PROGNOSTIC VALUE OF MULTIDETECTOR COMPUTED TOMOGRAPHY CORONARY ANGIOGRAPHY IN DIABETES: EXCELLENT LONG-TERM PROGNOSIS IN PATIENTS WITH NORMAL CORONARY ARTERIES

Objective: To assess the prognostic role of multidetector computed tomography coronary angiography (MDCT-CA) in diabetics with suspected coronary artery disease (CAD). Use of MDCT-CA is increasing in patients with suspected CAD. However, data supporting its prognostic value in diabetics are limited. Research Design and Methods: Between January 2006 and September 2007, 429 consecutive diabetic patients were prospectively studied with MDCT-CA for detecting presence and assessing extent of CAD (disease extension and coronary plaque scores). Patients were classified according to the presence of normal coronaries, non-obstructive (50%) coronary lesions. The composite rates of hard cardiac events (cardiac death, non-fatal myocardial infarction, unstable angina) and all cardiac events (including revascularization) were the end points of the study. Results: Twenty-four patients were excluded because MDCT-CA data were uninterpretable. Of the remaining 405 patients, clinical follow-up (mean 62\ub19 months) was obtained in 390 (98%). By multivariate analysis, predictors of hard and all events were obstructive CAD, 3-vessel CAD and left main coronary artery (LMCA) disease. Cumulative event-free survival was 100% for hard and all events in patients with normal coronary arteries, 78% for hard events and 56% for all events in patients with non-obstructive CAD, and 60% for hard events and 16% for all events in patients with obstructive CAD. Three-vessel CAD and LMCA disease was associated with higher rate of hard cardiac events. Conclusions: MDCT-CA provides long-term prognostic information in diabetics with suspected CAD, showing excellent prognosis when there is no evidence of atherosclerosis and allowing risk stratification when CAD is present

Large-Eddy Simulation of a Turbulent Spray Flame Using the Flamelet Generated Manifold Approach

Abstract In order to meet the increasingly stringent regulations in terms of pollutant emissions adopted by ICAO-CAEP in last years, a redesign of aero-engine combustors has been required and, today, lean combustion technology can be considered as the most effective solution. In this context, common design tools and standard RANS predictive techniques are often not capable of properly characterizing combustors performances. Thus, computational techniques have been rapidly evolving towards an extensive use of Large-Eddy Simulation (LES) or hybrid RANS methods. This paper presents the numerical analysis of an experimental partially premixed flame fed by a dilute spray of acetone [1] , exploiting a two-phase Eulerian-Lagrangian approach combined with the Flamelet Generated Manifold (FGM) combustion model in the context of LES techniques. All simulations have been performed with thecode Ansys Fluent 15.0. A comparison both in non-reactive and reactive conditions of the obtained results with experimental data and conventional RANS solution has been realized in order to highlight the LES capabilities to give a new insight into the physics of reactive two-phase flows, particularly on the unsteady evolution of turbulent spray flames involving particles dispersion, evaporation and combustion

Osteopontin as Candidate Biomarker of Coronary Disease despite Low Cardiovascular Risk: Insights from CAPIRE Study

Stratification according high cardiovascular (CV) risk categories, still represents a clinical challenge. In this analysis of the CAPIRE study (NCT02157662), we investigate whether inflammation could fit between CV risk factors (RFs) and the presence of coronary artery disease (CAD). In total, 544 patients were included and categorized according with the presence of CAD and CV risk factor burden (low/multiple). The primary endpoint was to verify any independent association of neutrophil-related biomarkers with CAD across CV risk categories. The highest values of osteo-pontin (OPN) were detected in the low RF group and associated with CAD (23.2 vs. 19.4 ng/mL; p = 0.001), although no correlation with plaque extent and/or composition were observed. Con-versely, myeloperoxidase (MPO) and resistin did not differ by CAD presence. Again, OPN was identified as independent variable associated with CAD but only in the low RF group (adjOR 8.42 [95% CI 8.42\u201346.83]; p-value = 0.015). As an ancillary finding, a correlation linked OPN with the neutrophil degranulation biomarker MPO (r = 0.085; p = 0.048) and resistin (r = 0.177; p = 3.4 7 10 125 ). In the present study, OPN further strengthens its role as biomarker of CAD, potentially bridging subclinical CV risk with development of atherosclerosis

Huge Left Atrial Myxoma and Concomitant Silent Coronary Artery Disease in a Young Man

