Muscular dystrophies are a group of genetic disorders that result in muscle weakness over time. Each type of muscular dystrophy is different from the others. It is important to get help as early as possible. Muscular dystrophy has no cure, but acting early may help an individual with muscular dystrophy get the services and treatments he or she needs to lead a full life.
A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease.
Wightman Douglas P et al. Nature genetics 2021 9 (9) 1276-1282
Late-onset Alzheimer's disease is a prevalent age-related polygenic disease that accounts for 50-70% of dementia cases. Currently, only a fraction of the genetic variants underlying Alzheimer's disease have been identified. Here we show that increased sample sizes allowed identification of seven previously unidentified genetic loci contributing to Alzheimer's disease. This study highlights microglia, immune cells and protein catabolism as relevant to late-onset Alzheimer's disease, while identifying and prioritizing previously unidentified genes of potential interest. We anticipate that these results can be included in larger meta-analyses of Alzheimer's disease to identify further genetic variants that contribute to Alzheimer's pathology.
Scoping review: the empowerment of Alzheimer’s Disease caregivers with mHealth applications
E Kim et al, NPJ Digital Medicine, September 7, 2021
We searched published literature in five electronic databases between January 2014 and January 2021. Twelve articles were included in the final review. Six themes emerged based on the functionalities provided by the reviewed applications for caregivers. They are tracking, task management, monitoring, caregiver mental support, education, and caregiver communication platform. The review revealed that mHealth applications for AD patients’ caregivers are inadequate.
Facial expressions can detect Parkinson’s disease: preliminary evidence from videos collected online
MR ALi et al, NPJ Digital Medicine, September 3, 2021
We analyzed the facial action units (AU) from 1812 videos of 604 individuals (61 with PD and 543 without PD, with a mean age 63.9?y/o, sd. 7.8) collected online through a web-based tool (www.parktest.net). In these videos, participants were asked to make three facial expressions followed by a neutral face. Using machine learning, we objectively measured the variance of the facial muscle movements and used it to distinguish between individuals with and without PD. The prediction accuracy using the facial micro-expressions was comparable to methodologies that utilize motor symptoms.
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