Annamalai University

About: Annamalai University is a education organization based out in Chidambaram, Tamil Nadu, India. It is known for research contribution in the topics: Lipid peroxidation & Antioxidant. The organization has 8098 authors who have published 10758 publications receiving 203872 citations.

Lipid peroxidation & antioxidants status in patients with oral squamous cell carcinoma.

Abstract: Background & objectives: Overproduction of lipid peroxidation byproducts and disturbances in antioxidant defense system have been implicated in the pathogenesis of several diseases including oral cancer. Though several studies have been done on the level of lipid peroxidation and antioxidants in oral cancer patients, there are no reports in patients with various clinical stages of oral squamous cell carcinoma. We carried out this study to assess the level of oxidative stress in oral cancer patients with various clinical stages. Methods: Blood samples of 48 adult male oral cancer patients with various clinical stages of oral cancer (stage II to stage IV, 16 of each) and 16 age and sex matched healthy subjects were collected. Plasma and erythrocytes levels of thiobarbituric acid reactive substances (TBARS), vitamin E, reduced glutathione (GSH), and activities of superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx) were assayed using specific colorimetric methods. The statistical comparisons were performed by one way analysis of variance (ANOVA) followed by Student’s t-test. Results: Elevated lipid peroxidation and decline in non-enzymatic and enzymatic antioxidants status were noticed in oral cancer patients as compared to healthy subjects. The TBARS levels were gradually increased whereas antioxidants were gradually reduced from stage II to stage IV of oral cancer patients. Interpretation & conclusion: The altered lipid peroxidation in plasma and erythrocytes of oral cancer patients may be related to their compensatory changes in the antioxidants defense system.

Cinnamon bark extract improves glucose metabolism and lipid profile in the fructose-fed rat.

Abstract: Introduction The present study was designed to investigate whether cinnamon bark extract (CBEt) mitigates the adverse effects of fructose loading on glucose metabolism and lipid profile in rats Methods Adult male albino rats of body weight 150-170 g were divided into five groups and fed with either control or high fructose diet (HFD) CBEt was administered to HFD-fed rats orally at two doses (a low and a high dose) while the control diet-fed rats were treated with a high dose of CBEt The treatment protocol was carried out for 60 days after which the oral glucose tolerance test was carried out Biochemical parameters related to glucose metabolism and lipid profile were assayed Results The levels of glucose, insulin and protein-bound sugars were higher and activities of enzymes of glucose metabolism were altered in HFD-fed rats, as compared to control animals The levels were brought back to near-normal when administered with CBEt at high dose CBEt also prevented the hyperlipidaemia observed in fructose-fed rats and improved glucose tolerance CBEt did not show any significant effect in fructose-fed rats when administered at low dose Conclusion These findings indicate the improvement of glucose metabolism in-vivo by CBEt in fructose-fed rats

Breast mass classification based on cytological patterns using RBFNN and SVM

Abstract: Correct diagnosis is one of the major problems in medical field. This includes the limitation of human expertise in diagnosing the disease manually. From the literature it has been found that pattern classification techniques such as support vector machines (SVM) and radial basis function neural network (RBFNN) can help them to improve in this domain. RBFNN and SVM with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. This paper compares the use of polynomial kernel of SVM and RBFNN in ascertaining the diagnostic accuracy of cytological data obtained from the Wisconsin breast cancer database. The data set includes nine different attributes and two categories of tumors namely benign and malignant. Known sets of cytologically proven tumor data was used to train the models to categorize cancer patients according to their diagnosis. Performance measures such as accuracy, specificity, sensitivity, F-score and other metrics used in medical diagnosis such as Youden's index and discriminant power were evaluated to convey and compare the qualities of the classifiers. This research has demonstrated that RBFNN outperformed the polynomial kernel of SVM for correctly classifying the tumors.