Human beta-defensin 2 and protease activated receptor-2 expression in patients with chronic periodontitis

Objective: Some previous studies have shown that gingipains, trypsin-like proteases produced by Porphyromonas gingivalis, up-regulate human beta defensin-2 (HBD-2) mRNA expression through protease-activated receptor-2 (PAR(2)) in gingival epithelial cells. This study aimed at investigating salivary HBD-2 levels and crevicular PAR(2) mRNA expression in human chronic periodontitis and evaluating whether periodontal treatment affected this process. Methods: Salivary and gingival crevicular fluid (GCF) samples were collected from periodontally healthy (control) and chronic periodontitis patients at baseline and 50 days after nonsurgical periodontal treatment. Salivary HBD-2, and GCF TNF-alpha levels were analysed by ELISA, and PAR(2) mRNA at the GCF was evaluated by RT-PCR. Results: P. gingivalis was significantly (p < 0.05) more prevalent in patients with chronic periodontitis when compared to controls. This prevalence decreased after periodontal therapy (p < 0.0001). The control group showed statistically significant lower levels of HBD-2, TNF-alpha, and PAR(2) expression when compared to the chronic periodontitis group. In addition, periodontal treatment significantly reduced PAR(2) expression and HBD-2 levels in chronic periodontitis patients (p < 0.001). Conclusions: Our results suggest that salivary HBD-2 levels and PAR(2) mRNA expression from GCF are higher in subjects with chronic periodontitis than in healthy subjects, and that periodontal treatment decreases both HBD-2 levels and PAR(2) expression. (C) 2012 Elsevier Ltd. All rights reserved.

A general class of zero-or-one inflated beta regression models

This paper proposes a general class of regression models for continuous proportions when the data contain zeros or ones. The proposed class of models assumes that the response variable has a mixed continuous-discrete distribution with probability mass at zero or one. The beta distribution is used to describe the continuous component of the model, since its density has a wide range of different shapes depending on the values of the two parameters that index the distribution. We use a suitable parameterization of the beta law in terms of its mean and a precision parameter. The parameters of the mixture distribution are modeled as functions of regression parameters. We provide inference, diagnostic, and model selection tools for this class of models. A practical application that employs real data is presented. (C) 2011 Elsevier B.V. All rights reserved.