Dementia

Large Language Model-Driven Prioritization of Alzheimer’s Disease Drug Targets Across Multidimensional Criteria

Large language models (LLMs) offer new opportunities to synthesize the vast and heterogeneous biomedical literature, yet their potential to support drug target prioritization in complex diseases such as Alzheimer’s disease (AD) remains largely unexplored. Here, we introduce an LLM-driven framework that evaluates and ranks AD therapeutic targets across six criteria central to pharmaceutical decisio...

Relationship of the Microbiome with Neurodegenerative Diseases: Development of AI Tools to Detect Alzheimer’s

This study aimed to develop an artificial intelligence (AI) algorithm capable of distinguishing Alzheimer’s disease (AD) from healthy patients using gut microbiome metagenomics data. To do so, 16S rRNA gene and Whole Genome Shotgun (WGS) datasets available in the literature were utilised. Data was pre-processed and filtered. Then, an initial analysis was done to study classical parameters of micro...

Acoustic Analysis of Primary Care Patient-provider Conversations to Screen for Cognitive Impairment

Cognitive impairment (CI) is often under detected in primary care due to time and resource constraints. Passive analysis of clinical dialogue may offe...

A foundation model for mapping the phenomic and genetic landscape of cerebral small vessel disease biomarkers

Cerebral small vessel disease (CSVD) is a leading cause of age-related cognitive decline and neurological disorders, yet its precise characterization ...

Clustering high-cost patients in England using machine learning: a population-based cohort study

To identify clusters of high-cost patients in England based on diagnoses and sociodemographic characteristics to inform targeted population health man...

A Pathway-Based Machine Learning Approach Identifies Region-Specific Markers and Patterns in Alzheimer’s Disease Patients Based on Spatial and Severity Metadata

Alzheimer’s disease (AD) exhibits profound spatial heterogeneity in its molecular and pathological features, yet the basis of this regional selectivit...

Machine Learning-Based Prediction of Cell-type Resolved Brain eQTLs Enhances Discovery of Variants Explaining Alzheimer’s Disease Heritability

The majority of causal genome-wide association studies (GWAS) variants for Alzheimer’s disease (AD) are believed to reside in noncoding regions of the...

Multiscale-Multistage Temporal Convolutional Network for EEG-Based Mild Cognitive Impairment Detection

Mild cognitive impairment (MCI) is an intermediate stage between normal ageing and dementia, with affected individuals at a higher risk of progressing...

Antidepressant Use at the Threshold: using electronic health records to characterise people prescribed antidepressants around the time of dementia diagnosis

Antidepressant use is common in people with dementia. Antidepressants may be started to manage symptoms of dementia, rather than depressive and anxiet...

A Comprehensive Investigation of Machine Learning Practices in Predictive Modeling of Alzheimer’s Disease and Related Dementias using Multisite Real-world Electronic Health Records

The irreversible progression and profound societal impact of Alzheimer’s disease and related dementias (AD/ADRD) underscore the pressing need for earl...

Brain Region-Centered MultiModal Hypergraph Fusion for MCI Conversion Prediction

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, with mild cognitive impairment (MCI) as its prodromal stage. Accurate MCI conver...

Dementia etiology classification using NULISA plasma biomarkers and machine learning

Accurate antemortem differentiation among dementia etiologies remains challenging, particularly for atypical or mixed clinical presentations. Multiple...

Alzheimer's disease detection based on large language model prompt engineering

In light of the growing proportion of older individuals in our society, the timely diagnosis of Alzheimer's disease has become a crucial aspect of h...

The translational power of Alzheimer's-based organoid models in personalized medicine: an integrated biological and digital approach embodying patient clinical history.

Alzheimer's disease (AD) is a complex neurodegenerative condition characterized by a multifaceted interplay of genetic, environmental, and pathologica...

Identification and validation of pyroptosis-related genes in Alzheimer's disease based on multi-transcriptome and machine learning.

BACKGROUND: Alzheimer's disease (AD) progression is characterized by persistent neuroinflammation, where pyroptosis-an inflammatory programmed cell de...

Nanocarrier-based targeted drug delivery for Alzheimer's disease: addressing neuroinflammation and enhancing clinical translation.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, amyloid-beta (Aβ) aggregation, tau pathology,...

Fully automated MRI-based analysis of the locus coeruleus in aging and Alzheimer's disease dementia using ELSI-Net.

INTRODUCTION: The locus coeruleus (LC) is linked to the development and pathophysiology of neurodegenerative diseases such as Alzheimer's disease (AD)...

Association of RDoC dimensions with brain transcriptional profiles in Alzheimer's disease.

INTRODUCTION: Neuropsychiatric symptoms are common in people with Alzheimer's disease (AD) across all severity stages. Their heterogeneous presentatio...

Comprehensive Machine Learning-Based Prediction Model for Delirium Risk in Older Patients with Dementia: Risk Factors Identification.

BACKGROUND: Delirium superimposed on dementia (DSD) is a severe complication in older adults with dementia, marked by fluctuating cognition, inattenti...

Artificial intelligence and omics-based autoantibody profiling in dementia.

INTRODUCTION: Dementia is a neurodegenerative syndrome marked by the accumulation of disease-specific proteins and immune dysregulation, including aut...