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Atrial fibrillation (AF) is the most common sustained arrhythmia in patients with chronic kidney disease (CKD). In this study, we examined the association between inflammation and AF in 3,762 adults with CKD, enrolled in the Chronic Renal Insufficiency Cohort (CRIC) study. AF was determined at baseline by self-report and electrocardiogram (ECG). Plasma concentrations of interleukin(IL)-1, IL-1 Receptor antagonist, IL-6, tumor necrosis factor (TNF)-α, transforming growth factor-β, high sensitivity C-Reactive protein, and fibrinogen, measured at baseline. At baseline, 642 subjects had history of AF, but only 44 had AF in ECG recording. During a mean follow-up of 3.7 years, 108 subjects developed new-onset AF. There was no significant association between inflammatory biomarkers and past history of AF. After adjustment for demographic characteristics, comorbid conditions, laboratory values, echocardiographic variables, and medication use, plasma IL-6 level was significantly associated with presence of AF at baseline (Odds ratio [OR], 1.61; 95% confidence interval [CI], 1.21 to 2.14; P = 0.001) and new-onset AF (OR, 1.25; 95% CI, 1.02 to 1.53; P = 0.03). To summarize, plasma IL-6 level is an independent and consistent predictor of AF in patients with CKD.

Areas of future GBM recurrence exhibit small but highly statistically significant differences in signal intensity on ADC maps and FLAIR images months before the development of abnormal enhancement occurs. A multiparametric logistic model calibrated to these changes can be used to estimate the burden of microscopic nonenhancing tumor and predict the location of recurrent disease. Computational big-data modeling performed at the voxel level is a powerful technique capable of discovering important but subtle patterns in imaging data.

Familial aggregation of early onset or juvenile Periodontitis (JP), a disorder that varies in expression and age of onset, has been recognized for some time. Autosomal recessive and X‐linked inheritance patterns have been suggested, and one large pedigree has demonstrated autosomal dominant inheritance. The variability and age limitations in clinical phenotypic diagnosis present several problems to genetic analysis, because information on members of the youngest and older generations may be lost to the analysis. The purpose of the present study was to elucidate the genetic basis of JP by formal pedigree analysis and comparison of competing genetic models. Twenty‐eight families were included, with general and specific autosomal models, and an X‐linked model being compared. The autosomal recessive model provided the most parsimonious explanation of the data, and its likelihood was not significantly different from the more general model. Likelihoods for the sporadic (nongenetic) and X‐linked models were considerably lower than the autosomal models. While comparison of genetic models suggests recessive inheritance of JP, the serious complications to pedigree analysis posed by limitations warns against acceptance of this conclusion, without more exhaustive evaluation of: (1) a more extensive collection of family data, (2) more complete investigation of the effects of age limitations on comparisons among competing models, and (3) elucidation of the importance of diagnosis and phenotype assignment of adults through past dental records.