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Abstract-Cardiac function is of paramount importance for both prognosis and treatment of different pathologies such as mitral regurgitation, ischemia, dyssynchrony and myocarditis. Cardiac behavior is determined by structural and functional features. In both cases, the analysis of medical imaging studies requires to detect and segment the myocardium. Nowadays, magnetic resonance imaging (MRI) is one of the most relevant and accurate non-invasive diagnostic tools for cardiac structure and function.In this work we propose to use a deep learning technique to assist the automatization of myocardial segmentation in cardiac MRI. We present several improvements to previous works in this paper: we propose to use the Jaccard distance as optimization objective function, we integrate a residual learning strategy into the code, and we introduce a batch normalization layer to train the fully convolutional neural network. Our results demonstrate that this architecture outperforms previous approaches based on a similar network architecture, and that provides a suitable approach for myocardial segmentation. Our benchmark shows that the automatic myocardial segmentation takes less than 22 seg. for a volume of 128 x 128 x 13 pixels in a 3.1 GHz intel core i7.

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Tigecycline is the first of a new class of antibiotics named glycylcyclines and it was approved for the treatment of complicated intra-abdominal infections and skin and skin structure infections and community-acquired bacterial pneumonia. Notwithstanding this, the tigecycline's pharmacological and microbiological profile encourage physicians' use of the drug in other infections. The aim of this study was to characterize the indications type, pathogens, and outcomes of patients who were treated with tigecycline. We analyzed the tigecycline prescriptions in 209 patients in 23 Latin American centres using an electronic form included in the website LatinUser (http://www.clinicalrec.com.ar). Sixty-six patients (31.5%) received tigecycline for approved indications, and 143 (68.5%) for "off label" indications (47% with scientific support and 21.5% with limited or without any scientific support). The most frequent "off label" use was ventilator-associated pneumonia (VAP) (76 patients). The etiology of infections was established in 88 patients (42%). Acinetobacter spp. (54.5%, in 65% of cases carbapenems-resistant), methicillin-resistant Staphylococcus aureus (12%), and extended spectrum β-lactamases-producing Enterobacteriaceae (10%) were the most common microorganisms isolated. Overall, attending physicians reported clinical success in 144 of the 209 patients (69%). Global mortality proportion was 35,5% (74/209 patients). Our study shows that the off label use of tigecycline is frequent, especially in VAP due to multidrug-resistant pathogens, where the therapeutic options are limited (eg: carbapenems-resistant Acinetobacter spp.). Physicians must evaluate the benefits/risks to use this antibiotic for indications that lack rigorous scientific support.

Speckle Tracking is one of the most prominent techniques used to estimate the regional movement of the heart based on ultrasound acquisitions. Many different approaches have been proposed, proving their suitability to obtain quantitative and qualitative information regarding myocardial deformation, motion and function assessment. New proposals to improve the basic algorithm usually focus on one of these three steps: (1) the similarity measure between images and the speckle model; (2) the transformation model, i.e. the type of motion considered between images; (3) the optimization strategies, such as the use of different optimization techniques in the transformation step or the inclusion of structural information. While many contributions have shown their good performance independently, it is not always clear how they perform when integrated in a whole pipeline. Every step will have a degree of influence over the following and hence over the final result. Thus, a Speckle Tracking pipeline must be analyzed as a whole when developing novel methods, since improvements in a particular step might be undermined by the choices taken in further steps. This work presents two main contributions: (1) We provide a complete analysis of the influence of the different steps in a Speckle Tracking pipeline over the motion and strain estimation accuracy. (2) The study proposes a methodology for the analysis of Speckle Tracking systems specifically designed to provide an easy and systematic way to include other strategies. We close the analysis with some conclusions and recommendations that can be used as an orientation of the degree of influence of the models for speckle, the transformation models, interpolation schemes and optimization strategies over the estimation of motion features. They can be further use to evaluate and design new strategy into a Speckle Tracking system.

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