Phenotypic modulation of cultured vascular smooth muscle cells: a functional analysis focusing on MLC and ERK1/2 phosphorylation

Primary cultures of vascular smooth muscle cells (VSMCs) from rats offer a good model system to examine the molecular basis of mechanism of vascular contraction-relaxation. However, during pathological conditions such as atherosclerosis and hypertension, VSMCs characteristically exhibit phenotypic modulation, change from a quiescent contractile to a proliferative synthetic phenotype, which impairs this mechanism of vascular contraction-relaxation. Taking in account that Myosin light chain (MLC) and ERK1/2 directly participate in the process of vascular contraction, the aim of the current study was to analyze the involvement of MLC and ERK1/2 signaling during the process of VSMCs phenotypic modulation. Primary cultures of VSMCs from rat thoracic aortas were isolated and submitted to different number of passages or to freezing condition. Semi-quantitative RT-PCR was used to evaluate the mRNA levels of VSMCs differentiation markers, and western blot assays were used to determine the MLC and ERK1/2 phosphorylation levels during VSMCs phenotypic modulation. Also, immunocytochemical experiments were performed to evaluate morphological alterations occurred during the phenotypic modulation. Elevated number of passages (up to 4) as well as the freezing/thawing process induced a significant phenotypic modulation in VSMCs, which was accompanied by diminished MLC and ERK1/2 phosphorylation levels. Phosphorylation of MLC was suppressed completely by the treatment with a synthetic inhibitor of MEK-1, a direct upstream of ERK1/2, PD98059. These findings provide that ERK1/2-promoted MLC phosphorylation is impaired during VSMCs phenotypic modulation, suggesting that ERK1/2 signaling pathway may represent a potential target for understanding the pathogenesis of several vascular disease processes frequently associated to this condition.

Microbiological diversity of generalized aggressive periodontitis by 16S rRNA clonal analysis

Background/aim: The purpose of this study was to determine the bacterial diversity in the subgingival plaque of subjects with generalized aggressive periodontitis by using culture-independent molecular methods based on 16S ribosomal DNA cloning. Methods: Samples from 10 subjects with generalized aggressive periodontitis were selected. DNA was extracted and the 16S rRNA gene was amplified with the universal primer pairs 9F and 1525R. Amplified genes were cloned, sequenced, and identified by comparison with known 16S rRNA sequences. Results: One hundred and ten species were identified from 10 subjects and 1007 clones were sequenced. Of these, 70 species were most prevalent. Fifty-seven percent of the clone (40 taxa) sequences represented phylotypes for which no cultivated isolates have been reported. Several species of Selenomonas and Streptococcus were found at high prevalence and proportion in all subjects. Overall, 50% of the clone libraries were formed by these two genera. Selenomonas sputigena, the species most commonly detected, was found in nine of 10 subjects. Other species of Selenomonas were often present at high levels, including S. noxia, Selenomonas sp. EW084, Selenomonas sp. EW076, Selenomonas FT050, Selenomonas sp. P2PA_80, and Selenomonas sp. strain GAA14. The classical putative periodontal pathogens, such as, Aggregatibacter actinomycetemcomitans, was below the limit of detection and was not detected. Conclusion: These data suggest that other species, notably species of Selenomonas, may be associated with disease in generalized aggressive periodontitis subjects.

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.

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.

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].

Infrared analysis of ion beam irradiated polymers

Irradiation with heavy ions can produce several modifications in the chain structure of polymers. These modifications can be related to scissioning and cross-linking of chemical bonds. which depend on the ion fluence and the density of energy deposited in the material. Stacked thin film Makrofol-KG (R) samples were irradiated with 350 MeV Au(26+) ions and FTIR absorption spectroscopy was used to determine the bond changes in the samples. Data on the absorption bands as a function of the fluence indicated a higher probability for simple-bonds scissioning than for double-bonds scissioning and no dependence on the number of double bonds breaking with ion fluence. Since sample irradiation was done in a non-track-overlapping regime, a novel process for double bonds formation is suggested: the excitation of a site in the material by only one incident ion followed by a double bond formation during the de-excitation process. (C) 2009 Elsevier B.V. All rights reserved.

