Abundance and evolution of galaxy clusters in cosmological models with massive neutrino

The time evolution of the number density of galaxy clusters and their mass and temperature functions are used to constrain cosmological parameters in the spatially flat dark matter models containing a fraction of hot particles (massive neutrino) additional to cold and baryonic matter. We test the modified MDM models with cosmic gravitational waves and show that they neither pass the cluster evolution test nor reproduce the observed height of the first acoustic peak in $\Delta T/T$ spectrum, and therefore should be ruled out. The models with a non-zero cosmological constant are in better agreement with observations. We estimate the free cosmological parameters in $\Lambda$MDM with a negligible abundance of gravitational waves, and find that within the parameter ranges $h\in (0.6, 0.7)$, $n\in (0.9, 1.1)$, (i) the value of $\Omega_\Lambda$ is strongly affected by a small fraction of hot dark matter, $f_\nu\equiv\Omega_\nu /\Omega_m\in (0, 0.2)$: $0.45 <\Omega_\Lambda <0.7$ ($1\sigma$ CL), and (ii) the redshift evolution of galaxy clusters alone reveals the following explicit correlation between $\Omega_\Lambda$ and $f_\nu$: $\Omega_\Lambda +0.5f_\nu =0.65\pm 0.1$. The present accuracy of observational data allows only to bound the fraction of hot matter, $f_\nu\in (0, 0.2)$ (the number of massive neutrino species remains undelimited, $N_\nu =1, 2, 3$).Comment: 9 pages, 7 figures, submitted in A&

Impact of clinical phenotypes on management and outcomes in European atrial fibrillation patients: a report from the ESC-EHRA EURObservational Research Programme in AF (EORP-AF) General Long-Term Registry

Background: Epidemiological studies in atrial fibrillation (AF) illustrate that clinical complexity increase the risk of major adverse outcomes. We aimed to describe European AF patients\u2019 clinical phenotypes and analyse the differential clinical course. Methods: We performed a hierarchical cluster analysis based on Ward\u2019s Method and Squared Euclidean Distance using 22 clinical binary variables, identifying the optimal number of clusters. We investigated differences in clinical management, use of healthcare resources and outcomes in a cohort of European AF patients from a Europe-wide observational registry. Results: A total of 9363 were available for this analysis. We identified three clusters: Cluster 1 (n = 3634; 38.8%) characterized by older patients and prevalent non-cardiac comorbidities; Cluster 2 (n = 2774; 29.6%) characterized by younger patients with low prevalence of comorbidities; Cluster 3 (n = 2955;31.6%) characterized by patients\u2019 prevalent cardiovascular risk factors/comorbidities. Over a mean follow-up of 22.5 months, Cluster 3 had the highest rate of cardiovascular events, all-cause death, and the composite outcome (combining the previous two) compared to Cluster 1 and Cluster 2 (all P &lt;.001). An adjusted Cox regression showed that compared to Cluster 2, Cluster 3 (hazard ratio (HR) 2.87, 95% confidence interval (CI) 2.27\u20133.62; HR 3.42, 95%CI 2.72\u20134.31; HR 2.79, 95%CI 2.32\u20133.35), and Cluster 1 (HR 1.88, 95%CI 1.48\u20132.38; HR 2.50, 95%CI 1.98\u20133.15; HR 2.09, 95%CI 1.74\u20132.51) reported a higher risk for the three outcomes respectively. Conclusions: In European AF patients, three main clusters were identified, differentiated by differential presence of comorbidities. Both non-cardiac and cardiac comorbidities clusters were found to be associated with an increased risk of major adverse outcomes