Dementia

Development of AI-assisted 3D-printed degradable hydrogel microneedles for transdermal delivery of progesterone-loaded solid lipid nanoparticles: a novel approach to slowing Alzheimer's disease progression.

Progesterone (PG) is used to slow the progression of neurodegenerative diseases, particularly Alzheimer's disease (AD) in postmenopausal women. However, PG exhibits high lipophilicity, resulting in strong binding to skin tissues and plasma proteins, which may limit its systemic transport via transdermal routes. Solid lipid nanoparticles (SLNs) have been highlighted for their potential to enhance d...

The geographic digital divide among dementia caregivers in the U.S.: a cross-sectional study.

BACKGROUND: Digital health tools, particularly patient portals, can support caregiving, but there is limited understanding of how sociodemographic and geographic factors influence digital health engagement among U.S. caregivers, especially dementia caregivers. Dementia caregiving involves complex coordination, medication management, and frequent healthcare interactions. Therefore, access to online...

Disproportionately Elevated Sulcal Index (DESI): An automatically driven index representing disproportionate subarachnoid space enlargement in brain MRI scans.

INTRODUCTION: Idiopathic normal pressure hydrocephalus (iNPH) is frequently underdiagnosed due to non-specific symptoms and the risks of invasive test...

Top five Alzheimer Disease trial eligibility criteria favor men compared to women in a clinic-based cohort.

BACKGROUND: Less women participate in Alzheimer Disease (AD) trials compared to their estimated representation in the global dementia population. OBJE...

Predicting first onset of suicide attempt among children with suicidal ideation or non-suicidal self-injury using machine learning: a prospective population-based cohort study.

While children with suicidal ideation or non-suicidal self-injury (NSSI) are at high risk of suicide, most do not attempt suicide. This study aims to ...

Osteoarthritis and dementia: contrasting disorders driven by mutual pathways of autophagy, mTOR, GLP-1, AMPK, wnt, and WISP1.

INTRODUCTION: Increased global lifespan is paralleled by a rise in non-communicable diseases with osteoarthritis and dementia, including Alzheimer's d...

Integration of glymphatic system function and hippocampal radiomics for diagnosis and conversion prediction of Alzheimer's disease.

BACKGROUND: Glymphatic system (GS) function and hippocampal microstructural changes are promising imaging markers of Alzheimer's disease (AD). This st...

A multifeature machine learning and resting-state EEG study reveals differences in beta oscillation in late-life depression with or without mild cognitive impairment.

BACKGROUND: Late-life depression (LLD) often co-occurs with mild cognitive impairment (MCI), and patients with LLD and MCI (LLD-MCI) have an increased...

Combining EEG, event-related potentials, and MRI biomarkers for detection of mild cognitive impairment: A machine learning approach.

OBJECTIVE: Mild cognitive impairment (MCI) is an intermediary stage between typical cognitive aging and dementia. Identifying reliable biomarkers for ...

Large language models for deductive qualitative content analysis in dementia-focused embedded pragmatic clinical trials: A comparative methodological study.

INTRODUCTION: Thematic coding helps researchers characterize intervention implementation in embedded pragmatic clinical trials (ePCTs), particularly i...

AI chatbots in strabismus care: A multidomain expert evaluation of caregiver-facing information.

AimTo evaluate and compare the performance of five artificial intelligence (AI) chatbots-ChatGPT (OpenAI 4), Google Gemini, Grok (xAI), DeepSeek, and ...

Factors Associated with Respite Care Use Among Veteran Caregivers: A Machine Learning Analysis.

BACKGROUND AND OBJECTIVES: Respite care provides temporary relief to family caregivers yet remains underused, and the factors shaping its utilization ...

Testing Near-Field Radio-Frequency Sensors to Predict Respiratory Distress in Patients Living With Dementia: A Pilot Feasibility and Acceptability Study.

BackgroundPeople living with dementia (PLWD) with advanced illness are prone to respiratory distress yet often cannot self-report dyspnea, delaying re...

Robust disease prognosis via diagnostic knowledge preservation: A sequential learning approach.

Accurate disease prognosis is essential for patient care but is often hindered by the scarcity of longitudinal data. This study explores deep learning...

The presymptomatic and early manifestations of semantic dementia.

People with semantic dementia (SD) or semantic variant primary progressive aphasia typically present with marked atrophy of the anterior temporal lobe...

Data-driven detection of lucid episodes in advanced dementia: Application of latent class analysis to observational data.

BackgroundLucid episodes (LEs) in advanced dementia are significant clinical events yet are challenging to investigate as they are characterized by a ...

Learning patient-specific spatial biomarker dynamics via operator learning for Alzheimer's disease progression.

Alzheimer's disease (AD) is a complex, multifactorial neurodegenerative disorder with substantial heterogeneity in progression and treatment response....

Reducing annotation burden in medical imaging with ADGNET: A semi-supervised deep learning strategy.

We propose ADGNET, a semi-supervised framework for Alzheimer's disease (AD) diagnosis that jointly optimizes image reconstruction and classification t...

Disentangling amyloid polymorphs in normal aging and Alzheimer's disease using dual-probe spectral imaging.

Variability in Alzheimer's disease (AD) clinical presentation complicates mechanistic studies and therapeutic outcome prediction. Brain protein aggreg...

Developing machine learning-enhanced WHODAS 2.0 short forms for persons with dementia.

BACKGROUND: Disability assessment in dementia is important for care planning, but the full World Health Organization Disability Assessment Schedule 2....