Combination therapy in dyslipidemia : where are we now?

Lowering low-density lipoprotein cholesterol (LDL-C) reduces the risk of cardiovascular disease: each 1.0mmol/L (38.7mg/dL) reduction in LDL-C reduces the incidence of major coronary events, coronary revascularizations, and ischemic stroke by approximately 20%. Statins are a well-established treatment option for dyslipidemia, with LDL-C reduction in the range of 27-55%.Several lipid goal-driven guidelines recommend reducing LDL-C to <2.59mmol/L (100mg/dL) or <1.81mmol/L (70mg/dL) in very high-risk patients. Many patients treated with statins do not reach these goals, and remain at risk of future cardiovascular events. The 2013 American College of Cardiology/American Heart Association guidelines move away from advocating LDL-C treatment targets with focus placed on identifying patients most likely to benefit from high-intensity or moderate-intensity statin therapy.While increasing the statin dose can prove efficacious in some patients, this approach typically offers limited additional LDL-C lowering, and is associated with increased incidence of adverse side effects. Indeed, this has led to the investigation of statins in combination with other lipid-modifying agents for the treatment of dyslipidemia.This review of the evidence for statin use in combination with fibrates, niacin, bile acid sequestrants, and the cholesterol absorption inhibitor, ezetimibe, in dyslipidemic patients at increased risk of cardiovascular disease, explores the impact of such combination therapies on lipids, attainment of lipid targets, inflammatory markers, and on cardiovascular outcomes and pathology. Additionally, new and emerging dyslipidemia treatments are summarized

A comparison of non-HDL and LDL cholesterol goal attainment in a large, multinational patient population: The Lipid Treatment Assessment Project 2

Cardiolog

Comparison of Lipid Profiles and Attainment of Lipid Goals in Patients = 65 Years (from the Lipid Treatment Assessment Project [L-TAP] 2)

Cardiolog

Non-linear unmixing of hyperspectral images using multiple-kernel self-organizing maps

International audienceThe spatial pixel resolution of common multispectral and hyperspectral sensors is generally not sufficient to avoid thatmultiple elementary materials contribute to the observed spectrum of a single pixel. To alleviate this limitation, spectral unmixingis a by-pass procedure which consists in decomposing the observed spectra associated with these mixed pixels into a set ofcomponent spectra, or endmembers, and a set of corresponding proportions, or abundances, that represent the proportion ofeach endmember in these pixels. In this study, a spectral unmixing technique is proposed to handle the challenging scenario of non-linear mixtures. This algorithm relies on a dedicated implementation of multiple-kernel learning using self-organising mapproposed as a solver for the non-linear unmixing problem. Based on a priori knowledge of the endmember spectra, it aims atestimating their relative abundances without specifying the non-linear model under consideration. It is compared to state-of-the-art algorithms using synthetic yet realistic and real hyperspectral images. Results obtained from experiments conducted onsynthetic and real hyperspectral images assess the potential and the effectiveness of this unmixing strategy. Finally, therelevance and potential parallel implementation of the proposed method is demonstrated

Estimativa de área de soja por classificação de imagens normalizada pela matriz de erros

O objetivo deste trabalho foi estimar a área plantada com soja por meio da normalização da matriz de erros gerada a partir da classificação supervisionada de imagens TM/Landsat‑5. Foram avaliados oito municípios no Estado do Paraná, com dados referentes à safra de 2003/2004. As classificações foram realizadas por meio dos métodos paralelepípedo e máxima verossimilhança, dando origem à "máscara de soja". Os valores do índice Kappa dos oito municípios ficaram acima de 0,6. As estimativas de área de soja, corrigidas por matriz de erros, apresentaram alta correlação com as estimativas oficiais do estado e com as estimativas geradas a partir de um método alternativo denominado "expansão direta". A estimativa de área de soja por meio da normalização da matriz de erros apresenta menor custo e pode subsidiar métodos convencionais na estimativa menos subjetiva de safras