Prognostic significance of cardiac magnetic resonance imaging: Update 2010

Cardiac magnetic resonance imaging (CMR) has become an indispensible imaging technique for the diagnosis and treatment of patients with cardiovascular diseases. Technical advances in the past have rendered CMR unique in the evaluation of cardiovascular anatomy, physiology, and pathophysiology due to its unique ability to produce high resolution tomographic images of the human heart and vessels in any arbitrary orientation, with soft tissue contrast that is superior to competing imaging modalities without the use of ionizing radiation. CMR imaging is the gold standard for assessing left and right ventricular function and for detecting myocardial tissue abnormalities like edema, infarction, or scars. For prognostic reasons abnormal structure and dysfunction of the heart, and the detection of myocardial ischemia and/or myocardial scars are the main targets for CMR imaging. In this review we briefly describe the prognostic significance of several CMR imaging techniques and special CMR parameters in patients with coronary artery disease (CAD), with cardiomyopathies, and with chronic heart failure. Myocardial ischemia proved to be a strong predictor of an adverse outcome in patients with CAD. Microvascular obstruction in acute myocardial infarction is a new and independent parameter of negative left ventricular remodeling and a worse prognosis. Myocardial scars in patients with CAD and unrecognized myocardial infarction heralds a negative outcome. Scar in patients with dilated or hypertrophic cardiomyopathy are a strong predictor of both life-threatening ventricular tachyarrhythmias and prognosis. CMR imaging may improve the assessment of inter- and intraventricular dyssynchrony and provide prognostic information by detecting myocardial scars. (Cardiol J 2010; 17, 6: 549-557

Galaxy training: A powerful framework for teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform