Hydroxytyrosol inhibits hydrogen peroxide-induced apoptotic signaling via labile iron chelation

Although it is known that Mediterranean diet plays an important role in maintaining human health, the underlying molecular mechanisms remain largely unknown. The aim of this investigation was to elucidate the potential role of ortho-dihydroxy group containing natural compounds in H2O2-induced DNA damage and apoptosis. For this purpose, the main phenolic alcohols of olive oil, namely hydroxytyrosol and tyrosol, were examined for their ability to protect cultured cells under conditions of oxidative stress. A strong correlation was observed between the ability of hydroxytyrosol to mitigate intracellular labile iron level and the protection offered against H2O2-induced DNA damage and apoptosis. On the other hand, tyrosol, which lacks the ortho-dihydroxy group, was ineffective. Moreover, hydroxytyrosol (but not tyrosol), was able to diminish the late sustained phase of H2O2-induced JNK and p38 phosphorylation. The derangement of intracellular iron homeostasis, following exposure of cells to H2O2, played pivotal role both in the induction of DNA damage and the initiation of apoptotic signaling. The presented results suggest that the protective effects exerted by ortho-dihydroxy group containing dietary compounds against oxidative stress-induced cell damage are linked to their ability to influence changes in the intracellular labile iron homeostasis

A deep learning oriented method for automated 3D reconstruction of carotid arterial trees from MR imaging

The scope of this paper is to present a new carotid vessel segmentation algorithm implementing the U-net based convolutional neural network architecture. With carotid atherosclerosis being the major cause of stroke in Europe, new methods that can provide more accurate image segmentation of the carotid arterial tree and plaque tissue can help improve early diagnosis, prevention and treatment of carotid disease. Herein, we present a novel methodology combining the U-net model and morphological active contours in an iterative framework that accurately segments the carotid lumen and outer wall. The method automatically produces a 3D meshed model of the carotid bifurcation and smaller branches, using multispectral MR image series obtained from two clinical centres of the TAXINOMISIS study. As indicated by a validation study, the algorithm succeeds high accuracy (99.1% for lumen area and 92.6% for the perimeter) for lumen segmentation. The proposed algorithm will be used in the TAXINOMISIS study to obtain more accurate 3D vessel models for improved computational fluid dynamics simulations and the development of models of atherosclerotic plaque progression

A Machine Learning Model for the Identification of High risk Carotid Atherosclerotic Plaques

Carotid artery disease is an inflammatory condition involving the deposition and accumulation of lipid species and leucocytes from blood into the arterial wall, which causes the narrowing of the carotid arteries on either side of the neck. Different imaging modalities can by implemented to determine the presence and the location of carotid artery stenosis, such as carotid ultrasound, computed tomography angiography (CTA), magnetic resonance angiography (MRA), or cerebral angiography. However, except of the presence and the degree of stenosis of the carotid arteries, the vulnerability of the carotid atherosclerotic plaques constitutes a significant factor for the progression of the disease and the presence of disease symptoms. In this study, our aim is to develop and present a machine learning model for the identification of high risk plaques using non imaging based features and non-invasive imaging based features. Firstly, we implemented statistical analysis to identify the most statistical significant features according to the defined output, and subsequently, we implemented different feature selection techniques and classification schemes for the development of our machine learning model. The overall methodology has been trained and tested using 208 cases of 107 cases of low risk plaques and 101 cases of high risk plaques. The highest accuracy of 0.76 was achieved using the relief feature selection technique and the support vector machine classification scheme. The innovative aspect of the proposed machine learning model is both the different categories of the utilized input features and the definition of the problem to be solved

Direct Binding of Bcl‑2 Family Proteins by Quercetin Triggers Its Pro-Apoptotic Activity

Bcl-2 family proteins are important regulators of apoptosis and its antiapoptotic members, which are overexpressed in many types of cancer, are of high prognostic significance, establishing them as attractive therapeutic targets. Quercetin, a natural flavonoid, has drawn much attention because it exerts anticancer effects, while sparing normal cells. A multidisciplinary approach has been employed herein, in an effort to reveal its mode of action including dose–response antiproliferative activity and induced apoptosis effect, biochemical and physicochemical assays, and computational calculations. It may be concluded that, quercetin binds directly to the BH3 domain of Bcl-2 and Bcl-xL proteins, thereby inhibiting their activity and promoting cancer cell apoptosis

The TAXINOMISIS Project: A multidisciplinary approach for the development of a new risk stratification model for patients with asymptomatic carotid artery stenosis

Introduction: Asymptomatic carotid artery stenosis (ACAS) may cause future stroke and therefore patients with ACAS require best medical treatment. Patients at high risk for stroke may opt for additional revascularization (either surgery or stenting) but the future stroke risk should outweigh the risk for peri/post-operative stroke/death. Current risk stratification for patients with ACAS is largely based on outdated randomized-controlled trials that lack the integration of improved medical therapies and risk factor control. Furthermore, recent circulating and imaging biomarkers for stroke have never been included in a risk stratification model. The TAXINOMISIS Project aims to develop a new risk stratification model for cerebrovascular complications in patients with ACAS and this will be tested through a prospective observational multicentre clinical trial performed in six major European vascular surgery centres. Methods and analysis: The risk stratification model will compromise clinical, circulating, plaque and imaging biomarkers. The prospective multicentre observational study will include 300 patients with 50%-99% ACAS. The primary endpoint is the three-year incidence of cerebrovascular complications. Biomarkers will be retrieved from plasma samples, brain MRI, carotid MRA and duplex ultrasound. The TAXINOMISIS Project will serve as a platform for the development of new computer tools that assess plaque progression based on radiology images and a lab-on-chip with genetic variants that could predict medication response in individual patients. Conclusion: Results from the TAXINOMISIS study could potentially improve future risk stratification in patients with ACAS to assist personalized evidence-based treatment decision-making