Evidence on the prevalence and geographic distribution of major cardiovascular risk factors in Italy

Objective: To assess the prevalence and geographic distribution of major cardiovascular risk factors in a large community-wide sample of the Italian population. Design: A cross-sectional survey. Standardized methods were used to collect and measure cardiovascular risk factors. Data were adjusted for survey weightings. Qualitative and quantitative variables were compared with parametric and non-parametric tests, as appropriate. Setting: Towns (n 193) across different Italian regions. Subjects: Unselected adults (n 24 213; 12 626 men; 11 587 women) aged 18–98 years (mean age 56·9 (sd 15·3) years), who volunteered to participate in a community-wide screening programme over a 2 d period in 2007. Results: Overall, the prevalence of major cardiovascular risk factors was: obesity, 22·7 % (women 18·9 %, men 26·1 %); overweight, 44·7 % (women 31·6 %, men 56·7 %); hypertension, 59·6 % (women 48·3 %, men 70·0 %); dyslipidaemia, 59·1 % (women 57·7 %, men 60·3 %); diabetes, 15·3 % (women 11·2 %, men 19·0 %) and smoking, 19·8 % (women 14·0 %, men 25·2 %). We found a high prevalence of unhealthy eating habits; fruit and vegetable consumption was below the recommended range in 60 % of the study population. Ninety per cent of the study population had more than one cardiovascular risk factor and 84 % had between two and five cardiovascular risk factors. There were differences among Italian macro-areas mainly for obesity, hypertension, dyslipidaemia and diabetes. Conclusions: The study provides alarming evidence on current prevalence data for major cardiovascular risk factors in a large sample of the Italian population. Particularly, obesity and hypertension represent a relevant public health problem. There is a pressing need for effective preventive health measures which must also take into account the differences among Italian macro-areas

An Approach to Model Resources Rationalisation in Hybrid Clouds through Users Activity Characterisation

In recent years, some strategies (e.g., server consolidation by means of virtualisation techniques) helped the managers of large Information Technology (IT) infrastructures to limit, when possible, the use of hardware resources in order to provide reliable services and to reduce the Total Cost of Ownership (TCO) of such infrastructures. Moreover, with the advent of Cloud computing, a resource usage rationalisation can be pursued also for the users applications, if this is compatible with the Quality of Service (QoS) which must be guaranteed. In this perspective, modern datacenters are “elastic”, i.e., able to shrink or enlarge the number of local physical or virtual resources from private/public Clouds. Moreover, many of large computing environments are integrated in distributed computing environment as the grid and cloud infrastructures. In this document, we report some advances in the realisation of a utility, we named Adaptive Scheduling Controller (ASC) which, interacting with the datacenter resource manager, allows an effective and efficient usage of resources, also by means of users jobs classification. Here, we focus both on some data mining algorithms which allows to classify the users activity and on the mathematical formalisation of the functional used by ASC to find the most suitable configuration for the datacenter’s resource manager. The presented case study concerns the SCoPE infrastructure, which has a twofold role: local computing resources provider for the University of Naples Federico II and remote resources provider for both the Italian Grid Infrastructure (IGI) and the European Grid Infrastructure (EGI) Federated Cloud

Biological properties of a human compact anti-ErbB2 antibody.

