on the qualities of groups of antibodies instead of the quantity of a single marker would make it possible to do a better job of reliably detecting the disease early.
Sarkar said, “When you look at these hundreds of measurements with just the human eye, even if you plot it with bar charts or pie charts, finding patterns can be challenging,” msn.com reported.
“It will seem like a jumble. That’s where these technologies come in. It brings into focus the part of the data that really matters,” Sarkar continued.