No abstract

Histone deacetylase (HDAC) is a key enzyme regulating gene expression, including angiogenic cytokine expression. We have previously identified a novel synthetic HDAC inhibitor, known as N-hydroxy-7-(2-naphthylthio) heptanomide (HNHA), with antitumor properties. Here, we investigated the antiangiogenic properties of this small synthetic molecule both in vitro and in vivo. HNHA inhibited nuclear HDAC enzyme activity in human umbilical endothelial cells (HUVECs), an effect accompanied by histone hyperacetylation, p21 upregulation, and cell cycle arrest. HNHA also inhibited vascular endothelial growth factor-induced tube formation and migration of HUVECs, in the absence of any detectable cellular toxicity. Intravitreous injection of HNHA into mice inhibited retinal neovascularization associated with oxygen-induced retinopathy (OIR) and laser-induced choroidal neovascularization (CNV), as determined through fluorescence angiography and vessel counting. Retinas from HNHA-treated animals had a normal histological appearance without any detectable increase in terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling-positive cells, showing that this compound did not induce retinal toxicity. These findings indicate that HNHA has direct antiangiogenic effects and may be an effective strategy for inhibiting the pathological retinal and choroidal neovascularization underlying blinding eye diseases.

서 론2015년 ABSTRACTDrowsy driving is a significant factor in traffic accidents, so driver drowsiness detection system based on computer vision for convenience and safety has been actively studied. However, it is difficult to accurately detect the driver drowsiness in complex background and environmental change. In this paper, it proposed the driver drowsiness detection algorithm to determine whether the driver is drowsy through the measurement standard of a yawn, eyes drowsy status, and nod based on facial features. The proposed algorithm detect the driver drowsiness in the complex background, and it is robust to changes in the environment. The algorithm can be applied in real time because of the processing speed faster. Throughout the experiment, we confirmed that the algorithm reliably detected driver drowsiness. The processing speed of the proposed algorithm is about 0.084ms. Also, the proposed algorithm can achieve an average detection rate of 98.48% and 97.37% for a yawn, drowsy eyes, and nod in the daytime and nighttime.