Distribution analysis of the putative cancer marker S100A4 across invasive squamous cell carcinoma penile tissue

MS-based proteomic methods were utilised for the first time in the discovery of novel penile cancer biomarkers. MALDI MS imaging was used to obtain the in situ biomolecular MS profile of squamous cell carcinoma of the penis which was then compared to benign epithelial MS profiles. Spectra from cancerous and benign tissue areas were examined to identify MS peaks that best distinguished normal epithelial cells from invasive squamous epithelial cells, providing crucial evidence to suggest S100A4 to be differentially expressed. Verification by immunohistochemistry resulted in positive staining for S100A4 in a sub-population of invasive but not benign epithelial cells.

The connective tissue of the adductor canal - a morphological study in fetal and adult specimens

The adductor canal is a conical or pyramid-shaped pathway that contains the femoral vessels, saphenous nerve and a varying amount of fibrous tissue. It is involved in adductor canal syndrome, a claudication syndrome involving young individuals. Our objective was to study modifications induced by aging on the connective tissue and to correlate them to the proposed pathophysiological mechanism. The bilateral adductor canals and femoral vessels of four adult and five fetal specimens were removed en bloc and analyzed. Sections 12 mu m thick were obtained and the connective tissue studied with Sirius Red, Verhoeff, Weigert and Azo stains. Scanning electron microscopy (SEM) photomicrographs of the surfaces of each adductor canal were also analyzed. Findings were homogeneous inside each group. The connective tissue of the canal was continuous with the outer layer of the vessels in both groups. The pattern of concentric, thick collagen type I bundles in fetal specimens was replaced by a diffuse network of compact collagen bundles with several transversal fibers and an impressive content of collagen III fibers. Elastic fibers in adults were not concentrated in the thick bundles but dispersed in line with the transversal fiber system. A dynamic compression mechanism with or without an evident constricting fibrous band has been proposed previously for adductor canal syndrome, possibly involving the connective tissue inside the canal. The vessels may not slide freely during movement. These age-related modifications in normal individuals may represent necessary conditions for this syndrome to develop.

High sodium intake enhances insulin-stimulated glucose uptake in rat epididymal adipose tissue

Objective: This study investigated the effect of different sodium content diets on rat adipose tissue carbohydrate metabolism and insulin sensitivity. Methods and Procedures: Male Wistar rats were fed on normal- (0.5% Na+; NS), high- (3.12% Na+; HS), or low-sodium (0.06% Na+; LS) diets for 3, 6, and 9 weeks after weaning. Blood pressure (BP) was measured using a computerized tail-cuff system. An intravenous insulin tolerance test (ivITT) was performed in fasted animals. At the end of each period, rats were killed and blood samples were collected for glucose and insulin determinations. The white adipose tissue (WAT) from abdominal and inguinal subcutaneous (SC) and periepididymal (PE) depots were weighed and processed for adipocyte isolation and measurement of in vitro rates of insulin-stimulated 2-deoxy-d-[H-3]-glucose uptake (2DGU) and conversion of -[U-C-14]-glucose into (CO2)-C-14. Results: After 6 weeks, HS diet significantly increased the BP, SC and PE WAT masses, PE adipocyte size, and plasma insulin concentration. The sodium dietary content did not influence the whole-body insulin sensitivity. A higher half-maximal effective insulin concentration (EC50) from the dose - response curve of 2DGU and an increase in the insulin-stimulated glucose oxidation rate were observed in the isolated PE adipocytes from HS rats. Discussion: The chronic salt overload enhanced the adipocyte insulin sensitivity for glucose uptake and the insulin-induced glucose metabolization, contributing to promote adipocyte hypertrophy and increase the mass of several adipose depots, particularly the PE fat pad.

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 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.

Robust linear mixed models with skew-normal independent distributions from a Bayesian perspective

Linear mixed models were developed to handle clustered data and have been a topic of increasing interest in statistics for the past 50 years. Generally. the normality (or symmetry) of the random effects is a common assumption in linear mixed models but it may, sometimes, be unrealistic, obscuring important features of among-subjects variation. In this article, we utilize skew-normal/independent distributions as a tool for robust modeling of linear mixed models under a Bayesian paradigm. The skew-normal/independent distributions is an attractive class of asymmetric heavy-tailed distributions that includes the skew-normal distribution, skew-t, skew-slash and the skew-contaminated normal distributions as special cases, providing an appealing robust alternative to the routine use of symmetric distributions in this type of models. The methods developed are illustrated using a real data set from Framingham cholesterol study. (C) 2009 Elsevier B.V. All rights reserved.

