New hybrids between triterpenoid acids and nucleoside HIV-RT inhibitors

One-pot synthesis of ester-linked conjugates of betulinic, ursolic or oleanolic acids with anti-HIV drugs AZT, derivatives of AZT and 3TC was performed, the resulting conjugates having shown high anti-HIV activities.

Title:

New hybrids between triterpenoid acids and nucleoside HIV-RT inhibitors
Authors: Dang, Anh Thi Tuyet
Pham, Chinh The
Le, Tuan Anh
Truong, Hieu Hong
Vu, Ha Thu Thi
Soldatenkov, Anatoly T.
Nguyen, Tuyen Van
Keywords: Hybrids
Triterpenoid acids
Nucleoside
HIV-RT inhibitors
Issue Date: 2015
Publisher: Elsevier Ltd
Citation: Scopus
Abstract: One-pot synthesis of ester-linked conjugates of betulinic, ursolic or oleanolic acids with anti-HIV drugs AZT, derivatives of AZT and 3TC was performed, the resulting conjugates having shown high anti-HIV activities.
Description: Mendeleev Communications, Volume 25, Issue 2, 1 March 2015, Pages 96-98
URI: http://www.sciencedirect.com/science/article/pii/S0959943615000425
http://repository.vnu.edu.vn/handle/VNU_123/32571
ISSN: 09599436
Appears in Collections:Bài báo của ĐHQGHN trong Scopus

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Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks

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This study tested the use of machine learning techniques for the estimation of above-ground biomass (AGB) of Sonneratia caseolaris in a coastal area of Hai Phong city, Vietnam. We employed a GIS database and multi-layer perceptron neural networks (MLPNN) to build and verify an AGB model, drawing upon data from a survey of 1508 mangrove trees in 18 sampling plots and ALOS-2 PALSAR imagery. We assessed the model’s performance using root-mean-square error, mean absolute error, coefficient of determination (R2), and leave-one-out cross-validation. We also compared the model’s usability with four machine learning techniques: support vector regression, radial basis function neural networks, Gaussian process, and random forest. The MLPNN model performed well and outperformed the machine learning techniques. The MLPNN model-estimated AGB ranged between 2.78 and 298.95 Mg ha−1 (average = 55.8 Mg ha−1); below-ground biomass ranged between 4.06 and 436.47 Mg ha−1 (average = 81.47 M...
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