A team of researchers is currently working to address the problems encountered by patients with cancer who don't experience the symptoms until their illness has started to spread, and there are no reliable screening tests for early detection.
As indicated in a Phys.org report, ovarian cancer kills 14,000 women in the United States each year. More so, it is the fifth leading cause of death from cancer among women, and it is so fatal partly because the illness is hard to catch in its early stages.
Perception-based nanosensor platform could advance detection of ovarian cancer https://t.co/WvQgybUclC via @physorg_com
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The team comprises researchers from Memorial Sloan Kettering Cancer Center, the University of Maryland, Weill Cornell Medicine, the National Institutes of Standards and Technology, and Lehigh University.
Two recent studies described their advancements toward a new detection approach for ovarian cancer. This method uses machine learning techniques to effectively examine spectral signatures of carbon nanotubes for the detection of biomarkers of the disease and the recognition of cancer itself.
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Perception-Based Nanosensor Platform
The first study was published in Science Advances. Here, postdoctoral research associate Yoona Yang from Lehigh's Department of Chemical and Biochemical Engineering demonstrated that a perception-based nanosensor platform could identify "ovarian cancer biomarkers using machine learning.
In this research, Yang, the co-first author, worked with Zvi Yaari, the postdoctoral research fellow at the New York-based Memorial Sloan Kettering Cancer Center.
One of the authors, professor of bioengineering and chemical and biomolecular engineering Anand Jagota from Leigh University, who also serves as associate dean of research for Lehigh's College of Health, is part of the Lehigh's Nano-Human Interfaces Presidential Initiatives, a multidisciplinary research initiative to change the way experts are working with data, as well as the sophisticated instruments of scientific discovery.
Traditionally, the detection of biomarkers for diseases needs a molecular recognition molecule such as an antibody to be matched with every marker.
Nonetheless, there is not a single analyte or biomarker for ovarian cancer that specifies the presence of cancer. When numerous analytes need to be gauged in a given sample, which can increase a test's accuracy, more antibodies are required, increasing the cost of both the test and the turnaround time.
Carbon Nanotubes
Yang explained that "perception-based sensing is functioning like the human brain." He added the system comprises a sensing array that catches a specific feature of the analytes in a specific manner, and then the ensemble reaction from the variety is assessed by the perceptive computational model. It can identify different analytes at once, making it much more efficient.
Moreover, for this particular research, the array comprises single-wall carbon nanotubes wrapped in DNA strands.
The manner in which the DNA was wrapped, and the variety of DNA sequences that were employed, created a variety of surfaces on the nanotubes.
Nanotube-DNA Combination
In the second paper, published in Nature Biomedical Engineering, the researchers said they were not looking at biomarkers anymore. Instead, they were looking at the illness itself.
The study author added that they wanted to find out if this particular technology could differentiate a blood specimen from a patient who has ovarian cancer from a patient who doesn't have ovarian cancer.
In this research, the nanotubes were functionalized with quantum defects which significantly increased the variety of responses the nanotubes would also offer.
According to Jagota, the nanotubes had a specific molecule bound to them that provided an extra signal when it came to data.
Therefore, richer data came from each nanotube-DNA combination. More so, the model was trained not on the biomarker but on the state of the diseases.
Related information about nanotechnology for cancer is shown on Institute for Molecular Bioscience's YouTube video below:
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