Neurology

Point positioning and counting network: A deep learning-based method for automatic axon counting.

Accurate quantification of axon density is pivotal in optic nerve-related studies, as axonal degeneration is a hallmark of numerous neurological disorders. This study introduces the Point Positioning and Counting Network (PPCNet), a novel point-annotation-based deep learning framework that overcomes the limitations of manual counting and existing automated tools. PPCNet integrates a VGG16 backbone...

Explicable intensity-aware 3D cerebrovascular segmentation with planar representation.

Cerebrovascular segmentation provides valuable cues for cerebrovascular diseases. Deep learning has achieved remarkable success in cerebrovascular segmentation, but relies on colossal computing power. To address existing challenges, we studied the intensity characteristics in cerebrovascular imaging and proposed an explicable intensity-aware cerebrovascular segmentation (EI-Seg) with 3D and tri-pl...

Establishing a relationship between iron-based blood measures and structural brain changes using neural networks in UK Biobank.

BACKGROUND: Disturbance of iron homeostasis in both the brain and blood is linked to cognitive impairment and neurodegenerative diseases. Investigatio...

Artificial intelligence and multiomics integration for Parkinson's disease drug development.

Recent advances underscore the potential of integrating multiomics, such as genomics, transcriptomics, proteomics, and metabolomics, and artificial in...

Behavioral screening defines the molecular Parkinsonism-related subgroups in Drosophila.

Parkinson's disease (PD) and related familial Parkinsonism are defined by motor dysfunction, but the specific upstream molecular causes of these clini...

HIF3A-mediated aberrant activation of TXNIP promotes Alzheimer's disease progression.

Oxidative stress (OS) is a hallmark of Alzheimer's disease (AD), yet the cell type-specific mechanisms remain unclear. We analyzed a single-cell RNA s...

Distinct Prescription Patterns Emerge Years Before ALS Diagnosis: A Nationwide Registry-Based Study.

OBJECTIVE: The prodromal phase of amyotrophic lateral sclerosis (ALS) is poorly defined. We aimed to characterize prescription drug use patterns in th...

Contralateral language network integration predicts and protects against naming decline after temporal lobe resection.

OBJECTIVE: Anterior temporal lobe resection (ATLR) is an effective treatment for drug-resistant temporal lobe epilepsy (TLE) but carries a substantial...

Plan A blocks in regional anaesthesia: a narrative review.

INTRODUCTION: The Plan A blocks framework was proposed in 2019 with the aim of promoting a small number of versatile, high-value regional anaesthetic ...

Pediatric SleepNet: A Deep Learning Network for Reliable Pediatric Sleep Staging Across Developmental Stages.

STUDY OBJECTIVES: Manual sleep staging in pediatric populations is challenging due to developmental variability and limited scoring consistency, espec...

Cross-Platform Proteomics and Machine Learning Algorithms Nominate Plasma Biomarkers of Stroke Diagnosis.

BACKGROUND: Blood-based biomarkers for stroke subtyping could improve triage in emergency settings. We used cross-platform proteomics to identify plas...

Self-Powered Multisensor with Electromagnetic and Triboelectric Energy Harvesting for Wireless Monitoring in Every Knee Joint Motion.

Artificial Intelligence of Things (AIoT) enables convenient human health monitoring but consumes massive data and energy. Traditional power supplies a...

Enhanced Prediction of Atrial Fibrillation in Patients With Ischemic Stroke Through Electronic Medical Records and Text Mining: Algorithm Development and Validation.

BACKGROUND: Stroke remains one of the leading causes of mortality and long-term disability worldwide. Atrial fibrillation (AF) is a major and often un...

Towards "greener" strategies in quality control: rapid volatilomics of cocoa based on HS-GC-IMS and machine learning.

Gas chromatography-ion mobility spectrometry (GC-IMS) has emerged as a powerful analytical platform in quality control of food, beverages, and flavor ...

Clinical and pathologic correlations of machine learning quantification of Aβ deposits across 3 brain regions of decedents with Alzheimer disease.

Machine learning enables scalable quantification of neuropathology, offering deeper phenotyping of Alzheimer's disease (AD). In this validation study,...

Quantification of Ki-67 labeling index in pediatric brain tumor immunohistochemistry images.

Quantification of the Kiel 67 (Ki-67) labeling index (LI) is critical for assessing proliferation and prognosis in tumors but manual scoring remains a...

EEG based detection of schizophrenia using asymmetry of entropy and CNN-LSTM model.

Schizophrenia is a severe neuropsychiatric disorder with a significant impact on individual's real-life functioning. It is characterized by abnormal a...

MedFusion-gP-AKI: development and multicenter validation of a machine learning fusion model for early prediction of KDIGO stage 3 acute kidney injury in critically ill traumatic cervicothoracic spinal cord injury patients.

KDIGO stage-3 acute kidney injury (AKI), a life-threatening complication in critically ill patients with traumatic cervicothoracic spinal cord injury ...

Functional brain network modulation following conventional and optimized HD-tDCS in major depressive disorder: A machine learning prediction of treatment response.

BACKGROUND: Major depressive disorder (MDD) is a prevalent and disabling condition that remains inadequately treated in many patients. Transcranial di...

Clinical Impact of Postrecanalization Hemorrhagic Transformation and Its Prediction Using Baseline Noncontrast CT.

BACKGROUND: Hemorrhagic transformation (HT) after recanalization therapy remains a critical concern in acute ischemic stroke management. While severe ...