Ermanno Cordelli1, Giuseppe Maulucci2, Marco De Spirito2, Alessandro Rizzi3, Dario Pitocco3, Paolo Soda4. 1. Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy. Electronic address: e.cordelli@unicampus.it. 2. Istituto di Fisica, Università Cattolica del Sacro Cuore, Rome, Italy. 3. Istituto di Medicina Interna, Fondazione Policlinico Gemelli, Rome, Italy. 4. Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy.
Abstract
BACKGROUND AND OBJECTIVE: Investigation of membrane fluidity by metabolic functional imaging opens up a new and important area of translational research in type 1 diabetes mellitus, being a useful and sensitive biomarker for disease monitoring and treatment. We investigate here how data on membrane fluidity can be used for diabetes monitoring. METHODS: We present a decision support system that distinguishes between healthy subjects, type 1 diabetes mellitus patients, and type 1 diabetes mellitus patients with complications. It leverages on dual channel data computed from the physical state of human red blood cells membranes by means of features based on first- and second-order statistical measures as well as on rotation invariant co-occurrence local binary patterns. The experiments were carried out on a dataset of more than 1000 images belonging to 27 subjects. RESULTS: Our method shows a global accuracy of 100%, outperforming also the state-of-the-art approach based on the glycosylated hemoglobin. CONCLUSIONS: The proposed recognition approach permits to achieve promising results.
BACKGROUND AND OBJECTIVE: Investigation of membrane fluidity by metabolic functional imaging opens up a new and important area of translational research in type 1 diabetes mellitus, being a useful and sensitive biomarker for disease monitoring and treatment. We investigate here how data on membrane fluidity can be used for diabetes monitoring. METHODS: We present a decision support system that distinguishes between healthy subjects, type 1 diabetes mellituspatients, and type 1 diabetes mellituspatients with complications. It leverages on dual channel data computed from the physical state of human red blood cells membranes by means of features based on first- and second-order statistical measures as well as on rotation invariant co-occurrence local binary patterns. The experiments were carried out on a dataset of more than 1000 images belonging to 27 subjects. RESULTS: Our method shows a global accuracy of 100%, outperforming also the state-of-the-art approach based on the glycosylated hemoglobin. CONCLUSIONS: The proposed recognition approach permits to achieve promising results.
Authors: Mario Ruiz; Rakesh Bodhicharla; Emma Svensk; Ranjan Devkota; Kiran Busayavalasa; Henrik Palmgren; Marcus Ståhlman; Jan Boren; Marc Pilon Journal: Elife Date: 2018-12-04 Impact factor: 8.140
Authors: Giada Bianchetti; Salome Azoulay-Ginsburg; Nimrod Yosef Keshet-Levy; Aviv Malka; Sofia Zilber; Edward E Korshin; Shlomo Sasson; Marco De Spirito; Arie Gruzman; Giuseppe Maulucci Journal: Int J Mol Sci Date: 2021-03-18 Impact factor: 5.923
Authors: Domiziana Santucci; Eliodoro Faiella; Ermanno Cordelli; Rosa Sicilia; Carlo de Felice; Bruno Beomonte Zobel; Giulio Iannello; Paolo Soda Journal: Cancers (Basel) Date: 2021-05-06 Impact factor: 6.639