Senior study author Tony Ho, PhD, the Weatherhead Chair in Biotechnology Innovation at Tulane, is working with colleagues to simplify the new tuberculosis test so that it can be administered in the community and read with a smartphone. Photography by Paula Porsche Celentano.
A new blood test developed by Tulane University researchers combines nanotechnology and artificial intelligence to diagnose tuberculosis (TB) in children in cases where the deadly disease may go undetected, according to a study conducted in The nature of biomedical engineering.
Although the current test requires a sophisticated lab to perform, researchers are working to simplify it so that it can be performed in the community and read with a smartphone.
Study senior author Tony Ho, Ph.D., Weatherhead Presidential Chair in Biotechnology Innovation at Tulane University said.
Tuberculosis is the second most common cause of death from infectious diseases worldwide, after COVID-19 only recently resolved it.
This disease is especially deadly in young children, especially those with HIV. Of the quarter of a million children under 15 years of age who die from tuberculosis each year, more than 80% were younger than 5. In nearly all of these cases, 96% were undiagnosed.
“This is a tragedy because when children are diagnosed and treated, they do better,” said Dr. Sylvia M. LaCourse, assistant professor in the departments of medicine and global health at the University of Washington School of Medicine. “But we have to find them first.”
The new blood test uses nanotechnology to allow scientists to see even tiny components of the bacteria that cause tuberculosis, a molecule called lipoarabinomanan (LAM) and a protein associated with it called LprG.
These molecules can be found in the blood of people with tuberculosis in small, membrane-bound sacs, called extracellular vesicles. Our cells constantly shed such vesicles, which is one of the ways cells get rid of unwanted substances. In the case of tuberculosis, immune cells called macrophages that engulf and attempt to digest tuberculosis shed vesicles studded with LAM and LprG.
“The problem is that every cell in our body produces thousands of alveoli per day,” Hu said. “The blood sample may contain billions of vesicles of which only a few hundred have been derived from tuberculosis-infected macrophages.”
To discover these vesicles, he and his colleagues coated the nanoparticles with antibodies that bind LAM and LprG. If the blood contains LAM or LprG vesicles, the antibodies will bind to the particles, which can be seen under a microscope.
To make the test more sensitive, the researchers also created an artificial intelligence algorithm that removes any background noise that other materials might cause on the nanoparticle surfaces. This automated process also allows samples to be analyzed quickly.
The researchers found that the test was highly sensitive: it accurately detected tuberculosis in 89% of children known to have had tuberculosis; It identified an additional 74% of children with unconfirmed TB who missed standard tests
“These preliminary results are very promising for diagnosing tuberculosis in children using small amounts of blood, including among children who missed our typical sputum-based tests. Levels of biomarkers also decreased after starting treatment, highlighting the potential of the test as a means of monitoring response, LaCourse said. for treatment.”
Researchers are evaluating the assay in future studies of children with suspected tuberculosis.
To develop the test, Tulane led an international collaboration of 24 different research organizations, including the University of Washington, Emory University, the University of Miami, Louisiana State University, Houston Methodist Hospital, and collaborators in the Dominican Republic, Kenya and Vietnam.
Tulane research scientist Wenshu Zheng, Ph.D., of the Center for Cellular and Molecular Diagnostics at Tulane University School of Medicine was the lead author of the study, which was co-authored by Tulane colleagues Xu Wang, Zhen Huang, Duran Bao, and Yating Xiao. , Li Yang, Lili Zhang, Jia Fan, Bo Ning, Zhenzhong Li, and Christopher Lyon.