University of Patras

About: University of Patras is a education organization based out in Pátrai, Greece. It is known for research contribution in the topics: Population & Catalysis. The organization has 13372 authors who have published 31263 publications receiving 677159 citations. The organization is also known as: Panepistímio Patrón.

Anticoagulation monitoring during cardiac surgery: a review of current and emerging techniques.

Abstract: The literature does not consistently support the importance of anticoagulation monitoring techniques during CPB. This is best reflected by studies that have evaluated the impact of the ACT method on blood loss and transfusion outcomes. Inconsistent findings from studies that evaluated the impact of ACT monitoring may be related to either suboptimal study design (i.e., retrospective, unblinded, nonrandomized) or possibly the diagnostic inprecision of the ACT method used in these studies. There are a small number of well-controlled studies, some of which suggest that bleeding and transfusion outcomes can be improved by refining heparin monitoring techniques, either by sustaining better anticoagulation during CPB or by optimizing protamine doses (i.e., when empiric protocols result in excessive protamine doses). More well-controlled studies are needed to better define the importance of anticoagulation management during CPB.

Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases.

Abstract: While the spread of COVID-19 is increased, new, automatic, and reliable methods for accurate detection are essential to reduce the exposure of the medical experts to the outbreak. X-ray imaging, although limited to specific visualizations, may be helpful for the diagnosis. In this study, the problem of automatic classification of pulmonary diseases, including the recently emerged COVID-19, from X-ray images, is considered. Deep Learning has proven to be a remarkable method to extract massive high-dimensional features from medical images. Specifically, in this paper, the state-of-the-art Convolutional Neural Network called Mobile Net is employed and trained from scratch to investigate the importance of the extracted features for the classification task. A large-scale dataset of 3905 X-ray images, corresponding to 6 diseases, is utilized for training MobileNet v2, which has been proven to achieve excellent results in related tasks. Training the CNNs from scratch outperforms the other transfer learning techniques, both in distinguishing the X-rays between the seven classes and between Covid-19 and non-Covid-19. A classification accuracy between the seven classes of 87.66% is achieved. Besides, this method achieves 99.18% accuracy, 97.36% Sensitivity, and 99.42% Specificity in the detection of COVID-19. The results suggest that training CNNs from scratch may reveal vital biomarkers related but not limited to the COVID-19 disease, while the top classification accuracy suggests further examination of the X-ray imaging potential.