Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/57348
Type of publication: Konferencijų tezės nerecenzuojamuose leidiniuose / Conference theses in non-peer-reviewed publications (T2)
Field of Science: Farmacija / Pharmacy (M003);Biologija / Biology (N010)
Author(s): Drevinskas, Tomas;Mickienė, Rūta;Maruška, Audrius Sigitas;Stankevičius, Mantas;Tiso, Nicola;Šalomskas, Algirdas;Lelešius, Raimundas;Karpovaitė, Agneta;Ragažinskienė, Ona
Title: Investigation of antiviral properties in medicinal aromatic plants using chemical and data analysis means / Tomas Drevinskas, Rūta Mickienė, Audrius Maruška, Mantas Stankevičius, Nicola Tiso, Algirdas Šalomskas, Raimundas Lelešius, Agneta Karpovaitė, Ona Ragažinskienė
Is part of: The 8th International Conference on Pharmaceutical Sciences and Pharmacy Practice dedicated to the 80th anniversary of the Museum of History of Lithuanian Medicine and Pharmacy : book of abstracts : December 15, 2017 Kaunas, Lithuania / organized by the Faculty of Pharmacy of Lithuanian Health Sciences University in collaboration with Lithuanian Pharmaceutical Association and LSMU FF Alumni Association. Kaunas : Lithuanian University of Health Sciences, 2017
Extent: p. 60-61
Date: 2017
Note: ISBN 978-9955-15-517-1
Keywords: Plants, medicinal;Oils, volatile;Antiviral agents
ISBN: 9789955155171
Abstract: In many cases only trivial methodology is used for defining the rules, or the factors that are responsible for antiviral activity. Typically, top-down, or bottom-up research strategies are used making the research work complex and expensive. In this work hybrid research strategy investigation of medicinal plants with respect to antiviral properties is presented. The investigation covers chemical analysis methods such as: (i) spectrophotometric analysis non-volatile compounds, (ii) capillary zone electrophoresis analysis of anions and cations, (iii) gas chromatography-mass spectrometric analysis of volatiles (iv) antiviral tests and (v) machine learning techniques. Proposed methodology was capable of identifying 8 antivirally active medicinal plants out of 16 plants. Using machine learning methods several rules were generated suggesting that phenolic compounds having a pKa value higher than 4.7 are directly and positively related to antiviral activity. [...]
Internet: https://hdl.handle.net/20.500.12259/57348
Affiliation(s): Biochemijos katedra
Biologijos katedra
Gamtos mokslų fakultetas
Lietuvos sveikatos mokslų universitetas. Veterinarijos akademija
Lietuvos sveikatos mokslų universitetas. Veterinarijos akademija. Mikrobiologijos ir virusologijos institutas
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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