Utilizing graph neural networks for adverse health detection and personalized decision making in sensor-based remote monitoring for dementia care.

Background: Sensor-based remote health monitoring is increasingly used to detect adverse health in people living with dementia (PLwD) at home, aiming to prevent hospitalizations and reduce caregiver burden. However, home sensor data is often noisy, overly granular, and suffers from unreliable labeling, data drift and high variability between households. Current anomaly detection methods lack generalizability and personalization, often requiring anomaly-free training data and frequent model updates.

Enriched Environment Contributes to the Recovery from Neurotoxin-Induced Parkinson's Disease Pathology.

Parkinson's disease (PD) is a neurological disorder that affects dopaminergic neurons. The lack of understanding of the underlying molecular mechanisms of PD pathology makes treating it a challenge. Several pieces of evidence support the protective role of enriched environment (EE) and exercise on dopaminergic neurons.

Vascular amyloid accumulation alters the gabaergic synapse and induces hyperactivity in a model of cerebral amyloid angiopathy.

Cerebral amyloid angiopathy (CAA) is typified by the cerebrovascular deposition of amyloid. The mechanisms underlying the contribution of CAA to neurodegeneration are not currently understood. Although CAA is highly associated with the accumulation of β-amyloid (Aβ), other amyloids are known to associate with the vasculature.