Microbial culture and checkerboard DNA-DNA hybridization assessment of bacteria in root canals of primary teeth pre- and post-endodontic therapy with a calcium hydroxide/chlorhexidine paste

Aim. To investigate the root canal microbiota of primary teeth with apical periodontitis and the in vivo antimicrobial effects of a calcium hydroxide/chlorhexidine paste used as root canal dressing. Design. Baseline samples were collected from 30 root canals of primary teeth with apical periodontitis. Then, the root canals were filled with a calcium hydroxide paste containing 1% chlorhexidine for 14 days and the second bacteriologic samples were taken prior to root canal filling. Samples were submitted to microbiologic culture procedure to detect root canal bacteria and processed for checkerboard DNA-DNA hybridization. Results. Baseline microbial culture revealed high prevalence and cfu number of anaerobic, black-pigmented bacteroides, Streptococcus, and aerobic microorganisms. Following root canal dressing, the overall number of cfu was dramatically diminished compared to initial contamination (P < 0.05), although prevalence did not change (P > 0.05). Of 35 probes used for checkerboard DNA-DNA hybridization, 31 (88.57%) were present at baseline, and following root canal dressing, the number of positive probes reduced to 13 (37.14%). Similarly, the number of bacterial cells diminished folowing application of calcium hydroxide/chlorhexidine root canal dressing (P = 0.006). Conclusion. Apical periodontitis is caused by a polymicrobial infection, and a calcium hydroxide/chlorhexidine paste is effective in reducing the number of bacteria inside root canals when applied as a root canal dressing.

Screening of bacteria to produce polyhydroxyalkanoates from xylose

Although xylose is a major constituent of lignocellulosic feedstock and the second most abundant sugar in nature, only 22% of 3,152 screened bacterial isolates showed significant growth in xylose in 24 h. Of those 684, only 24% accumulated polyhydroxyalkanoates after 72 h. A mangrove isolate, identified as Bacillus sp. MA3.3, yielded the best results in literature thus far for Gram-positive strains in experiments with glucose and xylose as the sole carbon source. When glucose or xylose were supplied, poly-3-hydroxybutyrate (PHB) contents of cell dry weight were, respectively, 62 and 64%, PHB yield 0.25 and 0.24 g g(-1) and PHB productivity (P(PHB)) 0.10 and 0.06 g l(-1) h(-1). This 40% P(PHB) difference may be related to the theoretical ATP production per 3-hydroxybutyrate (3HB) monomer calculated as 3 mol mol(-1) for xylose, less than half of the ATP/3HB produced from glucose (7 mol mol(-1)). In PHB production using sugar mixtures, all parameters were strongly reduced due to carbon catabolite repression. PHB production using Gram-positive strains is particularly interesting for medical applications because these bacteria do not produce lipopolysaccharide endotoxins which can induce immunogenic reactions. Moreover, the combination of inexpensive substrates and products of more value may lead to the economical sustainability of industrial PHB production.

Bayesian Estimation of the Logistic Positive Exponent IRT Model

A Bayesian inference approach using Markov Chain Monte Carlo (MCMC) is developed for the logistic positive exponent (LPE) model proposed by Samejima and for a new skewed Logistic Item Response Theory (IRT) model, named Reflection LPE model. Both models lead to asymmetric item characteristic curves (ICC) and can be appropriate because a symmetric ICC treats both correct and incorrect answers symmetrically, which results in a logical contradiction in ordering examinees on the ability scale. A data set corresponding to a mathematical test applied in Peruvian public schools is analyzed, where comparisons with other parametric IRT models also are conducted. Several model comparison criteria are discussed and implemented. The main conclusion is that the LPE and RLPE IRT models are easy to implement and seem to provide the best fit to the data set considered.

Bayesian estimation of regression parameters in elliptical measurement error models

The main object of this paper is to discuss the Bayes estimation of the regression coefficients in the elliptically distributed simple regression model with measurement errors. The posterior distribution for the line parameters is obtained in a closed form, considering the following: the ratio of the error variances is known, informative prior distribution for the error variance, and non-informative prior distributions for the regression coefficients and for the incidental parameters. We proved that the posterior distribution of the regression coefficients has at most two real modes. Situations with a single mode are more likely than those with two modes, especially in large samples. The precision of the modal estimators is studied by deriving the Hessian matrix, which although complicated can be computed numerically. The posterior mean is estimated by using the Gibbs sampling algorithm and approximations by normal distributions. The results are applied to a real data set and connections with results in the literature are reported. (C) 2011 Elsevier B.V. All rights reserved.

A semi-Markov multistate model for estimation of the mean quality-adjusted survival for non-progressive processes

We discuss the estimation of the expected value of the quality-adjusted survival, based on multistate models. We generalize an earlier work, considering the sojourn times in health states are not identically distributed, for a given vector of covariates. Approaches based on semiparametric and parametric (exponential and Weibull distributions) methodologies are considered. A simulation study is conducted to evaluate the performance of the proposed estimator and the jackknife resampling method is used to estimate the variance of such estimator. An application to a real data set is also included.

Estimation of the mean quality-adjusted survival using a multistate model for the sojourn times

In clinical trials, it may be of interest taking into account physical and emotional well-being in addition to survival when comparing treatments. Quality-adjusted survival time has the advantage of incorporating information about both survival time and quality-of-life. In this paper, we discuss the estimation of the expected value of the quality-adjusted survival, based on multistate models for the sojourn times in health states. Semiparametric and parametric (with exponential distribution) approaches are considered. A simulation study is presented to evaluate the performance of the proposed estimator and the jackknife resampling method is used to compute bias and variance of the estimator. (C) 2007 Elsevier B.V. All rights reserved.