Cellular and tissue effects induced by photogemA (R) and red LED in photodynamic therapy

In order to consider the photodynamic therapy (PDT) as a clinical treatment for candidosis, it is necessary to know its cytotoxic effect on normal cells and tissues. Therefore, this study evaluated the toxicity of PDT with PhotogemA (R) associated with red light-emitting diode (LED) on L929 and MDPC-23 cell cultures and healthy rat palatal mucosa. In the in vitro experiment, the cells (30000 cells/cm(2)) were seeded in 24-well plates for 48 h, incubated with PhotogemA (R) (50, 100, or 150 mg/l) and either irradiated or not with a red LED source (630 +/- 3 nm; 75 or 100 J/cm(2); 22 mW/cm(2)). Cell metabolism was evaluated by the MTT assay (ANOVA and Dunnet`s post hoc tests; p < 0.05) and cell morphology was examined by scanning electron microscopy. In the in vivo evaluation, PhotogemA (R) (500 mg/l) was applied to the palatal mucosa of Wistar rats during 30 min and exposed to red LED (630 nm) during 20 min (306 J/cm(2)). The palatal mucosa was photographed for macroscopic analysis at 0, 1, 3, and 7 days posttreatment and subjected to histological analysis after sacrifice of the rats. For both cell lines, there was a statistically significant decrease of the mitochondrial activity (90-97%) for all PhotogemA (R) concentrations associated with red LED regardless of the energy density. However, in the in vivo evaluation, the PDT-treated groups presented intact mucosa with normal characteristics both macroscopically and histologically. From these results, it may be concluded that the association of PhotogemA (R) and red LED caused severe toxic effects on normal cell cultures, characterized by the reduction of mitochondrial activity and morphological alterations, but did not cause damage to the rat palatal mucosa in vivo.

Inference and local influence assessment in skew-normal null intercept measurement error model

In this article, we discuss inferential aspects of the measurement error regression models with null intercepts when the unknown quantity x (latent variable) follows a skew normal distribution. We examine first the maximum-likelihood approach to estimation via the EM algorithm by exploring statistical properties of the model considered. Then, the marginal likelihood, the score function and the observed information matrix of the observed quantities are presented allowing direct inference implementation. In order to discuss some diagnostics techniques in this type of models, we derive the appropriate matrices to assessing the local influence on the parameter estimates under different perturbation schemes. The results and methods developed in this paper are illustrated considering part of a real data set used by Hadgu and Koch [1999, Application of generalized estimating equations to a dental randomized clinical trial. Journal of Biopharmaceutical Statistics, 9, 161-178].

Bimodal extension based on the skew-normal distribution with application to pollen data

This paper considers an extension to the skew-normal model through the inclusion of an additional parameter which can lead to both uni- and bi-modal distributions. The paper presents various basic properties of this family of distributions and provides a stochastic representation which is useful for obtaining theoretical properties and to simulate from the distribution. Moreover, the singularity of the Fisher information matrix is investigated and maximum likelihood estimation for a random sample with no covariates is considered. The main motivation is thus to avoid using mixtures in fitting bimodal data as these are well known to be complicated to deal with, particularly because of identifiability problems. Data-based illustrations show that such model can be useful. Copyright (C) 2009 John Wiley & Sons, Ltd.