Phase diagram and spectral properties of a new exactly integrable spin-1 quantum chain

The spectral properties and phase diagram of the exactly integrable spin-1 quantum chain introduced by Alcaraz and Bariev are presented. The model has a U(1) symmetry and its integrability is associated with an unknown R-matrix whose dependence on the spectral parameters is not of a different form. The associated Bethe ansatz equations that fix the eigenspectra are distinct from those associated with other known integrable spin models. The model has a free parameter t(p). We show that at the special point t(p) = 1, the model acquires an extra U(1) symmetry and reduces to the deformed SU(3) Perk-Schultz model at a special value of its anisotropy q = exp(i2 pi/3) and in the presence of an external magnetic field. Our analysis is carried out either by solving the associated Bethe ansatz equations or by direct diagonalization of the quantum Hamiltonian for small lattice sizes. The phase diagram is calculated by exploring the consequences of conformal invariance on the finite-size corrections of the Hamiltonian eigenspectrum. The model exhibits a critical phase ruled by the c = 1 conformal field theory separated from a massive phase by first-order phase transitions.

A Single Mutation at the Sheet Switch Region Results in Conformational Changes Favoring lambda 6 Light-Chain Fibrillogenesis

Systemic amyloid light-chain (LC) amyloidosis is a disease process characterized by the pathological deposition of monoclonal LCs in tissue. All LC subtypes are capable of fibril formation although lambda chains, particularly those belonging to the lambda 6 type, are overrepresented. Here, we report the thermodynamic and in vitro fibrillogenic properties of several mutants of the lambda 6 protein 6aJL2 in which Pro7 and/or His8 was substituted by Ser or Pro. The H8P and H8S mutants were almost as stable as the wildtype protein and were poorly fibrillogenic. In contrast, the P7S mutation decreased the thermodynamic stability of 6aJL2 and greatly enhanced its capacity to form amyloid-like fibrils in vitro. The crystal structure of the P7S mutant showed that the substitution induced both local and long-distance effects, such as the rearrangement of the V(L) (variable region of the light chain)-V(L) interface. This mutant crystallized in two orthorhombic polymorphs, P2(1)2(1)2(1) and C222(1). In the latter, a monomer that was not arranged in the typical Bence-Jones dimer was observed for the first time. Crystal-packing analysis of the C222(1) lattice showed the establishment of intermolecular beta-beta interactions that involved the N-terminus and beta-strand B and that these could be relevant in the mechanism of LC fibril formation. Our results strongly suggest that Pro7 is a key residue in the conformation of the N-terminal sheet switch motif and, through long-distance interactions, is also critically involved in the contacts that stabilized the V(L) interface in lambda 6 LCs. (C) 2009 Elsevier Ltd. All rights reserved.

Bayesian Analysis for Nonlinear Regression Model Under Skewed Errors, with Application in Growth Curves

We have considered a Bayesian approach for the nonlinear regression model by replacing the normal distribution on the error term by some skewed distributions, which account for both skewness and heavy tails or skewness alone. The type of data considered in this paper concerns repeated measurements taken in time on a set of individuals. Such multiple observations on the same individual generally produce serially correlated outcomes. Thus, additionally, our model does allow for a correlation between observations made from the same individual. We have illustrated the procedure using a data set to study the growth curves of a clinic measurement of a group of pregnant women from an obstetrics clinic in Santiago, Chile. Parameter estimation and prediction were carried out using appropriate posterior simulation schemes based in Markov Chain Monte Carlo methods. Besides the deviance information criterion (DIC) and the conditional predictive ordinate (CPO), we suggest the use of proper scoring rules based on the posterior predictive distribution for comparing models. For our data set, all these criteria chose the skew-t model as the best model for the errors. These DIC and CPO criteria are also validated, for the model proposed here, through a simulation study. As a conclusion of this study, the DIC criterion is not trustful for this kind of complex model.