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Journal of Pharmaceutical Analysis 2014年第4期4卷 234-241页

作者： Daniel J.Liput eleftheria Tsakalozou Dana C.Hammell Kalpana S.Paudel Kimberly Nixon Audra L.Stinchcomb Department of Pharmaceutical Sciences College of PharmacyUniversity of KentuckyLexingtonKY 40536USA AllTranz LexingtonKY 40505USA Department of Pharmaceutical Sciences College of PharmacySouth CollegeKnoxvilleTN 37922USA Department of Pharmaceutical Sciences School of PharmacyUniversity of MarylandBaltimoreMD 21201USA

Reported concentrations for endocannabinoids and related lipids in biological tissues can vary greatly; therefore, methods used to quantify these compounds need to be validated. This report describes a method to quantify anandamide （AEA）, oleoylethanolamide （OEA） and palmitoylethanolamide （PEA） from rodent brain tissue. Analytes were exwacted using acetonitfile without further sample clean up, resolved on a C 18 reverse-phase column using a gradient mobile phase and detected using eleclmspmy ionization in positive selected ion monitoring mode on a single quadrupole mass spectrometer. The method produced high recovery rates for AEA, OEA and PEA, ranging from 98.1% to 106.2%, 98.5% to 102.2% and 85.4% to 89.5%, respectively. The method resulted in adequate sensitivity with a lower limit of quantification for AEA, OEA

关键词： Endocarmabinoids Acylethanolamides Anandamide OEA PEA LC-MS

Detection of Angioectasias and Haemorrhages Incorporated into a Multi-Class Classification Tool for the GI Tract Anomalies by Using Binary CNNs

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Journal of Biomedical Science and Engineering 2021年第12期14卷 402-414页

作者： Christos Barbagiannis Alexios Polydorou Michail Zervakis Andreas Polydorou eleftheria Sergaki School of Electrical and Computer Engineering (ECE) School Technical University of Crete Chania Greece Department of Surgery School of Medicine National and Kapodistrian University of Athens Athens Greece

The proposed deep learning algorithm will be integrated as a binary classifier under the umbrella of a multi-class classification tool to facilitate the automated detection of non-healthy deformities, anatomical landmarks, pathological findings, other anomalies and normal cases, by examining medical endoscopic images of GI tract. Each binary classifier is trained to detect one specific non-healthy condition. The algorithm analyzed in the present work expands the ability of detection of this tool by classifying GI tract image snapshots into two classes, depicting haemorrhage and non-haemorrhage state. The proposed algorithm is the result of the collaboration between interdisciplinary specialists on AI and Data Analysis, Computer Vision, Gastroenterologists of four University Gastroenterology Departments of Greek Medical Schools. The data used are 195 videos (177 from non-healthy cases and 18 from healthy cases) videos captured from the PillCam(R) Medronics device, originated from 195 patients, all diagnosed with different forms of angioectasia, haemorrhages and other diseases from different sites of the gastrointestinal (GI), mainly including difficult cases of diagnosis. Our AI algorithm is based on convolutional neural network (CNN) trained on annotated images at image level, using a semantic tag indicating whether the image contains angioectasia and haemorrhage traces or not. At least 22 CNN architectures were created and evaluated some of which pre-trained applying transfer learning on ImageNet data. All the CNN variations were introduced, trained to a prevalence dataset of 50%, and evaluated of unseen data. On test data, the best results were obtained from our CNN architectures which do not utilize backbone of transfer learning. Across a balanced dataset from no-healthy images and healthy images from 39 videos from different patients, identified correct diagnosis with sensitivity 90%, specificity 92%, precision 91.8%, FPR 8%, FNR 10%. Besides, we comp

关键词： Capsule Endoscopy (CE) Small Bowel Bleeding (SBB) Angioectasia Haemorrhage Gatrointestinal (GI) Small Bowel Capsule Endoscopy (SBCE) Convolutional Neural Network (CNN) Computer Aided Diagnosis (CAD) Image Level Annotation Pixel Level Annotation Binary Classification

PEM Fuel Cell System Evaluation Using Operational Data and Updated Matlab/Simulink Simulation Tools

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Journal of Energy and Power Engineering 2012年第12期6卷 1911-1917页

作者： Naxakis Ioannis Marinis Andreas Nakis Stavros Pyrgioti eleftheria loannis Milias-Argitis High Voltage Laboratory University of Patras Patras 26500 Greece Laboratory of Production Transportation and Distribution of Electric Energy University of Patras Patras 26500 Greece

The aim of this paper is the evaluation of the performance of a low pressure PEM （proton exchange membrane） fuel cell stack to step load changes, which are characteristic of standalone fuel cell system applications. The goal is a better understanding of the electrical behavior of the FC （fuel cell）, as a result of the electrochemical processes, via the cell＇s voltage characteristic during transient response. While changing the load, the performance of significant parameters affected such as temperature, pressure, purge status etc. are registered and evaluated. The analysis and experiment are based on a low pressure 1.2 kW PEM fuel cell stack （NEXAS power module）. Then, the experiment is simulated using Matlab/Simulink tools, while PCU （power conditioning units） are added in order to control power flow for enhanced performance. Finally, both operational and simulation data are compared to each other showing that simple PCUs applications can improve system＇s efficiency.

