Alzheimer's Disease

Potent substoichiometric inhibition of alpha-synuclein aggregation by de novo oligomer-binding proteins

The non-amyloid {beta} component (NAC) domain of alpha synuclein (Syn) drives Syn aggregation in Parkinson disease, yet as an intrinsically disordered segment it lacks a stable epitope for conventional ligand discovery. Using deep learning-based protein design, we generated compact single-chain binders that take advantage of the propensity of the NAC domain to adopt an extended {beta}-strand confo...

Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts

Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of real-world clinical data. Left unaddressed, these barriers prevent reliable disease modelling and hinder effective clinical evaluation. Conventional imputation strategies introduce systematic bias, distort inter-feature relationships, and yield overconf...

General-Purpose vs. Domain-Specific Large Language Models in Antibiotic Clinical Decision-Making: A Double-Blind Evaluation with a 2X2 Factorial Design

Background: Antimicrobial resistance poses a major threat to global public health. Large language models (LLMs) offer new possibilities for optimizing...

Explainable Machine Learning Models for Alzheimer's Diagnosis Using Routine and Low-Cost Clinical Data

Emerging as a significant global health challenge, Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that causes memory loss and co...

Exploring Attitudes of Primary Caregivers Towards Pediatric Tissue-Based Research using Large Language Models: Insights from Rural and Urban Community Calls and Surveys

Objectives: Explore the perspectives of primary caregivers towards pediatric tissue-based research participation. Design: Cross-sectional. Setting: Tw...

Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts

Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of rea...

$\mathtt{Q^2SAR}$: overcoming classical bottlenecks in drug discovery via quantum multiple kernel learning

Quantitative Structure-Activity Relationship ($\mathtt{QSAR}$) modeling is a foundational computational methodology in early-stage drug discovery, hea...

Early Prediction of Parkinson's Disease Progression by Integrating Research Cohort and Real-World Data Using Knowledge-Anchored Graph Learning

Parkinson disease (PD) progression is highly heterogeneous. Deeply phenotyped longitudinal research cohorts have enabled characterization of PD progre...

The Patients' Voice in Clostridioides difficile Infection: Large Language Model-Assisted Thematic Analysis of Patient Testimonials

Background. Clostridioides difficile infection (CDI) imposes a burden that extends well beyond the gastrointestinal tract, yet existing outcome measur...

Effect of initiating an ARB- versus ACEI-based regimen on dementia risk, a target trial emulation of 2.5 million US Veterans

Background: Hypertension is a modifiable risk factor for dementia, yet the comparative effectiveness of angiotensin receptor blockers (ARBs) versus an...

Retina-derived Quantitative Biomarkers of Brain Health

Accurate and scalable assessment of quantitative neuroimaging biomarkers, such as white matter hyperintensities (WMH) and hippocampal (HIP) volumes, i...

Data-driven trajectories of atrophy explain clinical heterogeneity across Lewy body diseases

Background: Lewy body diseases (LBD) collectively share alpha-synuclein Lewy pathology, yet present wide clinical heterogeneity, with overlapping moto...

When Color Adds Nothing: A Causal Audit of Channel Triplication in Alzheimer's MRI Classification

Medical imaging pipelines routinely copy single-channel grayscale data into three identical RGB channels before classification, usually without justif...

AT-Attn: Temporal-Aware Cross-Attention for Longitudinal Multimodal Alzheimer's Disease Diagnosis

In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits. Integrating these...

ScaleSurfer: multi-scale anatomical segmentation and parcellation of the human brain

Human brain magnetic resonance imaging (MRI) revolutionized our ability to non-invasively probe individual differences in neuroanatomy. These anatomic...

Robust Longitudinal Dementia Prediction under Systemic Missingness via Hierarchical Fusion and Test-Time Adaptation

Longitudinal dementia progression prediction is essential for clinical decision-making. However, models often degrade on external cohorts due to syste...

CenSynCMB: Centre Maps and Physics-Guided Synthesis for Microbleed Detection

Cerebral microbleeds (CMBs) are MRI markers of small vessel disease and the microbleed component of amyloid related imaging abnormalities (ARIA-H), bu...

Predicting Early Stages Of Alzheimer's Disease And Identifying Key Biomarkers Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies

Alzheimers disease (AD) is a brain disorder that develops slowly and mainly affects memory, thinking, language, and daily activities. It is one of the...

NeuroBridge: Bridging Multi-Task MRI Knowledge for Neurodegenerative Disease Diagnosis

INTRODUCTION: Accurate MRI-based identification of Alzheimer's disease (AD), mild cognitive impairment (MCI), and related dementias remains challengin...

Attitudes of People Living with Dementia and their Carers towards the use of Generative Artificial Intelligence to inform Structured Medication Reviews

Background: Polypharmacy is common in people living with dementia (PLwD) and associated with adverse outcomes. Although Structured Medication Reviews ...