Expression Profile and Distribution of Efhc1 Gene Transcript During Rodent Brain Development

One of the putative causative genes for juvenile myoclonic epilepsy (JME) is EFHC1. We report here the expression profile and distribution of Efhc1 messenger RNA (mRNA) during mouse and rat brain development. Real-time polymerase chain reaction revealed that there is no difference in the expression of Efhc1 mRNA between right and left hemispheres in both species. In addition, the highest levels of Efhc1 mRNA were found at intra-uterine stages in mouse and in adulthood in rat. In common, there was a progressive decrease in Efhc1 expression from 1-day-old neonates to 14-day-old animals in both species. In situ hybridization studies showed that rat and mouse Efhc1 mRNAs are expressed in ependymal cells of ventricle walls. Our findings suggest that Efhc1 expression is more important during initial phases of brain development and that at this stage it could be involved in key developmental mechanisms underlying JME.

The role of proliferator-activated receptor gamma coactivator-1 alpha in the fatty-acid -dependent transcriptional control of interleukin-10 in hepatic cells of rodents

Interleukin-10 (IL-10) is an endogenous factor that restrains hepatic insulin resistance in diet-induced steatosis Reducing IL-10 expression increases proinflammatory activity in the steatotic liver and worsens insulin resistance As the transcriptional coactivator proliferator-activated receptor gamma coactivator-1 alpha (PGC-1 alpha) plays a central role in dysfunctional hepatocytic activity in diet-induced steatosis, we hypothesized that at least part of the action of PGC-1 alpha could be mediated by reducing the transcription of the IL-10 gene Here, we used immunoblotting, real-time polymerase chain reaction, immunocytochemistry, and chromatin immunoprecipitation assay to investigate the role of PGC-1 alpha in the control of IL-10 expression in hepatic cells First, we show that, in the intact steatotic liver, the expressions of IL-10 and PGC-1 alpha are increased Inhibiting PGC-1 alpha expression by antisense oligonucleotide increases IL-10 expression and reduces the steatotic phenotype. In cultured hepatocytes, the treatment with saturated and unsaturated fatty acids increased IL-10 expression. This was accompanied by increased association of PGC-1 alpha with c-Maf and p50-nuclear factor (NF) kappa B, 2 transcription factors known to modulate IL-10 expression In addition, after fatty acid treatment. PGC-1 alpha, c-Maf, and p50-NF kappa B migrate from the cytosol to the nuclei of hepatocytes and bind to the IL-10 promoter region Inhibiting NF kappa B activation with salicylate reduces IL-10 expression and the association of PGC-1 alpha with p50-NF kappa B Thus, PGC-1 alpha emerges as a potential transcriptional regulator of the inflammatory phenomenon taking place in the steatotic liver (C) 2010 Elsevier Inc All rights reserved

GTPases RhoA and Rac1 are important for amelogenin and DSPP expression during differentiation of ameloblasts and odontoblasts

Morphogenesis and cytodifferentiation are distinct processes in tooth development. Cell proliferation predominates in morphogenesis; differentiation involves changes in form and gene expression. The cytoskeleton is essential for both processes, being regulated by Rho GTPases. The aim of this study was to verify the expression, distribution, and role of Rho GTPases in ameloblasts and odontoblasts during tooth development in correlation with actin and tubulin arrangements and amelogenin and dentin sialophosphoprotein (DSPP) expression. RhoA, Rac1, and Cdc42 were strongly expressed during morphogenesis; during cytodifferentiation, RhoA was present in ameloblasts and odontoblasts, Rac1 and its effector Pak3 were observed in ameloblasts; and Cdc42 was present in all cells of the tooth germ and mesenchyme. The expression of RhoA mRNA and its effectors RockI and RockII, Rac1 and Pak3, as analyzed by real-time polymerase chain reaction, increased after ameloblast and odontoblast differentiation, according to the mRNA expression of amelogenin and DSPP. The inhibition of all Rho GTPases by Clostridium difficile toxin A completely abolished amelogenin and DSPP expression in tooth germs cultured in anterior eye chamber, whereas the specific inhibition of the Rocks showed only a partial effect. Thus, both GTPases are important during tooth morphogenesis. During cytodifferentiation, Rho proteins are essential for the complete differentiation of ameloblasts and odontoblasts by regulating the expression of amelogenin and DSPP. RhoA and its effector RockI contribute to this role. A specific function for Rac1 in ameloblasts remains to be elucidated; its punctate distribution indicates its possible role in exocytosis/endocytosis.

