Altered phenotype and function of dendritic cells in individuals with chronic periodontitis

OBJECTIVE: To investigate the effects of periodontal bacterial lysates on maturation and function of mature monocyte-derived dendritic cells (m-MDDCs) derived from individuals with chronic periodontitis (CP) or healthy periodontal tissue (HP). DESIGN: m-MDDCs derived from peripheral blood monocytes, cultured for 7 days in presence of interleukin (IL)-4 and granulocyte-macrophage colony stimulating factor (GM-CSF), were stimulated with lysates of Streptococcus sanguinis, Prevotella intermedia, Porphyromonas gingivalis, or Treponema denticola on day 4, and were then phenotyped. IL-10, IL-12 and IFN-gamma concentration in the supernatant of cultures were measured. RESULTS: Expression of HLA-DR was lower in bacterial-unstimulated mature m-MDDC from CP compared to HP (p=0.04), while expression of CD1a and CD123 were higher in CP. The expression pattern of HLA-DR, CD11c, CD123, and CD1a did not change on bacterial stimulation, regardless of the bacteria. Stimulation with P. intermedia upregulated CD80 and CD86 in CP cells (p≤0.05). Production of IL-12p70 by bacterial-unstimulated m-MDDCs was 5.8-fold greater in CP compared to HP. Bacterial stimulation further increased IL-12p70 production while decreasing IL-10. Significantly more IFN-gamma was produced in co-cultures of CP m-MDDCs than with HP m-MDDCs when cells were stimulated with P. intermedia (p=0.009). CONCLUSIONS: Bacterial-unstimulated m-MDDC from CP exhibited a more immature phenotype but a cytokine profile biased towards proinflammatory response; this pattern was maintained/exacerbated after bacterial stimulation. P. intermedia upregulated co-stimulatory molecules and IFN-gamma expression in CP m-MDDC. These events might contribute to periodontitis pathogenesis

Evaluation of physiologic complexity in time series using generalized sample entropy and surrogate data analysis

Complexity in time series is an intriguing feature of living dynamical systems, with potential use for identification of system state. Although various methods have been proposed for measuring physiologic complexity, uncorrelated time series are often assigned high values of complexity, errouneously classifying them as a complex physiological signals. Here, we propose and discuss a method for complex system analysis based on generalized statistical formalism and surrogate time series. Sample entropy (SampEn) was rewritten inspired in Tsallis generalized entropy, as function of q parameter (qSampEn). qSDiff curves were calculated, which consist of differences between original and surrogate series qSampEn. We evaluated qSDiff for 125 real heart rate variability (HRV) dynamics, divided into groups of 70 healthy, 44 congestive heart failure (CHF), and 11 atrial fibrillation (AF) subjects, and for simulated series of stochastic and chaotic process. The evaluations showed that, for nonperiodic signals, qSDiff curves have a maximum point (qSDiff(max)) for q not equal 1. Values of q where the maximum point occurs and where qSDiff is zero were also evaluated. Only qSDiff(max) values were capable of distinguish HRV groups (p-values 5.10 x 10(-3); 1.11 x 10(-7), and 5.50 x 10(-7) for healthy vs. CHF, healthy vs. AF, and CHF vs. AF, respectively), consistently with the concept of physiologic complexity, and suggests a potential use for chaotic system analysis. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4758815]