Osteoprotegerin is sensitive to actomyosin tension in human periodontal ligament fibroblasts

The Lancet, 366(9499), 1809-1820

The pathogenesis of periodontitis involves a complex immune/inflammatory cascade that is initiated by the bacteria of the oral biofilm that forms naturally on the teeth. The susceptibility to periodontitis appears to be determined by the host response; specifically, the magnitude of the inflammatory response and the differential activation of immune pathways. The purpose of this review was to delineate our current knowledge of the host response in periodontitis. The role of innate immunity, the failure of acute inflammation to resolve (thus becoming chronic), the cytokine pathways that regulate the activation of acquired immunity and the cells and products of the immune system are considered. New information relating to regulation of both inflammation and the immune response will be reviewed in the context of susceptibility to, and perhaps control of, periodontitis. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Background Even though real-time PCR has been broadly applied in biomedical sciences, data processing procedures for the analysis of quantitative real-time PCR are still lacking; specifically in the realm of appropriate statistical treatment. Confidence interval and statistical significance considerations are not explicit in many of the current data analysis approaches. Based on the standard curve method and other useful data analysis methods, we present and compare four statistical approaches and models for the analysis of real-time PCR data. Results In the first approach, a multiple regression analysis model was developed to derive ΔΔCt from estimation of interaction of gene and treatment effects. In the second approach, an ANCOVA (analysis of covariance) model was proposed, and the ΔΔCt can be derived from analysis of effects of variables. The other two models involve calculation ΔCt followed by a two group t-test and non-parametric analogous Wilcoxon test. SAS programs were developed for all four models and data output for analysis of a sample set are presented. In addition, a data quality control model was developed and implemented using SAS. Conclusion Practical statistical solutions with SAS programs were developed for real-time PCR data and a sample dataset was analyzed with the SAS programs. The analysis using the various models and programs yielded similar results. Data quality control and analysis procedures presented here provide statistical elements for the estimation of the relative expression of genes using real-time PCR.