Neurology

Causal inference in real-world dementia research: a systematic review protocol.

BACKGROUND: Dementia presents complex challenges for causal inference due to its multifactorial aetiology and slow, heterogeneous progression. Randomized controlled trials are often limited in their potential to fully address these challenges because of ethical and practical constraints. As the field evolves, observational studies incorporating advanced causal inference methods are increasingly us...

Artificial intelligence in sleep medicine I: Diagnosis, treatment, care, and research.

Artificial intelligence (AI) is transforming sleep medicine (SM) through improved diagnostics, therapeutics, and research. AI enhances diagnostic accuracy, treatment personalization, workflow efficiency. However, implementation requires careful validation and oversight. This review explores AI applications across the sleep disorders spectrum. AI in diagnostic SM covers applications including polys...

NeuroBACE-ML: A reliability-aware screening framework for high-throughput prioritization of potent BACE1 inhibitors.

Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) is a key enzyme in amyloid-β generation and remains an important target in Alzheimer's d...

Proteomics for precision nutrition: current evidence and future directions.

PURPOSE OF REVIEW: This review critically evaluates proteomics research applied to clinical nutrition and metabolism published between mid-2024 and ea...

Multiparametric AI-based perfusion analysis outperforms Tmax thresholding for critically hypoperfused tissue estimation in acute ischemic stroke undergoing admission MRI.

OBJECTIVE: Perfusion imaging is used to estimate critically hypoperfused tissue in acute ischemic stroke (AIS), commonly using threshold-based methods...

Persistent white matter disruption underlies apparent functional normalization in intractable temporal lobe epilepsy: Evidence from multimodal MRI.

BACKGROUND: Intractable temporal lobe epilepsy (ITLE) poses ongoing therapeutic challenges due to resistance to antiseizure medications and limited im...

SRSF1 as a promising biomarker and key player in the pathogenesis of Alzheimer's disease.

Given the unclear pathogenesis and insidious progression of Alzheimer's disease (AD), the aim of the present study was to identify reliable diagnostic...

Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review).

Acute leukemias (ALs) are a diverse group of hematological malignancies characterized by the abnormal proliferation of immature cells. Microscopic obs...

Speech-based digital biomarkers for early etiological stratification of Alzheimer's disease and frontotemporal degeneration: a biomarker-confirmed prospective study.

BACKGROUND: Early differentiation between Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) is a prerequisite for secondary preven...

Evaluation of Brain Age on Magnetic Resonance Imaging Using Machine Learning Techniques in Patients With Hearing Loss.

OBJECTIVES: Hearing impairment is strongly linked to cognitive decline, with individuals having mild-to-severe hearing loss estimated as having a twof...

Early Predictors of Long-Term Outcomes in Pediatric "Mild" Traumatic Brain Injury: A Machine Learning Approach.

While most children recover from pediatric "mild" traumatic brain injury (pmTBI), up to one-third experience persisting symptoms after concussion (PSa...

Machine Learning-Driven Ensemble Screening of Multitarget Kinase Inhibitors for Tauopathy-Associated Neurodegeneration Using All-Atom and Steered MD Simulations.

Tauopathies arise when normal functions of the tau protein in axonal transport and neuronal maintenance are disrupted by an imbalance between kinases ...

Transformation of Parkinson's care through SMART-PD (Self-Management, Remote monitoring and Timely review for Parkinson's Disease): Care pathway mapping with clinical experts.

Purpose of researchSMaRT-PD is a clinical decision support system (CDSS) for the home-based management and care of Parkinson's disease. It utilises re...

Personalizing treatment of pancreatitis-associated chronic pain: the need for an integrated omics approach.

BACKGROUND: Chronic pancreatitis (CP) is a progressive inflammatory disorder characterized by debilitating chronic pain and substantial healthcare bur...

Artificial intelligence in cancer survivorship: evaluating ChatGPT's performance in rehabilitation-related queries.

PURPOSE: This study aimed to evaluate the performance of ChatGPT in responding to patient-centered queries related to cancer rehabilitation. It examin...

Cogninet: an explainable deep learning model for multi-class MRI-based Alzheimer's disease staging.

PURPOSE: Alzheimer's Disease (AD) is a neurodegenerative condition which presents significant challenges in early diagnosis and clinical decision-maki...

Enhancing generalizability in classification of peripheral neural recordings with graph neural network.

The peripheral nervous system plays a crucial role in facilitating communication between biological systems. However, decoding neural signals from per...

Integrating metacognitive mechanisms optimizes EEG generative models via hierarchical regularization.

Obtaining sufficient electroencephalography (EEG) signals for training deep neural networks (DNNs) in brain-computer interfaces (BCIs) is challenging ...

DeepCRI: Real-time EEG-based Prognostication after Cardiac Arrest.

Accurate prediction of neurological outcome after cardiac arrest is essential for guiding intensive care decisions. Electroencephalography (EEG) suppo...

Incorporating normal periventricular changes for enhanced pathological white matter hyperintensity segmentation: on multiclass deep learning approaches.

White matter hyperintensities (WMH) detected on FLAIR MRI sequences serve as important biomarkers for cerebrovascular pathology, correlating with incr...