Basic Science and Pathogenesis.

Background: The Cognitive Function Index (CFI) is a validated test used to assess changes in self-perceived cognitive and functional status as reported by an individual and their study partner. Previous studies have demonstrated an inverse correlation between higher amyloid-beta (Aβ) burden and CFI, with certain CFI items exhibiting stronger associations than others. However, there is limited understanding of the association between declines in cognition and function, as assessed by CFI, and Tau levels measured by PET.

Clinical Manifestations.

Background: Alzheimer's disease exhibits heterogeneity through varied phenotypic and pathological manifestations. Here, we aimed to investigate the potential of semi-supervised pattern classification applied to volumetric MRI data in identifying relatively homogeneous subgroups of individuals exhibiting cognitive decline (CD) throughout the study period.

Method: We used data from the placebo arm of trial of Solanezumab for mild dementia due to AD (EXPEDITION-3 trial).

Clinical Manifestations.

Background: Alzheimer's disease (AD) is a heterogeneous disease with different clinical phenotypes and pathophysiological subtypes. Identifying cognitive/functional subtypes in AD could elucidate the diverse clinical progression patterns. The Cognitive Function Index (CFI), a 15-item questionnaire completed by participants and study partners, captures aspects of cognitive and functional decline.

Identifying drug interactions using machine learning.

The majority of Americans, accounting for 51% of the population, take 2 or more drugs daily. Unfortunately, nearly 100,000 people die annually as a result of adverse drug reactions (ADRs), making it the 4th most common cause of mortality in the USA. Drug-drug interactions (DDls) and their impact on patients represent critical challenges for the healthcare system.