Technique Can Rapidly Distinguish Positive Cases of COVID-19 and Gulf War Illness
By Edwin L. Aguirre
Noureddine Melikechi, dean of the Kennedy College of Sciences and professor in the Department of Physics and Applied Physics, is helping to advance the use of laser technology to diagnose serious illnesses, ranging from cancers and COVID-19 to Gulf War illness, a chronic condition that affects a quarter of the nearly 700,000 American veterans who served in the 1990-1991 Gulf War.
The technique – called laser-induced breakdown spectroscopy, or LIBS – is a rapid, sensitive and cost-effective method that is currently used in a wide range of applications, including geology, biology, manufacturing, the food industry and forensic science. It uses intense pulses of laser to vaporize a tiny portion of a sample of blood or other body fluids from a patient and produce a cloud of charged particles. An instrument called a spectrograph records the light emitted by the particles for analysis. Using special algorithms they have developed, Melikechi and his research group can process the data to look for telltale spectral signatures, or biomarkers, for a disease or syndrome.
“The benefit of LIBS is that it requires only a very small blood sample,” says Melikechi. “It’s also non-invasive, accurate and fast, with the potential to analyze up to hundreds of samples in an hour.”
The team has successfully demonstrated the use of LIBS in detecting biomarkers for epithelial ovarian cancer, melanoma and Alzheimer’s disease through a single drop of blood, as well as in analyzing onsite the surface composition of the planet Mars through NASA’s Perseverance rover.
A Mystery Illness
According to Melikechi, patients suffering from GWI experience multiple physical and emotional symptoms, including fatigue, joint and muscle pain, gastrointestinal disorders and cognitive difficulties, as well as depression and anxiety.
“The cause of GWI is still unknown, and many hypotheses have been investigated, including exposure to vaccines, medications, pesticides, chemical agents and inhalation of toxic fumes from burn pits and burning oil fields,” he says. “The health effects associated with GWI have significant impact on the veterans’ quality of life.”
Melikechi says there is no effective treatment available, and patients do not appear to recover with time.
“This is a complex condition that is not well-understood, and its management may require individualized health care plans,” he says.
For their work, Melikechi and his co-investigators examined the LIBS signatures of blood plasma collected from GWI patients and non-GWI patients and used various machine learning techniques to analyze the spectra.
“By comparing the LIBS spectra obtained from the blood plasma of GWI patients and non-GWI patients, we generated a classification model that was used to conduct a blind test. This test yielded a classification accuracy of 90% for the unknown cases,” says Melikechi.
Aside from Melikechi, other members of the team included former UML postdoctoral fellow Rosalba Gaudiuso as well as researchers from the Veterans Administration Bedford Healthcare System, Harvard Medical School and the Massachusetts School of Pharmacy and Health Sciences. The team’s findings were published in the journal Applied Spectroscopy.
“While our results may not be considered conclusive due to the small scale of this initial study, they nonetheless demonstrate the potential of LIBS in diagnosing GWI,” notes Melikechi. “Additional studies are currently underway, and we are using samples from a larger number of GWI patients and controls to further validate the diagnostic accuracy of our LIBS-based test.”
The Fight Against COVID-19
While the number of new cases, hospitalizations and deaths have been trending downward across the country over the summer, according to the Centers for Disease Control’s COVID Data Tracker, health care professionals are still concerned about a possible resurgence of the Omicron subvariants with the onset of winter, so continued testing, monitoring and vaccination are recommended.
Melikechi says LIBS, combined with machine learning, can be used for rapid screening to confirm infection and determine the status of SARS-CoV-2 immune response in the blood plasma of patients. (SARS-CoV-2 is the virus that causes COVID-19.)
“Our technique can distinguish plasma of people who previously tested positive for SARS-CoV-2 using the RT-PCR test from those who did not, with up to 95% accuracy,” he says.
The researchers obtained data from three different LIBS spectrographs in two different facilities – in Melikechi’s lab at UMass Lowell and in Associate Professor Kim Berlo’s lab at McGill University in Montreal, Canada. Analysis at McGill showed that LIBS could distinguish plasma from positive donors from that of negative donors by the former’s lower levels of the elements zinc and barium.
“Zinc has an important role in our body’s immune response and is being studied for its role in the treatment of COVID-19,” notes Melikechi.
The simplicity, reliability and fast analysis of LIBS, combined with no sample preparation and minimal training required to operate the system, makes it a powerful clinical lab diagnostic tool in the fight against SARS-CoV-2 and other pathogens, according to Melikechi.
Aside from Melikechi, Gaudiuso and Berlo, the team also included UML postdoctoral fellow Ebo Ewusi-Annan as well as researchers from McGill University, the VA Bedford Healthcare System/Boston University School of Medicine, and Gregory Chiklis ’92 of MRN Diagnostics. Their findings were published in the journal Nature Scientific Reports.
“Our results reported thus far, although encouraging, have been obtained with limited number of samples,” says Melikechi. “Much more work needs to be done.”