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Confirmation of the antiviral properties of medicinal plants via chemical analysis, machine learning methods and antiviral tests: a methodological approach

机译:通过化学分析,机器学习方法和抗病毒测试确认药用植物的抗病毒性质:一种方法论方法

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摘要

Medicinal Wants are reported to possess antiviral activity, but finding the substances that are responsible for antiviral activity in the complex mixture of the plant extract is an extremely difficult task. In this paper a methodology related to the determination of the antiviral properties of medicinal plant extracts and based on phytochemical analysis, antiviral tests and machine [earning methods is described. 16 potentially antiviral medicinal Wants were selected, and their chemometric characteristics and antiviral properties were investigated. Three different analytical methods were used for chemical analysis: (i) spectrophotometry, (ii) capillary electrophoresis with contactess conductivity detection, and (iii) gas chromatography-mass spectrometry. 14 attributes were obtained describing the composition of the plant extracts. Viral growth inhibition properties were investigated and 8 candidate plant extracts were selected as being active against viruses. Infectious bronchitis virus was used as a mode[virus. Machine [earning techniques including deep neural network classification, classification and regression tree induction and hierarchical clusterization were used for mining the factors that are responsible for antiviral effects. It was determined that (i) phenolic compounds providing high radical scavenging activity and fractions containing high content of phenolic compounds are positively related to antiviral activity in plant extracts, (ii) hydrophilic compounds that are positively charged (pK(a) 4.7) in acidic media and possess medium and Low electrophoretic mobility properties are negatively related to antiviral activity in medicinal Wants, (iii) phenolic acids with pK(a) Lower than 4.7 are not related to antiviral activity in the extracts, and (iv) volatile compounds in the extracts, including diversity, quantity and different volatility properties, do not affect the antiviral activity of plant extracts. Following the proposed methodological approach, it is possible to confirm which chemometric attributes are responsible for antiviral activity in medicinal plant extracts.
机译:据报道,药用的要求具有抗病毒活性,但发现负责植物提取物的复杂混合物中的抗病毒活性的物质是一个极其困难的任务。本文在鉴定药用植物提取物的抗病毒性质和基于植物化学分析,抗病毒试验和机器的方法中的一种方法[禁止方法。选择潜在的抗病毒药物,研究了它们的化学计量特性和抗病毒性能。三种不同的分析方法用于化学分析:(i)分光光度法,(ii)毛细管电泳,具有接触电导率检测,(III)气相色谱 - 质谱。获得14个属性,描述了植物提取物的组成。研究了病毒生长抑制性能,并选择8种候选植物提取物作为病毒活性。传染性支气管炎病毒用作模式[病毒。机器[收入技术,包括深度神经网络分类,分类和回归树感应和分级集群化用于开采负责抗病毒效应的因素。确定提供高自由基清除活性的酚类化合物和含有高含量的酚类化合物的级分与植物提取物中的抗病毒活性正相关,(ii)带正电荷的亲水性化合物(PK(a)& 4.7)在酸性介质和具有培养基中和低电泳迁移率特性与药用的抗病毒活性呈负相关,(III)具有低于4.7的PK(A)的酚酸与提取物中的抗病毒活性和(iv)挥发性化合物无关在提取物中,包括多样性,数量和不同的挥发性特性,不影响植物提取物的抗病毒活性。遵循所提出的方法方法,可以确认哪种化学计量属性对药用植物提取物中的抗病毒活性负责。

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  • 来源
    《Analytical methods》 |2018年第16期|共11页
  • 作者单位

    Vytautas Magnus Univ Instrumental Anal Open Access Ctr Fac Nat Sci Vileikos Str 8 LT-44404 Kaunas Lithuania;

    Vytautas Magnus Univ Instrumental Anal Open Access Ctr Fac Nat Sci Vileikos Str 8 LT-44404 Kaunas Lithuania;

    Vytautas Magnus Univ Instrumental Anal Open Access Ctr Fac Nat Sci Vileikos Str 8 LT-44404 Kaunas Lithuania;

    Vytautas Magnus Univ Instrumental Anal Open Access Ctr Fac Nat Sci Vileikos Str 8 LT-44404 Kaunas Lithuania;

    Vytautas Magnus Univ Instrumental Anal Open Access Ctr Fac Nat Sci Vileikos Str 8 LT-44404 Kaunas Lithuania;

    Lithuanian Univ Hlth Sci Vet Acad Dept Vet Pathobiol Tilzes Str 18 LT-47181 Kaunas Lithuania;

    Lithuanian Univ Hlth Sci Vet Acad Dept Vet Pathobiol Tilzes Str 18 LT-47181 Kaunas Lithuania;

    Lithuanian Univ Hlth Sci Vet Acad Dept Vet Pathobiol Tilzes Str 18 LT-47181 Kaunas Lithuania;

    Vytautas Magnus Univ Kaunas Bot Garden Sect Med Plants ZE Zilibero Str 6 LT-46324 Kaunas Lithuania;

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  • 正文语种 eng
  • 中图分类 分析化学;
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