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Single-nucleotide polymorphisms in genes associated with the vitamin D pathway related to clinical and therapeutic outcomes of American tegumentary leishmaniasis.
Frontiers in cellular and infection microbiology
Jan 8, 2025
BACKGROUND: The vitamin D pathway contributes to the microbicidal activity of macrophages against infection. In addition to induction of this pathway, interferon-gamma (IFNγ), interleukin (IL)-15, and IL32γ are part of a network of pro-inflammatory ...
EEG microstate analysis and machine learning classification in patients with obsessive-compulsive disorder.
Journal of psychiatric research
Jan 7, 2025
BACKGROUND: Microstate characterization of electroencephalogram (EEG) is a data-driven approach to explore the functional changes and interrelationships of multiple brain networks on a millisecond scale. This study aimed to explore the pathological c...
Enhancing repeatability of follicle counting with deep learning reconstruction high-resolution MRI in PCOS patients.
Scientific reports
Jan 7, 2025
Follicle count, a pivotal metric in the adjunct diagnosis of polycystic ovary syndrome (PCOS), is often underestimated when assessed via transvaginal ultrasonography compared to MRI. Nevertheless, the repeatability of follicle counting using traditio...
Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning.
PloS one
Jan 7, 2025
Polycystic ovary syndrome (PCOS) is a primary endocrine disorder affecting premenopausal women involving metabolic dysregulation. We aimed to screen serum biomarkers in PCOS patients using untargeted lipidomics and ensemble machine learning. Serum fr...
rU-Net, Multi-Scale Feature Fusion and Transfer Learning: Unlocking the Potential of Cuffless Blood Pressure Monitoring With PPG and ECG.
IEEE journal of biomedical and health informatics
Jan 7, 2025
This study introduces an innovative deep-learning model for cuffless blood pressure estimation using PPG and ECG signals, demonstrating state-of-the-art performance on the largest clean dataset, PulseDB. The rU-Net architecture, a fusion of U-Net and...
Interpretable Multi-Branch Architecture for Spatiotemporal Neural Networks and Its Application in Seizure Prediction.
IEEE journal of biomedical and health informatics
Jan 7, 2025
Currently, spatiotemporal convolutional neural networks (CNNs) for electroencephalogram (EEG) signals have emerged as promising tools for seizure prediction (SP), which explore the spatiotemporal biomarkers in an epileptic brain. Generally, these CNN...
Multiscale Spatial-Temporal Feature Fusion Neural Network for Motor Imagery Brain-Computer Interfaces.
IEEE journal of biomedical and health informatics
Jan 7, 2025
Motor imagery, one of the main brain-computer interface (BCI) paradigms, has been extensively utilized in numerous BCI applications, such as the interaction between disabled people and external devices. Precise decoding, one of the most significant a...
Step Width Estimation in Individuals With and Without Neurodegenerative Disease via a Novel Data-Augmentation Deep Learning Model and Minimal Wearable Inertial Sensors.
IEEE journal of biomedical and health informatics
Jan 7, 2025
Step width is vital for gait stability, postural balance control, and fall risk reduction. However, estimating step width typically requires either fixed cameras or a full kinematic body suit of wearable inertial measurement units (IMUs), both of whi...
Spatial Craving Patterns in Marijuana Users: Insights From fMRI Brain Connectivity Analysis With High-Order Graph Attention Neural Networks.
IEEE journal of biomedical and health informatics
Jan 7, 2025
The excessive consumption of marijuana can induce substantial psychological and social consequences. In this investigation, we propose an elucidative framework termed high-order graph attention neural networks (HOGANN) for the classification of Marij...
TCNN-KAN: Optimized CNN by Kolmogorov-Arnold Network and Pruning Techniques for sEMG Gesture Recognition.
IEEE journal of biomedical and health informatics
Jan 7, 2025
Surface electromyography (sEMG) is a non-invasive technique that records the electrical signals generated by muscle activity. sEMG signals are widely used in the field of biomedical and health informatics for diagnosing and monitoring neuromuscular d...
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