Expression of Pancreatic Endocrine Markers by Prolactin-Treated Rat Bone Marrow Mesenchymal Stem Cells

Background. Mesenchymal stem cells (MSCs) are an attractive source for generation of cells with beta-cell properties. Previous studies have demonstrated the ability of prolactin to induce an increase in beta-cell mass and maturation, which suggests beneficial effects of its use in MSC differentiation protocols. Objective. To evaluate the expression of endocrine differentiation markers in rat MSCs treated in vitro with prolactin. Methods. Mesenchymal stem cells from bone marrow of Wistar rats were isolated, expanded, and characterized. Differentiation of MSCs was induced in medium containing 23 mmol/L of glucose, and nicotinamide, 2-mercaptoethanol, and exendin-4, in the presence or absence of 500 ng/mL of rat recombinant prolactin. Expression of endocrine markers and prolactin receptor genes was evaluated using real-time polymerase chain reaction, and compared between culture stages and presence vs absence of prolactin in the culture medium. Expression of insulin, somatostatin, glucagon, and pancreatic and duodenal homeobox 1 was also evaluated at immunofluorescence microscopy. Results. Isolated cells were mostly MSCs, as confirmed at fluorescent-activated cell sorting and cytochemistry. Pax6, Ngn-3, Isl1, NeuroD1, Nkx2.2, and Nkx6.1 exhibited varied expression during culture stages. The long form of the prolactin receptor messenger RNA was induced in prolactin-treated cultures (P < .05). The somatostatin gene was induced in early stages of differentiation (P < .05), and its expression was induced by prolactin, as confirmed using immunofluorescence. Conclusion. Culture of rat bone marrow MSCs in differentiation medium induces expression of pancreatic endocrine-specific genes, and somatostatin and prolactin receptor expression was also induced by prolactin.

Tumor necrosis factor-alpha-308G/A single nucleotide polymorphism and red-complex periodontopathogens are independently associated with increased levels of tumor necrosis factor-alpha in diseased periodontal tissues

Background and Objective: Inflammatory cytokines such as tumor necrosis factor-alpha are involved in the pathogenesis of periodontal diseases. A high between-subject variation in the level of tumor necrosis factor-alpha mRNA has been verified, which may be a result of genetic polymorphisms and/or the presence of periodontopathogens such as Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola (called the red complex) and Aggregatibacter actinomycetemcomitans. In this study, we investigated the effect of the tumor necrosis factor-alpha (TNFA) -308G/A gene polymorphism and of periodontopathogens on the tumor necrosis factor-alpha levels in the periodontal tissues of nonsmoking patients with chronic periodontitis (n = 127) and in control subjects (n = 177). Material and Methods: The TNFA-308G/A single nucleotide polymorphism was investigated using polymerase chain reaction-restriction fragment length polymorphism analysis, whereas the tumor necrosis factor-alpha levels and the periodontopathogen load were determined using real-time polymerase chain reaction. Results: No statistically significant differences were found in the frequency of the TNFA-308 single nucleotide polymorphism in control and chronic periodontitis groups, in spite of the higher frequency of the A allele in the chronic periodontitis group. The concomitant analyses of genotypes and periodontopathogens demonstrated that TNFA-308 GA/AA genotypes and the red-complex periodontopathogens were independently associated with increased levels of tumor necrosis factor-alpha in periodontal tissues, and no additive effect was seen when both factors were present. P. gingivalis, T. forsythia and T. denticola counts were positively correlated with the level of tumor necrosis factor-alpha. TNFA-308 genotypes were not associated with the periodontopathogen detection odds or with the bacterial load. Conclusion: Our results demonstrate that the TNFA-308 A allele and red-complex periodontopathogens are independently associated with increased levels of tumor necrosis factor-alpha in diseased tissues of nonsmoking chronic periodontitis patients and consequently are potentially involved in determining the disease outcome.

Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients

In this paper, we present different ofrailtyo models to analyze longitudinal data in the presence of covariates. These models incorporate the extra-Poisson variability and the possible correlation among the repeated counting data for each individual. Assuming a CD4 counting data set in HIV-infected patients, we develop a hierarchical Bayesian analysis considering the different proposed models and using Markov Chain Monte Carlo methods. We also discuss some Bayesian discrimination aspects for the choice of the best model.

Regression Models with Heteroscedasticity using Bayesian Approach

In this paper, we compare the performance of two statistical approaches for the analysis of data obtained from the social research area. In the first approach, we use normal models with joint regression modelling for the mean and for the variance heterogeneity. In the second approach, we use hierarchical models. In the first case, individual and social variables are included in the regression modelling for the mean and for the variance, as explanatory variables, while in the second case, the variance at level 1 of the hierarchical model depends on the individuals (age of the individuals), and in the level 2 of the hierarchical model, the variance is assumed to change according to socioeconomic stratum. Applying these methodologies, we analyze a Colombian tallness data set to find differences that can be explained by socioeconomic conditions. We also present some theoretical and empirical results concerning the two models. From this comparative study, we conclude that it is better to jointly modelling the mean and variance heterogeneity in all cases. We also observe that the convergence of the Gibbs sampling chain used in the Markov Chain Monte Carlo method for the jointly modeling the mean and variance heterogeneity is quickly achieved.