ErbB2 is a prognostic factor and target of therapy for many carcinomas. In contrast with the other ErbB receptors, ErbB2 lacks a soluble direct ligand, but it is the preferred co-receptor for the ErbB family members, forming heterodimers with more potent and prolonged signalling activity than that of homodimers. We recently produced a new anti-ErbB2 antibody, Erb-hcAb, by fusion of Erbicin, a human, anti-ErbB2 scFv, selectively cytotoxic to ErbB2-positive cells, and a human Fc domain. This fully human antitumour antibody represents a compact version of an IgG1, with the cytotoxicity of the scFv moiety on target cells, combined with the ability of the Fc moiety to induce both antibody- and complement-dependent cytotoxicity. Here, we describe the main properties of Erb-hcAb, using as a reference Herceptin, an anti-ErbB2 humanized monoclonal currently employed in clinical immunotherapy. We found that both bivalent Erb-hcAb and Herceptin increase receptor phosphorylation and downregulation, whereas monovalent Erbicin does not. These results correlate with the finding that Erb-hcAb is capable of inducing apoptosis and inhibiting cell cycle progression in ErbB2-positive cells. Its powerful in vitro antitumour action matched that observed in vivo in experiments with human ErbB2-positive tumour xenografts established in athymic mice. Finally, Erb-hcAb displays a glycosylation profile virtually superimposable to that of a human IgG. These findings suggest that Erb-hcAb is a very promising new agent for the immunotherapy of carcinomas that overexpress the ErbB2 receptor

A PETSc parallel-in-time solver based on MGRIT algorithm

We address the development of a modular implementation of the MGRIT (MultiGrid-In-Time) algorithm to solve linear and nonlinear systems that arise from the discretization of evolutionary models with a parallel-in-time approach in the context of the PETSc (the Portable, Extensible Toolkit for Scientific computing) library. Our aim is to give the opportunity of predicting the performance gain achievable when using the MGRIT approach instead of the Time Stepping integrator (TS). To this end, we analyze the performance parameters of the algorithm that provide a-priori the best number of processing elements and grid levels to use to address the scaling of MGRIT, regarded as a parallel iterative algorithm proceeding along the time dimensio

Body composition and cardiovascular risk factors in pretransplant hemodialysis patients

BACKGROUND: Obesity, hyperlipemia and cardiovascular complications contribute to a significant proportion of morbidity and mortality of renal transplant patients and have negative effects on renal survival. Aim of the present study was to evaluate the main abnormalities in body composition and the prevalence of some cardiovascular risk factors in a population of hemodialyzed (HD) patients awaiting renal transplantation. METHODS: We studied 151 HD patients, all included in a waiting list for renal transplantation, 97 males and 54 females, with mean age 47.4+/-12 years. Patients were divided into three groups according to their body mass index (BMI) (kg/m2): 18.5 to 24.9 (normoweight, NW); 25.0 to 29.9 (overweight, OW); > or =30 (obese, OB). The body composition measurements were obtained the day after the mid-week HD session using bioelectrical impedance analysis (BIA). RESULTS: We found that 47 patients were NW (31%), while 56 were OW (37%), and 48 were OB (32%). BIA-measured body cell mass was (BCM) significantly increased in the OW as compared with the NW group (P<0.001), but, of note, no significant difference was found in OB group in comparison with the OW. Total cholesterol and triglycerides plasma levels were significantly elevated in OW and OB patients with respect to NW (P<0.05) and an increased prevalence of diabetes was seen in OB patients (NW: 6%, OW: 5%, OB: 12%). CONCLUSIONS: These data show that a large proportion of patients awaiting renal transplant are overweight or obese and a consistent part of them have other cardiovascular risk factors associated. Furthermore, obese HD patients have a BCM lower than predicted on the basis of BMI and show an altered metabolic profile. A better understanding of the characteristics of patients included in the renal transplant waiting list is crucial in order to design prospective studies that aim to define the proper risk profile for the selection of patients

On the Virtualization of CUDA Based GPU Remoting on ARM and X86 Machines in the GVirtuS Framework

The astonishing development of diverse and different hardware platforms is twofold: on one side, the challenge for the exascale performance for big data processing and management; on the other side, the mobile and embedded devices for data collection and human machine interaction. This drove to a highly hierarchical evolution of programming models. GVirtuS is the general virtualization system developed in 2009 and firstly introduced in 2010 enabling a completely transparent layer among GPUs and VMs. This paper shows the latest achievements and developments of GVirtuS, now supporting CUDA 6.5, memory management and scheduling. Thanks to the new and improved remoting capabilities, GVirtus now enables GPU sharing among physical and virtual machines based on x86 and ARM CPUs on local workstations, computing clusters and distributed cloud appliances