No abstract

Ablative techniques based on irreversible electroporation (IRE) have emerged as an effective cancer therapy. In these treatments electrical pulses are delivered in order to induce irrecoverable damage to the cell membranes, thus, causing cell death. An interesting feature of electroporation treatments is the capability to predict shape and extension of lesions by means of mathematical simulations. Those prediction methods have been refined thought in-vivo experimental observations. Aiming at minimization of animal experiments, vegetal models have been used to easily observe the IRE outcomes. However, these observations are limited at the surface of the tissue. Here we present an improved method able to observe the inner parts of the tissue and therefore evaluate in three-dimensions the results of IRE using potatoes as vegetal models. The technique consists in using a dye solution to enhance the IRE area in sliced potato tubers. After slice digitalization, the electroporated area is automatically identified and the resulting treated volume is calculated. In addition, a threedimensional reconstruction of both healthy tissue and IRE volume is generated. The proposed evaluation technique was used to assess different pulse protocols outcomes. Numerical simulations had been carried out to compare the numerical predictions to the experimental observations. The obtained results show a clear match between experimental and simulated volumes confirming the reliability of the proposed method.

Initiatives such as the UK Biobank provide joint cardiac and brain imaging information for thousands of individuals, representing a unique opportunity to study the relationship between heart and brain. Most of research on large multimodal databases has been focusing on studying the associations among the available measurements by means of univariate and multivariate association models. However, these approaches do not provide insights about the underlying mechanisms and are often hampered by the lack of prior knowledge on the physiological relationships between measurements. For instance, important indices of the cardiovascular function, such as cardiac contractility, cannot be measured in-vivo. While these non-observable parameters can be estimated by means of biophysical models, their personalisation is generally an ill-posed problem, often lacking critical data and only applied to small datasets. Therefore, to jointly study brain and heart, we propose an approach in which the parameter personalisation of a lumped cardiovascular model is constrained by the statistical relationships observed between model parameters and brain-volumetric indices extracted from imaging, i.e. ventricles or white matter hyperintensities volumes, and clinical information such as age or body surface area. We explored the plausibility of the learnt relationships by inferring the model parameters conditioned on the absence of part of the target clinical features, applying this framework in a cohort of more than 3 000 subjects and in a pathological subgroup of 59 subjects diagnosed with atrial fibrillation. Our

Cerebrovascular diseases have been associated with a variety of heart diseases like heart failure or atrial fibrillation, however the mechanistic relationship between these pathologies is largely unknown. Until now, the study of the underlying heart-brain link has been challenging due to the lack of databases containing data from both organs. Current large data collection initiatives such as the UK Biobank provide us with joint cardiac and brain imaging information for thousands of individuals, and represent a unique opportunity to gain insights about the heart and brain pathophysiology from a systems medicine point of view. Research has focused on standard statistical studies finding correlations in a phenomenological way. We propose a mechanistic analysis of the heart and brain interactions through the personalisation of the parameters of a lumped cardiovascular model under constraints provided by brain-volumetric parameters extracted from imaging, i.e: ventricles or white matter hyperintensities volumes, and clinical information such as age or body surface area. We applied this framework in a cohort of more than 3000 subjects and in a pathological subgroup of 53 subjects diagnosed with atrial fibrillation. Our results show that the use of brain feature constraints helps in improving the parameter estimation in order to identify significant differences associated to specific clinical conditions.