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Anuj Sharma

Researcher at Chandragupt Institute of Management

Publications -  145
Citations -  3043

Anuj Sharma is an academic researcher from Chandragupt Institute of Management. The author has contributed to research in topics: Chemistry & Venezuelan equine encephalitis virus. The author has an hindex of 23, co-authored 114 publications receiving 2186 citations. Previous affiliations of Anuj Sharma include AIIMS, New Delhi & Dr. B. R. Ambedkar National Institute of Technology Jalandhar.

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Journal ArticleDOI

Chikungunya Virus Infection Alters Expression of MicroRNAs Involved in Cellular Proliferation, Immune Response and Apoptosis.

TL;DR: Several miRNAs that may play important roles in early events after Chikungunya virus infection and can be potential therapeutic targets against CHIKV infection are identified.
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Picroliv accelerates epithelialization and angiogenesis in rat wounds

TL;DR: The findings suggest that picroliv could be developed as a therapeutic angiogenic agent for the restoration of the blood supply in diseases involving inadequate blood supply such as limb ischemia, ischemic myocardium and wound healing.
Journal Article

Inhibition of tumor angiogenesis by Brahma Rasayana (BR).

TL;DR: Findings suggest the possible mechanism(s) of action of BR in the reduction of tumor growth and metastatic spread and Methanolic extract of BR was found to inhibit the proliferation, tube formation, cell migration and attachment of HUVEC on matrigel in a dose dependant manner.
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1,5-Iodonaphthyl azide-inactivated V3526 protects against aerosol challenge with virulent venezuelan equine encephalitis virus.

TL;DR: It is demonstrated that among three routes of immunization, intramuscular immunization with INA-inactivate V3526 (INA-iV3526) provided complete protection against aerosol challenge with virulent VEEV.
Proceedings ArticleDOI

A Wordsets based document clustering algorithm for large datasets

TL;DR: In this paper, the authors proposed WDC (Wordsets-based Clustering), an efficient clustering algorithm based on closed words sets, which uses a hierarchical approach to cluster text documents having common words.