Bayesian inference for a skew-normal IRT model under the centred parameterization

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is interest in studying latent variables. These latent variables are directly considered in the Item Response Models (IRM) and they are usually called latent traits. A usual assumption for parameter estimation of the IRM, considering one group of examinees, is to assume that the latent traits are random variables which follow a standard normal distribution. However, many works suggest that this assumption does not apply in many cases. Furthermore, when this assumption does not hold, the parameter estimates tend to be biased and misleading inference can be obtained. Therefore, it is important to model the distribution of the latent traits properly. In this paper we present an alternative latent traits modeling based on the so-called skew-normal distribution; see Genton (2004). We used the centred parameterization, which was proposed by Azzalini (1985). This approach ensures the model identifiability as pointed out by Azevedo et al. (2009b). Also, a Metropolis Hastings within Gibbs sampling (MHWGS) algorithm was built for parameter estimation by using an augmented data approach. A simulation study was performed in order to assess the parameter recovery in the proposed model and the estimation method, and the effect of the asymmetry level of the latent traits distribution on the parameter estimation. Also, a comparison of our approach with other estimation methods (which consider the assumption of symmetric normality for the latent traits distribution) was considered. The results indicated that our proposed algorithm recovers properly all parameters. Specifically, the greater the asymmetry level, the better the performance of our approach compared with other approaches, mainly in the presence of small sample sizes (number of examinees). Furthermore, we analyzed a real data set which presents indication of asymmetry concerning the latent traits distribution. The results obtained by using our approach confirmed the presence of strong negative asymmetry of the latent traits distribution. (C) 2010 Elsevier B.V. All rights reserved.

On the Skew-Normal-Cauchy Distribution

In this article, we study further properties of a skew normal distribution, called the skew-normal-Cauchy (SNC) distribution by Nadarajah and Kotz (2003). A stochastic representation is obtained which allows alternative derivations for moments, moments generating function, and skewness and kurtosis coefficients. Issues related to singularity of the Fisher information matrix are investigated.

Multivariate measurement error models based on scale mixtures of the skew-normal distribution

Scale mixtures of the skew-normal (SMSN) distribution is a class of asymmetric thick-tailed distributions that includes the skew-normal (SN) distribution as a special case. The main advantage of these classes of distributions is that they are easy to simulate and have a nice hierarchical representation facilitating easy implementation of the expectation-maximization algorithm for the maximum-likelihood estimation. In this paper, we assume an SMSN distribution for the unobserved value of the covariates and a symmetric scale mixtures of the normal distribution for the error term of the model. This provides a robust alternative to parameter estimation in multivariate measurement error models. Specific distributions examined include univariate and multivariate versions of the SN, skew-t, skew-slash and skew-contaminated normal distributions. The results and methods are applied to a real data set.

On the skew-normal calibration model

In this article, we present the EM-algorithm for performing maximum likelihood estimation of an asymmetric linear calibration model with the assumption of skew-normally distributed error. A simulation study is conducted for evaluating the performance of the calibration estimator with interpolation and extrapolation situations. As one application in a real data set, we fitted the model studied in a dimensional measurement method used for calculating the testicular volume through a caliper and its calibration by using ultrasonography as the standard method. By applying this methodology, we do not need to transform the variables to have symmetrical errors. Another interesting aspect of the approach is that the developed transformation to make the information matrix nonsingular, when the skewness parameter is near zero, leaves the parameter of interest unchanged. Model fitting is implemented and the best choice between the usual calibration model and the model proposed in this article was evaluated by developing the Akaike information criterion, Schwarz`s Bayesian information criterion and Hannan-Quinn criterion.

Local Influence Analysis for Skew-Normal Linear Mixed Models

In this article, we consider local influence analysis for the skew-normal linear mixed model (SN-LMM). As the observed data log-likelihood associated with the SN-LMM is intractable, Cook`s well-known approach cannot be applied to obtain measures of local influence. Instead, we develop local influence measures following the approach of Zhu and Lee (2001). This approach is based on the use of an EM-type algorithm and is measurement invariant under reparametrizations. Four specific perturbation schemes are discussed. Results obtained for a simulated data set and a real data set are reported, illustrating the usefulness of the proposed methodology.

The log-bimodal-skew-normal model. A geochemical application

The main objective of this paper is to study a logarithm extension of the bimodal skew normal model introduced by Elal-Olivero et al. [1]. The model can then be seen as an alternative to the log-normal model typically used for fitting positive data. We study some basic properties such as the distribution function and moments, and discuss maximum likelihood for parameter estimation. We report results of an application to a real data set related to nickel concentration in soil samples. Model fitting comparison with several alternative models indicates that the model proposed presents the best fit and so it can be quite useful in real applications for chemical data on substance concentration. Copyright (C) 2011 John Wiley & Sons, Ltd.