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Breakthrough: Israeli Scientists Develop Early Parkinson’s Detection Method

Parkinson’s disease is the second most prevalent neurodegenerative disease in the world after Alzheimer’s – with about 8.5 million people with Parkinson’s living worldwide today

Parkinson's disease

Tel Aviv University researchers, in partnership with leading Israeli medical centers, have pioneered a groundbreaking method for early detection of Parkinson’s disease. By utilizing super-resolution microscopy and advanced computational analysis, the team has developed a technology capable of identifying protein aggregates, a hallmark of the disease, up to two decades before the onset of motor symptoms. This early detection breakthrough holds immense potential for facilitating timely treatment or even preventative measures against Parkinson’s, a currently incurable condition.

The study was piloted by researchers from the School of Neurobiology, Biochemistry & Biophysics at the Wise Faculty of Life Sciences, the Sagol School of Neuroscience, and the Faculty of Medical and Health Sciences at Tel Aviv University, led by Prof. Uri Ashery and PhD candidate Ofir Sade.

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“Parkinson’s disease is the second most prevalent neurodegenerative disease in the world after Alzheimer’s – with about 8.5 million people with Parkinson’s living worldwide today, and 1,200 new sufferers diagnosed annually in Israel,” explained Prof. Ashery. “The debilitating disease is characterized by the destruction of dopaminergic (dopamine producing) neurons in the brain’s Substantia Nigra area. Today, diagnosis of Parkinson’s disease is based mainly on clinical symptoms such as tremors or gait dysfunctions, alongside relevant questionnaires. However, these symptoms usually appear at a relatively advanced stage of the disease, when over 50%, and up to 80% of the dopaminergic neurons in the Substantia Nigra are already dead.”

Past studies have shown that alpha-synuclein aggregates form in other parts of the body as well, such as the skin and digestive system. In the current work the researchers examined skin biopsies from 7 people with and 7 people without Parkinson’s disease, received from the Sheba, Ichilov, and Meir Medical Centers.

With proof of concept obtained through the study, the researchers now plan to expand their work, supported by the Michael J. Fox Foundation for Parkinson’s Research. In the next phase they will increase the number of samples to 90 – 45 from healthy subjects and 45 from people without Parkinson’s disease – in order to identify differences between the two groups. Ofir Sade: “We intend to pinpoint the exact juncture at which a normal quantity of proteins turns into a pathological aggregate. In addition, we will collaborate with Prof. Lior Wolf of TAU’s School of Computer Science to develop a machine learning algorithm that will identify correlations between results of motor and cognitive tests and our findings under the microscope. Using this algorithm, we will be able to predict the future development and severity of various pathologies.”

The research employed super-resolution microscopy and computational analysis and has uncovered distinct tissue differences between individuals with and without Parkinson’s disease. To enhance their understanding and develop a predictive machine learning model, the scientists said future studies will involve a larger sample size.

“We are particularly interested in individuals with a familial history of Parkinson’s, especially those carrying mutations commonly found in Ashkenazi Jewish populations. A clinical trial is currently underway to evaluate a drug designed to inhibit the formation of protein aggregates, a key factor in Parkinson’s development,” said the scientists.

They hope that this research will pave the way for preventive treatments and real-time monitoring of medication effectiveness through microscopic observation.

It’s noteworthy that their developed methodology has the potential to be applied to the early diagnosis of other neurodegenerative diseases characterized by protein aggregation in neurons, such as Alzheimer’s.

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