One-step reverse transcriptase polymerase chain reaction for the diagnosis of respiratory syncytial virus in children

Human respiratory syncytial virus (HRSV) is the main cause of acute lower respiratory tract infections in infants and children. Rapid diagnosis is required to permit appropriate care and treatment and to avoid unnecessary antibiotic use. Reverse transcriptase (RT-PCR) and indirect immunofluorescence assay (IFA) methods have been considered important tools for virus detection due to their high sensitivity and specificity. In order to maximize use-simplicity and minimize the risk of sample cross-contamination inherent in two-step techniques, a RT-PCR method using only a single tube to detect HRSV in clinical samples was developed. Nasopharyngeal aspirates from 226 patients with acute respiratory illness, ranging from infants to 5 years old, were collected at the University Hospital of the University of Sao Paulo (HU-USP), and tested using IFA, one-step RT-PCR, and semi-nested RT-PCR. One hundred and two (45.1%) samples were positive by at least one of the three methods, and 75 (33.2%) were positive by all methods: 92 (40.7%) were positive by one-step RT-PCR, 84 (37.2%) by IFA, and 96 (42.5%) by the semi-nested RT-PCR technique. One-step RT-PCR was shown to be fast, sensitive, and specific for RSV diagnosis, without the added inconvenience and risk of false positive results associated with semi-nested PCR. The combined use of these two methods enhances HRSV detection. (C) 2007 Elsevier B.V. All rights reserved.

Effects of lipid-lowering drugs on reverse cholesterol transport gene expressions in peripheral blood mononuclear and HepG2 cells

Aims: The ATP-binding cassette transporters, ABCA1 and ABCG1, are LXR-target genes that play an important role in reverse cholesterol transport. We examined the effects of inhibitors of the cholesterol absorption (ezetimibe) and synthesis (statins) on expression of these transporters in HepG2 cells and peripheral blood mononuclear cells (PBMCs) of individuals with primary (and nonfamilial) hypercholesterolemia (HC). Materials & methods: A total of 48 HC individuals were treated with atorvastatin (10 mg/day/4 weeks) and 23 were treated with ezetimibe (10 mg/day/4 weeks), followed by simvastatin (10 mg/day/8 weeks) and simvastatin plus ezetimibe (10 mg of each/day/4 weeks). Gene expression was examined in statin- or ezetimibe-treated and control HepG2 cells as well as PBMCs using real-time PCR. Results: In PBMCs, statins and ezetimibe downregulated ABCA1 and ABCG1 mRNA expression but did not modulate NR1H2 (LxR-beta) and NR1H3 (LXR-alpha) levels. Positive correlations of ABCA1 with ABCG1 and of NR1H2 with NR1H3 expressions were found in all phases of the treatments. In HepG2 cells, ABCA1 mRNA levels remained unaltered while ABCG1 expression was increased by statin (1.0-10.0 mu M) or ezetimibe (5.0 mu M) treatments. Atorvastatin upregulated NR1H2 and NR1H3 only at 10.0 mu M, meanwhile ezetimibe (1.0-5.0 mu M) downregulated NR1H2 but did not change NR1H3 expression. Conclusion: Our findings reveal that lipid-lowering drugs downregulate ABCA1 and ABCG1 mRNA expression in PBMCs of HC individuals and exhibit differential effects on HepG2 cells. Moreover, they indicate that the ABCA1 and ABCG1 transcript levels were not correlated directly to LXR mRNA expression in both cell models treated with lipid-lowering drugs.

In silico screening of HIV-1 non-nucleoside reverse transcriptase and protease inhibitors

Two targets, reverse transcriptase (RT) and protease from HIV-1, were used during the past two decades to the discovery of non-nucleoside reverse transcriptase inhibitors (NNRTI) and protease inhibitors (PI) that belong to the arsenal of the antiretroviral therapy. Herein these enzymes were chosen as templates for conducting a computer-aided ligand design. Ligand and structure-based drug designs were the starting points to select compounds from a database bearing more than five million compounds by means of cheminformatic tools. New promising lead structures are retrieved from the database, which are open to acquisition and test. Classes of molecules already described as NNRTI or PI in the literature also came out and were useful to prove the reliability of the workflow, and thus validating the work carried out so far. (c) 2007 Elsevier Masson SAS. All rights reserved.

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.

Prediction in Multilevel Logistic Regression

The purpose of this article is to present a new method to predict the response variable of an observation in a new cluster for a multilevel logistic regression. The central idea is based on the empirical best estimator for the random effect. Two estimation methods for multilevel model are compared: penalized quasi-likelihood and Gauss-Hermite quadrature. The performance measures for the prediction of the probability for a new cluster observation of the multilevel logistic model in comparison with the usual logistic model are examined through simulations and an application.