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https://www.selleckchem.com/products/mek162.html Bronchiectasis (B), commonly seen in patients with chronic obstructive pulmonary disease (COPD), is associated with exacerbations and predicts mortality. To differentiate patient groups with COPD-(B+) or COPD-(B-) and their exacerbations by using inflammatory markers. Consecutive COPD patients were divided into two groups according to findings on high resolution thorax CT (HRCT) images using Smith and modified Reiff scores. Patients were prospectively followed for possible future exacerbations. Serum fibrinogen, C-reactive protein (CRP), soluble urokinase-type plasminogen activator receptor (suPAR) and Plasminogen activator inhibitor-1 (PAI-1) levels were studied during exacerbation and stable periods. Eighty-seven patients were included and (85M, 2 F), mean aged was 68.1±9 (46-87). HRCT confirmed bronchiectasis in 38 (43.7%) patients, most commonly in tubular form (89.4%) and in lower lobes. COPD-B(+) group had lower body mass index (P=0.036), more advanced stage of disease (P=0.004) and more frequenls of suPAR and PAI-1 suggest us their significant roles in increased systemic inflammation associated with coexisting of COPD and bronchiectasis.Multivariate count data are common in many disciplines. The variables in such data often exhibit complex positive or negative dependency structures. We propose three Bayesian approaches to modeling bivariate count data by simultaneously considering covariate-dependent means and correlation. A direct approach utilizes a bivariate negative binomial probability mass function developed in Famoye (2010, Journal of Applied Statistics). The second approach fits bivariate count data indirectly using a bivariate Poisson-gamma mixture model. The third approach is a bivariate Gaussian copula model. Based on the results from simulation analyses, the indirect and copula approaches perform better overall than the direct approach in terms of model fitting and identifying covariate-dependent as

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