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RISS 인기검색어
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- 저자 : Guangwei Lin (검색결과 5 건)
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Lin, Aiqun,Wu, Guangwei,Gu, Qianqun,Zhu, Tianjiao,Li, Dehai 대한약학회 2014 Archives of Pharmacal Research Vol.37 No.7
Chemical investigation of an Antarctic deep-sea derived fungus Penicillium sp. PR 19 N-1 yielded five new eremophilane-type sesquiterpenes 1-5 and a new rare lactam-type eremophilane 6, together with three known compounds 7-9. The structures of these diverse sesquiterpenes were determined by extensive NMR and mass spectroscopic analyses. Compounds 1, 2, 4-6, 8 and 9 were evaluated for their cytotoxities against HL-60 and A-549 human cancer cell lines, and 5 was the most active one with $IC_{50}$ value of $5.2{\mu}M$ against the A-549 cells.
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Aiqun Lin,Guangwei Wu,Qian Qun Gu,Tian-Jiao Zhu,De-Hai Li 대한약학회 2014 Archives of Pharmacal Research Vol.37 No.7
Chemical investigation of an Antarctic deepseaderived fungus Penicillium sp. PR19 N-1 yielded fivenew eremophilane-type sesquiterpenes 1–5 and a new rarelactam-type eremophilane 6, together with three knowncompounds 7–9. The structures of these diverse sesquiterpeneswere determined by extensive NMR and massspectroscopic analyses. Compounds 1, 2, 4–6, 8 and 9 wereevaluated for their cytotoxities against HL-60 and A-549human cancer cell lines, and 5 was the most active one withIC50 value of 5.2 lM against the A-549 cells.
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Huang Guangwei,Bao Hailong,Zhan Peng,Lu Xiyang,Duan Zonggang,Xiong Xinlin,Lin Muzhi,Wang Bing,An Hongxin,Xiahou Luanda,Zhou Haiyan,Luo Zhenhua,Li Wei 대한독성 유전단백체 학회 2024 Molecular & cellular toxicology Vol.20 No.2
Objectives This study aimed at investigating the role of the proprotein convertase subtilisin/Kexin type 9 (PCSK9)-mediated autophagy on myocardial ischemia/reperfusion injury (MIRI). To determine the relationship between autophagy, apoptosis, fibrosis, and inflammation in the myocardium, to provide experience in preventing and treating the myocardial ischemia/reperfusion (I/R) injury. Methods An AC16 hypoxia-reoxygenation model and a rat myocardial ischemia–reperfusion model were established. The concentrations of cardiac troponin T (cTnT) and creatine kinase-MB (CKMB) in plasma were measured by ELISA. To determine the size of the myocardial infarction, TTC/EB staining was performed. In addition to identifying pathological changes in myocardial tissue, Masson’s trichrome stains and H&E stains were used to identify pathological changes. Echocardiography was employed to detect cardiac function. Western blot analysis was then performed to detect the protein expression of Parkin, Pink1, and markers associated with autophagy (Beclin-1, p62, LC3). Results A significant increase in PCSK9 was observed in the myocardium during H/R. In the cardiac-specific PCSK9 knockdown model, cardiac autophagy was significantly inhibited, whereas cardiac-specific PCSK9 overexpression promoted cardiac autophagy. In vivo studies have demonstrated a significant decrease in cardiac autophagy when the PCSK9 inhibitor was administered. Apoptosis induced by I/R was greatly decreased, and myocardial infarction size and function were both improved by PCSK9 inhibitors. Mechanistically, the PCSK9 inhibitor improved the degree of myocardial fibrosis and inhibited the development of inflammation. Conclusions Our results demonstrated that increased PCSK9 via the parkin/pink1 signaling pathway contributes to I/R and H/R by exaggerating excessive autophagy during reperfusion/reoxygenation. In addition, the PCSK9 inhibitor blocked the development of inflammation and improved Infarct size, myocardial function, and myocardial fibrosis.
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An ensemble learning based Bayesian model updating approach for structural damage identification
Yi Zhang,Guangwei Lin,Enjian Cai,Taisen Zhao,Zhaoyan Li 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.32 No.1
This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.
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Yi Zhang,Guangwei Lin,Qinzhuo Liao 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.2
Model uncertainty is a key factor that could influence the accuracy and reliability of numerical model-based analysis. It is necessary to acquire an appropriate updating approach which could search and determine the realistic model parameter values from measurements. In this paper, the Bayesian model updating theory combined with the transitional Markov chain Monte Carlo (TMCMC) method and K-means cluster analysis is utilized in the updating of the structural model parameters. Kriging and polynomial chaos expansion (PCE) are employed to generate surrogate models to reduce the computational burden in TMCMC. The selected updating approaches are applied to three structural examples with different complexity, including a two-storey frame, a ten-storey frame, and the national stadium model. These models stand for the lowdimensional linear model, the high-dimensional linear model, and the nonlinear model, respectively. The performances of updating in these three models are assessed in terms of the prediction uncertainty, numerical efforts, and prior information. This study also investigates the updating scenarios using the analytical approach and surrogate models. The uncertainty quantification in the Bayesian approach is further discussed to verify the validity and accuracy of the surrogate models. Finally, the advantages and limitations of the surrogate model-based updating approaches are discussed for different structural complexity. The possibility of utilizing the boosting algorithm as an ensemble learning method for improving the surrogate models is also presented.
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