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Patricia Garcia recently published an article about a computational model of the fetal circulation to better understand the blood redistribution in fetuses with intrauterine growth restriction.
Intrauterine growth restriction (IUGR) is one of the leading causes of perinatal mortality and can be defined as a low birth weight together with signs of chronic hypoxia or malnutrition. It is mostly due to placental insufficiency resulting in a chronic restriction of oxygen and nutrients to the fetus. IUGR leads to cardiac dysfunction in uterus which can persist postnatally. Under these altered conditions, IUGR fetuses redistribute their blood in order to maintain delivery of oxygenated blood to the brain, known as brain sparing. Given that, in the fetus the aortic isthmus (AoI) is a key arterial connection between the cerebral and placental circulations, quantifying AoI blood flow has been proposed to assess this brain sparing effect in clinical practice. However, which remodeling or redistribution processes in the cardiovascular systems induce the observed changes in AoI flow in IUGR fetuses is not fully understood. We developed a computational model of the fetal circulation, including the key elements related to fetal blood redistribution. Using measured cardiac outflow profiles to allow personalization, we can recreate and better understand the blood flow changes in individual IUGR fetuses.
Manifold Learning techniques are expected to convert data from a high to lower dimensional representation while recovering the intrinsic geometry of the data. In the first part of the talk some insights into manifold learning will be given, starting with its applicability, the description of popular algorithms and finally its extension to combine multiple features. The second part of the talk will be focused on describing how we plan to apply these techniques to characterize the cardiac function of different populations (volunteers, patients suffering from heart failure, etc.).
Patricia Garcia recently published an article about automated quantification of cardiac tissue properties in microscopy images
This article proposes an automatic methodology for quantyfing cardiac muscle properties in Second Harmonic Generation Images (SHG), such as sarcomere length, A-band length, thick–thin interaction length, and fiber orientation. In this work, we evaluated the performance of our methodology in computer-generated muscle fibers modeling some artifacts that are present during the image acquisition. Then, we also evaluated it by comparing it to manual measurements in SHG images from cardiac tissue of fetal and adult rabbits. The results showed a good performance of our methodology at high signal-to-noise ratio of 20 dB. We conclude that our automated measurements enable reliable characterization of cardiac fiber tissues to systematically study cardiac tissue in a wide range of conditions.