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ISSN: 2155-9880

Journal of Clinical & Experimental Cardiology
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Research Article

Detection and Determination of Protein Network Associated with Atrial Fibrillation Phenotypes

Oliver Klein2#, Thorsten Hanke1#, Junfeng Yan1, Grit Nebrich2, Sophie Krause1, Herbert Thiele3 and Salah A Mohamed1*
1Department of Cardic and Thoracic Vascular Surgery, University of Luebeck, Luebeck, Germany
2Charité-Universitaetsmedizin, Berlin, Berlin-Brandenburger Centrum for Regenerative Therapien Campus Virchow-Klinikum, Berlin, Germany
3Fraunhofer Institute for Medical Image Computing MEVIS, Bremen, Germany
#Both authors have contributed equally to this work
Corresponding Author : Salah A Mohamed
Department of Cardic and Thoracic Vascular Surgery
University of Luebeck, Luebeck, Germany
Tel: +49-451-5006425
Email: [email protected]
Received: October 16, 2015; Accepted: November 25, 2015; Published: November 28, 2015
Citation: Klein O, Hanke T, Yan J, Nebrich G, Krause S, et al. (2015) Detection and Determination of Protein Network Associated with Atrial Fibrillation Phenotypes. J Clin Exp Cardiolog 6:410. doi:10.4172/2155-9880.1000410
Copyright: © 2015 Klein O, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Abstract

Atrial fibrillation (AF) is associated with increased risks of stroke, cardiac failure, and mortality. Since the discrimination of AF phenotype is inadequate, accurate diagnosis remains elusive. Left atrial appendage tissue resected routinely during the maze procedure was collected from patients with paroxysmal, persistent, and longstanding persistent arrhythmia. In situ comprehensive proteomic approaches of matrix-assisted laser desorption/ ionization imaging mass spectrometry was used to differentiate and classify the spatial molecular processes in the pathology of AF phenotypes. Using unsupervised computational evaluation strategy, probabilistic latent semantic clustering, and receiver operating characteristic analysis (SCiLS Lab), the acquired peptide signatures and characteristic m/z species could be used to assign the AF phenotype. Intensity distribution of the given m/z values, which are discriminative for the considered cluster, was determined to distinguish between paroxysmal and persistent AF (mean, 4.08 ± 1.21 vs 1.59 ± 0.12, p=0.09) and persistent and long-standing persistent AF (1.59 ± 0.12 vs 6.85 ± 3.02, p=0.02). Tissue-based proteomic approach provides clinically relevant information, which may be beneficial in improving risk stratification in AF patients.

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