Is distortion of the bioprosthesis ring a risk factor for early calcification ?

<p>Abstract</p> <p>Background</p> <p>As the population ages, bioprosthesis are increasingly being used in cardiac valve replacement. Pericardial bioprosthesis combine an excellent hemodynamic performance with low thrombogenicity, but valve failure associated with calcification remains a concern with these valves. We describe distortion of the bioprosthesis ring as a risk factor for early calcification.</p> <p>Methods</p> <p>A total of 510 patients over the age of 70 years underwent isolated aortic valve replacement with the Mitroflow (A12) pericardial bioprosthesis. Thirty two patients (6,2%) have undergone a second aortic valve replacement due to structural valve dysfunction resulting from valve calcification. In all patients a chest radiography and coronary angiography was performed before reoperation. A 64 Multidetector Computed Tomography (MDCT) with retrospective ECG gating study was performed in four patients to evaluate the aortic bioprosthesis.</p> <p>Results</p> <p>Chest radiography showed in all patients an irregular bioprosthesis ring. At preoperative coronary angiography a distorted bioprosthesis ring was detected in all patients. Macroscopic findings of the explanted bioprostheses included extensive calcification in all specimens.</p> <p>Conclusion</p> <p>There was a possible relationship between early bioprosthetic calcification and radiologic distortion of the bioprosthesis ring.</p

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts