Geriatrics

Machine-learning based strategy identifies a robust protein biomarker panel for Alzheimer's disease in cerebrospinal fluid.

BACKGROUND: The complex pathogenesis of Alzheimer's disease (AD) has resulted in limited current biomarkers for its classification and diagnosis, necessitating further investigation into reliable universal biomarkers or combinations.

BrainAGE latent representation clustering is associated with longitudinal disease progression in early-onset Alzheimer's disease.

INTRODUCTION: Early-onset Alzheimer's disease (EOAD) population is a clinically, genetically and pathologically heterogeneous condition. Identifying biomarkers related to disease progression is crucial for advancing clinical trials and improving therapeutic strategies. This study aims to differentiate EOAD patients with varying rates of progression using Brain Age Gap Estimation (BrainAGE)-based c...

Transformer attention-based neural network for cognitive score estimation from sMRI data.

Accurately predicting cognitive scores based on structural MRI holds significant clinical value for understanding the pathological stages of dementia ...

MIA and CD163 as promising diagnostic biomarkers in vascular dementia: A multi-method study combining WGCNA, machine learning with validation in animal models and clinical samples.

Vascular dementia (VaD), the second most common form of dementia, lacks reliable biomarkers for early diagnosis. Here, we integrated weighted gene co-...

Demystifying the Dermatology Residency Application Process, Part II - Application Season: Strategic Application, Interviewing, and Mentorship.

The dermatology residency match has become increasingly competitive, culminating in a 2025 United States senior MD match rate of 63 % according to the...

Discovery of novel cathepsin K inhibitors for osteoporosis treatment using a deep learning-based strategy.

BACKGROUND: Cathepsin K (CTSK), a cysteine protease of the papain family, exhibits high expression in activated osteoclasts, making it a key therapeut...

Hybrid Transformer for Early Alzheimer's Detection: Integration of Handwriting-Based 2D Images and 1D Signal Features.

Alzheimer's Disease (AD) is a prevalent neurodegenerative condition where early detection is vital. Handwriting, often affected early in AD, offers a ...

Design of a Mobile App and a Clinical Trial Management System for Cognitive Health and Dementia Risk Reduction: User-Centered Design Approach.

BACKGROUND: The rising prevalence of dementia is a major concern, with approximately 45% of cases linked to 14 modifiable risk factors. The European p...

Machine learning-assisted optimization of dietary intervention against dementia risk.

A healthy diet has been associated with a reduced risk of dementia. Here we devised a Machine learning-assisted Optimizing Dietary intERvention agains...

A dual path graph neural network framework for dementia diagnosis.

Dementia typically results from damage to neural pathways and the consequent degeneration of neuronal connections. Graph neural networks (GNNs) have b...

Character generation and visual quality enhancement in animated films using deep learning.

With the application and development of technologies such as artificial intelligence and deep learning in the generation of animated films, improving ...

Exploring the complex associations between community public spaces and healthy aging: an explainable analysis using catboost and SHAP.

BACKGROUND: As global aging accelerates, community public spaces (CPS) are increasingly recognized as vital for promoting healthy aging. However, exis...

Exploring the link between the ZJU index and sarcopenia in adults aged 20-59 using NHANES and machine learning.

Sarcopenia, characterized by progressive loss of muscle mass and function, is a growing public health concern. The ZJU index, a novel metabolic marker...

A multi-modal graph-based framework for Alzheimer's disease detection.

We propose a compositional graph-based Machine Learning (ML) framework for Alzheimer's disease (AD) detection that constructs complex ML predictors fr...

End-to-end feature fusion for jointly optimized speech enhancement and automatic speech recognition.

Speech enhancement (SE) and automatic speech recognition (ASR) in real-time processing involve improving the quality and intelligibility of speech sig...

Classifying and diagnosing Alzheimer's disease with deep learning using 6735 brain MRI images.

Traditional diagnostic methods for Alzheimer's disease often suffer from low accuracy and lengthy processing times, delaying crucial interventions and...

The impact of intelligent devices utilization on household medical expenditure of older adults in China.

With the rapid development of artificial intelligence, there is an increasing utilization of intelligent devices by older adults. The relationship bet...

A novel neuroimaging based early detection framework for alzheimer disease using deep learning.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function, posing a major global health chall...

Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery.

Hip fractures among the elderly population continue to present significant risks and high mortality rates despite advancements in surgical procedures....

Clinical validation of AI assisted animal ultrasound models for diagnosis of early liver trauma.

The study aimed to develop an AI-assisted ultrasound model for early liver trauma identification, using data from Bama miniature pigs and patients in ...