Synonymous Codon Usage Analysis of Thirty Two Mycobacteriophage Genomes

Synonymous codon usage of protein coding genes of thirty two completely sequenced mycobacteriophage genomes was studied using multivariate statistical analysis. One of the major factors influencing codon usage is identified to be compositional bias. Codons ending with either C or G are preferred in highly expressed genes among which C ending codons are highly preferred over G ending codons. A strong negative correlation between effective number of codons (Nc) and GC3s content was also observed, showing that the codon usage was effected by gene nucleotide composition. Translational selection is also identified to play a role in shaping the codon usage operative at the level of translational accuracy. High level of heterogeneity is seen among and between the genomes. Length of genes is also identified to influence the codon usage in 11 out of 32 phage genomes. Mycobacteriophage Cooper is identified to be the highly biased genome with better translation efficiency comparing well with the host specific tRNA genes

Insights from the clustering of microarray data associated with the heart disease

Heart failure (HF) is the major of cause of mortality and morbidity in the developed world. Gene expression profiles of animal model of heart failure have been used in number of studies to understand human cardiac disease. In this study, statistical methods of analysing microarray data on cardiac tissues from dogs with pacing induced HF were used to identify differentially expressed genes between normal and two abnormal tissues. The unsupervised techniques principal component analysis (PCA) and cluster analysis were explored to distinguish between three different groups of 12 arrays and to separate the genes which are up regulated in different conditions among 23912 genes in heart failure canines' microarray data. It was found that out of 23912 genes, 1802 genes were differentially expressed in the three groups at 5% level of significance and 496 genes were differentially expressed at 1% level of significance using one way analysis of variance (ANOVA). The genes clustered using PCA and clustering analysis were explored in the paper to understand HF and a small number of differentially expressed genes related to HF were identified

Bayesian structural equation modeling for post treatment health related quality of life among tuberculosis patients

BACKGROUND: The use of Bayesian Structural Equation Model (BSEM) to evaluate the impact of TB on self-reported health related quality of life (HRQoL) of TB patients has been not studied. OBJECTIVE: To identify the factors that contribute to the HRQoL of TB patients using BSEM. METHODS: This is a latent variable modeling with Bayesian approach using secondary data. HRQoL data collected after one year from newly diagnosed 436 TB patients who were registered and successfully completed treatment at Government health facilities in Tiruvallur district, south India under the National TB Elimination Programme (NTEP) were used for this analysis. In this study, the four independent latent variables such as physical well–being (PW = PW1-7), mental well-being (MW = MW1-7), social well-being (SW = SW1-4) and habits were considered. The BSEM was constructed using Markov Chain Monte Carlo algorithm for identifying the factors that contribute to the HRQoL of TB patients who completed treatment. RESULTS: Bayesian estimates were obtained using 46,300 observations after convergence and the standardized structural regression estimate of PW, MW, SW on HRQoL were 0.377 (p<0.001), 0.543 (p<0.001) and 0.208 (p<0.001) respectively. The latent variables PW, MW and SW were significantly associated with HRQoL of TB patients. The age was found to be significantly negatively associated with HRQoL of TB patients. CONCLUSIONS: The current study demonstrated the application of BSEM in evaluating HRQoL. This methodology may be used to study precise estimates of HRQoL of TB patients in different time points

Antihypercholesterolemic and Antioxidative Potential of an Extract of the Plant, Piper betle

Hypercholesterolemia is a dominant risk factor for atherosclerosis and cardiovascular diseases. In the present study, the putative antihypercholesterolemic and antioxidative properties of an ethanolic extract of Piper betle and of its active constituent, eugenol, were evaluated in experimental hypercholesterolemia induced by a single intraperitoneal injection of Triton WR-1339 (300 mg/kg b.wt) in Wistar rats. Saline-treated hypercholesterolemic rats revealed significantly higher mean blood/serum levels of glucose, total cholesterol, triglycerides, low density and very low density lipoprotein cholesterol, and of serum hepatic marker enzymes; in addition, significantly lower mean serum levels of high density lipoprotein cholesterol and significantly lower mean activities of enzymatic antioxidants and nonenzymatic antioxidants were noted in hepatic tissue samples from saline-treated hypercholesterolemic rats, compared to controls. However, in hypercholesterolemic rats receiving the Piper betle extract (500 mg/kg b.wt) or eugenol (5 mg/kg b.wt) for seven days orally, all these parameters were significantly better than those in saline-treated hypercholesterolemic rats. The hypercholesterolemia-ameliorating effect was better defined in eugenol-treated than in Piper betle extract-treated rats, being as effective as that of the standard lipid-lowering drug, lovastatin (10 mg/kg b.wt). These results suggest that eugenol, an active constituent of the Piper betle extract, possesses antihypercholesterolemic and other activities in experimental hypercholesterolemic Wistar rats

Structural equation modeling of latent growth curves of weight gain among treated tuberculosis patients.

Tuberculosis still remains a major public health problem even though it is treatable and curable. Weight gain measurement during anti tuberculosis (TB) treatment period is an important component to assess the progress of TB patients. In this study, Latent Growth Models (LGMs) were implemented in a longitudinal design to predict the change in weight of TB patients who were given three different regimens under randomized controlled clinical trial for anti-TB treatment. Linear and Quadratic LGMs were fitted using Mplus software. The age, sex and treatment response of the TB patients were used as time invariant independent variables of the growth trajectories. The quadratic trend was found to be better in explaining the changes in weight without grouping than the quadratic model for three group comparisons. A significant increase in the change of weight over time was identified while a significant quadratic effect indicated that weights were sustained over time. The growth rate was similar in both the groups. The treatment response had significant association with the growth rate of weight scores of the patients

Base line characteristic of the TB patients.

<p>Base line characteristic of the TB patients.</p

Parameter Estimates of quadratic LGM.

<p>*p<0.001, #p<0.005, +p<0.05, $p<0.01.</p