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Nagarjuna Reddy, G.
- Evaluation of anthelmintic activity of Eichhornia crassipes ischolar_mains
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Authors
Affiliations
1 KLR Pharmacy College, Paloncha, Khammam, Andhra Pradesh, IN
2 Koringa College of Pharmacy, Korangi-533 461, E.G. Dt., Andhra Pradesh, IN
3 Pydah College of Pharmacy, Patavala, E.G.Dt., Andhra Pradesh, IN
1 KLR Pharmacy College, Paloncha, Khammam, Andhra Pradesh, IN
2 Koringa College of Pharmacy, Korangi-533 461, E.G. Dt., Andhra Pradesh, IN
3 Pydah College of Pharmacy, Patavala, E.G.Dt., Andhra Pradesh, IN
Source
Research Journal of Pharmacology and Pharmacodynamics, Vol 5, No 3 (2013), Pagination: 183-184Abstract
Herbal medicine is having very old history. Plants are the typical manufacturers of complex drug molecules, which serve as a prototype to develop more effective and less toxic medicines. Helminth infections are distressing huge population in the world. These infections are contributing to the disorders like pneumonia, anaemia, eosinophilia and under nourishment. Anthelmintics are the drugs which expels the parasitic worms from the gastrointestinal tract by either paralyzing or killing the worms. So, there is a need of investigation of new anthelmintic molecules. Eichhornia crassipes belongs to the family Pontederiaceae, commonly known as "Water hyacinth" is selected as test drug based on ethno-botanical survey conducted in East Godavari Dist., Andhra Pradesh. In present study evaluation of anthelmintic activity of ischolar_main extracts was done by using adult Indian earth worm Pheritima posthuma. The above activity was carried out using the petroleum ether, ethanol extracts of different concentrations using piperazine citrate positive control and normal saline as negative control. Overall anthelmintic activity revealed that concentration dependent nature of extracts. The extract shows potent and significant anthelmintic activity as compared to the standards and it was investigated to be used as effective anthelmintic drug.Keywords
Helminth Infections, Anthelmintics, Eichhornia crassipes, Pheritima posthuma, Petroleum Ether & Ethanol ExtractReferences
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- Transfer Learning-Based Approach for Early Detection of Alzheimer’s Disease .
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1 no, IN
1 no, IN
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Artificial Intelligent Systems and Machine Learning, Vol 14, No 1 (2022), Pagination: 13 - 17Abstract
Alzheimer's disease is one of the world's main health concerns today. People with Alzheimer's disease who are diagnosed early have the best chance of receiving effective therapy. It's critical to catch the sickness as early as possible. Magnetic resonance imaging is one way to define Alzheimer's disease by finding structural abnormalities in the brain (MRI). We propose that machine learning, specifically trained convolutional neural networks (CNNs) with transfer learning capable of making predictions about similar brain imagery, can aid in early detection. CNN enables the extraction of MRI properties and classification as Alzheimer's disease or normal brain. We used the VGG19 architecture to categorize patients as having no signs of Alzheimer's disease or having signs of very mild, mild, or moderate Alzheimer's disease. Based on a transfer learning methodology, this method correctly classifies MRI images into four phases of Alzheimer's disease with an accuracy of 85 percent.Keywords
--Alzheimers Disease, Transfer Learning, VGG19, MRI, CNN.References
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