Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Bhattacharjee, Atanu
- Interactome Analysis of Devs Protein Involved in Persistence of Mycobacterium tuberculosis and Design of Inhibitor against its Interacting Persister Protein: an Approach to Inhibit Protein-protein Interaction
Abstract Views :585 |
PDF Views:454
Authors
Affiliations
1 Department of Biotechnology and Bioinformatics, North Eastern Hill University, Shillong-793022, IN
2 Centre for Advanced studies in Crystallography and Biophysics, University of Madras, Guindy (Maraimalai) Campus, Chennai- 600 025, IN
1 Department of Biotechnology and Bioinformatics, North Eastern Hill University, Shillong-793022, IN
2 Centre for Advanced studies in Crystallography and Biophysics, University of Madras, Guindy (Maraimalai) Campus, Chennai- 600 025, IN
Source
Indian Journal of Bioinformatics and Biotechnology, Vol 2, No 3 (2013), Pagination: 57-64Abstract
Background: Mycobacterium tuberculosis has been a potential threat for humans for ages. Its invulnerability to various drugs and persistency has emerged as a stumbling block in eradicating the pathogenecity of the bacteria. A protein-protein interaction network of redox sensor histidine kinase response regulator (devS), a member of the two- component regulatory system devR/devS is known to be involved in onset of the dormancy response acting as a redox sensor was studied. Methods: An interactome level analysis of devS with other proteins involved is essential to gain insights into the proteins involvement in persistence of tuberculosis. Folding pattern of the proteins involved in the interaction was analyzed and molecular docking was performed to understand the protein-ligand interaction. Result: DevS protein directly interacts with high confidence with transcriptional regulatory protein (devR) protein forming a two-component system, probable transcriptional regulatory (narL) protein and a universal stress protein (MT3220). Hypoxia sensor histidine kinase response regulator dosT (MT2086) interact with the two-component regulatory system devR/devS involved in dormancy and is structurally aligned with devS protein. The folding patterns of devS, MT2086 and MT0867 are similar but at a different folding rate. Conslusion: DevS is shown to interact with devR protein with high confidence, which is involved in the two-component system. A better interaction is seen with piperine, berberine and allin with all the four target proteins.Keywords
Mycobacterium tuberculosis, Persister Proteins, InteractomeFull Text
References
- Armstrong JA, Hart PD (1987) Response of cultured macrophages to Mycobacterium tuberculosis, with observations on fusion of lysosomes with phagosomes. J. Exp. Med. 134:713-40.
- Capriotti E, Casadio R (2007) K-Fold: a tool for the prediction of the protein folding kinetic order and rate. Bioinformatics 23 (3): 385-386.
- Chauhan S, Sharma D, Singh A, Surolia A, Tyagi JS (2011) Comprehensive insights into Mycobacterium tuberculosis DevR (DosR) regulon activation switch. Nucleic Acids Research 39(17):7400-14.
- Ellner JJ (1997) Review: the immune response in human tuberculosis--implications for tuberculosis control. J. Infect. Dis. 176 (5): 1351-1359
- Hingley-Wilson SM, Sambandamurthy VK, Jacobs WR Jr. (2003) Survival perspectives from the world’s most successful pathogen, Mycobacterium tuberculosis. Nat. Immunol. 4 (10): 949-955
- Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug. Delivery. 23: 4-25
- Nathan C, Shiloh MU (2000) Reactive oxygen and nitrogen intermediates in the relationship between mammalian hosts and microbial pathogens. Proc. Natl. Acad. Sci. U. S. A. 97(16): 8841–8848.
- Noss EH, Harding CV, Boom WH (2000) Mycobacterium tuberculosis inhibits MHC class II antigen processing in murine bone marrow macrophages. Cell Immunol. 201: 63
- Parrish NM, Dick JD, Bishai WR Mechanisms of latency in Mycobacterium tuberculosis Trends Microbiol. 6(3):107-12.10.
