Validation of a novel ELISA for measurement of electronegative low-density lipoprotein

Background: Oxidative modification of low-density lipoprotein (LDL) plays a key role in the pathogenesis of atherosclerosis. LDL(-) is present in blood plasma of healthy subjects and at higher concentrations in diseases with high cardiovascular risk, such as familial hypercholesterolemia or diabetes. Methods: We developed and validated a sandwich ELISA for LDL(-) in human plasma using two monoclonal antibodies against LDL(-) that do not bind to native LDL, extensively copper-oxidized LDL or malondialdehyde-modified LDL. The characteristics of assay performance, such as limits of detection and quantification, accuracy, inter- and intra-assay precision were evaluated. The linearity, interferences and stability tests were also performed. Results: The calibration range of the assay is 0.625-20.0 mU/L at 1: 2000 sample dilution. ELISA validation showed intra- and inter- assay precision and recovery within the required limits for immunoassays. The limits of detection and quantification were 0.423 mU/L and 0.517 mU/L LDL(-), respectively. The intra- and inter- assay coefficient of variation ranged from 9.5% to 11.5% and from 11.3% to 18.9%, respectively. Recovery of LDL(-) ranged from 92.8% to 105.1%. Conclusions: This ELISA represents a very practical tool for measuring LDL(-) in human blood for widespread research and clinical sample use. Clin Chem Lab Med 2008; 46: 1769-75.

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.

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].

Challenging measurement of the (16)O+(27)Al elastic and inelastic angular distributions up to large angles

The (16)O+(27)Al elastic and inelastic angular distributions have been measured in a broad angular range (13 degrees < theta(lab) < 52 degrees) at about 100 MeV incident energy. The use of the MAGNEX large acceptance magnetic spectrometer and of the ray-reconstruction analysis technique has been crucial in order to provide, in the same experiment, high-resolution energy spectra and cross-section measurements distributed over more than seven orders of magnitude down to hundreds of nb/sr. (C) 2011 Elsevier B.V. All rights reserved.

A dedicated setup for the measurement of the electron transport parameters in gases at large electric fields

Electron transport parameters are important in several areas ranging from particle detectors to plasma-assisted processing reactors. Nevertheless, especially at high fields strengths and for complex gases, relatively few data are published. A dedicated setup has been developed to measure the electron drift velocity and the first Townsend coefficient in parallel plate geometry. An RPC-like cell has been adopted to reach high field strengths without the risk of destructive sparks. The validation data obtained with pure Nitrogen will be presented and compared to a selection of the available literature and to calculations performed with Magboltz 2 version 8.6. The new data collected in pure Isobutane will then be discussed. This is the first time the electron drift velocity in pure Isobutane is measured well into the saturation region. Good agreement is found with expectations from Magboltz. (C) 2009 Elsevier B.V. All rights reserved.

Influence of different light sources and photo-activation methods on degree of conversion and polymerization shrinkage of a nanocomposite resin

The purpose of this study was to evaluate the influence of different light sources and photo-activation methods on degree of conversion (DC%) and polymerization shrinkage (PS) of a nanocomposite resin (Filtek (TM) Supreme XT, 3M/ESPE). Two light-curing units (LCUs), one halogen-lamp (QTH) and one light-emitting-diode (LED), and two different photo-activation methods (continuous and gradual) were investigated in this study. The specimens were divided in four groups: group 1-power density (PD) of 570 mW/cm(2) for 20 s (QTH); group 2-PD 0 at 570 mW/cm(2) for 10 s + 10 s at 570 mW/cm(2) (QTH); group 3-PD 860 mW/cm(2) for 20 s (LED), and group 4-PD 125 mW/cm(2) for 10 s + 10 s at 860 mW/cm(2) (LED). A testing machine EMIC with rectangular steel bases (6 x 1 x 2 mm) was used to record the polymerization shrinkage forces (MPa) for a period that started with the photo-activation and ended after two minutes of measurement. For each group, ten repetitions (n = 40) were performed. For DC% measurements, five specimens (n = 20) for each group were made in a metallic mold (2 mm thickness and 4 mm diameter, ISO 4049) and them pulverized, pressed with bromide potassium (KBr) and analyzed with FT-IR spectroscopy. The data of PS were analyzed by Analysis of Variance (ANOVA) with Welch`s correction and Tamhane`s test. The PS means (MPa) were: 0.60 (G1); 0.47 (G2); 0.52 (G3) and 0.45 (G4), showing significant differences between two photo-activation methods, regardless of the light source used. The continuous method provided the highest values for PS. The data of DC% were analyzed by Analysis of Variance (ANOVA) and shows significant differences for QTH LCUs, regardless of the photo-activation method used. The QTH provided the lowest values for DC%. The gradual method provides lower polymerization contraction, either with halogen lamp or LED. Degree of conversion (%) for continuous or gradual photo-activation method was influenced by the LCUs. Thus, the presented results suggest that gradual method photo-activation with LED LCU would suffice to ensure adequate degree of conversion and minimum polymerization shrinkage.

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].

Semiparametric Bayesian measurement error modeling

This work presents a Bayesian semiparametric approach for dealing with regression models where the covariate is measured with error. Given that (1) the error normality assumption is very restrictive, and (2) assuming a specific elliptical distribution for errors (Student-t for example), may be somewhat presumptuous; there is need for more flexible methods, in terms of assuming only symmetry of errors (admitting unknown kurtosis). In this sense, the main advantage of this extended Bayesian approach is the possibility of considering generalizations of the elliptical family of models by using Dirichlet process priors in dependent and independent situations. Conditional posterior distributions are implemented, allowing the use of Markov Chain Monte Carlo (MCMC), to generate the posterior distributions. An interesting result shown is that the Dirichlet process prior is not updated in the case of the dependent elliptical model. Furthermore, an analysis of a real data set is reported to illustrate the usefulness of our approach, in dealing with outliers. Finally, semiparametric proposed models and parametric normal model are compared, graphically with the posterior distribution density of the coefficients. (C) 2009 Elsevier Inc. All rights reserved.

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.

Influence Diagnostics for a Skew Extension of the Grubbs Measurement Error Model

Influence diagnostics methods are extended in this article to the Grubbs model when the unknown quantity x (latent variable) follows a skew-normal distribution. Diagnostic measures are derived from the case-deletion approach and the local influence approach under several perturbation schemes. The observed information matrix to the postulated model and Delta matrices to the corresponding perturbed models are derived. Results obtained for one real data set are reported, illustrating the usefulness of the proposed methodology.

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.

Measurement of the Viscoelastic Properties of the Vocal Folds

Objectives: Studies of the viscoelastic properties of the vocal folds are normally performed with rheometers that use parallel assigned a fixed value. In tissues subject to variation of thickness plates whose interplate space is usually at between samples, fixed gaps could result in different compressions, compromising the comparison among them. We performed,in experimental study to determine whether different compressions call lead to different results in measurements of dynamic viscosity (DV) of vocal fold samples. Methods: We Measured the DV of vocal fold samples of 10 larynges of cadavers under 3 different compression levels, corresponding to 0.2, 0.5, and 10 N on an 8-mm-diameter parallel-plate rheometer. Results: The DV directly varied with compression. We observed statistically significant differences between the results of 0.2 and 10 N (p = 0.0396) and 0.5 and 10 N (p = 0.0442). Conclusions: The study demonstrated that the level of compression influences the DV measure and Suggests that a defined compression level should be used in rheometric studies of biological tissues.