Neurology

Polygenic and polysocial risk scores in post-type 2 diabetes dementia: Risk stratification and predictive modeling in the UK Biobank cohort.

BackgroundDementia is a common complication of type 2 diabetes mellitus (T2DM), influenced by both genetic susceptibility and social disadvantages. While polygenic risk scores (PRS) have been widely applied to assess genetic vulnerability, the contribution of social determinants and their interaction with genetic risk are less understood.ObjectiveThis study aimed to investigate the independent and...

Enhancing Parkinson's Disease Staging: An Integrative Deep Learning Framework for Multimodal Feature Selection.

Parkinson's disease (PD) affects 10 million globally, with accurate staging essential for personalized treatment planning. Current UPDRS assessments achieve < 93% accuracy due to subjective clinical judgment and unimodal data limitations, failing to capture complex genetic-neuroimaging-clinical interactions driving disease heterogeneity. This study introduces MAFNet, a novel deep learning framewor...

Adaptive AI for Cardiovascular Event Adjudication: Cardiovascular Event Adjudication Across Different Definitions in the ODYSSEY OUTCOMES and EUCLID Trials.

BACKGROUND: Clinical endpoint classification (CEC) is the gold standard for cardiovascular endpoint measurement in clinical trials, but adds time and ...

A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification in neurodegenerative disease.

Quantitative PET underpins diagnosis and treatment monitoring in neurodegenerative disease, yet systematic biases between PET-MRI and PET-CT preclude ...

Dual-energy CT clot and peri-clot radiomics for predicting complete reperfusion and clinical outcome following endovascular therapy in acute ischemic stroke.

OBJECTIVES: Complete reperfusion is the optimal technical goal of endovascular therapy (EVT) and is closely linked to favorable outcomes in acute isch...

Brain volumetric variability and artificial intelligence diagnosis: Importance of race/ethnicity-specific reference standards and social determinant adjustment. A scoping review.

Neuroimaging techniques such as magnetic resonance imaging (MRI) are routinely used in diagnostic radiology to evaluate brain changes associated with ...

Altered degree centrality and resting-state functional connectivity in epilepsy patients with focal to bilateral tonic-clonic seizures.

Focal to bilateral tonic-clonic seizures (FBTCS) is a severe form of seizure associated with various adverse events. This study aimed to characterize ...

Comparative evaluation of magnetic metal oxide nanoparticles for microorganism concentration: From standard strains to cerebrospinal fluid samples using statistical and artificial neural network modeling.

Magnetic nanoparticle based concentration of microorganisms is an effective strategy for improving detection efficiency in low biomass biological samp...

Machine learning-based prediction of postoperative delirium from intraoperative EEG signal alterations in brain functional connectivity.

OBJECTIVES: Postoperative delirium (POD) is a frequent complication following cardiovascular surgery and requires timely intervention. While early ris...

A Machine learning framework for calf muscle fatigue assessment using IMU sensors and EMG-Based labeling.

PURPOSE: Calf muscle fatigue detection is essential for sports performance monitoring, injury prevention, rehabilitation, and tasks involving prolonge...

Isoginkgetin protects against degeneration of ALS motor neurons via regulating the GSK-3β-TFEB signaling axis.

Lysosomal dysfunction is a core pathological driver of neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS). Transcription factor EB...

A Position Statement on Endovascular Models and Effectiveness Metrics for Mechanical Thrombectomy Navigation, on Behalf of the Stakeholder Taskforce for Artificial Intelligence-Assisted Robotic Thrombectomy (START).

Although we are making progress in overcoming infectious diseases and cancer, one of the major medical challenges of the mid-21st century will be the ...

Comparative Mortality Risk of Aripiprazole, Olanzapine, Quetiapine, and Risperidone in Alzheimer's Disease: A Real‑World Retrospective Cohort Study with Treatment Effect Heterogeneity Analysis.

BACKGROUND: Second-generation antipsychotics (SGAs) are frequently used off-label to manage behavioral symptoms in Alzheimer's disease (AD), despite o...

Contrastive representation learning and capsule networks enable accurate identification of ferroptosis-related proteins.

Ferroptosis is a distinct iron-dependent form of regulated cell death that plays critical roles in cancer progression, neurodegenerative disorders, an...

A hybrid deep learning framework for epileptic seizure prediction using scalp and intracranial EEG data.

Epilepsy is a prevalent neurological disorder affecting over 50 million people globally, often impairing quality of life due to unpredictable and recu...

Multiphysics learning with graph neural networks for thrombosis prediction in intracranial aneurysms.

Intracranial aneurysms are pathological dilations of cerebral arteries affecting 3-5% of the population. Their rupture can cause subarachnoid hemorrha...

Machine learning prediction of discharge destination in patients with Parkinson's disease; a nationwide cohort study.

Risk stratification during hospitalization may support real-world discharge planning. We developed and validated machine learning models and an interp...

Circulatory dietary and gut-derived metabolites predict early cognitive decline.

INTRODUCTION: A key component of disease prevention is the identification of at-risk individuals. Microbial dysbiosis in the early stages of cognitive...

Can a novel computer vision-based framework detect head-on-head impacts during a rugby league tackle?

BACKGROUND: Head-on-head impacts are a risk factor for concussion, which is a concern for sports. Computer vision frameworks may provide an automated ...

Quantitative pathology and APOE genotype reveal dementia risk and progression in Lewy body disease.

Dementia in Lewy body diseases (LBD) is common and arises through heterogeneous and incompletely understood pathways. Evidence suggests contributions ...