Involvement of sensory nerves and TRPV1 receptors in the rat airway inflammatory response to two environment pollutants: diesel exhaust particles (DEP) and 1,2-naphthoquinone (1,2-NQ)

The environmental chemical 1,2-naphthoquinone (1,2-NQ) is implicated in the exacerbation of airways diseases induced by exposure to diesel exhaust particles (DEP), which involves a neurogenic-mediated mechanism. Plasma extravasation in trachea, main bronchus and lung was measured as the local (125)I-bovine albumin accumulation. RT-PCR quantification of TRPV1 and tachykinin (NK(1) and NK(2)) receptor gene expression were investigated in main bronchus. Intratracheal injection of DEP (1 and 5 mg/kg) or 1,2-NQ (35 and 100 nmol/kg) caused oedema in trachea and bronchus. 1,2-NQ markedly increased the DEP-induced responses in the rat airways in an additive rather than synergistic manner. This effect that was significantly reduced by L-732,138, an NK(1) receptor antagonist, and in a lesser extent by SR48968, an NK(2) antagonist. Neonatal capsaicin treatment also markedly reduced DEP and 1,2-NQ-induced oedema. Exposure to pollutants increased the TRPV1, NK(1) and NK(2) receptors gene expression in bronchus, an effect was partially suppressed by capsaicin treatment. In conclusion, our results are consistent with the hypothesis that DEP-induced airways oedema is highly influenced by increased ambient levels of 1,2-NQ and takes place by neurogenic mechanisms involving up-regulation of TRPV1 and tachykinin receptors.

Log-Burr XII regression models with censored data

In survival analysis applications, the failure rate function may frequently present a unimodal shape. In such case, the log-normal or log-logistic distributions are used. In this paper, we shall be concerned only with parametric forms, so a location-scale regression model based on the Burr XII distribution is proposed for modeling data with a unimodal failure rate function as an alternative to the log-logistic regression model. Assuming censored data, we consider a classic analysis, a Bayesian analysis and a jackknife estimator for the parameters of the proposed model. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the log-logistic and log-Burr XII regression models. Besides, we use sensitivity analysis to detect influential or outlying observations, and residual analysis is used to check the assumptions in the model. Finally, we analyze a real data set under log-Buff XII regression models. (C) 2008 Published by Elsevier B.V.