Reduced allergic lung inflammation in rats following formaldehyde exposure: Long-term effects on multiple effector systems

Clinical and experimental evidences show that formaldehyde (FA) exposure has an irritant effect on the upper airways. As being an indoor and outdoor pollutant, FA is known to be a causal factor of occupational asthma. This study aimed to investigate the repercussion of FA exposure on the course of a lung allergic process triggered by an antigen unrelated to FA. For this purpose, male Wistar rats were subjected to FA inhalation for 3 consecutive days (1%, 90-min daily), subsequently sensitized with ovalbumin (OVA)-alum via the intraperitoneal route, and 2 weeks later challenged with aerosolized OVA. The OVA challenge in rats after FA inhalation (FA/OVA group) evoked a low-intensity lung inflammation as indicated by the reduced enumerated number of inflammatory cells in bronchoalveolar lavage as compared to FA-untreated allergic rats (OVA/OVA group). Treatment with FA also reduced the number of bone marrow cells and blood leukocytes in sensitized animals challenged with OVA, which suggests that the effects of FA had not been only localized to the airways. As indicated by passive cutaneous anaphylactic reaction, FA treatment did not impair the anti-OVA IgE synthesis, but reduced the magnitude of OVA challenge-induced mast cell degranulation. Moreover, FA treatment was associated to a diminished lung expression of PECAM-1 (platelet-endothelial cell adhesion molecule 1) in lung endothelial cells after OVA challenge and an exacerbated release of nitrites by BAL-cultured cells. Keeping in mind that rats subjected solely to either FA or OVA challenge were able to significantly increase the cell influx into lung, our study shows that FA inhalation triggers long-lasting effects that affect multiple mediator systems associated to OVA-induced allergic lung such as the reduction of mast cells activation, PECAM-1 expression and exacerbation of NO generation, thereby contributing to the decrease of cell recruitment after the OVA challenge. In conclusion, repeated expositions to air-borne FA may impair the lung cell recruitment after an allergic stimulus, thereby leading to a non-responsive condition against inflammatory stimuli likely those where mast cells are involved. (C) 2008 Elsevier Ireland Ltd. All rights reserved.

Exercise training enhances rat pancreatic islets anaplerotic enzymes content despite reduced insulin secretion

Endurance exercise has been shown to reduce pancreatic islets glucose-stimulated insulin secretion (GSIS). Anaplerotic/cataplerotic pathways are directly related to GSIS signaling. However, the effect of endurance training upon pancreatic islets anaplerotic enzymes is still unknown. In this sense, we tested the hypothesis that endurance exercise decreases GSIS by reducing anaplerotic/cataplerotic enzymes content. Male Wistar rats were randomly assigned to one of the four experimental groups as follows: control sedentary group (CTL), trained 1 day per week (TRE1x), trained 3 days per week (TRE3x) and trained 5 days per week (TRE5x) and submitted to an 8 weeks endurance-training protocol. After the training protocol, pancreatic islets were isolated and incubated with basal (2.8 mM) and stimulating (16.7 mM) glucose concentrations for GSIS measurement by radioimmunoassay. In addition, pyruvate carboxylase (PYC), pyruvate dehydrogenase (PDH), pyruvate dehydrogenase kinase 4 (PDK4), ATP-citrate lyase (ACL) and glutamate dehydrogenase (GDH) content were quantified by western blotting. Our data showed that 8 weeks of chronic endurance exercise reduced GSIS by 50% in a dose-response manner according to weekly exercise frequency. PYC showed significant twofold increase in TRE3x. PYC enhancement was even higher in TRE5x (p < 0.0001). PDH and PDK4 reached significant 25 and 50% enhancement, respectively compared with CTL. ACL and GDH also reported significant 50 and 75% increase, respectively. The absence of exercise-induced correlations among GSIS and anaplerotic/cataplerotic enzymes suggests that exercise may control insulin release by activating other signaling pathways. The observed anaplerotic and cataplerotic enzymes enhancement might be related to beta-cell surviving rather than insulin secretion.

Reduced frequency of CD4(+)CD25(HIGH)FOXP3(+) cells and diminished FOXP3 expression in patients with Common Variable Immunodeficiency: A link to autoimmunity?

Common Variable Immunodeficiency (CVID) is a primary immunodeficiency disease characterized by defective immunoglobulin production and often associated with autoimmunity. We used flow cytometry to analyze CD4(+)CD25(HIGH)FOXP3(+) T regulatory (Treg) cells and ask whether perturbations in their frequency in peripheral blood could underlie the high incidence of autoimmune disorders in CVID patients. In this study, we report for the first time that CVID patients with autoimmune disease have a significantly reduced frequency of CD4(+)CD25(HIGH)FOXP3(+) cells in their peripheral blood accompanied by a decreased intensity of FOXP3 expression. Notably, although CVID patients in whom autoimmunity was not diagnosed had a reduced frequency of CD4(+)CD25(HIGH)FOXP3(+) cells, FOXP3 expression levels did not differ from those in healthy controls. In conclusion, these data suggest compromised homeostasis of CD4(+)CD25(HIGH)FOXP3(+) cells in a subset of CVID patients with autoimmunity, and may implicate Treg cells in pathological mechanisms of CVID. (C) 2009 Elsevier Inc. All rights reserved.

Heteroscedastic Nonlinear Regression Models

In this article, we present a generalization of the Bayesian methodology introduced by Cepeda and Gamerman (2001) for modeling variance heterogeneity in normal regression models where we have orthogonality between mean and variance parameters to the general case considering both linear and highly nonlinear regression models. Under the Bayesian paradigm, we use MCMC methods to simulate samples for the joint posterior distribution. We illustrate this algorithm considering a simulated data set and also considering a real data set related to school attendance rate for children in Colombia. Finally, we present some extensions of the proposed MCMC algorithm.