关键词： Distributed generation units). dynamic-transient response PEM fuel cell standalone power systems PCU (power conditioning

Improving CAD Hemorrhage Detection in Capsule Endoscopy

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Journal of Biomedical Science and Engineering 2021年第3期14卷 103-118页

作者： Polydorou Alexios Sergaki eleftheria Polydorou Andreas Barbagiannis Christos Vardiambasis Ioannis George Giakos Zervakis Michail School of Electrical and Computer Engineering Technical University of Crete Chania Greece Department of Surgery School of Medicine National and Kapodistrian University of Athens Athens Greece Department of Electronic Engineering Faculty of Engineering Hellenic Mediterranean University Chania Crete Greece School of ECE Manhattan College New York USA

This study explores an automated framework to assist the recognition of hemorrhage traces and bleeding lesions in video streams of small bowel capsule endoscopy (SBCE). The proposed methodology aims to achieve fast image control (<10 minutes), save valuable time of the physicians, and enable high performance diagnosis. A specialized elimination algorithm excludes all identical consecutive frames by utilizing the difference of gray levels in pixel luminance. An image filtering algorithm is proposed based on an experimentally calculated bleeding index and blood-color chart, which inspects all remaining frames of the footage and identifies pixels that reflect active or potential hemorrhage in color. The bleeding index and blood-color chart are estimated of the chromatic thresholds in RGB and HSV color spaces, and have been extracted after experimenting with more than 3200 training images, derived from 99 videos of a pool of 138 patients. The dataset has been provided by a team of expert gastroenterologist surgeons, who have also evaluated the results. The proposed algorithms are tested on a set of more than 1000 selected frame samples from the entire 39 testing videos, to a prevalence of 50% pathologic frames (balanced dataset). The frame elimination of identical and consecutive frames achieved a reduction of 36% of total frames. The best statistical performance for diagnosis of positive pathological frames from a video stream is achieved by utilizing masks in the HSV color model, with sensitivity up to 99%, precision 94.41% to a prevalence of 50%, accuracy up to 96.1%, FNR 1%, FPR 6.8%. The estimated blood-color chart will be clinically validated and used in feature extraction schemes supporting machine learning ML algorithms to improve the localization potential.

关键词： Capsule Endoscopy Small Bowel Bleeding Detection Computer Aided Diagnosis (CAD) Color Models Color Feature

Imaging in the COVID-19 era:Lessons learned during a pandemic

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World Journal of Radiology 2021年第6期13卷 192-222页

作者： Georgios Antonios Sideris Melina Nikolakea Aikaterini-eleftheria Karanikola Sofia Konstantinopoulou Dimitrios Giannis Lucy Modahl Department of Radiology University of Massachusetts Medical SchoolBaystate Medical CenterSpringfieldMA 01199United States Group Society of Junior DoctorsAthens 11527Greece Division of Pulmonary Medicine Department of PediatricsSheikh Khalifa Medical CityAbu Dhabi W13-01United Arab Emirates Institute of Health Innovations and Outcomes Research The Feinstein Institutes for Medical ResearchNorthwell HealthManhassetNY 11030United States

The first year of the coronavirus disease 2019(COVID-19)pandemic has been a year of unprecedented changes,scientific breakthroughs,and *** radiology community has not been spared from the challenges imposed on global healthcare *** has played a crucial part in tackling this pandemic,either by demonstrating the manifestations of the virus and guiding patient management,or by safely handling the patients and mitigating transmission within the *** modifications involving all aspects of daily radiology practice have occurred as a result of the pandemic,including workflow alterations,volume reductions,and strict infection control *** the ongoing challenges,considerable knowledge has been gained that will guide future *** aim of this review is to provide the latest evidence on the role of imaging in the diagnosis of the multifaceted manifestations of COVID-19,and to discuss the implications of the pandemic on radiology departments globally,including infection control strategies and delays in cancer ***,the promising contribution of artificial intelligence in the COVID-19 pandemic is explored.

关键词： COVID-19 Infectious diseases Diagnostic imaging Radiography Computed tomography Artificial intelligence

Gallstone Ileus: A Case Report and Our Clinic’s Experience

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Surgical Science 2014年第1期5卷 10-14页

作者： Konstantinos Alexiou Argyrios Ioannidis Nikolaos Sikalias eleftheria Konstantinidou Athanasios Fotopoulos Ioannis Karanikas Nikolaos Economou Department of Surgery Sismanoglion General Hospital Athens Greece

We present a 72-year-old female patient complaining of pain and distention of the abdomen, nausea and vomiting. No history of previous abdominal surgery, hernias or biliary disease was present. Plain abdominal x-rays showed small bowel obstruction. This mechanical obstruction of the gastrointestinal truct was caused by a gallstone in the terminal ileum.

关键词： Gallstone Ileus