- Roberts DM, Liao RP, Wisedchaisri G, Hol WG, Sherman DR (2004) Two sensor kinases contribute to the hypoxic response of Mycobacterium tuberculosis. J. Biol. Chem. 279:23082-23087
- Russell DG (2001) Mycobacterium tuberculosis: here today, and here tomorrow. Nat. Rev. Mol. Cell Biol. 2 (8): 569-577
- Sherman DR, Voskuil M, Schnappinger D, Liao R, Harrell MI, Schoolnik GK(2001) Regulation of the Mycobacterium tuberculosis hypoxic response gene encoding alpha -crystallin. Proc. Natl. Acad. Sci. U. S. A. 98:7534–7539.
- Shiloh MU, Manzanillo P, Cox JS (2008) Mycobacterium tuberculosis senses host-derived carbon monoxide during macrophage infection. Cell Host Microbe 3:323-330.
- Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, Jensen LJ, von Mering C (2011) The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 39:D561-8.
- Udwadia ZF, Amale RA, Ajbani KK, Rodrigues C (2012) Totally drug-resistant tuberculosis in India. Clin. Infect. Dis. 54(4):579-81.
- Voskuil MI, Schnappinger D, Harrell MI, Visconti KC, Dolganov G, Sherman DR, Schoolnik GK (2003) Inhibition of respiration by nitric oxide induces a Mycobacterium tuberculosis dormancy program. J. Exp. Med. 198:705–713.
- Wayne LG, Lin KY (1982) Glyoxylate metabolism and adaptation of Mycobacterium tuberculosis to survival under anaerobic conditions. Infect. Immun. 37:1042–1049.
- Wayne LG, Sohaskey CD (2001) Non-replicating persistence of Mycobacterium tuberculosis. Annu. Rev. Microbial. 55:139–163.
- WHO report, 2009
- WHO report, 2011
- An Application of Linear Mixed Effect Model to Compare the Drug Treatment Effect in Patients with Type 2 Diabetes
Abstract Views :382 |
PDF Views:0
Authors
Affiliations
1 Dept. of Statistics, Gauhati University
1 Dept. of Statistics, Gauhati University
Source
Indian Journal of Public Health Research & Development, Vol 4, No 1 (2013), Pagination: 24-27Abstract
In this article, different types of mixed effect models have been applied for drug effect comparison in type 2 diabetes patients. The mixed effect models have been applied through Bayesian approach and compared with frequency approach. The combination of metformin with pioglitazone is found to be effective compared to pioglitazone with gliclazide.Keywords
MCMC, FBS, AR(1)
References
- Metformin/glibenclamide (Glucovance) for type 2 diabetes mellitus avliable at Rational Assessment of Drugs and Research.2010.
- Antonio Ceriello and Don Johns, Mario Widel, David J. Eckland, Kathryn and Meng. Comparison of Effect of Pioglitazone With Metformin or Sulfonylurea (Monotherapy and Combination Therapy) on Postload Glycemia and Composite Insulin Sensitivity Index During an Oral Glucose Tolerance Test in Patients With Type 2 Diabetes. Diabetes Care 28:266–272, 2005.
- Laird, N. M. and Ware, J. H. (1982). Random effects models for longitudinal data. Biometrics 1982: 38:963–974.
- Wong, G. Y. and Mason, W. M. The hierarchical logistic regression model for multilevel analysis. Journal of the American Statistical Association 1985: 80, 513–524.
- West, M. Generalized linear models: Scale parameters, outlier accommodation and prior distributions. In J. M. Bernardo, M. H. DeGischolar_main, D. V. Lindley, and A. F. M. Smith (eds.),1985.
- Harville,David A(1977) “Maximum Likelihood Approaches to variance component Estimation and to Related Problem:Journal of the American Statistical Association; Vol 72,No.358.
- Kerl M, Martins M, Churchill G. Analysis of variance for gene expression, micro array data. J comput Biol.2000;7:819-837.
- Andrew Gelman(1995)Method of moments using Monter Carlo simulation. Journal of Computatuional and Graphical Statistics,36-54.
- Brown H and Prescott R. Applied mixed models in Medicine; 2006 John Wiley & Sons,Ltd.
- Horrace .C and Schmidt P.(2000) Multiple comparison with the best, with economic applications. J. Appl. Econometrics 15,1-26.
- Butler, S. and Louis, T. (1992). Random effects models with non-parametric priors. Stat. in Med.11, 1981±2000.
