A new model of outbred genetically selected mice which present a strong acute inflammatory response in the absence of complement component C5

Mice selected for a strong (AIRmax) or weak (AIRmin) acute inflammatory response present different susceptibilities to bacterial infections, autoimmune diseases and carcinogenesis. Variations in these phenotypes have been also detected in AIRmax and AIRmin mice rendered homozygous for Slc11a1 resistant (R) and susceptible (S) alleles. Our aim was to investigate if the phenotypic differences observed in these mice was related to the complement system. AIRmax and AIRmin mice and AIRmax and AIRmin groups homozygous for the resistance (R) or susceptibility (S) alleles of the solute carrier family 11a1 member (Slc11a1) gene, formerly designated Nramp-1. While no difference in complement activity was detected in sera from AIRmax and AIRmin strains, all sera from AIRmax Slc11a1 resistant mice (AIRmax(RR)) presented no complement-dependent hemolytic activity. Furthermore, C5 was not found in their sera by immunodiffusion and, polymerase chain reaction and DNA sequencing of its gene demonstrated that AIRmax(RR) mice are homozygous for the C5 deficient (D) mutation previously described in A/J. Therefore, the C5D allele was fixed in homozygosis in AIRmax(RR) line. The AIRmax(RR) line is a new experimental mouse model in which a strong inflammatory response can be triggered in vivo in the absence of C5.

Effects of exercise training on autonomic dysfunction management in an experimental model of menopause and myocardial infarction

Objective: The aim of this study was to investigate the effects of exercise training on cardiovascular autonomic dysfunction in ovariectomized rats submitted to myocardial infarction. Methods: Female Wistar rats were divided into the following ovariectomized groups: sedentary ovariectomized (SO), trained ovariectomized (TO), sedentary ovariectomized infarcted (SOI), and trained ovariectomized infarcted (TOI). Trained groups were submitted to an exercise training protocol on a treadmill (8 wk). Arterial baroreflex sensitivity was evaluated by heart rate responses to arterial pressure changes, and cardiopulmonary baroreflex sensitivity was tested by bradycardic and hypotension responses to serotonin injection. Vagal and sympathetic effects were calculated by pharmacological blockade. Results: Arterial pressure was reduced in the TO in comparison with the SO group and increased in the TOI in relation to the SOI group. Exercise training improved the baroreflex sensitivity in both the TO and TOI groups. The TOI group displayed improvement in cardiopulmonary reflex sensitivity compared with the SOI group at the 16 mu g/kg serotonin dose. Exercise training enhanced the vagal effect in both the TO (45%) and TOI (46%) animals compared with the SO and SOI animals and reduced the sympathetic effect in the TOI (38%) in comparison with the SOI animals. Significant correlations were obtained between bradycardic baroreflex responses and vagal (r = -0.7, P < 0.005) and sympathetic (r = 0.7, P < 0.001) effects. Conclusions: These results indicate that exercise training in ovariectomized rats submitted to myocardial infarction improves resting hemodynamic status and reflex control of the circulation, which may be due to an increase in the vagal component. This suggests a homeostatic role for exercise training in reducing the autonomic impairment of myocardial infarction in postmenopausal women.

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.

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.

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].

A robust Bayesian approach to null intercept measurement error model with application to dental data

Measurement error models often arise in epidemiological and clinical research. Usually, in this set up it is assumed that the latent variable has a normal distribution. However, the normality assumption may not be always correct. Skew-normal/independent distribution is a class of asymmetric thick-tailed distributions which includes the Skew-normal distribution as a special case. In this paper, we explore the use of skew-normal/independent distribution as a robust alternative to null intercept measurement error model under a Bayesian paradigm. We assume that the random errors and the unobserved value of the covariate (latent variable) follows jointly a skew-normal/independent distribution, providing an appealing robust alternative to the routine use of symmetric normal distribution in this type of model. Specific distributions examined include univariate and multivariate versions of the skew-normal distribution, the skew-t distributions, the skew-slash distributions and the skew contaminated normal distributions. The methods developed is illustrated using a real data set from a dental clinical trial. (C) 2008 Elsevier B.V. All rights reserved.