Romidepsin Exhibits Anti-Esophageal Squamous Cell Carcinoma Activity Through the DDIT4-mTORC1 Pathway

Romidepsin Exhibits Anti-Esophageal Squamous Cell Carcinoma Activity through DDIT4-mTORC1 Pathway Esophageal squamous cell carcinoma (ESCC) is one of the most common human malignancies globally, with high incidence and mortality rates. Given the limited current treatment options, there is an urgent need to develop new effective therapeutic drugs. I...

An Explicit Estimated Baseline Model for Robust Estimation of Fluorophores Using Multiple-Wavelength Excitation Fluorescence Spectroscopy

Research Background Fluorescence spectroscopy is a widely used method for identifying and quantifying fluorescent substances (fluorophores). However, quantifying the fluorophores of interest becomes challenging when the material contains other fluorophores (baseline fluorophores), especially when the emission spectrum of the baseline is not well-de...

Multi-Level Feature Exploration and Fusion Network for Prediction of IDH Status in Gliomas from MRI

Multi-Level Feature Exploration and Fusion Network for Prediction of IDH Status in MRI Background Glioma is the most common malignant primary brain tumor in adults. According to the 2021 World Health Organization (WHO) classification of tumors, genotype plays a significant role in the classification of tumor subtypes, especially the isocitrate dehy...

Normalizing Flow-Based Distribution Estimation of Pharmacokinetic Parameters in Dynamic Contrast-Enhanced Magnetic Resonance Imaging

In modern medical diagnostics and clinical research, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) technology provides significant information regarding tissue pathophysiology. By fitting a Tracer-Kinetic (TK) model, pharmacokinetic (PK) parameters can be extracted from time-series MRI signals. However, these estimated PK parameter...

A Siamese-Transport Domain Adaptation Framework for 3D MRI Classification of Gliomas and Alzheimer’s Diseases

Classification of 3D MRI Gliomas and Alzheimer’s Disease Based on the Siamese-Transport Domain Adaptation Framework Background In computer-aided diagnosis, 3D magnetic resonance imaging (MRI) screening plays a vital role in the early diagnosis of various brain diseases, effectively preventing the deterioration of the condition. Glioma is a common m...

DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG

DeepSleepNet: An Automatic Sleep Stage Scoring Model Based on Single-Channel EEG Background Introduction Sleep has a significant impact on human health, and monitoring sleep quality is crucial in medical research and practice. Typically, sleep experts score sleep stages by analyzing various physiological signals such as electroencephalogram (EEG), ...

Immersive Virtual Reality for the Cognitive Rehabilitation of Stroke Survivors

Immersive Virtual Reality for the Cognitive Rehabilitation of Stroke Survivors

In recent years, Virtual Reality (VR) technology has become increasingly common, with related hardware becoming more affordable. For example, current head-mounted displays (HMDs) on the market not only offer high-resolution displays but also feature precise head and handheld controller tracking. Initially, these technologies were mostly used in the...

Multi-view Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classification

Sleep stage classification is crucial for sleep quality assessment and disease diagnosis. However, existing classification methods still face numerous challenges in handling the spatial and temporal features of time-varying multi-channel brain signals, coping with individual differences in biological signals, and model interpretability. Traditional...

Multi-task Heterogeneous Ensemble Learning-based Cross-subject EEG Classification in Stroke Patients

Multi-task Heterogeneous Ensemble Learning-based Cross-subject EEG Classification in Stroke Patients

Background Introduction Motor Imagery (MI) refers to performing activities through imagination without actual muscle movement. This paradigm is widely used in Brain-Computer Interface (BCI) to decode brain activities into control commands for external devices. Specifically, Electroencephalography (EEG) is widely used in BCI due to its relative affo...

Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics from Surface EMG

Musculoskeletal models have been widely used in biomechanical analysis because they can estimate motion variables that are difficult to measure directly in living organisms, such as muscle forces and joint moments. Traditional physics-driven computational musculoskeletal models can explain the dynamic interactions between neural inputs to muscles, ...