Severe but silent coronary artery disease may rarely exist in young patients with a low-risk profile but with a family history of coronary artery disease. We describe the case of a 33-year-old Caucasian male with progressive shortness of breath caused by a huge left atrial myxoma who was diagnosed to have significant coronary artery disease in the preoperative assessment. After investigations, the patient underwent resection of the left atrial tumor and coronary artery bypass grafting (CABG) with a successful outcome. Even in the case of a young male, it may be prudent to investigate silent coronary artery disease in the presence of cardiovascular risk factors and family history of coronary artery disease. The learning objective of this case is to debate about the usefulness of a preoperative coronary study even in the young population with cardiac nonischemic pathologies (ie, valve pathology, cardiac tumors, etc.)

Predictive value of HDL function in patients with coronary artery disease: relationship with coronary plaque characteristics and clinical events

BACKGROUND: HDL is endowed with several metabolic, vascular, and immunoinflammatory protective functions. Among them, a key property is to promote reverse cholesterol transport from cells back to the liver. The aim of this study was to estimate the association of scavenger receptor class B type I (SR-BI)- and ATP binding cassette transporter A1 (ABCA1)-mediated cholesterol efflux (the two major routes for cholesterol efflux to HDL) with the presence, extent, and severity of coronary artery disease (CAD), vascular wall remodelling processes, coronary plaque characteristics, and the incidence of myocardial infarction in the different subgroups of patients from the CAPIRE study. METHODS: Patients (n = 525) from the CAPIRE study were divided into two groups: low-risk factors (RF), with 0–1 RF (n = 263), and multiple-RF, with ≥2 RFs; within each group, subjects were classified as no-CAD or CAD based on the segment involvement score (SIS) evaluated by coronary computed tomography angiography (SIS = 0 and SIS > 5, respectively). SR-BI- and ABCA1-mediated cholesterol efflux were measured using the plasma of all patients. RESULTS: SR-BI-mediated cholesterol efflux was significantly reduced in patients with CAD in both the low-RF and multiple-RF groups, whereas ABCA1-mediated cholesterol efflux was similar among all groups. In CAD patients, multivariable analysis showed that SR-BI-mediated cholesterol efflux <25(th) percentile predicted cardiovascular outcome (odds ratio 4.1; 95% CI: 1.3–13.7; p = .019), whereas ABCA-1-mediated cholesterol efflux and HDL-C levels significantly did not. Despite this finding, reduced SR-BI-mediated cholesterol efflux was not associated with changes in high-risk plaque features or changes in the prevalence of elevated total, non-calcified, and low-attenuation plaque volume. CONCLUSION: KEY MESSAGES: Increased cholesterol efflux capacity, an estimate of HDL function, is associated with a reduced CVD risk, regardless of HDL-C levels. HDL-C levels are significantly lower in patients with CAD. Lower SR-BI-mediated cholesterol efflux capacity is observed in patients with diffuse coronary atherosclerosis and is associated with the worst clinical outcomes in patients with CAD, independently of atherosclerotic plaque features

Predicting zinc binding at the proteome level

BACKGROUND: Metalloproteins are proteins capable of binding one or more metal ions, which may be required for their biological function, for regulation of their activities or for structural purposes. Metal-binding properties remain difficult to predict as well as to investigate experimentally at the whole-proteome level. Consequently, the current knowledge about metalloproteins is only partial. RESULTS: The present work reports on the development of a machine learning method for the prediction of the zinc-binding state of pairs of nearby amino-acids, using predictors based on support vector machines. The predictor was trained using chains containing zinc-binding sites and non-metalloproteins in order to provide positive and negative examples. Results based on strong non-redundancy tests prove that (1) zinc-binding residues can be predicted and (2) modelling the correlation between the binding state of nearby residues significantly improves performance. The trained predictor was then applied to the human proteome. The present results were in good agreement with the outcomes of previous, highly manually curated, efforts for the identification of human zinc-binding proteins. Some unprecedented zinc-binding sites could be identified, and were further validated through structural modelling. The software implementing the predictor is freely available at: CONCLUSION: The proposed approach constitutes a highly automated tool for the identification of metalloproteins, which provides results of comparable quality with respect to highly manually refined predictions. The ability to model correlations between pairwise residues allows it to obtain a significant improvement over standard 1D based approaches. In addition, the method permits the identification of unprecedented metal sites, providing important hints for the work of experimentalists