Endothelial progenitor cells: what use for the cardiologist?

Endothelial Progenitor Cells (EPC) were first described in 1997 and have since been the subject of numerous investigative studies exploring the potential of these cells in the process of cardiovascular damage and repair. Whilst their exact definition and mechanism of action remains unclear, they are directly influenced by different cardiovascular risk factors and have a definite role to play in defining cardiovascular risk. Furthermore, EPCs may have important therapeutic implications and further understanding of their pathophysiology has enabled us to explore new possibilities in the management of cardiovascular disease. This review article aims to provide an overview of the vast literature on EPCs in relation to clinical cardiology

Forecasting of Engine Performance for Gasoline-Ethanol Blends using Machine Learning

The incorporation of alternative fuels in the automotive domain has brought a new paradigm to tackle the environmental and energy crises. Therefore, it is of interest to test and forecast engine performance with blended fuels. This paper presents an experimental study on gasoline-ethanol blends to test and forecast engine behavior due to changes in the fuel. This study employed a machine learning (ML) technique called TOPSIS to forecast the performance of a slightly higher blend fuelled engine based on experimental data obtained from the same engine running on 0% ethanol blend (E0) and E10 fuels under full load conditions. The engine performance predictions of this ML model were validated for 15% ethanol blend (E15) and further used to predict the engine performance of 20% ethanol blend fuel. The prediction R2 score for the ML model was found to be greater than 0.95 and the MAPE range was 1% to 5% for all observed engine performance attributes. Thus, this paper presents the potential of TOPSIS methodology-based ML predictions on blended fuel engine performance to shorten the testing efforts of blended fuel engines. This methodology may help to faster incorporate higher blended fuels in the automotive sector

Forecasting of Engine Performance for Gasoline-Ethanol Blends using Machine Learning

The incorporation of alternative fuels in the automotive domain has brought a new paradigm to tackle the environmental and energy crises. Therefore, it is of interest to test and forecast engine performance with blended fuels. This paper presents an experimental study on gasoline-ethanol blends to test and forecast engine behavior due to changes in the fuel. This study employed a machine learning (ML) technique called TOPSIS to forecast the performance of a slightly higher blend fuelled engine based on experimental data obtained from the same engine running on 0% ethanol blend (E0) and E10 fuels under full load conditions. The engine performance predictions of this ML model were validated for 15% ethanol blend (E15) and further used to predict the engine performance of 20% ethanol blend fuel. The prediction R2 score for the ML model was found to be greater than 0.95 and the MAPE range was 1% to 5% for all observed engine performance attributes. Thus, this paper presents the potential of TOPSIS methodology-based ML predictions on blended fuel engine performance to shorten the testing efforts of blended fuel engines. This methodology may help to faster incorporate higher blended fuels in the automotive sector

Progressive breathlessness in an Afro Caribbean hypertensive subject

The sensitivity and specificity of structural assessment of the heart by echocardiography in black hypertensive patients presenting with symptoms of heart failure is often incomplete. Cardiovascular magnetic resonance, mainly by virtue of its ability to characterize myocardial tissue composition, may be of value in differentiating some of the common pathologies noninvasively. We present an illustrative case of hypertrophic cardiomyopathy in a British Afro Caribbean hypertensive patient where at least some features of familial amyloidosis were present on screening echocardiography. Cardiovascular magnetic resonance examination of this case established not only the usefulness of this technique, but also highlighted the importance of recognizing the variations and departure from the usual which one associates with hypertrophic cardiomyopathy, so as to arrive at the final diagnosis

Non-Fermi Liquid Fixed Point in 2+1 Dimensions

We construct models of excitations about a Fermi surface that display calculable deviations from Fermi liquid behavior in the low-energy limit. They arise as a consequence of coupling to a Chern-Simons gauge field, whose fluctations are controlled through a ${1\over{k^x}}$ interaction. The Fermi liquid fixed point is shown to be unstable in the infrared for $x<1$, and an infrared-stable fixed point is found in a $(1-x)$-expansion, analogous to the $\epsilon$-expansion of critical phenomena. $x=1$ corresponds to Coulomb interactions, and in this case we find a logarithmic approach to zero coupling. We describe the low-energy behavior of metals in the universality class of the new fixed point, and discuss its possible application to the compressible $\nu={1\over2}$ quantum Hall state and to the normal state of copper-oxide superconductors.Comment: 24 pages, 2 figures uuencoded at end, use Phyzzx and epsf, PUPT 1438, IASSNS-HEP 93/8

The EXCEL Trial: The Interventionalists’ Perspective

Left main stem (LMS) disease is identified in up to 5% of diagnostic angiography cases, and is associated with significant morbidity and mortality due to the proportion of myocardium it subtends. In the past 10 years, there has been a significant change in the way we contemplate treating lesions in the LMS due to evolving experience and evidence in percutaneous coronary intervention (PCI) strategies and technologies. This has been reflected in recent changes in European and International guidance on managing patients with this lesion subset. Here, the authors provide an overview of the current literature regarding the management of LMS disease using PCI in light of new developments and emerging concepts in this field, specifically looking at the recent EXCEL trial

Renormalization Group Approach to Low Temperature Properties of a Non-Fermi Liquid Metal

We expand upon on an earlier renormalization group analysis of a non-Fermi liquid fixed point that plausibly govers the two dimensional electron liquid in a magnetic field near filling fraction $\nu=1/2$. We give a more complete description of our somewhat unorthodox renormalization group transformation by relating both our field-theoretic approach to a direct mode elimination and our anisotropic scaling to the general problem of incorporating curvature of the Fermi surface. We derive physical consequences of the fixed point by showing how they follow from renormalization group equations for finite-size scaling, where the size may be set by the temperature or by the frequency of interest. In order fully to exploit this approach, it is necessary to take into account composite operators, including in some cases dangerous ``irrelevant'' operators. We devote special attention to gauge invariance, both as a formal requirement and in its positive role providing Ward identities constraining the renormalization of composite operators. We emphasize that new considerations arise in describing properties of the physical electrons (as opposed to the quasiparticles.) We propose an experiment which, if feasible, will allow the most characteristic feature of our results, that isComment: 42 pages, 5 figures upon request, uses Phyzzx, IASSNS-HEP 94/6