University of Madras

About: University of Madras is a education organization based out in Chennai, Tamil Nadu, India. It is known for research contribution in the topics: Ring (chemistry) & Lipid peroxidation. The organization has 8496 authors who have published 11369 publications receiving 211152 citations. The organization is also known as: Madras University & University of Chennai.

Oral cancer in Southern India: the influence of body size, diet, infections and sexual practices.

Abstract: Between 1996 and 1999, we carried out a study in Southern India on risk factors for oral cancer. The study included 591 incident cases of cancer of the oral cavity (282 women) and 582 hospital controls (290 women). Height was unrelated to oral cancer risk. Body mass index (weight in kilograms/height in metres squared) was inversely associated with risk (P for trend<0.001). Paan chewers with low BMI were at particularly high risk. Risk was increased among subjects consuming meat (odds ratio (OR) 1.54, 95% confidence interval (CI) 1.00-2.37), ham and salami (OR 4.40, 95% CI 2.88-6.71) two or more times per week. Frequent consumption of fish, eggs, raw green vegetables, cruciferous vegetables, carrots, pulses, apples or pears, citrus fruit, and overall consumption of vegetables and fruit decreased oral cancer risk (P for trend for each of these items less than or equal to 0.001). The risk associated with low consumption of vegetables was higher among smokers than among non-smokers. Men, but not women, who practised oral sex had an increased oral cancer risk (OR 3.14, 95% CI 1.15-8.63). Women with more than one sexual partner during life were at increased oral cancer risk (OR 9.93, 95% CI 1.57-62.9).

Purification, crystal structure and antimicrobial activity of phenazine-1-carboxamide produced by a growth-promoting biocontrol bacterium, Pseudomonas aeruginosa MML2212

Abstract: Aim: To purify and characterize an antimicrobial compound produced by a biocontrol bacterium, Pseudomonas aeruginosa MML2212, and evaluate its activity against rice pathogens, Rhizoctonia solani and Xanthomonas oryzae pv. oryzae. Methods and Results: Pseudomonas aeruginosa strain MML2212 isolated from the rice rhizosphere with wide-spectrum antimicrobial activity was cultured in Kings’B broth using a fermentor for 36 h. The extracellular metabolites were isolated from the fermented broth using ethyl acetate extraction and purified by two-step silica-gel column chromatography. Three fractions were separated, of which a major compound was obtained in pure state as yellow needles. It was crystallized after dissolving with chloroform followed by slow evaporation. It is odourless with a melting point of 220–222°C. It was soluble in most of the organic solvents and poorly soluble in water. The molecular mass of purified compound was estimated as 223·3 by mass spectral analysis. Further, it was characterized by IR, 1H and 13C NMR spectral analyses. The crystal structure of the compound was elucidated for the first time by X-ray diffraction study and deposited in the Cambridge Crystallographic Data Centre (http://www.ccde.com.ac.uk) with the accession no. CCDC 617344. Conclusion: The crystal compound was undoubtedly identified as phenazine-1-carboxamide (PCN) with the empirical formula of C13H9N3O. Significance and Impact of the Study: As this is the first report on the crystal structure of PCN, it provides additional information to the structural chemistry. Furthermore, the present study reports the antimicrobial activity of purified PCN on major rice pathogens, R. solani and X. oryzae pv. oryzae. Therefore, the PCN can be developed as an ideal agrochemical candidate for the control of both sheath blight and bacterial leaf blight diseases of rice.

Effect of silymarin on N-nitrosodiethylamine induced hepatocarcinogenesis in rats.

Abstract: AIM To study the effect of silymarin on the levels of tumor markers and MDA (malondialdehyde)-DNA adduct formation during N-nitrosodiethylamine induced hepatocellular carcinoma in male Wistar albino rats. METHODS The levels of AFP, CEA and activities of liver marker enzymes in serum, MDA-DNA immunohistochemistry were done according to standard procedures in the control and experimental groups of rats. RESULTS Hepatocellular carcinoma was evidenced from significant (p < 0.05) increases of alpha-fetoprotein, carcinoembryonic antigen, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, acid phosphatase, lactate dehydrogenase, gamma-glutamyltransferase and 5'-nucleotidase in serum and increased MDA-DNA adducts were also observed in the tissue sections of hepatocellular carcinoma. Silymarin treatment significantly attenuated the alteration of these markers and decreased the levels of MDA-DNA adduct formation. CONCLUSION Silymarin could be developed as a promising chemotherapeutic adjuvant for the treatment of liver cancer.

Harnessing Computational Biology for Exact Linear B-Cell Epitope Prediction: A Novel Amino Acid Composition-Based Feature Descriptor

Abstract: Proteins embody epitopes that serve as their antigenic determinants. Epitopes occupy a central place in integrative biology, not to mention as targets for novel vaccine, pharmaceutical, and systems diagnostics development. The presence of T-cell and B-cell epitopes has been extensively studied due to their potential in synthetic vaccine design. However, reliable prediction of linear B-cell epitope remains a formidable challenge. Earlier studies have reported discrepancy in amino acid composition between the epitopes and non-epitopes. Hence, this study proposed and developed a novel amino acid composition-based feature descriptor, Dipeptide Deviation from Expected Mean (DDE), to distinguish the linear B-cell epitopes from non-epitopes effectively. In this study, for the first time, only exact linear B-cell epitopes and non-epitopes have been utilized for developing the prediction method, unlike the use of epitope-containing regions in earlier reports. To evaluate the performance of the DDE feature vector, models have been developed with two widely used machine-learning techniques Support Vector Machine and AdaBoost-Random Forest. Five-fold cross-validation performance of the proposed method with error-free dataset and dataset from other studies achieved an overall accuracy between nearly 61% and 73%, with balance between sensitivity and specificity metrics. Performance of the DDE feature vector was better (with accuracy difference of about 2% to 12%), in comparison to other amino acid-derived features on different datasets. This study reflects the efficiency of the DDE feature vector in enhancing the linear B-cell epitope prediction performance, compared to other feature representations. The proposed method is made as a stand-alone tool available freely for researchers, particularly for those interested in vaccine design and novel molecular target development for systems therapeutics and diagnostics: https://github.com/brsaran/LBEEP.