Diagnostic efficacy of combining diffusion-weighted magnetic resonance imaging with serum Mucin 1, Mucin 13, and Mucin 16 in distinguishing borderline from malignant epithelial ovarian tumors.

Aims: To enhance ovarian tumor diagnosis beyond conventional methods, this study explored combining diffusion-weighted magnetic resonance imaging (DWI-MRI) and serum biomarkers (Mucin 1 [MUC1], MUC13, and MUC16) for distinguishing borderline from malignant epithelial ovarian tumors.

Methods: A total of 126 patients, including 71 diagnosed with borderline (BEOTs) and 55 with malignant epithelial ovarian tumors (MEOTs), underwent preoperative DWI-MRI. Region of interest (ROI) was manually drawn along the solid component's boundary of the largest tumor, focusing on areas with potentially the lowest apparent diffusion coefficient (ADC).

Graph Reasoning Module for Alzheimer's Disease Diagnosis: A Plug-and-Play Method.

Recent advances in deep learning have led to increased adoption of convolutional neural networks (CNN) for structural magnetic resonance imaging (sMRI)-based Alzheimer's disease (AD) detection. AD results in widespread damage to neurons in different brain regions and destroys their connections. However, current CNN-based methods struggle to relate spatially distant information effectively.

Controllable Fabrication of Vertical Graphene with Tunable Growth Nature by Remote Plasma-Enhanced Chemical Vapor Deposition.

As an important member of the graphene family, vertical graphene (VG) has broad applications like field emission, energy storage, and sensors owing to its fascinating physical and chemical properties. Among various fabrication methods for VG, plasma enhanced chemical vapor deposition (PECVD) is most employed because of the fast growth rate at relatively low temperature for the high-quality VG. However, to date, relations between growth manner of VG and growth parameters such as growth temperature, dosage of gaseous carbon source, and electric power to generate plasma are still less known, which in turn hinder the massive production of VG for further applications.

Comparing Multi-Dimensional fNIRS Features Using Bayesian Optimization-Based Neural Networks for Mild Cognitive Impairment (MCI) Detection.

The diagnosis of mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD), is essential for initiating timely treatment to delay the onset of AD. Previous studies have shown the potential of functional near-infrared spectroscopy (fNIRS) for diagnosing MCI. However, preprocessing fNIRS measurements requires extensive experience to identify poor-quality segments.

Brown Adipose Tissue and Novel Management Strategies for Polycystic Ovary Syndrome Therapy.

Brown adipose tissue (BAT), a unique tissue, plays a key role in metabolism and energy expenditure through adaptive nonshivering thermogenesis. It has recently become a therapeutic target in the treatment of obesity and metabolic diseases. The thermogenic effect of BAT occurs through uncoupling protein-1 by uncoupling adenosine triphosphate (ATP) synthesis from energy substrate oxidation.

TR-GAN: Multi-Session Future MRI Prediction With Temporal Recurrent Generative Adversarial Network.

Magnetic Resonance Imaging (MRI) has been proven to be an efficient way to diagnose Alzheimer's disease (AD). Recent dramatic progress on deep learning greatly promotes the MRI analysis based on data-driven CNN methods using a large-scale longitudinal MRI dataset. However, most of the existing MRI datasets are fragmented due to unexpected quits of volunteers.

Bilinear neural network with 3-D attention for brain decoding of motor imagery movements from the human EEG.

Deep learning has achieved great success in areas such as computer vision and natural language processing. In the past, some work used convolutional networks to process EEG signals and reached or exceeded traditional machine learning methods. We propose a novel network structure and call it QNet.