Boxing, Identifying Subtypes, Novel Cell Model Study and Symptom Tracking
Hello from beautiful Marmaris, Türkiye! Today, I bring you the latest updates on Parkinson's disease that have caught my eye. I am thrilled to share these exciting new updates with you.
Lifestyle
Interactive Boxing Class for Parkinson’s Patients:
At Intermountain Health McKay-Dee Hospital in Ogden, Utah, an innovative exercise program is helping Parkinson’s patients manage their symptoms. The Rock Steady Boxing Classes, led by Camille Kopa, a Physical Therapist Assistant and certified coach, involve non-contact boxing exercises that enhance coordination, strength, balance, and endurance. Participants have reported improvements in their motor functions, with some even regaining abilities such as walking with a narrow gait. The program also fosters a supportive community, encouraging patients to push their limits while ensuring safety. Classes are held twice a week with a physician's referral, offering a promising approach to mitigating Parkinson’s symptoms through high-intensity exercise.
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Treatment
Three Distinct Parkinson’s Subtypes Identified via Machine Learning:
A study by Cornell University researchers has identified three distinct subtypes of Parkinson’s disease using machine learning, potentially enabling more personalized treatments. The subtypes—Inching Pace (PD-I), Moderate Pace (PD-M), and Rapid Pace (PD-R)—each have unique genetic signatures and progression rates. This classification helps in understanding the heterogeneous nature of Parkinson’s and tailoring treatments accordingly. Notably, the diabetes drug metformin showed promise in alleviating symptoms, particularly cognitive deficits in PD-R patients. The findings support the need for customized treatment strategies based on the disease subtype, paving the way for precision medicine in Parkinson’s disease management.
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Research
Parkinson’s Disease Inclusionopathy Cell Models Aid Study of Disease Pathology:
Researchers at Brigham and Women's Hospital have developed a technology that rapidly converts stem cells into brain cells with protein structures characteristic of Parkinson’s disease, aiding in the study of the disease’s pathology. This new approach, led by Dr. Vikram Khurana, allows for the creation of patient-specific models within weeks, facilitating high-throughput genetic and drug screens. These models can reflect the diverse protein misfolding pathologies seen in Parkinson’s, providing insights into disease mechanisms and potential treatments. The technology aims to support personalized diagnostic and treatment strategies, making it a valuable tool for understanding and combating neurodegenerative diseases.
Technology
Machine Learning Revolutionizes Parkinson's Disease Symptom Tracking and Progression Prediction:
A study led by Leidy Guarin, MS, BCBA,LBA and published in IEEE Transactions on Neural Systems and Rehabilitation Engineering highlights a new machine learning system that quantifies motor symptoms in Parkinson’s disease and predicts disease progression. By analyzing video recordings of the finger-tapping test, researchers developed algorithms to assess bradykinesia severity with high accuracy. This novel tiered binary classification approach considers different movement features according to disease severity, improving upon traditional methods.
The video processing pipeline utilizes hand tracking results to measure angular displacement between vectors formed by the base of the hand and the tips of the index and thumb fingers, as identified by Google's MediaPipe in each video frame. By tracking the angular distance throughout the video, we generate an angular displacement signal. Kinematic features related to bradykinesia are then extracted from the peaks and valleys (green and red dots) of this signal.
The system’s ability to detect subtle changes in motor function offers a more sensitive tool for early diagnosis and treatment planning, enhancing the management of Parkinson’s disease.
These updates underscore the ongoing commitment to advancing Parkinson’s care, and I look forward to keeping you informed as these exciting developments unfold. Thank you for joining me on this journey to better Parkinson's disease management.
