MiR-452 negatively regulates osteoblast differentiation in periodontal ligament stem cells by targeting the polycomb-group protein, BMI1

Purpose: To determine whether miR-452 regulates osteoblast differentiation (OD) in human periodontal ligament stem cells (hPDLSCs) by targeting polycomb-group protein BMI1. Methods: hPDLSCs were stimulated to differentiate upon treatment with mineralization liquid. Quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting were used to measure mRNA and protein expressions, respectively. Alkaline phosphatase (ALP) activity and Alizarin red staining were used to determine the osteogenic differentiation (OD) of hPDLSCs. The bioinformatics software, Targetscan, was used to predict the potential target of miR-452, while luciferase assay, qRT- PCR, and western blot were employed to verify the target gene of miR-452, BMI1. Results: MiR-452 was downregulated during the OD of hPDLSCs, but miR-452 overexpression inhibited the OD of hPDLSCs. BMI1 was identified as a direct target gene of miR-452 during the OD of hPDLSCs, while miR-452 overexpression correlated inversely with BMI1 expression during OD of hPDLSCs. Conclusion: Overexpression of miR-452 suppresses the OD of hPDLSCs by targeting BMI1.This study may provide potential diagnostic and therapeutic basis for OD in hPDLSCs

Recent developments of neuroprotective agents for degenerative retinal disorders

Retinal degeneration is a debilitating ocular complication characterized by the progressive loss of photoreceptors and other retinal neurons, which are caused by a group of retinal diseases affecting various age groups, and increasingly prevalent in the elderly. Age-related macular degeneration, diabetic retinopathy and glaucoma are among the most common complex degenerative retinal disorders, posing significant public health problems worldwide largely due to the aging society and the lack of effective therapeutics. Whilst pathoetiologies vary, if left untreated, loss of retinal neurons can result in an acquired degeneration and ultimately severe visual impairment. Irrespective of underlined etiology, loss of neurons and supporting cells including retinal pigment epithelium, microvascular endothelium, and glia, converges as the common endpoint of retinal degeneration and therefore discovery or repurposing of therapies to protect retinal neurons directly or indirectly are under intensive investigation. This review overviews recent developments of potential neuroprotectants including neuropeptides, exosomes, mitochondrial-derived peptides, complement inhibitors, senolytics, autophagy enhancers and antioxidants either still experimentally or in clinical trials. Effective treatments that possess direct or indirect neuroprotective properties would significantly lift the burden of visual handicap

Treatment of diabetic retinopathy through neuropeptide Y-mediated enhancement of neurovascular microenvironment

Diabetic retinopathy (DR) is one of the most severe clinical manifestations of diabetes mellitus and a major cause of blindness. DR is principally a microvascular disease, although the pathogenesis also involves metabolic reactive intermediates which induce neuronal and glial activation resulting in disruption of the neurovascular unit and regulation of the microvasculature. However, the impact of neural/glial activation in DR remains controversial, notwithstanding our understanding as to when neural/glial activation occurs in the course of disease. The objective of this study was to determine a potential protective role of neuropeptide Y (NPY) using an established model of DR permissive to N‐methyl‐D‐aspartate (NMDA)‐induced excitotoxic apoptosis of retinal ganglion cells (RGC) and vascular endothelial growth factor (VEGF)‐induced vascular leakage. In vitro evaluation using primary retinal endothelial cells demonstrates that NPY promotes vascular integrity, demonstrated by maintained tight junction protein expression and reduced permeability in response to VEGF treatment. Furthermore, ex vivo assessment of retinal tissue explants shows that NPY can protect RGC from excitotoxic‐induced apoptosis. In vivo clinical imaging and ex vivo tissue analysis in the diabetic model permitted assessment of NPY treatment in relation to neural and endothelial changes. The neuroprotective effects of NPY were confirmed by attenuating NMDA‐induced retinal neural apoptosis and able to maintain inner retinal vascular integrity. These findings could have important clinical implications and offer novel therapeutic approaches for the treatment in the early stages of DR

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation. However, anomaly detection for these seasonal KPIs with various patterns and data quality has been a great challenge, especially without labels. In this paper, we proposed Donut, an unsupervised anomaly detection algorithm based on VAE. Thanks to a few of our key techniques, Donut greatly outperforms a state-of-arts supervised ensemble approach and a baseline VAE approach, and its best F-scores range from 0.75 to 0.9 for the studied KPIs from a top global Internet company. We come up with a novel KDE interpretation of reconstruction for Donut, making it the first VAE-based anomaly detection algorithm with solid theoretical explanation.Comment: 12 pages (including references), 17 figures, submitted to WWW 2018: The 2018 Web Conference, April 23--27, 2018, Lyon, France. The contents discarded from the conference version due to the 9-page limitation are also included in this versio

