Grey anti-inflammation analysis of phenolic acid phenethyl esters in human neutrophils.

IF 0.2 4区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI:10.1504/ijdmb.2015.066769
Ya-Ting Lee, Chian-Song Chiu
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引用次数: 1

Abstract

This paper presents grey structure activity relationship analysis for anti-inflammation of phenolic acid phenethyl esters in human neutrophils. To study the anti-inflammation effect, 14 compounds of phenolic acid phenethyl esters are synthesised, while the inhibition on superoxide anion generation (which is linked to an inflammation effect) induced by PMA and fMLP stimulants is detected. Next, the relationship weighting of each functional group of phenolic acid phenethyl esters is found by applying the grey system theory on the measured data. Moreover, evident structure activity relationships are established to regulate the anti-inflammation effect of such compounds, e.g. the most important functional group affecting the anti-inflammation in human neutrophils is revealed. In addition, some extending results are obtained based on the grey analysis. It is interesting that the analysed result is consistent with the actual circumstance. In comparison with traditional methods, this paper applying the grey theory indicates more characteristic information about the structure activity relationships of phenolic acid phenethyl esters while fewer data samples are required.

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嗜中性粒细胞中酚酸苯乙酯的灰色抗炎分析。
本文对人体中性粒细胞中酚酸苯乙酯的抗炎作用进行灰色结构-活性关系分析。为了研究其抗炎作用,我们合成了14种酚酸苯乙酯化合物,同时检测了PMA和fMLP兴奋剂诱导的超氧阴离子生成(与炎症作用有关)的抑制作用。其次,应用灰色系统理论对实测数据求出酚酸苯乙酯各官能团的关系权重。此外,还建立了明显的构效关系来调节这些化合物的抗炎作用,例如揭示了影响人中性粒细胞抗炎的最重要的官能团。此外,在灰色分析的基础上得到了一些扩展结果。有趣的是,分析结果与实际情况相符。与传统方法相比,应用灰色理论可以获得更多酚酸苯乙酯的结构活性关系的特征信息,所需的数据样本更少。
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来源期刊
CiteScore
1.00
自引率
0.00%
发文量
0
审稿时长
>12 weeks
期刊介绍: Mining bioinformatics data is an emerging area at the intersection between bioinformatics and data mining. The objective of IJDMB is to facilitate collaboration between data mining researchers and bioinformaticians by presenting cutting edge research topics and methodologies in the area of data mining for bioinformatics. This perspective acknowledges the inter-disciplinary nature of research in data mining and bioinformatics and provides a unified forum for researchers/practitioners/students/policy makers to share the latest research and developments in this fast growing multi-disciplinary research area.
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