Biomarkers.

Background: Dementia is a major public health problem affecting millions of people worldwide. Early diagnosis and intervention are essential to improve quality of life and reduce the burden of dementia. Recently, voice digital biomarkers have emerged as a promising approach for the early detection of dementia owing to its clinical utility and accessibility.

Biomarkers.

Background: Amyloid-β is widely known as a substantial biomarker in the diagnosis of Alzheimer's disease. However, detection of amyloid-β through neuroimaging techniques requires huge amounts of resources. There is a growing demand to detect these pathologies based on digital biomarkers.

EBP1 potentiates amyloid β pathology by regulating γ-secretase.

The abnormal deposition of amyloid β (Aβ), produced by proteolytic cleavage events of amyloid precursor protein involving the protease γ-secretase and subsequent polymerization into amyloid plaques, plays a key role in the neuropathology of Alzheimer's disease (AD). Here we show that ErbB3 binding protein 1 (EBP1)/proliferation-associated 2G4 (PA2G4) interacts with presenilin, a catalytic subunit of γ-secretase, inhibiting Aβ production. Mice lacking forebrain Ebp1/Pa2g4 recapitulate the representative phenotypes of late-onset sporadic AD, displaying an age-dependent increase in Aβ deposition, amyloid plaques and cognitive dysfunction.

Clinical predictors and noninvasive imaging in Fontan-associated liver disease: A systematic review and meta-analysis.

Background: Despite the development of several imaging modalities for diagnosing Fontan-associated liver disease (FALD), there is no optimal protocol for the follow-up of FALD. We conducted a systematic review and meta-analysis to identify factors related to liver fibrosis using biopsy reports and to identify alternative noninvasive modalities that could better reflect liver histological changes in FALD.

Methods: A systematic review and meta-analysis were conducted following the PRISMA guidelines Table S2.

Time-Series Representation Feature Refinement with a Learnable Masking Augmentation Framework in Contrastive Learning.

In this study, we propose a novel framework for time-series representation learning that integrates a learnable masking-augmentation strategy into a contrastive learning framework. Time-series data pose challenges due to their temporal dependencies and feature-extraction complexities. To address these challenges, we introduce a masking-based reconstruction approach within a contrastive learning context, aiming to enhance the model's ability to learn discriminative temporal features.