Prognostic value of new left atrial volume index severity partition cutoffs after cardiac rehabilitation program in patients undergoing cardiac surgery

Background: Previous studies showed that left atrial enlargement is an independent marker of adverse outcomes in both primary and secondary cardiovascular prevention. However, no data are available on long-term outcomes in patients undergoing valve surgery and/or coronary artery by-pass graft (CABG) surgery. Aim of the study was to evaluate long-term prognostic role of left atrial volume index (LAVi) after cardiac surgery, using the cutoff values recently proposed by the European Association of Cardiovascular Imaging and American Society of Echocardiography. Methods: We created a retrospective registry of 1703 consecutive patients who underwent cardiovascular rehabilitation program after cardiac surgery, including CABG, valve surgery and valve + CABG surgery. LAVi was calculated as ratio of left atrium volume to body surface area, in ml/m2 at discharge; 563 patients with available LAVi data were included in the study. Results: In the whole population LAVi was 36 ± 14 ml/m2 (mean ± SD) and the follow-up time was 5 ± 1. 5 years. Increased LAVi (>34 ml/m2) predicted major adverse cardiovascular and cerebrovascular events (MACCEs) (HR = 2.1; CI95 %: 1.4–3.1; p < 0.001) and cardiovascular mortality (HR = 2.2; CI95 %: 1.0–4.5; p = 0.032). An increased LAVi remained MACCEs predictor after adjustement for age, gender, diabetes, atrial fibrillation at discharge, echocardiographic E/A ratio and left ventricular ejection fraction (HR = 1.8; CI95 %: 1.0–3.0; p = 0. 036). When the study population was split according to increasing LAVi values, left atrium enlargement resulted a predictor of progressively worse adverse outcome. Conclusions: LAVi is a predictor of long-term adverse cardiovascular outcome after cardiac surgery, even after correction for main clinical and echocardiographic variables. The recently recommended LAVi severity cutoffs appear adequate to effectively stratify outcome in patients undergoing rehabilitation after cardiac surgery

Josephson currents and gap enhancement in graph arrays of superconductive islands

Evidence is reported that topological effects in graph-shaped arrays of superconducting islands can condition superconducting energy gap and transition temperature. The carriers giving rise to the new phase are couples of electrons (Cooper pairs) which, in the superconducting state, behave as predicted for bosons in our structures. The presented results have been obtained both on star and double comb-shaped arrays and the coupling between the islands is provided by Josephson junctions whose potential can be tuned by external magnetic field or temperature. Our peculiar technique for probing distribution on the islands is such that the hopping of bosons between the different islands occurs because their thermal energy is of the same order of the Josephson coupling energy between the islands. Both for star and double comb graph topologies the results are in qualitative and quantitative agreement with theoretical predictions

Forecasting real-world complex networks’ robustness to node attack using network structure indexes

In this study, we simulate the degree and betweenness node attack over a large set of 200 real-world networks from different areas of science. We perform an initial node attack approach, where the node centrality rank is computed at the beginning of the simulation, and it is not updated along the node removal process. We quantify the network damage by tracing the largest connected component (LCC ) and evaluate the network robustness with the “percolation threshold qc ,” i.e., the fraction of nodes removed, for which the size of the LCC is quasi-zero. We correlate qc with 20 network structural indicators (NSIs) from the literature using single linear regression (SLR), multiple linear regression (MLR) models, and the Pearson correlation coefficient test. The NSIs cover most of the essential structural features proposed in network science to describe real-world networks. We find that the Estrada heterogeneity (EH ) index, evaluating the degree difference of connected nodes, best predicts qc . The EH index measures the network node degree heterogeneity based on the difference of functions of node degrees for all pairs of linked nodes. We find that the qc value decreases as a function of the EH index, unveiling that heterogeneous real-world networks with a higher variance in the degree of connected nodes are more vulnerable to node attacks

Table1_Forecasting real-world complex networks’ robustness to node attack using network structure indexes.DOCX

In this study, we simulate the degree and betweenness node attack over a large set of 200 real-world networks from different areas of science. We perform an initial node attack approach, where the node centrality rank is computed at the beginning of the simulation, and it is not updated along the node removal process. We quantify the network damage by tracing the largest connected component (LCC) and evaluate the network robustness with the “percolation threshold qc,” i.e., the fraction of nodes removed, for which the size of the LCC is quasi-zero. We correlate qc with 20 network structural indicators (NSIs) from the literature using single linear regression (SLR), multiple linear regression (MLR) models, and the Pearson correlation coefficient test. The NSIs cover most of the essential structural features proposed in network science to describe real-world networks. We find that the Estrada heterogeneity (EH) index, evaluating the degree difference of connected nodes, best predicts qc. The EH index measures the network node degree heterogeneity based on the difference of functions of node degrees for all pairs of linked nodes. We find that the qc value decreases as a function of the EH index, unveiling that heterogeneous real-world networks with a higher variance in the degree of connected nodes are more vulnerable to node attacks.</p