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Arulmozhi, S.
- Effect of Leaf Galls of Piper nigrum Linn. Against Carageenan Induced Inflammation in Albino Rats
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PDF Views:507
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Journal of Natural Remedies, Vol 7, No 2 (2007), Pagination: 229-233Abstract
Objective: To evaluate the anti-inflammatory activity of leaf galls of Piper nigrum Linn. against the carageenan induced paw oedema in Albino rats. Materials and methods: The benzene, chloroform and ethanol extracts of leaf galls of Piper nigrum Linn. were obtained by continuous soxhlet extraction. Each extract was assessed for anti-inflammatory activity in carageenan induced Albino rats by measuring the paw volume and ulcerogenic activity was also carried out. Results: All the extracts showed significant (P<0.05) anti-inflammatory activity in which ethanol extract showed prominent (P<0.01) effect. The extracts were devoid of ulcerogenic action. Conclusion: From the results, it is revealed that, the active ethanol extract of leaf galls of Piper nigrum is worthwhile to develop the bioactive principle for inflammatory disorders.Keywords
Piper nigrum, Antiinflammatory, UlcerogenicFull Text
- Comparative Food Intake Inhibitory Activity of Sida cordifolia L. and Withania somnifera L. in Rats
Abstract Views :492 |
PDF Views:427
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Source
Journal of Natural Remedies, Vol 7, No 2 (2007), Pagination: 289-293Abstract
Objective: The purpose of the present study was to evaluate food intake inhibitory activity of aqueous extract of Sida cordifolia (AESC) and alcoholic extract of Withania somnifera (AEWS). Both Sida cordifolia Linn. (Malvaceae) and Withania somnifera Linn. (Solanaceae) are widely growing medicinal plants and have been reported to possess number of medicinal properties. Materials and methods: The food intake inhibitory activity of different concentrations of AESC and AEWS (0.5% w/w, 1% w/w and 1.5% w/w) were evaluated by supplementing them with normal feed of rats for seven days, measuring their body weight and food intake daily and compared with the control. Results: There was a significant decrease in food intake (P<0.001) and body weight (P<0.01) with 1 % w/w and 1.5 % w/w of AESC while decrease in food intake and body weight with 0.5 % w/w of AESC was not significant. AEWS showed a significant (P<0.05) decrease in food intake only, but no significant decrease in body weight was observed with AEWS at any dose level. Conclusion: Both extracts were found to have significant food intake inhibitory activity. However when compared to AEWS, AESC was found to be more effective in reducing the food intake and bodyweight.Full Text
- A Review: Deep Learning Techniques for Image Classification of Pancreatic Tumor
Abstract Views :238 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Chikkanna Government Arts College, IN
1 Department of Computer Science, Chikkanna Government Arts College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 11, No 1 (2020), Pagination: 2217-2223Abstract
Pancreatic Cancer (PC) may be a leading reason behind death worldwide and its prognosis is extremely poor within the present scenario. There are numerous methods and techniques for tumor identification in brain, breast, lungs, but limited work was done on pancreatic tumor detection. Pancreatic tumor image classification is usually provided by computer-aided screening (CAD), diagnosis and quantitative evaluations in radiology images like CT and MRI. Tumor classification through these methods may help to trace, predict and endorse customized therapy as part of effective treatment, without invasions of cancer. Nowadays, Convolutional Neural Networks (CNN) have shown promising results for precise pancreatic image classification. As a prominent, the algorithms are required to work out and classify the categories of pancreatic tumors at early stages for saving most of the life. Because of the various shapes, huge sample size, processing and analyzing big databases, new statistical methods are to be implemented. On the opposite hand, detection of tumors within the medical images also become difficult since the standard of input images. This paper mainly concentrates on a study of carcinoma and also the recent research on tumor detection and classification in medical images. The convolution neural network (CNN) developed in recent years has been widely utilized in the sector of image processing because it's good at handling image classification and recognition problems and has brought great improvement within the accuracy of the many machine learning tasks. One in every of the foremost powerful approaches to resolve image recognition and classification problem is that the CNN. The experimental results demonstrate that the proposed approach can improve the performance of the classification accuracy.Keywords
CNN, Classification, Deep Learning, Medical Image Analysis, Pancreatic Cancer, Adenocarcinomas.
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