[SIC Position paper: Treat to prevent the first event - intensive LDL cholesterol lowering in patients at very high cardiovascular risk without a previous cardiovascular event. From ESC guidelines to clinical practice]. / Position paper SIC: Trattare per prevenire ­ la riduzione intensiva del colesterolo LDL nel paziente a rischio cardiovascolare molto alto senza pregresso evento. Dalle linee guida ESC alla pratica clinica.

Cardiovascular (CV) diseases account for over 4 million deaths every year in Europe and over 220 000 deaths in Italy, representing the leading cause of morbidity and mortality worldwide. The European Society of Cardiology (ESC) guidelines have visionary included in the at very high CV risk group patients without previous acute ischemic events, such as those with subclinical atherosclerosis, chronic coronary syndrome or peripheral arterial disease, familial hypercholesterolemia, diabetes mellitus with target organ damage or multiple associated risk factors, and those with high calculated CV risk score, recommending to consider them and to achieve the same LDL-cholesterol targets as for secondary prevention patients. The aim of this position paper is to provide an updated overview of ESC guidelines that focuses on these patient categories to raise awareness within the clinical community regarding CV risk reduction in this specific epidemiological context.

Higher-order correlations reveal complex memory in temporal hypergraphs.

Many real-world complex systems are characterized by interactions in groups that change in time. Current temporal network approaches, however, are unable to describe group dynamics, as they are based on pairwise interactions only. Here, we use time-varying hypergraphs to describe such systems, and we introduce a framework based on higher-order correlations to characterize their temporal organization. The analysis of human interaction data reveals the existence of coherent and interdependent mesoscopic structures, thus capturing aggregation, fragmentation and nucleation processes in social systems. We introduce a model of temporal hypergraphs with non-Markovian group interactions, which reveals complex memory as a fundamental mechanism underlying the emerging pattern in the data.

MultiplexSAGE: A Multiplex Embedding Algorithm for Inter-Layer Link Prediction.

Research on graph representation learning has received great attention in recent years. However, most of the studies so far have focused on the embedding of single-layer graphs. The few studies dealing with the problem of representation learning of multilayer structures rely on the strong hypothesis that the inter-layer links are known, and this limits the range of possible applications. Here we propose MultiplexSAGE, a generalization of the GraphSAGE algorithm that allows embedding multiplex networks. We show that MultiplexSAGE is capable to reconstruct both the intra-layer and the inter-layer connectivity, outperforming competing methods. Next, through a comprehensive experimental analysis, we shed light also on the performance of the embedding, both in simple and multiplex networks, showing that both the density of the graph and the randomness of the links strongly influences the quality of the embedding.

Lack of practical identifiability may hamper reliable predictions in COVID-19 epidemic models.

Compartmental models are widely adopted to describe and predict the spreading of infectious diseases. The unknown parameters of these models need to be estimated from the data. Furthermore, when some of the model variables are not empirically accessible, as in the case of asymptomatic carriers of coronavirus disease 2019 (COVID-19), they have to be obtained as an outcome of the model. Here, we introduce a framework to quantify how the uncertainty in the data affects the determination of the parameters and the evolution of the unmeasured variables of a given model. We illustrate how the method is able to characterize different regimes of identifiability, even in models with few compartments. Last, we discuss how the lack of identifiability in a realistic model for COVID-19 may prevent reliable predictions of the epidemic dynamics.