Alzheimer's Disease

Deep-Learning-Assisted Stratification of Amyloid Beta Mutants Using Drying Droplet Patterns.

The development of simple and accurate methods to predict mutations in proteins remains an unsolved challenge in modern biochemistry. It is discovered that critical information about primary and secondary peptide structures can be inferred from the stains left behind by their drying droplets. To analyze the complex stain patterns, deep-learning neuronal networks are challenged with polarized light...

Brain network connectivity feature extraction using deep learning for Alzheimer's disease classification.

Early diagnosis and therapeutic intervention for Alzheimer's disease (AD) is currently the only viable option for improving clinical outcomes. Combining structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) to diagnose AD has yielded promising results. Most studies assume fixed time lags when constructing functional networks. Since the propa...

Identifying Blood Biomarkers for Dementia Using Machine Learning Methods in the Framingham Heart Study.

Blood biomarkers for dementia have the potential to identify preclinical disease and improve participant selection for clinical trials. Machine learni...

Therapeutic use of the humanoid robot, Telenoid, with older adults: A critical interpretive synthesis review.

This review sought to critically evaluate the use of the teleoperated humanoid robotic communications device, Telenoid, for therapeutic purposes with ...

Deep learning from multiple experts improves identification of amyloid neuropathologies.

Pathologists can label pathologies differently, making it challenging to yield consistent assessments in the absence of one ground truth. To address t...

Dietary administration of D-chiro-inositol attenuates sex-specific metabolic imbalances in the 5xFAD mouse model of Alzheimer's disease.

Increasing evidence shows that hypothalamic dysfunction, insulin resistance, and weight loss precede and progress along with the cognitive decline in ...

Predicting Abnormal Amyloid Burden: Can Machine Learning Become a Future Tool in Preventive Neurology?

Dementia risk predictions from German claims data using methods of machine learning.

INTRODUCTION: We examined whether German claims data are suitable for dementia risk prediction, how machine learning (ML) compares to classical regres...

Predicting brain structural network using functional connectivity.

Uncovering the non-trivial brain structure-function relationship is fundamentally important for revealing organizational principles of human brain. Ho...

Socially assistive robots for people with dementia: Systematic review and meta-analysis of feasibility, acceptability and the effect on cognition, neuropsychiatric symptoms and quality of life.

BACKGROUND: There is increasing interest in using robots to support dementia care but little consensus on the evidence for their use. The aim of the s...

Predicting Brain Age Using Machine Learning Algorithms: A Comprehensive Evaluation.

Machine learning (ML) algorithms play a vital role in the brain age estimation frameworks. The impact of regression algorithms on prediction accuracy ...

Formulation and optimization of silymarin-encapsulated binary micelles for enhanced amyloid disaggregation activity.

Silymarin (SLY) is a natural hydrophobic polyphenol that possesses antioxidant and amyloid fibril (Aβ) inhibition activity, but its activity is hinder...

A Single Model Deep Learning Approach for Alzheimer's Disease Diagnosis.

Early and accurate diagnosis of Alzheimer's disease (AD) and its prodromal period mild cognitive impairment (MCI) is essential for the delayed disease...

Assessment of the predictive potential of cognitive scores from retinal images and retinal fundus metadata via deep learning using the CLSA database.

Accumulation of beta-amyloid in the brain and cognitive decline are considered hallmarks of Alzheimer's disease. Knowing from previous studies that th...

The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects.

Research on segmentation of the hippocampus in magnetic resonance images through deep learning convolutional neural networks (CNNs) shows promising re...

A deep learning MRI approach outperforms other biomarkers of prodromal Alzheimer's disease.

BACKGROUND: The three core pathologies of Alzheimer's disease (AD) are amyloid pathology, tau pathology, and neurodegeneration. Biomarkers exist for e...

Analysis of Factors Affecting Cranial Nerve Function of Patients With Vascular Mild Cognitive Impairment Through Functional Magnetic Resonance Imaging Under Artificial Intelligence Environment.

The study aimed to explore the risk factors of effects of patients with vascular mild cognitive impairment (VaMCI) through functional magnetic resonan...

Reduction of multiple-caregiver assistance through the long-term use of a transfer support robot in a nursing facility.

The long-term use of transfer support robots in nursing facilities is an important option for improving the efficiency of care work. The "Resyone" tra...

Single-molecule fluorescence imaging and deep learning reveal highly heterogeneous aggregation of amyloid-β 42.

Polymorphism in the structure of amyloid fibrils suggests the existence of many different assembly pathways. Characterization of this heterogeneity is...

EvoRator: Prediction of Residue-level Evolutionary Rates from Protein Structures Using Machine Learning.

Measuring evolutionary rates at the residue level is indispensable for gaining structural and functional insights into proteins. State-of-the-art tool...