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<title cf:type="text"><![CDATA[Chinese Herbal Medicines -->Quality Analysis of TCM]]></title>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[How to identify â€˜Material basis - Quality markersâ€™ more accurately in Chinese herbal medicines from modern chromatography-mass spectrometry data-sets: Opportunities and challenges of chemometric tools]]></title>
<link><![CDATA[https://www.tiprpress.com/chmen/article/abstract/CHM202101002]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Modern chromatography - mass spectrometer (MS) technology is an essential weapon in the exploration of traditional Chinese medicines (TCMs) which is based on the ‘effectiveness-material basis-quality markers (Q-markers)’. Nevertheless, the hardware bottleneck and irregular operation will limit the accuracy and comprehensiveness of test results. Chemometrics was thereby used to solve the existing problems: 1) The method of ‘design-modeling-optimization’ can be adopted to solve the multi-factor and multi-level problems in sample preparation/ parameter setting; 2) The approaches of signal processing can be used to calibrate the deviation from retention time (rt) dimension and mass-to-charge ratio (m/z) dimension in different types of instruments; 3) The methods of multivariate calibration and multivariate resolution can be utilized to analyze the co-eluting peaks in complex samples. When the researchers need to capture essential information on raw data sets extracting the higher level of information on essential features, 1) The significant components which affects the drug properties/efficacy can be find by the pattern recognition and variable selection; 2) Fingerprint-efficacy modeling is explored to clarify the material basis, or to screen out the Q-markers of biological significance; 3) Chemometric tools can apply to integrate chemical (metabolic) fingerprints with network pharmacology, bioinformatics, omics and others from a multi-level perspective. Under these programs, the qualitative/quantitative works will achieve in chemical (metabolic) fingerprint and metabolic trajectories, which leads to an accurate reflection of ‘material basis and Q-markers’ in TCMs. Likewise, an in-depth hidden information can be disclosed, so that the components of drug properties/efficacy will be found. More importantly, multidimensional data can be integrated with fingerprints to acquire more hidden information.]]></description>
<pubDate>2021/1/18 13:58:59</pubDate>
<category><![CDATA[Quality Analysis of TCM]]></category>
<author><![CDATA[Min He, Yu Zhou]]></author>
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<atom:name>Min He, Yu Zhou</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[An integrated strategy toward comprehensive characterization and quantification of multiple components from herbal medicine: An application study in Gelsemium elegans]]></title>
<link><![CDATA[https://www.tiprpress.com/chmen/article/abstract/CHM202101003]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Objective: To develop a powerful integrated strategy based on liquid chromatography coupled with mass spectrometry (LC-MS) systems for the comprehensive characterization and quantification of multiple components of herbal medicines. Methods: Firstly, different mobile phase additives, analysis time, and MS acquisition modes were orthogonally tested with liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) in order to detect as many components of Gelsemium elegans as possible with high peak intensity. Secondly, several data mining strategies, including database searching, diagnostic ion filtering and neutral loss filtering, were utilized to perform chemical profiling. Subsequently, this study focused on the quantification and validation of the performance of a liquid chromatography-triple mass spectrometry (LC-QqQ/MS) assay based on derivative multiple reaction monitoring (DeMRM). 
Results: A total of 147 components from G. elegans were characterized, among them 116 nontarget components were reported for the first time. A sensitive and reproducible LC-QqQ/MS method was successfully developed and validated for the simultaneous relative quantification of 41 components of G. elegans. This LC-QqQ/MS method was then applied to compare the contents of components in the roots, stems and leaves. 
Conclusion: The present integrated strategy would significantly contribute to chemical studies on herbal medicine, and its utility could be extended to other research fields, such as metabolomics, quality control, and pharmacokinetics.]]></description>
<pubDate>2021/1/18 13:58:59</pubDate>
<category><![CDATA[Quality Analysis of TCM]]></category>
<author><![CDATA[Meng-ting Zuoa, Yan-chun Liu a, Zhi-liang Suna,b, Li Lina, Qi Tangc, Pi Chengc, Zhao-ying Liu a,b,c]]></author>
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<atom:name>Meng-ting Zuoa, Yan-chun Liu a, Zhi-liang Suna,b, Li Lina, Qi Tangc, Pi Chengc, Zhao-ying Liu a,b,c</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Identification and activity evaluation of Astragalus Radix from different germplasm resources based on specific oligosaccharide fragments]]></title>
<link><![CDATA[https://www.tiprpress.com/chmen/article/abstract/CHM202101004]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[]]></description>
<pubDate>2021/1/18 13:58:59</pubDate>
<category><![CDATA[Quality Analysis of TCM]]></category>
<author><![CDATA[Shu-ying Li, Di Wangb, c, Xian-rong Lid, e, Xue-mei Qinb, Yu-guang Duf, Ke Lib, f]]></author>
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<atom:name>Shu-ying Li, Di Wangb, c, Xian-rong Lid, e, Xue-mei Qinb, Yu-guang Duf, Ke Lib, f</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[A mathematical model for quality evaluation of total saponins of Panax japonicas based on hypolipidemic activity]]></title>
<link><![CDATA[https://www.tiprpress.com/chmen/article/abstract/CHM202101005]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Objective: The chemical finger printing-based methods for evaluating TCMs quality can report partial of TCMs quality without linking to effective constituents. In this study, a mathematical model was established for the quality evaluation of total saponins of Panax japonicus (TSPJ), a folk medicine in China and Japan for treating diseases, through coupling the dynamic changes of chemical constitutions with corresponding activities.
Methods: High-performance liquid chromatography (HPLC) fingerprints were applied to establish the chromatographic database of TSPJ. The associated hypolipidemic activity database was determined by TG assay using HepG2 cell model. Correlation analyses of two databases were performed by partial least squares (PLS) for calculating regression coefficients, and the interval value of YZL value (the ratio of positive and negative peak-to-peak area coefficient) closely related to hypolipidemic activity was refined by the formula of Norminv function to value the quality of TSPJ. 
Results: In this study, the chromatographic data of 16 common peaks were obtained from 20 batches of TSPJ. After the estimate by this mathematical evaluation model, seven peaks were positively correlated with hypolipidemic activity, and nine peaks were negatively correlated with hypolipidemic activity. When the YZL value was less than 0.7861, the quality of sample was inferior, while YZL value was more than 6.6992, and the quality of samples was superior. The quality of another ten batches of TSPJ was further assessed to verify this method. 
Conclusion: These results indicated that the established model could be usefully applied to evaluate the quality of TSPJ in the hypolipidemic activity.]]></description>
<pubDate>2021/1/18 13:58:59</pubDate>
<category><![CDATA[Quality Analysis of TCM]]></category>
<author><![CDATA[Dong-li Qia,c, Xiao-lin Yangb, Jing Chena,d, Fei Lia, Pei-pei Zhoua, Ping Chene, Li Yangb, Ru-feng Wangb]]></author>
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<atom:name>Dong-li Qia,c, Xiao-lin Yangb, Jing Chena,d, Fei Lia, Pei-pei Zhoua, Ping Chene, Li Yangb, Ru-feng Wangb</atom:name>
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