Alzheimer's Disease

Machine learning-assisted high-performance immunoSERS platform using silk fibroin as a natural etching mask for early diagnosis of Alzheimer's disease.

Early and accurate diagnosis of Alzheimer's disease (AD) is a major stride toward pharmacological interventions to delay the onset or progression of the disease in patients with mild symptoms. In this study, we developed a silk fibroin-templated surface-enhanced Raman spectroscopy (SERS)-activated double-sandwich immunoassay (immunoSERS) platform that enhances plasmonic hotspot formation for the u...

MRomicsNet: A morphomics-radiomics-driven adaptive topological model for AD diagnosis on clinically routine T1-weighted images.

BACKGROUND AND OBJECTIVE: Both morphomics and radiomics are typical features when constructing brain networks on the clinically routine T1-weighted images (T1WI) in Alzheimer's disease (AD) diagnosis but the integration was rarely reported. The study was to devise a morphomics-radiomics-driven adaptive topological model (MRomicsNet) by leveraging the strengths of morphomics and radiomics in AD rep...

Single-cell resolution spatial transcriptomic signature of the retrosplenial cortex during memory consolidation.

The retrosplenial cortex (RSC) is a critical brain region that is activated during spatial memory tasks and plays a crucial role in the consolidation ...

Systematic post-translational modification genome wide identifies therapeutic targets for Alzheimer's disease: evidence from multi-cohort analysis.

BACKGROUND: The rapid increase in the incidence of Alzheimer's disease (AD) has raised concerns, given its profound effects on both society and the ec...

Probabilistic maps of white matter hyperintensity in a Chinese aging cohort and application for clinical diagnosis.

OBJECTIVES: White matter hyperintensities (WMH) are abnormalities in brain imaging that contribute to cognitive decline and diseases. This study aimed...

Unveiling manganese metabolism-related biomarkers in Alzheimer's disease: Insights into diagnosis and therapeutic targets.

BACKGROUND: Alzheimer's disease (AD), a neurodegenerative disorder with multifactorial etiologies, has been closely associated with disturbances in ma...

Robust detection of femtogram-level Alzheimer's biomarkers using machine learning-enhanced graphene biosensors.

Early diagnosis of Alzheimer's disease (AD) requires blood biomarker tests sensitive to femtogram/mL concentrations. Graphene field-effect transistors...

Connectome-based markers predict the sub-types of frontotemporal dementia.

Frontotemporal dementia (FTD) presents a complex spectrum of neurodegenerative disorders, encompassing distinct subtypes with varied clinical manifest...

GAN-based novel feature selection approach with hybrid deep learning for heartbeat classification from ECG signal.

Heart arrhythmias are one of the most important categories of cardiovascular illness. A heartbeat that is abnormal like too early, too slow, too fast,...

Genetic and pathway complexity in Alzheimer's disease: Insights from multi-omic data about the immune response and mitochondrial function.

Structure and function of voltage-gated sodium channel Nav1.6: Involvement in the pathological process of neural injury.

The voltage-gated sodium channel Nav1.6, encoded by the sodium voltage-gated channel alpha subunit 8 gene, is a crucial regulator of neuronal excitabi...

MDSFD-Net: Alzheimer's disease diagnosis with missing modality via disentanglement learning and feature distillation.

Multi-modal analysis can provide complementary information and significantly aid in the early diagnosis and intervention of Alzheimer's Disease (AD). ...

Decision Strategies in AI-Based Ensemble Models in Opportunistic Alzheimer's Detection from Structural MRI.

Alzheimer's disease (AD) is a neurodegenerative condition and the most common form of dementia. Recent developments in AD treatment call for robust di...

Attention Gated-VGG with deep learning-based features for Alzheimer's disease classification.

BACKGROUND: Alzheimer's disease (AD) is considered to be one of the neurodegenerative diseases with possible cognitive deficits related to dementia in...

Multi-modal orthogonal fusion network via cross-layer guidance for Alzheimer's disease diagnosis.

Multi-modal neuroimaging techniques are widely employed for the accurate diagnosis of Alzheimer's Disease (AD). Existing fusion methods typically focu...

The Evolving Landscape of Amyloid Research.

The exponential growth of biomedical and life sciences literature, including research on amyloid biology, has made it increasingly challenging to trac...

Integrative machine learning approach to risk prediction for dementia and Alzheimer's disease.

Dementia, particularly Alzheimer's disease (AD), presents a growing global health challenge characterized by cognitive decline, behavioral changes, an...

AlzhiNet: Traversing from 2D-CNN to 3D-CNN, Towards Early Detection and Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with increasing prevalence among the ageing population, necessitating early and a...

Proteomic signature of dementia risk in type 2 diabetes.

INTRODUCTION: Type 2 diabetes (T2D) significantly increases dementia risk, yet the molecular mechanisms underlying this association remain unclear. OB...

Six promising drug repurposing candidates for Alzheimer's disease and their sex-specific mechanisms and efficacy.

Alzheimer's disease is a neurodegenerative disorder that leads to progressive memory loss, cognitive decline, and behavioral changes. Despite ongoing ...