The Correlation Between Major Adverse Cardiovascular and Cerebrovascular Events (MACCE) and miR-142-3p in Maintenance Hemodialysis Patients With End-Stage Renal Disease.

Background: Patients with end-stage renal disease (ESRD) on maintenance hemodialysis (MHD) are at high risk for major adverse cardiovascular and cerebrovascular events (MACCE), which are prone to be detrimental to patients' lives. Identifying risk factors for MACCE can help target measures to prevent or reduce the occurrence of MACCE.

Objective: The aim was to investigate the correlation between miR-142-3p and MACCE in ESRD patients on MHD and to provide a new predictor for MACCE occurrence.

A robust approach to 3D neuron shape representation for quantification and classification.

We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing "curve" skeletons which can only be applied for tubular shapes.

Geographic Atrophy Segmentation Using Multimodal Deep Learning.

Purpose: To examine deep learning (DL)-based methods for accurate segmentation of geographic atrophy (GA) lesions using fundus autofluorescence (FAF) and near-infrared (NIR) images.

Methods: This retrospective analysis utilized imaging data from study eyes of patients enrolled in Proxima A and B (NCT02479386; NCT02399072) natural history studies of GA. Two multimodal DL networks (UNet and YNet) were used to automatically segment GA lesions on FAF; segmentation accuracy was compared with annotations by experienced graders.

3D Neuron Morphology Analysis.

We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing"curve"skeletons which can only be applied for tubular shapes.

Segmentation, tracking, and sub-cellular feature extraction in 3D time-lapse images.

This paper presents a method for time-lapse 3D cell analysis. Specifically, we consider the problem of accurately localizing and quantitatively analyzing sub-cellular features, and for tracking individual cells from time-lapse 3D confocal cell image stacks. The heterogeneity of cells and the volume of multi-dimensional images presents a major challenge for fully automated analysis of morphogenesis and development of cells.

Corrigendum: Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.

[This corrects the article DOI: 10.3389/fnins.2019.

Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.

The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy.

The lncRNA PVT1 regulates nasopharyngeal carcinoma cell proliferation via activating the KAT2A acetyltransferase and stabilizing HIF-1α.

Long noncoding RNAs (lncRNAs) play important roles in regulating the development and progression of many cancers. However, the clinical significance of specific lncRNAs in the context of nasopharyngeal carcinoma (NPC) and the molecular mechanisms by which they regulate this form of cancer remain largely unclear. In this study we found that the lncRNA PVT1 was upregulated in NPC, and that in patients this upregulation was associated with reduced survival.

LncRNA DANCR upregulates PI3K/AKT signaling through activating serine phosphorylation of RXRA.

Conventional therapies and novel molecular targeted therapies against breast cancer have gained great advances over the past two decades. However, poor prognosis and low survival rate are far from expectation for improvement, particularly in patients with triple negative breast cancer (TNBC). Here, we found that lncRNA DANCR was significantly overregulated in TNBC tissues and cell lines compared with normal breast tissues or other type of breast cancer.

Enhanced Electrochemical Performances of BiO/rGO Nanocomposite via Chemical Bonding as Anode Materials for Lithium Ion Batteries.

Bismuth oxide/reduced graphene oxide (termed BiO@rGO) nanocomposite has been facilely prepared by a solvothermal method via introducing chemical bonding that has been demonstrated by Raman and X-ray photoelectron spectroscopy spectra. Tremendous single-crystal BiO nanoparticles with an average size of ∼5 nm are anchored and uniformly dispersed on rGO sheets. Such a nanostructure results in enhanced electrochemical reversibility and cycling stability of BiO@rGO composite materials as anodes for lithium ion batteries in comparison with agglomerated bare BiO nanoparticles.