This paper reports our study on the impact of transcatheter aortic valve replacement (TAVR) on the classification of aortic stenosis (AS) patients using cardio-mechanical modalities. Machine learning algorithms such as decision tree, random forest, and neural network were applied to conduct two tasks. Firstly, the pre- and post-TAVR data are evaluated with the classifiers trained in the literature. Secondly, new classifiers are trained to classify between pre- and post-TAVR data. Using analysis of variance, the features that are significantly different between pre- and post-TAVR patients are selected and compared to the features used in the pre-trained classifiers. The results suggest that pre-TAVR subjects could be classified as AS patients but post-TAVR could not be classified as healthy subjects. The features which differentiate pre- and post-TAVR patients reveal different distributions compared to the features that classify AS patients and healthy subjects. These results could guide future work in the classification of AS as well as the evaluation of the recovery status of patients after TAVR treatment.In this computational modelling work, we explored the mechanical roles that various glycosaminoglycans (GAGs) distributions may play in the porcine ascending aortic wall, by studying both the transmural residual stress as well as the opening angle in aortic ring samples. A finite element (FE) model was first constructed and validated against published data generated from rodent aortic rings. The FE model was then used to simulate the response of porcine ascending aortic rings with different GAG distributions prescribed through the wall of the aorta. The results indicated that a uniform GAG distribution within the aortic wall did not induce residual stresses, allowing the aortic ring to remain closed when subjected to a radial cut. By contrast, a heterogeneous GAG distribution led to the development of residual stresses which could be released by a radial cut, causing the ring to open. The residual stresses and opening angle were shown to be modulated by the GAG content, gradient, and the nature of the transmural distribution.Cardiovascular diseases are nowadays considered as the main cause of morbidity and mortality worldwide. Coronary Artery Disease (CAD), the most typical form of cardiovascular disease is diagnosed by a variety of imaging modalities, both invasive and non-invasive, which involve either risk implications or high cost. Therefore, several attempts have been undertaken to early diagnose and predict either the high CAD risk patients or the cardiovascular events, implementing machine learning techniques.  https://www.selleckchem.com/products/avelumab.html  The purpose of this study is to present a classification scheme for the prediction of Percutaneous Coronary Intervention (PCI) stenting placement, using image-based data. The proposed classification model is a gradient boosting classifier, incorporated into a class imbalance handling technique, the Easy ensemble scheme and aims to classify coronary segments into high CAD risk and low CAD risk, based on their PCI placement. Through this study, we investigate the importance of image based features, concluding that the combination of the coronary degree of stenosis and the fractional flow reserve achieves accuracy 78%.In this work we present a novel method for the prediction and generation of atherosclerotic plaques. This is performed in a two-step approach, by employing first a multilevel computational plaque growth model and second a correlation between the model's results and the 3D reconstructed follow-up plaques. In particular, computer tomography coronary angiography (CTCA) data and blood tests were collected from patients at two time points. Using the baseline data, the plaque growth is simulated using a multi-level computational model which includes i) modeling of the blood flow dynamics, ii) modeling of low and high density lipoproteins and monocytes' infiltration in the arterial wall, and the species reactions during the atherosclerotic process, and iii) modeling of the arterial wall thickening. The correlation between the followup plaques and the simulated plaque density distribution resulted to the extraction of a threshold of the plaque density, that can be used to identify plaque areas.Clinical Relevance- The methodology presented in this work is a first step to the prediction of the plaque shape and location of patients with atherosclerosis and could be used as an additional tool for patient-specific risk stratification.The advances in cardiovascular modelling over the past two decades have given the opportunity to create accurate three dimensional models of the coronary vasculature which, combined with advanced computational fluid dynamics algorithms can shed light to intriguing matters that concern clinicians. One of these issues is the presence of a stenosis near bifurcations in one of the major coronary vessels. In this work, we try to shed light on the aforementioned matter by creating a healthy arterial bifurcation reconstructed using the fusion of Optical Coherence Tomography and X-Ray angiography images. The healthy model was edited by adding an artificial stenosis of 50% diameter reduction into three different locations after the bifurcation, thus creating three diseased models. After performing the appropriate blood flow simulations, we observed that the location of the stenosis affects the Wall Shear Stress (WSS) distribution but it does not affect the functional significance of the stenosis itself.Cardiac biomechanical modelling is a promising new tool to be used in prognostic medicine and therapy planning for patients suffering from a variety of cardiovascular diseases and injuries. In order to have an accurate biomechanical model, personalized parameters to define loading, boundary conditions and mechanical properties are required. Achieving personalized modelling parameters often requires inverse optimization which is computationally expensive; hence techniques to reduce the multivariable complexity are in need. Presented in this paper is the fundamental blueprint to create a library of scar tissue mechanical properties to be used in modelling the healing mechanics of hearts that have suffered acute myocardial infarction. This library can be used to reduce the number of variables necessary to capture the scar tissue mechanical properties down to 1. This single parameter also carries information pertaining to staging of the scar tissue healing, predict its rate, and predict its collagen density. This information can be potentially used as valuable biomarkers to adjust existing or develop new treatment plans for patients.