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The aim of this study was to investigate the discrimination power of standard long-term heart rate variability (HRV) measures for the diagnosis of chronic heart failure (CHF). The authors performed a retrospective analysis on four public Holter databases, analyzing the data of 72 normal subjects and 44 patients suffering from CHF. To assess the(More)
Dual Oxidases (DUOX) 1 and 2 are efficiently expressed in thyroid, gut, lung and immune system. The function and the regulation of these enzymes in mammals are still largely unknown. We report here that DUOX 1 and 2 are expressed in human neuroblastoma SK-N-BE cells as well as in a human oligodendrocyte cell line (MO3-13) and in rat brain and they are(More)
OBJECTIVE The purpose of our study was to evaluate the diagnostic value of an imaging protocol combining dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI) in patients with suspicious breast lesions. MATERIALS AND METHODS A total of 31 breast lesions (15 malignant and 16 benign proved by histological examination) in 26 female(More)
Traditionally, tracer kinetic modelling and pixel classification of DCE-MRI studies are accomplished separately, although they could greatly benefit from each other. In this article, we propose an expectation-maximisation scheme for simultaneous pixel classification and compartmental modelling of DCE-MRI studies. The key point in the proposed scheme is the(More)
PURPOSE To assess the feasibility and effectiveness of quantitative intravoxel incoherent motion (IVIM) of Diffusion-weighted imaging (DWI) in the assessment of liver metastases treated with targeted chemotherapy agents. METHODS 12 patients with unresectable liver metastases from colorectal cancer were enrolled and received neoadjuvant FOLFIRI(More)
We performed a systematic review of several pattern analysis approaches for classifying breast lesions using dynamic, morphological, and textural features in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Several machine learning approaches, namely artificial neural networks (ANN), support vector machines (SVM), linear discriminant analysis(More)
Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in(More)