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A comparative study on the traditional Indian Shodhana and Chinese processing methods for aconite roots by characterization and determination of the major components

Overview of attention for article published in BMC Chemistry, October 2013
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Title
A comparative study on the traditional Indian Shodhana and Chinese processing methods for aconite roots by characterization and determination of the major components
Published in
BMC Chemistry, October 2013
DOI 10.1186/1752-153x-7-169
Pubmed ID
Authors

Yogini Jaiswal, Zhitao Liang, Peng Yong, Hubiao Chen, Zhongzhen Zhao

Abstract

Aconitum is an indispensable entity of the traditional medicine therapy in Ayurveda and Traditional Chinese medicine (TCM), in spite of its known fatal toxicity characteristics. The prolonged use of this drug, irrespective of its known lethal effects, is governed by the practice of effective detoxification processes that have been used for decades. However, the processing methods of Ayurveda and TCM are different, and no comparative study has been carried out to evaluate their differences.The objective of the present study was to carry out comparative chemical profiling of the roots of Aconitum heterophyllum Wall, A. carmichaelii Debx., and A. kusnezoffii Reichb. after application of two detoxification methods used in Ayurveda and one method used in TCM .

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Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hong Kong 1 3%
Unknown 34 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 23%
Student > Ph. D. Student 7 20%
Professor > Associate Professor 3 9%
Student > Bachelor 2 6%
Student > Master 2 6%
Other 2 6%
Unknown 11 31%
Readers by discipline Count As %
Medicine and Dentistry 5 14%
Pharmacology, Toxicology and Pharmaceutical Science 4 11%
Chemistry 4 11%
Agricultural and Biological Sciences 4 11%
Biochemistry, Genetics and Molecular Biology 3 9%
Other 4 11%
Unknown 11 31%