Alzheimer's Disease

QENNA: A quantum-enhanced neural network for early Alzheimer's detection using magnetic resonance imaging.

Early detection of Alzheimer's disease (AD) is essential for effective clinical intervention and disease management. However, conventional Deep Learning (DL) methods face limitations in analyzing complex brain magnetic resonance imaging (MRI), especially when training data are scarce. In this study, we propose a Quantum-Enhanced Neural Network Architecture (QENNA) that integrates quantum convoluti...

Delineating white matter phenotypes of sensori-/psychomotor functioning in large-scale cohorts of healthy individuals and patients with mental disorders across the lifespan (whiteSPAN): rationale and methods of an interdisciplinary bicentric project.

Aberrant sensori-/psychomotor functioning-including muscular hand weakness, sedentary behavior, psychomotor agitation, slowing, agitation, apathy, and anxiety-is increasingly recognized as a transdiagnostic feature across mental and neurodegenerative disorders. Objectively measured sensori-/psychomotor abnormalities serve as rapid, noninvasive indicators of cognitive and affective dysfunction, yet...

Occupational Exposure to Welding Fumes Associated With Pulmonary Surfactant Lipid Compositional Changes, Inflammatory Activity, and Pulmonary Functioning.

OBJECTIVE: This study aimed to investigate alterations in the respiratory tract lining fluid phospholipids and their association with pulmonary functi...

Mortality prediction among older people in primary care: a comparison of predictive performance of the frailty index with regression-based and machine learning models.

INTRODUCTION: The frailty index is widely used to identify vulnerable individuals at risk of adverse outcomes like mortality. However, its predictive ...

Bridging the Hearing Divide: Policy Solutions for Aging Americans.

Hearing loss affects approximately two thirds of adults in the United States aged 70 years or older and frequently remains untreated despite its well-...

Utility of Baseline Pathological, Neuroimaging and Clinical Markers for Prognosis in Early Parkinson's Disease.

BackgroundCurrently, prognosis of Parkinson's Disease (PD) is limited. Emerging literature highlights potential of multi-modal biomarkers and neuroima...

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 t...

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 im...

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,...

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...