Alzheimer's Disease

Automated detection of active and structural MRI lesions in the sacroiliac joints in axial spondyloarthritis: training and validation across 3 phase III clinical trial datasets.

OBJECTIVES: This study aims to assess the performance of a fully automated deep learning (DL) system for detecting active and structural magnetic resonance imaging (MRI) lesions of the sacroiliac joints (SIJs) in axial spondyloarthritis (axSpA), and validate its generalisability across independent clinical trial datasets. METHODS: A 2-stage automated pipeline was developed to delineate left and ri...

A CECT-Based 2PI System as a Novel Noninvasive Prognostic Tool for Hepatocellular Carcinoma: A Dual-Validation Study.

This study aimed to develop and validate machine learning (ML) models integrating clinical parameters and the 2PI system (Pathology and Prognosis-Informed Imaging System) for predicting postoperative recurrence risk in hepatocellular carcinoma (HCC). The multicenter retrospective study included 496 patients with solitary HCC (≤ 5 cm). Surgical resection (SR) patients from the primary center consti...

Targeting the mitochondrial functional network: Decoding upstream pathological mechanisms and novel therapeutic paradigms for Alzheimer's disease.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder worldwide. Conventional downstream interventions targeting β-amyloid (Aβ) an...

Breaking the Autophagy-Oxidative Stress Vicious Cycle in Alzheimer's Disease: Lactiflorin Unlocks P62 Dual Phosphorylation via ULK1 Targeting.

ETHNOPHARMACOLOGICAL RELEVANCE: The dried root of Paeonia lactiflora Pall. has a long history of medicinal use in traditional Chinese medicine. Classi...

From Volumetrics to 3D Tensors: A Multi-Cohort Evaluation of Machine Learning and Deep Learning for Alzheimer's Classification.

Automated Alzheimer's disease (AD) classification from structural MRI typically employs either feature-engineered machine learning (ML) or end-to-end ...

Toward harmonized reporting of Alzheimer's disease biomarkers in clinical practice.

Cerebrospinal fluid biomarkers, and more recently blood-based biomarkers, are playing a pivotal role in reshaping the clinical management of neurodege...

Machine learning classification of mild Alzheimer's disease using EEG during emotional processing.

BackgroundMild Alzheimer's disease (AD) is associated with alterations in brain activity, which can be detected using electroencephalography (EEG). In...

Bridging language markers and pathology: correlations between digital speech measures and surrogate CSF biomarkers in Alzheimer's disease.

INTRODUCTION: Digital language markers show promise in detecting early cognitive impairment related to Alzheimer's disease (AD), yet their relationshi...

Clinically altered brain activity may not look like aged brain activity: Implications for brain-age modeling and biomarker strategies.

INTRODUCTION: Brain-age gap (BAG), the difference between predicted age and chronological age, is studied as a biomarker for the natural progression o...

Bridging Machine Learning and Event-Based Analyses to Assess Maternal Contingent Responses to Infant Nondistress and Distress Vocalizations From Home Audio Recordings.

Caregiver-infant vocal interactions are foundational for early development, yet most evidence is derived from brief laboratory or home-visit observati...

Written in the Stars: Astrocyte Biology From Evolution to Disease.

In the 21st century, neuroglial research has entered a period of Renaissance, extending the views of prominent neuroanatomists and neurologists of the...

Toward Precision Neuropsychiatry of Dementia: Neuropsychiatric Symptoms as Multidimensional Clinical Phenotypes.

Neuropsychiatric symptoms (NPS) are the most clinically consequential manifestations of dementia, yet they are frequently underestimated as secondary ...

Cross-tissue immune profiling of APOE ε4 reveals early dysregulation in Alzheimer's disease.

INTRODUCTION: Apolipoprotein E (APOE) ε4 is the strongest genetic risk factor for late-onset Alzheimer's disease (AD), but its contribution to disease...

A strategic pathway for the ethical development of AI tools in dementia care.

Artificial intelligence (AI) is rapidly entering dementia clinical practice, offering opportunities across the care continuum. However, cognitive decl...

Elevated endogenous glucagon-like peptide-1 as a novel Alzheimer's disease biomarker associated with neuroinflammation and Aβ pathology.

BACKGROUND: Alzheimer's disease (AD), a neurodegenerative disorder, is pathologically defined by the accumulation of amyloid-β (Aβ) plaques, hyperphos...

The impact of artificial intelligence-based clinical training on burnout among practitioners treating serious mental illness.

Burnout is a pervasive challenge in the mental health field, particularly among practitioners treating serious mental illness. Although skills-based t...

Using large language models to investigate patients' and caregivers' perceptions on SUDEP: A case study with an online epilepsy population.

BACKGROUND: Sudden Unexpected Death in Epilepsy (SUDEP) is a leading cause of epilepsy-related mortality, yet remains under-communicated in clinical p...

Ultrafast (0.5-Minute) Deep Learning Based Choroid Plexus Segmentation: Links to Cognitive Impairment and Glymphatic Function.

BACKGROUND AND PURPOSE: Choroid plexus volume (CPV) may reflect cognitive impairment and glymphatic dysfunction. However, its clinical use is limited ...

Generative Brain Priors-Driven Self-Regulated and Clinically-Guided Multimodal Fusion for Early Alzheimer's Disease Diagnosis.

Alzheimer's disease (AD) and mild cognitive impairment (MCI) require accurate early diagnosis to support timely clinical intervention and disease mana...

Multitargeted drug strategies for Alzheimer's and Parkinson's diseases.

Neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD), are multifactorial diseases that are characterized by sev...