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Treatment of biliary tract cancer with NVP-AEW541:Mechanisms of action and resistance

World Journal of Gastroenterology 2010年第2期16卷 156-166页

作者： samuel wolf Jana Lorenz Joachim Mssner Marcus Wiedmann Department of Internal MedicineⅡ University of Leipzig Department of Internal MedicineⅠ St. Mary's Hospital

AIM:To investigate in vitro treatment with NVPAEW541,a small molecule inhibitor of insulin-like growth factor-1 receptor(IGF-1R),in biliary tract cancer (BTC),since this disease is associated with a poor prognosis due to wide resistance to chemotherapeutic agents and ***:Cell growth inhibition by NVP-AEW541 was studied in vitro in 7 human BTC cell lines by automated cell *** addition,the anti-tumoral mechanism of NVP-AEW541 was studied by Western blotting,cell cycle analysis and reverse transcription-polymerase chain reaction(RT-PCR).Anti-tumoral drug effect in combination with gemcitabine,5-fluorouracil(5-FU)and Polo-like kinase 1 inhibitor BI2536 was also studied. RESULTS:In vitro treatment with NVP-AEW541 suppressed growth in all human BTC cell lines,however response was lower in gallbladder *** withNVP-AEW541 was associated with dephosphorylation of IGF-1R and *** contrast,phosphorylation of p42/p44 and Stat3 and expression of Bcl-xL were inconsistently *** addition,treated cells showed cell cycle arrest at the G1/S-checkpoint and an increase in sub-G1 ***,IGF-1R and its ligands IGF-1 and IGF-2 were co-expressed in RT-PCR,suggesting an autocrine loop of tumor cell *** with gemcitabine,NVP-AEW541 exerted synergistic effects, particularly at low concentrations,while effects of combination with 5-FU or BI 2536 were only additive. CONCLUSION:Our findings suggest that NVP-AEW541 is active against BTC in vitro and potentiates the efficacy of gemcitabine.

关键词： Tyrosine kinase inhibitor Cholangiocarcinoma Gemcitabine NVP-AEW541

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Intelligent Islanding Detection of Multi-distributed Generation Using Artificial Neural Network Based on Intrinsic Mode Function Feature

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Journal of Modern Power Systems and Clean Energy 2020年第3期8卷 511-520页

作者： samuel Admasie Syed Basit Ali Bukhari Teke Gush Raza Haider Chul Hwan Kim Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwonSouth Korea U.S.-Pakistan Center for Advanced Studies in Energy(USPCAS-E) National University of Sciences and Technology(NUST)H-12 IslamabadPakistan Department of Electrical Engineering Balochistan University of Engineering and TechnologyKhuzdar 89100Pakistan

The integration of distributed energy resources(DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method(IIDM) using an intrinsic mode function(IMF)feature-based grey wolf optimized artificial neural network(GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.

关键词： Distributed energy resource(DER) intrinsic mode function(IMF) grey wolf optimized artificial neural network(GWO-ANN) intelligent islanding detection method(IIDM) microgrid

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