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- 저자 : ( Zhijing Yang ) (검색결과 3 건)
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Flavonoids in Resina Draconis protect against pulmonary fibrosis via the TGF‑β1/NOTCH1 pathway
Liteng Yang,Xin Liu,Ning Zhang,Gaohui Wu,Lifang Chen,Jingyi Xu,Xi Ren,Xiaoming Jiang,Zhijing Huang 대한독성 유전단백체 학회 2020 Molecular & cellular toxicology Vol.16 No.2
Background It is known that flavonoids in Resina Draconis (FRD) have anti-inflammatory and analgesic effects, but the function and mechanisms of FRD against pulmonary fibrosis remain unknown. Objective The study aimed to study the effect and mechanism of FRD on pulmonary fibrosis. Methods Pulmonary fibroblasts were isolated and identified. After treatment with transforming growth factor (TGF)-β1 and FRD-containing serum, expressions of TGF-β1, Jagged1, Notch1, alpha-smooth muscle actin, and collagen I were examined using real-time quantitative PCR and Western blot. Besides, the related proteins were verified in rats with bleomycin-induced pulmonary fibrosis. Results We successfully isolated and identified pulmonary fibroblasts and proved that FRD-containing serum inhibits proliferation and downregulates Notch1 expression in TGF-β1-induced fibroblasts. Moreover, our results indicate that FRD might alleviate pulmonary fibrosis via the Jagged1/Notch1 signaling pathways in vivo. Conclusion Flavonoids in Resina Draconis might play a key role in pulmonary fibrosis via critical pathways, especially the TGF-β1 and NOTCH1 signaling pathways.
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Yumeng Zhao,Bin Yang,Xinyu Hao,Zhijing Zhao 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2015 NANO Vol.10 No.6
CeOx/C supported PtCu thin film catalysts were prepared by ion beam sputtering (IBS) and subsequently annealed at 400℃ under vacuum environment and electrochemically dealloyed. Scanning transmission electronic microscope (STEM) and atomic force microscope (AFM) characterizations show that the surface of post-processed catalyst presents nanoporous structure and has a high root mean square roughness ( RMS=13.9 nm). Electrochemical measurements indicate that the post-processed PtCu – CeOx/C catalyst shows higher catalytic activity towards hydrogen evolution reaction than pure Pt/C. While inductively coupled plasma atomic emission spectroscopy (ICP-AES) analysis displays that the platinum (Pt) loading of the post-processed PtCu – CeOx/C is 0.1192 mg/cm2, decreasing by 20% compare to pure Pt/C (0.1490 mg/cm2). X-ray photoelectron spectroscopy (XPS) analysis confirms that the surface of post-processed PtCu – CeOx/C enrich Pt and analyzes the chemical valence of Pt element using depth profiling technology. It can be inferred that the enhancement in catalytic property is attributed to the combined action between geometric structure effect and electronic modification effect of Pt atoms from CeOx support.
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Spectral Clustering with Sparse Graph Construction Based on Markov Random Walk
( Jiangzhong Cao ),( Pei Chen ),( Bingo Wing-kuen Ling ),( Zhijing Yang ),( Qingyun Dai ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.7
Spectral clustering has become one of the most popular clustering approaches in recent years. Similarity graph constructed on the data is one of the key factors that influence the performance of spectral clustering. However, the similarity graphs constructed by existing methods usually contain some unreliable edges. To construct reliable similarity graph for spectral clustering, an efficient method based on Markov random walk (MRW) is proposed in this paper. In the proposed method, theMRW model is defined on the raw k-NN graph and the neighbors of each sample are determined by the probability of the MRW. Since the high order transition probabilities carry complex relationships among data, the neighbors in the graph determined by our proposed method are more reliable than those of the existing methods. Experiments are performed on the synthetic and real-world datasets for performance evaluation and comparison. The results show that the graph obtained by our proposed method reflects the structure of the data better than those of the state-of-the-art methods and can effectively improve the performance of spectral clustering.
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