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Background-Atrial fibrillation (AF) is the most common sustained arrhythmia observed in otherwise healthy individuals.Most lone AF cases are nonfamilial, leading to the assumption that a primary genetic origin is unlikely. In this study, we provide data supporting a novel paradigm that atrial tissue-specific genetic defects may be associated with sporadic cases of lone AF. Methods and Results-We sequenced the entire coding region of the connexin 43 (Cx43) gene (GJA1) from atrial tissue and lymphocytes of 10 unrelated subjects with nonfamilial, lone AF who had undergone surgical pulmonary vein isolation. In the atrial tissue of 1 patient, we identified a novel frameshift mutation caused by a single nucleotide deletion (c.932delC) that predicted 36 aberrant amino acids followed by a premature stop codon, leading to truncation of the C-terminal domain of Cx43. The mutation was absent from the lymphocyte DNA of the patient, indicating genetic mosaicism. Protein trafficking studies demonstrated intracellular retention of the mutant protein and a dominantnegative effect on gap junction formation of both wild-type Cx43 and Cx40. Electrophysiological studies revealed no electrical coupling of cells expressing the mutant protein alone and significant reductions in coupling when coexpressed with wild-type connexins. Conclusions-This study reports atrial tissue genetic mosaicism of a novel loss-of-function Cx43 mutation associated with lone AF. These findings implicate somatic genetic defects of Cx43 as a potential cause of AF and support the paradigm that sporadic, nonfamilial cases of lone AF may arise from genetic mosaicism that creates heterogeneous coupling patterns, predisposing the tissue to reentrant arrhythmias. (Circulation. 2010;122:236-244.)

Reexploration for bleeding is a lethal and morbid complication of cardiac surgery, with a detrimental effect that surpasses that of any other known potentially modifiable risk factor. All efforts should be made to minimize the incidence and burden of reexploration for bleeding, including further research on transfusion management during CPB.

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Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available in DICOM format in company with the labels of the training set. The labels of the test set are hidden at the time of writing this paper as they will be used for benchmarking machine learning algorithms on an open platform.

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