Arsenic Trioxide Inhibits Cell Growth and Induces Apoptosis through Inactivation of Notch Signaling Pathway in Breast Cancer

Arsenic trioxide has been reported to inhibit cell growth and induce apoptotic cell death in many human cancer cells including breast cancer. However, the precise molecular mechanisms underlying the anti-tumor activity of arsenic trioxide are still largely unknown. In the present study, we assessed the effects of arsenic trioxide on cell viability and apoptosis in breast cancer cells. For mechanistic studies, we used multiple cellular and molecular approaches such as MTT assay, apoptosis ELISA assay, gene transfection, RT-PCR, Western blotting, and invasion assays. For the first time, we found a significant reduction in cell viability in arsenic trioxide-treated cells in a dose-dependent manner, which was consistent with induction of apoptosis and also associated with down-regulation of Notch-1 and its target genes. Taken together, our findings provide evidence showing that the down-regulation of Notch-1 by arsenic trioxide could be an effective approach, to cause down-regulation of Bcl-2, and NF-κB, resulting in the inhibition of cell growth and invasion as well as induction of apoptosis. These results suggest that the anti-tumor activity of arsenic trioxide is in part mediated through a novel mechanism involving inactivation of Notch-1 and its target genes. We also suggest that arsenic trioxide could be further developed as a potential therapeutic agent for the treatment of breast cancer

Xia, J.; et al., Arsenic Trioxide Inhibits Cell Growth and Induces Apoptosis through Inactivation of Notch Signaling Pathway in Breast Cancer. Int. J. Mol. Sci. 2012, 13, 9627–9641

The authors wish to change Figure 5D of the paper published in IJMS [1]. In Figure 5D, the bands for NF-κB and Bcl-2 are similar with Notch-1 bands. The authors have carefully checked the original files and found that it is an inadvertent mistake in the published version of Figure 5D. Figure 5 is revised as follows. The authors would like to apologize for any inconvenience caused to the readers by these changes.[...

Lessons from Nature for Carbon‐Based Nanoarchitected Metamaterials

Bioinspired materials often achieve superior mechanical properties owing to their microscale architectures that resemble design motifs in biological materials. The bioinspired architectures can be extended to nanoscale, where carbon‐based materials, including graphene and carbon nanotubes, are excellent candidates as building blocks. This study introduces carbon‐based nanoarchitected metamaterials inspired by seven biological design motifs, i.e., cellular, gradient, tubular, fibrous, helicoidal, suture, and layered structures. Numerical studies based on molecular dynamics simulation along with continuum‐based finite element analysis are conducted for each bioinspired design to examine the unique mechanical properties, namely specific stiffness, specific strength, failure strain, and specific energy absorption, under tensile/shear loading conditions. Different deformation and failure mechanisms found by molecular simulation and continuum mechanics are discussed. The numerical results show that the mechanical properties of the introduced bioinspired and carbon‐based nanoscale designs may surpass the performance of the conventional carbon‐based counterparts. The developed nanoarchitected metamaterials demonstrate instances of possibilities for filling the empty regions in the Ashby charts to attain lightweight advanced materials that can also break the trade‐off between strength and failure strain. These findings impart lessons from the constitutive structure of biological materials to form the next generation of multifunctional architected metamaterials with rationally designed nano‐architectures

UCoL: Unsupervised Learning of Discriminative Facial Representations via Uncertainty-Aware Contrast

This paper presents Uncertainty-aware Contrastive Learning (UCoL): a fully unsupervised framework for discriminative facial representation learning. Our UCoL is built upon a momentum contrastive network, referred to as Dual-path Momentum Network. Specifically, two flows of pairwise contrastive training are conducted simultaneously: one is formed with intra-instance self augmentation, and the other is to identify positive pairs collected by online pairwise prediction. We introduce a novel uncertainty-aware consistency K-nearest neighbors algorithm to generate predicted positive pairs, which enables efficient discriminative learning from large-scale open-world unlabeled data. Experiments show that UCoL significantly improves the baselines of unsupervised models and performs on par with the semi-supervised and supervised face representation learning methods