Background-Mechanisms favoring the occurrence of paroxysmal atrial fibrillation (PAF) are complex and poorly defined. This study was designed to analyze dynamic changes in autonomic tone preceding the onset of PAF in a large group of patients. Methods and Results-Holter tapes from 77 unselected consecutive patients (63 men and 14 women aged 58Ϯ12 years) with PAF were analyzed. A total of 147 episodes of sustained AF (Ͼ30 minutes) were recorded and submitted to time-domain and frequency-domain heart rate variability analyses; 6 periods were studied using repeated measures ANOVA: the 24-hour period, the hour preceding PAF, and the 20 minutes before PAF divided into four 5-minute periods. In the time-domain analyses, a linear decrease in mean RR interval from 925Ϯ16 to 906Ϯ16 ms (PϽ0.0002) was observed before the onset of PAF, together with a significant increase in the standard deviation of NN intervals from 65Ϯ4 to 70Ϯ4 ms (PϽ0.02). In the frequency-domain analyses, a significant increase in high-frequency (HF, HF-NU) components was observed before PAF (PϽ0.001 and PϽ0.0001, respectively), together with a progressive decrease in low-frequency components (LF, LF-NU) (PϽ0.0001 and PϽ0.004, respectively). The low/high frequency ratio showed a linear increase until 10 minutes before PAF, followed by a sharp decrease immediately before PAF, suggesting a primary increase in adrenergic tone followed by a marked modulation toward vagal predominance. No difference was observed in these heart rate variability changes between patients with "lone" PAF and patients with structural heart disease. Conclusions-The occurrence of PAF greatly depends on variations of the autonomic tone, with a primary increase in adrenergic tone followed by an abrupt shift toward vagal predominance.

Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patientyears of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision recall curve of 63.0%. The model was externally validated in a large independent patient cohort.

Clinical presentation of AF in Cameroon is much more severe than in developed countries. A rate-control strategy is predominant in Cameroon and OAC is prescribed in only 34.2% of eligible patients, despite a high CHADS(2) score at inclusion. Death and stroke rate at 1 year are very high in Cameroon possibly because of a lower use of OAC, and a higher prevalence of rheumatic mitral disease and of more severe co-morbidities.

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The BRCA Challenge is a long-term data-sharing project initiated within the Global Alliance for Genomics and Health (GA4GH) to aggregate BRCA1 and BRCA2 data to support highly collaborative research activities. Its goal is to generate an informed and current understanding of the impact of genetic variation on cancer risk across the iconic cancer predisposition genes, BRCA1 and BRCA2. Initially, reported variants in BRCA1 and BRCA2 available from public databases were integrated into a single, newly created site, www.brcaexchange.org. The purpose of the BRCA Exchange is to provide the community with a reliable and easily accessible record of variants interpreted for a high-penetrance phenotype. More than 20,000 variants have been aggregated, three times the number found in the next-largest public database at the project’s outset, of which approximately 7,250 have expert classifications. The data set is based on shared information from existing clinical databases—Breast Cancer Information Core (BIC), ClinVar, and the Leiden Open Variation Database (LOVD)—as well as population databases, all linked to a single point of access. The BRCA Challenge has brought together the existing international Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) consortium expert panel, along with expert clinicians, diagnosticians, researchers, and database providers, all with a common goal of advancing our understanding of BRCA1 and BRCA2 variation. Ongoing work includes direct contact with national centers with access to BRCA1 and BRCA2 diagnostic data to encourage data sharing, development of methods suitable for extraction of genetic variation at the level of individual laboratory reports, and engagement with participant communities to enable a more comprehensive understanding of the clinical significance of genetic variation in BRCA1 and BRCA2.