A model for epileptic EEG detection and recognition based on Multi-Attention mechanism and Spatiotemporal.

In the field of neuroscience, epilepsy is a chronic non-communicable brain disease that affects approximately 50 million people worldwide. Electroencephalography (EEG) has become a key tool in detecting and characterizing human neurological diseases such as epilepsy. This rapid and accurate diagnosis allows doctors to provide timely and effective treatment to patients, significantly reducing the frequency of future seizures and the risk of related complications, which is crucial for ensuring the long-term health and quality of life of patients.

The role of genetic testing in evaluating surgical outcomes for pediatric focal cortical dysplasia associated with NPRL3 variant.

This case report describes a 5-year-and-10-month-old female patient who developed sleep-related hypermotor epilepsy, at the age of 2, exhibiting various forms of seizures since the age of 2. Initially, the seizures were controlled for one year with multiple anti-seizure medications; however, symptoms recurred when the patient was 3 years and 5 months old, leading to an increased seizure frequency and a poor response to combined drug therapy. Long-term video-EEG revealed discharges originating from the frontal lobe, while MRI and PET-CT scans indicated FCD in the left frontal region.

CaMK2A/CREB pathway activation is associated with enhanced mitophagy and neuronal apoptosis in diabetic retinopathy.

Diabetic retinopathy (DR) is a common complication of diabetes mellitus, characterized by progressive neurodegeneration and vision impairment. The Ca2+/calmodulin-dependent protein kinase II alpha (CaMK2A) and cAMP response element-binding protein (CREB) signaling pathway has been implicated in various neurological disorders. However, its role in DR pathogenesis remains elusive.

EEG detection and recognition model for epilepsy based on dual attention mechanism.

In the field of clinical neurology, automated detection of epileptic seizures based on electroencephalogram (EEG) signals has the potential to significantly accelerate the diagnosis of epilepsy. This rapid and accurate diagnosis enables doctors to provide timely and effective treatment for patients, significantly reducing the frequency of future epileptic seizures and the risk of related complications, which is crucial for safeguarding patients' long-term health and quality of life. Presently, deep learning techniques, particularly Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs), have demonstrated remarkable accuracy improvements across various domains.

Zinc finger transcription factor ZFP1 is associated with growth, conidiation, osmoregulation, and virulence in the Polygonatum kingianum pathogen Fusarium oxysporum.

Rhizome rot is a destructive soil-borne disease of Polygonatum kingianum and adversely affects the yield and sustenance of the plant. Understanding how the causal fungus Fusarium oxysporum infects P. kingianum may suggest effective control measures against rhizome rot.

Seizure, Motor, and Cognitive Outcomes After Epilepsy Surgery for Patients With Sturge-Weber Syndrome: Results From a Multicenter Study.

Background And Objectives: Surgery is widely performed for refractory epilepsy in patients with Sturge-Weber syndrome (SWS), but reports on its effectiveness are limited. This study aimed to analyze seizure, motor, and cognitive outcomes of surgery in these patients and to identify factors associated with the outcomes.

Methods: This was a multicenter retrospective observational study using data from patients with SWS and refractory epilepsy who underwent epilepsy surgery between 2000 and 2020 at 16 centers throughout China.

CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition.

EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input mode, which cannot obtain multiple feature information, and cannot meet the requirements of intelligent and high real-time brain computer interface. And because the EEG signal is nonlinear, the traditional methods of time domain or frequency domain are not suitable.