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.

Methods of Estimation in Multiple Linear Regression: Application to Clinical Data

Nesse artigo, tem-se o interesse em avaliar diferentes estratégias de estimação de parâmetros para um modelo de regressão linear múltipla. Para a estimação dos parâmetros do modelo foram utilizados dados de um ensaio clínico em que o interesse foi verificar se o ensaio mecânico da propriedade de força máxima (EM-FM) está associada com a massa femoral, com o diâmetro femoral e com o grupo experimental de ratas ovariectomizadas da raça Rattus norvegicus albinus, variedade Wistar. Para a estimação dos parâmetros do modelo serão comparadas três metodologias: a metodologia clássica, baseada no método dos mínimos quadrados; a metodologia Bayesiana, baseada no teorema de Bayes; e o método Bootstrap, baseado em processos de reamostragem.

Reduced gradient method combined with augmented Lagrangian and barrier for the optimal power flow problem

A new approach for solving the optimal power flow (OPF) problem is established by combining the reduced gradient method and the augmented Lagrangian method with barriers and exploring specific characteristics of the relations between the variables of the OPF problem. Computer simulations on IEEE 14-bus and IEEE 30-bus test systems illustrate the method. (c) 2007 Elsevier Inc. All rights reserved.

Global analytic regularity for structures of co-rank one

We consider real analytic involutive structures V, of co-rank one, defined on a real analytic paracompact orientable manifold M. To each such structure we associate certain connected subsets of M which we call the level sets of V. We prove that analytic regularity propagates along them. With a further assumption on the level sets of V we characterize the global analytic hypoellipticity of a differential operator naturally associated to V. As an application we study a case of tube structures.

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.

A bivariate regression model for matched paired survival data: local influence and residual analysis

The use of bivariate distributions plays a fundamental role in survival and reliability studies. In this paper, we consider a location scale model for bivariate survival times based on the proposal of a copula to model the dependence of bivariate survival data. For the proposed model, we consider inferential procedures based on maximum likelihood. Gains in efficiency from bivariate models are also examined in the censored data setting. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the bivariate regression model for matched paired survival data. Sensitivity analysis methods such as local and total influence are presented and derived under three perturbation schemes. The martingale marginal and the deviance marginal residual measures are used to check the adequacy of the model. Furthermore, we propose a new measure which we call modified deviance component residual. The methodology in the paper is illustrated on a lifetime data set for kidney patients.

A nonlinear regression model with skew-normal errors

In this paper we have discussed inference aspects of the skew-normal nonlinear regression models following both, a classical and Bayesian approach, extending the usual normal nonlinear regression models. The univariate skew-normal distribution that will be used in this work was introduced by Sahu et al. (Can J Stat 29:129-150, 2003), which is attractive because estimation of the skewness parameter does not present the same degree of difficulty as in the case with Azzalini (Scand J Stat 12:171-178, 1985) one and, moreover, it allows easy implementation of the EM-algorithm. As illustration of the proposed methodology, we consider a data set previously analyzed in the literature under normality.

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.

The log-exponentiated Weibull regression model for interval-censored data

In interval-censored survival data, the event of interest is not observed exactly but is only known to occur within some time interval. Such data appear very frequently. In this paper, we are concerned only with parametric forms, and so a location-scale regression model based on the exponentiated Weibull distribution is proposed for modeling interval-censored data. We show that the proposed log-exponentiated Weibull regression model for interval-censored data represents a parametric family of models that include other regression models that are broadly used in lifetime data analysis. Assuming the use of interval-censored data, we employ a frequentist analysis, a jackknife estimator, a parametric bootstrap and a Bayesian analysis for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Furthermore, for different parameter settings, sample sizes and censoring percentages, various simulations are performed; in addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to a modified deviance residual in log-exponentiated Weibull regression models for interval-censored data. (C) 2009 Elsevier B.V. All rights reserved.

Generalized log-gamma regression models with cure fraction

In this paper, the generalized log-gamma regression model is modified to allow the possibility that long-term survivors may be present in the data. This modification leads to a generalized log-gamma regression model with a cure rate, encompassing, as special cases, the log-exponential, log-Weibull and log-normal regression models with a cure rate typically used to model such data. The models attempt to simultaneously estimate the effects of explanatory variables on the timing acceleration/deceleration of a given event and the surviving fraction, that is, the proportion of the population for which the event never occurs. The normal curvatures of local influence are derived under some usual perturbation schemes and two martingale-type residuals are proposed to assess departures from the generalized log-gamma error assumption as well as to detect outlying observations. Finally, a data set from the medical area is analyzed.

Log-Burr XII regression models with censored data

In survival analysis applications, the failure rate function may frequently present a unimodal shape. In such case, the log-normal or log-logistic distributions are used. In this paper, we shall be concerned only with parametric forms, so a location-scale regression model based on the Burr XII distribution is proposed for modeling data with a unimodal failure rate function as an alternative to the log-logistic regression model. Assuming censored data, we consider a classic analysis, a Bayesian analysis and a jackknife estimator for the parameters of the proposed model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the log-logistic and log-Burr XII regression models. Besides, we use sensitivity analysis to detect influential or outlying observations, and residual analysis is used to check the assumptions in the model. Finally, we analyze a real data set under log-Buff XII regression models. (C) 2008 Published by Elsevier B.V.