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Kiran Kumari Patil

Researcher at Massachusetts Institute of Technology

Publications -  69
Citations -  512

Kiran Kumari Patil is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Wireless sensor network & Quality of service. The author has an hindex of 8, co-authored 60 publications receiving 291 citations. Previous affiliations of Kiran Kumari Patil include Narsee Monjee Institute of Management Studies & Reva Institute of Technology and Management.

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Natural anti-obesity agents and their therapeutic role in management of obesity: A future trend perspective.

TL;DR: A metanalysis of all the available data on the use of the plants with their biological source, active phytochemical constituents and a probable mechanism of action as natural anti-obesity agents reveals the need and scope of further research in the development of new natural phytoconstituents for the management of obesity.
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Evidence for gastroprotective, anti-inflammatory and antioxidant potential of methanolic extract of Cordia dichotoma leaves on indomethacin and stress induced gastric lesions in Wistar rats.

TL;DR: The pretreatment with methanolic extract of CD leaves (MECD) obtained using Soxhlet extractor has shown significant gastroprotective potential probably due to its antioxidant and anti-inflammatory properties.
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Recent therapeutic approaches for the management of tuberculosis: Challenges and opportunities

TL;DR: The recent approaches to the treatment of tuberculosis in terms of discovery of new chemical entity, repurposing of old drugs and the use of novel drug delivery technology such as liposomes, niosomes, liquid crystals, solid lipid nanoparticles, polymeric micelles, dendrimers, nanoemulsion and microparticles are reviewed.
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Deep Learning Based Weighted SOM to Forecast Weather and Crop Prediction for Agriculture Application

TL;DR: The proposed SOM strategy is one of the proper dimensionality reducing strategy to highlight the self-arranging outline and improved accuracy in weather and crop prediction up to 7-23% compared to the current approach.