Self-organized stable pacemakers near the onset of birhythmicity

General amplitude equations for reaction-diffusion systems near to the soft onset of birhythmicity described by a supercritical pitchfork-Hopf bifurcation are derived. Using these equations and applying singular perturbation theory, we show that stable autonomous pacemakers represent a generic kind of spatiotemporal patterns in such systems. This is verified by numerical simulations, which also show the existence of breathing and swinging pacemaker solutions. The drift of self-organized pacemakers in media with spatial parameter gradients is analytically and numerically investigated.Comment: 4 pages, 4 figure

Clinical and electrophysiological predictors of device-detected new-onset atrial fibrillation during 3 years after cardiac surgery

Postoperative atrial fibrillation (POAF) after cardiac surgery is an independent predictor of stroke and mortality late after discharge. We aimed to determine the burden and predictors of early (up to 5th postoperative day) and late (after 5th postoperative day) new-onset atrial fibrillation (AF) using implantable loop recorders (ILRs) in patients undergoing open chest cardiac surgery Seventy-nine patients without a history of AF undergoing cardiac surgery underwent peri-operative high-resolution mapping of electrically induced AF and were followed 36 months after surgery using an ILR (Reveal XTTM). Clinical and electrophysiological predictors of late POAF were assessed. POAF occurred in 46 patients (58%), with early POAF detected in 27 (34%) and late POAF in 37 patients (47%). Late POAF episodes were short-lasting (mostly between 2 min and 6 h) and showed a circadian rhythm pattern with a peak of episode initiation during daytime. In POAF patients, electrically induced AF showed more complex propagation patterns than in patients without POAF. Early POAF, right atrial (RA) volume, prolonged PR time, and advanced age were independent predictors of late POAF

Heart Failure, Female Sex, and Atrial Fibrillation Are the Main Drivers of Human Atrial Cardiomyopathy: Results From the CATCH ME Consortium

Background: Atrial cardiomyopathy (atCM) is an emerging prognostic factor in cardiovascular disease. Fibrotic remodeling, cardiomyocyte hypertrophy, and capillary density are hallmarks of atCM. The contribution of etiological factors and atrial fibrillation (AF) to the development of differential atCM phenotypes has not been quantified. This study aimed to evaluate the association between histological features of atCM and the clinical phenotype. Methods and results: We examined left atrial (LA, n=95) and right atrial (RA, n=76) appendages from a European cohort of patients undergoing cardiac surgery. Quantification of histological atCM features was performed following wheat germ agglutinin/CD31/vimentin staining. The contributions of AF, heart failure, sex, and age to histological characteristics were determined with multiple linear regression models. Persistent AF was associated with increased endomysial fibrosis (LA: +1.13±0.47 μm, P=0.038; RA: +0.94±0.38 μm, P=0.041), whereas total extracellular matrix content was not. Men had larger cardiomyocytes (LA: +1.92±0.72 μm, P<0.001), while women had more endomysial fibrosis (LA: +0.99±0.56 μm, P=0.003). Patients with heart failure showed more endomysial fibrosis (LA: +1.85±0.48 μm, P<0.001) and extracellular matrix content (LA: +3.07±1.29%, P=0.016), and a higher capillary density (LA: +0.13±0.06, P=0.007) and size (LA: +0.46±0.22 μm, P=0.044). Fuzzy k-means clustering of histological features identified 2 subtypes of atCM: 1 characterized by enhanced endomysial fibrosis (LA: +3.17 μm, P<0.001; RA: +2.86 μm, P<0.001), extracellular matrix content (LA: +3.53%, P<0.001; RA: +6.40%, P<0.001) and fibroblast density (LA: +4.38%, P<0.001), and 1 characterized by cardiomyocyte hypertrophy (LA: +1.16 μm, P=0.008; RA: +2.58 μm, P<0.001). Patients with fibrotic atCM were more frequently female (LA: odds ratio [OR], 1.33, P=0.002; RA: OR, 1.54, P=0.004), with persistent AF (LA: OR, 1.22, P=0.036) or heart failure (LA: OR, 1.62, P<0.001). Hypertrophic features were more common in men (LA: OR=1.33, P=0.002; RA: OR, 1.54, P=0.004). Conclusions: Fibrotic atCM is associated with female sex, persistent AF, and heart failure, while hypertrophic features are more common in men

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms. Design, Setting, and Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016. Main Outcomes and Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated. Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity. Conclusions and Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation

Identification of regulatory variants associated with genetic susceptibility to meningococcal disease

Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of such polymorphisms in a more systematic way by targeting specific regulatory regions relevant for the phenotype studied. We applied this method to meningococcal disease susceptibility, using the DNA binding pattern of RELA - a NF-kB subunit, master regulator of the response to infection - under bacterial stimuli in nasopharyngeal epithelial cells. We designed a custom panel to cover these RELA binding sites and used it for targeted sequencing in cases and controls. Variant calling and association analysis were performed followed by validation of candidate polymorphisms by genotyping in three independent cohorts. We identified two new polymorphisms, rs4823231 and rs11913168, showing signs of association with meningococcal disease susceptibility. In addition, using our genomic data as well as publicly available resources, we found evidences for these SNPs to have potential regulatory effects on ATXN10 and LIF genes respectively. The variants and related candidate genes are relevant for infectious diseases and may have important contribution for meningococcal disease pathology. Finally, we described a novel genetic association approach that could be applied to other phenotypes

Identification of regulatory variants associated with genetic susceptibility to meningococcal disease.

Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of such polymorphisms in a more systematic way by targeting specific regulatory regions relevant for the phenotype studied. We applied this method to meningococcal disease susceptibility, using the DNA binding pattern of RELA - a NF-kB subunit, master regulator of the response to infection - under bacterial stimuli in nasopharyngeal epithelial cells. We designed a custom panel to cover these RELA binding sites and used it for targeted sequencing in cases and controls. Variant calling and association analysis were performed followed by validation of candidate polymorphisms by genotyping in three independent cohorts. We identified two new polymorphisms, rs4823231 and rs11913168, showing signs of association with meningococcal disease susceptibility. In addition, using our genomic data as well as publicly available resources, we found evidences for these SNPs to have potential regulatory effects on ATXN10 and LIF genes respectively. The variants and related candidate genes are relevant for infectious diseases and may have important contribution for meningococcal disease pathology. Finally, we described a novel genetic association approach that could be applied to other phenotypes

Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection are often non-specific, and there is no definitive test for the accurate diagnosis of infection. The 'omics' approaches to identifying biomarkers from the host-response to bacterial infection are promising. In this study, lipidomic analysis was carried out with plasma samples obtained from febrile children with confirmed bacterial infection (n = 20) and confirmed viral infection (n = 20). We show for the first time that bacterial and viral infection produces distinct profile in the host lipidome. Some species of glycerophosphoinositol, sphingomyelin, lysophosphatidylcholine and cholesterol sulfate were higher in the confirmed virus infected group, while some species of fatty acids, glycerophosphocholine, glycerophosphoserine, lactosylceramide and bilirubin were lower in the confirmed virus infected group when compared with confirmed bacterial infected group. A combination of three lipids achieved an area under the receiver operating characteristic (ROC) curve of 0.911 (95% CI 0.81 to 0.98). This pilot study demonstrates the potential of metabolic biomarkers to assist clinicians in distinguishing bacterial from viral infection in febrile children, to facilitate effective clinical management and to the limit inappropriate use of antibiotics

Life-threatening infections in children in Europe (the EUCLIDS Project): a prospective cohort study

Background: Sepsis and severe focal infections represent a substantial disease burden in children admitted to hospital. We aimed to understand the burden of disease and outcomes in children with life-threatening bacterial infections in Europe. Methods: The European Union Childhood Life-threatening Infectious Disease Study (EUCLIDS) was a prospective, multicentre, cohort study done in six countries in Europe. Patients aged 1 month to 18 years with sepsis (or suspected sepsis) or severe focal infections, admitted to 98 participating hospitals in the UK, Austria, Germany, Lithuania, Spain, and the Netherlands were prospectively recruited between July 1, 2012, and Dec 31, 2015. To assess disease burden and outcomes, we collected demographic and clinical data using a secured web-based platform and obtained microbiological data using locally available clinical diagnostic procedures. Findings: 2844 patients were recruited and included in the analysis. 1512 (53·2%) of 2841 patients were male and median age was 39·1 months (IQR 12·4–93·9). 1229 (43·2%) patients had sepsis and 1615 (56·8%) had severe focal infections. Patients diagnosed with sepsis had a median age of 27·6 months (IQR 9·0–80·2), whereas those diagnosed with severe focal infections had a median age of 46·5 months (15·8–100·4; p<0·0001). Of 2844 patients in the entire cohort, the main clinical syndromes were pneumonia (511 [18·0%] patients), CNS infection (469 [16·5%]), and skin and soft tissue infection (247 [8·7%]). The causal microorganism was identified in 1359 (47·8%) children, with the most prevalent ones being Neisseria meningitidis (in 259 [9·1%] patients), followed by Staphylococcus aureus (in 222 [7·8%]), Streptococcus pneumoniae (in 219 [7·7%]), and group A streptococcus (in 162 [5·7%]). 1070 (37·6%) patients required admission to a paediatric intensive care unit. Of 2469 patients with outcome data, 57 (2·2%) deaths occurred: seven were in patients with severe focal infections and 50 in those with sepsis. Interpretation: Mortality in children admitted to hospital for sepsis or severe focal infections is low in Europe. The disease burden is mainly in children younger than 5 years and is largely due to vaccine-preventable meningococcal and pneumococcal infections. Despite the availability and application of clinical procedures for microbiological diagnosis, the causative organism remained unidentified in approximately 50% of patients

Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection ar

Plasma lipid profiles discriminate bacterial from viral infection in febrile children

Fever is the most common reason that children present to Emergency Departments. Clinical signs and symptoms suggestive of bacterial infection are often non-specific, and there is no definitive test for the accurate diagnosis of infection. The 'omics' approaches to identifying biomarkers from the host-response to bacterial infection are promising. In this study, lipidomic analysis was carried out with plasma samples obtained from febrile children with confirmed bacterial infection (n = 20) and confirmed viral infection (n = 20). We show for the first time that bacterial and viral infection produces distinct profile in the host lipidome. Some species of glycerophosphoinositol, sphingomyelin, lysophosphatidylcholine and cholesterol sulfate were higher in the confirmed virus infected group, while some species of fatty acids, glycerophosphocholine, glycerophosphoserine, lactosylceramide and bilirubin were lower in the confirmed virus infected group when compared with confirmed bacterial infected group. A combination of three lipids achieved an area under the receiver operating characteristic (ROC) curve of 0.911 (95% CI 0.81 to 0.98). This pilot study demonstrates the potential of metabolic biomarkers to assist clinicians in distinguishing bacterial from viral infection in febrile children, to facilitate effective clinical management and to the limit inappropriate use of antibiotics