A Bayesian Analysis in the Presence of Covariates for Multivariate Survival Data: An example of Application

In this paper, we introduce a Bayesian analysis for survival multivariate data in the presence of a covariate vector and censored observations. Different ""frailties"" or latent variables are considered to capture the correlation among the survival times for the same individual. We assume Weibull or generalized Gamma distributions considering right censored lifetime data. We develop the Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods.

Predator-prey models: an application for the plankton dynamics of lake Geneva

In this paper we present a hierarchical Bayesian analysis for a predator-prey model applied to ecology considering the use of Markov Chain Monte Carlo methods. We consider the introduction of a random effect in the model and the presence of a covariate vector. An application to ecology is considered using a data set related to the plankton dynamics of lake Geneva for the year 1990. We also discuss some aspects of discrimination of the proposed models.

Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics

The purpose of this paper is to develop a Bayesian analysis for nonlinear regression models under scale mixtures of skew-normal distributions. This novel class of models provides a useful generalization of the symmetrical nonlinear regression models since the error distributions cover both skewness and heavy-tailed distributions such as the skew-t, skew-slash and the skew-contaminated normal distributions. The main advantage of these class of distributions is that they have a nice hierarchical representation that allows the implementation of Markov chain Monte Carlo (MCMC) methods to simulate samples from the joint posterior distribution. In order to examine the robust aspects of this flexible class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. Further, some discussions on the model selection criteria are given. The newly developed procedures are illustrated considering two simulations study, and a real data previously analyzed under normal and skew-normal nonlinear regression models. (C) 2010 Elsevier B.V. All rights reserved.

Bayesian analysis of skew-t multivariate null intercept measurement error model

The multivariate skew-t distribution (J Multivar Anal 79:93-113, 2001; J R Stat Soc, Ser B 65:367-389, 2003; Statistics 37:359-363, 2003) includes the Student t, skew-Cauchy and Cauchy distributions as special cases and the normal and skew-normal ones as limiting cases. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis of repeated measures, pretest/post-test data, under multivariate null intercept measurement error model (J Biopharm Stat 13(4):763-771, 2003) where the random errors and the unobserved value of the covariate (latent variable) follows a Student t and skew-t distribution, respectively. The results and methods are numerically illustrated with an example in the field of dentistry.

On estimation and influence diagnostics for log-Birnbaum-Saunders Student-t regression models: Full Bayesian analysis

The purpose of this paper is to develop a Bayesian approach for log-Birnbaum-Saunders Student-t regression models under right-censored survival data. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the considered model. In order to attenuate the influence of the outlying observations on the parameter estimates, we present in this paper Birnbaum-Saunders models in which a Student-t distribution is assumed to explain the cumulative damage. Also, some discussions on the model selection to compare the fitted models are given and case deletion influence diagnostics are developed for the joint posterior distribution based on the Kullback-Leibler divergence. The developed procedures are illustrated with a real data set. (C) 2010 Elsevier B.V. All rights reserved.

A Bayesian Long-term Survival Model Parametrized in the Cured Fraction

The main goal of this paper is to investigate a cure rate model that comprehends some well-known proposals found in the literature. In our work the number of competing causes of the event of interest follows the negative binomial distribution. The model is conveniently reparametrized through the cured fraction, which is then linked to covariates by means of the logistic link. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis in the proposed model. The procedure is illustrated with a numerical example.

Bayesian analysis for a skew extension of the multivariate null intercept measurement error model

Skew-normal distribution is a class of distributions that includes the normal distributions as a special case. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis in a multivariate, null intercept, measurement error model [R. Aoki, H. Bolfarine, J.A. Achcar, and D. Leao Pinto Jr, Bayesian analysis of a multivariate null intercept error-in -variables regression model, J. Biopharm. Stat. 13(4) (2003b), pp. 763-771] where the unobserved value of the covariate (latent variable) follows a skew-normal distribution. The results and methods are applied to a real dental clinical trial presented in [A. Hadgu and G. Koch, Application of generalized estimating equations to a dental randomized clinical trial, J. Biopharm. Stat. 9 (1999), pp. 161-178].

Bayesian Estimation of the Logistic Positive Exponent IRT Model

A Bayesian inference approach using Markov Chain Monte Carlo (MCMC) is developed for the logistic positive exponent (LPE) model proposed by Samejima and for a new skewed Logistic Item Response Theory (IRT) model, named Reflection LPE model. Both models lead to asymmetric item characteristic curves (ICC) and can be appropriate because a symmetric ICC treats both correct and incorrect answers symmetrically, which results in a logical contradiction in ordering examinees on the ability scale. A data set corresponding to a mathematical test applied in Peruvian public schools is analyzed, where comparisons with other parametric IRT models also are conducted. Several model comparison criteria are discussed and implemented. The main conclusion is that the LPE and RLPE IRT models are easy to implement and seem to provide the best fit to the data set considered.