Alternanthera brasiliana Kuntz is an important herb which is having higher medicinal properties. Through all most all of the parts are used in the traditional system of medicines. The bioactive compounds or phytochemicals present in the plant show many pharmacological activities like wound healing, anti-inflammatory, antitumor, analgesic, immunostimulant and, antimicrobial and antiviral activities. Phytochemical screening of methanolic and ethanolic extracts of, A.brasiliana leaves showed the presence of phenol and alkaloids and A.brasiliana stem extracts showed the presence of different constituents like alkaloids flavonoids steroids glycosides and saponin. The DPPH and ABTS methods were followed for studying antioxidant activities of extracts. The wound healing potential of extracts is determined by the in-vitro method by using chick embryo fibroblast cell line study revealed that both stem and leaf showing wound healing activity. From the extracts the ethanolic extracts showing great wound healing potential at a time interval. Ethanolic stem extracts show comparatively higher wound healing potency. This paves way for future studies on this plant for isolation and commercialization of phytochemicals which are responsible for wound healing property of the plant Alternanthera brasiliana Kuntz. Keyword : Alternanthera brasiliana, antioxidant, DPPH and ABTS, wound healing, MTT assay.
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The explosion of affluent social networks, online communities, and jointly generated information resources has accelerated the convergence of technological and social networks producing environments that reveal both the framework of the underlying information arrangements and the collective formation of their members. In studying the consequences of these developments, we face the opportunity to analyze the POD repository at unprecedented scale levels and extract useful information from query log data. This chapter aim is to improve the performance of a POD repository from a different point of view. Firstly, we propose a novel query recommender system to help users shorten their query sessions. The idea is to find shortcuts to speed up the user interaction with the open data repository and decrease the number of queries submitted. The proposed model, based on pseudo-relevance feedback, formalizes exploiting the knowledge mined from query logs to help users rapidly satisfy their information need.
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