Alzheimer’s disease (AD), a neurological disorder that causes the brain to atrophy, begins with mild memory loss, and progresses to a physical and mental deterioration that seriously affects one’s ability to carry out daily activities. AD can disrupt people’s ability to handle money responsibly or complete once familiar tasks, and can cause severe changes in their mood, personality, and behaviors. According to the Centers for Disease Control and Prevention, the number of individuals diagnosed with AD is projected to reach 14 million people by 2060.
While a cure for AD doesn’t exist yet, there are ways that the disease can be managed to ease the burden on the individuals themselves and their caregivers. Some medications can mitigate memory loss symptoms, however, that’s only a short-term solution. Treatment typically revolves around creating a safe and supportive environment and establishing habits that minimize reliance on memory-dependent tasks.
As is the case with most diseases, early detection can be essential to improving the quality of life for people living with AD. At present, physicians rely on several biomarkers to detect AD. The problem with these biomarkers, according to researcher Fenglong Ma, PhD, is that it is often costly, time-consuming, and prone to error. Researchers from the Institute for Computational and Data Sciences at Pennsylvania State University received a $1.2 million grant from the National Institutes of Health to fund a project that’s aim is to “develop a novel and minimally-invasive system that integrates a multimodal biosensing platform and a machine-learning framework, which synergistically work together to significantly enhance the detection accuracy.”
Researchers plan to design a system, referred to as the Multimodal Optical, Mechanical, Electrochemical Nanosensor with Two-dimensional material Amplification (MOMENTA) platform, that uses biosensors to analyze biological samples for the sensitive and selective detection of AD biomarkers.
“The framework is able to identify potential new biomarkers based on a statistical analysis of the learned weights on the input signals and provide feedback information to further improve the MOMENTA platform design,” researchers wrote.
The multimodal, interdisciplinary research combines two-dimensional platforms for sensor enhancement, nanotechnology experts who advance sensor platforms, data scientists who analyze data with machine-learning methods to target early prediction of AD, and experts who help to identify potentially new AD biomarkers.
Sharon Huang, PhD, professor of information sciences and technology at the Huck Institutes of the Life Sciences and coresearcher stated, “We hope our project can result in a minimally-invasive technique that can detect Alzheimer’s disease in its early stages. The technique also has the potential to be high throughput, making it possible to be used in screening for the disease. We will also try our best to make the technique accurate, reducing false positives and false negatives in AD detection.”