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( Xiaoyuan Sun ),( Yu Kang ),( Shan Xue ),( Jing Zou ),( Jiabo Xu ),( Daoqiang Tang ),( Hui Qin ) 대한내과학회 2021 The Korean Journal of Internal Medicine Vol.36 No.0
Background/Aims: MicroRNAs (miRNAs) play critical regulatory roles in the pathogenesis of pulmonary fibrosis. The aim of this study was to explore whether miRNA antagomirs could serve as potential therapeutic agents in interstitial lung diseases. Methods: A mouse model of pulmonary fibrosis was established by intratracheal injection of bleomycin (BLM). Using microarray analysis, up-regulated miRNAs were identified during the development of pulmonary fibrosis. miR-155 was chosen as the candidate miRNA. Fifteen mice were then randomized into the following three groups: BLM + antagomiR-155 group, treated with BLM plus intravenously injected with antagomiR-155; BLM group, treated with intratracheal BLM plus phosphate-buffered saline (PBS); and a control group, treated with PBS only. Lung tissues were collected for histopathological analysis, hydroxyproline measurement, and Western blotting. Enzyme-linked immunosorbent assays were used for the measurement of cytokines associated with pulmonary fibrosis. Results: Histological changes and hydroxyproline levels induced by BLM were significantly inhibited by antagomiR-155. The levels of interleukin 4 (IL-4) and transforming growth factor-β (TGF-β) expression were increased after BLM treatment. However, miR-155 silencing decreased the expression of IL-4, TGF-β, and interferon-γ. TGF-β-activated kinase 1/mitogen-activated protein kinase kinase kinase 7 (MAP3K7)-binding protein 2 (TAB2) of the mitogen-activated protein kinase (MAPK) signaling pathway, was activated by BLM and inhibited by in vivo silencing of miR-155 via antagomiR-155. Conclusions: In vivo treatment with antagomiR-155 alleviated the pathological changes induced by BLM and may be a promising therapeutic strategy for pulmonary fibrosis.
Huang, Xinglu,Zhang, Fan,Wang, Yu,Sun, Xiaolian,Choi, Ki Young,Liu, Dingbin,Choi, Jin-sil,Shin, Tae-Hyun,Cheon, Jinwoo,Niu, Gang,Chen, Xiaoyuan American Chemical Society 2014 ACS NANO Vol.8 No.5
<P/><P>Stem-cell-based therapies have attracted considerable interest in regenerative medicine and oncological research. However, a major limitation of systemic delivery of stem cells is the low homing efficiency to the target site. Here, we report a serendipitous finding that various iron-based magnetic nanoparticles (MNPs) actively augment chemokine receptor CXCR4 expression of bone-marrow-derived mesenchymal stem cells (MSCs). On the basis of this observation, we designed an iron-based nanocluster that can effectively label MSCs, improve cell homing efficiency, and track the fate of the cells <I>in vivo</I>. Using this nanocluster, the labeled MSCs were accurately monitored by magnetic resonance imaging and improved the homing to both traumatic brain injury and glioblastoma models as compared to unlabeled MSCs. Our findings provide a simple and safe method for imaging and targeted delivery of stem cells and extend the potential applications of iron-based MNPs in regenerative medicine and oncology.</P>
Finger Vein Recognition Based on Multi-Orientation Weighted Symmetric Local Graph Structure
( Song Dong ),( Jucheng Yang ),( Yarui Chen ),( Chao Wang ),( Xiaoyuan Zhang ),( Dong Sun Park ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.10
Finger vein recognition is a biometric technology using finger veins to authenticate a person, and due to its high degree of uniqueness, liveness, and safety, it is widely used. The traditional Symmetric Local Graph Structure (SLGS) method only considers the relationship between the image pixels as a dominating set, and uses the relevant theories to tap image features. In order to better extract finger vein features, taking into account location information and direction information between the pixels of the image, this paper presents a novel finger vein feature extraction method, Multi-Orientation Weighted Symmetric Local Graph Structure (MOW-SLGS), which assigns weight to each edge according to the positional relationship between the edge and the target pixel. In addition, we use the Extreme Learning Machine (ELM) classifier to train and classify the vein feature extracted by the MOW-SLGS method. Experiments show that the proposed method has better performance than traditional methods.