by Protein clumping is a common feature of various neurodegenerative disorders, including Alzheimer’s and Parkinson’s disease, which impact the brain and affect over 100,000 Danes aged 65 and above, as well as more than 55 million individuals worldwide. The underlying cause of these diseases and effective treatment methods remain elusive due to the challenges in studying protein clumping.
Researchers from the Hatzakis lab at the University of Copenhagen’s Department of Chemistry have recently developed a novel machine learning algorithm that can detect and analyze protein clumps in real-time under a microscope. Their findings were published in the journal Nature Communications.
Neurodegenerative disorders arise when proteins, the body’s smallest building blocks, clump together and disrupt essential functions. Understanding the mechanisms behind protein clumping and developing targeted therapies have been difficult due to the lack of suitable tools.
The new algorithm, created by Ph.D. Jacob Kæstel-Hansen and his team, can automatically map and track the essential characteristics of clumped-up proteins that contribute to Alzheimer’s and other Neurodegenerative Disorders. Previously, this task would have taken researchers several weeks to accomplish manually.
“Our algorithm can now solve this challenge in just minutes,” says Kæstel-Hansen. “This will hopefully contribute to a greater understanding of these diseases and, in the long term, lead to new therapies for neurodegenerative brain disorders.”
The ability to study microscopic images of protein clumping more efficiently will provide valuable insights into the causes of these debilitating conditions and potentially pave the way for the development of effective treatments.
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1. Source: Coherent Market Insights, Public sources, Desk research.
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