- Escobar, M. and West, M. (1998) Computing nonparametric hierarchical models. In: Dey, D. et al. (eds.) Practical Nonparametric and Semiparametric Bayesian Statistics. New York, NY:Springer.
- Spiegelhalter,D.J., Thomas,A.,Best,N. and Lunn,D(2004) WinBUGS 1.4 User Manual, Version 1.4.1.Cambridge Medical Research Council Biostatistics Unit.
- Zhu,L. and Carlin, B.P(2000) Comparing hierarchical models for spatio-temporally misaligned data using deviance information criterion. Statistics in Medicine,19,2265-2278.
- An Application of Bootstrap Regression Method in Age Dependency Structure to the Community
Abstract Views :217 |
PDF Views:3
Authors
Affiliations
1 Department of Statistics, Gauhati University, Guwahati, IN
2 Jawaharlal Nehru School of Management, Assam University, Silchar, Assam, IN
1 Department of Statistics, Gauhati University, Guwahati, IN
2 Jawaharlal Nehru School of Management, Assam University, Silchar, Assam, IN
Source
Indian Journal of Public Health Research & Development, Vol 4, No 3 (2013), Pagination: 44-48Abstract
The age dependency is an important factor to contribute to the economic structure of a family. In this work, the effect of age dependency on the economic sustainability in the families of Varanasi city has been observed. To deal with small sample size problem the boot strapping method has been used. The relevant calculations are done using the software R. It has been found that the presences of young dependent people make the families economically poor. However, the presences of old age people in the family make it economically sustainable.Keywords
Bi-Square, Huber Estimator, M-Estimator, Re-Sampling MethodReferences
- Nair PS, Chacko AT, Nair S. Age Structural Transition in South Asia: Economic Consequences [Internet]. 2009 [cited 2011]. Available from: www.iussp2009.princeton.edu/download.aspx?submissionId=91038
- Parks K. Text book of Preventive and Social Medicine. Jabalpur, India: Bhanot Publishers; 2006.
- Kevin Kinsella and David R. Phillips. Global Aging: The Challenge of Success. Population Bulletin 2005; 60(1) [cited 2011]. Available from: ttp://secamlocal.ex.ac.uk/people/staff/ dbs202/cag/courses/MT37C/course/ node13.html
- Efron B. Bootstrap Methods: Another Look at the Jacknnife [Internet]. The Annals of Statistics 2005; 7(1):1-26.
- Huber PJ. Robust Estimation of a Location Parameter [Internet]. Annals of Mathematical Statistics 1964; 35:73-101 [cited 2011]. Available from: http://www.oecd.org/dataoecd/4/24/ 38148786.pdf
- John Fox. Robust Regression, 2002[cited 2011] Available from: http:/cran.r-project.orgdoc/ contrib./Fox-Companionappendix-robustregression. pdf.
- Leger C, Politis DN, Romano JP. Bootstrap Technology and Applications [Internet]. Technical Report 1991; 54-91 [cited 2011]. Available from: http://www.oecd.org/dataoecd/4/24/ 38148786.pdf.
- Evaluation of Acute and Sub-Chronic Oral Toxicity Study of Ethanolic Extract of Crataeva nurvala Buch-Ham Stem Bark on Experimental Wistar Rats
Abstract Views :173 |
PDF Views:0
Authors
Affiliations
1 Department of Pharmacognosy, NGSM Institute of Pharmaceutical Sciences, Deralakatte, Mangalore – 575 018, Karnataka, IN
2 Department of Pharmacology, NGSM Institute of Pharmaceutical Sciences, Deralakatte, Mangalore – 574 018, Karnataka, IN
3 Department of Pharmacognosy, NGSM Institute of Pharmaceutical Sciences, Deralakatte, Mangalore – 574 018, IN
1 Department of Pharmacognosy, NGSM Institute of Pharmaceutical Sciences, Deralakatte, Mangalore – 575 018, Karnataka, IN
2 Department of Pharmacology, NGSM Institute of Pharmaceutical Sciences, Deralakatte, Mangalore – 574 018, Karnataka, IN
3 Department of Pharmacognosy, NGSM Institute of Pharmaceutical Sciences, Deralakatte, Mangalore – 574 018, IN