Hemodynamic forces play a role in the development of atherosclerosis. Their variations with age have been assessed in cross-sectional, but not longitudinal, studies. The aim of the present study was to investigate in both sexes the age-dependent change in wall shear stress and arterial stiffness in subjects studied twice 12 years apart. Forty-eight subjects (15 women and 33 men) were studied twice 12 years apart. Subjects underwent blood viscosity measurement and echo-Doppler of carotid arteries, from which the intima-media thickness (IMT) was measured and the wall shear stress and Peterson's elastic modulus were calculated. Blood viscosity increased in both sexes, more markedly in women (+13.2%) than men (+7.2%). Common carotid diameter increased in both sexes, but in men (+7.4%) more than in women (+5.5%). Peak and mean velocity decreased at follow-up by 10.7% and 18.9% in women and by 14.2% and 18.1% in men. Peak and mean shear stress significantly decreased in men by 13.0% and 17.5%, respectively, while in women only the mean shear stress was reduced (−11.8%). The IMT of the common carotid artery increased by 29% in women and 20% in men. Arterial stiffness significantly increased (+74.5% in women and +28.0% in men). The percent change in mean shear stress was significantly and inversely associated with the percent change in IMT. The data of this study show that, in a middle-aged population observed for almost 12 years, the mean shear stress decreases significantly in both sexes, while peak shear stress decreases significantly only in men. The change in mean shear stress is inversely associated with changes in IMT. Arterial stiffness, on the other hand, increases with aging.

networks are largely used for modelling and analysing a wide range of biological data. As a consequence, many different research efforts have resulted in the introduction of a large number of algorithms for analysis and comparison of networks. Many of these algorithms can deal with networks with a single class of nodes and edges, also referred to as homogeneous networks. Recently, many different approaches tried to integrate into a single model the interplay of different molecules. A possible formalism to model such a scenario comes from node/edge coloured networks (also known as heterogeneous networks) implemented as node/ edge-coloured graphs. therefore, the need for the introduction of algorithms able to compare heterogeneous networks arises. We here focus on the local comparison of heterogeneous networks, and we formulate it as a network alignment problem. to the best of our knowledge, the local alignment of heterogeneous networks has not been explored in the past. We here propose L-HetnetAligner a novel algorithm that receives as input two heterogeneous networks (node-coloured graphs) and builds a local alignment of them. We also implemented and tested our algorithm. Our results confirm that our method builds high-quality alignments. The following website *contains Supplementary File 1 material and the code.Network Alignment (GNA) algorithms try to find a global mapping among all the nodes of the input networks, while Local Network Alignment (LNA) algorithms focus on mapping among (relatively) small single regions of input networks 12 . LNA has been defined in the past for homogeneous networks (LNA hom ), and it has been formalised in many papers, such as the first paper by Berg and Lassig 13 and the different formalisation proposed by Mina and Guzzi 14 . LNA algorithms try to find a mapping among (small) subregions of the input graphs 14 .Despite the existence of many algorithms for the local alignment of homogeneous networks 12 (see related work section for a detailed synopsis), they are not able to deal with heterogeneous networks since existing algorithms may process only homogeneous networks. Therefore they fail to discriminate among different node types. The alignment of heterogeneous networks is a relatively new field; Gu et al. 15 presented a novel GNA algorithm for heterogeneous networks, while to the best of our knowledge there are no available LNA algorithms designed for heterogeneous networks. Since the local alignment of networks reveals different knowledge compared to global alignment, there is a need for the introduction of novel LNA algorithms for heterogeneous networks.Here we propose L-HetNetAligner, a novel algorithm for local alignment of heterogeneous networks by proposing a two-step strategy as depicted in Fig. 2. Our algorithm takes as input two heterogeneous networks modelled as node-coloured graphs and a set of initial similarities among nodes of the networks, and it produces a set of graphs representing single local regions of the alignment. The algorithm merges two input graphs int...

Objectives: To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions. Design: Retrospective cohort study. Setting: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe. Participants: Patients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2. Primary and secondary outcome measures: Patients were categorized as ″ever-severe″ or ″never-severe″ using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction. Results: Of 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites. Conclusions: Laboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models.

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BackgroundRecently the study of the complex system of connections in neural systems, i.e. the connectome, has gained a central role in neurosciences. The modeling and analysis of connectomes are therefore a growing area. Here we focus on the representation of connectomes by using graph theory formalisms. Macroscopic human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. The atlas-based parcellations present some known limitations in cases of early brain development and abnormal anatomy. Consequently, it has been recently proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space, as a way to deal with the unknown correspondences of the parcels. Such process requires modeling of the brain using graph theory and the subsequent comparison of the structure of graphs. The latter step may be modeled as a network alignment (NA) problem.ResultsIn this work, we first define the problem formally, then we test six existing state of the art of network aligners on diffusion MRI-derived brain networks. We compare the performances of algorithms by assessing six topological measures. We also evaluated the robustness of algorithms to alterations of the dataset.ConclusionThe results confirm that NA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes. The analysis shows MAGNA++ is the best global alignment algorithm. The paper presented a new analysis methodology that uses network alignment for validating atlas-free parcellation brain connectomes. The methodology has been experimented on several brain datasets.