Neurology

Leveraging pathological markers of lower grade glioma to predict the occurrence of secondary epilepsy, a retrospective study.

Epilepsy is a common manifestation in patients with lower grade glioma (LGG), often presenting as the initial symptom in approximately 70% of cases. This study aimed to identify clinical and pathological markers for epileptic seizures in patients with LGG. Additionally, it sought to develop and validate a machine learning model that enables tailored risk-based anti-seizure treatment. Health record...

Machine learning-based prognostic prediction for acute ischemic stroke using whole-brain and infarct multi-PLD ASL radiomics.

INTRODUCTION: Accurate early prognostic prediction for acute ischemic stroke (AIS) is essential for guiding personalized treatment. This study aimed to assess the predictive value of radiomics features from whole-brain and infarct cerebral blood flow (CBF) images using multiple post-labeling delay arterial spin labeling (multi-PLD ASL), and to develop a prediction model incorporating clinical risk...

D-optimal candexch algorithm-enhanced machine learning UV-spectrophotometry for five-analyte determination in novel anti-glaucoma formulations and ocular fluids: four-color sustainability framework with NQS assessment and UN-SDG integration.

The novel anti-glaucoma ophthalmic preparation containing latanoprost, netarsudil, and benzalkonium chloride has posed a significant challenge due to ...

Machine-learning based strategy identifies a robust protein biomarker panel for Alzheimer's disease in cerebrospinal fluid.

BACKGROUND: The complex pathogenesis of Alzheimer's disease (AD) has resulted in limited current biomarkers for its classification and diagnosis, nece...

Diagnostic value of transcranial ultrasonography and clinical features for Parkinson's disease based on XGBoost model and SHAP visualization analysis: a retrospective study.

OBJECTIVES: Parkinson's disease (PD) requires early diagnosis for optimal management. This study aims to evaluate whether combining transcranial ultra...

The value of triglyceride-glucose index-related indices in evaluating migraine: perspectives from multi-centre cross-sectional studies and machine learning models.

BACKGROUND: This study employed representative data from the U.S. and China to delve into the correlation among migraine prevalence, the triglyceride‒...

Gaucher disease, state of the art and perspectives.

Knowledge about Gaucher disease (GD), considered a model for rare diseases, has considerably increased since its discovery. The pathophysiology of thi...

BrainAGE latent representation clustering is associated with longitudinal disease progression in early-onset Alzheimer's disease.

INTRODUCTION: Early-onset Alzheimer's disease (EOAD) population is a clinically, genetically and pathologically heterogeneous condition. Identifying b...

Transformer attention-based neural network for cognitive score estimation from sMRI data.

Accurately predicting cognitive scores based on structural MRI holds significant clinical value for understanding the pathological stages of dementia ...

Neural networks to estimate multiple sclerosis disability and predict progression using routinely collected healthcare data.

BACKGROUND AND OBJECTIVES: Multiple sclerosis (MS)-related disability is conventionally measured using the Expanded Disability Status Scale (EDSS), wh...

Glycoproteomics Analysis to Identify Potential Biomarkers and Investigate CD14 Monocyte Activation Mechanisms via Exosomal Proteins in Cerebral Infarction Using a Novel Glycogen-Functionalized Nanoprobe.

Cerebral infarction (CI) is a leading cause of disability and mortality, with activated plasma monocytes and altered protein glycosylation identified ...

Artificial Intelligence in Diagnosis and Prognosis of Cognitive Impairment in Parkinson's Disease.

Parkinson's disease (PD), a progressive neurodegenerative disorder, affects millions globally, with cognitive impairment as a significant non-motor co...

Beyond Recanalization: Machine Learning-Based Insights into Post-Thrombectomy Vascular Morphology in Stroke Patients.

Many stroke patients have poor outcomes despite successful endovascular therapy (EVT). We hypothesized that machine learning (ML)-based analysis of va...

Novel technologies for REM sleep behavior disorder detection for home screening in Parkinson's disease and related alpha-synucleinopathies.

Isolated REM sleep behavior disorder (iRBD) is a prodromal marker of Parkinson's disease (PD) and related alpha-synucleinopathies. Identification of R...

Machine learning algorithms for prediction of cerebrospinal fluid leakage after posterior surgery for thoracic ossification of the ligamentum flavum.

To develop and validate a machine-learning (ML) model that pre-operatively predicts cerebrospinal-fluid leakage (CSFL) after posterior decompression f...

Simultaneous determination of 7 thiols associated proteins in lymphoma patients'serum and cerebrospinal fluid by UHPLC-HRMS technique.

Thiol compounds can serve as markers for the antioxidant and prognostic status of lymphoma, playing a crucial role in early tumor diagnosis. However, ...

Individualized structural network deviations predict surgical outcome in mesial temporal lobe epilepsy: a multicentre validation study.

BACKGROUND: Surgical resection is an effective treatment for medically refractory mesial temporal lobe epilepsy (mTLE), however, more than one-third o...

MIA and CD163 as promising diagnostic biomarkers in vascular dementia: A multi-method study combining WGCNA, machine learning with validation in animal models and clinical samples.

Vascular dementia (VaD), the second most common form of dementia, lacks reliable biomarkers for early diagnosis. Here, we integrated weighted gene co-...

Identification of differentially co-expressed genes with lipid metabolism in Parkinson's disease by bioinformatics analysis.

There was increasing evidence that lipid metabolism disorders played a significant part in the maturation of Parkinson's disease (PD). The purpose of ...

Optic nerve sheath diameter in the prognosis of patients with traumatic optic neuropathy: Evaluation and predictive efficacy.

The purpose of this study was to evaluate and predict visual prognosis in patients with traumatic optic neuropathy by measuring the optic nerve sheath...